def test_subpixel_indices(position, subpixel_index):
"""
Test subpixel_indices utility function.
Test by asserting that the function returns correct results for
given test values.
"""
assert np.all(subpixel_indices(position, subsampling) == subpixel_index)
def test_subpixel_indices(position, subpixel_index):
"""
Test subpixel_indices utility function.
Test by asserting that the function returns correct results for
given test values.
"""
assert np.all(subpixel_indices(position, subsampling) == subpixel_index)
def evaluate(self, x, y, flux, x_0, y_0):
"""
Discrete PRF model evaluation.
Given a certain position and flux the corresponding image of
the PSF is chosen and scaled to the flux. If x and y are
outside the boundaries of the image, zero will be returned.
Parameters
----------
x : float
x coordinate array in pixel coordinates.
y : float
y coordinate array in pixel coordinates.
flux : float
Model flux.
x_0 : float
x position of the center of the PRF.
y_0 : float
y position of the center of the PRF.
"""
# Convert x and y to index arrays
x = (x - x_0 + 0.5 + self.prf_shape[1] // 2).astype('int')
y = (y - y_0 + 0.5 + self.prf_shape[0] // 2).astype('int')
# Get subpixel indices
y_sub, x_sub = subpixel_indices((y_0, x_0), self.subsampling)
# Out of boundary masks
x_bound = np.logical_or(x < 0, x >= self.prf_shape[1])
y_bound = np.logical_or(y < 0, y >= self.prf_shape[0])
out_of_bounds = np.logical_or(x_bound, y_bound)
# Set out of boundary indices to zero
x[x_bound] = 0
y[y_bound] = 0
result = flux * self._prf_array[int(y_sub), int(x_sub)][y, x]
# Set out of boundary values to zero
result[out_of_bounds] = 0
return result
def evaluate(self, x, y, flux, x_0, y_0):
"""
Discrete PRF model evaluation.
Given a certain position and flux the corresponding image of
the PSF is chosen and scaled to the flux. If x and y are
outside the boundaries of the image, zero will be returned.
Parameters
----------
x : float
x coordinate array in pixel coordinates.
y : float
y coordinate array in pixel coordinates.
flux : float
Model flux.
x_0 : float
x position of the center of the PRF.
y_0 : float
y position of the center of the PRF.
"""
# Convert x and y to index arrays
x = (x - x_0 + 0.5 + self.prf_shape[1] // 2).astype('int')
y = (y - y_0 + 0.5 + self.prf_shape[0] // 2).astype('int')
# Get subpixel indices
y_sub, x_sub = subpixel_indices((y_0, x_0), self.subsampling)
# Out of boundary masks
x_bound = np.logical_or(x < 0, x >= self.prf_shape[1])
y_bound = np.logical_or(y < 0, y >= self.prf_shape[0])
out_of_bounds = np.logical_or(x_bound, y_bound)
# Set out of boundary indices to zero
x[x_bound] = 0
y[y_bound] = 0
result = flux * self._prf_array[int(y_sub), int(x_sub)][y, x]
# Set out of boundary values to zero
result[out_of_bounds] = 0
return result
def create_from_image(cls,
imdata,
positions,
size,
fluxes=None,
mask=None,
mode='mean',
subsampling=1,
fix_nan=False):
"""
Create a discrete point response function (PRF) from image data.
Given a list of positions and size this function estimates an
image of the PRF by extracting and combining the individual PRFs
from the given positions.
NaN values are either ignored by passing a mask or can be
replaced by the mirrored value with respect to the center of the
PRF.
Note that if fluxes are *not* specified explicitly, it will be
flux estimated from an aperture of the same size as the PRF
image. This does *not* account for aperture corrections so often
will *not* be what you want for anything other than quick-look
needs.
Parameters
----------
imdata : array
Data array with the image to extract the PRF from
positions : List or array or `~astropy.table.Table`
List of pixel coordinate source positions to use in creating
the PRF. If this is a `~astropy.table.Table` it must have
columns called ``x_0`` and ``y_0``.
size : odd int
Size of the quadratic PRF image in pixels.
mask : bool array, optional
Boolean array to mask out bad values.
fluxes : array, optional
Object fluxes to normalize extracted PRFs. If not given (or
None), the flux is estimated from an aperture of the same
size as the PRF image.
mode : {'mean', 'median'}
One of the following modes to combine the extracted PRFs:
* 'mean': Take the pixelwise mean of the extracted PRFs.
* 'median': Take the pixelwise median of the extracted PRFs.
subsampling : int
Factor of subsampling of the PRF (default = 1).
fix_nan : bool
Fix NaN values in the data by replacing it with the
mirrored value. Assuming that the PRF is symmetrical.
Returns
-------
prf : `photutils.psf.sandbox.DiscretePRF`
Discrete PRF model estimated from data.
"""
# Check input array type and dimension.
if np.iscomplexobj(imdata):
raise TypeError('Complex type not supported')
if imdata.ndim != 2:
raise ValueError(f'{imdata.ndim}-d array not supported. '
'Only 2-d arrays supported.')
if size % 2 == 0:
raise TypeError("Size must be odd.")
if fluxes is not None and len(fluxes) != len(positions):
raise TypeError('Position and flux arrays must be of equal '
'length.')
if mask is None:
mask = np.isnan(imdata)
if isinstance(positions, (list, tuple)):
positions = np.array(positions)
if isinstance(positions, Table) or \
(isinstance(positions, np.ndarray) and
positions.dtype.names is not None):
# One can do clever things like
# positions['x_0', 'y_0'].as_array().view((positions['x_0'].dtype,
# 2))
# but that requires positions['x_0'].dtype is
# positions['y_0'].dtype.
# Better do something simple to allow type promotion if required.
pos = np.empty((len(positions), 2))
pos[:, 0] = positions['x_0']
pos[:, 1] = positions['y_0']
positions = pos
if isinstance(fluxes, (list, tuple)):
fluxes = np.array(fluxes)
if mode == 'mean':
combine = np.ma.mean
elif mode == 'median':
combine = np.ma.median
else:
raise Exception('Invalid mode to combine prfs.')
data_internal = np.ma.array(data=imdata, mask=mask)
prf_model = np.ndarray(shape=(subsampling, subsampling, size, size))
positions_subpixel_indices = \
np.array([subpixel_indices(_, subsampling) for _ in positions],
dtype=int)
for i in range(subsampling):
for j in range(subsampling):
extracted_sub_prfs = []
sub_prf_indices = np.all(positions_subpixel_indices == [j, i],
axis=1)
if not sub_prf_indices.any():
raise ValueError('The source coordinates do not sample '
'all sub-pixel positions. Reduce the '
'value of the subsampling parameter.')
positions_sub_prfs = positions[sub_prf_indices]
for k, position in enumerate(positions_sub_prfs):
x, y = position
extracted_prf = extract_array(data_internal, (size, size),
(y, x))
# Check shape to exclude incomplete PRFs at the boundaries
# of the image
if (extracted_prf.shape == (size, size)
and np.ma.sum(extracted_prf) != 0):
# Replace NaN values by mirrored value, with respect
# to the prf's center
if fix_nan:
prf_nan = extracted_prf.mask
if prf_nan.any():
if (prf_nan.sum() > 3
or prf_nan[size // 2, size // 2]):
continue
else:
extracted_prf = mask_to_mirrored_value(
extracted_prf, prf_nan,
(size // 2, size // 2))
# Normalize and add extracted PRF to data cube
if fluxes is None:
extracted_prf_norm = (np.ma.copy(extracted_prf) /
np.ma.sum(extracted_prf))
else:
fluxes_sub_prfs = fluxes[sub_prf_indices]
extracted_prf_norm = (np.ma.copy(extracted_prf) /
fluxes_sub_prfs[k])
extracted_sub_prfs.append(extracted_prf_norm)
else:
continue
prf_model[i, j] = np.ma.getdata(
combine(np.ma.dstack(extracted_sub_prfs), axis=2))
return cls(prf_model, subsampling=subsampling)
def create_from_image(cls, imdata, positions, size, fluxes=None,
mask=None, mode='mean', subsampling=1,
fix_nan=False):
"""
Create a discrete point response function (PRF) from image data.
Given a list of positions and size this function estimates an
image of the PRF by extracting and combining the individual PRFs
from the given positions.
NaN values are either ignored by passing a mask or can be
replaced by the mirrored value with respect to the center of the
PRF.
Note that if fluxes are *not* specified explicitly, it will be
flux estimated from an aperture of the same size as the PRF
image. This does *not* account for aperture corrections so often
will *not* be what you want for anything other than quick-look
needs.
Parameters
----------
imdata : array
Data array with the image to extract the PRF from
positions : List or array or `~astropy.table.Table`
List of pixel coordinate source positions to use in creating
the PRF. If this is a `~astropy.table.Table` it must have
columns called ``x_0`` and ``y_0``.
size : odd int
Size of the quadratic PRF image in pixels.
mask : bool array, optional
Boolean array to mask out bad values.
fluxes : array, optional
Object fluxes to normalize extracted PRFs. If not given (or
None), the flux is estimated from an aperture of the same
size as the PRF image.
mode : {'mean', 'median'}
One of the following modes to combine the extracted PRFs:
* 'mean': Take the pixelwise mean of the extracted PRFs.
* 'median': Take the pixelwise median of the extracted PRFs.
subsampling : int
Factor of subsampling of the PRF (default = 1).
fix_nan : bool
Fix NaN values in the data by replacing it with the
mirrored value. Assuming that the PRF is symmetrical.
Returns
-------
prf : `photutils.psf.sandbox.DiscretePRF`
Discrete PRF model estimated from data.
"""
# Check input array type and dimension.
if np.iscomplexobj(imdata):
raise TypeError('Complex type not supported')
if imdata.ndim != 2:
raise ValueError('{0}-d array not supported. '
'Only 2-d arrays supported.'.format(imdata.ndim))
if size % 2 == 0:
raise TypeError("Size must be odd.")
if fluxes is not None and len(fluxes) != len(positions):
raise TypeError('Position and flux arrays must be of equal '
'length.')
if mask is None:
mask = np.isnan(imdata)
if isinstance(positions, (list, tuple)):
positions = np.array(positions)
if isinstance(positions, Table) or \
(isinstance(positions, np.ndarray) and
positions.dtype.names is not None):
# One can do clever things like
# positions['x_0', 'y_0'].as_array().view((positions['x_0'].dtype,
# 2))
# but that requires positions['x_0'].dtype is
# positions['y_0'].dtype.
# Better do something simple to allow type promotion if required.
pos = np.empty((len(positions), 2))
pos[:, 0] = positions['x_0']
pos[:, 1] = positions['y_0']
positions = pos
if isinstance(fluxes, (list, tuple)):
fluxes = np.array(fluxes)
if mode == 'mean':
combine = np.ma.mean
elif mode == 'median':
combine = np.ma.median
else:
raise Exception('Invalid mode to combine prfs.')
data_internal = np.ma.array(data=imdata, mask=mask)
prf_model = np.ndarray(shape=(subsampling, subsampling, size, size))
positions_subpixel_indices = \
np.array([subpixel_indices(_, subsampling) for _ in positions],
dtype=np.int)
for i in range(subsampling):
for j in range(subsampling):
extracted_sub_prfs = []
sub_prf_indices = np.all(positions_subpixel_indices == [j, i],
axis=1)
if not sub_prf_indices.any():
raise ValueError('The source coordinates do not sample '
'all sub-pixel positions. Reduce the '
'value of the subsampling parameter.')
positions_sub_prfs = positions[sub_prf_indices]
for k, position in enumerate(positions_sub_prfs):
x, y = position
extracted_prf = extract_array(data_internal, (size, size),
(y, x))
# Check shape to exclude incomplete PRFs at the boundaries
# of the image
if (extracted_prf.shape == (size, size) and
np.ma.sum(extracted_prf) != 0):
# Replace NaN values by mirrored value, with respect
# to the prf's center
if fix_nan:
prf_nan = extracted_prf.mask
if prf_nan.any():
if (prf_nan.sum() > 3 or
prf_nan[size // 2, size // 2]):
continue
else:
extracted_prf = mask_to_mirrored_num(
extracted_prf, prf_nan,
(size // 2, size // 2))
# Normalize and add extracted PRF to data cube
if fluxes is None:
extracted_prf_norm = (np.ma.copy(extracted_prf) /
np.ma.sum(extracted_prf))
else:
fluxes_sub_prfs = fluxes[sub_prf_indices]
extracted_prf_norm = (np.ma.copy(extracted_prf) /
fluxes_sub_prfs[k])
extracted_sub_prfs.append(extracted_prf_norm)
else:
continue
prf_model[i, j] = np.ma.getdata(
combine(np.ma.dstack(extracted_sub_prfs), axis=2))
return cls(prf_model, subsampling=subsampling)
def create_prf(data,
positions,
size,
fluxes=None,
mask=None,
mode='mean',
subsampling=1,
fix_nan=False):
"""
Estimate point response function (PRF) from image data.
Given a list of positions and size this function estimates an image of
the PRF by extracting and combining the individual PRFs from the given
positions. Different modes of combining are available.
NaN values are either ignored by passing a mask or can be replaced by
the mirrored value with respect to the center of the PRF.
Furthermore it is possible to specify fluxes to have a correct
normalization of the individual PRFs. Otherwise the flux is estimated from
a quadratic aperture of the same size as the PRF image.
Parameters
----------
data : array
Data array
positions : List or array
List of pixel coordinate source positions to use in creating the PRF.
size : odd int
Size of the quadratic PRF image in pixels.
mask : bool array, optional
Boolean array to mask out bad values.
fluxes : array, optional
Object fluxes to normalize extracted PRFs.
mode : {'mean', 'median'}
One of the following modes to combine the extracted PRFs:
* 'mean'
Take the pixelwise mean of the extracted PRFs.
* 'median'
Take the pixelwise median of the extracted PRFs.
subsampling : int
Factor of subsampling of the PRF (default = 1).
fix_nan : bool
Fix NaN values in the data by replacing it with the
mirrored value. Assuming that the PRF is symmetrical.
Returns
-------
prf : `photutils.psf.DiscretePRF`
Discrete PRF model estimated from data.
Notes
-----
In Astronomy different definitions of Point Spread Function (PSF) and
Point Response Function (PRF) are used. Here we assume that the PRF is
an image of a point source after discretization e.g. with a CCD. This
definition is equivalent to the `Spitzer definiton of the PRF
<http://irsa.ipac.caltech.edu/data/SPITZER/docs/dataanalysistools/tools/mopex/mopexusersguide/89/>`_.
References
----------
`Spitzer PSF vs. PRF
<http://irsa.ipac.caltech.edu/data/SPITZER/docs/files/spitzer/PRF_vs_PSF.pdf>`_
`Kepler PSF calibration
<http://keplerscience.arc.nasa.gov/CalibrationPSF.shtml>`_
`The Kepler Pixel Response Function
<http://adsabs.harvard.edu/abs/2010ApJ...713L..97B>`_
"""
# Check input array type and dimension.
if np.iscomplexobj(data):
raise TypeError('Complex type not supported')
if data.ndim != 2:
raise ValueError('{0}-d array not supported. '
'Only 2-d arrays supported.'.format(data.ndim))
if size % 2 == 0:
raise TypeError("Size must be odd.")
if fluxes is not None and len(fluxes) != len(positions):
raise TypeError("Position and flux arrays must be of equal length.")
if mask is None:
mask = np.isnan(data)
if isinstance(positions, (list, tuple)):
positions = np.array(positions)
if isinstance(fluxes, (list, tuple)):
fluxes = np.array(fluxes)
if mode == 'mean':
combine = np.ma.mean
elif mode == 'median':
combine = np.ma.median
else:
raise Exception('Invalid mode to combine prfs.')
data_internal = np.ma.array(data=data, mask=mask)
prf_model = np.ndarray(shape=(subsampling, subsampling, size, size))
positions_subpixel_indices = np.array(
[subpixel_indices(_, subsampling) for _ in positions], dtype=np.int)
for i in range(subsampling):
for j in range(subsampling):
extracted_sub_prfs = []
sub_prf_indices = np.all(positions_subpixel_indices == [j, i],
axis=1)
positions_sub_prfs = positions[sub_prf_indices]
for k, position in enumerate(positions_sub_prfs):
x, y = position
extracted_prf = extract_array(data_internal, (size, size),
(y, x))
# Check shape to exclude incomplete PRFs at the boundaries
# of the image
if (extracted_prf.shape == (size, size)
and np.ma.sum(extracted_prf) != 0):
# Replace NaN values by mirrored value, with respect
# to the prf's center
if fix_nan:
prf_nan = extracted_prf.mask
if prf_nan.any():
if (prf_nan.sum() > 3
or prf_nan[size // 2, size // 2]):
continue
else:
extracted_prf = mask_to_mirrored_num(
extracted_prf, prf_nan,
(size // 2, size // 2))
# Normalize and add extracted PRF to data cube
if fluxes is None:
extracted_prf_norm = (np.ma.copy(extracted_prf) /
np.ma.sum(extracted_prf))
else:
fluxes_sub_prfs = fluxes[sub_prf_indices]
extracted_prf_norm = (np.ma.copy(extracted_prf) /
fluxes_sub_prfs[k])
extracted_sub_prfs.append(extracted_prf_norm)
else:
continue
prf_model[i, j] = np.ma.getdata(
combine(np.ma.dstack(extracted_sub_prfs), axis=2))
return DiscretePRF(prf_model, subsampling=subsampling)
def create_prf(data, positions, size, fluxes=None, mask=None, mode='mean',
subsampling=1, fix_nan=False):
"""
Estimate point response function (PRF) from image data.
Given a list of positions and size this function estimates an image of
the PRF by extracting and combining the individual PRFs from the given
positions. Different modes of combining are available.
NaN values are either ignored by passing a mask or can be replaced by
the mirrored value with respect to the center of the PRF.
Furthermore it is possible to specify fluxes to have a correct
normalization of the individual PRFs. Otherwise the flux is estimated from
a quadratic aperture of the same size as the PRF image.
Parameters
----------
data : array
Data array
positions : List or array
List of pixel coordinate source positions to use in creating the PRF.
size : odd int
Size of the quadratic PRF image in pixels.
mask : bool array, optional
Boolean array to mask out bad values.
fluxes : array, optional
Object fluxes to normalize extracted PRFs.
mode : {'mean', 'median'}
One of the following modes to combine the extracted PRFs:
* 'mean'
Take the pixelwise mean of the extracted PRFs.
* 'median'
Take the pixelwise median of the extracted PRFs.
subsampling : int
Factor of subsampling of the PRF (default = 1).
fix_nan : bool
Fix NaN values in the data by replacing it with the
mirrored value. Assuming that the PRF is symmetrical.
Returns
-------
prf : `photutils.psf.DiscretePRF`
Discrete PRF model estimated from data.
Notes
-----
In Astronomy different definitions of Point Spread Function (PSF) and
Point Response Function (PRF) are used. Here we assume that the PRF is
an image of a point source after discretization e.g. with a CCD. This
definition is equivalent to the `Spitzer definiton of the PRF
<http://irsa.ipac.caltech.edu/data/SPITZER/docs/dataanalysistools/tools/mopex/mopexusersguide/89/>`_.
References
----------
`Spitzer PSF vs. PRF
<http://irsa.ipac.caltech.edu/data/SPITZER/docs/files/spitzer/PRF_vs_PSF.pdf>`_
`Kepler PSF calibration
<http://keplerscience.arc.nasa.gov/CalibrationPSF.shtml>`_
`The Kepler Pixel Response Function
<http://adsabs.harvard.edu/abs/2010ApJ...713L..97B>`_
"""
# Check input array type and dimension.
if np.iscomplexobj(data):
raise TypeError('Complex type not supported')
if data.ndim != 2:
raise ValueError('{0}-d array not supported. '
'Only 2-d arrays supported.'.format(data.ndim))
if size % 2 == 0:
raise TypeError("Size must be odd.")
if fluxes is not None and len(fluxes) != len(positions):
raise TypeError("Position and flux arrays must be of equal length.")
if mask is None:
mask = np.isnan(data)
if isinstance(positions, (list, tuple)):
positions = np.array(positions)
if isinstance(fluxes, (list, tuple)):
fluxes = np.array(fluxes)
if mode == 'mean':
combine = np.ma.mean
elif mode == 'median':
combine = np.ma.median
else:
raise Exception('Invalid mode to combine prfs.')
data_internal = np.ma.array(data=data, mask=mask)
prf_model = np.ndarray(shape=(subsampling, subsampling, size, size))
positions_subpixel_indices = np.array([subpixel_indices(_, subsampling)
for _ in positions], dtype=np.int)
for i in range(subsampling):
for j in range(subsampling):
extracted_sub_prfs = []
sub_prf_indices = np.all(positions_subpixel_indices == [j, i],
axis=1)
positions_sub_prfs = positions[sub_prf_indices]
for k, position in enumerate(positions_sub_prfs):
x, y = position
extracted_prf = extract_array(data_internal, (size, size),
(y, x))
# Check shape to exclude incomplete PRFs at the boundaries
# of the image
if (extracted_prf.shape == (size, size) and
np.ma.sum(extracted_prf) != 0):
# Replace NaN values by mirrored value, with respect
# to the prf's center
if fix_nan:
prf_nan = extracted_prf.mask
if prf_nan.any():
if (prf_nan.sum() > 3 or
prf_nan[size // 2, size // 2]):
continue
else:
extracted_prf = mask_to_mirrored_num(
extracted_prf, prf_nan,
(size // 2, size // 2))
# Normalize and add extracted PRF to data cube
if fluxes is None:
extracted_prf_norm = (np.ma.copy(extracted_prf) /
np.ma.sum(extracted_prf))
else:
fluxes_sub_prfs = fluxes[sub_prf_indices]
extracted_prf_norm = (np.ma.copy(extracted_prf) /
fluxes_sub_prfs[k])
extracted_sub_prfs.append(extracted_prf_norm)
else:
continue
prf_model[i, j] = np.ma.getdata(
combine(np.ma.dstack(extracted_sub_prfs), axis=2))
return DiscretePRF(prf_model, subsampling=subsampling)
