semvec/pypi-packages huggingface dataset:
petropandas 0.1.3
Pandas accessors for petrologists
Requires Python: >=3.10

# petropandas

[![Release](https://img.shields.io/github/v/release/ondrolexa/petropandas)](https://img.shields.io/github/v/release/ondrolexa/petropandas)
[![Build status](https://img.shields.io/github/actions/workflow/status/ondrolexa/petropandas/testing.yml?branch=main)](https://github.com/ondrolexa/petropandas/actions/workflows/testing.yml?query=branch%3Amain)
[![Commit activity](https://img.shields.io/github/commit-activity/m/ondrolexa/petropandas)](https://img.shields.io/github/commit-activity/m/ondrolexa/petropandas)
[![License](https://img.shields.io/github/license/ondrolexa/petropandas)](https://img.shields.io/github/license/ondrolexa/petropandas)

Pandas accessors for petrologists

## Getting started

`petropandas` provides several `pandas.DataFrame` accessors to seemlesly integrate
common petrological calculations to your Python data analysis workflow.

```python
from petropandas import pd, mindb
```

For more details check the [documentation](https://petropandas.readthedocs.io/).

```python
df = pd.read_excel("some/folder/data.xlsx")
df.oxides.molprop()
df.oxides.cations(noxy=12)

df.ree.normalize(reservoir='CI Chondrites', reference='McDonough & Sun 1995')
```
epist-sdk 0.1.0
Official Python SDK for Epist.ai
Requires Python: >=3.8

# epist-sdk

Official Python SDK for the Epist.ai Audio RAG Platform.

## Installation

```bash
pip install epist-sdk
```

## Usage

```python
from epist import Epist

client = Epist(api_key="YOUR_API_KEY")

# 1. Upload File
status = client.upload_file("meeting_recording.mp3")
print(f"Task Started: {status['id']}")

# 2. Transcribe URL
task = client.transcribe_url("https://example.com/podcast.mp3")

# 3. Search
results = client.search("quarterly earnings")
for result in results:
    print(f"[{result['score']}] {result['text']}")
```
ray-velda 0.1.1
Ray node autoscaler based on Velda
Requires Python: >=3.8

# ray_velda

Support Ray cluster auto-scaling running on dev instances powered by Velda.

Usage
-----

1. Setup a ray cluster config. See [ray_config_example.yaml](/ray_config_example.yaml) for example.
2. To start a cluster:

```sh
ray up ray_config.ayml
```

3. After your cluster is up, you can

* Access Ray dashboard: `http://8265-ray-[cluster-name]-[instance-id].i.[velda-domain]`
* Submit job: `ray job submit --address http://ray-[cluster-name]:8265 -- python my_script.py`
small-cache 0.0.5
Small cache
Requires Python: >=3.11

small_cache
================

|      CI              | status |
|----------------------|--------|
| pip builds           | [![Pip Action Status][actions-pip-badge]][actions-pip-link] |
| wheels               | [![Wheel Action Status][actions-wheels-badge]][actions-wheels-link] |

[actions-pip-link]:        https://github.com/wjakob/small_cache/actions?query=workflow%3APip
[actions-pip-badge]:       https://github.com/wjakob/small_cache/workflows/Pip/badge.svg
[actions-wheels-link]:     https://github.com/wjakob/small_cache/actions?query=workflow%3AWheels
[actions-wheels-badge]:    https://github.com/wjakob/small_cache/workflows/Wheels/badge.svg


This repository contains a tiny project showing how to create C++ bindings
using [nanobind](https://github.com/wjakob/nanobind) and
[scikit-build-core](https://scikit-build-core.readthedocs.io/en/latest/index.html). It
was derived from the corresponding _pybind11_ [example
project](https://github.com/pybind/scikit_build_example/) developed by
[@henryiii](https://github.com/henryiii).

Furthermore, the [bazel](https://github.com/wjakob/small_cache/tree/bazel) branch contains an example
on how to build nanobind bindings extensions with Bazel using the [nanobind-bazel](https://github.com/nicholasjng/nanobind-bazel/) project.

Installation
------------

1. Clone this repository
2. Run `pip install ./small_cache`

Afterwards, you should be able to issue the following commands (shown in an
interactive Python session):

```pycon
>>> import small_cache
>>> small_cache.add(1, 2)
3
```

CI Examples
-----------

The `.github/workflows` directory contains two continuous integration workflows
for GitHub Actions. The first one (`pip`) runs automatically after each commit
and ensures that packages can be built successfully and that tests pass.

The `wheels` workflow uses
[cibuildwheel](https://cibuildwheel.readthedocs.io/en/stable/) to automatically
produce binary wheels for a large variety of platforms. If a `pypi_password`
token is provided using GitHub Action's _secrets_ feature, this workflow can
even automatically upload packages on PyPI.


License
-------

_nanobind_ and this example repository are both provided under a BSD-style
license that can be found in the [LICENSE](./LICENSE) file. By using,
distributing, or contributing to this project, you agree to the terms and
conditions of this license.