Object = cfp.Object(sys.argv[1]) Object.InitiateCase('MINUIT.FIRSTTRY') DataPrepPath = cfp.DataPrepPath FigurePath = cfp.FigurePath exec("from tmcmc.myfunc import %s as ModelFunc" % (Object.FuncName)) ModelParams = tmcmc.iomcmc.ReadStartParams(DataPrepPath+'GUESS.'+Object.name+'.par') APList = [] TTList = [] for fileName in os.listdir(DataPrepPath): if fileName.startswith(Object.name) and fileName.endswith('.data'): AP = dfp.getAP(fileName) APList.append(AP) TT = dfp.getTT(fileName,Object.name) TTList.append(TT) TTList = list(set(TTList)) APList = list(set(APList)) APList2 = APList rms = {} goodid_db = {} bad_err = {} Completeness = {} print 'Outlier rejection ' for AP in APList:
import numpy as np Object = cfp.Object(sys.argv[1]) Object.InitiateCase('MINUIT.FIRSTTRY') DataPrepPath = cfp.DataPrepPath exec("from tmcmc.myfunc import %s as ModelFunc" % (Object.FuncName)) ModelParams = tmcmc.iomcmc.ReadStartParams(DataPrepPath+'GUESS.'+Object.name+'.par') APList = [] TTList = [] for fileName in os.listdir(DataPrepPath): if fileName.startswith(Object.name) and fileName.endswith('.data'): AP = dfp.getAP(fileName) APList.append(AP) TT = dfp.getTT(fileName,Object.name) TTList.append(TT) TTList = list(set(TTList)) APList = list(set(APList)) #FracRej = pickle.load(open(cfp.PicklePath+Object.name+'.FracRej.pickle','rb')) if Object.name == 'WASP2' or Object.name == 'XO2': bcase = float(sys.argv[2]) APList = [] if Object.name == 'WASP2': SplitNum = 7e0 else:
import os import sys import cPickle as pickle from tmcmc import DataFuncPrep as dfp from tmcmc import class_fitprep as cfp Object = cfp.Object(sys.argv[1]) DataPrepPath = cfp.DataPrepPath APList = [] TTList = [] for fileName in os.listdir(DataPrepPath): if fileName.startswith(Object.name) and fileName.endswith(".data"): AP = dfp.getAP(fileName) APList.append(AP) TT = dfp.getTT(fileName, Object.name) TTList.append(TT) TTList = list(set(TTList)) APList = list(set(APList)) for AP in APList: LCListName = DataPrepPath + Object.name + ".LC.AP" + AP + ".list" NListName = DataPrepPath + Object.name + ".NUS.AP" + AP + ".onoff" LCListObject = open(LCListName, "w") NListObject = open(NListName, "w") print >> LCListObject, "# FileName | TTag " print >> NListObject, "# NUISANCE Data | TTag | " + dfp.detrendHeader.strip("#") for TT in TTList: print >> LCListObject, DataPrepPath + Object.name + ".AP" + AP + "." + TT + ".lc" + "|" + TT
import os import sys import tmcmc import cPickle as pickle from tmcmc import DataFuncPrep as dfp from tmcmc import class_fitprep as cfp Object = cfp.Object(sys.argv[1]) DataPrepPath = cfp.DataPrepPath APList = [] TTList = [] for fileName in os.listdir(DataPrepPath): if fileName.startswith(Object.name) and fileName.endswith('.data'): AP = dfp.getAP(fileName) TT = dfp.getTT(fileName,Object.name) APList.append(AP) TTList.append(TT) APPs = list(set(APList)) TTs = list(set(TTList)) FluxNorm = {} for AP in APPs: FluxNorm[AP] = {} for TT in TTs: FluxNorm[AP][TT] = 0 for fileName in os.listdir(DataPrepPath): if fileName.startswith(Object.name) and fileName.endswith('.data'):
exec("from tmcmc.myfunc import %s as ModelFunc" % (Object.FuncName)) ModelParams = tmcmc.iomcmc.ReadStartParams(DataPrepPath+'GUESS.'+Object.name+'.par') for fileName in os.listdir(DataPrepPath): if fileName.startswith(Object.name) and \ fileName.endswith('.nus'): name_split = map(str, fileName.split('.')) AP = name_split[1].strip('AP') TT = name_split[2] if TT in ['T8','T9','T10']: lcfile = Object.name+'.AP'+AP+'.'+TT+'.lc' nusfile = Object.name+'.AP'+AP+'.'+TT+'.nus' #os.system('wc %s' % DataPrepPath+nusfile ) #os.system('wc %s' % DataPrepPath+lcfile ) dt_data = dfp.readNusFile(DataPrepPath+nusfile) lc_data = tmcmc.iomcmc.ReadSingleDataFile(DataPrepPath+lcfile) time = np.array(lc_data['all']['x']) diffT = 45e0/86400e0 if len(time) > 0: #print dt_data.keys() T0 = time[0] new_index = (time-T0)//diffT dt_data['index'] = new_index dfp.writeUpdatedNuisance(DataPrepPath+nusfile, dt_data) #sys.exit() ## OPT AP X #LCListName = DataPrepPath+Object.name+'.LC.listx' #NListName = DataPrepPath+Object.name+'.NUS.onoffx' #ObservedData = tmcmc.iomcmc.ReadMultiList(LCListName)
LCListName = DataPrepPath+Object.name+'.LC.listx' NListName = DataPrepPath+Object.name+'.NUS.onoffx' ObservedData = tmcmc.iomcmc.ReadMultiList(LCListName) NuisanceData = tmcmc.iomcmc.ReadDetrendFile(NListName) ModelData = ModelFunc(ModelParams,ObservedData) HiResData = dfp.HiRes(ObservedData,5000) HiResModel = ModelFunc(ModelParams,HiResData) DetrendedData = tmcmc.mcmc.DetrendData(ObservedData,ModelData,NuisanceData,'',False) #for TT in DetrendedData.keys(): #print TT, DetrendedData[TT]['x'] #sys.exit() ChiSQ, DetrendedData = dfp.chisq(DetrendedData,ModelData) #print DetrendedData['T1']['x'] DOF = len(DetrendedData['all']['y'])-dfp.NOpen(ModelParams) print ChiSQ, DOF, ChiSQ/DOF OtherInfo = {} ScaleTT = {} #scaling individual nights for TT in ObservedData.keys(): if TT.startswith('T'): med_err = np.median(DetrendedData[TT]['yerr']) scale = OptAp[TT]['min_rms']/med_err DetrendedData[TT]['yerr'] = np.array(DetrendedData[TT]['yerr'])*scale ObservedData[TT]['yerr'] = np.array(ObservedData[TT]['yerr'])*scale ScaleTT[TT] = scale
import os import sys import cPickle as pickle from tmcmc import DataFuncPrep as dfp from tmcmc import class_fitprep as cfp Object = cfp.Object(sys.argv[1]) DataPrepPath = cfp.DataPrepPath APList = [] TTList = [] for fileName in os.listdir(DataPrepPath): if fileName.startswith(Object.name) and fileName.endswith('.data'): AP = dfp.getAP(fileName) APList.append(AP) TT = dfp.getTT(fileName,Object.name) TTList.append(TT) TTList = list(set(TTList)) APList = list(set(APList)) for AP in APList: LCListName = DataPrepPath+Object.name+'.LC.AP'+AP+'.list0' NListName = DataPrepPath+Object.name+'.NUS.AP'+AP+'.onoff0' LCListObject = open(LCListName,'w') NListObject = open(NListName,'w') print >> LCListObject, '# FileName | TTag ' print >> NListObject, '# NUISANCE Data | TTag | '+dfp.detrendHeader.strip('#') for TT in TTList: print >> LCListObject, DataPrepPath+Object.name+'.AP'+AP+'.'+TT+'.lc0'+'|'+TT SwitchLine = dfp.getSwitchLine(Object.name,TT,supress=True)