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{
"filename": "_valuessrc.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/table/header/_valuessrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ValuessrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(self, plotly_name="valuessrc", parent_name="table.header", **kwargs):
super(ValuessrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@table@header@_valuessrc.py@.PATH_END.py
|
{
"filename": "ClassFitTEC.py",
"repo_name": "saopicc/killMS",
"repo_path": "killMS_extracted/killMS-master/killMS/Other/ClassFitTEC.py",
"type": "Python"
}
|
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from DDFacet.Other import logger
log=logger.getLogger("ClassFitTEC")
import killMS.Array.ModLinAlg
K=8.4479745e9
import scipy.sparse
from DDFacet.Other import ClassTimeIt
logger.setSilent(["ClassFitTEC"])
def TECToPhase(TEC,freq):
phase=K*TEC*(1./freq)
return phase
def TECToZ(TEC,ConstPhase,freq):
if ConstPhase is None: ConstPhase=0
return np.exp(1j*(TECToPhase(TEC,freq)+ConstPhase))
def Dot(*args):
P=1.
for M in args:
#P=np.dot(np.complex128(P),np.complex128(M))
P=np.dot(P,M)
return P
# it=208; iDir=14; S=np.load("L229509_merged.npz"); G=S["Sols"]["G"][it,:,:,iDir,0,0]; f=S["FreqDomains"].mean(axis=1)
def Norm(G,iRef=0):
nf,na=G.shape
for iFreq in range(nf):
g0=G[iFreq,iRef]
G[iFreq]*=g0.conj()/np.abs(g0)
def test(G,f):
# nf,na=G.shape
# #na=3
# t=np.random.randn(na)*0.01
# c=np.random.randn(na)*np.pi/10
# G=TECToZ(t.reshape((1,-1)),c.reshape((1,-1)),f.reshape((-1,1)))
TECMachine=ClassFitTEC(G,f)
#TECMachine.DoFit()
TECMachine.findX0()
TECMachine.doFit()
class ClassFitTEC():
def __init__(self,gains,nu,Tol=5e-2,Incr=1,
#Mode=["TEC","CPhase"],
Mode=["TEC"]):
self.nf,self.na=gains.shape
self.Mode=Mode
self.LMode=len(Mode)
self.G=gains.copy()
Norm(self.G)
self.Tol=Tol
self.G/=np.abs(self.G)
self.CentralFreqs=self.nu=nu
self.NFreq=nu.size
na=self.na
self.nbl=(na**2-na)//2
self.CurrentX=None
log.print("Number of Antennas: %i"%self.na)
log.print("Number of Freqs: %i"%nu.size)
log.print("Number of Points: %i"%(nu.size*self.na**2))
self.Y=np.array([(self.G[iFreq].reshape((-1,1))*self.G[iFreq].conj().reshape((1,-1))).ravel() for iFreq in range(self.NFreq)]).ravel()
self.nu_Y=np.array([(self.nu[iFreq]*np.ones((self.na,self.na)).ravel()) for iFreq in range(self.NFreq)]).ravel()
self.A0=np.array([(np.mgrid[0:na:1,0:na:1][0]).ravel() for iFreq in range(self.NFreq)]).ravel()
self.A1=np.array([(np.mgrid[0:na:1,0:na:1][1]).ravel() for iFreq in range(self.NFreq)]).ravel()
self.Incr=Incr
Mask=np.where(self.A1>self.A0)[0]
self.Mask=Mask
self.Y=self.Y[Mask][::self.Incr]
self.nu_Y=self.nu_Y[Mask][::self.Incr]
self.A0=self.A0[Mask][::self.Incr]
self.A1=self.A1[Mask][::self.Incr]
self.x0=None
self.indA0=[np.where(self.A0==iAnt)[0] for iAnt in range(na)]
self.indA1=[np.where(self.A1==iAnt)[0] for iAnt in range(na)]
def doFit(self,NIter=100):
if self.x0 is None and self.CurrentX is None:
self.CurrentX=np.zeros((self.LMode*self.na,),np.float32)+1e-10
#self.CurrentX=np.random.randn(2*self.na)
self.Current_iIter=0
for iIter in range(NIter):
self.doLMIter()
#self.Plot()
self.Current_iIter=iIter
if self.Diff<self.Tol:
log.print("Convergence in %i steps"%(iIter+1))
break
return self.CurrentX
def GiveGPredict(self,X):
t=X[0:self.na].reshape((1,-1))
c=None
if "CPhase" in self.Mode:
c=X[self.na:].reshape((1,-1))
z=TECToZ(t,c,self.nu.reshape((-1,1)))
return z
def doLMIter(self):
T=ClassTimeIt.ClassTimeIt()
T.disable()
#J,H=
self.giveJacobianHessian()
T.timeit("J, H")
z=self.GiveGPredict(self.CurrentX)
Y=np.array([(z[iFreq].reshape((-1,1))*z[iFreq].conj().reshape((1,-1))).ravel() for iFreq in range(self.NFreq)]).ravel()
r=self.Y-Y[self.Mask][::self.Incr]
v=self.JHy(r)
H=self.DiagJHJ()
T.timeit("diff")
Hinv=killMS.Array.ModLinAlg.invSVD(H)
T.timeit("inv")
X = self.CurrentX + np.real(np.dot(Hinv,v.reshape((-1,1))).ravel())
xx=self.CurrentX.copy()
xx[xx==0]=1e-6
self.Diff=np.max(np.abs((X-xx)/xx))
z0=self.GiveGPredict(self.CurrentX)
Norm(z0)
self.CurrentX=X
z=self.GiveGPredict(self.CurrentX)
Norm(z)
self.Diff=np.max(np.abs(np.angle(z*z0.conj())))
#print self.Diff
return
# HinvJH=np.dot(scipy.sparse.coo_matrix(Hinv),J.T.conj())
# T.timeit("HinvJH")
# HinvJHy=np.dot(HinvJH,scipy.sparse.coo_matrix(r.reshape((-1,1))))
# T.timeit("HinvJHy")
# self.CurrentX+=np.real(np.array(HinvJHy.todense())).ravel()
# T.timeit("X")
# #self.CurrentX+=np.real(Dot(Hinv,J.T.conj(),r.reshape((-1,1))).ravel())
def setX0(self,x0):
self.CurrentX=x0
def findX0(self):
NTEC=101
NConstPhase=51
TECGridAmp=0.1
if self.LMode==2:
TECGrid,CPhase=np.mgrid[-TECGridAmp:TECGridAmp:NTEC*1j,-np.pi:np.pi:NConstPhase*1j]
Z=TECToZ(TECGrid.reshape((-1,1)),CPhase.reshape((-1,1)),self.CentralFreqs.reshape((1,-1)))
elif self.LMode==1:
TECGrid,CPhase=np.mgrid[-TECGridAmp:TECGridAmp:NTEC*1j],None
Z=TECToZ(TECGrid.reshape((-1,1)),CPhase,self.CentralFreqs.reshape((1,-1)))
self.Z=Z
self.TECGrid,self.CPhase=TECGrid,CPhase
self.CurrentX=np.zeros((self.LMode,self.na),np.float32)
for iAnt in range(self.na):
g=self.G[:,iAnt]
g0=g/np.abs(g)
W=np.ones(g0.shape,np.float32)
W[g==1.]=0
Z=self.Z
for iTry in range(5):
R=(g0.reshape((1,-1))-Z)*W.reshape((1,-1))
Chi2=np.sum(np.abs(R)**2,axis=1)
iTec=np.argmin(Chi2)
rBest=R[iTec]
if np.max(np.abs(rBest))==0: break
Sig=np.sum(np.abs(rBest*W))/np.sum(W)
ind=np.where(np.abs(rBest*W)>5.*Sig)[0]
if ind.size==0: break
W[ind]=0
self.CurrentX[0,iAnt]=self.TECGrid.ravel()[iTec]
if "CPhase" in self.Mode:
self.CurrentX[1,iAnt]=self.CPhase.ravel()[iTec]
self.CurrentX=self.CurrentX.ravel()
def Plot(self):
z=self.GiveGPredict(self.CurrentX)
Norm(z)
import pylab
pylab.clf()
pylab.plot(self.nu,np.angle(self.G),color="black")
pylab.plot(self.nu,np.angle(z),color="gray")
pylab.draw()
pylab.show(False)
pylab.pause(0.1)
def JHy(self,y):
T=ClassTimeIt.ClassTimeIt("JHy")
T.disable()
v=np.zeros((self.LMode,self.na),np.complex64)
for iAnt in range(self.na):
v[0,iAnt]+=np.sum(self.J_TEC[self.indA0[iAnt]].conj()*y[self.indA0[iAnt]])
v[0,iAnt]+=np.sum(-self.J_TEC[self.indA1[iAnt]].conj()*y[self.indA1[iAnt]])
if "CPhase" in self.Mode:
v[1,iAnt]+=np.sum(self.J_Phase[self.indA0[iAnt]].conj()*y[self.indA0[iAnt]])
v[1,iAnt]+=np.sum(-self.J_Phase[self.indA1[iAnt]].conj()*y[self.indA1[iAnt]])
v=v.ravel()
T.timeit("Prod")
# PSparse=np.array(np.dot(self.J.T.conj(),scipy.sparse.coo_matrix(y.reshape((-1,1)))).todense()).ravel()
# T.timeit("PSparse")
return v
def DiagJHJ(self):
T=ClassTimeIt.ClassTimeIt("JHy")
T.disable()
H=np.zeros((self.LMode,self.na,self.LMode,self.na),np.complex64)
for iAnt in range(self.na):
#Jt[self.indA0[iAnt],iAnt]=J_TEC[self.indA0[iAnt]]
#Jt[self.indA1[iAnt],iAnt]=-J_TEC[self.indA1[iAnt]]
H[0,iAnt,0,iAnt]+=np.sum(self.J_TEC[self.indA0[iAnt]].conj()*self.J_TEC[self.indA0[iAnt]])
H[0,iAnt,0,iAnt]+=np.sum(self.J_TEC[self.indA1[iAnt]].conj()*self.J_TEC[self.indA1[iAnt]])
if "CPhase" in self.Mode:
H[0,iAnt,1,iAnt]+=np.sum(self.J_TEC[self.indA0[iAnt]].conj()*self.J_Phase[self.indA0[iAnt]])
H[0,iAnt,1,iAnt]+=np.sum(self.J_TEC[self.indA1[iAnt]].conj()*self.J_Phase[self.indA1[iAnt]])
H[1,iAnt,0,iAnt]+=np.sum(self.J_Phase[self.indA0[iAnt]].conj()*self.J_TEC[self.indA0[iAnt]])
H[1,iAnt,0,iAnt]+=np.sum(self.J_Phase[self.indA1[iAnt]].conj()*self.J_TEC[self.indA1[iAnt]])
H[1,iAnt,1,iAnt]+=np.sum(self.J_Phase[self.indA0[iAnt]].conj()*self.J_Phase[self.indA0[iAnt]])
H[1,iAnt,1,iAnt]+=np.sum(self.J_Phase[self.indA1[iAnt]].conj()*self.J_Phase[self.indA1[iAnt]])
T.timeit("H")
H=H.reshape((self.LMode*self.na,self.LMode*self.na))
return H
A=np.log10(np.abs(self.H))
B=np.log10(np.abs(H))
vmin,vmax=A.min(),A.max()
import pylab
pylab.clf()
pylab.subplot(1,2,1)
pylab.imshow(A,interpolation="nearest",vmin=vmin,vmax=vmax)
pylab.colorbar()
pylab.subplot(1,2,2)
pylab.imshow(B,interpolation="nearest",vmin=vmin,vmax=vmax)
pylab.colorbar()
pylab.draw()
pylab.show(False)
stop
return H
def giveJacobianHessian(self):
T=ClassTimeIt.ClassTimeIt("J")
J=np.zeros((self.Y.size,self.na*2),np.complex64)
Jt=J[:,0:self.na]
Jc=J[:,self.na:]
TEC=self.CurrentX[0:self.na]
dTEC=TEC[self.A0]-TEC[self.A1]
if "CPhase" in self.Mode:
ConstPhase=self.CurrentX[self.na:]
dConstPhase=ConstPhase[self.A0]-ConstPhase[self.A1]
else:
dConstPhase=0
Phase=K/self.nu_Y*dTEC+dConstPhase
Z=np.exp(1j*Phase)
self.J_TEC=J_TEC=1j*K/self.nu_Y*Z
self.J_Phase=J_Phase=1j*Z
return
T.timeit("first")
for iAnt in range(self.na):
Jt[self.indA0[iAnt],iAnt]=J_TEC[self.indA0[iAnt]]
Jt[self.indA1[iAnt],iAnt]=-J_TEC[self.indA1[iAnt]]
Jc[self.indA0[iAnt],iAnt]=J_Phase[self.indA0[iAnt]]
Jc[self.indA1[iAnt],iAnt]=-J_Phase[self.indA1[iAnt]]
T.timeit("build")
self.J=J
self.Jsp=Jsp=scipy.sparse.coo_matrix(J)
T.timeit("sp")
# import pylab
# pylab.clf()
# pylab.subplot(1,2,1)
# pylab.imshow(Jt.real,interpolation="nearest",aspect="auto")
# pylab.subplot(1,2,2)
# pylab.imshow(Jc.real,interpolation="nearest",aspect="auto")
# pylab.draw()
# pylab.show(False)
# stop
#print np.count_nonzero(J)/float(J.size)
T.timeit("prod")
H=np.array(np.dot(Jsp.T.conj(),Jsp).todense())
self.H=H
T.timeit("Hsp")
return J,H
# import numpy as np
# from DDFacet.Other import logger
# log=logger.getLogger("ClassFitTEC")
# import killMS.Array.ModLinAlg
# K=8.4479745e9
# import scipy.sparse
# from DDFacet.Other import ClassTimeIt
# logger.setSilent(["ClassFitTEC"])
# def TECToPhase(TEC,freq):
# phase=K*TEC*(1./freq)
# return phase
# def TECToZ(TEC,ConstPhase,freq):
# return np.exp(1j*(TECToPhase(TEC,freq)+ConstPhase))
# def Dot(*args):
# P=1.
# for M in args:
# #P=np.dot(np.complex128(P),np.complex128(M))
# P=np.dot(P,M)
# return P
# # it=208; iDir=14; S=np.load("L229509_merged.npz"); G=S["Sols"]["G"][it,:,:,iDir,0,0]; f=S["FreqDomains"].mean(axis=1)
# def Norm(G,iRef=0):
# nf,na=G.shape
# for iFreq in range(nf):
# g0=G[iFreq,iRef]
# G[iFreq]*=g0.conj()/np.abs(g0)
# def test(G,f):
# # nf,na=G.shape
# # #na=3
# # t=np.random.randn(na)*0.01
# # c=np.random.randn(na)*np.pi/10
# # G=TECToZ(t.reshape((1,-1)),c.reshape((1,-1)),f.reshape((-1,1)))
# TECMachine=ClassFitTEC(G,f)
# #TECMachine.DoFit()
# TECMachine.findX0()
# TECMachine.doFit()
# class ClassFitTEC():
# def __init__(self,gains,nu,Tol=5e-2,Incr=1):
# self.nf,self.na=gains.shape
# self.G=gains.copy()
# Norm(self.G)
# self.Tol=Tol
# self.G/=np.abs(self.G)
# self.CentralFreqs=self.nu=nu
# self.NFreq=nu.size
# na=self.na
# self.nbl=(na**2-na)/2
# self.CurrentX=None
# log.print("Number of Antennas: %i"%self.na)
# log.print("Number of Freqs: %i"%nu.size)
# log.print("Number of Points: %i"%(nu.size*self.na**2))
# self.Y=np.array([(self.G[iFreq].reshape((-1,1))*self.G[iFreq].conj().reshape((1,-1))).ravel() for iFreq in range(self.NFreq)]).ravel()
# self.nu_Y=np.array([(self.nu[iFreq]*np.ones((self.na,self.na)).ravel()) for iFreq in range(self.NFreq)]).ravel()
# self.A0=np.array([(np.mgrid[0:na:1,0:na:1][0]).ravel() for iFreq in range(self.NFreq)]).ravel()
# self.A1=np.array([(np.mgrid[0:na:1,0:na:1][1]).ravel() for iFreq in range(self.NFreq)]).ravel()
# self.Incr=Incr
# Mask=np.where(self.A1>self.A0)[0]
# self.Mask=Mask
# self.Y=self.Y[Mask][::self.Incr]
# self.nu_Y=self.nu_Y[Mask][::self.Incr]
# self.A0=self.A0[Mask][::self.Incr]
# self.A1=self.A1[Mask][::self.Incr]
# self.x0=None
# self.indA0=[np.where(self.A0==iAnt)[0] for iAnt in range(na)]
# self.indA1=[np.where(self.A1==iAnt)[0] for iAnt in range(na)]
# def doFit(self,NIter=100):
# if self.x0 is None and self.CurrentX is None:
# self.CurrentX=np.zeros((2*self.na,),np.float32)+1e-10
# #self.CurrentX=np.random.randn(2*self.na)
# self.Current_iIter=0
# for iIter in range(NIter):
# self.doLMIter()
# #self.Plot()
# self.Current_iIter=iIter
# if self.Diff<self.Tol:
# log.print("Convergence in %i steps"%(iIter+1))
# break
# return self.CurrentX
# def GiveGPredict(self,X):
# t=X[0:self.na].reshape((1,-1))
# c=X[self.na:].reshape((1,-1))
# z=TECToZ(t,c,self.nu.reshape((-1,1)))
# return z
# def doLMIter(self):
# T=ClassTimeIt.ClassTimeIt()
# T.disable()
# #J,H=
# self.giveJacobianHessian()
# T.timeit("J, H")
# z=self.GiveGPredict(self.CurrentX)
# Y=np.array([(z[iFreq].reshape((-1,1))*z[iFreq].conj().reshape((1,-1))).ravel() for iFreq in range(self.NFreq)]).ravel()
# r=self.Y-Y[self.Mask][::self.Incr]
# v=self.JHy(r)
# H=self.DiagJHJ()
# T.timeit("diff")
# Hinv=killMS.Array.ModLinAlg.invSVD(H)
# T.timeit("inv")
# X = self.CurrentX + np.real(np.dot(Hinv,v.reshape((-1,1))).ravel())
# #print self.CurrentX
# xx=self.CurrentX.copy()
# xx[xx==0]=1e-6
# self.Diff=np.max(np.abs((X-xx)/xx))
# z0=self.GiveGPredict(self.CurrentX)
# Norm(z0)
# self.CurrentX=X
# z=self.GiveGPredict(self.CurrentX)
# Norm(z)
# self.Diff=np.max(np.abs(np.angle(z*z0.conj())))
# #print self.Diff
# return
# # HinvJH=np.dot(scipy.sparse.coo_matrix(Hinv),J.T.conj())
# # T.timeit("HinvJH")
# # HinvJHy=np.dot(HinvJH,scipy.sparse.coo_matrix(r.reshape((-1,1))))
# # T.timeit("HinvJHy")
# # self.CurrentX+=np.real(np.array(HinvJHy.todense())).ravel()
# # T.timeit("X")
# # #self.CurrentX+=np.real(Dot(Hinv,J.T.conj(),r.reshape((-1,1))).ravel())
# def setX0(self,x0):
# self.CurrentX=x0
# def findX0(self):
# NTEC=101
# NConstPhase=51
# TECGridAmp=0.1
# TECGrid,CPhase=np.mgrid[-TECGridAmp:TECGridAmp:NTEC*1j,-np.pi:np.pi:NConstPhase*1j]
# Z=TECToZ(TECGrid.reshape((-1,1)),CPhase.reshape((-1,1)),self.CentralFreqs.reshape((1,-1)))
# self.Z=Z
# self.TECGrid,self.CPhase=TECGrid,CPhase
# self.CurrentX=np.zeros((2,self.na),np.float32)
# for iAnt in range(self.na):
# g=self.G[:,iAnt]
# g0=g/np.abs(g)
# W=np.ones(g0.shape,np.float32)
# W[g==1.]=0
# Z=self.Z
# for iTry in range(5):
# R=(g0.reshape((1,-1))-Z)*W.reshape((1,-1))
# Chi2=np.sum(np.abs(R)**2,axis=1)
# iTec=np.argmin(Chi2)
# rBest=R[iTec]
# if np.max(np.abs(rBest))==0: break
# Sig=np.sum(np.abs(rBest*W))/np.sum(W)
# ind=np.where(np.abs(rBest*W)>5.*Sig)[0]
# if ind.size==0: break
# W[ind]=0
# self.CurrentX[0,iAnt]=self.TECGrid.ravel()[iTec]
# self.CurrentX[1,iAnt]=self.TECGrid.ravel()[iTec]
# self.CurrentX=self.CurrentX.ravel()
# def Plot(self):
# z=self.GiveGPredict(self.CurrentX)
# Norm(z)
# import pylab
# pylab.clf()
# pylab.plot(self.nu,np.angle(self.G),color="black")
# pylab.plot(self.nu,np.angle(z),color="gray")
# pylab.draw()
# pylab.show(False)
# pylab.pause(0.1)
# def JHy(self,y):
# T=ClassTimeIt.ClassTimeIt("JHy")
# T.disable()
# v=np.zeros((2,self.na),np.complex64)
# for iAnt in range(self.na):
# v[0,iAnt]+=np.sum(self.J_TEC[self.indA0[iAnt]].conj()*y[self.indA0[iAnt]])
# v[0,iAnt]+=np.sum(-self.J_TEC[self.indA1[iAnt]].conj()*y[self.indA1[iAnt]])
# v[1,iAnt]+=np.sum(self.J_Phase[self.indA0[iAnt]].conj()*y[self.indA0[iAnt]])
# v[1,iAnt]+=np.sum(-self.J_Phase[self.indA1[iAnt]].conj()*y[self.indA1[iAnt]])
# v=v.ravel()
# T.timeit("Prod")
# # PSparse=np.array(np.dot(self.J.T.conj(),scipy.sparse.coo_matrix(y.reshape((-1,1)))).todense()).ravel()
# # T.timeit("PSparse")
# return v
# def DiagJHJ(self):
# T=ClassTimeIt.ClassTimeIt("JHy")
# T.disable()
# H=np.zeros((2,self.na,2,self.na),np.complex64)
# for iAnt in range(self.na):
# #Jt[self.indA0[iAnt],iAnt]=J_TEC[self.indA0[iAnt]]
# #Jt[self.indA1[iAnt],iAnt]=-J_TEC[self.indA1[iAnt]]
# H[0,iAnt,0,iAnt]+=np.sum(self.J_TEC[self.indA0[iAnt]].conj()*self.J_TEC[self.indA0[iAnt]])
# H[0,iAnt,0,iAnt]+=np.sum(self.J_TEC[self.indA1[iAnt]].conj()*self.J_TEC[self.indA1[iAnt]])
# H[0,iAnt,1,iAnt]+=np.sum(self.J_TEC[self.indA0[iAnt]].conj()*self.J_Phase[self.indA0[iAnt]])
# H[0,iAnt,1,iAnt]+=np.sum(self.J_TEC[self.indA1[iAnt]].conj()*self.J_Phase[self.indA1[iAnt]])
# H[1,iAnt,0,iAnt]+=np.sum(self.J_Phase[self.indA0[iAnt]].conj()*self.J_TEC[self.indA0[iAnt]])
# H[1,iAnt,0,iAnt]+=np.sum(self.J_Phase[self.indA1[iAnt]].conj()*self.J_TEC[self.indA1[iAnt]])
# H[1,iAnt,1,iAnt]+=np.sum(self.J_Phase[self.indA0[iAnt]].conj()*self.J_Phase[self.indA0[iAnt]])
# H[1,iAnt,1,iAnt]+=np.sum(self.J_Phase[self.indA1[iAnt]].conj()*self.J_Phase[self.indA1[iAnt]])
# T.timeit("H")
# H=H.reshape((2*self.na,2*self.na))
# return H
# A=np.log10(np.abs(self.H))
# B=np.log10(np.abs(H))
# vmin,vmax=A.min(),A.max()
# import pylab
# pylab.clf()
# pylab.subplot(1,2,1)
# pylab.imshow(A,interpolation="nearest",vmin=vmin,vmax=vmax)
# pylab.colorbar()
# pylab.subplot(1,2,2)
# pylab.imshow(B,interpolation="nearest",vmin=vmin,vmax=vmax)
# pylab.colorbar()
# pylab.draw()
# pylab.show(False)
# stop
# return H
# def giveJacobianHessian(self):
# T=ClassTimeIt.ClassTimeIt("J")
# J=np.zeros((self.Y.size,self.na*2),np.complex64)
# Jt=J[:,0:self.na]
# Jc=J[:,self.na:]
# TEC=self.CurrentX[0:self.na]
# ConstPhase=self.CurrentX[self.na:]
# self.A0,self.A1
# dTEC=TEC[self.A0]-TEC[self.A1]
# dConstPhase=ConstPhase[self.A0]-ConstPhase[self.A1]
# Phase=K/self.nu_Y*dTEC+dConstPhase
# Z=np.exp(1j*Phase)
# self.J_TEC=J_TEC=1j*K/self.nu_Y*Z
# self.J_Phase=J_Phase=1j*Z
# return
# T.timeit("first")
# for iAnt in range(self.na):
# Jt[self.indA0[iAnt],iAnt]=J_TEC[self.indA0[iAnt]]
# Jt[self.indA1[iAnt],iAnt]=-J_TEC[self.indA1[iAnt]]
# Jc[self.indA0[iAnt],iAnt]=J_Phase[self.indA0[iAnt]]
# Jc[self.indA1[iAnt],iAnt]=-J_Phase[self.indA1[iAnt]]
# T.timeit("build")
# self.J=J
# self.Jsp=Jsp=scipy.sparse.coo_matrix(J)
# T.timeit("sp")
# # import pylab
# # pylab.clf()
# # pylab.subplot(1,2,1)
# # pylab.imshow(Jt.real,interpolation="nearest",aspect="auto")
# # pylab.subplot(1,2,2)
# # pylab.imshow(Jc.real,interpolation="nearest",aspect="auto")
# # pylab.draw()
# # pylab.show(False)
# # stop
# #print np.count_nonzero(J)/float(J.size)
# T.timeit("prod")
# H=np.array(np.dot(Jsp.T.conj(),Jsp).todense())
# self.H=H
# T.timeit("Hsp")
# return J,H
|
saopiccREPO_NAMEkillMSPATH_START.@killMS_extracted@killMS-master@killMS@Other@ClassFitTEC.py@.PATH_END.py
|
{
"filename": "plot_c_fitting.ipynb",
"repo_name": "jpierel14/sntd",
"repo_path": "sntd_extracted/sntd-master/docs/source/examples/plot_c_fitting.ipynb",
"type": "Jupyter Notebook"
}
|
```python
%matplotlib inline
```
# Measure Time Delays
A series of examples demonstrating various fitting options/features
with SNTD.
There are 3 methods built into SNTD to measure time delays
(parallel, series, color). They are accessed by the same
function: :py:func:`~sntd.fitting.fit_data` .
Here ``myMISN`` was generated in the `sphx_glr_examples_plot_b_sim.py` part
of the documentation, using the :py:func:`~sntd.simulation.createMultiplyImagedSN`
function. The true delay for all of these fits is 50 days.
You can batch process (with sbatch or multiprocessing) using any or all of these methods as well
(see `examples:Batch Processing Time Delay Measurements`)
## `Run this notebook with Google Colab <https://colab.research.google.com/github/jpierel14/sntd/blob/master/notebooks/docs_fitting.ipynb>`_.
**Parallel:**
```python
import sntd
myMISN=sntd.load_example_misn()
fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
params=['x0','t0','x1','c'],constants={'z':1.4},refImage='image_1',cut_time=[-30,40],
bounds={'t0':(-20,20),'x1':(-2,2),'c':(-1,1),'mu':(.5,2)},fitOrder=['image_1','image_2'],
method='parallel',microlensing=None,modelcov=False,npoints=100)
print(fitCurves.parallel.time_delays)
print(fitCurves.parallel.time_delay_errors)
print(fitCurves.parallel.magnifications)
print(fitCurves.parallel.magnification_errors)
fitCurves.plot_object(showFit=True,method='parallel')
fitCurves.plot_fit(method='parallel',par_image='image_1')
fitCurves.plot_fit(method='parallel',par_image='image_2')
```
Note that the bounds for the 't0' parameter are not absolute, the actual peak time will be estimated (unless t0_guess is defined)
and the defined bounds will be added to this value. Similarly for amplitude, where bounds are multiplicative
Other methods are called in a similar fashion, with a couple of extra arguments:
**Series:**
```python
fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
params=['x0','t0','x1','c'],constants={'z':1.4},refImage='image_1',cut_time=[-30,40],
bounds={'t0':(-20,20),'td':(-20,20),'mu':(.5,2),'x1':(-2,2),'c':(-.5,.5)},
method='series',npoints=100)
print(fitCurves.series.time_delays)
print(fitCurves.series.time_delay_errors)
print(fitCurves.series.magnifications)
print(fitCurves.series.magnification_errors)
fitCurves.plot_object(showFit=True,method='series')
fitCurves.plot_fit(method='series')
```
**Color:**
By default, this will attempt to fit every combination of colors possible from
the bands present in the data. You can define specific colors using the "fit_colors"
argument.
```python
fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
params=['t0','c'],constants={'z':1.4,'x1':fitCurves.images['image_1'].fits.model.get('x1')},refImage='image_1',
color_param_ignore=['x1'],bounds={'t0':(-20,20),'td':(-20,20),'mu':(.5,2),'c':(-.5,.5)},cut_time=[-30,40],
method='color',microlensing=None,modelcov=False,npoints=200,maxiter=None,minsnr=3)
print(fitCurves.color.time_delays)
print(fitCurves.color.time_delay_errors)
fitCurves.plot_object(showFit=True,method='color')
fitCurves.plot_fit(method='color')
```
You can include your fit from the parallel method as a prior on light curve and time delay parameters in the series/color methods with the "fit_prior" command:
```python
fitCurves_parallel=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
params=['x0','t0','x1','c'],constants={'z':1.4},refImage='image_1',
bounds={'t0':(-20,20),'x1':(-3,3),'c':(-.5,.5),'mu':(.5,2)},fitOrder=['image_1','image_2'],cut_time=[-30,40],
method='parallel',microlensing=None,modelcov=False,npoints=100,maxiter=None)
fitCurves_color=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],cut_time=[-50,30],
params=['t0','c'],constants={'z':1.4,'x1':fitCurves.images['image_1'].fits.model.get('x1')},refImage='image_1',
bounds={'t0':(-20,20),'td':(-20,20),'mu':(.5,2),'c':(-.5,.5)},fit_prior=fitCurves_parallel,
method='color',microlensing=None,modelcov=False,npoints=200,maxiter=None,minsnr=3)
print(fitCurves_parallel.parallel.time_delays)
print(fitCurves_parallel.parallel.time_delay_errors)
print(fitCurves_color.color.time_delays)
print(fitCurves_color.color.time_delay_errors)
```
**Fitting Using Extra Propagation Effects**
You might also want to include other propagation effects in your fitting model, and fit relevant parameters. This can be done by
simply adding effects to an SNCosmo model, in the same way as if you were fitting a single SN with SNCosmo. First we can add some
extreme dust in the source and lens frames (your final simulations may look slightly different as **c** is chosen randomly):
```python
myMISN2 = sntd.createMultiplyImagedSN(sourcename='salt2-extended', snType='Ia', redshift=1.4,z_lens=.53, bands=['F110W','F160W'],
zp=[26.9,26.2], cadence=8., epochs=30.,time_delays=[20., 70.], magnifications=[20,10],
objectName='My Type Ia SN',telescopename='HST',av_lens=1.5,
av_host=1)
print('lensebv:',myMISN2.images['image_1'].simMeta['lensebv'],
'hostebv:',myMISN2.images['image_1'].simMeta['hostebv'],
'c:',myMISN2.images['image_1'].simMeta['c'])
```
Okay, now we can fit the MISN first without taking these effects into account:
```python
fitCurves_dust=sntd.fit_data(myMISN2,snType='Ia', models='salt2-extended',bands=['F110W','F160W'],
params=['x0','x1','t0','c'],npoints=200,
constants={'z':1.4},minsnr=1,cut_time=[-30,40],
bounds={'t0':(-15,15),'x1':(-3,3),'c':(-1,1)})
print(fitCurves_dust.parallel.time_delays)
print(fitCurves_dust.parallel.time_delay_errors)
print('c:',fitCurves_dust.images['image_1'].fits.model.get('c'))
fitCurves_dust.plot_object(showFit=True)
```
We can see that the fitter has done reasonably well, and the time delay is still accurate (True delay is 50 days).
However, one issue is that the measured value for **c** is vastly different than the actual value
as it attempts to compensate for extinction without a propagation effect. Now let's add in the propagation effects:
```python
import sncosmo
dust = sncosmo.CCM89Dust()
salt2_model=sncosmo.Model('salt2-extended',effects=[dust,dust],effect_names=['lens','host'],effect_frames=['free','rest'])
fitCurves_dust=sntd.fit_data(myMISN2,snType='Ia', models=salt2_model,bands=['F110W','F160W'],npoints=200,
params=['x0','x1','t0','c','lensebv','hostebv'],minsnr=1,cut_time=[-30,40],
constants={'z':1.4,'lensr_v':3.1,'lensz':0.53,'hostr_v':3.1},
bounds={'t0':(-15,15),'x1':(-3,3),'c':(-.1,.1),'lensebv':(.2,1.),'hostebv':(.2,1.)})
print(fitCurves_dust.parallel.time_delays)
print(fitCurves_dust.parallel.time_delay_errors)
print('c:',fitCurves_dust.images['image_1'].fits.model.get('c'),
'lensebv:',fitCurves_dust.images['image_1'].fits.model.get('lensebv'),
'hostebv:',fitCurves_dust.images['image_1'].fits.model.get('hostebv'))
fitCurves_dust.plot_object(showFit=True)
```
Now the measured value for **c** is much closer to reality, and the measured times of peak are somewhat
more accurate.
|
jpierel14REPO_NAMEsntdPATH_START.@sntd_extracted@sntd-master@docs@source@examples@plot_c_fitting.ipynb@.PATH_END.py
|
{
"filename": "_z.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/layout/scene/camera/eye/_z.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ZValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="z", parent_name="layout.scene.camera.eye", **kwargs
):
super(ZValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "camera"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@layout@scene@camera@eye@_z.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "adrn/gala",
"repo_path": "gala_extracted/gala-main/gala/potential/frame/tests/__init__.py",
"type": "Python"
}
|
adrnREPO_NAMEgalaPATH_START.@gala_extracted@gala-main@gala@potential@frame@tests@__init__.py@.PATH_END.py
|
|
{
"filename": "units.py",
"repo_name": "gammapy/gammapy",
"repo_path": "gammapy_extracted/gammapy-main/gammapy/utils/units.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Units and Quantity related helper functions."""
import logging
from math import floor
import numpy as np
import astropy.units as u
__all__ = ["standardise_unit", "unit_from_fits_image_hdu"]
log = logging.getLogger(__name__)
def standardise_unit(unit):
"""Standardise unit.
Changes applied by this function:
* Drop "photon" == "ph" from the unit
* Drop "count" == "ct" from the unit
Parameters
----------
unit : `~astropy.units.Unit` or str
Any old unit.
Returns
-------
unit : `~astropy.units.Unit`
Shiny new, standardised unit.
Examples
--------
>>> from gammapy.utils.units import standardise_unit
>>> standardise_unit('ph cm-2 s-1')
Unit("1 / (s cm2)")
>>> standardise_unit('ct cm-2 s-1')
Unit("1 / (s cm2)")
>>> standardise_unit('cm-2 s-1')
Unit("1 / (s cm2)")
"""
unit = u.Unit(unit)
bases, powers = [], []
for base, power in zip(unit.bases, unit.powers):
if str(base) not in {"ph", "ct"}:
bases.append(base)
powers.append(power)
return u.CompositeUnit(scale=unit.scale, bases=bases, powers=powers)
def unit_from_fits_image_hdu(header):
"""Read unit from a FITS image HDU.
- The ``BUNIT`` key is used.
- `astropy.units.Unit` is called.
If the ``BUNIT`` value is invalid, a log warning
is emitted and the empty unit is used.
- `standardise_unit` is called
"""
unit = header.get("BUNIT", "")
try:
u.Unit(unit)
except ValueError:
log.warning(f"Invalid value BUNIT={unit!r} in FITS header. Setting empty unit.")
unit = ""
return standardise_unit(unit)
def energy_unit_format(E):
"""Format energy quantities to a string representation that is more comfortable to read.
This is done by switching to the most relevant unit (keV, MeV, GeV, TeV) and changing the float precision.
Parameters
----------
E: `~astropy.units.Quantity`
Quantity or list of quantities.
Returns
-------
str : str
A string or tuple of strings with energy unit formatted.
"""
try:
iter(E)
except TypeError:
pass
else:
return tuple(map(energy_unit_format, E))
i = floor(np.log10(E.to_value(u.eV)) / 3) # a new unit every 3 decades
unit = (u.eV, u.keV, u.MeV, u.GeV, u.TeV, u.PeV)[i] if i < 5 else u.PeV
v = E.to_value(unit)
i = floor(np.log10(v))
prec = (2, 1, 0)[i] if i < 3 else 0
return f"{v:0.{prec}f} {unit}"
|
gammapyREPO_NAMEgammapyPATH_START.@gammapy_extracted@gammapy-main@gammapy@utils@units.py@.PATH_END.py
|
{
"filename": "clova.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/langchain_community/embeddings/clova.py",
"type": "Python"
}
|
from __future__ import annotations
from typing import Any, Dict, List, Optional, cast
import requests
from langchain_core._api.deprecation import deprecated
from langchain_core.embeddings import Embeddings
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from pydantic import BaseModel, ConfigDict, SecretStr, model_validator
@deprecated(
since="0.3.4",
removal="1.0.0",
alternative_import="langchain_community.ClovaXEmbeddings",
)
class ClovaEmbeddings(BaseModel, Embeddings):
"""
Clova's embedding service.
To use this service,
you should have the following environment variables
set with your API tokens and application ID,
or pass them as named parameters to the constructor:
- ``CLOVA_EMB_API_KEY``: API key for accessing Clova's embedding service.
- ``CLOVA_EMB_APIGW_API_KEY``: API gateway key for enhanced security.
- ``CLOVA_EMB_APP_ID``: Application ID for identifying your application.
Example:
.. code-block:: python
from langchain_community.embeddings import ClovaEmbeddings
embeddings = ClovaEmbeddings(
clova_emb_api_key='your_clova_emb_api_key',
clova_emb_apigw_api_key='your_clova_emb_apigw_api_key',
app_id='your_app_id'
)
query_text = "This is a test query."
query_result = embeddings.embed_query(query_text)
document_text = "This is a test document."
document_result = embeddings.embed_documents([document_text])
"""
endpoint_url: str = (
"https://clovastudio.apigw.ntruss.com/testapp/v1/api-tools/embedding"
)
"""Endpoint URL to use."""
model: str = "clir-emb-dolphin"
"""Embedding model name to use."""
clova_emb_api_key: Optional[SecretStr] = None
"""API key for accessing Clova's embedding service."""
clova_emb_apigw_api_key: Optional[SecretStr] = None
"""API gateway key for enhanced security."""
app_id: Optional[SecretStr] = None
"""Application ID for identifying your application."""
model_config = ConfigDict(
extra="forbid",
)
@model_validator(mode="before")
@classmethod
def validate_environment(cls, values: Dict) -> Any:
"""Validate api key exists in environment."""
values["clova_emb_api_key"] = convert_to_secret_str(
get_from_dict_or_env(values, "clova_emb_api_key", "CLOVA_EMB_API_KEY")
)
values["clova_emb_apigw_api_key"] = convert_to_secret_str(
get_from_dict_or_env(
values, "clova_emb_apigw_api_key", "CLOVA_EMB_APIGW_API_KEY"
)
)
values["app_id"] = convert_to_secret_str(
get_from_dict_or_env(values, "app_id", "CLOVA_EMB_APP_ID")
)
return values
def embed_documents(self, texts: List[str]) -> List[List[float]]:
"""
Embed a list of texts and return their embeddings.
Args:
texts: The list of texts to embed.
Returns:
List of embeddings, one for each text.
"""
embeddings = []
for text in texts:
embeddings.append(self._embed_text(text))
return embeddings
def embed_query(self, text: str) -> List[float]:
"""
Embed a single query text and return its embedding.
Args:
text: The text to embed.
Returns:
Embeddings for the text.
"""
return self._embed_text(text)
def _embed_text(self, text: str) -> List[float]:
"""
Internal method to call the embedding API and handle the response.
"""
payload = {"text": text}
# HTTP headers for authorization
headers = {
"X-NCP-CLOVASTUDIO-API-KEY": cast(
SecretStr, self.clova_emb_api_key
).get_secret_value(),
"X-NCP-APIGW-API-KEY": cast(
SecretStr, self.clova_emb_apigw_api_key
).get_secret_value(),
"Content-Type": "application/json",
}
# send request
app_id = cast(SecretStr, self.app_id).get_secret_value()
response = requests.post(
f"{self.endpoint_url}/{self.model}/{app_id}",
headers=headers,
json=payload,
)
# check for errors
if response.status_code == 200:
response_data = response.json()
if "result" in response_data and "embedding" in response_data["result"]:
return response_data["result"]["embedding"]
raise ValueError(
f"API request failed with status {response.status_code}: {response.text}"
)
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@langchain_community@embeddings@clova.py@.PATH_END.py
|
{
"filename": "noxfile.py",
"repo_name": "cosmicrays/hermes",
"repo_path": "hermes_extracted/hermes-master/lib/pybind11/noxfile.py",
"type": "Python"
}
|
import os
import nox
nox.needs_version = ">=2022.1.7"
nox.options.sessions = ["lint", "tests", "tests_packaging"]
PYTHON_VERSIONS = [
"3.6",
"3.7",
"3.8",
"3.9",
"3.10",
"3.11",
"pypy3.7",
"pypy3.8",
"pypy3.9",
]
if os.environ.get("CI", None):
nox.options.error_on_missing_interpreters = True
@nox.session(reuse_venv=True)
def lint(session: nox.Session) -> None:
"""
Lint the codebase (except for clang-format/tidy).
"""
session.install("pre-commit")
session.run("pre-commit", "run", "-a", *session.posargs)
@nox.session(python=PYTHON_VERSIONS)
def tests(session: nox.Session) -> None:
"""
Run the tests (requires a compiler).
"""
tmpdir = session.create_tmp()
session.install("cmake")
session.install("-r", "tests/requirements.txt")
session.run(
"cmake",
"-S.",
f"-B{tmpdir}",
"-DPYBIND11_WERROR=ON",
"-DDOWNLOAD_CATCH=ON",
"-DDOWNLOAD_EIGEN=ON",
*session.posargs,
)
session.run("cmake", "--build", tmpdir)
session.run("cmake", "--build", tmpdir, "--config=Release", "--target", "check")
@nox.session
def tests_packaging(session: nox.Session) -> None:
"""
Run the packaging tests.
"""
session.install("-r", "tests/requirements.txt")
session.run("pytest", "tests/extra_python_package", *session.posargs)
@nox.session(reuse_venv=True)
def docs(session: nox.Session) -> None:
"""
Build the docs. Pass "serve" to serve.
"""
session.install("-r", "docs/requirements.txt")
session.chdir("docs")
if "pdf" in session.posargs:
session.run("sphinx-build", "-M", "latexpdf", ".", "_build")
return
session.run("sphinx-build", "-M", "html", ".", "_build")
if "serve" in session.posargs:
session.log("Launching docs at http://localhost:8000/ - use Ctrl-C to quit")
session.run("python", "-m", "http.server", "8000", "-d", "_build/html")
elif session.posargs:
session.error("Unsupported argument to docs")
@nox.session(reuse_venv=True)
def make_changelog(session: nox.Session) -> None:
"""
Inspect the closed issues and make entries for a changelog.
"""
session.install("ghapi", "rich")
session.run("python", "tools/make_changelog.py")
@nox.session(reuse_venv=True)
def build(session: nox.Session) -> None:
"""
Build SDists and wheels.
"""
session.install("build")
session.log("Building normal files")
session.run("python", "-m", "build", *session.posargs)
session.log("Building pybind11-global files (PYBIND11_GLOBAL_SDIST=1)")
session.run(
"python", "-m", "build", *session.posargs, env={"PYBIND11_GLOBAL_SDIST": "1"}
)
|
cosmicraysREPO_NAMEhermesPATH_START.@hermes_extracted@hermes-master@lib@pybind11@noxfile.py@.PATH_END.py
|
{
"filename": "1-bug_report.md",
"repo_name": "LSSTDESC/rail",
"repo_path": "rail_extracted/rail-main/.github/ISSUE_TEMPLATE/1-bug_report.md",
"type": "Markdown"
}
|
---
name: Bug report
about: Tell us about a problem to fix
title: 'Short description'
labels: 'bug'
assignees: ''
---
**Bug report**
**Before submitting**
Please check the following:
- [ ] I have described the situation in which the bug arose, including what code was executed, information about my environment, and any applicable data others will need to reproduce the problem.
- [ ] I have included available evidence of the unexpected behavior (including error messages, screenshots, and/or plots) as well as a descriprion of what I expected instead.
- [ ] If I have a solution in mind, I have provided an explanation and/or pseudocode and/or task list.
|
LSSTDESCREPO_NAMErailPATH_START.@rail_extracted@rail-main@.github@ISSUE_TEMPLATE@1-bug_report.md@.PATH_END.py
|
{
"filename": "spectra.py",
"repo_name": "gammapy/gammapy",
"repo_path": "gammapy_extracted/gammapy-main/gammapy/astro/darkmatter/spectra.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""Dark matter spectra."""
import numpy as np
import astropy.units as u
from astropy.table import Table
from gammapy.maps import Map, MapAxis, RegionGeom
from gammapy.modeling import Parameter
from gammapy.modeling.models import SpectralModel, TemplateNDSpectralModel
from gammapy.utils.scripts import make_path
__all__ = ["PrimaryFlux", "DarkMatterAnnihilationSpectralModel"]
class PrimaryFlux(TemplateNDSpectralModel):
"""DM-annihilation gamma-ray spectra.
Based on the precomputed models by Cirelli et al. (2016). All available
annihilation channels can be found there. The dark matter mass will be set
to the nearest available value. The spectra will be available as
`~gammapy.modeling.models.TemplateNDSpectralModel` for a chosen dark matter mass and
annihilation channel. Using a `~gammapy.modeling.models.TemplateNDSpectralModel`
allows the interpolation between different dark matter masses.
Parameters
----------
mDM : `~astropy.units.Quantity`
Dark matter particle mass as rest mass energy.
channel: str
Annihilation channel. List available channels with `~gammapy.spectrum.PrimaryFlux.allowed_channels`.
References
----------
* `2011JCAP...03..051 <https://ui.adsabs.harvard.edu/abs/2011JCAP...03..051C>`_
* Cirelli et al (2016): http://www.marcocirelli.net/PPPC4DMID.html
"""
channel_registry = {
"eL": "eL",
"eR": "eR",
"e": "e",
"muL": r"\[Mu]L",
"muR": r"\[Mu]R",
"mu": r"\[Mu]",
"tauL": r"\[Tau]L",
"tauR": r"\[Tau]R",
"tau": r"\[Tau]",
"q": "q",
"c": "c",
"b": "b",
"t": "t",
"WL": "WL",
"WT": "WT",
"W": "W",
"ZL": "ZL",
"ZT": "ZT",
"Z": "Z",
"g": "g",
"gamma": r"\[Gamma]",
"h": "h",
"nu_e": r"\[Nu]e",
"nu_mu": r"\[Nu]\[Mu]",
"nu_tau": r"\[Nu]\[Tau]",
"V->e": "V->e",
"V->mu": r"V->\[Mu]",
"V->tau": r"V->\[Tau]",
}
table_filename = "$GAMMAPY_DATA/dark_matter_spectra/AtProduction_gammas.dat"
tag = ["PrimaryFlux", "dm-pf"]
def __init__(self, mDM, channel):
self.table_path = make_path(self.table_filename)
if not self.table_path.exists():
raise FileNotFoundError(
f"\n\nFile not found: {self.table_filename}\n"
"You may download the dataset needed with the following command:\n"
"gammapy download datasets --src dark_matter_spectra"
)
else:
self.table = Table.read(
str(self.table_path),
format="ascii.fast_basic",
guess=False,
delimiter=" ",
)
self.channel = channel
# create RegionNDMap for channel
masses = np.unique(self.table["mDM"])
log10x = np.unique(self.table["Log[10,x]"])
mass_axis = MapAxis.from_nodes(masses, name="mass", interp="log", unit="GeV")
log10x_axis = MapAxis.from_nodes(log10x, name="energy_true")
channel_name = self.channel_registry[self.channel]
geom = RegionGeom(region=None, axes=[log10x_axis, mass_axis])
region_map = Map.from_geom(
geom=geom, data=self.table[channel_name].reshape(geom.data_shape)
)
interp_kwargs = {"extrapolate": True, "fill_value": 0, "values_scale": "lin"}
super().__init__(region_map, interp_kwargs=interp_kwargs)
self.mDM = mDM
self.mass.frozen = True
@property
def mDM(self):
"""Dark matter mass."""
return u.Quantity(self.mass.value, "GeV")
@mDM.setter
def mDM(self, mDM):
unit = self.mass.unit
_mDM = u.Quantity(mDM).to(unit)
_mDM_val = _mDM.to_value(unit)
min_mass = u.Quantity(self.mass.min, unit)
max_mass = u.Quantity(self.mass.max, unit)
if _mDM_val < self.mass.min or _mDM_val > self.mass.max:
raise ValueError(
f"The mass {_mDM} is out of the bounds of the model. Please choose a mass between {min_mass} < `mDM` < {max_mass}"
)
self.mass.value = _mDM_val
@property
def allowed_channels(self):
"""List of allowed annihilation channels."""
return list(self.channel_registry.keys())
@property
def channel(self):
"""Annihilation channel as a string."""
return self._channel
@channel.setter
def channel(self, channel):
if channel not in self.allowed_channels:
raise ValueError(
f"Invalid channel: {channel}\nAvailable: {self.allowed_channels}\n"
)
else:
self._channel = channel
def evaluate(self, energy, **kwargs):
"""Evaluate the primary flux."""
mass = {"mass": self.mDM}
kwargs.update(mass)
log10x = np.log10(energy / self.mDM)
dN_dlogx = super().evaluate(log10x, **kwargs)
dN_dE = dN_dlogx / (energy * np.log(10))
return dN_dE
class DarkMatterAnnihilationSpectralModel(SpectralModel):
r"""Dark matter annihilation spectral model.
The gamma-ray flux is computed as follows:
.. math::
\frac{\mathrm d \phi}{\mathrm d E} =
\frac{\langle \sigma\nu \rangle}{4\pi k m^2_{\mathrm{DM}}}
\frac{\mathrm d N}{\mathrm dE} \times J(\Delta\Omega)
Parameters
----------
mass : `~astropy.units.Quantity`
Dark matter mass.
channel : str
Annihilation channel for `~gammapy.astro.darkmatter.PrimaryFlux`, e.g. "b" for "bbar".
See `PrimaryFlux.channel_registry` for more.
scale : float
Scale parameter for model fitting.
jfactor : `~astropy.units.Quantity`
Integrated J-Factor needed when `~gammapy.modeling.models.PointSpatialModel`
is used.
z: float
Redshift value.
k: int
Type of dark matter particle (k:2 Majorana, k:4 Dirac).
Examples
--------
This is how to instantiate a `DarkMatterAnnihilationSpectralModel` model::
>>> import astropy.units as u
>>> from gammapy.astro.darkmatter import DarkMatterAnnihilationSpectralModel
>>> channel = "b"
>>> massDM = 5000*u.Unit("GeV")
>>> jfactor = 3.41e19 * u.Unit("GeV2 cm-5")
>>> modelDM = DarkMatterAnnihilationSpectralModel(mass=massDM, channel=channel, jfactor=jfactor) # noqa: E501
References
----------
* `2011JCAP...03..051 <https://ui.adsabs.harvard.edu/abs/2011JCAP...03..051C>`_
"""
THERMAL_RELIC_CROSS_SECTION = 3e-26 * u.Unit("cm3 s-1")
"""Thermally averaged annihilation cross-section"""
scale = Parameter(
"scale",
1,
unit="",
interp="log",
)
tag = ["DarkMatterAnnihilationSpectralModel", "dm-annihilation"]
def __init__(self, mass, channel, scale=scale.quantity, jfactor=1, z=0, k=2):
self.k = k
self.z = z
self.mass = u.Quantity(mass)
self.channel = channel
self.jfactor = u.Quantity(jfactor)
self.primary_flux = PrimaryFlux(mass, channel=self.channel)
super().__init__(scale=scale)
def evaluate(self, energy, scale):
"""Evaluate dark matter annihilation model."""
flux = (
scale
* self.jfactor
* self.THERMAL_RELIC_CROSS_SECTION
* self.primary_flux(energy=energy * (1 + self.z))
/ self.k
/ self.mass
/ self.mass
/ (4 * np.pi)
)
return flux
def to_dict(self, full_output=False):
"""Convert to dictionary."""
data = super().to_dict(full_output=full_output)
data["spectral"]["channel"] = self.channel
data["spectral"]["mass"] = self.mass.to_string()
data["spectral"]["jfactor"] = self.jfactor.to_string()
data["spectral"]["z"] = self.z
data["spectral"]["k"] = self.k
return data
@classmethod
def from_dict(cls, data):
"""Create spectral model from a dictionary.
Parameters
----------
data : dict
Dictionary with model data.
Returns
-------
model : `DarkMatterAnnihilationSpectralModel`
Dark matter annihilation spectral model.
"""
data = data["spectral"]
data.pop("type")
parameters = data.pop("parameters")
scale = [p["value"] for p in parameters if p["name"] == "scale"][0]
return cls(scale=scale, **data)
class DarkMatterDecaySpectralModel(SpectralModel):
r"""Dark matter decay spectral model.
The gamma-ray flux is computed as follows:
.. math::
\frac{\mathrm d \phi}{\mathrm d E} =
\frac{\Gamma}{4\pi m_{\mathrm{DM}}}
\frac{\mathrm d N}{\mathrm dE} \times J(\Delta\Omega)
Parameters
----------
mass : `~astropy.units.Quantity`
Dark matter mass.
channel : str
Annihilation channel for `~gammapy.astro.darkmatter.PrimaryFlux`, e.g. "b" for "bbar".
See `PrimaryFlux.channel_registry` for more.
scale : float
Scale parameter for model fitting
jfactor : `~astropy.units.Quantity`
Integrated J-Factor needed when `~gammapy.modeling.models.PointSpatialModel`
is used.
z: float
Redshift value.
Examples
--------
This is how to instantiate a `DarkMatterAnnihilationSpectralModel` model::
>>> import astropy.units as u
>>> from gammapy.astro.darkmatter import DarkMatterDecaySpectralModel
>>> channel = "b"
>>> massDM = 5000*u.Unit("GeV")
>>> jfactor = 3.41e19 * u.Unit("GeV cm-2")
>>> modelDM = DarkMatterDecaySpectralModel(mass=massDM, channel=channel, jfactor=jfactor) # noqa: E501
References
----------
* `2011JCAP...03..051 <https://ui.adsabs.harvard.edu/abs/2011JCAP...03..051C>`_
"""
LIFETIME_AGE_OF_UNIVERSE = 4.3e17 * u.Unit("s")
"""Use age of univserse as lifetime"""
scale = Parameter(
"scale",
1,
unit="",
interp="log",
)
tag = ["DarkMatterDecaySpectralModel", "dm-decay"]
def __init__(self, mass, channel, scale=scale.quantity, jfactor=1, z=0):
self.z = z
self.mass = u.Quantity(mass)
self.channel = channel
self.jfactor = u.Quantity(jfactor)
self.primary_flux = PrimaryFlux(mass, channel=self.channel)
super().__init__(scale=scale)
def evaluate(self, energy, scale):
"""Evaluate dark matter decay model."""
flux = (
scale
* self.jfactor
* self.primary_flux(energy=energy * (1 + self.z))
/ self.LIFETIME_AGE_OF_UNIVERSE
/ self.mass
/ (4 * np.pi)
)
return flux
def to_dict(self, full_output=False):
data = super().to_dict(full_output=full_output)
data["spectral"]["channel"] = self.channel
data["spectral"]["mass"] = self.mass.to_string()
data["spectral"]["jfactor"] = self.jfactor.to_string()
data["spectral"]["z"] = self.z
return data
@classmethod
def from_dict(cls, data):
"""Create spectral model from dictionary.
Parameters
----------
data : dict
Dictionary with model data.
Returns
-------
model : `DarkMatterDecaySpectralModel`
Dark matter decay spectral model.
"""
data = data["spectral"]
data.pop("type")
parameters = data.pop("parameters")
scale = [p["value"] for p in parameters if p["name"] == "scale"][0]
return cls(scale=scale, **data)
|
gammapyREPO_NAMEgammapyPATH_START.@gammapy_extracted@gammapy-main@gammapy@astro@darkmatter@spectra.py@.PATH_END.py
|
{
"filename": "modules.py",
"repo_name": "yqiuu/starduster",
"repo_path": "starduster_extracted/starduster-main/starduster/modules.py",
"type": "Python"
}
|
import torch
from torch import nn
from torch.nn import functional as F
from numpy import pi
__all__ = [
"Monotonic", "Unimodal", "Smooth", "PlankianMixture", "Transfer",
"LInfLoss", "create_mlp", "kld_trapz", "kld_binary", "reduce_loss"
]
class Monotonic(nn.Module):
def __init__(self, increase=True):
super().__init__()
self.increase = increase
def forward(self, x_in):
x = F.softplus(x_in)
x = torch.cumsum(x, dim=1)/x.size(1)
if self.increase:
return x - x[:, None, 0]
else:
return -x + x[:, None, -1]
class Unimodal(nn.Module):
def __init__(self, input_size, output_size):
super().__init__()
self.lin1 = nn.Linear(input_size, output_size)
self.lin2 = nn.Linear(input_size, output_size)
self.increase = Monotonic(increase=True)
self.decrease = Monotonic(increase=False)
def forward(self, x_in):
return self.increase(self.lin1(x_in))*self.decrease(self.lin2(x_in))
class Smooth(nn.Module):
def __init__(self, kernel_size):
super().__init__()
self.kernel_size = kernel_size
def forward(self, x_in):
x = F.avg_pool1d(x_in[:, None, :], self.kernel_size, 1)
return x[:, 0, :]
class PlankianMixture(nn.Module):
def __init__(self, input_size, n_mix, x):
super().__init__()
self.lin_mu = nn.Linear(input_size, n_mix)
self.lin_w = nn.Linear(input_size, n_mix)
self.const = 15/pi**4
self.register_buffer('x_inv', 1./x)
def planck(self, mu):
y = self.x_inv*mu[:, :, None]
f = torch.exp(-y)
return self.const*y**4*f/(1 - f)
def forward(self, x_in):
mu = torch.cumsum(torch.exp(self.lin_mu(x_in)), dim=1)
w = F.softmax(self.lin_w(x_in), dim=1)
return torch.sum(self.planck(mu)*w[:, :, None], dim=1)
class Transfer(nn.Module):
def __init__(self, input_size, output_size, dx):
super().__init__()
self.lin_neg = nn.Linear(input_size, output_size)
self.lin_pos = nn.Linear(input_size, output_size)
self.dx = dx
def forward(self, x, budget):
z_pos = F.softplus(self.lin_pos(x))
z_pos = z_pos/torch.trapz(z_pos, dx=self.dx)[:, None]
z_neg = torch.sigmoid(self.lin_neg(x))*budget
y = torch.trapz(z_neg, dx=self.dx)[:, None]*z_pos - z_neg
return y
class LInfLoss(nn.Module):
def __init__(self, reduction='mean'):
super().__init__()
self.reduction = reduction
def forward(self, y_true, y_pred):
loss = torch.linalg.norm(y_pred - y_true, ord=float('inf'), dim=1)
return reduce_loss(loss, self.reduction)
def create_mlp(input_size, layer_sizes, activations):
modules = []
size_in = input_size
for size_out, act in zip(layer_sizes, activations):
modules.append(nn.Linear(size_in, size_out))
if act is not None:
modules.append(act)
size_in = size_out
return nn.Sequential(*modules)
def kld_trapz(a_pred, a_true, dx, eps=1e-10):
"""Compute KL divergence using the trapezoidal rule."""
return -torch.trapz(a_true*torch.log((a_pred + eps)/a_true), dx=dx)
def kld_binary(a_pred, a_true, eps=1e-6):
"""Compute binary KL divergence."""
a_pred = F.hardtanh(a_pred, eps, 1 - eps)
b_pred = 1 - a_pred
b_true = 1 - a_true
return -a_true*torch.log(a_pred/a_true) - b_true*torch.log(b_pred/b_true)
def reduce_loss(loss, reduction):
if reduction == 'mean':
return torch.mean(loss)
elif reduction == 'sum':
return torch.sum(loss)
elif reduction == 'square_mean':
return torch.mean(loss*loss)
elif reduction == 'square_sum':
return torch.sum(loss*loss)
elif reduction == 'none':
return loss
else:
raise ValueError("Invalid reduction: {}".format(reduction))
|
yqiuuREPO_NAMEstardusterPATH_START.@starduster_extracted@starduster-main@starduster@modules.py@.PATH_END.py
|
{
"filename": "_uirevision.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/sankey/_uirevision.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class UirevisionValidator(_plotly_utils.basevalidators.AnyValidator):
def __init__(self, plotly_name="uirevision", parent_name="sankey", **kwargs):
super(UirevisionValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@sankey@_uirevision.py@.PATH_END.py
|
{
"filename": "mask_ops.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/core/ops/mask_ops.py",
"type": "Python"
}
|
"""
Ops for masked arrays.
"""
from __future__ import annotations
from typing import TYPE_CHECKING
import numpy as np
from pandas._libs import (
lib,
missing as libmissing,
)
if TYPE_CHECKING:
from pandas._typing import npt
def kleene_or(
left: bool | np.ndarray | libmissing.NAType,
right: bool | np.ndarray | libmissing.NAType,
left_mask: np.ndarray | None,
right_mask: np.ndarray | None,
) -> tuple[npt.NDArray[np.bool_], npt.NDArray[np.bool_]]:
"""
Boolean ``or`` using Kleene logic.
Values are NA where we have ``NA | NA`` or ``NA | False``.
``NA | True`` is considered True.
Parameters
----------
left, right : ndarray, NA, or bool
The values of the array.
left_mask, right_mask : ndarray, optional
The masks. Only one of these may be None, which implies that
the associated `left` or `right` value is a scalar.
Returns
-------
result, mask: ndarray[bool]
The result of the logical or, and the new mask.
"""
# To reduce the number of cases, we ensure that `left` & `left_mask`
# always come from an array, not a scalar. This is safe, since
# A | B == B | A
if left_mask is None:
return kleene_or(right, left, right_mask, left_mask)
if not isinstance(left, np.ndarray):
raise TypeError("Either `left` or `right` need to be a np.ndarray.")
raise_for_nan(right, method="or")
if right is libmissing.NA:
result = left.copy()
else:
result = left | right
if right_mask is not None:
# output is unknown where (False & NA), (NA & False), (NA & NA)
left_false = ~(left | left_mask)
right_false = ~(right | right_mask)
mask = (
(left_false & right_mask)
| (right_false & left_mask)
| (left_mask & right_mask)
)
else:
if right is True:
mask = np.zeros_like(left_mask)
elif right is libmissing.NA:
mask = (~left & ~left_mask) | left_mask
else:
# False
mask = left_mask.copy()
return result, mask
def kleene_xor(
left: bool | np.ndarray | libmissing.NAType,
right: bool | np.ndarray | libmissing.NAType,
left_mask: np.ndarray | None,
right_mask: np.ndarray | None,
) -> tuple[npt.NDArray[np.bool_], npt.NDArray[np.bool_]]:
"""
Boolean ``xor`` using Kleene logic.
This is the same as ``or``, with the following adjustments
* True, True -> False
* True, NA -> NA
Parameters
----------
left, right : ndarray, NA, or bool
The values of the array.
left_mask, right_mask : ndarray, optional
The masks. Only one of these may be None, which implies that
the associated `left` or `right` value is a scalar.
Returns
-------
result, mask: ndarray[bool]
The result of the logical xor, and the new mask.
"""
# To reduce the number of cases, we ensure that `left` & `left_mask`
# always come from an array, not a scalar. This is safe, since
# A ^ B == B ^ A
if left_mask is None:
return kleene_xor(right, left, right_mask, left_mask)
if not isinstance(left, np.ndarray):
raise TypeError("Either `left` or `right` need to be a np.ndarray.")
raise_for_nan(right, method="xor")
if right is libmissing.NA:
result = np.zeros_like(left)
else:
result = left ^ right
if right_mask is None:
if right is libmissing.NA:
mask = np.ones_like(left_mask)
else:
mask = left_mask.copy()
else:
mask = left_mask | right_mask
return result, mask
def kleene_and(
left: bool | libmissing.NAType | np.ndarray,
right: bool | libmissing.NAType | np.ndarray,
left_mask: np.ndarray | None,
right_mask: np.ndarray | None,
) -> tuple[npt.NDArray[np.bool_], npt.NDArray[np.bool_]]:
"""
Boolean ``and`` using Kleene logic.
Values are ``NA`` for ``NA & NA`` or ``True & NA``.
Parameters
----------
left, right : ndarray, NA, or bool
The values of the array.
left_mask, right_mask : ndarray, optional
The masks. Only one of these may be None, which implies that
the associated `left` or `right` value is a scalar.
Returns
-------
result, mask: ndarray[bool]
The result of the logical xor, and the new mask.
"""
# To reduce the number of cases, we ensure that `left` & `left_mask`
# always come from an array, not a scalar. This is safe, since
# A & B == B & A
if left_mask is None:
return kleene_and(right, left, right_mask, left_mask)
if not isinstance(left, np.ndarray):
raise TypeError("Either `left` or `right` need to be a np.ndarray.")
raise_for_nan(right, method="and")
if right is libmissing.NA:
result = np.zeros_like(left)
else:
result = left & right
if right_mask is None:
# Scalar `right`
if right is libmissing.NA:
mask = (left & ~left_mask) | left_mask
else:
mask = left_mask.copy()
if right is False:
# unmask everything
mask[:] = False
else:
# unmask where either left or right is False
left_false = ~(left | left_mask)
right_false = ~(right | right_mask)
mask = (left_mask & ~right_false) | (right_mask & ~left_false)
return result, mask
def raise_for_nan(value: object, method: str) -> None:
if lib.is_float(value) and np.isnan(value):
raise ValueError(f"Cannot perform logical '{method}' with floating NaN")
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@core@ops@mask_ops.py@.PATH_END.py
|
{
"filename": "split.py",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/tools/proto_splitter/split.py",
"type": "Python"
}
|
# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic interface for Python-based Splitter."""
import abc
from collections.abc import Sequence
import time
from typing import Optional, Union
from absl import logging
import riegeli
from google.protobuf import message
from tensorflow.python.lib.io import file_io
from tensorflow.tools.proto_splitter import chunk_pb2
from tensorflow.tools.proto_splitter import util
from tensorflow.tools.proto_splitter import version as version_lib
from tensorflow.tools.proto_splitter import versions_pb2
class Splitter(abc.ABC):
"""An abstract class for splitting and writing protos that are > 2GB.
See the README on how to use or subclass this class.
"""
@property
@abc.abstractmethod
def version_def(self) -> versions_pb2.VersionDef:
"""Version info about the splitter and merge implementation required."""
@abc.abstractmethod
def split(
self,
) -> tuple[Sequence[Union[message.Message, bytes]], chunk_pb2.ChunkedMessage]:
"""Splits proto message into a Sequence of protos/bytes."""
@abc.abstractmethod
def write(self, file_prefix: str) -> str:
"""Serializes proto to disk.
Args:
file_prefix: string prefix of the filepath.
Returns:
The actual path the proto is written to.
"""
class ComposableSplitter(Splitter):
"""A Splitter that can be composed with other splitters.
This Splitter writes to the riegeli file format.
See README for details.
"""
def __init__(
self,
proto,
*,
proto_as_initial_chunk: bool = True,
parent_splitter: Optional["ComposableSplitter"] = None,
fields_in_parent: Optional[util.FieldTypes] = None,
):
"""Initializes ComposableSplitter.
Args:
proto: Proto message to split.
proto_as_initial_chunk: Whether to initialize chunks with the
user-provided proto as the initial chunk.
parent_splitter: The parent `ComposableSplitter` object.
fields_in_parent: Fields to access `proto` from the parent splitter's
proto.
"""
self._proto = proto
self._parent_splitter = parent_splitter
self._fields_in_parent = fields_in_parent
# Whether chunks have been created. See `build_chunks()`.
self._built = False
# Keep a list of chunk ids in the order in which they were added to the
# list.
self._add_chunk_order = []
self._fix_chunk_order = False
# Initialize chunks and ChunkedMessage (optionally with the first chunk as
# the user-provided proto.
if parent_splitter is not None:
# If this is not the root Splitter class, skip the initialization of
# the chunks/message since the parent's will be updated instead.
self._chunks = None
self._chunked_message = None
elif proto_as_initial_chunk:
self._chunks = [self._proto]
self._chunked_message = chunk_pb2.ChunkedMessage(chunk_index=0)
self._add_chunk_order.append(id(self._proto))
else:
self._chunks = []
self._chunked_message = chunk_pb2.ChunkedMessage()
def build_chunks(self) -> None:
"""Builds the Splitter object by generating chunks from the proto.
Subclasses of `ComposableChunks` should only need to override this method.
This method should be called once per Splitter to create the chunks.
Users should call the methods `split` or `write` instead.
"""
@property
def version_def(self) -> versions_pb2.VersionDef:
"""Version info about the splitter and join implementation required."""
return versions_pb2.VersionDef(
splitter_version=1,
join_version=0,
bad_consumers=version_lib.get_bad_versions(),
)
def split(
self,
) -> tuple[Sequence[Union[message.Message, bytes]], chunk_pb2.ChunkedMessage]:
"""Splits a proto message into a Sequence of protos/bytes."""
if self._parent_splitter:
raise ValueError(
"A child ComposableSplitter's `split` method should not be called "
"directly, since it inherit chunks from a parent object. Please call "
"the parent's `split()` method instead."
)
assert self._chunks is not None
assert self._chunked_message is not None
if not self._built:
self.build_chunks()
self._fix_chunks()
self._built = True
return self._chunks, self._chunked_message
def write(
self, file_prefix: str, writer_options: Optional[str] = None
) -> str:
"""Serializes a proto to disk.
The writer writes all chunks into a riegeli file. The chunk metadata
(ChunkMetadata) is written at the very end.
Args:
file_prefix: string prefix of the filepath. The writer will automatically
attach a `.pb` or `.cpb` (chunked pb) suffix depending on whether the
proto is split.
writer_options: Optional writer options to pass to the riegeli writer. See
https://github.com/google/riegeli/blob/master/doc/record_writer_options.md
for options.
Returns:
The actual filepath the proto is written to. The filepath will be
different depending on whether the proto is split, i.e., whether it will
be a pb or not.
"""
if self._parent_splitter is not None:
raise ValueError(
"A child ComposableSplitter's `write` method should not be called "
"directly, since it inherits unrelated chunks from a parent object. "
"Please call the parent's `write()` method instead."
)
start_time = time.time()
chunks, chunked_message = self.split()
if not chunked_message.chunked_fields:
path = f"{file_prefix}.pb"
file_io.atomic_write_string_to_file(
path, self._proto.SerializeToString(deterministic=True)
)
logging.info("Unchunked file exported to %s", path)
return path
path = f"{file_prefix}.cpb"
writer_kwargs = {}
if writer_options is not None:
writer_kwargs["options"] = writer_options
with riegeli.RecordWriter(file_io.FileIO(path, "wb"), **writer_kwargs) as f:
metadata = chunk_pb2.ChunkMetadata(
message=chunked_message, version=self.version_def
)
for chunk in chunks:
if isinstance(chunk, message.Message):
f.write_message(chunk)
chunk_type = chunk_pb2.ChunkInfo.Type.MESSAGE
size = chunk.ByteSize()
else:
f.write_record(chunk)
chunk_type = chunk_pb2.ChunkInfo.Type.BYTES
size = len(chunk)
metadata.chunks.add(
type=chunk_type, size=size, offset=f.last_pos.numeric
)
f.write_message(metadata)
end = time.time()
logging.info("Chunked file exported to %s", path)
logging.info(
"Total time spent splitting and writing the message: %s",
end - start_time,
)
logging.info(
"Number of chunks created (including initial message): %s",
len(chunks),
)
return path
def add_chunk(
self,
chunk: Union[message.Message, bytes],
field_tags: util.FieldTypes,
index=None,
) -> None:
"""Adds a new chunk and updates the ChunkedMessage proto.
Args:
chunk: Proto message or bytes.
field_tags: Field information about the placement of the chunked data
within self._proto.
index: Optional index at which to insert the chunk. The chunk ordering is
important for merging.
"""
if self._parent_splitter is not None:
self._parent_splitter.add_chunk(
chunk, self._fields_in_parent + field_tags, index
)
else:
assert self._chunks is not None
assert self._chunked_message is not None
field = self._chunked_message.chunked_fields.add(
field_tag=util.get_field_tag(self._proto, field_tags)
)
new_chunk_index = len(self._chunks)
field.message.chunk_index = new_chunk_index
self._add_chunk_order.append(id(chunk))
if index is None:
self._chunks.append(chunk)
else:
self._chunks.insert(index, chunk)
self._fix_chunk_order = True
def _fix_chunks(self) -> None:
"""Fixes chunk indices in the ChunkedMessage."""
if not self._fix_chunk_order:
return
# The chunk_index of each nested ChunkedMessage is set to the length of the
# list when the chunk was added. This would be fine if the chunks were
# always added to the end of the list. However, this is not always the case
# the indices must be updated.
# Use the address of each chunk (python `id`) as lookup keys to the
# ordered chunk indices.
chunk_indices = {id(chunk): i for i, chunk in enumerate(self._chunks)}
to_fix = [self._chunked_message]
while to_fix:
for field in to_fix.pop().chunked_fields:
if field.message.chunked_fields:
to_fix.append(field.message)
if not field.message.HasField("chunk_index"):
continue
chunk_addr = self._add_chunk_order[field.message.chunk_index]
assert (
chunk_addr in chunk_indices
), f"Found unexpected chunk {chunk_addr}"
new_chunk_index = chunk_indices[chunk_addr]
field.message.chunk_index = new_chunk_index
self._add_chunk_order = [id(chunk) for chunk in self._chunks]
self._fix_chunk_order = False
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@tools@proto_splitter@split.py@.PATH_END.py
|
{
"filename": "tools.py",
"repo_name": "ladsantos/p-winds",
"repo_path": "p-winds_extracted/p-winds-main/p_winds/tools.py",
"type": "Python"
}
|
#! /usr/bin/env python
# -*- coding: utf-8 -*-
"""
This module contains useful tools to facilitate numerical calculations.
"""
from __future__ import (division, print_function, absolute_import,
unicode_literals)
import numpy as np
import astropy.units as u
import os
from warnings import warn
from astropy.io import fits
__all__ = ["nearest_index", "standard_spectrum", "generate_muscles_spectrum",
"make_spectrum_from_file"]
# Find $PWINDS_REFSPEC_DIR environment variable
try:
_PWINDS_REFSPEC_DIR = os.environ["PWINDS_REFSPEC_DIR"]
except KeyError:
_PWINDS_REFSPEC_DIR = None
warn("Environment variable PWINDS_REFSPEC_DIR is not set.")
def nearest_index(array, target_value):
"""
Finds the index of a value in ``array`` that is closest to ``target_value``.
Parameters
----------
array : ``numpy.array``
Target array.
target_value : ``float``
Target value.
Returns
-------
index : ``int``
Index of the value in ``array`` that is closest to ``target_value``.
"""
index = array.searchsorted(target_value)
index = np.clip(index, 1, len(array) - 1)
left = array[index - 1]
right = array[index]
index -= target_value - left < right - target_value
return index
def standard_spectrum(stellar_type, semi_major_axis,
reference_spectra_dir=_PWINDS_REFSPEC_DIR,
stellar_radius=None, truncate_wavelength_grid=False,
cutoff_thresh=13.6):
"""
Construct a dictionary containing an input spectrum for a given spectral
type. The code scales this to the spectrum received at your planet provided
a value for the scaled ``semi_major_axis``.
Spectrum of iota Horologii was kindly provided by Jorge Sanz-Forcada (priv.
comm.). Spectrum of HD 108147 and HR 8799 were obtained from the
X-exoplanets database and combined with PHOENIX atmosphere models for the
NUV. Solar spectrum comes from the Whole Heliosphere Interval (WHI)
Reference Spectra obtained from the LASP Interactive Solar Irradiance
Datacenter. All other spectra are from the MUSCLES survey.
Parameters
----------
stellar_type : ``str``
Define the stellar type. The available options are:
- ``'mid-A'`` (based on HR 8799)
- ``'early-F'`` (based on WASP-17)
- ``'late-F'`` (based on HD 108147)
- ``'early-G'`` (based on HD 149026)
- ``'solar'`` (based on the Sun)
- ``'young-Sun'`` (based on iota Horologii)
- ``'late-G'`` (based on TOI-193)
- ``'active-K'`` (based on epsilon Eridanii)
- ``'early-K'`` (based on HD 97658)
- ``'late-k'`` (based on WASP-43)
- ``'active-M'`` (based on Proxima Centauri)
- ``'early-M'`` (based on GJ 436)
- ``'late-M'`` (based on TRAPPIST-1)
semi_major_axis : ``float``
Semi-major axis of the planet in units of stellar radii. The code first
converts the MUSCLES spectrum to what it would be at R_star;
``semi_major_axis`` is needed to get the spectrum at the planet.
reference_spectra_dir : ``str``, optional
Path to the directory with the MUSCLES data. Default value is defined by
the environment variable ``$PWINDS_REFSPEC_DIR``.
stellar_radius : ``float``, optional
Stellar radius in unit of solar radii. Setting a value for this
parameter allows the spectrum to be scaled to an arbitrary stellar
radius instead of the radius of the MUSCLES star. If ``None``, then the
scaling is performed using the radius of the MUSCLES star. Default is
``None``.
truncate_wavelength_grid : ``bool``, optional
If ``True``, will only return the spectrum with energy > 13.6 eV. This
may be useful for computational expediency. If False, returns the whole
spectrum. Default is ``False``.
cutoff_thresh : ``float``, optional
If ``truncate_wavelength_grid`` is set to ``True``, then the truncation
happens for energies whose value in eV is above this threshold, also in
eV. Default is ``13.6``.
Returns
-------
spectrum : ``dict``
Spectrum dictionary with entries for the wavelength and flux, and their
units.
"""
muscles_match = {'early-A': None, 'late-A': None, 'early-F': 'wasp-17',
'late-F': None, 'early-G': 'hd-149026',
'late-G': 'toi-193', 'solar': None, 'young-Sun': None,
'active-K': 'v-eps-eri', 'early-K': 'hd97658',
'late-K': 'wasp-43', 'active-M': 'gj551',
'early-M': 'gj436', 'late-M': 'trappist-1'}
try:
spectrum = generate_muscles_spectrum(muscles_match[stellar_type],
semi_major_axis,
reference_spectra_dir,
stellar_radius,
truncate_wavelength_grid,
cutoff_thresh)
except KeyError:
prefix = reference_spectra_dir
# Check if prefix has a trailing forward slash
if prefix[-1] == '/':
pass
# If not, add it
else:
prefix += '/'
if stellar_type == 'solar':
spectrum_array = np.loadtxt(
prefix + 'ref_solar_irradiance_whi-2008_ver2.dat', skiprows=142,
usecols=(0, 2))
i1 = nearest_index(spectrum_array[:, 0], 300)
wavelength = (spectrum_array[:i1, 0] * u.nm).to(u.angstrom).value
flux = (spectrum_array[:i1, 1] * u.W / u.m ** 2 / u.nm).to(
u.erg / u.s / u.cm ** 2 / u.angstrom).value
r_star_origin = 1.00 * u.solRad
dist = 1 * u.au
elif stellar_type == 'young-Sun':
spectrum_array = np.loadtxt(prefix + 'spec_hr810_1au.dat')
wavelength = spectrum_array[:, 0]
flux = spectrum_array[:, 1]
r_star_origin = 1.00 * u.solRad # Assumption
dist = 1 * u.au
elif stellar_type == 'mid-A':
spectrum_array = np.loadtxt(prefix + 'spec_hr8799_1au.dat')
wavelength = spectrum_array[:, 0]
flux = spectrum_array[:, 1]
r_star_origin = 1.44 * u.solRad # From Gaia DR2 for HR 8799
dist = 1 * u.au
elif stellar_type == 'late-F':
spectrum_array = np.loadtxt(prefix + 'spec_hd108147_1au.dat')
wavelength = spectrum_array[:, 0]
flux = spectrum_array[:, 1]
r_star_origin = 1.23 * u.solRad # From Gaia DR2 for HD 108147
dist = 1 * u.au
else:
raise ValueError('Specified stellar type not recognized')
if stellar_radius is None:
r_star = r_star_origin
else:
r_star = stellar_radius * u.solRad
conv = float((dist / r_star) ** 2) # conversion to
# spectrum at R_star
spectrum = {'wavelength': wavelength,
'flux_lambda': flux * conv * semi_major_axis ** (-2),
'wavelength_unit': u.AA,
'flux_unit': u.erg / u.s / u.cm / u.cm / u.AA}
return spectrum
def generate_muscles_spectrum(host_star_name, semi_major_axis,
reference_spectra_dir=_PWINDS_REFSPEC_DIR,
stellar_radius=None,
truncate_wavelength_grid=False,
cutoff_thresh=13.6):
"""
Construct a dictionary containing an input spectrum from a MUSCLES spectrum.
MUSCLES reports spectra as observed at Earth, the code scales this to the
spectrum received at your planet provided a value for the scaled
``semi-major-axis``.
Parameters
----------
host_star_name : ``str``
Name of the MUSCLES stellar spectrum you want to use. Must be one of:
['gj176', 'gj436', 'gj551', 'gj581', 'gj667c', 'gj832', 'gj876',
'gj1214', 'hd40307', 'hd85512', 'hd97658', 'v-eps-eri', 'gj1132',
'hat-p-12', 'hat-p-26', 'hd-149026', 'l-98-59', 'l-678-39', 'l-980-5',
'lhs-2686', 'lp-791-18', 'toi-193', 'trappist-1', 'wasp-17', 'wasp-43',
'wasp-77a', 'wasp-127'].
semi_major_axis : ``float``
Semi-major axis of the planet in units of stellar radii. The code first
converts the MUSCLES spectrum to what it would be at R_star;
``semi_major_axis`` is needed to get the spectrum at the planet.
reference_spectra_dir : ``str``, optional
Path to the directory with the reference spectra. Default value is
defined by the environment variable ``$PWINDS_REFSPEC_DIR``.
stellar_radius : ``float``, optional
Stellar radius in unit of solar radii. Setting a value for this
parameter allows the spectrum to be scaled to an arbitrary stellar
radius instead of the radius of the MUSCLES star. If ``None``, then the
scaling is performed using the radius of the MUSCLES star. Default is
``None``.
truncate_wavelength_grid : ``bool``, optional
If ``True``, will only return the spectrum with energy > 13.6 eV. This
may be useful for computational expediency. If False, returns the whole
spectrum. Default is ``False``.
cutoff_thresh : ``float``, optional
If ``truncate_wavelength_grid`` is set to ``True``, then the truncation
happens for energies whose value in eV is above this threshold, also in
eV. Default is ``13.6``.
Returns
-------
spectrum : ``dict``
Spectrum dictionary with entries for the wavelength and flux, and their
units.
"""
# Hard coding some values
# The stellar radii and distances are taken from NASA Exoplanet Archive.
thresh = cutoff_thresh * u.eV
stars = [
# Old ones
'gj176', 'gj436', 'gj551', 'gj581', 'gj667c', 'gj832', 'gj876',
'gj1214', 'hd40307', 'hd85512', 'hd97658', 'v-eps-eri',
# New ones
#'gj15a', 'gj163', 'gj649', 'gj674', 'gj676a', 'gj699', 'gj729', 'gj849',
'gj1132', 'hat-p-12', 'hat-p-26', 'hd-149026', 'l-98-59', 'l-678-39',
'l-980-5', 'lhs-2686', 'lp-791-18', 'toi-193', 'trappist-1', 'wasp-17',
'wasp-43', 'wasp-77a', 'wasp-127'
]
versions = np.array([
# Old ones
'v22', 'v22', 'v22', 'v22', 'v22', 'v22', 'v22',
'v22', 'v22', 'v22', 'v22', 'v22',
# New ones
#'v23', 'v23', 'v23', 'v23', 'v23', 'v23', 'v23', 'v23',
'v23', 'v24', 'v24', 'v24', 'v24', 'v24',
'v23', 'v23', 'v24', 'v24', 'v23', 'v24',
'v24', 'v24', 'v24'
])
st_rads = np.array([
# Old ones
0.46, 0.449, 0.154, 0.3297020, 0.42, 0.45, 0.35, 0.22,
0.71, 0.69, 0.74, 0.77,
# New ones
#
0.21, 0.7, 0.87, 1.41, 0.3, 0.34,
0.22, # L 980-5 radius assumed to be the same as GJ 1214
0.449, # LHS 2686 radius assumed to be the same as GJ 436
0.18, 0.95, 0.12, 1.49, 0.6, 0.910, 1.33
]) * u.solRad
dists = np.array([
# Old ones
9.470450, 9.75321, 1.30119, 6.298100, 7.24396, 4.964350,
4.67517, 14.6427, 12.9363, 11.2810, 21.5618,
3.20260,
# New ones
#3.56244, 15.1353,
12.613, 142.751, 141.837, 75.8643, 10.6194, 9.44181, 13.3731, 12.1893,
26.4927, 80.4373, 12.4298888, 405.908, 86.7467, 105.6758, 159.507
]) * u.pc
muscles_dists = {starname: dist for starname, dist in zip(stars, dists)}
muscles_rstars = {starname: st_rad for starname, st_rad in zip(stars,
st_rads)}
muscles_versions = {starname: versions for starname, versions in zip(stars,
versions)}
# MUSCLES records spectra as observed at earth, so we need to convert it to
# spectrum at R_star. The user has the option of setting an arbitary stellar
# radius instead of the MUSCLES star radius to allow for more flexibility.
# This can be especially useful for slightly evolved stars, whose radius
# are larger than the MUSCLES stars.
dist = muscles_dists[host_star_name]
vnumber = muscles_versions[host_star_name]
if stellar_radius is None:
rstar = muscles_rstars[host_star_name]
else:
rstar = stellar_radius * u.solRad
conv = float((dist / rstar) ** 2) # conversion to spectrum at R_star
# First check if reference_spectra_dir has a trailing forward slash
if reference_spectra_dir[-1] == '/':
pass
# If not, add it
else:
reference_spectra_dir += '/'
# Read the MUSCLES spectrum
spec = fits.getdata(reference_spectra_dir +
f'hlsp_muscles_multi_multi_{host_star_name}_broadband_'
f'{vnumber}_adapt-const-res-sed.fits',
1)
if truncate_wavelength_grid:
mask = spec['WAVELENGTH'] * u.AA < thresh.to(u.AA,
equivalencies=u.spectral())
else:
mask = np.ones(spec.shape, dtype='bool')
spectrum = {'wavelength': spec['WAVELENGTH'][mask],
'flux_lambda': spec['FLUX'][mask] * conv *
semi_major_axis ** (-2),
'wavelength_unit': u.AA,
'flux_unit': u.erg / u.s / u.cm / u.cm / u.AA}
return spectrum
def make_spectrum_from_file(filename, units, path='', skiprows=0,
scale_flux=1.0, star_distance=None,
semi_major_axis=None):
"""
Construct a dictionary containing an input spectrum from a text file. The
input file must have two or more columns, in which the first is the
wavelength or frequency bin center and the second is the flux per bin of
wavelength or frequency. The code automatically figures out if the input
spectra are binned in wavelength or frequency based on the units the user
passes.
Parameters
----------
filename : ``str``
Name of the file containing the spectrum data.
units : ``dict``
Units of the spectrum. This dictionary must have the entries
``'wavelength'`` and ``'flux'``, or ``'frequency'`` and ``'flux'``.
The units must be set in ``astropy.units``.
path : ``str``, optional
Path to the spectrum data file.
skiprows : ``int``, optional
Number of rows to skip corresponding to the header of the input text
file.
scale_flux : ``float``, optional
Scaling factor for flux. Default value is 1.0 (no scaling).
star_distance : ``float`` or ``None``, optional
Distance to star in unit of parsec. This is used to scale the flux as
observed from Earth to the semi-major axis of the planet. If ``None``,
no scaling is applied. If not ``None``, then a value``semi_major_axis``
must be provided as well. Default is ``None``.
semi_major_axis : ``float`` or ``None``, optional
Semi-major axis of the planet in unit of au. This is used to scale the
flux as observed from Earth to the semi-major axis of the planet. Notice
that this parameter is different from the
``generate_muscles_spectrum()`` function, which uses the semi-major
axis in unit of stellar radii. If ``None``, no scaling is applied. If
not ``None``, then a value``star_distance`` must be provided as well.
Default is ``None``.
Returns
-------
spectrum : ``dict``
Spectrum dictionary with entries for the wavelength and flux, and their
units.
"""
spectrum_table = np.loadtxt(path + filename, usecols=(0, 1),
skiprows=skiprows, dtype=float)
try:
x_axis = 'wavelength'
x_axis_unit = units.pop(x_axis)
y_axis = 'flux_lambda'
except KeyError:
x_axis = 'frequency'
x_axis_unit = units.pop(x_axis)
y_axis = 'flux_nu'
y_axis_unit = units.pop('flux')
conv_pc_to_au = 206264.8062471 # Conversion from pc to au
if star_distance is not None and semi_major_axis is not None:
scale_to_planet = \
(star_distance * conv_pc_to_au / semi_major_axis) ** 2
else:
scale_to_planet = 1.0
spectrum = {x_axis: spectrum_table[:, 0],
y_axis: spectrum_table[:, 1] * scale_flux * scale_to_planet,
'{}_unit'.format(x_axis): x_axis_unit,
'flux_unit': y_axis_unit}
return spectrum
|
ladsantosREPO_NAMEp-windsPATH_START.@p-winds_extracted@p-winds-main@p_winds@tools.py@.PATH_END.py
|
{
"filename": "param_format.py",
"repo_name": "a-griffiths/AutoSpec",
"repo_path": "AutoSpec_extracted/AutoSpec-master/param_format.py",
"type": "Python"
}
|
# Default configuration file for AutoSpec v 1.1.2
# DATE: 20-09-2018
# ----------- Operating Mode -------------------------------------------------------------------------------------------------------------------------------------------------------------
MODE = 'param' # 'param' or 'cat' to use configuration or catalogue file respectively for extraction mode (string).
# ----------- Reference Spectra -----------------------------------------------------------------------------------------------------------------------------------------------------------
REF = '' # reference spectrum to use for cross correlation, must also be in image (IMG), aperture (APER) parameters or '' to use white light image.
# ----------- Required Files --------------------------------------------------------------------------------------------------------------------------------------------------------------
DATACUBE = '' # name of datacube file (string).
CATALOG = '' # name of catalog file (string).
# ----------- Datacube Extension: Use if datacube extensions are not specified in fits headers --------------------------------------------------------------------------------------------
DATA_EXT = () # The number/name of the data (int or str), or data and variance extensions (int, int or str, str), () if none.
# ----------- Spectral Extractions --------------------------------------------------------------------------------------------------------------------------------------------------------
APER = '' # aperture sizes (in arcseconds) for spectrum extraction (float or array or floats).
IMG = '' # name of additional image files for weighted spectra and segmentation extractions (string or comma seperated list of strings), '' if none.
# ----------- Object Masks ----------------------------------------------------------------------------------------------------------------------------------------------------------------
USE_IMGS = True # if AutoSpec should also use the segmentation maps created from the images in IMG parameter to create final masks (True or False).
OBJ_MASK = 'INTER' # object mask from union (UNION) or intersection (INTER) of segmentation maps (string).
SEG = '' # name of additional segmentation maps files to be used (string or comma seperated list of strings).
# ----------- Output Formatting -----------------------------------------------------------------------------------------------------------------------------------------------------------
OUTPUT = 'output' # name of output directory (string).
PRE_OUT = 'Source_' # string to prepend to output data files (string), '' if none.
# ----------- Extraction Parameters -------------------------------------------------------------------------------------------------------------------------------------------------------
SIZE = 5 # sub image/cube extraction size in arcseconds (float).
XCOR = True # perform cross correlation (True or False).
CONT_SUB = True # preform continuum subtraction, only runs if XCOR is True (True or False).
CONT_POLY = 5 # degree of polynomial for contiuum fitting (integer).
SKY_SUB = True # Perform sky subtraction when extracting spectra (True or False).
PLOTS = True # output plots (True or False).
# ----------- Outputs ---------------------------------------------------------------------------------------------------------------------------------------------------------------------
OUT_SUB = False # output extracted source subcubes (True or False), these make up the bulk of the output filesize.
OUT_IMG = True # output extracted source images (True or False).
OUT_SEG = True # output segmentation maps (True or False).
OUT_MASK = True # output object and sky masks (True or False).
OUT_XCOR = True # output cross-correlation map (True or False).
OUT_SPEC = True # output additional spectra (True) or final only (False).
# ----------- MISCELLANEOUS ---------------------------------------------------------------------------------------------------------------------------------------------------------------
CMAP = 'viridis' # colour map to use for image plots.
ORIG_FROM = '' # name of the detector software which creates this object (string).
ORIG_FROMV = '' # version of the detector software which creates this object (string).
ORIG_CUBE = '' # name of the FITS data cube from which this object has been extracted (string).
ORIGN_CUBEV = '' # version of the FITS data cube (string).
WARNINGS = False # turn warnings on (True) or off (False).
|
a-griffithsREPO_NAMEAutoSpecPATH_START.@AutoSpec_extracted@AutoSpec-master@param_format.py@.PATH_END.py
|
{
"filename": "another_test.ipynb",
"repo_name": "LucaMalavolta/PyORBIT",
"repo_path": "PyORBIT_extracted/PyORBIT-main/development/spleaf/another_test.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(0)
# Settings
P0 = 3.8
dP = 1.25
tmax = 20
amp = [6.0, 2.0, 0.33]
phase = [0, np.pi / 2, -3*np.pi / 4]
nt = [75, 100, 50]
# True signal
tsmooth = np.linspace(0, tmax, 400)
Psmooth = P0 + dP * (tsmooth / tmax - 1 / 2)
Ysignal = [
ak * np.sin(2 * np.pi * tsmooth / Psmooth + pk)
for ak, pk in zip(amp, phase)
]
# Generate observations calendars
T = [
np.sort(
np.concatenate((np.random.uniform(0, tmax / 2,
ntk // 2), np.random.uniform(2 * tmax / 3.5 , tmax, (ntk + 1) // 2))))
for ntk in nt
]
# Generate measurements with white noise
Yerr = [np.random.uniform(0.5, 1.5, ntk) for ntk in nt]
P = [P0 + dP * (tk / tmax - 1 / 2) for tk in T]
Y = [
amp[k] * np.sin(2 * np.pi * T[k] / P[k] + phase[k]) +
np.random.normal(0, Yerr[k]) for k in range(3)
]
# Plot
_, axs = plt.subplots(3, 1, sharex=True, figsize=(6, 10))
for k in range(3):
ax = axs[k]
ax.plot(tsmooth, Ysignal[k], 'r', label='truth')
ax.errorbar(T[k], Y[k], Yerr[k], fmt='.', color='k', label='meas.')
ax.set_ylabel(f'$y_{k}$')
ax.set_xlabel('$t$')
axs[0].legend()
```
<matplotlib.legend.Legend at 0x7f600b951210>
```python
from spleaf import cov, term
from scipy.optimize import fmin_l_bfgs_b
# Merge all 3 time series
t_full, y_full, yerr_full, series_index = cov.merge_series(T, Y, Yerr)
# Initialize the S+LEAF model
C = cov.Cov(t_full,
err=term.Error(yerr_full),
GP=term.MultiSeriesKernel(term.ESPKernel(1.0, 3.8, 1000.0, 0.35, nharm=4), series_index,
[6.0, 2.0, 0.33], np.ones(3)))
D = cov.Cov(t_full,
err=term.Error(yerr_full),
GP=term.MultiSeriesKernel(term.ESPKernel(1.0, 3.8, 1000.0, 0.35, nharm=4), series_index,
[6.0, 2.0, 2.33], np.ones(3)))
# Fit the hyperparameters using the fmin_l_bfgs_b function from scipy.optimize.
# List of parameters to fit
param = C.param[1:]
# The amplitude of the SHOKernel is fixed at 1 (not fitted),
# since it would be degenerated with the amplitudes alpha, \beta.
# Define the function to minimize
def negloglike(x, y, C):
C.set_param(x, param)
nll = -C.loglike(y)
# gradient
nll_grad = -C.loglike_grad()[1][1:]
return (nll, nll_grad)
# Fit
xbest, _, _ = fmin_l_bfgs_b(negloglike, C.get_param(param), args=(y_full, C))
# Use S+LEAF to predict the missing data
C.set_param(xbest, param)
_, axs = plt.subplots(3, 1, sharex=True, figsize=(6, 10))
for k in range(3):
# Predict time series k
C.kernel['GP'].set_conditional_coef(series_id=k)
mu_fit, var_fit = C.conditional(y_full, tsmooth, calc_cov='diag')
# Plot
ax = axs[k]
ax.plot(tsmooth, Ysignal[k], 'r', label='truth')
ax.errorbar(T[k], Y[k], Yerr[k], fmt='.', color='k', label='meas.')
ax.fill_between(tsmooth,
mu_fit - np.sqrt(var_fit),
mu_fit + np.sqrt(var_fit),
color='g',
alpha=0.5)
ax.plot(tsmooth, mu_fit, 'g', label='predict.')
ax.set_ylabel(f'$y_{k}$')
ax.set_xlabel('$t$')
axs[0].legend()
plt.show()
```
```python
print(xbest)
mod_best = xbest.copy()
mod_best[-2] = 12.99
print(mod_best)
```
[ 4.64977774e+00 9.99970528e+02 5.28794981e-01 1.86540839e+00
-8.90565869e-01 6.12527349e-02 2.35564873e+00 4.31216248e-01
-1.31963919e-01]
[ 4.64977774e+00 9.99970528e+02 5.28794981e-01 1.86540839e+00
-8.90565869e-01 6.12527349e-02 2.35564873e+00 1.29900000e+01
-1.31963919e-01]
```python
# Use S+LEAF to predict the missing data
D.set_param(mod_best, param)
_, axs = plt.subplots(3, 1, sharex=True, figsize=(6, 10))
for k in range(3):
# Predict time series k
D.kernel['GP'].set_conditional_coef(series_id=k)
mu_mod, var_mod = D.conditional(y_full, tsmooth, calc_cov='diag')
# Plot
ax = axs[k]
ax.plot(tsmooth, Ysignal[k], 'r', label='truth')
ax.errorbar(T[k], Y[k], Yerr[k], fmt='.', color='k', label='meas.')
ax.fill_between(tsmooth,
mu_mod - np.sqrt(var_mod),
mu_mod + np.sqrt(var_mod),
color='g',
alpha=0.5)
ax.plot(tsmooth, mu_mod, 'g', label='predict.')
ax.set_ylabel(f'$y_{k}$')
ax.set_xlabel('$t$')
axs[0].legend()
plt.show()
```
```python
```
```python
```
```python
```
```python
import sys
import jax
jax.config.update("jax_enable_x64", True)
import jax.numpy as jnp
from tinygp import kernels, GaussianProcess
#from tinygp.helpers import JAXArray
if sys.version_info[1] < 10:
raise Warning("You should be using Python 3.10 - tinygp may not work")
class LatentKernel(kernels.Kernel):
"""A custom kernel based on Rajpaul et al. (2015)
Args:
kernel: The kernel function describing the latent process. This can be any other
``tinygp`` kernel.
coeff_prim: The primal coefficients for each class. This can be thought of as how
much the latent process itself projects into the observations for that class.
This should be an array with an entry for each class of observation.
coeff_deriv: The derivative coefficients for each class. This should have the same
shape as ``coeff_prim``.
"""
try:
kernel : kernels.Kernel
coeff_prim: jax.Array | float
coeff_deriv: jax.Array | float
except:
pass
def __init__(self, kernel, coeff_prim, coeff_deriv):
self.kernel = kernel
self.coeff_prim, self.coeff_deriv = jnp.broadcast_arrays(
jnp.asarray(coeff_prim), jnp.asarray(coeff_deriv)
)
def evaluate(self, X1, X2):
t1, label1 = X1
t2, label2 = X2
# Differentiate the kernel function: the first derivative wrt x1
Kp = jax.grad(self.kernel.evaluate, argnums=0)
# ... and the second derivative
Kpp = jax.grad(Kp, argnums=1)
# Evaluate the kernel matrix and all of its relevant derivatives
K = self.kernel.evaluate(t1, t2)
d2K_dx1dx2 = Kpp(t1, t2)
# For stationary kernels, these are related just by a minus sign, but we'll
# evaluate them both separately for generality's sake
dK_dx2 = jax.grad(self.kernel.evaluate, argnums=1)(t1, t2)
dK_dx1 = Kp(t1, t2)
# Extract the coefficients
a1 = self.coeff_prim[label1]
a2 = self.coeff_prim[label2]
b1 = self.coeff_deriv[label1]
b2 = self.coeff_deriv[label2]
# Construct the matrix element
return (
a1 * a2 * K
+ a1 * b2 * dK_dx2
+ b1 * a2 * dK_dx1
+ b1 * b2 * d2K_dx1dx2
)
def _build_tinygp_multidimensional(params):
base_kernel = kernels.ExpSquared(scale=jnp.abs(params["Pdec"])) \
* kernels.ExpSineSquared(
scale=jnp.abs(params["Prot"]),
gamma=jnp.abs(params["gamma"]))
kernel = LatentKernel(base_kernel, params['coeff_prime'], params['coeff_deriv'])
return GaussianProcess(
kernel, params['X'], diag=jnp.abs(params['diag']), mean=0.0
)
@jax.jit
def _loss_tinygp(params):
gp = _build_tinygp_multidimensional(params)
return gp.log_probability(params['y'])
```
```python
dataset_x0 = []
dataset_res = []
dataset_label = []
dataser_er2 = []
temp_input = []
temp_label = []
for ii in range(0, 3):
temp_input = np.append(temp_input, tsmooth)
temp_label = np.append(temp_label, np.zeros_like(tsmooth, dtype=int) + ii)
X_input = (temp_input, temp_label.astype(int))
for k in range(0,3):
dataset_x0 = np.append(dataset_x0, T[k])
dataset_label = np.append(dataset_label, np.zeros_like(T[k], dtype=int) + k)
dataset_res = np.append(dataset_res, Y[k])
dataser_er2 = np.append(dataser_er2, Yerr[k]**2)
tinygp_X = (dataset_x0, dataset_label.astype(int))
```
```python
internal_parameter_values = xbest.copy()
theta_dict = dict(
gamma=1. / (2.*internal_parameter_values[2] ** 2),
Pdec=internal_parameter_values[1],
Prot=internal_parameter_values[0],
diag=dataser_er2,
X=tinygp_X,
y=dataset_res,
coeff_prime=internal_parameter_values[3:6],
coeff_deriv=internal_parameter_values[6:],
x_predict = X_input
)
gp = _build_tinygp_multidimensional(theta_dict)
_, cond_gp = gp.condition(theta_dict['y'], theta_dict['x_predict'])
#mu = cond_gp.mean
#std = np.sqrt(cond_gp.variance)
mu_full = cond_gp.loc # or cond_gp.mean?
```
```python
internal_parameter_values = mod_best.copy()
theta_dict = dict(
gamma=1. / (2.*internal_parameter_values[2] ** 2),
Pdec=internal_parameter_values[1],
Prot=internal_parameter_values[0],
diag=dataser_er2,
X=tinygp_X,
y=dataset_res,
coeff_prime=internal_parameter_values[3:6],
coeff_deriv=internal_parameter_values[6:],
x_predict = X_input
)
gp = _build_tinygp_multidimensional(theta_dict)
_, cond_gp_mod = gp.condition(theta_dict['y'], theta_dict['x_predict'])
#mu = cond_gp.mean
#std = np.sqrt(cond_gp.variance)
mu_full_mod = cond_gp_mod.loc # or cond_gp.mean?
```
```python
_, axs = plt.subplots(3, 1, sharex=True, figsize=(6, 10))
for k in range(3):
# Predict time series k
C.kernel['GP'].set_conditional_coef(series_id=k)
mu_fit, var_fit = C.conditional(y_full, tsmooth, calc_cov='diag')
# Predict time series k
l_nstart, l_nend = len(tsmooth)*k, len(tsmooth)*(k+1)
tinygp_mu = mu_full[l_nstart:l_nend]
tinygp_std = np.sqrt(cond_gp.variance)[l_nstart:l_nend]
# Plot
ax = axs[k]
ax.plot(tsmooth, Ysignal[k], 'r', label='truth')
ax.errorbar(T[k], Y[k], Yerr[k], fmt='.', color='k', label='meas.')
ax.fill_between(tsmooth,
mu_fit - np.sqrt(var_fit),
mu_fit + np.sqrt(var_fit),
color='g',
alpha=0.5)
ax.plot(tsmooth, mu_fit, 'g', label='predict.')
ax.fill_between(tsmooth,
tinygp_mu - tinygp_std,
tinygp_mu + tinygp_std,
color='C4',
alpha=0.5)
ax.plot(tsmooth, tinygp_mu, 'C5', label='tinygp.')
ax.set_ylabel(f'$y_{k}$')
ax.set_xlabel('$t$')
axs[0].legend()
plt.show()
```
```python
_, axs = plt.subplots(3, 1, sharex=True, figsize=(6, 10))
for k in range(3):
# Predict time series k
D.kernel['GP'].set_conditional_coef(series_id=k)
mu_mod, var_mod = D.conditional(y_full, tsmooth, calc_cov='diag')
# Predict time series k
l_nstart, l_nend = len(tsmooth)*k, len(tsmooth)*(k+1)
tinygp_mu_mod = mu_full_mod[l_nstart:l_nend]
tinygp_std_mod = np.sqrt(cond_gp_mod.variance)[l_nstart:l_nend]
# Plot
ax = axs[k]
ax.plot(tsmooth, Ysignal[k], 'r', label='truth')
ax.errorbar(T[k], Y[k], Yerr[k], fmt='.', color='k', label='meas.')
ax.fill_between(tsmooth,
mu_mod - np.sqrt(var_mod),
mu_mod + np.sqrt(var_mod),
color='g',
alpha=0.5)
ax.plot(tsmooth, mu_mod, 'g', label='predict.')
ax.fill_between(tsmooth,
tinygp_mu_mod - tinygp_std_mod,
tinygp_mu_mod + tinygp_std_mod,
color='C4',
alpha=0.5)
ax.plot(tsmooth, tinygp_mu_mod, 'C5', label='tinygp')
ax.set_ylabel(f'$y_{k}$')
ax.set_xlabel('$t$')
axs[0].legend()
plt.show()
```
```python
# Initialize the S+LEAF model
PP = cov.Cov(t_full,
err=term.Error(yerr_full),
GP=term.MultiSeriesKernel(term.ESPKernel(1.0, xbest[0], xbest[1], xbest[2], nharm=4), series_index,
[xbest[3], xbest[4], xbest[5]], [xbest[6], xbest[7], xbest[8]]))
_, axs = plt.subplots(3, 1, sharex=True, figsize=(6, 10))
for k in range(3):
PP.kernel['GP'].set_conditional_coef(series_id=k)
mu_pp, var_pp = PP.conditional(y_full, tsmooth, calc_cov='diag')
# Predict time series k
C.kernel['GP'].set_conditional_coef(series_id=k)
mu_fit, var_fit = C.conditional(y_full, tsmooth, calc_cov='diag')
# Predict time series k
l_nstart, l_nend = len(tsmooth)*k, len(tsmooth)*(k+1)
tinygp_mu = mu_full[l_nstart:l_nend]
tinygp_std = np.sqrt(cond_gp.variance)[l_nstart:l_nend]
# Plot
ax = axs[k]
ax.plot(tsmooth, Ysignal[k], 'r', label='truth')
ax.errorbar(T[k], Y[k], Yerr[k], fmt='.', color='k', label='meas.')
#ax.fill_between(tsmooth,
# mu_fit - np.sqrt(var_fit),
# mu_fit + np.sqrt(var_fit),
# color='g',
# alpha=0.5)
#ax.plot(tsmooth, mu_fit, 'g', label='predict.')
ax.fill_between(tsmooth,
mu_pp - np.sqrt(var_pp),
mu_pp + np.sqrt(var_pp),
color='C6',
alpha=0.5)
ax.plot(tsmooth, mu_pp, 'C7', label='predict.')
ax.fill_between(tsmooth,
tinygp_mu - tinygp_std,
tinygp_mu + tinygp_std,
color='C4',
alpha=0.5)
ax.plot(tsmooth, tinygp_mu, 'C5', label='tinygp.')
ax.set_ylabel(f'$y_{k}$')
ax.set_xlabel('$t$')
axs[0].legend()
plt.show()
```
```python
```
|
LucaMalavoltaREPO_NAMEPyORBITPATH_START.@PyORBIT_extracted@PyORBIT-main@development@spleaf@another_test.ipynb@.PATH_END.py
|
{
"filename": "planck_lite_py.py",
"repo_name": "heatherprince/planck-lite-py",
"repo_path": "planck-lite-py_extracted/planck-lite-py-master/planck_lite_py.py",
"type": "Python"
}
|
'''
Python version of Planck's plik-lite likelihood with the option to include
the low-ell temperature as two Gaussian bins
The official Planck likelihoods are availabe at https://pla.esac.esa.int/
The papers describing the Planck likelihoods are
Planck 2018: https://arxiv.org/abs/1907.12875
Planck 2015: https://arxiv.org/abs/1507.02704
The covariance matrix treatment is based on Zack Li's ACT likelihood code
available at: https://github.com/xzackli/actpols2_like_py
planck calibration is set to 1 by default but this can easily be modified
'''
import numpy as np
from scipy.io import FortranFile
import scipy.linalg
def main():
TTTEEE2018=PlanckLitePy(year=2018, spectra='TTTEEE', use_low_ell_bins=False)
TTTEEE2018.test()
TTTEEE2018_lowTTbins=PlanckLitePy(year=2018, spectra='TTTEEE', use_low_ell_bins=True)
TTTEEE2018_lowTTbins.test()
TT2018=PlanckLitePy(year=2018, spectra='TT', use_low_ell_bins=False)
TT2018.test()
TT2018_lowTTbins=PlanckLitePy(year=2018, spectra='TT', use_low_ell_bins=True)
TT2018_lowTTbins.test()
class PlanckLitePy:
def __init__(self, data_directory='data', year=2018, spectra='TT', use_low_ell_bins=False):
'''
data_directory = path from where you are running this to the folder
containing the planck2015/8_low_ell and planck2015/8_plik_lite data
year = 2015 or 2018
spectra = TT for just temperature or TTTEEE for temperature (TT),
E mode (EE) and cross (TE) spectra
use_low_ell_bins = True to use 2 low ell bins for the TT 2<=ell<30 data
or False to only use ell>=30
'''
self.year=year
self.spectra=spectra
self.use_low_ell_bins=use_low_ell_bins #False matches Plik_lite - just l>=30
if self.use_low_ell_bins:
self.nbintt_low_ell=2
self.plmin_TT=2
else:
self.nbintt_low_ell=0
self.plmin_TT=30
self.plmin=30
self.plmax=2508
self.calPlanck=1
if year==2015:
self.data_dir=data_directory+'/planck2015_plik_lite/'
version=18
elif year==2018:
self.data_dir=data_directory+'/planck2018_plik_lite/'
version=22
else:
print('Year must be 2015 or 2018')
return 1
if spectra=='TT':
self.use_tt=True
self.use_ee=False
self.use_te=False
elif spectra=='TTTEEE':
self.use_tt=True
self.use_ee=True
self.use_te=True
else:
print('Spectra must be TT or TTTEEE')
return 1
self.nbintt_hi = 215 #30-2508 #used when getting covariance matrix
self.nbinte = 199 #30-1996
self.nbinee = 199 #30-1996
self.nbin_hi=self.nbintt_hi+self.nbinte+self.nbinee
self.nbintt=self.nbintt_hi+self.nbintt_low_ell #mostly want this if using low ell
self.nbin_tot=self.nbintt+self.nbinte+self.nbinee
self.like_file = self.data_dir+'cl_cmb_plik_v'+str(version)+'.dat'
self.cov_file = self.data_dir+'c_matrix_plik_v'+str(version)+'.dat'
self.blmin_file = self.data_dir+'blmin.dat'
self.blmax_file = self.data_dir+'blmax.dat'
self.binw_file = self.data_dir+'bweight.dat'
# read in binned ell value, C(l) TT, TE and EE and errors
# use_tt etc to select relevant parts
self.bval, self.X_data, self.X_sig=np.genfromtxt(self.like_file, unpack=True)
self.blmin=np.loadtxt(self.blmin_file).astype(int)
self.blmax=np.loadtxt(self.blmax_file).astype(int)
self.bin_w=np.loadtxt(self.binw_file)
if self.use_low_ell_bins:
self.data_dir_low_ell=data_directory+'/planck'+str(year)+'_low_ell/'
self.bval_low_ell, self.X_data_low_ell, self.X_sig_low_ell=np.genfromtxt(self.data_dir_low_ell+'CTT_bin_low_ell_'+str(year)+'.dat', unpack=True)
self.blmin_low_ell=np.loadtxt(self.data_dir_low_ell+'blmin_low_ell.dat').astype(int)
self.blmax_low_ell=np.loadtxt(self.data_dir_low_ell+'blmax_low_ell.dat').astype(int)
self.bin_w_low_ell=np.loadtxt(self.data_dir_low_ell+'bweight_low_ell.dat')
self.bval=np.concatenate((self.bval_low_ell, self.bval))
self.X_data=np.concatenate((self.X_data_low_ell, self.X_data))
self.X_sig=np.concatenate((self.X_sig_low_ell, self.X_sig))
self.blmin_TT=np.concatenate((self.blmin_low_ell, self.blmin+len(self.bin_w_low_ell)))
self.blmax_TT=np.concatenate((self.blmax_low_ell, self.blmax+len(self.bin_w_low_ell)))
self.bin_w_TT=np.concatenate((self.bin_w_low_ell, self.bin_w))
else:
self.blmin_TT=self.blmin
self.blmax_TT=self.blmax
self.bin_w_TT=self.bin_w
self.fisher=self.get_inverse_covmat()
def get_inverse_covmat(self):
#read full covmat
f = FortranFile(self.cov_file, 'r')
covmat = f.read_reals(dtype=float).reshape((self.nbin_hi,self.nbin_hi))
for i in range(self.nbin_hi):
for j in range(i,self.nbin_hi):
covmat[i,j] = covmat[j,i]
#select relevant covmat
if self.use_tt and not(self.use_ee) and not(self.use_te):
#just tt
bin_no=self.nbintt_hi
start=0
end=start+bin_no
cov=covmat[start:end, start:end]
elif not(self.use_tt) and not(self.use_ee) and self.use_te:
#just te
bin_no=self.nbinte
start=self.nbintt_hi
end=start+bin_no
cov=covmat[start:end, start:end]
elif not(self.use_tt) and self.use_ee and not(self.use_te):
#just ee
bin_no=self.nbinee
start=self.nbintt_hi+self.nbinte
end=start+bin_no
cov=covmat[start:end, start:end]
elif self.use_tt and self.use_ee and self.use_te:
#use all
bin_no=self.nbin_hi
cov=covmat
else:
print("not implemented")
#invert high ell covariance matrix (cholesky decomposition should be faster)
fisher=scipy.linalg.cho_solve(scipy.linalg.cho_factor(cov), np.identity(bin_no))
fisher=fisher.transpose()
if self.use_low_ell_bins:
bin_no += self.nbintt_low_ell
inv_covmat_with_lo=np.zeros(shape=(bin_no, bin_no))
inv_covmat_with_lo[0:2, 0:2]=np.diag(1./self.X_sig_low_ell**2)
inv_covmat_with_lo[2:,2:]= fisher
fisher=inv_covmat_with_lo
return fisher
def loglike(self, Dltt, Dlte, Dlee, ellmin=2):
#convert model Dl's to Cls then bin them
ls=np.arange(len(Dltt))+ellmin
fac=ls*(ls+1)/(2*np.pi)
Cltt=Dltt/fac
Clte=Dlte/fac
Clee=Dlee/fac
# Fortran to python slicing: a:b becomes a-1:b
# need to subtract 1 to use 0 indexing for cl,
# then add one for weights because fortran includes top value
Cltt_bin=np.zeros(self.nbintt)
for i in range(self.nbintt):
Cltt_bin[i]=np.sum(Cltt[self.blmin_TT[i]+self.plmin_TT-ellmin:self.blmax_TT[i]+self.plmin_TT+1-ellmin]*self.bin_w_TT[self.blmin_TT[i]:self.blmax_TT[i]+1])
# bin widths and weights are the same for TT, TE and EE
Clte_bin=np.zeros(self.nbinte)
for i in range(self.nbinte):
Clte_bin[i]=np.sum(Clte[self.blmin[i]+self.plmin-ellmin:self.blmax[i]+self.plmin+1-ellmin]*self.bin_w[self.blmin[i]:self.blmax[i]+1])
# bin widths and weights are the same for TT, TE and EE
Clee_bin=np.zeros(self.nbinee)
for i in range(self.nbinee):
Clee_bin[i]=np.sum(Clee[self.blmin[i]+self.plmin-ellmin:self.blmax[i]+self.plmin+1-ellmin]*self.bin_w[self.blmin[i]:self.blmax[i]+1])
X_model=np.zeros(self.nbin_tot)
X_model[:self.nbintt]=Cltt_bin/self.calPlanck**2
X_model[self.nbintt:self.nbintt+self.nbinte]=Clte_bin/self.calPlanck**2
X_model[self.nbintt+self.nbinte:]=Clee_bin/self.calPlanck**2
Y=self.X_data-X_model
#choose relevant bits based on whether using TT, TE, EE
if self.use_tt and not(self.use_ee) and not(self.use_te):
#just tt
bin_no=self.nbintt
start=0
end=start+bin_no
diff_vec=Y[start:end]
elif not(self.use_tt) and not(self.use_ee) and self.use_te:
#just te
bin_no=self.nbinte
start=self.nbintt
end=start+bin_no
diff_vec=Y[start:end]
elif not(self.use_tt) and self.use_ee and not(self.use_te):
#just ee
bin_no=self.nbinee
start=self.nbintt+self.nbinte
end=start+bin_no
diff_vec=Y[start:end]
elif self.use_tt and self.use_ee and self.use_te:
#use all
bin_no=self.nbin_tot
diff_vec=Y
else:
print("not implemented")
return -0.5*diff_vec.dot(self.fisher.dot(diff_vec))
def test(self):
ls, Dltt, Dlte, Dlee = np.genfromtxt('data/Dl_planck2015fit.dat', unpack=True)
ellmin=int(ls[0])
loglikelihood=self.loglike(Dltt, Dlte, Dlee, ellmin)
if self.year==2018 and self.spectra=='TTTEEE' and not self.use_low_ell_bins:
print('Log likelihood for 2018 high-l TT, TE and EE:')
expected = -291.33481235418026
# Plik-lite within cobaya gives -291.33481235418003
elif self.year==2018 and self.spectra=='TTTEEE' and self.use_low_ell_bins:
print('Log likelihood for 2018 high-l TT, TE and EE + low-l TT bins:')
expected = -293.95586501795134
elif self.year==2018 and self.spectra=='TT' and not self.use_low_ell_bins:
print('Log likelihood for 2018 high-l TT:')
expected = -101.58123068722583
#Plik-lite within cobaya gives -101.58123068722568
elif self.year==2018 and self.spectra=='TT' and self.use_low_ell_bins:
print('Log likelihood for 2018 high-l TT + low-l TT bins:')
expected = -104.20228335099686
elif self.year==2015 and self.spectra=='TTTEEE' and not self.use_low_ell_bins:
print('NB: Don\'t use 2015 polarization!')
print('Log likelihood for 2015 high-l TT, TE and EE:')
expected = -280.9388125627618
# Plik-lite within cobaya gives -291.33481235418003
elif self.year==2015 and self.spectra=='TTTEEE' and self.use_low_ell_bins:
print('NB: Don\'t use 2015 polarization!')
print('Log likelihood for 2015 high-l TT, TE and EE + low-l TT bins:')
expected = -283.1905700256343
elif self.year==2015 and self.spectra=='TT' and not self.use_low_ell_bins:
print('Log likelihood for 2015 high-l TT:')
expected = -102.34403873289027
#Plik-lite within cobaya gives -101.58123068722568
elif self.year==2015 and self.spectra=='TT' and self.use_low_ell_bins:
print('Log likelihood for 2015 high-l TT + low-l TT bins:')
expected = -104.59579619576277
else:
expected=None
print('Planck-lite-py:',loglikelihood)
if(expected):
print('expected:', expected)
print('difference:', loglikelihood-expected, '\n')
if __name__=='__main__':
main()
|
heatherprinceREPO_NAMEplanck-lite-pyPATH_START.@planck-lite-py_extracted@planck-lite-py-master@planck_lite_py.py@.PATH_END.py
|
{
"filename": "_font.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/graph_objs/choropleth/legendgrouptitle/_font.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceHierarchyType as _BaseTraceHierarchyType
import copy as _copy
class Font(_BaseTraceHierarchyType):
# class properties
# --------------------
_parent_path_str = "choropleth.legendgrouptitle"
_path_str = "choropleth.legendgrouptitle.font"
_valid_props = {
"color",
"family",
"lineposition",
"shadow",
"size",
"style",
"textcase",
"variant",
"weight",
}
# color
# -----
@property
def color(self):
"""
The 'color' property is a color and may be specified as:
- A hex string (e.g. '#ff0000')
- An rgb/rgba string (e.g. 'rgb(255,0,0)')
- An hsl/hsla string (e.g. 'hsl(0,100%,50%)')
- An hsv/hsva string (e.g. 'hsv(0,100%,100%)')
- A named CSS color:
aliceblue, antiquewhite, aqua, aquamarine, azure,
beige, bisque, black, blanchedalmond, blue,
blueviolet, brown, burlywood, cadetblue,
chartreuse, chocolate, coral, cornflowerblue,
cornsilk, crimson, cyan, darkblue, darkcyan,
darkgoldenrod, darkgray, darkgrey, darkgreen,
darkkhaki, darkmagenta, darkolivegreen, darkorange,
darkorchid, darkred, darksalmon, darkseagreen,
darkslateblue, darkslategray, darkslategrey,
darkturquoise, darkviolet, deeppink, deepskyblue,
dimgray, dimgrey, dodgerblue, firebrick,
floralwhite, forestgreen, fuchsia, gainsboro,
ghostwhite, gold, goldenrod, gray, grey, green,
greenyellow, honeydew, hotpink, indianred, indigo,
ivory, khaki, lavender, lavenderblush, lawngreen,
lemonchiffon, lightblue, lightcoral, lightcyan,
lightgoldenrodyellow, lightgray, lightgrey,
lightgreen, lightpink, lightsalmon, lightseagreen,
lightskyblue, lightslategray, lightslategrey,
lightsteelblue, lightyellow, lime, limegreen,
linen, magenta, maroon, mediumaquamarine,
mediumblue, mediumorchid, mediumpurple,
mediumseagreen, mediumslateblue, mediumspringgreen,
mediumturquoise, mediumvioletred, midnightblue,
mintcream, mistyrose, moccasin, navajowhite, navy,
oldlace, olive, olivedrab, orange, orangered,
orchid, palegoldenrod, palegreen, paleturquoise,
palevioletred, papayawhip, peachpuff, peru, pink,
plum, powderblue, purple, red, rosybrown,
royalblue, rebeccapurple, saddlebrown, salmon,
sandybrown, seagreen, seashell, sienna, silver,
skyblue, slateblue, slategray, slategrey, snow,
springgreen, steelblue, tan, teal, thistle, tomato,
turquoise, violet, wheat, white, whitesmoke,
yellow, yellowgreen
Returns
-------
str
"""
return self["color"]
@color.setter
def color(self, val):
self["color"] = val
# family
# ------
@property
def family(self):
"""
HTML font family - the typeface that will be applied by the web
browser. The web browser will only be able to apply a font if
it is available on the system which it operates. Provide
multiple font families, separated by commas, to indicate the
preference in which to apply fonts if they aren't available on
the system. The Chart Studio Cloud (at https://chart-
studio.plotly.com or on-premise) generates images on a server,
where only a select number of fonts are installed and
supported. These include "Arial", "Balto", "Courier New",
"Droid Sans", "Droid Serif", "Droid Sans Mono", "Gravitas One",
"Old Standard TT", "Open Sans", "Overpass", "PT Sans Narrow",
"Raleway", "Times New Roman".
The 'family' property is a string and must be specified as:
- A non-empty string
Returns
-------
str
"""
return self["family"]
@family.setter
def family(self, val):
self["family"] = val
# lineposition
# ------------
@property
def lineposition(self):
"""
Sets the kind of decoration line(s) with text, such as an
"under", "over" or "through" as well as combinations e.g.
"under+over", etc.
The 'lineposition' property is a flaglist and may be specified
as a string containing:
- Any combination of ['under', 'over', 'through'] joined with '+' characters
(e.g. 'under+over')
OR exactly one of ['none'] (e.g. 'none')
Returns
-------
Any
"""
return self["lineposition"]
@lineposition.setter
def lineposition(self, val):
self["lineposition"] = val
# shadow
# ------
@property
def shadow(self):
"""
Sets the shape and color of the shadow behind text. "auto"
places minimal shadow and applies contrast text font color. See
https://developer.mozilla.org/en-US/docs/Web/CSS/text-shadow
for additional options.
The 'shadow' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["shadow"]
@shadow.setter
def shadow(self, val):
self["shadow"] = val
# size
# ----
@property
def size(self):
"""
The 'size' property is a number and may be specified as:
- An int or float in the interval [1, inf]
Returns
-------
int|float
"""
return self["size"]
@size.setter
def size(self, val):
self["size"] = val
# style
# -----
@property
def style(self):
"""
Sets whether a font should be styled with a normal or italic
face from its family.
The 'style' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'italic']
Returns
-------
Any
"""
return self["style"]
@style.setter
def style(self, val):
self["style"] = val
# textcase
# --------
@property
def textcase(self):
"""
Sets capitalization of text. It can be used to make text appear
in all-uppercase or all-lowercase, or with each word
capitalized.
The 'textcase' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'word caps', 'upper', 'lower']
Returns
-------
Any
"""
return self["textcase"]
@textcase.setter
def textcase(self, val):
self["textcase"] = val
# variant
# -------
@property
def variant(self):
"""
Sets the variant of the font.
The 'variant' property is an enumeration that may be specified as:
- One of the following enumeration values:
['normal', 'small-caps', 'all-small-caps',
'all-petite-caps', 'petite-caps', 'unicase']
Returns
-------
Any
"""
return self["variant"]
@variant.setter
def variant(self, val):
self["variant"] = val
# weight
# ------
@property
def weight(self):
"""
Sets the weight (or boldness) of the font.
The 'weight' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [1, 1000]
OR exactly one of ['normal', 'bold'] (e.g. 'bold')
Returns
-------
int
"""
return self["weight"]
@weight.setter
def weight(self, val):
self["weight"] = val
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
"""
def __init__(
self,
arg=None,
color=None,
family=None,
lineposition=None,
shadow=None,
size=None,
style=None,
textcase=None,
variant=None,
weight=None,
**kwargs,
):
"""
Construct a new Font object
Sets this legend group's title font.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.choropleth.leg
endgrouptitle.Font`
color
family
HTML font family - the typeface that will be applied by
the web browser. The web browser will only be able to
apply a font if it is available on the system which it
operates. Provide multiple font families, separated by
commas, to indicate the preference in which to apply
fonts if they aren't available on the system. The Chart
Studio Cloud (at https://chart-studio.plotly.com or on-
premise) generates images on a server, where only a
select number of fonts are installed and supported.
These include "Arial", "Balto", "Courier New", "Droid
Sans", "Droid Serif", "Droid Sans Mono", "Gravitas
One", "Old Standard TT", "Open Sans", "Overpass", "PT
Sans Narrow", "Raleway", "Times New Roman".
lineposition
Sets the kind of decoration line(s) with text, such as
an "under", "over" or "through" as well as combinations
e.g. "under+over", etc.
shadow
Sets the shape and color of the shadow behind text.
"auto" places minimal shadow and applies contrast text
font color. See https://developer.mozilla.org/en-
US/docs/Web/CSS/text-shadow for additional options.
size
style
Sets whether a font should be styled with a normal or
italic face from its family.
textcase
Sets capitalization of text. It can be used to make
text appear in all-uppercase or all-lowercase, or with
each word capitalized.
variant
Sets the variant of the font.
weight
Sets the weight (or boldness) of the font.
Returns
-------
Font
"""
super(Font, self).__init__("font")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.choropleth.legendgrouptitle.Font
constructor must be a dict or
an instance of :class:`plotly.graph_objs.choropleth.legendgrouptitle.Font`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("color", None)
_v = color if color is not None else _v
if _v is not None:
self["color"] = _v
_v = arg.pop("family", None)
_v = family if family is not None else _v
if _v is not None:
self["family"] = _v
_v = arg.pop("lineposition", None)
_v = lineposition if lineposition is not None else _v
if _v is not None:
self["lineposition"] = _v
_v = arg.pop("shadow", None)
_v = shadow if shadow is not None else _v
if _v is not None:
self["shadow"] = _v
_v = arg.pop("size", None)
_v = size if size is not None else _v
if _v is not None:
self["size"] = _v
_v = arg.pop("style", None)
_v = style if style is not None else _v
if _v is not None:
self["style"] = _v
_v = arg.pop("textcase", None)
_v = textcase if textcase is not None else _v
if _v is not None:
self["textcase"] = _v
_v = arg.pop("variant", None)
_v = variant if variant is not None else _v
if _v is not None:
self["variant"] = _v
_v = arg.pop("weight", None)
_v = weight if weight is not None else _v
if _v is not None:
self["weight"] = _v
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@graph_objs@choropleth@legendgrouptitle@_font.py@.PATH_END.py
|
{
"filename": "_line.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/waterfall/decreasing/marker/_line.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LineValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(
self, plotly_name="line", parent_name="waterfall.decreasing.marker", **kwargs
):
super(LineValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Line"),
data_docs=kwargs.pop(
"data_docs",
"""
color
Sets the line color of all decreasing values.
width
Sets the line width of all decreasing values.
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@waterfall@decreasing@marker@_line.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "RafiKueng/SpaghettiLens",
"repo_path": "SpaghettiLens_extracted/SpaghettiLens-master/_backup2/apps/lenses/templatetags/__init__.py",
"type": "Python"
}
|
RafiKuengREPO_NAMESpaghettiLensPATH_START.@SpaghettiLens_extracted@SpaghettiLens-master@_backup2@apps@lenses@templatetags@__init__.py@.PATH_END.py
|
|
{
"filename": "convolutions.ipynb",
"repo_name": "google/jax",
"repo_path": "jax_extracted/jax-main/docs/notebooks/convolutions.ipynb",
"type": "Jupyter Notebook"
}
|
# Generalized convolutions in JAX
<!--* freshness: { reviewed: '2024-04-08' } *-->
[](https://colab.research.google.com/github/jax-ml/jax/blob/main/docs/notebooks/convolutions.ipynb) [](https://kaggle.com/kernels/welcome?src=https://github.com/jax-ml/jax/blob/main/docs/notebooks/convolutions.ipynb)
JAX provides a number of interfaces to compute convolutions across data, including:
- {func}`jax.numpy.convolve` (also {func}`jax.numpy.correlate`)
- {func}`jax.scipy.signal.convolve` (also {func}`~jax.scipy.signal.correlate`)
- {func}`jax.scipy.signal.convolve2d` (also {func}`~jax.scipy.signal.correlate2d`)
- {func}`jax.lax.conv_general_dilated`
For basic convolution operations, the `jax.numpy` and `jax.scipy` operations are usually sufficient. If you want to do more general batched multi-dimensional convolution, the `jax.lax` function is where you should start.
## Basic one-dimensional convolution
Basic one-dimensional convolution is implemented by {func}`jax.numpy.convolve`, which provides a JAX interface for {func}`numpy.convolve`. Here is a simple example of 1D smoothing implemented via a convolution:
```python
import matplotlib.pyplot as plt
from jax import random
import jax.numpy as jnp
import numpy as np
key = random.key(1701)
x = jnp.linspace(0, 10, 500)
y = jnp.sin(x) + 0.2 * random.normal(key, shape=(500,))
window = jnp.ones(10) / 10
y_smooth = jnp.convolve(y, window, mode='same')
plt.plot(x, y, 'lightgray')
plt.plot(x, y_smooth, 'black');
```
The `mode` parameter controls how boundary conditions are treated; here we use `mode='same'` to ensure that the output is the same size as the input.
For more information, see the {func}`jax.numpy.convolve` documentation, or the documentation associated with the original {func}`numpy.convolve` function.
## Basic N-dimensional convolution
For *N*-dimensional convolution, {func}`jax.scipy.signal.convolve` provides a similar interface to that of {func}`jax.numpy.convolve`, generalized to *N* dimensions.
For example, here is a simple approach to de-noising an image based on convolution with a Gaussian filter:
```python
from scipy import misc
import jax.scipy as jsp
fig, ax = plt.subplots(1, 3, figsize=(12, 5))
# Load a sample image; compute mean() to convert from RGB to grayscale.
image = jnp.array(misc.face().mean(-1))
ax[0].imshow(image, cmap='binary_r')
ax[0].set_title('original')
# Create a noisy version by adding random Gaussian noise
key = random.key(1701)
noisy_image = image + 50 * random.normal(key, image.shape)
ax[1].imshow(noisy_image, cmap='binary_r')
ax[1].set_title('noisy')
# Smooth the noisy image with a 2D Gaussian smoothing kernel.
x = jnp.linspace(-3, 3, 7)
window = jsp.stats.norm.pdf(x) * jsp.stats.norm.pdf(x[:, None])
smooth_image = jsp.signal.convolve(noisy_image, window, mode='same')
ax[2].imshow(smooth_image, cmap='binary_r')
ax[2].set_title('smoothed');
```
Like in the one-dimensional case, we use `mode='same'` to specify how we would like edges to be handled. For more information on available options in *N*-dimensional convolutions, see the {func}`jax.scipy.signal.convolve` documentation.
## General convolutions
For the more general types of batched convolutions often useful in the context of building deep neural networks, JAX and XLA offer the very general N-dimensional __conv_general_dilated__ function, but it's not very obvious how to use it. We'll give some examples of the common use-cases.
A survey of the family of convolutional operators, [a guide to convolutional arithmetic](https://arxiv.org/abs/1603.07285), is highly recommended reading!
Let's define a simple diagonal edge kernel:
```python
# 2D kernel - HWIO layout
kernel = jnp.zeros((3, 3, 3, 3), dtype=jnp.float32)
kernel += jnp.array([[1, 1, 0],
[1, 0,-1],
[0,-1,-1]])[:, :, jnp.newaxis, jnp.newaxis]
print("Edge Conv kernel:")
plt.imshow(kernel[:, :, 0, 0]);
```
Edge Conv kernel:
And we'll make a simple synthetic image:
```python
# NHWC layout
img = jnp.zeros((1, 200, 198, 3), dtype=jnp.float32)
for k in range(3):
x = 30 + 60*k
y = 20 + 60*k
img = img.at[0, x:x+10, y:y+10, k].set(1.0)
print("Original Image:")
plt.imshow(img[0]);
```
Original Image:
### lax.conv and lax.conv_with_general_padding
These are the simple convenience functions for convolutions
️⚠️ The convenience `lax.conv`, `lax.conv_with_general_padding` helper function assume __NCHW__ images and __OIHW__ kernels.
```python
from jax import lax
out = lax.conv(jnp.transpose(img,[0,3,1,2]), # lhs = NCHW image tensor
jnp.transpose(kernel,[3,2,0,1]), # rhs = OIHW conv kernel tensor
(1, 1), # window strides
'SAME') # padding mode
print("out shape: ", out.shape)
print("First output channel:")
plt.figure(figsize=(10,10))
plt.imshow(np.array(out)[0,0,:,:]);
```
out shape: (1, 3, 200, 198)
First output channel:
```python
out = lax.conv_with_general_padding(
jnp.transpose(img,[0,3,1,2]), # lhs = NCHW image tensor
jnp.transpose(kernel,[2,3,0,1]), # rhs = IOHW conv kernel tensor
(1, 1), # window strides
((2,2),(2,2)), # general padding 2x2
(1,1), # lhs/image dilation
(1,1)) # rhs/kernel dilation
print("out shape: ", out.shape)
print("First output channel:")
plt.figure(figsize=(10,10))
plt.imshow(np.array(out)[0,0,:,:]);
```
out shape: (1, 3, 202, 200)
First output channel:
### Dimension Numbers define dimensional layout for conv_general_dilated
The important argument is the 3-tuple of axis layout arguments:
(Input Layout, Kernel Layout, Output Layout)
- __N__ - batch dimension
- __H__ - spatial height
- __W__ - spatial width
- __C__ - channel dimension
- __I__ - kernel _input_ channel dimension
- __O__ - kernel _output_ channel dimension
⚠️ To demonstrate the flexibility of dimension numbers we choose a __NHWC__ image and __HWIO__ kernel convention for `lax.conv_general_dilated` below.
```python
dn = lax.conv_dimension_numbers(img.shape, # only ndim matters, not shape
kernel.shape, # only ndim matters, not shape
('NHWC', 'HWIO', 'NHWC')) # the important bit
print(dn)
```
ConvDimensionNumbers(lhs_spec=(0, 3, 1, 2), rhs_spec=(3, 2, 0, 1), out_spec=(0, 3, 1, 2))
#### SAME padding, no stride, no dilation
```python
out = lax.conv_general_dilated(img, # lhs = image tensor
kernel, # rhs = conv kernel tensor
(1,1), # window strides
'SAME', # padding mode
(1,1), # lhs/image dilation
(1,1), # rhs/kernel dilation
dn) # dimension_numbers = lhs, rhs, out dimension permutation
print("out shape: ", out.shape)
print("First output channel:")
plt.figure(figsize=(10,10))
plt.imshow(np.array(out)[0,:,:,0]);
```
out shape: (1, 200, 198, 3)
First output channel:
#### VALID padding, no stride, no dilation
```python
out = lax.conv_general_dilated(img, # lhs = image tensor
kernel, # rhs = conv kernel tensor
(1,1), # window strides
'VALID', # padding mode
(1,1), # lhs/image dilation
(1,1), # rhs/kernel dilation
dn) # dimension_numbers = lhs, rhs, out dimension permutation
print("out shape: ", out.shape, "DIFFERENT from above!")
print("First output channel:")
plt.figure(figsize=(10,10))
plt.imshow(np.array(out)[0,:,:,0]);
```
out shape: (1, 198, 196, 3) DIFFERENT from above!
First output channel:
#### SAME padding, 2,2 stride, no dilation
```python
out = lax.conv_general_dilated(img, # lhs = image tensor
kernel, # rhs = conv kernel tensor
(2,2), # window strides
'SAME', # padding mode
(1,1), # lhs/image dilation
(1,1), # rhs/kernel dilation
dn) # dimension_numbers = lhs, rhs, out dimension permutation
print("out shape: ", out.shape, " <-- half the size of above")
plt.figure(figsize=(10,10))
print("First output channel:")
plt.imshow(np.array(out)[0,:,:,0]);
```
out shape: (1, 100, 99, 3) <-- half the size of above
First output channel:
#### VALID padding, no stride, rhs kernel dilation ~ Atrous convolution (excessive to illustrate)
```python
out = lax.conv_general_dilated(img, # lhs = image tensor
kernel, # rhs = conv kernel tensor
(1,1), # window strides
'VALID', # padding mode
(1,1), # lhs/image dilation
(12,12), # rhs/kernel dilation
dn) # dimension_numbers = lhs, rhs, out dimension permutation
print("out shape: ", out.shape)
plt.figure(figsize=(10,10))
print("First output channel:")
plt.imshow(np.array(out)[0,:,:,0]);
```
out shape: (1, 176, 174, 3)
First output channel:
#### VALID padding, no stride, lhs=input dilation ~ Transposed Convolution
```python
out = lax.conv_general_dilated(img, # lhs = image tensor
kernel, # rhs = conv kernel tensor
(1,1), # window strides
((0, 0), (0, 0)), # padding mode
(2,2), # lhs/image dilation
(1,1), # rhs/kernel dilation
dn) # dimension_numbers = lhs, rhs, out dimension permutation
print("out shape: ", out.shape, "<-- larger than original!")
plt.figure(figsize=(10,10))
print("First output channel:")
plt.imshow(np.array(out)[0,:,:,0]);
```
out shape: (1, 397, 393, 3) <-- larger than original!
First output channel:
We can use the last to, for instance, implement _transposed convolutions_:
```python
# The following is equivalent to tensorflow:
# N,H,W,C = img.shape
# out = tf.nn.conv2d_transpose(img, kernel, (N,2*H,2*W,C), (1,2,2,1))
# transposed conv = 180deg kernel rotation plus LHS dilation
# rotate kernel 180deg:
kernel_rot = jnp.rot90(jnp.rot90(kernel, axes=(0,1)), axes=(0,1))
# need a custom output padding:
padding = ((2, 1), (2, 1))
out = lax.conv_general_dilated(img, # lhs = image tensor
kernel_rot, # rhs = conv kernel tensor
(1,1), # window strides
padding, # padding mode
(2,2), # lhs/image dilation
(1,1), # rhs/kernel dilation
dn) # dimension_numbers = lhs, rhs, out dimension permutation
print("out shape: ", out.shape, "<-- transposed_conv")
plt.figure(figsize=(10,10))
print("First output channel:")
plt.imshow(np.array(out)[0,:,:,0]);
```
out shape: (1, 400, 396, 3) <-- transposed_conv
First output channel:
### 1D Convolutions
You aren't limited to 2D convolutions, a simple 1D demo is below:
```python
# 1D kernel - WIO layout
kernel = jnp.array([[[1, 0, -1], [-1, 0, 1]],
[[1, 1, 1], [-1, -1, -1]]],
dtype=jnp.float32).transpose([2,1,0])
# 1D data - NWC layout
data = np.zeros((1, 200, 2), dtype=jnp.float32)
for i in range(2):
for k in range(2):
x = 35*i + 30 + 60*k
data[0, x:x+30, k] = 1.0
print("in shapes:", data.shape, kernel.shape)
plt.figure(figsize=(10,5))
plt.plot(data[0]);
dn = lax.conv_dimension_numbers(data.shape, kernel.shape,
('NWC', 'WIO', 'NWC'))
print(dn)
out = lax.conv_general_dilated(data, # lhs = image tensor
kernel, # rhs = conv kernel tensor
(1,), # window strides
'SAME', # padding mode
(1,), # lhs/image dilation
(1,), # rhs/kernel dilation
dn) # dimension_numbers = lhs, rhs, out dimension permutation
print("out shape: ", out.shape)
plt.figure(figsize=(10,5))
plt.plot(out[0]);
```
in shapes: (1, 200, 2) (3, 2, 2)
ConvDimensionNumbers(lhs_spec=(0, 2, 1), rhs_spec=(2, 1, 0), out_spec=(0, 2, 1))
out shape: (1, 200, 2)
### 3D Convolutions
```python
import matplotlib as mpl
# Random 3D kernel - HWDIO layout
kernel = jnp.array([
[[0, 0, 0], [0, 1, 0], [0, 0, 0]],
[[0, -1, 0], [-1, 0, -1], [0, -1, 0]],
[[0, 0, 0], [0, 1, 0], [0, 0, 0]]],
dtype=jnp.float32)[:, :, :, jnp.newaxis, jnp.newaxis]
# 3D data - NHWDC layout
data = jnp.zeros((1, 30, 30, 30, 1), dtype=jnp.float32)
x, y, z = np.mgrid[0:1:30j, 0:1:30j, 0:1:30j]
data += (jnp.sin(2*x*jnp.pi)*jnp.cos(2*y*jnp.pi)*jnp.cos(2*z*jnp.pi))[None,:,:,:,None]
print("in shapes:", data.shape, kernel.shape)
dn = lax.conv_dimension_numbers(data.shape, kernel.shape,
('NHWDC', 'HWDIO', 'NHWDC'))
print(dn)
out = lax.conv_general_dilated(data, # lhs = image tensor
kernel, # rhs = conv kernel tensor
(1,1,1), # window strides
'SAME', # padding mode
(1,1,1), # lhs/image dilation
(1,1,1), # rhs/kernel dilation
dn) # dimension_numbers
print("out shape: ", out.shape)
# Make some simple 3d density plots:
def make_alpha(cmap):
my_cmap = cmap(jnp.arange(cmap.N))
my_cmap[:,-1] = jnp.linspace(0, 1, cmap.N)**3
return mpl.colors.ListedColormap(my_cmap)
my_cmap = make_alpha(plt.cm.viridis)
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(x.ravel(), y.ravel(), z.ravel(), c=data.ravel(), cmap=my_cmap)
ax.axis('off')
ax.set_title('input')
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
ax.scatter(x.ravel(), y.ravel(), z.ravel(), c=out.ravel(), cmap=my_cmap)
ax.axis('off')
ax.set_title('3D conv output');
```
in shapes: (1, 30, 30, 30, 1) (3, 3, 3, 1, 1)
ConvDimensionNumbers(lhs_spec=(0, 4, 1, 2, 3), rhs_spec=(4, 3, 0, 1, 2), out_spec=(0, 4, 1, 2, 3))
out shape: (1, 30, 30, 30, 1)
|
googleREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@docs@notebooks@convolutions.ipynb@.PATH_END.py
|
{
"filename": "convert_visibilities.py",
"repo_name": "ledatelescope/bifrost",
"repo_path": "bifrost_extracted/bifrost-master/python/bifrost/blocks/convert_visibilities.py",
"type": "Python"
}
|
# Copyright (c) 2016-2023, The Bifrost Authors. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of The Bifrost Authors nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from bifrost.map import map as bf_map
from bifrost.pipeline import TransformBlock
from bifrost.DataType import DataType
from copy import deepcopy
from math import sqrt
from bifrost import telemetry
telemetry.track_module()
class ConvertVisibilitiesBlock(TransformBlock):
def __init__(self, iring, fmt,
*args, **kwargs):
super(ConvertVisibilitiesBlock, self).__init__(iring, *args, **kwargs)
self.ofmt = fmt
def define_valid_input_spaces(self):
return ('cuda',)
def on_sequence(self, iseq):
ihdr = iseq.header
itensor = ihdr['_tensor']
ilabels = itensor['labels']
assert(ilabels[0] == 'time')
ohdr = deepcopy(ihdr)
otensor = ohdr['_tensor']
if ilabels[1:] == ['freq', 'station_i', 'pol_i', 'station_j', 'pol_j']:
nchan, nstand, npol, nstand_j, npol_j = itensor['shape'][1:]
assert(nstand_j == nstand)
assert( npol_j == npol)
self.ifmt = 'matrix'
if self.ofmt == 'matrix':
ohdr['matrix_fill_mode'] = 'hermitian'
elif self.ofmt == 'storage':
nbaseline = nstand*(nstand+1)//2
del ohdr['matrix_fill_mode']
otensor['labels'] = ['time', 'baseline', 'freq', 'stokes']
otensor['shape'] = [-1, nbaseline, nchan, npol*npol]
time_units, freq_units, stand_units, pol_units, _, _ = itensor['units']
otensor['units'] = [time_units, None, freq_units, ('I', 'Q', 'U', 'V')]
else:
raise NotImplementedError("Unsupported conversion from " +
self.ifmt + " to " + self.ofmt)
elif ilabels[1:] == ['baseline', 'freq', 'stokes']:
nbaseline, nchan, nstokes = itensor['shape'][1:]
assert(nstokes == 1 or nstokes == 4)
npol = 1 if nstokes == 1 else 2
nstand = int(sqrt(8 * nbaseline + 1) - 1) // 2
time_units, baseline_units, freq_units, stokes_units, = itensor['units']
pol_units = ('X', 'Y') # TODO: Support L/R (using additional metadata?)
self.ifmt = 'storage'
if self.ofmt == 'matrix':
otensor['labels'] = ['time', 'freq', 'station_i', 'pol_i', 'station_j', 'pol_j']
otensor['shape'] = [-1, nchan, nstand, npol, nstand, npol]
otensor['units'] = [time_units, freq_units, None, pol_units, None, pol_units]
else:
raise NotImplementedError("Cannot convert input from %s to %s"
% (ilabels, self.ofmt))
return ohdr
def on_data(self, ispan, ospan):
idata = ispan.data
odata = ospan.data
itype = DataType(idata.dtype)
otype = DataType(odata.dtype)
if self.ifmt == 'matrix' and self.ofmt == 'matrix':
# Make a full-matrix copy of the lower-only input matrix
# odata[t,c,i,p,j,q] = idata[t,c,i,p,j,q] (lower filled only)
shape_nopols = list(idata.shape)
del shape_nopols[5]
del shape_nopols[3]
idata = idata.view(itype.as_vector(2))
odata = odata.view(otype.as_vector(2))
bf_map(
'''
bool in_lower_triangle = (i > j);
if( in_lower_triangle ) {
odata(t,c,i,0,j,0) = idata(t,c,i,0,j,0);
odata(t,c,i,1,j,0) = idata(t,c,i,1,j,0);
} else {
auto x = idata(t,c,j,0,i,0);
auto y = idata(t,c,j,1,i,0);
auto x1 = x[1];
x[0] = x[0].conj();
x[1] = y[0].conj();
if( i != j ) {
y[0] = x1.conj();
}
y[1] = y[1].conj();
odata(t,c,i,0,j,0) = x;
odata(t,c,i,1,j,0) = y;
}
''',
shape=shape_nopols, axis_names=['t', 'c', 'i', 'j'],
data={'idata': idata, 'odata': odata})
elif self.ifmt == 'matrix' and self.ofmt == 'storage':
assert(idata.shape[2] <= 2048)
idata = idata.view(itype.as_vector(2))
odata = odata.view(otype.as_vector(4))
# TODO: Support L/R as well as X/Y pols
bf_map('''
// TODO: This only works up to 2048 in single-precision
#define project_triangular(i, j) ((i)*((i)+1)/2 + (j))
int i = int((sqrt(8.f*(b)+1)-1)/2);
int j = b - project_triangular(i, 0);
auto x = idata(t,c,i,0,j,0);
auto y = idata(t,c,i,1,j,0);
if( i == j ) {
x[1] = y[0].conj();
}
idata_type::value_type eye(0, 1);
auto I = (x[0] + y[1]);
auto Q = (x[0] - y[1]);
auto U = (x[1] + y[0]);
auto V = (x[1] - y[0]) * eye;
odata(t,b,c,0) = odata_type(I,Q,U,V);
''',
shape=odata.shape[:-1], axis_names=['t', 'b', 'c'],
data={'idata': idata, 'odata': odata},
block_shape=[64,8]) # TODO: Tune this
#elif self.ifmt == 'matrix' and self.ofmt == 'triangular':
elif self.ifmt == 'storage' and self.ofmt == 'matrix':
oshape_nopols = list(odata.shape)
del oshape_nopols[5]
del oshape_nopols[3]
idata = idata.view(itype.as_vector(4))
odata = odata.view(otype.as_vector(2))
bf_map('''
bool in_upper_triangle = (i < j);
auto b = in_upper_triangle ? j*(j+1)/2 + i : i*(i+1)/2 + j;
auto IQUV = idata(t,b,c,0);
auto I = IQUV[0], Q = IQUV[1], U = IQUV[2], V = IQUV[3];
idata_type::value_type eye(0, 1);
auto xx = 0.5f*(I + Q);
auto xy = 0.5f*(U - V*eye);
auto yx = 0.5f*(U + V*eye);
auto yy = 0.5f*(I - Q);
if( i == j ) {
xy = yx.conj();
}
if( in_upper_triangle ) {
auto tmp_xy = xy;
xx = xx.conj();
xy = yx.conj();
yx = tmp_xy.conj();
yy = yy.conj();
}
odata(t,c,i,0,j,0) = odata_type(xx, xy);
odata(t,c,i,1,j,0) = odata_type(yx, yy);
''',
shape=oshape_nopols, axis_names=['t', 'c', 'i', 'j'],
data={'idata': idata, 'odata': odata},
block_shape=[64,8]) # TODO: Tune this
else:
raise NotImplementedError
def convert_visibilities(iring, fmt, *args, **kwargs):
"""Convert visibility data to a new format.
Supported values of 'fmt' are:
matrix, storage
Args:
iring (Ring or Block): Input data source.
fmt (str): The desired output format: matrix, storage.
*args: Arguments to ``bifrost.pipeline.TransformBlock``.
**kwargs: Keyword Arguments to ``bifrost.pipeline.TransformBlock``.
**Tensor semantics**::
Input: ['time', 'freq', 'station_i', 'pol_i', 'station_j', 'pol_j'], dtype = any complex, space = CUDA
fmt = 'matrix' (produces a fully-filled matrix from a lower-filled one)
Output: ['time', 'freq', 'station_i', 'pol_i', 'station_j', 'pol_j'], dtype = any complex, space = CUDA
fmt = 'storage' (suitable for common on-disk data formats such as UVFITS, FITS-IDI, MS etc.)
Output: ['time', 'baseline', 'freq', 'stokes'], dtype = any complex, space = CUDA
Input: ['time', 'baseline', 'freq', 'stokes'], dtype = any complex, space = CUDA
fmt = 'matrix' (fully-filled matrix suitable for linear algebra operations)
Output: ['time', 'freq', 'station_i', 'pol_i', 'station_j', 'pol_j'], dtype = any complex, space = CUDA
Returns:
ConvertVisibilitiesBlock: A new block instance.
"""
return ConvertVisibilitiesBlock(iring, fmt, *args, **kwargs)
|
ledatelescopeREPO_NAMEbifrostPATH_START.@bifrost_extracted@bifrost-master@python@bifrost@blocks@convert_visibilities.py@.PATH_END.py
|
{
"filename": "introduction.ipynb",
"repo_name": "philippbaumeister/ExoMDN",
"repo_path": "ExoMDN_extracted/ExoMDN-main/introduction.ipynb",
"type": "Jupyter Notebook"
}
|
<img src="banner.png" width=500 style="margin-left:0; margin-right:auto; padding: 20px"/>
This notebook provides an introduction to ExoMDN, a machine-learning based model for the rapid characterization of exoplanet interiors. ExoMDN is based on Mixture Density Networks (MDNs), which output a mixture of Gaussian functions in order to approximate the distribution of interior structures which fit e.g. observed planet mass and planet radius.
For more details, see Baumeister and Tosi 2023
Contact: <philipp.baumeister@dlr.de>
```python
from exomdn import ExoMDN
from exomdn.plotting import cornerplot, cornerplot_logratios
```
# Setting up
</br>
Let's start by creating a new <code>ExoMDN</code> object. <code>ExoMDN</code> handles the MDN models and includes interactive widgets to facilitate loading models and running predictions.
```python
exo = ExoMDN(model_path="./models", data_path="./data")
```
# Loading a trained model
</br>
Next, we need to load a trained MDN model which we want to use for the interior prediction. To simplify things, we will use the included <code>load_model_widget</code>, which allows to interactively select which model to load. By default, <code>ExoMDN</code> searches for models in the <i>./models</i> path. This can be changed by setting <code>exomdn.model_path</code>
By default, two models are available:
* ***mass_radius_Teq*** (takes planet mass, radius, and equilibrium temperature as inputs)
* ***mass_radius_k2_Teq*** (takes planet mass, radius, fluid Love number $k_2$ and equilibrium temperature as inputs)
```python
exo.load_model_widget
```
# Making an interior prediction
</br>
<code>ExoMDN</code> provides a custom widget to run a prediction for a single planet. The output of the MDN is in terms of a distribution of log-ratios of the mass and radius fractions of each interior layer of the planet. To convert to mass and radius fractions, the model samples from the distribution and transforms each point. The number of samples can be specified in the "Options" section of the widget.
</br>
</br>
Uncertainties can be included by ticking the checkbox in "Planet parameters". The model then first samples a number of times from within the error bars (how often can be set with the "Uncertainty samples" option) and predicts an interior distribution from each. From each of these predictions a number of points is then sampled so that the total number fits as closely as possible to the specified total number of samples (e.g. with the default values of 10 000 total samples and 1000 uncertainty samples, the model predicts 1000 distributions from within the error bars and then samples 10 times from each predicted distribution to get to the total of 10 000)
```python
exo.prediction_widget
```
The output of the prediction widget is saved in <code>ExoMDN</code> in the form of [pandas DataFrames](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) as follows:
* `exomdn.input_prompt` contains the input(s) to the prediction
* `exomdn.prediction` contains the predicted samples of the interior
* `exomdn.mixture_components` contains the means, variances, and mixture weights of the predicted Gaussian mixture
```python
print(f"Length of input: {len(exo.input_prompt)}")
print(f"Number of mixture components: {len(exo.mixture_components)}")
print("=" * 40)
print("Prediction Summary:")
exo.prediction.describe()
```
```python
exo.mixture_components
```
# Visualization
We can visualize the output of the MDN with the `cornerplot_logratios` function. It takes as input the prediction data, the mixture components, and the log-ratio data columns one wants to visualize (' (`exo.rf_logratios` for radius fractions, `exo.mf_logratios` for mass fractions, `exo.logratios` for both).
The upper right plots also show the distribution of Gaussian kernels as predicted by the MDN, where the colors mark the respective weight in the distribution.
```python
# showing radius fractions
cornerplot_logratios(data=exo.prediction, data_components=exo.mixture_components, columns=exo.rf_logratios, height=2)
# showing mass fractions
# cornerplot_logratios(data=exomdn.prediction, data_components=exomdn.mixture_components, columns=exomdn.mf_logratios, height=2)
# showing both radius and mass fractions
# cornerplot_logratios(data=exomdn.prediction, data_components=exomdn.mixture_components, columns=exomdn.logratios, height=1.5)
```
The `cornerplot` function can be used to show the predicted interior in terms of true radius and mass fractions instead of log-ratios.
```python
# showing radius fractions
cornerplot(data=exo.prediction, columns=exo.rf, height=2)
# showing mass fractions
# cornerplot(data=exomdn.prediction, columns=exomdn.mf, height=2)
```
```python
```
|
philippbaumeisterREPO_NAMEExoMDNPATH_START.@ExoMDN_extracted@ExoMDN-main@introduction.ipynb@.PATH_END.py
|
{
"filename": "b3dplot.py",
"repo_name": "SpaceOdyssey/blobby3d",
"repo_path": "blobby3d_extracted/blobby3d-master/pyblobby3d/src/pyblobby3d/b3dplot.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""Plotting routines.
@author: Mathew Varidel
"""
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
class cmap:
flux = 'Greys_r'
v = 'RdYlBu_r'
vdisp = 'YlOrBr'
residuals = 'RdYlBu_r'
def plot_map(
ax, map_2d,
title=None,
xlabel=None,
ylabel=None,
xticks=False,
yticks=False,
xlim=None,
ylim=None,
colorbar=False,
colorbar_cax=None,
cbar_label=None,
clim=None,
cbarticks=None,
cbar_nticks=5,
logscale=False,
cmap=None,
title_fontsize='large',
label_fontsize='large',
tick_fontsize='large',
cb_label_fontsize='large',
cb_tick_fontsize='large',
fwhm=None,
aspect=None,
mask=None):
"""Plot singular 2d map to an axes."""
naxis = map_2d.shape
if clim is not None:
if clim[0] is None:
clim[0] = np.nanmin(map_2d)
if clim[1] is None:
clim[1] = np.nanmax(map_2d)
else:
clim = [np.nanmin(map_2d), np.nanmax(map_2d)]
if logscale:
norm = mpl.colors.LogNorm(vmin=clim[0], vmax=clim[1])
else:
norm = None
ax.imshow(
map_2d,
origin='lower',
interpolation='nearest',
norm=norm,
vmin=clim[0], vmax=clim[1],
cmap=cmap,
aspect=aspect
)
ax.tick_params(axis='both', direction='in', pad=4.0)
if isinstance(title, str):
ax.set_title(title, fontsize=title_fontsize)
if isinstance(xlabel, str):
ax.set_xlabel(xlabel, fontsize=label_fontsize)
if isinstance(ylabel, str):
ax.set_ylabel(ylabel, fontsize=label_fontsize)
set_ticks(
ax.xaxis,
ticks=xticks,
naxis=naxis[1],
lim=xlim,
tick_fontsize=tick_fontsize)
set_ticks(
ax.yaxis,
ticks=yticks,
naxis=naxis[0],
lim=ylim,
tick_fontsize=tick_fontsize)
# XTicks
# # TODO: Combine XTicks/YTicks settings
# if not xticks:
# ax.set_xticks([])
# elif xticks == 'all':
# x_dist = np.diff(self.x_lim)[0]/2.0
# ax.set_xticks([-0.5, (naxis[1]-1.0)/2.0, naxis[1]-0.5])
# xtick_values = [-round(x_dist, 1), 0.0, round(x_dist, 1)]
# ax.set_xticklabels(xtick_values, fontsize=tick_fontsize)
# elif xticks == 'upper':
# x_dist = np.diff(self.x_lim)[0]/2.0
# ax.set_xticks([(naxis[1]-1.0)/2.0, naxis[1]-0.5])
# xtick_values = [0.0, round(x_dist, 1)]
# ax.set_xticklabels(xtick_values, fontsize=tick_fontsize)
# elif xticks == 'lower':
# x_dist = np.diff(self.x_lim)[0]/2.0
# ax.set_xticks([-0.5, (naxis[1]-1.0)/2.0])
# xtick_values = [-round(x_dist, 1), 0.0]
# ax.set_xticklabels(xtick_values, fontsize=tick_fontsize)
# # YTicks
# if not yticks:
# ax.set_yticks([])
# elif yticks == 'all':
# y_dist = np.diff(self.y_lim)[0]/2.0
# ax.set_yticks([-0.5, (naxis[0]-1.0)/2.0, naxis[0]-0.5])
# ytick_values = [-round(y_dist, 1), 0.0, round(y_dist, 1)]
# ax.set_yticklabels(ytick_values, fontsize=tick_fontsize)
# elif yticks == 'upper':
# y_dist = np.diff(self.y_lim)[0]/2.0
# ax.set_yticks([(naxis[0]-1.0)/2.0, naxis[0]-0.5])
# ytick_values = [0.0, round(y_dist, 1)]
# ax.set_yticklabels(ytick_values, fontsize=tick_fontsize)
# elif yticks == 'lower':
# y_dist = np.diff(self.y_lim)[0]/2.0
# ax.set_yticks([-0.5, (naxis[0]-1.0)/2.0])
# ytick_values = [-round(y_dist, 1), 0.0]
# ax.set_yticklabels(ytick_values, fontsize=tick_fontsize)
if colorbar:
if colorbar_cax is None:
divider = make_axes_locatable(ax)
colorbar_cax = divider.append_axes(
'right', size='10%', pad=0.03)
plot_colorbar(
cax=colorbar_cax,
clim=clim,
label=cbar_label,
label_fontsize=cb_label_fontsize,
cbarticks=cbarticks,
cbar_nticks=cbar_nticks,
tick_fontsize=cb_tick_fontsize,
cmap=cmap
)
if fwhm is not None:
circle = plt.Circle(
(1.1*fwhm, 1.1*fwhm),
radius=fwhm,
fill=False, edgecolor='r', linewidth=1.0)
ax.add_artist(circle)
def set_ticks(ax, naxis, ticks=False, lim=None, tick_fontsize='large'):
if not ticks:
ax.set_ticks([])
elif ticks == 'all':
dist = np.diff(lim)[0]/2.0
ax.set_ticks([-0.5, (naxis-1.0)/2.0, naxis-0.5])
tick_values = [-round(dist, 1), 0.0, round(dist, 1)]
ax.set_ticklabels(tick_values, fontsize=tick_fontsize)
elif ticks == 'upper':
dist = np.diff(lim)[0]/2.0
ax.set_ticks([(naxis-1.0)/2.0, naxis-0.5])
tick_values = [0.0, round(dist, 1)]
ax.set_ticklabels(tick_values, fontsize=tick_fontsize)
elif ticks == 'lower':
dist = np.diff(lim)[0]/2.0
ax.set_ticks([-0.5, (naxis-1.0)/2.0])
tick_values = [-round(dist, 1), 0.0]
ax.set_ticklabels(tick_values, fontsize=tick_fontsize)
else:
raise ValueError('xticks must be False, all, upper, lower.')
def plot_colorbar(
cax,
mappable=None,
label=None,
xlabel=None,
ylabel=None,
clim=None,
cbarticks=None,
cbar_nticks=5,
logscale=False,
cmap=None,
label_fontsize='large',
tick_fontsize='large'):
"""
Plot colorbar.
Parameters
----------
cax : matplotlib.pyplot.axes
Colorbar axis.
mappable : matplotlib.cm.ScalarMappable, default None
Mappable object -- usually image. Default None uses a linear
mappable between limits constructed by clim.
title : str, default None
xlabel : str, default None
ylabel : str, default None
clim : list, default None
Color limits. Default uses mappable to construct color limits.
cbarticks : list, default None
cbarticks[0] corresponds to ticks.
cbarticks[1] corresponds to tick labels.
cbar_nticks : int, default 5
Number of colorbar ticks if cbarticks is None.
logscale : bool, default False
Not allowed at this time.
cmap : Matplotlib.colormap instance
label_fontsize : matplotlib fontsize property
tick_fontisze : matplotlib fontsize property
"""
assert (
(mappable is not None)
or (mappable is None and clim is not None)
)
# Deal with limits
if clim is not None:
if clim[0] is None:
clim[0] = np.nanmin(mappable)
if clim[1] is None:
clim[1] = np.nanmax(mappable)
else:
clim = [
np.nanmin(mappable),
np.nanmax(mappable)
]
# logscale
if logscale:
cb_norm = mpl.colors.LogNorm(vmin=clim[0], vmax=clim[1])
# tick formatter
def logformatter(x, pos):
value = np.exp(np.log(clim[0]) + x*np.log(clim[1]/clim[0]))
value = round(value, -int(np.floor(np.log10(abs(value)))))
return '%i' % (value)
else:
cb_norm = mpl.colors.Normalize(vmin=clim[0], vmax=clim[1])
# Construct default mappable
if mappable is None:
sm = plt.cm.ScalarMappable(cmap=cmap, norm=cb_norm)
sm.set_array([])
# Construct colorbar
cb = plt.colorbar(sm, cax=cax)
if label is not None:
cb.set_label(label=label, fontsize=label_fontsize)
cb.set_clim(clim[0], clim[1])
# Set ticks
if isinstance(cbarticks, list):
assert len(cbarticks) == 2
if logscale:
def logformatter(x, pos):
value = np.exp(np.log(clim[0]) + x*np.log(clim[1]/clim[0]))
value = round(value, -int(np.floor(np.log10(abs(value)))))
return '%i' % (value)
# values = [20.0, 50.0, 100.0, 200.0]
pct = list(map(
lambda x: np.log(x/clim[0])/np.log(clim[1]/clim[0]),
cbarticks[0]
))
cb.ax.yaxis.set_ticks(pct)
# cb.ax.yaxis.set_major_formatter(
# mpl.ticker.FuncFormatter(logformatter))
cb.ax.yaxis.set_major_formatter(
mpl.ticker.FixedFormatter(cbarticks[1])
)
cb.ax.yaxis.set_tick_params(
labelsize=tick_fontsize,
direction='in',
pad=4.0
)
else:
cb.set_ticks(cbarticks[0])
cb.set_ticklabels(cbarticks[1])
else:
if logscale:
tick_locator = mpl.ticker.LinearLocator(numticks=cbar_nticks)
cb.locator = tick_locator
else:
tick_locator = mpl.ticker.MaxNLocator(nbins=cbar_nticks)
cb.locator = tick_locator
cb.ax.tick_params(
labelsize=tick_fontsize,
direction='in',
pad=4.0
)
cb.update_ticks()
|
SpaceOdysseyREPO_NAMEblobby3dPATH_START.@blobby3d_extracted@blobby3d-master@pyblobby3d@src@pyblobby3d@b3dplot.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/carpet/baxis/title/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._text import TextValidator
from ._offset import OffsetValidator
from ._font import FontValidator
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__,
[],
["._text.TextValidator", "._offset.OffsetValidator", "._font.FontValidator"],
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@carpet@baxis@title@__init__.py@.PATH_END.py
|
{
"filename": "mmm.py",
"repo_name": "davidharvey1986/pyRRG",
"repo_path": "pyRRG_extracted/pyRRG-master/src/mmm.py",
"type": "Python"
}
|
#!/usr/bin/env python
# D. Jones - 2/13/14
"""This code is from the IDL Astronomy Users Library"""
import numpy as np
def mmm( sky_vector,
highbad = False,
debug = False,
readnoise = False,
nsky = False,
integer = "discrete",
mxiter = 50,
minsky = 20,
nan=True):
"""Estimate the sky background in a stellar contaminated field.
MMM assumes that contaminated sky pixel values overwhelmingly display
POSITIVE departures from the true value. Adapted from DAOPHOT
routine of the same name.
CALLING SEQUENCE:
skymod,sigma,skew = mmm.mmm( sky, highbad= , readnoise=, debug=,
minsky=, nsky=, integer=)
INPUTS:
sky - Array or Vector containing sky values. This version of
MMM does not require SKY to be sorted beforehand.
RETURNS:
skymod - Scalar giving estimated mode of the sky values
sigma - Scalar giving standard deviation of the peak in the sky
histogram. If for some reason it is impossible to derive
skymod, then SIGMA = -1.0
skew - Scalar giving skewness of the peak in the sky histogram
If no output variables are supplied or if "debug" is set
then the values of skymod, sigma and skew will be printed.
OPTIONAL KEYWORD INPUTS:
highbad - scalar value of the (lowest) "bad" pixel level (e.g. cosmic
rays or saturated pixels) If not supplied, then there is
assumed to be no high bad pixels.
minsky - Integer giving mininum number of sky values to be used. MMM
will return an error if fewer sky elements are supplied.
Default = 20.
maxiter - integer giving maximum number of iterations allowed,default=50
readnoise - Scalar giving the read noise (or minimum noise for any
pixel). Normally, MMM determines the (robust) median by
averaging the central 20% of the sky values. In some cases
where the noise is low, and pixel values are quantized a
larger fraction may be needed. By supplying the optional
read noise parameter, MMM is better able to adjust the
fraction of pixels used to determine the median.
integer - Set this keyword if the input SKY vector only contains
discrete integer values. This keyword is only needed if the
SKY vector is of type float or double precision, but contains
only discrete integer values. (Prior to July 2004, the
equivalent of /INTEGER was set for all data types)
debug - If this keyword is set and non-zero, then additional
information is displayed at the terminal.
OPTIONAL OUTPUT KEYWORD:
nsky - Integer scalar giving the number of pixels actually used for the
sky computation (after outliers have been removed).
NOTES:
(1) Program assumes that low "bad" pixels (e.g. bad CCD columns) have
already been deleted from the SKY vector.
(2) MMM was updated in June 2004 to better match more recent versions
of DAOPHOT.
(3) Does not work well in the limit of low Poisson integer counts
(4) MMM may fail for strongly skewed distributions.
METHOD:
The algorithm used by MMM consists of roughly two parts:
(1) The average and sigma of the sky pixels is computed. These values
are used to eliminate outliers, i.e. values with a low probability
given a Gaussian with specified average and sigma. The average
and sigma are then recomputed and the process repeated up to 20
iterations.
(2) The amount of contamination by stars is estimated by comparing the
mean and median of the remaining sky pixels. If the mean is larger
than the median then the true sky value is estimated by
3*median - 2*mean
REVISION HISTORY:
Adapted to IDL from 1986 version of DAOPHOT in STSDAS W. Landsman, STX Feb, 1987
Added HIGHBAD keyword W. Landsman January, 1991
Fixed occasional problem with integer inputs W. Landsman Feb, 1994
Avoid possible 16 bit integer overflow W. Landsman November, 2001
Added READNOISE, NSKY keywords, new median computation W. Landsman June, 2004
Added INTEGER keyword W. Landsman July, 2004
Improve numerical precision W. Landsman October, 2004
Fewer aborts on strange input sky histograms W. Landsman October, 2005
Added /SILENT keyword November, 2005
Fix too many /CON keywords to MESSAGE W.L. December, 2005
Fix bug introduced June 2004 removing outliers N. Cunningham/W. Landsman January, 2006
when READNOISE not set
Make sure that MESSAGE never aborts W. Landsman January, 2008
Add mxiter keyword and change default to 50 W. Landsman August, 2011
Added MINSKY keyword W.L. December, 2011
Converted to Python D. Jones January, 2014
"""
print("Getting the background of the image, this can take a while for large images")
if nan: sky_vector = sky_vector[np.where(sky_vector == sky_vector)]
nsky = len( sky_vector ) #Get number of sky elements
if nsky < minsky:
sigma=-1.0 ; skew = 0.0; skymod = np.nan
print(('ERROR -Input vector must contain at least '+str(minsky)+' elements'))
return(skymod,sigma,skew)
nlast = nsky-1 #Subscript of last pixel in SKY array
if debug:
print(('Processing '+str(nsky) + ' element array'))
sz_sky = np.shape(sky_vector)
sky = np.sort(sky_vector) #Sort SKY in ascending values
skymid = 0.5*sky[int((nsky-1)/2)] + 0.5*sky[int(nsky/2)] #Median value of all sky values
cut1 = np.min( [skymid-sky[0],sky[nsky-1] - skymid] )
if highbad:
cut1[np.where(cut1 > highbad - skymid)[0]] = highbad - skymid
cut2 = skymid + cut1
cut1 = skymid - cut1
# Select the pixels between Cut1 and Cut2
good = np.where( (sky <= cut2) & (sky >= cut1))[0]
Ngood = len(good)
if ( Ngood == 0 ):
sigma=-1.0 ; skew = 0.0; skymod = 0.0
print(('ERROR - No sky values fall within ' + str(cut1) + \
' and ' + str(cut2)))
return(skymod,sigma,skew)
delta = sky[good] - skymid #Subtract median to improve arithmetic accuracy
sum = np.sum(delta.astype('float64'))
sumsq = np.sum(delta.astype('float64')**2)
maximm = np.max( good) ; minimm = np.min(good) # Highest value accepted at upper end of vector
minimm = minimm -1 #Highest value reject at lower end of vector
# Compute mean and sigma (from the first pass).
medianIndex = int(np.floor((minimm+maximm+1)/2))
skymed = 0.5*sky[medianIndex] + \
0.5*sky[medianIndex + 1] #median
skymn = sum/(maximm-minimm) #mean
sigma = np.sqrt(sumsq/(maximm-minimm)-skymn**2) #sigma
skymn = skymn + skymid #Add median which was subtracted off earlier
# If mean is less than the mode, then the contamination is slight, and the
# mean value is what we really want.
# skymod = (skymed < skymn) ? 3.*skymed - 2.*skymn : skymn
if skymed < skymn:
skymod = 3.*skymed - 2.*skymn
else: skymod = skymn
# Rejection and recomputation loop:
niter = 0
clamp = 1
old = 0
# START_LOOP:
redo = True
while redo:
niter = niter + 1
if ( niter > mxiter ):
sigma=-1.0 ; skew = 0.0
print(('ERROR - Too many ('+str(mxiter) + ') iterations,' + \
' unable to compute sky'))
return(skymod,sigma,skew)
if ( maximm-minimm < minsky ): #Error?
sigma = -1.0 ; skew = 0.0
print(('ERROR - Too few ('+str(maximm-minimm) + \
') valid sky elements, unable to compute sky'))
return(skymod,sigma,skew)
# Compute Chauvenet rejection criterion.
r = np.log10( float( maximm-minimm ) )
r = np.max( [ 2., ( -0.1042*r + 1.1695)*r + 0.8895 ] )
# Compute rejection limits (symmetric about the current mode).
cut = r*sigma + 0.5*np.abs(skymn-skymod)
# if integer: cut = cut > 1.5
cut1 = skymod - cut ; cut2 = skymod + cut
#
# Recompute mean and sigma by adding and/or subtracting sky values
# at both ends of the interval of acceptable values.
redo = False
newmin = minimm
if sky[newmin+1] >= cut1: tst_min = 1 #Is minimm+1 above current CUT?
else: tst_min = 0
if (newmin == -1) and tst_min: done = 1 #Are we at first pixel of SKY?
else: done = 0
if not done:
if newmin > 0: skyind = newmin
else: skyind = 0
if (sky[skyind] < cut1) and tst_min: done = 1
if not done:
istep = 1 - 2*int(tst_min)
while not done:
newmin = newmin + istep
if (newmin == -1) | (newmin == nlast): done = 1
if not done:
if (sky[newmin] <= cut1) and (sky[newmin+1] >= cut1): done = 1
if tst_min: delta = sky[newmin+1:minimm+1] - skymid
else: delta = sky[minimm+1:newmin+1] - skymid
sum = sum - istep*np.sum(delta)
sumsq = sumsq - istep*np.sum(delta**2)
redo = True
minimm = newmin
newmax = maximm
if sky[maximm] <= cut2: tst_max = 1 #Is current maximum below upper cut?
else: tst_max = 0
if (maximm == nlast) and tst_max: done = 1
else: done = 0 #Are we at last pixel of SKY array?
if not done:
if maximm+1 < nlast: skyind = maximm+1
else: skyind = nlast
if ( tst_max ) and (sky[skyind] > cut2): done = 1
if not done: # keep incrementing newmax
istep = -1 + 2*int(tst_max) #Increment up or down?
while not done:
newmax = newmax + istep
if (newmax == nlast) or (newmax == -1): done = 1
if not done:
if ( sky[newmax] <= cut2 ) and ( sky[newmax+1] >= cut2 ): done = 1
if tst_max:
delta = sky[maximm+1:newmax+1] - skymid
else:
delta = sky[newmax+1:maximm+1] - skymid
sum = sum + istep*np.sum(delta)
sumsq = sumsq + istep*np.sum(delta**2)
redo = True
maximm = newmax
#
# Compute mean and sigma (from this pass).
#
nsky = maximm - minimm
if ( nsky < minsky ): # error?
sigma = -1.0 ; skew = 0.0
print('ERROR - Outlier rejection left too few sky elements')
return(skymod,sigma,skew)
skymn = sum/nsky
var = sumsq/nsky - skymn**2
if var < 0: var = 0
sigma = float( np.sqrt( var ))
skymn = skymn + skymid
# Determine a more robust median by averaging the central 20% of pixels.
# Estimate the median using the mean of the central 20 percent of sky
# values. Be careful to include a perfectly symmetric sample of pixels about
# the median, whether the total number is even or odd within the acceptance
# interval
center = (minimm + 1 + maximm)/2.
side = np.round(0.2*(maximm-minimm))/2. + 0.25
j = int(np.round(center-side))
k = int(np.round(center+side))
# In case the data has a large number of of the same (quantized)
# intensity, expand the range until both limiting values differ from the
# central value by at least 0.25 times the read noise.
if readnoise:
L = round(center-0.25)
M = round(center+0.25)
R = 0.25*readnoise
while ((j > 0) and (k < nsky-1) and \
( ((sky[L] - sky[j]) < R) or ((sky[k] - sky[M]) < R))):
j -= 1
k += 1
skymed = np.sum(sky[j:k+1])/(k-j+1)
# If the mean is less than the median, then the problem of contamination
# is slight, and the mean is what we really want.
if skymed < skymn :
dmod = 3.*skymed-2.*skymn-skymod
else: dmod = skymn - skymod
# prevent oscillations by clamping down if sky adjustments are changing sign
if dmod*old < 0: clamp = 0.5*clamp
skymod = skymod + clamp*dmod
old = dmod
# if redo then goto, START_LOOP
#
skew = float( (skymn-skymod)/max([1.,sigma]) )
nsky = maximm - minimm
if debug:
print(('% MMM: Number of unrejected sky elements: ', str(nsky,2), \
' Number of iterations: ', str(niter)))
print(('% MMM: Mode, Sigma, Skew of sky vector:', skymod, sigma, skew ))
return(skymod,sigma,skew)
|
davidharvey1986REPO_NAMEpyRRGPATH_START.@pyRRG_extracted@pyRRG-master@src@mmm.py@.PATH_END.py
|
{
"filename": "get_adr.py",
"repo_name": "grzeimann/Panacea",
"repo_path": "Panacea_extracted/Panacea-master/get_adr.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Sep 20 10:46:25 2021
@author: gregz
"""
from astropy.io import fits
from astropy.modeling.models import Gaussian2D
from astropy.modeling.fitting import LevMarLSQFitter
import numpy as np
import glob
import os.path as op
from input_utils import setup_logging
log = setup_logging('adr')
basedir = '/work/03946/hetdex/maverick/LRS2/STANDARDS'
filenames = glob.glob(op.join(basedir, 'multi_2021*uv.fits'))
N = 11
M = len(filenames)
k=0
XC, YC, WC = (np.zeros((M, 2*N)), np.zeros((M, 2*N)), np.zeros((2*N,)))
fitter = LevMarLSQFitter()
G = Gaussian2D()
for filename in filenames:
log.info('Working on %s' % filename)
j = 0
for name in ['uv', 'orange']:
f = fits.open(filename.replace('uv', name))
wave = f[6].data[0]
x = f[5].data[:, 0]
y = f[5].data[:, 1]
skysub = f[2].data
chunks = [np.nanmedian(xo, axis=1) for xo in np.array_split(skysub, N,
axis=1)]
wc = [np.nanmedian(xo) for xo in np.array_split(wave, N)]
xc = np.zeros((N,))
yc = np.zeros((N,))
wc = np.array(wc)
for i, chunk in enumerate(chunks):
ind = np.nanargmax(chunk)
d = np.sqrt((x-x[ind])**2 + (y-y[ind])**2)
xc[i] = x[ind]
yc[i] = y[ind]
G.amplitude.value = np.nanmax(chunk)
G.x_mean.value = xc[i]
G.y_mean.value = yc[i]
sel = (d < 3.0) * np.isfinite(chunk)
fit = fitter(G, x[sel], y[sel], chunk[sel])
xc[i] = fit.x_mean.value * 1.
yc[i] = fit.y_mean.value * 1.
XC[k, j:j+N] = xc
YC[k, j:j+N] = yc
WC[j:j+N] = wc
j += N
XC[k, WC<4650.] += 0.20
YC[k, WC<4650.] -= 0.24
sel = np.isfinite(XC[k]) * np.isfinite(YC[k])
p0 = np.polyfit(WC[sel], XC[k][sel], 3)
p1 = np.polyfit(WC[sel], YC[k][sel], 3)
xc = np.polyval(p0, 5500)
yc = np.polyval(p1, 5500)
XC[k] = XC[k] - xc
YC[k] = YC[k] - yc
if np.sqrt(xc**2 + yc**2)>2.5:
XC[k] = np.nan
YC[k] = np.nan
log.info('Centroid: %0.2f %0.2f' % (xc, yc))
k += 1
fits.HDUList([fits.PrimaryHDU(XC), fits.ImageHDU(YC), fits.ImageHDU(WC)]).writeto('test.fits', overwrite=True)
|
grzeimannREPO_NAMEPanaceaPATH_START.@Panacea_extracted@Panacea-master@get_adr.py@.PATH_END.py
|
{
"filename": "StatsAddMin.md",
"repo_name": "tensorflow/tensorflow",
"repo_path": "tensorflow_extracted/tensorflow-master/tensorflow/lite/g3doc/api_docs/python/tflite_support/metadata_schema_py_generated/StatsAddMin.md",
"type": "Markdown"
}
|
page_type: reference
<link rel="stylesheet" href="/site-assets/css/style.css">
<!-- DO NOT EDIT! Automatically generated file. -->
<div itemscope itemtype="http://developers.google.com/ReferenceObject">
<meta itemprop="name" content="tflite_support.metadata_schema_py_generated.StatsAddMin" />
<meta itemprop="path" content="Stable" />
</div>
# tflite_support.metadata_schema_py_generated.StatsAddMin
<!-- Insert buttons and diff -->
<table class="tfo-notebook-buttons tfo-api nocontent" align="left">
<td>
<a target="_blank" href="https://github.com/tensorflow/tflite-support/blob/v0.4.4/tensorflow_lite_support/metadata/metadata_schema_py_generated.py#L1874-L1875">
<img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />
View source on GitHub
</a>
</td>
</table>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link">
<code>tflite_support.metadata_schema_py_generated.StatsAddMin(
builder, min
)
</code></pre>
<!-- Placeholder for "Used in" -->
|
tensorflowREPO_NAMEtensorflowPATH_START.@tensorflow_extracted@tensorflow-master@tensorflow@lite@g3doc@api_docs@python@tflite_support@metadata_schema_py_generated@StatsAddMin.md@.PATH_END.py
|
{
"filename": "LaplaceErrorDistribution.py",
"repo_name": "dokester/BayesicFitting",
"repo_path": "BayesicFitting_extracted/BayesicFitting-master/BayesicFitting/source/LaplaceErrorDistribution.py",
"type": "Python"
}
|
import numpy as numpy
import math
from .Formatter import formatter as fmt
from .ScaledErrorDistribution import ScaledErrorDistribution
__author__ = "Do Kester"
__year__ = 2023
__license__ = "GPL3"
__version__ = "3.1.0"
__url__ = "https://www.bayesicfitting.nl"
__status__ = "Perpetual Beta"
# *
# * This file is part of the BayesicFitting package.
# *
# * BayesicFitting is free software: you can redistribute it and/or modify
# * it under the terms of the GNU Lesser General Public License as
# * published by the Free Software Foundation, either version 3 of
# * the License, or ( at your option ) any later version.
# *
# * BayesicFitting is distributed in the hope that it will be useful,
# * but WITHOUT ANY WARRANTY; without even the implied warranty of
# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# * GNU Lesser General Public License for more details.
# *
# * The GPL3 license can be found at <http://www.gnu.org/licenses/>.
# *
# * A JAVA version of this code was part of the Herschel Common
# * Science System (HCSS), also under GPL3.
# *
# * 2010 - 2014 Do Kester, SRON (Java code)
# * 2017 - 2023 Do Kester
class LaplaceErrorDistribution( ScaledErrorDistribution ):
"""
To calculate a Laplace likelihood.
For one residual, x, it holds
f( x ) = 1 / ( 2 s ) exp( - |x| / s )
where s is the scale.
s is a hyperparameter, which might be estimated from the data.
The variance of this function is σ^2 = 2 s ^ 2.
See: toSigma()
The function is mostly used to calculate the likelihood L over N
residuals, or easier using log likelihood, logL.
logL = log( N / ( 2 s ) ) - ∑( |x| / s )
Using weights this becomes:
logL = log( ∑( w ) / ( 2 s ) ) - ∑( w |x| / s )
Using this error distribution results in median-like solutions.
Author Do Kester.
"""
SQRT2 = math.sqrt( 2 )
LGSQ2 = math.log( SQRT2 )
LOG2 = math.log( 2.0 )
# *********CONSTRUCTORS***************************************************
def __init__( self, scale=1.0, limits=None, copy=None ) :
"""
Constructor of Laplace Distribution.
Parameters
----------
scale : float
noise scale
limits : None or list of 2 floats [low,high]
None : no limits implying fixed scale
low low limit on scale (needs to be >0)
high high limit on scale
when limits are set, the scale is *not* fixed.
copy : LaplaceErrorDistribution
distribution to be copied.
"""
super( LaplaceErrorDistribution, self ).__init__( scale=scale,
limits=limits, copy=copy )
def copy( self ):
""" Return copy of this. """
return LaplaceErrorDistribution( copy=self )
# *********DATA & WEIGHT***************************************************
def acceptWeight( self ):
"""
True if the distribution accepts weights.
Always true for this distribution.
"""
return True
def toSigma( self, scale ) :
"""
Return sigma, the squareroot of the variance.
Parameter
--------
scale : float
the scale of this Laplace distribution.
"""
return scale * math.sqrt( 2.0 )
def getScale( self, problem, allpars=None ) :
"""
Return the noise scale
Parameters
----------
problem : Problem
to be solved
allpars : array_like
None take parameters from problem.model
list of all parameters in the problem
"""
sumres = self.getSumRes( problem, allpars=allpars )
return sumres / problem.sumweight
def getSumRes( self, problem, allpars=None, scale=1 ):
"""
Return the sum of the absolute values of the residuals.
sum ( | res | )
Parameters
----------
problem : Problem
to be solved
allpars : array_like
None take parameters from problem.model
list of all parameters in the problem
scale : float or array_like
scale of residuals (from accuracies or noisescale of errdis)
"""
res = self.getResiduals( problem, allpars=allpars ) / scale
if problem.weights is not None :
res *= problem.weights
return numpy.sum( numpy.abs( res ) )
# *********LIKELIHOODS***************************************************
def logLikelihood_alt( self, problem, allpars ) :
"""
Return the log( likelihood ) for a Gaussian distribution.
Parameters
----------
problem : Problem
to be solved
allpars : array_like
parameters of the problem
"""
self.ncalls += 1
scale = allpars[-1] if not problem.hasAccuracy else problem.accuracy
sumres = self.getSumRes( problem, allpars, scale=scale )
if isinstance( scale, float ) :
norm = problem.sumweight * ( self.LOG2 + math.log( scale ) )
elif problem.hasWeights() :
norm = numpy.sum( problem.weights * ( self.LOG2 + numpy.log( scale ) ) )
else :
norm = numpy.sum( self.LOG2 + numpy.log( scale ) )
return -( norm + sumres )
def logLdata( self, problem, allpars, mockdata=None ) :
"""
Return the log( likelihood ) for each residual
logL = sum( logLdata )
Parameters
----------
problem : Problem
to be solved
allpars : array_like
list of all parameters in the problem
mockdata : array_like
as calculated by the model
"""
np = problem.npars
res = problem.residuals( allpars[:np], mockdata=mockdata )
scale = allpars[-1] if not problem.hasAccuracy else problem.accuracy
res = - numpy.abs( res ) / scale - ( self.LOG2 + numpy.log( scale ) )
if problem.weights is not None :
res = res * problem.weights
return res
def partialLogL_alt( self, problem, allpars, fitIndex ) :
"""
Return the partial derivative of log( likelihood ) to the parameters.
dL/ds is not implemented for problems with accuracy
Parameters
----------
problem : Problem
to be solved
allpars : array_like
list of all parameters in the problem
fitIndex : array_like
indices of parameters to be fitted
"""
self.nparts += 1
scale = allpars[-1] if not problem.hasAccuracy else problem.accuracy
dM = problem.partial( allpars[:-1] )
res = problem.residuals( allpars[:-1] )
wgt = numpy.ones_like( res, dtype=float ) if problem.weights is None else problem.weights
wgt = numpy.copysign( wgt, res ) / scale
dL = numpy.zeros( len( fitIndex ), dtype=float )
i = 0
for k in fitIndex :
if k >= 0 :
dL[i] = numpy.sum( wgt * dM[:,k] )
i += 1
else :
dL[-1] = self.getSumRes( problem, allpars=allpars, scale=scale ) - problem.sumweight
return dL
def nextPartialData( self, problem, allpars, fitIndex, mockdata=None ) :
"""
Return the partial derivative of elements of the log( likelihood )
to the parameters.
dL/ds is not implemented for problems with accuracy
Parameters
----------
problem : Problem
to be solved
allpars : array_like
list of all parameters in the problem
fitIndex : array_like
indices of parameters to be fitted
mockdata : array_like
as calculated by the model
"""
param = allpars[:-1]
scale = allpars[-1] if not problem.hasAccuracy else problem.accuracy
res = problem.residuals( param, mockdata=mockdata )
dM = problem.partial( param )
## TBD import mockdata into partial
# dM = model.partial( self.xdata, param, mockdata=mockdata )
wgt = numpy.ones_like( res, dtype=float ) if problem.weights is None else problem.weights
swgt = numpy.copysign( wgt, res )
res *= swgt / scale ## make all residuals >= 0
for k in fitIndex :
if k >= 0 :
yield ( swgt * dM[:,k] ) / scale
else :
yield ( res - wgt ) / scale
return
def __str__( self ) :
return "Laplace error distribution"
|
dokesterREPO_NAMEBayesicFittingPATH_START.@BayesicFitting_extracted@BayesicFitting-master@BayesicFitting@source@LaplaceErrorDistribution.py@.PATH_END.py
|
{
"filename": "_opacity.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/scattersmith/selected/marker/_opacity.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class OpacityValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self,
plotly_name="opacity",
parent_name="scattersmith.selected.marker",
**kwargs,
):
super(OpacityValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
max=kwargs.pop("max", 1),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@scattersmith@selected@marker@_opacity.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/partners/qdrant/langchain_qdrant/__init__.py",
"type": "Python"
}
|
from langchain_qdrant.fastembed_sparse import FastEmbedSparse
from langchain_qdrant.qdrant import QdrantVectorStore, RetrievalMode
from langchain_qdrant.sparse_embeddings import SparseEmbeddings, SparseVector
from langchain_qdrant.vectorstores import Qdrant
__all__ = [
"Qdrant",
"QdrantVectorStore",
"SparseEmbeddings",
"SparseVector",
"FastEmbedSparse",
"RetrievalMode",
]
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@partners@qdrant@langchain_qdrant@__init__.py@.PATH_END.py
|
{
"filename": "_size.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/volume/colorbar/title/font/_size.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class SizeValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(
self, plotly_name="size", parent_name="volume.colorbar.title.font", **kwargs
):
super(SizeValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 1),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@volume@colorbar@title@font@_size.py@.PATH_END.py
|
{
"filename": "test_covariance_kernels.py",
"repo_name": "ArgonneCPAC/diffmah",
"repo_path": "diffmah_extracted/diffmah-main/diffmah/diffmahpop_kernels/tests/test_covariance_kernels.py",
"type": "Python"
}
|
""""""
import numpy as np
from jax import random as jran
from ...tests.test_utils import _enforce_is_cov
from .. import covariance_kernels as ck
def test_param_u_param_names_propagate_properly():
gen = zip(ck.DEFAULT_COV_U_PARAMS._fields, ck.DEFAULT_COV_PARAMS._fields)
for u_key, key in gen:
assert u_key[:2] == "u_"
assert u_key[2:] == key
inferred_default_params = ck.get_bounded_cov_params(ck.DEFAULT_COV_U_PARAMS)
assert set(inferred_default_params._fields) == set(ck.DEFAULT_COV_PARAMS._fields)
inferred_default_u_params = ck.get_unbounded_cov_params(ck.DEFAULT_COV_PARAMS)
assert set(inferred_default_u_params._fields) == set(
ck.DEFAULT_COV_U_PARAMS._fields
)
def test_get_bounded_params_fails_when_passing_params():
try:
ck.get_bounded_cov_params(ck.DEFAULT_COV_PARAMS)
raise NameError("get_bounded_cov_params should not accept params")
except AttributeError:
pass
def test_get_unbounded_params_fails_when_passing_u_params():
try:
ck.get_unbounded_cov_params(ck.DEFAULT_COV_U_PARAMS)
raise NameError("get_unbounded_cov_params should not accept u_params")
except AttributeError:
pass
def test_param_u_param_inversion():
ran_key = jran.key(0)
n_tests = 100
for __ in range(n_tests):
ran_key, test_key = jran.split(ran_key, 2)
n_p = len(ck.DEFAULT_COV_PARAMS)
u_p = jran.uniform(test_key, minval=-100, maxval=100, shape=(n_p,))
u_p = ck.CovUParams(*u_p)
p = ck.get_bounded_cov_params(u_p)
u_p2 = ck.get_unbounded_cov_params(p)
for x, y in zip(u_p, u_p2):
assert np.allclose(x, y, rtol=0.01)
def test_default_params_are_in_bounds():
for key in ck.DEFAULT_COV_PARAMS._fields:
val = getattr(ck.DEFAULT_COV_PARAMS, key)
bound = getattr(ck.COV_PBOUNDS, key)
assert bound[0] < val < bound[1]
def test_covariances_are_always_covariances():
lgmarr = np.linspace(10, 15, 20)
ran_key = jran.key(0)
npars = len(ck.DEFAULT_COV_PARAMS)
ntests = 200
for __ in range(ntests):
ran_key, test_key = jran.split(ran_key, 2)
u_p = jran.uniform(test_key, minval=-1000, maxval=1000, shape=(npars,))
u_params = ck.CovUParams(*u_p)
cov_params = ck.get_bounded_cov_params(u_params)
for lgm in lgmarr:
cov = ck._get_diffmahpop_cov(cov_params, lgm)
assert cov.shape == (4, 4)
_enforce_is_cov(cov)
|
ArgonneCPACREPO_NAMEdiffmahPATH_START.@diffmah_extracted@diffmah-main@diffmah@diffmahpop_kernels@tests@test_covariance_kernels.py@.PATH_END.py
|
{
"filename": "tvtk_base_handler.py",
"repo_name": "enthought/mayavi",
"repo_path": "mayavi_extracted/mayavi-master/tvtk/tvtk_base_handler.py",
"type": "Python"
}
|
""" Handler and UI elements for tvtk objects.
"""
# Author: Vibha Srinivasan <vibha@enthought.com>
# Copyright (c) 2008-2020, Enthought, Inc.
# License: BSD Style.
from traits.api import HasTraits, Str, Instance, Property, Button, List, Enum
from traitsui.handler import Handler
from traitsui.ui_info import UIInfo
from traitsui.item import Item
from traitsui.view import View
from traits.trait_base import user_name_for, xgetattr
def TableEditor(*args, **kw):
from .value_column import ObjectColumn, ValueColumn
from traitsui.api import TableEditor as _E
return _E(columns=[ObjectColumn(name='name'), ValueColumn(name='value')])
class TraitsViewObject(HasTraits):
""" Wrapper for all items to be included in the full traits view of the TVTKBase
object.
"""
# Trait name (name of the trait that is to be included in the view).
name = Str
# The TVTKBase object for which we are building a view.
parent = Instance(HasTraits)
class TVTKBaseHandler(Handler):
""" A handler for the TVTKBase object.
"""
# A reference to the UIInfo object.
info = Instance(UIInfo)
# Type of view (of info.object) to display.
view_type = Enum(['Basic', 'Advanced'])
# The view for the object (that is, info.object)
view = Property(depends_on='view_type')
# List of TraitsViewObject items, where each item contains information
# about the trait to display as a row in a table editor.
_full_traits_list = Property(List, editor=TableEditor)
def init_info(self, info):
""" Informs the handler what the UIInfo object for a View will be.
Overridden here to save a reference to the info object.
"""
self.info = info
return
def _get__full_traits_list(self):
""" Returns a list of objects to be included in the table editor for
the full traits view.
"""
return [TraitsViewObject(name=name, parent = self.info.object)
for name in self.info.object._full_traitnames_list_]
def _get_view(self):
""" Returns the view (for info.object) to be displayed in the
InstanceEditor.
"""
if self.view_type == "Basic":
view = self.info.object.trait_view('view')
else:
view = self.info.object.trait_view('full_traits_view')
# This method is called when the default traits view for the object is
# displayed. The default traits view already has a title, so do not
# display a title for the contained view.
view.title = ''
return view
#### EOF ###################################################################
|
enthoughtREPO_NAMEmayaviPATH_START.@mayavi_extracted@mayavi-master@tvtk@tvtk_base_handler.py@.PATH_END.py
|
{
"filename": "test_empty.py",
"repo_name": "pandas-dev/pandas",
"repo_path": "pandas_extracted/pandas-main/pandas/tests/reshape/concat/test_empty.py",
"type": "Python"
}
|
import numpy as np
import pytest
import pandas as pd
from pandas import (
DataFrame,
RangeIndex,
Series,
concat,
date_range,
)
import pandas._testing as tm
class TestEmptyConcat:
def test_handle_empty_objects(self, sort, using_infer_string):
df = DataFrame(
np.random.default_rng(2).standard_normal((10, 4)), columns=list("abcd")
)
dfcopy = df[:5].copy()
dfcopy["foo"] = "bar"
empty = df[5:5]
frames = [dfcopy, empty, empty, df[5:]]
concatted = concat(frames, axis=0, sort=sort)
expected = df.reindex(columns=["a", "b", "c", "d", "foo"])
expected["foo"] = expected["foo"].astype(
object if not using_infer_string else "str"
)
expected.loc[0:4, "foo"] = "bar"
tm.assert_frame_equal(concatted, expected)
# empty as first element with time series
# GH3259
df = DataFrame(
{"A": range(10000)}, index=date_range("20130101", periods=10000, freq="s")
)
empty = DataFrame()
result = concat([df, empty], axis=1)
tm.assert_frame_equal(result, df)
result = concat([empty, df], axis=1)
tm.assert_frame_equal(result, df)
result = concat([df, empty])
tm.assert_frame_equal(result, df)
result = concat([empty, df])
tm.assert_frame_equal(result, df)
def test_concat_empty_series(self):
# GH 11082
s1 = Series([1, 2, 3], name="x")
s2 = Series(name="y", dtype="float64")
res = concat([s1, s2], axis=1)
exp = DataFrame(
{"x": [1, 2, 3], "y": [np.nan, np.nan, np.nan]},
index=RangeIndex(3),
)
tm.assert_frame_equal(res, exp)
s1 = Series([1, 2, 3], name="x")
s2 = Series(name="y", dtype="float64")
res = concat([s1, s2], axis=0)
# name will be reset
exp = Series([1, 2, 3], dtype="float64")
tm.assert_series_equal(res, exp)
# empty Series with no name
s1 = Series([1, 2, 3], name="x")
s2 = Series(name=None, dtype="float64")
res = concat([s1, s2], axis=1)
exp = DataFrame(
{"x": [1, 2, 3], 0: [np.nan, np.nan, np.nan]},
columns=["x", 0],
index=RangeIndex(3),
)
tm.assert_frame_equal(res, exp)
@pytest.mark.parametrize("tz", [None, "UTC"])
@pytest.mark.parametrize("values", [[], [1, 2, 3]])
def test_concat_empty_series_timelike(self, tz, values):
# GH 18447
first = Series([], dtype="M8[ns]").dt.tz_localize(tz)
dtype = None if values else np.float64
second = Series(values, dtype=dtype)
expected = DataFrame(
{
0: Series([pd.NaT] * len(values), dtype="M8[ns]").dt.tz_localize(tz),
1: values,
}
)
result = concat([first, second], axis=1)
tm.assert_frame_equal(result, expected)
@pytest.mark.parametrize(
"left,right,expected",
[
# booleans
(np.bool_, np.int32, np.object_), # changed from int32 in 2.0 GH#39817
(np.bool_, np.float32, np.object_),
# datetime-like
("m8[ns]", np.bool_, np.object_),
("m8[ns]", np.int64, np.object_),
("M8[ns]", np.bool_, np.object_),
("M8[ns]", np.int64, np.object_),
# categorical
("category", "category", "category"),
("category", "object", "object"),
],
)
def test_concat_empty_series_dtypes(self, left, right, expected):
# GH#39817, GH#45101
result = concat([Series(dtype=left), Series(dtype=right)])
assert result.dtype == expected
@pytest.mark.parametrize(
"dtype", ["float64", "int8", "uint8", "bool", "m8[ns]", "M8[ns]"]
)
def test_concat_empty_series_dtypes_match_roundtrips(self, dtype):
dtype = np.dtype(dtype)
result = concat([Series(dtype=dtype)])
assert result.dtype == dtype
result = concat([Series(dtype=dtype), Series(dtype=dtype)])
assert result.dtype == dtype
@pytest.mark.parametrize("dtype", ["float64", "int8", "uint8", "m8[ns]", "M8[ns]"])
@pytest.mark.parametrize(
"dtype2",
["float64", "int8", "uint8", "m8[ns]", "M8[ns]"],
)
def test_concat_empty_series_dtypes_roundtrips(self, dtype, dtype2):
# round-tripping with self & like self
if dtype == dtype2:
pytest.skip("same dtype is not applicable for test")
def int_result_type(dtype, dtype2):
typs = {dtype.kind, dtype2.kind}
if not len(typs - {"i", "u", "b"}) and (
dtype.kind == "i" or dtype2.kind == "i"
):
return "i"
elif not len(typs - {"u", "b"}) and (
dtype.kind == "u" or dtype2.kind == "u"
):
return "u"
return None
def float_result_type(dtype, dtype2):
typs = {dtype.kind, dtype2.kind}
if not len(typs - {"f", "i", "u"}) and (
dtype.kind == "f" or dtype2.kind == "f"
):
return "f"
return None
def get_result_type(dtype, dtype2):
result = float_result_type(dtype, dtype2)
if result is not None:
return result
result = int_result_type(dtype, dtype2)
if result is not None:
return result
return "O"
dtype = np.dtype(dtype)
dtype2 = np.dtype(dtype2)
expected = get_result_type(dtype, dtype2)
result = concat([Series(dtype=dtype), Series(dtype=dtype2)]).dtype
assert result.kind == expected
def test_concat_empty_series_dtypes_triple(self):
assert (
concat(
[Series(dtype="M8[ns]"), Series(dtype=np.bool_), Series(dtype=np.int64)]
).dtype
== np.object_
)
def test_concat_empty_series_dtype_category_with_array(self):
# GH#18515
assert (
concat(
[Series(np.array([]), dtype="category"), Series(dtype="float64")]
).dtype
== "float64"
)
def test_concat_empty_series_dtypes_sparse(self):
result = concat(
[
Series(dtype="float64").astype("Sparse"),
Series(dtype="float64").astype("Sparse"),
]
)
assert result.dtype == "Sparse[float64]"
result = concat(
[Series(dtype="float64").astype("Sparse"), Series(dtype="float64")]
)
expected = pd.SparseDtype(np.float64)
assert result.dtype == expected
result = concat(
[Series(dtype="float64").astype("Sparse"), Series(dtype="object")]
)
expected = pd.SparseDtype("object")
assert result.dtype == expected
def test_concat_empty_df_object_dtype(self):
# GH 9149
df_1 = DataFrame({"Row": [0, 1, 1], "EmptyCol": np.nan, "NumberCol": [1, 2, 3]})
df_2 = DataFrame(columns=df_1.columns)
result = concat([df_1, df_2], axis=0)
expected = df_1.astype(object)
tm.assert_frame_equal(result, expected)
def test_concat_empty_dataframe_dtypes(self):
df = DataFrame(columns=list("abc"))
df["a"] = df["a"].astype(np.bool_)
df["b"] = df["b"].astype(np.int32)
df["c"] = df["c"].astype(np.float64)
result = concat([df, df])
assert result["a"].dtype == np.bool_
assert result["b"].dtype == np.int32
assert result["c"].dtype == np.float64
result = concat([df, df.astype(np.float64)])
assert result["a"].dtype == np.object_
assert result["b"].dtype == np.float64
assert result["c"].dtype == np.float64
def test_concat_inner_join_empty(self):
# GH 15328
df_empty = DataFrame()
df_a = DataFrame({"a": [1, 2]}, index=[0, 1], dtype="int64")
df_expected = DataFrame({"a": []}, index=RangeIndex(0), dtype="int64")
result = concat([df_a, df_empty], axis=1, join="inner")
tm.assert_frame_equal(result, df_expected)
result = concat([df_a, df_empty], axis=1, join="outer")
tm.assert_frame_equal(result, df_a)
def test_empty_dtype_coerce(self):
# xref to #12411
# xref to #12045
# xref to #11594
# see below
# 10571
df1 = DataFrame(data=[[1, None], [2, None]], columns=["a", "b"])
df2 = DataFrame(data=[[3, None], [4, None]], columns=["a", "b"])
result = concat([df1, df2])
expected = df1.dtypes
tm.assert_series_equal(result.dtypes, expected)
def test_concat_empty_dataframe(self):
# 39037
df1 = DataFrame(columns=["a", "b"])
df2 = DataFrame(columns=["b", "c"])
result = concat([df1, df2, df1])
expected = DataFrame(columns=["a", "b", "c"])
tm.assert_frame_equal(result, expected)
df3 = DataFrame(columns=["a", "b"])
df4 = DataFrame(columns=["b"])
result = concat([df3, df4])
expected = DataFrame(columns=["a", "b"])
tm.assert_frame_equal(result, expected)
def test_concat_empty_dataframe_different_dtypes(self, using_infer_string):
# 39037
df1 = DataFrame({"a": [1, 2, 3], "b": ["a", "b", "c"]})
df2 = DataFrame({"a": [1, 2, 3]})
result = concat([df1[:0], df2[:0]])
assert result["a"].dtype == np.int64
assert result["b"].dtype == np.object_ if not using_infer_string else "str"
def test_concat_to_empty_ea(self):
"""48510 `concat` to an empty EA should maintain type EA dtype."""
df_empty = DataFrame({"a": pd.array([], dtype=pd.Int64Dtype())})
df_new = DataFrame({"a": pd.array([1, 2, 3], dtype=pd.Int64Dtype())})
expected = df_new.copy()
result = concat([df_empty, df_new])
tm.assert_frame_equal(result, expected)
|
pandas-devREPO_NAMEpandasPATH_START.@pandas_extracted@pandas-main@pandas@tests@reshape@concat@test_empty.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/graph_objs/scatter/hoverlabel/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._font import Font
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(__name__, [], ["._font.Font"])
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@graph_objs@scatter@hoverlabel@__init__.py@.PATH_END.py
|
{
"filename": "test_c_reader.py",
"repo_name": "waynebhayes/SpArcFiRe",
"repo_path": "SpArcFiRe_extracted/SpArcFiRe-master/scripts/SpArcFiRe-pyvenv/lib/python2.7/site-packages/astropy/io/ascii/tests/test_c_reader.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
try:
from cStringIO import StringIO
except ImportError: # cStringIO doesn't exist in Python 3
from io import BytesIO
StringIO = lambda x: BytesIO(x.encode('ascii'))
import os
import functools
from textwrap import dedent
import pytest
import numpy as np
from numpy import ma
from ....table import Table, MaskedColumn
from ... import ascii
from ...ascii.core import ParameterError, FastOptionsError
from ...ascii.cparser import CParserError
from ..fastbasic import FastBasic, FastCsv, FastTab, FastCommentedHeader, \
FastRdb, FastNoHeader
from .common import assert_equal, assert_almost_equal, assert_true
from ....extern import six
from ....extern.six.moves import range
TRAVIS = os.environ.get('TRAVIS', False)
def assert_table_equal(t1, t2, check_meta=False):
assert_equal(len(t1), len(t2))
assert_equal(t1.colnames, t2.colnames)
if check_meta:
assert_equal(t1.meta, t2.meta)
for name in t1.colnames:
if len(t1) != 0:
assert_equal(t1[name].dtype.kind, t2[name].dtype.kind)
if not isinstance(t1[name], MaskedColumn):
for i, el in enumerate(t1[name]):
try:
if not isinstance(el, six.string_types) and np.isnan(el):
assert_true(not isinstance(t2[name][i], six.string_types) and np.isnan(t2[name][i]))
elif isinstance(el, six.string_types):
assert_equal(el, t2[name][i])
else:
assert_almost_equal(el, t2[name][i])
except (TypeError, NotImplementedError):
pass # ignore for now
# Use this counter to create a unique filename for each file created in a test
# if this function is called more than once in a single test
_filename_counter = 0
def _read(tmpdir, table, Reader=None, format=None, parallel=False, check_meta=False, **kwargs):
# make sure we have a newline so table can't be misinterpreted as a filename
global _filename_counter
table += '\n'
reader = Reader(**kwargs)
t1 = reader.read(table)
t2 = reader.read(StringIO(table))
t3 = reader.read(table.splitlines())
t4 = ascii.read(table, format=format, guess=False, **kwargs)
t5 = ascii.read(table, format=format, guess=False, fast_reader=False, **kwargs)
assert_table_equal(t1, t2, check_meta=check_meta)
assert_table_equal(t2, t3, check_meta=check_meta)
assert_table_equal(t3, t4, check_meta=check_meta)
assert_table_equal(t4, t5, check_meta=check_meta)
if parallel:
if TRAVIS:
pytest.xfail("Multiprocessing can sometimes fail on Travis CI")
elif os.name == 'nt':
pytest.xfail("Multiprocessing is currently unsupported on Windows")
t6 = ascii.read(table, format=format, guess=False, fast_reader={
'parallel': True}, **kwargs)
assert_table_equal(t1, t6, check_meta=check_meta)
filename = str(tmpdir.join('table{0}.txt'.format(_filename_counter)))
_filename_counter += 1
with open(filename, 'wb') as f:
f.write(table.encode('ascii'))
f.flush()
t7 = ascii.read(filename, format=format, guess=False, **kwargs)
if parallel:
t8 = ascii.read(filename, format=format, guess=False, fast_reader={
'parallel': True}, **kwargs)
assert_table_equal(t1, t7, check_meta=check_meta)
if parallel:
assert_table_equal(t1, t8, check_meta=check_meta)
return t1
@pytest.fixture(scope='function')
def read_basic(tmpdir, request):
return functools.partial(_read, tmpdir, Reader=FastBasic, format='basic')
@pytest.fixture(scope='function')
def read_csv(tmpdir, request):
return functools.partial(_read, tmpdir, Reader=FastCsv, format='csv')
@pytest.fixture(scope='function')
def read_tab(tmpdir, request):
return functools.partial(_read, tmpdir, Reader=FastTab, format='tab')
@pytest.fixture(scope='function')
def read_commented_header(tmpdir, request):
return functools.partial(_read, tmpdir, Reader=FastCommentedHeader,
format='commented_header')
@pytest.fixture(scope='function')
def read_rdb(tmpdir, request):
return functools.partial(_read, tmpdir, Reader=FastRdb, format='rdb')
@pytest.fixture(scope='function')
def read_no_header(tmpdir, request):
return functools.partial(_read, tmpdir, Reader=FastNoHeader,
format='no_header')
@pytest.mark.parametrize("parallel", [True, False])
def test_simple_data(parallel, read_basic):
"""
Make sure the fast reader works with basic input data.
"""
table = read_basic("A B C\n1 2 3\n4 5 6", parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
def test_read_types():
"""
Make sure that the read() function takes filenames,
strings, and lists of strings in addition to file-like objects.
"""
t1 = ascii.read("a b c\n1 2 3\n4 5 6", format='fast_basic', guess=False)
# TODO: also read from file
t2 = ascii.read(StringIO("a b c\n1 2 3\n4 5 6"), format='fast_basic', guess=False)
t3 = ascii.read(["a b c", "1 2 3", "4 5 6"], format='fast_basic', guess=False)
assert_table_equal(t1, t2)
assert_table_equal(t2, t3)
@pytest.mark.parametrize("parallel", [True, False])
def test_supplied_names(parallel, read_basic):
"""
If passed as a parameter, names should replace any
column names found in the header.
"""
table = read_basic("A B C\n1 2 3\n4 5 6", names=('X', 'Y', 'Z'), parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('X', 'Y', 'Z'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_no_header(parallel, read_basic, read_no_header):
"""
The header should not be read when header_start=None. Unless names is
passed, the column names should be auto-generated.
"""
# Cannot set header_start=None for basic format
with pytest.raises(ValueError):
read_basic("A B C\n1 2 3\n4 5 6", header_start=None, data_start=0, parallel=parallel)
t2 = read_no_header("A B C\n1 2 3\n4 5 6", parallel=parallel)
expected = Table([['A', '1', '4'], ['B', '2', '5'], ['C', '3', '6']], names=('col1', 'col2', 'col3'))
assert_table_equal(t2, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_no_header_supplied_names(parallel, read_basic, read_no_header):
"""
If header_start=None and names is passed as a parameter, header
data should not be read and names should be used instead.
"""
table = read_no_header("A B C\n1 2 3\n4 5 6",
names=('X', 'Y', 'Z'), parallel=parallel)
expected = Table([['A', '1', '4'], ['B', '2', '5'], ['C', '3', '6']], names=('X', 'Y', 'Z'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_comment(parallel, read_basic):
"""
Make sure that line comments are ignored by the C reader.
"""
table = read_basic("# comment\nA B C\n # another comment\n1 2 3\n4 5 6", parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_empty_lines(parallel, read_basic):
"""
Make sure that empty lines are ignored by the C reader.
"""
table = read_basic("\n\nA B C\n1 2 3\n\n\n4 5 6\n\n\n\n", parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_lstrip_whitespace(parallel, read_basic):
"""
Test to make sure the reader ignores whitespace at the beginning of fields.
"""
text = """
1, 2, \t3
A,\t\t B, C
a, b, c
""" + ' \n'
table = read_basic(text, delimiter=',', parallel=parallel)
expected = Table([['A', 'a'], ['B', 'b'], ['C', 'c']], names=('1', '2', '3'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_rstrip_whitespace(parallel, read_basic):
"""
Test to make sure the reader ignores whitespace at the end of fields.
"""
text = ' 1 ,2 \t,3 \nA\t,B ,C\t \t \n \ta ,b , c \n'
table = read_basic(text, delimiter=',', parallel=parallel)
expected = Table([['A', 'a'], ['B', 'b'], ['C', 'c']], names=('1', '2', '3'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_conversion(parallel, read_basic):
"""
The reader should try to convert each column to ints. If this fails, the
reader should try to convert to floats. Failing this, it should fall back
to strings.
"""
text = """
A B C D E
1 a 3 4 5
2. 1 9 10 -5.3e4
4 2 -12 .4 six
"""
table = read_basic(text, parallel=parallel)
assert_equal(table['A'].dtype.kind, 'f')
assert table['B'].dtype.kind in ('S', 'U')
assert_equal(table['C'].dtype.kind, 'i')
assert_equal(table['D'].dtype.kind, 'f')
assert table['E'].dtype.kind in ('S', 'U')
@pytest.mark.parametrize("parallel", [True, False])
def test_delimiter(parallel, read_basic):
"""
Make sure that different delimiters work as expected.
"""
text = """
COL1 COL2 COL3
1 A -1
2 B -2
"""
expected = Table([[1, 2], ['A', 'B'], [-1, -2]], names=('COL1', 'COL2', 'COL3'))
for sep in ' ,\t#;':
table = read_basic(text.replace(' ', sep), delimiter=sep, parallel=parallel)
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_include_names(parallel, read_basic):
"""
If include_names is not None, the parser should read only those columns in include_names.
"""
table = read_basic("A B C D\n1 2 3 4\n5 6 7 8", include_names=['A', 'D'], parallel=parallel)
expected = Table([[1, 5], [4, 8]], names=('A', 'D'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_exclude_names(parallel, read_basic):
"""
If exclude_names is not None, the parser should exclude the columns in exclude_names.
"""
table = read_basic("A B C D\n1 2 3 4\n5 6 7 8", exclude_names=['A', 'D'], parallel=parallel)
expected = Table([[2, 6], [3, 7]], names=('B', 'C'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_include_exclude_names(parallel, read_basic):
"""
Make sure that include_names is applied before exclude_names if both are specified.
"""
text = """
A B C D E F G H
1 2 3 4 5 6 7 8
9 10 11 12 13 14 15 16
"""
table = read_basic(text, include_names=['A', 'B', 'D', 'F', 'H'],
exclude_names=['B', 'F'], parallel=parallel)
expected = Table([[1, 9], [4, 12], [8, 16]], names=('A', 'D', 'H'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_quoted_fields(parallel, read_basic):
"""
The character quotechar (default '"') should denote the start of a field which can
contain the field delimiter and newlines.
"""
if parallel:
pytest.xfail("Multiprocessing can fail with quoted fields")
text = """
"A B" C D
1.5 2.1 -37.1
a b " c
d"
"""
table = read_basic(text, parallel=parallel)
expected = Table([['1.5', 'a'], ['2.1', 'b'], ['-37.1', 'cd']], names=('A B', 'C', 'D'))
assert_table_equal(table, expected)
table = read_basic(text.replace('"', "'"), quotechar="'", parallel=parallel)
assert_table_equal(table, expected)
@pytest.mark.parametrize("key,val", [
('delimiter', ',,'), # multi-char delimiter
('comment', '##'), # multi-char comment
('data_start', None), # data_start=None
('data_start', -1), # data_start negative
('quotechar', '##'), # multi-char quote signifier
('header_start', -1), # negative header_start
('converters', dict((i + 1, ascii.convert_numpy(np.uint)) for i in range(3))), # passing converters
('Inputter', ascii.ContinuationLinesInputter), # passing Inputter
('header_Splitter', ascii.DefaultSplitter), # passing Splitter
('data_Splitter', ascii.DefaultSplitter)])
def test_invalid_parameters(key, val):
"""
Make sure the C reader raises an error if passed parameters it can't handle.
"""
with pytest.raises(ParameterError):
FastBasic(**{key: val}).read('1 2 3\n4 5 6')
with pytest.raises(ParameterError):
ascii.read('1 2 3\n4 5 6',
format='fast_basic', guess=False, **{key: val})
def test_invalid_parameters_other():
with pytest.raises(TypeError):
FastBasic(foo=7).read('1 2 3\n4 5 6') # unexpected argument
with pytest.raises(FastOptionsError): # don't fall back on the slow reader
ascii.read('1 2 3\n4 5 6', format='basic', fast_reader={'foo': 7})
with pytest.raises(ParameterError):
# Outputter cannot be specified in constructor
FastBasic(Outputter=ascii.TableOutputter).read('1 2 3\n4 5 6')
def test_too_many_cols1():
"""
If a row contains too many columns, the C reader should raise an error.
"""
text = """
A B C
1 2 3
4 5 6
7 8 9 10
11 12 13
"""
with pytest.raises(CParserError) as e:
table = FastBasic().read(text)
assert 'CParserError: an error occurred while parsing table data: too many ' \
'columns found in line 3 of data' in str(e)
def test_too_many_cols2():
text = """\
aaa,bbb
1,2,
3,4,
"""
with pytest.raises(CParserError) as e:
table = FastCsv().read(text)
assert 'CParserError: an error occurred while parsing table data: too many ' \
'columns found in line 1 of data' in str(e)
def test_too_many_cols3():
text = """\
aaa,bbb
1,2,,
3,4,
"""
with pytest.raises(CParserError) as e:
table = FastCsv().read(text)
assert 'CParserError: an error occurred while parsing table data: too many ' \
'columns found in line 1 of data' in str(e)
@pytest.mark.parametrize("parallel", [True, False])
def test_not_enough_cols(parallel, read_csv):
"""
If a row does not have enough columns, the FastCsv reader should add empty
fields while the FastBasic reader should raise an error.
"""
text = """
A,B,C
1,2,3
4,5
6,7,8
"""
table = read_csv(text, parallel=parallel)
assert table['B'][1] is not ma.masked
assert table['C'][1] is ma.masked
with pytest.raises(CParserError) as e:
table = FastBasic(delimiter=',').read(text)
@pytest.mark.parametrize("parallel", [True, False])
def test_data_end(parallel, read_basic, read_rdb):
"""
The parameter data_end should specify where data reading ends.
"""
text = """
A B C
1 2 3
4 5 6
7 8 9
10 11 12
"""
table = read_basic(text, data_end=3, parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
# data_end supports negative indexing
table = read_basic(text, data_end=-2, parallel=parallel)
assert_table_equal(table, expected)
text = """
A\tB\tC
N\tN\tS
1\t2\ta
3\t4\tb
5\t6\tc
"""
# make sure data_end works with RDB
table = read_rdb(text, data_end=-1, parallel=parallel)
expected = Table([[1, 3], [2, 4], ['a', 'b']], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
# positive index
table = read_rdb(text, data_end=3, parallel=parallel)
expected = Table([[1], [2], ['a']], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
# empty table if data_end is too small
table = read_rdb(text, data_end=1, parallel=parallel)
expected = Table([[], [], []], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_inf_nan(parallel, read_basic):
"""
Test that inf and nan-like values are correctly parsed on all platforms.
Regression test for https://github.com/astropy/astropy/pull/3525
"""
text = dedent("""\
A
nan
+nan
-nan
inf
infinity
+inf
+infinity
-inf
-infinity
""")
expected = Table({'A': [np.nan, np.nan, np.nan,
np.inf, np.inf, np.inf, np.inf,
-np.inf, -np.inf]})
table = read_basic(text, parallel=parallel)
assert table['A'].dtype.kind == 'f'
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_fill_values(parallel, read_basic):
"""
Make sure that the parameter fill_values works as intended. If fill_values
is not specified, the default behavior should be to convert '' to 0.
"""
text = """
A, B, C
, 2, nan
a, -999, -3.4
nan, 5, -9999
8, nan, 7.6e12
"""
table = read_basic(text, delimiter=',', parallel=parallel)
# The empty value in row A should become a masked '0'
assert isinstance(table['A'], MaskedColumn)
assert table['A'][0] is ma.masked
# '0' rather than 0 because there is a string in the column
assert_equal(table['A'].data.data[0], '0')
assert table['A'][1] is not ma.masked
table = read_basic(text, delimiter=',', fill_values=('-999', '0'), parallel=parallel)
assert isinstance(table['B'], MaskedColumn)
assert table['A'][0] is not ma.masked # empty value unaffected
assert table['C'][2] is not ma.masked # -9999 is not an exact match
assert table['B'][1] is ma.masked
# Numeric because the rest of the column contains numeric data
assert_equal(table['B'].data.data[1], 0.0)
assert table['B'][0] is not ma.masked
table = read_basic(text, delimiter=',', fill_values=[], parallel=parallel)
# None of the columns should be masked
for name in 'ABC':
assert not isinstance(table[name], MaskedColumn)
table = read_basic(text, delimiter=',', fill_values=[('', '0', 'A'),
('nan', '999', 'A', 'C')], parallel=parallel)
assert np.isnan(table['B'][3]) # nan filling skips column B
assert table['B'][3] is not ma.masked # should skip masking as well as replacing nan
assert table['A'][0] is ma.masked
assert table['A'][2] is ma.masked
assert_equal(table['A'].data.data[0], '0')
assert_equal(table['A'].data.data[2], '999')
assert table['C'][0] is ma.masked
assert_almost_equal(table['C'].data.data[0], 999.0)
assert_almost_equal(table['C'][1], -3.4) # column is still of type float
@pytest.mark.parametrize("parallel", [True, False])
def test_fill_include_exclude_names(parallel, read_csv):
"""
fill_include_names and fill_exclude_names should filter missing/empty value handling
in the same way that include_names and exclude_names filter output columns.
"""
text = """
A, B, C
, 1, 2
3, , 4
5, 5,
"""
table = read_csv(text, fill_include_names=['A', 'B'], parallel=parallel)
assert table['A'][0] is ma.masked
assert table['B'][1] is ma.masked
assert table['C'][2] is not ma.masked # C not in fill_include_names
table = read_csv(text, fill_exclude_names=['A', 'B'], parallel=parallel)
assert table['C'][2] is ma.masked
assert table['A'][0] is not ma.masked
assert table['B'][1] is not ma.masked # A and B excluded from fill handling
table = read_csv(text, fill_include_names=['A', 'B'], fill_exclude_names=['B'], parallel=parallel)
assert table['A'][0] is ma.masked
assert table['B'][1] is not ma.masked # fill_exclude_names applies after fill_include_names
assert table['C'][2] is not ma.masked
@pytest.mark.parametrize("parallel", [True, False])
def test_many_rows(parallel, read_basic):
"""
Make sure memory reallocation works okay when the number of rows
is large (so that each column string is longer than INITIAL_COL_SIZE).
"""
text = 'A B C\n'
for i in range(500): # create 500 rows
text += ' '.join([str(i) for i in range(3)])
text += '\n'
table = read_basic(text, parallel=parallel)
expected = Table([[0] * 500, [1] * 500, [2] * 500], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_many_columns(parallel, read_basic):
"""
Make sure memory reallocation works okay when the number of columns
is large (so that each header string is longer than INITIAL_HEADER_SIZE).
"""
# create a string with 500 columns and two data rows
text = ' '.join([str(i) for i in range(500)])
text += ('\n' + text + '\n' + text)
table = read_basic(text, parallel=parallel)
expected = Table([[i, i] for i in range(500)], names=[str(i) for i in range(500)])
assert_table_equal(table, expected)
def test_fast_reader():
"""
Make sure that ascii.read() works as expected by default and with
fast_reader specified.
"""
text = 'a b c\n1 2 3\n4 5 6'
with pytest.raises(ParameterError): # C reader can't handle regex comment
ascii.read(text, format='fast_basic', guess=False, comment='##')
# Enable multiprocessing and the fast converter
try:
ascii.read(text, format='basic', guess=False,
fast_reader={'parallel': True, 'use_fast_converter': True})
except NotImplementedError:
# Might get this on Windows, try without parallel...
if os.name == 'nt':
ascii.read(text, format='basic', guess=False,
fast_reader={'parallel': False,
'use_fast_converter': True})
else:
raise
# Should raise an error if fast_reader has an invalid key
with pytest.raises(FastOptionsError):
ascii.read(text, format='fast_basic', guess=False, fast_reader={'foo': True})
# Use the slow reader instead
ascii.read(text, format='basic', guess=False, comment='##', fast_reader=False)
# Will try the slow reader afterwards by default
ascii.read(text, format='basic', guess=False, comment='##')
@pytest.mark.parametrize("parallel", [True, False])
def test_read_tab(parallel, read_tab):
"""
The fast reader for tab-separated values should not strip whitespace, unlike
the basic reader.
"""
if parallel:
pytest.xfail("Multiprocessing can fail with quoted fields")
text = '1\t2\t3\n a\t b \t\n c\t" d\n e"\t '
table = read_tab(text, parallel=parallel)
assert_equal(table['1'][0], ' a') # preserve line whitespace
assert_equal(table['2'][0], ' b ') # preserve field whitespace
assert table['3'][0] is ma.masked # empty value should be masked
assert_equal(table['2'][1], ' d e') # preserve whitespace in quoted fields
assert_equal(table['3'][1], ' ') # preserve end-of-line whitespace
@pytest.mark.parametrize("parallel", [True, False])
def test_default_data_start(parallel, read_basic):
"""
If data_start is not explicitly passed to read(), data processing should
beginning right after the header.
"""
text = 'ignore this line\na b c\n1 2 3\n4 5 6'
table = read_basic(text, header_start=1, parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('a', 'b', 'c'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_commented_header(parallel, read_commented_header):
"""
The FastCommentedHeader reader should mimic the behavior of the
CommentedHeader by overriding the default header behavior of FastBasic.
"""
text = """
# A B C
1 2 3
4 5 6
"""
t1 = read_commented_header(text, parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('A', 'B', 'C'))
assert_table_equal(t1, expected)
text = '# first commented line\n # second commented line\n\n' + text
t2 = read_commented_header(text, header_start=2, data_start=0, parallel=parallel)
assert_table_equal(t2, expected)
t3 = read_commented_header(text, header_start=-1, data_start=0, parallel=parallel) # negative indexing allowed
assert_table_equal(t3, expected)
text += '7 8 9'
t4 = read_commented_header(text, header_start=2, data_start=2, parallel=parallel)
expected = Table([[7], [8], [9]], names=('A', 'B', 'C'))
assert_table_equal(t4, expected)
with pytest.raises(ParameterError):
read_commented_header(text, header_start=-1, data_start=-1, parallel=parallel) # data_start cannot be negative
@pytest.mark.parametrize("parallel", [True, False])
def test_rdb(parallel, read_rdb):
"""
Make sure the FastRdb reader works as expected.
"""
text = """
A\tB\tC
1n\tS\t4N
1\t 9\t4.3
"""
table = read_rdb(text, parallel=parallel)
expected = Table([[1], [' 9'], [4.3]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
assert_equal(table['A'].dtype.kind, 'i')
assert table['B'].dtype.kind in ('S', 'U')
assert_equal(table['C'].dtype.kind, 'f')
with pytest.raises(ValueError) as e:
text = 'A\tB\tC\nN\tS\tN\n4\tb\ta' # C column contains non-numeric data
read_rdb(text, parallel=parallel)
assert 'Column C failed to convert' in str(e)
with pytest.raises(ValueError) as e:
text = 'A\tB\tC\nN\tN\n1\t2\t3' # not enough types specified
read_rdb(text, parallel=parallel)
assert 'mismatch between number of column names and column types' in str(e)
with pytest.raises(ValueError) as e:
text = 'A\tB\tC\nN\tN\t5\n1\t2\t3' # invalid type for column C
read_rdb(text, parallel=parallel)
assert 'type definitions do not all match [num](N|S)' in str(e)
@pytest.mark.parametrize("parallel", [True, False])
def test_data_start(parallel, read_basic):
"""
Make sure that data parsing begins at data_start (ignoring empty and
commented lines but not taking quoted values into account).
"""
if parallel:
pytest.xfail("Multiprocessing can fail with quoted fields")
text = """
A B C
1 2 3
4 5 6
7 8 "9
\t1"
# comment
10 11 12
"""
table = read_basic(text, data_start=2, parallel=parallel)
expected = Table([[4, 7, 10], [5, 8, 11], [6, 91, 12]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
table = read_basic(text, data_start=3, parallel=parallel)
# ignore empty line
expected = Table([[7, 10], [8, 11], [91, 12]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
with pytest.raises(CParserError) as e:
# tries to begin in the middle of quoted field
read_basic(text, data_start=4, parallel=parallel)
assert 'not enough columns found in line 1 of data' in str(e)
table = read_basic(text, data_start=5, parallel=parallel)
# ignore commented line
expected = Table([[10], [11], [12]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
text = """
A B C
1 2 3
4 5 6
7 8 9
# comment
10 11 12
"""
# make sure reading works as expected in parallel
table = read_basic(text, data_start=2, parallel=parallel)
expected = Table([[4, 7, 10], [5, 8, 11], [6, 9, 12]], names=('A', 'B', 'C'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_quoted_empty_values(parallel, read_basic):
"""
Quoted empty values spanning multiple lines should be treated correctly.
"""
if parallel:
pytest.xfail("Multiprocessing can fail with quoted fields")
text = 'a b c\n1 2 " \n "'
table = read_basic(text, parallel=parallel)
assert table['c'][0] is ma.masked # empty value masked by default
@pytest.mark.parametrize("parallel", [True, False])
def test_csv_comment_default(parallel, read_csv):
"""
Unless the comment parameter is specified, the CSV reader should
not treat any lines as comments.
"""
text = 'a,b,c\n#1,2,3\n4,5,6'
table = read_csv(text, parallel=parallel)
expected = Table([['#1', '4'], [2, 5], [3, 6]], names=('a', 'b', 'c'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_whitespace_before_comment(parallel, read_tab):
"""
Readers that don't strip whitespace from data (Tab, RDB)
should still treat lines with leading whitespace and then
the comment char as comment lines.
"""
text = 'a\tb\tc\n # comment line\n1\t2\t3'
table = read_tab(text, parallel=parallel)
expected = Table([[1], [2], [3]], names=('a', 'b', 'c'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_strip_line_trailing_whitespace(parallel, read_basic):
"""
Readers that strip whitespace from lines should ignore
trailing whitespace after the last data value of each
row.
"""
text = 'a b c\n1 2 \n3 4 5'
with pytest.raises(CParserError) as e:
ascii.read(StringIO(text), format='fast_basic', guess=False)
assert 'not enough columns found in line 1' in str(e)
text = 'a b c\n 1 2 3 \t \n 4 5 6 '
table = read_basic(text, parallel=parallel)
expected = Table([[1, 4], [2, 5], [3, 6]], names=('a', 'b', 'c'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_no_data(parallel, read_basic):
"""
As long as column names are supplied, the C reader
should return an empty table in the absence of data.
"""
table = read_basic('a b c', parallel=parallel)
expected = Table([[], [], []], names=('a', 'b', 'c'))
assert_table_equal(table, expected)
table = read_basic('a b c\n1 2 3', data_start=2, parallel=parallel)
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_line_endings(parallel, read_basic, read_commented_header, read_rdb):
"""
Make sure the fast reader accepts CR and CR+LF
as newlines.
"""
text = 'a b c\n1 2 3\n4 5 6\n7 8 9\n'
expected = Table([[1, 4, 7], [2, 5, 8], [3, 6, 9]], names=('a', 'b', 'c'))
for newline in ('\r\n', '\r'):
table = read_basic(text.replace('\n', newline), parallel=parallel)
assert_table_equal(table, expected)
# Make sure the splitlines() method of FileString
# works with CR/CR+LF line endings
text = '#' + text
for newline in ('\r\n', '\r'):
table = read_commented_header(text.replace('\n', newline), parallel=parallel)
assert_table_equal(table, expected)
expected = Table([[1, 4, 7], [2, 5, 8], [3, 6, 9]], names=('a', 'b', 'c'), masked=True)
expected['a'][0] = np.ma.masked
expected['c'][0] = np.ma.masked
text = 'a\tb\tc\nN\tN\tN\n\t2\t\n4\t5\t6\n7\t8\t9\n'
for newline in ('\r\n', '\r'):
table = read_rdb(text.replace('\n', newline), parallel=parallel)
assert_table_equal(table, expected)
assert np.all(table == expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_store_comments(parallel, read_basic):
"""
Make sure that the output Table produced by the fast
reader stores any comment lines in its meta attribute.
"""
text = """
# header comment
a b c
# comment 2
# comment 3
1 2 3
4 5 6
"""
table = read_basic(text, parallel=parallel, check_meta=True)
assert_equal(table.meta['comments'],
['header comment', 'comment 2', 'comment 3'])
@pytest.mark.parametrize("parallel", [True, False])
def test_empty_quotes(parallel, read_basic):
"""
Make sure the C reader doesn't segfault when the
input data contains empty quotes. [#3407]
"""
table = read_basic('a b\n1 ""\n2 ""', parallel=parallel)
expected = Table([[1, 2], [0, 0]], names=('a', 'b'))
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_fast_tab_with_names(parallel, read_tab):
"""
Make sure the C reader doesn't segfault when the header for the
first column is missing [#3545]
"""
content = """#
\tdecDeg\tRate_pn_offAxis\tRate_mos2_offAxis\tObsID\tSourceID\tRADeg\tversion\tCounts_pn\tRate_pn\trun\tRate_mos1\tRate_mos2\tInserted_pn\tInserted_mos2\tbeta\tRate_mos1_offAxis\trcArcsec\tname\tInserted\tCounts_mos1\tInserted_mos1\tCounts_mos2\ty\tx\tCounts\toffAxis\tRot
-3.007559\t0.0000\t0.0010\t0013140201\t0\t213.462574\t0\t2\t0.0002\t0\t0.0001\t0.0001\t0\t1\t0.66\t0.0217\t3.0\tfakeXMMXCS J1413.8-0300\t3\t1\t2\t1\t398.000\t127.000\t5\t13.9\t72.3\t"""
head = ['A{0}'.format(i) for i in range(28)]
table = read_tab(content, data_start=1,
parallel=parallel, names=head)
@pytest.mark.skipif(not os.getenv('TEST_READ_HUGE_FILE'),
reason='Environment variable TEST_READ_HUGE_FILE must be '
'defined to run this test')
def test_read_big_table(tmpdir):
"""Test reading of a huge file.
This test generates a huge CSV file (~2.3Gb) before reading it (see
https://github.com/astropy/astropy/pull/5319). The test is run only if the
environment variable ``TEST_READ_HUGE_FILE`` is defined. Note that running
the test requires quite a lot of memory (~18Gb when reading the file) !!
"""
NB_ROWS = 250000
NB_COLS = 500
filename = str(tmpdir.join("big_table.csv"))
print("Creating a {} rows table ({} columns).".format(NB_ROWS, NB_COLS))
data = np.random.random(NB_ROWS)
t = Table(data=[data]*NB_COLS, names=[str(i) for i in range(NB_COLS)])
data = None
print("Saving the table to {}".format(filename))
t.write(filename, format='ascii.csv', overwrite=True)
t = None
print("Counting the number of lines in the csv, it should be {}"
" + 1 (header).".format(NB_ROWS))
assert sum(1 for line in open(filename)) == NB_ROWS + 1
print("Reading the file with astropy.")
t = Table.read(filename, format='ascii.csv', fast_reader=True)
assert len(t) == NB_ROWS
# fast_reader configurations: False| 'use_fast_converter'=False|True
@pytest.mark.parametrize('reader', [0, 1, 2])
# catch Windows environment since we cannot use _read() with custom fast_reader
@pytest.mark.parametrize("parallel", [False, True])
def test_data_out_of_range(parallel, reader):
"""
Numbers with exponents beyond float64 range (|~4.94e-324 to 1.7977e+308|)
shall be returned as 0 and +-inf respectively by the C parser, just like
the Python parser.
Test fast converter only to nominal accuracy.
"""
if os.name == 'nt':
pytest.xfail(reason="Multiprocessing is currently unsupported on Windows")
# Python reader and strtod() are expected to return precise results
rtol = 1.e-30
if reader > 1:
rtol = 1.e-15
# passing fast_reader dict with parametrize does not work!
if reader > 0:
fast_reader = {'parallel': parallel, 'use_fast_converter': reader > 1}
else:
fast_reader = False
if parallel:
if reader < 1:
pytest.skip("Multiprocessing only available in fast reader")
elif TRAVIS:
pytest.xfail("Multiprocessing can sometimes fail on Travis CI")
fields = ['10.1E+199', '3.14e+313', '2048e+306', '0.6E-325', '-2.e345']
values = np.array([1.01e200, np.inf, np.inf, 0.0, -np.inf])
t = ascii.read(StringIO(' '.join(fields)), format='no_header', guess=False,
fast_reader=fast_reader)
read_values = np.array([col[0] for col in t.itercols()])
assert_almost_equal(read_values, values, rtol=rtol, atol=1.e-324)
# test some additional corner cases
fields = ['.0101E202', '0.000000314E+314', '1777E+305', '-1799E+305', '0.2e-323',
'2500e-327', ' 0.0000000000000000000001024E+330']
values = np.array([1.01e200, 3.14e307, 1.777e308, -np.inf, 0.0, 4.94e-324, 1.024e308])
t = ascii.read(StringIO(' '.join(fields)), format='no_header', guess=False,
fast_reader=fast_reader)
read_values = np.array([col[0] for col in t.itercols()])
assert_almost_equal(read_values, values, rtol=rtol, atol=1.e-324)
# test corner cases again with non-standard exponent_style (auto-detection)
if reader < 2:
pytest.skip("Fortran exponent style only available in fast converter")
fast_reader.update({'exponent_style': 'A'})
fields = ['.0101D202', '0.000000314d+314', '1777+305', '-1799E+305', '0.2e-323',
'2500-327', ' 0.0000000000000000000001024Q+330']
t = ascii.read(StringIO(' '.join(fields)), format='no_header', guess=False,
fast_reader=fast_reader)
read_values = np.array([col[0] for col in t.itercols()])
assert_almost_equal(read_values, values, rtol=rtol, atol=1.e-324)
# catch Windows environment since we cannot use _read() with custom fast_reader
@pytest.mark.parametrize("parallel", [True, False])
def test_int_out_of_range(parallel):
"""
Integer numbers outside int range shall be returned as string columns
consistent with the standard (Python) parser (no 'upcasting' to float).
"""
if os.name == 'nt':
pytest.xfail(reason="Multiprocessing is currently unsupported on Windows")
imin = np.iinfo(np.int).min+1
imax = np.iinfo(np.int).max-1
huge = '{:d}'.format(imax+2)
text = 'P M S\n {:d} {:d} {:s}'.format(imax, imin, huge)
expected = Table([[imax], [imin], [huge]], names=('P', 'M', 'S'))
table = ascii.read(text, format='basic', guess=False,
fast_reader={'parallel': parallel})
assert_table_equal(table, expected)
# check with leading zeroes to make sure strtol does not read them as octal
text = 'P M S\n000{:d} -0{:d} 00{:s}'.format(imax, -imin, huge)
expected = Table([[imax], [imin], ['00'+huge]], names=('P', 'M', 'S'))
table = ascii.read(text, format='basic', guess=False,
fast_reader={'parallel': parallel})
assert_table_equal(table, expected)
# mixed columns should be returned as float, but if the out-of-range integer
# shows up first, it will produce a string column - with both readers
pytest.xfail("Integer fallback depends on order of rows")
text = 'A B\n 12.3 {0:d}9\n {0:d}9 45.6e7'.format(imax)
expected = Table([[12.3, 10.*imax], [10.*imax, 4.56e8]],
names=('A', 'B'))
table = ascii.read(text, format='basic', guess=False,
fast_reader={'parallel': parallel})
assert_table_equal(table, expected)
table = ascii.read(text, format='basic', guess=False, fast_reader=False)
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_fortran_reader(parallel):
"""
Make sure that ascii.read() can read Fortran-style exponential notation
using the fast_reader.
"""
if os.name == 'nt':
pytest.xfail(reason="Multiprocessing is currently unsupported on Windows")
text = 'A B C\n100.01{:s}+99 2.0 3\n 4.2{:s}-1 5.0{:s}-1 0.6{:s}4'
expected = Table([[1.0001e101, 0.42], [2, 0.5], [3.0, 6000]],
names=('A', 'B', 'C'))
expstyles = {'e': 4*('E'), 'D': ('D', 'd', 'd', 'D'), 'Q': 2*('q', 'Q'),
'fortran': ('D', 'E', 'Q', 'd')}
# C strtod (not-fast converter) can't handle Fortran exp
with pytest.raises(FastOptionsError) as e:
ascii.read(text.format(*(4*('D'))), format='basic', guess=False,
fast_reader={'use_fast_converter': False,
'parallel': parallel, 'exponent_style': 'D'})
assert 'fast_reader: exponent_style requires use_fast_converter' in str(e)
# enable multiprocessing and the fast converter
# iterate over all style-exponent combinations
for s, c in expstyles.items():
table = ascii.read(text.format(*c), format='basic', guess=False,
fast_reader={'parallel': parallel,
'exponent_style': s})
assert_table_equal(table, expected)
# mixes and triple-exponents without any character using autodetect option
text = 'A B C\n1.0001+101 2.0E0 3\n.42d0 0.5 6.+003'
table = ascii.read(text, format='basic', guess=False,
fast_reader={'parallel': parallel, 'exponent_style': 'fortran'})
assert_table_equal(table, expected)
# additional corner-case checks
text = 'A B C\n1.0001+101 2.0+000 3\n0.42+000 0.5 6000.-000'
table = ascii.read(text, format='basic', guess=False,
fast_reader={'parallel': parallel, 'exponent_style': 'fortran'})
assert_table_equal(table, expected)
@pytest.mark.parametrize("parallel", [True, False])
def test_fortran_invalid_exp(parallel):
"""
Test Fortran-style exponential notation in the fast_reader with invalid
exponent-like patterns (no triple-digits) to make sure they are returned
as strings instead, as with the standard C parser.
"""
if os.name == 'nt':
pytest.xfail(reason="Multiprocessing is currently unsupported on Windows")
if parallel and TRAVIS:
pytest.xfail("Multiprocessing can sometimes fail on Travis CI")
fields = ['1.0001+1', '.42d1', '2.3+10', '0.5', '3+1001', '3000.',
'2', '4.56e-2.3', '8000', '4.2-122']
values = ['1.0001+1', 4.2, '2.3+10', 0.5, '3+1001', 3.e3,
2, '4.56e-2.3', 8000, 4.2e-122]
t = ascii.read(StringIO(' '.join(fields)), format='no_header', guess=False,
fast_reader={'parallel': parallel, 'exponent_style': 'A'})
read_values = [col[0] for col in t.itercols()]
assert read_values == values
|
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|
{
"filename": "_textfont.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/validators/funnelarea/_textfont.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TextfontValidator(_plotly_utils.basevalidators.CompoundValidator):
def __init__(self, plotly_name="textfont", parent_name="funnelarea", **kwargs):
super(TextfontValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Textfont"),
data_docs=kwargs.pop(
"data_docs",
"""
color
colorsrc
Sets the source reference on Chart Studio Cloud
for color .
family
HTML font family - the typeface that will be
applied by the web browser. The web browser
will only be able to apply a font if it is
available on the system which it operates.
Provide multiple font families, separated by
commas, to indicate the preference in which to
apply fonts if they aren't available on the
system. The Chart Studio Cloud (at
https://chart-studio.plotly.com or on-premise)
generates images on a server, where only a
select number of fonts are installed and
supported. These include "Arial", "Balto",
"Courier New", "Droid Sans",, "Droid Serif",
"Droid Sans Mono", "Gravitas One", "Old
Standard TT", "Open Sans", "Overpass", "PT Sans
Narrow", "Raleway", "Times New Roman".
familysrc
Sets the source reference on Chart Studio Cloud
for family .
size
sizesrc
Sets the source reference on Chart Studio Cloud
for size .
""",
),
**kwargs
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@validators@funnelarea@_textfont.py@.PATH_END.py
|
{
"filename": "combined.py",
"repo_name": "jroulet/cogwheel",
"repo_path": "cogwheel_extracted/cogwheel-main/cogwheel/gw_prior/combined.py",
"type": "Python"
}
|
"""
Define some commonly used priors for the full set of parameters, for
convenience.
Prior classes defined here can be used for parameter estimation and
are registered in a dictionary ``prior_registry``.
"""
from cogwheel import utils
from cogwheel.prior import CombinedPrior, Prior, check_inheritance_order
from cogwheel.likelihood import (RelativeBinningLikelihood,
MarginalizedDistanceLikelihood,
MarginalizedDistancePhaseLikelihood,
MarginalizedExtrinsicLikelihood,
MarginalizedExtrinsicLikelihoodQAS)
from .extrinsic import (UniformPhasePrior,
IsotropicInclinationPrior,
IsotropicSkyLocationPrior,
UniformTimePrior,
UniformPolarizationPrior,
UniformLuminosityVolumePrior,
UniformComovingVolumePrior)
from .mass import UniformDetectorFrameMassesPrior
from .miscellaneous import (ZeroTidalDeformabilityPrior,
FixedIntrinsicParametersPrior,
FixedReferenceFrequencyPrior)
from .pn import PNCoordinatesPrior
from .spin import (
UniformEffectiveSpinPrior,
IsotropicSpinsAlignedComponentsPrior,
VolumetricSpinsAlignedComponentsPrior,
UniformDiskInplaneSpinsIsotropicInclinationPrior,
IsotropicSpinsInplaneComponentsIsotropicInclinationPrior,
UniformDiskInplaneSpinsIsotropicInclinationSkyLocationPrior,
IsotropicSpinsInplaneComponentsIsotropicInclinationSkyLocationPrior,
CartesianUniformDiskInplaneSpinsIsotropicInclinationPrior,
ZeroInplaneSpinsPrior)
from .tides import UniformTidalDeformabilitiesBNSPrior
prior_registry = {}
class ConditionedPriorError(Exception):
"""Indicates that a Prior is conditioned on some parameters."""
class ReferenceWaveformFinderMixin:
"""
Provide a constructor based on a `likelihood.ReferenceWaveformFinder`
instance to provide initialization arguments.
"""
@classmethod
def from_reference_waveform_finder(
cls, reference_waveform_finder, **kwargs):
"""
Instantiate `prior.Prior` subclass with help from a
`likelihood.ReferenceWaveformFinder` instance.
This will generate kwargs for:
* tgps
* par_dic_0
* f_avg
* f_ref
* ref_det_name
* detector_pair
* t0_refdet
* mchirp_range
Additional `**kwargs` can be passed to complete missing entries
or override these.
"""
return cls(**reference_waveform_finder.get_coordinate_system_kwargs()
| kwargs)
class RegisteredPriorMixin(ReferenceWaveformFinderMixin):
"""
Register existence of a `Prior` subclass in `prior_registry`.
Intended usage is to only register the final priors (i.e., for the
full set of GW parameters).
`RegisteredPriorMixin` should be inherited before `Prior` (otherwise
`PriorError` is raised) in order to test for conditioned-on
parameters.
"""
def __init_subclass__(cls):
"""Validate subclass and register it in prior_registry."""
super().__init_subclass__()
check_inheritance_order(cls, RegisteredPriorMixin, Prior)
if cls.conditioned_on:
raise ConditionedPriorError('Only register fully defined priors.')
prior_registry[cls.__name__] = cls
# ----------------------------------------------------------------------
# Default priors for the full set of variables, for convenience.
class IASPrior(RegisteredPriorMixin, CombinedPrior):
"""Precessing, flat in chieff, uniform luminosity volume."""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = [
FixedReferenceFrequencyPrior,
UniformDetectorFrameMassesPrior,
UniformEffectiveSpinPrior,
UniformDiskInplaneSpinsIsotropicInclinationSkyLocationPrior,
UniformPolarizationPrior,
UniformTimePrior,
UniformPhasePrior,
UniformLuminosityVolumePrior,
ZeroTidalDeformabilityPrior]
class AlignedSpinIASPrior(RegisteredPriorMixin, CombinedPrior):
"""Aligned spin, flat in chieff, uniform luminosity volume."""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = [UniformDetectorFrameMassesPrior,
IsotropicInclinationPrior,
IsotropicSkyLocationPrior,
UniformTimePrior,
UniformPolarizationPrior,
UniformPhasePrior,
UniformLuminosityVolumePrior,
UniformEffectiveSpinPrior,
ZeroInplaneSpinsPrior,
ZeroTidalDeformabilityPrior,
FixedReferenceFrequencyPrior]
class TidalIASPrior(RegisteredPriorMixin, CombinedPrior):
"""
Aligned spin, flat in tidal parameters, flat in chieff, uniform
luminosity volume.
"""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = [UniformDetectorFrameMassesPrior,
IsotropicInclinationPrior,
IsotropicSkyLocationPrior,
UniformTimePrior,
UniformPolarizationPrior,
UniformPhasePrior,
UniformLuminosityVolumePrior,
UniformEffectiveSpinPrior,
ZeroInplaneSpinsPrior,
UniformTidalDeformabilitiesBNSPrior,
FixedReferenceFrequencyPrior]
class CartesianIASPrior(RegisteredPriorMixin, CombinedPrior):
"""
Precessing, flat in chieff, uniform luminosity volume.
Physically equivalent to ``IntrinsicIASPrior`` but does not require
periodic parameters, which some samplers cannot deal with.
"""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = [
FixedReferenceFrequencyPrior,
UniformDetectorFrameMassesPrior,
UniformEffectiveSpinPrior,
CartesianUniformDiskInplaneSpinsIsotropicInclinationPrior,
IsotropicSkyLocationPrior,
UniformPolarizationPrior,
UniformTimePrior,
UniformPhasePrior,
UniformLuminosityVolumePrior,
ZeroTidalDeformabilityPrior]
class LVCPrior(RegisteredPriorMixin, CombinedPrior):
"""Precessing, isotropic spins, uniform luminosity volume."""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = [
FixedReferenceFrequencyPrior,
UniformDetectorFrameMassesPrior,
IsotropicSpinsAlignedComponentsPrior,
UniformPolarizationPrior,
IsotropicSpinsInplaneComponentsIsotropicInclinationSkyLocationPrior,
UniformTimePrior,
UniformPhasePrior,
UniformLuminosityVolumePrior,
ZeroTidalDeformabilityPrior]
class AlignedSpinLVCPrior(RegisteredPriorMixin, CombinedPrior):
"""
Aligned spin components from isotropic distribution, uniform
luminosity volume.
"""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = [UniformDetectorFrameMassesPrior,
IsotropicInclinationPrior,
IsotropicSkyLocationPrior,
UniformTimePrior,
UniformPolarizationPrior,
UniformPhasePrior,
UniformLuminosityVolumePrior,
IsotropicSpinsAlignedComponentsPrior,
ZeroInplaneSpinsPrior,
ZeroTidalDeformabilityPrior,
FixedReferenceFrequencyPrior]
class IASPriorComovingVT(RegisteredPriorMixin, CombinedPrior):
"""Precessing, flat in chieff, uniform comoving VT."""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = utils.replace(IASPrior.prior_classes,
UniformLuminosityVolumePrior,
UniformComovingVolumePrior)
class AlignedSpinIASPriorComovingVT(RegisteredPriorMixin,
CombinedPrior):
"""Aligned spin, flat in chieff, uniform comoving VT."""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = utils.replace(AlignedSpinIASPrior.prior_classes,
UniformLuminosityVolumePrior,
UniformComovingVolumePrior)
class LVCPriorComovingVT(RegisteredPriorMixin, CombinedPrior):
"""Precessing, isotropic spins, uniform comoving VT."""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = utils.replace(LVCPrior.prior_classes,
UniformLuminosityVolumePrior,
UniformComovingVolumePrior)
class AlignedSpinLVCPriorComovingVT(RegisteredPriorMixin,
CombinedPrior):
"""
Aligned spins from isotropic distribution, uniform comoving VT.
"""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = utils.replace(AlignedSpinLVCPrior.prior_classes,
UniformLuminosityVolumePrior,
UniformComovingVolumePrior)
class ExtrinsicParametersPrior(RegisteredPriorMixin, CombinedPrior):
"""Uniform luminosity volume, fixed intrinsic parameters."""
default_likelihood_class = RelativeBinningLikelihood
prior_classes = [FixedIntrinsicParametersPrior,
IsotropicInclinationPrior,
IsotropicSkyLocationPrior,
UniformTimePrior,
UniformPolarizationPrior,
UniformPhasePrior,
UniformLuminosityVolumePrior,
FixedReferenceFrequencyPrior]
class MarginalizedDistanceIASPrior(RegisteredPriorMixin, CombinedPrior):
"""
Prior for usage with ``MarginalizedDistanceLikelihood``.
Similar to ``IASPrior`` except it does not include distance.
Uniform in effective spin and detector-frame component masses.
"""
default_likelihood_class = MarginalizedDistanceLikelihood
prior_classes = IASPrior.prior_classes.copy()
prior_classes.remove(UniformLuminosityVolumePrior)
class MarginalizedDistanceAndPhaseIASPrior(RegisteredPriorMixin,
CombinedPrior):
"""
Prior for usage with ``MarginalizedDistanceLikelihood``.
Similar to ``IASPrior`` except it does not include distance or phase.
Uniform in effective spin and detector-frame component masses.
"""
default_likelihood_class = MarginalizedDistancePhaseLikelihood
prior_classes = IASPrior.prior_classes.copy()
prior_classes.remove(UniformLuminosityVolumePrior)
prior_classes.remove(UniformPhasePrior)
class MarginalizedDistanceLVCPrior(RegisteredPriorMixin, CombinedPrior):
"""
Prior for usage with ``MarginalizedDistanceLikelihood``.
Similar to ``LVCPrior`` except it does not include distance.
Isotropic spin orientations, uniform in component spin magnitudes
and detector-frame component masses.
"""
default_likelihood_class = MarginalizedDistanceLikelihood
prior_classes = LVCPrior.prior_classes.copy()
prior_classes.remove(UniformLuminosityVolumePrior)
class IntrinsicAlignedSpinIASPrior(RegisteredPriorMixin, CombinedPrior):
"""
Prior for usage with ``MarginalizedExtrinsicLikelihoodQAS``.
Intrinsic parameters only, aligned spins, uniform in effective spin
and detector frame component masses, no tides.
"""
default_likelihood_class = MarginalizedExtrinsicLikelihoodQAS
prior_classes = [UniformDetectorFrameMassesPrior,
UniformEffectiveSpinPrior,
ZeroTidalDeformabilityPrior,
FixedReferenceFrequencyPrior]
class IntrinsicAlignedSpinLVCPrior(RegisteredPriorMixin, CombinedPrior):
"""
Prior for usage with ``MarginalizedExtrinsicLikelihoodQAS``.
Intrinsic parameters only, aligned spins, uniform in effective spin
and detector frame component masses, no tides.
"""
default_likelihood_class = MarginalizedExtrinsicLikelihoodQAS
prior_classes = [UniformDetectorFrameMassesPrior,
IsotropicSpinsAlignedComponentsPrior,
ZeroTidalDeformabilityPrior,
FixedReferenceFrequencyPrior]
class IntrinsicIASPrior(RegisteredPriorMixin, CombinedPrior):
"""
Prior for usage with ``MarginalizedExtrinsicLikelihood``.
Intrinsic parameters only, precessing, uniform in effective spin
and detector frame component masses, no tides.
"""
default_likelihood_class = MarginalizedExtrinsicLikelihood
prior_classes = [FixedReferenceFrequencyPrior,
UniformDetectorFrameMassesPrior,
UniformEffectiveSpinPrior,
UniformDiskInplaneSpinsIsotropicInclinationPrior,
ZeroTidalDeformabilityPrior]
class IntrinsicLVCPrior(RegisteredPriorMixin, CombinedPrior):
"""
Prior for usage with ``MarginalizedExtrinsicLikelihood``.
Intrinsic parameters only, precessing, isotropic spins, uniform in
component spin magnitudes and detector frame masses, no tides.
"""
default_likelihood_class = MarginalizedExtrinsicLikelihood
prior_classes = [FixedReferenceFrequencyPrior,
UniformDetectorFrameMassesPrior,
IsotropicSpinsAlignedComponentsPrior,
IsotropicSpinsInplaneComponentsIsotropicInclinationPrior,
ZeroTidalDeformabilityPrior]
class CartesianIntrinsicIASPrior(RegisteredPriorMixin, CombinedPrior):
"""
Prior for usage with ``MarginalizedExtrinsicLikelihood``.
Physically equivalent to ``IntrinsicIASPrior`` but does not require
periodic parameters, which some samplers cannot deal with.
Intrinsic parameters only, precessing, uniform in effective spin
and detector frame component masses, no tides.
"""
default_likelihood_class = MarginalizedExtrinsicLikelihood
prior_classes = [FixedReferenceFrequencyPrior,
UniformDetectorFrameMassesPrior,
UniformEffectiveSpinPrior,
CartesianUniformDiskInplaneSpinsIsotropicInclinationPrior,
ZeroTidalDeformabilityPrior]
class IntrinsicVolumetricSpinPrior(RegisteredPriorMixin,
CombinedPrior):
"""
Prior for usage with ``MarginalizedExtrinsicLikelihood``.
Intrinsic parameters only, precessing, uniform in detector frame
component masses, volumetric spin prior (spin components uniform in
the ball |s| < 1), no tides.
For low mass systems, consider ``PNIntrinsicVolumetricSpinPrior``
instead.
"""
default_likelihood_class = MarginalizedExtrinsicLikelihood
prior_classes = utils.replace(IntrinsicIASPrior.prior_classes,
UniformEffectiveSpinPrior,
VolumetricSpinsAlignedComponentsPrior)
class PNIntrinsicVolumetricSpinPrior(RegisteredPriorMixin,
CombinedPrior):
"""
Prior for usage with ``MarginalizedExtrinsicLikelihood``.
Intrinsic parameters only, precessing, uniform in detector frame
component masses, volumetric spin prior (spin components uniform in
the ball |s| < 1), no tides.
Best suited for low masses where PN expansion is better justified.
"""
default_likelihood_class = MarginalizedExtrinsicLikelihood
prior_classes = [FixedReferenceFrequencyPrior,
PNCoordinatesPrior,
CartesianUniformDiskInplaneSpinsIsotropicInclinationPrior,
ZeroTidalDeformabilityPrior]
@classmethod
def from_reference_waveform_finder(
cls, reference_waveform_finder, **kwargs):
eigvecs = PNCoordinatesPrior.eigvecs_from_reference_waveform_finder(
reference_waveform_finder)
return cls(**reference_waveform_finder.get_coordinate_system_kwargs()
| {'eigvecs': eigvecs} | kwargs)
|
jrouletREPO_NAMEcogwheelPATH_START.@cogwheel_extracted@cogwheel-main@cogwheel@gw_prior@combined.py@.PATH_END.py
|
{
"filename": "iterative_fft_particle.py",
"repo_name": "cosmodesi/pyrecon",
"repo_path": "pyrecon_extracted/pyrecon-main/pyrecon/iterative_fft_particle.py",
"type": "Python"
}
|
"""Re-implementation of Bautista et al. 2018 (https://arxiv.org/pdf/1712.08064.pdf) algorithm."""
import numpy as np
from .recon import BaseReconstruction, ReconstructionError
from . import utils
class OriginalIterativeFFTParticleReconstruction(BaseReconstruction):
"""
Exact re-implementation of Bautista et al. 2018 (https://arxiv.org/pdf/1712.08064.pdf) algorithm
at https://github.com/julianbautista/eboss_clustering/blob/master/python/recon.py.
Numerical agreement in the Zeldovich displacements between original codes and this re-implementation is machine precision
(absolute and relative difference of 1e-12).
"""
def assign_data(self, positions, weights=None):
"""
Assign (paint) data to :attr:`mesh_data`.
Keeps track of input positions (for :meth:`run`) and weights (for :meth:`set_density_contrast`).
See :meth:`BaseReconstruction.assign_data` for parameters.
"""
if weights is None:
weights = np.ones_like(positions, shape=(len(positions),))
if self.wrap: positions = self.info.wrap(positions)
if self.mesh_data.value is None:
self._positions_data = positions
self._weights_data = weights
else:
self._positions_data = np.concatenate([self._positions_data, positions], axis=0)
self._weights_data = np.concatenate([self._weights_data, weights], axis=0)
self.mesh_data.assign_cic(positions, weights=weights, wrap=self.wrap)
def set_density_contrast(self, ran_min=0.01, smoothing_radius=15.):
r"""
Set :math:`\delta` field :attr:`mesh_delta` from data and randoms fields :attr:`mesh_data` and :attr:`mesh_randoms`.
Note
----
This method follows Julian's reconstruction code.
:attr:`mesh_data` and :attr:`mesh_randoms` fields are assumed to be smoothed already.
Parameters
----------
ran_min : float, default=0.01
:attr:`mesh_randoms` points below this threshold times mean random weights have their density contrast set to 0.
"""
self.ran_min = ran_min
self.smoothing_radius = smoothing_radius
if self.has_randoms:
sum_data, sum_randoms = np.sum(self.mesh_data.value), np.sum(self.mesh_randoms.value)
alpha = sum_data * 1. / sum_randoms
self.mesh_delta = self.mesh_data - alpha * self.mesh_randoms
threshold = ran_min * sum_randoms / self._size_randoms
mask = self.mesh_randoms > threshold
self.mesh_delta[mask] /= (self.bias * alpha * self.mesh_randoms[mask])
self.mesh_delta[~mask] = 0.
else:
self.mesh_delta = self.mesh_data / np.mean(self.mesh_data) - 1.
self.mesh_delta /= self.bias
def run(self, niterations=3):
"""
Run reconstruction, i.e. compute reconstructed data real-space positions (:attr:`_positions_rec_data`)
and Zeldovich displacements fields :attr:`mesh_psi`.
Parameters
----------
niterations : int
Number of iterations.
"""
self._iter = 0
# Gaussian smoothing before density contrast calculation
self.mesh_data.smooth_gaussian(self.smoothing_radius, method='fft')
if self.has_randoms: self.mesh_randoms.smooth_gaussian(self.smoothing_radius, method='fft')
self._positions_rec_data = self._positions_data.copy()
for iter in range(niterations):
self.mesh_psi = self._iterate(return_psi=iter == niterations - 1)
del self.mesh_randoms
def _iterate(self, return_psi=False):
self.log_info('Running iteration {:d}.'.format(self._iter))
if self._iter > 0:
self.mesh_data = self.mesh_randoms.copy()
self.mesh_data.value = None # to reset mesh values
# Painting reconstructed data real-space positions
wrap = self.wrap; self.wrap = True # enforce wrapping
super(OriginalIterativeFFTParticleReconstruction, self).assign_data(self._positions_rec_data, weights=self._weights_data) # super in order not to save positions_rec_data
self.wrap = wrap
# Gaussian smoothing before density contrast calculation
self.mesh_data.smooth_gaussian(self.smoothing_radius, method='fft')
self.set_density_contrast(ran_min=self.ran_min, smoothing_radius=self.smoothing_radius)
del self.mesh_data
delta_k = self.mesh_delta.to_complex()
del self.mesh_delta
k = utils.broadcast_arrays(*delta_k.coords())
delta_k.prod_sum([k**2 for k in delta_k.coords()], exp=-1)
delta_k[0, 0, 0] = 0.
# k = utils.broadcast_arrays(*delta_k.coords())
# k2 = sum(kk**2 for kk in k)
# k2[0,0,0] = 1. # to avoid dividing by 0
# delta_k /= k2
self.log_info('Computing displacement field.')
shifts = np.empty_like(self._positions_rec_data)
psis = []
for iaxis in range(delta_k.ndim):
# no need to compute psi on axis where los is 0
if not return_psi and self.los is not None and self.los[iaxis] == 0:
shifts[:, iaxis] = 0.
continue
psi = (delta_k * 1j * k[iaxis]).to_real()
# Reading shifts at reconstructed data real-space positions
shifts[:, iaxis] = psi.read_cic(self._positions_rec_data, wrap=True)
if return_psi: psis.append(psi)
# self.log_info('A few displacements values:')
# for s in shifts[:3]: self.log_info('{}'.format(s))
if self.los is None:
los = self._positions_data / utils.distance(self._positions_data)[:, None]
else:
los = self.los
# Comments in Julian's code:
# For first loop need to approximately remove RSD component from psi to speed up convergence
# See Burden et al. 2015: 1504.02591v2, eq. 12 (flat sky approximation)
if self._iter == 0:
shifts -= self.beta / (1 + self.beta) * np.sum(shifts * los, axis=-1)[:, None] * los
# Comments in Julian's code:
# Remove RSD from original positions of galaxies to give new positions
# these positions are then used in next determination of psi,
# assumed to not have RSD.
# The iterative procedure then uses the new positions as if they'd been read in from the start
self._positions_rec_data = self._positions_data - self.f * np.sum(shifts * los, axis=-1)[:, None] * los
diff = self._positions_rec_data - self.mesh_randoms.offset
if (not self.wrap) and np.any((diff < 0) | (diff > self.mesh_randoms.boxsize - self.mesh_randoms.cellsize)):
self.log_warning('Some particles are out-of-bounds.')
self._iter += 1
if return_psi:
return psis
def read_shifts(self, positions, field='disp+rsd'):
"""
Read displacement at input positions.
Note
----
Data shifts are read at the reconstructed real-space positions,
while random shifts are read at the redshift-space positions, is that consistent?
Parameters
----------
positions : array of shape (N, 3), string
Cartesian positions.
Pass string 'data' to get the displacements for the input data positions passed to :meth:`assign_data`.
Note that in this case, shifts are read at the reconstructed data real-space positions.
field : string, default='disp+rsd'
Either 'disp' (Zeldovich displacement), 'rsd' (RSD displacement), or 'disp+rsd' (Zeldovich + RSD displacement).
Returns
-------
shifts : array of shape (N, 3)
Displacements.
"""
field = field.lower()
allowed_fields = ['disp', 'rsd', 'disp+rsd']
if field not in allowed_fields:
raise ReconstructionError('Unknown field {}. Choices are {}'.format(field, allowed_fields))
def read_cic(positions, wrap=False):
shifts = np.empty_like(positions)
for iaxis, psi in enumerate(self.mesh_psi):
shifts[:, iaxis] = psi.read_cic(positions, wrap=wrap)
return shifts
if isinstance(positions, str) and positions == 'data':
# _positions_rec_data already wrapped during iteration
shifts = read_cic(self._positions_rec_data, wrap=True)
if field == 'disp':
return shifts
rsd = self._positions_data - self._positions_rec_data
if field == 'rsd':
return rsd
# field == 'disp+rsd'
shifts += rsd
return shifts
if self.wrap: positions = self.info.wrap(positions) # wrap here for local los
shifts = read_cic(positions, wrap=False) # aleady wrapped
if field == 'disp':
return shifts
if self.los is None:
los = positions / utils.distance(positions)[:, None]
else:
los = self.los
rsd = self.f * np.sum(shifts * los, axis=-1)[:, None] * los
if field == 'rsd':
return rsd
# field == 'disp+rsd'
# we follow convention of original algorithm: remove RSD first,
# then remove Zeldovich displacement
real_positions = positions - rsd
diff = real_positions - self.mesh_psi[0].offset
if (not self.wrap) and np.any((diff < 0) | (diff > self.mesh_psi[0].boxsize - self.mesh_psi[0].cellsize)):
self.log_warning('Some particles are out-of-bounds.')
shifts = read_cic(real_positions, wrap=True)
return shifts + rsd
def read_shifted_positions(self, positions, field='disp+rsd'):
"""
Read shifted positions i.e. the difference ``positions - self.read_shifts(positions, field=field)``.
Output (and input) positions are wrapped if :attr:`wrap`.
Parameters
----------
positions : array of shape (N, 3), string
Cartesian positions.
Pass string 'data' to get the shift positions for the input data positions passed to :meth:`assign_data`.
Note that in this case, shifts are read at the reconstructed data real-space positions.
field : string, default='disp+rsd'
Apply either 'disp' (Zeldovich displacement), 'rsd' (RSD displacement), or 'disp+rsd' (Zeldovich + RSD displacement).
Returns
-------
positions : array of shape (N, 3)
Shifted positions.
"""
shifts = self.read_shifts(positions, field=field)
if isinstance(positions, str) and positions == 'data':
positions = self._positions_data
positions = positions - shifts
if self.wrap: positions = self.info.wrap(positions)
return positions
class IterativeFFTParticleReconstruction(OriginalIterativeFFTParticleReconstruction):
"""Any update / test / improvement upon original algorithm."""
|
cosmodesiREPO_NAMEpyreconPATH_START.@pyrecon_extracted@pyrecon-main@pyrecon@iterative_fft_particle.py@.PATH_END.py
|
{
"filename": "Executor.py",
"repo_name": "rat-pac/rat-pac",
"repo_path": "rat-pac_extracted/rat-pac-master/python/SCons/Executor.py",
"type": "Python"
}
|
"""SCons.Executor
A module for executing actions with specific lists of target and source
Nodes.
"""
#
# Copyright (c) 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009 The SCons Foundation
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to
# permit persons to whom the Software is furnished to do so, subject to
# the following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY
# KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE
# WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
#
__revision__ = "src/engine/SCons/Executor.py 4043 2009/02/23 09:06:45 scons"
import string
import UserList
from SCons.Debug import logInstanceCreation
import SCons.Errors
import SCons.Memoize
class Batch:
"""Remembers exact association between targets
and sources of executor."""
def __init__(self, targets=[], sources=[]):
self.targets = targets
self.sources = sources
class TSList(UserList.UserList):
"""A class that implements $TARGETS or $SOURCES expansions by wrapping
an executor Method. This class is used in the Executor.lvars()
to delay creation of NodeList objects until they're needed.
Note that we subclass UserList.UserList purely so that the
is_Sequence() function will identify an object of this class as
a list during variable expansion. We're not really using any
UserList.UserList methods in practice.
"""
def __init__(self, func):
self.func = func
def __getattr__(self, attr):
nl = self.func()
return getattr(nl, attr)
def __getitem__(self, i):
nl = self.func()
return nl[i]
def __getslice__(self, i, j):
nl = self.func()
i = max(i, 0); j = max(j, 0)
return nl[i:j]
def __str__(self):
nl = self.func()
return str(nl)
def __repr__(self):
nl = self.func()
return repr(nl)
class TSObject:
"""A class that implements $TARGET or $SOURCE expansions by wrapping
an Executor method.
"""
def __init__(self, func):
self.func = func
def __getattr__(self, attr):
n = self.func()
return getattr(n, attr)
def __str__(self):
n = self.func()
if n:
return str(n)
return ''
def __repr__(self):
n = self.func()
if n:
return repr(n)
return ''
def rfile(node):
"""
A function to return the results of a Node's rfile() method,
if it exists, and the Node itself otherwise (if it's a Value
Node, e.g.).
"""
try:
rfile = node.rfile
except AttributeError:
return node
else:
return rfile()
class Executor:
"""A class for controlling instances of executing an action.
This largely exists to hold a single association of an action,
environment, list of environment override dictionaries, targets
and sources for later processing as needed.
"""
if SCons.Memoize.use_memoizer:
__metaclass__ = SCons.Memoize.Memoized_Metaclass
memoizer_counters = []
def __init__(self, action, env=None, overridelist=[{}],
targets=[], sources=[], builder_kw={}):
if __debug__: logInstanceCreation(self, 'Executor.Executor')
self.set_action_list(action)
self.pre_actions = []
self.post_actions = []
self.env = env
self.overridelist = overridelist
if targets or sources:
self.batches = [Batch(targets[:], sources[:])]
else:
self.batches = []
self.builder_kw = builder_kw
self._memo = {}
def get_lvars(self):
try:
return self.lvars
except AttributeError:
self.lvars = {
'CHANGED_SOURCES' : TSList(self._get_changed_sources),
'CHANGED_TARGETS' : TSList(self._get_changed_targets),
'SOURCE' : TSObject(self._get_source),
'SOURCES' : TSList(self._get_sources),
'TARGET' : TSObject(self._get_target),
'TARGETS' : TSList(self._get_targets),
'UNCHANGED_SOURCES' : TSList(self._get_unchanged_sources),
'UNCHANGED_TARGETS' : TSList(self._get_unchanged_targets),
}
return self.lvars
def _get_changes(self):
cs = []
ct = []
us = []
ut = []
for b in self.batches:
if b.targets[0].is_up_to_date():
us.extend(map(rfile, b.sources))
ut.extend(b.targets)
else:
cs.extend(map(rfile, b.sources))
ct.extend(b.targets)
self._changed_sources_list = SCons.Util.NodeList(cs)
self._changed_targets_list = SCons.Util.NodeList(ct)
self._unchanged_sources_list = SCons.Util.NodeList(us)
self._unchanged_targets_list = SCons.Util.NodeList(ut)
def _get_changed_sources(self, *args, **kw):
try:
return self._changed_sources_list
except AttributeError:
self._get_changes()
return self._changed_sources_list
def _get_changed_targets(self, *args, **kw):
try:
return self._changed_targets_list
except AttributeError:
self._get_changes()
return self._changed_targets_list
def _get_source(self, *args, **kw):
#return SCons.Util.NodeList([rfile(self.batches[0].sources[0]).get_subst_proxy()])
return rfile(self.batches[0].sources[0]).get_subst_proxy()
def _get_sources(self, *args, **kw):
return SCons.Util.NodeList(map(lambda n: rfile(n).get_subst_proxy(), self.get_all_sources()))
def _get_target(self, *args, **kw):
#return SCons.Util.NodeList([self.batches[0].targets[0].get_subst_proxy()])
return self.batches[0].targets[0].get_subst_proxy()
def _get_targets(self, *args, **kw):
return SCons.Util.NodeList(map(lambda n: n.get_subst_proxy(), self.get_all_targets()))
def _get_unchanged_sources(self, *args, **kw):
try:
return self._unchanged_sources_list
except AttributeError:
self._get_changes()
return self._unchanged_sources_list
def _get_unchanged_targets(self, *args, **kw):
try:
return self._unchanged_targets_list
except AttributeError:
self._get_changes()
return self._unchanged_targets_list
def get_action_targets(self):
if not self.action_list:
return []
targets_string = self.action_list[0].get_targets(self.env, self)
if targets_string[0] == '$':
targets_string = targets_string[1:]
return self.get_lvars()[targets_string]
def set_action_list(self, action):
import SCons.Util
if not SCons.Util.is_List(action):
if not action:
import SCons.Errors
raise SCons.Errors.UserError, "Executor must have an action."
action = [action]
self.action_list = action
def get_action_list(self):
return self.pre_actions + self.action_list + self.post_actions
def get_all_targets(self):
"""Returns all targets for all batches of this Executor."""
result = []
for batch in self.batches:
# TODO(1.5): remove the list() cast
result.extend(list(batch.targets))
return result
def get_all_sources(self):
"""Returns all sources for all batches of this Executor."""
result = []
for batch in self.batches:
# TODO(1.5): remove the list() cast
result.extend(list(batch.sources))
return result
def get_all_children(self):
"""Returns all unique children (dependencies) for all batches
of this Executor.
The Taskmaster can recognize when it's already evaluated a
Node, so we don't have to make this list unique for its intended
canonical use case, but we expect there to be a lot of redundancy
(long lists of batched .cc files #including the same .h files
over and over), so removing the duplicates once up front should
save the Taskmaster a lot of work.
"""
result = SCons.Util.UniqueList([])
for target in self.get_all_targets():
result.extend(target.children())
return result
def get_all_prerequisites(self):
"""Returns all unique (order-only) prerequisites for all batches
of this Executor.
"""
result = SCons.Util.UniqueList([])
for target in self.get_all_targets():
# TODO(1.5): remove the list() cast
result.extend(list(target.prerequisites))
return result
def get_action_side_effects(self):
"""Returns all side effects for all batches of this
Executor used by the underlying Action.
"""
result = SCons.Util.UniqueList([])
for target in self.get_action_targets():
result.extend(target.side_effects)
return result
memoizer_counters.append(SCons.Memoize.CountValue('get_build_env'))
def get_build_env(self):
"""Fetch or create the appropriate build Environment
for this Executor.
"""
try:
return self._memo['get_build_env']
except KeyError:
pass
# Create the build environment instance with appropriate
# overrides. These get evaluated against the current
# environment's construction variables so that users can
# add to existing values by referencing the variable in
# the expansion.
overrides = {}
for odict in self.overridelist:
overrides.update(odict)
import SCons.Defaults
env = self.env or SCons.Defaults.DefaultEnvironment()
build_env = env.Override(overrides)
self._memo['get_build_env'] = build_env
return build_env
def get_build_scanner_path(self, scanner):
"""Fetch the scanner path for this executor's targets and sources.
"""
env = self.get_build_env()
try:
cwd = self.batches[0].targets[0].cwd
except (IndexError, AttributeError):
cwd = None
return scanner.path(env, cwd,
self.get_all_targets(),
self.get_all_sources())
def get_kw(self, kw={}):
result = self.builder_kw.copy()
result.update(kw)
result['executor'] = self
return result
def do_nothing(self, target, kw):
return 0
def do_execute(self, target, kw):
"""Actually execute the action list."""
env = self.get_build_env()
kw = self.get_kw(kw)
status = 0
for act in self.get_action_list():
#args = (self.get_all_targets(), self.get_all_sources(), env)
args = ([], [], env)
status = apply(act, args, kw)
if isinstance(status, SCons.Errors.BuildError):
status.executor = self
raise status
elif status:
msg = "Error %s" % status
raise SCons.Errors.BuildError(
errstr=msg,
node=self.batches[0].targets,
executor=self,
action=act)
return status
# use extra indirection because with new-style objects (Python 2.2
# and above) we can't override special methods, and nullify() needs
# to be able to do this.
def __call__(self, target, **kw):
return self.do_execute(target, kw)
def cleanup(self):
self._memo = {}
def add_sources(self, sources):
"""Add source files to this Executor's list. This is necessary
for "multi" Builders that can be called repeatedly to build up
a source file list for a given target."""
# TODO(batch): extend to multiple batches
assert (len(self.batches) == 1)
# TODO(batch): remove duplicates?
sources = filter(lambda x, s=self.batches[0].sources: x not in s, sources)
self.batches[0].sources.extend(sources)
def get_sources(self):
return self.batches[0].sources
def add_batch(self, targets, sources):
"""Add pair of associated target and source to this Executor's list.
This is necessary for "batch" Builders that can be called repeatedly
to build up a list of matching target and source files that will be
used in order to update multiple target files at once from multiple
corresponding source files, for tools like MSVC that support it."""
self.batches.append(Batch(targets, sources))
def prepare(self):
"""
Preparatory checks for whether this Executor can go ahead
and (try to) build its targets.
"""
for s in self.get_all_sources():
if s.missing():
msg = "Source `%s' not found, needed by target `%s'."
raise SCons.Errors.StopError, msg % (s, self.batches[0].targets[0])
def add_pre_action(self, action):
self.pre_actions.append(action)
def add_post_action(self, action):
self.post_actions.append(action)
# another extra indirection for new-style objects and nullify...
def my_str(self):
env = self.get_build_env()
get = lambda action, t=self.get_all_targets(), s=self.get_all_sources(), e=env: \
action.genstring(t, s, e)
return string.join(map(get, self.get_action_list()), "\n")
def __str__(self):
return self.my_str()
def nullify(self):
self.cleanup()
self.do_execute = self.do_nothing
self.my_str = lambda S=self: ''
memoizer_counters.append(SCons.Memoize.CountValue('get_contents'))
def get_contents(self):
"""Fetch the signature contents. This is the main reason this
class exists, so we can compute this once and cache it regardless
of how many target or source Nodes there are.
"""
try:
return self._memo['get_contents']
except KeyError:
pass
env = self.get_build_env()
get = lambda action, t=self.get_all_targets(), s=self.get_all_sources(), e=env: \
action.get_contents(t, s, e)
result = string.join(map(get, self.get_action_list()), "")
self._memo['get_contents'] = result
return result
def get_timestamp(self):
"""Fetch a time stamp for this Executor. We don't have one, of
course (only files do), but this is the interface used by the
timestamp module.
"""
return 0
def scan_targets(self, scanner):
# TODO(batch): scan by batches
self.scan(scanner, self.get_all_targets())
def scan_sources(self, scanner):
# TODO(batch): scan by batches
if self.batches[0].sources:
self.scan(scanner, self.get_all_sources())
def scan(self, scanner, node_list):
"""Scan a list of this Executor's files (targets or sources) for
implicit dependencies and update all of the targets with them.
This essentially short-circuits an N*M scan of the sources for
each individual target, which is a hell of a lot more efficient.
"""
env = self.get_build_env()
# TODO(batch): scan by batches)
deps = []
if scanner:
for node in node_list:
node.disambiguate()
s = scanner.select(node)
if not s:
continue
path = self.get_build_scanner_path(s)
deps.extend(node.get_implicit_deps(env, s, path))
else:
kw = self.get_kw()
for node in node_list:
node.disambiguate()
scanner = node.get_env_scanner(env, kw)
if not scanner:
continue
scanner = scanner.select(node)
if not scanner:
continue
path = self.get_build_scanner_path(scanner)
deps.extend(node.get_implicit_deps(env, scanner, path))
deps.extend(self.get_implicit_deps())
for tgt in self.get_all_targets():
tgt.add_to_implicit(deps)
def _get_unignored_sources_key(self, node, ignore=()):
return (node,) + tuple(ignore)
memoizer_counters.append(SCons.Memoize.CountDict('get_unignored_sources', _get_unignored_sources_key))
def get_unignored_sources(self, node, ignore=()):
key = (node,) + tuple(ignore)
try:
memo_dict = self._memo['get_unignored_sources']
except KeyError:
memo_dict = {}
self._memo['get_unignored_sources'] = memo_dict
else:
try:
return memo_dict[key]
except KeyError:
pass
if node:
# TODO: better way to do this (it's a linear search,
# but it may not be critical path)?
sourcelist = []
for b in self.batches:
if node in b.targets:
sourcelist = b.sources
break
else:
sourcelist = self.get_all_sources()
if ignore:
idict = {}
for i in ignore:
idict[i] = 1
sourcelist = filter(lambda s, i=idict: not i.has_key(s), sourcelist)
memo_dict[key] = sourcelist
return sourcelist
def get_implicit_deps(self):
"""Return the executor's implicit dependencies, i.e. the nodes of
the commands to be executed."""
result = []
build_env = self.get_build_env()
for act in self.get_action_list():
deps = act.get_implicit_deps(self.get_all_targets(),
self.get_all_sources(),
build_env)
result.extend(deps)
return result
_batch_executors = {}
def GetBatchExecutor(key):
return _batch_executors[key]
def AddBatchExecutor(key, executor):
assert not _batch_executors.has_key(key)
_batch_executors[key] = executor
nullenv = None
def get_NullEnvironment():
"""Use singleton pattern for Null Environments."""
global nullenv
import SCons.Util
class NullEnvironment(SCons.Util.Null):
import SCons.CacheDir
_CacheDir_path = None
_CacheDir = SCons.CacheDir.CacheDir(None)
def get_CacheDir(self):
return self._CacheDir
if not nullenv:
nullenv = NullEnvironment()
return nullenv
class Null:
"""A null Executor, with a null build Environment, that does
nothing when the rest of the methods call it.
This might be able to disapper when we refactor things to
disassociate Builders from Nodes entirely, so we're not
going to worry about unit tests for this--at least for now.
"""
def __init__(self, *args, **kw):
if __debug__: logInstanceCreation(self, 'Executor.Null')
self.batches = [Batch(kw['targets'][:], [])]
def get_build_env(self):
return get_NullEnvironment()
def get_build_scanner_path(self):
return None
def cleanup(self):
pass
def prepare(self):
pass
def get_unignored_sources(self, *args, **kw):
return tuple(())
def get_action_targets(self):
return []
def get_action_list(self):
return []
def get_all_targets(self):
return self.batches[0].targets
def get_all_sources(self):
return self.batches[0].targets[0].sources
def get_all_children(self):
return self.get_all_sources()
def get_all_prerequisites(self):
return []
def get_action_side_effects(self):
return []
def __call__(self, *args, **kw):
return 0
def get_contents(self):
return ''
def _morph(self):
"""Morph this Null executor to a real Executor object."""
batches = self.batches
self.__class__ = Executor
self.__init__([])
self.batches = batches
# The following methods require morphing this Null Executor to a
# real Executor object.
def add_pre_action(self, action):
self._morph()
self.add_pre_action(action)
def add_post_action(self, action):
self._morph()
self.add_post_action(action)
def set_action_list(self, action):
self._morph()
self.set_action_list(action)
# Local Variables:
# tab-width:4
# indent-tabs-mode:nil
# End:
# vim: set expandtab tabstop=4 shiftwidth=4:
|
rat-pacREPO_NAMErat-pacPATH_START.@rat-pac_extracted@rat-pac-master@python@SCons@Executor.py@.PATH_END.py
|
{
"filename": "backend_qt4.py",
"repo_name": "waynebhayes/SpArcFiRe",
"repo_path": "SpArcFiRe_extracted/SpArcFiRe-master/scripts/SpArcFiRe-pyvenv/lib/python2.7/site-packages/matplotlib/backends/backend_qt4.py",
"type": "Python"
}
|
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import six
from six import unichr
import os
import re
import signal
import sys
from matplotlib._pylab_helpers import Gcf
from matplotlib.backend_bases import (
FigureCanvasBase, FigureManagerBase, NavigationToolbar2, TimerBase,
cursors)
from matplotlib.figure import Figure
from matplotlib.widgets import SubplotTool
from .qt_compat import QtCore, QtWidgets, _getSaveFileName, __version__
from .backend_qt5 import (
backend_version, SPECIAL_KEYS, SUPER, ALT, CTRL, SHIFT, MODIFIER_KEYS,
cursord, _create_qApp, _BackendQT5, TimerQT, MainWindow, FigureManagerQT,
NavigationToolbar2QT, SubplotToolQt, error_msg_qt, exception_handler)
from .backend_qt5 import FigureCanvasQT as FigureCanvasQT5
DEBUG = False
class FigureCanvasQT(FigureCanvasQT5):
def wheelEvent(self, event):
x = event.x()
# flipy so y=0 is bottom of canvas
y = self.figure.bbox.height - event.y()
# from QWheelEvent::delta doc
steps = event.delta()/120
if (event.orientation() == QtCore.Qt.Vertical):
FigureCanvasBase.scroll_event(self, x, y, steps)
if DEBUG:
print('scroll event: delta = %i, '
'steps = %i ' % (event.delta(), steps))
@_BackendQT5.export
class _BackendQT4(_BackendQT5):
FigureCanvas = FigureCanvasQT
|
waynebhayesREPO_NAMESpArcFiRePATH_START.@SpArcFiRe_extracted@SpArcFiRe-master@scripts@SpArcFiRe-pyvenv@lib@python2.7@site-packages@matplotlib@backends@backend_qt4.py@.PATH_END.py
|
{
"filename": "version.py",
"repo_name": "tgrassi/prizmo",
"repo_path": "prizmo_extracted/prizmo-main/src_py/ChiantiPy/version.py",
"type": "Python"
}
|
'''
the current version of the ChiantiPy package
'''
__version_info__ = ('0','11', '1')
__version__ = '.'.join(__version_info__)
|
tgrassiREPO_NAMEprizmoPATH_START.@prizmo_extracted@prizmo-main@src_py@ChiantiPy@version.py@.PATH_END.py
|
{
"filename": "test_friendli.py",
"repo_name": "langchain-ai/langchain",
"repo_path": "langchain_extracted/langchain-master/libs/community/tests/unit_tests/chat_models/test_friendli.py",
"type": "Python"
}
|
"""Test Friendli LLM for chat."""
from unittest.mock import AsyncMock, MagicMock, Mock
import pytest
from pydantic import SecretStr
from pytest import CaptureFixture, MonkeyPatch
from langchain_community.adapters.openai import aenumerate
from langchain_community.chat_models import ChatFriendli
@pytest.fixture
def mock_friendli_client() -> Mock:
"""Mock instance of Friendli client."""
return Mock()
@pytest.fixture
def mock_friendli_async_client() -> AsyncMock:
"""Mock instance of Friendli async client."""
return AsyncMock()
@pytest.fixture
def chat_friendli(
mock_friendli_client: Mock, mock_friendli_async_client: AsyncMock
) -> ChatFriendli:
"""Friendli LLM for chat with mock clients."""
return ChatFriendli(
friendli_token=SecretStr("personal-access-token"),
client=mock_friendli_client,
async_client=mock_friendli_async_client,
)
@pytest.mark.requires("friendli")
def test_friendli_token_is_secret_string(capsys: CaptureFixture) -> None:
"""Test if friendli token is stored as a SecretStr."""
fake_token_value = "personal-access-token"
chat = ChatFriendli(friendli_token=fake_token_value) # type: ignore[arg-type]
assert isinstance(chat.friendli_token, SecretStr)
assert chat.friendli_token.get_secret_value() == fake_token_value
print(chat.friendli_token, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
@pytest.mark.requires("friendli")
def test_friendli_token_read_from_env(
monkeypatch: MonkeyPatch, capsys: CaptureFixture
) -> None:
"""Test if friendli token can be parsed from environment."""
fake_token_value = "personal-access-token"
monkeypatch.setenv("FRIENDLI_TOKEN", fake_token_value)
chat = ChatFriendli()
assert isinstance(chat.friendli_token, SecretStr)
assert chat.friendli_token.get_secret_value() == fake_token_value
print(chat.friendli_token, end="") # noqa: T201
captured = capsys.readouterr()
assert captured.out == "**********"
@pytest.mark.requires("friendli")
def test_friendli_invoke(
mock_friendli_client: Mock, chat_friendli: ChatFriendli
) -> None:
"""Test invocation with friendli."""
mock_message = Mock()
mock_message.content = "Hello Friendli"
mock_message.role = "assistant"
mock_choice = Mock()
mock_choice.message = mock_message
mock_response = Mock()
mock_response.choices = [mock_choice]
mock_friendli_client.chat.completions.create.return_value = mock_response
result = chat_friendli.invoke("Hello langchain")
assert result.content == "Hello Friendli"
mock_friendli_client.chat.completions.create.assert_called_once_with(
messages=[{"role": "user", "content": "Hello langchain"}],
stream=False,
model=chat_friendli.model,
frequency_penalty=None,
presence_penalty=None,
max_tokens=None,
stop=None,
temperature=None,
top_p=None,
)
@pytest.mark.requires("friendli")
async def test_friendli_ainvoke(
mock_friendli_async_client: AsyncMock, chat_friendli: ChatFriendli
) -> None:
"""Test async invocation with friendli."""
mock_message = Mock()
mock_message.content = "Hello Friendli"
mock_message.role = "assistant"
mock_choice = Mock()
mock_choice.message = mock_message
mock_response = Mock()
mock_response.choices = [mock_choice]
mock_friendli_async_client.chat.completions.create.return_value = mock_response
result = await chat_friendli.ainvoke("Hello langchain")
assert result.content == "Hello Friendli"
mock_friendli_async_client.chat.completions.create.assert_awaited_once_with(
messages=[{"role": "user", "content": "Hello langchain"}],
stream=False,
model=chat_friendli.model,
frequency_penalty=None,
presence_penalty=None,
max_tokens=None,
stop=None,
temperature=None,
top_p=None,
)
@pytest.mark.requires("friendli")
def test_friendli_stream(
mock_friendli_client: Mock, chat_friendli: ChatFriendli
) -> None:
"""Test stream with friendli."""
mock_delta_0 = Mock()
mock_delta_0.content = "Hello "
mock_delta_1 = Mock()
mock_delta_1.content = "Friendli"
mock_choice_0 = Mock()
mock_choice_0.delta = mock_delta_0
mock_choice_1 = Mock()
mock_choice_1.delta = mock_delta_1
mock_chunk_0 = Mock()
mock_chunk_0.choices = [mock_choice_0]
mock_chunk_1 = Mock()
mock_chunk_1.choices = [mock_choice_1]
mock_stream = MagicMock()
mock_chunks = [mock_chunk_0, mock_chunk_1]
mock_stream.__iter__.return_value = mock_chunks
mock_friendli_client.chat.completions.create.return_value = mock_stream
stream = chat_friendli.stream("Hello langchain")
for i, chunk in enumerate(stream):
assert chunk.content == mock_chunks[i].choices[0].delta.content
mock_friendli_client.chat.completions.create.assert_called_once_with(
messages=[{"role": "user", "content": "Hello langchain"}],
stream=True,
model=chat_friendli.model,
frequency_penalty=None,
presence_penalty=None,
max_tokens=None,
stop=None,
temperature=None,
top_p=None,
)
@pytest.mark.requires("friendli")
async def test_friendli_astream(
mock_friendli_async_client: AsyncMock, chat_friendli: ChatFriendli
) -> None:
"""Test async stream with friendli."""
mock_delta_0 = Mock()
mock_delta_0.content = "Hello "
mock_delta_1 = Mock()
mock_delta_1.content = "Friendli"
mock_choice_0 = Mock()
mock_choice_0.delta = mock_delta_0
mock_choice_1 = Mock()
mock_choice_1.delta = mock_delta_1
mock_chunk_0 = Mock()
mock_chunk_0.choices = [mock_choice_0]
mock_chunk_1 = Mock()
mock_chunk_1.choices = [mock_choice_1]
mock_stream = AsyncMock()
mock_chunks = [mock_chunk_0, mock_chunk_1]
mock_stream.__aiter__.return_value = mock_chunks
mock_friendli_async_client.chat.completions.create.return_value = mock_stream
stream = chat_friendli.astream("Hello langchain")
async for i, chunk in aenumerate(stream):
assert chunk.content == mock_chunks[i].choices[0].delta.content
mock_friendli_async_client.chat.completions.create.assert_awaited_once_with(
messages=[{"role": "user", "content": "Hello langchain"}],
stream=True,
model=chat_friendli.model,
frequency_penalty=None,
presence_penalty=None,
max_tokens=None,
stop=None,
temperature=None,
top_p=None,
)
|
langchain-aiREPO_NAMElangchainPATH_START.@langchain_extracted@langchain-master@libs@community@tests@unit_tests@chat_models@test_friendli.py@.PATH_END.py
|
{
"filename": "image.py",
"repo_name": "achael/eht-imaging",
"repo_path": "eht-imaging_extracted/eht-imaging-main/ehtim/image.py",
"type": "Python"
}
|
# image.py
# an interferometric image class
#
# Copyright (C) 2018 Andrew Chael
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from __future__ import division
from __future__ import print_function
from builtins import str
from builtins import range
from builtins import object
import sys
import copy
import math
import numpy as np
import numpy.matlib as matlib
import matplotlib as mpl
import matplotlib.pyplot as plt
import scipy.optimize as opt
import scipy.signal
import scipy.ndimage.filters as filt
import scipy.interpolate
from scipy import ndimage as ndi
try:
from skimage.feature import canny
from skimage.transform import hough_circle, hough_circle_peaks
except ImportError:
print("Warning: scikit-image not installed! Cannot use hough transform")
import ehtim.observing.obs_simulate as simobs
import ehtim.observing.pulses as pulses
import ehtim.io.save
import ehtim.io.load
import ehtim.const_def as ehc
import ehtim.observing.obs_helpers as obsh
# TODO : add time to all images
# TODO : add arbitrary center location
###################################################################################################
# Image object
###################################################################################################
class Image(object):
"""A polarimetric image (in units of Jy/pixel).
Attributes:
pulse (function): The function convolved with the pixel values for continuous image.
psize (float): The pixel dimension in radians
xdim (int): The number of pixels along the x dimension
ydim (int): The number of pixels along the y dimension
mjd (int): The integer MJD of the image
time (float): The observing time of the image (UTC hours)
source (str): The astrophysical source name
ra (float): The source Right Ascension in fractional hours
dec (float): The source declination in fractional degrees
rf (float): The image frequency in Hz
polrep (str): polarization representation, either 'stokes' or 'circ'
pol_prim (str): The default image: I,Q,U or V for Stokes, or RR,LL,LR,RL for Circular
_imdict (dict): The dictionary with the polarimetric images
_mflist (list): List of spectral index images (and higher order terms)
"""
def __init__(self, image, psize, ra, dec, pa=0.0,
polrep='stokes', pol_prim=None,
rf=ehc.RF_DEFAULT, pulse=ehc.PULSE_DEFAULT, source=ehc.SOURCE_DEFAULT,
mjd=ehc.MJD_DEFAULT, time=0.):
"""A polarimetric image (in units of Jy/pixel).
Args:
image (numpy.array): The 2D intensity values in a Jy/pixel array
polrep (str): polarization representation, either 'stokes' or 'circ'
pol_prim (str): The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular
psize (float): The pixel dimension in radians
ra (float): The source Right Ascension in fractional hours
dec (float): The source declination in fractional degrees
pa (float): logical positional angle of the image
rf (float): The image frequency in Hz
pulse (function): The function convolved with the pixel values for continuous image.
source (str): The source name
mjd (int): The integer MJD of the image
time (float): The observing time of the image (UTC hours)
Returns:
(Image): the Image object
"""
if len(image.shape) != 2:
raise Exception("image must be a 2D numpy array")
if polrep not in ['stokes', 'circ']:
raise Exception("only 'stokes' and 'circ' are supported polreps!")
# Save the image vector
imvec = image.flatten()
if polrep == 'stokes':
if pol_prim is None:
pol_prim = 'I'
if pol_prim == 'I':
self._imdict = {'I': imvec, 'Q': np.array([]), 'U': np.array([]), 'V': np.array([])}
elif pol_prim == 'V':
self._imdict = {'I': np.array([]), 'Q': np.array([]), 'U': np.array([]), 'V': imvec}
elif pol_prim == 'Q':
self._imdict = {'I': np.array([]), 'Q': imvec, 'U': np.array([]), 'V': np.array([])}
elif pol_prim == 'U':
self._imdict = {'I': np.array([]), 'Q': np.array([]), 'U': imvec, 'V': np.array([])}
else:
raise Exception("for polrep=='stokes', pol_prim must be 'I','Q','U', or 'V'!")
elif polrep == 'circ':
if pol_prim is None:
print("polrep is 'circ' and no pol_prim specified! Setting pol_prim='RR'")
pol_prim = 'RR'
if pol_prim == 'RR':
self._imdict = {'RR': imvec, 'LL': np.array([]), 'RL': np.array([]), 'LR': np.array([])}
elif pol_prim == 'LL':
self._imdict = {'RR': np.array([]), 'LL': imvec, 'RL': np.array([]), 'LR': np.array([])}
else:
raise Exception("for polrep=='circ', pol_prim must be 'RR' or 'LL'!")
else:
raise Exception("polrep must be 'circ' or 'stokes'!")
# multifrequency spectral index, curvature arrays
# TODO -- higher orders?
# TODO -- don't initialize to zero?
avec = np.array([]) # np.zeros(imvec.shape)
bvec = np.array([]) # np.zeros(imvec.shape)
self._mflist = [avec, bvec]
# Save the image dimension data
self.pol_prim = pol_prim
self.polrep = polrep
self.pulse = pulse
self.psize = float(psize)
self.xdim = image.shape[1]
self.ydim = image.shape[0]
# Save the image metadata
self.ra = float(ra)
self.dec = float(dec)
self.pa = float(pa)
self.rf = float(rf)
self.source = str(source)
self.mjd = int(mjd)
# Cached FFT of the image
self.cached_fft = {}
if time > 24:
self.mjd += int((time - time % 24) / 24)
self.time = float(time % 24)
else:
self.time = time
@property
def imvec(self):
imvec = self._imdict[self.pol_prim]
return imvec
@imvec.setter
def imvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("imvec size is not consistent with xdim*ydim!")
self._imdict[self.pol_prim] = vec
@property
def specvec(self):
specvec = self._mflist[0]
return specvec
@specvec.setter
def specvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
self._mflist[0] = vec
@property
def curvvec(self):
curvvec = self._mflist[1]
return curvvec
@curvvec.setter
def curvvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
self._mflist[1] = vec
@property
def ivec(self):
# if self.polrep != 'stokes':
# raise Exception("ivec is not defined unless self.polrep=='stokes'")
ivec = np.array([])
if self.polrep == 'stokes':
ivec = self._imdict['I']
elif self.polrep == 'circ':
if len(self.rrvec) != 0 and len(self.llvec) != 0:
ivec = 0.5 * (self.rrvec + self.llvec)
return ivec
@ivec.setter
def ivec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'stokes':
raise Exception("ivec cannot be set unless self.polrep=='stokes'")
self._imdict['I'] = vec
@property
def qvec(self):
# if self.polrep != 'stokes':
# raise Exception("qvec is not defined unless self.polrep=='stokes'")
qvec = np.array([])
if self.polrep == 'stokes':
qvec = self._imdict['Q']
elif self.polrep == 'circ':
if len(self.rlvec) != 0 and len(self.lrvec) != 0:
qvec = np.real(0.5 * (self.lrvec + self.rlvec))
return qvec
@qvec.setter
def qvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'stokes':
raise Exception("ivec cannot be set unless self.polrep=='stokes'")
self._imdict['Q'] = vec
@property
def uvec(self):
# if self.polrep != 'stokes':
# raise Exception("qvec is not defined unless self.polrep=='stokes'")
uvec = np.array([])
if self.polrep == 'stokes':
uvec = self._imdict['U']
elif self.polrep == 'circ':
if len(self.rlvec) != 0 and len(self.lrvec) != 0:
uvec = np.real(0.5j * (self.lrvec - self.rlvec))
return uvec
@uvec.setter
def uvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'stokes':
raise Exception("uvec cannot be set unless self.polrep=='stokes'")
self._imdict['U'] = vec
@property
def vvec(self):
# if self.polrep != 'stokes':
# raise Exception("vvec is not defined unless self.polrep=='stokes'")
vvec = np.array([])
if self.polrep == 'stokes':
vvec = self._imdict['V']
elif self.polrep == 'circ':
if len(self.rrvec) != 0 and len(self.llvec) != 0:
vvec = 0.5 * (self.rrvec - self.llvec)
return vvec
@vvec.setter
def vvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'stokes':
raise Exception("vvec cannot be set unless self.polrep=='stokes'")
self._imdict['V'] = vec
@property
def rrvec(self):
# if self.polrep != 'circ':
# raise Exception("rrvec is not defined unless self.polrep=='circ'")
rrvec = np.array([])
if self.polrep == 'circ':
rrvec = self._imdict['RR']
elif self.polrep == 'stokes':
if len(self.ivec) != 0 and len(self.vvec) != 0:
rrvec = (self.ivec + self.vvec)
return rrvec
@rrvec.setter
def rrvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'circ':
raise Exception("rrvec cannot be set unless self.polrep=='circ'")
self._imdict['RR'] = vec
@property
def llvec(self):
# if self.polrep != 'circ':
# raise Exception("llvec is not defined unless self.polrep=='circ'")
llvec = np.array([])
if self.polrep == 'circ':
llvec = self._imdict['LL']
elif self.polrep == 'stokes':
if len(self.ivec) != 0 and len(self.vvec) != 0:
llvec = (self.ivec - self.vvec)
return llvec
@llvec.setter
def llvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'circ':
raise Exception("llvec cannot be set unless self.polrep=='circ'")
self._imdict['LL'] = vec
@property
def rlvec(self):
# if self.polrep != 'circ':
# raise Exception("rlvec is not defined unless self.polrep=='circ'")
rlvec = np.array([])
if self.polrep == 'circ':
rlvec = self._imdict['RL']
elif self.polrep == 'stokes':
if len(self.qvec) != 0 and len(self.uvec) != 0:
rlvec = (self.qvec + 1j * self.uvec)
return rlvec
@rlvec.setter
def rlvec(self, vec):
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'circ':
raise Exception("rlvec cannot be set unless self.polrep=='circ'")
self._imdict['RL'] = vec
@property
def lrvec(self):
"""Return the imvec of LR"""
# if self.polrep != 'circ':
# raise Exception("lrvec is not defined unless self.polrep=='circ'")
lrvec = np.array([])
if self.polrep == 'circ':
lrvec = self._imdict['LR']
elif self.polrep == 'stokes':
if len(self.qvec) != 0 and len(self.uvec) != 0:
lrvec = (self.qvec - 1j * self.uvec)
return lrvec
@lrvec.setter
def lrvec(self, vec):
"""Set the imvec"""
if len(vec) != self.xdim * self.ydim:
raise Exception("vec size is not consistent with xdim*ydim!")
if self.polrep != 'circ':
raise Exception("lrvec cannot be set unless self.polrep=='circ'")
self._imdict['LR'] = vec
@property
def pvec(self):
"""Return the polarization magnitude for each pixel"""
if self.polrep == 'circ':
pvec = np.abs(self.rlvec)
elif self.polrep == 'stokes':
pvec = np.abs(self.qvec + 1j * self.uvec)
return pvec
@property
def mvec(self):
"""Return the fractional polarization for each pixel"""
if self.polrep == 'circ':
mvec = 2 * np.abs(self.rlvec) / (self.rrvec + self.llvec)
elif self.polrep == 'stokes':
mvec = np.abs(self.qvec + 1j * self.uvec) / self.ivec
return mvec
@property
def chivec(self):
"""Return the fractional polarization angle for each pixel"""
if self.polrep == 'circ':
chivec = 0.5 * np.angle(self.rlvec / (self.rrvec + self.llvec))
elif self.polrep == 'stokes':
chivec = 0.5 * np.angle((self.qvec + 1j * self.uvec) / self.ivec)
return chivec
@property
def evpavec(self):
"""Return the fractional polarization angle for each pixel"""
return self.chivec
@property
def evec(self):
"""Return the E mode image vector"""
if self.polrep == 'circ':
qvec = np.real(0.5 * (self.lrvec + self.rlvec))
uvec = np.real(0.5j * (self.lrvec - self.rlvec))
elif self.polrep == 'stokes':
qvec = self.qvec
uvec = self.uvec
qarr = qvec.reshape((self.ydim, self.xdim))
uarr = uvec.reshape((self.ydim, self.xdim))
qarr_fft = np.fft.fftshift(np.fft.fft2(qarr))
uarr_fft = np.fft.fftshift(np.fft.fft2(uarr))
# TODO -- check conventions for u,v angle
s, t = np.meshgrid(np.flip(np.fft.fftshift(np.fft.fftfreq(self.xdim, d=1.0 / self.xdim))),
np.flip(np.fft.fftshift(np.fft.fftfreq(self.ydim, d=1.0 / self.ydim))))
s = s + .5 # .5 offset to reference to pixel center
t = t + .5 # .5 offset to reference to pixel center
uvangle = np.arctan2(s, t)
# TODO -- these conventions for e,b are from kaminokowski aara 54:227-69 sec 4.1
# TODO -- check!
cos2arr = np.round(np.cos(2 * uvangle), decimals=10)
sin2arr = np.round(np.sin(2 * uvangle), decimals=10)
earr_fft = (cos2arr * qarr_fft + sin2arr * uarr_fft)
earr = np.fft.ifft2(np.fft.ifftshift(earr_fft))
return np.real(earr.flatten())
@property
def bvec(self):
"""Return the B mode image vector"""
if self.polrep == 'circ':
qvec = np.real(0.5 * (self.lrvec + self.rlvec))
uvec = np.real(0.5j * (self.lrvec - self.rlvec))
elif self.polrep == 'stokes':
qvec = self.qvec
uvec = self.uvec
# TODO -- check conventions for u,v angle
qarr = qvec.reshape((self.ydim, self.xdim))
uarr = uvec.reshape((self.ydim, self.xdim))
qarr_fft = np.fft.fftshift(np.fft.fft2(qarr))
uarr_fft = np.fft.fftshift(np.fft.fft2(uarr))
# TODO -- are these conventions for u,v right?
s, t = np.meshgrid(np.flip(np.fft.fftshift(np.fft.fftfreq(self.xdim, d=1.0 / self.xdim))),
np.flip(np.fft.fftshift(np.fft.fftfreq(self.ydim, d=1.0 / self.ydim))))
s = s + .5 # .5 offset to reference to pixel center
t = t + .5 # .5 offset to reference to pixel center
uvangle = np.arctan2(s, t)
# TODO -- check!
cos2arr = np.round(np.cos(2 * uvangle), decimals=10)
sin2arr = np.round(np.sin(2 * uvangle), decimals=10)
barr_fft = (-sin2arr * qarr_fft + cos2arr * uarr_fft)
barr = np.fft.ifft2(np.fft.ifftshift(barr_fft))
return np.real(barr.flatten())
def get_polvec(self, pol):
"""Get the imvec corresponding to the chosen polarization
"""
if self.polrep == 'stokes' and pol is None:
pol = 'I'
elif self.polrep == 'circ' and pol is None:
pol = 'RR'
if pol.lower() == 'i':
outvec = self.ivec
elif pol.lower() == 'q':
outvec = self.qvec
elif pol.lower() == 'u':
outvec = self.uvec
elif pol.lower() == 'v':
outvec = self.vvec
elif pol.lower() == 'rr':
outvec = self.rrvec
elif pol.lower() == 'll':
outvec = self.llvec
elif pol.lower() == 'lr':
outvec = self.lrvec
elif pol.lower() == 'rl':
outvec = self.rlvec
elif pol.lower() == 'p':
outvec = self.pvec
elif pol.lower() == 'm':
outvec = self.mvec
elif pol.lower() == 'chi' or pol.lower() =='evpa':
outvec = self.chivec
elif pol.lower() == 'e':
outvec = self.evec
elif pol.lower() == 'b':
outvec = self.bvec
else:
raise Exception("Requested polvec type not recognized!")
return outvec
def image_args(self):
"""Copy arguments for making a new Image into a list and dictonary
"""
arglist = [self.imarr(), self.psize, self.ra, self.dec]
argdict = {'rf': self.rf, 'pa': self.pa,
'polrep': self.polrep, 'pol_prim': self.pol_prim,
'pulse': self.pulse, 'source': self.source,
'mjd': self.mjd, 'time': self.time}
return (arglist, argdict)
def copy(self):
"""Return a copy of the Image object.
Args:
Returns:
(Image): copy of the Image.
"""
# Make new image with primary polarization
arglist, argdict = self.image_args()
newim = Image(*arglist, **argdict)
# Copy over all polarization images
newim.copy_pol_images(self)
# Copy over spectral index information
newim._mflist = copy.deepcopy(self._mflist)
return newim
def copy_pol_images(self, old_image):
"""Copy polarization images from old_image over to self.
Args:
old_image (Image): image object to copy from
"""
for pol in list(self._imdict.keys()):
if (pol == self.pol_prim):
continue
polvec = old_image._imdict[pol]
if len(polvec):
self.add_pol_image(polvec.reshape(self.ydim, self.xdim), pol)
def add_pol_image(self, image, pol):
"""Add another image polarization.
Args:
image (list): 2D image frame (possibly complex) in a Jy/pixel array
pol (str): The image type: 'I','Q','U','V' for stokes, 'RR','LL','RL','LR' for circ
"""
if pol == self.pol_prim:
raise Exception("new pol in add_pol_image is the same as pol_prim!")
if image.shape != (self.ydim, self.xdim):
raise Exception("add_pol_image image shapes incompatible with primary image!")
if not (pol in list(self._imdict.keys())):
raise Exception("for polrep==%s, pol in add_pol_image in " %
self.polrep + ",".join(list(self._imdict.keys())))
if self.polrep == 'stokes':
if pol == 'I':
self.ivec = image.flatten()
elif pol == 'Q':
self.qvec = image.flatten()
elif pol == 'U':
self.uvec = image.flatten()
elif pol == 'V':
self.vvec = image.flatten()
elif self.polrep == 'circ':
if pol == 'RR':
self.rrvec = image.flatten()
elif pol == 'LL':
self.llvec = image.flatten()
elif pol == 'RL':
self.rlvec = image.flatten()
elif pol == 'LR':
self.lrvec = image.flatten()
return
# TODO deprecated -- replace with generic add_pol_image
def add_qu(self, qimage, uimage):
"""Add Stokes Q and U images. self.polrep must be 'stokes'
Args:
qimage (numpy.array): The 2D Stokes Q values in Jy/pixel array
uimage (numpy.array): The 2D Stokes U values in Jy/pixel array
Returns:
"""
if self.polrep != 'stokes':
raise Exception("polrep must be 'stokes' for add_qu() !")
self.add_pol_image(qimage, 'Q')
self.add_pol_image(uimage, 'U')
return
# TODO deprecated -- replace with generic add_pol_image
def add_v(self, vimage):
"""Add Stokes V image. self.polrep must be 'stokes'
Args:
vimage (numpy.array): The 2D Stokes Q values in Jy/pixel array
"""
if self.polrep != 'stokes':
raise Exception("polrep must be 'stokes' for add_v() !")
self.add_pol_image(vimage, 'V')
return
def switch_polrep(self, polrep_out='stokes', pol_prim_out=None):
"""Return a new image with the polarization representation changed
Args:
polrep_out (str): the polrep of the output data
pol_prim_out (str): The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for circ
Returns:
(Image): new Image object with potentially different polrep
"""
if polrep_out not in ['stokes', 'circ']:
raise Exception("polrep_out must be either 'stokes' or 'circ'")
if pol_prim_out is None:
if polrep_out == 'stokes':
pol_prim_out = 'I'
elif polrep_out == 'circ':
pol_prim_out = 'RR'
# Simply copy if the polrep is unchanged
if polrep_out == self.polrep and pol_prim_out == self.pol_prim:
return self.copy()
# Assemble a dictionary of new polarization vectors
if polrep_out == 'stokes':
if self.polrep == 'stokes':
imdict = {'I': self.ivec, 'Q': self.qvec, 'U': self.uvec, 'V': self.vvec}
else:
if len(self.rrvec) == 0 or len(self.llvec) == 0:
ivec = np.array([])
vvec = np.array([])
else:
ivec = 0.5 * (self.rrvec + self.llvec)
vvec = 0.5 * (self.rrvec - self.llvec)
if len(self.rlvec) == 0 or len(self.lrvec) == 0:
qvec = np.array([])
uvec = np.array([])
else:
qvec = np.real(0.5 * (self.lrvec + self.rlvec))
uvec = np.real(0.5j * (self.lrvec - self.rlvec))
imdict = {'I': ivec, 'Q': qvec, 'U': uvec, 'V': vvec}
elif polrep_out == 'circ':
if self.polrep == 'circ':
imdict = {'RR': self.rrvec, 'LL': self.llvec, 'RL': self.rlvec, 'LR': self.lrvec}
else:
if len(self.ivec) == 0 or len(self.vvec) == 0:
rrvec = np.array([])
llvec = np.array([])
else:
rrvec = (self.ivec + self.vvec)
llvec = (self.ivec - self.vvec)
if len(self.qvec) == 0 or len(self.uvec) == 0:
rlvec = np.array([])
lrvec = np.array([])
else:
rlvec = (self.qvec + 1j * self.uvec)
lrvec = (self.qvec - 1j * self.uvec)
imdict = {'RR': rrvec, 'LL': llvec, 'RL': rlvec, 'LR': lrvec}
# Assemble the new image
imvec = imdict[pol_prim_out]
if len(imvec) == 0:
raise Exception("for switch_polrep to %s with pol_prim_out=%s, \n" %
(polrep_out, pol_prim_out) + "output image is not defined")
arglist, argdict = self.image_args()
arglist[0] = imvec.reshape(self.ydim, self.xdim)
argdict['polrep'] = polrep_out
argdict['pol_prim'] = pol_prim_out
newim = Image(*arglist, **argdict)
# Add in any other polarizations
for pol in list(imdict.keys()):
if pol == newim.pol_prim:
continue
polvec = imdict[pol]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim)
newim.add_pol_image(polarr, pol)
# Add in spectral index
newim._mflist = copy.deepcopy(self._mflist)
return newim
def orth_chi(self):
"""Rotate the EVPA 90 degrees
Args:
Returns:
(Image): image with rotated EVPA
"""
im = self.copy()
if im.polrep == 'stokes':
im.qvec *= -1
im.uvec *= -1
elif im.polrep == 'circ':
im.lrvec *= -1# np.conjugate(im.rlvec)
im.rlvec *= -1#np.conjugate(im.rlvec)
#im.lrvec = np.conjugate(im.rlvec)
#im.rlvec = np.conjugate(im.rlvec)
return im
def get_image_mf(self, nu):
"""Get image at a given frequency given the spectral information in self._mflist
Args:
nu (float): frequency in Hz
Returns:
(Image): image at the desired frequency
"""
# TODO -- what to do about polarization? Faraday rotation?
nuref = self.rf
log_nufrac = np.log(nu / nuref)
log_imvec = np.log(self.imvec)
for n, mfvec in enumerate(self._mflist):
if len(mfvec):
log_imvec += mfvec * (log_nufrac**(n + 1))
imvec = np.exp(log_imvec)
arglist, argdict = self.image_args()
arglist[0] = imvec.reshape(self.ydim, self.xdim)
argdict['rf'] = nu
outim = Image(*arglist, **argdict)
# Copy over all polarization images -- unchanged for now
outim.copy_pol_images(self)
# DON'T copy over spectral index information for now
# outim._mflist = copy.deepcopy(self._mflist)
return outim
def imarr(self, pol=None):
"""Return the 2D image array of a given pol parameter.
Args:
pol (str): I,Q,U or V for Stokes, or RR,LL,LR,RL for Circ
Returns:
(numpy.array): 2D image array of dimension (ydim, xdim)
"""
if pol is None:
pol = self.pol_prim
imvec = self.get_polvec(pol)
if len(imvec):
imarr = imvec.reshape(self.ydim, self.xdim)
else:
imarr = np.array([])
return imarr
# imarr = np.array([])
# if self.polrep == 'stokes':
# if pol == "I" and len(self.ivec):
# imarr = self.ivec.reshape(self.ydim, self.xdim)
# elif pol == "Q" and len(self.qvec):
# imarr = self.qvec.reshape(self.ydim, self.xdim)
# elif pol == "U" and len(self.uvec):
# imarr = self.uvec.reshape(self.ydim, self.xdim)
# elif pol == "V" and len(self.vvec):
# imarr = self.vvec.reshape(self.ydim, self.xdim)
# elif self.polrep == 'circ':
# if pol == "RR" and len(self.rrvec):
# imarr = self.rrvec.reshape(self.ydim, self.xdim)
# elif pol == "LL" and len(self.llvec):
# imarr = self.llvec.reshape(self.ydim, self.xdim)
# elif pol == "RL" and len(self.rlvec):
# imarr = self.rlvec.reshape(self.ydim, self.xdim)
# elif pol == "LR" and len(self.lrvec):
# imarr = self.lrvec.reshape(self.ydim, self.xdim)
return imarr
def sourcevec(self):
"""Return the source position vector in geocentric coordinates at 0h GMST.
Args:
Returns:
(numpy.array): normal vector pointing to source in geocentric coordinates (m)
"""
sourcevec = np.array([np.cos(self.dec * ehc.DEGREE), 0, np.sin(self.dec * ehc.DEGREE)])
return sourcevec
def fovx(self):
"""Return the image fov in x direction in radians.
Args:
Returns:
(float) : image fov in x direction (radian)
"""
return self.psize * self.xdim
def fovy(self):
"""Returns the image fov in y direction in radians.
Args:
Returns:
(float) : image fov in y direction (radian)
"""
return self.psize * self.ydim
def total_flux(self):
"""Return the total flux of the image in Jy.
Args:
Returns:
(float) : image total flux (Jy)
"""
if self.polrep == 'stokes':
flux = np.sum(self.ivec)
elif self.polrep == 'circ':
flux = 0.5 * (np.sum(self.rrvec) + np.sum(self.llvec))
return flux
def lin_polfrac(self):
"""Return the total fractional linear polarized flux
Args:
Returns:
(float) : image fractional linear polarized flux
"""
if self.polrep == 'stokes':
frac = np.abs(np.sum(self.qvec + 1j * self.uvec)) / np.abs(np.sum(self.ivec))
elif self.polrep == 'circ':
frac = 2 * np.abs(np.sum(self.rlvec)) / np.abs(np.sum(self.rrvec + self.llvec))
return frac
def evpa(self):
"""Return the total evpa
Args:
Returns:
(float) : image average evpa (E of N) in radian
"""
if self.polrep == 'stokes':
frac = 0.5 * np.angle(np.sum(self.qvec + 1j * self.uvec))
elif self.polrep == 'circ':
frac = np.angle(np.sum(self.rlvec))
return frac
def circ_polfrac(self):
"""Return the total fractional circular polarized flux
Args:
Returns:
(float) : image fractional circular polarized flux
"""
if self.polrep == 'stokes':
frac = np.sum(self.vvec) / np.abs(np.sum(self.ivec))
elif self.polrep == 'circ':
frac = np.sum(self.rrvec - self.llvec) / np.abs(np.sum(self.rrvec + self.llvec))
return frac
def center(self, pol=None):
"""Center the image based on the coordinates of the centroid().
A non-integer shift is used, which wraps the image when rotating.
Args:
pol (str): The polarization for which to find the image centroid
Returns:
(np.array): centroid positions (x0,y0) in radians
"""
return self.shift_fft(-self.centroid(pol=pol))
def centroid(self, pol=None):
"""Compute the location of the image centroid (corresponding to the polarization pol)
Args:
pol (str): The polarization for which to find the image centroid
Returns:
(np.array): centroid positions (x0,y0) in radians
"""
if pol is None:
pol = self.pol_prim
imvec = self.get_polvec(pol)
pdim = self.psize
# if not (pol in list(self._imdict.keys())):
# raise Exception("for polrep==%s, pol must be in " %
# self.polrep + ",".join(list(self._imdict.keys())))
# imvec = self._imdict[pol]
if len(imvec):
xlist = np.arange(0, -self.xdim, -1) * pdim + (pdim * self.xdim) / 2.0 - pdim / 2.0
ylist = np.arange(0, -self.ydim, -1) * pdim + (pdim * self.ydim) / 2.0 - pdim / 2.0
x0 = np.sum(np.outer(0.0 * ylist + 1.0, xlist).ravel() * imvec) / np.sum(imvec)
y0 = np.sum(np.outer(ylist, 0.0 * xlist + 1.0).ravel() * imvec) / np.sum(imvec)
centroid = np.array([x0, y0])
else:
raise Exception("No %s image found!" % pol)
return centroid
def pad(self, fovx, fovy):
"""Pad an image to new fov_x by fov_y in radian.
Args:
fovx (float): new fov in x dimension (rad)
fovy (float): new fov in y dimension (rad)
Returns:
im_pad (Image): padded image
"""
# Find pad widths
fovoldx = self.fovx()
fovoldy = self.fovy()
padx = int(0.5 * (fovx - fovoldx) / self.psize)
pady = int(0.5 * (fovy - fovoldy) / self.psize)
# Pad main image vector
imarr = self.imvec.reshape(self.ydim, self.xdim)
imarr = np.pad(imarr, ((pady, pady), (padx, padx)), 'constant')
# Make new image
arglist, argdict = self.image_args()
arglist[0] = imarr
outim = Image(*arglist, **argdict)
# Pad all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim)
polarr = np.pad(polarr, ((pady, pady), (padx, padx)), 'constant')
outim.add_pol_image(polarr, pol)
# Add in spectral index
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = mfvec.reshape(self.ydim, self.xdim)
mfarr = np.pad(mfarr, ((pady, pady), (padx, padx)), 'constant')
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def resample_square(self, xdim_new, ker_size=5):
"""Exactly resample a square image to new dimensions using the pulse function.
Args:
xdim_new (int): new pixel dimension
ker_size (int): kernel size for resampling
Returns:
im_resampled (Image): resampled image
"""
if self.xdim != self.ydim:
raise Exception("Image must be square to use Image.resample_square!")
if self.pulse == pulses.deltaPulse2D:
raise Exception("Image.resample_squre does not work with delta pulses!")
ydim_new = xdim_new
fov = self.xdim * self.psize
psize_new = float(fov) / float(xdim_new)
# Define an interpolation function using the pulse
ij = np.array([[[i * self.psize + (self.psize * self.xdim) / 2.0 - self.psize / 2.0,
j * self.psize + (self.psize * self.ydim) / 2.0 - self.psize / 2.0]
for i in np.arange(0, -self.xdim, -1)]
for j in np.arange(0, -self.ydim, -1)]).reshape((self.xdim * self.ydim, 2))
def im_new_val(imvec, x_idx, y_idx):
x = x_idx * psize_new + (psize_new * xdim_new) / 2.0 - psize_new / 2.0
y = y_idx * psize_new + (psize_new * ydim_new) / 2.0 - psize_new / 2.0
mask = (((x - ker_size * self.psize / 2.0) < ij[:, 0]) *
(ij[:, 0] < (x + ker_size * self.psize / 2.0)) *
((y - ker_size * self.psize / 2.0) < ij[:, 1]) *
(ij[:, 1] < (y + ker_size * self.psize / 2.0))
).flatten()
interp = np.sum([imvec[n] * self.pulse(x - ij[n, 0], y - ij[n, 1], self.psize, dom="I")
for n in np.arange(len(imvec))[mask]])
return interp
def im_new(imvec):
imarr_new = np.array([[im_new_val(imvec, x_idx, y_idx)
for x_idx in np.arange(0, -xdim_new, -1)]
for y_idx in np.arange(0, -ydim_new, -1)])
return imarr_new
# Compute new primary image vector
imarr_new = im_new(self.imvec)
# Normalize
scaling = np.sum(self.imvec) / np.sum(imarr_new)
imarr_new *= scaling
# Make new image
arglist, argdict = self.image_args()
arglist[0] = imarr_new
arglist[1] = psize_new
outim = Image(*arglist, **argdict)
# Interpolate all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polarr_new = im_new(polvec)
polarr_new *= scaling
outim.add_pol_image(polarr_new, pol)
# Interpolate spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
print("WARNING: resample_squre not debugged for spectral index resampling!")
if len(mfvec):
mfarr = im_new(mfvec)
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def regrid_image(self, targetfov, npix, interp='linear'):
"""Resample the image to new (square) dimensions.
Args:
targetfov (float): new field of view (radian)
npix (int): new pixel dimension
interp ('linear', 'cubic', 'quintic'): type of interpolation. default is linear
Returns:
(Image): resampled image
"""
psize_new = float(targetfov) / float(npix)
fov_x = self.fovx()
fov_y = self.fovy()
# define an interpolation function
x = np.linspace(-fov_x / 2, fov_x / 2, self.xdim)
y = np.linspace(-fov_y / 2, fov_y / 2, self.ydim)
xtarget = np.linspace(-targetfov / 2, targetfov / 2, npix)
ytarget = np.linspace(-targetfov / 2, targetfov / 2, npix)
def interp_imvec(imvec, specind=False):
if np.any(np.imag(imvec) != 0):
return interp_imvec(np.real(imvec)) + 1j * interp_imvec(np.imag(imvec))
interpfunc = scipy.interpolate.interp2d(y, x, np.reshape(imvec, (self.ydim, self.xdim)),
kind=interp)
tmpimg = interpfunc(ytarget, xtarget)
tmpimg[np.abs(xtarget) > fov_x / 2., :] = 0.0
tmpimg[:, np.abs(ytarget) > fov_y / 2.] = 0.0
if not specind: # adjust pixel size if not a spectral index map
tmpimg = tmpimg * (psize_new)**2 / self.psize**2
return tmpimg
# Make new image
imarr_new = interp_imvec(self.imvec)
arglist, argdict = self.image_args()
arglist[0] = imarr_new
arglist[1] = psize_new
outim = Image(*arglist, **argdict)
# Interpolate all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polarr_new = interp_imvec(polvec)
outim.add_pol_image(polarr_new, pol)
# Interpolate spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = interp_imvec(mfvec, specind=True)
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def rotate(self, angle, interp='cubic'):
"""Rotate the image counterclockwise by the specified angle.
Args:
angle (float): CCW angle to rotate the image (radian)
interp ('linear', 'cubic', 'quintic'): type of interpolation. default is cubic
Returns:
(Image): resampled image
"""
order = 3
if interp == 'linear':
order = 1
elif interp == 'cubic':
order = 3
elif interp == 'quintic':
order = 5
# Define an interpolation function
def rot_imvec(imvec):
if np.any(np.imag(imvec) != 0):
return rot_imvec(np.real(imvec)) + 1j * rot_imvec(np.imag(imvec))
imarr_rot = scipy.ndimage.interpolation.rotate(imvec.reshape((self.ydim, self.xdim)),
angle * 180.0 / np.pi, reshape=False,
order=order, mode='constant',
cval=0.0, prefilter=True)
return imarr_rot
# pol_prim needs to be RR,LL,I,or V for a simple rotation to work!
if(not (self.pol_prim in ['RR', 'LL', 'I', 'V'])):
raise Exception("im.pol_prim must be a scalar ('I','V','RR','LL') for simple rotation!")
# Make new image
imarr_rot = rot_imvec(self.imvec)
arglist, argdict = self.image_args()
arglist[0] = imarr_rot
outim = Image(*arglist, **argdict)
# Rotate all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polarr_rot = rot_imvec(polvec)
if pol == 'RL':
polarr_rot *= np.exp(1j * 2 * angle)
elif pol == 'LR':
polarr_rot *= np.exp(-1j * 2 * angle)
elif pol == 'Q':
polarr_rot = polarr_rot + 1j * rot_imvec(self._imdict['U'])
polarr_rot = np.real(np.exp(1j * 2 * angle) * polarr_rot)
elif pol == 'U':
polarr_rot = rot_imvec(self._imdict['Q']) + 1j * polarr_rot
polarr_rot = np.imag(np.exp(1j * 2 * angle) * polarr_rot)
outim.add_pol_image(polarr_rot, pol)
# Rotate spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = rot_imvec(mfvec)
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def shift(self, shiftidx):
"""Shift the image by a given number of pixels.
Args:
shiftidx (list): pixel offsets [x_offset, y_offset] for the image shift
Returns:
(Image): shifted images
"""
# Define shifting function
def shift_imvec(imvec):
im_shift = np.roll(imvec.reshape(self.ydim, self.xdim), shiftidx[0], axis=0)
im_shift = np.roll(im_shift, shiftidx[1], axis=1)
return im_shift
# Make new image
imarr_shift = shift_imvec(self.imvec)
arglist, argdict = self.image_args()
arglist[0] = imarr_shift
outim = Image(*arglist, **argdict)
# Shift all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polarr_shift = shift_imvec(polvec)
outim.add_pol_image(polarr_shift, pol)
# Shift spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = shift_imvec(mfvec)
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def shift_fft(self, shift):
"""Shift the image by a given vector in radians.
This allows non-integer pixel shifts, via FFT.
Args:
shift (list): offsets [x_offset, y_offset] for the image shift in radians
Returns:
(Image): shifted image
"""
Nx = self.xdim
Ny = self.ydim
[dx_pixels, dy_pixels] = np.array(shift) / self.psize
s, t = np.meshgrid(np.fft.fftfreq(Nx, d=1.0 / Nx), np.fft.fftfreq(Ny, d=1.0 / Ny))
rotate = np.exp(2.0 * np.pi * 1j * (s * dx_pixels + t * dy_pixels) / float(Nx))
imarr = self.imvec.reshape((Ny, Nx))
imarr_rotate = np.real(np.fft.ifft2(np.fft.fft2(imarr) * rotate))
# make new Image
arglist, argdict = self.image_args()
arglist[0] = imarr_rotate
outim = Image(*arglist, **argdict)
# Shift all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
imarr = polvec.reshape((Ny, Nx))
imarr_rotate = np.real(np.fft.ifft2(np.fft.fft2(imarr) * rotate))
outim.add_pol_image(imarr_rotate, pol)
# Shift spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = mfvec.reshape((Ny, Nx))
mfarr = np.real(np.fft.ifft2(np.fft.fft2(mfarr) * rotate))
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def blur_gauss(self, beamparams, frac=1., frac_pol=0):
"""Blur image with a Gaussian beam w/ beamparams [fwhm_max, fwhm_min, theta] in radians.
Args:
beamparams (list): [fwhm_maj, fwhm_min, theta, x, y] in radians
frac (float): fractional beam size for blurring the main image
frac_pol (float): fractional beam size for blurring the other polarizations
Returns:
(Image): output image
"""
if frac <= 0.0 or beamparams[0] <= 0:
return self.copy()
# Make a Gaussian image
xlist = np.arange(0, -self.xdim, -1) * self.psize + \
(self.psize * self.xdim) / 2.0 - self.psize / 2.0
ylist = np.arange(0, -self.ydim, -1) * self.psize + \
(self.psize * self.ydim) / 2.0 - self.psize / 2.0
sigma_maj = beamparams[0] / (2. * np.sqrt(2. * np.log(2.)))
sigma_min = beamparams[1] / (2. * np.sqrt(2. * np.log(2.)))
cth = np.cos(beamparams[2])
sth = np.sin(beamparams[2])
def gaussim(blurfrac):
gauss = np.array([[np.exp(-(j * cth + i * sth)**2 / (2 * (blurfrac * sigma_maj)**2) -
(i * cth - j * sth)**2 / (2 * (blurfrac * sigma_min)**2))
for i in xlist]
for j in ylist])
gauss = gauss[0:self.ydim, 0:self.xdim]
gauss = gauss / np.sum(gauss) # normalize to 1
return gauss
gauss = gaussim(frac)
if frac_pol:
gausspol = gaussim(frac_pol)
# Define a convolution function
def blur(imarr, gauss):
imarr_blur = scipy.signal.fftconvolve(gauss, imarr, mode='same')
return imarr_blur
# Convolve the primary image
imarr = (self.imvec).reshape(self.ydim, self.xdim).astype('float64')
imarr_blur = blur(imarr, gauss)
# Make new image object
arglist, argdict = self.image_args()
arglist[0] = imarr_blur
outim = Image(*arglist, **argdict)
# Blur all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).astype('float64')
if frac_pol:
polarr = blur(polarr, gausspol)
outim.add_pol_image(polarr, pol)
# Blur spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = mfvec.reshape(self.ydim, self.xdim).astype('float64')
mfarr = blur(mfarr, gauss)
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def blur_circ(self, fwhm_i, fwhm_pol=0, filttype='gauss'):
"""Apply a circular gaussian filter to the image, with FWHM in radians.
Args:
fwhm_i (float): circular beam size for Stokes I blurring in radian
fwhm_pol (float): circular beam size for Stokes Q,U,V blurring in radian
filttype (str): "gauss" or "butter"
Returns:
(Image): output image
"""
sigma = fwhm_i / (2. * np.sqrt(2. * np.log(2.)))
sigmap = sigma / self.psize
fwhmp = fwhm_i / self.psize
fwhmp_pol = fwhm_pol / self.psize
# Define a convolution function
def blur_gauss(imarr, fwhm):
sigma = fwhmp / (2. * np.sqrt(2. * np.log(2.)))
if np.any(np.imag(imarr) != 0):
return blur(np.real(imarr), sigma) + 1j * blur(np.imag(imarr), sigma)
imarr_blur = filt.gaussian_filter(imarr, (sigma, sigma))
return imarr_blur
def blur_butter(imarr, size):
#bfilt = scipy.signal.butter(2,freq,btype='low',output='sos')
#if np.any(np.imag(imarr) != 0):
# return blur(np.real(imarr), sigma) + 1j * blur(np.imag(imarr), sigma)
#imarr_blur = scipy.signal.sosfilt(bfilt, imarr, axis=0)
#imarr_blur = scipy.signal.sosfilt(bfilt, imarr_blur, axis=1)
if size==0:
return imarr
cutoff = 1/size
Nx = self.xdim
Ny = self.ydim
s, t = np.meshgrid(np.fft.fftfreq(Nx, d=1.0 ), np.fft.fftfreq(Ny, d=1.0 ))
#s, t = np.meshgrid(np.fft.fftfreq(Nx, d=1.0 / Nx), np.fft.fftfreq(Ny, d=1.0 / Ny))
r = np.sqrt(s**2 + t**2)
bfilt = 1./np.sqrt(1 + (r/cutoff)**4)
imarr = self.imvec.reshape((Ny, Nx))
imarr_filt = np.real(np.fft.ifft2(np.fft.fft2(imarr) * bfilt))
return imarr_filt
if filttype=='gauss':
blur = blur_gauss
elif filttype=='butter':
blur = blur_butter
else:
raise Exception("filttype not recognized in blur_circ!")
# Blur the primary image
imarr = self.imvec.reshape(self.ydim, self.xdim)
imarr_blur = blur(imarr, fwhmp)
arglist, argdict = self.image_args()
arglist[0] = imarr_blur
outim = Image(*arglist, **argdict)
# Blur spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = mfvec.reshape(self.ydim, self.xdim)
mfarr = blur(mfarr, fwhmp)
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
# Blur all polarizations and copy overi
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim)
if fwhm_pol:
#print("Blurring polarization")
polarr = blur(polarr, fwhmp_pol)
outim.add_pol_image(polarr, pol)
return outim
def blur_mf(self, freqs, fwhm, fit_order=1, filttype='gauss'):
"""Blur image correctly across multiple frequencies
WARNING: does not currently do polarization correctly!
Args:
freqs (float): Frequencies to include in the blurring & spectral index fit
fwhm (float): circular beam size
fit_order (int): how many orders to fit spectrum: 1 or 2
filttype (str): "gauss" or "butter"
Returns:
(Image): output image
"""
if fit_order not in [1,2]:
raise Exception("fit_order must be 1 or 2 in blur_mf!")
reffreq = self.rf
# remove any zeros in the images
imlist = [self.get_image_mf(rf).blur_circ(kernel, filttype=filttype) for rf in freqs]
for image in imlist:
image.imvec[image.imvec<=0] = np.min(image.imvec[image.imvec!=0])
xfit = np.log(np.array(freqs)/reffreq)
yfit = np.log(np.array([im.imvec for im in imlist]))
if fit_order == 2:
coeffs = np.polyfit(xfit,yfit,2)
beta = coeffs[0]
alpha = coeffs[1]
elif fit_order == 1:
coeffs = np.polyfit(xfit,yfit,1)
alpha = coeffs[0]
beta = 0*alpha
else:
alpha = 0*yfit
beta = 0*yfit
outim = self.blur_circ(kernel, filttype=filttype)
outim.specvec = alpha
outim.curvvec = beta
return outim
def grad(self, gradtype='abs'):
"""Return the gradient image
Args:
gradtype (str): 'x','y',or 'abs' for the image gradient dimension
Returns:
Image : an image object containing the gradient image
"""
# Define the desired gradient function
def gradim(imvec):
if np.any(np.imag(imvec) != 0):
return gradim(np.real(imvec)) + 1j * gradim(np.imag(imvec))
imarr = imvec.reshape(self.ydim, self.xdim)
#sx = ndi.sobel(imarr, axis=0, mode='constant')
#sy = ndi.sobel(imarr, axis=1, mode='constant')
sx = ndi.sobel(imarr, axis=0, mode='nearest')
sy = ndi.sobel(imarr, axis=1, mode='nearest')
# TODO: are these in the right order??
if gradtype == 'x':
gradarr = sx
if gradtype == 'y':
gradarr = sy
else:
gradarr = np.hypot(sx, sy)
return gradarr
# Find the gradient for the primary image
gradarr = gradim(self.imvec)
arglist, argdict = self.image_args()
arglist[0] = gradarr
outim = Image(*arglist, **argdict)
# Find the gradient for all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
gradarr = gradim(polvec)
outim.add_pol_image(gradarr, pol)
# Find the spectral index gradients and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfarr = gradim(mfvec)
mfvec_out = mfarr.flatten()
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def mask(self, cutoff=0.05, beamparams=None, frac=0.0):
"""Produce an image mask that shows all pixels above the specified cutoff frac of the max
Works off the primary image
Args:
cutoff (float): mask pixels with intensities greater than cuttoff * max
beamparams (list): either [fwhm_maj, fwhm_min, pos_ang] or a single fwhm
frac (float): the fraction of nominal beam to blur with
Returns:
(Image): output mask image
"""
# Blur the image
if beamparams is not None:
try:
len(beamparams)
except TypeError:
beamparams = [beamparams, beamparams, 0]
if len(beamparams) == 3:
mask = self.blur_gauss(beamparams, frac)
else:
raise Exception("beamparams should be a length 3 array [maj, min, posang]!")
else:
mask = self.copy()
# Mask pixels outside the desired intensity range
maxval = np.max(mask.imvec)
minval = np.min(mask.imvec)
intensityrange = maxval - minval
thresh = intensityrange * cutoff + minval
maskvec = (mask.imvec > thresh).astype(int)
# make the primary image
maskarr = maskvec.reshape(mask.ydim, mask.xdim)
arglist, argdict = self.image_args()
arglist[0] = maskarr
mask = Image(*arglist, **argdict)
# Replace all polarization imvecs with mask
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
mask.add_pol_image(maskarr, pol)
# No spectral index information in mask
return mask
# TODO make this work with a mask image of different dimensions & fov
def apply_mask(self, mask_im, fill_val=0.):
"""Apply a mask to the image
Args:
mask_im (Image): a mask image with the same dimensions as the Image
fill_val (float): masked pixels of all polarizations are set to this value
Returns:
(Image): the masked image
"""
if ((self.psize != mask_im.psize) or
(self.xdim != mask_im.xdim) or (self.ydim != mask_im.ydim)):
raise Exception("mask image does not match dimensions of the current image!")
# Get the mask vector
maskvec = mask_im.imvec.astype(bool)
maskvec[maskvec <= 0] = 0
maskvec[maskvec > 0] = 1
# Mask the primary image
imvec = self.imvec
imvec[~maskvec] = fill_val
imarr = imvec.reshape(self.ydim, self.xdim)
arglist, argdict = self.image_args()
arglist[0] = imarr
outim = Image(*arglist, **argdict)
# Apply mask to all polarizations and copy over
for pol in list(self._imdict.keys()):
if pol == self.pol_prim:
continue
polvec = self._imdict[pol]
if len(polvec):
polvec[~maskvec] = fill_val
polarr = polvec.reshape(self.ydim, self.xdim)
outim.add_pol_image(polarr, pol)
# Apply mask to spectral index and copy over
mflist_out = []
for mfvec in self._mflist:
if len(mfvec):
mfvec_out = copy.deepcopy(mfvec)
mfvec_out[~maskvec] = 0.
else:
mfvec_out = np.array([])
mflist_out.append(mfvec_out)
outim._mflist = mflist_out
return outim
def threshold(self, cutoff=0.05, beamparams=None, frac=0.0, fill_val=None):
"""Apply a hard threshold to the primary polarization image.
Leave other polarizations untouched.
Args:
cutoff (float): Mask pixels with intensities greater than cuttoff * max
beamparams (list): either [fwhm_maj, fwhm_min, pos_ang] or a single fwhm
frac (float): the fraction of nominal beam to blur with
fill_val (float): masked pixels are set to this value.
If fill_val==None, they are set to the min unmasked value
Returns:
(Image): output mask image
"""
if fill_val is None or fill_val is False:
maxval = np.max(self.imvec)
minval = np.min(self.imvec)
intensityrange = maxval - minval
fill_val = (intensityrange * cutoff + minval)
mask = self.mask(cutoff=cutoff, beamparams=beamparams, frac=frac)
out = self.apply_mask(mask, fill_val=fill_val)
return out
def add_flat(self, flux, pol=None):
"""Add a flat background flux to the main polarization image.
Args:
flux (float): total flux to add to image
pol (str): the polarization to add the flux to. None defaults to pol_prim.
Returns:
(Image): output image
"""
if pol is None:
pol = self.pol_prim
if not (pol in list(self._imdict.keys())):
raise Exception("for polrep==%s, pol must be in " %
self.polrep + ",".join(list(self._imdict.keys())))
if not len(self._imdict[pol]):
raise Exception("no image for pol %s" % pol)
# Make a flat image array
flatarr = ((flux / float(len(self.imvec))) * np.ones(len(self.imvec)))
flatarr = flatarr.reshape(self.ydim, self.xdim)
# Add to the main image and create the new image object
imarr = self.imvec.reshape(self.ydim, self.xdim).copy()
if pol == self.pol_prim:
imarr += flatarr
arglist, argdict = self.image_args()
arglist[0] = imarr
outim = Image(*arglist, **argdict)
# Copy over the rest of the polarizations
for pol2 in list(self._imdict.keys()):
if pol2 == self.pol_prim:
continue
polvec = self._imdict[pol2]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).copy()
if pol2 == pol:
polarr += flatarr
outim.add_pol_image(polarr, pol2)
# Copy the spectral index (unchanged)
outim._mflist = copy.deepcopy(self._mflist)
return outim
def add_tophat(self, flux, radius, pol=None):
"""Add centered tophat flux to the Stokes I image inside a given radius.
Args:
flux (float): total flux to add to image
radius (float): radius of top hat flux in radians
pol (str): the polarization to add the flux to. None defaults to pol_prim
Returns:
(Image): output image
"""
if pol is None:
pol = self.pol_prim
if not (pol in list(self._imdict.keys())):
raise Exception("for polrep==%s, pol must be in " %
self.polrep + ",".join(list(self._imdict.keys())))
if not len(self._imdict[pol]):
raise Exception("no image for pol %s" % pol)
# Make a tophat image array
xlist = np.arange(0, -self.xdim, -1) * self.psize + \
(self.psize * self.xdim) / 2.0 - self.psize / 2.0
ylist = np.arange(0, -self.ydim, -1) * self.psize + \
(self.psize * self.ydim) / 2.0 - self.psize / 2.0
hatarr = np.array([[1.0 if np.sqrt(i**2 + j**2) <= radius else 0.
for i in xlist]
for j in ylist])
hatarr = hatarr[0:self.ydim, 0:self.xdim]
hatarr *= flux / np.sum(hatarr)
# Add to the main image and create the new image object
imarr = self.imvec.reshape(self.ydim, self.xdim).copy()
if pol == self.pol_prim:
imarr += hatarr
arglist, argdict = self.image_args()
arglist[0] = imarr
outim = Image(*arglist, **argdict)
# Copy over the rest of the polarizations
for pol2 in list(self._imdict.keys()):
if pol2 == self.pol_prim:
continue
polvec = self._imdict[pol2]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).copy()
if pol2 == pol:
polarr += hatarr
outim.add_pol_image(polarr, pol2)
# Copy the spectral index (unchanged)
outim._mflist = copy.deepcopy(self._mflist)
return outim
def add_gauss(self, flux, beamparams, pol=None):
"""Add a gaussian to an image.
Args:
flux (float): the total flux contained in the Gaussian in Jy
beamparams (list): [fwhm_maj, fwhm_min, theta, x, y], all in radians
pol (str): the polarization to add the flux to. None defaults to pol_prim.
Returns:
(Image): output image
"""
if pol is None:
pol = self.pol_prim
if not (pol in list(self._imdict.keys())):
raise Exception("for polrep==%s, pol must be in " %
self.polrep + ",".join(list(self._imdict.keys())))
if not len(self._imdict[pol]):
raise Exception("no image for pol %s" % pol)
# Make a Gaussian image
try:
x = beamparams[3]
y = beamparams[4]
except IndexError:
x = y = 0.0
sigma_maj = beamparams[0] / (2. * np.sqrt(2. * np.log(2.)))
sigma_min = beamparams[1] / (2. * np.sqrt(2. * np.log(2.)))
cth = np.cos(beamparams[2])
sth = np.sin(beamparams[2])
xlist = np.arange(0, -self.xdim, -1) * self.psize + \
(self.psize * self.xdim) / 2.0 - self.psize / 2.0
ylist = np.arange(0, -self.ydim, -1) * self.psize + \
(self.psize * self.ydim) / 2.0 - self.psize / 2.0
def gaussian(x2, y2):
gauss = np.exp(-((y2) * cth + (x2) * sth)**2 / (2 * sigma_maj**2) +
-((x2) * cth - (y2) * sth)**2 / (2 * sigma_min**2))
return gauss
gaussarr = np.array([[gaussian(i - x, j - y) for i in xlist] for j in ylist])
gaussarr = gaussarr[0:self.ydim, 0:self.xdim]
gaussarr *= flux / np.sum(gaussarr)
# TODO: if we want to add a gaussian to V, we might also want to make sure we add it to I
# Add to the main image and create the new image object
imarr = self.imvec.reshape(self.ydim, self.xdim).copy()
if pol == self.pol_prim:
imarr += gaussarr
arglist, argdict = self.image_args()
arglist[0] = imarr
outim = Image(*arglist, **argdict)
# Copy over the rest of the polarizations
for pol2 in list(self._imdict.keys()):
if pol2 == self.pol_prim:
continue
polvec = self._imdict[pol2]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).copy()
if pol2 == pol:
polarr += gaussarr
outim.add_pol_image(polarr, pol2)
# Copy the spectral index (unchanged)
outim._mflist = copy.deepcopy(self._mflist)
return outim
def add_crescent(self, flux, Rp, Rn, a, b, x=0, y=0, pol=None):
"""Add a crescent to an image; see Kamruddin & Dexter (2013).
Args:
flux (float): the total flux contained in the crescent in Jy
Rp (float): the larger radius in radians
Rn (float): the smaller radius in radians
a (float): the relative x offset of smaller disk in radians
b (float): the relative y offset of smaller disk in radians
x (float): the center x coordinate of the larger disk in radians
y (float): the center y coordinate of the larger disk in radians
pol (str): the polarization to add the flux to. None defaults to pol_prim.
Returns:
(Image): output image add_gaus
"""
if pol is None:
pol = self.pol_prim
if not (pol in list(self._imdict.keys())):
raise Exception("for polrep==%s, pol must be in " %
self.polrep + ",".join(list(self._imdict.keys())))
if not len(self._imdict[pol]):
raise Exception("no image for pol %s" % pol)
# Make a crescent image
xlist = np.arange(0, -self.xdim, -1) * self.psize + \
(self.psize * self.xdim) / 2.0 - self.psize / 2.0
ylist = np.arange(0, -self.ydim, -1) * self.psize + \
(self.psize * self.ydim) / 2.0 - self.psize / 2.0
def crescent(x2, y2):
if (x2 - a)**2 + (y2 - b)**2 > Rn**2 and x2**2 + y2**2 < Rp**2:
return 1.0
else:
return 0.0
crescarr = np.array([[crescent(i - x, j - y) for i in xlist] for j in ylist])
crescarr = crescarr[0:self.ydim, 0:self.xdim]
crescarr *= flux / np.sum(crescarr)
# Add to the main image and create the new image object
imarr = self.imvec.reshape(self.ydim, self.xdim).copy()
if pol == self.pol_prim:
imarr += crescarr
arglist, argdict = self.image_args()
arglist[0] = imarr
outim = Image(*arglist, **argdict)
# Copy over the rest of the polarizations
for pol2 in list(self._imdict.keys()):
if pol2 == self.pol_prim:
continue
polvec = self._imdict[pol2]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).copy()
if pol2 == pol:
polarr += crescarr
outim.add_pol_image(polarr, pol2)
# Copy the spectral index (unchanged)
outim._mflist = copy.deepcopy(self._mflist)
return outim
def add_ring_m1(self, I0, I1, r0, phi, sigma, x=0, y=0, pol=None):
"""Add a ring to an image with an m=1 mode
Args:
I0 (float):
I1 (float):
r0 (float): the radius
phi (float): angle of m1 mode
sigma (float): the blurring size
x (float): the center x coordinate of the larger disk in radians
y (float): the center y coordinate of the larger disk in radians
pol (str): the polarization to add the flux to. None defaults to pol_prim.
Returns:
(Image): output image add_gaus
"""
if pol is None:
pol = self.pol_prim
if not (pol in list(self._imdict.keys())):
raise Exception("for polrep==%s, pol must be in " %
self.polrep + ",".join(list(self._imdict.keys())))
if not len(self._imdict[pol]):
raise Exception("no image for pol %s" % pol)
# Make a ring image
flux = I0 - 0.5 * I1
phi = phi + np.pi
psize = self.psize
xlist = np.arange(0, -self.xdim, -1) * self.psize + \
(self.psize * self.xdim) / 2.0 - self.psize / 2.0
ylist = np.arange(0, -self.ydim, -1) * self.psize + \
(self.psize * self.ydim) / 2.0 - self.psize / 2.0
def ringm1(x2, y2):
if (x2**2 + y2**2) > (r0 - psize)**2 and (x2**2 + y2**2) < (r0 + psize)**2:
theta = np.arctan2(y2, x2)
flux = (I0 - 0.5 * I1 * (1 + np.cos(theta - phi))) / (2 * np.pi * r0)
return flux
else:
return 0.0
ringarr = np.array([[ringm1(i - x, j - y)
for i in xlist]
for j in ylist])
ringarr = ringarr[0:self.ydim, 0:self.xdim]
arglist, argdict = self.image_args()
arglist[0] = ringarr
outim = Image(*arglist, **argdict)
outim = outim.blur_circ(sigma)
outim.imvec *= flux / (outim.total_flux())
ringarr = outim.imvec.reshape(self.ydim, self.xdim)
# Add to the main image and create the new image object
imarr = self.imvec.reshape(self.ydim, self.xdim).copy()
if pol == self.pol_prim:
imarr += ringarr
arglist[0] = imarr
outim = Image(*arglist, **argdict)
# Copy over the rest of the polarizations
for pol2 in list(self._imdict.keys()):
if pol2 == self.pol_prim:
continue
polvec = self._imdict[pol2]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).copy()
if pol2 == pol:
polarr += ringarr
outim.add_pol_image(polarr, pol2)
# Copy the spectral index (unchanged)
outim._mflist = copy.deepcopy(self._mflist)
return outim
def add_const_pol(self, mag, angle, cmag=0, csign=1):
"""Return an with constant fractional linear and circular polarization
Args:
mag (float): constant polarization fraction to add to the image
angle (float): constant EVPA
cmag (float): constant circular polarization fraction to add to the image
cmag (int): constant circular polarization sign +/- 1
Returns:
(Image): output image
"""
if not (0 <= mag < 1):
raise Exception("fractional polarization magnitude must be between 0 and 1!")
if not (0 <= cmag < 1):
raise Exception("circular polarization magnitude must be between 0 and 1!")
if self.polrep == 'stokes':
im_stokes = self
elif self.polrep == 'circ':
im_stokes = self.switch_polrep(polrep_out='stokes')
ivec = im_stokes.ivec.copy()
qvec = obsh.qimage(ivec, mag * np.ones(len(ivec)), angle * np.ones(len(ivec)))
uvec = obsh.uimage(ivec, mag * np.ones(len(ivec)), angle * np.ones(len(ivec)))
vvec = cmag * np.sign(csign) * ivec
# create the new stokes image object
iarr = ivec.reshape(self.ydim, self.xdim).copy()
arglist, argdict = self.image_args()
arglist[0] = iarr
argdict['polrep'] = 'stokes'
argdict['pol_prim'] = 'I'
outim = Image(*arglist, **argdict)
# Copy over the rest of the polarizations
imdict = {'I': ivec, 'Q': qvec, 'U': uvec, 'V': vvec}
for pol in list(imdict.keys()):
if pol == 'I':
continue
polvec = imdict[pol]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).copy()
outim.add_pol_image(polarr, pol)
# Copy the spectral index (unchanged)
outim._mflist = copy.deepcopy(self._mflist)
return outim
def add_random_pol(self, mag, corr, cmag=0., ccorr=0., seed=0):
"""Return an image random linear and circular polarizations with certain correlation lengths
Args:
mag (float): linear polarization fraction
corr (float): EVPA correlation length (radians)
cmag (float): circular polarization fraction
ccorr (float): CP correlation length (radians)
seed (int): Seed for random number generation
Returns:
(Image): output image
"""
import ehtim.scattering.stochastic_optics as so
if not (0 <= mag < 1):
raise Exception("fractional polarization magnitude must be between 0 and 1!")
if not (0 <= cmag < 1):
raise Exception("circular polarization magnitude must be between 0 and 1!")
if self.polrep == 'stokes':
im_stokes = self
elif self.polrep == 'circ':
im_stokes = self.switch_polrep(polrep_out='stokes')
ivec = im_stokes.ivec.copy()
# create the new stokes image object
iarr = ivec.reshape(self.ydim, self.xdim).copy()
arglist, argdict = self.image_args()
arglist[0] = iarr
argdict['polrep'] = 'stokes'
argdict['pol_prim'] = 'I'
outim = Image(*arglist, **argdict)
# Make a random phase screen using the scattering tools
# Use this screen to define the EVPA
dist = 1.0 * 3.086e21
rdiff = np.abs(corr) * dist / 1e3
theta_mas = 0.37 * 1.0 / rdiff * 1000. * 3600. * 180. / np.pi
sm = so.ScatteringModel(scatt_alpha=1.67, observer_screen_distance=dist,
source_screen_distance=1.e5 * dist,
theta_maj_mas_ref=theta_mas, theta_min_mas_ref=theta_mas,
r_in=rdiff * 2, r_out=1e30)
ep = so.MakeEpsilonScreen(self.xdim, self.ydim, rngseed=seed)
ps = np.array(sm.MakePhaseScreen(ep, outim, obs_frequency_Hz=29.979e9).imvec)
ps = ps / 1000**(1.66 / 2)
qvec = ivec * mag * np.sin(ps)
uvec = ivec * mag * np.cos(ps)
# Make a random phase screen using the scattering tools
# Use this screen to define the CP magnitude
if cmag != 0.0 and ccorr > 0.0:
dist = 1.0 * 3.086e21
rdiff = np.abs(ccorr) * dist / 1e3
theta_mas = 0.37 * 1.0 / rdiff * 1000. * 3600. * 180. / np.pi
sm = so.ScatteringModel(scatt_alpha=1.67, observer_screen_distance=dist,
source_screen_distance=1.e5 * dist,
theta_maj_mas_ref=theta_mas, theta_min_mas_ref=theta_mas,
r_in=rdiff * 2, r_out=1e30)
ep = so.MakeEpsilonScreen(self.xdim, self.ydim, rngseed=seed * 2)
ps = np.array(sm.MakePhaseScreen(ep, outim, obs_frequency_Hz=29.979e9).imvec)
ps = ps / 1000**(1.66 / 2)
vvec = ivec * cmag * np.sin(ps)
else:
vvec = ivec * cmag
# Copy over the rest of the polarizations
imdict = {'I': ivec, 'Q': qvec, 'U': uvec, 'V': vvec}
for pol in list(imdict.keys()):
if pol == 'I':
continue
polvec = imdict[pol]
if len(polvec):
polarr = polvec.reshape(self.ydim, self.xdim).copy()
outim.add_pol_image(polarr, pol)
# Copy the spectral index (unchanged)
outim._mflist = copy.deepcopy(self._mflist)
return outim
def add_const_mf(self, alpha, beta=0.):
"""Add a constant spectral index and curvature term
Args:
alpha (float): spectral index (with no - sign)
beta (float): curvature
Returns:
(Image): output image with constant mf information added
"""
avec = alpha * np.ones(len(self.imvec))
bvec = beta * np.ones(len(self.imvec))
# create the new image object
outim = self.copy()
outim._mflist = [avec, bvec]
return outim
def add_zblterm(self, obs, uv_min, zblval=None, new_fov=False,
gauss_sz=False, gauss_sz_factor=0.75, debias=True):
"""Add a large Gaussian term to account for missing flux in the zero baseline.
Args:
obs : an Obsdata object to determine min non-zero baseline and 0-bl flux
uv_min (float): The cutoff in Glambada used to determine what is a 0-bl
new_fov (rad): The size of the padded image once the Gaussian is added
(if False it will be set to 3 x the gaussian fwhm)
gauss_sz (rad): The size of the Gaussian added to add flux to the 0-bl.
(if False it is computed from the min non-zero baseline)
gauss_sz_factor (float): The fraction of the min non-zero baseline
used to caluclate the Gaussian FWHM.
debias (bool): True if you use debiased amplitudes to caluclate the 0-bl flux in Jy
Returns:
(Image): a padded image with a large Gaussian component
"""
if gauss_sz is False:
obs_flag = obs.flag_uvdist(uv_min=uv_min)
minuvdist = np.min(np.sqrt(obs_flag.data['u']**2 + obs_flag.data['v']**2))
gauss_sz_sigma = (1 / (gauss_sz_factor * minuvdist))
gauss_sz = gauss_sz_sigma * 2.355 # convert from stdev to fwhm
factor = 5.0
if new_fov is False:
im_fov = np.max((self.xdim * self.psize, self.ydim * self.psize))
new_fov = np.max((factor * (gauss_sz / 2.355), im_fov))
if new_fov < factor * (gauss_sz / 2.355):
print('WARNING! The specified new fov may not be large enough')
# calculate the amount of flux to include in the Gaussian
obs_zerobl = obs.flag_uvdist(uv_max=uv_min)
obs_zerobl.add_amp(debias=debias)
orig_totflux = np.sum(obs_zerobl.amp['amp'] * (1 / obs_zerobl.amp['sigma']**2))
orig_totflux /= np.sum(1 / obs_zerobl.amp['sigma']**2)
if zblval is None:
addedflux = orig_totflux - np.sum(self.imvec)
else:
addedflux = orig_totflux - zblval
print('Adding a ' + str(addedflux) + ' Jy circular Gaussian of FWHM size ' +
str(gauss_sz / ehc.RADPERUAS) + ' uas')
im_new = self.copy()
im_new = im_new.pad(new_fov, new_fov)
im_new = im_new.add_gauss(addedflux, (gauss_sz, gauss_sz, 0, 0, 0))
return im_new
def sample_uv(self, uv, polrep_obs='stokes',
sgrscat=False, ttype='nfft',
cache=False, fft_pad_factor=2,
zero_empty_pol=True, verbose=True):
"""Sample the image on the selected uv points without creating an Obsdata object.
Args:
uv (ndarray): an array of uv points
polrep_obs (str): 'stokes' or 'circ' sets the data polarimetric representation
sgrscat (bool): if True, the visibilites will be blurred by the Sgr A* kernel
ttype (str): "fast" or "nfft" or "direct"
cache (bool): Use cached fft for 'fast' mode -- deprecated, use nfft instead!
fft_pad_factor (float): zero pad the image to fft_pad_factor * image size in FFT
zero_empty_pol (bool): if True, returns zero vec if the polarization doesn't exist.
Otherwise return None
verbose (bool): Boolean value controls output prints.
Returns:
(list): a list of [I,Q,U,V] visibilities
"""
if polrep_obs not in ['stokes', 'circ']:
raise Exception("polrep_obs must be either 'stokes' or 'circ'")
data = simobs.sample_vis(self, uv, polrep_obs=polrep_obs, sgrscat=sgrscat,
ttype=ttype, cache=cache, fft_pad_factor=fft_pad_factor,
zero_empty_pol=zero_empty_pol, verbose=verbose)
return data
def observe_same_nonoise(self, obs, sgrscat=False, ttype="nfft",
cache=False, fft_pad_factor=2,
zero_empty_pol=True, verbose=True):
"""Observe the image on the same baselines as an existing observation without noise.
Args:
obs (Obsdata): the existing observation
sgrscat (bool): if True, the visibilites will be blurred by the Sgr A* kernel
ttype (str): "fast" or "nfft" or "direct"
cache (bool): Use cached fft for 'fast' mode -- deprecated, use nfft instead!
fft_pad_factor (float): zero pad the image to fft_pad_factor * image size in FFT
zero_empty_pol (bool): if True, returns zero vec if the polarization doesn't exist.
Otherwise return None
verbose (bool): Boolean value controls output prints.
Returns:
(Obsdata): an observation object with no noise
"""
# Check for agreement in coordinates and frequency
tolerance = 1e-8
if (np.abs(self.ra - obs.ra) > tolerance) or (np.abs(self.dec - obs.dec) > tolerance):
raise Exception("Image coordinates are not the same as observtion coordinates!")
if (np.abs(self.rf - obs.rf) / obs.rf > tolerance):
raise Exception("Image frequency is not the same as observation frequency!")
if (ttype == 'direct' or ttype == 'fast' or ttype == 'nfft'):
if verbose: print("Producing clean visibilities from image with " + ttype + " FT . . . ")
else:
raise Exception("ttype=%s, options for ttype are 'direct', 'fast', 'nfft'" % ttype)
# Copy data to be safe
obsdata = copy.deepcopy(obs.data)
# Extract uv datasample
uv = obsh.recarr_to_ndarr(obsdata[['u', 'v']], 'f8')
data = simobs.sample_vis(self, uv, sgrscat=sgrscat, polrep_obs=obs.polrep,
ttype=ttype, cache=cache, fft_pad_factor=fft_pad_factor,
zero_empty_pol=zero_empty_pol, verbose=verbose)
# put visibilities into the obsdata
if obs.polrep == 'stokes':
obsdata['vis'] = data[0]
if not(data[1] is None):
obsdata['qvis'] = data[1]
obsdata['uvis'] = data[2]
obsdata['vvis'] = data[3]
elif obs.polrep == 'circ':
obsdata['rrvis'] = data[0]
if not(data[1] is None):
obsdata['llvis'] = data[1]
if not(data[2] is None):
obsdata['rlvis'] = data[2]
obsdata['lrvis'] = data[3]
obs_no_noise = ehtim.obsdata.Obsdata(self.ra, self.dec, obs.rf, obs.bw, obsdata, obs.tarr,
source=self.source, mjd=self.mjd, polrep=obs.polrep,
ampcal=True, phasecal=True, opacitycal=True,
dcal=True, frcal=True,
timetype=obs.timetype, scantable=obs.scans)
return obs_no_noise
def observe_same(self, obs_in,
ttype='nfft', fft_pad_factor=2,
sgrscat=False, add_th_noise=True,
jones=False, inv_jones=False,
opacitycal=True, ampcal=True, phasecal=True,
frcal=True, dcal=True, rlgaincal=True,
stabilize_scan_phase=False, stabilize_scan_amp=False,
neggains=False,
taup=ehc.GAINPDEF,
gain_offset=ehc.GAINPDEF, gainp=ehc.GAINPDEF,
phase_std=-1,
dterm_offset=ehc.DTERMPDEF,
rlratio_std=0., rlphase_std=0.,
sigmat=None, phasesigmat=None, rlgsigmat=None,rlpsigmat=None,
caltable_path=None, seed=False, verbose=True):
"""Observe the image on the same baselines as an existing observation object and add noise.
Args:
obs_in (Obsdata): the existing observation
ttype (str): "fast" or "nfft" or "direct"
fft_pad_factor (float): zero pad the image to fft_pad_factor * image size in FFT
sgrscat (bool): if True, the visibilites will be blurred by the Sgr A* kernel
add_th_noise (bool): if True, baseline-dependent thermal noise is added
jones (bool): if True, uses Jones matrix to apply mis-calibration effects
inv_jones (bool): if True, applies estimated inverse Jones matrix
(not including random terms) to a priori calibrate data
opacitycal (bool): if False, time-dependent gaussian errors are added to opacities
ampcal (bool): if False, time-dependent gaussian errors are added to station gains
phasecal (bool): if False, time-dependent station-based random phases are added
frcal (bool): if False, feed rotation angle terms are added to Jones matrices.
dcal (bool): if False, time-dependent gaussian errors added to D-terms.
rlgaincal (bool): if False, time-dependent gains are not equal for R and L pol
stabilize_scan_phase (bool): if True, random phase errors are constant over scans
stabilize_scan_amp (bool): if True, random amplitude errors are constant over scans
neggains (bool): if True, force the applied gains to be <1
taup (float): the fractional std. dev. of the random error on the opacities
gainp (float): the fractional std. dev. of the random error on the gains
or a dict giving one std. dev. per site
gain_offset (float): the base gain offset at all sites,
or a dict giving one gain offset per site
phase_std (float): std. dev. of LCP phase,
or a dict giving one std. dev. per site
a negative value samples from uniform
dterm_offset (float): the base std. dev. of random additive error at all sites,
or a dict giving one std. dev. per site
rlratio_std (float): the fractional std. dev. of the R/L gain offset
or a dict giving one std. dev. per site
rlphase_std (float): std. dev. of R/L phase offset,
or a dict giving one std. dev. per site
a negative value samples from uniform
sigmat (float): temporal std for a Gaussian Process used to generate gains.
If sigmat=None then an iid gain noise is applied.
phasesigmat (float): temporal std for a Gaussian Process used to generate phases.
If phasesigmat=None then an iid gain noise is applied.
rlgsigmat (float): temporal std deviation for a Gaussian Process used to generate R/L gain ratios.
If rlgsigmat=None then an iid gain noise is applied.
rlpsigmat (float): temporal std deviation for a Gaussian Process used to generate R/L phase diff.
If rlpsigmat=None then an iid gain noise is applied.
caltable_path (string): If not None, path and prefix for saving the applied caltable
seed (int): seeds the random component of the noise terms. DO NOT set to 0!
verbose (bool): print updates and warnings
Returns:
(Obsdata): an observation object
"""
if seed:
np.random.seed(seed=seed)
obs = self.observe_same_nonoise(obs_in, sgrscat=sgrscat,ttype=ttype,
cache=False, fft_pad_factor=fft_pad_factor,
zero_empty_pol=True, verbose=verbose)
# Jones Matrix Corruption & Calibration
if jones:
obsdata = simobs.add_jones_and_noise(obs, add_th_noise=add_th_noise,
opacitycal=opacitycal, ampcal=ampcal,
phasecal=phasecal, frcal=frcal, dcal=dcal,
rlgaincal=rlgaincal,
stabilize_scan_phase=stabilize_scan_phase,
stabilize_scan_amp=stabilize_scan_amp,
neggains=neggains,
taup=taup,
gain_offset=gain_offset, gainp=gainp,
phase_std=phase_std,
dterm_offset=dterm_offset,
rlratio_std=rlratio_std, rlphase_std=rlphase_std,
sigmat=sigmat, phasesigmat=phasesigmat,
rlgsigmat=rlgsigmat,rlpsigmat=rlpsigmat,
caltable_path=caltable_path, seed=seed,verbose=verbose)
obs = ehtim.obsdata.Obsdata(obs.ra, obs.dec, obs.rf, obs.bw, obsdata, obs.tarr,
source=obs.source, mjd=obs.mjd, polrep=obs_in.polrep,
ampcal=ampcal, phasecal=phasecal, opacitycal=opacitycal,
dcal=dcal, frcal=frcal,
timetype=obs.timetype, scantable=obs.scans)
if inv_jones:
obsdata = simobs.apply_jones_inverse(obs,
opacitycal=opacitycal, dcal=dcal, frcal=frcal,
verbose=verbose)
obs = ehtim.obsdata.Obsdata(obs.ra, obs.dec, obs.rf, obs.bw, obsdata, obs.tarr,
source=obs.source, mjd=obs.mjd, polrep=obs_in.polrep,
ampcal=ampcal, phasecal=phasecal,
opacitycal=True, dcal=True, frcal=True,
timetype=obs.timetype, scantable=obs.scans)
# No Jones Matrices, Add noise the old way
# NOTE There is an asymmetry here - in the old way, we don't offer the ability to
# *not* unscale estimated noise.
else:
if caltable_path:
print('WARNING: the caltable is only saved if you apply noise with a Jones Matrix')
# TODO -- clean up arguments
obsdata = simobs.add_noise(obs, add_th_noise=add_th_noise,
opacitycal=opacitycal, ampcal=ampcal, phasecal=phasecal,
stabilize_scan_phase=stabilize_scan_phase,
stabilize_scan_amp=stabilize_scan_amp,
neggains=neggains,
taup=taup,
gain_offset=gain_offset, gainp=gainp,
sigmat=sigmat,
caltable_path=caltable_path, seed=seed,
verbose=verbose)
obs = ehtim.obsdata.Obsdata(obs.ra, obs.dec, obs.rf, obs.bw, obsdata, obs.tarr,
source=obs.source, mjd=obs.mjd, polrep=obs_in.polrep,
ampcal=ampcal, phasecal=phasecal,
opacitycal=True, dcal=True, frcal=True,
timetype=obs.timetype, scantable=obs.scans)
return obs
def observe(self, array, tint, tadv, tstart, tstop, bw,
mjd=None, timetype='UTC', polrep_obs=None,
elevmin=ehc.ELEV_LOW, elevmax=ehc.ELEV_HIGH,
no_elevcut_space=False,
ttype='nfft', fft_pad_factor=2, fix_theta_GMST=False,
sgrscat=False, add_th_noise=True,
jones=False, inv_jones=False,
opacitycal=True, ampcal=True, phasecal=True,
frcal=True, dcal=True, rlgaincal=True,
stabilize_scan_phase=False, stabilize_scan_amp=False,
neggains=False,
tau=ehc.TAUDEF, taup=ehc.GAINPDEF,
gain_offset=ehc.GAINPDEF, gainp=ehc.GAINPDEF,
phase_std=-1,
dterm_offset=ehc.DTERMPDEF,
rlratio_std=0.,rlphase_std=0.,
sigmat=None, phasesigmat=None, rlgsigmat=None,rlpsigmat=None,
caltable_path=None, seed=False, verbose=True):
"""Generate baselines from an array object and observe the image.
Args:
array (Array): an array object containing sites with which to generate baselines
tint (float): the scan integration time in seconds
tadv (float): the uniform cadence between scans in seconds
tstart (float): the start time of the observation in hours
tstop (float): the end time of the observation in hours
bw (float): the observing bandwidth in Hz
mjd (int): the mjd of the observation, if set as different from the image mjd
timetype (str): how to interpret tstart and tstop; either 'GMST' or 'UTC'
polrep_obs (str): 'stokes' or 'circ' sets the data polarimetric representation
elevmin (float): station minimum elevation in degrees
elevmax (float): station maximum elevation in degrees
no_elevcut_space (bool): if True, do not apply elevation cut to orbiters
ttype (str): "fast", "nfft" or "dtft"
fft_pad_factor (float): zero pad the image to fft_pad_factor * image size in the FFT
fix_theta_GMST (bool): if True, stops earth rotation to sample fixed u,v
sgrscat (bool): if True, the visibilites will be blurred by the Sgr A* kernel
add_th_noise (bool): if True, baseline-dependent thermal noise is added
jones (bool): if True, uses Jones matrix to apply mis-calibration effects
otherwise uses old formalism without D-terms
inv_jones (bool): if True, applies estimated inverse Jones matrix
(not including random terms) to calibrate data
opacitycal (bool): if False, time-dependent gaussian errors are added to opacities
ampcal (bool): if False, time-dependent gaussian errors are added to station gains
phasecal (bool): if False, time-dependent station-based random phases are added
frcal (bool): if False, feed rotation angle terms are added to Jones matrix.
dcal (bool): if False, time-dependent gaussian errors added to Jones matrix D-terms.
rlgaincal (bool): if False, time-dependent gains are not equal for R and L pol
stabilize_scan_phase (bool): if True, random phase errors are constant over scans
stabilize_scan_amp (bool): if True, random amplitude errors are constant over scans
neggains (bool): if True, force the applied gains to be <1
tau (float): the base opacity at all sites, or a dict giving one opacity per site
taup (float): the fractional std. dev. of the random error on the opacities
gainp (float): the fractional std. dev. of the random error on the gains
or a dict giving one std. dev. per site
gain_offset (float): the base gain offset at all sites,
or a dict giving one gain offset per site
phase_std (float): std. dev. of LCP phase,
or a dict giving one std. dev. per site
a negative value samples from uniform
dterm_offset (float): the base std. dev. of random additive error at all sites,
or a dict giving one std. dev. per site
rlratio_std (float): the fractional std. dev. of the R/L gain offset
or a dict giving one std. dev. per site
rlphase_std (float): std. dev. of R/L phase offset,
or a dict giving one std. dev. per site
a negative value samples from uniform
sigmat (float): temporal std for a Gaussian Process used to generate gains.
If sigmat=None then an iid gain noise is applied.
phasesigmat (float): temporal std for a Gaussian Process used to generate phases.
If phasesigmat=None then an iid gain noise is applied.
rlgsigmat (float): temporal std deviation for a Gaussian Process used to generate R/L gain ratios.
If rlgsigmat=None then an iid gain noise is applied.
rlpsigmat (float): temporal std deviation for a Gaussian Process used to generate R/L phase diff.
If rlpsigmat=None then an iid gain noise is applied.
caltable_path (string): If not None, path and prefix for saving the applied caltable
seed (int): seeds the random component of the noise terms. DO NOT set to 0!
verbose (bool): print updates and warnings
Returns:
(Obsdata): an observation object
"""
# Generate empty observation
if verbose: print("Generating empty observation file . . . ")
if mjd is None:
mjd = self.mjd
if polrep_obs is None:
polrep_obs = self.polrep
obs = array.obsdata(self.ra, self.dec, self.rf, bw, tint, tadv, tstart, tstop, mjd=mjd,
polrep=polrep_obs, tau=tau,
elevmin=elevmin, elevmax=elevmax,
no_elevcut_space=no_elevcut_space,
timetype=timetype, fix_theta_GMST=fix_theta_GMST)
# Observe on the same baselines as the empty observation and add noise
obs = self.observe_same(obs, ttype=ttype, fft_pad_factor=fft_pad_factor,
sgrscat=sgrscat, add_th_noise=add_th_noise,
jones=jones, inv_jones=inv_jones,
opacitycal=opacitycal, ampcal=ampcal,
phasecal=phasecal, dcal=dcal,
frcal=frcal, rlgaincal=rlgaincal,
stabilize_scan_phase=stabilize_scan_phase,
stabilize_scan_amp=stabilize_scan_amp,
neggains=neggains,
taup=taup,
gain_offset=gain_offset, gainp=gainp,
phase_std=phase_std,
dterm_offset=dterm_offset,
rlratio_std=rlratio_std,rlphase_std=rlphase_std,
sigmat=sigmat,phasesigmat=phasesigmat,
rlgsigmat=rlgsigmat,rlpsigmat=rlpsigmat,
caltable_path=caltable_path, seed=seed, verbose=verbose)
obs.mjd = mjd
return obs
def observe_vex(self, vex, source, t_int=0.0, tight_tadv=False,
polrep_obs=None, ttype='nfft', fft_pad_factor=2,
fix_theta_GMST=False,
sgrscat=False, add_th_noise=True,
jones=False, inv_jones=False,
opacitycal=True, ampcal=True, phasecal=True,
frcal=True, dcal=True, rlgaincal=True,
stabilize_scan_phase=False, stabilize_scan_amp=False,
neggains=False,
tau=ehc.TAUDEF, taup=ehc.GAINPDEF,
gain_offset=ehc.GAINPDEF, gainp=ehc.GAINPDEF,
phase_std=-1,
dterm_offset=ehc.DTERMPDEF,
rlratio_std=0.,rlphase_std=0.,
sigmat=None, phasesigmat=None, rlgsigmat=None,rlpsigmat=None,
caltable_path=None, seed=False, verbose=True):
"""Generate baselines from a vex file and observes the image.
Args:
vex (Vex): an vex object containing sites and scan information
source (str): the source to observe
t_int (float): if not zero, overrides the vex scan lengths
tight_tadv (float): if True, advance right after each integration,
otherwise advance after 2x the scan length
polrep_obs (str): 'stokes' or 'circ' sets the data polarimetric representation
ttype (str): "fast" or "nfft" or "dtft"
fft_pad_factor (float): zero pad the image to fft_pad_factor * image size in FFT
fix_theta_GMST (bool): if True, stops earth rotation to sample fixed u,v
sgrscat (bool): if True, the visibilites will be blurred by the Sgr A* kernel
add_th_noise (bool): if True, baseline-dependent thermal noise is added
jones (bool): if True, uses Jones matrix to apply mis-calibration effects
otherwise uses old formalism without D-terms
inv_jones (bool): if True, applies estimated inverse Jones matrix
(not including random terms) to calibrate data
opacitycal (bool): if False, time-dependent gaussian errors are added to opacities
ampcal (bool): if False, time-dependent gaussian errors are added to station gains
phasecal (bool): if False, time-dependent station-based random phases are added
frcal (bool): if False, feed rotation angle terms are added to Jones matrix.
dcal (bool): if False, time-dependent gaussian errors added to Jones matrix D-terms.
rlgaincal (bool): if False, time-dependent gains are not equal for R and L pol
stabilize_scan_phase (bool): if True, random phase errors are constant over scans
stabilize_scan_amp (bool): if True, random amplitude errors are constant over scans
neggains (bool): if True, force the applied gains to be <1
tau (float): the base opacity at all sites,
or a dict giving one opacity per site
taup (float): the fractional std. dev. of the random error on the opacities
gainp (float): the fractional std. dev. of the random error on the gains
or a dict giving one std. dev. per site
gain_offset (float): the base gain offset at all sites,
or a dict giving one gain offset per site
phase_std (float): std. dev. of LCP phase,
or a dict giving one std. dev. per site
a negative value samples from uniform
dterm_offset (float): the base std. dev. of random additive error at all sites,
or a dict giving one std. dev. per site
rlratio_std (float): the fractional std. dev. of the R/L gain offset
or a dict giving one std. dev. per site
rlphase_std (float): std. dev. of R/L phase offset,
or a dict giving one std. dev. per site
a negative value samples from uniform
sigmat (float): temporal std for a Gaussian Process used to generate gains.
If sigmat=None then an iid gain noise is applied.
phasesigmat (float): temporal std for a Gaussian Process used to generate phases.
If phasesigmat=None then an iid gain noise is applied.
rlgsigmat (float): temporal std deviation for a Gaussian Process used to generate R/L gain ratios.
If rlgsigmat=None then an iid gain noise is applied.
rlpsigmat (float): temporal std deviation for a Gaussian Process used to generate R/L phase diff.
If rlpsigmat=None then an iid gain noise is applied.
caltable_path (string): If not None, path and prefix for saving the applied caltable
seed (int): seeds the random component of the noise terms. DO NOT set to 0!
verbose (bool): print updates and warnings
Returns:
(Obsdata): an observation object
"""
if polrep_obs is None:
polrep_obs = self.polrep
t_int_flag = False
if t_int == 0.0:
t_int_flag = True
# Loop over all scans and assemble a list of scan observations
obs_List = []
for i_scan in range(len(vex.sched)):
if t_int_flag:
t_int = vex.sched[i_scan]['scan'][0]['scan_sec']
if tight_tadv:
t_adv = t_int
else:
t_adv = 2.0 * vex.sched[i_scan]['scan'][0]['scan_sec']
# If this scan doesn't observe the source, advance
if vex.sched[i_scan]['source'] != source:
continue
# What subarray is observing now?
scankeys = list(vex.sched[i_scan]['scan'].keys())
subarray = vex.array.make_subarray([vex.sched[i_scan]['scan'][key]['site']
for key in scankeys])
# Observe with the subarray over the scan interval
t_start = vex.sched[i_scan]['start_hr']
t_stop = t_start + vex.sched[i_scan]['scan'][0]['scan_sec']/3600.0 - ehc.EP
obs = self.observe(subarray, t_int, t_adv, t_start, t_stop, vex.bw_hz,
mjd=vex.sched[i_scan]['mjd_floor'], timetype='UTC',
polrep_obs=polrep_obs,
elevmin=.01, elevmax=89.99,
ttype=ttype, fft_pad_factor=fft_pad_factor,
fix_theta_GMST=fix_theta_GMST,
sgrscat=sgrscat,
add_th_noise=add_th_noise,
jones=jones, inv_jones=inv_jones,
opacitycal=opacitycal, ampcal=ampcal, phasecal=phasecal,
frcal=frcal, dcal=dcal, rlgaincal=rlgaincal,
stabilize_scan_phase=stabilize_scan_phase,
stabilize_scan_amp=stabilize_scan_amp,
neggains=neggains,
tau=tau, taup=taup,
gain_offset=gain_offset, gainp=gainp,
phase_std=phase_std,
dterm_offset=dterm_offset,
rlratio_std=rlratio_std,rlphase_std=rlphase_std,
sigmat=sigmat,phasesigmat=phasesigmat,
rlgsigmat=rlgsigmat,rlpsigmat=rlpsigmat,
caltable_path=caltable_path, seed=seed, verbose=verbose)
obs_List.append(obs)
# Merge the scans together
obs = ehtim.obsdata.merge_obs(obs_List)
return obs
def compare_images(self, im_compare, pol=None, psize=None,target_fov=None, blur_frac=0.0,
beamparams=[1., 1., 1.], metric=['nxcorr', 'nrmse', 'rssd'],
blursmall=False, shift=True):
"""Compare to another image by computing normalized cross correlation,
normalized root mean squared error, or square root of the sum of squared differences.
Returns metrics only for the primary polarization imvec!
Args:
im_compare (Image): the image to compare to
pol (str): which polarization image to compare. Default is self.pol_prim
psize (float): pixel size of comparison image (rad).
If None it is the smallest of the input image pizel sizes
target_fov (float): fov of the comparison image (rad).
If None it is twice the largest fov of the input images
beamparams (list): the nominal Gaussian beam parameters [fovx, fovy, position angle]
blur_frac (float): fractional beam to blur each image to before comparison
metric (list) : a list of fidelity metrics from ['nxcorr','nrmse','rssd']
blursmall (bool) : True to blur the unpadded image rather than the large image.
shift (int): manual image shift, otherwise use shift from maximum cross-correlation
Returns:
(tuple): [errormetric, im1_pad, im2_shift]
"""
im1 = self.copy()
im2 = im_compare.switch_polrep(polrep_out=im1.polrep, pol_prim_out=im1.pol_prim)
if im1.polrep != im2.polrep:
raise Exception("In find_shift, im1 and im2 must have the same polrep!")
if im1.pol_prim != im2.pol_prim:
raise Exception("In find_shift, im1 and im2 must have the same pol_prim!")
# Shift the comparison image to maximize normalized cross-corr.
[idx, xcorr, im1_pad, im2_pad] = im1.find_shift(im2, psize=psize, target_fov=target_fov,
beamparams=beamparams, pol=pol,
blur_frac=blur_frac, blursmall=blursmall)
if not isinstance(shift, bool):
idx = shift
im2_shift = im2_pad.shift(idx)
# Compute error metrics
error = []
imvec1 = im1_pad.get_polvec(pol)
imvec2 = im2_shift.get_polvec(pol)
if 'nxcorr' in metric:
error.append(xcorr[idx[0], idx[1]] / (im1_pad.xdim * im1_pad.ydim))
if 'nrmse' in metric:
error.append(np.sqrt(np.sum((np.abs(imvec1 - imvec2)**2 * im1_pad.psize**2)) /
np.sum((imvec1)**2 * im1_pad.psize**2)))
if 'rssd' in metric:
error.append(np.sqrt(np.sum(np.abs(imvec1 - imvec2)**2) * im1_pad.psize**2))
return (error, im1_pad, im2_shift)
def align_images(self, im_list, pol=None, shift=True, final_fov=False, scale='lin',
gamma=0.5, dynamic_range=[1.e3]):
"""Align all the images in im_list to the current image (self)
Aligns all images by comparison of the primary pol image.
Args:
im_list (list): list of images to align to the current image
shift (list): list of manual image shifts,
otherwise use the shift from maximum cross-correlation
pol (str): which polarization image to compare. Default is self.pol_prim
final_fov (float): fov of the comparison image (rad).
If False it is the largestinput image fov
scale (str) : compare images in 'log','lin',or 'gamma' scale
gamma (float): exponent for gamma scale comparison
dynamic_range (float): dynamic range for log and gamma scale comparisons
Returns:
(tuple): (im_list_shift, shifts, im0_pad)
"""
im0 = self.copy()
if not np.all(im0.polrep == np.array([im.polrep for im in im_list])):
raise Exception("In align_images, all images must have the same polrep!")
if not np.all(im0.pol_prim == np.array([im.pol_prim for im in im_list])):
raise Exception("In find_shift, all images must have the same pol_prim!")
if len(dynamic_range) == 1:
dynamic_range = dynamic_range * np.ones(len(im_list) + 1)
useshift = True
if isinstance(shift, bool):
useshift = False
# Find the minimum psize and the maximum field of view
psize = im0.psize
max_fov = np.max([im0.xdim * im0.psize, im0.ydim * im0.psize])
for i in range(0, len(im_list)):
psize = np.min([psize, im_list[i].psize])
max_fov = np.max([max_fov,
im_list[i].xdim * im_list[i].psize,
im_list[i].ydim * im_list[i].psize])
if not final_fov:
final_fov = max_fov
# Shift all images in the list
im_list_shift = []
shifts = []
for i in range(0, len(im_list)):
(idx, _, im0_pad_orig, im_pad) = im0.find_shift(im_list[i], target_fov=2 * max_fov,
psize=psize, pol=pol,
scale=scale, gamma=gamma,
dynamic_range=dynamic_range[i + 1])
if i == 0:
npix = int(im0_pad_orig.xdim / 2)
im0_pad = im0_pad_orig.regrid_image(final_fov, npix)
if useshift:
idx = shift[i]
tmp = im_pad.shift(idx)
shifts.append(idx)
im_list_shift.append(tmp.regrid_image(final_fov, npix))
return (im_list_shift, shifts, im0_pad)
def find_shift(self, im_compare, pol=None, psize=None, target_fov=None,
beamparams=[1., 1., 1.], blur_frac=0.0, blursmall=False,
scale='lin', gamma=0.5, dynamic_range=1.e3):
"""Find image shift that maximizes normalized cross correlation with a second image im2.
Finds shift only by comparison of the primary pol image.
Args:
im_compare (Image): image with respect with to switch
pol (str): which polarization image to compare. Default is self.pol_prim
psize (float): pixel size of comparison image (rad).
If None it is the smallest of the input image pizel sizes
target_fov (float): fov of the comparison image (rad).
If None it is twice the largest fov of the input images
beamparams (list): the nominal Gaussian beam parameters [fovx, fovy, position angle]
blur_frac (float): fractional beam to blur each image to before comparison
blursmall (bool) : True to blur the unpadded image rather than the large image.
scale (str) : compare images in 'log','lin',or 'gamma' scale
gamma (float): exponent for gamma scale comparison
dynamic_range (float): dynamic range for log and gamma scale comparisons
Returns:
(tuple): (errormetric, im1_pad, im2_shift)
"""
im1 = self.copy()
im2 = im_compare.switch_polrep(polrep_out=im1.polrep, pol_prim_out=im1.pol_prim)
if pol=='RL' or pol=='LR':
raise Exception("Find_shift currently doesn't work with complex RL or LR imvecs!")
if im1.polrep != im2.polrep:
raise Exception("In find_shift, im1 and im2 must have the same polrep!")
if im1.pol_prim != im2.pol_prim:
raise Exception("In find_shift, im1 and im2 must have the same pol_prim!")
# Find maximum FOV and minimum pixel size for comparison
if target_fov is None:
max_fov = np.max([im1.fovx(), im1.fovy(), im2.fovx(), im2.fovy()])
target_fov = 2 * max_fov
if psize is None:
psize = np.min([im1.psize, im2.psize])
npix = int(target_fov / psize)
# Blur images, then pad
if ((blur_frac > 0.0) and (blursmall is True)):
im1 = im1.blur_gauss(beamparams, blur_frac, blur_frac)
im2 = im2.blur_gauss(beamparams, blur_frac, blur_frac)
im1_pad = im1.regrid_image(target_fov, npix)
im2_pad = im2.regrid_image(target_fov, npix)
# or, pad images, then blur
if ((blur_frac > 0.0) and (blursmall is False)):
im1_pad = im1_pad.blur_gauss(beamparams, blur_frac, blur_frac)
im2_pad = im2_pad.blur_gauss(beamparams, blur_frac, blur_frac)
# Rescale the image vectors into log or gamma scale
# TODO -- what about negative values? complex values?
im1_pad_vec = im1_pad.get_polvec(pol)
im2_pad_vec = im2_pad.get_polvec(pol)
if scale == 'log':
im1_pad_vec[im1_pad_vec < 0.0] = 0.0
im1_pad_vec = np.log(im1_pad_vec + np.max(im1_pad_vec) / dynamic_range)
im2_pad_vec[im2_pad_vec < 0.0] = 0.0
im2_pad_vec = np.log(im2_pad_vec + np.max(im2_pad_vec) / dynamic_range)
if scale == 'gamma':
im1_pad_vec[im1_pad_vec < 0.0] = 0.0
im1_pad_vec = (im1_pad_vec + np.max(im1_pad_vec) / dynamic_range)**(gamma)
im2_pad_vec[im2_pad_vec < 0.0] = 0.0
im2_pad_vec = (im2_pad_vec + np.max(im2_pad_vec) / dynamic_range)**(gamma)
# Normalize images and compute cross correlation with FFT
im1_norm = (im1_pad_vec.reshape(im1_pad.ydim, im1_pad.xdim) - np.mean(im1_pad_vec))
im1_norm /= np.std(im1_pad_vec)
im2_norm = (im2_pad_vec.reshape(im2_pad.ydim, im2_pad.xdim) - np.mean(im2_pad_vec))
im2_norm /= np.std(im2_pad_vec)
fft_im1 = np.fft.fft2(im1_norm)
fft_im2 = np.fft.fft2(im2_norm)
xcorr = np.real(np.fft.ifft2(fft_im1 * np.conj(fft_im2)))
# Find idx of shift that maximized cross-correlation
idx = np.unravel_index(xcorr.argmax(), xcorr.shape)
return [idx, xcorr, im1_pad, im2_pad]
def hough_ring(self, edgetype='canny', thresh=0.2, num_circles=3, radius_range=None,
return_type='rad', display_results=True):
"""Use a circular hough transform to find a circle in the image
Returns metrics only for the primary polarization imvec!
Args:
num_circles (int) : number of circles to return
radius_range (tuple): range of radii to search in Hough transform, in radian
edgetype (str): edge detection type, 'gradient' or 'canny'
thresh(float): fractional threshold for the gradient image
display_results (bool): True to display results of the fit
return_type (str): 'rad' to return in radian, 'pixel' to return in pixel units
Returns:
list : a list of fitted circles (xpos, ypos, radius, objFunc), in radian
"""
if 'skimage' not in sys.modules:
raise Exception("scikit-image not installed: cannot use hough_ring!")
# coordinate values
pdim = self.psize
xlist = np.arange(0, -self.xdim, -1) * pdim + (pdim * self.xdim) / 2.0 - pdim / 2.0
ylist = np.arange(0, -self.ydim, -1) * pdim + (pdim * self.ydim) / 2.0 - pdim / 2.0
# normalize to range 0, 1
im = self.copy()
maxval = np.max(im.imvec)
meanval = np.mean(im.imvec)
im_norm = im.imvec / (maxval + .01 * meanval)
im_norm = im_norm.astype('float') # is it a problem if it's double??
im_norm[np.isnan(im.imvec)] = 0 # mask nans to 0
im.imvec = im_norm
# detect edges
if edgetype == 'canny':
imarr = im.imvec.reshape(self.ydim, self.xdim)
edges = canny(imarr, sigma=0, high_threshold=thresh, low_threshold=0.01)
im_edges = self.copy()
im_edges.imvec = edges.flatten()
elif edgetype == 'grad':
im_edges = self.grad()
if not (thresh is None):
thresh_val = thresh * np.max(im_edges.imvec)
mask = im_edges.imvec > thresh_val
# im_edges.imvec[mask] = 1
im_edges.imvec[~mask] = 0
edges = im_edges.imvec.reshape(self.ydim, self.xdim)
else:
im_edges = im.copy()
if not (thresh is None):
thresh_val = thresh * np.max(im_edges.imvec)
mask = im_edges.imvec > thresh_val
# im_edges.imvec[mask] = 1f
im_edges.imvec[~mask] = 0
edges = im_edges.imvec.reshape(self.ydim, self.xdim)
# define radius range for Hough transform search
if radius_range is None:
hough_radii = np.arange(int(10 * ehc.RADPERUAS / self.psize),
int(50 * ehc.RADPERUAS / self.psize))
else:
hough_radii = np.linspace(
radius_range[0] /
self.psize,
radius_range[0] /
self.psize,
25)
# perform the hough transform and select the most prominent circles
hough_res = hough_circle(edges, hough_radii)
accums, cy, cx, radii = hough_circle_peaks(hough_res, hough_radii,
total_num_peaks=num_circles)
accum_tot = np.sum(accums)
# print results, plot circles, and return
outlist = []
if display_results:
plt.ion()
fig = self.display()
ax = fig.gca()
i = 0
colors = ['b', 'r', 'w', 'lime', 'magenta', 'aqua']
for accum, center_y, center_x, radius in zip(accums, cy, cx, radii):
accum_frac = accum / accum_tot
if return_type == 'rad':
x_rad = xlist[int(np.round(center_x))]
y_rad = ylist[int(np.round(center_y))]
r_rad = radius * self.psize
outlist.append([x_rad, y_rad, r_rad, accum_frac])
else:
outlist.append([center_x, center_y, radius, accum_frac])
print(accum_frac)
print("%i ring diameter: %0.1f microarcsec" % (i, 2 * radius * pdim / ehc.RADPERUAS))
if display_results:
if i > len(colors):
color = colors[-1]
else:
color = colors[i]
circ = mpl.patches.Circle((center_y, center_x), radius, fill=False, color=color)
ax.add_patch(circ)
i += 1
return outlist
def fit_gauss(self, units='rad'):
"""Determine the Gaussian parameters that short baselines would measure for the source
by diagonalizing the image covariance matrix.
Returns parameters only for the primary polarization!
Args:
units (string): 'rad' returns values in radians,
'natural' returns FWHM in uas and PA in degrees
Returns:
(tuple) : a tuple (fwhm_maj, fwhm_min, theta) of the fit Gaussian parameters
"""
(x1, y1) = self.centroid()
pdim = self.psize
im = self.imvec
xlist = np.arange(0, -self.xdim, -1) * pdim + (pdim * self.xdim) / 2.0 - pdim / 2.0
ylist = np.arange(0, -self.ydim, -1) * pdim + (pdim * self.ydim) / 2.0 - pdim / 2.0
x2 = (np.sum(np.outer(0.0 * ylist + 1.0, (xlist - x1)**2).ravel() * im) / np.sum(im))
y2 = (np.sum(np.outer((ylist - y1)**2, 0.0 * xlist + 1.0).ravel() * im) / np.sum(im))
xy = (np.sum(np.outer(ylist - y1, xlist - x1).ravel() * im) / np.sum(im))
eig = np.linalg.eigh(np.array(((x2, xy), (xy, y2))))
gauss_params = np.array((eig[0][1]**0.5 * (8. * np.log(2.))**0.5,
eig[0][0]**0.5 * (8. * np.log(2.))**0.5,
np.mod(np.arctan2(eig[1][1][0], eig[1][1][1]) + np.pi, np.pi)))
if units == 'natural':
gauss_params[0] /= ehc.RADPERUAS
gauss_params[1] /= ehc.RADPERUAS
gauss_params[2] *= 180. / np.pi
return gauss_params
def fit_gauss_empirical(self, paramguess=None):
"""Determine the Gaussian parameters that short baselines would measure
Returns parameters only for the primary polarization!
Args:
paramguess (tuple): Initial guess (fwhm_maj, fwhm_min, theta) of fit parameters
Returns:
(tuple) : a tuple (fwhm_maj, fwhm_min, theta) of the fit Gaussian parameters.
"""
# This could be done using moments of the intensity distribution (self.fit_gauss)
# but we'll use the visibility approach
u_max = 1.0 / (self.psize * self.xdim) / 5.0
uv = np.array([[u, v]
for u in np.arange(-u_max, u_max * 1.001, u_max / 4.0)
for v in np.arange(-u_max, u_max * 1.001, u_max / 4.0)])
u = uv[:, 0]
v = uv[:, 1]
vis = np.dot(obsh.ftmatrix(self.psize, self.xdim, self.ydim, uv, pulse=self.pulse),
self.imvec)
if paramguess is None:
paramguess = (self.psize * self.xdim / 4.0, self.psize * self.xdim / 4.0, 0.)
def errfunc(p):
vismodel = obsh.gauss_uv(u, v, self.total_flux(), p, x=0., y=0.)
err = np.sum((np.abs(vis) - np.abs(vismodel))**2)
return err
# minimizer params
optdict = {'maxiter': 5000, 'maxfev': 5000, 'xtol': paramguess[0] / 1e9, 'ftol': 1e-10}
res = opt.minimize(errfunc, paramguess, method='Nelder-Mead', options=optdict)
# Return in the form [maj, min, PA]
x = res.x
x[0] = np.abs(x[0])
x[1] = np.abs(x[1])
x[2] = np.mod(x[2], np.pi)
if x[0] < x[1]:
maj = x[1]
x[1] = x[0]
x[0] = maj
x[2] = np.mod(x[2] + np.pi / 2.0, np.pi)
return x
def contour(self, contour_levels=[0.1, 0.25, 0.5, 0.75],
contour_cfun=None, color='w', legend=True, show_im=True,
cfun='afmhot', scale='lin', interp='gaussian', gamma=0.5, dynamic_range=1.e3,
plotp=False, nvec=20, pcut=0.01, mcut=0.1, label_type='ticks', has_title=True,
has_cbar=True, cbar_lims=(), cbar_unit=('Jy', 'pixel'),
contour_im=False, power=0, beamcolor='w',
export_pdf="", show=True, beamparams=None, cbar_orientation="vertical",
scale_lw=1, beam_lw=1, cbar_fontsize=12, axis=None, scale_fontsize=12):
"""Display the image in a contour plot.
Args:
contour_levels (arr): the fractional contour levels relative to the max flux plotted
contour_cfun (pyplot colormap function): the function used to get the RGB colors
legend (bool): True to show a legend that says what each contour line corresponds to
cfun (str): matplotlib.pyplot color function
scale (str): image scaling in ['log','gamma','lin']
interp (str): image interpolation 'gauss' or 'lin'
gamma (float): index for gamma scaling
dynamic_range (float): dynamic range for log and gamma scaling
plotp (bool): True to plot linear polarimetic image
nvec (int): number of polarimetric vectors to plot
pcut (float): minimum stokes P value for displaying polarimetric vectors
as fraction of maximum Stokes I pixel
mcut (float): minimum fractional polarization for plotting vectors
label_type (string): specifies the type of axes labeling: 'ticks', 'scale', 'none'
has_title (bool): True if you want a title on the plot
has_cbar (bool): True if you want a colorbar on the plot
cbar_lims (tuple): specify the lower and upper limit of the colorbar
cbar_unit (tuple of strings): the unit of each pixel for the colorbar:
'Jy', 'm-Jy', '$\mu$Jy'
export_pdf (str): path to exported PDF with plot
show (bool): Display the plot if true
show_im (bool): Display the image with the contour plot if True
Returns:
(matplotlib.figure.Figure): figure object with image
"""
image = self.copy()
# or some generalized version for image sizes
y = np.linspace(0, image.ydim, image.ydim)
x = np.linspace(0, image.xdim, image.xdim)
# make the image grid
z = image.imvec.reshape((image.ydim, image.xdim))
maxz = max(image.imvec)
if axis is None:
ax = plt.gca()
elif axis is not None:
ax = axis
plt.sca(axis)
if show_im:
if axis is not None:
axis = image.display(cfun=cfun, scale=scale, interp=interp, gamma=gamma,
dynamic_range=dynamic_range,
plotp=plotp, nvec=nvec, pcut=pcut, mcut=mcut,
label_type=label_type, has_title=has_title,
has_cbar=has_cbar, cbar_lims=cbar_lims,
cbar_unit=cbar_unit,
beamparams=beamparams,
cbar_orientation=cbar_orientation, scale_lw=1, beam_lw=1,
cbar_fontsize=cbar_fontsize, axis=axis,
scale_fontsize=scale_fontsize, power=power,
beamcolor=beamcolor)
else:
image.display(cfun=cfun, scale=scale, interp=interp, gamma=gamma,
dynamic_range=dynamic_range,
plotp=plotp, nvec=nvec, pcut=pcut, mcut=mcut, label_type=label_type,
has_title=has_title, has_cbar=has_cbar,
cbar_lims=cbar_lims, cbar_unit=cbar_unit, beamparams=beamparams,
cbar_orientation=cbar_orientation, scale_lw=1, beam_lw=1,
cbar_fontsize=cbar_fontsize,
axis=None, scale_fontsize=scale_fontsize,
power=power, beamcolor=beamcolor)
else:
if contour_im is False:
image.imvec = 0.0 * image.imvec
else:
image = contour_im.copy()
if axis is not None:
axis = image.display(cfun=cfun, scale=scale, interp=interp, gamma=gamma,
dynamic_range=dynamic_range,
plotp=plotp, nvec=nvec, pcut=pcut, mcut=mcut,
label_type=label_type, has_title=has_title,
has_cbar=has_cbar, cbar_lims=cbar_lims, cbar_unit=cbar_unit,
beamparams=beamparams,
cbar_orientation=cbar_orientation, scale_lw=1, beam_lw=1,
cbar_fontsize=cbar_fontsize,
axis=axis,
scale_fontsize=scale_fontsize, power=power,
beamcolor=beamcolor)
else:
image.display(cfun=cfun, scale=scale, interp=interp, gamma=gamma,
dynamic_range=dynamic_range,
plotp=plotp, nvec=nvec, pcut=pcut, mcut=mcut, label_type=label_type,
has_title=has_title,
has_cbar=has_cbar, cbar_lims=cbar_lims, cbar_unit=cbar_unit,
beamparams=beamparams,
cbar_orientation=cbar_orientation, scale_lw=1, beam_lw=1,
cbar_fontsize=cbar_fontsize, axis=None,
scale_fontsize=scale_fontsize, power=power, beamcolor=beamcolor)
if axis is None:
ax = plt.gcf()
if axis is not None:
ax = axis
if axis is not None:
ax = axis
plt.sca(axis)
count = 0.
for level in contour_levels:
if not(contour_cfun is None):
rgbval = contour_cfun(count / len(contour_levels))
rgbstring = '#%02x%02x%02x' % (rgbval[0] * 256, rgbval[1] * 256, rgbval[2] * 256)
else:
rgbstring = color
cs = plt.contour(x, y, z, levels=[level * maxz], colors=rgbstring, cmap=None)
count += 1
cs.collections[0].set_label(str(int(level * 100)) + '%')
if legend:
plt.legend()
if show:
#plt.show(block=False)
ehc.show_noblock()
if export_pdf != "":
ax.savefig(export_pdf, bbox_inches='tight', pad_inches=0)
elif axis is not None:
return axis
return ax
def display(self, pol=None, cfun=False, interp='gaussian',
scale='lin', gamma=0.5, dynamic_range=1.e3,
plotp=False, plot_stokes=False, nvec=20,
vec_cfun=None,
scut=0, pcut=0.1, mcut=0.01, scale_ticks=False,
log_offset=False,
label_type='ticks', has_title=True, alpha=1,
has_cbar=True, only_cbar=False, cbar_lims=(), cbar_unit=('Jy', 'pixel'),
export_pdf="", pdf_pad_inches=0.0, show=True, beamparams=None,
cbar_orientation="vertical", scinot=False,
scale_lw=1, beam_lw=1, cbar_fontsize=12, axis=None,
scale_fontsize=12,
power=0,
beamcolor='w', beampos='right', scalecolor='w',dpi=500):
"""Display the image.
Args:
pol (str): which polarization image to plot. Default is self.pol_prim
pol='spec' will plot spectral index
pol='curv' will plot spectral curvature
cfun (str): matplotlib.pyplot color function.
False changes with 'pol', but is 'afmhot' for most
interp (str): image interpolation 'gauss' or 'lin'
scale (str): image scaling in ['log','gamma','lin']
gamma (float): index for gamma scaling
dynamic_range (float): dynamic range for log and gamma scaling
plotp (bool): True to plot linear polarimetic image
plot_stokes (bool): True to plot stokes subplots along with plotp
nvec (int): number of polarimetric vectors to plot
vec_cfun (str): color function for vectors colored by lin pol frac
scut (float): minimum stokes I value for displaying spectral index
pcut (float): minimum stokes I value for displaying polarimetric vectors
(fraction of maximum Stokes I)
mcut (float): minimum fractional polarization value for displaying vectors
label_type (string): specifies the type of axes labeling: 'ticks', 'scale', 'none'
has_title (bool): True if you want a title on the plot
has_cbar (bool): True if you want a colorbar on the plot
cbar_lims (tuple): specify the lower and upper limit of the colorbar
cbar_unit (tuple): specifies the unit of the colorbar: e.g.,
('Jy','pixel'),('m-Jy','$\mu$as$^2$'),['Tb']
beamparams (list): [fwhm_maj, fwhm_min, theta], set to plot beam contour
export_pdf (str): path to exported PDF with plot
show (bool): Display the plot if true
scinot (bool): Display numbers/units in scientific notation
scale_lw (float): Linewidth of the scale overlay
beam_lw (float): Linewidth of the beam overlay
cbar_fontsize (float): Fontsize of the text elements of the colorbar
axis (matplotlib.axes.Axes): An axis object
scale_fontsize (float): Fontsize of the scale label
power (float): Passed to colorbar for division of ticks by 1e(power)
beamcolor (str): color of the beam overlay
scalecolor (str): color of the scale label overlay
Returns:
(matplotlib.figure.Figure): figure object with image
"""
if (interp in ['gauss', 'gaussian', 'Gaussian', 'Gauss']):
interp = 'gaussian'
elif (interp in ['linear','bilinear']):
interp = 'bilinear'
else:
interp = 'none'
if not(beamparams is None or beamparams is False):
if beamparams[0] > self.fovx() or beamparams[1] > self.fovx():
raise Exception("beam FWHM must be smaller than fov!")
if self.polrep == 'stokes' and pol is None:
pol = 'I'
elif self.polrep == 'circ' and pol is None:
pol = 'RR'
if only_cbar:
has_cbar = True
label_type = 'none'
has_title = False
if axis is None:
f = plt.figure()
plt.clf()
if axis is not None:
plt.sca(axis)
f = plt.gcf()
# Get unit scale factor
factor = 1.
fluxunit = 'Jy'
areaunit = 'pixel'
if cbar_unit[0] in ['m-Jy', 'mJy']:
fluxunit = 'mJy'
factor *= 1.e3
elif cbar_unit[0] in ['muJy', r'$\mu$-Jy', r'$\mu$Jy']:
fluxunit = r'$\mu$Jy'
factor *= 1.e6
elif cbar_unit[0] == 'Tb':
factor = 3.254e13 / (self.rf**2 * self.psize**2)
fluxunit = 'Brightness Temperature (K)'
areaunit = ''
if power != 0:
fluxunit = (r'Brightness Temperature ($10^{{' + str(power) + '}}$ K)')
else:
fluxunit = 'Brightness Temperature (K)'
elif cbar_unit[0] in ['Jy']:
fluxunit = 'Jy'
factor *= 1.
else:
factor = 1
fluxunit = cbar_unit[0]
areaunit = ''
if len(cbar_unit) == 1 or cbar_unit[0] == 'Tb':
factor *= 1.
elif cbar_unit[1] == 'pixel':
factor *= 1.
if power != 0:
areaunit = areaunit + (r' ($10^{{' + str(power) + '}}$ K)')
elif cbar_unit[1] in ['$arcseconds$^2$', 'as$^2$', 'as2']:
areaunit = 'as$^2$'
fovfactor = self.xdim * self.psize * (1 / ehc.RADPERAS)
factor *= (1. / fovfactor)**2 / (1. / self.xdim)**2
if power != 0:
areaunit = areaunit + (r' ($10^{{' + str(power) + '}}$ K)')
elif cbar_unit[1] in [r'$\m-arcseconds$^2$', 'mas$^2$', 'mas2']:
areaunit = 'mas$^2$'
fovfactor = self.xdim * self.psize * (1 / ehc.RADPERUAS) / 1000.
factor *= (1. / fovfactor)**2 / (1. / self.xdim)**2
if power != 0:
areaunit = areaunit + (r' ($10^{{' + str(power) + '}}$ K)')
elif cbar_unit[1] in [r'$\mu$-arcseconds$^2$', r'$\mu$as$^2$', 'muas2']:
areaunit = r'$\mu$as$^2$'
fovfactor = self.xdim * self.psize * (1 / ehc.RADPERUAS)
factor *= (1. / fovfactor)**2 / (1. / self.xdim)**2
if power != 0:
areaunit = areaunit + (r' ($10^{{' + str(power) + '}}$ K)')
elif cbar_unit[1] == 'beam':
if (beamparams is None or beamparams is False):
print("Cannot convert to Jy/beam without beamparams!")
else:
areaunit = 'beam'
beamarea = (2.0 * np.pi * beamparams[0] * beamparams[1] / (8.0 * np.log(2)))
factor *= beamarea / (self.psize**2)
if power != 0:
areaunit = areaunit + (r' ($10^{{' + str(power) + '}}$ K)')
else:
raise ValueError('cbar_unit ' + cbar_unit[1] + ' is not a possible option')
if not plotp: # Plot a single polarization image
cbar_lims_p = ()
if pol.lower() == 'spec':
imvec = self.specvec.copy()
# mask out low total intensity values
mask = self.imvec < (scut * np.max(self.imvec))
imvec[mask] = np.nan
unit = r'$\alpha$'
factor = 1
cbar_lims_p = [-5, 5]
cfun_p = 'seismic'
elif pol.lower() == 'curv':
imvec = self.curvvec.copy()
# mask out low total intensity values
mask = self.imvec < (scut * np.max(self.imvec))
imvec[mask] = np.nan
unit = r'$\beta$'
factor = 1
cbar_lims_p = [-5, 5]
cfun_p = 'seismic'
elif pol.lower() == 'm':
imvec = self.mvec.copy()
unit = r'$\|\breve{m}|$'
factor = 1
cbar_lims_p = [0, 1]
cfun_p = 'cool'
elif pol.lower() == 'p':
imvec = self.mvec * self.ivec
unit = r'$\|P|$'
cfun_p = 'afmhot'
elif pol.lower() == 'chi' or pol.lower() == 'evpa':
imvec = self.chivec.copy() / ehc.DEGREE
unit = r'$\chi (^\circ)$'
factor = 1
cbar_lims_p = [0, 180]
cfun_p = 'hsv'
elif pol.lower() == 'e':
imvec = self.evec.copy()
unit = r'$E$-mode'
cfun_p = 'Spectral'
elif pol.lower() == 'b':
imvec = self.bvec.copy()
unit = r'$B$-mode'
cfun_p = 'Spectral'
else:
pol = pol.upper()
if pol == 'V':
cfun_p = 'bwr'
else:
cfun_p = 'afmhot'
try:
imvec = np.array(self._imdict[pol]).reshape(-1) / (10.**power)
except KeyError:
try:
if self.polrep == 'stokes':
im2 = self.switch_polrep('circ')
elif self.polrep == 'circ':
im2 = self.switch_polrep('stokes')
imvec = np.array(im2._imdict[pol]).reshape(-1) / (10.**power)
except KeyError:
raise Exception("Cannot make pol %s image in display()!" % pol)
unit = fluxunit
if areaunit != '':
unit += ' / ' + areaunit
if np.any(np.imag(imvec)):
print('casting complex image to abs value')
imvec = np.real(imvec)
imvec = imvec * factor
imarr = imvec.reshape(self.ydim, self.xdim)
if scale == 'log':
if (imarr < 0.0).any():
print('clipping values less than 0 in display')
imarr[imarr < 0.0] = 0.0
if log_offset:
imarr = np.log10(imarr + log_offset / dynamic_range)
else:
imarr = np.log10(imarr + np.max(imarr) / dynamic_range)
unit = r'$\log_{10}$(' + unit + ')'
if scale == 'gamma':
if (imarr < 0.0).any():
print('clipping values less than 0 in display')
imarr[imarr < 0.0] = 0.0
imarr = (imarr + np.max(imarr) / dynamic_range)**(gamma)
unit = '(' + unit + ')^' + str(gamma)
if not cbar_lims and cbar_lims_p:
cbar_lims = cbar_lims_p
if cbar_lims:
cbar_lims[0] = cbar_lims[0] / (10.**power)
cbar_lims[1] = cbar_lims[1] / (10.**power)
imarr[imarr > cbar_lims[1]] = cbar_lims[1]
imarr[imarr < cbar_lims[0]] = cbar_lims[0]
if has_title:
plt.title("%s %.2f GHz %s" % (self.source, self.rf / 1e9, pol), fontsize=16)
if not cfun:
cfun = cfun_p
cmap = plt.get_cmap(cfun).copy()
cmap.set_bad(color='whitesmoke')
if cbar_lims:
im = plt.imshow(imarr, alpha=alpha, cmap=cmap, interpolation=interp,
vmin=cbar_lims[0], vmax=cbar_lims[1])
else:
im = plt.imshow(imarr, alpha=alpha, cmap=cmap, interpolation=interp)
if not(beamparams is None or beamparams is False):
if beampos=='left':
beamparams = [beamparams[0], beamparams[1], beamparams[2],
+.4 * self.fovx(), -.4 * self.fovy()]
else:
beamparams = [beamparams[0], beamparams[1], beamparams[2],
-.35 * self.fovx(), -.35 * self.fovy()]
beamimage = self.copy()
beamimage.imvec *= 0
beamimage = beamimage.add_gauss(1, beamparams)
halflevel = 0.5 * np.max(beamimage.imvec)
beamimarr = (beamimage.imvec).reshape(beamimage.ydim, beamimage.xdim)
plt.contour(beamimarr, levels=[halflevel], colors=beamcolor, linewidths=beam_lw)
if has_cbar:
if only_cbar:
im.set_visible(False)
cb = plt.colorbar(im, fraction=0.046, pad=0.04, orientation=cbar_orientation)
cb.set_label(unit, fontsize=float(cbar_fontsize))
if cbar_fontsize != 12:
cb.set_label(unit, fontsize=float(cbar_fontsize) / 1.5)
cb.ax.tick_params(labelsize=cbar_fontsize)
if cbar_lims:
plt.clim(cbar_lims[0], cbar_lims[1])
if scinot:
cb.formatter.set_powerlimits((0, 0))
cb.update_ticks()
else: # plot polarization with ticks!
im_stokes = self.switch_polrep(polrep_out='stokes')
imvec = np.array(im_stokes.imvec).reshape(-1) / (10**power)
qvec = np.array(im_stokes.qvec).reshape(-1) / (10**power)
uvec = np.array(im_stokes.uvec).reshape(-1) / (10**power)
vvec = np.array(im_stokes.vvec).reshape(-1) / (10**power)
if len(imvec) == 0:
imvec = np.zeros(im_stokes.ydim * im_stokes.xdim)
if len(qvec) == 0:
qvec = np.zeros(im_stokes.ydim * im_stokes.xdim)
if len(uvec) == 0:
uvec = np.zeros(im_stokes.ydim * im_stokes.xdim)
if len(vvec) == 0:
vvec = np.zeros(im_stokes.ydim * im_stokes.xdim)
imvec *= factor
qvec *= factor
uvec *= factor
vvec *= factor
imarr = (imvec).reshape(im_stokes.ydim, im_stokes.xdim)
qarr = (qvec).reshape(im_stokes.ydim, im_stokes.xdim)
uarr = (uvec).reshape(im_stokes.ydim, im_stokes.xdim)
varr = (vvec).reshape(im_stokes.ydim, im_stokes.xdim)
unit = fluxunit
if areaunit != '':
unit = fluxunit + ' / ' + areaunit
# only the stokes I image gets transformed! TODO
imarr2 = imarr.copy()
if scale == 'log':
if (imarr2 < 0.0).any():
print('clipping values less than 0 in display')
imarr2[imarr2 < 0.0] = 0.0
imarr2 = np.log10(imarr2 + np.max(imarr2) / dynamic_range)
unit = r'$\log_{10}$(' + unit + ')'
if scale == 'gamma':
if (imarr2 < 0.0).any():
print('clipping values less than 0 in display')
imarr2[imarr2 < 0.0] = 0.0
imarr2 = (imarr2 + np.max(imarr2) / dynamic_range)**(gamma)
unit = '(' + unit + ')^gamma'
if cbar_lims:
cbar_lims[0] = cbar_lims[0] / (10.**power)
cbar_lims[1] = cbar_lims[1] / (10.**power)
imarr2[imarr2 > cbar_lims[1]] = cbar_lims[1]
imarr2[imarr2 < cbar_lims[0]] = cbar_lims[0]
# polarization ticks
m = (np.abs(qvec + 1j * uvec) / imvec).reshape(self.ydim, self.xdim)
thin = self.xdim // nvec
maska = (imvec).reshape(self.ydim, self.xdim) > pcut * np.max(imvec)
maskb = (np.abs(qvec + 1j * uvec) / imvec).reshape(self.ydim, self.xdim) > mcut
mask = maska * maskb
mask2 = mask[::thin, ::thin]
x = (np.array([[i for i in range(self.xdim)]
for j in range(self.ydim)])[::thin, ::thin])
x = x[mask2]
y = (np.array([[j for i in range(self.xdim)]
for j in range(self.ydim)])[::thin, ::thin])
y = y[mask2]
a = (-np.sin(np.angle(qvec + 1j * uvec) /
2).reshape(self.ydim, self.xdim)[::thin, ::thin])
a = a[mask2]
b = (np.cos(np.angle(qvec + 1j * uvec) /
2).reshape(self.ydim, self.xdim)[::thin, ::thin])
b = b[mask2]
m = (np.abs(qvec + 1j * uvec) / imvec).reshape(self.ydim, self.xdim)
p = (np.abs(qvec + 1j * uvec)).reshape(self.ydim, self.xdim)
m[np.logical_not(mask)] = np.nan
p[np.logical_not(mask)] = np.nan
qarr[np.logical_not(mask)] = np.nan
uarr[np.logical_not(mask)] = np.nan
voi = (vvec / imvec).reshape(self.ydim, self.xdim)
voi[np.logical_not(mask)] = np.nan
if scale_ticks:
pticks = ((np.abs(qvec + 1j * uvec)).reshape(self.ydim, self.xdim))[::thin, ::thin][mask2]
pscale = (pticks - np.min(pticks))/(np.max(pticks) - np.min(pticks))
a *= pscale
b *= pscale
# Little pol plots
if plot_stokes:
maxval = 1.1 * np.max((np.max(np.abs(uarr)),
np.max(np.abs(qarr)), np.max(np.abs(varr))))
# P Plot
ax = plt.subplot2grid((2, 5), (0, 0))
im = plt.imshow(p, cmap=plt.get_cmap('bwr'), interpolation=interp,
vmin=-maxval, vmax=maxval)
plt.contour(imarr, colors='k', linewidths=.25)
ax.set_xticks([])
ax.set_yticks([])
if has_title:
plt.title('P')
if has_cbar:
cbaxes = plt.gcf().add_axes([0.1, 0.2, 0.01, 0.6])
cbar = plt.colorbar(im, fraction=0.046, pad=0.04, cax=cbaxes,
label=unit, orientation='vertical')
cbar.ax.tick_params(labelsize=cbar_fontsize)
cbaxes.yaxis.set_ticks_position('left')
cbaxes.yaxis.set_label_position('left')
if cbar_lims:
plt.clim(-maxval, maxval)
cmap = plt.get_cmap('bwr')
cmap.set_bad('whitesmoke')
# V Plot
ax = plt.subplot2grid((2, 5), (0, 1))
plt.imshow(varr, cmap=cmap, interpolation=interp,
vmin=-maxval, vmax=maxval)
ax.set_xticks([])
ax.set_yticks([])
if has_title:
plt.title('V')
# Q Plot
ax = plt.subplot2grid((2, 5), (1, 0))
plt.imshow(qarr, cmap=cmap, interpolation=interp,
vmin=-maxval, vmax=maxval)
plt.contour(imarr, colors='k', linewidths=.25)
ax.set_xticks([])
ax.set_yticks([])
if has_title:
plt.title('Q')
# U Plot
ax = plt.subplot2grid((2, 5), (1, 1))
plt.imshow(uarr, cmap=cmap, interpolation=interp,
vmin=-maxval, vmax=maxval)
plt.contour(imarr, colors='k', linewidths=.25)
ax.set_xticks([])
ax.set_yticks([])
if has_title:
plt.title('U')
# V/I plot
ax = plt.subplot2grid((2, 5), (0, 2))
cmap = plt.get_cmap('seismic')
cmap.set_bad('whitesmoke')
im = plt.imshow(voi, cmap=cmap, interpolation=interp,
vmin=-1, vmax=1)
if has_title:
plt.title('V/I')
plt.contour(imarr, colors='k', linewidths=.25)
ax.set_xticks([])
ax.set_yticks([])
if has_cbar:
cbaxes = plt.gcf().add_axes([0.125, 0.1, 0.425, 0.01])
cbar = plt.colorbar(im, fraction=0.046, pad=0.04, cax=cbaxes,
label='|m|', orientation='horizontal')
cbar.ax.tick_params(labelsize=cbar_fontsize)
cbaxes.yaxis.set_ticks_position('right')
cbaxes.yaxis.set_label_position('right')
if cbar_lims:
plt.clim(-1, 1)
# m plot
ax = plt.subplot2grid((2, 5), (1, 2))
plt.imshow(m, cmap=plt.get_cmap('seismic'), interpolation=interp, vmin=-1, vmax=1)
ax.set_xticks([])
ax.set_yticks([])
if has_title:
plt.title('m')
plt.contour(imarr, colors='k', linewidths=.25)
plt.quiver(x, y, a, b,
headaxislength=20, headwidth=1, headlength=.01, minlength=0, minshaft=1,
width=.01 * self.xdim, units='x', pivot='mid', color='k', angles='uv',
scale=1.0 / thin)
plt.quiver(x, y, a, b,
headaxislength=20, headwidth=1, headlength=.01, minlength=0, minshaft=1,
width=.005 * self.xdim, units='x', pivot='mid', color='w', angles='uv',
scale=1.1 / thin)
# Big Stokes I plot --axis
ax = plt.subplot2grid((2, 5), (0, 3), rowspan=2, colspan=2)
else:
ax = plt.gca()
if not cfun:
cfun = 'afmhot'
cmap = plt.get_cmap(cfun)
cmap.set_bad(color='whitesmoke')
# Big Stokes I plot
if cbar_lims:
im = plt.imshow(imarr2, cmap=cmap, interpolation=interp,
vmin=cbar_lims[0], vmax=cbar_lims[1])
else:
im = plt.imshow(imarr2, cmap, interpolation=interp)
if vec_cfun is None:
plt.quiver(x, y, a, b,
headaxislength=20, headwidth=1, headlength=.01, minlength=0, minshaft=1,
width=.01 * self.xdim, units='x', pivot='mid', color='k', angles='uv',
scale=1.0 / thin)
plt.quiver(x, y, a, b,
headaxislength=20, headwidth=1, headlength=.01, minlength=0, minshaft=1,
width=.005 * self.xdim, units='x', pivot='mid', color='w', angles='uv',
scale=1.1 / thin)
else:
mthin = (
np.abs(
qvec +
1j *
uvec) /
imvec).reshape(
self.ydim,
self.xdim)[
::thin,
::thin]
mthin = mthin[mask2]
plt.quiver(x, y, a, b,
headaxislength=20, headwidth=1, headlength=.01, minlength=0, minshaft=1,
width=.01 * self.xdim, units='x', pivot='mid', color='w', angles='uv',
scale=1.0 / thin)
plt.quiver(x, y, a, b, mthin,
norm=mpl.colors.Normalize(vmin=0, vmax=1.), cmap=vec_cfun,
headaxislength=20, headwidth=1, headlength=.01, minlength=0, minshaft=1,
width=.007 * self.xdim, units='x', pivot='mid', angles='uv',
scale=1.1 / thin)
if not(beamparams is None or beamparams is False):
beamparams = [beamparams[0], beamparams[1], beamparams[2],
-.35 * self.fovx(), -.35 * self.fovy()]
beamimage = self.copy()
beamimage.imvec *= 0
beamimage = beamimage.add_gauss(1, beamparams)
halflevel = 0.5 * np.max(beamimage.imvec)
beamimarr = (beamimage.imvec).reshape(beamimage.ydim, beamimage.xdim)
plt.contour(beamimarr, levels=[halflevel], colors=beamcolor, linewidths=beam_lw)
if has_cbar:
cbar = plt.colorbar(im, fraction=0.046, pad=0.04,
label=unit, orientation=cbar_orientation)
cbar.ax.tick_params(labelsize=cbar_fontsize)
if cbar_lims:
plt.clim(cbar_lims[0], cbar_lims[1])
if has_title:
plt.title("%s %.1f GHz : m=%.1f%% , v=%.1f%%" % (self.source, self.rf / 1e9,
self.lin_polfrac() * 100,
self.circ_polfrac() * 100),
fontsize=12)
f.subplots_adjust(hspace=.1, wspace=0.3)
# Label the plot
ax = plt.gca()
if label_type == 'ticks':
xticks = obsh.ticks(self.xdim, self.psize / ehc.RADPERAS / 1e-6)
yticks = obsh.ticks(self.ydim, self.psize / ehc.RADPERAS / 1e-6)
plt.xticks(xticks[0], xticks[1])
plt.yticks(yticks[0], yticks[1])
plt.xlabel(r'Relative RA ($\mu$as)')
plt.ylabel(r'Relative Dec ($\mu$as)')
elif label_type == 'scale':
plt.axis('off')
fov_uas = self.xdim * self.psize / ehc.RADPERUAS # get the fov in uas
roughfactor = 1. / 3. # make the bar about 1/3 the fov
fov_scale = int(math.ceil(fov_uas * roughfactor / 10.0)) * 10
start = self.xdim * roughfactor / 3.0 # select the start location
end = start + fov_scale / fov_uas * self.xdim # determine the end location
plt.plot([start, end], [self.ydim - start - 5, self.ydim - start - 5],
color=scalecolor, lw=scale_lw) # plot a line
plt.text(x=(start + end) / 2.0, y=self.ydim - start + self.ydim / 30,
s=str(fov_scale) + r" $\mu$as", color=scalecolor,
ha="center", va="center", fontsize=scale_fontsize)
ax = plt.gca()
if axis is None:
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
elif label_type == 'none' or label_type is None:
plt.axis('off')
ax = plt.gca()
if axis is None:
ax.axes.get_xaxis().set_visible(False)
ax.axes.get_yaxis().set_visible(False)
# Show or save to file
if axis is not None:
return axis
if show:
#plt.show(block=False)
ehc.show_noblock()
if export_pdf != "":
f.savefig(export_pdf, bbox_inches='tight', pad_inches=pdf_pad_inches, dpi=dpi)
return f
def overlay_display(self, im_list, color_coding=np.array([[1, 0, 1], [0, 1, 0]]),
export_pdf="", show=True, f=False,
shift=[0, 0], final_fov=False, interp='gaussian',
scale='lin', gamma=0.5, dynamic_range=[1.e3], rescale=True):
"""Overlay primary polarization images of a list of images to compare structures.
Args:
im_list (list): list of images to align to the current image
color_coding (numpy.array): Color coding of each image in the composite
f (matplotlib.pyplot.figure): Figure to overlay on top of
export_pdf (str): path to exported PDF with plot
show (bool): Display the plot if true
shift (list): list of manual image shifts,
otherwise use the shift from maximum cross-correlation
final_fov (float): fov of the comparison image (rad).
If False it is the largestinput image fov
scale (str) : compare images in 'log','lin',or 'gamma' scale
gamma (float): exponent for gamma scale comparison
dynamic_range (float): dynamic range for log and gamma scale comparisons
Returns:
(matplotlib.figure.Figure): figure object with image
"""
if not f:
f = plt.figure()
plt.clf()
if len(dynamic_range) == 1:
dynamic_range = dynamic_range * np.ones(len(im_list) + 1)
if not isinstance(shift, np.ndarray) and not isinstance(shift, bool):
shift = matlib.repmat(shift, len(im_list), 1)
psize = self.psize
max_fov = np.max([self.xdim * self.psize, self.ydim * self.psize])
for i in range(0, len(im_list)):
psize = np.min([psize, im_list[i].psize])
max_fov = np.max([max_fov, im_list[i].xdim * im_list[i].psize,
im_list[i].ydim * im_list[i].psize])
if not final_fov:
final_fov = max_fov
(im_list_shift, shifts, im0_pad) = self.align_images(im_list, shift=shift,
final_fov=final_fov,
scale=scale, gamma=gamma,
dynamic_range=dynamic_range)
# unit = 'Jy/pixel'
if scale == 'log':
# unit = 'log(Jy/pixel)'
log_offset = np.max(im0_pad.imvec) / dynamic_range[0]
im0_pad.imvec = np.log10(im0_pad.imvec + log_offset)
for i in range(0, len(im_list)):
log_offset = np.max(im_list_shift[i].imvec) / dynamic_range[i + 1]
im_list_shift[i].imvec = np.log10(im_list_shift[i].imvec + log_offset)
if scale == 'gamma':
# unit = '(Jy/pixel)^gamma'
log_offset = np.max(im0_pad.imvec) / dynamic_range[0]
im0_pad.imvec = (im0_pad.imvec + log_offset)**(gamma)
for i in range(0, len(im_list)):
log_offset = np.max(im_list_shift[i].imvec) / dynamic_range[i + 1]
im_list_shift[i].imvec = (im_list_shift[i].imvec + log_offset)**(gamma)
composite_img = np.zeros((im0_pad.ydim, im0_pad.xdim, 3))
for i in range(-1, len(im_list)):
if i == -1:
immtx = im0_pad.imvec.reshape(im0_pad.ydim, im0_pad.xdim)
else:
immtx = im_list_shift[i].imvec.reshape(im0_pad.ydim, im0_pad.xdim)
if rescale:
immtx = immtx - np.min(np.min(immtx))
immtx = immtx / np.max(np.max(immtx))
for c in range(0, 3):
composite_img[:, :, c] = composite_img[:, :, c] + (color_coding[i + 1, c] * immtx)
if rescale is False:
composite_img = composite_img - np.min(np.min(np.min(composite_img)))
composite_img = composite_img / np.max(np.max(np.max(composite_img)))
plt.subplot(111)
plt.title('%s MJD %i %.2f GHz' % (self.source, self.mjd, self.rf / 1e9), fontsize=20)
plt.imshow(composite_img, interpolation=interp)
xticks = obsh.ticks(im0_pad.xdim, im0_pad.psize / ehc.RADPERAS / 1e-6)
yticks = obsh.ticks(im0_pad.ydim, im0_pad.psize / ehc.RADPERAS / 1e-6)
plt.xticks(xticks[0], xticks[1])
plt.yticks(yticks[0], yticks[1])
plt.xlabel(r'Relative RA ($\mu$as)')
plt.ylabel(r'Relative Dec ($\mu$as)')
if show:
#plt.show(block=False)
ehc.show_noblock()
if export_pdf != "":
f.savefig(export_pdf, bbox_inches='tight')
return (f, shift)
def save_txt(self, fname):
"""Save image data to text file.
Args:
fname (str): path to output text file
Returns:
"""
ehtim.io.save.save_im_txt(self, fname)
return
def save_fits(self, fname):
"""Save image data to a fits file.
Args:
fname (str): path to output fits file
Returns:
"""
ehtim.io.save.save_im_fits(self, fname)
return
###################################################################################################
# Image creation functions
###################################################################################################
def make_square(obs, npix, fov, pulse=ehc.PULSE_DEFAULT, polrep='stokes', pol_prim=None):
"""Make an empty square image.
Args:
obs (Obsdata): an obsdata object with the image metadata
npix (int): the pixel size of each axis
fov (float): the field of view of each axis in radians
pulse (function): the function convolved with the pixel values for continuous image
polrep (str): polarization representation, either 'stokes' or 'circ'
pol_prim (str): The default image: I,Q,U or V for Stokes, or RR,LL,LR,RL for Circular
Returns:
(Image): an image object
"""
outim = make_empty(npix, fov, obs.ra, obs.dec, rf=obs.rf, source=obs.source,
polrep=polrep, pol_prim=pol_prim, pulse=pulse,
mjd=obs.mjd, time=obs.tstart)
return outim
def make_empty(npix, fov, ra, dec, rf=ehc.RF_DEFAULT, source=ehc.SOURCE_DEFAULT,
polrep='stokes', pol_prim=None, pulse=ehc.PULSE_DEFAULT,
mjd=ehc.MJD_DEFAULT, time=0.):
"""Make an empty square image.
Args:
npix (int): the pixel size of each axis
fov (float): the field of view of each axis in radians
ra (float): The source Right Ascension in fractional hours
dec (float): The source declination in fractional degrees
rf (float): The image frequency in Hz
source (str): The source name
polrep (str): polarization representation, either 'stokes' or 'circ'
pol_prim (str): The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular
pulse (function): The function convolved with the pixel values for continuous image.
mjd (int): The integer MJD of the image
time (float): The observing time of the image (UTC hours)
Returns:
(Image): an image object
"""
pdim = fov / float(npix)
npix = int(npix)
imarr = np.zeros((npix, npix))
outim = Image(imarr, pdim, ra, dec,
polrep=polrep, pol_prim=pol_prim,
rf=rf, source=source, mjd=mjd, time=time, pulse=pulse)
return outim
def load_image(image, display=False, aipscc=False):
"""Read in an image from a text, .fits, .h5, or ehtim.image.Image object
Args:
image (str/Image): path to input file
display (boolean): determine whether to display the image default
aipscc (boolean): if True, then AIPS CC table will be loaded instead
of the original brightness distribution.
Returns:
(Image): loaded image object
(boolean): False if the image cannot be read
"""
is_unicode = False
try:
if isinstance(image, basestring):
is_unicode = True
except NameError: # python 3
pass
if isinstance(image, str) or is_unicode:
if image.endswith('.fits'):
im = ehtim.io.load.load_im_fits(image, aipscc=aipscc)
elif image.endswith('.txt'):
im = ehtim.io.load.load_im_txt(image)
elif image.endswith('.h5'):
im = ehtim.io.load.load_im_hdf5(image)
else:
print("Image format is not recognized. Was expecting .fits, .txt, or Image.")
print(" Got <.{0}>. Returning False.".format(image.split('.')[-1]))
return False
elif isinstance(image, ehtim.image.Image):
im = image
else:
print("Image format is not recognized. Was expecting .fits, .txt, or Image.")
print(" Got {0}. Returning False.".format(type(image)))
return False
if display:
im.display()
return im
def load_txt(fname, polrep='stokes', pol_prim=None, pulse=ehc.PULSE_DEFAULT, zero_pol=True):
"""Read in an image from a text file.
Args:
fname (str): path to input text file
pulse (function): The function convolved with the pixel values for continuous image.
polrep (str): polarization representation, either 'stokes' or 'circ'
pol_prim (str): The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular
zero_pol (bool): If True, loads any missing polarizations as zeros
Returns:
(Image): loaded image object
"""
return ehtim.io.load.load_im_txt(fname, pulse=pulse, polrep=polrep,
pol_prim=pol_prim, zero_pol=True)
def load_fits(fname, aipscc=False, pulse=ehc.PULSE_DEFAULT,
polrep='stokes', pol_prim=None, zero_pol=False):
"""Read in an image from a FITS file.
Args:
fname (str): path to input fits file
aipscc (bool): if True, then AIPS CC table will be loaded
pulse (function): The function convolved with the pixel values for continuous image.
polrep (str): polarization representation, either 'stokes' or 'circ'
pol_prim (str): The default image: I,Q,U or V for Stokes, RR,LL,LR,RL for Circular
zero_pol (bool): If True, loads any missing polarizations as zeros
Returns:
(Image): loaded image object
"""
return ehtim.io.load.load_im_fits(fname, aipscc=aipscc, pulse=pulse,
polrep=polrep, pol_prim=pol_prim, zero_pol=zero_pol)
def avg_imlist(imlist):
"""Average a list of images.
Args:
imlist (list): list of image objects
Returns:
(Image): average image object
"""
imavg = imlist[0]
if np.any(np.array([im.polrep for im in imlist]) != imavg.polrep):
raise Exception("im.polrep in all images are not the same in avg_imlist!")
if np.any(np.array([im.source for im in imlist]) != imavg.source):
raise Exception("im.source in all images are not the same in avg_imlist!")
if np.any(np.array([im.rf for im in imlist]) != imavg.rf):
raise Exception("im.rf in all images are not the same in avg_imlist!")
keys = imavg._imdict.keys()
for im in imlist[1:]:
for key in keys:
imavg._imdict[key] += im._imdict[key]
for key in keys:
imavg._imdict[key] /= float(len(imlist))
return imavg
def get_specim(imlist, reffreq, fit_order=2):
"""get the spectral index/curvature from a list of images"""
freqs = [im.rf for im in imlist]
# remove any zeros in the images
for im in imlist:
im.imvec[im.imvec<=0] = np.min(im.imvec[im.imvec!=0])
# fit
xfit = np.log(np.array(freqs)/reffreq)
yfit = np.log(np.array([im.imvec for im in imlist]))
if fit_order == 2:
coeffs = np.polyfit(xfit,yfit,2)
beta = coeffs[0]
alpha = coeffs[1]
imvec = np.exp(coeffs[2])
elif fit_order == 1:
coeffs = np.polyfit(xfit,yfit,1)
alpha = coeffs[0]
beta = 0*alpha
imvec = np.exp(coeffs[1])
else:
raise Exception()
outim = imlist[0].copy() #TODO no polarization
outim.imvec = imvec
outim.rf = reffreq
outim.specvec = alpha
outim.curvvec = beta
return outim
def blur_mf(im,freqs,kernel,fit_order=2):
"""blur multifrequncy images with the same beam"""
reffreq = im.rf
# remove any zeros in the images
imlist = [im.get_image_mf(rf).blur_circ(kernel) for rf in freqs]
for image in imlist:
image.imvec[image.imvec<=0] = np.min(image.imvec[image.imvec!=0])
xfit = np.log(np.array(freqs)/reffreq)
yfit = np.log(np.array([im.imvec for im in imlist]))
if fit_order == 2:
coeffs = np.polyfit(xfit,yfit,2)
beta = coeffs[0]
alpha = coeffs[1]
elif fit_order == 1:
coeffs = np.polyfit(xfit,yfit,1)
alpha = coeffs[0]
beta = 0*alpha
else:
alpha = 0*yfit
beta = 0*yfit
outim = im.blur_circ(kernel)
outim.specvec = alpha
outim.curvvec = beta
return outim
|
achaelREPO_NAMEeht-imagingPATH_START.@eht-imaging_extracted@eht-imaging-main@ehtim@image.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/decorator/py2/tests/__init__.py",
"type": "Python"
}
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@decorator@py2@tests@__init__.py@.PATH_END.py
|
|
{
"filename": "fft_ops.py",
"repo_name": "simonsobs/sotodlib",
"repo_path": "sotodlib_extracted/sotodlib-master/sotodlib/tod_ops/fft_ops.py",
"type": "Python"
}
|
"""FFTs and related operations
"""
import sys
import numdifftools as ndt
import numpy as np
import pyfftw
import so3g
from scipy.optimize import minimize
from scipy.signal import welch
from sotodlib import core
from . import detrend_tod
def _get_num_threads():
# Guess how many threads we should be using in FFT ops...
return so3g.useful_info().get("omp_num_threads", 4)
def rfft(
aman,
detrend="linear",
resize="zero_pad",
window=np.hanning,
axis_name="samps",
signal_name="signal",
delta_t=None,
):
"""Return the real fft of aman.signal_name along the axis axis_name.
Does not change the data in the axis manager.
Arguments:
aman: axis manager
detrend: Method of detrending to be done before ffting. Can
be 'linear', 'mean', or None. Note that detrending here can be slow
for large arrays.
resize: How to resize the axis to increase fft speed. 'zero_pad'
will increase to the next 2**N. 'trim' will cut out so the
factorization of N contains only low primes. None will not
change the axis length and might be quite slow.
window: a function that takes N are returns an fft window
Can be None if no windowing
axis_name: name of axis you would like to fft along
signal_name: name of the variable in aman to fft
delta_t: if none, it will look for 'timestamps' in the axis manager
and will otherwise assume 1. if not None, it should be the
sampling rate along the axis you're ffting
Returns:
fft: the fft'd data
freqs: the frequencies it is value at (since resizing is an option)
"""
if len(aman._assignments[signal_name]) > 2:
raise ValueError("rfft only works for 1D or 2D data streams")
axis = getattr(aman, axis_name)
if len(aman._assignments[signal_name]) == 1:
n_det = 1
main_idx = 0
other_idx = None
elif len(aman._assignments[signal_name]) == 2:
checks = np.array(
[x == axis_name for x in aman._assignments[signal_name]], dtype="bool"
)
main_idx = np.where(checks)[0][0]
other_idx = np.where(~checks)[0][0]
other_axis = getattr(aman, aman._assignments[signal_name][other_idx])
n_det = other_axis.count
if detrend is None:
signal = np.atleast_2d(getattr(aman, signal_name))
else:
signal = detrend_tod(
aman, detrend, axis_name=axis_name, signal_name=signal_name, in_place=True
)
if other_idx is not None and other_idx != 0:
signal = signal.transpose()
if window is not None:
signal = signal * window(axis.count)[None, :]
if resize == "zero_pad":
k = int(np.ceil(np.log(axis.count) / np.log(2)))
n = 2**k
elif resize == "trim":
n = find_inferior_integer(axis.count)
elif resize is None:
n = axis.count
else:
raise ValueError('resize must be "zero_pad", "trim", or None')
a, b, t_fun = build_rfft_object(n_det, n, "FFTW_FORWARD")
if resize == "zero_pad":
a[:, : axis.count] = signal
a[:, axis.count :] = 0
elif resize == "trim":
a[:] = signal[:, :n]
else:
a[:] = signal[:]
t_fun()
if delta_t is None:
if "timestamps" in aman:
delta_t = (aman.timestamps[-1] - aman.timestamps[0]) / axis.count
else:
delta_t = 1
freqs = np.fft.rfftfreq(n, delta_t)
if other_idx is not None and other_idx != 0:
return b.transpose(), freqs
return b, freqs
def build_rfft_object(n_det, n, direction="FFTW_FORWARD", **kwargs):
"""Build PyFFTW object for fft-ing
Arguments:
n_det: number of detectors (or just the arr.shape[0] for the
array you are going to fft)
n: number of samples in timestream
direction: fft direction. Can be FFTW_FORWARD, FFTW_BACKWARD, or BOTH
kwargs: additional arguments to pass to pyfftw.FFTW
Returns:
a: array for the real valued side of the fft
b: array for the the complex side of the fft
t_fun: function for performing FFT (two are returned if direction=='BOTH')
"""
fftargs = {"threads": _get_num_threads(), "flags": ["FFTW_ESTIMATE"]}
fftargs.update(kwargs)
a = pyfftw.empty_aligned((n_det, n), dtype="float32")
b = pyfftw.empty_aligned((n_det, (n + 2) // 2), dtype="complex64")
if direction == "FFTW_FORWARD":
t_fun = pyfftw.FFTW(a, b, direction=direction, **fftargs)
elif direction == "FFTW_BACKWARD":
t_fun = pyfftw.FFTW(b, a, direction=direction, **fftargs)
elif direction == "BOTH":
t_1 = pyfftw.FFTW(a, b, direction="FFTW_FORWARD", **fftargs)
t_2 = pyfftw.FFTW(b, a, direction="FFTW_BACKWARD", **fftargs)
return a, b, t_1, t_2
else:
raise ValueError("direction must be FFTW_FORWARD or FFTW_BACKWARD")
return a, b, t_fun
def find_inferior_integer(target, primes=[2, 3, 5, 7, 11, 13]):
"""Find the largest integer less than or equal to target whose prime
factorization contains only the integers listed in primes.
"""
p = primes[0]
n = np.floor(np.log(target) / np.log(p))
best = p**n
if len(primes) == 1:
return int(best)
while n > 0:
n -= 1
base = p**n
best_friend = find_inferior_integer(target / base, primes[1:])
if (best_friend * base) >= best:
best = best_friend * base
return int(best)
def find_superior_integer(target, primes=[2, 3, 5, 7, 11, 13]):
"""Find the smallest integer less than or equal to target whose prime
factorization contains only the integers listed in primes.
"""
p = primes[0]
n = np.ceil(np.log(target) / np.log(p))
best = p**n
if len(primes) == 1:
return int(best)
while n > 0:
n -= 1
base = p**n
best_friend = find_superior_integer(target / base, primes[1:])
if (best_friend * base) <= best:
best = best_friend * base
return int(best)
def calc_psd(
aman,
signal=None,
timestamps=None,
max_samples=2**18,
prefer='center',
freq_spacing=None,
merge=False,
overwrite=True,
subscan=False,
**kwargs
):
"""Calculates the power spectrum density of an input signal using signal.welch().
Data defaults to aman.signal and times defaults to aman.timestamps.
By default the nperseg will be set to power of 2 closest to the 1/50th of
the samples used, this can be overridden by providing nperseg or freq_spacing.
Arguments:
aman (AxisManager): with (dets, samps) OR (channels, samps)axes.
signal (float ndarray): data signal to pass to scipy.signal.welch().
timestamps (float ndarray): timestamps associated with the data signal.
max_samples (int): maximum samples along sample axis to send to welch.
prefer (str): One of ['left', 'right', 'center'], indicating what
part of the array we would like to send to welch if cuts are
required.
freq_spacing (float): The approximate desired frequency spacing of the PSD.
If None the default nperseg of ~1/50th the signal length is used.
If an nperseg is explicitly passed then that will be used.
merge (bool): if True merge results into axismanager.
overwrite (bool): if true will overwrite f, pxx axes.
subscan (bool): if True, compute psd on subscans.
**kwargs: keyword args to be passed to signal.welch().
Returns:
freqs: array of frequencies corresponding to PSD calculated from welch.
Pxx: array of PSD values.
"""
if signal is None:
signal = aman.signal
if subscan:
freqs, Pxx = _calc_psd_subscan(aman, signal=signal, freq_spacing=freq_spacing, **kwargs)
axis_map_pxx = [(0, "dets"), (1, "nusamps"), (2, "subscans")]
else:
if timestamps is None:
timestamps = aman.timestamps
n_samps = signal.shape[-1]
if n_samps <= max_samples:
start = 0
stop = n_samps
else:
offset = n_samps - max_samples
if prefer == "left":
offset = 0
elif prefer == "center":
offset //= 2
elif prefer == "right":
pass
else:
raise ValueError(f"Invalid choice prefer='{prefer}'")
start = offset
stop = offset + max_samples
fs = 1 / np.nanmedian(np.diff(timestamps[start:stop]))
if "nperseg" not in kwargs:
if freq_spacing is not None:
nperseg = int(2 ** (np.around(np.log2(fs / freq_spacing))))
else:
nperseg = int(2 ** (np.around(np.log2((stop - start) / 50.0))))
kwargs["nperseg"] = nperseg
freqs, Pxx = welch(signal[:, start:stop], fs, **kwargs)
axis_map_pxx = [(0, aman.dets), (1, "nusamps")]
if merge:
aman.merge( core.AxisManager(core.OffsetAxis("nusamps", len(freqs))))
if overwrite:
if "freqs" in aman._fields:
aman.move("freqs", None)
if "Pxx" in aman._fields:
aman.move("Pxx", None)
aman.wrap("freqs", freqs, [(0,"nusamps")])
aman.wrap("Pxx", Pxx, axis_map_pxx)
return freqs, Pxx
def _calc_psd_subscan(aman, signal=None, freq_spacing=None, **kwargs):
"""
Calculate the power spectrum density of subscans using signal.welch().
Data defaults to aman.signal. aman.timestamps is used for times.
aman.subscan_info is used to identify subscans.
See calc_psd for arguments.
"""
from .flags import get_subscan_signal
if signal is None:
signal = aman.signal
fs = 1 / np.nanmedian(np.diff(aman.timestamps))
if "nperseg" not in kwargs:
if freq_spacing is not None:
nperseg = int(2 ** (np.around(np.log2(fs / freq_spacing))))
else:
duration_samps = np.asarray([np.ptp(x.ranges()) if x.ranges().size > 0 else 0 for x in aman.subscan_info.subscan_flags])
duration_samps = duration_samps[duration_samps > 0]
nperseg = int(2 ** (np.around(np.log2(np.median(duration_samps) / 4))))
kwargs["nperseg"] = nperseg
Pxx = []
for iss in range(aman.subscan_info.subscans.count):
signal_ss = get_subscan_signal(aman, signal, iss)
axis = -1 if "axis" not in kwargs else kwargs["axis"]
if signal_ss.shape[axis] >= kwargs["nperseg"]:
freqs, pxx_sub = welch(signal_ss, fs, **kwargs)
Pxx.append(pxx_sub)
else:
Pxx.append(np.full((signal.shape[0], kwargs["nperseg"]//2+1), np.nan)) # Add nans if subscan is too short
Pxx = np.array(Pxx)
Pxx = Pxx.transpose(1, 2, 0) # Dets, nusamps, subscans
return freqs, Pxx
def calc_wn(aman, pxx=None, freqs=None, low_f=5, high_f=10):
"""
Function that calculates the white noise level as a median PSD value between
two frequencies. Defaults to calculation of white noise between 5 and 10Hz.
Defaults frequency information to a wrapped "freqs" field in aman.
Arguments
---------
aman (AxisManager):
Uses aman.freq as frequency information associated with the PSD, pxx.
pxx (Float array):
Psd information to calculate white noise. Defaults to aman.pxx
freqs (1d Float array):
frequency information related to the psd. Defaults to aman.freqs
low_f (Float):
low frequency cutoff to calculate median psd value. Defaults to 5Hz
high_f (float):
high frequency cutoff to calculate median psd value. Defaults to 10Hz
Returns
-------
wn: Float array of white noise levels for each psd passed into argument.
"""
if freqs is None:
freqs = aman.freqs
if pxx is None:
pxx = aman.Pxx
fmsk = np.all([freqs >= low_f, freqs <= high_f], axis=0)
if pxx.ndim == 1:
wn2 = np.median(pxx[fmsk])
else:
wn2 = np.median(pxx[:, fmsk], axis=1)
wn = np.sqrt(wn2)
return wn
def noise_model(f, p):
"""
Noise model for power spectrum with white noise, and 1/f noise.
"""
fknee, w, alpha = p[0], p[1], p[2]
return w * (1 + (fknee / f) ** alpha)
def neglnlike(params, x, y):
model = noise_model(x, params)
output = np.sum(np.log(model) + y / model)
if not np.isfinite(output):
return 1.0e30
return output
def fit_noise_model(
aman,
signal=None,
f=None,
pxx=None,
psdargs={},
fwhite=(10, 100),
lowf=1,
merge_fit=False,
f_max=100,
merge_name="noise_fit_stats",
merge_psd=True,
freq_spacing=None,
subscan=False
):
"""
Fits noise model with white and 1/f noise to the PSD of signal.
This uses a MLE method that minimizes a log likelihood. This is
better for chi^2 distributed data like the PSD.
Reference: http://keatonb.github.io/archivers/powerspectrumfits
Args
----
aman : AxisManager
Axis manager which has samps axis aligned with signal.
signal : nparray
Signal sized ndets x nsamps to fit noise model to.
Default is None which corresponds to aman.signal.
f : nparray
Frequency of PSD of signal.
Default is None which calculates f, pxx from signal.
pxx : nparray
PSD sized ndets x len(f) which is fit to with model.
Default is None which calculates f, pxx from signal.
psdargs : dict
Dictionary of optional argument for ``scipy.signal.welch``
fwhite : tuple
Low and high frequency used to estimate white noise for initial
guess passed to ``scipy.signal.curve_fit``.
lowf : tuple
Frequency below which estimate of 1/f noise index and knee are estimated
for initial guess passed to ``scipy.signal.curve_fit``.
merge_fit : bool
Merges fit and fit statistics into input axis manager.
f_max : float
Maximum frequency to include in the fitting. This is particularly
important for lowpass filtered data such as that post demodulation
if the data is not downsampled after lowpass filtering.
merge_name : bool
If ``merge_fit`` is True then addes into axis manager with merge_name.
merge_psd : bool
If ``merg_psd`` is True then adds fres and Pxx to the axis manager.
freq_spacing : float
The approximate desired frequency spacing of the PSD. Passed to calc_psd.
subscan : bool
If True, fit noise on subscans.
Returns
-------
noise_fit_stats : AxisManager
If merge_fit is False then axis manager with fit and fit statistics
is returned otherwise nothing is returned and axis manager is wrapped
into input aman.
"""
if signal is None:
signal = aman.signal
if f is None or pxx is None:
f, pxx = calc_psd(
aman,
signal=signal,
timestamps=aman.timestamps,
freq_spacing=freq_spacing,
merge=merge_psd,
subscan=subscan,
**psdargs,
)
if subscan:
fitout, covout = _fit_noise_model_subscan(aman, signal, f, pxx, psdargs=psdargs,
fwhite=fwhite, lowf=lowf, f_max=f_max,
freq_spacing=freq_spacing)
axis_map_fit = [(0, "dets"), (1, "noise_model_coeffs"), (2, aman.subscans)]
axis_map_cov = [(0, "dets"), (1, "noise_model_coeffs"), (2, "noise_model_coeffs"), (3, aman.subscans)]
else:
eix = np.argmin(np.abs(f - f_max))
f = f[1:eix]
pxx = pxx[:, 1:eix]
fitout = np.zeros((aman.dets.count, 3))
# This is equal to np.sqrt(np.diag(cov)) when doing curve_fit
covout = np.zeros((aman.dets.count, 3, 3))
for i in range(aman.dets.count):
p = pxx[i]
wnest = np.median(p[((f > fwhite[0]) & (f < fwhite[1]))])
pfit = np.polyfit(np.log10(f[f < lowf]), np.log10(p[f < lowf]), 1)
fidx = np.argmin(np.abs(10 ** np.polyval(pfit, np.log10(f)) - wnest))
p0 = [f[fidx], wnest, -pfit[0]]
bounds = [(0, None), (sys.float_info.min, None), (None, None)]
res = minimize(neglnlike, p0, args=(f, p), bounds=bounds, method="Nelder-Mead")
try:
Hfun = ndt.Hessian(lambda params: neglnlike(params, f, p), full_output=True)
hessian_ndt, _ = Hfun(res["x"])
# Inverse of the hessian is an estimator of the covariance matrix
# sqrt of the diagonals gives you the standard errors.
covout[i] = np.linalg.inv(hessian_ndt)
except np.linalg.LinAlgError:
covout[i] = np.full((3, 3), np.nan)
fitout[i] = res.x
axis_map_fit = [(0, "dets"), (1, "noise_model_coeffs")]
axis_map_cov = [(0, "dets"), (1, "noise_model_coeffs"), (2, "noise_model_coeffs")]
noise_model_coeffs = ["fknee", "white_noise", "alpha"]
noise_fit_stats = core.AxisManager(
aman.dets,
core.LabelAxis(
name="noise_model_coeffs", vals=np.array(noise_model_coeffs, dtype="<U8")
),
)
noise_fit_stats.wrap("fit", fitout, axis_map_fit)
noise_fit_stats.wrap("cov", covout, axis_map_cov)
if merge_fit:
aman.wrap(merge_name, noise_fit_stats)
return noise_fit_stats
def _fit_noise_model_subscan(
aman,
signal,
f,
pxx,
psdargs={},
fwhite=(10, 100),
lowf=1,
f_max=100,
freq_spacing=None,
):
"""
Fits noise model with white and 1/f noise to the PSD of signal subscans.
Args are as for fit_noise_model.
"""
fitout = np.empty((aman.dets.count, 3, aman.subscan_info.subscans.count))
covout = np.empty((aman.dets.count, 3, 3, aman.subscan_info.subscans.count))
for isub in range(aman.subscan_info.subscans.count):
if np.all(np.isnan(pxx[...,isub])): # Subscan has been fully cut
fitout[..., isub] = np.full((aman.dets.count, 3), np.nan)
covout[..., isub] = np.full((aman.dets.count, 3, 3), np.nan)
else:
noise_model = fit_noise_model(aman, f=f, pxx=pxx[...,isub], fwhite=fwhite, lowf=lowf, merge_fit=False,
f_max=f_max, merge_psd=False, subscan=False)
fitout[..., isub] = noise_model.fit
covout[..., isub] = noise_model.cov
return fitout, covout
def build_hpf_params_dict(
filter_name,
noise_fit=None,
filter_params=None
):
"""
Build the filter parameter dictionary from a provided
dictionary or from noise fit results.
Args
----
filter_name : str
Name of the filter to build the parameter dict for.
noise_fit: AxisManager
AxisManager containing the result of the noise model fit sized nparams x ndets.
filter_params: dict
Filter parameters dictionary to complement parameters
derived from the noise fit (or to be used if noise fit is None).
Returns
-------
filter_params : dict
Returns a dictionary of the median values of the noise model fit parameters
if noise_fit is not None, otherwise return the provided filter_params.
"""
if noise_fit is not None:
pars_mapping = {
"high_pass_butter4": {
"fc": "fknee",
},
"counter_1_over_f": {
"fk": "fknee",
"n": "alpha"
},
"high_pass_sine2": {
"cutoff": "fknee",
"width": None
}
}
if filter_name not in pars_mapping.keys():
raise NotImplementedError(
f"{filter_name} params from noise fit is not implemented"
)
noise_fit_array = noise_fit.fit
noise_fit_params = noise_fit.noise_model_coeffs.vals
median_params = np.median(noise_fit_array, axis=0)
median_dict = {
k: median_params[i]
for i, k in enumerate(noise_fit_params)
}
params_dict = {}
for k, v in pars_mapping[filter_name].items():
if v is None:
if (filter_params is None) or (k not in filter_params):
raise ValueError(
f"Required parameters {k} not found in config "
"and cannot be derived from noise fit."
)
else:
params_dict.update({k: filter_params[k]})
else:
params_dict[k] = median_dict[v]
filter_params = params_dict
return filter_params
|
simonsobsREPO_NAMEsotodlibPATH_START.@sotodlib_extracted@sotodlib-master@sotodlib@tod_ops@fft_ops.py@.PATH_END.py
|
{
"filename": "multikernelmanager.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/jupyter-client/py3/jupyter_client/multikernelmanager.py",
"type": "Python"
}
|
"""A kernel manager for multiple kernels"""
# Copyright (c) Jupyter Development Team.
# Distributed under the terms of the Modified BSD License.
from __future__ import annotations
import asyncio
import json
import os
import socket
import typing as t
import uuid
from functools import wraps
from pathlib import Path
import zmq
from traitlets import Any, Bool, Dict, DottedObjectName, Instance, Unicode, default, observe
from traitlets.config.configurable import LoggingConfigurable
from traitlets.utils.importstring import import_item
from .connect import KernelConnectionInfo
from .kernelspec import NATIVE_KERNEL_NAME, KernelSpecManager
from .manager import KernelManager
from .utils import ensure_async, run_sync, utcnow
class DuplicateKernelError(Exception):
pass
def kernel_method(f: t.Callable) -> t.Callable:
"""decorator for proxying MKM.method(kernel_id) to individual KMs by ID"""
@wraps(f)
def wrapped(
self: t.Any, kernel_id: str, *args: t.Any, **kwargs: t.Any
) -> t.Callable | t.Awaitable:
# get the kernel
km = self.get_kernel(kernel_id)
method = getattr(km, f.__name__)
# call the kernel's method
r = method(*args, **kwargs)
# last thing, call anything defined in the actual class method
# such as logging messages
f(self, kernel_id, *args, **kwargs)
# return the method result
return r
return wrapped
class MultiKernelManager(LoggingConfigurable):
"""A class for managing multiple kernels."""
default_kernel_name = Unicode(
NATIVE_KERNEL_NAME, help="The name of the default kernel to start"
).tag(config=True)
kernel_spec_manager = Instance(KernelSpecManager, allow_none=True)
kernel_manager_class = DottedObjectName(
"jupyter_client.ioloop.IOLoopKernelManager",
help="""The kernel manager class. This is configurable to allow
subclassing of the KernelManager for customized behavior.
""",
).tag(config=True)
@observe("kernel_manager_class")
def _kernel_manager_class_changed(self, change: t.Any) -> None:
self.kernel_manager_factory = self._create_kernel_manager_factory()
kernel_manager_factory = Any(help="this is kernel_manager_class after import")
@default("kernel_manager_factory")
def _kernel_manager_factory_default(self) -> t.Callable:
return self._create_kernel_manager_factory()
def _create_kernel_manager_factory(self) -> t.Callable:
kernel_manager_ctor = import_item(self.kernel_manager_class)
def create_kernel_manager(*args: t.Any, **kwargs: t.Any) -> KernelManager:
if self.shared_context:
if self.context.closed:
# recreate context if closed
self.context = self._context_default()
kwargs.setdefault("context", self.context)
km = kernel_manager_ctor(*args, **kwargs)
return km
return create_kernel_manager
shared_context = Bool(
True,
help="Share a single zmq.Context to talk to all my kernels",
).tag(config=True)
context = Instance("zmq.Context")
_created_context = Bool(False)
_pending_kernels = Dict()
@property
def _starting_kernels(self) -> dict:
"""A shim for backwards compatibility."""
return self._pending_kernels
@default("context")
def _context_default(self) -> zmq.Context:
self._created_context = True
return zmq.Context()
connection_dir = Unicode("")
external_connection_dir = Unicode(None, allow_none=True)
_kernels = Dict()
def __init__(self, *args: t.Any, **kwargs: t.Any) -> None:
super().__init__(*args, **kwargs)
self.kernel_id_to_connection_file: dict[str, Path] = {}
def __del__(self) -> None:
"""Handle garbage collection. Destroy context if applicable."""
if self._created_context and self.context and not self.context.closed:
if self.log:
self.log.debug("Destroying zmq context for %s", self)
self.context.destroy()
try:
super_del = super().__del__ # type:ignore[misc]
except AttributeError:
pass
else:
super_del()
def list_kernel_ids(self) -> list[str]:
"""Return a list of the kernel ids of the active kernels."""
if self.external_connection_dir is not None:
external_connection_dir = Path(self.external_connection_dir)
if external_connection_dir.is_dir():
connection_files = [p for p in external_connection_dir.iterdir() if p.is_file()]
# remove kernels (whose connection file has disappeared) from our list
k = list(self.kernel_id_to_connection_file.keys())
v = list(self.kernel_id_to_connection_file.values())
for connection_file in list(self.kernel_id_to_connection_file.values()):
if connection_file not in connection_files:
kernel_id = k[v.index(connection_file)]
del self.kernel_id_to_connection_file[kernel_id]
del self._kernels[kernel_id]
# add kernels (whose connection file appeared) to our list
for connection_file in connection_files:
if connection_file in self.kernel_id_to_connection_file.values():
continue
try:
connection_info: KernelConnectionInfo = json.loads(
connection_file.read_text()
)
except Exception: # noqa: S112
continue
self.log.debug("Loading connection file %s", connection_file)
if not ("kernel_name" in connection_info and "key" in connection_info):
continue
# it looks like a connection file
kernel_id = self.new_kernel_id()
self.kernel_id_to_connection_file[kernel_id] = connection_file
km = self.kernel_manager_factory(
parent=self,
log=self.log,
owns_kernel=False,
)
km.load_connection_info(connection_info)
km.last_activity = utcnow()
km.execution_state = "idle"
km.connections = 1
km.kernel_id = kernel_id
km.kernel_name = connection_info["kernel_name"]
km.ready.set_result(None)
self._kernels[kernel_id] = km
# Create a copy so we can iterate over kernels in operations
# that delete keys.
return list(self._kernels.keys())
def __len__(self) -> int:
"""Return the number of running kernels."""
return len(self.list_kernel_ids())
def __contains__(self, kernel_id: str) -> bool:
return kernel_id in self._kernels
def pre_start_kernel(
self, kernel_name: str | None, kwargs: t.Any
) -> tuple[KernelManager, str, str]:
# kwargs should be mutable, passing it as a dict argument.
kernel_id = kwargs.pop("kernel_id", self.new_kernel_id(**kwargs))
if kernel_id in self:
raise DuplicateKernelError("Kernel already exists: %s" % kernel_id)
if kernel_name is None:
kernel_name = self.default_kernel_name
# kernel_manager_factory is the constructor for the KernelManager
# subclass we are using. It can be configured as any Configurable,
# including things like its transport and ip.
constructor_kwargs = {}
if self.kernel_spec_manager:
constructor_kwargs["kernel_spec_manager"] = self.kernel_spec_manager
km = self.kernel_manager_factory(
connection_file=os.path.join(self.connection_dir, "kernel-%s.json" % kernel_id),
parent=self,
log=self.log,
kernel_name=kernel_name,
**constructor_kwargs,
)
return km, kernel_name, kernel_id
def update_env(self, *, kernel_id: str, env: t.Dict[str, str]) -> None:
"""
Allow to update the environment of the given kernel.
Forward the update env request to the corresponding kernel.
.. version-added: 8.5
"""
if kernel_id in self:
self._kernels[kernel_id].update_env(env=env)
async def _add_kernel_when_ready(
self, kernel_id: str, km: KernelManager, kernel_awaitable: t.Awaitable
) -> None:
try:
await kernel_awaitable
self._kernels[kernel_id] = km
self._pending_kernels.pop(kernel_id, None)
except Exception as e:
self.log.exception(e)
async def _remove_kernel_when_ready(
self, kernel_id: str, kernel_awaitable: t.Awaitable
) -> None:
try:
await kernel_awaitable
self.remove_kernel(kernel_id)
self._pending_kernels.pop(kernel_id, None)
except Exception as e:
self.log.exception(e)
def _using_pending_kernels(self) -> bool:
"""Returns a boolean; a clearer method for determining if
this multikernelmanager is using pending kernels or not
"""
return getattr(self, "use_pending_kernels", False)
async def _async_start_kernel(self, *, kernel_name: str | None = None, **kwargs: t.Any) -> str:
"""Start a new kernel.
The caller can pick a kernel_id by passing one in as a keyword arg,
otherwise one will be generated using new_kernel_id().
The kernel ID for the newly started kernel is returned.
"""
km, kernel_name, kernel_id = self.pre_start_kernel(kernel_name, kwargs)
if not isinstance(km, KernelManager):
self.log.warning( # type:ignore[unreachable]
"Kernel manager class ({km_class}) is not an instance of 'KernelManager'!".format(
km_class=self.kernel_manager_class.__class__
)
)
kwargs["kernel_id"] = kernel_id # Make kernel_id available to manager and provisioner
starter = ensure_async(km.start_kernel(**kwargs))
task = asyncio.create_task(self._add_kernel_when_ready(kernel_id, km, starter))
self._pending_kernels[kernel_id] = task
# Handling a Pending Kernel
if self._using_pending_kernels():
# If using pending kernels, do not block
# on the kernel start.
self._kernels[kernel_id] = km
else:
await task
# raise an exception if one occurred during kernel startup.
if km.ready.exception():
raise km.ready.exception() # type: ignore[misc]
return kernel_id
start_kernel = run_sync(_async_start_kernel)
async def _async_shutdown_kernel(
self,
kernel_id: str,
now: bool | None = False,
restart: bool | None = False,
) -> None:
"""Shutdown a kernel by its kernel uuid.
Parameters
==========
kernel_id : uuid
The id of the kernel to shutdown.
now : bool
Should the kernel be shutdown forcibly using a signal.
restart : bool
Will the kernel be restarted?
"""
self.log.info("Kernel shutdown: %s", kernel_id)
# If the kernel is still starting, wait for it to be ready.
if kernel_id in self._pending_kernels:
task = self._pending_kernels[kernel_id]
try:
await task
km = self.get_kernel(kernel_id)
await t.cast(asyncio.Future, km.ready)
except asyncio.CancelledError:
pass
except Exception:
self.remove_kernel(kernel_id)
return
km = self.get_kernel(kernel_id)
# If a pending kernel raised an exception, remove it.
if not km.ready.cancelled() and km.ready.exception():
self.remove_kernel(kernel_id)
return
stopper = ensure_async(km.shutdown_kernel(now, restart))
fut = asyncio.ensure_future(self._remove_kernel_when_ready(kernel_id, stopper))
self._pending_kernels[kernel_id] = fut
# Await the kernel if not using pending kernels.
if not self._using_pending_kernels():
await fut
# raise an exception if one occurred during kernel shutdown.
if km.ready.exception():
raise km.ready.exception() # type: ignore[misc]
shutdown_kernel = run_sync(_async_shutdown_kernel)
@kernel_method
def request_shutdown(self, kernel_id: str, restart: bool | None = False) -> None:
"""Ask a kernel to shut down by its kernel uuid"""
@kernel_method
def finish_shutdown(
self,
kernel_id: str,
waittime: float | None = None,
pollinterval: float | None = 0.1,
) -> None:
"""Wait for a kernel to finish shutting down, and kill it if it doesn't"""
self.log.info("Kernel shutdown: %s", kernel_id)
@kernel_method
def cleanup_resources(self, kernel_id: str, restart: bool = False) -> None:
"""Clean up a kernel's resources"""
def remove_kernel(self, kernel_id: str) -> KernelManager:
"""remove a kernel from our mapping.
Mainly so that a kernel can be removed if it is already dead,
without having to call shutdown_kernel.
The kernel object is returned, or `None` if not found.
"""
return self._kernels.pop(kernel_id, None)
async def _async_shutdown_all(self, now: bool = False) -> None:
"""Shutdown all kernels."""
kids = self.list_kernel_ids()
kids += list(self._pending_kernels)
kms = list(self._kernels.values())
futs = [self._async_shutdown_kernel(kid, now=now) for kid in set(kids)]
await asyncio.gather(*futs)
# If using pending kernels, the kernels will not have been fully shut down.
if self._using_pending_kernels():
for km in kms:
try:
await km.ready
except asyncio.CancelledError:
self._pending_kernels[km.kernel_id].cancel()
except Exception:
# Will have been logged in _add_kernel_when_ready
pass
shutdown_all = run_sync(_async_shutdown_all)
def interrupt_kernel(self, kernel_id: str) -> None:
"""Interrupt (SIGINT) the kernel by its uuid.
Parameters
==========
kernel_id : uuid
The id of the kernel to interrupt.
"""
kernel = self.get_kernel(kernel_id)
if not kernel.ready.done():
msg = "Kernel is in a pending state. Cannot interrupt."
raise RuntimeError(msg)
out = kernel.interrupt_kernel()
self.log.info("Kernel interrupted: %s", kernel_id)
return out
@kernel_method
def signal_kernel(self, kernel_id: str, signum: int) -> None:
"""Sends a signal to the kernel by its uuid.
Note that since only SIGTERM is supported on Windows, this function
is only useful on Unix systems.
Parameters
==========
kernel_id : uuid
The id of the kernel to signal.
signum : int
Signal number to send kernel.
"""
self.log.info("Signaled Kernel %s with %s", kernel_id, signum)
async def _async_restart_kernel(self, kernel_id: str, now: bool = False) -> None:
"""Restart a kernel by its uuid, keeping the same ports.
Parameters
==========
kernel_id : uuid
The id of the kernel to interrupt.
now : bool, optional
If True, the kernel is forcefully restarted *immediately*, without
having a chance to do any cleanup action. Otherwise the kernel is
given 1s to clean up before a forceful restart is issued.
In all cases the kernel is restarted, the only difference is whether
it is given a chance to perform a clean shutdown or not.
"""
kernel = self.get_kernel(kernel_id)
if self._using_pending_kernels() and not kernel.ready.done():
msg = "Kernel is in a pending state. Cannot restart."
raise RuntimeError(msg)
await ensure_async(kernel.restart_kernel(now=now))
self.log.info("Kernel restarted: %s", kernel_id)
restart_kernel = run_sync(_async_restart_kernel)
@kernel_method
def is_alive(self, kernel_id: str) -> bool: # type:ignore[empty-body]
"""Is the kernel alive.
This calls KernelManager.is_alive() which calls Popen.poll on the
actual kernel subprocess.
Parameters
==========
kernel_id : uuid
The id of the kernel.
"""
def _check_kernel_id(self, kernel_id: str) -> None:
"""check that a kernel id is valid"""
if kernel_id not in self:
raise KeyError("Kernel with id not found: %s" % kernel_id)
def get_kernel(self, kernel_id: str) -> KernelManager:
"""Get the single KernelManager object for a kernel by its uuid.
Parameters
==========
kernel_id : uuid
The id of the kernel.
"""
self._check_kernel_id(kernel_id)
return self._kernels[kernel_id]
@kernel_method
def add_restart_callback(
self, kernel_id: str, callback: t.Callable, event: str = "restart"
) -> None:
"""add a callback for the KernelRestarter"""
@kernel_method
def remove_restart_callback(
self, kernel_id: str, callback: t.Callable, event: str = "restart"
) -> None:
"""remove a callback for the KernelRestarter"""
@kernel_method
def get_connection_info(self, kernel_id: str) -> dict[str, t.Any]: # type:ignore[empty-body]
"""Return a dictionary of connection data for a kernel.
Parameters
==========
kernel_id : uuid
The id of the kernel.
Returns
=======
connection_dict : dict
A dict of the information needed to connect to a kernel.
This includes the ip address and the integer port
numbers of the different channels (stdin_port, iopub_port,
shell_port, hb_port).
"""
@kernel_method
def connect_iopub( # type:ignore[empty-body]
self, kernel_id: str, identity: bytes | None = None
) -> socket.socket:
"""Return a zmq Socket connected to the iopub channel.
Parameters
==========
kernel_id : uuid
The id of the kernel
identity : bytes (optional)
The zmq identity of the socket
Returns
=======
stream : zmq Socket or ZMQStream
"""
@kernel_method
def connect_shell( # type:ignore[empty-body]
self, kernel_id: str, identity: bytes | None = None
) -> socket.socket:
"""Return a zmq Socket connected to the shell channel.
Parameters
==========
kernel_id : uuid
The id of the kernel
identity : bytes (optional)
The zmq identity of the socket
Returns
=======
stream : zmq Socket or ZMQStream
"""
@kernel_method
def connect_control( # type:ignore[empty-body]
self, kernel_id: str, identity: bytes | None = None
) -> socket.socket:
"""Return a zmq Socket connected to the control channel.
Parameters
==========
kernel_id : uuid
The id of the kernel
identity : bytes (optional)
The zmq identity of the socket
Returns
=======
stream : zmq Socket or ZMQStream
"""
@kernel_method
def connect_stdin( # type:ignore[empty-body]
self, kernel_id: str, identity: bytes | None = None
) -> socket.socket:
"""Return a zmq Socket connected to the stdin channel.
Parameters
==========
kernel_id : uuid
The id of the kernel
identity : bytes (optional)
The zmq identity of the socket
Returns
=======
stream : zmq Socket or ZMQStream
"""
@kernel_method
def connect_hb( # type:ignore[empty-body]
self, kernel_id: str, identity: bytes | None = None
) -> socket.socket:
"""Return a zmq Socket connected to the hb channel.
Parameters
==========
kernel_id : uuid
The id of the kernel
identity : bytes (optional)
The zmq identity of the socket
Returns
=======
stream : zmq Socket or ZMQStream
"""
def new_kernel_id(self, **kwargs: t.Any) -> str:
"""
Returns the id to associate with the kernel for this request. Subclasses may override
this method to substitute other sources of kernel ids.
:param kwargs:
:return: string-ized version 4 uuid
"""
return str(uuid.uuid4())
class AsyncMultiKernelManager(MultiKernelManager):
kernel_manager_class = DottedObjectName(
"jupyter_client.ioloop.AsyncIOLoopKernelManager",
config=True,
help="""The kernel manager class. This is configurable to allow
subclassing of the AsyncKernelManager for customized behavior.
""",
)
use_pending_kernels = Bool(
False,
help="""Whether to make kernels available before the process has started. The
kernel has a `.ready` future which can be awaited before connecting""",
).tag(config=True)
context = Instance("zmq.asyncio.Context")
@default("context")
def _context_default(self) -> zmq.asyncio.Context:
self._created_context = True
return zmq.asyncio.Context()
start_kernel: t.Callable[..., t.Awaitable] = MultiKernelManager._async_start_kernel # type:ignore[assignment]
restart_kernel: t.Callable[..., t.Awaitable] = MultiKernelManager._async_restart_kernel # type:ignore[assignment]
shutdown_kernel: t.Callable[..., t.Awaitable] = MultiKernelManager._async_shutdown_kernel # type:ignore[assignment]
shutdown_all: t.Callable[..., t.Awaitable] = MultiKernelManager._async_shutdown_all # type:ignore[assignment]
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@jupyter-client@py3@jupyter_client@multikernelmanager.py@.PATH_END.py
|
{
"filename": "_value.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/bar/error_x/_value.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class ValueValidator(_plotly_utils.basevalidators.NumberValidator):
def __init__(self, plotly_name="value", parent_name="bar.error_x", **kwargs):
super(ValueValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@bar@error_x@_value.py@.PATH_END.py
|
{
"filename": "design.md",
"repo_name": "jax-ml/jax",
"repo_path": "jax_extracted/jax-main/docs/pallas/design/design.md",
"type": "Markdown"
}
|
# Pallas Design
<!--* freshness: { reviewed: '2024-04-15' } *-->
In this document, we explain the initial Pallas design.
This is a snapshot of some of the earlier design decisions made
and Pallas's specific APIs might have changed since.
## Introduction
JAX is being used for a diverse set of workloads, from large scale machine
learning to scientific computing.
JAX’s success story is as much a success story for XLA,
the primary compiler that JAX targets – XLA compiles JAX
programs for accelerators and has enabled JAX to scale to the largest ML
models.
JAX describes logical computations in XLA’s representation, HLO.
HLO describes how computations happen logically but not physically.
Given a logical HLO computation, XLA decides how that computation is to be
executed physically.
For a wide variety of ML applications, XLA does a good
job of compiling user programs but inevitably some users hit XLA's
limitations.
In these cases, we need to provide an “escape hatch” to allow
experts to write hand-tuned kernels that outperform XLA at that
point in time.
Furthermore, advances in ML systems research take some time to be
incorporated into XLA and users often want to run ahead with them.
Over time, the compiler can incorporate the optimizations that were proven
out experimentally through hand-tuned kernels.
XLA does offer the `CustomCall` mechanism as an escape hatch, but it
requires users to write C++ and on GPU it requires users to learn the
CUDA programming model.
The CUDA programming model is arguably too low-level for many machine
learning GPU kernels, like matrix multiplication,
and even expert users will have trouble using CUDA to implement efficient
matrix multiplication or multi-headed attention.
Not only this, JAX users are usually familiar with Python and NumPy-style
array programming which doesn’t involve writing any C++ or thinking about
GPU parallelism.
All popular machine learning frameworks share this
idea: manipulating (usually) arrays with high level operations
like `matmul` or `convolution`.
Unfortunately, this means implementing a custom operation via `CustomCall`
is a big investment, involving potentially learning C++ and/or GPU
programming.
[Triton](https://triton-lang.org/main/index.html), a GPU compiler built
and maintained by OpenAI, has taken the ML compiler world by storm.
Triton offers the best of both worlds: an array-based programming model
for GPU kernels. Triton is the primary code generation route
for `torch.compile` in PyTorch 2.0, via the Torch Inductor library.
Triton actively hides some aspects of GPU programming in the name of a
more accessible programming model that can be used from Python and to
generate optimized code from a higher-level representation.
While GPUs are more flexible than what Triton offers, in the ML domain,
Triton seems to be expressive enough for many applications.
In this document, we describe Pallas, an extension to JAX that enables
kernel programming for both GPUs and TPUs using a Triton-like model.
A JAX-based kernel language offers several advantages:
* Although Triton exposes a TPU-like programming model to users,
i.e. writing programs for tiles of arrays in L1-cache, it is specialized
enough to GPU that we cannot directly compile Triton for TPU.
For example, Triton offers atomic operations specifically meant to
handle parallel writes that don’t necessarily make sense on TPU.
A higher level front end can abstract away details of the platform
while surfacing just that tile-based programming model.
The kernels will thus be portable across different hardware platforms.
* JAX as a tracing-based frontend for numerical computing is both
mature and well-used.
By embedding the kernel programming language in JAX itself,
we can re-use JAX’s tracing infrastructure and provide a
NumPy-like frontend that’s already familiar to users.
* JAX transformations are key to its success, allowing users to
express simple programs but transform them to achieve complex
functionality.
We can leverage the same transformations (vmap, jvp, etc.) to
transform user-written kernels.
The open question is: is JAX a good fit for a kernel language at all?
We think so.
Triton demonstrates that an array programming language can be
practical for writing GPU kernels and JAX is just that.
JAX has also proven to be a flexible front-end for compilers and
for program transformations.
We describe Pallas as follows: we first describe the ways in which
we extend JAX to support writing custom kernels.
We then show how we can lower Pallas to both Triton and Mosaic.
We conclude by describing existing and potential ways to transform
Pallas kernels via JAX transformations.
<center>
Visualization of Pallas lowering paths
</center>
## Pallas: Extending JAX for kernels
The key point we’d like to make is that Pallas is just JAX, with some
extensions:
1. Users now use reference types called `Ref`s in their JAX code.
This gives users more precise control over memory access and
layout in JAX will more closely resemble physical layout.
2. Users write their JAX programs using a subset of JAX primitives,
along with a set of Pallas-specific primitives.
3. Users embed their Pallas kernels in an outer JAX program via a
special `pallas_call` higher-order function, that executes the
kernel in a map. It is analogous to `pmap` or `shard_map`,
except with references to shared memory.
We’ll go over these three extensions one at a time, by example.
Note that these APIs are still experimental and subject to change.
### Reference types
Let’s look at an example Pallas program for adding two vectors:
```python
import jax
import jax.numpy as jnp
from jax.experimental import pallas as pl
def add_kernel(x_ref, y_ref, o_ref):
# In this code, `x_ref`, `y_ref` and `o_ref` are (8,)-shaped `Ref`s
x = x_ref[:]
y = y_ref[:]
o_ref[:] = x + y
x, y = jnp.arange(8), jnp.arange(8, 16)
add = pl.pallas_call(add_kernel, out_shape=jax.ShapeDtypeStruct((8,), jnp.int32))
add(x, y)
```
Unlike a regular JAX program, `add_kernel` does not receive immutable
array arguments.
Instead, it’s provided with references that can be read from and
updated in-place using NumPy-like syntax.
`Ref`s are not a Pallas-specific concept – they were introduced to
JAX to represent stateful computations.
However, we can leverage them when writing kernels that operate on
mutable memory too.
Pallas kernels not only receive `Ref`s corresponding to the inputs
to the kernel, but also receive `Ref`s for the outputs as well
(specified in `pallas_call` via `out_shape`).
`Ref`s are special types that cannot be passed into the usual set of
JAX primitives without being read from first.
When you read from a `Ref` you get a JAX `Array` type out, and you
must write an `Array` into a `Ref`.
#### Reading from/writing into Refs
Reading from a `Ref` corresponds to loading an array into the
lowest level of the memory hierarchy (L1-cache on GPU and vector
registers on TPU). Writing into a `Ref` is analogous.
```python
def f(x_ref, o_ref):
# Using vanilla Python indexing
x = x_ref[0, 2:5, :]
# Or via Numpy advanced int indexing
o_ref[jnp.arange(3), :] = x
# Note that in order to use NumPy advanced int indexing, you need to broadcast the indices against each other into the desired multidimensional shape:
def f(x_ref):
# Assume x_ref is (8, 4) and we want to read out a (2, 3) slice
x = x_ref[jnp.arange(2)[..., None], jnp.arange(3)[None, ...]]
```
Writing to `Ref`s can be done via analogous `__setitem__` style
indexing.
Other forms of indexing (for example, dynamic slicing) can be done
via `pallas.load` and `pallas.store`, new JAX primitives designed to
make loading from/storing into memory easier.
We’ll discuss these new primitives later.
### Extending JAX with new Pallas primitives
Because JAX was designed with HLO in mind, the set of JAX primitives
closely mirrors the set of HLO operations.
Targeting a new compiler (e.g. Triton or Mosaic) means we might need
to supplement JAX’s primitives with new ones specific to the new
compiler.
At the same time, we may not be able to lower all JAX primitives,
so we need to restrict it to a subset.
Because Pallas was initially designed with Triton in mind,
we offer a set of new primitives targeting the Triton programming model.
As we’ll show later, we can lower these primitives to Mosaic as well.
#### `pallas.load` and `pallas.store`
`pallas.load` and `pallas.store` are primitives that allow loading
from memory and storing into memory.
Unlike `__getitem__` and `__setitem__` they are more flexible at the
cost of being more verbose.
Specifically, you can use the `pallas.dynamic_slice` (`pallas.ds` for
short) construct (which should maybe be upstreamed into JAX to be
used with Ref `__getitem__` and `__setitem__`).
```python
def f(x_ref, o_ref):
# Reading from memory via pallas.load
x = pl.load(x_ref, (0, slice(2, 5), slice(None)))
# Using integer indexing automatically broadcasts
x = pl.load(x_ref, (0, 2 + jnp.arange(3), slice(None)))
# You can also use `pl.dynamic_slice` (`pl.ds` for short) objects as well
pl.store(o_ref, (0, pl.ds(start=2, size=3), slice(None)), x)
```
`pallas.load` and `pallas.store` also support masking via the mask
argument.
```python
def f(x_ref, o_ref):
# Reading from memory via pallas.load
idx = jnp.arange(8)
mask = idx < 5
x = pl.load(x_ref, (idx,), mask=mask, other=float('-inf'))
```
Masking is important when doing out-of-bounds loads/stores.
The operational semantics of masking can be compiler-determined
(if we understand the documentation properly, Triton avoids the read
from/write to memory if it’s masked).
#### `pallas.program_id` and `pallas.num_programs`
As we’ll soon see, we’ll be executing the same Pallas kernels many
times (either in parallel or in a pipeline depending on the backend).
These new primitives tell us “where” we are in the execution of the
kernel.
`pallas.program_id` takes in an axis argument, which tells us which
index in an axis of a multidimensional grid this kernel is currently
executing in (analogous to `threadId` from CUDA programming or
`lax.axis_index` in `jax.pmap`).
Note that we are currently borrowing the “program” terminology from
Triton and in the future we might want to change it to something more
familiar to JAX users.
```python
def f(x_ref, o_ref):
i = pl.program_id(axis=0) # execution index in the first axis of the grid
o_ref[i] = jnp.exp(x_ref[i])
```
`pallas.num_programs` also takes in an axis and returns the grid size
for that axis.
Note that while `program_id` and `num_programs` are Triton-specific
terminology they are easily generalized to make sense on TPU as well.
#### Using a subset of JAX primitives in Pallas
Because we’re writing kernels, not high-level HLO programs, some JAX
primitives may not be able to be represented in our underlying
substrate efficiently.
However, we know we can support most elementwise operations,
simple dot products, and JAX control flow.
While we haven’t yet mapped out exactly all the JAX primitives that
we can support in Pallas kernels, we can certainly identify some that
are not easy to lower or are unlikely to be useful:
* `conv_general` - convolution usually isn’t offered as primitive in
the underlying hardware.
* `gather/scatter` - the underlying compiler may not support
noncontiguous memory reads and writes
### Executing Pallas kernels with `pallas_call`
Now that we’ve written our Pallas kernels (a.k.a. JAX with `Ref`s and
the extra Pallas primitives), how do we execute them on a GPU or TPU?
We use `pallas_call`, a higher order function (akin to `jax.jit` and
`jax.pmap`) that executes the kernel.
The signature of `pallas_call` is as follows:
```python
def pallas_call(
kernel: Callable,
out_shape: Sequence[jax.ShapeDtypeStruct],
*,
in_specs: Sequence[Spec],
out_specs: Sequence[Spec],
grid: Optional[Tuple[int, ...]] = None) -> Callable:
...
```
When we provide a kernel to `pallas_call` we provide additional
information. The first is `out_shape` which tells the kernel what the
outputs look like (`pallas_call` will pass a `Ref` corresponding to
these into the kernel to be written to).
The rest of the information (`in_specs`, `out_specs`, and `grid`) are
information about how the kernel will be scheduled on the accelerator.
The (rough) semantics for `pallas_call` are as follows:
```python
def pallas_call(kernel, out_shape, *, in_specs, out_specs, grid):
def execute(*args):
outputs = map(empty_ref, out_shape)
grid_indices = map(range, grid)
for indices in itertools.product(*grid_indices): # Could run in parallel!
local_inputs = [in_spec.transform(arg, indices) for arg, in_spec in
zip(args, in_specs)]
local_outputs = [out_spec.transform(arg, indices) for arg, out_spec in
zip(outputs, out_specs)]
kernel(*local_inputs, *local_outputs) # writes to outputs
return execute
```
Specifically, `pallas_call` will “loop” over grid iteration space,
applying a transformation to the inputs and outputs specified via
the `in_specs` and `out_specs`.
In each iteration, the kernel will be called on the transformed
inputs and outputs. Note that the “loop” over the iteration space
could be executed in parallel (e.g. on GPU).
`pallas_call` also provides no guarantees on the order of loop
iterations over the iteration space, just that every member of the
iteration space will be looped over.
Compilers like Triton and Mosaic will have more specific operational
semantics associated with the grid.
#### Transformation functions
The `in_specs` and `out_specs` arguments to `pallas_call` allow
inputs and outputs to be transformed in some way.
The two options that Pallas offers right now are an identity
transformation (where inputs and outputs are left unchanged),
and `BlockSpec`s, take fixed-size slices of `Ref`s determined by the
loop index.
A `BlockSpec` takes an `index_map` function and a `block_shape`.
Logically, it takes an array and slices it along each axis into
`block_shape` sizes blocks.
The `index_map` function takes loop indices (from the grid index set)
and maps them to block indices.
The transform function converts `Ref`s into logical views of the
`Ref` at the corresponding block.
When we specify `None` in an entry in block_shape,
that corresponds to “mapping” over that dimension,
removing it from the block within the kernel.
```python
class BlockSpec:
index_map: Callable[[Tuple[Int, ...]], Tuple[Int, ...]]
block_shape: Tuple[Optional[int], ...]
def transform(self, ref, *loop_indices):
block_indices = self.transform_function(loop_indices)
# Returns a view of `ref` starting at `block_indices` of shape self.block_shape
...
```
We could also imagine other `Spec`s that are used with `pallas_call`,
for example a `Spec` that corresponds to overlapping windows to, say,
implement convolutions.
### Immediate benefits of Pallas as a front-end
By offering a JAX front-end for kernel writing, we can immediately
reap some benefits.
#### More flexible front end
The first is that JAX users are already accustomed to the benefits
(and limitations) of programming with JAX and its tracing-based
transformations.
This means users can use closures and other familiar Python constructs
when writing Pallas kernels.
This is unlike the existing AST-parsing-based Triton front end or the
MLIR builders for Mosaic.
For example, this makes Pallas far more amenable to templating than
Triton.
See this example of how we can use higher-order functions in Python
to template a kernel.
```python
def make_kernel(eltwise_kernel):
def add(x_ref, y_ref, o_ref):
x = pl.load(x_ref, ())
y = pl.load(y_ref, ())
pl.store(o_ref, (), eltwise_kernel(x + y))
return add
kernel1 = make_kernel(lambda x: x * 2)
kernel2 = make_kernel(jnp.exp)
pl.pallas_call(kernel1, out_shape=x, grid=1)(1., 1.)
pl.pallas_call(kernel2, out_shape=x, grid=1)(1., 1.)
```
#### Emulation mode
By representing kernels as programs with JAX primitives and some new
Pallas primitives, we can also lower Pallas programs to StableHLO
directly and compile/execute them with XLA.
Specifically, a `pallas_call` can be implemented as a `lax.scan` over
the grid.
This enables us to develop GPU or TPU kernels on any XLA-supported
platform (even CPU!) and debug them using JAX/XLA debugging tools
(like `jax.debug.print`).
We can also use the more reliable and better tested XLA numerics to
verify the correctness of the Triton and Mosaic compilers.
One could also imagine perturbing the `scan` ordering to simulate the
parallel reads and writes that happen on GPU.
### GPU Examples
Note all the following examples are for GPU only. They will require tweaks to
the block sizes to work on TPUs.
#### `add`
We modify our `add_kernel` example to operate over (2,)-sized blocks
using `BlockSpec`s.
```python
def add_kernel(x_ref, y_ref, o_ref):
# In this code, `x_ref`, `y_ref` and `o_ref` are (2,)-shaped `Ref`s
x = x_ref[:]
y = y_ref[:]
o_ref[:] = x + y
x, y = jnp.arange(8), jnp.arange(8, 16)
add = pl.pallas_call(
add_kernel,
out_shape=jax.ShapeDtypeStruct((8,), jnp.int32),
in_specs=[
pl.BlockSpec((2,), lambda i: i),
pl.BlockSpec((2,), lambda i: i)
],
out_specs=pl.BlockSpec((2,), lambda i: i),
grid=(4,))
add(x, y)
```
#### Templated matmul
In this example, we compute tiles of the output by doing an unrolled
accumulation over blocks of rows and columns from our input arrays.
We inline an activation function into the body of the kernel using a
higher order function so we can emit a fused kernel.
```python
def matmul_kernel(x_ref, y_ref, o_ref, *, activation, block_k):
acc = jnp.zeros((x_ref.shape[0], y_ref.shape[1]), jnp.float32)
for k in range(x_ref.shape[1] // block_k):
x = x_ref[:, k*block_k:(k+1)*block_k]
y = y_ref[k*block_k:(k+1)*block_k, :]
acc += x @ y
o_ref[:, :] = activation(acc).astype(o_ref.dtype)
x, y = jnp.ones((512, 256)), jnp.ones((256, 1024))
block_shape = 128, 256, 128
@partial(jax.jit, static_argnames=["block_shape", "activation"])
def matmul(x, y, *, block_shape, activation):
block_m, block_n, block_k = block_shape
fused_matmul = pl.pallas_call(
partial(matmul_kernel, block_k=block_k, activation=activation),
out_shape=jax.ShapeDtypeStruct((x.shape[0], y.shape[1],), jnp.float32),
in_specs=[
pl.BlockSpec((block_m, x.shape[1]), lambda i, j: (i, 0)),
pl.BlockSpec((y.shape[0], block_n), lambda i, j: (0, j))
],
out_specs=pl.BlockSpec((block_m, block_n), lambda i, j: (i, j)),
grid=(4, 4),
)
return fused_matmul(x, y)
z = matmul(x, y, block_shape=block_shape, activation=jax.nn.gelu)
```
### Lowering Pallas
After users express their Pallas kernels, we lower them to different
representations depending on the target backend.
On GPUs, we lower Pallas to Triton IR, and on TPU we lower Pallas to
Mosaic.
#### Lowering Pallas to Triton for GPU
Lowering Pallas to Triton is easy because Pallas was designed with
Triton as a target language in mind.
The main differences between Pallas and Triton is that Triton doesn’t
have a notion of `BlockSpec`s and also uses pointers when doing
memory loads and stores as opposed to indices.
Triton supports pointers as an array element type in its language
and in Triton you can load from and store to arrays of pointers.
In Pallas, when given a `(4, 5)`-shaped `Ref`, `x_ref`, and then do
like `x_ref[3, 2]`, we need to lower this to computing a Triton
pointer to the appropriate row-major position in `x_ref` (that is,
doing 5 * 3 + 2 * 1).
Similarly, when we lower slices to Triton, e.g. `x_ref[4, :]` we need
to produce an array of pointers `5 * 4 + jnp.arange(3)`.
Other than that, lowering to Triton is fairly straightforward.
JAX dot products can be lowered to Triton dot products and JAX unary
primitives are lowered to their Triton equivalents.
Triton’s atomic operations are lowered via new Pallas atomic
primitives.
#### Lowering Pallas to Mosaic for TPU
Mosaic consumes (mostly) standard dialect MLIR and emits LLO to be
compiled for TPU.
Pallas can be lowered to Mosaic via translating JAX primitives to
MLIR (mostly the `vector` and `arith` dialects).
The `BlockSpec`s can be converted into pipeline schedules
(i.e. the `transform_func`s in Mosaic).
### Transforming Pallas
A natural question is how do JAX transformations interact with Pallas
kernels?
There are two main ways: transformations inside Pallas kernels and
transformations outside Pallas kernels.
Transformation inside Pallas kernels should actually “just work”,
so long as we are able to lower the transformed code.
For example, we could use `jax.grad(jnp.sin)(...)` inside of a JAX
kernel because we can lower a `cos` to both Triton and Mosaic.
However, we might not be able to lower a `jax.vmap(lax.dynamic_slice)`
because it could turn into a gather that we cannot lower.
Transformations of Pallas kernels from the outer JAX programs is
perhaps the more interesting case. How do we handle things like
`vmap(pallas_call)` and `grad(pallas_call)`?
#### `vmap-of-pallas_call`
vmap automatically vectorizes JAX programs. While kernel writers might
want precise control over how a batched kernel will behave differently
from its unbatched variant, we can offer a reasonable default `vmap`
rule for `pallas_call` while offering the `jax.custom_vmap`
customization mechanism. When `pallas_call` is `vmap`-ed, we augment
the `pallas_call` to have an extra grid dimension corresponding to the
new batch dimension and transform the `BlockSpec`s to handle indexing
along that dimension.
#### `grad-of-pallas_call`
`grad` of `pallas_call` enables automatic differentiation of kernels.
`jax.grad` breaks down into applications of three distinct transforms:
`jvp`, `partial_eval` and `transpose`.
In principle, we can re-use most of JAX’s infrastructure when
implementing these rules for `pallas_call` (since it behaves much like
existing JAX higher order primitives).
However, automatic differentiation of kernels can result in a
performance hit due to how memory access is transposed.
If we write a GPU kernel with overlapping-and-parallel reads and
disjoint-but-parallel writes, we automatically transpose it into a
kernel that has overlapping-but-parallel writes (which are slow when
done atomically) and disjoint-and-parallel reads.
To emit a kernel that better uses parallelism with shared memory,
we would need to reorder loops and change how the kernel is vectorized.
Unfortunately, we do not have a program representation amenable to
that in Pallas.
A potential direction to automatically differentiating kernels
efficiently is to explore a different representation, perhaps one
like that in Dex.
We could also look at how Enzyme approaches this problem.
However, AD of Pallas kernels may still be useful for a class of
kernels that does transpose efficiently (for example elementwise
kernels).
In general, though, `jax.custom_vjp` is a viable escape hatch to
express Pallas kernels that work with `jax.grad`.
#### Other transformations
We could imagine other JAX transformations applying to Pallas kernels
that we haven’t explicitly explored yet.
For example, `checkify` is a JAX transformation that does functional
error handling.
We could imagine using `checkify` with pallas_call to allow plumbing
out error codes from GPU kernels that indicate if OOB access or NaNs
were produced.
Another potential transformation to integrate with is
custom_partitioning to enable automatically partitionable kernels to
be used with pjit.
|
jax-mlREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@docs@pallas@design@design.md@.PATH_END.py
|
{
"filename": "test_commands.py",
"repo_name": "simonsobs/sorunlib",
"repo_path": "sorunlib_extracted/sorunlib-main/tests/test_commands.py",
"type": "Python"
}
|
import os
os.environ["OCS_CONFIG_DIR"] = "./test_util/"
import pytest
import datetime as dt
from unittest.mock import MagicMock, patch
from sorunlib.commands import wait_until
def mkts(offset):
"""Make timestamp.
Args:
offset (int): Offset from current time in seconds.
Returns:
str: ISO formatted timestamp 'offset' seconds from now, i.e.
'2023-04-22T00:59:56.264293+00:00'
Examples:
An example called at '2023-04-24T21:14:27.790440+00:00':
>>> mkts(1)
'2023-04-24T21:14:28.790440+00:00'
"""
now = dt.datetime.now(dt.timezone.utc)
delta = dt.timedelta(seconds=offset)
ts = now + delta
return ts.isoformat()
# patch out time.sleep so we don't actually sleep during testing
@patch('sorunlib.commands.time.sleep', MagicMock())
@pytest.mark.parametrize("timestamp,tolerance", [
# timestamp in past, not high enough tolerance
(mkts(-10), 5),
# timestamp in future, but past tolerance timestamp
(mkts(1), mkts(-1))])
def test_wait_until_past_tolerance(timestamp, tolerance):
with pytest.raises(ValueError):
wait_until(timestamp, tolerance)
@patch('sorunlib.commands.time.sleep', MagicMock())
def test_wait_until_unsupported_tolerance():
with pytest.raises(ValueError):
wait_until(mkts(1), tolerance=[1, 2])
def test_wait_until_unsupported_tz():
with pytest.raises(ValueError):
tz = dt.timezone(offset=dt.timedelta(hours=5))
t = dt.datetime.now(tz).isoformat() # i.e. '2023-04-22T00:59:56.264293+05:00'
wait_until(t)
@patch('sorunlib.commands.time.sleep', MagicMock())
@pytest.mark.parametrize("timestamp,tolerance", [
# timestamps in future, future or no tolerance
(mkts(1), None),
(mkts(1), 5),
(mkts(1), mkts(10)),
# timestamps in past, high enough or no tolerance
(mkts(-1), None),
(mkts(-1), 5),
(mkts(-1), mkts(10)),
# test mix of tz aware timestamp, naive tolerance timestamp
(mkts(0), mkts(10)[:-6]),
(mkts(0)[:-6], mkts(10)),
# testing TZ detection w/past timestamps, no tolerance
("2020-01-01T00:00:00", None),
("2020-01-01T00:00:00+00:00", None)])
def test_wait_until(timestamp, tolerance):
wait_until(timestamp, tolerance)
|
simonsobsREPO_NAMEsorunlibPATH_START.@sorunlib_extracted@sorunlib-main@tests@test_commands.py@.PATH_END.py
|
{
"filename": "setup.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/numpy/py3/numpy/_typing/setup.py",
"type": "Python"
}
|
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('_typing', parent_package, top_path)
config.add_data_files('*.pyi')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@numpy@py3@numpy@_typing@setup.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "EranOfek/AstroPack",
"repo_path": "AstroPack_extracted/AstroPack-main/matlab/doc/README.md",
"type": "Markdown"
}
|
# MATLAB Doc Folder
|
EranOfekREPO_NAMEAstroPackPATH_START.@AstroPack_extracted@AstroPack-main@matlab@doc@README.md@.PATH_END.py
|
{
"filename": "test_tolerancing.py",
"repo_name": "chandra-marx/marxs",
"repo_path": "marxs_extracted/marxs-main/marxs/design/tests/test_tolerancing.py",
"type": "Python"
}
|
import os
import tempfile
import numpy as np
import pytest
import astropy.units as u
from astropy.table import Table
from astropy.coordinates import SkyCoord
from astropy.utils.data import get_pkg_data_filename
from marxs.design.tolerancing import (oneormoreelements,
wiggle, moveglobal, moveindividual, moveelem,
varyperiod, varyorderselector, varyattribute,
run_tolerances,
generate_6d_wigglelist,
select_1dof_changed,
DispersedWigglePlotter,
run_tolerances_for_energies,
run_tolerances_for_energies2,
)
from marxs.optics import (FlatGrating, OrderSelector, RadialMirrorScatter,
RectangleAperture, ThinLens, FlatDetector)
from marxs.design import RowlandTorus, GratingArrayStructure
from marxs.utils import generate_test_photons
from marxs.source import PointSource, FixedPointing
from marxs.simulator import Sequence
from marxs.analysis.gratings import CaptureResAeff
try:
import matplotlib.pyplot as plt
HAS_MPL = True
except ImportError:
HAS_MPL = False
mytorus = RowlandTorus(0.5, 0.5, position=[1.5, 0, -3])
def gsa(elem_class=FlatGrating):
'''make a parallel structure - fresh for every test'''
g = GratingArrayStructure(rowland=mytorus,
d_element=[0.1, 0.1], radius=[0.1,.2],
elem_class=elem_class,
elem_args={'zoom':0.2, 'd':0.002,
'order_selector': OrderSelector([1])
})
return g
elempos = np.stack([e.pos4d for e in gsa().elements])
def test_oneormore():
@oneormoreelements
def func(a, b, c):
a.value += 1
class HoldData():
def __init__(self, value):
self.value = value
obj1 = HoldData(2)
obj2 = HoldData(4)
obj3 = HoldData(6)
listin = [obj2, obj3]
# First, make sure that func works, otherwise the remaining test is useless.
func(obj1, 2, c=4)
assert obj1.value == 3
func(listin, 'a', None)
assert listin[0].value == 5
assert listin[1].value == 7
@pytest.mark.parametrize('function', [wiggle, moveglobal, moveindividual])
def test_change_parallel_elements(function):
'''Check that parameters work and elements are in fact changed.
More detailed checks that the type of change is correct are
implemented as separate tests, but those tests don't check out
every parameter.
'''
g = gsa()
function(g, 0., 0., 0.)
assert np.all(np.stack([e.pos4d for e in g.elements]) == elempos)
for key in ['dx', 'dy', 'dz', 'rx', 'ry', 'rz']:
d = {key: 1.23}
function(g, **d)
assert not np.all(np.stack([e.pos4d for e in g.elements]) == elempos)
def test_moveelements_translate():
'''Check that the element movers work. If the whole structure is translated
or individual elements are translated by the same amount, the positions
should be the same.'''
g0 = gsa()
g1 = gsa()
g2 = gsa()
moveglobal(g1, dy=-20)
moveindividual(g2, dy=-20)
assert np.allclose(np.stack([e.pos4d for e in g1.elements]),
np.stack([e.pos4d for e in g2.elements]))
assert not np.allclose(np.stack([e.pos4d for e in g0.elements]),
np.stack([e.pos4d for e in g2.elements]))
def test_moveelements_rotate():
'''Check that the element movers work.
Unlike test_moveelements_translate we expect different results because
there are different center of the rotation.
This test does not check that the rotation is correct, only that its
different because the validity of the rotation matrix itself is already
covered by the tests in the transforms3d package.
'''
g1 = gsa()
g2 = gsa()
moveglobal(g1, rz=-1, ry=.2)
moveindividual(g2, rz=-1, ry=.2)
assert not np.allclose(np.stack([e.pos4d for e in g1.elements]),
np.stack([e.pos4d for e in g2.elements]))
def test_moveelem():
'''Move an individual element'''
det = FlatDetector(zoom=[1, 100, 100])
assert det.geometry['center'][2] == 0
moveelem(det, dz=5)
assert det.geometry['center'][2] == 5
assert np.all(det.geometry.pos4d[:3, :3] == np.eye(3) * [1, 100, 100])
def test_wiggle():
'''Check wiggle function'''
g = gsa()
wiggle(g, dx=10, dy=.1)
diff = elempos - np.stack([e.pos4d for e in g.elements])
# Given the numbers, wiggle in x must be larger than y
# This also tests that not all diff numbers are the same
# (as they would be with move).
assert np.std(diff[:, 0, 3]) > np.std(diff[:, 1, 3])
@pytest.mark.parametrize('function', [varyperiod, varyorderselector])
def test_errormessage(function):
'''Check that check is performed for right type of object.
Some function just set an attribute and there is no function call after
that that would fail or do anything if called with the wrong type of object.
Thus, it's very simple to call these with an object where it does not make
any sense to apply them. So, they have some error check. Here, we check
this check.
'''
with pytest.raises(ValueError) as e:
# All functions accept two parameters.
# Error should be raised before they are used, so the value does not
# matter
function(gsa, 1., 2.)
assert 'does not have' in str(e.value)
def test_gratings_d():
'''Change the grating constant.'''
g = gsa()
varyperiod(g.elements, 1., .1)
periods = [e._d for e in g.elements]
assert np.std(periods) > 0.01
assert np.std(periods) < 5.
assert np.mean(periods) > .5
def test_scatter():
'''Check that the right properties are set.'''
scat = RadialMirrorScatter(inplanescatter=1. * u.arcmin,
perpplanescatter=.1 * u.arcmin)
varyattribute(scat, inplanescatter=2. * u.arcsec,
perpplanescatter=.2 * u.degree)
assert scat.inplanescatter == 2. * u.arcsec
assert scat.perpplanescatter == .2 * u.degree
def test_errormessage_attribute():
'''Test error message for generic attributechanger'''
with pytest.raises(ValueError) as e:
# All functions accept two parameters.
# Error should be raised before they are used, so the value does not
# matter
varyattribute(gsa, attributenotpresent=1., notpresenteither=2.)
assert 'does not have' in str(e.value)
def test_orderselector():
'''Test setting the order selector properties.'''
photons = generate_test_photons(5)
grat = FlatGrating(d=1., order_selector=OrderSelector([1]))
p = grat(photons.copy())
assert np.all(p['order'] == 1)
varyorderselector(grat, OrderSelector, [2])
p = grat(photons.copy())
assert np.all(p['order'] == 2)
def test_runtolerances():
'''Test the loop with mock functions.
This is not a complete functional test, just making sure all calling
signatures work.
'''
photons = generate_test_photons(20)
grat = FlatGrating(d=1., order_selector=OrderSelector([1]))
parameters =[{'order_selector': OrderSelector, 'orderlist': [2]},
{'order_selector': OrderSelector, 'orderlist': [1, 2], 'p': [.8, 0.]}]
def afunc(photons):
return {'meanorder': np.nanmean(photons['order'])}
out = run_tolerances(photons, grat, varyorderselector, grat,
parameters, afunc)
assert out[0]['meanorder'] == 2
assert out[1]['meanorder'] == 1
# check parameters are in output
assert out[1]['orderlist'] == [1, 2]
# check original parameter is still intact and can be used again
# Regression test: If results are inserted into the same dict
# 'meanorder' will appear which is not valid for varyorderselector
assert 'meanorder' not in parameters[0]
def test_run_tolerances_for_energies():
'''For this test, we need to define an instrument. The instrument is not
very realistic (an X-ray mirror with r=0 won't work), but the
point here is just to check the tolerancing for several
energies. To make that calculation reasonably fast, we need to
keep the number of elements in the optical system small.
'''
coords = SkyCoord(12. * u.deg, -45 * u.deg)
src = PointSource(coords=coords)
pnt = FixedPointing(coords=coords)
aper = RectangleAperture(position=[5000, 0, 0], zoom=[1, 10, 10])
lens = ThinLens(position=[4900, 0, 0], zoom=[1, 10, 10], focallength=4900)
grat = FlatGrating(d=.002, order_selector=OrderSelector([0, 1]),
position=[4800, 0, 0], zoom=[1, 10, 10])
det = FlatDetector(zoom=[1, 100, 100])
instrum = Sequence(elements=[pnt, aper, lens, grat, det])
parameters = [{'period_mean': 0.003, 'period_sigma': 0.},
{'period_mean': 0.004, 'period_sigma': 0.}]
res = run_tolerances_for_energies(src, [.1, 1] * u.keV,
Sequence(elements=[pnt, aper, lens]),
Sequence(elements=[grat, det]),
varyperiod, grat,
parameters,
CaptureResAeff(orders=[0, 1, 2]),
reset={'period_mean': 0.005,
'period_sigma': 0.},
t_source=1. * u.ks)
# Check the reset worked
assert grat._d == 0.005
# Check both energy have been calculated
assert 1 in res['energy']
assert .1 in res['energy']
assert len(res) == 4
# check results are reasonable
assert np.all(res['R'].data[:, 0] == 0)
assert not np.any(np.isfinite(res['R'].data[:, 2]))
assert res['R'].data[2, 1] > res['R'].data[0, 1]
def test_run_tolerances_for_energies2():
'''Same, as above, but with different calling sequence
'''
coords = SkyCoord(12. * u.deg, -45 * u.deg)
src = PointSource(coords=coords)
pnt = FixedPointing(coords=coords)
aper = RectangleAperture(position=[5000, 0, 0], zoom=[1, 10, 10])
lens = ThinLens(position=[4900, 0, 0], zoom=[1, 10, 10], focallength=4900)
grat = FlatGrating(d=.002, order_selector=OrderSelector([0, 1]),
position=[4800, 0, 0], zoom=[1, 10, 10])
det = FlatDetector(zoom=[1, 100, 100])
instrum = Sequence(elements=[pnt, aper, lens, grat, det])
parameters = [{'period_mean': 0.003, 'period_sigma': 0.},
{'period_mean': 0.004, 'period_sigma': 0.}]
res = run_tolerances_for_energies2(src, [.1, 1] * u.keV,
instrum, FlatGrating,
varyperiod,
parameters,
CaptureResAeff(orders=[0, 1, 2]),
reset={'period_mean': 0.005,
'period_sigma': 0.},
t_source=1. * u.ks)
# Check the reset worked
assert grat._d == 0.005
# Check both energy have been calculated
assert 1 in res['energy']
assert .1 in res['energy']
assert len(res) == 4
# check results are reasonable
assert np.all(res['R'].data[:, 0] == 0)
assert not np.any(np.isfinite(res['R'].data[:, 2]))
assert res['R'].data[2, 1] > res['R'].data[0, 1]
def test_6dlist():
'''Check the list of dicts in 3 translations dof and 3 rotations'''
cglob, cind = generate_6d_wigglelist([0, 1.] * u.cm, [0., 1.] * u.degree,
names=['x', 'y', 'z', 'rx', 'ry', 'rz'])
assert len(cind) == 7
assert len(cglob) == 13
assert set(cind[5].keys()) == set(['x', 'y', 'z', 'rx', 'ry', 'rz'])
tab = Table(cind)
for col in ['x', 'y', 'z']:
assert np.max(tab[col]) == 10
assert np.min(tab[col]) == 0
for col in ['x', 'y', 'z']:
assert np.max(tab[col]) == 10
assert np.min(tab[col]) == 0
tab = Table(cglob)
for col in tab.colnames:
assert - np.min(tab[col]) == np.max(tab[col])
def test_6d_warning():
with pytest.warns(UserWarning):
cglob, cind = generate_6d_wigglelist([1.] * u.cm, [0., 1.] * u.degree)
def test_find_changed():
'''Test that we find the row where only one parameter was changed.'''
tab = Table({'par1': [-1, -1, 0, 0, 0, 1],
'par2': [-1, 0, 0, 3, 0, 0],
'id': [ 0, 1, 2, 3, 4, 5]})
t = select_1dof_changed(tab, 'par1', parlist=['par1', 'par2'])
assert set(t['id']) == set([1, 2, 4, 5])
def test_plot_wiggle():
'''Test that wiggle plot works. This does not test that the result
looks correct, only that running through the plot function does not
raise any errors.
This is one of the few plotting functions in the entire package, so
setting up the infrastructure to compare output pixel-by-pixel does not
seem worth it as this point.
'''
plt = pytest.importorskip("matplotlib.pyplot")
fig, ax = plt.subplots()
tab = Table({'wave': [1, 1],
'dd': [0, 1],
'Rgrat': [500, 500],
'Aeff': [20, 50]})
wiggle_plotter = DispersedWigglePlotter()
wiggle_plotter.plot_wiggle(tab, 'dd', ['dd'], ax, Aeff_col='Aeff')
@pytest.mark.skipif('not HAS_MPL')
def test_plot_6dof():
tab = Table({'wave': [1, 1, 2, 2, 1, 1, 1, 1],
'dd': [0, 1, 0, 2, 0, 0, 0, 0],
'rr': [0, 0, 0, 0, 2, 4, 6, 8],
'R': np.random.rand(8),
'Aeffgrat': np.arange(8)})
wiggle_plotter = DispersedWigglePlotter()
with tempfile.TemporaryDirectory() as tmpdirname:
name = os.path.join(tmpdirname, 'var_global.fits')
tab.write(name)
fig, ax = wiggle_plotter.load_and_plot(name, ['dd', 'rr'], R_col='R')
@pytest.mark.skipif('not HAS_MPL')
def test_plot_6dof_real_file():
'''Repeat previous test with static data file. This is a more realistic file
but it takes too long to generate every time. This file is used in the docs
in design/tolerancing (see docs/pyplot/chandra_tolerancing) so if this test
breaks, the docs will likely have to be changed, too.
'''
wiggle_plotter = DispersedWigglePlotter()
filename = get_pkg_data_filename('data/wiggle_global.fits', 'marxs.design.tests')
fig, ax = wiggle_plotter.load_and_plot(filename)
|
chandra-marxREPO_NAMEmarxsPATH_START.@marxs_extracted@marxs-main@marxs@design@tests@test_tolerancing.py@.PATH_END.py
|
{
"filename": "fitting.py",
"repo_name": "jpierel14/sntd",
"repo_path": "sntd_extracted/sntd-master/sntd/fitting.py",
"type": "Python"
}
|
import warnings
import sncosmo
import os
import sys
import pyParz
import pickle
import subprocess
import glob
import math
import time
import tarfile
import numpy as np
import matplotlib.pyplot as plt
from copy import copy
from scipy import stats
from astropy.table import Table
import nestle
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF
import scipy
import itertools
from sncosmo import nest_lc
from itertools import combinations
from collections import OrderedDict
from .util import *
from .util import _filedir_, _current_dir_
from .curve_io import _sntd_deepcopy
from .models import BazinSource
from .ml import *
__all__ = ['fit_data']
_thetaSN_ = ['z', 'hostebv', 'screenz',
'rise', 'fall', 'sigma', 'k', 'x1', 'c']
_thetaL_ = ['t0', 'amplitude', 'screenebv', 'dt0',
'A', 'B', 't1', 'psi', 'phi', 's', 'x0']
_needs_bounds = {'z'}
def fit_data(curves=None, snType='Ia', bands=None, models=None, params=None, bounds={}, ignore=None, constants={}, ignore_models=[],
method='parallel', t0_guess=None, effect_names=[], effect_frames=[], batch_init=None, cut_time=None, force_positive_param=[],
dust=None, microlensing=None, fitOrder=None, color_bands=None, color_param_ignore=[], min_points_per_band=3, identify_micro=False,
min_n_bands=1, max_n_bands=None, n_cores_per_node=1, npar_cores=4, max_batch_jobs=199, max_cadence=None, fit_colors=None,
fit_prior=None, par_or_batch='parallel', batch_partition=None, nbatch_jobs=None, batch_python_path=None, n_per_node=None, fast_model_selection=True,
wait_for_batch=False, band_order=None, set_from_simMeta={}, guess_amplitude=True, trial_fit=False, clip_data=False, use_MLE=False,
kernel='RBF', refImage='image_1', nMicroSamples=100, color_curve=None, warning_supress=True,
micro_fit_bands='all', verbose=True, **kwargs):
"""The main high-level fitting function.
Parameters
----------
curves: :class:`~sntd.curve_io.MISN`
The MISN object containing the multiple images to fit.
snType: str
The supernova classification
bands: :class:`~list` of :class:`~sncosmo.Bandpass` or :class:`~str`, or :class:`~sncosmo.Bandpass` or :class:`~str`
The band(s) to be fit
models: :class:`~list` of :class:`~sncosmo.Model` or str, or :class:`~sncosmo.Model` or :class:`~str`
The model(s) to be used for fitting to the data
params: :class:`~list` of :class:`~str`
The parameters to be fit for the models inside of the parameter models
bounds: :class:`dict`
A dictionary with parameters in params as keys and a tuple of bounds as values
ignore: :class:`~list` of :class:`~str`
List of parameters to ignore
constants: :class:`dict`
Dictionary with parameters as keys and the constant value you want to set them to as values
ignore_models: class:`~list`
List of model names to ignore, usually used if you did not specify the "models" parameter
and let all models for a given SN type be chosen, but you want to ignore one or more.
method: :class:`~str` or :class:`~list`
Needs to be 'parallel', 'series', or 'color', or a list containting one or more of these
t0_guess: :class:`dict`
Dictionary with image names (i.e. 'image_1','image_2') as keys and a guess for time of peak as values
effect_names: :class:`~list` of :class:`~str`
List of effect names if model contains a :class:`~sncosmo.PropagationEffect`.
effect_frames: :class:`~list` of :class:`~str`
List of the frames (e.g. obs or rest) that correspond to the effects in effect_names
batch_init: :class:`~str`
A string to be pasted into the batch python file (e.g. extra imports or filters added to sncosmo.)
cut_time: :class:`~list`
The start and end (rest frame) phase that you want to fit in, default accept all phases.
force_positive_param: :class:`~list`
Optional list of parameters to always make positive.
dust: :class:`sncosmo.PropagationEffect`
An sncosmo dust propagation effect to include in the model
microlensing: str
If None microlensing is ignored, otherwise should be str (e.g. achromatic, chromatic)
fitOrder: :class:`~list`
The order you want to fit the images if using parallel method (default chooses by npoints/SNR)
color_bands: :class:`~list`
If using multiple methods (in batch mode), the subset of bands to use for color fitting.
color_param_ignore: :class:`~list`
If using multiple methods, parameters you may want to fit for one method but not
for color method (e.g. stretch)
min_points_per_band: int
Only accept bands to fit with this number of points fitting other criterion (e.g. minsnr)
identify_micro: bool
If True, function is run to attempt to identify bands where microlensing is least problematic.
min_n_bands: int
Checks the SN to make sure it has this number of bands (with min_points_per_band in each)
max_n_bands: int
The best n bands are chosen from the data.
n_cores_per_node: int
The number of cores to run parallelization on per node
npar_cores: int
The number of cores to devote to parallelization
max_batch_jobs: int
The maximum number of jobs allowed by your slurm task manager.
max_cadence: int
To clip each image of a MISN to this cadence
fit_colors: list
List of colors to use in color fitting (e.g. ['bessellb-bessellv','bessellb-bessellr'])
fit_prior: :class:`~sntd.curve_io.MISN` or bool
if implementing parallel method alongside others and fit_prior is True, will use output of parallel as prior
for series/color. If SNTD MISN object, used as prior for series or color.
par_or_batch: str
if providing a list of SNe, par means multiprocessing and batch means sbatch. Must supply other batch
parameters if batch is chosen, so parallel is default.
batch_partition: str
The name of the partition for sbatch command
nbatch_jobs: int
number of jobs (10 jobs for 100 light curves is 10 light curves per job)
batch_python_path: str
path to python you want to use for batch mode (if different from current)
n_per_node: int
Number of SNe to fit per node (in series) in batch mode. If none, just distributes all SNe across the number
of jobs you have by default.
fast_model_selection: bool
If you are providing a list of models and want the best fit, turning this on will make the fitter choose based
on a simple minuit fit before moving to the full sntd fitting. If false, each model will be fitted with the full
sntd fitting and the best will be chosen.
wait_for_batch: bool
if false, submits job in the background. If true, waits for job to finish (shows progress bar) and returns output.
band_order: :class:`~list`
If you want colors to be fit in a specific order (e.g. B-V instead of V-B depending on band order)
set_from_simMeta: :class:`~dict`
Dictionary where keys are model parameters and values are the corresponding key in the
:class:`~sntd.curve_io.MISN`.images.simMeta dictionary (e.g. {'z':'sim_redshift'} if you want to set the model
redshift based on a simulated redshift in simMeta called 'sim_redshfit')
guess_amplitude: bool
If True, the amplitude parameter for the model is estimated, as well as its bounds
trial_fit: bool
If true, a simple minuit fit is performed to locate the parameter space for nestle fits, otherwise the full parameter
range in bounds is used.
clip_data: bool
If true, criterion like minsnr and cut_time actually will remove data from the light curve, as opposed to simply not
fitting those data points.
use_MLE: bool
If true, uses MLE as the parameter estimator instead of the median of the nested sampling samples
kernel: str
The kernel to use for microlensing GPR
refImage: str
The name of the image you want to be the reference image (i.e. image_1,image_2, etc.)
nMicroSamples: int
The number of pulls from the GPR posterior you want to use for microlensing uncertainty estimation
color_curve: :class:`astropy.Table`
A color curve to define the relationship between bands for parameterized light curve model.
warning_supress: bool
Turns on or off warnings
micro_fit_bands: str or list of str
The band(s) to fit microlensing. All assumes achromatic, and will fit all bands together.
verbose: bool
Turns on/off the verbosity flag
Returns
-------
fitted_MISN: :class:`~sntd.curve_io.MISN` or :class:`~list`
The same MISN that was passed to fit_data, but with new fits and time delay measurements included. List
if list was provided.
Examples
--------
>>> fitCurves=sntd.fit_data(myMISN,snType='Ia', models='salt2-extended',bands=['F110W','F125W'],
params=['x0','x1','t0','c'],constants={'z':1.33},bounds={'t0':(-15,15),'x1':(-2,2),'c':(0,1)},
method='parallel',microlensing=None)
"""
# get together user arguments
locs = locals()
args = copy(locs)
for k in kwargs.keys():
args[k] = kwargs[k]
if isinstance(curves, (list, tuple, np.ndarray)):
if isinstance(curves[0], str): # then its a filename list
filelist = True
else:
filelist = False
args['curves'] = []
for i in range(len(curves)):
temp = _sntd_deepcopy(curves[i])
temp.nsn = i+1
args['curves'].append(temp)
args['parlist'] = True
else:
args['curves'] = _sntd_deepcopy(curves)
args['parlist'] = False
if method != 'color' or identify_micro:
args['bands'] = [bands] if bands is not None and not isinstance(
bands, (tuple, list, np.ndarray)) else bands
args['bands'] = list(set(bands)) if bands is not None else None
# sets the bands to user's if defined (set, so that they're unique), otherwise to all the bands that exist in curves
if args['bands'] is None:
args['bands'] = list(curves.bands) if not isinstance(
curves, (list, tuple, np.ndarray)) and not isinstance(args['curves'][0], str) else None
args['bands'] = _bandCheck(args['curves'],args['bands'])
# get together the model(s) needed for fitting
models = [models] if models is not None and not isinstance(
models, (tuple, list, np.ndarray)) else models
if models is None:
mod, types = np.loadtxt(os.path.join(
_filedir_, 'data', 'sncosmo', 'models.ref'), dtype='str', unpack=True)
modDict = {mod[i]: types[i] for i in range(len(mod))}
if isinstance(snType, str):
if snType != 'Ia':
mods = [x[0] for x in sncosmo.models._SOURCES._loaders.keys(
) if x[0] in modDict.keys() and modDict[x[0]][:len(snType)] == snType]
elif snType == 'Ia':
mods = [x[0] for x in sncosmo.models._SOURCES._loaders.keys()
if 'salt2' in x[0]]
else:
mods = []
for t in snType:
if t != 'Ia':
mods = np.append(mods, [x[0] for x in sncosmo.models._SOURCES._loaders.keys(
) if x[0] in modDict.keys() and modDict[x[0]][:len(t)] == t])
elif t == 'Ia':
mods = np.append(
mods, [x[0] for x in sncosmo.models._SOURCES._loaders.keys() if 'salt2' in x[0]])
else:
mods = models
mods = np.unique(mods)
for ig_mod in ignore_models:
if ig_mod not in mods:
temp = snana_to_sncosmo(ig_mod)
if temp is not None:
mods = [x for x in mods if x != temp[1]]
else:
mods = [x for x in mods if x != ig_mod]
args['models'] = mods
if warning_supress:
warnings.simplefilter('ignore')
if identify_micro and not args['parlist']:
all_bands, color_bands = identify_micro_func(args)
args['color_bands'] = color_bands
args['bands'] = all_bands
args['curves'].micro_bands = all_bands
args['curves'].micro_color_bands = color_bands
if fit_prior is False:
args['fit_prior'] = None
if args['parlist'] and n_per_node is None and par_or_batch == 'batch':
if nbatch_jobs is None:
print('Must set n_per_node node and/or nbatch_jobs')
n_per_node = math.ceil(len(args['curves'])/nbatch_jobs)
if isinstance(method, (list, np.ndarray, tuple)):
if len(method) == 1:
method = method[0]
elif 'parallel' in method and fit_prior == True: # Run parallel first if using as prior
method = np.append(
['parallel'], [x for x in method if x != 'parallel'])
if args['parlist']:
if par_or_batch == 'parallel':
print('Have not yet set up parallelized multi-fit processing')
sys.exit(1)
else:
if n_cores_per_node > 1:
parallelize = n_cores_per_node
n_per_node = max(n_per_node, n_cores_per_node)
micro_par = None
elif microlensing is not None:
parallelize = None
micro_par = npar_cores
else:
parallelize = None
micro_par = None
total_jobs = math.ceil(len(args['curves'])/n_per_node)
if nbatch_jobs is None:
nbatch_jobs = min(total_jobs, max_batch_jobs)
script_name_init, folder_name = make_sbatch(partition=batch_partition,
njobs=min(total_jobs, nbatch_jobs), njobstotal=min(total_jobs, max_batch_jobs), python_path=batch_python_path, init=True, parallelize=parallelize, microlensing_cores=micro_par)
script_name, folder_name = make_sbatch(partition=batch_partition, folder=folder_name,
njobs=min(total_jobs, nbatch_jobs), python_path=batch_python_path, init=False, parallelize=parallelize, microlensing_cores=micro_par)
pickle.dump(constants, open(os.path.join(
folder_name, 'sntd_constants.pkl'), 'wb'))
pickle.dump(args['curves'], open(
os.path.join(folder_name, 'sntd_data.pkl'), 'wb'))
pyfiles = ['run_sntd_init.py', 'run_sntd.py'] if parallelize is None else [
'run_sntd_init_par.py', 'run_sntd_par.py']
for pyfile in pyfiles:
with open(os.path.join(_filedir_, 'batch', pyfile)) as f:
batch_py = f.read()
if 'init' in pyfile:
batch_py = batch_py.replace('nlcsreplace', str(
min(int(n_per_node*min(total_jobs, max_batch_jobs)), len(args['curves']))))
batch_py = batch_py.replace(
'njobsreplace', str(min(total_jobs, max_batch_jobs)))
else:
batch_py = batch_py.replace(
'nlcsreplace', str(n_per_node))
if batch_init is None:
batch_py = batch_py.replace(
'batchinitreplace', 'print("Nothing to initialize...")')
else:
batch_py = batch_py.replace(
'batchinitreplace', batch_init)
batch_py = batch_py.replace(
'ncores', str(n_cores_per_node))
indent1 = batch_py.find('fitCurves=')
indent = batch_py.find('try:')+len('try:')+1
sntd_command = ''
for i in range(len(method)):
fit_method = method[i]
sntd_command += 'sntd.fit_data('
for par, val in locs.items():
if par == 'curves':
if i == 0:
if parallelize is None:
sntd_command += 'curves=all_dat[i],'
else:
sntd_command += 'curves=all_input,'
else:
sntd_command += 'curves=fitCurves,'
elif par == 'constants':
if parallelize is None:
sntd_command += 'constants=all_dat[i].constants,'
else:
sntd_command += 'constants={'+'},'
elif par == 'batch_init':
sntd_command += 'batch_init=None,'
elif par == 'identify_micro' and identify_micro:
if i > 0:
sntd_command += 'identify_micro=False,'
else:
sntd_command += 'identify_micro=True,'
elif par == 'bands' and identify_micro:
if i > 0:
if parallelize is None:
if fit_method != 'color':
sntd_command += 'bands=fitCurves.micro_bands,'
else:
sntd_command += 'bands=fitCurves.micro_color_bands,'
else:
print('Have not implemented this yet.')
sys.exit(1)
else:
sntd_command += 'bands=None,'
elif fit_method == 'color' and par == 'bands':
if color_bands is not None:
sntd_command += 'bands=' + \
str(color_bands)+','
else:
sntd_command += 'bands='+str(val)+','
elif par == 'method':
sntd_command += 'method="'+fit_method+'",'
elif par == 'fit_prior' and fit_method != 'parallel' and (fit_prior is not None and fit_prior is not False):
if parallelize is None:
sntd_command += 'fit_prior=fitCurves,'
else:
sntd_command += 'fit_prior=True,'
elif par == 'par_or_batch' and parallelize is not None:
sntd_command += 'par_or_batch="parallel",'
elif par == 'npar_cores' and parallelize is not None:
sntd_command += 'npar_cores=%i,' % n_cores_per_node
elif isinstance(val, str):
sntd_command += str(par)+'="'+str(val)+'",'
elif par == 'kwargs':
for par2, val2 in val.items():
if isinstance(val, str):
sntd_command += str(par2) + \
'="'+str(val2)+'",'
else:
sntd_command += str(par2) + \
'='+str(val2)+','
else:
sntd_command += str(par)+'='+str(val)+','
sntd_command = sntd_command[:-1]+')\n'
if i < len(method)-1:
sntd_command += ' '*(indent1-indent)+'fitCurves='
batch_py = batch_py.replace(
'sntdcommandreplace', sntd_command)
with open(os.path.join(os.path.abspath(folder_name), pyfile), 'w') as f:
f.write(batch_py)
return run_sbatch(folder_name, script_name_init, script_name, total_jobs, max_batch_jobs, n_per_node, wait_for_batch, parallelize, len(args['curves']), verbose)
else:
initBounds = copy(args['bounds'])
if 'parallel' in method:
if verbose:
print('Starting parallel method...')
curves = _fitparallel(args)
if args['fit_prior'] == True:
args['fit_prior'] = curves
args['curves'] = curves
args['bounds'] = copy(initBounds)
if 'series' in method:
if verbose:
print('Starting series method...')
if 'td' not in args['bounds']:
if verbose:
print(
'td not in bounds for series method, choosing based on parallel bounds...')
args['bounds']['td'] = args['bounds']['t0']
if 'mu' not in args['bounds']:
if verbose:
print(
'mu not in bounds for series method, choosing defaults...')
args['bounds']['mu'] = [0, 10]
curves = _fitseries(args)
args['curves'] = curves
args['bounds'] = copy(initBounds)
if 'color' in method:
if verbose:
print('Starting color method...')
if 'td' not in args['bounds']:
if verbose:
print(
'td not in bounds for color method, choosing based on parallel bounds...')
args['bounds']['td'] = args['bounds']['t0']
curves = _fitColor(args)
elif method not in ['parallel', 'series', 'color']:
raise RuntimeError(
'Parameter "method" must be "parallel","series", or "color".')
elif method == 'parallel':
if args['parlist']:
if par_or_batch == 'parallel':
par_arg_vals = []
for i in range(len(args['curves'])):
temp_args = {}
for par_key in ['snType', 'bounds', 'constants', 't0_guess']:
if isinstance(args[par_key], (list, tuple, np.ndarray)):
try:
temp_args[par_key] = args[par_key][i]
except:
pass
for par_key in ['bands', 'models', 'ignore', 'params']:
if isinstance(args[par_key], (list, tuple, np.ndarray)) and np.any([isinstance(x, (list, tuple, np.ndarray)) for x in args[par_key]]):
try:
temp_args[par_key] = args[par_key][i]
except:
pass
par_arg_vals.append([args['curves'][i], temp_args])
curves = pyParz.foreach(par_arg_vals, _fitparallel, [
args], numThreads=min(npar_cores, len(par_arg_vals)))
else:
if n_cores_per_node > 1:
parallelize = n_cores_per_node
n_per_node = max(n_per_node, n_cores_per_node)
micro_par = None
elif microlensing is not None:
parallelize = None
micro_par = npar_cores
else:
parallelize = None
micro_par = None
total_jobs = math.ceil(len(args['curves'])/n_per_node)
if nbatch_jobs is None:
nbatch_jobs = min(total_jobs, max_batch_jobs)
script_name_init, folder_name = make_sbatch(partition=batch_partition,
njobs=min(total_jobs, nbatch_jobs), njobstotal=min(total_jobs, max_batch_jobs), python_path=batch_python_path, init=True, parallelize=parallelize, microlensing_cores=micro_par)
script_name, folder_name = make_sbatch(partition=batch_partition, folder=folder_name,
njobs=min(total_jobs, nbatch_jobs), python_path=batch_python_path, init=False, parallelize=parallelize, microlensing_cores=micro_par)
pickle.dump(constants, open(os.path.join(
folder_name, 'sntd_constants.pkl'), 'wb'))
pickle.dump(args['curves'], open(
os.path.join(folder_name, 'sntd_data.pkl'), 'wb'))
pyfiles = ['run_sntd_init.py', 'run_sntd.py'] if parallelize is None else [
'run_sntd_init_par.py', 'run_sntd_par.py']
for pyfile in pyfiles:
with open(os.path.join(_filedir_, 'batch', pyfile)) as f:
batch_py = f.read()
if 'init' in pyfile:
batch_py = batch_py.replace('nlcsreplace', str(
min(int(n_per_node*min(total_jobs, max_batch_jobs)), len(args['curves']))))
batch_py = batch_py.replace(
'njobsreplace', str(min(total_jobs, max_batch_jobs)))
else:
batch_py = batch_py.replace(
'nlcsreplace', str(n_per_node))
if batch_init is None:
batch_py = batch_py.replace(
'batchinitreplace', 'print("Nothing to initialize...")')
else:
batch_py = batch_py.replace(
'batchinitreplace', batch_init)
batch_py = batch_py.replace(
'ncores', str(n_cores_per_node))
indent1 = batch_py.find('fitCurves=')
indent = batch_py.find('try:')+len('try:')+1
sntd_command = 'sntd.fit_data('
for par, val in locs.items():
if par == 'curves':
if parallelize is None:
sntd_command += 'curves=all_dat[i],'
else:
sntd_command += 'curves=all_input,'
elif par == 'batch_init':
sntd_command += 'batch_init=None,'
elif par == 'constants':
if parallelize is None:
sntd_command += 'constants=all_dat[i].constants,'
else:
sntd_command += 'constants=const_list,'
elif par == 'method':
sntd_command += 'method="parallel",'
elif par == 'par_or_batch' and parallelize is not None:
sntd_command += 'par_or_batch="parallel",'
elif par == 'npar_cores' and parallelize is not None:
sntd_command += 'npar_cores=%i,' % n_cores_per_node
elif isinstance(val, str):
sntd_command += str(par)+'="'+str(val)+'",'
elif par == 'kwargs':
for par2, val2 in val.items():
if isinstance(val, str):
sntd_command += str(par2) + \
'="'+str(val2)+'",'
else:
sntd_command += str(par2)+'='+str(val2)+','
else:
sntd_command += str(par)+'='+str(val)+','
sntd_command = sntd_command[:-1]+')'
batch_py = batch_py.replace(
'sntdcommandreplace', sntd_command)
with open(os.path.join(os.path.abspath(folder_name), pyfile), 'w') as f:
f.write(batch_py)
return run_sbatch(folder_name, script_name_init, script_name, total_jobs, max_batch_jobs, n_per_node, wait_for_batch, parallelize, len(args['curves']), verbose)
else:
curves = _fitparallel(args)
elif method == 'series':
if args['parlist']:
if par_or_batch == 'parallel':
par_arg_vals = []
for i in range(len(args['curves'])):
temp_args = {}
try:
for par_key in ['snType', 'bounds', 'constants', 't0_guess']:
if isinstance(args[par_key], (list, tuple, np.ndarray)):
temp_args[par_key] = args[par_key][i]
for par_key in ['bands', 'models', 'ignore', 'params']:
if isinstance(args[par_key], (list, tuple, np.ndarray)) and np.any([isinstance(x, (list, tuple, np.ndarray)) for x in args[par_key]]):
temp_args[par_key] = args[par_key][i]
except:
pass
par_arg_vals.append([args['curves'][i], temp_args])
curves = pyParz.foreach(par_arg_vals, _fitseries, [
args], numThreads=min(npar_cores, len(par_arg_vals)))
else:
if n_cores_per_node > 1:
parallelize = n_cores_per_node
n_per_node = max(n_per_node, n_cores_per_node)
micro_par = None
elif microlensing is not None:
parallelize = None
micro_par = npar_cores
else:
parallelize = None
micro_par = None
total_jobs = math.ceil(len(args['curves'])/n_per_node)
if nbatch_jobs is None:
nbatch_jobs = min(total_jobs, max_batch_jobs)
script_name_init, folder_name = make_sbatch(partition=batch_partition,
njobs=min(total_jobs, nbatch_jobs), njobstotal=min(total_jobs, max_batch_jobs), python_path=batch_python_path, init=True, parallelize=parallelize, microlensing_cores=micro_par)
script_name, folder_name = make_sbatch(partition=batch_partition, folder=folder_name,
njobs=min(total_jobs, nbatch_jobs), python_path=batch_python_path, init=False, parallelize=parallelize, microlensing_cores=micro_par)
pickle.dump(constants, open(os.path.join(
folder_name, 'sntd_constants.pkl'), 'wb'))
pickle.dump(args['curves'], open(
os.path.join(folder_name, 'sntd_data.pkl'), 'wb'))
pyfiles = ['run_sntd_init.py', 'run_sntd.py'] if parallelize is None else [
'run_sntd_init_par.py', 'run_sntd_par.py']
for pyfile in pyfiles:
with open(os.path.join(_filedir_, 'batch', pyfile)) as f:
batch_py = f.read()
if 'init' in pyfile:
batch_py = batch_py.replace('nlcsreplace', str(
min(int(n_per_node*min(total_jobs, max_batch_jobs)), len(args['curves']))))
batch_py = batch_py.replace(
'njobsreplace', str(min(total_jobs, max_batch_jobs)))
else:
batch_py = batch_py.replace(
'nlcsreplace', str(n_per_node))
if batch_init is None:
batch_py = batch_py.replace(
'batchinitreplace', 'print("Nothing to initialize...")')
else:
batch_py = batch_py.replace(
'batchinitreplace', batch_init)
batch_py = batch_py.replace(
'ncores', str(n_cores_per_node))
indent1 = batch_py.find('fitCurves=')
indent = batch_py.find('try:')+len('try:')+1
sntd_command = 'sntd.fit_data('
for par, val in locs.items():
if par == 'curves':
if parallelize is None:
sntd_command += 'curves=all_dat[i],'
else:
sntd_command += 'curves=all_input,'
elif par == 'batch_init':
sntd_command += 'batch_init=None,'
elif par == 'constants':
if parallelize is None:
sntd_command += 'constants=all_dat[i].constants,'
else:
sntd_command += 'constants={'+'},'
elif par == 'method':
sntd_command += 'method="series",'
elif par == 'par_or_batch' and parallelize is not None:
sntd_command += 'par_or_batch="parallel",'
elif par == 'npar_cores' and parallelize is not None:
sntd_command += 'npar_cores=%i,' % n_cores_per_node
elif isinstance(val, str):
sntd_command += str(par)+'="'+str(val)+'",'
elif par == 'kwargs':
for par2, val2 in val.items():
if isinstance(val, str):
sntd_command += str(par2) + \
'="'+str(val2)+'",'
else:
sntd_command += str(par2)+'='+str(val2)+','
else:
sntd_command += str(par)+'='+str(val)+','
sntd_command = sntd_command[:-1]+')'
batch_py = batch_py.replace(
'sntdcommandreplace', sntd_command)
with open(os.path.join(os.path.abspath(folder_name), pyfile), 'w') as f:
f.write(batch_py)
return run_sbatch(folder_name, script_name_init, script_name, total_jobs, max_batch_jobs, n_per_node, wait_for_batch, parallelize, len(args['curves']), verbose)
else:
curves = _fitseries(args)
elif method == 'color':
if args['parlist']:
if par_or_batch == 'parallel':
par_arg_vals = []
for i in range(len(args['curves'])):
temp_args = {}
try:
for par_key in ['snType', 'bounds', 'constants', 't0_guess']:
if isinstance(args[par_key], (list, tuple, np.ndarray)):
temp_args[par_key] = args[par_key][i]
for par_key in ['bands', 'models', 'ignore', 'params']:
if isinstance(args[par_key], (list, tuple, np.ndarray)) and np.any([isinstance(x, (list, tuple, np.ndarray)) for x in args[par_key]]):
temp_args[par_key] = args[par_key][i]
except:
pass
par_arg_vals.append([args['curves'][i], temp_args])
curves = pyParz.foreach(par_arg_vals, _fitColor, [
args], numThreads=min(npar_cores, len(par_arg_vals)))
else:
if n_cores_per_node > 1:
parallelize = n_cores_per_node
n_per_node = max(n_per_node, n_cores_per_node)
micro_par = None
elif microlensing is not None:
parallelize = None
micro_par = npar_cores
else:
parallelize = None
micro_par = None
total_jobs = math.ceil(len(args['curves'])/n_per_node)
if nbatch_jobs is None:
nbatch_jobs = min(total_jobs, max_batch_jobs)
script_name_init, folder_name = make_sbatch(partition=batch_partition,
njobs=min(total_jobs, nbatch_jobs), njobstotal=min(total_jobs, max_batch_jobs), python_path=batch_python_path, init=True, parallelize=parallelize, microlensing_cores=micro_par)
script_name, folder_name = make_sbatch(partition=batch_partition, folder=folder_name,
njobs=min(total_jobs, nbatch_jobs), python_path=batch_python_path, init=False, parallelize=parallelize, microlensing_cores=micro_par)
pickle.dump(constants, open(os.path.join(
folder_name, 'sntd_constants.pkl'), 'wb'))
pickle.dump(args['curves'], open(
os.path.join(folder_name, 'sntd_data.pkl'), 'wb'))
pyfiles = ['run_sntd_init.py', 'run_sntd.py'] if parallelize is None else [
'run_sntd_init_par.py', 'run_sntd_par.py']
for pyfile in pyfiles:
with open(os.path.join(_filedir_, 'batch', pyfile)) as f:
batch_py = f.read()
if 'init' in pyfile:
batch_py = batch_py.replace('nlcsreplace', str(
min(int(n_per_node*min(total_jobs, max_batch_jobs)), len(args['curves']))))
batch_py = batch_py.replace(
'njobsreplace', str(min(total_jobs, max_batch_jobs)))
else:
batch_py = batch_py.replace(
'nlcsreplace', str(n_per_node))
if batch_init is None:
batch_py = batch_py.replace(
'batchinitreplace', 'print("Nothing to initialize...")')
else:
batch_py = batch_py.replace(
'batchinitreplace', batch_init)
batch_py = batch_py.replace(
'ncores', str(n_cores_per_node))
indent1 = batch_py.find('fitCurves=')
indent = batch_py.find('try:')+len('try:')+1
sntd_command = 'sntd.fit_data('
for par, val in locs.items():
if par == 'curves':
if parallelize is None:
sntd_command += 'curves=all_dat[i],'
else:
sntd_command += 'curves=all_input,'
elif par == 'batch_init':
sntd_command += 'batch_init=None,'
elif par == 'constants':
if parallelize is None:
sntd_command += 'constants=all_dat[i].constants,'
else:
sntd_command += 'constants={'+'},'
elif par == 'method':
sntd_command += 'method="color",'
elif par == 'par_or_batch' and parallelize is not None:
sntd_command += 'par_or_batch="parallel",'
elif par == 'npar_cores' and parallelize is not None:
sntd_command += 'npar_cores=%i,' % n_cores_per_node
elif isinstance(val, str):
sntd_command += str(par)+'="'+str(val)+'",'
elif par == 'kwargs':
for par2, val2 in val.items():
if isinstance(val, str):
sntd_command += str(par2) + \
'="'+str(val2)+'",'
else:
sntd_command += str(par2)+'='+str(val2)+','
else:
sntd_command += str(par)+'='+str(val)+','
sntd_command = sntd_command[:-1]+')'
batch_py = batch_py.replace(
'sntdcommandreplace', sntd_command)
with open(os.path.join(os.path.abspath(folder_name), pyfile), 'w') as f:
f.write(batch_py)
return run_sbatch(folder_name, script_name_init, script_name, total_jobs, max_batch_jobs, n_per_node, wait_for_batch, parallelize, len(args['curves']), verbose)
else:
if args['color_bands'] is not None:
args['bands'] = args['color_bands']
curves = _fitColor(args)
return curves
def _bandCheck(curves,bands):
final_bands = []
for b in bands:
for im in curves.images.keys():
if b in curves.images[im].table['band']:
final_bands.append(b)
break
elif b.upper() in curves.images[im].table['band']:
final_bands.append(b.upper())
break
elif b.lower() in curves.images[im].table['band']:
final_bands.append(b.lower())
break
return final_bands
def _fitColor(all_args):
fit_start = time.time()
# Check if parallelized or single fit
if isinstance(all_args, (list, tuple, np.ndarray)):
curves, args = all_args
if isinstance(args, list):
args = args[0]
if isinstance(curves, list):
curves, single_par_vars = curves
for key in single_par_vars:
args[key] = single_par_vars[key]
if isinstance(curves, str):
args['curves'] = pickle.load(open(curves, 'rb'))
else:
args['curves'] = curves
if args['verbose']:
print('Fitting MISN number %i...' % curves.nsn)
else:
args = all_args
for p in args['curves'].constants.keys():
if p not in args['constants'].keys():
args['constants'][p] = args['curves'].constants[p]
if args['clip_data']:
for im in args['curves'].images.keys():
args['curves'].clip_data(im=im, minsnr=args.get(
'minsnr', 0), max_cadence=args['max_cadence'])
else:
for im in args['curves'].images.keys():
args['curves'].clip_data(im=im, rm_NaN=True)
args['bands'] = list(args['bands'])
_, band_SNR, _ = getBandSNR(
args['curves'], args['bands'], args['min_points_per_band'])
if len(args['bands']) < 2:
raise RuntimeError(
"If you want to analyze color curves, you need two bands!")
else:
if args['fit_colors'] is None:
# Try and determine the best bands to use in the fit
final_bands = []
for band in np.unique(args['curves'].images[args['refImage']].table['band']):
to_add = True
for im in args['curves'].images.keys():
if len(np.where(args['curves'].images[im].table['band'] == band)[0]) < args['min_points_per_band']:
to_add = False
if to_add:
final_bands.append(band)
if np.any([x not in final_bands for x in args['bands']]):
all_SNR = []
for band in final_bands:
ims = []
for d in args['curves'].images.keys():
inds = np.where(
args['curves'].images[d].table['band'] == band)[0]
if len(inds) == 0:
ims.append(0)
else:
ims.append(np.sum(args['curves'].images[d].table['flux'][inds]/args['curves'].images[d].table['fluxerr'][inds]) *
np.sqrt(len(inds)))
all_SNR.append(np.sum(ims))
sorted = np.flip(np.argsort(all_SNR))
args['bands'] = np.array(final_bands)[sorted]
if args['max_n_bands'] is not None:
args['bands'] = args['bands'][:args['max_n_bands']]
colors_to_fit = [x for x in combinations(args['bands'], 2)]
if args['color_bands'] is not None:
for i in range(len(colors_to_fit)):
colors_to_fit[i] = [
x for x in args['color_bands'] if x in colors_to_fit[i]]
else:
colors_to_fit = [x.split('-') for x in args['fit_colors']]
imnums = [x[-1] for x in args['curves'].images.keys()]
if args['fit_prior'] is not None:
if args['fit_prior'] == True:
args['fit_prior'] = args['curves']
ref = args['fit_prior'].parallel.fitOrder[0]
refnum = ref[-1]
else:
ref = args['refImage']
refnum = ref[-1]
inds = np.arange(0, len(args['curves'].images[ref].table), 1).astype(int)
nimage = len(imnums)
snParams = ['dt_%s' % i for i in imnums if i != refnum]
all_vparam_names = np.append(args['params'],
snParams).flatten()
if 'td' in args['constants'].keys():
all_vparam_names = np.array(
[x for x in all_vparam_names if 'dt_' not in x])
ims = list(args['curves'].images.keys())
for param in all_vparam_names:
if param in args['color_param_ignore'] and args['fit_prior'] is not None and param not in args['constants']:
par_ref = args['fit_prior'].parallel.fitOrder[0]
args['constants'][param] = args['fit_prior'].images[par_ref].param_quantiles[param][1]
if param in all_vparam_names:
all_vparam_names = np.array(
[x for x in all_vparam_names if x != param])
if param not in args['bounds'].keys():
if param.startswith('dt_'):
if args['fit_prior'] is not None:
im = [x for x in ims if x[-1] == param[-1]][0]
args['bounds'][param] = np.array([-1, 1])*3*np.sqrt(args['fit_prior'].parallel.time_delay_errors[im]**2 +
args['fit_prior'].parallel.time_delay_errors[ref]**2) +\
(args['fit_prior'].parallel.time_delays[im] -
args['fit_prior'].parallel.time_delays[ref])
else:
args['bounds'][param] = np.array(
args['bounds']['td'])
elif args['fit_prior'] is not None:
par_ref = args['fit_prior'].parallel.fitOrder[0]
if param not in args['fit_prior'].images[par_ref].param_quantiles.keys():
continue
args['bounds'][param] = 3*np.array([args['fit_prior'].images[par_ref].param_quantiles[param][0] -
args['fit_prior'].images[par_ref].param_quantiles[param][1],
args['fit_prior'].images[par_ref].param_quantiles[param][2] -
args['fit_prior'].images[par_ref].param_quantiles[param][1]]) + \
args['fit_prior'].images[par_ref].param_quantiles[param][1]
if args['dust'] is not None:
if isinstance(args['dust'], str):
dust_dict = {'CCM89Dust': sncosmo.CCM89Dust,
'OD94Dust': sncosmo.OD94Dust, 'F99Dust': sncosmo.F99Dust}
dust = dust_dict[args['dust']]()
else:
dust = args['dust']
else:
dust = []
effect_names = args['effect_names']
effect_frames = args['effect_frames']
effects = [dust for i in range(len(effect_names))] if effect_names else []
effect_names = effect_names if effect_names else []
effect_frames = effect_frames if effect_frames else []
if not isinstance(effect_names, (list, tuple)):
effects = [effect_names]
if not isinstance(effect_frames, (list, tuple)):
effects = [effect_frames]
if 'ignore_models' in args['set_from_simMeta'].keys():
to_ignore = args['curves'].images[ref].simMeta[args['set_from_simMeta']
['ignore_models']]
if isinstance(to_ignore, str):
to_ignore = [to_ignore]
args['models'] = [x for x in np.array(
args['models']).flatten() if x not in to_ignore]
if args['fit_prior'] is not None and args['fit_prior'].images[args['fit_prior'].parallel.fitOrder[0]].fits.model._source.name not in args['models']:
print('Wanted to use a fit prior but do not have the same model as an option.')
raise RuntimeError
elif args['fit_prior'] is not None:
args['models'] = args['fit_prior'].images[args['fit_prior'].parallel.fitOrder[0]
].fits.model._source.name
if not args['curves'].quality_check(min_n_bands=2,
min_n_points_per_band=args['min_points_per_band'],
clip=False, method='parallel'):
if args['verbose']:
print("Curve(s) not passing quality check.")
return
all_fit_dict = {}
if args['fast_model_selection'] and len(np.array(args['models']).flatten()) > 1:
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
minchisq = np.inf
init_inds = copy(inds)
for mod in np.array(args['models']).flatten():
inds = copy(init_inds)
if isinstance(mod, str):
if mod.upper() in ['BAZIN', 'BAZINSOURCE']:
mod = 'BAZINSOURCE'
if len(np.unique(args['curves'].images[ref].table['band'])) > 1 and args['color_curve'] is None:
best_band = band_SNR[args['fitOrder'][0]][0]
inds = np.where(
args['curves'].images[ref].table['band'] == best_band)[0]
source = BazinSource(
data=args['curves'].images[ref].table[inds], colorCurve=args['color_curve'])
else:
source = sncosmo.get_source(mod)
tempMod = sncosmo.Model(
source=source, effects=effects, effect_names=effect_names, effect_frames=effect_frames)
else:
tempMod = copy(mod)
tempMod.set(**{k: args['constants'][k]
for k in args['constants'].keys() if k in tempMod.param_names})
tempMod.set(**{k: args['curves'].images[args['refImage']].simMeta[args['set_from_simMeta'][k]]
for k in args['set_from_simMeta'].keys() if k in tempMod.param_names})
if mod == 'BAZINSOURCE':
tempMod.set(z=0)
try:
res, fit = sncosmo.fit_lc(args['curves'].images[ref].table[inds], tempMod, [x for x in args['params'] if x in tempMod.param_names],
bounds={b: args['bounds'][b] for b in args['bounds'] if b not in [
't0', tempMod.param_names[2]]},
minsnr=args.get('minsnr', 0))
except:
if args['verbose']:
print('Issue with %s, skipping...' % mod)
continue
tempchisq = res.chisq / \
(len(inds)+len([x for x in args['params']
if x in tempMod.param_names])-1)
if tempchisq < minchisq:
minchisq = tempchisq
bestres = copy(res)
bestfit = copy(fit)
bestmodname = copy(mod)
all_fit_dict[mod] = [copy(fit), copy(res)]
try:
args['models'] = [bestmodname]
except:
print('Every model had an error.')
sys.exit(1)
finallogz = -np.inf
for mod in np.array(args['models']).flatten():
if isinstance(mod, str):
source = sncosmo.get_source(mod)
tempMod = sncosmo.Model(source=source, effects=effects,
effect_names=effect_names, effect_frames=effect_frames)
else:
tempMod = copy(mod)
tempMod.set(**{k: args['constants'][k]
for k in args['constants'].keys() if k in tempMod.param_names})
tempMod.set(**{k: args['curves'].images[args['refImage']].simMeta[args['set_from_simMeta'][k]]
for k in args['set_from_simMeta'].keys() if k in tempMod.param_names})
if args['fit_prior'] is not None:
par_ref = args['fit_prior'].parallel.fitOrder[0]
if mod != args['fit_prior'].images[par_ref].fits.model._source.name:
continue
temp_delays = {k: args['fit_prior'].parallel.time_delays[k]-args['fit_prior'].parallel.time_delays[par_ref]
for k in args['fit_prior'].parallel.fitOrder}
args['curves'].color_table([x[0] for x in colors_to_fit], [x[1] for x in colors_to_fit], time_delays={im: 0 for im in args['curves'].images.keys()},
minsnr=args.get('minsnr', 0))
args['curves'].color.meta['reft0'] = args['fit_prior'].images[par_ref].fits.model.get(
't0')
args['curves'].color.meta['td'] = temp_delays
else:
par_ref = args['refImage']
im_name = args['refImage'][:-1]
if args['trial_fit']:
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
best_bands = band_SNR[args['refImage']][:min(
len(band_SNR[args['refImage']]), 2)]
temp_delays = {}
temp_mags = {}
fit_order = np.flip(args['fitOrder']) if args['fitOrder'] is not None else \
[x for x in args['curves'].images.keys(
) if x != args['refImage']]+[args['refImage']]
for im in fit_order:
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[im].table['band'] == b)[0])
temp_inds = temp_bands.astype(int)
res, fit = sncosmo.fit_lc(copy(args['curves'].images[im].table[temp_inds]), tempMod,
[x for x in args['params'] if x in tempMod.param_names and x in args['bounds'].keys()] +
[tempMod.param_names[2]],
bounds={b: args['bounds'][b] for b in args['bounds'].keys() if b not in [
't0', tempMod.param_names[2]]},
minsnr=args.get('minsnr', 0))
temp_delays[im] = fit.get('t0')
for param in args['color_param_ignore']:
if param not in args['constants']:
args['constants'][param] = fit.get(param)
if param in all_vparam_names:
all_vparam_names = np.array(
[x for x in all_vparam_names if x != param])
tempMod.set(**{k: args['constants'][k]
for k in args['constants'].keys() if k in tempMod.param_names})
args['curves'].color.meta['reft0'] = temp_delays[args['refImage']]
temp_delays = {
im: temp_delays[im]-temp_delays[args['refImage']] for im in temp_delays.keys()}
for b in args['bounds']:
if b in list(res.errors.keys()):
if b not in all_vparam_names:
tempMod.set(**{b: fit.get(b)})
elif b != 't0':
args['bounds'][b] = np.array([np.max([args['bounds'][b][0], (args['bounds'][b][0]-np.median(args['bounds'][b]))/2+fit.get(b)]),
np.min([args['bounds'][b][1], (args['bounds'][b][1]-np.median(args['bounds'][b]))/2+fit.get(b)])])
else:
args['bounds'][b] = (np.array(
args['bounds'][b])-np.median(args['bounds'][b]))/2+args['curves'].color.meta['reft0']
elif b.startswith('dt_'):
args['bounds'][b] = np.array(
args['bounds']['td'])/2+temp_delays[im_name+b[-1]]
if 't0' not in args['bounds'].keys():
args['bounds']['t0'] = np.array(
args['bounds']['td'])/2+args['curves'].color.meta['reft0']
args['curves'].color_table([x[0] for x in colors_to_fit], [x[1] for x in colors_to_fit], time_delays={im: 0 for im in args['curves'].images.keys()},
minsnr=args.get('minsnr', 0))
args['curves'].color.meta['td'] = temp_delays
else:
if args['t0_guess'] is not None:
args['curves'].color_table([x[0] for x in colors_to_fit], [x[1] for x in colors_to_fit],
referenceImage=args['refImage'], static=True, model=tempMod,
minsnr=args.get('minsnr', 0),
time_delays={im: args['t0_guess'][im]-args['t0_guess'][args['refImage']] for
im in args['t0_guess'].keys()})
args['curves'].color.meta['reft0'] = args['t0_guess'][args['refImage']]
else:
args['curves'].color_table([x[0] for x in colors_to_fit], [x[1] for x in colors_to_fit],
referenceImage=args['refImage'], static=True, model=tempMod,
minsnr=args.get('minsnr', 0))
for b in args['bounds']:
if b.startswith('dt_'):
args['bounds'][b] = np.array(
args['bounds']['td'])+args['curves'].color.meta['td'][im_name+b[-1]]
elif b == 't0':
args['bounds'][b] = np.array(
args['bounds'][b])+args['curves'].color.meta['reft0']
if 't0' not in args['bounds'].keys():
args['bounds']['t0'] = np.array(
args['bounds']['td'])+args['curves'].color.meta['reft0']
# if td is constant, overwrite here
if 'td' in args['constants'].keys():
args['curves'].color_table([x[0] for x in colors_to_fit], [x[1] for x in colors_to_fit],
referenceImage=args['refImage'], static=False, model=tempMod,
minsnr=args.get('minsnr', 0),
time_delays=args['constants']['td'])
if args['cut_time'] is not None:
for im in args['curves'].images.keys():
args['curves'].color.table = args['curves'].color.table[np.where(np.logical_or(args['curves'].color.table['image'] != im,
np.logical_and(args['curves'].color.table['time'] >=
args['cut_time'][0]*(1+tempMod.get('z'))+args['curves'].color.meta['reft0'] +
args['curves'].color.meta['td'][im],
args['curves'].color.table['time'] <=
args['cut_time'][1]*(1+tempMod.get('z'))+args['curves'].color.meta['reft0'] +
args['curves'].color.meta['td'][im])))[0]]
all_vparam_names = np.array(
[x for x in all_vparam_names if x != tempMod.param_names[2]])
if args['band_order'] is not None:
args['bands'] = [x for x in args['band_order'] if x in args['bands']]
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
if not args['curves'].quality_check(min_n_bands=args['min_n_bands'],
min_n_points_per_band=args['min_points_per_band'], clip=args['clip_data'], method='color'):
print("Error: Did not pass quality check.")
return
params, res, model = nest_color_lc(args['curves'].color.table, tempMod, nimage, colors=colors_to_fit,
bounds=args['bounds'], use_MLE=args['use_MLE'],
vparam_names=[x for x in all_vparam_names if x in tempMod.param_names or x in snParams], ref=par_ref,
minsnr=args.get('minsnr', 5.), priors=args.get('priors', None), ppfs=args.get('ppfs', None),
method=args.get('nest_method', 'single'), maxcall=args.get('maxcall', None),
modelcov=args.get('modelcov', None), rstate=args.get('rstate', None),
maxiter=args.get('maxiter', None), npoints=args.get('npoints', 100))
if finallogz < res.logz:
finallogz = res.logz
finalres, finalmodel = res, model
time_delays = args['curves'].color.meta['td']
final_param_quantiles = params
args['curves'].color.time_delays = dict([])
args['curves'].color.time_delay_errors = dict([])
args['curves'].color.t_peaks = dict([])
finalres_max = finalres.logl.argmax()
if 'td' in args['constants'].keys():
args['curves'].color.time_delays = args['constants']['td']
args['curves'].color.time_delay_errors = {
im: 0 for im in args['curves'].color.time_delays.keys()}
args['curves'].color.meta['fit_colors'] = colors_to_fit
args['curves'].color.refImage = args['refImage']
args['curves'].color.priorImage = par_ref
args['curves'].color.bands = args['bands']
args['curves'].color.fits = newDict()
args['curves'].color.fits['model'] = finalmodel
args['curves'].color.fits['res'] = finalres
return args['curves']
if par_ref == args['refImage']:
args['curves'].color.time_delays[par_ref] = 0
args['curves'].color.time_delay_errors[par_ref] = np.array([0, 0])
if not args['use_MLE']:
args['curves'].color.t_peaks[par_ref] = weighted_quantile(
finalres.samples[:, finalres.vparam_names.index('t0')], .5, finalres.weights)
else:
args['curves'].color.t_peaks[par_ref] = finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
for k in args['curves'].images.keys():
if k == par_ref:
continue
else:
if not args['use_MLE']:
args['curves'].color.t_peaks[k] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index('dt_'+k[-1])] +
finalres.samples[:, finalres.vparam_names.index(
't0')],
.5, finalres.weights)
dt_quant = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(
'dt_'+k[-1])], [.16, .5, .84], finalres.weights)
else:
args['curves'].color.t_peaks[k] = finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])] +\
finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
dt_quant = [finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]-finalres.errors['dt_'+k[-1]],
finalres.samples[finalres_max, finalres.vparam_names.index(
'dt_'+k[-1])],
finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]+finalres.errors['dt_'+k[-1]]]
args['curves'].color.time_delays[k] = dt_quant[1]
args['curves'].color.time_delay_errors[k] = np.array(
[dt_quant[0]-dt_quant[1], dt_quant[2]-dt_quant[1]])
else:
args['curves'].color.time_delays[args['refImage']] = 0
args['curves'].color.time_delay_errors[args['refImage']] = np.array([
0, 0])
trefSamples = finalres.samples[:, finalres.vparam_names.index(
'dt_'+args['refImage'][-1])]
if not args['use_MLE']:
args['curves'].color.t_peaks[args['refImage']] = weighted_quantile(
trefSamples+finalres.samples[:, finalres.vparam_names.index('t0')], .5, finalres.weights)
else:
args['curves'].color.t_peaks[args['refImage']] = trefSamples[finalres_max] + \
finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
for k in args['curves'].images.keys():
if k == args['refImage']:
continue
elif k == par_ref:
if not args['use_MLE']:
args['curves'].color.t_peaks[k] = weighted_quantile(
finalres.samples[:, finalres.vparam_names.index('t0')], .5, finalres.weights)
dt_quant = weighted_quantile(-1*trefSamples,
[.16, .5, .84], finalres.weights)
else:
args['curves'].color.t_peaks[k] = finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
dt_quant = [-1*trefSamples[finalres_max]-finalres.errors['t0'],
-1*trefSamples[finalres_max],
-1*trefSamples[finalres_max]+finalres.errors['t0']]
args['curves'].color.time_delays[k] = dt_quant[1]
args['curves'].color.time_delay_errors[k] = np.array(
[dt_quant[0]-dt_quant[1], dt_quant[2]-dt_quant[1]])
else:
if not args['use_MLE']:
args['curves'].color.t_peaks[k] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index('dt_'+k[-1])] +
finalres.samples[:, finalres.vparam_names.index(
't0')],
.5, finalres.weights)
dt_quant = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(
'dt_'+k[-1])]-trefSamples, [.16, .5, .84], finalres.weights)
else:
args['curves'].color.t_peaks[k] = finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])] +\
finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
dt_quant = [finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]-trefSamples[finalres_max]-finalres.errors['dt_'+k[-1]],
finalres.samples[finalres_max, finalres.vparam_names.index(
'dt_'+k[-1])]-trefSamples[finalres_max],
finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]-trefSamples[finalres_max]+finalres.errors['dt_'+k[-1]]]
args['curves'].color.time_delays[k] = dt_quant[1]
args['curves'].color.time_delay_errors[k] = np.array(
[dt_quant[0]-dt_quant[1], dt_quant[2]-dt_quant[1]])
finalmodel.set(t0=args['curves'].color.t_peaks[args['refImage']])
args['curves'].color_table([x[0] for x in colors_to_fit], [x[1] for x in colors_to_fit],
time_delays=args['curves'].color.time_delays, minsnr=args.get('minsnr', 0))
args['curves'].color.meta['td'] = time_delays
args['curves'].color.meta['fit_colors'] = colors_to_fit
args['curves'].color.refImage = args['refImage']
args['curves'].color.priorImage = par_ref
args['curves'].color.bands = args['bands']
args['curves'].color.fits = newDict()
args['curves'].color.fits['model'] = finalmodel
args['curves'].color.fits['res'] = finalres
fit_end = time.time()
args['curves'].color.fit_time = fit_end - fit_start
return args['curves']
def nest_color_lc(data, model, nimage, colors, vparam_names, bounds, ref='image_1', use_MLE=False,
minsnr=5., priors=None, ppfs=None, npoints=100, method='single',
maxiter=None, maxcall=None, modelcov=False, rstate=None,
verbose=False, warn=True, **kwargs):
# Taken from SNCosmo nest_lc
# experimental parameters
tied = kwargs.get("tied", None)
vparam_names = list(vparam_names)
if ppfs is None:
ppfs = {}
if tied is None:
tied = {}
# Convert bounds/priors combinations into ppfs
if bounds is not None:
for key, val in bounds.items():
if key in ppfs:
continue # ppfs take priority over bounds/priors
a, b = val
if priors is not None and key in priors:
# solve ppf at discrete points and return interpolating
# function
x_samples = np.linspace(0., 1., 101)
ppf_samples = sncosmo.utils.ppf(priors[key], x_samples, a, b)
f = sncosmo.utils.Interp1D(0., 1., ppf_samples)
else:
f = sncosmo.utils.Interp1D(0., 1., np.array([a, b]))
ppfs[key] = f
# NOTE: It is important that iparam_names is in the same order
# every time, otherwise results will not be reproducible, even
# with same random seed. This is because iparam_names[i] is
# matched to u[i] below and u will be in a reproducible order,
# so iparam_names must also be.
iparam_names = [key for key in vparam_names if key in ppfs]
ppflist = [ppfs[key] for key in iparam_names]
npdim = len(iparam_names) # length of u
ndim = len(vparam_names) # length of v
# Check that all param_names either have a direct prior or are tied.
for name in vparam_names:
if name in iparam_names:
continue
if name in tied:
continue
raise ValueError("Must supply ppf or bounds or tied for parameter '{}'"
.format(name))
def prior_transform(u):
d = {}
for i in range(npdim):
d[iparam_names[i]] = ppflist[i](u[i])
v = np.empty(ndim, dtype=np.float)
for i in range(ndim):
key = vparam_names[i]
if key in d:
v[i] = d[key]
else:
v[i] = tied[key](d)
return v
if np.any(['dt_' in x for x in vparam_names]):
doTd = True
nTdParam = nimage-1
else:
doTd = False
nTdParam = 0
model_param_names = [
x for x in vparam_names[:len(vparam_names)-nTdParam]]
model_idx = np.array([vparam_names.index(name)
for name in model_param_names])
td_params = [x for x in vparam_names[len(
vparam_names)-nimage:] if x.startswith('dt')]
td_idx = np.array([vparam_names.index(name) for name in td_params])
im_indices = [np.where(data['image'] == i)[0]
for i in np.unique(data['image']) if i != ref]
obs_dict = {}
err_dict = {}
zp_dict = {}
time_dict = {}
im_dict = {}
for color in colors:
col_inds = np.where(~np.isnan(data[color[0]+'-'+color[1]]))[0]
time_dict[color[0]+'-'+color[1]] = np.array(data['time'][col_inds])
im_dict[color[0]+'-'+color[1]] = {i[i.find('_')+1:]: np.where(data[col_inds]['image'] == i)[0] for
i in np.unique(data[col_inds]['image']) if i != ref}
obs_dict[color[0]+'-'+color[1]
] = np.array(data[color[0]+'-'+color[1]][col_inds])
err_dict[color[0]+'-'+color[1]
] = np.array(data[color[0]+'-'+color[1]+'_err'][col_inds])
zpsys = data['zpsys'][0]
def chisq_likelihood(parameters):
model.set(**{model_param_names[k]: parameters[model_idx[k]]
for k in range(len(model_idx))})
mod_dict = {}
cov_dict = {}
chisq = 0
for color in colors:
obs = obs_dict[color[0]+'-'+color[1]]
err = err_dict[color[0]+'-'+color[1]]
time = copy(time_dict[color[0]+'-'+color[1]])
if doTd:
for i in range(len(td_idx)):
time[im_dict[color[0]+'-'+color[1]]
[td_params[i][-1]]] -= parameters[td_idx[i]]
timesort = np.argsort(time)
mod_color = model.color(color[0], color[1], zpsys, time[timesort])
if np.any(np.isnan(mod_color)):
return(-np.inf)
if modelcov:
for b in color:
_, mcov = model.bandfluxcov(b,
time[timesort],
zp=zp_dict[b],
zpsys=zpsys)
cov_dict[b] = mcov
cov = np.diag(err[timesort])
mcov1 = cov_dict[color[0]][:, np.array(color_inds1)[timesort]]
mcov1 = mcov1[np.array(color_inds1)[timesort], :]
mcov2 = cov_dict[color[1]][:, np.array(color_inds2)[timesort]]
mcov2 = mcov2[np.array(color_inds2)[timesort], :]
cov = cov + np.sqrt(mcov1**2+mcov2**2)
invcov = np.linalg.pinv(cov)
diff = obs-model_observations
chisq += np.dot(np.dot(diff, invcov), diff)
else:
chi = (obs[timesort]-mod_color)/err[timesort]
chisq += np.dot(chi, chi)
return chisq
def loglike(parameters):
chisq = chisq_likelihood(parameters)
if not np.isfinite(chisq):
return -np.inf
return(-.5*chisq)
res = nestle.sample(loglike, prior_transform, ndim, npdim=npdim,
npoints=npoints, method=method, maxiter=maxiter,
maxcall=maxcall, rstate=rstate,
callback=(nestle.print_progress if verbose else None))
vparameters, cov = nestle.mean_and_cov(res.samples, res.weights)
res = sncosmo.utils.Result(niter=res.niter,
ncall=res.ncall,
logz=res.logz,
logzerr=res.logzerr,
h=res.h,
samples=res.samples,
weights=res.weights,
logvol=res.logvol,
logl=res.logl,
errors=OrderedDict(zip(vparam_names,
np.sqrt(np.diagonal(cov)))),
vparam_names=copy(vparam_names),
bounds=bounds)
if use_MLE:
best_ind = res.logl.argmax()
params = [[res.samples[best_ind, i]-res.errors[vparam_names[i]], res.samples[best_ind, i], res.samples[best_ind, i]+res.errors[vparam_names[i]]]
for i in range(len(vparam_names))]
else:
params = [weighted_quantile(
res.samples[:, i], [.16, .5, .84], res.weights) for i in range(len(vparam_names))]
model.set(**{model_param_names[k]: params[model_idx[k]][1]
for k in range(len(model_idx))})
return params, res, model
def _fitseries(all_args):
fit_start = time.time()
if isinstance(all_args, (list, tuple, np.ndarray)):
curves, args = all_args
if isinstance(args, list):
args = args[0]
if isinstance(curves, list):
curves, single_par_vars = curves
for key in single_par_vars:
args[key] = single_par_vars[key]
if isinstance(curves, str):
args['curves'] = pickle.load(open(curves, 'rb'))
else:
args['curves'] = curves
if args['verbose']:
print('Fitting MISN number %i...' % curves.nsn)
else:
args = all_args
for p in args['curves'].constants.keys():
if p not in args['constants'].keys():
args['constants'][p] = args['curves'].constants[p]
if args['clip_data']:
for im in args['curves'].images.keys():
args['curves'].clip_data(im=im, minsnr=args.get(
'minsnr', 0), max_cadence=args['max_cadence'])
else:
for im in args['curves'].images.keys():
args['curves'].clip_data(im=im, rm_NaN=True)
args['bands'], band_SNR, _ = getBandSNR(
args['curves'], args['bands'], args['min_points_per_band'])
args['curves'].series.bands = args['bands'][:args['max_n_bands']
]if args['max_n_bands'] is not None else args['bands']
imnums = [x[-1] for x in args['curves'].images.keys()]
if args['fit_prior'] is not None:
if args['fit_prior'] == True:
args['fit_prior'] = args['curves']
ref = args['fit_prior'].parallel.fitOrder[0]
refnum = ref[-1]
else:
ref = args['refImage']
refnum = ref[-1]
nimage = len(imnums)
snParams = [['dt_%s' % i, 'mu_%s' % i] for i in imnums if i != refnum]
all_vparam_names = np.append(args['params'],
snParams).flatten()
if 'mu' in args['constants'].keys():
all_vparam_names = [x for x in all_vparam_names if 'mu_' not in x]
if 'td' in args['constants'].keys():
all_vparam_names = [x for x in all_vparam_names if 'dt_' not in x]
ims = list(args['curves'].images.keys())
for param in all_vparam_names:
if param not in args['bounds'].keys():
if param.startswith('dt_'):
if args['fit_prior'] is not None:
im = [x for x in ims if x[-1] == param[-1]][0]
args['bounds'][param] = np.array([-1, 1])*3*np.sqrt(args['fit_prior'].parallel.time_delay_errors[im]**2 +
args['fit_prior'].parallel.time_delay_errors[ref]**2) +\
(args['fit_prior'].parallel.time_delays[im] -
args['fit_prior'].parallel.time_delays[ref])
else:
args['bounds'][param] = np.array(
args['bounds']['td']) # +time_delays[im]
elif param.startswith('mu_'):
if args['fit_prior'] is not None:
im = [x for x in ims if x[-1] == param[-1]][0]
args['bounds'][param] = np.array([-1, 1])*3*(args['fit_prior'].parallel.magnifications[im]/args['fit_prior'].parallel.magnifications[ref]) *\
np.sqrt((args['fit_prior'].parallel.magnification_errors[im]/args['fit_prior'].parallel.magnifications[im])**2 +
(args['fit_prior'].parallel.magnification_errors[ref]/args['fit_prior'].parallel.magnifications[ref])**2)\
+ (args['fit_prior'].parallel.magnifications[im] /
args['fit_prior'].parallel.magnifications[ref])
else:
args['bounds'][param] = np.array(
args['bounds']['mu']) # *magnifications[im]
elif args['fit_prior'] is not None:
par_ref = args['fit_prior'].parallel.fitOrder[0]
if param not in args['fit_prior'].images[par_ref].param_quantiles.keys():
continue
args['bounds'][param] = 3*np.array([args['fit_prior'].images[par_ref].param_quantiles[param][0] -
args['fit_prior'].images[par_ref].param_quantiles[param][1],
args['fit_prior'].images[par_ref].param_quantiles[param][2] -
args['fit_prior'].images[par_ref].param_quantiles[param][1]]) + \
args['fit_prior'].images[par_ref].param_quantiles[param][1]
elif args['fit_prior'] is not None:
par_ref = args['fit_prior'].parallel.fitOrder[0]
if param not in args['fit_prior'].images[par_ref].param_quantiles.keys():
continue
args['bounds'][param] = 3*np.array([args['fit_prior'].images[par_ref].param_quantiles[param][0] -
args['fit_prior'].images[par_ref].param_quantiles[param][1],
args['fit_prior'].images[par_ref].param_quantiles[param][2] -
args['fit_prior'].images[par_ref].param_quantiles[param][1]]) + \
args['fit_prior'].images[par_ref].param_quantiles[param][1]
finallogz = -np.inf
if args['dust'] is not None:
if isinstance(args['dust'], str):
dust_dict = {'CCM89Dust': sncosmo.CCM89Dust,
'OD94Dust': sncosmo.OD94Dust, 'F99Dust': sncosmo.F99Dust}
dust = dust_dict[args['dust']]()
else:
dust = args['dust']
else:
dust = []
effect_names = args['effect_names']
effect_frames = args['effect_frames']
effects = [dust for i in range(len(effect_names))] if effect_names else []
effect_names = effect_names if effect_names else []
effect_frames = effect_frames if effect_frames else []
if not isinstance(effect_names, (list, tuple)):
effects = [effect_names]
if not isinstance(effect_frames, (list, tuple)):
effects = [effect_frames]
if args['fit_prior'] is not None and args['fit_prior'].images[args['fit_prior'].parallel.fitOrder[0]].fits.model._source.name not in args['models']:
print('Wanted to use a fit prior but do not have the same model as an option.')
raise RuntimeError
elif args['fit_prior'] is not None:
args['models'] = args['fit_prior'].images[args['fit_prior'].parallel.fitOrder[0]
].fits.model._source.name
if args['max_n_bands'] is not None:
best_bands = band_SNR[ref][:min(
len(band_SNR[ref]), args['max_n_bands'])]
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[ref].table['band'] == b)[0])
inds = temp_bands.astype(int)
else:
best_bands = args['bands']
inds = np.arange(
0, len(args['curves'].images[ref].table), 1).astype(int)
if 'ignore_models' in args['set_from_simMeta'].keys():
to_ignore = args['curves'].images[ref].simMeta[args['set_from_simMeta']
['ignore_models']]
if isinstance(to_ignore, str):
to_ignore = [to_ignore]
args['models'] = [x for x in np.array(
args['models']).flatten() if x not in to_ignore]
all_fit_dict = {}
if not args['curves'].quality_check(min_n_bands=args['min_n_bands'],
min_n_points_per_band=args['min_points_per_band'], clip=False, method='parallel'):
return
if args['fast_model_selection'] and len(np.array(args['models']).flatten()) > 1:
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
minchisq = np.inf
init_inds = copy(inds)
for mod in np.array(args['models']).flatten():
inds = copy(init_inds)
if isinstance(mod, str):
if mod.upper() in ['BAZIN', 'BAZINSOURCE']:
mod = 'BAZINSOURCE'
if len(np.unique(args['curves'].images[ref].table['band'])) > 1 and args['color_curve'] is None:
best_band = band_SNR[args['fitOrder'][0]][0]
inds = np.where(
args['curves'].images[ref].table['band'] == best_band)[0]
source = BazinSource(
data=args['curves'].images[ref].table[inds], colorCurve=args['color_curve'])
else:
source = sncosmo.get_source(mod)
tempMod = sncosmo.Model(
source=source, effects=effects, effect_names=effect_names, effect_frames=effect_frames)
else:
tempMod = copy(mod)
tempMod.set(**{k: args['constants'][k]
for k in args['constants'].keys() if k in tempMod.param_names})
tempMod.set(**{k: args['curves'].images[args['refImage']].simMeta[args['set_from_simMeta'][k]]
for k in args['set_from_simMeta'].keys() if k in tempMod.param_names})
if not np.all([tempMod.bandoverlap(x) for x in best_bands]):
if args['verbose']:
print('Skipping %s because it does not cover the bands...')
continue
if mod == 'BAZINSOURCE':
tempMod.set(z=0)
try:
res, fit = sncosmo.fit_lc(args['curves'].images[ref].table[inds], tempMod, [x for x in args['params'] if x in tempMod.param_names],
bounds={b: args['bounds'][b] for b in args['bounds'] if b not in [
't0', tempMod.param_names[2]]},
minsnr=args.get('minsnr', 0))
except:
if args['verbose']:
print('Issue with %s, skipping...' % mod)
continue
tempchisq = res.chisq / \
(len(inds)+len([x for x in args['params']
if x in tempMod.param_names])-1)
if tempchisq < minchisq:
minchisq = tempchisq
bestres = copy(res)
bestfit = copy(fit)
bestmodname = copy(mod)
all_fit_dict[mod] = [copy(fit), copy(res)]
try:
args['models'] = [bestmodname]
except:
print('Every model had an error.')
sys.exit(1)
for mod in np.array(args['models']).flatten():
if isinstance(mod, str):
if mod.upper() in ['BAZIN', 'BAZINSOURCE']:
source = BazinSource(
data=args['curves'].images[args['fitOrder'][0]].table)
else:
source = sncosmo.get_source(mod)
tempMod = sncosmo.Model(source=source, effects=effects,
effect_names=effect_names, effect_frames=effect_frames)
else:
tempMod = copy(mod)
tempMod.set(**{k: args['constants'][k]
for k in args['constants'].keys() if k in tempMod.param_names})
tempMod.set(**{k: args['curves'].images[args['refImage']].simMeta[args['set_from_simMeta'][k]]
for k in args['set_from_simMeta'].keys() if k in tempMod.param_names})
if args['fit_prior'] is not None:
par_ref = args['fit_prior'].parallel.fitOrder[0]
if mod != args['fit_prior'].images[par_ref].fits.model._source.name:
continue
temp_delays = {k: args['fit_prior'].parallel.time_delays[k]-args['fit_prior'].parallel.time_delays[par_ref]
for k in args['fit_prior'].parallel.fitOrder}
temp_mags = {k: args['fit_prior'].parallel.magnifications[k]/args['fit_prior'].parallel.magnifications[par_ref]
for k in args['fit_prior'].parallel.fitOrder}
args['curves'].combine_curves(time_delays={im: 0 for im in args['curves'].images.keys()},
magnifications={im: 1 for im in args['curves'].images.keys()}, minsnr=args.get('minsnr', 0))
args['curves'].series.meta['reft0'] = args['fit_prior'].images[par_ref].fits.model.get(
't0')
args['curves'].series.meta['refamp'] = args['fit_prior'].images[par_ref].fits.model.get(
tempMod.param_names[2])
args['curves'].series.meta['td'] = temp_delays
args['curves'].series.meta['mu'] = temp_mags
else:
par_ref = args['refImage']
im_name = args['refImage'][:-1]
if args['trial_fit']:
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
nbands = args['max_n_bands'] if args['max_n_bands'] is not None else 2
best_bands = band_SNR[args['refImage']][:min(
len(band_SNR[args['refImage']]), nbands)]
temp_delays = {}
temp_mags = {}
fit_order = np.flip(args['fitOrder']) if args['fitOrder'] is not None else \
[x for x in args['curves'].images.keys(
) if x != args['refImage']]+[args['refImage']]
for im in fit_order:
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[im].table['band'] == b)[0])
temp_inds = temp_bands.astype(int)
res, fit = sncosmo.fit_lc(copy(args['curves'].images[im].table[temp_inds]), tempMod, [x for x in args['params'] if x in tempMod.param_names],
bounds={b: args['bounds'][b] for b in args['bounds'].keys() if b not in [
't0', tempMod.param_names[2]]},
minsnr=args.get('minsnr', 0))
temp_delays[im] = fit.get('t0')
temp_mags[im] = fit.parameters[2]
args['curves'].series.meta['reft0'] = temp_delays[args['refImage']]
args['curves'].series.meta['refamp'] = temp_mags[args['refImage']]
temp_delays = {
im: temp_delays[im]-temp_delays[args['refImage']] for im in temp_delays.keys()}
temp_mags = {
im: temp_mags[im]/temp_mags[args['refImage']] for im in temp_mags}
for b in args['bounds']:
if b in list(res.errors.keys()):
if b not in ['t0', tempMod.param_names[2]]:
args['bounds'][b] = np.array([np.max([args['bounds'][b][0], (args['bounds'][b][0]-np.median(args['bounds'][b]))/2+fit.get(b)]),
np.min([args['bounds'][b][1], (args['bounds'][b][1]-np.median(args['bounds'][b]))/2+fit.get(b)])])
elif b == 't0':
args['bounds'][b] = (np.array(
args['bounds'][b])-np.median(args['bounds'][b]))/2+args['curves'].series.meta['reft0']
else:
args['bounds'][b] = (np.array(
args['bounds'][b])-np.median(args['bounds'][b]))/2+args['curves'].series.meta['refamp']
elif b.startswith('dt_'):
args['bounds'][b] = np.array(
args['bounds']['td'])/2+temp_delays[im_name+b[-1]]
elif b.startswith('mu_'):
args['bounds'][b] = (np.array(
args['bounds']['mu'])*temp_mags[im_name+b[-1]]+temp_mags[im_name+b[-1]])/2
if tempMod.param_names[2] not in args['bounds'].keys():
if 'mu' in args['bounds'].keys():
args['bounds'][tempMod.param_names[2]] = (np.array(
args['bounds']['mu'])*args['curves'].series.meta['refamp']+args['curves'].series.meta['refamp'])/2
else:
args['bounds'][tempMod.param_names[2]] = (np.array(
[.1, 10])*args['curves'].series.meta['refamp']+args['curves'].series.meta['refamp'])/2
if 't0' not in args['bounds'].keys():
args['bounds']['t0'] = np.array(
args['bounds']['td'])/2+args['curves'].series.meta['reft0']
if args['curves'].series.table is None:
args['curves'].combine_curves(time_delays={im: 0 for im in args['curves'].images.keys()},
magnifications={im: 1 for im in args['curves'].images.keys()}, minsnr=args.get('minsnr', 0))
args['curves'].series.meta['td'] = temp_delays
args['curves'].series.meta['mu'] = temp_mags
else:
if args['curves'].series.table is None:
args['curves'].combine_curves(
referenceImage=args['refImage'], static=True, model=tempMod, minsnr=args.get('minsnr', 0))
if args['t0_guess'] is not None:
args['curves'].series.meta['td'] = {
im: args['t0_guess'][im]-args['t0_guess'][args['refImage']] for im in args['t0_guess'].keys()}
if 'reft0' not in args['curves'].series.meta.keys():
args['curves'].series.meta['reft0'] = args['t0_guess'][args['refImage']]
elif 'reft0' not in args['curves'].series.meta.keys():
guess_t0, guess_amp = sncosmo.fitting.guess_t0_and_amplitude(sncosmo.photdata.photometric_data(
args['curves'].series.table),
tempMod, args.get('minsnr', 0))
args['curves'].series.meta['reft0'] = guess_t0
if 'refamp' not in args['curves'].series.meta.keys():
args['curves'].series.meta['refamp'] = guess_amp
for b in args['bounds']:
if b.startswith('dt_'):
args['bounds'][b] = np.array(
args['bounds']['td'])+args['curves'].series.meta['td'][im_name+b[-1]]
elif b.startswith('mu_'):
args['bounds'][b] = np.array(
args['bounds']['mu'])*args['curves'].series.meta['mu'][im_name+b[-1]]
elif b == 't0':
args['bounds'][b] = np.array(
args['bounds'][b])+args['curves'].series.meta['reft0']
if tempMod.param_names[2] not in args['bounds'].keys():
args['bounds'][tempMod.param_names[2]] = (np.array(
[.1, 10])*args['curves'].series.meta['refamp']+args['curves'].series.meta['refamp'])/2
if 't0' not in args['bounds'].keys():
args['bounds']['t0'] = np.array(
args['bounds']['td'])+args['curves'].series.meta['reft0']
# if constant td/mag, overwrite previous sets
if 'td' in args['constants'].keys() or 'mu' in args['constants'].keys():
if 'td' in args['constants'].keys():
args['curves'].series.meta['td'] = args['constants']['td']
temp_delays = args['constants']['td']
else:
temp_delays = {
im: 0 for im in args['curves'].series.meta['td'].keys()}
if 'mu' in args['constants'].keys():
args['curves'].series.meta['mu'] = args['constants']['mu']
temp_mags = args['constants']['mu']
else:
temp_mags = {
im: 1 for im in args['curves'].series.meta['mu'].keys()}
args['curves'].combine_curves(
referenceImage=args['refImage'], static=False, model=tempMod, minsnr=args.get('minsnr', 0),
time_delays=temp_delays, magnifications=temp_mags)
if args['cut_time'] is not None:
for im in args['curves'].images.keys():
args['curves'].series.table = args['curves'].series.table[np.where(np.logical_or(args['curves'].series.table['image'] != im,
np.logical_and(args['curves'].series.table['time'] >=
args['cut_time'][0]*(1+tempMod.get('z'))+args['curves'].series.meta['reft0'] +
args['curves'].series.meta['td'][im],
args['curves'].series.table['time'] <=
args['cut_time'][1]*(1+tempMod.get('z'))+args['curves'].series.meta['reft0'] +
args['curves'].series.meta['td'][im])))[0]]
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
for b in [x for x in np.unique(args['curves'].series.table['band']) if x not in args['curves'].series.bands]:
args['curves'].series.table = args['curves'].series.table[args['curves'].series.table['band'] != b]
if not args['curves'].quality_check(min_n_bands=args['min_n_bands'],
min_n_points_per_band=args['min_points_per_band'], clip=args['clip_data'], method='series'):
print('Error: Did not pass quality check.')
return
vparam_names_final = [
x for x in all_vparam_names if x in tempMod.param_names or x in np.array(snParams).flatten()]
params, res, model = nest_series_lc(args['curves'].series.table, tempMod, nimage, bounds=args['bounds'], use_MLE=args['use_MLE'],
vparam_names=vparam_names_final, ref=par_ref,
minsnr=args.get('minsnr', 5.), priors=args.get('priors', None), ppfs=args.get('ppfs', None),
method=args.get('nest_method', 'single'), maxcall=args.get('maxcall', None),
modelcov=args.get('modelcov', None), rstate=args.get('rstate', None),
maxiter=args.get('maxiter', None), npoints=args.get('npoints', 100))
if finallogz < res.logz:
finallogz = res.logz
final_param_quantiles, finalres, finalmodel = params, res, model
time_delays = args['curves'].series.meta['td']
magnifications = args['curves'].series.meta['mu']
args['curves'].series.param_quantiles = {d: final_param_quantiles[finalres.vparam_names.index(d)]
for d in finalres.vparam_names}
if 'td' in args['constants'].keys():
args['curves'].series.time_delays = args['constants']['td']
else:
args['curves'].series.time_delays = {
im: 0 for im in args['curves'].images.keys()}
if 'mu' in args['constants'].keys():
args['curves'].series.magnifications = args['constants']['mu']
else:
args['curves'].series.magnifications = {
im: 1 for im in args['curves'].images.keys()}
args['curves'].series.magnification_errors = {
im: 1 for im in args['curves'].images.keys()}
args['curves'].series.time_delay_errors = {
im: np.array([0, 0]) for im in args['curves'].images.keys()}
args['curves'].series.t_peaks = dict([])
args['curves'].series.a_peaks = dict([])
finalres_max = finalres.logl.argmax()
if not np.any(['mu' in x for x in vparam_names_final]):
doMu = False
else:
doMu = True
if not np.any(['dt' in x for x in vparam_names_final]):
doTd = False
else:
doTd = True
if not doMu and not doTd:
args['curves'].series.refImage = args['refImage']
args['curves'].series.priorImage = par_ref
args['curves'].series.fits = newDict()
args['curves'].series.fits['model'] = finalmodel
args['curves'].series.fits['res'] = finalres
return args['curves']
if par_ref == args['refImage']:
if not args['use_MLE']:
args['curves'].series.t_peaks[par_ref] = weighted_quantile(
finalres.samples[:, finalres.vparam_names.index('t0')], .5, finalres.weights)
args['curves'].series.a_peaks[par_ref] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(finalmodel.param_names[2])],
.5, finalres.weights)
else:
args['curves'].series.t_peaks[par_ref] = finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
args['curves'].series.a_peaks[par_ref] = finalres.samples[finalres_max,
finalres.vparam_names.index(finalmodel.param_names[2])]
for k in args['curves'].images.keys():
if k == par_ref:
continue
else:
if not args['use_MLE']:
if doTd:
args['curves'].series.t_peaks[k] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index('dt_'+k[-1])] +
finalres.samples[:, finalres.vparam_names.index(
't0')],
.5, finalres.weights)
if doMu:
args['curves'].series.a_peaks[k] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index('mu_'+k[-1])] *
finalres.samples[:, finalres.vparam_names.index(
finalmodel.param_names[2])],
.5, finalres.weights)
if doTd:
dt_quant = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(
'dt_'+k[-1])], [.16, .5, .84], finalres.weights)
if doMu:
mu_quant = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(
'mu_'+k[-1])], [.16, .5, .84], finalres.weights)
else:
if doTd:
args['curves'].series.t_peaks[k] = finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])] +\
finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
if doMu:
args['curves'].series.a_peaks[k] = finalres.samples[finalres_max, finalres.vparam_names.index('mu_'+k[-1])] *\
finalres.samples[finalres_max, finalres.vparam_names.index(
finalmodel.param_names[2])]
if doTd:
dt_quant = [finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]-finalres.errors['dt_'+k[-1]],
finalres.samples[finalres_max, finalres.vparam_names.index(
'dt_'+k[-1])],
finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]+finalres.errors['dt_'+k[-1]]]
if doMu:
mu_quant = [finalres.samples[finalres_max, finalres.vparam_names.index('mu_'+k[-1])]-finalres.errors['mu_'+k[-1]],
finalres.samples[finalres_max, finalres.vparam_names.index(
'mu_'+k[-1])],
finalres.samples[finalres_max, finalres.vparam_names.index('mu_'+k[-1])]+finalres.errors['mu_'+k[-1]]]
if doTd:
args['curves'].series.time_delays[k] = dt_quant[1]
args['curves'].series.time_delay_errors[k] = np.array(
[dt_quant[0]-dt_quant[1], dt_quant[2]-dt_quant[1]])
if doMu:
args['curves'].series.magnifications[k] = mu_quant[1]
args['curves'].series.magnification_errors[k] = np.array(
[mu_quant[0]-mu_quant[1], mu_quant[2]-mu_quant[1]])
else:
args['curves'].series.time_delays[args['refImage']] = 0
args['curves'].series.time_delay_errors[args['refImage']] = np.array([
0, 0])
args['curves'].series.magnifications[args['refImage']] = 1
args['curves'].series.magnification_errors[args['refImage']] = np.array([
0, 0])
if doTd:
trefSamples = finalres.samples[:, finalres.vparam_names.index(
'dt_'+args['refImage'][-1])]
if doMu:
arefSamples = finalres.samples[:, finalres.vparam_names.index(
'mu_'+args['refImage'][-1])]
if not args['use_MLE']:
if doTd:
args['curves'].series.t_peaks[args['refImage']] = weighted_quantile(
trefSamples+finalres.samples[:, finalres.vparam_names.index('t0')], .5, finalres.weights)
if doMu:
args['curves'].series.a_peaks[args['refImage']] = weighted_quantile(arefSamples*finalres.samples[:, finalres.vparam_names.index(finalmodel.param_names[2])],
.5, finalres.weights)
else:
if doTd:
args['curves'].series.t_peaks[args['refImage']] = trefSamples[finalres_max] + \
finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
if doMu:
args['curves'].series.a_peaks[args['refImage']] = arefSamples[finalres_max] * \
finalres.samples[finalres_max, finalres.vparam_names.index(
finalmodel.param_names[2])]
for k in args['curves'].images.keys():
if k == args['refImage']:
continue
elif k == par_ref:
if not args['use_MLE']:
if doTd:
args['curves'].series.t_peaks[k] = weighted_quantile(
finalres.samples[:, finalres.vparam_names.index('t0')], .5, finalres.weights)
if doMu:
args['curves'].series.a_peaks[k] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(finalmodel.param_names[2])],
.5, finalres.weights)
if doTd:
dt_quant = weighted_quantile(-1*trefSamples,
[.16, .5, .84], finalres.weights)
if doMu:
mu_quant = weighted_quantile(
1./arefSamples, [.16, .5, .84], finalres.weights)
else:
if doTd:
args['curves'].series.t_peaks[k] = finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
if doMu:
args['curves'].series.a_peaks[k] = finalres.samples[finalres_max,
finalres.vparam_names.index(finalmodel.param_names[2])]
if doTd:
dt_quant = [-1*trefSamples[finalres_max]-finalres.errors['dt_'+args['refImage'][-1]],
-1*trefSamples[finalres_max],
-1*trefSamples[finalres_max]+finalres.errors['dt_'+args['refImage'][-1]]]
if doMu:
mu_quant = [1./arefSamples-finalres.errors['mu_'+args['refImage'][-1]],
1./arefSamples,
1./arefSamples+finalres.errors['mu_'+args['refImage'][-1]]]
if doTd:
args['curves'].series.time_delays[k] = dt_quant[1]
args['curves'].series.time_delay_errors[k] = np.array(
[dt_quant[0]-dt_quant[1], dt_quant[2]-dt_quant[1]])
if doMu:
args['curves'].series.magnifications[k] = mu_quant[1]
args['curves'].series.magnification_errors[k] = np.array(
[mu_quant[0]-mu_quant[1], mu_quant[2]-mu_quant[1]])
else:
if not args['use_MLE']:
if doTd:
args['curves'].series.t_peaks[k] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index('dt_'+k[-1])] +
finalres.samples[:, finalres.vparam_names.index(
't0')],
.5, finalres.weights)
if doMu:
args['curves'].series.a_peaks[k] = weighted_quantile(finalres.samples[:, finalres.vparam_names.index('mu_'+k[-1])] *
finalres.samples[:, finalres.vparam_names.index(
finalmodel.param_names[2])],
.5, finalres.weights)
if doTd:
dt_quant = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(
'dt_'+k[-1])]-trefSamples, [.16, .5, .84], finalres.weights)
if doMu:
mu_quant = weighted_quantile(finalres.samples[:, finalres.vparam_names.index(
'mu_'+k[-1])]/arefSamples, [.16, .5, .84], finalres.weights)
else:
if doTd:
args['curves'].series.t_peaks[k] = finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])] +\
finalres.samples[finalres_max,
finalres.vparam_names.index('t0')]
if doMu:
args['curves'].series.a_peaks[k] = finalres.samples[finalres_max, finalres.vparam_names.index('mu_'+k[-1])] *\
finalres.samples[finalres_max, finalres.vparam_names.index(
finalmodel.param_names[2])]
if doTd:
terr = np.sqrt(
finalres.errors['dt_'+k[-1]]**2+finalres.errors['dt_'+args['refImage'][-1]]**2)
dt_quant = [finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]-trefSamples[finalres_max]-terr,
finalres.samples[finalres_max, finalres.vparam_names.index(
'dt_'+k[-1])]-trefSamples[finalres_max],
finalres.samples[finalres_max, finalres.vparam_names.index('dt_'+k[-1])]-trefSamples[finalres_max]+terr]
if doMu:
m = finalres.samples[finalres_max, finalres.vparam_names.index(
'mu_'+k[-1])]/arefSamples[finalres_max]
merr = m*np.sqrt((finalres.errors['mu_'+k[-1]]/finalres.samples[finalres_max, finalres.vparam_names.index('mu_'+k[-1])])**2 +
(finalres.errors['mu_'+args['refImage'][-1]]/arefSamples[finalres_max])**2)
if doTd:
args['curves'].series.time_delays[k] = dt_quant[1]
args['curves'].series.time_delay_errors[k] = np.array(
[dt_quant[0]-dt_quant[1], dt_quant[2]-dt_quant[1]])
if doMu:
args['curves'].series.magnifications[k] = mu_quant[1]
args['curves'].series.magnification_errors[k] = np.array(
[mu_quant[0]-mu_quant[1], mu_quant[2]-mu_quant[1]])
args['curves'].combine_curves(time_delays=args['curves'].series.time_delays,
magnifications=args['curves'].series.magnifications, referenceImage=args['refImage'])
args['curves'].series.meta['td'] = time_delays
args['curves'].series.meta['mu'] = magnifications
finalmodel.set(t0=args['curves'].series.t_peaks[args['refImage']])
finalmodel.parameters[2] = args['curves'].series.a_peaks[args['refImage']]
args['curves'].series.refImage = args['refImage']
args['curves'].series.priorImage = par_ref
args['curves'].series.fits = newDict()
args['curves'].series.fits['model'] = finalmodel
args['curves'].series.fits['res'] = finalres
if args['microlensing'] is not None:
tempTable = copy(args['curves'].series.table)
micro, sigma, x_pred, y_pred, samples, x_resid, y_resid, err_resid = fit_micro(args['curves'].series.fits.model, tempTable,
tempTable['zpsys'][0], args['nMicroSamples'],
micro_type=args['microlensing'], kernel=args['kernel'])
temp_vparam_names = args['curves'].series.fits.res.vparam_names + \
[finalmodel.param_names[2]]+['t0']
for im in args['curves'].images.keys():
try:
temp_vparam_names.remove('dt_'+str(im[-1]))
temp_vparam_names.remove('mu_'+str(im[-1]))
except:
pass
temp_bounds = {p: args['curves'].series.param_quantiles[p][[0, 2]]
for p in args['curves'].series.fits.res.vparam_names}
temp_bounds['t0'] = args['bounds']['td'] + \
args['curves'].series.t_peaks[args['refImage']]
temp_bounds = {b: temp_bounds[b] for b in temp_bounds.keys(
) if b != args['curves'].series.fits.model.param_names[2]}
args['curves'].series.microlensing = newDict()
args['curves'].series.microlensing.micro_propagation_effect = micro
args['curves'].series.microlensing.micro_x = x_pred
args['curves'].series.microlensing.micro_y = y_pred
args['curves'].series.microlensing.samples_y = samples
args['curves'].series.microlensing.sigma = sigma
args['curves'].series.microlensing.resid_x = x_resid
args['curves'].series.microlensing.resid_y = y_resid
args['curves'].series.microlensing.resid_err = err_resid
try:
t0s = pyParz.foreach(samples.T, _micro_uncertainty,
[args['curves'].series.fits.model, np.array(tempTable), tempTable.colnames,
x_pred, temp_vparam_names,
temp_bounds, None, args.get('minsnr', 0), args.get('maxcall', None), args['npoints']])
except:
if args['verbose']:
print('Issue with series microlensing identification, skipping...')
return args['curves']
t0s = np.array(t0s)
t0s = t0s[np.isfinite(t0s)]
mu, sigma = scipy.stats.norm.fit(t0s)
args['curves'].series.param_quantiles['micro'] = np.sqrt((args['curves'].series.fits.model.get('t0')-mu)**2
+ sigma**2)
fit_end = time.time()
args['curves'].series.fit_time = fit_end - fit_start
return args['curves']
def nest_series_lc(data, model, nimage, vparam_names, bounds, ref='image_1', use_MLE=False,
minsnr=5., priors=None, ppfs=None, npoints=100, method='single',
maxiter=None, maxcall=None, modelcov=False, rstate=None,
verbose=False, warn=True, **kwargs):
# Taken from SNCosmo nest_lc
# experimental parameters
tied = kwargs.get("tied", None)
vparam_names = list(vparam_names)
if ppfs is None:
ppfs = {}
if tied is None:
tied = {}
# Convert bounds/priors combinations into ppfs
if bounds is not None:
for key, val in bounds.items():
if key in ppfs:
continue # ppfs take priority over bounds/priors
a, b = val
if priors is not None and key in priors:
# solve ppf at discrete points and return interpolating
# function
x_samples = np.linspace(0., 1., 101)
ppf_samples = sncosmo.utils.ppf(priors[key], x_samples, a, b)
f = sncosmo.utils.Interp1D(0., 1., ppf_samples)
else:
f = sncosmo.utils.Interp1D(0., 1., np.array([a, b]))
ppfs[key] = f
# NOTE: It is important that iparam_names is in the same order
# every time, otherwise results will not be reproducible, even
# with same random seed. This is because iparam_names[i] is
# matched to u[i] below and u will be in a reproducible order,
# so iparam_names must also be.
iparam_names = [key for key in vparam_names if key in ppfs]
ppflist = [ppfs[key] for key in iparam_names]
npdim = len(iparam_names) # length of u
ndim = len(vparam_names) # length of v
# Check that all param_names either have a direct prior or are tied.
for name in vparam_names:
if name in iparam_names:
continue
if name in tied:
continue
raise ValueError("Must supply ppf or bounds or tied for parameter '{}'"
.format(name))
def prior_transform(u):
d = {}
for i in range(npdim):
d[iparam_names[i]] = ppflist[i](u[i])
v = np.empty(ndim, dtype=np.float)
for i in range(ndim):
key = vparam_names[i]
if key in d:
v[i] = d[key]
else:
v[i] = tied[key](d)
return v
if np.any(['mu_' in x for x in vparam_names]):
doMu = True
nParams = 2
else:
doMu = False
nParams = 1
if np.any(['dt_' in x for x in vparam_names]):
doTd = True
else:
doTd = False
nParams -= 1
model_param_names = [
x for x in vparam_names[:len(vparam_names)-(nimage-1)*nParams]]
model_idx = np.array([vparam_names.index(name)
for name in model_param_names])
td_params = [x for x in vparam_names[len(
vparam_names)-nimage*nParams:] if x.startswith('dt')]
td_idx = np.array([vparam_names.index(name) for name in td_params])
amp_params = [x for x in vparam_names[len(
vparam_names)-nimage*nParams:] if x.startswith('mu')]
amp_idx = np.array([vparam_names.index(name) for name in amp_params])
model_param_index = [model.param_names.index(
name) for name in model_param_names]
# mindat=model.mintime()
# maxdat=model.maxtime()
# data=data[np.where(np.logical_and(data['time']>=mindat,data['time']<=maxdat))]
im_indices = [np.where(data['image'] == i)[0]
for i in np.unique(data['image']) if i != ref]
cov = np.diag(data['fluxerr']**2)
zp = np.array(data['zp'])
zpsys = np.array(data['zpsys'])
time = np.array(data['time'])
flux = np.array(data['flux'])
fluxerr = np.array(data['fluxerr'])
band = np.array(data['band'])
def chisq_likelihood(parameters):
model.parameters[model_param_index] = parameters[model_idx]
tempTime = copy(time)
tempFlux = copy(flux)
for i in range(len(im_indices)):
if doTd:
tempTime[im_indices[i]] -= parameters[td_idx[i]]
if doMu:
tempFlux[im_indices[i]] /= parameters[amp_idx[i]]
timesort = np.argsort(tempTime)
model_observations = model.bandflux(band, tempTime[timesort],
zp=zp, zpsys=zpsys)
if modelcov:
_, mcov = model.bandfluxcov(band, tempTime[timesort],
zp=zp, zpsys=zpsys)
cov = cov[timesort,timesort] + mcov
invcov = np.linalg.pinv(cov)
diff = tempFlux[timesort]-model_observations
chisq = np.dot(np.dot(diff, invcov), diff)
else:
chi = (tempFlux[timesort]-model_observations)/np.array(fluxerr[timesort])
chisq = np.dot(chi, chi)
return chisq
def loglike(parameters):
chisq = chisq_likelihood(parameters)
return(-.5*chisq)
res = nestle.sample(loglike, prior_transform, ndim, npdim=npdim,
npoints=npoints, method=method, maxiter=maxiter,
maxcall=maxcall, rstate=rstate,
callback=(nestle.print_progress if verbose else None))
vparameters, cov = nestle.mean_and_cov(res.samples, res.weights)
res = sncosmo.utils.Result(niter=res.niter,
ncall=res.ncall,
logz=res.logz,
logzerr=res.logzerr,
h=res.h,
samples=res.samples,
weights=res.weights,
logvol=res.logvol,
logl=res.logl,
errors=OrderedDict(zip(vparam_names,
np.sqrt(np.diagonal(cov)))),
vparam_names=copy(vparam_names),
bounds=bounds)
if use_MLE:
best_ind = res.logl.argmax()
params = [[res.samples[best_ind, i]-res.errors[vparam_names[i]], res.samples[best_ind, i], res.samples[best_ind, i]+res.errors[vparam_names[i]]]
for i in range(len(vparam_names))]
else:
params = [weighted_quantile(
res.samples[:, i], [.16, .5, .84], res.weights) for i in range(len(vparam_names))]
model.set(**{model_param_names[k]: params[model_idx[k]][1]
for k in range(len(model_idx))})
return params, res, model
def getBandSNR(curves, bands, min_points_per_band):
final_bands = []
band_dict = {im: [] for im in curves.images.keys()}
for band in list(bands):
to_add = True
for im in curves.images.keys():
if len(np.where(curves.images[im].table['band'] == band)[0]) < min_points_per_band:
to_add = False
else:
band_dict[im].append(band)
if to_add:
final_bands.append(band)
all_SNR = []
band_SNR = {im: [] for im in curves.images.keys()}
for d in curves.images.keys():
for band in final_bands:
inds = np.where(curves.images[d].table['band'] == band)[0]
if len(inds) == 0:
band_SNR[d].append(0)
else:
band_SNR[d].append(np.sum(curves.images[d].table['flux'][inds]/curves.images[d].table['fluxerr'][inds]) *
np.sqrt(len(inds)))
band_SNR = {k: np.array(final_bands)[np.flip(
np.argsort(band_SNR[k]))] for k in band_SNR.keys()}
return(np.array(final_bands), band_SNR, band_dict)
def _fitparallel(all_args):
fit_start = time.time()
if isinstance(all_args, (list, tuple, np.ndarray)):
curves, args = all_args
if isinstance(args, list):
args = args[0]
if isinstance(curves, list):
curves, single_par_vars = curves
for key in single_par_vars:
args[key] = single_par_vars[key]
if isinstance(curves, str):
args['curves'] = pickle.load(open(curves, 'rb'))
else:
args['curves'] = curves
if args['verbose']:
print('Fitting MISN number %i...' % curves.nsn)
else:
args = all_args
for p in args['curves'].constants.keys():
if p not in args['constants'].keys():
args['constants'][p] = args['curves'].constants[p]
if 't0' in args['bounds']:
t0Bounds = copy(args['bounds']['t0'])
if args['clip_data']:
for im in args['curves'].images.keys():
args['curves'].clip_data(im=im, minsnr=args.get(
'minsnr', 0), max_cadence=args['max_cadence'])
else:
for im in args['curves'].images.keys():
args['curves'].clip_data(im=im, rm_NaN=True)
args['bands'], band_SNR, band_dict = getBandSNR(
args['curves'], args['bands'], args['min_points_per_band'])
args['curves'].bands = args['bands']
if len(args['bands']) == 0:
if args['verbose']:
print('Not enough data based on cuts.')
return(None)
for d in args['curves'].images.keys():
for b in [x for x in np.unique(args['curves'].images[d].table['band']) if x not in band_dict[d]]:
args['curves'].images[d].table = args['curves'].images[d].table[args['curves'].images[d].table['band'] != b]
if 'amplitude' in args['bounds']:
args['guess_amplitude'] = False
if args['fitOrder'] is None:
all_SNR = [np.sum(args['curves'].images[d].table['flux']/args['curves'].images[d].table['fluxerr'])
for d in np.sort(list(args['curves'].images.keys()))]
sorted = np.flip(np.argsort(all_SNR))
args['fitOrder'] = np.sort(list(args['curves'].images.keys()))[sorted]
args['curves'].parallel.fitOrder = args['fitOrder']
if args['t0_guess'] is not None:
if 't0' in args['bounds']:
args['bounds']['t0'] = (t0Bounds[0]+args['t0_guess'][args['fitOrder'][0]],
t0Bounds[1]+args['t0_guess'][args['fitOrder'][0]])
guess_t0 = args['t0_guess']
else:
print('If you supply a t0 guess, you must also supply bounds.')
sys.exit(1)
if args['max_n_bands'] is not None:
best_bands = band_SNR[args['fitOrder'][0]][:min(
len(band_SNR[args['fitOrder'][0]]), args['max_n_bands'])]
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[args['fitOrder'][0]].table['band'] == b)[0])
inds = temp_bands.astype(int)
else:
best_bands = args['bands']
inds = np.arange(
0, len(args['curves'].images[args['fitOrder'][0]].table), 1).astype(int)
initial_bounds = copy(args['bounds'])
finallogz = -np.inf
if args['dust'] is not None:
if isinstance(args['dust'], str):
dust_dict = {'CCM89Dust': sncosmo.CCM89Dust,
'OD94Dust': sncosmo.OD94Dust, 'F99Dust': sncosmo.F99Dust}
dust = dust_dict[args['dust']]()
else:
dust = args['dust']
else:
dust = []
effect_names = args['effect_names']
effect_frames = args['effect_frames']
effects = [dust for i in range(len(effect_names))] if effect_names else []
effect_names = effect_names if effect_names else []
effect_frames = effect_frames if effect_frames else []
if not isinstance(effect_names, (list, tuple)):
effects = [effect_names]
if not isinstance(effect_frames, (list, tuple)):
effects = [effect_frames]
if 'ignore_models' in args['set_from_simMeta'].keys():
to_ignore = args['curves'].images[args['fitOrder'][0]
].simMeta[args['set_from_simMeta']['ignore_models']]
if isinstance(to_ignore, str):
to_ignore = [to_ignore]
args['models'] = [x for x in np.array(
args['models']).flatten() if x not in to_ignore]
if not args['curves'].quality_check(min_n_bands=args['min_n_bands'],
min_n_points_per_band=args['min_points_per_band'], clip=args['clip_data']):
return
all_fit_dict = {}
if args['fast_model_selection'] and len(np.array(args['models']).flatten()) > 1:
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
minchisq = np.inf
init_inds = copy(inds)
for mod in np.array(args['models']).flatten():
inds = copy(init_inds)
if isinstance(mod, str):
if mod.upper() in ['BAZIN', 'BAZINSOURCE']:
mod = 'BAZINSOURCE'
if len(np.unique(args['curves'].images[args['fitOrder'][0]].table['band'])) > 1:
if args['color_curve'] is None:
best_band = band_SNR[args['fitOrder'][0]][0]
inds = np.where(
args['curves'].images[args['fitOrder'][0]].table['band'] == best_band)[0]
else:
inds = np.arange(
0, len(args['curves'].images[args['fitOrder'][0]].table), 1)
else:
best_band = band_SNR[args['fitOrder'][0]][0]
inds = np.arange(
0, len(args['curves'].images[args['fitOrder'][0]].table), 1)
source = BazinSource(
data=args['curves'].images[args['fitOrder'][0]].table[inds], colorCurve=args['color_curve'])
else:
source = sncosmo.get_source(mod)
tempMod = sncosmo.Model(
source=source, effects=effects, effect_names=effect_names, effect_frames=effect_frames)
else:
tempMod = copy(mod)
tempMod.set(**{k: args['constants'][k]
for k in args['constants'].keys() if k in tempMod.param_names})
tempMod.set(**{k: args['curves'].images[args['refImage']].simMeta[args['set_from_simMeta'][k]]
for k in args['set_from_simMeta'].keys() if k in tempMod.param_names})
if not np.all([tempMod.bandoverlap(x) for x in best_bands]):
if args['verbose']:
print('Skipping %s because it does not cover the bands...' % mod)
continue
if mod == 'BAZINSOURCE':
tempMod.set(z=0)
try:
res, fit = sncosmo.fit_lc(args['curves'].images[args['fitOrder'][0]].table[inds], tempMod, [x for x in args['params'] if x in tempMod.param_names],
bounds={b: args['bounds'][b] for b in args['bounds'] if b not in [
't0', tempMod.param_names[2]]},
minsnr=args.get('minsnr', 0))
except:
if args['verbose']:
print('Issue with %s, skipping...' % mod)
continue
tempchisq = res.chisq / \
(len(inds)+len([x for x in args['params']
if x in tempMod.param_names])-1)
if tempchisq < minchisq:
minchisq = tempchisq
bestres = copy(res)
bestfit = copy(fit)
bestmodname = copy(mod)
all_fit_dict[mod] = [copy(fit), copy(res)]
try:
args['models'] = [bestmodname]
except:
print('Every model had an error.')
return None
for mod in np.array(args['models']).flatten():
if isinstance(mod, str):
if mod.upper() in ['BAZIN', 'BAZINSOURCE']:
mod = 'BAZINSOURCE'
if len(np.unique(args['curves'].images[args['fitOrder'][0]].table['band'])) > 1:
if args['color_curve'] is None:
best_band = band_SNR[args['fitOrder'][0]][0]
inds = np.where(
args['curves'].images[args['fitOrder'][0]].table['band'] == best_band)[0]
else:
inds = np.arange(
0, len(args['curves'].images[args['fitOrder'][0]].table), 1)
else:
best_band = band_SNR[args['fitOrder'][0]][0]
inds = np.arange(
0, len(args['curves'].images[args['fitOrder'][0]].table), 1)
source = BazinSource(
data=args['curves'].images[args['fitOrder'][0]].table[inds], colorCurve=args['color_curve'])
else:
source = sncosmo.get_source(mod)
tempMod = sncosmo.Model(source=source, effects=effects,
effect_names=effect_names, effect_frames=effect_frames)
else:
tempMod = copy(mod)
tempMod.set(**{k: args['constants'][k]
for k in args['constants'].keys() if k in tempMod.param_names})
if args['set_from_simMeta'] is not None:
tempMod.set(**{k: args['curves'].images[args['refImage']].simMeta[args['set_from_simMeta'][k]]
for k in args['set_from_simMeta'].keys() if k in tempMod.param_names})
if mod == 'BAZINSOURCE':
tempMod.set(z=0)
if args['trial_fit'] and args['t0_guess'] is None:
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
if args['max_n_bands'] is None:
best_bands = band_SNR[args['fitOrder'][0]][:min(
len(band_SNR[args['fitOrder'][0]]), 2)]
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[args['fitOrder'][0]].table['band'] == b)[0])
temp_inds = temp_bands.astype(int)
else:
temp_inds = copy(inds)
res, fit = sncosmo.fit_lc(args['curves'].images[args['fitOrder'][0]].table[temp_inds], tempMod, [x for x in args['params'] if x in tempMod.param_names],
bounds={b: args['bounds'][b]+(args['bounds'][b]-np.median(
args['bounds'][b]))*2 for b in args['bounds'].keys() if b not in ['t0', tempMod.param_names[2]]},
minsnr=args.get('minsnr', 0))
for b in args['bounds'].keys():
if b in res.param_names:
if b != 't0':
if args['bounds'][b][0] <= fit.get(b) <= args['bounds'][b][1]:
args['bounds'][b] = np.array([np.max([args['bounds'][b][0], (args['bounds'][b][0]-np.median(args['bounds'][b]))/2+fit.get(b)]),
np.min([args['bounds'][b][1], (args['bounds'][b][1]-np.median(args['bounds'][b]))/2+fit.get(b)])])
else:
args['bounds'][b] = np.array([(args['bounds'][b][0]-np.median(args['bounds'][b]))/2+fit.get(b),
(args['bounds'][b][1]-np.median(args['bounds'][b]))/2+fit.get(b)])
else:
args['bounds'][b] = args['bounds'][b]+fit.get('t0')
if tempMod.param_names[2] not in args['bounds'].keys():
args['bounds'][tempMod.param_names[2]] = np.array(
[.1, 10])*fit.parameters[2]
guess_t0 = fit.get('t0')
elif args['guess_amplitude']:
guess_t0, guess_amp = sncosmo.fitting.guess_t0_and_amplitude(
sncosmo.photdata.photometric_data(
args['curves'].images[args['fitOrder'][0]].table[inds]),
tempMod, args.get('minsnr', 5.))
if args['t0_guess'] is None:
args['bounds']['t0'] = np.array(initial_bounds['t0'])+guess_t0
if tempMod.param_names[2] in args['bounds']:
args['bounds'][tempMod.param_names[2]] = np.array(args['bounds'][tempMod.param_names[2]]) *\
guess_amp
else:
args['bounds'][tempMod.param_names[2]
] = [.1*guess_amp, 10*guess_amp]
if args['clip_data']:
args['curves'].images[args['fitOrder'][0]
].table = args['curves'].images[args['fitOrder'][0]].table[inds]
if args['cut_time'] is not None:
args['curves'].clip_data(im=args['fitOrder'][0], minsnr=args.get('minsnr', 0), mintime=args['cut_time'][0]*(1+tempMod.get('z')),
maxtime=args['cut_time'][1]*(1+tempMod.get('z')), peak=guess_t0)
else:
args['curves'].clip_data(
im=args['fitOrder'][0], minsnr=args.get('minsnr', 0))
fit_table = args['curves'].images[args['fitOrder'][0]].table
elif args['cut_time'] is not None:
fit_table = copy(
args['curves'].images[args['fitOrder'][0]].table)
fit_table = fit_table[inds]
fit_table = fit_table[fit_table['time'] >=
guess_t0+(args['cut_time'][0]*(1+tempMod.get('z')))]
fit_table = fit_table[fit_table['time'] <=
guess_t0+(args['cut_time'][1]*(1+tempMod.get('z')))]
fit_table = fit_table[fit_table['flux'] /
fit_table['fluxerr'] >= args.get('minsnr', 0)]
else:
fit_table = copy(
args['curves'].images[args['fitOrder'][0]].table)
fit_table = fit_table[inds]
for b in args['force_positive_param']:
if b in args['bounds'].keys():
args['bounds'][b] = np.array(
[max([args['bounds'][b][0], 0]), max([args['bounds'][b][1], 0])])
else:
args['bounds'][b] = np.array([0, np.inf])
res, fit = sncosmo.nest_lc(fit_table, tempMod, [x for x in args['params'] if x in tempMod.param_names],
bounds=args['bounds'],
priors=args.get('priors', None), ppfs=args.get('ppfs', None),
minsnr=args.get('minsnr', 5.0), method=args.get('nest_method', 'single'),
maxcall=args.get('maxcall', None), modelcov=args.get('modelcov', False),
rstate=args.get('rstate', None), guess_amplitude_bound=False,
zpsys=args['curves'].images[args['fitOrder'][0]].zpsys,
maxiter=args.get('maxiter', None), npoints=args.get('npoints', 100))
all_fit_dict[mod] = [copy(fit), copy(res)]
if finallogz < res.logz:
first_res = [args['fitOrder'][0], copy(fit), copy(res)]
finallogz = res.logz
if not args['use_MLE']:
first_params = [weighted_quantile(first_res[2].samples[:, i], [.16, .5, .84], first_res[2].weights)
for i in range(len(first_res[2].vparam_names))]
else:
best_ind = first_res[2].logl.argmax()
first_params = [[first_res[2].samples[best_ind, i]-first_res[2].errors[first_res[2].vparam_names[i]],
first_res[2].samples[best_ind, i],
first_res[2].samples[best_ind, i]+first_res[2].errors[first_res[2].vparam_names[i]]] for
i in range(len(first_res[2].vparam_names))]
first_res[1].set(**{first_res[2].vparam_names[k]: first_params[k][1]
for k in range(len(first_res[2].vparam_names))})
args['curves'].images[args['fitOrder'][0]].fits = newDict()
args['curves'].images[args['fitOrder'][0]].fits['model'] = first_res[1]
args['curves'].images[args['fitOrder'][0]].fits['res'] = first_res[2]
t0ind = first_res[2].vparam_names.index('t0')
ampind = first_res[2].vparam_names.index(first_res[1].param_names[2])
args['curves'].images[args['fitOrder'][0]].param_quantiles = {k: first_params[first_res[2].vparam_names.index(k)] for
k in first_res[2].vparam_names}
# for i in range(len(first_res[2].vparam_names)):
# if first_res[2].vparam_names[i]==first_res[1].param_names[2] or first_res[2].vparam_names[i]=='t0':
# continue
# initial_bounds[first_res[2].vparam_names[i]]=3*np.array([first_params[i][0],first_params[i][2]])-2*first_params[i][1]
for d in args['fitOrder'][1:]:
if args['max_n_bands'] is not None:
best_bands = band_SNR[d][:min(
len(band_SNR[d]), args['max_n_bands'])]
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[d].table['band'] == b)[0])
inds = temp_bands.astype(int)
else:
inds = np.arange(
0, len(args['curves'].images[d].table), 1).astype(int)
args['curves'].images[d].fits = newDict()
initial_bounds['t0'] = copy(t0Bounds)
if args['t0_guess'] is not None:
if 't0' in args['bounds']:
initial_bounds['t0'] = (
t0Bounds[0]+args['t0_guess'][d], t0Bounds[1]+args['t0_guess'][d])
guess_t0_start = False
else:
best_bands = band_SNR[d][:min(len(band_SNR[d]), 2)]
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[d].table['band'] == b)[0])
inds = temp_bands.astype(int)
if mod == 'BAZINSOURCE':
minds = np.where(
args['curves'].images[d].table['band'] == best_band)[0]
inds = None
else:
minds = np.arange(
0, len(args['curves'].images[d].table), 1).astype(int)
if args['clip_data']:
fit_table = args['curves'].images[d].table[minds]
else:
fit_table = copy(args['curves'].images[d].table)
fit_table = fit_table[minds]
if args['trial_fit'] and args['t0_guess'] is None:
if args['max_n_bands'] is None:
best_bands = band_SNR[d][:min(len(band_SNR[d]), 2)]
temp_bands = []
for b in best_bands:
temp_bands = np.append(temp_bands, np.where(
args['curves'].images[d].table['band'] == b)[0])
temp_inds = temp_bands.astype(int)
else:
temp_inds = copy(inds)
res, fit = sncosmo.fit_lc(args['curves'].images[d].table[temp_inds], args['curves'].images[args['fitOrder'][0]].fits['model'],
['t0', args['curves'].images[args['fitOrder']
[0]].fits['model'].param_names[2]],
minsnr=args.get('minsnr', 0))
image_bounds = {b: initial_bounds[b] if b != 't0' else initial_bounds['t0']+fit.get(
't0') for b in initial_bounds.keys()}
guess_t0_start = False
else:
image_bounds = copy(initial_bounds)
if args['t0_guess'] is None:
guess_t0_start = True
else:
guess_t0_start = False
par_output = nest_parallel_lc(fit_table, first_res[1], first_res[2], image_bounds, min_n_bands=args['min_n_bands'],
min_n_points_per_band=args[
'min_points_per_band'], guess_t0_start=guess_t0_start, use_MLE=args['use_MLE'],
guess_amplitude_bound=True, priors=args.get('priors', None), ppfs=args.get('None'),
method=args.get('nest_method', 'single'), cut_time=args['cut_time'], snr_band_inds=inds,
maxcall=args.get('maxcall', None), modelcov=args.get('modelcov', False),
rstate=args.get('rstate', None), minsnr=args.get('minsnr', 5),
maxiter=args.get('maxiter', None), npoints=args.get('npoints', 1000))
if par_output is None:
return
params, args['curves'].images[d].fits['model'], args['curves'].images[d].fits['res'] = par_output
sample_dict = {args['fitOrder'][0]: [
first_res[2].samples[:, t0ind], first_res[2].samples[:, ampind]]}
arg_max_dict = {args['fitOrder'][0]: first_res[2].logl.argmax()}
for k in args['fitOrder'][1:]:
sample_dict[k] = [args['curves'].images[k].fits['res'].samples[:, t0ind],
args['curves'].images[k].fits['res'].samples[:, ampind]]
args['curves'].images[k].param_quantiles = {d: params[args['curves'].images[k].fits['res'].vparam_names.index(d)]
for d in args['curves'].images[k].fits['res'].vparam_names}
arg_max_dict[k] = args['curves'].images[k].fits['res'].logl.argmax()
trefSamples, arefSamples = sample_dict[args['refImage']]
refWeights = args['curves'].images[args['refImage']].fits['res'].weights
args['curves'].parallel.time_delays = {args['refImage']: 0}
args['curves'].parallel.magnifications = {args['refImage']: 1}
args['curves'].parallel.time_delay_errors = {
args['refImage']: np.array([0, 0])}
args['curves'].parallel.magnification_errors = {
args['refImage']: np.array([0, 0])}
for k in args['curves'].images.keys():
if k == args['refImage']:
continue
else:
ttempSamples, atempSamples = sample_dict[k]
if not args['use_MLE']:
if len(ttempSamples) > len(trefSamples):
inds = np.flip(np.argsort(args['curves'].images[k].fits['res'].weights)[
len(ttempSamples)-len(trefSamples):])
inds_ref = np.flip(np.argsort(refWeights))
else:
inds_ref = np.flip(np.argsort(refWeights)[
len(trefSamples)-len(ttempSamples):])
inds = np.flip(np.argsort(
args['curves'].images[k].fits['res'].weights))
t_quant = weighted_quantile(ttempSamples[inds]-trefSamples[inds_ref], [.16, .5, .84], refWeights[inds_ref] *
args['curves'].images[k].fits['res'].weights[inds])
a_quant = weighted_quantile(atempSamples[inds]/arefSamples[inds_ref], [.16, .5, .84], refWeights[inds_ref] *
args['curves'].images[k].fits['res'].weights[inds])
else:
terr = np.sqrt((args['curves'].images[k].param_quantiles['t0'][1]-args['curves'].images[k].param_quantiles['t0'][0])**2 +
(args['curves'].images[args['refImage']].param_quantiles['t0'][1]-args['curves'].images[args['refImage']].param_quantiles['t0'][0])**2)
a = atempSamples[arg_max_dict[k]] / \
arefSamples[arg_max_dict[args['refImage']]]
aname = args['curves'].images[k].fits.model.param_names[2]
aerr = a*np.sqrt(((args['curves'].images[k].param_quantiles[aname][1]-args['curves'].images[k].param_quantiles[aname][0]) /
atempSamples[arg_max_dict[k]])**2 +
((args['curves'].images[args['refImage']].param_quantiles[aname][1]-args['curves'].images[args['refImage']].param_quantiles[aname][0]) /
arefSamples[arg_max_dict[args['refImage']]])**2)
t_quant = [ttempSamples[arg_max_dict[k]]-trefSamples[arg_max_dict[args['refImage']]]-terr,
ttempSamples[arg_max_dict[k]] -
trefSamples[arg_max_dict[args['refImage']]],
ttempSamples[arg_max_dict[k]]-trefSamples[arg_max_dict[args['refImage']]]+terr]
a_quant = [atempSamples[arg_max_dict[k]]/arefSamples[arg_max_dict[args['refImage']]]-aerr,
atempSamples[arg_max_dict[k]] /
arefSamples[arg_max_dict[args['refImage']]],
atempSamples[arg_max_dict[k]]/arefSamples[arg_max_dict[args['refImage']]]+aerr]
args['curves'].parallel.time_delays[k] = t_quant[1]
args['curves'].parallel.magnifications[k] = a_quant[1]
args['curves'].parallel.time_delay_errors[k] = np.array(
[t_quant[0]-t_quant[1], t_quant[2]-t_quant[1]])
args['curves'].parallel.magnification_errors[k] = \
np.array([a_quant[0]-a_quant[1], a_quant[2]-a_quant[1]])
if args['clip_data']:
if args['cut_time'] is not None:
args['curves'].clip_data(im=k, minsnr=args.get('minsnr', 0), mintime=args['cut_time'][0]*(1+args['curves'].images[k].fits.model.get('z')),
maxtime=args['cut_time'][1] *
(1+args['curves'].images[k].fits.model.get('z')),
peak=args['curves'].images[k].fits.model.get('t0'))
else:
args['curves'].clip_data(im=k, minsnr=args.get('minsnr', 0))
if args['microlensing'] is not None:
for k in args['curves'].images.keys():
tempTable = copy(args['curves'].images[k].table)
micro, sigma, x_pred, y_pred, samples, x_resid, y_resid, err_resid = fit_micro(args['curves'].images[k].fits.model,
tempTable, args['curves'].images[
k].zpsys, args['nMicroSamples'],
micro_type=args[
'microlensing'], kernel=args['kernel'],
bands=args['micro_fit_bands'])
args['curves'].images[k].microlensing.micro_propagation_effect = micro
args['curves'].images[k].microlensing.micro_x = x_pred
args['curves'].images[k].microlensing.micro_y = y_pred
args['curves'].images[k].microlensing.samples_y = samples
args['curves'].images[k].microlensing.sigma = sigma
args['curves'].images[k].microlensing.resid_x = x_resid
args['curves'].images[k].microlensing.resid_y = y_resid
args['curves'].images[k].microlensing.resid_err = err_resid
try:
t0s = pyParz.foreach(samples.T, _micro_uncertainty,
[args['curves'].images[k].fits.model, np.array(tempTable), tempTable.colnames,
x_pred, args['curves'].images[k].fits.res.vparam_names,
{p: args['curves'].images[k].param_quantiles[p][[0, 2]]
for p in args['curves'].images[k].fits.res.vparam_names if p !=
args['curves'].images[k].fits.model.param_names[2]}, None,
args.get('minsnr', 0), args.get('maxcall', None), args['npoints']], numThreads=args['npar_cores'])
except RuntimeError:
if args['verbose']:
print('Issue with microlensing identification, skipping...')
return args['curves']
t0s = np.array(t0s)
t0s = t0s[np.isfinite(t0s)]
mu, sigma = scipy.stats.norm.fit(t0s)
args['curves'].images[k].param_quantiles['micro'] = np.sqrt((args['curves'].images[k].fits.model.get('t0')-mu)**2
+ sigma**2)
fit_end = time.time()
args['curves'].parallel.fit_time = fit_end - fit_start
return args['curves']
def nest_parallel_lc(data, model, prev_res, bounds, guess_amplitude_bound=False, guess_t0_start=True,
cut_time=None, snr_band_inds=None, vparam_names=None, use_MLE=False,
min_n_bands=1, min_n_points_per_band=3,
minsnr=5., priors=None, ppfs=None, npoints=100, method='single',
maxiter=None, maxcall=None, modelcov=False, rstate=None,
verbose=False, warn=True, **kwargs):
# Taken from SNCosmo nest_lc
# experimental parameters
tied = kwargs.get("tied", None)
if prev_res is not None:
vparam_names = list(prev_res.vparam_names)
if ppfs is None:
ppfs = {}
if tied is None:
tied = {}
model = copy(model)
if guess_amplitude_bound:
if snr_band_inds is None:
snr_band_inds = np.arange(0, len(data), 1).astype(int)
guess_t0, guess_amp = sncosmo.fitting.guess_t0_and_amplitude(sncosmo.photdata.photometric_data(data[snr_band_inds]),
model, minsnr)
if guess_t0_start:
model.set(t0=guess_t0)
bounds['t0'] = np.array(bounds['t0'])+guess_t0
else:
model.set(t0=np.median(bounds['t0']))
model.parameters[2] = guess_amp
bounds[model.param_names[2]] = (0, 10*guess_amp)
if cut_time is not None and (guess_amplitude_bound or not guess_t0_start):
data = data[data['time'] >= cut_time[0]*(1+model.get('z'))+guess_t0]
data = data[data['time'] <= cut_time[1]*(1+model.get('z'))+guess_t0]
if prev_res is not None:
data, quality = check_table_quality(
data, min_n_bands=min_n_bands, min_n_points_per_band=min_n_points_per_band, clip=True)
if not quality:
return
# Convert bounds/priors combinations into ppfs
if bounds is not None:
for key, val in bounds.items():
if key in ppfs:
continue # ppfs take priority over bounds/priors
a, b = val
if priors is not None and key in priors:
# solve ppf at discrete points and return interpolating
# function
x_samples = np.linspace(0., 1., 101)
ppf_samples = sncosmo.utils.ppf(priors[key], x_samples, a, b)
f = sncosmo.utils.Interp1D(0., 1., ppf_samples)
else:
f = sncosmo.utils.Interp1D(0., 1., np.array([a, b]))
ppfs[key] = f
# NOTE: It is important that iparam_names is in the same order
# every time, otherwise results will not be reproducible, even
# with same random seed. This is because iparam_names[i] is
# matched to u[i] below and u will be in a reproducible order,
# so iparam_names must also be.
if prev_res is not None:
prior_inds = [i for i in range(
len(vparam_names)) if vparam_names[i] in _thetaSN_]
if len(prior_inds) == 0:
doPrior = False
else:
doPrior = True
prior_dist = NDposterior('temp')
prior_func = prior_dist._logpdf([tuple(prev_res.samples[i, prior_inds]) for i in range(prev_res.samples.shape[0])],
prev_res.weights)
else:
doPrior = False
iparam_names = [key for key in vparam_names if key in ppfs]
ppflist = [ppfs[key] for key in iparam_names]
npdim = len(iparam_names) # length of u
ndim = len(vparam_names) # length of v
# Check that all param_names either have a direct prior or are tied.
for name in vparam_names:
if name in iparam_names:
continue
if name in tied:
continue
raise ValueError("Must supply ppf or bounds or tied for parameter '{}'"
.format(name))
def prior_transform(u):
d = {}
for i in range(npdim):
d[iparam_names[i]] = ppflist[i](u[i])
v = np.empty(ndim, dtype=np.float)
for i in range(ndim):
key = vparam_names[i]
if key in d:
v[i] = d[key]
else:
v[i] = tied[key](d)
return v
model_idx = np.array([model.param_names.index(name)
for name in vparam_names])
flux = np.array(data['flux'])
fluxerr = np.array(data['fluxerr'])
zp = np.array(data['zp'])
zpsys = np.array(data['zpsys'])
chi1 = flux/fluxerr
def chisq_likelihood(parameters):
model.parameters[model_idx] = parameters
model_observations = model.bandflux(data['band'], data['time'],
zp=zp, zpsys=zpsys)
if modelcov:
cov = np.diag(data['fluxerr']*data['fluxerr'])
_, mcov = model.bandfluxcov(data['band'], data['time'],
zp=zp, zpsys=zpsys)
cov = cov + mcov
invcov = np.linalg.pinv(cov)
diff = flux-model_observations
chisq = np.dot(np.dot(diff, invcov), diff)
else:
chi = chi1-model_observations/fluxerr
chisq = np.dot(chi, chi)
return chisq
def loglike(parameters):
if doPrior:
prior_val = prior_func(*parameters[prior_inds])
else:
prior_val = 0
chisq = chisq_likelihood(parameters)
return(prior_val-.5*chisq)
res = nestle.sample(loglike, prior_transform, ndim, npdim=npdim,
npoints=npoints, method=method, maxiter=maxiter,
maxcall=maxcall, rstate=rstate,
callback=(nestle.print_progress if verbose else None))
vparameters, cov = nestle.mean_and_cov(res.samples, res.weights)
res = sncosmo.utils.Result(niter=res.niter,
ncall=res.ncall,
logz=res.logz,
logzerr=res.logzerr,
h=res.h,
samples=res.samples,
weights=res.weights,
logvol=res.logvol,
logl=res.logl,
errors=OrderedDict(zip(vparam_names,
np.sqrt(np.diagonal(cov)))),
vparam_names=copy(vparam_names),
bounds=bounds)
if use_MLE:
best_ind = res.logl.argmax()
params = [[res.samples[best_ind, i]-res.errors[vparam_names[i]], res.samples[best_ind, i], res.samples[best_ind, i]+res.errors[vparam_names[i]]]
for i in range(len(vparam_names))]
else:
params = [weighted_quantile(
res.samples[:, i], [.16, .5, .84], res.weights) for i in range(len(vparam_names))]
model.set(**{vparam_names[k]: params[k][1]
for k in range(len(vparam_names))})
return params, model, res
def _micro_uncertainty(args):
sample, other = args
nest_fit, data, colnames, x_pred, vparam_names, bounds, priors, minsnr, maxcall, npoints = other
data = Table(data, names=colnames)
tempMicro = AchromaticMicrolensing(
x_pred/(1+nest_fit.get('z')), sample, magformat='multiply')
# Assumes achromatic
temp = tempMicro.propagate((data['time']-nest_fit.get('t0'))/(1+nest_fit.get('z')), [],
np.atleast_2d(np.array(data['flux'])))
data['flux'] = temp[0]
try:
tempRes, tempMod = nest_lc(data, nest_fit, vparam_names=vparam_names, bounds=bounds,
minsnr=minsnr, maxcall=maxcall,
guess_amplitude_bound=True, maxiter=None, npoints=npoints,
priors=priors)
except:
return(np.nan)
return float(tempMod.get('t0'))
def fit_micro(fit, dat, zpsys, nsamples, micro_type='achromatic', kernel='RBF', bands='all'):
t0 = fit.get('t0')
fit.set(t0=t0)
data = copy(dat)
data['time'] -= t0
if len(data) == 0:
data = copy(dat)
achromatic = micro_type.lower() == 'achromatic'
if achromatic:
allResid = []
allErr = []
allTime = []
else:
allResid = dict([])
allErr = dict([])
allTime = dict([])
if bands == 'all':
bands = np.unique(data['band'])
elif isinstance(bands, str):
bands = [bands]
for b in bands:
tempData = data[data['band'] == b]
tempData = tempData[tempData['flux'] > 0]
tempTime = copy(tempData['time'])
mod = fit.bandflux(b, tempTime+t0, zpsys=zpsys, zp=tempData['zp'])
residual = tempData['flux']/mod
tempData = tempData[~np.isnan(residual)]
residual = residual[~np.isnan(residual)]
tempTime = tempTime[~np.isnan(residual)]
_, mcov = fit.bandfluxcov(b, tempTime,
zp=tempData['zp'], zpsys=zpsys)
if achromatic:
allResid = np.append(allResid, residual)
totalErr = np.abs(residual*np.sqrt((tempData['fluxerr']/tempData['flux'])**2 +
np.array([mcov[i][i] for i in range(len(tempData))])/mod**2))
allErr = np.append(
allErr, residual*tempData['fluxerr']/tempData['flux'])
allTime = np.append(allTime, tempTime)
else:
allResid[b] = residual
allErr[b] = residual*tempData['fluxerr']/tempData['flux']
allTime[b] = tempTime
if kernel == 'RBF':
kernel = RBF(0.1, (.001, 20.))
good_inds = np.where(np.logical_and(np.isfinite(allResid),
np.logical_and(np.isfinite(allErr),
np.isfinite(allTime))))
allResid = allResid[good_inds]
allErr = allErr[good_inds]
allTime = allTime[good_inds]
if achromatic:
gp = GaussianProcessRegressor(kernel=kernel, alpha=allErr ** 2,
n_restarts_optimizer=100)
try:
gp.fit(np.atleast_2d(allTime).T, allResid.ravel())
except RuntimeError:
temp = np.atleast_2d(allTime).T
temp2 = allResid.ravel()
temp = temp[np.isfinite(temp2)]
temp2 = temp2[np.isfinite(temp2)]
gp.fit(temp, temp2)
X = np.atleast_2d(np.linspace(
np.min(allTime), np.max(allTime), 1000)).T
y_pred, sigma = gp.predict(X, return_std=True)
samples = gp.sample_y(X, nsamples)
tempX = X[:, 0]
tempX = np.append([fit._source._phase[0]*(1+fit.get('z'))],
np.append(tempX, [fit._source._phase[-1]*(1+fit.get('z'))]))
temp_y_pred = np.append([1.], np.append(y_pred, [1.]))
temp_sigma = np.append([0.], np.append(sigma, [0.]))
result = AchromaticMicrolensing(
tempX/(1+fit.get('z')), temp_y_pred, magformat='multiply')
else:
pass
# TODO make chromatic microlensing a thing
return result, sigma, X[:, 0], y_pred, samples, allTime, allResid, allErr
def param_fit(args, modName, fit=False):
sources = {'BazinSource': BazinSource}
source = sources[modName](
args['curve'].table, colorCurve=args['color_curve'])
mod = sncosmo.Model(source)
if args['constants']:
mod.set(**args['constants'])
if not fit:
res = sncosmo.utils.Result()
res.vparam_names = args['params']
else:
#res,mod=sncosmo.fit_lc(args['curve'].table,mod,args['params'], bounds=args['bounds'],guess_amplitude=True,guess_t0=True,maxcall=1)
if 'amplitude' in args['bounds']:
guess_amp_bound = False
else:
guess_amp_bound = True
res, mod = nest_lc(args['curve'].table, mod, vparam_names=args['params'], bounds=args['bounds'],
guess_amplitude_bound=guess_amp_bound, maxiter=1000, npoints=200)
return({'res': res, 'model': mod})
def identify_micro_func(args):
print('Only a development function for now!')
return args['bands'], args['bands']
if len(args['bands']) <= 2:
return args['bands'], args['bands']
res_dict = {}
original_args = copy(args)
combos = []
for r in range(len(args['bands'])-1):
temp = [x for x in itertools.combinations(original_args['bands'], r)]
for t in temp:
combos.append(t)
if 'td' not in args['bounds'].keys():
args['bounds']['td'] = args['bounds']['t0']
for bands in itertools.combinations(args['bands'], 2):
good = True
for b in bands:
if not np.all([len(np.where(original_args['curves'].images[im].table['band'] == b)[0]) >= 3 for im in original_args['curves'].images.keys()]):
good = False
if not good:
continue
temp_args = copy(original_args)
temp_args['bands'] = [x for x in bands]
temp_args['npoints'] = 200
temp_args['fit_prior'] = None
fitCurves = _fitColor(temp_args)
if np.all([np.isfinite(fitCurves.color.time_delays[x]) for x in fitCurves.images.keys()]):
res_dict[bands[0]+'-'+bands[1]] = copy(fitCurves.color.fits.res)
if len(list(res_dict.keys())) == 0:
print('No good fitting.', args['bands'])
return(args['bands'], args['bands'])
ind = res_dict[list(res_dict.keys())[0]].vparam_names.index('c')
print([(x, weighted_quantile(res_dict[x].samples[:, ind],
[.16, .5, .84], res_dict[x].weights)) for x in res_dict.keys()])
dev_dict = {}
for bs in combos:
dev_dict[','.join(list(bs))] = (np.average([weighted_quantile(res_dict[x].samples[:, ind], .5, res_dict[x].weights)
for x in res_dict.keys() if np.all([b not in x for b in bs])], weights=1/np.abs([res_dict[x].logz for x in res_dict.keys() if np.all([b not in x for b in bs])])),
np.std([weighted_quantile(res_dict[x].samples[:, ind], .5, res_dict[x].weights)
for x in res_dict.keys() if np.all([b not in x for b in bs])]))
print(dev_dict)
to_remove = None
best_std = dev_dict[''][1]/np.sqrt(len(args['bands']))
if len(args['bands']) > 3:
for bands in dev_dict.keys():
# len(args['bands'])-len(bands.split(','))==2:
if dev_dict[bands][1] != 0:
print(bands, dev_dict[bands])
if dev_dict[bands][1]/np.sqrt(len(args['bands'])-len(bands.split(','))) < best_std:
to_remove = bands.split(',')
best_std = dev_dict[bands][1] / \
np.sqrt(len(args['bands'])-len(to_remove))
print(to_remove, best_std)
sys.exit()
final_color_bands = None
best_logz = -np.inf
best_logzerr = 0
for bands in res_dict.keys():
logz, logzerr = calc_ev(res_dict[bands], args['npoints'])
if logz > best_logz:
final_color_bands = bands
best_logz = logz
best_logzerr = logzerr
print(bands, best_logz, best_logzerr)
final_all_bands = []
for bands in res_dict.keys():
logz, logzerr = calc_ev(res_dict[bands], args['npoints'])
print(bands, logz, logzerr)
if logz+3*logzerr >= best_logz-3*best_logzerr:
final_all_bands = np.append(final_all_bands, bands.split('-'))
print(np.unique(final_all_bands), np.array(final_color_bands.split('-')))
sys.exit()
return(np.unique(final_all_bands), np.array(final_color_bands.split('-')))
# else:
# print([[x for x in args['bands'] if x not in to_remove]]*2)
# sys.exit()
# return [[x for x in args['bands'] if x not in to_remove]]*2
# else:
# best_bands=None
# best_logz=-np.inf
# for bands in res_dict.keys():
#
# if res_dict[bands].logz>best_logz:
# best_bands=bands
# best_logz=res_dict[bands].logz
#
# return [best_bands.split('-')]*2
def calc_ev(res, nlive):
logZnestle = res.logz # value of logZ
# value of the information gain in nats
infogainnestle = res.h
if not np.isfinite(infogainnestle):
infogainnestle = .1*logZnestle
# /nlive) # estimate of the statistcal uncertainty on logZ
logZerrnestle = np.sqrt(infogainnestle)
return logZnestle, logZerrnestle
|
jpierel14REPO_NAMEsntdPATH_START.@sntd_extracted@sntd-master@sntd@fitting.py@.PATH_END.py
|
{
"filename": "plot_disk_fit.py",
"repo_name": "mkenworthy/exorings",
"repo_path": "exorings_extracted/exorings-master/plot_disk_fit.py",
"type": "Python"
}
|
import sys, getopt
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import exorings
from astropy.io import ascii
# set sensible imshow defaults
mpl.rc('image', interpolation='nearest', origin='lower', cmap='gray')
mpl.rc('axes.formatter', limits=(-7, 7))
def plot_gradient_fit(t, f, fn, xt, yt, p):
# f = gradient of fit at points of measurement
p.plot(xt, yt, lw=3.0, color='black', zorder=1)
p.scatter(t, f, facecolor='1.0', s=60, color='black', zorder=2, lw=1)
p.scatter(t, f, facecolor='None', s=60, color='black', zorder=3, lw=1)
p.scatter(t, fn, facecolor='0.0', s=60, zorder=4, lw=1)
p.vlines(t, f, fn, zorder=1, lw=2, color='0.5', linestyles='dotted')
p.set_xlabel('HJD - 2450000 [Days]')
p.set_ylabel('Light curve gradient [$L_\star/day$]')
################################################################################
# BEGIN main program
################################################################################
def helpme():
print ('plot_disk_fit.py -d <disk input FITS> -o <output plot file>')
print ('Example: ')
print (' plot_disk_fit.py -d 54220.65.try3.fits -o disk_fit.pdf')
sys.exit()
# parse command line options
try:
opts, args = getopt.getopt(sys.argv[1:], "hd:o:", ["dfile=", "ofile="])
except getopt.GetoptError:
helpme()
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
help()
elif opt in ("-d", "--dfile"):
fitsin_disk = arg
read_in_disk_parameters = True
elif opt in ("-o", "--ofile"):
plotfileout = arg
# get light curve gradients
grad = ascii.read("gradients.txt")
grad_time = grad['col1'] + 54222.
grad_mag = np.abs(grad['col2'])
# read in or create the ring system tau and radii
print ('Reading in disk parameters from %s' % fitsin_disk)
(res, taun_ringsxx, rad_ringsxx, dstar) = exorings.read_ring_fits(fitsin_disk)
# make the radius and projected gradient for the measured gradient points
(ring_disk_fit, grad_disk_fit) = \
exorings.make_ring_grad_line(grad_time, res[0], res[1], res[2], res[3])
# produce fine grained gradient and ring values
samp_t = np.arange(-100, 100, 0.001) + 54222.
(samp_r, samp_g) = exorings.make_ring_grad_line(samp_t, res[0], res[1], res[2], res[3])
hjd_minr = samp_t[np.argmin(samp_g)]
exorings.print_disk_parameters(res, hjd_minr, samp_r)
# plotting fit of gradients from ellipse curve to J1407 gradients
plt.rc('font', **{'family':'sans-serif', 'sans-serif':['Helvetica']})
plt.rc('text', usetex=True)
figfit = plt.figure(figsize=(10, 6))
f2 = figfit.add_subplot(111)
f2.set_ylim([0, 1.1*np.max(grad_mag)])
f2.set_xlim([np.min(samp_t), np.max(samp_t)])
plot_gradient_fit(grad_time, grad_disk_fit * np.max(grad_mag), grad_mag, \
samp_t, samp_g*np.max(grad_mag), f2)
# make ticks thicker
for ax in figfit.axes: # go over all the subplots in the figure fig
for i in ax.spines.itervalues(): # ... and go over all the axes too...
i.set_linewidth(2)
ax.minorticks_on() # switch on the minor ticks
# set the tick lengths and tick widths
ax.tick_params('both', length=15, width=2, which='major')
ax.tick_params('both', length=6, width=1, which='minor')
# adjust text size on the axes
f2.tick_params(axis='both', which='major', labelsize=14)
print ('writing plot out to file %s' % plotfileout)
plt.savefig(plotfileout)
|
mkenworthyREPO_NAMEexoringsPATH_START.@exorings_extracted@exorings-master@plot_disk_fit.py@.PATH_END.py
|
{
"filename": "dataset.py",
"repo_name": "facebookresearch/faiss",
"repo_path": "faiss_extracted/faiss-main/demos/offline_ivf/dataset.py",
"type": "Python"
}
|
# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import os
import numpy as np
import faiss
from typing import List
import random
import logging
from functools import lru_cache
def create_dataset_from_oivf_config(cfg, ds_name):
normalise = cfg["normalise"] if "normalise" in cfg else False
return MultiFileVectorDataset(
cfg["datasets"][ds_name]["root"],
[
FileDescriptor(
f["name"], f["format"], np.dtype(f["dtype"]), f["size"]
)
for f in cfg["datasets"][ds_name]["files"]
],
cfg["d"],
normalise,
cfg["datasets"][ds_name]["size"],
)
@lru_cache(maxsize=100)
def _memmap_vecs(
file_name: str, format: str, dtype: np.dtype, size: int, d: int
) -> np.array:
"""
If the file is in raw format, the file size will
be divisible by the dimensionality and by the size
of the data type.
Otherwise,the file contains a header and we assume
it is of .npy type. It the returns the memmapped file.
"""
assert os.path.exists(file_name), f"file does not exist {file_name}"
if format == "raw":
fl = os.path.getsize(file_name)
nb = fl // d // dtype.itemsize
assert nb == size, f"{nb} is different than config's {size}"
assert fl == d * dtype.itemsize * nb # no header
return np.memmap(file_name, shape=(nb, d), dtype=dtype, mode="r")
elif format == "npy":
vecs = np.load(file_name, mmap_mode="r")
assert vecs.shape[0] == size, f"size:{size},shape {vecs.shape[0]}"
assert vecs.shape[1] == d
assert vecs.dtype == dtype
return vecs
else:
ValueError("The file cannot be loaded in the current format.")
class FileDescriptor:
def __init__(self, name: str, format: str, dtype: np.dtype, size: int):
self.name = name
self.format = format
self.dtype = dtype
self.size = size
class MultiFileVectorDataset:
def __init__(
self,
root: str,
file_descriptors: List[FileDescriptor],
d: int,
normalize: bool,
size: int,
):
assert os.path.exists(root)
self.root = root
self.file_descriptors = file_descriptors
self.d = d
self.normalize = normalize
self.size = size
self.file_offsets = [0]
t = 0
for f in self.file_descriptors:
xb = _memmap_vecs(
f"{self.root}/{f.name}", f.format, f.dtype, f.size, self.d
)
t += xb.shape[0]
self.file_offsets.append(t)
assert (
t == self.size
), "the sum of num of embeddings per file!=total num of embeddings"
def iterate(self, start: int, batch_size: int, dt: np.dtype):
buffer = np.empty(shape=(batch_size, self.d), dtype=dt)
rem = 0
for f in self.file_descriptors:
if start >= f.size:
start -= f.size
continue
logging.info(f"processing: {f.name}...")
xb = _memmap_vecs(
f"{self.root}/{f.name}",
f.format,
f.dtype,
f.size,
self.d,
)
if start > 0:
xb = xb[start:]
start = 0
req = min(batch_size - rem, xb.shape[0])
buffer[rem:rem + req] = xb[:req]
rem += req
if rem == batch_size:
if self.normalize:
faiss.normalize_L2(buffer)
yield buffer.copy()
rem = 0
for i in range(req, xb.shape[0], batch_size):
j = i + batch_size
if j <= xb.shape[0]:
tmp = xb[i:j].astype(dt)
if self.normalize:
faiss.normalize_L2(tmp)
yield tmp
else:
rem = xb.shape[0] - i
buffer[:rem] = xb[i:j]
if rem > 0:
tmp = buffer[:rem]
if self.normalize:
faiss.normalize_L2(tmp)
yield tmp
def get(self, idx: List[int]):
n = len(idx)
fidx = np.searchsorted(self.file_offsets, idx, "right")
res = np.empty(shape=(len(idx), self.d), dtype=np.float32)
for r, id, fid in zip(range(n), idx, fidx):
assert fid > 0 and fid <= len(self.file_descriptors), f"{fid}"
f = self.file_descriptors[fid - 1]
# deferring normalization until after reading the vec
vecs = _memmap_vecs(
f"{self.root}/{f.name}", f.format, f.dtype, f.size, self.d
)
i = id - self.file_offsets[fid - 1]
assert i >= 0 and i < vecs.shape[0]
res[r, :] = vecs[i] # TODO: find a faster way
if self.normalize:
faiss.normalize_L2(res)
return res
def sample(self, n, idx_fn, vecs_fn):
if vecs_fn and os.path.exists(vecs_fn):
vecs = np.load(vecs_fn)
assert vecs.shape == (n, self.d)
return vecs
if idx_fn and os.path.exists(idx_fn):
idx = np.load(idx_fn)
assert idx.size == n
else:
idx = np.array(sorted(random.sample(range(self.size), n)))
if idx_fn:
np.save(idx_fn, idx)
vecs = self.get(idx)
if vecs_fn:
np.save(vecs_fn, vecs)
return vecs
def get_first_n(self, n, dt):
assert n <= self.size
return next(self.iterate(0, n, dt))
|
facebookresearchREPO_NAMEfaissPATH_START.@faiss_extracted@faiss-main@demos@offline_ivf@dataset.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "Keck-DataReductionPipelines/KPF-Pipeline",
"repo_path": "KPF-Pipeline_extracted/KPF-Pipeline-master/modules/var_exts/src/__init__.py",
"type": "Python"
}
|
Keck-DataReductionPipelinesREPO_NAMEKPF-PipelinePATH_START.@KPF-Pipeline_extracted@KPF-Pipeline-master@modules@var_exts@src@__init__.py@.PATH_END.py
|
|
{
"filename": "gen_qa_models.py",
"repo_name": "triton-inference-server/server",
"repo_path": "server_extracted/server-main/qa/common/gen_qa_models.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# Copyright 2018-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# * Neither the name of NVIDIA CORPORATION nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
# EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
# OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import argparse
import os
from builtins import range
import gen_ensemble_model_utils as emu
import numpy as np
from gen_common import (
np_dtype_bfloat16,
np_to_model_dtype,
np_to_onnx_dtype,
np_to_tf_dtype,
np_to_torch_dtype,
np_to_trt_dtype,
)
FLAGS = None
np_dtype_string = np.dtype(object)
from typing import List, Tuple
def create_graphdef_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap=False,
):
if not tu.validate_for_tf_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
tf_input_dtype = np_to_tf_dtype(input_dtype)
tf_output0_dtype = np_to_tf_dtype(output0_dtype)
tf_output1_dtype = np_to_tf_dtype(output1_dtype)
# Create the model. If non-batching then don't include the batch
# dimension.
tf.compat.v1.reset_default_graph()
if max_batch == 0:
in0 = tf.compat.v1.placeholder(
tf_input_dtype, tu.shape_to_tf_shape(input_shape), "INPUT0"
)
in1 = tf.compat.v1.placeholder(
tf_input_dtype, tu.shape_to_tf_shape(input_shape), "INPUT1"
)
else:
in0 = tf.compat.v1.placeholder(
tf_input_dtype,
[
None,
]
+ tu.shape_to_tf_shape(input_shape),
"INPUT0",
)
in1 = tf.compat.v1.placeholder(
tf_input_dtype,
[
None,
]
+ tu.shape_to_tf_shape(input_shape),
"INPUT1",
)
# If the input is a string, then convert each string to the
# equivalent int32 value.
if tf_input_dtype == tf.string:
in0 = tf.strings.to_number(in0, tf.int32)
in1 = tf.strings.to_number(in1, tf.int32)
add = tf.add(in0, in1, "ADD")
sub = tf.subtract(in0, in1, "SUB")
# Cast or convert result to the output dtype.
if tf_output0_dtype == tf.string:
cast0 = tf.strings.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.strings.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
out0 = tf.identity(cast0, "OUTPUT0")
out1 = tf.identity(cast1, "OUTPUT1")
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"graphdef_nobatch" if max_batch == 0 else "graphdef",
input_dtype,
output0_dtype,
output1_dtype,
)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with tf.compat.v1.Session() as sess:
graph_io.write_graph(
sess.graph.as_graph_def(),
model_version_dir,
"model.graphdef",
as_text=False,
)
def create_graphdef_modelconfig(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
):
if not tu.validate_for_tf_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
if version_policy is not None:
type, val = version_policy
if type == "latest":
version_policy_str = "{{ latest {{ num_versions: {} }}}}".format(val)
elif type == "specific":
version_policy_str = "{{ specific {{ versions: {} }}}}".format(val)
else:
version_policy_str = "{ all { }}"
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"graphdef_nobatch" if max_batch == 0 else "graphdef",
input_dtype,
output0_dtype,
output1_dtype,
)
config_dir = models_dir + "/" + model_name
config = """
name: "{}"
platform: "tensorflow_graphdef"
max_batch_size: {}
version_policy: {}
input [
{{
name: "INPUT0"
data_type: {}
dims: [ {} ]
}},
{{
name: "INPUT1"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT0"
data_type: {}
dims: [ {} ]
label_filename: "output0_labels.txt"
}},
{{
name: "OUTPUT1"
data_type: {}
dims: [ {} ]
}}
]
""".format(
model_name,
max_batch,
version_policy_str,
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(output0_dtype),
tu.shape_to_dims_str(output0_shape),
np_to_model_dtype(output1_dtype),
tu.shape_to_dims_str(output1_shape),
)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
with open(config_dir + "/output0_labels.txt", "w") as lfile:
for l in range(output0_label_cnt):
lfile.write("label" + str(l) + "\n")
def create_savedmodel_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap=False,
):
if not tu.validate_for_tf_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
tf_input_dtype = np_to_tf_dtype(input_dtype)
tf_output0_dtype = np_to_tf_dtype(output0_dtype)
tf_output1_dtype = np_to_tf_dtype(output1_dtype)
# Create the model. If non-batching then don't include the batch
# dimension.
tf.compat.v1.reset_default_graph()
if max_batch == 0:
in0 = tf.compat.v1.placeholder(
tf_input_dtype, tu.shape_to_tf_shape(input_shape), "TENSOR_INPUT0"
)
in1 = tf.compat.v1.placeholder(
tf_input_dtype, tu.shape_to_tf_shape(input_shape), "TENSOR_INPUT1"
)
else:
in0 = tf.compat.v1.placeholder(
tf_input_dtype,
[
None,
]
+ tu.shape_to_tf_shape(input_shape),
"TENSOR_INPUT0",
)
in1 = tf.compat.v1.placeholder(
tf_input_dtype,
[
None,
]
+ tu.shape_to_tf_shape(input_shape),
"TENSOR_INPUT1",
)
# If the input is a string, then convert each string to the
# equivalent float value.
if tf_input_dtype == tf.string:
in0 = tf.strings.to_number(in0, tf.int32)
in1 = tf.strings.to_number(in1, tf.int32)
add = tf.add(in0, in1, "ADD")
sub = tf.subtract(in0, in1, "SUB")
# Cast or convert result to the output dtype.
if tf_output0_dtype == tf.string:
cast0 = tf.strings.as_string(add if not swap else sub, name="TOSTR0")
else:
cast0 = tf.cast(add if not swap else sub, tf_output0_dtype, "CAST0")
if tf_output1_dtype == tf.string:
cast1 = tf.strings.as_string(sub if not swap else add, name="TOSTR1")
else:
cast1 = tf.cast(sub if not swap else add, tf_output1_dtype, "CAST1")
tf.identity(cast0, "TENSOR_OUTPUT0")
tf.identity(cast1, "TENSOR_OUTPUT1")
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel",
input_dtype,
output0_dtype,
output1_dtype,
)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with tf.compat.v1.Session() as sess:
input0_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name(
"TENSOR_INPUT0:0"
)
input1_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name(
"TENSOR_INPUT1:0"
)
output0_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name(
"TENSOR_OUTPUT0:0"
)
output1_tensor = tf.compat.v1.get_default_graph().get_tensor_by_name(
"TENSOR_OUTPUT1:0"
)
tf.compat.v1.saved_model.simple_save(
sess,
model_version_dir + "/model.savedmodel",
inputs={"INPUT0": input0_tensor, "INPUT1": input1_tensor},
outputs={"OUTPUT0": output0_tensor, "OUTPUT1": output1_tensor},
)
def create_savedmodel_modelconfig(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
):
if not tu.validate_for_tf_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
if version_policy is not None:
type, val = version_policy
if type == "latest":
version_policy_str = "{{ latest {{ num_versions: {} }}}}".format(val)
elif type == "specific":
version_policy_str = "{{ specific {{ versions: {} }}}}".format(val)
else:
version_policy_str = "{ all { }}"
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"savedmodel_nobatch" if max_batch == 0 else "savedmodel",
input_dtype,
output0_dtype,
output1_dtype,
)
config_dir = models_dir + "/" + model_name
config = """
name: "{}"
platform: "tensorflow_savedmodel"
max_batch_size: {}
version_policy: {}
input [
{{
name: "INPUT0"
data_type: {}
dims: [ {} ]
}},
{{
name: "INPUT1"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT0"
data_type: {}
dims: [ {} ]
label_filename: "output0_labels.txt"
}},
{{
name: "OUTPUT1"
data_type: {}
dims: [ {} ]
}}
]
""".format(
model_name,
max_batch,
version_policy_str,
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(output0_dtype),
tu.shape_to_dims_str(output0_shape),
np_to_model_dtype(output1_dtype),
tu.shape_to_dims_str(output1_shape),
)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
with open(config_dir + "/output0_labels.txt", "w") as lfile:
for l in range(output0_label_cnt):
lfile.write("label" + str(l) + "\n")
def create_plan_dynamic_rf_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
min_dim,
max_dim,
):
trt_input_dtype = np_to_trt_dtype(input_dtype)
trt_output0_dtype = np_to_trt_dtype(output0_dtype)
trt_output1_dtype = np_to_trt_dtype(output1_dtype)
trt_memory_format = trt.TensorFormat.LINEAR
# Create the model
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
if max_batch == 0:
input_with_batchsize = [i for i in input_shape]
else:
input_with_batchsize = [-1] + [i for i in input_shape]
in0 = network.add_input("INPUT0", trt_input_dtype, input_with_batchsize)
in1 = network.add_input("INPUT1", trt_input_dtype, input_with_batchsize)
# TRT uint8 cannot be used to represent quantized floating-point value yet
# uint8 must be converted to float16 or float32 before any operation
# FIXME: Remove support check when jetson supports TRT 8.5 (DLIS-4256)
if tu.support_trt_uint8():
if trt_input_dtype == trt.uint8:
in0_cast = network.add_identity(in0)
in0_cast.set_output_type(0, trt.float32)
in0 = in0_cast.get_output(0)
in1_cast = network.add_identity(in1)
in1_cast.set_output_type(0, trt.float32)
in1 = in1_cast.get_output(0)
add = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUM)
sub = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUB)
out0 = add if not swap else sub
out1 = sub if not swap else add
# uint8 conversion after operations
# FIXME: Remove support check when jetson supports TRT 8.5 (DLIS-4256)
if tu.support_trt_uint8():
if trt_output0_dtype == trt.uint8:
out0 = network.add_identity(out0.get_output(0))
out0.set_output_type(0, trt.uint8)
if trt_output1_dtype == trt.uint8:
out1 = network.add_identity(out1.get_output(0))
out1.set_output_type(0, trt.uint8)
out0.get_output(0).name = "OUTPUT0"
out1.get_output(0).name = "OUTPUT1"
network.mark_output(out0.get_output(0))
network.mark_output(out1.get_output(0))
out0.get_output(0).dtype = trt_output0_dtype
out1.get_output(0).dtype = trt_output1_dtype
in0.allowed_formats = 1 << int(trt_memory_format)
in1.allowed_formats = 1 << int(trt_memory_format)
out0.get_output(0).allowed_formats = 1 << int(trt_memory_format)
out1.get_output(0).allowed_formats = 1 << int(trt_memory_format)
if trt_input_dtype == trt.int8:
in0.dynamic_range = (-128.0, 127.0)
in1.dynamic_range = (-128.0, 127.0)
if trt_output0_dtype == trt.int8:
out0.get_output(0).dynamic_range = (-128.0, 127.0)
if trt_output1_dtype == trt.int8:
out1.get_output(0).dynamic_range = (-128.0, 127.0)
min_shape = []
opt_shape = []
max_shape = []
if max_batch != 0:
min_shape = min_shape + [1]
opt_shape = opt_shape + [max(1, max_batch)]
max_shape = max_shape + [max(1, max_batch)]
for i in input_shape:
if i == -1:
min_shape = min_shape + [min_dim]
opt_shape = opt_shape + [int((max_dim + min_dim) / 2)]
max_shape = max_shape + [max_dim]
else:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
profile = builder.create_optimization_profile()
profile.set_shape("INPUT0", min_shape, opt_shape, max_shape)
profile.set_shape("INPUT1", min_shape, opt_shape, max_shape)
flags = 1 << int(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS)
flags |= 1 << int(trt.BuilderFlag.REJECT_EMPTY_ALGORITHMS)
datatype_set = set([trt_input_dtype, trt_output0_dtype, trt_output1_dtype])
for dt in datatype_set:
if dt == trt.int8:
flags |= 1 << int(trt.BuilderFlag.INT8)
elif dt == trt.float16:
flags |= 1 << int(trt.BuilderFlag.FP16)
config = builder.create_builder_config()
config.flags = flags
config.add_optimization_profile(profile)
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
# Use a different model name for different kinds of models
model_name = tu.get_model_name(
"plan_nobatch" if max_batch == 0 else "plan",
input_dtype,
output0_dtype,
output1_dtype,
)
if min_dim != 1 or max_dim != 32:
model_name = "{}-{}-{}".format(model_name, min_dim, max_dim)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_dynamic_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
min_dim,
max_dim,
):
trt_input_dtype = np_to_trt_dtype(input_dtype)
trt_output0_dtype = np_to_trt_dtype(output0_dtype)
trt_output1_dtype = np_to_trt_dtype(output1_dtype)
# Create the model
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
if max_batch == 0:
input_with_batchsize = [i for i in input_shape]
else:
input_with_batchsize = [-1] + [i for i in input_shape]
in0 = network.add_input("INPUT0", trt_input_dtype, input_with_batchsize)
in1 = network.add_input("INPUT1", trt_input_dtype, input_with_batchsize)
add = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUM)
sub = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUB)
out0 = add if not swap else sub
out1 = sub if not swap else add
out0.get_output(0).name = "OUTPUT0"
out1.get_output(0).name = "OUTPUT1"
network.mark_output(out0.get_output(0))
network.mark_output(out1.get_output(0))
min_shape = []
opt_shape = []
max_shape = []
for i in input_shape:
if i == -1:
min_shape = min_shape + [min_dim]
opt_shape = opt_shape + [int((max_dim + min_dim) / 2)]
max_shape = max_shape + [max_dim]
else:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
config = builder.create_builder_config()
# create multiple profiles with same shape for testing
# with decreasing batch sizes
profile = []
for i in range(4):
profile.append(builder.create_optimization_profile())
if max_batch == 0:
profile[i].set_shape("INPUT0", min_shape, opt_shape, max_shape)
profile[i].set_shape("INPUT1", min_shape, opt_shape, max_shape)
else:
bs = [max_batch - i if max_batch > i else 1]
opt_bs = [1 + i if 1 + i < max_batch - 1 else max_batch - 1]
# Hardcoded 'max_shape[0] += 1' in default profile for
# L0_trt_dynamic_shape, to differentiate whether default profile
# is used if no profile is specified
max_shape_override = max_shape
if i == 0 and (min_dim == 1 and max_dim == 32):
max_shape_override[0] += 1
profile[i].set_shape(
"INPUT0", [1] + min_shape, opt_bs + opt_shape, bs + max_shape_override
)
profile[i].set_shape(
"INPUT1", [1] + min_shape, opt_bs + opt_shape, bs + max_shape_override
)
config.add_optimization_profile(profile[i])
# some profiles with non-one min shape for first dim to test autofiller
for i in range(2):
profile.append(builder.create_optimization_profile())
if max_batch == 0:
profile[i + 4].set_shape("INPUT0", min_shape, opt_shape, max_shape)
profile[i + 4].set_shape("INPUT1", min_shape, opt_shape, max_shape)
else:
profile[i + 4].set_shape(
"INPUT0", [5 + i] + min_shape, [6] + opt_shape, [max_batch] + max_shape
)
profile[i + 4].set_shape(
"INPUT1", [5 + i] + min_shape, [6] + opt_shape, [max_batch] + max_shape
)
config.add_optimization_profile(profile[i + 4])
# Will repeat another profile with same min and max shapes as the first profile to test non-zero profile
# for infer_variable test.
profile.append(builder.create_optimization_profile())
if max_batch == 0:
profile[6].set_shape("INPUT0", min_shape, opt_shape, max_shape)
profile[6].set_shape("INPUT1", min_shape, opt_shape, max_shape)
else:
profile[6].set_shape(
"INPUT0", [1] + min_shape, [1] + opt_shape, [max_batch] + max_shape
)
profile[6].set_shape(
"INPUT1", [1] + min_shape, [1] + opt_shape, [max_batch] + max_shape
)
config.add_optimization_profile(profile[6])
# Will add some profiles with static shapes to test the cases where min_shape=opt_shape=max_shape
for i in range(3):
profile.append(builder.create_optimization_profile())
if max_batch == 0:
static_shape = max_shape
profile[7 + i].set_shape("INPUT0", static_shape, static_shape, static_shape)
profile[7 + i].set_shape("INPUT1", static_shape, static_shape, static_shape)
else:
# Skipping alternate batch sizes for testing unsupported batches in L0_trt_dynamic_shape.
full_static_shape = [1 + (2 * i)] + max_shape
profile[7 + i].set_shape(
"INPUT0", full_static_shape, full_static_shape, full_static_shape
)
profile[7 + i].set_shape(
"INPUT1", full_static_shape, full_static_shape, full_static_shape
)
config.add_optimization_profile(profile[7 + i])
# Add profiles where each profile supports a specific batch size
if max_batch != 0:
for i in range(max_batch):
profile.append(builder.create_optimization_profile())
profile[10 + i].set_shape(
"INPUT0", [1 + i] + min_shape, [1 + i] + opt_shape, [1 + i] + max_shape
)
profile[10 + i].set_shape(
"INPUT1", [1 + i] + min_shape, [1 + i] + opt_shape, [1 + i] + max_shape
)
config.add_optimization_profile(profile[10 + i])
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
# Use a different model name for different kinds of models
model_name = tu.get_model_name(
"plan_nobatch" if max_batch == 0 else "plan",
input_dtype,
output0_dtype,
output1_dtype,
)
if min_dim != 1 or max_dim != 32:
model_name = "{}-{}-{}".format(model_name, min_dim, max_dim)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_fixed_rf_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
):
trt_input_dtype = np_to_trt_dtype(input_dtype)
trt_output0_dtype = np_to_trt_dtype(output0_dtype)
trt_output1_dtype = np_to_trt_dtype(output1_dtype)
trt_memory_format = trt.TensorFormat.LINEAR
# Create the model
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
if max_batch == 0:
input_with_batchsize = [i for i in input_shape]
else:
input_with_batchsize = [-1] + [i for i in input_shape]
in0 = network.add_input("INPUT0", trt_input_dtype, input_with_batchsize)
in1 = network.add_input("INPUT1", trt_input_dtype, input_with_batchsize)
add = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUM)
sub = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUB)
out0 = add if not swap else sub
out1 = sub if not swap else add
out0.get_output(0).name = "OUTPUT0"
out1.get_output(0).name = "OUTPUT1"
network.mark_output(out0.get_output(0))
network.mark_output(out1.get_output(0))
out0.get_output(0).dtype = trt_output0_dtype
out1.get_output(0).dtype = trt_output1_dtype
in0.allowed_formats = 1 << int(trt_memory_format)
in1.allowed_formats = 1 << int(trt_memory_format)
out0.get_output(0).allowed_formats = 1 << int(trt_memory_format)
out1.get_output(0).allowed_formats = 1 << int(trt_memory_format)
if trt_input_dtype == trt.int8:
in0.dynamic_range = (-128.0, 127.0)
in1.dynamic_range = (-128.0, 127.0)
if trt_output0_dtype == trt.int8:
out0.get_output(0).dynamic_range = (-128.0, 127.0)
if trt_output1_dtype == trt.int8:
out1.get_output(0).dynamic_range = (-128.0, 127.0)
config = builder.create_builder_config()
min_shape = []
opt_shape = []
max_shape = []
if max_batch != 0:
min_shape = min_shape + [1]
opt_shape = opt_shape + [max(1, max_batch)]
max_shape = max_shape + [max(1, max_batch)]
for i in input_shape:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
profile = builder.create_optimization_profile()
profile.set_shape("INPUT0", min_shape, opt_shape, max_shape)
profile.set_shape("INPUT1", min_shape, opt_shape, max_shape)
flags = 1 << int(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS)
flags |= 1 << int(trt.BuilderFlag.REJECT_EMPTY_ALGORITHMS)
datatype_set = set([trt_input_dtype, trt_output0_dtype, trt_output1_dtype])
for dt in datatype_set:
if dt == trt.int8:
flags |= 1 << int(trt.BuilderFlag.INT8)
elif dt == trt.float16:
flags |= 1 << int(trt.BuilderFlag.FP16)
config = builder.create_builder_config()
config.flags = flags
config.add_optimization_profile(profile)
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
model_name = tu.get_model_name(
"plan_nobatch" if max_batch == 0 else "plan",
input_dtype,
output0_dtype,
output1_dtype,
)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_fixed_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
):
trt_input_dtype = np_to_trt_dtype(input_dtype)
trt_output0_dtype = np_to_trt_dtype(output0_dtype)
trt_output1_dtype = np_to_trt_dtype(output1_dtype)
# Create the model
TRT_LOGGER = trt.Logger(trt.Logger.INFO)
builder = trt.Builder(TRT_LOGGER)
network = builder.create_network()
if max_batch == 0:
input_with_batchsize = [i for i in input_shape]
else:
input_with_batchsize = [-1] + [i for i in input_shape]
in0 = network.add_input("INPUT0", trt_input_dtype, input_with_batchsize)
in1 = network.add_input("INPUT1", trt_input_dtype, input_with_batchsize)
add = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUM)
sub = network.add_elementwise(in0, in1, trt.ElementWiseOperation.SUB)
out0 = add if not swap else sub
out1 = sub if not swap else add
out0.get_output(0).name = "OUTPUT0"
out1.get_output(0).name = "OUTPUT1"
network.mark_output(out0.get_output(0))
network.mark_output(out1.get_output(0))
config = builder.create_builder_config()
min_shape = []
opt_shape = []
max_shape = []
if max_batch != 0:
min_shape = min_shape + [1]
opt_shape = opt_shape + [max(1, max_batch)]
max_shape = max_shape + [max(1, max_batch)]
for i in input_shape:
min_shape = min_shape + [i]
opt_shape = opt_shape + [i]
max_shape = max_shape + [i]
profile = builder.create_optimization_profile()
profile.set_shape("INPUT0", min_shape, opt_shape, max_shape)
profile.set_shape("INPUT1", min_shape, opt_shape, max_shape)
config.add_optimization_profile(profile)
config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 20)
try:
engine_bytes = builder.build_serialized_network(network, config)
except AttributeError:
engine = builder.build_engine(network, config)
engine_bytes = engine.serialize()
del engine
del network
model_name = tu.get_model_name(
"plan_nobatch" if max_batch == 0 else "plan",
input_dtype,
output0_dtype,
output1_dtype,
)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
with open(model_version_dir + "/model.plan", "wb") as f:
f.write(engine_bytes)
def create_plan_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap=False,
min_dim=1,
max_dim=32,
):
if not tu.validate_for_trt_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
if (
input_dtype == np.uint8
or output0_dtype == np.uint8
or output1_dtype == np.uint8
):
# TRT uint8 cannot be used to represent quantized floating-point value yet
create_plan_dynamic_rf_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
min_dim,
max_dim,
)
elif (
input_dtype != np.float32
or output0_dtype != np.float32
or output1_dtype != np.float32
):
if (
not tu.shape_is_fixed(input_shape)
or not tu.shape_is_fixed(output0_shape)
or not tu.shape_is_fixed(output1_shape)
):
create_plan_dynamic_rf_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
min_dim,
max_dim,
)
else:
create_plan_fixed_rf_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
)
else:
if (
not tu.shape_is_fixed(input_shape)
or not tu.shape_is_fixed(output0_shape)
or not tu.shape_is_fixed(output1_shape)
):
create_plan_dynamic_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
min_dim,
max_dim,
)
else:
create_plan_fixed_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap,
)
def create_plan_modelconfig(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
min_dim=1,
max_dim=32,
):
if not tu.validate_for_trt_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
if version_policy is not None:
type, val = version_policy
if type == "latest":
version_policy_str = "{{ latest {{ num_versions: {} }}}}".format(val)
elif type == "specific":
version_policy_str = "{{ specific {{ versions: {} }}}}".format(val)
else:
version_policy_str = "{ all { }}"
# Use a different model name for different kinds of models
model_name = tu.get_model_name(
"plan_nobatch" if max_batch == 0 else "plan",
input_dtype,
output0_dtype,
output1_dtype,
)
if min_dim != 1 or max_dim != 32:
model_name = "{}-{}-{}".format(model_name, min_dim, max_dim)
config_dir = models_dir + "/" + model_name
if -1 in input_shape:
# Selects the sixth profile for FP32 datatype
# Note the min and max shapes of first and sixth
# profile are identical.
profile_index = 6 if input_dtype == np.float32 else 0
config = """
name: "{}"
platform: "tensorrt_plan"
max_batch_size: {}
version_policy: {}
input [
{{
name: "INPUT0"
data_type: {}
dims: [ {} ]
}},
{{
name: "INPUT1"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT0"
data_type: {}
dims: [ {} ]
label_filename: "output0_labels.txt"
}},
{{
name: "OUTPUT1"
data_type: {}
dims: [ {} ]
}}
]
instance_group [
{{
profile:"{}"
}}
]
""".format(
model_name,
max_batch,
version_policy_str,
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(output0_dtype),
tu.shape_to_dims_str(output0_shape),
np_to_model_dtype(output1_dtype),
tu.shape_to_dims_str(output1_shape),
profile_index,
)
else:
config = """
name: "{}"
platform: "tensorrt_plan"
max_batch_size: {}
version_policy: {}
input [
{{
name: "INPUT0"
data_type: {}
dims: [ {} ]
}},
{{
name: "INPUT1"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT0"
data_type: {}
dims: [ {} ]
label_filename: "output0_labels.txt"
}},
{{
name: "OUTPUT1"
data_type: {}
dims: [ {} ]
}}
]
""".format(
model_name,
max_batch,
version_policy_str,
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(output0_dtype),
tu.shape_to_dims_str(output0_shape),
np_to_model_dtype(output1_dtype),
tu.shape_to_dims_str(output1_shape),
)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
with open(config_dir + "/output0_labels.txt", "w") as lfile:
for l in range(output0_label_cnt):
lfile.write("label" + str(l) + "\n")
def create_onnx_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap=False,
):
if not tu.validate_for_onnx_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
onnx_input_dtype = np_to_onnx_dtype(input_dtype)
onnx_output0_dtype = np_to_onnx_dtype(output0_dtype)
onnx_output1_dtype = np_to_onnx_dtype(output1_dtype)
onnx_input_shape, idx = tu.shape_to_onnx_shape(input_shape, 0)
onnx_output0_shape, idx = tu.shape_to_onnx_shape(input_shape, idx)
onnx_output1_shape, idx = tu.shape_to_onnx_shape(input_shape, idx)
# Create the model
model_name = tu.get_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx",
input_dtype,
output0_dtype,
output1_dtype,
)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
batch_dim = [] if max_batch == 0 else [None]
in0 = onnx.helper.make_tensor_value_info(
"INPUT0", onnx_input_dtype, batch_dim + onnx_input_shape
)
in1 = onnx.helper.make_tensor_value_info(
"INPUT1", onnx_input_dtype, batch_dim + onnx_input_shape
)
out0 = onnx.helper.make_tensor_value_info(
"OUTPUT0", onnx_output0_dtype, batch_dim + onnx_output0_shape
)
out1 = onnx.helper.make_tensor_value_info(
"OUTPUT1", onnx_output1_dtype, batch_dim + onnx_output1_shape
)
internal_in0 = onnx.helper.make_node("Identity", ["INPUT0"], ["_INPUT0"])
internal_in1 = onnx.helper.make_node("Identity", ["INPUT1"], ["_INPUT1"])
# cast int8, int16 input to higher precision int as Onnx Add/Sub operator doesn't support those type
# Also casting String data type to int32
if (
(onnx_input_dtype == onnx.TensorProto.INT8)
or (onnx_input_dtype == onnx.TensorProto.INT16)
or (onnx_input_dtype == onnx.TensorProto.STRING)
):
internal_in0 = onnx.helper.make_node(
"Cast", ["INPUT0"], ["_INPUT0"], to=onnx.TensorProto.INT32
)
internal_in1 = onnx.helper.make_node(
"Cast", ["INPUT1"], ["_INPUT1"], to=onnx.TensorProto.INT32
)
add = onnx.helper.make_node(
"Add", ["_INPUT0", "_INPUT1"], ["CAST0" if not swap else "CAST1"]
)
sub = onnx.helper.make_node(
"Sub", ["_INPUT0", "_INPUT1"], ["CAST1" if not swap else "CAST0"]
)
cast0 = onnx.helper.make_node("Cast", ["CAST0"], ["OUTPUT0"], to=onnx_output0_dtype)
cast1 = onnx.helper.make_node("Cast", ["CAST1"], ["OUTPUT1"], to=onnx_output1_dtype)
# Avoid cast from float16 to float16
# (bug in Onnx Runtime, cast from float16 to float16 will become cast from float16 to float32)
if onnx_input_dtype == onnx.TensorProto.FLOAT16:
if onnx_output0_dtype == onnx_input_dtype:
cast0 = onnx.helper.make_node("Identity", ["CAST0"], ["OUTPUT0"])
if onnx_output1_dtype == onnx_input_dtype:
cast1 = onnx.helper.make_node("Identity", ["CAST1"], ["OUTPUT1"])
onnx_nodes = [internal_in0, internal_in1, add, sub, cast0, cast1]
onnx_inputs = [in0, in1]
onnx_outputs = [out0, out1]
graph_proto = onnx.helper.make_graph(
onnx_nodes, model_name, onnx_inputs, onnx_outputs
)
if FLAGS.onnx_opset > 0:
model_opset = onnx.helper.make_operatorsetid("", FLAGS.onnx_opset)
model_def = onnx.helper.make_model(
graph_proto, producer_name="triton", opset_imports=[model_opset]
)
else:
model_def = onnx.helper.make_model(graph_proto, producer_name="triton")
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
onnx.save(model_def, model_version_dir + "/model.onnx")
def create_onnx_modelconfig(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
):
if not tu.validate_for_onnx_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
return
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"onnx_nobatch" if max_batch == 0 else "onnx",
input_dtype,
output0_dtype,
output1_dtype,
)
config_dir = models_dir + "/" + model_name
# [TODO] move create_general_modelconfig() out of emu as it is general
# enough for all backends to use
config = emu.create_general_modelconfig(
model_name,
"onnxruntime_onnx",
max_batch,
emu.repeat(input_dtype, 2),
emu.repeat(input_shape, 2),
emu.repeat(None, 2),
[output0_dtype, output1_dtype],
[output0_shape, output1_shape],
emu.repeat(None, 2),
["output0_labels.txt", None],
version_policy=version_policy,
force_tensor_number_suffix=True,
)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
with open(config_dir + "/output0_labels.txt", "w") as lfile:
for l in range(output0_label_cnt):
lfile.write("label" + str(l) + "\n")
def create_libtorch_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap=False,
):
if not tu.validate_for_libtorch_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
max_batch,
):
return
torch_output0_dtype = np_to_torch_dtype(output0_dtype)
torch_output1_dtype = np_to_torch_dtype(output1_dtype)
model_name = tu.get_model_name(
"libtorch_nobatch" if max_batch == 0 else "libtorch",
input_dtype,
output0_dtype,
output1_dtype,
)
# handle for -1 (when variable) since can't create tensor with shape of [-1]
input_shape = [abs(ips) for ips in input_shape]
# Create the model
if (
(input_dtype == np_dtype_string)
and (output0_dtype != np_dtype_string)
and (output1_dtype != np_dtype_string)
):
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(self, INPUT0: List[str], INPUT1: List[str]):
input0_int = torch.tensor([int(i) for i in INPUT0])
input1_int = torch.tensor([int(i) for i in INPUT1])
op0 = (
input0_int + input1_int
if not self.swap
else input0_int - input1_int
)
op1 = (
input0_int - input1_int
if not self.swap
else input0_int + input1_int
)
return op0.to(self.output0_dtype), op1.to(self.output1_dtype)
elif (
(input_dtype == np_dtype_string)
and (output0_dtype == np_dtype_string)
and (output1_dtype == np_dtype_string)
):
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(
self, INPUT0: List[str], INPUT1: List[str]
) -> Tuple[List[str], List[str]]:
input0_int = torch.tensor([int(i) for i in INPUT0])
input1_int = torch.tensor([int(i) for i in INPUT1])
op0 = [
str(i.item())
for i in (
input0_int + input1_int
if not self.swap
else input0_int - input1_int
)
]
op1 = [
str(i.item())
for i in (
input0_int - input1_int
if not self.swap
else input0_int + input1_int
)
]
return op0, op1
elif (
(input_dtype == np_dtype_string)
and (output0_dtype == np_dtype_string)
and (output1_dtype != np_dtype_string)
):
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(
self, INPUT0: List[str], INPUT1: List[str]
) -> Tuple[List[str], torch.Tensor]:
input0_int = torch.tensor([int(i) for i in INPUT0])
input1_int = torch.tensor([int(i) for i in INPUT1])
op0 = [
str(i.item())
for i in (
input0_int + input1_int
if not self.swap
else input0_int - input1_int
)
]
op1 = (
input0_int - input1_int
if not self.swap
else input0_int + input1_int
).to(self.output1_dtype)
return op0, op1
elif (
(input_dtype == np_dtype_string)
and (output0_dtype != np_dtype_string)
and (output1_dtype == np_dtype_string)
):
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(
self, INPUT0: List[str], INPUT1: List[str]
) -> Tuple[torch.Tensor, List[str]]:
input0_int = torch.tensor([int(i) for i in INPUT0])
input1_int = torch.tensor([int(i) for i in INPUT1])
op0 = (
input0_int + input1_int
if not self.swap
else input0_int - input1_int
).to(self.output0_dtype)
op1 = [
str(i.item())
for i in (
input0_int - input1_int
if not self.swap
else input0_int + input1_int
)
]
return op0, op1
elif (
(input_dtype != np_dtype_string)
and (output0_dtype == np_dtype_string)
and (output1_dtype == np_dtype_string)
):
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(self, INPUT0, INPUT1) -> Tuple[List[str], List[str]]:
op0 = [
str(i.item())
for i in (INPUT0 + INPUT1 if not self.swap else INPUT0 - INPUT1)
]
op1 = [
str(i.item())
for i in (INPUT0 - INPUT1 if not self.swap else INPUT0 + INPUT1)
]
return op0, op1
elif (
(input_dtype != np_dtype_string)
and (output0_dtype != np_dtype_string)
and (output1_dtype == np_dtype_string)
):
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(self, INPUT0, INPUT1) -> Tuple[torch.Tensor, List[str]]:
op0 = (INPUT0 + INPUT1 if not self.swap else INPUT0 - INPUT1).to(
self.output0_dtype
)
op1 = [
str(i.item())
for i in (INPUT0 - INPUT1 if not self.swap else INPUT0 + INPUT1)
]
return op0, op1
elif (
(input_dtype != np_dtype_string)
and (output0_dtype == np_dtype_string)
and (output1_dtype != np_dtype_string)
):
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(self, INPUT0, INPUT1) -> Tuple[List[str], torch.Tensor]:
op0 = [
str(i.item())
for i in (INPUT0 + INPUT1 if not self.swap else INPUT0 - INPUT1)
]
op1 = (INPUT0 - INPUT1 if not self.swap else INPUT0 + INPUT1).to(
self.output1_dtype
)
return op0, op1
else:
class AddSubNet(nn.Module):
def __init__(self, *args):
self.output0_dtype = args[0][0]
self.output1_dtype = args[0][1]
self.swap = args[0][2]
super(AddSubNet, self).__init__()
def forward(self, INPUT0, INPUT1):
op0 = (INPUT0 + INPUT1 if not self.swap else INPUT0 - INPUT1).to(
self.output0_dtype
)
op1 = (INPUT0 - INPUT1 if not self.swap else INPUT0 + INPUT1).to(
self.output1_dtype
)
return op0, op1
addSubModel = AddSubNet((torch_output0_dtype, torch_output1_dtype, swap))
traced = torch.jit.script(addSubModel)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
traced.save(model_version_dir + "/model.pt")
def create_libtorch_modelconfig(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
):
if not tu.validate_for_libtorch_model(
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
max_batch,
):
return
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
if version_policy is not None:
type, val = version_policy
if type == "latest":
version_policy_str = "{{ latest {{ num_versions: {} }}}}".format(val)
elif type == "specific":
version_policy_str = "{{ specific {{ versions: {} }}}}".format(val)
else:
version_policy_str = "{ all { }}"
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"libtorch_nobatch" if max_batch == 0 else "libtorch",
input_dtype,
output0_dtype,
output1_dtype,
)
config_dir = models_dir + "/" + model_name
config = """
name: "{}"
platform: "pytorch_libtorch"
max_batch_size: {}
version_policy: {}
input [
{{
name: "INPUT0"
data_type: {}
dims: [ {} ]
}},
{{
name: "INPUT1"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT__0"
data_type: {}
dims: [ {} ]
label_filename: "output0_labels.txt"
}},
{{
name: "OUTPUT__1"
data_type: {}
dims: [ {} ]
}}
]
""".format(
model_name,
max_batch,
version_policy_str,
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(output0_dtype),
tu.shape_to_dims_str(output0_shape),
np_to_model_dtype(output1_dtype),
tu.shape_to_dims_str(output1_shape),
)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
with open(config_dir + "/output0_labels.txt", "w") as lfile:
for l in range(output0_label_cnt):
lfile.write("label" + str(l) + "\n")
def create_openvino_modelfile(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
swap=False,
):
batch_dim = () if max_batch == 0 else (max_batch,)
if not tu.validate_for_openvino_model(
input_dtype,
output0_dtype,
output1_dtype,
batch_dim + input_shape,
batch_dim + output0_shape,
batch_dim + output1_shape,
):
return
# Create the model
model_name = tu.get_model_name(
"openvino_nobatch" if max_batch == 0 else "openvino",
input_dtype,
output0_dtype,
output1_dtype,
)
model_version_dir = models_dir + "/" + model_name + "/" + str(model_version)
in0 = ov.opset1.parameter(
shape=batch_dim + input_shape, dtype=input_dtype, name="INPUT0"
)
in1 = ov.opset1.parameter(
shape=batch_dim + input_shape, dtype=input_dtype, name="INPUT1"
)
r0 = ov.opset1.add(in0, in1) if not swap else ov.opset1.subtract(in0, in1)
r1 = ov.opset1.subtract(in0, in1) if not swap else ov.opset1.add(in0, in1)
result0 = ov.opset1.reshape(r0, batch_dim + output0_shape, special_zero=False)
result1 = ov.opset1.reshape(r1, batch_dim + output1_shape, special_zero=False)
op0 = ov.opset1.convert(result0, destination_type=output0_dtype, name="OUTPUT0")
op1 = ov.opset1.convert(result1, destination_type=output1_dtype, name="OUTPUT1")
model = ov.Model([op0, op1], [in0, in1], model_name)
try:
os.makedirs(model_version_dir)
except OSError as ex:
pass # ignore existing dir
ov.serialize(
model, model_version_dir + "/model.xml", model_version_dir + "/model.bin"
)
def create_openvino_modelconfig(
models_dir,
max_batch,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
):
batch_dim = () if max_batch == 0 else (max_batch,)
if not tu.validate_for_openvino_model(
input_dtype,
output0_dtype,
output1_dtype,
batch_dim + input_shape,
batch_dim + output0_shape,
batch_dim + output1_shape,
):
return
# Unpack version policy
version_policy_str = "{ latest { num_versions: 1 }}"
if version_policy is not None:
type, val = version_policy
if type == "latest":
version_policy_str = "{{ latest {{ num_versions: {} }}}}".format(val)
elif type == "specific":
version_policy_str = "{{ specific {{ versions: {} }}}}".format(val)
else:
version_policy_str = "{ all { }}"
# Use a different model name for the non-batching variant
model_name = tu.get_model_name(
"openvino_nobatch" if max_batch == 0 else "openvino",
input_dtype,
output0_dtype,
output1_dtype,
)
config_dir = models_dir + "/" + model_name
# platform is empty and backend is 'openvino' for openvino model
config = """
name: "{}"
backend: "openvino"
max_batch_size: {}
version_policy: {}
input [
{{
name: "INPUT0"
data_type: {}
dims: [ {} ]
}},
{{
name: "INPUT1"
data_type: {}
dims: [ {} ]
}}
]
output [
{{
name: "OUTPUT0"
data_type: {}
dims: [ {} ]
label_filename: "output0_labels.txt"
}},
{{
name: "OUTPUT1"
data_type: {}
dims: [ {} ]
}}
]
""".format(
model_name,
max_batch,
version_policy_str,
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(input_dtype),
tu.shape_to_dims_str(input_shape),
np_to_model_dtype(output0_dtype),
tu.shape_to_dims_str(output0_shape),
np_to_model_dtype(output1_dtype),
tu.shape_to_dims_str(output1_shape),
)
try:
os.makedirs(config_dir)
except OSError as ex:
pass # ignore existing dir
with open(config_dir + "/config.pbtxt", "w") as cfile:
cfile.write(config)
with open(config_dir + "/output0_labels.txt", "w") as lfile:
for l in range(output0_label_cnt):
lfile.write("label" + str(l) + "\n")
def create_models(
models_dir,
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
output0_label_cnt,
version_policy=None,
):
model_version = 1
# Create two models, one that supports batching with a max-batch
# of 8, and one that does not with a max-batch of 0
if FLAGS.graphdef:
# max-batch 8
create_graphdef_modelconfig(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_graphdef_modelfile(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
# max-batch 0
create_graphdef_modelconfig(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_graphdef_modelfile(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
if FLAGS.savedmodel:
# max-batch 8
create_savedmodel_modelconfig(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_savedmodel_modelfile(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
# max-batch 0
create_savedmodel_modelconfig(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_savedmodel_modelfile(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
if FLAGS.tensorrt:
# max-batch 8
suffix = ()
if (
input_dtype == np.int8
or output0_dtype == np.int8
or output1_dtype == np.int8
):
suffix = (1, 1)
create_plan_modelconfig(
models_dir,
8,
model_version,
input_shape + suffix,
output0_shape + suffix,
output1_shape + suffix,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_plan_modelfile(
models_dir,
8,
model_version,
input_shape + suffix,
output0_shape + suffix,
output1_shape + suffix,
input_dtype,
output0_dtype,
output1_dtype,
)
# max-batch 0
create_plan_modelconfig(
models_dir,
0,
model_version,
input_shape + suffix,
output0_shape + suffix,
output1_shape + suffix,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_plan_modelfile(
models_dir,
0,
model_version,
input_shape + suffix,
output0_shape + suffix,
output1_shape + suffix,
input_dtype,
output0_dtype,
output1_dtype,
)
if -1 in input_shape:
# models for testing optimization profiles
create_plan_modelconfig(
models_dir,
8,
model_version,
input_shape + suffix,
output0_shape + suffix,
output1_shape + suffix,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
min_dim=4,
max_dim=32,
)
create_plan_modelfile(
models_dir,
8,
model_version,
input_shape + suffix,
output0_shape + suffix,
output1_shape + suffix,
input_dtype,
output0_dtype,
output1_dtype,
min_dim=4,
max_dim=32,
)
if FLAGS.onnx:
# max-batch 8
create_onnx_modelconfig(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_onnx_modelfile(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
# max-batch 0
create_onnx_modelconfig(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_onnx_modelfile(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
if FLAGS.libtorch:
# max-batch 8
create_libtorch_modelconfig(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_libtorch_modelfile(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
# max-batch 0
create_libtorch_modelconfig(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_libtorch_modelfile(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
if FLAGS.openvino:
# max-batch 8
create_openvino_modelconfig(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_openvino_modelfile(
models_dir,
8,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
# max-batch 0
create_openvino_modelconfig(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
create_openvino_modelfile(
models_dir,
0,
model_version,
input_shape,
output0_shape,
output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
if FLAGS.ensemble:
for pair in emu.platform_types_and_validation():
if not pair[1](
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
):
continue
config_input_shape = input_shape
config_output0_shape = output0_shape
config_output1_shape = output1_shape
if pair[0] == "plan":
if len(input_shape) == 1 and input_dtype == np.int8:
config_input_shape = (input_shape[0], 1, 1)
if len(output0_shape) == 1 and output0_dtype == np.int8:
config_output0_shape = (output0_shape[0], 1, 1)
if len(output1_shape) == 1 and output1_dtype == np.int8:
config_output1_shape = (output1_shape[0], 1, 1)
# max-batch 0
emu.create_ensemble_modelconfig(
pair[0],
models_dir,
0,
model_version,
config_input_shape,
config_output0_shape,
config_output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
emu.create_ensemble_modelfile(
pair[0],
models_dir,
0,
model_version,
config_input_shape,
config_output0_shape,
config_output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
# max-batch 8 (Skip for PyTorch models with String I/O)
if (pair[0] == "libtorch") and not pair[1](
input_dtype,
output0_dtype,
output1_dtype,
input_shape,
output0_shape,
output1_shape,
8,
):
continue
emu.create_ensemble_modelconfig(
pair[0],
models_dir,
8,
model_version,
config_input_shape,
config_output0_shape,
config_output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
output0_label_cnt,
version_policy,
)
emu.create_ensemble_modelfile(
pair[0],
models_dir,
8,
model_version,
config_input_shape,
config_output0_shape,
config_output1_shape,
input_dtype,
output0_dtype,
output1_dtype,
)
def create_fixed_models(
models_dir, input_dtype, output0_dtype, output1_dtype, version_policy=None
):
input_size = 16
create_models(
models_dir,
input_dtype,
output0_dtype,
output1_dtype,
(input_size,),
(input_size,),
(input_size,),
input_size,
version_policy,
)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"--models_dir", type=str, required=True, help="Top-level model directory"
)
parser.add_argument(
"--graphdef",
required=False,
action="store_true",
help="Generate GraphDef models",
)
parser.add_argument(
"--savedmodel",
required=False,
action="store_true",
help="Generate SavedModel models",
)
parser.add_argument(
"--tensorrt",
required=False,
action="store_true",
help="Generate TensorRT PLAN models",
)
parser.add_argument(
"--onnx",
required=False,
action="store_true",
help="Generate Onnx Runtime Onnx models",
)
parser.add_argument(
"--onnx_opset",
type=int,
required=False,
default=0,
help="Opset used for Onnx models. Default is to use ONNXRT default",
)
parser.add_argument(
"--libtorch",
required=False,
action="store_true",
help="Generate Pytorch LibTorch models",
)
parser.add_argument(
"--openvino",
required=False,
action="store_true",
help="Generate Openvino models",
)
parser.add_argument(
"--variable",
required=False,
action="store_true",
help="Used variable-shape tensors for input/output",
)
parser.add_argument(
"--ensemble",
required=False,
action="store_true",
help="Generate ensemble models against the models"
+ " in all platforms. Note that the models generated"
+ " are not completed.",
)
FLAGS, unparsed = parser.parse_known_args()
if FLAGS.graphdef or FLAGS.savedmodel:
import tensorflow as tf
from tensorflow.python.framework import graph_io
tf.compat.v1.disable_eager_execution()
if FLAGS.tensorrt:
import tensorrt as trt
if FLAGS.onnx:
import onnx
if FLAGS.libtorch:
import torch
from torch import nn
if FLAGS.openvino:
import openvino.runtime as ov
import test_util as tu
# Tests with models that accept fixed-shape input/output tensors
if not FLAGS.variable:
create_fixed_models(
FLAGS.models_dir, np.uint8, np.uint8, np.uint8, ("latest", 3)
)
create_fixed_models(FLAGS.models_dir, np.int8, np.int8, np.int8, ("latest", 1))
create_fixed_models(
FLAGS.models_dir, np.int16, np.int16, np.int16, ("latest", 2)
)
create_fixed_models(
FLAGS.models_dir, np.int32, np.int32, np.int32, ("all", None)
)
create_fixed_models(FLAGS.models_dir, np.int64, np.int64, np.int64)
create_fixed_models(
FLAGS.models_dir,
np.float16,
np.float16,
np.float16,
(
"specific",
[
1,
],
),
)
create_fixed_models(
FLAGS.models_dir, np.float32, np.float32, np.float32, ("specific", [1, 3])
)
create_fixed_models(FLAGS.models_dir, np.float16, np.float32, np.float32)
create_fixed_models(FLAGS.models_dir, np.int32, np.int8, np.int8)
create_fixed_models(FLAGS.models_dir, np.int8, np.int32, np.int32)
create_fixed_models(FLAGS.models_dir, np.int32, np.int8, np.int16)
create_fixed_models(FLAGS.models_dir, np.float32, np.uint8, np.uint8)
create_fixed_models(FLAGS.models_dir, np.uint8, np.float32, np.float32)
create_fixed_models(FLAGS.models_dir, np.float32, np.uint8, np.float16)
create_fixed_models(FLAGS.models_dir, np.int32, np.float32, np.float32)
create_fixed_models(FLAGS.models_dir, np.float32, np.int32, np.int32)
create_fixed_models(FLAGS.models_dir, np.int32, np.float16, np.int16)
create_fixed_models(FLAGS.models_dir, np_dtype_string, np.int32, np.int32)
create_fixed_models(
FLAGS.models_dir, np_dtype_string, np_dtype_string, np_dtype_string
)
create_fixed_models(
FLAGS.models_dir, np_dtype_string, np.int32, np_dtype_string
)
create_fixed_models(
FLAGS.models_dir, np_dtype_string, np_dtype_string, np.int32
)
create_fixed_models(
FLAGS.models_dir, np.int32, np_dtype_string, np_dtype_string
)
create_fixed_models(FLAGS.models_dir, np.int32, np.int32, np_dtype_string)
create_fixed_models(FLAGS.models_dir, np.int32, np_dtype_string, np.int32)
# Make multiple versions of some models for version testing
# (they use different version policies when created above)
if FLAGS.graphdef:
for vt in [np.float16, np.float32, np.int8, np.int16, np.int32]:
create_graphdef_modelfile(
FLAGS.models_dir, 8, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_graphdef_modelfile(
FLAGS.models_dir, 8, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_graphdef_modelfile(
FLAGS.models_dir, 0, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_graphdef_modelfile(
FLAGS.models_dir, 0, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
if FLAGS.savedmodel:
for vt in [np.float16, np.float32, np.int8, np.int16, np.int32]:
create_savedmodel_modelfile(
FLAGS.models_dir, 8, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_savedmodel_modelfile(
FLAGS.models_dir, 8, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_savedmodel_modelfile(
FLAGS.models_dir, 0, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_savedmodel_modelfile(
FLAGS.models_dir, 0, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
if FLAGS.tensorrt:
if tu.check_gpus_compute_capability(min_capability=8.0):
create_fixed_models(
FLAGS.models_dir,
np_dtype_bfloat16,
np_dtype_bfloat16,
np_dtype_bfloat16,
)
else:
print(
"Skipping the generation of TensorRT PLAN models for the BF16 datatype!"
)
for vt in [np.float32, np.float16, np.int32, np.uint8]:
create_plan_modelfile(
FLAGS.models_dir, 8, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_plan_modelfile(
FLAGS.models_dir, 8, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_plan_modelfile(
FLAGS.models_dir, 0, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_plan_modelfile(
FLAGS.models_dir, 0, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
vt = np.int8
# handle INT8 separately as it doesn't allow 1d tensors
create_plan_modelfile(
FLAGS.models_dir,
8,
2,
(16, 1, 1),
(16, 1, 1),
(16, 1, 1),
vt,
vt,
vt,
swap=True,
)
create_plan_modelfile(
FLAGS.models_dir,
8,
3,
(16, 1, 1),
(16, 1, 1),
(16, 1, 1),
vt,
vt,
vt,
swap=True,
)
create_plan_modelfile(
FLAGS.models_dir,
0,
2,
(16, 1, 1),
(16, 1, 1),
(16, 1, 1),
vt,
vt,
vt,
swap=True,
)
create_plan_modelfile(
FLAGS.models_dir,
0,
3,
(16, 1, 1),
(16, 1, 1),
(16, 1, 1),
vt,
vt,
vt,
swap=True,
)
if FLAGS.onnx:
for vt in [np.float16, np.float32, np.int8, np.int16, np.int32]:
create_onnx_modelfile(
FLAGS.models_dir, 8, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_onnx_modelfile(
FLAGS.models_dir, 8, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_onnx_modelfile(
FLAGS.models_dir, 0, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_onnx_modelfile(
FLAGS.models_dir, 0, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
if FLAGS.libtorch:
for vt in [np.float32, np.int32, np.int16, np.int8]:
create_libtorch_modelfile(
FLAGS.models_dir, 8, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_libtorch_modelfile(
FLAGS.models_dir, 8, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_libtorch_modelfile(
FLAGS.models_dir, 0, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_libtorch_modelfile(
FLAGS.models_dir, 0, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
if FLAGS.openvino:
for vt in [np.float16, np.float32, np.int8, np.int16, np.int32]:
create_openvino_modelfile(
FLAGS.models_dir, 8, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_openvino_modelfile(
FLAGS.models_dir, 8, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_openvino_modelfile(
FLAGS.models_dir, 0, 2, (16,), (16,), (16,), vt, vt, vt, swap=True
)
create_openvino_modelfile(
FLAGS.models_dir, 0, 3, (16,), (16,), (16,), vt, vt, vt, swap=True
)
if FLAGS.ensemble:
for pair in emu.platform_types_and_validation():
for vt in [np.float16, np.float32, np.int8, np.int16, np.int32]:
shape = (
(16, 1, 1) if (pair[0] == "plan" and vt == np.int8) else (16,)
)
if not pair[1](vt, vt, vt, shape, shape, shape):
continue
emu.create_ensemble_modelfile(
pair[0],
FLAGS.models_dir,
8,
2,
shape,
shape,
shape,
vt,
vt,
vt,
swap=True,
)
emu.create_ensemble_modelfile(
pair[0],
FLAGS.models_dir,
8,
3,
shape,
shape,
shape,
vt,
vt,
vt,
swap=True,
)
emu.create_ensemble_modelfile(
pair[0],
FLAGS.models_dir,
0,
2,
shape,
shape,
shape,
vt,
vt,
vt,
swap=True,
)
emu.create_ensemble_modelfile(
pair[0],
FLAGS.models_dir,
0,
3,
shape,
shape,
shape,
vt,
vt,
vt,
swap=True,
)
# Tests with models that accept variable-shape input/output tensors
if FLAGS.variable:
create_models(
FLAGS.models_dir,
np.float32,
np.float32,
np.float32,
(-1,),
(-1,),
(-1,),
16,
)
create_models(
FLAGS.models_dir,
np.float32,
np.int32,
np.int32,
(-1, -1),
(-1, -1),
(-1, -1),
16,
)
create_models(
FLAGS.models_dir,
np.float32,
np.int64,
np.int64,
(8, -1),
(8, -1),
(8, -1),
32,
)
create_models(
FLAGS.models_dir,
np.float32,
np.int32,
np.int64,
(-1, 8, -1),
(-1, 8, -1),
(-1, 8, -1),
32,
)
create_models(
FLAGS.models_dir, np.float32, np.float32, np.int32, (-1,), (-1,), (-1,), 16
)
create_models(
FLAGS.models_dir,
np.int32,
np.int32,
np.int32,
(-1, -1),
(-1, -1),
(-1, -1),
16,
)
create_models(
FLAGS.models_dir,
np.int32,
np.int32,
np.float32,
(-1, 8, -1),
(-1, 8, -1),
(-1, 8, -1),
32,
)
create_models(
FLAGS.models_dir,
np_dtype_string,
np_dtype_string,
np_dtype_string,
(-1,),
(-1,),
(-1,),
16,
)
create_models(
FLAGS.models_dir,
np_dtype_string,
np.int32,
np.int32,
(-1, -1),
(-1, -1),
(-1, -1),
16,
)
create_models(
FLAGS.models_dir,
np_dtype_string,
np_dtype_string,
np.int32,
(8, -1),
(8, -1),
(8, -1),
32,
)
create_models(
FLAGS.models_dir,
np_dtype_string,
np.int32,
np_dtype_string,
(-1, 8, -1),
(-1, 8, -1),
(-1, 8, -1),
32,
)
if FLAGS.tensorrt:
if tu.check_gpus_compute_capability(min_capability=8.0):
create_models(
FLAGS.models_dir,
np_dtype_bfloat16,
np_dtype_bfloat16,
np_dtype_bfloat16,
(-1, -1),
(-1, -1),
(-1, -1),
0,
)
else:
print(
"Skipping the generation of TensorRT PLAN models for the BF16 datatype!"
)
if FLAGS.ensemble:
# Create utility models used in ensemble
# nop (only creates model config, should add model file before use)
model_dtypes = ["TYPE_BOOL", "TYPE_STRING"]
for s in [8, 16, 32, 64]:
for t in ["INT", "UINT", "FP"]:
if t == "FP" and s == 8:
continue
model_dtypes.append("TYPE_{}{}".format(t, s))
for model_dtype in model_dtypes:
# Use variable size to handle all shape. Note: piping variable size output
# to fixed size model is not safe but doable
for model_shape in [(-1,), (-1, -1), (-1, -1, -1)]:
emu.create_nop_modelconfig(FLAGS.models_dir, model_shape, model_dtype)
|
triton-inference-serverREPO_NAMEserverPATH_START.@server_extracted@server-main@qa@common@gen_qa_models.py@.PATH_END.py
|
{
"filename": "LICENSE.md",
"repo_name": "martinjameswhite/litemangle",
"repo_path": "litemangle_extracted/litemangle-master/LICENSE.md",
"type": "Markdown"
}
|
The MIT License (MIT)
Copyright (c) 2015 Martin White
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
martinjameswhiteREPO_NAMElitemanglePATH_START.@litemangle_extracted@litemangle-master@LICENSE.md@.PATH_END.py
|
{
"filename": "asciidata-checkpoint.py",
"repo_name": "davidharvey1986/pyRRG",
"repo_path": "pyRRG_extracted/pyRRG-master/lib/asciidata/.ipynb_checkpoints/asciidata-checkpoint.py",
"type": "Python"
}
|
"""
Main class of the asciidata module
@author: Martin Kuemmel, Jonas Haase
@organization: Space Telescope - European Coordinating Facility (ST-ECF)
@license: Gnu Public Licence
@contact: mkuemmel@eso.org
@since: 2005/09/13
$LastChangedBy: mkuemmel $
$LastChangedDate: 2008-01-08 18:17:08 +0100 (Tue, 08 Jan 2008) $
$LastChangedRevision: $
$HeadURL: $
"""
__version__ = "Version 1.1 $LastChangedRevision: 330 $"
import string, sys, os, types,copy
from .asciiheader import *
from .asciicolumn import *
from .asciisorter import *
from .asciierror import *
from .asciiutils import *
class NullData(object):
"""
Null class as a parent class for the AsciiData class
This parent classs of the AsciiData class offers to create
a new AsciiData instance without a file to read from.
All elements are set to None, but of course can later
be filled by the user.
"""
def __init__(self, ncols, nrows, null=None):
"""
Constructor for the NullData Class
Creates an empty AsciiData instance with columns and
rows as specified. All entries are 'None'.
@param ncols: the number of columns to be created
@type ncols: integer
@param nrows: the number of rows to be created
@type nrows: integer
@param null: string to be interpretet as NULL
@type null: string
"""
# set the default null string
if null:
self._null = [null.strip()]
else:
self._null = ['Null']
# create the colum list
self.columns = []
for index in range(ncols):
# get the column name
colname = self._def_colname(index)
# create and append an empty column
self.columns.append(AsciiColumn(nrows=nrows, colname=colname,
null=self._null))
def _def_colname(self, index):
"""
Gives the default column name.
The method composes and returns the
default column name for a column at a
given index.
@param index: the index of the column
@type index: integer
"""
return 'column'+str(index+1)
class AsciiData(NullData):
"""
Basic class in the AstroAsciiData project
This class and its methods forms the complete API for the
for the
"""
def __init__(self, filename=None, ncols=0, nrows=0, null=None,
delimiter=None, comment_char=None, columnInfo=0, headerComment=1):
"""
Constructor for the AsciiData Class
The data is taken from a file specified in the input.
As addition, a NULL string, a delimiter string and a comment_char
string can be specified. The ascii data is read in from the
file and stored in a list of Columns
@param filename: the filename to create the AsciiData from
@type filename: string
@param ncols: the number of columns to be created
@type ncols: integer
@param nrows: the number of rows to be created
@type nrows: integer
@param null: string to be interpretet as NULL
@type null: string
@param delimiter: string to be used as delimiter
@type delimiter: string
@param comment_char: string to be used as comment character
@type comment: string
"""
self.ncols = 0
self.nrows = 0
# set the default comment_char
if comment_char:
self._comment_char = comment_char
else:
self._comment_char = '#'
# set the default null string
if null:
self._null = [null.strip()]
else:
self._null = ['Null', 'NULL', 'None', '*']
# set the delimiter
self._delimiter = delimiter
# set the separator
self._separator = Separator(delimiter)
# create the header
self.header = Header(filename, self._comment_char)
# check whether a filename is given
if filename != None:
# check whether the file exists
if os.path.exists(filename):
self.filename = filename
else:
err_msg = "Filename: " + filename + " does not exist!"
raise Exception(err_msg)
# set public output flags
if self.header.SExtractorFlag:
self.columnInfo = 1
self.headerComment = 1
else:
self.columnInfo = 0
self.headerComment = 1
# load in all data from the files
self.columns = self._load_columns(filename, self._null,
self._comment_char, self._separator)
else:
# set the filename to none
self.filename = None
# check whether valid numbers where given
if nrows > 0 and ncols > 0:
# create the empty instance
super(AsciiData, self).__init__(ncols, nrows, null)
else:
err_msg = "Number of columns, rows: " \
+ str(ncols) + str(nrows) + " are not reasonable!"
raise Exception(err_msg)
# set the public output flags
# as the corresponding parameters
self.columnInfo = columnInfo
self.headerComment = headerComment
# find the number of undefined columns
self._undef_cols = self._find_undefined_cols(self.columns)
# find the number of columns and rows
self.ncols = len(self.columns)
if self.ncols:
self.nrows = self.columns[0].get_nrows()
def __getitem__(self, element):
"""
Defines the list operator for indexing
This method returns the index or indices as specified
in the input. In the current class therefore returns
either a column or a column slice as specified in the input.
@param element: either column index or slice or name
@type element: string/integer
@return: a column
@rtype: AsciiColumn(s)
"""
# this part deals with slices
if type(element) == slice:
# FIXME this must be possible to do more elegantly
start,stop,step = element.indices(self.ncols)
newAD = copy.deepcopy(self)
all = list(range(self.ncols))
inclusive = [x for x in all[start:stop:step]]
while all:
idx = all.pop()
if not idx in inclusive:
del newAD[idx]
return newAD
# this part deals with individual
# columns, specified by index or name
try:
index = self._loc_column(element)
except ColumnError:
index = self.append(element)
# return the desired column
return self.columns[index]
def __setitem__(self, element, column):
"""
Defines the list operator for indexed assignement
The method inserts a column to the class at the
specified index. As of now, it is not possible
to create extra columns with this method,
only existing columns can be overwritten.
@param element: either column index or name
@type element: string/integer
@param column: the column to assign to an index
@type column: AsciiColumn
"""
index = self._loc_column(element)
# check whether the column does have the same number
# of rows as the class
# raise an error if not
if column.get_nrows() != self.nrows:
err_msg = 'Nrows: '+str(column.get_nrows())+' different than nrows: '\
+str(self.nrows)+'!!'
raise Exception(err_msg)
# check whether the column has a name
if not column.colname:
# give it a default name
column.colname = self._def_colname(index)
# assign the new column
self.columns[index] = column.copy()
# transfer the null element to the new column
self.columns[index]._null[0] = self._null[0]
def __delitem__(self, element):
"""
Deletes an index.
The method deletes a column specified in the input.
The column can be specified either by the column
name or the index.
@param element: either column index or name
@type element: string/integer
"""
# get the index from the input
index = self._loc_column(element)
# delete the column
del self.columns[index]
# adjust the number of columns
self.ncols -= 1
def __iter__(self):
"""
Provide an iterator object.
The function provides and returns an interator object
for the AstroAsciiData class. Due to this iterator object
sequences like:
for column in ascii_data_object:
<do something with column>
are possible.
"""
return AsciiLenGetIter(self)
def __len__(self):
"""
Defines a length method for the object
@return: the length of the object
@rtype: integer
"""
return self.ncols
def str(self):
"""
Defines a string method for the object.
Gives a simple string method such that str(AsciiData)
does work. The formatting is close to the formatting
for the output to files.
@return: the string representation of the object
@rtype: string
"""
bigstring = ''
# take the object delimiter or ' '
if not self._delimiter:
delim = ' '
else:
delim = self._delimiter
# add the header to the string
bigstring = bigstring + str(self.header)
# go over each row
for ii in range(self.nrows):
# create the string list
strlist = self._row_tostring(ii)
# treat the first line different
if ii:
# transform the listing to one string and append it
# put a linefeed at the beginning
bigstring = bigstring + '\n' + delim.join(strlist)
else:
# transform the listing to one string and append it
bigstring = bigstring + delim.join(strlist)
return bigstring
def __str__(self):
"""
Defines a string method for the object.
Gives a simple string method such that str(AsciiData)
does work. The formatting is close to the formatting
for the output to files.
@return: the string representation of the object
@rtype: string
"""
bigstring = ''
# take the object delimiter or ' '
if not self._delimiter:
delim = ' '
else:
delim = self._delimiter
# print the column information
if self.columnInfo:
for n, col in enumerate(self.columns):
bigstring += str(col.collheader(n,self._comment_char))
# print the header
if self.headerComment:
bigstring += str(self.header)
# go over each row
for ii in range(self.nrows):
# create the string list
strlist = self._row_tostring(ii)
# treat the first line different
if ii:
# transform the listing to one string and append it
# put a linefeed at the beginning
bigstring = bigstring + '\n' + delim.join(strlist)
else:
# transform the listing to one string and append it
bigstring = bigstring + delim.join(strlist)
# return the string
return bigstring
def flush(self):
"""
Prints the current status to the file.
The methods gives the opportunity to replace the data in
the AsciiData with the current version in memory.
"""
if self.filename != None:
# well, that an easy job
self.writeto(self.filename)
else:
raise Exception('No filename given. Use "writeto()" instead.')
def writeto(self, filename, colInfo=None, headComment=None):
"""
Prints the AsciiData to a new file.
The method prints the current status of the
object to a new file. The name of the file
is given in the input. An already existing
file is replaced.
@param filename: the filename to write the object to
@type filename: string
"""
# check whether the parameter is set
if colInfo==None:
# if not, take the class variable
colInfo = self.columnInfo
# check whether the parameter is set
if headComment == None:
# if not, take the class calue
headComment = self.headerComment
# open the file
fstream = open(filename,'w+')
# open a printstream
nprinter = NicePrinter(fstream, delimiter=self._delimiter)
# print everything to the stream
self._print_tostream(nprinter, colInfo, headComment)
#close the file
fstream.close()
# use the given name as class filename
# if no one is yet defined
if self.filename == None:
self.filename = filename
def tofits(self):
"""
Transforms the AsciiData object to fits
@return: pointer to the fits object
@rtype: binary table HDU
"""
from . import asciifits
# create an AsciiFits object
asciiFits = asciifits.AsciiFits(self)
# return the table HDU
return asciiFits.tabhdu
def writetofits(self, fits_name=None):
"""
Prints the AsciiData to a new file.
@param fits_name: the name for the fits file
@type fits_name: string
@return: the name of the fits file
@rtype: string
"""
from . import asciifits
# check whether a file name is given
if fits_name == None:
# check wheter the instance has a filename
if self.filename == None:
# no automatic filename possible; raise error
raise Exception('Please specify a name for the fits-file!')
else:
# determine a filename for the fits
fits_name = self._get_fitsname(self.filename)
# create an AsciiFits object
asciiFits = asciifits.AsciiFits(self)
# write out the object onto disk
asciiFits.flush(fits_name)
# return the name of the fits object
return fits_name
def writetohtml(self, html_name=None, tr_attr=None, td_attr=None):
"""
Prints the AsciiData object as table in a html-file
@param filename: the filename to write the object to
@type filename: string
@param tr_attr: the attributes for the tr-tag
@type tr_att: string
@param td_attr: the attributes for the td-tag
@type td_att: string
@return: the name of the html-file
@rtype: string
"""
# check whether a file name is given
if html_name == None:
# check wheter the instance has a filename
if self.filename == None:
# no automatic filename possible; raise error
raise Exception('Please specify a name for the html-file!')
else:
# determine a filename for the html-file
html_name = self._get_htmlname(self.filename)
# determine the line start, element delimiter and the line end
l_start, l_delim, l_end = self._get_lineparams(tr_attr, td_attr)
# open the file
fstream = open(html_name,'w+')
# open a printstream
nprinter = NicePrinter(fstream, delimiter=l_delim,
linestart=l_start, linend=l_end)
# print the data
# go over each row
for ii in range(self.nrows):
# create the string list
strlist = self._row_tostring(ii)
# send the list to the printer
nprinter.print_list(strlist)
#close the file
fstream.close()
# return the filename
return html_name
def writetolatex(self, latex_name=None):
"""
Prints the AsciiData object as table in a latex-file
@param filename: the filename to write the object to
@type filename: string
@return: the name of the latex-file
@rtype: string
"""
# check whether a file name is given
if latex_name == None:
# check wheter the instance has a filename
if self.filename == None:
# no automatic filename possible; raise error
raise Exception('Please specify a name for the latex-file!')
else:
# determine a filename for the latex-file
latex_name = self._get_latexname(self.filename)
# open the file
fstream = open(latex_name,'w+')
# open a printstream with the correct parameters
# please note that each '\' must be protected by
# another '\' to be interpreted as string
nprinter = NicePrinter(fstream, delimiter='&', linend='\\\\\n')
# print the data
# go over each row
for ii in range(self.nrows):
# create the string list
strlist = self._row_tostring(ii)
# send the list to the printer
nprinter.print_list(strlist)
#close the file
fstream.close()
# return the filename
return latex_name
def info(self):
"""
Print class info to the screen.
The method gives some basic information on the
class. The output is directly written onto
the screen.
@return: the string representing the information
@rtype: string
"""
# define the return string
bigstring = ''
# assemble the basic table information
bigstring += 'File: ' + str(self.filename) +'\n'
bigstring += 'Ncols: ' + str(self.ncols) + '\n'
bigstring += 'Nrows: ' + str(self.nrows) + '\n'
bigstring += 'Delimiter: ' + str(self._delimiter) + '\n'
bigstring += 'Null value: ' + str(self._null) + '\n'
bigstring += 'comment_char: ' + str(self._comment_char) + '\n'
# go over each column and add
# the individual column info
for col in self.columns:
bigstring += col.info()
# return the result
return bigstring
def append(self, colname):
"""
Appends a new column to the object.
This method creates and appends a new column to the
object. The new column must be specified with a name.
The new column doe have only Null entries.
@param colname: the name of the column
@type colname: string
"""
# check whether the column name does exist
# raise a warning if yes
if self.find(colname) > -1:
err_msg = 'Column with name: '+colname+' does just exist!'
raise Exception(err_msg)
# get the index of the new column
index = self.ncols
# create and append the new column
self.columns.append(AsciiColumn(nrows=self.nrows, colname=colname,
null=self._null))
# adjust the number of columns
self.ncols +=1
#return the index of the column
return index
def find(self, colname):
"""
Finds the column number for a name.
The method looks through all columns of the instance
for a matching column name. In case the column name exists,
the column index is returned. If the column name does
not exist, -1 is returned.
@param colname: the name of the column
@type colname: string
@return: the index of the column, or -1
@rtype: integer
"""
for index in range(len(self.columns)):
if self.columns[index].colname == colname:
return index
return -1
def delete(self, start, end=None):
"""
Deletes a row slice or element from all columns.
The method deletes one or several rows from all columns.
It uses the __delelte__ or __delitem__ operators
in the AsciiColumn class.
@param start: the starting row index
@type start: integer
@param end: the end row index
@type end: integer
"""
if end:
if start < self.nrows:
# go over each column
for col in self.columns:
# delete the row
del col[start: end]
# adjust the number of rows
self.nrows -= end-start
else:
# go over each column
for col in self.columns:
# delete the row
del col[start]
# adjust the number of rows
self.nrows -= 1
# make a lower limit to the number of rows
if self.nrows < 0:
self.nrows = 0
def newcomment_char(self, comment_char):
"""
Define a new comment_char string
@param comment_char: the new null string
@type comment_char: string
"""
# store the new null element
self._comment_char = comment_char
self.header.set_comment_char(comment_char)
def newnull(self, newnull):
"""
Define a new null string
@param newnull: the new null string
@type newnull: string
"""
# store the new null element
self._null[0] = newnull
# store the new null in the columns
for column in self.columns:
column._null[0] = newnull
def newdelimiter(self, delimiter):
"""
Set a new delimiter string
@param delimiter: the new delimiter string
@type delimiter: string
"""
# set the new delimiter
self._delimiter = delimiter
# set the separator
self._separator = Separator(delimiter)
def insert(self, nrows, start=0):
"""
Inserts one or several rows
The method inserts one or several rows into all
columns of the class instance. The number of rows
as well as the positioning of the new rows are
specified in the input. The parameter 'start'
gives the index which the first inserted row
will have.
Setting "start=-1" means appending the rows at
the end of the columns
@param nrows: the number of rows to add
@type nrows: integer
@param start: the position of the inserted rows
@type start: integer
"""
# go over all columns
for col in self.columns:
# add none elements at the end
for ii in range(nrows):
col.add_element(None)
# check whether the new rows are inserted inside
# the old rows, then the elements must be moved
if start < self.nrows and start != -1:
# go over all columns
for col in self.columns:
# repeat over rows to be inserted
for ii in range(self.nrows-start):
# reorder the column elements
index = self.nrows - ii - 1
col[index+nrows] = col[index]
# repeat over rows to be inserted
for ii in range(nrows):
# insert None in the new rows
index = ii + start
col[index] = None
# update the number of rows
self.nrows = self.columns[0].get_nrows()
def sort(self, colname, descending=0, ordered=0):
"""
Sorts the entries along the values in one column
The method sorts all columns of the AsciiData object according
to the order in one specified column. Both, sorting in ascending
and descending order is possible.
@param colname: the column to use for sorting
@type colname: string/integer
@param descending: indicates ascending (=0) or descending (=1) sorting
@type descending: integer
@param ordered: indicates ordered (1) or non-ordered sorting
@type ordered: integer
"""
# initialize a temporary array
sort_data = []
# transfer the data from the sort column
# to the temporary array
for index in range(self.nrows):
sort_data.append(self[colname][index])
# create the sorting index
sorter = ColumnIndex()
# sort according to the data in the temporary array
sorter.sort(sort_data, descending, ordered)
# go over all colums
for index in range(self.ncols):
# reorder the data in the column according
# to the sorting order
self[index]._data = sorter.enindex(self[index]._data)
def rstrip(self,x=None):
'''
Removes trailing rows which contain the value of x
null is default (and the only value which really works)
syntactic sugar for _strip(-1,x)
@param x: Data value in rows to strip of - defaults to Null
@type x: any legal asciidata type
'''
self._strip(-1,x)
def lstrip(self,x=None):
'''
Removes leading rows which contain the value of x
null is default (and the only value which really works)
syntactic sugar for _strip(0,x)
@param x: Data value in rows to strip of - defaults to Null
@type x: any legal asciidata type
'''
self._strip(0,x)
def strip(self,x=None):
'''
Removes both leading and trailing rows which contain the value of x
null is default (and the only value which really works)
syntactic sugar for _strip
@param x: Data value in rows to strip of - defaults to Null
@type x: any legal asciidata type
'''
self._strip(-1,x)
self._strip(0,x)
def toSExtractor(self):
"""
convenience function to set the ouput to be in SEextractor style
"""
self.headerComment = 1
self.columnInfo = 1
self.newcomment_char('#')
self.newdelimiter(' ')
def toplain(self):
"""
convenience procedure to toggle to plain ACSII output
delimiters are not changed
"""
self.headerComment = 1
self.columnInfo = 0
def _get_fitsname(self, filename):
"""
Determines the fitsname for a given file name
@param filename: the input filename
@type filename: string
@return: the name of the fits file
@rtype: string
"""
# search for the extension
dot_pos = filename.rfind('.')
# if an extension exists
if dot_pos > -1:
# replace the old extension with '.fits'
fits_name = filename[:dot_pos] + '.fits'
else:
# append the extension '.fits'
fits_name = filename + '.fits'
# return the fits name
return fits_name
def _get_htmlname(self, filename):
"""
Determines the html name for a given file name
@param filename: the input filename
@type filename: string
@return: the name for the html file
@rtype: string
"""
# search for the extension
dot_pos = filename.rfind('.')
# if an extension exists
if dot_pos > -1:
# replace the old extension with '.html'
html_name = filename[:dot_pos] + '.html'
else:
# append the extension '.html'
html_name = filename + '.html'
# return the html name
return html_name
def _get_latexname(self, filename):
"""
Determines the latex filename for a given file name
@param filename: the input filename
@type filename: string
@return: the name for the latex file
@rtype: string
"""
# search for the extension
dot_pos = filename.rfind('.')
# if an extension exists
if dot_pos > -1:
# replace the old extension with '.html'
latex_name = filename[:dot_pos] + '.tex'
else:
# append the extension '.html'
latex_name = filename + '.tex'
# return the html name
return latex_name
def _get_lineparams(self, tr_attr=None, td_attr=None):
"""
Prints the AsciiData object as table in html-file
@param tr_attr: attributes for the tr-tag
@type tr_attr: string
@param td_attr: attributes for the td-tag
@type td_attr: string
@return: the html-table linestart, delimiter and lineend
@rtype: string, string, string
"""
# form the string for the tr-attributes
if tr_attr == None:
str_tr_add = ''
else:
str_tr_add = ' ' + tr_attr
# form the string for the td-attributes
if td_attr == None:
str_td_add = ''
else:
str_td_add = ' ' + td_attr
# compose linestart, delimiter and lineend
lstart = '<tr'+str_tr_add+'><td'+str_td_add+'>'
delim = '</td><td'+str_td_add+'>'
lend = '</td></tr>\n'
# return linestart, delimiter, lineend
return lstart, delim, lend
def _loc_column(self, element):
"""
Localizes a column
The method localizes the column from any possible input.
Possible input is either the column name or column index.
Basic checks are done whether the column exists.
@param element: either column index or name
@type element: string/integer
@return: the column index
@rtype: integer
"""
# create an element
elem = Element(element)
# check the types and derive the column index
if elem.get_type() == int:
# check for -1, which indicates the last column
if element == -1:
# set the index of the last column
index = self.ncols-1
else:
# set the index to the input index
index = element
elif elem.get_type() == bytes:
index = self.find(element)
# check whether the column index exists
# raise an error if not
if index > self.ncols-1:
err_msg = 'Index: '+str(index)+' is larger than ncols: ' +str(self.ncols)+'!!'
raise Exception(err_msg)
elif index < 0:
raise ColumnError('Column name: "'+element+'" does not exist!')
# return the index
return index
def _load_columns(self, filename, null, comment_char, separator):
"""
Transforms the content of a file into columns
Opens the file, defines the columns, adds all data rows,
and returns the columns.
@param filename: the filename to create the AsciiData from
@type filename: string
@param null: string to be interpreted as NULL
@type null: string
@param separator: string to be used as delimiter
@type separator: string
@param comment_char: string to be used as comment character
@type comment_char: string
@return: the columns loaded
@rtype: [AsciiColumn]
"""
undef_cols = []
collist = []
# open the file, and parse through all rows
for line in open(filename, 'r'):
# throw away trailing and leading whitespaces
str_line = line.strip()
if len(str_line) < 1 or str_line[0] == comment_char:
continue
# if collumns exist, add a row
if collist:
self._add_row(collist, line, null, separator)
# if columns do not exist, define them
else:
collist = self._define_cols(line, null, separator)
# return the column list
return collist
def _find_undefined_cols(self, collist):
"""
Finds undefined columns
The method finds undefined columns in a column list.
An undefined column is a column with the flag "self._defined"
not set. This means that column type and column format
are not specified, and the column elements are Null.
The indices of the undefined columns is returned as a list
@param collist: the list of existing columns
@type collist: list of AsciiColumns
@return: a list with the indices of undefined columns
@rtype: [integer]
"""
undefined = []
# go over each column
index=0
for col in collist:
# check whether the column is defined
# append the index to the list if not
if not col.get_defined():
undefined.append(index)
# increment the index
index = index+1
# return the list
return undefined
def _add_row(self, collist, line, null, separator):
"""
Adds a line from the file to the column list.
The method gets a line from the input file.
The line is split up into its items.
Then each item is added to the column
it belongs to. Items matching the NULL
string are added as "None". A delimiter
is taken into account in the splitting,
if specified.
@param collist: the list of existing columns
@type collist: list of AsciiColumns
@param line: the line to be added to the columns
@type line: string
@param null: string to be interpretet as NULL
@type null: string
@param separator: string to be used as delimiter
@type separator: string
"""
# split the line, either according toa whitespace,
# or according to a specified delimiter
items = separator.separate(line)
# check whether there is an item for each column
if len(collist) != len(items):
err_msg = "Number of columns does not fit to number of items in " + line
raise Exception(err_msg)
# go over each item
index = 0
for item in items:
# check whether the item is NULL.
# add the item to the column,
# using 'None' for NULL items
if null.count(item.strip) > 0:
collist[index].add_element(None)
else:
collist[index].add_element(item)
# increment the index
index += 1
def _define_cols(self, line, null, separator):
"""
Defines the columns from an input line.
The method splits an ascii line from the input file into its
items. For each item a new column is created and added
to a column list. The column list is finally returned.
@param line: the line to be added to the columns
@type line: string
@param null: string to be interpretet as NULL
@type null: string
@param separator: string to be used as delimiter
@type separator: string
@return: the columns created
@rtype: [AsciiColumn]
"""
collist = []
# split the line, either according toa whitespace,
# or according to a specified delimiter
items = separator.separate(line)
# go over each item, and create a column
# for each. NULL items are transformed to 'None'
index = 0
for item in items:
# set the default column unit and comment
colunit = ''
colcomment = ''
# check whether there is column
# information from the header
if self.header.SExtractorFlag:
# extract the header information
colname,colunit,colcomment = self.header.getCollInfo(index)
else:
# make the default column name
colname = self._def_colname(index)
# check whether the element is a NULL-value
if null.count(item.strip()) > 0:
# append an undefined column
collist.append(AsciiColumn(element=[None], colname=colname,
null=null))
else:
# append a defined column
collist.append(AsciiColumn(element=[item], colname=colname,
null=null))
# transfer the resto of the column information
if colunit:
collist[-1].set_unit(colunit)
if colcomment:
collist[-1].set_colcomment(colcomment)
# increment the index
index += 1
# return the column list
return collist
def _print_tostream(self, nprinter, colInfo, headComment):
"""
Prints the AsciiData to a stream
The method forms for each row in the AsciiData a list
with formated strings, each list element representing
one element. The list is sent to a printing stream
which is responsible for the output.
@param nprinter: the NicePrinter object with the stream
@type nprinter: NicePrinter
"""
# print the column information
if colInfo:
for n, col in enumerate(self.columns):
nprinter.print_string(col.collheader(n,self._comment_char))
# print the header
if headComment:
nprinter.print_string(str(self.header))
# print the data
# go over each row
for ii in range(self.nrows):
# create the string list
strlist = self._row_tostring(ii)
# send the list to the printer
nprinter.print_list(strlist)
def _row_tostring(self, index):
"""
Creates the formatted string list for one row.
The method extracts from each column the formatted
string representation of the element in a specified
row. The list of strings is returned.
@param index:
@type index: integer
@return: the list with formatted strings
@rtype: [string]
"""
# initialize the list
strlist = []
# go over each column
for jj in range(self.ncols):
# append the string of the requested
# element to the list
strlist.append(self.columns[jj].fprint_elem(index))
# return the list
return strlist
def _strip(self,rowindex, x=None):
'''
Removes rows which contain the value of x
null is default (and the only value which really works)
@param rowindex: select if it is lstrip (0) or rstrip (-1)
@type rowindex: int
'''
while self.nrows>0:
equal = True
for col in self.columns:
equal = equal and (col[rowindex] == x)
if equal:
self.delete(rowindex)
else:
break
|
davidharvey1986REPO_NAMEpyRRGPATH_START.@pyRRG_extracted@pyRRG-master@lib@asciidata@.ipynb_checkpoints@asciidata-checkpoint.py@.PATH_END.py
|
{
"filename": "parameters_f2_image.py",
"repo_name": "GeminiDRSoftware/DRAGONS",
"repo_path": "DRAGONS_extracted/DRAGONS-master/geminidr/f2/parameters_f2_image.py",
"type": "Python"
}
|
# This parameter file contains the parameters related to the primitives located
# in the primitives_f2.py file, in alphabetical order.
from gempy.library import config
from astrodata import AstroData
from geminidr.core import parameters_photometry, parameters_stack, parameters_nearIR
class addDQConfig(parameters_nearIR.addDQConfig):
def setDefaults(self):
self.add_illum_mask = True
class detectSourcesConfig(parameters_photometry.detectSourcesConfig):
def setDefaults(self):
self.mask = True
#class makeLampFlatConfig(parameters_nearIR.makeLampFlatConfig):
# dark = config.Field("Name of dark frame (for K-band flats)", (str, AstroData), None, optional=True)
class makeBPMConfig(parameters_nearIR.makeBPMConfig):
def setDefaults(self):
self.dark_lo_thresh = -150.
self.dark_hi_thresh = 650.
self.flat_lo_thresh = 0.68
self.flat_hi_thresh = 1.28
|
GeminiDRSoftwareREPO_NAMEDRAGONSPATH_START.@DRAGONS_extracted@DRAGONS-master@geminidr@f2@parameters_f2_image.py@.PATH_END.py
|
{
"filename": "Obiwan_fibflux.ipynb",
"repo_name": "desihub/LSS",
"repo_path": "LSS_extracted/LSS-main/Sandbox/Obiwan_fibflux.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import fitsio
import numpy as np
from matplotlib import pyplot as plt
```
```python
params = {'legend.fontsize': 'x-large',
'axes.labelsize': 'x-large',
'axes.titlesize':'x-large',
'xtick.labelsize':'x-large',
'ytick.labelsize':'x-large',
'figure.facecolor':'w'}
plt.rcParams.update(params)
```
```python
fs = fitsio.read('/global/cscratch1/sd/adematti/legacysim/dr9/ebv1000shaper/south/file0_rs0_skip0/merged/matched_input.fits') #obiwan outputs in DECaLS
```
```python
seld = fs['flux_g']*0 == 0 #select detected
seld &= fs['flux_g'] > 0.2 #also select within this relevant gflux range
seld &= fs['flux_g'] < 2
```
```python
fsd = fs[seld]
```
```python
a = plt.hist(fsd['fiberflux_g']/fsd['flux_g'],bins=30)
plt.xlabel('output fiberflux_g/flux_g')
plt.ylabel('# in bin')
plt.title('Obiwan outputs with 0.2 < flux_g < 2')
plt.show()
```
The spike is for type PSF, take a look at what the histograms look like by type:
```python
for tp in np.unique(fsd['type']):
wt = fsd['type'] == tp
if tp != 'PSF':
plt.hist(fsd[wt]['fiberflux_g']/fsd[wt]['flux_g'],bins=a[1],density=True,label=tp,histtype='step',linewidth=3)
plt.legend(loc='upper left')
plt.xlabel('output fiberflux_g/flux_g')
plt.ylabel('relative fraction in bin')
plt.title('Obiwan outputs with 0.2 < flux_g < 2')
plt.show()
```
Generally, this makes sense. The more complex the type, the more extended it is and the smaller the fraction of within the fiber.
What happens if we now look at the recovered fiberflux vs input?
```python
b = plt.hist(fsd['fiberflux_g']/fsd['input_flux_g'],bins=30,range=(0,1.5))
plt.xlabel('output fiberflux_g / input flux_g')
plt.ylabel('# in bin')
plt.title('Obiwan outputs with 0.2 < flux_g < 2')
plt.show()
```
```python
for tp in np.unique(fsd['type']):
wt = fsd['type'] == tp
#if tp != 'PSF':
plt.hist(fsd[wt]['fiberflux_g']/fsd[wt]['input_flux_g'],bins=b[1],density=True,label=tp,histtype='step',linewidth=3)
plt.legend(loc='upper left')
plt.xlabel('output fiberflux_g / input flux_g')
plt.ylabel('relative fraction in bin')
plt.title('Obiwan outputs with 0.2 < flux_g < 2')
plt.show()
```
```python
a = plt.hist(fsd['input_galdepth_g'],range=(200,3000))
b = plt.hist(fsd['input_galdepth_g'],weights=fsd['fiberflux_g']/fsd['input_flux_g'],bins=a[1])
c = plt.hist(fsd['input_galdepth_g'],weights=fsd['flux_g']/fsd['input_flux_g'],bins=a[1])
plt.clf()
plt.plot(a[1][:-1],b[0]/a[0],label='fiberflux_g / input_flux_g')
plt.plot(a[1][:-1],c[0]/a[0]*.56,label='0.56 x flux_g / input_flux_g')
plt.legend()
plt.xlabel('galdepth_g')
plt.ylabel('ratio to input flux')
plt.title('Obiwan outputs with 0.2 < flux_g < 2')
plt.grid(alpha=0.5)
plt.show()
```
```python
```
|
desihubREPO_NAMELSSPATH_START.@LSS_extracted@LSS-main@Sandbox@Obiwan_fibflux.ipynb@.PATH_END.py
|
{
"filename": "test_peakfinder.py",
"repo_name": "astropy/photutils",
"repo_path": "photutils_extracted/photutils-main/photutils/detection/tests/test_peakfinder.py",
"type": "Python"
}
|
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
Tests for the peakfinder module.
"""
import astropy.units as u
import numpy as np
import pytest
from astropy.tests.helper import assert_quantity_allclose
from numpy.testing import assert_array_equal, assert_equal
from photutils.centroids import centroid_com
from photutils.datasets import make_gwcs, make_wcs
from photutils.detection import find_peaks
from photutils.utils._optional_deps import HAS_GWCS
from photutils.utils.exceptions import NoDetectionsWarning
class TestFindPeaks:
def test_box_size(self, data):
"""
Test with box_size.
"""
tbl = find_peaks(data, 0.1, box_size=3)
assert tbl['id'][0] == 1
assert len(tbl) == 25
columns = ['id', 'x_peak', 'y_peak', 'peak_value']
assert all(column in tbl.colnames for column in columns)
assert np.min(tbl['x_peak']) > 0
assert np.max(tbl['x_peak']) < 101
assert np.min(tbl['y_peak']) > 0
assert np.max(tbl['y_peak']) < 101
assert np.max(tbl['peak_value']) < 13.2
# test with units
unit = u.Jy
tbl2 = find_peaks(data << unit, 0.1 << unit, box_size=3)
columns = ['id', 'x_peak', 'y_peak']
for column in columns:
assert_equal(tbl[column], tbl2[column])
col = 'peak_value'
assert tbl2[col].unit == unit
assert_equal(tbl[col], tbl2[col].value)
def test_footprint(self, data):
"""
Test with footprint.
"""
tbl0 = find_peaks(data, 0.1, box_size=3)
tbl1 = find_peaks(data, 0.1, footprint=np.ones((3, 3)))
assert_array_equal(tbl0, tbl1)
def test_mask(self, data):
"""
Test with mask.
"""
mask = np.zeros(data.shape, dtype=bool)
mask[0:50, :] = True
tbl0 = find_peaks(data, 0.1, box_size=3)
tbl1 = find_peaks(data, 0.1, box_size=3, mask=mask)
assert len(tbl1) < len(tbl0)
def test_maskshape(self, data):
"""
Test if make shape doesn't match data shape.
"""
match = 'data and mask must have the same shape'
with pytest.raises(ValueError, match=match):
find_peaks(data, 0.1, mask=np.ones((5, 5)))
def test_thresholdshape(self, data):
"""
Test if threshold shape doesn't match data shape.
"""
match = 'threshold array must have the same shape as the input data'
with pytest.raises(ValueError, match=match):
find_peaks(data, np.ones((2, 2)))
def test_npeaks(self, data):
"""
Test npeaks.
"""
tbl = find_peaks(data, 0.1, box_size=3, npeaks=1)
assert len(tbl) == 1
def test_border_width(self, data):
"""
Test border exclusion.
"""
tbl0 = find_peaks(data, 0.1, box_size=3)
tbl1 = find_peaks(data, 0.1, box_size=3, border_width=0)
tbl2 = find_peaks(data, 0.1, box_size=3, border_width=(0, 0))
assert len(tbl0) == len(tbl1)
assert len(tbl1) == len(tbl2)
tbl3 = find_peaks(data, 0.1, box_size=3, border_width=25)
tbl4 = find_peaks(data, 0.1, box_size=3, border_width=(25, 25))
assert len(tbl3) == len(tbl4)
assert len(tbl3) < len(tbl0)
tbl0 = find_peaks(data, 0.1, box_size=3, border_width=(34, 0))
tbl1 = find_peaks(data, 0.1, box_size=3, border_width=(0, 36))
assert np.min(tbl0['y_peak']) >= 34
assert np.min(tbl1['x_peak']) >= 36
match = 'border_width must be >= 0'
with pytest.raises(ValueError, match=match):
find_peaks(data, 0.1, box_size=3, border_width=-1)
match = 'border_width must have integer values'
with pytest.raises(ValueError, match=match):
find_peaks(data, 0.1, box_size=3, border_width=3.1)
def test_box_size_int(self, data):
"""
Test non-integer box_size.
"""
tbl1 = find_peaks(data, 0.1, box_size=5.0)
tbl2 = find_peaks(data, 0.1, box_size=5.5)
assert_array_equal(tbl1, tbl2)
def test_centroid_func_callable(self, data):
"""
Test that centroid_func is callable.
"""
match = 'centroid_func must be a callable object'
with pytest.raises(TypeError, match=match):
find_peaks(data, 0.1, box_size=2, centroid_func=True)
def test_wcs(self, data):
"""
Test with astropy WCS.
"""
columns = ['skycoord_peak', 'skycoord_centroid']
fits_wcs = make_wcs(data.shape)
tbl = find_peaks(data, 1, wcs=fits_wcs, centroid_func=centroid_com)
for column in columns:
assert column in tbl.colnames
assert tbl.colnames == ['id', 'x_peak', 'y_peak', 'skycoord_peak',
'peak_value', 'x_centroid', 'y_centroid',
'skycoord_centroid']
@pytest.mark.skipif(not HAS_GWCS, reason='gwcs is required')
def test_gwcs(self, data):
"""
Test with gwcs.
"""
columns = ['skycoord_peak', 'skycoord_centroid']
gwcs_obj = make_gwcs(data.shape)
tbl = find_peaks(data, 1, wcs=gwcs_obj, centroid_func=centroid_com)
for column in columns:
assert column in tbl.colnames
@pytest.mark.skipif(not HAS_GWCS, reason='gwcs is required')
def test_wcs_values(self, data):
fits_wcs = make_wcs(data.shape)
gwcs_obj = make_gwcs(data.shape)
tbl1 = find_peaks(data, 1, wcs=fits_wcs, centroid_func=centroid_com)
tbl2 = find_peaks(data, 1, wcs=gwcs_obj, centroid_func=centroid_com)
columns = ['skycoord_peak', 'skycoord_centroid']
for column in columns:
assert_quantity_allclose(tbl1[column].ra, tbl2[column].ra)
assert_quantity_allclose(tbl1[column].dec, tbl2[column].dec)
def test_constant_array(self):
"""
Test for empty output table when data is constant.
"""
data = np.ones((10, 10))
match = 'Input data is constant'
with pytest.warns(NoDetectionsWarning, match=match):
tbl = find_peaks(data, 0.0)
assert tbl is None
def test_no_peaks(self, data):
"""
Tests for when no peaks are found.
"""
fits_wcs = make_wcs(data.shape)
match = 'No local peaks were found'
with pytest.warns(NoDetectionsWarning, match=match):
tbl = find_peaks(data, 10000)
assert tbl is None
with pytest.warns(NoDetectionsWarning, match=match):
tbl = find_peaks(data, 100000, centroid_func=centroid_com)
assert tbl is None
with pytest.warns(NoDetectionsWarning, match=match):
tbl = find_peaks(data, 100000, wcs=fits_wcs)
assert tbl is None
with pytest.warns(NoDetectionsWarning, match=match):
tbl = find_peaks(data, 100000, wcs=fits_wcs,
centroid_func=centroid_com)
assert tbl is None
def test_data_nans(self, data):
"""
Test that data with NaNs does not issue Runtime warning.
"""
data = np.copy(data)
data[50:, :] = np.nan
find_peaks(data, 0.1)
|
astropyREPO_NAMEphotutilsPATH_START.@photutils_extracted@photutils-main@photutils@detection@tests@test_peakfinder.py@.PATH_END.py
|
{
"filename": "_title.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/histogram2dcontour/colorbar/_title.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TitleValidator(_plotly_utils.basevalidators.TitleValidator):
def __init__(
self, plotly_name="title", parent_name="histogram2dcontour.colorbar", **kwargs
):
super(TitleValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
data_class_str=kwargs.pop("data_class_str", "Title"),
data_docs=kwargs.pop(
"data_docs",
"""
font
Sets this color bar's title font. Note that the
title's font used to be set by the now
deprecated `titlefont` attribute.
side
Determines the location of color bar's title
with respect to the color bar. Defaults to
"top" when `orientation` if "v" and defaults
to "right" when `orientation` if "h". Note that
the title's location used to be set by the now
deprecated `titleside` attribute.
text
Sets the title of the color bar. Note that
before the existence of `title.text`, the
title's contents used to be defined as the
`title` attribute itself. This behavior has
been deprecated.
""",
),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@histogram2dcontour@colorbar@_title.py@.PATH_END.py
|
{
"filename": "act_dr6_lenslike.py",
"repo_name": "ACTCollaboration/act_dr6_lenslike",
"repo_path": "act_dr6_lenslike_extracted/act_dr6_lenslike-main/act_dr6_lenslike/act_dr6_lenslike.py",
"type": "Python"
}
|
import numpy as np
import warnings
from scipy.interpolate import interp1d
try:
from cobaya.likelihoods.base_classes import InstallableLikelihood
except:
InstallableLikelihood = object
import os
default_version = "v1.2"
variants =[x.strip() for x in '''
act_baseline,
act_extended,
actplanck_baseline,
actplanck_extended,
act_polonly,
act_cibdeproj,
act_cinpaint
'''.strip().replace('\n','').split(',')]
# ================
# HELPER FUNCTIONS
# ================
def download(url, filename):
# thanks to https://stackoverflow.com/a/63831344
# this function can be considered CC-BY-SA 4.0
import functools
import pathlib
import shutil
import requests
from tqdm.auto import tqdm
r = requests.get(url, stream=True, allow_redirects=True)
if r.status_code != 200:
r.raise_for_status() # Will only raise for 4xx codes, so...
raise RuntimeError(f"Request to {url} returned status code {r.status_code}")
file_size = int(r.headers.get('Content-Length', 0))
path = pathlib.Path(filename).expanduser().resolve()
path.parent.mkdir(parents=True, exist_ok=True)
desc = "(Unknown total file size)" if file_size == 0 else ""
r.raw.read = functools.partial(r.raw.read, decode_content=True) # Decompress if needed
with tqdm.wrapattr(r.raw, "read", total=file_size, desc=desc) as r_raw:
with path.open("wb") as f:
shutil.copyfileobj(r_raw, f)
return path
def get_data(data_url="https://lambda.gsfc.nasa.gov/data/suborbital/ACT/ACT_dr6/likelihood/data/",
data_filename_root="ACT_dr6_likelihood",version=None):
if version is None:
version = default_version
data_filename = f"f{data_filename_root}_{version}.tgz"
file_dir = os.path.abspath(os.path.dirname(__file__))
data_dir = f"{file_dir}/data/{version}/"
if os.path.exists(os.path.join(file_dir, data_dir)):
print('Data already exists at {}, not downloading again.'.format(os.path.join(file_dir, data_dir)))
else:
import tarfile
orig_cwd = os.getcwd()
os.mkdir(os.path.join(file_dir, data_dir))
os.chdir(os.path.join(file_dir, data_dir))
print('Downloading data {} and placing it in likelihood folder.'.format(data_filename))
download(data_url+data_filename, data_filename)
tar = tarfile.open(data_filename)
tar.extractall(path=os.path.join(file_dir, data_dir).rstrip(f'{version}/')) # this is not great
tar.close()
os.remove(data_filename)
os.chdir(orig_cwd)
def pp_to_kk(clpp,ell):
return clpp * (ell*(ell+1.))**2. / 4.
def get_corrected_clkk(data_dict,clkk,cltt,clte,clee,clbb,suff='',
do_norm_corr=True, do_N1kk_corr=True, do_N1cmb_corr=True,
act_calib=False, no_like_cmb_corrections=False):
if no_like_cmb_corrections:
do_norm_corr = False
do_N1cmb_corr = False
clkk_fid = data_dict['fiducial_cl_kk']
cl_dict = {'tt':cltt,'te':clte,'ee':clee,'bb':clbb}
if do_N1kk_corr:
N1_kk_corr = data_dict[f'dN1_kk{suff}'] @ (clkk-clkk_fid)
else:
N1_kk_corr = 0
dNorm = data_dict[f'dAL_dC{suff}']
fid_norm = data_dict[f'fAL{suff}']
N1_cmb_corr = 0.
norm_corr = 0.
if act_calib and not('planck' in suff):
ocl = cl_dict['tt']
fcl = data_dict[f'fiducial_cl_tt']
ols = np.arange(ocl.size)
cal_ell_min = 1000
cal_ell_max = 2000
sel = np.s_[np.logical_and(ols>cal_ell_min,ols<cal_ell_max)]
cal_fact = (ocl[sel]/fcl[sel]).mean()
else:
cal_fact = 1.0
for i,s in enumerate(['tt','ee','bb','te']):
icl = cl_dict[s]
cldiff = ((icl/cal_fact)-data_dict[f'fiducial_cl_{s}'])
if do_N1cmb_corr:
N1_cmb_corr = N1_cmb_corr + (data_dict[f'dN1_{s}{suff}']@cldiff)
if do_norm_corr:
c = - 2. * (dNorm[i] @ cldiff)
if i==0:
ls = np.arange(c.size)
c[ls>=2] = c[ls>=2] / fid_norm[ls>=2]
norm_corr = norm_corr + c
nclkk = clkk + norm_corr*clkk_fid + N1_kk_corr + N1_cmb_corr
return nclkk
def standardize(ls,cls,trim_lmax,lbuffer=2,extra_dims="y"):
cstart = int(ls[0])
diffs = np.diff(ls)
if not(np.all(np.isclose(diffs,1.))): raise ValueError("Multipoles are not spaced by 1")
if not(cstart<=2): raise ValueError("Multipoles start at value greater than 2")
nlen = trim_lmax+lbuffer
cend = nlen - cstart
if extra_dims=="xyy":
out = np.zeros((cls.shape[0],nlen,nlen))
out[:,cstart:,cstart:] = cls[:,:cend,:cend]
elif extra_dims=="yy":
out = np.zeros((nlen,nlen))
out[cstart:,cstart:] = cls[:cend,:cend]
elif extra_dims=="xy":
out = np.zeros((cls.shape[0],nlen))
out[:,cstart:] = cls[:,:cend]
elif extra_dims=="y":
out = np.zeros(nlen)
out[cstart:] = cls[:cend]
else:
raise ValueError
return out
def get_limber_clkk_flat_universe(results,Pfunc,lmax,kmax,nz,zsrc=None):
# Adapting code from Antony Lewis' CAMB notebook
if zsrc is None:
chistar = results.conformal_time(0)- results.tau_maxvis
else:
chistar = results.comoving_radial_distance(zsrc)
chis = np.linspace(0,chistar,nz)
zs=results.redshift_at_comoving_radial_distance(chis)
dchis = (chis[2:]-chis[:-2])/2
chis = chis[1:-1]
zs = zs[1:-1]
#Get lensing window function (flat universe)
win = ((chistar-chis)/(chis**2*chistar))**2
#Do integral over chi
ls = np.arange(0,lmax+2, dtype=np.float64)
cl_kappa=np.zeros(ls.shape)
w = np.ones(chis.shape) #this is just used to set to zero k values out of range of interpolation
for i, l in enumerate(ls[2:]):
k=(l+0.5)/chis
w[:]=1
w[k<1e-4]=0
w[k>=kmax]=0
cl_kappa[i+2] = np.dot(dchis, w*Pfunc.P(zs, k, grid=False)*win/k**4)
cl_kappa*= (ls*(ls+1))**2
return cl_kappa
def get_camb_lens_obj(nz,kmax,zmax=None):
import camb
pars = camb.CAMBparams()
# This cosmology is purely to go from chis->zs for limber integration;
# the details do not matter
pars.set_cosmology(H0=67.5, ombh2=0.022, omch2=0.122)
pars.InitPower.set_params(ns=0.965)
results= camb.get_background(pars)
nz = nz
if zmax is None:
chistar = results.conformal_time(0)- results.tau_maxvis
else:
chistar = results.comoving_radial_distance(zmax)
chis = np.linspace(0,chistar,nz)
zs=results.redshift_at_comoving_radial_distance(chis)
cobj = {"CAMBdata": None,
"Pk_interpolator": { "z": zs,
"k_max": kmax,
"nonlinear": True,
"vars_pairs": ([["Weyl", "Weyl"]])}}
return cobj
def parse_variant(variant):
variant = variant.lower().strip()
if variant not in variants: raise ValueError
v = None
if '_extended' in variant:
baseline = False
else:
baseline = True
if '_baseline' not in variant:
v = variant.split('_')[-1]
include_planck = True if 'actplanck' in variant else False
return v,baseline,include_planck
# ==================
# Generic likelihood
# ==================
"""
data_dict = load_data(data_directory) # pre-load data
# for each predicted spectra in chain
# cl_kk is CMB lensing convergence power spectrum (dimensionless,
# no ell or 2pi factors)
# cl_tt, cl_ee, cl_te, cl_bb are lensed CMB power spectra
# (muK^2 units, no ell or 2pi factors)
lnlike = generic_lnlike(data_dict,cl_kk,cl_tt,cl_ee,cl_te,cl_bb)
This returns ln(Likelihood)
so for example,
chi_square = -2 lnlike
"""
def load_data(variant, ddir=None,
lens_only=False,
apply_hartlap=True,like_corrections=True,mock=False,
nsims_act=796,nsims_planck=400,trim_lmax=2998,scale_cov=None,
version=None, act_cmb_rescale=False, act_calib=False):
"""
Given a data directory path, this function loads into a dictionary
the data products necessary for evaluating the DR6 lensing likelihood.
This includes:
1. the ACT lensing bandpowers. Planck lensing bandpowers will be
appended if include_planck is True.
2. the associated binning matrix to be applied to a theory curve
3. the associated covariance matrix
4. data products associated with applying likelihood corrections
All these products will be standardized so that they apply
to theory curves specified from L=0 to trim_lmax.
A Hartlap correction will be applied to the covariance matrix
corresponding to the lower of the number of simulations involved.
"""
# TODO: review defaults
if version is None:
version = default_version
if ddir is None:
file_dir = os.path.abspath(os.path.dirname(__file__))
ddir = f"{file_dir}/data/{version}/"
if not os.path.exists(ddir):
raise FileNotFoundError("Requested data directory {} does not exist.\
Please place the data there. Default data can \
be downloaded to the default location \
with the act_dr6_lenslike.get_data() function.".format(ddir))
print(f"Loading ACT DR6 lensing likelihood {version}...")
v,baseline,include_planck = parse_variant(variant)
if include_planck and act_cmb_rescale: raise ValueError
# output data
d = {}
if lens_only and like_corrections: raise ValueError("Likelihood corrections should not be used in lens_only runs.")
if not(lens_only) and not(like_corrections):
warnings.warn("Neither using CMB-marginalized covariance matrix nor including likelihood corrections. Effective covariance may be underestimated.")
d['include_planck'] = include_planck
d['likelihood_corrections'] = like_corrections
# Fiducial spectra
if like_corrections:
f_ls, f_tt, f_ee, f_bb, f_te = np.loadtxt(f"{ddir}/like_corrs/cosmo2017_10K_acc3_lensedCls.dat",unpack=True)
f_tt = f_tt / (f_ls * (f_ls+1.)) * 2. * np.pi
f_ee = f_ee / (f_ls * (f_ls+1.)) * 2. * np.pi
f_bb = f_bb / (f_ls * (f_ls+1.)) * 2. * np.pi
f_te = f_te / (f_ls * (f_ls+1.)) * 2. * np.pi
fd_ls, f_dd = np.loadtxt(f"{ddir}/like_corrs/cosmo2017_10K_acc3_lenspotentialCls.dat",unpack=True,usecols=[0,5])
f_kk = f_dd * 2. * np.pi / 4.
d['fiducial_cl_tt'] = standardize(f_ls,f_tt,trim_lmax)
d['fiducial_cl_te'] = standardize(f_ls,f_te,trim_lmax)
d['fiducial_cl_ee'] = standardize(f_ls,f_ee,trim_lmax)
d['fiducial_cl_bb'] = standardize(f_ls,f_bb,trim_lmax)
d['fiducial_cl_kk'] = standardize(fd_ls,f_kk,trim_lmax)
# Return data bandpowers, covariance matrix and binning matrix
if baseline:
start = 2
end = -6
else:
start = 2
end = -3
if v is None:
y = np.loadtxt(f'{ddir}/clkk_bandpowers_act.txt')
elif v=='cinpaint':
y = np.loadtxt(f'{ddir}/clkk_bandpowers_act_cinpaint.txt')
elif v=='polonly':
y = np.loadtxt(f'{ddir}/clkk_bandpowers_act_polonly.txt')
elif v=='cibdeproj':
y = np.loadtxt(f'{ddir}/clkk_bandpowers_act_cibdeproj.txt')
nbins_tot_act = y.size
d['full_data_binned_clkk_act'] = y.copy()
data_act = y[start:end].copy()
d['data_binned_clkk'] = data_act
nbins_act = data_act.size
binmat = np.loadtxt(f'{ddir}/binning_matrix_act.txt')
d['full_binmat_act'] = binmat.copy()
pells = np.arange(binmat.shape[1])
bcents = binmat@pells
ls = np.arange(binmat.shape[1])
d['binmat_act'] = standardize(ls,binmat[start:end,:],trim_lmax,extra_dims="xy")
d['bcents_act'] = bcents[start:end].copy()
if act_cmb_rescale:
# load A_L_fid / A_L_ACT and standardize it
r = np.loadtxt("ratio_fid_over_act_wmap.txt")
rls = np.arange(r.size)
r[rls<2] = 0
rs = standardize(rls,r,trim_lmax)
# bin it
rb = d['binmat_act'] @ rs
# correct data
print("Binned ACT rescaling corrections: ", rb)
d['data_binned_clkk'] = d['data_binned_clkk'] / rb**2
if lens_only:
if act_cmb_rescale: raise ValueError
if act_calib: raise ValueError
if include_planck:
if v not in [None,'cinpaint']: raise ValueError(f"Combination of {v} with Planck is not available")
fcov = np.loadtxt(f'{ddir}/covmat_actplanck_cmbmarg.txt')
else:
if v=='cibdeproj':
fcov = np.loadtxt(f"{ddir}/covmat_act_cibdeproj_cmbmarg.txt")
elif v=='pol':
fcov = np.loadtxt(f"{ddir}/covmat_act_polonly_cmbmarg.txt")
else:
fcov = np.loadtxt(f"{ddir}/covmat_act_cmbmarg.txt")
else:
if v not in [None,'cinpaint']: raise ValueError(f"Covmat for {v} without CMB marginalization is not available")
if include_planck:
fcov = np.loadtxt(f'{ddir}/covmat_actplanck.txt')
else:
fcov = np.loadtxt(f'{ddir}/covmat_act.txt')
d['full_act_cov'] = fcov.copy()
# Remove trailing bins from ACT part
sel = np.s_[nbins_tot_act+end:nbins_tot_act]
cov = np.delete(np.delete(fcov,sel,0),sel,1)
# Remove leading bins from ACT part
sel = np.s_[:start]
cov = np.delete(np.delete(cov,sel,0),sel,1)
# Test
covmat = np.loadtxt(f'{ddir}/covmat_act.txt')
covmat1 = covmat[start:end,start:end]
cdiff = cov[:nbins_act,:nbins_act] - covmat1
if not(np.all(np.isclose(cdiff,0))): raise ValueError
if include_planck:
data_planck = np.loadtxt(f'{ddir}/clkk_bandpowers_planck.txt')
d['data_binned_clkk'] = np.append(d['data_binned_clkk'],data_planck)
binmat = np.loadtxt(f'{ddir}/binning_matrix_planck.txt')
pells = np.arange(binmat.shape[1])
bcents = binmat@pells
ls = np.arange(binmat.shape[1])
d['binmat_planck'] = standardize(ls,binmat,trim_lmax,extra_dims="xy")
d['bcents_planck'] = bcents.copy()
if like_corrections:
# Load matrices
cmat = np.load(f"{ddir}/like_corrs/norm_correction_matrix_Lmin0_Lmax4000.npy")
ls = np.arange(cmat.shape[1])
d['dAL_dC'] = standardize(ls,cmat,trim_lmax,extra_dims="xyy")
if include_planck:
cmat = np.load(f"{ddir}/like_corrs/P18_norm_correction_matrix_Lmin0_Lmax3000.npy")
ls = np.arange(cmat.shape[1])
d['dAL_dC_planck'] = standardize(ls,cmat,trim_lmax,extra_dims="xyy")
fAL_ls,fAL = np.loadtxt(f"{ddir}/like_corrs/n0mv_fiducial_lmin600_lmax3000_Lmin0_Lmax4000.txt")
d['fAL'] = standardize(fAL_ls,fAL,trim_lmax,extra_dims="y")
if include_planck:
fAL_ls,fAL = np.loadtxt(f"{ddir}/like_corrs/PLANCK_n0mv_fiducial_lmin600_lmax3000_Lmin0_Lmax3000.txt")
d['fAL_planck'] = standardize(fAL_ls,fAL,trim_lmax,extra_dims="y")
for spec in ['kk','tt','ee','bb','te']:
n1mat = np.loadtxt(f"{ddir}/like_corrs/N1der_{spec.upper()}_lmin600_lmax3000_full.txt")
d[f'dN1_{spec}'] = standardize(fAL_ls,n1mat,trim_lmax,extra_dims="yy")
if include_planck:
n1mat = np.loadtxt(f"{ddir}/like_corrs/N1_planck_der_{spec.upper()}_lmin100_lmax2048.txt")
d[f'dN1_{spec}_planck'] = standardize(fAL_ls,n1mat,trim_lmax,extra_dims="yy")
nbins = d['data_binned_clkk'].size
nsims = min(nsims_act,nsims_planck) if include_planck else nsims_act
hartlap_correction = (nsims-nbins-2.)/(nsims-1.)
if apply_hartlap:
warnings.warn(f"Hartlap correction to cinv: {hartlap_correction}")
else:
warnings.warn(f"Disabled Hartlap correction to cinv: {hartlap_correction}")
hartlap_correction = 1.0
if scale_cov is not None:
warnings.warn(f"Covariance has been artificially scaled by: {scale_cov}")
cov = cov * scale_cov
d['cov'] = cov
cinv = np.linalg.inv(cov) * hartlap_correction
d['cinv'] = cinv
if mock:
mclpp = np.loadtxt(f"{ddir}/cls_default_dr6_accuracy.txt",usecols=[5])
ls = np.arange(mclpp.size)
mclkk = mclpp * 2. * np.pi / 4.
self.clkk_data = self.binning_matrix @ mclkk[:self.kLmax]
return d
def generic_lnlike(data_dict,ell_kk,cl_kk,ell_cmb,cl_tt,cl_ee,cl_te,cl_bb,trim_lmax=2998,
return_theory=False,do_norm_corr=True,act_calib=False,no_actlike_cmb_corrections=False):
cl_kk = standardize(ell_kk,cl_kk,trim_lmax)
cl_tt = standardize(ell_cmb,cl_tt,trim_lmax)
cl_ee = standardize(ell_cmb,cl_ee,trim_lmax)
cl_bb = standardize(ell_cmb,cl_bb,trim_lmax)
cl_te = standardize(ell_cmb,cl_te,trim_lmax)
d = data_dict
cinv = d['cinv']
clkk_act = get_corrected_clkk(data_dict,cl_kk,cl_tt,cl_te,cl_ee,cl_bb,
do_norm_corr=do_norm_corr,act_calib=act_calib,
no_like_cmb_corrections=no_actlike_cmb_corrections) if d['likelihood_corrections'] else cl_kk
bclkk = d['binmat_act'] @ clkk_act
if d['include_planck']:
clkk_planck = get_corrected_clkk(data_dict,cl_kk,cl_tt,cl_te,cl_ee,cl_bb,'_planck') if d['likelihood_corrections'] else cl_kk
bclkk = np.append(bclkk, d['binmat_planck'] @ clkk_planck)
delta = d['data_binned_clkk'] - bclkk
lnlike = -0.5 * np.dot(delta,np.dot(cinv,delta))
if return_theory:
return lnlike, bclkk
else:
return lnlike
# =================
# Cobaya likelihood
# =================
class ACTDR6LensLike(InstallableLikelihood):
lmax: int = 4000
mock = False
nsims_act = 792. # Number of sims used for covmat; used in Hartlap correction
nsims_planck = 400. # Number of sims used for covmat; used in Hartlap correction
no_like_corrections = False
no_actlike_cmb_corrections = False
lens_only = False
# Any ells above this will be discarded; likelihood must at least request ells up to this
trim_lmax = 2998
variant = "act_baseline"
apply_hartlap = True
# Limber integral parameters
limber = False
nz = 100
kmax = 10
zmax = None
scale_cov = None
varying_cmb_alens = False # Whether to divide the theory spectrum by Alens
version = None
act_cmb_rescale = False
act_calib = False
def initialize(self):
if self.lens_only: self.no_like_corrections = True
if self.lmax<self.trim_lmax: raise ValueError(f"An lmax of at least {self.trim_lmax} is required.")
self.data = load_data(variant=self.variant,lens_only=self.lens_only,
like_corrections=not(self.no_like_corrections),apply_hartlap=self.apply_hartlap,
mock=self.mock,nsims_act=self.nsims_act,nsims_planck=self.nsims_planck,
trim_lmax=self.trim_lmax,scale_cov=self.scale_cov,version=self.version,
act_cmb_rescale=self.act_cmb_rescale,act_calib=self.act_calib)
if self.no_like_corrections:
self.requested_cls = ["pp"]
else:
self.requested_cls = ["tt", "te", "ee", "bb", "pp"]
def get_requirements(self):
if self.no_like_corrections:
ret = {'Cl': {'tt': self.lmax,'te': self.lmax,'ee': self.lmax,'pp':self.lmax}}
else:
ret = {'Cl': {'pp':self.lmax}}
if self.limber:
cobj = get_camb_lens_obj(self.nz,self.kmax,self.zmax)
ret.update(cobj)
return ret
def logp(self, **params_values):
cl = self.provider.get_Cl(ell_factor=False, units='FIRASmuK2')
return self.loglike(cl, **params_values)
def get_limber_clkk(self,**params_values):
Pfunc = self.provider.get_Pk_interpolator(var_pair=("Weyl", "Weyl"), nonlinear=True, extrap_kmax=30.)
results = self.provider.get_CAMBdata()
return get_limber_clkk_flat_universe(results,Pfunc,self.trim_lmax,self.kmax,nz,zstar=None)
def loglike(self, cl, **params_values):
ell = cl['ell']
Alens = 1
if self.varying_cmb_alens:
Alens = self.provider.get_param('Alens')
clpp = cl['pp'] / Alens
if self.limber:
cl_kk = self.get_limber_clkk( **params_values)
else:
cl_kk = pp_to_kk(clpp,ell)
logp = generic_lnlike(self.data,ell,cl_kk,ell,cl['tt'],cl['ee'],cl['te'],cl['bb'],self.trim_lmax,
do_norm_corr=not(self.act_cmb_rescale),act_calib=self.act_calib,
no_actlike_cmb_corrections=self.no_actlike_cmb_corrections)
self.log.debug(
f"ACT-DR6-lensing-like lnLike value = {logp} (chisquare = {-2 * logp})")
return logp
|
ACTCollaborationREPO_NAMEact_dr6_lenslikePATH_START.@act_dr6_lenslike_extracted@act_dr6_lenslike-main@act_dr6_lenslike@act_dr6_lenslike.py@.PATH_END.py
|
{
"filename": "bls.py",
"repo_name": "RadioAstronomySoftwareGroup/pyuvdata",
"repo_path": "pyuvdata_extracted/pyuvdata-main/src/pyuvdata/utils/bls.py",
"type": "Python"
}
|
# Copyright (c) 2024 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Utilities for baseline numbers."""
import copy
import re
import warnings
import numpy as np
from . import _bls
from .pol import conj_pol, polnum2str, polstr2num
__all__ = ["baseline_to_antnums", "antnums_to_baseline"]
def baseline_to_antnums(baseline, *, Nants_telescope): # noqa: N803
"""
Get the antenna numbers corresponding to a given baseline number.
Parameters
----------
baseline : int or array_like of ints
baseline number
Nants_telescope : int
number of antennas
Returns
-------
int or array_like of int
first antenna number(s)
int or array_like of int
second antenna number(s)
"""
if Nants_telescope > 2147483648:
raise ValueError(f"error Nants={Nants_telescope}>2147483648 not supported")
if np.any(np.asarray(baseline) < 0):
raise ValueError("negative baseline numbers are not supported")
if np.any(np.asarray(baseline) > 4611686018498691072):
raise ValueError("baseline numbers > 4611686018498691072 are not supported")
return_array = isinstance(baseline, np.ndarray | list | tuple)
ant1, ant2 = _bls.baseline_to_antnums(
np.ascontiguousarray(baseline, dtype=np.uint64)
)
if return_array:
return ant1.astype(int), ant2.astype(int)
else:
return int(ant1.item(0)), int(ant2.item(0))
def antnums_to_baseline(
ant1,
ant2,
*,
Nants_telescope, # noqa: N803
attempt256=False,
use_miriad_convention=False,
):
"""
Get the baseline number corresponding to two given antenna numbers.
Parameters
----------
ant1 : int or array_like of int
first antenna number
ant2 : int or array_like of int
second antenna number
Nants_telescope : int
number of antennas
attempt256 : bool
Option to try to use the older 256 standard used in
many uvfits files. If there are antenna numbers >= 256, the 2048
standard will be used unless there are antenna numbers >= 2048
or Nants_telescope > 2048. In that case, the 2147483648 standard
will be used. Default is False.
use_miriad_convention : bool
Option to use the MIRIAD convention where BASELINE id is
`bl = 256 * ant1 + ant2` if `ant2 < 256`, otherwise
`bl = 2048 * ant1 + ant2 + 2**16`.
Note antennas should be 1-indexed (start at 1, not 0)
Returns
-------
int or array of int
baseline number corresponding to the two antenna numbers.
"""
if Nants_telescope is not None and Nants_telescope > 2147483648:
raise ValueError(
"cannot convert ant1, ant2 to a baseline index "
f"with Nants={Nants_telescope}>2147483648."
)
if np.any(np.concatenate((np.unique(ant1), np.unique(ant2))) >= 2147483648):
raise ValueError(
"cannot convert ant1, ant2 to a baseline index "
"with antenna numbers greater than 2147483647."
)
if np.any(np.concatenate((np.unique(ant1), np.unique(ant2))) < 0):
raise ValueError(
"cannot convert ant1, ant2 to a baseline index "
"with antenna numbers less than zero."
)
nants_less2048 = True
if Nants_telescope is not None and Nants_telescope > 2048:
nants_less2048 = False
return_array = isinstance(ant1, np.ndarray | list | tuple)
baseline = _bls.antnums_to_baseline(
np.ascontiguousarray(ant1, dtype=np.uint64),
np.ascontiguousarray(ant2, dtype=np.uint64),
attempt256=attempt256,
nants_less2048=nants_less2048,
use_miriad_convention=use_miriad_convention,
)
if return_array:
return baseline
else:
return baseline.item(0)
def baseline_index_flip(baseline, *, Nants_telescope): # noqa: N803
"""Change baseline number to reverse antenna order."""
ant1, ant2 = baseline_to_antnums(baseline, Nants_telescope=Nants_telescope)
return antnums_to_baseline(ant2, ant1, Nants_telescope=Nants_telescope)
def parse_ants(uv, ant_str, *, print_toggle=False, x_orientation=None):
"""
Get antpair and polarization from parsing an aipy-style ant string.
Used to support the select function. Generates two lists of antenna pair
tuples and polarization indices based on parsing of the string ant_str.
If no valid polarizations (pseudo-Stokes params, or combinations of [lr]
or [xy]) or antenna numbers are found in ant_str, ant_pairs_nums and
polarizations are returned as None.
Parameters
----------
uv : UVBase Object
A UVBased object that supports the following functions and parameters:
- get_ants
- get_antpairs
- get_pols
These are used to construct the baseline ant_pair_nums
and polarizations returned.
ant_str : str
String containing antenna information to parse. Can be 'all',
'auto', 'cross', or combinations of antenna numbers and polarization
indicators 'l' and 'r' or 'x' and 'y'. Minus signs can also be used
in front of an antenna number or baseline to exclude it from being
output in ant_pairs_nums. If ant_str has a minus sign as the first
character, 'all,' will be appended to the beginning of the string.
See the tutorial for examples of valid strings and their behavior.
print_toggle : bool
Boolean for printing parsed baselines for a visual user check.
x_orientation : str, optional
Orientation of the physical dipole corresponding to what is
labelled as the x polarization ("east" or "north") to allow for
converting from E/N strings. If input uv object has an `x_orientation`
parameter and the input to this function is `None`, the value from the
object will be used. Any input given to this function will override the
value on the uv object. See corresonding parameter on UVData
for more details.
Returns
-------
ant_pairs_nums : list of tuples of int or None
List of tuples containing the parsed pairs of antenna numbers, or
None if ant_str is 'all' or a pseudo-Stokes polarizations.
polarizations : list of int or None
List of desired polarizations or None if ant_str does not contain a
polarization specification.
"""
required_attrs = ["get_ants", "get_antpairs", "get_pols"]
if not all(hasattr(uv, attr) for attr in required_attrs):
raise ValueError(
"UVBased objects must have all the following attributes in order "
f"to call 'parse_ants': {required_attrs}."
)
if x_orientation is None and (
hasattr(uv.telescope, "x_orientation")
and uv.telescope.x_orientation is not None
):
x_orientation = uv.telescope.x_orientation
ant_re = r"(\(((-?\d+[lrxy]?,?)+)\)|-?\d+[lrxy]?)"
bl_re = f"(^({ant_re}_{ant_re}|{ant_re}),?)"
str_pos = 0
ant_pairs_nums = []
polarizations = []
ants_data = uv.get_ants()
ant_pairs_data = uv.get_antpairs()
pols_data = uv.get_pols()
warned_ants = []
warned_pols = []
if ant_str.startswith("-"):
ant_str = "all," + ant_str
while str_pos < len(ant_str):
m = re.search(bl_re, ant_str[str_pos:])
if m is None:
if ant_str[str_pos:].upper().startswith("ALL"):
if len(ant_str[str_pos:].split(",")) > 1:
ant_pairs_nums = uv.get_antpairs()
elif ant_str[str_pos:].upper().startswith("AUTO"):
for pair in ant_pairs_data:
if pair[0] == pair[1] and pair not in ant_pairs_nums:
ant_pairs_nums.append(pair)
elif ant_str[str_pos:].upper().startswith("CROSS"):
for pair in ant_pairs_data:
if not (pair[0] == pair[1] or pair in ant_pairs_nums):
ant_pairs_nums.append(pair)
elif ant_str[str_pos:].upper().startswith("PI"):
polarizations.append(polstr2num("pI"))
elif ant_str[str_pos:].upper().startswith("PQ"):
polarizations.append(polstr2num("pQ"))
elif ant_str[str_pos:].upper().startswith("PU"):
polarizations.append(polstr2num("pU"))
elif ant_str[str_pos:].upper().startswith("PV"):
polarizations.append(polstr2num("pV"))
else:
raise ValueError(f"Unparsable argument {ant_str}")
comma_cnt = ant_str[str_pos:].find(",")
if comma_cnt >= 0:
str_pos += comma_cnt + 1
else:
str_pos = len(ant_str)
else:
m = m.groups()
str_pos += len(m[0])
if m[2] is None:
ant_i_list = [m[8]]
ant_j_list = list(uv.get_ants())
else:
if m[3] is None:
ant_i_list = [m[2]]
else:
ant_i_list = m[3].split(",")
if m[6] is None:
ant_j_list = [m[5]]
else:
ant_j_list = m[6].split(",")
for ant_i in ant_i_list:
include_i = True
if isinstance(ant_i, str) and ant_i.startswith("-"):
ant_i = ant_i[1:] # nibble the - off the string
include_i = False
for ant_j in ant_j_list:
include_j = True
if isinstance(ant_j, str) and ant_j.startswith("-"):
ant_j = ant_j[1:]
include_j = False
pols = None
ant_i, ant_j = str(ant_i), str(ant_j)
if not ant_i.isdigit():
ai = re.search(r"(\d+)([x,y,l,r])", ant_i).groups()
if not ant_j.isdigit():
aj = re.search(r"(\d+)([x,y,l,r])", ant_j).groups()
if ant_i.isdigit() and ant_j.isdigit():
ai = [ant_i, ""]
aj = [ant_j, ""]
elif ant_i.isdigit() and not ant_j.isdigit():
if "x" in ant_j or "y" in ant_j:
pols = ["x" + aj[1], "y" + aj[1]]
else:
pols = ["l" + aj[1], "r" + aj[1]]
ai = [ant_i, ""]
elif not ant_i.isdigit() and ant_j.isdigit():
if "x" in ant_i or "y" in ant_i:
pols = [ai[1] + "x", ai[1] + "y"]
else:
pols = [ai[1] + "l", ai[1] + "r"]
aj = [ant_j, ""]
elif not ant_i.isdigit() and not ant_j.isdigit():
pols = [ai[1] + aj[1]]
ant_tuple = (abs(int(ai[0])), abs(int(aj[0])))
# Order tuple according to order in object
if ant_tuple in ant_pairs_data:
pass
elif ant_tuple[::-1] in ant_pairs_data:
ant_tuple = ant_tuple[::-1]
else:
if not (
ant_tuple[0] in ants_data or ant_tuple[0] in warned_ants
):
warned_ants.append(ant_tuple[0])
if not (
ant_tuple[1] in ants_data or ant_tuple[1] in warned_ants
):
warned_ants.append(ant_tuple[1])
if pols is not None:
for pol in pols:
if not (pol.lower() in pols_data or pol in warned_pols):
warned_pols.append(pol)
continue
if include_i and include_j:
if ant_tuple not in ant_pairs_nums:
ant_pairs_nums.append(ant_tuple)
if pols is not None:
for pol in pols:
if (
pol.lower() in pols_data
and polstr2num(pol, x_orientation=x_orientation)
not in polarizations
):
polarizations.append(
polstr2num(pol, x_orientation=x_orientation)
)
elif not (
pol.lower() in pols_data or pol in warned_pols
):
warned_pols.append(pol)
else:
if pols is not None:
for pol in pols:
if pol.lower() in pols_data:
if uv.Npols == 1 and [pol.lower()] == pols_data:
ant_pairs_nums.remove(ant_tuple)
if (
polstr2num(pol, x_orientation=x_orientation)
in polarizations
):
polarizations.remove(
polstr2num(pol, x_orientation=x_orientation)
)
elif not (
pol.lower() in pols_data or pol in warned_pols
):
warned_pols.append(pol)
elif ant_tuple in ant_pairs_nums:
ant_pairs_nums.remove(ant_tuple)
if (
ant_str.upper() == "ALL"
or len(ant_pairs_nums) == 0
and ant_str.upper() not in ["AUTO", "CROSS"]
):
ant_pairs_nums = None
if len(polarizations) == 0:
polarizations = None
else:
polarizations.sort(reverse=True)
if print_toggle:
print("\nParsed antenna pairs:")
if ant_pairs_nums is not None:
for pair in ant_pairs_nums:
print(pair)
print("\nParsed polarizations:")
if polarizations is not None:
for pol in polarizations:
print(polnum2str(pol, x_orientation=x_orientation))
if len(warned_ants) > 0:
warnings.warn(
"Warning: Antenna number {a} passed, but not present "
"in the ant_1_array or ant_2_array".format(
a=(",").join(map(str, warned_ants))
)
)
if len(warned_pols) > 0:
warnings.warn(
"Warning: Polarization {p} is not present in the polarization_array".format(
p=(",").join(warned_pols).upper()
)
)
return ant_pairs_nums, polarizations
def _extract_bls_pol(
*, bls, polarizations, baseline_array, ant_1_array, ant_2_array, nants_telescope
):
if isinstance(bls, list) and all(
isinstance(bl_ind, int | np.integer) for bl_ind in bls
):
for bl_ind in bls:
if bl_ind not in baseline_array:
raise ValueError(
f"Baseline number {bl_ind} is not present in the baseline_array"
)
bls = list(
zip(*baseline_to_antnums(bls, Nants_telescope=nants_telescope), strict=True)
)
elif isinstance(bls, tuple) and (len(bls) == 2 or len(bls) == 3):
bls = [bls]
if len(bls) == 0 or not all(isinstance(item, tuple) for item in bls):
raise ValueError(
"bls must be a list of tuples of antenna numbers "
"(optionally with polarization) or a list of baseline numbers."
)
if not all(
[isinstance(item[0], int | np.integer) for item in bls]
+ [isinstance(item[1], int | np.integer) for item in bls]
):
raise ValueError(
"bls must be a list of tuples of antenna numbers "
"(optionally with polarization) or a list of baseline numbers."
)
if any(len(item) == 3 for item in bls):
if polarizations is not None:
raise ValueError(
"Cannot provide any length-3 tuples and also specify polarizations."
)
bls_2 = copy.deepcopy(bls)
bl_pols = set()
for bl_i, bl in enumerate(bls):
if len(bl) != 3:
raise ValueError("If some bls are 3-tuples, all bls must be 3-tuples.")
if not isinstance(bl[2], str):
raise ValueError(
"The third element in a bl tuple must be a polarization string"
)
wh1 = np.where(np.logical_and(ant_1_array == bl[0], ant_2_array == bl[1]))[
0
]
if len(wh1) > 0:
bls_2[bl_i] = (bl[0], bl[1])
bl_pols.add(bl[2])
else:
wh2 = np.where(
np.logical_and(ant_1_array == bl[1], ant_2_array == bl[0])
)[0]
if len(wh2) > 0:
bls_2[bl_i] = (bl[1], bl[0])
# find conjugate polarization
bl_pols.add(conj_pol(bl[2]))
else:
raise ValueError(
f"Antenna pair {bl} does not have any data "
"associated with it."
)
polarizations = list(bl_pols)
bls = bls_2
return bls, polarizations
|
RadioAstronomySoftwareGroupREPO_NAMEpyuvdataPATH_START.@pyuvdata_extracted@pyuvdata-main@src@pyuvdata@utils@bls.py@.PATH_END.py
|
{
"filename": "inkpot.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/Pygments/py2/pygments/styles/inkpot.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
pygments.styles.inkpot
~~~~~~~~~~~~~~~~~~~~~~
A highlighting style for Pygments, inspired by the Inkpot theme for VIM.
:copyright: Copyright 2006-2019 by the Pygments team, see AUTHORS.
:license: BSD, see LICENSE for details.
"""
from pygments.style import Style
from pygments.token import Text, Other, Keyword, Name, Comment, String, \
Error, Number, Operator, Generic, Whitespace, Punctuation
class InkPotStyle(Style):
background_color = "#1e1e27"
default_style = ""
styles = {
Text: "#cfbfad",
Other: "#cfbfad",
Whitespace: "#434357",
Comment: "#cd8b00",
Comment.Preproc: "#409090",
Comment.PreprocFile: "bg:#404040 #ffcd8b",
Comment.Special: "#808bed",
Keyword: "#808bed",
Keyword.Pseudo: "nobold",
Keyword.Type: "#ff8bff",
Operator: "#666666",
Punctuation: "#cfbfad",
Name: "#cfbfad",
Name.Attribute: "#cfbfad",
Name.Builtin.Pseudo: '#ffff00',
Name.Builtin: "#808bed",
Name.Class: "#ff8bff",
Name.Constant: "#409090",
Name.Decorator: "#409090",
Name.Exception: "#ff0000",
Name.Function: "#c080d0",
Name.Label: "#808bed",
Name.Namespace: "#ff0000",
Name.Variable: "#cfbfad",
String: "bg:#404040 #ffcd8b",
String.Doc: "#808bed",
Number: "#f0ad6d",
Generic.Heading: "bold #000080",
Generic.Subheading: "bold #800080",
Generic.Deleted: "#A00000",
Generic.Inserted: "#00A000",
Generic.Error: "#FF0000",
Generic.Emph: "italic",
Generic.Strong: "bold",
Generic.Prompt: "bold #000080",
Generic.Output: "#888",
Generic.Traceback: "#04D",
Error: "bg:#6e2e2e #ffffff"
}
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@Pygments@py2@pygments@styles@inkpot.py@.PATH_END.py
|
{
"filename": "_nticks.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/contourcarpet/colorbar/_nticks.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class NticksValidator(_plotly_utils.basevalidators.IntegerValidator):
def __init__(
self, plotly_name="nticks", parent_name="contourcarpet.colorbar", **kwargs
):
super(NticksValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "colorbars"),
min=kwargs.pop("min", 0),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@contourcarpet@colorbar@_nticks.py@.PATH_END.py
|
{
"filename": "thermalFlags.py",
"repo_name": "barentsen/dave",
"repo_path": "dave_extracted/dave-master/susanplay/thermalFlags.py",
"type": "Python"
}
|
# -*- coding: utf-8 -*-
"""
Created on Mon Mar 7 16:58:43 2016
@author: smullall
"""
import dave.susanplay.mainSusan as mS
import dave.pipeline.pipeline as pipe
import numpy as np
import dave.pipeline.clipboard as c
def countThermFlags(clip):
thermal=dict()
#Create the light curves.
clip['config']['dataStorePath']='/home/smullall/Science/datastore'
clip=pipe.serveTask(clip)
clip=pipe.trapezoidFitTask(clip)
#Get just the interesting flags
thruster=2**20;
safemode=2**1;
desat=2**5
isbad=np.bitwise_and(clip.serve.flags,thruster+safemode+desat) != 0
thermal['isBad'] = isbad
time=clip.serve.time
period=clip.trapFit.period_days
epoch=clip.trapFit.epoch_bkjd
#phi = np.fmod(time-epoch + .25*period, period)
phiorig = (time-epoch + .25*period) % period
phi = phiorig[np.isfinite(phiorig)]
dur=clip.trapFit.duration_hrs/24;
#Calculate the phase range of the folded transit model.
phi1=0.25*period - 0.5*dur;
phi2=0.25*period + 0.5*dur;
thermal['phimin']=phi1;
thermal['phimax']=phi2;
thermal['inTransCadTot'] = len( phi[[phi>phi1] and [phi<phi2]] )
#How many isbads exist in that phase range.
phiisbad=phiorig[isbad]
countBad=0
for (i,v) in enumerate(phiisbad):
if v > phi1 and v< phi2:
countBad=countBad+1
thermal['inTransCadBad'] = countBad
thermal['numTrans']=np.floor((time[-1]-time[0])/period)
clip['thermal']=thermal
return clip
def getFractionThermal(clip):
clip=countThermFlags(clip)
if clip.thermal.numTrans >= 2:
frac=clip.thermal.inTransCadBad/clip.thermal.numTrans
else:
frac=0;
return frac;
|
barentsenREPO_NAMEdavePATH_START.@dave_extracted@dave-master@susanplay@thermalFlags.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/graph_objs/isosurface/caps/__init__.py",
"type": "Python"
}
|
import sys
if sys.version_info < (3, 7):
from ._x import X
from ._y import Y
from ._z import Z
else:
from _plotly_utils.importers import relative_import
__all__, __getattr__, __dir__ = relative_import(
__name__, [], ["._x.X", "._y.Y", "._z.Z"]
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@graph_objs@isosurface@caps@__init__.py@.PATH_END.py
|
{
"filename": "test_sigma_clip.py",
"repo_name": "RuthAngus/starspot",
"repo_path": "starspot_extracted/starspot-master/tests/test_sigma_clip.py",
"type": "Python"
}
|
# import numpy as np
# import matplotlib.pyplot as plt
# from starspot.rotation_tools import filter_sigma_clip, sigma_clip
# def test_sigma_clip():
# np.random.seed(42)
# N, Nout = 1000, 20
# t0 = np.linspace(0, 100, N)
# p = 10
# w = 2*np.pi/p
# y0 = np.sin(w*t0) + np.random.randn(N)*.1
# inds = np.random.choice(np.arange(len(y0)), Nout)
# y0[inds] += np.random.randn(Nout)*10.
# # Initial removal of extreme outliers.
# m = sigma_clip(y0, nsigma=7)
# t, y = t0[m], y0[m]
# # Sigma clip
# smooth, mask = filter_sigma_clip(t, y, polyorder=2)
# resids = y - smooth
# # Plot results
# plt.figure(figsize=(16, 9))
# plt.subplot(2, 1, 1)
# plt.plot(t0, y0, ".", label="Original")
# plt.plot(t, y, ".", label="initial clip")
# plt.plot(t, smooth, label="smoothed")
# plt.legend()
# plt.subplot(2, 1, 2)
# plt.plot(t, resids, ".", label="Whole lc")
# plt.plot(t[~mask], resids[~mask], ".", label="Detected outliers")
# plt.legend()
# plt.savefig("test.png")
# if __name__ == "__main__":
# test_sigma_clip()
|
RuthAngusREPO_NAMEstarspotPATH_START.@starspot_extracted@starspot-master@tests@test_sigma_clip.py@.PATH_END.py
|
{
"filename": "plot.py",
"repo_name": "Q3D/q3dfit",
"repo_path": "q3dfit_extracted/q3dfit-main/q3dfit/plot.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import math
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from q3dfit.q3dmath import cmplin
from q3dfit.q3dutil import lmlabel
from q3dfit.exceptions import InitializationError
from q3dfit.questfitfcn import readcf
from matplotlib import rcParams
from matplotlib import pyplot as plt
def plotcont(q3do, savefig=False, outfile=None, ct_coeff=None, q3di=None,
compspec=None, comptitles=None, ps=None,
title=None, fitran=None, yranminmax=None, IR=False,
compcols=None, xstyle='log', ystyle='log',
waveunit_in='micron',
waveunit_out='micron',
figsize=(10, 5), fluxunit_in='flambda',
fluxunit_out='flambda',
mode='light'
):
'''
Created on Tue Jun 1 13:32:37 2021
@author: annamurphree
Plots continuum fit of optical data (fit by fitqsohost or ppxf)
or IR data (fit by questfit).
Init file optional parameters ('argscontplot'):
xstyle = log or lin (linear),
ystyle = log or lin (linear),
waveunit_in = micron or Angstrom,
waveunit_out = micron or Angstrom,
fluxunit_in = flambda, lambdaflambda (= nufnu), or fnu,
fluxunit_out = flambda, lambdaflambda (= nufnu), or fnu,
mode = light or dark
The first options are the defaults.
'''
# dark mode just for fun:
if mode == 'dark':
pltstyle = 'dark_background'
dcolor = 'w'
else:
pltstyle = 'seaborn-v0_8-ticks'
dcolor = 'k'
wave = q3do.wave
specstars = q3do.cont_dat
modstars = q3do.cont_fit
# for optical spectra fit by fitqsohost or ppxf:
if not IR:
if compspec is not None:
if len(compspec) > 1:
ncomp = len(compspec)
else:
ncomp = 1
compcolors = ['c', 'plum', 'm']
complabels = ['QSO', 'Host', 'Wind']
if comptitles is not None:
complabels = comptitles
if compcols is not None:
compcolors = compcols
else:
ncomp = 0
if fitran is not None:
xran = fitran
else:
xran = q3do.fitrange
if waveunit_in == 'Angstrom' and waveunit_out == 'micron':
# convert angstrom to microns
xran = list(np.divide(xran, 10**4))
wave = list(np.divide(wave, 10**4))
# speed of light in microns/s
c = 2.998e+14
elif waveunit_in == 'micron' and waveunit_out == 'Angstrom':
# convert microns to angstroms
xran = list(np.multiply(xran, 10**4))
wave = list(np.multiply(wave, 10**4))
# speed of light in angstroms/s
c = 2.998e+18
if fluxunit_in == 'flambda' and fluxunit_out == 'lambdaflambda':
# multiply the flux by wavelength
specstars = list(np.multiply(specstars, wave))
modstars = list(np.multiply(modstars, wave))
if ncomp > 0:
for i in range(0, ncomp):
compspec[i] = list(np.multiply(compspec[i], wave))
ytit = '$\lambda$F$_\lambda$'
elif fluxunit_in == 'flambda' and fluxunit_out == 'fnu':
# multiply the flux by wavelength^2/c
specstars = \
list(np.multiply(specstars,
np.divide(np.multiply(wave, wave), c)))
modstars = \
list(np.multiply(modstars,
np.divide(np.multiply(wave, wave), c)))
if ncomp > 0:
for i in range(0, ncomp):
compspec[i] = \
list(np.multiply(compspec[i],
np.divide(np.multiply(wave, wave), c)))
ytit = 'F$_\u03BD$'
else:
ytit = 'F$_\lambda$'
# plot on a log scale:
if xstyle == 'log' or ystyle == 'log':
plt.style.use(pltstyle)
# CB: Otherwise the background becomes black and the axes ticks
# unreadable when saving the figure
if mode == 'light':
rcParams['savefig.facecolor'] = 'white'
fig = plt.figure(figsize=figsize)
# fig = plt.figure()
plt.axis('off') # so the subplots don't share a y-axis
fig.add_subplot(1, 1, 1)
ydat = specstars
ymod = modstars
# plotting
plt.xlim(xran[0], xran[1])
fig.axes[0].axis('off') # so the subplots don't share a y-axis
fig.axes[1].axis('off') # so the subplots don't share a y-axis
gs = fig.add_gridspec(4, 1)
ax1 = fig.add_subplot(gs[:3, :])
# ax1.legend(ncol=2)
if xstyle == 'log':
ax1.set_xscale('log')
# ax1.set_xticklabels([])
if ystyle == 'log':
ax1.set_yscale('log')
ax1.set_ylabel(ytit, fontsize=20)
if title == 'QSO':
ax1.set_ylim(10e-7)
# actually plotting
plt.plot(wave, ydat, dcolor, linewidth=1)
plt.plot(wave, ymod, 'r', linewidth=3, label='Total')
if ncomp > 0:
for i in range(0, ncomp):
plt.plot(wave, compspec[i], compcolors[i], linewidth=3,
label=complabels[i])
# tick formatting
yticks_used = ax1.get_yticks()
ylim_used = ax1.get_ylim()
yticks_used = np.append(np.append(ylim_used[0], yticks_used),
ylim_used[1])
ax1.set_yticks(yticks_used)
ax1.set_ylim(ylim_used)
ax1.minorticks_on()
ax1.tick_params(which='major', length=20, pad=10, labelsize=20)
ax1.tick_params(which='minor', length=7, labelsize=17)
l = ax1.legend(loc='upper right', fontsize=16)
for text in l.get_texts():
text.set_color(dcolor)
ax2 = fig.add_subplot(gs[-1, :], sharex=ax1)
ax2.plot(wave, np.divide(specstars, modstars), color=dcolor)
ax2.axhline(1, color='grey', linestyle='--', alpha=0.7, zorder=0)
ax2.set_ylabel('Data/Model', fontsize=19)
ax2.tick_params(which='major', length=20, pad=20, labelsize=18)
ax2.tick_params(which='minor', length=7, labelsize=17)
if waveunit_out == 'micron':
ax2.set_xlabel('Wavelength ($\mu$m)', fontsize=20)
elif waveunit_out == 'Angstrom':
ax2.set_xlabel('Wavelength ($\AA$)', fontsize=20)
gs.update(wspace=0.0, hspace=0.05)
plt.gcf().subplots_adjust(bottom=0.1)
if title is not None:
plt.suptitle(title, fontsize=30)
if savefig and outfile is not None:
plt.savefig(outfile[0] + '.jpg')
elif xstyle == 'lin' or ystyle == 'lin':
dxran = xran[1] - xran[0]
xran1 = [xran[0], xran[0] + np.around(dxran/3.0, 3)]
xran2 = [xran[0] + np.around(dxran/3.0, 3),
xran[0] + 2.0 * np.around(dxran/3.0, 3)]
xran3 = [xran[0] + 2.0 * np.around(dxran/3.0, 3),
xran[1]]
i1 = [None]
i2 = [None]
i3 = [None]
i1.pop(0)
i2.pop(0)
i3.pop(0)
ydat = specstars
ymod = modstars
for i in range(0, len(wave)):
if wave[i] > xran1[0] and wave[i] < xran1[1]:
i1.append(i)
if wave[i] > xran2[0] and wave[i] < xran2[1]:
i2.append(i)
if wave[i] > xran3[0] and wave[i] < xran3[1]:
i3.append(i)
maxthresh = 0.2
ntop = 20
nbottom = 20
if len(wave) < 100:
ntop = 10
nbottom = 10
++ntop
--nbottom
if waveunit_out == 'micron':
xtit = 'Observed Wavelength ($\mu$m)'
elif waveunit_out == 'Angstrom':
xtit = 'Observed Wavelength ($\AA$)'
plt.style.use(pltstyle)
fig = plt.figure(figsize=figsize)
plt.axis('off') # so the subplots don't share a y-axis
maximum = 0
minimum = 0
''
idict = {1: i1, 2: i2, 3: i3}
xrans = {1: xran1, 2: xran2, 3: xran3}
for group in range(1, 4):
if len(idict[group]) > 0:
fig.add_subplot(3, 1, group)
# finding min/max values at indices from idict
dat_et_mod = np.concatenate((ydat[idict[group]],
ymod[idict[group]]))
maximum = np.nanmax(dat_et_mod)
minimum = np.nanmin(dat_et_mod)
# set min and max in yran
if yranminmax is not None:
yran = [minimum, maximum]
else:
yran = [0, maximum]
# finding yran[1] aka max
ydi = np.zeros(len(idict[group]))
ydi = np.array(ydat)[idict[group]]
ymodi = np.zeros(len(idict[group]))
ymodi = np.array(ymod)[idict[group]]
y = np.array(ydi - ymodi)
ny = len(y)
iysort = np.argsort(y)
ysort = np.array(y)[iysort]
ymodisort = ymodi[iysort]
if ysort[ny - ntop] < ysort[ny - 1] * maxthresh:
yran[1] = np.nanmax(ysort[0:ny - ntop] +
ymodisort[0:ny - ntop])
# plotting
plt.xlim(xrans[group][0], xrans[group][1])
plt.ylim(yran[0], yran[1])
plt.ylabel(ytit, fontsize=15)
if group == 3:
plt.xlabel(xtit, fontsize=15, labelpad=10)
if ystyle == 'log':
plt.yscale('log')
# tick formatting
plt.minorticks_on()
plt.tick_params(which='major', length=10, pad=5)
plt.tick_params(which='minor', length=5)
if waveunit_out == 'micron':
xticks = np.arange(np.around(xrans[group][0],1)-0.025,
np.around(xrans[group][1],1), 0.025)[:-1]
plt.xticks(xticks, fontsize=10)
elif waveunit_out == 'Angstrom':
xticks = np.arange(math.floor(xrans[group][0]/100.0)*100,
(math.floor(xrans[group][1]/100)*100)+100, 100)
plt.xticks(xticks, fontsize=10)
if np.nanmin(ydat) > 1e-10:
# this will fail if fluxes are very low (<~1e-10)
plt.yticks(np.arange(yran[0], yran[1],
np.around((yran[1] - yran[0])/5.,
decimals=2)), fontsize=10)
else:
plt.yticks()
# actually plotting
plt.plot(wave, ydat, dcolor, linewidth=1)
if ncomp > 0:
for i in range(0, ncomp):
plt.plot(wave, compspec[i], compcolors[i], linewidth=3, label=complabels[i])
plt.plot(wave, ymod, 'r', linewidth=4, label=title)
if group == 1:
plt.legend(loc='upper right')
# more formatting
plt.subplots_adjust(hspace=0.25)
#plt.tight_layout(pad=5)
#plt.gcf().subplots_adjust(bottom=0.1)
if title is not None:
plt.suptitle(title, fontsize=40)
if savefig and outfile is not None:
if len(outfile[0])>1:
plt.savefig(outfile[0] + '.jpg')
else:
plt.savefig(outfile + '.jpg')
# for IR spectra fit with questfit:
else:
comp_best_fit = q3do.ct_coeff['comp_best_fit']
if xstyle == 'log' or ystyle == 'log':
if IR:
fig = plt.figure(figsize=figsize)
gs = fig.add_gridspec(4,1)
ax1 = fig.add_subplot(gs[:3, :])
MIRgdlambda = wave #[q3do.ct_indx]
MIRgdflux = q3do.spec #[q3do.ct_indx]
MIRcontinuum = modstars #[q3do.ct_indx]
if waveunit_in =='micron' and waveunit_out == 'Angstrom':
# convert microns to angstroms
MIRgdlambda = list(np.multiply(MIRgdlambda, 10**4))
elif waveunit_in =='Angstrom' and waveunit_out == 'micron':
# convert angstroms to microns
MIRgdlambda = list(np.divide(MIRgdlambda, 10**4))
if fluxunit_in == 'flambda' and fluxunit_out == 'lambdaflambda':
# multiply the flux by wavelength
MIRgdflux = list(np.multiply(MIRgdflux, MIRgdlambda))
MIRcontinuum = list(np.multiply(MIRcontinuum, MIRgdlambda))
if len(comp_best_fit.keys()) > 0:
for i in range(0, len(comp_best_fit.keys())):
comp_best_fit[list(comp_best_fit.keys())[i]] = \
np.multiply(comp_best_fit[list(comp_best_fit.keys())[i]],
MIRgdlambda)
ytit = '$\lambda$F$_\lambda$'
elif fluxunit_in == 'flambda' and fluxunit_out == 'fnu':
# multiply the flux by wavelength^2/c
MIRgdflux = list(np.multiply(MIRgdflux, MIRgdlambda))
MIRcontinuum = list(np.multiply(MIRgdflux, MIRgdlambda))
ytit = 'F$_\u03BD$'
else:
ytit = 'F$_\lambda$'
plt.style.use(pltstyle)
ax1.plot(MIRgdlambda, MIRgdflux, label='Data',color=dcolor)
ax1.plot(MIRgdlambda, MIRcontinuum, label='Model', color='r')
if 'global_ext_model' in q3di.argscontfit:
for i in np.arange(0,len(comp_best_fit.keys())-2,1):
ax1.plot(MIRgdlambda,
np.multiply(comp_best_fit[list(comp_best_fit.keys())[i]],
np.multiply(comp_best_fit[list(comp_best_fit.keys())[-2]],
comp_best_fit[list(comp_best_fit.keys())[-1]])),
label=list(comp_best_fit.keys())[i],
linestyle='--',alpha=0.5)
else:
for i in np.arange(0,len(comp_best_fit.keys()),3):
ax1.plot(MIRgdlambda,
np.multiply(comp_best_fit[list(comp_best_fit.keys())[i]],
np.multiply(comp_best_fit[list(comp_best_fit.keys())[i+1]],
comp_best_fit[list(comp_best_fit.keys())[i+2]])),
label=list(comp_best_fit.keys())[i],
linestyle='--',alpha=0.5)
for comp_i in comp_best_fit.keys():
if 'ext' not in comp_i and 'abs' not in comp_i:
spec_out = comp_best_fit[comp_i]
if comp_i+'_ext' in comp_best_fit.keys():
spec_out *= comp_best_fit[comp_i+'_ext']
if comp_i+'_abs' in comp_best_fit.keys():
spec_out *= comp_best_fit[comp_i+'_abs']
plt.plot(MIRgdlambda, spec_out, label=comp_i,linestyle='--',alpha=0.5)
#ax1.legend(ncol=2)
ax1.legend(loc='upper right',bbox_to_anchor=(1.15, 1),prop={'size': 10})
if xstyle == 'log':
ax1.set_xscale('log')
if ystyle == 'log':
ax1.set_yscale('log')
ax1.set_ylim(1e-4)
ax1.set_ylabel(ytit, fontsize=12)
ax2 = fig.add_subplot(gs[-1, :], sharex=ax1)
ax2.plot(MIRgdlambda,np.divide(MIRgdflux,MIRcontinuum),color=dcolor)
ax2.axhline(1, color='grey', linestyle='--', alpha=0.7, zorder=0)
ax2.set_ylabel('Data/Model', fontsize=12)
if waveunit_out == 'Angstrom':
ax2.set_xlabel('Wavelength ($\AA$)', fontsize=12)
elif waveunit_out == 'micron':
ax2.set_xlabel('Wavelength ($\mu$m)', fontsize=12)
gs.update(wspace=0.0, hspace=0.05)
plt.suptitle('Total', fontsize=30)
elif xstyle == 'lin' or ystyle == 'lin':
if fitran is not None:
xran = fitran
else:
xran = q3do.fitran
MIRgdlambda = wave #[q3do.ct_indx]
MIRgdflux = q3do.spec #[q3do.ct_indx]
MIRcontinuum = modstars #[q3do.ct_indx]
xtit = ''
if waveunit_in == 'microns' and waveunit_out == 'Angstrom':
# convert wave list from microns to angstroms
MIRgdlambda = list(np.multiply(MIRgdlambda, 10**4))
xtit = 'Observed Wavelength ($\AA$)'
elif waveunit_in == 'Angstrom' and waveunit_out == 'micron':
# convert wave list from angstroms to microns
MIRgdlambda = list(np.divide(MIRgdlambda, 10**4))
xtit = 'Observed Wavelength ($\mu$m)'
if fluxunit_in == 'flambda' and fluxunit_out == 'lambdaflambda':
# multiply the flux by wavelength
MIRgdflux = list(np.multiply(MIRgdflux, MIRgdlambda))
MIRcontinuum = list(np.multiply(MIRcontinuum, MIRgdlambda))
if len(comp_best_fit.keys()) > 0:
for i in range(0, len(comp_best_fit.keys())):
comp_best_fit[list(comp_best_fit.keys())[i]] = \
list(np.multiply(comp_best_fit[list(comp_best_fit.keys())[i]],
MIRgdlambda))
ytit = '$\lambda$F$_\lambda$'
elif fluxunit_in == 'flambda' and fluxunit_out == 'fnu':
# multiply the flux by wavelength
MIRgdflux = list(np.multiply(MIRgdflux, MIRgdlambda))
MIRcontinuum = list(np.multiply(MIRgdflux, MIRgdlambda))
ytit = 'F$_\u03BD$'
else:
ytit = 'F$_\lambda$'
wave = MIRgdlambda
ydat = MIRgdflux
ymod = MIRcontinuum
dxran = xran[1] - xran[0]
xran1 = [xran[0], xran[0] + np.around(dxran/3.0,3)]
xran2 = [xran[0] + np.around(dxran/3.0,3), xran[0] + 2.0 * np.around(dxran/3.0,3)]
xran3 = [xran[0] + 2.0 * np.around(dxran/3.0,3), xran[1]]
i1 = [None]
i2 = [None]
i3 = [None]
i1.pop(0)
i2.pop(0)
i3.pop(0)
for i in range(0, len(wave)):
if wave[i] > xran1[0] and wave[i] < xran1[1]:
i1.append(i)
if wave[i] > xran2[0] and wave[i] < xran2[1]:
i2.append(i)
if wave[i] > xran3[0] and wave[i] < xran3[1]:
i3.append(i)
maxthresh = 0.2
ntop = 20
nbottom = 20
if len(wave) < 100:
ntop = 10
nbottom = 10
++ntop
--nbottom
plt.style.use(pltstyle)
fig = plt.figure(figsize=figsize)
#fig = plt.figure()
plt.axis('off') # so the subplots don't share a y-axis
maximum = 0
minimum = 0
idict = {1:i1, 2:i2, 3:i3}
xrans = {1:xran1, 2:xran2, 3:xran3}
for group in range(1,4):
if len(idict[group]) > 0:
fig.add_subplot(3, 1, group)
ax = plt.subplot(3, 1, group)
# shrink current axis by 10% to fit legend on side
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
# finding max value between ydat and ymod at indices from i1
for i in idict[group]:
bigboy = np.nanmax([ydat[i], ymod[i]])
if bigboy > maximum:
maximum = bigboy
# finding min
for i in idict[group]:
smallboy = np.nanmin([ydat[i], ymod[i]])
if smallboy < minimum:
minimum = smallboy
# set min and max in yran
if yranminmax is not None:
yran = [minimum, maximum]
else:
yran = [0, maximum]
# finding yran[1] aka max
ydi = np.zeros(len(idict[group]))
ydi = np.array(ydat)[idict[group]]
ymodi = np.zeros(len(idict[group]))
ymodi = np.array(ymod)[idict[group]]
y = np.array(ydi - ymodi)
ny = len(y)
iysort = np.argsort(y)
ysort = np.array(y)[iysort]
ymodisort = ymodi[iysort]
if ysort[ny - ntop] < ysort[ny - 1] * maxthresh:
yran[1] = np.nanmax(ysort[0:ny - ntop] +
ymodisort[0:ny - ntop])
# plotting
plt.xlim(xrans[group][0], xrans[group][1])
plt.ylim(yran[0], yran[1])
plt.ylabel(ytit, fontsize=15)
if group == 3:
plt.xlabel(xtit, fontsize=15, labelpad=10)
if ystyle == 'log':
plt.yscale('log')
# tick formatting
plt.minorticks_on()
plt.tick_params(which='major', length=10, pad=5)
plt.tick_params(which='minor', length=5)
if waveunit_out == 'micron':
xticks = np.arange(np.around(xrans[group][0]), np.around(xrans[group][1]), 1)
plt.xticks(xticks, fontsize=10)
elif waveunit_out == 'Angstrom':
xticks = np.arange(math.floor(xrans[group][0]/1000.0)*1000,
(math.floor(xrans[group][1]/1000.0)*1000)+1000, 10000)
plt.xticks(xticks, fontsize=10)
if fluxunit_out != 'fnu':
# this will fail if fluxes are very low (<~1e-10)
plt.yticks(np.arange(yran[0], yran[1],
np.around((yran[1] - yran[0])/5.,
decimals=2)),
fontsize=10)
else:
plt.yticks()
# actually plotting
plt.plot(MIRgdlambda, MIRgdflux, label='Data',
color=dcolor)
plt.plot(MIRgdlambda, MIRcontinuum, label='Model',
color='red')
if 'global_ext_model' in q3di.argscontfit:
for i in np.arange(0,len(comp_best_fit.keys())-2,1):
plt.plot(MIRgdlambda,
np.multiply(comp_best_fit[list(comp_best_fit.keys())[i]],
np.multiply(comp_best_fit[list(comp_best_fit.keys())[-2]],
comp_best_fit[list(comp_best_fit.keys())[-1]])),
label=list(comp_best_fit.keys())[i],linestyle='--',alpha=0.5)
else:
for comp_i in comp_best_fit.keys():
if 'ext' not in comp_i and 'abs' not in comp_i:
spec_out = comp_best_fit[comp_i]
if comp_i+'_ext' in comp_best_fit.keys():
spec_out *= comp_best_fit[comp_i+'_ext']
if comp_i+'_abs' in comp_best_fit.keys():
spec_out *= comp_best_fit[comp_i+'_abs']
plt.plot(MIRgdlambda, spec_out, label=comp_i,linestyle='--',alpha=0.5)
if group == 1:
ax.legend(loc='upper right',bbox_to_anchor=(1.22, 1),prop={'size': 10})
# more formatting
plt.subplots_adjust(hspace=0.25)
plt.tight_layout(pad=15)
plt.gcf().subplots_adjust(bottom=0.1)
plt.gcf().subplots_adjust(right=0.85)
if title is not None:
plt.suptitle(title, fontsize=30)
if savefig and outfile is not None:
if len(outfile[0])>1:
plt.savefig(outfile[0] + '.jpg')
else:
plt.savefig(outfile + '.jpg')
def plotline(q3do, nx=1, ny=1, figsize=(16,13), line=None, center_obs=None,
center_rest=None, size=300., savefig=False, outfile=None,
specConv=None):
"""
Plot emission line fit and output to JPG
Parameters
----------
q3do : dict
contains results of fit
label=np.arrange(Nlines)
label= str(label)
line labels for plot
wave= np.arrange((Nlines), float)
rest wavelengths of lines
lineoth= np.arrange((Notherlines, Ncomp), float)
wavelengths of other lines to plot
nx # of plot columns
ny # of plot rows
outfile : str
Full path and name of output plot.
"""
ncomp = q3do.maxncomp
colors = ['Magenta', 'Green', 'Orange', 'Teal']
wave = q3do.wave
spectot = q3do.spec
specstars = q3do.cont_dat
modstars = q3do.cont_fit
modlines = q3do.line_fit
modtot = modstars + modlines
# To-do: Allow output wavelengths in Angstrom
#'waveunit_out' = 'micron'
# if 'waveunit_out' in pltpar:
# if pltpar['waveunit_out = 'Angstrom':
# waveunit_out = 'Angstrom'
# To-do: Get masking code from pltcont
# lines
linelist = q3do.linelist['lines']
linelabel = q3do.linelist['name']
linetext = q3do.linelist['linelab']
# Sort in wavelength order
isortlam = np.argsort(linelist)
linelist = linelist[isortlam]
linelabel = linelabel[isortlam]
linetext = linetext[isortlam]
#
# Plotting parameters
#
# Look for line list, then determine center of plot window from fitted
# wavelength
if line is not None:
sub_linlab = line
linwav = np.empty(len(sub_linlab), dtype='float32')
for i in range(0, len(sub_linlab)):
# Get wavelength from zeroth component
if sub_linlab[i] != '':
lmline = lmlabel(sub_linlab[i])
# if ncomp > 0
if f'{lmline.lmlabel}_0_cwv' in q3do.param.keys():
linwav[i] = q3do.param[f'{lmline.lmlabel}_0_cwv']
# otherwise
else:
idx = np.where(q3do.linelist['name'] == sub_linlab[i])
if len(idx) > 0:
linwav[i] = q3do.linelist['lines'][idx] * \
(1. + q3do.zstar)
else:
raise InitializationError(f'Line {sub_linlab[i]} not fit.')
else:
linwav[i] = 0.
# If linelist not present, get cwavelength enter of plot window from list
# first option: wavelength center specified in observed (plotted) frame
elif center_obs is not None:
linwav = np.array(center_obs)
# second option: wavelength center specified in rest frame, then converted
# to observed (plotted) frame
elif center_rest is not None:
linwav = np.array(center_rest) * q3do.zstar
else:
raise InitializationError('LINE, CENTER_OBS, or CENTER_REST ' +
'list not given in ARGSPLTLIN dictionary')
nlin = len(linwav)
# Size of plot in wavelength, in observed frame
# case of single size for all panels
if isinstance(size, float):
size = np.full(nlin, size) # default size currently 300 A ... fix for
# case of array of sizes
else:
size = np.array(size)
# other units!
off = np.array([-1.*size/2., size/2.])
off = off.transpose()
plt.style.use('dark_background')
fig = plt.figure(figsize=figsize)
for i in range(0, nlin):
outer = gridspec.GridSpec(ny, nx, wspace=0.2, hspace=0.2)
inner = \
gridspec.GridSpecFromSubplotSpec(2, 1,
subplot_spec=outer[i],
wspace=0.1, hspace=0,
height_ratios=[4, 2],
width_ratios=None)
# create xran and ind
linwavtmp = linwav[i]
offtmp = off[i, :]
xran = linwavtmp + offtmp
ind = np.array([0])
for h in range(0, len(wave)):
if wave[h] > xran[0] and wave[h] < xran[1]:
ind = np.append(ind, h)
ind = np.delete(ind, [0])
ct = len(ind)
if ct > 0:
# create subplots
ax0 = plt.Subplot(fig, inner[0])
ax1 = plt.Subplot(fig, inner[1])
fig.add_subplot(ax0)
fig.add_subplot(ax1)
# create x-ticks
xticks = np.linspace(xran[0],xran[1],num=5,endpoint=False)
xticks = np.delete(xticks, [0])
# if waveunit_out == 'Angstrom':
# xticks = xticks * 1.E4
# create minor x-ticks
xmticks = np.linspace(xran[0],xran[1],num=25,endpoint=False)
xmticks = np.delete(xmticks, [0])
# if waveunit_out == 'Angstrom':
# xmticks = xticks * 1.E4
# set ticks
ax0.set_xticks(xticks)
ax1.set_xticks(xticks)
ax0.set_xticks(xmticks, minor=True)
ax1.set_xticks(xmticks, minor=True)
ax0.tick_params('x', which='major', direction='in', length=7,
width=2, color='white')
ax0.tick_params('x', which='minor', direction='in', length=5,
width=1, color='white')
ax1.tick_params('x', which='major', direction='in', length=7,
width=2, color='white')
ax1.tick_params('x', which='minor', direction='in', length=5,
width=1, color='white')
# create yran
ydat = spectot
ymod = modtot
ydattmp = np.zeros((ct), dtype=float)
ymodtmp = np.zeros((ct), dtype=float)
for j in range(0, len(ind)):
ydattmp[j] = ydat[(ind[j])]
ymodtmp[j] = ymod[(ind[j])]
ydatmin = min(ydattmp)
ymodmin = min(ymodtmp)
if ydatmin <= ymodmin:
yranmin = ydatmin
else:
yranmin = ymodmin
ydatmax = max(ydattmp)
ymodmax = max(ymodtmp)
if ydatmax >= ymodmax:
yranmax = ydatmax
else:
yranmax = ymodmax
yran = [yranmin, yranmax]
icol = (float(i))/(float(nx))
if icol % 1 == 0:
ytit = 'Fit'
else:
ytit = ''
ax0.set(ylabel=ytit)
ax0.set_xlim([xran[0], xran[1]])
ax0.set_ylim([yran[0], yran[1]])
# plots on ax0
ax0.plot(wave, ydat, color='White', linewidth=1)
xtit = 'Observed Wavelength ($\mu$m)'
# if waveunit_out == 'Angstrom':
# xtit = 'Observed Wavelength ($\AA$)'
ytit = ''
ax0.plot(wave, ymod, color='Red', linewidth=2)
# Plot all lines visible in plot range
for j in range(0, ncomp):
ylaboff = 0.07
for k, line in enumerate(linelabel):
lmline = lmlabel(line)
if f'{lmline.lmlabel}_{j}_cwv' in q3do.param.keys():
refwav = q3do.param[f'{lmline.lmlabel}_{j}_cwv']
else:
irefwav = np.where(q3do.linelist['name'] == line)
refwav = q3do.linelist['lines'][irefwav] * \
(1. + q3do.zstar)
if refwav >= xran[0] and refwav <= xran[1]:
if f'{lmline.lmlabel}_{j}_cwv' in \
q3do.param.keys():
flux = cmplin(q3do, line, j, velsig=True)
if specConv is not None:
conv = specConv.spect_convolver(wave, flux, refwav)
else:
conv = flux
ax0.plot(wave, yran[0] + conv, color=colors[j],
linewidth=2, linestyle='dashed')
ax0.annotate(linetext[k], (0.05, 1. - ylaboff),
xycoords='axes fraction',
va='center', fontsize=15)
ylaboff += 0.07
# if nmasked > 0:
# for r in range(0,nmasked):
# ax0.plot([masklam[r,0], masklam[r,1]], [yran[0], yran[0]],linewidth=8, color='Cyan')
# set new value for yran
ydat = specstars
ymod = modstars
ydattmp = np.zeros((len(ind)), dtype=float)
ymodtmp = np.zeros((len(ind)), dtype=float)
for j in range(0, len(ind)):
ydattmp[j] = ydat[(ind[j])]
ymodtmp[j] = ymod[(ind[j])]
ydatmin = min(ydattmp)
ymodmin = min(ymodtmp)
if ydatmin <= ymodmin:
yranmin = ydatmin
else:
yranmin = ymodmin
ydatmax = max(ydattmp)
ymodmax = max(ymodtmp)
if ydatmax >= ymodmax:
yranmax = ydatmax
else:
yranmax = ymodmax
yran = [yranmin, yranmax]
if icol % 1 == 0:
ytit = 'Residual'
else:
ytit = ''
ax1.set(ylabel=ytit)
# plots on ax1
ax1.set_xlim([xran[0], xran[1]])
ax1.set_ylim([yran[0], yran[1]])
ax1.plot(wave, ydat, linewidth=1)
ax1.plot(wave, ymod, color='Red')
# title
xtit = 'Observed Wavelength ($\mu$m)'
# if waveunit_out == 'Angstrom':
# xtit = 'Observed Wavelength ($\AA$)'
fig.suptitle(xtit, fontsize=25)
if savefig and outfile is not None:
if len(outfile[0])>1:
fig.savefig(outfile[0] + '.jpg')
else:
fig.savefig(outfile + '.jpg')
def adjust_ax(ax, fig, fs=20, minor=False):
'''CB: Function defined to adjust the sizes of xlabel, ylabel, and the ticklabels (in an inelegant way for the latter)
Parameters
-----
ax: matplotlib axis object
ax object of the plot you want to adjust
fig: matplotlib fig object
fig object that contains the ax object
returns
-------
Nothing
'''
fig.canvas.draw()
xlabel = ax.get_xlabel()
ylabel = ax.get_ylabel()
ax.set_xlabel(xlabel, fontsize=fs)
ax.set_ylabel(ylabel, fontsize=fs)
ax.tick_params(labelsize=fs-3)
# -- Trying to prune xtickslabels if increasing the fontsize made them overlap
xticks_old = ax.get_xticks()
if minor:
xticks_old = ax.get_xticks(minor=True)
xfigsize = fig.get_size_inches()[0] # in inches
textstrlen = len(ax.get_xticklabels()[0]._text.replace('\\mathdefault', '')) # length of tick labels depends on nr of decimals specified
textwidth_inch = textstrlen * (fs-3)*0.7 / 72. # Assume width of number in text = 0.7* height. Matplotlib uses 72 Points per inch (ppi): https://stackoverflow.com/questions/47633546/relationship-between-dpi-and-figure-size
if (len(xticks_old)+1)*textwidth_inch > 0.9* xfigsize * ax.get_position().width:
xticks_new = np.array([])
for i in range(len(xticks_old)):
if i%2==1:
xticks_new = np.append(xticks_new, xticks_old[i])
if not minor:
ax.set_xticks(xticks_new, fontsize=fs-3)
else:
ax.set_xticks(xticks_new, fontsize=fs-3, minor=True)
ax.set_xticklabels(ax.get_xticks(), fontsize=fs-3)
ax.tick_params(axis='x', which='both', labelsize=fs-3)
fig.tight_layout()
def plotdecomp(q3do, q3di, savefig=True, outfile=None, templ_mask=[], do_lines=False, show=False,
mode='light', ymin=-1, ymax=-1, try_adjust_ax=True):
wave = q3do.wave
specstars = q3do.cont_dat
modstars = q3do.cont_fit
MIRgdlambda = wave
MIRgdflux = q3do.spec
MIRcontinuum = modstars
if outfile is None:
outfile=q3do.filelab + '_decomp'
if do_lines:
plotquest(q3do.wave, q3do.spec, q3do.cont_fit, q3do.ct_coeff, q3di, zstar=q3do.zstar, savefig=savefig, outfile=outfile,
templ_mask=templ_mask, lines=q3do.linelist['lines'], linespec=q3do.line_fit, show=show, mode=mode, ymin=ymin, ymax=ymax,
try_adjust_ax=try_adjust_ax, row=q3do.row, col=q3do.col)
else:
plotquest(q3do.wave, q3do.spec, q3do.cont_fit, q3do.ct_coeff, q3di, zstar=q3do.zstar, savefig=savefig, outfile=outfile,
templ_mask=templ_mask, show=show, mode=mode, ymin=ymin, ymax=ymax, try_adjust_ax=try_adjust_ax, row=q3do.row, col=q3do.col)
def plotquest(MIRgdlambda, MIRgdflux, MIRcontinuum, ct_coeff, q3di, zstar=0.,
savefig=True, outfile=None, templ_mask=[], lines=[], linespec=[], show=False,
mode='light', ymin=-1, ymax=-1, try_adjust_ax=True, row=-1, col=-1):
# dark mode just for fun:
if mode == 'dark':
pltstyle = 'dark_background'
dcolor = 'w'
else:
pltstyle = 'seaborn-v0_8-ticks'
dcolor = 'k'
plt.style.use(pltstyle)
# CB: Otherwise the background becomes black and the axes ticks
# unreadable when saving the figure
if mode == 'light':
rcParams['savefig.facecolor'] = 'white'
comp_best_fit = ct_coeff['comp_best_fit']
plot_noext = False # Remove dust contribution and plot intrinstic components
if 'plot_decomp' in q3di.argscontfit:
config_file = readcf(q3di.argscontfit['config_file'])
global_extinction = False
for key in config_file:
try:
if 'global' in config_file[key][3]:
global_extinction = True
except:
continue
fig = plt.figure(figsize=(6, 9))
gs = fig.add_gridspec(6,1, top=0.95, bottom=0.08, left=0.2)
ax1 = fig.add_subplot(gs[:5, :])
ax1.plot(MIRgdlambda, MIRgdflux,color='black')
if len(lines)==0:
ax1.plot(MIRgdlambda, MIRcontinuum, color='r')
else:
ax1.plot(MIRgdlambda, MIRcontinuum + linespec, color='darkorange')
if len(templ_mask)>0:
MIRgdlambda_temp = MIRgdlambda[templ_mask]
else:
MIRgdlambda_temp = MIRgdlambda
if len(lines)>0:
for line_i in lines:
ax1.axvline(line_i * (1. + zstar), color='grey', linestyle='--', alpha=0.7, zorder=0)
#ax1.axvspan(line_i-max(q3di.siglim_gas), line_i+max(q3di.siglim_gas))
ax1.plot(MIRgdlambda, linespec, color='r', linestyle='-', alpha=0.7, linewidth=1.5)
colour_list = ['dodgerblue', 'mediumblue', 'salmon', 'palegreen', 'orange', 'purple', 'forestgreen', 'darkgoldenrod', 'mediumblue', 'magenta', 'plum', 'yellowgreen']
if global_extinction:
str_global_ext = list(comp_best_fit.keys())[-2]
str_global_ice = list(comp_best_fit.keys())[-1]
# global_ext is a multi-dimensional array
if len(comp_best_fit[str_global_ext].shape) > 1:
comp_best_fit[str_global_ext] = comp_best_fit[str_global_ext] [:,0,0]
# global_ice is a multi-dimensional array
if len(comp_best_fit[str_global_ice].shape) > 1:
comp_best_fit[str_global_ice] = comp_best_fit[str_global_ice] [:,0,0]
count = 0
for i, el in enumerate(comp_best_fit):
if (el != str_global_ext) and (el != str_global_ice):
if len(comp_best_fit[el].shape) > 1: # component is a multi-dimensional array
comp_best_fit[el] = comp_best_fit[el] [:,0,0]
if plot_noext:
if count>len(colour_list)-1:
ax1.plot(MIRgdlambda_temp, comp_best_fit[el]/comp_best_fit[str_global_ext]/comp_best_fit[str_global_ice], label=el,linestyle='--',alpha=0.5)
else:
ax1.plot(MIRgdlambda_temp, comp_best_fit[el]/comp_best_fit[str_global_ext]/comp_best_fit[str_global_ice], color=colour_list[count], label=el,linestyle='--',alpha=0.5)
else:
if count>len(colour_list)-1:
ax1.plot(MIRgdlambda_temp, comp_best_fit[el], label=el,linestyle='--',alpha=0.5)
else:
ax1.plot(MIRgdlambda_temp, comp_best_fit[el], color=colour_list[count], label=el,linestyle='--',alpha=0.5)
count += 1
else:
count = 0
for i, el in enumerate(comp_best_fit):
if len(comp_best_fit[el].shape) > 1:
comp_best_fit[el] = comp_best_fit[el] [:,0,0]
if not ('_ext' in el or '_abs' in el):
spec_i = comp_best_fit[el]
label_i = el
if not plot_noext:
if el+'_ext' in comp_best_fit.keys():
spec_i = spec_i*comp_best_fit[el+'_ext']
if el+'_abs' in comp_best_fit.keys():
spec_i = spec_i*comp_best_fit[el+'_abs']
if count>len(colour_list)-1:
ax1.plot(MIRgdlambda_temp, spec_i, label=label_i,linestyle='--',alpha=0.5)
else:
ax1.plot(MIRgdlambda_temp, spec_i, label=label_i, color=colour_list[i], linestyle='--',alpha=0.5)
count += 1
ax1.legend(ncol=2)
ax1.set_xscale('log')
ax1.set_yscale('log')
#ax1.set_ylim(1e-5,1e2)
ax1.set_ylabel('Flux')
if try_adjust_ax:
adjust_ax(ax1, fig, minor=True)
ax1.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False) # turn off major & minor ticks on the x-axis
ax2 = fig.add_subplot(gs[5:6, :], sharex=ax1)
if len(lines)>=1:
ax1.set_ylim(min(MIRcontinuum)/1e3, 3*max(MIRcontinuum + linespec))
ax2.plot(MIRgdlambda,MIRgdflux/(MIRcontinuum + linespec),color='black')
else:
ax1.set_ylim(min(MIRcontinuum)/1e3, 3*max(max(MIRgdflux), max(MIRcontinuum)))
ax2.plot(MIRgdlambda,MIRgdflux/MIRcontinuum,color='black')
if ymin>0.:
ax1.set_ylim(bottom=ymin)
if ymax>0.:
ax1.set_ylim(top=ymax)
ax2.axhline(1, color='grey', linestyle='--', alpha=0.7, zorder=0)
ax2.set_ylabel('Data/Model')
ax2.set_xlabel('Wavelength [micron]')
from matplotlib.ticker import ScalarFormatter
ax2.xaxis.set_major_formatter(ScalarFormatter())
ax2.xaxis.set_minor_formatter(ScalarFormatter())
ax2.ticklabel_format(style='plain')
if row>-1 and col>-1:
ax1.set_title('Spaxel [{}, {}]'.format(col, row), fontsize=20)
gs.update(wspace=0.0, hspace=0.05)
adjust_ax(ax2, fig)
if savefig and outfile is not None:
if len(outfile[0])>1:
plt.savefig(outfile[0]+'.jpg')
else:
plt.savefig(outfile+'.jpg')
else:
fig.savefig(outfile + '.jpg')
if show:
plt.show()
|
Q3DREPO_NAMEq3dfitPATH_START.@q3dfit_extracted@q3dfit-main@q3dfit@plot.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "LSSTDESC/Spectractor",
"repo_path": "Spectractor_extracted/Spectractor-master/spectractor/__init__.py",
"type": "Python"
}
|
LSSTDESCREPO_NAMESpectractorPATH_START.@Spectractor_extracted@Spectractor-master@spectractor@__init__.py@.PATH_END.py
|
|
{
"filename": "GalRotpy-checkpoint.ipynb",
"repo_name": "andresGranadosC/GalRotpy",
"repo_path": "GalRotpy_extracted/GalRotpy-master/notebook/.ipynb_checkpoints/GalRotpy-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
```python
from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons, TextBox # Matplotlib widgets
import matplotlib.pylab as plt # Plotting interface
import numpy as np
from galpy.potential import MiyamotoNagaiPotential, NFWPotential, RazorThinExponentialDiskPotential, BurkertPotential # GALPY potentials
from galpy.potential import calcRotcurve # composed rotation curve calculation for plotting
from astropy import units # Physical/real units data managing
from astropy import table as Table # For fast and easy reading / writing with tables using numpy library
import emcee
import corner
import time
import pandas as pd
import multiprocessing as mp
from scipy.optimize import fsolve
import ipywidgets as widgets
```
galpyWarning: libgalpy C extension module not loaded, because of error 'dlopen(/Library/Python/3.7/site-packages/libgalpy.cpython-37m-darwin.so, 6): Library not loaded: @rpath/libgsl.25.dylib
Referenced from: /Library/Python/3.7/site-packages/libgalpy.cpython-37m-darwin.so
Reason: image not found'
```python
def boolString_to_bool(boolString):
if boolString == 'True':
return True
elif boolString == 'False':
return False
else:
return None
```
```python
init_guess_params = Table.Table.read('../M33_guess_params.txt', format='ascii.tab')
```
```python
init_guess_params
```
<i>Table length=6</i>
<table id="table4790125960" class="table-striped table-bordered table-condensed">
<thead><tr><th>component</th><th>mass</th><th>a (kpc)</th><th>b (kpc)</th><th>checked</th></tr></thead>
<thead><tr><th>str12</th><th>float64</th><th>float64</th><th>float64</th><th>str5</th></tr></thead>
<tr><td>BULGE</td><td>110000000.0</td><td>0.0</td><td>0.495</td><td>False</td></tr>
<tr><td>THIN DISC</td><td>38837296969.03567</td><td>10.069858729947157</td><td>2.499954901776149</td><td>True</td></tr>
<tr><td>THICK DISC</td><td>39000000000.0</td><td>2.6</td><td>0.8</td><td>False</td></tr>
<tr><td>EXP. DISC</td><td>500.0</td><td>5.3</td><td>0.0</td><td>False</td></tr>
<tr><td>DARK HALO</td><td>1196921394849.7383</td><td>18.682199726086495</td><td>0.0</td><td>True</td></tr>
<tr><td>BURKERT HALO</td><td>8000000.0</td><td>20.0</td><td>0.0</td><td>False</td></tr>
</table>
```python
c_bulge, amp1, a1, b1, include_bulge = init_guess_params[0]
c_tn, amp2, a2, b2, include_tn = init_guess_params[1]
c_tk, amp3, a3, b3, include_tk = init_guess_params[2]
c_ex, amp4, h_r, vertical_ex, include_ex = init_guess_params[3]
c_dh, amp5, a5, b5, include_dh = init_guess_params[4]
c_bh, amp6, a6, b6, include_bh = init_guess_params[5]
```
```python
visibility = [ boolString_to_bool(include_bulge), boolString_to_bool(include_tn), boolString_to_bool(include_tk), boolString_to_bool(include_ex), boolString_to_bool(include_dh), boolString_to_bool(include_bh)]
```
```python
input_params=Table.Table.read('../input_params.txt', format='ascii.tab') # Initial parameters
input_params
```
---------------------------------------------------------------------------
FileNotFoundError Traceback (most recent call last)
<ipython-input-2-2b535d68edb6> in <module>
----> 1 input_params=Table.Table.read('../input_params.txt', format='ascii.tab') # Initial parameters
2 input_params
/Library/Python/3.7/site-packages/astropy/table/connect.py in __call__(self, *args, **kwargs)
50 def __call__(self, *args, **kwargs):
51 cls = self._cls
---> 52 out = registry.read(cls, *args, **kwargs)
53
54 # For some readers (e.g., ascii.ecsv), the returned `out` class is not
/Library/Python/3.7/site-packages/astropy/io/registry.py in read(cls, format, *args, **kwargs)
521
522 reader = get_reader(format, cls)
--> 523 data = reader(*args, **kwargs)
524
525 if not isinstance(data, cls):
/Library/Python/3.7/site-packages/astropy/io/ascii/connect.py in io_read(format, filename, **kwargs)
16 format = re.sub(r'^ascii\.', '', format)
17 kwargs['format'] = format
---> 18 return read(filename, **kwargs)
19
20
/Library/Python/3.7/site-packages/astropy/io/ascii/ui.py in read(table, guess, **kwargs)
285 # through below to the non-guess way so that any problems result in a
286 # more useful traceback.
--> 287 dat = _guess(table, new_kwargs, format, fast_reader)
288 if dat is None:
289 guess = False
/Library/Python/3.7/site-packages/astropy/io/ascii/ui.py in _guess(table, read_kwargs, format, fast_reader)
445
446 reader.guessing = True
--> 447 dat = reader.read(table)
448 _read_trace.append({'kwargs': copy.deepcopy(guess_kwargs),
449 'Reader': reader.__class__,
/Library/Python/3.7/site-packages/astropy/io/ascii/fastbasic.py in read(self, table)
115 data_start=self.data_start,
116 fill_extra_cols=self.fill_extra_cols,
--> 117 **self.kwargs)
118 conversion_info = self._read_header()
119 self.check_header()
astropy/io/ascii/cparser.pyx in astropy.io.ascii.cparser.CParser.__cinit__()
astropy/io/ascii/cparser.pyx in astropy.io.ascii.cparser.CParser.setup_tokenizer()
astropy/io/ascii/cparser.pyx in astropy.io.ascii.cparser.FileString.__cinit__()
FileNotFoundError: [Errno 2] No such file or directory: '../input_params.txt'
```python
tt=Table.Table.read('../M31_rot_curve.txt', format='ascii.tab') # Rotation curve
x_offset = 0.0 # It defines a radial coordinate offset as user input
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
# Real data:
r_data=tt['r']-x_offset # The txt file must contain the radial coordinate values in kpc
v_c_data=tt['vel'] # velocity in km/s
v_c_err_data = tt['e_vel'] # and velocity error in km/s
# This loop is needed since galpy fails when r=0 or very close to 0
for i in range(len(r_data)):
if r_data[i]<1e-3:
r_data[i]=1e-3
```
```python
tt
```
<i>Table length=28</i>
<table id="table4731257240" class="table-striped table-bordered table-condensed">
<thead><tr><th>r</th><th>r2</th><th>vel</th><th>e_vel</th></tr></thead>
<thead><tr><th>float64</th><th>float64</th><th>float64</th><th>float64</th></tr></thead>
<tr><td>25.0</td><td>5.68</td><td>235.5</td><td>17.8</td></tr>
<tr><td>30.0</td><td>6.81</td><td>242.9</td><td>0.8</td></tr>
<tr><td>35.0</td><td>7.95</td><td>251.1</td><td>0.7</td></tr>
<tr><td>40.0</td><td>9.08</td><td>262.0</td><td>2.1</td></tr>
<tr><td>45.0</td><td>10.22</td><td>258.9</td><td>6.9</td></tr>
<tr><td>50.0</td><td>11.35</td><td>255.1</td><td>5.7</td></tr>
<tr><td>55.0</td><td>12.49</td><td>251.8</td><td>17.1</td></tr>
<tr><td>60.0</td><td>13.62</td><td>252.1</td><td>7.4</td></tr>
<tr><td>65.0</td><td>14.76</td><td>251.0</td><td>18.6</td></tr>
<tr><td>70.0</td><td>15.89</td><td>245.5</td><td>28.8</td></tr>
<tr><td>...</td><td>...</td><td>...</td><td>...</td></tr>
<tr><td>112.5</td><td>25.54</td><td>227.4</td><td>28.8</td></tr>
<tr><td>117.0</td><td>26.56</td><td>225.6</td><td>28.8</td></tr>
<tr><td>121.5</td><td>27.58</td><td>224.4</td><td>28.8</td></tr>
<tr><td>126.0</td><td>28.6</td><td>222.3</td><td>28.8</td></tr>
<tr><td>130.5</td><td>29.62</td><td>222.1</td><td>28.8</td></tr>
<tr><td>135.0</td><td>30.65</td><td>224.9</td><td>28.8</td></tr>
<tr><td>139.5</td><td>31.67</td><td>228.1</td><td>28.8</td></tr>
<tr><td>144.0</td><td>32.69</td><td>231.1</td><td>28.8</td></tr>
<tr><td>148.5</td><td>33.71</td><td>230.4</td><td>28.8</td></tr>
<tr><td>153.0</td><td>34.73</td><td>226.8</td><td>28.8</td></tr>
</table>
```python
lista=np.linspace(0.001, 1.02*np.max(r_data), 10*len(r_data)) # radial coordinate for the rotation curve calculation
lista
```
array([1.00000000e-03, 5.60351254e-01, 1.11970251e+00, 1.67905376e+00,
2.23840502e+00, 2.79775627e+00, 3.35710753e+00, 3.91645878e+00,
4.47581004e+00, 5.03516129e+00, 5.59451254e+00, 6.15386380e+00,
6.71321505e+00, 7.27256631e+00, 7.83191756e+00, 8.39126882e+00,
8.95062007e+00, 9.50997133e+00, 1.00693226e+01, 1.06286738e+01,
1.11880251e+01, 1.17473763e+01, 1.23067276e+01, 1.28660789e+01,
1.34254301e+01, 1.39847814e+01, 1.45441326e+01, 1.51034839e+01,
1.56628351e+01, 1.62221864e+01, 1.67815376e+01, 1.73408889e+01,
1.79002401e+01, 1.84595914e+01, 1.90189427e+01, 1.95782939e+01,
2.01376452e+01, 2.06969964e+01, 2.12563477e+01, 2.18156989e+01,
2.23750502e+01, 2.29344014e+01, 2.34937527e+01, 2.40531039e+01,
2.46124552e+01, 2.51718065e+01, 2.57311577e+01, 2.62905090e+01,
2.68498602e+01, 2.74092115e+01, 2.79685627e+01, 2.85279140e+01,
2.90872652e+01, 2.96466165e+01, 3.02059677e+01, 3.07653190e+01,
3.13246703e+01, 3.18840215e+01, 3.24433728e+01, 3.30027240e+01,
3.35620753e+01, 3.41214265e+01, 3.46807778e+01, 3.52401290e+01,
3.57994803e+01, 3.63588315e+01, 3.69181828e+01, 3.74775341e+01,
3.80368853e+01, 3.85962366e+01, 3.91555878e+01, 3.97149391e+01,
4.02742903e+01, 4.08336416e+01, 4.13929928e+01, 4.19523441e+01,
4.25116953e+01, 4.30710466e+01, 4.36303978e+01, 4.41897491e+01,
4.47491004e+01, 4.53084516e+01, 4.58678029e+01, 4.64271541e+01,
4.69865054e+01, 4.75458566e+01, 4.81052079e+01, 4.86645591e+01,
4.92239104e+01, 4.97832616e+01, 5.03426129e+01, 5.09019642e+01,
5.14613154e+01, 5.20206667e+01, 5.25800179e+01, 5.31393692e+01,
5.36987204e+01, 5.42580717e+01, 5.48174229e+01, 5.53767742e+01,
5.59361254e+01, 5.64954767e+01, 5.70548280e+01, 5.76141792e+01,
5.81735305e+01, 5.87328817e+01, 5.92922330e+01, 5.98515842e+01,
6.04109355e+01, 6.09702867e+01, 6.15296380e+01, 6.20889892e+01,
6.26483405e+01, 6.32076918e+01, 6.37670430e+01, 6.43263943e+01,
6.48857455e+01, 6.54450968e+01, 6.60044480e+01, 6.65637993e+01,
6.71231505e+01, 6.76825018e+01, 6.82418530e+01, 6.88012043e+01,
6.93605556e+01, 6.99199068e+01, 7.04792581e+01, 7.10386093e+01,
7.15979606e+01, 7.21573118e+01, 7.27166631e+01, 7.32760143e+01,
7.38353656e+01, 7.43947168e+01, 7.49540681e+01, 7.55134194e+01,
7.60727706e+01, 7.66321219e+01, 7.71914731e+01, 7.77508244e+01,
7.83101756e+01, 7.88695269e+01, 7.94288781e+01, 7.99882294e+01,
8.05475806e+01, 8.11069319e+01, 8.16662832e+01, 8.22256344e+01,
8.27849857e+01, 8.33443369e+01, 8.39036882e+01, 8.44630394e+01,
8.50223907e+01, 8.55817419e+01, 8.61410932e+01, 8.67004444e+01,
8.72597957e+01, 8.78191470e+01, 8.83784982e+01, 8.89378495e+01,
8.94972007e+01, 9.00565520e+01, 9.06159032e+01, 9.11752545e+01,
9.17346057e+01, 9.22939570e+01, 9.28533082e+01, 9.34126595e+01,
9.39720108e+01, 9.45313620e+01, 9.50907133e+01, 9.56500645e+01,
9.62094158e+01, 9.67687670e+01, 9.73281183e+01, 9.78874695e+01,
9.84468208e+01, 9.90061720e+01, 9.95655233e+01, 1.00124875e+02,
1.00684226e+02, 1.01243577e+02, 1.01802928e+02, 1.02362280e+02,
1.02921631e+02, 1.03480982e+02, 1.04040333e+02, 1.04599685e+02,
1.05159036e+02, 1.05718387e+02, 1.06277738e+02, 1.06837090e+02,
1.07396441e+02, 1.07955792e+02, 1.08515143e+02, 1.09074495e+02,
1.09633846e+02, 1.10193197e+02, 1.10752548e+02, 1.11311900e+02,
1.11871251e+02, 1.12430602e+02, 1.12989953e+02, 1.13549305e+02,
1.14108656e+02, 1.14668007e+02, 1.15227358e+02, 1.15786710e+02,
1.16346061e+02, 1.16905412e+02, 1.17464763e+02, 1.18024115e+02,
1.18583466e+02, 1.19142817e+02, 1.19702168e+02, 1.20261520e+02,
1.20820871e+02, 1.21380222e+02, 1.21939573e+02, 1.22498925e+02,
1.23058276e+02, 1.23617627e+02, 1.24176978e+02, 1.24736330e+02,
1.25295681e+02, 1.25855032e+02, 1.26414384e+02, 1.26973735e+02,
1.27533086e+02, 1.28092437e+02, 1.28651789e+02, 1.29211140e+02,
1.29770491e+02, 1.30329842e+02, 1.30889194e+02, 1.31448545e+02,
1.32007896e+02, 1.32567247e+02, 1.33126599e+02, 1.33685950e+02,
1.34245301e+02, 1.34804652e+02, 1.35364004e+02, 1.35923355e+02,
1.36482706e+02, 1.37042057e+02, 1.37601409e+02, 1.38160760e+02,
1.38720111e+02, 1.39279462e+02, 1.39838814e+02, 1.40398165e+02,
1.40957516e+02, 1.41516867e+02, 1.42076219e+02, 1.42635570e+02,
1.43194921e+02, 1.43754272e+02, 1.44313624e+02, 1.44872975e+02,
1.45432326e+02, 1.45991677e+02, 1.46551029e+02, 1.47110380e+02,
1.47669731e+02, 1.48229082e+02, 1.48788434e+02, 1.49347785e+02,
1.49907136e+02, 1.50466487e+02, 1.51025839e+02, 1.51585190e+02,
1.52144541e+02, 1.52703892e+02, 1.53263244e+02, 1.53822595e+02,
1.54381946e+02, 1.54941297e+02, 1.55500649e+02, 1.56060000e+02])
```python
class MiyamotoNagaiP:
def __init__(self, dict_params):
self.amp = dict_params['amp']
self.a = dict_params['a']
self.b = dict_params['b']
def __str__(self):
return f"amp={self.amp}, a={self.a}, b={self.b}"
def __repr__(self):
return f"amp={self.amp}, a={self.a}, b={self.b}"
class MassScaleP:
def __init__(self, dict_params):
self.amp = dict_params['amp']
self.a = dict_params['a']
def __str__(self):
return f"amp={self.amp}, a={self.a}"
def __repr__(self):
return f"amp={self.amp}, a={self.a}"
```
```python
amp1 = widgets.FloatSlider(min=110000000.0*10**(-1), max=110000000.0*10**(1), step=110000000.0*0.1)
b1 = widgets.FloatSlider(min=0.495*(100-70)/100, max=0.495*(100+70)/100, step=0.495*0.1)
ui = widgets.HBox([amp1, b1])
def f(amp1, b1):
global bulge_potential
bulge_dict = {'amp': amp1, 'a':0, 'b': b1 }
bulge_potential = MiyamotoNagaiP(bulge_dict)
print((amp1, b1))
bulge_params = widgets.interactive_output(f, {'amp1': amp1, 'b1': b1})
display(ui, bulge_params)
```
HBox(children=(FloatSlider(value=11000000.0, max=1100000000.0, min=11000000.0, step=11000000.0), FloatSlider(v…
Output()
```python
amp2 = widgets.FloatSlider(min=3900000000.0*10**(-1), max=3900000000.0*10**(1), step=3900000000.0*0.1)
a2 = widgets.FloatSlider(min=5.3*(100-90)/100, max=5.3*(100+90)/100, step=5.3*0.1)
b2 = widgets.FloatSlider(min=0.25*(100-90)/100, max=0.25*(100+90)/100, step=0.25*0.1)
ui = widgets.HBox([amp2, a2, b2])
def f(amp2, a2, b2):
global thin_disk_potential
thin_disk_dict = {'amp': amp2, 'a':a2, 'b': b2 }
thin_disk_potential = MiyamotoNagaiP(thin_disk_dict)
print((amp2, a2, b2))
thin_disk_params = widgets.interactive_output(f, {'amp2': amp2, 'a2': a2, 'b2': b2})
display(ui, thin_disk_params)
```
HBox(children=(FloatSlider(value=390000000.0, max=39000000000.0, min=390000000.0, step=390000000.0), FloatSlid…
Output()
```python
amp3 = widgets.FloatSlider(min=39000000000.0*10**(-0.5), max=39000000000.0*10**(0.5), step=39000000000.0*0.1)
a3 = widgets.FloatSlider(min=2.6*(100-20)/100, max=2.6*(100+20)/100, step=2.6*0.1)
b3 = widgets.FloatSlider(min=0.8*(100-90)/100, max=0.8*(100+90)/100, step=0.8*0.1)
ui = widgets.HBox([amp3, a3, b3])
def f(amp3, a3, b3):
global thick_disk_potential
thick_disk_dict = {'amp': amp3, 'a':a3, 'b': b3 }
thick_disk_potential = MiyamotoNagaiP(thick_disk_dict)
print((amp3, a3, b3))
thick_disk_params = widgets.interactive_output(f, {'amp3': amp3, 'a3': a3, 'b3': b3})
display(ui, thin_disk_params)
```
HBox(children=(FloatSlider(value=12332882874.65668, max=123328828746.5668, min=12332882874.65668, step=3900000…
Output(outputs=({'output_type': 'stream', 'text': '(390000000.0, 0.53, 0.025)\n', 'name': 'stdout'},))
```python
amp4 = widgets.FloatSlider(min=500.0*10**(-0.5), max=500.0*10**(0.5), step=500.0*0.1)
h_r = widgets.FloatSlider(min=5.3*(100-90)/100, max=5.3*(100+90)/100, step=5.3*0.1)
ui = widgets.HBox([amp4, h_r ])
def f(amp4, h_r):
global exp_disk_potential
exp_disk_dict = {'amp': amp4, 'a': h_r}
exp_disk_potential = MassScaleP(exp_disk_dict)
print((amp4, h_r))
exp_disk_params = widgets.interactive_output(f, {'amp4': amp4, 'h_r': h_r})
display(ui, exp_disk_params)
```
HBox(children=(FloatSlider(value=158.11388300841898, max=1581.1388300841897, min=158.11388300841898, step=50.0…
Output()
```python
amp5 = widgets.FloatSlider(min=140000000000.0*10**(-1), max=140000000000.0*10**(1), step=140000000000.0*0.1)
a5 = widgets.FloatSlider(min=13*(100-90)/100, max=13*(100+90)/100, step=13*0.1)
ui = widgets.HBox([amp5, a5 ])
def f(amp5, a5):
global dark_halo_potential
dark_halo_dict = {'amp': amp5, 'a': a5}
dark_halo_potential = MassScaleP(dark_halo_dict)
print((amp5, a5))
dark_halo_params = widgets.interactive_output(f, {'amp5': amp5, 'a5': a5})
display(ui, dark_halo_params)
```
HBox(children=(FloatSlider(value=14000000000.0, max=1400000000000.0, min=14000000000.0, step=14000000000.0), F…
Output()
```python
amp6 = widgets.FloatSlider(min=8000000.0*10**(-1), max=8000000.0*10**(1), step=8000000.0*0.1)
a6 = widgets.FloatSlider(min=20*(100-90)/100, max=20*(100+90)/100, step=20*0.1)
ui = widgets.HBox([amp6, a6 ])
def f(amp6, a6):
global burkert_halo_potential
burkert_halo_dict = {'amp': amp6, 'a': a6}
burkert_halo_potential = MassScaleP(burkert_halo_dict)
print((amp6, a6))
burkert_halo_params = widgets.interactive_output(f, {'amp6': amp6, 'a6': a6})
display(ui, burkert_halo_params)
```
HBox(children=(FloatSlider(value=800000.0, max=80000000.0, min=800000.0, step=800000.0), FloatSlider(value=2.0…
Output()
```python
lista=np.linspace(0.001, 1.02*np.max(r_data), 10*len(r_data))
```
```python
bulge_potential, thin_disk_potential, thick_disk_potential, exp_disk_potential, dark_halo_potential, burkert_halo_potential
```
(amp=11000000.0, a=0, b=0.1485,
amp=390000000.0, a=0.53, b=0.025,
amp=12332882874.65668, a=2.08, b=0.08,
amp=158.11388300841898, a=0.53,
amp=14000000000.0, a=1.3,
amp=800000.0, a=2.0)
```python
data_rows = [('BULGE', 110000000.0, 1.0, 0.0, 20, 0.495, 70),
('THIN DISK', 3900000000.0, 1.0, 5.3, 90, 0.25, 1),
('THICK DISK', 39000000000.0, 0.5, 2.6, 20, 0.8, 1),
('EXP DISK', 500.0, 0.5, 5.3, 90, 0.0, 0),
('DARK HALO', 140000000000.0, 1.0, 13.0, 90, 0.0, 0),
('BURKERT HALO', 8000000.0, 1.0, 20.0, 90, 0.0, 0)]
input_params = Table.Table(rows=data_rows, names=('component', 'mass', 'threshold_mass', 'a (kpc)', 'threshold_a', 'b (kpc)', 'threshold_b'))
def input_component(component, guess_mass, guess_a, guess_b):
component_mass, component_scale_a, component_scale_b = guess_mass, guess_a, guess_b
print('Set the guess parameters for', component)
try:
component_mass = float(input('Mass (in M_sun):'))
except:
print('No valid Mass for', component, '. It will be taken the default mass:', component_mass, 'M_sun')
try:
component_scale_a = float(input('Radial Scale Length (in kpc):'))
except:
print('No valid Radial Scale Length for', component, '. It will be taken the default Radial Scale Lenght:', component_scale_a, 'kpc')
if component not in ['EXP DISK', 'DARK HALO', 'BURKERT HALO' ]:
try:
component_scale_b = float(input('Vertical Scale Length (in kpc):'))
except:
print('No valid Vertical Scale Length for', component, '. It will be taken the default Vertical Scale Lenght:', component_scale_b, 'kpc')
return component_mass, component_scale_a, component_scale_b
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
x_offset = 0.0 # It defines a radial coordinate offset as user input
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
# Real data:
r_data=tt['r']-x_offset # The txt file must contain the radial coordinate values in kpc
v_c_data=tt['vel'] # velocity in km/s
v_c_err_data = tt['e_vel'] # and velocity error in km/s
# This loop is needed since galpy fails when r=0 or very close to 0
for i in range(len(r_data)):
if r_data[i]<1e-3:
r_data[i]=1e-3
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Initial parameters:
c_bulge, amp1, delta_mass_bulge, a1, delta_radial_bulge, b1, delta_vertical_bulge = input_params[0]
amp1, a1, b1 = input_component(c_bulge, amp1, a1, b1)
#print(mass, radial, vertical)
c_tn, amp2, delta_mass_tn, a2, delta_radial_tn, b2, delta_vertical_tn = input_params[1]
amp2, a2, b2 = input_component(c_tn, amp2, a2, b2)
#print(mass, radial, vertical)
c_tk, amp3, delta_mass_tk, a3, delta_radial_tk, b3, delta_vertical_tk = input_params[2]
amp3, a3, b3 = input_component(c_tk, amp3, a3, b3)
#print(mass, radial, vertical)
c_ex, amp4, delta_mass_ex, h_r, delta_radial_ex, vertical_ex, delta_vertical_ex = input_params[3]
amp4, h_r, vertical_ex = input_component(c_ex, amp4, h_r, vertical_ex)
#print(mass, radial, vertical)
c_dh, amp5, delta_mass_dh, a5, delta_radial_dh, b5, delta_vertical_dh = input_params[4]
amp5, a5, b5 = input_component(c_dh, amp5, a5, b5)
#print(mass, radial, vertical)
c_bh, amp6, delta_mass_bh, a6, delta_radial_bh, b6, delta_vertical_bh = input_params[5]
amp6, a6, b6 = input_component(c_bh, amp6, a6, b6)
```
Set the guess parameters for BULGE
Mass (in M_sun):
No valid Mass for BULGE . It will be taken the default mass: 110000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for BULGE . It will be taken the default Radial Scale Lenght: 0.0 kpc
Vertical Scale Length (in kpc):
No valid Vertical Scale Length for BULGE . It will be taken the default Vertical Scale Lenght: 0.495 kpc
Set the guess parameters for THIN DISK
Mass (in M_sun):
No valid Mass for THIN DISK . It will be taken the default mass: 3900000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for THIN DISK . It will be taken the default Radial Scale Lenght: 5.3 kpc
Vertical Scale Length (in kpc):
No valid Vertical Scale Length for THIN DISK . It will be taken the default Vertical Scale Lenght: 0.25 kpc
Set the guess parameters for THICK DISK
Mass (in M_sun):
No valid Mass for THICK DISK . It will be taken the default mass: 39000000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for THICK DISK . It will be taken the default Radial Scale Lenght: 2.6 kpc
Vertical Scale Length (in kpc):
No valid Vertical Scale Length for THICK DISK . It will be taken the default Vertical Scale Lenght: 0.8 kpc
Set the guess parameters for EXP DISK
Mass (in M_sun):
No valid Mass for EXP DISK . It will be taken the default mass: 500.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for EXP DISK . It will be taken the default Radial Scale Lenght: 5.3 kpc
Set the guess parameters for DARK HALO
Mass (in M_sun):
No valid Mass for DARK HALO . It will be taken the default mass: 140000000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for DARK HALO . It will be taken the default Radial Scale Lenght: 13.0 kpc
Set the guess parameters for BURKERT HALO
Mass (in M_sun):
No valid Mass for BURKERT HALO . It will be taken the default mass: 8000000.0 M_sun
Radial Scale Length (in kpc):
No valid Radial Scale Length for BURKERT HALO . It will be taken the default Radial Scale Lenght: 20.0 kpc
```python
MN_Bulge_p= MiyamotoNagaiPotential(amp=bulge_potential.amp*units.Msun,
a=bulge_potential.a*units.kpc,
b=bulge_potential.b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=thin_disk_potential.amp*units.Msun,
a=thin_disk_potential.a*units.kpc,
b=thin_disk_potential.b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=thick_disk_potential.amp*units.Msun,
a=thick_disk_potential.a*units.kpc,
b=thick_disk_potential.b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
EX_Disk_p = RazorThinExponentialDiskPotential(amp=exp_disk_potential.amp*(units.Msun/(units.pc**2)),
hr=exp_disk_potential.a*units.kpc, maxiter=20, tol=0.001,
normalize=False, ro=r_0, vo=v_0, new=True, glorder=100)
NFW_p = NFWPotential(amp=dark_halo_potential.amp*units.Msun,
a=dark_halo_potential.a*units.kpc, normalize=False, ro=r_0, vo=v_0)
BK_p = BurkertPotential(amp=burkert_halo_potential.amp*units.Msun/(units.kpc)**3,
a=burkert_halo_potential.a*units.kpc, normalize=False, ro=r_0, vo=v_0)
# Circular velocities in km/s
MN_Bulge = calcRotcurve(MN_Bulge_p, lista, phi=None)*220
MN_Thin_Disk = calcRotcurve(MN_Thin_Disk_p, lista, phi=None)*220
MN_Thick_Disk = calcRotcurve(MN_Thick_Disk_p, lista, phi=None)*220
EX_Disk = calcRotcurve(EX_Disk_p, lista, phi=None)*220
NFW = calcRotcurve(NFW_p, lista, phi=None)*220
BK = calcRotcurve(BK_p, lista, phi=None)*220
# Circular velocity for the composition of 5 potentials in km/s
v_circ_comp = calcRotcurve([MN_Bulge_p,MN_Thin_Disk_p,MN_Thick_Disk_p, EX_Disk_p, NFW_p, BK_p], lista, phi=None)*220
```
```python
bulge_potential
```
amp=11000000.0, a=0, b=0.1485
```python
c_bulge, amp1, delta_mass_bulge, a1, delta_radial_bulge, b1, delta_vertical_bulge
```
('BULGE', 110000000.0, 1.0, 0.0, 20, 0.495, 70)
```python
c_dh, amp5, delta_mass_dh, a5, delta_radial_dh, b5, delta_vertical_dh
```
('DARK HALO', 140000000000.0, 1.0, 13.0, 90, 0.0, 0)
```python
from scipy.optimize import curve_fit
```
# Bulge_NFW_potentials
```python
def Bulge_NFW_potentials( r, delta_r, bulge_amp, bulge_a, bulge_b, dark_halo_amp, dark_halo_a ):
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
MN_Bulge_p= MiyamotoNagaiPotential(amp=bulge_amp*units.Msun,
a=bulge_a*units.kpc,
b=bulge_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
NFW_p = NFWPotential(amp=dark_halo_amp*units.Msun,
a=dark_halo_a*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
v_circ_comp = calcRotcurve([MN_Bulge_p, NFW_p], r-delta_r , phi=None)*220
return v_circ_comp
bounds = (( -10, amp1/(10**delta_mass_bulge), a1, b1*(1-0.01*delta_vertical_bulge), amp5/(10*delta_mass_dh), a5*(1-0.01*delta_radial_dh) ),
( 10, amp1*(10**delta_mass_bulge), 0.1*delta_radial_bulge, b1*(1+0.01*delta_vertical_bulge), amp5*(10**delta_mass_dh), a5*(1+0.01*delta_radial_dh) ) )
bounds
```
((-10,
11000000.0,
0.0,
0.14849999999999997,
14000000000.0,
1.2999999999999998),
(10, 1100000000.0, 2.0, 0.8415, 1400000000000.0, 24.7))
```python
popt, pcov = curve_fit(Bulge_NFW_potentials,
r_data, v_c_data.data,
p0=[0, amp1, a1, b1, amp5, a5 ],
bounds=bounds )
print(popt, np.sqrt(np.diag(pcov)))
plt.scatter( r_data, v_c_data.data )
plt.plot( r_data, Bulge_NFW_potentials( r_data, *popt ) )
```
[-3.52351441e-01 1.73198523e+08 8.56968382e-01 5.44170287e-01
1.87733502e+11 1.09055832e+01] [3.00891852e-01 9.65470703e+08 2.41854286e+03 2.41447933e+03
3.41114598e+10 1.85516675e+00]
[<matplotlib.lines.Line2D at 0x1229b5c88>]
# Bulge_ThinDisk_NFW_potentials
```python
c_tn, amp2, delta_mass_tn, a2, delta_radial_tn, b2, delta_vertical_tn
```
('THIN DISK', 3900000000.0, 1.0, 5.3, 90, 0.25, 1)
```python
def Bulge_ThinDisk_NFW_potentials( r, delta_r, bulge_amp, bulge_a, bulge_b, tn_amp, tn_a, tn_b, dark_halo_amp, dark_halo_a ):
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
MN_Bulge_p= MiyamotoNagaiPotential(amp=bulge_amp*units.Msun,
a=bulge_a*units.kpc,
b=bulge_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=tn_amp*units.Msun,
a=tn_a*units.kpc,
b=tn_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
NFW_p = NFWPotential(amp=dark_halo_amp*units.Msun,
a=dark_halo_a*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
v_circ_comp = calcRotcurve([MN_Bulge_p, MN_Thin_Disk_p, NFW_p], r-delta_r , phi=None)*220
return v_circ_comp
bounds = (( -10, amp1/(10**delta_mass_bulge), a1, b1*(1-0.01*delta_vertical_bulge), amp2/(10**delta_mass_tn), a2*(1-0.01*delta_radial_tn), b2/(10**delta_vertical_tn), amp5/(10*delta_mass_dh), a5*(1-0.01*delta_radial_dh) ),
( 10, amp1*(10**delta_mass_bulge), 0.1*delta_radial_bulge, b1*(1+0.01*delta_vertical_bulge), amp2*(10**delta_mass_tn), a2*(1+0.01*delta_radial_tn), b2*(10**delta_vertical_tn), amp5*(10**delta_mass_dh), a5*(1+0.01*delta_radial_dh) ) )
bounds
```
((-10,
11000000.0,
0.0,
0.14849999999999997,
390000000.0,
0.5299999999999999,
0.025,
14000000000.0,
1.2999999999999998),
(10,
1100000000.0,
2.0,
0.8415,
39000000000.0,
10.069999999999999,
2.5,
1400000000000.0,
24.7))
```python
popt, pcov = curve_fit(Bulge_ThinDisk_NFW_potentials,
r_data, v_c_data.data,
p0=[0, amp1, a1, b1, amp2, a2, b2, amp5, a5 ],
bounds=bounds )
print(popt, np.sqrt(np.diag(pcov)))
plt.scatter( r_data, v_c_data.data )
plt.plot( r_data, Bulge_ThinDisk_NFW_potentials( r_data, *popt ) )
```
[-2.78902447e-01 1.24430536e+07 1.46675591e+00 7.61836853e-01
7.13107384e+09 2.68408162e+00 1.71493848e-01 2.57425690e+11
1.62738596e+01] [2.38046289e-01 2.88592063e+11 7.18582305e+05 7.18997942e+05
2.81004025e+11 8.08245548e+05 8.08244419e+05 1.49677219e+11
9.47114773e+00]
[<matplotlib.lines.Line2D at 0x120e158d0>]
```python
```
# ThinDisk_NFW_potentials
```python
def ThinDisk_NFW_potentials( r, delta_r, tn_amp, tn_a, tn_b, dark_halo_amp, dark_halo_a ):
r_0=1*units.kpc # units
v_0=220*units.km/units.s # units
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=tn_amp*units.Msun,
a=tn_a*units.kpc,
b=tn_b*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
NFW_p = NFWPotential(amp=dark_halo_amp*units.Msun,
a=dark_halo_a*units.kpc,
normalize=False,
ro=r_0, vo=v_0)
v_circ_comp = calcRotcurve([ MN_Thin_Disk_p, NFW_p], r-delta_r , phi=None)*220
return v_circ_comp
bounds = (( -10, amp2/(10**delta_mass_tn), a2*(1-0.01*delta_radial_tn), b2/(10**delta_vertical_tn), amp5/(10*delta_mass_dh), a5*(1-0.01*delta_radial_dh) ),
( 10, amp2*(10**delta_mass_tn), a2*(1+0.01*delta_radial_tn), b2*(10**delta_vertical_tn), amp5*(10**delta_mass_dh), a5*(1+0.01*delta_radial_dh) ) )
bounds
```
((-10,
390000000.0,
0.5299999999999999,
0.025,
14000000000.0,
1.2999999999999998),
(10, 39000000000.0, 10.069999999999999, 2.5, 1400000000000.0, 24.7))
```python
popt, pcov = curve_fit(ThinDisk_NFW_potentials,
r_data, v_c_data.data,
p0=[0, amp2, a2, b2, amp5, a5 ],
bounds=bounds )
print(popt, np.sqrt(np.diag(pcov)))
plt.scatter( r_data, v_c_data.data )
plt.plot( r_data, ThinDisk_NFW_potentials( r_data, *popt ) )
```
[-2.78025944e-01 7.11898506e+09 2.74051174e+00 1.07097147e-01
2.57638242e+11 1.62771507e+01] [1.08072512e-01 2.90526942e+09 6.63797844e+05 6.63797792e+05
7.57432197e+10 4.37991102e+00]
[<matplotlib.lines.Line2D at 0x120d19e80>]
```python
```
```python
True and False
```
False
```python
run args_input.py a b
```
3
['args_input.py', 'a', 'b']
('a', 'is delicious. Would you like to try some?\n')
Or would you rather have the b ?
```python
args = [1, 2, 3]
flag = True
for i in args:
if i not in [1, 2, 4, 5]:
flag = False
```
```python
flag
```
False
```python
```
```python
fig = plt.figure(1)
ax = fig.add_axes((0.41, 0.1, 0.55, 0.85))
#ax.yaxis.set_ticks_position('both')
#ax.tick_params(axis='y', which='both', labelleft=True, labelright=True)
# Data
CV_galaxy = ax.errorbar(r_data, v_c_data, v_c_err_data, c='k', fmt='', ls='none')
CV_galaxy_dot = ax.scatter(r_data, v_c_data, c='k')
# A plot for each rotation curve with the colors indicated below
MN_b_plot, = ax.plot(lista, MN_Bulge, linestyle='--', c='gray')
MN_td_plot, = ax.plot(lista, MN_Thin_Disk, linestyle='--', c='purple')
MN_tkd_plot, = ax.plot(lista, MN_Thick_Disk, linestyle='--', c='blue')
EX_d_plot, = ax.plot(lista, EX_Disk, linestyle='--', c='cyan')
NFW_plot, = ax.plot(lista, NFW, linestyle='--', c='green')
BK_plot, = ax.plot(lista, BK, linestyle='--', c='orange')
# Composed rotation curve
v_circ_comp_plot, = ax.plot(lista, v_circ_comp, c='k')
ax.set_xlabel(r'$R(kpc)$', fontsize=20)
ax.set_ylabel(r'$v_c(km/s)$', fontsize=20)
ax.tick_params(axis='both', which='both', labelsize=15)
```
```python
rax = plt.axes((0.07, 0.8, 0.21, 0.15))
check = CheckButtons(rax, ('MN Bulge (GRAY)', 'MN Thin Disc (PURPLE)', 'MN Thick Disc (BLUE)', 'Exp. Disc (CYAN)', 'NFW - Halo (GREEN)', 'Burkert - Halo (ORANGE)'), (True, True, True, True, True, True))
for r in check.rectangles: # Checkbox options-colors
r.set_facecolor("lavender")
r.set_edgecolor("black")
#r.set_alpha(0.2)
[ll.set_color("black") for l in check.lines for ll in l]
[ll.set_linewidth(2) for l in check.lines for ll in l]
```
[None, None, None, None, None, None, None, None, None, None, None, None]
```python
MN_b_amp_ax = fig.add_axes((0.09,0.75,0.17,0.03))
MN_b_amp_s = Slider(MN_b_amp_ax, r"$M$($M_\odot$)", input_params['mass'][0]/(10**input_params['threshold_mass'][0]), input_params['mass'][0]*(10**input_params['threshold_mass'][0]), valinit=input_params['mass'][0], color='gray', valfmt='%1.3E')
MN_b_a_ax = fig.add_axes((0.09,0.72,0.17,0.03))
MN_b_a_s = Slider(MN_b_a_ax, "$a$ ($kpc$)", 0, 0.1*input_params['threshold_a'][0], valinit=input_params['a (kpc)'][0], color='gray')
MN_b_b_ax = fig.add_axes((0.09,0.69,0.17,0.03))
MN_b_b_s = Slider(MN_b_b_ax, "$b$ ($kpc$)", input_params['b (kpc)'][0]*(1-0.01*input_params['threshold_b'][0]), input_params['b (kpc)'][0]*(1+0.01*input_params['threshold_b'][0]), valinit=input_params['b (kpc)'][0], color='gray')
# Thin disk - purple
MN_td_amp_ax = fig.add_axes((0.09,0.63,0.17,0.03))
MN_td_amp_s = Slider(MN_td_amp_ax, r"$M$($M_\odot$)", input_params['mass'][1]/(10**input_params['threshold_mass'][1]), input_params['mass'][1]*(10**input_params['threshold_mass'][1]), valinit=input_params['mass'][1], color='purple', valfmt='%1.3E')
MN_td_a_ax = fig.add_axes((0.09,0.60,0.17,0.03))
MN_td_a_s = Slider(MN_td_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][1]*(1-0.01*input_params['threshold_a'][1]), input_params['a (kpc)'][1]*(1+0.01*input_params['threshold_a'][1]), valinit=input_params['a (kpc)'][1], color='purple')
MN_td_b_ax = fig.add_axes((0.09,0.57,0.17,0.03))
MN_td_b_s = Slider(MN_td_b_ax, "$b$ ($kpc$)", input_params['b (kpc)'][1]/(10**input_params['threshold_b'][1]), input_params['b (kpc)'][1]*(10**input_params['threshold_b'][1]), valinit=input_params['b (kpc)'][1], color='purple')
# Thick disk - Blue
MN_tkd_amp_ax = fig.add_axes((0.09,0.51,0.17,0.03))
MN_tkd_amp_s = Slider(MN_tkd_amp_ax, r"$M$($M_\odot$)", input_params['mass'][2]/(10**input_params['threshold_mass'][2]), input_params['mass'][2]*(10**input_params['threshold_mass'][2]), valinit=input_params['mass'][2], color='blue', valfmt='%1.3E')
MN_tkd_a_ax = fig.add_axes((0.09,0.48,0.17,0.03))
MN_tkd_a_s = Slider(MN_tkd_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][2]*(1-0.01*input_params['threshold_a'][2]), input_params['a (kpc)'][2]*(1+0.01*input_params['threshold_a'][2]), valinit=input_params['a (kpc)'][2], color='blue')
MN_tkd_b_ax = fig.add_axes((0.09,0.45,0.17,0.03))
MN_tkd_b_s = Slider(MN_tkd_b_ax, "$b$ ($kpc$)", input_params['b (kpc)'][2]/(10**input_params['threshold_b'][2]), input_params['b (kpc)'][2]*(10**input_params['threshold_b'][2]), valinit=input_params['b (kpc)'][2], color='blue')
# Exponential disk - Cyan
MN_ed_amp_ax = fig.add_axes((0.09,0.39,0.17,0.03))
MN_ed_amp_s = Slider(MN_ed_amp_ax, r"$\Sigma_0$($M_\odot/pc^2$)", input_params['mass'][3]/(10**input_params['threshold_mass'][3]), input_params['mass'][3]*(10**input_params['threshold_mass'][3]), valinit=input_params['mass'][3], color='cyan', valfmt='%1.3E')
MN_ed_a_ax = fig.add_axes((0.09,0.36,0.17,0.03))
MN_ed_a_s = Slider(MN_ed_a_ax, "$h_r$ ($kpc$)", input_params['a (kpc)'][3]*(1-0.01*input_params['threshold_a'][3]), input_params['a (kpc)'][3]*(1+0.01*input_params['threshold_a'][3]), valinit=input_params['a (kpc)'][3], color='cyan')
# NFW Halo - green
NFW_amp_ax = fig.add_axes((0.09,0.30,0.17,0.03))
NFW_amp_s = Slider(NFW_amp_ax, r"$M_0$($M_\odot$)", input_params['mass'][4]/(10*input_params['threshold_mass'][4]), input_params['mass'][4]*(10**input_params['threshold_mass'][4]), valinit=input_params['mass'][4], color='green', valfmt='%1.3E')
NFW_a_ax = fig.add_axes((0.09,0.27,0.17,0.03))
NFW_a_s = Slider(NFW_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][4]*(1-0.01*input_params['threshold_a'][4]), input_params['a (kpc)'][4]*(1+0.01*input_params['threshold_a'][4]), valinit=input_params['a (kpc)'][4], color='green')
# Burkert Halo - orange
BK_amp_ax = fig.add_axes((0.09,0.21,0.17,0.03))
BK_amp_s = Slider(BK_amp_ax, r"$\rho_0$($M_\odot/kpc^3$)", input_params['mass'][5]/(10*input_params['threshold_mass'][5]), input_params['mass'][5]*(10**input_params['threshold_mass'][5]), valinit=input_params['mass'][5], color='orange', valfmt='%1.3E')
BK_a_ax = fig.add_axes((0.09,0.18,0.17,0.03))
BK_a_s = Slider(BK_a_ax, "$a$ ($kpc$)", input_params['a (kpc)'][5]*(1-0.01*input_params['threshold_a'][5]), input_params['a (kpc)'][5]*(1+0.01*input_params['threshold_a'][5]), valinit=input_params['a (kpc)'][5], color='orange')
```
```python
# Bulge
def MN_b_amp_s_func(val):
if MN_b_plot.get_visible() == True:
global MN_Bulge_p, amp1, a1, b1
amp1=val*1
MN_Bulge_p = MiyamotoNagaiPotential(amp=val*units.Msun,a=a1*units.kpc,b=b1*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_b_a_s_func(val):
if MN_b_plot.get_visible() == True:
global MN_Bulge_p, amp1, a1, b1
a1=val*1
MN_Bulge_p = MiyamotoNagaiPotential(amp=amp1*units.Msun,a=val*units.kpc,b=b1*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_b_b_s_func(val):
if MN_b_plot.get_visible() == True:
global MN_Bulge_p, amp1, a1, b1
b1=val*1
MN_Bulge_p = MiyamotoNagaiPotential(amp=amp1*units.Msun,a=a1*units.kpc,b=val*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
# Thin disk
def MN_td_amp_s_func(val):
if MN_td_plot.get_visible() == True:
global MN_Thin_Disk_p, amp2, a2, b2
amp2=val*1
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=val*units.Msun,a=a2*units.kpc,b=b2*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_td_a_s_func(val):
if MN_td_plot.get_visible() == True:
global MN_Thin_Disk_p, amp2, a2, b2
a2=val*1
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=amp2*units.Msun,a=val*units.kpc,b=b2*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_td_b_s_func(val):
if MN_td_plot.get_visible() == True:
global MN_Thin_Disk_p, amp2, a2, b2
b2=val*1
MN_Thin_Disk_p= MiyamotoNagaiPotential(amp=amp2*units.Msun,a=a2*units.kpc,b=val*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
# Thick disk
def MN_tkd_amp_s_func(val):
if MN_tkd_plot.get_visible() == True:
global MN_Thick_Disk_p, amp3, a3, b3
amp3=val*1
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=val*units.Msun,a=a3*units.kpc,b=b3*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_tkd_a_s_func(val):
if MN_tkd_plot.get_visible() == True:
global MN_Thick_Disk_p, amp3, a3, b3
a3=val*1
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=amp3*units.Msun,a=val*units.kpc,b=b3*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
def MN_tkd_b_s_func(val):
if MN_tkd_plot.get_visible() == True:
global MN_Thick_Disk_p, amp3, a3, b3
b3=val*1
MN_Thick_Disk_p= MiyamotoNagaiPotential(amp=amp3*units.Msun,a=a3*units.kpc,b=val*units.kpc,normalize=False,ro=r_0, vo=v_0)
update_rot_curve()
# Exponential disk
def MN_ed_amp_s_func(val):
if EX_d_plot.get_visible() == True:
global EX_Disk_p, amp4,h_r
amp4=val*1
EX_Disk_p = RazorThinExponentialDiskPotential(amp=val*(units.Msun/(units.pc**2)), hr=h_r*units.kpc, maxiter=20, tol=0.001, normalize=False, ro=r_0, vo=v_0, new=True, glorder=100)
update_rot_curve()
def MN_ed_a_s_func(val):
if EX_d_plot.get_visible() == True:
global EX_Disk_p, amp4,h_r
h_r=val*1
EX_Disk_p = RazorThinExponentialDiskPotential(amp=amp4*(units.Msun/(units.pc**2)), hr=val*units.kpc, maxiter=20, tol=0.001, normalize=False, ro=r_0, vo=v_0, new=True, glorder=100)
update_rot_curve()
# NFW Halo
def NFW_amp_s_func(val):
if NFW_plot.get_visible() == True:
global NFW_p, amp5,a5
amp5=val*1
NFW_p = NFWPotential(amp=val*units.Msun, a=a5*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
def NFW_a_s_func(val):
if NFW_plot.get_visible() == True:
global NFW_p, amp5,a5
a5=val*1
NFW_p = NFWPotential(amp=amp5*units.Msun, a=val*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
# Burkert Halo
def BK_amp_s_func(val):
if BK_plot.get_visible() == True:
global BK_p, amp6,a6
amp6=val*1
BK_p = BurkertPotential(amp=val*units.Msun/(units.kpc)**3, a=a6*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
def BK_a_s_func(val):
if BK_plot.get_visible() == True:
global BK_p, amp6,a6
a6=val*1
BK_p = BurkertPotential(amp=amp6*units.Msun/(units.kpc)**3, a=val*units.kpc, normalize=False, ro=r_0, vo=v_0)
update_rot_curve()
```
```python
def update_rot_curve():
ax.clear()
global MN_b_plot, MN_Bulge_p, MN_Thin_Disk_p,MN_Thick_Disk_p, MN_td_plot,MN_tkd_plot, NFW_p, NFW_plot, EX_d_plot, EX_Disk_p, CV_galaxy, CV_galaxy_dot, BK_p, BK_plot
composite_pot_array=[]
ax.set_xlabel(r'$R(kpc)$', fontsize=20)
ax.set_ylabel(r'$v_c(km/s)$', fontsize=20)
ax.tick_params(axis='both', which='both', labelsize=15)
#ax.xaxis.set_major_locator(ticker.MultipleLocator(5))
ax.set_xlim([0, 1.02*r_data[-1]])
ax.set_ylim([0,np.max(v_c_data)*1.2])
if MN_b_plot.get_visible() == True:
MN_Bulge = calcRotcurve(MN_Bulge_p, lista, phi=None)*220
MN_b_plot, = ax.plot(lista, MN_Bulge, linestyle='--', c='gray')
composite_pot_array.append(MN_Bulge_p)
if MN_td_plot.get_visible() == True:
MN_Thin_Disk = calcRotcurve(MN_Thin_Disk_p, lista, phi=None)*220
MN_td_plot, = ax.plot(lista, MN_Thin_Disk, linestyle='--', c='purple')
composite_pot_array.append(MN_Thin_Disk_p)
if MN_tkd_plot.get_visible() == True:
MN_Thick_Disk = calcRotcurve(MN_Thick_Disk_p, lista, phi=None)*220
MN_tkd_plot, = ax.plot(lista, MN_Thick_Disk, linestyle='--', c='blue')
composite_pot_array.append(MN_Thick_Disk_p)
if NFW_plot.get_visible() == True:
NFW = calcRotcurve(NFW_p, lista, phi=None)*220
NFW_plot, = ax.plot(lista, NFW, linestyle='--', c='green')
composite_pot_array.append(NFW_p)
if EX_d_plot.get_visible() == True:
EX_Disk = calcRotcurve(EX_Disk_p, lista, phi=None)*220
EX_d_plot, = ax.plot(lista, EX_Disk, linestyle='--', c='cyan')
composite_pot_array.append(EX_Disk_p)
if BK_plot.get_visible() == True:
BK = calcRotcurve(BK_p, lista, phi=None)*220
BK_plot, = ax.plot(lista, BK, linestyle='--', c='orange')
composite_pot_array.append(BK_p)
CV_galaxy = ax.errorbar(r_data, v_c_data, v_c_err_data, c='k', fmt='', ls='none')
CV_galaxy_dot = ax.scatter(r_data, v_c_data, c='k')
v_circ_comp = calcRotcurve(composite_pot_array, lista, phi=None)*220
v_circ_comp_plot, = ax.plot(lista, v_circ_comp, c='k')
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Here we define the sliders update functions
MN_b_amp_s.on_changed(MN_b_amp_s_func)
MN_b_a_s.on_changed(MN_b_a_s_func)
MN_b_b_s.on_changed(MN_b_b_s_func)
MN_td_amp_s.on_changed(MN_td_amp_s_func)
MN_td_a_s.on_changed(MN_td_a_s_func)
MN_td_b_s.on_changed(MN_td_b_s_func)
MN_tkd_amp_s.on_changed(MN_tkd_amp_s_func)
MN_tkd_a_s.on_changed(MN_tkd_a_s_func)
MN_tkd_b_s.on_changed(MN_tkd_b_s_func)
NFW_amp_s.on_changed(NFW_amp_s_func)
NFW_a_s.on_changed(NFW_a_s_func)
BK_amp_s.on_changed(BK_amp_s_func)
BK_a_s.on_changed(BK_a_s_func)
MN_ed_amp_s.on_changed(MN_ed_amp_s_func)
MN_ed_a_s.on_changed(MN_ed_a_s_func)
```
0
```python
def reset(event):
MN_b_amp_s.reset()
MN_b_a_s.reset()
MN_b_b_s.reset()
MN_td_amp_s.reset()
MN_td_a_s.reset()
MN_td_b_s.reset()
MN_tkd_amp_s.reset()
MN_tkd_a_s.reset()
MN_tkd_b_s.reset()
MN_ed_amp_s.reset()
MN_ed_a_s.reset()
NFW_amp_s.reset()
NFW_a_s.reset()
BK_amp_s.reset()
BK_a_s.reset()
axcolor="lavender"
resetax = fig.add_axes((0.07, 0.08, 0.08, 0.05))
button_reset = Button(resetax, 'Reset', color=axcolor)
button_reset.on_clicked(reset)
```
0
```python
def check_on_clicked(label):
if label == 'MN Bulge (GRAY)':
MN_b_plot.set_visible(not MN_b_plot.get_visible())
update_rot_curve()
elif label == 'MN Thin Disc (PURPLE)':
MN_td_plot.set_visible(not MN_td_plot.get_visible())
update_rot_curve()
elif label == 'MN Thick Disc (BLUE)':
MN_tkd_plot.set_visible(not MN_tkd_plot.get_visible())
update_rot_curve()
elif label == 'Exp. Disc (CYAN)':
EX_d_plot.set_visible(not EX_d_plot.get_visible())
update_rot_curve()
elif label == 'NFW - Halo (GREEN)':
NFW_plot.set_visible(not NFW_plot.get_visible())
update_rot_curve()
elif label == 'Burkert - Halo (ORANGE)':
BK_plot.set_visible(not BK_plot.get_visible())
update_rot_curve()
plt.draw()
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Plotting all the curves
ax.set_xlabel(r'$R(kpc)$', fontsize=20)
ax.set_ylabel(r'$v_c(km/s)$', fontsize=20)
ax.tick_params(axis='both', which='both', labelsize=15)
#ax.xaxis.set_major_locator(ticker.MultipleLocator(5))
#ax.set_xlim([0, np.max(lista)])
#ax.set_ylim([0,np.max(v_c_data)*1.2])
check.on_clicked(check_on_clicked)
```
0
```python
ax
```
<matplotlib.axes._axes.Axes at 0x120acb748>
```python
from matplotlib.widgets import Slider, Button, RadioButtons, CheckButtons, TextBox # Matplotlib widgets
```
```python
CheckButtons?
```
```python
%matplotlib
t = np.arange(0.0, 2.0, 0.01)
s0 = np.sin(2*np.pi*t)
s1 = np.sin(4*np.pi*t)
s2 = np.sin(6*np.pi*t)
fig, ax = plt.subplots()
l0, = ax.plot(t, s0, visible=False, lw=2, color='k', label='2 Hz')
l1, = ax.plot(t, s1, lw=2, color='r', label='4 Hz')
l2, = ax.plot(t, s2, lw=2, color='g', label='6 Hz')
plt.subplots_adjust(left=0.2)
lines = [l0, l1, l2]
# Make checkbuttons with all plotted lines with correct visibility
rax = plt.axes([0.05, 0.4, 0.1, 0.15])
labels = [str(line.get_label()) for line in lines]
visibility = [line.get_visible() for line in lines]
check = CheckButtons(rax, labels, visibility)
def func(label):
index = labels.index(label)
lines[index].set_visible(not lines[index].get_visible())
plt.draw()
check.on_clicked(func)
```
Using matplotlib backend: MacOSX
0
```python
visibility
```
[False, True, True]
```python
check.get_status()
```
[True, False, False]
```python
l1.set_visible?
```
```python
print( check.get_status() )
check_visibility = check.get_status()
MN_b_plot.set_visible(check_visibility[0])
MN_td_plot.set_visible(check_visibility[1])
MN_tkd_plot.set_visible(check_visibility[2])
EX_d_plot.set_visible(check_visibility[3])
NFW_plot.set_visible(check_visibility[4])
BK_plot.set_visible(check_visibility[5])
```
|
andresGranadosCREPO_NAMEGalRotpyPATH_START.@GalRotpy_extracted@GalRotpy-master@notebook@.ipynb_checkpoints@GalRotpy-checkpoint.ipynb@.PATH_END.py
|
{
"filename": "pretty_printer.py",
"repo_name": "google/jax",
"repo_path": "jax_extracted/jax-main/jax/_src/pretty_printer.py",
"type": "Python"
}
|
# Copyright 2021 The JAX Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# Wadler-Lindig pretty printer.
#
# References:
# Wadler, P., 1998. A prettier printer. Journal of Functional Programming,
# pp.223-244.
#
# Lindig, C. 2000. Strictly Pretty.
# https://lindig.github.io/papers/strictly-pretty-2000.pdf
#
# Hafiz, A. 2021. Strictly Annotated: A Pretty-Printer With Support for
# Annotations. https://ayazhafiz.com/articles/21/strictly-annotated
#
from __future__ import annotations
from collections.abc import Sequence
import enum
from functools import partial
import sys
from typing import Any, NamedTuple
from jax._src import config
from jax._src import util
try:
import colorama # pytype: disable=import-error
except ImportError:
colorama = None
_PPRINT_USE_COLOR = config.bool_flag(
'jax_pprint_use_color',
config.bool_env('JAX_PPRINT_USE_COLOR', True),
help='Enable jaxpr pretty-printing with colorful syntax highlighting.'
)
def _can_use_color() -> bool:
try:
# Check if we're in IPython or Colab
ipython = get_ipython() # type: ignore[name-defined]
shell = ipython.__class__.__name__
if shell == "ZMQInteractiveShell":
# Jupyter Notebook
return True
elif "colab" in str(ipython.__class__):
# Google Colab (external or internal)
return True
except NameError:
pass
# Otherwise check if we're in a terminal
return hasattr(sys.stdout, 'isatty') and sys.stdout.isatty()
CAN_USE_COLOR = _can_use_color()
class Doc(util.StrictABC):
__slots__ = ()
def format(
self, width: int = 80, *, use_color: bool | None = None,
annotation_prefix: str = " # ",
source_map: list[list[tuple[int, int, Any]]] | None = None
) -> str:
"""
Formats a pretty-printer document as a string.
Args:
source_map: for each line in the output, contains a list of
(start column, end column, source) tuples. Each tuple associates a
region of output text with a source.
"""
if use_color is None:
use_color = CAN_USE_COLOR and _PPRINT_USE_COLOR.value
return _format(self, width, use_color=use_color,
annotation_prefix=annotation_prefix, source_map=source_map)
def __str__(self):
return self.format()
def __add__(self, other: Doc) -> Doc:
return concat([self, other])
class _NilDoc(Doc):
def __repr__(self): return "nil"
_nil = _NilDoc()
class _TextDoc(Doc):
__slots__ = ("text", "annotation")
text: str
annotation: str | None
def __init__(self, text: str, annotation: str | None = None):
assert isinstance(text, str), text
assert annotation is None or isinstance(annotation, str), annotation
self.text = text
self.annotation = annotation
def __repr__(self):
if self.annotation is not None:
return f"text(\"{self.text}\", annotation=\"{self.annotation}\")"
else:
return f"text(\"{self.text}\")"
class _ConcatDoc(Doc):
__slots__ = ("children",)
children: list[Doc]
def __init__(self, children: Sequence[Doc]):
self.children = list(children)
assert all(isinstance(doc, Doc) for doc in self.children), self.children
def __repr__(self): return f"concat({self.children})"
class _BreakDoc(Doc):
__slots__ = ("text",)
text: str
def __init__(self, text: str):
assert isinstance(text, str), text
self.text = text
def __repr__(self): return f"break({self.text})"
class _GroupDoc(Doc):
__slots__ = ("child",)
child: Doc
def __init__(self, child: Doc):
assert isinstance(child, Doc), child
self.child = child
def __repr__(self): return f"group({self.child})"
class _NestDoc(Doc):
__slots__ = ("n", "child",)
n: int
child: Doc
def __init__(self, n: int, child: Doc):
assert isinstance(child, Doc), child
self.n = n
self.child = child
def __repr__(self): return f"nest({self.n, self.child})"
_NO_SOURCE = object()
class _SourceMapDoc(Doc):
__slots__ = ("child", "source")
child: Doc
source: Any
def __init__(self, child: Doc, source: Any):
assert isinstance(child, Doc), child
self.child = child
self.source = source
def __repr__(self): return f"source({self.child}, {self.source})"
Color = enum.Enum("Color", ["BLACK", "RED", "GREEN", "YELLOW", "BLUE",
"MAGENTA", "CYAN", "WHITE", "RESET"])
Intensity = enum.Enum("Intensity", ["DIM", "NORMAL", "BRIGHT"])
class _ColorDoc(Doc):
__slots__ = ("foreground", "background", "intensity", "child")
foreground: Color | None
background: Color | None
intensity: Intensity | None
child: Doc
def __init__(self, child: Doc, *, foreground: Color | None = None,
background: Color | None = None,
intensity: Intensity | None = None):
assert isinstance(child, Doc), child
self.child = child
self.foreground = foreground
self.background = background
self.intensity = intensity
_BreakMode = enum.Enum("_BreakMode", ["FLAT", "BREAK"])
# In Lindig's paper fits() and format() are defined recursively. This is a
# non-recursive formulation using an explicit stack, necessary because Python
# doesn't have a tail recursion optimization.
def _fits(doc: Doc, width: int, agenda: list[tuple[int, _BreakMode, Doc]]
) -> bool:
while width >= 0 and len(agenda) > 0:
i, m, doc = agenda.pop()
if isinstance(doc, _NilDoc):
pass
elif isinstance(doc, _TextDoc):
width -= len(doc.text)
elif isinstance(doc, _ConcatDoc):
agenda.extend((i, m, d) for d in reversed(doc.children))
elif isinstance(doc, _BreakDoc):
if m == _BreakMode.BREAK:
return True
width -= len(doc.text)
elif isinstance(doc, _NestDoc):
agenda.append((i + doc.n, m, doc.child))
elif isinstance(doc, _GroupDoc):
agenda.append((i, _BreakMode.FLAT, doc.child))
elif isinstance(doc, _ColorDoc) or isinstance(doc, _SourceMapDoc):
agenda.append((i, m, doc.child))
else:
raise ValueError("Invalid document ", doc)
return width >= 0
# Annotation layout: A flat group is sparse if there are no breaks between
# annotations.
def _sparse(doc: Doc) -> bool:
agenda = [doc]
num_annotations = 0
seen_break = False
while len(agenda) > 0:
doc = agenda.pop()
if isinstance(doc, _NilDoc):
pass
elif isinstance(doc, _TextDoc):
if doc.annotation is not None:
if num_annotations >= 1 and seen_break:
return False
num_annotations += 1
elif isinstance(doc, _ConcatDoc):
agenda.extend(reversed(doc.children))
elif isinstance(doc, _BreakDoc):
seen_break = True
elif isinstance(doc, _NestDoc):
agenda.append(doc.child)
elif isinstance(doc, _GroupDoc):
agenda.append(doc.child)
elif isinstance(doc, _ColorDoc) or isinstance(doc, _SourceMapDoc):
agenda.append(doc.child)
else:
raise ValueError("Invalid document ", doc)
return True
class _ColorState(NamedTuple):
foreground: Color
background: Color
intensity: Intensity
class _State(NamedTuple):
indent: int
mode: _BreakMode
doc: Doc
color: _ColorState
source_map: Any
class _Line(NamedTuple):
text: str
width: int
annotations: str | None | list[str]
def _update_color(use_color: bool, state: _ColorState, update: _ColorState
) -> tuple[_ColorState, str]:
if not use_color or colorama is None:
return update, ""
color_str = ""
if state.foreground != update.foreground:
color_str += getattr(colorama.Fore, str(update.foreground.name))
if state.background != update.background:
color_str += getattr(colorama.Back, str(update.background.name))
if state.intensity != update.intensity:
color_str += colorama.Style.NORMAL # pytype: disable=unsupported-operands
color_str += getattr(colorama.Style, str(update.intensity.name))
return update, color_str
def _align_annotations(lines):
# TODO: Hafiz also implements a local alignment mode, where groups of lines
# with annotations are aligned together.
maxlen = max(l.width for l in lines)
out = []
for l in lines:
if len(l.annotations) == 0:
out.append(l._replace(annotations=None))
elif len(l.annotations) == 1:
out.append(l._replace(text=l.text + " " * (maxlen - l.width),
annotations=l.annotations[0]))
else:
out.append(l._replace(text=l.text + " " * (maxlen - l.width),
annotations=l.annotations[0]))
for a in l.annotations[1:]:
out.append(_Line(text=" " * maxlen, width=l.width, annotations=a))
return out
def _format(
doc: Doc, width: int, *, use_color: bool, annotation_prefix: str,
source_map: list[list[tuple[int, int, Any]]] | None
) -> str:
lines = []
default_colors = _ColorState(Color.RESET, Color.RESET, Intensity.NORMAL)
annotation_colors = _ColorState(Color.RESET, Color.RESET, Intensity.DIM)
color_state = default_colors
source_start = 0 # The column at which the current source region starts.
source = _NO_SOURCE # The currently active source region.
line_source_map = [] # Source maps for the current line of text.
agenda = [_State(0, _BreakMode.BREAK, doc, default_colors, source)]
k = 0
line_text = ""
line_annotations = []
while len(agenda) > 0:
i, m, doc, color, agenda_source = agenda.pop()
if source_map is not None and agenda_source != source:
pos = len(line_text)
if source_start != pos and source is not _NO_SOURCE:
line_source_map.append((source_start, pos, source))
source = agenda_source
source_start = pos
if isinstance(doc, _NilDoc):
pass
elif isinstance(doc, _TextDoc):
color_state, color_str = _update_color(use_color, color_state, color)
line_text += color_str
line_text += doc.text
if doc.annotation is not None:
line_annotations.append(doc.annotation)
k += len(doc.text)
elif isinstance(doc, _ConcatDoc):
agenda.extend(_State(i, m, d, color, source)
for d in reversed(doc.children))
elif isinstance(doc, _BreakDoc):
if m == _BreakMode.BREAK:
if len(line_annotations) > 0:
color_state, color_str = _update_color(use_color, color_state,
annotation_colors)
line_text += color_str
lines.append(_Line(line_text, k, line_annotations))
if source_map is not None:
pos = len(line_text)
if source_start != pos and source is not _NO_SOURCE:
line_source_map.append((source_start, pos, source))
source_map.append(line_source_map)
line_source_map = []
source_start = i
line_text = " " * i
line_annotations = []
k = i
else:
color_state, color_str = _update_color(use_color, color_state, color)
line_text += color_str
line_text += doc.text
k += len(doc.text)
elif isinstance(doc, _NestDoc):
agenda.append(_State(i + doc.n, m, doc.child, color, source))
elif isinstance(doc, _GroupDoc):
# In Lindig's paper, _fits is passed the remainder of the document.
# I'm pretty sure that's a bug and we care only if the current group fits!
if (_sparse(doc)
and _fits(doc, width - k, [(i, _BreakMode.FLAT, doc.child)])):
agenda.append(_State(i, _BreakMode.FLAT, doc.child, color, source))
else:
agenda.append(_State(i, _BreakMode.BREAK, doc.child, color, source))
elif isinstance(doc, _ColorDoc):
color = _ColorState(doc.foreground or color.foreground,
doc.background or color.background,
doc.intensity or color.intensity)
agenda.append(_State(i, m, doc.child, color, source))
elif isinstance(doc, _SourceMapDoc):
agenda.append(_State(i, m, doc.child, color, doc.source))
else:
raise ValueError("Invalid document ", doc)
if len(line_annotations) > 0:
color_state, color_str = _update_color(use_color, color_state,
annotation_colors)
line_text += color_str
if source_map is not None:
pos = len(line_text)
if source_start != pos and source is not _NO_SOURCE:
line_source_map.append((source_start, pos, source))
source_map.append(line_source_map)
lines.append(_Line(line_text, k, line_annotations))
lines = _align_annotations(lines)
out = "\n".join(
l.text if l.annotations is None
else f"{l.text}{annotation_prefix}{l.annotations}" for l in lines)
color_state, color_str = _update_color(use_color, color_state,
default_colors)
return out + color_str
# Public API.
def nil() -> Doc:
"""An empty document."""
return _nil
def text(s: str, annotation: str | None = None) -> Doc:
"""Literal text."""
return _TextDoc(s, annotation)
def concat(docs: Sequence[Doc]) -> Doc:
"""Concatenation of documents."""
docs = list(docs)
if len(docs) == 1:
return docs[0]
return _ConcatDoc(docs)
def brk(text: str = " ") -> Doc:
"""A break.
Prints either as a newline or as `text`, depending on the enclosing group.
"""
return _BreakDoc(text)
def group(doc: Doc) -> Doc:
"""Layout alternative groups.
Prints the group with its breaks as their text (typically spaces) if the
entire group would fit on the line when printed that way. Otherwise, breaks
inside the group as printed as newlines.
"""
return _GroupDoc(doc)
def nest(n: int, doc: Doc) -> Doc:
"""Increases the indentation level by `n`."""
return _NestDoc(n, doc)
def color(doc: Doc, *, foreground: Color | None = None,
background: Color | None = None,
intensity: Intensity | None = None):
"""ANSI colors.
Overrides the foreground/background/intensity of the text for the child doc.
Requires use_colors=True to be set when printing and the `colorama` package
to be installed; otherwise does nothing.
"""
return _ColorDoc(doc, foreground=foreground, background=background,
intensity=intensity)
def source_map(doc: Doc, source: Any):
"""Source mapping.
A source map associates a region of the pretty-printer's text output with a
source location that produced it. For the purposes of the pretty printer a
``source`` may be any object: we require only that we can compare sources for
equality. A text region to source object mapping can be populated as a side
output of the ``format`` method.
"""
return _SourceMapDoc(doc, source)
type_annotation = partial(color, intensity=Intensity.NORMAL,
foreground=Color.MAGENTA)
keyword = partial(color, intensity=Intensity.BRIGHT, foreground=Color.BLUE)
def join(sep: Doc, docs: Sequence[Doc]) -> Doc:
"""Concatenates `docs`, separated by `sep`."""
docs = list(docs)
if len(docs) == 0:
return nil()
xs = [docs[0]]
for doc in docs[1:]:
xs.append(sep)
xs.append(doc)
return concat(xs)
|
googleREPO_NAMEjaxPATH_START.@jax_extracted@jax-main@jax@_src@pretty_printer.py@.PATH_END.py
|
{
"filename": "__init__.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/data/__init__.py",
"type": "Python"
}
|
"""
Built-in datasets for demonstration, educational and test purposes.
"""
def gapminder(datetimes=False, centroids=False, year=None, pretty_names=False):
"""
Each row represents a country on a given year.
https://www.gapminder.org/data/
Returns:
A `pandas.DataFrame` with 1704 rows and the following columns:
`['country', 'continent', 'year', 'lifeExp', 'pop', 'gdpPercap',
'iso_alpha', 'iso_num']`.
If `datetimes` is True, the 'year' column will be a datetime column
If `centroids` is True, two new columns are added: ['centroid_lat', 'centroid_lon']
If `year` is an integer, the dataset will be filtered for that year
"""
df = _get_dataset("gapminder")
if year:
df = df[df["year"] == year]
if datetimes:
df["year"] = (df["year"].astype(str) + "-01-01").astype("datetime64[ns]")
if not centroids:
df = df.drop(["centroid_lat", "centroid_lon"], axis=1)
if pretty_names:
df.rename(
mapper=dict(
country="Country",
continent="Continent",
year="Year",
lifeExp="Life Expectancy",
gdpPercap="GDP per Capita",
pop="Population",
iso_alpha="ISO Alpha Country Code",
iso_num="ISO Numeric Country Code",
centroid_lat="Centroid Latitude",
centroid_lon="Centroid Longitude",
),
axis="columns",
inplace=True,
)
return df
def tips(pretty_names=False):
"""
Each row represents a restaurant bill.
https://vincentarelbundock.github.io/Rdatasets/doc/reshape2/tips.html
Returns:
A `pandas.DataFrame` with 244 rows and the following columns:
`['total_bill', 'tip', 'sex', 'smoker', 'day', 'time', 'size']`."""
df = _get_dataset("tips")
if pretty_names:
df.rename(
mapper=dict(
total_bill="Total Bill",
tip="Tip",
sex="Payer Gender",
smoker="Smokers at Table",
day="Day of Week",
time="Meal",
size="Party Size",
),
axis="columns",
inplace=True,
)
return df
def iris():
"""
Each row represents a flower.
https://en.wikipedia.org/wiki/Iris_flower_data_set
Returns:
A `pandas.DataFrame` with 150 rows and the following columns:
`['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species', 'species_id']`."""
return _get_dataset("iris")
def wind():
"""
Each row represents a level of wind intensity in a cardinal direction, and its frequency.
Returns:
A `pandas.DataFrame` with 128 rows and the following columns:
`['direction', 'strength', 'frequency']`."""
return _get_dataset("wind")
def election():
"""
Each row represents voting results for an electoral district in the 2013 Montreal
mayoral election.
Returns:
A `pandas.DataFrame` with 58 rows and the following columns:
`['district', 'Coderre', 'Bergeron', 'Joly', 'total', 'winner', 'result', 'district_id']`."""
return _get_dataset("election")
def election_geojson():
"""
Each feature represents an electoral district in the 2013 Montreal mayoral election.
Returns:
A GeoJSON-formatted `dict` with 58 polygon or multi-polygon features whose `id`
is an electoral district numerical ID and whose `district` property is the ID and
district name."""
import gzip
import json
import os
path = os.path.join(
os.path.dirname(os.path.dirname(__file__)),
"package_data",
"datasets",
"election.geojson.gz",
)
with gzip.GzipFile(path, "r") as f:
result = json.loads(f.read().decode("utf-8"))
return result
def carshare():
"""
Each row represents the availability of car-sharing services near the centroid of a zone
in Montreal over a month-long period.
Returns:
A `pandas.DataFrame` with 249 rows and the following columns:
`['centroid_lat', 'centroid_lon', 'car_hours', 'peak_hour']`."""
return _get_dataset("carshare")
def stocks(indexed=False, datetimes=False):
"""
Each row in this wide dataset represents closing prices from 6 tech stocks in 2018/2019.
Returns:
A `pandas.DataFrame` with 100 rows and the following columns:
`['date', 'GOOG', 'AAPL', 'AMZN', 'FB', 'NFLX', 'MSFT']`.
If `indexed` is True, the 'date' column is used as the index and the column index
If `datetimes` is True, the 'date' column will be a datetime column
is named 'company'"""
df = _get_dataset("stocks")
if datetimes:
df["date"] = df["date"].astype("datetime64[ns]")
if indexed:
df = df.set_index("date")
df.columns.name = "company"
return df
def experiment(indexed=False):
"""
Each row in this wide dataset represents the results of 100 simulated participants
on three hypothetical experiments, along with their gender and control/treatment group.
Returns:
A `pandas.DataFrame` with 100 rows and the following columns:
`['experiment_1', 'experiment_2', 'experiment_3', 'gender', 'group']`.
If `indexed` is True, the data frame index is named "participant" """
df = _get_dataset("experiment")
if indexed:
df.index.name = "participant"
return df
def medals_wide(indexed=False):
"""
This dataset represents the medal table for Olympic Short Track Speed Skating for the
top three nations as of 2020.
Returns:
A `pandas.DataFrame` with 3 rows and the following columns:
`['nation', 'gold', 'silver', 'bronze']`.
If `indexed` is True, the 'nation' column is used as the index and the column index
is named 'medal'"""
df = _get_dataset("medals")
if indexed:
df = df.set_index("nation")
df.columns.name = "medal"
return df
def medals_long(indexed=False):
"""
This dataset represents the medal table for Olympic Short Track Speed Skating for the
top three nations as of 2020.
Returns:
A `pandas.DataFrame` with 9 rows and the following columns:
`['nation', 'medal', 'count']`.
If `indexed` is True, the 'nation' column is used as the index."""
df = _get_dataset("medals").melt(
id_vars=["nation"], value_name="count", var_name="medal"
)
if indexed:
df = df.set_index("nation")
return df
def _get_dataset(d):
import pandas
import os
return pandas.read_csv(
os.path.join(
os.path.dirname(os.path.dirname(__file__)),
"package_data",
"datasets",
d + ".csv.gz",
)
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@data@__init__.py@.PATH_END.py
|
{
"filename": "grid_parameters.py",
"repo_name": "dmvandamt/beyonce",
"repo_path": "beyonce_extracted/beyonce-main/beyonce/shallot/grid_parameters.py",
"type": "Python"
}
|
"""
This module contains the parameter class used to hold grid information and
instantiate new shallot grids.
"""
from __future__ import annotations
import os
import numpy as np
import beyonce.validate as validate
from beyonce.shallot.errors import LoadError, InvalidBoundsError, OriginMissingError
class Parameters:
"""
This class contains all the information pertaining to the grid:
dx (x-coordinate of disk centre w.r.t. eclipse centre [t_ecl])
dy (y-coordinate of disk centre w.r.t. eclipse centre [t_ecl])
rf (extent of rf_array in one-direction)
rf_array (radius factor that compares with the smallest disk at a
given point)
grid_shape (shape of the total grid)
slice_shape (shape of dy, dx slice)
extendable (whether the grid contains point (0, 0))
It also contains methods to save and load the grid parameters.
"""
def __init__(self,
min_x: float,
max_x: float,
num_x: int,
min_y: float,
max_y: float,
num_y: int,
max_rf: float,
num_rf: int
) -> None:
"""
This is the constructor for the disk grid parameter class
Parameters
----------
num_xy : integer
This is the resolution of the grid in the dx and dy directions.
maximum_radius : float
This is the maximum radius of the disk [t_ecl].
num_rf : integer
This is the resolution of the grid in the rf direction. Note that
the size of this grid dimension is then (2 * num_rf - 1).
maximum_rf : float
This is the maximum rf value.
"""
self.dx = self._determine_dx(min_x, max_x, num_x)
self.dy = self._determine_dy(min_y, max_y, num_y)
self.rf, self.rf_array = self._determine_rf(max_rf, num_rf)
self.grid_shape, self.slice_shape = self._determine_grid_and_slice_shape()
self.extendable = self._determine_extendable()
def __str__(self) -> str:
"""
This returns the string representation of the class.
Returns
-------
str_string : str
String representation of the Parameters class.
"""
return self.__repr__()
def __repr__(self) -> str:
"""
This generates a string representation of the grid parameters object.
Returns
-------
repr_string : str
Representation string of the grid parameters class.
"""
dy, dx, rf_array = self.get_vectors()
lines: list[str] = [""]
lines.append("Grid Parameters")
lines.append(28 * "-")
dx_min = f'{f"{dx[0]:.2f}":>6}'
dx_max = f'{f"{dx[-1]:.2f}":>6}'
lines.append(f"dx: {dx_min} -> {dx_max} ({len(dx)})")
dy_min = f'{f"{dy[0]:.2f}":>6}'
dy_max = f'{f"{dy[-1]:.2f}":>6}'
lines.append(f"dy: {dy_min} -> {dy_max} ({len(dy)})")
rf_min = f'{f"{1:.2f}":>6}'
rf_max = f'{f"{rf_array[0]:.2f}":>6}'
rf_num = len(rf_array)
lines.append(f"rf: {rf_max} -> {rf_min} -> {rf_max} ({rf_num})")
lines.append(f"grid_shape: {self.grid_shape}")
repr_string = "\n".join(lines)
return repr_string
def __eq__(self, other: Parameters) -> bool:
"""
This method is used to determine whether or not two instances are
equal to each other
`
Returns
-------
equal : bool
Whether two class instances contain the same information.
"""
dx_equal = np.all(self.dx == other.dx)
dy_equal = np.all(self.dy == other.dy)
rf_equal = np.all(self.rf == other.rf)
equal = dx_equal and dy_equal and rf_equal
return equal
def _generate_vector(self,
min_value : float,
max_value : float,
num_points : int,
name_vector : str
) -> np.ndarray:
"""
This method generates a linspace array defined by the input parameters
Parameters
----------
min_value : float
The minimum value of the vector.
max_value : float
The maximum value of the vector.
num_points : int
The length of the vector.
name_vector : str
The name of the vector attached to error messages.
"""
name_vector = validate.string(name_vector, "name_vector")
min_value = validate.number(min_value, "min_value")
max_value = validate.number(max_value, "max_value")
if min_value >= max_value:
raise InvalidBoundsError(
f"min_{name_vector}", min_value, f"max_{name_vector}", max_value
)
num_points = validate.number(num_points, "num_points", check_integer=True,
lower_bound=1)
return np.linspace(min_value, max_value, num_points)
def _determine_dx(self,
min_x: float,
max_x: float,
num_x: int
) -> np.ndarray:
"""
This method is used to determine the dx vector
Parameters
----------
min_x : float
The minimum value of x [t_ecl].
max_x : float
The maximum value of x [t_ecl].
num_x : int
The number of dx elements.
Returns
-------
dx : np.ndarray
Grid dx dimension vector.
"""
return self._generate_vector(min_x, max_x, num_x, "x")[None, :, None]
def _determine_dy(self,
min_y: float,
max_y: float,
num_y: int
) -> np.ndarray:
"""
This method is used to determine the dx vector
Parameters
----------
min_y : float
The minimum value of y [t_ecl].
max_y : float
The maximum value of y [t_ecl].
num_y : int
The number of dy elements.
Returns
-------
dy : np.ndarray
Grid dy dimension vector.
"""
return self._generate_vector(min_y, max_y, num_y, "y")[:, None, None]
def _determine_rf(self,
max_rf: float,
num_rf: int
) -> tuple[np.ndarray, np.ndarray]:
"""
This method is used to determine the dx vector
Parameters
----------
max_rf : float
The maximum value of rf [-].
num_rf : int
The number of rf elements (in one direction).
Returns
-------
rf : np.ndarray
Rf range from 1 to max_rf in num_rf.
rf_array : np.ndarray
Grid rf dimension vector.
"""
rf = self._generate_vector(1, max_rf, num_rf, "rf")
rf_array = np.concatenate((np.flip(rf), rf[1:]), 0)
return rf, rf_array
def _determine_grid_and_slice_shape(self) -> tuple[
tuple[int, int, int],
tuple[int, int]
]:
"""
This method sets useful grid parameters (grid shape and slice shape).
Returns
-------
grid_shape : tuple
"""
dy, dx, rf_array = self.get_vectors()
grid_shape = (len(dy), len(dx), len(rf_array))
slice_shape = (len(dy), len(dx))
return grid_shape, slice_shape
def _determine_extendable(self) -> bool:
"""
This method is used to determine whether this particular set of grid
parameters can be extended.
Returns
-------
extendable : bool
Whether the parameters object can be pe
"""
dy, dx, _ = self.get_vectors()
extendable = dy[0] == 0 and dx[0] == 0
return extendable
def extend_grid(self) -> None:
"""
This method is used to reflect the grid parameters about the x and y
axes.
"""
if not self.extendable:
raise OriginMissingError("grid")
num_y, num_x = self.slice_shape
max_y = self.dy[-1, 0, 0]
max_x = self.dx[0, -1, 0]
self.dx = self._determine_dx(-max_x, max_x, 2 * num_x - 1)
self.dy = self._determine_dy(-max_y, max_y, 2 * num_y - 1)
self.grid_shape, self.slice_shape = self._determine_grid_and_slice_shape()
self.extendable = self._determine_extendable()
def get_vectors(self) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
"""
This method returns the flattened dy, dx, and rf grid vectors.
Returns
-------
dy : np.ndarray
The y coordinates of the centre of the ellipse [t_ecl]
dx : np.ndarray
The x coordinates of the centre of the ellipse [t_ecl]
rf_array : np.ndarray
The rf radius stretch factors of the ellipse [-]
"""
return self.dy.flatten(), self.dx.flatten(), self.rf_array
def save(self, directory: str) -> None:
"""
This method saves all the information of this object to a specified
directory.
Parameters
----------
directory : str
File path for the saved information.
"""
directory = validate.string(directory, "directory")
if not os.path.exists(directory):
os.mkdir(directory)
np.save(f"{directory}/dx", self.dx)
np.save(f"{directory}/dy", self.dy)
np.save(f"{directory}/rf", self.rf)
np.save(f"{directory}/rf_array", self.rf_array)
@classmethod
def load(cls, directory: str) -> Parameters:
"""
This method loads all the information of this object from a specified
directory.
Parameters
----------
directory : str
File path for the saved information.
Returns
-------
parameters : Parameters
This is the loaded object.
"""
directory = validate.string(directory, "directory")
try:
parameters = cls(0, 1, 1, 0, 1, 1, 2, 1)
parameters.dx = np.load(f"{directory}/dx.npy")
parameters.dy = np.load(f"{directory}/dy.npy")
parameters.rf = np.load(f"{directory}/rf.npy")
parameters.rf_array = np.load(f"{directory}/rf_array.npy")
parameters.grid_shape, parameters.slice_shape = (
parameters._determine_grid_and_slice_shape()
)
parameters.extendable = parameters._determine_extendable()
except Exception:
raise LoadError("parameters", directory)
return parameters
|
dmvandamtREPO_NAMEbeyoncePATH_START.@beyonce_extracted@beyonce-main@beyonce@shallot@grid_parameters.py@.PATH_END.py
|
{
"filename": "_linepositionsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattersmith/hoverlabel/font/_linepositionsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class LinepositionsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name="linepositionsrc",
parent_name="scattersmith.hoverlabel.font",
**kwargs,
):
super(LinepositionsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattersmith@hoverlabel@font@_linepositionsrc.py@.PATH_END.py
|
{
"filename": "ChangeLog.md",
"repo_name": "PhaseTracer/PhaseTracer",
"repo_path": "PhaseTracer_extracted/PhaseTracer-master/ChangeLog.md",
"type": "Markdown"
}
|
## PhaseTracer-1.0.0 [March 4, 2020]
* Initial release
## PhaseTracer-1.0.1 [April 10, 2020]
* Setup automatic build & tests etc
* BugFix: calculate_sm_masses setting in FlexibleSUSY should be set to 1 or 0.
* Fix cmake building of FS example to always use tagged version v2.4.1 of the code.
## PhaseTracer-1.0.2 [April 11, 2020]
* Specify axis limits for phase_plotter
* Update FS version used to 2.4.2, avoids compilation complaining about an unused Mathematica interafce problem on Mac OS with Mathamatica 12.
## PhaseTracer-1.0.3 [April 15, 2020]
* Many thanks to Jingwei Lian for pointing out a bug in THDMIISNMSSMBCsimple.hpp. get_vector_debye_sq() returns two W boson masses, instead of one W boson mass and one Z boson mass.
## PhaseTracer-1.1.0 [January 4, 2021]
* Update BSMPT used in PhaseTracer to version 2
* Add requirement on cmake version, >=3.9, because of OpenMP support
* Add a setting about BOOST in cmake to fix compiling problem with BOOST.1.72
* Add functions, 'get_minima_at_t_low' and 'get_minima_at_t_high', to get minima at lowest and highest temperature.
* Add function, 'get_deepest_phase_at_T', to get the deepest phase at T.
* Change 'V1' function in 'one_loop_potential' class to virtual function
* Fix a bug that counter_term is not added to zero-temperature potential
## PhaseTracer-2.0 [November 5, 2021]
* Added xi to OneLoopPotential class, as well as the relevant contributions to V1
* Add high-temperature expansions into the one-loop potential class
* Add On-shell like scheme example
* Add covariant gauge example
* Add h_bar_expansion method to obtain TC
* Merge interface with TransitionSlover
* Modify the one-loop potential class to interface with DRalgo
* Add calculation of action, and relevant outputs
* Add calculation of nucleation temperature
* Add calculation of alpha, beta
* Add calculation of GW spectrum, and relevant outputs
* Add calculation of GW SNR, and relevant outputs
|
PhaseTracerREPO_NAMEPhaseTracerPATH_START.@PhaseTracer_extracted@PhaseTracer-master@ChangeLog.md@.PATH_END.py
|
{
"filename": "plot_P_Pdot_search.py",
"repo_name": "nhurleywalker/GPMTransient",
"repo_path": "GPMTransient_extracted/GPMTransient-main/P_Pdot_diagram/plot_P_Pdot_search.py",
"type": "Python"
}
|
#from astropy.table import Table
#from astropy.coordinates import SkyCoord
#from astropy import unit as u
#import scipy.ndimage
#from scipy.ndimage.filters import gaussian_filter
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import markers
from matplotlib.path import Path
from matplotlib.colors import LogNorm
import matplotlib.font_manager
from matplotlib import rc
import pandas as pd
import scipy.stats
from matplotlib.ticker import FormatStrFormatter
SOURCE_CSV = "chi2_grid.csv"
def P(f0):
return 1/f0
def Pdot(f0, f1):
return - f1 / (f0**2)
# https://www.cv.nrao.edu/~sransom/web/Ch6.html
def Edot(P, Pdot, I=1.e45):
return 4 * np.pi**2 * I * Pdot / P**3
def B(P, Pdot):
return 3.2e19 * np.sqrt(P*Pdot)
def tau(P, Pdot):
return P / (2*Pdot)
def s_to_Myr(t):
return t / (60 * 60 * 24 * 365.25 * 1.e6)
# Nature requires sans-serif fonts
plt.rcParams.update({
"font.size": 7,
"font.sans-serif": ["Helvetica"]})
cm = 1/2.54 # centimeters in inches
arrowprops = dict(arrowstyle="->", lw=0.5, shrinkA=0.0)
DOF = 57 # 59 TOAs minus 2 fitted parameters
df = pd.read_csv(SOURCE_CSV, delimiter=",")
#df.columns = ["Name", "RA", "Dec"]
print(df.keys())
xsize = len(np.unique(df['F0']))
ysize = len(np.unique(df['F1']))
arr = np.array(df['chi2']).reshape((ysize, xsize))
F0 = np.array(df['F0']).reshape((ysize, xsize))
F1 = np.array(df['F1']).reshape((ysize, xsize))
# First island: minimum chi^2 in main ellipse:
best_f0 = df['F0'][np.argmin(arr)]
best_f1 = df['F1'][np.argmin(arr)]
best_P = P(best_f0)
best_Pdot = Pdot(best_f0, best_f1)
bestfit = np.min(arr)
# Second island: minimum chi^2 in lower-left ellipse:
# 1, 2, and 3 sigma confidence limits
nsigma = np.arange(1, 4)
# These are the CDFs going from -infinity to nsigma. So subtract away 0.5 and double for the 2-sided values
CIs = (scipy.stats.norm().cdf(nsigma) - 0.5) * 2
print(f"Confidence intervals for {nsigma} sigma: {CIs}")
# chi^2 random variable for 2 parameters
rv = scipy.stats.chi2(2)
# The ppf = Percent point function is the inverse of the CDF
contour_levels = rv.ppf(CIs)
print(f"Contour levels for {nsigma} sigma and 2 parameters: {contour_levels}")
# Do the same for a 1 parameter case
#CIs = (scipy.stats.norm().cdf(nsigma) - 0.5) * 2
#print(f"Confidence intervals for {nsigma} sigma: {CIs}")
# chi^2 random variable for 1 parameters
#rv = scipy.stats.chi2(1)
#contour_levels_1param = rv.ppf(CIs)
#print(f"Contour levels for {nsigma} sigma and 1 parameter: {contour_levels_1param}")
# Plot the grid/contour results
#fig, ax = plt.subplots(figsize=(16, 9))
# Just plot the values offset from the best-fit values
#https://www.nature.com/nature/for-authors/final-submission#:~:text=For%20guidance%2C%20Nature's%20standard%20figure,(120%E2%80%93136%20mm).
fig = plt.figure(figsize=(8.9*cm,7.8*cm))
ax = fig.add_subplot(111)
twod = ax.contour(
1.e9*(F0 - best_f0),
1.e18*F1,
arr - bestfit,
levels=contour_levels,
colors="b",
linewidths=[0.5],
)
fmt = {}
strs = ['$1\\sigma$', '$2\\sigma$', '$3\\sigma$']
for l, s in zip(twod.levels, strs):
fmt[l] = s
ax.clabel(twod, twod.levels, inline=True, fmt=fmt, fontsize=5)
xy = twod.collections[2].get_paths()[0].vertices
sig3_f0, sig3_f1 = xy[np.argmin(xy.T[1])]
sig3_P = P(sig3_f0/1.e9 + best_f0)
sig3_Pdot = Pdot(sig3_f0/1.e9 + best_f0, sig3_f1/1.e18)
sig3_B = B(sig3_P, sig3_Pdot)
sig3_Edot = Edot(sig3_P, sig3_Pdot)
sig3_tau = s_to_Myr(tau(sig3_P, sig3_Pdot))
xy = twod.collections[1].get_paths()[0].vertices
sig2_f0, sig2_f1 = xy[np.argmin(xy.T[1])]
sig2_P = P(sig2_f0/1.e9 + best_f0)
sig2_Pdot = Pdot(sig2_f0/1.e9 + best_f0, sig2_f1/1.e18)
sig2_B = B(sig2_P, sig2_Pdot)
sig2_Edot = Edot(sig2_P, sig2_Pdot)
sig2_tau = s_to_Myr(tau(sig2_P, sig2_Pdot))
xy = twod.collections[0].get_paths()[0].vertices
sig1_f0, sig1_f1 = xy[np.argmin(xy.T[1])]
sig1_P = P(sig1_f0/1.e9 + best_f0)
sig1_Pdot = Pdot(sig1_f0/1.e9 + best_f0, sig1_f1/1.e18)
sig1_B = B(sig1_P, sig1_Pdot)
sig1_Edot = Edot(sig1_P, sig1_Pdot)
sig1_tau = s_to_Myr(tau(sig1_P, sig1_Pdot))
im = ax.imshow(arr/DOF, origin="lower", extent=[1.e9*(np.nanmin(df['F0'])- best_f0), 1.e9*(np.nanmax(df['F0'])-best_f0), 1.e18*np.nanmin(df['F1']), 1.e18*np.nanmax(df['F1'])], interpolation="none", aspect="auto", cmap="bone_r", norm=LogNorm(vmin=1, vmax=10))
ax.set_ylim([-1.5, 1.5])
ax.set_xlim([-0.2, 0.2])
ax.set_xlabel(r'$\Delta f$ / nHz')
ax.set_ylabel(r'$\Delta \dot{f}$ / $10^{-18}$')
ax.axvline(0, lw=0.5, ls="--", color="k", alpha=0.5)
ax.axhline(0, lw=0.5, ls="--", color="k", alpha=0.5)
ax.scatter(best_f0 - best_f0, 1.e18*best_f1, marker="o", zorder=30, lw=0.5, s=10)
#ax.errorbar(sig1_f0 - best_f0/1.e9, sig1_f1, color='magenta', **errargs)
arrowlength = 0.15
ax.annotate("", xy=(sig1_f0 - best_f0/1.e9, sig1_f1 + arrowlength), xytext=(sig1_f0 - best_f0/1.e9, sig1_f1), arrowprops = {'color' : 'magenta', **arrowprops})
ax.annotate("", xy=(sig2_f0 - best_f0/1.e9, sig2_f1 + arrowlength), xytext=(sig2_f0 - best_f0/1.e9, sig2_f1), arrowprops = {'color' : 'violet', **arrowprops})
ax.annotate("", xy=(sig3_f0 - best_f0/1.e9, sig3_f1 + arrowlength), xytext=(sig3_f0 - best_f0/1.e9, sig3_f1), arrowprops = {'color' : 'thistle', **arrowprops})
#ax.errorbar(sig2_f0 - best_f0/1.e9, sig2_f1, color='violet', **errargs)
#ax.errorbar(sig3_f0 - best_f0/1.e9, sig3_f1, color='thistle', **errargs)
cb = plt.colorbar(im, label=r"reduced $\chi^2$", format=FormatStrFormatter('%2.0f'))
cb.ax.yaxis.set_minor_formatter(FormatStrFormatter('%2.0f'))
fig.savefig("Ppdot_search.pdf", bbox_inches="tight", dpi=300)
fig.savefig("Ppdot_search.eps", bbox_inches="tight", dpi=300)
print(f"Best F0 = {best_f0}, Best F1 = {best_f1}")
print(f"Best P = {best_P}, Best Pdot = {best_Pdot}")
print(f"1-sigma limit F0 = {sig1_f0/1.e9 + best_f0}, 1-sigma limit F1 = {sig1_f1/1.e18}")
print(f"1-sigma limit P = {sig1_P}, 1-sigma limit Pdot = {sig1_Pdot}")
print(f"1-sigma limit Edot = {sig1_Edot:2.2g} erg/s, B = {sig1_B:2.2g} G, tau = {sig1_tau:2.2g} Myr")
print(f"2-sigma limit F0 = {sig2_f0/1.e9 + best_f0}, 2-sigma limit F1 = {sig2_f1/1.e18}")
print(f"2-sigma limit P = {sig2_P}, 2-sigma limit Pdot = {sig2_Pdot}")
print(f"2-sigma limit Edot = {sig2_Edot:2.2g} erg/s, B = {sig2_B:2.2g} G, tau = {sig2_tau:2.2g} Myr")
print(f"3-sigma limit F0 = {sig3_f0/1.e9 + best_f0}, 3-sigma limit F1 = {sig3_f1/1.e18}")
print(f"3-sigma limit P = {sig3_P}, 3-sigma limit Pdot = {sig3_Pdot}")
print(f"3-sigma limit Edot = {sig3_Edot:2.2g} erg/s, B = {sig3_B:2.2g} G, tau = {sig3_tau:2.2g} Myr")
print(f"Reduced chi^2 of fit = {bestfit/DOF}")
#fig.savefig("P_Pdot.pdf", bbox_inches="tight", dpi=300)
#fig.savefig("P_Pdot.pdf", bbox_inches="tight", dpi=300)
#fig.savefig("P_Pdot.pdf", bbox_inches="tight", dpi=300)
#fig.savefig("P_Pdot.png", bbox_inches="tight", dpi=300)
#fig.savefig("P_Pdot.eps", bbox_inches="tight", dpi=300)
|
nhurleywalkerREPO_NAMEGPMTransientPATH_START.@GPMTransient_extracted@GPMTransient-main@P_Pdot_diagram@plot_P_Pdot_search.py@.PATH_END.py
|
{
"filename": "README.md",
"repo_name": "EranOfek/AstroPack",
"repo_path": "AstroPack_extracted/AstroPack-main/matlab/apps/app_snr/README.md",
"type": "Markdown"
}
|
# SNR Applications
|
EranOfekREPO_NAMEAstroPackPATH_START.@AstroPack_extracted@AstroPack-main@matlab@apps@app_snr@README.md@.PATH_END.py
|
{
"filename": "makeMassFunctionPlotsCCL_recovered.py",
"repo_name": "simonsobs/nemo",
"repo_path": "nemo_extracted/nemo-main/examples/SOSims/validationScripts/makeMassFunctionPlotsCCL_recovered.py",
"type": "Python"
}
|
"""
Plot the mass function in z bins.
Range adjusted to drop the last bin, which is more incomplete in the sense that it may not cover that
full mass bin (whereas all other bins are guaranteed to by definition).
"""
import os
import sys
import astropy.table as atpy
import astropy.io.fits as pyfits
import IPython
import numpy as np
from nemo import plotSettings, completeness, signals
import pylab as plt
from scipy import stats
from astLib import *
import pyccl as ccl
from colossus.lss import mass_function
#------------------------------------------------------------------------------------------------------------
# Options
SNRCut=4.0
selFnDir="../MFMF_SOSim_3freq_tiles/selFn"
footprintLabel=None
massCol='M200m'
zBinEdges=[0.2, 0.5, 0.9, 1.2]
zMin=min(zBinEdges)
zMax=max(zBinEdges)
log10MBinEdges=np.linspace(13.8, 15.5, 18)
symbs=['D', 's', 'o']
# Handling different mass definitions
if massCol == 'M500c':
delta=500
rhoType="critical"
elif massCol == 'M200m':
delta=200
rhoType="matter"
else:
raise Exception("Unsupported massCol - should be M500c or M200m")
deltaLabel="%d%s" % (delta, rhoType[0])
log10MBinCentres=(log10MBinEdges[1:]+log10MBinEdges[:-1])/2
# Set up Websky cosmology
H0, Om0, Ob0, sigma_8, ns = 68.0, 0.31, 0.049, 0.81, 0.965
selFn=completeness.SelFn(selFnDir, SNRCut, footprintLabel = footprintLabel, zStep = 0.02,
delta = delta, rhoType = rhoType)
scalingRelationDict=selFn.scalingRelationDict
selFn.update(H0, Om0, Ob0, sigma_8, ns, scalingRelationDict = scalingRelationDict)
print("Total area = %.3f square degrees" % (selFn.totalAreaDeg2))
# Load Nemo catalog
tab=atpy.Table().read('../MFMF_SOSim_3freq_tiles/MFMF_SOSim_3freq_tiles_M500.fits')
tab.rename_column("M500", "M500c")
# All the analysis first ------------------------------------------------------------------------------------
# WARNING: We're using halo catalogs, so disabled completeness correction
results={}
predMz=selFn.mockSurvey.clusterCount
for i in range(len(zBinEdges)-1):
zMin=zBinEdges[i]
zMax=zBinEdges[i+1]
label='%.1f < z < %.1f' % (zMin, zMax)
fSky=selFn.mockSurvey.areaDeg2/(4*np.pi*(180/np.pi)**2)
shellVolumeMpc3=fSky*(selFn.mockSurvey._comovingVolume(zMax)-selFn.mockSurvey._comovingVolume(zMin))
zMask=np.logical_and(selFn.mockSurvey.z >= zMin, selFn.mockSurvey.z < zMax)
countsByMass=predMz[zMask, :].sum(axis = 0)
predCounts=np.zeros(len(log10MBinEdges)-1)
predNumDensity=np.zeros(len(log10MBinEdges)-1)
obsCounts=np.zeros(len(log10MBinEdges)-1)
obsCountsErr=np.zeros(len(log10MBinEdges)-1)
obsNumDensity=np.zeros(len(log10MBinEdges)-1)
obsNumDensityErr=np.zeros(len(log10MBinEdges)-1)
complCorr=np.zeros(len(log10MBinEdges)-1) # Holds average completeness in each mass bin
h=H0/100.
binTab=tab[np.logical_and(tab['redshift'] >= zMin, tab['redshift'] < zMax)]
obsLog10Ms=np.log10(binTab[massCol]*1e14)
for j in range(len(log10MBinEdges)-1):
mMin=log10MBinEdges[j]
mMax=log10MBinEdges[j+1]
mMask=np.logical_and(selFn.mockSurvey.log10M >= mMin, selFn.mockSurvey.log10M < mMax)
predCounts[j]=countsByMass[mMask].sum()
obsMask=np.logical_and(obsLog10Ms >= mMin, obsLog10Ms < mMax)
obsCounts[j]=obsMask.sum()
obsCountsErr[j]=np.sqrt(obsCounts[j])
predNumDensity[j]=predCounts[j]/shellVolumeMpc3
obsNumDensity[j]=obsCounts[j]/shellVolumeMpc3
complCorr[j]=selFn.compMz[zMask, :].mean(axis = 0)[mMask].mean()
validMask=(obsCounts > 0)
fracErr=obsCountsErr[validMask]/obsCounts[validMask]
results[label]={'log10MBinCentres': log10MBinCentres[validMask],
'predCounts': predCounts[validMask],
'obsCounts': obsCounts[validMask],
'obsCountsErr': obsCountsErr[validMask],
'predNumDensity': predNumDensity[validMask],
'obsNumDensity': obsNumDensity[validMask],
'obsNumDensityErr': fracErr*obsNumDensity[validMask],
# Completeness corrected
'corr_obsCounts': obsCounts[validMask]/complCorr[validMask],
'corr_obsCountsErr': fracErr*(obsCounts[validMask]/complCorr[validMask]),
'corr_obsNumDensity': obsNumDensity[validMask]/complCorr[validMask],
'corr_obsNumDensityErr': fracErr*(obsNumDensity[validMask]/complCorr[validMask]),
}
# Counts comparison plot (just N as a function of mass) -----------------------------------------------------
plotSettings.update_rcParams()
plt.figure(figsize=(9,6.5))
ax=plt.axes([0.15, 0.12, 0.84, 0.85])
for key, symb in zip(results.keys(), symbs):
plotLog10MBinCentres=results[key]['log10MBinCentres']
pred=results[key]['predCounts']
obs=results[key]['obsCounts']
obsErr=results[key]['obsCountsErr']
corr_obs=results[key]['corr_obsCounts']
corr_obsErr=results[key]['corr_obsCountsErr']
plt.errorbar(plotLog10MBinCentres, obs, yerr = obsErr, color = 'none', markeredgecolor = 'k',
elinewidth = 3, fmt = symb, ms = 6, zorder = 900)
plt.errorbar(plotLog10MBinCentres, corr_obs, yerr = corr_obsErr,
elinewidth = 3, fmt = symb, ms = 6, zorder = 900, label = key)
plt.plot(plotLog10MBinCentres, pred, 'k-')
plt.semilogy()
plt.ylim(0.1, 5e5)
plt.xlim(14.0, log10MBinEdges.max())
plt.xlabel("log$_{10}$($M_{\\rm %s}$ / $M_{\odot}$)" % (deltaLabel))
plt.ylabel("$N$")
plt.legend()
plt.savefig("Recovered_%s_counts.png" % (massCol))
plt.close()
# Counts per unit volume (N per Mpc^3) ----------------------------------------------------------------------
plotSettings.update_rcParams()
plt.figure(figsize=(9,6.5))
ax=plt.axes([0.15, 0.12, 0.84, 0.85])
for key, symb in zip(results.keys(), symbs):
plotLog10MBinCentres=results[key]['log10MBinCentres']
pred=results[key]['predNumDensity']
obs=results[key]['obsNumDensity']
obsErr=results[key]['obsNumDensityErr']
corr_obs=results[key]['corr_obsNumDensity']
corr_obsErr=results[key]['corr_obsNumDensityErr']
plt.errorbar(plotLog10MBinCentres, obs, yerr = obsErr, color = 'none', markeredgecolor = 'k',
elinewidth = 3, fmt = symb, ms = 6, zorder = 900)
plt.errorbar(plotLog10MBinCentres, corr_obs, yerr = corr_obsErr,
elinewidth = 3, fmt = symb, ms = 6, zorder = 900, label = key)
plt.plot(plotLog10MBinCentres, pred, 'k-')
plt.semilogy()
#plt.ylim(0.1, 5e5)
plt.xlim(14.0, log10MBinEdges.max())
plt.xlabel("log$_{10}$($M_{\\rm %s}$ / $M_{\odot}$)" % (deltaLabel))
plt.ylabel("$N$ (Mpc$^{-3}$)")
plt.legend()
plt.savefig("Recovered_%s_numDensity.png" % (massCol))
plt.close()
IPython.embed()
sys.exit()
|
simonsobsREPO_NAMEnemoPATH_START.@nemo_extracted@nemo-main@examples@SOSims@validationScripts@makeMassFunctionPlotsCCL_recovered.py@.PATH_END.py
|
{
"filename": "ryota_plot.py",
"repo_name": "grzeimann/Remedy",
"repo_path": "Remedy_extracted/Remedy-master/ryota_plot.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Sep 13 09:29:15 2019
@author: gregz
"""
from astroquery.sdss import SDSS
from astropy.table import Table
import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator
import seaborn as sns
import numpy as np
import matplotlib
from tables import open_file
from astropy.io import fits
sns.set_context('talk')
ML = MultipleLocator(500)
ml = MultipleLocator(100)
MLy = MultipleLocator(1)
mly = MultipleLocator(0.2)
ncat = Table.read('ryota.dat', format='ascii.fixed_width_two_line')
sp = SDSS.get_spectra(matches=ncat)
index = -5
out_pdf = 'ryota_hetdex.pdf'
F = fits.open('ryota_hetdex_spectra.fits')
pdf = matplotlib.backends.backend_pdf.PdfPages(out_pdf)
hdfile = open_file('survey_hdr1.h5')
t = Table(hdfile.root.Survey[:])
cnt = 0
for index in np.arange(len(ncat)):
if (cnt % 3) == 0:
plot_num = 311
fig = plt.figure(figsize=(8.5, 11)) # inches
plt.subplot(plot_num)
plt.plot(10**(sp[index][1].data['loglam']), sp[index][1].data['flux'], lw=1,
alpha=0.5, label='SDSS')
flam = 10**(-0.4 * (ncat[index]['g']-23.9)) * 1e-29 * 3e18 / 5000.**2 * 1e17
plt.scatter(5000., flam, marker='x', color='k', s=150)
for ind in np.where(ncat['specobjid'][index] == F[1].data['source_id'])[0]:
st = str(F[1].data['obs_id'][ind])
sel1 = np.where((t['date'] == int(st[:8])) * (t['obsid'] == int(st[-3:])))[0]
x = t[sel1[0]]
sel = F[4].data[ind] < 0.8
data = F[2].data[ind] * 1.
data[sel] = np.nan
plt.plot(np.linspace(3470, 5540, 1036), data, 'r-', alpha=0.4,
label=st)
plt.xlim([3450, 5550])
plt.xlabel(r'Wavelength ($\AA$)')
plt.ylabel(r'F$_{\lambda}$ (1e-17 ergs/s/cm^2/$\AA$)')
if (cnt % 3) == 2:
pdf.savefig(fig)
if index == (len(ncat)-1):
pdf.savefig(fig)
plot_num +=1
cnt += 1
pdf.close()
|
grzeimannREPO_NAMERemedyPATH_START.@Remedy_extracted@Remedy-master@ryota_plot.py@.PATH_END.py
|
{
"filename": "quickguide.py",
"repo_name": "exosports/BART",
"repo_path": "BART_extracted/BART-master/scripts/quickguide.py",
"type": "Python"
}
|
# ::: Frequently-Used Scripts :::
# -------------------------------
# Index:
# ( 0) Make a log-scaled pressure profile.
# ( 1) Make a Temperature profile.
# ( 2) Make an atmospheric file with uniform abundances.
# ( 3) Read and plot a transit spectrum.
# ( 4) Calculate the minimum and maximum line-profile widths.
# ( 5) Plot the posterior TP profiles.
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# Preamble:
import sys
import numpy as np
import matplotlib.pyplot as plt
import scipy.constants as sc
# Assuming that the current working directory is /home/.../BART/scripts/
sys.path.append("../code/")
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# ( 0) Make a log-scaled pressure profile:
import makeP as mp
# Output pressure file:
pfile = "layers.press"
# Pressure variables:
nlayers = 100
ptop = 1e-5 # bar
pbottom = 1e2 # bar
# Write the pressure to file:
mp.makeP(nlayers, ptop, pbottom, pfile, log=True)
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# ( 1) Make a Temperature profile using model from Line et al. (2013):
import PT as PT
# [Run script ( 0) to make a pressure profile file]
# Read the pressure file to an array:
press = PT.read_press_file(pfile)
# System parameters:
Rplanet = 1.0*6.995e8 # m
Tstar = 5700.0 # K
Tint = 100.0 # K
gplanet = 2200.0 # cm s-2
smaxis = 0.050*sc.au # m
# Fitting parameters: [log10(kappa), log10(g1), log10(g2), alpha, beta]
params = np.asarray( [-2.0, -0.55, -0.8, 0.5, 1.0])
# Calculate the temperature profile:
temp = PT.PT_line(press, params, Rplanet, Tstar, Tint, smaxis, gplanet)
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# ( 2) Make an atmospheric file with uniform abundances:
import makeatm as ma
# Output filename:
atmfile = "uniform.atm"
# Elemental abundances file:
elemabun = "../inputs/abundances_Asplund2009.txt"
# Transiting extrasolar planet filename:
tep = "../inputs/tep/HD209458b.tep"
# Atmospheric species:
species = "He H2 CO CO2 CH4 H2O NH3 C2H2 C2H4"
# Abundances (mole mixing ratio):
abundances = "0.15 0.85 1e-4 1e-4 1e-4 1e-4 1e-10 1e-10 1e-10"
# [Run script ( 2) to make a temperature profile]
# Make the atmospheric file:
ma.uniform(atmfile, pfile, elemabun, tep, species, abundances, temp)
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# ( 3) Read and plot a transit spectrum:
import readtransit as rt
wl, spectrum = rt.readspectrum("eclipse_out.dat.-Flux", 0)
plt.figure(1)
plt.clf()
plt.semilogx(wl, spectrum, "b", label="Planet spectrum")
plt.xlim(wl[-1], wl[0])
plt.legend(loc="best")
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# ( 4) Calculate the minimum and maximum line-profile widths:
# TBD
# ::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
# ( 5) Plot the posterior TP profiles:
# TBD
|
exosportsREPO_NAMEBARTPATH_START.@BART_extracted@BART-master@scripts@quickguide.py@.PATH_END.py
|
{
"filename": "readCoREAS.py",
"repo_name": "nu-radio/NuRadioMC",
"repo_path": "NuRadioMC_extracted/NuRadioMC-master/NuRadioReco/modules/io/coreas/readCoREAS.py",
"type": "Python"
}
|
from NuRadioReco.modules.base.module import register_run
import h5py
import NuRadioReco.framework.event
import NuRadioReco.framework.station
import NuRadioReco.framework.radio_shower
from radiotools import coordinatesystems as cstrafo
from NuRadioReco.modules.io.coreas import coreas
from NuRadioReco.utilities import units
import numpy as np
import numpy.random
import logging
import time
import os
class readCoREAS:
def __init__(self):
self.__t = 0
self.__t_event_structure = 0
self.__t_per_event = 0
self.__input_files = None
self.__station_id = None
self.__n_cores = None
self.__max_distace = None
self.__current_input_file = None
self.__random_generator = None
self.logger = logging.getLogger('NuRadioReco.readCoREAS')
def begin(self, input_files, station_id, n_cores=10, max_distance=2 * units.km, seed=None):
"""
begin method
initialize readCoREAS module
Parameters
----------
input_files: input files
list of coreas hdf5 files
station_id: station id
id number of the station
n_cores: number of cores (integer)
the number of random core positions to generate for each input file
max_distance: radius of random cores (double or None)
if None: max distance is set to the maximum ground distance of the
star pattern simulation
seed: int (default: None)
Seed for the random number generation. If None is passed, no seed is set
"""
self.__input_files = input_files
self.__station_id = station_id
self.__n_cores = n_cores
self.__max_distace = max_distance
self.__current_input_file = 0
self.__random_generator = numpy.random.RandomState(seed)
@register_run()
def run(self, detector, output_mode=0):
"""
Read in a random sample of stations from a CoREAS file.
A number of random positions is selected within a certain radius.
For each position the closest observer is selected and a simulated
event is created for that observer.
Parameters
----------
detector: Detector object
Detector description of the detector that shall be simulated
output_mode: integer (default 0)
* 0: only the event object is returned
* 1: the function reuturns the event object, the current inputfilename, the distance between the choosen station and the requested core position,
and the area in which the core positions are randomly distributed
"""
while (self.__current_input_file < len(self.__input_files)):
t = time.time()
t_per_event = time.time()
filesize = os.path.getsize(self.__input_files[self.__current_input_file])
if(filesize < 18456 * 2): # based on the observation that a file with such a small filesize is corrupt
self.logger.warning("file {} seems to be corrupt, skipping to next file".format(self.__input_files[self.__current_input_file]))
self.__current_input_file += 1
continue
corsika = h5py.File(self.__input_files[self.__current_input_file], "r")
self.logger.info(
"using coreas simulation {} with E={:2g} theta = {:.0f}".format(
self.__input_files[self.__current_input_file],
corsika['inputs'].attrs["ERANGE"][0] * units.GeV,
corsika['inputs'].attrs["THETAP"][0]
)
)
positions = []
for i, observer in enumerate(corsika['CoREAS']['observers'].values()):
position = observer.attrs['position']
positions.append(np.array([-position[1], position[0], 0]) * units.cm)
self.logger.debug("({:.0f}, {:.0f})".format(position[0], position[1]))
positions = np.array(positions)
max_distance = self.__max_distace
if(max_distance is None):
max_distance = np.max(np.abs(positions[:, 0:2]))
area = np.pi * max_distance ** 2
if(output_mode == 0):
n_cores = self.__n_cores * 100 # for output mode 1 we want always n_cores in star pattern. Therefore we generate more core positions to be able to select n_cores in the star pattern afterwards
elif(output_mode == 1):
n_cores = self.__n_cores
else:
raise ValueError('output mode {} not defined.'.format(output_mode))
theta = self.__random_generator.rand(n_cores) * 2 * np.pi
r = (self.__random_generator.rand(n_cores)) ** 0.5 * max_distance
cores = np.array([r * np.cos(theta), r * np.sin(theta), np.zeros(n_cores)]).T
zenith, azimuth, magnetic_field_vector = coreas.get_angles(corsika)
cs = cstrafo.cstrafo(zenith, azimuth, magnetic_field_vector)
positions_vBvvB = cs.transform_from_magnetic_to_geographic(positions.T)
positions_vBvvB = cs.transform_to_vxB_vxvxB(positions_vBvvB).T
dd = (positions_vBvvB[:, 0] ** 2 + positions_vBvvB[:, 1] ** 2) ** 0.5
ddmax = dd.max()
self.logger.info("star shape from: {} - {}".format(-dd.max(), dd.max()))
cores_vBvvB = cs.transform_from_magnetic_to_geographic(cores.T)
cores_vBvvB = cs.transform_to_vxB_vxvxB(cores_vBvvB).T
dcores = (cores_vBvvB[:, 0] ** 2 + cores_vBvvB[:, 1] ** 2) ** 0.5
mask_cores_in_starpattern = dcores <= ddmax
if((not np.sum(mask_cores_in_starpattern)) and (output_mode == 1)): # handle special case of no core position being generated within star pattern
observer = corsika['CoREAS']['observers'].values()[0]
evt = NuRadioReco.framework.event.Event(corsika['inputs'].attrs['RUNNR'], corsika['inputs'].attrs['EVTNR']) # create empty event
station = NuRadioReco.framework.station.Station(self.__station_id)
sim_station = coreas.make_sim_station(self.__station_id, corsika, observer, detector.get_channel_ids(self.__station_id))
station.set_sim_station(sim_station)
evt.set_station(station)
yield evt, self.__current_input_file, None, area
cores_to_iterate = cores_vBvvB[mask_cores_in_starpattern]
if(output_mode == 0): # select first n_cores that are in star pattern
if(np.sum(mask_cores_in_starpattern) < self.__n_cores):
self.logger.warning("only {0} cores contained in star pattern, returning {0} cores instead of {1} cores that were requested".format(np.sum(mask_cores_in_starpattern), self.__n_cores))
else:
cores_to_iterate = cores_vBvvB[mask_cores_in_starpattern][:self.__n_cores]
self.__t_per_event += time.time() - t_per_event
self.__t += time.time() - t
for iCore, core in enumerate(cores_to_iterate):
t = time.time()
# check if out of bounds
distances = np.linalg.norm(core[:2] - positions_vBvvB[:, :2], axis=1)
index = np.argmin(distances)
distance = distances[index]
key = list(corsika['CoREAS']['observers'].keys())[index]
self.logger.info(
"generating core at ground ({:.0f}, {:.0f}), vBvvB({:.0f}, {:.0f}), nearest simulated station is {:.0f}m away at ground ({:.0f}, {:.0f}), vBvvB({:.0f}, {:.0f})".format(
cores[iCore][0],
cores[iCore][1],
core[0],
core[1],
distance / units.m,
positions[index][0],
positions[index][1],
positions_vBvvB[index][0],
positions_vBvvB[index][1]
)
)
t_event_structure = time.time()
observer = corsika['CoREAS']['observers'].get(key)
evt = NuRadioReco.framework.event.Event(self.__current_input_file, iCore) # create empty event
station = NuRadioReco.framework.station.Station(self.__station_id)
channel_ids = detector.get_channel_ids(self.__station_id)
sim_station = coreas.make_sim_station(self.__station_id, corsika, observer, channel_ids)
station.set_sim_station(sim_station)
evt.set_station(station)
sim_shower = coreas.make_sim_shower(corsika, observer, detector, self.__station_id)
evt.add_sim_shower(sim_shower)
rd_shower = NuRadioReco.framework.radio_shower.RadioShower(station_ids=[station.get_id()])
evt.add_shower(rd_shower)
if(output_mode == 0):
self.__t += time.time() - t
self.__t_event_structure += time.time() - t_event_structure
yield evt
elif(output_mode == 1):
self.__t += time.time() - t
self.__t_event_structure += time.time() - t_event_structure
yield evt, self.__current_input_file, distance, area
else:
self.logger.debug("output mode > 1 not implemented")
raise NotImplementedError
self.__current_input_file += 1
def end(self):
from datetime import timedelta
self.logger.setLevel(logging.INFO)
dt = timedelta(seconds=self.__t)
self.logger.info("total time used by this module is {}".format(dt))
self.logger.info("\tcreate event structure {}".format(timedelta(seconds=self.__t_event_structure)))
self.logger.info("per event {}".format(timedelta(seconds=self.__t_per_event)))
return dt
|
nu-radioREPO_NAMENuRadioMCPATH_START.@NuRadioMC_extracted@NuRadioMC-master@NuRadioReco@modules@io@coreas@readCoREAS.py@.PATH_END.py
|
{
"filename": "_family.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/cone/legendgrouptitle/font/_family.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class FamilyValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(
self, plotly_name="family", parent_name="cone.legendgrouptitle.font", **kwargs
):
super(FamilyValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "style"),
no_blank=kwargs.pop("no_blank", True),
strict=kwargs.pop("strict", True),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@cone@legendgrouptitle@font@_family.py@.PATH_END.py
|
{
"filename": "_ticktextsrc.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py3/plotly/validators/scattermap/marker/colorbar/_ticktextsrc.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TicktextsrcValidator(_plotly_utils.basevalidators.SrcValidator):
def __init__(
self,
plotly_name="ticktextsrc",
parent_name="scattermap.marker.colorbar",
**kwargs,
):
super(TicktextsrcValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "none"),
**kwargs,
)
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py3@plotly@validators@scattermap@marker@colorbar@_ticktextsrc.py@.PATH_END.py
|
{
"filename": "puntopi-checkpoint.ipynb",
"repo_name": "Monsalves-Gonzalez-N/Paper_OGLE",
"repo_path": "Paper_OGLE_extracted/Paper_OGLE-main/.ipynb_checkpoints/puntopi-checkpoint.ipynb",
"type": "Jupyter Notebook"
}
|
```python
from CNN_2dhist_function import *
```
2024-01-11 18:04:18.640756: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-01-11 18:04:18.677020: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-11 18:04:18.677050: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-11 18:04:18.678017: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-01-11 18:04:18.683542: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
INFO: Pandarallel will run on 24 workers.
INFO: Pandarallel will use Memory file system to transfer data between the main process and workers.
```python
# Establecer la semilla para TensorFlow
tf.random.set_seed(42)
# Obtén el número de CPUs
num_cpus = psutil.cpu_count(logical=False)
path_data = "/home/nicolas/nico/Data/data_Paper_OGLE/"
datos = f"{path_data}Data/datos_ogle/datos"
path_datos_4 = datos + "/datos_ogle_4/I"
path_datos_3 = datos + "/datos_ogle_3/I"
path_datos = ["_","_","_",path_datos_3,path_datos_4]
rng = np.random.default_rng(42)
gyr = ["#ffa600",
'#003f5c',
"#58508d",
"#ff6361",
"#ffd380",
"#bc5090",
"#129675"
]
palet = sns.palplot(sns.color_palette(gyr))
sns.set_context("paper")
path = "/home/nicolas/nico/Data/data_Paper_OGLE/7_01_2024/"
```
```python
train_number_ELL = pd.read_csv(f"{path}/train_number_ELL.csv")
train_number_DST = pd.read_csv(f"{path}/train_number_DST.csv")
train_number_M = pd.read_csv(f"{path}/train_number_M.csv")
prueba_8mil = pd.read_csv(f"{path}/prueba_8mil.csv")
```
```python
data = h5py.File(f"{path}/Data.hdf5", 'r+')
```
```python
df_lista = [prueba_8mil,train_number_ELL,train_number_DST,train_number_M]
keys_lista = ['Number_CEP','Number_ELL','Number_DST', 'Number_M']
```
```python
train_models(df_lista, keys_lista, data, prueba_8mil,path,epochs=1000, use_balanced_generator=False)
```
Use balanced Generator [False]
Data: 67232
-----------------------------------------------------------------------------------
Epoch 1/1000
2024-01-11 18:05:38.745700: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.809019: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.809163: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.813242: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.813522: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.813698: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.927569: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.927657: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.927722: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:901] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355
2024-01-11 18:05:38.927769: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5943 MB memory: -> device: 0, name: NVIDIA GeForce RTX 4060 Laptop GPU, pci bus id: 0000:01:00.0, compute capability: 8.9
2024-01-11 18:05:39.874701: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8906
2024-01-11 18:05:40.783496: I external/local_xla/xla/service/service.cc:168] XLA service 0x4b7d110 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2024-01-11 18:05:40.783514: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA GeForce RTX 4060 Laptop GPU, Compute Capability 8.9
2024-01-11 18:05:40.788351: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1704996340.856163 570820 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.
701/701 [==============================] - ETA: 0s - loss: 2.0800 - acc: 0.1382
Epoch 1: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 9s 8ms/step - loss: 2.0800 - acc: 0.1382 - val_loss: 2.0772 - val_acc: 0.2170
Epoch 2/1000
699/701 [============================>.] - ETA: 0s - loss: 2.0758 - acc: 0.1713
Epoch 2: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 2.0758 - acc: 0.1713 - val_loss: 2.0722 - val_acc: 0.2659
Epoch 3/1000
700/701 [============================>.] - ETA: 0s - loss: 2.0703 - acc: 0.1958
Epoch 3: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 2.0703 - acc: 0.1958 - val_loss: 2.0648 - val_acc: 0.2593
Epoch 4/1000
693/701 [============================>.] - ETA: 0s - loss: 2.0614 - acc: 0.2044
Epoch 4: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 2.0613 - acc: 0.2045 - val_loss: 2.0506 - val_acc: 0.2616
Epoch 5/1000
695/701 [============================>.] - ETA: 0s - loss: 2.0419 - acc: 0.2207
Epoch 5: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 2.0417 - acc: 0.2206 - val_loss: 2.0183 - val_acc: 0.2845
Epoch 6/1000
700/701 [============================>.] - ETA: 0s - loss: 1.9969 - acc: 0.2462
Epoch 6: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 1.9968 - acc: 0.2462 - val_loss: 1.9443 - val_acc: 0.3626
Epoch 7/1000
694/701 [============================>.] - ETA: 0s - loss: 1.9038 - acc: 0.3092
Epoch 7: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 1.9032 - acc: 0.3094 - val_loss: 1.7959 - val_acc: 0.4407
Epoch 8/1000
696/701 [============================>.] - ETA: 0s - loss: 1.7345 - acc: 0.3711
Epoch 8: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 1.7338 - acc: 0.3713 - val_loss: 1.5590 - val_acc: 0.5006
Epoch 9/1000
696/701 [============================>.] - ETA: 0s - loss: 1.5584 - acc: 0.4250
Epoch 9: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 1.5579 - acc: 0.4250 - val_loss: 1.3741 - val_acc: 0.5638
Epoch 10/1000
694/701 [============================>.] - ETA: 0s - loss: 1.4129 - acc: 0.4905
Epoch 10: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 6s 8ms/step - loss: 1.4123 - acc: 0.4910 - val_loss: 1.2110 - val_acc: 0.6057
Epoch 11/1000
690/701 [============================>.] - ETA: 0s - loss: 1.2605 - acc: 0.5510
Epoch 11: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 6s 8ms/step - loss: 1.2602 - acc: 0.5510 - val_loss: 1.0563 - val_acc: 0.6435
Epoch 12/1000
695/701 [============================>.] - ETA: 0s - loss: 1.1200 - acc: 0.6013
Epoch 12: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 1.1200 - acc: 0.6012 - val_loss: 0.9307 - val_acc: 0.6770
Epoch 13/1000
700/701 [============================>.] - ETA: 0s - loss: 1.0101 - acc: 0.6358
Epoch 13: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 1.0097 - acc: 0.6359 - val_loss: 0.8406 - val_acc: 0.7089
Epoch 14/1000
697/701 [============================>.] - ETA: 0s - loss: 0.9312 - acc: 0.6634
Epoch 14: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.9313 - acc: 0.6633 - val_loss: 0.7775 - val_acc: 0.7290
Epoch 15/1000
697/701 [============================>.] - ETA: 0s - loss: 0.8759 - acc: 0.6818
Epoch 15: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.8758 - acc: 0.6819 - val_loss: 0.7354 - val_acc: 0.7441
Epoch 16/1000
699/701 [============================>.] - ETA: 0s - loss: 0.8354 - acc: 0.6966
Epoch 16: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.8356 - acc: 0.6967 - val_loss: 0.7024 - val_acc: 0.7543
Epoch 17/1000
701/701 [==============================] - ETA: 0s - loss: 0.8025 - acc: 0.7093
Epoch 17: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.8025 - acc: 0.7093 - val_loss: 0.6813 - val_acc: 0.7593
Epoch 18/1000
700/701 [============================>.] - ETA: 0s - loss: 0.7752 - acc: 0.7190
Epoch 18: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.7753 - acc: 0.7189 - val_loss: 0.6584 - val_acc: 0.7697
Epoch 19/1000
695/701 [============================>.] - ETA: 0s - loss: 0.7509 - acc: 0.7265
Epoch 19: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.7507 - acc: 0.7266 - val_loss: 0.6390 - val_acc: 0.7788
Epoch 20/1000
701/701 [==============================] - ETA: 0s - loss: 0.7359 - acc: 0.7330
Epoch 20: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.7359 - acc: 0.7330 - val_loss: 0.6253 - val_acc: 0.7807
Epoch 21/1000
699/701 [============================>.] - ETA: 0s - loss: 0.7169 - acc: 0.7422
Epoch 21: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.7170 - acc: 0.7421 - val_loss: 0.6112 - val_acc: 0.7877
Epoch 22/1000
701/701 [==============================] - ETA: 0s - loss: 0.7028 - acc: 0.7457
Epoch 22: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.7028 - acc: 0.7457 - val_loss: 0.6002 - val_acc: 0.7900
Epoch 23/1000
700/701 [============================>.] - ETA: 0s - loss: 0.6875 - acc: 0.7495
Epoch 23: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.6874 - acc: 0.7496 - val_loss: 0.5929 - val_acc: 0.7904
Epoch 24/1000
697/701 [============================>.] - ETA: 0s - loss: 0.6749 - acc: 0.7557
Epoch 24: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.6749 - acc: 0.7556 - val_loss: 0.5811 - val_acc: 0.7949
Epoch 25/1000
698/701 [============================>.] - ETA: 0s - loss: 0.6645 - acc: 0.7595
Epoch 25: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.6649 - acc: 0.7595 - val_loss: 0.5724 - val_acc: 0.7947
Epoch 26/1000
693/701 [============================>.] - ETA: 0s - loss: 0.6533 - acc: 0.7631
Epoch 26: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.6540 - acc: 0.7628 - val_loss: 0.5657 - val_acc: 0.7978
Epoch 27/1000
694/701 [============================>.] - ETA: 0s - loss: 0.6459 - acc: 0.7653
Epoch 27: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.6452 - acc: 0.7655 - val_loss: 0.5604 - val_acc: 0.8030
Epoch 28/1000
695/701 [============================>.] - ETA: 0s - loss: 0.6342 - acc: 0.7710
Epoch 28: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.6340 - acc: 0.7712 - val_loss: 0.5530 - val_acc: 0.8048
Epoch 29/1000
701/701 [==============================] - ETA: 0s - loss: 0.6286 - acc: 0.7718
Epoch 29: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.6286 - acc: 0.7718 - val_loss: 0.5486 - val_acc: 0.8022
Epoch 30/1000
697/701 [============================>.] - ETA: 0s - loss: 0.6217 - acc: 0.7754
Epoch 30: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.6214 - acc: 0.7756 - val_loss: 0.5409 - val_acc: 0.8052
Epoch 31/1000
696/701 [============================>.] - ETA: 0s - loss: 0.6164 - acc: 0.7760
Epoch 31: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.6163 - acc: 0.7761 - val_loss: 0.5367 - val_acc: 0.8067
Epoch 32/1000
695/701 [============================>.] - ETA: 0s - loss: 0.6077 - acc: 0.7811
Epoch 32: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.6081 - acc: 0.7810 - val_loss: 0.5300 - val_acc: 0.8128
Epoch 33/1000
693/701 [============================>.] - ETA: 0s - loss: 0.6024 - acc: 0.7821
Epoch 33: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.6027 - acc: 0.7819 - val_loss: 0.5283 - val_acc: 0.8124
Epoch 34/1000
696/701 [============================>.] - ETA: 0s - loss: 0.5949 - acc: 0.7831
Epoch 34: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 6s 8ms/step - loss: 0.5948 - acc: 0.7831 - val_loss: 0.5268 - val_acc: 0.8128
Epoch 35/1000
698/701 [============================>.] - ETA: 0s - loss: 0.5908 - acc: 0.7862
Epoch 35: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5908 - acc: 0.7861 - val_loss: 0.5177 - val_acc: 0.8168
Epoch 36/1000
700/701 [============================>.] - ETA: 0s - loss: 0.5838 - acc: 0.7874
Epoch 36: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5840 - acc: 0.7873 - val_loss: 0.5157 - val_acc: 0.8180
Epoch 37/1000
698/701 [============================>.] - ETA: 0s - loss: 0.5764 - acc: 0.7923
Epoch 37: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5764 - acc: 0.7922 - val_loss: 0.5111 - val_acc: 0.8185
Epoch 38/1000
693/701 [============================>.] - ETA: 0s - loss: 0.5729 - acc: 0.7935
Epoch 38: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5727 - acc: 0.7935 - val_loss: 0.5074 - val_acc: 0.8190
Epoch 39/1000
699/701 [============================>.] - ETA: 0s - loss: 0.5694 - acc: 0.7941
Epoch 39: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5697 - acc: 0.7940 - val_loss: 0.5071 - val_acc: 0.8221
Epoch 40/1000
700/701 [============================>.] - ETA: 0s - loss: 0.5643 - acc: 0.7968
Epoch 40: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5642 - acc: 0.7968 - val_loss: 0.5014 - val_acc: 0.8243
Epoch 41/1000
695/701 [============================>.] - ETA: 0s - loss: 0.5614 - acc: 0.7967
Epoch 41: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5611 - acc: 0.7969 - val_loss: 0.5013 - val_acc: 0.8238
Epoch 42/1000
698/701 [============================>.] - ETA: 0s - loss: 0.5601 - acc: 0.7982
Epoch 42: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5602 - acc: 0.7981 - val_loss: 0.4951 - val_acc: 0.8257
Epoch 43/1000
694/701 [============================>.] - ETA: 0s - loss: 0.5546 - acc: 0.7981
Epoch 43: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5544 - acc: 0.7982 - val_loss: 0.4931 - val_acc: 0.8259
Epoch 44/1000
697/701 [============================>.] - ETA: 0s - loss: 0.5486 - acc: 0.8015
Epoch 44: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5492 - acc: 0.8014 - val_loss: 0.4943 - val_acc: 0.8260
Epoch 45/1000
694/701 [============================>.] - ETA: 0s - loss: 0.5464 - acc: 0.8030
Epoch 45: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5468 - acc: 0.8028 - val_loss: 0.4900 - val_acc: 0.8291
Epoch 46/1000
700/701 [============================>.] - ETA: 0s - loss: 0.5436 - acc: 0.8041
Epoch 46: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5436 - acc: 0.8041 - val_loss: 0.4871 - val_acc: 0.8274
Epoch 47/1000
700/701 [============================>.] - ETA: 0s - loss: 0.5396 - acc: 0.8041
Epoch 47: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5397 - acc: 0.8040 - val_loss: 0.4843 - val_acc: 0.8275
Epoch 48/1000
690/701 [============================>.] - ETA: 0s - loss: 0.5350 - acc: 0.8084
Epoch 48: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5352 - acc: 0.8084 - val_loss: 0.4847 - val_acc: 0.8283
Epoch 49/1000
700/701 [============================>.] - ETA: 0s - loss: 0.5310 - acc: 0.8090
Epoch 49: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5313 - acc: 0.8089 - val_loss: 0.4809 - val_acc: 0.8295
Epoch 50/1000
699/701 [============================>.] - ETA: 0s - loss: 0.5311 - acc: 0.8097
Epoch 50: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5310 - acc: 0.8097 - val_loss: 0.4786 - val_acc: 0.8294
Epoch 51/1000
698/701 [============================>.] - ETA: 0s - loss: 0.5271 - acc: 0.8092
Epoch 51: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.5274 - acc: 0.8090 - val_loss: 0.4769 - val_acc: 0.8319
Epoch 52/1000
695/701 [============================>.] - ETA: 0s - loss: 0.5241 - acc: 0.8115
Epoch 52: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5244 - acc: 0.8115 - val_loss: 0.4736 - val_acc: 0.8319
Epoch 53/1000
695/701 [============================>.] - ETA: 0s - loss: 0.5218 - acc: 0.8124
Epoch 53: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5220 - acc: 0.8123 - val_loss: 0.4726 - val_acc: 0.8335
Epoch 54/1000
693/701 [============================>.] - ETA: 0s - loss: 0.5197 - acc: 0.8130
Epoch 54: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5194 - acc: 0.8131 - val_loss: 0.4688 - val_acc: 0.8362
Epoch 55/1000
692/701 [============================>.] - ETA: 0s - loss: 0.5153 - acc: 0.8141
Epoch 55: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5158 - acc: 0.8141 - val_loss: 0.4702 - val_acc: 0.8332
Epoch 56/1000
698/701 [============================>.] - ETA: 0s - loss: 0.5132 - acc: 0.8156
Epoch 56: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5133 - acc: 0.8156 - val_loss: 0.4685 - val_acc: 0.8354
Epoch 57/1000
699/701 [============================>.] - ETA: 0s - loss: 0.5151 - acc: 0.8152
Epoch 57: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.5149 - acc: 0.8153 - val_loss: 0.4653 - val_acc: 0.8380
Epoch 58/1000
699/701 [============================>.] - ETA: 0s - loss: 0.5102 - acc: 0.8168
Epoch 58: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5103 - acc: 0.8167 - val_loss: 0.4632 - val_acc: 0.8375
Epoch 59/1000
695/701 [============================>.] - ETA: 0s - loss: 0.5075 - acc: 0.8182
Epoch 59: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5072 - acc: 0.8183 - val_loss: 0.4648 - val_acc: 0.8357
Epoch 60/1000
701/701 [==============================] - ETA: 0s - loss: 0.5044 - acc: 0.8189
Epoch 60: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5044 - acc: 0.8189 - val_loss: 0.4627 - val_acc: 0.8371
Epoch 61/1000
693/701 [============================>.] - ETA: 0s - loss: 0.5030 - acc: 0.8197
Epoch 61: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.5028 - acc: 0.8198 - val_loss: 0.4595 - val_acc: 0.8391
Epoch 62/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4997 - acc: 0.8193
Epoch 62: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.5000 - acc: 0.8192 - val_loss: 0.4580 - val_acc: 0.8372
Epoch 63/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4974 - acc: 0.8206
Epoch 63: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4972 - acc: 0.8206 - val_loss: 0.4570 - val_acc: 0.8398
Epoch 64/1000
700/701 [============================>.] - ETA: 0s - loss: 0.4954 - acc: 0.8214
Epoch 64: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4954 - acc: 0.8215 - val_loss: 0.4539 - val_acc: 0.8419
Epoch 65/1000
691/701 [============================>.] - ETA: 0s - loss: 0.4926 - acc: 0.8246
Epoch 65: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4923 - acc: 0.8245 - val_loss: 0.4541 - val_acc: 0.8399
Epoch 66/1000
700/701 [============================>.] - ETA: 0s - loss: 0.4918 - acc: 0.8222
Epoch 66: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4919 - acc: 0.8222 - val_loss: 0.4542 - val_acc: 0.8406
Epoch 67/1000
692/701 [============================>.] - ETA: 0s - loss: 0.4882 - acc: 0.8242
Epoch 67: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4886 - acc: 0.8240 - val_loss: 0.4519 - val_acc: 0.8439
Epoch 68/1000
692/701 [============================>.] - ETA: 0s - loss: 0.4898 - acc: 0.8241
Epoch 68: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4901 - acc: 0.8240 - val_loss: 0.4535 - val_acc: 0.8418
Epoch 69/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4832 - acc: 0.8264
Epoch 69: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4832 - acc: 0.8262 - val_loss: 0.4479 - val_acc: 0.8436
Epoch 70/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4834 - acc: 0.8261
Epoch 70: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4834 - acc: 0.8261 - val_loss: 0.4511 - val_acc: 0.8436
Epoch 71/1000
701/701 [==============================] - ETA: 0s - loss: 0.4810 - acc: 0.8277
Epoch 71: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.4810 - acc: 0.8277 - val_loss: 0.4488 - val_acc: 0.8421
Epoch 72/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4787 - acc: 0.8280
Epoch 72: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4785 - acc: 0.8279 - val_loss: 0.4471 - val_acc: 0.8431
Epoch 73/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4800 - acc: 0.8273
Epoch 73: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4799 - acc: 0.8272 - val_loss: 0.4465 - val_acc: 0.8457
Epoch 74/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4761 - acc: 0.8282
Epoch 74: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4763 - acc: 0.8281 - val_loss: 0.4459 - val_acc: 0.8447
Epoch 75/1000
697/701 [============================>.] - ETA: 0s - loss: 0.4742 - acc: 0.8296
Epoch 75: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4745 - acc: 0.8295 - val_loss: 0.4423 - val_acc: 0.8470
Epoch 76/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4746 - acc: 0.8292
Epoch 76: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4741 - acc: 0.8293 - val_loss: 0.4418 - val_acc: 0.8474
Epoch 77/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4733 - acc: 0.8302
Epoch 77: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4733 - acc: 0.8302 - val_loss: 0.4387 - val_acc: 0.8483
Epoch 78/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4663 - acc: 0.8307
Epoch 78: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4663 - acc: 0.8306 - val_loss: 0.4432 - val_acc: 0.8467
Epoch 79/1000
695/701 [============================>.] - ETA: 0s - loss: 0.4674 - acc: 0.8310
Epoch 79: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4674 - acc: 0.8311 - val_loss: 0.4369 - val_acc: 0.8480
Epoch 80/1000
695/701 [============================>.] - ETA: 0s - loss: 0.4667 - acc: 0.8336
Epoch 80: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.4672 - acc: 0.8334 - val_loss: 0.4367 - val_acc: 0.8506
Epoch 81/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4650 - acc: 0.8334
Epoch 81: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 3s 5ms/step - loss: 0.4650 - acc: 0.8335 - val_loss: 0.4365 - val_acc: 0.8468
Epoch 82/1000
701/701 [==============================] - ETA: 0s - loss: 0.4634 - acc: 0.8345
Epoch 82: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4634 - acc: 0.8345 - val_loss: 0.4394 - val_acc: 0.8468
Epoch 83/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4601 - acc: 0.8344
Epoch 83: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4600 - acc: 0.8342 - val_loss: 0.4341 - val_acc: 0.8499
Epoch 84/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4595 - acc: 0.8351
Epoch 84: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4595 - acc: 0.8352 - val_loss: 0.4387 - val_acc: 0.8482
Epoch 85/1000
701/701 [==============================] - ETA: 0s - loss: 0.4573 - acc: 0.8348
Epoch 85: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4573 - acc: 0.8348 - val_loss: 0.4317 - val_acc: 0.8522
Epoch 86/1000
694/701 [============================>.] - ETA: 0s - loss: 0.4564 - acc: 0.8357
Epoch 86: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4561 - acc: 0.8357 - val_loss: 0.4309 - val_acc: 0.8504
Epoch 87/1000
700/701 [============================>.] - ETA: 0s - loss: 0.4555 - acc: 0.8382
Epoch 87: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4555 - acc: 0.8382 - val_loss: 0.4336 - val_acc: 0.8499
Epoch 88/1000
690/701 [============================>.] - ETA: 0s - loss: 0.4537 - acc: 0.8373
Epoch 88: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4531 - acc: 0.8375 - val_loss: 0.4334 - val_acc: 0.8491
Epoch 89/1000
700/701 [============================>.] - ETA: 0s - loss: 0.4526 - acc: 0.8384
Epoch 89: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4525 - acc: 0.8385 - val_loss: 0.4321 - val_acc: 0.8488
Epoch 90/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4513 - acc: 0.8385
Epoch 90: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4513 - acc: 0.8386 - val_loss: 0.4289 - val_acc: 0.8520
Epoch 91/1000
701/701 [==============================] - ETA: 0s - loss: 0.4502 - acc: 0.8378
Epoch 91: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4502 - acc: 0.8378 - val_loss: 0.4295 - val_acc: 0.8521
Epoch 92/1000
697/701 [============================>.] - ETA: 0s - loss: 0.4493 - acc: 0.8384
Epoch 92: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4493 - acc: 0.8384 - val_loss: 0.4263 - val_acc: 0.8523
Epoch 93/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4470 - acc: 0.8385
Epoch 93: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4474 - acc: 0.8381 - val_loss: 0.4243 - val_acc: 0.8536
Epoch 94/1000
695/701 [============================>.] - ETA: 0s - loss: 0.4459 - acc: 0.8396
Epoch 94: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4460 - acc: 0.8396 - val_loss: 0.4248 - val_acc: 0.8546
Epoch 95/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4436 - acc: 0.8402
Epoch 95: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4440 - acc: 0.8400 - val_loss: 0.4253 - val_acc: 0.8537
Epoch 96/1000
692/701 [============================>.] - ETA: 0s - loss: 0.4430 - acc: 0.8419
Epoch 96: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4434 - acc: 0.8415 - val_loss: 0.4254 - val_acc: 0.8540
Epoch 97/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4420 - acc: 0.8420
Epoch 97: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4421 - acc: 0.8420 - val_loss: 0.4228 - val_acc: 0.8551
Epoch 98/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4403 - acc: 0.8424
Epoch 98: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4407 - acc: 0.8424 - val_loss: 0.4236 - val_acc: 0.8531
Epoch 99/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4386 - acc: 0.8436
Epoch 99: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4387 - acc: 0.8436 - val_loss: 0.4215 - val_acc: 0.8571
Epoch 100/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4376 - acc: 0.8443
Epoch 100: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4374 - acc: 0.8443 - val_loss: 0.4227 - val_acc: 0.8555
Epoch 101/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4368 - acc: 0.8440
Epoch 101: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4366 - acc: 0.8441 - val_loss: 0.4188 - val_acc: 0.8565
Epoch 102/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4353 - acc: 0.8444
Epoch 102: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4356 - acc: 0.8443 - val_loss: 0.4154 - val_acc: 0.8590
Epoch 103/1000
695/701 [============================>.] - ETA: 0s - loss: 0.4346 - acc: 0.8436
Epoch 103: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4344 - acc: 0.8437 - val_loss: 0.4205 - val_acc: 0.8578
Epoch 104/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4376 - acc: 0.8435
Epoch 104: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4373 - acc: 0.8437 - val_loss: 0.4149 - val_acc: 0.8583
Epoch 105/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4320 - acc: 0.8453
Epoch 105: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4322 - acc: 0.8452 - val_loss: 0.4155 - val_acc: 0.8580
Epoch 106/1000
694/701 [============================>.] - ETA: 0s - loss: 0.4289 - acc: 0.8463
Epoch 106: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4296 - acc: 0.8463 - val_loss: 0.4166 - val_acc: 0.8555
Epoch 107/1000
694/701 [============================>.] - ETA: 0s - loss: 0.4302 - acc: 0.8468
Epoch 107: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4301 - acc: 0.8468 - val_loss: 0.4134 - val_acc: 0.8600
Epoch 108/1000
701/701 [==============================] - ETA: 0s - loss: 0.4305 - acc: 0.8457
Epoch 108: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4305 - acc: 0.8457 - val_loss: 0.4137 - val_acc: 0.8573
Epoch 109/1000
697/701 [============================>.] - ETA: 0s - loss: 0.4279 - acc: 0.8469
Epoch 109: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 6s 8ms/step - loss: 0.4283 - acc: 0.8468 - val_loss: 0.4118 - val_acc: 0.8591
Epoch 110/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4293 - acc: 0.8464
Epoch 110: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4285 - acc: 0.8467 - val_loss: 0.4114 - val_acc: 0.8594
Epoch 111/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4265 - acc: 0.8476
Epoch 111: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4262 - acc: 0.8475 - val_loss: 0.4148 - val_acc: 0.8575
Epoch 112/1000
694/701 [============================>.] - ETA: 0s - loss: 0.4222 - acc: 0.8508
Epoch 112: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4222 - acc: 0.8507 - val_loss: 0.4079 - val_acc: 0.8603
Epoch 113/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4220 - acc: 0.8497
Epoch 113: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4223 - acc: 0.8496 - val_loss: 0.4076 - val_acc: 0.8603
Epoch 114/1000
697/701 [============================>.] - ETA: 0s - loss: 0.4237 - acc: 0.8485
Epoch 114: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4240 - acc: 0.8484 - val_loss: 0.4113 - val_acc: 0.8589
Epoch 115/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4200 - acc: 0.8490
Epoch 115: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4201 - acc: 0.8489 - val_loss: 0.4086 - val_acc: 0.8608
Epoch 116/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4202 - acc: 0.8508
Epoch 116: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4202 - acc: 0.8507 - val_loss: 0.4065 - val_acc: 0.8604
Epoch 117/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4186 - acc: 0.8495
Epoch 117: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.4187 - acc: 0.8496 - val_loss: 0.4079 - val_acc: 0.8591
Epoch 118/1000
694/701 [============================>.] - ETA: 0s - loss: 0.4166 - acc: 0.8519
Epoch 118: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4161 - acc: 0.8521 - val_loss: 0.4062 - val_acc: 0.8609
Epoch 119/1000
700/701 [============================>.] - ETA: 0s - loss: 0.4177 - acc: 0.8506
Epoch 119: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4176 - acc: 0.8507 - val_loss: 0.4086 - val_acc: 0.8588
Epoch 120/1000
695/701 [============================>.] - ETA: 0s - loss: 0.4149 - acc: 0.8517
Epoch 120: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4151 - acc: 0.8515 - val_loss: 0.4052 - val_acc: 0.8617
Epoch 121/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4174 - acc: 0.8512
Epoch 121: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4171 - acc: 0.8512 - val_loss: 0.4044 - val_acc: 0.8608
Epoch 122/1000
694/701 [============================>.] - ETA: 0s - loss: 0.4134 - acc: 0.8537
Epoch 122: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4134 - acc: 0.8538 - val_loss: 0.4026 - val_acc: 0.8620
Epoch 123/1000
695/701 [============================>.] - ETA: 0s - loss: 0.4116 - acc: 0.8524
Epoch 123: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4115 - acc: 0.8524 - val_loss: 0.4052 - val_acc: 0.8619
Epoch 124/1000
699/701 [============================>.] - ETA: 0s - loss: 0.4119 - acc: 0.8531
Epoch 124: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4117 - acc: 0.8533 - val_loss: 0.4035 - val_acc: 0.8622
Epoch 125/1000
696/701 [============================>.] - ETA: 0s - loss: 0.4090 - acc: 0.8551
Epoch 125: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4087 - acc: 0.8551 - val_loss: 0.4008 - val_acc: 0.8620
Epoch 126/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4087 - acc: 0.8526
Epoch 126: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4089 - acc: 0.8525 - val_loss: 0.4035 - val_acc: 0.8632
Epoch 127/1000
692/701 [============================>.] - ETA: 0s - loss: 0.4066 - acc: 0.8563
Epoch 127: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4067 - acc: 0.8563 - val_loss: 0.4006 - val_acc: 0.8637
Epoch 128/1000
700/701 [============================>.] - ETA: 0s - loss: 0.4081 - acc: 0.8538
Epoch 128: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4080 - acc: 0.8538 - val_loss: 0.4030 - val_acc: 0.8630
Epoch 129/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4085 - acc: 0.8534
Epoch 129: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4085 - acc: 0.8534 - val_loss: 0.4012 - val_acc: 0.8625
Epoch 130/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4072 - acc: 0.8556
Epoch 130: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4071 - acc: 0.8555 - val_loss: 0.3986 - val_acc: 0.8646
Epoch 131/1000
695/701 [============================>.] - ETA: 0s - loss: 0.4055 - acc: 0.8554
Epoch 131: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4052 - acc: 0.8554 - val_loss: 0.4019 - val_acc: 0.8640
Epoch 132/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4037 - acc: 0.8567
Epoch 132: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.4038 - acc: 0.8565 - val_loss: 0.3972 - val_acc: 0.8650
Epoch 133/1000
693/701 [============================>.] - ETA: 0s - loss: 0.4038 - acc: 0.8559
Epoch 133: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4038 - acc: 0.8561 - val_loss: 0.3999 - val_acc: 0.8610
Epoch 134/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4030 - acc: 0.8554
Epoch 134: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.4030 - acc: 0.8554 - val_loss: 0.3959 - val_acc: 0.8652
Epoch 135/1000
694/701 [============================>.] - ETA: 0s - loss: 0.4015 - acc: 0.8568
Epoch 135: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4017 - acc: 0.8566 - val_loss: 0.3997 - val_acc: 0.8651
Epoch 136/1000
698/701 [============================>.] - ETA: 0s - loss: 0.4006 - acc: 0.8571
Epoch 136: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.4004 - acc: 0.8571 - val_loss: 0.3958 - val_acc: 0.8639
Epoch 137/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3996 - acc: 0.8578
Epoch 137: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3991 - acc: 0.8580 - val_loss: 0.3958 - val_acc: 0.8662
Epoch 138/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3962 - acc: 0.8589
Epoch 138: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3963 - acc: 0.8588 - val_loss: 0.3965 - val_acc: 0.8657
Epoch 139/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3971 - acc: 0.8579
Epoch 139: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3970 - acc: 0.8578 - val_loss: 0.3921 - val_acc: 0.8669
Epoch 140/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3967 - acc: 0.8578
Epoch 140: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3973 - acc: 0.8577 - val_loss: 0.3903 - val_acc: 0.8678
Epoch 141/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3950 - acc: 0.8604
Epoch 141: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 6s 8ms/step - loss: 0.3950 - acc: 0.8604 - val_loss: 0.3967 - val_acc: 0.8632
Epoch 142/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3967 - acc: 0.8581
Epoch 142: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3968 - acc: 0.8580 - val_loss: 0.3960 - val_acc: 0.8646
Epoch 143/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3967 - acc: 0.8606
Epoch 143: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3967 - acc: 0.8604 - val_loss: 0.3917 - val_acc: 0.8673
Epoch 144/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3946 - acc: 0.8593
Epoch 144: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3947 - acc: 0.8592 - val_loss: 0.3923 - val_acc: 0.8649
Epoch 145/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3911 - acc: 0.8614
Epoch 145: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3910 - acc: 0.8614 - val_loss: 0.3913 - val_acc: 0.8681
Epoch 146/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3916 - acc: 0.8608
Epoch 146: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3918 - acc: 0.8608 - val_loss: 0.3914 - val_acc: 0.8673
Epoch 147/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3875 - acc: 0.8613
Epoch 147: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3877 - acc: 0.8611 - val_loss: 0.3882 - val_acc: 0.8667
Epoch 148/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3885 - acc: 0.8615
Epoch 148: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3885 - acc: 0.8613 - val_loss: 0.3905 - val_acc: 0.8677
Epoch 149/1000
701/701 [==============================] - ETA: 0s - loss: 0.3886 - acc: 0.8622
Epoch 149: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3886 - acc: 0.8622 - val_loss: 0.3901 - val_acc: 0.8666
Epoch 150/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3853 - acc: 0.8617
Epoch 150: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3854 - acc: 0.8616 - val_loss: 0.3864 - val_acc: 0.8683
Epoch 151/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3853 - acc: 0.8620
Epoch 151: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3853 - acc: 0.8620 - val_loss: 0.3886 - val_acc: 0.8674
Epoch 152/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3853 - acc: 0.8630
Epoch 152: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3853 - acc: 0.8630 - val_loss: 0.3877 - val_acc: 0.8691
Epoch 153/1000
701/701 [==============================] - ETA: 0s - loss: 0.3833 - acc: 0.8635
Epoch 153: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3833 - acc: 0.8635 - val_loss: 0.3883 - val_acc: 0.8679
Epoch 154/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3865 - acc: 0.8623
Epoch 154: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3866 - acc: 0.8624 - val_loss: 0.3890 - val_acc: 0.8662
Epoch 155/1000
691/701 [============================>.] - ETA: 0s - loss: 0.3821 - acc: 0.8645
Epoch 155: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3821 - acc: 0.8646 - val_loss: 0.3864 - val_acc: 0.8673
Epoch 156/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3835 - acc: 0.8628
Epoch 156: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3831 - acc: 0.8630 - val_loss: 0.3857 - val_acc: 0.8701
Epoch 157/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3822 - acc: 0.8639
Epoch 157: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3821 - acc: 0.8639 - val_loss: 0.3873 - val_acc: 0.8699
Epoch 158/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3798 - acc: 0.8659
Epoch 158: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3798 - acc: 0.8659 - val_loss: 0.3835 - val_acc: 0.8706
Epoch 159/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3781 - acc: 0.8658
Epoch 159: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3780 - acc: 0.8657 - val_loss: 0.3860 - val_acc: 0.8707
Epoch 160/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3782 - acc: 0.8654
Epoch 160: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3781 - acc: 0.8654 - val_loss: 0.3829 - val_acc: 0.8700
Epoch 161/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3795 - acc: 0.8649
Epoch 161: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3794 - acc: 0.8649 - val_loss: 0.3842 - val_acc: 0.8679
Epoch 162/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3811 - acc: 0.8647
Epoch 162: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3814 - acc: 0.8647 - val_loss: 0.3819 - val_acc: 0.8678
Epoch 163/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3772 - acc: 0.8655
Epoch 163: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3770 - acc: 0.8655 - val_loss: 0.3799 - val_acc: 0.8704
Epoch 164/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3743 - acc: 0.8668
Epoch 164: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3739 - acc: 0.8667 - val_loss: 0.3833 - val_acc: 0.8688
Epoch 165/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3734 - acc: 0.8675
Epoch 165: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3732 - acc: 0.8676 - val_loss: 0.3828 - val_acc: 0.8702
Epoch 166/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3747 - acc: 0.8673
Epoch 166: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3751 - acc: 0.8673 - val_loss: 0.3818 - val_acc: 0.8682
Epoch 167/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3769 - acc: 0.8660
Epoch 167: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3764 - acc: 0.8663 - val_loss: 0.3820 - val_acc: 0.8706
Epoch 168/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3728 - acc: 0.8675
Epoch 168: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3729 - acc: 0.8675 - val_loss: 0.3863 - val_acc: 0.8679
Epoch 169/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3704 - acc: 0.8678
Epoch 169: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3704 - acc: 0.8676 - val_loss: 0.3792 - val_acc: 0.8701
Epoch 170/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3710 - acc: 0.8681
Epoch 170: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3709 - acc: 0.8681 - val_loss: 0.3808 - val_acc: 0.8710
Epoch 171/1000
691/701 [============================>.] - ETA: 0s - loss: 0.3691 - acc: 0.8676
Epoch 171: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3689 - acc: 0.8678 - val_loss: 0.3805 - val_acc: 0.8709
Epoch 172/1000
691/701 [============================>.] - ETA: 0s - loss: 0.3701 - acc: 0.8667
Epoch 172: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 8ms/step - loss: 0.3707 - acc: 0.8668 - val_loss: 0.3779 - val_acc: 0.8706
Epoch 173/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3669 - acc: 0.8692
Epoch 173: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3670 - acc: 0.8692 - val_loss: 0.3792 - val_acc: 0.8739
Epoch 174/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3697 - acc: 0.8699
Epoch 174: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3697 - acc: 0.8699 - val_loss: 0.3798 - val_acc: 0.8695
Epoch 175/1000
690/701 [============================>.] - ETA: 0s - loss: 0.3696 - acc: 0.8680
Epoch 175: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.3689 - acc: 0.8681 - val_loss: 0.3789 - val_acc: 0.8719
Epoch 176/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3661 - acc: 0.8704
Epoch 176: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.3660 - acc: 0.8705 - val_loss: 0.3774 - val_acc: 0.8697
Epoch 177/1000
701/701 [==============================] - ETA: 0s - loss: 0.3663 - acc: 0.8693
Epoch 177: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 3s 5ms/step - loss: 0.3663 - acc: 0.8693 - val_loss: 0.3800 - val_acc: 0.8694
Epoch 178/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3657 - acc: 0.8696
Epoch 178: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3652 - acc: 0.8697 - val_loss: 0.3780 - val_acc: 0.8714
Epoch 179/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3669 - acc: 0.8696
Epoch 179: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3669 - acc: 0.8696 - val_loss: 0.3764 - val_acc: 0.8735
Epoch 180/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3656 - acc: 0.8707
Epoch 180: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3655 - acc: 0.8706 - val_loss: 0.3733 - val_acc: 0.8726
Epoch 181/1000
701/701 [==============================] - ETA: 0s - loss: 0.3655 - acc: 0.8701
Epoch 181: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3655 - acc: 0.8701 - val_loss: 0.3784 - val_acc: 0.8703
Epoch 182/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3642 - acc: 0.8706
Epoch 182: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3643 - acc: 0.8707 - val_loss: 0.3741 - val_acc: 0.8722
Epoch 183/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3619 - acc: 0.8699
Epoch 183: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3621 - acc: 0.8699 - val_loss: 0.3740 - val_acc: 0.8746
Epoch 184/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3614 - acc: 0.8718
Epoch 184: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3609 - acc: 0.8720 - val_loss: 0.3741 - val_acc: 0.8740
Epoch 185/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3620 - acc: 0.8708
Epoch 185: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.3618 - acc: 0.8709 - val_loss: 0.3723 - val_acc: 0.8744
Epoch 186/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3598 - acc: 0.8736
Epoch 186: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3599 - acc: 0.8736 - val_loss: 0.3759 - val_acc: 0.8715
Epoch 187/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3589 - acc: 0.8711
Epoch 187: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3581 - acc: 0.8715 - val_loss: 0.3727 - val_acc: 0.8736
Epoch 188/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3570 - acc: 0.8728
Epoch 188: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3568 - acc: 0.8729 - val_loss: 0.3715 - val_acc: 0.8746
Epoch 189/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3582 - acc: 0.8727
Epoch 189: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3583 - acc: 0.8727 - val_loss: 0.3743 - val_acc: 0.8746
Epoch 190/1000
701/701 [==============================] - ETA: 0s - loss: 0.3545 - acc: 0.8737
Epoch 190: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3545 - acc: 0.8737 - val_loss: 0.3748 - val_acc: 0.8743
Epoch 191/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3559 - acc: 0.8733
Epoch 191: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3554 - acc: 0.8735 - val_loss: 0.3747 - val_acc: 0.8745
Epoch 192/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3568 - acc: 0.8719
Epoch 192: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3568 - acc: 0.8721 - val_loss: 0.3720 - val_acc: 0.8741
Epoch 193/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3580 - acc: 0.8724
Epoch 193: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3582 - acc: 0.8723 - val_loss: 0.3717 - val_acc: 0.8760
Epoch 194/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3546 - acc: 0.8747
Epoch 194: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.3547 - acc: 0.8747 - val_loss: 0.3689 - val_acc: 0.8760
Epoch 195/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3540 - acc: 0.8741
Epoch 195: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3540 - acc: 0.8741 - val_loss: 0.3726 - val_acc: 0.8736
Epoch 196/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3533 - acc: 0.8747
Epoch 196: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3534 - acc: 0.8746 - val_loss: 0.3730 - val_acc: 0.8736
Epoch 197/1000
690/701 [============================>.] - ETA: 0s - loss: 0.3541 - acc: 0.8743
Epoch 197: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3539 - acc: 0.8743 - val_loss: 0.3733 - val_acc: 0.8742
Epoch 198/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3504 - acc: 0.8750
Epoch 198: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3504 - acc: 0.8749 - val_loss: 0.3686 - val_acc: 0.8756
Epoch 199/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3502 - acc: 0.8766
Epoch 199: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3502 - acc: 0.8766 - val_loss: 0.3718 - val_acc: 0.8747
Epoch 200/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3501 - acc: 0.8750
Epoch 200: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3502 - acc: 0.8749 - val_loss: 0.3665 - val_acc: 0.8748
Epoch 201/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3511 - acc: 0.8752
Epoch 201: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3511 - acc: 0.8753 - val_loss: 0.3694 - val_acc: 0.8763
Epoch 202/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3511 - acc: 0.8747
Epoch 202: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3514 - acc: 0.8747 - val_loss: 0.3669 - val_acc: 0.8773
Epoch 203/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3489 - acc: 0.8756
Epoch 203: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3487 - acc: 0.8756 - val_loss: 0.3710 - val_acc: 0.8747
Epoch 204/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3494 - acc: 0.8759
Epoch 204: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3494 - acc: 0.8759 - val_loss: 0.3662 - val_acc: 0.8768
Epoch 205/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3468 - acc: 0.8777
Epoch 205: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3471 - acc: 0.8775 - val_loss: 0.3685 - val_acc: 0.8756
Epoch 206/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3465 - acc: 0.8771
Epoch 206: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3465 - acc: 0.8771 - val_loss: 0.3657 - val_acc: 0.8770
Epoch 207/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3443 - acc: 0.8775
Epoch 207: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3443 - acc: 0.8775 - val_loss: 0.3648 - val_acc: 0.8774
Epoch 208/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3461 - acc: 0.8777
Epoch 208: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3461 - acc: 0.8776 - val_loss: 0.3669 - val_acc: 0.8764
Epoch 209/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3449 - acc: 0.8774
Epoch 209: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3450 - acc: 0.8774 - val_loss: 0.3703 - val_acc: 0.8758
Epoch 210/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3440 - acc: 0.8773
Epoch 210: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3438 - acc: 0.8774 - val_loss: 0.3648 - val_acc: 0.8763
Epoch 211/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3425 - acc: 0.8793
Epoch 211: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3424 - acc: 0.8793 - val_loss: 0.3687 - val_acc: 0.8742
Epoch 212/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3418 - acc: 0.8782
Epoch 212: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3417 - acc: 0.8782 - val_loss: 0.3667 - val_acc: 0.8777
Epoch 213/1000
701/701 [==============================] - ETA: 0s - loss: 0.3427 - acc: 0.8804
Epoch 213: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3427 - acc: 0.8804 - val_loss: 0.3663 - val_acc: 0.8774
Epoch 214/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3396 - acc: 0.8799
Epoch 214: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3397 - acc: 0.8800 - val_loss: 0.3674 - val_acc: 0.8774
Epoch 215/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3384 - acc: 0.8793
Epoch 215: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3384 - acc: 0.8791 - val_loss: 0.3632 - val_acc: 0.8777
Epoch 216/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3402 - acc: 0.8788
Epoch 216: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.3401 - acc: 0.8788 - val_loss: 0.3649 - val_acc: 0.8763
Epoch 217/1000
701/701 [==============================] - ETA: 0s - loss: 0.3388 - acc: 0.8796
Epoch 217: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3388 - acc: 0.8796 - val_loss: 0.3676 - val_acc: 0.8758
Epoch 218/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3377 - acc: 0.8805
Epoch 218: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3378 - acc: 0.8804 - val_loss: 0.3642 - val_acc: 0.8764
Epoch 219/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3376 - acc: 0.8794
Epoch 219: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.3378 - acc: 0.8793 - val_loss: 0.3651 - val_acc: 0.8778
Epoch 220/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3353 - acc: 0.8815
Epoch 220: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3348 - acc: 0.8815 - val_loss: 0.3653 - val_acc: 0.8769
Epoch 221/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3356 - acc: 0.8813
Epoch 221: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3355 - acc: 0.8813 - val_loss: 0.3703 - val_acc: 0.8747
Epoch 222/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3377 - acc: 0.8798
Epoch 222: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3379 - acc: 0.8798 - val_loss: 0.3613 - val_acc: 0.8774
Epoch 223/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3342 - acc: 0.8812
Epoch 223: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3347 - acc: 0.8812 - val_loss: 0.3618 - val_acc: 0.8785
Epoch 224/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3330 - acc: 0.8817
Epoch 224: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3329 - acc: 0.8817 - val_loss: 0.3632 - val_acc: 0.8770
Epoch 225/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3325 - acc: 0.8815
Epoch 225: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.3322 - acc: 0.8817 - val_loss: 0.3710 - val_acc: 0.8745
Epoch 226/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3351 - acc: 0.8806
Epoch 226: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3349 - acc: 0.8806 - val_loss: 0.3632 - val_acc: 0.8769
Epoch 227/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3342 - acc: 0.8818
Epoch 227: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3342 - acc: 0.8818 - val_loss: 0.3604 - val_acc: 0.8796
Epoch 228/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3319 - acc: 0.8823
Epoch 228: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3319 - acc: 0.8822 - val_loss: 0.3623 - val_acc: 0.8776
Epoch 229/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3287 - acc: 0.8827
Epoch 229: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3291 - acc: 0.8827 - val_loss: 0.3609 - val_acc: 0.8779
Epoch 230/1000
691/701 [============================>.] - ETA: 0s - loss: 0.3310 - acc: 0.8829
Epoch 230: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3310 - acc: 0.8830 - val_loss: 0.3612 - val_acc: 0.8787
Epoch 231/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3312 - acc: 0.8822
Epoch 231: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3312 - acc: 0.8822 - val_loss: 0.3633 - val_acc: 0.8774
Epoch 232/1000
701/701 [==============================] - ETA: 0s - loss: 0.3314 - acc: 0.8823
Epoch 232: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3314 - acc: 0.8823 - val_loss: 0.3578 - val_acc: 0.8799
Epoch 233/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3290 - acc: 0.8833
Epoch 233: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3283 - acc: 0.8836 - val_loss: 0.3586 - val_acc: 0.8793
Epoch 234/1000
691/701 [============================>.] - ETA: 0s - loss: 0.3291 - acc: 0.8817
Epoch 234: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3290 - acc: 0.8817 - val_loss: 0.3589 - val_acc: 0.8791
Epoch 235/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3257 - acc: 0.8843
Epoch 235: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3261 - acc: 0.8840 - val_loss: 0.3599 - val_acc: 0.8802
Epoch 236/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3271 - acc: 0.8842
Epoch 236: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3267 - acc: 0.8844 - val_loss: 0.3581 - val_acc: 0.8801
Epoch 237/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3244 - acc: 0.8848
Epoch 237: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3244 - acc: 0.8848 - val_loss: 0.3572 - val_acc: 0.8799
Epoch 238/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3246 - acc: 0.8830
Epoch 238: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.3245 - acc: 0.8829 - val_loss: 0.3605 - val_acc: 0.8776
Epoch 239/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3269 - acc: 0.8845
Epoch 239: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3265 - acc: 0.8847 - val_loss: 0.3605 - val_acc: 0.8791
Epoch 240/1000
701/701 [==============================] - ETA: 0s - loss: 0.3258 - acc: 0.8840
Epoch 240: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3258 - acc: 0.8840 - val_loss: 0.3667 - val_acc: 0.8770
Epoch 241/1000
688/701 [============================>.] - ETA: 0s - loss: 0.3248 - acc: 0.8852
Epoch 241: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3251 - acc: 0.8851 - val_loss: 0.3595 - val_acc: 0.8796
Epoch 242/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3216 - acc: 0.8865
Epoch 242: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3217 - acc: 0.8865 - val_loss: 0.3592 - val_acc: 0.8795
Epoch 243/1000
701/701 [==============================] - ETA: 0s - loss: 0.3230 - acc: 0.8847
Epoch 243: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3230 - acc: 0.8847 - val_loss: 0.3573 - val_acc: 0.8797
Epoch 244/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3197 - acc: 0.8870
Epoch 244: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3196 - acc: 0.8871 - val_loss: 0.3569 - val_acc: 0.8794
Epoch 245/1000
701/701 [==============================] - ETA: 0s - loss: 0.3201 - acc: 0.8863
Epoch 245: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3201 - acc: 0.8863 - val_loss: 0.3606 - val_acc: 0.8793
Epoch 246/1000
691/701 [============================>.] - ETA: 0s - loss: 0.3202 - acc: 0.8856
Epoch 246: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3200 - acc: 0.8858 - val_loss: 0.3618 - val_acc: 0.8784
Epoch 247/1000
690/701 [============================>.] - ETA: 0s - loss: 0.3205 - acc: 0.8850
Epoch 247: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.3201 - acc: 0.8852 - val_loss: 0.3568 - val_acc: 0.8789
Epoch 248/1000
701/701 [==============================] - ETA: 0s - loss: 0.3235 - acc: 0.8848
Epoch 248: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.3235 - acc: 0.8848 - val_loss: 0.3573 - val_acc: 0.8790
Epoch 249/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3204 - acc: 0.8865
Epoch 249: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3202 - acc: 0.8866 - val_loss: 0.3581 - val_acc: 0.8804
Epoch 250/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3173 - acc: 0.8880
Epoch 250: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3179 - acc: 0.8879 - val_loss: 0.3556 - val_acc: 0.8811
Epoch 251/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3169 - acc: 0.8877
Epoch 251: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3165 - acc: 0.8877 - val_loss: 0.3570 - val_acc: 0.8795
Epoch 252/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3164 - acc: 0.8874
Epoch 252: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.3163 - acc: 0.8875 - val_loss: 0.3549 - val_acc: 0.8808
Epoch 253/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3179 - acc: 0.8876
Epoch 253: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3177 - acc: 0.8876 - val_loss: 0.3605 - val_acc: 0.8792
Epoch 254/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3148 - acc: 0.8880
Epoch 254: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3146 - acc: 0.8881 - val_loss: 0.3573 - val_acc: 0.8789
Epoch 255/1000
690/701 [============================>.] - ETA: 0s - loss: 0.3167 - acc: 0.8878
Epoch 255: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3167 - acc: 0.8879 - val_loss: 0.3606 - val_acc: 0.8805
Epoch 256/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3152 - acc: 0.8881
Epoch 256: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3153 - acc: 0.8882 - val_loss: 0.3529 - val_acc: 0.8820
Epoch 257/1000
701/701 [==============================] - ETA: 0s - loss: 0.3158 - acc: 0.8889
Epoch 257: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3158 - acc: 0.8889 - val_loss: 0.3561 - val_acc: 0.8797
Epoch 258/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3122 - acc: 0.8898
Epoch 258: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3129 - acc: 0.8896 - val_loss: 0.3538 - val_acc: 0.8787
Epoch 259/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3148 - acc: 0.8871
Epoch 259: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 6ms/step - loss: 0.3145 - acc: 0.8872 - val_loss: 0.3540 - val_acc: 0.8809
Epoch 260/1000
696/701 [============================>.] - ETA: 0s - loss: 0.3132 - acc: 0.8895
Epoch 260: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3133 - acc: 0.8894 - val_loss: 0.3578 - val_acc: 0.8791
Epoch 261/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3109 - acc: 0.8904
Epoch 261: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3107 - acc: 0.8903 - val_loss: 0.3548 - val_acc: 0.8817
Epoch 262/1000
693/701 [============================>.] - ETA: 0s - loss: 0.3116 - acc: 0.8887
Epoch 262: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3115 - acc: 0.8887 - val_loss: 0.3538 - val_acc: 0.8807
Epoch 263/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3102 - acc: 0.8903
Epoch 263: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3104 - acc: 0.8902 - val_loss: 0.3552 - val_acc: 0.8811
Epoch 264/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3142 - acc: 0.8889
Epoch 264: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3142 - acc: 0.8888 - val_loss: 0.3542 - val_acc: 0.8804
Epoch 265/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3122 - acc: 0.8893
Epoch 265: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3121 - acc: 0.8894 - val_loss: 0.3544 - val_acc: 0.8807
Epoch 266/1000
700/701 [============================>.] - ETA: 0s - loss: 0.3092 - acc: 0.8900
Epoch 266: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3093 - acc: 0.8901 - val_loss: 0.3550 - val_acc: 0.8812
Epoch 267/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3090 - acc: 0.8901
Epoch 267: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3093 - acc: 0.8901 - val_loss: 0.3548 - val_acc: 0.8813
Epoch 268/1000
701/701 [==============================] - ETA: 0s - loss: 0.3074 - acc: 0.8901
Epoch 268: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3074 - acc: 0.8901 - val_loss: 0.3537 - val_acc: 0.8824
Epoch 269/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3059 - acc: 0.8905
Epoch 269: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3058 - acc: 0.8907 - val_loss: 0.3539 - val_acc: 0.8815
Epoch 270/1000
695/701 [============================>.] - ETA: 0s - loss: 0.3070 - acc: 0.8906
Epoch 270: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3069 - acc: 0.8907 - val_loss: 0.3510 - val_acc: 0.8831
Epoch 271/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3066 - acc: 0.8913
Epoch 271: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.3063 - acc: 0.8913 - val_loss: 0.3539 - val_acc: 0.8822
Epoch 272/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3059 - acc: 0.8919
Epoch 272: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3060 - acc: 0.8919 - val_loss: 0.3523 - val_acc: 0.8814
Epoch 273/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3033 - acc: 0.8936
Epoch 273: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3040 - acc: 0.8934 - val_loss: 0.3533 - val_acc: 0.8820
Epoch 274/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3040 - acc: 0.8924
Epoch 274: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.3039 - acc: 0.8925 - val_loss: 0.3532 - val_acc: 0.8823
Epoch 275/1000
699/701 [============================>.] - ETA: 0s - loss: 0.3068 - acc: 0.8910
Epoch 275: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3065 - acc: 0.8910 - val_loss: 0.3509 - val_acc: 0.8811
Epoch 276/1000
698/701 [============================>.] - ETA: 0s - loss: 0.3045 - acc: 0.8921
Epoch 276: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3046 - acc: 0.8921 - val_loss: 0.3507 - val_acc: 0.8822
Epoch 277/1000
692/701 [============================>.] - ETA: 0s - loss: 0.3038 - acc: 0.8923
Epoch 277: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3034 - acc: 0.8926 - val_loss: 0.3558 - val_acc: 0.8813
Epoch 278/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3015 - acc: 0.8930
Epoch 278: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.3018 - acc: 0.8930 - val_loss: 0.3516 - val_acc: 0.8819
Epoch 279/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3032 - acc: 0.8915
Epoch 279: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 3s 5ms/step - loss: 0.3030 - acc: 0.8915 - val_loss: 0.3493 - val_acc: 0.8840
Epoch 280/1000
700/701 [============================>.] - ETA: 0s - loss: 0.2987 - acc: 0.8935
Epoch 280: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 3s 4ms/step - loss: 0.2988 - acc: 0.8935 - val_loss: 0.3551 - val_acc: 0.8806
Epoch 281/1000
698/701 [============================>.] - ETA: 0s - loss: 0.2998 - acc: 0.8932
Epoch 281: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2999 - acc: 0.8931 - val_loss: 0.3520 - val_acc: 0.8833
Epoch 282/1000
694/701 [============================>.] - ETA: 0s - loss: 0.3005 - acc: 0.8917
Epoch 282: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2998 - acc: 0.8919 - val_loss: 0.3498 - val_acc: 0.8834
Epoch 283/1000
691/701 [============================>.] - ETA: 0s - loss: 0.2980 - acc: 0.8928
Epoch 283: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2985 - acc: 0.8926 - val_loss: 0.3514 - val_acc: 0.8827
Epoch 284/1000
699/701 [============================>.] - ETA: 0s - loss: 0.2985 - acc: 0.8946
Epoch 284: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2982 - acc: 0.8946 - val_loss: 0.3509 - val_acc: 0.8805
Epoch 285/1000
697/701 [============================>.] - ETA: 0s - loss: 0.3000 - acc: 0.8931
Epoch 285: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2998 - acc: 0.8932 - val_loss: 0.3527 - val_acc: 0.8831
Epoch 286/1000
699/701 [============================>.] - ETA: 0s - loss: 0.2996 - acc: 0.8929
Epoch 286: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2995 - acc: 0.8929 - val_loss: 0.3507 - val_acc: 0.8841
Epoch 287/1000
698/701 [============================>.] - ETA: 0s - loss: 0.2985 - acc: 0.8927
Epoch 287: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2983 - acc: 0.8928 - val_loss: 0.3511 - val_acc: 0.8827
Epoch 288/1000
701/701 [==============================] - ETA: 0s - loss: 0.2947 - acc: 0.8963
Epoch 288: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2947 - acc: 0.8963 - val_loss: 0.3494 - val_acc: 0.8843
Epoch 289/1000
696/701 [============================>.] - ETA: 0s - loss: 0.2990 - acc: 0.8936
Epoch 289: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2992 - acc: 0.8935 - val_loss: 0.3490 - val_acc: 0.8836
Epoch 290/1000
697/701 [============================>.] - ETA: 0s - loss: 0.2957 - acc: 0.8937
Epoch 290: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2957 - acc: 0.8938 - val_loss: 0.3535 - val_acc: 0.8827
Epoch 291/1000
694/701 [============================>.] - ETA: 0s - loss: 0.2957 - acc: 0.8946
Epoch 291: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2954 - acc: 0.8946 - val_loss: 0.3522 - val_acc: 0.8819
Epoch 292/1000
693/701 [============================>.] - ETA: 0s - loss: 0.2943 - acc: 0.8946
Epoch 292: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2940 - acc: 0.8947 - val_loss: 0.3511 - val_acc: 0.8830
Epoch 293/1000
700/701 [============================>.] - ETA: 0s - loss: 0.2945 - acc: 0.8946
Epoch 293: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2947 - acc: 0.8945 - val_loss: 0.3492 - val_acc: 0.8838
Epoch 294/1000
689/701 [============================>.] - ETA: 0s - loss: 0.2933 - acc: 0.8957
Epoch 294: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2932 - acc: 0.8958 - val_loss: 0.3481 - val_acc: 0.8839
Epoch 295/1000
696/701 [============================>.] - ETA: 0s - loss: 0.2924 - acc: 0.8951
Epoch 295: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2925 - acc: 0.8950 - val_loss: 0.3495 - val_acc: 0.8841
Epoch 296/1000
692/701 [============================>.] - ETA: 0s - loss: 0.2934 - acc: 0.8963
Epoch 296: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 3s 5ms/step - loss: 0.2931 - acc: 0.8964 - val_loss: 0.3491 - val_acc: 0.8832
Epoch 297/1000
696/701 [============================>.] - ETA: 0s - loss: 0.2923 - acc: 0.8953
Epoch 297: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2919 - acc: 0.8953 - val_loss: 0.3516 - val_acc: 0.8836
Epoch 298/1000
695/701 [============================>.] - ETA: 0s - loss: 0.2922 - acc: 0.8957
Epoch 298: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2923 - acc: 0.8957 - val_loss: 0.3494 - val_acc: 0.8840
Epoch 299/1000
693/701 [============================>.] - ETA: 0s - loss: 0.2906 - acc: 0.8967
Epoch 299: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.2909 - acc: 0.8966 - val_loss: 0.3480 - val_acc: 0.8837
Epoch 300/1000
691/701 [============================>.] - ETA: 0s - loss: 0.2893 - acc: 0.8966
Epoch 300: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2893 - acc: 0.8965 - val_loss: 0.3496 - val_acc: 0.8827
Epoch 301/1000
700/701 [============================>.] - ETA: 0s - loss: 0.2872 - acc: 0.8983
Epoch 301: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2873 - acc: 0.8982 - val_loss: 0.3482 - val_acc: 0.8843
Epoch 302/1000
692/701 [============================>.] - ETA: 0s - loss: 0.2883 - acc: 0.8978
Epoch 302: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2887 - acc: 0.8976 - val_loss: 0.3491 - val_acc: 0.8844
Epoch 303/1000
692/701 [============================>.] - ETA: 0s - loss: 0.2892 - acc: 0.8967
Epoch 303: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.2892 - acc: 0.8967 - val_loss: 0.3477 - val_acc: 0.8826
Epoch 304/1000
699/701 [============================>.] - ETA: 0s - loss: 0.2883 - acc: 0.8968
Epoch 304: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2883 - acc: 0.8968 - val_loss: 0.3507 - val_acc: 0.8842
Epoch 305/1000
700/701 [============================>.] - ETA: 0s - loss: 0.2887 - acc: 0.8965
Epoch 305: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2887 - acc: 0.8965 - val_loss: 0.3517 - val_acc: 0.8840
Epoch 306/1000
694/701 [============================>.] - ETA: 0s - loss: 0.2882 - acc: 0.8967
Epoch 306: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2882 - acc: 0.8970 - val_loss: 0.3482 - val_acc: 0.8845
Epoch 307/1000
695/701 [============================>.] - ETA: 0s - loss: 0.2879 - acc: 0.8968
Epoch 307: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2878 - acc: 0.8968 - val_loss: 0.3484 - val_acc: 0.8847
Epoch 308/1000
697/701 [============================>.] - ETA: 0s - loss: 0.2853 - acc: 0.8975
Epoch 308: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2854 - acc: 0.8974 - val_loss: 0.3514 - val_acc: 0.8852
Epoch 309/1000
697/701 [============================>.] - ETA: 0s - loss: 0.2845 - acc: 0.8982
Epoch 309: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2846 - acc: 0.8982 - val_loss: 0.3475 - val_acc: 0.8834
Epoch 310/1000
698/701 [============================>.] - ETA: 0s - loss: 0.2841 - acc: 0.8991
Epoch 310: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2845 - acc: 0.8990 - val_loss: 0.3490 - val_acc: 0.8831
Epoch 311/1000
698/701 [============================>.] - ETA: 0s - loss: 0.2840 - acc: 0.8982
Epoch 311: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2842 - acc: 0.8980 - val_loss: 0.3461 - val_acc: 0.8841
Epoch 312/1000
693/701 [============================>.] - ETA: 0s - loss: 0.2844 - acc: 0.8980
Epoch 312: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2845 - acc: 0.8980 - val_loss: 0.3484 - val_acc: 0.8851
Epoch 313/1000
696/701 [============================>.] - ETA: 0s - loss: 0.2846 - acc: 0.8998
Epoch 313: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2848 - acc: 0.8998 - val_loss: 0.3503 - val_acc: 0.8842
Epoch 314/1000
697/701 [============================>.] - ETA: 0s - loss: 0.2852 - acc: 0.8987
Epoch 314: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2851 - acc: 0.8988 - val_loss: 0.3466 - val_acc: 0.8848
Epoch 315/1000
699/701 [============================>.] - ETA: 0s - loss: 0.2826 - acc: 0.8985
Epoch 315: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2829 - acc: 0.8985 - val_loss: 0.3501 - val_acc: 0.8825
Epoch 316/1000
691/701 [============================>.] - ETA: 0s - loss: 0.2817 - acc: 0.8992
Epoch 316: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2817 - acc: 0.8993 - val_loss: 0.3488 - val_acc: 0.8837
Epoch 317/1000
692/701 [============================>.] - ETA: 0s - loss: 0.2821 - acc: 0.8988
Epoch 317: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2817 - acc: 0.8989 - val_loss: 0.3478 - val_acc: 0.8842
Epoch 318/1000
701/701 [==============================] - ETA: 0s - loss: 0.2810 - acc: 0.8992
Epoch 318: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 3s 5ms/step - loss: 0.2810 - acc: 0.8992 - val_loss: 0.3492 - val_acc: 0.8859
Epoch 319/1000
700/701 [============================>.] - ETA: 0s - loss: 0.2815 - acc: 0.9006
Epoch 319: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2813 - acc: 0.9006 - val_loss: 0.3458 - val_acc: 0.8867
Epoch 320/1000
701/701 [==============================] - ETA: 0s - loss: 0.2801 - acc: 0.8999
Epoch 320: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2801 - acc: 0.8999 - val_loss: 0.3492 - val_acc: 0.8829
Epoch 321/1000
701/701 [==============================] - ETA: 0s - loss: 0.2814 - acc: 0.8997
Epoch 321: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2814 - acc: 0.8997 - val_loss: 0.3454 - val_acc: 0.8840
Epoch 322/1000
697/701 [============================>.] - ETA: 0s - loss: 0.2784 - acc: 0.9002
Epoch 322: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.2785 - acc: 0.9002 - val_loss: 0.3518 - val_acc: 0.8836
Epoch 323/1000
699/701 [============================>.] - ETA: 0s - loss: 0.2762 - acc: 0.9012
Epoch 323: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2762 - acc: 0.9012 - val_loss: 0.3491 - val_acc: 0.8844
Epoch 324/1000
698/701 [============================>.] - ETA: 0s - loss: 0.2758 - acc: 0.9019
Epoch 324: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2758 - acc: 0.9019 - val_loss: 0.3480 - val_acc: 0.8849
Epoch 325/1000
688/701 [============================>.] - ETA: 0s - loss: 0.2765 - acc: 0.9009
Epoch 325: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2764 - acc: 0.9010 - val_loss: 0.3471 - val_acc: 0.8849
Epoch 326/1000
696/701 [============================>.] - ETA: 0s - loss: 0.2761 - acc: 0.9015
Epoch 326: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2759 - acc: 0.9016 - val_loss: 0.3507 - val_acc: 0.8850
Epoch 327/1000
698/701 [============================>.] - ETA: 0s - loss: 0.2758 - acc: 0.9015
Epoch 327: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.2759 - acc: 0.9015 - val_loss: 0.3502 - val_acc: 0.8841
Epoch 328/1000
699/701 [============================>.] - ETA: 0s - loss: 0.2762 - acc: 0.9016
Epoch 328: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 5s 7ms/step - loss: 0.2761 - acc: 0.9016 - val_loss: 0.3464 - val_acc: 0.8844
Epoch 329/1000
696/701 [============================>.] - ETA: 0s - loss: 0.2733 - acc: 0.9029
Epoch 329: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2738 - acc: 0.9026 - val_loss: 0.3473 - val_acc: 0.8846
Epoch 330/1000
690/701 [============================>.] - ETA: 0s - loss: 0.2746 - acc: 0.9026
Epoch 330: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2744 - acc: 0.9028 - val_loss: 0.3474 - val_acc: 0.8836
Epoch 331/1000
694/701 [============================>.] - ETA: 0s - loss: 0.2739 - acc: 0.9021
Epoch 331: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2745 - acc: 0.9019 - val_loss: 0.3470 - val_acc: 0.8853
Epoch 332/1000
701/701 [==============================] - ETA: 0s - loss: 0.2761 - acc: 0.9018
Epoch 332: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2761 - acc: 0.9018 - val_loss: 0.3496 - val_acc: 0.8853
Epoch 333/1000
700/701 [============================>.] - ETA: 0s - loss: 0.2730 - acc: 0.9013
Epoch 333: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2731 - acc: 0.9013 - val_loss: 0.3462 - val_acc: 0.8865
Epoch 334/1000
691/701 [============================>.] - ETA: 0s - loss: 0.2715 - acc: 0.9029
Epoch 334: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 5ms/step - loss: 0.2716 - acc: 0.9031 - val_loss: 0.3458 - val_acc: 0.8855
Epoch 335/1000
695/701 [============================>.] - ETA: 0s - loss: 0.2751 - acc: 0.9028
Epoch 335: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2752 - acc: 0.9028 - val_loss: 0.3460 - val_acc: 0.8866
Epoch 336/1000
699/701 [============================>.] - ETA: 0s - loss: 0.2695 - acc: 0.9042
Epoch 336: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_CEP/cp.ckpt
701/701 [==============================] - 4s 6ms/step - loss: 0.2695 - acc: 0.9042 - val_loss: 0.3469 - val_acc: 0.8854
Epoch 336: early stopping
Use balanced Generator [False]
Data: 179632
-----------------------------------------------------------------------------------
Epoch 1/1000
1867/1872 [============================>.] - ETA: 0s - loss: 2.0787 - acc: 0.1411
Epoch 1: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 11s 5ms/step - loss: 2.0787 - acc: 0.1411 - val_loss: 2.0715 - val_acc: 0.1812
Epoch 2/1000
1872/1872 [==============================] - ETA: 0s - loss: 2.0628 - acc: 0.1758
Epoch 2: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 2.0628 - acc: 0.1758 - val_loss: 2.0421 - val_acc: 0.2249
Epoch 3/1000
1862/1872 [============================>.] - ETA: 0s - loss: 1.9864 - acc: 0.2432
Epoch 3: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 1.9858 - acc: 0.2435 - val_loss: 1.8454 - val_acc: 0.3950
Epoch 4/1000
1872/1872 [==============================] - ETA: 0s - loss: 1.6097 - acc: 0.4198
Epoch 4: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 6ms/step - loss: 1.6097 - acc: 0.4198 - val_loss: 1.2439 - val_acc: 0.5860
Epoch 5/1000
1869/1872 [============================>.] - ETA: 0s - loss: 1.1678 - acc: 0.5826
Epoch 5: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 1.1675 - acc: 0.5828 - val_loss: 0.8820 - val_acc: 0.6827
Epoch 6/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.9135 - acc: 0.6696
Epoch 6: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.9135 - acc: 0.6696 - val_loss: 0.7367 - val_acc: 0.7438
Epoch 7/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.8048 - acc: 0.7091
Epoch 7: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.8048 - acc: 0.7091 - val_loss: 0.6685 - val_acc: 0.7660
Epoch 8/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.7413 - acc: 0.7327
Epoch 8: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.7413 - acc: 0.7327 - val_loss: 0.6281 - val_acc: 0.7789
Epoch 9/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.6990 - acc: 0.7483
Epoch 9: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 6ms/step - loss: 0.6991 - acc: 0.7482 - val_loss: 0.5980 - val_acc: 0.7871
Epoch 10/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.6671 - acc: 0.7598
Epoch 10: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.6669 - acc: 0.7598 - val_loss: 0.5718 - val_acc: 0.7987
Epoch 11/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.6417 - acc: 0.7684
Epoch 11: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.6415 - acc: 0.7685 - val_loss: 0.5551 - val_acc: 0.8053
Epoch 12/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.6224 - acc: 0.7765
Epoch 12: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 5ms/step - loss: 0.6227 - acc: 0.7763 - val_loss: 0.5422 - val_acc: 0.8077
Epoch 13/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.6045 - acc: 0.7823
Epoch 13: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.6045 - acc: 0.7823 - val_loss: 0.5303 - val_acc: 0.8096
Epoch 14/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.5885 - acc: 0.7882
Epoch 14: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.5884 - acc: 0.7883 - val_loss: 0.5196 - val_acc: 0.8154
Epoch 15/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.5773 - acc: 0.7930
Epoch 15: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.5773 - acc: 0.7930 - val_loss: 0.5136 - val_acc: 0.8196
Epoch 16/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.5669 - acc: 0.7960
Epoch 16: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.5668 - acc: 0.7960 - val_loss: 0.5046 - val_acc: 0.8199
Epoch 17/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.5550 - acc: 0.8004
Epoch 17: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.5550 - acc: 0.8004 - val_loss: 0.4967 - val_acc: 0.8251
Epoch 18/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.5468 - acc: 0.8030
Epoch 18: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.5470 - acc: 0.8031 - val_loss: 0.4907 - val_acc: 0.8251
Epoch 19/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.5361 - acc: 0.8077
Epoch 19: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 6ms/step - loss: 0.5360 - acc: 0.8077 - val_loss: 0.4831 - val_acc: 0.8310
Epoch 20/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.5304 - acc: 0.8100
Epoch 20: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 11s 6ms/step - loss: 0.5303 - acc: 0.8100 - val_loss: 0.4799 - val_acc: 0.8319
Epoch 21/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.5236 - acc: 0.8125
Epoch 21: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 6ms/step - loss: 0.5236 - acc: 0.8126 - val_loss: 0.4741 - val_acc: 0.8357
Epoch 22/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.5169 - acc: 0.8152
Epoch 22: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.5169 - acc: 0.8153 - val_loss: 0.4731 - val_acc: 0.8337
Epoch 23/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.5104 - acc: 0.8180
Epoch 23: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.5103 - acc: 0.8181 - val_loss: 0.4653 - val_acc: 0.8364
Epoch 24/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.5063 - acc: 0.8202
Epoch 24: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.5061 - acc: 0.8202 - val_loss: 0.4589 - val_acc: 0.8407
Epoch 25/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.4988 - acc: 0.8217
Epoch 25: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4989 - acc: 0.8217 - val_loss: 0.4582 - val_acc: 0.8396
Epoch 26/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.4941 - acc: 0.8236
Epoch 26: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4941 - acc: 0.8236 - val_loss: 0.4524 - val_acc: 0.8420
Epoch 27/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.4913 - acc: 0.8252
Epoch 27: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4915 - acc: 0.8251 - val_loss: 0.4517 - val_acc: 0.8422
Epoch 28/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.4854 - acc: 0.8267
Epoch 28: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4854 - acc: 0.8267 - val_loss: 0.4449 - val_acc: 0.8447
Epoch 29/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.4798 - acc: 0.8285
Epoch 29: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.4800 - acc: 0.8284 - val_loss: 0.4411 - val_acc: 0.8461
Epoch 30/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.4769 - acc: 0.8306
Epoch 30: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4769 - acc: 0.8306 - val_loss: 0.4387 - val_acc: 0.8469
Epoch 31/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.4726 - acc: 0.8324
Epoch 31: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4726 - acc: 0.8324 - val_loss: 0.4377 - val_acc: 0.8473
Epoch 32/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.4684 - acc: 0.8346
Epoch 32: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4682 - acc: 0.8347 - val_loss: 0.4380 - val_acc: 0.8490
Epoch 33/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.4649 - acc: 0.8356
Epoch 33: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4648 - acc: 0.8357 - val_loss: 0.4330 - val_acc: 0.8486
Epoch 34/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.4624 - acc: 0.8347
Epoch 34: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4624 - acc: 0.8347 - val_loss: 0.4279 - val_acc: 0.8512
Epoch 35/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.4585 - acc: 0.8378
Epoch 35: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4584 - acc: 0.8378 - val_loss: 0.4250 - val_acc: 0.8548
Epoch 36/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.4532 - acc: 0.8388
Epoch 36: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.4532 - acc: 0.8388 - val_loss: 0.4241 - val_acc: 0.8534
Epoch 37/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.4507 - acc: 0.8402
Epoch 37: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 11s 6ms/step - loss: 0.4507 - acc: 0.8402 - val_loss: 0.4273 - val_acc: 0.8507
Epoch 38/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.4488 - acc: 0.8407
Epoch 38: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4485 - acc: 0.8408 - val_loss: 0.4254 - val_acc: 0.8508
Epoch 39/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.4452 - acc: 0.8425
Epoch 39: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4450 - acc: 0.8425 - val_loss: 0.4172 - val_acc: 0.8544
Epoch 40/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.4421 - acc: 0.8437
Epoch 40: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4421 - acc: 0.8437 - val_loss: 0.4148 - val_acc: 0.8573
Epoch 41/1000
1858/1872 [============================>.] - ETA: 0s - loss: 0.4388 - acc: 0.8443
Epoch 41: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.4389 - acc: 0.8444 - val_loss: 0.4121 - val_acc: 0.8588
Epoch 42/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.4371 - acc: 0.8445
Epoch 42: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.4371 - acc: 0.8444 - val_loss: 0.4157 - val_acc: 0.8582
Epoch 43/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.4333 - acc: 0.8469
Epoch 43: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.4332 - acc: 0.8470 - val_loss: 0.4099 - val_acc: 0.8578
Epoch 44/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.4314 - acc: 0.8466
Epoch 44: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4315 - acc: 0.8466 - val_loss: 0.4078 - val_acc: 0.8587
Epoch 45/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.4291 - acc: 0.8488
Epoch 45: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4291 - acc: 0.8488 - val_loss: 0.4068 - val_acc: 0.8593
Epoch 46/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.4259 - acc: 0.8495
Epoch 46: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.4259 - acc: 0.8495 - val_loss: 0.4063 - val_acc: 0.8609
Epoch 47/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.4235 - acc: 0.8503
Epoch 47: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.4236 - acc: 0.8502 - val_loss: 0.4068 - val_acc: 0.8587
Epoch 48/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.4215 - acc: 0.8514
Epoch 48: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4213 - acc: 0.8515 - val_loss: 0.4069 - val_acc: 0.8601
Epoch 49/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.4191 - acc: 0.8522
Epoch 49: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4189 - acc: 0.8523 - val_loss: 0.4007 - val_acc: 0.8610
Epoch 50/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.4177 - acc: 0.8520
Epoch 50: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 6ms/step - loss: 0.4177 - acc: 0.8521 - val_loss: 0.4043 - val_acc: 0.8608
Epoch 51/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.4159 - acc: 0.8529
Epoch 51: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4159 - acc: 0.8529 - val_loss: 0.3963 - val_acc: 0.8635
Epoch 52/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.4138 - acc: 0.8545
Epoch 52: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4139 - acc: 0.8545 - val_loss: 0.3943 - val_acc: 0.8631
Epoch 53/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.4111 - acc: 0.8557
Epoch 53: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4111 - acc: 0.8557 - val_loss: 0.3944 - val_acc: 0.8635
Epoch 54/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.4100 - acc: 0.8555
Epoch 54: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4099 - acc: 0.8555 - val_loss: 0.3899 - val_acc: 0.8676
Epoch 55/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.4079 - acc: 0.8562
Epoch 55: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4080 - acc: 0.8562 - val_loss: 0.3892 - val_acc: 0.8651
Epoch 56/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.4067 - acc: 0.8568
Epoch 56: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.4068 - acc: 0.8567 - val_loss: 0.3879 - val_acc: 0.8674
Epoch 57/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.4032 - acc: 0.8576
Epoch 57: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4033 - acc: 0.8576 - val_loss: 0.3878 - val_acc: 0.8659
Epoch 58/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.4006 - acc: 0.8590
Epoch 58: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.4007 - acc: 0.8591 - val_loss: 0.3853 - val_acc: 0.8688
Epoch 59/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.3990 - acc: 0.8600
Epoch 59: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3991 - acc: 0.8600 - val_loss: 0.3854 - val_acc: 0.8667
Epoch 60/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.3981 - acc: 0.8599
Epoch 60: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3980 - acc: 0.8599 - val_loss: 0.3848 - val_acc: 0.8691
Epoch 61/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3966 - acc: 0.8597
Epoch 61: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3965 - acc: 0.8598 - val_loss: 0.3811 - val_acc: 0.8694
Epoch 62/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3938 - acc: 0.8611
Epoch 62: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3940 - acc: 0.8610 - val_loss: 0.3815 - val_acc: 0.8688
Epoch 63/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3923 - acc: 0.8620
Epoch 63: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3924 - acc: 0.8621 - val_loss: 0.3777 - val_acc: 0.8701
Epoch 64/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3926 - acc: 0.8621
Epoch 64: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 6ms/step - loss: 0.3924 - acc: 0.8621 - val_loss: 0.3798 - val_acc: 0.8705
Epoch 65/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.3904 - acc: 0.8632
Epoch 65: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3905 - acc: 0.8631 - val_loss: 0.3810 - val_acc: 0.8699
Epoch 66/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.3876 - acc: 0.8630
Epoch 66: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3875 - acc: 0.8630 - val_loss: 0.3790 - val_acc: 0.8703
Epoch 67/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3863 - acc: 0.8637
Epoch 67: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3863 - acc: 0.8637 - val_loss: 0.3736 - val_acc: 0.8723
Epoch 68/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.3841 - acc: 0.8645
Epoch 68: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3841 - acc: 0.8645 - val_loss: 0.3738 - val_acc: 0.8713
Epoch 69/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3825 - acc: 0.8659
Epoch 69: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3825 - acc: 0.8659 - val_loss: 0.3714 - val_acc: 0.8739
Epoch 70/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3804 - acc: 0.8662
Epoch 70: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3804 - acc: 0.8662 - val_loss: 0.3706 - val_acc: 0.8735
Epoch 71/1000
1857/1872 [============================>.] - ETA: 0s - loss: 0.3804 - acc: 0.8665
Epoch 71: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3803 - acc: 0.8666 - val_loss: 0.3703 - val_acc: 0.8729
Epoch 72/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.3783 - acc: 0.8667
Epoch 72: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3780 - acc: 0.8669 - val_loss: 0.3731 - val_acc: 0.8738
Epoch 73/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3773 - acc: 0.8676
Epoch 73: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3774 - acc: 0.8675 - val_loss: 0.3689 - val_acc: 0.8736
Epoch 74/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.3755 - acc: 0.8684
Epoch 74: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3755 - acc: 0.8684 - val_loss: 0.3671 - val_acc: 0.8754
Epoch 75/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.3735 - acc: 0.8683
Epoch 75: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 5ms/step - loss: 0.3735 - acc: 0.8683 - val_loss: 0.3700 - val_acc: 0.8753
Epoch 76/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.3733 - acc: 0.8697
Epoch 76: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3734 - acc: 0.8697 - val_loss: 0.3655 - val_acc: 0.8776
Epoch 77/1000
1860/1872 [============================>.] - ETA: 0s - loss: 0.3718 - acc: 0.8690
Epoch 77: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3719 - acc: 0.8690 - val_loss: 0.3658 - val_acc: 0.8766
Epoch 78/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.3705 - acc: 0.8702
Epoch 78: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3705 - acc: 0.8702 - val_loss: 0.3612 - val_acc: 0.8764
Epoch 79/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3684 - acc: 0.8709
Epoch 79: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3684 - acc: 0.8709 - val_loss: 0.3613 - val_acc: 0.8771
Epoch 80/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3685 - acc: 0.8707
Epoch 80: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3685 - acc: 0.8708 - val_loss: 0.3613 - val_acc: 0.8777
Epoch 81/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.3663 - acc: 0.8720
Epoch 81: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3664 - acc: 0.8719 - val_loss: 0.3592 - val_acc: 0.8779
Epoch 82/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3645 - acc: 0.8719
Epoch 82: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3645 - acc: 0.8719 - val_loss: 0.3578 - val_acc: 0.8785
Epoch 83/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.3632 - acc: 0.8732
Epoch 83: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 11s 6ms/step - loss: 0.3633 - acc: 0.8731 - val_loss: 0.3628 - val_acc: 0.8758
Epoch 84/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3617 - acc: 0.8731
Epoch 84: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3617 - acc: 0.8731 - val_loss: 0.3592 - val_acc: 0.8780
Epoch 85/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3604 - acc: 0.8746
Epoch 85: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3604 - acc: 0.8746 - val_loss: 0.3567 - val_acc: 0.8790
Epoch 86/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.3604 - acc: 0.8735
Epoch 86: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3602 - acc: 0.8736 - val_loss: 0.3586 - val_acc: 0.8782
Epoch 87/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.3590 - acc: 0.8748
Epoch 87: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3590 - acc: 0.8748 - val_loss: 0.3562 - val_acc: 0.8778
Epoch 88/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3571 - acc: 0.8751
Epoch 88: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3571 - acc: 0.8751 - val_loss: 0.3567 - val_acc: 0.8782
Epoch 89/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3559 - acc: 0.8756
Epoch 89: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3559 - acc: 0.8756 - val_loss: 0.3549 - val_acc: 0.8798
Epoch 90/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3545 - acc: 0.8760
Epoch 90: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3546 - acc: 0.8760 - val_loss: 0.3521 - val_acc: 0.8782
Epoch 91/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3543 - acc: 0.8760
Epoch 91: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3542 - acc: 0.8760 - val_loss: 0.3514 - val_acc: 0.8804
Epoch 92/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3532 - acc: 0.8762
Epoch 92: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3532 - acc: 0.8762 - val_loss: 0.3518 - val_acc: 0.8824
Epoch 93/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.3496 - acc: 0.8774
Epoch 93: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3497 - acc: 0.8774 - val_loss: 0.3508 - val_acc: 0.8806
Epoch 94/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.3492 - acc: 0.8775
Epoch 94: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3491 - acc: 0.8775 - val_loss: 0.3518 - val_acc: 0.8803
Epoch 95/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3485 - acc: 0.8781
Epoch 95: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3485 - acc: 0.8781 - val_loss: 0.3470 - val_acc: 0.8819
Epoch 96/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.3478 - acc: 0.8779
Epoch 96: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3478 - acc: 0.8779 - val_loss: 0.3509 - val_acc: 0.8801
Epoch 97/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3463 - acc: 0.8792
Epoch 97: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3463 - acc: 0.8792 - val_loss: 0.3515 - val_acc: 0.8800
Epoch 98/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3465 - acc: 0.8787
Epoch 98: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3463 - acc: 0.8787 - val_loss: 0.3473 - val_acc: 0.8818
Epoch 99/1000
1858/1872 [============================>.] - ETA: 0s - loss: 0.3450 - acc: 0.8792
Epoch 99: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3447 - acc: 0.8793 - val_loss: 0.3438 - val_acc: 0.8824
Epoch 100/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3428 - acc: 0.8800
Epoch 100: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3428 - acc: 0.8800 - val_loss: 0.3501 - val_acc: 0.8790
Epoch 101/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3415 - acc: 0.8801
Epoch 101: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3414 - acc: 0.8801 - val_loss: 0.3457 - val_acc: 0.8820
Epoch 102/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3409 - acc: 0.8803
Epoch 102: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3408 - acc: 0.8803 - val_loss: 0.3445 - val_acc: 0.8804
Epoch 103/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.3411 - acc: 0.8802
Epoch 103: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3411 - acc: 0.8802 - val_loss: 0.3454 - val_acc: 0.8832
Epoch 104/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3393 - acc: 0.8820
Epoch 104: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3394 - acc: 0.8819 - val_loss: 0.3475 - val_acc: 0.8831
Epoch 105/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3388 - acc: 0.8813
Epoch 105: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3388 - acc: 0.8814 - val_loss: 0.3443 - val_acc: 0.8823
Epoch 106/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3380 - acc: 0.8817
Epoch 106: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3382 - acc: 0.8816 - val_loss: 0.3434 - val_acc: 0.8830
Epoch 107/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.3351 - acc: 0.8832
Epoch 107: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.3352 - acc: 0.8831 - val_loss: 0.3399 - val_acc: 0.8851
Epoch 108/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.3339 - acc: 0.8827
Epoch 108: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3339 - acc: 0.8827 - val_loss: 0.3423 - val_acc: 0.8843
Epoch 109/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3354 - acc: 0.8828
Epoch 109: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3353 - acc: 0.8828 - val_loss: 0.3390 - val_acc: 0.8853
Epoch 110/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.3322 - acc: 0.8839
Epoch 110: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3321 - acc: 0.8839 - val_loss: 0.3382 - val_acc: 0.8866
Epoch 111/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.3311 - acc: 0.8843
Epoch 111: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3311 - acc: 0.8843 - val_loss: 0.3397 - val_acc: 0.8854
Epoch 112/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.3316 - acc: 0.8839
Epoch 112: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3319 - acc: 0.8839 - val_loss: 0.3397 - val_acc: 0.8843
Epoch 113/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.3304 - acc: 0.8849
Epoch 113: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3305 - acc: 0.8848 - val_loss: 0.3404 - val_acc: 0.8851
Epoch 114/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.3293 - acc: 0.8857
Epoch 114: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3293 - acc: 0.8856 - val_loss: 0.3415 - val_acc: 0.8838
Epoch 115/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.3289 - acc: 0.8850
Epoch 115: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3289 - acc: 0.8850 - val_loss: 0.3376 - val_acc: 0.8848
Epoch 116/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3270 - acc: 0.8862
Epoch 116: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3271 - acc: 0.8861 - val_loss: 0.3385 - val_acc: 0.8830
Epoch 117/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.3262 - acc: 0.8852
Epoch 117: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3262 - acc: 0.8852 - val_loss: 0.3340 - val_acc: 0.8867
Epoch 118/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3264 - acc: 0.8862
Epoch 118: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3264 - acc: 0.8862 - val_loss: 0.3346 - val_acc: 0.8860
Epoch 119/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.3235 - acc: 0.8870
Epoch 119: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3237 - acc: 0.8870 - val_loss: 0.3354 - val_acc: 0.8870
Epoch 120/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3243 - acc: 0.8868
Epoch 120: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3242 - acc: 0.8868 - val_loss: 0.3337 - val_acc: 0.8872
Epoch 121/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3235 - acc: 0.8869
Epoch 121: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3234 - acc: 0.8869 - val_loss: 0.3321 - val_acc: 0.8877
Epoch 122/1000
1860/1872 [============================>.] - ETA: 0s - loss: 0.3210 - acc: 0.8885
Epoch 122: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3208 - acc: 0.8885 - val_loss: 0.3340 - val_acc: 0.8872
Epoch 123/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.3214 - acc: 0.8881
Epoch 123: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3213 - acc: 0.8881 - val_loss: 0.3323 - val_acc: 0.8866
Epoch 124/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.3211 - acc: 0.8876
Epoch 124: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3210 - acc: 0.8876 - val_loss: 0.3292 - val_acc: 0.8890
Epoch 125/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.3208 - acc: 0.8876
Epoch 125: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3206 - acc: 0.8876 - val_loss: 0.3317 - val_acc: 0.8876
Epoch 126/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.3188 - acc: 0.8892
Epoch 126: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3185 - acc: 0.8893 - val_loss: 0.3321 - val_acc: 0.8874
Epoch 127/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.3198 - acc: 0.8886
Epoch 127: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3199 - acc: 0.8886 - val_loss: 0.3337 - val_acc: 0.8869
Epoch 128/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3161 - acc: 0.8897
Epoch 128: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3161 - acc: 0.8897 - val_loss: 0.3299 - val_acc: 0.8886
Epoch 129/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.3169 - acc: 0.8892
Epoch 129: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3170 - acc: 0.8892 - val_loss: 0.3271 - val_acc: 0.8885
Epoch 130/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3152 - acc: 0.8905
Epoch 130: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3151 - acc: 0.8906 - val_loss: 0.3290 - val_acc: 0.8882
Epoch 131/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.3148 - acc: 0.8901
Epoch 131: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 5ms/step - loss: 0.3151 - acc: 0.8900 - val_loss: 0.3273 - val_acc: 0.8897
Epoch 132/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.3143 - acc: 0.8907
Epoch 132: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3143 - acc: 0.8907 - val_loss: 0.3270 - val_acc: 0.8897
Epoch 133/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3127 - acc: 0.8907
Epoch 133: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3127 - acc: 0.8907 - val_loss: 0.3294 - val_acc: 0.8881
Epoch 134/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3123 - acc: 0.8907
Epoch 134: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3124 - acc: 0.8907 - val_loss: 0.3265 - val_acc: 0.8892
Epoch 135/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.3117 - acc: 0.8915
Epoch 135: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3118 - acc: 0.8915 - val_loss: 0.3263 - val_acc: 0.8902
Epoch 136/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3098 - acc: 0.8915
Epoch 136: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3099 - acc: 0.8915 - val_loss: 0.3251 - val_acc: 0.8901
Epoch 137/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.3100 - acc: 0.8916
Epoch 137: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3098 - acc: 0.8916 - val_loss: 0.3258 - val_acc: 0.8888
Epoch 138/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.3097 - acc: 0.8924
Epoch 138: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3097 - acc: 0.8923 - val_loss: 0.3248 - val_acc: 0.8901
Epoch 139/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3095 - acc: 0.8921
Epoch 139: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3095 - acc: 0.8921 - val_loss: 0.3255 - val_acc: 0.8891
Epoch 140/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3074 - acc: 0.8929
Epoch 140: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3073 - acc: 0.8929 - val_loss: 0.3223 - val_acc: 0.8916
Epoch 141/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3062 - acc: 0.8937
Epoch 141: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3060 - acc: 0.8938 - val_loss: 0.3227 - val_acc: 0.8921
Epoch 142/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.3060 - acc: 0.8926
Epoch 142: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3059 - acc: 0.8926 - val_loss: 0.3289 - val_acc: 0.8896
Epoch 143/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.3047 - acc: 0.8940
Epoch 143: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3046 - acc: 0.8940 - val_loss: 0.3229 - val_acc: 0.8915
Epoch 144/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3042 - acc: 0.8940
Epoch 144: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 5ms/step - loss: 0.3042 - acc: 0.8940 - val_loss: 0.3202 - val_acc: 0.8923
Epoch 145/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.3040 - acc: 0.8946
Epoch 145: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3040 - acc: 0.8946 - val_loss: 0.3249 - val_acc: 0.8896
Epoch 146/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3033 - acc: 0.8939
Epoch 146: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.3033 - acc: 0.8939 - val_loss: 0.3215 - val_acc: 0.8917
Epoch 147/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.3017 - acc: 0.8954
Epoch 147: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.3016 - acc: 0.8954 - val_loss: 0.3172 - val_acc: 0.8906
Epoch 148/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.3004 - acc: 0.8948
Epoch 148: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.3004 - acc: 0.8948 - val_loss: 0.3188 - val_acc: 0.8913
Epoch 149/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2990 - acc: 0.8959
Epoch 149: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2990 - acc: 0.8959 - val_loss: 0.3235 - val_acc: 0.8877
Epoch 150/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2991 - acc: 0.8956
Epoch 150: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2992 - acc: 0.8956 - val_loss: 0.3191 - val_acc: 0.8921
Epoch 151/1000
1857/1872 [============================>.] - ETA: 0s - loss: 0.2986 - acc: 0.8960
Epoch 151: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2984 - acc: 0.8960 - val_loss: 0.3246 - val_acc: 0.8889
Epoch 152/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2969 - acc: 0.8968
Epoch 152: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2968 - acc: 0.8969 - val_loss: 0.3156 - val_acc: 0.8924
Epoch 153/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2974 - acc: 0.8960
Epoch 153: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2975 - acc: 0.8960 - val_loss: 0.3166 - val_acc: 0.8919
Epoch 154/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2971 - acc: 0.8963
Epoch 154: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2973 - acc: 0.8963 - val_loss: 0.3201 - val_acc: 0.8908
Epoch 155/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2958 - acc: 0.8966
Epoch 155: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2958 - acc: 0.8965 - val_loss: 0.3188 - val_acc: 0.8902
Epoch 156/1000
1858/1872 [============================>.] - ETA: 0s - loss: 0.2963 - acc: 0.8968
Epoch 156: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2961 - acc: 0.8970 - val_loss: 0.3166 - val_acc: 0.8921
Epoch 157/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.2935 - acc: 0.8977
Epoch 157: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2935 - acc: 0.8977 - val_loss: 0.3162 - val_acc: 0.8931
Epoch 158/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2950 - acc: 0.8982
Epoch 158: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2950 - acc: 0.8982 - val_loss: 0.3162 - val_acc: 0.8913
Epoch 159/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2943 - acc: 0.8970
Epoch 159: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2943 - acc: 0.8970 - val_loss: 0.3167 - val_acc: 0.8918
Epoch 160/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.2923 - acc: 0.8984
Epoch 160: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2924 - acc: 0.8984 - val_loss: 0.3155 - val_acc: 0.8912
Epoch 161/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2924 - acc: 0.8985
Epoch 161: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2926 - acc: 0.8984 - val_loss: 0.3175 - val_acc: 0.8931
Epoch 162/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2916 - acc: 0.8984
Epoch 162: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2914 - acc: 0.8985 - val_loss: 0.3140 - val_acc: 0.8928
Epoch 163/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2897 - acc: 0.8985
Epoch 163: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2897 - acc: 0.8985 - val_loss: 0.3156 - val_acc: 0.8918
Epoch 164/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.2887 - acc: 0.8999
Epoch 164: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2888 - acc: 0.8999 - val_loss: 0.3158 - val_acc: 0.8918
Epoch 165/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2885 - acc: 0.8993
Epoch 165: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2885 - acc: 0.8993 - val_loss: 0.3153 - val_acc: 0.8931
Epoch 166/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2871 - acc: 0.9003
Epoch 166: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2870 - acc: 0.9003 - val_loss: 0.3156 - val_acc: 0.8927
Epoch 167/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2868 - acc: 0.8997
Epoch 167: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2867 - acc: 0.8997 - val_loss: 0.3128 - val_acc: 0.8934
Epoch 168/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2854 - acc: 0.9000
Epoch 168: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2854 - acc: 0.9000 - val_loss: 0.3124 - val_acc: 0.8945
Epoch 169/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2862 - acc: 0.9000
Epoch 169: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 5ms/step - loss: 0.2860 - acc: 0.9001 - val_loss: 0.3129 - val_acc: 0.8929
Epoch 170/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2847 - acc: 0.9002
Epoch 170: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2847 - acc: 0.9002 - val_loss: 0.3124 - val_acc: 0.8940
Epoch 171/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2851 - acc: 0.9011
Epoch 171: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2852 - acc: 0.9011 - val_loss: 0.3116 - val_acc: 0.8954
Epoch 172/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.2836 - acc: 0.9009
Epoch 172: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2837 - acc: 0.9009 - val_loss: 0.3123 - val_acc: 0.8954
Epoch 173/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2842 - acc: 0.9006
Epoch 173: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2841 - acc: 0.9007 - val_loss: 0.3118 - val_acc: 0.8939
Epoch 174/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.2823 - acc: 0.9009
Epoch 174: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2822 - acc: 0.9009 - val_loss: 0.3115 - val_acc: 0.8942
Epoch 175/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2825 - acc: 0.9015
Epoch 175: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2825 - acc: 0.9015 - val_loss: 0.3127 - val_acc: 0.8934
Epoch 176/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2801 - acc: 0.9018
Epoch 176: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2802 - acc: 0.9018 - val_loss: 0.3109 - val_acc: 0.8941
Epoch 177/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2806 - acc: 0.9020
Epoch 177: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2806 - acc: 0.9020 - val_loss: 0.3109 - val_acc: 0.8935
Epoch 178/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.2791 - acc: 0.9028
Epoch 178: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2791 - acc: 0.9028 - val_loss: 0.3131 - val_acc: 0.8924
Epoch 179/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2778 - acc: 0.9033
Epoch 179: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2780 - acc: 0.9032 - val_loss: 0.3106 - val_acc: 0.8935
Epoch 180/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2781 - acc: 0.9033
Epoch 180: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2781 - acc: 0.9033 - val_loss: 0.3120 - val_acc: 0.8938
Epoch 181/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2769 - acc: 0.9034
Epoch 181: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2767 - acc: 0.9035 - val_loss: 0.3090 - val_acc: 0.8944
Epoch 182/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2773 - acc: 0.9031
Epoch 182: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2773 - acc: 0.9030 - val_loss: 0.3108 - val_acc: 0.8941
Epoch 183/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2767 - acc: 0.9035
Epoch 183: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2766 - acc: 0.9036 - val_loss: 0.3080 - val_acc: 0.8950
Epoch 184/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2752 - acc: 0.9036
Epoch 184: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2752 - acc: 0.9036 - val_loss: 0.3073 - val_acc: 0.8969
Epoch 185/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2735 - acc: 0.9042
Epoch 185: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2735 - acc: 0.9042 - val_loss: 0.3056 - val_acc: 0.8962
Epoch 186/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2738 - acc: 0.9045
Epoch 186: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2739 - acc: 0.9045 - val_loss: 0.3043 - val_acc: 0.8986
Epoch 187/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2735 - acc: 0.9039
Epoch 187: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2736 - acc: 0.9039 - val_loss: 0.3065 - val_acc: 0.8953
Epoch 188/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2728 - acc: 0.9048
Epoch 188: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2726 - acc: 0.9049 - val_loss: 0.3063 - val_acc: 0.8969
Epoch 189/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2721 - acc: 0.9047
Epoch 189: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2723 - acc: 0.9046 - val_loss: 0.3076 - val_acc: 0.8956
Epoch 190/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2719 - acc: 0.9046
Epoch 190: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2720 - acc: 0.9046 - val_loss: 0.3066 - val_acc: 0.8956
Epoch 191/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2695 - acc: 0.9055
Epoch 191: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2694 - acc: 0.9056 - val_loss: 0.3053 - val_acc: 0.8962
Epoch 192/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2688 - acc: 0.9058
Epoch 192: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2688 - acc: 0.9058 - val_loss: 0.3045 - val_acc: 0.8980
Epoch 193/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2688 - acc: 0.9067
Epoch 193: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2687 - acc: 0.9067 - val_loss: 0.3077 - val_acc: 0.8958
Epoch 194/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2680 - acc: 0.9065
Epoch 194: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2679 - acc: 0.9065 - val_loss: 0.3057 - val_acc: 0.8955
Epoch 195/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.2670 - acc: 0.9067
Epoch 195: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2669 - acc: 0.9067 - val_loss: 0.3036 - val_acc: 0.8974
Epoch 196/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2676 - acc: 0.9061
Epoch 196: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2679 - acc: 0.9061 - val_loss: 0.3047 - val_acc: 0.8976
Epoch 197/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2649 - acc: 0.9076
Epoch 197: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2649 - acc: 0.9076 - val_loss: 0.3040 - val_acc: 0.8962
Epoch 198/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2649 - acc: 0.9074
Epoch 198: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2650 - acc: 0.9074 - val_loss: 0.3038 - val_acc: 0.8985
Epoch 199/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2647 - acc: 0.9075
Epoch 199: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2647 - acc: 0.9075 - val_loss: 0.3024 - val_acc: 0.8969
Epoch 200/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2637 - acc: 0.9077
Epoch 200: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2638 - acc: 0.9077 - val_loss: 0.3051 - val_acc: 0.8967
Epoch 201/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2636 - acc: 0.9078
Epoch 201: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2636 - acc: 0.9077 - val_loss: 0.3022 - val_acc: 0.8976
Epoch 202/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2632 - acc: 0.9077
Epoch 202: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2631 - acc: 0.9077 - val_loss: 0.3032 - val_acc: 0.8972
Epoch 203/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2633 - acc: 0.9076
Epoch 203: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2634 - acc: 0.9076 - val_loss: 0.3042 - val_acc: 0.8972
Epoch 204/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2614 - acc: 0.9089
Epoch 204: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2613 - acc: 0.9089 - val_loss: 0.3056 - val_acc: 0.8961
Epoch 205/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2613 - acc: 0.9094
Epoch 205: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2613 - acc: 0.9094 - val_loss: 0.3019 - val_acc: 0.8994
Epoch 206/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2598 - acc: 0.9097
Epoch 206: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2598 - acc: 0.9097 - val_loss: 0.3008 - val_acc: 0.8986
Epoch 207/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2598 - acc: 0.9096
Epoch 207: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2598 - acc: 0.9096 - val_loss: 0.3005 - val_acc: 0.8993
Epoch 208/1000
1857/1872 [============================>.] - ETA: 0s - loss: 0.2590 - acc: 0.9091
Epoch 208: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2591 - acc: 0.9091 - val_loss: 0.3014 - val_acc: 0.8980
Epoch 209/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2582 - acc: 0.9089
Epoch 209: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2581 - acc: 0.9089 - val_loss: 0.3015 - val_acc: 0.8975
Epoch 210/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2571 - acc: 0.9102
Epoch 210: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2571 - acc: 0.9102 - val_loss: 0.2978 - val_acc: 0.8993
Epoch 211/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2571 - acc: 0.9102
Epoch 211: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2571 - acc: 0.9102 - val_loss: 0.3006 - val_acc: 0.8986
Epoch 212/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2558 - acc: 0.9110
Epoch 212: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2558 - acc: 0.9109 - val_loss: 0.2982 - val_acc: 0.9010
Epoch 213/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2565 - acc: 0.9107
Epoch 213: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2563 - acc: 0.9109 - val_loss: 0.2980 - val_acc: 0.9011
Epoch 214/1000
1857/1872 [============================>.] - ETA: 0s - loss: 0.2546 - acc: 0.9115
Epoch 214: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2546 - acc: 0.9116 - val_loss: 0.2994 - val_acc: 0.8988
Epoch 215/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2544 - acc: 0.9107
Epoch 215: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2544 - acc: 0.9107 - val_loss: 0.3016 - val_acc: 0.8990
Epoch 216/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2539 - acc: 0.9114
Epoch 216: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2538 - acc: 0.9114 - val_loss: 0.3009 - val_acc: 0.8983
Epoch 217/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2543 - acc: 0.9114
Epoch 217: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2542 - acc: 0.9115 - val_loss: 0.2992 - val_acc: 0.8986
Epoch 218/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2536 - acc: 0.9112
Epoch 218: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2536 - acc: 0.9113 - val_loss: 0.2971 - val_acc: 0.9014
Epoch 219/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2521 - acc: 0.9120
Epoch 219: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2523 - acc: 0.9120 - val_loss: 0.2966 - val_acc: 0.9007
Epoch 220/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2519 - acc: 0.9121
Epoch 220: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2520 - acc: 0.9121 - val_loss: 0.3002 - val_acc: 0.8988
Epoch 221/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2514 - acc: 0.9126
Epoch 221: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2516 - acc: 0.9125 - val_loss: 0.3001 - val_acc: 0.8997
Epoch 222/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2518 - acc: 0.9119
Epoch 222: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2519 - acc: 0.9119 - val_loss: 0.3000 - val_acc: 0.9009
Epoch 223/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2514 - acc: 0.9123
Epoch 223: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2513 - acc: 0.9123 - val_loss: 0.3009 - val_acc: 0.9004
Epoch 224/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2497 - acc: 0.9125
Epoch 224: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2497 - acc: 0.9125 - val_loss: 0.3000 - val_acc: 0.8998
Epoch 225/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2495 - acc: 0.9127
Epoch 225: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2493 - acc: 0.9128 - val_loss: 0.2971 - val_acc: 0.9012
Epoch 226/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2482 - acc: 0.9135
Epoch 226: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2481 - acc: 0.9136 - val_loss: 0.2987 - val_acc: 0.9004
Epoch 227/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2469 - acc: 0.9133
Epoch 227: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2470 - acc: 0.9132 - val_loss: 0.2965 - val_acc: 0.9014
Epoch 228/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2472 - acc: 0.9136
Epoch 228: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2470 - acc: 0.9136 - val_loss: 0.2968 - val_acc: 0.8993
Epoch 229/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2469 - acc: 0.9135
Epoch 229: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2468 - acc: 0.9135 - val_loss: 0.2991 - val_acc: 0.8993
Epoch 230/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2470 - acc: 0.9142
Epoch 230: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2469 - acc: 0.9142 - val_loss: 0.2939 - val_acc: 0.9025
Epoch 231/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2457 - acc: 0.9142
Epoch 231: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 5ms/step - loss: 0.2456 - acc: 0.9142 - val_loss: 0.2993 - val_acc: 0.9000
Epoch 232/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2442 - acc: 0.9146
Epoch 232: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2441 - acc: 0.9146 - val_loss: 0.2975 - val_acc: 0.8998
Epoch 233/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2442 - acc: 0.9148
Epoch 233: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2442 - acc: 0.9148 - val_loss: 0.2951 - val_acc: 0.9002
Epoch 234/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2445 - acc: 0.9146
Epoch 234: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2446 - acc: 0.9146 - val_loss: 0.2955 - val_acc: 0.9009
Epoch 235/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2436 - acc: 0.9145
Epoch 235: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2437 - acc: 0.9145 - val_loss: 0.2952 - val_acc: 0.9018
Epoch 236/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2430 - acc: 0.9146
Epoch 236: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2429 - acc: 0.9146 - val_loss: 0.2964 - val_acc: 0.9017
Epoch 237/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2412 - acc: 0.9159
Epoch 237: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2409 - acc: 0.9160 - val_loss: 0.3019 - val_acc: 0.9009
Epoch 238/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2413 - acc: 0.9157
Epoch 238: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2415 - acc: 0.9157 - val_loss: 0.2963 - val_acc: 0.9023
Epoch 239/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2399 - acc: 0.9165
Epoch 239: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2399 - acc: 0.9165 - val_loss: 0.2938 - val_acc: 0.9024
Epoch 240/1000
1858/1872 [============================>.] - ETA: 0s - loss: 0.2413 - acc: 0.9153
Epoch 240: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2411 - acc: 0.9154 - val_loss: 0.2931 - val_acc: 0.9011
Epoch 241/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2387 - acc: 0.9164
Epoch 241: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2388 - acc: 0.9163 - val_loss: 0.2947 - val_acc: 0.9019
Epoch 242/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2400 - acc: 0.9161
Epoch 242: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2400 - acc: 0.9161 - val_loss: 0.2935 - val_acc: 0.9031
Epoch 243/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2402 - acc: 0.9158
Epoch 243: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2402 - acc: 0.9158 - val_loss: 0.2986 - val_acc: 0.8989
Epoch 244/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2384 - acc: 0.9167
Epoch 244: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2384 - acc: 0.9167 - val_loss: 0.2938 - val_acc: 0.9027
Epoch 245/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2379 - acc: 0.9164
Epoch 245: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2382 - acc: 0.9163 - val_loss: 0.2941 - val_acc: 0.9025
Epoch 246/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2362 - acc: 0.9167
Epoch 246: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2362 - acc: 0.9167 - val_loss: 0.2969 - val_acc: 0.9016
Epoch 247/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2352 - acc: 0.9178
Epoch 247: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2353 - acc: 0.9177 - val_loss: 0.2930 - val_acc: 0.9035
Epoch 248/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.2371 - acc: 0.9173
Epoch 248: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2371 - acc: 0.9173 - val_loss: 0.2920 - val_acc: 0.9035
Epoch 249/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.2364 - acc: 0.9174
Epoch 249: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2362 - acc: 0.9174 - val_loss: 0.2927 - val_acc: 0.9032
Epoch 250/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.2352 - acc: 0.9176
Epoch 250: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 10s 5ms/step - loss: 0.2350 - acc: 0.9177 - val_loss: 0.2920 - val_acc: 0.9040
Epoch 251/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2336 - acc: 0.9184
Epoch 251: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2338 - acc: 0.9183 - val_loss: 0.2956 - val_acc: 0.9016
Epoch 252/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2336 - acc: 0.9183
Epoch 252: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2336 - acc: 0.9183 - val_loss: 0.2945 - val_acc: 0.9024
Epoch 253/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.2339 - acc: 0.9187
Epoch 253: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2338 - acc: 0.9188 - val_loss: 0.2923 - val_acc: 0.9025
Epoch 254/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2317 - acc: 0.9183
Epoch 254: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2316 - acc: 0.9183 - val_loss: 0.2973 - val_acc: 0.9017
Epoch 255/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2330 - acc: 0.9179
Epoch 255: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2330 - acc: 0.9180 - val_loss: 0.2936 - val_acc: 0.9028
Epoch 256/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2305 - acc: 0.9191
Epoch 256: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2306 - acc: 0.9190 - val_loss: 0.2925 - val_acc: 0.9023
Epoch 257/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.2315 - acc: 0.9190
Epoch 257: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2315 - acc: 0.9191 - val_loss: 0.2920 - val_acc: 0.9021
Epoch 258/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2309 - acc: 0.9189
Epoch 258: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2310 - acc: 0.9189 - val_loss: 0.2945 - val_acc: 0.9035
Epoch 259/1000
1860/1872 [============================>.] - ETA: 0s - loss: 0.2305 - acc: 0.9191
Epoch 259: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2304 - acc: 0.9191 - val_loss: 0.2919 - val_acc: 0.9017
Epoch 260/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2293 - acc: 0.9200
Epoch 260: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2293 - acc: 0.9200 - val_loss: 0.2920 - val_acc: 0.9036
Epoch 261/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2292 - acc: 0.9198
Epoch 261: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2294 - acc: 0.9198 - val_loss: 0.2915 - val_acc: 0.9025
Epoch 262/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2273 - acc: 0.9203
Epoch 262: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2273 - acc: 0.9203 - val_loss: 0.2919 - val_acc: 0.9035
Epoch 263/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2277 - acc: 0.9201
Epoch 263: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2277 - acc: 0.9201 - val_loss: 0.2921 - val_acc: 0.9047
Epoch 264/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2280 - acc: 0.9204
Epoch 264: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2278 - acc: 0.9204 - val_loss: 0.2918 - val_acc: 0.9020
Epoch 265/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2279 - acc: 0.9203
Epoch 265: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2281 - acc: 0.9202 - val_loss: 0.2902 - val_acc: 0.9029
Epoch 266/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2267 - acc: 0.9203
Epoch 266: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2269 - acc: 0.9203 - val_loss: 0.2893 - val_acc: 0.9052
Epoch 267/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2262 - acc: 0.9212
Epoch 267: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2262 - acc: 0.9212 - val_loss: 0.2906 - val_acc: 0.9030
Epoch 268/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2260 - acc: 0.9212
Epoch 268: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2260 - acc: 0.9212 - val_loss: 0.2945 - val_acc: 0.9020
Epoch 269/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2246 - acc: 0.9212
Epoch 269: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2248 - acc: 0.9212 - val_loss: 0.2916 - val_acc: 0.9030
Epoch 270/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.2245 - acc: 0.9217
Epoch 270: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2247 - acc: 0.9217 - val_loss: 0.2908 - val_acc: 0.9043
Epoch 271/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2246 - acc: 0.9205
Epoch 271: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2246 - acc: 0.9205 - val_loss: 0.2909 - val_acc: 0.9034
Epoch 272/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2233 - acc: 0.9221
Epoch 272: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2234 - acc: 0.9221 - val_loss: 0.2906 - val_acc: 0.9042
Epoch 273/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2233 - acc: 0.9220
Epoch 273: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2234 - acc: 0.9220 - val_loss: 0.2909 - val_acc: 0.9043
Epoch 274/1000
1860/1872 [============================>.] - ETA: 0s - loss: 0.2236 - acc: 0.9211
Epoch 274: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2235 - acc: 0.9211 - val_loss: 0.2922 - val_acc: 0.9036
Epoch 275/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.2218 - acc: 0.9215
Epoch 275: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2218 - acc: 0.9215 - val_loss: 0.2889 - val_acc: 0.9052
Epoch 276/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2219 - acc: 0.9220
Epoch 276: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2220 - acc: 0.9220 - val_loss: 0.2936 - val_acc: 0.9051
Epoch 277/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.2218 - acc: 0.9218
Epoch 277: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2218 - acc: 0.9218 - val_loss: 0.2933 - val_acc: 0.9041
Epoch 278/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2194 - acc: 0.9234
Epoch 278: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2193 - acc: 0.9234 - val_loss: 0.2910 - val_acc: 0.9045
Epoch 279/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2196 - acc: 0.9230
Epoch 279: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2197 - acc: 0.9230 - val_loss: 0.2908 - val_acc: 0.9039
Epoch 280/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2188 - acc: 0.9228
Epoch 280: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2188 - acc: 0.9229 - val_loss: 0.2888 - val_acc: 0.9047
Epoch 281/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.2194 - acc: 0.9230
Epoch 281: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2193 - acc: 0.9230 - val_loss: 0.2908 - val_acc: 0.9046
Epoch 282/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2175 - acc: 0.9239
Epoch 282: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2177 - acc: 0.9239 - val_loss: 0.2893 - val_acc: 0.9036
Epoch 283/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.2168 - acc: 0.9237
Epoch 283: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2169 - acc: 0.9236 - val_loss: 0.2908 - val_acc: 0.9027
Epoch 284/1000
1869/1872 [============================>.] - ETA: 0s - loss: 0.2193 - acc: 0.9224
Epoch 284: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2193 - acc: 0.9223 - val_loss: 0.2889 - val_acc: 0.9041
Epoch 285/1000
1862/1872 [============================>.] - ETA: 0s - loss: 0.2177 - acc: 0.9231
Epoch 285: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2177 - acc: 0.9231 - val_loss: 0.2912 - val_acc: 0.9040
Epoch 286/1000
1871/1872 [============================>.] - ETA: 0s - loss: 0.2174 - acc: 0.9241
Epoch 286: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2173 - acc: 0.9241 - val_loss: 0.2910 - val_acc: 0.9032
Epoch 287/1000
1860/1872 [============================>.] - ETA: 0s - loss: 0.2157 - acc: 0.9246
Epoch 287: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2156 - acc: 0.9246 - val_loss: 0.2909 - val_acc: 0.9045
Epoch 288/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2177 - acc: 0.9236
Epoch 288: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2177 - acc: 0.9236 - val_loss: 0.2885 - val_acc: 0.9057
Epoch 289/1000
1859/1872 [============================>.] - ETA: 0s - loss: 0.2151 - acc: 0.9245
Epoch 289: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2152 - acc: 0.9245 - val_loss: 0.2877 - val_acc: 0.9058
Epoch 290/1000
1867/1872 [============================>.] - ETA: 0s - loss: 0.2150 - acc: 0.9249
Epoch 290: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2151 - acc: 0.9248 - val_loss: 0.2875 - val_acc: 0.9057
Epoch 291/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2139 - acc: 0.9248
Epoch 291: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2139 - acc: 0.9248 - val_loss: 0.2924 - val_acc: 0.9040
Epoch 292/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2137 - acc: 0.9248
Epoch 292: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2137 - acc: 0.9248 - val_loss: 0.2886 - val_acc: 0.9041
Epoch 293/1000
1864/1872 [============================>.] - ETA: 0s - loss: 0.2131 - acc: 0.9248
Epoch 293: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2131 - acc: 0.9248 - val_loss: 0.2902 - val_acc: 0.9050
Epoch 294/1000
1865/1872 [============================>.] - ETA: 0s - loss: 0.2139 - acc: 0.9250
Epoch 294: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2138 - acc: 0.9250 - val_loss: 0.2916 - val_acc: 0.9041
Epoch 295/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2135 - acc: 0.9251
Epoch 295: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2135 - acc: 0.9251 - val_loss: 0.2931 - val_acc: 0.9016
Epoch 296/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2108 - acc: 0.9258
Epoch 296: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2108 - acc: 0.9258 - val_loss: 0.2915 - val_acc: 0.9046
Epoch 297/1000
1870/1872 [============================>.] - ETA: 0s - loss: 0.2120 - acc: 0.9258
Epoch 297: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2120 - acc: 0.9258 - val_loss: 0.2896 - val_acc: 0.9053
Epoch 298/1000
1866/1872 [============================>.] - ETA: 0s - loss: 0.2102 - acc: 0.9262
Epoch 298: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 9s 5ms/step - loss: 0.2102 - acc: 0.9262 - val_loss: 0.2938 - val_acc: 0.9035
Epoch 299/1000
1863/1872 [============================>.] - ETA: 0s - loss: 0.2102 - acc: 0.9264
Epoch 299: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2103 - acc: 0.9263 - val_loss: 0.2898 - val_acc: 0.9044
Epoch 300/1000
1858/1872 [============================>.] - ETA: 0s - loss: 0.2101 - acc: 0.9265
Epoch 300: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2104 - acc: 0.9264 - val_loss: 0.2942 - val_acc: 0.9046
Epoch 301/1000
1872/1872 [==============================] - ETA: 0s - loss: 0.2100 - acc: 0.9263
Epoch 301: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2100 - acc: 0.9263 - val_loss: 0.2919 - val_acc: 0.9047
Epoch 302/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2105 - acc: 0.9259
Epoch 302: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 8s 4ms/step - loss: 0.2106 - acc: 0.9258 - val_loss: 0.2902 - val_acc: 0.9054
Epoch 303/1000
1857/1872 [============================>.] - ETA: 0s - loss: 0.2087 - acc: 0.9266
Epoch 303: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2084 - acc: 0.9267 - val_loss: 0.2888 - val_acc: 0.9060
Epoch 304/1000
1868/1872 [============================>.] - ETA: 0s - loss: 0.2094 - acc: 0.9260
Epoch 304: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2094 - acc: 0.9260 - val_loss: 0.2897 - val_acc: 0.9054
Epoch 305/1000
1861/1872 [============================>.] - ETA: 0s - loss: 0.2083 - acc: 0.9273
Epoch 305: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_ELL/cp.ckpt
1872/1872 [==============================] - 7s 4ms/step - loss: 0.2082 - acc: 0.9273 - val_loss: 0.2951 - val_acc: 0.9035
Epoch 305: early stopping
Use balanced Generator [False]
Data: 296360
-----------------------------------------------------------------------------------
Epoch 1/1000
3079/3088 [============================>.] - ETA: 0s - loss: 2.0492 - acc: 0.1693
Epoch 1: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 14s 4ms/step - loss: 2.0490 - acc: 0.1695 - val_loss: 1.9645 - val_acc: 0.2918
Epoch 2/1000
3081/3088 [============================>.] - ETA: 0s - loss: 1.6388 - acc: 0.3918
Epoch 2: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 1.6378 - acc: 0.3922 - val_loss: 1.0857 - val_acc: 0.6336
Epoch 3/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.9874 - acc: 0.6419
Epoch 3: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.9870 - acc: 0.6420 - val_loss: 0.7262 - val_acc: 0.7524
Epoch 4/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.7830 - acc: 0.7172
Epoch 4: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.7830 - acc: 0.7172 - val_loss: 0.6310 - val_acc: 0.7800
Epoch 5/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.7012 - acc: 0.7463
Epoch 5: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.7010 - acc: 0.7464 - val_loss: 0.5850 - val_acc: 0.7960
Epoch 6/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.6492 - acc: 0.7661
Epoch 6: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.6493 - acc: 0.7661 - val_loss: 0.5553 - val_acc: 0.8044
Epoch 7/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.6177 - acc: 0.7770
Epoch 7: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.6176 - acc: 0.7771 - val_loss: 0.5359 - val_acc: 0.8122
Epoch 8/1000
3088/3088 [==============================] - ETA: 0s - loss: 0.5930 - acc: 0.7863
Epoch 8: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.5930 - acc: 0.7863 - val_loss: 0.5215 - val_acc: 0.8181
Epoch 9/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.5734 - acc: 0.7940
Epoch 9: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.5735 - acc: 0.7940 - val_loss: 0.5070 - val_acc: 0.8245
Epoch 10/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.5562 - acc: 0.8003
Epoch 10: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.5561 - acc: 0.8003 - val_loss: 0.4987 - val_acc: 0.8255
Epoch 11/1000
3088/3088 [==============================] - ETA: 0s - loss: 0.5450 - acc: 0.8046
Epoch 11: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.5450 - acc: 0.8046 - val_loss: 0.4855 - val_acc: 0.8317
Epoch 12/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.5331 - acc: 0.8082
Epoch 12: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.5331 - acc: 0.8082 - val_loss: 0.4780 - val_acc: 0.8352
Epoch 13/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.5225 - acc: 0.8132
Epoch 13: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.5225 - acc: 0.8132 - val_loss: 0.4740 - val_acc: 0.8349
Epoch 14/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.5124 - acc: 0.8162
Epoch 14: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.5123 - acc: 0.8162 - val_loss: 0.4706 - val_acc: 0.8382
Epoch 15/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.5041 - acc: 0.8201
Epoch 15: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.5041 - acc: 0.8201 - val_loss: 0.4657 - val_acc: 0.8390
Epoch 16/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.4965 - acc: 0.8226
Epoch 16: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.4965 - acc: 0.8226 - val_loss: 0.4622 - val_acc: 0.8374
Epoch 17/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.4874 - acc: 0.8267
Epoch 17: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4874 - acc: 0.8267 - val_loss: 0.4553 - val_acc: 0.8424
Epoch 18/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.4826 - acc: 0.8275
Epoch 18: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4826 - acc: 0.8275 - val_loss: 0.4471 - val_acc: 0.8454
Epoch 19/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.4754 - acc: 0.8314
Epoch 19: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4754 - acc: 0.8314 - val_loss: 0.4436 - val_acc: 0.8480
Epoch 20/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.4704 - acc: 0.8326
Epoch 20: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4704 - acc: 0.8326 - val_loss: 0.4383 - val_acc: 0.8502
Epoch 21/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.4629 - acc: 0.8353
Epoch 21: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4629 - acc: 0.8353 - val_loss: 0.4387 - val_acc: 0.8478
Epoch 22/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.4586 - acc: 0.8375
Epoch 22: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 14s 4ms/step - loss: 0.4587 - acc: 0.8374 - val_loss: 0.4353 - val_acc: 0.8516
Epoch 23/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.4540 - acc: 0.8392
Epoch 23: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4539 - acc: 0.8392 - val_loss: 0.4275 - val_acc: 0.8546
Epoch 24/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.4501 - acc: 0.8402
Epoch 24: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4503 - acc: 0.8402 - val_loss: 0.4251 - val_acc: 0.8547
Epoch 25/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.4446 - acc: 0.8427
Epoch 25: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4446 - acc: 0.8427 - val_loss: 0.4254 - val_acc: 0.8522
Epoch 26/1000
3074/3088 [============================>.] - ETA: 0s - loss: 0.4406 - acc: 0.8439
Epoch 26: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4406 - acc: 0.8438 - val_loss: 0.4187 - val_acc: 0.8558
Epoch 27/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.4370 - acc: 0.8453
Epoch 27: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4370 - acc: 0.8453 - val_loss: 0.4142 - val_acc: 0.8583
Epoch 28/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.4310 - acc: 0.8470
Epoch 28: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 14s 4ms/step - loss: 0.4311 - acc: 0.8470 - val_loss: 0.4151 - val_acc: 0.8577
Epoch 29/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.4285 - acc: 0.8490
Epoch 29: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4285 - acc: 0.8490 - val_loss: 0.4101 - val_acc: 0.8591
Epoch 30/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.4258 - acc: 0.8496
Epoch 30: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4258 - acc: 0.8496 - val_loss: 0.4096 - val_acc: 0.8593
Epoch 31/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.4214 - acc: 0.8507
Epoch 31: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4214 - acc: 0.8507 - val_loss: 0.4069 - val_acc: 0.8612
Epoch 32/1000
3075/3088 [============================>.] - ETA: 0s - loss: 0.4199 - acc: 0.8516
Epoch 32: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4200 - acc: 0.8515 - val_loss: 0.4039 - val_acc: 0.8629
Epoch 33/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.4146 - acc: 0.8534
Epoch 33: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.4146 - acc: 0.8534 - val_loss: 0.4011 - val_acc: 0.8640
Epoch 34/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.4130 - acc: 0.8539
Epoch 34: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4132 - acc: 0.8538 - val_loss: 0.4046 - val_acc: 0.8598
Epoch 35/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.4099 - acc: 0.8552
Epoch 35: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4100 - acc: 0.8551 - val_loss: 0.3972 - val_acc: 0.8650
Epoch 36/1000
3088/3088 [==============================] - ETA: 0s - loss: 0.4058 - acc: 0.8569
Epoch 36: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4058 - acc: 0.8569 - val_loss: 0.3935 - val_acc: 0.8664
Epoch 37/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.4040 - acc: 0.8576
Epoch 37: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.4040 - acc: 0.8576 - val_loss: 0.3917 - val_acc: 0.8670
Epoch 38/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.4017 - acc: 0.8587
Epoch 38: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.4016 - acc: 0.8587 - val_loss: 0.3906 - val_acc: 0.8680
Epoch 39/1000
3074/3088 [============================>.] - ETA: 0s - loss: 0.3994 - acc: 0.8589
Epoch 39: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3996 - acc: 0.8588 - val_loss: 0.3888 - val_acc: 0.8680
Epoch 40/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.3950 - acc: 0.8602
Epoch 40: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3950 - acc: 0.8602 - val_loss: 0.3864 - val_acc: 0.8687
Epoch 41/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.3936 - acc: 0.8608
Epoch 41: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3936 - acc: 0.8608 - val_loss: 0.3841 - val_acc: 0.8715
Epoch 42/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.3903 - acc: 0.8621
Epoch 42: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3903 - acc: 0.8621 - val_loss: 0.3822 - val_acc: 0.8703
Epoch 43/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3879 - acc: 0.8638
Epoch 43: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.3879 - acc: 0.8638 - val_loss: 0.3809 - val_acc: 0.8697
Epoch 44/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.3852 - acc: 0.8642
Epoch 44: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3853 - acc: 0.8642 - val_loss: 0.3795 - val_acc: 0.8705
Epoch 45/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.3843 - acc: 0.8649
Epoch 45: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3842 - acc: 0.8649 - val_loss: 0.3775 - val_acc: 0.8724
Epoch 46/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.3798 - acc: 0.8661
Epoch 46: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.3798 - acc: 0.8662 - val_loss: 0.3761 - val_acc: 0.8731
Epoch 47/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.3785 - acc: 0.8667
Epoch 47: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3785 - acc: 0.8667 - val_loss: 0.3746 - val_acc: 0.8741
Epoch 48/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.3760 - acc: 0.8680
Epoch 48: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3760 - acc: 0.8680 - val_loss: 0.3701 - val_acc: 0.8755
Epoch 49/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.3741 - acc: 0.8686
Epoch 49: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 14s 4ms/step - loss: 0.3741 - acc: 0.8686 - val_loss: 0.3681 - val_acc: 0.8769
Epoch 50/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.3732 - acc: 0.8692
Epoch 50: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.3732 - acc: 0.8692 - val_loss: 0.3684 - val_acc: 0.8758
Epoch 51/1000
3074/3088 [============================>.] - ETA: 0s - loss: 0.3705 - acc: 0.8697
Epoch 51: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3704 - acc: 0.8698 - val_loss: 0.3698 - val_acc: 0.8745
Epoch 52/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.3678 - acc: 0.8705
Epoch 52: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3678 - acc: 0.8705 - val_loss: 0.3675 - val_acc: 0.8758
Epoch 53/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.3668 - acc: 0.8710
Epoch 53: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3669 - acc: 0.8710 - val_loss: 0.3638 - val_acc: 0.8769
Epoch 54/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3648 - acc: 0.8715
Epoch 54: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3648 - acc: 0.8715 - val_loss: 0.3708 - val_acc: 0.8731
Epoch 55/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.3629 - acc: 0.8726
Epoch 55: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3629 - acc: 0.8726 - val_loss: 0.3610 - val_acc: 0.8785
Epoch 56/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.3602 - acc: 0.8742
Epoch 56: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3602 - acc: 0.8742 - val_loss: 0.3597 - val_acc: 0.8769
Epoch 57/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.3588 - acc: 0.8749
Epoch 57: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 14s 4ms/step - loss: 0.3588 - acc: 0.8749 - val_loss: 0.3573 - val_acc: 0.8782
Epoch 58/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.3558 - acc: 0.8754
Epoch 58: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3556 - acc: 0.8755 - val_loss: 0.3574 - val_acc: 0.8793
Epoch 59/1000
3088/3088 [==============================] - ETA: 0s - loss: 0.3556 - acc: 0.8756
Epoch 59: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.3556 - acc: 0.8756 - val_loss: 0.3580 - val_acc: 0.8772
Epoch 60/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.3532 - acc: 0.8764
Epoch 60: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3531 - acc: 0.8764 - val_loss: 0.3565 - val_acc: 0.8803
Epoch 61/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.3512 - acc: 0.8769
Epoch 61: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3511 - acc: 0.8770 - val_loss: 0.3557 - val_acc: 0.8793
Epoch 62/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.3493 - acc: 0.8776
Epoch 62: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3494 - acc: 0.8775 - val_loss: 0.3512 - val_acc: 0.8816
Epoch 63/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.3470 - acc: 0.8783
Epoch 63: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3471 - acc: 0.8783 - val_loss: 0.3518 - val_acc: 0.8809
Epoch 64/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.3464 - acc: 0.8789
Epoch 64: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3463 - acc: 0.8789 - val_loss: 0.3511 - val_acc: 0.8813
Epoch 65/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3450 - acc: 0.8788
Epoch 65: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3450 - acc: 0.8789 - val_loss: 0.3491 - val_acc: 0.8827
Epoch 66/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.3424 - acc: 0.8802
Epoch 66: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3425 - acc: 0.8802 - val_loss: 0.3481 - val_acc: 0.8848
Epoch 67/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.3411 - acc: 0.8805
Epoch 67: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.3412 - acc: 0.8805 - val_loss: 0.3456 - val_acc: 0.8825
Epoch 68/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.3386 - acc: 0.8814
Epoch 68: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3388 - acc: 0.8813 - val_loss: 0.3452 - val_acc: 0.8838
Epoch 69/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.3377 - acc: 0.8825
Epoch 69: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3376 - acc: 0.8825 - val_loss: 0.3441 - val_acc: 0.8848
Epoch 70/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.3366 - acc: 0.8827
Epoch 70: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.3366 - acc: 0.8827 - val_loss: 0.3434 - val_acc: 0.8838
Epoch 71/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3345 - acc: 0.8834
Epoch 71: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.3345 - acc: 0.8834 - val_loss: 0.3402 - val_acc: 0.8863
Epoch 72/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.3331 - acc: 0.8839
Epoch 72: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3332 - acc: 0.8839 - val_loss: 0.3409 - val_acc: 0.8863
Epoch 73/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.3316 - acc: 0.8846
Epoch 73: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3316 - acc: 0.8845 - val_loss: 0.3401 - val_acc: 0.8867
Epoch 74/1000
3073/3088 [============================>.] - ETA: 0s - loss: 0.3295 - acc: 0.8853
Epoch 74: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3296 - acc: 0.8853 - val_loss: 0.3389 - val_acc: 0.8866
Epoch 75/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3270 - acc: 0.8859
Epoch 75: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3270 - acc: 0.8859 - val_loss: 0.3354 - val_acc: 0.8871
Epoch 76/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.3257 - acc: 0.8867
Epoch 76: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3258 - acc: 0.8867 - val_loss: 0.3383 - val_acc: 0.8876
Epoch 77/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.3244 - acc: 0.8866
Epoch 77: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3244 - acc: 0.8866 - val_loss: 0.3320 - val_acc: 0.8903
Epoch 78/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3226 - acc: 0.8873
Epoch 78: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3226 - acc: 0.8873 - val_loss: 0.3375 - val_acc: 0.8855
Epoch 79/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.3218 - acc: 0.8883
Epoch 79: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3218 - acc: 0.8883 - val_loss: 0.3328 - val_acc: 0.8875
Epoch 80/1000
3073/3088 [============================>.] - ETA: 0s - loss: 0.3198 - acc: 0.8891
Epoch 80: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3197 - acc: 0.8891 - val_loss: 0.3292 - val_acc: 0.8901
Epoch 81/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.3195 - acc: 0.8882
Epoch 81: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3196 - acc: 0.8882 - val_loss: 0.3290 - val_acc: 0.8878
Epoch 82/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.3172 - acc: 0.8900
Epoch 82: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3171 - acc: 0.8900 - val_loss: 0.3322 - val_acc: 0.8876
Epoch 83/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3155 - acc: 0.8901
Epoch 83: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3156 - acc: 0.8901 - val_loss: 0.3317 - val_acc: 0.8894
Epoch 84/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.3146 - acc: 0.8901
Epoch 84: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3147 - acc: 0.8901 - val_loss: 0.3264 - val_acc: 0.8918
Epoch 85/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.3132 - acc: 0.8909
Epoch 85: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3132 - acc: 0.8909 - val_loss: 0.3277 - val_acc: 0.8894
Epoch 86/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.3122 - acc: 0.8907
Epoch 86: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.3122 - acc: 0.8907 - val_loss: 0.3284 - val_acc: 0.8890
Epoch 87/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.3101 - acc: 0.8920
Epoch 87: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3101 - acc: 0.8921 - val_loss: 0.3228 - val_acc: 0.8915
Epoch 88/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.3078 - acc: 0.8925
Epoch 88: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3078 - acc: 0.8925 - val_loss: 0.3214 - val_acc: 0.8916
Epoch 89/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.3069 - acc: 0.8934
Epoch 89: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.3069 - acc: 0.8933 - val_loss: 0.3245 - val_acc: 0.8917
Epoch 90/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.3067 - acc: 0.8933
Epoch 90: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3066 - acc: 0.8933 - val_loss: 0.3203 - val_acc: 0.8949
Epoch 91/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.3046 - acc: 0.8936
Epoch 91: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3045 - acc: 0.8936 - val_loss: 0.3226 - val_acc: 0.8938
Epoch 92/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.3028 - acc: 0.8945
Epoch 92: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3028 - acc: 0.8945 - val_loss: 0.3194 - val_acc: 0.8942
Epoch 93/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.3029 - acc: 0.8946
Epoch 93: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.3029 - acc: 0.8946 - val_loss: 0.3200 - val_acc: 0.8951
Epoch 94/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.3002 - acc: 0.8957
Epoch 94: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3002 - acc: 0.8957 - val_loss: 0.3191 - val_acc: 0.8942
Epoch 95/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2999 - acc: 0.8957
Epoch 95: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.3000 - acc: 0.8957 - val_loss: 0.3183 - val_acc: 0.8933
Epoch 96/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.2984 - acc: 0.8962
Epoch 96: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2984 - acc: 0.8962 - val_loss: 0.3178 - val_acc: 0.8957
Epoch 97/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2978 - acc: 0.8966
Epoch 97: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2978 - acc: 0.8966 - val_loss: 0.3159 - val_acc: 0.8929
Epoch 98/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2956 - acc: 0.8976
Epoch 98: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2956 - acc: 0.8977 - val_loss: 0.3134 - val_acc: 0.8945
Epoch 99/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.2958 - acc: 0.8971
Epoch 99: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2957 - acc: 0.8971 - val_loss: 0.3123 - val_acc: 0.8981
Epoch 100/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.2936 - acc: 0.8977
Epoch 100: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2935 - acc: 0.8978 - val_loss: 0.3162 - val_acc: 0.8929
Epoch 101/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.2926 - acc: 0.8984
Epoch 101: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2925 - acc: 0.8985 - val_loss: 0.3142 - val_acc: 0.8956
Epoch 102/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.2903 - acc: 0.8989
Epoch 102: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2903 - acc: 0.8989 - val_loss: 0.3160 - val_acc: 0.8951
Epoch 103/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.2899 - acc: 0.8997
Epoch 103: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2899 - acc: 0.8997 - val_loss: 0.3123 - val_acc: 0.8964
Epoch 104/1000
3075/3088 [============================>.] - ETA: 0s - loss: 0.2891 - acc: 0.8996
Epoch 104: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2891 - acc: 0.8995 - val_loss: 0.3118 - val_acc: 0.8948
Epoch 105/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.2880 - acc: 0.9001
Epoch 105: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2880 - acc: 0.9001 - val_loss: 0.3072 - val_acc: 0.8976
Epoch 106/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2858 - acc: 0.9004
Epoch 106: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2857 - acc: 0.9005 - val_loss: 0.3097 - val_acc: 0.8977
Epoch 107/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.2864 - acc: 0.9007
Epoch 107: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2864 - acc: 0.9007 - val_loss: 0.3056 - val_acc: 0.8982
Epoch 108/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2857 - acc: 0.9008
Epoch 108: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2857 - acc: 0.9008 - val_loss: 0.3131 - val_acc: 0.8947
Epoch 109/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.2842 - acc: 0.9007
Epoch 109: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2843 - acc: 0.9007 - val_loss: 0.3099 - val_acc: 0.8974
Epoch 110/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.2831 - acc: 0.9017
Epoch 110: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2831 - acc: 0.9018 - val_loss: 0.3091 - val_acc: 0.8985
Epoch 111/1000
3074/3088 [============================>.] - ETA: 0s - loss: 0.2828 - acc: 0.9021
Epoch 111: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2830 - acc: 0.9021 - val_loss: 0.3131 - val_acc: 0.8962
Epoch 112/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.2818 - acc: 0.9019
Epoch 112: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2818 - acc: 0.9019 - val_loss: 0.3115 - val_acc: 0.8972
Epoch 113/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.2807 - acc: 0.9021
Epoch 113: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2806 - acc: 0.9021 - val_loss: 0.3043 - val_acc: 0.8996
Epoch 114/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.2803 - acc: 0.9030
Epoch 114: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2802 - acc: 0.9030 - val_loss: 0.3116 - val_acc: 0.8961
Epoch 115/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.2789 - acc: 0.9034
Epoch 115: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2788 - acc: 0.9034 - val_loss: 0.3080 - val_acc: 0.8995
Epoch 116/1000
3088/3088 [==============================] - ETA: 0s - loss: 0.2777 - acc: 0.9034
Epoch 116: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2777 - acc: 0.9034 - val_loss: 0.3026 - val_acc: 0.8993
Epoch 117/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.2775 - acc: 0.9036
Epoch 117: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2774 - acc: 0.9036 - val_loss: 0.3067 - val_acc: 0.8978
Epoch 118/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.2752 - acc: 0.9043
Epoch 118: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2752 - acc: 0.9043 - val_loss: 0.3006 - val_acc: 0.8986
Epoch 119/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.2749 - acc: 0.9043
Epoch 119: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2749 - acc: 0.9044 - val_loss: 0.2998 - val_acc: 0.9011
Epoch 120/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.2743 - acc: 0.9049
Epoch 120: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2743 - acc: 0.9048 - val_loss: 0.2978 - val_acc: 0.9015
Epoch 121/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2723 - acc: 0.9052
Epoch 121: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2723 - acc: 0.9052 - val_loss: 0.2997 - val_acc: 0.9005
Epoch 122/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2728 - acc: 0.9059
Epoch 122: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2729 - acc: 0.9059 - val_loss: 0.3009 - val_acc: 0.9016
Epoch 123/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.2714 - acc: 0.9061
Epoch 123: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2714 - acc: 0.9061 - val_loss: 0.3076 - val_acc: 0.8976
Epoch 124/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.2723 - acc: 0.9052
Epoch 124: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2722 - acc: 0.9052 - val_loss: 0.3021 - val_acc: 0.8991
Epoch 125/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.2696 - acc: 0.9066
Epoch 125: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2695 - acc: 0.9066 - val_loss: 0.3063 - val_acc: 0.8984
Epoch 126/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.2688 - acc: 0.9066
Epoch 126: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2688 - acc: 0.9066 - val_loss: 0.3003 - val_acc: 0.8998
Epoch 127/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.2679 - acc: 0.9065
Epoch 127: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2679 - acc: 0.9065 - val_loss: 0.2964 - val_acc: 0.9014
Epoch 128/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2668 - acc: 0.9074
Epoch 128: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2667 - acc: 0.9075 - val_loss: 0.3043 - val_acc: 0.8996
Epoch 129/1000
3074/3088 [============================>.] - ETA: 0s - loss: 0.2664 - acc: 0.9077
Epoch 129: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2665 - acc: 0.9077 - val_loss: 0.3010 - val_acc: 0.8988
Epoch 130/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2650 - acc: 0.9078
Epoch 130: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 10s 3ms/step - loss: 0.2650 - acc: 0.9078 - val_loss: 0.3009 - val_acc: 0.8990
Epoch 131/1000
3075/3088 [============================>.] - ETA: 0s - loss: 0.2658 - acc: 0.9071
Epoch 131: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2657 - acc: 0.9072 - val_loss: 0.2992 - val_acc: 0.9009
Epoch 132/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.2636 - acc: 0.9084
Epoch 132: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2635 - acc: 0.9084 - val_loss: 0.2981 - val_acc: 0.8998
Epoch 133/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2629 - acc: 0.9082
Epoch 133: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2629 - acc: 0.9082 - val_loss: 0.2938 - val_acc: 0.9025
Epoch 134/1000
3074/3088 [============================>.] - ETA: 0s - loss: 0.2619 - acc: 0.9091
Epoch 134: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2619 - acc: 0.9091 - val_loss: 0.2968 - val_acc: 0.9017
Epoch 135/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.2615 - acc: 0.9097
Epoch 135: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2614 - acc: 0.9097 - val_loss: 0.2964 - val_acc: 0.9027
Epoch 136/1000
3075/3088 [============================>.] - ETA: 0s - loss: 0.2621 - acc: 0.9087
Epoch 136: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2621 - acc: 0.9087 - val_loss: 0.2946 - val_acc: 0.9014
Epoch 137/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2601 - acc: 0.9104
Epoch 137: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2600 - acc: 0.9104 - val_loss: 0.2941 - val_acc: 0.9031
Epoch 138/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2589 - acc: 0.9103
Epoch 138: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2589 - acc: 0.9103 - val_loss: 0.2918 - val_acc: 0.9038
Epoch 139/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2575 - acc: 0.9102
Epoch 139: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2576 - acc: 0.9102 - val_loss: 0.2929 - val_acc: 0.9030
Epoch 140/1000
3076/3088 [============================>.] - ETA: 0s - loss: 0.2571 - acc: 0.9102
Epoch 140: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2574 - acc: 0.9101 - val_loss: 0.2972 - val_acc: 0.9013
Epoch 141/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2575 - acc: 0.9104
Epoch 141: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2576 - acc: 0.9103 - val_loss: 0.2938 - val_acc: 0.9012
Epoch 142/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2563 - acc: 0.9113
Epoch 142: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2564 - acc: 0.9113 - val_loss: 0.2923 - val_acc: 0.9042
Epoch 143/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2556 - acc: 0.9115
Epoch 143: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2557 - acc: 0.9115 - val_loss: 0.2912 - val_acc: 0.9037
Epoch 144/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2544 - acc: 0.9116
Epoch 144: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2543 - acc: 0.9116 - val_loss: 0.2923 - val_acc: 0.9025
Epoch 145/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.2542 - acc: 0.9120
Epoch 145: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2541 - acc: 0.9120 - val_loss: 0.2952 - val_acc: 0.9028
Epoch 146/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2541 - acc: 0.9112
Epoch 146: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2540 - acc: 0.9113 - val_loss: 0.2934 - val_acc: 0.9038
Epoch 147/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.2533 - acc: 0.9122
Epoch 147: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2531 - acc: 0.9122 - val_loss: 0.2976 - val_acc: 0.9017
Epoch 148/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2531 - acc: 0.9122
Epoch 148: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2531 - acc: 0.9122 - val_loss: 0.2954 - val_acc: 0.9028
Epoch 149/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2510 - acc: 0.9134
Epoch 149: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2511 - acc: 0.9134 - val_loss: 0.2960 - val_acc: 0.9029
Epoch 150/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.2490 - acc: 0.9131
Epoch 150: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2491 - acc: 0.9131 - val_loss: 0.2945 - val_acc: 0.9018
Epoch 151/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.2502 - acc: 0.9132
Epoch 151: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 13s 4ms/step - loss: 0.2503 - acc: 0.9132 - val_loss: 0.2895 - val_acc: 0.9041
Epoch 152/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.2488 - acc: 0.9133
Epoch 152: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2488 - acc: 0.9133 - val_loss: 0.2906 - val_acc: 0.9030
Epoch 153/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2491 - acc: 0.9132
Epoch 153: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2491 - acc: 0.9132 - val_loss: 0.2917 - val_acc: 0.9037
Epoch 154/1000
3077/3088 [============================>.] - ETA: 0s - loss: 0.2476 - acc: 0.9141
Epoch 154: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2476 - acc: 0.9141 - val_loss: 0.2940 - val_acc: 0.9009
Epoch 155/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.2478 - acc: 0.9136
Epoch 155: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2479 - acc: 0.9136 - val_loss: 0.2900 - val_acc: 0.9047
Epoch 156/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.2461 - acc: 0.9146
Epoch 156: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2461 - acc: 0.9146 - val_loss: 0.2908 - val_acc: 0.9036
Epoch 157/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.2457 - acc: 0.9149
Epoch 157: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2456 - acc: 0.9149 - val_loss: 0.2853 - val_acc: 0.9064
Epoch 158/1000
3088/3088 [==============================] - ETA: 0s - loss: 0.2455 - acc: 0.9147
Epoch 158: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 10s 3ms/step - loss: 0.2455 - acc: 0.9147 - val_loss: 0.2878 - val_acc: 0.9040
Epoch 159/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.2445 - acc: 0.9150
Epoch 159: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2446 - acc: 0.9149 - val_loss: 0.2914 - val_acc: 0.9036
Epoch 160/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2447 - acc: 0.9151
Epoch 160: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2447 - acc: 0.9151 - val_loss: 0.2920 - val_acc: 0.9025
Epoch 161/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.2445 - acc: 0.9150
Epoch 161: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2446 - acc: 0.9150 - val_loss: 0.2823 - val_acc: 0.9065
Epoch 162/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2425 - acc: 0.9161
Epoch 162: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2425 - acc: 0.9161 - val_loss: 0.2876 - val_acc: 0.9057
Epoch 163/1000
3081/3088 [============================>.] - ETA: 0s - loss: 0.2418 - acc: 0.9158
Epoch 163: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2418 - acc: 0.9158 - val_loss: 0.2856 - val_acc: 0.9060
Epoch 164/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2410 - acc: 0.9163
Epoch 164: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2411 - acc: 0.9163 - val_loss: 0.2921 - val_acc: 0.9030
Epoch 165/1000
3080/3088 [============================>.] - ETA: 0s - loss: 0.2410 - acc: 0.9161
Epoch 165: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2409 - acc: 0.9161 - val_loss: 0.2917 - val_acc: 0.9053
Epoch 166/1000
3084/3088 [============================>.] - ETA: 0s - loss: 0.2406 - acc: 0.9163
Epoch 166: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2406 - acc: 0.9163 - val_loss: 0.2914 - val_acc: 0.9033
Epoch 167/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2399 - acc: 0.9168
Epoch 167: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2399 - acc: 0.9168 - val_loss: 0.2902 - val_acc: 0.9033
Epoch 168/1000
3085/3088 [============================>.] - ETA: 0s - loss: 0.2401 - acc: 0.9160
Epoch 168: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2402 - acc: 0.9160 - val_loss: 0.2840 - val_acc: 0.9083
Epoch 169/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.2380 - acc: 0.9170
Epoch 169: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2381 - acc: 0.9170 - val_loss: 0.2839 - val_acc: 0.9068
Epoch 170/1000
3079/3088 [============================>.] - ETA: 0s - loss: 0.2374 - acc: 0.9176
Epoch 170: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2374 - acc: 0.9176 - val_loss: 0.2875 - val_acc: 0.9054
Epoch 171/1000
3072/3088 [============================>.] - ETA: 0s - loss: 0.2368 - acc: 0.9172
Epoch 171: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 3ms/step - loss: 0.2367 - acc: 0.9172 - val_loss: 0.2862 - val_acc: 0.9064
Epoch 172/1000
3083/3088 [============================>.] - ETA: 0s - loss: 0.2365 - acc: 0.9181
Epoch 172: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2365 - acc: 0.9181 - val_loss: 0.2873 - val_acc: 0.9051
Epoch 173/1000
3078/3088 [============================>.] - ETA: 0s - loss: 0.2351 - acc: 0.9182
Epoch 173: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2351 - acc: 0.9182 - val_loss: 0.2900 - val_acc: 0.9051
Epoch 174/1000
3082/3088 [============================>.] - ETA: 0s - loss: 0.2349 - acc: 0.9185
Epoch 174: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2349 - acc: 0.9185 - val_loss: 0.2855 - val_acc: 0.9066
Epoch 175/1000
3087/3088 [============================>.] - ETA: 0s - loss: 0.2353 - acc: 0.9184
Epoch 175: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 12s 4ms/step - loss: 0.2352 - acc: 0.9184 - val_loss: 0.2909 - val_acc: 0.9036
Epoch 176/1000
3086/3088 [============================>.] - ETA: 0s - loss: 0.2339 - acc: 0.9185
Epoch 176: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_DST/cp.ckpt
3088/3088 [==============================] - 11s 4ms/step - loss: 0.2338 - acc: 0.9186 - val_loss: 0.2864 - val_acc: 0.9062
Epoch 176: early stopping
Use balanced Generator [False]
Data: 482544
-----------------------------------------------------------------------------------
Epoch 1/1000
5019/5027 [============================>.] - ETA: 0s - loss: 2.0362 - acc: 0.1799
Epoch 1: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 20s 4ms/step - loss: 2.0359 - acc: 0.1801 - val_loss: 1.8234 - val_acc: 0.3845
Epoch 2/1000
5025/5027 [============================>.] - ETA: 0s - loss: 1.3299 - acc: 0.5214
Epoch 2: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 1.3298 - acc: 0.5215 - val_loss: 0.7968 - val_acc: 0.7256
Epoch 3/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.8058 - acc: 0.7099
Epoch 3: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.8057 - acc: 0.7100 - val_loss: 0.6354 - val_acc: 0.7735
Epoch 4/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.6783 - acc: 0.7549
Epoch 4: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.6783 - acc: 0.7549 - val_loss: 0.5769 - val_acc: 0.7926
Epoch 5/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.6183 - acc: 0.7762
Epoch 5: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.6182 - acc: 0.7763 - val_loss: 0.5393 - val_acc: 0.8032
Epoch 6/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.5805 - acc: 0.7907
Epoch 6: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.5804 - acc: 0.7907 - val_loss: 0.5247 - val_acc: 0.8105
Epoch 7/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.5533 - acc: 0.8005
Epoch 7: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.5533 - acc: 0.8005 - val_loss: 0.5043 - val_acc: 0.8186
Epoch 8/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.5330 - acc: 0.8076
Epoch 8: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.5330 - acc: 0.8077 - val_loss: 0.4947 - val_acc: 0.8229
Epoch 9/1000
5021/5027 [============================>.] - ETA: 0s - loss: 0.5145 - acc: 0.8151
Epoch 9: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.5145 - acc: 0.8151 - val_loss: 0.4850 - val_acc: 0.8296
Epoch 10/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.4998 - acc: 0.8207
Epoch 10: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.4998 - acc: 0.8207 - val_loss: 0.4754 - val_acc: 0.8292
Epoch 11/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.4876 - acc: 0.8243
Epoch 11: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.4876 - acc: 0.8243 - val_loss: 0.4601 - val_acc: 0.8386
Epoch 12/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.4752 - acc: 0.8291
Epoch 12: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.4752 - acc: 0.8291 - val_loss: 0.4607 - val_acc: 0.8385
Epoch 13/1000
5026/5027 [============================>.] - ETA: 0s - loss: 0.4666 - acc: 0.8332
Epoch 13: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.4666 - acc: 0.8333 - val_loss: 0.4534 - val_acc: 0.8427
Epoch 14/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.4574 - acc: 0.8363
Epoch 14: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.4573 - acc: 0.8363 - val_loss: 0.4459 - val_acc: 0.8462
Epoch 15/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.4502 - acc: 0.8391
Epoch 15: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.4501 - acc: 0.8391 - val_loss: 0.4372 - val_acc: 0.8502
Epoch 16/1000
5012/5027 [============================>.] - ETA: 0s - loss: 0.4441 - acc: 0.8416
Epoch 16: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.4439 - acc: 0.8417 - val_loss: 0.4346 - val_acc: 0.8503
Epoch 17/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.4369 - acc: 0.8448
Epoch 17: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.4369 - acc: 0.8448 - val_loss: 0.4307 - val_acc: 0.8500
Epoch 18/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.4309 - acc: 0.8473
Epoch 18: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.4309 - acc: 0.8473 - val_loss: 0.4206 - val_acc: 0.8564
Epoch 19/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.4237 - acc: 0.8497
Epoch 19: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.4238 - acc: 0.8497 - val_loss: 0.4211 - val_acc: 0.8566
Epoch 20/1000
5021/5027 [============================>.] - ETA: 0s - loss: 0.4192 - acc: 0.8515
Epoch 20: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.4192 - acc: 0.8514 - val_loss: 0.4135 - val_acc: 0.8589
Epoch 21/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.4135 - acc: 0.8536
Epoch 21: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.4135 - acc: 0.8536 - val_loss: 0.4111 - val_acc: 0.8614
Epoch 22/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.4092 - acc: 0.8552
Epoch 22: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.4092 - acc: 0.8552 - val_loss: 0.4078 - val_acc: 0.8610
Epoch 23/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.4054 - acc: 0.8566
Epoch 23: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.4054 - acc: 0.8566 - val_loss: 0.4063 - val_acc: 0.8617
Epoch 24/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.4003 - acc: 0.8586
Epoch 24: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.4003 - acc: 0.8586 - val_loss: 0.4003 - val_acc: 0.8612
Epoch 25/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.3964 - acc: 0.8601
Epoch 25: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.3964 - acc: 0.8600 - val_loss: 0.4008 - val_acc: 0.8646
Epoch 26/1000
5021/5027 [============================>.] - ETA: 0s - loss: 0.3919 - acc: 0.8615
Epoch 26: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3919 - acc: 0.8615 - val_loss: 0.3982 - val_acc: 0.8662
Epoch 27/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.3884 - acc: 0.8631
Epoch 27: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3884 - acc: 0.8631 - val_loss: 0.3944 - val_acc: 0.8660
Epoch 28/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.3848 - acc: 0.8644
Epoch 28: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3849 - acc: 0.8644 - val_loss: 0.3905 - val_acc: 0.8665
Epoch 29/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.3815 - acc: 0.8658
Epoch 29: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.3815 - acc: 0.8658 - val_loss: 0.3857 - val_acc: 0.8691
Epoch 30/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.3768 - acc: 0.8675
Epoch 30: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3767 - acc: 0.8675 - val_loss: 0.3850 - val_acc: 0.8688
Epoch 31/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.3748 - acc: 0.8686
Epoch 31: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3748 - acc: 0.8686 - val_loss: 0.3833 - val_acc: 0.8681
Epoch 32/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.3709 - acc: 0.8697
Epoch 32: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.3709 - acc: 0.8697 - val_loss: 0.3829 - val_acc: 0.8697
Epoch 33/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.3681 - acc: 0.8713
Epoch 33: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3681 - acc: 0.8713 - val_loss: 0.3771 - val_acc: 0.8734
Epoch 34/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.3653 - acc: 0.8721
Epoch 34: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3653 - acc: 0.8721 - val_loss: 0.3756 - val_acc: 0.8737
Epoch 35/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.3629 - acc: 0.8724
Epoch 35: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3628 - acc: 0.8724 - val_loss: 0.3714 - val_acc: 0.8759
Epoch 36/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.3588 - acc: 0.8743
Epoch 36: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3588 - acc: 0.8743 - val_loss: 0.3733 - val_acc: 0.8723
Epoch 37/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.3567 - acc: 0.8749
Epoch 37: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3567 - acc: 0.8749 - val_loss: 0.3735 - val_acc: 0.8706
Epoch 38/1000
5020/5027 [============================>.] - ETA: 0s - loss: 0.3550 - acc: 0.8756
Epoch 38: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3550 - acc: 0.8756 - val_loss: 0.3611 - val_acc: 0.8779
Epoch 39/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.3522 - acc: 0.8769
Epoch 39: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3522 - acc: 0.8770 - val_loss: 0.3640 - val_acc: 0.8759
Epoch 40/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.3496 - acc: 0.8784
Epoch 40: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.3496 - acc: 0.8784 - val_loss: 0.3678 - val_acc: 0.8747
Epoch 41/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.3465 - acc: 0.8792
Epoch 41: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3466 - acc: 0.8792 - val_loss: 0.3594 - val_acc: 0.8763
Epoch 42/1000
5013/5027 [============================>.] - ETA: 0s - loss: 0.3441 - acc: 0.8799
Epoch 42: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3441 - acc: 0.8799 - val_loss: 0.3598 - val_acc: 0.8775
Epoch 43/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.3408 - acc: 0.8811
Epoch 43: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3407 - acc: 0.8811 - val_loss: 0.3537 - val_acc: 0.8817
Epoch 44/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.3385 - acc: 0.8822
Epoch 44: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.3386 - acc: 0.8821 - val_loss: 0.3571 - val_acc: 0.8783
Epoch 45/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.3361 - acc: 0.8831
Epoch 45: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3361 - acc: 0.8831 - val_loss: 0.3517 - val_acc: 0.8787
Epoch 46/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.3345 - acc: 0.8833
Epoch 46: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3345 - acc: 0.8833 - val_loss: 0.3492 - val_acc: 0.8822
Epoch 47/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.3308 - acc: 0.8854
Epoch 47: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3308 - acc: 0.8854 - val_loss: 0.3452 - val_acc: 0.8822
Epoch 48/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.3281 - acc: 0.8860
Epoch 48: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.3281 - acc: 0.8860 - val_loss: 0.3414 - val_acc: 0.8854
Epoch 49/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.3266 - acc: 0.8866
Epoch 49: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3266 - acc: 0.8866 - val_loss: 0.3401 - val_acc: 0.8847
Epoch 50/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.3238 - acc: 0.8878
Epoch 50: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3237 - acc: 0.8878 - val_loss: 0.3438 - val_acc: 0.8853
Epoch 51/1000
5020/5027 [============================>.] - ETA: 0s - loss: 0.3211 - acc: 0.8885
Epoch 51: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3212 - acc: 0.8885 - val_loss: 0.3424 - val_acc: 0.8847
Epoch 52/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.3188 - acc: 0.8893
Epoch 52: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3187 - acc: 0.8893 - val_loss: 0.3460 - val_acc: 0.8814
Epoch 53/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.3158 - acc: 0.8904
Epoch 53: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3158 - acc: 0.8904 - val_loss: 0.3332 - val_acc: 0.8875
Epoch 54/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.3133 - acc: 0.8916
Epoch 54: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.3133 - acc: 0.8916 - val_loss: 0.3361 - val_acc: 0.8861
Epoch 55/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.3112 - acc: 0.8916
Epoch 55: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3112 - acc: 0.8916 - val_loss: 0.3334 - val_acc: 0.8876
Epoch 56/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.3097 - acc: 0.8925
Epoch 56: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3097 - acc: 0.8925 - val_loss: 0.3304 - val_acc: 0.8897
Epoch 57/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.3079 - acc: 0.8933
Epoch 57: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3080 - acc: 0.8932 - val_loss: 0.3254 - val_acc: 0.8906
Epoch 58/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.3054 - acc: 0.8945
Epoch 58: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.3054 - acc: 0.8945 - val_loss: 0.3340 - val_acc: 0.8878
Epoch 59/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.3042 - acc: 0.8948
Epoch 59: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3042 - acc: 0.8948 - val_loss: 0.3238 - val_acc: 0.8904
Epoch 60/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.3020 - acc: 0.8956
Epoch 60: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.3020 - acc: 0.8956 - val_loss: 0.3229 - val_acc: 0.8909
Epoch 61/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.3004 - acc: 0.8961
Epoch 61: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.3004 - acc: 0.8961 - val_loss: 0.3261 - val_acc: 0.8905
Epoch 62/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2988 - acc: 0.8967
Epoch 62: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2988 - acc: 0.8967 - val_loss: 0.3234 - val_acc: 0.8934
Epoch 63/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.2969 - acc: 0.8972
Epoch 63: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2969 - acc: 0.8972 - val_loss: 0.3204 - val_acc: 0.8941
Epoch 64/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2951 - acc: 0.8978
Epoch 64: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2951 - acc: 0.8978 - val_loss: 0.3179 - val_acc: 0.8929
Epoch 65/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.2931 - acc: 0.8987
Epoch 65: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2930 - acc: 0.8987 - val_loss: 0.3237 - val_acc: 0.8893
Epoch 66/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.2924 - acc: 0.8989
Epoch 66: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2923 - acc: 0.8990 - val_loss: 0.3198 - val_acc: 0.8941
Epoch 67/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2915 - acc: 0.8993
Epoch 67: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2915 - acc: 0.8993 - val_loss: 0.3197 - val_acc: 0.8934
Epoch 68/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2898 - acc: 0.8999
Epoch 68: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2898 - acc: 0.8999 - val_loss: 0.3125 - val_acc: 0.8956
Epoch 69/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2875 - acc: 0.9008
Epoch 69: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2875 - acc: 0.9008 - val_loss: 0.3188 - val_acc: 0.8897
Epoch 70/1000
5013/5027 [============================>.] - ETA: 0s - loss: 0.2865 - acc: 0.9009
Epoch 70: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2865 - acc: 0.9009 - val_loss: 0.3158 - val_acc: 0.8961
Epoch 71/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2845 - acc: 0.9018
Epoch 71: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2846 - acc: 0.9018 - val_loss: 0.3166 - val_acc: 0.8941
Epoch 72/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2837 - acc: 0.9018
Epoch 72: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2837 - acc: 0.9018 - val_loss: 0.3128 - val_acc: 0.8958
Epoch 73/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2819 - acc: 0.9031
Epoch 73: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2819 - acc: 0.9031 - val_loss: 0.3118 - val_acc: 0.8970
Epoch 74/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2809 - acc: 0.9028
Epoch 74: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2809 - acc: 0.9028 - val_loss: 0.3199 - val_acc: 0.8937
Epoch 75/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2805 - acc: 0.9029
Epoch 75: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2805 - acc: 0.9029 - val_loss: 0.3142 - val_acc: 0.8949
Epoch 76/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2787 - acc: 0.9037
Epoch 76: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2787 - acc: 0.9037 - val_loss: 0.3080 - val_acc: 0.8979
Epoch 77/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2779 - acc: 0.9037
Epoch 77: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2780 - acc: 0.9036 - val_loss: 0.3109 - val_acc: 0.8967
Epoch 78/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2762 - acc: 0.9043
Epoch 78: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.2762 - acc: 0.9043 - val_loss: 0.3175 - val_acc: 0.8919
Epoch 79/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2754 - acc: 0.9047
Epoch 79: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2754 - acc: 0.9047 - val_loss: 0.3084 - val_acc: 0.8976
Epoch 80/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2747 - acc: 0.9049
Epoch 80: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.2747 - acc: 0.9049 - val_loss: 0.3086 - val_acc: 0.8970
Epoch 81/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2730 - acc: 0.9051
Epoch 81: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2730 - acc: 0.9051 - val_loss: 0.3056 - val_acc: 0.8996
Epoch 82/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2720 - acc: 0.9064
Epoch 82: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2720 - acc: 0.9064 - val_loss: 0.3052 - val_acc: 0.8988
Epoch 83/1000
5021/5027 [============================>.] - ETA: 0s - loss: 0.2716 - acc: 0.9061
Epoch 83: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2716 - acc: 0.9061 - val_loss: 0.3043 - val_acc: 0.8978
Epoch 84/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.2701 - acc: 0.9063
Epoch 84: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.2701 - acc: 0.9063 - val_loss: 0.3014 - val_acc: 0.8982
Epoch 85/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2699 - acc: 0.9069
Epoch 85: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2698 - acc: 0.9069 - val_loss: 0.3059 - val_acc: 0.8988
Epoch 86/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2680 - acc: 0.9074
Epoch 86: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2681 - acc: 0.9074 - val_loss: 0.3071 - val_acc: 0.8967
Epoch 87/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.2670 - acc: 0.9076
Epoch 87: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2670 - acc: 0.9076 - val_loss: 0.3038 - val_acc: 0.8997
Epoch 88/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2665 - acc: 0.9079
Epoch 88: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2666 - acc: 0.9079 - val_loss: 0.3015 - val_acc: 0.8995
Epoch 89/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2651 - acc: 0.9084
Epoch 89: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 20s 4ms/step - loss: 0.2652 - acc: 0.9084 - val_loss: 0.3022 - val_acc: 0.8997
Epoch 90/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2639 - acc: 0.9091
Epoch 90: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2638 - acc: 0.9091 - val_loss: 0.3008 - val_acc: 0.8998
Epoch 91/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2638 - acc: 0.9087
Epoch 91: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2639 - acc: 0.9087 - val_loss: 0.3022 - val_acc: 0.8987
Epoch 92/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2626 - acc: 0.9096
Epoch 92: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2626 - acc: 0.9096 - val_loss: 0.2980 - val_acc: 0.9003
Epoch 93/1000
5021/5027 [============================>.] - ETA: 0s - loss: 0.2626 - acc: 0.9091
Epoch 93: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2626 - acc: 0.9091 - val_loss: 0.3093 - val_acc: 0.8956
Epoch 94/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.2614 - acc: 0.9098
Epoch 94: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2614 - acc: 0.9098 - val_loss: 0.2963 - val_acc: 0.9020
Epoch 95/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.2596 - acc: 0.9104
Epoch 95: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2596 - acc: 0.9104 - val_loss: 0.3097 - val_acc: 0.8957
Epoch 96/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.2585 - acc: 0.9106
Epoch 96: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2587 - acc: 0.9105 - val_loss: 0.2966 - val_acc: 0.9016
Epoch 97/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2581 - acc: 0.9108
Epoch 97: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2581 - acc: 0.9108 - val_loss: 0.2976 - val_acc: 0.9006
Epoch 98/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2576 - acc: 0.9114
Epoch 98: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2576 - acc: 0.9114 - val_loss: 0.2977 - val_acc: 0.9018
Epoch 99/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2565 - acc: 0.9113
Epoch 99: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2564 - acc: 0.9114 - val_loss: 0.2956 - val_acc: 0.9012
Epoch 100/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2551 - acc: 0.9118
Epoch 100: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2551 - acc: 0.9118 - val_loss: 0.2999 - val_acc: 0.8982
Epoch 101/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2550 - acc: 0.9119
Epoch 101: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2550 - acc: 0.9119 - val_loss: 0.2994 - val_acc: 0.8995
Epoch 102/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2536 - acc: 0.9123
Epoch 102: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2536 - acc: 0.9123 - val_loss: 0.2928 - val_acc: 0.9015
Epoch 103/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.2530 - acc: 0.9124
Epoch 103: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2530 - acc: 0.9124 - val_loss: 0.2986 - val_acc: 0.9007
Epoch 104/1000
5013/5027 [============================>.] - ETA: 0s - loss: 0.2530 - acc: 0.9129
Epoch 104: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2529 - acc: 0.9129 - val_loss: 0.3019 - val_acc: 0.8993
Epoch 105/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.2516 - acc: 0.9128
Epoch 105: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2516 - acc: 0.9128 - val_loss: 0.2952 - val_acc: 0.9004
Epoch 106/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.2509 - acc: 0.9131
Epoch 106: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2509 - acc: 0.9131 - val_loss: 0.2936 - val_acc: 0.9016
Epoch 107/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2504 - acc: 0.9136
Epoch 107: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2503 - acc: 0.9136 - val_loss: 0.2924 - val_acc: 0.9045
Epoch 108/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.2495 - acc: 0.9138
Epoch 108: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2495 - acc: 0.9137 - val_loss: 0.3039 - val_acc: 0.8988
Epoch 109/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2489 - acc: 0.9137
Epoch 109: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2488 - acc: 0.9137 - val_loss: 0.2957 - val_acc: 0.9012
Epoch 110/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2473 - acc: 0.9145
Epoch 110: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2473 - acc: 0.9145 - val_loss: 0.2953 - val_acc: 0.9010
Epoch 111/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2472 - acc: 0.9146
Epoch 111: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2472 - acc: 0.9146 - val_loss: 0.2950 - val_acc: 0.9009
Epoch 112/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2460 - acc: 0.9152
Epoch 112: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2460 - acc: 0.9152 - val_loss: 0.2907 - val_acc: 0.9022
Epoch 113/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2461 - acc: 0.9150
Epoch 113: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2461 - acc: 0.9150 - val_loss: 0.2944 - val_acc: 0.9014
Epoch 114/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2455 - acc: 0.9152
Epoch 114: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2455 - acc: 0.9152 - val_loss: 0.2894 - val_acc: 0.9030
Epoch 115/1000
5013/5027 [============================>.] - ETA: 0s - loss: 0.2446 - acc: 0.9155
Epoch 115: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2446 - acc: 0.9155 - val_loss: 0.2921 - val_acc: 0.9014
Epoch 116/1000
5020/5027 [============================>.] - ETA: 0s - loss: 0.2442 - acc: 0.9156
Epoch 116: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2442 - acc: 0.9156 - val_loss: 0.2875 - val_acc: 0.9050
Epoch 117/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2428 - acc: 0.9162
Epoch 117: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2428 - acc: 0.9162 - val_loss: 0.2947 - val_acc: 0.8998
Epoch 118/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2422 - acc: 0.9165
Epoch 118: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2422 - acc: 0.9165 - val_loss: 0.2885 - val_acc: 0.9040
Epoch 119/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2418 - acc: 0.9160
Epoch 119: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2418 - acc: 0.9161 - val_loss: 0.2885 - val_acc: 0.9038
Epoch 120/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2406 - acc: 0.9168
Epoch 120: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2406 - acc: 0.9168 - val_loss: 0.2895 - val_acc: 0.9030
Epoch 121/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2401 - acc: 0.9168
Epoch 121: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2401 - acc: 0.9168 - val_loss: 0.2840 - val_acc: 0.9048
Epoch 122/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2400 - acc: 0.9172
Epoch 122: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2400 - acc: 0.9173 - val_loss: 0.2916 - val_acc: 0.9039
Epoch 123/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2387 - acc: 0.9175
Epoch 123: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2387 - acc: 0.9175 - val_loss: 0.2918 - val_acc: 0.9019
Epoch 124/1000
5013/5027 [============================>.] - ETA: 0s - loss: 0.2388 - acc: 0.9174
Epoch 124: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2388 - acc: 0.9175 - val_loss: 0.2921 - val_acc: 0.9025
Epoch 125/1000
5026/5027 [============================>.] - ETA: 0s - loss: 0.2378 - acc: 0.9179
Epoch 125: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2379 - acc: 0.9179 - val_loss: 0.2860 - val_acc: 0.9041
Epoch 126/1000
5026/5027 [============================>.] - ETA: 0s - loss: 0.2372 - acc: 0.9180
Epoch 126: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2372 - acc: 0.9180 - val_loss: 0.2903 - val_acc: 0.9023
Epoch 127/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2373 - acc: 0.9178
Epoch 127: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2372 - acc: 0.9178 - val_loss: 0.2907 - val_acc: 0.9011
Epoch 128/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.2356 - acc: 0.9187
Epoch 128: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2356 - acc: 0.9187 - val_loss: 0.2845 - val_acc: 0.9055
Epoch 129/1000
5025/5027 [============================>.] - ETA: 0s - loss: 0.2363 - acc: 0.9187
Epoch 129: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2363 - acc: 0.9187 - val_loss: 0.2865 - val_acc: 0.9042
Epoch 130/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.2349 - acc: 0.9187
Epoch 130: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.2349 - acc: 0.9187 - val_loss: 0.2923 - val_acc: 0.9041
Epoch 131/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.2336 - acc: 0.9190
Epoch 131: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2336 - acc: 0.9190 - val_loss: 0.2822 - val_acc: 0.9063
Epoch 132/1000
5011/5027 [============================>.] - ETA: 0s - loss: 0.2337 - acc: 0.9194
Epoch 132: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2337 - acc: 0.9194 - val_loss: 0.2867 - val_acc: 0.9048
Epoch 133/1000
5020/5027 [============================>.] - ETA: 0s - loss: 0.2327 - acc: 0.9197
Epoch 133: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2327 - acc: 0.9197 - val_loss: 0.2875 - val_acc: 0.9038
Epoch 134/1000
5026/5027 [============================>.] - ETA: 0s - loss: 0.2319 - acc: 0.9198
Epoch 134: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2319 - acc: 0.9198 - val_loss: 0.2818 - val_acc: 0.9052
Epoch 135/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2320 - acc: 0.9200
Epoch 135: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2319 - acc: 0.9200 - val_loss: 0.2826 - val_acc: 0.9078
Epoch 136/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.2314 - acc: 0.9199
Epoch 136: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2314 - acc: 0.9199 - val_loss: 0.2849 - val_acc: 0.9049
Epoch 137/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2308 - acc: 0.9202
Epoch 137: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2308 - acc: 0.9202 - val_loss: 0.2818 - val_acc: 0.9054
Epoch 138/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.2301 - acc: 0.9203
Epoch 138: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2301 - acc: 0.9203 - val_loss: 0.2835 - val_acc: 0.9047
Epoch 139/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2299 - acc: 0.9207
Epoch 139: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2299 - acc: 0.9207 - val_loss: 0.2799 - val_acc: 0.9047
Epoch 140/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2293 - acc: 0.9210
Epoch 140: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2293 - acc: 0.9210 - val_loss: 0.2951 - val_acc: 0.8989
Epoch 141/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2286 - acc: 0.9208
Epoch 141: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2286 - acc: 0.9208 - val_loss: 0.2819 - val_acc: 0.9052
Epoch 142/1000
5018/5027 [============================>.] - ETA: 0s - loss: 0.2279 - acc: 0.9212
Epoch 142: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2279 - acc: 0.9212 - val_loss: 0.2803 - val_acc: 0.9071
Epoch 143/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.2277 - acc: 0.9212
Epoch 143: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2277 - acc: 0.9212 - val_loss: 0.2936 - val_acc: 0.8995
Epoch 144/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2267 - acc: 0.9219
Epoch 144: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2267 - acc: 0.9219 - val_loss: 0.2952 - val_acc: 0.9001
Epoch 145/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.2261 - acc: 0.9217
Epoch 145: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2260 - acc: 0.9217 - val_loss: 0.2848 - val_acc: 0.9052
Epoch 146/1000
5019/5027 [============================>.] - ETA: 0s - loss: 0.2262 - acc: 0.9218
Epoch 146: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2263 - acc: 0.9218 - val_loss: 0.2797 - val_acc: 0.9062
Epoch 147/1000
5026/5027 [============================>.] - ETA: 0s - loss: 0.2251 - acc: 0.9222
Epoch 147: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2251 - acc: 0.9222 - val_loss: 0.2792 - val_acc: 0.9073
Epoch 148/1000
5020/5027 [============================>.] - ETA: 0s - loss: 0.2249 - acc: 0.9221
Epoch 148: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2249 - acc: 0.9221 - val_loss: 0.2829 - val_acc: 0.9057
Epoch 149/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.2236 - acc: 0.9225
Epoch 149: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2235 - acc: 0.9225 - val_loss: 0.2788 - val_acc: 0.9069
Epoch 150/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2235 - acc: 0.9227
Epoch 150: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2235 - acc: 0.9227 - val_loss: 0.2824 - val_acc: 0.9053
Epoch 151/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2233 - acc: 0.9228
Epoch 151: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2233 - acc: 0.9228 - val_loss: 0.2796 - val_acc: 0.9072
Epoch 152/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2229 - acc: 0.9228
Epoch 152: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2229 - acc: 0.9228 - val_loss: 0.2798 - val_acc: 0.9059
Epoch 153/1000
5013/5027 [============================>.] - ETA: 0s - loss: 0.2214 - acc: 0.9231
Epoch 153: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2214 - acc: 0.9231 - val_loss: 0.2886 - val_acc: 0.9037
Epoch 154/1000
5020/5027 [============================>.] - ETA: 0s - loss: 0.2222 - acc: 0.9232
Epoch 154: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2223 - acc: 0.9232 - val_loss: 0.2820 - val_acc: 0.9045
Epoch 155/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2210 - acc: 0.9235
Epoch 155: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2210 - acc: 0.9235 - val_loss: 0.2854 - val_acc: 0.9025
Epoch 156/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2206 - acc: 0.9236
Epoch 156: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 16s 3ms/step - loss: 0.2206 - acc: 0.9237 - val_loss: 0.2771 - val_acc: 0.9076
Epoch 157/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2200 - acc: 0.9237
Epoch 157: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 3ms/step - loss: 0.2200 - acc: 0.9237 - val_loss: 0.2941 - val_acc: 0.8996
Epoch 158/1000
5012/5027 [============================>.] - ETA: 0s - loss: 0.2195 - acc: 0.9239
Epoch 158: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2196 - acc: 0.9239 - val_loss: 0.2828 - val_acc: 0.9050
Epoch 159/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2183 - acc: 0.9243
Epoch 159: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2182 - acc: 0.9243 - val_loss: 0.2758 - val_acc: 0.9092
Epoch 160/1000
5012/5027 [============================>.] - ETA: 0s - loss: 0.2182 - acc: 0.9248
Epoch 160: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2181 - acc: 0.9249 - val_loss: 0.2854 - val_acc: 0.9046
Epoch 161/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2176 - acc: 0.9245
Epoch 161: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2176 - acc: 0.9245 - val_loss: 0.2806 - val_acc: 0.9050
Epoch 162/1000
5012/5027 [============================>.] - ETA: 0s - loss: 0.2181 - acc: 0.9246
Epoch 162: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2180 - acc: 0.9246 - val_loss: 0.2804 - val_acc: 0.9055
Epoch 163/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2170 - acc: 0.9247
Epoch 163: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2170 - acc: 0.9247 - val_loss: 0.2793 - val_acc: 0.9056
Epoch 164/1000
5022/5027 [============================>.] - ETA: 0s - loss: 0.2160 - acc: 0.9251
Epoch 164: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2160 - acc: 0.9251 - val_loss: 0.2815 - val_acc: 0.9071
Epoch 165/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2156 - acc: 0.9254
Epoch 165: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2156 - acc: 0.9254 - val_loss: 0.2825 - val_acc: 0.9064
Epoch 166/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.2156 - acc: 0.9253
Epoch 166: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 18s 4ms/step - loss: 0.2156 - acc: 0.9253 - val_loss: 0.2776 - val_acc: 0.9068
Epoch 167/1000
5023/5027 [============================>.] - ETA: 0s - loss: 0.2148 - acc: 0.9252
Epoch 167: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2148 - acc: 0.9252 - val_loss: 0.2882 - val_acc: 0.9022
Epoch 168/1000
5015/5027 [============================>.] - ETA: 0s - loss: 0.2143 - acc: 0.9256
Epoch 168: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2143 - acc: 0.9256 - val_loss: 0.2813 - val_acc: 0.9038
Epoch 169/1000
5024/5027 [============================>.] - ETA: 0s - loss: 0.2139 - acc: 0.9259
Epoch 169: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2138 - acc: 0.9259 - val_loss: 0.2798 - val_acc: 0.9065
Epoch 170/1000
5027/5027 [==============================] - ETA: 0s - loss: 0.2138 - acc: 0.9260
Epoch 170: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2138 - acc: 0.9260 - val_loss: 0.2797 - val_acc: 0.9070
Epoch 171/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2124 - acc: 0.9262
Epoch 171: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2124 - acc: 0.9262 - val_loss: 0.2811 - val_acc: 0.9073
Epoch 172/1000
5014/5027 [============================>.] - ETA: 0s - loss: 0.2132 - acc: 0.9260
Epoch 172: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 17s 3ms/step - loss: 0.2131 - acc: 0.9261 - val_loss: 0.2848 - val_acc: 0.9030
Epoch 173/1000
5016/5027 [============================>.] - ETA: 0s - loss: 0.2123 - acc: 0.9266
Epoch 173: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2123 - acc: 0.9266 - val_loss: 0.2779 - val_acc: 0.9056
Epoch 174/1000
5017/5027 [============================>.] - ETA: 0s - loss: 0.2119 - acc: 0.9264
Epoch 174: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_Number_M/cp.ckpt
5027/5027 [==============================] - 19s 4ms/step - loss: 0.2120 - acc: 0.9264 - val_loss: 0.2810 - val_acc: 0.9064
Epoch 174: early stopping
```python
#train_models(df_lista, keys_lista, data, prueba_8mil,path,epochs=1000, use_balanced_generator=True)
```
Use balanced Generator [True]
Data: 253128
-----------------------------------------------------------------------------------
Epoch 1/1000
694/696 [============================>.] - ETA: 0s - loss: 2.0791 - acc: 0.1325
Epoch 1: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 13s 17ms/step - loss: 2.0790 - acc: 0.1325 - val_loss: 2.0752 - val_acc: 0.2019
Epoch 2/1000
693/696 [============================>.] - ETA: 0s - loss: 2.0728 - acc: 0.1709
Epoch 2: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 2.0727 - acc: 0.1710 - val_loss: 2.0670 - val_acc: 0.2440
Epoch 3/1000
696/696 [==============================] - ETA: 0s - loss: 2.0633 - acc: 0.1981
Epoch 3: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 2.0633 - acc: 0.1981 - val_loss: 2.0533 - val_acc: 0.2113
Epoch 4/1000
693/696 [============================>.] - ETA: 0s - loss: 2.0447 - acc: 0.2089
Epoch 4: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 2.0446 - acc: 0.2089 - val_loss: 2.0224 - val_acc: 0.2134
Epoch 5/1000
693/696 [============================>.] - ETA: 0s - loss: 1.9999 - acc: 0.2277
Epoch 5: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.9997 - acc: 0.2278 - val_loss: 1.9476 - val_acc: 0.2638
Epoch 6/1000
696/696 [==============================] - ETA: 0s - loss: 1.8992 - acc: 0.2873
Epoch 6: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 1.8992 - acc: 0.2873 - val_loss: 1.7757 - val_acc: 0.4219
Epoch 7/1000
693/696 [============================>.] - ETA: 0s - loss: 1.7046 - acc: 0.3744
Epoch 7: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.7041 - acc: 0.3745 - val_loss: 1.4922 - val_acc: 0.5204
Epoch 8/1000
696/696 [==============================] - ETA: 0s - loss: 1.4922 - acc: 0.4542
Epoch 8: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.4922 - acc: 0.4542 - val_loss: 1.2614 - val_acc: 0.5868
Epoch 9/1000
694/696 [============================>.] - ETA: 0s - loss: 1.2991 - acc: 0.5307
Epoch 9: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.2988 - acc: 0.5308 - val_loss: 1.0698 - val_acc: 0.6365
Epoch 10/1000
695/696 [============================>.] - ETA: 0s - loss: 1.1317 - acc: 0.5917
Epoch 10: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.1316 - acc: 0.5917 - val_loss: 0.9281 - val_acc: 0.6793
Epoch 11/1000
696/696 [==============================] - ETA: 0s - loss: 1.0136 - acc: 0.6334
Epoch 11: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.0136 - acc: 0.6334 - val_loss: 0.8354 - val_acc: 0.7110
Epoch 12/1000
696/696 [==============================] - ETA: 0s - loss: 0.9319 - acc: 0.6636
Epoch 12: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.9319 - acc: 0.6636 - val_loss: 0.7737 - val_acc: 0.7321
Epoch 13/1000
696/696 [==============================] - ETA: 0s - loss: 0.8760 - acc: 0.6842
Epoch 13: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.8760 - acc: 0.6842 - val_loss: 0.7304 - val_acc: 0.7460
Epoch 14/1000
694/696 [============================>.] - ETA: 0s - loss: 0.8335 - acc: 0.6997
Epoch 14: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.8336 - acc: 0.6996 - val_loss: 0.6979 - val_acc: 0.7566
Epoch 15/1000
694/696 [============================>.] - ETA: 0s - loss: 0.8015 - acc: 0.7112
Epoch 15: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.8014 - acc: 0.7112 - val_loss: 0.6735 - val_acc: 0.7672
Epoch 16/1000
693/696 [============================>.] - ETA: 0s - loss: 0.7728 - acc: 0.7220
Epoch 16: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7727 - acc: 0.7220 - val_loss: 0.6529 - val_acc: 0.7747
Epoch 17/1000
695/696 [============================>.] - ETA: 0s - loss: 0.7516 - acc: 0.7298
Epoch 17: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7516 - acc: 0.7298 - val_loss: 0.6354 - val_acc: 0.7794
Epoch 18/1000
693/696 [============================>.] - ETA: 0s - loss: 0.7305 - acc: 0.7368
Epoch 18: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7305 - acc: 0.7368 - val_loss: 0.6199 - val_acc: 0.7838
Epoch 19/1000
694/696 [============================>.] - ETA: 0s - loss: 0.7128 - acc: 0.7435
Epoch 19: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7128 - acc: 0.7435 - val_loss: 0.6086 - val_acc: 0.7859
Epoch 20/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6978 - acc: 0.7487
Epoch 20: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.6977 - acc: 0.7487 - val_loss: 0.5969 - val_acc: 0.7914
Epoch 21/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6842 - acc: 0.7542
Epoch 21: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.6841 - acc: 0.7542 - val_loss: 0.5860 - val_acc: 0.7927
Epoch 22/1000
695/696 [============================>.] - ETA: 0s - loss: 0.6711 - acc: 0.7589
Epoch 22: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6710 - acc: 0.7589 - val_loss: 0.5767 - val_acc: 0.7974
Epoch 23/1000
696/696 [==============================] - ETA: 0s - loss: 0.6596 - acc: 0.7633
Epoch 23: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6596 - acc: 0.7633 - val_loss: 0.5688 - val_acc: 0.7997
Epoch 24/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6502 - acc: 0.7662
Epoch 24: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6502 - acc: 0.7663 - val_loss: 0.5609 - val_acc: 0.8023
Epoch 25/1000
695/696 [============================>.] - ETA: 0s - loss: 0.6391 - acc: 0.7704
Epoch 25: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6392 - acc: 0.7704 - val_loss: 0.5541 - val_acc: 0.8040
Epoch 26/1000
695/696 [============================>.] - ETA: 0s - loss: 0.6312 - acc: 0.7728
Epoch 26: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6312 - acc: 0.7728 - val_loss: 0.5470 - val_acc: 0.8083
Epoch 27/1000
694/696 [============================>.] - ETA: 0s - loss: 0.6249 - acc: 0.7762
Epoch 27: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6248 - acc: 0.7762 - val_loss: 0.5412 - val_acc: 0.8094
Epoch 28/1000
694/696 [============================>.] - ETA: 0s - loss: 0.6166 - acc: 0.7786
Epoch 28: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6166 - acc: 0.7786 - val_loss: 0.5350 - val_acc: 0.8113
Epoch 29/1000
695/696 [============================>.] - ETA: 0s - loss: 0.6110 - acc: 0.7810
Epoch 29: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6109 - acc: 0.7810 - val_loss: 0.5314 - val_acc: 0.8140
Epoch 30/1000
694/696 [============================>.] - ETA: 0s - loss: 0.6041 - acc: 0.7833
Epoch 30: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6041 - acc: 0.7833 - val_loss: 0.5256 - val_acc: 0.8142
Epoch 31/1000
696/696 [==============================] - ETA: 0s - loss: 0.5978 - acc: 0.7860
Epoch 31: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5978 - acc: 0.7860 - val_loss: 0.5212 - val_acc: 0.8170
Epoch 32/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5907 - acc: 0.7887
Epoch 32: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5907 - acc: 0.7887 - val_loss: 0.5160 - val_acc: 0.8186
Epoch 33/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5855 - acc: 0.7905
Epoch 33: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5855 - acc: 0.7905 - val_loss: 0.5137 - val_acc: 0.8191
Epoch 34/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5810 - acc: 0.7916
Epoch 34: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5811 - acc: 0.7915 - val_loss: 0.5092 - val_acc: 0.8218
Epoch 35/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5759 - acc: 0.7941
Epoch 35: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5759 - acc: 0.7940 - val_loss: 0.5058 - val_acc: 0.8231
Epoch 36/1000
695/696 [============================>.] - ETA: 0s - loss: 0.5718 - acc: 0.7950
Epoch 36: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5717 - acc: 0.7951 - val_loss: 0.5021 - val_acc: 0.8251
Epoch 37/1000
695/696 [============================>.] - ETA: 0s - loss: 0.5667 - acc: 0.7970
Epoch 37: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5667 - acc: 0.7970 - val_loss: 0.4986 - val_acc: 0.8248
Epoch 38/1000
696/696 [==============================] - ETA: 0s - loss: 0.5629 - acc: 0.7984
Epoch 38: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5629 - acc: 0.7984 - val_loss: 0.4957 - val_acc: 0.8262
Epoch 39/1000
695/696 [============================>.] - ETA: 0s - loss: 0.5577 - acc: 0.8006
Epoch 39: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5577 - acc: 0.8006 - val_loss: 0.4922 - val_acc: 0.8271
Epoch 40/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5539 - acc: 0.8019
Epoch 40: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5540 - acc: 0.8018 - val_loss: 0.4895 - val_acc: 0.8266
Epoch 41/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5495 - acc: 0.8032
Epoch 41: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5496 - acc: 0.8032 - val_loss: 0.4865 - val_acc: 0.8308
Epoch 42/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5474 - acc: 0.8046
Epoch 42: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5475 - acc: 0.8046 - val_loss: 0.4834 - val_acc: 0.8304
Epoch 43/1000
696/696 [==============================] - ETA: 0s - loss: 0.5420 - acc: 0.8057
Epoch 43: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5420 - acc: 0.8057 - val_loss: 0.4801 - val_acc: 0.8329
Epoch 44/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5394 - acc: 0.8074
Epoch 44: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5394 - acc: 0.8074 - val_loss: 0.4791 - val_acc: 0.8318
Epoch 45/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5360 - acc: 0.8083
Epoch 45: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5360 - acc: 0.8083 - val_loss: 0.4761 - val_acc: 0.8340
Epoch 46/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5345 - acc: 0.8083
Epoch 46: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5345 - acc: 0.8084 - val_loss: 0.4723 - val_acc: 0.8346
Epoch 47/1000
696/696 [==============================] - ETA: 0s - loss: 0.5301 - acc: 0.8098
Epoch 47: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5301 - acc: 0.8098 - val_loss: 0.4706 - val_acc: 0.8362
Epoch 48/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5271 - acc: 0.8117
Epoch 48: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5270 - acc: 0.8117 - val_loss: 0.4678 - val_acc: 0.8364
Epoch 49/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5245 - acc: 0.8126
Epoch 49: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5243 - acc: 0.8126 - val_loss: 0.4654 - val_acc: 0.8374
Epoch 50/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5219 - acc: 0.8135
Epoch 50: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.5219 - acc: 0.8134 - val_loss: 0.4633 - val_acc: 0.8383
Epoch 51/1000
695/696 [============================>.] - ETA: 0s - loss: 0.5189 - acc: 0.8153
Epoch 51: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5189 - acc: 0.8153 - val_loss: 0.4619 - val_acc: 0.8400
Epoch 52/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5152 - acc: 0.8158
Epoch 52: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5152 - acc: 0.8158 - val_loss: 0.4576 - val_acc: 0.8405
Epoch 53/1000
695/696 [============================>.] - ETA: 0s - loss: 0.5120 - acc: 0.8173
Epoch 53: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5120 - acc: 0.8173 - val_loss: 0.4580 - val_acc: 0.8403
Epoch 54/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5100 - acc: 0.8182
Epoch 54: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5099 - acc: 0.8183 - val_loss: 0.4554 - val_acc: 0.8413
Epoch 55/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5072 - acc: 0.8189
Epoch 55: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5072 - acc: 0.8189 - val_loss: 0.4529 - val_acc: 0.8420
Epoch 56/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5045 - acc: 0.8202
Epoch 56: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5046 - acc: 0.8201 - val_loss: 0.4513 - val_acc: 0.8430
Epoch 57/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5028 - acc: 0.8211
Epoch 57: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5028 - acc: 0.8211 - val_loss: 0.4493 - val_acc: 0.8434
Epoch 58/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4996 - acc: 0.8218
Epoch 58: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4997 - acc: 0.8218 - val_loss: 0.4471 - val_acc: 0.8438
Epoch 59/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4974 - acc: 0.8217
Epoch 59: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4974 - acc: 0.8218 - val_loss: 0.4464 - val_acc: 0.8448
Epoch 60/1000
696/696 [==============================] - ETA: 0s - loss: 0.4963 - acc: 0.8231
Epoch 60: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4963 - acc: 0.8231 - val_loss: 0.4438 - val_acc: 0.8448
Epoch 61/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4934 - acc: 0.8239
Epoch 61: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4934 - acc: 0.8239 - val_loss: 0.4423 - val_acc: 0.8454
Epoch 62/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4910 - acc: 0.8251
Epoch 62: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4910 - acc: 0.8250 - val_loss: 0.4404 - val_acc: 0.8463
Epoch 63/1000
696/696 [==============================] - ETA: 0s - loss: 0.4892 - acc: 0.8253
Epoch 63: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4892 - acc: 0.8253 - val_loss: 0.4389 - val_acc: 0.8475
Epoch 64/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4866 - acc: 0.8267
Epoch 64: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4866 - acc: 0.8268 - val_loss: 0.4372 - val_acc: 0.8480
Epoch 65/1000
696/696 [==============================] - ETA: 0s - loss: 0.4844 - acc: 0.8271
Epoch 65: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4844 - acc: 0.8271 - val_loss: 0.4351 - val_acc: 0.8489
Epoch 66/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4834 - acc: 0.8282
Epoch 66: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4833 - acc: 0.8283 - val_loss: 0.4336 - val_acc: 0.8493
Epoch 67/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4813 - acc: 0.8285
Epoch 67: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4813 - acc: 0.8285 - val_loss: 0.4333 - val_acc: 0.8493
Epoch 68/1000
696/696 [==============================] - ETA: 0s - loss: 0.4797 - acc: 0.8285
Epoch 68: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.4797 - acc: 0.8285 - val_loss: 0.4317 - val_acc: 0.8506
Epoch 69/1000
696/696 [==============================] - ETA: 0s - loss: 0.4765 - acc: 0.8303
Epoch 69: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4765 - acc: 0.8303 - val_loss: 0.4307 - val_acc: 0.8513
Epoch 70/1000
696/696 [==============================] - ETA: 0s - loss: 0.4756 - acc: 0.8304
Epoch 70: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4756 - acc: 0.8304 - val_loss: 0.4282 - val_acc: 0.8508
Epoch 71/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4725 - acc: 0.8321
Epoch 71: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4725 - acc: 0.8321 - val_loss: 0.4272 - val_acc: 0.8525
Epoch 72/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4721 - acc: 0.8319
Epoch 72: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4721 - acc: 0.8319 - val_loss: 0.4257 - val_acc: 0.8531
Epoch 73/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4693 - acc: 0.8329
Epoch 73: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4694 - acc: 0.8328 - val_loss: 0.4248 - val_acc: 0.8532
Epoch 74/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4689 - acc: 0.8327
Epoch 74: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4689 - acc: 0.8327 - val_loss: 0.4230 - val_acc: 0.8518
Epoch 75/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4675 - acc: 0.8340
Epoch 75: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4674 - acc: 0.8340 - val_loss: 0.4218 - val_acc: 0.8537
Epoch 76/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4658 - acc: 0.8342
Epoch 76: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4658 - acc: 0.8342 - val_loss: 0.4210 - val_acc: 0.8541
Epoch 77/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4638 - acc: 0.8350
Epoch 77: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4638 - acc: 0.8350 - val_loss: 0.4210 - val_acc: 0.8539
Epoch 78/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4628 - acc: 0.8354
Epoch 78: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4628 - acc: 0.8354 - val_loss: 0.4177 - val_acc: 0.8550
Epoch 79/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4603 - acc: 0.8363
Epoch 79: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4604 - acc: 0.8363 - val_loss: 0.4176 - val_acc: 0.8559
Epoch 80/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4599 - acc: 0.8359
Epoch 80: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4599 - acc: 0.8358 - val_loss: 0.4159 - val_acc: 0.8549
Epoch 81/1000
696/696 [==============================] - ETA: 0s - loss: 0.4577 - acc: 0.8377
Epoch 81: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4577 - acc: 0.8377 - val_loss: 0.4150 - val_acc: 0.8566
Epoch 82/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4565 - acc: 0.8374
Epoch 82: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4564 - acc: 0.8374 - val_loss: 0.4142 - val_acc: 0.8562
Epoch 83/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4551 - acc: 0.8383
Epoch 83: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4551 - acc: 0.8383 - val_loss: 0.4125 - val_acc: 0.8571
Epoch 84/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4526 - acc: 0.8387
Epoch 84: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4526 - acc: 0.8387 - val_loss: 0.4107 - val_acc: 0.8577
Epoch 85/1000
696/696 [==============================] - ETA: 0s - loss: 0.4512 - acc: 0.8396
Epoch 85: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4512 - acc: 0.8396 - val_loss: 0.4108 - val_acc: 0.8571
Epoch 86/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4503 - acc: 0.8400
Epoch 86: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4503 - acc: 0.8400 - val_loss: 0.4092 - val_acc: 0.8589
Epoch 87/1000
696/696 [==============================] - ETA: 0s - loss: 0.4486 - acc: 0.8401
Epoch 87: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4486 - acc: 0.8401 - val_loss: 0.4093 - val_acc: 0.8586
Epoch 88/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4471 - acc: 0.8410
Epoch 88: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4471 - acc: 0.8411 - val_loss: 0.4068 - val_acc: 0.8591
Epoch 89/1000
696/696 [==============================] - ETA: 0s - loss: 0.4478 - acc: 0.8411
Epoch 89: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4478 - acc: 0.8411 - val_loss: 0.4069 - val_acc: 0.8601
Epoch 90/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4448 - acc: 0.8421
Epoch 90: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4447 - acc: 0.8421 - val_loss: 0.4061 - val_acc: 0.8594
Epoch 91/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4451 - acc: 0.8422
Epoch 91: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4451 - acc: 0.8422 - val_loss: 0.4040 - val_acc: 0.8599
Epoch 92/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4415 - acc: 0.8432
Epoch 92: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4414 - acc: 0.8432 - val_loss: 0.4046 - val_acc: 0.8615
Epoch 93/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4420 - acc: 0.8435
Epoch 93: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4420 - acc: 0.8434 - val_loss: 0.4039 - val_acc: 0.8610
Epoch 94/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4406 - acc: 0.8436
Epoch 94: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4408 - acc: 0.8436 - val_loss: 0.4015 - val_acc: 0.8625
Epoch 95/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4390 - acc: 0.8441
Epoch 95: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4390 - acc: 0.8441 - val_loss: 0.4007 - val_acc: 0.8620
Epoch 96/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4371 - acc: 0.8454
Epoch 96: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4372 - acc: 0.8454 - val_loss: 0.4009 - val_acc: 0.8622
Epoch 97/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4369 - acc: 0.8451
Epoch 97: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4367 - acc: 0.8452 - val_loss: 0.3998 - val_acc: 0.8616
Epoch 98/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4348 - acc: 0.8456
Epoch 98: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.4348 - acc: 0.8456 - val_loss: 0.3987 - val_acc: 0.8626
Epoch 99/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4339 - acc: 0.8459
Epoch 99: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4339 - acc: 0.8460 - val_loss: 0.3969 - val_acc: 0.8632
Epoch 100/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4325 - acc: 0.8465
Epoch 100: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4324 - acc: 0.8466 - val_loss: 0.3973 - val_acc: 0.8626
Epoch 101/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4319 - acc: 0.8473
Epoch 101: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4319 - acc: 0.8473 - val_loss: 0.3951 - val_acc: 0.8649
Epoch 102/1000
696/696 [==============================] - ETA: 0s - loss: 0.4307 - acc: 0.8474
Epoch 102: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4307 - acc: 0.8474 - val_loss: 0.3943 - val_acc: 0.8644
Epoch 103/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4299 - acc: 0.8480
Epoch 103: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.4299 - acc: 0.8480 - val_loss: 0.3941 - val_acc: 0.8645
Epoch 104/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4289 - acc: 0.8483
Epoch 104: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4290 - acc: 0.8483 - val_loss: 0.3930 - val_acc: 0.8651
Epoch 105/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4269 - acc: 0.8490
Epoch 105: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4269 - acc: 0.8489 - val_loss: 0.3930 - val_acc: 0.8662
Epoch 106/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4255 - acc: 0.8497
Epoch 106: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4255 - acc: 0.8497 - val_loss: 0.3919 - val_acc: 0.8653
Epoch 107/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4260 - acc: 0.8495
Epoch 107: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4260 - acc: 0.8495 - val_loss: 0.3908 - val_acc: 0.8660
Epoch 108/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4243 - acc: 0.8501
Epoch 108: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4243 - acc: 0.8500 - val_loss: 0.3903 - val_acc: 0.8674
Epoch 109/1000
696/696 [==============================] - ETA: 0s - loss: 0.4228 - acc: 0.8503
Epoch 109: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4228 - acc: 0.8503 - val_loss: 0.3887 - val_acc: 0.8671
Epoch 110/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4218 - acc: 0.8504
Epoch 110: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4218 - acc: 0.8504 - val_loss: 0.3902 - val_acc: 0.8666
Epoch 111/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4211 - acc: 0.8510
Epoch 111: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4210 - acc: 0.8510 - val_loss: 0.3886 - val_acc: 0.8671
Epoch 112/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4209 - acc: 0.8511
Epoch 112: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4208 - acc: 0.8512 - val_loss: 0.3868 - val_acc: 0.8667
Epoch 113/1000
696/696 [==============================] - ETA: 0s - loss: 0.4188 - acc: 0.8517
Epoch 113: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4188 - acc: 0.8517 - val_loss: 0.3863 - val_acc: 0.8682
Epoch 114/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4179 - acc: 0.8523
Epoch 114: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4179 - acc: 0.8523 - val_loss: 0.3862 - val_acc: 0.8680
Epoch 115/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4178 - acc: 0.8522
Epoch 115: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4179 - acc: 0.8521 - val_loss: 0.3854 - val_acc: 0.8682
Epoch 116/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4168 - acc: 0.8527
Epoch 116: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4168 - acc: 0.8528 - val_loss: 0.3850 - val_acc: 0.8681
Epoch 117/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4147 - acc: 0.8537
Epoch 117: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4147 - acc: 0.8537 - val_loss: 0.3832 - val_acc: 0.8695
Epoch 118/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4150 - acc: 0.8530
Epoch 118: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4150 - acc: 0.8530 - val_loss: 0.3824 - val_acc: 0.8696
Epoch 119/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4130 - acc: 0.8539
Epoch 119: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4130 - acc: 0.8539 - val_loss: 0.3824 - val_acc: 0.8696
Epoch 120/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4123 - acc: 0.8541
Epoch 120: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4122 - acc: 0.8541 - val_loss: 0.3817 - val_acc: 0.8691
Epoch 121/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4106 - acc: 0.8554
Epoch 121: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4107 - acc: 0.8553 - val_loss: 0.3820 - val_acc: 0.8701
Epoch 122/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4105 - acc: 0.8552
Epoch 122: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4106 - acc: 0.8552 - val_loss: 0.3812 - val_acc: 0.8694
Epoch 123/1000
696/696 [==============================] - ETA: 0s - loss: 0.4105 - acc: 0.8552
Epoch 123: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4105 - acc: 0.8552 - val_loss: 0.3802 - val_acc: 0.8707
Epoch 124/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4091 - acc: 0.8555
Epoch 124: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4090 - acc: 0.8555 - val_loss: 0.3793 - val_acc: 0.8697
Epoch 125/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4077 - acc: 0.8558
Epoch 125: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4077 - acc: 0.8558 - val_loss: 0.3793 - val_acc: 0.8709
Epoch 126/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4068 - acc: 0.8565
Epoch 126: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.4068 - acc: 0.8566 - val_loss: 0.3773 - val_acc: 0.8721
Epoch 127/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4065 - acc: 0.8568
Epoch 127: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4065 - acc: 0.8568 - val_loss: 0.3773 - val_acc: 0.8705
Epoch 128/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4061 - acc: 0.8569
Epoch 128: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4061 - acc: 0.8569 - val_loss: 0.3771 - val_acc: 0.8707
Epoch 129/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4055 - acc: 0.8570
Epoch 129: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4055 - acc: 0.8570 - val_loss: 0.3769 - val_acc: 0.8699
Epoch 130/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4034 - acc: 0.8576
Epoch 130: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4033 - acc: 0.8576 - val_loss: 0.3753 - val_acc: 0.8730
Epoch 131/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4028 - acc: 0.8573
Epoch 131: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4028 - acc: 0.8574 - val_loss: 0.3753 - val_acc: 0.8721
Epoch 132/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4017 - acc: 0.8588
Epoch 132: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4017 - acc: 0.8588 - val_loss: 0.3744 - val_acc: 0.8721
Epoch 133/1000
696/696 [==============================] - ETA: 0s - loss: 0.4013 - acc: 0.8587
Epoch 133: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4013 - acc: 0.8587 - val_loss: 0.3732 - val_acc: 0.8729
Epoch 134/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4012 - acc: 0.8584
Epoch 134: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4011 - acc: 0.8585 - val_loss: 0.3726 - val_acc: 0.8742
Epoch 135/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3999 - acc: 0.8592
Epoch 135: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4000 - acc: 0.8591 - val_loss: 0.3730 - val_acc: 0.8719
Epoch 136/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3981 - acc: 0.8599
Epoch 136: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3982 - acc: 0.8599 - val_loss: 0.3714 - val_acc: 0.8732
Epoch 137/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3972 - acc: 0.8598
Epoch 137: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3973 - acc: 0.8598 - val_loss: 0.3718 - val_acc: 0.8725
Epoch 138/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3963 - acc: 0.8605
Epoch 138: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3962 - acc: 0.8606 - val_loss: 0.3700 - val_acc: 0.8733
Epoch 139/1000
696/696 [==============================] - ETA: 0s - loss: 0.3963 - acc: 0.8604
Epoch 139: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3963 - acc: 0.8604 - val_loss: 0.3697 - val_acc: 0.8747
Epoch 140/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3956 - acc: 0.8606
Epoch 140: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3956 - acc: 0.8605 - val_loss: 0.3710 - val_acc: 0.8737
Epoch 141/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3948 - acc: 0.8605
Epoch 141: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3948 - acc: 0.8605 - val_loss: 0.3694 - val_acc: 0.8740
Epoch 142/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3942 - acc: 0.8614
Epoch 142: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3943 - acc: 0.8615 - val_loss: 0.3677 - val_acc: 0.8741
Epoch 143/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3930 - acc: 0.8617
Epoch 143: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3930 - acc: 0.8617 - val_loss: 0.3682 - val_acc: 0.8734
Epoch 144/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3924 - acc: 0.8613
Epoch 144: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3924 - acc: 0.8614 - val_loss: 0.3683 - val_acc: 0.8749
Epoch 145/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3916 - acc: 0.8620
Epoch 145: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3916 - acc: 0.8620 - val_loss: 0.3675 - val_acc: 0.8747
Epoch 146/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3904 - acc: 0.8623
Epoch 146: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3903 - acc: 0.8624 - val_loss: 0.3669 - val_acc: 0.8752
Epoch 147/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3896 - acc: 0.8627
Epoch 147: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3896 - acc: 0.8627 - val_loss: 0.3651 - val_acc: 0.8748
Epoch 148/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3894 - acc: 0.8627
Epoch 148: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3892 - acc: 0.8628 - val_loss: 0.3651 - val_acc: 0.8757
Epoch 149/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3887 - acc: 0.8629
Epoch 149: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3887 - acc: 0.8629 - val_loss: 0.3646 - val_acc: 0.8750
Epoch 150/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3875 - acc: 0.8630
Epoch 150: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3875 - acc: 0.8630 - val_loss: 0.3643 - val_acc: 0.8751
Epoch 151/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3867 - acc: 0.8640
Epoch 151: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3867 - acc: 0.8639 - val_loss: 0.3644 - val_acc: 0.8767
Epoch 152/1000
696/696 [==============================] - ETA: 0s - loss: 0.3879 - acc: 0.8637
Epoch 152: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3879 - acc: 0.8637 - val_loss: 0.3626 - val_acc: 0.8756
Epoch 153/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3866 - acc: 0.8637
Epoch 153: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3866 - acc: 0.8637 - val_loss: 0.3641 - val_acc: 0.8745
Epoch 154/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3847 - acc: 0.8642
Epoch 154: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3847 - acc: 0.8642 - val_loss: 0.3626 - val_acc: 0.8759
Epoch 155/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3846 - acc: 0.8645
Epoch 155: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3846 - acc: 0.8645 - val_loss: 0.3618 - val_acc: 0.8776
Epoch 156/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3839 - acc: 0.8651
Epoch 156: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3839 - acc: 0.8651 - val_loss: 0.3604 - val_acc: 0.8779
Epoch 157/1000
696/696 [==============================] - ETA: 0s - loss: 0.3835 - acc: 0.8657
Epoch 157: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3835 - acc: 0.8657 - val_loss: 0.3608 - val_acc: 0.8767
Epoch 158/1000
696/696 [==============================] - ETA: 0s - loss: 0.3827 - acc: 0.8655
Epoch 158: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3827 - acc: 0.8655 - val_loss: 0.3597 - val_acc: 0.8760
Epoch 159/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3827 - acc: 0.8658
Epoch 159: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3825 - acc: 0.8658 - val_loss: 0.3594 - val_acc: 0.8758
Epoch 160/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3808 - acc: 0.8656
Epoch 160: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3808 - acc: 0.8656 - val_loss: 0.3591 - val_acc: 0.8775
Epoch 161/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3802 - acc: 0.8660
Epoch 161: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.3802 - acc: 0.8660 - val_loss: 0.3593 - val_acc: 0.8776
Epoch 162/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3800 - acc: 0.8660
Epoch 162: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3801 - acc: 0.8660 - val_loss: 0.3584 - val_acc: 0.8770
Epoch 163/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3790 - acc: 0.8665
Epoch 163: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3792 - acc: 0.8664 - val_loss: 0.3582 - val_acc: 0.8791
Epoch 164/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3776 - acc: 0.8671
Epoch 164: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3776 - acc: 0.8671 - val_loss: 0.3581 - val_acc: 0.8788
Epoch 165/1000
696/696 [==============================] - ETA: 0s - loss: 0.3775 - acc: 0.8670
Epoch 165: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3775 - acc: 0.8670 - val_loss: 0.3576 - val_acc: 0.8763
Epoch 166/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3776 - acc: 0.8674
Epoch 166: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3777 - acc: 0.8674 - val_loss: 0.3578 - val_acc: 0.8767
Epoch 167/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3757 - acc: 0.8679
Epoch 167: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3757 - acc: 0.8679 - val_loss: 0.3563 - val_acc: 0.8772
Epoch 168/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3759 - acc: 0.8677
Epoch 168: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3760 - acc: 0.8677 - val_loss: 0.3559 - val_acc: 0.8779
Epoch 169/1000
696/696 [==============================] - ETA: 0s - loss: 0.3746 - acc: 0.8679
Epoch 169: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3746 - acc: 0.8679 - val_loss: 0.3550 - val_acc: 0.8801
Epoch 170/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3744 - acc: 0.8682
Epoch 170: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3745 - acc: 0.8682 - val_loss: 0.3543 - val_acc: 0.8803
Epoch 171/1000
696/696 [==============================] - ETA: 0s - loss: 0.3742 - acc: 0.8688
Epoch 171: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3742 - acc: 0.8688 - val_loss: 0.3549 - val_acc: 0.8795
Epoch 172/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3731 - acc: 0.8690
Epoch 172: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3731 - acc: 0.8690 - val_loss: 0.3543 - val_acc: 0.8776
Epoch 173/1000
696/696 [==============================] - ETA: 0s - loss: 0.3724 - acc: 0.8690
Epoch 173: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3724 - acc: 0.8690 - val_loss: 0.3535 - val_acc: 0.8785
Epoch 174/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3714 - acc: 0.8695
Epoch 174: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3715 - acc: 0.8694 - val_loss: 0.3528 - val_acc: 0.8790
Epoch 175/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3707 - acc: 0.8695
Epoch 175: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3707 - acc: 0.8695 - val_loss: 0.3523 - val_acc: 0.8797
Epoch 176/1000
696/696 [==============================] - ETA: 0s - loss: 0.3710 - acc: 0.8699
Epoch 176: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3710 - acc: 0.8699 - val_loss: 0.3525 - val_acc: 0.8789
Epoch 177/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3701 - acc: 0.8701
Epoch 177: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3701 - acc: 0.8701 - val_loss: 0.3525 - val_acc: 0.8791
Epoch 178/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3698 - acc: 0.8702
Epoch 178: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3698 - acc: 0.8701 - val_loss: 0.3512 - val_acc: 0.8806
Epoch 179/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3691 - acc: 0.8703
Epoch 179: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3692 - acc: 0.8702 - val_loss: 0.3517 - val_acc: 0.8785
Epoch 180/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3690 - acc: 0.8705
Epoch 180: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3690 - acc: 0.8705 - val_loss: 0.3505 - val_acc: 0.8797
Epoch 181/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3682 - acc: 0.8706
Epoch 181: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3682 - acc: 0.8706 - val_loss: 0.3502 - val_acc: 0.8785
Epoch 182/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3666 - acc: 0.8709
Epoch 182: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3667 - acc: 0.8709 - val_loss: 0.3502 - val_acc: 0.8796
Epoch 183/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3663 - acc: 0.8715
Epoch 183: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3664 - acc: 0.8714 - val_loss: 0.3487 - val_acc: 0.8811
Epoch 184/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3664 - acc: 0.8714
Epoch 184: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3666 - acc: 0.8713 - val_loss: 0.3483 - val_acc: 0.8804
Epoch 185/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3648 - acc: 0.8718
Epoch 185: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3648 - acc: 0.8719 - val_loss: 0.3481 - val_acc: 0.8798
Epoch 186/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3646 - acc: 0.8719
Epoch 186: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3646 - acc: 0.8719 - val_loss: 0.3486 - val_acc: 0.8795
Epoch 187/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3646 - acc: 0.8726
Epoch 187: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3645 - acc: 0.8726 - val_loss: 0.3466 - val_acc: 0.8811
Epoch 188/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3638 - acc: 0.8726
Epoch 188: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3639 - acc: 0.8725 - val_loss: 0.3479 - val_acc: 0.8794
Epoch 189/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3630 - acc: 0.8721
Epoch 189: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3629 - acc: 0.8721 - val_loss: 0.3470 - val_acc: 0.8794
Epoch 190/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3627 - acc: 0.8729
Epoch 190: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3627 - acc: 0.8729 - val_loss: 0.3463 - val_acc: 0.8805
Epoch 191/1000
696/696 [==============================] - ETA: 0s - loss: 0.3624 - acc: 0.8728
Epoch 191: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3624 - acc: 0.8728 - val_loss: 0.3460 - val_acc: 0.8812
Epoch 192/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3620 - acc: 0.8729
Epoch 192: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3620 - acc: 0.8729 - val_loss: 0.3467 - val_acc: 0.8803
Epoch 193/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3610 - acc: 0.8733
Epoch 193: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3610 - acc: 0.8733 - val_loss: 0.3451 - val_acc: 0.8809
Epoch 194/1000
696/696 [==============================] - ETA: 0s - loss: 0.3606 - acc: 0.8734
Epoch 194: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3606 - acc: 0.8734 - val_loss: 0.3462 - val_acc: 0.8807
Epoch 195/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3588 - acc: 0.8746
Epoch 195: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3588 - acc: 0.8746 - val_loss: 0.3451 - val_acc: 0.8800
Epoch 196/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3600 - acc: 0.8738
Epoch 196: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3601 - acc: 0.8738 - val_loss: 0.3454 - val_acc: 0.8823
Epoch 197/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3588 - acc: 0.8742
Epoch 197: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3587 - acc: 0.8742 - val_loss: 0.3444 - val_acc: 0.8805
Epoch 198/1000
696/696 [==============================] - ETA: 0s - loss: 0.3582 - acc: 0.8742
Epoch 198: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3582 - acc: 0.8742 - val_loss: 0.3443 - val_acc: 0.8815
Epoch 199/1000
696/696 [==============================] - ETA: 0s - loss: 0.3581 - acc: 0.8742
Epoch 199: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3581 - acc: 0.8742 - val_loss: 0.3437 - val_acc: 0.8812
Epoch 200/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3572 - acc: 0.8747
Epoch 200: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3571 - acc: 0.8747 - val_loss: 0.3422 - val_acc: 0.8822
Epoch 201/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3569 - acc: 0.8752
Epoch 201: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3570 - acc: 0.8752 - val_loss: 0.3418 - val_acc: 0.8811
Epoch 202/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3562 - acc: 0.8753
Epoch 202: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3562 - acc: 0.8753 - val_loss: 0.3410 - val_acc: 0.8822
Epoch 203/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3552 - acc: 0.8753
Epoch 203: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3553 - acc: 0.8753 - val_loss: 0.3419 - val_acc: 0.8826
Epoch 204/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3541 - acc: 0.8757
Epoch 204: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3542 - acc: 0.8756 - val_loss: 0.3417 - val_acc: 0.8818
Epoch 205/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3549 - acc: 0.8759
Epoch 205: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3549 - acc: 0.8759 - val_loss: 0.3415 - val_acc: 0.8834
Epoch 206/1000
696/696 [==============================] - ETA: 0s - loss: 0.3546 - acc: 0.8755
Epoch 206: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3546 - acc: 0.8755 - val_loss: 0.3425 - val_acc: 0.8817
Epoch 207/1000
696/696 [==============================] - ETA: 0s - loss: 0.3527 - acc: 0.8767
Epoch 207: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3527 - acc: 0.8767 - val_loss: 0.3404 - val_acc: 0.8830
Epoch 208/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3528 - acc: 0.8763
Epoch 208: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3529 - acc: 0.8762 - val_loss: 0.3418 - val_acc: 0.8808
Epoch 209/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3527 - acc: 0.8759
Epoch 209: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3527 - acc: 0.8759 - val_loss: 0.3401 - val_acc: 0.8831
Epoch 210/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3531 - acc: 0.8763
Epoch 210: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3531 - acc: 0.8763 - val_loss: 0.3397 - val_acc: 0.8831
Epoch 211/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3518 - acc: 0.8770
Epoch 211: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3518 - acc: 0.8769 - val_loss: 0.3392 - val_acc: 0.8834
Epoch 212/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3510 - acc: 0.8769
Epoch 212: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3511 - acc: 0.8768 - val_loss: 0.3382 - val_acc: 0.8837
Epoch 213/1000
696/696 [==============================] - ETA: 0s - loss: 0.3508 - acc: 0.8775
Epoch 213: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3508 - acc: 0.8775 - val_loss: 0.3379 - val_acc: 0.8831
Epoch 214/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3494 - acc: 0.8780
Epoch 214: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3495 - acc: 0.8780 - val_loss: 0.3378 - val_acc: 0.8820
Epoch 215/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3502 - acc: 0.8772
Epoch 215: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3503 - acc: 0.8772 - val_loss: 0.3370 - val_acc: 0.8847
Epoch 216/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3486 - acc: 0.8780
Epoch 216: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3486 - acc: 0.8779 - val_loss: 0.3388 - val_acc: 0.8834
Epoch 217/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3491 - acc: 0.8776
Epoch 217: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3491 - acc: 0.8777 - val_loss: 0.3368 - val_acc: 0.8837
Epoch 218/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3478 - acc: 0.8787
Epoch 218: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3478 - acc: 0.8787 - val_loss: 0.3370 - val_acc: 0.8836
Epoch 219/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3477 - acc: 0.8785
Epoch 219: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3477 - acc: 0.8785 - val_loss: 0.3362 - val_acc: 0.8838
Epoch 220/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3470 - acc: 0.8784
Epoch 220: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3470 - acc: 0.8784 - val_loss: 0.3364 - val_acc: 0.8820
Epoch 221/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3459 - acc: 0.8787
Epoch 221: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3459 - acc: 0.8787 - val_loss: 0.3356 - val_acc: 0.8842
Epoch 222/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3465 - acc: 0.8787
Epoch 222: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3465 - acc: 0.8787 - val_loss: 0.3352 - val_acc: 0.8842
Epoch 223/1000
696/696 [==============================] - ETA: 0s - loss: 0.3456 - acc: 0.8788
Epoch 223: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3456 - acc: 0.8788 - val_loss: 0.3352 - val_acc: 0.8839
Epoch 224/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3461 - acc: 0.8790
Epoch 224: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3460 - acc: 0.8790 - val_loss: 0.3340 - val_acc: 0.8842
Epoch 225/1000
696/696 [==============================] - ETA: 0s - loss: 0.3437 - acc: 0.8797
Epoch 225: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3437 - acc: 0.8797 - val_loss: 0.3336 - val_acc: 0.8838
Epoch 226/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3442 - acc: 0.8791
Epoch 226: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3442 - acc: 0.8791 - val_loss: 0.3346 - val_acc: 0.8841
Epoch 227/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3434 - acc: 0.8799
Epoch 227: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3434 - acc: 0.8799 - val_loss: 0.3338 - val_acc: 0.8843
Epoch 228/1000
696/696 [==============================] - ETA: 0s - loss: 0.3435 - acc: 0.8792
Epoch 228: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3435 - acc: 0.8792 - val_loss: 0.3337 - val_acc: 0.8850
Epoch 229/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3425 - acc: 0.8802
Epoch 229: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3425 - acc: 0.8802 - val_loss: 0.3342 - val_acc: 0.8849
Epoch 230/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3424 - acc: 0.8801
Epoch 230: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3425 - acc: 0.8801 - val_loss: 0.3338 - val_acc: 0.8845
Epoch 231/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3420 - acc: 0.8805
Epoch 231: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3419 - acc: 0.8805 - val_loss: 0.3334 - val_acc: 0.8850
Epoch 232/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3399 - acc: 0.8811
Epoch 232: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3400 - acc: 0.8811 - val_loss: 0.3339 - val_acc: 0.8850
Epoch 233/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3406 - acc: 0.8803
Epoch 233: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3406 - acc: 0.8803 - val_loss: 0.3338 - val_acc: 0.8837
Epoch 234/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3396 - acc: 0.8812
Epoch 234: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3396 - acc: 0.8813 - val_loss: 0.3318 - val_acc: 0.8861
Epoch 235/1000
696/696 [==============================] - ETA: 0s - loss: 0.3395 - acc: 0.8810
Epoch 235: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3395 - acc: 0.8810 - val_loss: 0.3316 - val_acc: 0.8853
Epoch 236/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3388 - acc: 0.8812
Epoch 236: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3387 - acc: 0.8813 - val_loss: 0.3307 - val_acc: 0.8851
Epoch 237/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3388 - acc: 0.8817
Epoch 237: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3389 - acc: 0.8817 - val_loss: 0.3311 - val_acc: 0.8863
Epoch 238/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3386 - acc: 0.8820
Epoch 238: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3386 - acc: 0.8819 - val_loss: 0.3310 - val_acc: 0.8863
Epoch 239/1000
696/696 [==============================] - ETA: 0s - loss: 0.3382 - acc: 0.8814
Epoch 239: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3382 - acc: 0.8814 - val_loss: 0.3318 - val_acc: 0.8838
Epoch 240/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3379 - acc: 0.8819
Epoch 240: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3379 - acc: 0.8819 - val_loss: 0.3301 - val_acc: 0.8854
Epoch 241/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3372 - acc: 0.8826
Epoch 241: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3372 - acc: 0.8826 - val_loss: 0.3308 - val_acc: 0.8851
Epoch 242/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3366 - acc: 0.8822
Epoch 242: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3366 - acc: 0.8822 - val_loss: 0.3288 - val_acc: 0.8867
Epoch 243/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3364 - acc: 0.8821
Epoch 243: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3364 - acc: 0.8821 - val_loss: 0.3286 - val_acc: 0.8865
Epoch 244/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3356 - acc: 0.8827
Epoch 244: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3356 - acc: 0.8827 - val_loss: 0.3289 - val_acc: 0.8855
Epoch 245/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3351 - acc: 0.8825
Epoch 245: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3351 - acc: 0.8825 - val_loss: 0.3284 - val_acc: 0.8854
Epoch 246/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3345 - acc: 0.8831
Epoch 246: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3345 - acc: 0.8831 - val_loss: 0.3286 - val_acc: 0.8860
Epoch 247/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3341 - acc: 0.8827
Epoch 247: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3341 - acc: 0.8827 - val_loss: 0.3293 - val_acc: 0.8860
Epoch 248/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3334 - acc: 0.8834
Epoch 248: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3334 - acc: 0.8834 - val_loss: 0.3270 - val_acc: 0.8871
Epoch 249/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3320 - acc: 0.8842
Epoch 249: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3321 - acc: 0.8841 - val_loss: 0.3269 - val_acc: 0.8861
Epoch 250/1000
696/696 [==============================] - ETA: 0s - loss: 0.3327 - acc: 0.8837
Epoch 250: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3327 - acc: 0.8837 - val_loss: 0.3276 - val_acc: 0.8876
Epoch 251/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3326 - acc: 0.8839
Epoch 251: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3325 - acc: 0.8839 - val_loss: 0.3281 - val_acc: 0.8865
Epoch 252/1000
696/696 [==============================] - ETA: 0s - loss: 0.3317 - acc: 0.8843
Epoch 252: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3317 - acc: 0.8843 - val_loss: 0.3270 - val_acc: 0.8876
Epoch 253/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3320 - acc: 0.8837
Epoch 253: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3320 - acc: 0.8837 - val_loss: 0.3272 - val_acc: 0.8877
Epoch 254/1000
696/696 [==============================] - ETA: 0s - loss: 0.3315 - acc: 0.8844
Epoch 254: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3315 - acc: 0.8844 - val_loss: 0.3257 - val_acc: 0.8878
Epoch 255/1000
696/696 [==============================] - ETA: 0s - loss: 0.3311 - acc: 0.8848
Epoch 255: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3311 - acc: 0.8848 - val_loss: 0.3245 - val_acc: 0.8880
Epoch 256/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3304 - acc: 0.8845
Epoch 256: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3304 - acc: 0.8845 - val_loss: 0.3252 - val_acc: 0.8865
Epoch 257/1000
696/696 [==============================] - ETA: 0s - loss: 0.3288 - acc: 0.8853
Epoch 257: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3288 - acc: 0.8853 - val_loss: 0.3247 - val_acc: 0.8872
Epoch 258/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3293 - acc: 0.8853
Epoch 258: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3292 - acc: 0.8853 - val_loss: 0.3261 - val_acc: 0.8872
Epoch 259/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3285 - acc: 0.8851
Epoch 259: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3287 - acc: 0.8851 - val_loss: 0.3252 - val_acc: 0.8883
Epoch 260/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3285 - acc: 0.8851
Epoch 260: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3285 - acc: 0.8850 - val_loss: 0.3243 - val_acc: 0.8889
Epoch 261/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3289 - acc: 0.8853
Epoch 261: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3289 - acc: 0.8852 - val_loss: 0.3243 - val_acc: 0.8886
Epoch 262/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3278 - acc: 0.8856
Epoch 262: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3278 - acc: 0.8856 - val_loss: 0.3241 - val_acc: 0.8885
Epoch 263/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3272 - acc: 0.8857
Epoch 263: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3270 - acc: 0.8857 - val_loss: 0.3230 - val_acc: 0.8890
Epoch 264/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3276 - acc: 0.8856
Epoch 264: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3275 - acc: 0.8856 - val_loss: 0.3226 - val_acc: 0.8891
Epoch 265/1000
696/696 [==============================] - ETA: 0s - loss: 0.3267 - acc: 0.8863
Epoch 265: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.3267 - acc: 0.8863 - val_loss: 0.3231 - val_acc: 0.8887
Epoch 266/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3265 - acc: 0.8861
Epoch 266: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3265 - acc: 0.8861 - val_loss: 0.3234 - val_acc: 0.8889
Epoch 267/1000
696/696 [==============================] - ETA: 0s - loss: 0.3248 - acc: 0.8868
Epoch 267: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3248 - acc: 0.8868 - val_loss: 0.3229 - val_acc: 0.8891
Epoch 268/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3263 - acc: 0.8861
Epoch 268: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3263 - acc: 0.8861 - val_loss: 0.3236 - val_acc: 0.8884
Epoch 269/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3257 - acc: 0.8862
Epoch 269: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3257 - acc: 0.8862 - val_loss: 0.3226 - val_acc: 0.8896
Epoch 270/1000
696/696 [==============================] - ETA: 0s - loss: 0.3247 - acc: 0.8869
Epoch 270: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3247 - acc: 0.8869 - val_loss: 0.3221 - val_acc: 0.8892
Epoch 271/1000
696/696 [==============================] - ETA: 0s - loss: 0.3251 - acc: 0.8867
Epoch 271: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3251 - acc: 0.8867 - val_loss: 0.3218 - val_acc: 0.8904
Epoch 272/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3241 - acc: 0.8869
Epoch 272: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3241 - acc: 0.8869 - val_loss: 0.3206 - val_acc: 0.8898
Epoch 273/1000
696/696 [==============================] - ETA: 0s - loss: 0.3234 - acc: 0.8869
Epoch 273: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3234 - acc: 0.8869 - val_loss: 0.3201 - val_acc: 0.8911
Epoch 274/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3231 - acc: 0.8871
Epoch 274: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3232 - acc: 0.8871 - val_loss: 0.3208 - val_acc: 0.8904
Epoch 275/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3226 - acc: 0.8873
Epoch 275: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3224 - acc: 0.8873 - val_loss: 0.3211 - val_acc: 0.8902
Epoch 276/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3229 - acc: 0.8871
Epoch 276: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3229 - acc: 0.8871 - val_loss: 0.3192 - val_acc: 0.8904
Epoch 277/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3221 - acc: 0.8881
Epoch 277: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3221 - acc: 0.8881 - val_loss: 0.3197 - val_acc: 0.8903
Epoch 278/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3213 - acc: 0.8880
Epoch 278: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3212 - acc: 0.8880 - val_loss: 0.3200 - val_acc: 0.8899
Epoch 279/1000
696/696 [==============================] - ETA: 0s - loss: 0.3214 - acc: 0.8877
Epoch 279: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3214 - acc: 0.8877 - val_loss: 0.3195 - val_acc: 0.8904
Epoch 280/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3203 - acc: 0.8880
Epoch 280: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3203 - acc: 0.8880 - val_loss: 0.3190 - val_acc: 0.8902
Epoch 281/1000
696/696 [==============================] - ETA: 0s - loss: 0.3203 - acc: 0.8882
Epoch 281: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3203 - acc: 0.8882 - val_loss: 0.3186 - val_acc: 0.8920
Epoch 282/1000
696/696 [==============================] - ETA: 0s - loss: 0.3199 - acc: 0.8883
Epoch 282: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3199 - acc: 0.8883 - val_loss: 0.3191 - val_acc: 0.8902
Epoch 283/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3190 - acc: 0.8888
Epoch 283: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3189 - acc: 0.8889 - val_loss: 0.3182 - val_acc: 0.8911
Epoch 284/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3177 - acc: 0.8893
Epoch 284: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3177 - acc: 0.8893 - val_loss: 0.3182 - val_acc: 0.8900
Epoch 285/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3188 - acc: 0.8887
Epoch 285: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3190 - acc: 0.8887 - val_loss: 0.3177 - val_acc: 0.8911
Epoch 286/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3182 - acc: 0.8893
Epoch 286: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3181 - acc: 0.8893 - val_loss: 0.3183 - val_acc: 0.8898
Epoch 287/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3178 - acc: 0.8888
Epoch 287: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3177 - acc: 0.8888 - val_loss: 0.3184 - val_acc: 0.8904
Epoch 288/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3174 - acc: 0.8892
Epoch 288: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3174 - acc: 0.8892 - val_loss: 0.3180 - val_acc: 0.8912
Epoch 289/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3173 - acc: 0.8895
Epoch 289: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3172 - acc: 0.8895 - val_loss: 0.3180 - val_acc: 0.8902
Epoch 290/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3167 - acc: 0.8895
Epoch 290: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3167 - acc: 0.8895 - val_loss: 0.3170 - val_acc: 0.8907
Epoch 291/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3162 - acc: 0.8900
Epoch 291: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3163 - acc: 0.8899 - val_loss: 0.3152 - val_acc: 0.8913
Epoch 292/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3145 - acc: 0.8904
Epoch 292: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3145 - acc: 0.8904 - val_loss: 0.3161 - val_acc: 0.8914
Epoch 293/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3145 - acc: 0.8904
Epoch 293: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3145 - acc: 0.8904 - val_loss: 0.3166 - val_acc: 0.8910
Epoch 294/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3143 - acc: 0.8903
Epoch 294: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3144 - acc: 0.8903 - val_loss: 0.3165 - val_acc: 0.8904
Epoch 295/1000
696/696 [==============================] - ETA: 0s - loss: 0.3142 - acc: 0.8900
Epoch 295: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3142 - acc: 0.8900 - val_loss: 0.3156 - val_acc: 0.8914
Epoch 296/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3143 - acc: 0.8903
Epoch 296: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3143 - acc: 0.8902 - val_loss: 0.3151 - val_acc: 0.8930
Epoch 297/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3139 - acc: 0.8904
Epoch 297: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3140 - acc: 0.8904 - val_loss: 0.3157 - val_acc: 0.8917
Epoch 298/1000
696/696 [==============================] - ETA: 0s - loss: 0.3127 - acc: 0.8907
Epoch 298: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.3127 - acc: 0.8907 - val_loss: 0.3155 - val_acc: 0.8916
Epoch 299/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3133 - acc: 0.8908
Epoch 299: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3133 - acc: 0.8908 - val_loss: 0.3139 - val_acc: 0.8916
Epoch 300/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3119 - acc: 0.8915
Epoch 300: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3119 - acc: 0.8915 - val_loss: 0.3154 - val_acc: 0.8932
Epoch 301/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3111 - acc: 0.8914
Epoch 301: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3111 - acc: 0.8915 - val_loss: 0.3140 - val_acc: 0.8940
Epoch 302/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3122 - acc: 0.8906
Epoch 302: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3121 - acc: 0.8906 - val_loss: 0.3151 - val_acc: 0.8934
Epoch 303/1000
696/696 [==============================] - ETA: 0s - loss: 0.3115 - acc: 0.8913
Epoch 303: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3115 - acc: 0.8913 - val_loss: 0.3141 - val_acc: 0.8924
Epoch 304/1000
696/696 [==============================] - ETA: 0s - loss: 0.3112 - acc: 0.8915
Epoch 304: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3112 - acc: 0.8915 - val_loss: 0.3128 - val_acc: 0.8924
Epoch 305/1000
696/696 [==============================] - ETA: 0s - loss: 0.3106 - acc: 0.8916
Epoch 305: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3106 - acc: 0.8916 - val_loss: 0.3151 - val_acc: 0.8929
Epoch 306/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3104 - acc: 0.8922
Epoch 306: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3105 - acc: 0.8922 - val_loss: 0.3135 - val_acc: 0.8923
Epoch 307/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3101 - acc: 0.8918
Epoch 307: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3102 - acc: 0.8918 - val_loss: 0.3128 - val_acc: 0.8932
Epoch 308/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3093 - acc: 0.8920
Epoch 308: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3093 - acc: 0.8920 - val_loss: 0.3132 - val_acc: 0.8931
Epoch 309/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3086 - acc: 0.8924
Epoch 309: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3086 - acc: 0.8924 - val_loss: 0.3141 - val_acc: 0.8916
Epoch 310/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3090 - acc: 0.8923
Epoch 310: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3090 - acc: 0.8923 - val_loss: 0.3114 - val_acc: 0.8937
Epoch 311/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3076 - acc: 0.8927
Epoch 311: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3077 - acc: 0.8926 - val_loss: 0.3111 - val_acc: 0.8938
Epoch 312/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3078 - acc: 0.8926
Epoch 312: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3078 - acc: 0.8926 - val_loss: 0.3126 - val_acc: 0.8936
Epoch 313/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3071 - acc: 0.8931
Epoch 313: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3072 - acc: 0.8930 - val_loss: 0.3131 - val_acc: 0.8929
Epoch 314/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3077 - acc: 0.8932
Epoch 314: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3077 - acc: 0.8932 - val_loss: 0.3124 - val_acc: 0.8917
Epoch 315/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3066 - acc: 0.8931
Epoch 315: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3066 - acc: 0.8931 - val_loss: 0.3109 - val_acc: 0.8934
Epoch 316/1000
696/696 [==============================] - ETA: 0s - loss: 0.3067 - acc: 0.8933
Epoch 316: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3067 - acc: 0.8933 - val_loss: 0.3124 - val_acc: 0.8934
Epoch 317/1000
696/696 [==============================] - ETA: 0s - loss: 0.3074 - acc: 0.8933
Epoch 317: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3074 - acc: 0.8933 - val_loss: 0.3101 - val_acc: 0.8940
Epoch 318/1000
696/696 [==============================] - ETA: 0s - loss: 0.3061 - acc: 0.8933
Epoch 318: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3061 - acc: 0.8933 - val_loss: 0.3108 - val_acc: 0.8929
Epoch 319/1000
696/696 [==============================] - ETA: 0s - loss: 0.3060 - acc: 0.8939
Epoch 319: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3060 - acc: 0.8939 - val_loss: 0.3104 - val_acc: 0.8929
Epoch 320/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3060 - acc: 0.8937
Epoch 320: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3060 - acc: 0.8937 - val_loss: 0.3096 - val_acc: 0.8935
Epoch 321/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3040 - acc: 0.8942
Epoch 321: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3039 - acc: 0.8942 - val_loss: 0.3091 - val_acc: 0.8955
Epoch 322/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3041 - acc: 0.8937
Epoch 322: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3041 - acc: 0.8937 - val_loss: 0.3099 - val_acc: 0.8950
Epoch 323/1000
696/696 [==============================] - ETA: 0s - loss: 0.3041 - acc: 0.8937
Epoch 323: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3041 - acc: 0.8937 - val_loss: 0.3084 - val_acc: 0.8960
Epoch 324/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3034 - acc: 0.8947
Epoch 324: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3035 - acc: 0.8947 - val_loss: 0.3094 - val_acc: 0.8949
Epoch 325/1000
696/696 [==============================] - ETA: 0s - loss: 0.3022 - acc: 0.8948
Epoch 325: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3022 - acc: 0.8948 - val_loss: 0.3089 - val_acc: 0.8948
Epoch 326/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3032 - acc: 0.8945
Epoch 326: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3032 - acc: 0.8945 - val_loss: 0.3082 - val_acc: 0.8937
Epoch 327/1000
696/696 [==============================] - ETA: 0s - loss: 0.3026 - acc: 0.8945
Epoch 327: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3026 - acc: 0.8945 - val_loss: 0.3086 - val_acc: 0.8940
Epoch 328/1000
696/696 [==============================] - ETA: 0s - loss: 0.3021 - acc: 0.8950
Epoch 328: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3021 - acc: 0.8950 - val_loss: 0.3094 - val_acc: 0.8940
Epoch 329/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3033 - acc: 0.8945
Epoch 329: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3032 - acc: 0.8946 - val_loss: 0.3102 - val_acc: 0.8933
Epoch 330/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3018 - acc: 0.8949
Epoch 330: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3018 - acc: 0.8949 - val_loss: 0.3082 - val_acc: 0.8953
Epoch 331/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3018 - acc: 0.8950
Epoch 331: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3019 - acc: 0.8950 - val_loss: 0.3065 - val_acc: 0.8946
Epoch 332/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3014 - acc: 0.8951
Epoch 332: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3014 - acc: 0.8951 - val_loss: 0.3081 - val_acc: 0.8945
Epoch 333/1000
696/696 [==============================] - ETA: 0s - loss: 0.3004 - acc: 0.8956
Epoch 333: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3004 - acc: 0.8956 - val_loss: 0.3071 - val_acc: 0.8943
Epoch 334/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3001 - acc: 0.8958
Epoch 334: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3001 - acc: 0.8958 - val_loss: 0.3071 - val_acc: 0.8948
Epoch 335/1000
696/696 [==============================] - ETA: 0s - loss: 0.2997 - acc: 0.8957
Epoch 335: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2997 - acc: 0.8957 - val_loss: 0.3066 - val_acc: 0.8961
Epoch 336/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3002 - acc: 0.8955
Epoch 336: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3001 - acc: 0.8956 - val_loss: 0.3060 - val_acc: 0.8950
Epoch 337/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2994 - acc: 0.8963
Epoch 337: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2995 - acc: 0.8962 - val_loss: 0.3074 - val_acc: 0.8954
Epoch 338/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2992 - acc: 0.8962
Epoch 338: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2993 - acc: 0.8962 - val_loss: 0.3062 - val_acc: 0.8961
Epoch 339/1000
696/696 [==============================] - ETA: 0s - loss: 0.2991 - acc: 0.8962
Epoch 339: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2991 - acc: 0.8962 - val_loss: 0.3065 - val_acc: 0.8954
Epoch 340/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2972 - acc: 0.8966
Epoch 340: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2972 - acc: 0.8966 - val_loss: 0.3064 - val_acc: 0.8940
Epoch 341/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2977 - acc: 0.8965
Epoch 341: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2976 - acc: 0.8965 - val_loss: 0.3057 - val_acc: 0.8956
Epoch 342/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2973 - acc: 0.8964
Epoch 342: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2974 - acc: 0.8963 - val_loss: 0.3053 - val_acc: 0.8961
Epoch 343/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2976 - acc: 0.8963
Epoch 343: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2977 - acc: 0.8963 - val_loss: 0.3055 - val_acc: 0.8951
Epoch 344/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2967 - acc: 0.8969
Epoch 344: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2967 - acc: 0.8969 - val_loss: 0.3048 - val_acc: 0.8956
Epoch 345/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2964 - acc: 0.8968
Epoch 345: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2963 - acc: 0.8968 - val_loss: 0.3051 - val_acc: 0.8953
Epoch 346/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2959 - acc: 0.8969
Epoch 346: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2959 - acc: 0.8969 - val_loss: 0.3056 - val_acc: 0.8950
Epoch 347/1000
696/696 [==============================] - ETA: 0s - loss: 0.2956 - acc: 0.8971
Epoch 347: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2956 - acc: 0.8971 - val_loss: 0.3067 - val_acc: 0.8958
Epoch 348/1000
696/696 [==============================] - ETA: 0s - loss: 0.2948 - acc: 0.8978
Epoch 348: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2948 - acc: 0.8978 - val_loss: 0.3037 - val_acc: 0.8961
Epoch 349/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2955 - acc: 0.8973
Epoch 349: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2955 - acc: 0.8972 - val_loss: 0.3032 - val_acc: 0.8974
Epoch 350/1000
696/696 [==============================] - ETA: 0s - loss: 0.2947 - acc: 0.8973
Epoch 350: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2947 - acc: 0.8973 - val_loss: 0.3045 - val_acc: 0.8970
Epoch 351/1000
696/696 [==============================] - ETA: 0s - loss: 0.2938 - acc: 0.8977
Epoch 351: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2938 - acc: 0.8977 - val_loss: 0.3046 - val_acc: 0.8957
Epoch 352/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2933 - acc: 0.8979
Epoch 352: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2933 - acc: 0.8978 - val_loss: 0.3040 - val_acc: 0.8956
Epoch 353/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2931 - acc: 0.8981
Epoch 353: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2930 - acc: 0.8981 - val_loss: 0.3034 - val_acc: 0.8965
Epoch 354/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2931 - acc: 0.8985
Epoch 354: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2931 - acc: 0.8985 - val_loss: 0.3028 - val_acc: 0.8976
Epoch 355/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2939 - acc: 0.8977
Epoch 355: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2939 - acc: 0.8977 - val_loss: 0.3030 - val_acc: 0.8962
Epoch 356/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2925 - acc: 0.8982
Epoch 356: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.2926 - acc: 0.8982 - val_loss: 0.3038 - val_acc: 0.8961
Epoch 357/1000
696/696 [==============================] - ETA: 0s - loss: 0.2928 - acc: 0.8980
Epoch 357: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2928 - acc: 0.8980 - val_loss: 0.3030 - val_acc: 0.8973
Epoch 358/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2923 - acc: 0.8981
Epoch 358: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2923 - acc: 0.8981 - val_loss: 0.3037 - val_acc: 0.8953
Epoch 359/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2911 - acc: 0.8989
Epoch 359: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2913 - acc: 0.8988 - val_loss: 0.3038 - val_acc: 0.8962
Epoch 360/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2913 - acc: 0.8988
Epoch 360: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2912 - acc: 0.8988 - val_loss: 0.3028 - val_acc: 0.8984
Epoch 361/1000
696/696 [==============================] - ETA: 0s - loss: 0.2914 - acc: 0.8983
Epoch 361: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2914 - acc: 0.8983 - val_loss: 0.3024 - val_acc: 0.8976
Epoch 362/1000
696/696 [==============================] - ETA: 0s - loss: 0.2903 - acc: 0.8991
Epoch 362: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2903 - acc: 0.8991 - val_loss: 0.3009 - val_acc: 0.8966
Epoch 363/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2895 - acc: 0.8993
Epoch 363: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2895 - acc: 0.8992 - val_loss: 0.3014 - val_acc: 0.8967
Epoch 364/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2900 - acc: 0.8986
Epoch 364: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2901 - acc: 0.8986 - val_loss: 0.3018 - val_acc: 0.8958
Epoch 365/1000
696/696 [==============================] - ETA: 0s - loss: 0.2898 - acc: 0.8992
Epoch 365: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2898 - acc: 0.8992 - val_loss: 0.3027 - val_acc: 0.8972
Epoch 366/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2900 - acc: 0.8998
Epoch 366: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2900 - acc: 0.8998 - val_loss: 0.3014 - val_acc: 0.8970
Epoch 367/1000
696/696 [==============================] - ETA: 0s - loss: 0.2891 - acc: 0.8994
Epoch 367: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2891 - acc: 0.8994 - val_loss: 0.3017 - val_acc: 0.8967
Epoch 368/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2888 - acc: 0.8995
Epoch 368: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2888 - acc: 0.8995 - val_loss: 0.3015 - val_acc: 0.8981
Epoch 369/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2879 - acc: 0.8994
Epoch 369: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2879 - acc: 0.8994 - val_loss: 0.3017 - val_acc: 0.8987
Epoch 370/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2879 - acc: 0.8999
Epoch 370: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2879 - acc: 0.8999 - val_loss: 0.3004 - val_acc: 0.8970
Epoch 371/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2874 - acc: 0.9002
Epoch 371: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2874 - acc: 0.9002 - val_loss: 0.3018 - val_acc: 0.8959
Epoch 372/1000
696/696 [==============================] - ETA: 0s - loss: 0.2873 - acc: 0.9002
Epoch 372: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2873 - acc: 0.9002 - val_loss: 0.3004 - val_acc: 0.8978
Epoch 373/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2869 - acc: 0.9005
Epoch 373: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2869 - acc: 0.9005 - val_loss: 0.3003 - val_acc: 0.8970
Epoch 374/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2871 - acc: 0.9000
Epoch 374: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2871 - acc: 0.9000 - val_loss: 0.2991 - val_acc: 0.8990
Epoch 375/1000
696/696 [==============================] - ETA: 0s - loss: 0.2879 - acc: 0.9000
Epoch 375: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2879 - acc: 0.9000 - val_loss: 0.3003 - val_acc: 0.8976
Epoch 376/1000
696/696 [==============================] - ETA: 0s - loss: 0.2868 - acc: 0.9005
Epoch 376: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2868 - acc: 0.9005 - val_loss: 0.2990 - val_acc: 0.8970
Epoch 377/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2858 - acc: 0.9009
Epoch 377: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2857 - acc: 0.9009 - val_loss: 0.2972 - val_acc: 0.8998
Epoch 378/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2861 - acc: 0.9004
Epoch 378: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2861 - acc: 0.9004 - val_loss: 0.2991 - val_acc: 0.8977
Epoch 379/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2855 - acc: 0.9009
Epoch 379: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2855 - acc: 0.9009 - val_loss: 0.2991 - val_acc: 0.8984
Epoch 380/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2848 - acc: 0.9010
Epoch 380: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2847 - acc: 0.9010 - val_loss: 0.3005 - val_acc: 0.8970
Epoch 381/1000
696/696 [==============================] - ETA: 0s - loss: 0.2849 - acc: 0.9009
Epoch 381: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2849 - acc: 0.9009 - val_loss: 0.2997 - val_acc: 0.8982
Epoch 382/1000
696/696 [==============================] - ETA: 0s - loss: 0.2845 - acc: 0.9008
Epoch 382: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2845 - acc: 0.9008 - val_loss: 0.2994 - val_acc: 0.8988
Epoch 383/1000
696/696 [==============================] - ETA: 0s - loss: 0.2840 - acc: 0.9012
Epoch 383: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2840 - acc: 0.9012 - val_loss: 0.2990 - val_acc: 0.8987
Epoch 384/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2846 - acc: 0.9011
Epoch 384: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2846 - acc: 0.9011 - val_loss: 0.2992 - val_acc: 0.8975
Epoch 385/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2840 - acc: 0.9010
Epoch 385: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2841 - acc: 0.9009 - val_loss: 0.2986 - val_acc: 0.8987
Epoch 386/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2823 - acc: 0.9021
Epoch 386: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2824 - acc: 0.9021 - val_loss: 0.2985 - val_acc: 0.8999
Epoch 387/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2829 - acc: 0.9019
Epoch 387: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2829 - acc: 0.9018 - val_loss: 0.2977 - val_acc: 0.8989
Epoch 388/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2828 - acc: 0.9017
Epoch 388: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2829 - acc: 0.9017 - val_loss: 0.2970 - val_acc: 0.8995
Epoch 389/1000
696/696 [==============================] - ETA: 0s - loss: 0.2824 - acc: 0.9020
Epoch 389: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2824 - acc: 0.9020 - val_loss: 0.2979 - val_acc: 0.8980
Epoch 390/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2823 - acc: 0.9017
Epoch 390: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2823 - acc: 0.9017 - val_loss: 0.2983 - val_acc: 0.8982
Epoch 391/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2816 - acc: 0.9020
Epoch 391: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2817 - acc: 0.9020 - val_loss: 0.2975 - val_acc: 0.8983
Epoch 392/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2816 - acc: 0.9021
Epoch 392: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2816 - acc: 0.9020 - val_loss: 0.2959 - val_acc: 0.8998
Epoch 393/1000
696/696 [==============================] - ETA: 0s - loss: 0.2818 - acc: 0.9023
Epoch 393: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2818 - acc: 0.9023 - val_loss: 0.2975 - val_acc: 0.8984
Epoch 394/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2804 - acc: 0.9021
Epoch 394: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2804 - acc: 0.9021 - val_loss: 0.2983 - val_acc: 0.8979
Epoch 395/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2805 - acc: 0.9027
Epoch 395: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2804 - acc: 0.9027 - val_loss: 0.2975 - val_acc: 0.8977
Epoch 396/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2806 - acc: 0.9023
Epoch 396: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2806 - acc: 0.9023 - val_loss: 0.2967 - val_acc: 0.8979
Epoch 397/1000
696/696 [==============================] - ETA: 0s - loss: 0.2801 - acc: 0.9026
Epoch 397: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2801 - acc: 0.9026 - val_loss: 0.2967 - val_acc: 0.8979
Epoch 398/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2792 - acc: 0.9030
Epoch 398: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2791 - acc: 0.9030 - val_loss: 0.2963 - val_acc: 0.8992
Epoch 399/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2792 - acc: 0.9030
Epoch 399: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2792 - acc: 0.9030 - val_loss: 0.2961 - val_acc: 0.8989
Epoch 400/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2793 - acc: 0.9029
Epoch 400: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2793 - acc: 0.9029 - val_loss: 0.2978 - val_acc: 0.8988
Epoch 401/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2791 - acc: 0.9028
Epoch 401: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2790 - acc: 0.9028 - val_loss: 0.2989 - val_acc: 0.8975
Epoch 402/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2787 - acc: 0.9033
Epoch 402: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2787 - acc: 0.9033 - val_loss: 0.2958 - val_acc: 0.8988
Epoch 403/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2772 - acc: 0.9038
Epoch 403: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2771 - acc: 0.9038 - val_loss: 0.2963 - val_acc: 0.8993
Epoch 404/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2784 - acc: 0.9032
Epoch 404: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2784 - acc: 0.9031 - val_loss: 0.2955 - val_acc: 0.8984
Epoch 405/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2783 - acc: 0.9033
Epoch 405: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2783 - acc: 0.9033 - val_loss: 0.2962 - val_acc: 0.9009
Epoch 406/1000
696/696 [==============================] - ETA: 0s - loss: 0.2774 - acc: 0.9034
Epoch 406: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2774 - acc: 0.9034 - val_loss: 0.2941 - val_acc: 0.9004
Epoch 407/1000
696/696 [==============================] - ETA: 0s - loss: 0.2768 - acc: 0.9040
Epoch 407: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2768 - acc: 0.9040 - val_loss: 0.2948 - val_acc: 0.9014
Epoch 408/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2770 - acc: 0.9039
Epoch 408: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2770 - acc: 0.9039 - val_loss: 0.2950 - val_acc: 0.9003
Epoch 409/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2773 - acc: 0.9035
Epoch 409: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2772 - acc: 0.9035 - val_loss: 0.2955 - val_acc: 0.8990
Epoch 410/1000
696/696 [==============================] - ETA: 0s - loss: 0.2767 - acc: 0.9037
Epoch 410: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2767 - acc: 0.9037 - val_loss: 0.2954 - val_acc: 0.8993
Epoch 411/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2758 - acc: 0.9041
Epoch 411: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2758 - acc: 0.9041 - val_loss: 0.2968 - val_acc: 0.8987
Epoch 412/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2756 - acc: 0.9043
Epoch 412: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2756 - acc: 0.9044 - val_loss: 0.2960 - val_acc: 0.8998
Epoch 413/1000
696/696 [==============================] - ETA: 0s - loss: 0.2763 - acc: 0.9041
Epoch 413: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2763 - acc: 0.9041 - val_loss: 0.2935 - val_acc: 0.9009
Epoch 414/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2756 - acc: 0.9043
Epoch 414: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2757 - acc: 0.9043 - val_loss: 0.2930 - val_acc: 0.9018
Epoch 415/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2752 - acc: 0.9042
Epoch 415: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2752 - acc: 0.9042 - val_loss: 0.2941 - val_acc: 0.8996
Epoch 416/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2750 - acc: 0.9044
Epoch 416: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2750 - acc: 0.9044 - val_loss: 0.2934 - val_acc: 0.8999
Epoch 417/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2742 - acc: 0.9048
Epoch 417: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2743 - acc: 0.9048 - val_loss: 0.2939 - val_acc: 0.9006
Epoch 418/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2747 - acc: 0.9047
Epoch 418: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2747 - acc: 0.9047 - val_loss: 0.2934 - val_acc: 0.9020
Epoch 419/1000
696/696 [==============================] - ETA: 0s - loss: 0.2735 - acc: 0.9046
Epoch 419: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2735 - acc: 0.9046 - val_loss: 0.2947 - val_acc: 0.9010
Epoch 420/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2736 - acc: 0.9052
Epoch 420: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2736 - acc: 0.9052 - val_loss: 0.2934 - val_acc: 0.9004
Epoch 421/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2735 - acc: 0.9051
Epoch 421: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2735 - acc: 0.9051 - val_loss: 0.2935 - val_acc: 0.9017
Epoch 422/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2724 - acc: 0.9053
Epoch 422: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2723 - acc: 0.9053 - val_loss: 0.2934 - val_acc: 0.9008
Epoch 423/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2725 - acc: 0.9049
Epoch 423: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2725 - acc: 0.9049 - val_loss: 0.2936 - val_acc: 0.9006
Epoch 424/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2721 - acc: 0.9050
Epoch 424: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2721 - acc: 0.9050 - val_loss: 0.2934 - val_acc: 0.9014
Epoch 425/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2720 - acc: 0.9055
Epoch 425: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2720 - acc: 0.9056 - val_loss: 0.2926 - val_acc: 0.9007
Epoch 426/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2717 - acc: 0.9058
Epoch 426: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2717 - acc: 0.9058 - val_loss: 0.2925 - val_acc: 0.9022
Epoch 427/1000
696/696 [==============================] - ETA: 0s - loss: 0.2712 - acc: 0.9058
Epoch 427: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2712 - acc: 0.9058 - val_loss: 0.2922 - val_acc: 0.9021
Epoch 428/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2718 - acc: 0.9054
Epoch 428: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2718 - acc: 0.9054 - val_loss: 0.2936 - val_acc: 0.9011
Epoch 429/1000
696/696 [==============================] - ETA: 0s - loss: 0.2702 - acc: 0.9058
Epoch 429: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2702 - acc: 0.9058 - val_loss: 0.2922 - val_acc: 0.9020
Epoch 430/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2705 - acc: 0.9060
Epoch 430: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2705 - acc: 0.9060 - val_loss: 0.2915 - val_acc: 0.9003
Epoch 431/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2704 - acc: 0.9060
Epoch 431: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2704 - acc: 0.9060 - val_loss: 0.2919 - val_acc: 0.9006
Epoch 432/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2697 - acc: 0.9064
Epoch 432: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2696 - acc: 0.9065 - val_loss: 0.2940 - val_acc: 0.8995
Epoch 433/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2697 - acc: 0.9064
Epoch 433: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2697 - acc: 0.9064 - val_loss: 0.2916 - val_acc: 0.9014
Epoch 434/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2695 - acc: 0.9061
Epoch 434: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2694 - acc: 0.9062 - val_loss: 0.2922 - val_acc: 0.9009
Epoch 435/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2684 - acc: 0.9064
Epoch 435: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2684 - acc: 0.9064 - val_loss: 0.2917 - val_acc: 0.9020
Epoch 436/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2691 - acc: 0.9063
Epoch 436: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2690 - acc: 0.9063 - val_loss: 0.2932 - val_acc: 0.9012
Epoch 437/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2692 - acc: 0.9061
Epoch 437: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2691 - acc: 0.9061 - val_loss: 0.2923 - val_acc: 0.9016
Epoch 438/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2683 - acc: 0.9064
Epoch 438: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2683 - acc: 0.9064 - val_loss: 0.2908 - val_acc: 0.9005
Epoch 439/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2687 - acc: 0.9066
Epoch 439: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2687 - acc: 0.9067 - val_loss: 0.2930 - val_acc: 0.9003
Epoch 440/1000
696/696 [==============================] - ETA: 0s - loss: 0.2677 - acc: 0.9071
Epoch 440: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2677 - acc: 0.9071 - val_loss: 0.2922 - val_acc: 0.9024
Epoch 441/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2684 - acc: 0.9069
Epoch 441: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2684 - acc: 0.9069 - val_loss: 0.2907 - val_acc: 0.9018
Epoch 442/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2675 - acc: 0.9070
Epoch 442: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2675 - acc: 0.9069 - val_loss: 0.2912 - val_acc: 0.9004
Epoch 443/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2674 - acc: 0.9071
Epoch 443: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2674 - acc: 0.9071 - val_loss: 0.2907 - val_acc: 0.9004
Epoch 444/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2672 - acc: 0.9069
Epoch 444: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2673 - acc: 0.9068 - val_loss: 0.2902 - val_acc: 0.9019
Epoch 445/1000
696/696 [==============================] - ETA: 0s - loss: 0.2661 - acc: 0.9077
Epoch 445: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2661 - acc: 0.9077 - val_loss: 0.2901 - val_acc: 0.9041
Epoch 446/1000
696/696 [==============================] - ETA: 0s - loss: 0.2650 - acc: 0.9080
Epoch 446: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2650 - acc: 0.9080 - val_loss: 0.2912 - val_acc: 0.9038
Epoch 447/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2660 - acc: 0.9078
Epoch 447: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2660 - acc: 0.9077 - val_loss: 0.2908 - val_acc: 0.9013
Epoch 448/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2659 - acc: 0.9077
Epoch 448: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2658 - acc: 0.9077 - val_loss: 0.2902 - val_acc: 0.9017
Epoch 449/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2660 - acc: 0.9073
Epoch 449: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2661 - acc: 0.9073 - val_loss: 0.2900 - val_acc: 0.9011
Epoch 450/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2656 - acc: 0.9077
Epoch 450: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2657 - acc: 0.9077 - val_loss: 0.2906 - val_acc: 0.9035
Epoch 451/1000
696/696 [==============================] - ETA: 0s - loss: 0.2641 - acc: 0.9079
Epoch 451: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2641 - acc: 0.9079 - val_loss: 0.2898 - val_acc: 0.9024
Epoch 452/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2659 - acc: 0.9074
Epoch 452: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2659 - acc: 0.9074 - val_loss: 0.2904 - val_acc: 0.9027
Epoch 453/1000
696/696 [==============================] - ETA: 0s - loss: 0.2642 - acc: 0.9082
Epoch 453: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2642 - acc: 0.9082 - val_loss: 0.2893 - val_acc: 0.9019
Epoch 454/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2639 - acc: 0.9079
Epoch 454: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2638 - acc: 0.9080 - val_loss: 0.2889 - val_acc: 0.9027
Epoch 455/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2642 - acc: 0.9077
Epoch 455: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2642 - acc: 0.9077 - val_loss: 0.2885 - val_acc: 0.9036
Epoch 456/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2635 - acc: 0.9083
Epoch 456: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2636 - acc: 0.9083 - val_loss: 0.2882 - val_acc: 0.9031
Epoch 457/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2642 - acc: 0.9085
Epoch 457: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2642 - acc: 0.9085 - val_loss: 0.2886 - val_acc: 0.9024
Epoch 458/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2641 - acc: 0.9080
Epoch 458: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2640 - acc: 0.9080 - val_loss: 0.2892 - val_acc: 0.9050
Epoch 459/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2632 - acc: 0.9082
Epoch 459: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2633 - acc: 0.9082 - val_loss: 0.2892 - val_acc: 0.9024
Epoch 460/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2623 - acc: 0.9089
Epoch 460: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2623 - acc: 0.9089 - val_loss: 0.2890 - val_acc: 0.9023
Epoch 461/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2620 - acc: 0.9088
Epoch 461: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2621 - acc: 0.9087 - val_loss: 0.2894 - val_acc: 0.9027
Epoch 462/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2630 - acc: 0.9079
Epoch 462: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2631 - acc: 0.9078 - val_loss: 0.2913 - val_acc: 0.9028
Epoch 463/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2617 - acc: 0.9091
Epoch 463: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2617 - acc: 0.9091 - val_loss: 0.2888 - val_acc: 0.9030
Epoch 464/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2608 - acc: 0.9095
Epoch 464: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2607 - acc: 0.9095 - val_loss: 0.2892 - val_acc: 0.9038
Epoch 465/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2619 - acc: 0.9089
Epoch 465: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2618 - acc: 0.9089 - val_loss: 0.2880 - val_acc: 0.9038
Epoch 466/1000
696/696 [==============================] - ETA: 0s - loss: 0.2611 - acc: 0.9088
Epoch 466: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2611 - acc: 0.9088 - val_loss: 0.2873 - val_acc: 0.9037
Epoch 467/1000
696/696 [==============================] - ETA: 0s - loss: 0.2612 - acc: 0.9092
Epoch 467: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2612 - acc: 0.9092 - val_loss: 0.2870 - val_acc: 0.9050
Epoch 468/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2610 - acc: 0.9089
Epoch 468: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2611 - acc: 0.9089 - val_loss: 0.2889 - val_acc: 0.9025
Epoch 469/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2601 - acc: 0.9094
Epoch 469: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2601 - acc: 0.9094 - val_loss: 0.2881 - val_acc: 0.9030
Epoch 470/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2605 - acc: 0.9098
Epoch 470: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2605 - acc: 0.9098 - val_loss: 0.2887 - val_acc: 0.9042
Epoch 471/1000
696/696 [==============================] - ETA: 0s - loss: 0.2611 - acc: 0.9092
Epoch 471: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2611 - acc: 0.9092 - val_loss: 0.2877 - val_acc: 0.9026
Epoch 472/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2598 - acc: 0.9096
Epoch 472: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2598 - acc: 0.9096 - val_loss: 0.2871 - val_acc: 0.9052
Epoch 473/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2594 - acc: 0.9098
Epoch 473: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2594 - acc: 0.9098 - val_loss: 0.2882 - val_acc: 0.9046
Epoch 474/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2588 - acc: 0.9099
Epoch 474: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2589 - acc: 0.9099 - val_loss: 0.2872 - val_acc: 0.9046
Epoch 475/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2587 - acc: 0.9099
Epoch 475: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2586 - acc: 0.9099 - val_loss: 0.2880 - val_acc: 0.9026
Epoch 476/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2589 - acc: 0.9100
Epoch 476: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2589 - acc: 0.9100 - val_loss: 0.2860 - val_acc: 0.9054
Epoch 477/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2587 - acc: 0.9101
Epoch 477: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2586 - acc: 0.9101 - val_loss: 0.2878 - val_acc: 0.9025
Epoch 478/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2584 - acc: 0.9100
Epoch 478: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2584 - acc: 0.9100 - val_loss: 0.2876 - val_acc: 0.9034
Epoch 479/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2576 - acc: 0.9104
Epoch 479: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2577 - acc: 0.9103 - val_loss: 0.2881 - val_acc: 0.9032
Epoch 480/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2583 - acc: 0.9103
Epoch 480: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2583 - acc: 0.9103 - val_loss: 0.2870 - val_acc: 0.9037
Epoch 481/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2574 - acc: 0.9104
Epoch 481: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2574 - acc: 0.9104 - val_loss: 0.2891 - val_acc: 0.9032
Epoch 482/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2573 - acc: 0.9104
Epoch 482: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2574 - acc: 0.9104 - val_loss: 0.2871 - val_acc: 0.9033
Epoch 483/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2568 - acc: 0.9107
Epoch 483: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2568 - acc: 0.9107 - val_loss: 0.2863 - val_acc: 0.9036
Epoch 484/1000
696/696 [==============================] - ETA: 0s - loss: 0.2568 - acc: 0.9110
Epoch 484: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2568 - acc: 0.9110 - val_loss: 0.2866 - val_acc: 0.9045
Epoch 485/1000
696/696 [==============================] - ETA: 0s - loss: 0.2571 - acc: 0.9104
Epoch 485: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2571 - acc: 0.9104 - val_loss: 0.2855 - val_acc: 0.9037
Epoch 486/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2560 - acc: 0.9107
Epoch 486: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2561 - acc: 0.9107 - val_loss: 0.2867 - val_acc: 0.9052
Epoch 487/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2560 - acc: 0.9107
Epoch 487: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2560 - acc: 0.9107 - val_loss: 0.2844 - val_acc: 0.9046
Epoch 488/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2557 - acc: 0.9111
Epoch 488: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2556 - acc: 0.9111 - val_loss: 0.2853 - val_acc: 0.9048
Epoch 489/1000
696/696 [==============================] - ETA: 0s - loss: 0.2557 - acc: 0.9111
Epoch 489: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2557 - acc: 0.9111 - val_loss: 0.2858 - val_acc: 0.9046
Epoch 490/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2555 - acc: 0.9112
Epoch 490: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2555 - acc: 0.9111 - val_loss: 0.2856 - val_acc: 0.9039
Epoch 491/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2556 - acc: 0.9108
Epoch 491: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2556 - acc: 0.9108 - val_loss: 0.2869 - val_acc: 0.9054
Epoch 492/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2549 - acc: 0.9108
Epoch 492: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2550 - acc: 0.9107 - val_loss: 0.2871 - val_acc: 0.9046
Epoch 493/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2552 - acc: 0.9114
Epoch 493: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2551 - acc: 0.9114 - val_loss: 0.2843 - val_acc: 0.9057
Epoch 494/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2534 - acc: 0.9120
Epoch 494: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2534 - acc: 0.9120 - val_loss: 0.2858 - val_acc: 0.9041
Epoch 495/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2540 - acc: 0.9114
Epoch 495: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2539 - acc: 0.9114 - val_loss: 0.2860 - val_acc: 0.9037
Epoch 496/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2532 - acc: 0.9119
Epoch 496: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2531 - acc: 0.9120 - val_loss: 0.2843 - val_acc: 0.9054
Epoch 497/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2536 - acc: 0.9116
Epoch 497: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2535 - acc: 0.9116 - val_loss: 0.2853 - val_acc: 0.9051
Epoch 498/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2536 - acc: 0.9118
Epoch 498: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2536 - acc: 0.9118 - val_loss: 0.2839 - val_acc: 0.9057
Epoch 499/1000
696/696 [==============================] - ETA: 0s - loss: 0.2532 - acc: 0.9118
Epoch 499: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2532 - acc: 0.9118 - val_loss: 0.2848 - val_acc: 0.9052
Epoch 500/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2531 - acc: 0.9115
Epoch 500: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2531 - acc: 0.9115 - val_loss: 0.2849 - val_acc: 0.9044
Epoch 501/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2524 - acc: 0.9121
Epoch 501: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2524 - acc: 0.9121 - val_loss: 0.2856 - val_acc: 0.9046
Epoch 502/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2519 - acc: 0.9119
Epoch 502: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2519 - acc: 0.9119 - val_loss: 0.2854 - val_acc: 0.9053
Epoch 503/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2521 - acc: 0.9123
Epoch 503: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2521 - acc: 0.9123 - val_loss: 0.2862 - val_acc: 0.9047
Epoch 504/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2521 - acc: 0.9121
Epoch 504: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2522 - acc: 0.9121 - val_loss: 0.2850 - val_acc: 0.9054
Epoch 505/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2518 - acc: 0.9125
Epoch 505: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2519 - acc: 0.9124 - val_loss: 0.2845 - val_acc: 0.9050
Epoch 506/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2518 - acc: 0.9125
Epoch 506: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2518 - acc: 0.9125 - val_loss: 0.2845 - val_acc: 0.9052
Epoch 507/1000
696/696 [==============================] - ETA: 0s - loss: 0.2520 - acc: 0.9122
Epoch 507: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2520 - acc: 0.9122 - val_loss: 0.2837 - val_acc: 0.9051
Epoch 508/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2509 - acc: 0.9130
Epoch 508: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2509 - acc: 0.9130 - val_loss: 0.2840 - val_acc: 0.9058
Epoch 509/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2509 - acc: 0.9126
Epoch 509: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2509 - acc: 0.9126 - val_loss: 0.2824 - val_acc: 0.9068
Epoch 510/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2504 - acc: 0.9127
Epoch 510: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2506 - acc: 0.9127 - val_loss: 0.2862 - val_acc: 0.9052
Epoch 511/1000
696/696 [==============================] - ETA: 0s - loss: 0.2505 - acc: 0.9129
Epoch 511: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2505 - acc: 0.9129 - val_loss: 0.2851 - val_acc: 0.9062
Epoch 512/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2506 - acc: 0.9130
Epoch 512: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2505 - acc: 0.9130 - val_loss: 0.2860 - val_acc: 0.9055
Epoch 513/1000
696/696 [==============================] - ETA: 0s - loss: 0.2505 - acc: 0.9129
Epoch 513: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2505 - acc: 0.9129 - val_loss: 0.2849 - val_acc: 0.9041
Epoch 514/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2494 - acc: 0.9130
Epoch 514: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2494 - acc: 0.9130 - val_loss: 0.2836 - val_acc: 0.9052
Epoch 515/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2505 - acc: 0.9128
Epoch 515: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2505 - acc: 0.9127 - val_loss: 0.2858 - val_acc: 0.9062
Epoch 516/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2495 - acc: 0.9131
Epoch 516: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2495 - acc: 0.9131 - val_loss: 0.2842 - val_acc: 0.9063
Epoch 517/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2491 - acc: 0.9129
Epoch 517: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2491 - acc: 0.9129 - val_loss: 0.2828 - val_acc: 0.9061
Epoch 518/1000
696/696 [==============================] - ETA: 0s - loss: 0.2488 - acc: 0.9132
Epoch 518: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2488 - acc: 0.9132 - val_loss: 0.2841 - val_acc: 0.9045
Epoch 519/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2490 - acc: 0.9132
Epoch 519: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2490 - acc: 0.9132 - val_loss: 0.2833 - val_acc: 0.9061
Epoch 520/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2485 - acc: 0.9134
Epoch 520: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2486 - acc: 0.9134 - val_loss: 0.2833 - val_acc: 0.9062
Epoch 521/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2473 - acc: 0.9140
Epoch 521: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2474 - acc: 0.9139 - val_loss: 0.2843 - val_acc: 0.9046
Epoch 522/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2479 - acc: 0.9136
Epoch 522: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2480 - acc: 0.9136 - val_loss: 0.2837 - val_acc: 0.9047
Epoch 523/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2471 - acc: 0.9136
Epoch 523: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2471 - acc: 0.9136 - val_loss: 0.2810 - val_acc: 0.9059
Epoch 524/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2471 - acc: 0.9135
Epoch 524: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2471 - acc: 0.9135 - val_loss: 0.2850 - val_acc: 0.9056
Epoch 525/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2478 - acc: 0.9137
Epoch 525: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2478 - acc: 0.9137 - val_loss: 0.2818 - val_acc: 0.9056
Epoch 526/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2474 - acc: 0.9135
Epoch 526: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2473 - acc: 0.9136 - val_loss: 0.2834 - val_acc: 0.9050
Epoch 527/1000
696/696 [==============================] - ETA: 0s - loss: 0.2467 - acc: 0.9138
Epoch 527: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2467 - acc: 0.9138 - val_loss: 0.2826 - val_acc: 0.9062
Epoch 528/1000
696/696 [==============================] - ETA: 0s - loss: 0.2466 - acc: 0.9143
Epoch 528: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2466 - acc: 0.9143 - val_loss: 0.2829 - val_acc: 0.9040
Epoch 529/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2462 - acc: 0.9142
Epoch 529: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2462 - acc: 0.9142 - val_loss: 0.2851 - val_acc: 0.9046
Epoch 530/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2460 - acc: 0.9145
Epoch 530: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2460 - acc: 0.9145 - val_loss: 0.2822 - val_acc: 0.9068
Epoch 531/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2462 - acc: 0.9143
Epoch 531: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2461 - acc: 0.9143 - val_loss: 0.2816 - val_acc: 0.9061
Epoch 532/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2456 - acc: 0.9144
Epoch 532: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2456 - acc: 0.9144 - val_loss: 0.2818 - val_acc: 0.9056
Epoch 533/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2457 - acc: 0.9143
Epoch 533: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2457 - acc: 0.9144 - val_loss: 0.2821 - val_acc: 0.9071
Epoch 534/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2461 - acc: 0.9145
Epoch 534: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2461 - acc: 0.9145 - val_loss: 0.2811 - val_acc: 0.9076
Epoch 535/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2443 - acc: 0.9151
Epoch 535: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2444 - acc: 0.9151 - val_loss: 0.2821 - val_acc: 0.9068
Epoch 536/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2437 - acc: 0.9154
Epoch 536: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2438 - acc: 0.9154 - val_loss: 0.2817 - val_acc: 0.9071
Epoch 537/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2448 - acc: 0.9146
Epoch 537: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2448 - acc: 0.9147 - val_loss: 0.2836 - val_acc: 0.9068
Epoch 538/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2446 - acc: 0.9149
Epoch 538: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_DST/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2446 - acc: 0.9149 - val_loss: 0.2822 - val_acc: 0.9074
Epoch 538: early stopping
Use balanced Generator [True]
Data: 369493
-----------------------------------------------------------------------------------
Epoch 1/1000
693/696 [============================>.] - ETA: 0s - loss: 2.0794 - acc: 0.1420
Epoch 1: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 13s 17ms/step - loss: 2.0794 - acc: 0.1421 - val_loss: 2.0770 - val_acc: 0.1858
Epoch 2/1000
696/696 [==============================] - ETA: 0s - loss: 2.0755 - acc: 0.1734
Epoch 2: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 2.0755 - acc: 0.1734 - val_loss: 2.0718 - val_acc: 0.2193
Epoch 3/1000
696/696 [==============================] - ETA: 0s - loss: 2.0697 - acc: 0.2090
Epoch 3: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 2.0697 - acc: 0.2090 - val_loss: 2.0635 - val_acc: 0.2772
Epoch 4/1000
696/696 [==============================] - ETA: 0s - loss: 2.0596 - acc: 0.2379
Epoch 4: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 2.0596 - acc: 0.2379 - val_loss: 2.0476 - val_acc: 0.3085
Epoch 5/1000
694/696 [============================>.] - ETA: 0s - loss: 2.0372 - acc: 0.2632
Epoch 5: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 2.0372 - acc: 0.2632 - val_loss: 2.0091 - val_acc: 0.3455
Epoch 6/1000
695/696 [============================>.] - ETA: 0s - loss: 1.9771 - acc: 0.2946
Epoch 6: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.9770 - acc: 0.2946 - val_loss: 1.9000 - val_acc: 0.4137
Epoch 7/1000
695/696 [============================>.] - ETA: 0s - loss: 1.8226 - acc: 0.3425
Epoch 7: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.8225 - acc: 0.3425 - val_loss: 1.6592 - val_acc: 0.4609
Epoch 8/1000
695/696 [============================>.] - ETA: 0s - loss: 1.6128 - acc: 0.3987
Epoch 8: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.6127 - acc: 0.3988 - val_loss: 1.4250 - val_acc: 0.5397
Epoch 9/1000
693/696 [============================>.] - ETA: 0s - loss: 1.4453 - acc: 0.4693
Epoch 9: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.4451 - acc: 0.4694 - val_loss: 1.2469 - val_acc: 0.5849
Epoch 10/1000
696/696 [==============================] - ETA: 0s - loss: 1.2892 - acc: 0.5364
Epoch 10: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.2892 - acc: 0.5364 - val_loss: 1.0850 - val_acc: 0.6285
Epoch 11/1000
695/696 [============================>.] - ETA: 0s - loss: 1.1413 - acc: 0.5891
Epoch 11: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.1412 - acc: 0.5892 - val_loss: 0.9506 - val_acc: 0.6642
Epoch 12/1000
693/696 [============================>.] - ETA: 0s - loss: 1.0219 - acc: 0.6287
Epoch 12: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 1.0217 - acc: 0.6289 - val_loss: 0.8516 - val_acc: 0.6973
Epoch 13/1000
696/696 [==============================] - ETA: 0s - loss: 0.9365 - acc: 0.6577
Epoch 13: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.9365 - acc: 0.6577 - val_loss: 0.7835 - val_acc: 0.7225
Epoch 14/1000
696/696 [==============================] - ETA: 0s - loss: 0.8773 - acc: 0.6788
Epoch 14: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.8773 - acc: 0.6788 - val_loss: 0.7365 - val_acc: 0.7398
Epoch 15/1000
693/696 [============================>.] - ETA: 0s - loss: 0.8329 - acc: 0.6949
Epoch 15: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.8328 - acc: 0.6950 - val_loss: 0.7024 - val_acc: 0.7516
Epoch 16/1000
693/696 [============================>.] - ETA: 0s - loss: 0.7999 - acc: 0.7085
Epoch 16: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7998 - acc: 0.7086 - val_loss: 0.6761 - val_acc: 0.7620
Epoch 17/1000
693/696 [============================>.] - ETA: 0s - loss: 0.7718 - acc: 0.7181
Epoch 17: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7716 - acc: 0.7182 - val_loss: 0.6544 - val_acc: 0.7704
Epoch 18/1000
693/696 [============================>.] - ETA: 0s - loss: 0.7477 - acc: 0.7273
Epoch 18: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7475 - acc: 0.7273 - val_loss: 0.6359 - val_acc: 0.7745
Epoch 19/1000
695/696 [============================>.] - ETA: 0s - loss: 0.7287 - acc: 0.7349
Epoch 19: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7287 - acc: 0.7349 - val_loss: 0.6199 - val_acc: 0.7807
Epoch 20/1000
694/696 [============================>.] - ETA: 0s - loss: 0.7100 - acc: 0.7416
Epoch 20: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.7100 - acc: 0.7416 - val_loss: 0.6056 - val_acc: 0.7857
Epoch 21/1000
696/696 [==============================] - ETA: 0s - loss: 0.6951 - acc: 0.7477
Epoch 21: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6951 - acc: 0.7477 - val_loss: 0.5937 - val_acc: 0.7902
Epoch 22/1000
695/696 [============================>.] - ETA: 0s - loss: 0.6799 - acc: 0.7528
Epoch 22: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6799 - acc: 0.7529 - val_loss: 0.5835 - val_acc: 0.7932
Epoch 23/1000
695/696 [============================>.] - ETA: 0s - loss: 0.6679 - acc: 0.7571
Epoch 23: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6678 - acc: 0.7571 - val_loss: 0.5728 - val_acc: 0.7973
Epoch 24/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6554 - acc: 0.7618
Epoch 24: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6554 - acc: 0.7618 - val_loss: 0.5652 - val_acc: 0.8000
Epoch 25/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6453 - acc: 0.7664
Epoch 25: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6455 - acc: 0.7663 - val_loss: 0.5569 - val_acc: 0.8038
Epoch 26/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6351 - acc: 0.7701
Epoch 26: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6351 - acc: 0.7701 - val_loss: 0.5484 - val_acc: 0.8040
Epoch 27/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6262 - acc: 0.7732
Epoch 27: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6263 - acc: 0.7732 - val_loss: 0.5412 - val_acc: 0.8082
Epoch 28/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6193 - acc: 0.7756
Epoch 28: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6192 - acc: 0.7757 - val_loss: 0.5357 - val_acc: 0.8109
Epoch 29/1000
694/696 [============================>.] - ETA: 0s - loss: 0.6104 - acc: 0.7791
Epoch 29: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6104 - acc: 0.7791 - val_loss: 0.5291 - val_acc: 0.8128
Epoch 30/1000
693/696 [============================>.] - ETA: 0s - loss: 0.6046 - acc: 0.7813
Epoch 30: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.6047 - acc: 0.7813 - val_loss: 0.5232 - val_acc: 0.8154
Epoch 31/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5961 - acc: 0.7846
Epoch 31: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5961 - acc: 0.7846 - val_loss: 0.5178 - val_acc: 0.8162
Epoch 32/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5900 - acc: 0.7864
Epoch 32: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5899 - acc: 0.7864 - val_loss: 0.5136 - val_acc: 0.8194
Epoch 33/1000
696/696 [==============================] - ETA: 0s - loss: 0.5840 - acc: 0.7896
Epoch 33: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5840 - acc: 0.7896 - val_loss: 0.5090 - val_acc: 0.8188
Epoch 34/1000
696/696 [==============================] - ETA: 0s - loss: 0.5778 - acc: 0.7918
Epoch 34: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5778 - acc: 0.7918 - val_loss: 0.5037 - val_acc: 0.8218
Epoch 35/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5740 - acc: 0.7926
Epoch 35: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5738 - acc: 0.7927 - val_loss: 0.5008 - val_acc: 0.8232
Epoch 36/1000
696/696 [==============================] - ETA: 0s - loss: 0.5679 - acc: 0.7958
Epoch 36: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5679 - acc: 0.7958 - val_loss: 0.4963 - val_acc: 0.8245
Epoch 37/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5638 - acc: 0.7967
Epoch 37: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.5639 - acc: 0.7966 - val_loss: 0.4933 - val_acc: 0.8277
Epoch 38/1000
696/696 [==============================] - ETA: 0s - loss: 0.5589 - acc: 0.7985
Epoch 38: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5589 - acc: 0.7985 - val_loss: 0.4894 - val_acc: 0.8281
Epoch 39/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5550 - acc: 0.8004
Epoch 39: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5551 - acc: 0.8003 - val_loss: 0.4870 - val_acc: 0.8290
Epoch 40/1000
696/696 [==============================] - ETA: 0s - loss: 0.5511 - acc: 0.8017
Epoch 40: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.5511 - acc: 0.8017 - val_loss: 0.4827 - val_acc: 0.8310
Epoch 41/1000
696/696 [==============================] - ETA: 0s - loss: 0.5461 - acc: 0.8038
Epoch 41: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5461 - acc: 0.8038 - val_loss: 0.4809 - val_acc: 0.8315
Epoch 42/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5426 - acc: 0.8051
Epoch 42: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5426 - acc: 0.8051 - val_loss: 0.4777 - val_acc: 0.8313
Epoch 43/1000
696/696 [==============================] - ETA: 0s - loss: 0.5392 - acc: 0.8056
Epoch 43: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5392 - acc: 0.8056 - val_loss: 0.4745 - val_acc: 0.8332
Epoch 44/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5361 - acc: 0.8075
Epoch 44: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5359 - acc: 0.8076 - val_loss: 0.4711 - val_acc: 0.8339
Epoch 45/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5318 - acc: 0.8094
Epoch 45: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5317 - acc: 0.8094 - val_loss: 0.4689 - val_acc: 0.8363
Epoch 46/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5288 - acc: 0.8101
Epoch 46: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5289 - acc: 0.8101 - val_loss: 0.4672 - val_acc: 0.8355
Epoch 47/1000
695/696 [============================>.] - ETA: 0s - loss: 0.5259 - acc: 0.8109
Epoch 47: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5259 - acc: 0.8108 - val_loss: 0.4647 - val_acc: 0.8373
Epoch 48/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5241 - acc: 0.8126
Epoch 48: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5239 - acc: 0.8126 - val_loss: 0.4627 - val_acc: 0.8378
Epoch 49/1000
694/696 [============================>.] - ETA: 0s - loss: 0.5197 - acc: 0.8140
Epoch 49: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5197 - acc: 0.8140 - val_loss: 0.4599 - val_acc: 0.8385
Epoch 50/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5164 - acc: 0.8146
Epoch 50: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5164 - acc: 0.8146 - val_loss: 0.4582 - val_acc: 0.8392
Epoch 51/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5152 - acc: 0.8157
Epoch 51: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5151 - acc: 0.8157 - val_loss: 0.4573 - val_acc: 0.8388
Epoch 52/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5108 - acc: 0.8176
Epoch 52: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5108 - acc: 0.8175 - val_loss: 0.4542 - val_acc: 0.8416
Epoch 53/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5087 - acc: 0.8174
Epoch 53: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5088 - acc: 0.8173 - val_loss: 0.4515 - val_acc: 0.8437
Epoch 54/1000
693/696 [============================>.] - ETA: 0s - loss: 0.5060 - acc: 0.8194
Epoch 54: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5059 - acc: 0.8194 - val_loss: 0.4502 - val_acc: 0.8436
Epoch 55/1000
696/696 [==============================] - ETA: 0s - loss: 0.5044 - acc: 0.8199
Epoch 55: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5044 - acc: 0.8199 - val_loss: 0.4478 - val_acc: 0.8443
Epoch 56/1000
695/696 [============================>.] - ETA: 0s - loss: 0.5017 - acc: 0.8209
Epoch 56: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.5018 - acc: 0.8210 - val_loss: 0.4464 - val_acc: 0.8445
Epoch 57/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4990 - acc: 0.8215
Epoch 57: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4990 - acc: 0.8215 - val_loss: 0.4448 - val_acc: 0.8445
Epoch 58/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4982 - acc: 0.8220
Epoch 58: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4982 - acc: 0.8220 - val_loss: 0.4428 - val_acc: 0.8446
Epoch 59/1000
696/696 [==============================] - ETA: 0s - loss: 0.4942 - acc: 0.8234
Epoch 59: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4942 - acc: 0.8234 - val_loss: 0.4419 - val_acc: 0.8455
Epoch 60/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4928 - acc: 0.8240
Epoch 60: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4928 - acc: 0.8241 - val_loss: 0.4397 - val_acc: 0.8473
Epoch 61/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4909 - acc: 0.8248
Epoch 61: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4909 - acc: 0.8248 - val_loss: 0.4387 - val_acc: 0.8467
Epoch 62/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4887 - acc: 0.8249
Epoch 62: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4886 - acc: 0.8250 - val_loss: 0.4366 - val_acc: 0.8465
Epoch 63/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4863 - acc: 0.8268
Epoch 63: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4864 - acc: 0.8268 - val_loss: 0.4353 - val_acc: 0.8472
Epoch 64/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4855 - acc: 0.8268
Epoch 64: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4856 - acc: 0.8267 - val_loss: 0.4332 - val_acc: 0.8495
Epoch 65/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4831 - acc: 0.8282
Epoch 65: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4831 - acc: 0.8282 - val_loss: 0.4322 - val_acc: 0.8487
Epoch 66/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4805 - acc: 0.8290
Epoch 66: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4804 - acc: 0.8289 - val_loss: 0.4309 - val_acc: 0.8501
Epoch 67/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4786 - acc: 0.8292
Epoch 67: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4786 - acc: 0.8293 - val_loss: 0.4298 - val_acc: 0.8496
Epoch 68/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4773 - acc: 0.8306
Epoch 68: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4774 - acc: 0.8306 - val_loss: 0.4289 - val_acc: 0.8496
Epoch 69/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4750 - acc: 0.8310
Epoch 69: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4750 - acc: 0.8310 - val_loss: 0.4273 - val_acc: 0.8502
Epoch 70/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4733 - acc: 0.8317
Epoch 70: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4733 - acc: 0.8317 - val_loss: 0.4260 - val_acc: 0.8518
Epoch 71/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4711 - acc: 0.8330
Epoch 71: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4711 - acc: 0.8330 - val_loss: 0.4247 - val_acc: 0.8513
Epoch 72/1000
696/696 [==============================] - ETA: 0s - loss: 0.4703 - acc: 0.8326
Epoch 72: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.4703 - acc: 0.8326 - val_loss: 0.4232 - val_acc: 0.8512
Epoch 73/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4686 - acc: 0.8331
Epoch 73: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4686 - acc: 0.8330 - val_loss: 0.4219 - val_acc: 0.8522
Epoch 74/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4665 - acc: 0.8344
Epoch 74: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.4665 - acc: 0.8344 - val_loss: 0.4221 - val_acc: 0.8528
Epoch 75/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4654 - acc: 0.8346
Epoch 75: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4654 - acc: 0.8346 - val_loss: 0.4208 - val_acc: 0.8535
Epoch 76/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4644 - acc: 0.8351
Epoch 76: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4644 - acc: 0.8351 - val_loss: 0.4180 - val_acc: 0.8543
Epoch 77/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4616 - acc: 0.8362
Epoch 77: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4616 - acc: 0.8363 - val_loss: 0.4174 - val_acc: 0.8546
Epoch 78/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4607 - acc: 0.8363
Epoch 78: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4608 - acc: 0.8363 - val_loss: 0.4161 - val_acc: 0.8560
Epoch 79/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4609 - acc: 0.8363
Epoch 79: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4609 - acc: 0.8364 - val_loss: 0.4157 - val_acc: 0.8565
Epoch 80/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4581 - acc: 0.8374
Epoch 80: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4581 - acc: 0.8374 - val_loss: 0.4153 - val_acc: 0.8566
Epoch 81/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4558 - acc: 0.8388
Epoch 81: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4557 - acc: 0.8388 - val_loss: 0.4130 - val_acc: 0.8571
Epoch 82/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4553 - acc: 0.8388
Epoch 82: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4553 - acc: 0.8388 - val_loss: 0.4112 - val_acc: 0.8586
Epoch 83/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4552 - acc: 0.8388
Epoch 83: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4551 - acc: 0.8388 - val_loss: 0.4112 - val_acc: 0.8576
Epoch 84/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4525 - acc: 0.8393
Epoch 84: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4526 - acc: 0.8393 - val_loss: 0.4103 - val_acc: 0.8582
Epoch 85/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4519 - acc: 0.8401
Epoch 85: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4519 - acc: 0.8402 - val_loss: 0.4098 - val_acc: 0.8581
Epoch 86/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4500 - acc: 0.8398
Epoch 86: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4501 - acc: 0.8399 - val_loss: 0.4081 - val_acc: 0.8600
Epoch 87/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4488 - acc: 0.8412
Epoch 87: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4488 - acc: 0.8412 - val_loss: 0.4072 - val_acc: 0.8587
Epoch 88/1000
696/696 [==============================] - ETA: 0s - loss: 0.4471 - acc: 0.8418
Epoch 88: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4471 - acc: 0.8418 - val_loss: 0.4065 - val_acc: 0.8590
Epoch 89/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4454 - acc: 0.8422
Epoch 89: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4455 - acc: 0.8422 - val_loss: 0.4052 - val_acc: 0.8598
Epoch 90/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4449 - acc: 0.8424
Epoch 90: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4448 - acc: 0.8425 - val_loss: 0.4046 - val_acc: 0.8604
Epoch 91/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4438 - acc: 0.8431
Epoch 91: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4437 - acc: 0.8431 - val_loss: 0.4033 - val_acc: 0.8603
Epoch 92/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4423 - acc: 0.8437
Epoch 92: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4423 - acc: 0.8436 - val_loss: 0.4028 - val_acc: 0.8615
Epoch 93/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4424 - acc: 0.8431
Epoch 93: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4422 - acc: 0.8432 - val_loss: 0.4009 - val_acc: 0.8625
Epoch 94/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4397 - acc: 0.8447
Epoch 94: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4397 - acc: 0.8448 - val_loss: 0.4017 - val_acc: 0.8621
Epoch 95/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4394 - acc: 0.8451
Epoch 95: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.4394 - acc: 0.8451 - val_loss: 0.4000 - val_acc: 0.8620
Epoch 96/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4370 - acc: 0.8455
Epoch 96: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4371 - acc: 0.8455 - val_loss: 0.3991 - val_acc: 0.8629
Epoch 97/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4365 - acc: 0.8460
Epoch 97: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4365 - acc: 0.8460 - val_loss: 0.3975 - val_acc: 0.8633
Epoch 98/1000
696/696 [==============================] - ETA: 0s - loss: 0.4350 - acc: 0.8465
Epoch 98: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4350 - acc: 0.8465 - val_loss: 0.3975 - val_acc: 0.8627
Epoch 99/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4354 - acc: 0.8465
Epoch 99: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4353 - acc: 0.8466 - val_loss: 0.3975 - val_acc: 0.8635
Epoch 100/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4340 - acc: 0.8466
Epoch 100: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4339 - acc: 0.8467 - val_loss: 0.3961 - val_acc: 0.8647
Epoch 101/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4317 - acc: 0.8474
Epoch 101: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4316 - acc: 0.8474 - val_loss: 0.3943 - val_acc: 0.8663
Epoch 102/1000
696/696 [==============================] - ETA: 0s - loss: 0.4308 - acc: 0.8483
Epoch 102: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4308 - acc: 0.8483 - val_loss: 0.3946 - val_acc: 0.8638
Epoch 103/1000
696/696 [==============================] - ETA: 0s - loss: 0.4293 - acc: 0.8484
Epoch 103: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4293 - acc: 0.8484 - val_loss: 0.3939 - val_acc: 0.8650
Epoch 104/1000
696/696 [==============================] - ETA: 0s - loss: 0.4288 - acc: 0.8482
Epoch 104: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4288 - acc: 0.8482 - val_loss: 0.3920 - val_acc: 0.8660
Epoch 105/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4285 - acc: 0.8492
Epoch 105: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.4285 - acc: 0.8492 - val_loss: 0.3915 - val_acc: 0.8661
Epoch 106/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4282 - acc: 0.8491
Epoch 106: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4282 - acc: 0.8491 - val_loss: 0.3911 - val_acc: 0.8657
Epoch 107/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4246 - acc: 0.8503
Epoch 107: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4246 - acc: 0.8503 - val_loss: 0.3915 - val_acc: 0.8654
Epoch 108/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4239 - acc: 0.8505
Epoch 108: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4241 - acc: 0.8504 - val_loss: 0.3902 - val_acc: 0.8653
Epoch 109/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4246 - acc: 0.8504
Epoch 109: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4244 - acc: 0.8504 - val_loss: 0.3904 - val_acc: 0.8649
Epoch 110/1000
694/696 [============================>.] - ETA: 0s - loss: 0.4227 - acc: 0.8510
Epoch 110: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4226 - acc: 0.8510 - val_loss: 0.3889 - val_acc: 0.8644
Epoch 111/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4215 - acc: 0.8512
Epoch 111: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4216 - acc: 0.8512 - val_loss: 0.3866 - val_acc: 0.8664
Epoch 112/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4208 - acc: 0.8515
Epoch 112: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4207 - acc: 0.8515 - val_loss: 0.3862 - val_acc: 0.8672
Epoch 113/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4204 - acc: 0.8519
Epoch 113: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4203 - acc: 0.8519 - val_loss: 0.3868 - val_acc: 0.8683
Epoch 114/1000
696/696 [==============================] - ETA: 0s - loss: 0.4189 - acc: 0.8525
Epoch 114: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4189 - acc: 0.8525 - val_loss: 0.3851 - val_acc: 0.8672
Epoch 115/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4172 - acc: 0.8529
Epoch 115: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4173 - acc: 0.8528 - val_loss: 0.3838 - val_acc: 0.8680
Epoch 116/1000
696/696 [==============================] - ETA: 0s - loss: 0.4164 - acc: 0.8537
Epoch 116: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4164 - acc: 0.8537 - val_loss: 0.3832 - val_acc: 0.8676
Epoch 117/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4152 - acc: 0.8535
Epoch 117: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4153 - acc: 0.8535 - val_loss: 0.3828 - val_acc: 0.8673
Epoch 118/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4140 - acc: 0.8544
Epoch 118: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4139 - acc: 0.8545 - val_loss: 0.3832 - val_acc: 0.8677
Epoch 119/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4141 - acc: 0.8544
Epoch 119: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4141 - acc: 0.8544 - val_loss: 0.3822 - val_acc: 0.8673
Epoch 120/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4136 - acc: 0.8547
Epoch 120: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4135 - acc: 0.8547 - val_loss: 0.3817 - val_acc: 0.8682
Epoch 121/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4124 - acc: 0.8546
Epoch 121: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4124 - acc: 0.8547 - val_loss: 0.3800 - val_acc: 0.8681
Epoch 122/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4111 - acc: 0.8555
Epoch 122: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.4111 - acc: 0.8555 - val_loss: 0.3794 - val_acc: 0.8694
Epoch 123/1000
696/696 [==============================] - ETA: 0s - loss: 0.4097 - acc: 0.8558
Epoch 123: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4097 - acc: 0.8558 - val_loss: 0.3785 - val_acc: 0.8691
Epoch 124/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4096 - acc: 0.8563
Epoch 124: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4095 - acc: 0.8563 - val_loss: 0.3780 - val_acc: 0.8698
Epoch 125/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4088 - acc: 0.8561
Epoch 125: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4088 - acc: 0.8562 - val_loss: 0.3767 - val_acc: 0.8702
Epoch 126/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4097 - acc: 0.8557
Epoch 126: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4097 - acc: 0.8557 - val_loss: 0.3772 - val_acc: 0.8703
Epoch 127/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4070 - acc: 0.8570
Epoch 127: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4071 - acc: 0.8570 - val_loss: 0.3763 - val_acc: 0.8697
Epoch 128/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4067 - acc: 0.8568
Epoch 128: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4066 - acc: 0.8569 - val_loss: 0.3766 - val_acc: 0.8693
Epoch 129/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4056 - acc: 0.8575
Epoch 129: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4057 - acc: 0.8575 - val_loss: 0.3757 - val_acc: 0.8705
Epoch 130/1000
696/696 [==============================] - ETA: 0s - loss: 0.4029 - acc: 0.8589
Epoch 130: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4029 - acc: 0.8589 - val_loss: 0.3749 - val_acc: 0.8710
Epoch 131/1000
696/696 [==============================] - ETA: 0s - loss: 0.4039 - acc: 0.8581
Epoch 131: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4039 - acc: 0.8581 - val_loss: 0.3741 - val_acc: 0.8715
Epoch 132/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4024 - acc: 0.8589
Epoch 132: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4025 - acc: 0.8588 - val_loss: 0.3734 - val_acc: 0.8709
Epoch 133/1000
696/696 [==============================] - ETA: 0s - loss: 0.4007 - acc: 0.8591
Epoch 133: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4007 - acc: 0.8591 - val_loss: 0.3721 - val_acc: 0.8716
Epoch 134/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4010 - acc: 0.8593
Epoch 134: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4011 - acc: 0.8593 - val_loss: 0.3717 - val_acc: 0.8708
Epoch 135/1000
695/696 [============================>.] - ETA: 0s - loss: 0.4013 - acc: 0.8593
Epoch 135: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4013 - acc: 0.8593 - val_loss: 0.3714 - val_acc: 0.8722
Epoch 136/1000
693/696 [============================>.] - ETA: 0s - loss: 0.4008 - acc: 0.8593
Epoch 136: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.4007 - acc: 0.8593 - val_loss: 0.3715 - val_acc: 0.8716
Epoch 137/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3983 - acc: 0.8601
Epoch 137: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3983 - acc: 0.8601 - val_loss: 0.3707 - val_acc: 0.8711
Epoch 138/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3997 - acc: 0.8595
Epoch 138: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3997 - acc: 0.8595 - val_loss: 0.3708 - val_acc: 0.8724
Epoch 139/1000
696/696 [==============================] - ETA: 0s - loss: 0.3968 - acc: 0.8605
Epoch 139: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3968 - acc: 0.8605 - val_loss: 0.3683 - val_acc: 0.8738
Epoch 140/1000
696/696 [==============================] - ETA: 0s - loss: 0.3962 - acc: 0.8613
Epoch 140: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3962 - acc: 0.8613 - val_loss: 0.3684 - val_acc: 0.8726
Epoch 141/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3947 - acc: 0.8617
Epoch 141: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3947 - acc: 0.8617 - val_loss: 0.3686 - val_acc: 0.8726
Epoch 142/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3950 - acc: 0.8614
Epoch 142: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3950 - acc: 0.8614 - val_loss: 0.3675 - val_acc: 0.8726
Epoch 143/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3946 - acc: 0.8612
Epoch 143: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3947 - acc: 0.8612 - val_loss: 0.3666 - val_acc: 0.8729
Epoch 144/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3940 - acc: 0.8620
Epoch 144: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3939 - acc: 0.8620 - val_loss: 0.3661 - val_acc: 0.8734
Epoch 145/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3935 - acc: 0.8620
Epoch 145: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3936 - acc: 0.8620 - val_loss: 0.3651 - val_acc: 0.8746
Epoch 146/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3926 - acc: 0.8622
Epoch 146: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3927 - acc: 0.8622 - val_loss: 0.3654 - val_acc: 0.8736
Epoch 147/1000
696/696 [==============================] - ETA: 0s - loss: 0.3915 - acc: 0.8629
Epoch 147: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3915 - acc: 0.8629 - val_loss: 0.3642 - val_acc: 0.8740
Epoch 148/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3906 - acc: 0.8626
Epoch 148: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3906 - acc: 0.8626 - val_loss: 0.3637 - val_acc: 0.8734
Epoch 149/1000
696/696 [==============================] - ETA: 0s - loss: 0.3902 - acc: 0.8633
Epoch 149: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3902 - acc: 0.8633 - val_loss: 0.3637 - val_acc: 0.8742
Epoch 150/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3898 - acc: 0.8635
Epoch 150: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3898 - acc: 0.8635 - val_loss: 0.3622 - val_acc: 0.8749
Epoch 151/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3877 - acc: 0.8646
Epoch 151: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3876 - acc: 0.8646 - val_loss: 0.3620 - val_acc: 0.8750
Epoch 152/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3878 - acc: 0.8643
Epoch 152: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3877 - acc: 0.8643 - val_loss: 0.3624 - val_acc: 0.8744
Epoch 153/1000
696/696 [==============================] - ETA: 0s - loss: 0.3867 - acc: 0.8645
Epoch 153: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3867 - acc: 0.8645 - val_loss: 0.3612 - val_acc: 0.8753
Epoch 154/1000
695/696 [============================>.] - ETA: 0s - loss: 0.3863 - acc: 0.8647
Epoch 154: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3863 - acc: 0.8647 - val_loss: 0.3615 - val_acc: 0.8742
Epoch 155/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3849 - acc: 0.8655
Epoch 155: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3850 - acc: 0.8654 - val_loss: 0.3598 - val_acc: 0.8754
Epoch 156/1000
696/696 [==============================] - ETA: 0s - loss: 0.3849 - acc: 0.8651
Epoch 156: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3849 - acc: 0.8651 - val_loss: 0.3590 - val_acc: 0.8756
Epoch 157/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3846 - acc: 0.8652
Epoch 157: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3847 - acc: 0.8652 - val_loss: 0.3592 - val_acc: 0.8768
Epoch 158/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3845 - acc: 0.8654
Epoch 158: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3845 - acc: 0.8654 - val_loss: 0.3590 - val_acc: 0.8756
Epoch 159/1000
696/696 [==============================] - ETA: 0s - loss: 0.3835 - acc: 0.8653
Epoch 159: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3835 - acc: 0.8653 - val_loss: 0.3587 - val_acc: 0.8757
Epoch 160/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3828 - acc: 0.8657
Epoch 160: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3829 - acc: 0.8656 - val_loss: 0.3585 - val_acc: 0.8763
Epoch 161/1000
694/696 [============================>.] - ETA: 0s - loss: 0.3816 - acc: 0.8667
Epoch 161: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3816 - acc: 0.8666 - val_loss: 0.3579 - val_acc: 0.8767
Epoch 162/1000
696/696 [==============================] - ETA: 0s - loss: 0.3807 - acc: 0.8667
Epoch 162: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 18ms/step - loss: 0.3807 - acc: 0.8667 - val_loss: 0.3584 - val_acc: 0.8759
Epoch 163/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3798 - acc: 0.8670
Epoch 163: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3798 - acc: 0.8670 - val_loss: 0.3576 - val_acc: 0.8758
Epoch 164/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3800 - acc: 0.8665
Epoch 164: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3799 - acc: 0.8665 - val_loss: 0.3562 - val_acc: 0.8765
Epoch 165/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3799 - acc: 0.8666
Epoch 165: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3800 - acc: 0.8666 - val_loss: 0.3566 - val_acc: 0.8772
Epoch 166/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3795 - acc: 0.8670
Epoch 166: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3796 - acc: 0.8670 - val_loss: 0.3548 - val_acc: 0.8779
Epoch 167/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3784 - acc: 0.8674
Epoch 167: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3783 - acc: 0.8674 - val_loss: 0.3551 - val_acc: 0.8766
Epoch 168/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3772 - acc: 0.8675
Epoch 168: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3774 - acc: 0.8674 - val_loss: 0.3552 - val_acc: 0.8767
Epoch 169/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3771 - acc: 0.8677
Epoch 169: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3772 - acc: 0.8677 - val_loss: 0.3534 - val_acc: 0.8774
Epoch 170/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3762 - acc: 0.8683
Epoch 170: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3762 - acc: 0.8683 - val_loss: 0.3533 - val_acc: 0.8778
Epoch 171/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3753 - acc: 0.8686
Epoch 171: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3753 - acc: 0.8686 - val_loss: 0.3536 - val_acc: 0.8779
Epoch 172/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3753 - acc: 0.8683
Epoch 172: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3754 - acc: 0.8683 - val_loss: 0.3523 - val_acc: 0.8780
Epoch 173/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3754 - acc: 0.8686
Epoch 173: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3753 - acc: 0.8687 - val_loss: 0.3519 - val_acc: 0.8775
Epoch 174/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3741 - acc: 0.8696
Epoch 174: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3741 - acc: 0.8695 - val_loss: 0.3518 - val_acc: 0.8775
Epoch 175/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3733 - acc: 0.8693
Epoch 175: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3732 - acc: 0.8694 - val_loss: 0.3507 - val_acc: 0.8795
Epoch 176/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3717 - acc: 0.8698
Epoch 176: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3717 - acc: 0.8698 - val_loss: 0.3502 - val_acc: 0.8784
Epoch 177/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3714 - acc: 0.8698
Epoch 177: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3713 - acc: 0.8698 - val_loss: 0.3504 - val_acc: 0.8806
Epoch 178/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3714 - acc: 0.8698
Epoch 178: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3714 - acc: 0.8699 - val_loss: 0.3507 - val_acc: 0.8779
Epoch 179/1000
696/696 [==============================] - ETA: 0s - loss: 0.3708 - acc: 0.8703
Epoch 179: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3708 - acc: 0.8703 - val_loss: 0.3504 - val_acc: 0.8776
Epoch 180/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3701 - acc: 0.8708
Epoch 180: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3700 - acc: 0.8708 - val_loss: 0.3491 - val_acc: 0.8799
Epoch 181/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3702 - acc: 0.8700
Epoch 181: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3701 - acc: 0.8701 - val_loss: 0.3479 - val_acc: 0.8804
Epoch 182/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3691 - acc: 0.8713
Epoch 182: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3691 - acc: 0.8713 - val_loss: 0.3495 - val_acc: 0.8785
Epoch 183/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3687 - acc: 0.8709
Epoch 183: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3687 - acc: 0.8709 - val_loss: 0.3500 - val_acc: 0.8790
Epoch 184/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3687 - acc: 0.8712
Epoch 184: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3686 - acc: 0.8712 - val_loss: 0.3483 - val_acc: 0.8812
Epoch 185/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3673 - acc: 0.8714
Epoch 185: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3673 - acc: 0.8714 - val_loss: 0.3473 - val_acc: 0.8802
Epoch 186/1000
696/696 [==============================] - ETA: 0s - loss: 0.3670 - acc: 0.8714
Epoch 186: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3670 - acc: 0.8714 - val_loss: 0.3479 - val_acc: 0.8806
Epoch 187/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3671 - acc: 0.8715
Epoch 187: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3670 - acc: 0.8716 - val_loss: 0.3484 - val_acc: 0.8795
Epoch 188/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3664 - acc: 0.8718
Epoch 188: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3665 - acc: 0.8717 - val_loss: 0.3461 - val_acc: 0.8808
Epoch 189/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3657 - acc: 0.8721
Epoch 189: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3657 - acc: 0.8721 - val_loss: 0.3460 - val_acc: 0.8802
Epoch 190/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3646 - acc: 0.8723
Epoch 190: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3647 - acc: 0.8723 - val_loss: 0.3464 - val_acc: 0.8800
Epoch 191/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3641 - acc: 0.8732
Epoch 191: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3644 - acc: 0.8731 - val_loss: 0.3455 - val_acc: 0.8806
Epoch 192/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3646 - acc: 0.8730
Epoch 192: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3646 - acc: 0.8730 - val_loss: 0.3450 - val_acc: 0.8810
Epoch 193/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3635 - acc: 0.8728
Epoch 193: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3636 - acc: 0.8728 - val_loss: 0.3447 - val_acc: 0.8820
Epoch 194/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3629 - acc: 0.8729
Epoch 194: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3629 - acc: 0.8729 - val_loss: 0.3447 - val_acc: 0.8813
Epoch 195/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3627 - acc: 0.8732
Epoch 195: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3627 - acc: 0.8733 - val_loss: 0.3429 - val_acc: 0.8823
Epoch 196/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3613 - acc: 0.8737
Epoch 196: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3615 - acc: 0.8736 - val_loss: 0.3435 - val_acc: 0.8830
Epoch 197/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3613 - acc: 0.8735
Epoch 197: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3613 - acc: 0.8735 - val_loss: 0.3438 - val_acc: 0.8813
Epoch 198/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3604 - acc: 0.8738
Epoch 198: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3603 - acc: 0.8738 - val_loss: 0.3431 - val_acc: 0.8827
Epoch 199/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3603 - acc: 0.8735
Epoch 199: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3604 - acc: 0.8735 - val_loss: 0.3419 - val_acc: 0.8828
Epoch 200/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3603 - acc: 0.8741
Epoch 200: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3602 - acc: 0.8742 - val_loss: 0.3430 - val_acc: 0.8822
Epoch 201/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3599 - acc: 0.8744
Epoch 201: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3599 - acc: 0.8744 - val_loss: 0.3417 - val_acc: 0.8818
Epoch 202/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3586 - acc: 0.8750
Epoch 202: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3586 - acc: 0.8750 - val_loss: 0.3413 - val_acc: 0.8820
Epoch 203/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3581 - acc: 0.8750
Epoch 203: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3580 - acc: 0.8750 - val_loss: 0.3411 - val_acc: 0.8827
Epoch 204/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3575 - acc: 0.8752
Epoch 204: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3574 - acc: 0.8752 - val_loss: 0.3406 - val_acc: 0.8828
Epoch 205/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3571 - acc: 0.8754
Epoch 205: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3571 - acc: 0.8754 - val_loss: 0.3396 - val_acc: 0.8828
Epoch 206/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3562 - acc: 0.8760
Epoch 206: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3562 - acc: 0.8760 - val_loss: 0.3405 - val_acc: 0.8824
Epoch 207/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3557 - acc: 0.8754
Epoch 207: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3558 - acc: 0.8754 - val_loss: 0.3404 - val_acc: 0.8831
Epoch 208/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3560 - acc: 0.8759
Epoch 208: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3560 - acc: 0.8759 - val_loss: 0.3390 - val_acc: 0.8833
Epoch 209/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3555 - acc: 0.8760
Epoch 209: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3555 - acc: 0.8760 - val_loss: 0.3386 - val_acc: 0.8842
Epoch 210/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3548 - acc: 0.8758
Epoch 210: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3548 - acc: 0.8758 - val_loss: 0.3387 - val_acc: 0.8838
Epoch 211/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3535 - acc: 0.8765
Epoch 211: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3534 - acc: 0.8766 - val_loss: 0.3385 - val_acc: 0.8832
Epoch 212/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3532 - acc: 0.8767
Epoch 212: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3532 - acc: 0.8767 - val_loss: 0.3377 - val_acc: 0.8833
Epoch 213/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3522 - acc: 0.8769
Epoch 213: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3524 - acc: 0.8768 - val_loss: 0.3372 - val_acc: 0.8854
Epoch 214/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3523 - acc: 0.8767
Epoch 214: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3523 - acc: 0.8767 - val_loss: 0.3369 - val_acc: 0.8839
Epoch 215/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3524 - acc: 0.8771
Epoch 215: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3524 - acc: 0.8771 - val_loss: 0.3374 - val_acc: 0.8842
Epoch 216/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3517 - acc: 0.8774
Epoch 216: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3517 - acc: 0.8774 - val_loss: 0.3368 - val_acc: 0.8834
Epoch 217/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3517 - acc: 0.8773
Epoch 217: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3517 - acc: 0.8773 - val_loss: 0.3370 - val_acc: 0.8846
Epoch 218/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3509 - acc: 0.8771
Epoch 218: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3508 - acc: 0.8771 - val_loss: 0.3354 - val_acc: 0.8851
Epoch 219/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3506 - acc: 0.8775
Epoch 219: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3507 - acc: 0.8775 - val_loss: 0.3365 - val_acc: 0.8843
Epoch 220/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3493 - acc: 0.8774
Epoch 220: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3493 - acc: 0.8775 - val_loss: 0.3361 - val_acc: 0.8839
Epoch 221/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3495 - acc: 0.8779
Epoch 221: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3496 - acc: 0.8778 - val_loss: 0.3347 - val_acc: 0.8843
Epoch 222/1000
696/696 [==============================] - ETA: 0s - loss: 0.3488 - acc: 0.8775
Epoch 222: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3488 - acc: 0.8775 - val_loss: 0.3354 - val_acc: 0.8851
Epoch 223/1000
696/696 [==============================] - ETA: 0s - loss: 0.3491 - acc: 0.8784
Epoch 223: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3491 - acc: 0.8784 - val_loss: 0.3350 - val_acc: 0.8838
Epoch 224/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3486 - acc: 0.8780
Epoch 224: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3486 - acc: 0.8780 - val_loss: 0.3340 - val_acc: 0.8846
Epoch 225/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3473 - acc: 0.8782
Epoch 225: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3473 - acc: 0.8782 - val_loss: 0.3336 - val_acc: 0.8855
Epoch 226/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3476 - acc: 0.8783
Epoch 226: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3477 - acc: 0.8783 - val_loss: 0.3337 - val_acc: 0.8859
Epoch 227/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3472 - acc: 0.8792
Epoch 227: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3472 - acc: 0.8792 - val_loss: 0.3336 - val_acc: 0.8846
Epoch 228/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3462 - acc: 0.8791
Epoch 228: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3462 - acc: 0.8791 - val_loss: 0.3330 - val_acc: 0.8851
Epoch 229/1000
696/696 [==============================] - ETA: 0s - loss: 0.3463 - acc: 0.8790
Epoch 229: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3463 - acc: 0.8790 - val_loss: 0.3327 - val_acc: 0.8868
Epoch 230/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3453 - acc: 0.8795
Epoch 230: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3452 - acc: 0.8796 - val_loss: 0.3325 - val_acc: 0.8852
Epoch 231/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3452 - acc: 0.8791
Epoch 231: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3454 - acc: 0.8791 - val_loss: 0.3319 - val_acc: 0.8851
Epoch 232/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3443 - acc: 0.8798
Epoch 232: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3444 - acc: 0.8797 - val_loss: 0.3327 - val_acc: 0.8849
Epoch 233/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3442 - acc: 0.8796
Epoch 233: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3443 - acc: 0.8796 - val_loss: 0.3305 - val_acc: 0.8868
Epoch 234/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3440 - acc: 0.8798
Epoch 234: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3440 - acc: 0.8798 - val_loss: 0.3313 - val_acc: 0.8854
Epoch 235/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3429 - acc: 0.8805
Epoch 235: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3429 - acc: 0.8806 - val_loss: 0.3302 - val_acc: 0.8877
Epoch 236/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3427 - acc: 0.8807
Epoch 236: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3427 - acc: 0.8808 - val_loss: 0.3303 - val_acc: 0.8865
Epoch 237/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3425 - acc: 0.8806
Epoch 237: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3426 - acc: 0.8806 - val_loss: 0.3301 - val_acc: 0.8868
Epoch 238/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3424 - acc: 0.8808
Epoch 238: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3423 - acc: 0.8808 - val_loss: 0.3302 - val_acc: 0.8854
Epoch 239/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3415 - acc: 0.8809
Epoch 239: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3414 - acc: 0.8809 - val_loss: 0.3297 - val_acc: 0.8870
Epoch 240/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3413 - acc: 0.8808
Epoch 240: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3412 - acc: 0.8809 - val_loss: 0.3297 - val_acc: 0.8861
Epoch 241/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3400 - acc: 0.8815
Epoch 241: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3400 - acc: 0.8815 - val_loss: 0.3292 - val_acc: 0.8873
Epoch 242/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3404 - acc: 0.8813
Epoch 242: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3404 - acc: 0.8813 - val_loss: 0.3299 - val_acc: 0.8863
Epoch 243/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3403 - acc: 0.8815
Epoch 243: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3404 - acc: 0.8814 - val_loss: 0.3298 - val_acc: 0.8860
Epoch 244/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3399 - acc: 0.8815
Epoch 244: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3398 - acc: 0.8814 - val_loss: 0.3287 - val_acc: 0.8869
Epoch 245/1000
696/696 [==============================] - ETA: 0s - loss: 0.3383 - acc: 0.8819
Epoch 245: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3383 - acc: 0.8819 - val_loss: 0.3280 - val_acc: 0.8876
Epoch 246/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3384 - acc: 0.8819
Epoch 246: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3385 - acc: 0.8819 - val_loss: 0.3270 - val_acc: 0.8886
Epoch 247/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3382 - acc: 0.8823
Epoch 247: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3383 - acc: 0.8822 - val_loss: 0.3277 - val_acc: 0.8872
Epoch 248/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3378 - acc: 0.8823
Epoch 248: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3378 - acc: 0.8823 - val_loss: 0.3264 - val_acc: 0.8885
Epoch 249/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3370 - acc: 0.8824
Epoch 249: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3370 - acc: 0.8824 - val_loss: 0.3275 - val_acc: 0.8862
Epoch 250/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3368 - acc: 0.8826
Epoch 250: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3369 - acc: 0.8825 - val_loss: 0.3273 - val_acc: 0.8886
Epoch 251/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3360 - acc: 0.8827
Epoch 251: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3359 - acc: 0.8827 - val_loss: 0.3271 - val_acc: 0.8887
Epoch 252/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3365 - acc: 0.8824
Epoch 252: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3365 - acc: 0.8824 - val_loss: 0.3270 - val_acc: 0.8880
Epoch 253/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3354 - acc: 0.8828
Epoch 253: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3354 - acc: 0.8828 - val_loss: 0.3259 - val_acc: 0.8877
Epoch 254/1000
696/696 [==============================] - ETA: 0s - loss: 0.3363 - acc: 0.8834
Epoch 254: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3363 - acc: 0.8834 - val_loss: 0.3269 - val_acc: 0.8881
Epoch 255/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3339 - acc: 0.8833
Epoch 255: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3340 - acc: 0.8833 - val_loss: 0.3251 - val_acc: 0.8882
Epoch 256/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3345 - acc: 0.8838
Epoch 256: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3344 - acc: 0.8838 - val_loss: 0.3246 - val_acc: 0.8886
Epoch 257/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3337 - acc: 0.8835
Epoch 257: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3337 - acc: 0.8836 - val_loss: 0.3244 - val_acc: 0.8877
Epoch 258/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3331 - acc: 0.8839
Epoch 258: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3331 - acc: 0.8839 - val_loss: 0.3243 - val_acc: 0.8892
Epoch 259/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3330 - acc: 0.8845
Epoch 259: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3329 - acc: 0.8845 - val_loss: 0.3241 - val_acc: 0.8900
Epoch 260/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3319 - acc: 0.8842
Epoch 260: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3318 - acc: 0.8843 - val_loss: 0.3249 - val_acc: 0.8887
Epoch 261/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3321 - acc: 0.8842
Epoch 261: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3321 - acc: 0.8842 - val_loss: 0.3229 - val_acc: 0.8893
Epoch 262/1000
696/696 [==============================] - ETA: 0s - loss: 0.3318 - acc: 0.8845
Epoch 262: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3318 - acc: 0.8845 - val_loss: 0.3245 - val_acc: 0.8893
Epoch 263/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3310 - acc: 0.8846
Epoch 263: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3310 - acc: 0.8846 - val_loss: 0.3237 - val_acc: 0.8896
Epoch 264/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3310 - acc: 0.8849
Epoch 264: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3311 - acc: 0.8849 - val_loss: 0.3225 - val_acc: 0.8890
Epoch 265/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3313 - acc: 0.8850
Epoch 265: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3313 - acc: 0.8850 - val_loss: 0.3225 - val_acc: 0.8896
Epoch 266/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3304 - acc: 0.8848
Epoch 266: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3304 - acc: 0.8848 - val_loss: 0.3223 - val_acc: 0.8900
Epoch 267/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3303 - acc: 0.8845
Epoch 267: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3303 - acc: 0.8845 - val_loss: 0.3224 - val_acc: 0.8895
Epoch 268/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3292 - acc: 0.8858
Epoch 268: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3292 - acc: 0.8857 - val_loss: 0.3238 - val_acc: 0.8877
Epoch 269/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3292 - acc: 0.8855
Epoch 269: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3292 - acc: 0.8855 - val_loss: 0.3230 - val_acc: 0.8894
Epoch 270/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3290 - acc: 0.8857
Epoch 270: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3291 - acc: 0.8856 - val_loss: 0.3212 - val_acc: 0.8907
Epoch 271/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3285 - acc: 0.8858
Epoch 271: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3284 - acc: 0.8859 - val_loss: 0.3206 - val_acc: 0.8906
Epoch 272/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3283 - acc: 0.8857
Epoch 272: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3282 - acc: 0.8857 - val_loss: 0.3213 - val_acc: 0.8903
Epoch 273/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3271 - acc: 0.8859
Epoch 273: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3271 - acc: 0.8859 - val_loss: 0.3203 - val_acc: 0.8903
Epoch 274/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3281 - acc: 0.8860
Epoch 274: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3280 - acc: 0.8860 - val_loss: 0.3202 - val_acc: 0.8909
Epoch 275/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3273 - acc: 0.8865
Epoch 275: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3275 - acc: 0.8864 - val_loss: 0.3210 - val_acc: 0.8897
Epoch 276/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3265 - acc: 0.8866
Epoch 276: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3265 - acc: 0.8866 - val_loss: 0.3206 - val_acc: 0.8901
Epoch 277/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3261 - acc: 0.8862
Epoch 277: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3261 - acc: 0.8862 - val_loss: 0.3200 - val_acc: 0.8908
Epoch 278/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3257 - acc: 0.8865
Epoch 278: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3258 - acc: 0.8865 - val_loss: 0.3203 - val_acc: 0.8909
Epoch 279/1000
696/696 [==============================] - ETA: 0s - loss: 0.3241 - acc: 0.8873
Epoch 279: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3241 - acc: 0.8873 - val_loss: 0.3184 - val_acc: 0.8902
Epoch 280/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3253 - acc: 0.8868
Epoch 280: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3252 - acc: 0.8867 - val_loss: 0.3194 - val_acc: 0.8915
Epoch 281/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3246 - acc: 0.8868
Epoch 281: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3246 - acc: 0.8868 - val_loss: 0.3189 - val_acc: 0.8914
Epoch 282/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3243 - acc: 0.8872
Epoch 282: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3243 - acc: 0.8872 - val_loss: 0.3196 - val_acc: 0.8906
Epoch 283/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3238 - acc: 0.8875
Epoch 283: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3238 - acc: 0.8875 - val_loss: 0.3179 - val_acc: 0.8916
Epoch 284/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3227 - acc: 0.8877
Epoch 284: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3226 - acc: 0.8877 - val_loss: 0.3180 - val_acc: 0.8919
Epoch 285/1000
696/696 [==============================] - ETA: 0s - loss: 0.3234 - acc: 0.8875
Epoch 285: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3234 - acc: 0.8875 - val_loss: 0.3191 - val_acc: 0.8903
Epoch 286/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3229 - acc: 0.8875
Epoch 286: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3229 - acc: 0.8875 - val_loss: 0.3182 - val_acc: 0.8918
Epoch 287/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3216 - acc: 0.8883
Epoch 287: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3217 - acc: 0.8884 - val_loss: 0.3176 - val_acc: 0.8930
Epoch 288/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3215 - acc: 0.8882
Epoch 288: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3215 - acc: 0.8882 - val_loss: 0.3170 - val_acc: 0.8920
Epoch 289/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3212 - acc: 0.8882
Epoch 289: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3213 - acc: 0.8881 - val_loss: 0.3173 - val_acc: 0.8926
Epoch 290/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3206 - acc: 0.8882
Epoch 290: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3206 - acc: 0.8882 - val_loss: 0.3175 - val_acc: 0.8910
Epoch 291/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3197 - acc: 0.8885
Epoch 291: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3197 - acc: 0.8885 - val_loss: 0.3170 - val_acc: 0.8928
Epoch 292/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3204 - acc: 0.8884
Epoch 292: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3204 - acc: 0.8884 - val_loss: 0.3173 - val_acc: 0.8915
Epoch 293/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3203 - acc: 0.8890
Epoch 293: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3202 - acc: 0.8890 - val_loss: 0.3160 - val_acc: 0.8919
Epoch 294/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3185 - acc: 0.8892
Epoch 294: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3185 - acc: 0.8892 - val_loss: 0.3168 - val_acc: 0.8928
Epoch 295/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3197 - acc: 0.8888
Epoch 295: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3196 - acc: 0.8888 - val_loss: 0.3155 - val_acc: 0.8919
Epoch 296/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3190 - acc: 0.8892
Epoch 296: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3189 - acc: 0.8892 - val_loss: 0.3148 - val_acc: 0.8934
Epoch 297/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3192 - acc: 0.8888
Epoch 297: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3192 - acc: 0.8889 - val_loss: 0.3156 - val_acc: 0.8925
Epoch 298/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3177 - acc: 0.8895
Epoch 298: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3177 - acc: 0.8895 - val_loss: 0.3150 - val_acc: 0.8927
Epoch 299/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3176 - acc: 0.8894
Epoch 299: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3175 - acc: 0.8895 - val_loss: 0.3146 - val_acc: 0.8937
Epoch 300/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3171 - acc: 0.8899
Epoch 300: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3170 - acc: 0.8899 - val_loss: 0.3148 - val_acc: 0.8924
Epoch 301/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3173 - acc: 0.8895
Epoch 301: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3173 - acc: 0.8895 - val_loss: 0.3162 - val_acc: 0.8921
Epoch 302/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3167 - acc: 0.8904
Epoch 302: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3166 - acc: 0.8904 - val_loss: 0.3126 - val_acc: 0.8946
Epoch 303/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3165 - acc: 0.8901
Epoch 303: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3165 - acc: 0.8901 - val_loss: 0.3142 - val_acc: 0.8938
Epoch 304/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3159 - acc: 0.8900
Epoch 304: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3159 - acc: 0.8900 - val_loss: 0.3140 - val_acc: 0.8935
Epoch 305/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3155 - acc: 0.8905
Epoch 305: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3155 - acc: 0.8905 - val_loss: 0.3147 - val_acc: 0.8923
Epoch 306/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3151 - acc: 0.8907
Epoch 306: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3151 - acc: 0.8907 - val_loss: 0.3132 - val_acc: 0.8928
Epoch 307/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3151 - acc: 0.8902
Epoch 307: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3151 - acc: 0.8902 - val_loss: 0.3135 - val_acc: 0.8935
Epoch 308/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3150 - acc: 0.8908
Epoch 308: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3150 - acc: 0.8908 - val_loss: 0.3137 - val_acc: 0.8931
Epoch 309/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3150 - acc: 0.8905
Epoch 309: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3150 - acc: 0.8905 - val_loss: 0.3146 - val_acc: 0.8926
Epoch 310/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3135 - acc: 0.8910
Epoch 310: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3134 - acc: 0.8910 - val_loss: 0.3118 - val_acc: 0.8944
Epoch 311/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3140 - acc: 0.8909
Epoch 311: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3140 - acc: 0.8909 - val_loss: 0.3114 - val_acc: 0.8932
Epoch 312/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3135 - acc: 0.8913
Epoch 312: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3135 - acc: 0.8914 - val_loss: 0.3124 - val_acc: 0.8934
Epoch 313/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3122 - acc: 0.8916
Epoch 313: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3122 - acc: 0.8916 - val_loss: 0.3109 - val_acc: 0.8933
Epoch 314/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3116 - acc: 0.8918
Epoch 314: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3116 - acc: 0.8918 - val_loss: 0.3119 - val_acc: 0.8940
Epoch 315/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3122 - acc: 0.8917
Epoch 315: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3121 - acc: 0.8917 - val_loss: 0.3117 - val_acc: 0.8944
Epoch 316/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3114 - acc: 0.8918
Epoch 316: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3115 - acc: 0.8918 - val_loss: 0.3112 - val_acc: 0.8945
Epoch 317/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3113 - acc: 0.8920
Epoch 317: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3113 - acc: 0.8920 - val_loss: 0.3100 - val_acc: 0.8953
Epoch 318/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3112 - acc: 0.8921
Epoch 318: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3114 - acc: 0.8920 - val_loss: 0.3113 - val_acc: 0.8946
Epoch 319/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3104 - acc: 0.8921
Epoch 319: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3104 - acc: 0.8921 - val_loss: 0.3090 - val_acc: 0.8950
Epoch 320/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3100 - acc: 0.8924
Epoch 320: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3101 - acc: 0.8924 - val_loss: 0.3106 - val_acc: 0.8943
Epoch 321/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3096 - acc: 0.8925
Epoch 321: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3097 - acc: 0.8924 - val_loss: 0.3103 - val_acc: 0.8930
Epoch 322/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3102 - acc: 0.8923
Epoch 322: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3102 - acc: 0.8922 - val_loss: 0.3104 - val_acc: 0.8952
Epoch 323/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3086 - acc: 0.8929
Epoch 323: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3086 - acc: 0.8929 - val_loss: 0.3097 - val_acc: 0.8949
Epoch 324/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3079 - acc: 0.8932
Epoch 324: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3079 - acc: 0.8932 - val_loss: 0.3093 - val_acc: 0.8950
Epoch 325/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3081 - acc: 0.8928
Epoch 325: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3081 - acc: 0.8928 - val_loss: 0.3094 - val_acc: 0.8950
Epoch 326/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3079 - acc: 0.8930
Epoch 326: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3080 - acc: 0.8930 - val_loss: 0.3077 - val_acc: 0.8950
Epoch 327/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3082 - acc: 0.8932
Epoch 327: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3081 - acc: 0.8932 - val_loss: 0.3095 - val_acc: 0.8952
Epoch 328/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3067 - acc: 0.8935
Epoch 328: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3067 - acc: 0.8935 - val_loss: 0.3083 - val_acc: 0.8968
Epoch 329/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3080 - acc: 0.8933
Epoch 329: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3079 - acc: 0.8933 - val_loss: 0.3088 - val_acc: 0.8962
Epoch 330/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3072 - acc: 0.8934
Epoch 330: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3072 - acc: 0.8934 - val_loss: 0.3075 - val_acc: 0.8956
Epoch 331/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3073 - acc: 0.8931
Epoch 331: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3072 - acc: 0.8931 - val_loss: 0.3076 - val_acc: 0.8954
Epoch 332/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3054 - acc: 0.8941
Epoch 332: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3055 - acc: 0.8941 - val_loss: 0.3071 - val_acc: 0.8956
Epoch 333/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3052 - acc: 0.8940
Epoch 333: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3052 - acc: 0.8939 - val_loss: 0.3073 - val_acc: 0.8966
Epoch 334/1000
696/696 [==============================] - ETA: 0s - loss: 0.3055 - acc: 0.8941
Epoch 334: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3055 - acc: 0.8941 - val_loss: 0.3079 - val_acc: 0.8961
Epoch 335/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3047 - acc: 0.8941
Epoch 335: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3047 - acc: 0.8941 - val_loss: 0.3080 - val_acc: 0.8948
Epoch 336/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3040 - acc: 0.8943
Epoch 336: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3040 - acc: 0.8943 - val_loss: 0.3062 - val_acc: 0.8963
Epoch 337/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3048 - acc: 0.8941
Epoch 337: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3046 - acc: 0.8941 - val_loss: 0.3057 - val_acc: 0.8959
Epoch 338/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3037 - acc: 0.8943
Epoch 338: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3037 - acc: 0.8942 - val_loss: 0.3069 - val_acc: 0.8959
Epoch 339/1000
696/696 [==============================] - ETA: 0s - loss: 0.3031 - acc: 0.8951
Epoch 339: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3031 - acc: 0.8951 - val_loss: 0.3066 - val_acc: 0.8953
Epoch 340/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3037 - acc: 0.8946
Epoch 340: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3037 - acc: 0.8946 - val_loss: 0.3055 - val_acc: 0.8968
Epoch 341/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3032 - acc: 0.8948
Epoch 341: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3032 - acc: 0.8948 - val_loss: 0.3080 - val_acc: 0.8956
Epoch 342/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3031 - acc: 0.8950
Epoch 342: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3031 - acc: 0.8950 - val_loss: 0.3062 - val_acc: 0.8950
Epoch 343/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3025 - acc: 0.8948
Epoch 343: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3025 - acc: 0.8947 - val_loss: 0.3045 - val_acc: 0.8974
Epoch 344/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3017 - acc: 0.8950
Epoch 344: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3016 - acc: 0.8951 - val_loss: 0.3060 - val_acc: 0.8966
Epoch 345/1000
696/696 [==============================] - ETA: 0s - loss: 0.3013 - acc: 0.8956
Epoch 345: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3013 - acc: 0.8956 - val_loss: 0.3055 - val_acc: 0.8965
Epoch 346/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3008 - acc: 0.8951
Epoch 346: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.3008 - acc: 0.8951 - val_loss: 0.3052 - val_acc: 0.8964
Epoch 347/1000
696/696 [==============================] - ETA: 0s - loss: 0.3012 - acc: 0.8958
Epoch 347: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3012 - acc: 0.8958 - val_loss: 0.3037 - val_acc: 0.8979
Epoch 348/1000
693/696 [============================>.] - ETA: 0s - loss: 0.3006 - acc: 0.8960
Epoch 348: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.3005 - acc: 0.8960 - val_loss: 0.3040 - val_acc: 0.8966
Epoch 349/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2998 - acc: 0.8961
Epoch 349: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2997 - acc: 0.8962 - val_loss: 0.3069 - val_acc: 0.8960
Epoch 350/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2996 - acc: 0.8961
Epoch 350: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2997 - acc: 0.8961 - val_loss: 0.3036 - val_acc: 0.8982
Epoch 351/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2993 - acc: 0.8968
Epoch 351: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2992 - acc: 0.8968 - val_loss: 0.3032 - val_acc: 0.8971
Epoch 352/1000
696/696 [==============================] - ETA: 0s - loss: 0.2990 - acc: 0.8964
Epoch 352: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2990 - acc: 0.8964 - val_loss: 0.3043 - val_acc: 0.8964
Epoch 353/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2979 - acc: 0.8971
Epoch 353: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2979 - acc: 0.8972 - val_loss: 0.3037 - val_acc: 0.8976
Epoch 354/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2982 - acc: 0.8965
Epoch 354: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2982 - acc: 0.8965 - val_loss: 0.3035 - val_acc: 0.8973
Epoch 355/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2988 - acc: 0.8966
Epoch 355: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2989 - acc: 0.8965 - val_loss: 0.3023 - val_acc: 0.8973
Epoch 356/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2985 - acc: 0.8964
Epoch 356: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2985 - acc: 0.8964 - val_loss: 0.3026 - val_acc: 0.8966
Epoch 357/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2984 - acc: 0.8962
Epoch 357: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2985 - acc: 0.8962 - val_loss: 0.3021 - val_acc: 0.8970
Epoch 358/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2972 - acc: 0.8969
Epoch 358: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2972 - acc: 0.8969 - val_loss: 0.3033 - val_acc: 0.8984
Epoch 359/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2975 - acc: 0.8969
Epoch 359: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2975 - acc: 0.8969 - val_loss: 0.3020 - val_acc: 0.8970
Epoch 360/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2973 - acc: 0.8968
Epoch 360: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2972 - acc: 0.8969 - val_loss: 0.3012 - val_acc: 0.8976
Epoch 361/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2965 - acc: 0.8975
Epoch 361: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2964 - acc: 0.8975 - val_loss: 0.3025 - val_acc: 0.8993
Epoch 362/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2967 - acc: 0.8972
Epoch 362: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2967 - acc: 0.8972 - val_loss: 0.3021 - val_acc: 0.8972
Epoch 363/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2966 - acc: 0.8971
Epoch 363: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2966 - acc: 0.8971 - val_loss: 0.3017 - val_acc: 0.8981
Epoch 364/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2957 - acc: 0.8978
Epoch 364: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2957 - acc: 0.8978 - val_loss: 0.3000 - val_acc: 0.8982
Epoch 365/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2957 - acc: 0.8976
Epoch 365: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2958 - acc: 0.8975 - val_loss: 0.3002 - val_acc: 0.8979
Epoch 366/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2950 - acc: 0.8976
Epoch 366: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2950 - acc: 0.8976 - val_loss: 0.3035 - val_acc: 0.8963
Epoch 367/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2937 - acc: 0.8980
Epoch 367: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2936 - acc: 0.8979 - val_loss: 0.2989 - val_acc: 0.8989
Epoch 368/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2947 - acc: 0.8979
Epoch 368: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2947 - acc: 0.8979 - val_loss: 0.2996 - val_acc: 0.8982
Epoch 369/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2927 - acc: 0.8984
Epoch 369: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2927 - acc: 0.8984 - val_loss: 0.2998 - val_acc: 0.8979
Epoch 370/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2934 - acc: 0.8978
Epoch 370: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2934 - acc: 0.8978 - val_loss: 0.2994 - val_acc: 0.8989
Epoch 371/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2930 - acc: 0.8983
Epoch 371: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2929 - acc: 0.8984 - val_loss: 0.2997 - val_acc: 0.8990
Epoch 372/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2926 - acc: 0.8984
Epoch 372: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2926 - acc: 0.8984 - val_loss: 0.2989 - val_acc: 0.8972
Epoch 373/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2929 - acc: 0.8986
Epoch 373: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2927 - acc: 0.8986 - val_loss: 0.2999 - val_acc: 0.8981
Epoch 374/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2916 - acc: 0.8989
Epoch 374: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2914 - acc: 0.8989 - val_loss: 0.3007 - val_acc: 0.8981
Epoch 375/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2921 - acc: 0.8990
Epoch 375: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2921 - acc: 0.8990 - val_loss: 0.3001 - val_acc: 0.8993
Epoch 376/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2915 - acc: 0.8990
Epoch 376: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2915 - acc: 0.8990 - val_loss: 0.2992 - val_acc: 0.8983
Epoch 377/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2917 - acc: 0.8990
Epoch 377: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2916 - acc: 0.8990 - val_loss: 0.2986 - val_acc: 0.8991
Epoch 378/1000
696/696 [==============================] - ETA: 0s - loss: 0.2915 - acc: 0.8989
Epoch 378: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2915 - acc: 0.8989 - val_loss: 0.2987 - val_acc: 0.8986
Epoch 379/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2900 - acc: 0.8997
Epoch 379: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2901 - acc: 0.8997 - val_loss: 0.2977 - val_acc: 0.8993
Epoch 380/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2907 - acc: 0.8991
Epoch 380: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2908 - acc: 0.8991 - val_loss: 0.2983 - val_acc: 0.8972
Epoch 381/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2900 - acc: 0.8997
Epoch 381: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2899 - acc: 0.8998 - val_loss: 0.2969 - val_acc: 0.8999
Epoch 382/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2895 - acc: 0.8999
Epoch 382: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2895 - acc: 0.8999 - val_loss: 0.2970 - val_acc: 0.8997
Epoch 383/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2891 - acc: 0.8995
Epoch 383: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2890 - acc: 0.8995 - val_loss: 0.2966 - val_acc: 0.9004
Epoch 384/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2883 - acc: 0.9002
Epoch 384: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2884 - acc: 0.9001 - val_loss: 0.2977 - val_acc: 0.8982
Epoch 385/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2889 - acc: 0.9000
Epoch 385: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2890 - acc: 0.9000 - val_loss: 0.2962 - val_acc: 0.8998
Epoch 386/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2886 - acc: 0.9002
Epoch 386: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2885 - acc: 0.9002 - val_loss: 0.2979 - val_acc: 0.8990
Epoch 387/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2884 - acc: 0.9002
Epoch 387: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2884 - acc: 0.9002 - val_loss: 0.2955 - val_acc: 0.8989
Epoch 388/1000
696/696 [==============================] - ETA: 0s - loss: 0.2878 - acc: 0.9002
Epoch 388: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2878 - acc: 0.9002 - val_loss: 0.2973 - val_acc: 0.8989
Epoch 389/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2885 - acc: 0.9004
Epoch 389: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2885 - acc: 0.9004 - val_loss: 0.2957 - val_acc: 0.9001
Epoch 390/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2879 - acc: 0.8999
Epoch 390: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2878 - acc: 0.9000 - val_loss: 0.2958 - val_acc: 0.8990
Epoch 391/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2876 - acc: 0.9003
Epoch 391: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2878 - acc: 0.9002 - val_loss: 0.2963 - val_acc: 0.8987
Epoch 392/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2874 - acc: 0.9006
Epoch 392: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2873 - acc: 0.9006 - val_loss: 0.2957 - val_acc: 0.8985
Epoch 393/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2865 - acc: 0.9004
Epoch 393: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2865 - acc: 0.9004 - val_loss: 0.2955 - val_acc: 0.8996
Epoch 394/1000
696/696 [==============================] - ETA: 0s - loss: 0.2864 - acc: 0.9008
Epoch 394: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2864 - acc: 0.9008 - val_loss: 0.2956 - val_acc: 0.8989
Epoch 395/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2852 - acc: 0.9012
Epoch 395: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2852 - acc: 0.9012 - val_loss: 0.2957 - val_acc: 0.8984
Epoch 396/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2871 - acc: 0.9006
Epoch 396: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2871 - acc: 0.9006 - val_loss: 0.2939 - val_acc: 0.9000
Epoch 397/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2860 - acc: 0.9008
Epoch 397: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2860 - acc: 0.9008 - val_loss: 0.2951 - val_acc: 0.8993
Epoch 398/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2846 - acc: 0.9014
Epoch 398: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2847 - acc: 0.9014 - val_loss: 0.2957 - val_acc: 0.8992
Epoch 399/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2843 - acc: 0.9010
Epoch 399: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2843 - acc: 0.9010 - val_loss: 0.2941 - val_acc: 0.9007
Epoch 400/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2845 - acc: 0.9014
Epoch 400: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2845 - acc: 0.9014 - val_loss: 0.2935 - val_acc: 0.9009
Epoch 401/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2842 - acc: 0.9016
Epoch 401: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2841 - acc: 0.9016 - val_loss: 0.2947 - val_acc: 0.8987
Epoch 402/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2837 - acc: 0.9020
Epoch 402: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2837 - acc: 0.9020 - val_loss: 0.2949 - val_acc: 0.8993
Epoch 403/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2837 - acc: 0.9020
Epoch 403: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2836 - acc: 0.9020 - val_loss: 0.2942 - val_acc: 0.8998
Epoch 404/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2837 - acc: 0.9017
Epoch 404: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2837 - acc: 0.9017 - val_loss: 0.2940 - val_acc: 0.9001
Epoch 405/1000
696/696 [==============================] - ETA: 0s - loss: 0.2828 - acc: 0.9020
Epoch 405: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2828 - acc: 0.9020 - val_loss: 0.2944 - val_acc: 0.8988
Epoch 406/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2824 - acc: 0.9020
Epoch 406: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2824 - acc: 0.9020 - val_loss: 0.2938 - val_acc: 0.8996
Epoch 407/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2830 - acc: 0.9018
Epoch 407: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2829 - acc: 0.9018 - val_loss: 0.2936 - val_acc: 0.9004
Epoch 408/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2817 - acc: 0.9019
Epoch 408: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2817 - acc: 0.9019 - val_loss: 0.2943 - val_acc: 0.9009
Epoch 409/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2828 - acc: 0.9021
Epoch 409: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2828 - acc: 0.9022 - val_loss: 0.2943 - val_acc: 0.8997
Epoch 410/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2823 - acc: 0.9021
Epoch 410: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2822 - acc: 0.9021 - val_loss: 0.2951 - val_acc: 0.8998
Epoch 411/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2816 - acc: 0.9022
Epoch 411: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2817 - acc: 0.9021 - val_loss: 0.2928 - val_acc: 0.9000
Epoch 412/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2810 - acc: 0.9030
Epoch 412: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2810 - acc: 0.9030 - val_loss: 0.2938 - val_acc: 0.9007
Epoch 413/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2813 - acc: 0.9023
Epoch 413: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2814 - acc: 0.9023 - val_loss: 0.2916 - val_acc: 0.9015
Epoch 414/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2800 - acc: 0.9035
Epoch 414: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2800 - acc: 0.9035 - val_loss: 0.2927 - val_acc: 0.8993
Epoch 415/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2799 - acc: 0.9027
Epoch 415: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2798 - acc: 0.9027 - val_loss: 0.2921 - val_acc: 0.9005
Epoch 416/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2805 - acc: 0.9027
Epoch 416: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2805 - acc: 0.9027 - val_loss: 0.2918 - val_acc: 0.9014
Epoch 417/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2796 - acc: 0.9032
Epoch 417: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2795 - acc: 0.9032 - val_loss: 0.2939 - val_acc: 0.8995
Epoch 418/1000
696/696 [==============================] - ETA: 0s - loss: 0.2798 - acc: 0.9030
Epoch 418: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2798 - acc: 0.9030 - val_loss: 0.2930 - val_acc: 0.9008
Epoch 419/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2785 - acc: 0.9034
Epoch 419: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2785 - acc: 0.9035 - val_loss: 0.2908 - val_acc: 0.9020
Epoch 420/1000
696/696 [==============================] - ETA: 0s - loss: 0.2785 - acc: 0.9037
Epoch 420: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2785 - acc: 0.9037 - val_loss: 0.2916 - val_acc: 0.9005
Epoch 421/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2781 - acc: 0.9038
Epoch 421: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2780 - acc: 0.9039 - val_loss: 0.2918 - val_acc: 0.9010
Epoch 422/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2786 - acc: 0.9034
Epoch 422: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2786 - acc: 0.9035 - val_loss: 0.2904 - val_acc: 0.9016
Epoch 423/1000
696/696 [==============================] - ETA: 0s - loss: 0.2785 - acc: 0.9031
Epoch 423: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2785 - acc: 0.9031 - val_loss: 0.2900 - val_acc: 0.9012
Epoch 424/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2781 - acc: 0.9038
Epoch 424: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2781 - acc: 0.9038 - val_loss: 0.2910 - val_acc: 0.8999
Epoch 425/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2777 - acc: 0.9041
Epoch 425: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2776 - acc: 0.9041 - val_loss: 0.2904 - val_acc: 0.9025
Epoch 426/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2773 - acc: 0.9037
Epoch 426: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2773 - acc: 0.9037 - val_loss: 0.2919 - val_acc: 0.9006
Epoch 427/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2774 - acc: 0.9039
Epoch 427: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2774 - acc: 0.9040 - val_loss: 0.2899 - val_acc: 0.9015
Epoch 428/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2765 - acc: 0.9040
Epoch 428: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2765 - acc: 0.9039 - val_loss: 0.2921 - val_acc: 0.8999
Epoch 429/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2762 - acc: 0.9045
Epoch 429: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2762 - acc: 0.9045 - val_loss: 0.2906 - val_acc: 0.9009
Epoch 430/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2754 - acc: 0.9044
Epoch 430: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2753 - acc: 0.9044 - val_loss: 0.2905 - val_acc: 0.9008
Epoch 431/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2750 - acc: 0.9049
Epoch 431: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2750 - acc: 0.9048 - val_loss: 0.2899 - val_acc: 0.9009
Epoch 432/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2747 - acc: 0.9049
Epoch 432: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2746 - acc: 0.9049 - val_loss: 0.2900 - val_acc: 0.9006
Epoch 433/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2755 - acc: 0.9047
Epoch 433: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2755 - acc: 0.9047 - val_loss: 0.2885 - val_acc: 0.9006
Epoch 434/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2750 - acc: 0.9042
Epoch 434: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2750 - acc: 0.9042 - val_loss: 0.2894 - val_acc: 0.8997
Epoch 435/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2746 - acc: 0.9052
Epoch 435: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2748 - acc: 0.9052 - val_loss: 0.2905 - val_acc: 0.9011
Epoch 436/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2743 - acc: 0.9051
Epoch 436: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2744 - acc: 0.9050 - val_loss: 0.2909 - val_acc: 0.9013
Epoch 437/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2747 - acc: 0.9047
Epoch 437: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2747 - acc: 0.9047 - val_loss: 0.2901 - val_acc: 0.9007
Epoch 438/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2737 - acc: 0.9051
Epoch 438: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2737 - acc: 0.9051 - val_loss: 0.2895 - val_acc: 0.9025
Epoch 439/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2743 - acc: 0.9050
Epoch 439: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2742 - acc: 0.9049 - val_loss: 0.2893 - val_acc: 0.9015
Epoch 440/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2726 - acc: 0.9056
Epoch 440: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2727 - acc: 0.9056 - val_loss: 0.2895 - val_acc: 0.9015
Epoch 441/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2728 - acc: 0.9053
Epoch 441: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2729 - acc: 0.9052 - val_loss: 0.2884 - val_acc: 0.9025
Epoch 442/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2728 - acc: 0.9055
Epoch 442: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2730 - acc: 0.9054 - val_loss: 0.2889 - val_acc: 0.9011
Epoch 443/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2723 - acc: 0.9056
Epoch 443: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2723 - acc: 0.9057 - val_loss: 0.2872 - val_acc: 0.9030
Epoch 444/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2721 - acc: 0.9057
Epoch 444: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2721 - acc: 0.9057 - val_loss: 0.2876 - val_acc: 0.9025
Epoch 445/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2726 - acc: 0.9057
Epoch 445: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2726 - acc: 0.9057 - val_loss: 0.2887 - val_acc: 0.9020
Epoch 446/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2720 - acc: 0.9054
Epoch 446: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2720 - acc: 0.9054 - val_loss: 0.2882 - val_acc: 0.9014
Epoch 447/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2722 - acc: 0.9059
Epoch 447: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2721 - acc: 0.9060 - val_loss: 0.2878 - val_acc: 0.9020
Epoch 448/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2719 - acc: 0.9058
Epoch 448: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2719 - acc: 0.9058 - val_loss: 0.2884 - val_acc: 0.9013
Epoch 449/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2713 - acc: 0.9058
Epoch 449: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2714 - acc: 0.9058 - val_loss: 0.2874 - val_acc: 0.9023
Epoch 450/1000
696/696 [==============================] - ETA: 0s - loss: 0.2706 - acc: 0.9062
Epoch 450: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2706 - acc: 0.9062 - val_loss: 0.2885 - val_acc: 0.9022
Epoch 451/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2703 - acc: 0.9062
Epoch 451: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2703 - acc: 0.9062 - val_loss: 0.2886 - val_acc: 0.9005
Epoch 452/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2707 - acc: 0.9066
Epoch 452: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2707 - acc: 0.9066 - val_loss: 0.2883 - val_acc: 0.9004
Epoch 453/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2702 - acc: 0.9065
Epoch 453: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2702 - acc: 0.9065 - val_loss: 0.2878 - val_acc: 0.9025
Epoch 454/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2704 - acc: 0.9065
Epoch 454: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2704 - acc: 0.9065 - val_loss: 0.2867 - val_acc: 0.9030
Epoch 455/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2695 - acc: 0.9071
Epoch 455: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2695 - acc: 0.9071 - val_loss: 0.2889 - val_acc: 0.9003
Epoch 456/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2699 - acc: 0.9063
Epoch 456: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2700 - acc: 0.9062 - val_loss: 0.2878 - val_acc: 0.9013
Epoch 457/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2693 - acc: 0.9071
Epoch 457: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2692 - acc: 0.9071 - val_loss: 0.2874 - val_acc: 0.9021
Epoch 458/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2686 - acc: 0.9067
Epoch 458: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2687 - acc: 0.9067 - val_loss: 0.2879 - val_acc: 0.9024
Epoch 459/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2696 - acc: 0.9066
Epoch 459: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2695 - acc: 0.9066 - val_loss: 0.2864 - val_acc: 0.9024
Epoch 460/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2683 - acc: 0.9069
Epoch 460: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2683 - acc: 0.9069 - val_loss: 0.2873 - val_acc: 0.9030
Epoch 461/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2677 - acc: 0.9073
Epoch 461: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2678 - acc: 0.9073 - val_loss: 0.2875 - val_acc: 0.9030
Epoch 462/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2679 - acc: 0.9071
Epoch 462: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2678 - acc: 0.9072 - val_loss: 0.2876 - val_acc: 0.9020
Epoch 463/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2679 - acc: 0.9071
Epoch 463: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2680 - acc: 0.9072 - val_loss: 0.2869 - val_acc: 0.9017
Epoch 464/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2676 - acc: 0.9072
Epoch 464: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2676 - acc: 0.9072 - val_loss: 0.2861 - val_acc: 0.9026
Epoch 465/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2660 - acc: 0.9078
Epoch 465: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2661 - acc: 0.9078 - val_loss: 0.2861 - val_acc: 0.9021
Epoch 466/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2676 - acc: 0.9072
Epoch 466: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2675 - acc: 0.9072 - val_loss: 0.2853 - val_acc: 0.9033
Epoch 467/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2669 - acc: 0.9077
Epoch 467: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2669 - acc: 0.9077 - val_loss: 0.2869 - val_acc: 0.9022
Epoch 468/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2663 - acc: 0.9076
Epoch 468: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2663 - acc: 0.9077 - val_loss: 0.2865 - val_acc: 0.9011
Epoch 469/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2666 - acc: 0.9076
Epoch 469: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2665 - acc: 0.9076 - val_loss: 0.2855 - val_acc: 0.9041
Epoch 470/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2657 - acc: 0.9079
Epoch 470: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2658 - acc: 0.9079 - val_loss: 0.2848 - val_acc: 0.9034
Epoch 471/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2660 - acc: 0.9074
Epoch 471: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2659 - acc: 0.9074 - val_loss: 0.2842 - val_acc: 0.9030
Epoch 472/1000
696/696 [==============================] - ETA: 0s - loss: 0.2657 - acc: 0.9076
Epoch 472: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2657 - acc: 0.9076 - val_loss: 0.2859 - val_acc: 0.9034
Epoch 473/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2649 - acc: 0.9086
Epoch 473: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2648 - acc: 0.9086 - val_loss: 0.2843 - val_acc: 0.9046
Epoch 474/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2649 - acc: 0.9078
Epoch 474: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2650 - acc: 0.9077 - val_loss: 0.2864 - val_acc: 0.9011
Epoch 475/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2642 - acc: 0.9082
Epoch 475: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2643 - acc: 0.9081 - val_loss: 0.2853 - val_acc: 0.9030
Epoch 476/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2648 - acc: 0.9080
Epoch 476: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2649 - acc: 0.9080 - val_loss: 0.2843 - val_acc: 0.9030
Epoch 477/1000
696/696 [==============================] - ETA: 0s - loss: 0.2639 - acc: 0.9082
Epoch 477: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2639 - acc: 0.9082 - val_loss: 0.2871 - val_acc: 0.9011
Epoch 478/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2641 - acc: 0.9085
Epoch 478: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2641 - acc: 0.9085 - val_loss: 0.2857 - val_acc: 0.9038
Epoch 479/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2643 - acc: 0.9085
Epoch 479: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2643 - acc: 0.9085 - val_loss: 0.2834 - val_acc: 0.9041
Epoch 480/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2635 - acc: 0.9086
Epoch 480: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2635 - acc: 0.9087 - val_loss: 0.2850 - val_acc: 0.9037
Epoch 481/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2627 - acc: 0.9091
Epoch 481: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2627 - acc: 0.9091 - val_loss: 0.2846 - val_acc: 0.9038
Epoch 482/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2629 - acc: 0.9087
Epoch 482: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 15ms/step - loss: 0.2629 - acc: 0.9087 - val_loss: 0.2845 - val_acc: 0.9019
Epoch 483/1000
696/696 [==============================] - ETA: 0s - loss: 0.2631 - acc: 0.9084
Epoch 483: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2631 - acc: 0.9084 - val_loss: 0.2848 - val_acc: 0.9026
Epoch 484/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2627 - acc: 0.9091
Epoch 484: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2628 - acc: 0.9091 - val_loss: 0.2849 - val_acc: 0.9023
Epoch 485/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2621 - acc: 0.9087
Epoch 485: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2620 - acc: 0.9088 - val_loss: 0.2851 - val_acc: 0.9020
Epoch 486/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2626 - acc: 0.9089
Epoch 486: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2626 - acc: 0.9089 - val_loss: 0.2879 - val_acc: 0.9025
Epoch 487/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2630 - acc: 0.9088
Epoch 487: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2630 - acc: 0.9089 - val_loss: 0.2860 - val_acc: 0.9002
Epoch 488/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2616 - acc: 0.9093
Epoch 488: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2616 - acc: 0.9093 - val_loss: 0.2860 - val_acc: 0.9020
Epoch 489/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2609 - acc: 0.9096
Epoch 489: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2608 - acc: 0.9096 - val_loss: 0.2847 - val_acc: 0.9023
Epoch 490/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2610 - acc: 0.9095
Epoch 490: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2610 - acc: 0.9094 - val_loss: 0.2863 - val_acc: 0.9026
Epoch 491/1000
696/696 [==============================] - ETA: 0s - loss: 0.2601 - acc: 0.9094
Epoch 491: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2601 - acc: 0.9094 - val_loss: 0.2840 - val_acc: 0.9031
Epoch 492/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2610 - acc: 0.9095
Epoch 492: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2611 - acc: 0.9095 - val_loss: 0.2830 - val_acc: 0.9036
Epoch 493/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2604 - acc: 0.9098
Epoch 493: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2604 - acc: 0.9098 - val_loss: 0.2819 - val_acc: 0.9038
Epoch 494/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2596 - acc: 0.9098
Epoch 494: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2595 - acc: 0.9098 - val_loss: 0.2846 - val_acc: 0.9030
Epoch 495/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2607 - acc: 0.9097
Epoch 495: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2607 - acc: 0.9098 - val_loss: 0.2838 - val_acc: 0.9029
Epoch 496/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2601 - acc: 0.9101
Epoch 496: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2600 - acc: 0.9101 - val_loss: 0.2838 - val_acc: 0.9026
Epoch 497/1000
696/696 [==============================] - ETA: 0s - loss: 0.2594 - acc: 0.9099
Epoch 497: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2594 - acc: 0.9099 - val_loss: 0.2832 - val_acc: 0.9042
Epoch 498/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2596 - acc: 0.9098
Epoch 498: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2596 - acc: 0.9098 - val_loss: 0.2832 - val_acc: 0.9035
Epoch 499/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2585 - acc: 0.9102
Epoch 499: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2585 - acc: 0.9102 - val_loss: 0.2828 - val_acc: 0.9041
Epoch 500/1000
696/696 [==============================] - ETA: 0s - loss: 0.2590 - acc: 0.9101
Epoch 500: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2590 - acc: 0.9101 - val_loss: 0.2853 - val_acc: 0.9020
Epoch 501/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2582 - acc: 0.9107
Epoch 501: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2583 - acc: 0.9106 - val_loss: 0.2837 - val_acc: 0.9021
Epoch 502/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2577 - acc: 0.9106
Epoch 502: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2577 - acc: 0.9106 - val_loss: 0.2830 - val_acc: 0.9028
Epoch 503/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2587 - acc: 0.9099
Epoch 503: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2586 - acc: 0.9099 - val_loss: 0.2866 - val_acc: 0.9004
Epoch 504/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2582 - acc: 0.9105
Epoch 504: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2583 - acc: 0.9104 - val_loss: 0.2838 - val_acc: 0.9029
Epoch 505/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2581 - acc: 0.9104
Epoch 505: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2581 - acc: 0.9104 - val_loss: 0.2826 - val_acc: 0.9041
Epoch 506/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2578 - acc: 0.9107
Epoch 506: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2577 - acc: 0.9107 - val_loss: 0.2828 - val_acc: 0.9043
Epoch 507/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2577 - acc: 0.9105
Epoch 507: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2577 - acc: 0.9105 - val_loss: 0.2816 - val_acc: 0.9036
Epoch 508/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2573 - acc: 0.9109
Epoch 508: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2573 - acc: 0.9108 - val_loss: 0.2825 - val_acc: 0.9032
Epoch 509/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2581 - acc: 0.9106
Epoch 509: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2581 - acc: 0.9106 - val_loss: 0.2841 - val_acc: 0.9017
Epoch 510/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2567 - acc: 0.9108
Epoch 510: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2567 - acc: 0.9108 - val_loss: 0.2823 - val_acc: 0.9051
Epoch 511/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2563 - acc: 0.9111
Epoch 511: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2562 - acc: 0.9111 - val_loss: 0.2822 - val_acc: 0.9031
Epoch 512/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2559 - acc: 0.9112
Epoch 512: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2560 - acc: 0.9112 - val_loss: 0.2828 - val_acc: 0.9051
Epoch 513/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2562 - acc: 0.9111
Epoch 513: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2561 - acc: 0.9111 - val_loss: 0.2811 - val_acc: 0.9034
Epoch 514/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2559 - acc: 0.9113
Epoch 514: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2559 - acc: 0.9113 - val_loss: 0.2821 - val_acc: 0.9042
Epoch 515/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2563 - acc: 0.9112
Epoch 515: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2563 - acc: 0.9112 - val_loss: 0.2828 - val_acc: 0.9041
Epoch 516/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2558 - acc: 0.9112
Epoch 516: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2558 - acc: 0.9112 - val_loss: 0.2819 - val_acc: 0.9041
Epoch 517/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2547 - acc: 0.9114
Epoch 517: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2548 - acc: 0.9114 - val_loss: 0.2823 - val_acc: 0.9037
Epoch 518/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2553 - acc: 0.9114
Epoch 518: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2553 - acc: 0.9114 - val_loss: 0.2816 - val_acc: 0.9039
Epoch 519/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2542 - acc: 0.9117
Epoch 519: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2542 - acc: 0.9118 - val_loss: 0.2809 - val_acc: 0.9042
Epoch 520/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2544 - acc: 0.9118
Epoch 520: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2544 - acc: 0.9118 - val_loss: 0.2807 - val_acc: 0.9052
Epoch 521/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2548 - acc: 0.9114
Epoch 521: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2548 - acc: 0.9114 - val_loss: 0.2814 - val_acc: 0.9038
Epoch 522/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2542 - acc: 0.9115
Epoch 522: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2541 - acc: 0.9115 - val_loss: 0.2834 - val_acc: 0.9025
Epoch 523/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2547 - acc: 0.9118
Epoch 523: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2548 - acc: 0.9118 - val_loss: 0.2829 - val_acc: 0.9016
Epoch 524/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2537 - acc: 0.9120
Epoch 524: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2538 - acc: 0.9119 - val_loss: 0.2807 - val_acc: 0.9045
Epoch 525/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2543 - acc: 0.9117
Epoch 525: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2543 - acc: 0.9117 - val_loss: 0.2814 - val_acc: 0.9034
Epoch 526/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2532 - acc: 0.9121
Epoch 526: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2532 - acc: 0.9121 - val_loss: 0.2809 - val_acc: 0.9048
Epoch 527/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2528 - acc: 0.9123
Epoch 527: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2527 - acc: 0.9123 - val_loss: 0.2811 - val_acc: 0.9034
Epoch 528/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2529 - acc: 0.9119
Epoch 528: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2529 - acc: 0.9119 - val_loss: 0.2819 - val_acc: 0.9046
Epoch 529/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2525 - acc: 0.9124
Epoch 529: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2524 - acc: 0.9124 - val_loss: 0.2809 - val_acc: 0.9052
Epoch 530/1000
696/696 [==============================] - ETA: 0s - loss: 0.2523 - acc: 0.9128
Epoch 530: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2523 - acc: 0.9128 - val_loss: 0.2807 - val_acc: 0.9041
Epoch 531/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2520 - acc: 0.9125
Epoch 531: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2519 - acc: 0.9125 - val_loss: 0.2814 - val_acc: 0.9042
Epoch 532/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2520 - acc: 0.9125
Epoch 532: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2521 - acc: 0.9124 - val_loss: 0.2807 - val_acc: 0.9041
Epoch 533/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2518 - acc: 0.9122
Epoch 533: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2519 - acc: 0.9122 - val_loss: 0.2797 - val_acc: 0.9054
Epoch 534/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2525 - acc: 0.9122
Epoch 534: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2524 - acc: 0.9123 - val_loss: 0.2821 - val_acc: 0.9035
Epoch 535/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2510 - acc: 0.9130
Epoch 535: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2509 - acc: 0.9131 - val_loss: 0.2803 - val_acc: 0.9046
Epoch 536/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2512 - acc: 0.9127
Epoch 536: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2512 - acc: 0.9127 - val_loss: 0.2804 - val_acc: 0.9056
Epoch 537/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2507 - acc: 0.9133
Epoch 537: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2507 - acc: 0.9133 - val_loss: 0.2812 - val_acc: 0.9052
Epoch 538/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2499 - acc: 0.9133
Epoch 538: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2499 - acc: 0.9133 - val_loss: 0.2809 - val_acc: 0.9042
Epoch 539/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2516 - acc: 0.9128
Epoch 539: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2515 - acc: 0.9128 - val_loss: 0.2825 - val_acc: 0.9020
Epoch 540/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2508 - acc: 0.9128
Epoch 540: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2507 - acc: 0.9128 - val_loss: 0.2806 - val_acc: 0.9031
Epoch 541/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2495 - acc: 0.9132
Epoch 541: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2495 - acc: 0.9132 - val_loss: 0.2819 - val_acc: 0.9036
Epoch 542/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2500 - acc: 0.9133
Epoch 542: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2500 - acc: 0.9133 - val_loss: 0.2809 - val_acc: 0.9040
Epoch 543/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2491 - acc: 0.9135
Epoch 543: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2491 - acc: 0.9135 - val_loss: 0.2823 - val_acc: 0.9040
Epoch 544/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2493 - acc: 0.9134
Epoch 544: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2492 - acc: 0.9135 - val_loss: 0.2798 - val_acc: 0.9044
Epoch 545/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2490 - acc: 0.9137
Epoch 545: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2489 - acc: 0.9137 - val_loss: 0.2804 - val_acc: 0.9047
Epoch 546/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2483 - acc: 0.9138
Epoch 546: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2482 - acc: 0.9139 - val_loss: 0.2794 - val_acc: 0.9047
Epoch 547/1000
696/696 [==============================] - ETA: 0s - loss: 0.2488 - acc: 0.9139
Epoch 547: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2488 - acc: 0.9139 - val_loss: 0.2796 - val_acc: 0.9053
Epoch 548/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2490 - acc: 0.9133
Epoch 548: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2490 - acc: 0.9133 - val_loss: 0.2782 - val_acc: 0.9052
Epoch 549/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2485 - acc: 0.9140
Epoch 549: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2484 - acc: 0.9140 - val_loss: 0.2789 - val_acc: 0.9051
Epoch 550/1000
694/696 [============================>.] - ETA: 0s - loss: 0.2479 - acc: 0.9140
Epoch 550: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2479 - acc: 0.9141 - val_loss: 0.2804 - val_acc: 0.9056
Epoch 551/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2475 - acc: 0.9138
Epoch 551: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2476 - acc: 0.9138 - val_loss: 0.2798 - val_acc: 0.9056
Epoch 552/1000
695/696 [============================>.] - ETA: 0s - loss: 0.2479 - acc: 0.9142
Epoch 552: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2478 - acc: 0.9142 - val_loss: 0.2787 - val_acc: 0.9045
Epoch 553/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2477 - acc: 0.9137
Epoch 553: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2476 - acc: 0.9138 - val_loss: 0.2809 - val_acc: 0.9051
Epoch 554/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2478 - acc: 0.9142
Epoch 554: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2477 - acc: 0.9142 - val_loss: 0.2795 - val_acc: 0.9054
Epoch 555/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2475 - acc: 0.9138
Epoch 555: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2475 - acc: 0.9138 - val_loss: 0.2785 - val_acc: 0.9044
Epoch 556/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2464 - acc: 0.9146
Epoch 556: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2464 - acc: 0.9146 - val_loss: 0.2809 - val_acc: 0.9042
Epoch 557/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2468 - acc: 0.9141
Epoch 557: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2467 - acc: 0.9141 - val_loss: 0.2795 - val_acc: 0.9046
Epoch 558/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2469 - acc: 0.9144
Epoch 558: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2469 - acc: 0.9144 - val_loss: 0.2796 - val_acc: 0.9057
Epoch 559/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2465 - acc: 0.9146
Epoch 559: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2465 - acc: 0.9146 - val_loss: 0.2792 - val_acc: 0.9047
Epoch 560/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2468 - acc: 0.9142
Epoch 560: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2468 - acc: 0.9142 - val_loss: 0.2804 - val_acc: 0.9055
Epoch 561/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2458 - acc: 0.9149
Epoch 561: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2458 - acc: 0.9149 - val_loss: 0.2794 - val_acc: 0.9054
Epoch 562/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2463 - acc: 0.9147
Epoch 562: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2463 - acc: 0.9147 - val_loss: 0.2805 - val_acc: 0.9042
Epoch 563/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2457 - acc: 0.9147
Epoch 563: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2457 - acc: 0.9146 - val_loss: 0.2780 - val_acc: 0.9054
Epoch 564/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2458 - acc: 0.9150
Epoch 564: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2458 - acc: 0.9149 - val_loss: 0.2810 - val_acc: 0.9054
Epoch 565/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2458 - acc: 0.9146
Epoch 565: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2458 - acc: 0.9146 - val_loss: 0.2794 - val_acc: 0.9057
Epoch 566/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2454 - acc: 0.9149
Epoch 566: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2454 - acc: 0.9149 - val_loss: 0.2776 - val_acc: 0.9064
Epoch 567/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2445 - acc: 0.9150
Epoch 567: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2445 - acc: 0.9150 - val_loss: 0.2773 - val_acc: 0.9044
Epoch 568/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2445 - acc: 0.9154
Epoch 568: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2444 - acc: 0.9154 - val_loss: 0.2798 - val_acc: 0.9055
Epoch 569/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2442 - acc: 0.9155
Epoch 569: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2443 - acc: 0.9154 - val_loss: 0.2798 - val_acc: 0.9056
Epoch 570/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2442 - acc: 0.9150
Epoch 570: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2442 - acc: 0.9150 - val_loss: 0.2774 - val_acc: 0.9064
Epoch 571/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2434 - acc: 0.9157
Epoch 571: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2435 - acc: 0.9157 - val_loss: 0.2795 - val_acc: 0.9057
Epoch 572/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2436 - acc: 0.9154
Epoch 572: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2437 - acc: 0.9154 - val_loss: 0.2792 - val_acc: 0.9049
Epoch 573/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2442 - acc: 0.9153
Epoch 573: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2443 - acc: 0.9153 - val_loss: 0.2777 - val_acc: 0.9068
Epoch 574/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2442 - acc: 0.9153
Epoch 574: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2442 - acc: 0.9153 - val_loss: 0.2794 - val_acc: 0.9053
Epoch 575/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2441 - acc: 0.9153
Epoch 575: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2441 - acc: 0.9154 - val_loss: 0.2776 - val_acc: 0.9053
Epoch 576/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2434 - acc: 0.9153
Epoch 576: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2434 - acc: 0.9153 - val_loss: 0.2778 - val_acc: 0.9072
Epoch 577/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2428 - acc: 0.9158
Epoch 577: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2427 - acc: 0.9159 - val_loss: 0.2773 - val_acc: 0.9070
Epoch 578/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2419 - acc: 0.9160
Epoch 578: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2419 - acc: 0.9160 - val_loss: 0.2799 - val_acc: 0.9057
Epoch 579/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2431 - acc: 0.9156
Epoch 579: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2431 - acc: 0.9155 - val_loss: 0.2791 - val_acc: 0.9043
Epoch 580/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2420 - acc: 0.9157
Epoch 580: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2419 - acc: 0.9157 - val_loss: 0.2780 - val_acc: 0.9059
Epoch 581/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2418 - acc: 0.9160
Epoch 581: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2418 - acc: 0.9160 - val_loss: 0.2785 - val_acc: 0.9055
Epoch 582/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2422 - acc: 0.9160
Epoch 582: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2422 - acc: 0.9160 - val_loss: 0.2770 - val_acc: 0.9065
Epoch 583/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2416 - acc: 0.9160
Epoch 583: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2416 - acc: 0.9160 - val_loss: 0.2773 - val_acc: 0.9062
Epoch 584/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2417 - acc: 0.9164
Epoch 584: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2418 - acc: 0.9164 - val_loss: 0.2772 - val_acc: 0.9062
Epoch 585/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2415 - acc: 0.9156
Epoch 585: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2415 - acc: 0.9156 - val_loss: 0.2755 - val_acc: 0.9062
Epoch 586/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2417 - acc: 0.9159
Epoch 586: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2416 - acc: 0.9159 - val_loss: 0.2766 - val_acc: 0.9058
Epoch 587/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2405 - acc: 0.9162
Epoch 587: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2406 - acc: 0.9161 - val_loss: 0.2785 - val_acc: 0.9064
Epoch 588/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2407 - acc: 0.9165
Epoch 588: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2406 - acc: 0.9165 - val_loss: 0.2764 - val_acc: 0.9070
Epoch 589/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2405 - acc: 0.9163
Epoch 589: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2405 - acc: 0.9162 - val_loss: 0.2788 - val_acc: 0.9063
Epoch 590/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2402 - acc: 0.9168
Epoch 590: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2403 - acc: 0.9168 - val_loss: 0.2767 - val_acc: 0.9065
Epoch 591/1000
696/696 [==============================] - ETA: 0s - loss: 0.2400 - acc: 0.9167
Epoch 591: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2400 - acc: 0.9167 - val_loss: 0.2786 - val_acc: 0.9059
Epoch 592/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2407 - acc: 0.9165
Epoch 592: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2406 - acc: 0.9165 - val_loss: 0.2784 - val_acc: 0.9056
Epoch 593/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2409 - acc: 0.9164
Epoch 593: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2408 - acc: 0.9165 - val_loss: 0.2784 - val_acc: 0.9047
Epoch 594/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2387 - acc: 0.9172
Epoch 594: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2387 - acc: 0.9172 - val_loss: 0.2757 - val_acc: 0.9062
Epoch 595/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2400 - acc: 0.9164
Epoch 595: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2399 - acc: 0.9163 - val_loss: 0.2782 - val_acc: 0.9055
Epoch 596/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2385 - acc: 0.9171
Epoch 596: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2386 - acc: 0.9171 - val_loss: 0.2761 - val_acc: 0.9068
Epoch 597/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2388 - acc: 0.9172
Epoch 597: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2389 - acc: 0.9172 - val_loss: 0.2779 - val_acc: 0.9062
Epoch 598/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2384 - acc: 0.9171
Epoch 598: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 12s 17ms/step - loss: 0.2384 - acc: 0.9171 - val_loss: 0.2770 - val_acc: 0.9066
Epoch 599/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2389 - acc: 0.9171
Epoch 599: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2388 - acc: 0.9171 - val_loss: 0.2784 - val_acc: 0.9062
Epoch 600/1000
693/696 [============================>.] - ETA: 0s - loss: 0.2373 - acc: 0.9175
Epoch 600: saving model to /project/nico/Data/data_Paper_OGLE/7_01_2024/training_softmax_batchBalanced_Number_M/cp.ckpt
696/696 [==============================] - 11s 16ms/step - loss: 0.2373 - acc: 0.9175 - val_loss: 0.2761 - val_acc: 0.9060
Epoch 600: early stopping
|
Monsalves-Gonzalez-NREPO_NAMEPaper_OGLEPATH_START.@Paper_OGLE_extracted@Paper_OGLE-main@.ipynb_checkpoints@puntopi-checkpoint.ipynb@.PATH_END.py
|
{
"filename": "test_scene.py",
"repo_name": "rennehan/yt-swift",
"repo_path": "yt-swift_extracted/yt-swift-main/yt/visualization/volume_rendering/tests/test_scene.py",
"type": "Python"
}
|
import os
import shutil
import tempfile
from unittest import TestCase
import numpy as np
from yt.testing import assert_fname, fake_random_ds, fake_vr_orientation_test_ds
from yt.visualization.volume_rendering.api import (
create_scene,
create_volume_source,
volume_render,
)
def setup():
"""Test specific setup."""
from yt.config import ytcfg
ytcfg["yt", "internals", "within_testing"] = True
class RotationTest(TestCase):
# This toggles using a temporary directory. Turn off to examine images.
use_tmpdir = True
def setUp(self):
if self.use_tmpdir:
self.curdir = os.getcwd()
# Perform I/O in safe place instead of yt main dir
self.tmpdir = tempfile.mkdtemp()
os.chdir(self.tmpdir)
else:
self.curdir, self.tmpdir = None, None
def tearDown(self):
if self.use_tmpdir:
os.chdir(self.curdir)
shutil.rmtree(self.tmpdir)
def test_rotation(self):
ds = fake_random_ds(32)
ds2 = fake_random_ds(32)
dd = ds.sphere(ds.domain_center, ds.domain_width[0] / 2)
dd2 = ds2.sphere(ds2.domain_center, ds2.domain_width[0] / 2)
im, sc = volume_render(dd, field=("gas", "density"))
im.write_png("test.png")
vol = sc.get_source(0)
tf = vol.transfer_function
tf.clear()
mi, ma = dd.quantities.extrema(("gas", "density"))
mi = np.log10(mi)
ma = np.log10(ma)
mi_bound = ((ma - mi) * (0.10)) + mi
ma_bound = ((ma - mi) * (0.90)) + mi
tf.map_to_colormap(mi_bound, ma_bound, scale=0.01, colormap="Blues_r")
vol2 = create_volume_source(dd2, field=("gas", "density"))
sc.add_source(vol2)
tf = vol2.transfer_function
tf.clear()
mi, ma = dd2.quantities.extrema(("gas", "density"))
mi = np.log10(mi)
ma = np.log10(ma)
mi_bound = ((ma - mi) * (0.10)) + mi
ma_bound = ((ma - mi) * (0.90)) + mi
tf.map_to_colormap(mi_bound, ma_bound, scale=0.01, colormap="Reds_r")
fname = "test_scene.pdf"
sc.save(fname, sigma_clip=6.0)
assert_fname(fname)
fname = "test_rot.png"
sc.camera.pitch(np.pi)
sc.render()
sc.save(fname, sigma_clip=6.0, render=False)
assert_fname(fname)
def test_annotations():
from matplotlib.image import imread
curdir = os.getcwd()
tmpdir = tempfile.mkdtemp()
os.chdir(tmpdir)
ds = fake_vr_orientation_test_ds(N=16)
sc = create_scene(ds)
sc.annotate_axes()
sc.annotate_domain(ds)
sc.render()
# ensure that there are actually red, green, blue, and white pixels
# in the image. see Issue #1595
im = sc._last_render
for c in ([1, 0, 0, 1], [0, 1, 0, 1], [0, 0, 1, 1], [1, 1, 1, 1]):
assert np.where((im == c).all(axis=-1))[0].shape[0] > 0
sc[0].tfh.tf.add_layers(10, colormap="cubehelix")
sc.save_annotated(
"test_scene_annotated.png",
text_annotate=[[(0.1, 1.05), "test_string"]],
)
image = imread("test_scene_annotated.png")
assert image.shape == sc.camera.resolution + (4,)
os.chdir(curdir)
shutil.rmtree(tmpdir)
|
rennehanREPO_NAMEyt-swiftPATH_START.@yt-swift_extracted@yt-swift-main@yt@visualization@volume_rendering@tests@test_scene.py@.PATH_END.py
|
{
"filename": "test_wap.py",
"repo_name": "crossbario/crossbar",
"repo_path": "crossbar_extracted/crossbar-master/crossbar/webservice/test/test_wap.py",
"type": "Python"
}
|
#####################################################################################
#
# Copyright (c) typedef int GmbH
# SPDX-License-Identifier: EUPL-1.2
#
#####################################################################################
import os
from twisted.trial.unittest import TestCase
from werkzeug.routing import Map, Rule
from jinja2 import Environment, FileSystemLoader
from jinja2.environment import Template
from crossbar.webservice.wap import WapResource
class WapTestCase(TestCase):
"""
Tests for :class:`crossbar.webservice.wap.WapResource`.
"""
_WAP1 = {
"type":
"wap",
"templates":
"../templates",
"sandbox":
True,
"routes": [{
"path": "/greeting/<name>",
"method": "GET",
"call": "com.example.greeting",
"render": "greeting.html"
}, {
"path": "/product/<int:product_id>/<report>/<int:year>/<int:month>",
"method": "GET",
"call": "com.example.get_product_report",
"render": "product_report.html"
}],
"wamp": {
"realm": "realm1",
"authrole": "anonymous"
}
}
def setUp(self):
self._templates_dir = os.path.join(os.path.dirname(__file__), 'templates')
self._jinja_env = Environment(loader=FileSystemLoader(self._templates_dir), autoescape=True)
def test_map_adapter(self):
# https://werkzeug.palletsprojects.com/en/2.1.x/routing/#werkzeug.routing.MapAdapter.match
test_map = Map()
url = '/reports/product/<int:product_id>/<report>/<int:year>/<int:month>'
endpoint = 'endpoint1'
rule = Rule(url, methods=['GET'], endpoint=endpoint)
test_map.add(rule)
test_adapter = test_map.bind('localhost', '/')
test_url = '/reports/product/123/total/2016/12'
test_data = {'product_id': 123, 'report': 'total', 'year': 2016, 'month': 12}
_endpoint, _kwargs = test_adapter.match(test_url, method='GET', query_args={})
self.assertEqual(_endpoint, endpoint)
self.assertEqual(_kwargs, test_data)
def test_map_adapter_factory(self):
map_adapter = WapResource._create_map_adapter(self._jinja_env, self._WAP1, 'localhost', 'reports')
test_url = '/reports/product/123/total/2016/12'
test_data = {'product_id': 123, 'report': 'total', 'year': 2016, 'month': 12}
_endpoint, _kwargs = map_adapter.match(test_url, method='GET', query_args={})
# ('com.example.get_product_report', <Template 'product_report.html'>) != 'localhost'
self.assertEqual(_endpoint[0], 'com.example.get_product_report')
self.assertIsInstance(_endpoint[1], Template)
self.assertEqual(_kwargs, test_data)
|
crossbarioREPO_NAMEcrossbarPATH_START.@crossbar_extracted@crossbar-master@crossbar@webservice@test@test_wap.py@.PATH_END.py
|
{
"filename": "Plots.ipynb",
"repo_name": "ricardoclandim/NIRVANA",
"repo_path": "NIRVANA_extracted/NIRVANA-master/Plots.ipynb",
"type": "Jupyter Notebook"
}
|
```python
import matplotlib.pyplot as plt
import numpy as np
import multiprocessing as mp
from glob import glob
from tqdm import tqdm_notebook as tqdm
from nirvana.data.manga import MaNGAGasKinematics, MaNGAStellarKinematics
from nirvana.models.higher_order import bisym_model
from nirvana.util.fits_prep import fileprep
from nirvana.models.geometry import projected_polar
from nirvana.util.fits_prep import makealltable
from mpl_toolkits.axes_grid1 import make_axes_locatable as mal
from matplotlib.lines import Line2D
from matplotlib.cm import ScalarMappable
from matplotlib.colors import Normalize, LinearSegmentedColormap
from scipy.signal import savgol_filter
%matplotlib notebook
```
```python
args, resdict = fileprep('/media/brian/bdigiorg/nirvana/lux/barred/sample/nirvana_8078-12703_Gas.fits', rootdir='/media/brian/bdigiorg/manga/spectro/')
```
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/8078/images/12703.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/redux/DR17/8078/stack/manga-8078-12703-LOGCUBE.fits.gz ...
Done
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/8078/12703/manga-8078-12703-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 7.5 +/- 0.0
Inclination: 30.5 +/- 0.1
Systemic Velocity: 3.3 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 159.3 +/- 0.1
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 2773
Velocity chi-square: 145872.16459275514
Reduced chi-square: 52.737586620663464
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 7.5 +/- 0.0
Inclination: 30.5 +/- 0.1
Systemic Velocity: 3.3 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 159.3 +/- 0.1
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 2772
Velocity chi-square: 145870.79628685315
Reduced chi-square: 52.756165022370034
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: 7.5 +/- 0.0
Inclination: 28.4 +/- 0.1
Systemic Velocity: 2.1 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 159.3 +/- 0.1
RC: Scale: 3.7 +/- 0.0
----------
Velocity measurements: 2261
Velocity chi-square: 121157.43749039598
Reduced chi-square: 53.75219054587222
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
```python
resdict['pa'] = 0
pabs = [0,45,90,180]
plt.figure(figsize=(4,5.5))
for i in range(len(pabs)):
plt.subplot(2,2,i+1)
resdict['pab'] = pabs[i]
velmodel, sigmodel = bisym_model(args,resdict)
im = plt.imshow(args.kin.remap(velmodel), cmap='Spectral')
plt.title(f'$\phi - \phi_b = {{{pabs[i]}}}^\circ$')
plt.tick_params(left=False, labelleft=False, bottom=False, labelbottom=False)
cax = mal(plt.gca()).append_axes('bottom', size='5%', pad=0)
plt.colorbar(im, cax=cax, orientation='horizontal',label='km/s')
plt.tight_layout()
plt.savefig('relpabcomparison.pdf',format='pdf')
```
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_188833/544502730.py in <module>
----> 1 resdict['pa'] = 0
2 pabs = [0,45,90,180]
3 plt.figure(figsize=(4,5.5))
4 for i in range(len(pabs)):
5 plt.subplot(2,2,i+1)
NameError: name 'resdict' is not defined
```python
def projectedpab(pab, pa, inc, degrees=True, relpab=False):
_pab, _pa, _inc = np.radians((pab, pa, inc)) if degrees else (pab, pa, inc)
adjust = _pab > np.pi
if not relpab: _pab -= _pa
projpab = np.arctan(np.tan(_pab) * np.cos(_inc)) + _pa
return (np.degrees(projpab) % 180 + 180*adjust) % 360
angs = []
pabs = []
pabes = []
deprojs = []
vts = []
v2ts = []
v2rs = []
#margs, mresdict = fileprep('data/lux/fullrun/nirvana_8078-12703_Gas.fits')
for i,ang in enumerate(np.arange(0,181,15)):
try:
f = fits.open(f'/media/brian/bdigiorg/nirvana/lux/mocks/pabbias/807812703fixednewpab{ang}.fits')
pabs += [f[1].data['pab'][0]]
pabes += [f[1].data['pabu'][0] - f[1].data['pabl'][0]]
angs += [ang]
vts += [f[1].data['vt'][~f[1].data['velmask']]]
v2ts += [f[1].data['v2t'][~f[1].data['velmask']]]
v2rs += [f[1].data['v2r'][~f[1].data['velmask']]]
pab = projectedpab(f[1].data['pab'][0], f[1].data['pa'][0], f[1].data['inc'][0], relpab=True)
deprojs += [pab]
print(f'{ang}: deproj: {pab} proj: {f[1].data["pab"]} pa: {f[1].data["pa"]} inc: {f[1].data["inc"]}')
#plt.figure(figsize=(12,4))
#plt.subplot(131)
#plt.suptitle(ang)
#plt.imshow(f['vel'].data, cmap = 'RdBu')
#plt.subplot(132)
#plt.imshow(f['vel_model'].data, cmap='RdBu')
#plt.subplot(133)
#plt.imshow(f['vel'].data - f['vel_model'].data, cmap='RdBu')
#plt.colorbar()
#plt.tight_layout()
except Exception:
print(f'{ang}: failed')
```
0: deproj: 149.84148597717285 proj: [141.84021] pa: [0.34956843] inc: [41.4212]
15: deproj: 23.820663452148438 proj: [26.746199] pa: [3.4751008] inc: [42.624626]
30: deproj: 46.19218444824219 proj: [50.402943] pa: [3.7508006] inc: [40.850357]
45: deproj: 62.48583984375 proj: [65.26833] pa: [3.465709] inc: [39.89614]
60: deproj: 73.7020034790039 proj: [74.75807] pa: [3.0712361] inc: [39.18797]
75: deproj: 82.3406753540039 proj: [81.878075] pa: [2.679419] inc: [38.529835]
90: deproj: 89.67969512939453 proj: [87.88167] pa: [2.3622835] inc: [37.866306]
105: deproj: 95.81602478027344 proj: [92.96744] pa: [2.0931454] inc: [37.186337]
120: deproj: 102.56729125976562 proj: [98.67598] pa: [1.8057312] inc: [36.597397]
135: deproj: 109.554931640625 proj: [104.88458] pa: [1.349601] inc: [36.083893]
150: failed
165: deproj: 143.36630249023438 proj: [137.54152] pa: [359.7944] inc: [36.236855]
180: deproj: 179.38037538528442 proj: [179.01633] pa: [0.08559021] inc: [44.202393]
```python
plt.figure(figsize=(3.5,3.5))
#plt.errorbar(angs, pabs, yerr=pabes, marker='.', ls='-', c='olivedrab', label='In plane')
plt.errorbar(angs, deprojs, yerr=pabes, marker='.', ls='-', c='olivedrab', label='In plane')
#plt.legend()
plt.plot(angs, angs, 'k:', lw=1)
plt.xlabel('Input Relative PA (deg)')
plt.ylabel('Output Relative PA (deg)')
plt.gca().set_aspect(1)
plt.xlim((-5,185))
plt.ylim((-5,185))
plt.tight_layout()
plt.savefig('pabbias.pdf', format='pdf')
```
<IPython.core.display.Javascript object>
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" width="350">
```python
fs = glob('/media/brian/bdigiorg/nirvana/lux/barred/sample/*Gas*fits')
pas = np.zeros(len(fs))
pabs = np.zeros(len(fs))
pabus = np.zeros(len(fs))
pabes = np.zeros(len(fs))
pabls = np.zeros(len(fs))
incs = np.zeros(len(fs))
deprojs = np.zeros(len(fs))
vts = []
v2ts = []
v2rs = []
pis = []
ids = []
for i in tqdm(range(len(fs))):
with fits.open(fs[i]) as f:
pas[i] = f[1].data['pa']
pabs[i] = f[1].data['pab']
pabus[i] = f[1].data['pabu']
pabls[i] = f[1].data['pabl']
pabes += [f[1].data['pabu'][0] - f[1].data['pabl'][0]]
incs[i] = f[1].data['inc']
deprojs[i] = projectedpab(pabs[i], pas[i], incs[i], relpab=False)
pis += [f[0].header['plateifu']]
ids += [f[0].header['mangaid']]
vts += [f[1].data['vt'][~f[1].data['velmask']]]
v2ts += [f[1].data['v2t'][~f[1].data['velmask']]]
v2rs += [f[1].data['v2r'][~f[1].data['velmask']]]
```
TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0
Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`
0%| | 0/1118 [00:00<?, ?it/s]
```python
plt.figure(figsize=(3.5,2))
diffs = (np.array(deprojs)) % 360
hist, bins, plot = plt.hist(np.abs(180-diffs), bins=30, density=True, range=(0,180), color='olivedrab')
#[plt.axvline(v, c='k', ls='--', lw=1) for v in [0,90,180,270,360]]
plt.xticks([0,45,90,135,180])
plt.xlabel('Relative PA (deg)')
plt.tight_layout()
plt.savefig('relpabhist.pdf', format='pdf')
```
<IPython.core.display.Javascript object>
<img 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CUsXj94Rb2Aw7TvV7Zk4ZUxcwr4rdszrR2/snGK5YIJsngTaPH6wSvqRRimfb+ycX5B1FayFPBbtwJvsuaror1x8XQrdwB+8PJbwOW2V5g/TPt+ZcP2TeXTq4DfuhV40zu3znseBLx+8AmzABmgX/mwIvj1P2z9Kl+dCvitW4G3OtdF0VELvFoMUsCNAgKvNtdKXkkCb8lSKaMUOKMCAq/AW/JXJIng9eu83wL3Ky9Xg59C+jyIAn7rUq6GIKpmtIzAm9GJ1bBiV0DglcVb8qLLInhl0ZY8/croUAGBV+AteTkJvCVLpYxSILEK+EG/nI4HYUJh/XqBwkfxICL7WZR+i8CvvN8iibp+v/b1uRRImgJ+34ly+huECQJvOQobY4KI7AdOv0XgV95vCFHX79e+PpcCSVPA7ztRTn+DMEHgLUdhgbdMtZRdCiRTAYE3mfNSZ6+CPN38LFa/ReBX3k/CqOv3a1+fS4G0KeD3nckfTxAmyOItc0UEETksOMvsYq3sfouo0v0LOz6VlwKuFfD7zgi8rhX3qU/gjVlwNScFKqCAwFsB0c/UpMCbsAlRd6RABAoIvBGIGqZKgTeMeiorBdKhgMCbsHkSeBM2IeqOFIhAAYE3AlHDVCnwhlFPZaVAOhQQeBM2TwJvwiZE3ZECESgg8EYgapgqBd4w6qmsFEiHAgJvwuZJ4E3YhKg7UiACBQTeCEQNU6XAG0Y9lZUC6VBA4E3YPKURvAmTUN2RAolXQOBN2BQJvAmbEHVHCkSggMAbgahhqhR4w6inslIgHQoIvAmbJ4E3YROi7kiBCBQQeCMQNUyVAm8Y9VRWCqRDAYE3YfMk8CZsQtQdKRCBAgJvBKKGqVLgDaOeykqBdCgg8CZsngTehE2IuiMFIlBA4I1A1DBVCrxh1FNZKZAOBQTehM2TwJuwCVF3pEAECgi8EYgapkqBN4x6KisF0qGAwJuweRJ4EzYh6o4UiEABgTcCUcNUKfCGUU9lpUA6FBB4EzZPAm/CJkTdkQIRKJBp8M6fP9/Mnj3bHDlyxHTo0MHMnTvX9OrVq04ZN2/ebMaPH2927txpmjdvbiZMmGBGjRpVI//atWvNHXfcYZ555hlz+eWXm+nTp5sBAwbUyFNuu/mFBd4IVrmqlAIJUyCz4F21apUZMmSIAYI9evQwixYtMkuXLjW7du0yrVq1qjUN+/fvNx07djQjRowwI0eONFu2bDFjxowxK1euNAMHDrT5t23bZsF91113WdiuW7fOTJkyxTz++OOma9euNk+57RZ2ROBN2DdE3ZECESiQWfACws6dO5sFCxbkZGvXrp3p37+/mTlzZi0pJ06caNavX292796d+wxrd8eOHRa4pOuvv94Axo0bN+by9O3b11xwwQUW0KRy2xV4I1jVqlIKJFyBTIL31KlTpkGDBmb16tU13ABjx44127dvN7gUClPv3r1Np06dzLx583IfYdEOGjTInDhxwtSvX99ayuPGjbN/XpozZ451YRw8eNAEaVfgTfg3RN2TAhEokEnwHj582LRo0cK6C7p3756TbcaMGWbZsmVm7969taRs27atGTp0qJk8eXLus61bt1o3BfU1a9bMnHPOOebee+81N954Yy7P/fffb4YNG2ZOnjxp85XbLuX489KxY8cs4J999lnTsGHDkqb81m+9s6R8yiQFpEAyFPjqZ7aX3BF+Zbds2dK89NJLplGjRiWXy89Y79VXX301UMkyCnkABJzdunXLlWQjbMWKFWbPnj1FwQtAJ02alPsMcPfs2dNuzjVt2tSCF3APHjw4l+e+++4zw4cPN6+88koOvOW0O3XqVDNt2rQyRqesUkAKVKMCGGMXX3xxoKHHAt4gP/kr5WootHhPnz5t/va3v5kLL7zQ1KtXz1dk72lYjoXsW2mCM1TTeDXWBC/EEF0rd16xVY8fP25PWp111lmBWo4FvPSMTa4uXbrYUw1eat++venXr1+dm2sbNmywpx68NHr0aOsTzt9cQ4BHHnkkl+fqq682jRs3rrG5Vk67gVTMKxTkFETYNitZvprGq7FWcqVF13Yl5jU28HrHuhYuXGjdDYsXLzZLliyxZ3Rbt25tXQqHDh0yy5cvtwp7x8k4SsaRMmDLqYb842S4ELCMcVkA8IcfftjcfvvtRY+T1dWu6+msxCS6HkM59VXTeDXWclZGevJWYl5jAy/TgLU7a9Ys66PljC4nEAAniY20AwcOmE2bNuVmjNMOnFjwXqDgiFnhCxRr1qyxsN23b1/uBYrrrruuxqyfqV3Xy6MSk+h6DOXUV03j1VjLWRnpyVuJeY0VvOmZiuA9xUfMuWQs+HPPPTd4RSkpWU3j1VhTsijL7GYl5lXgLXOSlF0KSAEpEFYBgTesgiovBaSAFChTAYG3TMGUXQpIASkQVgGBN6yCKi8FpIAUKFMBgbdMwfyyhwlB6Vd3Ej5n45DXuImzQUwMEidSeIMwP3Fu+4knnkhCl8vuA2fDCTVKbJCjR4/mYoa8+93vtnVxgJ63GzkS+fe//92eUf/Wt75lQ50mOT322GM2LOtTTz1lTxYxPoJUeYm3Nh944AH7ejxvhXL+naOaXqQ/8r33ve+tFVuFYFWUS1LyG+s///lPc9ttt5mHHnrI/PWvfzWXXHKJ+dznPmd4V8BLbLrdeuut9gjrv/71L3PllVfak1lB31bL10fgdbhawoagdNiVSKp68sknbZAiYla8733vqwHeF154wXz3u9/NtcsX9w1veEMk/Yi6UkDy+9//3kbS4+2k733ve/boIy/zEPvjK1/5igUScUKIKXL33XcbvujEHDn//POj7l7g+onix2v3RAkktGoheIlz8qY3vclcdtllFjSMmcBWf/rTn0yTJk1su4CXMd955525frzuda8LHLMg8GB8CvqNlXcDfv7zn9vQtED3xz/+sQ07S3xv3gkgAWFe4mKeeXP185//vH2LlQfX2WefHarrAm8o+WoWDhuC0mFXnFeFhcAXlic+oHnnO99ZA7wEDMF6SHsCOMCTl3E+/OEP54bDeD/ykY/Y2M/A+JZbbjGcKydhGV100UUWyLzwk4bE6++F4C3st3e+9dFHH7XWngfe/LlP61h5j4AHLL9svISFf80119g5JjgWDxtiyZCPRMwZguPwpuyHPvShUEMXeEPJ97/CQeJROGo6lmo++clPWgsWKwirpxC8QBcrl9e1+/TpYy1CrKe0JdwMWPT5sGEMvG3JuezvfOc79kWd3/zmN9YF4SWsJMZe6HJJ6vj9wMt6vueee+xDFov3jW98Yw68vNCEu4WHDa/of+lLX0q0pV9srLyIheXKuuVByotbH/3oR21sbwJx/exnP7MPGyxc4nt76R3veId1z4QNpCXwOvpmBAlB6ajpyKvBfwdIcTW89rWvrQVeXCznnXeeffWbV72xIv7zn//YhZ3Gl0gIXcpDhJ/ewAUf3yc+8Qnz5je/2bpTCE3K6+18Yb1088032xjQP/rRjyKfDxcN1AXe73//++aGG26wMa8JvQqYPN827fKa/6WXXmqjA+KO4UWhNm3amJ/85CcuuhVJHcXGyoMFdwMhCl7zmtfYYDe4Hbglh5QfXja/Ux/84Aft+LlBJ0wSeMOol1c2SOhLR01HWg0bLe9617usD4ynPanQ4i3sABs3QBhgF76+HWlnHVXO/X2f+tSnrN8WXx4uFvyaWLl8OfNjQntN8iVGqx/+8IeOehFtNXWB9+WXX7Ybby+++KKFLJbfL3/5yzp/vfBwZX3wX3RKYio21q9+9at2fPyXtcpc8xDB/fKBD3ygTvBeddVV9hcPsV/CJIE3jHp5ZbPqasDi4T67/M2E//73vzZEJlYC/s1iGw1Yh5/+9KdzflBHMsdaDRDCz4nlh58PP/c3vvGNqnA1eEIzjzyE8uNi508CLgd+1eT7QmOdpBIaKwQvfnwCmAPZfD8+6/W5556zD0+5GkoQNilZyg19mZR+n6kf+Dz5CZ2fCFD/1re+1UKVTYrCxPEcdv85bsVP9LQnjozx85IAT1i2uBgI3sSt1yQeuvizs7a5xthwI3z84x83HDUrlnA3vO1tb7NHzLyAV0mb70LwepuGbJLho/YSG6O4yvh1522ucaKFkzwkfglwlEybawmbYb/QlwnrbuDu5LsasAL5UnI8CcuQCHOc8/3zn/9sLypN8vGqugTAT4sl95a3vMVuLH3hC1+wVh23V3PXH4DlPDP+XixCrrBicybpx8mYK8ZDYmPw61//uj0WyKYpx6Xw47PBxDzy8OQEC+DBjcAZZVww3PDCzj+bbRyv44gVx8nw/4c9YhV4QRYpeKaxcpUXaxh3yje/+U3rauDBwfExNPHO8vJffN4cJ0MjzvSii46TuZwpR3XFGYLSUZfLriYfvPxsY5f3t7/9rb2Dii8tX2aO5HD0Jo3pwQcftD+t+dnJF46HClDy7tfyXqBggyX/BYpi1n+Sxs/DgbkpTJxYwWfJ3YX4cwESIGZTjZCr3uYaPmysX6xcwMb88lOdUw1JO7N9prEC0ueff97OMdYtJxeALxuk/JLxbprh+jAeumy05b9A4WJdy8ebpG+G+iIFpEBVKCDwVsU0a5BSQAokSQGBN0mzob5IASlQFQoIvFUxzRqkFJACSVJA4E3SbKgvUkAKVIUCAm9VTLMGKQWkQJIUEHiTNBvqixSQAlWhgMBbFdOsQUoBKZAkBQTeJM2G+iIFpEBVKCDwVsU0J3+QvGpMPATegCPWb9Dkqp6g7YctR9yH9u3b27i+REErlqIaI2+o8TZXGiPKhdU97vICb9yKZ7C9/DvXeF+fIDK8SkoMg/wg0mcaehCY0G7hzRdETvvLX/5iYwkQZzWq5L1WSv3EIiauAzEq8qHFa6ZoQV7i9xLTwC8RO4CoWT/96U/rzBpEK792+Xz9+vU2HsGePXts5Dml6BQQeKPTtmpqBoDenWsEQCd4CqEEe/XqZYOIl5KCwKQYeEtpy0UeYEqQnL59+1r4c4kkMQAIpMNtFSQCzBDPgdgOBFy56aabfJsG4AQdGjx4cOzg5aHFg4Jx5Eft8u20MpStgMBbtmQqUKhAMQAStYovMNGcvASoCK1I6D3vVlcuGCQVghcIELSEuKgENCGiFHm53ZgEnAqvX+HyQur1XBZvf/vbbTkCvXDVi5cIaM79WkTb4mJHQgASDIXYwwRGIbA3Vxx5gd+LzXhhqMF///vf9uofbqolchmJgDTc5gB4CbzDWM6U6Bc/9wm8w/VDXvrVr35l73Ij2huBeL74xS9ayzrfLcPDDmuVgN6vf/3rDTclMAbvyh7Ce6IBY6RuQlpyr1zh/WmE/ER7bmZQik4BgTc6baum5kLw7tu3z1x77bUWukCTRLR/oljxU5qQhECD2LaE4SM6ViF4ARn3fXHBJPDYunWrBTHwJj4q0bGGDx9uA5V7txsTIYubQPJ9xcCIiFu/+MUvcvPBv1Eff0ARy5yyU6ZMsRHIsFJ5aPzhD3+oM+pWsVsNKMuYuNUAqBNKkRiutIElCRwBfV1p7ty5tm0A6yWCsTOe97///fZKJR5aPHzQ2AMvbfCQoW3iH+PiIFYyvz482PMZ1/N8+9vfttcZMVbuleOXCe16iZuVvYdj1SzgCgxU4K2A6FlrEvDys5r72LCWsBpJQJUweyQsT+LY5v+EBqwElQaApbgaPvOZz1iXxpo1a2ydxSztwnqAE9YtwCL03+nTp21f8MdiQQMmbtg4evRojfvhCACOVQjs/SxebuHA1QAYveDaWKWAFn8tidCZWKuMua7EzcVPP/10Df8uweTZ8CIkY4MGDWxRQjjiuvDAC0R5uOTf90ZIS8IXEiOYUJ2EeSS84cc+9jFbB1Y+DwOAnA9e/LzowYNPft7ovqkCb3TaVk3NAJDNI6wlLknkXjKsRYJIs8HFZhc3NLC5lP9lxiLDSgSmxcALYKiLGzCw4tjx56cxP71LBS/5OCWAJXjbbbcZ3BFczY1ljCUNMPn3wo0v2sMy5mFRF3h50LCZ6F3w45n8AAAEZUlEQVQlA2xxsfDwAfLz5s2zsXxJPCx4CDHOugKG404AmD/4wQ9yTVJmx44dNdwU/H908MDLRibWLBd05iesZR4EAJb86MhDx0vckcatEfngpR7cFMxjKZuBVbPIHQ9U4HUsaDVWV8zyxL/JNdkERAes3EqLVcz1SPkJCPFTuhC8+ERxQXzta1+zm1XcZAEksey2b99eFngJYs7tIL/73e+shYf7Y8OGDbYOwMo9agTOLkz4bD0faeFnuBp40HAxIj7T/KvsgR0wLAQsQC68bia/XsDNg4FfAF7CCqbf+f7hQvCyEYY1XOwhgbX7xz/+0bp3uBUkP4g3/9anT58a4EUn3A9AWyk6BQTe6LStmpqLgReQAQR8nVhc3FWFRcfP8WKpELyf/exn7U/1/GNVQI7bETzw4gbAv+lBlHqLWc64GfCt/vrXv7agBJhsepGw8OgnV+KwMVdqquuWXspj5WJ9AtL89OUvf9m6YTxXSWFb/DtjwjfuHVfzXA1Ywp4Fih+YjTLP4qWdtWvX2pshih2hY2MNVwMnTDwLHN8488IFj/kWL/PD3OX7xEvVRPlKV0DgLV0r5axDgbqOdXE64IorrrAbargMvB1/QIdfFBCygz9+/PhawORnOr5LLF8sYm6xveeee+z/9sDLOWEgxPUtgAW3BS6PYi9i8DICVhwPAvy5HsTY+OLnNnDCYuQ4F24ILFP8soyhWKoLvLhVuOgTXylHzfITkMcSpo9NmjSpVS3ABYbc6eVdI8QmIuPhWnFOZ/BgYXONB4UHXvqLKwHrldMZWOl8/sADD9hNTSxvLH0eYmyuYZ2z0YlubFBy+sFLXOt05ZVX1vmA1JfAjQICrxsdq7qWusDLZg7Hk4AAP3H5/7gLsGQ58sTttPyUZjOn0FIFzFh1bE4BOTblAOvGjRtz4AVynI3dtm2bPeVQeJws/w047sJjcw5fL2+F5Seg61mN1IlbBBhzLKyu+7XqAi+uETbQgDsXY+YnfNqcKKAtHjbFEuPE8vaOpJHniSeesFpw2gF/NVYplmv+cTLcCZxkQAO0w8cM+NngpK/FjpMBZk5LeG15Dy1OTPALRSk6BQTe6LRVzVKgbAU41YA7hIdVlDc0Y/1jmfOgwOolYS1z2gH3hlK0Cgi80eqr2qVA2QpgkXPigF8ErhLWMa8Cv+c977FwvfPOO60vF8B7G4j8GuEXAVa5UrQKCLzR6qvapUAiFAC8bKRxrpeNP84244ZwCfdEDDQlnRB4UzJR6qYUkALZUUDgzc5caiRSQAqkRAGBNyUTpW5KASmQHQUE3uzMpUYiBaRAShQQeFMyUeqmFJAC2VFA4M3OXGokUkAKpEQBgTclE6VuSgEpkB0FBN7szKVGIgWkQEoUEHhTMlHqphSQAtlRQODNzlxqJFJACqREAYE3JROlbkoBKZAdBQTe7MylRiIFpEBKFBB4UzJR6qYUkALZUUDgzc5caiRSQAqkRAGBNyUTpW5KASmQHQX+P3+nNiAfD1oCAAAAAElFTkSuQmCC" width="350">
```python
incbins = [15,30,45,60,75]
plt.figure(figsize=(3.5,5))
for i in range(len(incbins)-1):
plt.subplot(4,1,i+1)
cut = (incs > incbins[i]) & (incs < incbins[i+1])
print(f'{incbins[i]}-{incbins[i+1]}: {cut.sum()}')
absdiffs = np.abs(180 - ((deprojs[cut]) % 360))
plt.hist(absdiffs, bins=30, density=True, range=(0,180), color='olivedrab')
#plt.hist(relgz[cut], bins=30, histtype='step', color='k', density=True, label='GZ')
#[plt.axvline(v, c='.5', ls='--', lw=1) for v in [0,90,180]]
plt.text(.74,.8,f'$i={{{incbins[i]}}}-{{{incbins[i+1]}}}^\circ$', transform=plt.gca().transAxes,
horizontalalignment='center', fontsize=12)
plt.xticks([0,45,90,135,180])
plt.tick_params(labelbottom=False, direction='in')
plt.ylim((0,.018))
plt.tick_params(labelbottom=True, direction='in')
plt.xlabel('Relative PA (deg)')
plt.tight_layout(h_pad=-1)
plt.savefig('relpabinchists.pdf', format='pdf')
```
<IPython.core.display.Javascript object>
<img 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" width="350">
15-30: 82
30-45: 319
45-60: 423
60-75: 278
```python
def gzbarang(gz,plot=False, returncen=False):
bar = gz[4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
r = np.sqrt(x**2 + y**2)
th = (np.degrees(np.arctan2(y,x))) % 180
degs = np.linspace(0,180,181)
tots = np.zeros(180)
for i in range(180):
cut = (th > degs[i]) & (th < degs[i+1])
tots[i] = np.sum(bar[cut])
smoothtots = savgol_filter(tots, 19, 2)
maxbin = degs[np.argmax(smoothtots)]
centtots = np.zeros(180)
centdegs = (degs-maxbin)
for i in range(180):
cut = ((th-maxbin)%180 > degs[i]) & ((th-maxbin)%180 < degs[i+1])
centtots[i] = np.sum(bar[cut])
cen = (np.average((degs[:-1]+90)%180 - 90, weights=centtots))
pab = (maxbin + cen + 90) % 180
r = np.sqrt(x**2 + y**2)
th = np.degrees(np.arctan2(y,x))
#plt.figure(figsize=(8,8))
#plt.subplot(221)
#plt.imshow(r)
#plt.subplot(222)
#plt.imshow(th)
major = ((th-95)%180 < pab) & ((th-85)%180 > pab) * (bar > .2*len(gz[10].data))
minor = ((th-5)%180 < pab) & ((th+5)%180 > pab) * (bar > .2*len(gz[10].data))
#plt.subplot(223)
#plt.imshow(major)
#plt.subplot(224)
#plt.imshow(minor)
barlength = np.max(r[major])
barwidth = np.max(r[minor])
if returncen: return pab, (xcen, ycen), barlength, barwidth
return pab, (xcen, ycen), tots, smoothtots, maxbin, centtots, cen
def prepfiles(plate, ifu, stellar=False,dir='barred',root='/media/brian/bdigiorg/'):
vftype = 'Stars' if stellar else 'Gas'
f = fits.open(f'/media/brian/bdigiorg/nirvana/lux/{dir}/nirvana_{plate}-{ifu}_{vftype}.fits')
maps = fits.open(f'{root}/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/{plate}/{ifu}/manga-{plate}-{ifu}-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz')
projpab = projectedpab(f[1].data['pab'], f[1].data['pa'], f[1].data['inc'])
gz = fits.open(glob(f'{root}/GZ3D/gz3d_{f[0].header["mangaid"]}*.fits.gz')[0])
drpall = fits.open(f'{root}/manga/spectro/redux/DR17/drpall-v3_1_1.fits')[1].data
drp = drpall[drpall['plateifu'] == f'{plate}-{ifu}']
d = {}
for k in ['f','maps','gz','projpab','drp']:
exec(f'd["{k}"] = {k}')
return d
plate, ifu, vftype = (8078,12703,'Gas')
fname = f'data/lux/{dir}/nirvana_{plate}-{ifu}_{vftype}.fits'
d = prepfiles(plate,ifu,vftype=='Stars',dir='barred/sample/')
for i in range(len(d['gz'])):
d['gz'][i].data = np.flip(d['gz'][i].data, 0)
drpall = fits.open('/media/brian/bdigiorg/manga/spectro/redux/DR17/drpall-v3_1_1.fits')[1].data
drp = drpall[drpall['plateifu']==f'{plate}-{ifu}']
vel = np.ma.array(d['maps']['emline_gvel'].data[0], mask=d['maps']['emline_gvel_mask'].data[0])
nang = d['f'][1].data['pab']
ncen = (d['f'][1].data['xc'], d['f'][1].data['yc'])
ppa = drp['nsa_elpetro_phi']
f = plt.figure(figsize=(3.5,6))
gs = f.add_gridspec(3,1,height_ratios=[2,1,1], hspace=.1, top=1, bottom=.07)
#plt.subplot(211)
ax1 = f.add_subplot(gs[0])
plt.imshow(d['gz'][0].data, origin='lower')
plt.tick_params(left=None, bottom=None, labelleft=None, labelbottom=None)
bar = d['gz'][4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
if bar.any():
gzang, gzcen, tots, smoothtots, maxbin, centtots, cen = gzbarang(d['gz'])#, returncen=True)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
gzpab = gzang
npab = d['projpab']
gzpa = ppa
npa = d['f'][1].data['pa']
plt.contour(d['gz'][4].data,cmap='Greens',levels=2, linestyles=':', alpha=1, linewidths=1)
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(gzpab - 90)) + gzcen[1], 'olivedrab', label='GZ:3D')
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(npab - 90)) + gzcen[1], 'w--', label='Nirvana')
plt.plot(*gzcen, 'o', c='olivedrab')
else:
xcen, ycen = (0,0)
plt.xlim((0,d['gz'][0].data.shape[0]))
plt.ylim((0,d['gz'][0].data.shape[1]))
handles, labels = plt.gca().get_legend_handles_labels()
handles.append(Line2D([0], [0], label='Bar', color='g', ls=':'))
handles.append(Line2D([0], [0], label='MaNGA IFU', color='m', ls='-'))
plt.legend(handles=handles)
plt.axis('off')
#plt.subplot(413)
f.add_subplot(gs[1])
degs = np.linspace(0,181,180)
plt.plot(degs, tots, '--', c='k', lw=1, label='Bar votes')
plt.plot(degs, smoothtots, '-', c='olivedrab', label='Smoothed')
plt.axvline(maxbin, c='r', ls=':', label='Max votes')
plt.tick_params(direction='in')
plt.legend()
plt.ylabel('Number of Votes')
#plt.subplot(414)
f.add_subplot(gs[2])
plt.plot((degs+90)%180 - 90, centtots, 'k--', lw=1)#, label='Recentered votes')
plt.axvline(cen, c='r', ls=':', label='Center\nof Mass')
plt.xlabel('Position Angle (deg)')
plt.legend()
plt.tick_params(direction='in')
plt.ylabel('Number of Votes')
#plt.tight_layout(h_pad=0)
plt.savefig('gzbarang.pdf', format='pdf')
```
<IPython.core.display.Javascript object>
<img 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" width="350">
```python
def gzbarang(gz,plot=False, returncen=False):
#bar = gz[4].data
#xx = np.arange(bar.shape[0])
#yy = np.arange(bar.shape[0])
#x,y = np.meshgrid(xx,yy)
#fit = np.polyfit(x.flatten(),y.flatten(),1,w=bar.flatten())
#return np.degrees(np.arctan(fit[0])) % 180, fit[0]
bar = gz[4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
r = np.sqrt(x**2 + y**2)
th = (np.degrees(np.arctan2(y,x))) % 180
degs = np.linspace(0,180,181)
tots = np.zeros(180)
for i in range(180):
cut = (th > degs[i]) & (th < degs[i+1])
tots[i] = np.sum(bar[cut])
smoothtots = savgol_filter(tots, 19, 2)
pab = degs[np.argmax(smoothtots)]
centtots = np.zeros(180)
centdegs = (degs-pab)
for i in range(180):
cut = ((th-pab)%180 > degs[i]) & ((th-pab)%180 < degs[i+1])
centtots[i] = np.sum(bar[cut])
cen = (np.average((degs[:-1]+90)%180 - 90, weights=centtots))
if returncen: return pab+cen, (xcen, ycen)
return (pab + cen + 90) % 180
def barangparallel(mangaid):
try:
f = glob(f'/media/brian/bdigiorg/GZ3D/gz3d_{mangaid}*.fits.gz')[0]
with fits.open(f) as gz:
for i in range(len(gz)):
gz[i].data = np.flip(gz[i].data, 0)
return gzbarang(gz, returncen=False)
except ZeroDivisionError as e:
print(mangaid, 'failed', e)
return np.nan
#barids = drp['mangaid'][bars2]
with mp.Pool(6) as p:
barangsmp = p.map(barangparallel, ids)
barangs90 = np.array(barangsmp)
```
1-837 failed Weights sum to zero, can't be normalized
1-411 failed Weights sum to zero, can't be normalized
1-380 failed Weights sum to zero, can't be normalized
1-54815 failed Weights sum to zero, can't be normalized
1-50480 failed Weights sum to zero, can't be normalized
1-50537 failed Weights sum to zero, can't be normalized
1-2333 failed Weights sum to zero, can't be normalized
1-2431 failed Weights sum to zero, can't be normalized
1-36456 failed Weights sum to zero, can't be normalized
1-36457 failed Weights sum to zero, can't be normalized
1-53488 failed Weights sum to zero, can't be normalized
1-207 failed Weights sum to zero, can't be normalized
1-323 failed Weights sum to zero, can't be normalized
1-277 failed Weights sum to zero, can't be normalized
1-46266 failed Weights sum to zero, can't be normalized
1-60709 failed Weights sum to zero, can't be normalized
1-27404 failed Weights sum to zero, can't be normalized
1-27654 failed Weights sum to zero, can't be normalized
1-34106 failed Weights sum to zero, can't be normalized
1-4109 failed Weights sum to zero, can't be normalized
1-38802 failed Weights sum to zero, can't be normalized
1-35900 failed Weights sum to zero, can't be normalized
1-36382 failed Weights sum to zero, can't be normalized
1-92 failed Weights sum to zero, can't be normalized
1-40700 failed Weights sum to zero, can't be normalized
1-2511 failed Weights sum to zero, can't be normalized
1-23929 failed Weights sum to zero, can't be normalized
1-36832 failed Weights sum to zero, can't be normalized
1-36899 failed Weights sum to zero, can't be normalized
1-37908 failed Weights sum to zero, can't be normalized
1-46562 failed Weights sum to zero, can't be normalized
1-37996 failed Weights sum to zero, can't be normalized
1-38380 failed Weights sum to zero, can't be normalized
1-26611 failed Weights sum to zero, can't be normalized
1-51668 failed Weights sum to zero, can't be normalized
1-31996 failed Weights sum to zero, can't be normalized
1-46428 failed Weights sum to zero, can't be normalized
1-24416 failed Weights sum to zero, can't be normalized
1-51523 failed Weights sum to zero, can't be normalized
1-51378 failed Weights sum to zero, can't be normalized
1-42030 failed Weights sum to zero, can't be normalized
1-45581 failed Weights sum to zero, can't be normalized
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_188833/2157326586.py in <module>
48 barangsmp = p.map(barangparallel, ids)
49 barangs90 = np.array(barangsmp)
---> 50 np.save('GZ3Dbarpas2', barangs)
NameError: name 'barangs' is not defined
```python
np.save('GZ3Dbarpas2', barangs90)
```
```python
drp = fits.open('drpall-v3_1_1.fits')[1].data
ppas = np.zeros(len(fs))
indexes = np.zeros(len(fs))
for i,pi in enumerate(pis):
indexes[i] = np.where(drp['plateifu'] == pi)[0][0]
ppas[i] = drp['nsa_elpetro_phi'][drp['plateifu'] == pi]
ppabs = projectedpab(pabs, pas, incs, relpab=False)
barangs90 = np.load('GZ3Dbarpas2.npy')
diffs = (barangs90 - ppabs)%180
relgz = (barangs90 - ppas)%180
reln = (ppabs-90 - pas)%180
vtmaxs = np.array([np.max(v) for v in vts])
v2tmaxs = np.array([np.max(v) for v in v2ts])
v2rmaxs = np.array([np.max(v) for v in v2rs])
strong = (v2rmaxs/vtmaxs > .1) & (pabus-pabls < 30)
```
RuntimeWarning: invalid value encountered in remainder
RuntimeWarning: invalid value encountered in remainder
```python
def recenter(arr, mod=180):
return (arr - mod/2) % mod - mod/2
plt.figure(figsize=(3.5,6))
plt.subplot(5,1,1)
hist, bins, plot = plt.hist(recenter(relgz), bins=30, density=True, range=(-90,90), color='olivedrab', label='GZ:3D')
plt.hist(recenter(np.abs(180-deprojs)), bins=30, density=True, range=(-90,90), color='k', histtype='step', label='Nirvana')
#[plt.axvline(v, c='k', ls='--', lw=1) for v in [-90,0,90]]
plt.tick_params(labelbottom=False, direction='in')
plt.text(.58,.8,'All', transform=plt.gca().transAxes, fontsize=12)
plt.legend(loc=2)
plt.ylim((0,.05))
incbins = [15,30,45,60,75]
for i in range(len(incbins)-1):
plt.subplot(5,1,i+2)
cut = (incs > incbins[i]) & (incs < incbins[i+1])
print(f'{incbins[i]}-{incbins[i+1]}: {cut.sum()}')
absdiffs = np.abs(180 - ((deprojs[cut]) % 360))
plt.hist(recenter(relgz[cut]), bins=30, density=True, range=(-90,90), color='olivedrab')
plt.hist(recenter(absdiffs), bins=30, density=True, range=(-90,90), color='k', histtype='step')
#[plt.axvline(v, c='k', ls='--', lw=1) for v in [-90,0,90]]
plt.text(.74,.8,f'$i={{{incbins[i]}}}-{{{incbins[i+1]}}}^\circ$', transform=plt.gca().transAxes,
horizontalalignment='center', fontsize=12)
plt.xticks([-90,-45,0,45,90])
plt.tick_params(labelbottom=False, direction='in')
plt.ylim((0,.05))
plt.tick_params(labelbottom=True, direction='in')
plt.xlabel('Relative PA (deg)')
plt.tight_layout(h_pad=-1)
plt.savefig('allrelpabhists.pdf', format='pdf')
```
<IPython.core.display.Javascript object>
<img src="data:image/png;base64,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" width="350">
RuntimeWarning: invalid value encountered in remainder
RuntimeWarning: invalid value encountered in remainder
RuntimeWarning: invalid value encountered in remainder
RuntimeWarning: invalid value encountered in remainder
RuntimeWarning: invalid value encountered in remainder
15-30: 82
30-45: 319
45-60: 423
60-75: 278
```python
apis = np.array(pis)
relgz[apis=='8078-12703']
reln[apis=='8078-12703']
```
array([152.30773823])
```python
def recenter(arr, mod=180):
return (arr - mod/2) % mod - mod/2
plt.figure(figsize=(3.5,2))
plt.hist(recenter(diffs), bins=20, color='olivedrab', density=True)
plt.xticks([-90,-45,0,45,90])
#plt.axvline(np.nanmedian(recenter(diffs)), c='k', ls='--', label='Median')
#plt.legend()
plt.xlabel(r'GZ:3D Bar PA - Nirvana $\phi_b$ (deg)')
plt.tight_layout()
print(np.nanmedian(recenter(diffs)))
plt.savefig('gzdiffshist.pdf',format='pdf')
```
<IPython.core.display.Javascript object>
<img 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" width="350">
-1.5418951400969974
RuntimeWarning: invalid value encountered in remainder
```python
def projectedpab(pab, pa, inc, degrees=True, relpab=False):
_pab, _pa, _inc = np.radians((pab, pa, inc)) if degrees else (pab, pa, inc)
adjust = _pab > np.pi
if not relpab: _pab -= _pa
projpab = np.arctan(np.tan(_pab) * np.cos(_inc)) + _pa
return (np.degrees(projpab) % 180 + 180*adjust) % 360
def prepfiles(plate, ifu, stellar=False,dir='barred'):
vftype = 'Stars' if stellar else 'Gas'
f = fits.open(f'/media/brian/bdigiorg/nirvana/lux/{dir}/nirvana_{plate}-{ifu}_{vftype}.fits')
maps = fits.open(f'/media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/{plate}/{ifu}/manga-{plate}-{ifu}-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz')
projpab = projectedpab(f[1].data['pab'], f[1].data['pa'], f[1].data['inc'])
gz = fits.open(glob(f'/media/brian/bdigiorg/GZ3D/gz3d_{f[0].header["mangaid"]}*.fits.gz')[0])
drpall = fits.open('drpall-v3_1_1.fits')[1].data
drp = drpall[drpall['plateifu'] == f'{plate}-{ifu}']
d = {}
for k in ['f','maps','gz','projpab','drp']:
exec(f'd["{k}"] = {k}')
return d
def plotpabs(plate,ifu,vftype='Gas', dir='barred/sample', minus=False):
fname = f'/media/brian/bdigiorg/nirvana/lux/{dir}/nirvana_{plate}-{ifu}_{vftype}.fits'
d = prepfiles(plate,ifu,vftype=='Stars',dir=dir)
if minus: d['projpab'] *= -1
for i in range(len(d['gz'])):
d['gz'][i].data = np.flip(d['gz'][i].data, 0)
drpall = fits.open('drpall-v3_1_1.fits')[1].data
drp = drpall[drpall['plateifu']==f'{plate}-{ifu}']
vel = np.ma.array(d['maps']['emline_gvel'].data[0], mask=d['maps']['emline_gvel_mask'].data[0])
nang = d['f'][1].data['pab']
ncen = (d['f'][1].data['xc'], d['f'][1].data['yc'])
ppa = drp['nsa_elpetro_phi']
plt.figure(figsize=(12,4))
plt.subplot(131)
plt.imshow(d['gz'][0].data, origin='lower')
bar = d['gz'][4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
if bar.any():
gzang, gzcen, l, w = gzbaranglw(d['gz'], returncen=True)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
gzpab = gzang
npab = d['projpab']
gzpa = ppa
npa = d['f'][1].data['pa']
plt.contour(d['gz'][4].data,colors='w',levels=1, linestyles='--', alpha=.5)
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(gzpab - 90)) + gzcen[1], 'g', label='GZ')
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(npab - 90)) + gzcen[1], 'w', label='N pab')
else:
xcen, ycen = (0,0)
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(npa - 90)) + gzcen[1], 'w--', label='N pa')
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(gzpa - 90)) + gzcen[1], 'g--', label='pPA')
plt.xlim((0,d['gz'][0].data.shape[0]))
plt.ylim((0,d['gz'][0].data.shape[1]))
plt.legend()
plt.axis('off')
#return
plt.subplot(132)
x = d['maps']['spx_skycoo'].data[0]
xcen = x.shape[0]//2
plt.imshow(vel, cmap='RdBu', origin='lower')
args, resdict = fileprep(fname, rootdir='/media/brian/bdigiorg/manga/spectro')
z = np.zeros(len(resdict['vt']))
vtdict, v2tdict, v2rdict = [resdict.copy(), resdict.copy(), resdict.copy()]
vtdict['v2t'] = z
vtdict['v2r'] = z
v2tdict['vt'] = z
v2tdict['v2r'] = z
v2rdict['vt'] = z
v2rdict['v2t'] = z
velmodel, sigmodel = bisym_model(args, resdict, plot=True)
vtmodel, sigmodel = bisym_model(args, vtdict, plot=True)
v2tmodel, sigmodel = bisym_model(args, v2tdict, plot=True)
v2rmodel, sigmodel = bisym_model(args, v2rdict, plot=True)
plt.axis('off')
if bar.any():
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpab - 90)) + xcen, 'g', label='GZ')
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(npab - 90)) + xcen, 'k', label='N pab')
plt.plot(x[0]+xcen, x[0]*np.tan(np.radians(npa - 90)) + xcen, 'k--', label='N pa')
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpa - 90)) + xcen, 'g--', label='pPA')
plt.contour(velmodel-vtmodel-v2tmodel,colors='k',levels=5, linestyles='--', alpha=.5)
plt.xlim((0,x.shape[0]))
plt.ylim((0,x.shape[1]))
plt.legend()
plt.subplot(133)
plt.imshow(velmodel-vtmodel-v2tmodel,cmap='RdBu', origin='lower')
if bar.any():
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpab - 90)) + xcen, 'g', label='GZ')
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(npab - 90)) + xcen, 'k', label='Nirv')
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpa - 90)) + xcen, 'g--', label='pPA')
plt.plot(x[0]+xcen, x[0]*np.tan(np.radians(npa - 90)) + xcen, 'k--', label='N pa')
plt.xlim((0,x.shape[0]))
plt.ylim((0,x.shape[1]))
plt.legend()
plt.tight_layout()
print(gzpab, npab)
def gzbaranglw(gz,plot=False, returncen=False):
bar = gz[4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
r = np.sqrt(x**2 + y**2)
th = (np.degrees(np.arctan2(y,x))) % 180
degs = np.linspace(0,180,181)
tots = np.zeros(180)
for i in range(180):
cut = (th > degs[i]) & (th < degs[i+1])
tots[i] = np.sum(bar[cut])
smoothtots = savgol_filter(tots, 19, 2)
maxbin = degs[np.argmax(smoothtots)]
centtots = np.zeros(180)
centdegs = (degs-maxbin)
for i in range(180):
cut = ((th-maxbin)%180 > degs[i]) & ((th-maxbin)%180 < degs[i+1])
centtots[i] = np.sum(bar[cut])
cen = (np.average((degs[:-1]+90)%180 - 90, weights=centtots))
pab = (maxbin + cen + 90) % 180
r = np.sqrt(x**2 + y**2)
th = np.degrees(np.arctan2(y,x))
#plt.figure(figsize=(8,8))
#plt.subplot(221)
#plt.imshow(r)
#plt.subplot(222)
#plt.imshow(th)
major = ((th-95)%180 < pab) & ((th-85)%180 > pab) * (bar > .2*len(gz[10].data))
minor = ((th-5)%180 < pab) & ((th+5)%180 > pab) * (bar > .2*len(gz[10].data))
#plt.subplot(223)
#plt.imshow(major)
#plt.subplot(224)
#plt.imshow(minor)
barlength = np.max(r[major])
barwidth = np.max(r[minor])
if returncen: return pab, (xcen, ycen), barlength, barwidth
return pab
plate,ifu = (8078, 12703)
plotpabs(plate, ifu)
print(barangs[apis==f'{plate}-{ifu}'], ppabs[apis==f'{plate}-{ifu}'])
```
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" width="1200">
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/8078/images/12703.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/redux/DR17/8078/stack/manga-8078-12703-LOGCUBE.fits.gz ...
Done
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/8078/12703/manga-8078-12703-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 7.5 +/- 0.0
Inclination: 30.5 +/- 0.1
Systemic Velocity: 3.3 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 159.3 +/- 0.1
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 2773
Velocity chi-square: 145872.16459275514
Reduced chi-square: 52.737586620663464
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 7.5 +/- 0.0
Inclination: 30.5 +/- 0.1
Systemic Velocity: 3.3 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 159.3 +/- 0.1
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 2772
Velocity chi-square: 145870.79628685315
Reduced chi-square: 52.756165022370034
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: 7.5 +/- 0.0
Inclination: 28.4 +/- 0.1
Systemic Velocity: 2.1 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 159.3 +/- 0.1
RC: Scale: 3.7 +/- 0.0
----------
Velocity measurements: 2261
Velocity chi-square: 121157.43749039598
Reduced chi-square: 53.75219054587222
----------------------------------------------------------------------
72.35009562551227 [76.97776031]
RuntimeWarning: divide by zero encountered in true_divide
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
/tmp/ipykernel_192962/3189066527.py in <module>
156 plate,ifu = (8078, 12703)
157 plotpabs(plate, ifu)
--> 158 print(barangs[apis==f'{plate}-{ifu}'], ppabs[apis==f'{plate}-{ifu}'])
NameError: name 'barangs' is not defined
```python
def gzbarang3(gz,plot=False, returncen=False):
bar = gz[4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
r = np.sqrt(x**2 + y**2)
th = (np.degrees(np.arctan2(y,x))) % 180
degs = np.linspace(0,180,181)
tots = np.zeros(180)
for i in range(180):
cut = (th > degs[i]) & (th < degs[i+1])
tots[i] = np.sum(bar[cut])
smoothtots = savgol_filter(tots, 19, 2)
maxbin = degs[np.argmax(smoothtots)]
centtots = np.zeros(180)
centdegs = (degs-maxbin)
for i in range(180):
cut = ((th-maxbin)%180 > degs[i]) & ((th-maxbin)%180 < degs[i+1])
centtots[i] = np.sum(bar[cut])
cen = (np.average((degs[:-1]+90)%180 - 90, weights=centtots))
pab = (maxbin + cen + 90) % 180
r = np.sqrt(x**2 + y**2)
th = np.degrees(np.arctan2(y,x))
#plt.figure(figsize=(8,8))
#plt.subplot(221)
#plt.imshow(r)
#plt.subplot(222)
#plt.imshow(th)
major = ((th-95)%180 < pab) & ((th-85)%180 > pab) * (bar > .2*len(gz[10].data))
minor = ((th-5)%180 < pab) & ((th+5)%180 > pab) * (bar > .2*len(gz[10].data))
#plt.subplot(223)
#plt.imshow(major)
#plt.subplot(224)
#plt.imshow(minor)
barlength = np.max(r[major])
barwidth = np.max(r[minor])
if returncen: return pab, (xcen, ycen), barlength, barwidth
return pab, (xcen, ycen), tots, smoothtots, maxbin, centtots, cen
plt.figure(figsize=(9,3))
drpall = fits.open('/media/brian/bdigiorg/manga/spectro/redux/DR17/drpall-v3_1_1.fits')[1].data
n = 0
while n < 12:
try:
pi = np.random.choice(pis)
plt.subplot(2,6,n+1)
plate,ifu,vftype = (*pi.split('-'), 'Gas')
d = prepfiles(plate,ifu,vftype=='Stars',dir='barred/sample/')
for i in range(len(d['gz'])):
d['gz'][i].data = np.flip(d['gz'][i].data, 0)
drp = drpall[drpall['plateifu']==f'{plate}-{ifu}']
vel = np.ma.array(d['maps']['emline_gvel'].data[0], mask=d['maps']['emline_gvel_mask'].data[0])
nang = d['f'][1].data['pab']
ncen = (d['f'][1].data['xc'], d['f'][1].data['yc'])
ppa = drp['nsa_elpetro_phi']
plt.imshow(d['gz'][0].data, origin='lower')
plt.tick_params(left=None, bottom=None, labelleft=None, labelbottom=None)
bar = d['gz'][4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
if bar.any():
gzang, gzcen, tots, smoothtots, maxbin, centtots, cen = gzbarang3(d['gz'])#, returncen=True)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
gzpab = gzang
npab = d['projpab']
gzpa = ppa
npa = d['f'][1].data['pa']
plt.contour(d['gz'][4].data,cmap='Greens',levels=2, linestyles=':', alpha=1, linewidths=1)
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(gzpab - 90)) + gzcen[1], 'olivedrab', label='GZ:3D')
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(npab - 90)) + gzcen[1], 'w--', label='Nirvana')
plt.plot(*gzcen, 'o', c='olivedrab')
else:
xcen, ycen = (0,0)
plt.xlim((0,d['gz'][0].data.shape[0]))
plt.ylim((0,d['gz'][0].data.shape[1]))
handles, labels = plt.gca().get_legend_handles_labels()
handles.append(Line2D([0], [0], label='Bar', color='g', ls=':'))
handles.append(Line2D([0], [0], label='MaNGA\nIFU', color='m', ls='-'))
plt.axis('off')
plt.text(.5,.05,pi,transform=plt.gca().transAxes,horizontalalignment='center',fontsize=12,c='w')
n += 1
except:
print('failed')
plt.cla()
plt.axis('off')
plt.legend(handles=handles, facecolor='.7', loc=4)
plt.tight_layout(pad=0, h_pad=-.5, w_pad=-.5)
#plt.savefig('imagemosaic.pdf', format='pdf')
```
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" width="900">
```python
def prepfiles(plate, ifu, stellar=False,dir='barred',root='/media/brian/bdigiorg/'):
vftype = 'Stars' if stellar else 'Gas'
f = fits.open(f'/media/brian/bdigiorg/nirvana/lux/{dir}/nirvana_{plate}-{ifu}_{vftype}.fits')
maps = fits.open(f'{root}/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/{plate}/{ifu}/manga-{plate}-{ifu}-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz')
projpab = projectedpab(f[1].data['pab'], f[1].data['pa'], f[1].data['inc'])
gz = fits.open(glob(f'{root}/GZ3D/gz3d_{f[0].header["mangaid"]}*.fits.gz')[0])
drpall = fits.open(f'{root}/manga/spectro/redux/DR17/drpall-v3_1_1.fits')[1].data
drp = drpall[drpall['plateifu'] == f'{plate}-{ifu}']
d = {}
for k in ['f','maps','gz','projpab','drp']:
exec(f'd["{k}"] = {k}')
return d
plt.figure(figsize=(6,8))
drpall = fits.open('/media/brian/bdigiorg/manga/spectro/redux/DR17/drpall-v3_1_1.fits')[1].data
n = 0
pabes = pabus - pabls
good = pabes < 20
pis = np.array(pis)
while n < 5:
#try:
pi = np.random.choice(pis[good])
plt.subplot(5,3,3*n+1)
plate,ifu,vftype = (*pi.split('-'), 'Gas')
d = prepfiles(plate,ifu,vftype=='Stars',dir='barred/sample/')
for i in range(len(d['gz'])):
d['gz'][i].data = np.flip(d['gz'][i].data, 0)
drp = drpall[drpall['plateifu']==f'{plate}-{ifu}']
vel = np.ma.array(d['maps']['emline_gvel'].data[0], mask=d['maps']['emline_gvel_mask'].data[0]) - d['f'][1].data['vsys']
nang = d['f'][1].data['pab']
ncen = (d['f'][1].data['xc'], d['f'][1].data['yc'])
ppa = drp['nsa_elpetro_phi']
plt.imshow(d['gz'][0].data, origin='lower')
plt.tick_params(left=None, bottom=None, labelleft=None, labelbottom=None)
bar = d['gz'][4].data
xx = np.linspace(0,bar.shape[0],bar.shape[0])
yy = np.linspace(0,bar.shape[1],bar.shape[1])
x,y = np.meshgrid(xx,yy)
if bar.any():
gzang, gzcen, tots, smoothtots, maxbin, centtots, cen = gzbarang3(d['gz'])#, returncen=True)
xcen = np.average(x, weights=bar)
ycen = np.average(y, weights=bar)
x -= xcen
y -= ycen
gzpab = gzang
npab = d['projpab']
gzpa = ppa
npa = d['f'][1].data['pa']
plt.contour(d['gz'][4].data,cmap='Greens',levels=2, linestyles=':', alpha=.5, linewidths=1)
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(gzpab - 90)) + gzcen[1], 'olivedrab', label='GZ:3D')
plt.plot(x[0]+gzcen[0], x[0]*np.tan(np.radians(npab - 90)) + gzcen[1], 'w--', label='Nirvana')
plt.plot(*gzcen, 'o', c='olivedrab')
else:
xcen, ycen = (0,0)
plt.xlim((0,d['gz'][0].data.shape[0]))
plt.ylim((0,d['gz'][0].data.shape[1]))
handles, labels = plt.gca().get_legend_handles_labels()
handles.append(Line2D([0], [0], label='Bar', color='g', ls=':'))
handles.append(Line2D([0], [0], label='MaNGA\nIFU', color='m', ls='-'))
plt.axis('off')
plt.text(.5,.05,pi,transform=plt.gca().transAxes,horizontalalignment='center',fontsize=12,c='w')
plt.subplot(5,3,3*n+2)
x = d['maps']['spx_skycoo'].data[0]
xcen = x.shape[0]//2
vmax = np.max(np.abs(vel))
plt.imshow(vel, cmap='RdBu_r', origin='lower', vmin=-vmax, vmax=vmax)
fname = f'/media/brian/bdigiorg/nirvana/lux/barred/sample/nirvana_{plate}-{ifu}_{vftype}.fits'
args, resdict = fileprep(fname, rootdir='/media/brian/bdigiorg/manga/spectro')
args.clip()
z = np.zeros(len(resdict['vt']))
vtdict, v2tdict, v2rdict = [resdict.copy(), resdict.copy(), resdict.copy()]
vtdict['v2t'] = z
vtdict['v2r'] = z
v2tdict['vt'] = z
v2tdict['v2r'] = z
v2rdict['vt'] = z
v2rdict['v2t'] = z
velmodel, sigmodel = bisym_model(args, resdict, plot=True)
vtmodel, sigmodel = bisym_model(args, vtdict, plot=True)
v2tmodel, sigmodel = bisym_model(args, v2tdict, plot=True)
v2rmodel, sigmodel = bisym_model(args, v2rdict, plot=True)
plt.axis('off')
if bar.any():
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpab - 90)) + xcen, 'olivedrab', label='GZ')
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(npab - 90)) + xcen, 'k--', label='N pab')
#plt.plot(x[0]+xcen, x[0]*np.tan(np.radians(npa - 90)) + xcen, 'k--', label='N pa')
#plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpa - 90)) + xcen, 'g--', label='pPA')
plt.contour(velmodel-vtmodel-v2tmodel-d['f'][1].data['vsys'],colors='k',levels=5, linestyles='--', alpha=.5)
plt.xlim((0,x.shape[0]))
plt.ylim((0,x.shape[1]))
#plt.legend()
plt.subplot(5,3,3*n+3)
plt.imshow(velmodel-vtmodel,cmap='RdBu_r', origin='lower', vmin=-vmax, vmax=vmax)
if bar.any():
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpab - 90)) + xcen, 'olivedrab', label='GZ')
plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(npab - 90)) + xcen, 'k--', label='Nirv')
#plt.plot(x[xcen]+xcen, x[xcen]*np.tan(np.radians(gzpa - 90)) + xcen, 'g--', label='pPA')
#plt.plot(x[0]+xcen, x[0]*np.tan(np.radians(npa - 90)) + xcen, 'k--', label='N pa')
plt.xlim((0,x.shape[0]))
plt.ylim((0,x.shape[1]))
plt.axis('off')
#plt.legend()
n += 1
#except:
# print('failed')
#plt.cla()
#plt.axis('off')
#plt.legend(handles=handles, facecolor='.7', loc=4)
plt.tight_layout(pad=0, h_pad=-.5, w_pad=-.5)
#plt.savefig('imagemosaic.pdf', format='pdf')
```
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" width="600">
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/9095/stack/manga-9095-12703-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/9095/images/12703.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/9095/12703/manga-9095-12703-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -83.6 +/- 0.0
Inclination: 48.1 +/- 0.1
Systemic Velocity: -4.8 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 237.5 +/- 0.2
RC: Scale: 1.8 +/- 0.0
----------
Velocity measurements: 2642
Velocity chi-square: 62555.89972739115
Reduced chi-square: 23.740379403184498
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -83.4 +/- 0.0
Inclination: 49.3 +/- 0.1
Systemic Velocity: -4.9 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 237.6 +/- 0.2
RC: Scale: 1.8 +/- 0.0
----------
Velocity measurements: 1490
Velocity chi-square: 28293.130027035702
Reduced chi-square: 19.078307503058465
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -83.3 +/- 0.0
Inclination: 49.4 +/- 0.1
Systemic Velocity: -4.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 238.2 +/- 0.2
RC: Scale: 1.8 +/- 0.0
----------
Velocity measurements: 1327
Velocity chi-square: 26696.024008459586
Reduced chi-square: 20.224260612469383
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -83.3 +/- 0.0
Inclination: 49.4 +/- 0.1
Systemic Velocity: -4.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 238.2 +/- 0.2
RC: Scale: 1.8 +/- 0.0
----------
Velocity measurements: 1327
Velocity chi-square: 26696.024008459586
Reduced chi-square: 20.224260612469383
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -83.3 +/- 0.0
Inclination: 49.4 +/- 0.1
Systemic Velocity: -4.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 238.2 +/- 0.2
RC: Scale: 1.8 +/- 0.0
----------
Velocity measurements: 1327
Velocity chi-square: 26696.024008459586
Reduced chi-square: 20.224260612469383
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -83.3 +/- 0.0
Inclination: 49.4 +/- 0.1
Systemic Velocity: -4.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 238.2 +/- 0.2
RC: Scale: 1.8 +/- 0.0
----------
Velocity measurements: 1327
Velocity chi-square: 26696.024008459586
Reduced chi-square: 20.224260612469383
----------------------------------------------------------------------
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/12490/stack/manga-12490-3701-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/12490/images/3701.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/12490/3701/manga-12490-3701-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.3 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -7.3 +/- 0.1
Inclination: 46.0 +/- 0.4
Systemic Velocity: -7.9 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 132.9 +/- 0.5
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 748
Velocity chi-square: 10132.521528733405
Reduced chi-square: 13.67411812244724
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.3 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -7.4 +/- 0.1
Inclination: 45.7 +/- 0.4
Systemic Velocity: -7.8 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 132.6 +/- 0.5
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 724
Velocity chi-square: 9863.788925040588
Reduced chi-square: 13.757027789456886
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.3 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -7.3 +/- 0.1
Inclination: 45.7 +/- 0.4
Systemic Velocity: -8.1 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 133.2 +/- 0.5
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 660
Velocity chi-square: 9371.163053856972
Reduced chi-square: 14.350938826733493
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.3 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -7.3 +/- 0.1
Inclination: 45.7 +/- 0.4
Systemic Velocity: -8.1 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 133.2 +/- 0.5
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 660
Velocity chi-square: 9371.163053856972
Reduced chi-square: 14.350938826733493
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.3 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -7.3 +/- 0.1
Inclination: 45.7 +/- 0.4
Systemic Velocity: -8.1 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 133.2 +/- 0.5
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 660
Velocity chi-square: 9371.163053856972
Reduced chi-square: 14.350938826733493
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.3 +/- 0.0
Y center: -0.1 +/- 0.0
Position Angle: -7.3 +/- 0.1
Inclination: 45.7 +/- 0.4
Systemic Velocity: -8.1 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 133.2 +/- 0.5
RC: Scale: 3.4 +/- 0.0
----------
Velocity measurements: 660
Velocity chi-square: 9371.163053856972
Reduced chi-square: 14.350938826733493
----------------------------------------------------------------------
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/11951/stack/manga-11951-12704-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/11951/images/12704.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/11951/12704/manga-11951-12704-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.1 +/- 0.0
Y center: -0.0 +/- 0.0
Position Angle: 134.3 +/- 0.0
Inclination: 56.3 +/- 0.1
Systemic Velocity: -0.2 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 252.7 +/- 0.3
RC: Scale: 5.3 +/- 0.0
----------
Velocity measurements: 2507
Velocity chi-square: 28259.761785590323
Reduced chi-square: 11.303904714236129
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.1 +/- 0.0
Y center: -0.0 +/- 0.0
Position Angle: 134.3 +/- 0.0
Inclination: 55.9 +/- 0.1
Systemic Velocity: 0.2 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 253.1 +/- 0.3
RC: Scale: 5.4 +/- 0.0
----------
Velocity measurements: 1924
Velocity chi-square: 15793.687604825063
Reduced chi-square: 8.238752010863362
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.1 +/- 0.0
Y center: -0.0 +/- 0.0
Position Angle: 134.3 +/- 0.0
Inclination: 55.9 +/- 0.1
Systemic Velocity: 0.1 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 253.0 +/- 0.3
RC: Scale: 5.4 +/- 0.0
----------
Velocity measurements: 1923
Velocity chi-square: 15551.154877331146
Reduced chi-square: 8.116469142657174
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.1 +/- 0.0
Y center: -0.0 +/- 0.0
Position Angle: 133.9 +/- 0.0
Inclination: 55.4 +/- 0.1
Systemic Velocity: 0.9 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 253.8 +/- 0.3
RC: Scale: 5.4 +/- 0.0
----------
Velocity measurements: 1540
Velocity chi-square: 12221.464925170296
Reduced chi-square: 7.972253702002802
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.1 +/- 0.0
Y center: -0.0 +/- 0.0
Position Angle: 133.9 +/- 0.0
Inclination: 55.4 +/- 0.1
Systemic Velocity: 0.9 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 253.8 +/- 0.3
RC: Scale: 5.4 +/- 0.0
----------
Velocity measurements: 1540
Velocity chi-square: 12221.464925170296
Reduced chi-square: 7.972253702002802
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.1 +/- 0.0
Y center: -0.0 +/- 0.0
Position Angle: 133.9 +/- 0.0
Inclination: 55.4 +/- 0.1
Systemic Velocity: 0.9 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 253.8 +/- 0.3
RC: Scale: 5.4 +/- 0.0
----------
Velocity measurements: 1540
Velocity chi-square: 12221.464925170296
Reduced chi-square: 7.972253702002802
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.1 +/- 0.0
Y center: -0.0 +/- 0.0
Position Angle: 133.9 +/- 0.0
Inclination: 55.4 +/- 0.1
Systemic Velocity: 0.9 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 253.8 +/- 0.3
RC: Scale: 5.4 +/- 0.0
----------
Velocity measurements: 1540
Velocity chi-square: 12221.464925170296
Reduced chi-square: 7.972253702002802
----------------------------------------------------------------------
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/8440/stack/manga-8440-12704-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/8440/images/12704.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/8440/12704/manga-8440-12704-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: The maximum number of function evaluations is exceeded.
Fit status: 0
Fit success: False
----------
Base parameters:
X center: 11.2 +/- 0.0
Y center: 3.5 +/- 0.0
Position Angle: 217.6 +/- 0.0
Inclination: 54.7 +/- 0.0
Systemic Velocity: -285.1 +/- 0.2
----------
Rotation curve parameters:
RC: Asymptotic value: 475.6 +/- 0.2
RC: Scale: 0.1 +/- 0.0
----------
Velocity measurements: 2602
Velocity chi-square: 1321879.323401892
Reduced chi-square: 509.3947296346405
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.0 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 148.1 +/- 0.0
Inclination: 44.0 +/- 0.2
Systemic Velocity: 22.9 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 162.4 +/- 0.2
RC: Scale: 3.1 +/- 0.0
----------
Velocity measurements: 1734
Velocity chi-square: 10331.143700377066
Reduced chi-square: 5.982133005429685
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.0 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 148.0 +/- 0.0
Inclination: 43.6 +/- 0.2
Systemic Velocity: 22.8 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 162.0 +/- 0.2
RC: Scale: 3.0 +/- 0.0
----------
Velocity measurements: 1471
Velocity chi-square: 9193.437063079866
Reduced chi-square: 6.279670125054553
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.0 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 148.0 +/- 0.0
Inclination: 43.6 +/- 0.2
Systemic Velocity: 22.8 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 162.0 +/- 0.2
RC: Scale: 3.0 +/- 0.0
----------
Velocity measurements: 1471
Velocity chi-square: 9193.437063079866
Reduced chi-square: 6.279670125054553
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.0 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 148.0 +/- 0.0
Inclination: 43.6 +/- 0.2
Systemic Velocity: 22.8 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 162.0 +/- 0.2
RC: Scale: 3.0 +/- 0.0
----------
Velocity measurements: 1471
Velocity chi-square: 9193.437063079866
Reduced chi-square: 6.279670125054553
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: -0.0 +/- 0.0
Y center: 0.0 +/- 0.0
Position Angle: 148.0 +/- 0.0
Inclination: 43.6 +/- 0.2
Systemic Velocity: 22.8 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 162.0 +/- 0.2
RC: Scale: 3.0 +/- 0.0
----------
Velocity measurements: 1471
Velocity chi-square: 9193.437063079866
Reduced chi-square: 6.279670125054553
----------------------------------------------------------------------
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/10213/stack/manga-10213-12705-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/10213/images/12705.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/10213/12705/manga-10213-12705-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.4 +/- 0.0
Position Angle: -28.1 +/- 0.0
Inclination: 36.6 +/- 0.2
Systemic Velocity: 7.6 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 144.1 +/- 0.1
RC: Scale: 6.0 +/- 0.0
----------
Velocity measurements: 2711
Velocity chi-square: 99952.28713550561
Reduced chi-square: 36.9646032305864
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.2 +/- 0.0
Y center: -0.4 +/- 0.0
Position Angle: -28.2 +/- 0.0
Inclination: 35.8 +/- 0.2
Systemic Velocity: 7.2 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 144.6 +/- 0.1
RC: Scale: 6.1 +/- 0.0
----------
Velocity measurements: 2450
Velocity chi-square: 95571.73211080642
Reduced chi-square: 39.12064351649874
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.3 +/- 0.0
Y center: -0.5 +/- 0.0
Position Angle: -28.2 +/- 0.0
Inclination: 36.0 +/- 0.2
Systemic Velocity: 5.5 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 145.7 +/- 0.2
RC: Scale: 6.3 +/- 0.0
----------
Velocity measurements: 2436
Velocity chi-square: 80037.13807998202
Reduced chi-square: 32.950653799910256
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.4 +/- 0.0
Y center: -0.6 +/- 0.0
Position Angle: -28.4 +/- 0.0
Inclination: 35.0 +/- 0.2
Systemic Velocity: 4.7 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 146.5 +/- 0.2
RC: Scale: 6.4 +/- 0.0
----------
Velocity measurements: 2433
Velocity chi-square: 77306.84051750305
Reduced chi-square: 31.865968886027638
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.5 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -28.5 +/- 0.0
Inclination: 34.3 +/- 0.2
Systemic Velocity: 3.4 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 146.8 +/- 0.2
RC: Scale: 6.5 +/- 0.0
----------
Velocity measurements: 2427
Velocity chi-square: 72234.15124755514
Reduced chi-square: 29.848822829568242
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.5 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -28.6 +/- 0.0
Inclination: 34.2 +/- 0.2
Systemic Velocity: 3.2 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 146.6 +/- 0.2
RC: Scale: 6.4 +/- 0.0
----------
Velocity measurements: 2425
Velocity chi-square: 70746.76115063585
Reduced chi-square: 29.258379301338234
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.5 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -28.6 +/- 0.0
Inclination: 33.9 +/- 0.2
Systemic Velocity: 2.8 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 146.7 +/- 0.2
RC: Scale: 6.5 +/- 0.0
----------
Velocity measurements: 2422
Velocity chi-square: 68802.47711665143
Reduced chi-square: 28.48963855761964
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.6 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -29.5 +/- 0.0
Inclination: 28.5 +/- 0.3
Systemic Velocity: 1.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 145.2 +/- 0.2
RC: Scale: 6.4 +/- 0.0
----------
Velocity measurements: 1784
Velocity chi-square: 61322.422533600744
Reduced chi-square: 34.50896034530149
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.6 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -29.5 +/- 0.0
Inclination: 28.5 +/- 0.3
Systemic Velocity: 1.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 145.2 +/- 0.2
RC: Scale: 6.4 +/- 0.0
----------
Velocity measurements: 1784
Velocity chi-square: 61322.422533600744
Reduced chi-square: 34.50896034530149
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.6 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -29.5 +/- 0.0
Inclination: 28.5 +/- 0.3
Systemic Velocity: 1.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 145.2 +/- 0.2
RC: Scale: 6.4 +/- 0.0
----------
Velocity measurements: 1784
Velocity chi-square: 61322.422533600744
Reduced chi-square: 34.50896034530149
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.6 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -29.5 +/- 0.0
Inclination: 28.5 +/- 0.3
Systemic Velocity: 1.3 +/- 0.1
----------
Rotation curve parameters:
RC: Asymptotic value: 145.2 +/- 0.2
RC: Scale: 6.4 +/- 0.0
----------
Velocity measurements: 1784
Velocity chi-square: 61322.422533600744
Reduced chi-square: 34.50896034530149
----------------------------------------------------------------------
```python
datadir='/media/brian/bdigiorg/nirvana/lux/marchrun/'
mgas = makealltable('nirvana_',datadir=datadir,vftype='Gas', mangadir='/media/brian/bdigiorg/manga/')
#stars = makealltable('nirvana_',datadir=datadir,vftype='Stars', mangadir='/media/brian/bdigiorg/manga/')
mgpi = [f"{mgas['plate'][i]}-{mgas['ifu'][i]}" for i in range(len(mgas))]
#spi = [f"{stars['plate'][i]}-{stars['ifu'][i]}" for i in range(len(stars))]
```
2392 files found...
100%|█████████████████████████████████████████████████████████████████| 2392/2392 [00:39<00:00, 60.06it/s]
```python
datadir='/media/brian/bdigiorg/nirvana/lux/christmasrun/'
cgas = makealltable('nirvana_',datadir=datadir,vftype='Gas', mangadir='/media/brian/bdigiorg/manga/')
#stars = makealltable('nirvana_',datadir=datadir,vftype='Stars', mangadir='/media/brian/bdigiorg/manga/')
cgpi = [f"{cgas['plate'][i]}-{cgas['ifu'][i]}" for i in range(len(cgas))]
#spi = [f"{stars['plate'][i]}-{stars['ifu'][i]}" for i in range(len(stars))]
```
5357 files found...
100%|█████████████████████████████████████████████████████████████████| 5357/5357 [01:34<00:00, 56.68it/s]
```python
def makeplot(xdata, ydata, color, alpha=.7):
plt.scatter(xdata, ydata, c=color, s=5,alpha=alpha, cmap='jet')
ax = plt.gca()
ax.set_aspect(1)
xmin, xmax = ax.get_xlim()
ymin, ymax = ax.get_ylim()
line = np.linspace(min(xmin,ymin),max(xmax,ymax))
plt.plot(line,line,'k--')
drp = fits.open('/media/brian/bdigiorg/manga/spectro/redux/DR17/drpall-v3_1_1.fits')[1].data
ppi = [f"{drp['plate'][i]}-{drp['ifudsgn'][i]}" for i in range(len(drp))]
ppa = drp['nsa_elpetro_phi']
#pinc = np.degrees(np.arccos(drp['nsa_elpetro_ba']))
q0 = .2
pinc = np.degrees(np.arccos(np.sqrt((drp['nsa_elpetro_ba']**2-q0**2) / (1 - q0**2))))
pinc[q < q0] = 90
plt.figure(figsize=(3,5))
a = ((mgas,mgpi,'Unif.'), (cgas,cgpi, 'Gauss.'))
for j in range(2):
gas, gpi, prior = a[j]
pmatch = np.zeros(len(gpi),dtype=int)
for i in range(len(gpi)):
try: pmatch[i] = ppi.index(gpi[i])
except: pass
good = (pmatch != 0) & (ppa[pmatch] > -1000)
plt.subplot(211+j)
print(len(pinc[pmatch][good]), len(gas['inc']))
plt.hist2d(pinc[pmatch][good], gas['inc'][good], cmap='Greens',range=((0,90),(0,90)),bins=30)
plt.plot(np.linspace(0,90,90), np.linspace(0,90,90), 'k--')
plt.xlabel('Photometric inc. (deg)')
plt.ylabel('Nirvana gas inc. (deg)')
if j==0:
plt.tick_params(direction='in', labelbottom=False)
plt.plot(np.linspace(0,90,90), np.linspace(0,90,90)+20, 'k:',label='Prior\nbound')
plt.plot(np.linspace(0,90,90), np.linspace(0,90,90)-20, 'k:')
plt.legend(loc=4)
plt.gca().set_aspect(1)
plt.text(.1,.85,f'{prior} prior', transform=plt.gca().transAxes,
horizontalalignment='left', fontsize=12)
plt.tick_params(direction='in', labelbottom=True)
plt.tight_layout(h_pad=-1.3)
plt.gcf().subplots_adjust(hspace=0)
plt.savefig('incpriors.pdf', format='pdf')
```
RuntimeWarning: invalid value encountered in sqrt
RuntimeWarning: invalid value encountered in arccos
<IPython.core.display.Javascript object>
<img 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" 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```python
datadir='/media/brian/bdigiorg/nirvana/lux/barred/sample/'
sgas = makealltable('nirvana_',datadir=datadir,vftype='Gas', mangadir='/media/brian/bdigiorg/manga/')
#stars = makealltable('nirvana_',datadir=datadir,vftype='Stars', mangadir='/media/brian/bdigiorg/manga/')
sgpi = [f"{mgas['plate'][i]}-{mgas['ifu'][i]}" for i in range(len(mgas))]
```
1118 files found...
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```python
drp = fits.open('/media/brian/bdigiorg/manga/spectro/redux/DR17/drpall-v3_1_1.fits')[1].data
ppi = [f"{drp['plate'][i]}-{drp['ifudsgn'][i]}" for i in range(len(drp))]
ppa = drp['nsa_elpetro_phi']
#pinc = np.degrees(np.arccos(drp['nsa_elpetro_ba']))
q0 = .2
pinc = np.degrees(np.arccos(np.sqrt((drp['nsa_elpetro_ba']**2-q0**2) / (1 - q0**2))))
pinc[q < q0] = 90
plt.figure(figsize=(3,5))
pmatch = np.zeros(len(gpi),dtype=int)
for i in range(len(gpi)):
try: pmatch[i] = ppi.index(gpi[i])
except: pass
good = (pmatch != 0) & (ppa[pmatch] > -1000)
xdata = pinc[pmatch][good]
ydata = gas['inc'][good]
plt.subplot(211)
plt.hist2d(xdata, ydata, cmap='Greens',range=((0,90),(0,90)),bins=30)
plt.plot(np.linspace(0,90,90), np.linspace(0,90,90), 'k--')
plt.xlabel('Photometric inc. (deg)')
plt.ylabel('Nirvana gas inc. (deg)')
plt.tick_params(direction='in', labelbottom=False)
plt.gca().set_aspect(1)
plt.subplot(212)
plt.hist2d(xdata, ydata-xdata, cmap='Greens',range=((0,90),(-23,23)),bins=30)
plt.axhline(0, c='k', ls='--')
plt.axhline(np.nanmedian(ydata-xdata), c='r', ls=':', label='Median')
plt.xlabel('Photometric inc. (deg)')
plt.ylabel('Nirvana - Photometric (deg)')
plt.tick_params(direction='in', labelbottom=True)
plt.legend(loc=4)
plt.tight_layout(h_pad=-1.3)
plt.gcf().subplots_adjust(hspace=0)
print(np.nanmedian(ydata-xdata))
plt.savefig('incbias.pdf', format='pdf')
```
RuntimeWarning: invalid value encountered in sqrt
RuntimeWarning: invalid value encountered in arccos
<IPython.core.display.Javascript object>
<img 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" width="300">
4.393298603476385
```python
q = drp[pmatch][good]['nsa_elpetro_ba']
q0 = .2
pinc = np.degrees(np.arccos(np.sqrt((q**2-q0**2) / (1 - q0**2))))
pinc[q < q0] = 90
```
RuntimeWarning: invalid value encountered in sqrt
```python
plt.figure()
plt.hist(pinc)
```
<IPython.core.display.Javascript object>
<img 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" width="640">
(array([ 43., 230., 543., 778., 886., 909., 789., 626., 335., 67.]),
array([ 5.54670606, 13.99203546, 22.43736485, 30.88269424, 39.32802364,
47.77335303, 56.21868242, 64.66401182, 73.10934121, 81.55467061,
90. ]),
<BarContainer object of 10 artists>)
```python
def one2oneplot(plate,ifu,priordir='gaussprior',color=None):
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/{priordir}/nirvana_{plate}-{ifu}_Gas_mock_i??_r*.fits')
for fi in fs:
inc = int(fi[fi.find('_i')+2:fi.find('_r')])
with fits.open(fi) as f:
if color is not None:
if color == 'vsig':
c = np.log10(np.max(f[1].data['vt'])/np.max(f[1].data['sig']))
plt.scatter(inc, f[1].data['inc'], c=c, cmap='jet',s=5,vmin=-1, vmax=1)
if color == 'v2r':
c = np.max(f[1].data['v2r'])
plt.scatter(inc, f[1].data['inc'], c=c, cmap='jet',s=5, vmin=0, vmax=200)
else:
plt.plot(inc, f[1].data['inc'], 'k.')
plt.xlabel('Input Inc. (deg)')
plt.ylabel('Output Inc. (deg)')
x = np.linspace(10,80,100)
plt.plot(x,x,'k--')
plt.gca().set_aspect(1)
plt.tight_layout()
plt.figure(figsize=(12,8))
color = 'v2r'
plt.subplot(231)
one2oneplot(7965,3704,'500pen',color)
plt.title('Regular, High Penalty')
plt.subplot(232)
one2oneplot(7965,3704,'gaussprior',color)
plt.title('Regular, Gaussian')
plt.subplot(233)
one2oneplot(7965,3704,'unifprior',color)
plt.title('Regular, Uniform')
plt.subplot(234)
one2oneplot(11021,3703,'500pen',color)
plt.title('Irregular, High Penalty')
plt.subplot(235)
one2oneplot(11021,3703,'gaussprior',color)
plt.title('Irregular, Gaussian')
plt.subplot(236)
one2oneplot(11021,3703,'unifprior',color)
plt.title('Irregular, Uniform')
plt.tight_layout()
```
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" width="1200">
```python
plt.figure(figsize=(3,4.5))
cmap = 'viridis'
plt.subplot(211)
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/500pen/nirvana_7965-3704_Gas_mock_i??_r*.fits')
cs1 = []
cs2 = []
vmin, vmax = (0,75)
x = np.linspace(0,90,100)
plt.plot(x,x,'k--')
for fi in fs:
inc = int(fi[fi.find('_i')+2:fi.find('_r')])
with fits.open(fi) as f:
c = np.max(f[1].data['v2r'])
cs1 += [c]
plt.scatter(inc+2, f[1].data['inc'], c=c, cmap=cmap,s=10, vmin=vmin, vmax=vmax)
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/gaussprior/nirvana_7965-3704_Gas_mock_i??_r*.fits')
for fi in fs:
inc = int(fi[fi.find('_i')+2:fi.find('_r')])
with fits.open(fi) as f:
c = np.max(f[1].data['v2r'])
cs2 += [c]
plt.scatter(inc-2, f[1].data['inc'], c=c, cmap=cmap,s=30, marker='+', vmin=vmin, vmax=vmax)
plt.ylabel('Output Inc. (deg)')
plt.gca().set_aspect(1)
plt.xticks([0,15,30,45,60,75,90])
plt.yticks([0,15,30,45,60,75,90])
plt.xlim(0,90)
plt.ylim(0,90)
plt.tick_params(direction='in', labelbottom=False)
plt.text(.1,.8,f'7965-3704\n(unbarred)', transform=plt.gca().transAxes,
horizontalalignment='left', fontsize=12)
#csmax = max((np.max(cs1),np.max(cs2)))
#cm = plt.get_cmap(cmap)
#colors = cm(np.linspace(1.-csmax/float(csmax), 1, cm.N))
#color_map = LinearSegmentedColormap.from_list('cut_'+cmap, colors)
cax = mal(plt.gca()).append_axes('right', size='5%', pad=0)
#plt.colorbar(ScalarMappable(plt.Normalize(0,max((np.max(cs1),np.max(cs2)))), cmap),
plt.colorbar(ScalarMappable(plt.Normalize(vmin,vmax), cmap),
cax=cax, orientation='vertical',label=r'$V_{2r}$ (km/s)')
plt.tick_params(direction='in')
plt.subplot(212)
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/500pen/nirvana_11021-3703_Gas_mock_i??_r*.fits')
cs1 = []
cs2 = []
vmin, vmax = (0,120)
x = np.linspace(0,90,100)
plt.plot(x,x,'k--')
for fi in fs:
inc = int(fi[fi.find('_i')+2:fi.find('_r')])
with fits.open(fi) as f:
c = np.max(f[1].data['v2r'])
cs1 += [c]
if fi == fs[-1]:
plt.scatter(inc+2, f[1].data['inc'], c=c, cmap=cmap,s=10, vmin=vmin, vmax=vmax, label='Penalty')
else:
plt.scatter(inc+2, f[1].data['inc'], c=c, cmap=cmap,s=10, vmin=vmin, vmax=vmax)
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/gaussprior/nirvana_7965-3704_Gas_mock_i??_r*.fits')
for fi in fs:
inc = int(fi[fi.find('_i')+2:fi.find('_r')])
with fits.open(fi) as f:
c = np.max(f[1].data['v2r'])
cs2 += [c]
if fi == fs[-1]:
plt.scatter(inc-2, f[1].data['inc'], c=c, cmap=cmap,s=30, marker='+', vmin=vmin, vmax=vmax, label='No Penalty')
else:
plt.scatter(inc-2, f[1].data['inc'], c=c, cmap=cmap,s=30, marker='+', vmin=vmin, vmax=vmax)
plt.xlabel('Input Inc. (deg)')
plt.ylabel('Output Inc. (deg)')
plt.gca().set_aspect(1)
plt.xticks([0,15,30,45,60,75,90])
plt.yticks([0,15,30,45,60,75])
plt.xlim(0,90)
plt.ylim(0,90)
plt.tick_params(direction='in')
plt.text(.1,.8,f'11021-3703\n(barred)', transform=plt.gca().transAxes,
horizontalalignment='left', fontsize=12)
plt.legend(loc=4)
cax = mal(plt.gca()).append_axes('right', size='5%', pad=0)
plt.colorbar(ScalarMappable(plt.Normalize(0,max((np.max(cs1),np.max(cs2)))),cmap),
cax=cax, orientation='vertical',label=r'$V_{2r}$ (km/s)')
plt.tick_params(direction='in')
plt.tight_layout(rect=(.04,0,.96,1))
plt.gcf().subplots_adjust(hspace=0)
plt.savefig('penaltybias.pdf', format='pdf')
```
<IPython.core.display.Javascript object>
<img 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" width="300">
```python
plt.figure()
cut = np.abs(90-relgz) < 45
print(cut.sum())
plt.hist(pabes[cut], bins=30, density=True)
plt.hist(pabes, bins=30, density=True, histtype='step')
```
<IPython.core.display.Javascript object>
<img 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" width="640">
100
(array([4.30545809e-02, 1.59489143e-02, 3.81875413e-03, 2.39608102e-03,
1.19804051e-03, 6.73897788e-04, 7.48775320e-04, 2.99510128e-04,
3.74387660e-04, 1.49755064e-04, 3.74387660e-04, 5.24142724e-04,
4.49265192e-04, 8.98530384e-04, 9.73407916e-04, 1.27291804e-03,
2.17144843e-03, 2.17144843e-03, 1.64730570e-03, 1.72218324e-03,
1.04828545e-03, 5.99020256e-04, 7.48775320e-05, 7.48775320e-05,
1.49755064e-04, 7.48775320e-05, 1.49755064e-04, 0.00000000e+00,
7.48775320e-05, 5.99020256e-04]),
array([1.42414622e-01, 1.20879791e+01, 2.40335435e+01, 3.59791079e+01,
4.79246724e+01, 5.98702368e+01, 7.18158012e+01, 8.37613657e+01,
9.57069301e+01, 1.07652495e+02, 1.19598059e+02, 1.31543623e+02,
1.43489188e+02, 1.55434752e+02, 1.67380317e+02, 1.79325881e+02,
1.91271446e+02, 2.03217010e+02, 2.15162574e+02, 2.27108139e+02,
2.39053703e+02, 2.50999268e+02, 2.62944832e+02, 2.74890397e+02,
2.86835961e+02, 2.98781526e+02, 3.10727090e+02, 3.22672654e+02,
3.34618219e+02, 3.46563783e+02, 3.58509348e+02]),
[<matplotlib.patches.Polygon at 0x7fca60ccde50>])
```python
pabes = pabus-pabls
print(np.sum(pabes < 50))
```
877
```python
def recenter(arr, mod=180):
return (arr - mod/2) % mod - mod/2
def recoveryplot(plate,ifu,param):
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/newmocks/penalty/nirvana_{plate}-{ifu}_Gas_mock_i??_r*.fits')
recoveries = np.zeros(len(fs))
for i in range(len(fs)):
with fits.open(fs[i]) as f:
recoveries[i] = f[1].data[param] - resdict[param]
plt.hist(recoveries, bins=20, color='olivedrab',density=True)
plt.tick_params(direction='in', labelleft=False)
plt.text(.1,.8,param,fontsize=14,transform=plt.gca().transAxes)
plt.axvline(0, c='k', ls='--')
plate, ifu = (11021,3703)
if plate == 11021: args, resdict = fileprep(f'/media/brian/bdigiorg/nirvana/lux/barred/sample/nirvana_{plate}-{ifu}_Gas.fits', rootdir='/media/brian/bdigiorg/manga/spectro/')
elif plate == 7965: args, resdict = fileprep(f'/media/brian/bdigiorg/nirvana/lux/mocks/newmocks/nirvana_{plate}-{ifu}_Gas.fits', rootdir='/media/brian/bdigiorg/manga/spectro/')
params = ['inc','xc','pa','yc','pab']
plt.figure(figsize=(3,4))
for i in range(len(params)):
plt.subplot(3,2,i+1)
recoveryplot(plate,ifu,params[i])
plt.tight_layout()
```
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/11021/stack/manga-11021-3703-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/11021/images/3703.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/11021/3703/manga-11021-3703-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.9 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -110.7 +/- 0.1
Inclination: 1.0 +/- 21.8
Systemic Velocity: -88.0 +/- 1.0
----------
Rotation curve parameters:
RC: Asymptotic value: 183.5 +/- 1.3
RC: Scale: 4.5 +/- 0.0
----------
Velocity measurements: 742
Velocity chi-square: 69096.61087255078
Reduced chi-square: 94.00899438442283
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.8 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -110.8 +/- 0.1
Inclination: 1.0 +/- 21.9
Systemic Velocity: -85.7 +/- 0.9
----------
Rotation curve parameters:
RC: Asymptotic value: 178.6 +/- 1.2
RC: Scale: 4.3 +/- 0.0
----------
Velocity measurements: 696
Velocity chi-square: 68680.2853778356
Reduced chi-square: 99.68111085317213
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.5 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -111.8 +/- 0.1
Inclination: 1.0 +/- 23.6
Systemic Velocity: -77.0 +/- 0.8
----------
Rotation curve parameters:
RC: Asymptotic value: 176.7 +/- 1.2
RC: Scale: 4.4 +/- 0.0
----------
Velocity measurements: 692
Velocity chi-square: 58428.32197484034
Reduced chi-square: 85.29682040122678
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.2 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -112.4 +/- 0.1
Inclination: 1.0 +/- 24.9
Systemic Velocity: -71.4 +/- 0.7
----------
Rotation curve parameters:
RC: Asymptotic value: 176.4 +/- 1.2
RC: Scale: 4.5 +/- 0.0
----------
Velocity measurements: 689
Velocity chi-square: 51771.26572446772
Reduced chi-square: 75.91094681006997
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.2 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -112.5 +/- 0.1
Inclination: 1.0 +/- 25.4
Systemic Velocity: -72.1 +/- 0.8
----------
Rotation curve parameters:
RC: Asymptotic value: 177.1 +/- 1.2
RC: Scale: 4.5 +/- 0.0
----------
Velocity measurements: 654
Velocity chi-square: 51566.84140597743
Reduced chi-square: 79.70145503242261
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
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kZYSMCH3/qoM8AhA00yJAAhNAToInsaghtCCCGEELqIfpTpgwCE0Ad1oU4IIc4aDdAtxSb58FsfdYogkCB1BCCEAZosDcGNGSFmhJgRBjh4oEmxCEAIY2FzmwlCiBmhWw9zU7oPv/VRpxt6KNUnAQihT/ol6k5DcGNGiBkhZoQBDh5oUiwCEMJY2NxmghBiRujWw9yU7sNvfdTphh5K9UkAQuiTPmaERQnwbHP50lp6Zszs/P3Bw7pRTXMcsRagu+ab5EOUfNQZsg3QtngEFi1aRMOGDctnHjNmDLVu3bpkYSZvxELwWRyxFs9PGuUycQSpCS7Lluq2cT/t7Y/LwEeA+6gzLh/kqxwCJjEegs9CCC35ookjSE1wWbZUt437aW9/XAY+AtxHnXH5IF/lEDCJ8RB8FkJoyRdNHEFqgsuypbpt3E97++My8BHgPuqMywf5KoeASYyH4LMQQku+aOIIUhNcli3VbeN+2tsfl4GPAPdRZ1w+yFc5BExiPASfhRBa8kUTR5CawGWvXFlH0z76MZ+054BW1KRJlfo7fo9QIujnvo8A91GnH7qo1SWB5cuX07hx4/JVDBkyhGpqakpWaTL+heCzEEJNb5IMLRVjIlb49YnSdCW7mHCXbCrd9xHgXOeUKVOoT58+RZs3adIkqdm4DwKE7RMBOoGPAaUQgzTgSthMBmQIIYRQ8q/cfQihLimkKzfGLV28kkaf82U+ybx586hDhw6YEfp0GwghNtSX8j/pAcXkAcTU5334rY86TTkhfxgE6scShDAMmzRoRQjBLQ24EjaTARkzQswIJf+qPyPkP+MVqC4xpMsRgBAG7gsQQswIMSPUC9IQYkWvpUgVGgEIYWgWKWhPCMGNGWF8J5HYmc6Wy7XMpOz4PV6V04ff+qjTlBPyh0EAQhiGHUq2IoTglgZzCaHJgIxXo3g1KvkXXo3qEkI6nbcr+EYYoJ9ACPFqVCd4i6UxeQAxDQUffuujTlNOyB8GAcwIw7ADZoQlCGBGiBmhbohCCHVJIV0hAQhh4D4RQnDj1Wh8J5HYmczaXJYdv8ercvrwWx91mnJC/jAIQAjDsANmhJgRRvZECGFDZBDCyC6EDD8RgBAG7gohBLc04EoITWc9tSvraObUJflquvduQdU4a5Qku5hwl2wq3ffhtz7qlDjgfjoI1I+lwvHmvmumUdOmTUt2xCQOQ/BZnDWq6aOSoaViTAZkqW6TsqV227jvsv0uyzbtu48A91GnKSfkD4NAuViSxhiTOAzBZyGEmj4oGVoqRnKkcvmluk3Kltpt477L9rss27TvPgLcR52mnJA/DAIQwjDsULIVIQS3NOBKCE3ESqrbpGyp3Tbuu2y/y7JN++7Db33UacoJ+cMgACEMww4QwhIEQh7sdVzHZftdlq3Tt3JpfIiSjzpNOSF/GAQghGHYAUJYRgiXLa2lcaNn5lMMGd6dmjWvVn/HjLC0A/tk40OUfNQZ+PCB5mkSqC+EhePNp+9/R23atClZkskDaQg+i2+EMZxEM0uDZCYDMjtZ4ZFHw0f2ouYtm0AIr+pb1hwm3OPYuX4eHwHuo05TTsgfBgFsnwjDDpgRlpkRQgiLwzF5EnXt9j5EyUedrjmifDsEpFipXwvOGrXD3GopNoJbcgJp5iDllzoslV8uf6XPCMv1XeIm2cV1/nJtt+G3kl8V3vdRZ9Q2Ir0fAlKsQAj92EW7VhvBLTmB6YApdUYqH0JYnIDEzbVdpfohhJLn434oBKRYgRCGYqkS7YAQVvY3QswI7QWgjVix1xqUFBIBCGFpa2RmsYzkBNKTv5RfcnipfMwIMSOUfEjnPoRQh1I200QZw/CNMEAfsRHckhNIQiXll7BJ5UMIIYSSD+nctxErOvUgTfoIRBnDIIQB2tdGcEtOIAmVlF/CJpUPIYQQSj6kc99GrOjUgzTpIxBlDIMQBmhfG8EtOYEkVFJ+CZtUPoQQQij5kM59G7GiUw/SpI9AlDEMQhigfTm4p0yZQn369CnaukmTJomtlpxAEiopv9QAqXwIIYRQ8iGd+xBCHUrZTBNlDIMQBugjEMK+tGxJLT0w6uu8dfY/eQ1q1qIyjlgr53LSA4QU3K7zl2u7D1HyUWeAQwaaVISAFCv1sxSON1M/mUNt27YtyVUqu1wchuCzWDX6k2lNB0wp8qTypRmhiVhIbXN9XwoSk75JZUvcTfNDCF17D8q3RUDydV9xCCHUtLANUJITmA6YUlek8iGEeDUq+ZDOfRuxolMP0qSPgDQGQggDt6mN4JacQBIqKb+EUCofQgghlHxI576NWNGpB2nSR8BkDJPGL6lsvBq14C82gtvEUNwFKb/UTcmRIIQQQsmHdO7biBWdepAmfQRMxjBp/JLKhhBa8BcbwW1iqBCEsLa2jr6fuSxPs1P3ZlRdXaX+LjmpBRMYFSGx9/VKRseuJmxt+G1U8D7qjNpGpPdDIEocFo43Yy/7kpo0WfWzb8UuqWwIoQWb2whuE0PpDJhSN00GVG57Jf8ME4RQ8h79+zZiRb82pEwTAWkMrN8XbJ8I0LI2gltyAkmopPwSNqn8cvkhhKXpSHaRuJvmL2c3G34r+VXhfR91Rm0j0vshIPk6hNCPXbRrtRHckhOYDphSZ6TyIYTFCUjcXNtVqh9CKHk+7odCQIqVckI4fGQvat6y9KtRqY94NSoR0rgPIcSr0VJuIgW3JGSm+SGEGgGMJEEQkHwdQhiEmUo3AkIIIYQQ6gWpjVjRqwmp0kYAQljaYjhZ5ic2pjMHKSik8vFqFK9GJR/SuQ8h1KGUzTQQwgwIYRQjF8MhCZXr8tMshKZsyvXd1C6u8+PVaDZFJY29jhKn5Vapx+k7vhHGoVaQR+cpN4qRIYQWjFKvCFP2EEJ79tCJFXu1oaQ0EYgSpxDCAC2rE9xRjAwhtGtkU/YQQnv20IkVe7WhpDQRiBKnEMIALasT3FGMDCG0a2RT9hBCe/bQiRV7taGkNBGIEqcQwgAtqxPcUYwMIbRrZFP2EEJ79tCJFXu1oaQ0EYgSpxDCAC2rE9xRjJxGIVy5oo4+e3dhvun9N2lLTZqGcdaoKXsIob2g04kVe7WhpDQRiBKn5cabOH3GYpk41Ary6AR3FCOnUQhNxMKCCcoWYcrepG9S3Vg16tr6KD8tBKRYcdkPCKEFuhDCvmUpSoO9BRNACGNA1PHbGMWWzeKjTtt9QHluCEAIS3OtmA31pkaWxMR1+eVcX6pbarubsPq5VKl9JvVLfZPqdp2/XN98iJKPOk3si7zJEZBixWVLMCO0QFcnuE2NbDpgSt2UyocQFicgcZPs7jo/hFDyfNwPhYAUKy7bCSG0QBdC2JeWL6ull++fk6e53QGdqaZZtfq7NNhbMAFejcaAqOO3MYrFq1Hb0DJSXhQhLDfexMEFIYxDrSCPzoASxcjFmiSJievypRlhyD/Ma8qmXN9N7eI6P2aEFgIcRSRCIEqcYvtEIiaJVgmEMOxfn4gSYNEsL892pbohhFGJI32lEpBipX6/IYQBegGEEEJYyi2l4IYQBhjQaJIXAlKsQAi9mEW/UgghhBBCqBcvOrGiVxJSVRoBCGFpi2L7xE9sTGcOUtBI5eMbYXECEjcpuF3nxzdCyfNxPxQCUqxgRhiKpUq0Q+cpN4qRi1VjOmBKCKXyIYQQQsmHdO7rxIpOOUhTeQSijJH4Rhig/XWCO4qRIYR2jWzKvlxrpAcIqW7X+TEjtOtLKM0dASlWMCN0x95KyRBCfCMs5UhScEMIrYQgCqkAAlKsQAgDN3IlCKEpYpN9hFECwMds2ZSNyxml1LbQNgrrxIrUJ9yvTAJRxgG8Gg3QB3SCO4qR0zjYQwjjOabpjFCqFUIoEcL9UAhEGSMhhKFYrV47IIRE7Jh3XDw9T+WIv/Sg5i2bqL/7HOy5/igBlrR7+WSj47e2efio03YffJZXzpclX3Ld7iTjrNx4E6efoT0wFvYB2yd+IiI5eZJOGNXRXLfddflR+xslveu2hxbgEMIo3tE4LYTQjF+p3KHFCYSwhKVcD5hu3GtVqa7b7rr8SmXjQ5R81OnSfkmXDSF0QxxCaIGrTnCbztgw2Jc2FNjEY6PjtxbCo0ERPuq03Qef5UEI3dCHEFrgqhPcEMLSoMHGDxsdv7UQHhBCixAhhBZh1isKQmiBq86AUumDfV1dHS1bUpun2axFNVVVVeHVqOBfrmezoQW4TqxYCMmKLQJCuMq05cabOMYPLU4K+4DFMj8RcT1gxnGe+nmwfSIeQdd2DS3AIYTx/CSXC0K4igS2T5j5kZPcOsFd6TNCCGE814IQxuOW1VwQQghhsL4PISz/hOZzsGenMX0Icel4Ptno+K3tvvuo03YffJYHIYQQ+vS/snXrBLfpYOx6wDSFixlhPIKu7ZrGV6NSrEjM4lkiHblMhNA1V6l8m4TxatQmTUtlQQgxIyzlSmMvW3XazmF/6lE0iTSomw4uEEJLQV6vGJ14t1/rqhIhhJgRuvIt43J1AsPlgCYFiHEHNQrAjLA4JAhhQy42YkV6eNBwV6MkOn0wqqBMZgghhNCVbxmXqxMYEMLSmCuZDYQQQmg8wNQrAEIIIbTpT1bLghDi1ShejeqFlI1YwYywOGuJi/TAKeWXLCyVL+WPch/fCKPQSiitjeCWmio5aZJOWKyteDWKV6OSD/N9G7EixYJOO0zS6PTBpPxyeTEjxIzQlW8Zl6sTGKZCJQW/afmmECCEEEIdH7IRK1Is6LTDJI1OH0zKhxDK9DAjlBklnqJt27a0aNEiatasWdG6+/TpQzO/n2zUru6d+pXNb1q+UePUkUdE879dni+mQ9ca+umENXLddtflm7BZMGcVk/ada4oW47rt5cqfMmUK1dTU0MKFC026GCmvjViRmEVqUIzEzI0vjuukr3JxLnGRxggpv9RXqXwpf5T75cabKOXk0oYWJ4V9SMURa6uvvjp99913alApdvkImDjOgDzZIjB9+nRq3bo1zZo1K7GOI1YSQ42KLBHwESepFEJLvFEMCIAACIAACDQikIoZIewGAiAAAiAAAq4IQAhdkUW5IAACIAACqSAAIUyFmdBIEAABEAABVwQghK7IolwQAAEQAIFUEIAQpsJMaCQIgAAIgIArAhBCV2RRLgiAAAiAQCoIQAhTYKYlS5bQpZdeSnfffTfxnpuOHTvS7rvvThdddBGtueaaKehBtCb++OOP9PTTT9Ojjz5Kb731Fn355Ze0cuVK6tu3Lx1wwAF0xhlnUJs2bRoUesEFF9CFF15YsqI//elP9Le//S1aQ5DaKYFPPvmEHn74YWXryZMn0+zZs2m11VajbbbZhk4//XTadtttI9V/22230dFHH10yz69//Wu65557IpVZSYltjiPz588njrkHH3xQ7ZPl/atDhw5VMdihQ4fUYYMQBm4ydt6dd96ZXnvtNerevbsaHFgY3nzzTerSpQtNmDDBywkcLrGNHj2ajj32WFUFH7c1YMAA+uGHHxQDPqXll7/8Jb300kvUtWvXfDNyQjhw4EAlmIXXkCFD6KCDDnLZbJQdkQA/xH399dfUrl072nLLLZUIfvTRRzRx4kSqqqqiK6+8kk477TTtUnNCuOGGG9JGG23UKB/XceKJJ2qXV0kJbY4j33//PW299dbq4WXttdemzTbbjCZNmqT+49h7/fXXqVOnTqnCByEM3FznnXceXXzxxcrx+Mk5NxPiQeLMM8+k7bbbTolCJV133HGHCiaeFfTr9/PRdzNnziQWtPfee48OPfRQGjt2bCMhvPXWW+moo46qJBwV25ddd91VzeB4ll//+MQbb7yRTjjhBGrSpAl9+OGH6kFI58oJ4fnnn69mK7h+JmBzHDniiCPozjvvpP3335/+85//UNOmTVVFp5xyCl133XXE92+//fZU4YcQBmyu5cuXq1kPv4Z49913aeONN27QWn7y5YHi7bffpk033TTgnthrGs+A+dVZ8+bN1SwxN4DmZoQQQnusfZa02267qQc/tisLm84FISw/+OCFAAAgAElEQVROyeY4wq9B11hjDfWQMmPGDOrWrVu+0qVLl9Jaa61Fc+fOVTP9+vd07OczDYTQJ32h7hdeeIF22mkn9erz888/b5SaZ4r8pJelJ2D+fsjnd/L1zTffqNfFfEEIA3bkGE374x//SP/4xz/ouOOOI54h6lwQwuKUbI4j/KD529/+Vn2uefbZZxtVeMwxx9Att9xCaXsghRDqRJinNFdffbV6Pcjftu69995GrRg3bhzttdde6iM1f7TOwsXfjzbYYIP8rzrwzLC+EP7mN79Ri4n4mwh/g9pjjz0yM1uuJPsfeOCBdP/996sHvXKLoOr3OSeEHBP8HZnfGPAiDn6Y3H777SsJT6S+2BxH+JvtNddcQyNGjKC///3vjdpx/fXX08knn6y+7V511VWR2ukzMYTQJ32hbl4dyc7EYsjfBAuvDz74QC0K2GSTTeidd94JuCf2msaLaHgxzd57702PPPJIvuByq0b5GxQPkoUrTe21CiXZJMA/w8SLpPhVW5TX/uVWjbIQ8vesNL2us8XU5jjC3wX5oZvFkL8JFl68CpgfzDkdP8ik5YIQBmwpfi3073//m8455xy65JJLGrWUX5fyYpL+/fvTp59+GnBP7DTt8ccfVzNg/jjP2yr4G2nuuuuuu9Tye54B9uzZk+bNm0cvv/wy8Ss2/l6RpVmzHdp+SlmxYgXtuOOO9Oqrr1LU7Q5PPfWUWmS17777qtWMixcvVqur2Qd4qwZ/R3/jjTfU960sXTbHEV7g9Mwzz6hxafjw4Y0w8uvSwYMHE6dje6TlghAGbKnc7Ofcc89VK0cLL16+zCKYBSH8+OOPibdGsMDxq55TTz1Vy3K80pRfpfKS7/Hjx6uFNrjsEODXl/yqOsrFK4K32GKLkll4e8MNN9yghIwfdvg1t+n1v//9T4ngZ599RmPGjKHDDjvMtMhU5bc5jrDIsdjxWxn+Hlh4sUiyCEIIU+UiYTfW5iuNsHtavnVfffWVEkE+TICZXHHFFZG6w98zLr/88kjfmyJVkNHEvH8s6it5Xrixww47FCXGB0Twwi9+fckzwmL7QeOizn27SuPS/rh9zuWzOY7g1aipNZA/MgGbH7kjVx5Ihjlz5qhDBPjVFu85u/nmm9Vm6yjXTTfdRMcff7zapM9/xhUegZxQtW/fnl588cWiG+JNWs1bMXhLBs9o+M9ZumyOI1gskyXPCaSvusueo6ysC6RrWs3gU2R4xR8vmOAnUV45G+f7zmWXXUZnnXVWyUVHWo1BImcE+HUlr/Zt2bKlEime/du+eKHMIYccQvvttx898MADtosPujyb44ju9gneQlHuuLvQgOEbYWgWqdeeZcuWqQ31CxYsKLuhnhcEbL755gH3JHrTeMUgL3zhIOYneV4hWv/0Ed0S6+rq1Kk8vEiCF9QMGzZMNyvSJUCAF0Dx4pbq6mp1tix/W3JxHXzwwXTfffepRWe8+CxLl81xhL+587YkXrDGG+rrH3OY21DP3+N5gRpvXUnLBSEM3FK8UGbkyJFqkQc/Lec2k+eOWBs0aBC98sorgfciWvP4gG3eO8nLtPm16JNPPkmtWrUqWQi/PuUBlVcZ5vYVcmJeJPGHP/xBbcjmoORl+eXKidZKpDYlwIuX+FUln3zCIsUre3Uu3iPI13PPPadOOcld1157rdrsXX+bDJf917/+VR24wDNOXmBWP49OfZWQJuo4MmrUKOL/eAbNB/7Xvw4//HC16Ii3JfEh5rkj1ngBG9uA7/MRbGm6IISBW4s3hvPiAp7R5A7dnjZtmvo7H2zLy8VtLioIAQfvUcodtsyByIcyF7t4AUznzp3VIeS9e/dW6dZdd13q0aNH/lg6fjrl0/Afe+wxJ6/cQuCV1jbwIdt8fCDbjs/MLXbxg17hMv3cN+KpU6dSr1698tn431ns+GxS3kLDsfP++++rE4hatGihBm9+xZ7FK+o4ktuXe+SRR6o9uPUvfvDcaqut1IMln3qVO3SbVxDz33lM4rhM0wUhTIG1eD8UP5XxIdP8OoIHEP4ZJt5SwWf7Vdol/aRSrr+5gZC/JfKsmQOQ91ZyoPK3RB5gmRMfSJDFWUDofqGz6KnYQFxKCHnFKZ9Fywur2Af4tTi/xuPjwNgH1llnndCROG1flHGknBByI3kbE/N+6KGH1P5dXunLr7j5FCAbW16cgihSOIQwaeKoDwRAAARAICgCEMKgzIHGgAAIgAAIJE0AQpg0cdQHAiAAAiAQFAEIYVDmQGNAAARAAASSJgAhTJo46gMBEAABEAiKAIQwKHOgMSAAAiAAAkkTgBAmTRz1gQAIgAAIBEUAQhiUOdAYEAABEACBpAlACJMmjvpAAARAAASCIgAhDMocaAwIgAAIgEDSBCCESRNHfSAAAiAAAkERgBAGZQ40BgRAAARAIGkCEMKkiaM+EAABEACBoAhACIMyBxoDAiAAAiCQNAEIYdLEUR8IgAAIgEBQBCCEQZkDjQEBEAABEEiaAIQwaeKoDwRAAARAICgCEMKgzIHGgAAIgAAIJE0AQpg0cdQHAiAAAiAQFAEIYVDmQGNAAARAAASSJgAhTJo46gMBEAABEAiKAIQwKHOgMSAAAiAAAkkTgBAmTRz1gQAIgAAIBEUAQhiUOdAYEAABEACBpAlACJMmjvpAAARAAASCIgAhDMocaAwIgAAIgEDSBFIhhKuvvjotWrSIevTokTQf1AcCsQlMnz6dWrduTbNmzYpdRtSMiJWoxJDeNwEfcVLY51QIYdu2bWn58uXUp08f3zZD/RkhUFdXR3PmzMn3tnPnzlRVVRWp91OmTKGamhpauHBhpHwmiRErq+jZsJ+JHZBXn4CPOEmlEK633nqq3ZMmTdKni5QgYEBg/vz5tNpqq+VLmDdvHnXo0CFSiT781kedkaAklNiG/RJqauarCcFnUzEjDAFU5r01YwBsDKQ+/NZHnSG6hg37hdivSmxTCD4LIaxEz0KfjAnYGEh9BLiPOo1hOyjAhv0cNAtFFiEQgs9CCOGaIFCEgI2B1EeA+6gzRAeyYb8Q+1WJbQrBZyGElehZ6JMxARsDqY8A91GnMWwHBdiwn4NmoUjMCOP7AII7PjvkjEfAxkDqw2+5Tl6FV2qFdVYWnNmwXzzPQa6oBHzESWEbMSOMajWkrygCJ1/Vt2h/li5eSaPP+TJ/L02rRrMshDl7Ftpv+Mhe1LxlExp1+ucV5b+hdKZUHOXaV447hFDTiiGA0mwqkqWMQCUKIZsgKzO/QneDEPoJQAhhAtwhhAlAzmgVEMLKMjyE0I89IYQJcIcQJgA5o1WYBLCEzIff+qhT4pDkfZf2TLIfaavLhHsIPotvhGnzOLTXKgGTAJYa4iPAfdQpcUjyvkt7JtmPtNVlwj0En82sEN5222109NFHK3974YUXaIcddqCxY8fSLbfcQhMnTiRedda9e3caPHgwjRgxgvr161fUN1euXKnyP/300/T666/TZ599RnPnzqVmzZoRH4C85ZZbqnp22WWXtPl2JtprEsASIB8BbrNOPuh+jTXWoAULFtDmm29Ob775ptRlOvzww2nMmDHqXNZPP/20UdwsXryYbr/9dho3bhy9//776jzXFi1aqHoGDBhAQ4cOpb322ovatWsn1lUsgUt7xmpQRjKZcLfps3FxQwiJ6JlnnqHrr7+eHnrooaIcmzdvTrfeeisdeuihje6fdtppdM0114j8DznkEGLx5bJwhUPAJIClXvgIcNt1nnLKKXTdddeprr777ru08cYbl+w2r6z9xS9+QUuWLKGdd96Znn322QZpn3/+eSWUM2fOLIvu/PPPpwsuuEDCW/S+S3vGalBGMplwt+2zcZBDCInUrO2NN96grbbain7/+9/TOuusQxzUDz74IN14443Es74mTZoowdxxxx0bcD755JNVun322Ufl5/1b/NM7s2fPpo8//phGjRpFX3zxhcrDaXODShxjIY99AiYBLLXGR4DbrvOjjz6iXJknnHAC/etf/yrZ7auvvppOP/10df/ee++lgw46KJ/2ySefVDM9jiW+OF4OPvhg6tu3r4qtGTNm0Pjx4+n++++nI488EkIoOVdg903iyLbPxkEDIfyJGgfl3XffTdXV1Q04Pvroo+p1TW1tLa299trq1ScHbu7iPVs9e/akpk2bFuXPgc9Pwffcc4/Kl0sfx1jIY58AVo3KTPmzwUsvvaReV37zzTfqQa/YxQMaC2fXrl3pq6++Uj9BxRc/VPIDIv+f/41jYf/99y8ZL/z7jfyqNM6FVaNxqJnngRCaMxRLcPHEUP8bYceOHdWsrX379kXbcswxx6hvh3zx7I+FMcrF30H4eyGLIj81n3rqqVGyI60BASlASxWd5g313Ceb+wj/85//EL/a52v06NHE8VB4vfrqq7Ttttuqfz7rrLPo0ksvzSe56KKLiF938sV//stf/mJg0fJZIYTO0JYtWIozbKi3YBfXQnjiiSfSP//5z5Itzb025QTHH3883XDDDSXT8gIDFj7+P88ic9dOO+1E3333HR1xxBFqsQCuZAhIAQohlO3AP4q91lprqdf9W2yxhfqMUHixX995551qkcznn3+u3p7krk022YTee+899X2cyyj1wCm3RE4BIZQZuUghxRmE0AJ110LIwsSBXOrigYBfB/H/+TvghAkTGiSdOnWqmuk99thjxH/mX8cude25555qxRyuZAhIAQoh1LPDOeecQ3/9619VYl7tueGGG+Yz8gprXiTDK0J33XVXeuqpp/L3VqxYoQSQHwq33npreu211/QqjJkKQhgTnGE2Kc4ghIaAObtrIeQP+bvttlvZlvJWCv52wR/3J0+enE/LiwJYRJcuXarVU/7ewtstcCVDQApQCKGeHaZNm6ZmeSxoJ510klplnbuuvfba/Ot+XuxS//vft99+S926dVNJDzjgAPrvf/+rV2HMVBDCmOAMs0lxBiE0BJyEEPITLD/Jlrv4Gx+/1uH9hLxghi9+BbT++usrEWzVqpUaDHbffXcllp06dVJ7CflVEV89evRQK+O23357evHFFy1QQRE6BKQAhRDqUFyVZu+991ZvPfjVJi+aYZ/na4MNNlB7b/lhcfr06Q0WjnHMcOzwdeCBB9J9992nX2GMlBDCGNAsZJHiDEJoAbLrGaHOq9E2bdrQsmXLGrwaPfvss/OLAp544gklgqUuzs/fDSGEFhwiQhFSgEII9WE+/vjjNGTIEJWBF4/xQRH8qnPgwIHq3/j16SWXXNKgQLwa1eeb5pRSnEEILVjXtRDGXSyTe0LmVafff/99yZ7yCj6eOfIFIbTgEBGKkAIUQqgPk1+L8tsO/g6e+1Z+1FFHqcVfvO2IV17zVqLCa9NNN1Wb8UNYLCP1Fj/TJBEqfl+KMwhhPK4NcrkWQn6NyUFc6lin4cOH080336za9MADD9B+++2n/rzHHnsQf19s2bIlLVy4sMH+wvod4JWmN910E4TQgi9ELUIKUAhhNKKXXXaZ2h7B18svv6zegvz4448qFnjGWOziWWJuy4Tv7RNSbyGEEiEIYTxCFnK5FkJuIh+fdtdddzXaUM8rPPkUDH4a7t27t/o+mNs8X/94Nd6XyCdiFF5cJi+mya0kxYzQgkNEKAJCGAGWRlLeArTmmmuqzwT8TTB3XNrDDz+s4qTYxatKeUM9n8ErbajnOOMyXW2ol7oIIZQIQQjjEbKQy7UQ5o5Y22abbdQRa/3791eHbvPmed4zyN85+NUPH6zNZyjmLl5GznukWOR4YQwfQcXbI7p06UK8yo5PquFVclweDwI8iEAILThEhCIghBFgaSYdNmyYOqA+d7Fosb/XP3GpsKjCI9b23XffBkes8Uk0vC0ptwrb9KzRUr9QL3URQigRghDGI2Qhl2shZIHj5eD8VFvs4m8bfKIGH5VWeF111VV05plnltw7yKtMeVbJv2LBgwWE0IJDRCgirhCuWFZLE8bNpR03OUrVxiel8CvwKJcLv5XqT6LO+qfIcHt0D8nms3pZRPmBsNylW16xMnL2ztkvl2brIR2pabOGxycWyw8hlDwMQhiPkIVcLoK78GeYWKD4ZAz+d14Kzj89w8u+cz/DxAdxl7peeeUVtaGeDw3mRTMdOnRQiwp4PxXvueLN+L169YIQWvCFqEXEFcJcPSYDowu/lfqfVJ38gMfbh3gWyItn+OQZnYtXTvNDJW/D4DjjeGnbtq3akM8LyjhmeGVqbmuGTpn10/i0d9S2VlJ6iTsWy1iwtovgLvZ7hBaaiiICIyAFqNRcCGFjQnx4Nn8f5P2zvHL6kUcekTAmdt+nvRPrZIAVSdwhhBaMBiG0ADGjRUgBKmGBEDYmdOWVV6rPAXzxa3/+Lh7K5dPeoTDw0Q6JO4TQglUghBYgZrQIKUAlLBDChoR4qwQv/vr666/V639eRZ07PUlimcR9n/ZOon+h1iFxhxBasByE0ALEjBYhBWgpLCtX1NHE8QvogB3OVUn40AVeNBXlcuG3Uv0u6mTR4wO1+f8jR45UP1DNl3Qik9RWF/dz9s7ZL1fH+gPbU5Omq447LHeZPPhIZVfyfSnOIIQWrO8iuPGN0IJhUlCEFKCluoDfI/yZTO6Heeuz4g30/Fo0pNkgt086a1RyWQihRKj4fSnOIITxuDbIxULIv+zOm3KLXXF+hBRCaMEwKShCClAIoWzEnBDyjJhXP/MWiD/84Q+Rt5PINZmngBCaM4xTghRnEMI4VAvyuBBCC81CESkgIAUohDAFRozQRAhhBFgWk0pxBiG0ANvFq1ELzUIRKSAgBSiEMAVGjNBECGEEWBaTSnEGIbQAG0JoAWJGi5ACFEJYWY4BIfRjTynOIIQW7AIhtAAxo0VIAQohrCzHgBD6sacUZxBCC3aBEFqAmNEipACFEFaWY0AI/dhTijMIoQW7QAgtQMxoEVKAQggryzEghH7sKcUZhNCCXSCEFiBmtAgpQCGEleUYEEI/9pTiDEJowS4QQgsQM1qEFKAQwspyDAihH3tKcQYhtGAXCKEFiBktQgpQCGFlOQaE0I89pTiDEFqwC4TQAsSMFiEFaCksdbV1tHD+CrromJdUkh49elB1tfzDrvXL8+G3PuoMybVy9s7ZL9e2th2aUlU1zhp1ZSspziCEFshnPbgtIMxsEVKASmBMzp704bc+6pQYJnnfp72T7GdodUncIYQWLJb14LaAMLNFSAEqgYEQSoTCuu/T3mGRSLY1EncIoQV7QAgtQMxoEVKASlgghBKhsO77tHdYJJJtjcQdQmjBHhBCCxAzWoQUoBIWCKFEKKz7Pu0dFolkWyNxhxBasAeE0ALEjBYhBWgpLMuW1NI9/5hBHdutoZJ8+OGH1K5du0gUffitjzojQXGcOGfvnP1y1R0yYi1q1kJe7GTy4OO4a0EXL8UZhNCC+bIe3BYQZrYIKUBLgcEP86bTZbB9wo/dpDiDEFqwC4TQAsSMFiEFKISwshwDQujHnlKcQQgt2AVCaAFiRouQAhRCWFmOASH0Y08pziCEFuwCIbQAMaNFSAEKIawsx4AQ+rGnFGcQQgt2gRBagJjRIqQAhRBWlmNACP3YU4ozCKEFu0AILUDMaBFSgEIIK8sxIIR+7CnFGYTQgl0ghBYgZrQIKUAhhJXlGBBCP/aU4gxCaMEuEEILEDNahBSgEMLKcgwIoR97SnEGIbRgFwihBYgZLUIKUAhhZTkGhNCPPaU4gxBasAuE0ALEjBYhBSiEsLIcA0Lox55SnEEILdgFQmgBYkaLkAK0FJblS2vpmTGz6Vd9BqskY8aModatW0ei6MNvfdQZCYrjxDl75+yXq27wsG5U0xxHrLnCL8UZhNAC+awHtwWEmS1CClAJjMnZkz781kedEsMk7/u0d5L9DK0uiTuE0ILFsh7cFhBmtggpQCUwEEKJUFj3fdo7LBLJtkbiDiG0YA8IoQWIGS1CClAJC4RQIhTWfZ/2DotEsq2RuEMILdgDQmgBYkaLkAJUwgIhlAiFdd+nvcMikWxrJO4QQgv2gBBagJjRIqQALYVl5co6mvbRj3TcPv9SSYYMGUI1NTWRKPrwWx91RoLiOHHO3jn75arrOaAVNWlSJdZu8uAjFl7BCaQ4gxBaMH7Wg9sCwswWIQVoKTD4PcJ0ugy2T/ixmxRnEEILdoEQWoCY0SKkAIUQVpZjQAj92FOKMwihBbtACC1AzGgRUoBCCCvLMSCEfuwpxRmE0IJdIIQWIGa0CClAIYSV5RgQQj/2lOIMQmjBLhBCCxAzWoQUoBDCynIMCKEfe0pxBiG0YBcIoQWIGS1CClAIYWU5BoTQjz2lOIMQWrALhNACxIwWIQUohLCyHANC6MeeUpxBCC3YBUJoAWJGi5ACFEJYWY4BIfRjTynOIIQW7AIhtAAxo0VIAVqJQjhlyhTq06dP0a5NmjSpoj0BQujHvFKcQQgt2AVCaAFiRouQAhRCWFmOASH0Y08pziCEFuwCIbQAMaNFSAFaiULIfar0mV8pu0EI/QS6FGcQQgt2gRBagJjRIqQALYWldmUdzZy6hE49aIxKMmjQIGratGkkij781kedkaA4Tpyzd85+ueq6925B1Thr1Bl9Kc4ghBbQZz24LSDMbBFSgEpgTA5h9uG3PuqUGCZ536e9k+xnaHVJ3CGEFiyW9eC2gDCzRUgBKoGBEEqEwrrv095hkUi2NRJ3CKEFe0AILUDMaBFSgEpYIIQSobDu+7R3WCSSbY3EHUJowR4QQgsQM1qEFKASFgihRCis+z7tHRaJZFsjcYcQWrAHhNACxIwWIQVoKSzLltbSuNEzqd+aW6okjz32GLVp0yYSRR9+66POSFAcJ87ZO2e/XHVDhnenZs2rxdpNHnzEwis4gRRnEEILxs96cFtAmNkipAAtBQY/zBumy+jas9B+w0f2ouYtm4idghCKiIomkOwCIYzHtUEuCKEFiBktQgpQCGG6HEPXnhDCZO0q2QVCaMEeEEILEDNahBSgEMJ0OYauPSGEydpVsguE0II9IIQWIGa0CClAIYTpcgxde0IIk7WrZBcIoQV7QAgtQMxoEVKAQgjT5Ri69oQQJmtXyS4QQgv2YCHM8on6FhBmtggpQCGE6XINXXtCCJO1q2QXCKEFe0AILUDMaBFSgOoKYalVh6EFeKW/PdG1J4Qw2YCX7BJanBTSqaqrq6tLFln02io9uKMTQQ5dAlKAQgh1SYaRTteeEMJk7SXZBUJowR4QQgsQM1qEFKAQwnQ5hq49IYTJ2lWyC4TQgj0ghBYgZrQIKUAhhOlyDF17QgiTtatkFwihBXtACC1AzGgRUoCWwrJsSS09MOrr/O39T16DmrVofERXaAFe6bGia09d+xXaHyfLxBsoJLuEFif4RhjPzsiVUgJSgJp2K7QAhxCaWRRCGI+fFGehxQmEMJ6dkSulBKQANe1WaAEOITSzKIQwHj8pzkKLEwhhPDsjV0oJSAFq2q3QAhxCaGZRCGE8flKchRYnEMJ4dkaulBKQAtS0W6EFOITQzKIQwnj8pDgLLU4ghPHsjFwpJSAFaKlu1dbW0fczl+Vvd+rejKqrqxolDy3AIYSrTKRrv0KDlrOn5EtZFlETNiH4LDbUp3SAR7P1CEgBWqoU3eX3EEI9O9hKpWtPXftBCO1YRrJLaHGCGaEdu6OUlBCQAhRCmBJD/tRMXXtCCJO1q2QXCKEFe4QwdbbQDRThgYAUoBBCD0YxqFLXnhBCA8gxskp2gRDGgFqYBUJoAWJGi5ACFEKYLsfQtSeEMFm7SnaBEFqwB4TQAsSMFiEFKIQwXY6ha08IYbJ2lewCIbRgDwihBYgZLUIKUAhhuhxD154QwmTtKtkFQmjBHhBCCxAzWoQUoBDCdDmGrj0hhMnaVbILhNCCPSCEFiBmtAgpQCGE6XIMXXtCCJO1q2QXCKEFe0AILUDMaBFSgEII0+UYuvaEECZrV8kuEEIL9oAQWoCY0SKkAIUQpssxdO0JIUzWrpJdIIQW7AEhtAAxo0VIAQohTJdj6NoTQpisXSW7QAgt2ANCaAFiRouQArQUlpUr6uizdxfmb/ffpC01aYqzRn27ka49de1X2B+cNRrPwpJdIITxuDbIBSG0ADGjRUgBaooltACv9FjxaU+pbhy6XTqaQouTwpbi0G3TkRD5gyYgDV6mjQ8twCGEZhbFjDAePynOQosTCGE8OyNXSglIAWrardACXEcIJSY+ZzZS20zt5TK/xE3qm+v8Lvtu0jcdn3XZdi4bM0LXhFG+VwJSgJo2DkJoSrBhftf2stvahqW5FjKJjVS/y76btA1CqGmZEEBpNhXJAiMgBWip5i5fVksv3z8nf3u7AzpTTbPqRskhhHYNHtdeha3QtZ/N1ktCJPXNdX6bfS0sy6RvIYzvmBG69A6UbUxACjDjCkoUoLv8HkJo1wK27K1rP5utdy1kEhupfpt9hRC6pFmi7BCeGDx0G1USkRT8riDpDqQQQrsWsGVvXfvZbL0kRFLfXOe32VcIoUuaEEIPdMOuUho8XLVedyCFENq1gC1769rPZutdC5nERqrfZl8hhC5pQgg90A27Sin4XbVedyCFENq1gC1769rPZuslIZL65jq/zb5CCF3ShBB6oBt2ldLg4ar1ugMphNCuBWzZW9d+NlvvWsgkNlL9NvsKIXRJE0LogW7YVUrB76r1ugMphNCuBWzZW9d+NlsvCZHUN9f5bfYVQuiSJoTQA92wq5QGD1et1x1IIYR2LWDL3rr2s9l610ImsZHqt9lXCKFLmhBCD3TDrlIKflet1x1IIYR2LWDL3rr2s9l6SYikvrnOb7OvEEKXNCGEHuiGXaU0eLhqve5ACiG0awFb9ta1n83WuxYyiY1Uv82+Qghd0oQQeqAbdpVS8Ltqve5ACiG0awFb9ta1n83WS0Ik9c11fpt9hRC6pAkh9EA37HG1FScAACAASURBVCqlwcNV63kgvePi6fnij/hLD2reskmj6iCEdi1gy9669rPZetdCJrGR6rfZVwihS5oQQg90w65SCn7frYcQ2rVA6PYu11tJiKS+uc5v11INSzPpWwgnh+GsUZfegbKNCUgBZlyBYQEQQnsDIpcUur1DFkLJlSWhlfKXuy/ZLbQ4KewLhNDE+sjrnIAUYM4bIFQQWoDrPF1LTE0GTNOypfy+7Q0hLE5AsltocQIhDDmS0LZGBKQA840stACHEPrzCOkBQvJl0/xSz6XypfyYEZoQspBXJ7gtVIMiAiQgDR6umlxXV0fLltTmi2/WopqqqqoaVQchDPPVqK79bPqPJDSSL5vml/oilS/lhxCaELKQF0JoAWJKi5AGD1fd0l1+DyEMUwh17WfTfyShkXzZNL/UF6l8KT+E0ISQhbwQQgsQU1qENHi46pbuQAohhBDmCEhCI/myaX4pFqTypfwQQhNCFvLqCKGpk0nNlMqX8rt0Qqnu0O+bsnXRPwhhPKqSLaU4kPLrtkrXfrrl6aQz7ZtpfqmNUvlSfgihCSELeSGEFiAGXIStwc9mF3UHUswIMSPEjFDe9hJanBSOFRWzfYIH07GXrToJ5LA/9Wg0Jpo+DZkO1qb12xzkQyvLlK2L/kAI41GVbCnFgZRft1W69tMtTyedad9M80ttlMqX8sedEfK43L1TP5o0aVLRInQmOiZt08kLIdShZGGjr0sn1OxCsMlsDX42O6g7kIb2pKszqEi8TXzVtGwpv66Nde2nW55OOomb1DfT/FIbpfKl/BBCE0IW8uoGN2aEFmB7KEIaIDw0iXQHUghhQ+tItpQGYym/ri/o2k+3PJ10pn0zzS+1USpfyg8hNCFkIS+E0ALEgIuwNfjZ7KLuQAohhBDmCEhCI/m5aX7J/6XypfwQQhNCFvJCCC1ADLgIaYDw0XQIYTzqki2lwVjKr9sqXfvplqeTzrRvpvmlNkrlS/khhCaELOSFEFqAGHARtgY/m13UHUgxI8SMEDPC8qtGsVjG0sjUtm1bWrRoETVr1qxoiX369KGZ30+mBXOWq/vtO9c0SserlkwuLt/kMq3fpO7Q85qyddG/ujqi+d+u8ie+OnStoSInrKnVcKWuKVOmUE1NDS1cuNBFE4uWqRsr5Rpk4quSLaWypfy6IHXtp1ueTjrTvpnml9oolS/lL3e/nN14XK5p2px4nC52+YiTwnakYtXo6quvTt99950aVIpdpQCbGBZ5QcCUwPTp06l169Y0a9Ys06K08yNWtFEhYSAEfMRJKoUwEHuhGSAAAiAAAhVIIBUzwgrkji6BAAiAAAgEQgBCGIgh0AwQAAEQAAE/BCCEfrijVhAAARAAgUAIQAgDMQSaAQIgAAIg4IcAhNAPd9QKAiAAAiAQCIHUCCHvI3zggQfozTffpDfeeIM++OADWrZsGV166aV01llnlcX51Vdf0XnnnUdPPvkkzZ07l3r06EGHHHIInX322dSiRYuieZcsWaLKvvvuu4mX93bs2JF23313uuiii2jNNdcMxHxohm0CsLttovbKs2mb+fPn0wUXXEAPPvig2t7C206GDh1KF154IXXo0MFeoyuopB9//JGefvppevTRR+mtt96iL7/8klauXEl9+/alAw44gM444wxq06aNdo979epF06ZNK5n+448/pl/+8pfa5ZkkTI0Qvv/++7Txxhs36qskhLxZc+utt1b7ENdff30aMGAAvf322/TFF1+of3/hhReoefPmDcrlgNt5553ptddeo+7du9O2226rjM4i3KVLF5owYULJzaEmxkBevwRgd7/8y9Vu0zbff/+9iv3JkyfT2muvTZtttpn6iSD+jwf1119/nTp16hQuDE8tGz16NB177LGqdj7ti8fSH374QY2TfGgEi9ZLL71EXbt21WphTgiPPPLIoul5bOfxN4krNULIgsZgtthiC9p8883p/vvvp5EjR4ozwu23355efvllOuWUU+iaa65RTFesWEEHH3ywehrkmSI/Bda/+N8uvvhiFSz8BJR7yrnyyivpzDPPpO22204ZHFdlEYDdw7WnTdscccQRdOedd9L+++9P//nPf6hp06aq4zxGXHfddcT3b7/99nBheGrZHXfcoR4STj/9dOrX7+cTlWbOnElDhgyh9957jw499FAaO3asVgtzQljHxwB5vlIjhIWc+LUGC1i5GSFP31k4+QmFX2/Wn/nNnj2b1lprLSVy/OfcqTXLly9X6fnVybvvvttoFrrhhhvShx9+qGaVm266qWfzoXpbBGB3WyTtl2PTNvwadI011qAmTZrQjBkzqFu3bvkGL126VI0J/Pnk66+/bnDPfq8qq0R+S7bNNtuoMZZniaWOw6zfawihBR/QEcLzzz9ffdM75phjiKf1hRe//nz++efV69EddthB3eY/77TTTurV5+eff94oD88U+emUy+Y24KoMArB7uHa0aZtbb72Vfvvb36pPH88++2yjTvNYccsttxCnO+qoo8KFEljL+PshHyfI1zfffKP1ShNCaMGIOkLIH78ffvhhuv766+mkk05qVOuIESPo8ssvp6uvvppOPfVUdZ//zFP/gw46iO69995GecaNG0d77bWX+rDOr1ZxVQYB2D1cO9q0zWmnnaY+kXDs//3vf2/UaR4rTj75ZOJ0V111VbhQAmvZxIkTaYMNNsgfMl+47qJYc3NCyHbgT1+ch7897rfffmotRpJXRb8a3WSTTdR7axbDffbZpxFXDgh2eF7tdMUVV6j7/GcOABZD/iZYePFq1Y022oi47HfeeSdJW6EuhwRgd4dwDYu2aRv+LsgPsBz7/E2w8OKxgh9yOR2vQ8ClR4AX0fBbt7333pseeeQRrUylVo22atWKrr32WvUmL6mrooWwf//+amXYM888Q7vssksjprlVUMcddxzdeOON6j7/+d///jedc845dMkllzTKw69L+UMxl/3pp58mZSfU45gA7O4YsEHxNm2z6667qvGAY3z48OGNWsWvSwcPHkyc7qmnnjJodXayPv744+otGS864nUZvI5C5+IHkR133FGtteAZIK/k59fS/JDC2zL4gYUfSpK4EhPCAw88kHj6HOXiVUq82KXYpfNqlAWLhYudm78JFF4cDBxk9YUw92Rz7rnnqpWjhRcLK4sghDCKJcNPC7uHayObtmGR4/GAH4KLzThYJFkEIYR6/sB7/QYOHEjz5s1r8IlJL3fxVDfddBMdf/zxiY6xiQkh79WJ+iqx/iKWQmQ6QohXoybumK28Nl+/ZYuc+97atA1ejdqzFx9UwiLIK/Lrf14yraG2tlYttvn222/VLLF3796mRYr5ExNCsSURE+gIIRbLRISa4eQ2F2RkGKOTrtu0DRbL2DHRnDlz1EEjn3zyCR199NF08803U1VVlZ3CidRWDN6SMX78ePVn11dFC6Hu9gneQsHvqvnSXapdbCO+a2OhfHcEYHd3bE1Ltmkb3e0T/K2KB3hcjQnwKTK8xYz3UvMMm1fX875Mm9e6666rRJYXJ/7qV7+yWXTRsipaCPlItC233LLshnpeocTHr+U21PP5pbyhfsGCBWU31HPZfMINrsogALuHa0ebtuFTUPisYF7YwRvq6x8HlttQz0ew8YZ6Pn8UV0MCzGiPPfZQE4bddttNrRDV2TwfhSMfdcdbMVq2bKm+Pdouv1hbKloIucODBg1S02veJ8ivWPjiI9Z+/etfq0O8iy2K4X/j49t4Ss5HrOU2iuaOWOMyX3nllSi2RdoUEIDdwzVSVNuMGjWK+D/ek8anT9W/Dj/8cBozZow6KPqee+7JH7HGYwQv2+f7fAQbroYEeCUn76/m1Zz8WpR/xIAnEuWuUnbgFbmdO3dudDoXn9rFP4jAi3DqH4vp2hapEkJ2an6i44s/1PJTGx+J9Itf/EL9G39gLdzkzqs8+cxQfsrjpww+KJaX+PJHWJ4tvvjii41+gYIP+OWTZvhXLnKHbvMp6fx3PoyXz9vjw3lxVRYB2D1ce0a1TW4NAR/ofNtttzXoGH/f2mqrrdQmbj5BKnfoNq9q579zfPMgjashgdy+a/5XHovbtWtXFBEfUpLjV8oOuX/v2bOnYs7bJ6ZOnarewvFEhc+I5m0ZktDaslGqhFD62Q6Gyr8SUXjxK5D6P8PE4smHw/LPMPH0u9i1ePFi9STJB8hy/tVWW039DBNvqeD8uCqTAOwerl2j2KacEHIP+ZUbryF46KGH1FnDfObovvvuq84v5p9cw9WYQI6pxIYFjcdqvkrZgRfC8BYWnpTwkWz8KYqFlb8HDhs2TH2ftf3dsVy7UyWEkgFwHwRAAARAAASiEoAQRiWG9CAAAiAAAhVFAEJYUeZEZ0AABEAABKISgBBGJYb0IAACIAACFUUAQlhR5kRnQAAEQAAEohKAEEYlhvQgAAIgAAIVRQBCWFHmRGdAAARAAASiEoAQRiWG9CAAAiAAAhVFAEJYUeZEZ0AABEAABKISgBBGJYb0IAACIAACFUUAQlhR5kRnQAAEQAAEohKAEEYlhvQgAAIgAAIVRQBCWFHmRGdAAARAAASiEoAQRiWG9CAAAiAAAhVFAEJYUeZEZ0AABEAABKISgBBGJYb0IAACIAACFUUAQlhR5kRnQAAEQAAEohKAEEYlhvQgAAIgAAIVRQBCWFHmRGdAAARAAASiEoAQRiWG9CAAAiAAAhVFAEJYUeZEZ0AABEAABKISgBBGJYb0IAACIAACFUUAQlhR5kRnQAAEQAAEohKAEEYlhvQgAAIgAAIVRQBCWFHmRGdAAARAAASiEoAQRiWG9CAAAiAAAhVFIBVCuPrqq9OiRYuoR48eFQUfnalsAtOnT6fWrVvTrFmzKruj6B0IpJxAKoSwbdu2tHz5curTp0/KcTdsfl1dHc2ZMyf/j507d6aqqqqK6mOWOzNlyhSqqamhhQsXZhkD+g4CwRNIhRCut956CuSkSZOCBxqlgfPnz6fVVlstn2XevHnUoUOHKEUgbcAEKtVvA0aOpoFALAIQwljY7GSCENrhGGopEMJQLYN2gUBDAhBCjx4BIfQIP4GqIYQJQEYVIGCBAITQAsS4RUAI45JLRz4IYTrshFaCAITQow9ACD3CT6BqCGECkFEFCFggACG0ADFuERDCuOTSkQ9CmA47oZUgACFMyAdOvqpvo5qWLl5Jo8/5ssG/H3nkkXTbbbcl1CpUIxEoZrf6eUad/nnJIiCEEl3cB4EwCEAIE7IDhDAh0JargRBaBoriQCBAAhDChIwCIUwItOVqIISWgaI4EAiQAIQwIaNIA+r1Z0xRLcGr0YQMolmNZDe8GtUEiWQgEDABCGFCxpEGVAhhQoaIWI1kNwhhRKBIDgIBEoAQJmQUaUCFECZkiIjVSHaDEEYEiuQgECCBzAohr8w8+uijlUleeOEF2mGHHWjs2LF0yy230MSJE4m3NnTv3p0GDx5MI0aMoH79+hU138qVK1X+p59+ml5//XX67LPPaO7cudSsWTPiX83YcsstVT0P/d8JZc1fKITchmuuuYaee+45mjlzJrVp04Y23nhjOvbYY+mggw4K0JUqs0kQwsq0K3oFAvUJQAiJ6JlnnqHrr7+eHnrooaLe0bx5c7r11lvp0EMPbXT/tNNOU4IlXf02bkM7H9qVmjQt/usS9YWQxfeYY46hpUuXFi32wAMPVKLNv2yAyy0BCKFbvigdBEIgACEkUrO2N954g7baaiv6/e9/T+ussw7xL0E8+OCDdOONNxLP+po0aaIEc8cdd2xgt5NPPlml22effVR+/qko/g262bNn08cff0yjRo2iL774QuXZYFA72m7/Lvn8xfYRbrTRRvTRRx+pMngmuv3226u633rrLbrsssvoq6++Uvl5lsmzV1xuCUAI3fJF6SAQAgEI4U9WOPjgg+nuu++m6urqBnZ59NFHaejQoVRbW0trr722evXJwpS7+DfnevbsSU2bNi1qTxbRww8/nO655x6qqiY6/Owe1K7jqplcMSHkf+/WrRuNHz++0e8vfv/99zRw4ED69NNPVf7nn3++kTCH4FSV1AYIYSVZE30BgeIEIIRE1LFjRzVra9++fVFK/JoyN/vi2R8LY5SLf3y3a7cuVFdLNGhoJ9pwu1W/OVhKCO+8804lnsWuV155hbbbbjt1a9999y35OjdK+5C2NAEIIbwDBCqfAISQiE488UT65z//WdLaudemnOD444+nG264oWTaRYsWqV+d5//zLDJ3bbH1hrT4f7W0zmZtaJfDupUUQhbjb7/9Vi22KXWtu+669Mknn6jXpwsWLGgwQ618l022hxDCZHmjNhDwQQBCSES33347HXHEESX5L1++XIkO/5+/A06YMKFB2qlTp9LVV19Njz32GPGf6+rqSpbVc91WtNex3UsKIX+D5Fee5S7+Ppg7j5QFkb9p4nJDAELohitKBYGQCEAIiejJJ5+k3XbbraxdeCvFrFmzqG/fvjR58uR82nvvvVeJaKkVnoWFrtGnBQ393RolhfCQQw5R3yrLXX/+85/pb3/7m0ry6quvqu+GuNwQgBC64YpSQSAkAhBCInrqqado1113LWsX3hPIK0F5PyEvmOHr888/p/XXX1+JYKtWrejUU0+l3XffXYllp06d1OvNqqpV2yXarlZD/5u/gn7RpwXtV0YIeYsGb40od5111llqBSlfvKhmm222CcmnKqotEMKKMic6AwJFCUAINV+N8ob2ZcuWNXg1evbZZ9Oll16qwD7xxBNKBEtdNc2racWyOlEI8Wo0rEiFEIZlD7QGBFwQgBAaLJbZe++91XdBXnXKWxtKXZMmTVIzR76kGaHOYpkBAwaoPYr83ZJPwCm1dcOFw2StTAhh1iyO/maRAISQSL3G5O0T7dq1K+oDw4cPp5tvvlnde+CBB2i//fZTf95jjz3U98WWLVvSwoULS67e5JWmN910k5YQcqK77rqLhg0bVrQt/E1w2223Vfd4E//DDz+cRb9NrM8QwsRQoyIQ8EYAQvgTev42xwJUuKF+3LhxSnB4K0Tv3r3V98HcDKz+8Wq8ipN/Qqnw4jJ5MU1uJak0I+T8/D3ytddeU/XVv/gM00GDBqnZIF98DulOO+3kzXmyUDGEMAtWRh+zTgBCWO+INV50wkes9e/fX71y5M3zvGdwxYoVSiD5YO2dd9457zPvv/8+bbLJJkrkeGHMCSecQHvuuSd16dKFpk2bplZ//ve//1XlTf96stpHWF8IVyyrpQnj5qryPnxlgfp/7og1/iZZ7Ii1GTNmqHT43cJkQhdCmAxn1AICPglACImUwPGh26VeM/Kh26NHjy562stVV11FZ555Zsm9g7zKlGeVm245gBbOa7hqtL7h6x+6vcsuu6hDt3lxTrGLX83ykW3lNt37dKpKqhtCWEnWRF9AoDgBCOFPP8PEh1vz0Wb8ipN/AolPbOFXlLmfYSq3aZ2PPeMN9byVgRfNdOjQQW2h2H///emkk05Si1r4fFFdIeQ2fPDBB+pXLfgnnvhnmLgMnn3y98pf//rX8OeECEAIEwKNakDAIwEIYb3fI3RpB5MB1WW7UHZ5AiZ2W2+99VThvGoYFwiAQLgEIISaQmgyILL5pfzlXKTcr6CH61qV0TLJbviF+sqwM3qRbQIQQo9CuHJFHU0cv2qRDF/rD2xf9Id7IYT+ghRC6I89agaBpAhACD0KYeHPMA0f2Yuat/z5tw5zTgAhTCocGtcDIfTHHjWDQFIEIIQQwqR8LZX1QAhTaTY0GgQiEYAQQggjOUzWEkMIs2Zx9DeLBDIrhFGNbTIgcl3F8uPVaFQrJJ/exO5YNZq8vVAjCMQhACHUpGYyIEIINSEHmMzE7hDCAA2KJoFAEQIQQk23MBkQIYSakANMZmJ3CGGABkWTQABCGN8HTAZECGF87r5zmtgdQujbeqgfBPQIYEaox0ncEC9tccA3Qk3QgSWDEAZmEDQHBBwQgBBqQjUZEDEj1IQcYDITu2NGGKBB0SQQwKvR+D5gMiBCCONz953TxO4QQt/WQ/0goEcAM0I9Tng1qsmp0pJBCCvNougPCDQmACHU9AqTAbHUjLCuto4Wzl+Rb0HbDk2pqrqqUYuk74+aXUCyGARM7I4ZYQzgyAICHghACDWhmwyIpYRQs2qCEOqSsp/OxO4QQvv2QIkg4IIAhFCTqsmACCHUhBxgMhO7QwgDNCiaBAJFCEAINd3CZECEEGpCDjCZid0hhAEaFE0CAQhhfB8wGRAhhPG5+85pYncIoW/roX4Q0COAGaEeJyerRpctqaV7/jEj34JDRqxFzVpUN2oRvhFqGslBMgihA6goEgQCIwAh1DSIyYBYakaIX5/QhO8xmYndMSP0aDhUDQIRCEAINWGZDIgQQk3IASYzsTuEMECDokkgUIQAhFDTLUwGRAihJuQAk5nYHUIYoEHRJBCAEMb3AZMB0bUQmrYtPhX3OX33zaR+CKF7/0ANIGCDAGaEmhRNBkQIoSbkIslMuceveVVOk/ohhKb0kR8EkiEAIdTkbDIgQgg1IUMI44NCThAAgdgEIISa6CCEmqAsJzPlbtock/oxIzSlj/wgkAwBCKEmZ5MBETNCTciYEcYHhZwgAAKxCUAINdFBCDVBWU5myt20OSb1Y0ZoSh/5QSAZAhBCTc4mAyJmhJqQMSOMDwo5QQAEYhOAEGqicyGEy5fW0jNjZudbMHhYN6ppHv2INdO2aSLwksx330zqx4zQi8ugUhCITABCqInMZEAsNSPUrFr8PULTtum2w0c6330zqR9C6MNjUCcIRCcAIdRkZjIgQgg1IePVaHxQyAkCIBCbAIRQEx2EUBOU5WSm3E2bY1I/ZoSm9JEfBJIhACHU5GwyIGJGqAkZM8L4oJATBEAgNgEIoSY6F0K4cmUdTfvox3wLeg5oRU2aVDVqkfR7hKZt00TgJZnvvpnUjxmhF5dBpSAQmUBqhHDKlCnUp0+foh2cNGlS5I5HzWAyIJaaEdr6PULTtkVlkWR6330zqR9CmKSnoC4QiE8AQqjJzmRAhBBqQsar0figkBMEQCA2gdQIIfcwiZlfKZIQwtg+ZpTRlLtR5fj1CVN8yA8CqSAAIdQ0k+mAXCw/Xo3K8E25yzWUT2FSP16NmtJHfhBIhgCEUJOzyYCIV6OakPFqND4o5AQBEIhNAEKoiQ5CqAnKcjJT7qbNMakfM0JT+sgPAskQgBBqcjYZEDEj1ISMGWF8UMgJAiAQmwCEUBMdhFATlOVkptxNm2NSP2aEpvSRHwSSIQAh1ORsMiBiRqgJGTPC+KCQEwRAIDYBCKEmOgihJijLyUy5mzbHpH7MCE3pIz8IJEOgYoTQZMDSQS2Vr1NGYRpsn5CpSdyl4+fkGsqnMKkfQmhKH/lBIBkCEEJNztKAqFlMg2S1K+to5tQl+X/r3rsFVeOs0QaMJO4QwjiehzwgAAL1CUAINf1BGpA1i4mVTBrspbZJ+WM1KqFMvvtmUj9mhAk5CaoBAUMCEEJNgNKAqFlMrGSSkEltk/LHalRCmXz3zaR+CGFCToJqQMCQAIRQE6A0IGoWEyuZJGRS26T8sRqVUCbffTOpH0KYkJOgGhAwJAAh1AQoDYiaxcRKJgmZ1DYpf6xGJZTJd99M6ocQJuQkqAYEDAlACDUBSgOiZjENki1bWkvjRs/M/9uQ4d2pWfPqRkVJQia1Tcofp+1J5fHdN5P6IYRJeQnqAQEzAhBCTX7SgKhZTINk2D4hU5O4uxZ5k/ohhLJ9kQIEQiAAIdS0gjQgahYDIYwISuIOIYwIFMlBAAQaEYAQajqFNCBrFgMhjAhK4g4hjAgUyUEABCCEcX1AGpDjlItXozI1iTuEUGaIFCAAAuUJYEao6SHSgKxZDGaEEUFJ3CGEEYEiOQiAAGaEcX1AGpDjlIsZoUxN4g4hlBkiBQiAAGaEioDpgCkNyHEcDUIoU5O4m9pVaoFJ/Vg1KtHFfRAIgwBejWraQRoQNYvBq9GIoCTuEMKIQJEcBEAAr0bj+oA0IMcpFzNCmZrEHUIoM0QKEAABvBrFq9HTP09tHEAIU2s6NBwEUkMAr0Y1TSUNyJrFNEi2bEktPTDq6/y/7X/yGtSsBY5Yqw9J4o4ZYRzPQx4QAIH6BCCEmv4gDciaxcRKJg32Utuk/LEalVAm330zqR+LZRJyElQDAoYEIISaAKUBUbOYWMkkIZPaJuWP1aiEMvnum0n9EMKEnATVgIAhAQihJkBpQNQsJlYySciktkn5YzUqoUy++2ZSP4QwISdBNSBgSABCqAlQGhA1i4mVTBIyqW1S/liNSiiT776Z1A8hTMhJUA0IGBKAEGoClAZEzWIaJKutraPvZy7L/1un7s2ouroqTlFGeUIWSom7adul8iWw5eqHEEr0cB8EwiAAIdS0g+mAWawa3X2Emk2MncxUTGJXrJFR4m7adql8qYkQQokQ7oNA+AQghJo2Mh0wIYSaoAuSSdwhhPG4IhcIgMDPBCCEmt4gDciaxTRIhhmhTE3iDiGUGSIFCIBAeQIQQk0PkQZkzWIghBFBSdwhhBGBIjkIgEAjAhBCTaeQBmTNYiCEEUFJ3CGEEYEiOQiAAIQwrg9IA3KccvFqVKYmcYcQygyRAgRAAK9GFQHfA2YxM0AI5fCEEMqMkAIEQMCMAF6NavKTBmTNYvBqNCIoibvvBxxsn4hoUCQHgQAJQAg1jSINyJrFQAgjgpK4QwgjAkVyEACBRgQghJpOIQ3ImsVACCOCkrhDCCMCRXIQAAEIYVwfkAbkOOXiG6FMTeIOIZQZIgUIgEB5ApgRanqINCBrFtMg2coVdfTZuwvz/9Z/k7bUpCnOGq0PSeIOIYzjecgDAiBQnwCEUNMfpAFZs5ggk5mKictOSdxN2y6VL/UNi2UkQrgPAuETgBBq2sh0wNSsxksyUzFxqiYeQwAADDxJREFU2WiJu2nbpfKlvkEIJUK4DwLhE4AQatrIdMDUrMZLMlMxcdloibtp26Xypb5BCCVCuA8C4ROAEGrayHTA1KzGSzJTMXHZaIm7adul8qW+QQglQrgPAuETgBBq2sh0wCxWzfJltfTy/XPyt7Y7oDPVNKvWbJG9ZKZiYq8ljUuSuJu2XSpf6huEUCKE+yAQPgEIoaaNTAfMYtVg+4QMX+IOIZQZIgUIgEB5AhBCTQ+RBmTNYhokgxDK1CTuEEKZIVKAAAhACBUB3wMmZoTxQhFCGI8bcoEACOgTwIxQk5U0IGsWgxlhRFASd98POPhGGNGgSA4CARKAEGoaRRqQNYuBEEYEJXGHEEYEiuQgAAKNCEAINZ1CGpA1i4EQFgHlgm0ce8TJgxlhHGrIAwJhEYAQatrDxWCNxTKr4Ltgq2lW42QQQmOEKAAEvBOAEGqawMVgDSGEEGq6H5KBAAg4JAAh1IQLIdQEFSOZC7YxmhErC2aEsbAhEwgERQBCqGkOF4M1ZoSYEWq6H5KBAAg4JAAh1ITrSgjvuHh6vgVH/KUHNW/ZRLNF9pKZrrw0bYkLtqZt0s2PGaEuKaQDgXAJQAg1bZPmwVrqIoRQIlT6PoQwPjvkBIFQCEAINS0BIdQEFSNZmtlCCGMYHFlAIDACEEJNg6R5sJa6iBmhRAgzwviEkBMEwicAIdS0EYRQE1SMZGlmixlhDIMjCwgERgBCqGkQF4N1XV0dLVtSm29BsxbVVFVVpdkie8kwI4zPEkIYnx1ygkAoBCCEmpZwIYTYPrEKvgu2mmY1TgYhNEaIAkDAOwEIoaYJXAzWEEIIoab7IRkIgIBDAhBCTbgQQk1QMZK5YBujGbGyYEYYCxsygUBQBCCEmuZwMVhjRogZoab7IRkIgIBDAhBCTbgQQk1QMZK5YBujGUWzjL1s1ck/h/2pR9H7mBHaIo1yQMAfAQihJnsXgzVmhOHPCCGEmgGCZCCQYgIQQk3jQQg1QcVI5oJtjGZgRmgLGsoBgZQRgBBqGszFYI0ZIWaEmu6HZCAAAg4JQAg14UIINUHFSOaCbYxmYEZoCxrKAYGUEYAQahrMxWCNGSFmhJruh2QgAAIOCaRCCNu2bUuLFi2iZs2aFUXRp08fmvn95LKYunfqZ4RRKj9O4XV1RPO/XZ7P2qFrDXk4YY1M2cTpe/08LtiatimXf8GcVfZp37mmaJHl2E2ZMoVqampo4cKFtpqDckAABBwQSIUQrr766vTdd9+pQaXYxUKICwRCIzB9+nRq3bo1zZo1K7SmoT0gAAL1CKRCCGExEAABEAABEHBFAELoiizKBQEQAAEQSAUBCGEqzIRGggAIgAAIuCIAIXRFFuWCAAiAAAikggCEMBVmQiNBAARAAARcEYAQuiKLckEABEAABFJBAELoyUxLliyhSy+9lO6++27iZfYdO3ak3XffnS666CJac801PbWqsqr98ccf6emnn6ZHH32U3nrrLfryyy9p5cqV1LdvXzrggAPojDPOoDZt2hTt9B133EGjRo2ijz76SO1f3Wqrrejcc8+lbbbZpiSk1157jS655BJ6/fXXadmyZTRgwAD63e9+R0ceeWRlgUVvQKDCCEAIPRiURXDnnXcmHji7d+9O2267rRqk33zzTerSpQtNmDCBsDfS3DCjR4+mY489VhW03nrrKWH64YcfFHfe5P7LX/6SXnrpJeratWuDylggr7rqKmrZsiXtuuuuxPZ67rnnqK6uju677z7ab7/9GjXuwQcfpIMOOohqa2tpu+22o86dO6s88+fPp9NPP52uvPJK8w6hBBAAAScEIIROsJYv9LzzzqOLL76Ytt56azVjyc1KeLA888wz1UDKAzQuMwI8q+PZGQtRv34/nyw0c+ZMGjJkCL333nt06KGH0tixY/MVPf/88+ohpVOnTuqBJJeP/7zDDjsocZw6dSqtttpq+Tzz5s2j3r1704IFC+j++++n/fffX92bPXs2DRo0iD7//HPicnfccUezDiE3CICAEwIQQidYSxe6fPlyNQPhmcK7775LG2+8cYPEG264IX344Yf09ttv06abbppw67JTHQsbv+Zs3ry5miXmju9jgXz88cfVjPC0005rAOTUU0+la6+9li6//HL1wJK7/vGPf9Af//hH2nfffemhhx5qkIdniiyMe+21l3pFiwsEQCA8AhDChG3ywgsv0E477aReffJMofDimSLPGM8//3y64IILEm5ddqrj74d8/Blf33zzjXpFza9AO3ToQEuXLqUZM2Y0+lb7yiuvqNn69ttvTy+++GIeFv/95ZdfpjvvvJMOP/zwBhD5W2H79u3Vv/HMsUWLFtmBjJ6CQEoIQAgTNtTVV1+tXtXx96R77723Ue3jxo1Ts4ehQ4cSzyZwuSEwceJE2mCDDfKHYvPM8P3331czdP5O++233zaqmA9+59fY/Fp07ty5+fv8d57hT5o0SX2HLLw233xzNcPn8nnGjwsEQCAsAhDChO2RW4hRagHFBx98QBtttBFtsskm9M477yTcuuxUx4toeDHN3nvvTY888ojqOP+fX2+yGPJr62JXTvT4dSr/Kgr/Pzfj42+E7dq1a5SNF9fwK1Mun+vDBQIgEBYBCGHC9jjuuOPo3//+N51zzjlqqX3hxa9LeYFG//796dNPP024ddmojr8B8qy7adOmaltFbpbGi2aGDRtGAwcOpFdffbUoDN7a8vXXX+dfp/Jr1TXWWEOl5e+/XGbhxa9Lx4wZoxbl8OIcXCAAAmERgBAmbI/cTIT3pPH3wMJr8uTJSgQhhG4M8/HHHyuh4+91/JqaF8DkLhYrFi1e6cnfA4tdLHosfrnviiyKuX2fpYSQxZVFEELoxqYoFQRMCUAITQlGzI9XoxGBWUz+1VdfKRHkAwzYDldccUWD0vFq1CJsFAUCKSIAIUzYWFgskzDwn6qbM2eOOrjgk08+oaOPPppuvvlmqqqqatAY3cUyvLKUZ5S5i//O3wexWMaPbVErCJgSgBCaEoyYX3f7BG+huPDCCyOWjuTFCPApMrxlhVdu8p4+Xq3bpEmTRkkXL16sVoRK2ycKDzwot32CX5fyAho+lYZXlmL7BHwUBMIjACFM2Ca8r4w31PMMotyGej5ujZfd4zIjwKK2xx57ED+A7LbbbmrlZm7zfLGS99xzT3riiSfKbqj/+9//TiNGjMhn57//6U9/KruhnsvlrTG4QAAEwiMAIfRgE14oM3LkSHWyCR+xltvYnTtirdxiDQ/NTW2VfMA279fk/Zj8WvTJJ5+kVq1ale3Ps88+S4MHDy56xBofkcb7DfmINT4kPXfxnkI+Yo23UtQ/Yo33IvI3SV4JzOXy0W24QAAEwiMAIfRgEz7BhM+tfOONN/KHbk+bNk39nc+45PMx+RcScJkRuOaaa/LHpPFevmJ7/LgGPjKND8nOXXy0Gudl0WRR5Fn8M888ow7U5teq/MsVhRcL4MEHH6xegfKrUi6PxY9fh55yyimqPFwgAAJhEoAQerILf4/in2HiJfV8nBd/m+KfYeItFWuttZanVlVWtXxEnc53Vp7h9erVq0Hnb7vtNvUzTLzdoqamJv8zTDxbL3WNHz++wc8wrbvuuupnmHhxDi4QAIFwCUAIw7UNWgYCIAACIJAAAQhhApBRBQiAAAiAQLgEIITh2gYtAwEQAAEQSIAAhDAByKgCBEAABEAgXAIQwnBtg5aBAAiAAAgkQABCmABkVAECIAACIBAuAQhhuLZBy0AABEAABBIgACFMADKqAAEQAAEQCJcAhDBc26BlIAACIAACCRCAECYAGVWAAAiAAAiESwBCGK5t0DIQAAEQAIEECEAIE4CMKkAABEAABMIlACEM1zZoGQiAAAiAQAIEIIQJQEYVIAACIAAC4RKAEIZrG7QMBEAABEAgAQIQwgQgowoQAAEQAIFwCUAIw7UNWgYCIAACIJAAAQhhApBRBQiAAAiAQLgEIITh2gYtAwEQAAEQSIAAhDAByKgCBEAABEAgXAIQwnBtg5aBAAiAAAgkQABCmABkVAECIAACIBAuAQhhuLZBy0AABEAABBIgACFMADKqAAEQAAEQCJcAhDBc26BlIAACIAACCRCAECYAGVWAAAiAAAiESwBCGK5t0DIQAAEQAIEECEAIE4CMKkAABEAABMIl8P9g8/vHTs1ZbAAAAABJRU5ErkJggg==" width="300">
```python
plate, ifu = (7965,3704)
if plate == 11021: args, resdict = fileprep(f'/media/brian/bdigiorg/nirvana/lux/barred/sample/nirvana_{plate}-{ifu}_Gas.fits', rootdir='/media/brian/bdigiorg/manga/spectro/')
elif plate == 7965: args, resdict = fileprep(f'/media/brian/bdigiorg/nirvana/lux/mocks/newmocks/nirvana_{plate}-{ifu}_Gas.fits', rootdir='/media/brian/bdigiorg/manga/spectro/')
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/newmocks/penalty/nirvana_{plate}-{ifu}_Gas_mock_i??_r*.fits')
plt.figure(figsize=(10,18))
for i in range(len(fs)):
with fits.open(fs[i]) as f:
plt.subplot(9,5,i+1)
plt.imshow(f['vel_model'].data-f['vel'].data, cmap='RdBu', origin='lower',vmin=-20,vmax=20)
plt.axis('off')
plt.tick_params(left=False,bottom=False,labelleft=False,labelbottom=False)
plt.tight_layout(h_pad=0,w_pad=0)
```
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/7965/stack/manga-7965-3704-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/7965/images/3704.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/7965/3704/manga-7965-3704-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.0 +/- 0.0
Y center: 0.1 +/- 0.0
Position Angle: -77.3 +/- 0.1
Inclination: 21.4 +/- 1.0
Systemic Velocity: 1.7 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 28.9 +/- 0.1
RC: Scale: 3.9 +/- 0.0
----------
Velocity measurements: 818
Velocity chi-square: 9074.859985890593
Reduced chi-square: 11.18971638210924
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.0 +/- 0.0
Y center: 0.1 +/- 0.0
Position Angle: -77.3 +/- 0.1
Inclination: 21.4 +/- 1.0
Systemic Velocity: 1.7 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 28.9 +/- 0.1
RC: Scale: 3.9 +/- 0.0
----------
Velocity measurements: 818
Velocity chi-square: 9074.859985890593
Reduced chi-square: 11.18971638210924
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 0.0 +/- 0.0
Y center: 0.1 +/- 0.0
Position Angle: -77.1 +/- 0.1
Inclination: 20.9 +/- 1.0
Systemic Velocity: 1.8 +/- 0.0
----------
Rotation curve parameters:
RC: Asymptotic value: 28.6 +/- 0.1
RC: Scale: 3.8 +/- 0.0
----------
Velocity measurements: 741
Velocity chi-square: 8287.02771951823
Reduced chi-square: 11.290228500706036
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
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width="1000">
```python
plate, ifu = (11021,3703)
if plate == 11021: args, resdict = fileprep(f'/media/brian/bdigiorg/nirvana/lux/barred/sample/nirvana_{plate}-{ifu}_Gas.fits', rootdir='/media/brian/bdigiorg/manga/spectro/')
elif plate == 7965: args, resdict = fileprep(f'/media/brian/bdigiorg/nirvana/lux/mocks/newmocks/nirvana_{plate}-{ifu}_Gas.fits', rootdir='/media/brian/bdigiorg/manga/spectro/')
fs = glob(f'/media/brian/bdigiorg/nirvana/lux/mocks/newmocks/penalty/nirvana_{plate}-{ifu}_Gas_mock_i??_r*.fits')
plt.figure(figsize=(10,18))
for i in range(len(fs)):
with fits.open(fs[i]) as f:
plt.subplot(9,5,i+1)
plt.imshow(f['vel_model'].data-f['vel'].data, cmap='RdBu', origin='lower',vmin=-20,vmax=20)
plt.axis('off')
plt.tick_params(left=False,bottom=False,labelleft=False,labelbottom=False)
plt.tight_layout(h_pad=0,w_pad=0)
```
UserWarning: Datacube file /media/brian/bdigiorg/manga/spectro/redux/DR17/11021/stack/manga-11021-3703-LOGCUBE.fits.gz does not exist!
UserWarning: Image file /media/brian/bdigiorg/manga/spectro/redux/DR17/11021/images/3703.png does not exist!
Reading /media/brian/bdigiorg/manga/spectro/analysis/DR17/HYB10-MILESHC-MASTARHC2/11021/3703/manga-11021-3703-MAPS-HYB10-MILESHC-MASTARHC2.fits.gz ...
Done
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.9 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -110.7 +/- 0.1
Inclination: 1.0 +/- 21.8
Systemic Velocity: -88.0 +/- 1.0
----------
Rotation curve parameters:
RC: Asymptotic value: 183.5 +/- 1.3
RC: Scale: 4.5 +/- 0.0
----------
Velocity measurements: 742
Velocity chi-square: 69096.61087255078
Reduced chi-square: 94.00899438442283
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.8 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -110.8 +/- 0.1
Inclination: 1.0 +/- 21.9
Systemic Velocity: -85.7 +/- 0.9
----------
Rotation curve parameters:
RC: Asymptotic value: 178.6 +/- 1.2
RC: Scale: 4.3 +/- 0.0
----------
Velocity measurements: 696
Velocity chi-square: 68680.2853778356
Reduced chi-square: 99.68111085317213
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.5 +/- 0.0
Y center: -0.8 +/- 0.0
Position Angle: -111.8 +/- 0.1
Inclination: 1.0 +/- 23.6
Systemic Velocity: -77.0 +/- 0.8
----------
Rotation curve parameters:
RC: Asymptotic value: 176.7 +/- 1.2
RC: Scale: 4.4 +/- 0.0
----------
Velocity measurements: 692
Velocity chi-square: 58428.32197484034
Reduced chi-square: 85.29682040122678
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.2 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -112.4 +/- 0.1
Inclination: 1.0 +/- 24.9
Systemic Velocity: -71.4 +/- 0.7
----------
Rotation curve parameters:
RC: Asymptotic value: 176.4 +/- 1.2
RC: Scale: 4.5 +/- 0.0
----------
Velocity measurements: 689
Velocity chi-square: 51771.26572446772
Reduced chi-square: 75.91094681006997
----------------------------------------------------------------------
----------------------------------------------------------------------
Fit Result
----------------------------------------------------------------------
Fit status message: `ftol` termination condition is satisfied.
Fit status: 2
Fit success: True
----------
Base parameters:
X center: 3.2 +/- 0.0
Y center: -0.7 +/- 0.0
Position Angle: -112.5 +/- 0.1
Inclination: 1.0 +/- 25.4
Systemic Velocity: -72.1 +/- 0.8
----------
Rotation curve parameters:
RC: Asymptotic value: 177.1 +/- 1.2
RC: Scale: 4.5 +/- 0.0
----------
Velocity measurements: 654
Velocity chi-square: 51566.84140597743
Reduced chi-square: 79.70145503242261
----------------------------------------------------------------------
RuntimeWarning: divide by zero encountered in true_divide
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" width="1000">
```python
```
|
ricardoclandimREPO_NAMENIRVANAPATH_START.@NIRVANA_extracted@NIRVANA-master@Plots.ipynb@.PATH_END.py
|
{
"filename": "core.py",
"repo_name": "riogroup/SORA",
"repo_path": "SORA_extracted/SORA-master/sora/body/core.py",
"type": "Python"
}
|
import warnings
import astropy.constants as const
import astropy.units as u
import numpy as np
from astropy.coordinates import SkyCoord, Longitude, Latitude
from astropy.time import Time
from sora.config import input_tests
from .frame import get_archinal_frame
from .meta import BaseBody, PhysicalData
from .utils import search_sbdb, search_satdb, apparent_magnitude
__all__ = ['Body']
class Body(BaseBody):
"""Class that contains and manages the information of the body.
Attributes
----------
name : `str`, required
The name of the object. It can be the used `spkid` or `designation
number` to query the SBDB (Small-Body DataBase). In this case, the name
is case insensitive.
database : `str`, optional, default='auto'
The database to query the object. It can be ``satdb`` for our temporary
hardcoded satellite database, or ``'sbdb'`` to query on the SBDB. If
database is set as ``auto`` it will try first with ``satdb``,
then ``sbdb``. If the user wants to use their own information,
database must be given as ``None``. In this case, `spkid` parameter
must be given.
ephem : `sora.EphemKernel`, `sora.EphemHorizons`, `sora.EphemJPL`, `sora.EphemPlanete`
An Ephem Class that contains information about the ephemeris. It can be
"horizons" to automatically defined an EphemHorizons object or a list of
kernels to automatically define an EphemKernel object.
orbit_class : `str`
It defines the Orbital class of the body. It can be ``TNO``,
``Satellite``, ``Centaur``, ``comet``, ``asteroid``, ``trojan``, ``neo``,
and ``planet``. It is important for a better characterization of the
object. If a different value is given, it will be defined as
``unclassified``.
spkid : `str`, `int`, `float`
If ``database=None``, the user must give a `spkid` or an `ephem`
which has the `spkid` parameter.
shape : `str`, `sora.body.shape.Shape3D`
It defines the input shape of the body. It can be a body.shape object
or the path to OBJ file.
albedo : `float`, `int`
The albedo of the object.
H : `float`, `int`
The absolute magnitude.
G : `float`, `int`
The phase slope.
diameter : `float`, `int`, `astropy.quantity.Quantity`
The diameter of the object, in km.
density : `float`, `int`, `astropy.quantity.Quantity`
The density of the object, in g/cm³.
GM : `float`, `int`, `astropy.quantity.Quantity`
The Standard Gravitational Parameter, in km³/s².
rotation : `float`, `int`, `astropy.quantity.Quantity`
The Rotation of the object, in hours.
pole : `str`, `astropy.coordinates.SkyCoord`
The Pole coordinates of the object. It can be a `SkyCoord object` or a
string in the format ``'hh mm ss.ss +dd mm ss.ss'``.
BV : `float`, `int`
The B-V color.
UB : `float`, `int`
The U-B color.
smass : `str`
The spectral type in SMASS classification.
tholen : `str`
The spectral type in Tholen classification.
Note
----
The following attributes are are returned from the Small-Body DataBase when
``database='sbdb'`` or from our temporary hardcoded Satellite DataBase when
``database='satdb'``:
`orbit_class`, `spkid`, `albedo`, `H`, `G`, `diameter`, `density`, `GM`,
`rotation`, `pole`, `BV`, `UB`, `smass`, and `tholen`.
These are physical parameters the user can give to the object. If a query is
made and user gives a parameter, the parameter given by the user is defined
in the *Body* object.
"""
def __init__(self, name, database='auto', **kwargs):
allowed_kwargs = ["albedo", "H", "G", "diameter", "density", "GM", "rotation", "pole", "BV", "UB", "smass",
"orbit_class", "spkid", "tholen", "ephem", "frame", "shape"]
input_tests.check_kwargs(kwargs, allowed_kwargs=allowed_kwargs)
self._shared_with = {'ephem': {}, 'occultation': {}}
if database not in ['auto', 'satdb', 'sbdb', None]:
raise ValueError(f'{database} is not a valid database argument.')
if database is None:
self.__from_local(name=name, spkid=kwargs.get('spkid'))
if database in ['auto', 'satdb']:
try:
self.__from_satdb(name=name)
except ValueError:
pass
else:
database = 'satdb'
if database in ['auto', 'sbdb']:
try:
self.__from_sbdb(name=name)
except ValueError:
pass
else:
database = 'sbdb'
if database == 'auto':
raise ValueError('Object was not located on satdb or sbdb.')
# set the physical parameters based on the kwarg name.
if 'smass' in kwargs:
self.spectral_type['SMASS']['value'] = kwargs.pop('smass')
if 'tholen' in kwargs:
self.spectral_type['Tholen']['value'] = kwargs.pop('tholen')
for key in kwargs:
setattr(self, key, kwargs[key])
try:
shape = self.shape
except AttributeError:
self.shape = self.radius.value
self._shared_with['ephem']['search_name'] = self._search_name
self._shared_with['ephem']['id_type'] = self._id_type
if getattr(self, "frame", None) is None:
try:
self.frame = get_archinal_frame(self.spkid)
except ValueError:
if not np.isnan(self.pole.ra) and not np.isnan(self.rotation):
from .frame import PlanetocentricFrame
self.frame = PlanetocentricFrame(epoch='J2000', pole=self.pole, alphap=0, deltap=0, prime_angle=0,
rotation_velocity=360*u.deg / self.rotation, right_hand=True,
reference="")
if 'ephem' not in kwargs:
self.ephem = 'horizons'
def __from_sbdb(self, name):
"""Searches the object in the SBDB and defines its physical parameters.
Parameters
----------
name : `str`
The `name`, `spkid` or `designation number` of the Small Body.
"""
sbdb = search_sbdb(name)
self.meta_sbdb = sbdb
self.name = sbdb['object']['fullname']
self.shortname = sbdb['object'].get('shortname', self.name)
self.orbit_class = sbdb['object']['orbit_class']['name']
pp = sbdb['phys_par'] # get the physical parameters (pp) of the sbdb
if 'extent' in pp:
extent = np.array(pp['extent'].split('x'), dtype=float)/2
self.shape = extent
self.albedo = PhysicalData('Albedo', pp.get('albedo'), pp.get('albedo_sig'), pp.get('albedo_ref'), pp.get('albedo_note'))
self.H = PhysicalData('Absolute Magnitude', pp.get('H'), pp.get('H_sig'), pp.get('H_ref'), pp.get('H_note'), unit=u.mag)
self.G = PhysicalData('Phase Slope', pp.get('G'), pp.get('G_sig'), pp.get('G_ref'), pp.get('G_note'))
self.diameter = PhysicalData('Diameter', pp.get('diameter'), pp.get('diameter_sig'), pp.get('diameter_ref'),
pp.get('diameter_note'), unit=u.km)
self.density = PhysicalData('Density', pp.get('density'), pp.get('density_sig'), pp.get('density_ref'),
pp.get('density_note'), unit=u.g/u.cm**3)
self.GM = PhysicalData('Standard Gravitational Parameter', pp.get('GM'), pp.get('GM_sig'), pp.get('GM_ref'),
pp.get('GM_note'), unit=u.km**3/u.s**2)
self.rotation = PhysicalData('Rotation', pp.get('rot_per'), pp.get('rot_per_sig'), pp.get('rot_per_ref'),
pp.get('rot_per_note'), unit=u.h)
self.BV = PhysicalData('B-V color', pp.get('BV'), pp.get('BV_sig'), pp.get('BV_ref'), pp.get('BV_note'))
self.UB = PhysicalData('U-B color', pp.get('UB'), pp.get('UB_sig'), pp.get('UB_ref'), pp.get('UB_note'))
if 'pole' in pp:
delimiters = [",", "|", ";", "/"]
pole = pp['pole']
for delimiter in delimiters:
pole = pole.replace(delimiter, " ")
if len(pole.split()) == 2:
self.pole = SkyCoord(pole, unit=('deg', 'deg'))
# Removed uncertainty due to different SBDB formats.
# pole_err = pp['pole_sig'].split('/')
# self.pole.ra.uncertainty = Longitude(pole_err[0], unit=u.deg)
# self.pole.dec.uncertainty = Latitude(pole_err[0] if len(pole_err) == 1 else pole_err[1], unit=u.deg)
self.pole.reference = pp['pole_ref'] or ""
self.pole.notes = pp['pole_note'] or ""
else:
self.pole = None
else:
self.pole = None
self.spectral_type = {
"SMASS": {"value": pp.get('spec_B'), "reference": pp.get('spec_B_ref'), "notes": pp.get('spec_B_note')},
"Tholen": {"value": pp.get('spec_T'), "reference": pp.get('spec_T_ref'), "notes": pp.get('spec_T_note')}}
self.spkid = sbdb['object']['spkid']
self._des_name = sbdb['object']['des']
self.discovery = "Discovered {} by {} at {}".format(sbdb['discovery'].get('date'), sbdb['discovery'].get('who'),
sbdb['discovery'].get('location'))
def __from_satdb(self, name):
satdb = search_satdb(name)
self.name = name.capitalize()
self.shortname = name.capitalize()
self.orbit_class = satdb['class']
self.albedo = PhysicalData('Albedo', *satdb.get('albedo', [None, None, None]))
self.H = PhysicalData('Absolute Magnitude', *satdb.get('H', [None, None, None]), unit=u.mag)
self.G = PhysicalData('Phase Slope', *satdb.get('G', [None, None, None]))
self.diameter = PhysicalData('Diameter', *satdb.get('diameter', [None, None, None]), unit=u.km)
self.density = PhysicalData('Density', *satdb.get('density', [None, None, None]), unit=u.g / u.cm ** 3)
self.GM = PhysicalData('Standard Gravitational Parameter', *satdb.get('GM', [None, None, None]),
unit=u.km ** 3 / u.s ** 2)
self.rotation = PhysicalData('Rotation', *satdb.get('rotation', [None, None, None]), unit=u.h)
if 'pole' in satdb:
self.pole = SkyCoord(satdb['pole'][0].replace('/', ' '), unit=('deg', 'deg'))
self.pole.ra.uncertainty = Longitude(satdb['pole'][1].split('/')[0], unit=u.deg)
self.pole.dec.uncertainty = Latitude(satdb['pole'][1].split('/')[1], unit=u.deg)
self.pole.reference = satdb['pole'][2] or ""
self.pole.notes = ""
else:
self.pole = None
self.BV = None
self.UB = None
self.spectral_type = {
"SMASS": {"value": None, "reference": "", "notes": ""},
"Tholen": {"value": None, "reference": "", "notes": ""}}
self.spkid = satdb['spkid']
self._des_name = name
self.discovery = ""
def __from_local(self, name, spkid):
"""Defines Body object with default values for mode='local'.
"""
self.name = name
self.shortname = name
self.orbit_class = None
if not spkid:
raise ValueError("'spkid' must be given.")
self.spkid = spkid
self.albedo = None
self.H = None
self.G = None
self.diameter = None
self.density = None
self.GM = None
self.rotation = None
self.pole = None
self.BV = None
self.UB = None
self.spectral_type = {"SMASS": {"value": None, "reference": None, "notes": None},
"Tholen": {"value": None, "reference": None, "notes": None}}
self.discovery = ""
def get_position(self, time, observer='geocenter'):
"""Returns the object position as seen by an observer
Parameters
----------
time : `str`, `astropy.time.Time`
Reference time to calculate the object position. It can be a string
in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.
observer : `str`, `sora.Observer`, `sora.Spacecraft`
IAU code of the observer (must be present in given list of kernels),
a SORA observer object or a string: ['geocenter', 'barycenter']
Returns
-------
coord : `astropy.coordinates.SkyCoord`
Astropy SkyCoord object with the object coordinates at the given time.
"""
return self.ephem.get_position(time=time, observer=observer)
def get_pole_position_angle(self, time, observer='geocenter'):
"""Returns the pole position angle and the aperture angle relative to
the geocenter.
Parameters
----------
time : `str`, `astropy.time.Time`
Time from which to calculate the position.
It can be a string in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.
observer : `str`, `sora.Observer`, `sora.Spacecraft`
IAU code of the observer (must be present in given list of kernels),
a SORA observer object or a string: ['geocenter', 'barycenter']
Returns
-------
position_angle, aperture_angle : `float` array
Position angle and aperture angle of the object's pole, in degrees.
"""
time = Time(time)
pole = self.pole
if np.isnan(pole.ra):
raise ValueError("Pole coordinates are not defined")
obj = self.ephem.get_position(time, observer=observer)
position_angle = obj.position_angle(pole).rad*u.rad
aperture_angle = np.arcsin(
-(np.sin(pole.dec)*np.sin(obj.dec) +
np.cos(pole.dec)*np.cos(obj.dec)*np.cos(pole.ra-obj.ra))
)
return position_angle.to('deg'), aperture_angle.to('deg')
def apparent_magnitude(self, time, observer='geocenter'):
"""Calculates the object's apparent magnitude.
Parameters
----------
time : `str`, `astropy.time.Time`
Reference time to calculate the object's apparent magnitude.
It can be a string in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.
observer : `str`, `sora.Observer`, `sora.Spacecraft`
IAU code of the observer (must be present in given list of kernels),
a SORA observer object or a string: ['geocenter', 'barycenter']
Returns
-------
ap_mag : `float`
Object apparent magnitude.
"""
from astroquery.jplhorizons import Horizons
time = Time(time)
if np.isnan(self.H) or np.isnan(self.G):
from sora.observer import Observer, Spacecraft
warnings.warn('H and/or G is not defined for {}. Searching into JPL Horizons service'.format(self.shortname))
origins = {'geocenter': '@399', 'barycenter': '@0'}
location = origins.get(observer)
if not location and isinstance(observer, str):
location = observer
if isinstance(observer, (Observer, Spacecraft)):
location = f'{getattr(observer, "code", "")}@{getattr(observer, "spkid", "")}'
if not location:
raise ValueError("observer must be 'geocenter', 'barycenter' or an observer object.")
obj = Horizons(id=self._search_name, id_type=self._id_type, location=location, epochs=time.jd)
eph = obj.ephemerides(extra_precision=True)
if 'H' in eph.keys():
self.H = eph['H'][0]
self.H.reference = "JPL Horizons"
self.G = eph['G'][0]
self.G.reference = "JPL Horizons"
if len(eph['V']) == 1:
return eph['V'][0]
else:
return eph['V'].tolist()
else:
obs_obj = self.ephem.get_position(time, observer=observer)
sun_obj = self.ephem.get_position(time, observer='10')
# Calculates the phase angle between the 2-vectors
unit_vector_1 = -obs_obj.cartesian.xyz / np.linalg.norm(obs_obj.cartesian.xyz)
unit_vector_2 = -sun_obj.cartesian.xyz / np.linalg.norm(sun_obj.cartesian.xyz)
dot_product = np.dot(unit_vector_1, unit_vector_2)
phase = np.arccos(dot_product).to(u.deg).value
return apparent_magnitude(self.H.value, self.G.value, obs_obj.distance.to(u.AU).value,
sun_obj.distance.to(u.AU).value, phase)
def to_log(self, namefile):
"""Saves the body log to a file.
Parameters
----------
namefile : `str`
Filename to save the log.
"""
f = open(namefile, 'w')
f.write(self.__str__())
f.close()
def get_orientation(self, time, observer='geocenter'):
"""Returns the object orientation as seen by an observer.
Parameters
----------
time : `str`, `astropy.time.Time`
Epoch of observation to calculate the object orientation. It can be a string
in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.
observer : `str`, `sora.Observer`, `sora.Spacecraft`
IAU code of the observer (must be present in given list of kernels),
a SORA observer object or a string: ['geocenter', 'barycenter']
to compute ephemeris.
Returns
-------
orientation : `dict`
A dictionary with the following orientation parameters:
- `sub_observer`: `str`
the longitude and latitude of the body in the direction of the observer.
- `sub_solar` : `str`
The sub-solar coordinate.
- `pole_position_angle` : `astropy.coordinates.Angle`
Apparent position angle of the pole.
- `pole_aperture_angle` : `astropy.coordinates.Angle`
Apparent aperture angle of the pole.
"""
time = Time(time)
pos = self.ephem.get_position(time=time, observer=observer)
orientation = {}
try:
epoch = time - pos.spherical.distance / const.c
frame = self.frame.frame_at(epoch=epoch)
pole = frame.pole
subobs = SkyCoord(-pos.cartesian).transform_to(frame=frame)
orientation['sub_observer'] = subobs.to_string('decimal')
# TODO(subsun is technically wrong. We must correct to an observer on the body.)
pos_sun = self.ephem.get_position(time=time, observer='10')
subsun = SkyCoord(-pos_sun.cartesian).transform_to(frame=frame)
orientation['sub_solar'] = subsun.to_string('decimal')
except AttributeError:
warnings.warn('Frame attribute is not defined')
pole = self.pole
if not np.isnan(pole.ra):
position_angle = pos.position_angle(pole).rad * u.rad
aperture_angle = np.arcsin(
-(np.sin(pole.dec) * np.sin(pos.dec) +
np.cos(pole.dec) * np.cos(pos.dec) * np.cos(pole.ra - pos.ra))
)
orientation['pole_position_angle'] = position_angle.to('deg')
orientation['pole_aperture_angle'] = aperture_angle.to('deg')
else:
warnings.warn("Pole coordinates are not defined")
return orientation
def plot(self, time=None, observer='geocenter', center_f=0, center_g=0, contour=False, ax=None, plot_pole=True, **kwargs):
"""Plots the body shape as viewed by observer at some time given the body orientation.
If the user wants to dictate the orientation, please use `shape.plot()` instead.
Parameters
----------
time : `str`, `astropy.time.Time`
Reference time to calculate the object's apparent magnitude.
It can be a string in the ISO format (yyyy-mm-dd hh:mm:ss.s) or an astropy Time object.
It must be only one value.
observer : `str`, `sora.Observer`, `sora.Spacecraft`
IAU code of the observer (must be present in given list of kernels),
a SORA observer object or a string: ['geocenter', 'barycenter']
center_f : `int`, `float`
Offset of the center of the body in the East direction, in km
center_g : `int`, `float`
Offset of the center of the body in the North direction, in km
radial_offset : `int`, `float`
Offset of the center of the body in the direction of observation, in km
ax : `matplotlib.pyplot.Axes`
The axes where to make the plot. If None, it will use the default axes.
contour : `bool`
If True, it plots the limb of the projected shape.
If False, it plots the 3D shape. Default: False.
plot_pole : `bool`
If True, the direction of the pole is plotted.
Ignored if `contour=True`
"""
if not hasattr(self, 'shape'):
raise ValueError('{} does not have a shape or size to be plotted'.format(self.__class__.__name__))
if time is None or getattr(self, 'frame', None) is None:
warnings.warn('No time is giving or frame is not defined. Plotting without computing orientation. '
'To provide orientation, please plot from shape directly.')
orientation = {}
else:
time = Time(time)
if not time.isscalar and len(time) > 1:
raise ValueError('time keyword must refer to only one instant')
orientation = self.get_orientation(time=time, observer=observer)
orientation.pop('pole_aperture_angle')
if 'pole_aperture_angle' in kwargs:
kwargs.pop('pole_aperture_angle')
if contour:
orientation.pop('sub_solar')
self.shape.get_limb(**orientation).plot(center_f=center_f, center_g=center_g, ax=ax, **kwargs)
else:
self.shape.plot(**orientation, center_f=center_f, center_g=center_g, ax=ax, plot_pole=plot_pole, **kwargs)
def __str__(self):
from .values import smass, tholen
out = ['#' * 79 + '\n{:^79s}\n'.format(self.name) + '#' * 79 + '\n',
'Object Orbital Class: {}\n'.format(self.orbit_class)]
if self.spectral_type['Tholen']['value'] or self.spectral_type['SMASS']['value']:
out += 'Spectral Type:\n'
value = self.spectral_type['SMASS']['value']
if value:
out.append(' SMASS: {} [Reference: {}]\n'.format(value, self.spectral_type['SMASS']['reference']))
value = self.spectral_type['Tholen']['value']
if value:
out.append(' Tholen: {} [Reference: {}]\n'.format(value, self.spectral_type['Tholen']['reference']))
out += " "*7 + (smass.get(self.spectral_type['SMASS']['value']) or
tholen.get(self.spectral_type['Tholen']['value'])) + "\n"
out.append(self.discovery)
out.append('\n\nPhysical parameters:\n')
out.append(self.diameter.__str__())
out.append(self.mass.__str__())
out.append(self.density.__str__())
out.append(self.rotation.__str__())
if not np.isnan(self.pole.ra):
out.append('Pole\n RA:{} +/- {}\n DEC:{} +/- {}\n Reference: {}, {}\n'.format(
self.pole.ra.__str__(), self.pole.ra.uncertainty.__str__(), self.pole.dec.__str__(),
self.pole.dec.uncertainty.__str__(), self.pole.reference, self.pole.notes))
out.append(self.H.__str__())
out.append(self.G.__str__())
out.append(self.albedo.__str__())
out.append(self.BV.__str__())
out.append(self.UB.__str__())
if hasattr(self, 'frame'):
out.append('\n' + self.frame.__str__() + '\n')
if hasattr(self, 'shape'):
out.append('\n' + self.shape.__str__() + '\n')
if hasattr(self, 'ephem'):
out.append('\n' + self.ephem.__str__() + '\n')
return ''.join(out)
|
riogroupREPO_NAMESORAPATH_START.@SORA_extracted@SORA-master@sora@body@core.py@.PATH_END.py
|
{
"filename": "lock.py",
"repo_name": "sdss/idlspec2d",
"repo_path": "idlspec2d_extracted/idlspec2d-master/python/boss_drp/utils/lock.py",
"type": "Python"
}
|
import os
import time
def lock(file, pause=5, niter=None):
"""Attempt to acquire a file lock by creating a symlink. Retry on failure."""
i = 0
while True:
if niter is not None and i == niter:
break
i += 1
try:
os.symlink(file, file + '.lock')
return True
except FileExistsError:
print(f"Lock already acquired ({file}). Retrying in {pause} seconds...")
time.sleep(pause)
except Exception as e:
print(f"An error occurred: {e}")
return False
return False
def unlock(file):
"""Release the file lock by removing the symlink."""
try:
os.unlink(file + '.lock')
return(True)
except FileNotFoundError:
return(True)
except Exception as e:
print(f"An error occurred while releasing the lock: {e}")
return(False)
return(False)
"""
# Example usage
file = 'path/to/your/file.txt'
if lock(file):
try:
# Perform your file operations here
print("Performing file operations.")
time.sleep(10) # Simulate long-running task
finally:
unlock(file)
else:
print("Could not acquire lock. Exiting.")
"""
|
sdssREPO_NAMEidlspec2dPATH_START.@idlspec2d_extracted@idlspec2d-master@python@boss_drp@utils@lock.py@.PATH_END.py
|
{
"filename": "_token.py",
"repo_name": "plotly/plotly.py",
"repo_path": "plotly.py_extracted/plotly.py-master/packages/python/plotly/plotly/validators/carpet/stream/_token.py",
"type": "Python"
}
|
import _plotly_utils.basevalidators
class TokenValidator(_plotly_utils.basevalidators.StringValidator):
def __init__(self, plotly_name="token", parent_name="carpet.stream", **kwargs):
super(TokenValidator, self).__init__(
plotly_name=plotly_name,
parent_name=parent_name,
edit_type=kwargs.pop("edit_type", "calc"),
no_blank=kwargs.pop("no_blank", True),
strict=kwargs.pop("strict", True),
**kwargs,
)
|
plotlyREPO_NAMEplotly.pyPATH_START.@plotly.py_extracted@plotly.py-master@packages@python@plotly@plotly@validators@carpet@stream@_token.py@.PATH_END.py
|
{
"filename": "theoretical_lf.py",
"repo_name": "cylammarco/WDPhotTools",
"repo_path": "WDPhotTools_extracted/WDPhotTools-main/src/WDPhotTools/theoretical_lf.py",
"type": "Python"
}
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""For computing theorectical WDLFs"""
import warnings
import os
import numpy as np
from scipy import optimize, integrate
from scipy.interpolate import interp1d
from matplotlib import pyplot as plt
from .atmosphere_model_reader import AtmosphereModelReader
from .cooling_model_reader import CoolingModelReader
from .util import load_ms_lifetime_datatable
class WDLF(AtmosphereModelReader, CoolingModelReader):
"""
Computing the theoretical WDLFs based on the input IFMR, WD cooling and
MS lifetime models.
We are using little m for WD mass and big M for MS mass throughout this
package.
All the models are reporting in different set of units. They are all
converted by the formatter to this set of units: (1) mass is in solar mass,
(2) luminosity is in erg/s, (3) time/age is in year.
For conversion, we use (1) M_sun = 1.98847E30 and (2) L_sun = 3.826E33.
"""
def __init__(
self,
imf_model="C03",
ifmr_model="C08",
low_mass_cooling_model="montreal_co_da_20",
intermediate_mass_cooling_model="montreal_co_da_20",
high_mass_cooling_model="montreal_co_da_20",
ms_model="PARSECz0017",
):
super(WDLF, self).__init__()
self.cooling_interpolator = None
self.wdlf_params = {
"imf_model": None,
"ifmr_model": None,
"sfr_mode": None,
"ms_model": None,
}
self.imf_model_list = ["K01", "C03", "C03b", "manual"]
self.ifmr_model_list = [
"C08",
"C08b",
"S09",
"S09b",
"W09",
"K09",
"K09b",
"C18",
"EB18",
"manual",
]
self.sfr_mode_list = ["constant", "burst", "decay", "manual"]
self.ms_model_list = [
"PARSECz00001",
"PARSECz00002",
"PARSECz00005",
"PARSECz0001",
"PARSECz0002",
"PARSECz0004",
"PARSECz0006",
"PARSECz0008",
"PARSECz001",
"PARSECz0014",
"PARSECz0017",
"PARSECz002",
"PARSECz003",
"PARSECz004",
"PARSECz006",
"GENEVAz002",
"GENEVAz006",
"GENEVAz014",
"MISTFem400",
"MISTFem350",
"MISTFem300",
"MISTFem250",
"MISTFem200",
"MISTFem175",
"MISTFem150",
"MISTFem125",
"MISTFem100",
"MISTFem075",
"MISTFem050",
"MISTFem025",
"MISTFe000",
"MISTFe025",
"MISTFe050",
"manual",
]
# The IFMR, WD cooling and MS lifetime models are required to
# initialise the object.
self.set_imf_model(imf_model)
self.set_ifmr_model(ifmr_model)
self.set_low_mass_cooling_model(low_mass_cooling_model)
self.set_intermediate_mass_cooling_model(
intermediate_mass_cooling_model
)
self.set_high_mass_cooling_model(high_mass_cooling_model)
self.set_ms_model(ms_model)
self.set_sfr_model()
self._update_filename()
self.mag = None
self.mag_to_mbol_itp = None
self.number_density = None
def _update_filename(self):
self._filename_middle = (
f"_{self.wdlf_params['sfr_mode']}"
f"_{self.wdlf_params['ms_model']}"
f"_{self.wdlf_params['ifmr_model']}"
f"_{self.cooling_models['low_mass_cooling_model']}"
f"_{self.cooling_models['intermediate_mass_cooling_model']}"
f"_{self.cooling_models['high_mass_cooling_model']}."
)
def _imf(self, mass_ms):
"""
Compute the initial mass function based on the pre-selected initial
mass_function model and the given mass (mass_ms).
See set_imf_model() for more details.
Parameters
----------
M: float, list of float or array of float
Input MS mass
Returns
-------
mass_function: array
Array of mass_function, normalised to 1 at 1 M_sun.
"""
mass_ms = np.asarray(mass_ms).reshape(-1)
if self.wdlf_params["imf_model"] == "K01":
mass_function = mass_ms**-2.3
# mass lower than 0.08 is impossible, so that range is ignored.
if (mass_ms < 0.5).any():
m_mask = mass_ms < 0.5
# (0.5**-2.3) / (0.5**-1.3) = 2.0
mass_function[m_mask] = mass_ms[m_mask] ** -1.3 * 2.0
elif self.wdlf_params["imf_model"] == "C03":
mass_function = mass_ms**-2.3
if (mass_ms < 1).any():
m_mask = np.array(mass_ms < 1.0)
# 0.158 / (ln(10) * mass_ms) = 0.06861852814 / mass_ms
# log(0.079) = -1.1023729087095586
# 2 * 0.69**2. = 0.9522
# Normalisation factor (at mass_ms=1) is 0.01915058
mass_function[m_mask] = (
(0.06861852814 / mass_ms[m_mask])
* np.exp(
-(
(np.log10(mass_ms[m_mask]) + 1.1023729087095586)
** 2.0
)
/ 0.9522
)
/ 0.01915058
)
elif self.wdlf_params["imf_model"] == "C03b":
mass_function = mass_ms**-2.3
if (mass_ms <= 1).any():
m_mask = np.array(mass_ms <= 1.0)
# 0.086 * 1. / (ln(10) * M) = 0.03734932544 / M
# log(0.22) = -0.65757731917
# 2 * 0.57**2. = 0.6498
# Normalisation factor (at M=1) is 0.01919917
mass_function[m_mask] = (
(0.03734932544 / mass_ms[m_mask])
* np.exp(
-((np.log10(mass_ms[m_mask]) + 0.65757731917) ** 2.0)
/ 0.6498
)
/ 0.01919917
)
else:
mass_function = self.imf_function(mass_ms)
return mass_function
def _ms_age(self, mass_ms):
"""
Compute the main sequence lifetime based on the pre-selected MS model
and the given solar mass (mass_ms).
See set_ms_model() for more details.
Parameters
----------
M: float, list of float or array of float
Input MS mass
Returns
-------
age: array
Array of total MS lifetime, same size as M.
"""
mass_ms = np.asarray(mass_ms).reshape(-1)
age = None
if self.wdlf_params["ms_model"] == "PARSECz00001":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz00001.csv")
elif self.wdlf_params["ms_model"] == "PARSECz00002":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz00002.csv")
elif self.wdlf_params["ms_model"] == "PARSECz00005":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz00005.csv")
elif self.wdlf_params["ms_model"] == "PARSECz0001":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz0001.csv")
elif self.wdlf_params["ms_model"] == "PARSECz0002":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz0002.csv")
elif self.wdlf_params["ms_model"] == "PARSECz0004":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz0004.csv")
elif self.wdlf_params["ms_model"] == "PARSECz0006":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz0006.csv")
elif self.wdlf_params["ms_model"] == "PARSECz0008":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz0008.csv")
elif self.wdlf_params["ms_model"] == "PARSECz001":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz001.csv")
elif self.wdlf_params["ms_model"] == "PARSECz0014":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz0014.csv")
elif self.wdlf_params["ms_model"] == "PARSECz0017":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz0017.csv")
elif self.wdlf_params["ms_model"] == "PARSECz002":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz002.csv")
elif self.wdlf_params["ms_model"] == "PARSECz003":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz003.csv")
elif self.wdlf_params["ms_model"] == "PARSECz004":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz004.csv")
elif self.wdlf_params["ms_model"] == "PARSECz006":
# https://people.sissa.it/~sbressan/parsec.html
datatable = load_ms_lifetime_datatable("PARSECz006.csv")
elif self.wdlf_params["ms_model"] == "GENEVAz014":
# https://obswww.unige.ch/Research/evol/tables_grids2011/
datatable = load_ms_lifetime_datatable("geneva2011z014.csv")
elif self.wdlf_params["ms_model"] == "GENEVAz006":
# https://obswww.unige.ch/Research/evol/tables_grids2011/
datatable = load_ms_lifetime_datatable("geneva2011z006.csv")
elif self.wdlf_params["ms_model"] == "GENEVAz002":
# https://obswww.unige.ch/Research/evol/tables_grids2011/
datatable = load_ms_lifetime_datatable("geneva2011z002.csv")
elif self.wdlf_params["ms_model"] == "MISTFe050":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fe050.csv")
elif self.wdlf_params["ms_model"] == "MISTFe025":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fe025.csv")
elif self.wdlf_params["ms_model"] == "MISTFe000":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fe000.csv")
elif self.wdlf_params["ms_model"] == "MISTFem025":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem025.csv")
elif self.wdlf_params["ms_model"] == "MISTFem050":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem050.csv")
elif self.wdlf_params["ms_model"] == "MISTFem075":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem075.csv")
elif self.wdlf_params["ms_model"] == "MISTFem100":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem100.csv")
elif self.wdlf_params["ms_model"] == "MISTFem125":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem125.csv")
elif self.wdlf_params["ms_model"] == "MISTFem150":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem150.csv")
elif self.wdlf_params["ms_model"] == "MISTFem175":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem175.csv")
elif self.wdlf_params["ms_model"] == "MISTFem200":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem200.csv")
elif self.wdlf_params["ms_model"] == "MISTFem250":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem250.csv")
elif self.wdlf_params["ms_model"] == "MISTFem300":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem300.csv")
elif self.wdlf_params["ms_model"] == "MISTFem350":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem350.csv")
elif self.wdlf_params["ms_model"] == "MISTFem400":
# http://waps.cfa.harvard.edu/MIST/
datatable = load_ms_lifetime_datatable("MISTv1p2Fem400.csv")
else:
age = self.ms_function(mass_ms)
if age is None:
massi = np.array(datatable[:, 0]).astype(np.float64)
time = np.array(datatable[:, 1]).astype(np.float64)
age = interp1d(
massi, time, kind="cubic", fill_value="extrapolate"
)(mass_ms)
return age
def _ifmr(self, mass_ms):
"""
Compute the final mass (i.e. WD mass) based on the pre-selected IFMR
model and the zero-age MS mass (M).
See set_ifmr_model() for more details.
Parameters
----------
M: float, list of float or array of float
Input MS mass
Returns
-------
mass: array
Array of WD mass, same size as M.
"""
mass_ms = np.asarray(mass_ms).reshape(-1)
if self.wdlf_params["ifmr_model"] == "C08":
mass = 0.117 * mass_ms + 0.384
if (mass < 0.4349).any():
mass[mass < 0.4349] = 0.4349
elif self.wdlf_params["ifmr_model"] == "C08b":
mass = 0.096 * mass_ms + 0.429
if (mass_ms >= 2.7).any():
mass[mass_ms >= 2.7] = 0.137 * mass_ms[mass_ms >= 2.7] + 0.318
if (mass < 0.4746).any():
mass[mass < 0.4746] = 0.4746
elif self.wdlf_params["ifmr_model"] == "S09":
mass = 0.084 * mass_ms + 0.466
if (mass < 0.5088).any():
mass[mass < 0.5088] = 0.5088
elif self.wdlf_params["ifmr_model"] == "S09b":
mass = 0.134 * mass_ms[mass_ms < 4.0] + 0.331
if (mass_ms >= 4.0).any():
mass = 0.047 * mass_ms[mass_ms >= 4.0] + 0.679
if (mass < 0.3823).any():
mass[mass < 0.3823] = 0.3823
elif self.wdlf_params["ifmr_model"] == "W09":
mass = 0.129 * mass_ms + 0.339
if (mass < 0.3893).any():
mass[mass < 0.3893] = 0.3893
elif self.wdlf_params["ifmr_model"] == "K09":
mass = 0.109 * mass_ms + 0.428
if (mass < 0.4804).any():
mass[mass < 0.4804] = 0.4804
elif self.wdlf_params["ifmr_model"] == "K09b":
mass = 0.101 * mass_ms + 0.463
if (mass < 0.4804).any():
mass[mass < 0.4804] = 0.4804
elif self.wdlf_params["ifmr_model"] == "C18":
mass = interp1d(
(0.83, 2.85, 3.60, 7.20),
(0.5554, 0.71695, 0.8572, 1.2414),
fill_value="extrapolate",
bounds_error=False,
)(mass_ms)
elif self.wdlf_params["ifmr_model"] == "EB18":
mass = interp1d(
(0.95, 2.75, 3.54, 5.21, 8.0),
(0.5, 0.67, 0.81, 0.91, 1.37),
fill_value="extrapolate",
bounds_error=False,
)(mass_ms)
else:
mass = self.ifmr_function(mass_ms)
return mass
def _find_mass_ms_min(self, mass_ms, mag):
"""
A function to be minimised to find the minimum mass limit that a MS
star could have turned into a WD in the given age of the
population (which is given by the SFR).
Parameters
----------
mass_ms: float
MS mass.
logL: float
log WD luminosity.
Return
------
The difference between the total time and the sum of the cooling time
and main sequence lifetime.
"""
# Get the WD mass
mass = self._ifmr(mass_ms)
# Get the bolometric magnitude
mbol = self.mag_to_mbol_itp(mass, mag)
if mbol == -np.inf:
return np.inf
logL = (4.75 - mbol) / 2.5 + 33.582744965691276
# Get the cooling age from the WD mass and the luminosity
t_cool = self.cooling_interpolator(logL, mass)
if t_cool <= 0.0:
return np.inf
# Get the MS life time
t_ms = self._ms_age(mass_ms)
if t_ms <= 0.0:
return np.inf
# Time since star formation
time = self.t_start - t_cool - t_ms
if time < 0.0:
return np.inf
else:
return mass_ms**2.0
def _integrand(self, mass_ms, mag):
"""
The integrand of the number density computation based on the
pre-selected (1) MS lifetime model, (2) initial mass function,
(3) initial-final mass relation, and (4) WD cooling model.
Parameters
----------
M: float
Main sequence stellar mass
mag: float
Absolute magnitude in a given passband
T0: float
Look-back time
passband: str (Default: mbol)
passband to be integrated in
Return
------
The product for integrating to the number density.
"""
# Get the WD mass
mass = self._ifmr(mass_ms)
# Get the mass function
mass_function = self._imf(mass_ms)
mbol = self.mag_to_mbol_itp(mass, mag)
if (mbol < -2.0) or (mbol > 20.0) or (not np.isfinite(mbol)):
return 0.0
logL = (4.75 - mbol) / 2.5 + 33.582744965691276
# Get the WD cooling time
t_cool = self.cooling_interpolator(logL, mass)
if t_cool < 0.0:
return 0.0
# Get the MS lifetime
t_ms = self._ms_age(mass_ms)
if t_ms < 0:
return 0.0
# Get the time since star formation
# and then the SFR
sfr = self.sfr(t_cool + t_ms)
if sfr < 0.0:
return 0.0
# Get the cooling rate
dLdt = -self.cooling_rate_interpolator(logL, mass)
total_contribution = mass_function * sfr * dLdt
if np.isfinite(total_contribution):
if total_contribution < 0.0:
return 0.0
return total_contribution
else:
return 0.0
def set_sfr_model(
self,
mode="constant",
age=10e9,
duration=1e9,
mean_lifetime=3e9,
sfr_model=None,
):
"""
Set the SFR scenario, we only provide a few basic forms, free format
can be supplied as a callable function through sfr_model.
The SFR function accepts the time in unit of year, which is the
lookback time (i.e. today is 0, age of the university is ~13.8E9).
For burst and constant SFH, tophat functions are used:
- t1 is the beginning of the star burst
- t2 is the end
- t0 and t3 are tiny deviations from t1 and t2 required for
interpolation
>>> SFR
>>> ^ x-------x
>>> | | |
>>> | | |
>>> | x-----------x x-----------------x
>>> -30E9 0 t3/t2 t1/t0 13.8E9 30E9
>>> Lookback Time
Parameters
----------
mode: str (Default: 'constant')
Choice of SFR mode:
1. constant
2. burst
3. decay
4. manual
age: float (Default: 10E9)
Lookback time in unit of years.
duration: float (Default: 1E9)
Duration of the starburst, only used if mode is 'burst'.
mean_lifetime: float (Default: 3E9)
Only used if mode is 'decay'. The default value has a SFR mean
lifetime of 3 Gyr (SFR dropped by a factor of e after 3 Gyr).
sfr_model: callable function (Default: None)
The free-form star formation rate, in unit of years. If not
callable, it reverts to using a constant star formation rate.
It is necessary to fill in the age argument.
"""
if mode not in self.sfr_mode_list:
raise ValueError("Please provide a valid SFR mode.")
else:
if mode == "manual":
if callable(sfr_model):
self.sfr = sfr_model
else:
warnings.warn(
"The sfr_model provided is not callable, "
"None is applied, i.e. constant star fomration."
)
mode = "constant"
elif mode == "constant":
t_1 = age
t_0 = t_1 * 1.00001
# current time = 0.
t_2 = 0.0
t_3 = t_2 * 0.99999
self.sfr = interp1d(
np.array((30e9, t_0, t_1, t_2, t_3, -30e9)),
np.array((0.0, 0.0, 1.0, 1.0, 0.0, 0.0)),
fill_value="extrapolate",
)
elif mode == "burst":
t_1 = age
t_0 = t_1 * 1.00001
t_2 = t_1 - duration
t_3 = t_2 * 0.99999
self.sfr = interp1d(
np.array((30e9, t_0, t_1, t_2, t_3, -30e9)),
np.array((0.0, 0.0, 1.0, 1.0, 0.0, 0.0)),
fill_value="extrapolate",
)
else:
_t = 10.0 ** np.linspace(0, np.log10(age), 10000)
_sfr = np.exp((_t - age) / mean_lifetime)
self.sfr = interp1d(
_t, _sfr, bounds_error=False, fill_value=0.0
)
self.t_start = age
self.wdlf_params["sfr_mode"] = mode
self._update_filename()
def set_imf_model(self, model, imf_function=None):
"""
Set the initial mass function.
Parameters
----------
model: str (Default: 'C03')
Choice of IFMR model:
1. K01 - Kroupa 2001
2. C03 - Charbrier 2003
3. C03b - Charbrier 2003 (including binary)
4. manual
imf_function: callable function (Default: None)
A callable imf function, only used if model is 'manual'.
"""
if model in self.imf_model_list:
self.wdlf_params["imf_model"] = model
else:
raise ValueError("Please provide a valid Imass_function model.")
self.imf_function = imf_function
self._update_filename()
def set_ms_model(self, model, ms_function=None):
"""
Set the total stellar evolution lifetime model.
Parameters
----------
model: str (Default: 'PARSECz0017')
Choice of MS model are from the PARSEC, Geneva and MIST stellar
evolution models. The complete list of available models is as
follow:
1. PARSECz00001 - Z = 0.0001, Y = 0.249
2. PARSECz00002 - Z = 0.0002, Y = 0.249
3. PARSECz00005 - Z = 0.0005, Y = 0.249
4. PARSECz0001 - Z = 0.001, Y = 0.25
5. PARSECz0002 - Z = 0.002, Y = 0.252
6. PARSECz0004 - Z = 0.004, Y = 0.256
7. PARSECz0006 - Z = 0.006, Y = 0.259
8. PARSECz0008 - Z = 0.008, Y = 0.263
9. PARSECz001 - Z = 0.01, Y = 0.267
10. PARSECz0014 - Z = 0.014, Y = 0.273
11. PARSECz0017 - Z = 0.017, Y = 0.279
12. PARSECz002 - Z = 0.02, Y = 0.284
13. PARSECz003 - Z = 0.03, Y = 0.302
14. PARSECz004 - Z = 0.04, Y = 0.321
15. PARSECz006 - Z = 0.06, Y = 0.356
16. GENEVAz002 - Z = 0.002
17. GENEVAz006 - Z = 0.006
18. GENEVAz014 - Z = 0.014
19. MISTFem400 - [Fe/H] = -4.0
20. MISTFem350 - [Fe/H] = -3.5
21. MISTFem300 - [Fe/H] = -3.0
22. MISTFem250 - [Fe/H] = -2.5
23. MISTFem200 - [Fe/H] = -2.0
24. MISTFem175 - [Fe/H] = -1.75
25. MISTFem150 - [Fe/H] = -1.5
26. MISTFem125 - [Fe/H] = -1.25
27. MISTFem100 - [Fe/H] = -1.0
28. MISTFem075 - [Fe/H] = -0.75
29. MISTFem050 - [Fe/H] = -0.5
30. MISTFem025 - [Fe/H] = -0.25
31. MISTFe000 - [Fe/H] = 0.0
32. MISTFe025 - [Fe/H] = 0.25
33. MISTFe050 - [Fe/H] = 0.5
ms_function: callable function (Default: None)
A callable MS lifetime function, only used if model is 'manual'.
"""
if model in self.ms_model_list:
self.wdlf_params["ms_model"] = model
else:
raise ValueError("Please provide a valid MS model.")
self.ms_function = ms_function
self._update_filename()
def set_ifmr_model(self, model, ifmr_function=None):
"""
Set the initial-final mass relation (IFMR).
Parameters
----------
model: str (Default: 'EB18')
Choice of IFMR model:
1. C08 - Catalan et al. 2008
2. C08b - Catalan et al. 2008 (two-part)
3. S09 - Salaris et al. 2009
4. S09b - Salaris et al. 2009 (two-part)
5. W09 - Williams, Bolte & Koester 2009
6. K09 - Kalirai et al. 2009
7. K09b - Kalirai et al. 2009 (two-part)
8. C18 - Cummings et al. 2018
9. EB18 - El-Badry et al. 2018
10. manual
ifmr_function: callable function (Default: None)
A callable ifmr function, only used if model is 'manual'.
"""
if model in self.ifmr_model_list:
self.wdlf_params["ifmr_model"] = model
else:
raise ValueError("Please provide a valid IFMR mode.")
self.ifmr_function = ifmr_function
self._update_filename()
def compute_density(
self,
mag,
passband="Mbol",
atmosphere="H",
interpolator="CT",
mass_ms_max=8.0,
limit=10000,
n_points=100,
epsabs=1e-6,
epsrel=1e-6,
normed=True,
save_csv=False,
folder=None,
filename=None,
):
"""
Compute the density based on the pre-selected models: (1) MS lifetime
model, (2) initial mass function, (3) initial-final mass relation, and
(4) WD cooling model. It integrates over the function _integrand().
Parameters
----------
mag: float or array of float
Absolute magnitude in the given passband
passband: str (Default: "Mbol")
The passband to be integrated in.
atmosphere: str (Default: H)
The atmosphere type.
interpolator: str (Default: CT)
Choose between 'CT' and 'RBF.'
mass_ms_max: float (Deafult: 8.0)
The upper limit of the main sequence stellar mass. This may not
be used if it exceeds the upper bound of the IFMR model.
limit: int (Default: 10000)
The maximum number of steps of integration
n_points: int (Default: 100)
The number of points for initial integration sampling,
too small a value will lead to failed integration because the
integrato can underestimate the density if the star formation
periods are short. While too large a value will lead to
low performance due to oversampling, though the accuracy is
guaranteed. The default value is sufficient to compute WDLF
for star burst as short as 1E8 years. For burst as short as
1E7, we recommand an n_points of 1000 or larger.
epsabs: float (Default: 1e-6)
The absolute tolerance of the integration step. For star burst,
we recommend a step smaller than 1e-8.
epsrel: float (Default: 1e-6)
The relative tolerance of the integration step. For star burst,
we recommend a step smaller than 1e-8.
normed: boolean (Default: True)
Set to True to return a WDLF sum to 1. Otherwise, it is arbitrary
to the integrator.
save_csv: boolean (Default: False)
Set to True to save the WDLF as CSV files. One CSV per T0.
folder: str (Default: None)
The relative or absolute path to destination, the current working
directory will be used if None.
filename: str (Default: None)
The filename of the csv. The default filename will be used
if None.
Returns
-------
mag: array of float
The magnitude at which the number density is computed.
number_density: array of float
The (arbitrary) number density at that magnitude.
"""
if self.cooling_interpolator is None:
self.compute_cooling_age_interpolator()
mag = np.asarray(mag).reshape(-1)
number_density = np.zeros_like(mag)
self.mag_to_mbol_itp = self.interp_am(
dependent="Mbol",
atmosphere=atmosphere,
independent=["mass", passband],
interpolator=interpolator,
)
mass_ms_upper_bound = mass_ms_max
for i, mag_i in enumerate(mag):
mass_ms_min = optimize.fminbound(
self._find_mass_ms_min,
0.5,
mass_ms_upper_bound,
args=[mag_i],
xtol=1e-8,
maxfun=10000,
)
points = 10.0 ** np.linspace(
np.log10(mass_ms_min), np.log10(mass_ms_max), n_points
)
# Note that the points are needed because it can fail to
# integrate if the star burst is too short
number_density[i] = integrate.quad(
self._integrand,
mass_ms_min,
mass_ms_max,
args=[mag_i],
limit=limit,
points=points,
epsabs=epsabs,
epsrel=epsrel,
)[0]
mass_ms_upper_bound = mass_ms_min
number_density[
np.isnan(number_density) | (number_density <= 0.0)
] = +0.0
# Normalise the WDLF only if the function returned is not all zero
if normed & (number_density > 0.0).any():
number_density /= np.nansum(number_density)
self.mag = mag
self.number_density = number_density
if save_csv:
if folder is None:
_folder = os.getcwd()
else:
_folder = os.path.abspath(folder)
if filename is None:
_filename = (
f"{self.t_start / 1e9:.2f}Gyr"
f"{self._filename_middle}csv"
)
else:
_filename = filename
np.savetxt(
os.path.join(_folder, _filename),
np.column_stack((mag, number_density)),
delimiter=",",
)
return mag, number_density
def plot_input_models(
self,
figsize=(15, 15),
title=None,
display=True,
savefig=False,
folder=None,
filename=None,
ext=["png"],
sfh_log=False,
imf_log=True,
ms_time_log=True,
cooling_model_use_mag=True,
**kwargs,
):
"""
Plot the input cooling model.
Parameters
----------
use_mag: bool (Default: True)
Set to use magnitude instead of luminosity
figsize: array of size 2 (Default: (12, 8))
Set the dimension of the figure.
title: str (Default: None)
Set the title of the figure.
display: bool (Default: True)
Set to display the figure.
savefig: bool (Default: False)
Set to save the figure.
folder: str (Default: None)
The relative or absolute path to destination, the current working
directory will be used if None.
filename: str (Default: None)
The filename of the figure. The default filename will be used
if None.
ext: str (Default: ['png'])
Image type to be saved, multiple extensions can be provided. The
supported types are those available in `matplotlib.pyplot.savefig`.
sfh_log: bool (Default: False)
Set to plot the SFH in logarithmic space
imf_log: bool (Default: False)
Set to plot the Imass_function in logarithmic space
ms_time_log: bool (Default: True)
Set to plot the MS lifetime in logarithmic space
cooling_model_use_mag: bool (Default: True)
Set to plot the Cooling model in logarithmic space
fig: matplotlib.figure.Figure (Default: None)
Overplotting on an existing Figure.
kwargs: dict (Default: {})
Keyword arguments for the colorbar()
"""
fig, axs = plt.subplots(nrows=3, ncols=2, figsize=figsize)
# top row
ax1 = axs[0, 0] # Initial Mass Function
ax2 = axs[0, 1] # Star Formation History
# middle row
ax3 = axs[1, 0] # MS lifetime
ax4 = axs[1, 1] # Initial-Final Mass Relation
# bottom row
ax5 = axs[2, 0] # Cooling Model: Mobl(t) or L(t)
ax6 = axs[2, 1] # Cooling Model: d(Mobl)/d(t) or d(L)/d(t)
#
# Initial Mass Function
#
mass = np.linspace(0.25, 8.25, 1000)
if imf_log:
ax1.plot(mass, np.log10(self._imf(mass)))
ax1.set_ylabel("log(Imass_function)")
else:
ax1.plot(mass, self._imf(mass))
ax1.set_ylabel("Imass_function")
ax1.set_xlabel(r"Mass / M$_\odot$")
ax1.set_xlim(0.25, 8.25)
ax1.grid()
ax1.set_title("Initial Mass Function")
#
# Star formation History
#
_t = np.linspace(0, self.t_start, 1000)
ax2.plot(_t / 1e9, self.sfr(_t))
if sfh_log:
ax2.set_yscale("log")
ax2.set_ylabel("log(Relative SFR)")
else:
ax2.set_ylabel("Relative SFR")
ax2.set_xlabel("Look-back Time / Gyr")
ax2.set_title("Star Formation History")
ax2.grid()
#
# Main Sequence Lifetime
#
ax3.plot(mass, self._ms_age(mass))
if ms_time_log:
ax3.set_yscale("log")
ax3.set_ylabel("log(MS Lifetime / yr)")
else:
ax3.set_ylabel("MS Lifetime / yr")
ax3.set_xlabel(r"ZAMS Mass / M$_\odot$")
ax3.set_title("MS Lifetime")
ax3.grid()
#
# Initial-Final Mass Relation
#
ax4.plot(mass, self._ifmr(mass))
ax4.set_ylabel(r"Final Mass / M$_\odot$")
ax4.set_xlabel(r"Initial Mass / M$_\odot$")
ax4.set_xlim(0.25, 8.25)
ax4.grid()
ax4.set_title("Initial-Final Mass Relation")
#
# Cooling Model : Mobl(t) or L(t)
#
if cooling_model_use_mag:
# Get absolute magnitude from the bolometric luminosity
brightness = (
4.75 - (np.log10(self.luminosity) - 33.582744965691276) * 2.5
)
else:
brightness = self.luminosity
sc5 = ax5.scatter(self.age / 1e9, brightness, c=self.mass, s=5)
# colorbar
cbar5 = plt.colorbar(mappable=sc5, ax=ax5, **kwargs)
cbar5.ax.set_ylabel("Solar Mass", rotation=270, labelpad=15)
# y axis
if cooling_model_use_mag:
ax5.set_ylabel(r"M$_{\mathrm{bol}}$ / mag")
else:
ax5.set_ylabel(r"L$_{\mathrm{bol}}$")
ax5.set_yscale("log")
ax5.set_ylim(np.nanmin(brightness), np.nanmax(brightness))
# x axis
ax5.set_xlabel(r"Age / Gyr")
ax5.set_xlim(0.0, 16.0)
ax5.grid()
ax5.set_title("Cooling Model")
#
# Cooling Model: d(mbol)/d(t) or d(L)/d(t)
#
if cooling_model_use_mag:
# 2.5 * 1e9 * (365.25 * 24. * 60. * 60.) / np.log(10) =
# 3.426322886e16
rate_of_change = -3.426322886e16 / self.luminosity * self.dLdt
else:
rate_of_change = self.dLdt * -1.0
rate_of_change[np.isnan(rate_of_change)] = 0.0
rate_of_change[~np.isfinite(rate_of_change)] = 0.0
sc6 = ax6.scatter(self.age / 1e9, rate_of_change, c=self.mass, s=5)
cbar6 = plt.colorbar(mappable=sc6, ax=ax6, **kwargs)
cbar6.ax.set_ylabel("Solar Mass", rotation=270, labelpad=15)
# y axis
if cooling_model_use_mag:
ax6.set_ylabel(r"d(M$_{\mathrm{bol}})/dt (Gyr)$")
ax6.set_ylim(-0.005, np.nanmax(rate_of_change) * 0.6)
else:
ax6.set_ylabel(r"-d(L$_{\mathrm{bol}})/dt (s)$")
ax6.set_yscale("log")
ax6.set_ylim(np.nanmin(rate_of_change), np.nanmax(rate_of_change))
# x axis
ax6.set_xlabel(r"Age / Gyr")
ax6.set_xlim(0.0, 16.0)
ax6.grid()
ax6.set_title("Cooling Rate")
plt.subplots_adjust(
top=0.95,
bottom=0.075,
left=0.075,
right=0.99,
hspace=0.4,
wspace=0.225,
)
if title is not None:
plt.suptitle(title)
if savefig:
if isinstance(ext, str):
ext = [ext]
if folder is None:
_folder = os.getcwd()
else:
_folder = os.path.abspath(folder)
if not os.path.exists(_folder):
os.makedirs(_folder)
# Loop through the ext list to save figure into each image type
for _e in ext:
if filename is None:
_filename = "input_model." + _e
else:
_filename = filename + "." + _e
plt.savefig(os.path.join(_folder, _filename))
if display:
plt.show()
return fig
def plot_wdlf(
self,
log=True,
figsize=(12, 8),
title=None,
display=True,
savefig=False,
folder=None,
filename=None,
ext=["png"],
fig=None,
):
"""
Plot the input Initial-Final Mass Relation.
Parameters
----------
log: bool (Default: True)
Set to plot the WDLF in logarithmic space
figsize: array of size 2 (Default: (12, 8))
Set the dimension of the figure.
title: str (Default: None)
Set the title of the figure.
display: bool (Default: True)
Set to display the figure.
savefig: bool (Default: False)
Set to save the figure.
folder: str (Default: None)
The relative or absolute path to destination, the current working
directory will be used if None.
filename: str (Default: None)
The filename of the figure. The default filename will be used
if None.
ext: str (Default: ['png'])
Image type to be saved, multiple extensions can be provided. The
supported types are those available in `matplotlib.pyplot.savefig`.
fig: matplotlib.figure.Figure (Default: None)
Overplotting on an existing Figure.
"""
if fig is None:
fig = plt.figure(figsize=figsize)
_density = self.number_density
plt.plot(
self.mag,
_density,
label=f"{self.t_start / 1e90:.2f} Gyr",
)
plt.xlim(0, 20)
plt.xlabel(r"M$_{\mathrm{bol}}$ / mag")
_density_finite = _density[np.isfinite(_density)]
# If there is nothing to plot...
if (len(_density_finite) == 0) or (_density_finite == 0.0).all():
return 0
ymin = np.floor(np.nanmin(_density_finite))
ymax = np.ceil(np.nanmax(_density_finite))
plt.ylim(ymin, ymax)
plt.ylabel(r"$\log{(N)}$")
if log:
plt.yscale("log")
plt.grid()
plt.legend()
if title is None:
title = f"WDLF: {self.t_start / 1e9:.2f} Gyr"
plt.title(title)
plt.tight_layout()
if savefig:
if isinstance(ext, str):
ext = [ext]
if folder is None:
_folder = os.getcwd()
else:
_folder = os.path.abspath(folder)
if not os.path.exists(_folder):
os.makedirs(_folder)
# Loop through the ext list to save figure into each image type
for _e in ext:
if filename is None:
_filename = (
f"{self.t_start / 1e9:.2f}Gyr"
f"{self._filename_middle}{_e}"
)
else:
_filename = filename + "." + _e
plt.savefig(os.path.join(_folder, _filename))
if display:
plt.show()
return fig
|
cylammarcoREPO_NAMEWDPhotToolsPATH_START.@WDPhotTools_extracted@WDPhotTools-main@src@WDPhotTools@theoretical_lf.py@.PATH_END.py
|
{
"filename": "analysis.py",
"repo_name": "Samreay/ChainConsumer",
"repo_path": "ChainConsumer_extracted/ChainConsumer-master/src/chainconsumer/analysis.py",
"type": "Python"
}
|
from __future__ import annotations
import logging
from collections.abc import Callable
from pathlib import Path
import numpy as np
from pydantic import Field
from scipy.integrate import simpson as simps
from scipy.interpolate import interp1d
from scipy.ndimage import gaussian_filter
from .base import BetterBase
from .chain import Chain, ChainName, ColumnName, MaxPosterior, Named2DMatrix
from .helpers import get_bins, get_grid_bins, get_latex_table_frame, get_smoothed_bins
from .kde import MegKDE
from .statistics import SummaryStatistic
class Bound(BetterBase):
lower: float | None = Field(default=None)
center: float | None = Field(default=None)
upper: float | None = Field(default=None)
@property
def array(self) -> np.ndarray:
return np.array(
[
self.lower if self.lower is not None else np.nan,
self.center if self.center is not None else np.nan,
self.upper if self.upper is not None else np.nan,
]
)
@property
def all_none(self) -> bool:
return self.lower is None and self.center is None and self.upper is None
@classmethod
def from_array(cls, array: np.ndarray | list[float]) -> Bound:
assert len(array) == 3, "Array must have 3 elements"
lower, center, upper = array
return cls(lower=lower, center=center, upper=upper)
class Analysis:
def __init__(self, parent: ChainConsumer):
self.parent = parent
self._logger = logging.getLogger("chainconsumer")
self._summaries: dict[SummaryStatistic, Callable[[Chain, ColumnName], Bound | None]] = {
SummaryStatistic.MAX: self.get_parameter_summary_max,
SummaryStatistic.MEAN: self.get_parameter_summary_mean,
SummaryStatistic.CUMULATIVE: self.get_parameter_summary_cumulative,
SummaryStatistic.MAX_CENTRAL: self.get_parameter_summary_max_central,
}
def get_latex_table(
self,
chains: list[ChainName | Chain] | None = None,
columns: list[ColumnName] | None = None,
transpose: bool = False,
caption: str | None = None,
label: str = "tab:model_params",
hlines: bool = True,
blank_fill: str = "--",
filename: str | Path | None = None,
) -> str: # pragma: no cover
"""Generates a LaTeX table from parameter summaries.
Args:
chains:
Used to specify which chain to show if more than one chain is loaded in.
Can be an integer, specifying the
chain index, or a str, specifying the chain name.
columns:
If set, only creates a plot for those specific parameters (if list). If an
integer is given, only plots the fist so many parameters.
transpose : bool, optional
Defaults to False, which gives each column as a parameter, each chain (framework)
as a row. You can swap it so that you have a parameter each row and a framework
each column by setting this to True
caption : str, optional
If you want to generate a caption for the table through Python, use this.
Defaults to an empty string
label : str, optional
If you want to generate a label for the table through Python, use this.
Defaults to an empty string
hlines : bool, optional
Inserts ``\\hline`` before and after the header, and at the end of table.
blank_fill : str, optional
If a framework does not have a particular parameter, will fill that cell of
the table with this string.
filename : str | Path, optional
The file to save the output string to
Returns:
str: the LaTeX table.
"""
final_chains = self.parent.plotter._sanitise_chains(chains)
final_columns = self.parent.plotter._sanitise_columns(columns, final_chains)
blind = self.parent.plotter._sanitise_blinds(self.parent.plotter.config.blind, final_columns)
final_columns = [c for c in final_columns if c not in blind]
num_chains = len(final_chains)
num_parameters = len(final_columns)
fit_values = self.get_summary(chains=final_chains)
if label is None:
label = ""
if caption is None:
caption = ""
end_text = " \\\\ \n"
column_text = "c" * (num_chains + 1) if transpose else "c" * (num_parameters + 1)
center_text = ""
hline_text = "\\hline\n"
if hlines:
center_text += hline_text + "\t\t"
if transpose:
center_text += " & ".join(["Parameter"] + [c.name for c in final_chains]) + end_text
if hlines:
center_text += "\t\t" + hline_text
for p in final_columns:
arr = ["\t\t" + self.parent.plotter.config.get_label(p)]
for _, column_results in fit_values.items():
if p in column_results:
arr.append(self.get_parameter_text(column_results[p], wrap=True))
else:
arr.append(blank_fill)
center_text += " & ".join(arr) + end_text
else:
center_text += (
" & ".join(["Model", *[self.parent.plotter.config.get_label(c) for c in final_columns]]) + end_text
)
if hlines:
center_text += "\t\t" + hline_text
for name, chain_res in fit_values.items():
arr = ["\t\t" + name]
for p in final_columns:
if p in chain_res:
arr.append(self.get_parameter_text(chain_res[p], wrap=True))
else:
arr.append(blank_fill)
center_text += " & ".join(arr) + end_text
if hlines:
center_text += "\t\t" + hline_text
final_text = get_latex_table_frame(caption, label) % (column_text, center_text)
if filename is not None:
if isinstance(filename, str):
filename = Path(filename)
with Path.open(filename, "w") as f:
f.write(final_text)
return final_text
def get_summary(
self,
chains: list[Chain] | None = None,
columns: list[ColumnName] | None = None,
) -> dict[ChainName, dict[ColumnName, Bound]]:
"""Gets a summary of the marginalised parameter distributions.
Args:
parameters (list[str], optional): A list of parameters which to generate summaries for.
chains (dict[str, Chain] | list[str], optional): A list of chains to generate summaries for.
Returns:
dict[ChainName, dict[ColumnName, Bound]]: A map from chain name to column name to bound.
"""
results = {}
if chains is None:
chains = self.parent.plotter._sanitise_chains(None, include_skip=True)
if columns is None:
columns = self.parent.plotter._sanitise_columns(None, chains)
for chain in chains:
res = {}
params_to_find = columns if columns is not None else chain.data_columns
for p in params_to_find:
if p not in chain.samples:
continue
summary = self.get_parameter_summary(chain, p)
res[p] = summary
results[chain.name] = res
return results
def get_max_posteriors(self, chains: dict[str, Chain] | list[str] | None = None) -> dict[ChainName, MaxPosterior]:
"""Gets the maximum posterior point in parameter space from the passed parameters.
Requires the chains to have set `posterior` values.
Args:
chains (dict[str, Chain] | list[str], optional): A list of chains to generate summaries for.
Returns:
dict[ChainName, MaxPosterior]: A map from chain name to max posterior point.
"""
results = {}
if chains is None:
chains = self.parent._chains
if isinstance(chains, list):
chains = {c: self.parent._chains[c] for c in chains}
for chain_name, chain in chains.items():
max_posterior = chain.get_max_posterior_point()
if max_posterior is None:
continue
results[chain_name] = max_posterior
return results
def get_parameter_summary(self, chain: Chain, column: ColumnName) -> Bound | None:
callback = self._summaries[chain.statistics]
return callback(chain, column)
def get_correlation_table(
self,
chain: str | Chain,
columns: list[str] | None = None,
caption: str = "Parameter Correlations",
label: str = "tab:parameter_correlations",
) -> str:
"""
Gets a LaTeX table of parameter correlations.
Args:
chain (str|Chain, optional_: The chain index or name. Defaults to first chain.
columns (list[str], optional): The list of parameters to compute correlations. Defaults to all columns
caption (str, optional): The LaTeX table caption.
label (str, optional): The LaTeX table label.
Returns:
str: The LaTeX table ready to go!
"""
if isinstance(chain, str):
assert chain in self.parent._chains, f"Chain {chain} not found!"
chain = self.parent._chains[chain]
if chain is None:
assert len(self.parent._chains) == 1, "You must specify a chain if there are multiple chains"
chain = next(iter(self.parent._chains.values()))
correlations = chain.get_correlation(columns=columns)
return self._get_2d_latex_table(correlations, caption, label)
def get_covariance_table(
self,
chain: str | Chain,
columns: list[str] | None = None,
caption: str = "Parameter Covariance",
label: str = "tab:parameter_covariance",
) -> str:
"""
Gets a LaTeX table of parameter covariances.
Args:
chain (str|Chain, optional_: The chain index or name. Defaults to first chain.
columns (list[str], optional): The list of parameters to compute covariances on. Defaults to all columns
caption (str, optional): The LaTeX table caption.
label (str, optional): The LaTeX table label.
Returns:
str: The LaTeX table ready to go!
"""
if isinstance(chain, str):
assert chain in self.parent._chains, f"Chain {chain} not found!"
chain = self.parent._chains[chain]
if chain is None:
assert len(self.parent._chains) == 1, "You must specify a chain if there are multiple chains"
chain = next(iter(self.parent._chains.values()))
covariance = chain.get_covariance(columns=columns)
return self._get_2d_latex_table(covariance, caption, label)
def _get_smoothed_histogram(
self, chain: Chain, column: ColumnName, pad: bool = False
) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
data = chain.get_data(column)
if chain.grid:
bins = get_grid_bins(data)
else:
bins, _ = get_smoothed_bins(chain.smooth, get_bins(chain), data, chain.weights, pad=pad)
hist, edges = np.histogram(data, bins=bins, density=True, weights=chain.weights)
if chain.power is not None:
hist = hist**chain.power
edge_centers = 0.5 * (edges[1:] + edges[:-1])
xs = np.linspace(edge_centers[0], edge_centers[-1], 10000)
if chain.smooth:
hist = gaussian_filter(hist, chain.smooth, mode="reflect")
if chain.kde:
kde_xs = np.linspace(edge_centers[0], edge_centers[-1], max(200, int(bins.max())))
factor = chain.kde if isinstance(chain.kde, int | float) else 1.0
ys = MegKDE(data.to_numpy(), chain.weights, factor=factor).evaluate(kde_xs)
area = simps(ys, x=kde_xs)
ys = ys / area
ys = interp1d(kde_xs, ys, kind="linear")(xs)
else:
ys = interp1d(edge_centers, hist, kind="linear")(xs)
cs = ys.cumsum()
cs /= cs.max()
return xs, ys, cs
def _get_2d_latex_table(self, named_matrix: Named2DMatrix, caption: str, label: str) -> str:
parameters = [self.parent.plotter.config.get_label(c) for c in named_matrix.columns]
matrix = named_matrix.matrix
latex_table = get_latex_table_frame(caption=caption, label=label)
column_def = "c|%s" % ("c" * len(parameters))
hline_text = " \\hline\n"
table = ""
table += " & ".join(["", *parameters]) + "\\\\ \n"
table += hline_text
max_len = max([len(s) for s in parameters])
format_string = " %%%ds" % max_len
for p, row in zip(parameters, matrix):
table += format_string % p
for r in row:
table += f" & {r:5.2f}"
table += " \\\\ \n"
table += hline_text
return latex_table % (column_def, table)
def get_parameter_text(self, bound: Bound, wrap: bool = False):
"""Generates LaTeX appropriate text from marginalised parameter bounds.
Parameters
----------
lower : float
The lower bound on the parameter
maximum : float
The value of the parameter with maximum probability
upper : float
The upper bound on the parameter
wrap : bool
Wrap output text in dollar signs for LaTeX
Returns
-------
str
The formatted text given the parameter bounds
"""
if bound.lower is None or bound.upper is None or bound.center is None:
return ""
upper_error = bound.upper - bound.center
lower_error = bound.center - bound.lower
if upper_error != 0 and lower_error != 0:
resolution = min(np.floor(np.log10(np.abs(upper_error))), np.floor(np.log10(np.abs(lower_error))))
elif upper_error == 0 and lower_error != 0:
resolution = np.floor(np.log10(np.abs(lower_error)))
elif upper_error != 0 and lower_error == 0:
resolution = np.floor(np.log10(np.abs(upper_error)))
else:
resolution = np.floor(np.log10(np.abs(bound.center)))
factor = 0
fmt = "%0.1f"
r = 1
if np.abs(resolution) > 2:
factor = -resolution
if resolution == 2:
fmt = "%0.0f"
factor = -1
r = 0
if resolution == 1:
fmt = "%0.0f"
if resolution == -1:
fmt = "%0.2f"
r = 2
elif resolution == -2:
fmt = "%0.3f"
r = 3
upper_error *= 10**factor
lower_error *= 10**factor
maximum = bound.center * 10**factor
upper_error = round(upper_error, r)
lower_error = round(lower_error, r)
maximum = round(maximum, r)
if maximum == -0.0:
maximum = 0.0
if resolution == 2:
upper_error *= 10**-factor
lower_error *= 10**-factor
maximum *= 10**-factor
factor = 0
fmt = "%0.0f"
upper_error_text = fmt % upper_error
lower_error_text = fmt % lower_error
if upper_error_text == lower_error_text:
text = r"{}\pm {}".format(fmt, "%s") % (maximum, lower_error_text)
else:
text = r"{}^{{+{}}}_{{-{}}}".format(fmt, "%s", "%s") % (maximum, upper_error_text, lower_error_text)
if factor != 0:
text = r"\left( %s \right) \times 10^{%d}" % (text, -factor)
if wrap:
text = f"${text}$"
return text
def get_parameter_summary_mean(self, chain: Chain, column: ColumnName) -> Bound | None:
xs, _, cs = self._get_smoothed_histogram(chain, column)
vals = [0.5 - chain.summary_area / 2, 0.5, 0.5 + chain.summary_area / 2]
bounds = interp1d(cs, xs)(vals)
bounds[1] = 0.5 * (bounds[0] + bounds[2])
return Bound(lower=bounds[0], center=bounds[1], upper=bounds[2])
def get_parameter_summary_cumulative(self, chain: Chain, column: ColumnName) -> Bound | None:
xs, _, cs = self._get_smoothed_histogram(chain, column)
vals = [0.5 - chain.summary_area / 2, 0.5, 0.5 + chain.summary_area / 2]
bounds = interp1d(cs, xs)(vals)
return Bound(lower=bounds[0], center=bounds[1], upper=bounds[2])
def get_parameter_summary_max(self, chain: Chain, column: ColumnName) -> Bound | None:
xs, ys, cs = self._get_smoothed_histogram(chain, column)
n_pad = 1000
x_start = xs[0] * np.ones(n_pad)
x_end = xs[-1] * np.ones(n_pad)
y_start = np.linspace(0, ys[0], n_pad)
y_end = np.linspace(ys[-1], 0, n_pad)
xs = np.concatenate((x_start, xs, x_end))
ys = np.concatenate((y_start, ys, y_end))
cs = ys.cumsum()
cs = cs / cs.max()
start_index = ys.argmax()
max_val = ys[start_index]
min_val = 0
threshold = 0.003
x1 = None
x2 = None
count = 0
while x1 is None:
mid = (max_val + min_val) / 2.0
count += 1
try:
if count > 50:
raise ValueError("Failed to converge") # noqa: TRY301
i1 = start_index - np.where(ys[:start_index][::-1] < mid)[0][0]
i2 = start_index + np.where(ys[start_index:] < mid)[0][0]
area = cs[i2] - cs[i1]
deviation = np.abs(area - chain.summary_area)
if deviation < threshold:
x1 = float(xs[i1])
x2 = float(xs[i2])
elif area < chain.summary_area:
max_val = mid
elif area > chain.summary_area:
min_val = mid
except ValueError:
self._logger.warning(f"Parameter {column} in chain {chain.name} is not constrained")
return Bound(lower=None, center=float(xs[start_index]), upper=None)
return Bound(lower=x1, center=float(xs[start_index]), upper=x2)
def get_parameter_summary_max_central(self, chain, parameter):
xs, ys, cs = self._get_smoothed_histogram(chain, parameter)
c_to_x = interp1d(cs, xs)
max_index = ys.argmax()
x = xs[max_index]
vals = [0.5 - 0.5 * chain.summary_area, 0.5 + 0.5 * chain.summary_area]
xvals = c_to_x(vals)
return Bound(lower=xvals[0], center=x, upper=xvals[1])
if __name__ == "__main__":
from .chainconsumer import ChainConsumer
|
SamreayREPO_NAMEChainConsumerPATH_START.@ChainConsumer_extracted@ChainConsumer-master@src@chainconsumer@analysis.py@.PATH_END.py
|
{
"filename": "cadence_metrics.py",
"repo_name": "lsst/rubin_sim",
"repo_path": "rubin_sim_extracted/rubin_sim-main/rubin_sim/maf/metrics/cadence_metrics.py",
"type": "Python"
}
|
__all__ = (
"TemplateExistsMetric",
"UniformityMetric",
"GeneralUniformityMetric",
"RapidRevisitUniformityMetric",
"RapidRevisitMetric",
"NRevisitsMetric",
"IntraNightGapsMetric",
"InterNightGapsMetric",
"VisitGapMetric",
)
import numpy as np
from .base_metric import BaseMetric
class FSMetric(BaseMetric):
"""Calculate the fS value (Nvisit-weighted delta(M5-M5srd))."""
def __init__(self, filter_col="filter", metric_name="fS", **kwargs):
self.filter_col = filter_col
cols = [self.filter_col]
super().__init__(cols=cols, metric_name=metric_name, units="fS", **kwargs)
def run(self, data_slice, slice_point=None):
# We could import this from the m5_flat_sed values,
# but it makes sense to calculate the m5
# directly from the throughputs. This is easy enough to do and
# will allow variation of
# the throughput curves and readnoise and visit length, etc.
pass
class TemplateExistsMetric(BaseMetric):
"""Calculate the fraction of images with a previous template
image of desired quality."""
def __init__(
self,
seeing_col="seeingFwhmGeom",
observation_start_mjd_col="observationStartMJD",
metric_name="TemplateExistsMetric",
**kwargs,
):
cols = [seeing_col, observation_start_mjd_col]
super(TemplateExistsMetric, self).__init__(
col=cols, metric_name=metric_name, units="fraction", **kwargs
)
self.seeing_col = seeing_col
self.observation_start_mjd_col = observation_start_mjd_col
def run(self, data_slice, slice_point=None):
# Check that data is sorted in observationStartMJD order
data_slice.sort(order=self.observation_start_mjd_col)
# Find the minimum seeing up to a given time
seeing_mins = np.minimum.accumulate(data_slice[self.seeing_col])
# Find the difference between the seeing and the minimum seeing
# at the previous visit
seeing_diff = data_slice[self.seeing_col] - np.roll(seeing_mins, 1)
# First image never has a template; check how many others do
good = np.where(seeing_diff[1:] >= 0.0)[0]
frac = (good.size) / float(data_slice[self.seeing_col].size)
return frac
class UniformityMetric(BaseMetric):
"""Calculate how uniformly the observations are spaced in time.
This is based on how a KS-test works:
look at the cumulative distribution of observation dates,
and compare to a perfectly uniform cumulative distribution.
Perfectly uniform observations = 0, perfectly non-uniform = 1.
Parameters
----------
mjd_col : `str`, optional
The column containing time for each observation.
Default "observationStartMJD".
survey_length : `float`, optional
The overall duration of the survey. Default 10.
"""
def __init__(self, mjd_col="observationStartMJD", units="", survey_length=10.0, **kwargs):
"""survey_length = time span of survey (years)"""
self.mjd_col = mjd_col
super(UniformityMetric, self).__init__(col=self.mjd_col, units=units, **kwargs)
self.survey_length = survey_length
def run(self, data_slice, slice_point=None):
# If only one observation, there is no uniformity
if data_slice[self.mjd_col].size == 1:
return 1
# Scale dates to lie between 0 and 1,
# where 0 is the first observation date and 1 is surveyLength
dates = (data_slice[self.mjd_col] - data_slice[self.mjd_col].min()) / (self.survey_length * 365.25)
dates.sort() # Just to be sure
n_cum = np.arange(1, dates.size + 1) / float(dates.size)
d_max = np.max(np.abs(n_cum - dates - dates[1]))
return d_max
class GeneralUniformityMetric(BaseMetric):
"""Calculate how uniformly any values are distributed.
This is based on how a KS-test works:
look at the cumulative distribution of data,
and compare to a perfectly uniform cumulative distribution.
Perfectly uniform observations = 0, perfectly non-uniform = 1.
To be "perfectly uniform" here, the endpoints need to be included.
Parameters
----------
col : `str`, optional
The column of data to use for the metric.
The default is "observationStartMJD" as this is most
typically used with time.
min_value : `float`, optional
The minimum value expected for the data.
Default None will calculate use the minimum value in this dataslice
(which may not cover the full range).
max_value : `float`, optional
The maximum value expected for the data.
Default None will calculate use the maximum value in this dataslice
(which may not cover the full range).
"""
def __init__(self, col="observationStartMJD", units="", min_value=None, max_value=None, **kwargs):
self.col = col
super().__init__(col=self.col, units=units, **kwargs)
self.min_value = min_value
self.max_value = max_value
def run(self, data_slice, slice_point=None):
# If only one observation, there is no uniformity
if data_slice[self.col].size == 1:
return 1
# Scale values to lie between 0 and 1,
# where 0 is the min_value and 1 is max_value
if self.min_value is None:
min_value = data_slice[self.col].min()
else:
min_value = self.min_value
if self.max_value is None:
max_value = data_slice[self.col].max()
else:
max_value = self.max_value
scaled_values = (data_slice[self.col] - min_value) / max_value
scaled_values.sort() # Just to be sure
n_cum = np.arange(0, scaled_values.size) / float(scaled_values.size - 1)
d_max = np.max(np.abs(n_cum - scaled_values))
return d_max
class RapidRevisitUniformityMetric(BaseMetric):
"""Calculate uniformity of time between consecutive visits on
short timescales (for RAV1).
Uses the same 'uniformity' calculation as the UniformityMetric,
based on the KS-test.
A value of 0 is perfectly uniform; a value of 1 is purely non-uniform.
Parameters
----------
mjd_col : `str`, optional
The column containing the 'time' value. Default observationStartMJD.
min_nvisits : `int`, optional
The minimum number of visits required within the
time interval (d_tmin to d_tmax).
Default 100.
d_tmin : `float`, optional
The minimum dTime to consider (in days). Default 40 seconds.
d_tmax : `float`, optional
The maximum dTime to consider (in days). Default 30 minutes.
"""
def __init__(
self,
mjd_col="observationStartMJD",
min_nvisits=100,
d_tmin=40.0 / 60.0 / 60.0 / 24.0,
d_tmax=30.0 / 60.0 / 24.0,
metric_name="RapidRevisitUniformity",
**kwargs,
):
self.mjd_col = mjd_col
self.min_nvisits = min_nvisits
self.d_tmin = d_tmin
self.d_tmax = d_tmax
super().__init__(col=self.mjd_col, metric_name=metric_name, **kwargs)
# Update min_nvisits, as 0 visits will crash algorithm
# and 1 is nonuniform by definition.
if self.min_nvisits <= 1:
self.min_nvisits = 2
def run(self, data_slice, slice_point=None):
# Calculate consecutive visit time intervals
dtimes = np.diff(np.sort(data_slice[self.mjd_col]))
# Identify dtimes within interval from dTmin/dTmax.
good = np.where((dtimes >= self.d_tmin) & (dtimes <= self.d_tmax))[0]
# If there are not enough visits in this time range, return bad value.
if good.size < self.min_nvisits:
return self.badval
# Throw out dtimes outside desired range, and sort, then scale to 0-1.
dtimes = np.sort(dtimes[good])
dtimes = (dtimes - dtimes.min()) / float(self.d_tmax - self.d_tmin)
# Set up a uniform distribution between 0-1 (to match dtimes).
uniform_dtimes = np.arange(1, dtimes.size + 1, 1) / float(dtimes.size)
# Look at the differences between our times and the uniform times.
dmax = np.max(np.abs(uniform_dtimes - dtimes - dtimes[1]))
return dmax
class RapidRevisitMetric(BaseMetric):
def __init__(
self,
mjd_col="observationStartMJD",
metric_name="RapidRevisit",
d_tmin=40.0 / 60.0 / 60.0 / 24.0,
d_tpairs=20.0 / 60.0 / 24.0,
d_tmax=30.0 / 60.0 / 24.0,
min_n1=28,
min_n2=82,
**kwargs,
):
self.mjd_col = mjd_col
self.d_tmin = d_tmin
self.d_tpairs = d_tpairs
self.d_tmax = d_tmax
self.min_n1 = min_n1
self.min_n2 = min_n2
super().__init__(col=self.mjd_col, metric_name=metric_name, **kwargs)
def run(self, data_slice, slice_point=None):
dtimes = np.diff(np.sort(data_slice[self.mjd_col]))
n1 = len(np.where((dtimes >= self.d_tmin) & (dtimes <= self.d_tpairs))[0])
n2 = len(np.where((dtimes >= self.d_tmin) & (dtimes <= self.d_tmax))[0])
if (n1 >= self.min_n1) and (n2 >= self.min_n2):
val = 1
else:
val = 0
return val
class NRevisitsMetric(BaseMetric):
"""Calculate the number of consecutive visits with
time differences less than d_t.
Parameters
----------
d_t : `float`, optional
The time interval to consider (in minutes). Default 30.
normed : `bool`, optional
Flag to indicate whether to return the total number of
consecutive visits with time differences less than d_t (False),
or the fraction of overall visits (True).
Note that we would expect (if all visits occur in pairs within d_t)
this fraction would be 0.5!
"""
def __init__(self, mjd_col="observationStartMJD", d_t=30.0, normed=False, metric_name=None, **kwargs):
units = ""
if metric_name is None:
if normed:
metric_name = "Fraction of revisits faster than %.1f minutes" % (d_t)
else:
metric_name = "Number of revisits faster than %.1f minutes" % (d_t)
units = "#"
self.mjd_col = mjd_col
self.d_t = d_t / 60.0 / 24.0 # convert to days
self.normed = normed
super(NRevisitsMetric, self).__init__(
col=self.mjd_col, units=units, metric_name=metric_name, **kwargs
)
def run(self, data_slice, slice_point=None):
dtimes = np.diff(np.sort(data_slice[self.mjd_col]))
n_fast_revisits = np.size(np.where(dtimes <= self.d_t)[0])
if self.normed:
n_fast_revisits = n_fast_revisits / float(np.size(data_slice[self.mjd_col]))
return n_fast_revisits
class IntraNightGapsMetric(BaseMetric):
"""
Calculate the (reduce_func) of the gap between consecutive
observations within a night, in hours.
Parameters
----------
reduce_func : function, optional
Function that can operate on array-like structures.
Typically numpy function.
Default np.median.
"""
def __init__(
self,
mjd_col="observationStartMJD",
night_col="night",
reduce_func=np.median,
metric_name="Median Intra-Night Gap",
**kwargs,
):
units = "hours"
self.mjd_col = mjd_col
self.night_col = night_col
self.reduce_func = reduce_func
super(IntraNightGapsMetric, self).__init__(
col=[self.mjd_col, self.night_col], units=units, metric_name=metric_name, **kwargs
)
def run(self, data_slice, slice_point=None):
data_slice.sort(order=self.mjd_col)
dt = np.diff(data_slice[self.mjd_col])
dn = np.diff(data_slice[self.night_col])
good = np.where(dn == 0)
if np.size(good[0]) == 0:
result = self.badval
else:
result = self.reduce_func(dt[good]) * 24
return result
class InterNightGapsMetric(BaseMetric):
"""Calculate the (reduce_func) of the gap between consecutive
observations in different nights, in days.
Parameters
----------
reduce_func : function, optional
Function that can operate on array-like structures.
Typically numpy function.
Default np.median.
"""
def __init__(
self,
mjd_col="observationStartMJD",
night_col="night",
reduce_func=np.median,
metric_name="Median Inter-Night Gap",
**kwargs,
):
units = "days"
self.mjd_col = mjd_col
self.night_col = night_col
self.reduce_func = reduce_func
super().__init__(col=[self.mjd_col, self.night_col], units=units, metric_name=metric_name, **kwargs)
def run(self, data_slice, slice_point=None):
data_slice.sort(order=self.mjd_col)
unights = np.unique(data_slice[self.night_col])
if np.size(unights) < 2:
result = self.badval
else:
# Find the first and last observation of each night
first_of_night = np.searchsorted(data_slice[self.night_col], unights)
last_of_night = np.searchsorted(data_slice[self.night_col], unights, side="right") - 1
diff = data_slice[self.mjd_col][first_of_night[1:]] - data_slice[self.mjd_col][last_of_night[:-1]]
result = self.reduce_func(diff)
return result
class VisitGapMetric(BaseMetric):
"""Calculate the (reduce_func) of the gap between any
consecutive observations, in hours, regardless of night boundaries.
Different from inter-night and intra-night gaps,
because this is really just counting all of the times between consecutive
observations (not time between nights or time within a night).
Parameters
----------
reduce_func : function, optional
Function that can operate on array-like structures.
Typically numpy function.
Default np.median.
"""
def __init__(
self,
mjd_col="observationStartMJD",
night_col="night",
reduce_func=np.median,
metric_name="VisitGap",
**kwargs,
):
units = "hours"
self.mjd_col = mjd_col
self.night_col = night_col
self.reduce_func = reduce_func
super().__init__(col=[self.mjd_col, self.night_col], units=units, metric_name=metric_name, **kwargs)
def run(self, data_slice, slice_point=None):
data_slice.sort(order=self.mjd_col)
diff = np.diff(data_slice[self.mjd_col])
result = self.reduce_func(diff) * 24.0
return result
|
lsstREPO_NAMErubin_simPATH_START.@rubin_sim_extracted@rubin_sim-main@rubin_sim@maf@metrics@cadence_metrics.py@.PATH_END.py
|
{
"filename": "_histogram2d.py",
"repo_name": "catboost/catboost",
"repo_path": "catboost_extracted/catboost-master/contrib/python/plotly/py2/plotly/graph_objs/_histogram2d.py",
"type": "Python"
}
|
from plotly.basedatatypes import BaseTraceType as _BaseTraceType
import copy as _copy
class Histogram2d(_BaseTraceType):
# class properties
# --------------------
_parent_path_str = ""
_path_str = "histogram2d"
_valid_props = {
"autobinx",
"autobiny",
"autocolorscale",
"bingroup",
"coloraxis",
"colorbar",
"colorscale",
"customdata",
"customdatasrc",
"histfunc",
"histnorm",
"hoverinfo",
"hoverinfosrc",
"hoverlabel",
"hovertemplate",
"hovertemplatesrc",
"ids",
"idssrc",
"legendgroup",
"marker",
"meta",
"metasrc",
"name",
"nbinsx",
"nbinsy",
"opacity",
"reversescale",
"showlegend",
"showscale",
"stream",
"type",
"uid",
"uirevision",
"visible",
"x",
"xaxis",
"xbingroup",
"xbins",
"xcalendar",
"xgap",
"xsrc",
"y",
"yaxis",
"ybingroup",
"ybins",
"ycalendar",
"ygap",
"ysrc",
"z",
"zauto",
"zhoverformat",
"zmax",
"zmid",
"zmin",
"zsmooth",
"zsrc",
}
# autobinx
# --------
@property
def autobinx(self):
"""
Obsolete: since v1.42 each bin attribute is auto-determined
separately and `autobinx` is not needed. However, we accept
`autobinx: true` or `false` and will update `xbins` accordingly
before deleting `autobinx` from the trace.
The 'autobinx' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["autobinx"]
@autobinx.setter
def autobinx(self, val):
self["autobinx"] = val
# autobiny
# --------
@property
def autobiny(self):
"""
Obsolete: since v1.42 each bin attribute is auto-determined
separately and `autobiny` is not needed. However, we accept
`autobiny: true` or `false` and will update `ybins` accordingly
before deleting `autobiny` from the trace.
The 'autobiny' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["autobiny"]
@autobiny.setter
def autobiny(self, val):
self["autobiny"] = val
# autocolorscale
# --------------
@property
def autocolorscale(self):
"""
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`colorscale`. In case `colorscale` is unspecified or
`autocolorscale` is true, the default palette will be chosen
according to whether numbers in the `color` array are all
positive, all negative or mixed.
The 'autocolorscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["autocolorscale"]
@autocolorscale.setter
def autocolorscale(self, val):
self["autocolorscale"] = val
# bingroup
# --------
@property
def bingroup(self):
"""
Set the `xbingroup` and `ybingroup` default prefix For example,
setting a `bingroup` of 1 on two histogram2d traces will make
them their x-bins and y-bins match separately.
The 'bingroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["bingroup"]
@bingroup.setter
def bingroup(self, val):
self["bingroup"] = val
# coloraxis
# ---------
@property
def coloraxis(self):
"""
Sets a reference to a shared color axis. References to these
shared color axes are "coloraxis", "coloraxis2", "coloraxis3",
etc. Settings for these shared color axes are set in the
layout, under `layout.coloraxis`, `layout.coloraxis2`, etc.
Note that multiple color scales can be linked to the same color
axis.
The 'coloraxis' property is an identifier of a particular
subplot, of type 'coloraxis', that may be specified as the string 'coloraxis'
optionally followed by an integer >= 1
(e.g. 'coloraxis', 'coloraxis1', 'coloraxis2', 'coloraxis3', etc.)
Returns
-------
str
"""
return self["coloraxis"]
@coloraxis.setter
def coloraxis(self, val):
self["coloraxis"] = val
# colorbar
# --------
@property
def colorbar(self):
"""
The 'colorbar' property is an instance of ColorBar
that may be specified as:
- An instance of :class:`plotly.graph_objs.histogram2d.ColorBar`
- A dict of string/value properties that will be passed
to the ColorBar constructor
Supported dict properties:
bgcolor
Sets the color of padded area.
bordercolor
Sets the axis line color.
borderwidth
Sets the width (in px) or the border enclosing
this color bar.
dtick
Sets the step in-between ticks on this axis.
Use with `tick0`. Must be a positive number, or
special strings available to "log" and "date"
axes. If the axis `type` is "log", then ticks
are set every 10^(n*dtick) where n is the tick
number. For example, to set a tick mark at 1,
10, 100, 1000, ... set dtick to 1. To set tick
marks at 1, 100, 10000, ... set dtick to 2. To
set tick marks at 1, 5, 25, 125, 625, 3125, ...
set dtick to log_10(5), or 0.69897000433. "log"
has several special values; "L<f>", where `f`
is a positive number, gives ticks linearly
spaced in value (but not position). For example
`tick0` = 0.1, `dtick` = "L0.5" will put ticks
at 0.1, 0.6, 1.1, 1.6 etc. To show powers of 10
plus small digits between, use "D1" (all
digits) or "D2" (only 2 and 5). `tick0` is
ignored for "D1" and "D2". If the axis `type`
is "date", then you must convert the time to
milliseconds. For example, to set the interval
between ticks to one day, set `dtick` to
86400000.0. "date" also has special values
"M<n>" gives ticks spaced by a number of
months. `n` must be a positive integer. To set
ticks on the 15th of every third month, set
`tick0` to "2000-01-15" and `dtick` to "M3". To
set ticks every 4 years, set `dtick` to "M48"
exponentformat
Determines a formatting rule for the tick
exponents. For example, consider the number
1,000,000,000. If "none", it appears as
1,000,000,000. If "e", 1e+9. If "E", 1E+9. If
"power", 1x10^9 (with 9 in a super script). If
"SI", 1G. If "B", 1B.
len
Sets the length of the color bar This measure
excludes the padding of both ends. That is, the
color bar length is this length minus the
padding on both ends.
lenmode
Determines whether this color bar's length
(i.e. the measure in the color variation
direction) is set in units of plot "fraction"
or in *pixels. Use `len` to set the value.
minexponent
Hide SI prefix for 10^n if |n| is below this
number. This only has an effect when
`tickformat` is "SI" or "B".
nticks
Specifies the maximum number of ticks for the
particular axis. The actual number of ticks
will be chosen automatically to be less than or
equal to `nticks`. Has an effect only if
`tickmode` is set to "auto".
outlinecolor
Sets the axis line color.
outlinewidth
Sets the width (in px) of the axis line.
separatethousands
If "true", even 4-digit integers are separated
showexponent
If "all", all exponents are shown besides their
significands. If "first", only the exponent of
the first tick is shown. If "last", only the
exponent of the last tick is shown. If "none",
no exponents appear.
showticklabels
Determines whether or not the tick labels are
drawn.
showtickprefix
If "all", all tick labels are displayed with a
prefix. If "first", only the first tick is
displayed with a prefix. If "last", only the
last tick is displayed with a suffix. If
"none", tick prefixes are hidden.
showticksuffix
Same as `showtickprefix` but for tick suffixes.
thickness
Sets the thickness of the color bar This
measure excludes the size of the padding, ticks
and labels.
thicknessmode
Determines whether this color bar's thickness
(i.e. the measure in the constant color
direction) is set in units of plot "fraction"
or in "pixels". Use `thickness` to set the
value.
tick0
Sets the placement of the first tick on this
axis. Use with `dtick`. If the axis `type` is
"log", then you must take the log of your
starting tick (e.g. to set the starting tick to
100, set the `tick0` to 2) except when
`dtick`=*L<f>* (see `dtick` for more info). If
the axis `type` is "date", it should be a date
string, like date data. If the axis `type` is
"category", it should be a number, using the
scale where each category is assigned a serial
number from zero in the order it appears.
tickangle
Sets the angle of the tick labels with respect
to the horizontal. For example, a `tickangle`
of -90 draws the tick labels vertically.
tickcolor
Sets the tick color.
tickfont
Sets the color bar's tick label font
tickformat
Sets the tick label formatting rule using d3
formatting mini-languages which are very
similar to those in Python. For numbers, see:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format
And for dates see:
https://github.com/d3/d3-time-
format#locale_format We add one item to d3's
date formatter: "%{n}f" for fractional seconds
with n digits. For example, *2016-10-13
09:15:23.456* with tickformat "%H~%M~%S.%2f"
would display "09~15~23.46"
tickformatstops
A tuple of :class:`plotly.graph_objects.histogr
am2d.colorbar.Tickformatstop` instances or
dicts with compatible properties
tickformatstopdefaults
When used in a template (as layout.template.dat
a.histogram2d.colorbar.tickformatstopdefaults),
sets the default property values to use for
elements of
histogram2d.colorbar.tickformatstops
ticklabelposition
Determines where tick labels are drawn.
ticklen
Sets the tick length (in px).
tickmode
Sets the tick mode for this axis. If "auto",
the number of ticks is set via `nticks`. If
"linear", the placement of the ticks is
determined by a starting position `tick0` and a
tick step `dtick` ("linear" is the default
value if `tick0` and `dtick` are provided). If
"array", the placement of the ticks is set via
`tickvals` and the tick text is `ticktext`.
("array" is the default value if `tickvals` is
provided).
tickprefix
Sets a tick label prefix.
ticks
Determines whether ticks are drawn or not. If
"", this axis' ticks are not drawn. If
"outside" ("inside"), this axis' are drawn
outside (inside) the axis lines.
ticksuffix
Sets a tick label suffix.
ticktext
Sets the text displayed at the ticks position
via `tickvals`. Only has an effect if
`tickmode` is set to "array". Used with
`tickvals`.
ticktextsrc
Sets the source reference on Chart Studio Cloud
for ticktext .
tickvals
Sets the values at which ticks on this axis
appear. Only has an effect if `tickmode` is set
to "array". Used with `ticktext`.
tickvalssrc
Sets the source reference on Chart Studio Cloud
for tickvals .
tickwidth
Sets the tick width (in px).
title
:class:`plotly.graph_objects.histogram2d.colorb
ar.Title` instance or dict with compatible
properties
titlefont
Deprecated: Please use
histogram2d.colorbar.title.font instead. Sets
this color bar's title font. Note that the
title's font used to be set by the now
deprecated `titlefont` attribute.
titleside
Deprecated: Please use
histogram2d.colorbar.title.side instead.
Determines the location of color bar's title
with respect to the color bar. Note that the
title's location used to be set by the now
deprecated `titleside` attribute.
x
Sets the x position of the color bar (in plot
fraction).
xanchor
Sets this color bar's horizontal position
anchor. This anchor binds the `x` position to
the "left", "center" or "right" of the color
bar.
xpad
Sets the amount of padding (in px) along the x
direction.
y
Sets the y position of the color bar (in plot
fraction).
yanchor
Sets this color bar's vertical position anchor
This anchor binds the `y` position to the
"top", "middle" or "bottom" of the color bar.
ypad
Sets the amount of padding (in px) along the y
direction.
Returns
-------
plotly.graph_objs.histogram2d.ColorBar
"""
return self["colorbar"]
@colorbar.setter
def colorbar(self, val):
self["colorbar"] = val
# colorscale
# ----------
@property
def colorscale(self):
"""
Sets the colorscale. The colorscale must be an array containing
arrays mapping a normalized value to an rgb, rgba, hex, hsl,
hsv, or named color string. At minimum, a mapping for the
lowest (0) and highest (1) values are required. For example,
`[[0, 'rgb(0,0,255)'], [1, 'rgb(255,0,0)']]`. To control the
bounds of the colorscale in color space, use`zmin` and `zmax`.
Alternatively, `colorscale` may be a palette name string of the
following list: Greys,YlGnBu,Greens,YlOrRd,Bluered,RdBu,Reds,Bl
ues,Picnic,Rainbow,Portland,Jet,Hot,Blackbody,Earth,Electric,Vi
ridis,Cividis.
The 'colorscale' property is a colorscale and may be
specified as:
- A list of colors that will be spaced evenly to create the colorscale.
Many predefined colorscale lists are included in the sequential, diverging,
and cyclical modules in the plotly.colors package.
- A list of 2-element lists where the first element is the
normalized color level value (starting at 0 and ending at 1),
and the second item is a valid color string.
(e.g. [[0, 'green'], [0.5, 'red'], [1.0, 'rgb(0, 0, 255)']])
- One of the following named colorscales:
['aggrnyl', 'agsunset', 'algae', 'amp', 'armyrose', 'balance',
'blackbody', 'bluered', 'blues', 'blugrn', 'bluyl', 'brbg',
'brwnyl', 'bugn', 'bupu', 'burg', 'burgyl', 'cividis', 'curl',
'darkmint', 'deep', 'delta', 'dense', 'earth', 'edge', 'electric',
'emrld', 'fall', 'geyser', 'gnbu', 'gray', 'greens', 'greys',
'haline', 'hot', 'hsv', 'ice', 'icefire', 'inferno', 'jet',
'magenta', 'magma', 'matter', 'mint', 'mrybm', 'mygbm', 'oranges',
'orrd', 'oryel', 'oxy', 'peach', 'phase', 'picnic', 'pinkyl',
'piyg', 'plasma', 'plotly3', 'portland', 'prgn', 'pubu', 'pubugn',
'puor', 'purd', 'purp', 'purples', 'purpor', 'rainbow', 'rdbu',
'rdgy', 'rdpu', 'rdylbu', 'rdylgn', 'redor', 'reds', 'solar',
'spectral', 'speed', 'sunset', 'sunsetdark', 'teal', 'tealgrn',
'tealrose', 'tempo', 'temps', 'thermal', 'tropic', 'turbid',
'turbo', 'twilight', 'viridis', 'ylgn', 'ylgnbu', 'ylorbr',
'ylorrd'].
Appending '_r' to a named colorscale reverses it.
Returns
-------
str
"""
return self["colorscale"]
@colorscale.setter
def colorscale(self, val):
self["colorscale"] = val
# customdata
# ----------
@property
def customdata(self):
"""
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note that,
"scatter" traces also appends customdata items in the markers
DOM elements
The 'customdata' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["customdata"]
@customdata.setter
def customdata(self, val):
self["customdata"] = val
# customdatasrc
# -------------
@property
def customdatasrc(self):
"""
Sets the source reference on Chart Studio Cloud for customdata
.
The 'customdatasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["customdatasrc"]
@customdatasrc.setter
def customdatasrc(self, val):
self["customdatasrc"] = val
# histfunc
# --------
@property
def histfunc(self):
"""
Specifies the binning function used for this histogram trace.
If "count", the histogram values are computed by counting the
number of values lying inside each bin. If "sum", "avg", "min",
"max", the histogram values are computed using the sum, the
average, the minimum or the maximum of the values lying inside
each bin respectively.
The 'histfunc' property is an enumeration that may be specified as:
- One of the following enumeration values:
['count', 'sum', 'avg', 'min', 'max']
Returns
-------
Any
"""
return self["histfunc"]
@histfunc.setter
def histfunc(self, val):
self["histfunc"] = val
# histnorm
# --------
@property
def histnorm(self):
"""
Specifies the type of normalization used for this histogram
trace. If "", the span of each bar corresponds to the number of
occurrences (i.e. the number of data points lying inside the
bins). If "percent" / "probability", the span of each bar
corresponds to the percentage / fraction of occurrences with
respect to the total number of sample points (here, the sum of
all bin HEIGHTS equals 100% / 1). If "density", the span of
each bar corresponds to the number of occurrences in a bin
divided by the size of the bin interval (here, the sum of all
bin AREAS equals the total number of sample points). If
*probability density*, the area of each bar corresponds to the
probability that an event will fall into the corresponding bin
(here, the sum of all bin AREAS equals 1).
The 'histnorm' property is an enumeration that may be specified as:
- One of the following enumeration values:
['', 'percent', 'probability', 'density', 'probability
density']
Returns
-------
Any
"""
return self["histnorm"]
@histnorm.setter
def histnorm(self, val):
self["histnorm"] = val
# hoverinfo
# ---------
@property
def hoverinfo(self):
"""
Determines which trace information appear on hover. If `none`
or `skip` are set, no information is displayed upon hovering.
But, if `none` is set, click and hover events are still fired.
The 'hoverinfo' property is a flaglist and may be specified
as a string containing:
- Any combination of ['x', 'y', 'z', 'text', 'name'] joined with '+' characters
(e.g. 'x+y')
OR exactly one of ['all', 'none', 'skip'] (e.g. 'skip')
- A list or array of the above
Returns
-------
Any|numpy.ndarray
"""
return self["hoverinfo"]
@hoverinfo.setter
def hoverinfo(self, val):
self["hoverinfo"] = val
# hoverinfosrc
# ------------
@property
def hoverinfosrc(self):
"""
Sets the source reference on Chart Studio Cloud for hoverinfo
.
The 'hoverinfosrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hoverinfosrc"]
@hoverinfosrc.setter
def hoverinfosrc(self, val):
self["hoverinfosrc"] = val
# hoverlabel
# ----------
@property
def hoverlabel(self):
"""
The 'hoverlabel' property is an instance of Hoverlabel
that may be specified as:
- An instance of :class:`plotly.graph_objs.histogram2d.Hoverlabel`
- A dict of string/value properties that will be passed
to the Hoverlabel constructor
Supported dict properties:
align
Sets the horizontal alignment of the text
content within hover label box. Has an effect
only if the hover label text spans more two or
more lines
alignsrc
Sets the source reference on Chart Studio Cloud
for align .
bgcolor
Sets the background color of the hover labels
for this trace
bgcolorsrc
Sets the source reference on Chart Studio Cloud
for bgcolor .
bordercolor
Sets the border color of the hover labels for
this trace.
bordercolorsrc
Sets the source reference on Chart Studio Cloud
for bordercolor .
font
Sets the font used in hover labels.
namelength
Sets the default length (in number of
characters) of the trace name in the hover
labels for all traces. -1 shows the whole name
regardless of length. 0-3 shows the first 0-3
characters, and an integer >3 will show the
whole name if it is less than that many
characters, but if it is longer, will truncate
to `namelength - 3` characters and add an
ellipsis.
namelengthsrc
Sets the source reference on Chart Studio Cloud
for namelength .
Returns
-------
plotly.graph_objs.histogram2d.Hoverlabel
"""
return self["hoverlabel"]
@hoverlabel.setter
def hoverlabel(self, val):
self["hoverlabel"] = val
# hovertemplate
# -------------
@property
def hovertemplate(self):
"""
Template string used for rendering the information that appear
on hover box. Note that this will override `hoverinfo`.
Variables are inserted using %{variable}, for example "y:
%{y}". Numbers are formatted using d3-format's syntax
%{variable:d3-format}, for example "Price: %{y:$.2f}".
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format for details on
the formatting syntax. Dates are formatted using d3-time-
format's syntax %{variable|d3-time-format}, for example "Day:
%{2019-01-01|%A}". https://github.com/d3/d3-time-
format#locale_format for details on the date formatting syntax.
The variables available in `hovertemplate` are the ones emitted
as event data described at this link
https://plotly.com/javascript/plotlyjs-events/#event-data.
Additionally, every attributes that can be specified per-point
(the ones that are `arrayOk: true`) are available. variable `z`
Anything contained in tag `<extra>` is displayed in the
secondary box, for example "<extra>{fullData.name}</extra>". To
hide the secondary box completely, use an empty tag
`<extra></extra>`.
The 'hovertemplate' property is a string and must be specified as:
- A string
- A number that will be converted to a string
- A tuple, list, or one-dimensional numpy array of the above
Returns
-------
str|numpy.ndarray
"""
return self["hovertemplate"]
@hovertemplate.setter
def hovertemplate(self, val):
self["hovertemplate"] = val
# hovertemplatesrc
# ----------------
@property
def hovertemplatesrc(self):
"""
Sets the source reference on Chart Studio Cloud for
hovertemplate .
The 'hovertemplatesrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["hovertemplatesrc"]
@hovertemplatesrc.setter
def hovertemplatesrc(self, val):
self["hovertemplatesrc"] = val
# ids
# ---
@property
def ids(self):
"""
Assigns id labels to each datum. These ids for object constancy
of data points during animation. Should be an array of strings,
not numbers or any other type.
The 'ids' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["ids"]
@ids.setter
def ids(self, val):
self["ids"] = val
# idssrc
# ------
@property
def idssrc(self):
"""
Sets the source reference on Chart Studio Cloud for ids .
The 'idssrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["idssrc"]
@idssrc.setter
def idssrc(self, val):
self["idssrc"] = val
# legendgroup
# -----------
@property
def legendgroup(self):
"""
Sets the legend group for this trace. Traces part of the same
legend group hide/show at the same time when toggling legend
items.
The 'legendgroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["legendgroup"]
@legendgroup.setter
def legendgroup(self, val):
self["legendgroup"] = val
# marker
# ------
@property
def marker(self):
"""
The 'marker' property is an instance of Marker
that may be specified as:
- An instance of :class:`plotly.graph_objs.histogram2d.Marker`
- A dict of string/value properties that will be passed
to the Marker constructor
Supported dict properties:
color
Sets the aggregation data.
colorsrc
Sets the source reference on Chart Studio Cloud
for color .
Returns
-------
plotly.graph_objs.histogram2d.Marker
"""
return self["marker"]
@marker.setter
def marker(self, val):
self["marker"] = val
# meta
# ----
@property
def meta(self):
"""
Assigns extra meta information associated with this trace that
can be used in various text attributes. Attributes such as
trace `name`, graph, axis and colorbar `title.text`, annotation
`text` `rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta` values in
an attribute in the same trace, simply use `%{meta[i]}` where
`i` is the index or key of the `meta` item in question. To
access trace `meta` in layout attributes, use
`%{data[n[.meta[i]}` where `i` is the index or key of the
`meta` and `n` is the trace index.
The 'meta' property accepts values of any type
Returns
-------
Any|numpy.ndarray
"""
return self["meta"]
@meta.setter
def meta(self, val):
self["meta"] = val
# metasrc
# -------
@property
def metasrc(self):
"""
Sets the source reference on Chart Studio Cloud for meta .
The 'metasrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["metasrc"]
@metasrc.setter
def metasrc(self, val):
self["metasrc"] = val
# name
# ----
@property
def name(self):
"""
Sets the trace name. The trace name appear as the legend item
and on hover.
The 'name' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["name"]
@name.setter
def name(self, val):
self["name"] = val
# nbinsx
# ------
@property
def nbinsx(self):
"""
Specifies the maximum number of desired bins. This value will
be used in an algorithm that will decide the optimal bin size
such that the histogram best visualizes the distribution of the
data. Ignored if `xbins.size` is provided.
The 'nbinsx' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int
"""
return self["nbinsx"]
@nbinsx.setter
def nbinsx(self, val):
self["nbinsx"] = val
# nbinsy
# ------
@property
def nbinsy(self):
"""
Specifies the maximum number of desired bins. This value will
be used in an algorithm that will decide the optimal bin size
such that the histogram best visualizes the distribution of the
data. Ignored if `ybins.size` is provided.
The 'nbinsy' property is a integer and may be specified as:
- An int (or float that will be cast to an int)
in the interval [0, 9223372036854775807]
Returns
-------
int
"""
return self["nbinsy"]
@nbinsy.setter
def nbinsy(self, val):
self["nbinsy"] = val
# opacity
# -------
@property
def opacity(self):
"""
Sets the opacity of the trace.
The 'opacity' property is a number and may be specified as:
- An int or float in the interval [0, 1]
Returns
-------
int|float
"""
return self["opacity"]
@opacity.setter
def opacity(self, val):
self["opacity"] = val
# reversescale
# ------------
@property
def reversescale(self):
"""
Reverses the color mapping if true. If true, `zmin` will
correspond to the last color in the array and `zmax` will
correspond to the first color.
The 'reversescale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["reversescale"]
@reversescale.setter
def reversescale(self, val):
self["reversescale"] = val
# showlegend
# ----------
@property
def showlegend(self):
"""
Determines whether or not an item corresponding to this trace
is shown in the legend.
The 'showlegend' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showlegend"]
@showlegend.setter
def showlegend(self, val):
self["showlegend"] = val
# showscale
# ---------
@property
def showscale(self):
"""
Determines whether or not a colorbar is displayed for this
trace.
The 'showscale' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["showscale"]
@showscale.setter
def showscale(self, val):
self["showscale"] = val
# stream
# ------
@property
def stream(self):
"""
The 'stream' property is an instance of Stream
that may be specified as:
- An instance of :class:`plotly.graph_objs.histogram2d.Stream`
- A dict of string/value properties that will be passed
to the Stream constructor
Supported dict properties:
maxpoints
Sets the maximum number of points to keep on
the plots from an incoming stream. If
`maxpoints` is set to 50, only the newest 50
points will be displayed on the plot.
token
The stream id number links a data trace on a
plot with a stream. See https://chart-
studio.plotly.com/settings for more details.
Returns
-------
plotly.graph_objs.histogram2d.Stream
"""
return self["stream"]
@stream.setter
def stream(self, val):
self["stream"] = val
# uid
# ---
@property
def uid(self):
"""
Assign an id to this trace, Use this to provide object
constancy between traces during animations and transitions.
The 'uid' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["uid"]
@uid.setter
def uid(self, val):
self["uid"] = val
# uirevision
# ----------
@property
def uirevision(self):
"""
Controls persistence of some user-driven changes to the trace:
`constraintrange` in `parcoords` traces, as well as some
`editable: true` modifications such as `name` and
`colorbar.title`. Defaults to `layout.uirevision`. Note that
other user-driven trace attribute changes are controlled by
`layout` attributes: `trace.visible` is controlled by
`layout.legend.uirevision`, `selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)` (accessible
with `config: {editable: true}`) is controlled by
`layout.editrevision`. Trace changes are tracked by `uid`,
which only falls back on trace index if no `uid` is provided.
So if your app can add/remove traces before the end of the
`data` array, such that the same trace has a different index,
you can still preserve user-driven changes if you give each
trace a `uid` that stays with it as it moves.
The 'uirevision' property accepts values of any type
Returns
-------
Any
"""
return self["uirevision"]
@uirevision.setter
def uirevision(self, val):
self["uirevision"] = val
# visible
# -------
@property
def visible(self):
"""
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as a
legend item (provided that the legend itself is visible).
The 'visible' property is an enumeration that may be specified as:
- One of the following enumeration values:
[True, False, 'legendonly']
Returns
-------
Any
"""
return self["visible"]
@visible.setter
def visible(self, val):
self["visible"] = val
# x
# -
@property
def x(self):
"""
Sets the sample data to be binned on the x axis.
The 'x' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["x"]
@x.setter
def x(self, val):
self["x"] = val
# xaxis
# -----
@property
def xaxis(self):
"""
Sets a reference between this trace's x coordinates and a 2D
cartesian x axis. If "x" (the default value), the x coordinates
refer to `layout.xaxis`. If "x2", the x coordinates refer to
`layout.xaxis2`, and so on.
The 'xaxis' property is an identifier of a particular
subplot, of type 'x', that may be specified as the string 'x'
optionally followed by an integer >= 1
(e.g. 'x', 'x1', 'x2', 'x3', etc.)
Returns
-------
str
"""
return self["xaxis"]
@xaxis.setter
def xaxis(self, val):
self["xaxis"] = val
# xbingroup
# ---------
@property
def xbingroup(self):
"""
Set a group of histogram traces which will have compatible
x-bin settings. Using `xbingroup`, histogram2d and
histogram2dcontour traces (on axes of the same axis type) can
have compatible x-bin settings. Note that the same `xbingroup`
value can be used to set (1D) histogram `bingroup`
The 'xbingroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["xbingroup"]
@xbingroup.setter
def xbingroup(self, val):
self["xbingroup"] = val
# xbins
# -----
@property
def xbins(self):
"""
The 'xbins' property is an instance of XBins
that may be specified as:
- An instance of :class:`plotly.graph_objs.histogram2d.XBins`
- A dict of string/value properties that will be passed
to the XBins constructor
Supported dict properties:
end
Sets the end value for the x axis bins. The
last bin may not end exactly at this value, we
increment the bin edge by `size` from `start`
until we reach or exceed `end`. Defaults to the
maximum data value. Like `start`, for dates use
a date string, and for category data `end` is
based on the category serial numbers.
size
Sets the size of each x axis bin. Default
behavior: If `nbinsx` is 0 or omitted, we
choose a nice round bin size such that the
number of bins is about the same as the typical
number of samples in each bin. If `nbinsx` is
provided, we choose a nice round bin size
giving no more than that many bins. For date
data, use milliseconds or "M<n>" for months, as
in `axis.dtick`. For category data, the number
of categories to bin together (always defaults
to 1).
start
Sets the starting value for the x axis bins.
Defaults to the minimum data value, shifted
down if necessary to make nice round values and
to remove ambiguous bin edges. For example, if
most of the data is integers we shift the bin
edges 0.5 down, so a `size` of 5 would have a
default `start` of -0.5, so it is clear that
0-4 are in the first bin, 5-9 in the second,
but continuous data gets a start of 0 and bins
[0,5), [5,10) etc. Dates behave similarly, and
`start` should be a date string. For category
data, `start` is based on the category serial
numbers, and defaults to -0.5.
Returns
-------
plotly.graph_objs.histogram2d.XBins
"""
return self["xbins"]
@xbins.setter
def xbins(self, val):
self["xbins"] = val
# xcalendar
# ---------
@property
def xcalendar(self):
"""
Sets the calendar system to use with `x` date data.
The 'xcalendar' property is an enumeration that may be specified as:
- One of the following enumeration values:
['gregorian', 'chinese', 'coptic', 'discworld',
'ethiopian', 'hebrew', 'islamic', 'julian', 'mayan',
'nanakshahi', 'nepali', 'persian', 'jalali', 'taiwan',
'thai', 'ummalqura']
Returns
-------
Any
"""
return self["xcalendar"]
@xcalendar.setter
def xcalendar(self, val):
self["xcalendar"] = val
# xgap
# ----
@property
def xgap(self):
"""
Sets the horizontal gap (in pixels) between bricks.
The 'xgap' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["xgap"]
@xgap.setter
def xgap(self, val):
self["xgap"] = val
# xsrc
# ----
@property
def xsrc(self):
"""
Sets the source reference on Chart Studio Cloud for x .
The 'xsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["xsrc"]
@xsrc.setter
def xsrc(self, val):
self["xsrc"] = val
# y
# -
@property
def y(self):
"""
Sets the sample data to be binned on the y axis.
The 'y' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["y"]
@y.setter
def y(self, val):
self["y"] = val
# yaxis
# -----
@property
def yaxis(self):
"""
Sets a reference between this trace's y coordinates and a 2D
cartesian y axis. If "y" (the default value), the y coordinates
refer to `layout.yaxis`. If "y2", the y coordinates refer to
`layout.yaxis2`, and so on.
The 'yaxis' property is an identifier of a particular
subplot, of type 'y', that may be specified as the string 'y'
optionally followed by an integer >= 1
(e.g. 'y', 'y1', 'y2', 'y3', etc.)
Returns
-------
str
"""
return self["yaxis"]
@yaxis.setter
def yaxis(self, val):
self["yaxis"] = val
# ybingroup
# ---------
@property
def ybingroup(self):
"""
Set a group of histogram traces which will have compatible
y-bin settings. Using `ybingroup`, histogram2d and
histogram2dcontour traces (on axes of the same axis type) can
have compatible y-bin settings. Note that the same `ybingroup`
value can be used to set (1D) histogram `bingroup`
The 'ybingroup' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["ybingroup"]
@ybingroup.setter
def ybingroup(self, val):
self["ybingroup"] = val
# ybins
# -----
@property
def ybins(self):
"""
The 'ybins' property is an instance of YBins
that may be specified as:
- An instance of :class:`plotly.graph_objs.histogram2d.YBins`
- A dict of string/value properties that will be passed
to the YBins constructor
Supported dict properties:
end
Sets the end value for the y axis bins. The
last bin may not end exactly at this value, we
increment the bin edge by `size` from `start`
until we reach or exceed `end`. Defaults to the
maximum data value. Like `start`, for dates use
a date string, and for category data `end` is
based on the category serial numbers.
size
Sets the size of each y axis bin. Default
behavior: If `nbinsy` is 0 or omitted, we
choose a nice round bin size such that the
number of bins is about the same as the typical
number of samples in each bin. If `nbinsy` is
provided, we choose a nice round bin size
giving no more than that many bins. For date
data, use milliseconds or "M<n>" for months, as
in `axis.dtick`. For category data, the number
of categories to bin together (always defaults
to 1).
start
Sets the starting value for the y axis bins.
Defaults to the minimum data value, shifted
down if necessary to make nice round values and
to remove ambiguous bin edges. For example, if
most of the data is integers we shift the bin
edges 0.5 down, so a `size` of 5 would have a
default `start` of -0.5, so it is clear that
0-4 are in the first bin, 5-9 in the second,
but continuous data gets a start of 0 and bins
[0,5), [5,10) etc. Dates behave similarly, and
`start` should be a date string. For category
data, `start` is based on the category serial
numbers, and defaults to -0.5.
Returns
-------
plotly.graph_objs.histogram2d.YBins
"""
return self["ybins"]
@ybins.setter
def ybins(self, val):
self["ybins"] = val
# ycalendar
# ---------
@property
def ycalendar(self):
"""
Sets the calendar system to use with `y` date data.
The 'ycalendar' property is an enumeration that may be specified as:
- One of the following enumeration values:
['gregorian', 'chinese', 'coptic', 'discworld',
'ethiopian', 'hebrew', 'islamic', 'julian', 'mayan',
'nanakshahi', 'nepali', 'persian', 'jalali', 'taiwan',
'thai', 'ummalqura']
Returns
-------
Any
"""
return self["ycalendar"]
@ycalendar.setter
def ycalendar(self, val):
self["ycalendar"] = val
# ygap
# ----
@property
def ygap(self):
"""
Sets the vertical gap (in pixels) between bricks.
The 'ygap' property is a number and may be specified as:
- An int or float in the interval [0, inf]
Returns
-------
int|float
"""
return self["ygap"]
@ygap.setter
def ygap(self, val):
self["ygap"] = val
# ysrc
# ----
@property
def ysrc(self):
"""
Sets the source reference on Chart Studio Cloud for y .
The 'ysrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["ysrc"]
@ysrc.setter
def ysrc(self, val):
self["ysrc"] = val
# z
# -
@property
def z(self):
"""
Sets the aggregation data.
The 'z' property is an array that may be specified as a tuple,
list, numpy array, or pandas Series
Returns
-------
numpy.ndarray
"""
return self["z"]
@z.setter
def z(self, val):
self["z"] = val
# zauto
# -----
@property
def zauto(self):
"""
Determines whether or not the color domain is computed with
respect to the input data (here in `z`) or the bounds set in
`zmin` and `zmax` Defaults to `false` when `zmin` and `zmax`
are set by the user.
The 'zauto' property must be specified as a bool
(either True, or False)
Returns
-------
bool
"""
return self["zauto"]
@zauto.setter
def zauto(self, val):
self["zauto"] = val
# zhoverformat
# ------------
@property
def zhoverformat(self):
"""
Sets the hover text formatting rule using d3 formatting mini-
languages which are very similar to those in Python. See:
https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format
The 'zhoverformat' property is a string and must be specified as:
- A string
- A number that will be converted to a string
Returns
-------
str
"""
return self["zhoverformat"]
@zhoverformat.setter
def zhoverformat(self, val):
self["zhoverformat"] = val
# zmax
# ----
@property
def zmax(self):
"""
Sets the upper bound of the color domain. Value should have the
same units as in `z` and if set, `zmin` must be set as well.
The 'zmax' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["zmax"]
@zmax.setter
def zmax(self, val):
self["zmax"] = val
# zmid
# ----
@property
def zmid(self):
"""
Sets the mid-point of the color domain by scaling `zmin` and/or
`zmax` to be equidistant to this point. Value should have the
same units as in `z`. Has no effect when `zauto` is `false`.
The 'zmid' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["zmid"]
@zmid.setter
def zmid(self, val):
self["zmid"] = val
# zmin
# ----
@property
def zmin(self):
"""
Sets the lower bound of the color domain. Value should have the
same units as in `z` and if set, `zmax` must be set as well.
The 'zmin' property is a number and may be specified as:
- An int or float
Returns
-------
int|float
"""
return self["zmin"]
@zmin.setter
def zmin(self, val):
self["zmin"] = val
# zsmooth
# -------
@property
def zsmooth(self):
"""
Picks a smoothing algorithm use to smooth `z` data.
The 'zsmooth' property is an enumeration that may be specified as:
- One of the following enumeration values:
['fast', 'best', False]
Returns
-------
Any
"""
return self["zsmooth"]
@zsmooth.setter
def zsmooth(self, val):
self["zsmooth"] = val
# zsrc
# ----
@property
def zsrc(self):
"""
Sets the source reference on Chart Studio Cloud for z .
The 'zsrc' property must be specified as a string or
as a plotly.grid_objs.Column object
Returns
-------
str
"""
return self["zsrc"]
@zsrc.setter
def zsrc(self, val):
self["zsrc"] = val
# type
# ----
@property
def type(self):
return self._props["type"]
# Self properties description
# ---------------------------
@property
def _prop_descriptions(self):
return """\
autobinx
Obsolete: since v1.42 each bin attribute is auto-
determined separately and `autobinx` is not needed.
However, we accept `autobinx: true` or `false` and will
update `xbins` accordingly before deleting `autobinx`
from the trace.
autobiny
Obsolete: since v1.42 each bin attribute is auto-
determined separately and `autobiny` is not needed.
However, we accept `autobiny: true` or `false` and will
update `ybins` accordingly before deleting `autobiny`
from the trace.
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`colorscale`. In case `colorscale` is unspecified or
`autocolorscale` is true, the default palette will be
chosen according to whether numbers in the `color`
array are all positive, all negative or mixed.
bingroup
Set the `xbingroup` and `ybingroup` default prefix For
example, setting a `bingroup` of 1 on two histogram2d
traces will make them their x-bins and y-bins match
separately.
coloraxis
Sets a reference to a shared color axis. References to
these shared color axes are "coloraxis", "coloraxis2",
"coloraxis3", etc. Settings for these shared color axes
are set in the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple color
scales can be linked to the same color axis.
colorbar
:class:`plotly.graph_objects.histogram2d.ColorBar`
instance or dict with compatible properties
colorscale
Sets the colorscale. The colorscale must be an array
containing arrays mapping a normalized value to an rgb,
rgba, hex, hsl, hsv, or named color string. At minimum,
a mapping for the lowest (0) and highest (1) values are
required. For example, `[[0, 'rgb(0,0,255)'], [1,
'rgb(255,0,0)']]`. To control the bounds of the
colorscale in color space, use`zmin` and `zmax`.
Alternatively, `colorscale` may be a palette name
string of the following list: Greys,YlGnBu,Greens,YlOrR
d,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,H
ot,Blackbody,Earth,Electric,Viridis,Cividis.
customdata
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note
that, "scatter" traces also appends customdata items in
the markers DOM elements
customdatasrc
Sets the source reference on Chart Studio Cloud for
customdata .
histfunc
Specifies the binning function used for this histogram
trace. If "count", the histogram values are computed by
counting the number of values lying inside each bin. If
"sum", "avg", "min", "max", the histogram values are
computed using the sum, the average, the minimum or the
maximum of the values lying inside each bin
respectively.
histnorm
Specifies the type of normalization used for this
histogram trace. If "", the span of each bar
corresponds to the number of occurrences (i.e. the
number of data points lying inside the bins). If
"percent" / "probability", the span of each bar
corresponds to the percentage / fraction of occurrences
with respect to the total number of sample points
(here, the sum of all bin HEIGHTS equals 100% / 1). If
"density", the span of each bar corresponds to the
number of occurrences in a bin divided by the size of
the bin interval (here, the sum of all bin AREAS equals
the total number of sample points). If *probability
density*, the area of each bar corresponds to the
probability that an event will fall into the
corresponding bin (here, the sum of all bin AREAS
equals 1).
hoverinfo
Determines which trace information appear on hover. If
`none` or `skip` are set, no information is displayed
upon hovering. But, if `none` is set, click and hover
events are still fired.
hoverinfosrc
Sets the source reference on Chart Studio Cloud for
hoverinfo .
hoverlabel
:class:`plotly.graph_objects.histogram2d.Hoverlabel`
instance or dict with compatible properties
hovertemplate
Template string used for rendering the information that
appear on hover box. Note that this will override
`hoverinfo`. Variables are inserted using %{variable},
for example "y: %{y}". Numbers are formatted using
d3-format's syntax %{variable:d3-format}, for example
"Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format for
details on the formatting syntax. Dates are formatted
using d3-time-format's syntax %{variable|d3-time-
format}, for example "Day: %{2019-01-01|%A}".
https://github.com/d3/d3-time-format#locale_format for
details on the date formatting syntax. The variables
available in `hovertemplate` are the ones emitted as
event data described at this link
https://plotly.com/javascript/plotlyjs-events/#event-
data. Additionally, every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. variable `z` Anything contained in tag
`<extra>` is displayed in the secondary box, for
example "<extra>{fullData.name}</extra>". To hide the
secondary box completely, use an empty tag
`<extra></extra>`.
hovertemplatesrc
Sets the source reference on Chart Studio Cloud for
hovertemplate .
ids
Assigns id labels to each datum. These ids for object
constancy of data points during animation. Should be an
array of strings, not numbers or any other type.
idssrc
Sets the source reference on Chart Studio Cloud for
ids .
legendgroup
Sets the legend group for this trace. Traces part of
the same legend group hide/show at the same time when
toggling legend items.
marker
:class:`plotly.graph_objects.histogram2d.Marker`
instance or dict with compatible properties
meta
Assigns extra meta information associated with this
trace that can be used in various text attributes.
Attributes such as trace `name`, graph, axis and
colorbar `title.text`, annotation `text`
`rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta`
values in an attribute in the same trace, simply use
`%{meta[i]}` where `i` is the index or key of the
`meta` item in question. To access trace `meta` in
layout attributes, use `%{data[n[.meta[i]}` where `i`
is the index or key of the `meta` and `n` is the trace
index.
metasrc
Sets the source reference on Chart Studio Cloud for
meta .
name
Sets the trace name. The trace name appear as the
legend item and on hover.
nbinsx
Specifies the maximum number of desired bins. This
value will be used in an algorithm that will decide the
optimal bin size such that the histogram best
visualizes the distribution of the data. Ignored if
`xbins.size` is provided.
nbinsy
Specifies the maximum number of desired bins. This
value will be used in an algorithm that will decide the
optimal bin size such that the histogram best
visualizes the distribution of the data. Ignored if
`ybins.size` is provided.
opacity
Sets the opacity of the trace.
reversescale
Reverses the color mapping if true. If true, `zmin`
will correspond to the last color in the array and
`zmax` will correspond to the first color.
showlegend
Determines whether or not an item corresponding to this
trace is shown in the legend.
showscale
Determines whether or not a colorbar is displayed for
this trace.
stream
:class:`plotly.graph_objects.histogram2d.Stream`
instance or dict with compatible properties
uid
Assign an id to this trace, Use this to provide object
constancy between traces during animations and
transitions.
uirevision
Controls persistence of some user-driven changes to the
trace: `constraintrange` in `parcoords` traces, as well
as some `editable: true` modifications such as `name`
and `colorbar.title`. Defaults to `layout.uirevision`.
Note that other user-driven trace attribute changes are
controlled by `layout` attributes: `trace.visible` is
controlled by `layout.legend.uirevision`,
`selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)`
(accessible with `config: {editable: true}`) is
controlled by `layout.editrevision`. Trace changes are
tracked by `uid`, which only falls back on trace index
if no `uid` is provided. So if your app can add/remove
traces before the end of the `data` array, such that
the same trace has a different index, you can still
preserve user-driven changes if you give each trace a
`uid` that stays with it as it moves.
visible
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as
a legend item (provided that the legend itself is
visible).
x
Sets the sample data to be binned on the x axis.
xaxis
Sets a reference between this trace's x coordinates and
a 2D cartesian x axis. If "x" (the default value), the
x coordinates refer to `layout.xaxis`. If "x2", the x
coordinates refer to `layout.xaxis2`, and so on.
xbingroup
Set a group of histogram traces which will have
compatible x-bin settings. Using `xbingroup`,
histogram2d and histogram2dcontour traces (on axes of
the same axis type) can have compatible x-bin settings.
Note that the same `xbingroup` value can be used to set
(1D) histogram `bingroup`
xbins
:class:`plotly.graph_objects.histogram2d.XBins`
instance or dict with compatible properties
xcalendar
Sets the calendar system to use with `x` date data.
xgap
Sets the horizontal gap (in pixels) between bricks.
xsrc
Sets the source reference on Chart Studio Cloud for x
.
y
Sets the sample data to be binned on the y axis.
yaxis
Sets a reference between this trace's y coordinates and
a 2D cartesian y axis. If "y" (the default value), the
y coordinates refer to `layout.yaxis`. If "y2", the y
coordinates refer to `layout.yaxis2`, and so on.
ybingroup
Set a group of histogram traces which will have
compatible y-bin settings. Using `ybingroup`,
histogram2d and histogram2dcontour traces (on axes of
the same axis type) can have compatible y-bin settings.
Note that the same `ybingroup` value can be used to set
(1D) histogram `bingroup`
ybins
:class:`plotly.graph_objects.histogram2d.YBins`
instance or dict with compatible properties
ycalendar
Sets the calendar system to use with `y` date data.
ygap
Sets the vertical gap (in pixels) between bricks.
ysrc
Sets the source reference on Chart Studio Cloud for y
.
z
Sets the aggregation data.
zauto
Determines whether or not the color domain is computed
with respect to the input data (here in `z`) or the
bounds set in `zmin` and `zmax` Defaults to `false`
when `zmin` and `zmax` are set by the user.
zhoverformat
Sets the hover text formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. See: https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format
zmax
Sets the upper bound of the color domain. Value should
have the same units as in `z` and if set, `zmin` must
be set as well.
zmid
Sets the mid-point of the color domain by scaling
`zmin` and/or `zmax` to be equidistant to this point.
Value should have the same units as in `z`. Has no
effect when `zauto` is `false`.
zmin
Sets the lower bound of the color domain. Value should
have the same units as in `z` and if set, `zmax` must
be set as well.
zsmooth
Picks a smoothing algorithm use to smooth `z` data.
zsrc
Sets the source reference on Chart Studio Cloud for z
.
"""
def __init__(
self,
arg=None,
autobinx=None,
autobiny=None,
autocolorscale=None,
bingroup=None,
coloraxis=None,
colorbar=None,
colorscale=None,
customdata=None,
customdatasrc=None,
histfunc=None,
histnorm=None,
hoverinfo=None,
hoverinfosrc=None,
hoverlabel=None,
hovertemplate=None,
hovertemplatesrc=None,
ids=None,
idssrc=None,
legendgroup=None,
marker=None,
meta=None,
metasrc=None,
name=None,
nbinsx=None,
nbinsy=None,
opacity=None,
reversescale=None,
showlegend=None,
showscale=None,
stream=None,
uid=None,
uirevision=None,
visible=None,
x=None,
xaxis=None,
xbingroup=None,
xbins=None,
xcalendar=None,
xgap=None,
xsrc=None,
y=None,
yaxis=None,
ybingroup=None,
ybins=None,
ycalendar=None,
ygap=None,
ysrc=None,
z=None,
zauto=None,
zhoverformat=None,
zmax=None,
zmid=None,
zmin=None,
zsmooth=None,
zsrc=None,
**kwargs
):
"""
Construct a new Histogram2d object
The sample data from which statistics are computed is set in
`x` and `y` (where `x` and `y` represent marginal
distributions, binning is set in `xbins` and `ybins` in this
case) or `z` (where `z` represent the 2D distribution and
binning set, binning is set by `x` and `y` in this case). The
resulting distribution is visualized as a heatmap.
Parameters
----------
arg
dict of properties compatible with this constructor or
an instance of :class:`plotly.graph_objs.Histogram2d`
autobinx
Obsolete: since v1.42 each bin attribute is auto-
determined separately and `autobinx` is not needed.
However, we accept `autobinx: true` or `false` and will
update `xbins` accordingly before deleting `autobinx`
from the trace.
autobiny
Obsolete: since v1.42 each bin attribute is auto-
determined separately and `autobiny` is not needed.
However, we accept `autobiny: true` or `false` and will
update `ybins` accordingly before deleting `autobiny`
from the trace.
autocolorscale
Determines whether the colorscale is a default palette
(`autocolorscale: true`) or the palette determined by
`colorscale`. In case `colorscale` is unspecified or
`autocolorscale` is true, the default palette will be
chosen according to whether numbers in the `color`
array are all positive, all negative or mixed.
bingroup
Set the `xbingroup` and `ybingroup` default prefix For
example, setting a `bingroup` of 1 on two histogram2d
traces will make them their x-bins and y-bins match
separately.
coloraxis
Sets a reference to a shared color axis. References to
these shared color axes are "coloraxis", "coloraxis2",
"coloraxis3", etc. Settings for these shared color axes
are set in the layout, under `layout.coloraxis`,
`layout.coloraxis2`, etc. Note that multiple color
scales can be linked to the same color axis.
colorbar
:class:`plotly.graph_objects.histogram2d.ColorBar`
instance or dict with compatible properties
colorscale
Sets the colorscale. The colorscale must be an array
containing arrays mapping a normalized value to an rgb,
rgba, hex, hsl, hsv, or named color string. At minimum,
a mapping for the lowest (0) and highest (1) values are
required. For example, `[[0, 'rgb(0,0,255)'], [1,
'rgb(255,0,0)']]`. To control the bounds of the
colorscale in color space, use`zmin` and `zmax`.
Alternatively, `colorscale` may be a palette name
string of the following list: Greys,YlGnBu,Greens,YlOrR
d,Bluered,RdBu,Reds,Blues,Picnic,Rainbow,Portland,Jet,H
ot,Blackbody,Earth,Electric,Viridis,Cividis.
customdata
Assigns extra data each datum. This may be useful when
listening to hover, click and selection events. Note
that, "scatter" traces also appends customdata items in
the markers DOM elements
customdatasrc
Sets the source reference on Chart Studio Cloud for
customdata .
histfunc
Specifies the binning function used for this histogram
trace. If "count", the histogram values are computed by
counting the number of values lying inside each bin. If
"sum", "avg", "min", "max", the histogram values are
computed using the sum, the average, the minimum or the
maximum of the values lying inside each bin
respectively.
histnorm
Specifies the type of normalization used for this
histogram trace. If "", the span of each bar
corresponds to the number of occurrences (i.e. the
number of data points lying inside the bins). If
"percent" / "probability", the span of each bar
corresponds to the percentage / fraction of occurrences
with respect to the total number of sample points
(here, the sum of all bin HEIGHTS equals 100% / 1). If
"density", the span of each bar corresponds to the
number of occurrences in a bin divided by the size of
the bin interval (here, the sum of all bin AREAS equals
the total number of sample points). If *probability
density*, the area of each bar corresponds to the
probability that an event will fall into the
corresponding bin (here, the sum of all bin AREAS
equals 1).
hoverinfo
Determines which trace information appear on hover. If
`none` or `skip` are set, no information is displayed
upon hovering. But, if `none` is set, click and hover
events are still fired.
hoverinfosrc
Sets the source reference on Chart Studio Cloud for
hoverinfo .
hoverlabel
:class:`plotly.graph_objects.histogram2d.Hoverlabel`
instance or dict with compatible properties
hovertemplate
Template string used for rendering the information that
appear on hover box. Note that this will override
`hoverinfo`. Variables are inserted using %{variable},
for example "y: %{y}". Numbers are formatted using
d3-format's syntax %{variable:d3-format}, for example
"Price: %{y:$.2f}". https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format for
details on the formatting syntax. Dates are formatted
using d3-time-format's syntax %{variable|d3-time-
format}, for example "Day: %{2019-01-01|%A}".
https://github.com/d3/d3-time-format#locale_format for
details on the date formatting syntax. The variables
available in `hovertemplate` are the ones emitted as
event data described at this link
https://plotly.com/javascript/plotlyjs-events/#event-
data. Additionally, every attributes that can be
specified per-point (the ones that are `arrayOk: true`)
are available. variable `z` Anything contained in tag
`<extra>` is displayed in the secondary box, for
example "<extra>{fullData.name}</extra>". To hide the
secondary box completely, use an empty tag
`<extra></extra>`.
hovertemplatesrc
Sets the source reference on Chart Studio Cloud for
hovertemplate .
ids
Assigns id labels to each datum. These ids for object
constancy of data points during animation. Should be an
array of strings, not numbers or any other type.
idssrc
Sets the source reference on Chart Studio Cloud for
ids .
legendgroup
Sets the legend group for this trace. Traces part of
the same legend group hide/show at the same time when
toggling legend items.
marker
:class:`plotly.graph_objects.histogram2d.Marker`
instance or dict with compatible properties
meta
Assigns extra meta information associated with this
trace that can be used in various text attributes.
Attributes such as trace `name`, graph, axis and
colorbar `title.text`, annotation `text`
`rangeselector`, `updatemenues` and `sliders` `label`
text all support `meta`. To access the trace `meta`
values in an attribute in the same trace, simply use
`%{meta[i]}` where `i` is the index or key of the
`meta` item in question. To access trace `meta` in
layout attributes, use `%{data[n[.meta[i]}` where `i`
is the index or key of the `meta` and `n` is the trace
index.
metasrc
Sets the source reference on Chart Studio Cloud for
meta .
name
Sets the trace name. The trace name appear as the
legend item and on hover.
nbinsx
Specifies the maximum number of desired bins. This
value will be used in an algorithm that will decide the
optimal bin size such that the histogram best
visualizes the distribution of the data. Ignored if
`xbins.size` is provided.
nbinsy
Specifies the maximum number of desired bins. This
value will be used in an algorithm that will decide the
optimal bin size such that the histogram best
visualizes the distribution of the data. Ignored if
`ybins.size` is provided.
opacity
Sets the opacity of the trace.
reversescale
Reverses the color mapping if true. If true, `zmin`
will correspond to the last color in the array and
`zmax` will correspond to the first color.
showlegend
Determines whether or not an item corresponding to this
trace is shown in the legend.
showscale
Determines whether or not a colorbar is displayed for
this trace.
stream
:class:`plotly.graph_objects.histogram2d.Stream`
instance or dict with compatible properties
uid
Assign an id to this trace, Use this to provide object
constancy between traces during animations and
transitions.
uirevision
Controls persistence of some user-driven changes to the
trace: `constraintrange` in `parcoords` traces, as well
as some `editable: true` modifications such as `name`
and `colorbar.title`. Defaults to `layout.uirevision`.
Note that other user-driven trace attribute changes are
controlled by `layout` attributes: `trace.visible` is
controlled by `layout.legend.uirevision`,
`selectedpoints` is controlled by
`layout.selectionrevision`, and `colorbar.(x|y)`
(accessible with `config: {editable: true}`) is
controlled by `layout.editrevision`. Trace changes are
tracked by `uid`, which only falls back on trace index
if no `uid` is provided. So if your app can add/remove
traces before the end of the `data` array, such that
the same trace has a different index, you can still
preserve user-driven changes if you give each trace a
`uid` that stays with it as it moves.
visible
Determines whether or not this trace is visible. If
"legendonly", the trace is not drawn, but can appear as
a legend item (provided that the legend itself is
visible).
x
Sets the sample data to be binned on the x axis.
xaxis
Sets a reference between this trace's x coordinates and
a 2D cartesian x axis. If "x" (the default value), the
x coordinates refer to `layout.xaxis`. If "x2", the x
coordinates refer to `layout.xaxis2`, and so on.
xbingroup
Set a group of histogram traces which will have
compatible x-bin settings. Using `xbingroup`,
histogram2d and histogram2dcontour traces (on axes of
the same axis type) can have compatible x-bin settings.
Note that the same `xbingroup` value can be used to set
(1D) histogram `bingroup`
xbins
:class:`plotly.graph_objects.histogram2d.XBins`
instance or dict with compatible properties
xcalendar
Sets the calendar system to use with `x` date data.
xgap
Sets the horizontal gap (in pixels) between bricks.
xsrc
Sets the source reference on Chart Studio Cloud for x
.
y
Sets the sample data to be binned on the y axis.
yaxis
Sets a reference between this trace's y coordinates and
a 2D cartesian y axis. If "y" (the default value), the
y coordinates refer to `layout.yaxis`. If "y2", the y
coordinates refer to `layout.yaxis2`, and so on.
ybingroup
Set a group of histogram traces which will have
compatible y-bin settings. Using `ybingroup`,
histogram2d and histogram2dcontour traces (on axes of
the same axis type) can have compatible y-bin settings.
Note that the same `ybingroup` value can be used to set
(1D) histogram `bingroup`
ybins
:class:`plotly.graph_objects.histogram2d.YBins`
instance or dict with compatible properties
ycalendar
Sets the calendar system to use with `y` date data.
ygap
Sets the vertical gap (in pixels) between bricks.
ysrc
Sets the source reference on Chart Studio Cloud for y
.
z
Sets the aggregation data.
zauto
Determines whether or not the color domain is computed
with respect to the input data (here in `z`) or the
bounds set in `zmin` and `zmax` Defaults to `false`
when `zmin` and `zmax` are set by the user.
zhoverformat
Sets the hover text formatting rule using d3 formatting
mini-languages which are very similar to those in
Python. See: https://github.com/d3/d3-3.x-api-
reference/blob/master/Formatting.md#d3_format
zmax
Sets the upper bound of the color domain. Value should
have the same units as in `z` and if set, `zmin` must
be set as well.
zmid
Sets the mid-point of the color domain by scaling
`zmin` and/or `zmax` to be equidistant to this point.
Value should have the same units as in `z`. Has no
effect when `zauto` is `false`.
zmin
Sets the lower bound of the color domain. Value should
have the same units as in `z` and if set, `zmax` must
be set as well.
zsmooth
Picks a smoothing algorithm use to smooth `z` data.
zsrc
Sets the source reference on Chart Studio Cloud for z
.
Returns
-------
Histogram2d
"""
super(Histogram2d, self).__init__("histogram2d")
if "_parent" in kwargs:
self._parent = kwargs["_parent"]
return
# Validate arg
# ------------
if arg is None:
arg = {}
elif isinstance(arg, self.__class__):
arg = arg.to_plotly_json()
elif isinstance(arg, dict):
arg = _copy.copy(arg)
else:
raise ValueError(
"""\
The first argument to the plotly.graph_objs.Histogram2d
constructor must be a dict or
an instance of :class:`plotly.graph_objs.Histogram2d`"""
)
# Handle skip_invalid
# -------------------
self._skip_invalid = kwargs.pop("skip_invalid", False)
self._validate = kwargs.pop("_validate", True)
# Populate data dict with properties
# ----------------------------------
_v = arg.pop("autobinx", None)
_v = autobinx if autobinx is not None else _v
if _v is not None:
self["autobinx"] = _v
_v = arg.pop("autobiny", None)
_v = autobiny if autobiny is not None else _v
if _v is not None:
self["autobiny"] = _v
_v = arg.pop("autocolorscale", None)
_v = autocolorscale if autocolorscale is not None else _v
if _v is not None:
self["autocolorscale"] = _v
_v = arg.pop("bingroup", None)
_v = bingroup if bingroup is not None else _v
if _v is not None:
self["bingroup"] = _v
_v = arg.pop("coloraxis", None)
_v = coloraxis if coloraxis is not None else _v
if _v is not None:
self["coloraxis"] = _v
_v = arg.pop("colorbar", None)
_v = colorbar if colorbar is not None else _v
if _v is not None:
self["colorbar"] = _v
_v = arg.pop("colorscale", None)
_v = colorscale if colorscale is not None else _v
if _v is not None:
self["colorscale"] = _v
_v = arg.pop("customdata", None)
_v = customdata if customdata is not None else _v
if _v is not None:
self["customdata"] = _v
_v = arg.pop("customdatasrc", None)
_v = customdatasrc if customdatasrc is not None else _v
if _v is not None:
self["customdatasrc"] = _v
_v = arg.pop("histfunc", None)
_v = histfunc if histfunc is not None else _v
if _v is not None:
self["histfunc"] = _v
_v = arg.pop("histnorm", None)
_v = histnorm if histnorm is not None else _v
if _v is not None:
self["histnorm"] = _v
_v = arg.pop("hoverinfo", None)
_v = hoverinfo if hoverinfo is not None else _v
if _v is not None:
self["hoverinfo"] = _v
_v = arg.pop("hoverinfosrc", None)
_v = hoverinfosrc if hoverinfosrc is not None else _v
if _v is not None:
self["hoverinfosrc"] = _v
_v = arg.pop("hoverlabel", None)
_v = hoverlabel if hoverlabel is not None else _v
if _v is not None:
self["hoverlabel"] = _v
_v = arg.pop("hovertemplate", None)
_v = hovertemplate if hovertemplate is not None else _v
if _v is not None:
self["hovertemplate"] = _v
_v = arg.pop("hovertemplatesrc", None)
_v = hovertemplatesrc if hovertemplatesrc is not None else _v
if _v is not None:
self["hovertemplatesrc"] = _v
_v = arg.pop("ids", None)
_v = ids if ids is not None else _v
if _v is not None:
self["ids"] = _v
_v = arg.pop("idssrc", None)
_v = idssrc if idssrc is not None else _v
if _v is not None:
self["idssrc"] = _v
_v = arg.pop("legendgroup", None)
_v = legendgroup if legendgroup is not None else _v
if _v is not None:
self["legendgroup"] = _v
_v = arg.pop("marker", None)
_v = marker if marker is not None else _v
if _v is not None:
self["marker"] = _v
_v = arg.pop("meta", None)
_v = meta if meta is not None else _v
if _v is not None:
self["meta"] = _v
_v = arg.pop("metasrc", None)
_v = metasrc if metasrc is not None else _v
if _v is not None:
self["metasrc"] = _v
_v = arg.pop("name", None)
_v = name if name is not None else _v
if _v is not None:
self["name"] = _v
_v = arg.pop("nbinsx", None)
_v = nbinsx if nbinsx is not None else _v
if _v is not None:
self["nbinsx"] = _v
_v = arg.pop("nbinsy", None)
_v = nbinsy if nbinsy is not None else _v
if _v is not None:
self["nbinsy"] = _v
_v = arg.pop("opacity", None)
_v = opacity if opacity is not None else _v
if _v is not None:
self["opacity"] = _v
_v = arg.pop("reversescale", None)
_v = reversescale if reversescale is not None else _v
if _v is not None:
self["reversescale"] = _v
_v = arg.pop("showlegend", None)
_v = showlegend if showlegend is not None else _v
if _v is not None:
self["showlegend"] = _v
_v = arg.pop("showscale", None)
_v = showscale if showscale is not None else _v
if _v is not None:
self["showscale"] = _v
_v = arg.pop("stream", None)
_v = stream if stream is not None else _v
if _v is not None:
self["stream"] = _v
_v = arg.pop("uid", None)
_v = uid if uid is not None else _v
if _v is not None:
self["uid"] = _v
_v = arg.pop("uirevision", None)
_v = uirevision if uirevision is not None else _v
if _v is not None:
self["uirevision"] = _v
_v = arg.pop("visible", None)
_v = visible if visible is not None else _v
if _v is not None:
self["visible"] = _v
_v = arg.pop("x", None)
_v = x if x is not None else _v
if _v is not None:
self["x"] = _v
_v = arg.pop("xaxis", None)
_v = xaxis if xaxis is not None else _v
if _v is not None:
self["xaxis"] = _v
_v = arg.pop("xbingroup", None)
_v = xbingroup if xbingroup is not None else _v
if _v is not None:
self["xbingroup"] = _v
_v = arg.pop("xbins", None)
_v = xbins if xbins is not None else _v
if _v is not None:
self["xbins"] = _v
_v = arg.pop("xcalendar", None)
_v = xcalendar if xcalendar is not None else _v
if _v is not None:
self["xcalendar"] = _v
_v = arg.pop("xgap", None)
_v = xgap if xgap is not None else _v
if _v is not None:
self["xgap"] = _v
_v = arg.pop("xsrc", None)
_v = xsrc if xsrc is not None else _v
if _v is not None:
self["xsrc"] = _v
_v = arg.pop("y", None)
_v = y if y is not None else _v
if _v is not None:
self["y"] = _v
_v = arg.pop("yaxis", None)
_v = yaxis if yaxis is not None else _v
if _v is not None:
self["yaxis"] = _v
_v = arg.pop("ybingroup", None)
_v = ybingroup if ybingroup is not None else _v
if _v is not None:
self["ybingroup"] = _v
_v = arg.pop("ybins", None)
_v = ybins if ybins is not None else _v
if _v is not None:
self["ybins"] = _v
_v = arg.pop("ycalendar", None)
_v = ycalendar if ycalendar is not None else _v
if _v is not None:
self["ycalendar"] = _v
_v = arg.pop("ygap", None)
_v = ygap if ygap is not None else _v
if _v is not None:
self["ygap"] = _v
_v = arg.pop("ysrc", None)
_v = ysrc if ysrc is not None else _v
if _v is not None:
self["ysrc"] = _v
_v = arg.pop("z", None)
_v = z if z is not None else _v
if _v is not None:
self["z"] = _v
_v = arg.pop("zauto", None)
_v = zauto if zauto is not None else _v
if _v is not None:
self["zauto"] = _v
_v = arg.pop("zhoverformat", None)
_v = zhoverformat if zhoverformat is not None else _v
if _v is not None:
self["zhoverformat"] = _v
_v = arg.pop("zmax", None)
_v = zmax if zmax is not None else _v
if _v is not None:
self["zmax"] = _v
_v = arg.pop("zmid", None)
_v = zmid if zmid is not None else _v
if _v is not None:
self["zmid"] = _v
_v = arg.pop("zmin", None)
_v = zmin if zmin is not None else _v
if _v is not None:
self["zmin"] = _v
_v = arg.pop("zsmooth", None)
_v = zsmooth if zsmooth is not None else _v
if _v is not None:
self["zsmooth"] = _v
_v = arg.pop("zsrc", None)
_v = zsrc if zsrc is not None else _v
if _v is not None:
self["zsrc"] = _v
# Read-only literals
# ------------------
self._props["type"] = "histogram2d"
arg.pop("type", None)
# Process unknown kwargs
# ----------------------
self._process_kwargs(**dict(arg, **kwargs))
# Reset skip_invalid
# ------------------
self._skip_invalid = False
|
catboostREPO_NAMEcatboostPATH_START.@catboost_extracted@catboost-master@contrib@python@plotly@py2@plotly@graph_objs@_histogram2d.py@.PATH_END.py
|
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