def test_convertLSRXYZUVWToAstrometry():
"""Checks Beta Pictoris is accurately handled"""
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_bp_helio = np.array([-3.4, -16.4, -9.9, -11.0, -16.0, -9.1])
xyzuvw_bp_lsr = cc.convert_helio2lsr(xyzuvw_bp_helio)
calculated_astr_bp = cc.convert_lsrxyzuvw2astrometry(xyzuvw_bp_lsr)
assert np.allclose(astr_bp, calculated_astr_bp, rtol=1e-2)
calculated_xyzuvw_bp_lsr = cc.convert_astrometry2lsrxyzuvw(astr_bp)
assert np.allclose(calculated_xyzuvw_bp_lsr, xyzuvw_bp_lsr, rtol=0.15)
calculated_astr_bp2 = cc.convert_lsrxyzuvw2astrometry(xyzuvw_bp_lsr)
calculated_xyzuvw_bp_lsr2 = cc.convert_astrometry2lsrxyzuvw(
calculated_astr_bp2)
assert np.allclose(calculated_xyzuvw_bp_lsr2, xyzuvw_bp_lsr)
def test_convertLSRXYZUVWToAstrometry():
"""Checks Beta Pictoris is accurately handled"""
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_bp_helio = np.array([-3.4, -16.4, -9.9, -11.0, -16.0, -9.1])
xyzuvw_bp_lsr = cc.convert_helio2lsr(xyzuvw_bp_helio)
calculated_astr_bp = cc.convert_lsrxyzuvw2astrometry(xyzuvw_bp_lsr)
assert np.allclose(astr_bp, calculated_astr_bp, rtol=1e-2)
calculated_xyzuvw_bp_lsr = cc.convert_astrometry2lsrxyzuvw(astr_bp)
assert np.allclose(calculated_xyzuvw_bp_lsr, xyzuvw_bp_lsr, rtol=0.15)
calculated_astr_bp2 = cc.convert_lsrxyzuvw2astrometry(xyzuvw_bp_lsr)
calculated_xyzuvw_bp_lsr2 = cc.convert_astrometry2lsrxyzuvw(
calculated_astr_bp2
)
assert np.allclose(calculated_xyzuvw_bp_lsr2, xyzuvw_bp_lsr)
def test_convertAstrometryToLSRXYZUVW():
"""Check edge case of nearby sun, also compare with output of astropy"""
# pick an astrometry which should be right near the sun
sun_astro = (0., 90., 1e15, 0., 0., 0.)
sun_xyzuvw_lsr = (0., 0., 25., 11.1, 12.24, 7.25)
assert np.allclose(cc.convert_astrometry2lsrxyzuvw(sun_astro),
sun_xyzuvw_lsr)
# TODO: compare astropy coordinates
# will do this when upgraded to python 3
star_astros = np.array([
[86.82, -51.067, 51.44, 4.65, 83.1, 20], # beta Pic
[165.466, -34.705, 18.62, -66.19, -13.9, 13.4], # TW Hya
[82.187, -65.45, 65.93, 33.16, 150.83, 32.4], # AB Dor
[100.94, -71.977, 17.17, 6.17, 61.15, 20.7] # HIP 32235
])
def test_convertAstrometryToLSRXYZUVW():
"""Check edge case of nearby sun, also compare with output of astropy"""
# pick an astrometry which should be right near the sun
sun_astro = (0., 90., 1e15, 0.,0.,0.)
sun_xyzuvw_lsr = (0., 0., 25., 11.1, 12.24, 7.25)
assert np.allclose(
cc.convert_astrometry2lsrxyzuvw(sun_astro), sun_xyzuvw_lsr
)
# TODO: compare astropy coordinates
# will do this when upgraded to python 3
star_astros = np.array([
[86.82, -51.067, 51.44, 4.65, 83.1, 20], # beta Pic
[165.466, -34.705, 18.62, -66.19, -13.9, 13.4], # TW Hya
[82.187, -65.45, 65.93, 33.16, 150.83, 32.4], # AB Dor
[100.94, -71.977, 17.17, 6.17, 61.15, 20.7] # HIP 32235
])
def convertRowToCartesian(row, row_ix=None, nrows=None):
dim=6
cart_col_names = ['X', 'Y', 'Z', 'U', 'V', 'W',
'dX', 'dY', 'dZ', 'dU', 'dV', 'dW',
'c_XY', 'c_XZ', 'c_XU', 'c_XV', 'c_XW',
'c_YZ', 'c_YU', 'c_YV', 'c_YW',
'c_ZU', 'c_ZV', 'c_ZW',
'c_UV', 'c_UW',
'c_VW']
try:
if row_ix % 100 == 0:
print("{:010.7f}% done".format(row_ix / float(nrows) * 100.))
except TypeError:
pass
astr_mean, astr_cov = datatool.convertRecToArray(row)
xyzuvw_mean = coordinate.convert_astrometry2lsrxyzuvw(astr_mean)
xyzuvw_cov = transform.transform_covmatrix(
astr_cov,
coordinate.convert_astrometry2lsrxyzuvw,
astr_mean,
dim=dim,
)
# fill in cartesian mean
for col_ix, col_name in enumerate(cart_col_names[:6]):
row[col_name] = xyzuvw_mean[col_ix]
# fill in standard deviations
xyzuvw_stds = np.sqrt(xyzuvw_cov[np.diag_indices(dim)])
for col_ix, col_name in enumerate(cart_col_names[6:12]):
row[col_name] = xyzuvw_stds[col_ix]
correl_matrix = xyzuvw_cov / xyzuvw_stds / xyzuvw_stds.reshape(6, 1)
# fill in correlations
for col_ix, col_name in enumerate(cart_col_names[12:]):
row[col_name] = correl_matrix[
np.triu_indices(dim, k=1)[0][col_ix],
np.triu_indices(dim, k=1)[1][col_ix]
]
star_tab['radial_velocity'] = 16.44
star_tab['radial_velocity_error'] = 1.
if np.isnan(star_tab['radial_velocity']):
print("Its nan")
# extend proper motion uncertainty
star_tab['pmra_error'] *= 100.
star_tab['pmdec_error'] *= 100.
# star_tab['radial_velocity_error'] *= 10
astr_mean, astr_cov = dt.convertRecToArray(star_tab[0])
xyzuvw_cov = cv.transformAstrCovsToCartesian(np.array([astr_cov]),
np.array([astr_mean])
)[0]
xyzuvw = cc.convert_astrometry2lsrxyzuvw(astr_mean)
ln_bg_ols = dt.getKernelDensities(gaia_xyzuvw_file, [xyzuvw])
star_pars = {'xyzuvw':np.array([xyzuvw]),
'xyzuvw_cov':np.array([xyzuvw_cov])}
nbp_stars = 100
ln_bp_ols = np.log(nbp_stars) + chronostar.likelihood.get_lnoverlaps(beta_fit.getInternalSphericalPars(), star_pars)
combined_lnols = np.hstack((ln_bp_ols, ln_bg_ols))
membership_probs = em.calc_membership_probs(combined_lnols)
print(membership_probs)
cart_col_names = [
'X', 'Y', 'Z', 'U', 'V', 'W', 'dX', 'dY', 'dZ', 'dU', 'dV', 'dW',
'c_XY', 'c_XZ', 'c_XU', 'c_XV', 'c_XW', 'c_YZ', 'c_YU', 'c_YV', 'c_YW',
'c_ZU', 'c_ZV', 'c_ZW', 'c_UV', 'c_UW', 'c_VW'
]
# insert empty columns
for col_name in cart_col_names:
gt[col_name] = empty_col
for row_ix, gt_row in enumerate(gt):
dim = 6
if row_ix % 10 == 0:
print("{:02.2f}% done".format(row_ix / float(nrows) * 100.))
astr_mean, astr_cov = gc.convertRecToArray(gt_row)
xyzuvw_mean = coord.convert_astrometry2lsrxyzuvw(astr_mean)
xyzuvw_cov = tf.transform_covmatrix(
astr_cov,
coord.convert_astrometry2lsrxyzuvw,
astr_mean,
dim=dim,
)
# fill in cartesian mean
for col_ix, col_name in enumerate(cart_col_names[:6]):
gt_row[col_name] = xyzuvw_mean[col_ix]
# fill in standard deviations
xyzuvw_stds = np.sqrt(xyzuvw_cov[np.diag_indices(dim)])
for col_ix, col_name in enumerate(cart_col_names[6:12]):
gt_row[col_name] = xyzuvw_stds[col_ix]
'c_XY', 'c_XZ', 'c_XU', 'c_XV', 'c_XW',
'c_YZ', 'c_YU', 'c_YV', 'c_YW',
'c_ZU', 'c_ZV', 'c_ZW',
'c_UV', 'c_UW',
'c_VW']
# insert empty columns
for col_name in cart_col_names:
gt[col_name] = empty_col
for row_ix, gt_row in enumerate(gt):
dim = 6
if row_ix%10 == 0:
print("{:02.2f}% done".format(row_ix / float(nrows) * 100.))
astr_mean, astr_cov = gc.convertRecToArray(gt_row)
xyzuvw_mean = coord.convert_astrometry2lsrxyzuvw(astr_mean)
xyzuvw_cov = tf.transform_covmatrix(
astr_cov,
coord.convert_astrometry2lsrxyzuvw,
astr_mean,
dim=dim,
)
# fill in cartesian mean
for col_ix, col_name in enumerate(cart_col_names[:6]):
gt_row[col_name] = xyzuvw_mean[col_ix]
# fill in standard deviations
xyzuvw_stds = np.sqrt(xyzuvw_cov[np.diag_indices(dim)])
for col_ix, col_name in enumerate(cart_col_names[6:12]):
gt_row[col_name] = xyzuvw_stds[col_ix]
star_tab['radial_velocity'] = 16.44
star_tab['radial_velocity_error'] = 1.
if np.isnan(star_tab['radial_velocity']):
print("Its nan")
# extend proper motion uncertainty
star_tab['pmra_error'] *= 100.
star_tab['pmdec_error'] *= 100.
# star_tab['radial_velocity_error'] *= 10
astr_mean, astr_cov = dt.convertRecToArray(star_tab[0])
xyzuvw_cov = cv.transformAstrCovsToCartesian(np.array([astr_cov]),
np.array([astr_mean]))[0]
xyzuvw = cc.convert_astrometry2lsrxyzuvw(astr_mean)
ln_bg_ols = dt.getKernelDensities(gaia_xyzuvw_file, [xyzuvw])
star_pars = {
'xyzuvw': np.array([xyzuvw]),
'xyzuvw_cov': np.array([xyzuvw_cov])
}
nbp_stars = 100
ln_bp_ols = np.log(nbp_stars) + chronostar.likelihood.get_lnoverlaps(
beta_fit.getInternalSphericalPars(), star_pars)
combined_lnols = np.hstack((ln_bp_ols, ln_bg_ols))
membership_probs = em.calc_membership_probs(combined_lnols)
print(membership_probs)