semvec/pypi-packages huggingface dataset:
zensols.dsprov 0.0.1
This library provides integrated MIMIC-III with discharge summary provenance of data annotations and Pythonic classes.
Keywords: tooling

# Provenience of discharge summaries Pythonic access

[![PyPI][pypi-badge]][pypi-link]
[![Python 3.9][python39-badge]][python39-link]
[![Python 3.10][python310-badge]][python310-link]
[![Build Status][build-badge]][build-link]

This library provides integrated MIMIC-III with discharge summary provenance of
data annotations and Pythonic classes.


## Documentation

See the [full documentation](https://plandes.github.io/dsprov/index.html).
The [API reference](https://plandes.github.io/dsprov/api.html) is also
available.


## Obtaining

The easiest way to install the command line program is via the `pip` installer:
```bash
pip3 install --use-deprecated=legacy-resolver zensols.dsprov
```

Binaries are also available on [pypi].


## Usage

The package includes a command line interface, which is probably most useful by
dumping selected admission annotations.

### Command line

```bash
# help
$ dsprov -h

# get two admission IDs (hadm_id)
$ dsprov ids -l 2

# print out two admissions
$ dsprov show -l 2

# print out admissions 139676
$ dsprov show -d 139676

# output the JSON of two admissions with indent 4
$ dsprov show -i 4 -f json -d $(dsprov ids -l 2 | awk '{print $1}' | paste -s -d, -)
```

### API

The package can be used directly in your research to provide Python object
oriented access to the annotations:

```python
>>> from zensols.nlp import FeatureDocument
>>> from zensols.dsprov import ApplicationFactory, AdmissionMatch
>>> stash = ApplicationFactory.get_stash()
>>> am: AdmissionMatch = next(iter(stash.values()))
>>> doc: FeatureDocument = am.note_matches[0].discharge_summary.note.doc
>>> print(f'hadm: {am.hadm_id}')
>>> print(f'sentences: {len(doc.sents)}')
>>> print(f'tokens: {doc.token_len}')
>>> print(f'entities: {doc.entities}')
hadm: 120334
sentences: 46
tokens: 1039
entities: (<Admission>, <Date>, <Discharge>, <Date>, <Date of Birth>, <Sex>, ...)
```


## Changelog

An extensive changelog is available [here](CHANGELOG.md).


## License

[MIT License](LICENSE.md)

Copyright (c) 2023 Paul Landes


<!-- links -->
[pypi]: https://pypi.org/project/zensols.dsprov/
[pypi-link]: https://pypi.python.org/pypi/zensols.dsprov
[pypi-badge]: https://img.shields.io/pypi/v/zensols.dsprov.svg
[python39-badge]: https://img.shields.io/badge/python-3.9-blue.svg
[python39-link]: https://www.python.org/downloads/release/python-390
[python310-badge]: https://img.shields.io/badge/python-3.10-blue.svg
[python310-link]: https://www.python.org/downloads/release/python-310
[build-badge]: https://github.com/plandes/dsprov/workflows/CI/badge.svg
[build-link]: https://github.com/plandes/dsprov/actions
yaghm 0.3.1
Minimal git hook manager for the command line.

# Yet Another Git Hook Manager
Yaghm is a minimal [git hook](https://git-scm.com/book/en/v2/Customizing-Git-Git-Hooks) manager. You write a simple YAML config file listing the hooks you want. `yaghm enable` enables them in your repo. `yaghm disable` disables them.:

* Yaghm is simple. Hooks are not rocket science. The config is just a list of shell commands. When you enable them, yaghm writes a wrapper script in `.git/hooks` that calls the commands you gave. If they work in your shell they'll work in the hook.
* Because it's so simple, the documentation is complete, troubleshooting is easy and writing your own extensions is a breeze.
* **Your** workflow, not mine. You can store the config wherever you want. Maybe you version your hooks, maybe you don't, maybe you keep them in a separate repo, or your home dir, or your dotfiles...

## Usage
To use yaghm, you create a config that lists your hook commands (see [detailed syntax](doc/config.md)):

```yaml
pre-commit:
  - ...
  # Assuming you have a program called current_branch_not
  - current_branch_not master 
  - enable: black --check .
    install: pip install black
    update: pip install -U black
  - enable: require_version_bump master setup.py
    update: pip install -U metovhooks

post-commit:
  - ...
```

You can use `yaghm -h` to see general help. There are three main subcommands (which also support `-h`): 

* Enable hooks: `yaghm enable`
* List enabled hooks: `yaghm list`
* Disable hooks: `yaghm disable`

When using `enable` and `disable`, if you pass `--dryrun` no files will actually be written.

You can also run custom hook commands (e.g. update) with `yaghm update`. If any hooks in your config [specify the command](doc/config.md), it will be executed.

## Install
Install with `pip install yaghm`

## Further reading

* [How it works](doc/technical.md)
* [Config syntax](doc/config.md)
* [Developing hooks](doc/custom-hooks.md)
ss13-tools 2.3.3
Python toolchain for SS13
Requires Python: >=3.9.1,<3.12
Keywords: ss13,tgstation

# SS13 tools

[![PyLint](https://github.com/RigglePrime/SS13-tools/actions/workflows/pylint.yml/badge.svg)](https://github.com/RigglePrime/SS13-tools/actions/workflows/pylint.yml)
[![Flake8](https://github.com/RigglePrime/SS13-tools/actions/workflows/flake8-lint.yml/badge.svg)](https://github.com/RigglePrime/SS13-tools/actions/workflows/flake8-lint.yml)
[![Build](https://github.com/RigglePrime/SS13-tools/actions/workflows/publish.yml/badge.svg)](https://github.com/RigglePrime/SS13-tools/actions/workflows/publish.yml)

A set of tools to help with automating tasks for the /tg/ branch of SS13.

If you're looking for LogBuddy help, click [here](./ss13_tools/log_buddy/README.md)

Made for Python 3.9+

## How to run

`pip install ss13-tools; ss13tools` (pip3 on Linux) or dowload the executable [here](https://github.com/RigglePrime/SS13-tools/releases/latest) (Windows only).
If the latter is giving you trouble, try the former. Python can be installed [here](https://www.python.org/downloads/)

Remember, if you ever screw an input up try pressing the up arrow.

## Contents

- Root script that displays all options: `ss13tools`
- auth: authenticate and download from raw logs
- byond: tools for working with BYOND, such as checking if a ckey exists
- [centcom](https://centcom.melonmesa.com/), comes as a script `centcom`
- log_downloader: downloads logs from tg's [parsed logs](https://tgstation13.org/parsed-logs)
- [LogBuddy](./ss13_tools/log_buddy/README.md), comes as a script `logbuddy`
- scrubby: scrubby tools
- slur_detector: what it says on the tin

For examples on how to use please see [this file](ss13_tools/menu.py)

## Downloading

For compiled versions, see [this link](https://github.com/RigglePrime/admin-tools/releases).

If the above link is giving you trouble, try installing it with `pip`.

1. Download python from [here](https://www.python.org/downloads/)
2. Install, make sure to check "add to environment variables" or "add to PATH", and use `py` launcher
3. Run `pip install ss13-tools`. If this is giving you trouble, try logging out and back in.
4. Run `ss13tools` or `logbuddy` directly

Now also available on [PyPi](https://pypi.org/project/ss13-tools/)!

If you know your way around python, you may try using [PyPy](https://www.pypy.org/) for faster performance.
Leye 0.0.1
A sentiment lexicon algorithm to classify pidgin English and English text into positive, negative or neutral

Hello, you are welcome to Leye classifier

A sentiment lexicon algorithm to classify pidgin English and English text into positive, negative or neutral


To use this system, you can enter raw text directly or enter your document name which should be in a csv file format (for example, data.csv). You need to name the column as 'text'. 
The system creates new column for score and class.  You should expect the output as shown below. 

            text     score     class
the bank is good      2       positive


### How to use the packages

pip install Leye

from sentileye.polarity import result

Follow the instructions, enter your csv filename (for example, data.csv) or your raw text directly to classify. 
NB: filename - the csv file must be in your current working directory
PB-Sent 0.0.1
A sentiment lexicon algorithm to classify pidgin English and English text into positive, negative or neutral

Hello, you are welcome to PB_Sent classifier

A sentiment lexicon algorithm to classify pidgin English and English text into positive, negative or neutral


To use this system, you can enter raw text directly or enter your document name which should be in a csv file format (for example, data.csv). You need to name the column as 'text'. 
The system creates new column for score and class.  You should expect the output as shown below. 

            text     score     class
the bank is good      2       positive


### How to use the packages

pip install PB_Sent

from sentileye.polarity import result

Follow the instructions, enter your csv filename (for example, data.csv) or your raw text directly to classify. 
NB: filename - the csv file must be in your current working directory