def test_dl_to_rphi_2d():
#This is a tangent point
l= numpy.arcsin(0.75)
d= 6./numpy.tan(l)
r,phi= bovy_coords.dl_to_rphi_2d(d,l,degree=False,ro=8.,phio=0.)
assert numpy.fabs(r-6.) < 10.**-10., 'dl_to_rphi_2d conversion did not work as expected'
assert numpy.fabs(phi-numpy.arccos(0.75)) < 10.**-10., 'dl_to_rphi_2d conversion did not work as expected'
#This is a different point
d,l= 2., 45.
r,phi= bovy_coords.dl_to_rphi_2d(d,l,degree=True,ro=2.*numpy.sqrt(2.),
phio=10.)
assert numpy.fabs(r-2.) < 10.**-10., 'dl_to_rphi_2d conversion did not work as expected'
assert numpy.fabs(phi-55.) < 10.**-10., 'dl_to_rphi_2d conversion did not work as expected'
#This is a different point, for array
d,l= 2., 45.
os= numpy.ones(2)
r,phi= bovy_coords.dl_to_rphi_2d(os*d,os*l,degree=True,
ro=2.*numpy.sqrt(2.),
phio=0.)
assert numpy.all(numpy.fabs(r-2.) < 10.**-10.), 'dl_to_rphi_2d conversion did not work as expected'
assert numpy.all(numpy.fabs(phi-45.) < 10.**-10.), 'dl_to_rphi_2d conversion did not work as expected'
#This is a different point, for list (which I support for some reason)
d,l= 2., 45.
r,phi= bovy_coords.dl_to_rphi_2d([d,d],[l,l],degree=True,
ro=2.*numpy.sqrt(2.),
phio=0.)
r= numpy.array(r)
phi= numpy.array(phi)
assert numpy.all(numpy.fabs(r-2.) < 10.**-10.), 'dl_to_rphi_2d conversion did not work as expected'
assert numpy.all(numpy.fabs(phi-45.) < 10.**-10.), 'dl_to_rphi_2d conversion did not work as expected'
return None
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]
