def getNextLumInterval(params,sampleSize=3):
#lumMerits=read.getGridInt(params['divSpec'])
lumMerits=2.*read.getGridInt(params['subSpec'])/read.getGridInt(params['addSpec'])
paramSortID=np.argsort(np.abs(lumMerits))
bestParam=paramSortID[:sampleSize]
m,t=np.polyfit(params['lum'][bestParam],lumMerits[bestParam],1)
bestLum=(-t)/m
lumDev=min(np.abs(params['lum']-bestLum))
interval=[bestLum-lumDev,bestLum+lumDev]
minIntervalCheck=False
steadParam=checkSteadiness(lumMerits)
if steadParam<4:
print "Warning merits not steady suggestions might be problematic (steadParam=%s)"%steadParam
#WARNING. CHECK SAMPLES THIS WILL BE BAD LATER ON
minInterval=0.02
if 2*lumDev<minInterval:
minIntervalCheck=True
print "Interval too small"
intervalIDs=[0,1]
i=2
while True:
intervalPoints=params['lum'][paramSortID][intervalIDs]
minIntervalPoint,maxIntervalPoint=min(intervalPoints),max(intervalPoints)
if np.abs(minIntervalPoint-maxIntervalPoint)>minInterval:
interval=[minIntervalPoint,maxIntervalPoint]
break
if len(intervalIDs)>len(lumMerits)-3:
interval=[bestLum-2*lumDev,bestLum+2*lumDev]
break
intervalIDs.append(i)
i+=1
evalDic={'suggestValue':bestLum,
'bestFitID':bestParam[0],
'merit':abs(lumMerits[bestParam[0]]),
'merits':lumMerits,
'interval':interval,
'dev':lumDev,
'sortedModelIDX':paramSortID,
'fitKey':'lum',
'mininterval':minIntervalCheck,
'steady':steadParam}
return evalDic
return bestLum,bestParam[0],abs(lumMerits[bestParam[0]]),[bestLum-lumDev,bestLum+lumDev]
def getNextLumGuess(params,noForFit=2):
lums=params['lum']
lumMerits=read.getGridInt(params['subspec'])
selLumMerits=np.argsort(lumMerits)[:noForFit]
print lums[selLumMerits],lumMerits[selLumMerits]
return np.polyfit(lums[selLumMerits],lumMerits[selLumMerits],1)
def getLumScale(params,noForFit=2):
lums=params['lum']
lumMerits=read.getGridInt(params['divSpec'])
selLumMerits=np.argsort(lumMerits)[:noForFit]
return np.polyfit(lums[selLumMerits],lumMerits[selLumMerits],1)