def getParamsFromModel(modelGrid):
merits=[]
i=0
origSpec=modelGrid.origSpec
for j,(aSpec,subSpec,llist,w,t) in enumerate(zip(modelGrid['aspec'],modelGrid['subspec'],modelGrid['llist'],modelGrid['w'],modelGrid['t'])):
print i
i+=1
#subSpec=model['subspec']
addSpec=aSpec+origSpec
intTotal=fit.getInt(subSpec)/fit.getInt(addSpec)
intUV=fit.getUVInt(subSpec)/fit.getUVInt(addSpec)
slope=fit.getSlope(subSpec)
elementMerits=[]
for element in ['C','O','Na','Mg','Si','S','Ca','Ti','Cr','Ni','Fe']:
try:
lmerits,weights=fitelem.getElementMerit(element,llist,subSpec)
elementMerits.append(np.average(lmerits,weights=weights))
#pdb.set_trace()
except:
elementMerits.append(-1)
allMerit=[intTotal,intUV,slope,w,t]+elementMerits
tmpParam=modelGrid.grid[j].param
#tmpParam.update(modelGrid.grid[j].param.comp.data)
merits.append((np.array(allMerit),tmpParam))
return merits
def getFeaturesFromBestFit(bestFit):
merits=[]
i=0
origSpec=bestFit.origSpec
aSpec,subSpec,llist,w,t=bestFit['aspec'],bestFit['subspec'],bestFit['llist'],bestFit['w'],bestFit['t']
addSpec=aSpec+origSpec
intTotal=fit.getInt(subSpec)/fit.getInt(addSpec)
intUV=fit.getUVInt(subSpec)/fit.getUVInt(addSpec)
slope=fit.getSlope(subSpec)
chiSquared=fit.getChiSquared(subSpec)
modChiSquared=fit.getModChiSquared(subSpec)
elementMerits=[]
for element in ['C','O','Na','Mg','Si','S','Ca','Ti','Cr','Ni','Fe']:
try:
lmerits,weights=fitelem.getElementMerit(element,llist,subSpec)
elementMerits.append(np.average(lmerits,weights=weights))
#pdb.set_trace()
except:
elementMerits.append(-1)
allMerit=[intTotal,intUV,chiSquared,modChiSquared,slope,w,t]+elementMerits
print "modincl"
tmpParam=bestFit.param
#tmpParam.update(modelGrid.grid[j].param.comp.data)
merits.append((np.array(allMerit),tmpParam))
return merits
def calcTargetFeatureRelDistance(params,ids,bestFitDict):
#Calculates the target with relative distance to features
bestFitTarget={}
targets=[]
for bestFitName,bestFitModel in bestFitDict.items():
origSpec=bestFitModel.origSpec
aSpec=bestFitModel.aSpec
subSpec=fit.getSubSpec(aSpec,origSpec)
addSpec=aSpec+origSpec
intTotal=fit.getInt(subSpec)/fit.getInt(addSpec)
intUV=fit.getUVInt(subSpec)/fit.getUVInt(addSpec)
slope=fit.getSlope(subSpec)
elementMerits=[]
for element in ['C','O','Na','Mg','Si','S','Ca','Ti','Cr','Ni','Fe']:
try:
lmerits,weights=fitelem.getElementMerit(element,bestFitModel.llist,subSpec)
elementMerits.append(np.average(lmerits,weights=weights))
#pdb.set_trace()
except:
elementMerits.append(-1)
allMerit=[intTotal,intUV,slope,bestFitModel['w'],bestFitModel['t']]+elementMerits
bestFitTarget[bestFitName]=allMerit
for id,param in zip(ids,params):
bestFitName=re.search('(.+)ts\d+\.pkl',id[0]).groups()[0]+'bf.pkl'
#tmpTarget=[abs(iparam-itarget)/abs(iparam+itarget) for iparam,itarget in zip(param[0],bestFitTarget[bestFitName])]
tmpTarget=[abs(iparam/itarget)-1 for iparam,itarget in zip(param[0],bestFitTarget[bestFitName]) if itarget!=0]
targets.append(tmpTarget)
return np.array(targets)
def getInputFromModel(modelGrid):
merits=[]
i=0
origSpec=modelGrid.origSpec
for aSpec,subSpec,llist,w,t in zip(modelGrid['aspec'],modelGrid['subspec'],modelGrid['llist'],modelGrid['w'],modelGrid['t']):
print i
i+=1
#subSpec=model['subspec']
addSpec=aSpec+origSpec
intTotal=fit.getInt(subSpec)/fit.getInt(addSpec)
intUV=fit.getUVInt(subSpec)/fit.getUVInt(addSpec)
slope=fit.getSlope(subSpec)
chiSquared=fit.getChiSquared(subSpec)
modChiSquared=fit.getModChiSquared(subSpec)
MAD=fit.getMAD(aSpec,origSpec)
elementMerits=[]
for element in ['C','O','Na','Mg','Si','S','Ca','Ti','Cr','Ni','Fe']:
try:
lmerits,weights=fitelem.getElementMerit(element,llist,subSpec)
elementMerits.append(np.average(lmerits,weights=weights))
#pdb.set_trace()
except:
elementMerits.append(-1)
allMerit=[intTotal,intUV,chiSquared,modChiSquared,MAD,slope,w,t]+elementMerits
print 'MAD'
merits.append(allMerit)
return np.array(merits)
self.specFlag = vecFunc(self.grid)
def _getDivSpec(self):
self.divSpec = np.array([item.divSpec for item in self.grid])
def _getFitness(self):
self.fitness = np.array([item.fitness for item in self.grid])
# def divSpecGetter(item):
# tmpAspec=item['aspec']
# tmpAspec=tmpAspec.interpolate(xref=self.origSpec.x)
# return tmpAspec/self.origSpec
# vecFunc=np.vectorize(divSpecGetter)
# self.divSpec=vecFunc(self.grid)
def toDB(self, conn, dicaID=None, storeLList=None, storeWParam=None):
modelGridIDs = []
# saving all models to db
for model in self.grid:
modelGridIDs.append(model.toDB(conn, dicaID=dicaID, storeLList=storeLList, storeWParam=storeWParam))
return np.array(modelGridIDs)
# simple Functions to extract merits
# getGridInt=np.vectorize(lambda item: fit.getDiffIntBin(item,1)[1][0])
getGridInt = np.vectorize(lambda item: fit.getInt(item))
getGridUV = np.vectorize(lambda item: fit.getUVInt(item, norm=False))
getGridOptical = np.vectorize(lambda item: fit.getInt(item, lower=3950.0, norm=False))
getGridUVComp = np.vectorize(lambda item: fit.getUVIntComp(item))
getGridSlope = np.vectorize(lambda item: fit.getIntSlope(item))
getGridSlope = np.vectorize(lambda item: fit.getSlope(item))