def ex15(exclude=sc.array([1,2,3,4]),plotfilename='ex15.png',
bovyprintargs={}):
"""ex15: solve exercise 15
Input:
exclude - ID numbers to exclude from the analysis
plotfilename - filename for the output plot
Output:
plot
History:
2010-05-07 - Written - Bovy (NYU)
"""
#Read the data
data= read_data('data_allerr.dat',allerr=True)
ndata= len(data)
nsample= ndata- len(exclude)
#Put the dat in the appropriate arrays and matrices
Y= sc.zeros(nsample)
X= sc.zeros(nsample)
Z= sc.zeros((nsample,2))
jj= 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
pass
else:
Y[jj]= data[ii][1][1]
X[jj]= data[ii][1][0]
Z[jj,0]= X[jj]
Z[jj,1]= Y[jj]
jj= jj+1
#Now compute the PCA solution
Zm= sc.mean(Z,axis=0)
Q= sc.cov(Z.T)
eigs= linalg.eig(Q)
maxindx= sc.argmax(eigs[0])
V= eigs[1][maxindx]
V= V/linalg.norm(V)
m= sc.sqrt(1/V[0]**2.-1)
bestfit= sc.array([-m*Zm[0]+Zm[1],m])
#Plot result
plot.bovy_print(**bovyprintargs)
xrange=[0,300]
yrange=[0,700]
plot.bovy_plot(sc.array(xrange),bestfit[1]*sc.array(xrange)+bestfit[0],
'k--',xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',zorder=2)
plot.bovy_plot(X,Y,marker='o',color='k',linestyle='None',
zorder=0,overplot=True)
plot.bovy_text(r'$y = %4.2f \,x %4.0f' % (bestfit[1], bestfit[0])+r'$',
bottom_right=True)
plot.bovy_end_print(plotfilename)
def exNew(exclude=sc.array([1,2,3,4]),
plotfilename='exNew.png',nburn=20000,nsamples=200000,
parsigma=[5,.075,.01,1,.1],dsigma=1.):
"""exMix1: solve the new exercise using MCMC sampling
Input:
exclude - ID numbers to exclude from the analysis (can be None)
plotfilename - filename for the output plot
nburn - number of burn-in samples
nsamples - number of samples to take after burn-in
parsigma - proposal distribution width (Gaussian)
dsigma - divide uncertainties by this amount
Output:
plot
History:
2010-04-28 - Written - Bovy (NYU)
"""
sc.random.seed(1) #In the interest of reproducibility (if that's a word)
#Read the data
data= read_data('data_yerr.dat')
ndata= len(data)
if not exclude == None:
nsample= ndata- len(exclude)
else:
nsample= ndata
#First find the chi-squared solution, which we will use as an
#initial guess
#Put the data in the appropriate arrays and matrices
Y= sc.zeros(nsample)
X= sc.zeros(nsample)
A= sc.ones((nsample,2))
C= sc.zeros((nsample,nsample))
yerr= sc.zeros(nsample)
jj= 0
for ii in range(ndata):
if not exclude == None and sc.any(exclude == data[ii][0]):
pass
else:
Y[jj]= data[ii][1][1]
X[jj]= data[ii][1][0]
A[jj,1]= data[ii][1][0]
C[jj,jj]= data[ii][2]**2./dsigma**2.
yerr[jj]= data[ii][2]/dsigma
jj= jj+1
#Now compute the best fit and the uncertainties
bestfit= sc.dot(linalg.inv(C),Y.T)
bestfit= sc.dot(A.T,bestfit)
bestfitvar= sc.dot(linalg.inv(C),A)
bestfitvar= sc.dot(A.T,bestfitvar)
bestfitvar= linalg.inv(bestfitvar)
bestfit= sc.dot(bestfitvar,bestfit)
initialguess= sc.array([bestfit[0],bestfit[1],0.,sc.mean(Y),m.log(sc.var(Y))])#(m,b,Pb,Yb,Vb)
#With this initial guess start off the sampling procedure
initialX= objective(initialguess,X,Y,yerr)
currentX= initialX
bestX= initialX
bestfit= initialguess
currentguess= initialguess
naccept= 0
samples= []
samples.append(currentguess)
for jj in range(nburn+nsamples):
#Draw a sample from the proposal distribution
newsample= sc.zeros(5)
newsample[0]= currentguess[0]+stats.norm.rvs()*parsigma[0]
newsample[1]= currentguess[1]+stats.norm.rvs()*parsigma[1]
#newsample[2]= stats.uniform.rvs()
newsample[2]= currentguess[2]+stats.norm.rvs()*parsigma[2]
newsample[3]= currentguess[3]+stats.norm.rvs()*parsigma[3]
newsample[4]= currentguess[4]+stats.norm.rvs()*parsigma[4]
#Calculate the objective function for the newsample
newX= objective(newsample,X,Y,yerr)
#Accept or reject
#Reject with the appropriate probability
u= stats.uniform.rvs()
if u < m.exp(newX-currentX):
#Accept
currentX= newX
currentguess= newsample
naccept= naccept+1
if currentX > bestX:
bestfit= currentguess
bestX= currentX
samples.append(currentguess)
if double(naccept)/(nburn+nsamples) < .2 or double(naccept)/(nburn+nsamples) > .6:
print "Acceptance ratio was "+str(double(naccept)/(nburn+nsamples))
samples= sc.array(samples).T[:,nburn:-1]
print "Best-fit, overall"
print bestfit, sc.mean(samples[2,:]), sc.median(samples[2,:])
histmb,edges= sc.histogramdd(samples.T[:,0:2],bins=round(sc.sqrt(nsamples)/5.))
indxi= sc.argmax(sc.amax(histmb,axis=1))
indxj= sc.argmax(sc.amax(histmb,axis=0))
print "Best-fit, marginalized"
print edges[0][indxi-1], edges[1][indxj-1]
print edges[0][indxi], edges[1][indxj]
print edges[0][indxi+1], edges[1][indxj+1]
#2D histogram
plot.bovy_print()
levels= special.erf(0.5*sc.arange(1,4))
#xrange=[edges[0][0],edges[0][-1]]
#yrange=[edges[1][0],edges[1][-1]]
xrange=[-120,120]
yrange=[1.5,3.2]
histmb,edges= sc.histogramdd(samples.T[:,0:2],
range=[[-120,120],[1.5,3.2]],
bins=(round(sc.sqrt(nsamples)/5.)/(edges[0][-1]-edges[0][0])*(xrange[1]-xrange[0]),
round(sc.sqrt(nsamples)/5.)/(edges[1][-1]-edges[1][0])*(yrange[1]-yrange[0])))
aspect=(xrange[1]-xrange[0])/(yrange[1]-yrange[0])
plot.bovy_dens2d(histmb.T,origin='lower',cmap='gist_yarg',
contours=True,cntrmass=True,
xrange=xrange,yrange=yrange,
levels=levels,
aspect=aspect,
xlabel=r'$b$',ylabel=r'$m$')
if dsigma == 1.:
plot.bovy_text(r'$\mathrm{using\ correct\ data\ uncertainties}$',
top_right=True)
else:
plot.bovy_text(r'$\mathrm{using\ data\ uncertainties\ /\ 2}$',
top_right=True)
if dsigma == 1.:
plot.bovy_end_print('exNew1a.png')
else:
plot.bovy_end_print('exNew2a.png')
#Data with MAP line and sampling
plot.bovy_print()
bestb= edges[0][indxi]
bestm= edges[1][indxj]
xrange=[0,300]
yrange=[0,700]
plot.bovy_plot(xrange,bestm*sc.array(xrange)+bestb,'k-',
xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',zorder=2)
errorbar(X,Y,yerr,color='k',marker='o',color='k',linestyle='None',zorder=1)
for ii in range(10):
#Random sample
ransample= sc.floor((stats.uniform.rvs()*nsamples))
ransample= samples.T[ransample,0:2]
bestb= ransample[0]
bestm= ransample[1]
plot.bovy_plot(xrange,bestm*sc.array(xrange)+bestb,
overplot=True,xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',color='0.75',zorder=1)
if dsigma == 1.:
plot.bovy_text(r'$\mathrm{using\ correct\ data\ uncertainties}$',
top_right=True)
else:
plot.bovy_text(r'$\mathrm{using\ data\ uncertainties\ /\ 2}$',
top_right=True)
if dsigma == 1.:
plot.bovy_end_print('exNew1b.png')
else:
plot.bovy_end_print('exNew2b.png')
#Pb plot
plot.bovy_print()
plot.bovy_hist(samples.T[:,2],color='k',bins=round(sc.sqrt(nsamples)/5.),
xlabel=r'$P_\mathrm{b}$',normed=True,histtype='step',
range=[0,1])
if dsigma == 1.:
plot.bovy_text(r'$\mathrm{using\ correct\ data\ uncertainties}$',
top_right=True)
else:
plot.bovy_text(r'$\mathrm{using\ data\ uncertainties\ /\ 2}$',
top_right=True)
if dsigma == 1.:
plot.bovy_end_print('exNew1c.png')
else:
plot.bovy_end_print('exNew2c.png')
return
def ex10(exclude=sc.array([1,2,3,4]),
plotfilenameA='ex10a.png',
plotfilenameB='ex10b.png',
nburn=1000,nsamples=200000,
parsigma=[5,.075,0.1],
bovyprintargs={}):
"""ex10: solve exercise 10 using MCMC sampling
Input:
exclude - ID numbers to exclude from the analysis (can be None)
plotfilename - filename for the output plot
nburn - number of burn-in samples
nsamples - number of samples to take after burn-in
parsigma - proposal distribution width (Gaussian)
Output:
plot
History:
2010-05-07 - Written - Bovy (NYU)
"""
sc.random.seed(-1) #In the interest of reproducibility (if that's a word)
#Read the data
data= read_data('data_yerr.dat')
ndata= len(data)
if not exclude == None:
nsample= ndata- len(exclude)
else:
nsample= ndata
#First find the chi-squared solution, which we will use as an
#initial guess
#Put the data in the appropriate arrays and matrices
Y= sc.zeros(nsample)
X= sc.zeros(nsample)
A= sc.ones((nsample,2))
C= sc.zeros((nsample,nsample))
yerr= sc.zeros(nsample)
jj= 0
for ii in range(ndata):
if not exclude == None and sc.any(exclude == data[ii][0]):
pass
else:
Y[jj]= data[ii][1][1]
X[jj]= data[ii][1][0]
A[jj,1]= data[ii][1][0]
C[jj,jj]= data[ii][2]**2.
yerr[jj]= data[ii][2]
jj= jj+1
#Now compute the best fit and the uncertainties
bestfit= sc.dot(linalg.inv(C),Y.T)
bestfit= sc.dot(A.T,bestfit)
bestfitvar= sc.dot(linalg.inv(C),A)
bestfitvar= sc.dot(A.T,bestfitvar)
bestfitvar= linalg.inv(bestfitvar)
bestfit= sc.dot(bestfitvar,bestfit)
initialguess= sc.array([bestfit[0],bestfit[1],0.])#(m,b,logS)
#With this initial guess start off the sampling procedure
initialX= objective(initialguess,X,Y,yerr)
currentX= initialX
bestX= initialX
bestfit= initialguess
currentguess= initialguess
naccept= 0
samples= []
samples.append(currentguess)
for jj in range(nburn+nsamples):
#Draw a sample from the proposal distribution
newsample= sc.zeros(3)
newsample[0]= currentguess[0]+stats.norm.rvs()*parsigma[0]
newsample[1]= currentguess[1]+stats.norm.rvs()*parsigma[1]
newsample[2]= currentguess[2]+stats.norm.rvs()*parsigma[2]
#Calculate the objective function for the newsample
newX= objective(newsample,X,Y,yerr)
#Accept or reject
#Reject with the appropriate probability
u= stats.uniform.rvs()
accept=False
try:
test= m.exp(newX-currentX)
if u < test:
accept= True
except OverflowError:
accept= True
if accept:
#Accept
currentX= newX
currentguess= newsample
naccept= naccept+1
if currentX > bestX:
bestfit= currentguess
bestX= currentX
samples.append(currentguess)
if double(naccept)/(nburn+nsamples) < .5 or double(naccept)/(nburn+nsamples) > .8:
print "Acceptance ratio was "+str(double(naccept)/(nburn+nsamples))
samples= sc.array(samples).T[:,nburn:-1]
print "Best-fit, overall"
print bestfit, sc.mean(samples[2,:]), sc.median(samples[2,:])
histmb,edges= sc.histogramdd(samples.T[:,0:2],bins=round(sc.sqrt(nsamples)/2.))
indxi= sc.argmax(sc.amax(histmb,axis=1))
indxj= sc.argmax(sc.amax(histmb,axis=0))
print "Best-fit, marginalized"
print edges[0][indxi-1], edges[1][indxj-1]
print edges[0][indxi], edges[1][indxj]
print edges[0][indxi+1], edges[1][indxj+1]
print "Best-fit for S marginalized"
histS,edgesS= sc.histogram(samples.T[:,2],bins=round(sc.sqrt(nsamples)/2.))
indx= sc.argmax(histS)
#Data with MAP line and sampling
plot.bovy_print(**bovyprintargs)
bestb= bestfit[0]
bestm= bestfit[1]
xrange=[0,300]
yrange=[0,700]
plot.bovy_plot(xrange,bestm*sc.array(xrange)+bestb,'k-',
xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',zorder=2)
errorbar(X,Y,sc.exp(bestfit[2]/2.),
marker='o',color='k',linestyle='None',zorder=1)
plot.bovy_text(r'$\mathrm{MAP}\ :\ y = %4.2f \,x+ %4.0f' % (bestfit[1], bestfit[0])+r'$'+'\n'+r'$\mathrm{MAP}\ :\ \sqrt{S} = %3.1f$' % (sc.exp(bestfit[2]/2.)),
bottom_right=True)
plot.bovy_end_print(plotfilenameA)
#Data with MAP line and sampling
plot.bovy_print(**bovyprintargs)
bestb= edges[0][indxi]
bestm= edges[1][indxj]
bestS= edgesS[indx]
xrange=[0,300]
yrange=[0,700]
plot.bovy_plot(xrange,bestm*sc.array(xrange)+bestb,'k-',
xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',zorder=2)
errorbar(X,Y,sc.exp(bestS/2.),
marker='o',color='k',linestyle='None',zorder=1)
plot.bovy_text(r'$\mathrm{marginalized\ over\ S}\ :\ y = %4.2f \,x+ %4.0f' % (bestm, bestb)+r'$'+'\n'+r'$\mathrm{marginalized\ over}\ (m,b)\ :\ \sqrt{S} = %3.1f$' % (sc.exp(bestS/2.)),
bottom_right=True)
plot.bovy_end_print(plotfilenameB)
return
def ex15(
exclude=sc.array([1, 2, 3, 4]), plotfilename='ex15.png',
bovyprintargs={}):
"""ex15: solve exercise 15
Input:
exclude - ID numbers to exclude from the analysis
plotfilename - filename for the output plot
Output:
plot
History:
2010-05-07 - Written - Bovy (NYU)
"""
#Read the data
data = read_data('data_allerr.dat', allerr=True)
ndata = len(data)
nsample = ndata - len(exclude)
#Put the dat in the appropriate arrays and matrices
Y = sc.zeros(nsample)
X = sc.zeros(nsample)
Z = sc.zeros((nsample, 2))
jj = 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
pass
else:
Y[jj] = data[ii][1][1]
X[jj] = data[ii][1][0]
Z[jj, 0] = X[jj]
Z[jj, 1] = Y[jj]
jj = jj + 1
#Now compute the PCA solution
Zm = sc.mean(Z, axis=0)
Q = sc.cov(Z.T)
eigs = linalg.eig(Q)
maxindx = sc.argmax(eigs[0])
V = eigs[1][maxindx]
V = V / linalg.norm(V)
m = sc.sqrt(1 / V[0]**2. - 1)
bestfit = sc.array([-m * Zm[0] + Zm[1], m])
#Plot result
plot.bovy_print(**bovyprintargs)
xrange = [0, 300]
yrange = [0, 700]
plot.bovy_plot(sc.array(xrange),
bestfit[1] * sc.array(xrange) + bestfit[0],
'k--',
xrange=xrange,
yrange=yrange,
xlabel=r'$x$',
ylabel=r'$y$',
zorder=2)
plot.bovy_plot(X,
Y,
marker='o',
color='k',
linestyle='None',
zorder=0,
overplot=True)
plot.bovy_text(r'$y = %4.2f \,x %4.0f' % (bestfit[1], bestfit[0]) + r'$',
bottom_right=True)
plot.bovy_end_print(plotfilename)
def ex10(exclude=sc.array([1, 2, 3, 4]),
plotfilenameA='ex10a.png',
plotfilenameB='ex10b.png',
nburn=1000,
nsamples=200000,
parsigma=[5, .075, 0.1],
bovyprintargs={}):
"""ex10: solve exercise 10 using MCMC sampling
Input:
exclude - ID numbers to exclude from the analysis (can be None)
plotfilename - filename for the output plot
nburn - number of burn-in samples
nsamples - number of samples to take after burn-in
parsigma - proposal distribution width (Gaussian)
Output:
plot
History:
2010-05-07 - Written - Bovy (NYU)
"""
sc.random.seed(-1) #In the interest of reproducibility (if that's a word)
#Read the data
data = read_data('data_yerr.dat')
ndata = len(data)
if not exclude == None:
nsample = ndata - len(exclude)
else:
nsample = ndata
#First find the chi-squared solution, which we will use as an
#initial guess
#Put the data in the appropriate arrays and matrices
Y = sc.zeros(nsample)
X = sc.zeros(nsample)
A = sc.ones((nsample, 2))
C = sc.zeros((nsample, nsample))
yerr = sc.zeros(nsample)
jj = 0
for ii in range(ndata):
if not exclude == None and sc.any(exclude == data[ii][0]):
pass
else:
Y[jj] = data[ii][1][1]
X[jj] = data[ii][1][0]
A[jj, 1] = data[ii][1][0]
C[jj, jj] = data[ii][2]**2.
yerr[jj] = data[ii][2]
jj = jj + 1
#Now compute the best fit and the uncertainties
bestfit = sc.dot(linalg.inv(C), Y.T)
bestfit = sc.dot(A.T, bestfit)
bestfitvar = sc.dot(linalg.inv(C), A)
bestfitvar = sc.dot(A.T, bestfitvar)
bestfitvar = linalg.inv(bestfitvar)
bestfit = sc.dot(bestfitvar, bestfit)
initialguess = sc.array([bestfit[0], bestfit[1], 0.]) #(m,b,logS)
#With this initial guess start off the sampling procedure
initialX = objective(initialguess, X, Y, yerr)
currentX = initialX
bestX = initialX
bestfit = initialguess
currentguess = initialguess
naccept = 0
samples = []
samples.append(currentguess)
for jj in range(nburn + nsamples):
#Draw a sample from the proposal distribution
newsample = sc.zeros(3)
newsample[0] = currentguess[0] + stats.norm.rvs() * parsigma[0]
newsample[1] = currentguess[1] + stats.norm.rvs() * parsigma[1]
newsample[2] = currentguess[2] + stats.norm.rvs() * parsigma[2]
#Calculate the objective function for the newsample
newX = objective(newsample, X, Y, yerr)
#Accept or reject
#Reject with the appropriate probability
u = stats.uniform.rvs()
accept = False
try:
test = m.exp(newX - currentX)
if u < test:
accept = True
except OverflowError:
accept = True
if accept:
#Accept
currentX = newX
currentguess = newsample
naccept = naccept + 1
if currentX > bestX:
bestfit = currentguess
bestX = currentX
samples.append(currentguess)
if double(naccept) / (nburn + nsamples) < .5 or double(naccept) / (
nburn + nsamples) > .8:
print "Acceptance ratio was " + str(
double(naccept) / (nburn + nsamples))
samples = sc.array(samples).T[:, nburn:-1]
print "Best-fit, overall"
print bestfit, sc.mean(samples[2, :]), sc.median(samples[2, :])
histmb, edges = sc.histogramdd(samples.T[:, 0:2],
bins=round(sc.sqrt(nsamples) / 2.))
indxi = sc.argmax(sc.amax(histmb, axis=1))
indxj = sc.argmax(sc.amax(histmb, axis=0))
print "Best-fit, marginalized"
print edges[0][indxi - 1], edges[1][indxj - 1]
print edges[0][indxi], edges[1][indxj]
print edges[0][indxi + 1], edges[1][indxj + 1]
print "Best-fit for S marginalized"
histS, edgesS = sc.histogram(samples.T[:, 2],
bins=round(sc.sqrt(nsamples) / 2.))
indx = sc.argmax(histS)
#Data with MAP line and sampling
plot.bovy_print(**bovyprintargs)
bestb = bestfit[0]
bestm = bestfit[1]
xrange = [0, 300]
yrange = [0, 700]
plot.bovy_plot(xrange,
bestm * sc.array(xrange) + bestb,
'k-',
xrange=xrange,
yrange=yrange,
xlabel=r'$x$',
ylabel=r'$y$',
zorder=2)
errorbar(X,
Y,
sc.exp(bestfit[2] / 2.),
marker='o',
color='k',
linestyle='None',
zorder=1)
plot.bovy_text(r'$\mathrm{MAP}\ :\ y = %4.2f \,x+ %4.0f' %
(bestfit[1], bestfit[0]) + r'$' + '\n' +
r'$\mathrm{MAP}\ :\ \sqrt{S} = %3.1f$' %
(sc.exp(bestfit[2] / 2.)),
bottom_right=True)
plot.bovy_end_print(plotfilenameA)
#Data with MAP line and sampling
plot.bovy_print(**bovyprintargs)
bestb = edges[0][indxi]
bestm = edges[1][indxj]
bestS = edgesS[indx]
xrange = [0, 300]
yrange = [0, 700]
plot.bovy_plot(xrange,
bestm * sc.array(xrange) + bestb,
'k-',
xrange=xrange,
yrange=yrange,
xlabel=r'$x$',
ylabel=r'$y$',
zorder=2)
errorbar(X,
Y,
sc.exp(bestS / 2.),
marker='o',
color='k',
linestyle='None',
zorder=1)
plot.bovy_text(
r'$\mathrm{marginalized\ over\ S}\ :\ y = %4.2f \,x+ %4.0f' %
(bestm, bestb) + r'$' + '\n' +
r'$\mathrm{marginalized\ over}\ (m,b)\ :\ \sqrt{S} = %3.1f$' %
(sc.exp(bestS / 2.)),
bottom_right=True)
plot.bovy_end_print(plotfilenameB)
return
def ex14(exclude=sc.array([1,2,3,4]),plotfilename='ex14.png',
bovyprintargs={}):
"""ex12: solve exercise 14
Input:
exclude - ID numbers to exclude from the analysis
plotfilename - filename for the output plot
Output:
plot
History:
2010-05-07 - Written - Bovy (NYU)
"""
#Read the data
data= read_data('data_allerr.dat',allerr=True)
ndata= len(data)
nsample= ndata- len(exclude)
#First find the chi-squared solution, which we will use as an
#initial gues
#Put the dat in the appropriate arrays and matrices
Y1= sc.zeros(nsample)
X1= sc.zeros(nsample)
A1= sc.ones((nsample,2))
C1= sc.zeros((nsample,nsample))
Y2= sc.zeros(nsample)
X2= sc.zeros(nsample)
A2= sc.ones((nsample,2))
C2= sc.zeros((nsample,nsample))
yerr= sc.zeros(nsample)
xerr= sc.zeros(nsample)
ycovar= sc.zeros((2,nsample,2))#Makes the sc.dot easier
jj= 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
pass
else:
Y1[jj]= data[ii][1][1]
X1[jj]= data[ii][1][0]
A1[jj,1]= data[ii][1][0]
C1[jj,jj]= data[ii][2]**2.
yerr[jj]= data[ii][2]
Y2[jj]= data[ii][1][0]
X2[jj]= data[ii][1][1]
A2[jj,1]= data[ii][1][1]
C2[jj,jj]= data[ii][3]**2.
xerr[jj]= data[ii][3]
jj= jj+1
#Now compute the best fit and the uncertainties: forward
bestfit1= sc.dot(linalg.inv(C1),Y1.T)
bestfit1= sc.dot(A1.T,bestfit1)
bestfitvar1= sc.dot(linalg.inv(C1),A1)
bestfitvar1= sc.dot(A1.T,bestfitvar1)
bestfitvar1= linalg.inv(bestfitvar1)
bestfit1= sc.dot(bestfitvar1,bestfit1)
#Now compute the best fit and the uncertainties: backward
bestfit2= sc.dot(linalg.inv(C2),Y2.T)
bestfit2= sc.dot(A2.T,bestfit2)
bestfitvar2= sc.dot(linalg.inv(C2),A2)
bestfitvar2= sc.dot(A2.T,bestfitvar2)
bestfitvar2= linalg.inv(bestfitvar2)
bestfit2= sc.dot(bestfitvar2,bestfit2)
#Propagate to y=mx+b
linerrprop= sc.array([[-1./bestfit2[1],bestfit2[0]/bestfit2[1]**2],
[0.,-1./bestfit2[1]**2.]])
bestfit2= sc.array([-bestfit2[0]/bestfit2[1],1./bestfit2[1]])
bestfitvar2= sc.dot(linerrprop,sc.dot(bestfitvar2,linerrprop.T))
#Plot result
plot.bovy_print(**bovyprintargs)
xrange=[0,300]
yrange=[0,700]
plot.bovy_plot(sc.array(xrange),bestfit1[1]*sc.array(xrange)+bestfit1[0],
'k--',xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',zorder=2)
plot.bovy_plot(sc.array(xrange),bestfit2[1]*sc.array(xrange)+bestfit2[0],
'k-.',overplot=True,zorder=2)
#Plot data
errorbar(A1[:,1],Y1,yerr,xerr,color='k',marker='o',
linestyle='None',zorder=0)
plot.bovy_text(r'$\mathrm{forward}\ ---\:\ y = ( '+'%4.2f \pm %4.2f )\,x+ ( %4.0f\pm %4.0f' % (bestfit1[1], m.sqrt(bestfitvar1[1,1]), bestfit1[0],m.sqrt(bestfitvar1[0,0]))+r')$'+'\n'+
r'$\mathrm{reverse}\ -\cdot -\:\ y = ( '+'%4.2f \pm %4.2f )\,x+ ( %4.0f\pm %4.0f' % (bestfit2[1], m.sqrt(bestfitvar2[1,1]), bestfit2[0],m.sqrt(bestfitvar2[0,0]))+r')$',bottom_right=True)
plot.bovy_end_print(plotfilename)
def ex14(
exclude=sc.array([1, 2, 3, 4]), plotfilename='ex14.png',
bovyprintargs={}):
"""ex12: solve exercise 14
Input:
exclude - ID numbers to exclude from the analysis
plotfilename - filename for the output plot
Output:
plot
History:
2010-05-07 - Written - Bovy (NYU)
"""
#Read the data
data = read_data('data_allerr.dat', allerr=True)
ndata = len(data)
nsample = ndata - len(exclude)
#First find the chi-squared solution, which we will use as an
#initial gues
#Put the dat in the appropriate arrays and matrices
Y1 = sc.zeros(nsample)
X1 = sc.zeros(nsample)
A1 = sc.ones((nsample, 2))
C1 = sc.zeros((nsample, nsample))
Y2 = sc.zeros(nsample)
X2 = sc.zeros(nsample)
A2 = sc.ones((nsample, 2))
C2 = sc.zeros((nsample, nsample))
yerr = sc.zeros(nsample)
xerr = sc.zeros(nsample)
ycovar = sc.zeros((2, nsample, 2)) #Makes the sc.dot easier
jj = 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
pass
else:
Y1[jj] = data[ii][1][1]
X1[jj] = data[ii][1][0]
A1[jj, 1] = data[ii][1][0]
C1[jj, jj] = data[ii][2]**2.
yerr[jj] = data[ii][2]
Y2[jj] = data[ii][1][0]
X2[jj] = data[ii][1][1]
A2[jj, 1] = data[ii][1][1]
C2[jj, jj] = data[ii][3]**2.
xerr[jj] = data[ii][3]
jj = jj + 1
#Now compute the best fit and the uncertainties: forward
bestfit1 = sc.dot(linalg.inv(C1), Y1.T)
bestfit1 = sc.dot(A1.T, bestfit1)
bestfitvar1 = sc.dot(linalg.inv(C1), A1)
bestfitvar1 = sc.dot(A1.T, bestfitvar1)
bestfitvar1 = linalg.inv(bestfitvar1)
bestfit1 = sc.dot(bestfitvar1, bestfit1)
#Now compute the best fit and the uncertainties: backward
bestfit2 = sc.dot(linalg.inv(C2), Y2.T)
bestfit2 = sc.dot(A2.T, bestfit2)
bestfitvar2 = sc.dot(linalg.inv(C2), A2)
bestfitvar2 = sc.dot(A2.T, bestfitvar2)
bestfitvar2 = linalg.inv(bestfitvar2)
bestfit2 = sc.dot(bestfitvar2, bestfit2)
#Propagate to y=mx+b
linerrprop = sc.array([[-1. / bestfit2[1], bestfit2[0] / bestfit2[1]**2],
[0., -1. / bestfit2[1]**2.]])
bestfit2 = sc.array([-bestfit2[0] / bestfit2[1], 1. / bestfit2[1]])
bestfitvar2 = sc.dot(linerrprop, sc.dot(bestfitvar2, linerrprop.T))
#Plot result
plot.bovy_print(**bovyprintargs)
xrange = [0, 300]
yrange = [0, 700]
plot.bovy_plot(sc.array(xrange),
bestfit1[1] * sc.array(xrange) + bestfit1[0],
'k--',
xrange=xrange,
yrange=yrange,
xlabel=r'$x$',
ylabel=r'$y$',
zorder=2)
plot.bovy_plot(sc.array(xrange),
bestfit2[1] * sc.array(xrange) + bestfit2[0],
'k-.',
overplot=True,
zorder=2)
#Plot data
errorbar(A1[:, 1],
Y1,
yerr,
xerr,
color='k',
marker='o',
linestyle='None',
zorder=0)
plot.bovy_text(r'$\mathrm{forward}\ ---\:\ y = ( ' +
'%4.2f \pm %4.2f )\,x+ ( %4.0f\pm %4.0f' %
(bestfit1[1], m.sqrt(bestfitvar1[1, 1]), bestfit1[0],
m.sqrt(bestfitvar1[0, 0])) + r')$' + '\n' +
r'$\mathrm{reverse}\ -\cdot -\:\ y = ( ' +
'%4.2f \pm %4.2f )\,x+ ( %4.0f\pm %4.0f' %
(bestfit2[1], m.sqrt(bestfitvar2[1, 1]), bestfit2[0],
m.sqrt(bestfitvar2[0, 0])) + r')$',
bottom_right=True)
plot.bovy_end_print(plotfilename)
def ex12(exclude=sc.array([1,2,3,4]),plotfilename='ex12.png',
bovyprintargs={}):
"""ex12: solve exercise 12 by optimization of the objective function
Input:
exclude - ID numbers to exclude from the analysis
plotfilename - filename for the output plot
Output:
plot
History:
2010-05-06 - Written - Bovy (NYU)
"""
#Read the data
data= read_data('data_allerr.dat',allerr=True)
ndata= len(data)
nsample= ndata- len(exclude)
#First find the chi-squared solution, which we will use as an
#initial gues
#Put the dat in the appropriate arrays and matrices
Y= sc.zeros(nsample)
X= sc.zeros(nsample)
A= sc.ones((nsample,2))
C= sc.zeros((nsample,nsample))
Z= sc.zeros((nsample,2))
yerr= sc.zeros(nsample)
ycovar= sc.zeros((2,nsample,2))#Makes the sc.dot easier
jj= 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
pass
else:
Y[jj]= data[ii][1][1]
X[jj]= data[ii][1][0]
Z[jj,0]= X[jj]
Z[jj,1]= Y[jj]
A[jj,1]= data[ii][1][0]
C[jj,jj]= data[ii][2]**2.
yerr[jj]= data[ii][2]
ycovar[0,jj,0]= data[ii][3]**2.
ycovar[1,jj,1]= data[ii][2]**2.
ycovar[0,jj,1]= data[ii][4]*m.sqrt(ycovar[0,jj,0]*ycovar[1,jj,1])
ycovar[1,jj,0]= ycovar[0,jj,1]
jj= jj+1
#Now compute the best fit and the uncertainties
bestfit= sc.dot(linalg.inv(C),Y.T)
bestfit= sc.dot(A.T,bestfit)
bestfitvar= sc.dot(linalg.inv(C),A)
bestfitvar= sc.dot(A.T,bestfitvar)
bestfitvar= linalg.inv(bestfitvar)
bestfit= sc.dot(bestfitvar,bestfit)
#Now optimize
bestfit2d1= optimize.fmin(objective,bestfit,(Z,ycovar),disp=False)
#Restart the optimization once using a different method
bestfit2d= optimize.fmin_powell(objective,bestfit,
(Z,ycovar),disp=False)
if linalg.norm(bestfit2d-bestfit2d1) > 10**-12:
if linalg.norm(bestfit2d-bestfit2d1) < 10**-6:
print "Different optimizers give slightly different results..."
else:
print "Different optimizers give rather different results..."
print "The norm of the results differs by %g" % linalg.norm(bestfit2d-bestfit2d1)
try:
x=raw_input('continue to plot? [yn]\n')
except EOFError:
print "Since you are in non-interactive mode I will assume 'y'"
x='y'
if x == 'n':
print "returning..."
return -1
#Plot result
plot.bovy_print(**bovyprintargs)
xrange=[0,300]
yrange=[0,700]
plot.bovy_plot(sc.array(xrange),bestfit2d[1]*sc.array(xrange)+bestfit2d[0],
'k-',xrange=xrange,yrange=yrange,
xlabel=r'$x$',ylabel=r'$y$',zorder=2)
#Plot the data OMG straight from plot_data.py
data= read_data('data_allerr.dat',True)
ndata= len(data)
#Create the ellipses and the data points
id= sc.zeros(nsample)
x= sc.zeros(nsample)
y= sc.zeros(nsample)
ellipses=[]
ymin, ymax= 0, 0
xmin, xmax= 0,0
jj= 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
continue
id[jj]= data[ii][0]
x[jj]= data[ii][1][0]
y[jj]= data[ii][1][1]
#Calculate the eigenvalues and the rotation angle
ycovar= sc.zeros((2,2))
ycovar[0,0]= data[ii][3]**2.
ycovar[1,1]= data[ii][2]**2.
ycovar[0,1]= data[ii][4]*m.sqrt(ycovar[0,0]*ycovar[1,1])
ycovar[1,0]= ycovar[0,1]
eigs= linalg.eig(ycovar)
angle= m.atan(-eigs[1][0,1]/eigs[1][1,1])/m.pi*180.
thisellipse= Ellipse(sc.array([x[jj],y[jj]]),2*m.sqrt(eigs[0][0]),
2*m.sqrt(eigs[0][1]),angle)
ellipses.append(thisellipse)
if (x[jj]+m.sqrt(ycovar[0,0])) > xmax:
xmax= (x[jj]+m.sqrt(ycovar[0,0]))
if (x[jj]-m.sqrt(ycovar[0,0])) < xmin:
xmin= (x[jj]-m.sqrt(ycovar[0,0]))
if (y[jj]+m.sqrt(ycovar[1,1])) > ymax:
ymax= (y[jj]+m.sqrt(ycovar[1,1]))
if (y[jj]-m.sqrt(ycovar[1,1])) < ymin:
ymin= (y[jj]-m.sqrt(ycovar[1,1]))
jj= jj+1
#Add the error ellipses
ax=gca()
for e in ellipses:
ax.add_artist(e)
e.set_facecolor('none')
ax.plot(x,y,color='k',marker='o',linestyle='None')
plot.bovy_text(r'$y = %4.2f \,x+ %4.0f' % (bestfit2d[1], bestfit2d[0])+r'$',
bottom_right=True)
plot.bovy_end_print(plotfilename)
def exMix1(
exclude=None,
plotfilenameA="exMix1a.png",
plotfilenameB="exMix1b.png",
plotfilenameC="exMix1c.png",
nburn=20000,
nsamples=1000000,
parsigma=[5, 0.075, 0.2, 1, 0.1],
dsigma=1.0,
bovyprintargs={},
sampledata=None,
):
"""exMix1: solve exercise 5 (mixture model) using MCMC sampling
Input:
exclude - ID numbers to exclude from the analysis (can be None)
plotfilename* - filenames for the output plot
nburn - number of burn-in samples
nsamples - number of samples to take after burn-in
parsigma - proposal distribution width (Gaussian)
dsigma - divide uncertainties by this amount
Output:
plot
History:
2010-04-28 - Written - Bovy (NYU)
"""
sc.random.seed(-1) # In the interest of reproducibility (if that's a word)
# Read the data
data = read_data("data_yerr.dat")
ndata = len(data)
if not exclude == None:
nsample = ndata - len(exclude)
else:
nsample = ndata
# First find the chi-squared solution, which we will use as an
# initial guess
# Put the data in the appropriate arrays and matrices
Y = sc.zeros(nsample)
X = sc.zeros(nsample)
A = sc.ones((nsample, 2))
C = sc.zeros((nsample, nsample))
yerr = sc.zeros(nsample)
jj = 0
for ii in range(ndata):
if not exclude == None and sc.any(exclude == data[ii][0]):
pass
else:
Y[jj] = data[ii][1][1]
X[jj] = data[ii][1][0]
A[jj, 1] = data[ii][1][0]
C[jj, jj] = data[ii][2] ** 2.0 / dsigma ** 2.0
yerr[jj] = data[ii][2] / dsigma
jj = jj + 1
brange = [-120, 120]
mrange = [1.5, 3.2]
# This matches the order of the parameters in the "samples" vector
mbrange = [brange, mrange]
if sampledata is None:
sampledata = runSampler(X, Y, A, C, yerr, nburn, nsamples, parsigma, mbrange)
(histmb, edges, mbsamples, pbhist, pbedges) = sampledata
# Hack -- produce fake Pbad samples from Pbad histogram.
pbsamples = hstack([array([x] * N) for x, N in zip((pbedges[:-1] + pbedges[1:]) / 2, pbhist)])
indxi = sc.argmax(sc.amax(histmb, axis=1))
indxj = sc.argmax(sc.amax(histmb, axis=0))
print "Best-fit, marginalized"
print edges[0][indxi - 1], edges[1][indxj - 1]
print edges[0][indxi], edges[1][indxj]
print edges[0][indxi + 1], edges[1][indxj + 1]
# 2D histogram
plot.bovy_print(**bovyprintargs)
levels = special.erf(0.5 * sc.arange(1, 4))
xe = [edges[0][0], edges[0][-1]]
ye = [edges[1][0], edges[1][-1]]
aspect = (xe[1] - xe[0]) / (ye[1] - ye[0])
plot.bovy_dens2d(
histmb.T,
origin="lower",
cmap=cm.gist_yarg,
interpolation="nearest",
contours=True,
cntrmass=True,
extent=xe + ye,
levels=levels,
aspect=aspect,
xlabel=r"$b$",
ylabel=r"$m$",
)
xlim(brange)
ylim(mrange)
plot.bovy_end_print(plotfilenameA)
# Data with MAP line and sampling
plot.bovy_print(**bovyprintargs)
bestb = edges[0][indxi]
bestm = edges[1][indxj]
xrange = [0, 300]
yrange = [0, 700]
plot.bovy_plot(
xrange,
bestm * sc.array(xrange) + bestb,
"k-",
xrange=xrange,
yrange=yrange,
xlabel=r"$x$",
ylabel=r"$y$",
zorder=2,
)
errorbar(X, Y, yerr, marker="o", color="k", linestyle="None", zorder=1)
for m, b in mbsamples:
plot.bovy_plot(
xrange,
m * sc.array(xrange) + b,
overplot=True,
xrange=xrange,
yrange=yrange,
xlabel=r"$x$",
ylabel=r"$y$",
color="0.75",
zorder=1,
)
plot.bovy_end_print(plotfilenameB)
# Pb plot
if not "text_fontsize" in bovyprintargs:
bovyprintargs["text_fontsize"] = 11
plot.bovy_print(**bovyprintargs)
plot.bovy_hist(
pbsamples,
bins=round(sc.sqrt(nsamples) / 5.0),
xlabel=r"$P_\mathrm{b}$",
normed=True,
histtype="step",
range=[0, 1],
edgecolor="k",
)
ylim(0, 4.0)
if dsigma == 1.0:
plot.bovy_text(r"$\mathrm{using\ correct\ data\ uncertainties}$", top_right=True)
else:
plot.bovy_text(r"$\mathrm{using\ data\ uncertainties\ /\ 2}$", top_left=True)
plot.bovy_end_print(plotfilenameC)
return sampledata
def ex12(
exclude=sc.array([1, 2, 3, 4]), plotfilename='ex12.png',
bovyprintargs={}):
"""ex12: solve exercise 12 by optimization of the objective function
Input:
exclude - ID numbers to exclude from the analysis
plotfilename - filename for the output plot
Output:
plot
History:
2010-05-06 - Written - Bovy (NYU)
"""
#Read the data
data = read_data('data_allerr.dat', allerr=True)
ndata = len(data)
nsample = ndata - len(exclude)
#First find the chi-squared solution, which we will use as an
#initial gues
#Put the dat in the appropriate arrays and matrices
Y = sc.zeros(nsample)
X = sc.zeros(nsample)
A = sc.ones((nsample, 2))
C = sc.zeros((nsample, nsample))
Z = sc.zeros((nsample, 2))
yerr = sc.zeros(nsample)
ycovar = sc.zeros((2, nsample, 2)) #Makes the sc.dot easier
jj = 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
pass
else:
Y[jj] = data[ii][1][1]
X[jj] = data[ii][1][0]
Z[jj, 0] = X[jj]
Z[jj, 1] = Y[jj]
A[jj, 1] = data[ii][1][0]
C[jj, jj] = data[ii][2]**2.
yerr[jj] = data[ii][2]
ycovar[0, jj, 0] = data[ii][3]**2.
ycovar[1, jj, 1] = data[ii][2]**2.
ycovar[0, jj, 1] = data[ii][4] * m.sqrt(
ycovar[0, jj, 0] * ycovar[1, jj, 1])
ycovar[1, jj, 0] = ycovar[0, jj, 1]
jj = jj + 1
#Now compute the best fit and the uncertainties
bestfit = sc.dot(linalg.inv(C), Y.T)
bestfit = sc.dot(A.T, bestfit)
bestfitvar = sc.dot(linalg.inv(C), A)
bestfitvar = sc.dot(A.T, bestfitvar)
bestfitvar = linalg.inv(bestfitvar)
bestfit = sc.dot(bestfitvar, bestfit)
#Now optimize
bestfit2d1 = optimize.fmin(objective, bestfit, (Z, ycovar), disp=False)
#Restart the optimization once using a different method
bestfit2d = optimize.fmin_powell(objective,
bestfit, (Z, ycovar),
disp=False)
if linalg.norm(bestfit2d - bestfit2d1) > 10**-12:
if linalg.norm(bestfit2d - bestfit2d1) < 10**-6:
print("Different optimizers give slightly different results...")
else:
print("Different optimizers give rather different results...")
print("The norm of the results differs by %g" %
linalg.norm(bestfit2d - bestfit2d1))
try:
x = raw_input('continue to plot? [yn]\n')
except EOFError:
print("Since you are in non-interactive mode I will assume 'y'")
x = 'y'
if x == 'n':
print("returning...")
return -1
#Plot result
plot.bovy_print(**bovyprintargs)
xrange = [0, 300]
yrange = [0, 700]
plot.bovy_plot(sc.array(xrange),
bestfit2d[1] * sc.array(xrange) + bestfit2d[0],
'k-',
xrange=xrange,
yrange=yrange,
xlabel=r'$x$',
ylabel=r'$y$',
zorder=2)
#Plot the data OMG straight from plot_data.py
data = read_data('data_allerr.dat', True)
ndata = len(data)
#Create the ellipses and the data points
id = sc.zeros(nsample)
x = sc.zeros(nsample)
y = sc.zeros(nsample)
ellipses = []
ymin, ymax = 0, 0
xmin, xmax = 0, 0
jj = 0
for ii in range(ndata):
if sc.any(exclude == data[ii][0]):
continue
id[jj] = data[ii][0]
x[jj] = data[ii][1][0]
y[jj] = data[ii][1][1]
#Calculate the eigenvalues and the rotation angle
ycovar = sc.zeros((2, 2))
ycovar[0, 0] = data[ii][3]**2.
ycovar[1, 1] = data[ii][2]**2.
ycovar[0, 1] = data[ii][4] * m.sqrt(ycovar[0, 0] * ycovar[1, 1])
ycovar[1, 0] = ycovar[0, 1]
eigs = linalg.eig(ycovar)
angle = m.atan(-eigs[1][0, 1] / eigs[1][1, 1]) / m.pi * 180.
thisellipse = Ellipse(sc.array([x[jj], y[jj]]), 2 * m.sqrt(eigs[0][0]),
2 * m.sqrt(eigs[0][1]), angle)
ellipses.append(thisellipse)
if (x[jj] + m.sqrt(ycovar[0, 0])) > xmax:
xmax = (x[jj] + m.sqrt(ycovar[0, 0]))
if (x[jj] - m.sqrt(ycovar[0, 0])) < xmin:
xmin = (x[jj] - m.sqrt(ycovar[0, 0]))
if (y[jj] + m.sqrt(ycovar[1, 1])) > ymax:
ymax = (y[jj] + m.sqrt(ycovar[1, 1]))
if (y[jj] - m.sqrt(ycovar[1, 1])) < ymin:
ymin = (y[jj] - m.sqrt(ycovar[1, 1]))
jj = jj + 1
#Add the error ellipses
ax = gca()
for e in ellipses:
ax.add_artist(e)
e.set_facecolor('none')
ax.plot(x, y, color='k', marker='o', linestyle='None')
plot.bovy_text(r'$y = %4.2f \,x+ %4.0f' % (bestfit2d[1], bestfit2d[0]) +
r'$',
bottom_right=True)
plot.bovy_end_print(plotfilename)
def bar_detectability(parser,
dx=_XWIDTH/20.,dy=_YWIDTH/20.,
nx=100,ny=20,
ngrid=201,rrange=[0.7,1.3],
phirange=[-m.pi/2.,m.pi/2.],
saveDir='../bar/1dLarge/'):
"""
NAME:
bar_detectability
PURPOSE:
analyze the detectability of the Hercules moving group in the
los-distribution around the Galaxy
INPUT:
nx - number of plots in the x-direction
ny - number of plots in the y direction
dx - x-spacing
dy - y-spacing
ngrid - number of gridpoints to evaluate the density on
rrange - range of Galactocentric radii to consider
phirange - range of Galactic azimuths to consider
saveDir - directory to save the pickles in
OUTPUT:
plot in plotfilename
HISTORY:
2010-05-09 - Written - Bovy (NYU)
"""
(options,args)= parser.parse_args()
if len(args) == 0:
parser.print_help()
return
if not options.convolve == None:
bar_detectability_convolve(parser,dx=dx,dy=dy,nx=nx,ny=ny,ngrid=ngrid,
rrange=rrange,phirange=phirange,
saveDir=saveDir)
return
vloslinspace= (-.9,.9,ngrid)
vloss= sc.linspace(*vloslinspace)
picklebasename= '1d_%i_%i_%i_%.1f_%.1f_%.1f_%.1f' % (nx,ny,ngrid,rrange[0],rrange[1],phirange[0],phirange[1])
detect= sc.zeros((nx,ny))
losd= sc.zeros((nx,ny))
gall= sc.zeros((nx,ny))
for ii in range(nx):
for jj in range(ny):
thisR= (rrange[0]+(rrange[1]-rrange[0])/
(ny*_YWIDTH+(ny-1)*dy)*(jj*(_YWIDTH+dy)+_YWIDTH/2.))
thisphi= (phirange[0]+(phirange[1]-phirange[0])/
(nx*_XWIDTH+(nx-1)*dx)*(ii*(_XWIDTH+dx)+_XWIDTH/2.))
thissavefilename= os.path.join(saveDir,picklebasename+'_%i_%i.sav' %(ii,jj))
if os.path.exists(thissavefilename):
print "Restoring los-velocity distribution at %.2f, %.2f ..." %(thisR,thisphi)
savefile= open(thissavefilename,'r')
vlosd= pickle.load(savefile)
axivlosd= pickle.load(savefile)
savefile.close()
else:
print "Did not find the los-velocity distribution at at %.2f, %.2f ..." %(thisR,thisphi)
print "returning ..."
return
ddx= 1./sc.sum(axivlosd)
#skipCenter
if not options.skipCenter == 0.:
skipIndx= (sc.fabs(vloss) < options.skipCenter)
indx= (sc.fabs(vloss) >= options.skipCenter)
vlosd= vlosd/sc.sum(vlosd[indx])/ddx
axivlosd= axivlosd/sc.sum(axivlosd[indx])/ddx
vlosd[skipIndx]= 1.
axivlosd[skipIndx]= 1.
vlosd_zeroindx= (vlosd == 0.)
axivlosd_zeroindx= (axivlosd == 0.)
vlosd[vlosd_zeroindx]= 1.
axivlosd[vlosd_zeroindx]= 1.
vlosd[axivlosd_zeroindx]= 1.
axivlosd[axivlosd_zeroindx]= 1.
detect[ii,jj]= probDistance.kullbackLeibler(vlosd,axivlosd,ddx,nan=True)
#los distance and Galactic longitude
d= m.sqrt(thisR**2.+1.-2.*thisR*m.cos(thisphi))
losd[ii,jj]= d
if 1./m.cos(thisphi) < thisR and m.cos(thisphi) > 0.:
l= m.pi-m.asin(thisR/d*m.sin(thisphi))
else:
l= m.asin(thisR/d*m.sin(thisphi))
gall[ii,jj]= l
#Find maximum, further than 3 kpc away
detectformax= detect.flatten()
detectformax[losd.flatten() < 3./8.2]= 0.
x= sc.argmax(detectformax)
indx = sc.unravel_index(x,detect.shape)
maxR= (rrange[0]+(rrange[1]-rrange[0])/
(ny*_YWIDTH+(ny-1)*dy)*(indx[1]*(_YWIDTH+dy)+_YWIDTH/2.))
maxphi= (phirange[0]+(phirange[1]-phirange[0])/
(nx*_XWIDTH+(nx-1)*dx)*(indx[0]*(_XWIDTH+dx)+_XWIDTH/2.))
print maxR, maxphi, losd[indx[0],indx[1]], detect[indx[0],indx[1]], gall[indx[0],indx[1]]*180./sc.pi
#Now plot
plot.bovy_print()
plot.bovy_dens2d(detect.T,origin='lower',#interpolation='nearest',
xlabel=r'$\mathrm{Galactocentric\ azimuth}\ [\mathrm{deg}]$',
ylabel=r'$\mathrm{Galactocentric\ radius}\ /R_0$',
cmap='gist_yarg',xrange=sc.array(phirange)*_RADTODEG,
yrange=rrange,
aspect=(phirange[1]-phirange[0])*_RADTODEG/(rrange[1]-rrange[0]))
#contour the los distance and gall
#plot.bovy_text(-22.,1.1,r'$\mathrm{apogee}$',color='w',
# rotation=105.)
plot.bovy_text(-18.,1.1,r'$\mathrm{APOGEE}$',color='w',
rotation=285.)
levels= [2/8.2*(ii+1/2.) for ii in range(10)]
contour(losd.T,levels,colors='0.25',origin='lower',linestyles='--',
aspect=(phirange[1]-phirange[0])*_RADTODEG/(rrange[1]-rrange[0]),
extent=(phirange[0]*_RADTODEG,phirange[1]*_RADTODEG,
rrange[0],rrange[1]))
gall[gall < 0.]+= sc.pi*2.
levels= [0.,sc.pi/2.,sc.pi,3.*sc.pi/2.]
contour(gall.T,levels,colors='w',origin='lower',linestyles='--',
aspect=(phirange[1]-phirange[0])*_RADTODEG/(rrange[1]-rrange[0]),
extent=(phirange[0]*_RADTODEG,phirange[1]*_RADTODEG,
rrange[0],rrange[1]))
levels= [-5/180.*sc.pi,250/180.*sc.pi]
contour(gall.T,levels,colors='w',origin='lower',linestyles='-.',
aspect=(phirange[1]-phirange[0])*_RADTODEG/(rrange[1]-rrange[0]),
extent=(phirange[0]*_RADTODEG,phirange[1]*_RADTODEG,
rrange[0],rrange[1]))
if options.skipCenter == 0.:
plot.bovy_text(r'$\mathrm{KL\ divergence\ / \ all}\ v_{\mathrm{los}}$',
title=True)
else:
plot.bovy_text(r'$\mathrm{KL\ divergence\ / }\ |v_{\mathrm{los}}| \geq %.2f \ v_0$' % options.skipCenter,
title=True)
plot.bovy_end_print(args[0])
def exMix1(exclude=None,
plotfilenameA='exMix1a.png',
plotfilenameB='exMix1b.png',
plotfilenameC='exMix1c.png',
nburn=20000,
nsamples=1000000,
parsigma=[5, .075, .2, 1, .1],
dsigma=1.,
bovyprintargs={},
sampledata=None):
"""exMix1: solve exercise 5 (mixture model) using MCMC sampling
Input:
exclude - ID numbers to exclude from the analysis (can be None)
plotfilename* - filenames for the output plot
nburn - number of burn-in samples
nsamples - number of samples to take after burn-in
parsigma - proposal distribution width (Gaussian)
dsigma - divide uncertainties by this amount
Output:
plot
History:
2010-04-28 - Written - Bovy (NYU)
"""
sc.random.seed(-1) #In the interest of reproducibility (if that's a word)
#Read the data
data = read_data('data_yerr.dat')
ndata = len(data)
if not exclude == None:
nsample = ndata - len(exclude)
else:
nsample = ndata
#First find the chi-squared solution, which we will use as an
#initial guess
#Put the data in the appropriate arrays and matrices
Y = sc.zeros(nsample)
X = sc.zeros(nsample)
A = sc.ones((nsample, 2))
C = sc.zeros((nsample, nsample))
yerr = sc.zeros(nsample)
jj = 0
for ii in range(ndata):
if not exclude == None and sc.any(exclude == data[ii][0]):
pass
else:
Y[jj] = data[ii][1][1]
X[jj] = data[ii][1][0]
A[jj, 1] = data[ii][1][0]
C[jj, jj] = data[ii][2]**2. / dsigma**2.
yerr[jj] = data[ii][2] / dsigma
jj = jj + 1
brange = [-120, 120]
mrange = [1.5, 3.2]
# This matches the order of the parameters in the "samples" vector
mbrange = [brange, mrange]
if sampledata is None:
sampledata = runSampler(X, Y, A, C, yerr, nburn, nsamples, parsigma,
mbrange)
(histmb, edges, mbsamples, pbhist, pbedges) = sampledata
# Hack -- produce fake Pbad samples from Pbad histogram.
pbsamples = hstack([
array([x] * N)
for x, N in zip((pbedges[:-1] + pbedges[1:]) / 2, pbhist)
])
indxi = sc.argmax(sc.amax(histmb, axis=1))
indxj = sc.argmax(sc.amax(histmb, axis=0))
print "Best-fit, marginalized"
print edges[0][indxi - 1], edges[1][indxj - 1]
print edges[0][indxi], edges[1][indxj]
print edges[0][indxi + 1], edges[1][indxj + 1]
#2D histogram
plot.bovy_print(**bovyprintargs)
levels = special.erf(0.5 * sc.arange(1, 4))
xe = [edges[0][0], edges[0][-1]]
ye = [edges[1][0], edges[1][-1]]
aspect = (xe[1] - xe[0]) / (ye[1] - ye[0])
plot.bovy_dens2d(histmb.T,
origin='lower',
cmap=cm.gist_yarg,
interpolation='nearest',
contours=True,
cntrmass=True,
extent=xe + ye,
levels=levels,
aspect=aspect,
xlabel=r'$b$',
ylabel=r'$m$')
xlim(brange)
ylim(mrange)
plot.bovy_end_print(plotfilenameA)
#Data with MAP line and sampling
plot.bovy_print(**bovyprintargs)
bestb = edges[0][indxi]
bestm = edges[1][indxj]
xrange = [0, 300]
yrange = [0, 700]
plot.bovy_plot(xrange,
bestm * sc.array(xrange) + bestb,
'k-',
xrange=xrange,
yrange=yrange,
xlabel=r'$x$',
ylabel=r'$y$',
zorder=2)
errorbar(X, Y, yerr, marker='o', color='k', linestyle='None', zorder=1)
for m, b in mbsamples:
plot.bovy_plot(xrange,
m * sc.array(xrange) + b,
overplot=True,
xrange=xrange,
yrange=yrange,
xlabel=r'$x$',
ylabel=r'$y$',
color='0.75',
zorder=1)
plot.bovy_end_print(plotfilenameB)
#Pb plot
if not 'text_fontsize' in bovyprintargs:
bovyprintargs['text_fontsize'] = 11
plot.bovy_print(**bovyprintargs)
plot.bovy_hist(pbsamples,
bins=round(sc.sqrt(nsamples) / 5.),
xlabel=r'$P_\mathrm{b}$',
normed=True,
histtype='step',
range=[0, 1],
edgecolor='k')
ylim(0, 4.)
if dsigma == 1.:
plot.bovy_text(r'$\mathrm{using\ correct\ data\ uncertainties}$',
top_right=True)
else:
plot.bovy_text(r'$\mathrm{using\ data\ uncertainties\ /\ 2}$',
top_left=True)
plot.bovy_end_print(plotfilenameC)
return sampledata