semvec/pypi-packages huggingface dataset:
majorityvoteproba 0.1.0
merge probability predictions for majority votes
License: MIT
Requires Python: >=3.6

[![Build Status](https://travis-ci.org/kmedian/majorityvoteproba.svg?branch=master)](https://travis-ci.org/kmedian/majorityvoteproba)
[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/kmedian/majorityvoteproba/master?urlpath=lab)

# majorityvoteproba


## Table of Contents
* [Installation](#installation)
* [Usage](#usage)
* [Commands](#commands)
* [Support](#support)
* [Contributing](#contributing)


## Installation
The `majorityvoteproba` [git repo](http://github.com/kmedian/majorityvoteproba) is available as [PyPi package](https://pypi.org/project/majorityvoteproba)

```
pip install majorityvoteproba
```


## Usage
Check the [examples](http://github.com/kmedian/majorityvoteproba/examples) folder for notebooks.


## Commands
* Check syntax: `flake8 --ignore=F401`
* Run Unit Tests: `python -W ignore -m unittest discover`
* Remove `.pyc` files: `find . -type f -name "*.pyc" | xargs rm`
* Remove `__pycache__` folders: `find . -type d -name "__pycache__" | xargs rm -rf`
* Upload to PyPi: `python setup.py sdist upload -r pypi`
* Upload to PyPi with twine: `python setup.py sdist && twine upload -r pypi dist/*`


## Debugging
* Notebooks to profile python code are in the [profile](http://github.com/kmedian/majorityvoteproba/profile) folder


## Support
Please [open an issue](https://github.com/kmedian/majorityvoteproba/issues/new) for support.


## Contributing
Please contribute using [Github Flow](https://guides.github.com/introduction/flow/). Create a branch, add commits, and [open a pull request](https://github.com/kmedian/majorityvoteproba/compare/).
hbrs-grader 1.0.1
A jupyter workflow for grading exams
License: MIT

# HBRS Grader

[![pipeline status](https://gitlab.com/joergbrech/hbrs_grader/badges/master/pipeline.svg)](https://gitlab.com/joergbrech/hbrs_grader/commits/master)

**A Jupyter Notebook workflow for grading exams at the Hochschule Bonn-Rhein-Sieg** for everyone who hates spreadsheets as much as I do.

This workflow allows you to
 - import a csv-file (e.g. obtained from DIAS), 
 - enter points for each student for an exam consisting of a specified number of assignments, 
 - decide on a passing grade and the limit of a *"perfect"* grade and 
 - create publishable results with some additional exam statistics.

Check out [this example notebook](hbrs_grader/template/template.ipynb). 
The [exported HTML-document](https://gitlab.com/joergbrech/hbrs_grader/-/jobs/artifacts/master/download?job=test) will not contain the code cells. 


Since the export format is HTML and most online teaching tools such as MOODLE or ILIAS support HTML, you can simply copy the HTML source to a text node of your online course.

## Installation

Run 

```bash
pip install hbrs_grader
```

## Usage

### Step 1/3: Create a Notebook from template

Run 

```bash
hbrs-grader create 2019_MyExam
```

to create a Jupyter notebook called `2010_MyExam.ipynb` in your current working directory.

### Step 2/3: Grade your exam

Execute the newly created notebook using `jupyter-notebook` or `jupyter-lab`:

```bash
jupyter-notebook ./2019_MyExam.ipynb
```

Then just follow the instructions in the code comments. You can add additional code cells if you like, just make sure that all code cells contain the following metadata

```json
{
    "hideCode": true,
    "hidePrompt": true
}
```

so that they don't show up in the published html.

### Step 3/3: Publish the results

Once you are done grading, you can export the notebook to HTML with the following command:

```bash
hbrs-grader export-notebook 2019_MyExam.ipynb
```
reppy 0.4.14
Replacement robots.txt Parser
License: MIT

Replaces the built-in robotsparser with a
RFC-conformant implementation that supports modern robots.txt constructs like
Sitemaps, Allow, and Crawl-delay. Main features:

- Memoization of fetched robots.txt
- Expiration taken from the `Expires` header
- Batch queries
- Configurable user agent for fetching robots.txt
- Automatic refetching based on expiration