def atd9():
"""
Verify that transformation preserve flux.
The test creates a mosaic from an input AstroData object and displays it
in the DS9 display. Using IRAF imexam we measure objects that are on the
the middle block for GMOS or the lower left block for GSAOI. We also measure
the same objects in the input amplifers. We calculate the magnitude difference.
"""
from astrodata import AstroData
from gempy.adlibrary.mosaicAD import MosaicAD
# This is the default Mosaic function
from gempy.mosaic.gemMosaicFunction import gemini_mosaic_function
try:
from stsci.numdisplay import display
except ImportError:
from numdisplay import display
print '\n atd9 REQUIREMENT.......'
print ('***** From a given AstroData object, the system shall conserve flux'
' after "transforming"')
ad = AstroData(file)
# Now make a mosaicAD object using the input ad
# and the default mosaic function named
# gemini_mosaic_function.
mo = MosaicAD(ad, gemini_mosaic_function)
mosaic_data = mo.mosaic_image_data()
display(mosaic_data,frame=1)
# display input amplifier 3. Assumning that there is one
# amplifier per block
display(ad['SCI',3].data,frame=2)
def __init__(self,pfile=None):
if pfile != None:
self.xyr = np.zeros([20,2])
self.xyb = np.zeros([20,2])
self.npin = 0
self.imr = pf.getdata(pfile,1)
self.imb = pf.getdata(pfile,2)
#if not ndis.checkDisplay():
# print "\n ****ERROR: ds9 is not running."
# return
ndis.display(self.imr,frame=1)
ndis.display(self.imb,frame=2)
def _findxycenter(hdr, im,ext,log):
def xy_tran(out):
"""
Function to transform blue coordinates to red frame coordinates
"""
xref = out[1] - 990.5897
yref = out[0] - 37.82109
x = -0.9985259*xref - 0.0178984*yref
y = -0.0181331*xref + 0.9991414*yref
return y,x
#hdr = pf.getheader(file)
#im = pf.getdata(file)
xcen = hdr.get('xcen')
ycen = hdr.get('ycen')
if (xcen == None):
#ratio,xc,yc = peak2halo('',image=im)
#xcen = xc
#ycen = yc
#if (xcen < 0 or ycen < 0):
if True:
if ext == 2:
# register blue frame to red's frame coordinate system
im = nd.geometric_transform (im,xy_tran)
try:
ndis.display(im,zscale=False)
except IOError,err:
sys.stderr.write('\n ***** ERROR: %s Start DS9.\n' % str(err))
sys.exit(1)
print " Mark center with left button, then use 'q' to continue, 's' to skip."
cursor = ndis.readcursor(sample=0)
cursor = cursor.split()
if cursor[3] == 's':
hdr.update("XCEN",-1, "Start mask x-center")
hdr.update("YCEN",-1, "Start mask y-center")
updated = True
print '\nFrame skipped... ****Make sure not to use it in your science script. ***\n'
#return updated,xcen,ycen,im
return xcen,ycen,im
x1 = float(cursor[0])
y1 = float(cursor[1])
xcen,ycen = gcentroid(im, x1,y1,9)
xcen = xcen[0]
ycen = ycen[0]
hdr.update("XCEN",xcen, "Start mask x-center")
hdr.update("YCEN",ycen, "Start mask y-center")
log.info(' (%.2f,%.2f) :: ' % (xcen,ycen)+('red','blue')[ext-1])
def plot_footprint_cut(data,x,y,orientation,evf1,evf2,region,filename,instru):
"""
Plot the footprint cut inside a rectangle then
display the same cut on frame 2.
"""
try:
from stsci.numdisplay import display
except ImportError:
from numdisplay import display
pl.clf()
bval = data.max()
bval += bval/10.
#rx1,rx2,ry1,ry2 = region
ny,nx = data.shape
rxmin,rymin=(nx,ny)
rxmax,rymax=(0,0)
if (True):
if orientation == 0:
x = np.arange(nx)
rxmin,rxmax = (0,nx)
for evf,color in zip([evf1,evf2],['b','r']):
zy = evf(x)
pl.plot(x,zy,color)
imin = np.argmin(zy)
imax = np.argmax(zy)
rymin = min(rymin,zy[imin])
rymax = max(rymax,zy[imax])
pl.fill([rxmin,rxmax,rxmax,rxmin], [rymin,rymin,rymax,rymax],fill=False)
else: # Orientation is 90
y = np.arange(ny)
rymin,rymax = (0,ny)
for evf,color in zip([evf1,evf2],['b','r']):
zx = evf(y)
pl.plot(zx,y,color)
imin = np.argmin(zx)
imax = np.argmax(zx)
rxmin = min(rxmin,zx[imin])
rxmax = max(rxmax,zx[imax])
pl.fill([rxmin,rxmax,rxmax,rxmin], [rymin,rymin,rymax,rymax],fill=False)
fname = os.path.basename(filename)
pl.title(fname+' ('+instru+')')
#pl.xlabel(str(region))
pl.draw()
display(data,frame=2,z1=0,z2=bval,quiet=True)
time.sleep(1)
def atd9():
"""
Verify that transformation preserve flux.
The test creates a mosaic from an input AstroData object and displays it
in the DS9 display. Using IRAF imexam we measure objects that are on the
the middle block for GMOS or the lower left block for GSAOI. We also measure
the same objects in the input amplifers. We calculate the magnitude difference.
"""
print('\n atd9 REQUIREMENT.......')
print('Conserve flux after "transforming"')
ad = astrodata.open(file)
# Now make a mosaicAD object using the input ad
# and the default mosaic function named
# gemini_mosaic_function.
mo = MosaicAD(ad, gemini_mosaic_function)
mosaic_data = mo.mosaic_image_data()
display(mosaic_data,frame=1)
display(ad['SCI',3].data,frame=2)
return
def _plot_footprints(image,footprints):
"""
NOTE: This for development. Not for
public release
Plot the edges in the list array self.trace
Plot the footprint edges using the fitting functions.
"""
try:
from stsci.numdisplay import display
except ImportError:
from numdisplay import display
orientation = footprints[0].edges[0].orientation
pl.clf()
med = np.median(np.where(image>0,image,0))
for k,footprint in enumerate(footprints):
edge1 = footprint.edges[0]; edge2 = footprint.edges[1]
xx1,yy1 = np.asarray(edge1.trace,int)
xx2,yy2 = np.asarray(edge2.trace,int)
# Plot (x_array,y_array)
evf1 = edge1.evalfunction
evf2 = edge2.evalfunction
if orientation == 90:
xx1 = np.asarray(evf1(yy1),int)
xx2 = np.asarray(evf2(yy2),int)
pl.plot(xx1,yy1,'b',xx2,yy2,'r')
else:
yy1 = np.asarray(evf1(xx1),int)
yy2 = np.asarray(evf2(xx2),int)
pl.plot(xx1,yy1,'b',xx2,yy2,'r')
image[yy1,xx1]=med*2
image[yy2,xx2]=med*2
display(image,frame=2,quiet=True)
def plot_footprint(ad):
""" Plot and display the edges found by trace_footprints.
This information is the TRACEFP bintable
extension in the AD object.
"""
try:
from stsci.numdisplay import display
except ImportError:
from numdisplay import display
if type(ad) is list: ad=ad[0]
if ad.instrument() == 'F2':
orientation = 90
else:
if ad['SCI',1].dispersion_axis() == 1:
orientation = 0
else:
orientation = 90
pl.clf()
tb = ad['TRACEFP'].data
data = ad['SCI',1].data
for rec in tb:
region = rec.field('region')
cutrange1 = rec.field('cutrange1')
cutrange2 = rec.field('cutrange2')
coeff1 = rec.field('cutcoeff1')
coeff2 = rec.field('cutcoeff2')
evf1 = set_evalfunction(coeff1)
evf2 = set_evalfunction(coeff2)
if orientation == 0:
x1,x2,y1,y2 = region
x = np.arange(x1,x2+1)
pl.plot(x,evf1(x)+y1,'b') # edge_1
pl.plot(x,evf2(x)+y1,'r') # edge_2
for x in np.arange(int(x1),int(x2+1.),20):
xi = slice(x,x+5)
xr = np.arange(x,x+5)
yi = list(evf1(xr)+y1) + list(evf2(xr)+y1)
data[yi,xi] = 9999
else: # Orientation is 90
x1,x2,y1,y2 = region
y = np.arange(y1,y2+1)
pl.plot(evf1(y)+x1,y,'b') # edge 1
pl.plot(evf2(y)+x1,y,'r') # edges 2
off = evf1(y1)
for y in range(int(y1),int(y2+1.),20):
yi = slice(y,y+5)
xi = list(evf1(np.arange(y,y+5))+x1-off) + \
list(evf2(np.arange(y,y+5))+x1-off)
data[yi,xi] = 99999
fname = os.path.basename(ad.filename)
pl.title(fname+' ('+ad.instrument()+')')
display(data,frame=1,quiet=True)
time.sleep(3)
def ncqlook(inputs, idir='', odir='./', log=True,lists=True, saturate=5000, nodisplay=False, full=False, port=5137):
"""
nqlook
Get NICI data from SBF: /net/petrohue/dataflow repository
for last night date. Produces a quick display of each
frame plus log file and lists that can be used with
niciprepare, nicimkflats and niciscience scripts.
The output lists are written in the working directory.
nqlook inputs='20081116' saturate=2000 display=False
Get data from 20081116 with a saturation limit at 2000
and does not display (for speed)'
nqlook @inlis idir='/data/myfiles' saturate=2000 display=False
Get data from 'inlis' which is a list of input FITS files
pathnames -one per line. The actual files are in the directory
'idir'
Use a saturation limit at 2000 and does not display (for speed)'
Even though you have 'inputs' parameter in here, it is not
taken into account since all the FITS files from the input
directory will be read.
NOTE: The files bad_r.fits ans bad_b.fits are read from
the nici directory wherever is it installed.
"""
if (inputs == None):
today = datetime.date.today()
dd = today.timetuple()
inputs = str(dd[0]) + '%.2d'%dd[1] + '%.2d'%dd[2]
if nodisplay == False:
# NOTE: pyds9 should be installed in ritchie.
# Needs to be part of the general installation and
# an explanatory should be given in the installation Guide
#ds9 = sao.ds9()
try:
pp = 'inet:%d' % port
ndis.open(imtdev=pp)
except:
print "\n ****************** WARNING: Please start ds9."
return
#else:
# ndis.close()
# We need to know were we are. 'nici' is the package name
fp, pathname, description = imp.find_module('nici')
pathname += '/'
#loads the badpixels list for the blue and red channel
bad_b = pf.getdata(pathname+'bad_b.fits')
bad_r = pf.getdata(pathname+'bad_r.fits')
# Chech that we can write in the current directory
# the output FITS file is written here.
odir = os.path.join(odir,'') # Make sure odir has '/'
if not os.access(odir,os.W_OK):
print "\nERROR: You do not have write access to \
this directory (output FITS file)\n"
return
if idir != '':
idir = os.path.join(idir,'') # Make sure odir has '/'
if not os.access(idir,os.R_OK):
print "\nERROR: No access to input directory:",idir
return
pathS = '/net/petrohue/dataflow/S'
froot = 'ncqlook'
if 'list' not in str(type(inputs)):
date = str(inputs)
fits_files = getFileList(date)
# see if it date format 20yymmdd. Will not work for 20?.fits
if len(fits_files) == 1:
dd = fits_files[0]
if len(dd) == 8 and dd[:2] == '20':
print 'Looking for files: ' +pathS+date+'S*.fits'
fits_files = glob.glob(pathS+date+'S*.fits')
froot = date
else:
# it is already a list:
fits_files = inputs
command = 'ncqlook ('+str(inputs)+',idir='+idir+',odir='+odir+',log=' \
+str(log)+',lists='+str(lists)+',saturate='+str(saturate)\
+',nodisplay='+str(nodisplay)+',full='+str(full)+')'
fits_files = np.sort(fits_files)
if len(fits_files)==0:
print 'couldn''t find files for :'+pathS +inputs+'S*.fits'
return
print '\nfound '+str(len(fits_files))+ ' files.'
previous_angle = -999 #define a few dummy variables
previous_time = -999
previous_ra = -99
previous_dec = -999
previous_mode = 'NONE'
nfiles = 0
nff = len(fits_files)
cube = np.zeros((nff,256,512),dtype=np.float32)
hh = 'NAME DATALAB EXPTIME CEN_WAVE FILTER_R FILTER_B (MIN-MAX rms)'+\
' [ext1,ext2 median] OBJECT CLASS MODE NCOADD CORE2HALO'
print command
print hh
lg = 0
if nff > 0 and (log or lists): # Open outputfiles
if log:
lg = open(odir+froot+'.log','w+')
lg.write('COMMAND: '+command+'\n')
lg.write(hh+'\n')
if lists:
flis_1 = open(odir+froot+'.1_flats','w+')
flis_2 = open(odir+froot+'.2_flats','w+')
slis_adi = open(odir+froot+'.adi','w+')
slis_sdi = open(odir+froot+'.sdi','w+')
slis_asdi = open(odir+froot+'.asdi','w+')
slis_other = open(odir+froot+'.other','w+')
parser = mathtext.MathTextParser("Bitmap")
for fitsname in fits_files:
t1=time.time()
fd = pf.open(idir+fitsname) # open the Fits file
fname = os.path.basename(fitsname)
if len(fd) == 0:
print fname," is EMPTY."
continue
phu = fd[0].header
inst = phu['instrume'] # instrument name for this image
if inst != 'NICI': #if not NICI, get the next filename
# print fname+' .. '+inst
fd.close()
continue
if len(fd) != 3:
line = ' Nici file '+fitsname+' does not have 2 extensions.'
print line
if log:
lg.write(line+'\n')
fd.close()
continue
nfiles += 1
im_ext1 = fd[1].data
hd1 = fd[1].header # reads extension header 1
im_ext2 = fd[2].data
hd2 = fd[2].header # reads extension header 1
shift_status1=-1
shift_status2=-1
# extension 1 and blue channel in
# extension 2.
# -1 if we do not have red channel
# 0 is all good
# 1 is shifted rows
if hd1.get('filter_b') == None and hd1.get('filter_r') != None:
shift_status1 = check_missing_rows (im_ext1,bad_r,log,lg)
if hd2.get('filter_b') != None and hd1.get('filter_r') == None:
shift_status2 = check_missing_rows (im_ext2,bad_b,log,lg)
devs = np.zeros(8, dtype=np.float32)
# Extracting the RMS of the
# 8x8-folded image sample. A high value may
# indicate readout noise problems
for sample in range(4):
x1 = 200+600*(sample % 2)
x2 = 263+600*(sample % 2)
y1 = 200+600*(sample/2)
y2 = 263+600*(sample/2)
devs[sample] = np.std(fold_88(im_ext1[x1:x2,y1:y2]))
for sample in range(4):
x1 = 200+600*(sample % 2)
x2 = 263+600*(sample % 2)
y1 = 200+600*(sample/2)
y2 = 263+600*(sample/2)
devs[sample+4] = np.std(fold_88(im_ext2[x1:x2,y1:y2]))
ncoadd_r = hd1['ncoadd_r']
ncoadd_b = hd2['ncoadd_b']
itime_r = hd1['itime_r']
crmode = phu.get('crmode')
if crmode == None:
line = 'Header keyword CRMODE not found in '+fname+' -skipped.'
print line
if log:
lg.write(line+'\n')
fd.close()
continue
# Start forming the output line.
obstype = str(phu.get('obstype'))
oclass = str(phu.get('obsclass'))
dichr = str(phu.get('dichroic')).lower()
#adi = ''
chan = ''
if 'mirror' in dichr:
chan = 'B'
if 'open' in dichr:
chan = 'R'
mask = phu.get('fpmw')
if 'clear' in mask.lower():
mask = '-NoMASK'
else:
mask = ''
tmp = obstype
if obstype == 'FLAT':
tmp = 'FLAT['+phu.get('gcalshut')+']'
# 1-Channel (ADI) (Dichroic is Mirror*(100% blue) or Open (100% red))
# 2-Channel (SDI or ASDI) (Dichroic is H-50/50*, H-CH4, H/K)
mode = "OTHER"
if (obstype == 'FLAT' or obstype == 'DARK'):
if chan:
mode = 'ADI-'+chan
else:
mode = 'SDI'
elif (oclass == 'science'):
if (crmode == 'FIXED'):
if chan:
mode = 'ADI-'+chan+mask
else:
mode = 'ASDI'+mask
elif (crmode == 'FOLLOW'):
if chan:
mode = 'Normal'+chan+mask
else:
mode = 'SDI'+mask
elif '-ENG' in phu.get('OBSID'):
mode = '* ENGINEERING DATA *'
else:
mode = '***WARNING***: Not ADI, SDI nor ASDI-- '+crmode+' '+dichr
line = ' '+str(phu.get('object'))+','+ oclass+','+tmp+','+mode
ti_coad = ' '+str(itime_r)+','+str(ncoadd_r)
dlab =' '+phu.get('DATALAB')
exp_time =' %.2f'%(itime_r*ncoadd_r/60.) # exposure time ... in minutes!
cenwave =' %.3f'%(float(phu.get('WAVELENG'))/10000.) # Central wavelenght in microns
filter_r = ' %10.10s'%phu.get('FILTER_R')
filter_b = ' %10.10s'%phu.get('FILTER_B')
mindevs = ' (%8.2f,'%min(devs)
maxdevs = '%8.2f)'%max(devs)
medext1 = ' [%8.2f,'%np.median(im_ext1)
medext2 = '%8.2f]'%np.median(im_ext2)
longl = fname+dlab+exp_time+cenwave+filter_r+filter_b+mindevs+maxdevs+medext1+medext2+line+ti_coad
if 'ENGINE' in mode:
print longl
if log:
lg.write(longl+'\n')
continue
# hh = 'MIN-MAX rms 8x8 boxes: (min - max) MEDIAN: [ext1,ext2],(OBJECT),'\
# '(OBSCLASS),(OBSTYPE),(CRMODE),(DICHROIC),(FPMW),(ITIME),(NCOADD)'
# Don't print until core2halo value is defined below.
# print longl
if lists:
if (obstype == 'FLAT' or obstype == 'DARK'):
if chan:
flis_1.write(fname+'\n')
else:
flis_2.write(fname+'\n')
elif oclass == 'science':
md = mode.lower()
if 'mask' in md:
slis_other.write(fname+'\n')
elif 'adi' in md:
slis_adi.write(fname+'\n')
elif 'asdi' in md:
slis_asdi.write(fname+'\n')
elif 'sdi' in md:
slis_sdi.write(fname+'\n')
else:
slis_other.write(fname+'\n')
# extast,hd1,astr ;extract the astrometric structure (astr) from the
# first extension header
# cd=astr.cd ;extract the CD matrix from the astrometric structure,
# this matrix defines scale/rotation/shear
ra_pointing = hd1['crval1'] # pointing RA from astrometry
dec_pointing = hd1['crval2'] # pointing DEC
st = np.asfarray(phu['st'].split(':'))
st = dmstod(phu['st']) # sidereal time
HA = st - ra_pointing/15. # defining the hour angle
harr = HA + np.asfarray([0,-1/60.])
para_angle = parangle(harr, dec_pointing, -30.240750)
# computing the paralactic angle. -30.240750 is the
# latitude of Cerro Pachon
obs_time = (dmstod(phu['LT'])*60.+720) % 1440
# here time is expressed in minutes after noon
angular_rate = para_angle[0] - para_angle[1]
# computing the rate of change of the paralactic angle
current_angle = para_angle[0]
astrom_angular_rate = (current_angle - previous_angle)/ \
(obs_time - previous_time)
# angle change as found by taking the difference in
# angle between this frame and the previous one
telescope_offset = np.sqrt( ((ra_pointing - previous_ra)*\
math.cos(np.radians(dec_pointing)))**2+\
(dec_pointing - previous_dec)**2 )
if (crmode == 'FIXED') and (previous_mode == 'FIXED'):
# we are in ADI mode
if (abs(angular_rate - astrom_angular_rate) > 1/10.) \
and (telescope_offset < 1E-3) and \
((obs_time - previous_time) < 3*exp_time):
print '---------- !!!!!!!! ---------------'
print 'Expected angular rate : '+str(angular_rate)+\
' deg/minute'
print 'Astrometric angular rate : '+\
str(astrom_angular_rate)+' deg/minute'
# CROP THE FRAMES and get core2halo ratios
crop1=0; crop2=0; c2h_1=-1; c2h_2=-1
if full:
out_ext1 = rebin(im_ext1,256,256)
out_ext2 = rebin(im_ext2,256,256)
else:
Ndev = lambda im : (np.amax(im) - np.median(im))/robust_sigma(im)
tmp = rebin(im_ext1,32,32)
if Ndev(tmp) > 50:
# We might have a peak:
crop1 = 1
c2h_1,x,y = peak2halo('',image=im_ext1)
if x-128 < 0 or x+128 > 1024 or y-128 < 0 or y+128 > 1024:
out_ext1 = rebin(im_ext1,256,256)
crop1 = 0
else:
#shift frame to center
out_ext1 = im_ext1[y-128:y+128,x-128:x+128]
else:
out_ext1 = rebin(im_ext1,256,256)
tmp = rebin(im_ext2,32,32)
if Ndev(tmp) > 50:
# We might have a peak:
crop2 = 1
c2h_2,x,y = peak2halo('',image=im_ext2)
if x-128 < 0 or x+128 > 1024 or y-128 < 0 or y+128 > 1024:
out_ext2 = rebin(im_ext2,256,256)
crop2 = 0
else:
#shift frame to center
out_ext2 = im_ext2[y-128:y+128,x-128:x+128]
else:
out_ext2 = rebin(im_ext2,256,256)
if 1: # Do not process if there is no red flux
a = out_ext1/ncoadd_r
saturated_1 = a > saturate
saturated_1[0:20,0:140] = 0
sat_red = np.sum(saturated_1)
#IDL out_ext1>=out_ext1[ (sort(out_ext1))[.01*n_elements(out_ext1)]]
szx1 = np.size(out_ext1)
sout = np.sort(out_ext1.ravel())
limit = sout[.01*szx1]
xmin = sout[0]; xmax= sout[np.size(sout)-1]
out_ext1 = np.clip(out_ext1, limit, xmax)
# we put lower and upper limits and the 1% and
# 99.5% (OR NCOADD*3500) of the image
#IDL out_ext1<=( out_ext1[ (sort(out_ext1))[.995*n_elements(out_ext1)]]
# < (sxpar(hd1,'ncoadd_r')*saturate) )
szx1 = np.size(out_ext1)
if sout[.995*szx1] < ncoadd_r*saturate:
limit = ncoadd_r*saturate
else:
limit = sout[.995*szx1]
out_ext1 = np.clip(out_ext1, xmin, limit)
if 1:
a = out_ext2/ncoadd_b
saturated_2 = nd.rotate(a > saturate,5,reshape=False)
saturated_2[0:20,0:140] = 0
sat_blue = np.sum(saturated_2)
szx2 = np.size(out_ext2)
sout = np.sort(out_ext2.ravel())
limit = sout[.01*szx2]
xmin = sout[0]; xmax= sout[np.size(sout)-1]
out_ext2 = np.clip(out_ext2, limit, xmax)
# we put lower and upper limits and the 1% and
# 99.5% (OR NCOADD*3500) of the image
#IDL: out_ext2<=( out_ext2[ (sort(out_ext2))[.995*n_elements(out_ext2)]]
# < (sxpar(hd2,'ncoadd_b')*saturate) )
szx2 = np.size(out_ext2)
if sout[.995*szx2] < ncoadd_b*saturate:
limit = ncoadd_b*saturate
else:
limit = sout[.995*szx2]
out_ext2 = np.clip(out_ext2, xmin, limit)
out = np.zeros((256,512),dtype=np.float32)
#create the image that will receive the frames to display
out[:,:256] = ((out_ext1 - np.amin(out_ext1)) / \
(np.amax(out_ext1) - np.amin(out_ext1)))[::-1,:]
a = out_ext2 - np.amin(out_ext2)
b = np.amax(out_ext2) - np.amin(out_ext2)
out[:,256:] = nd.rotate( (a/b), 5, reshape=False)[::-1,::-1]
#extract the root name of the file.
name_out = os.path.splitext(os.path.basename(fname))[0]
name_out += '['+('full','crop')[crop1]+'/'+('full','crop')[crop2]+']'
# Form the text to overlay onto the lower left of the output array.
rgba, depth1 = parser.to_rgba(name_out, color='gray', fontsize=10, dpi=200)
c = rgba[::2,::2,3]
far = np.asarray(c,dtype=np.float32)
stx = np.shape(far)
out[:stx[0],:stx[1]] = far[::-1,:]/256
if not full:
if sat_red != 0:
tmp1=out[:,:256]
tmp1[np.where(saturated_1)] = np.nan
out[:,:256]=tmp1
if not full:
if sat_blue != 0:
tmp2=out[:,256:]
tmp2[np.where(saturated_2)] = np.nan
out[:,256:]=tmp2
if nodisplay == False:
ndis.display(out,z1=-1,z2=1,zscale=False)
#ds9.view(out)
cube[nfiles-1,:,:] = out[::-1,:]
# Now print
if oclass == 'science':
if chan == 'red': c2h_2=-1
if chan == 'blue': c2h_1=-1
if c2h_1 > 0 and c2h_2 > 0:
longl += ',['+'%.2f,%.2f' % (c2h_1,c2h_2)+']'
elif c2h_1*c2h_2 == 1: # both -1
longl += ',[,]'
elif c2h_1 > 0:
longl += ',['+'%.2f,' % (c2h_1)+']'
else:
longl += ',['+',%.2f' % (c2h_2)+']'
if sat_red or sat_blue:
longl += ' Saturated('+str(sat_red)+','+str(sat_blue)+')'
print longl
if log:
lg.write(longl+'\n')
previous_angle=current_angle
#keep track of some variables for next iteration
previous_time =obs_time
previous_ra =ra_pointing
previous_dec =dec_pointing
previous_mode =crmode
fd.close()
if log:
lg.close()
if lists:
flis_1.close()
flis_2.close()
slis_adi.close()
slis_sdi.close()
slis_asdi.close()
slis_other.close()
if (nfiles > 0):
pf.writeto(odir+froot+'_cube.fits',cube[:nfiles,::-1,:], clobber=True)
# write the output cube with cropped/binned images
else:
print 'No NICI files found for the specified input: ',inputs
return
def at5():
"""
The test instantiates a MosaicGeometry object from an input dictionary,
a MosaicData object from a set of 4 data ndarrays and with these 2 object
as input, instantiates a Mosaic object.
"""
import numpy as np
try:
from stsci.numdisplay import display
except ImportError:
from numdisplay import display
print('\n at5 REQUIREMENT.......')
print ('MosaicData uses the geometry dictionary to mosaic the input ndarrays.')
geometry = {
'transformation': {
'shift': [(0., 0.), (-10, 20), (-10, 20), (0, 0)],
'rotation': (0.0, 0.0, 45.0, 45.),
'magnification': (1., 1., 1., .5),
},
'interpolator': 'linear',
'gap_dict': { 'tile_gaps': {(0,0):(20,30),(1,0):(20,30),
(0,1):(20,30),(1,1):(20,30)
},
'transform_gaps': {(0, 0): (20, 30), (1, 0): (20, 30),
(0, 1): (20, 30), (1, 1): (20, 30)
},
},
'blocksize': (200, 300),
'ref_block': (0, 0), # 0-based
'mosaic_grid': (2, 2)
}
mosaic_geometry = MosaicGeometry(geometry)
# Make a rectangle (200,300) (wide,high).
data = np.ones((300,200),dtype=np.float32)
data = data * 20 # make the background 20
# Make a 2x2 array with this rectangle
data_list = [data,data,data,data]
# make a box 50x50 starting at (50,50) with value 100
for k in range(4):
data_list[k][50: 101, 50: 101] = 100.
# Mark the block borders with value 400
data_list[k][:,0] = 400
data_list[k][:,199]= 400
data_list[k][0,:] = 400
data_list[k][299,:]= 400
mosaic_data = MosaicData(data_list)
# With these two objects we instantiate a Mosaic object
mo = Mosaic(mosaic_data, mosaic_geometry)
# Finally make the mosaic
mosaic_data = mo.mosaic_image_data()
# Success Criterion 1.
ref_block = mo.geometry.ref_block
blksz_x,blksz_y = mo.geometry.blocksize
print(int(mosaic_data.shape[0]/blksz_y), int(mosaic_data.shape[1]/blksz_x))
# Success Criterion 2.
x_gap,y_gap= mo.geometry.gap_dict['tile_gaps'][ref_block]
nblkx,nblky = mo.geometry.mosaic_grid
print('nx:', nblkx*blksz_x + (x_gap)*( nblkx-1))
print('ny:', nblky*blksz_y + (y_gap)*( nblky-1))
# Success Criterion 3.
# Now display the mosaic and the mask
display(mosaic_data,frame=1,z1=0,z2=400.5)
# Results:
# 1) Lower left block, top right corner of
# test box: (101,101)
# 2) Lower right block, top right corner
# of test box: (200+20-10+101, 101+20) or (311,121);
# gap: (20,0), shift: (-10,20)
# 3) Top left block, top right corner of
# test box gets rotated 45 degrees w/r to the
# block center: (50*cos(45),(50-50*sin(45)) or (35.4,14.6)
# in distance change of this corner. Now the new
# positioning with respect to the mosaic origin is
# (135.4-10,300+20+30+114.6) or (125.4, 464.6);
# gap: (0,30), shift: (-10,20)
# 4) top right block, top right corner of
# test box gets rotated 45 degrees but we have a
# magnification of 0.5 so the distance between the
# block center and the top right corner is 25 now.
# The distance change for rotation is then: (17.7,7.4)
# This corner position with no rotation is (100,125)
# now adding the rotation we have: (117.7,132.4).
# The position w/r to the mosaic origin is:
# (200+20+117.7, 300+30+132.4) or (337.7, 362.4)
return
def nici_cntrd(im,hdr,center_im=True):
"""
Read xcen,ycen and update header if necessary
If the automatic finding of the center mask fails, then
the interactive session will start. The SAOIMAGE/ds9
display must be up. If the default port is busy, then it will
use port 5199, so make sure you start "ds9 -port 5199".
"""
xcen = hdr.get('xcen')
ycen = hdr.get('ycen')
updated = False
if (xcen == None):
ratio,xc,yc = peak2halo('',image=im)
#xcen = xc[0]
#ycen = yc[0]
xcen = xc
ycen = yc
if (xcen < 0 or ycen < 0):
try:
ndis.display(im)
except IOError,err:
sys.stderr.write('\n ***** ERROR: %s Start DS9.\n' % str(err))
sys.exit(1)
print " Mark center with left button, then use 'q' to continue, 's' to skip."
cursor = ndis.readcursor(sample=0)
cursor = cursor.split()
if cursor[3] == 's':
hdr.update("XCEN",-1, "Start mask x-center")
hdr.update("YCEN",-1, "Start mask y-center")
updated = True
print '\nFrame skipped... ****Make sure not to use it in your science script. ***\n'
#return updated,xcen,ycen,im
return xcen,ycen,im
x1 = float(cursor[0])
y1 = float(cursor[1])
box = im[y1-64:y1+64,x1-64:x1+64].copy()
box -= scipy.signal.signaltools.medfilt2d(np.float32(box),11)
box = box[32:32+64,32:32+64]
bbx = box * ((box>(-robust_sigma(box)*5)) & \
(box <(15*robust_sigma(box))))
imbb = congrid(bbx,(1024,1024))
ndis.display(imbb, name='bbx')
del imbb
cursor = ndis.readcursor(sample=0)
cursor = cursor.split()
x2 = float(cursor[0])
y2 = float(cursor[1])
xcen,ycen = gcentroid(box, x2/16., y2/16., 4)
xcen = (xcen+x1)[0] - 32
ycen = (ycen+y1)[0] - 32
hdr.update("XCEN",xcen, "Start mask x-center")
hdr.update("YCEN",ycen, "Start mask y-center")
updated = True
def pyexam(scidata, function='both', pixelscale=1, frame=1, \
outFile='testout.txt', verbose=True, pymark=True, \
residuals=False, clip=True, sigma=3, boxSize=9., \
debug=False, niters=4.):
import pylab
from iqtool.gemplotlib import overlay
# Get box size around each object
apertureSize = fit.getApSize(pixelscale, boxSize)
outstring = '%10s%12s%12s%12s%12s%12s%12s%12s%12s%12s%12s%12s%12s%12s%12s%12s%12s\n'%('function', 'Cx', 'Cy', 'Bg', 'Peak', 'Wx', 'Wy', 'CooX', 'CooY', 'Ellipticity', 'FWHM_pix','FWHM_arcsec', 'Theta', 'PA_deg', ' FWHMx', 'FWHMy', 'Beta')
outFile.write(outstring)
print 'PYEXAM - Please select objects using spacebar, and press \'q\' when finished'
print ' pressing k, j, r, or e will make plots along with fits'
keystroke = ''
numdisplay.display(scidata, z1=scidata.mean()-scidata.std(),
z2=scidata.mean()+scidata.std(), frame=frame)
if verbose:
print '%10s%12s%12s%12s%12s%12s%12s'%('function', 'Coox', 'CooY', 'FWHMpix',
'FWHMarcsec', 'Ellipticity', 'PAdeg')
gAllstars = []
mAllstars = []
done = 'no'
while done == 'no':
# Get x,y
cursorInput = numdisplay.readcursor(sample=0)
components = cursorInput.split()
xPos = float(components[0])
yPos = float(components[1])
keystroke = components[3]
option = ''
if keystroke == '\\040' or keystroke == 'a' or keystroke == 'k' or keystroke == 'j' or keystroke == 'e' or keystroke == 'r':
print 'PYEXAM - X:',xPos,'Y:',yPos
gfitArray=None
mfitArray=None
gfxn=None
mfxn=None
positionCoords = (xPos,yPos)
stampArray, stampPars = fit.makeStamp(scidata, positionCoords,
apertureSize, outFile, debug=debug)
if stampArray == None: return None, None
gfitPars, gReturnModel, mfitPars, mReturnModel = \
fit.fitprofile(stampArray, function, positionCoords, \
outFile, pixelscale, stampPars, debug=debug, frame=frame)
imageDim = list(scidata.shape) # Gives [y,x] dimensions
#imageDim.reverse() #give [x,y] dimensions
if gfitPars != None:
clipobsPars = ('gauss', gfitPars['CooX'], gfitPars['CooY'],
gfitPars['FWHMpix'],
gfitPars['FWHMarcsec'], gfitPars['Ellip'],
gfitPars['PAdeg'])
iqUtil.printPars(clipobsPars, verbose)
gAllstars.append(gfitPars)
if pymark:
overlay.circle(x=gfitPars['CooX']-1,y=gfitPars['CooY']-1, \
radius=gfitPars['FWHMpix'],frame=frame,\
color=204)
gfxn = gReturnModel((gfitPars['Bg'], gfitPars['Peak'], \
gfitPars['Cy'], \
gfitPars['Cx'], gfitPars['Wy'], \
gfitPars['Wx'], gfitPars['Theta']+90))
gfitArray = np.zeros(imageDim)
gfitArray[0:stampPars[3],0:stampPars[1]] = gfxn(*np.indices((stampPars[3], stampPars[1])))
if mfitPars != None:
clipobsPars = ('moffat', mfitPars['CooX'],mfitPars['CooY'],
mfitPars['FWHMpix'],
mfitPars['FWHMarcsec'], mfitPars['Ellip'],
mfitPars['PAdeg'])
iqUtil.printPars(clipobsPars, verbose)
print mfitPars['CooX']
print mfitPars['CooY']
print mfitPars['Cx']
print mfitPars['Cy']
mAllstars.append(mfitPars)
if pymark:
overlay.circle(x=mfitPars['CooX'],
y=mfitPars['CooY'], frame=frame,
radius=mfitPars['FWHMpix'], color=212)
mfxn = mReturnModel((mfitPars['Bg'], mfitPars['Peak'], \
mfitPars['Cy'],\
mfitPars['Cx'], mfitPars['Wy'], \
mfitPars['Wx'], mfitPars['Theta']+90, \
mfitPars['Beta']))
mfitArray = np.zeros(imageDim)
mfitArray[0:stampPars[3],0:stampPars[1]] = mfxn(*np.indices((stampPars[3], stampPars[1])))
if keystroke == 'k':
print "PYEXAM - Yslice Plotting"
pylab.clf()
pylab.plot(stampArray.max(0), 'ro')
if mfitArray != None:
pylab.plot(mfitArray.max(0), 'b', label='moffat fit FWHM='+str(mfitPars['FWHMarcsec']))
pylab.axis([0,stampPars[1]-stampPars[0],mfitPars['Bg'],1.3*(mfitPars['Bg']+mfitPars['Peak'])])
if gfitArray != None:
pylab.plot(gfitArray.max(0), 'g',label='gauss fit FWHM='+str(gfitPars['FWHMarcsec']))
pylab.axis([0,stampPars[1]-stampPars[0],gfitPars['Bg'],1.3*(gfitPars['Bg']+gfitPars['Peak'])])
pylab.legend()
if keystroke == 'j':
print "PYEXAM - Xslice Plotting"
pylab.clf()
pylab.plot(stampArray.max(1), 'ro')
if mfitArray != None:
pylab.plot(mfitArray.max(1), 'b', label='moffat fit FWHM='+str(mfitPars['FWHMarcsec']))
pylab.axis([0,stampPars[1]-stampPars[0],mfitPars['Bg'],1.3*(mfitPars['Bg']+mfitPars['Peak'])])
if gfitArray != None:
pylab.plot(gfitArray.max(1), 'g',label='gauss fit FWHM='+str(gfitPars['FWHMarcsec']))
pylab.axis([0,stampPars[1]-stampPars[0],gfitPars['Bg'],1.3*(gfitPars['Bg']+gfitPars['Peak'])])
pylab.legend()
if keystroke == 'r':
print "PYEXAM - radial Plotting doesnt work yet!"
print "new version"
#rad1 = np.sqrt(stampArray.max(1)**2 + stampArray.max(0)**2)
pylab.clf()
pylab.plot(stampArray.max(1), 'ro')
if mfitArray != None:
pylab.plot(mfitArray.max(1), 'b', label='moffat fit FWHM='+str(mfitPars['FWHMarcsec']))
pylab.axis([0,stampPars[1]-stampPars[0],mfitPars['Bg'],1.3*(mfitPars['Bg']+mfitPars['Peak'])])
if gfitArray != None:
pylab.plot(gfitArray.max(1), 'g',label='gauss fit FWHM='+str(gfitPars['FWHMarcsec']))
pylab.axis([0,stampPars[1]-stampPars[0],gfitPars['Bg'],1.3*(gfitPars['Bg']+gfitPars['Peak'])])
pylab.legend()
if keystroke == 'e':
print 'PYEXAM - Plotting contours'
pylab.clf()
pylab.contour(stampArray)
elif keystroke == 'q':
done = 'yes'
if verbose: print "Done looping through stars"
if clip:
if gAllstars:
if verbose: print "# PYEXAM - performing Gaussian clipping"
gAllstars, gEllMean, gEllSigma, gFWHMMean, gFWHMSigma = \
iqUtil.sigmaClip(gAllstars, outFile, sigma, verbose, niters, garbageStat=garbageStat)
if verbose:
print "PYEXAM - Mean Gaussian Ellipticity:", gEllMean
print "PYEXAM - Mean Gaussian FWHM (""):", gFWHMMean
if mAllstars:
if verbose: print "# PYEXAM - performing Moffat clipping"
mAllstars, mEllMean, mEllSigma, mFWHMMean, mFWHMSigma = \
iqUtil.sigmaClip(mAllstars, outFile, sigma, verbose, niters, garbageStat=garbageStat)
if verbose:
print "PYEXAM - Mean Moffat Ellipticity:", mEllMean
print "PYEXAM - Mean Moffat FWHM (""):", mFWHMMean
for star in gAllstars:
iqUtil.writePars(star, outFile, 'gaussian')
for star in mAllstars:
iqUtil.writePars(star, outFile, 'moffat')
def at5():
"""
The test instantiates a MosaicGeometry object from an input dictionary,
a MosaicData object from a set of 4 data ndarrays and with these 2 object
as input, instantiates a Mosaic object.
"""
import numpy as np
from gempy.library.mosaic import Mosaic,MosaicGeometry, MosaicData
try:
from stsci.numdisplay import display
except ImportError:
from numdisplay import display
print '\n at5 REQUIREMENT.......'
print ('*****MosaicData shall correctly use the geometry dictionary to mosaic the input ndarrays.')
geometry = {
'transformation': {
'shift': [(0.,0.), (-10,20), (-10,20), (0,0)],
'rotation': (0.0, 0.0, 45.0, 45.),
'magnification': (1., 1., 1., .5),
},
'interpolator': 'linear',
'gap_dict': { 'tile_gaps': {(0,0):(20,30),(1,0):(20,30),
(0,1):(20,30),(1,1):(20,30)},
'transform_gaps': {(0,0):(20,30),(1,0):(20,30),
(0,1):(20,30),(1,1):(20,30)},
},
'blocksize': (200,300),
'ref_block': (0,0), # 0-based
'mosaic_grid': (2,2) }
mosaic_geometry = MosaicGeometry(geometry)
# Make a rectangle (200,300) (wide,high).
data = np.ones((300,200),dtype=np.float32)
data = data*20 # make the background 20
# Make a 2x2 array with this rectangle
data_list = [data,data,data,data]
# Inside each block, make a small box 50x50
# starting at (50,50) with value 100
for k in range(4):
data_list[k][50:101,50:101] = 100.
# Mark the block borders with value 400
data_list[k][:,0] =400
data_list[k][:,199]=400
data_list[k][0,:] =400
data_list[k][299,:]=400
# Now create the MosaicData object
mosaic_data = MosaicData(data_list)
# With these two objects we instantiate a Mosaic object
mo = Mosaic(mosaic_data, mosaic_geometry)
# Finally make the mosaic
mosaic_data = mo.mosaic_image_data()
# Success Criteria 1.
ref_block = mo.geometry.ref_block
blksz_x,blksz_y = mo.geometry.blocksize
print (int(mosaic_data.shape[0]/blksz_y), int(mosaic_data.shape[1]/blksz_x))
# Success Criteria 2.
x_gap,y_gap= mo.geometry.gap_dict['tile_gaps'][ref_block]
nblkx,nblky = mo.geometry.mosaic_grid
print 'nx:', nblkx*blksz_x + (x_gap)*( nblkx-1)
print 'ny:', nblky*blksz_y + (y_gap)*( nblky-1)
# Success Criteria 3.
# Now display the mosaic and the mask
display(mosaic_data,frame=1,z1=0,z2=400.5)
def frcheck(infile):
#<--- count number of lines in the list and backup the orignal file --->
# open list
try:
f = open(infile)
except IOError:
print >> sys.stderr, 'cannot open %s' % infile
return 1
nline = 0
orgfile = infile + ".org"
f_org = open(orgfile, 'w')
for line in f:
f_org.write(line)
if not line.startswith('#'):
nline += 1
# close list
f.close()
f_org.close()
#<--- input the file names into array --->#
# open list
f = open(infile)
fname = [[] for i in range(nline)]
frchk = [[] for i in range(nline)]
i = 0
for line in f:
if not line.startswith('#'):
fname[i] = line[:-1]
if not os.access(fname[i], os.R_OK):
print >> sys.stderr, "cannot read %s" % (fname[i])
f.close()
return 1
i += 1
# close list
f.close()
#<--- check frame --->
for i in range(nline):
im1 = pyfits.open(fname[i])
coadds = float(im1[0].header['COADDS'])
ndr = float(im1[0].header['NDR'])
base1, ext = os.path.splitext(os.path.basename(fname[i]))
if i != nline - 1:
im2 = pyfits.open(fname[i + 1])
base2, ext = os.path.splitext(os.path.basename(fname[i + 1]))
else:
im2 = pyfits.open(fname[i - 1])
base2, ext = os.path.splitext(os.path.basename(fname[i - 1]))
name = '%s-%s' % (base1, base2)
sub = im1[0].data - im2[0].data
z1_tmp = -1000.0 * float(coadds) * float(ndr)
z2_tmp = 1000.0 * float(coadds) * float(ndr)
zs = nd.zscale.zscale(sub, nsamples=1000, contrast=0.25)
#print 'test: %s %s' % (str(zs[0]),str(zs[1]))
if str(zs[0]) != 'nan':
z1_tmp = zs[0]
if str(zs[1]) != 'nan':
z2_tmp = zs[1]
nd.display(sub, bufname='imt1024', name=name, z1=z1_tmp, z2=z2_tmp)
d.set('zoom to fit')
check = ''
while check != 'OK' and check != 'NG':
check = raw_input('%s - %s (ok/ng): ' % (base1, base2))
if check.lower() == '' or check.lower() == 'ok':
check = 'OK'
elif check.lower() == 'ng':
check = 'NG'
else:
print 'Error: unknown answer (%s)' % (check)
print check
if check == 'OK':
frchk[i] = 0
else:
frchk[i] = 1
# close fits files
im1.close()
im2.close()
#<--- output bad removed list --->#
# open list
try:
f = open(orgfile)
except IOError:
print >> sys.stderr, 'cannot read original file %s' % (orgfile)
f_out = open(infile, 'w')
i = 0
for line in f:
if not line.startswith('#'):
if i == 0:
if frchk[i] == 0:
f_out.write('%s\n' % fname[i])
else:
f_out.write('#%s\n' % fname[i])
i += 1
else:
if frchk[i] == 0 or frchk[i - 1] == 0:
f_out.write('%s\n' % fname[i])
else:
f_out.write('#%s\n' % fname[i])
i += 1
else:
f_out.write(line)
# close list
f.close()
f_out.close()
return 0
