def _evaluate(self,l,b,d,_lbIndx=None):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc) can be array
OUTPUT:
extinction
HISTORY:
2015-03-08 - Started - Bovy (IAS)
"""
if isinstance(l,numpy.ndarray) or isinstance(b,numpy.ndarray):
raise NotImplementedError("array input for l and b for Sale et al. (2014) dust map not implemented")
if _lbIndx is None: lbIndx= self._lbIndx(l,b)
else: lbIndx= _lbIndx
if self._intps[lbIndx] != 0:
out= self._intps[lbIndx](d)
else:
tlbData= self.lbData(l,b)
interpData=\
interpolate.InterpolatedUnivariateSpline(self._ds,
tlbData['a0'],
k=1)
out= interpData(d)
self._intps[lbIndx]= interpData
if self._filter is None: # Sale et al. say A0/Aks = 11
return out/11./aebv('2MASS Ks',sf10=self._sf10)
else: # if sf10, first put ebv on SFD scale
return out/11./aebv('2MASS Ks',sf10=self._sf10)\
*aebv(self._filter,sf10=self._sf10)
def _evaluate(self,l,b,d):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc) can be array
OUTPUT:
extinction
HISTORY:
2013-12-12 - Started - Bovy (IAS)
"""
if isinstance(l,numpy.ndarray) or isinstance(b,numpy.ndarray):
raise NotImplementedError("array input for l and b for Drimmel dust map not implemented")
lbIndx= self._lbIndx(l,b)
if self._intps[lbIndx] != 0:
out= self._intps[lbIndx](d)
else:
tlbData= self.lbData(l,b,addBC=True)
interpData=\
interpolate.InterpolatedUnivariateSpline(tlbData['dist'],
tlbData['aks'],
k=1)
out= interpData(d)
self._intps[lbIndx]= interpData
if self._filter is None:
return out/aebv('2MASS Ks',sf10=self._sf10)
else:
return out/aebv('2MASS Ks',sf10=self._sf10)\
*aebv(self._filter,sf10=self._sf10)
def _evaluate(self, l, b, d):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc) can be array
OUTPUT:
extinction
HISTORY:
2013-12-12 - Started - Bovy (IAS)
"""
if isinstance(l, numpy.ndarray) or isinstance(b, numpy.ndarray):
raise NotImplementedError(
"array input for l and b for Drimmel dust map not implemented")
lbIndx = self._lbIndx(l, b)
if self._intps[lbIndx] != 0:
out = self._intps[lbIndx](d)
else:
tlbData = self.lbData(l, b, addBC=True)
interpData=\
interpolate.InterpolatedUnivariateSpline(tlbData['dist'],
tlbData['aks'],
k=1)
out = interpData(d)
self._intps[lbIndx] = interpData
if self._filter is None:
return out / aebv('2MASS Ks', sf10=self._sf10)
else:
return out/aebv('2MASS Ks',sf10=self._sf10)\
*aebv(self._filter,sf10=self._sf10)
def plotData(self,l,b,*args,**kwargs):
"""
NAME:
plotData
PURPOSE:
plot the Marshall et al. (2006) extinction values
along a given line of sight as a function of
distance
INPUT:
l,b - Galactic longitude and latitude (degree)
bovy_plot.bovy_plot args and kwargs
OUTPUT:
plot to output device
HISTORY:
2013-12-15 - Written - Bovy (IAS)
"""
if not _BOVY_PLOT_LOADED:
raise NotImplementedError("galpy.util.bovy_plot could not be loaded, so there is no plotting; might have to install galpy (http://github.com/jobovy/galpy) for plotting")
#First get the data
tdata= self.lbData(l,b)
#Filter
if self._filter is None:
filterFac= 1./aebv('2MASS Ks',sf10=self._sf10)
else:
filterFac= 1./aebv('2MASS Ks',sf10=self._sf10)\
*aebv(self._filter,sf10=self._sf10)
#Plot
out= bovy_plot.bovy_plot(tdata['dist'],tdata['aks']*filterFac,
*args,**kwargs)
#uncertainties
pyplot.errorbar(tdata['dist'],tdata['aks']*filterFac,
xerr=tdata['e_dist'],
yerr=tdata['e_aks']*filterFac,
ls='none',marker=None,color='k')
return out
def plotData(self,l,b,*args,**kwargs):
"""
NAME:
plotData
PURPOSE:
plot the Marshall et al. (2006) extinction values
along a given line of sight as a function of
distance
INPUT:
l,b - Galactic longitude and latitude (degree)
bovy_plot.bovy_plot args and kwargs
OUTPUT:
plot to output device
HISTORY:
2013-12-15 - Written - Bovy (IAS)
"""
if not _BOVY_PLOT_LOADED:
raise NotImplementedError("galpy.util.bovy_plot could not be loaded, so there is no plotting; might have to install galpy (http://github.com/jobovy/galpy) for plotting")
#First get the data
tdata= self.lbData(l,b)
#Filter
if self._filter is None:
filterFac= 1./aebv('2MASS Ks',sf10=self._sf10)
else:
filterFac= 1./aebv('2MASS Ks',sf10=self._sf10)\
*aebv(self._filter,sf10=self._sf10)
#Plot
out= bovy_plot.bovy_plot(tdata['dist'],tdata['aks']*filterFac,
*args,**kwargs)
#uncertainties
pyplot.errorbar(tdata['dist'],tdata['aks']*filterFac,
xerr=tdata['e_dist'],
yerr=tdata['e_aks']*filterFac,
ls='none',marker=None,color='k')
return out
def _evaluate(self,l,b,d):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc) can be array
OUTPUT:
extinction E(B-V)
HISTORY:
2015-03-02 - Started - Bovy (IAS)
"""
distmod= 5.*numpy.log10(d)+10.
if isinstance(l,numpy.ndarray) or isinstance(b,numpy.ndarray):
raise NotImplementedError("array input for l and b for combined dust map not implemented")
lbIndx= self._lbIndx(l,b)
if self._intps[lbIndx] != 0:
out= self._intps[lbIndx][0](distmod)
else:
interpData=\
interpolate.InterpolatedUnivariateSpline(self._distmods,
self._best_fit[lbIndx],
k=self._interpk)
out= interpData(distmod)
self._intps[lbIndx]= interpData
if self._filter is None:
return out
else:
return out*aebv(self._filter,sf10=self._sf10)
def _evaluate(self, l, b, d):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc) can be array
OUTPUT:
extinction E(B-V)
HISTORY:
2015-03-02 - Started - Bovy (IAS)
"""
distmod = 5. * numpy.log10(d) + 10.
if isinstance(l, numpy.ndarray) or isinstance(b, numpy.ndarray):
raise NotImplementedError(
"array input for l and b for combined dust map not implemented"
)
lbIndx = self._lbIndx(l, b)
if self._intps[lbIndx] != 0:
out = self._intps[lbIndx][0](distmod)
else:
interpData=\
interpolate.InterpolatedUnivariateSpline(self._distmods,
self._best_fit[lbIndx],
k=self._interpk)
out = interpData(distmod)
self._intps[lbIndx] = interpData
if self._filter is None:
return out
else:
return out * aebv(self._filter, sf10=self._sf10)
def _evaluate(self, l, b, d):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc)
OUTPUT:
extinction
HISTORY:
2013-11-24 - Started - Bovy (IAS)
"""
tebv = read_SFD_EBV(l,
b,
interp=self._interp,
noloop=self._noloop,
verbose=False)
if self._filter is None:
return tebv * numpy.ones_like(d)
else:
return tebv * aebv(self._filter,
sf10=self._sf10) * numpy.ones_like(d)
def dust_vals_disk(self, lcen, bcen, dist, radius):
"""
NAME:
dust_vals_disk
PURPOSE:
return the distribution of extinction within a small disk as samples
INPUT:
lcen, bcen - Galactic longitude and latitude of the center of the disk (deg)
dist - distance in kpc
radius - radius of the disk (deg)
OUTPUT:
(pixarea,extinction) - arrays of pixel-area in sq rad and extinction value
HISTORY:
2015-03-06 - Written - Bovy (IAS)
"""
# Convert the disk center to a HEALPIX vector
vec = healpy.pixelfunc.ang2vec((90. - bcen) * _DEGTORAD,
lcen * _DEGTORAD)
distmod = 5. * numpy.log10(dist) + 10.
# Query the HEALPIX map for pixels that lie within the disk
pixarea = []
extinction = []
for nside in self._nsides:
# Find the pixels at this resolution that fall within the disk
ipixs = healpy.query_disc(nside,
vec,
radius * _DEGTORAD,
inclusive=False,
nest=True)
# Get indices of all pixels within the disk at current nside level
nsideindx = self._pix_info['nside'] == nside
potenIndxs = self._indexArray[nsideindx]
nsidepix = self._pix_info['healpix_index'][nsideindx]
# Loop through the pixels in the (small) disk
tout = []
for ii, ipix in enumerate(ipixs):
lbIndx = potenIndxs[ipix == nsidepix]
if numpy.sum(lbIndx) == 0: continue
if self._intps[lbIndx] != 0:
tout.append(self._intps[lbIndx][0](distmod))
else:
interpData=\
interpolate.InterpolatedUnivariateSpline(self._distmods,
self._best_fit[lbIndx],
k=self._interpk)
tout.append(interpData(distmod))
self._intps[lbIndx] = interpData
tarea = healpy.pixelfunc.nside2pixarea(nside)
tarea = [tarea for ii in range(len(tout))]
pixarea.extend(tarea)
extinction.extend(tout)
pixarea = numpy.array(pixarea)
extinction = numpy.array(extinction)
if not self._filter is None:
extinction = extinction * aebv(self._filter, sf10=self._sf10)
return (pixarea, extinction)
def dust_vals_disk(self,lcen,bcen,dist,radius):
"""
NAME:
dust_vals_disk
PURPOSE:
return the distribution of extinction within a small disk as samples
INPUT:
lcen, bcen - Galactic longitude and latitude of the center of the disk (deg)
dist - distance in kpc
radius - radius of the disk (deg)
OUTPUT:
(pixarea,extinction) - arrays of pixel-area in sq rad and extinction value
HISTORY:
2015-03-06 - Written - Bovy (IAS)
"""
# Convert the disk center to a HEALPIX vector
vec= healpy.pixelfunc.ang2vec((90.-bcen)*_DEGTORAD,lcen*_DEGTORAD)
distmod= 5.*numpy.log10(dist)+10.
# Query the HEALPIX map for pixels that lie within the disk
pixarea= []
extinction= []
for nside in self._nsides:
# Find the pixels at this resolution that fall within the disk
ipixs= healpy.query_disc(nside,vec,radius*_DEGTORAD,
inclusive=False,nest=True)
# Get indices of all pixels within the disk at current nside level
nsideindx= self._pix_info['nside'] == nside
potenIndxs= self._indexArray[nsideindx]
nsidepix= self._pix_info['healpix_index'][nsideindx]
# Loop through the pixels in the (small) disk
tout= []
for ii,ipix in enumerate(ipixs):
lbIndx= potenIndxs[ipix == nsidepix]
if numpy.sum(lbIndx) == 0: continue
if self._intps[lbIndx] != 0:
tout.append(self._intps[lbIndx][0](distmod))
else:
interpData=\
interpolate.InterpolatedUnivariateSpline(self._distmods,
self._best_fit[lbIndx],
k=self._interpk)
tout.append(interpData(distmod))
self._intps[lbIndx]= interpData
tarea= healpy.pixelfunc.nside2pixarea(nside)
tarea= [tarea for ii in range(len(tout))]
pixarea.extend(tarea)
extinction.extend(tout)
pixarea= numpy.array(pixarea)
extinction= numpy.array(extinction)
if not self._filter is None:
extinction= extinction*aebv(self._filter,sf10=self._sf10)
return (pixarea,extinction)
def _evaluate(self,l,b,d):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc)
OUTPUT:
extinction
HISTORY:
2013-11-24 - Started - Bovy (IAS)
"""
tebv= read_SFD_EBV(l,b,interp=self._interp,
noloop=self._noloop,verbose=False)
if self._filter is None:
return tebv*numpy.ones_like(d)
else:
return tebv*aebv(self._filter,sf10=self._sf10)*numpy.ones_like(d)
def _evaluate(self, l, b, d, norescale=False, _fd=1., _fs=1., _fo=1.):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc) can be array
norescale= (False) if True, don't apply re-scalings
_fd, _fs, _fo= (1.) amplitudes of the different components
OUTPUT:
extinction
HISTORY:
2013-12-10 - Started - Bovy (IAS)
"""
if isinstance(l, numpy.ndarray) or isinstance(b, numpy.ndarray):
raise NotImplementedError(
"array input for l and b for Drimmel dust map not implemented")
cl = numpy.cos(l * _DEGTORAD)
sl = numpy.sin(l * _DEGTORAD)
cb = numpy.cos(b * _DEGTORAD)
sb = numpy.sin(b * _DEGTORAD)
#Setup arrays
avori = numpy.zeros_like(d)
avspir = numpy.zeros_like(d)
avdisk = numpy.zeros_like(d)
#Find nearest pixel in COBE map for the re-scaling
rfIndx = numpy.argmax(
cos_sphere_dist(self._rf_sintheta, self._rf_costheta,
self._rf_sinphi, self._rf_cosphi,
numpy.sin(numpy.pi / 2. - b * _DEGTORAD),
numpy.cos(numpy.pi / 2. - b * _DEGTORAD), sl, cl))
rfdisk, rfspir, rfori = 1., 1., 1,
if self._drimmelMaps['rf_comp'][rfIndx] == 1 and not norescale:
rfdisk = self._drimmelMaps['rf'][rfIndx]
elif self._drimmelMaps['rf_comp'][rfIndx] == 2 and not norescale:
rfspir = self._drimmelMaps['rf'][rfIndx]
elif self._drimmelMaps['rf_comp'][rfIndx] == 3 and not norescale:
rfori = self._drimmelMaps['rf'][rfIndx]
#Find maximum distance
dmax = 100.
if b != 0.: dmax = .49999 / numpy.fabs(sb) - self._zsun / sb
if cl != 0.:
tdmax = (14.9999 / numpy.fabs(cl) - self._xsun / cl)
if tdmax < dmax: dmax = tdmax
if sl != 0.:
tdmax = 14.9999 / numpy.fabs(sl)
if tdmax < dmax: dmax = tdmax
d = copy.copy(d)
d[d > dmax] = dmax
#Rectangular coordinates
X = d * cb * cl
Y = d * cb * sl
Z = d * sb + self._zsun
#Local grid
#Orion
locIndx = (numpy.fabs(X) < 1.) * (numpy.fabs(Y) < 2.)
if numpy.sum(locIndx) > 0:
xi = X[locIndx] / self._dx_ori2 + float(self._nx_ori2 - 1) / 2.
yj = Y[locIndx] / self._dy_ori2 + float(self._ny_ori2 - 1) / 2.
zk = Z[locIndx] / self._dz_ori2 + float(self._nz_ori2 - 1) / 2.
avori[locIndx] = map_coordinates(self._drimmelMaps['avori2'],
[xi, yj, zk],
mode='constant',
cval=0.)
#local disk
locIndx = (numpy.fabs(X) < 0.75) * (numpy.fabs(Y) < 0.75)
if numpy.sum(locIndx) > 0:
xi = X[locIndx] / self._dx_diskloc + float(self._nx_diskloc -
1) / 2.
yj = Y[locIndx] / self._dy_diskloc + float(self._ny_diskloc -
1) / 2.
zk = Z[locIndx] / self._dz_diskloc + float(self._nz_diskloc -
1) / 2.
avdisk[locIndx] = map_coordinates(self._drimmelMaps['avdloc'],
[xi, yj, zk],
mode='constant',
cval=0.)
#Go to Galactocentric coordinates
X = X + self._xsun
#Stars beyond the local grid
#Orion
globIndx = True - (numpy.fabs(X - self._xsun) < 1.) * (numpy.fabs(Y) <
2.)
if numpy.sum(globIndx) > 0:
#Orion grid is different from other global grids, so has its own dmax
dmax = 100.
if b != 0.: dmax = .49999 / numpy.fabs(sb) - self._zsun / sb
if cl > 0.:
tdmax = (2.374999 / numpy.fabs(cl))
if tdmax < dmax: dmax = tdmax
if cl < 0.:
tdmax = (1.374999 / numpy.fabs(cl))
if tdmax < dmax: dmax = tdmax
if sl != 0.:
tdmax = (3.749999 / numpy.fabs(sl))
if tdmax < dmax: dmax = tdmax
dori = copy.copy(d)
dori[dori > dmax] = dmax
Xori = dori * cb * cl + self._xsun
Yori = dori * cb * sl
Zori = dori * sb + self._zsun
xi = Xori[globIndx] / self._dx_ori + 2.5 * float(self._nx_ori - 1)
yj = Yori[globIndx] / self._dy_ori + float(self._ny_ori - 1) / 2.
zk = Zori[globIndx] / self._dz_ori + float(self._nz_ori - 1) / 2.
avori[globIndx] = map_coordinates(self._drimmelMaps['avori'],
[xi, yj, zk],
mode='constant',
cval=0.)
#disk & spir
xi = X / self._dx_disk + float(self._nx_disk - 1) / 2.
yj = Y / self._dy_disk + float(self._ny_disk - 1) / 2.
zk = Z / self._dz_disk + float(self._nz_disk - 1) / 2.
avspir = map_coordinates(self._drimmelMaps['avspir'], [xi, yj, zk],
mode='constant',
cval=0.)
globIndx = True - (numpy.fabs(X - self._xsun) < 0.75) * (numpy.fabs(Y)
< 0.75)
if numpy.sum(globIndx) > 0:
avdisk[globIndx] = map_coordinates(self._drimmelMaps['avdisk'],
[xi, yj, zk],
mode='constant',
cval=0.)[globIndx]
#Return
out = _fd * rfdisk * avdisk + _fs * rfspir * avspir + _fo * rfori * avori
if self._filter is None: # From Rieke & Lebovksy (1985); if sf10, first put ebv on SFD scale
return out / 3.09 / ((1 - self._sf10) + self._sf10 * 0.86)
else:
return out/3.09/((1-self._sf10)+self._sf10*0.86)\
*aebv(self._filter,sf10=self._sf10)
def _evaluate(self,l,b,d,norescale=False,
_fd=1.,_fs=1.,_fo=1.):
"""
NAME:
_evaluate
PURPOSE:
evaluate the dust-map
INPUT:
l- Galactic longitude (deg)
b- Galactic latitude (deg)
d- distance (kpc) can be array
norescale= (False) if True, don't apply re-scalings
_fd, _fs, _fo= (1.) amplitudes of the different components
OUTPUT:
extinction
HISTORY:
2013-12-10 - Started - Bovy (IAS)
"""
if isinstance(l,numpy.ndarray) or isinstance(b,numpy.ndarray):
raise NotImplementedError("array input for l and b for Drimmel dust map not implemented")
cl= numpy.cos(l*_DEGTORAD)
sl= numpy.sin(l*_DEGTORAD)
cb= numpy.cos(b*_DEGTORAD)
sb= numpy.sin(b*_DEGTORAD)
#Setup arrays
avori= numpy.zeros_like(d)
avspir= numpy.zeros_like(d)
avdisk= numpy.zeros_like(d)
#Find nearest pixel in COBE map for the re-scaling
rfIndx= numpy.argmax(cos_sphere_dist(self._rf_sintheta,
self._rf_costheta,
self._rf_sinphi,
self._rf_cosphi,
numpy.sin(numpy.pi/2.-b*_DEGTORAD),
numpy.cos(numpy.pi/2.-b*_DEGTORAD),
sl,cl))
rfdisk, rfspir, rfori= 1., 1., 1,
if self._drimmelMaps['rf_comp'][rfIndx] == 1 and not norescale:
rfdisk= self._drimmelMaps['rf'][rfIndx]
elif self._drimmelMaps['rf_comp'][rfIndx] == 2 and not norescale:
rfspir= self._drimmelMaps['rf'][rfIndx]
elif self._drimmelMaps['rf_comp'][rfIndx] == 3 and not norescale:
rfori= self._drimmelMaps['rf'][rfIndx]
#Find maximum distance
dmax= 100.
if b != 0.: dmax= .49999/numpy.fabs(sb) - self._zsun/sb
if cl != 0.:
tdmax= (14.9999/numpy.fabs(cl)-self._xsun/cl)
if tdmax < dmax: dmax= tdmax
if sl != 0.:
tdmax = 14.9999/numpy.fabs(sl)
if tdmax < dmax: dmax= tdmax
d= copy.copy(d)
d[d > dmax]= dmax
#Rectangular coordinates
X= d*cb*cl
Y= d*cb*sl
Z= d*sb+self._zsun
#Local grid
#Orion
locIndx= (numpy.fabs(X) < 1.)*(numpy.fabs(Y) < 2.)
if numpy.sum(locIndx) > 0:
xi = X[locIndx]/self._dx_ori2+float(self._nx_ori2-1)/2.
yj = Y[locIndx]/self._dy_ori2+float(self._ny_ori2-1)/2.
zk = Z[locIndx]/self._dz_ori2+float(self._nz_ori2-1)/2.
avori[locIndx]= map_coordinates(self._drimmelMaps['avori2'],
[xi,yj,zk],
mode='constant',cval=0.)
#local disk
locIndx= (numpy.fabs(X) < 0.75)*(numpy.fabs(Y) < 0.75)
if numpy.sum(locIndx) > 0:
xi = X[locIndx]/self._dx_diskloc+float(self._nx_diskloc-1)/2.
yj = Y[locIndx]/self._dy_diskloc+float(self._ny_diskloc-1)/2.
zk = Z[locIndx]/self._dz_diskloc+float(self._nz_diskloc-1)/2.
avdisk[locIndx]= map_coordinates(self._drimmelMaps['avdloc'],
[xi,yj,zk],
mode='constant',cval=0.)
#Go to Galactocentric coordinates
X= X+self._xsun
#Stars beyond the local grid
#Orion
globIndx= True-(numpy.fabs(X-self._xsun) < 1.)*(numpy.fabs(Y) < 2.)
if numpy.sum(globIndx) > 0:
#Orion grid is different from other global grids, so has its own dmax
dmax= 100.
if b != 0.: dmax= .49999/numpy.fabs(sb) - self._zsun/sb
if cl > 0.:
tdmax = (2.374999/numpy.fabs(cl))
if tdmax < dmax: dmax= tdmax
if cl < 0.:
tdmax = (1.374999/numpy.fabs(cl))
if tdmax < dmax: dmax= tdmax
if sl != 0.:
tdmax = (3.749999/numpy.fabs(sl))
if tdmax < dmax: dmax= tdmax
dori= copy.copy(d)
dori[dori > dmax]= dmax
Xori= dori*cb*cl+self._xsun
Yori= dori*cb*sl
Zori= dori*sb+self._zsun
xi = Xori[globIndx]/self._dx_ori + 2.5*float(self._nx_ori-1)
yj = Yori[globIndx]/self._dy_ori + float(self._ny_ori-1)/2.
zk = Zori[globIndx]/self._dz_ori + float(self._nz_ori-1)/2.
avori[globIndx]= map_coordinates(self._drimmelMaps['avori'],
[xi,yj,zk],
mode='constant',cval=0.)
#disk & spir
xi = X/self._dx_disk+float(self._nx_disk-1)/2.
yj = Y/self._dy_disk+float(self._ny_disk-1)/2.
zk = Z/self._dz_disk+float(self._nz_disk-1)/2.
avspir= map_coordinates(self._drimmelMaps['avspir'],
[xi,yj,zk],
mode='constant',cval=0.)
globIndx= True-(numpy.fabs(X-self._xsun) < 0.75)*(numpy.fabs(Y) < 0.75)
if numpy.sum(globIndx) > 0:
avdisk[globIndx]= map_coordinates(self._drimmelMaps['avdisk'],
[xi,yj,zk],
mode='constant',
cval=0.)[globIndx]
#Return
out=_fd*rfdisk*avdisk+_fs*rfspir*avspir+_fo*rfori*avori
if self._filter is None: # From Rieke & Lebovksy (1985); if sf10, first put ebv on SFD scale
return out/3.09/((1-self._sf10)+self._sf10*0.78)
else:
return out/3.09/((1-self._sf10)+self._sf10*0.78)\
*aebv(self._filter,sf10=self._sf10)
def plot_mollweide(self, d, **kwargs):
"""
NAME:
plot_mollweide
PURPOSE:
plot the extinction across the sky in Galactic coordinates out to a given distance using a Mollweide projection
INPUT:
d - distance in kpc (nearest distance to this in the map is plotted)
nside_plot= (2048) nside of the plotted map
healpy.visufunc.mollview kwargs
OUTPUT:
plot to output device
HISTORY:
2019-12-06 - Written - Bovy (UofT)
"""
# Distance modulus
dm = 5. * numpy.log10(d) + 10.
# Get factor to apply to map to obtain extinction in object's filter
filter_fac= aebv(self._filter,sf10=self._sf10) \
if not self._filter is None else 1.
# Map the dust map to a common nside, first find nearest distance pixel
tpix = numpy.argmin(numpy.fabs(dm - self._distmods))
# Construct an empty map at the highest HEALPix resolution present in the map; code snippets adapted from http://argonaut.skymaps.info/usage
nside_max = numpy.max(self._pix_info['nside'])
npix = healpy.pixelfunc.nside2npix(nside_max)
pix_val = numpy.empty(npix, dtype='f8')
pix_val[:] = healpy.UNSEEN
# Fill the upsampled map
for nside in numpy.unique(self._pix_info['nside']):
# Get indices of all pixels at current nside level
indx = self._pix_info['nside'] == nside
# Extract A_X of each selected pixel
pix_val_n = filter_fac * self._best_fit[indx, tpix]
# Determine nested index of each selected pixel in upsampled map
mult_factor = (nside_max // nside)**2
pix_idx_n = self._pix_info['healpix_index'][indx] * mult_factor
# Write the selected pixels into the upsampled map
for offset in range(mult_factor):
pix_val[pix_idx_n + offset] = pix_val_n[:]
# If the desired nside is less than the maximum nside in the map, degrade
nside_plot = kwargs.get('nside_plot', 2048)
if not nside_plot is None and nside_plot < nside_max:
pix_val = healpy.pixelfunc.ud_grade(pix_val,
nside_plot,
pess=False,
order_in='NEST',
order_out='NEST')
pix_val[pix_val == healpy.UNSEEN] = -1.
if not self._filter is None:
kwargs['unit'] = r'$A_{%s}\,(\mathrm{mag})$' % (
self._filter.split(' ')[-1])
else:
kwargs['unit'] = r'$E(B-V)\,(\mathrm{mag})$'
kwargs['title'] = kwargs.get('title', "")
healpy.visufunc.mollview(pix_val,
nest=True,
xsize=4000,
min=0.,
max=numpy.quantile(pix_val, 0.99),
format=r'$%g$',
cmap='gist_yarg',
**kwargs)
return None
