semvec/pypi-packages huggingface dataset:
sahl-employment-confirmation-common 3.6.0
Common modules for the Employment Confirmation tooling
Requires Python: >=3.7,<4.0

# Employment Confirmation Common Modules

An internal SAHL Python package to supply common modules to the Employment Confirmation API and Frontend tools. 

# Contents

* **models.py** - Pydantic modules for data trasnfer from API to frontend 
* **s3_logging.py** - Tooling to write JSON logs including the API results to an S3 bucket
quantile-data-kit 0.0.48
An internal Quantile development kit for making working with data easier
Requires Python: >=3.8

# Quantile Data Kit 🔍

## Publish to pypi
How to deploy a new version of the QDK?
1) Update the package version in `setup.py`.
2) Run the Makefile `make publish`

## Components
There are four types of base components in the QDK. 
1) `LoadComponent`. Takes nothing as input and outputs a DataFrame.
2) `TransformComponent`. Takes a DataFrame as input and outputs a DataFrame.
3) `TrainingComponent`. Takes data and a model as input and outputs a trained model.
4) `InferenceComponent`. Takes data and a model as input and ouputs prediction data.

## Adding a new component?
Adding a new component to the QDK requires the following steps:
1) **Type of component:** Decide which type of the four components above you are adding. 
2) **Add component:** Once you decide which type of component you are adding, add in the corresponding folder (e.g. `qdk/loader`) a new Python file that inherits from the parent component. In this file you can optionally overwrite `input_defs`, `output_defs` and `config_schema`. When adding a new component, you are required to add a classmethod with the same name as the `compute_function` attribute on the parent class. The keys in the `config_schema` are injected into the parameters of the compute function. Lastly, you need to import the new component to `qdk/__init__.py`. This allows you to import it from top-level.
4) **Write tests**: To continuously check the robustness of the components, we highly encourage you to add tests using `pytest`. The tests can be added at `qdk/tests`. Reminder to prefix the folder, files and functions with `test_`. One is able to test the components using either VScode testing or the terminal (e.g. with `pytest -s qdk/tests/test_loaders`).
convert2snn 0.0.3
Convert conventional to spiking neural networks.
Requires Python: >=3.7

# Convert2SNN
Tool for converting conventional neural networks to spiking neural networks.

***Currently under construction. Please check again later until everything is implemented.***

## About
This tool can be used to convert and optimize conventional neural networks that were trained in TensorFlow to spiking neural networks (SNN).

## Installation
Run the following to install:
```bash
pip install convert2snn
```

## Usage
```python
from convert2snn import convert
```

## Developing
To install Convert2SNN alongside the tools you need to develop and run tests, run the following:
```bash
pip install -e.[dev]
```

## References
- [Mueller, Auge, Klimaschka, Knoll, "Neural Oscillations for Energy-Efficient Hardware Implementation of Sparsely Activated Deep Spiking Neural Networks", AAAI Practical DL, 2022](https://scholar.google.de/citations?view_op=view_citation&hl=de&user=xVfcQwsAAAAJ&authuser=1&citation_for_view=xVfcQwsAAAAJ:Tyk-4Ss8FVUC)
- [Mueller, Studenyak, Auge, Knoll, "Spiking Transformer Networks: A Rate Coded Approach for Processing Sequential Data", ICSAI, 2021](https://mediatum.ub.tum.de/1633751)
- [Mueller, Auge, Knoll, "Normalization Hyperparameter Search for Converted Spiking Neural Networks", Bernstein Conference, 2021](https://abstracts.g-node.org/conference/BC21/abstracts#/uuid/30534c50-fe09-4842-9ee6-f0127c52ce73)
- [Mueller, Hansjakob, Auge, Knoll, "Minimizing Inference Time: Optimization Methods for Converted Deep Spiking Neural Networks", IJCNN, 2021](https://ieeexplore.ieee.org/abstract/document/9533874)
- [Mueller, Hansjakob, Auge, "Faster Conversion of Analog to Spiking Neural Networks by Error Centering", Bernstein Conference, 2020](https://abstracts.g-node.org/abstracts/c4ee2b6a-340f-4955-9629-63f67ec63584)
scalablewritingpyxel 1.3.6
A module that give a scalable text writing function for pyxel

# pyxel_scalable_writing
**This module is made to run with pyxel**

This module allow to write with a scaling text function

you MUST have a pyxres file that contain a white square of 20 pyxel width named writing_module.pyxres, you can use the one of the github

WON'T work with a white square superior to 20 px

To show your personnalised text, you must use the method parsing of class Character

`Characters.parsing(string, x_depart, y_depart, unity_width, transparence)`

`string` is the text you want to show us

`x_depart` and `y_depart` is the top left position coordinates of your text

`unity_width` is an optional paremeter, it's the width of an unity of your image (like a pixel but bigger), its default value is 5

`transparence` is an optional element that is True by default, if you want to have your text on a black background, call it as False

[on pypi !](https://pypi.org/project/scalablewritingpyxel/)
youtubedanyukla 0.0.1
Ushbu kutubxona yordamida Youtubedan videolarni osonlik bilan yuklab olishingiz mumkin!
Requires Python: >=3.8,<4.0

```python
from youtubedanyukla import Yukla
Yukla("https://youtu.be/9_A5sK1Kdzw", "H")
```

```html
<h4>M - videoni musiqa ko'rinishida yuklab olish</h4>
<h4>H - videoni yuqori sifatda yuklab olish</h4>
<h4>L - videoni past sifatda yuklab olish</h4>
```