semvec/pypi-packages huggingface dataset:
qimage2ndarray 1.10.0
Conversion between QImages and numpy.ndarrays.
Keywords: QImage,numpy,ndarray,image,convert,PyQt4,PyQt5,PyQt6,PySide,PySide6

qimage2ndarray is a small python extension for quickly converting
between QImages and numpy.ndarrays (in both directions).  These are
very common tasks when programming e.g. scientific visualizations in
Python using PyQt4 as the GUI library.

* Supports conversion of scalar and RGB data, with arbitrary dtypes
  and memory layout, with and without alpha channels, into QImages
  (e.g. for display or saving using Qt).

* qimage2ndarray makes it possible to create ndarrays that are
  *views* into a given QImage's memory.

  This allows for very efficient data handling and makes it possible
  to modify Qt image data in-place (e.g. for brightness/gamma or alpha
  mask modifications).

* Masked arrays are also supported and are converted into QImages
  with transparent pixels.

* Supports recarrays (and comes with an appropriate dtype) for
  convenient access to RGB(A) channels.

* Supports value scaling / normalization to 0..255 for convenient
  display of arbitrary NumPy arrays.

* qimage2ndarray is stable and unit-tested.
snmp-rp 0.3.1
A SNMP Libray based in Rust SNMP
Requires Python: >=3.7
Keywords: SNMP,Rust,SNMPv2,get,OID

SNMP-RP
=======

A Python SNMP library developed in Rust with PyO3.

About
------------

SNMP-RP is intended to be an interface between `SNMP Rust libray <https://github.com/hroi/rust-snmp/>`__ and Python.
Bringing compiled language performance and optimization to Python.


Installation
------------
.. code:: python

   pip install snmp-rp

Examples
------------

GET

.. code:: python

   import snmp_rp

   snmp_oid       = '1.3.6.1.4.1.367.3.2.1.2.1.4.0'
   snmp_host      = '172.16.0.53'
   snmp_community = 'public'
   snmp_port      = '161'

   sys_descr = snmp_rp.get(snmp_oid, snmp_host, snmp_port, snmp_community)

   print(sys_descr)

Roadmap
-------

-  Add support for GETNEXT
-  Add support for GETBULK
-  Add support for SET

License
-------

.. |MIT License| image:: https://img.shields.io/badge/License-MIT-green.svg
   :target: https://choosealicense.com/licenses/mit/
valda 0.1.10
A Data Valuation Package for Machine Learning
Requires Python: >=3.6
Keywords: data valuation

# Valda

## Introduction

Valda is a Python package for data valuation in machine learning. If you are interested in 

- analyzing the contribution of individual training examples to the final classification performance, or 
- identifying some noisy examples in the training set, 

you may be interested in the functions provided by this package.


The current version supports five different data valuation methods. It supports all the classifiers from Sklearn for valuation, and also user-defined classifier using PyTorch. 

- Leave-one-out (LOO), 
- Data Shapley with the TMC algorithm (TMC-Shapley) from [Ghorbani and Zou (2019)](https://proceedings.mlr.press/v97/ghorbani19c.html), 
- Beta Shapley from [Kwon and Zou (2022)](https://arxiv.org/abs/2110.14049)
- Class-wise Shapley (CS-Shapley) from [Schoch et al. (2022)](https://arxiv.org/abs/2211.06800)
- Influence Function (IF) from [Koh and Liang (2017)](https://arxiv.org/abs/1703.04730)
  - IF only works with the classifiers built with PyTorch, because it requires gradient computation.
  - v0.1.8 only support the first-order gradient computation, and we will add the second-order computation soon.




## Tutorial

Please checkout a simple tutorial on [Google Colab](https://colab.research.google.com/drive/1agsMNqZan-3RnJLQtBGATRHHWYMe7C9H?usp=sharing), for how to use this package.
pybaker 2.0.2
A build system library
Requires Python: >=3.6


# 🥧pybaker🥧

Pybaker is an easy to use flexible build system, provided as a python library. Born from my frustrations with existing build systems.

The library provides a Builder object and also allows you to create your own compiler, linker, dependency scanner and language configuration.
It comes with a few Premade configurations but I would recomend using the clang configuration.

Install it from pip.

Example build script:

``` Python
# If clang is installed this will just work.

from pybaker import *


def build():
    builder = Builder("test", build_type=BuildType.DEBUG, cores = 4)

    # To see how to implement a language configuration check out 
    # the Languages class.
    builder.add_language(Languages.C())

    builder.add_path("src")

    if builder.build():
        # Do something on failure...
        pass

    if builder.link(["-lUser32.lib", "-lGdi32.lib"]):
        # Do something on failure...
        pass


if __name__ == "__main__":
    build()
```

To run it you simply call from your base directory  

``` shell
python build.py 
```

This will create a new directory where it will dump its private files. A well as the build results.

</br>

## 2.0.0 Release Notes:

---

1. Many classes and functions have been renamed for clarity.
2. The language presets are now functions tha treturn a language rather than static members so they can be safely modified.
3. The dependency scanner API has been simplified.
4. Added gcc as a linker option similar to clang.
5. Added a way to extend existing language configurations without needing to create a new one.
6. The precentage of compilation done is now printed to the teminal along with the building file message. This requires passing an extra paramete to the compile function.
7. Now allows for multithreaded building. You can select the number of cores by setting the cores parameter in the Builder constructor.
8. Added a tcc configuration.
9. Added static library linker options.

The MIT License (MIT)

Copyright © 2022 oatslaboats

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, copy, modify, merge, publish, distribute, sublicense, and/or sell 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, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.