semvec/pypi-packages huggingface dataset:
api-extractor 0.1.4
Extract Python docstring written in Markdown format
License: MIT
Requires Python: >=3.7
Keywords: api,docs,sphinx,extractor,python,markdown,rst,autodoc

# api-extractor

[![ci](https://github.com/ydcjeff/api-extractor/actions/workflows/ci.yaml/badge.svg?branch=main)](https://github.com/ydcjeff/api-extractor/actions/workflows/ci.yaml)
[![PyPI](https://img.shields.io/pypi/v/api-extractor)](https://pypi.org/project/api-extractor)
![PyPI - Downloads](https://img.shields.io/pypi/dm/api-extractor)

> Extract Python docstring written in Markdown format.

## Installation

`api-extractor` can be installed with `pip`.

```sh
pip install api-extractor -U
```

## Documentation

[Read the Docs for the usage](https://api-extractor.netlify.app).

## Contribution

See [Contributing Guide](./.github/contributing.md).

## License

[MIT](./LICENSE)
binoculars 0.1.3
Various calculations for binomial confidence intervals.
License: MIT
Keywords: statistics,binomial,confidence

# Binoculars: Binomial Confidence Intervals

![https://deepnote.com/project/e17fa473-51c6-45aa-8de0-980be7d2dc5f](demo.png)

![Unit Tests](https://github.com/nolanbconaway/binoculars/workflows/Unit%20Tests/badge.svg)
[![codecov](https://codecov.io/gh/nolanbconaway/binoculars/branch/main/graph/badge.svg?token=EOH4M3PIYL)](https://codecov.io/gh/nolanbconaway/binoculars)
[![PyPI](https://img.shields.io/pypi/v/binoculars)](https://pypi.org/project/binoculars/)


This is a small package that provides functions to compute the confidence interval for a binomial proportion. I made it because I spend altogether too much time staring at the Binomial proportion confidence interval [wiki page](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval).

Presently, the package implements:

- [The Normal Approximation](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Normal_approximation_interval)
- [The Wilson Interval](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Wilson_score_interval) (no continuity correction)
- [Jeffrey's interval](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Jeffreys_interval) (via scipy.stats.beta)
- [Clopper-Pearson interval](https://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval#Clopper%E2%80%93Pearson_interval) (also via scipy.stats.beta)

If you haven't spent a lot of time thinking about which interval _you_ should use (and why would you want to?), I suggest using the Wilson interval or Jeffrey's interval. Jeffrey's interval is returned by default by the `binomial_confidence` function in this package. 

> You _oughtn't_ use the normal approximation if you don't have to. It produces patently inaccurate values with low/high probabilities at low Ns. The plot at the top of this readme shows the normal approximation producing lower lower bounds of _less than 0_ in these cases.

## Install

```sh
pip install binoculars
```

## Usage

```py
from binoculars import binomial_confidence

N, p = 100, 0.2

binomial_confidence(p, N)  # default to jeffrey's interval
# (0.1307892803998113, 0.28628125447599173)

binomial_confidence(p, N, tail='lower') # grab one tail
# 0.1307892803998113

# set Z value
binomial_confidence(p, N, tail='lower', z=2.58)
# 0.11212431621448567

# choose your method

binomial_confidence(p, N, method='normal')
# (0.12160000000000001, 0.2784)

binomial_confidence(p, N, method='wilson')
# (0.1333659225590988, 0.28883096192650237)
```

## Development

I honestly do not imagine touching this a lot. But maybe you want to add one of the other interval methods?

1. Make a python 3.6+ venv
3. `pip install -e .[test]`
4. `black lib --check`
5. `pytest`

## Later (?)

 - [ ] Add confidence intervals for odds ratios, differences
 - [ ] Add the unimplemented intervals
 - [x] Add plots comparing the intervals to readme.
onto-lib 1.0.0
Tools for loading and manipulating ontologies in Python
License: MIT
Keywords: ontology utility obo graph bioinformatics


    Most of the ontologies under consideration come from the OBO Foundry and are thus 
    interoperable. Specifically, the ontologies link together via edges between terms that may span multiple 
    ontologies. Thus, a single "ontology graph" may span multiple ontologies. The central object that is used in this 
    package is an "ontology graph" object which represents a subset of the union of all ontologies. The file, 
    ontology_configurations.json denotes all of the ontology graphs that can be queried. Note, each ontology graph 
    has an ID. For example, ontology graph 17 represents the union of the Cell Ontology, Uberon, Disease Ontology, 
    Experimental Factors Ontology, and Cellosaurus. 
    The program general_ontology_tools.py contains the API for querying these ontology configurations. Specifically, 
    it addresses the following tasks: 
    1. Find all ancestors or descendants of a given ontology term 
    2. Given two ontology terms, determine whether one is an ancestor of another 
    3. Given a set of ontology terms, filter the set for all most-specific terms in the set. A term is most-specific 
    if no other term in the set is a descendant of the term. 
    4. Given a set of ontology terms, filter the set for all 
    most-general terms in the set. A term is most-general if no other term in the set is a descendant of the term.
hydra-python-core 0.3.1
Core functions for Hydrus
License: MIT

# hydra-python-core
This library provides the core functions to implement Hydra Official Specification in Python.

Currently the library mainly consists of 2 modules `doc_writer` and `doc_maker` which help hydrus generalise a lot of things. The documentation is available at
https://hydra-python-core.readthedocs.io/en/develop/

-> `doc_writer` creates a new API Documentation as well as a `HydraDoc` object while,
-> `doc_maker` uses an existing API Documentation to create a `HydraDoc` object 



### Installation

To install the library:

```bash
pip install git+https://github.com/HTTP-APIs/hydra-python-core.git#egg=hydra_python_core
```

**Note :-** If using hydrus, the library doesn't need to be installed separately as it is already a part of `requirements.txt` for hydrus.



### Usage

To import the modules:

```python
from hydra_python_core import doc_writer
from hydra_python_core import doc_maker
```

*Porting out from hydrus the hydraspecs directory*