def convert_catalog(cfile):
tycho2 = pyfits.open('../data/tycho2.fits')[1].data
sfile=re.split('\.',cfile)[0]+'.txt'
print(sfile)
try:
df = load_data.load_catalog(sfile)
except IOError:
print('skip')
return None
c = SkyCoord(df['gl']*u.degree, df['gb']*u.degree, frame='galactic')
catalog = SkyCoord(tycho2['Glon']*u.degree, tycho2['Glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask=d2d<0.001*u.degree
print(np.sum(mask))
dtype = np.dtype([('tycho_num', int), ('Glon', '>f4'), ('Glat', '>f4'), ('RAJ2000', '>f4'), ('DEJ2000', '>f4'),
('flux', float), ('nuv', float), ('gl', float), ('gb', float)])
matched_tycho = tycho2[idx[mask]]
matched_df = df[mask]
matched_catalog = np.core.records.fromarrays(np.array([idx[mask], matched_tycho['Glon'], matched_tycho['Glat'],
matched_tycho['RAJ2000'], matched_tycho['DEJ2000'], np.array(matched_df['FLUX_AUTO']),
np.array(matched_df['nuv']), np.array(matched_df['gl']),
np.array(matched_df['gb'])]), dtype=dtype)
print(matched_catalog.shape)
np.save(cfile, matched_catalog)
return matched_catalog
scans = ['0023', '0212']
scans = ['0023', '0212', '0464', '0689']
date = '08-21-2017' #'08-21-2017'
scan_f = sys.argv[1]
scans = []
with open(scan_f) as f:
scans = f.read().splitlines()
print scans
size = 800
Nb = 6000
ap = 0.004
for scan in scans:
print(scan)
cfile = '/scratch/dw1519/galex/fits/scan_map/catalog/' + date + '/starcat_' + scan + 'mapweight_fec_fwhm.txt'
df = load_data.load_catalog(cfile)
print len(df)
mask = df['nuv'] < 14.5
c1 = SkyCoord(df['gl'][mask] * u.degree,
df['gb'][mask] * u.degree,
frame='galactic')
mask = (df['nuv'] >= 14.5) & (df['nuv'] < 16.5)
c2 = SkyCoord(df['gl'][mask] * u.degree,
df['gb'][mask] * u.degree,
frame='galactic')
mask = (df['nuv'] >= 16.5)
c3 = SkyCoord(df['gl'][mask] * u.degree,
df['gb'][mask] * u.degree,
frame='galactic')
print(c1.shape[0] + c2.shape[0] + c3.shape[0])
def plot(date, scan):
#date = '08-21-2017'
print(scan)
tycho2 = pyfits.open('/scratch/dw1519/galex/data/tycho2.fits')[1].data
cfile='/scratch/dw1519/galex/fits/scan_map/catalog/'+date+'/starcat_'+scan+'mapweight_fec_fwhm.txt'
df = load_data.load_catalog(cfile)
c = SkyCoord(df['gl']*u.degree, df['gb']*u.degree, frame='galactic')
'''
if scan=='3488':
cut = df['gb']>1.
c = c[cut]
df = df[cut]
'''
print(c.shape)
catalog = SkyCoord(tycho2['Glon']*u.degree, tycho2['Glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask=d2d<0.001*u.degree
N = np.sum(mask)
print(N)
f = plt.figure(figsize=(12,10))
ax = plt.subplot(2, 2, 1)
ax.grid(False)
plt.scatter((c[mask].l.deg-catalog[idx[mask]].l.deg)*3600,\
(c[mask].b.deg-catalog[idx[mask]].b.deg)*3600,\
c=df['nuv'][mask], alpha=0.2, edgecolors='face', cmap=plt.get_cmap('jet'))
plt.axis('equal')
plt.axhline(y=0, color='b', linestyle='dashed', linewidth=1)
plt.axvline(x=0, color='b', linestyle='dashed', linewidth=1)
#plt.title('0023 NUV<15, star={0} {1:.0f}%, photon={2} {3:.0f}%'.format(len(bright), len(bright)/total_star*100,\
# len(bright_co), len(bright_co)/total_photon*100))
plt.xlim(-3.0,3.0)
plt.ylim(-3.0,3.0)
plt.ylabel('gb(SEx-T2)')
plt.title('gl(SEx-T2), N={0}'.format(N))
plt.colorbar()
ais = pyfits.open('/scratch/dw1519/galex/data/MyTable_4_jvc2688.fit')[1].data
ais_cat = SkyCoord(ais['glon']*u.degree, ais['glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(ais_cat)
mask=d2d<0.001*u.degree
N = np.sum(mask)
print('ais match:{0}'.format(N))
plt.subplot(2, 2, 2)
plt.scatter(ais['nuv'][idx[mask]],\
df['nuv'][mask]-ais['nuv'][idx[mask]],\
c=ais['glat'][idx[mask]], alpha=0.5, edgecolors='face', s=0.2, cmap=plt.get_cmap('jet'), vmin=-10, vmax=10)
plt.axhline(y=0, color='k', linestyle='dashed', linewidth=1)
nuv_list = [[14,15],[15,16],[16,17],[17,18],[18,19],[19,20]]
mask_list = [(e[0]<=ais['nuv'][idx[mask]]) & (e[1]>ais['nuv'][idx[mask]]) for e in nuv_list]
x = [np.median(ais['nuv'][idx[mask]][m]) for m in mask_list]
y = [np.median((df['nuv'][mask]-ais['nuv'][idx[mask]])[m]) for m in mask_list]
ye = [np.std((df['nuv'][mask]-ais['nuv'][idx[mask]])[m]) for m in mask_list]
plt.errorbar(x, y, yerr=ye)
plt.xlim(12,22)
plt.ylabel('NUV (SEx-AIS)')
plt.title('NUV AIS, N={0}'.format(N))
plt.ylim(-1,1)
plt.colorbar()
plt.subplot(2, 2, 3)
plt.hist(df['nuv'],bins=18,range=(12,22))
plt.title('NUV SExtractor')
plt.xlim(12,22)
plt.subplot(2, 2, 4)
plt.plot(df['nuv'], df['FWHM_IMAGE'],'.k',markersize=0.7)
plt.title('NUV SExtractor, N={0}'.format(len(df)))
plt.ylabel('FWHM [pix]')
plt.ylim(0,8)
plt.xlim(12,22)
plt.suptitle(scan)
plt.tight_layout()
#plt.subplots_adjust(top=0.9)
plt.savefig('/scratch/dw1519/galex/fits/scan_map/catalog/'+date+'/'+scan+'_match_new.png', dpi=190)
plt.clf()
#ranges = [(144.,154.), (153.,163.), (162.,172.), (171.,181.), (180,190)]
ranges = [(198.,208.), (207.,217.), (216.,226.), (225.,235.), (234,244)]
ranges = [(252.,262.), (261.,271.), (270.,280.), (279.,289.), (288,298), (297,307)]
ranges = [(315.,325.), (324.,334.), (333.,343.), (351.,1.)]
ran = (234,244)
date = '12-06-2017/6'
nrange = 0
for scan in scans:
print(scan)
ran = ranges[nrange]
tycho2 = pyfits.open('../data/tycho2.fits')[1].data
cfile='/scratch/dw1519/galex/fits/scan_map/catalog/'+date+'/starcat_'+scan+'mapweight_fec_fwhm.txt'
df = load_data.load_catalog(cfile)
c = SkyCoord(df['gl']*u.degree, df['gb']*u.degree, frame='galactic')
print(c.shape)
catalog = SkyCoord(tycho2['Glon']*u.degree, tycho2['Glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask=d2d<0.001*u.degree
N = np.sum(mask)
print(N)
exp = pyfits.open('/scratch/dw1519/galex/fits/scan_map/'+date+'/count_map_'+scan+'_exp.fits')[0]
w = wcs.WCS(exp.header)
e = exp.data
mask = e>0
nmask = np.sum(mask, axis=0)
nsort = np.argsort(nmask)
sortm = nmask[nsort]
def plot(date, scan):
#date = '08-21-2017'
print(scan)
tycho2 = pyfits.open('/scratch/dw1519/galex/data/tycho2.fits')[1].data
cfile = '/scratch/dw1519/galex/fits/scan_map/catalog/' + date + '/starcat_' + scan + 'mapweight_fec_fwhm.txt'
df = load_data.load_catalog(cfile)
c = SkyCoord(df['gl'] * u.degree, df['gb'] * u.degree, frame='galactic')
'''
if scan=='3488':
cut = df['gb']>1.
c = c[cut]
df = df[cut]
'''
print(c.shape)
catalog = SkyCoord(tycho2['Glon'] * u.degree,
tycho2['Glat'] * u.degree,
frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask = d2d < 0.001 * u.degree
N = np.sum(mask)
print(N)
f = plt.figure(figsize=(12, 10))
ax = plt.subplot(2, 2, 1)
ax.grid(False)
plt.scatter((c[mask].l.deg-catalog[idx[mask]].l.deg)*3600,\
(c[mask].b.deg-catalog[idx[mask]].b.deg)*3600,\
c=df['nuv'][mask], alpha=0.2, edgecolors='face', cmap=plt.get_cmap('jet'))
plt.axis('equal')
plt.axhline(y=0, color='b', linestyle='dashed', linewidth=1)
plt.axvline(x=0, color='b', linestyle='dashed', linewidth=1)
#plt.title('0023 NUV<15, star={0} {1:.0f}%, photon={2} {3:.0f}%'.format(len(bright), len(bright)/total_star*100,\
# len(bright_co), len(bright_co)/total_photon*100))
plt.xlim(-3.0, 3.0)
plt.ylim(-3.0, 3.0)
plt.ylabel('gb(SEx-T2)')
plt.title('gl(SEx-T2), N={0}'.format(N))
plt.colorbar()
ais = pyfits.open(
'/scratch/dw1519/galex/data/MyTable_4_jvc2688.fit')[1].data
ais_cat = SkyCoord(ais['glon'] * u.degree,
ais['glat'] * u.degree,
frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(ais_cat)
mask = d2d < 0.001 * u.degree
N = np.sum(mask)
print('ais match:{0}'.format(N))
plt.subplot(2, 2, 2)
plt.scatter(ais['nuv'][idx[mask]],\
df['nuv'][mask]-ais['nuv'][idx[mask]],\
c=ais['glat'][idx[mask]], alpha=0.5, edgecolors='face', s=0.2, cmap=plt.get_cmap('jet'), vmin=-10, vmax=10)
plt.axhline(y=0, color='k', linestyle='dashed', linewidth=1)
nuv_list = [[14, 15], [15, 16], [16, 17], [17, 18], [18, 19], [19, 20]]
mask_list = [
(e[0] <= ais['nuv'][idx[mask]]) & (e[1] > ais['nuv'][idx[mask]])
for e in nuv_list
]
x = [np.median(ais['nuv'][idx[mask]][m]) for m in mask_list]
y = [
np.median((df['nuv'][mask] - ais['nuv'][idx[mask]])[m])
for m in mask_list
]
ye = [
np.std((df['nuv'][mask] - ais['nuv'][idx[mask]])[m]) for m in mask_list
]
plt.errorbar(x, y, yerr=ye)
plt.xlim(12, 22)
plt.ylabel('NUV (SEx-AIS)')
plt.title('NUV AIS, N={0}'.format(N))
plt.ylim(-1, 1)
plt.colorbar()
plt.subplot(2, 2, 3)
plt.hist(df['nuv'], bins=18, range=(12, 22))
plt.title('NUV SExtractor')
plt.xlim(12, 22)
plt.subplot(2, 2, 4)
plt.plot(df['nuv'], df['FWHM_IMAGE'], '.k', markersize=0.7)
plt.title('NUV SExtractor, N={0}'.format(len(df)))
plt.ylabel('FWHM [pix]')
plt.ylim(0, 8)
plt.xlim(12, 22)
plt.suptitle(scan)
plt.tight_layout()
#plt.subplots_adjust(top=0.9)
plt.savefig('/scratch/dw1519/galex/fits/scan_map/catalog/' + date + '/' +
scan + '_match_new.png',
dpi=190)
plt.clf()
def correct(name):
#name = sys.argv[1]
print name
scan = '{0:04d}'.format(int(re.split('_', name)[3])) #'3191'
print scan
#name = 'AIS_GAL_SCAN_0'+scan+'_0002'
out = '/scratch/dw1519/galex/data/'+name
date = '08-21-2017_1'
cfile = '/scratch/dw1519/galex/fits/scan_map/catalog/'+date+'/starcat_'+scan+'mapweight_fec_fwhm.txt'
asprta = '/scratch/dw1519/galex/AIS_GAL_SCAN/asprta/'+name+'-cal-sec-dis-cut-asprta.fits'
scst = '/scratch/dw1519/galex/AIS_GAL_SCAN/scst/'+name+'-scst.fits'
num = int(scan)
if num>=5 and num<=185:
dis_l = '/scratch/dw1519/galex/data/distortion/5-185-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/5-185-xa_high-centroids.npy'
elif num>=194 and num<=437:
dis_l = '/scratch/dw1519/galex/data/distortion/194-437-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/194-437-xa_high-centroids.npy'
elif num>=446 and num<=887:
dis_l = '/scratch/dw1519/galex/data/distortion/446-887-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/446-887-xa_high-centroids.npy'
#elif num>=896 and num<=1157:
# dis_l = '/scratch/dw1519/galex/data/distortion/896-1157-xa_low-centroids.npy'
# dis_h = '/scratch/dw1519/galex/data/distortion/896-1157-xa_high-centroids.npy'
elif num>=1166 and num<=1373:
dis_l = '/scratch/dw1519/galex/data/distortion/1166-1373-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/1166-1373-xa_high-centroids.npy'
else:
dis_l = '/scratch/dw1519/galex/data/distortion/194-437-xa_low-centroids.npy'
dis_h = '/scratch/dw1519/galex/data/distortion/194-437-xa_high-centroids.npy'
print dis_l
print dis_h
data = np.load('/scratch/dw1519/galex/data/photons/'+name+'.npy')
print('data:{0}'.format(data.shape))
tycho2 = pyfits.open('/scratch/dw1519/galex/data/tycho2.fits')[1].data
df = ld.load_catalog(cfile)
c = SkyCoord(df['gl']*u.degree, df['gb']*u.degree, frame='galactic')
catalog = SkyCoord(tycho2['Glon']*u.degree, tycho2['Glat']*u.degree, frame='galactic')
idx, d2d, d3d = c.match_to_catalog_sky(catalog)
mask=d2d<0.001*u.degree
'''
dtype = np.dtype([('tycho_num', int), ('Glon', '>f4'), ('Glat', '>f4'), ('RAJ2000', '>f4'), ('DEJ2000', '>f4'),
('flux', float), ('nuv', float), ('gl', float), ('gb', float)])
matched_tycho = tycho2[idx[mask]]
matched_df = df[mask]
matched_catalog = np.core.records.fromarrays(np.array([idx[mask], matched_tycho['Glon'], matched_tycho['Glat'],
matched_tycho['RAJ2000'], matched_tycho['DEJ2000'], np.array(matched_df['FLUX_AUTO']),
np.array(matched_df['nuv']), np.array(matched_df['gl']),
np.array(matched_df['gb'])]), dtype=dtype)
'''
resolution = 0.5
hdulist = pyfits.open(asprta)
co_data = hdulist[1].data
n=resolution/0.005 #100
T = co_data['T']
ra = co_data['ra']
dec = co_data['dec']
roll = co_data['roll']
t_new = np.arange((T.shape[0]-1)*n)*0.005+T[0]
spl_ra = splrep(T, ra)
spl_dec = splrep(T, dec)
spl_roll = splrep(T, roll)
ra_new = splev(t_new, spl_ra)
dec_new = splev(t_new, spl_dec)
roll_new = splev(t_new, spl_roll)
asp = np.array([t_new, ra_new, dec_new, roll_new]).T
#np.save('../data/photon_list/%s_asp_fix.npy'%name, asp_cal)
hdulist.close()
#asp = np.load('../data/photon_list/{0}{1}-correct_asp_new.npy'.format(name, suffix))
scst_file = pyfits.open(scst)
scst_data = scst_file[1].data
scst_time = scst_data['pktime'].copy()
hv = scst_data['hvnom_nuv'].copy()
scst_file.close()
dis_map = ld.load_dis(dis_l, dis_h)
scst_ix = np.digitize(data[:,0]/1000., scst_time)
ix_mask = scst_ix<scst_time.shape[0]
scst_ix = scst_ix[ix_mask]
data = data[ix_mask]
data = data[hv[scst_ix]>0]
dx_list = []
dy_list = []
ya_list = []
q_list = []
n = 50
step = int(data.shape[0]/n)
print step
for i in range(n-1):
print i
data_tmp = ld.dis_correct(data[i*step:(i+1)*step], dis_map, asp, None, None)
sky_data = SkyCoord(data_tmp[:,0:2], unit='deg', frame='fk5', equinox='J2000.0')
print 'sky_data: {0}'.format(sky_data.shape)
idxc, idxcatalog, d2d_p, d3d_p = sky_data.search_around_sky(catalog[idx[mask]], 0.0045*u.deg)
print 'idxc: {0}'.format(idxc.shape)
xi, eta = gn.gnomfwd_simple(tycho2['RAJ2000'][idx[mask][idxc]], tycho2['DEJ2000'][idx[mask][idxc]],
data_tmp[idxcatalog,-3], data_tmp[idxcatalog,-2], -data_tmp[idxcatalog,-1], 1/36000., 0)
dx = (data_tmp[idxcatalog,6]-xi)/36000./800/0.001666*2400
dy = (data_tmp[idxcatalog,7]-eta)/36000./800/0.001666*2400
ya = data_tmp[idxcatalog,4]
q = data_tmp[idxcatalog,5]
dx_list.append(dx)
dy_list.append(dy)
ya_list.append(ya)
q_list.append(q)
data_tmp=sky_data=xi=eta=None
gc.collect()
print n-1
data_tmp = ld.dis_correct(data[(n-1)*step:], dis_map, asp, None, None)
sky_data = SkyCoord(data_tmp[:,0:2], unit='deg', frame='fk5', equinox='J2000.0')
print 'sky_data: {0}'.format(sky_data.shape)
idxc, idxcatalog, d2d_p, d3d_p = sky_data.search_around_sky(catalog[idx[mask]], 0.0045*u.deg)
print 'idxc: {0}'.format(idxc.shape)
xi, eta = gn.gnomfwd_simple(tycho2['RAJ2000'][idx[mask][idxc]], tycho2['DEJ2000'][idx[mask][idxc]],
data_tmp[idxcatalog,-3], data_tmp[idxcatalog,-2], -data_tmp[idxcatalog,-1], 1/36000., 0)
dx = (data_tmp[idxcatalog,6]-xi)/36000./800/0.001666*2400
dy = (data_tmp[idxcatalog,7]-eta)/36000./800/0.001666*2400
ya = data_tmp[idxcatalog,4]
q = data_tmp[idxcatalog,5]
dx_list.append(dx)
dy_list.append(dy)
ya_list.append(ya)
q_list.append(q)
data_tmp=sky_data=xi=eta=None
gc.collect()
dx = np.concatenate(dx_list, axis=0)
dy = np.concatenate(dy_list, axis=0)
ya = np.concatenate(ya_list, axis=0)
q = np.concatenate(q_list, axis=0)
data=scst_ix=ix_mask=None
gc.collect()
print dx.shape
print 'max dy: {0}'.format(np.max(dy))
print 'max dx: {0}'.format(np.max(dx))
mask = (dy>=-8) & (dy<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dy_q = []
for i in range(24):
q_mask = (q[mask] == i)
dy_c = np.median(dy[mask][q_mask])
dy_q.append(dy_c)
plt.plot(dy_q, np.arange(24), '.r')
print('dy_q:')
print dy_q
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-y_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(24), dy_q, '.r')
plt.savefig(out+'/q-y_c.pdf', dpi=190)
plt.clf()
mask = (dx>=-8) & (dx<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dx_q = []
for i in range(24):
q_mask = (q[mask] == i)
dx_c = np.median(dx[mask][q_mask])
dx_q.append(dx_c)
print('dx_q:')
print dx_q
plt.plot(dx_q, np.arange(24), '.r')
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-x_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(24), dx_q, '.r')
plt.savefig(out+'/q-x_c.pdf', dpi=190)
plt.clf()
q_corr = np.array([np.arange(24), np.array(dx_q), np.array(dy_q)]).T
np.save(out+'/q_corr.npy', q_corr)
q_mask = q<24
dy[q_mask] = dy[q_mask]-q_corr[q[q_mask].astype(int),-1]
dx[q_mask] = dx[q_mask]-q_corr[q[q_mask].astype(int),-2]
mask = (dy>=-8) & (dy<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dy_ya = []
for i in range(2,32):
ya_mask = (ya[mask] == i)
dy_c = np.median(dy[mask][ya_mask])
dy_ya.append(dy_c)
print('dy_ya:')
print dy_ya
plt.plot(dy_ya, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-y_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, dy_ya, '.k')
plt.savefig(out+'/ya-y_c.pdf', dpi=190)
plt.clf()
mask = (dx>=-8) & (dx<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dx_ya = []
for i in range(2,32):
ya_mask = (ya[mask] == i)
dx_c = np.median(dx[mask][ya_mask])
dx_ya.append(dx_c)
print('dx_ya:')
print dx_ya
plt.plot(dx_ya, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-x_tot.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, np.array(dx_ya), '.k')
plt.savefig(out+'/ya-x_c.pdf', dpi=190)
plt.clf()
ya_corr = np.array([np.arange(30)+2, np.array(dx_ya), np.array(dy_ya)]).T
np.save(out+'/ya_corr.npy', ya_corr)
ya_mask = q<24
dy = dy-ya_corr[ya.astype(int)-2,-1]
dx = dx-ya_corr[ya.astype(int)-2,-2]
#plot corrected
mask = (dy>=-8) & (dy<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(24):
q_mask = q[mask] == i
dy_c = np.median(dy[mask][q_mask])
dy_cs.append(dy_c)
print('dy_q:')
print dy_cs
plt.plot(dy_cs, np.arange(24), '.r')
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-y_tot_new.pdf', dpi=190)
plt.clf()
#np.save(out+'/q-y.npy', np.array([np.arange(24),dy_cs]).T)
plt.plot(np.arange(24), dy_cs, '.r')
plt.savefig(out+'/q-y_c_new.pdf', dpi=190)
plt.clf()
mask = (dx>=-8) & (dx<=8) & (q<24)
imsz = [24, 64]
H,xedges,yedges=np.histogram2d(q[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 0, 24], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(24):
q_mask = q[mask] == i
dy_c = np.median(dx[mask][q_mask])
dy_cs.append(dy_c)
print('dx_q:')
print dy_cs
plt.plot(dy_cs, np.arange(24), '.r')
plt.xlim(-8,8)
plt.ylim(0,24)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('q')
plt.savefig(out+'/q-x_tot_new.pdf', dpi=190)
plt.clf()
#np.save(out+'/q-x.npy', np.array([np.arange(24),dy_cs]).T)
plt.plot(np.arange(24), dy_cs, '.r')
plt.savefig(out+'/q-x_c_new.pdf', dpi=190)
plt.clf()
mask = (dy>=-8) & (dy<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dy[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(2,32):
ya_mask = ya[mask] == i
dy_c = np.median(dy[mask][ya_mask])
dy_cs.append(dy_c)
print('dy_ya:')
print dy_cs
plt.plot(dy_cs, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta y$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-y_tot_new.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, dy_cs, '.k')
plt.savefig(out+'/ya-y_c_new.pdf', dpi=190)
plt.clf()
#np.save(out+'/ya-y.npy', np.array([np.arange(30)+2,dy_cs]).T)
mask = (dx>=-8) & (dx<=8)
imsz = [30, 64]
H,xedges,yedges=np.histogram2d(ya[mask], dx[mask],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-8, 8, 2, 32], origin='lower', norm=LogNorm())
dy_cs = []
for i in range(2,32):
ya_mask = ya[mask] == i
dy_c = np.median(dx[mask][ya_mask])
dy_cs.append(dy_c)
print('dx_ya:')
print dy_cs
plt.plot(dy_cs, np.arange(30)+2, '.r')
plt.xlim(-8,8)
plt.ylim(2,32)
plt.title('scan{0:>5}'.format(scan))
plt.xlabel(r'$\Delta x$ [pixel]')
plt.ylabel('ya')
plt.savefig(out+'/ya-x_tot_new.pdf', dpi=190)
plt.clf()
plt.plot(np.arange(30)+2, dy_cs, '.k')
plt.savefig(out+'/ya-x_c_new.pdf', dpi=190)
plt.clf()
