semvec/pypi-packages huggingface dataset:
textual-slider 0.2.0
A Textual widget for a simple slider
Requires Python: >=3.8

# textual-slider

A Textual widget for a simple slider.

![screenshot](https://raw.githubusercontent.com/TomJGooding/textual-slider/main/assets/screenshot.png)

## Installation

Install textual-slider using pip:

```
pip install textual-slider
```

## Usage

textual-slider provides a simple `Slider` widget for use in
[Textual](https://github.com/Textualize/textual), that allows selecting an
**integer** value within a given range.

The initial value of the slider if not specified is the minimum value.
You can also optionally specify a step size between valid values.

```python
yield Slider(0, 10)

yield Slider(min=200, max=500, step=100, value=300)
```

You can find more complete usage examples of the `Slider` widget in the
`/examples/` directory of this repo.

## Limitations

Textual apps run in the terminal, which work in terms of character cells rather
than pixels. This means you obviously can't have the same fine-grained control
for this slider as usual, depending on the size of the slider range and the
styled width.

Currently this slider widget only works with **integer** values. Any suggestions
for how to work with floating point values would be welcome!

## Contributing

I created this simple slider widget as a learning exercise to better
understand Textual and it is still a work in progress.

I'd really appreciate any feedback or suggestions, but I'm afraid I
probably won't be accepting any PRs at the moment.

## Licence

Licensed under the [GNU General Public License v3.0](LICENSE).
geogif 0.3
Render xarray timestacks into GIFs
Requires Python: <4.0,>=3.8

# GeoGIF
[![Documentation Status](https://readthedocs.org/projects/geogif/badge/?version=latest)](https://geogif.readthedocs.io/en/latest/?badge=latest)


Make GIFs from time-stacked `xarray.DataArray`s (`time`, [optional `band`], `y`, `x`), dead-simple.

```python
from geogif import gif, dgif
gif(data_array)
dgif(dask_data_array).compute()
```

![Animation of shoreline moving on the coast of Cape Cod](docs/capecod.gif)

The "geo" part is a lie, actually. The arrays don't have to be geospatial in nature. But I called it GeoGIF because:

1. Wanting to animate a time-stack of imagery (like you'd get from [stackstac](https://stackstac.readthedocs.io/)) is a common task in the earth-observation/geospatial world.
1. I think `GeoGIF` is a hilarious idea<sup>[1](#geotiff)</sup>.


<a name="geotiff">1</a>: To ruin the joke, it sounds like GeoTIFF, a ubiquitous geospatial image format. If you also think this is a funny idea, and believe you'd have a better use for the name than I do, I'd happily cede it to you.

## Installation

```bash
pip install geogif
```

## Documentation

See https://geogif.readthedocs.io/en/latest/.

## Development

GeoGIF is managed by [Poetry](https://python-poetry.org/), so be sure that's installed first. To develop locally, first fork or clone the repo. Then, to set up a virtual environment and install the necessary dependencies:

```bash
cd geogif
poetry install
```

### Running Tests

GeoGIF has some basic end-to-end tests, written with [Hypothesis](https://hypothesis.readthedocs.io/en/latest/index.html). To run:

```bash
pytest
```

This will take ~30 seconds (longer the first time), as Hypothesis generates fake data to root out possible errors.

### Code style

GeoGIF is formatted with [shed](https://github.com/Zac-HD/shed), in order to allow for as few opinions as possible.
test-abasjdlajskajksdlajlcuvileiwwjrlkfsdjcklas 2
test---abasjdlajskajksdlajlcuvileiwwjrlkfsdjcklas
Requires Python: >=3.6

# test---abasjdlajskajksdlajlcuvileiwwjrlkfsdjcklas

- readme content
- more
impulse-mcmc 1.0.0
A rewrite of PTMCMCSampler focusing on modularity
Requires Python: >=3.10

# impulse
  A rewrite of PTMCMCSampler focusing on modularity

## Installation

  This package can be installed via pip: `pip install impulse_mcmc`

## Currently working:
  * Metropolis-Hastings sampling with adaptive jumps
  * Parallel tempering with adaptive temperature spacing (as in Vousden et al. 2016)
  * Vectorized likelihoods and priors

## Currently under development:
  * Resume function