def sodom(findstr, exten=0):
angles = {}
file_list = glob.glob(findstr)
for image in file_list:
angle = pyfits.open(image)[exten].header['ANGLE']
if angle not in angles.keys():
angles[angle] = []
angles[angle].append(image)
iraf.imcombine.combine = 'average'
iraf.imcombin.reject = 'none'
for theta in angles.keys():
if angles[theta][0].find('../') > -1: s = 3
else: s = 0
name = 'final_'+angles[theta][0][s:angles[theta][0].rfind('_')]+'.fits'
iraf.imcombine(','.join([s+'['+str(exten)+']' for s in angles[theta]]),name)
ADE.mediclean(name,name)
return
def clean_VI(searchstr):
"""Takes FITS files generated by LabView and:
1) Poor man's bkgrnd subtraction with ADE.mediclean
2) Sets the Primary Hdu to 0 instead of LabView's 1
"""
in_files = glob(searchstr)
for image in in_files:
HDU = pyfits.open(image)[1]
cleaned_data = ADE.mediclean(HDU.data[220:870,290:960])
clean_name = image.split('.fits')[0].split('/')[-1] + '.cleaned.fits'
pyfits.PrimaryHDU(cleaned_data,HDU.header).writeto(clean_name)
return
def fat_angel(findstr, num_ap, phi, dp, f, 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. The images
are first cleaned using a median subtraction algorithm, which
assumes that your S/N is very good. 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.
phi - Float
The angle between the screen normal and the detector normal
in RADIANS.
dp - Float
The distance, in millimeters, from the center of the screen
to the front glass of the camera lens.
f - Float
The nominal focal length of the camera lens in millimeters.
Read this number off of the lens body.
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.
Output:
Output is a tuple of Numpy vectors that each contain the folling info:
Field: Description:
0 input angle
1 ring radius (mm)
2 ring width (mm)
3 inner ring radius (mm)
4 outer ring radius (mm)
5,6 the x and y coordinates of the ring center (pixels)
Example:
Assume you have a bunch of data called X_red.fits where X is some
data iterator.
>>lb.fat_angel('*_red.fits',150,0.319,1084,26,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)
xcent = np.zeros(numfiles)
ycent = np.zeros(numfiles)
t1 = time.time()
for i in range(numfiles):
print(file_list[i])
huds = pyfits.open(file_list[i])
data = np.float32(huds[exten].data)
'get the angle from the FITS header. MUCH easier in python than IDL!'
angles[i] = huds[exten].header['ANGLE']
'''get rid of the background noise with mediclean. This algorithm
is very good if you have bodacious S/N'''
data = ADE.mediclean(data)
'''given the nominal focal length, f, and object distance, dp, we
can approximate the image distance, sp, using the thin lens eq.'''
sp = (1/float(f) - 1/float(dp))**-1
'''find the center of the image by minimizing the reported ring width'''
center = cent_test(data,phi,dp,sp,pixelpitch)
# center = ADE.centroid(data)
'''t_dist holds the transformation from the detector space to screen space'''
t_dist = metatron(data,center,phi,dp,sp,pixelpitch)
'Annulize!'
if debug:
t0 = time.time()
(r_vec,fluxes) = ADE.annulize(data,num_ap,distances=t_dist)
print "Annulize took "+str(time.time() - t0)+" seconds"
else:
(r_vec,fluxes) = ADE.annulize(data,num_ap,distances=t_dist)
'''find_peak uses the CDF of the fluxes to find the peak
and interpolation to find the limits of the FWHM. 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]
if debug:
'''plot a bunch of stuff'''
fig1 = plt.figure(fignum)
plt.clf()
sp0 = fig1.add_subplot(212)
sp0.plot(r_vec,
np.cumsum(fluxes)/np.max(np.cumsum(fluxes)))
sp0.axvline(x=rm,ls='--',lw=0.3)
sp0.axvline(x=r1,ls='--',lw=0.3)
sp0.axvline(x=r2,ls='--',lw=0.3)
sp0.set_xlabel("Radius (mm)")
sp0.set_ylabel("% of total counts")
sp0.set_title("Normalized CDF")
sp1 = fig1.add_subplot(211)
sp1.plot(r_vec,fluxes)
sp1.axvline(x=rm,ls='--',lw=0.3)
sp1.axvline(x=r1,ls='--',lw=0.3)
sp1.axvline(x=r2,ls='--',lw=0.3)
sp1.set_xlabel("Radius (mm)")
sp1.set_ylabel("Counts")
sp1.set_title("Ring Profile")
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
xcent[i] = center[0]
ycent[i] = center[1]
print "Total annulize time was "+str(time.time()-t1)+" seconds"
'We sort the data by angle to make the output a little more readable'
sort_idx = np.argsort(angles)
if output:
f = open(output, 'w')
f.write('#angle radius width r1 r2 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],
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],
xcent[sort_idx],
ycent[sort_idx])
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])
def fat_angel(name, num_ap, phi, dp, f, fsg, pixelpitch, exten, q, lock):
'''
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. The images
are first cleaned using a median subtraction algorithm, which
assumes that your S/N is very good. 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.
phi - Float
The angle between the screen normal and the detector normal
in RADIANS.
dp - Float
The distance, in millimeters, from the center of the screen
to the front glass of the camera lens.
f - Float
The nominal focal length of the camera lens in millimeters.
Read this number off of the lens body.
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.
Output:
Output is a tuple of Numpy vectors that each contain the folling info:
Field: Description:
0 input angle
1 ring radius (mm)
2 ring width (mm)
3 inner ring radius (mm)
4 outer ring radius (mm)
5,6 the x and y coordinates of the ring center (pixels)
Example:
Assume you have a bunch of data called X_red.fits where X is some
data iterator.
>>lb.fat_angel('*_red.fits',150,0.319,1084,26,exten=1,output='my_data.dat')
'''
print name
huds = pyfits.open(name)
data = np.float32(huds[exten].data)
'get the angle from the FITS header. MUCH easier in python than IDL!'
angle = huds[exten].header['ANGLE']
'''get rid of the background noise with mediclean. This algorithm
is very good if you have bodacious S/N'''
data = ADE.mediclean(data)
'''given the nominal focal length, f, and object distance, dp, we
can approximate the image distance, sp, using the thin lens eq.'''
sp = (1/float(f) - 1/float(dp))**-1
'''find the center of the image by minimizing the reported ring width'''
center = cent_test(data,phi,dp,sp,pixelpitch) #slow and right
# center = ADE.centroid(data) #fast and wrong
'''t_dist holds the transformation from the detector space to
screen space'''
t_dist = metatron(data,center,phi,dp,sp,pixelpitch)
'Annulize!'
(r_vec,fluxes,errors) = ADE.annulize(data,num_ap,distances=t_dist)
'''find_peak uses the CDF of the fluxes to find the peak
and interpolation to find the limits of the FWHM. Working with
the CDF allows us to assume that the annulus is gaussian-ish
without having to worry about the particulars'''
(rp,r1,r2) = find_peak(r_vec,fluxes)
ap = np.arctan(rp/fsg)*180/np.pi
a1 = np.arctan(r1/fsg)*180/np.pi
a2 = np.arctan(r2/fsg)*180/np.pi
awidth = a2 - a1
rwidth = r2 - r1
xcent = center[0]
ycent = center[1]
if full_output:
'write power profile info for use by theModule'
if not os.path.exists(full_dir): os.makedirs(full_dir)
angs = np.arctan(r_vec/fsg)*180/np.pi
write_full(angs,fluxes,angle,ap)
q.put((angle,ap,rp,awidth,rwidth,r1,r2,xcent,ycent))
return
