semvec/pypi-packages huggingface dataset:
crfsuite 0.3.1
Python binding for crfsuite

# crfsuite-rs

[![Rust](https://github.com/messense/crfsuite-rs/workflows/Rust/badge.svg)](https://github.com/messense/crfsuite-rs/actions?query=workflow%3ARust)
[![Python](https://github.com/messense/crfsuite-rs/workflows/Python/badge.svg)](https://github.com/messense/crfsuite-rs/actions?query=workflow%3APython)
[![codecov](https://codecov.io/gh/messense/crfsuite-rs/branch/master/graph/badge.svg)](https://codecov.io/gh/messense/crfsuite-rs)
[![Crates.io](https://img.shields.io/crates/v/crfsuite.svg)](https://crates.io/crates/crfsuite)
[![docs.rs](https://docs.rs/crfsuite/badge.svg)](https://docs.rs/crfsuite/)
[![PyPI](https://img.shields.io/pypi/v/crfsuite)](https://pypi.org/project/crfsuite)

Rust binding to [crfsuite](https://github.com/chokkan/crfsuite)

## Installation

Add it to your ``Cargo.toml``:

```toml
[dependencies]
crfsuite = "0.3"
```

Add ``extern crate crfsuite`` to your crate root and your're good to go!

## Python package

There is also a Python package named `crfsuite`, you can install it via `pip`:

```bash
pip install -U crfsuite
```

Usage example:

```python
from crfsuite import Model

if __name__ == '__main__':
    model = Model('path/to/crfsuite/model.crf')
    tagged = model.tag(["abc", "def"])
    print(tagged)
```

## License

This work is released under the MIT license. A copy of the license is provided in the [LICENSE](./LICENSE) file.
dogcrud 1.6.0
Datadog CRUD resources from the command line.
Requires Python: <3.14,>=3.12
Keywords: cli, datadog

# dogcrud

`dogcrud` is a command line tool for working with Datadog CRUD resources, like
dashboards, monitors, and log pipelines.

[![CI - Test](https://github.com/drichardson/dogcrud/actions/workflows/ci.yaml/badge.svg)](https://github.com/drichardson/dogcrud/actions/workflows/ci.yaml)
[![PyPI - Version](https://img.shields.io/pypi/v/dogcrud.svg)](https://pypi.org/project/dogcrud)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/dogcrud.svg)](https://pypi.org/project/dogcrud)


## Installation

The recommended installation is to use [uv](https://docs.astral.sh/uv/guides/tools/), a tool
for installing Python based command line tools like `dogcrud`.

```console
uv tool install dogcrud
```


# Usage

Save all Datadog sources supported by `dogcrud`:

```console
dogcrud save all
```

Run `dogcrud` to see how to restore and open saved JSON files.

# Shell Completion

`dogcrud` supports command line completion.

For `bash`, add this to `~/.bashrc`:

```bash
eval "$(_DOGCRUD_COMPLETE=bash_source dogcrud)"
```

For `zsh`, add this to `~/.zshrc`:

```zsh
eval "$(_DOGCRUD_COMPLETE=zsh_source dogcrud)"
```

For `fish`, add this to `~/.config/fish/completions/dogcrud.fish`:

```fish
_DOGCRUD_COMPLETE=fish_source dogcrud | source
```
pysynthbio 4.0.5
A package to retrieve data and models from Synthesize Bio's API

# <img src="https://assets.synthesize.bio/logomark.png" style="width: 30px; height: 30px;" alt="Logomark">&nbsp;pysynthbio

`pysynthbio` is an Python package that provides a convenient interface to the [Synthesize Bio](https://www.synthesize.bio/) API, allowing users to generate realistic gene expression data based on specified biological conditions. This package enables researchers to easily access AI-generated transcriptomic data for various modalities including bulk RNA-seq and single-cell RNA-seq.

If you'd prefer 1-click dataset generation and analysis, try our [web platform](https://app.synthesize.bio/datasets/).

## Prerequisites

Create a [Synthesize Bio](https://app.synthesize.bio/) account and generate an [API Key](https://app.synthesize.bio/account/api-key).

## Installation

```
pip install pysynthbio
```

## Quickstart

```python
import pysynthbio

# Set your API token (or use environment variable SYNTHESIZE_API_KEY)
pysynthbio.set_synthesize_token(use_keyring=True)

# Get an example query for the bulk RNA-seq baseline model
query = pysynthbio.get_example_query(model_id="gem-1-bulk")["example_query"]

# Generate synthetic expression data
result = pysynthbio.predict_query(query, model_id="gem-1-bulk")

# Access the results
metadata = result["metadata"]
expression = result["expression"]
```

## Documentation

[Get Started](https://synthesizebio.github.io/pysynthbio/usage/getting-started.html) | [Full Documentation](https://synthesizebio.github.io/pysynthbio/)

## Questions? Suggestions? Support?

Email us at [support@synthesize.bio](mailto:support@synthesize.bio). We'd love to hear from you!