astro-ndslice

Slice NumPy arrays, convert FITS/IRAF sections, and align images by pixel offsets.

Install

pip install astro-ndslice

Python >= 3.10.

For astronomers who need WCS/header offset helpers:

pip install "astro-ndslice[full]"

Slice an image

import numpy as np
from astro_ndslice import slicefy

data = np.arange(100).reshape(10, 10)
cutout = data[slicefy("[2:5,3:7]")]  # Same as data[2:7, 1:5].
trimmed = data[slicefy(1)]          # Trim one pixel from each edge - bezeling.

• FITS sections use 1-based, inclusive bounds in x, y order.
• NumPy uses 0-based, exclusive stops in row, column order.
• slicefy() assumes FITS strings
• slice_from_string() assumes Python strings unless you pass fits_convention=True.

Align images

import numpy as np
from astro_ndslice import offseted_shape, offsets2slice

images = [np.ones((3, 4)), np.full((2, 3), 2)]
shapes = [image.shape for image in images]  # NumPy axis order.
offsets = [(0, 0), (2, 1)]                # x, y offsets.
_, shape = offseted_shape(shapes, offsets)
indices = offsets2slice(shapes, offsets)
stack = np.full((len(images), *shape), np.nan)
for image, index in zip(images, indices):
    stack[index] = image

Both helpers round relative offsets to pixel positions. Use method="inner" to find the region shared by every image; no shared pixels raises ValueError.

Function guide

calc_offset_physical() corrects LTV translation terms for LTM scaling in shared physical-coordinate units. For example, LTV1=6, LTM1_1=2 gives 3 along x; ignore_ltm=True gives the raw value 6. This preserves the LTV sign; differing LTM matrices require resampling for image alignment.

See CHANGELOG.md for return-type changes and stricter validation. For version bumps and PyPI publishing, see RELEASING.md.