# Licensed under a 3-clause BSD style license - see LICENSE.rst
from ..common import _reproject_dispatcher
from ..utils import parse_input_data, parse_output_projection
from .core import _reproject_adaptive_2d
__all__ = ["reproject_adaptive"]
[docs]
def reproject_adaptive(
input_data,
output_projection,
shape_out=None,
hdu_in=0,
center_jacobian=False,
despike_jacobian=False,
roundtrip_coords=True,
conserve_flux=False,
kernel="gaussian",
kernel_width=1.3,
sample_region_width=4,
boundary_mode="strict",
boundary_fill_value=0,
boundary_ignore_threshold=0.5,
x_cyclic=False,
y_cyclic=False,
bad_value_mode="strict",
bad_fill_value=0,
output_array=None,
output_footprint=None,
return_footprint=True,
block_size=None,
parallel=False,
return_type=None,
):
"""
Reproject a 2D array from one WCS to another using the DeForest (2004)
adaptive, anti-aliased resampling algorithm, with optional flux
conservation. This algorithm smoothly transitions between filtered
interpolation and spatial averaging, depending on the scaling applied by
the transformation at each output location.
Parameters
----------
input_data : object
The input data to reproject. This can be:
* The name of a FITS file as a `str` or a `pathlib.Path` object
* An `~astropy.io.fits.HDUList` object
* An image HDU object such as a `~astropy.io.fits.PrimaryHDU`,
`~astropy.io.fits.ImageHDU`, or `~astropy.io.fits.CompImageHDU`
instance
* A tuple where the first element is a `~numpy.ndarray` and the
second element is either a
`~astropy.wcs.wcsapi.BaseLowLevelWCS`,
`~astropy.wcs.wcsapi.BaseHighLevelWCS`, or a
`~astropy.io.fits.Header` object
* An `~astropy.nddata.NDData` object from which the ``.data`` and
``.wcs`` attributes will be used as the input data.
If the data array contains more dimensions than are described by the
input header or WCS, the extra dimensions (assumed to be the first
dimensions) are taken to represent multiple images with the same
coordinate information. The coordinate transformation will be computed
once and then each image will be reprojected, offering a speedup over
reprojecting each image individually.
output_projection : `~astropy.wcs.wcsapi.BaseLowLevelWCS` or `~astropy.wcs.wcsapi.BaseHighLevelWCS` or `~astropy.io.fits.Header`
The output projection, which can be either a
`~astropy.wcs.wcsapi.BaseLowLevelWCS`,
`~astropy.wcs.wcsapi.BaseHighLevelWCS`, or a `~astropy.io.fits.Header`
instance.
shape_out : tuple, optional
If ``output_projection`` is a WCS instance, the shape of the output
data should be specified separately.
hdu_in : int or str, optional
If ``input_data`` is a FITS file or an `~astropy.io.fits.HDUList`
instance, specifies the HDU to use.
center_jacobian : bool
A Jacobian matrix is calculated, representing
d(input image coordinate) / d(output image coordinate),
a local linearization of the coordinate transformation. When this flag
is ``True``, the Jacobian is calculated at pixel grid points by
calculating the transformation at locations offset by half a pixel.
This is more accurate but carries the cost of tripling the number of
coordinate transforms done by this routine. This is recommended if your
coordinate transform varies significantly and non-smoothly between
output pixels. When ``False``, the Jacobian is calculated using
pixel-grid-point transforms, which produces Jacobian values at
locations between pixel grid points, and nearby Jacobian values are
averaged to produce values at the pixel grid points. This is more
efficient, and the loss of accuracy is extremely small for
transformations that vary smoothly between pixels. Defaults to
``False``.
despike_jacobian : bool
Whether to despike the computed Jacobian values. In some situations
(e.g. an all-sky map, with a wrap point in the longitude), extremely
large Jacobian values may be computed which are artifacts of the
coordinate system definition, rather than reflecting the actual nature
of the coordinate transformation. This may result in a band of ``nan``
pixels in the output image. In these situations, if the actual
transformation is approximately constant in the region of these
artifacts, this option should be enabled. If enabled, the typical
magnitude (distance from the determinant) of the Jacobian matrix,
``Jmag2 = sum_j sum_i (J_ij**2)``, is computed for each pixel and
compared to the 25th percentile of that value in the local 3x3
neighborhood (i.e. the third-lowest value). If it exceeds that
percentile value by more than 10 times, the Jacobian matrix is deemed
to be "spiking" and it is replaced by the average of the non-spiking
values in the 3x3 neighborhood.
roundtrip_coords : bool
Whether to verify that coordinate transformations are defined in both
directions.
conserve_flux : bool
Whether to rescale output pixel values so flux is conserved.
kernel : str
The averaging kernel to use. Allowed values are 'Hann' and 'Gaussian'.
Case-insensitive. The Gaussian kernel produces better photometric
accuracy and stronger anti-aliasing at the cost of some blurring (on
the scale of a few pixels). If not specified, the Gaussain kernel is
used by default.
kernel_width : double
The width of the kernel in pixels, measuring to +/- 1 sigma for the
Gaussian window. Does not apply to the Hann window. Reducing this width
may introduce photometric errors or leave input pixels under-sampled,
while increasing it may improve the degree of anti-aliasing but will
increase blurring of the output image. If this width is changed from
the default, a proportional change should be made to the value of
sample_region_width to maintain an equivalent degree of photometric
accuracy.
sample_region_width : double
The width in pixels of the output-image region which, when transformed
to the input plane, defines the region to be sampled for each output
pixel. Used only for the Gaussian kernel, which otherwise has infinite
extent. This value sets a trade-off between accuracy and computation
time, with better accuracy at higher values. The default value of 4,
with the default kernel width, should limit the most extreme errors to
less than one percent. Higher values will offer even more photometric
accuracy.
boundary_mode : str
How to handle when the sampling region includes regions outside the
bounds of the input image. The default is ``strict``. Allowed values
are:
* ``strict`` --- Output pixels will be NaN if any input sample
falls outside the input image.
* ``constant`` --- Samples outside the input image are replaced by
a constant value, set with the ``boundary_fill_value`` argument.
Output values become NaN if there are no valid input samples.
* ``grid-constant`` --- Samples outside the input image are
replaced by a constant value, set with the
``boundary_fill_value`` argument. Output values will be
``boundary_fill_value`` if there are no valid input samples.
* ``ignore`` --- Samples outside the input image are simply
ignored, contributing neither to the output value nor the
sum-of-weights normalization.
* ``ignore_threshold`` --- Acts as ``ignore``, unless the total
weight of the ignored samples exceeds a set fraction of the total
weight across the entire sampling region, set by the
``boundary_ignore_threshold`` argument. In that case, acts as
``strict``.
* ``nearest`` --- Samples outside the input image are replaced by
the nearest in-bounds input pixel.
boundary_fill_value : double
The constant value used by the ``constant`` boundary mode.
boundary_ignore_threshold : double
The threshold used by the ``ignore_threshold`` boundary mode. Should be
a value between 0 and 1, representing a fraction of the total weight
across the sampling region.
x_cyclic, y_cyclic : bool
Indicates that the x or y axis of the input image should be treated as
cyclic or periodic. Overrides the boundary mode for that axis, so that
out-of-bounds samples wrap to the other side of the image.
bad_value_mode : str
How to handle values of ``nan`` and ``inf`` in the input data. The
default is ``strct``. Allowed values are:
* ``strict`` --- Values of ``nan`` or ``inf`` in the input data are
propagated to every output value which samples them.
* ``ignore`` --- When a sampled input value is ``nan`` or ``inf``,
that input pixel is ignored (affected neither the accumulated sum
of weighted samples nor the accumulated sum of weights).
* ``constant`` --- Input values of ``nan`` and ``inf`` are replaced
with a constant value, set via the ``bad_fill_value`` argument.
bad_fill_value : double
The constant value used by the ``constant`` bad-value mode.
output_array : None or `~numpy.ndarray`
An array in which to store the reprojected data. This can be any numpy
array including a memory map, which may be helpful when dealing with
extremely large files.
output_footprint : `~numpy.ndarray`, optional
An array in which to store the footprint of reprojected data. This can be
any numpy array including a memory map, which may be helpful when dealing with
extremely large files.
return_footprint : bool
Whether to return the footprint in addition to the output array.
block_size : tuple or 'auto', optional
The size of blocks in terms of output array pixels that each block will handle
reprojecting. Extending out from (0,0) coords positively, block sizes
are clamped to output space edges when a block would extend past edge.
Specifying ``'auto'`` means that reprojection will be done in blocks with
the block size automatically determined. If ``block_size`` is not
specified or set to `None`, the reprojection will not be carried out in
blocks.
parallel : bool or int, optional
If `True`, the reprojection is carried out in parallel, and if a
positive integer, this specifies the number of processes to use.
The reprojection will be parallelized over output array blocks specified
by ``block_size`` (if the block size is not set, it will be determined
automatically).
return_type : {'numpy', 'dask'}, optional
Whether to return numpy or dask arrays - defaults to 'numpy'.
Returns
-------
array_new : `~numpy.ndarray`
The reprojected array.
footprint : `~numpy.ndarray`
Footprint of the input array in the output array. Values of 0 indicate
no coverage or valid values in the input image, while values of 1
indicate valid values.
"""
# TODO: add support for output_array
array_in, wcs_in = parse_input_data(input_data, hdu_in=hdu_in)
wcs_out, shape_out = parse_output_projection(
output_projection, shape_in=array_in.shape, shape_out=shape_out
)
return _reproject_dispatcher(
_reproject_adaptive_2d,
array_in=array_in,
wcs_in=wcs_in,
wcs_out=wcs_out,
shape_out=shape_out,
array_out=output_array,
parallel=parallel,
block_size=block_size,
return_footprint=return_footprint,
output_footprint=output_footprint,
reproject_func_kwargs=dict(
center_jacobian=center_jacobian,
despike_jacobian=despike_jacobian,
roundtrip_coords=roundtrip_coords,
conserve_flux=conserve_flux,
kernel=kernel,
kernel_width=kernel_width,
sample_region_width=sample_region_width,
boundary_mode=boundary_mode,
boundary_fill_value=boundary_fill_value,
boundary_ignore_threshold=boundary_ignore_threshold,
x_cyclic=x_cyclic,
y_cyclic=y_cyclic,
bad_value_mode=bad_value_mode,
bad_fill_value=bad_fill_value,
),
return_type=return_type,
)