semvec/pypi-packages huggingface dataset:
repostruc 0.0.6
Python module to generate Directory tree structure.
License: MIT
Requires Python: >=3.5.0
Keywords: Folder_structure folder structure repository directory tree python py


# REPOSTRUC

>`repostruc` is a cross-platform library that returns the directory structure in a formated form. The structure can be copied to your clipboard or a .md file can be made available.[`.md example`](PROJECTINFO.md)

## Ignored files

>`repostruc` ignores files from [`.gitinore`](.gitinore) file if present else ignores commonly ignored files and directories.

## Install the Requirements

```bash
 pip install repostruc
```

## Usage

### To clipboard

```bash
repostruc -c
```

### To file

```bash
repostruc -f filepath
```

### To [`PROJECTINFO.md`](PROJECTINFO.md)

```bash
repostruc -r -f
```

## Example

 View [`PROJECTINFO.md`](PROJECTINFO.md) which was made using `repostruc`

## Built With

* [`pathlib`](https://pypi.org/project/pathlib/) - pathlib offers a set of classes to handle filesystem paths.
* [`pyperclip`](https://pypi.org/project/pyperclip3/) - Cross-platform clipboard utilities supporting both binary and text data.
* [`gitignore_parser`](https://pypi.org/project/gitignore-parser/) - A spec-compliant gitignore parser for Python 3.5+
* [`docopt`](https://pypi.org/project/docopt/) - creates beautiful command-line interfaces

## Contributing

Please read [CONTRIBUTING.md](https://github.com/) for details on our code of conduct, and the process for submitting pull requests.

## Authors

* **Atharva Gundawar** - *Initial work* - [Github handle](https://github.com/Atharva-Gundawar)

See also the list of [contributors](https://github.com/your/project/contributors) who participated in this project.

## License

This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details

## Acknowledgments

* Generator function from [*Aaron Hall's*](https://stackoverflow.com/users/541136/aaron-hall) answer [here](https://stackoverflow.com/questions/9727673/list-directory-tree-structure-in-python)
coep-package 0.1.4
to create csv file and create latex code
License: MIT
Keywords: coep

This is a Python package which will help do 2 things
1. Create a csv file for the MCQ format questions
2. It will give Latex formatter function

Change Log
===========

0.0.2 (1/4/2021)
-----------------
-Added feature to create a textfile
-Added a feature to check duplicate values

0.0.3 (2/4/2021)
-----------------
-Modified the csv_module to handle duplicates of Image format questions.

0.0.4 (3/4/2021)
-----------------
-Modified putInCsv function such that it will append the new entries in the variation file

0.0.5 (5/1/2021)
-----------------
-Introduced utf-8 encoding to the module.

0.0.6 (24/6/2021)
-----------------
-discarded

0.0.7 (24/6/2021)
-----------------
-discarded

0.0.8 (24/6/2021)
-----------------
-Introduced drive API as drive_api
-added new CSV attribute to putInCsv 

0.0.9 (25/6/2021)
-----------------
-discarded

0.1.0 (26/6/2021)
-----------------
-removed colab bug

0.1.1 (29/6/2021)
-----------------
-discarded

0.1.2 (29/6/2021)
-----------------
-discarded

0.1.3 (29/6/2021)
-----------------
-discarded

0.1.4 (29/6/2021)
-----------------
-Remove Duplicates Bugfix
drf-yasg-stubs 0.1.4
Typing stubs for drf-yasg library (PEP 484 stubs for Mypy and PyCharm)
License: MIT
Requires Python: >=3.6

<a href="http://mypy-lang.org/">
<img src="http://mypy-lang.org/static/mypy_light.svg" alt="mypy logo" width="300px"/>
</a>

# Typing stubs for drf-yasg library

[![PyPI version](https://badge.fury.io/py/drf-yasg-stubs.svg)](https://badge.fury.io/py/drf-yasg-stubs)
[![Tests status](https://github.com/intgr/drf-yasg-stubs/workflows/Tests/badge.svg?branch=master)](https://github.com/intgr/drf-yasg-stubs/actions?query=workflow:Tests)

PEP 484 type stubs for Mypy and PyCharm.

This package contains type stubs to provide more precise static types and
type inference for
[drf-yasg](https://drf-yasg.readthedocs.io/en/stable/), the OpenAPI/Swagger
generator for Django REST Framework.

## Installation

```bash
pip install drf-yasg-stubs
```

That's all, run Mypy and it should be able to pick up these stubs.

## Changelog

#### 0.1.4 (2021-06-29)
* Allow passing `request_body=no_body` to `swagger_auto_schema` (#14)

  Contributed by Étienne Noss @etene

#### 0.1.3 (2021-01-15)
* Add re-exports to `inspectors/__init__.py` (#13)
* get_schema_view() now expects list arguments instead of tuple, as in
  drf-yasg 1.20 (#11)
* Type hints for OpenAPISchemaGenerator (#10)

#### 0.1.2 (2020-08-19)
* Fixed get_schema_view() arguments authentication_classes, permission_classes (#6)

#### 0.1.1 (2020-08-18)
* Fixed SchemaView method annotations (#2)
* Fixed @swagger_serializer_method decorator hints
* Improved hints for EndpointEnumerator, OpenAPISchemaGenerator

#### 0.1.0 (2020-01-13)
* Initial release

## Development

Release process: https://twine.readthedocs.io/en/stable/#using-twine

* docker build --tag=drf-yasg-stubs .
* docker run --rm -it drf-yasg-stubs twine upload 'dist/*'
* Create tag at https://github.com/intgr/drf-yasg-stubs/releases/new
webkio 0.3.1
Easy to use websocket event handler
License: MIT
Requires Python: >=3.7

# WebKio
> Easy to use websocket event handler
## Example
### Server
```python
from webkio import WebKio, Receiver

server = WebKio()


@server.event_handler()
async def print_hi(receiver: Receiver):
    print("Hi!")
    await receiver.send_ok()


if __name__ == "__main__":
    server.serve(True)

```
### Client
```python
import asyncio

from webkio.client import WKClient

if __name__ == "__main__":
    client = WKClient()
    asyncio.get_event_loop().run_until_complete(client.send_event("print_hi"))

```
hivae 0.19
HIVAE (Handling incomplete heterogeneous data using VAEs. - by Nazabal, et al., DOI: 10.1016/j.patcog.2020.107501, 2020) - Extenstion of implementations as easy to use Python library
License: MIT

# hivae

This repository contains the Modular reimplemenation of the Heterogeneous Incomplete Variational Autoencoder model (HI-VAE)written by Alfredo Nazabal and co-workers.
The package provided here is to a large part baseed on this implementation, but adheres to a more pythonic way, omitting the need for supplying parameters via I/O , as well as aligning the modelling more with sklearn.
It was written by A. Karwath (a.karwath@bham.ac.uk) and the CardAIc group @ University of Birmingham.

The details of the background of this model are included in this [paper](http://dx.doi.org/10.1016/j.patcog.2020.107501). 

## Install

The package can be installed using pip:

```pip install hivae```


## Examples

Once checked out, there are a number of example datasets (Wine, Adult and Diabetes), which can be found in ./hivae/examples/data. To evaluate the package, please use ./hivae/examples/hivae_general_example.py. The example should give a general explaination of how to use the package. More details will folow.


## Files description


* **hivae.py**: The main script of the library, it needs to imported to work with the library and is connected to all the other scripts.
* **loglik_ models_ missing_normalize.py**: In this file, the different likelihood models for the different types of variables considered (real, positive, count, categorical and ordinal) are included.
* **model_ HIVAE_inputDropout.py**: Contains the HI-VAE with input dropout encoder model.
* **model_ HIVAE_factorized.py**: Contains the HI-VAE with factorized encoder model
* **Github page with examples to follow**: 

## Contact

* **For questions this library --> Andreas Karwath**: a.karwath@bham.ac.uk


## More details regarding the hivae_general_example.py and use of the model (please note that this is under construction)

main_directory: project folder

dataset_name: the name of the database (required)

types_list_d: a dictionary where the key is the dataset name, which contains a list with tuples that indicates the column names, types, the number of dimensions and classes 

types:

•	count: real values

•	cat: categorical 0 or 1

•	pos: positive real values

•	ordinal: ordinal number

number of dimensions:

•	number of possibilities in the categorical variables or 1 in numerical

number of classes:

•	number of options (same of number of dimensions for categorical variables)

dataset_path: this is the folder of the csv files

results_path: the output folder for results (currently not used)

network_path: where the models are going to be stored

types_list: the specific type for the dataset you are going to use
data_file: the full dataset
train_file/ test_file: if the dataset was already splitted

train_data/test_data: pandas dataframes

dim_y: the depth of the network

dim_s/dim_z: dimensions of the embedding