def save_matched_hipp_ids(spec_survey):
if spec_survey == "rave":
# Xmatch with RAVE, taking into account the epoch difference.
tgas = gload.tgas()
rave_cat = gload.rave()
m1, m2, sep = gx.xmatch(rave_cat, tgas, colRA1='RAdeg', colDec1='DEdeg',
colRA2='ra', colDec2='dec', epoch1=2000.,
epoch2=2015.,swap=True)
rave_cat = rave_cat[m1]
tgas_rave = tgas[m2]
tgas_rave_hip = masked_array_to_simple_array(tgas_rave)
tgas_rave_hip_df = pd.DataFrame({"hip": tgas_rave_hip})
tgas_rave_hip_df.to_csv("tgas_rave_hip_df.csv")
elif spec_survey == "lamost":
# Match LAMOST to TGAS
tgas = gload.tgas()
lamost_cat = gload.lamost()
m1, m2, sep = gx.xmatch(lamost_cat, tgas, colRA1='ra', colDec1='dec',
colRA2='ra', colDec2='dec', epoch1=2000.,
epoch2=2015., swap=True)
lamost_cat = lamost_cat[m1]
tgas_lamost = tgas[m2]
tgas_lamost_hip_id = masked_array_to_simple_array(tgas_lamost)
tgas_lamost_hip_df = pd.DataFrame({"tycho2_id": tgas_lamost_hip_id})
tgas_lamost_hip_df.to_csv("tgas_lamost_hip_df.csv")
def tgas_match(cat, **kwargs):
"""
This cross-matches a pandas DataFrame to the TGAS catalog. The first
argument should be a data frame and the other keyword arguments are passed
to the ``gaia_tools.xmatch`` function.
"""
# Load the catalogs
tgas = gload.tgas()
# Set the default columns
kwargs["colRA1"] = kwargs.get("colRA1", "ra")
kwargs["colDec1"] = kwargs.get("colDec1", "dec")
kwargs["epoch1"] = kwargs.get("epoch1", 2000.0)
kwargs["colRA2"] = kwargs.get("colRA2", "ra")
kwargs["colDec2"] = kwargs.get("colDec2", "dec")
kwargs["epoch2"] = kwargs.get("epoch2", 2015.0)
# Do the cross-match using gaia_tools
m1, m2, dist = xmatch.xmatch(cat.to_records(), tgas, **kwargs)
# Build a pandas DataFrame out of the match and save the TGAS columns
matched = pd.DataFrame(cat.iloc[m1])
tgas_matched = tgas[m2]
for k in tgas_matched.dtype.names:
# Ugliness to deal with byte ordering
matched["tgas_" + k] = tgas_matched[k].byteswap().newbyteorder()
matched["tgas_match_distance"] = dist
return matched
"colDec1": "DEdeg",
"epoch1": 2000.0,
},
"tgas": {
"colRA2": "tgas_ra",
"colDec2": "tgas_dec",
"epoch2": 2015.0,
"colpmRA2": "tgas_pmra",
"colpmDec2": "tgas_pmdec"
}
}
# Do the cross-match with lamost using gaia_tools
lamost_cat = gload.lamost(cat='star')
# Dictionary unpacking syntax only works on python 3.5
m1, m2, dist = xmatch(lamost_cat, planets_tgas.to_records(),
dict(xmatch_kwargs['lamost'], xmatch_kwargs['tgas']))
# Add lamost columns to planets_tga dataframe
lamost_columns = ['teff', 'teff_err', 'logg', 'logg_err', 'feh', 'feh_err']
for k in lamost_columns:
planets_tgas["lamost_" + k] = np.nan
planets_tgas.loc[planets_tgas.iloc[m2].index,
"lamost_" + k] = lamost_cat[k][m1]
# Do the cross-match with rave using gaia_tools
rave_columns = [
'RAdeg', 'DEdeg', 'Teff_K', 'eTeff_K', 'logg_K', 'elogg_K', 'Met_K',
'eMet_K'
]
# Find column numbers to speed up loading of the data
@author: tao
"""
import gaia_tools.load as gload
from gaia_tools import xmatch
import matplotlib.pyplot as plt
import numpy as np
import time
tgas_cat=gload.tgas()
apogee_cat=gload.apogee()
tic = time.clock()
m1,m2,sep= xmatch.xmatch(apogee_cat,tgas_cat,colRA2='ra',colDec2='dec',epoch1=2000.,epoch2=2015.,swap=True)
toc = time.clock()
dt_astropy=toc - tic
plt.figure(1)
plt.xlabel('J')
plt.ylabel('delta_theta(arcsec)')
plt.scatter(apogee_cat['J'][m1],sep*3600,c='k',s=0.5,marker='.')
delta_ra_gaia=apogee_cat['RA'][m1]-tgas_cat['ra'][m2]
delta_dec_gaia=apogee_cat['DEC'][m1]-tgas_cat['dec'][m2]
plt.figure(2)
plt.xlabel('delta_dec (arcsec)')
plt.ylabel('delta_ra (arcsec)')
plt.scatter(delta_dec_gaia*3600,delta_ra_gaia*3600,c='k',s=0.5,marker='.')
# save a fits
hdu = fits.PrimaryHDU(spec)
flag_hdu = fits.ImageHDU(good_flag)
hdul = fits.HDUList([hdu, flag_hdu])
hdul.writeto(contspac_file_name)
good_flag = good_flag.astype(bool)
if corr_flag: # correct north-south
north_spec_idx = np.where(
allstar_data['TELESCOPE'][good_flag] == 'apo25m')[0]
south_spec_idx = np.where(
allstar_data['TELESCOPE'][good_flag] == 'lco25m')[0]
idx1, idx2, _ = xmatch(allstar_data[good_flag][north_spec_idx],
allstar_data[good_flag][south_spec_idx])
spec = fits.getdata(contspac_file_name)
lco_apo_mediff = np.median(spec[good_flag][south_spec_idx][idx2] -
spec[good_flag][north_spec_idx][idx1],
axis=0)
np.save("lco_apo_median_dr17sync.npy", lco_apo_mediff)
spec[good_flag][south_spec_idx] -= lco_apo_mediff
os.rename(
contspac_file_name,
"{0}_{2}{1}".format(*os.path.splitext(contspac_file_name) +
("uncorrected", )))
def query_along_orbit(
orbit,
frame_point,
frame_philims,
# points
points_inds=[],
points_philims=None,
# options
frame_dt=1.9 * u.Myr,
points_dt=None,
max_separation=.5 * u.kpc,
return_transforms=True,
# for make_query
local=False,
_random_index=None,
#
distance=23.2 * u.kpc,
duststr='output/ps1dust_{}.dat',
# saving and logging
pprint=False,
save_window=False,
save_frame=False,
logger=_LOGFILE,
verbose=None):
"""
Parameters
----------
orbit : SkyCoord
icrs orbit
frame_point : SkyCoord
points_philims : list
list (as long points_inds) of philim lists (len 4)
save_window : bool or str
False : nothing
True : save to 'output/window.fits'
str : save at this location
save_frame : bool or str
save the main Frame and coordtransforms
False : nothing
True : save to 'output/window.fits'
str : save at this location
"""
# -------------------------------------------------------------------------
# Preparing
## checks
assert isinstance(save_window, (bool, str))
assert isinstance(save_frame, (bool, str))
## points_philims
if points_philims is None:
points_philims = (frame_philims, ) * len(points_inds)
elif len(points_philims) != len(points_inds):
raise ValueError('points_philims do not match points_inds in length')
## dt
if points_dt is None:
points_dt = (frame_dt, ) * len(points_inds)
elif len(points_dt) != len(points_inds):
return ValueError('points_dt do not match points_inds in length')
# -------------------------------------------------------------------------
# preplotting
plt.plot(orbit.icrs.ra, orbit.icrs.dec, c='k')
plt.scatter(frame_point.icrs.ra, frame_point.icrs.dec, s=100, c='r')
for i in points_inds:
plt.scatter(orbit.icrs.ra[i], orbit.icrs.dec[i], s=50, c='k')
plt.set(title='Query Along Orbit',
xlabel='\alpha [deg]',
ylabel='\delta [deg]',
savefig='plots/query_along_point.png')
# -------------------------------------------------------------------------
# the master custom frame
logfile.newsection(title=f'Starting Query:', div='=')
df, MasterFrame, crd_xfm = query_at_point(orbit,
frame_point,
frame_philims,
dt=frame_dt,
is_nbody=False,
max_separation=max_separation,
return_transforms=True,
as_qtable=False,
local=local,
_random_index=_random_index,
pprint=pprint,
save_window=False,
save_frame=save_frame,
logger=logfile,
verbose=verbose)
# -------------------------------------------------------------------------
# Querying at points
for i, (ind, pdt,
philims) in enumerate(zip(points_inds, points_dt, points_philims)):
point = orbit[ind]
logfile.newsection(title=f'Query @ Point {i+1}:', div='=')
pointdf = query_at_point(orbit,
point,
philims,
dt=pdt,
is_nbody=False,
max_separation=max_separation,
return_transforms=False,
as_qtable=False,
local=local,
_random_index=_random_index,
pprint=pprint,
logger=logfile,
verbose=verbose)
# xmatch the catalogs to identify duplicates
_, idx2, _ = xmatch(df,
pointdf,
colRA1='ra',
colDec1='dec',
epoch1=2015.5,
colRA2='ra',
colDec2='dec',
epoch2=2015.5)
# finding distinct values in pointdf
inds = np.arange(len(pointdf)) # full index array for comparison
nm = list(set(inds).difference(idx2)) # not matched values
# merging tables
df = vstack([df, pointdf[nm]])
# -------------------------------------------------------------------------
# modifying table
register_stream_frame(crd_xfm.R_icrs_to_cust)
# replace the custom coordinates by values from MasterFrame
sc = SkyCoord(ra=df['ra'],
dec=df['dec'],
pm_ra_cosdec=df['pmra'],
pm_dec=df['pmdec'],
frame='icrs',
representation_type='spherical').transform_to(MasterFrame)
sc.phi1.wrap_angle = '180d'
df['phi1'] = sc.phi1.to(deg)
df['phi2'] = sc.phi2.to(deg)
df['pmphi1'] = sc.pm_phi1_cosphi2
df['pmphi2'] = sc.pm_phi2
df = QTable(df)
df['prlx'][df['prlx'] < 0] = np.NaN
table_utils.add_color_col(df, 'g', 'r')
table_utils.add_color_col(df, 'g', 'i')
table_utils.add_abs_pm_col(df, 'pmra', 'pmdec')
logfile.write("adding color columns",
"did g-r",
"did g-i",
"did |pm|",
sep='\n\t',
end='\n')
# -------------------------------------------------------------------------
# Dust
# distance = pal5['sc'][0].distance.to_value(u.kpc)
ps1dust = load_dust_gri(duststr, df=df, distance=distance)
## Making color corrections
logfile.write("Dextincting",
"g -> g dx",
"r -> r dx",
"i -> i dx",
"g-r -> g-r dx",
"g-i -> g-i dx",
"r-i -> r-i dx",
sep='\n\t',
end='\n')
df['g dx'] = df['g'] - ps1dust['g']
df['r dx'] = df['r'] - ps1dust['r']
df['i dx'] = df['i'] - ps1dust['i']
df['g-r dx'] = df['g dx'] - df['r dx']
df['g-i dx'] = df['g dx'] - df['i dx']
df['r-i dx'] = df['r dx'] - df['i dx']
for c in ('g', 'r', 'i', 'g-r', 'g-i', 'r-i'):
setattr(df[c + ' dx'].info, 'description', 'de-extincted w/ mwdust')
## Adding in Conversion to MegaCam
df['g MC'] = MCG1_PS1.G_MP9401(df, g='g dx', r='r dx', gmi='g-r dx')
df['r MC'] = MCG1_PS1.R_MP9601(df, g='g dx', r='r dx', gmi='g-r dx')
df['g-r MC'] = df['g MC'] - df['r MC']
for c in ('g MC', 'r MC', 'g-r MC'):
setattr(df[c].info, 'description',
'de-extincted & converted to MegaCam')
# -------------------------------------------------------------------------
# Saving
df = QTable(df)
if save_window: # True or str
if isinstance(save_window, str):
fpath = save_window
else:
fpath = 'output/window.fits'
logfile.write(f'saving to {fpath}')
df.write(fpath, format='fits', overwrite=True)
# -------------------------------------------------------------------------
# postplotting
plt.scatter(df['ra'].to_value(u.deg),
df['dec'].to_value(u.deg),
s=.1,
c='k')
plt.plot(orbit.icrs.ra, orbit.icrs.dec, c='k')
plt.scatter(frame_point.icrs.ra, frame_point.icrs.dec, s=100, c='r')
for i in points_inds:
plt.scatter(orbit.icrs.ra[i], orbit.icrs.dec[i], s=50, c='k')
plt.set(title='Query Along Orbit',
xlabel='\alpha [deg]',
ylabel='\delta [deg]',
savefig='plots/query_along_point.png')
# plt.scatter(df['phi1'], df['phi2'], s=.1, c='k', fig='new')
# plt.plot(orbit.icrs.ra, orbit.icrs.dec, c='k')
# plt.scatter(frame_point.icrs.ra, frame_point.icrs.dec, s=100, c='r')
# for i in points_inds:
# plt.scatter(orbit.icrs.ra[i], orbit.icrs.dec[i], s=50, c='k')
# plt.set(title='Query Along Orbit',
# xlabel='\alpha [deg]', ylabel='\delta [deg]',
# savefig='plots/query_along_point.png')
# -------------------------------------------------------------------------
# returning
return df, MasterFrame, crd_xfm