Metadata-Version: 2.1
Name: aafitrans
Version: 0.2.0
Summary: match coordinate lists
Author-email: Prajwel Joseph <prajwel.pj@gmail.com>
Project-URL: Homepage, https://github.com/prajwel/aafitrans
Project-URL: Bug Tracker, https://github.com/prajwel/aafitrans/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE.txt
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-image

# Aafitrans (AstroAlign FInd TRANSform)

Aafitrans is a Python package that builds upon the capabilities of the Astroalign package's `find_transform` function. It incorporates several modifications to improve its functionality and performance:

1. Enhanced RANSAC Algorithm: The RANSAC algorithm used in Aafitrans has been optimized to provide a solution that minimizes the sum of squared residuals. This improvement ensures a more accurate transformation estimation.

2. Arun and Horn's Method: Aafitrans replaces Umeyama's method from `scikit-image` with Arun and Horn's method for estimating `'euclidean'` or `'similarity'` transformations. 

3. Reflection Support: Unlike Astroalign, Aafitrans enables the matching of coordinate lists that include reflection along one axis. This enhancement expands the range of transformations that can be accurately estimated.

4. Extended Transformation Options: Aafitrans supports all transformations available in the `scikit-image` library, providing a comprehensive set of options for aligning and transforming images.

5. Improved Matching Efficiency: The `kdtree_search_radius` parameter in Aafitrans allows users to set the search radius for matches, enabling faster and more efficient matching of corresponding points between images.

6. Reproducible Results: Aafitrans introduces the `seed` parameter, which can be set during each run to ensure the reproducibility of results. This feature is particularly useful for research and debugging purposes.

7. Dependency Optimization: Aafitrans eliminates the need for the `sep` and `bottleneck` packages as dependencies, streamlining the installation process and reducing potential compatibility issues.

Please see the original Astroalign software at https://github.com/quatrope/astroalign
  
## Citation:
```
Astroalign: A Python module for astronomical image registration.
Beroiz, M., Cabral, J. B., & Sanchez, B.
Astronomy & Computing, Volume 32, July 2020, 100384.
```


## Installation
```bash
pip install aafitrans
```

## Usage 
It is similar to Astroalign's [`find_transform`](https://astroalign.quatrope.org/en/latest/tutorial.html#finding-the-transformation) function. However, there are many parameters available for the user to modify. 
```python
from aafitrans import find_transform
transf, (matched_source_xy, matched_target_xy) = find_transform(source_xy, target_xy,
                                                                max_control_points=50,
                                                                ttype='similarity',
                                                                pixel_tolerance=2,
                                                                min_matches=4,
                                                                num_nearest_neighbors=8,
                                                                kdtree_search_radius=0.02,
                                                                n_samples=1,
                                                                get_best_fit=True,
                                                                seed=None)

```

## Documentation for `find_transform` function

The `find_transform` function estimates the transform between two sets of control points, source and target. It returns a GeometricTransform object `T` ([see scikit-image documentation for details](https://scikit-image.org/docs/stable/auto_examples/transform/plot_transform_types.html#sphx-glr-auto-examples-transform-plot-transform-types-py)) that maps pixel x, y indices from the source image s = (x, y) into the target (destination) image t = (x, y). 

### Parameters:
- `source`: An iterable of (x, y) coordinates of the source control points.
- `target`: An iterable of (x, y) coordinates of the target control points.
- `max_control_points`: Default value is 50. The maximum number of control points to find the transformation.
- `ttype`: Default value is `'similarity'`. The type of transform to be estimated. One of the following should be set: {`'euclidean'`, `'similarity'`, `'affine'`, `'piecewise-affine'`, `'projective'`, `'polynomial'`}. For details, see [scikit-image documentation](https://scikit-image.org/docs/stable/api/skimage.transform.html#skimage.transform.estimate_transform). 
- `pixel_tolerance`: The maximum residual error for the estimated tranform.            
- `min_matches`: The minimum number of matches to be found. A value of 1 refers to 1 triangle, corresponding to 3 pairs of coordinates. 
- `num_nearest_neighbors`: The number of nearest neighbors of a given star (including itself) to construct the triangle invariants.                      
- `kdtree_search_radius`: The default is 0.02. This radius is used to find nearest neighbours while conducting a KD tree search of invariant features. 
- `n_samples`: The minimum number of data points to fit the model to. A value of 1 refers to 1 triangle, corresponding to 3 pairs of coordinates. 
- `get_best_fit`: Whether to minimize the total error.                          
- `seed`: Seed value for Numpy Random Generator.

### Returns:
- `T`: GeometricTransform object that maps pixel x, y indices from the source image s = (x, y) into the target (destination) image t = (x, y). It contains parameters of the transformation.
- `(source_pos_array, target_pos_array)`: A tuple of corresponding star positions in source and target.

### Raises:
- `TypeError`: If input type of `source` or `target` is not supported.
- `ValueError`: If it cannot find more than 3 stars on any input.
- `MaxIterError`: If no transformation is found.
