def test_generateEllGaussianImage(fitsName):
n = 100
data = np.zeros((n,n))
s0 = 0
s1 = 1000
phi = np.pi/4 # 45 degree
sigma1 = 4.95
sigma2 = 5.00
A = (np.cos(phi)/sigma1)**2 + (np.sin(phi)/sigma2)**2
B = (np.sin(phi)/sigma1)**2 + (np.cos(phi)/sigma2)**2
C = 2*np.sin(phi)*np.cos(phi)*(1/(sigma1**2)-1/(sigma2**2))
print C
x0 = n/2.0
y0 = n/2.0
for i in range(n):
for j in range(n):
data[i][j] = s0+s1*np.exp(-0.5*(A*(i-x0)**2 + B*(j-y0)**2 +C*(i-x0)*(j-y0)))
commons.writeFitsImage(data, fitsName )
def procedure():
# initialization:
s, B, const, imgList, varList, phi, lambdaS = prepare()
LM_SIE = models.PTMASS(critRad=6.16, centerX=0.0, centerY=5.0)
#LM_SIE = models.SIE(critRad=6.16, axisRatio=0.7, pa=0.0, centerX=0.0, centerY=0.0)
mappingDict = getMappingDict(imgList,varList, LM_SIE, const=const)
imgPointList = mappingDict.keys()
srcPointList = mappingDict.values()
dim = len(imgPointList)
srcPosition = []
s = []
r = np.zeros((1, 2*dim))
for i in range(dim):
x,y, bri, _, _ = srcPointList[i]
srcPosition.append((x,y))
s.append(bri)
r[0][i] = bri
srcMap = commons.pixelizeSource(srcPosition, s, const)
commons.writeFitsImage(srcMap, "example/outputSrc.fits")
plt.imshow(srcMap, origin="lower", interpolation="nearest")
plt.colorbar()
plt.show()
#return
chi2List = []
#critRadRange = np.arange(5.9, 6.3, 0.02)
#qRange = np.arange(0.1, 0.9, 0.05)
#paRange = np.arange(0, 20, 10)
centerYrange= np.arange(5, 15, 1)
index = []
i = 0
# = []
#for critRad in critRadRange:
# r = np.reshape(r, (1,2*const.length))
# r = scipy.sparse.coo_matrix(np.transpose(r))
for yshift in centerYrange:
i = i+1
#LM_SIE.critRad = critRad
#LM_SIE.axisRatio = q
#LM_SIE.pa = pa
LM_SIE.centerY = yshift
mappingDict = getMappingDict(imgList,varList, MODEL = LM_SIE, const=const)
srcPosition, srcPointList, L, normV, C, indexWeightList, d, RTR , HsTHs, imgPointList= construction.getLensOperator(mappingDict, s)
chi2, res = construction.getChiSquare(M = L, r = r, d =d, C=C, const=const)
chi2 = chi2.toarray()[0][0]
res = res.toarray()
# image = construction.buildModelImage(imgPointList, res, const)
# plt.imshow(image, origin="lower", interpolation="nearest")
# plt.colorbar()
# plt.show()
print yshift, chi2
index.append(yshift)
chi2List.append(chi2)
plt.plot(index, chi2List, '*-')
plt.xlabel("Parameter")
plt.ylabel("Chi2")
plt.show()
#critRadRange = np.arange(1.3, 4.0, 0.1)
# for i in range(1):
# mappingDict = getMappingDict(imgList,varList, model='SIE', critRad = 3.0, const=const)
# srcPosition, srcPointList, L, normV, C, indexWeightList, d, RTR, HsTHs = construction.getLensOperator(mappingDict, s)
# Ds = construction.getMatrixDs(normV,indexWeightList, const)
# Dphi =construction.getMatrixDphi(const)
#
#
# D = scipy.sparse.coo_matrix(Ds)*scipy.sparse.coo_matrix(Dphi)
#
#
# M =scipy.sparse.hstack([L,D])
#
# r = np.zeros(2*const.length)
# for i in range(const.length):
# r[i] = s[i]
#
#
# r =scipy.sparse.vstack(r)
# M_t = scipy.sparse.coo_matrix.transpose(M)
#
# C = scipy.sparse.coo_matrix(C)
# RTR = scipy.sparse.coo_matrix(RTR)
#
# C_inv = scipy.sparse.linalg.inv(C)
# b = M_t*C_inv*d
#
# A = M_t*C_inv*M+RTR
# r = scipy.sparse.linalg.spsolve(A, b)
#
# s = np.zeros((const.length,1))
# for i in range(const.length):
# s[i][0] = r[i]
# s_t = scipy.sparse.coo_matrix.transpose(scipy.sparse.coo_matrix(s))
# chi2 = construction.getChiSquare(M = M, r = r, d =d, C=C)
# G = chi2 + s_t*scipy.sparse.coo_matrix(HsTHs)*s
# print i, chi2, G
# srcMap = commons.pixelizeSource(srcPosition, s, const)
# print srcMap
# commons.writeFitsImage(srcMap, "model_test.fits")
# plt.imshow(srcMap, origin="lower", interpolation="nearest")
# plt.show()
return