def test_cov_dvrpmllbb_to_vxyz():
l,b,d= 90., 0., 2.
e_d, e_vr= 0.2, 2.
cov_pmllpmbb= numpy.array([[100.,0.],[0.,400.]])
pmll,pmbb= 20.,30.
cov_vxvyvz= bovy_coords.cov_dvrpmllbb_to_vxyz(d,e_d,e_vr,
pmll,pmbb,
cov_pmllpmbb,
l,b,
degree=True,
plx=False)
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[0,0])
-d*4.74047*pmll*numpy.sqrt((e_d/d)**2.+(10./pmll)**2.)) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[1,1])-e_vr) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[2,2])
-d*4.74047*pmbb*numpy.sqrt((e_d/d)**2.+(20./pmbb)**2.)) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
#Another one
l,b,d= 180., 0., 1./2.
e_d, e_vr= 0.05, 2.
cov_pmllpmbb= numpy.array([[100.,0.],[0.,400.]])
pmll,pmbb= 20.,30.
cov_vxvyvz= bovy_coords.cov_dvrpmllbb_to_vxyz(d,e_d,e_vr,
pmll,pmbb,
cov_pmllpmbb,
l/180.*numpy.pi,
b/180.*numpy.pi,
degree=False,
plx=True)
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[0,0])-e_vr) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[1,1])
-1./d*4.74047*pmll*numpy.sqrt((e_d/d)**2.+(10./pmll)**2.)) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[2,2])
-1./d*4.74047*pmbb*numpy.sqrt((e_d/d)**2.+(20./pmbb)**2.)) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
#Another one, w/ arrays
l,b,d= 90., 90., 2.
e_d, e_vr= 0.2, 2.
tcov_pmllpmbb= numpy.array([[100.,0.],[0.,400.]])
cov_pmllpmbb= numpy.empty((3,2,2))
for ii in range(3): cov_pmllpmbb[ii,:,:]= tcov_pmllpmbb
pmll,pmbb= 20.,30.
os= numpy.ones(3)
cov_vxvyvz= bovy_coords.cov_dvrpmllbb_to_vxyz(os*d,os*e_d,os*e_vr,
os*pmll,os*pmbb,
cov_pmllpmbb,
os*l,os*b,
degree=True,
plx=False)
for ii in range(3):
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[ii,0,0])
-d*4.74047*pmll*numpy.sqrt((e_d/d)**2.+(10./pmll)**2.)) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[ii,1,1])
-d*4.74047*pmbb*numpy.sqrt((e_d/d)**2.+(20./pmbb)**2.)) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
assert numpy.fabs(numpy.sqrt(cov_vxvyvz[ii,2,2])-e_vr) < 10.**-10., 'cov_dvrpmllbb_to_vxyz coversion did not work as expected'
return None
def uvw_unc(req_dict, opt_dict):
cov_radec = np.zeros((2, 2))
cov_radec[0, 0] = opt_dict['epmra']**2
cov_radec[1, 1] = opt_dict['epmdec']**2
covar_pmllbb = b_c.cov_pmrapmdec_to_pmllpmbb(cov_radec,
req_dict['ra'],
req_dict['dec'],
degree=True,
epoch=2000.0)
cov_vxvyvz = b_c.cov_dvrpmllbb_to_vxyz(req_dict['dist'],
opt_dict['edist'],
opt_dict['erv'],
req_dict['pml'],
req_dict['pmb'],
covar_pmllbb,
req_dict['l'],
req_dict['b'],
plx=False,
degree=True)
du = vx_e = m.sqrt(cov_vxvyvz[0, 0])
dv = vy_e = m.sqrt(cov_vxvyvz[1, 1])
dw = vz_e = m.sqrt(cov_vxvyvz[2, 2])
return du, dv, dw
def calc_spacevel_uncer_var_tensor(self):
"""
Using vscatter for error in RV (km/s) and 2.5% distance errors
"""
dist_uncer = 0.025 * self.get_col('RC_DIST')
uncer_tensor = bcoords.cov_dvrpmllbb_to_vxyz(self.get_col('RC_DIST'),
dist_uncer,
self.get_col('VSCATTER'),
self.pmll_pmbb[:, 0], self.pmll_pmbb[:, 1],
self.covar_pmllpmbb, self.get_col('GLON'),
self.get_col('GLAT'), degree=self.degree)
self.spacevel_uncer_var_tensor = uncer_tensor
def obs_to_galcen(ra,
dec,
dist,
pmra,
pmdec,
rv,
pmra_err,
pmdec_err,
pmra_pmdec_corr,
dist_err,
rv_err,
return_cov=True,
verbose=True,
return_rphiz=True,
ro=8.,
vo=220.,
zo=0.025,
parallax=False):
vxvv = np.dstack([ra, dec, dist, pmra, pmdec, rv])[0]
ra, dec = vxvv[:, 0], vxvv[:, 1]
lb = bovy_coords.radec_to_lb(ra, dec, degree=True)
pmra, pmdec = vxvv[:, 3], vxvv[:, 4]
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra,
pmdec,
ra,
dec,
degree=True)
d, vlos = vxvv[:, 2], vxvv[:, 5]
if parallax:
d = 1. / d
rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0],
lb[:, 1],
d,
vlos,
pmllpmbb[:, 0],
pmllpmbb[:, 1],
degree=True)
vsolar = np.array([-10.1, 4.0, 6.7])
vsun = np.array([
0.,
1.,
0.,
]) + vsolar / vo
X = rectgal[:, 0] / ro
Y = rectgal[:, 1] / ro
Z = rectgal[:, 2] / ro
vx = rectgal[:, 3] / vo
vy = rectgal[:, 4] / vo
vz = rectgal[:, 5] / vo
XYZ = np.dstack([X, Y, Z])[0]
vxyz = np.dstack([vx, vy, vz])[0]
if return_rphiz:
Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Zsun=zo / ro)
vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0],
vxyz[:, 1],
vxyz[:, 2],
Rpz[:, 0],
Rpz[:, 1],
Rpz[:, 2],
vsun=vsun,
Xsun=1.,
Zsun=zo / ro,
galcen=True)
if return_cov == True:
cov_pmradec = np.empty([len(pmra_err), 2, 2])
cov_pmradec[:, 0, 0] = pmra_err**2
cov_pmradec[:, 1, 1] = pmdec_err**2
cov_pmradec[:, 0, 1] = pmra_pmdec_corr * pmra_err * pmdec_err
cov_pmradec[:, 1, 0] = pmra_pmdec_corr * pmra_err * pmdec_err
if verbose:
print('propagating covariance in pmra pmdec -> pmll pmbb')
cov_pmllbb = bovy_coords.cov_pmrapmdec_to_pmllpmbb(cov_pmradec,
vxvv[:, 0],
vxvv[:, 1],
degree=True,
epoch='J2015')
if verbose:
print('propagating covariance in pmll pmbb -> vx vy vz')
cov_vxyz = bovy_coords.cov_dvrpmllbb_to_vxyz(vxvv[:, 2], dist_err,
rv_err, pmllpmbb[:, 0],
pmllpmbb[:,
1], cov_pmllbb,
lb[:, 0], lb[:, 1])
if not return_rphiz:
return XYZ, vxyz, cov_vxyz
if verbose:
print('propagating covariance in vx vy vz -> vR vT vz')
cov_galcencyl = bovy_coords.cov_vxyz_to_galcencyl(cov_vxyz,
Rpz[:, 1],
Xsun=1.,
Zsun=zo / ro)
return XYZ, vxyz, cov_vxyz, Rpz, vRvTvz, cov_galcencyl
if not return_rphiz:
return XYZ, vxyz
return XYZ, vxyz, Rpz, vRvTvz
def dat_to_galcen(
dat,
return_cov=True,
return_rphiz=True,
verbose=False,
ro=8.,
vo=220.,
zo=0.025,
keys=['ra', 'dec', 'BPG_meandist', 'pmra', 'pmdec', 'VHELIO_AVG'],
cov_keys=[
'pmra_error', 'pmdec_error', 'pmra_pmdec_corr', 'BPG_diststd',
'VERR'
],
parallax=False):
vxvv = np.dstack([dat[keys[i]] for i in range(len(keys))])[0]
ra, dec = vxvv[:, 0], vxvv[:, 1]
lb = bovy_coords.radec_to_lb(ra, dec, degree=True)
pmra, pmdec = vxvv[:, 3], vxvv[:, 4]
pmllpmbb = bovy_coords.pmrapmdec_to_pmllpmbb(pmra,
pmdec,
ra,
dec,
degree=True)
d, vlos = vxvv[:, 2], vxvv[:, 5]
if parallax:
d = 1. / d
rectgal = bovy_coords.sphergal_to_rectgal(lb[:, 0],
lb[:, 1],
d,
vlos,
pmllpmbb[:, 0],
pmllpmbb[:, 1],
degree=True)
vsolar = np.array([-11.1, 245.6,
7.25]) #use SBD10 vR and vZ and SGR proper motion vT
vsun = np.array([
0.,
0.,
0.,
]) + vsolar / vo
X = rectgal[:, 0] / ro
Y = rectgal[:, 1] / ro
Z = rectgal[:, 2] / ro
vx = rectgal[:, 3] / vo
vy = rectgal[:, 4] / vo
vz = rectgal[:, 5] / vo
XYZ = np.dstack([X, Y, Z])[0]
vxyz = np.dstack([vx, vy, vz])[0]
if return_rphiz:
Rpz = bovy_coords.XYZ_to_galcencyl(XYZ[:, 0],
XYZ[:, 1],
XYZ[:, 2],
Zsun=zo / ro)
vRvTvz = bovy_coords.vxvyvz_to_galcencyl(vxyz[:, 0],
vxyz[:, 1],
vxyz[:, 2],
Rpz[:, 0],
Rpz[:, 1],
Rpz[:, 2],
vsun=vsun,
Xsun=1.,
Zsun=zo / ro,
galcen=True)
if return_cov == True:
cov_pmradec = np.array([[[
dat[cov_keys[0]][i]**2,
dat[cov_keys[2]][i] * dat[cov_keys[0]][i] * dat[cov_keys[1]][i]
],
[
dat[cov_keys[2]][i] *
dat[cov_keys[0]][i] * dat[cov_keys[1]][i],
dat[cov_keys[1]][i]**2
]] for i in range(len(dat))])
if verbose:
print('propagating covariance in pmra pmdec -> pmll pmbb')
cov_pmllbb = bovy_coords.cov_pmrapmdec_to_pmllpmbb(cov_pmradec,
vxvv[:, 0],
vxvv[:, 1],
degree=True,
epoch='J2015')
if verbose:
print('propagating covariance in pmll pmbb -> vx vy vz')
cov_vxyz = bovy_coords.cov_dvrpmllbb_to_vxyz(vxvv[:,
2], dat[cov_keys[3]],
dat[cov_keys[4]],
pmllpmbb[:,
0], pmllpmbb[:,
1],
cov_pmllbb, lb[:,
0], lb[:,
1])
if not return_rphiz:
return XYZ, vxyz, cov_vxyz
if verbose:
print('propagating covariance in vx vy vz -> vR vT vz')
cov_galcencyl = bovy_coords.cov_vxyz_to_galcencyl(cov_vxyz,
Rpz[:, 1],
Xsun=1.,
Zsun=zo / ro)
return XYZ, vxyz, cov_vxyz, Rpz, vRvTvz, cov_galcencyl
if not return_rphiz:
return XYZ, vxyz
return XYZ, vxyz, Rpz, vRvTvz