semvec/pypi-packages huggingface dataset:
podstar 1.0.0
An RSS-compatible podcast feed client.
License: MIT
Requires Python: >=3.3
Keywords: rss,podcast,feed

# Pod⭐

_Podstar_ is a Python library for working with podcasts. It optimizes for robustness, efficiency, and standards support while providing a clean and simple API. Podstar has some neat features:

+ robust parsing of RSS-based podcast feeds
+ transparent use of paginated feeds (via RSS pagination)
+ support for HTTP caching headers (`ETag` or `Last-Modified`), enabling long-running aggregator processes to request as little data as possible
+ easy access to audio information, including:
  + duration (as determined by iTunes DTD metadata when available, or by streaming audio files to memory until necessary metadata is available)
  + bitrate
  + sample rate

## Getting Started

Ensure that you've got Python `>= 3.3` installed, and a new Python virtual environment activated. Install the library using:

```sh
$ pip install podstar
```

You're done!

## Using Podstar

The following code iterates over a podcast feed, listing each episode and its duration:

```python
import time
import podstar

feed = podstar.Feed('http://feeds.sceneonradio.org/SceneOnRadio')

# print some information about the podcast, including a text description (HTML in the description will be removed automatically)
print(feed.title)
print(feed.description + "\n")

# list each episode with its title, date published, and duration
for episode in feed.episodes():
    pub_date = episode.published_at.strftime("%a %b %d, %Y")
    duration = time.strftime("%H:%M:%S", time.gmtime(episode.duration))
    print(f"– \"{episode.title}\" ({pub_date}) [{duration}]")
```

For further examples of how to use Podstar, see the `examples/` directory.

## Development

Getting started with development on Podstar is relatively straightforward. 

Ensure that you have Python `>= 3.3` installed, clone the repository, and `cd` to it

```sh
$ git clone <repo url>
$ cd podstar/
```

Use `pip` to install the dependencies specified in `requirements.txt` (if you haven't already) as well as the development dependencies specified in `dev_requirements.txt`

```sh
$ pip install -r requirements.txt
$ pip install -r dev_requirements.txt
```

To run all tests, execute

```sh
$ python -m pytest tests
```

To check test coverage, execute:

```sh
python -m pytest --cov=podstar --cov-report term-missing -s tests
```

### Examples

`examples/download.py` – The original sketch used when designing the API for this library. Run it from the project's root directory by executing:

```sh
$ python -m examples.sketch
```

`examples/crawl.py` – Parses a good number of feeds in parallel using digitalpodcast.com as a convenient redirect. From the project's root directory, try it (on a decent internet connection) by executing:

```sh
$ python -m examples.crawl
```
CoBaye 1.0.0
Bayesian Correlation with pymc3
License: MIT
Requires Python: ~=3.7
Keywords: correlation analysis pymc3

# CoBaye

`CoBaye` is a simple `python3` program that uses `pymc3` probabilistic programming to calculate correlation coefficients under a Bayesian framework. It provides a formally correct and robust determination of the correlation strength and significance, including important statistics such as coefficient standard deviation and confidence intervals.

<img src="CoBaye/images/Guinea_Pig_eating_apple.JPG" title='CoBaye at inference' alt="CoBaye at inference" width="300"/>

## Installation

`
$ python setup.py install
`

## Usage

CoBaye loads information from an ASCII textfile containing an (*X*, *Y*) dataset. When available, errorbars should be included. The quantities should be provided in tab-separated columns in the order

- *X, Y*
- *X, X_err, Y, Y_err*.

To run the analysis perform

`
$ python CoBaye.py -f input_ASCII.txt
`

where *input_ASCII.txt* is your input file. To perform a test run with the demo data provided run

`
$ python CoBaye.py
`

For more info on the different options type

`
$ python CoBaye.py -h
`

Please check the [*example analysis notebook*](CoBaye/notebooks/ExampleAnalysis.ipynb), the recommended entry point for new `CoBaye` users. A step-by-step walkthrough over the statistical background and general lines of implementation is provided in the [*statistical background notebook*](CoBaye/notebooks/StatisticalBackground.ipynb). The [*advanced usage one*](CoBaye/notebooks/AdvancedUsage.ipynb) is recommended for those who want to include `CoBaye` in their codes.

## License
CoBaye is released under MIT license. See LICENSE for details.

The guinea pig eating apple by Jg4817 - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=25582578 .

## Contact
Contact me on pedro.figueira (at) eso.org .
triq 0.0.4
Use trio async framework with PySide2/PyQt5
License: MIT
Keywords: pyqt pyside async trio

# triq
Use [trio](https://github.com/python-trio/trio) async framework
with PyQt5/PySide2

## Caution
This is an alpha-quality software.

## How to
```python
...
import triq
import trio

async def some_async_func(message):
    await trio.sleep(0.5)
    print(message)

app = QApplication([])

hitme = QPushButton('Hit me')
hitme.clicked.connect(lambda: triq.call_async(some_async_func, 'hello, world!'))
hitme.show()

bye = QPushButton('Bye')
bye.clicked.connect(triq.exit)
bye.show()

triq.run(app)
```

The above should work in PyQt5 and PySide2