def range(min_val, max_val, bin_size):
"""range function for the binning of the histogram"""
# checks
if min_val > max_val:
min_val, max_val = max_val, min_val
# calculate start and stop bins
min_bin = int(sp.floor_divide(min_val, bin_size))
max_bin = int(sp.floor_divide(max_val, bin_size) + 1)
# return
return sp.arange(min_bin, max_bin) * bin_size
def range(min_val, max_val, bin_size):
"""range function for the binning of the histogram"""
# checks
if min_val > max_val:
min_val, max_val = max_val, min_val
# calculate start and stop bins
min_bin = int(sp.floor_divide(min_val, bin_size))
max_bin = int(sp.floor_divide(max_val, bin_size) + 1)
# return
return sp.arange(min_bin, max_bin) * bin_size
def GenerateProjectionMat2(x,y,cpx,cpy,gridsize):
tempcpx = cpx.reshape((-1,))
tempcpy = cpy.reshape((-1,))
positionx=sp.floor_divide(tempcpx+2,4./(gridsize-1))
positiony=sp.floor_divide(tempcpy+2,4./(gridsize-1))
mat = scipy.sparse.lil_matrix((tempcpx.size,gridsize**2))
gridscalar = (gridsize-1)**2/16.
for idx in range(0,tempcpx.size):
px = int(positionx[idx])
py = int(positiony[idx])
mat[idx,py*gridsize+px] = gridscalar*(tempcpx[idx]-x[0,px])*(tempcpy[idx]-y[py,0])
mat[idx,py*gridsize+px+1] = gridscalar*(x[0,px+1]-tempcpx[idx])*(tempcpy[idx]-y[py,0])
mat[idx,(py+1)*gridsize+px] = gridscalar*(tempcpx[idx]-x[0,px])*(y[py+1,0]-tempcpy[idx])
mat[idx,(py+1)*gridsize+px+1] = gridscalar*(x[0,px+1]-tempcpx[idx])*(y[py+1,0]-tempcpy[idx])
return mat
def GenerateProjectionMat(x,y,cpx,cpy,gridsize):
positionx=sp.floor_divide(cpx+2,4./(gridsize-1))
positiony=sp.floor_divide(cpy+2,4./(gridsize-1))
mat = scipy.sparse.lil_matrix((gridsize**2,gridsize**2))
gridscalar = (gridsize-1)**2/16.
for idxy in range(0,gridsize):
tempidxy = idxy*gridsize
for idxx in range(0,gridsize):
px = int(positionx[idxy,idxx])
py = int(positiony[idxy,idxx])
mat[tempidxy+idxx,py*gridsize+px] = gridscalar*(cpx[idxy,idxx]-x[0,px])*(cpy[idxy,idxx]-y[py,0])
mat[tempidxy+idxx,py*gridsize+px+1] = gridscalar*(x[0,px+1]-cpx[idxy,idxx])*(cpy[idxy,idxx]-y[py,0])
mat[tempidxy+idxx,(py+1)*gridsize+px] = gridscalar*(cpx[idxy,idxx]-x[0,px])*(y[py+1,0]-cpy[idxy,idxx])
mat[tempidxy+idxx,(py+1)*gridsize+px+1] = gridscalar*(x[0,px+1]-cpx[idxy,idxx])*(y[py+1,0]-cpy[idxy,idxx])
return mat
def SetProjMatPETSC(cpCorArray,ProjMat,DA,vg):
m = DA.getSizes()[0]
dx = 4./m
AO = DA.getAO()
psnx = sp.int32(sp.floor_divide(cpCorArray[:,0]+2.,dx))
psny = sp.int32(sp.floor_divide(cpCorArray[:,1]+2.,dx))
idxbl = psnx+psny*m
idxbr = psnx+1+psny*m
idxul = psnx+(psny+1)*m
idxur = psnx+1+(psny+1)*m
idxbl = AO.app2petsc(idxbl)
idxbr = AO.app2petsc(idxbr)
idxul = AO.app2petsc(idxul)
idxur = AO.app2petsc(idxur)
start,end = vg.getOwnershipRange()
modx = sp.mod(cpCorArray[:,0]+2.,dx)
mody = sp.mod(cpCorArray[:,1]+2.,dx)
dx2 = dx**2
for i in sp.arange(end-start):
ProjMat[i+start,idxbl[i]] = (dx-modx[i])*(dx-mody[i])/dx2
ProjMat[i+start,idxbr[i]] = modx[i]*(dx-mody[i])/dx2
ProjMat[i+start,idxul[i]] = (dx-modx[i])*mody[i]/dx2
ProjMat[i+start,idxur[i]] = modx[i]*mody[i]/dx2
return
def fastMonteCarlo(self, mig, kw):
dic_init = self.dic_init
### Get parameters
try:
if sp.remainder(len(dic_init['fastMonteCarlo']), 2) != 0: raise
nb_fMC = int(dic_init['fastMonteCarlo'][0])
seed_fMC = int(dic_init['fastMonteCarlo'][1])
sp.random.seed(seed=seed_fMC)
nb_expected_values = sp.floor_divide(
len(dic_init['fastMonteCarlo']) - 2, 2)
for i in range(nb_expected_values):
key = dic_init['fastMonteCarlo'][2 * (i + 1)]
val = dic_init['fastMonteCarlo'][2 * (i + 1) + 1]
if len(key) > 4 and key[:4] == 'fix_':
kw[key] = bool(val)
kw['error_' + key[4:]] = 0.
elif len(key) > 5 and key[:5] == 'free_':
kw[key] = bool(val)
else:
val = float(val)
mig.values[key] = val
kw[key] = val
except Exception as error:
print(
' ERROR::picca/py/picca/fitter/Chi2.py:: error in fast Monte-Carlo = ',
error)
print(' Exit')
sys.exit(0)
### Get bes fit
if not self.auto is None:
rp = self.auto.rp
rt = self.auto.rt
z = self.auto.z
bestFit_auto = self.cosmo.valueAuto(rp, rt, z, {
p: mig.values[p]
for p in self.cosmo.pglob + self.cosmo.pauto
})
### Metals
met = None
if not self.met is None:
met = self.met.valueAuto(mig.values)
bestFit_auto += met
bestFit_auto = sp.dot(self.auto.dm, bestFit_auto)
### Broadband
bb = None
if not self.bb is None:
p, b = self.bb.value(self.auto.da -
bestFit_auto[self.auto.cuts])
bb = self.bb(rt, rp, p)
if self.dic_init['distort_bb_auto']:
bb = sp.dot(self.auto.dm, bb)
bestFit_auto += bb
if not self.cross is None:
rp = self.cross.rp
rt = self.cross.rt
z = self.cross.z
bestFit_cross = self.cosmo.valueCross(rp, rt, z, {
p: mig.values[p]
for p in self.cosmo.pglob + self.cosmo.pcross
})
### Metals
met = None
if not self.met is None:
met = self.met.valueCross(mig.values)
bestFit_cross += met
bestFit_cross = sp.dot(self.cross.dm, bestFit_cross)
### Broadband
bb = None
if not self.bb_cross is None:
p, b = self.bb_cross.value(
self.cross.da - bestFit_cross[self.cross.cuts],
mig.values['drp'])
bb = self.bb_cross(rt, rp, mig.values['drp'], p)
if self.dic_init['distort_bb_cross']:
bb = sp.dot(self.cross.dm, bb)
bestFit_cross += bb
if not self.autoQSO is None:
rp = self.autoQSO.rp
rt = self.autoQSO.rt
z = self.autoQSO.z
bestFit_autoQSO = self.cosmo.valueAutoQSO(rp, rt, z, {
p: mig.values[p]
for p in self.cosmo.pglob + self.cosmo.pautoQSO
})
bestFit_autoQSO = sp.dot(self.autoQSO.dm, bestFit_autoQSO)
### File to save into
output_name = dic_init['output_prefix']
output_name += "save.pars.fastMonteCarlo"
f = open(output_name, "w", 0)
f.write("#\n#seed = {}\n#\n".format(seed_fMC))
for p in mig.parameters:
f.write("{} ".format(p))
f.write("chi2 ")
for p in mig.parameters:
f.write("{} ".format("err_" + p))
f.write("err_chi2\n")
### Get realisation fastMonteCarlo
for i in range(nb_fMC):
print(' fastMonteCarlo: ', i, ' over ', nb_fMC)
sys.stdout.flush()
### Get realisation fastMonteCarlo
if not dic_init['data_auto'] is None:
self.auto.get_realisation_fastMonteCarlo(bestFit=bestFit_auto)
if not dic_init['data_cross'] is None:
self.cross.get_realisation_fastMonteCarlo(
bestFit=bestFit_cross)
if not dic_init['data_autoQSO'] is None:
self.autoQSO.get_realisation_fastMonteCarlo(
bestFit=bestFit_autoQSO)
### Fit
mig_fMC = iminuit.Minuit(self,
forced_parameters=self.pname,
errordef=1,
print_level=0,
**kw)
try:
mig_fMC.migrad()
chi2_result = mig_fMC.get_fmin().fval
except:
chi2_result = sp.nan
### Save
for p in mig_fMC.parameters:
f.write("{} ".format(mig_fMC.values[p]))
f.write("{} ".format(chi2_result))
for p in mig_fMC.parameters:
f.write("{} ".format(mig_fMC.errors[p]))
f.write("{}\n".format(0.))
f.close()
return
