def getstar(reg, out_pickle, filename_h, filename_j, filename_k, \
ap_h=None,ap_j=None,ap_k=None,triggerid=None, calibration_reg=None, caliblimit=False):
'''
After creating a calibration deep-stack for all images, use this function
to perform photomotery of all calibration stars in the calibration region
file, and outputs a pickle file which is to be used as a photometry
dictionary which is to be used to replace 2mass.
Requirements: q_phot and qPickle.
note:
The keyword calibration_reg is for the calibration stars used to
calibrate these calibration stars. For now, just leave as 'none'
'''
stardict = {}
stardict_h = {}
stardict_j = {}
stardict_k = {}
if not ap_h:
ap_h = raw_input('Enter H aperture: ')
if not ap_j:
ap_j = raw_input('Enter J aperture: ')
if not ap_k:
ap_k = raw_input('Enter K aperture: ')
regpath = storepath + reg
regfile = open(regpath, 'r')
reglist = regfile.readlines()
temppath = storepath + 'temp.reg'
star_pos_list = []
##################################################################
#This part is actually not needed, but in case we want to get the star's postition...
for line in reglist:
if 'circle' in line:
star_str = line.strip('circle').strip().strip('")').strip(
'(').split(',')
ra_str = star_str[0]
dec_str = star_str[1]
star_pos = (float(ra_str), float(dec_str))
star_pos_list += [star_pos]
else:
pass
#End uneeded part of uneededness
###################################################################
callist = []
for line in reglist:
if 'circle' in line:
callist += [line]
else:
pass
keylist = []
for index, star_reg in enumerate(callist):
if os.path.exists(temppath):
os.remove(temppath)
starname = 'star' + str(index)
tempreg = open(temppath, 'w')
tempreg.write('# Region file format: DS9 version 4.1\n')
secondstr='global color=green dashlist=8 3 width=2 font="helvetica '+ \
'16 normal" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 '+ \
'delete=1 include=1 source=1\n'
tempreg.write(secondstr)
tempreg.write('fk5\n')
tmp_str = star_reg
tempreg.write(tmp_str)
tempreg.close()
star_str = star_reg.strip('circle').strip().strip('")').strip(
'(').split(',')
ra_str = star_str[0]
dec_str = star_str[1]
ra_round = ra_str[0:8]
dec_round = dec_str[0:7]
star_pos = (ra_round, dec_round)
star_pos_str = str(star_pos)
data_h = q_phot.dophot(filename_h,
temppath,
calreg=calibration_reg,
ap=ap_h,
caliblimit=caliblimit)
parent_label = star_pos_str
time = float(t_mid.t_mid(filename_h, trigger=triggerid))
terr = float(t_mid.t_mid(filename_h, trigger=triggerid,
delta=True)) / 2.
timetuple = (time, terr)
data_h.update({'t_mid': timetuple})
this_star_dict_h = {parent_label: data_h}
stardict_h.update(this_star_dict_h)
keylist.append(parent_label)
data_j = q_phot.dophot(filename_j,
temppath,
calreg=calibration_reg,
ap=ap_j,
caliblimit=caliblimit)
parent_label = star_pos_str
time = float(t_mid.t_mid(filename_j, trigger=triggerid))
terr = float(t_mid.t_mid(filename_j, trigger=triggerid,
delta=True)) / 2.
timetuple = (time, terr)
data_j.update({'t_mid': timetuple})
this_star_dict_j = {parent_label: data_j}
stardict_j.update(this_star_dict_j)
data_k = q_phot.dophot(filename_k,
temppath,
calreg=calibration_reg,
ap=ap_k,
caliblimit=caliblimit)
parent_label = star_pos_str
time = float(t_mid.t_mid(filename_k, trigger=triggerid))
terr = float(t_mid.t_mid(filename_k, trigger=triggerid,
delta=True)) / 2.
timetuple = (time, terr)
data_k.update({'t_mid': timetuple})
this_star_dict_k = {parent_label: data_k}
stardict_k.update(this_star_dict_k)
h_dict = {'h': stardict_h}
j_dict = {'j': stardict_j}
k_dict = {'k': stardict_k}
stardict.update(h_dict)
stardict.update(j_dict)
stardict.update(k_dict)
picklepath = storepath + out_pickle + '.data'
qPickle.save(stardict, picklepath, clobber=True)
print 'Created a dictionary for the following star locations:'
print keylist
return stardict
def magplot(reg,
filelist,
out_pickle=None,
ap=None,
triggerid=None,
globit=False,
noerr=False,
magrange=None,
caliblimit=True):
'''
Plot magnitudes of calibration stars as a function of time.
Do after the initial coaddition of triplestacks to plot the magnitudes of
calibration stars as a function of time for each science image.
Do once for EACH BAND
Requirements: q_phot and t_mid.
Arguments:
reg: region file of calibration stars to test and plot
filelist: EITHER: list of files to run photometry on, or a string
to do a glob search of files in a directory (if globit=True)
out_pickle: override the filename of the pickle file to write to.
Will use default naming convention if not specified.
triggerid: Swift trigger id of the GRB (if applicable; otherwise None)
globit: if True, do a glob search to get a list of filenames to run
photometry on instead of specifying an explicit list.
testind: index of image in the filelist from which to run the initial
photometry on to get the calibration star keywords. make sure
all your calib stars are present and viewable in this image.
noerr: if True, do not plot error bars
'''
if globit == True:
globstr1 = str(filelist) + '*coadd*[0-9].fits'
globstr2 = str(filelist) + '*coadd.fits'
globlist1 = glob.glob(globstr1)
globlist2 = glob.glob(globstr2)
filelist = globlist1 + globlist2
print 'globit actiavated'
print filelist
unique_name = (filelist[0].split('_'))[2]
filt = filelist[0][0]
calib_star_keys = []
testind = 0
caldict = {}
matplotlib.pyplot.clf()
regpath = storepath + reg
temppath = storepath + 'temp.reg'
regfile = open(regpath, 'r')
reglist = regfile.readlines()
callist = []
for line in reglist:
if 'circle' in line:
callist += [line]
else:
pass
colornumber = len(callist)
n_stars = len(callist)
while ((len(calib_star_keys) < len(callist)) and testind < len(filelist)):
tempreg = open(temppath, 'w')
tempreg.write('# Region file format: DS9 version 4.1\n')
secondstr='global color=green dashlist=8 3 width=2 font="helvetica '+ \
'16 normal" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 '+ \
'delete=1 include=1 source=1\n'
tempreg.write(secondstr)
tempreg.write('fk5\n')
#tmp_str = star_reg
print callist[0]
# Add a few arcseconds to the first position to make sure we don't use it as a target
test_ra = float(callist[0].lstrip('circle(').split(',')[0]) + 0.005
test_dec = float(callist[0].lstrip('circle(').split(',')[1]) + 0.005
tmp_str = 'circle(%f,%f,4") # width=2 font="helvetica 16 normal"\n' % (
test_ra, test_dec)
tempreg.write(tmp_str)
tempreg.close()
# Grab the calib stars we will be looping over:
calregion = '/calstarregs/' + os.path.basename(reg)
print "Using image #%i to get the calib stars; if not all are present \
in final plot, try a different image" % (testind)
testimage = filelist[testind]
photdict = q_phot.photreturn(os.path.basename(reg),
testimage,
reg=temppath,
calregion=calregion,
aper=ap,
auto_upper=False,
caliblimit=caliblimit,
trigger_id=triggerid)
for key in photdict[testimage]['calib_stars'].keys():
if not key in calib_star_keys:
calib_star_keys.append(key)
testind += 1
print 'length of stuff'
print len(callist)
print len(calib_star_keys)
for index, ra_str in enumerate(calib_star_keys):
# if os.path.exists(temppath):
# os.remove(temppath)
datalist = []
dataerrlist = []
timelist = []
timeerrlist = []
colorstr = str(float((1 / colornumber)) * float(index + 1))
colortuple = (colorstr, 0.5, 0)
starname = 'star' + str(index)
precal_dict = {}
for image in filelist:
print '**************************************'
print 'Photometry of star' + str(index)
print 'doing image ' + image
calregion = '/calstarregs/' + os.path.basename(reg)
data = q_phot.photreturn(os.path.basename(reg),
image,
reg=temppath,
calregion=calregion,
aper=ap,
auto_upper=False,
caliblimit=caliblimit)
image_data = data[image]
if image[0] != filt:
raise ValueError('Filter for %s does not match the others') % (
image)
if ra_str in image_data['calib_stars']:
datalist += [image_data['calib_stars'][ra_str]['new_mag']]
dataerrlist += [image_data['calib_stars'][ra_str]['new_e_mag']]
time = float(t_mid.t_mid(image, trigger=triggerid))
terr = float(t_mid.t_mid(image, trigger=triggerid,
delta=True)) / 2.
timetuple = (time, terr)
image_data.update({'t_mid': timetuple})
timelist += [time]
timeerrlist += [terr]
dec_str = str(image_data['calib_stars'][ra_str]['dec'])[0:7]
parent_label = image
precal_dict.update({parent_label: image_data})
else:
print 'WARNING: CALIB STAR %s IS NOT USABLE FOR THIS IMAGE' % (
ra_str)
datarr = array(datalist)
daterrarr = array(dataerrlist)
timarr = array(timelist)
timerrarr = array(timeerrlist)
if noerr == True:
pylab.plot(timarr, datarr, 'o', label=str((ra_str, dec_str)))
else:
pylab.errorbar(timarr,
datarr,
yerr=daterrarr,
fmt='o',
label=str((ra_str, dec_str))) #star_pos_str)
caldict.update({ra_str: precal_dict})
#matplotlib.pyplot.errorbar(timarr, datarr, yerr = daterrarr, label = starname, fmt='k.', color = colortuple)
star_stdv = numpy.std(datarr)
print 'The standard deviation of the calibration stars is: (DISREGARD THIS, THIS STDV IS PROBABLY WRONG)'
print star_stdv
plottitle = '%s Calibration Stars Magnitude vs. t_mid' % (filt)
plotylabel = '%s Magnitude' % (filt)
matplotlib.pyplot.title(plottitle)
matplotlib.pyplot.xlabel('Time After Burst (s)')
matplotlib.pyplot.ylabel(plotylabel)
ax = matplotlib.pyplot.gca()
ax.set_ylim(ax.get_ylim()[::-1])
ax.set_xlim((ax.get_xlim()[0]), (ax.get_xlim()[1]) * 1.2)
matplotlib.pyplot.legend()
if magrange:
ax.set_ylim(magrange)
F = pylab.gcf()
DefaultSize = F.get_size_inches()
DPI = F.get_dpi()
# F.set_size_inches( (DefaultSize[0]*2.5, DefaultSize[1]*2.5) )
# was getting incresingly larger with multiple runs
F.set_size_inches((20, 15))
n_stars_str = str(n_stars)
if not out_pickle:
picklepath = storepath + unique_name + '_' + filt + '_' + 'ap' + str(
ap) + '_' + n_stars_str + '_cal_stars.data'
else:
picklepath = out_pickle
filepath = storepath + unique_name + '_' + filt + '_' + 'ap' + str(
ap) + '_' + n_stars_str + '_cal_stars.png'
#matplotlib.pyplot.savefig(filepath)
qPickle.save(caldict, picklepath, clobber=True)
F.savefig(filepath)
return caldict
def getstar(reg, out_pickle, filename_h, filename_j, filename_k, \
ap_h=None,ap_j=None,ap_k=None,triggerid=None, calibration_reg=None, caliblimit=False):
'''
After creating a calibration deep-stack for all images, use this function
to perform photomotery of all calibration stars in the calibration region
file, and outputs a pickle file which is to be used as a photometry
dictionary which is to be used to replace 2mass.
Requirements: q_phot and qPickle.
note:
The keyword calibration_reg is for the calibration stars used to
calibrate these calibration stars. For now, just leave as 'none'
'''
stardict = {}
stardict_h = {}
stardict_j = {}
stardict_k = {}
if not ap_h:
ap_h = raw_input('Enter H aperture: ')
if not ap_j:
ap_j = raw_input('Enter J aperture: ')
if not ap_k:
ap_k = raw_input('Enter K aperture: ')
regpath = storepath + reg
regfile = open(regpath, 'r')
reglist = regfile.readlines()
temppath = storepath + 'temp.reg'
star_pos_list = []
##################################################################
#This part is actually not needed, but in case we want to get the star's postition...
for line in reglist:
if 'circle' in line:
star_str = line.strip('circle').strip().strip('")').strip('(').split(',')
ra_str = star_str[0]
dec_str = star_str[1]
star_pos = (float(ra_str), float(dec_str))
star_pos_list += [star_pos]
else:
pass
#End uneeded part of uneededness
###################################################################
callist = []
for line in reglist:
if 'circle' in line:
callist += [line]
else:
pass
keylist = []
for index, star_reg in enumerate(callist):
if os.path.exists(temppath):
os.remove(temppath)
starname = 'star'+str(index)
tempreg = open(temppath, 'w')
tempreg.write('# Region file format: DS9 version 4.1\n')
secondstr='global color=green dashlist=8 3 width=2 font="helvetica '+ \
'16 normal" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 '+ \
'delete=1 include=1 source=1\n'
tempreg.write(secondstr)
tempreg.write('fk5\n')
tmp_str = star_reg
tempreg.write(tmp_str)
tempreg.close()
star_str = star_reg.strip('circle').strip().strip('")').strip('(').split(',')
ra_str = star_str[0]
dec_str = star_str[1]
ra_round = ra_str[0:8]
dec_round =dec_str[0:7]
star_pos = (ra_round, dec_round)
star_pos_str = str(star_pos)
data_h = q_phot.dophot(filename_h, temppath, calreg=calibration_reg, ap=ap_h, caliblimit=caliblimit)
parent_label = star_pos_str
time = float(t_mid.t_mid(filename_h, trigger=triggerid))
terr = float(t_mid.t_mid(filename_h, trigger=triggerid,delta = True))/2.
timetuple = (time, terr)
data_h.update({'t_mid':timetuple})
this_star_dict_h = {parent_label:data_h}
stardict_h.update(this_star_dict_h)
keylist.append(parent_label)
data_j = q_phot.dophot(filename_j, temppath, calreg=calibration_reg, ap=ap_j, caliblimit=caliblimit)
parent_label = star_pos_str
time = float(t_mid.t_mid(filename_j, trigger=triggerid))
terr = float(t_mid.t_mid(filename_j, trigger=triggerid,delta = True))/2.
timetuple = (time, terr)
data_j.update({'t_mid':timetuple})
this_star_dict_j = {parent_label:data_j}
stardict_j.update(this_star_dict_j)
data_k = q_phot.dophot(filename_k, temppath, calreg=calibration_reg, ap=ap_k, caliblimit=caliblimit)
parent_label = star_pos_str
time = float(t_mid.t_mid(filename_k, trigger=triggerid))
terr = float(t_mid.t_mid(filename_k, trigger=triggerid,delta = True))/2.
timetuple = (time, terr)
data_k.update({'t_mid':timetuple})
this_star_dict_k = {parent_label:data_k}
stardict_k.update(this_star_dict_k)
h_dict = {'h':stardict_h}
j_dict = {'j':stardict_j}
k_dict = {'k':stardict_k}
stardict.update(h_dict)
stardict.update(j_dict)
stardict.update(k_dict)
picklepath = storepath + out_pickle + '.data'
qPickle.save(stardict, picklepath, clobber = True)
print 'Created a dictionary for the following star locations:'
print keylist
return stardict
def magplot(reg, filelist, out_pickle=None, ap=None, triggerid = None, globit = False, noerr=False, magrange=None, caliblimit=True):
'''
Plot magnitudes of calibration stars as a function of time.
Do after the initial coaddition of triplestacks to plot the magnitudes of
calibration stars as a function of time for each science image.
Do once for EACH BAND
Requirements: q_phot and t_mid.
Arguments:
reg: region file of calibration stars to test and plot
filelist: EITHER: list of files to run photometry on, or a string
to do a glob search of files in a directory (if globit=True)
out_pickle: override the filename of the pickle file to write to.
Will use default naming convention if not specified.
triggerid: Swift trigger id of the GRB (if applicable; otherwise None)
globit: if True, do a glob search to get a list of filenames to run
photometry on instead of specifying an explicit list.
testind: index of image in the filelist from which to run the initial
photometry on to get the calibration star keywords. make sure
all your calib stars are present and viewable in this image.
noerr: if True, do not plot error bars
'''
if globit == True:
globstr1 = str(filelist) + '*coadd*[0-9].fits'
globstr2 = str(filelist) + '*coadd.fits'
globlist1 = glob.glob(globstr1)
globlist2 = glob.glob(globstr2)
filelist = globlist1 + globlist2
print 'globit actiavated'
print filelist
unique_name = (filelist[0].split('_'))[2]
filt = filelist[0][0]
calib_star_keys = []
testind = 0
caldict = {}
matplotlib.pyplot.clf()
regpath = storepath + reg
temppath = storepath + 'temp.reg'
regfile = open(regpath, 'r')
reglist = regfile.readlines()
callist = []
for line in reglist:
if 'circle' in line:
callist += [line]
else:
pass
colornumber = len(callist)
n_stars = len(callist)
while((len(calib_star_keys) < len(callist)) and testind < len(filelist)):
tempreg = open(temppath, 'w')
tempreg.write('# Region file format: DS9 version 4.1\n')
secondstr='global color=green dashlist=8 3 width=2 font="helvetica '+ \
'16 normal" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 '+ \
'delete=1 include=1 source=1\n'
tempreg.write(secondstr)
tempreg.write('fk5\n')
#tmp_str = star_reg
print callist[0]
# Add a few arcseconds to the first position to make sure we don't use it as a target
test_ra = float(callist[0].lstrip('circle(').split(',')[0]) + 0.005
test_dec = float(callist[0].lstrip('circle(').split(',')[1]) + 0.005
tmp_str = 'circle(%f,%f,4") # width=2 font="helvetica 16 normal"\n' % (test_ra,test_dec)
tempreg.write(tmp_str)
tempreg.close()
# Grab the calib stars we will be looping over:
calregion = '/calstarregs/' + os.path.basename(reg)
print "Using image #%i to get the calib stars; if not all are present \
in final plot, try a different image" % (testind)
testimage = filelist[testind]
photdict = q_phot.photreturn(os.path.basename(reg), testimage, reg=temppath, calregion=calregion, aper=ap, auto_upper=False, caliblimit=caliblimit, trigger_id=triggerid)
for key in photdict[testimage]['calib_stars'].keys():
if not key in calib_star_keys:
calib_star_keys.append(key)
testind += 1
print 'length of stuff'
print len(callist)
print len(calib_star_keys)
for index, ra_str in enumerate(calib_star_keys):
# if os.path.exists(temppath):
# os.remove(temppath)
datalist = []
dataerrlist = []
timelist = []
timeerrlist = []
colorstr = str(float((1/colornumber))*float(index + 1))
colortuple = (colorstr, 0.5, 0)
starname = 'star'+str(index)
precal_dict = {}
for image in filelist:
print '**************************************'
print 'Photometry of star' + str(index)
print 'doing image ' + image
calregion = '/calstarregs/' + os.path.basename(reg)
data = q_phot.photreturn(os.path.basename(reg), image, reg=temppath, calregion=calregion, aper=ap, auto_upper=False, caliblimit=caliblimit)
image_data = data[image]
if image[0] != filt:
raise ValueError('Filter for %s does not match the others') % (image)
if ra_str in image_data['calib_stars']:
datalist += [image_data['calib_stars'][ra_str]['new_mag']]
dataerrlist += [image_data['calib_stars'][ra_str]['new_e_mag']]
time = float(t_mid.t_mid(image, trigger=triggerid))
terr = float(t_mid.t_mid(image, trigger=triggerid,delta = True))/2.
timetuple = (time, terr)
image_data.update({'t_mid':timetuple})
timelist += [time]
timeerrlist += [terr]
dec_str = str(image_data['calib_stars'][ra_str]['dec'])[0:7]
parent_label = image
precal_dict.update({parent_label:image_data})
else:
print 'WARNING: CALIB STAR %s IS NOT USABLE FOR THIS IMAGE' % (ra_str)
datarr = array(datalist)
daterrarr = array(dataerrlist)
timarr = array(timelist)
timerrarr = array(timeerrlist)
if noerr==True:
pylab.plot(timarr,datarr,'o',label=str((ra_str,dec_str)))
else:
pylab.errorbar(timarr,datarr,yerr=daterrarr,fmt='o',label=str((ra_str,dec_str))) #star_pos_str)
caldict.update({ra_str:precal_dict})
#matplotlib.pyplot.errorbar(timarr, datarr, yerr = daterrarr, label = starname, fmt='k.', color = colortuple)
star_stdv = numpy.std(datarr)
print 'The standard deviation of the calibration stars is: (DISREGARD THIS, THIS STDV IS PROBABLY WRONG)'
print star_stdv
plottitle = '%s Calibration Stars Magnitude vs. t_mid' % (filt)
plotylabel = '%s Magnitude' % (filt)
matplotlib.pyplot.title(plottitle)
matplotlib.pyplot.xlabel('Time After Burst (s)')
matplotlib.pyplot.ylabel(plotylabel)
ax = matplotlib.pyplot.gca()
ax.set_ylim(ax.get_ylim()[::-1])
ax.set_xlim((ax.get_xlim()[0]),(ax.get_xlim()[1])*1.2)
matplotlib.pyplot.legend()
if magrange:
ax.set_ylim(magrange)
F = pylab.gcf()
DefaultSize = F.get_size_inches()
DPI = F.get_dpi()
# F.set_size_inches( (DefaultSize[0]*2.5, DefaultSize[1]*2.5) )
# was getting incresingly larger with multiple runs
F.set_size_inches((20, 15))
n_stars_str = str(n_stars)
if not out_pickle:
picklepath = storepath + unique_name + '_' + filt + '_' + 'ap' + str(ap) + '_' + n_stars_str + '_cal_stars.data'
else:
picklepath = out_pickle
filepath = storepath + unique_name + '_' + filt + '_' + 'ap' + str(ap) + '_' + n_stars_str + '_cal_stars.png'
#matplotlib.pyplot.savefig(filepath)
qPickle.save(caldict, picklepath, clobber=True)
F.savefig(filepath)
return caldict
def p_photreturn(self,outname,ap,limsigma=3.0,plotcalib=True,\
offset_calc_type='weighted_mean',clobber=False, \
utburst=None):
'''
attempt to build up same structure as the photdict from q_phot
keys: filename
'''
if utburst == None:
utburst = datetime.datetime(1858, 11, 17) #just use mjd
offset_calc_type=offset_calc_type.lower()
photdict={}
newname = self.imagefilename + '_ap' + str(ap)
filepath = storepath + 'phot_'+ outname #outname is the filepath
# if calregion:
# calibration_list = openCalRegion(calregion)
# n_calstars = len(calibration_list)
# filepath += '_WithCalReg' + str(n_calstars)
# if stardict:
# filepath += '_WithDeepStack'
filepath += '.data'
while clobber == False: # why did i make this a while loop?? lol
if os.path.isfile(filepath) == True:
data = qPickle.load(filepath)
if newname in data:
return data
else:
clobber = True
else:
clobber = True
while clobber == True:
if os.path.isfile(filepath) == False:
photdict = {}
else:
#f = file(filepath)
photdict = qPickle.load(filepath) # This line loads the pickle file, enabling photLoop to work
# create dictionary for file
data = self.do_phot(ap=ap,limsigma=limsigma,plotcalib=plotcalib,offset_calc_type=offset_calc_type)
if not data:
print "Photometry failed. No data returned."
return
#rerun to get upper limit??
label = newname
# somehow update time here?
if self.scope == 'pairitel':
tdict = {'utburst':utburst,'STOP_CPU':data['STOP_CPU'],'STRT_CPU':data['STRT_CPU']}
time = float(t_mid.t_mid(time_dict=tdict))
terr = float(t_mid.t_mid(delta = True, time_dict=tdict))/2.
timetuple = (time, terr)
data.update({'t_mid':timetuple})
elif self.scope == 'kait':
# untested
tmid = startexp2tmid(utburst,data['STRT_CPU'],data['EXPTIME'])
terr = data['EXPTIME']
timetuple = (time, terr)
data.update({'t_mid':timetuple})
photdict.update({label:data})
qPickle.save(photdict, filepath, clobber = True)
return photdict
qPickle.save(photdict, filepath, clobber = True)
return photdict
def p_photreturn(self,outname,ap,limsigma=3.0,plotcalib=True,\
offset_calc_type='weighted_mean',clobber=False, \
utburst=None):
'''
attempt to build up same structure as the photdict from q_phot
keys: filename
'''
if utburst == None:
utburst = datetime.datetime(1858, 11, 17) #just use mjd
offset_calc_type = offset_calc_type.lower()
photdict = {}
newname = self.imagefilename + '_ap' + str(ap)
filepath = storepath + 'phot_' + outname #outname is the filepath
# if calregion:
# calibration_list = openCalRegion(calregion)
# n_calstars = len(calibration_list)
# filepath += '_WithCalReg' + str(n_calstars)
# if stardict:
# filepath += '_WithDeepStack'
filepath += '.data'
while clobber == False: # why did i make this a while loop?? lol
if os.path.isfile(filepath) == True:
data = qPickle.load(filepath)
if newname in data:
return data
else:
clobber = True
else:
clobber = True
while clobber == True:
if os.path.isfile(filepath) == False:
photdict = {}
else:
#f = file(filepath)
photdict = qPickle.load(
filepath
) # This line loads the pickle file, enabling photLoop to work
# create dictionary for file
data = self.do_phot(ap=ap,
limsigma=limsigma,
plotcalib=plotcalib,
offset_calc_type=offset_calc_type)
if not data:
print "Photometry failed. No data returned."
return
#rerun to get upper limit??
label = newname
# somehow update time here?
if self.scope == 'pairitel':
tdict = {
'utburst': utburst,
'STOP_CPU': data['STOP_CPU'],
'STRT_CPU': data['STRT_CPU']
}
time = float(t_mid.t_mid(time_dict=tdict))
terr = float(t_mid.t_mid(delta=True, time_dict=tdict)) / 2.
timetuple = (time, terr)
data.update({'t_mid': timetuple})
elif self.scope == 'kait':
# untested
tmid = startexp2tmid(utburst, data['STRT_CPU'],
data['EXPTIME'])
terr = data['EXPTIME']
timetuple = (time, terr)
data.update({'t_mid': timetuple})
photdict.update({label: data})
qPickle.save(photdict, filepath, clobber=True)
return photdict
qPickle.save(photdict, filepath, clobber=True)
return photdict