semvec/pypi-packages huggingface dataset:
betsy-ros 0.2.1
In this time of revolution between the regimes of ROS 1 and ROS 2, Betsy ROS creates a unified banner.
Requires Python: >=3.8

![image of Betsy Ross](logo.jpg)

# Betsy ROS
In this time of revolution between the regimes of ROS 1 and ROS 2, Betsy ROS creates a unified banner

Translation: This package is a unified Python API for certain "meta" ROS API calls that work the same in ROS 1 and ROS 2.

## Installation

[![PyPI version](https://badge.fury.io/py/betsy-ros.svg)](https://badge.fury.io/py/betsy-ros)

    sudo pip3 install betsy-ros

## Functionality
### Workspace
 * `get_workspace_root` returns a `BuildType` and `pathlib.Path` indicating the type and location of the current workspace.
   * `BuildType` can be `CATKIN_MAKE`, `CATKIN_TOOLS`, or `COLCON`
 * `get_ros_version` returns an `int` and `str` indicating the ROS version (1 or 2) and the distro short-name (e.g. `lunar`, `bouncy`)

### Packages
 * `get_package_name_from_path` returns the name of the package that the initial path is in (including subfolders)
 * `get_package_names` returns a set of strings of all the current package names, including the binary packages and the workspace packages
 * `find_package` returns the path to a given package
 * `is_binary_ros` returns True if the path passed to it is in the `/opt/ros` folder

### Interfaces
(i.e. messages, services and actions)

 * The class `ROSInterface` has three key attributes:
  * package
  * type
  * name
 * `list_interfaces` yields `ROSInterface` instances for all messages/services/actions in the environment.

### Environment
(i.e. active topics, service providers and action servers)

 * `get_topics`, `get_services`, and `get_actions` return the names of all active ROS resources
   * By default, it just returns the names.
   * If `include_types` is set to `True`, the type will also be included as a `ROSInterface`.

## Credit
Package logo from artwork by Jean Leon Gerome Ferris (1863–1930):
[Betsy Ross 1777](https://commons.wikimedia.org/wiki/File:Betsy_Ross_1777_cph.3g09905.jpg)
glypy 1.0.17
A Glycoinformatics Toolkit
Keywords: glycomics glycan carbohydrate glycoinformatics glypy n-linked o-linked glycosaminoglycan

glypy Glycan Analysis and Glycoinformatics Library for Python
-------------------------------------------------------------

|https://img.shields.io/travis/mobiusklein/glypy.svg| |Documentation
Status|


Glycobiology is the study of the biological functions, properties, and
structures of carbohydrate biomolecules, also called *glycans*. These
large, tree-like molecules are complex, having a wide variety of
building blocks as well as modifications and substitutions on those
building blocks.

`glypy` is a Python library providing code for reading, writing, and
manipulating glycan structures, glycan compositions, monosaccharides, and
their substituents. It also includes interfaces to popular glycan structure
databases, `GlyTouCan <https://glytoucan.org/>`_ and `UnicarbKB <http://www.unicarbkb.org/>`_
using `SPARQL` queries and an RDF-object mapper.

Example Use Cases
~~~~~~~~~~~~~~~~~

1. Traverse structures using either canonical or residue-level rule
   ordering.
2. Operate on monosaccharide and substituents as nodes and bonds as
   edges.
3. Add, remove, and modify these structures to alter glycan properties.
4. Identify substructures and motifs, classifying glycans.
5. Evaluate structural similarities with one of several ordering and
   comparator methods.
6. Plot tree structures with MatPlotLib, rendering using a configurable
   symbol nomenclature, such as SNFG, CFG, or IUPAC. Layout using vector
   graphics for lossless scaling.
7. Calculate the mass of a native or derivatized glycan.
8. Generate glycosidic and cross ring cleavage fragments for a
   collection of glycan structures for performing MS/MS database search.
9. Perform substructure similarity searches with exact ordering or
   topological comparison and exact or fuzzy per-residue matching to
   classify a structure as an N-linked glycan.
10. Annotate MS spectra with glycan structures, labeling which peaks
    matched a database entry.
11. Download all N-Glycans from `GlyTouCan <https://glytoucan.org/>`__
12. Find all glycans in a list which contain a particular subtree, or
    find common subtrees in a database of glycans, performing treelet
    enrichment analysis.
13. Synthesize all possible glycans using a set of enzymes starting from
    a set of seed structures.

.. |https://img.shields.io/travis/mobiusklein/glypy.svg| image:: https://img.shields.io/travis/mobiusklein/glypy.svg
   :target: https://travis-ci.org/mobiusklein/glypy
.. |Documentation Status| image:: https://readthedocs.org/projects/glypy/badge/?version=latest
   :target: http://glypy.readthedocs.org/en/latest/?badge=latest


Citing
~~~~~~

If you use `glypy` in a publication please cite:

    Klein, J., & Zaia, J. (2019). glypy - An open source glycoinformatics library.
    Journal of Proteome Research.
    https://doi.org/10.1021/acs.jproteome.9b00367
genrm 0.0.1
Generative Reward Models
Requires Python: <4.0,>=3.9
Keywords: python

# genrm

[![Release](https://img.shields.io/github/v/release/nlile/genrm)](https://img.shields.io/github/v/release/nlile/genrm)
[![Build status](https://img.shields.io/github/actions/workflow/status/nlile/genrm/master.yml?branch=master)](https://github.com/nlile/genrm/actions/workflows/master.yml?query=branch%3Amaster)
[![codecov](https://codecov.io/gh/nlile/genrm/branch/master/graph/badge.svg)](https://codecov.io/gh/nlile/genrm)
[![Commit activity](https://img.shields.io/github/commit-activity/m/nlile/genrm)](https://img.shields.io/github/commit-activity/m/nlile/genrm)
[![License](https://img.shields.io/github/license/nlile/genrm)](https://img.shields.io/github/license/nlile/genrm)

Generative Reward Models

- **Github repository**: <https://github.com/nlile/genrm/>
- **Documentation** <https://nlile.github.io/genrm/>

## Getting started with your project

### 1. Create a New Repository

First, create a repository on GitHub with the same name as this project, and then run the following commands:

```bash
git init -b master
git add .
git commit -m "init commit"
git remote add origin git@github.com:nlile/genrm.git
git push -u origin master
```

### 2. Set Up Your Development Environment

Then, install the environment and the pre-commit hooks with

```bash
make install
```

This will also generate your `uv.lock` file

### 3. Run the pre-commit hooks

Initially, the CI/CD pipeline might be failing due to formatting issues. To resolve those run:

```bash
uv run pre-commit run -a
```

### 4. Commit the changes

Lastly, commit the changes made by the two steps above to your repository.

```bash
git add .
git commit -m 'Fix formatting issues'
git push origin master
```

You are now ready to start development on your project!
The CI/CD pipeline will be triggered when you open a pull request, merge to master, or when you create a new release.

To finalize the set-up for publishing to PyPI, see [here](https://fpgmaas.github.io/cookiecutter-uv/features/publishing/#set-up-for-pypi).
For activating the automatic documentation with MkDocs, see [here](https://fpgmaas.github.io/cookiecutter-uv/features/mkdocs/#enabling-the-documentation-on-github).
To enable the code coverage reports, see [here](https://fpgmaas.github.io/cookiecutter-uv/features/codecov/).

## Releasing a new version

- Create an API Token on [PyPI](https://pypi.org/).
- Add the API Token to your projects secrets with the name `PYPI_TOKEN` by visiting [this page](https://github.com/nlile/genrm/settings/secrets/actions/new).
- Create a [new release](https://github.com/nlile/genrm/releases/new) on Github.
- Create a new tag in the form `*.*.*`.

For more details, see [here](https://fpgmaas.github.io/cookiecutter-uv/features/cicd/#how-to-trigger-a-release).

---

Repository initiated with [fpgmaas/cookiecutter-uv](https://github.com/fpgmaas/cookiecutter-uv).