def MovePix(self, indiv, iPix, Flux, FluxWeighted=True, InReg=None):
dx, dy = np.mgrid[-1:1:3 * 1j, -1:1:3 * 1j]
Dx = np.int32(np.concatenate((dx.flatten()[0:4], dx.flatten()[5::])))
Dy = np.int32(np.concatenate((dy.flatten()[0:4], dy.flatten()[5::])))
# ArrayModel=self.PM.GiveModelArray(indiv)
# ArrayModel_S=self.PM.ArrayToSubArray(indiv,Type="S")
ArrayModel_S = indiv # ArrayModel_S.reshape((1,ArrayModel_S.size))*np.ones((2,1))
A = self.PM.ModelToSquareArray(ArrayModel_S,
TypeInOut=("Parms", "Parms"),
DomainOut="Parms")
nf, npol, nx, nx = A.shape
#A=np.mean(A,axis=0).reshape(1,npol,nx,nx)
mapi, mapj = self.PM.SquareGrids["Parms"]["MappingIndexToXYPix"]
i0, j0 = mapi[iPix], mapj[iPix]
FluxWeighted = False
if FluxWeighted:
iAround = i0 + Dx
jAround = j0 + Dy
cx = ((iAround >= 0) & (iAround < nx))
cy = ((jAround >= 0) & (jAround < nx))
indIN = (cx & cy)
iAround = iAround[indIN]
jAround = jAround[indIN]
sAround = A[0, 0, iAround, jAround].copy()
#sInt=np.sum(sAround)
#sAround[sAround==0]=sInt*0.05
X = np.arange(iAround.size)
DM = ClassDistMachine()
DM.setRefSample(X, W=sAround, Ns=10)
ind = int(round(DM.GiveSample(1)[0]))
ind = np.max([0, ind])
ind = np.min([ind, iAround.size - 1])
ind = indIN[ind]
# else:
# if InReg is None:
# reg=random.random()
# else:
# reg=InReg
# ind=int(reg*8)
i1 = i0 + Dx[InReg]
j1 = j0 + Dy[InReg]
f0 = Flux #alpha*A[0,0,i0,j0]
_, _, nx, ny = A.shape
condx = ((i1 > 0) & (i1 < nx))
condy = ((j1 > 0) & (j1 < ny))
if condx & condy:
A[0, 0, i1, j1] += f0
A[0, 0, i0, j0] -= f0
AParm = self.PM.SquareArrayToModel(A, TypeInOut=("Parms", "Parms"))
ArrayModel_S.flat[:] = AParm.flat[:]
return indiv
def GiveCompacity(self,S):
DM=ClassDistMachine()
#S.fill(1)
#S[0]=100
DM.setRefSample(np.arange(S.size),W=np.sort(S),Ns=100,xmm=[0,S.size-1])#,W=sAround,Ns=10)
#DM.setRefSample(S)#,W=sAround,Ns=10)
xs,ys=DM.xyCumulD
dx=xs[1]-xs[0]
I=2.*(S.size-np.sum(ys)*dx)/S.size-1.
return I