def pick_positions(catalog, filename, separation, refimage=None, wcs_origin=1):
"""
Assigns positions to fake star list generated by pick_models
INPUTS:
-------
filename: string
Name of AST list generated by pick_models
separation: float
Minimum pixel separation between AST and star in photometry
catalog provided in the datamodel.
refimage: string
Name of the reference image. If supplied, the method will use the
reference image header to convert from RA and DEC to X and Y.
wcs_origin : 0 or 1 (default=1)
As described in the WCS documentation: "the coordinate in the upper
left corner of the image. In FITS and Fortran standards, this is 1.
In Numpy and C standards this is 0."
OUTPUTS:
--------
Ascii table that replaces [filename] with a new version of
[filename] that contains the necessary position columns for running
the ASTs though DOLPHOT
"""
noise = (
3.0
) # Spreads the ASTs in a circular annulus of 3 pixel width instead of all being
# precisely [separation] from an observed star.
colnames = catalog.data.columns
if "X" or "x" in colnames:
if "X" in colnames:
x_positions = catalog.data["X"][:]
y_positions = catalog.data["Y"][:]
if "x" in colnames:
x_positions = catalog.data["x"][:]
y_positions = catalog.data["y"][:]
else:
if refimage:
if ("RA" in colnames) or ("ra" in colnames):
if "RA" in colnames:
ra_positions = catalog.data["RA"][:]
dec_positions = catalog.data["DEC"][:]
if "ra" in colnames:
ra_positions = catalog.data["ra"][:]
dec_positions = catalog.data["dec"][:]
else:
raise RuntimeError(
"Your catalog does not supply X, Y or RA, DEC information for spatial AST distribution"
)
else:
raise RuntimeError(
"You must supply a Reference Image to determine spatial AST distribution."
)
wcs = WCS(refimage)
x_positions, y_positions = wcs.all_world2pix(ra_positions, dec_positions, wcs_origin)
astmags = ascii.read(filename)
n_asts = len(astmags)
# keep is defined to ensure that no fake stars are put outside of the image boundaries
keep = (
(x_positions > np.min(x_positions) + separation + noise)
& (x_positions < np.max(x_positions) - separation - noise)
& (y_positions > np.min(y_positions) + separation + noise)
& (y_positions < np.max(y_positions) - separation - noise)
)
x_positions = x_positions[keep]
y_positions = y_positions[keep]
ncat = len(x_positions)
ind = np.random.random(n_asts) * ncat
ind = ind.astype("int")
# Here we generate the circular distribution of ASTs surrounding random observed stars
separation = np.random.random(n_asts) * noise + separation
theta = np.random.random(n_asts) * 2.0 * np.pi
xvar = separation * np.cos(theta)
yvar = separation * np.sin(theta)
new_x = x_positions[ind] + xvar
new_y = y_positions[ind] + yvar
column1 = 0 * new_x
column2 = column1 + 1
column1 = Column(name="zeros", data=column1.astype("int"))
column2 = Column(name="ones", data=column2.astype("int"))
column3 = Column(name="X", data=new_x, format="%.2f")
column4 = Column(name="Y", data=new_y, format="%.2f")
astmags.add_column(column1, 0)
astmags.add_column(column2, 1)
astmags.add_column(column3, 2)
astmags.add_column(column4, 3)
ascii.write(astmags, filename, overwrite=True)
def pick_positions(catalog, filename, separation, refimage=None):
"""
Assigns positions to fake star list generated by pick_models
INPUTS:
-------
filename: string
Name of AST list generated by pick_models
separation: float
Minimum pixel separation between AST and star in photometry
catalog provided in the datamodel.
refimage: Name of the reference image. If supplied, the method will use the
reference image header to convert from RA and DEC to X and Y.
OUTPUTS:
--------
Ascii table that replaces [filename] with a new version of
[filename] that contains the necessary position columns for running
the ASTs though DOLPHOT
"""
noise = 3.0 #Spreads the ASTs in a circular annulus of 3 pixel width instead of all being
#precisely [separation] from an observed star.
colnames = catalog.data.columns
if 'X' or 'x' in colnames:
if 'X' in colnames:
x_positions = catalog.data['X'][:]
y_positions = catalog.data['Y'][:]
if 'x' in colnames:
x_positions = catalog.data['x'][:]
y_positions = catalog.data['y'][:]
else:
if refimage:
if 'RA' or 'ra' in colnames:
if 'RA' in colnames:
ra_positions = catalog.data['RA'][:]
dec_positions = catalog.data['DEC'][:]
if 'ra' in colnames:
ra_positions = catalog.data['ra'][:]
dec_positions = catalog.data['dec'][:]
else:
raise RuntimeError(
"Your catalog does not supply X, Y or RA, DEC information for spatial AST distribution"
)
else:
raise RuntimeError(
"You must supply a Reference Image to determine spatial AST distribution."
)
wcs = WCS(refimage)
x_positions, y_positions = wcs.all_world2pix(ra_positions,
dec_positions, 0)
astmags = ascii.read(filename)
n_asts = len(astmags)
# keep is defined to ensure that no fake stars are put outside of the image boundaries
keep = (x_positions > np.min(x_positions) + separation + noise) & (x_positions < np.max(x_positions) - separation - noise) & \
(y_positions > np.min(y_positions) + separation + noise) & (y_positions < np.max(y_positions) - separation - noise)
x_positions = x_positions[keep]
y_positions = y_positions[keep]
ncat = len(x_positions)
ind = np.random.random(n_asts) * ncat
ind = ind.astype('int')
# Here we generate the circular distribution of ASTs surrounding random observed stars
separation = np.random.random(n_asts) * noise + separation
theta = np.random.random(n_asts) * 2.0 * np.pi
xvar = separation * np.cos(theta)
yvar = separation * np.sin(theta)
new_x = x_positions[ind] + xvar
new_y = y_positions[ind] + yvar
column1 = 0 * new_x
column2 = column1 + 1
column1 = Column(name='zeros', data=column1.astype('int'))
column2 = Column(name='ones', data=column2.astype('int'))
column3 = Column(name='X', data=new_x, format='%.2f')
column4 = Column(name='Y', data=new_y, format='%.2f')
astmags.add_column(column1, 0)
astmags.add_column(column2, 1)
astmags.add_column(column3, 2)
astmags.add_column(column4, 3)
ascii.write(astmags, filename, overwrite=True)
