| Name | lazylinop JSON |
| Version |
1.18.2
JSON |
| download |
| home_page | None |
| Summary | A package dedicated to lazy linear operators based on diverse backends/libraries. |
| upload_time | 2025-07-17 12:23:38 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.8 |
| license | None |
| keywords |
butterfly
lazy computation
signal processing
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
## Lazylinop
[](https://pepy.tech/project/lazylinop)
[](https://pepy.tech/project/lazylinop)
[](https://pypi.org/project/lazylinop/)
A `LazyLinOp` is a high-level linear operator based on an arbitrary underlying implementation, such as:
- custom Python functions,
- a `NumPy array`,
- a `SciPy matrix`,
- a `Faust` object,
- any Python linear operator.
Thanks to the Lazylinop API, this operator can be easily manipulated, transformed or aggregated with other linear operators to form more complex `LazyLinOp` objects.
Thus, many operations are available such as the addition, concatenation, adjoint etc.
These operations are all ruled by the **lazy paradigm**: their evaluation is delayed until the resulting `LazyLinOp` is actually applied to a vector (or to a collection of vectors, seen as a matrix).
## Get started with ``lazylinop``
Run the following command to install the ``lazylinop`` package from PyPI:
```bash
pip install lazylinop
```
Run the following commands to install from conda:
```bash
conda config --add channels conda-forge
conda config --add channels lazylinop
conda install lazylinop
```
## First steps
Build 2D FFT from 1D FFT and Kronecker product:
```python3
from lazylinop.signal import fft
from lazylinop.basicops import kron
fft2d = kron(fft(N), fft(N))
x = np.random.randn(fft2d.shape[1])
y = fft2d @ x
```
Build circular convolution from 1D FFT:
```python3
from lazylinop.signal import fft
from lazylinop.basicops import diag
DFT = fft(N) * np.sqrt(N)
D = diag(DFT @ filter, k=0)
L = (DFT / N).H @ D @ DFT
```
## Authors
- Pascal Carrivain
- Simon Delamare
- Hakim Hadj-Djilani
- Remi Gribonval
## Contribute to ``lazylinop``
You can contribute to ``lazylinop`` with bug report, feature request and merge request.
## Useful links
- Link to the documentation: [Gitlab pages](https://faustgrp.gitlabpages.inria.fr/lazylinop/index.html)
## Running unit tests
```bash
cd tests
python3 -m unittest TestLazyLinOp.py
python3 -m unittest TestSignal.py
```
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"description": "## Lazylinop\n\n\n[](https://pepy.tech/project/lazylinop)\n[](https://pepy.tech/project/lazylinop)\n[](https://pypi.org/project/lazylinop/)\n\nA `LazyLinOp` is a high-level linear operator based on an arbitrary underlying implementation, such as:\n\n - custom Python functions,\n - a `NumPy array`,\n - a `SciPy matrix`,\n - a `Faust` object,\n - any Python linear operator.\n\nThanks to the Lazylinop API, this operator can be easily manipulated, transformed or aggregated with other linear operators to form more complex `LazyLinOp` objects.\nThus, many operations are available such as the addition, concatenation, adjoint etc.\nThese operations are all ruled by the **lazy paradigm**: their evaluation is delayed until the resulting `LazyLinOp` is actually applied to a vector (or to a collection of vectors, seen as a matrix).\n\n## Get started with ``lazylinop``\n\nRun the following command to install the ``lazylinop`` package from PyPI:\n\n```bash\npip install lazylinop\n```\n\nRun the following commands to install from conda:\n\n```bash\nconda config --add channels conda-forge\nconda config --add channels lazylinop\nconda install lazylinop\n```\n\n## First steps\n\nBuild 2D FFT from 1D FFT and Kronecker product:\n\n```python3\nfrom lazylinop.signal import fft\nfrom lazylinop.basicops import kron\nfft2d = kron(fft(N), fft(N))\nx = np.random.randn(fft2d.shape[1])\ny = fft2d @ x\n```\n\nBuild circular convolution from 1D FFT:\n\n```python3\nfrom lazylinop.signal import fft\nfrom lazylinop.basicops import diag\nDFT = fft(N) * np.sqrt(N)\nD = diag(DFT @ filter, k=0)\nL = (DFT / N).H @ D @ DFT\n```\n\n## Authors\n\n- Pascal Carrivain\n- Simon Delamare\n- Hakim Hadj-Djilani\n- Remi Gribonval\n\n## Contribute to ``lazylinop``\n\nYou can contribute to ``lazylinop`` with bug report, feature request and merge request.\n\n## Useful links\n\n- Link to the documentation: [Gitlab pages](https://faustgrp.gitlabpages.inria.fr/lazylinop/index.html)\n\n## Running unit tests\n\n```bash\n cd tests\n python3 -m unittest TestLazyLinOp.py\n python3 -m unittest TestSignal.py\n```",
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