semvec/pypi-packages huggingface dataset:
dau 0.1.3
Automatic user creation at Django startup.
License: MIT
Requires Python: >=3.6,<4.0


[![Build Status](https://travis-ci.org/AndreGuerra123/django_autouser.png)](https://travis-ci.org/AndreGuerra123/django_autouser)
[![codecov](https://codecov.io/gh/AndreGuerra123/django_autouser/branch/master/graph/badge.svg)](https://codecov.io/gh/AndreGuerra123/django_autouser)

# Django Auto User

Django Auto User (DAU) is a Django (Python) library for the automatic creation of users.

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Django Auto User.

```bash

pip install dau

```

## Usage

Add 'dau' to INSTALLED APPS after 'django.contrib.admin' in your settings.py:


```python

INSTALLED_APPS = [
    ...
    'django.contrib.admin',
    'dau',
    ...
]

```

Then, just add DJANGO_AUTO_USER settings to your settings.py file as:

```python

DJANGO_AUTO_USER = [
    {
        'username':'admin',
        'email':'admin@admin.com',
        'password':'adminpass',
        'is_superuser':True,
        'is_staff':True,
        'is_active':True
    }
]

```

Make your migrations with:

```bash

python manage.py makemigrations
python manage.py migrate

```


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
neuroptica 0.1.0
Nanophotonic Neural Network Simulator
License: MIT

# neuroptica [![Documentation Status](https://readthedocs.org/projects/neuroptica/badge/?version=latest)](https://neuroptica.readthedocs.io/en/latest/?badge=latest) [![Build Status](https://travis-ci.com/fancompute/neuroptica.svg?token=CSoUuvqmixfJpdwkLqet&branch=master)](https://travis-ci.com/fancompute/neuroptica)

`neuroptica` is a flexible chip-level simulation platform for nanophotonic neural networks written in Python/NumPy. It provides a wide range of abstracton levels for simulating optical NN's: the lowest-level functionality allows you to manipulate the arrangement and properties of individual phase shifters on a simulated chip, and the highest-level features provide a Keras-like API for designing optical NN by stacking network layers.


## Installation

The easiest way to get started with `neuroptica` is to install directly from the Python package manager:

```
pip install neuroptica
```

Alternately, you can clone the repository source code and edit it as needed with 

```
git clone https://github.com/fancompute/neuroptica.git
```

and in your program or notebook, add

```python
import sys
sys.path.append('path/to/neuroptica')
``` 


## Getting started

For an overview of `neuroptica`, read the [documentation](https://neuroptica.readthedocs.io). Example notebooks are included in the [`neuroptica-notebooks`](https://github.com/fancompute/neuroptica-notebooks) repository:

- [Planar data classification using electro-optic activation functions](https://github.com/fancompute/neuroptica-notebooks/blob/master/neuroptica_demo.ipynb)

![neuroptica training demo](https://raw.githubusercontent.com/fancompute/neuroptica/master/img/neuroptica_demo.gif)


## Authors

`neuroptica` was written by [Ben Bartlett](https://github.com/bencbartlett), [Momchil Minkov](https://github.com/momchilmm), [Tyler Hughes](https://github.com/twhughes), and  [Ian Williamson](https://github.com/ianwilliamson).
dxl2 0.1
A library for building robots using Dynamixel servos
License: MIT
Requires Python: >=3.6.0


# dxl2

`dxl2` aims to provide higher levels of abstraction for building robots powered by Dynamixel servos. It is a replacement for the lower level `dynamixel-sdk` library.

## Installation

```
git clone https://github.com/xrisk/dxl2.git
cd dxl2
pip3 install -r requirements.txt
```
type-checker 1.0.2
Type checker helpers for Python.
License: MIT
Keywords: Type,Checker,Validator

# Type Checker
Type check Python types with support for nested lists and dictionaries.

[![coverage report](https://gitlab.com/robjampar/type-checker/badges/master/coverage.svg)](https://gitlab.com/robjampar/type-checker/commits/master)

[![pipeline status](https://gitlab.com/robjampar/type-checker/badges/master/pipeline.svg)](https://gitlab.com/robjampar/type-checker/commits/master)

## Installation
Type Checker is a Python package, and is compatible with `Python 3` only (for now). It can be installed through `pip`.

##### Pip
```
pip install type-checker
```

## Run the Unit Tests
To run the tests.
```
pip install pipenv
pipenv install --dev
pipenv run python -m pytest tests --cov=type_checker
```

## Docs

The documentation is public, and is generated using Sphinx.

[Type Checker Documentation](https://robjampar.gitlab.io/type-checker)

##### Build documentation
To build a local static HTML version of the documentation.
```
pip install pipenv
pipenv install sphinx
pipenv run sphinx-build docs ./public -b html
```

## Type Checking Example
``` python
from typing import Union
from type_checker import type_check

# the type defintion to check a value against
type_def = {
    "hello": {
        "world": int
    },
    "a_key": str,
    "a_list": [
        float
    ],
    int: bool,
    float: Union[bool, int]
}

# a valid test value
test_value = {
    "hello": {
        "world": 1
    },
    "a_key": "this_is_a_string",
    "a_list": [
        3.2,
        4.7
        5.9
    ],
    5: True,
    5.2: False,
    5.7: 10

}

# use validate from the type_checker package to validate the value
is_valid = type_check(test_value, type_def)

print(f"is valid: {is_valid}")
```

``` text
$ python example.py
>>> is valid: True
```