def res_star(pid, scan_name, start, end, return_dict):
cata = spi.load_obj('../data/%s_starset_new' % name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
for star_num in range(start, end + 1):
order = 0
star = 'star_%d-%d' % (star_num, order)
if not os.path.exists('../data/%s/star/list/%s.csv' %
(scan_name, star)):
print 'skip:%s' % star
continue
csv_file = '../data/%s/star/list/%s.csv' % (scan_name, star)
data = []
time_list = []
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
first = reader.next()
last = float(first[0])
time_list.append(last)
data.append([])
data[0].append(first[6:8])
i = 0
for row in reader:
time = float(row[0])
if time - last > 500:
data.append([])
i += 1
data[i].append(row[6:8])
last = time
time_list.append(last)
else:
data[i].append(row[6:8])
output = "../plots/%s/star/res/%s.csv" % (scan_name, scan_name)
dir = os.path.dirname(output)
if not os.path.exists(dir):
os.makedirs(dir)
res_list = map(get_res, data)
print map(get_pos, data)
'''
def res_star(pid, scan_name, start, end, return_dict):
cata = spi.load_obj('../data/%s_starset_new'%name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
for star_num in range(start, end+1):
order = 0
star = 'star_%d-%d'%(star_num, order)
if not os.path.exists('../data/%s/star/list/%s.csv'%(scan_name, star)):
print 'skip:%s'%star
continue
csv_file = '../data/%s/star/list/%s.csv'%(scan_name, star)
data = []
time_list = []
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
first = reader.next()
last = float(first[0])
time_list.append(last)
data.append([])
data[0].append(first[6:8])
i = 0
for row in reader:
time = float(row[0])
if time - last > 500:
data.append([])
i += 1
data[i].append(row[6:8])
last = time
time_list.append(last)
else:
data[i].append(row[6:8])
output = "../plots/%s/star/res/%s.csv"%(scan_name, scan_name)
dir = os.path.dirname(output)
if not os.path.exists(dir):
os.makedirs(dir)
res_list = map(get_res, data)
print map(get_pos, data)
'''
def main_single():
name = 'AIS_GAL_SCAN_00014_0001'
cata = spi.load_obj('../data/%s_starset_extra_full'%name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector"%name)
star_list = sorted(star_list, key=getKey)
#print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(5989):
flux_list = []
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d'%i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv"%(name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv"%(name, star_num)
star_co = cata_a[i, 0:2]
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
bkg_count, edges = np.histogram(bkg_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
star_flux = np.array(star_count) - np.array(bkg_count)*64./40.
#star_flux[star_flux<0] = 0
star_len = cut_tail(star_flux)
time_array = np.arange(star_flux.shape[0])
mag = cata_list[i, 5]
flux_list.append((time_array, star_flux, i, cata_list[i, 5]))
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
res_flux = flux_list[plot_num][-1]
axes[plot_num,0].plot(time_array, star_flux, '-o')
axes[plot_num,0].text(0.95, 0.85, 'NUV: %.3f'%flux_list[plot_num][3], verticalalignment='bottom', horizontalalignment='right', transform=axes[plot_num,0].transAxes, color='green', fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num,0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num,0].get_xlim()
axes[plot_num,0].set_xlim([0, 61])
axes[plot_num,0].set_ylim([ylim[0]+np.absolute(ylim[1]-ylim[0])*0.01, ylim[1]])
axes[plot_num,0].set_xlabel('time/s')
plt.legend()
plt.savefig('../plots/%s/star/extra/single/star%d.png'%(name, flux_list[plot_num][2]),dpi=190)
plt.clf()
def main_res():
name = 'AIS_GAL_SCAN_00032_0001'
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00032_0001_asp_cal.npy')
asp_uni, uni_index=np.unique(asp[:,0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:,0]-asp_uni[0,0])+1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits'%name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0]-1
ix_tmp[ix_tmp>limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
cata = spi.load_obj('../data/%s_starset_extra_full'%name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector"%name)
star_list = sorted(star_list, key=getKey)
#print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1,len(star_list)):
if star_list[i][2] - istar[2]<=100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
flux_stat = []
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
print('group_num:%d'%group_num)
plot_num = 0
xlim = [0,60]
if len(plot) == 1:
group_num += 1
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d'%i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv"%(name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv"%(name, star_num)
star_co = cata_a[i, 0:2]
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
bkg_count, edges = np.histogram(bkg_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
index_0 = (star_data[0,0]-asp_uni[0,0]*1000)/5
index_1 = (star_data[-1,0]-asp_uni[0,0]*1000)/5
#print i, cata_list[i, 0]
#print star_co
#print np.median(star_data[:,-3:-1], axis=0)
if index_1-index_0>12400:
continue
star_track = asp_uni[index_0:index_1+1,:]
dead_t = dead_tmp[index_0:index_1+1]
ra = np.ones(star_track.shape[0])*star_co[0]
dec = np.ones(star_track.shape[0])*star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:,1], star_track[:,2], -star_track[:,3], 1/36000., 0.0)
res_list = res_star.get_res_new(xi, eta, '../fits/flat/NUV_flat.fits')
if res_list == None:
continue
res_list_d = res_list*(1-dead_t)
res_flux = running_sum(res_list_d, 200, 61)
res_len = cut_tail(res_flux)
star_flux = np.array(star_count) - np.array(bkg_count)*64./40.
star_flux[star_flux<0] = 0
star_len = cut_tail(star_flux)
delta = star_len-res_len
if delta>0 and delta<3:
star_flux = star_flux[:star_len]
res_flux = res_flux[:star_len]
elif delta >= 3 or delta<=-3:
continue
else:
star_flux = star_flux[:res_len]
res_flux = res_flux[:res_len]
if np.mean(star_flux)<5:
continue
res_flux = res_flux/np.mean(res_flux)
star_flux = star_flux/np.mean(star_flux)
time_array = np.arange(res_flux.shape[0])
var, likeli = likelihood(star_flux, res_flux)
mag = cata_list[i, 5]
flux_stat.append((mag, likeli, var, star_flux, res_flux))
print '%.3f %.3f %.3f'%(mag, var, likeli)
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append((time_array, star_flux, i, cata_list[i, 5], res_flux))
if len(flux_list)>1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
res_flux = flux_list[plot_num][-1]
axes[plot_num,0].plot(time_array, star_flux, '-o')
axes[plot_num,0].plot(time_array, res_flux, '-o')
axes[plot_num,0].text(0.95, 0.8, 'NUV: %.3f'%flux_list[plot_num][3], verticalalignment='bottom', horizontalalignment='right', transform=axes[plot_num,0].transAxes, color='green', fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num,0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num,0].get_xlim()
axes[plot_num,0].set_xlim([0, 61])
axes[plot_num,0].set_ylim([ylim[0]+np.absolute(ylim[1]-ylim[0])*0.01, ylim[1]])
if plot_num<len(flux_list)-1:
plt.setp( axes[plot_num,0].get_xticklabels(), visible=False)
else:
axes[plot_num,0].set_xlabel('time/s')
plt.setp( axes[plot_num,0].get_yticklabels(), visible=False)
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=0, hspace=0)
plt.legend()
plt.savefig('../plots/%s/star/extra/cons_res_flat/cons%d_track.png'%(name, group_num),dpi=190)
plt.clf()
group_num += 1
save_obj(flux_stat, '../plots/%s/star/extra/cons_res_flat/flux_stat'%(name))
def main_new():
name = 'AIS_GAL_SCAN_00014_0001'
cata = spi.load_obj('../data/%s_starset_extra_full'%name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector"%name)
star_list = sorted(star_list, key=getKey)
print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1,len(star_list)):
if star_list[i][2] - istar[2]<=100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
plot_num = 0
xlim = [0,60]
if len(plot) == 1:
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d'%i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv"%(name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv"%(name, star_num)
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
bkg_count, edges = np.histogram(bkg_data[:,0]-star_data[0,0], bins=np.arange(62)*1000)
star_flux = np.array(star_count) - np.array(bkg_count)*64./40.
star_flux[star_flux<0] = 0
if np.mean(star_flux)<5:
continue
time_array = np.arange(61)
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append((time_array, star_flux, i, cata_list[i, 5]))
if len(flux_list)>1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
axes[plot_num,0].plot(time_array, star_flux)
axes[plot_num,0].text(0.95, 0.85, 'NUV: %.3f'%flux_list[plot_num][3], verticalalignment='bottom', horizontalalignment='right', transform=axes[plot_num,0].transAxes, color='green', fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num,0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num,0].get_xlim()
axes[plot_num,0].set_xlim([0, xlim[1]])
axes[plot_num,0].set_ylim([ylim[0]+np.absolute(ylim[1]-ylim[0])*0.01, ylim[1]])
if plot_num<len(flux_list)-1:
plt.setp( axes[plot_num,0].get_xticklabels(), visible=False)
else:
axes[plot_num,0].set_xlabel('time/s')
#plot_num+=1
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=0, hspace=0)
plt.legend()
plt.savefig('../plots/%s/star/extra/cons_new/cons%d_track.png'%(name, group_num),dpi=190)
plt.clf()
group_num += 1
def res_star_new(pid, scan_name, start, end, return_dict):
cata = spi.load_obj('../data/%s_starset_new' % scan_name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00014_0001_asp_cal.npy')
asp_uni, uni_index = np.unique(asp[:, 0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:, 0] - asp_uni[0, 0]) + 1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits' %
scan_name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0] - 1
ix_tmp[ix_tmp > limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
for star_num in range(start, end + 1):
star_co = cata_a[star_num, 0:2]
order = 1
star = 'star_%d-%d' % (star_num, order)
if not os.path.exists('../data/%s/star/list/%s.csv' %
(scan_name, star)):
print 'skip:%s' % star
continue
csv_file = '../data/%s/star/list/%s.csv' % (scan_name, star)
count = []
time_list = []
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
first = float(reader.next()[0])
final = 0
last = 0
reader = csv.reader(file)
for row in reader:
time = float(row[0]) #time = int(float(row[0])/1000.)
if time - last > 200:
count.append(1)
last = time
time_list.append(last)
else:
count[-1] += 1
final = time
time_list = np.array(time_list) / 1000.
index_0 = (first - asp_uni[0, 0] * 1000) / 5
index_1 = (final - asp_uni[0, 0] * 1000) / 5
star_track = asp_uni[index_0:index_1 + 1, :]
dead_t = dead_tmp[index_0:index_1 + 1]
ra = np.ones(star_track.shape[0]) * star_co[0]
dec = np.ones(star_track.shape[0]) * star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:, 1], star_track[:,
2],
-star_track[:, 3], 1 / 36000., 0.0)
output = "../plots/%s/star/res/%s.csv" % (scan_name, scan_name)
dir = os.path.dirname(output)
if not os.path.exists(dir):
os.makedirs(dir)
print dead_t
res_list = get_res_new(xi, eta)
res_list_d = res_list * (1 - dead_t)
'''
plt.plot(star_track[:,0], res_list_d)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track_d.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
plt.plot(star_track[:,0], res_list)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
'''
f, axes = plt.subplots(2, 1)
axes[0].plot(time_list, count)
axes[0].set_ylabel('Num of Photons')
ylim = axes[0].get_ylim()
axes[0].set_ylim(ylim + np.absolute(ylim) * 0.01)
plt.setp(axes[0].get_xticklabels(), visible=False)
#plt.setp( axes[0].get_yticklabels(), visible=False)
axes[1].plot(star_track[:, 0], res_list_d)
axes[1].set_ylabel('Response')
axes[1].set_xlabel('time/s')
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
plt.savefig('../plots/%s/star/res/2p/res%d-%d_track.png' %
(scan_name, star_num, order),
dpi=190)
plt.clf()
def res_star_new(pid, scan_name, start, end, return_dict):
cata = spi.load_obj('../data/%s_starset_new'%scan_name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00014_0001_asp_cal.npy')
asp_uni, uni_index=np.unique(asp[:,0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:,0]-asp_uni[0,0])+1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits'%scan_name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0]-1
ix_tmp[ix_tmp>limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
for star_num in range(start, end+1):
star_co = cata_a[star_num, 0:2]
order = 1
star = 'star_%d-%d'%(star_num, order)
if not os.path.exists('../data/%s/star/list/%s.csv'%(scan_name, star)):
print 'skip:%s'%star
continue
csv_file = '../data/%s/star/list/%s.csv'%(scan_name, star)
count = []
time_list = []
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
first = float(reader.next()[0])
final = 0
last = 0
reader = csv.reader(file)
for row in reader:
time = float(row[0])#time = int(float(row[0])/1000.)
if time - last > 200:
count.append(1)
last = time
time_list.append(last)
else:
count[-1] += 1
final = time
time_list = np.array(time_list)/1000.
index_0 = (first-asp_uni[0,0]*1000)/5
index_1 = (final-asp_uni[0,0]*1000)/5
star_track = asp_uni[index_0:index_1+1,:]
dead_t = dead_tmp[index_0:index_1+1]
ra = np.ones(star_track.shape[0])*star_co[0]
dec = np.ones(star_track.shape[0])*star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:,1], star_track[:,2], -star_track[:,3], 1/36000., 0.0)
output = "../plots/%s/star/res/%s.csv"%(scan_name, scan_name)
dir = os.path.dirname(output)
if not os.path.exists(dir):
os.makedirs(dir)
print dead_t
res_list = get_res_new(xi, eta)
res_list_d = res_list*(1-dead_t)
'''
plt.plot(star_track[:,0], res_list_d)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track_d.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
plt.plot(star_track[:,0], res_list)
plt.xlabel('t/s')
plt.ylabel('response')
plt.savefig('../plots/%s/star/res/res%d-%d_track.png'%(scan_name, star_num, order), dpi=190)
plt.clf()
'''
f, axes = plt.subplots(2, 1)
axes[0].plot(time_list, count)
axes[0].set_ylabel('Num of Photons')
ylim = axes[0].get_ylim()
axes[0].set_ylim(ylim+np.absolute(ylim)*0.01)
plt.setp( axes[0].get_xticklabels(), visible=False)
#plt.setp( axes[0].get_yticklabels(), visible=False)
axes[1].plot(star_track[:,0], res_list_d)
axes[1].set_ylabel('Response')
axes[1].set_xlabel('time/s')
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=0, hspace=0)
plt.savefig('../plots/%s/star/res/2p/res%d-%d_track.png'%(scan_name,star_num,order),dpi=190)
plt.clf()
hdulist = pyfits.open('../data/asprta/AIS_GAL_SCAN_00005_0001-asprta.fits')
co_data = hdulist[1].data
step = 10
offsets = np.load('../data/05_r/cata/offsets300-1343_10_new.npy')
print offsets.shape
initial = int((co_data[300][0]-0.5)*1000)
skypos_f, skyrange_f = ([0, 0], [1.33333336,1.33333336])
imsz_f = imagetools.deg2pix(skypos_f, skyrange_f, 0.0016666667)
count_f = np.zeros(imsz_f)
wcs_f = imagetools.define_wcs(skypos_f,skyrange_f,width=False,height=False,verbose=0,pixsz=0.0016666667)
print imsz_f
cata = spi.load_obj('stars05')
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_a = cata_a*np.pi/180.
X, Y, Z = pycoo.spherical_to_cartesian(1, cata_a[:,1], cata_a[:,0])
cata_car = np.array([X,Y,Z], dtype='float64').T
rad = np.cos(0.00250*np.pi/180.)
with open('../data/csv_list05_full') as f:
csv_list = f.read().splitlines()
for csv_file in csv_list:
print csv_file
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
data = []
data_f = []
def main_single():
name = 'AIS_GAL_SCAN_00014_0001'
cata = spi.load_obj('../data/%s_starset_extra_full' % name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector" % name)
star_list = sorted(star_list, key=getKey)
#print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(5989):
flux_list = []
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d' % i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv" % (name,
star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv" % (
name, star_num)
star_co = cata_a[i, 0:2]
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
bkg_count, edges = np.histogram(bkg_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
star_flux = np.array(star_count) - np.array(bkg_count) * 64. / 40.
#star_flux[star_flux<0] = 0
star_len = cut_tail(star_flux)
time_array = np.arange(star_flux.shape[0])
mag = cata_list[i, 5]
flux_list.append((time_array, star_flux, i, cata_list[i, 5]))
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
res_flux = flux_list[plot_num][-1]
axes[plot_num, 0].plot(time_array, star_flux, '-o')
axes[plot_num, 0].text(0.95,
0.85,
'NUV: %.3f' % flux_list[plot_num][3],
verticalalignment='bottom',
horizontalalignment='right',
transform=axes[plot_num, 0].transAxes,
color='green',
fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num, 0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num, 0].get_xlim()
axes[plot_num, 0].set_xlim([0, 61])
axes[plot_num, 0].set_ylim(
[ylim[0] + np.absolute(ylim[1] - ylim[0]) * 0.01, ylim[1]])
axes[plot_num, 0].set_xlabel('time/s')
plt.legend()
plt.savefig('../plots/%s/star/extra/single/star%d.png' %
(name, flux_list[plot_num][2]),
dpi=190)
plt.clf()
def main_res():
name = 'AIS_GAL_SCAN_00032_0001'
asp = np.load('../data/photon_list/AIS_GAL_SCAN_00032_0001_asp_cal.npy')
asp_uni, uni_index = np.unique(asp[:, 0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:, 0] - asp_uni[0, 0]) + 1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits' % name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0] - 1
ix_tmp[ix_tmp > limit] = limit
dead_tmp = scst_tmp['t_dead_nuv'][ix_tmp]
cata = spi.load_obj('../data/%s_starset_extra_full' % name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector" % name)
star_list = sorted(star_list, key=getKey)
#print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1, len(star_list)):
if star_list[i][2] - istar[2] <= 100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
flux_stat = []
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
print('group_num:%d' % group_num)
plot_num = 0
xlim = [0, 60]
if len(plot) == 1:
group_num += 1
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d' % i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv" % (
name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv" % (
name, star_num)
star_co = cata_a[i, 0:2]
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
bkg_count, edges = np.histogram(bkg_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
index_0 = (star_data[0, 0] - asp_uni[0, 0] * 1000) / 5
index_1 = (star_data[-1, 0] - asp_uni[0, 0] * 1000) / 5
#print i, cata_list[i, 0]
#print star_co
#print np.median(star_data[:,-3:-1], axis=0)
if index_1 - index_0 > 12400:
continue
star_track = asp_uni[index_0:index_1 + 1, :]
dead_t = dead_tmp[index_0:index_1 + 1]
ra = np.ones(star_track.shape[0]) * star_co[0]
dec = np.ones(star_track.shape[0]) * star_co[1]
xi, eta = gn.gnomfwd_simple(ra, dec, star_track[:, 1],
star_track[:, 2], -star_track[:, 3],
1 / 36000., 0.0)
res_list = res_star.get_res_new(xi, eta,
'../fits/flat/NUV_flat.fits')
if res_list == None:
continue
res_list_d = res_list * (1 - dead_t)
res_flux = running_sum(res_list_d, 200, 61)
res_len = cut_tail(res_flux)
star_flux = np.array(star_count) - np.array(bkg_count) * 64. / 40.
star_flux[star_flux < 0] = 0
star_len = cut_tail(star_flux)
delta = star_len - res_len
if delta > 0 and delta < 3:
star_flux = star_flux[:star_len]
res_flux = res_flux[:star_len]
elif delta >= 3 or delta <= -3:
continue
else:
star_flux = star_flux[:res_len]
res_flux = res_flux[:res_len]
if np.mean(star_flux) < 5:
continue
res_flux = res_flux / np.mean(res_flux)
star_flux = star_flux / np.mean(star_flux)
time_array = np.arange(res_flux.shape[0])
var, likeli = likelihood(star_flux, res_flux)
mag = cata_list[i, 5]
flux_stat.append((mag, likeli, var, star_flux, res_flux))
print '%.3f %.3f %.3f' % (mag, var, likeli)
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append(
(time_array, star_flux, i, cata_list[i, 5], res_flux))
if len(flux_list) > 1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
res_flux = flux_list[plot_num][-1]
axes[plot_num, 0].plot(time_array, star_flux, '-o')
axes[plot_num, 0].plot(time_array, res_flux, '-o')
axes[plot_num, 0].text(0.95,
0.8,
'NUV: %.3f' % flux_list[plot_num][3],
verticalalignment='bottom',
horizontalalignment='right',
transform=axes[plot_num, 0].transAxes,
color='green',
fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num, 0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num, 0].get_xlim()
axes[plot_num, 0].set_xlim([0, 61])
axes[plot_num, 0].set_ylim(
[ylim[0] + np.absolute(ylim[1] - ylim[0]) * 0.01, ylim[1]])
if plot_num < len(flux_list) - 1:
plt.setp(axes[plot_num, 0].get_xticklabels(),
visible=False)
else:
axes[plot_num, 0].set_xlabel('time/s')
plt.setp(axes[plot_num, 0].get_yticklabels(), visible=False)
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
plt.legend()
plt.savefig(
'../plots/%s/star/extra/cons_res_flat/cons%d_track.png' %
(name, group_num),
dpi=190)
plt.clf()
group_num += 1
save_obj(flux_stat,
'../plots/%s/star/extra/cons_res_flat/flux_stat' % (name))
def main_new():
name = 'AIS_GAL_SCAN_00014_0001'
cata = spi.load_obj('../data/%s_starset_extra_full' % name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector" % name)
star_list = sorted(star_list, key=getKey)
print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1, len(star_list)):
if star_list[i][2] - istar[2] <= 100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
plot_num = 0
xlim = [0, 60]
if len(plot) == 1:
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d' % i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv" % (
name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv" % (
name, star_num)
star_data = np.genfromtxt(csv_file, delimiter=',')
bkg_data = np.genfromtxt(bkg_csv_file, delimiter=',')
star_count, edges = np.histogram(star_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
bkg_count, edges = np.histogram(bkg_data[:, 0] - star_data[0, 0],
bins=np.arange(62) * 1000)
star_flux = np.array(star_count) - np.array(bkg_count) * 64. / 40.
star_flux[star_flux < 0] = 0
if np.mean(star_flux) < 5:
continue
time_array = np.arange(61)
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append((time_array, star_flux, i, cata_list[i, 5]))
if len(flux_list) > 1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
axes[plot_num, 0].plot(time_array, star_flux)
axes[plot_num, 0].text(0.95,
0.85,
'NUV: %.3f' % flux_list[plot_num][3],
verticalalignment='bottom',
horizontalalignment='right',
transform=axes[plot_num, 0].transAxes,
color='green',
fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num, 0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num, 0].get_xlim()
axes[plot_num, 0].set_xlim([0, xlim[1]])
axes[plot_num, 0].set_ylim(
[ylim[0] + np.absolute(ylim[1] - ylim[0]) * 0.01, ylim[1]])
if plot_num < len(flux_list) - 1:
plt.setp(axes[plot_num, 0].get_xticklabels(),
visible=False)
else:
axes[plot_num, 0].set_xlabel('time/s')
#plot_num+=1
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
plt.legend()
plt.savefig('../plots/%s/star/extra/cons_new/cons%d_track.png' %
(name, group_num),
dpi=190)
plt.clf()
group_num += 1
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()
'''
offsets = np.load('../data/05_r/cata/offsets300-1343_10_new.npy')
print offsets.shape
initial = int((co_data[300][0] - 0.5) * 1000)
skypos_f, skyrange_f = ([0, 0], [1.33333336, 1.33333336])
imsz_f = imagetools.deg2pix(skypos_f, skyrange_f, 0.0016666667)
count_f = np.zeros(imsz_f)
wcs_f = imagetools.define_wcs(skypos_f,
skyrange_f,
width=False,
height=False,
verbose=0,
pixsz=0.0016666667)
print imsz_f
cata = spi.load_obj('stars05')
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_a = cata_a * np.pi / 180.
X, Y, Z = pycoo.spherical_to_cartesian(1, cata_a[:, 1], cata_a[:, 0])
cata_car = np.array([X, Y, Z], dtype='float64').T
rad = np.cos(0.00250 * np.pi / 180.)
with open('../data/csv_list05_full') as f:
csv_list = f.read().splitlines()
for csv_file in csv_list:
print csv_file
with open(csv_file, 'rb') as file:
reader = csv.reader(file)
data = []
data_f = []
if __name__ == '__main__':
if False:
name = sys.argv[1]
#name = 'AIS_GAL_SCAN_00032_0001'
print name
output = '../data/%s/star/extra/star.csv' % (name)
path = os.path.dirname(output)
if not os.path.exists(path):
os.makedirs(path)
else:
print 'exists'
cata = spi.load_obj('../data/%s_starset_extra_full' % name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
count_list, imsz_list, wcs_list = [], [], []
skypos_list, skyrange_list = [], []
for i in range(cata_list.shape[0]):
skypos, skyrange = ([cata_list[i, 1], cata_list[i,
2]], [0.03, 0.03])
print skypos
print skyrange
skypos_list.append(skypos)
skyrange_list.append(skyrange)
imsz = imagetools.deg2pix(skypos, skyrange, 0.000416666666666667)
count = np.zeros(imsz)
def main():
name = 'AIS_GAL_SCAN_00014_0001'
cata = spi.load_obj('../data/%s_starset_extra_full'%name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector"%name)
star_list = sorted(star_list, key=getKey)
print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1,len(star_list)):
if star_list[i][2] - istar[2]<=100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
plot_num = 0
xlim = [0,60]
if len(plot) == 1:
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d'%i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv"%(name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv"%(name, star_num)
star_file = open(csv_file, 'rb')
bkg_file = open(bkg_csv_file, 'rb')
star_reader = csv.reader(star_file)
bkg_reader = csv.reader(bkg_file)
last = 0
for row in star_reader:
time = float(row[0])#time = int(float(row[0])/1000.)
if len(time_list) > 0:
if time - time_list[0] > 59000:
break
if time - last > 1000:
star_count.append(1)
last = time
time_list.append(last)
else:
star_count[-1] += 1
last = 0
bkg_count = np.zeros(len(star_count))
for row in bkg_reader:
time = float(row[0])#time = int(float(row[0])/1000.)
for j in range(len(star_count)-1):
if time>=time_list[j] and time<time_list[j+1]:
bkg_count[j] += 1
break
if time>=time_list[-1]:
bkg_count[-1] += 1
'''
if time - last > 1000:
bkg_count.append(1)
last = time
bkg_time_list.append(last)
else:
bkg_count[-1] += 1
'''
star_flux = np.array(star_count) - np.array(bkg_count)*64./40.
star_flux[star_flux<0] = 0
if np.mean(star_flux)<5:
continue
#without bkg sub
star_flux = np.array(star_count)
time_array = (np.array(time_list)-time_list[0])/1000.
star_file.close()
bkg_file.close()
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append((time_array, star_flux, i, cata_list[i, 5]))
if len(flux_list)>1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
axes[plot_num,0].plot(time_array, star_flux)
axes[plot_num,0].text(0.95, 0.85, 'NUV: %.3f'%flux_list[plot_num][3], verticalalignment='bottom', horizontalalignment='right', transform=axes[plot_num,0].transAxes, color='green', fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num,0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num,0].get_xlim()
axes[plot_num,0].set_xlim([0, xlim[1]])
axes[plot_num,0].set_ylim([ylim[0]+np.absolute(ylim[1]-ylim[0])*0.01, ylim[1]])
if plot_num<len(flux_list)-1:
plt.setp( axes[plot_num,0].get_xticklabels(), visible=False)
else:
axes[plot_num,0].set_xlabel('time/s')
#plot_num+=1
plt.subplots_adjust(left=None, bottom=None, right=None, top=None,
wspace=0, hspace=0)
plt.legend()
plt.savefig('../plots/%s/star/extra/cons_no/cons%d_track.png'%(name, group_num),dpi=190)
plt.clf()
group_num += 1
def main():
name = 'AIS_GAL_SCAN_00014_0001'
cata = spi.load_obj('../data/%s_starset_extra_full' % name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
star_list = load_obj("../data/%s/star/extra/list_new/star_detector" % name)
star_list = sorted(star_list, key=getKey)
print star_list
plot_list = []
stars = []
istar = star_list[0]
stars.append(istar[0])
for i in range(1, len(star_list)):
if star_list[i][2] - istar[2] <= 100:
stars.append(star_list[i][0])
else:
plot_list.append(stars)
istar = star_list[i]
stars = []
stars.append(istar[0])
group_num = 0
for plot in plot_list:
#f, axes = plt.subplots(len(plot), 1, squeeze=False)
plot_num = 0
xlim = [0, 60]
if len(plot) == 1:
continue
flux_list = []
for i in plot:
#for i in range(397):
star_count = []
time_list = []
bkg_time_list = []
bkg_count = []
star_num = '%d' % i
csv_file = "../data/%s/star/extra/list_new/star_%s-0.csv" % (
name, star_num)
bkg_csv_file = "../data/%s/star/extra/bkg/star_bkg_%s-0.csv" % (
name, star_num)
star_file = open(csv_file, 'rb')
bkg_file = open(bkg_csv_file, 'rb')
star_reader = csv.reader(star_file)
bkg_reader = csv.reader(bkg_file)
last = 0
for row in star_reader:
time = float(row[0]) #time = int(float(row[0])/1000.)
if len(time_list) > 0:
if time - time_list[0] > 59000:
break
if time - last > 1000:
star_count.append(1)
last = time
time_list.append(last)
else:
star_count[-1] += 1
last = 0
bkg_count = np.zeros(len(star_count))
for row in bkg_reader:
time = float(row[0]) #time = int(float(row[0])/1000.)
for j in range(len(star_count) - 1):
if time >= time_list[j] and time < time_list[j + 1]:
bkg_count[j] += 1
break
if time >= time_list[-1]:
bkg_count[-1] += 1
'''
if time - last > 1000:
bkg_count.append(1)
last = time
bkg_time_list.append(last)
else:
bkg_count[-1] += 1
'''
star_flux = np.array(star_count) - np.array(bkg_count) * 64. / 40.
star_flux[star_flux < 0] = 0
if np.mean(star_flux) < 5:
continue
#without bkg sub
star_flux = np.array(star_count)
time_array = (np.array(time_list) - time_list[0]) / 1000.
star_file.close()
bkg_file.close()
'''
plt.plot(time_array, star_flux)
plt.xlabel('t/s')
plt.ylabel('Number of Photons')
plt.savefig('../plots/%s/star/star_flux/%s-0.png'%(name, star_num), dpi=190)
plt.clf()
'''
flux_list.append((time_array, star_flux, i, cata_list[i, 5]))
if len(flux_list) > 1:
flux_list = sorted(flux_list, key=getMag)
f, axes = plt.subplots(len(flux_list), 1, squeeze=False)
for plot_num in range(len(flux_list)):
star_flux = flux_list[plot_num][1]
time_array = flux_list[plot_num][0]
axes[plot_num, 0].plot(time_array, star_flux)
axes[plot_num, 0].text(0.95,
0.85,
'NUV: %.3f' % flux_list[plot_num][3],
verticalalignment='bottom',
horizontalalignment='right',
transform=axes[plot_num, 0].transAxes,
color='green',
fontsize=10)
#axes[plot_num,0].set_ylabel('Num of Photons')
ylim = axes[plot_num, 0].get_ylim()
if plot_num == 0:
xlim = axes[plot_num, 0].get_xlim()
axes[plot_num, 0].set_xlim([0, xlim[1]])
axes[plot_num, 0].set_ylim(
[ylim[0] + np.absolute(ylim[1] - ylim[0]) * 0.01, ylim[1]])
if plot_num < len(flux_list) - 1:
plt.setp(axes[plot_num, 0].get_xticklabels(),
visible=False)
else:
axes[plot_num, 0].set_xlabel('time/s')
#plot_num+=1
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
wspace=0,
hspace=0)
plt.legend()
plt.savefig('../plots/%s/star/extra/cons_no/cons%d_track.png' %
(name, group_num),
dpi=190)
plt.clf()
group_num += 1
if __name__ == '__main__':
if False:
name = sys.argv[1]
#name = 'AIS_GAL_SCAN_00032_0001'
print name
output = '../data/%s/star/extra/star.csv'%(name)
path = os.path.dirname(output)
if not os.path.exists(path):
os.makedirs(path)
else:
print 'exists'
cata = spi.load_obj('../data/%s_starset_extra_full'%name)
cata_a = np.array(list(cata))
cata_len = len(cata)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
count_list, imsz_list, wcs_list = [],[],[]
skypos_list, skyrange_list = [],[]
for i in range(cata_list.shape[0]):
skypos, skyrange = ([cata_list[i,1], cata_list[i,2]] , [0.03, 0.03])
print skypos
print skyrange
skypos_list.append(skypos)
skyrange_list.append(skyrange)
imsz = imagetools.deg2pix(skypos, skyrange, 0.000416666666666667)
count = np.zeros(imsz)
wcs = imagetools.define_wcs(skypos,skyrange,width=False,height=False,verbose=0,pixsz=0.000416666666666667)
print num
res_star_new(0, scan_name, num, num, return_dict)
if False:
scan_name = sys.argv[1]
#scan_name = 'AIS_GAL_SCAN_00014_0001'
output = '../fits/%s/extra/flat.fits' % (scan_name)
print output
path = os.path.dirname(output)
if not os.path.exists(path):
os.makedirs(path)
else:
print 'exists'
cata = spi.load_obj('../data/%s_starset_extra_all_star' % scan_name)
cata_a = np.array(list(cata))
cata_a = cata_a[np.logical_and(cata_a[:, 4] > 15.5,
cata_a[:, 4] < 17.5)]
cata_len = len(cata_a)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
print cata_len
asp = np.load('../data/photon_list/%s_asp_cal.npy' % scan_name)
asp_uni, uni_index = np.unique(asp[:, 0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:, 0] - asp_uni[0, 0]) + 1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits' %
scan_name)[1].data
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()
'''
print num
res_star_new(0, scan_name, num, num, return_dict)
if False:
scan_name = sys.argv[1]
#scan_name = 'AIS_GAL_SCAN_00014_0001'
output = '../fits/%s/extra/flat.fits'%(scan_name)
print output
path = os.path.dirname(output)
if not os.path.exists(path):
os.makedirs(path)
else:
print 'exists'
cata = spi.load_obj('../data/%s_starset_extra_all_star'%scan_name)
cata_a = np.array(list(cata))
cata_a = cata_a[np.logical_and(cata_a[:,4]>15.5,cata_a[:,4]<17.5)]
cata_len = len(cata_a)
cata_list = np.insert(cata_a, 0, np.arange(cata_len), axis=1)
print cata_len
asp = np.load('../data/photon_list/%s_asp_cal.npy'%scan_name)
asp_uni, uni_index=np.unique(asp[:,0], True)
asp_uni = asp[uni_index]
ix_tmp = (np.round(asp_uni[:,0]-asp_uni[0,0])+1).astype(int)
dead_tmp = np.zeros(ix_tmp.shape[0])
scst_tmp = pyfits.open('../AIS_GAL_SCAN/scst/%s-scst.fits'%scan_name)[1].data
limit = scst_tmp['t_dead_nuv'].shape[0]-1
ix_tmp[ix_tmp>limit] = limit
