def extract_ap_file(imgfile, apfiles, wlfiles=None, ap_width=60, \
target_path=None):
'''
Extract the apertures using ap_tracing solution and wavelength data
- inputs:
1. imgfile (input FITS)
2. apfiles (a list of input ap_tracing files)
3. wlfiles (a list of wavelength fitting coefficient files)
4. ap_width (to be extracted with this pixel width)
5. target_path (output directory)
'''
if len(apfiles) != len(wlfiles): wlfiles = None
img, header = ip.readfits(imgfile)
fpath, fname = ip.split_path(imgfile)
if ip.exist_path(target_path) == False: target_path = fpath
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
ostrips, owaves, ohdrs = \
extract_ap(img, apfiles, wlfiles=wlfiles, \
header=header, ap_width=ap_width, target_path=target_path )
for strip, wave, hdr in zip(ostrips, owaves, ohdrs):
ap_num = hdr.get('AP-NUM')
ip.savefits(target_path+name+'.%03d.fits' % (ap_num,), strip, header=hdr)
def extract_ap_file(imgfile, apfiles, wlfiles=None, ap_width=60, \
target_path=None):
'''
Extract the apertures using ap_tracing solution and wavelength data
- inputs:
1. imgfile (input FITS)
2. apfiles (a list of input ap_tracing files)
3. wlfiles (a list of wavelength fitting coefficient files)
4. ap_width (to be extracted with this pixel width)
5. target_path (output directory)
'''
if len(apfiles) != len(wlfiles): wlfiles = None
img, header = ip.readfits(imgfile)
fpath, fname = ip.split_path(imgfile)
if ip.exist_path(target_path) == False: target_path = fpath
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
ostrips, owaves, ohdrs = \
extract_ap(img, apfiles, wlfiles=wlfiles, \
header=header, ap_width=ap_width, target_path=target_path )
for strip, wave, hdr in zip(ostrips, owaves, ohdrs):
ap_num = hdr.get('AP-NUM')
ip.savefits(target_path + name + '.%03d.fits' % (ap_num, ),
strip,
header=hdr)
def transform_ap_file(stripfile, wave_step=False, outputfile=None): #2014-01-13 cksim
'''
Apply linear interpolation to the strip with a regular wavelength
- INPUTS :
1. stripfile (FITS)
2. wave_step (to be extracted with this wavelength step for a pixel)
3. outputfile (FITS filename)
- OUTPUTS:
1. tranformed 2D strip data with header
'''
astrip, ahdr = ip.readfits(stripfile)
fpath, fname = ip.split_path(stripfile)
if outputfile == None: outputfile = fpath+fname+'.tr'
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
ny, nx = astrip.shape
yy, xx = np.indices(astrip.shape)
if 'WV-DIM' in ahdr.keys():
xdim, ydim = np.array(ahdr.get('WV-DIM').split(','), dtype=np.int)
wl_coeff = np.zeros([xdim*ydim])
for i in range(xdim):
tmp = ahdr.get('WV-X%03d' % (i,))
wl_coeff[(i*ydim):(i*ydim+ydim)] = np.array(tmp.split(','), dtype=np.double)
awave = ip.polyval2d(xx, yy, wl_coeff, deg=[xdim-1, ydim-1])
else:
print 'No wavelength data in FITS header'
return None, None
if wave_step == False: wave_step = ( (np.max(awave) - np.min(awave)) ) / (nx-1) #2013-01-14 cksim
#--new version--
tstrip, xwave = transform_ap(astrip, awave, wave_step=wave_step)
wv1, wv2 = np.min(xwave), np.max(xwave)
'''old version
wv1, wv2 = (np.min(awave), np.max(awave))
xwave = np.arange(wv1, wv2, wave_step)
nwave = len(xwave)
#print nx, ny, nwave, np.min(awave), np.max(awave)
tstrip = np.zeros([ny,nwave])
for i in range(ny):
row = astrip[i,:]
wv = awave[i,:]
xrow = np.interp(xwave, wv, row)
tstrip[i,:] = xrow
'''
thdr = ahdr.copy()
thdr.update('TRN-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
# WCS header ========================================
thdr.update('WAT0_001', 'system=world')
thdr.update('WAT1_001', 'wtype=linear label=Wavelength units=microns units_display=microns')
thdr.update('WAT2_001', 'wtype=linear')
thdr.update('WCSDIM', 2)
thdr.update('DISPAXIS', 1)
thdr.update('DC-FLAG', 0)
# wavelength axis header =============================
thdr.update('CTYPE1', 'LINEAR ')
thdr.update('LTV1', 1)
thdr.update('LTM1_1', 1.0)
thdr.update('CRPIX1', 1.0)
thdr.update('CDELT1', wave_step)
thdr.update('CRVAL1', wv1)
thdr.update('CD1_1', wave_step)
# slit-position axis header ==========================
thdr.update('CTYPE2', 'LINEAR ')
thdr.update('LTV2', 1)
thdr.update('LTM2_2', 1.0)
thdr.update('CRPIX2', 1)
thdr.update('CRVAL2', 1)
thdr.update('CD2_2', 1)
ip.savefits(outputfile, tstrip, header=thdr)
np.savetxt('.'.join(outputfile.split('.')[:-1])+'.wave', xwave)
'''plt.subplot(211)
plt.imshow(astrip, aspect=2)
plt.xlim(0,nx)
plt.subplot(212)
plt.imshow(tstrip, aspect=2)
plt.xlim(0,nwave)
plt.show()
'''
##2013-11-21 cksim inserted below draw_strips_file()
#draw_strips_file('.'.join(outputfile.split('.')[:-1])+'.fits', '.'.join(outputfile.split('.')[:-1])+'.wave', linefile='ohlines.dat', \
# target_path=outputfile.split('SDC')[0], desc='SDC'+outputfile.split('SDC')[1].split('.fits')[0])
return tstrip, thdr
def extract_strips(filename, band, apnum=[], pdeg=PDEGREE, \
PA=0, offset=[1023.5,1023.5], pscale=0.018, \
slit_len=[-1,1], slit_step=0.025, wave_step=0.00001, \
fitting_path=FITTING_PATH, \
target_path=ONESTEP_PATH):
'''
Extract the strips directly based on ZEMAX analysis fitting data
(using mapping parameters like position angle, pixel scale, ...
- input : for each band
1. FITTING DATA (fitting_path)
2. MAPPING DATA (PA,offset,pscale)
'''
fpath, fname = ip.split_path(filename)
if ip.exist_path(target_path) == False: target_path = fpath
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
img, hdr = ip.readfits(filename)
# read order information from file
onum, odesc, owv1, owv2 = ip.read_orderinfo(band)
if len(apnum) == 0:
apnum = range(len(onum))
# read image size
ny, nx = img.shape
#==============================================================================
# Extract strips based on ZEMAX fitting data
#==============================================================================
descs = []
strips = []
wavelengths = []
for k in apnum:
desc, wv1, wv2 = (odesc[k], owv1[k], owv2[k])
print "order # = %s, wrr = [%f, %f]" % (desc, wv1, wv2)
# read the echellogram fitting data
mx = np.loadtxt(FITTING_PATH+'mx_%s_%02d_%02d.dat' % (desc, pdeg[0], pdeg[1]))
my = np.loadtxt(FITTING_PATH+'my_%s_%02d_%02d.dat' % (desc, pdeg[0], pdeg[1]))
# make X dimension array (for wavelength)
twave = np.arange(wv1, wv2, wave_step, dtype=np.float64)
n_wave = len(twave)
# make Y dimension array (for slit)
tslit = np.arange(slit_len[0],slit_len[1]+slit_step, slit_step, dtype=np.float64)
n_slit = len(tslit)
# make 2D array for wavelength, slit-positions
swave = np.zeros([n_slit,n_wave], dtype=np.float64)
sslit = np.zeros([n_slit,n_wave], dtype=np.float64)
for i in range(n_wave):
sslit[:,i] = tslit
for i in range(n_slit):
swave[i,:] = twave
# find X, Y positions for each wavelength and slit-position
sx = ip.polyval2d(swave, sslit, mx, deg=pdeg)
sy = ip.polyval2d(swave, sslit, my, deg=pdeg)
# transform into pixel units
px, py = ip.xy2pix(sx, sy, PA=PA, offset=offset, pscale=pscale)
# check image range 0 < x < 2048
xmin, xmax = (0,n_wave)
for i in range(n_slit):
vv = np.where((px[i,:] >= 0) & (px[i,:] < nx))[0]
if np.min(vv) > xmin: xmin = np.min(vv)
if np.max(vv) < xmax: xmax = np.max(vv)
# extract the aperture by using interpolation from image
tstrip = ip.imextract(img, px[:,xmin:xmax], py[:,xmin:xmax])
twave = twave[xmin:xmax]
print ' + Wavelength valid range = [%f, %f]' % (twave[0], twave[-1])
descs.append(desc)
wavelengths.append(twave)
strips.append(tstrip)
#==============================================================================
# Save the strips in FITS format
#==============================================================================
for d, w, s in zip(descs, wavelengths, strips):
shdr = header.copy()
shdr.update('GEN-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
shdr.update('LNAME', lname)
shdr.update('ECH-ORD', d)
# WCS header ========================================
shdr.update('WAT0_001', 'system=world')
shdr.update('WAT1_001', 'wtype=linear label=Wavelength units=microns units_display=microns')
shdr.update('WAT2_001', 'wtype=linear')
shdr.update('WCSDIM', 2)
shdr.update('DISPAXIS', 1)
shdr.update('DC-FLAG', 0)
# wavelength axis header =============================
shdr.update('CTYPE1', 'LINEAR ')
shdr.update('LTV1', 1)
shdr.update('LTM1_1', 1.0)
shdr.update('CRPIX1', 1.0)
#header.update('CDELT1', w[1]-w[0])
shdr.update('CRVAL1', w[0])
shdr.update('CD1_1', w[1]-w[0])
# slit-position axis header ==========================
shdr.update('CTYPE2', 'LINEAR ')
shdr.update('LTV2', 1)
shdr.update('LTM2_2', 1.0)
shdr.update('CRPIX2', 1.0)
#header.update('CDELT1', w[1]-w[0])
shdr.update('CRVAL2', -1.0)
shdr.update('CD2_2', slit_step)
# save FITS with header
ip.savefits(EXTRACT_PATH+'IGRINS_%s_%s.fits' % (lname,d), s, header=shdr)
np.savetxt(EXTRACT_PATH+'IGRINS_%s_%s.wave' % (lname,d), w)
def _ap_extract2(band, filename, ap_coeffs1 ,ap_coeffs2, \
width=60, ap_num=[], target_path=MANUAL_PATH,
savepng=False):
'''
Extract the strips based on the FLAT image
- inputs
0. band
1. image file name FITS
2. coefficients (1) (a list of 1d-array) - start position of y-axis
3. *coefficients (2) (a list of 1d-array) - end position of y-axis
4. width of output strip 2D-array
5. ap_num (a list of aperture number, which will be extracted)
- outputs
0. FITS files
1. strips (a list of 2d-array)
2. headers (a list of header object)
'''
img, hdr = ip.readfits(filename)
fpath, fname = ip.split_path(filename)
if ip.exist_path(target_path) == False: target_path = fpath
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
ny, nx = img.shape
if len(ap_num) == 0:
ap_num = range(len(ap_coeffs1))
n_ap = len(ap_num)
strips = []
hdrs = []
f1 = plt.figure(1, figsize=(15,15))
f2 = plt.figure(2, figsize=(15,2))
a1 = f1.add_subplot(111)
a2 = f2.add_subplot(111)
a1.imshow(img, cmap='gray')
for i in ap_num:
# generate the output 2d-strips
tstrip = np.zeros([width, nx], dtype=np.float64)
'''
# (case 1) if ap_coeffs2 is exist, for arbitrary aperture width
if ap_coeffs2 != None:
c1 = ap_coeff[i]
c2 = ap_coeffs2[i]
# define the x-positions and y-positions
xpos = np.arange(nx)
ypos1 = np.polynomial.polynomial.polyval(xpos, c1)
ypos2 = np.polynomial.polynomial.polyval(xpos, c2)
# use interpolation
for x0, y1, y2 in zip(xpos, ypos1, ypos2):
ypos = np.linspace(y1, y2, width)
yinp = np.interp(ypos, np.arange(ny), img[:,x0])
tstrip[:,x0] = yinp
'''
# (case 2) using just aperture width for extracting from the ap_coeffs1
c1 = ap_coeffs1[i]
c2 = ap_coeffs2[i]
# define the x-positions and y-positions
xpos = np.arange(nx)
ypos1 = np.polynomial.polynomial.polyval(xpos, c1)
ypos2 = ypos1 + width
# use the cropping method
yy, xx = np.indices(tstrip.shape)
# add the y-direction aperture curve to the y-coordinates
ayy = yy + ypos1
iyy = np.array(np.floor(ayy), dtype=np.int)
fyy = ayy - iyy
# find the valid points
vv = np.where((iyy >= 0) & (iyy <= ny-2))
tstrip[yy[vv],xx[vv]] = \
img[iyy[vv],xx[vv]] * (1.0 - fyy[yy[vv],xx[vv]]) + \
img[(iyy[vv]+1),xx[vv]] * fyy[yy[vv],xx[vv]]
# draw the extracting position
a1.plot(xpos, ypos1, 'g-', linewidth=3, alpha=0.5)
a1.plot(xpos, ypos2, 'r-', linewidth=3, alpha=0.5)
if savepng == True :
a2.imshow(tstrip, cmap='gray')
f2.savefig(target_path+name+'.%03d.png' % (i,))
a2.cla()
print 'ap[%d] : extracted ' % (i,)
strips.append(tstrip)
if hdr == None:
thdr = None
else:
thdr = hdr.copy()
thdr.update('AP-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
thdr.update('AP-MODE', 'manual')
thdr.update('AP-NUM', i)
c1list = []
for c in c1:
c1list.append('%.8E' % (c,))
thdr.update('AP-COEF1', ','.join(c1list))
c2list = []
for c in c2:
c2list.append('%.8E' % (c,))
thdr.update('AP-COEF2', ','.join(c2list))
thdr.update('AP-WIDTH', '%d' % (width,))
hdrs.append(thdr)
ip.savefits(target_path+name+'.%03d.fits' % (i,), tstrip, header=thdr)
a1.set_xlim(0,nx)
a1.set_ylim(0,ny)
f1.savefig(target_path+name+'.all.png')
plt.close('all')
return strips, hdrs
def _ap_extract2(band, filename, ap_coeffs1 ,ap_coeffs2, \
width=60, ap_num=[], target_path=MANUAL_PATH,
savepng=False):
'''
Extract the strips based on the FLAT image
- inputs
0. band
1. image file name FITS
2. coefficients (1) (a list of 1d-array) - start position of y-axis
3. *coefficients (2) (a list of 1d-array) - end position of y-axis
4. width of output strip 2D-array
5. ap_num (a list of aperture number, which will be extracted)
- outputs
0. FITS files
1. strips (a list of 2d-array)
2. headers (a list of header object)
'''
img, hdr = ip.readfits(filename)
fpath, fname = ip.split_path(filename)
if ip.exist_path(target_path) == False: target_path = fpath
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
ny, nx = img.shape
if len(ap_num) == 0:
ap_num = range(len(ap_coeffs1))
n_ap = len(ap_num)
strips = []
hdrs = []
f1 = plt.figure(1, figsize=(15, 15))
f2 = plt.figure(2, figsize=(15, 2))
a1 = f1.add_subplot(111)
a2 = f2.add_subplot(111)
a1.imshow(img, cmap='gray')
for i in ap_num:
# generate the output 2d-strips
tstrip = np.zeros([width, nx], dtype=np.float64)
'''
# (case 1) if ap_coeffs2 is exist, for arbitrary aperture width
if ap_coeffs2 != None:
c1 = ap_coeff[i]
c2 = ap_coeffs2[i]
# define the x-positions and y-positions
xpos = np.arange(nx)
ypos1 = np.polynomial.polynomial.polyval(xpos, c1)
ypos2 = np.polynomial.polynomial.polyval(xpos, c2)
# use interpolation
for x0, y1, y2 in zip(xpos, ypos1, ypos2):
ypos = np.linspace(y1, y2, width)
yinp = np.interp(ypos, np.arange(ny), img[:,x0])
tstrip[:,x0] = yinp
'''
# (case 2) using just aperture width for extracting from the ap_coeffs1
c1 = ap_coeffs1[i]
c2 = ap_coeffs2[i]
# define the x-positions and y-positions
xpos = np.arange(nx)
ypos1 = np.polynomial.polynomial.polyval(xpos, c1)
ypos2 = ypos1 + width
# use the cropping method
yy, xx = np.indices(tstrip.shape)
# add the y-direction aperture curve to the y-coordinates
ayy = yy + ypos1
iyy = np.array(np.floor(ayy), dtype=np.int)
fyy = ayy - iyy
# find the valid points
vv = np.where((iyy >= 0) & (iyy <= ny - 2))
tstrip[yy[vv],xx[vv]] = \
img[iyy[vv],xx[vv]] * (1.0 - fyy[yy[vv],xx[vv]]) + \
img[(iyy[vv]+1),xx[vv]] * fyy[yy[vv],xx[vv]]
# draw the extracting position
a1.plot(xpos, ypos1, 'g-', linewidth=3, alpha=0.5)
a1.plot(xpos, ypos2, 'r-', linewidth=3, alpha=0.5)
if savepng == True:
a2.imshow(tstrip, cmap='gray')
f2.savefig(target_path + name + '.%03d.png' % (i, ))
a2.cla()
print 'ap[%d] : extracted ' % (i, )
strips.append(tstrip)
if hdr == None:
thdr = None
else:
thdr = hdr.copy()
thdr.update('AP-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
thdr.update('AP-MODE', 'manual')
thdr.update('AP-NUM', i)
c1list = []
for c in c1:
c1list.append('%.8E' % (c, ))
thdr.update('AP-COEF1', ','.join(c1list))
c2list = []
for c in c2:
c2list.append('%.8E' % (c, ))
thdr.update('AP-COEF2', ','.join(c2list))
thdr.update('AP-WIDTH', '%d' % (width, ))
hdrs.append(thdr)
ip.savefits(target_path + name + '.%03d.fits' % (i, ),
tstrip,
header=thdr)
a1.set_xlim(0, nx)
a1.set_ylim(0, ny)
f1.savefig(target_path + name + '.all.png')
plt.close('all')
return strips, hdrs
def calibration(aperture, stripfile, lxx, lwave, datpath='', datapath='', outputname=''):
imgv1, ahdr = ip.readfits(stripfile)
col, line = imgv1.shape
img = imgv1[5:(line-5), :]
ny, nx = img.shape
# Identify emission lines
# Make range of the emission line (When we have identified the imission line
# peak, then range of the line will be [peak-npix, peak+npix]
npix = 13 # This number will be updated or edit by appropriate formular
# This will be identify the peak of the emission line
# from the range has applied for the line
i_peakline, lwidth = peak_reidentify(img, npix, lxx)
npix = np.zeros(len(lxx))
for i in range(len(lxx)):
npix[i] = round(np.average(lwidth[i]))*2 + 1
print 'npix', npix
ilxx = np.zeros(len(lxx))
for i in range(len(lxx)):
ilxx[i] = i_peakline[i][2]
#print 'identify lxx', ilxx
# Using Gaussian fitting to find the center of the line.
peakline = reidentify(img, npix, ilxx)
# Check peak line stable of Gaussian profile
for i in range(len(peakline)):
for j in range(len(peakline[i])):
if (peakline[i][j] - np.median(peakline[i])) > 3:
peakline[i][j] = np.median(peakline[i])
if (peakline[i][j] - np.median(peakline[i])) < -3:
peakline[i][j] = np.median(peakline[i])
# Using polynomial to fit the emission lines
fit_x, vmiddle = linefitting(img, peakline, lxx)
#print 'fit x', fit_x
#print 'value middle', vmiddle
# Test distortion of emission lines
residual(peakline, vmiddle)
# Shifting rows with delta x from identify lines and fitting.
xx = np.zeros(len(lwave))
for i in range(0, len(lwave)):
xx[i] = peakline[i][2] # Sum of 10 lines in the middle stripfile
#print 'center of identify lines', xx
line_xx = list(xx)
# Correct distortion correction by delta_x values
k = [[] for _ in range(ny)]
#print 'k', k, len(k)
for i in range(len(k)):
for c in range(len(fit_x)):
k[i].append(fit_x[c][ny-1]-fit_x[c][i])
#print 'k index', i, c
#print 'k -test', k
delta_x = np.zeros(ny)
for i in range(ny):
delta_x[i] = k[i][0] #np.average(k[i])
#print 'delta x', delta_x
'''
f = open('distor_table_t.dat','w')
A = np.arange(len(k))
for i in A:
temp = "%.4d %.6f \n" %(i, delta_x[i])
f.write(temp)
f.close()
'''
xx = line_xx # Using line_xx as list file
# Wavelength calibration
npixel = range(nx) # Array with len of 2047 pixels
wavel = np.array(lwave) # Make array of wavelength
# Polynomial function
coeff = np.polynomial.polynomial.polyfit(wavel, xx, 2)
#print 'cofficient fitting before transform', coeff
# Finding lambda min and max from coefficient fitting
# X = f(lamda) = a_0 + a_1 * X + a_2 * X^2
# Second order
p_min = [coeff[2], coeff[1], coeff[0]]
value_min = np.roots(p_min)
#print 'lamda min values', np.roots(p_min)
p_max = [coeff[2], coeff[1], coeff[0] - 2047]
value_max = np.roots(p_max)
lamda_min = find_nearest(value_min, min(wavel))
lamda_max = find_nearest(value_max, max(wavel))
# Position array
position = np.where(value_max == lamda_max)[0]
#print 'lamda max values', np.roots(p_max)
# Check in case of complex values
if (np.iscomplexobj(lamda_min)):
lamda_min = np.real(lamda_min)
elif (np.iscomplexobj(lamda_max)):
lamda_max = np.real(lamda_max)
#print 'position', position
#print 'lambda min and max', lamda_min, lamda_max
ptlamda = np.zeros(nx)
for i in range(nx):
pt = [coeff[2], coeff[1], coeff[0] - i]
if (np.iscomplexobj(np.roots(pt))):
temp = np.real(np.roots(pt))
ptlamda[i] = temp[position]
else:
ptlamda[i] = np.roots(pt)[position]
#print 'ptlamda', ptlamda
'''
# Or using 4th order poly function
# Polynomial function
coeff = np.polynomial.polynomial.polyfit(wavel, xx, 4)
print 'cofficient fitting before transform', coeff
p_min = [coeff[4], coeff[3], coeff[2], coeff[1], coeff[0]]
value_min = np.roots(p_min)
p_max = [coeff[4], coeff[3], coeff[2], coeff[1], coeff[0] - 2047]
value_max = np.roots(p_max)
lamda_min = find_nearest(value_min, min(wavel))
lamda_max = find_nearest(value_max, max(wavel))
print 'position', np.where(value_min == lamda_min)[0], np.where(value_max == lamda_max)[0]
print 'lambda min and max', lamda_min, lamda_max
#Solve 4th oder poly function to find the output solution
#for lambda min and max values
ptlamda = np.zeros(nx)
for i in range(nx):
pt = [coeff[4], coeff[3], coeff[2], coeff[1], coeff[0] - i]
ptlamda[i] = np.roots(pt)[np.where(value_min == lamda_min)[0][0]]
'''
'''
Plot the fitting before transform
'''
#fig = plt.figure(3, figsize=(10,8))
#a = fig.add_subplot(111)
#a.plot(ptlamda, range(nx), 'r', wavel, xx, 'ko')
#plt.xlabel('Wavelength [microns]')
#plt.ylabel('X-position [pixels]')
#plt.legend(['Polyfit', 'X-before transform'])
#plt.show()
# Finding linear equation from poly fitting function
# g(lamda) = b_0 + b1 * lamda
# Solve equation with two varible
# b_0 + b_1 x lamda_min = 0
# b_0 + b_1 x lamda_max = 2047
# Define linear function
def func(b):
f = [b[0] + b[1] * lamda_min, b[0] + b[1]*lamda_max - 2047]
return f
b = optimize.fsolve(func, [0, 0])
#print 'linear coeff', b
# Linear equation has value of b[0] and b[1]
# X' = b[0] + b[1] * lambda
# Delta lambda wavelength
delta_lamda = (lamda_max - lamda_min) / 2047
#print 'delta_lamda', delta_lamda
# Linear wavelength from the lambda min and max values
linear_wave = np.zeros(nx)
for i in range(nx):
linear_wave[i] = lamda_min + i*delta_lamda
#lwave = np.linspace(lamda_min, lamda_max, nx)
#print 'linear wavelength', linear_wave
# Create text file with i=0-2047, lamda, x, x'
x_fit = np.zeros(len(linear_wave)) # x position values calculated from the 4th order function.
lx_fit = np.zeros(len(linear_wave)) # x position values calculated from linear function.
# Second order
for i in range(len(linear_wave)):
x_fit[i] = coeff[0] + coeff[1] * linear_wave[i] + coeff[2] * pow(linear_wave[i],2)
lx_fit[i] = b[0] + b[1] * linear_wave[i]
'''
# 4nd order
for i in range(len(linear_wave)):
x_fit[i] = coeff[0] + coeff[1] * linear_wave[i] + coeff[2] * pow(linear_wave[i],2) + \
coeff[3] * pow(linear_wave[i],3) + coeff[4] * pow(linear_wave[i],4)
lx_fit[i] = b[0] + b[1] * linear_wave[i]
'''
# Transform x to x', delta is the values have to be shifted to convert 4th order
# poly function to linear function
delta = x_fit - lx_fit
#print 'delta x', delta
# Combine distortion correction and wavelength calbration
# Delta_s are the values have to be applied to the transform
# function.
delta_s = np.zeros([ny,nx])
for j in range(ny):
if delta_x[j] < 0:
delta_s[j] = delta + delta_x[j]
if delta_x[j] > 0:
delta_s[j] = delta - delta_x[j]
f = open(datpath + 'wavemap_H_02_ohline.' + str(aperture) + '.dat','w')
A = np.arange(len(linear_wave))
for i in A:
temp = "%.4d %.6f %.6f %.6f %.6f \n" %(i, linear_wave[i], x_fit[i], lx_fit[i], delta[i])
f.write(temp)
f.close()
# x-position after transform
#print 'xx', xx
lxx_t = np.zeros(len(lxx))
for i in range(len(lxx)):
lxx_t[i] = xx[i] - delta[lxx[i]]
#print 'delta', delta[lxx[i]]
#print 'lxx transform', lxx_t
tstrip = transform_p(img, delta_s) # Tranfsorm function
#z1, z2 = ip.zscale(tstrip)
#plt.imshow(tstrip, cmap='hot',vmin=z1, vmax=z2, aspect='auto')
#plt.show()
thdr = ahdr.copy()
thdr.update('TRN-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
# WCS header ========================================
thdr.update('WAT0_001', 'system=world')
thdr.update('WAT1_001', 'wtype=linear label=Wavelength units=microns units_display=microns')
# wavelength axis header =============================
thdr.update('CTYPE1', 'LINEAR ')
thdr.update('LTV1', 1)
thdr.update('LTM1_1', 1.0)
thdr.update('CRPIX1', 1.0)
thdr.update('CDELT1', delta_lamda)
thdr.update('CRVAL1', min(linear_wave))
thdr.update('LCOEFF1', b[0])
thdr.update('LCOEFF2', b[1])
ip.savefits(datapath + outputname + str(aperture) + '.fits', tstrip, header=thdr)
np.savetxt(datpath + outputname + str(aperture) + '.wave', linear_wave)
ip.savefits(datpath + 'wavemap_H_02_ohlines.' + str(aperture) + '.fits', delta_s, thdr)
return tstrip, delta_s, coeff, b, linear_wave, lxx_t
def transform_ap_file(stripfile,
wave_step=False,
outputfile=None): #2014-01-13 cksim
'''
Apply linear interpolation to the strip with a regular wavelength
- INPUTS :
1. stripfile (FITS)
2. wave_step (to be extracted with this wavelength step for a pixel)
3. outputfile (FITS filename)
- OUTPUTS:
1. tranformed 2D strip data with header
'''
astrip, ahdr = ip.readfits(stripfile)
fpath, fname = ip.split_path(stripfile)
if outputfile == None: outputfile = fpath + fname + '.tr'
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
ny, nx = astrip.shape
yy, xx = np.indices(astrip.shape)
if 'WV-DIM' in ahdr.keys():
xdim, ydim = np.array(ahdr.get('WV-DIM').split(','), dtype=np.int)
wl_coeff = np.zeros([xdim * ydim])
for i in range(xdim):
tmp = ahdr.get('WV-X%03d' % (i, ))
wl_coeff[(i * ydim):(i * ydim + ydim)] = np.array(tmp.split(','),
dtype=np.double)
awave = ip.polyval2d(xx, yy, wl_coeff, deg=[xdim - 1, ydim - 1])
else:
print 'No wavelength data in FITS header'
return None, None
if wave_step == False:
wave_step = (
(np.max(awave) - np.min(awave))) / (nx - 1) #2013-01-14 cksim
#--new version--
tstrip, xwave = transform_ap(astrip, awave, wave_step=wave_step)
wv1, wv2 = np.min(xwave), np.max(xwave)
'''old version
wv1, wv2 = (np.min(awave), np.max(awave))
xwave = np.arange(wv1, wv2, wave_step)
nwave = len(xwave)
#print nx, ny, nwave, np.min(awave), np.max(awave)
tstrip = np.zeros([ny,nwave])
for i in range(ny):
row = astrip[i,:]
wv = awave[i,:]
xrow = np.interp(xwave, wv, row)
tstrip[i,:] = xrow
'''
thdr = ahdr.copy()
thdr.update('TRN-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
# WCS header ========================================
thdr.update('WAT0_001', 'system=world')
thdr.update(
'WAT1_001',
'wtype=linear label=Wavelength units=microns units_display=microns')
thdr.update('WAT2_001', 'wtype=linear')
thdr.update('WCSDIM', 2)
thdr.update('DISPAXIS', 1)
thdr.update('DC-FLAG', 0)
# wavelength axis header =============================
thdr.update('CTYPE1', 'LINEAR ')
thdr.update('LTV1', 1)
thdr.update('LTM1_1', 1.0)
thdr.update('CRPIX1', 1.0)
thdr.update('CDELT1', wave_step)
thdr.update('CRVAL1', wv1)
thdr.update('CD1_1', wave_step)
# slit-position axis header ==========================
thdr.update('CTYPE2', 'LINEAR ')
thdr.update('LTV2', 1)
thdr.update('LTM2_2', 1.0)
thdr.update('CRPIX2', 1)
thdr.update('CRVAL2', 1)
thdr.update('CD2_2', 1)
ip.savefits(outputfile, tstrip, header=thdr)
np.savetxt('.'.join(outputfile.split('.')[:-1]) + '.wave', xwave)
'''plt.subplot(211)
plt.imshow(astrip, aspect=2)
plt.xlim(0,nx)
plt.subplot(212)
plt.imshow(tstrip, aspect=2)
plt.xlim(0,nwave)
plt.show()
'''
##2013-11-21 cksim inserted below draw_strips_file()
#draw_strips_file('.'.join(outputfile.split('.')[:-1])+'.fits', '.'.join(outputfile.split('.')[:-1])+'.wave', linefile='ohlines.dat', \
# target_path=outputfile.split('SDC')[0], desc='SDC'+outputfile.split('SDC')[1].split('.fits')[0])
return tstrip, thdr
def extract_strips(filename, band, apnum=[], pdeg=PDEGREE, \
PA=0, offset=[1023.5,1023.5], pscale=0.018, \
slit_len=[-1,1], slit_step=0.025, wave_step=0.00001, \
fitting_path=FITTING_PATH, \
target_path=ONESTEP_PATH):
'''
Extract the strips directly based on ZEMAX analysis fitting data
(using mapping parameters like position angle, pixel scale, ...
- input : for each band
1. FITTING DATA (fitting_path)
2. MAPPING DATA (PA,offset,pscale)
'''
fpath, fname = ip.split_path(filename)
if ip.exist_path(target_path) == False: target_path = fpath
# extract the file name only without extension
name = '.'.join(fname.split('.')[:-1])
img, hdr = ip.readfits(filename)
# read order information from file
onum, odesc, owv1, owv2 = ip.read_orderinfo(band)
if len(apnum) == 0:
apnum = range(len(onum))
# read image size
ny, nx = img.shape
#==============================================================================
# Extract strips based on ZEMAX fitting data
#==============================================================================
descs = []
strips = []
wavelengths = []
for k in apnum:
desc, wv1, wv2 = (odesc[k], owv1[k], owv2[k])
print "order # = %s, wrr = [%f, %f]" % (desc, wv1, wv2)
# read the echellogram fitting data
mx = np.loadtxt(FITTING_PATH + 'mx_%s_%02d_%02d.dat' %
(desc, pdeg[0], pdeg[1]))
my = np.loadtxt(FITTING_PATH + 'my_%s_%02d_%02d.dat' %
(desc, pdeg[0], pdeg[1]))
# make X dimension array (for wavelength)
twave = np.arange(wv1, wv2, wave_step, dtype=np.float64)
n_wave = len(twave)
# make Y dimension array (for slit)
tslit = np.arange(slit_len[0],
slit_len[1] + slit_step,
slit_step,
dtype=np.float64)
n_slit = len(tslit)
# make 2D array for wavelength, slit-positions
swave = np.zeros([n_slit, n_wave], dtype=np.float64)
sslit = np.zeros([n_slit, n_wave], dtype=np.float64)
for i in range(n_wave):
sslit[:, i] = tslit
for i in range(n_slit):
swave[i, :] = twave
# find X, Y positions for each wavelength and slit-position
sx = ip.polyval2d(swave, sslit, mx, deg=pdeg)
sy = ip.polyval2d(swave, sslit, my, deg=pdeg)
# transform into pixel units
px, py = ip.xy2pix(sx, sy, PA=PA, offset=offset, pscale=pscale)
# check image range 0 < x < 2048
xmin, xmax = (0, n_wave)
for i in range(n_slit):
vv = np.where((px[i, :] >= 0) & (px[i, :] < nx))[0]
if np.min(vv) > xmin: xmin = np.min(vv)
if np.max(vv) < xmax: xmax = np.max(vv)
# extract the aperture by using interpolation from image
tstrip = ip.imextract(img, px[:, xmin:xmax], py[:, xmin:xmax])
twave = twave[xmin:xmax]
print ' + Wavelength valid range = [%f, %f]' % (twave[0], twave[-1])
descs.append(desc)
wavelengths.append(twave)
strips.append(tstrip)
#==============================================================================
# Save the strips in FITS format
#==============================================================================
for d, w, s in zip(descs, wavelengths, strips):
shdr = header.copy()
shdr.update('GEN-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
shdr.update('LNAME', lname)
shdr.update('ECH-ORD', d)
# WCS header ========================================
shdr.update('WAT0_001', 'system=world')
shdr.update(
'WAT1_001',
'wtype=linear label=Wavelength units=microns units_display=microns'
)
shdr.update('WAT2_001', 'wtype=linear')
shdr.update('WCSDIM', 2)
shdr.update('DISPAXIS', 1)
shdr.update('DC-FLAG', 0)
# wavelength axis header =============================
shdr.update('CTYPE1', 'LINEAR ')
shdr.update('LTV1', 1)
shdr.update('LTM1_1', 1.0)
shdr.update('CRPIX1', 1.0)
#header.update('CDELT1', w[1]-w[0])
shdr.update('CRVAL1', w[0])
shdr.update('CD1_1', w[1] - w[0])
# slit-position axis header ==========================
shdr.update('CTYPE2', 'LINEAR ')
shdr.update('LTV2', 1)
shdr.update('LTM2_2', 1.0)
shdr.update('CRPIX2', 1.0)
#header.update('CDELT1', w[1]-w[0])
shdr.update('CRVAL2', -1.0)
shdr.update('CD2_2', slit_step)
# save FITS with header
ip.savefits(EXTRACT_PATH + 'IGRINS_%s_%s.fits' % (lname, d),
s,
header=shdr)
np.savetxt(EXTRACT_PATH + 'IGRINS_%s_%s.wave' % (lname, d), w)
def calibration(aperture,
stripfile,
lxx,
lwave,
datpath='',
datapath='',
outputname=''):
imgv1, ahdr = ip.readfits(stripfile)
col, line = imgv1.shape
img = imgv1[5:(line - 5), :]
ny, nx = img.shape
# Identify emission lines
# Make range of the emission line (When we have identified the imission line
# peak, then range of the line will be [peak-npix, peak+npix]
npix = 13 # This number will be updated or edit by appropriate formular
# This will be identify the peak of the emission line
# from the range has applied for the line
i_peakline, lwidth = peak_reidentify(img, npix, lxx)
npix = np.zeros(len(lxx))
for i in range(len(lxx)):
npix[i] = round(np.average(lwidth[i])) * 2 + 1
print 'npix', npix
ilxx = np.zeros(len(lxx))
for i in range(len(lxx)):
ilxx[i] = i_peakline[i][2]
#print 'identify lxx', ilxx
# Using Gaussian fitting to find the center of the line.
peakline = reidentify(img, npix, ilxx)
# Check peak line stable of Gaussian profile
for i in range(len(peakline)):
for j in range(len(peakline[i])):
if (peakline[i][j] - np.median(peakline[i])) > 3:
peakline[i][j] = np.median(peakline[i])
if (peakline[i][j] - np.median(peakline[i])) < -3:
peakline[i][j] = np.median(peakline[i])
# Using polynomial to fit the emission lines
fit_x, vmiddle = linefitting(img, peakline, lxx)
#print 'fit x', fit_x
#print 'value middle', vmiddle
# Test distortion of emission lines
residual(peakline, vmiddle)
# Shifting rows with delta x from identify lines and fitting.
xx = np.zeros(len(lwave))
for i in range(0, len(lwave)):
xx[i] = peakline[i][2] # Sum of 10 lines in the middle stripfile
#print 'center of identify lines', xx
line_xx = list(xx)
# Correct distortion correction by delta_x values
k = [[] for _ in range(ny)]
#print 'k', k, len(k)
for i in range(len(k)):
for c in range(len(fit_x)):
k[i].append(fit_x[c][ny - 1] - fit_x[c][i])
#print 'k index', i, c
#print 'k -test', k
delta_x = np.zeros(ny)
for i in range(ny):
delta_x[i] = k[i][0] #np.average(k[i])
#print 'delta x', delta_x
'''
f = open('distor_table_t.dat','w')
A = np.arange(len(k))
for i in A:
temp = "%.4d %.6f \n" %(i, delta_x[i])
f.write(temp)
f.close()
'''
xx = line_xx # Using line_xx as list file
# Wavelength calibration
npixel = range(nx) # Array with len of 2047 pixels
wavel = np.array(lwave) # Make array of wavelength
# Polynomial function
coeff = np.polynomial.polynomial.polyfit(wavel, xx, 2)
#print 'cofficient fitting before transform', coeff
# Finding lambda min and max from coefficient fitting
# X = f(lamda) = a_0 + a_1 * X + a_2 * X^2
# Second order
p_min = [coeff[2], coeff[1], coeff[0]]
value_min = np.roots(p_min)
#print 'lamda min values', np.roots(p_min)
p_max = [coeff[2], coeff[1], coeff[0] - 2047]
value_max = np.roots(p_max)
lamda_min = find_nearest(value_min, min(wavel))
lamda_max = find_nearest(value_max, max(wavel))
# Position array
position = np.where(value_max == lamda_max)[0]
#print 'lamda max values', np.roots(p_max)
# Check in case of complex values
if (np.iscomplexobj(lamda_min)):
lamda_min = np.real(lamda_min)
elif (np.iscomplexobj(lamda_max)):
lamda_max = np.real(lamda_max)
#print 'position', position
#print 'lambda min and max', lamda_min, lamda_max
ptlamda = np.zeros(nx)
for i in range(nx):
pt = [coeff[2], coeff[1], coeff[0] - i]
if (np.iscomplexobj(np.roots(pt))):
temp = np.real(np.roots(pt))
ptlamda[i] = temp[position]
else:
ptlamda[i] = np.roots(pt)[position]
#print 'ptlamda', ptlamda
'''
# Or using 4th order poly function
# Polynomial function
coeff = np.polynomial.polynomial.polyfit(wavel, xx, 4)
print 'cofficient fitting before transform', coeff
p_min = [coeff[4], coeff[3], coeff[2], coeff[1], coeff[0]]
value_min = np.roots(p_min)
p_max = [coeff[4], coeff[3], coeff[2], coeff[1], coeff[0] - 2047]
value_max = np.roots(p_max)
lamda_min = find_nearest(value_min, min(wavel))
lamda_max = find_nearest(value_max, max(wavel))
print 'position', np.where(value_min == lamda_min)[0], np.where(value_max == lamda_max)[0]
print 'lambda min and max', lamda_min, lamda_max
#Solve 4th oder poly function to find the output solution
#for lambda min and max values
ptlamda = np.zeros(nx)
for i in range(nx):
pt = [coeff[4], coeff[3], coeff[2], coeff[1], coeff[0] - i]
ptlamda[i] = np.roots(pt)[np.where(value_min == lamda_min)[0][0]]
'''
'''
Plot the fitting before transform
'''
#fig = plt.figure(3, figsize=(10,8))
#a = fig.add_subplot(111)
#a.plot(ptlamda, range(nx), 'r', wavel, xx, 'ko')
#plt.xlabel('Wavelength [microns]')
#plt.ylabel('X-position [pixels]')
#plt.legend(['Polyfit', 'X-before transform'])
#plt.show()
# Finding linear equation from poly fitting function
# g(lamda) = b_0 + b1 * lamda
# Solve equation with two varible
# b_0 + b_1 x lamda_min = 0
# b_0 + b_1 x lamda_max = 2047
# Define linear function
def func(b):
f = [b[0] + b[1] * lamda_min, b[0] + b[1] * lamda_max - 2047]
return f
b = optimize.fsolve(func, [0, 0])
#print 'linear coeff', b
# Linear equation has value of b[0] and b[1]
# X' = b[0] + b[1] * lambda
# Delta lambda wavelength
delta_lamda = (lamda_max - lamda_min) / 2047
#print 'delta_lamda', delta_lamda
# Linear wavelength from the lambda min and max values
linear_wave = np.zeros(nx)
for i in range(nx):
linear_wave[i] = lamda_min + i * delta_lamda
#lwave = np.linspace(lamda_min, lamda_max, nx)
#print 'linear wavelength', linear_wave
# Create text file with i=0-2047, lamda, x, x'
x_fit = np.zeros(
len(linear_wave
)) # x position values calculated from the 4th order function.
lx_fit = np.zeros(
len(linear_wave)) # x position values calculated from linear function.
# Second order
for i in range(len(linear_wave)):
x_fit[i] = coeff[0] + coeff[1] * linear_wave[i] + coeff[2] * pow(
linear_wave[i], 2)
lx_fit[i] = b[0] + b[1] * linear_wave[i]
'''
# 4nd order
for i in range(len(linear_wave)):
x_fit[i] = coeff[0] + coeff[1] * linear_wave[i] + coeff[2] * pow(linear_wave[i],2) + \
coeff[3] * pow(linear_wave[i],3) + coeff[4] * pow(linear_wave[i],4)
lx_fit[i] = b[0] + b[1] * linear_wave[i]
'''
# Transform x to x', delta is the values have to be shifted to convert 4th order
# poly function to linear function
delta = x_fit - lx_fit
#print 'delta x', delta
# Combine distortion correction and wavelength calbration
# Delta_s are the values have to be applied to the transform
# function.
delta_s = np.zeros([ny, nx])
for j in range(ny):
if delta_x[j] < 0:
delta_s[j] = delta + delta_x[j]
if delta_x[j] > 0:
delta_s[j] = delta - delta_x[j]
f = open(datpath + 'wavemap_H_02_ohline.' + str(aperture) + '.dat', 'w')
A = np.arange(len(linear_wave))
for i in A:
temp = "%.4d %.6f %.6f %.6f %.6f \n" % (i, linear_wave[i], x_fit[i],
lx_fit[i], delta[i])
f.write(temp)
f.close()
# x-position after transform
#print 'xx', xx
lxx_t = np.zeros(len(lxx))
for i in range(len(lxx)):
lxx_t[i] = xx[i] - delta[lxx[i]]
#print 'delta', delta[lxx[i]]
#print 'lxx transform', lxx_t
tstrip = transform_p(img, delta_s) # Tranfsorm function
#z1, z2 = ip.zscale(tstrip)
#plt.imshow(tstrip, cmap='hot',vmin=z1, vmax=z2, aspect='auto')
#plt.show()
thdr = ahdr.copy()
thdr.update('TRN-TIME', time.strftime('%Y-%m-%d %H:%M:%S'))
# WCS header ========================================
thdr.update('WAT0_001', 'system=world')
thdr.update(
'WAT1_001',
'wtype=linear label=Wavelength units=microns units_display=microns')
# wavelength axis header =============================
thdr.update('CTYPE1', 'LINEAR ')
thdr.update('LTV1', 1)
thdr.update('LTM1_1', 1.0)
thdr.update('CRPIX1', 1.0)
thdr.update('CDELT1', delta_lamda)
thdr.update('CRVAL1', min(linear_wave))
thdr.update('LCOEFF1', b[0])
thdr.update('LCOEFF2', b[1])
ip.savefits(datapath + outputname + str(aperture) + '.fits',
tstrip,
header=thdr)
np.savetxt(datpath + outputname + str(aperture) + '.wave', linear_wave)
ip.savefits(datpath + 'wavemap_H_02_ohlines.' + str(aperture) + '.fits',
delta_s, thdr)
return tstrip, delta_s, coeff, b, linear_wave, lxx_t