def to_model(data, meta):
"""
Create a photutils GriddedPSFModel object from input data and meta information
Parameters
----------
data : ndarray
3D numpy array of PSFs at different points across the detector
meta : dict
Dictionary containing meta data
Returns
-------
model : GriddedPSFModel
Photutils object with 3D data array and metadata with specified grid_xypos
and oversampling keys
"""
try:
from photutils import GriddedPSFModel
except ImportError:
raise ImportError("This method requires photutils >= 0.6")
ndd = NDData(data, meta=meta, copy=True)
ndd.meta['grid_xypos'] = [((float(ndd.meta[key][0].split(',')[1].split(')')[0])),
(float(ndd.meta[key][0].split(',')[0].split('(')[1])))
for key in ndd.meta.keys() if "DET_YX" in key]
ndd.meta['oversampling'] = meta["OVERSAMP"][0] # just pull the value
ndd.meta = {key.lower(): ndd.meta[key] for key in ndd.meta}
model = GriddedPSFModel(ndd)
return model
def to_model(data, meta):
"""
Create a photutils GriddedPSFModel object from input data and meta information
Parameters
----------
data : ndarray
3D numpy array of PSFs at different points across the detector
meta : dict
Dictionary containing meta data
Returns
-------
model : GriddedPSFModel
Photutils object with 3D data array and metadata with specified grid_xypos
and oversampling keys
"""
try:
from photutils import GriddedPSFModel
except ImportError:
raise ImportError("This method requires photutils >= 0.6")
ndd = NDData(data, meta=meta, copy=True)
ndd.meta['grid_xypos'] = [((float(ndd.meta[key][0].split(',')[1].split(')')[0])),
(float(ndd.meta[key][0].split(',')[0].split('(')[1])))
for key in ndd.meta.keys() if "DET_YX" in key]
ndd.meta['oversampling'] = meta["OVERSAMP"][0] # just pull the value
ndd.meta = {key.lower(): ndd.meta[key] for key in ndd.meta}
model = GriddedPSFModel(ndd)
return model
def to_griddedpsfmodel(HDUlist_or_filename=None, ext=0):
"""
Create a photutils GriddedPSFModel object from either a FITS file or
an HDUlist object. The input must have header keywords "DET_YX{}" and
"OVERSAMP" (will be present if psf_grid() is used to create the
file).
Parameters
----------
HDUlist_or_filename : string
Either a fits.HDUList object or a filename of a FITS file on disk
ext : int
Extension in that FITS file
Returns
-------
model : GriddedPSFModel
Photutils object with 3D data array and metadata with specified
grid_xypos and oversampling keys
"""
try:
from photutils import GriddedPSFModel
except ImportError:
raise ImportError("This method requires photutils >= 0.6")
if isinstance(HDUlist_or_filename, str):
HDUlist = fits.open(HDUlist_or_filename)
elif isinstance(HDUlist_or_filename, fits.HDUList):
HDUlist = HDUlist_or_filename
else:
raise ValueError('Input must be a filename or HDUlist')
data = HDUlist[ext].data
header = HDUlist[ext].header
# Check necessary keys are there
if not any("DET_YX" in key for key in header.keys()):
raise KeyError("You are missing 'DET_YX{}' keys: which are the detector locations of the PSFs")
if 'OVERSAMP' not in header.keys():
raise KeyError("You are missing 'OVERSAMP' key: which is the oversampling factor of the PSFs")
# Convert header to meta dict
header = header.copy(strip=True)
header.pop('COMMENT', None)
header.pop('', None)
header.pop('HISTORY', None)
meta = OrderedDict((a, (b, c)) for (a, b, c) in header.cards)
ndd = NDData(data, meta=meta, copy=True)
# Edit meta dictionary for GriddedPSFLibrary specifics
ndd.meta['grid_xypos'] = [((float(ndd.meta[key][0].split(',')[1].split(')')[0])),
(float(ndd.meta[key][0].split(',')[0].split('(')[1])))
for key in ndd.meta.keys() if "DET_YX" in key] # from (y,x) to (x,y)
if 'oversampling' not in ndd.meta:
ndd.meta['oversampling'] = ndd.meta['OVERSAMP'][0] # pull the value
# Turn all metadata keys into lowercase
ndd.meta = {key.lower(): ndd.meta[key] for key in ndd.meta}
# Create model
model = GriddedPSFModel(ndd)
return model
def to_griddedpsfmodel(HDUlist_or_filename=None, ext_data=0, ext_header=0):
"""
Create a photutils GriddedPSFModel object from either a FITS file or
an HDUlist object. The input must have header keywords "DET_YX{}" and
"OVERSAMP" (will already be present if psf_grid() is used to create
the file).
Parameters
----------
HDUlist_or_filename : HDUList or str
Either a fits.HDUList object or a filename of a FITS file on disk
ext_data : int
Extension of the data in the FITS file
ext_header : int
Extension of the header in the FITS file
Returns
-------
model : GriddedPSFModel
Photutils object with 3D data array and metadata with specified
grid_xypos and oversampling keys
"""
try:
from photutils import GriddedPSFModel
except ImportError:
raise ImportError("This method requires photutils >= 0.6")
if isinstance(HDUlist_or_filename, str):
HDUlist = fits.open(HDUlist_or_filename)
elif isinstance(HDUlist_or_filename, fits.HDUList):
HDUlist = HDUlist_or_filename
else:
raise ValueError('Input must be a filename or HDUlist')
data = HDUlist[ext_data].data
header = HDUlist[ext_header].header
# If there's only 1 PSF and the data is 2D, make the data 3D for photutils can use it
if len(data.shape) == 2 and len(header['DET_YX*']) == 1:
data = np.array([data])
# Check necessary keys are there
if not any("DET_YX" in key for key in header.keys()):
raise KeyError(
"You are missing 'DET_YX{}' keys: which are the detector locations of the PSFs"
)
if 'OVERSAMP' not in header.keys():
raise KeyError(
"You are missing 'OVERSAMP' key: which is the oversampling factor of the PSFs"
)
# Convert header to meta dict
header = header.copy(strip=True)
header.pop('COMMENT', None)
header.pop('', None)
header.pop('HISTORY', None)
meta = OrderedDict((a, (b, c)) for (a, b, c) in header.cards)
ndd = NDData(data, meta=meta, copy=True)
# Edit meta dictionary for GriddedPSFLibrary specifics
ndd.meta['grid_xypos'] = [
((float(ndd.meta[key][0].split(',')[1].split(')')[0])),
(float(ndd.meta[key][0].split(',')[0].split('(')[1])))
for key in ndd.meta.keys() if "DET_YX" in key
] # from (y,x) to (x,y)
if 'oversampling' not in ndd.meta:
ndd.meta['oversampling'] = ndd.meta['OVERSAMP'][0] # pull the value
# Turn all metadata keys into lowercase
ndd.meta = {key.lower(): ndd.meta[key] for key in ndd.meta}
# Create model
model = GriddedPSFModel(ndd)
return model