Metadata-Version: 2.4
Name: cloud2cloud
Version: 0.2.2
Summary: Cloud interpolator
Author: Eloi Demolis
Author-email: CoopTeam-CERFACS <coop@cerfacs.fr>
License: MIT License
        -----------
        
        Copyright (c) 2021 CERFACS
        Permission is hereby granted, free of charge, to any person
        obtaining a copy of this software and associated documentation
        files (the "Software"), to deal in the Software without
        restriction, including without limitation the rights to use,
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        copies of the Software, and to permit persons to whom the
        Software is furnished to do so, subject to the following
        conditions:
        
        The above copyright notice and this permission notice shall be
        included in all copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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Project-URL: Homepage, https://gitlab.com/cerfacs/cloud2cloud
Project-URL: Issues, https://gitlab.com/cerfacs/cloud2cloud/-/issues
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: scipy
Provides-Extra: tests
Requires-Dist: pytest; extra == "tests"
Requires-Dist: pytest-coverage; extra == "tests"
Dynamic: license-file

# cloud2cloud
This is a simple tool to interpolate N-dimensional data between two M-dimensional meshes as fast as possible.
At any point of the target mesh the data is computed from K nearest neighbours in the source mesh.


## cloud2cloud interface
For structured meshes.
```py
result = cloud2cloud(source, values, target)

source # ndarray(*shape_sce, dim_msh)
target # ndarray(*shape_tgt, dim_msh)
values # ndarray(*shape_sce, *shape_val)
result # ndarray(*shape_tgt, *shape_val)
```


## Raw interface
The underlying method used to interpolate.
```py
base = CloudInterpolator(source, target)
result = base.interp(values)

source # ndarray(dim_msh, points_sce) or tuple(ndarray(points_sce),...)
target # ndarray(dim_msh, points_tgt) or tuple(ndarray(points_tgt),...)
values # ndarray(points_sce, *shape_val)
result # ndarray(points_tgt, *shape_val)
```
