semvec/pypi-packages huggingface dataset:
docly 0.3.0
Generate docstrings for your python functions. Automatically!
License: MIT

# docly

![Docly - Automatic source code commenting](https://github.com/autosoft-dev/docly/blob/master/logo/docly.png)

[![parser: tree-hugger](https://img.shields.io/badge/parser-tree--hugger-lightgrey)](https://github.com/autosoft-dev/tree-hugger/)

Automatically generate docstrings for your python functions

## Main documentation (FAQ and all)

http://beta.thedocly.io/


## Installing

Requires python 3.6+

_NOTE THAT, if you are getting an error (in a fresh virtualenv in ubuntu it is observed) like `error: invalid command 'bdist_wheel'` then please install wheel by doing `pip install wheel`_

_ALSO NOTE that if you are getting an error to build tree-sitter because you do not have gcc installed then you can install it using `sudo apt-get install gcc python3-dev` for other distros please check [here](https://stackoverflow.com/questions/21530577/fatal-error-python-h-no-such-file-or-directory)_

First install setuptools-rust by 

```
pip install setuptools-rust
```

Then

```
pip install docly
```

## Using

To generate comments - 

```
docly-gen /path/to/file_or_folder_with_python_files
```
_Please note that if you do not have the necessary engine (models) downloaded before running the command (which is going to be case the first time you run this command) then it will download and set them up. Which may take a bit of time_


It will produce something like this (Shown on a single file but you can run it on a directory full of files also)

```
The diff has been generated, do you want to see the suggestions for missing Docstrings? [Y/n]
Y
+-----------------+------------------------------+---------------------------------------+
| File Name       | Function Name                | Docstring                             |
+-----------------+------------------------------+---------------------------------------+
| simple_funcs.py | add                          | Add two numbers .                     |
| simple_funcs.py | check_if_even                | Checks if number is even .            |
| simple_funcs.py | check_even_numbers_in_a_list | Return list of numbers in base_list . |
| simple_funcs.py | open_file                    | Open a file .                         |
+-----------------+------------------------------+---------------------------------------+
Do you want to apply the suggestions? [Y/n]
Y
Applying diff
Diff applied. Good bye!
```

Instead if you just want the above report and not to apply the chages then do this - 

```
docly-gen --no_generate_diff --print_report /path/to/file_or_folder_with_python_files
```

If you want to revert the changes we applied then use

```
docly-restore
```

This will bring back ALL the files that we had touched to the exact state before we applied the changes
threedb-preview 0.0.8
A framework for analyzing computer vision models with simulated data
License: MIT

# 3DB: A framework for analyzing computer vision models with simulated data

## Installation

Follow instructions on: https://github.com/3db/installers

## Complete demo

For detailed, step by step demonstration of the usage of the framework, please visit: https://github.com/3db/demo

## Documentation

You can find in-depth documentation for the package [here](https://3db.github.io/3db), including a
quickstart guide, an explanation on the internal layout of the package, as well
as guides for extending 3DB to new modalities, tasks, and output formats.

## Primer data

We offer data for users to quickly get started 3DB and reproduce the results of the paper mentioned below. It is available on dropbox at this link: https://www.dropbox.com/s/2gdprhp8jvku4zf/threedb_starting_kit.tar.gz?dl=0. (One can use `wget` to download it).

It contains:

- 3D models
- Environments: our studio and HDRI backgrounds
- Replacement textures to use with `threedb.controls.blender.material`
- Licensing/credits for the data

## Citation

If you find 3DB helpful, please cite it as:
```
@inproceedings{leclerc2021three,
    title={3DB: A Framework for Chungus Chungus},
    author={Guillaume Leclerc, Hadi Salman, Andrew Ilyas, Sai Vemprala, ..., Ashish Kapoor, Aleksander Madry},
    year={2021},
    booktitle={Arxiv preprint arXiv:TODO}
}
```
plasp 1.0.0
Learning with Partial Supervision
License: MIT
Requires Python: >=3.6
Keywords: partial labeling weakly supervised learning plasp scalablemethod structured prediction

Partial Labelling Approached with through Structured Prediction (PLASP)
=======================================================================
:Topic: Generic implementations of weakly supervision algorithms
   developped in [CAB20]_, [CAB21a]_, [CAB21b]_.
:Author: Vivien Cabannes
:Version: 1.0.0 of 2021/06/07

Installation
------------
To install our package, run the setup file

.. code:: shell

   $ python <path to code folder>/setup.py install

You can also install it in develop mode, eventually with pip

.. code:: shell

    $ cd <path to code folder>
    $ pip install -e .

Usage
-----
See files:
 - ``problems/classification/libsvm_experiments.py``
 - ``problems/classification/semi_supervision_experiments.py``
 - and more generally ``*_experiements.py``

Package Requirements
--------------------
Most of the code is based on the following python libraries:
 - numpy
 - numba
 - matplotlib
 
Some testing done with notebook are based on:
 - jupyter-notebook
 - ipywidgets

For ranking, we used the following lp solver library:
 - cplex

To load LIBSVM files, more precisely to read libsvm files format we used:
 - scikit-learn
 
To load MULAN files, more precisely to read mulan files format we used:
 - arff
 - skmultilearn

Datasets links
--------------
Datasets can be download at:
 - LIBSVM: https://www.csie.ntu.edu.tw/~cjlin/libsvm/oldfiles/index-1.0.html
 - MULAN: http://mulan.sourceforge.net/datasets-mlc.html

Change path in config file ``dataloader/config.py`` to specify path to your data.

References
----------
.. [CAB20] Structured Prediction with Partial Labelling through the Infimum Loss,
   Cabannes et al., *ICML*, 2020

.. [CAB21a] Disambiguation of weak supervision with exponential convergence rates,
   Cabannes et al., *ICML*, 2021

.. [CAB21b] Overcoming the curse of dimensionality with Laplacian regularization
   in semi-supervised learning, Cabannes et al., *Preprint*, 2021