def get_corr_map(co, sec, pixsz, bound, weights):
imsz = [2 * bound / pixsz, 2 * bound / pixsz]
H,xedges,yedges=np.histogram2d(co[:,1], co[:,0],\
bins=imsz, range=[[-bound,bound],[-bound,bound]], weights=weights*10)
print np.max(H)
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 = [-bound + cx * pixsz, -bound + cy * pixsz]
else:
centroid = [0., 0.]
max_value = 0
flux = 500
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, bound)
plt.ylim(-bound, bound)
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 imatrix(data,H): M = int((data.shape[0])**.5) data = data.reshape(M,M) center = c3.find_centroid(data) dx , dy = center[0] , center[1] chi = np.dot(np.linalg.inv(ms.B(H*M)) , ms.I(H*M)) hx = np.dot(phi(-dx, H , M) , chi) hy = np.dot(chi.T , phi(-dy, H , M).T) hf = np.kron(hx.T, hy) return hf
def get_corr_map(co, sec, pixsz, bound, weights):
imsz = [2*bound/pixsz, 2*bound/pixsz]
H,xedges,yedges=np.histogram2d(co[:,1], co[:,0],\
bins=imsz, range=[[-bound,bound],[-bound,bound]], weights=weights*10)
print np.max(H)
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 = [-bound+cx*pixsz, -bound+cy*pixsz]
else:
centroid = [0.,0.]
max_value = 0
flux = 500
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, bound)
plt.ylim(-bound, bound)
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 _find_centroid(filename):
try:
hdulist = pyfits.open(filename)
except IOError as e:
print "I/O error({0}): {1}".format(e.errno, e.strerror)
hdulist = None
except:
print "Unexpected error:", sys.exc_info()[0]
raise
if hdulist is not None:
w = pywcs.WCS(hdulist[0].header, hdulist)
data = hdulist[0].data
data = data.byteswap(True).newbyteorder()
cy, cx = c3.find_centroid(data)
else:
return None
return cx, cy
def _find_centroid(filename):
try:
hdulist = pyfits.open(filename)
except IOError as e:
print "I/O error({0}): {1}".format(e.errno, e.strerror)
hdulist = None
except:
print "Unexpected error:", sys.exc_info()[0]
raise
if hdulist is not None:
w = pywcs.WCS(hdulist[0].header, hdulist)
data = hdulist[0].data
data = data.byteswap(True).newbyteorder()
cy, cx = c3.find_centroid(data)
else:
return None
return cx,cy
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 find_centroid(filename):
try:
hdulist = pyfits.open(filename)
except IOError as e:
print "I/O error({0}): {1}: {2}".format(e.errno, e.strerror, filename)
hdulist = None
except:
print "Unexpected error:", sys.exc_info()[0]
raise
if hdulist is not None:
hdulist = pyfits.open(filename)
w = pywcs.WCS(hdulist[0].header, hdulist)
data = hdulist[0].data
data = data.byteswap(True).newbyteorder()
cy, cx = c3.find_centroid(data)
centroid = w.wcs_pix2world(w.sip_foc2pix([[cx, cy]],1),1)[0]
if centroid[0]>1:
centroid[0] = centroid[0]-360.
else:
centroid = [0,0]
return centroid
def centroid(t,H,wcs):
print 'threshold:{0}'.format(t)
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]
print(cx,cy)
print(centroid)
else:
centroid = [0.,0.]
max_value = 0
flux = 500
if centroid[0]>1:
centroid[0] = centroid[0]-360.
return centroid
def find_centroid(filename):
try:
hdulist = pyfits.open(filename)
except IOError as e:
print "I/O error({0}): {1}: {2}".format(e.errno, e.strerror, filename)
hdulist = None
except:
print "Unexpected error:", sys.exc_info()[0]
raise
if hdulist is not None:
hdulist = pyfits.open(filename)
w = pywcs.WCS(hdulist[0].header, hdulist)
data = hdulist[0].data
data = data.byteswap(True).newbyteorder()
cy, cx = c3.find_centroid(data)
centroid = w.wcs_pix2world(w.sip_foc2pix([[cx, cy]], 1), 1)[0]
if centroid[0] > 1:
centroid[0] = centroid[0] - 360.
else:
centroid = [0, 0]
return centroid
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 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, 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):
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
plt.subplot(1,3,2)
plt.imshow(qw, interpolation = "None" , origin = "lower")
plt.colorbar()
plt.subplot(1,3,3)
plt.imshow(qq, interpolation = "None" , origin = "lower")
plt.colorbar()
plt.show()
M , H = 25 , 3
y = profile.makeGaussian(M, M/5. ,0.1, (M/2.+0.83, M/2.+1.45))
y /= y.sum()
import c3
print c3.find_centroid(y) #data
model = profile.makeGaussian(H*M, H*M/5. ,0.1, (H*M/2., H*M/2.)) #model
hh = sampler.imatrix(y.flatten() , H) #sampling matrix
ww = model.flatten().dot(hh).reshape(M,M) #model rendered on data grid
ww = ww/ww.sum()
plt.subplot(1,4,1)
plt.imshow(y, interpolation = "None" , origin = "lower")
plt.title("data")
plt.colorbar()
plt.subplot(1,4,2)
plt.imshow(model, interpolation = "None" , origin = "lower")
plt.title("model")
plt.colorbar()
plt.subplot(1,4,3)
#anderson = pf.open("anderson.fits")[0].data
#X = np.exp(np.loadtxt("noregwfc_mean_iter_2.txt")).reshape(100,100)
#X1 = np.exp(np.loadtxt("noregwfc_mean_iter_1.txt")).reshape(100,100)
fl= 1e-5
f = .05
g = 0.01
H = 3
X = np.exp(np.loadtxt("superb_wfc_mean_iter_5_nfl.txt"))
Z = (X).reshape(25*H,25*H)
print Z.sum()
import c3
x0 , y0 = c3.find_centroid(Z)
print x0, y0
import scipy
from scipy import ndimage
Z2 = scipy.ndimage.interpolation.shift(Z, [x0,y0], output=None, order=3, mode='nearest', prefilter=True)
k = np.array([[True, False, True],
[False, False, False],
[True, False, True]])
Z2 = ndimage.convolve(Z2, k, mode='nearest')
Z2 = Z2 * Z.sum() /Z2.sum()
data = pf.open("f160w_25_457_557_457_557_pixels.fits")[0].data
mask = pf.open("f160w_25_457_557_457_557_dq.fits")[0].data
def q_fit(out, sacn):
#out='/scratch/dw1519/galex/co/co32_1-10_new'
co_list = []
info_list = []
co_files = sorted(glob.glob(out+"/[0-9]*.npy"), key=get_key)
info_files = sorted(glob.glob(out+"/info_*"), key=get_key_info)
'''
for i in range(0, 27):
co = np.load(out+'/radec/{0}.npy'.format((i+1)*100))/36000./800/0.001666*2400
info = np.load(out+'/info_{0}.npy'.format((i+1)*100))[1:]
'''
for cf, inf in zip(co_files, info_files):
co = np.load(cf)[1:]/36000./800/0.001666*2400
info = np.load(inf)[1:]
co_list.append(co)
info_list.append(info)
co = np.concatenate(co_list, axis=0)
info = np.concatenate(info_list, axis=0)
mask1 = (co[:,1]>=-15) & (co[:,1]<=15)
mask2 = (co[:,0]>=-15) & (co[:,0]<=15)
centroids = []
for q in range(0,32):
print('q={0}'.format(q))
mask3 = info[:,2] == q
hist_size = [100,100]
H,xedges,yedges=np.histogram2d(co[mask1&mask2&mask3,1], co[mask1&mask2&mask3,0],\
bins=hist_size, range=([ [-15,15],[-15,15] ]))
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
centroids.append([-15.+cx*30./hist_size[0], -15.+cy*30./hist_size[0]])
else:
centroids.append([0,0])
np.save(out+'/centroids_q.npy', centroids)
q = np.arange(32)
centroids = np.array(centroids)
plt.plot(q, centroids[:,0], '.k')
plt.xlabel('q')
plt.ylabel(r'mean $\Delta x$ [pixel]')
plt.title(r'scan{0:>5}'.format(scan))
plt.savefig(out+'/q_cx.png', dpi=190)
plt.clf()
plt.plot(q, centroids[:,1], '.k')
plt.xlabel('q')
plt.ylabel(r'mean $\Delta y$ [pixel]')
plt.title(r'scan{0:>5}'.format(scan))
plt.savefig(out+'/q_cy.png', dpi=190)
plt.clf()
'''
c = np.load(out+'/centroids.npy')
ya = np.arange(30)+2
slope, intercept, r_value, p_value, std_err = stats.linregress(ya, c[:,1])
print(slope, intercept, r_value, p_value, std_err)
np.save(out+'/q_fit.npy', [slope, intercept, r_value, p_value, std_err])
'''
#q hist
mask = (co[:,0]>=-15) & (co[:,0]<=15) & (info[:,2]<24)
imsz = [24, 60]
H,xedges,yedges=np.histogram2d(info[mask,2], co[mask,0],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-15, 15, 0, 24], origin='lower', norm=LogNorm())
q = np.arange(32)
plt.plot(centroids[:,0], q, 'or')
plt.xlim(-15,15)
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_fit.png', dpi=190)
plt.clf()
mask = (co[:,1]>=-15) & (co[:,1]<=15) & (info[:,2]<24)
imsz = [24, 60]
H,xedges,yedges=np.histogram2d(info[mask,2], co[mask,1],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-15, 15, 0, 24], origin='lower', norm=LogNorm())
plt.plot(centroids[:,1], q, 'or')
plt.xlim(-15,15)
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_fit.png', dpi=190)
plt.clf()
h = hist_g(photon_data, wcs, imsz)
count += h
'''
tranges = [[0,0]]
hdu = pyfits.PrimaryHDU(count)
hdu = imagetools.fits_header('NUV', skypos, tranges, skyrange, hdu=hdu, wcs=wcs)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('../fits/scan_map/fake2.fits', clobber=False)
'''
t = 1
H = h.copy()
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#0.5
cx+=0#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)
fig = plt.gcf()
fig.set_size_inches(8,8)
plt.imshow(h,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal', extent=[0.025,-0.025,0.025,-0.025], origin='upper', imlim=[-0.02,0.02, -0.02,0.02])
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(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):
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 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
import numpy as np
from matplotlib.colors import LogNorm
from mpl_toolkits.axes_grid1 import make_axes_locatable
fl=1e-5
f = .05
g = .01
H = 3
#X = np.exp(np.loadtxt("superb_wfc_mean_iter_5_nfl.txt"))
X = np.exp(np.loadtxt("superb_wfc_mean_iter_0.txt"))
Z = (X).reshape(25*H,25*H)
print Z.sum()
import c3
x0 , y0 = c3.find_centroid(Z)
print x0, y0
import scipy
from scipy import ndimage
Z2 = scipy.ndimage.interpolation.shift(Z, [-x0,-y0], output=None, order=3, mode='nearest', prefilter=True)
k = np.array([[0, 1, 0],
[1, 1, 1],
[0, 1, 0]])
Z2 = ndimage.convolve(Z2, k, mode='nearest')
Z2 = Z2 * Z.sum() /Z2.sum()
plt.figure(figsize=(10,10))
ax = plt.gca()
def q_fit(out, sacn):
#out='/scratch/dw1519/galex/co/co32_1-10_new'
co_list = []
info_list = []
co_files = sorted(glob.glob(out + "/[0-9]*.npy"), key=get_key)
info_files = sorted(glob.glob(out + "/info_*"), key=get_key_info)
'''
for i in range(0, 27):
co = np.load(out+'/radec/{0}.npy'.format((i+1)*100))/36000./800/0.001666*2400
info = np.load(out+'/info_{0}.npy'.format((i+1)*100))[1:]
'''
for cf, inf in zip(co_files, info_files):
co = np.load(cf)[1:] / 36000. / 800 / 0.001666 * 2400
info = np.load(inf)[1:]
co_list.append(co)
info_list.append(info)
co = np.concatenate(co_list, axis=0)
info = np.concatenate(info_list, axis=0)
mask1 = (co[:, 1] >= -15) & (co[:, 1] <= 15)
mask2 = (co[:, 0] >= -15) & (co[:, 0] <= 15)
centroids = []
for q in range(0, 32):
print('q={0}'.format(q))
mask3 = info[:, 2] == q
hist_size = [100, 100]
H,xedges,yedges=np.histogram2d(co[mask1&mask2&mask3,1], co[mask1&mask2&mask3,0],\
bins=hist_size, range=([ [-15,15],[-15,15] ]))
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
centroids.append([
-15. + cx * 30. / hist_size[0], -15. + cy * 30. / hist_size[0]
])
else:
centroids.append([0, 0])
np.save(out + '/centroids_q.npy', centroids)
q = np.arange(32)
centroids = np.array(centroids)
plt.plot(q, centroids[:, 0], '.k')
plt.xlabel('q')
plt.ylabel(r'mean $\Delta x$ [pixel]')
plt.title(r'scan{0:>5}'.format(scan))
plt.savefig(out + '/q_cx.png', dpi=190)
plt.clf()
plt.plot(q, centroids[:, 1], '.k')
plt.xlabel('q')
plt.ylabel(r'mean $\Delta y$ [pixel]')
plt.title(r'scan{0:>5}'.format(scan))
plt.savefig(out + '/q_cy.png', dpi=190)
plt.clf()
'''
c = np.load(out+'/centroids.npy')
ya = np.arange(30)+2
slope, intercept, r_value, p_value, std_err = stats.linregress(ya, c[:,1])
print(slope, intercept, r_value, p_value, std_err)
np.save(out+'/q_fit.npy', [slope, intercept, r_value, p_value, std_err])
'''
#q hist
mask = (co[:, 0] >= -15) & (co[:, 0] <= 15) & (info[:, 2] < 24)
imsz = [24, 60]
H,xedges,yedges=np.histogram2d(info[mask,2], co[mask,0],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-15, 15, 0, 24], origin='lower', norm=LogNorm())
q = np.arange(32)
plt.plot(centroids[:, 0], q, 'or')
plt.xlim(-15, 15)
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_fit.png', dpi=190)
plt.clf()
mask = (co[:, 1] >= -15) & (co[:, 1] <= 15) & (info[:, 2] < 24)
imsz = [24, 60]
H,xedges,yedges=np.histogram2d(info[mask,2], co[mask,1],\
bins=imsz)
plt.imshow(H,interpolation='None', cmap=plt.get_cmap('Greys'), aspect='equal',\
extent=[-15, 15, 0, 24], origin='lower', norm=LogNorm())
plt.plot(centroids[:, 1], q, 'or')
plt.xlim(-15, 15)
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_fit.png', dpi=190)
plt.clf()
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]
import c3 import profile y = profile.makeGaussian(33,6,0,(14.78,18.1)) print -16.5+14.78 , -16.5+18.1 print c3.find_centroid(y)
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 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)
'''
plt.imshow(np.log2(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', 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(-0.01*3600/2, 0.01*3600/2)
plt.ylim(-0.01*3600/2, 0.01*3600/2)
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.plot(c_sex[0]*3600,c_sex[1]*3600,'+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
h = hist_g(photon_data, wcs, imsz)
count += h
'''
tranges = [[0,0]]
hdu = pyfits.PrimaryHDU(count)
hdu = imagetools.fits_header('NUV', skypos, tranges, skyrange, hdu=hdu, wcs=wcs)
hdulist = pyfits.HDUList([hdu])
hdulist.writeto('../fits/scan_map/fake2.fits', clobber=False)
'''
t = 1
H = h.copy()
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 #0.5
cx += 0 #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)
fig = plt.gcf()
fig.set_size_inches(8, 8)
plt.imshow(h,
