semvec/pypi-packages huggingface dataset:
pyzamzar 1.0.4
Python SDK for the Zamzar file conversion API

# pyzamzar

Python SDK for the Zamzar file conversion API

## Installation

```bash
pip install pyzamzar
```

## Usage

### Obtain your API key

[Create an account](https://developers.zamzar.com/pricing), [log in](https://developers.zamzar.com/login) and obtain your API key [here](https://developers.zamzar.com/user)

### Initialize Zamzar client

```python
from zamzar import ZamzarClient

client = ZamzarClient('YOUR_API_KEY')
```

### Start conversion job

Example:

```python
job_info = client.start_conversion_job('README.md', 'pdf')
job_id = job_info['id']
```

### Poll job status until its completion

Example:

```python
while True:
    job_status = client.get_job_status(job_id)
    if job_status['status'] == 'successful':
        break
    elif job_status['status'] == 'failed':
        print("Conversion failed.")
        return
    else:
        print("Conversion in progress. Please wait...")
```

### Download converted file(s)

Example:

```python
file_id = job_status['target_files'][0]['id']
client.download_converted_file(file_id, 'readme.pdf')
print("File downloaded successfully as 'readme.pdf'.")
```
clld-audio-plugin 1.2.0
Render linked audio files in clld apps.

# clld-audio-plugin

[![Build Status](https://github.com/clld/clld-audio-plugin/workflows/tests/badge.svg)](https://github.com/clld/clld-audio-plugin/actions?query=workflow%3Atests)
[![PyPI](https://img.shields.io/pypi/v/clld-audio-plugin.svg)](https://pypi.org/project/clld-audio-plugin)

A plugin for the [`clld`](https://pypi.org/project/clld) package.


## Usage

`clld-audio-plugin` provides
- A model class [`clld_audio_plugin.models.Counterpart`](src/clld_audio_plugin/models.py) augmented with an `audio` column to store the URL to an audio file.
- A column for a `DataTable` [`clld_audio_plugin.datatables.AudioCol`](src/clld_audio_plugin/datatables.py) to display objects with an `audio` attribute as audio player.
- A map class [`clld_audio_plugin.maps.ParamMap`](src/clld_audio_plugin/maps.py) specifying a parameter map integrating clld audioplayer functionality.
- Static resources for the configuration/layout of the UI elements contributed by the plugin.

To use map and static resources, include the plugin in your app's `main` function:
```python
    config.include('clld_audio_plugin')
```

To use model and column, just import in your app's `datatables.py` or `models.py` as needed.
multifutures 0.3.2
Multiprocessing/multithreading made easy
Requires Python: >=3.9

# multifutures

[![PyPI - Version](https://img.shields.io/pypi/v/multifutures.svg)](https://pypi.org/project/multifutures)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/multifutures.svg)](https://pypi.org/project/multifutures)
[![ci](https://github.com/pmav99/multifutures/workflows/test/badge.svg)](https://github.com/pmav99/multifutures/actions?query=workflow%3Atest)

Multiprocessing/multithreading made easy!

---

**Table of Contents**

- [Installation](#installation)
- [Usage](#usage)
- [License](#license)

## Installation

```console
python -mpip install multifutures
```

## Usage

``` python
import multifutures as mf


def return_square(number: float) -> float:
    squared = number**2
    return squared


results = mf.multiprocess(
    func=return_square,
    func_kwargs=[{"number": i} for i in range(10)],
)

for result in results:
    print(result)
```

Will print:

```
FutureResult(exception=None, kwargs={'number': 0}, result=0)
FutureResult(exception=None, kwargs={'number': 1}, result=1)
FutureResult(exception=None, kwargs={'number': 2}, result=4)
FutureResult(exception=None, kwargs={'number': 3}, result=9)
FutureResult(exception=None, kwargs={'number': 4}, result=16)
FutureResult(exception=None, kwargs={'number': 5}, result=25)
FutureResult(exception=None, kwargs={'number': 6}, result=36)
FutureResult(exception=None, kwargs={'number': 7}, result=49)
FutureResult(exception=None, kwargs={'number': 8}, result=64)
FutureResult(exception=None, kwargs={'number': 9}, result=81)
```


## License

`multifutures` is distributed under the terms of the [MIT](https://spdx.org/licenses/MIT.html) license.
ichandbook 0.0.1
Library to help formating and publication of organisation technical handbook.
Requires Python: >=3.8
Keywords: Publishing, Markdown, Handbook

# ichandbook

This project is a set of tools to support publication and maintenance of the [ICHEC technical handbook](https://git.ichec.ie/performance/ichec-handbook). It may be useful for maintaining other technical projects too.

# Running Tests

In a Python virtual environment do:

```sh
pip install .'[test]'
```

## Unit Tests

```sh
pytest
```

## Linting and Static Analysis

```sh
black src test
mypy src test
```

## All Tests

Requires `tox`:

```sh
tox
```
Teras 0.3.1
A Unified Deep Learning Library for Tabular Data
Requires Python: >=3.9

# teras — A Unified Deep Learning Library for Tabular Data

![Teras logo banner](./data/imgs/teras_banner.jpg)



teras (short for Tabular Keras) is a unified deep learning library for Tabular 
Data that aims to be your one stop for everything related to deep learing with 
tabular data.

**IMPORTANT** teras v0.3 is now fully based on Keras 3, making everything available backend agnostic. It supports TensorFlow, JAX and PyTorch backends.

teras provides state of the art layers, models and architectures for all purposes, be it classification, regression or even data generation and imputation using state of the art deep learning architectures.

It also includes functions and classes for preprocessing data for complex architectures, making it extremely simple to transform your data in the expected format, saving you loads of hassle and time!

While these state of the art architectures can be quite sophisticated, teras, thanks to the incredible design of Keras, abstracts away all the complications and sophistication and makes it easy as ever to access those models and put them to use.

Not only that, everything available is highly customizable and modular, allowing for all variety of use cases.
## Installation:
You can install teras using pip as follows,
```
pip install teras
```

## Getting Started
Read our [Getting Started Guide](https://teras.readthedocs.io/en/latest/getting_started.html) to...*drum roll* get started with teras.


## Documentation:
You can access the documentation on ReadTheDocs.io: https://teras.readthedocs.io/en/latest/index.html


## Motivation
The main purposes of teras are to:
1. Provide a uniform interface for all the different proposed architectures to abstract away the complexities to make them accessible to everyone!
2. Further bridge the gap between research and application.
3. Be a one-stop for everything concerning deep learning for tabular data.
4. Accelerate research in tabular domain of deep learning by making it easier for researchers to access, use and experiment with exisiting architectures — saving them lots of valuable time.


## Support
If you find teras useful, consider supporting the project. I've been working on this for the past several months, and as you may guess such software consume a lot of your time. I also have many future plans for it but my current laptop is quite old which makes it impossible for me to test highly demanding workflows let alone rapidly test and iterate. So your support will be very vital in the betterment of this project, and many others that I plan to build!
Thank you!

[![BuyMeACoffee](https://img.shields.io/badge/Buy%20Me%20a%20Coffee-ffdd00?style=for-the-badge&logo=buy-me-a-coffee&logoColor=black)](https://www.buymeacoffee.com/KhawajaAbaid)
[![Patreon](https://img.shields.io/badge/Patreon-F96854?style=for-the-badge&logo=patreon&logoColor=white)](https://www.patreon.com/KhawajaAbaid)
bearclaw 0.0.4
Pipeline components to extract features from variants.
Requires Python: >=3.10

# Bearclaw
Components to help extract features from variants, to be used as part of a pipeline.

## Installation
```bash
pip3 install bearclaw
```

## Usage
```python
from bearclaw.preprocessing import VariantDataGenerator
from bearclaw.transforms import spectrum


# Dataframe containing locations of VCF files and labels.
dataframe = DataFrame({
    "vcf": [
        "src/test/resources/GRCh37/sample1.vcf",
        "src/test/resources/GRCh37/sample2.vcf",
    ],
    "class": [1, 0],
})
# Transform VCF files into features using `spectrum`, which counts the number of variants by flanking context.
dg = VariantDataGenerator(transform=spectrum)

# Convert dataframe to label `y` and features `X_spectrum` using `spectrum`.
X_spectrum, y = dg.flow_from_dataframe(dataframe, x_col="vcf")
```

## Reference documentation
https://hylkedonker.gitlab.io/bearclaw/


## License
The code in this repository is licensed under the  [MIT License](LICENSE).
spin-templete 0.1.2
spin templete is the layer of Spin-python-sdk for render html
Keywords: spin-python-sdk, spinpython, python webassembly, webassembly, webassembly in python, webassembly api, build python webassembly api, python to webassembly


    

# What is Spin
Spin is a framework for building and running event-driven microservice applications with WebAssembly (Wasm) components.

# Spin Templete

spin templete is the layer of Spin-python-sdk for render html

# How to install spin_templete

```python
pip install spin_templete
```

### setup
```
mkdir templete
cd templete
touch index.html
```
### index.html
```
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Document</title>
</head>
<body>
    <h1>Hello World Again Momin</h1>

    <ul>
        {% for item in items %}
            <li>{{ item }}</li>
        {% endfor %}
        </ul>   
    <ul>
        {% for item in mylist %}
            <li>{{ item }}</li>
        {% endfor %}
        </ul>   



<h1>{{name}}</h1>

</body>
</html>
```

### app.py
```python
from spin_sdk.http import IncomingHandler, Request, Response
from spintemplete.templete import Render


html = Render("templete")

class IncomingHandler(IncomingHandler):
    def handle_request(self, request: Request) -> Response:
        return html.render("index.html",items=[1,2,3,4,5],mylist=["hello","world"],name="momin")
```

### spin.toml
Add more html files your requirements
```

[component.myapp]
source = "app.wasm"
files = ["templete/index.html"]
```

Check Our Site : https://mefiz.com/about </br>
Developed by : Momin Iqbal