for name in name_list:
csvs = glob.glob("../data/%s/split/*.csv"%name)
csv_lists[name] = csvs
total_num_csv += len(csvs)
asprta = np.load('../data/photon_list/%s_asp.npy'%name)
sky_data = SkyCoord(asprta[:,1:3], unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
asprta = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
gal_l.append(np.mean(asprta[:,0]))
skypos = [np.mean(gal_l), 0.]
skyrange = [np.max(gal_l)-np.min(gal_l)+2., 24.]
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix_g(skypos, skyrange, 0.002)
wcs = imagetools.define_wcs_g(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.002)
count = np.zeros(imsz)
print skypos, skyrange
print imsz
step = 10
total_p_num = 10
manager = Manager()
return_dict = manager.dict()
p_list = []
pid = 0
for name in name_list:
csvs = csv_lists[name]
flat =flat_hdu[0].data
size=flat_hdu[0].header['CDELT2']
#flat = np.load('/scratch/dw1519/galex/data/star_photon/super_iterate3/flat.npy')
resample=3 #4
hrflat = scipy.ndimage.interpolation.zoom(flat, resample, order=0,prefilter=False)#scipy.ndimage.interpolation.zoom(flat,size/pixsz,order=1,prefilter=False)
print hrflat.shape
#hrflat = np.swapaxes(hrflat,0,1)
#print hrflat.shape
#hrflat = scipy.ndimage.interpolation.rotate(hrflat,-23.4,reshape=False,order=1,prefilter=False)
#print hrflat.shape
skypos = [np.mean(gal_l), 0.]
#skyrange = [np.max(gal_l)-np.min(gal_l)+1.5, 21.5]
skyrange = [1.55, 21.5]
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix_g(skypos, skyrange, pixsz).astype(int)
wcs = imagetools.define_wcs_g(skypos,skyrange,width=False,height=False,verbose=0,pixsz=pixsz)
focs = wcs.sip_pix2foc(wcs.wcs_world2pix(coo,0),0)
idx = np.argsort(focs[:,1])
focs = focs[idx]
asp_solution = asp_solution[idx]
dead = dead[idx]
cut = cut[idx]
foc_list = np.array_split(focs, num_t)
asp_solutions = np.array_split(asp_solution, num_t)
deads = np.array_split(dead, num_t)
cuts = np.array_split(cut, num_t)
flat, resample, order=0, prefilter=False
) #scipy.ndimage.interpolation.zoom(flat,size/pixsz,order=1,prefilter=False)
print hrflat.shape
hrflat = np.swapaxes(hrflat, 0, 1)
print hrflat.shape
hrflat = scipy.ndimage.interpolation.rotate(hrflat,
-23.4,
reshape=False,
order=1,
prefilter=False)
print hrflat.shape
skypos = [np.mean(gal_l), 0.]
skyrange = [np.max(gal_l) - np.min(gal_l) + 1.5, 21.5]
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix_g(skypos, skyrange, pixsz).astype(int)
wcs = imagetools.define_wcs_g(skypos,
skyrange,
width=False,
height=False,
verbose=0,
pixsz=pixsz)
manager = Manager()
return_dict = manager.dict()
p_list = []
for file_num in range(0, num_t):
p = Process(target=exp_dead_new,
args=(file_num, name_file, imsz, wcs, hrflat,
asp_solutions[file_num], deads[file_num],
asprta = np.concatenate(
(np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
print np.min(asprta[:, 0])
print np.max(asprta[:, 0])
print asprta
gal_l.append(np.mean(asprta[:, 0]))
skypos = [np.mean(gal_l), 0.]
print skypos
print np.max(gal_l)
print np.min(gal_l)
skyrange = [np.max(gal_l) - np.min(gal_l) + 2., 22.]
print skyrange
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix_g(skypos, skyrange, pixsz)
wcs = imagetools.define_wcs_g(skypos,
skyrange,
width=False,
height=False,
verbose=0,
pixsz=pixsz)
count = np.zeros(imsz)
print skypos, skyrange
print imsz
detector_size = [50, 50]
dis_map_l = np.load('../data/distortion/5-185-xa_low-centroids.npy')
dis_map_h = np.load('../data/distortion/5-185-xa_high-centroids.npy')
def run_one_r_sec(pid, scan_name, step, start, end, return_dict):
pixsz = 0.000416666666666667
skypos_count = [5.,-9.5]
skyrange_count = [2., 3.]
tranges_count = [[0,0]]
imsz_count = imsz = imagetools.deg2pix_g(skypos_count, skyrange_count, pixsz)
wcs_count = imagetools.define_wcs_g(skypos_count,skyrange_count,width=False,height=False,verbose=0,pixsz=pixsz)
count = np.zeros(imsz_count)
num_co = int(1/step)
hdulist = pyfits.open('../AIS_GAL_SCAN/asprta/%s-asprta.fits'%scan_name)
co_data = hdulist[1].data
length = co_data.shape[0]
cal_initial = int((co_data[1][0]-0.5)*1000)
offsets = np.load('../data/%s/cata/offsets_inter_half_sec.npy'%scan_name)
angle_list = [-0.035, -0.03, -0.025, -0.02, -0.015, -0.01, -0.005, 0, 0.005, 0.01, 0.015, 0.02, 0.025, 0.03, 0.035]
print length
for initial_sec in range(start, end):
print 'time:%d'%initial_sec
file_path = '../data/%s/cata/centroids_sec%d.npy'%(scan_name, initial_sec)
'''
if os.path.exists(file_path):
print 'skip:%d'%initial_sec
continue
'''
intitial_asp = co_data[initial_sec]
rot = intitial_asp[3]
center = np.array([intitial_asp[1], intitial_asp[2]])
#use the first order calibrated asp instead of the original one
center = cal_asp_r(offsets, cal_initial, intitial_asp[0]*1000, intitial_asp[1:3], 200)
#print(intitial_asp)
skypos = [0.0, 0.0]
skyrange = [0.02, 0.02]
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.0001)
initial_time = intitial_asp[0]-0.5
tranges = []
for sec in range(num_co):
tranges.append([initial_time+step*sec, initial_time+step*(sec+1)])
print tranges
data = get_data_cal(tranges, scan_name, cal_initial, offsets)
print len(data)
output = "../data/%s/cata/%s.csv"%(scan_name, scan_name)
dir = os.path.dirname(output)
if not os.path.exists(dir):
os.makedirs(dir)
centroids = []
for sec in range(num_co):
if len(data[sec]) ==0:
centroid = np.array([0.0, 0.0])
centroids.append(centroid)
print centroid
continue
coo1 = np.array(data[sec], dtype='float64')[:,-3:-1]
coo2 = catalog_fits.get_catalog(center, 0.69)
coo1 = angle_filter(coo1, center, 1.)
coo2 = angle_filter(coo2, center, 1.)
xi, eta = gn.gnomfwd_simple(coo1[:,0], coo1[:,1], center[0], center[1], -rot, 1/36000., 0.0)
max_now = -1000
cent_now = []
for angle in angle_list:
coo1[:,0], coo1[:,1] = gn.gnomrev_simple(xi,eta,center[0],center[1],-(rot+angle),1/36000.,0.)
centroid, max_value = get_corr_map(coo2, coo1, skypos, skyrange, sec, 0.02, 0.004)
if max_value>max_now:
max_now = max_value
cent_now = np.append(centroid, angle) #centroid.append(angle)
coo1[:,0], coo1[:,1] = gn.gnomrev_simple(xi,eta,center[0]-cent_now[0],center[1]-cent_now[1],-(rot+cent_now[2]),1/36000.,0.)
sky_data = SkyCoord(coo1, unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
data_count = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
H = hist_g(data_count, wcs_count, imsz_count)
count += H
sky_data=gal=data_count=H = None
gc.collect()
#xi2, eta2 = gn.gnomfwd_simple(coo2[:,0], coo2[:,1], center[0], center[1], -rot, 1/36000., 0.0)
#r, phi = cart2pol(xi, eta)
#r2, phi2 = cart2pol(xi2, eta2)
#coo1 = np.array([r,phi]).T
#coo2 = np.array([r2,phi2]).T
#get_corr_map(coo2, coo1, skypos, skyrange, sec, 0.05, 0.0015)
centroids.append(cent_now)
print cent_now
print centroids
#np.save('../data/%s/cata/centroids_rot%d.npy'%(scan_name, initial_sec), centroids)
hdu = pyfits.PrimaryHDU(count)
hdu = imagetools.fits_header('NUV', skypos_count, tranges_count, skyrange_count, hdu=hdu, wcs=wcs_count)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('../fits/count_map_%s_gal_sec_rot_count.fits'%(scan_name), clobber=False)
gal = sky_data.transform_to(Galactic)
asprta = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
print np.min(asprta[:,0])
print np.max(asprta[:,0])
print asprta
gal_l.append(np.mean(asprta[:,0]))
skypos = [np.mean(gal_l), 0.]
print skypos
print np.max(gal_l)
print np.min(gal_l)
skyrange = [np.max(gal_l)-np.min(gal_l)+2., 22.]
print skyrange
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix_g(skypos, skyrange, pixsz)
wcs = imagetools.define_wcs_g(skypos,skyrange,width=False,height=False,verbose=0,pixsz=pixsz)
count = np.zeros(imsz)
print skypos, skyrange
print imsz
detector_size = [50,50]
dis_map_l = np.load('../data/distortion/5-185-xa_low-centroids.npy')
dis_map_h = np.load('../data/distortion/5-185-xa_high-centroids.npy')
dis_map_l = dis_map_l.reshape(50*50,2)/detector_size[0]*36000*800*0.001666
dis_map_h = dis_map_h.reshape(50*50,2)/detector_size[0]*36000*800*0.001666
cen_x = np.arange(detector_size[0])+0.5
cen_y = np.arange(detector_size[1])+0.5
xi = np.repeat(detector2gon(cen_x, detector_size[0]), detector_size[1])
asp_solution = np.concatenate(asp_solution_list, axis=0)
sky_data = SkyCoord(asp_solution[:, 1:3],
unit='deg',
frame=FK5,
equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
asp_solution[:, 1:3] = np.concatenate(
(np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
asp_solutions = np.array_split(asp_solution, num_t)
skypos = [np.mean(gal_l), 0.]
skyrange = [np.max(gal_l) - np.min(gal_l) + 2., 24.]
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix_g(skypos, skyrange, 0.00166667)
wcs = imagetools.define_wcs_g(skypos,
skyrange,
width=False,
height=False,
verbose=0,
pixsz=0.00166667)
count = np.zeros(imsz)
cata = spi.load_obj('../data/%s_starset_extra_all_star' % scan_name)
cata_a = np.array(list(cata))
cata_len = len(cata_a)
print cata_a
mask_faint = cata_a[:, 3] < 40
