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)
