def exp_dead_new(file_num, name_file, imsz, wcs, flat_list, foc_list, asp_solution, dead, cut, flat_idx, step, out_path, return_dict):
print imsz
count = np.zeros(imsz)
x_lim = imsz[0]
y_lim = imsz[1]
length = flat_list[0].shape[0]
half_len = length/2.
print half_len
l = imsz[0]/10
start = foc_list[0,1]-half_len
print foc_list.shape
print start.shape
ox = np.repeat(np.arange(l)+start,length+1000)
oy = np.tile(np.arange(length+1000)+foc_list[0,0]-half_len-500,l)
omask = (ox>=0) & (ox<imsz[0]) & (oy>=0) & (oy<imsz[1])
ox = ox[omask]
oy = oy[omask]
gl,gb = wcs.all_pix2world(oy,ox,0)
c = SkyCoord(gl*u.degree, gb*u.degree, frame='galactic')
rd = c.transform_to(FK5)
for i in range(asp_solution.shape[0]):
hrflat = flat_list[flat_idx[i]]
foc = foc_list[i,:]#wcs.sip_pix2foc(wcs.wcs_world2pix(coo,1),1)
if (foc[1]+half_len)>=(start+l):
print 'update'
start = foc[1]-half_len
ox = np.repeat(np.arange(l)+start,length+1000)
oy = np.tile(np.arange(length+1000)+foc[0]-half_len-500,l)
omask = (ox>=0) & (ox<imsz[0]) & (oy>=0) & (oy<imsz[1])
if np.sum(omask)==0:
break
ox = ox[omask]
oy = oy[omask]
gl,gb = wcs.all_pix2world(oy,ox,0)
c = SkyCoord(gl*u.degree, gb*u.degree, frame='galactic')
rd = c.transform_to(FK5)
fmask = (ox>=(foc[1]-length/2)) & (ox<(foc[1]+length/2)) & (oy>=(foc[0]-length/2)) & (oy<(foc[0]+length/2))
if np.sum(fmask)==0:
continue
x = ox[fmask]
y = oy[fmask]
xi, eta = gn.gnomfwd_simple(rd.ra.deg[fmask], rd.dec.deg[fmask],
asp_solution[i,1], asp_solution[i,2], -asp_solution[i,3],1/36000.,0.)
px = ((xi/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*length
py = ((eta/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*length
pmask = (px>=0) & (px<length) & (py>=0) & (py<length)
if np.sum(pmask)==0:
continue
count[x[pmask].astype(int),y[pmask].astype(int)] += \
hrflat[px[pmask].astype(int),py[pmask].astype(int)]*step*(1-dead[i])*cut[i]
if i%100==0:
with open('/scratch/dw1519/galex/fits/scan_map/%s_gal_sec_exp_tmp%d.dat'%(name_file, file_num),'w') as f:
f.write('%d'%i)
print i
print '%d done'%file_num
#return_dict[file_num] = count
np.save('%s/%s_gal_sec_exp_tmp%d.npy'%(out_path, name_file, file_num), count)
def rrhr(band,skypos,tranges,skyrange,width=False,height=False,stepsz=1.,
verbose=0,calpath='../cal/',tscale=1000.,response=True,hdu=False,
retries=20):
"""Generate a high resolution relative response (rrhr) map."""
imsz = gxt.deg2pix(skypos,skyrange)
# TODO the if width / height
flat = get_fits_data(flat_filename(band,calpath),verbose=verbose)
flatinfo = get_fits_header(flat_filename(band,calpath))
npixx,npixy = flat.shape
fltsz = flat.shape
pixsz = flatinfo['CDELT2']
detsize = 1.25
# Rotate the flat into the correct orientation to start.
flat = np.flipud(np.rot90(flat))
# NOTE: This upsample interpolation is done _last_ in the canonical
# pipeline as part of the poissonbg.c routine.
# The interpolation function is "congrid" in the same file.
# TODO: Should this be first order interpolation? (i.e. bilinear)
hrflat = scipy.ndimage.interpolation.zoom(flat,4.,order=0,prefilter=False)
img = np.zeros(hrflat.shape)[
hrflat.shape[0]/2.-imsz[0]/2.:hrflat.shape[0]/2.+imsz[0]/2.,
hrflat.shape[1]/2.-imsz[1]/2.:hrflat.shape[1]/2+imsz[1]/2.]
for trange in tranges:
t0,t1=trange
entries = gQuery.getArray(gQuery.aspect(t0,t1),retries=retries)
n = len(entries)
asptime = np.float64(np.array(entries)[:,2])/tscale
aspra = np.float32(np.array(entries)[:,3])
aspdec = np.float32(np.array(entries)[:,4])
asptwist= np.float32(np.array(entries)[:,5])
aspflags= np.float32(np.array(entries)[:,6])
asptwist= np.float32(np.array(entries)[:,9])
aspra0 = np.zeros(n)+skypos[0]
aspdec0 = np.zeros(n)+skypos[1]
xi_vec, eta_vec = gnomonic.gnomfwd_simple(
aspra,aspdec,aspra0,aspdec0,-asptwist,1.0/36000.,0.)
col = 4.*( ((( xi_vec/36000.)/(detsize/2.)*(detsize/(fltsz[0]*pixsz)) + 1.)/2. * fltsz[0]) - (fltsz[0]/2.) )
row = 4.*( (((eta_vec/36000.)/(detsize/2.)*(detsize/(fltsz[1]*pixsz)) + 1.)/2. * fltsz[1]) - (fltsz[1]/2.) )
vectors = rotvec(np.array([col,row]),-asptwist)
for i in range(n):
if verbose>1:
print_inline('Stamping '+str(asptime[i]))
# FIXME: Clean this mess up a little just for clarity.
img += scipy.ndimage.interpolation.shift(scipy.ndimage.interpolation.rotate(hrflat,-asptwist[i],reshape=False,order=0,prefilter=False),[vectors[1,i],vectors[0,i]],order=0,prefilter=False)[hrflat.shape[0]/2.-imsz[0]/2.:hrflat.shape[0]/2.+imsz[0]/2.,hrflat.shape[1]/2.-imsz[1]/2.:hrflat.shape[1]/2+imsz[1]/2.]*dbt.compute_exptime(band,[asptime[i],asptime[i]+1],verbose=verbose,retries=retries)*gxt.compute_flat_scale(asptime[i]+0.5,band,verbose=0)
return img
def run_one_r_sec(pid, scan_name, step, resolution, duration, asp_cal, start, end, dis_map, return_dict):
skypos = [0.0, 0.0]
skyrange = [0.02, 0.02]
if step == 1:
skyrange = [0.04, 0.04]#[0.3, 0.3]
elif step == 0.5:
skyrange = [0.02, 0.02]
data = np.loadtxt()
for asp in asp_cal:
time = asp[0]
print time
center = asp[1:3]
aperture = 0.69
stars = catalog_fits.get_catalog_tycho(center, aperture)
xi, eta = gn.gnomfwd_simple (stars[:,0], stars[:,1], center[0], center[1], -asp[3], 1/36000., 0)
photons = data[data[:,0]==int(time*1000)]
co =
arg = np.argmin(np.absolute(asp_cal[:,0]-time_c[sec]))
center = asp_cal[arg, 1:3]
data_sec = dis_correct(np.array(data[sec], dtype='float64'), dis_map, asp_cal)
coo1 = np.array(data_sec, dtype='float64')
aperture = 0.69
#coo1 = np.array(data[sec], dtype='float64')[:,-3:-1]
coo1, mask = angle_filter(coo1, center, 0.6)
time_sec = np.array(data[sec], dtype='float64')[mask,0]/1000.
weights = gaussian(time_sec-time_c[sec], 0, np.std(time_sec-time_c[sec]))
coo2, mask = angle_filter(coo2, center, 0.6)
#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:
centroid, max_value, flux = get_corr_map(coo2, coo1, skypos, skyrange, sec, 0.0002, initial_sec, '1', weights)
if max_value>max_now:
max_now = max_value
cent_now = np.append(centroid, angle) #centroid.append(angle)
centroids.append(cent_now)
print centroids
np.save('../data/%s/cata/time_rot%d.npy'%(scan_name, initial_sec), center_time)
np.save('../data/%s/cata/centroids_rot%d.npy'%(scan_name, initial_sec), centroids)
def exp_dead_new(file_num, name_file, imsz, wcs, hrflat, asp_solution, dead,
cut, step, return_dict):
count = np.zeros(imsz)
rotate = scipy.ndimage.interpolation.rotate
x_lim = imsz[0]
y_lim = imsz[1]
length = hrflat.shape[0]
half_len = length / 2.
print half_len
coo = asp_solution[:, 1:3]
foc_list = wcs.sip_pix2foc(wcs.wcs_world2pix(coo, 0), 0)
print asp_solution.shape[0]
ox = np.repeat(np.arange(length), length)
oy = np.tile(np.arange(length), length)
ocx = length / 2
ocy = length / 2
for i in range(asp_solution.shape[0]):
foc = foc_list[i, :] #wcs.sip_pix2foc(wcs.wcs_world2pix(coo,1),1)
x = ox + (foc[1] - ocx)
y = oy + (foc[0] - ocy)
fmask = (x >= 0) & (x < imsz[0]) & (y >= 0) & (y < imsz[1])
x = x[fmask]
y = y[fmask]
gl, gb = wcs.all_pix2world(y, x, 0)
c = SkyCoord(gl * u.degree, gb * u.degree, frame='galactic')
rd = c.transform_to(FK5)
xi, eta = gn.gnomfwd_simple(rd.ra.deg, rd.dec.deg, asp_solution[i, 1],
asp_solution[i, 2], -asp_solution[i, 3],
1 / 36000., 0.)
px = ((xi / 36000.) / (1.25 / 2.) *
(1.25 / (800 * 0.001666)) + 1.) / 2. * length
py = ((eta / 36000.) / (1.25 / 2.) *
(1.25 / (800 * 0.001666)) + 1.) / 2. * length
pmask = (px >= 0) & (px < length) & (py >= 0) & (py < length)
count[x[pmask].astype(int),y[pmask].astype(int)] = \
hrflat[px[pmask].astype(int),py[pmask].astype(int)]*step*(1-dead[i])*cut[i]
if i % 2000 == 0:
with open(
'/scratch/dw1519/galex/fits/scan_map/%s_gal_sec_exp_tmp%d.dat'
% (name_file, file_num), 'w') as f:
f.write('%d' % i)
print i
print '%d done' % file_num
return_dict[file_num] = count
def exp_dead_new(file_num, name_file, imsz, wcs, hrflat, asp_solution, dead, cut, step, return_dict):
count = np.zeros(imsz)
rotate = scipy.ndimage.interpolation.rotate
x_lim = imsz[0]
y_lim = imsz[1]
length = hrflat.shape[0]
half_len = length/2.
print half_len
coo = asp_solution[:,1:3]
foc_list = wcs.sip_pix2foc(wcs.wcs_world2pix(coo,0),0)
print asp_solution.shape[0]
ox = np.repeat(np.arange(length),length)
oy = np.tile(np.arange(length),length)
ocx = length/2
ocy = length/2
for i in range(asp_solution.shape[0]):
foc = foc_list[i,:]#wcs.sip_pix2foc(wcs.wcs_world2pix(coo,1),1)
x = ox+(foc[1]-ocx)
y = oy+(foc[0]-ocy)
fmask = (x>=0) & (x<imsz[0]) & (y>=0) & (y<imsz[1])
x = x[fmask]
y = y[fmask]
gl,gb = wcs.all_pix2world(y,x,0)
c = SkyCoord(gl*u.degree, gb*u.degree, frame='galactic')
rd = c.transform_to(FK5)
xi, eta = gn.gnomfwd_simple(rd.ra.deg, rd.dec.deg,
asp_solution[i,1], asp_solution[i,2], -asp_solution[i,3],1/36000.,0.)
px = ((xi/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*length
py = ((eta/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*length
pmask = (px>=0) & (px<length) & (py>=0) & (py<length)
count[x[pmask].astype(int),y[pmask].astype(int)] = \
hrflat[px[pmask].astype(int),py[pmask].astype(int)]*step*(1-dead[i])*cut[i]
if i%2000==0:
with open('/scratch/dw1519/galex/fits/scan_map/%s_gal_sec_exp_tmp%d.dat'%(name_file, file_num),'w') as f:
f.write('%d'%i)
print i
print '%d done'%file_num
return_dict[file_num] = count
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)
photon_list = photon_list[qmask]
star_list = np.load('/scratch/dw1519/galex/data/star_photon/back/' +
scan + '_bstar.npy')
print star_list.shape
stars, ct = np.unique(photon_list[:, 0], return_counts=True)
print stars.shape
star_mask = (ct > 10)
print np.sum(star_mask)
stars = stars[star_mask]
for star in stars:
star_mask = (photon_list[:, 0] == star)
p = photon_list[star_mask]
ix = np.digitize(p[:, 1] / 1000., asp[:, 0]) - 1
xi, eta = gn.gnomfwd_simple(c_radec[star].ra.deg,
c_radec[star].dec.deg, asp[ix, 1],
asp[ix,
2], -asp[ix, 3], 1 / 36000., 0.)
pos = np.array([xi, eta]).T
pos = ((pos / 36000.) / (1.25 / 2.) *
(1.25 / (800 * 0.001666)) + 1.) / 2. * size
Hc, xedge, yedge = np.histogram2d(pos[:, 0],
pos[:, 1],
bins=[size, size],
range=([[0, size], [0, size]]))
xi, eta = gn.gnomfwd_simple(c_radec[star].ra.deg,
c_radec[star].dec.deg, asp[:, 1],
asp[:, 2], -asp[:, 3], 1 / 36000., 0.)
pos = np.array([xi, eta]).T
pos = ((pos / 36000.) / (1.25 / 2.) *
def main_res():
name = 'AIS_GAL_SCAN_00032_0001'
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00032_0001_asp_cal.npy')
asp_uni, uni_index=np.unique(asp[:,0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:,0]-asp_uni[0,0])+1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits'%name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0]-1
ix_tmp[ix_tmp>limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
cata = spi.load_obj('../data/%s_starset_extra_full'%name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector"%name)
star_list = sorted(star_list, key=getKey)
#print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1,len(star_list)):
if star_list[i][2] - istar[2]<=100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
flux_stat = []
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
print('group_num:%d'%group_num)
plot_num = 0
xlim = [0,60]
if len(plot) == 1:
group_num += 1
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d'%i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv"%(name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv"%(name, star_num)
star_co = cata_a[i, 0:2]
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
bkg_count, edges = np.histogram(bkg_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
index_0 = (star_data[0,0]-asp_uni[0,0]*1000)/5
index_1 = (star_data[-1,0]-asp_uni[0,0]*1000)/5
#print i, cata_list[i, 0]
#print star_co
#print np.median(star_data[:,-3:-1], axis=0)
if index_1-index_0>12400:
continue
star_track = asp_uni[index_0:index_1+1,:]
dead_t = dead_tmp[index_0:index_1+1]
ra = np.ones(star_track.shape[0])*star_co[0]
dec = np.ones(star_track.shape[0])*star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:,1], star_track[:,2], -star_track[:,3], 1/36000., 0.0)
res_list = res_star.get_res_new(xi, eta, '../fits/flat/NUV_flat.fits')
if res_list == None:
continue
res_list_d = res_list*(1-dead_t)
res_flux = running_sum(res_list_d, 200, 61)
res_len = cut_tail(res_flux)
star_flux = np.array(star_count) - np.array(bkg_count)*64./40.
star_flux[star_flux<0] = 0
star_len = cut_tail(star_flux)
delta = star_len-res_len
if delta>0 and delta<3:
star_flux = star_flux[:star_len]
res_flux = res_flux[:star_len]
elif delta >= 3 or delta<=-3:
continue
else:
star_flux = star_flux[:res_len]
res_flux = res_flux[:res_len]
if np.mean(star_flux)<5:
continue
res_flux = res_flux/np.mean(res_flux)
star_flux = star_flux/np.mean(star_flux)
time_array = np.arange(res_flux.shape[0])
var, likeli = likelihood(star_flux, res_flux)
mag = cata_list[i, 5]
flux_stat.append((mag, likeli, var, star_flux, res_flux))
print '%.3f %.3f %.3f'%(mag, var, likeli)
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append((time_array, star_flux, i, cata_list[i, 5], res_flux))
if len(flux_list)>1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
res_flux = flux_list[plot_num][-1]
axes[plot_num,0].plot(time_array, star_flux, '-o')
axes[plot_num,0].plot(time_array, res_flux, '-o')
axes[plot_num,0].text(0.95, 0.8, 'NUV: %.3f'%flux_list[plot_num][3], verticalalignment='bottom', horizontalalignment='right', transform=axes[plot_num,0].transAxes, color='green', fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num,0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num,0].get_xlim()
axes[plot_num,0].set_xlim([0, 61])
axes[plot_num,0].set_ylim([ylim[0]+np.absolute(ylim[1]-ylim[0])*0.01, ylim[1]])
if plot_num<len(flux_list)-1:
plt.setp( axes[plot_num,0].get_xticklabels(), visible=False)
else:
axes[plot_num,0].set_xlabel('time/s')
plt.setp( axes[plot_num,0].get_yticklabels(), visible=False)
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=0, hspace=0)
plt.legend()
plt.savefig('../plots/%s/star/extra/cons_res_flat/cons%d_track.png'%(name, group_num),dpi=190)
plt.clf()
group_num += 1
save_obj(flux_stat, '../plots/%s/star/extra/cons_res_flat/flux_stat'%(name))
def res_star_new(pid, scan_name, start, end, return_dict):
cata = spi.load_obj('../data/%s_starset_new' % scan_name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00014_0001_asp_cal.npy')
asp_uni, uni_index = np.unique(asp[:, 0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:, 0] - asp_uni[0, 0]) + 1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits' %
scan_name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0] - 1
ix_tmp[ix_tmp > limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
for star_num in range(start, end + 1):
star_co = cata_a[star_num, 0:2]
order = 1
star = 'star_%d-%d' % (star_num, order)
if not os.path.exists('../data/%s/star/list/%s.csv' %
(scan_name, star)):
print 'skip:%s' % star
continue
csv_file = '../data/%s/star/list/%s.csv' % (scan_name, star)
count = []
time_list = []
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
first = float(reader.next()[0])
final = 0
last = 0
reader = csv.reader(file)
for row in reader:
time = float(row[0]) #time = int(float(row[0])/1000.)
if time - last > 200:
count.append(1)
last = time
time_list.append(last)
else:
count[-1] += 1
final = time
time_list = np.array(time_list) / 1000.
index_0 = (first - asp_uni[0, 0] * 1000) / 5
index_1 = (final - asp_uni[0, 0] * 1000) / 5
star_track = asp_uni[index_0:index_1 + 1, :]
dead_t = dead_tmp[index_0:index_1 + 1]
ra = np.ones(star_track.shape[0]) * star_co[0]
dec = np.ones(star_track.shape[0]) * star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:, 1], star_track[:,
2],
-star_track[:, 3], 1 / 36000., 0.0)
output = "../plots/%s/star/res/%s.csv" % (scan_name, scan_name)
dir = os.path.dirname(output)
if not os.path.exists(dir):
os.makedirs(dir)
print dead_t
res_list = get_res_new(xi, eta)
res_list_d = res_list * (1 - dead_t)
'''
plt.plot(star_track[:,0], res_list_d)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track_d.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
plt.plot(star_track[:,0], res_list)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
'''
f, axes = plt.subplots(2, 1)
axes[0].plot(time_list, count)
axes[0].set_ylabel('Num of Photons')
ylim = axes[0].get_ylim()
axes[0].set_ylim(ylim + np.absolute(ylim) * 0.01)
plt.setp(axes[0].get_xticklabels(), visible=False)
#plt.setp( axes[0].get_yticklabels(), visible=False)
axes[1].plot(star_track[:, 0], res_list_d)
axes[1].set_ylabel('Response')
axes[1].set_xlabel('time/s')
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
plt.savefig('../plots/%s/star/res/2p/res%d-%d_track.png' %
(scan_name, star_num, order),
dpi=190)
plt.clf()
def run_model_chunk(pid, scan_name, chunk, cata_target, cata_lists, size_list,
cata_target_len, dead_tmp, asp_uni, return_dict):
count_list = []
exp_list = []
chunk_num = 0
for trange in chunk:
output = '../fits/%s/extra/new_black/exposure_chunk%d.fits' % (
scan_name, chunk_num)
if os.path.exists(output):
print 'exists %d' % (chunk_num)
chunk_num += 1
continue
t_mask = np.zeros(asp_uni.shape[0])
begin = np.where((asp_uni[:, 0] * 1000).astype(int) -
trange[0] == 0)[0]
end = np.where((asp_uni[:, 0] * 1000).astype(int) - trange[1] == 0)[0]
t_mask[begin:end + 1] = 1
t_asp = asp_uni[t_mask > 0]
t_dead = dead_tmp[t_mask > 0]
t_length = end + 1 - begin
print('time length:%d, %f, %f' % (t_length, trange[0], trange[1]))
count = np.zeros([t_length, 800, 800])
exp_count = np.zeros([t_length, 800, 800])
for star_num in range(cata_target_len):
#print star_num
star_co = cata_target[star_num, 0:2]
star_flux = cata_target[star_num, 3]
#print star_flux
ra = np.ones(t_asp.shape[0]) * star_co[0]
dec = np.ones(t_asp.shape[0]) * star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, t_asp[:, 1], t_asp[:, 2],
-t_asp[:, 3], 1 / 36000., 0.0)
coo = np.array([xi, eta]).T
coo = ((coo / 36000.) / (1.25 / 2.) *
(1.25 / (800 * 0.001666)) + 1.) / 2. * 800
for i in [-1, 0, 1]:
for j in [-1, 0, 1]:
coo_tmp = np.zeros([t_length, 3])
coo_tmp[:, 0] = np.arange(t_length)
coo_tmp[:, 1] = coo[:, 0] + i
coo_tmp[:, 2] = coo[:, 1] + j
mask_0 = np.logical_and(coo_tmp[:, 1] >= 0,
coo_tmp[:, 1] < 800)
mask_1 = np.logical_and(coo_tmp[:, 2] >= 0,
coo_tmp[:, 2] < 800)
mask = np.logical_and(mask_0, mask_1)
total = 1 + 4 * 0.0625 + 4 * 0.00390625
coo_tmp = coo_tmp[mask]
t_dead_tmp = t_dead[mask]
coo_tmp = np.array(coo_tmp, dtype=int)
factor = 1.
if (i, j) in set([(-1, 0), (1, 0), (0, -1), (0, 1)]):
factor = 0.0625
elif (i, j) in set([(-1, -1), (1, -1), (1, 1), (-1, 1)]):
factor = 0.00390625
else:
factor = 1.
#count[coo_tmp[:,0], coo_tmp[:,1]] += 0.005*factor*star_flux/total
count[coo_tmp[:, 0], coo_tmp[:, 1],
coo_tmp[:, 2]] += factor * star_flux / total
for mask_num in range(3):
mask_cata = cata_lists[mask_num]
size = size_list[mask_num]
radius = size[-1] + 0.5
for star_num in range(mask_cata.shape[0]):
#print star_num
star_co = mask_cata[star_num, 0:2]
#print star_flux
ra = np.ones(t_asp.shape[0]) * star_co[0]
dec = np.ones(t_asp.shape[0]) * star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, t_asp[:, 1], t_asp[:, 2],
-t_asp[:, 3], 1 / 36000., 0.0)
coo = np.array([xi, eta]).T
coo = ((coo / 36000.) / (1.25 / 2.) *
(1.25 / (800 * 0.001666)) + 1.) / 2. * 800
for i in size:
for j in size:
if i**2 + j**2 > radius**2:
continue
coo_tmp = np.zeros([t_length, 3])
coo_tmp[:, 0] = np.arange(t_length)
coo_tmp[:, 1] = coo[:, 0] + i
coo_tmp[:, 2] = coo[:, 1] + j
mask_0 = np.logical_and(coo_tmp[:, 1] >= 0,
coo_tmp[:, 1] < 800)
mask_1 = np.logical_and(coo_tmp[:, 2] >= 0,
coo_tmp[:, 2] < 800)
mask = np.logical_and(mask_0, mask_1)
coo_tmp = coo_tmp[mask]
coo_tmp = np.array(coo_tmp, dtype=int)
exp_count[coo_tmp[:, 0], coo_tmp[:, 1],
coo_tmp[:, 2]] -= 1
for t_step in range(t_length):
exp_count[t_step][
exp_count[t_step] >= 0] = 0.005 * (1 - t_dead[t_step])
exp_count[exp_count < 0] = 0.
count *= exp_count
exp_count_sum = np.sum(exp_count, axis=0)
count_sum = np.sum(count, axis=0)
exp_list.append(exp_count_sum)
count_list.append(count_sum)
exp_count = count = None
gc.collect()
chunk_num += 1
return_dict[pid] = (count_list, exp_list)
def main_res():
name = 'AIS_GAL_SCAN_00032_0001'
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00032_0001_asp_cal.npy')
asp_uni, uni_index = np.unique(asp[:, 0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:, 0] - asp_uni[0, 0]) + 1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits' % name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0] - 1
ix_tmp[ix_tmp > limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
cata = spi.load_obj('../data/%s_starset_extra_full' % name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector" % name)
star_list = sorted(star_list, key=getKey)
#print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1, len(star_list)):
if star_list[i][2] - istar[2] <= 100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
flux_stat = []
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
print('group_num:%d' % group_num)
plot_num = 0
xlim = [0, 60]
if len(plot) == 1:
group_num += 1
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d' % i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv" % (
name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv" % (
name, star_num)
star_co = cata_a[i, 0:2]
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
bkg_count, edges = np.histogram(bkg_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
index_0 = (star_data[0, 0] - asp_uni[0, 0] * 1000) / 5
index_1 = (star_data[-1, 0] - asp_uni[0, 0] * 1000) / 5
#print i, cata_list[i, 0]
#print star_co
#print np.median(star_data[:,-3:-1], axis=0)
if index_1 - index_0 > 12400:
continue
star_track = asp_uni[index_0:index_1 + 1, :]
dead_t = dead_tmp[index_0:index_1 + 1]
ra = np.ones(star_track.shape[0]) * star_co[0]
dec = np.ones(star_track.shape[0]) * star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:, 1],
star_track[:, 2], -star_track[:, 3],
1 / 36000., 0.0)
res_list = res_star.get_res_new(xi, eta,
'../fits/flat/NUV_flat.fits')
if res_list == None:
continue
res_list_d = res_list * (1 - dead_t)
res_flux = running_sum(res_list_d, 200, 61)
res_len = cut_tail(res_flux)
star_flux = np.array(star_count) - np.array(bkg_count) * 64. / 40.
star_flux[star_flux < 0] = 0
star_len = cut_tail(star_flux)
delta = star_len - res_len
if delta > 0 and delta < 3:
star_flux = star_flux[:star_len]
res_flux = res_flux[:star_len]
elif delta >= 3 or delta <= -3:
continue
else:
star_flux = star_flux[:res_len]
res_flux = res_flux[:res_len]
if np.mean(star_flux) < 5:
continue
res_flux = res_flux / np.mean(res_flux)
star_flux = star_flux / np.mean(star_flux)
time_array = np.arange(res_flux.shape[0])
var, likeli = likelihood(star_flux, res_flux)
mag = cata_list[i, 5]
flux_stat.append((mag, likeli, var, star_flux, res_flux))
print '%.3f %.3f %.3f' % (mag, var, likeli)
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append(
(time_array, star_flux, i, cata_list[i, 5], res_flux))
if len(flux_list) > 1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
res_flux = flux_list[plot_num][-1]
axes[plot_num, 0].plot(time_array, star_flux, '-o')
axes[plot_num, 0].plot(time_array, res_flux, '-o')
axes[plot_num, 0].text(0.95,
0.8,
'NUV: %.3f' % flux_list[plot_num][3],
verticalalignment='bottom',
horizontalalignment='right',
transform=axes[plot_num, 0].transAxes,
color='green',
fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num, 0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num, 0].get_xlim()
axes[plot_num, 0].set_xlim([0, 61])
axes[plot_num, 0].set_ylim(
[ylim[0] + np.absolute(ylim[1] - ylim[0]) * 0.01, ylim[1]])
if plot_num < len(flux_list) - 1:
plt.setp(axes[plot_num, 0].get_xticklabels(),
visible=False)
else:
axes[plot_num, 0].set_xlabel('time/s')
plt.setp(axes[plot_num, 0].get_yticklabels(), visible=False)
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
plt.legend()
plt.savefig(
'../plots/%s/star/extra/cons_res_flat/cons%d_track.png' %
(name, group_num),
dpi=190)
plt.clf()
group_num += 1
save_obj(flux_stat,
'../plots/%s/star/extra/cons_res_flat/flux_stat' % (name))
limit = scst_tmp['t_dead_nuv'].shape[0] - 1
ix_tmp[ix_tmp > limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
count = np.zeros([800, 800])
#for star_num in xrange(1,10):
for star_num in range(cata_len):
print star_num
star_co = cata_a[star_num, 0:2]
star_flux = cata_a[star_num, 3]
print star_flux
ra = np.ones(asp_uni.shape[0]) * star_co[0]
dec = np.ones(asp_uni.shape[0]) * star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, asp_uni[:, 1], asp_uni[:, 2],
-asp_uni[:, 3], 1 / 36000., 0.0)
coo = np.array([xi, eta]).T
#print coo.shape
#print coo
coo = ((coo / 36000.) / (1.25 / 2.) *
(1.25 / (800 * 0.001666)) + 1.) / 2. * 800
'''
mask_0 = np.logical_and(coo[:,0]>=0, coo[:,0]<800)
mask_1 = np.logical_and(coo[:,1]>=0, coo[:,1]<800)
mask = np.logical_and(mask_0, mask_1)
coo = coo[mask]
dead_t = dead_tmp[mask]
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix, info_list, d3, outdir):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
bound = skyrange[0]
co_rel = np.array([[0,0]])
infos = np.array([[0,0,0]])
rot = np.array([0])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=pixsz)
#with open('../data/try2_%d.csv'%sec, 'wb') as csvfile:
#writer = csv.writer(csvfile)
if len2>len1:
for i in range(len1):
#print(i)
tmp_co = coo2-coo1[i,:]
mask = (np.absolute(tmp_co[:,0])<=bound) & (np.absolute(tmp_co[:,1])<=bound)
tmp_co = tmp_co[np.absolute(tmp_co[:,0])<=bound,:]
tmp_co = tmp_co[np.absolute(tmp_co[:,1])<=bound,:]
co_rel = np.concatenate((co_rel, tmp_co), axis = 0)
infos = np.concatenate((infos,info_list[mask]), axis=0)
rot = np.concatenate((rot, d3[mask]), axis=0)
else:
for i in range(len2):
#print(i)
tmp_co = coo2[i,:]-coo1
tmp_co = tmp_co[np.absolute(tmp_co[:,0])<=bound,:]
tmp_co = tmp_co[np.absolute(tmp_co[:,1])<=bound,:]
co_rel = np.concatenate((co_rel, tmp_co), axis = 0)
infos = np.concatenate((infos,np.repeat(info_list[i], tmp_co.shape[0])), axis=0)
rot = np.concatenate((rot, np.repeat(d3[i], tmp_co.shape[0])), axis=0)
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(co_rel[1:],1),1)
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,\
bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]))
t = np.percentile(H, 90)
t = 500
print 'threshold:{0}'.format(t)
hp = H.copy()
#plt.colorbar()
#plt.plot(co_rel[:,0],co_rel[:,1], 'o', alpha=0.05)
#plt.xlim(-0.005,0.005)
#plt.ylim(-0.005,0.005)
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
c_data = c3.find_centroid(data, t)
if c_data is not None:
cy, cx, max_value, flux = c_data
cy+=0.5
cx+=0.5
centroid = wcs.wcs_pix2world(wcs.sip_foc2pix([[cx, cy]],1),1)[0]
else:
centroid = [0.,0.]
max_value = 0
flux = 500
if centroid[0]>1:
centroid[0] = centroid[0]-360.
#print 'max:{0}'.format(max_value)
print hp.shape
#plt.imshow(np.log2(hp+10**-10),interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.02*3600, -0.02*3600, 0.02*3600, -0.02*3600], origin='upper', vmin=0)
'''
plt.imshow(hp+10**-10,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.02*3600, -0.02*3600, 0.02*3600, -0.02*3600], origin='upper', norm=LogNorm(10**0,np.max(hp)))
plt.colorbar()
plt.xlim(-0.01*3600, 0.01*3600)
plt.ylim(-0.01*3600, 0.01*3600)
plt.ylabel('gb')
plt.xlabel('gl')
plt.axhline(y=0, color='b', linestyle='dashed', linewidth=1)
plt.axvline(x=0, color='b', linestyle='dashed', linewidth=1)
plt.plot(centroid[0]*3600,centroid[1]*3600,'+r', markersize=8)
#plt.show()
rot = np.mean(d3)/180.*np.pi
l = np.linspace(0, 8., 5)
x_y = np.sin(-rot)*l
x_x = np.cos(-rot)*l
y_y = np.sin(-rot+np.pi/2.)*l
y_x = np.cos(-rot+np.pi/2.)*l
plt.plot(x_x, x_y, '--g')
plt.plot(y_x, y_y, '--g')
co_rel = co_rel*3600
mask = (co_rel[1:,0]**2+co_rel[1:,1]**2) <= (2.5**2)
num = np.sum(mask)
plt.title('t={0}-{1}s, photons within d=5\" circle:{2}'.format(idx/2-50, idx/2, num))
plt.savefig('{0}/{1}.pdf'.format(outdir,idx))
plt.clf()
np.save('{0}/{1}.npy'.format(outdir,idx), co_rel)
'''
#co_rel = co_rel*3600
xi, eta = gn.gnomfwd_simple(co_rel[1:,0], co_rel[1:,1], 0, 0, -rot[1:], 1/36000., 0)
#co_radec = np.array([xi,eta]).T
path = os.path.dirname('{0}/radec/test'.format(outdir))
if not os.path.exists(path):
os.makedirs(path)
np.save('{0}/{1}.npy'.format(outdir, idx), co_rel*3600)
#np.save('{0}/radec/{1}.npy'.format(outdir, idx), co_radec)
#np.save('{0}/info_{1}.npy'.format(outdir, idx), infos)
'''
plt.scatter(co_rel[1:,0], co_rel[1:,1], c=infos[1:,0], s=.5, alpha=0.3, cmap=plt.get_cmap('jet'), marker='o',edgecolors='face')
cb1 = plt.colorbar()
cb1.set_label('xa')
plt.xlim(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*3600)
plt.savefig('{0}/xa_{1}.png'.format(outdir, idx))
plt.clf()
plt.scatter(co_rel[1:,0], co_rel[1:,1], c=infos[1:,1], s=.5, alpha=0.3, cmap=plt.get_cmap('jet'), marker='o',edgecolors='face')
cb1 = plt.colorbar()
cb1.set_label('ya')
plt.xlim(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*3600)
plt.savefig('{0}/ya_{1}.png'.format(outdir, idx))
plt.clf()
plt.scatter(co_rel[1:,0], co_rel[1:,1], c=infos[1:,2], s=.5, alpha=0.3, cmap=plt.get_cmap('jet'), marker='o',edgecolors='face')
cb1 = plt.colorbar()
cb1.set_label('q')
plt.xlim(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*3600)
plt.savefig('{0}/q_{1}.png'.format(outdir, idx), dpi=190)
plt.clf()
'''
'''
n, bins, patches = plt.hist(infos[1:,1], 30, normed=1, facecolor='green', alpha=0.5)
plt.savefig('/home/dw1519/dw1519/galex/plots/co239_s/yahist_{0}.pdf'.format(idx), dpi=190)
plt.clf()
mask = (co_rel[:,0]<-25) & (co_rel[:,0]>-45) & (co_rel[:,1]>42) & (co_rel[:,1]<66)
n, bins, patches = plt.hist(infos[mask,1], 30, normed=1, facecolor='green', alpha=0.5)
plt.savefig('/home/dw1519/dw1519/galex/plots/co239_s/yahist_gh_{0}.pdf'.format(idx), dpi=190)
plt.clf()
'''
return centroid, max_value, flux
def correct(name):
#name = sys.argv[1]
print name
scan = '{0:04d}'.format(int(re.split('_', name)[3])) #'3191'
print scan
#name = 'AIS_GAL_SCAN_0'+scan+'_0002'
out = '/scratch/dw1519/galex/data/'+name
date = '08-21-2017_1'
cfile = '/scratch/dw1519/galex/fits/scan_map/catalog/'+date+'/starcat_'+scan+'mapweight_fec_fwhm.txt'
asprta = '/scratch/dw1519/galex/AIS_GAL_SCAN/asprta/'+name+'-cal-sec-dis-cut-asprta.fits'
scst = '/scratch/dw1519/galex/AIS_GAL_SCAN/scst/'+name+'-scst.fits'
num = int(scan)
if num>=5 and num<=185:
dis_l = '/scratch/dw1519/galex/data/distortion/5-185-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/5-185-xa_high-centroids.npy'
elif num>=194 and num<=437:
dis_l = '/scratch/dw1519/galex/data/distortion/194-437-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/194-437-xa_high-centroids.npy'
elif num>=446 and num<=887:
dis_l = '/scratch/dw1519/galex/data/distortion/446-887-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/446-887-xa_high-centroids.npy'
#elif num>=896 and num<=1157:
# dis_l = '/scratch/dw1519/galex/data/distortion/896-1157-xa_low-centroids.npy'
# dis_h = '/scratch/dw1519/galex/data/distortion/896-1157-xa_high-centroids.npy'
elif num>=1166 and num<=1373:
dis_l = '/scratch/dw1519/galex/data/distortion/1166-1373-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/1166-1373-xa_high-centroids.npy'
else:
dis_l = '/scratch/dw1519/galex/data/distortion/194-437-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/194-437-xa_high-centroids.npy'
print dis_l
print dis_h
data = np.load('/scratch/dw1519/galex/data/photons/'+name+'.npy')
print('data:{0}'.format(data.shape))
tycho2 = pyfits.open('/scratch/dw1519/galex/data/tycho2.fits')[1].data
df = ld.load_catalog(cfile)
c = SkyCoord(df['gl']*u.degree, df['gb']*u.degree, frame='galactic')
catalog = SkyCoord(tycho2['Glon']*u.degree, tycho2['Glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask=d2d<0.001*u.degree
'''
dtype = np.dtype([('tycho_num', int), ('Glon', '>f4'), ('Glat', '>f4'), ('RAJ2000', '>f4'), ('DEJ2000', '>f4'),
('flux', float), ('nuv', float), ('gl', float), ('gb', float)])
matched_tycho = tycho2[idx[mask]]
matched_df = df[mask]
matched_catalog = np.core.records.fromarrays(np.array([idx[mask], matched_tycho['Glon'], matched_tycho['Glat'],
matched_tycho['RAJ2000'], matched_tycho['DEJ2000'], np.array(matched_df['FLUX_AUTO']),
np.array(matched_df['nuv']), np.array(matched_df['gl']),
np.array(matched_df['gb'])]), dtype=dtype)
'''
resolution = 0.5
hdulist = pyfits.open(asprta)
co_data = hdulist[1].data
n=resolution/0.005 #100
T = co_data['T']
ra = co_data['ra']
dec = co_data['dec']
roll = co_data['roll']
t_new = np.arange((T.shape[0]-1)*n)*0.005+T[0]
spl_ra = splrep(T, ra)
spl_dec = splrep(T, dec)
spl_roll = splrep(T, roll)
ra_new = splev(t_new, spl_ra)
dec_new = splev(t_new, spl_dec)
roll_new = splev(t_new, spl_roll)
asp = np.array([t_new, ra_new, dec_new, roll_new]).T
#np.save('../data/photon_list/%s_asp_fix.npy'%name, asp_cal)
hdulist.close()
#asp = np.load('../data/photon_list/{0}{1}-correct_asp_new.npy'.format(name, suffix))
scst_file = pyfits.open(scst)
scst_data = scst_file[1].data
scst_time = scst_data['pktime'].copy()
hv = scst_data['hvnom_nuv'].copy()
scst_file.close()
dis_map = ld.load_dis(dis_l, dis_h)
scst_ix = np.digitize(data[:,0]/1000., scst_time)
ix_mask = scst_ix<scst_time.shape[0]
scst_ix = scst_ix[ix_mask]
data = data[ix_mask]
data = data[hv[scst_ix]>0]
dx_list = []
dy_list = []
ya_list = []
q_list = []
n = 50
step = int(data.shape[0]/n)
print step
for i in range(n-1):
print i
data_tmp = ld.dis_correct(data[i*step:(i+1)*step], dis_map, asp, None, None)
sky_data = SkyCoord(data_tmp[:,0:2], unit='deg', frame='fk5', equinox='J2000.0')
print 'sky_data: {0}'.format(sky_data.shape)
idxc, idxcatalog, d2d_p, d3d_p = sky_data.search_around_sky(catalog[idx[mask]], 0.0045*u.deg)
print 'idxc: {0}'.format(idxc.shape)
xi, eta = gn.gnomfwd_simple(tycho2['RAJ2000'][idx[mask][idxc]], tycho2['DEJ2000'][idx[mask][idxc]],
data_tmp[idxcatalog,-3], data_tmp[idxcatalog,-2], -data_tmp[idxcatalog,-1], 1/36000., 0)
dx = (data_tmp[idxcatalog,6]-xi)/36000./800/0.001666*2400
dy = (data_tmp[idxcatalog,7]-eta)/36000./800/0.001666*2400
ya = data_tmp[idxcatalog,4]
q = data_tmp[idxcatalog,5]
dx_list.append(dx)
dy_list.append(dy)
ya_list.append(ya)
q_list.append(q)
data_tmp=sky_data=xi=eta=None
gc.collect()
print n-1
data_tmp = ld.dis_correct(data[(n-1)*step:], dis_map, asp, None, None)
sky_data = SkyCoord(data_tmp[:,0:2], unit='deg', frame='fk5', equinox='J2000.0')
print 'sky_data: {0}'.format(sky_data.shape)
idxc, idxcatalog, d2d_p, d3d_p = sky_data.search_around_sky(catalog[idx[mask]], 0.0045*u.deg)
print 'idxc: {0}'.format(idxc.shape)
xi, eta = gn.gnomfwd_simple(tycho2['RAJ2000'][idx[mask][idxc]], tycho2['DEJ2000'][idx[mask][idxc]],
data_tmp[idxcatalog,-3], data_tmp[idxcatalog,-2], -data_tmp[idxcatalog,-1], 1/36000., 0)
dx = (data_tmp[idxcatalog,6]-xi)/36000./800/0.001666*2400
dy = (data_tmp[idxcatalog,7]-eta)/36000./800/0.001666*2400
ya = data_tmp[idxcatalog,4]
q = data_tmp[idxcatalog,5]
dx_list.append(dx)
dy_list.append(dy)
ya_list.append(ya)
q_list.append(q)
data_tmp=sky_data=xi=eta=None
gc.collect()
dx = np.concatenate(dx_list, axis=0)
dy = np.concatenate(dy_list, axis=0)
ya = np.concatenate(ya_list, axis=0)
q = np.concatenate(q_list, axis=0)
data=scst_ix=ix_mask=None
gc.collect()
print dx.shape
print 'max dy: {0}'.format(np.max(dy))
print 'max dx: {0}'.format(np.max(dx))
mask = (dy>=-8) & (dy<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dy_q = []
for i in range(24):
q_mask = (q[mask] == i)
dy_c = np.median(dy[mask][q_mask])
dy_q.append(dy_c)
plt.plot(dy_q, np.arange(24), '.r')
print('dy_q:')
print dy_q
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-y_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(24), dy_q, '.r')
plt.savefig(out+'/q-y_c.pdf', dpi=190)
plt.clf()
mask = (dx>=-8) & (dx<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dx_q = []
for i in range(24):
q_mask = (q[mask] == i)
dx_c = np.median(dx[mask][q_mask])
dx_q.append(dx_c)
print('dx_q:')
print dx_q
plt.plot(dx_q, np.arange(24), '.r')
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-x_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(24), dx_q, '.r')
plt.savefig(out+'/q-x_c.pdf', dpi=190)
plt.clf()
q_corr = np.array([np.arange(24), np.array(dx_q), np.array(dy_q)]).T
np.save(out+'/q_corr.npy', q_corr)
q_mask = q<24
dy[q_mask] = dy[q_mask]-q_corr[q[q_mask].astype(int),-1]
dx[q_mask] = dx[q_mask]-q_corr[q[q_mask].astype(int),-2]
mask = (dy>=-8) & (dy<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dy_ya = []
for i in range(2,32):
ya_mask = (ya[mask] == i)
dy_c = np.median(dy[mask][ya_mask])
dy_ya.append(dy_c)
print('dy_ya:')
print dy_ya
plt.plot(dy_ya, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-y_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, dy_ya, '.k')
plt.savefig(out+'/ya-y_c.pdf', dpi=190)
plt.clf()
mask = (dx>=-8) & (dx<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dx_ya = []
for i in range(2,32):
ya_mask = (ya[mask] == i)
dx_c = np.median(dx[mask][ya_mask])
dx_ya.append(dx_c)
print('dx_ya:')
print dx_ya
plt.plot(dx_ya, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-x_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, np.array(dx_ya), '.k')
plt.savefig(out+'/ya-x_c.pdf', dpi=190)
plt.clf()
ya_corr = np.array([np.arange(30)+2, np.array(dx_ya), np.array(dy_ya)]).T
np.save(out+'/ya_corr.npy', ya_corr)
ya_mask = q<24
dy = dy-ya_corr[ya.astype(int)-2,-1]
dx = dx-ya_corr[ya.astype(int)-2,-2]
#plot corrected
mask = (dy>=-8) & (dy<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(24):
q_mask = q[mask] == i
dy_c = np.median(dy[mask][q_mask])
dy_cs.append(dy_c)
print('dy_q:')
print dy_cs
plt.plot(dy_cs, np.arange(24), '.r')
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-y_tot_new.pdf', dpi=190)
plt.clf()
#np.save(out+'/q-y.npy', np.array([np.arange(24),dy_cs]).T)
plt.plot(np.arange(24), dy_cs, '.r')
plt.savefig(out+'/q-y_c_new.pdf', dpi=190)
plt.clf()
mask = (dx>=-8) & (dx<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(24):
q_mask = q[mask] == i
dy_c = np.median(dx[mask][q_mask])
dy_cs.append(dy_c)
print('dx_q:')
print dy_cs
plt.plot(dy_cs, np.arange(24), '.r')
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-x_tot_new.pdf', dpi=190)
plt.clf()
#np.save(out+'/q-x.npy', np.array([np.arange(24),dy_cs]).T)
plt.plot(np.arange(24), dy_cs, '.r')
plt.savefig(out+'/q-x_c_new.pdf', dpi=190)
plt.clf()
mask = (dy>=-8) & (dy<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(2,32):
ya_mask = ya[mask] == i
dy_c = np.median(dy[mask][ya_mask])
dy_cs.append(dy_c)
print('dy_ya:')
print dy_cs
plt.plot(dy_cs, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-y_tot_new.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, dy_cs, '.k')
plt.savefig(out+'/ya-y_c_new.pdf', dpi=190)
plt.clf()
#np.save(out+'/ya-y.npy', np.array([np.arange(30)+2,dy_cs]).T)
mask = (dx>=-8) & (dx<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(2,32):
ya_mask = ya[mask] == i
dy_c = np.median(dx[mask][ya_mask])
dy_cs.append(dy_c)
print('dx_ya:')
print dy_cs
plt.plot(dy_cs, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-x_tot_new.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, dy_cs, '.k')
plt.savefig(out+'/ya-x_c_new.pdf', dpi=190)
plt.clf()
def run_one_r_sec(pid, scan_name, step, start, end, return_dict):
print('run one r sec')
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.]
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 = [0.2, 0.2]
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)
data = get_data(tranges, scan_name)
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, 0.5)
coo2 = angle_filter(coo2, center, 0.5)
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, 0.2, 0.004)
if max_value>max_now:
max_now = max_value
cent_now = np.append(centroid, angle) #centroid.append(angle)
centroids.append(cent_now)
print centroids
np.save('../data/%s/cata/centroids_rot%d.npy'%(scan_name, initial_sec), centroids)
limit = scst_tmp['t_dead_nuv'].shape[0]-1 ix_tmp[ix_tmp>limit] = limit dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp] count = np.zeros([800,800]) #for star_num in xrange(1,10): for star_num in range(cata_len): print star_num star_co = cata_a[star_num, 0:2] star_flux = cata_a[star_num, 3] print star_flux ra = np.ones(asp_uni.shape[0])*star_co[0] dec = np.ones(asp_uni.shape[0])*star_co[1] xi, eta = gn.gnomfwd_simple(ra, dec, asp_uni[:,1], asp_uni[:,2], -asp_uni[:,3], 1/36000., 0.0) coo = np.array([xi, eta]).T #print coo.shape #print coo coo = ((coo/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*800 ''' mask_0 = np.logical_and(coo[:,0]>=0, coo[:,0]<800) mask_1 = np.logical_and(coo[:,1]>=0, coo[:,1]<800) mask = np.logical_and(mask_0, mask_1) coo = coo[mask] dead_t = dead_tmp[mask]
qmask = photon_list[:,-1]>5
photon_list = photon_list[qmask]
star_list = np.load('/scratch/dw1519/galex/data/star_photon/'+in_dir+'/'+name+'_star.npy')
print star_list.shape
stars, ct = np.unique(photon_list[:,0], return_counts=True)
nuv = star_list[stars.astype(int),-1]
print stars.shape
star_mask = (ct>10) & (nuv<17.5) & (nuv>15.5)
print np.sum(star_mask)
stars = stars[star_mask]
for star in stars:
star_mask = (photon_list[:,0]==star)
p = photon_list[star_mask]
ix = np.digitize(p[:,1]/1000., asp[:,0])-1
xi, eta = gn.gnomfwd_simple(c_radec[star].ra.deg, c_radec[star].dec.deg,
asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
pos = np.array([xi,eta]).T
pos=((pos/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*size
Hc, xedge, yedge = np.histogram2d(pos[:,0], pos[:,1],
bins=[size,size], range=([ [0,size],[0,size] ]))
xi, eta = gn.gnomfwd_simple(c_radec[star].ra.deg, c_radec[star].dec.deg,
asp[:,1], asp[:,2], -asp[:,3],1/36000.,0.)
pos = np.array([xi,eta]).T
pos=((pos/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*size
H, xedge, yedge = np.histogram2d(pos[:,0], pos[:,1],
bins=[size,size], range=([ [0,size],[0,size] ]), weights=step*(1-dead)*cut)
def res_star_new(pid, scan_name, start, end, return_dict):
cata = spi.load_obj('../data/%s_starset_new'%scan_name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00014_0001_asp_cal.npy')
asp_uni, uni_index=np.unique(asp[:,0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:,0]-asp_uni[0,0])+1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits'%scan_name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0]-1
ix_tmp[ix_tmp>limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
for star_num in range(start, end+1):
star_co = cata_a[star_num, 0:2]
order = 1
star = 'star_%d-%d'%(star_num, order)
if not os.path.exists('../data/%s/star/list/%s.csv'%(scan_name, star)):
print 'skip:%s'%star
continue
csv_file = '../data/%s/star/list/%s.csv'%(scan_name, star)
count = []
time_list = []
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
first = float(reader.next()[0])
final = 0
last = 0
reader = csv.reader(file)
for row in reader:
time = float(row[0])#time = int(float(row[0])/1000.)
if time - last > 200:
count.append(1)
last = time
time_list.append(last)
else:
count[-1] += 1
final = time
time_list = np.array(time_list)/1000.
index_0 = (first-asp_uni[0,0]*1000)/5
index_1 = (final-asp_uni[0,0]*1000)/5
star_track = asp_uni[index_0:index_1+1,:]
dead_t = dead_tmp[index_0:index_1+1]
ra = np.ones(star_track.shape[0])*star_co[0]
dec = np.ones(star_track.shape[0])*star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:,1], star_track[:,2], -star_track[:,3], 1/36000., 0.0)
output = "../plots/%s/star/res/%s.csv"%(scan_name, scan_name)
dir = os.path.dirname(output)
if not os.path.exists(dir):
os.makedirs(dir)
print dead_t
res_list = get_res_new(xi, eta)
res_list_d = res_list*(1-dead_t)
'''
plt.plot(star_track[:,0], res_list_d)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track_d.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
plt.plot(star_track[:,0], res_list)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
'''
f, axes = plt.subplots(2, 1)
axes[0].plot(time_list, count)
axes[0].set_ylabel('Num of Photons')
ylim = axes[0].get_ylim()
axes[0].set_ylim(ylim+np.absolute(ylim)*0.01)
plt.setp( axes[0].get_xticklabels(), visible=False)
#plt.setp( axes[0].get_yticklabels(), visible=False)
axes[1].plot(star_track[:,0], res_list_d)
axes[1].set_ylabel('Response')
axes[1].set_xlabel('time/s')
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=0, hspace=0)
plt.savefig('../plots/%s/star/res/2p/res%d-%d_track.png'%(scan_name,star_num,order),dpi=190)
plt.clf()
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix,
info_list, d3, outdir):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
bound = skyrange[0] / 0.000554 / 2400. * 36000. * 800 * 0.001666
co_rel = np.array([[0, 0]])
infos = np.array([[0, 0, 0]])
rot = np.array([0])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1, len2)
wcs = imagetools.define_wcs(skypos,
skyrange,
width=False,
height=False,
verbose=0,
pixsz=pixsz)
#with open('../data/try2_%d.csv'%sec, 'wb') as csvfile:
#writer = csv.writer(csvfile)
'''
photon_len = coo2.shape[0]
star_len = coo1.shape[0]
print 'repeat coo1'
coo1 = np.repeat(coo1, photon_len, axis=0)
print coo1.shape
print 'repeat d3'
d3 = np.tile(d3, (star_len,1))
print d3.shape
print 'convert coo1'
xi, eta = gn.gnomfwd_simple(coo1[:,0], coo1[:,1], d3[:,1], d3[:,2], -d3[:,3], 1/36000., 0)
print 'tile coo2'
co_rel = np.tile(coo2, (star_len,1)) - np.array([xi,eta]).T
mask = (np.absolute(co_rel[:,0])<=bound) & (np.absolute(co_rel[:,1])<=bound)
co_rel = co_rel[mask]
print 'tile infos'
infos = np.tile(info_list, (star_len,1))[mask]
print infos.shape
'''
if len2 > len1:
for i in range(len1):
ones = np.ones(d3.shape[0])
xi, eta = gn.gnomfwd_simple(coo1[i, 0] * ones, coo1[i, 1] * ones,
d3[:, 1], d3[:, 2], -d3[:, 3],
1 / 36000., 0)
tmp_co = coo2 - np.array([xi, eta]).T
mask = (np.absolute(tmp_co[:, 0]) <= bound) & (np.absolute(
tmp_co[:, 1]) <= bound)
tmp_co = tmp_co[np.absolute(tmp_co[:, 0]) <= bound, :]
tmp_co = tmp_co[np.absolute(tmp_co[:, 1]) <= bound, :]
co_rel = np.concatenate((co_rel, tmp_co), axis=0)
infos = np.concatenate((infos, info_list[mask]), axis=0)
#rot = np.concatenate((rot, d3[mask]), axis=0)
else:
for i in range(len2):
#print(i)
tmp_co = coo2[i, :] - coo1
tmp_co = tmp_co[np.absolute(tmp_co[:, 0]) <= bound, :]
tmp_co = tmp_co[np.absolute(tmp_co[:, 1]) <= bound, :]
co_rel = np.concatenate((co_rel, tmp_co), axis=0)
infos = np.concatenate(
(infos, np.repeat(info_list[i], tmp_co.shape[0])), axis=0)
rot = np.concatenate((rot, np.repeat(d3[i], tmp_co.shape[0])),
axis=0)
path = os.path.dirname('{0}/test/test'.format(outdir))
if not os.path.exists(path):
os.makedirs(path)
np.save('{0}/test/{1}.npy'.format(outdir, idx), co_rel)
#np.save('{0}/radec/{1}.npy'.format(outdir, idx), co_radec)
np.save('{0}/test/info_{1}.npy'.format(outdir, idx), infos)
return [0, 0], 0, 0
def run_one_r_sec(pid, scan_name, step, start, end, return_dict):
print('run one r sec')
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.]
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 = [0.2, 0.2]
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)
data = get_data(tranges, scan_name)
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, 0.5)
coo2 = angle_filter(coo2, center, 0.5)
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, 0.2, 0.004)
if max_value > max_now:
max_now = max_value
cent_now = np.append(centroid,
angle) #centroid.append(angle)
centroids.append(cent_now)
print centroids
np.save(
'../data/%s/cata/centroids_rot%d.npy' % (scan_name, initial_sec),
centroids)
def run_model_chunk(pid, scan_name, chunk, cata_target, cata_lists, size_list, cata_target_len, dead_tmp, asp_uni, return_dict):
count_list=[]
exp_list=[]
chunk_num = 0
for trange in chunk:
output = '../fits/%s/extra/new_black/exposure_chunk%d.fits'%(scan_name, chunk_num)
if os.path.exists(output):
print 'exists %d'%(chunk_num)
chunk_num+=1
continue
t_mask = np.zeros(asp_uni.shape[0])
begin = np.where((asp_uni[:,0]*1000).astype(int) - trange[0] == 0)[0]
end = np.where((asp_uni[:,0]*1000).astype(int) - trange[1] == 0)[0]
t_mask[begin:end+1] = 1
t_asp = asp_uni[t_mask>0]
t_dead = dead_tmp[t_mask>0]
t_length = end+1-begin
print('time length:%d, %f, %f'%(t_length, trange[0], trange[1]))
count = np.zeros([t_length,800,800])
exp_count = np.zeros([t_length,800,800])
for star_num in range(cata_target_len):
#print star_num
star_co = cata_target[star_num, 0:2]
star_flux = cata_target[star_num, 3]
#print star_flux
ra = np.ones(t_asp.shape[0])*star_co[0]
dec = np.ones(t_asp.shape[0])*star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, t_asp[:,1], t_asp[:,2], -t_asp[:,3], 1/36000., 0.0)
coo = np.array([xi, eta]).T
coo = ((coo/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*800
for i in [-1,0,1]:
for j in [-1,0,1]:
coo_tmp = np.zeros([t_length, 3])
coo_tmp[:,0] = np.arange(t_length)
coo_tmp[:,1] = coo[:,0]+i
coo_tmp[:,2] = coo[:,1]+j
mask_0 = np.logical_and(coo_tmp[:,1]>=0, coo_tmp[:,1]<800)
mask_1 = np.logical_and(coo_tmp[:,2]>=0, coo_tmp[:,2]<800)
mask = np.logical_and(mask_0, mask_1)
total = 1+4*0.0625+4*0.00390625
coo_tmp = coo_tmp[mask]
t_dead_tmp = t_dead[mask]
coo_tmp = np.array(coo_tmp, dtype=int)
factor = 1.
if (i,j) in set([(-1,0),(1,0),(0,-1),(0,1)]):
factor = 0.0625
elif (i,j) in set([(-1,-1),(1,-1),(1,1),(-1,1)]):
factor = 0.00390625
else:
factor = 1.
#count[coo_tmp[:,0], coo_tmp[:,1]] += 0.005*factor*star_flux/total
count[coo_tmp[:,0], coo_tmp[:,1], coo_tmp[:,2]] += factor*star_flux/total
for mask_num in range(3):
mask_cata = cata_lists[mask_num]
size = size_list[mask_num]
radius = size[-1]+0.5
for star_num in range(mask_cata.shape[0]):
#print star_num
star_co = mask_cata[star_num, 0:2]
#print star_flux
ra = np.ones(t_asp.shape[0])*star_co[0]
dec = np.ones(t_asp.shape[0])*star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, t_asp[:,1], t_asp[:,2], -t_asp[:,3], 1/36000., 0.0)
coo = np.array([xi, eta]).T
coo = ((coo/36000.)/(1.25/2.)*(1.25/(800* 0.001666))+1.)/2.*800
for i in size:
for j in size:
if i**2+j**2>radius**2:
continue
coo_tmp = np.zeros([t_length, 3])
coo_tmp[:,0] = np.arange(t_length)
coo_tmp[:,1] = coo[:,0]+i
coo_tmp[:,2] = coo[:,1]+j
mask_0 = np.logical_and(coo_tmp[:,1]>=0, coo_tmp[:,1]<800)
mask_1 = np.logical_and(coo_tmp[:,2]>=0, coo_tmp[:,2]<800)
mask = np.logical_and(mask_0, mask_1)
coo_tmp = coo_tmp[mask]
coo_tmp = np.array(coo_tmp, dtype=int)
exp_count[coo_tmp[:,0], coo_tmp[:,1], coo_tmp[:,2]] -= 1
for t_step in range(t_length):
exp_count[t_step][exp_count[t_step]>=0] = 0.005*(1-t_dead[t_step])
exp_count[exp_count<0] = 0.
count *= exp_count
exp_count_sum = np.sum(exp_count, axis=0)
count_sum = np.sum(count, axis=0)
exp_list.append(exp_count_sum)
count_list.append(count_sum)
exp_count=count=None
gc.collect()
chunk_num += 1
return_dict[pid] = (count_list, exp_list)
def run_one_r_sec(pid, scan_name, step, resolution, asp_cal, start, end, dis_map, return_dict):
print('run one r sec')
num_co = int(resolution/step)
#load asp file
hdulist = pyfits.open('../AIS_GAL_SCAN/asprta/%s-asprta.fits'%scan_name)
co_data = hdulist[1].data
length = co_data.shape[0]
'''
try:
asp_cal = np.load('../data/photon_list/%s_asp_new.npy'%scan_name)
except IOError:
T = co_data['T']
ra = co_data['ra']
dec = co_data['dec']
roll = co_data['roll']
t_new = np.arange((T.shape[0]-1)*200)*0.005+T[0]
ra_new = np.interp(t_new, T, ra)
dec_new = np.interp(t_new, T, dec)
roll_new = np.interp(t_new, T, roll)
asp_cal = np.array([t_new, ra_new, dec_new, roll_new]).T
np.save('../data/photon_list/%s_asp_new.npy'%scan_name, asp_cal)
'''
#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.]
start =600#1000
print length
for initial_sec in range(start, end):
print 'time:%d'%initial_sec
file_path = '../data/%s/cata/centroids_rot%d.npy'%(scan_name, initial_sec)
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]
if step == 1:
skyrange = [0.04, 0.04]#[0.3, 0.3]
elif step == 0.5:
skyrange = [0.02, 0.02]
initial_time = intitial_asp[0]-step/2.
tranges = []
for sec in range(num_co):
tranges.append([initial_time+step*sec, initial_time+step*(sec+1)])
print tranges
time_c = np.mean(tranges, axis=1)
print 'center time:'
print time_c
#data = get_data_cal(tranges, scan_name, cal_initial, offsets)
data = get_data(tranges, scan_name)
print 'number of the photons:'
print len(data)
centroids = []
center_time = []
for sec in range(num_co):
center_time.append(time_c[sec])
if len(data[sec]) == 0:
centroid = np.array([0.0, 0.0, 0.0])
centroids.append(centroid)
print centroid
continue
arg = np.argmin(np.absolute(asp_cal[:,0]-time_c[sec]))
center = asp_cal[arg, 1:3]
data_sec = dis_correct(np.array(data[sec], dtype='float64'), dis_map, asp_cal)
coo1 = np.array(data_sec, dtype='float64')
aperture = 0.69
#coo1 = np.array(data[sec], dtype='float64')[:,-3:-1]
coo3 = catalog_fits.get_catalog_tycho(center, aperture)
coo2 = catalog_fits.get_catalog(center, aperture)
#coo2[:,0] -= 0.0052 #- 0.01005915
#coo2[:,1] -= -0.006 #- 0.00804992
#data_sec = dis_correct(np.array(data[sec+1], dtype='float64'), dis_map, asp_cal)
#coo4 = np.array(data_sec, dtype='float64')
#coo2 = catalog_fits.get_catalog(center, 2.)
#coo2 = np.concatenate([coo2,coo3], axis=0)
coo1 = angle_filter(coo1, center, 0.6)
coo2 = angle_filter(coo2, center, 0.6)
coo3 = angle_filter(coo3, center, 0.6)
#coo4 = angle_filter(coo4, center, 0.6)
fig = plt.gcf()
fig.set_size_inches(8,8)
plt.plot(coo1[:,0], coo1[:,1], '.k', markersize=0.1)
plt.plot(coo3[:,0], coo3[:,1], '+r', markersize=12)
plt.plot(coo2[:,0], coo2[:,1], '+b', markersize=8)
#plt.show()
plt.ylabel('Dec')
plt.xlabel('RA')
plt.xlim(center[0]-0.65, center[0]+0.65)
plt.ylim(center[1]-0.65, center[1]+0.65)
plt.tight_layout()
plt.savefig('/home/dw1519/galex/plots/photons.png', dpi=190)
plt.clf()
plt.close()
'''
#plot field
#plt.plot(asp_cal[:,1],'.b')
#plt.show()
print center
print coo1.shape
print np.min(coo1[:,0]), np.max(coo1[:,0])
print np.min(coo1[:,1]), np.max(coo1[:,1])
wcs = imagetools.define_wcs(center,[1.5,1.5],width=False,height=False,verbose=0,pixsz=0.002)
imsz = imagetools.deg2pix(center, [1.5,1.5], 0.002)
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(coo1,1),1)
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,\
bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]))
H_new = H.copy()
data = H_new.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
c_data = c3.find_source(data, 1)
if c_data is not None:
cx, cy, max_value = c_data
print cy, cx
centroid = wcs.wcs_pix2world(wcs.sip_foc2pix([[cx, cy]],1),1)[0]
coo1 = angle_filter_out(coo1, centroid, 0.01)
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(coo1,1),1)
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,\
bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]))
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), vmax=10)
plt.colorbar()
plt.show()
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(coo2,1),1)
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,\
bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]))
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'))
plt.show()
'''
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, flux = get_corr_map(coo3, coo1, skypos, skyrange, sec, 0.0002, initial_sec, '2')
print centroid
centroid, max_value, flux = get_corr_map(coo2, coo1, skypos, skyrange, sec, 0.0002, initial_sec, '1')
print centroid
#centroid, max_value, flux = get_corr_map(coo4, coo1, skypos, skyrange, sec, 0.0002, initial_sec, '3')
#print centroid
if max_value>max_now:
max_now = max_value
cent_now = np.append(centroid, angle) #centroid.append(angle)
centroids.append(cent_now)
print centroids
sys.exit(0)
def load_data(name, dis_l, dis_h, ya, c_file, asprta, resolution):
#name = 'AIS_GAL_SCAN_00023_0001'
suffix = '-cal-sec'
#dis_l = '/scratch/dw1519/galex/data/distortion/5-185-xa_low-centroids.npy'
#dis_h = '/scratch/dw1519/galex/data/distortion/5-185-xa_high-centroids.npy'
#ya = '/scratch/dw1519/galex/co/co23_1-10/test/ya_fit.npy'
hdulist = pyfits.open(asprta)
co_data = hdulist[1].data
n=resolution/0.005 #100
T = co_data['T']
ra = co_data['ra']
dec = co_data['dec']
roll = co_data['roll']
t_new = np.arange((T.shape[0]-1)*n)*0.005+T[0]
ra_new = np.interp(t_new, T, ra)
dec_new = np.interp(t_new, T, dec)
roll_new = np.interp(t_new, T, roll)
asp = np.array([t_new, ra_new, dec_new, roll_new]).T
#np.save('../data/photon_list/%s_asp_fix.npy'%name, asp_cal)
hdulist.close()
#asp = np.load('../data/photon_list/{0}{1}-correct_asp_new.npy'.format(name, suffix))
scst_file = pyfits.open('../AIS_GAL_SCAN/scst/{0}-scst.fits'.format(name))
scst_data = scst_file[1].data
scst_time = scst_data['pktime'].copy()
hv = scst_data['hvnom_nuv'].copy()
scst_file.close()
dis_map = load_dis(dis_l, dis_h)
if ya is not None:
ya_info = load_ya(ya)
else:
ya_info = [None, None]
tycho2 = pyfits.open('../data/tycho2.fits')[1].data
df = load_catalog(c_file)
c = SkyCoord(df['gl']*u.degree, df['gb']*u.degree, frame='galactic')
catalog = SkyCoord(tycho2['Glon']*u.degree, tycho2['Glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask=d2d<0.001*u.degree
photon_list = sorted(glob.glob("../data/"+name+suffix+"/split/*.csv"), key=get_key)
data_list = []
for f in photon_list:
print(f)
data = np.loadtxt(f,delimiter=',', usecols=[0,3,4,5,6,7])
if len(data.shape)<2:
continue
scst_ix = np.digitize(data[:,0]/1000., scst_time)
ix_mask = scst_ix<scst_time.shape[0]
scst_ix = scst_ix[ix_mask]
data = data[ix_mask]
print(data[-1,0]/1000.)
print(scst_time[scst_ix[-1]])
print(hv[scst_ix[-1]])
data = data[hv[scst_ix]>0]
if len(data)==0:
print('skip')
continue
data = dis_correct(data, dis_map, asp, ya_info[0], ya_info[1])
sky_data = SkyCoord(data[:,0:2], unit='deg', frame='fk5', equinox='J2000.0')
gal = sky_data.transform_to('galactic')
idxc, idxcatalog, d2d_p, d3d_p = gal.search_around_sky(catalog[idx[mask]], 0.0045*u.deg)
catalog_id = np.zeros((gal.shape[0], 3))-1
#catalog_id[idxcatalog,0] =
if len(idxc)>0:
xi, eta = gn.gnomfwd_simple(tycho2['RAJ2000'][idx[mask][idxc]], tycho2['DEJ2000'][idx[mask][idxc]],
data[idxcatalog,-3], data[idxcatalog,-2], -data[idxcatalog,-1], 1/36000., 0)
#print xi.shape
#print eta.shape
#print data[idxcatalog,6].shape
#print (data[idxcatalog,7]-eta).shape
catalog_id[idxcatalog] = np.column_stack([idx[mask][idxc], (data[idxcatalog,6]-xi),\
(data[idxcatalog,7]-eta)])
else:
print 'no star'
#catalog_id = np.array([catalog_id]).T
data = np.concatenate([np.array([gal.l.deg]).T, np.array([gal.b.deg]).T, data[:,2:8],catalog_id], axis=1)
data_list.append(data)
gc.collect()
return np.concatenate(data_list, axis=0)
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix, info_list, d3, outdir):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
bound = skyrange[0]/0.000554/2400.*36000.*800*0.001666
co_rel = np.array([[0,0]])
infos = np.array([[0,0,0]])
rot = np.array([0])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=pixsz)
#with open('../data/try2_%d.csv'%sec, 'wb') as csvfile:
#writer = csv.writer(csvfile)
'''
photon_len = coo2.shape[0]
star_len = coo1.shape[0]
print 'repeat coo1'
coo1 = np.repeat(coo1, photon_len, axis=0)
print coo1.shape
print 'repeat d3'
d3 = np.tile(d3, (star_len,1))
print d3.shape
print 'convert coo1'
xi, eta = gn.gnomfwd_simple(coo1[:,0], coo1[:,1], d3[:,1], d3[:,2], -d3[:,3], 1/36000., 0)
print 'tile coo2'
co_rel = np.tile(coo2, (star_len,1)) - np.array([xi,eta]).T
mask = (np.absolute(co_rel[:,0])<=bound) & (np.absolute(co_rel[:,1])<=bound)
co_rel = co_rel[mask]
print 'tile infos'
infos = np.tile(info_list, (star_len,1))[mask]
print infos.shape
'''
if len2>len1:
for i in range(len1):
ones = np.ones(d3.shape[0])
xi, eta = gn.gnomfwd_simple(coo1[i,0]*ones, coo1[i,1]*ones, d3[:,1], d3[:,2], -d3[:,3], 1/36000., 0)
tmp_co = coo2-np.array([xi,eta]).T
mask = (np.absolute(tmp_co[:,0])<=bound) & (np.absolute(tmp_co[:,1])<=bound)
tmp_co = tmp_co[np.absolute(tmp_co[:,0])<=bound,:]
tmp_co = tmp_co[np.absolute(tmp_co[:,1])<=bound,:]
co_rel = np.concatenate((co_rel, tmp_co), axis = 0)
infos = np.concatenate((infos,info_list[mask]), axis=0)
#rot = np.concatenate((rot, d3[mask]), axis=0)
else:
for i in range(len2):
#print(i)
tmp_co = coo2[i,:]-coo1
tmp_co = tmp_co[np.absolute(tmp_co[:,0])<=bound,:]
tmp_co = tmp_co[np.absolute(tmp_co[:,1])<=bound,:]
co_rel = np.concatenate((co_rel, tmp_co), axis = 0)
infos = np.concatenate((infos,np.repeat(info_list[i], tmp_co.shape[0])), axis=0)
rot = np.concatenate((rot, np.repeat(d3[i], tmp_co.shape[0])), axis=0)
path = os.path.dirname('{0}/test/test'.format(outdir))
if not os.path.exists(path):
os.makedirs(path)
np.save('{0}/test/{1}.npy'.format(outdir, idx), co_rel)
#np.save('{0}/radec/{1}.npy'.format(outdir, idx), co_radec)
np.save('{0}/test/info_{1}.npy'.format(outdir, idx), infos)
return [0,0], 0, 0
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix,
info_list, d3, outdir):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
bound = skyrange[0]
co_rel = np.array([[0, 0]])
infos = np.array([[0, 0, 0]])
rot = np.array([0])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1, len2)
wcs = imagetools.define_wcs(skypos,
skyrange,
width=False,
height=False,
verbose=0,
pixsz=pixsz)
#with open('../data/try2_%d.csv'%sec, 'wb') as csvfile:
#writer = csv.writer(csvfile)
if len2 > len1:
for i in range(len1):
#print(i)
tmp_co = coo2 - coo1[i, :]
mask = (np.absolute(tmp_co[:, 0]) <= bound) & (np.absolute(
tmp_co[:, 1]) <= bound)
tmp_co = tmp_co[np.absolute(tmp_co[:, 0]) <= bound, :]
tmp_co = tmp_co[np.absolute(tmp_co[:, 1]) <= bound, :]
co_rel = np.concatenate((co_rel, tmp_co), axis=0)
infos = np.concatenate((infos, info_list[mask]), axis=0)
rot = np.concatenate((rot, d3[mask]), axis=0)
else:
for i in range(len2):
#print(i)
tmp_co = coo2[i, :] - coo1
tmp_co = tmp_co[np.absolute(tmp_co[:, 0]) <= bound, :]
tmp_co = tmp_co[np.absolute(tmp_co[:, 1]) <= bound, :]
co_rel = np.concatenate((co_rel, tmp_co), axis=0)
infos = np.concatenate(
(infos, np.repeat(info_list[i], tmp_co.shape[0])), axis=0)
rot = np.concatenate((rot, np.repeat(d3[i], tmp_co.shape[0])),
axis=0)
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(co_rel[1:], 1), 1)
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,\
bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]))
t = np.percentile(H, 90)
t = 500
print 'threshold:{0}'.format(t)
hp = H.copy()
#plt.colorbar()
#plt.plot(co_rel[:,0],co_rel[:,1], 'o', alpha=0.05)
#plt.xlim(-0.005,0.005)
#plt.ylim(-0.005,0.005)
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
c_data = c3.find_centroid(data, t)
if c_data is not None:
cy, cx, max_value, flux = c_data
cy += 0.5
cx += 0.5
centroid = wcs.wcs_pix2world(wcs.sip_foc2pix([[cx, cy]], 1), 1)[0]
else:
centroid = [0., 0.]
max_value = 0
flux = 500
if centroid[0] > 1:
centroid[0] = centroid[0] - 360.
#print 'max:{0}'.format(max_value)
print hp.shape
#plt.imshow(np.log2(hp+10**-10),interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.02*3600, -0.02*3600, 0.02*3600, -0.02*3600], origin='upper', vmin=0)
'''
plt.imshow(hp+10**-10,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.02*3600, -0.02*3600, 0.02*3600, -0.02*3600], origin='upper', norm=LogNorm(10**0,np.max(hp)))
plt.colorbar()
plt.xlim(-0.01*3600, 0.01*3600)
plt.ylim(-0.01*3600, 0.01*3600)
plt.ylabel('gb')
plt.xlabel('gl')
plt.axhline(y=0, color='b', linestyle='dashed', linewidth=1)
plt.axvline(x=0, color='b', linestyle='dashed', linewidth=1)
plt.plot(centroid[0]*3600,centroid[1]*3600,'+r', markersize=8)
#plt.show()
rot = np.mean(d3)/180.*np.pi
l = np.linspace(0, 8., 5)
x_y = np.sin(-rot)*l
x_x = np.cos(-rot)*l
y_y = np.sin(-rot+np.pi/2.)*l
y_x = np.cos(-rot+np.pi/2.)*l
plt.plot(x_x, x_y, '--g')
plt.plot(y_x, y_y, '--g')
co_rel = co_rel*3600
mask = (co_rel[1:,0]**2+co_rel[1:,1]**2) <= (2.5**2)
num = np.sum(mask)
plt.title('t={0}-{1}s, photons within d=5\" circle:{2}'.format(idx/2-50, idx/2, num))
plt.savefig('{0}/{1}.pdf'.format(outdir,idx))
plt.clf()
np.save('{0}/{1}.npy'.format(outdir,idx), co_rel)
'''
#co_rel = co_rel*3600
xi, eta = gn.gnomfwd_simple(co_rel[1:, 0], co_rel[1:, 1], 0, 0, -rot[1:],
1 / 36000., 0)
#co_radec = np.array([xi,eta]).T
path = os.path.dirname('{0}/radec/test'.format(outdir))
if not os.path.exists(path):
os.makedirs(path)
np.save('{0}/{1}.npy'.format(outdir, idx), co_rel * 3600)
#np.save('{0}/radec/{1}.npy'.format(outdir, idx), co_radec)
#np.save('{0}/info_{1}.npy'.format(outdir, idx), infos)
'''
plt.scatter(co_rel[1:,0], co_rel[1:,1], c=infos[1:,0], s=.5, alpha=0.3, cmap=plt.get_cmap('jet'), marker='o',edgecolors='face')
cb1 = plt.colorbar()
cb1.set_label('xa')
plt.xlim(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*3600)
plt.savefig('{0}/xa_{1}.png'.format(outdir, idx))
plt.clf()
plt.scatter(co_rel[1:,0], co_rel[1:,1], c=infos[1:,1], s=.5, alpha=0.3, cmap=plt.get_cmap('jet'), marker='o',edgecolors='face')
cb1 = plt.colorbar()
cb1.set_label('ya')
plt.xlim(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*3600)
plt.savefig('{0}/ya_{1}.png'.format(outdir, idx))
plt.clf()
plt.scatter(co_rel[1:,0], co_rel[1:,1], c=infos[1:,2], s=.5, alpha=0.3, cmap=plt.get_cmap('jet'), marker='o',edgecolors='face')
cb1 = plt.colorbar()
cb1.set_label('q')
plt.xlim(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*3600)
plt.savefig('{0}/q_{1}.png'.format(outdir, idx), dpi=190)
plt.clf()
'''
'''
n, bins, patches = plt.hist(infos[1:,1], 30, normed=1, facecolor='green', alpha=0.5)
plt.savefig('/home/dw1519/dw1519/galex/plots/co239_s/yahist_{0}.pdf'.format(idx), dpi=190)
plt.clf()
mask = (co_rel[:,0]<-25) & (co_rel[:,0]>-45) & (co_rel[:,1]>42) & (co_rel[:,1]<66)
n, bins, patches = plt.hist(infos[mask,1], 30, normed=1, facecolor='green', alpha=0.5)
plt.savefig('/home/dw1519/dw1519/galex/plots/co239_s/yahist_gh_{0}.pdf'.format(idx), dpi=190)
plt.clf()
'''
return centroid, max_value, flux