def get_corr_map_photon(coo1, coo2, skypos, skyrange, sec, bound, bandwidth):
imsz = imagetools.deg2pix(skypos, skyrange, 0.0001)
count = np.zeros(imsz)
print(imsz)
co_rel = np.array([[0,0]])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.0001)
if len2<=80 or len1<=80:
return count, np.array([0.0, 0.0])
if len2>len1:
for i in range(len2):
#print(i)
tmp_co = np.roll(coo2, i, axis=0)[0:len1,:]-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)
else:
for i in range(len1):
#print(i)
tmp_co = coo2-np.roll(coo1, i, axis=0)[0:len2,:]
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)
print co_rel.shape
if co_rel.shape[0]>1000:
centroid = ck.find_centroid(co_rel[1:], bandwidth, 11, bound)
else:
return count, np.array([0.0, 0.0])
return count, centroid
def get_corr_map_(coo1, coo2, skypos, skyrange, sec):
imsz = imagetools.deg2pix(skypos, skyrange, 0.0001)
count = np.zeros(imsz)
print(imsz)
co_rel = np.array([[0,0]])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.0001)
#with open('../data/try2_%d.csv'%sec, 'wb') as csvfile:
#writer = csv.writer(csvfile)
if len2>len1:
for i in range(len2):
print(i)
tmp_co = np.roll(coo2, i, axis=0)[0:len1,:]-coo1
tmp_co = tmp_co[np.absolute(tmp_co[:,0])<=0.01,:]
tmp_co = tmp_co[np.absolute(tmp_co[:,1])<=0.01,:]
co_rel = np.concatenate((co_rel, tmp_co), axis = 0)
else:
for i in range(len1):
print(i)
tmp_co = coo2-np.roll(coo1, i, axis=0)[0:len2,:]
tmp_co = tmp_co[np.absolute(tmp_co[:,0])<=0.01,:]
tmp_co = tmp_co[np.absolute(tmp_co[:,1])<=0.01,:]
co_rel = np.concatenate((co_rel, tmp_co), axis = 0)
return co_rel[1:]
def get_corr_map_fast(coo1, coo2, skypos, skyrange, sec, bound, bandwidth):
imsz = imagetools.deg2pix(skypos, skyrange, 0.0001)
count = np.zeros(imsz)
print(imsz)
co_rel = np.array([[0,0]])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.0001)
#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,:]
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)
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)
print co_rel.shape
if co_rel.shape[0]>50:
centroid = ck.find_centroid(co_rel[1:], bandwidth, 11, bound)
else:
return count, np.array([0.0, 0.0])
return count, centroid
def get_corr_map(coo1, coo2, skypos, skyrange):
imsz = imagetools.deg2pix(skypos, skyrange, 0.001)
count = np.zeros(imsz)
print(imsz)
co_rel = np.array([[0,0]])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.001)
if len2>len1:
for i in range(len2):
print(i)
co_rel = np.concatenate((co_rel, np.roll(coo2, i, axis=0)[0:len1,:]-coo1), axis = 0)
if (i+1)%200 == 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]] ]))
count += H
co_rel = np.array([[0, 0]])
else:
for i in range(len1):
print(i)
co_rel = np.concatenate((co_rel, coo2-np.roll(coo1, i, axis=0)[0:len2,:]), axis = 0)
if (i+1)%200 == 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]] ]))
count += H
co_rel = np.array([[0, 0]])
if co_rel.shape[0]>1:
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]] ]))
count += H
return count
def get_corr_map(coo1, coo2, skypos, skyrange, sec, bound, bandwidth):
imsz = imagetools.deg2pix(skypos, skyrange, 0.0001)
count = np.zeros(imsz)
print(imsz)
co_rel = np.array([[0,0]])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.0001)
#with open('../data/try2_%d.csv'%sec, 'wb') as csvfile:
#writer = csv.writer(csvfile)
if len2>len1:
for i in range(len2):
#print(i)
tmp_co = np.roll(coo2, i, axis=0)[0:len1,:]-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)
'''
if (i+1)%200 == 0:
writer.writerows(co_rel[1:])
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]] ]))
count += H
co_rel = np.array([[0, 0]])
'''
else:
for i in range(len1):
#print(i)
tmp_co = coo2-np.roll(coo1, i, axis=0)[0:len2,:]
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)
'''
if (i+1)%200 == 0:
writer.writerows(co_rel[1:])
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]] ]))
count += H
co_rel = np.array([[0, 0]])
'''
'''
if co_rel.shape[0]>1:
#writer.writerows(co_rel[1:])
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]] ]))
count += H
#csvfile.close()
'''
print co_rel.shape
if co_rel.shape[0]>50:
centroid = ck.find_centroid(co_rel[1:], bandwidth, 11, bound)
else:
return count, np.array([0.0, 0.0])
return count, centroid
def run_one_r(pid, scan_name, step, start, end, return_dict):
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]
print length
for initial_sec in range(start, end):
file_path = '../data/%s/cata/centroids%d_half.npy'%(scan_name, initial_sec)
if os.path.exists(file_path):
print 'skip:%d'%initial_sec
continue
intitial_asp = co_data[initial_sec]
center = np.array([intitial_asp[1], intitial_asp[2]])
print(intitial_asp)
skypos = [0.0, 0.0]
skyrange = [0.3, 0.3]
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(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, 0.4)
coo2 = angle_filter(coo2, center, 0.4)
count, centroid = get_corr_map_fast(coo2, coo1, skypos, skyrange, sec, 0.20, 0.008)
centroids.append(centroid)
print centroid
print centroids
np.save('../data/%s/cata/centroids%d_half.npy'%(scan_name, initial_sec), centroids)
def plot_co(co, pixsz, skypos, skyrange, weights=None):
bound = skyrange[0]/2.
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=pixsz)
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(co[:,0:2],1),1)
if weights is not None:
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]), weights=weights)
else:
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]))
h = H.copy()
#np.flip(h,axis=0)
'''
hist_size = [100,100]
H,xedges,yedges=np.histogram2d(co[:,1], co[:,0],\
bins=hist_size, range=([ [-bound,bound],[-bound,bound] ]))
h = H.copy()
'''
plt.imshow(h+10**-10,interpolation='None', cmap=plt.get_cmap('Greys'),\
aspect='equal', extent=[bound*3600, -bound*3600, -bound*3600, bound*3600], origin='lower',vmin=0)
#norm=LogNorm(10**0,np.max(h)))
plt.axhline(y=0, color='b', linestyle='dashed', linewidth=1)
plt.axvline(x=0, color='b', linestyle='dashed', linewidth=1)
plt.xlabel(r'$\Delta gl$')
plt.ylabel(r'$\Delta gb$')
plt.xlim(-bound*3600,bound*3600)
plt.ylim(-bound*3600,bound*3600)
'''
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
t=50
c_data = c3.find_centroid(data, t)
if c_data is not None:
cy, cx, max_value, flux = c_data
c = np.array([-bound+cx*bound*2/hist_size[0], -bound+cy*bound*2/hist_size[0]])
print c*3600
'''
c = centroid(5,H,wcs)
print(c*3600)
plt.plot(c[0]*3600,c[1]*3600,'+r', markersize=8, mew=1)
def get_corr_map(coo1, coo2, skypos, skyrange, sec, bound, bandwidth):
imsz = imagetools.deg2pix(skypos, skyrange, 0.0001)
count = np.zeros(imsz)
#print(imsz)
co_rel = np.array([[0,0]])
len1 = coo1.shape[0]
len2 = coo2.shape[0]
print(len1,len2)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.0001)
#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,:]
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)
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)
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]] ]))
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
cy, cx, max_value = c3.find_centroid(data)
centroid = wcs.wcs_pix2world(wcs.sip_foc2pix([[cx, cy]],1),1)[0]
if centroid[0]>1:
centroid[0] = centroid[0]-360.
return centroid, max_value
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.]
print length
data1 = []
data2 = []
data3 = []
start = 700
c_list = []
for start in range(701, 1500, 50):
print start
data1 = []
data2 = []
data3 = []
for initial_sec in range(start, start+50):
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]
#coo2 = catalog_fits.get_catalog_tycho(center, aperture)
coo2 = catalog_fits.get_catalog(center, aperture)
#coo1 = angle_filter(coo1, center, 0.6)
#coo2 = angle_filter(coo2, 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
def get_corr_map(pid, coo1, coo2, skypos, skyrange, pixsz, time, suffix, weight, flux=None):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
bound = skyrange[0]/2.
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=pixsz)
catalog = SkyCoord(coo1, unit='deg', frame='fk5', equinox='J2000.0')
sky_data = SkyCoord(coo2, unit='deg', frame='fk5', equinox='J2000.0')
print '{1}:catalog:{0}'.format(catalog.shape[0], pid)
print '{1}:data:{0}'.format(sky_data.shape[0], pid)
idxc, idxcatalog, d2d_p, d3d_p = sky_data.search_around_sky(catalog, 0.0045*u.deg)
print '{1}:match:{0}'.format(idxc.shape[0], pid)
if idxc.shape[0]<10:
centroid = [0.,0.]
max_value = 0
flux = 500
return centroid, max_value, flux, idxc.shape[0]
co_rel = coo2[idxcatalog] - coo1[idxc]
if flux is not None:
weights = weight[idxcatalog]/flux[idxc]*50.
else:
weights = weight[idxcatalog]
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(co_rel,1),1)
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,\
bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]), weights=weights)
t = np.percentile(H, 90)
t = 1
print '{1}:threshold:{0}'.format(t, pid)
#hp = H.copy()
#plt.colorbar()
'''
plt.plot(co_rel[:,0],co_rel[:,1], '.k')
plt.xlim(-0.02,0.02)
plt.ylim(-0.02,0.02)
'''
#plt.savefig('/home/dw1519/galex/plots/co3/{0}.png'.format(idx))
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
c_data = c3.find_centroid(data, t)
#print c_data
if c_data is not None:
#cy, cx, max_value, flux, x, y = c_data
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]
#c_sex = wcs.wcs_pix2world(wcs.sip_foc2pix([[x+0.5, y+0.5]],1),1)[0]
#print c_sex
else:
centroid = [0.,0.]
max_value = 0
flux = 500
if centroid[0]>1:
centroid[0] = centroid[0]-360.
#if c_sex[0]>1:
# c_sex[0] = c_sex[0]-360.
#print 'max:{0}'.format(max_value)
#print cx,cy
'''
plt.imshow(np.log2(hp+10**-10),interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[bound, -bound, -bound, bound], origin='lower', vmin=0)
#plt.imshow(hp+10**-10,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.01*3600, -0.01*3600, 0.01*3600, -0.01*3600], origin='upper', norm=LogNorm(10**0,np.max(hp)))
plt.colorbar()
plt.xlim(-bound/3, bound/3)
plt.ylim(-bound/3, bound/3)
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],centroid[1],'+r', markersize=8)
#plt.plot(c_sex[0],c_sex[1],'+g', markersize=8)
plt.show()
#plt.savefig('/home/dw1519/dw1519/galex/plots/co239_fix/{0}.pdf'.format(idx))
#plt.clf()
'''
return centroid, max_value, flux, idxc.shape[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]])
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)
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)
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 = 50
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(-0.02 * 3600, 0.02 * 3600)
plt.ylim(-0.02 * 3600, 0.02 * 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)
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')
#mask = (co_rel[1:,0]**2+co_rel[1:,1]**2) <= (2.5**2)
#num = np.sum(mask)
plt.title('t={0}-{1}s'.format(idx / 2 - 50, idx / 2))
plt.savefig('{0}/zoom_{1}.pdf'.format(outdir, idx))
plt.clf()
'''
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('/home/dw1519/dw1519/galex/plots/co32-10/xa_{0}.png'.format(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('/home/dw1519/dw1519/galex/plots/co32-10/ya_{0}.png'.format(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('/home/dw1519/dw1519/galex/plots/co32-10/q_{0}.png'.format(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 run_one_r_sec(pid, scan_name, step, start, end, return_dict):
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/offsets1_10_new_inter_half_fine.npy' %
scan_name)
print length
for initial_sec in range(start, end):
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]
#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 = [0.1, 0.1]
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.)
count, centroid = get_corr_map_fast(coo2, coo1, skypos, skyrange,
sec, 0.03, 0.0015)
centroids.append(centroid)
print centroid
print centroids
np.save(
'../data/%s/cata/centroids_sec%d.npy' % (scan_name, initial_sec),
centroids)
data = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
H = hist_g(data, wcs, imsz)
count += H
trange[1] = float(time)/1000
tranges.append(trange)
#trange = [0, 0]
return_dict[pid] = (count, tranges)
if __name__ == '__main__':
if False:
skypos, skyrange = pos_range.get_pos_range(name_list='../data/name_list')
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix(skypos, skyrange, 0.002)
count = np.zeros(imsz)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.002)
print imsz
hdulist = pyfits.open('../data/AIS_GAL_SCAN_00005_0001-asprta.fits')
co_data = hdulist[1].data
step = 10
offsets = np.load('../data/new/offsets300-1343_10_new.npy')
length = offsets.shape[0]
print length
for i in range(length-1):
offsets[i+1] = offsets[i+1] + offsets[i]
offsets = np.append(offsets, np.array([[0.,0.]]), axis=0)
print offsets.shape
initial = int((co_data[300][0]-0.5)*1000)
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix,
weight):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
weights = np.array([])
bound = skyrange[0]
co_rel = np.array([[0, 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)
sky_data = SkyCoord(coo1, unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
coo1 = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T),
axis=1)
sky_data = SkyCoord(coo2, unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
coo2 = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T),
axis=1)
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)
weights = np.concatenate((weights, weight[mask]), axis=0)
else:
for i in range(len2):
#print(i)
tmp_co = coo2[i, :] - coo1
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)
weights = np.concatenate(
(weights, np.repeat(weight[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]] ]))#, weights=weights)
t = np.percentile(H, 90)
t = 1
print 'threshold:{0}'.format(t)
hp = H.copy()
#plt.colorbar()
'''
plt.plot(co_rel[:,0],co_rel[:,1], '.k')
plt.xlim(-0.02,0.02)
plt.ylim(-0.02,0.02)
'''
#plt.savefig('/home/dw1519/galex/plots/co3/{0}.png'.format(idx))
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
c_data = c3.find_centroid(data, t)
#print c_data
if c_data is not None:
#cy, cx, max_value, flux, x, y = c_data
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]
#c_sex = wcs.wcs_pix2world(wcs.sip_foc2pix([[x+0.5, y+0.5]],1),1)[0]
#print c_sex
else:
centroid = [0., 0.]
max_value = 0
flux = 500
if centroid[0] > 1:
centroid[0] = centroid[0] - 360.
#if c_sex[0]>1:
# c_sex[0] = c_sex[0]-360.
#print 'max:{0}'.format(max_value)
#print cx,cy
'''
plt.imshow(np.log2(hp+10**-10),interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.3, -0.3, 0.3, -0.3], origin='upper', vmin=0)
#plt.imshow(hp+10**-10,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.01*3600, -0.01*3600, 0.01*3600, -0.01*3600], origin='upper', norm=LogNorm(10**0,np.max(hp)))
plt.colorbar()
plt.xlim(-0.3, 0.3)
plt.ylim(-0.3, 0.3)
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],centroid[1],'+r', markersize=8)
#plt.plot(c_sex[0],c_sex[1],'+g', markersize=8)
plt.show()
#plt.savefig('/home/dw1519/dw1519/galex/plots/co239_fix/{0}.pdf'.format(idx))
#plt.clf()
'''
return centroid, max_value, flux
def get_corr_map(pid,
coo1,
coo2,
skypos,
skyrange,
pixsz,
time,
suffix,
weight,
flux=None):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
bound = skyrange[0] / 2.
wcs = imagetools.define_wcs(skypos,
skyrange,
width=False,
height=False,
verbose=0,
pixsz=pixsz)
catalog = SkyCoord(coo1, unit='deg', frame='fk5', equinox='J2000.0')
sky_data = SkyCoord(coo2, unit='deg', frame='fk5', equinox='J2000.0')
print '{1}:catalog:{0}'.format(catalog.shape[0], pid)
print '{1}:data:{0}'.format(sky_data.shape[0], pid)
idxc, idxcatalog, d2d_p, d3d_p = sky_data.search_around_sky(
catalog, 0.0045 * u.deg)
print '{1}:match:{0}'.format(idxc.shape[0], pid)
if idxc.shape[0] < 10:
centroid = [0., 0.]
max_value = 0
flux = 500
return centroid, max_value, flux, idxc.shape[0]
co_rel = coo2[idxcatalog] - coo1[idxc]
if flux is not None:
weights = weight[idxcatalog] / flux[idxc] * 50.
else:
weights = weight[idxcatalog]
foc = wcs.sip_pix2foc(wcs.wcs_world2pix(co_rel, 1), 1)
H,xedges,yedges=np.histogram2d(foc[:,1]-0.5, foc[:,0]-0.5,\
bins=imsz, range=([ [0,imsz[0]],[0,imsz[1]] ]), weights=weights)
t = np.percentile(H, 90)
t = 1
print '{1}:threshold:{0}'.format(t, pid)
#hp = H.copy()
#plt.colorbar()
'''
plt.plot(co_rel[:,0],co_rel[:,1], '.k')
plt.xlim(-0.02,0.02)
plt.ylim(-0.02,0.02)
'''
#plt.savefig('/home/dw1519/galex/plots/co3/{0}.png'.format(idx))
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
c_data = c3.find_centroid(data, t)
#print c_data
if c_data is not None:
#cy, cx, max_value, flux, x, y = c_data
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]
#c_sex = wcs.wcs_pix2world(wcs.sip_foc2pix([[x+0.5, y+0.5]],1),1)[0]
#print c_sex
else:
centroid = [0., 0.]
max_value = 0
flux = 500
if centroid[0] > 1:
centroid[0] = centroid[0] - 360.
#if c_sex[0]>1:
# c_sex[0] = c_sex[0]-360.
#print 'max:{0}'.format(max_value)
#print cx,cy
'''
plt.imshow(np.log2(hp+10**-10),interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[bound, -bound, -bound, bound], origin='lower', vmin=0)
#plt.imshow(hp+10**-10,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.01*3600, -0.01*3600, 0.01*3600, -0.01*3600], origin='upper', norm=LogNorm(10**0,np.max(hp)))
plt.colorbar()
plt.xlim(-bound/3, bound/3)
plt.ylim(-bound/3, bound/3)
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],centroid[1],'+r', markersize=8)
#plt.plot(c_sex[0],c_sex[1],'+g', markersize=8)
plt.show()
#plt.savefig('/home/dw1519/dw1519/galex/plots/co239_fix/{0}.pdf'.format(idx))
#plt.clf()
'''
return centroid, max_value, flux, idxc.shape[0]
def run_one_r(pid, scan_name, step, start, end, return_dict):
print 'run_one_r'
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]
plt.plot(co_data['T'])
plt.show()
cal_initial = int((co_data[1][0] - 0.5) * 1000)
print cal_initial
offsets = np.load('../data/%s/cata/offsets1_10_new_inter_half_fine.npy' %
scan_name)
print length
print co_data[-10]
print co_data[0]
print co_data[-1][0] - co_data[0][0]
#print offsets[300*200:301*200]
for initial_sec in range(start, end):
file_path = '../data/%s/cata/centroids%d_half.npy' % (scan_name,
initial_sec)
'''
if os.path.exists(file_path):
print 'skip:%d'%initial_sec
continue
'''
intitial_asp = co_data[initial_sec]
center = np.array([intitial_asp[1], intitial_asp[2]])
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)
get_corr_map(coo2, coo1, skypos, skyrange, sec, 0.05, 0.008)
'''
def run_one_photon_half(pid, scan_name, step, start, end, return_dict):
num_co = int(0.5 / step)
hdulist = pyfits.open('../AIS_GAL_SCAN/asprta/%s-asprta.fits' % scan_name)
co_data = hdulist[1].data
length = co_data.shape[0]
print length
for initial_sec in range(start, end):
file_path = '../data/%s/cata/centroids_half_photon%d.npy' % (
scan_name, initial_sec)
if os.path.exists(file_path):
print 'skip:%d' % initial_sec
continue
intitial_asp = co_data[initial_sec]
print(intitial_asp)
center = np.array([intitial_asp[1], intitial_asp[2]])
centroids = []
for part in range(2):
print 'part:%d' % part
skypos = [0.0, 0.0]
skyrange = [0.1, 0.1]
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 + part * 0.5,
initial_time + step * (sec + 1) + part * 0.5
])
print tranges
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)
if len(data[2]) == 0:
for sec in range(4):
centroid = np.array([0.0, 0.0])
centroids.append(centroid)
print centroid
continue
else:
coo2 = np.array(data[2], dtype='float64')[:, -3:-1]
coo2 = angle_filter(coo2, center, 0.4)
for sec in [0, 1, 3, 4]:
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]
coo1 = angle_filter(coo1, center, 0.4)
count, centroid = get_corr_map(coo2, coo1, skypos, skyrange,
sec, 0.05, 0.004)
centroids.append(centroid)
print centroid
print centroids
np.save(
'../data/%s/cata/centroids_half_photon%d.npy' %
(scan_name, initial_sec), centroids)
def run_one_photon(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)
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)
for star_num in range(start, end + 1):
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
data, time_list, count = get_data_star(scan_name, star)
length = len(data)
mean = np.mean(count)
if mean <= 80:
print 'skip:%s' % star
continue
output = "../data/%s/star/co_photon/%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(length):
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 = np.array(data[sec+1], dtype='float64')[:,-3:-1]
coo2 = np.array([cata_a[star_num, :]])
count, centroid = get_corr_map(coo2, coo1, skypos, skyrange, sec,
0.03, 0.004)
centroids.append(centroid)
print centroid
print centroids
np.save(
'../data/%s/star/co_photon/centroids%d-%d_c.npy' %
(scan_name, star_num, order), centroids)
time = np.array(time_list)
centers = np.array(centroids)
'''
for i in range(1,centers.shape[0]):
centers[i,:] += centers[i-1,:]
'''
print time
print centers
f, axes = plt.subplots(2, 1)
axes[0].plot(time, centers[:, 0])
axes[0].set_ylabel(r'$\delta RA/degree$')
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(time, centers[:, 1])
axes[1].set_ylabel(r'$\delta DEC/degree$')
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/co_photon/offset%d-%d_c.png' %
(scan_name, star_num, order),
dpi=190)
plt.clf()
'''
def run_one_r_o(pid, scan_name, step, start, end, return_dict):
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]
print length
for initial_sec in range(start, end):
file_path = '../data/%s/cata/centroids_new%d.npy' % (scan_name,
initial_sec)
if os.path.exists(file_path):
print 'skip:%d' % initial_sec
continue
intitial_asp = co_data[initial_sec]
center = np.array([intitial_asp[1], intitial_asp[2]])
print(intitial_asp)
skypos = [0.0, 0.0]
skyrange = [0.3, 0.3]
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(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 = []
out_mask = []
for sec in range(num_co):
if len(data[sec]) == 0:
centroid = np.array([0.0, 0.0])
out_mask.append(1)
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.)
count, centroid, out = get_corr_map_o(coo2, coo1, skypos, skyrange,
sec, 0.15, 0.008)
centroids.append(centroid)
out_mask.append(out)
print centroid
print centroids
np.save(
'../data/%s/cata/centroids_new%d.npy' % (scan_name, initial_sec),
centroids)
np.save('../data/%s/cata/out_mask%d.npy' % (scan_name, initial_sec),
out_mask)
def get_corr_map(coo1, coo2, skypos, skyrange, sec, bound, bandwidth):
imsz = imagetools.deg2pix(skypos, skyrange, 0.0001)
count = np.zeros(imsz)
print(imsz)
co_rel = np.array([[0, 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=0.0001)
#with open('../data/try2_%d.csv'%sec, 'wb') as csvfile:
#writer = csv.writer(csvfile)
if len2 > len1:
for i in range(len2):
#print(i)
tmp_co = np.roll(coo2, i, axis=0)[0:len1, :] - 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)
'''
if (i+1)%200 == 0:
writer.writerows(co_rel[1:])
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]] ]))
count += H
co_rel = np.array([[0, 0]])
'''
else:
for i in range(len1):
#print(i)
tmp_co = coo2 - np.roll(coo1, i, axis=0)[0:len2, :]
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)
'''
if (i+1)%200 == 0:
writer.writerows(co_rel[1:])
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]] ]))
count += H
co_rel = np.array([[0, 0]])
'''
'''
if co_rel.shape[0]>1:
#writer.writerows(co_rel[1:])
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]] ]))
count += H
#csvfile.close()
'''
print co_rel.shape
if co_rel.shape[0] > 50:
centroid = ck.find_centroid(co_rel[1:], bandwidth, 11, bound)
else:
return count, np.array([0.0, 0.0])
return count, centroid
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
bound = skyrange[0]
co_rel = np.array([[0,0]])
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,:]
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)
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)
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 = 1
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(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*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()
plt.savefig('/home/dw1519/dw1519/galex/plots/co239_orignal/{0}.pdf'.format(idx))
plt.clf()
return centroid, max_value, flux
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix, weight, flux=None):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
weights = np.array([])
bound = skyrange[0]/2.
co_rel = np.array([[0,0]])
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)
'''
sky_data = SkyCoord(coo1, unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
coo1 = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
sky_data = SkyCoord(coo2, unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
coo2 = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
'''
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)
if flux is not None:
f = flux[i]
else:
f = 1.
weights = np.concatenate((weights,weight[mask]/f*50.), axis=0)
else:
for i in range(len2):
#print(i)
tmp_co = coo2[i,:]-coo1
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)
if flux is not None:
f = flux[mask]
else:
f = np.ones(tmp_co.shape[0])
weights = np.concatenate((weights,np.repeat(weight[i], tmp_co.shape[0])/f), 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]] ]), weights=weights)
print H.shape
t = np.percentile(H, 90)
t = 1
print 'threshold:{0}'.format(t)
#hp = H.copy()
#plt.colorbar()
'''
plt.plot(co_rel[:,0],co_rel[:,1], '.k')
plt.xlim(-0.02,0.02)
plt.ylim(-0.02,0.02)
'''
#plt.savefig('/home/dw1519/galex/plots/co3/{0}.png'.format(idx))
data = H.byteswap(True).newbyteorder()
data = data.copy(order='C')
data = data.byteswap(True).newbyteorder()
c_data = c3.find_centroid(data, t)
#print c_data
if c_data is not None:
#cy, cx, max_value, flux, x, y = c_data
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]
#c_sex = wcs.wcs_pix2world(wcs.sip_foc2pix([[x+0.5, y+0.5]],1),1)[0]
#print c_sex
else:
centroid = [0.,0.]
max_value = 0
flux = 500
if centroid[0]>1:
centroid[0] = centroid[0]-360.
#if c_sex[0]>1:
# c_sex[0] = c_sex[0]-360.
#print 'max:{0}'.format(max_value)
#print cx,cy
'''
plt.imshow(np.log2(hp+10**-10),interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[bound, -bound, -bound, bound], origin='lower', vmin=0)
#plt.imshow(hp+10**-10,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.01*3600, -0.01*3600, 0.01*3600, -0.01*3600], origin='upper', norm=LogNorm(10**0,np.max(hp)))
plt.colorbar()
plt.xlim(-bound/3, bound/3)
plt.ylim(-bound/3, bound/3)
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],centroid[1],'+r', markersize=8)
#plt.plot(c_sex[0],c_sex[1],'+g', markersize=8)
plt.show()
#plt.savefig('/home/dw1519/dw1519/galex/plots/co239_fix/{0}.pdf'.format(idx))
#plt.clf()
'''
return centroid, max_value, flux
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_one_r(pid, scan_name, step, start, end, return_dict):
print 'run_one_r'
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]
plt.plot(co_data['T'])
plt.show()
cal_initial = int((co_data[1][0]-0.5)*1000)
print cal_initial
offsets = np.load('../data/%s/cata/offsets1_10_new_inter_half_fine.npy'%scan_name)
print length
print co_data[-10]
print co_data[0]
print co_data[-1][0]-co_data[0][0]
#print offsets[300*200:301*200]
for initial_sec in range(start, end):
file_path = '../data/%s/cata/centroids%d_half.npy'%(scan_name, initial_sec)
'''
if os.path.exists(file_path):
print 'skip:%d'%initial_sec
continue
'''
intitial_asp = co_data[initial_sec]
center = np.array([intitial_asp[1], intitial_asp[2]])
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)
get_corr_map(coo2, coo1, skypos, skyrange, sec, 0.05, 0.008)
'''
else:
step = 0.5
num_co = int(1/step)
hdulist = pyfits.open('../data/AIS_GAL_SCAN_00005_0001-asprta.fits')
co_data = hdulist[1].data
intitial_asp = co_data[initial_sec]
center = np.array([intitial_asp[1], intitial_asp[2]])
print(intitial_asp)
skypos = [0.0, 0.0]
skyrange = [0.04, 0.04]
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+1):
tranges.append([initial_time+step*sec, initial_time+step*(sec+1)])
print tranges
data = get_data(tranges)
print len(data)
centroids = []
for sec in range(num_co):
coo1 = np.array(data[sec], dtype='float64')[:,-3:-1]
def run_one_r_sec(pid, scan_name, step, start, end, return_dict):
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/offsets1_10_new_inter_half_fine.npy'%scan_name)
print length
for initial_sec in range(start, end):
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]
#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 = [0.1, 0.1]
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.)
count, centroid = get_corr_map_fast(coo2, coo1, skypos, skyrange, sec, 0.03, 0.0015)
centroids.append(centroid)
print centroid
print centroids
np.save('../data/%s/cata/centroids_sec%d.npy'%(scan_name, initial_sec), centroids)
if __name__ == '__main__':
initial_sec = 451
hdulist = pyfits.open('../data/AIS_GAL_SCAN_00005_0001-asprta.fits')
co_data = hdulist[1].data
intitial_asp = co_data[initial_sec]
skypos = [intitial_asp[1], intitial_asp[2]]
skyrange = [1.38,1.38]
tranges = []
trange = [0, 0]
imsz = imagetools.deg2pix(skypos, skyrange, 0.002)
count = np.zeros(imsz)
print imsz
print skypos
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.002)
with open('../data/csv_list') as f:
csv_list = f.read().splitlines()
for csv_file in csv_list:
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
data = []
i = 1
initial_time = trange[0] = int(intitial_asp[0]*1000)
print initial_time
for row in reader:
time = int(row[0])
if time < (initial_time + 0):
continue
if time >= (initial_time + 1000):
def get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
bound = skyrange[0]
co_rel = np.array([[0,0]])
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,:]
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)
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)
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 = 1
print 'threshold:{0}'.format(t)
hp = H.copy()
#plt.colorbar()
'''
plt.plot(co_rel[:,0],co_rel[:,1], '.k')
plt.xlim(-0.02,0.02)
plt.ylim(-0.02,0.02)
'''
#plt.savefig('/home/dw1519/galex/plots/co3/{0}.png'.format(idx))
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
return centroid, max_value, flux
def run_one_photon(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)
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)
for star_num in range(start, end+1):
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
data, time_list, count = get_data_star(scan_name, star)
length = len(data)
mean = np.mean(count)
if mean <= 80:
print 'skip:%s'%star
continue
output = "../data/%s/star/co_photon/%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(length):
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 = np.array(data[sec+1], dtype='float64')[:,-3:-1]
coo2 = np.array([cata_a[star_num,:]])
count, centroid = get_corr_map(coo2, coo1, skypos, skyrange, sec, 0.03, 0.004)
centroids.append(centroid)
print centroid
print centroids
np.save('../data/%s/star/co_photon/centroids%d-%d_c.npy'%(scan_name, star_num, order), centroids)
time = np.array(time_list)
centers = np.array(centroids)
'''
for i in range(1,centers.shape[0]):
centers[i,:] += centers[i-1,:]
'''
print time
print centers
f, axes = plt.subplots(2, 1)
axes[0].plot(time, centers[:,0])
axes[0].set_ylabel(r'$\delta RA/degree$')
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(time, centers[:,1])
axes[1].set_ylabel(r'$\delta DEC/degree$')
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/co_photon/offset%d-%d_c.png'%(scan_name,star_num,order),dpi=190)
plt.clf()
'''
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)
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]])
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)
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)
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 = 50
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(-0.02*3600, 0.02*3600)
plt.ylim(-0.02*3600, 0.02*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)
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')
mask = (co_rel[1:,0]**2+co_rel[1:,1]**2) <= (2.5**2)
num = np.sum(mask)
plt.title('t={0}-{1}s'.format(idx/2-50, idx/2))
plt.savefig('{0}/zoom_{1}.pdf'.format(outdir,idx))
plt.clf()
'''
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('/home/dw1519/dw1519/galex/plots/co32-10/xa_{0}.png'.format(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('/home/dw1519/dw1519/galex/plots/co32-10/ya_{0}.png'.format(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('/home/dw1519/dw1519/galex/plots/co32-10/q_{0}.png'.format(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 get_corr_map(coo1, coo2, skypos, skyrange, sec, pixsz, idx, suffix):
imsz = imagetools.deg2pix(skypos, skyrange, pixsz).astype(int)
count = np.zeros(imsz)
#print(imsz)
bound = skyrange[0]
co_rel = np.array([[0,0]])
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,:]
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)
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)
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 = 1
print 'threshold:{0}'.format(t)
hp = H.copy()
#plt.colorbar()
'''
plt.plot(co_rel[:,0],co_rel[:,1], '.k')
plt.xlim(-0.02,0.02)
plt.ylim(-0.02,0.02)
'''
#plt.savefig('/home/dw1519/galex/plots/co3/{0}.png'.format(idx))
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
fig = plt.gcf()
fig.set_size_inches(8,8)
plt.imshow(hp,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.02,-0.02, 0.02,-0.02], origin='upper', imlim=[-0.02,0.02, -0.02,0.02])
#plt.imshow(hp,interpolation='None', cmap=plt.get_cmap('Greys'), extent=[0.02,-0.02,0.02,-0.02])
plt.xlabel(r'$\Delta RA$')
plt.ylabel(r'$\Delta Dec$')
plt.plot(centroid[0],centroid[1],'+r', markersize=8)
plt.xlim(0.02, -0.02)
plt.ylim(0.02, -0.02)
plt.tight_layout()
#plt.subplots_adjust(left=0.12, bottom=0.07, right=0.95, top=0.97, wspace=0, hspace=0)
plt.savefig('/home/dw1519/galex/plots/co%s.png'%suffix, dpi=190)
#plt.show()
plt.clf()
return centroid, max_value, flux
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)
