/usr/lib/python3/dist-packages/photutils/psf/sandbox.py is in python3-photutils 0.3-3.
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"""
This module stores work related to photutils.psf that is not quite ready
for prime-time (i.e., is not considered a stable public API), but is
included either for experimentation or as legacy code.
"""
from __future__ import division
import numpy as np
from astropy.table import Table
from astropy.modeling import Parameter, Fittable2DModel
from astropy.modeling.fitting import LevMarLSQFitter
from astropy.nddata.utils import subpixel_indices
from ..utils import mask_to_mirrored_num
from ..extern.nddata_compat import extract_array
__all__ = ['DiscretePRF']
class DiscretePRF(Fittable2DModel):
"""
A discrete Pixel Response Function (PRF) model.
The discrete PRF model stores images of the PRF at different
subpixel positions or offsets as a lookup table. The resolution is
given by the subsampling parameter, which states in how many
subpixels a pixel is divided.
In the typical case of wanting to create a PRF from an image with
many point sources, use the `~DiscretePRF.create_from_image` method,
rather than directly initializing this class.
The discrete PRF model class in initialized with a 4 dimensional
array, that contains the PRF images at different subpixel positions.
The definition of the axes is as following:
1. Axis: y subpixel position
2. Axis: x subpixel position
3. Axis: y direction of the PRF image
4. Axis: x direction of the PRF image
The total array therefore has the following shape
(subsampling, subsampling, prf_size, prf_size)
Parameters
----------
prf_array : ndarray
Array containing PRF images.
normalize : bool
Normalize PRF images to unity. Equivalent to saying there is
*no* flux outside the bounds of the PRF images.
subsampling : int, optional
Factor of subsampling. Default = 1.
Notes
-----
See :ref:`psf-terminology` for more details on the distinction
between PSF and PRF as used in this module.
"""
flux = Parameter('flux')
x_0 = Parameter('x_0')
y_0 = Parameter('y_0')
def __init__(self, prf_array, normalize=True, subsampling=1):
# Array shape and dimension check
if subsampling == 1:
if prf_array.ndim == 2:
prf_array = np.array([[prf_array]])
if prf_array.ndim != 4:
raise TypeError('Array must have 4 dimensions.')
if prf_array.shape[:2] != (subsampling, subsampling):
raise TypeError('Incompatible subsampling and array size')
if np.isnan(prf_array).any():
raise Exception("Array contains NaN values. Can't create PRF.")
# Normalize if requested
if normalize:
for i in range(prf_array.shape[0]):
for j in range(prf_array.shape[1]):
prf_array[i, j] /= prf_array[i, j].sum()
# Set PRF asttributes
self._prf_array = prf_array
self.subsampling = subsampling
constraints = {'fixed': {'x_0': True, 'y_0': True}}
x_0 = 0
y_0 = 0
flux = 1
super(DiscretePRF, self).__init__(n_models=1, x_0=x_0, y_0=y_0,
flux=flux, **constraints)
self.fitter = LevMarLSQFitter()
@property
def prf_shape(self):
"""Shape of the PRF image."""
return self._prf_array.shape[-2:]
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
@classmethod
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)
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