def dis_correct(data, asp, dis_map=None, ya_corr=None, q_corr=None, cut=False):
if cut:
ya_mask = data[:,4]>=2
q_mask = data[:,5]>5
data = data[ya_mask&q_mask]
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
if dis_map is not None:
low_mask = data[:,3]<16
high_mask = data[:,3]>=16
coo = data[low_mask, 6:8]
data[low_mask, 6] -= dis_map['xi_l'](coo)
data[low_mask, 7] -= dis_map['eta_l'](coo)
coo = data[high_mask, 6:8]
data[high_mask, 6] -= dis_map['xi_h'](coo)
data[high_mask, 7] -= dis_map['eta_h'](coo)
if q_corr is not None:
q_mask = data[:,5]<24
data[q_mask,6] -= q_corr[data[q_mask,5].astype(int),-2]*36000*800*0.001666/2400.
data[q_mask,7] -= q_corr[data[q_mask,5].astype(int),-1]*36000*800*0.001666/2400.
if ya_corr is not None:
data[:,6] -= ya_corr[data[:,4].astype(int)-2,-2]*36000*800*0.001666/2400.
data[:,7] -= ya_corr[data[:,4].astype(int)-2,-1]*36000*800*0.001666/2400.
ra, dec = gn.gnomrev_simple(data[:,6], data[:,7], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.array([ra,dec]).T, data[:,0]
def dis_correct(data, dis_map, asp, slope, inter):
ya_mask = data[:, 2] >= 2
q_mask = np.ones(data.shape[0], dtype=bool)
#q_mask = data[:,3]>5
data_new = data[ya_mask & q_mask]
print data.shape
ix = np.digitize(data_new[:, 0] / 1000., asp[:, 0]) - 1
low_mask = data_new[:, 1] < 16
high_mask = data_new[:, 1] >= 16
coo = data_new[low_mask, 4:6]
data_new[low_mask, 4] -= dis_map['xi_l'](coo)
data_new[low_mask, 5] -= dis_map['eta_l'](coo)
coo = data_new[high_mask, 4:6]
data_new[high_mask, 4] -= dis_map['xi_h'](coo)
data_new[high_mask, 5] -= dis_map['eta_h'](coo)
if slope is not None:
data_new[:, 5] -= (slope * data_new[:, 2] +
inter) * 36000 * 800 * 0.001666 / 2400.
ra, dec = gn.gnomrev_simple(data_new[:, 4], data_new[:, 5], asp[ix, 1],
asp[ix, 2], -asp[ix, 3], 1 / 36000., 0.)
return np.concatenate(
[np.array([ra, dec]).T, data[ya_mask & q_mask, 0:6], asp[ix, 1:4]],
axis=1)
def dis_correct(data, asp, dis_map=None, slope=None, inter=None, cut=False):
if cut:
ya_mask = data[:, 2] >= 2
q_mask = data[:, 3] > 5
data = data[ya_mask & q_mask]
ix = np.digitize(data[:, 0] / 1000., asp[:, 0]) - 1
if dis_map is not None:
low_mask = data[:, 1] < 16
high_mask = data[:, 1] >= 16
coo = data[low_mask, 4:6]
data[low_mask, 4] -= dis_map['xi_l'](coo)
data[low_mask, 5] -= dis_map['eta_l'](coo)
coo = data[high_mask, 4:6]
data[high_mask, 4] -= dis_map['xi_h'](coo)
data[high_mask, 5] -= dis_map['eta_h'](coo)
if slope is not None:
data[:, 5] -= (slope * data[:, 2] +
inter) * 36000 * 800 * 0.001666 / 2400.
ra, dec = gn.gnomrev_simple(data[:, 4], data[:, 5], asp[ix, 1], asp[ix, 2],
-asp[ix, 3], 1 / 36000., 0.)
return np.array([ra, dec]).T
def dis_correct(data, asp, dis_map=None, ya_corr=None, q_corr=None, cut=False):
if cut:
ya_mask = data[:, 2] >= 2
q_mask = data[:, 3] > 5
data = data[ya_mask & q_mask]
ix = np.digitize(data[:, 0] / 1000., asp[:, 0]) - 1
if dis_map is not None:
low_mask = data[:, 1] < 16
high_mask = data[:, 1] >= 16
coo = data[low_mask, 4:6]
data[low_mask, 4] -= dis_map['xi_l'](coo)
data[low_mask, 5] -= dis_map['eta_l'](coo)
coo = data[high_mask, 4:6]
data[high_mask, 4] -= dis_map['xi_h'](coo)
data[high_mask, 5] -= dis_map['eta_h'](coo)
if q_corr is not None:
q_mask = data[:, 3] < 24
data[q_mask, 4] -= q_corr[data[q_mask, 3].astype(int),
-2] * 36000 * 800 * 0.001666 / 2400.
data[q_mask, 5] -= q_corr[data[q_mask, 3].astype(int),
-1] * 36000 * 800 * 0.001666 / 2400.
if ya_corr is not None:
data[:, 4] -= ya_corr[data[:, 2].astype(int) - 2,
-2] * 36000 * 800 * 0.001666 / 2400.
data[:, 5] -= ya_corr[data[:, 2].astype(int) - 2,
-1] * 36000 * 800 * 0.001666 / 2400.
ra, dec = gn.gnomrev_simple(data[:, 4], data[:, 5], asp[ix, 1], asp[ix, 2],
-asp[ix, 3], 1 / 36000., 0.)
return np.array([data[:, 0], ra, dec, data[:, 4], data[:, 5]]).T
def dis_correct(data, dis_map, asp):
ix = np.digitize(data['time']/1000., asp[:,0])-1
low_mask = data['xa']<16
high_mask = data['xa']>=16
coo = np.array([data['xi'][low_mask],data['eta'][low_mask]]).T
data['xi'][low_mask] -= dis_map['xi_l'](coo)
data['eta'][low_mask] -= dis_map['eta_l'](coo)
coo = np.array([data['xi'][high_mask],data['eta'][high_mask]]).T
data['xi'][high_mask] -= dis_map['xi_h'](coo)
data['eta'][high_mask] -= dis_map['eta_h'](coo)
ra, dec = gn.gnomrev_simple(data['xi'], data['eta'], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.array([ra,dec]).T
def dis_correct(data, dis_map, asp):
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
low_mask = data[:,1]<16
high_mask = data[:,1]>=16
coo = data[low_mask, 3:5]
data[low_mask, 3] -= dis_map['xi_l'](coo)
data[low_mask, 4] -= dis_map['eta_l'](coo)
coo = data[high_mask, 3:5]
data[high_mask, 3] -= dis_map['xi_h'](coo)
data[high_mask, 4] -= dis_map['eta_h'](coo)
ra, dec = gn.gnomrev_simple(data[:,3], data[:,4], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.array([ra,dec]).T
def no_dis(data, dis_map, asp):
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
low_mask = data[:,3]<16
high_mask = data[:,3]>=16
coo = data[low_mask, 6:8]
data[low_mask, 6] -= dis_map['xi_l'](coo)
data[low_mask, 7] -= dis_map['eta_l'](coo)
coo = data[high_mask, 6:8]
data[high_mask, 6] -= dis_map['xi_h'](coo)
data[high_mask, 7] -= dis_map['eta_h'](coo)
ra, dec = gn.gnomrev_simple(data[:,6], data[:,7], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.array([ra,dec]).T
def no_dis(data, dis_map, asp):
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
low_mask = data[:,3]<16
high_mask = data[:,3]>=16
coo = data[low_mask, 6:8]
data[low_mask, 6] -= dis_map['xi_l'](coo)
data[low_mask, 7] -= dis_map['eta_l'](coo)
coo = data[high_mask, 6:8]
data[high_mask, 6] -= dis_map['xi_h'](coo)
data[high_mask, 7] -= dis_map['eta_h'](coo)
ra, dec = gn.gnomrev_simple(data[:,6], data[:,7], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return data[:,8:10], data[:,3:6]#np.array([ra,dec]).T
def dis_correct(data, dis_map, asp):
ix = np.digitize(data['time'] / 1000., asp[:, 0]) - 1
low_mask = data['xa'] < 16
high_mask = data['xa'] >= 16
coo = np.array([data['xi'][low_mask], data['eta'][low_mask]]).T
data['xi'][low_mask] -= dis_map['xi_l'](coo)
data['eta'][low_mask] -= dis_map['eta_l'](coo)
coo = np.array([data['xi'][high_mask], data['eta'][high_mask]]).T
data['xi'][high_mask] -= dis_map['xi_h'](coo)
data['eta'][high_mask] -= dis_map['eta_h'](coo)
ra, dec = gn.gnomrev_simple(data['xi'], data['eta'], asp[ix, 1],
asp[ix, 2], -asp[ix, 3], 1 / 36000., 0.)
return np.array([ra, dec]).T
def dis_correct(data, dis_map, asp):
ya_mask = data[:,4]>=2
data = data[ya_mask]
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
low_mask = data[:,3]<16
high_mask = data[:,3]>=16
coo = data[low_mask, 6:8]
data[low_mask, 6] -= dis_map['xi_l'](coo)
data[low_mask, 7] -= dis_map['eta_l'](coo)
coo = data[high_mask, 6:8]
data[high_mask, 6] -= dis_map['xi_h'](coo)
data[high_mask, 7] -= dis_map['eta_h'](coo)
data[:,7] -= (-0.0281724222131*data[:,4]+0.489247048825)*36000*800*0.001666/2400.
#data[:,7] -= (-0.0426715749118*data[:,4]+0.714434337023)*36000*800*0.001666/2400.
ra, dec = gn.gnomrev_simple(data[:,6], data[:,7], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.array([ra,dec]).T, data[:,3:6]
def dis_correct(data, dis_map, asp, slope, inter):
ya_mask = data[:, 4] >= 2
data = data[ya_mask]
ix = np.digitize(data[:, 0] / 1000., asp[:, 0]) - 1
low_mask = data[:, 3] < 16
high_mask = data[:, 3] >= 16
coo = data[low_mask, 6:8]
data[low_mask, 6] -= dis_map['xi_l'](coo)
data[low_mask, 7] -= dis_map['eta_l'](coo)
coo = data[high_mask, 6:8]
data[high_mask, 6] -= dis_map['xi_h'](coo)
data[high_mask, 7] -= dis_map['eta_h'](coo)
data[:, 7] -= (slope * data[:, 4] + inter) * 36000 * 800 * 0.001666 / 2400.
#data[:,7] -= (-0.0426715749118*data[:,4]+0.714434337023)*36000*800*0.001666/2400.
ra, dec = gn.gnomrev_simple(data[:, 6], data[:, 7], asp[ix, 1], asp[ix, 2],
-asp[ix, 3], 1 / 36000., 0.)
return np.array([ra, dec]).T, data[:, 3:6]
def dis_correct(data, asp, dis_map=None, slope=None, inter=None, cut=False):
if cut:
ya_mask = data[:,2]>=2
q_mask = data[:,3]>5
data = data[ya_mask&q_mask]
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
if dis_map is not None:
low_mask = data[:,1]<16
high_mask = data[:,1]>=16
coo = data[low_mask, 4:6]
data[low_mask, 4] -= dis_map['xi_l'](coo)
data[low_mask, 5] -= dis_map['eta_l'](coo)
coo = data[high_mask, 4:6]
data[high_mask, 4] -= dis_map['xi_h'](coo)
data[high_mask, 5] -= dis_map['eta_h'](coo)
if slope is not None:
data[:,5] -= (slope*data[:,2]+inter)*36000*800*0.001666/2400.
ra, dec = gn.gnomrev_simple(data[:,4], data[:,5], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.array([ra,dec]).T
def dis_correct(data, dis_map, asp, slope, inter):
ya_mask = data[:,2]>=2
q_mask = np.ones(data.shape[0], dtype=bool)
#q_mask = data[:,3]>5
data = data[ya_mask&q_mask]
print data.shape
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
low_mask = data[:,1]<16
high_mask = data[:,1]>=16
coo = data[low_mask, 4:6]
data[low_mask, 4] -= dis_map['xi_l'](coo)
data[low_mask, 5] -= dis_map['eta_l'](coo)
coo = data[high_mask, 4:6]
data[high_mask, 4] -= dis_map['xi_h'](coo)
data[high_mask, 5] -= dis_map['eta_h'](coo)
if slope is not None:
data[:,5] -= (slope*data[:,2]+inter)*36000*800*0.001666/2400.
ra, dec = gn.gnomrev_simple(data[:,4], data[:,5], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.concatenate([np.array([ra,dec]).T, data[:,0:6], asp[ix,1:4]], axis=1)
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 dis_correct_non(data, dis_map, asp):
ix = np.digitize(data[:,0]/1000., asp[:,0])-1
ra, dec = gn.gnomrev_simple(data[:,6], data[:,7], asp[ix,1], asp[ix,2], -asp[ix,3],1/36000.,0.)
return np.array([ra,dec]).T
scst_time = scst_tmp['pktime']
ix_tmp = np.digitize(asp_solution_tmp[:, 0], scst_time) - 1
ix_mask = (ix_tmp >= 0) & (ix_tmp < scst_time.shape[0])
ix_tmp = ix_tmp[ix_mask]
asp_solution_tmp = asp_solution_tmp[ix_mask]
hv = scst_tmp['hvnom_nuv']
mask = hv[ix_tmp] > 0
ix_tmp = ix_tmp[mask]
asp_solution_tmp = asp_solution_tmp[mask]
x = np.array([0, 0])
y = np.array([0, 10000.])
ra, dec = gn.gnomrev_simple(x[0], y[0], asp_solution_tmp[:, 1],
asp_solution_tmp[:, 2],
-asp_solution_tmp[:, 3], 1 / 36000., 0.)
sky_data = SkyCoord(np.array([ra, dec]).T,
unit='deg',
frame=FK5,
equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
data0 = np.concatenate(
(np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
ra, dec = gn.gnomrev_simple(x[1], y[1], asp_solution_tmp[:, 1],
asp_solution_tmp[:, 2],
-asp_solution_tmp[:, 3], 1 / 36000., 0.)
sky_data = SkyCoord(np.array([ra, dec]).T,
unit='deg',
frame=FK5,
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_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)
scst_tmp = pyfits.open('{0}/{1}-scst.fits'.format(scst, name))[1].data
scst_time = scst_tmp['pktime']
ix_tmp = np.digitize(asp_solution_tmp[:,0], scst_time)-1
ix_mask = (ix_tmp>=0) & (ix_tmp<scst_time.shape[0])
ix_tmp = ix_tmp[ix_mask]
asp_solution_tmp = asp_solution_tmp[ix_mask]
hv = scst_tmp['hvnom_nuv']
mask = hv[ix_tmp]>0
ix_tmp = ix_tmp[mask]
asp_solution_tmp = asp_solution_tmp[mask]
x = np.array([0,0])
y = np.array([0,10000.])
ra, dec = gn.gnomrev_simple(x[0], y[0], asp_solution_tmp[:,1], asp_solution_tmp[:,2], -asp_solution_tmp[:,3],1/36000.,0.)
sky_data = SkyCoord(np.array([ra,dec]).T, unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
data0 = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
ra, dec = gn.gnomrev_simple(x[1], y[1], asp_solution_tmp[:,1], asp_solution_tmp[:,2], -asp_solution_tmp[:,3],1/36000.,0.)
sky_data = SkyCoord(np.array([ra,dec]).T, unit='deg', frame=FK5, equinox='J2000.0')
gal = sky_data.transform_to(Galactic)
data1 = np.concatenate((np.array([gal.l.deg]).T, np.array([gal.b.deg]).T), axis=1)
up = np.array([0,1.])
vector = ((data1-data0).T/np.linalg.norm((data1-data0),axis=1)).T
angle = [ang(vector[i],up) for i in range(vector.shape[0])]
mean_gl.append(np.mean(data0[:,0]))
mean_a.append(np.mean(angle))
plt.plot(angle, label=re.split('_',name)[3]+re.split('_',name)[4])
def dis_correct_non(data, dis_map, asp):
ix = np.digitize(data[:, 0] / 1000., asp[:, 0]) - 1
ra, dec = gn.gnomrev_simple(data[:, 6], data[:, 7], asp[ix, 1], asp[ix, 2],
-asp[ix, 3], 1 / 36000., 0.)
return np.array([ra, dec]).T
