def hipeqgal():
"""Print summary of EQ-GAL comparison with SLALIB eqgal (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_eqgal.txt")
dummy = np.zeros((len(hip_tab['px']), ))
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], dummy, dummy,
dummy, dummy, tpm.CJ)
v6o = convert.convertv6(v6l, s1=6, s2=4)
# The galactic coordinates are at epoch J2000. But SLALIB
# results are for B1950. So apply proper motion here.
v6o = convert.proper_motion(v6o, tpm.B1950, tpm.J2000)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - sla_tab[:, 0]
ra_diff = np.abs(ra_diff * 3600.0)
dec_diff = np.degrees(cat['delta']) - sla_tab[:, 1]
dec_diff = np.abs(dec_diff * 3600.0)
print("Comparison with SLALIB eqgal using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
def hipgaleq():
"""Print summary of GAL-EQ comparison with SLALIB galeq (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_galeq.txt")
dummy = np.zeros((len(hip_tab['px']), ))
v6l = convert.cat2v6(hip_tab['glon'], hip_tab['glat'], dummy, dummy, dummy,
dummy, tpm.CJ)
# The actual epoch of galactic data is J2000. But in SLALIB
# the input is taken to be B1950.0. So use tpm.B1950 as epoch
# in the conversion.
v6o = convert.convertv6(v6l, s1=4, s2=6, epoch=tpm.B1950)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - sla_tab[:, 0]
ra_diff = np.abs(ra_diff * 3600.0)
dec_diff = np.degrees(cat['delta']) - sla_tab[:, 1]
dec_diff = np.abs(dec_diff * 3600.0)
print("Comparison with SLALIB galeq using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
def hipfk524():
"""Print summary of FK5-FK4 comparison with SLALIB fk524 (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_fk524.txt")
rv = np.zeros((len(hip_tab['px'],)))
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], hip_tab['pma'],
hip_tab['pmd'], hip_tab['px'], rv, tpm.CJ)
v6o = convert.convertv6(v6l, s1=6, s2=5, epoch=tpm.J2000)
v6o = convert.proper_motion(v6o, tpm.B1950, tpm.J2000)
cat = (tpm.v62cat(v, tpm.CB) for v in v6o)
d = cat2array(cat)
ra_diff = np.degrees(d['alpha']) - sla_tab[:, 0]
ra_diff *= 3600.0
dec_diff = np.degrees(d['delta']) - sla_tab[:, 1]
dec_diff *= 3600.0
px_diff = d['px'] * 1000.0 - sla_tab[:, 2]
pma_diff = d['pma'] * 1000.0 / 100.0 - sla_tab[:, 3]
pmd_diff = d['pmd'] * 1000.0 / 100.0 - sla_tab[:, 4]
rv_diff = d['rv'] - sla_tab[:, 5]
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = [stats.describe(np.abs(i)) for i in
[ra_diff, dec_diff, px_diff, pma_diff, pmd_diff, rv_diff]]
print("Comparison with SLALIB fk524 using HIPPARCOS data.")
for name, unit, s in zip(
["ra_diff", "dec_diff", "px_diff", "pma_diff", "pmd_diff",
"rv_diff"],
["arsec", "arcsec", "milliarcsec", "milli-arsec/trop. yr",
"milli-arcsec/trop. yr", "km/s"],
x):
print(fs.format(name, unit, s[1][0], s[1][1], s[2], s[3] ** 0.5))
def hipecleq():
"""Print summary of ECL-EQ comparison with SLALIB ecleq (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_ecleq.txt")
dummy = np.zeros((len(hip_tab['px']), ))
v6l = convert.cat2v6(hip_tab['elon2'], hip_tab['elat2'], dummy, dummy,
dummy, dummy, tpm.CJ)
v6o = convert.convertv6(v6l, s1=3, s2=6)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - sla_tab[:, 0]
ra_diff = np.abs(ra_diff * 3600.0)
dec_diff = np.degrees(cat['delta']) - sla_tab[:, 1]
dec_diff = np.abs(dec_diff * 3600.0)
print("Comparison with SLALIB ecleq using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
def hipgaleq():
"""Print summary of GAL-EQ comparison with SLALIB galeq (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_galeq.txt")
dummy = np.zeros((len(hip_tab['px']),))
v6l = convert.cat2v6(hip_tab['glon'], hip_tab['glat'], dummy, dummy,
dummy, dummy, tpm.CJ)
# The actual epoch of galactic data is J2000. But in SLALIB
# the input is taken to be B1950.0. So use tpm.B1950 as epoch
# in the conversion.
v6o = convert.convertv6(v6l, s1=4, s2=6, epoch=tpm.B1950)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - sla_tab[:, 0]
ra_diff = np.abs(ra_diff * 3600.0)
dec_diff = np.degrees(cat['delta']) - sla_tab[:, 1]
dec_diff = np.abs(dec_diff * 3600.0)
print("Comparison with SLALIB galeq using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2],
x[3] ** 0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2],
x[3] ** 0.5))
def hipeqgal():
"""Print summary of EQ-GAL comparison with SLALIB eqgal (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_eqgal.txt")
dummy = np.zeros((len(hip_tab['px']),))
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], dummy, dummy,
dummy, dummy, tpm.CJ)
v6o = convert.convertv6(v6l, s1=6, s2=4)
# The galactic coordinates are at epoch J2000. But SLALIB
# results are for B1950. So apply proper motion here.
v6o = convert.proper_motion(v6o, tpm.B1950, tpm.J2000)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - sla_tab[:, 0]
ra_diff = np.abs(ra_diff * 3600.0)
dec_diff = np.degrees(cat['delta']) - sla_tab[:, 1]
dec_diff = np.abs(dec_diff * 3600.0)
print("Comparison with SLALIB eqgal using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2],
x[3] ** 0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2],
x[3] ** 0.5))
def hipecleq():
"""Print summary of ECL-EQ comparison with SLALIB ecleq (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_ecleq.txt")
dummy = np.zeros((len(hip_tab['px']),))
v6l = convert.cat2v6(hip_tab['elon2'], hip_tab['elat2'], dummy, dummy,
dummy, dummy, tpm.CJ)
v6o = convert.convertv6(v6l, s1=3, s2=6)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - sla_tab[:, 0]
ra_diff = np.abs(ra_diff * 3600.0)
dec_diff = np.degrees(cat['delta']) - sla_tab[:, 1]
dec_diff = np.abs(dec_diff * 3600.0)
print("Comparison with SLALIB ecleq using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2],
x[3] ** 0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2],
x[3] ** 0.5))
def hipfk425():
"""Print summary of FK4-FK5 comparison with SLALIB fk425 (HIP).
The input FK4 data is the same generated for the the FK5-FK4
conversion test. I read that data into slalib and perform the
reverse conversion. The result is then compared with that from
PyTPM.
"""
sla_tabb = get_sla("slalib_hip_fk524.txt")
sla_tab = get_sla("slalib_hip_fk524_fk425.txt")
r = np.radians(sla_tabb[:, 0])
d = np.radians(sla_tabb[:, 1])
px = sla_tabb[:, 2] / 1000.0
pma = sla_tabb[:, 3] / 1000.0 * 100.0
pmd = sla_tabb[:, 4] / 1000.0 * 100.0
rv = sla_tabb[:, 5]
v6l = convert.cat2v6(r, d, pma, pmd, px, rv, tpm.CB)
v6o = convert.convertv6(v6l, s1=5, s2=6, epoch=tpm.B1950)
v6o = convert.proper_motion(v6o, tpm.J2000, tpm.B1950)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
r = np.degrees(cat['alpha'])
d = np.degrees(cat['delta'])
# arc-sec/cent. to milli-arcsec/Jul. year.
pma = cat['pma'] * 1000.0 / 100.0
pmd = cat['pmd'] * 1000.0 / 100.0
# arc-sec to milli-arcsec
px = cat['px'] * 1000.0
ra_diff = np.abs(r - sla_tab[:, 0]) * 3600.0
dec_diff = np.abs(d - sla_tab[:, 1]) * 3600.0
px_diff = np.abs(px - sla_tab[:, 2])
pma_diff = np.abs(pma - sla_tab[:, 3])
pmd_diff = np.abs(pmd - sla_tab[:, 4])
rv_diff = np.abs(rv - sla_tab[:, 5])
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = [
stats.describe(np.abs(i))
for i in [ra_diff, dec_diff, px_diff, pma_diff, pmd_diff, rv_diff]
]
print("Comparison with SLALIB fk425 using HIPPARCOS data.")
for name, unit, s in zip(
["ra_diff", "dec_diff", "px_diff", "pma_diff", "pmd_diff", "rv_diff"],
[
"arsec", "arcsec", "milliarcsec", "milli-arsec/trop. yr",
"milli-arcsec/trop. yr", "km/s"
], x):
print(fs.format(name, unit, s[1][0], s[1][1], s[2], s[3]**0.5))
def hipfk425():
"""Print summary of FK4-FK5 comparison with SLALIB fk425 (HIP).
The input FK4 data is the same generated for the the FK5-FK4
conversion test. I read that data into slalib and perform the
reverse conversion. The result is then compared with that from
PyTPM.
"""
sla_tabb = get_sla("slalib_hip_fk524.txt")
sla_tab = get_sla("slalib_hip_fk524_fk425.txt")
r = np.radians(sla_tabb[:, 0])
d = np.radians(sla_tabb[:, 1])
px = sla_tabb[:, 2] / 1000.0
pma = sla_tabb[:, 3] / 1000.0 * 100.0
pmd = sla_tabb[:, 4] / 1000.0 * 100.0
rv = sla_tabb[:, 5]
v6l = convert.cat2v6(r, d, pma, pmd, px, rv, tpm.CB)
v6o = convert.convertv6(v6l, s1=5, s2=6, epoch=tpm.B1950)
v6o = convert.proper_motion(v6o, tpm.J2000, tpm.B1950)
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
r = np.degrees(cat['alpha'])
d = np.degrees(cat['delta'])
# arc-sec/cent. to milli-arcsec/Jul. year.
pma = cat['pma'] * 1000.0 / 100.0
pmd = cat['pmd'] * 1000.0 / 100.0
# arc-sec to milli-arcsec
px = cat['px'] * 1000.0
ra_diff = np.abs(r - sla_tab[:, 0]) * 3600.0
dec_diff = np.abs(d - sla_tab[:, 1]) * 3600.0
px_diff = np.abs(px - sla_tab[:, 2])
pma_diff = np.abs(pma - sla_tab[:, 3])
pmd_diff = np.abs(pmd - sla_tab[:, 4])
rv_diff = np.abs(rv - sla_tab[:, 5])
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = [stats.describe(np.abs(i)) for i in
[ra_diff, dec_diff, px_diff, pma_diff, pmd_diff, rv_diff]]
print("Comparison with SLALIB fk425 using HIPPARCOS data.")
for name, unit, s in zip(
["ra_diff", "dec_diff", "px_diff", "pma_diff", "pmd_diff",
"rv_diff"],
["arsec", "arcsec", "milliarcsec", "milli-arsec/trop. yr",
"milli-arcsec/trop. yr", "km/s"],
x):
print(fs.format(name, unit, s[1][0], s[1][1], s[2], s[3] ** 0.5))
def hipfk524():
"""Print summary of FK5-FK4 comparison with SLALIB fk524 (HIP)."""
hip_tab = get_hipdata()
sla_tab = get_sla("slalib_hip_fk524.txt")
rv = np.zeros((len(hip_tab['px'], )))
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], hip_tab['pma'],
hip_tab['pmd'], hip_tab['px'], rv, tpm.CJ)
v6o = convert.convertv6(v6l, s1=6, s2=5, epoch=tpm.J2000)
v6o = convert.proper_motion(v6o, tpm.B1950, tpm.J2000)
cat = (tpm.v62cat(v, tpm.CB) for v in v6o)
d = cat2array(cat)
ra_diff = np.degrees(d['alpha']) - sla_tab[:, 0]
ra_diff *= 3600.0
dec_diff = np.degrees(d['delta']) - sla_tab[:, 1]
dec_diff *= 3600.0
px_diff = d['px'] * 1000.0 - sla_tab[:, 2]
pma_diff = d['pma'] * 1000.0 / 100.0 - sla_tab[:, 3]
pmd_diff = d['pmd'] * 1000.0 / 100.0 - sla_tab[:, 4]
rv_diff = d['rv'] - sla_tab[:, 5]
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = [
stats.describe(np.abs(i))
for i in [ra_diff, dec_diff, px_diff, pma_diff, pmd_diff, rv_diff]
]
print("Comparison with SLALIB fk524 using HIPPARCOS data.")
for name, unit, s in zip(
["ra_diff", "dec_diff", "px_diff", "pma_diff", "pmd_diff", "rv_diff"],
[
"arsec", "arcsec", "milliarcsec", "milli-arsec/trop. yr",
"milli-arcsec/trop. yr", "km/s"
], x):
print(fs.format(name, unit, s[1][0], s[1][1], s[2], s[3]**0.5))
# Create V6C array from catalog data.
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], hip_tab['pma'],
hip_tab['pmd'], hip_tab['px'], rv, tpm.CJ)
# UTC and TDB for mid-night of 2010/1/1.
utc = tpm.gcal2j(2010, 1, 1) - 0.5 # midnight
tt = tpm.utc2tdb(utc)
# Apply proper motion from J2000 to date.
v6o = convert.proper_motion(v6l, tt, tpm.J2000)
# Convert from mean equinox J2000 to true equinox and epoch of date.
v6o = convert.convertv6(v6o, s1=6, s2=11, utc=utc)
# Convert to Numpy rec-array.
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - tab[:, 0]
ra_diff = np.abs(ra_diff) * 3600.0
dec_diff = np.degrees(cat['delta']) - tab[:, 1]
dec_diff = np.abs(dec_diff) * 3600.0
print("Comparison with SLALIB map using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2], x[3] ** 0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2], x[3] ** 0.5))
# Dummy radial velocity.
rv = np.zeros_like(hip_tab['px'])
# Create array of TPM V6C vectors.
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], hip_tab['pma'],
hip_tab['pmd'], hip_tab['px'], rv, tpm.CJ)
# Time for the observations.
utc = tpm.gcal2j(2010, 1, 1) - 0.5 # midnight
tt = tpm.utc2tdb(utc)
# Convert J2000 RA, DEC to Az, EL and ZD at given UTC.
v6o = convert.proper_motion(v6l, tt, tpm.J2000)
v619 = convert.convertv6(v6o, s1=6, s2=19, utc=utc)
cat19 = convert.v62cat(v619, tpm.CJ)
cat19 = cat2array(cat19)
az = np.degrees(cat19['alpha'])
el = np.degrees(cat19['delta'])
zd = 90.0 - el
# Keep only those objects with ZD < 75.0 degrees.
indx = np.where(zd < 75.0)
# Difference in AZ and ZD, using TPM and SLALIB.
az_diff = np.abs(az[indx] - az_sla[indx]) * 3600.0
zd_diff = np.abs(zd[indx] - zd_sla[indx]) * 3600.0
# Az, El to HA and Dec.
v620 = convert.convertv6(v619, s1=19, s2=20, utc=utc)
cat20 = convert.v62cat(v620, tpm.CJ)
cat20 = cat2array(cat20)
# Dummy radial velocity.
rv = np.zeros_like(hip_tab['px'])
# Create array of TPM V6C vectors.
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], hip_tab['pma'],
hip_tab['pmd'], hip_tab['px'], rv, tpm.CJ)
# Time for the observations.
utc = tpm.gcal2j(2010, 1, 1) - 0.5 # midnight
tt = tpm.utc2tdb(utc)
# Convert J2000 RA, DEC to Az, EL and ZD at given UTC.
v6o = convert.proper_motion(v6l, tt, tpm.J2000)
v619 = convert.convertv6(v6o, s1=6, s2=19, utc=utc)
cat19 = convert.v62cat(v619, tpm.CJ)
cat19 = cat2array(cat19)
az = np.degrees(cat19['alpha'])
el = np.degrees(cat19['delta'])
zd = 90.0 - el
# Keep only those objects with ZD < 75.0 degrees.
indx = np.where(zd < 75.0)
# Difference in AZ and ZD, using TPM and SLALIB.
az_diff = np.abs(az[indx] - az_sla[indx]) * 3600.0
zd_diff = np.abs(zd[indx] - zd_sla[indx]) * 3600.0
# Az, El to HA and Dec.
v620 = convert.convertv6(v619, s1=19, s2=20, utc=utc)
cat20 = convert.v62cat(v620, tpm.CJ)
cat20 = cat2array(cat20)
# Create V6C array from catalog data.
v6l = convert.cat2v6(hip_tab['raj2'], hip_tab['decj2'], hip_tab['pma'],
hip_tab['pmd'], hip_tab['px'], rv, tpm.CJ)
# UTC and TDB for mid-night of 2010/1/1.
utc = tpm.gcal2j(2010, 1, 1) - 0.5 # midnight
tt = tpm.utc2tdb(utc)
# Apply proper motion from J2000 to date.
v6o = convert.proper_motion(v6l, tt, tpm.J2000)
# Convert from mean equinox J2000 to true equinox and epoch of date.
v6o = convert.convertv6(v6o, s1=6, s2=11, utc=utc)
# Convert to Numpy rec-array.
cat = convert.v62cat(v6o, tpm.CJ)
cat = cat2array(cat)
ra_diff = np.degrees(cat['alpha']) - tab[:, 0]
ra_diff = np.abs(ra_diff) * 3600.0
dec_diff = np.degrees(cat['delta']) - tab[:, 1]
dec_diff = np.abs(dec_diff) * 3600.0
print("Comparison with SLALIB map using HIPPARCOS data.")
fs = "{0} {1}\n" + \
"Min: {2:.4f} Max: {3:.4f} \nMean: {4:.4f} Std: {5:.4f}\n"
x = stats.describe(ra_diff)
print(fs.format("ra_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
x = stats.describe(dec_diff)
print(fs.format("dec_diff", "arcsec", x[1][0], x[1][1], x[2], x[3]**0.5))
