semvec/pypi-packages huggingface dataset:
django-tools-socialnetworks 1.1.4
Simple Django app to manage a website social network links

# Django Tools SocialNetworks

Simple Django app to manage a website social network links

## Installation

1. Install with pip install `django-tools-socialnetworks`.

2. Add `djtools.socialnetworks` to your INSTALLED_APPS setting like this:
```
INSTALLED_APPS = [
    ...
    'djtools.socialnetworks',
]
```

3.1 Add the context processor to your settings file if need the social networks in all your pages.
```
TEMPLATES = [
    {
        ...
        'OPTIONS': {
            'context_processors': [
                ...
                'djtools.socialnetworks.context_processors.social_networks',
            ],
        },
    },
]
```
3.2 Or extend the `get_context` function of your view like this:
```
def get_context_data(self, **kwargs):
    context = super().get_context_data(**kwargs)
    context['social_networks'] = SocialNetwork.get_social_networks()
    return context
```

4. Run migrations commands `python manage.py migrate`

5. Include social_networks.html snippet in your template or override it.
```
{% include "djtools/socialnetworks/social_networks.html" %}
```
bidobe 0.1.1
Doppler beaming simulation in binary systems.
Keywords: binary,doppler,beaming

# Binary-doppler-beaming
[![Build Status](https://travis-ci.org/pbrus/binary-doppler-beaming.svg?branch=master)](https://travis-ci.org/pbrus/binary-doppler-beaming)
[![Code](https://img.shields.io/badge/code-Python-blue.svg "Python")](https://www.python.org/)
[![PyPI version](https://badge.fury.io/py/bidobe.svg)](https://badge.fury.io/py/bidobe)
[![License](https://img.shields.io/badge/license-MIT-yellow.svg "MIT license")](https://github.com/pbrus/binary-doppler-beaming/blob/master/LICENSE)

This software generates a light curve of a binary system which is caused by the photometric beaming. For more details see [arXiv:0708.2100](https://arxiv.org/pdf/0708.2100.pdf). You can set up any configuration of a binary system seen from Earth.

![Beaming visualization](http://www.astro.uni.wroc.pl/ludzie/brus/img/github/doppler.gif)

## Installation

To install the package please type from the command line:
```bash
$ sudo pip3 install bidobe
```
or alternatively:
```bash
$ git clone https://github.com/pbrus/binary-doppler-beaming
$ cd binary-doppler-beaming
$ sudo python3 setup.py install
```

## Usage

All you need are the `doppler_beaming.py` script and the `binary.conf` file. Edit the last one and configure your own binary system (see comments inside this file to find out which units are used):
```python
[OBJECTS]
mass1 = 1
mass2 = 2
temperature1 = 6000
temperature2 = 8000
radius1 = 1.0
radius2 = 1.5
distance = 1000

[ORBITS]
sum_major_axis = 8e10
eccentricity = 0.4
longitude_node = 70.0
inclination = 60.0
periastron_argument = 110.0
...
```
Now you can run the `doppler_beaming.py`:
```bash
$ python3 doppler_beaming.py
```
The main module *bidobe* (**bi**nary **do**ppler **be**aming) provides the interface to display, save to files and animate orbits, radial velocities and light curves. Moreover, it allows to convert [SI](https://en.wikipedia.org/wiki/International_System_of_Units) units from and to astronomical units. For example:
```python
orbit1_position = orbit1.convert_m_to_au(orbit1_position)
```
converts position in meters to position in [AU](https://en.wikipedia.org/wiki/Astronomical_unit)s. The following commands:
```python
plot_projected_orbits(orbit1_position, orbit2_position, "AU", "AU")
plot_projected_orbits(orbit1_position, orbit2_position, "AU", "AU", "orbits.eps")
animate_projected_orbits(orbit1_position, orbit2_position, "AU", "AU")
```
can be used to display orbits projected on the sky, save them to *orbits.eps* file and animate them on the screen, respectively.

I encourage to visit my website to see more detailed description of this project. The current link can be found on my [GitHub profile](https://github.com/pbrus).

## License

**Binary-doppler-beaming** is licensed under the [MIT license](http://opensource.org/licenses/MIT).
aresy 0.0.8
An industrial-strength data science toolkits

## Home
Built on popular scientific computing toolkits, aresy aims to make it easy to process large-scale data on a single server. aresy treats the whole data flow as a computation graph, where the input data will be put into placeholders, and you can perform nearly any python-based transformation by extending the graph. aresy was designed with the following principles:

- Compatible with python data science ecosystem, such as numpy, pytorch, tensorflow modules.
- Able to process cross-type, cross-domain, cross-granularity data simultaneously.
- Easy to build a large feature generator, a transformer or even a data loader to feed preprocessed data into deep learning models.

## Installation
```shell
python setup.py install
```

## Package Reference

- aresy.formatting
    - aresy.formatting.TSVDataLoader
    - aresy.formatting.TSVPlaceholder

- aresy.preprocessing
    - aresy.preprocessing.WrangleFloat
    - aresy.preprocessing.WrangleStr
    - aresy.preprocessing.WrangleNormalDatetime

- aresy.aggregating
    - aresy.aggregating.Sum
    - aresy.aggregating.Mean
    - aresy.aggregating.Std
    - aresy.aggregating.Max
    - aresy.aggregating.Min
    - aresy.aggregating.Quantile

- aresy.utils
    - aresy.wrap\_outputs\_to\_json