semvec/pypi-packages huggingface dataset:
another-setuptools-git-version 0.1.2
Automatically set package version using git tags.
License: MIT
Keywords: setuptools git version-control

# another-setuptools-git-version

[![PackageVersion][pypi-version]][pypi-home]
[![PythonVersion][python-version]][python-home]
[![Stable][pypi-status]][pypi-home]
[![Format][pypi-format]][pypi-home]
[![License][pypi-license]](LICENSE)

[pypi-version]: https://badge.fury.io/py/bad-setuptools-git-version.svg
[pypi-license]: https://img.shields.io/pypi/l/bad-setuptools-git-version.svg
[pypi-status]: https://img.shields.io/pypi/status/bad-setuptools-git-version.svg
[pypi-format]: https://img.shields.io/pypi/format/bad-setuptools-git-version.svg
[pypi-home]: https://badge.fury.io/py/bad-setuptools-git-version
[python-version]: https://img.shields.io/pypi/pyversions/bad-setuptools-git-version.svg
[python-home]: https://python.org

Automatically set package version from Git. This is a re-release of
[bad-setuptools-git-version][] with fixes and improvements, which is itself a re-release of [setuptools-git-version][]

[setuptools-git-version]: https://github.com/pyfidelity/setuptools-git-version


## Introduction

Instead of hard-coding the package version in ``setup.py`` like:

```python
setup(
    name='foobar',
    version='1.0.0',
    ...
)
```

this package allows to extract it from tags in the underlying Git repository:

```python
setup(
    name='foobar',
    version_config={
        "version_format": "{tag}.{cc}",
        "starting_version": "0.1.0"
    },
    setup_requires=['bad-setuptools-git-version'],
    ...
)
```

The tool uses the semantically-latest tag as the base version. If there are no annotated tags, the version specified by `starting_version` will be used. If `HEAD` is at the tag, the version will be the tag itself. If there are commits ahead of the tag, the first 8 characters of the sha of the `HEAD` commit will be included.
asyncimg 1.0.1
An async image manipulation lib. Can be used for discord.
License: MIT
Keywords: discord discord-rank discord-profile discord-leveling

### asyncimg
An async image manipulation lib. Can be used for discord.

### Usage
```python
from asyncimg import Generator

generator = Generator()
image = await generator.lovers(profile_pic_link1, profile_pic_link2)

# In discord command
file = discord.File(fp=image, filename='image.png')
await ctx.send(file=file)
```

### Available methods
```python
lovers(profile_pic_link1, profile_pic_link2)
stars(profile_pic_link)
colors(profile_pic_link)
frame(profile_pic_link)
envelop(profile_pic_link)
knockout(profile_pic_link1, profile_pic_link2)
fart(profile_pic_link)
```

`More coming soon`
 
Any help need or better suggesions? [Join here](https://discord.gg/7SaE8v2) to contribute.Change Log
===========

0.0.1 (3/8/2020)
--------------------------------
- Release (Buggy)

1.0.0 (3/8/2020)
--------------------------------
- Working release

1.0.1 (3/8/2020)
--------------------------------
- Attempt to fix frame (Images do not match)
galaxy2galaxy 0.0.1rc5
Galaxy2Galaxy
License: MIT
Keywords: astronomy machine learning

# Galaxy2Galaxy [![Build Status](https://travis-ci.org/ml4astro/galaxy2galaxy.svg?branch=master)](https://travis-ci.org/ml4astro/galaxy2galaxy) [![Documentation Status](https://readthedocs.org/projects/galaxy2galaxy/badge/?version=latest)](https://galaxy2galaxy.readthedocs.io/en/latest/?badge=latest) [![Join the chat at https://gitter.im/ml4astro/galaxy2galaxy](https://badges.gitter.im/ml4astro/galaxy2galaxy.svg)](https://gitter.im/ml4astro/galaxy2galaxy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) 

Galaxy2Galaxy, or G2G for short, is a library of models, datasets, and utilities to build generative models for astronomical images, based on the Tensor2Tensor library. Similarly to T2T, the goal of this project is to accelerate research in machine
learning models applied to astronomical image processing problems.

## Install

We recommend users create a conda environment before installing galaxy2galaxy. This makes installing tensorflow and galsim very easy:
```
$ conda install tensorflow-gpu==1.15
$ conda install -c conda-forge galsim
```
G2G can then easily be installed using pip inside the environment:
```
$ pip install git+https://github.com/ml4astro/pixel-cnn.git
$ pip install galaxy2galaxy
```

## Usage

To generate the COSMOS 25.2 sample at native pixel scale and stamp size:

```bash
$ g2g-datagen --problem=img2img_cosmos --data_dir=data/img2img_cosmos
```
This uses GalSim to draw postage stamps and save them in TFRecord format which can then be used for training.  This assumes that you have downloaded the GalSim COSMOS sample, if that's not the case, you can dowload it with: `galsim_download_cosmos -s 25.2`

To  train an autoencoder with this data:
```bash
$ g2g-trainer --data_dir=data/img2img_cosmos --output_dir=training/cosmos_ae   --problem=img2img_cosmos --model=continuous_autoencoder_basic  --train_steps=2000  --eval_steps=100 --hparams_set=continuous_autoencoder_basic
```