def setHistogram(self, hist):
hist = af.string2nestlist(hist)
jj = 0
for ii in hist:
if len(ii) == 1:
self._Histogram.append([ii[0], 'Histogram_%s.png' % ii[0]])
elif len(ii) == 2:
self._Histogram.append([ii[0], '%s.png' % ii[1]])
self._FigNames.append(self._Histogram[jj][1])
jj += 1
def setScatter(self, scatter):
scatter = af.string2nestlist(scatter)
jj = 0
for ii in scatter:
if len(ii) == 2:
self._Scatter.append(
[ii[0], ii[1],
'Scatter_%s_%s.png' % (ii[0], ii[1])])
elif len(ii) == 3:
self._Scatter.append([ii[0], ii[1], '%s.png' % ii[2]])
self._FigNames.append(self._Scatter[jj][2])
jj += 1
def setContour(self, Contour):
Contour = af.string2nestlist(Contour)
jj = 0
for ii in Contour:
if len(ii) == 3:
self._Contour.append([
ii[0], ii[1], ii[2],
'Contour_%s_%s_%s.png' % (ii[0], ii[1], ii[2])
])
elif len(ii) == 4:
self._Contour.append([ii[0], ii[1], ii[2], '%s.png' % ii[3]])
self._FigNames.append(self._Contour[jj][3])
jj += 1
def setColor(self, color):
color = af.string2nestlist(color)
jj = 0
for ii in color:
if len(ii) == 3:
self._Color.append([
ii[0], ii[1], ii[2],
'Color_%s_%s_%s.png' % (ii[0], ii[1], ii[2])
])
elif len(ii) == 4:
self._Color.append([ii[0], ii[1], ii[2], '%s.png' % ii[3]])
self._FigNames.append(self._Color[jj][3])
jj += 1
def setFreeFormChi2(self, var):
var = af.string2nestlist(var)
af.Info('FreeFormChi2:')
for ii in var:
if len(ii) in [1, 2]:
if len(ii) == 1:
jj = ii + ['FreeFormChi2_%s' % ii[0]]
else:
jj = ii
self._FreeFormChi2.append(jj)
af.Info(' varID= %s\tName= %s' % (jj[0], jj[1]))
self.Chi2[jj[1]] = af.NaN
else:
af.ErrorStop(
'The "FreeFormChi2" constraint on "%s" need 1 item or 2 items( VarID [, Name] ).'
% (ii[0]))
self.Chi2 = af.sortDic(self.Chi2)
def setGaussian(self, var):
var = af.string2nestlist(var)
af.Info('Gaussian Constraint:')
for ii in var:
if len(ii) in [3]:
jj = ii + ['symm', 'Gaussian_%s' % ii[0]]
self._Gaussian.append(jj)
self.Chi2[jj[4]] = af.NaN
af.Info(
' varID= %s\tMean= %e\tDeviation= %e\tType= %s\tName= %s'
% (jj[0], jj[1], jj[2], jj[3], jj[4]))
elif len(ii) in [4, 5]:
if not ii[3].lower() in ['symm', 'lower', 'upper']:
af.ErrorStop(
'For the "Gaussian" constraint on "%s", the "Type" can only be "symm", "upper" or "lower", not "%s".'
% (ii[0], ii[3]))
## new 20180428 liang
if len(ii) == 4:
jj = ii + ['Gaussian_%s' % ii[0]]
else:
jj = ii
self._Gaussian.append(jj)
self.Chi2[jj[4]] = af.NaN
af.Info(
' varID= %s\tMean= %e\tDeviation= %e\tType= %s\tName= %s'
% (jj[0], jj[1], jj[2], jj[3], jj[4]))
else:
af.ErrorStop(
'The "Gaussian" constraint on "%s" need 4 or 5 items( ID, Mean, Deviation, Type [, Name] ).'
% (ii[0]))
## new 20180428 liang
self.Chi2 = af.sortDic(self.Chi2)
def setInputPar(self, inputvar):
inputvar = af.string2nestlist(inputvar)
# inputvar is list of list of input parameters define in section [scan]
af.Info('Input parameters = ')
for ii in inputvar:
lenii = len(ii)
if self._ScanMethod == af._postprocess:
self.InPar[ii[0]] = af.NaN
self.AllPar[ii[0]] = af.NaN
af.Info(' ID= %s, read from previous '%(ii[0]))
continue
if lenii < 3 :
af.ErrorStop(self.InputCheck(ii[0], 3, "Value"))
# Set fixed par
if ii[1].upper() == "FIXED":
if lenii > 3 :
af.WarningNoWait(self.InputCheck(ii[0], 3, "Value"))
af.WarningWait("The rest %i values will be ignore."%(lenii-3) )
af.Info(' ID= %s\tPrior= %s\t =%f'%(ii[0],ii[1],ii[2]))
self.FixedPar[ii[0]] = ii[2]
self.AllPar[ii[0]] = ii[2]
continue
# Initialize other input par to NaN
self.InputPar[ii[0]] = ii
self.InPar[ii[0]] = af.NaN
self.AllPar[ii[0]] = af.NaN
if lenii < 4 :
af.ErrorStop(self.InputCheck(ii[0], 4, "Minimum, Maximum"))
if self._ScanMethod in [af._random, af._multinest]:
if lenii > 4 :
af.WarningNoWait(self.InputCheck(ii[0], 4, "Minimum, Maximum"))
af.WarningWait("The rest %i values will be ignore."%(lenii-4) )
af.Info(' ID= %s\tPrior= %s\tMin= %f\tMax= %f'%(ii[0],ii[1],ii[2],ii[3]))
continue
if self._ScanMethod == af._grid:
if lenii == 4:
self.GridBin[ii[0]]=20
af.WarningNoWait(self.InputCheck(ii[0], 5, "Minimum, Maximum, Number of bins"))
af.WarningWait("'Number of bins' will take default value, 20.")
else:
self.GridBin[ii[0]]=ii[4]
if self.GridBin[ii[0]] < 0 or type(ii[4]) != int:
af.WarningNoWait(InputCheck(ii[0], 5, "Minimum, Maximum, Number of bins"))
af.ErrorStop("'Number of bins' is not a positive integer.")
if lenii> 5:
af.WarningNoWait(self.InputCheck(ii[0], 5, "Minimum, Maximum, Number of bins"))
af.WarningWait("The rest %i values will be ignore."%(lenii-5) )
af.Info(' ID= %s\tPrior= %s\tMin= %f\tMax= %f\tNbin=%i'%(ii[0],ii[1],ii[2],ii[3],self.GridBin[ii[0]]))
continue
if self._ScanMethod == af._mcmc:
if lenii < 6:
af.WarningNoWait(self.InputCheck(ii[0], 6, "Minimum, Maximum, Interval, Initial value"))
self.MCMCiv[ii[0]] = 1./2.
IniV = float(ii[3]+ii[2])/2.
af.WarningWait("'Initial value' will take default value, (Max-Min)/2.")
if lenii < 5:
self.MCMCss[ii[0]] = 1./30.
Step = float(ii[3]-ii[2])/30.
af.WarningWait("'Interval' will take default value, (Max-Min)/30.")
else:
# The scan range is normalized to 1
self.MCMCss[ii[0]] = 1.0/float(ii[4])
Step = float(ii[3]-ii[2])/float(ii[4])
if ii[1].lower() == 'flat':
self.MCMCiv[ii[0]] = float(ii[5]-ii[2])/float(ii[3]-ii[2])
elif ii[1].lower() == 'log':
self.MCMCiv[ii[0]] = (log10(ii[5])-log10(ii[2]))/(log10(ii[3]) - log10(ii[2]))
IniV = ii[5]
if lenii > 6:
af.WarningNoWait(self.InputCheck(ii[0], 6, "Minimum, Maximum, Interval, Initial value"))
af.WarningWait("The rest %i values will be ignore."%(lenii-6) )
af.Info(' ID= %s\tPrior= %s\tMin= %f\tMax= %f\tStep=%f\tIniV=%f'%(ii[0],ii[1],ii[2],ii[3],Step,self.MCMCiv[ii[0]]))
continue