semvec/pypi-packages huggingface dataset:
tomoscan 2.3.6
Utility to access tomography data at ESRF
License: MIT
Requires Python: >=3.10
Keywords: NXtomo, nexus, tomography, esrf, bliss-tomo

# tomoscan

This library offers an abstraction to:

* access tomography data from spec acquisitions (EDF) and bliss acquisitions (HDF5)
* read and write volumes from / to HDF5, JP2K, TIFF and EDF


## installation


### using pypi

To install the latest 'tomoscan' pip package

``` bash
pip install tomoscan
```

### using gitlab repository

``` bash
pip install git+https://gitlab.esrf.fr/tomotools/tomoscan.git
```

## documentation

General documentation can be found here: [https://tomotools.gitlab-pages.esrf.fr/tomoscan/](https://tomotools.gitlab-pages.esrf.fr/tomoscan/)
pycuda 2025.1.3
Python wrapper for Nvidia CUDA
License: MIT
Requires Python: ~=3.8

PyCUDA: Pythonic Access to CUDA, with Arrays and Algorithms
=============================================================

.. image:: https://gitlab.tiker.net/inducer/pycuda/badges/main/pipeline.svg
    :alt: Gitlab Build Status
    :target: https://gitlab.tiker.net/inducer/pycuda/commits/main
.. image:: https://badge.fury.io/py/pycuda.png
    :target: https://pypi.org/project/pycuda
.. image:: https://zenodo.org/badge/1575319.svg
    :alt: Zenodo DOI for latest release
    :target: https://zenodo.org/badge/latestdoi/1575319

PyCUDA lets you access `Nvidia <https://nvidia.com>`_'s `CUDA
<https://nvidia.com/cuda/>`_ parallel computation API from Python.
Several wrappers of the CUDA API already exist-so what's so special
about PyCUDA?

* Object cleanup tied to lifetime of objects. This idiom, often
  called
  `RAII <https://en.wikipedia.org/wiki/Resource_Acquisition_Is_Initialization>`_
  in C++, makes it much easier to write correct, leak- and
  crash-free code. PyCUDA knows about dependencies, too, so (for
  example) it won't detach from a context before all memory
  allocated in it is also freed.

* Convenience. Abstractions like pycuda.driver.SourceModule and
  pycuda.gpuarray.GPUArray make CUDA programming even more
  convenient than with Nvidia's C-based runtime.

* Completeness. PyCUDA puts the full power of CUDA's driver API at
  your disposal, if you wish. It also includes code for
  interoperability with OpenGL.

* Automatic Error Checking. All CUDA errors are automatically
  translated into Python exceptions.

* Speed. PyCUDA's base layer is written in C++, so all the niceties
  above are virtually free.

* Helpful `Documentation <https://documen.tician.de/pycuda>`_.

Relatedly, like-minded computing goodness for `OpenCL <https://www.khronos.org/registry/OpenCL/>`_
is provided by PyCUDA's sister project `PyOpenCL <https://pypi.org/project/pyopencl>`_.