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 fit_rings(file, flatfile=None, disp=None):
if flatfile:
flat = np.loadtxt(flatfile)
hdu = pyfits.open(file)
(data, header) = (hdu[1].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)
# first guess is the center of the aperture (assume 4x4 binning here)
xc = 4 * 513.5 / binning
yc = 4 * 514.5 / binning
mask_val = 0.0
f = {}
f['MR'] = 22848.0
f['LR'] = 24169.32
# find brightest ring and refine center
cenwidth = 20
if flatfile:
prof, r = FP_profile(fp_im, xc, yc,
trim_rad=len(flat), mask=mask_val)
prof = prof / flat
else:
prof, r = FP_profile(fp_im, xc, yc,
trim_rad=4 * 450.0 / binning, mask=mask_val)
wave = cenwave / np.sqrt(1.0 + (r * binning / f[etname]) ** 2)
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
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))
back = 100.0
fwhm = 1.0
gam = 1.0
init = [back]
# keep brightest
if len(peak_list) > 1:
peaks = peak_list[0:1]
else:
peaks = peak_list
for peak in peaks:
# position
init.append(cenwave / np.sqrt(1.0 + (peak * binning / f[etname]) ** 2))
# amplitude
init.append(prof[peak])
# FWHM
init.append(fwhm)
# gamma
init.append(gam)
#fit = opt.fmin_slsqp(fit_func, init,
# args=(prof, rsq), bounds=bounds)
#fit = opt.fmin_tnc(fit_func, init,
# args=(prof, rsq), bounds=bounds, approx_grad=True)
fit = opt.fmin_powell(fit_func,
init,
args=(prof, wave),
ftol=0.00001,
full_output=False,
disp=False)
pars = {}
fit_v = fit[0]
print "Background = %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 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[1].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
# 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)
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 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[1].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
# 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)
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
