reproject

library0.20.0pypypi✓ verified 87d ago

The `reproject` package is a Python library designed for re-gridding astronomical images from one World Coordinate System (WCS) to another. It provides a uniform interface for various reprojection techniques, including interpolation-based methods, the adaptive and anti-aliased algorithm by DeForest (2004), and flux-conserving spherical polygon intersection. The library also supports reprojection to and from HEALPIX projections through integration with `astropy-healpix`. It is currently at version 0.19.0 and is actively maintained as part of the Astropy project, with a regular release cadence aligned with Astropy's development cycle.

data serialization

pip install reproject

package ↗homepage ↗

INSTALL

IMPORT

SIG · REPROJECT

reproject

datapythonv0.20.0

Install

16.4s avg

Import

3575ms

Disk

510MB

Pass rate

5/ 10

Env Coverage5 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.14.1 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.910 runs

build_error

glibc

py 3.10–3.910 runs

installs and imports cleanly · install 16.4s · import 3.575s · 347MB

510MB installed

● package 510MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

reproject_interp

✓ from reproject import reproject_interp

reproject_adaptive

✓ from reproject import reproject_adaptive

reproject_exact

✓ from reproject import reproject_exact

reproject_from_healpix

✓ from reproject import reproject_from_healpix

reproject_to_healpix

✓ from reproject import reproject_to_healpix

This quickstart demonstrates how to reproject a simple 2D NumPy array from one Astropy WCS object to another using the `reproject_interp` function. It creates dummy input data and WCS, defines a target WCS with a different scale and rotation, performs the reprojection, and then visualizes both the original and reprojected images. For real-world applications, input data often comes from FITS files.

import numpy as np from astropy.wcs import WCS from reproject import reproject_interp import matplotlib.pyplot as plt # 1. Create dummy input data and WCS data_in = np.arange(100.0).reshape((10, 10)) + 100.0 wcs_in = WCS(naxis=2) wcs_in.wcs.crpix = [5, 5] wcs_in.wcs.cdelt = np.array([-0.1, 0.1]) wcs_in.wcs.crval = [0, 0] wcs_in.wcs.ctype = ["RA---TAN", "DEC--TAN"] # 2. Create target output WCS with a different projection/scale/rotation wcs_out = WCS(naxis=2) wcs_out.wcs.crpix = [10, 10] wcs_out.wcs.cdelt = np.array([-0.05, 0.05]) # Different pixel scale wcs_out.wcs.crval = [0, 0] wcs_out.wcs.ctype = ["RA---TAN", "DEC--TAN"] wcs_out.wcs.pc = [[np.cos(np.deg2rad(15)), -np.sin(np.deg2rad(15))], [np.sin(np.deg2rad(15)), np.cos(np.deg2rad(15))]] # 15-deg rotation shape_out = (20, 20) # Target output shape # 3. Reproject the data using interpolation data_out, footprint = reproject_interp((data_in, wcs_in), wcs_out, shape_out=shape_out) # 4. Visualize the results (optional) plt.figure(figsize=(10, 5)) plt.subplot(1, 2, 1, projection=wcs_in) plt.imshow(data_in, origin='lower', cmap='viridis') plt.title('Original Data') plt.xlabel('X (pixels)') plt.ylabel('Y (pixels)') plt.subplot(1, 2, 2, projection=wcs_out) plt.imshow(data_out, origin='lower', cmap='viridis') plt.title('Reprojected Data') plt.xlabel('X (pixels)') plt.ylabel('Y (pixels)') plt.tight_layout() plt.show()

Debug

Known issues

gotchaThe `reproject` library assumes that the input World Coordinate System (WCS) information is accurate. It performs image re-gridding, not image registration (alignment of images where WCS might be inaccurate).

fix

Ensure your input data has correct and well-defined WCS information before using `reproject`. If alignment is needed, perform image registration with other tools first.

affects: All versions

gotchaInterpolation-based reprojection (`reproject_interp`) is generally faster but does not guarantee flux conservation by default. For scientific applications requiring precise flux preservation, this can lead to incorrect results.

fix

For flux-conserving reprojection, use `reproject_exact` or `reproject_adaptive` with the `conserve_flux=True` parameter. Be aware that these methods are computationally more intensive.

affects: All versions

gotchaWhen dealing with very large datasets, direct reprojection can consume significant memory. The `block_size` and `parallel` parameters are available for out-of-core and multi-threaded processing.

fix

For large arrays, consider setting `block_size='auto'` or a specific tuple, and `parallel=True` (which requires `dask` to be installed) to enable chunked processing and reduce memory footprint.

affects: All versions

Errors

Common errors & fixes

ValueError: The input WCS does not contain enough dimensions (expected 2, got X)

The `astropy.wcs.WCS` object provided for the input data does not have the expected number of celestial dimensions (typically 2 for images) that match the actual data array used for reprojection.

fix

Verify that your `WCS` object is correctly initialized with `naxis=2` (or the appropriate number of celestial axes) and that the celestial projection axes align with the dimensions of your input data array. When passing `(data, wcs)`, ensure `wcs` corresponds to the celestial dimensions of `data`.

Reprojected image appears shifted, distorted, or misaligned from its expected position.

This is often caused by inaccuracies in the input or output WCS definitions, discrepancies in coordinate system datums (e.g., ICRS vs. FK5), or issues with the spherical representation of coordinates during transformation. It can also stem from an incorrect understanding of how projections work versus reprojection.

fix

Carefully inspect both the input and output `astropy.wcs.WCS` objects. Check all relevant FITS keywords (`CRVAL`, `CRPIX`, `CDELT`, `CTYPE`, `PC`, `CUNIT`, `RADECSYS`, `EQUINOX`) for correctness. Ensure consistent coordinate frames. If dealing with different datums, confirm that `astropy` is configured to handle the transformations correctly. Small errors in the WCS can lead to significant shifts.

Upgrade

Version history

0.20.0latest on PyPI · released Jun 11, 2026

Audit

Dependencies

PythonrequiredRuntime environment

NumpyrequiredCore array manipulation

AstropyrequiredWorld Coordinate System (WCS) handling and core astronomical utilities

ScipyrequiredInterpolation and image processing algorithms

astropy-healpixoptionalRequired for HEALPIX image reprojection functionality

daskoptionalOptional, for parallel and out-of-core processing with `block_size`

zarroptionalOptional, for Dask array support

fsspecoptionalOptional, for Dask array support

shapelyoptionalOptional, for some mosaicking functionality

Agent activity

12 hits · last 30 days

node

OpenAI (training)

Resources

packagehttps://pypi.org/project/reproject/ ↗

homepagehttps://reproject.readthedocs.io ↗