def make_calring(hdu, method=None, thresh=5, niter=3, conv=0.05, minsize=10, axc=None, ayc=None):
"""Open each image and measure the position of the ring including its center and radius
Return the information about the calibration ring
"""
# setup the data
data = hdu[0].data
# extract the time and convert to decimal hours
utctime = saltkey.get("UTC-OBS", hdu[0])
utctime = salttime.time_obs2hr((utctime.split()[-1]))
# determine the correct etalon and information to extract
etstate = saltkey.get("ET-STATE", hdu[0])
if etstate.count("S2"):
etz = saltkey.get("ET1Z", hdu[0])
elif etstate.count("S3"):
etz = saltkey.get("ET2Z", hdu[0])
else:
msg = "This etalon state is not currently supported"
raise SaltError(msg)
# extract the ring
ring_list = findrings(data, thresh=thresh, niter=niter, minsize=minsize, axc=axc, ayc=ayc)
# assumes only one ring in the data set
ring = ring_list[0]
# determine the center and radius of the ring
if method is not None:
ring = findcenter(data, ring, method, niter=niter, conv=conv)
if axc:
ring.xc = axc
if ayc:
ring.yc = ayc
return ring.xc, ring.yc, ring.prad, ring.prad_err, etz, utctime
def fit_rings(file, trim_rad=470, disp=None):
"""
main routine to take a FITS file, read it in, azimuthally average it,
find the rings, and then fit voigt profiles to them.
Parameters
----------
file : string filename of FITS file to analyze
trim_rad : int maximum radial extent of profile
disp : boolean to control DS9 display of image
Returns
-------
list containing:
boolean - success of finding peaks or not
numpy array - wavelengths of profile
numpy array - radial flux profile data
numpy array - best-fit radial flux profile
dict - parameters of best-fit
"""
hdu = pyfits.open(file)
(data, header) = (hdu[0].data, hdu[0].header)
etalon = int(header['ET-STATE'].split()[3])
etwave_key = "ET%dWAVE0" % etalon
name_key = "ET%dMODE" % etalon
etname = header[name_key]
cenwave = float(header[etwave_key])
binning = int(header['CCDSUM'].split()[0])
if header['OBSMODE'] != 'FABRY-PEROT':
return False, np.empty(1), np.empty(1), np.empty(1), np.empty(1)
ysize, xsize = data.shape
# cut FP image down to square
fp_im = data[:,
(xsize - ysize) / 2:
(xsize + ysize) / 2]
if disp:
disp.set_np2arr(fp_im, dtype=np.int32)
# mask those gaps
fp_im = ma.masked_less_equal(data[:,
(xsize - ysize) / 2:
(xsize + ysize) / 2], 0.0)
# define center based on FP ghost imaging with special mask
xc = 2054 / binning
yc = 2008 / binning
# print "FP ring centre should be at x= %.1f y= %.1f" % (xc+280, yc)
#Find the list of rings:
ring_list=findrings(data, thresh=5, niter=5, minsize=10)
print "%1d ring(s) found" % (len(ring_list))
sxc=syc=0
for i in range(len(ring_list)):
ring_list[i]=findcenter(data, ring_list[i], method='center')
print "Ring number %1d: centre at X= %.1f, Y=%.1f, Radius= %.1f" % (i+1, ring_list[i].xc, ring_list[i].yc,ring_list[i].prad)
if ring_list[i].prad > 250 and ring_list[i].prad < 390:
sxc=ring_list[i].xc
syc=ring_list[i].yc
# xcn,ycn=find_center(fp_im,xc,yc)
if sxc >0 and syc > 0:
print "Found ring centre at x= %.1f y= %.1f" % (sxc, syc)
xc=sxc-280
yc=syc
#Xc0 and Yc0 is the ring centre for a good ring
# Ted's values:
xc0=802
yc0=503
#From Tim's:
# xc0=800
# yc0=500
deltaX=int(3.6*(syc-yc0))
deltaY=int(4.7*((sxc)-xc0))
# print "With this new centre, recommend dX= %1d, dY=%1d" % (deltaX, deltaY)
else:
print "Found ring centre at x= %.1f y= %.1f" % (sxc, syc)
print "Using FP ghost ring centre at x= %.1f y= %.1f" % (xc+280, yc)
# we use the 4x4 version of trim_rad since the vast majority of FP
# data is taken with 4x4 binning
trim_rad *= 4 / binning
mask_val = 0.0
f = {}
if cenwave < 5200:
f['MR'] = 22149.6
f['LR'] = 22795.92
f['TF'] = 24360.32
if cenwave > 6500 and cenwave < 6600:
f['MR'] = 22713.0
f['LR'] = 24191.40
f['TF'] = 24360.32
if cenwave >= 6600 and cenwave < 6700:
f['MR'] = 22848.0
f['LR'] = 24169.32
f['TF'] = 23830.20
if cenwave >= 6700 and cenwave < 6900:
f['MR'] = 22828.92
f['LR'] = 24087.68
f['TF'] = 24553.32
if cenwave >= 7300 and cenwave < 7700:
f['MR'] = 22864.06
f['LR'] = 24087.68
f['TF'] = 24553.32
else:
f['MR'] = 22828.92
f['LR'] = 24400.32
f['TF'] = 24553.32
# get the radial profile and flatten it with a default QTH flat profile
prof, r = FP_profile(fp_im, xc, yc, trim_rad=trim_rad, mask=mask_val)
# Not flatfielding the profile as ring already flat-fielded using saltflat.
# prof = flatprof(prof, binning)
wave = cenwave / np.sqrt(1.0 + (r * binning / f[etname]) ** 2)
# find the peaks and bail out if none found
npeaks, peak_list = find_peaks(prof, width=40)
if npeaks < 1:
print "No peaks found."
return False, np.empty(1), np.empty(1), np.empty(1), np.empty(1)
print "Found %d rings at:" % npeaks
cenwidth = 20
for peak in peak_list:
cen_peak = centroid(prof, peak, cenwidth)
if np.isnan(cen_peak):
cen_peak = peak
print "\t R %f" % cen_peak
if disp:
disp.set("regions command {circle %f %f %f # color=red}" %
(xc, yc, cen_peak))
# max_r = peak_list[0]
# pmax = prof[max_r]
back = 250.0
fwhm = 5.0
gam = 1.0
init = [back]
bounds = [(0.0, 2.0e8)]
# keep brightest
peak = peak_list[0]
# position
init.append(cenwave / np.sqrt(1.0 + (peak * binning / f[etname]) ** 2))
bounds.append((cenwave - 30, cenwave + 30))
# amplitude
init.append(prof[peak])
bounds.append((0.0, 1.0e8))
# FWHM
init.append(fwhm)
bounds.append((0.1, 20.0))
# gamma
init.append(gam)
bounds.append((0.0, 5.0))
### keep these around in case we want to try again someday
#
#fit = opt.fmin_slsqp(fit_func, init, args=(prof, wave),
# bounds=bounds)
#fit, nfeval, rc = opt.fmin_tnc(fit_func, init, args=(prof, wave),
# epsilon=0.0001,
# bounds=bounds,
# approx_grad=True,
# disp=0,
# maxfun=5000)
# good ol' powell method FTW
fit = opt.fmin_powell(fit_func, init, args=(prof, wave),
ftol=0.00001, full_output=False, disp=False)
#print "Return code: %s" % opt.tnc.RCSTRINGS[rc]
pars = {}
fit_v = fit[0]
print "\nBackground = %f" % fit[0]
pars['Background'] = fit[0]
pars['R'] = []
pars['Amplitude'] = []
pars['Gauss FWHM'] = []
pars['Gamma'] = []
pars['FWHM'] = []
for i in range(1, len(fit), 4):
fwhm = voigt_fwhm(fit[i + 2], fit[i + 3])
pars['R'].append(fit[i])
pars['Amplitude'].append(fit[i + 1])
pars['Gauss FWHM'].append(fit[i + 2])
pars['Gamma'].append(fit[i + 3])
pars['FWHM'].append(fwhm)
fit_v = fit_v + qvoigt(wave, fit[i + 1], fit[i],
fit[i + 2], fit[i + 3])
return True, wave, prof, fit_v, pars
def saltfpringfind(images, method=None, section=None, thresh=5, minsize=10, niter=5, conv=0.05,
displayimage=True, clobber=False, logfile='salt.log',verbose=True):
with logging(logfile,debug) as log:
# Check the input images
infiles = saltio.argunpack ('Input',images)
#check the method
method=saltio.checkfornone(method)
# read in the section
section=saltio.checkfornone(section)
if section is None:
pass
else:
section=saltio.getSection(section)
# open each raw image file
for img in infiles:
#open the fits file
struct=saltio.openfits(img)
data=struct[0].data
#determine the background value for the image
if section is None:
#if section is none, just use all pixels greater than zero
bdata=data[data>0]
else:
y1,y2,x1,x2=section
bdata=data[y1:y2,x1:x2]
bmean, bmedian, bstd=iterstat(bdata, sig=thresh, niter=niter, verbose=False)
message="Image Background Statistics\n%30s %6s %8s %8s\n%30s %5.4f %5.4f %5.4f\n" % \
('Image', 'Mean', 'Median', 'Std',img, bmean, bmedian, bstd)
log.message(message, with_stdout=verbose)
mdata=data*(data-bmean>thresh*bstd)
#prepare the first guess for the image
ring_list=findrings(data, thresh=thresh, niter=niter, minsize=minsize)
#if specified, find the center of the ring
if method is not None:
for i in range(len(ring_list)):
ring_list[i]=findcenter(data, ring_list[i], method, niter=niter, conv=conv)
#if one peak: no rings. If two peaks: one ring, if two peaks: four rings
if len(ring_list)==1:
msg="One ring dected in image"
else:
msg="%i rings found in image" % len(ring_list)
log.message(message, with_stdout=verbose)
if displayimage:
regfile=img.replace('.fits', '.reg')
if clobber and os.path.isfile(regfile): fout=saltio.delete(regfile)
fout=open(regfile, 'w')
fout.write("""# Region file format: DS9 version 4.1
# Filename: %s
global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1
physical
""" % img)
for ring in ring_list:
fout.write('circle(%f, %f, %f)\n' % (ring.xc,ring.yc,ring.prad))
fout.write('circle(%f, %f, %f)\n' % (ring.xc,ring.yc,ring.prad-3*ring.sigma))
fout.write('circle(%f, %f, %f)\n' % (ring.xc,ring.yc,ring.prad+3*ring.sigma))
fout.close()
display(img, catname=regfile, rformat='reg')
message = 'Ring Parameters\n%30s %6s %6s %6s\n' % ('Image', 'XC', 'YC', 'Radius')
log.message(message, with_stdout=verbose)
for ring in ring_list:
msg='%30s %6.2f %6.2f %6.2f\n' % (img, ring.xc, ring.yc, ring.prad)
log.message(msg, with_header=False, with_stdout=verbose)
def saltfpringfind(images,
method=None,
section=None,
thresh=5,
minsize=10,
niter=5,
conv=0.05,
displayimage=True,
clobber=False,
logfile='salt.log',
verbose=True):
with logging(logfile, debug) as log:
# Check the input images
infiles = saltio.argunpack('Input', images)
#check the method
method = saltio.checkfornone(method)
# read in the section
section = saltio.checkfornone(section)
if section is None:
pass
else:
section = saltio.getSection(section)
# open each raw image file
for img in infiles:
#open the fits file
struct = saltio.openfits(img)
data = struct[0].data
#determine the background value for the image
if section is None:
#if section is none, just use all pixels greater than zero
bdata = data[data > 0]
else:
y1, y2, x1, x2 = section
bdata = data[y1:y2, x1:x2]
bmean, bmedian, bstd = iterstat(bdata,
sig=thresh,
niter=niter,
verbose=False)
message="Image Background Statistics\n%30s %6s %8s %8s\n%30s %5.4f %5.4f %5.4f\n" % \
('Image', 'Mean', 'Median', 'Std',img, bmean, bmedian, bstd)
log.message(message, with_stdout=verbose)
mdata = data * (data - bmean > thresh * bstd)
#prepare the first guess for the image
ring_list = findrings(data,
thresh=thresh,
niter=niter,
minsize=minsize)
#if specified, find the center of the ring
if method is not None:
for i in range(len(ring_list)):
ring_list[i] = findcenter(data,
ring_list[i],
method,
niter=niter,
conv=conv)
#if one peak: no rings. If two peaks: one ring, if two peaks: four rings
if len(ring_list) == 1:
msg = "One ring dected in image"
else:
msg = "%i rings found in image" % len(ring_list)
log.message(message, with_stdout=verbose)
if displayimage:
regfile = img.replace('.fits', '.reg')
if clobber and os.path.isfile(regfile):
fout = saltio.delete(regfile)
fout = open(regfile, 'w')
fout.write("""# Region file format: DS9 version 4.1
# Filename: %s
global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1
physical
""" % img)
for ring in ring_list:
fout.write('circle(%f, %f, %f)\n' %
(ring.xc, ring.yc, ring.prad))
fout.write('circle(%f, %f, %f)\n' %
(ring.xc, ring.yc, ring.prad - 3 * ring.sigma))
fout.write('circle(%f, %f, %f)\n' %
(ring.xc, ring.yc, ring.prad + 3 * ring.sigma))
fout.close()
display(img, catname=regfile, rformat='reg')
message = 'Ring Parameters\n%30s %6s %6s %6s\n' % ('Image', 'XC',
'YC', 'Radius')
log.message(message, with_stdout=verbose)
for ring in ring_list:
msg = '%30s %6.2f %6.2f %6.2f\n' % (img, ring.xc, ring.yc,
ring.prad)
log.message(msg, with_header=False, with_stdout=verbose)
def fit_rings(file, trim_rad=470, disp=None):
"""
main routine to take a FITS file, read it in, azimuthally average it,
find the rings, and then fit voigt profiles to them.
Parameters
----------
file : string filename of FITS file to analyze
trim_rad : int maximum radial extent of profile
disp : boolean to control DS9 display of image
Returns
-------
list containing:
boolean - success of finding peaks or not
numpy array - wavelengths of profile
numpy array - radial flux profile data
numpy array - best-fit radial flux profile
dict - parameters of best-fit
"""
hdu = pyfits.open(file)
(data, header) = (hdu[0].data, hdu[0].header)
etalon = int(header['ET-STATE'].split()[3])
etwave_key = "ET%dWAVE0" % etalon
name_key = "ET%dMODE" % etalon
etname = header[name_key]
cenwave = float(header[etwave_key])
binning = int(header['CCDSUM'].split()[0])
if header['OBSMODE'] != 'FABRY-PEROT':
return False, np.empty(1), np.empty(1), np.empty(1), np.empty(1)
ysize, xsize = data.shape
# cut FP image down to square
fp_im = data[:,
(xsize - ysize) / 2:
(xsize + ysize) / 2]
if disp:
disp.set_np2arr(fp_im, dtype=np.int32)
# mask those gaps
fp_im = ma.masked_less_equal(data[:,
(xsize - ysize) / 2:
(xsize + ysize) / 2], 0.0)
# define center based on FP ghost imaging with special mask
xc = 2054 / binning
yc = 2008 / binning
print "FP ghost ring centre at x= %.1f y= %.1f" % (xc+280, yc)
#Find the list of rings:
ring_list=findrings(data, thresh=5, niter=5, minsize=10)
print "%1d ring(s) found" % (len(ring_list))
sxc=syc=0
for i in range(len(ring_list)):
ring_list[i]=findcenter(data, ring_list[i], method='center')
print "Ring number %1d: centre at X= %.1f, Y=%.1f, Radius= %.1f" % (i+1, ring_list[i].xc, ring_list[i].yc,ring_list[i].prad)
if ring_list[i].prad > 250 and ring_list[i].prad < 390:
sxc=ring_list[i].xc
syc=ring_list[i].yc
# xcn,ycn=find_center(fp_im,xc,yc)
if sxc >0 and syc > 0:
print "Using centre at x= %.1f y= %.1f" % (sxc, syc)
xc=sxc-280
yc=syc
#Xc0 and Yc0 is the ring centre for a good ring
# Ted's values:
xc0=802
yc0=503
#From Tim's:
# xc0=800
# yc0=500
deltaX=int(3.6*(syc-yc0))
deltaY=int(4.7*((sxc)-xc0))
print "With this new centre, recommend dX= %1d, dY=%1d" % (deltaX, deltaY)
else:
print "Using FP ghost ring centre at x= %.1f y= %.1f" % (xc+280, yc)
# we use the 4x4 version of trim_rad since the vast majority of FP
# data is taken with 4x4 binning
trim_rad *= 4 / binning
mask_val = 0.0
f = {}
if cenwave < 5200:
f['MR'] = 22149.6
f['LR'] = 22795.92
f['TF'] = 24360.32
if cenwave > 6500 and cenwave < 6600:
f['MR'] = 22713.0
f['LR'] = 24191.40
f['TF'] = 24360.32
if cenwave >= 6600 and cenwave < 6700:
f['MR'] = 22848.0
f['LR'] = 24169.32
f['TF'] = 23830.20
if cenwave >= 6700 and cenwave < 6900:
f['MR'] = 22828.92
f['LR'] = 24087.68
f['TF'] = 24553.32
else:
f['MR'] = 22828.92
f['LR'] = 24400.32
f['TF'] = 24553.32
# get the radial profile and flatten it with a default QTH flat profile
prof, r = FP_profile(fp_im, xc, yc, trim_rad=trim_rad, mask=mask_val)
# Not flatfielding the profile as ring already flat-fielded using saltflat.
# prof = flatprof(prof, binning)
wave = cenwave / np.sqrt(1.0 + (r * binning / f[etname]) ** 2)
# find the peaks and bail out if none found
npeaks, peak_list = find_peaks(prof, width=40)
if npeaks < 1:
print "No peaks found."
return False, np.empty(1), np.empty(1), np.empty(1), np.empty(1)
print "Found %d rings at:" % npeaks
cenwidth = 20
for peak in peak_list:
cen_peak = centroid(prof, peak, cenwidth)
if np.isnan(cen_peak):
cen_peak = peak
print "\t R %f" % cen_peak
if disp:
disp.set("regions command {circle %f %f %f # color=red}" %
(xc, yc, cen_peak))
# max_r = peak_list[0]
# pmax = prof[max_r]
back = 250.0
fwhm = 5.0
gam = 1.0
init = [back]
bounds = [(0.0, 2.0e8)]
# keep brightest
peak = peak_list[0]
# position
init.append(cenwave / np.sqrt(1.0 + (peak * binning / f[etname]) ** 2))
bounds.append((cenwave - 30, cenwave + 30))
# amplitude
init.append(prof[peak])
bounds.append((0.0, 1.0e8))
# FWHM
init.append(fwhm)
bounds.append((0.1, 20.0))
# gamma
init.append(gam)
bounds.append((0.0, 5.0))
### keep these around in case we want to try again someday
#
#fit = opt.fmin_slsqp(fit_func, init, args=(prof, wave),
# bounds=bounds)
#fit, nfeval, rc = opt.fmin_tnc(fit_func, init, args=(prof, wave),
# epsilon=0.0001,
# bounds=bounds,
# approx_grad=True,
# disp=0,
# maxfun=5000)
# good ol' powell method FTW
fit = opt.fmin_powell(fit_func, init, args=(prof, wave),
ftol=0.00001, full_output=False, disp=False)
#print "Return code: %s" % opt.tnc.RCSTRINGS[rc]
pars = {}
fit_v = fit[0]
print "\nBackground = %f" % fit[0]
pars['Background'] = fit[0]
pars['R'] = []
pars['Amplitude'] = []
pars['Gauss FWHM'] = []
pars['Gamma'] = []
pars['FWHM'] = []
for i in range(1, len(fit), 4):
fwhm = voigt_fwhm(fit[i + 2], fit[i + 3])
pars['R'].append(fit[i])
pars['Amplitude'].append(fit[i + 1])
pars['Gauss FWHM'].append(fit[i + 2])
pars['Gamma'].append(fit[i + 3])
pars['FWHM'].append(fwhm)
fit_v = fit_v + qvoigt(wave, fit[i + 1], fit[i],
fit[i + 2], fit[i + 3])
return True, wave, prof, fit_v, pars
