def cone_search(self, ra, dec, radius):
"""
Searches for sources in a cone of given radius and center
:param ra: decimal degrees, R.A. of the center of the cone
:param dec: decimal degrees, Dec. of the center of the cone
:param radius: radius in degrees
:return: a table with the list of sources
"""
skycoord = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame='icrs')
# First check that we have an active internet connection
if not internet_connection_is_active(): # pragma: no cover
raise ConeSearchFailed("It looks like you don't have an active internet connection. Cannot continue.")
with warnings.catch_warnings():
#Ignore all warnings, which are many from the conesearch module
warnings.simplefilter('ignore')
try:
votable = conesearch.conesearch(skycoord, radius,
catalog_db=self.catalog,
verb=3, verbose=True,
cache=False)
except VOSError as exc: # Pragma: no cover
# Download failed
raise ConeSearchFailed("Cone search failed. Reason: %s" % exc.message)
else:
# Download successful
table = votable.to_table()
self._last_query_results = table.to_pandas().set_index('name').sort_values("Search_Offset")
out = self.apply_format(table)
#This is needed to avoid strange errors
del votable
del table
# Save coordinates of center of cone search
self._ra = ra
self._dec = dec
# Make a DataFrame with the name of the source as index
return out
def main( ra, dec, radius, output=False ):
"""
important params:
'RAJ2000','DEJ2000','B1mag','R1mag','B2mag','R2mag','Imag'
"""
if output != False:
if os.path.exists(output) == True:
t = ascii.read(output)
return t
conf.conesearch_dbname = 'conesearch_warn'
catalog = 'The USNO-B1.0 Catalog (Monet+ 2003) 1'
c = SkyCoord(ra=ra,dec=dec,unit="degree")
t = conesearch.conesearch(c, radius*u.degree, catalog_db=catalog).to_table()
if len(t) == 0:
print "No USNO-B1.0 Catalog (%s,%s,%s)" % (ra,dec,radius)
return None
name_ls = ["B1mag","B2mag","R1mag","R2mag","Imag"]
for name in name_ls:
select = np.ma.getmask(t[name])
t[name][select] = np.nan
t.rename_column('RAJ2000','RA')
t.rename_column('DEJ2000','DEC')
filt_ls = ["B","R","I"]
for filt in filt_ls:
if filt in ["B","R"]:
name1 = "%s1mag"%filt
n1 = len(t[name1][~np.isnan(t[name1])])
name2 = "%s2mag"%filt
n2 = len(t[name2][~np.isnan(t[name2])])
if filt == "R":filt="Rc"
if n1 <= n2:
t.rename_column(name2,'%sMAG'%filt)
elif n2 < n1:
t.rename_column(name1,'%sMAG'%filt)
if filt == "I":
t.rename_column("%smag"%filt,'%scMAG'%filt)
if output != False:
ascii.write(t,output)
#print t
return t
def main(ra, dec, radius, output=False):
"""
important params:
'ra','dec','j_m','j_cmsig','j_msigcom','j_snr',h...,k....,'glon','glat'
"""
if output != False:
if os.path.exists(output) == True:
t = ascii.read(output)
return t
conf.conesearch_dbname = 'conesearch_good'
catalog = 'Two Micron All Sky Survey (2MASS) 1'
c = SkyCoord(ra=ra, dec=dec, unit="degree")
t = conesearch.conesearch(c, radius * u.degree,
catalog_db=catalog).to_table()
if len(t) == 0:
print "No 2MASS Catalog (%s,%s,%s)" % (ra, dec, radius)
return None
t.rename_column('ra', 'RA')
t.rename_column('dec', 'DEC')
filt_ls = ["J", "H", "K"]
for filt in filt_ls:
if filt in ["J", "H"]:
t.rename_column('%s_m' % filt.lower(), '%sMAG' % filt)
t.rename_column('%s_cmsig' % filt.lower(), '%sMAGERR' % filt)
elif filt == "K":
t.rename_column('%s_m' % filt.lower(), '%ssMAG' % filt)
t.rename_column('%s_cmsig' % filt.lower(), '%ssMAGERR' % filt)
if output != False:
ascii.write(t, output)
#print t
return t
from astroquery.vo_conesearch.conesearch import conesearch
from astropy import units as u
from astropy.coordinates import SkyCoord
center = SkyCoord(189.1 * u.deg, 3 * u.deg, frame='galactic')
search = conesearch(
center=center,
radius=1,
verb=1,
catalog_db=
"http://vizier.u-strasbg.fr/viz-bin/votable/-A?-source=IPHAS2&-out.all&")
search.to_table().write('../data/snrpic/iphas-data.fits',
format='fits',
overwrite=True)
def search(self, **params):
"""
For compatibility with generic star catalog search.
"""
self.logger.debug("search params=%s" % (str(params)))
ra, dec = params['ra'], params['dec']
if not (':' in ra):
# Assume RA and DEC are in degrees
ra_deg = float(ra)
dec_deg = float(dec)
else:
# Assume RA and DEC are in standard string notation
ra_deg = wcs.hmsStrToDeg(ra)
dec_deg = wcs.dmsStrToDeg(dec)
# Convert to degrees for search radius
radius_deg = float(params['r']) / 60.0
# radius_deg = float(params['r'])
# Note requires astropy 0.3.x+
c = coordinates.SkyCoord(ra_deg * units.degree,
dec_deg * units.degree,
frame='icrs')
self.logger.info("Querying catalog: %s" % (self.full_name))
time_start = time.time()
with warnings.catch_warnings(): # Ignore VO warnings
warnings.simplefilter('ignore')
results = conesearch.conesearch(c,
radius_deg * units.degree,
catalog_db=self.full_name,
verbose=False)
time_elapsed = time.time() - time_start
numsources = results.array.size
self.logger.info("Found %d sources in %.2f sec" %
(numsources, time_elapsed))
# Scan the returned fields to find ones we need to extract
# particulars from (ra, dec, id, magnitude)
mags = []
ext = {}
fields = results.array.dtype.names
for name in fields:
ucd = results.get_field_by_id(name).ucd
ucd = str(ucd).lower()
if ucd == 'id_main':
ext['id'] = name
elif ucd == 'pos_eq_ra_main':
ext['ra'] = name
elif ucd == 'pos_eq_dec_main':
ext['dec'] = name
if ('phot_' in ucd) or ('phot.' in ucd):
mags.append(name)
self.logger.debug("possible magnitude fields: %s" % str(mags))
if len(mags) > 0:
magfield = mags[0]
else:
magfield = None
# prepare the result list
starlist = []
arr = results.array
for i in range(numsources):
source = dict(zip(fields, arr[i]))
starlist.append(self.toStar(source, ext, magfield))
# metadata about the list
columns = [
('Name', 'name'),
('RA', 'ra'),
('DEC', 'dec'),
('Mag', 'mag'),
('Preference', 'preference'),
('Priority', 'priority'),
('Description', 'description'),
]
# Append extra columns returned by search to table header
# TODO: what if not all sources have same record structure?
# is this possible with VO?
cols = list(fields)
cols.remove(ext['ra'])
cols.remove(ext['dec'])
cols.remove(ext['id'])
columns.extend(zip(cols, cols))
# which column is the likely one to color source circles
colorCode = 'Mag'
info = Bunch.Bunch(columns=columns, color=colorCode)
return starlist, info
def cone_search(self, ra, dec, radius):
"""
Searches for sources in a cone of given radius and center
:param ra: decimal degrees, R.A. of the center of the cone
:param dec: decimal degrees, Dec. of the center of the cone
:param radius: radius in degrees
:return: a table with the list of sources
"""
skycoord = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame="icrs")
# First check that we have an active internet connection
if not internet_connection_is_active(): # pragma: no cover
raise ConeSearchFailed(
"It looks like you don't have an active internet connection. Cannot continue."
)
with warnings.catch_warnings():
# Ignore all warnings, which are many from the conesearch module
warnings.simplefilter("ignore")
try:
votable = conesearch.conesearch(
skycoord,
radius,
catalog_db=self.catalog,
verb=3,
verbose=True,
cache=False,
)
except VOSError as exc: # Pragma: no cover
# Download failed
raise ConeSearchFailed("Cone search failed. Reason: %s" %
exc.message)
else:
# Download successful
table = votable
# Workaround to comply with newer versions of astroquery
if isinstance(votable, astropy.io.votable.tree.Table):
table = votable.to_table()
if table is None:
log.error("Your search returned nothing")
return None
table.convert_bytestring_to_unicode()
pandas_df = (table.to_pandas().set_index("name").sort_values(
"Search_Offset"))
str_df = pandas_df.select_dtypes([object])
if astropy_old:
str_df = str_df.stack().str.decode("utf-8").unstack()
for col in str_df:
pandas_df[col] = str_df[col]
if astropy_old:
new_index = [x.decode("utf-8") for x in pandas_df.index]
pandas_df.index = new_index
self._last_query_results = pandas_df
out = self.apply_format(table)
# This is needed to avoid strange errors
del votable
del table
# Save coordinates of center of cone search
self._ra = ra
self._dec = dec
# Make a DataFrame with the name of the source as index
return out
try:
lun = open(file_stars_pkl, 'rb')
cat = pickle.load(lun)
lun.close()
print("Loaded: " + file_stars_pkl)
except FileNotFoundError:
name_cat = u'Guide Star Catalog v2 1'
# name_cat = u'The HST Guide Star Catalog, Version 1.1 (Lasker+ 1992) 1' # works, but 1' errors; investigating
# stars = conesearch.conesearch(w.wcs.crval, 0.3, cache=False, catalog_db = name_cat)
with data.conf.set_temp('remote_timeout', 200): # This is the very strange syntax to set a timeout delay.
# The default is 3 seconds, and that times out often.
stars = conesearch.conesearch(w.wcs.crval, radius, cache=False, catalog_db = name_cat)
# Extract proper fields
ra_cat = np.array(stars.array['ra'])*hbt.d2r # Convert to radians
dec_cat = np.array(stars.array['dec'])*hbt.d2r # Convert to radians
mag_cat = np.array(stars.array['Mag'])
# Sort the stars. I don't know why stars.sort('mag') doesn't work, but it fails.
order = np.argsort(mag_cat)
ra_cat = ra_cat[order]
dec_cat = dec_cat[order]
mag_cat = mag_cat[order]
def get_fits_table_from_server(catalog, server, ra, dec, sr, verbose=False):
"""
This function fetches sources from the specified catalog from the specified
server within the search radius and returns a Table containing them.
Parameters
----------
catalog : {'sdss9', '2mass', 'ukidss9', 'gmos'}
Name of catalog to search.
server : str
Name of server to query [sdss9_mko | sdss9_cpo | sdss9_vizier |
2mass_mko | 2mass_cpo | 2mass_vizier | ukidss9_mko | ukidss9_cpo]
ra : float
Right ascension of search center, decimal degrees.
dec : float
Declination of search center, decimal degrees.
sr : float
Search radius, decimal degrees.
verbose : bool
Verbose output.
Returns
-------
`astropy.table.Table`
Sources within the search cone.
"""
# OK, do the query
url = SERVER_URLS[server]
cols = CAT_COLS[catalog]
server_cols = SERVER_COLMAP[server]
if verbose:
print("RA, Dec, radius :", ra, dec, sr)
print("catalog :", catalog)
print("server :", server)
print("url :", url)
print("cols :", cols)
print("server_cols :", server_cols)
print("\n\n")
# turn on verbose for debug to stdout.
# Need verb=3 to get the right cols from vizier
# astroquery 0.4 removed the pedantic keyword in favor of the config
# item, and switched to returning an astropy table by default (hence
# return_astropy_table=False below).
# Another change is that conesearch returns None and issue of
# NoResultsWarning if no results are found, instead of raising a
# VOSError: https://github.com/astropy/astroquery/pull/1528
try:
try:
table = conesearch((ra, dec),
sr,
verb=3,
catalog_db=url,
return_astropy_table=False,
verbose=False)
except TypeError:
# astroquery < 0.4
table = conesearch((ra, dec),
sr,
verb=3,
catalog_db=url,
pedantic=False,
verbose=False)
except VOSError:
log.stdinfo("VO conesearch produced no results")
return
# Did we get any results?
if table is None or table.is_empty() or len(table.array) == 0:
log.stdinfo(f"No results returned from {server}")
return
array = table.array
if server == 'sdss9_vizier':
# Vizier uses the photoObj table from SDSS9, whereas the internal
# server uses the calibObj, AKA "datasweep", which contains a subset
# of photoObj, "designed for those who want to work with essentially
# every well measured object, but only need the most commonly used
# parameters".
#
# To get results similar to calibObj, we filter below on mode=1 to get
# only the primary sources (the 'main' photometric observation of an
# object). calibObj also uses a cut on magnitudes (see
# http://www.sdss3.org/dr9/imaging/catalogs.php) but this is difficult
# to reproduce here since the cuts apply to extinction-corrected
# magnitudes, and we don't have the extinction values in the Vizier
# table.
array = array[array['mode'] == 1]
# It turns out to be not viable to use UCDs to select the columns,
# even for the id, ra, and dec. Even with vizier. <sigh>
# The first column is our running integer column
ret_table = Table([list(range(1,
len(array) + 1))],
names=('Id', ),
dtype=('i4', ))
ret_table.add_column(
Column(array[server_cols[0]], name='Cat_Id', dtype='a'))
ret_table.add_column(
Column(array[server_cols[1]], name='RAJ2000', dtype='f8', unit='deg'))
ret_table.add_column(
Column(array[server_cols[2]], name='DEJ2000', dtype='f8', unit='deg'))
# Now the photometry columns
for col in range(3, len(cols)):
ret_table.add_column(
Column(array[server_cols[col]],
name=cols[col],
dtype='f4',
unit='mag',
format='8.4f'))
header = add_header_to_table(ret_table)
header['CATALOG'] = (catalog.upper(), 'Origin of source catalog')
# Add comments to the header to describe it
header.add_comment(f'Source catalog derived from the {catalog} catalog')
header.add_comment(f'Source catalog fetched from server at {url}')
header.add_comment(f'Delivered Table name from server: {table.name}')
for col in range(len(cols)):
header.add_comment('UCD for field {} is {}'.format(
cols[col],
table.get_field_by_id(server_cols[col]).ucd))
return ret_table
def nh_jring_create_objectlist(file_in, do_stars=True, bodies = [], num_stars_max = 100):
'''
Creates a text file which lists all the stars in a file, with lines like
'star', <xpos>, <ypos>, mag [optional]
<xpos> and <ypos> are the x and y coordinate centers, in pixels.
The output is sorted by magnitude, if it is available.
<bodies> is a list, like ['Adrastea', 'Amalthea'], etc.
It is OK to have xpos and ypos be outside the screen. We might want to know that sometimes, for satellites.
'''
# file_in = 'lor_0034962025_0x630_sci_1_opnav.fit'
dir_images = '/Users/throop/data/NH_Jring/data/jupiter/level2/lor/all/'
dir_out = '/Users/throop/data/NH_Jring/out/'
file_in_base = file_in.split('/')[-1] # Strip the pathname
file_out_base = file_in_base.replace('.fit', '_objects.txt')
file_out = dir_out + file_out_base
# If we were passed a
file = dir_images + file_in_base
header = hbt.get_image_header(dir_images + file_in_base)
dx_pix = header['NAXIS1']
dy_pix = header['NAXIS2']
radius_search = 0.2 * u.deg
with warnings.catch_warnings():
warnings.simplefilter("ignore")
w = WCS(file)
name_cat = u'Guide Star Catalog v2 1'# Works on gobi only (no tomato laptop)
name_cat = 'The HST Guide Star Catalog, Version GSC-ACT (Lasker+ 1996-99) 1'
url_cat = 'http://gsss.stsci.edu/webservices/vo/ConeSearch.aspx?CAT=GSC23&' # Works always
with data.conf.set_temp('remote_timeout', 30): # This is the very strange syntax to set a timeout delay.
# The default is 3 seconds, and that times out often.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
pos = (w.wcs.crval[0], w.wcs.crval[1]) # Get errors about invalid coords. Convert from np arr → tuple, ugh.
stars = conesearch.conesearch(pos, radius_search, cache=True, catalog_db = url_cat)
# NB: the returned value is a Table, but I have to access via .array[] -- not sure why.
ra_stars = np.array(stars.array['ra'])*hbt.d2r # Convert to radians
dec_stars = np.array(stars.array['dec'])*hbt.d2r # Convert to radians
mag_stars = np.array(stars.array['Mag'])
print("Stars downloaded: {}; mag = {} .. {}".format(np.size(mag_stars), np.nanmin(mag_stars), np.nanmax(mag_stars)))
print("RA = {} .. {}".format(np.nanmin(ra_stars)*hbt.r2d, np.nanmax(ra_stars)*hbt.r2d))
# Now sort by magnitude, and keep the 100 brightest
order = np.argsort(mag_stars)
order = np.array(order)[0:num_stars_max]
ra_stars = ra_stars[order]
dec_stars = dec_stars[order]
mag_stars = mag_stars[order]
radec_stars = np.transpose(np.array((ra_stars,dec_stars)))
x_stars, y_stars = w.wcs_world2pix(radec_stars[:,0]*hbt.r2d, radec_stars[:,1]*hbt.r2d, 0)
is_good = np.logical_and( np.logical_and(x_stars >=0, x_stars <= dx_pix),
np.logical_and(y_stars >=0, y_stars <= dy_pix) )
# Now make a table
t_stars = Table()
t_stars['name'] = np.zeros(np.shape(mag_stars[is_good]), dtype='U30')
t_stars['name'][:] = u'star'
t_stars['x_pix'] = x_stars[is_good]
t_stars['y_pix'] = y_stars[is_good]
t_stars['mag'] = mag_stars[is_good]
#==============================================================================
# Now find the satellite locations
#==============================================================================
if np.size(bodies) > 0:
# Look up satellite positions
et = header['SPCSCET']
utc = sp.et2utc(et, 'C', 0)
x_bodies, y_bodies = hbt.get_pos_bodies(et, bodies, units='pixels', wcs=w)
t_sats = Table()
t_sats['x_pix'] = x_bodies
t_sats['y_pix'] = y_bodies
t_sats['name'] = np.array(bodies).astype('U30')
#==============================================================================
# Merge the stars and sats into one table
#==============================================================================
t_merged = vstack([t_stars, t_sats])
else:
t_merged = t_stars
#==============================================================================
# And write the table to disk
#==============================================================================
t_merged.write(file_out, format = 'csv', overwrite = True)
print("Wrote: {} ({} objects)".format(file_out, np.shape(t_merged)[0]))
#
return t_merged
def cone_search(self, ra, dec, radius):
"""
Searches for sources in a cone of given radius and center
:param ra: decimal degrees, R.A. of the center of the cone
:param dec: decimal degrees, Dec. of the center of the cone
:param radius: radius in degrees
:return: a table with the list of sources
"""
skycoord = SkyCoord(ra=ra * u.degree, dec=dec * u.degree, frame='icrs')
# First check that we have an active internet connection
if not internet_connection_is_active(): # pragma: no cover
raise ConeSearchFailed(
"It looks like you don't have an active internet connection. Cannot continue."
)
with warnings.catch_warnings():
#Ignore all warnings, which are many from the conesearch module
warnings.simplefilter('ignore')
try:
votable = conesearch.conesearch(skycoord,
radius,
catalog_db=self.catalog,
verb=3,
verbose=True,
cache=False)
except VOSError as exc: # Pragma: no cover
# Download failed
raise ConeSearchFailed("Cone search failed. Reason: %s" %
exc.message)
else:
# Download successful
table = votable.to_table()
self._last_query_results = table.to_pandas().set_index(
'name').sort_values("Search_Offset")
out = self.apply_format(table)
#This is needed to avoid strange errors
del votable
del table
# Save coordinates of center of cone search
self._ra = ra
self._dec = dec
# Make a DataFrame with the name of the source as index
return out
def search(self, **params):
"""
For compatibility with generic star catalog search.
"""
self.logger.debug("search params=%s" % (str(params)))
ra, dec = params['ra'], params['dec']
if not (':' in ra):
# Assume RA and DEC are in degrees
ra_deg = float(ra)
dec_deg = float(dec)
else:
# Assume RA and DEC are in standard string notation
ra_deg = wcs.hmsStrToDeg(ra)
dec_deg = wcs.dmsStrToDeg(dec)
# Convert to degrees for search radius
radius_deg = float(params['r']) / 60.0
# radius_deg = float(params['r'])
# Note requires astropy 0.3.x+
c = coordinates.SkyCoord(ra_deg * units.degree,
dec_deg * units.degree,
frame='icrs')
self.logger.info("Querying catalog: %s" % (self.full_name))
time_start = time.time()
with warnings.catch_warnings(): # Ignore VO warnings
warnings.simplefilter('ignore')
results = conesearch.conesearch(
c, radius_deg * units.degree, catalog_db=self.full_name,
verbose=False)
time_elapsed = time.time() - time_start
numsources = results.array.size
self.logger.info("Found %d sources in %.2f sec" % (
numsources, time_elapsed))
# Scan the returned fields to find ones we need to extract
# particulars from (ra, dec, id, magnitude)
mags = []
ext = {}
fields = results.array.dtype.names
for name in fields:
ucd = results.get_field_by_id(name).ucd
ucd = str(ucd).lower()
if ucd == 'id_main':
ext['id'] = name
elif ucd == 'pos_eq_ra_main':
ext['ra'] = name
elif ucd == 'pos_eq_dec_main':
ext['dec'] = name
if ('phot_' in ucd) or ('phot.' in ucd):
mags.append(name)
self.logger.debug("possible magnitude fields: %s" % str(mags))
if len(mags) > 0:
magfield = mags[0]
else:
magfield = None
# prepare the result list
starlist = []
arr = results.array
for i in range(numsources):
source = dict(zip(fields, arr[i]))
starlist.append(self.toStar(source, ext, magfield))
# metadata about the list
columns = [('Name', 'name'),
('RA', 'ra'),
('DEC', 'dec'),
('Mag', 'mag'),
('Preference', 'preference'),
('Priority', 'priority'),
('Description', 'description'),
]
# Append extra columns returned by search to table header
# TODO: what if not all sources have same record structure?
# is this possible with VO?
cols = list(fields)
cols.remove(ext['ra'])
cols.remove(ext['dec'])
cols.remove(ext['id'])
columns.extend(zip(cols, cols))
# which column is the likely one to color source circles
colorCode = 'Mag'
info = Bunch.Bunch(columns=columns, color=colorCode)
return starlist, info
# Set up matplotlib
import matplotlib.pyplot as plt
import streamlit as st
st.title('Conesearch Examples')
sbar_text = 'This page uses the `astroquery.vo_conesearch` package '
sbar_text += 'and borrows heavily from an astropy-tutorials demo.'
st.sidebar.markdown(sbar_text)
# options = {'angular diameter': angular_diameter,
# 'lookback time: Matplotlib':lookback_mpl,
# 'lookback time: Altair': lookback_altair,
# 'lookback time: Bokeh': lookback_bokeh,
# 'lookback time: Plotly': lookback_plotly,
# }
# to_plot = st.sidebar.radio("Type of plot", list(options.keys()), 0)
st.markdown('## Catalogs available')
st.write(conesearch.list_catalogs())
target = st.sidebar.text_input('Astronomical object:', value='m31')
st.markdown('## Target object')
st.markdown(target)
c = coord.SkyCoord.from_name(target, frame='icrs')
st.write(c)
result = conesearch.conesearch(c, 0.1 * u.degree)
st.markdown(f'First non-empty table returned by {result.url}')
st.markdown(f'Number of rows is {result.nrows}')
plt.imshow(stretch(image))
# Load matching stars
DO_GSC1 = False # Stopped working 2-Oct-2016
DO_GSC2 = True
DO_USNOA2 = False
#==============================================================================
# Get stars from star catalogs
#==============================================================================
if (DO_GSC1):
name_cat = u'The HST Guide Star Catalog, Version 1.1 (Lasker+ 1992) 1' # works, but 1' errors; investigating
stars = conesearch.conesearch(w.wcs.crval, radius_search, cache=False, catalog_db = name_cat)
ra_stars = np.array(stars.array['RAJ2000'])*d2r # Convert to radians
dec_stars = np.array(stars.array['DEJ2000'])*d2r # Convert to radians
# table_stars = Table(stars.array.data)
if (DO_GSC2):
name_cat = u'Guide Star Catalog v2 1'
file_pickle = dir_out + file_short.replace('.fit', '') + '.stars_gsc.pkl'
# # If there is already a saved pickle file, then load from disk
#
# if os.path.isfile(file_pickle):
#
# print("Loading file: " + file_pickle)
# lun = open(file_pickle, 'rb')
# (ra_stars, dec_stars) = pickle.load(lun)
def navigate_image_stellar(im, wcs_in, name_catalog='', do_plot=True, method='fft', title=''):
"""
Navigate frame based on stellar images.
Result returns is pixel shift (dy, dx).
WCS paramaters are returned, *and* modified in place.
"""
import imreg_dft as ird
from astropy.wcs import WCS
# from astropy.vo.client import conesearch # Virtual Observatory, ie star catalogs # DEPRECATED!
from astroquery.vo_conesearch import conesearch # New home of conesearch
# Inputs are the image array, and the WCS structure.
# This routine does not do any file IO. The image array and header must be already loaded.
# The image is assumed to be stretched properly s.t. stars can be found using DAOphot.
NUM_STARS_PHOT = 100 # How many stars to use from DAOPhot. For noisy images, DAO will find a lot of
# fake stars, so we need to crank this up higher than the # of cat stars.
NUM_STARS_CAT = 50 # How many stars to use from star catalog
DO_GSC1 = False
DO_GSC12 = True
DO_USNOA2 = False
#==============================================================================
# Calculate the image radius, in radians, based on the size and the pixel scale
#==============================================================================
dx_pix = hbt.sizex(im)
dy_pix = hbt.sizey(im)
radec_corner = wcs_in.wcs_pix2world(0, dy_pix/2, 0)
radec_center = wcs_in.wcs_pix2world(dx_pix/2, dy_pix/2, 0)
(ra_corner, dec_corner) = radec_corner
(ra_center, dec_center) = radec_center
radius_image = math.sqrt((dec_corner-dec_center)**2 +
((ra_corner-ra_center) / np.cos(dec_corner*hbt.d2r))**2) * hbt.d2r
radius_search_deg = radius_image * hbt.r2d
# Read the WCS coordinates
center_deg = wcs_in.wcs.crval # degrees. # crval is a two-element array of [RA, Dec], in degrees
# Stretch the image. This is just for display -- no processing.
stretch_percent = 90
stretch = astropy.visualization.PercentileInterval(stretch_percent) # PI(90) scales array to 5th .. 95th %ile.
# Display it
if (do_plot):
plt.imshow(stretch(im))
#==============================================================================
# Get stars from star catalogs
#==============================================================================
if (DO_GSC1):
name_cat = u'The HST Guide Star Catalog, Version 1.1 (Lasker+ 1992) 1' # works, but 1' errors; investigating
stars = conesearch.conesearch(center_deg, radius_search_deg, cache=True, catalog_db = name_cat)
ra_stars = np.array(stars.array['RAJ2000'])*hbt.d2r # Convert to radians
dec_stars = np.array(stars.array['DEJ2000'])*hbt.d2r # Convert to radians
# table_stars = Table(stars.array.data)
if (DO_GSC12):
# name_cat = u'The HST Guide Star Catalog, Version 1.2 (Lasker+ 1996) 1'
name_cat = u'Guide Star Catalog v2 1' # Works from gobi, not tomato
url_cat = 'http://gsss.stsci.edu/webservices/vo/ConeSearch.aspx?CAT=GSC23&' # Works always
with data.conf.set_temp('remote_timeout', 30): # This is the very strange syntax to set a timeout delay.
# The default is 3 seconds, and that times out often.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# stars = conesearch.conesearch(wcs_in.wcs.crval, radius_search_deg, cache=True, catalog_db = url_cat)
# The various functions of conesearch/ConeSearch/etc are quite confusing, and are in flux.
# This line below seems to work. It does not allow an explicit catalog suggstion, but it does the job.
c = astropy.coordinates.SkyCoord(wcs_in.wcs.crval[0], wcs_in.wcs.crval[1], unit='deg')
stars = ConeSearch.query_region(c, f'{radius_search_deg} deg')
ra_stars = np.array(stars.array['ra'])*hbt.d2r # Convert to radians
dec_stars = np.array(stars.array['dec'])*hbt.d2r # Convert to radians
mag = np.array(stars.array['Mag'])
print("Stars downloaded: N = {}; mag = {:.2f} .. {:.2f}".format(np.size(mag), np.nanmin(mag), np.nanmax(mag)))
print("RA = {:.2f} .. {:.2f}".format(np.nanmin(ra_stars)*hbt.r2d, np.nanmax(ra_stars)*hbt.r2d))
# Now sort by magnitude, and keep the 100 brightest
# This is because this GSC catalog is huge -- typically 2000 stars in LORRI FOV.
# We need to reduce its size to fit in our fixed astropy table string length.
order = np.argsort(mag)
order = np.array(order)[0:NUM_STARS_CAT]
ra_stars = ra_stars[order] # Returned as radians
dec_stars = dec_stars[order]
if (DO_USNOA2):
name_cat = u'The USNO-A2.0 Catalogue (Monet+ 1998) 1' # Works but gives stars down to v=17; I want to v=13
stars = conesearch.conesearch(wcs_in.wcs.crval, 0.3, cache=False, catalog_db = name_cat)
table_stars = Table(stars.array.data)
mask = table_stars['Bmag'] < 13
table_stars_m = table_stars[mask]
ra_stars = table_stars_m['RAJ2000']*hbt.d2r # Convert to radians
dec_stars = table_stars_m['DEJ2000']*hbt.d2r # Convert to radians
ra_stars_cat = ra_stars
dec_stars_cat = dec_stars
radec_stars_cat = np.transpose(np.array((ra_stars_cat, dec_stars_cat)))
(x_stars_cat, y_stars_cat) = wcs_in.wcs_world2pix(
radec_stars_cat[:,0]*hbt.r2d,
radec_stars_cat[:,1]*hbt.r2d, 0)
points_stars_cat = np.transpose((y_stars_cat, x_stars_cat)) # Yes, order is supposed to be (y,x)
#==============================================================================
# Use DAOphot to search the image for stars.
#==============================================================================
points_stars_phot = hbt.find_stars(im, num=NUM_STARS_PHOT) # Returns N x 2 aray. 0 = Row = y; 1 = Column = x.
y_stars_phot =(points_stars_phot[:,0]) # xy is correct -- see above
x_stars_phot =(points_stars_phot[:,1]) #
#==============================================================================
# Make a plot showing the DAO stars on the image
#==============================================================================
color_phot = 'red' # Color for stars found photometrically
color_cat = 'lightgreen' # Color for stars in catalog
DO_PLOT_DAO = False # Plot an intermediate result?
if (DO_PLOT_DAO):
plt.imshow(stretch(im))
plt.plot(x_stars_phot, y_stars_phot, linestyle='none',
marker='o', markersize=9, mec=color_cat, mew=1, color='none',
label = 'DAO photometric stars') # plot() uses x, y
plt.plot(x_stars_cat, y_stars_cat, linestyle='none',
marker='o', markersize=5, color='lightgreen',
label = 'Cat stars') # plot() uses x, y
plt.title(title)
plt.ylim((hbt.sizey(im)),0)
plt.xlim((0,hbt.sizex(im)))
plt.legend(loc = 'upper left')
plt.show()
# Up til here, x and y are correct
#==============================================================================
# Look up the shift between the photometry and the star catalog.
# Do this by making a pair of fake images, and then looking up image registration on them.
#==============================================================================
# I call this pointing process 'opnav'.
# It is returned in order (y,x) because that is what imreg_dft uses, even though it is a bit weird.
diam_kernel = 11 # How many pixels across are our synthetic stellar images? Should be odd number. Not critical.
do_binary = True # For the stellar images, do a binary 1/0 (recommended), or a pixel distance?
shape = np.shape(im) # Set shape of output array
image_cat = hbt.image_from_list_points(points_stars_cat, shape, diam_kernel, do_binary=do_binary)
image_phot = hbt.image_from_list_points(points_stars_phot, shape, diam_kernel, do_binary=do_binary)
if (method == 'fft'): # Very fast method
# Set up a constraint for the fit. It should be different for 1x1 and 4x4.
# For 1x1, it works well to be 100 pixels.
if (hbt.sizex(im) == 1024): # For LORRI 1x1
constraint_tx = (0,100) # Mean and stdev. i.e., returned value will be within stdev of mean.
constraint_ty = (0,100)
if (hbt.sizex(im) == 256): # For LORRI 4x4
constraint_tx = (0,25) # Mean and stdev. i.e., returned value will be within stdev of mean.
constraint_ty = (0,25)
constraint_angle = 0 # With one value, it is a fixed constraint.
constraints = {'tx' : constraint_tx, 'ty' : constraint_ty, 'angle' : constraint_angle}
ird.translation(image_cat, image_phot, constraints=constraints)
(dy, dx) = ird.translation(image_cat, image_phot, constraints=constraints)['tvec']
dy_opnav = -dy
dx_opnav = -dx
if (method == 'bruteforce'): # Very slow method
((dx, dy), mat) = hbt.get_translation_images_bruteforce(image_cat, image_phot)
dx_opnav = -dx
dy_opnav = -dy
#==============================================================================
# Make a plot, showing DAO positions + catalog positions
#==============================================================================
do_plot = True
if (do_plot):
# hbt.figsize((10,10))
plt.imshow(stretch(im))
# Plot the stars -- catalog, and DAO
plt.plot(x_stars_cat + dx_opnav, y_stars_cat + dy_opnav,
marker='o', ls='None',
color=color_cat, alpha = 0.5, ms=12, mew=1, label = 'Cat Stars, adjusted')
plt.plot(x_stars_cat, y_stars_cat,
marker='o', ls='None',
color=color_cat, alpha = 1, ms=4, mew=1, label = 'Cat Stars, raw')
plt.plot(x_stars_phot, y_stars_phot,
marker='o', ls='None',
color='none', markersize=10, mew=1, mec=color_phot, alpha = 1, label = 'DAOfind Stars')
plt.title('After navigation, with dx = {:.1f}, dy = {:.1f}, {}'.format(dx_opnav, dy_opnav, title))
plt.legend() # Draw legend. Might be irrel since remove() might keep it; not sure.
plt.imshow(stretch(im))
plt.show()
#==============================================================================
# Return results and exit
#==============================================================================
# Results are returned in terms of pixel offset and a revised WCS structure.
# I don't seem to be able to copy a WCS structure, so I modify the one in place!
# Get the pixel location of the center position
crpix = wcs_in.wcs.crpix # Center position, in pixels, old
# Get the new RA, Dec center of the array. It is just the old location, plus the offset
ORIGIN_FORMAT = 1 # 0 for Numpy-style indexing, 1 for Fortran-style and FITS-style.
# So what do I used for FITS files in python? Experimentally, 1 is right and 0 is not.
(ra_new, dec_new) = wcs_in.wcs_pix2world(crpix[0] - dx_opnav, crpix[1] - dy_opnav, ORIGIN_FORMAT)
# Set it
wcs_in.wcs.crval = (ra_new, dec_new)
return(wcs_in, (dy_opnav, dx_opnav))
def get_fits_table_from_server(catalog, server, ra, dec, sr):
"""
This function fetches sources from the specified catalog from the specified
server within the search radius and returns a Table containing them.
Parameters
----------
catalog: str [sdss9 | 2mass | ukidss9 | gmos]
name of catalog to search
server: str
name of server to query [sdss9_mko | sdss9_cpo | sdss9_vizier |
2mass_mko | 2mass_cpo | 2mass_vizier | ukidss9_mko | ukidss9_cpo]
ra: float
right ascension of search center, decimal degrees
dec: float
declination of search center, decimal degrees
sr: float
search radius, decimal degrees
Returns
-------
Table
sources within the search cone
"""
# This defines the URL for each server
# There must be an entry in this dictionary for each server
# listed in cat_servers above
server_urls = {
'sdss9_mko': "http://mkocatalog2/cgi-bin/conesearch.py?CATALOG=sdss9&",
'sdss9_cpo': "http://cpocatalog2/cgi-bin/conesearch.py?CATALOG=sdss9&",
'sdss9_vizier':
"http://vizier.u-strasbg.fr/viz-bin/votable/-A?-source=sdss9&",
'2mass_mko':
"http://mkocatalog2/cgi-bin/conesearch.py?CATALOG=twomass_psc&",
'2mass_cpo':
"http://cpocatalog2/cgi-bin/conesearch.py?CATALOG=twomass_psc&",
'2mass_vizier':
"http://vizier.u-strasbg.fr/viz-bin/votable/-A?-source=B/2mass&",
'ukidss9_mko':
"http://mkocatalog2/cgi-bin/conesearch.py?CATALOG=ukidss&",
'ukidss9_cpo':
"http://cpocatalog2/cgi-bin/conesearch.py?CATALOG=ukidss&",
'gmos_mko': "http://mkocatalog2/cgi-bin/conesearch.py?CATALOG=gmos&",
'gmos_cpo': "http://cpocatalog2/cgi-bin/conesearch.py?CATALOG=gmos&",
}
# This defines the column names *we* will use for that catalog.
# There must be one entry in this list for each catalog listed
# in cat_servers above
cat_cols = {
'sdss9': ['catid', 'raj2000', 'dej2000', 'umag', 'umag_err',
'gmag', 'gmag_err', 'rmag', 'rmag_err', 'imag',
'imag_err', 'zmag', 'zmag_err'],
'2mass': ['catid', 'raj2000', 'dej2000', 'jmag', 'jmag_err',
'hmag', 'hmag_err', 'kmag', 'kmag_err'],
'ukidss9': ['catid', 'raj2000', 'dej2000', 'ymag', 'ymag_err',
'zmag', 'zmag_err', 'jmag', 'jmag_err',
'hmag', 'hmag_err', 'kmag', 'kmag_err'],
'gmos': ['name', 'raj2000', 'dej2000', 'umag', 'umag_err',
'gmag', 'gmag_err', 'rmag', 'rmag_err', 'imag',
'imag_err', 'zmag', 'zmag_err']
}
# This defines the column name mapping for each catalog server to our
# column names. This copes with both variable server conventions, and
# also allows us to point to different columns in the upstream catalog
# - eg different model fits magnitides - if we wish
# ***** These need to be in the same order as the list in our_cols *****
server_colmap = {
'sdss9_mko': ['objid', 'raj2000', 'dej2000', 'umag', 'umag_err',
'gmag', 'gmag_err', 'rmag', 'rmag_err', 'imag',
'imag_err', 'zmag', 'zmag_err'],
'sdss9_cpo': ['objid', 'raj2000', 'dej2000', 'umag', 'umag_err',
'gmag', 'gmag_err', 'rmag', 'rmag_err', 'imag',
'imag_err', 'zmag', 'zmag_err'],
'sdss9_vizier': ['objID', 'RAJ2000', 'DEJ2000', 'umag', 'e_umag',
'gmag', 'e_gmag', 'rmag', 'e_rmag', 'imag', 'e_imag',
'zmag', 'e_zmag'],
'2mass_mko': ['designation', 'ra', 'decl', 'j_m', 'j_cmsig',
'h_m', 'h_cmsig', 'k_m', 'k_cmsig'],
'2mass_cpo': ['designation', 'ra', 'decl', 'j_m', 'j_cmsig',
'h_m', 'h_cmsig', 'k_m', 'k_cmsig'],
'2mass_vizier': ['_2MASS', 'RAJ2000', 'DEJ2000', 'Jmag', 'Jcmsig',
'Hmag', 'Hcmsig', 'Kmag', 'Kcmsig'],
'ukidss9_mko': ['id', 'raj2000', 'dej2000', 'y_mag', 'y_mag_err',
'z_mag', 'z_mag_err', 'j_mag', 'j_mag_err',
'h_mag', 'h_mag_err', 'k_mag', 'k_mag_err'],
'ukidss9_cpo': ['id', 'raj2000', 'dej2000', 'y_mag', 'y_mag_err',
'z_mag', 'z_mag_err', 'j_mag', 'j_mag_err',
'h_mag', 'h_mag_err', 'k_mag', 'k_mag_err'],
'gmos_mko': ['name', 'raj2000', 'dej2000', 'umag', 'umag_err',
'gmag', 'gmag_err', 'rmag', 'rmag_err', 'imag',
'imag_err', 'zmag', 'zmag_err'],
'gmos_cpo': ['name', 'raj2000', 'dej2000', 'umag', 'umag_err',
'gmag', 'gmag_err', 'rmag', 'rmag_err', 'imag',
'imag_err', 'zmag', 'zmag_err']
}
# OK, do the query
url = server_urls[server]
cols = cat_cols[catalog]
server_cols = server_colmap[server]
# print "RA, Dec, radius:", ra, dec, sr
# print "catalog: %s" % catalog
# print "server: %s" % server
# print "url: %s" % url
# print "cols : %s" % cols
# print "server_cols: %s" % server_cols
# print "\n\n"
# turn on verbose for debug to stdout.
# Need verb=3 to get the right cols from vizier
try:
from astroquery.vo_conesearch.conesearch import conesearch
from astroquery.vo_conesearch.exceptions import VOSError
except ImportError:
# conesearch was deprecated in astropy 2.0 and removed in 3.0, and
# moved to astroquery 0.3.5
from astropy.vo.client.conesearch import conesearch
from astropy.vo.client.vos_catalog import VOSError
# The following phrase is implemented to handle differing function
# signatures and return behaviours of vo conesearch function. Under
# astropy, conesearch throws a VOSError exception on no results. Which
# seems a bit extreme. See the import phrase at top.
try:
try:
# astroquery 0.4 removed the pedantic keyword in favor of the
# config item, and switched to returning an astropy table by
# default (hence return_astropy_table=False below).
# Another change is that conesearch returns None and issue of
# NoResultsWarning if no results are found, instead of raising
# a VOSError.
# https://github.com/astropy/astroquery/pull/1528
table = conesearch((ra, dec), sr, verb=3, catalog_db=url,
return_astropy_table=False, verbose=False)
except TypeError:
table = conesearch((ra, dec), sr, verb=3, catalog_db=url,
pedantic=False, verbose=False)
except VOSError:
log.stdinfo("VO conesearch produced no results")
return
# Did we get any results?
if(table is None or table.is_empty() or len(table.array) == 0):
log.stdinfo("No results returned")
return
# It turns out to be not viable to use UCDs to select the columns,
# even for the id, ra, and dec. Even with vizier. <sigh>
# The first column is our running integer column
ret_table = Table([list(range(1, len(table.array[server_cols[0]])+1))],
names=('Id',), dtype=('i4',))
ret_table.add_column(Column(table.array[server_cols[0]], name='Cat_Id',
dtype='a'))
ret_table.add_column(Column(table.array[server_cols[1]], name='RAJ2000',
dtype='f8', unit='deg'))
ret_table.add_column(Column(table.array[server_cols[2]], name='DEJ2000',
dtype='f8', unit='deg'))
# Now the photometry columns
for col in range(3, len(cols)):
ret_table.add_column(Column(table.array[server_cols[col]], name=cols[col],
dtype='f4', unit='mag', format='8.4f'))
header = add_header_to_table(ret_table)
header['CATALOG'] = (catalog.upper(), 'Origin of source catalog')
# Add comments to the header to describe it
header.add_comment('Source catalog derived from the {} catalog'.
format(catalog))
header.add_comment('Source catalog fetched from server at {}'.format(url))
header.add_comment('Delivered Table name from server: {}'.
format(table.name))
for col in range(len(cols)):
header.add_comment('UCD for field {} is {}'.format(
cols[col], table.get_field_by_id(server_cols[col]).ucd))
return ret_table
def navigate_image_stellar(im,
wcs_in,
name_catalog='',
do_plot=True,
method='fft',
title=''):
"""
Navigate frame based on stellar images.
Result returns is pixel shift (dy, dx).
WCS paramaters are returned, *and* modified in place.
"""
import imreg_dft as ird
from astropy.wcs import WCS
# from astropy.vo.client import conesearch # Virtual Observatory, ie star catalogs # DEPRECATED!
from astroquery.vo_conesearch import conesearch # New home of conesearch
# Inputs are the image array, and the WCS structure.
# This routine does not do any file IO. The image array and header must be already loaded.
# The image is assumed to be stretched properly s.t. stars can be found using DAOphot.
NUM_STARS_PHOT = 100 # How many stars to use from DAOPhot. For noisy images, DAO will find a lot of
# fake stars, so we need to crank this up higher than the # of cat stars.
NUM_STARS_CAT = 50 # How many stars to use from star catalog
DO_GSC1 = False
DO_GSC12 = True
DO_USNOA2 = False
#==============================================================================
# Calculate the image radius, in radians, based on the size and the pixel scale
#==============================================================================
dx_pix = hbt.sizex(im)
dy_pix = hbt.sizey(im)
radec_corner = wcs_in.wcs_pix2world(0, dy_pix / 2, 0)
radec_center = wcs_in.wcs_pix2world(dx_pix / 2, dy_pix / 2, 0)
(ra_corner, dec_corner) = radec_corner
(ra_center, dec_center) = radec_center
radius_image = math.sqrt((dec_corner - dec_center)**2 + (
(ra_corner - ra_center) / np.cos(dec_corner * hbt.d2r))**2) * hbt.d2r
radius_search_deg = radius_image * hbt.r2d
# Read the WCS coordinates
center_deg = wcs_in.wcs.crval # degrees. # crval is a two-element array of [RA, Dec], in degrees
# Stretch the image. This is just for display -- no processing.
stretch_percent = 90
stretch = astropy.visualization.PercentileInterval(
stretch_percent) # PI(90) scales array to 5th .. 95th %ile.
# Display it
if (do_plot):
plt.imshow(stretch(im))
#==============================================================================
# Get stars from star catalogs
#==============================================================================
if (DO_GSC1):
name_cat = u'The HST Guide Star Catalog, Version 1.1 (Lasker+ 1992) 1' # works, but 1' errors; investigating
stars = conesearch.conesearch(center_deg,
radius_search_deg,
cache=True,
catalog_db=name_cat)
ra_stars = np.array(
stars.array['RAJ2000']) * hbt.d2r # Convert to radians
dec_stars = np.array(
stars.array['DEJ2000']) * hbt.d2r # Convert to radians
# table_stars = Table(stars.array.data)
if (DO_GSC12):
# name_cat = u'The HST Guide Star Catalog, Version 1.2 (Lasker+ 1996) 1'
name_cat = u'Guide Star Catalog v2 1' # Works from gobi, not tomato
url_cat = 'http://gsss.stsci.edu/webservices/vo/ConeSearch.aspx?CAT=GSC23&' # Works always
with data.conf.set_temp(
'remote_timeout',
30): # This is the very strange syntax to set a timeout delay.
# The default is 3 seconds, and that times out often.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
# stars = conesearch.conesearch(wcs_in.wcs.crval, radius_search_deg, cache=True, catalog_db = url_cat)
# The various functions of conesearch/ConeSearch/etc are quite confusing, and are in flux.
# This line below seems to work. It does not allow an explicit catalog suggstion, but it does the job.
c = astropy.coordinates.SkyCoord(wcs_in.wcs.crval[0],
wcs_in.wcs.crval[1],
unit='deg')
stars = ConeSearch.query_region(c, f'{radius_search_deg} deg')
ra_stars = np.array(stars.array['ra']) * hbt.d2r # Convert to radians
dec_stars = np.array(
stars.array['dec']) * hbt.d2r # Convert to radians
mag = np.array(stars.array['Mag'])
print("Stars downloaded: N = {}; mag = {:.2f} .. {:.2f}".format(
np.size(mag), np.nanmin(mag), np.nanmax(mag)))
print("RA = {:.2f} .. {:.2f}".format(
np.nanmin(ra_stars) * hbt.r2d,
np.nanmax(ra_stars) * hbt.r2d))
# Now sort by magnitude, and keep the 100 brightest
# This is because this GSC catalog is huge -- typically 2000 stars in LORRI FOV.
# We need to reduce its size to fit in our fixed astropy table string length.
order = np.argsort(mag)
order = np.array(order)[0:NUM_STARS_CAT]
ra_stars = ra_stars[order] # Returned as radians
dec_stars = dec_stars[order]
if (DO_USNOA2):
name_cat = u'The USNO-A2.0 Catalogue (Monet+ 1998) 1' # Works but gives stars down to v=17; I want to v=13
stars = conesearch.conesearch(wcs_in.wcs.crval,
0.3,
cache=False,
catalog_db=name_cat)
table_stars = Table(stars.array.data)
mask = table_stars['Bmag'] < 13
table_stars_m = table_stars[mask]
ra_stars = table_stars_m['RAJ2000'] * hbt.d2r # Convert to radians
dec_stars = table_stars_m['DEJ2000'] * hbt.d2r # Convert to radians
ra_stars_cat = ra_stars
dec_stars_cat = dec_stars
radec_stars_cat = np.transpose(np.array((ra_stars_cat, dec_stars_cat)))
(x_stars_cat,
y_stars_cat) = wcs_in.wcs_world2pix(radec_stars_cat[:, 0] * hbt.r2d,
radec_stars_cat[:, 1] * hbt.r2d, 0)
points_stars_cat = np.transpose(
(y_stars_cat, x_stars_cat)) # Yes, order is supposed to be (y,x)
#==============================================================================
# Use DAOphot to search the image for stars.
#==============================================================================
points_stars_phot = hbt.find_stars(
im,
num=NUM_STARS_PHOT) # Returns N x 2 aray. 0 = Row = y; 1 = Column = x.
y_stars_phot = (points_stars_phot[:, 0]) # xy is correct -- see above
x_stars_phot = (points_stars_phot[:, 1]) #
#==============================================================================
# Make a plot showing the DAO stars on the image
#==============================================================================
color_phot = 'red' # Color for stars found photometrically
color_cat = 'lightgreen' # Color for stars in catalog
DO_PLOT_DAO = False # Plot an intermediate result?
if (DO_PLOT_DAO):
plt.imshow(stretch(im))
plt.plot(x_stars_phot,
y_stars_phot,
linestyle='none',
marker='o',
markersize=9,
mec=color_cat,
mew=1,
color='none',
label='DAO photometric stars') # plot() uses x, y
plt.plot(x_stars_cat,
y_stars_cat,
linestyle='none',
marker='o',
markersize=5,
color='lightgreen',
label='Cat stars') # plot() uses x, y
plt.title(title)
plt.ylim((hbt.sizey(im)), 0)
plt.xlim((0, hbt.sizex(im)))
plt.legend(loc='upper left')
plt.show()
# Up til here, x and y are correct
#==============================================================================
# Look up the shift between the photometry and the star catalog.
# Do this by making a pair of fake images, and then looking up image registration on them.
#==============================================================================
# I call this pointing process 'opnav'.
# It is returned in order (y,x) because that is what imreg_dft uses, even though it is a bit weird.
diam_kernel = 11 # How many pixels across are our synthetic stellar images? Should be odd number. Not critical.
do_binary = True # For the stellar images, do a binary 1/0 (recommended), or a pixel distance?
shape = np.shape(im) # Set shape of output array
image_cat = hbt.image_from_list_points(points_stars_cat,
shape,
diam_kernel,
do_binary=do_binary)
image_phot = hbt.image_from_list_points(points_stars_phot,
shape,
diam_kernel,
do_binary=do_binary)
if (method == 'fft'): # Very fast method
# Set up a constraint for the fit. It should be different for 1x1 and 4x4.
# For 1x1, it works well to be 100 pixels.
if (hbt.sizex(im) == 1024): # For LORRI 1x1
constraint_tx = (
0, 100
) # Mean and stdev. i.e., returned value will be within stdev of mean.
constraint_ty = (0, 100)
if (hbt.sizex(im) == 256): # For LORRI 4x4
constraint_tx = (
0, 25
) # Mean and stdev. i.e., returned value will be within stdev of mean.
constraint_ty = (0, 25)
constraint_angle = 0 # With one value, it is a fixed constraint.
constraints = {
'tx': constraint_tx,
'ty': constraint_ty,
'angle': constraint_angle
}
ird.translation(image_cat, image_phot, constraints=constraints)
(dy, dx) = ird.translation(image_cat,
image_phot,
constraints=constraints)['tvec']
dy_opnav = -dy
dx_opnav = -dx
if (method == 'bruteforce'): # Very slow method
((dx, dy),
mat) = hbt.get_translation_images_bruteforce(image_cat, image_phot)
dx_opnav = -dx
dy_opnav = -dy
#==============================================================================
# Make a plot, showing DAO positions + catalog positions
#==============================================================================
do_plot = True
if (do_plot):
# hbt.figsize((10,10))
plt.imshow(stretch(im))
# Plot the stars -- catalog, and DAO
plt.plot(x_stars_cat + dx_opnav,
y_stars_cat + dy_opnav,
marker='o',
ls='None',
color=color_cat,
alpha=0.5,
ms=12,
mew=1,
label='Cat Stars, adjusted')
plt.plot(x_stars_cat,
y_stars_cat,
marker='o',
ls='None',
color=color_cat,
alpha=1,
ms=4,
mew=1,
label='Cat Stars, raw')
plt.plot(x_stars_phot,
y_stars_phot,
marker='o',
ls='None',
color='none',
markersize=10,
mew=1,
mec=color_phot,
alpha=1,
label='DAOfind Stars')
plt.title('After navigation, with dx = {:.1f}, dy = {:.1f}, {}'.format(
dx_opnav, dy_opnav, title))
plt.legend(
) # Draw legend. Might be irrel since remove() might keep it; not sure.
plt.imshow(stretch(im))
plt.show()
#==============================================================================
# Return results and exit
#==============================================================================
# Results are returned in terms of pixel offset and a revised WCS structure.
# I don't seem to be able to copy a WCS structure, so I modify the one in place!
# Get the pixel location of the center position
crpix = wcs_in.wcs.crpix # Center position, in pixels, old
# Get the new RA, Dec center of the array. It is just the old location, plus the offset
ORIGIN_FORMAT = 1 # 0 for Numpy-style indexing, 1 for Fortran-style and FITS-style.
# So what do I used for FITS files in python? Experimentally, 1 is right and 0 is not.
(ra_new, dec_new) = wcs_in.wcs_pix2world(crpix[0] - dx_opnav,
crpix[1] - dy_opnav,
ORIGIN_FORMAT)
# Set it
wcs_in.wcs.crval = (ra_new, dec_new)
return (wcs_in, (dy_opnav, dx_opnav))
index_asymptote = 0 if (encounter_phase == 'Outbound') else 1
# We make one call to SPICE to look up the asymptote position
# Most SPICE calls are later, but we need to do this one now, ahead of order.
(st,lt) = sp.spkezr('MU69', et[index_asymptote], 'J2000', 'LT', 'New Horizons')
(junk, ra_asymptote, dec_asymptote) = sp.recrad(st[0:3])
crval = np.array([ra_asymptote, dec_asymptote]) * hbt.r2d
radius_search = 0.15 # degrees # We only use these catalog for very fine searches, so narrow is OK.
DO_PLOT_GSC = False # Goes to about v=12
if DO_PLOT_GSC:
name_cat = u'The HST Guide Star Catalog, Version 1.1 (Lasker+ 1992) 1' # works, but 1' errors; investigating
stars = conesearch.conesearch(crval, radius_search, cache=False, catalog_db = name_cat)
ra_stars = np.array(stars.array['RAJ2000'])*hbt.d2r # Convert to radians
dec_stars = np.array(stars.array['DEJ2000'])*hbt.d2r # Convert to radians
mag_stars = np.array(stars.array['Pmag'])
if DO_PLOT_USNO:
name_cat = u'The USNO-A2.0 Catalogue (Monet+ 1998) 1'
stars = conesearch.conesearch(crval, radius_search, cache=False, catalog_db = name_cat)
ra_stars = np.array(stars.array['RAJ2000'])*hbt.d2r # Convert to radians
id_stars = np.array(stars.array['USNO-A2.0']) # ID
id_stars = id_stars.astype('U') # Convert from byte string to Unicode, ugh.
dec_stars = np.array(stars.array['DEJ2000'])*hbt.d2r # Convert to radians
mag_b_stars = np.array(stars.array['Bmag'])
mag_r_stars = np.array(stars.array['Rmag'])
usno = Table([id_stars, ra_stars, dec_stars, mag_b_stars, mag_r_stars],