def mvlosnonaxi(params, interpObj, interpObjAxi, thesedata, options, logpiso,
phio):
l = thesedata['GLON'] * _DEGTORAD
b = thesedata['GLAT'] * _DEGTORAD
cosb = math.cos(b)
sinb = math.sin(b)
out = 0.
norm = 0.
ds = numpy.linspace(_BINTEGRATEDMAX, _BINTEGRATEDMIN,
_BINTEGRATENBINS) / params[1] / _REFR0
for ii in range(_BINTEGRATENBINS):
#Calculate R and phi
R, phi = bovy_coords.dl_to_rphi_2d(ds[ii], l, degree=False, phio=phio)
if R < 0.5 or R > 2.: continue #Not in the model
logpd = _logpd(params, ds[ii], l, b, 0., 0., None, options, R, phi,
cosb, sinb, logpiso[ii])
out += math.exp(logpd) * (
(interpObj.meanvt(R, phi) * numpy.sin(phi + l - phio) -
interpObj.meanvr(R, phi) * numpy.cos(phi + l - phio)) -
(interpObjAxi.meanvt(R, phi) * numpy.sin(phi + l - phio) -
interpObjAxi.meanvr(R, phi) * numpy.cos(phi + l - phio)))
norm += math.exp(logpd)
if norm == 0.: return 0.
out /= norm
#if l < 35.*_DEGTORAD: print out
if options.dwarf:
out *= (1. - params[5 - options.nooutliermean])
R, phi = 1., phio
out += params[5 - options.nooutliermean] * (
(interpObj.meanvt(R, phi) * numpy.sin(phi + l - phio) -
interpObj.meanvr(R, phi) * numpy.cos(phi + l - phio)) -
(interpObjAxi.meanvt(R, phi) * numpy.sin(phi + l - phio) -
interpObjAxi.meanvr(R, phi) * numpy.cos(phi + l - phio)))
return out[0][0]
def mvlosnonaxi(params, interpObj, interpObjAxi, thesedata, options, logpiso, phio):
l = thesedata["GLON"] * _DEGTORAD
b = thesedata["GLAT"] * _DEGTORAD
cosb = math.cos(b)
sinb = math.sin(b)
out = 0.0
norm = 0.0
ds = numpy.linspace(_BINTEGRATEDMAX, _BINTEGRATEDMIN, _BINTEGRATENBINS) / params[1] / _REFR0
for ii in range(_BINTEGRATENBINS):
# Calculate R and phi
R, phi = bovy_coords.dl_to_rphi_2d(ds[ii], l, degree=False, phio=phio)
if R < 0.5 or R > 2.0:
continue # Not in the model
logpd = _logpd(params, ds[ii], l, b, 0.0, 0.0, None, options, R, phi, cosb, sinb, logpiso[ii])
out += math.exp(logpd) * (
(
interpObj.meanvt(R, phi) * numpy.sin(phi + l - phio)
- interpObj.meanvr(R, phi) * numpy.cos(phi + l - phio)
)
- (
interpObjAxi.meanvt(R, phi) * numpy.sin(phi + l - phio)
- interpObjAxi.meanvr(R, phi) * numpy.cos(phi + l - phio)
)
)
norm += math.exp(logpd)
if norm == 0.0:
return 0.0
out /= norm
# if l < 35.*_DEGTORAD: print out
if options.dwarf:
out *= 1.0 - params[5 - options.nooutliermean]
R, phi = 1.0, phio
out += params[5 - options.nooutliermean] * (
(
interpObj.meanvt(R, phi) * numpy.sin(phi + l - phio)
- interpObj.meanvr(R, phi) * numpy.cos(phi + l - phio)
)
- (
interpObjAxi.meanvt(R, phi) * numpy.sin(phi + l - phio)
- interpObjAxi.meanvr(R, phi) * numpy.cos(phi + l - phio)
)
)
return out[0][0]
def plot_distanceprior(parser):
(options,args)= parser.parse_args()
#Read the data
print "Reading the data ..."
data= readVclosData(postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
l= data['GLON']*_DEGTORAD
b= data['GLAT']*_DEGTORAD
sinl= numpy.sin(l)
cosl= numpy.cos(l)
sinb= numpy.sin(b)
cosb= numpy.cos(b)
jk= data['J0MAG']-data['K0MAG']
jk[(jk < 0.5)]= 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL
h= data['H0MAG']
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile= open(options.isofile,'rb')
iso= pickle.load(isofile)
if options.indivfeh:
zs= pickle.load(isofile)
elif options.varfeh:
locl= pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso= []
zs= numpy.arange(0.0005,0.03005,0.0005)
for ii in range(len(zs)):
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs= list(set(data['LOCATION']))
iso= []
for ii in range(len(locs)):
indx= (data['LOCATION'] == locs[ii])
locl= numpy.mean(data['GLON'][indx]*_DEGTORAD)
iso.append(isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso= isomodel.isomodel(imfmodel=options.imfmodel,Z=options.Z,
expsfh=options.expsfh)
#Set up polar grid
res= 51
xgrid= numpy.linspace(0.,2.*math.pi*(1.-1./res/2.),
2*res)
ygrid= numpy.linspace(0.5,2.8,res)
plotxgrid= numpy.linspace(xgrid[0]-(xgrid[1]-xgrid[0])/2.,
xgrid[-1]+(xgrid[1]-xgrid[0])/2.,
len(xgrid)+1)
plotygrid= numpy.linspace(ygrid[0]-(ygrid[1]-ygrid[0])/2.,
ygrid[-1]+(ygrid[1]-ygrid[0])/2.,
len(ygrid)+1)
plotthis= numpy.zeros((2*res,res,len(data)))-numpy.finfo(numpy.dtype(numpy.float64)).max
#_BINTEGRATENBINS= 11 #For quick testing
ds= numpy.linspace(_BINTEGRATEDMIN,_BINTEGRATEDMAX,_BINTEGRATENBINS)
logpiso= numpy.zeros((len(data),_BINTEGRATENBINS))
dm= _dm(ds)
for ii in range(len(data)):
mh= h[ii]-dm
if options.indivfeh:
#Find closest Z
thisZ= isodist.FEH2Z(data[ii]['FEH'])
indx= numpy.argmin(numpy.fabs(thisZ-zs))
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+jk[ii],mh)
elif options.varfeh:
#Find correct iso
indx= (locl == data[ii]['LOCATION'])
logpiso[ii,:]= iso[0][indx](numpy.zeros(_BINTEGRATENBINS)+jk[ii],mh)
else:
logpiso[ii,:]= iso(numpy.zeros(_BINTEGRATENBINS)+jk[ii],mh)
for jj in range(_BINTEGRATENBINS):
d= ds[jj]/_REFR0
R= numpy.sqrt(1.+d**2.-2.*d*cosl)
indx= (R == 0.)
R[indx]+= 0.0001
theta= numpy.arcsin(d/R*sinl)
indx= (1./cosl < d)*(cosl > 0.)
theta[indx]= numpy.pi-theta[indx]
indx= (theta < 0.)
theta[indx]+= 2.*math.pi
thisout= _logpd([0.,1.],d,None,None,
None,None,None,
options,R,theta,
1.,0.,logpiso[:,jj])
#Find bin to which these contribute
thetabin= numpy.floor((theta-xgrid[0])/(xgrid[1]-xgrid[0])+0.5)
Rbin= numpy.floor((R-plotygrid[0])/(plotygrid[1]-plotygrid[0]))
indx= (thetabin < 0)
thetabin[indx]= 0
Rbin[indx]= 0
thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max
indx= (thetabin >= 2*res)
thetabin[indx]= 0. #Has to be
#Rbin[indx]= 0
#thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max
indx= (Rbin < 0)
thetabin[indx]= 0
Rbin[indx]= 0
thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max
indx= (Rbin >= res)
thetabin[indx]= 0
Rbin[indx]= 0
thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max
thetabin= thetabin.astype('int')
Rbin= Rbin.astype('int')
for ii in range(len(data)):
plotthis[thetabin,Rbin,ii]= thisout[ii]
#Normalize
for ii in range(2*res):
for jj in range(res):
plotthis[ii,jj,0]= logsumexp(plotthis[ii,jj,:])
plotthis= plotthis[:,:,0]
plotthis-= numpy.amax(plotthis)
plotthis= numpy.exp(plotthis)
plotthis[(plotthis == 0.)]= numpy.nan
#Get los
locations= list(set(data['LOCATION']))
nlocs= len(locations)
l_plate= numpy.zeros(nlocs)
for ii in range(nlocs):
indx= (data['LOCATION'] == locations[ii])
l_plate[ii]= numpy.mean(data['GLON'][indx])
bovy_plot.bovy_print()
ax= pyplot.subplot(111,projection='galpolar')#galpolar is in bovy_plot
vmin, vmax= 0., 1.
out= ax.pcolor(plotxgrid,plotygrid,plotthis.T,cmap='gist_yarg',
vmin=vmin,vmax=vmax,zorder=2)
#Overlay los
for ii in range(nlocs):
lds= numpy.linspace(0.,2.95,501)
lt= numpy.zeros(len(lds))
lr= numpy.zeros(len(lds))
lr= numpy.sqrt(1.+lds**2.-2.*lds*numpy.cos(l_plate[ii]*_DEGTORAD))
lt= numpy.arcsin(lds/lr*numpy.sin(l_plate[ii]*_DEGTORAD))
indx= (1./numpy.cos(l_plate[ii]*_DEGTORAD) < lds)*(numpy.cos(l_plate[ii]*_DEGTORAD) > 0.)
lt[indx]= numpy.pi-lt[indx]
ax.plot(lt,lr,
ls='--',color='w',zorder=3)
from matplotlib.patches import Arrow, FancyArrowPatch
arr= FancyArrowPatch(posA=(-math.pi/2.,1.8),
posB=(-math.pi/4.,1.8),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (-math.pi/16.),
shrinkA=2.0, shrinkB=2.0, mutation_scale=20.0,
mutation_aspect=None,fc='k')
ax.add_patch(arr)
bovy_plot.bovy_text(-math.pi/2.,1.97,r'$\mathrm{Galactic\ rotation}$',
rotation=-22.5)
radii= numpy.array([0.5,1.,1.5,2.,2.5])
labels= []
for r in radii:
ax.plot(numpy.linspace(0.,2.*math.pi,501,),
numpy.zeros(501)+r,ls='-',color='0.65',zorder=1,lw=0.5)
labels.append(r'$%i$' % int(r*8.))
pyplot.rgrids(radii,labels=labels,angle=-32.5)
bovy_plot.bovy_text(5.785,2.82,r'$\mathrm{kpc}$')
azs= numpy.array([0.,45.,90.,135.,180.,225.,270.,315.])*_DEGTORAD
for az in azs:
ax.plot(numpy.zeros(501)+az,
numpy.linspace(0.,2.8,501),'-',color='0.6',lw=0.5,zorder=1)
#Sun
bovy_plot.bovy_text(0.065,.9075,r'$\odot$')
pyplot.ylim(0.,2.8)
bovy_plot.bovy_end_print(options.plotfile)
def plot_distanceprior(parser):
(options, args) = parser.parse_args()
#Read the data
print "Reading the data ..."
data = readVclosData(
postshutdown=options.postshutdown,
fehcut=options.fehcut,
cohort=options.cohort,
lmin=options.lmin,
bmax=options.bmax,
ak=True,
cutmultiples=options.cutmultiples,
validfeh=options.indivfeh, #if indivfeh, we need validfeh
jkmax=options.jkmax,
datafilename=options.fakedata)
l = data['GLON'] * _DEGTORAD
b = data['GLAT'] * _DEGTORAD
sinl = numpy.sin(l)
cosl = numpy.cos(l)
sinb = numpy.sin(b)
cosb = numpy.cos(b)
jk = data['J0MAG'] - data['K0MAG']
jk[(jk < 0.5)] = 0.5 #BOVY: FIX THIS HACK BY EMAILING GAIL
h = data['H0MAG']
#Set up the isochrone
if not options.isofile is None and os.path.exists(options.isofile):
print "Loading the isochrone model ..."
isofile = open(options.isofile, 'rb')
iso = pickle.load(isofile)
if options.indivfeh:
zs = pickle.load(isofile)
elif options.varfeh:
locl = pickle.load(isofile)
isofile.close()
else:
print "Setting up the isochrone model ..."
if options.indivfeh:
#Load all isochrones
iso = []
zs = numpy.arange(0.0005, 0.03005, 0.0005)
for ii in range(len(zs)):
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
Z=zs[ii]))
elif options.varfeh:
locs = list(set(data['LOCATION']))
iso = []
for ii in range(len(locs)):
indx = (data['LOCATION'] == locs[ii])
locl = numpy.mean(data['GLON'][indx] * _DEGTORAD)
iso.append(
isomodel.isomodel(imfmodel=options.imfmodel,
expsfh=options.expsfh,
marginalizefeh=True,
glon=locl))
else:
iso = isomodel.isomodel(imfmodel=options.imfmodel,
Z=options.Z,
expsfh=options.expsfh)
#Set up polar grid
res = 51
xgrid = numpy.linspace(0., 2. * math.pi * (1. - 1. / res / 2.), 2 * res)
ygrid = numpy.linspace(0.5, 2.8, res)
plotxgrid = numpy.linspace(xgrid[0] - (xgrid[1] - xgrid[0]) / 2.,
xgrid[-1] + (xgrid[1] - xgrid[0]) / 2.,
len(xgrid) + 1)
plotygrid = numpy.linspace(ygrid[0] - (ygrid[1] - ygrid[0]) / 2.,
ygrid[-1] + (ygrid[1] - ygrid[0]) / 2.,
len(ygrid) + 1)
plotthis = numpy.zeros((2 * res, res, len(data))) - numpy.finfo(
numpy.dtype(numpy.float64)).max
#_BINTEGRATENBINS= 11 #For quick testing
ds = numpy.linspace(_BINTEGRATEDMIN, _BINTEGRATEDMAX, _BINTEGRATENBINS)
logpiso = numpy.zeros((len(data), _BINTEGRATENBINS))
dm = _dm(ds)
for ii in range(len(data)):
mh = h[ii] - dm
if options.indivfeh:
#Find closest Z
thisZ = isodist.FEH2Z(data[ii]['FEH'])
indx = numpy.argmin(numpy.fabs(thisZ - zs))
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
jk[ii], mh)
elif options.varfeh:
#Find correct iso
indx = (locl == data[ii]['LOCATION'])
logpiso[ii, :] = iso[0][indx](numpy.zeros(_BINTEGRATENBINS) +
jk[ii], mh)
else:
logpiso[ii, :] = iso(numpy.zeros(_BINTEGRATENBINS) + jk[ii], mh)
for jj in range(_BINTEGRATENBINS):
d = ds[jj] / _REFR0
R = numpy.sqrt(1. + d**2. - 2. * d * cosl)
indx = (R == 0.)
R[indx] += 0.0001
theta = numpy.arcsin(d / R * sinl)
indx = (1. / cosl < d) * (cosl > 0.)
theta[indx] = numpy.pi - theta[indx]
indx = (theta < 0.)
theta[indx] += 2. * math.pi
thisout = _logpd([0., 1.], d, None, None, None, None, None, options, R,
theta, 1., 0., logpiso[:, jj])
#Find bin to which these contribute
thetabin = numpy.floor((theta - xgrid[0]) / (xgrid[1] - xgrid[0]) +
0.5)
Rbin = numpy.floor((R - plotygrid[0]) / (plotygrid[1] - plotygrid[0]))
indx = (thetabin < 0)
thetabin[indx] = 0
Rbin[indx] = 0
thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max
indx = (thetabin >= 2 * res)
thetabin[indx] = 0. #Has to be
#Rbin[indx]= 0
#thisout[indx]= -numpy.finfo(numpy.dtype(numpy.float64)).max
indx = (Rbin < 0)
thetabin[indx] = 0
Rbin[indx] = 0
thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max
indx = (Rbin >= res)
thetabin[indx] = 0
Rbin[indx] = 0
thisout[indx] = -numpy.finfo(numpy.dtype(numpy.float64)).max
thetabin = thetabin.astype('int')
Rbin = Rbin.astype('int')
for ii in range(len(data)):
plotthis[thetabin, Rbin, ii] = thisout[ii]
#Normalize
for ii in range(2 * res):
for jj in range(res):
plotthis[ii, jj, 0] = logsumexp(plotthis[ii, jj, :])
plotthis = plotthis[:, :, 0]
plotthis -= numpy.amax(plotthis)
plotthis = numpy.exp(plotthis)
plotthis[(plotthis == 0.)] = numpy.nan
#Get los
locations = list(set(data['LOCATION']))
nlocs = len(locations)
l_plate = numpy.zeros(nlocs)
for ii in range(nlocs):
indx = (data['LOCATION'] == locations[ii])
l_plate[ii] = numpy.mean(data['GLON'][indx])
bovy_plot.bovy_print()
ax = pyplot.subplot(111, projection='galpolar') #galpolar is in bovy_plot
vmin, vmax = 0., 1.
out = ax.pcolor(plotxgrid,
plotygrid,
plotthis.T,
cmap='gist_yarg',
vmin=vmin,
vmax=vmax,
zorder=2)
#Overlay los
for ii in range(nlocs):
lds = numpy.linspace(0., 2.95, 501)
lt = numpy.zeros(len(lds))
lr = numpy.zeros(len(lds))
lr = numpy.sqrt(1. + lds**2. -
2. * lds * numpy.cos(l_plate[ii] * _DEGTORAD))
lt = numpy.arcsin(lds / lr * numpy.sin(l_plate[ii] * _DEGTORAD))
indx = (1. / numpy.cos(l_plate[ii] * _DEGTORAD) < lds) * (numpy.cos(
l_plate[ii] * _DEGTORAD) > 0.)
lt[indx] = numpy.pi - lt[indx]
ax.plot(lt, lr, ls='--', color='w', zorder=3)
from matplotlib.patches import Arrow, FancyArrowPatch
arr = FancyArrowPatch(posA=(-math.pi / 2., 1.8),
posB=(-math.pi / 4., 1.8),
arrowstyle='->',
connectionstyle='arc3,rad=%4.2f' % (-math.pi / 16.),
shrinkA=2.0,
shrinkB=2.0,
mutation_scale=20.0,
mutation_aspect=None,
fc='k')
ax.add_patch(arr)
bovy_plot.bovy_text(-math.pi / 2.,
1.97,
r'$\mathrm{Galactic\ rotation}$',
rotation=-22.5)
radii = numpy.array([0.5, 1., 1.5, 2., 2.5])
labels = []
for r in radii:
ax.plot(numpy.linspace(
0.,
2. * math.pi,
501,
),
numpy.zeros(501) + r,
ls='-',
color='0.65',
zorder=1,
lw=0.5)
labels.append(r'$%i$' % int(r * 8.))
pyplot.rgrids(radii, labels=labels, angle=-32.5)
bovy_plot.bovy_text(5.785, 2.82, r'$\mathrm{kpc}$')
azs = numpy.array([0., 45., 90., 135., 180., 225., 270., 315.]) * _DEGTORAD
for az in azs:
ax.plot(numpy.zeros(501) + az,
numpy.linspace(0., 2.8, 501),
'-',
color='0.6',
lw=0.5,
zorder=1)
#Sun
bovy_plot.bovy_text(0.065, .9075, r'$\odot$')
pyplot.ylim(0., 2.8)
bovy_plot.bovy_end_print(options.plotfile)
