# change from None to the value you want.
ls, Nls, ellbb, dlbb, efficiency = lensNoise(Config,
expName,
lensName,
beamOverride=None,
noiseTOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None)
efficiencies.append(efficiency)
printC("Delensing efficiency: " + str(efficiency) + " %",
color="green",
bold=True)
# File root name for Fisher derivatives
derivRoot = Config.get("fisher", "derivRoot")
# Get list of parameters
paramList = Config.get("fisher", "paramList").split(',')
# Load fiducials and derivatives
fidCls = tryLoad(derivRoot + '_fCls.csv', ',')
dCls = {}
for paramName in paramList:
dCls[paramName] = tryLoad(derivRoot + '_dCls_' + paramName + '.csv',
',')
# Get lensing noise curve. If you want to override something from the Config file in order to make plots varying it,
# change from None to the value you want.
ls, Nls, ellbb, dlbb, efficiency = lensNoise(Config,
expName,
lensName,
beamOverride=None,
noiseTOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None)
printC("Delensing efficiency: " + str(efficiency) + " %",
color="green",
bold=True)
# File root name for Fisher derivatives
derivRoot = Config.get("fisher", "derivRoot")
# Get list of parameters
paramList = Config.get("fisher", "paramList").split(',')
# Load fiducials and derivatives
fidCls = tryLoad(derivRoot + '_fCls.csv', ',')
dCls = {}
for paramName in paramList:
dCls[paramName] = tryLoad(derivRoot + '_dCls_' + paramName + '.csv', ',')
def getStats(self,keyData,keyTheory,auto=False,show=False,numbins=-1):
dataVector = self.datas[keyData]['binned'][:numbins]
dofs = len(dataVector) - 2
if auto: dofs -= 1
chisqNull = self.chisq(keyData)
chisqTheory = self.chisq(keyData,keyTheory,numbins=numbins)
stats = {}
stats['reduced chisquare'] = chisqTheory / dofs
stats['pte'] = chi2.sf(chisqTheory,dofs)
stats['null sig'] = np.sqrt(chisqNull)
stats['theory sig'] = np.sqrt(chisqNull-chisqTheory)
if show:
printC("="*len(keyTheory),color='y')
printC(keyTheory,color='y')
printC('-'*len(keyTheory),color='y')
printC("amplitude",color='b')
bf,err = self.datas[keyTheory]['amp']
printC('{0:.2f}'.format(bf)+"+-"+'{:04.2f}'.format(err),color='p')
for key,val in list(stats.items()):
printC(key,color='b')
printC('{0:.2f}'.format(val),color='p')
printC("="*len(keyTheory),color='y')
return stats
# change from None to the value you want.
ls, Nls, ellbb, dlbb, efficiency = lensNoise(Config,
expName,
lensName,
beamOverride=None,
noiseTOverride=noiseNow,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None)
efficiencies.append(efficiency)
printC("Delensing efficiency: " + str(efficiency) + " %",
color="green",
bold=True)
# File root name for Fisher derivatives
derivRoot = Config.get("fisher", "derivRoot")
# Get list of parameters
paramList = Config.get("fisher", "paramList").split(',')
# Load fiducials and derivatives
fidCls = tryLoad(derivRoot + '_fCls.csv', ',')
dCls = {}
for paramName in paramList:
dCls[paramName] = tryLoad(derivRoot + '_dCls_' + paramName + '.csv',
',')
else:
ls, Nls, ellbb, dlbb, efficiency = lensNoise(Config,
expName,
lensName,
beamOverride=None,
noiseTOverride=None,
lkneeTOverride=None,
lkneePOverride=None,
alphaTOverride=None,
alphaPOverride=None)
pickle.dump((ls, Nls, ellbb, dlbb, efficiency),
open("data/lastNls.pkl", 'wb'))
printC("Delensing efficiency: " + str(efficiency) + " %",
color="green",
bold=True)
# File root name for Fisher derivatives
derivRoot = Config.get("fisher", "derivRoot")
# Get list of parameters
paramList = Config.get("fisher", "paramList").split(',')
# Load fiducials and derivatives
fidCls = tryLoad(derivRoot + '_fCls.csv', ',')
dCls = {}
for paramName in paramList:
dCls[paramName] = tryLoad(derivRoot + '_dCls_' + paramName + '.csv', ',')
# Load other Fisher matrices to add