def test_convertManyLSRXYZUVWToAstrometry():
return
xyzuvw_bp_helio = np.array([-3.4, -16.4, -9.9, -11.0, -16.0, -9.1])
xyzuvw_bp_lsr = xyzuvw_bp_helio + XYZUVWSOLARNOW_pc
astr_bp = [ # astrometry from wikiepdia
86.82125, #deg
-51.0664, #deg
51.44, #mas
4.65, #mas/yr
83.1, #mas/yr
20.0 #km/s
]
xyzuvw_lsrs = np.array([
xyzuvw_bp_lsr,
xyzuvw_bp_lsr,
xyzuvw_bp_lsr,
])
astros = np.array([
astr_bp,
astr_bp,
astr_bp,
])
calculated_astros = cc.convert_many_lsrxyzuvw2astrometry(xyzuvw_lsrs)
assert np.allclose(calculated_astros, astros, rtol=1e-2)
recalculated_xyzuvws = cc.convert_many_astrometry2lsrxyzuvw(calculated_astros)
assert np.allclose(recalculated_xyzuvws, xyzuvw_lsrs)
def test_convertManyLSRXYZUVWToAstrometry():
return
xyzuvw_bp_helio = np.array([-3.4, -16.4, -9.9, -11.0, -16.0, -9.1])
xyzuvw_bp_lsr = xyzuvw_bp_helio + XYZUVWSOLARNOW_pc
astr_bp = [ # astrometry from wikiepdia
86.82125, #deg
-51.0664, #deg
51.44, #mas
4.65, #mas/yr
83.1, #mas/yr
20.0 #km/s
]
xyzuvw_lsrs = np.array([
xyzuvw_bp_lsr,
xyzuvw_bp_lsr,
xyzuvw_bp_lsr,
])
astros = np.array([
astr_bp,
astr_bp,
astr_bp,
])
calculated_astros = cc.convert_many_lsrxyzuvw2astrometry(xyzuvw_lsrs)
assert np.allclose(calculated_astros, astros, rtol=1e-2)
recalculated_xyzuvws = cc.convert_many_astrometry2lsrxyzuvw(
calculated_astros)
assert np.allclose(recalculated_xyzuvws, xyzuvw_lsrs)
def convertMeasurementsToCartesian(t=None, loadfile='', astr_dict=None):
"""
Parameters
----------
t : astropy Table with the following columns:
name : id or name of star
radeg : right ascension in degrees
dedeg : declination in degrees
plx : parallax in mas
e_plx : error of parallax in mas
pmra : proper motion in right ascension in mas/yr
e_pmra : error of pm in right ascension in mas/yr
pmde : proper motion in declination in mas/yr
e_pmde : error of pm in declination in mas/yr
rv : radial velocity in km/s
e_rv : error of radial velocity in km/s
loadfile : (String {''})
if t is None, try and load table from loadfile
savefile : (String {''})
if non-empty, will save a fits file with this filename. Appropriate
file extension is applied if not there.
Returns
-------
dict :
t : astropy table
xyzuvw : [nstars, 6] array
space positions and velocities of each star
xyzuvw_cov : [nstars, 6, 6] array
covariance of positions and velocities of each star
"""
while True:
if t:
nstars = len(t)
astr_arr, err_arr = convertTableToArray(t)
astr_covs = convertAstrErrsToCovs(err_arr)
break
if loadfile:
t = Table.read(loadfile, format='ascii')
nstars = len(t)
astr_arr, err_arr = convertTableToArray(t)
astr_covs = convertAstrErrsToCovs(err_arr)
break
if astr_dict:
astr_arr = astr_dict['astr_mns']
astr_covs = astr_dict['astr_covs']
nstars = astr_arr.shape[0]
break
raise StandardError
xyzuvw = coordinate.convert_many_astrometry2lsrxyzuvw(astr_arr, mas=True)
xyzuvw_cov = transformAstrCovsToCartesian(astr_covs, astr_arr)
xyzuvw_dict = {'table':t, 'xyzuvw':xyzuvw, 'xyzuvw_cov':xyzuvw_cov}
return xyzuvw_dict
def convertMeasurementsToCartesian(t=None, loadfile='', astr_dict=None):
"""
Parameters
----------
t : astropy Table with the following columns:
name : id or name of star
radeg : right ascension in degrees
dedeg : declination in degrees
plx : parallax in mas
e_plx : error of parallax in mas
pmra : proper motion in right ascension in mas/yr
e_pmra : error of pm in right ascension in mas/yr
pmde : proper motion in declination in mas/yr
e_pmde : error of pm in declination in mas/yr
rv : radial velocity in km/s
e_rv : error of radial velocity in km/s
loadfile : (String {''})
if t is None, try and load table from loadfile
savefile : (String {''})
if non-empty, will save a fits file with this filename. Appropriate
file extension is applied if not there.
Returns
-------
dict :
t : astropy table
xyzuvw : [nstars, 6] array
space positions and velocities of each star
xyzuvw_cov : [nstars, 6, 6] array
covariance of positions and velocities of each star
"""
while True:
if t:
# nstars = len(t)
astr_arr, err_arr = convertTableToArray(t)
astr_covs = convertAstrErrsToCovs(err_arr)
break
if loadfile:
t = Table.read(loadfile, format='ascii')
# nstars = len(t)
astr_arr, err_arr = convertTableToArray(t)
astr_covs = convertAstrErrsToCovs(err_arr)
break
if astr_dict:
astr_arr = astr_dict['astr_mns']
astr_covs = astr_dict['astr_covs']
# nstars = astr_arr.shape[0]
break
raise StandardError
xyzuvw = coordinate.convert_many_astrometry2lsrxyzuvw(astr_arr, mas=True)
xyzuvw_cov = transformAstrCovsToCartesian(astr_covs, astr_arr)
xyzuvw_dict = {'table': t, 'xyzuvw': xyzuvw, 'xyzuvw_cov': xyzuvw_cov}
return xyzuvw_dict
def convertGaiaMeansToXYZUVW(astr_file="all_rvs_w_ok_plx", server=False):
"""
Generate mean XYZUVW for eac star in provided fits file (Gaia format)
"""
if server:
rdir = '/data/mash/tcrun/'
else:
rdir = '../data/'
#gaia_astr_file = rdir+'all_rvs_w_ok_plx.fits'
gaia_astr_file = rdir + astr_file + ".fits"
hdul = fits.open(gaia_astr_file) #, memmap=True)
nstars = hdul[1].data.shape[0]
means = np.zeros((nstars, 6))
means[:, 0] = hdul[1].data['ra']
means[:, 1] = hdul[1].data['dec']
means[:, 2] = hdul[1].data['parallax']
means[:, 3] = hdul[1].data['pmra']
means[:, 4] = hdul[1].data['pmdec']
means[:, 5] = hdul[1].data['radial_velocity']
xyzuvw_mns = cc.convert_many_astrometry2lsrxyzuvw(means, mas=True)
np.save(rdir + astr_file + "mean_xyzuvw.npy", xyzuvw_mns)
def convertGaiaMeansToXYZUVW(astr_file="all_rvs_w_ok_plx", server=False):
"""
Generate mean XYZUVW for eac star in provided fits file (Gaia format)
"""
if server:
rdir = '/data/mash/tcrun/'
else:
rdir = '../data/'
#gaia_astr_file = rdir+'all_rvs_w_ok_plx.fits'
gaia_astr_file = rdir+astr_file+".fits"
hdul = fits.open(gaia_astr_file)#, memmap=True)
nstars = hdul[1].data.shape[0]
means = np.zeros((nstars,6))
means[:,0] = hdul[1].data['ra']
means[:,1] = hdul[1].data['dec']
means[:,2] = hdul[1].data['parallax']
means[:,3] = hdul[1].data['pmra']
means[:,4] = hdul[1].data['pmdec']
means[:,5] = hdul[1].data['radial_velocity']
xyzuvw_mns = cc.convert_many_astrometry2lsrxyzuvw(means, mas=True)
np.save(rdir + astr_file + "mean_xyzuvw.npy", xyzuvw_mns)
return_group=True,
xyzuvw_savefile=xyzuvw_init_savefile,
group_savefile=group_savefile)
logging.info("Age is: {} Myr".format(group.age))
xyzuvw_now_true = to.trace_many_cartesian_orbit(xyzuvw_init, np.array([0., group.age]))[:, 1]
#assert np.allclose(np.mean(xyzuvw_now, axis=0), group.mean, rtol=1e-1)
logging.info("Mean of initial stars: {}".format(np.mean(xyzuvw_init, axis=0)))
logging.info("Mean of final stars: {}".format(np.mean(xyzuvw_now_true, axis=0)))
star_table = chronostar.synthdata.measureXYZUVW(xyzuvw_now_true, 20.0, astro_savefile)
astr_arr, err_arr = ms.convertTableToArray(star_table)
nstars = len(star_table)
astr_covs = cv.convertAstroErrsToCovs(err_arr)
xyzuvw_now = cc.convert_many_astrometry2lsrxyzuvw(astr_arr, mas=True)
logging.info("Mean of retrieved stars: {}".format(np.mean(xyzuvw_now, axis=0)))
if plot_it:
plt.clf()
plt.plot(xyzuvw_now[:,1], xyzuvw_now[:,2], '.')
chronostar.fitplotter.plotCovEllipse(group.generateEllipticalCovMatrix()[1:3, 1:3],
group.mean[1:3], with_line=True)
xyzuvw_covs = np.zeros((nstars,6,6))
for ix in range(nstars):
xyzuvw_covs[ix] = tf.transform_covmatrix(
astr_covs[ix], cc.convert_astrometry2lsrxyzuvw, astr_arr[ix], dim=6
)
if plot_it:
xyzuvw_now_true = to.trace_many_cartesian_orbit(xyzuvw_init,
np.array([0., group.age]))[:,
1]
#assert np.allclose(np.mean(xyzuvw_now, axis=0), group.mean, rtol=1e-1)
logging.info("Mean of initial stars: {}".format(np.mean(xyzuvw_init, axis=0)))
logging.info("Mean of final stars: {}".format(np.mean(xyzuvw_now_true,
axis=0)))
star_table = chronostar.synthdata.measureXYZUVW(xyzuvw_now_true, 20.0,
astro_savefile)
astr_arr, err_arr = ms.convertTableToArray(star_table)
nstars = len(star_table)
astr_covs = cv.convertAstroErrsToCovs(err_arr)
xyzuvw_now = cc.convert_many_astrometry2lsrxyzuvw(astr_arr, mas=True)
logging.info("Mean of retrieved stars: {}".format(np.mean(xyzuvw_now, axis=0)))
if plot_it:
plt.clf()
plt.plot(xyzuvw_now[:, 1], xyzuvw_now[:, 2], '.')
chronostar.fitplotter.plotCovEllipse(
group.generateEllipticalCovMatrix()[1:3, 1:3],
group.mean[1:3],
with_line=True)
xyzuvw_covs = np.zeros((nstars, 6, 6))
for ix in range(nstars):
xyzuvw_covs[ix] = tf.transform_covmatrix(astr_covs[ix],
cc.convert_astrometry2lsrxyzuvw,
astr_arr[ix],