semvec/pypi-packages huggingface dataset:
example-package-reyniel26 0.0.5
A small example package
Requires Python: >=3.7

# packaging_playground
My own playground on how to create a python package

- https://packaging.python.org/en/latest/tutorials/packaging-projects/

```
py -m pip install --upgrade build
```

```
py -m build
```

RUN ON BASH
```
pylint $(git ls-files '*.py')
```
pysnmptui-K0HAX 0.1.2022112704
Python SNMP TUI (Terminal User Interface)
Requires Python: >=3.9
Keywords: snmp,tui

# pysnmptui

Python SNMP TUI (Terminal User Interface)

## Configuration

You can use a configuration file instead of command line flags.

Invoke pysnmptui with `pysnmptui -c config.yaml` if your config file is named "config.yaml" and in the current directory.

Below is an example config.yaml.

```
Host: 127.0.0.1
Username: v3user
AuthProto: SHA
AuthPassword: password
PrivProto: AES
PrivPassword: password
Version: 3
ExtraMibs:
  - IF-MIB
  - ENTITY-MIB
  - ./mibs/CISCO-SMI
  - ./mibs/CISCO-TC
  - ./mibs/CISCO-IF-EXTENSION-MIB
  - ./mibs/CISCO-VTP-MIB
  - ./mibs/CISCO-CDP-MIB
```
bezierview 0.0.3
bezier curve visualization
Requires Python: >=3.6

# BezierView

View Bezier Curve transformation by Python.

## Install

```shell
pip install bezierview
```

# Play

Play Bezier Curve with pure python.

![play](https://github.com/MorvanZhou/bezierview/raw/main/curve_play.gif)

```python
import bezierview

bezierview.play()
```

# Animation

View an animated creation of pre-defined points.

![animation](https://github.com/MorvanZhou/bezierview/raw/main/curve.gif)

```python
import bezierview

points = [[50, 350], [250, 100], [330, 350], [450, 100]]
bezierview.animate(points)
```

# View static curve

View a static plot of pre-defined points.

```python
import bezierview

points = [[50, 350], [250, 100], [330, 350], [450, 100]]
bezierview.show(points)
```

![pic](https://github.com/MorvanZhou/bezierview/raw/main/curve.png)
pytorch-gan-zoo 0.0.6
Implementation of GAN models in PyTorch.
Requires Python: >=3.6.0

# GAN-zoo

<img src="./notebooks/snapshots/cover-image.png" width="500"/>

Train and evaluate basic FC-GAN:
```
# train on fashion-mnist
python -m ganzoo.examples.basic_gan.train_fc \
    --z_dim=100 --num_hidden_units=256 \
    --network_type=fc-large \
    --dataset_name=fashion-mnist

# evaluate
python -m ganzoo.examples.basic_gan.eval_fc \
    lightning_logs/version_1/checkpoints/epoch\=99-step\=337600.ckpt \
    --output_dir lightning_logs/version_1/outputs
```


A collection of GAN models implemented in PyTorch:

 * Basic GAN on MNIST [code](https://github.com/vmirly/PyTorch-GAN-zoo/tree/master/basic_gan)
 * Conditional GAN on MNIST [code](https://github.com/vmirly/PyTorch-GAN-zoo/tree/master/cond_gan)
 * Pix2Pix on FACADES dataset [code](https://github.com/vmirly/PyTorch-GAN-zoo/tree/master/pix2pix)


## References - part 1

 * GAN [arxiv](https://arxiv.org/abs/1406.2661)
 * Wasserstein GAN [arxiv](https://arxiv.org/abs/1701.07875)
 * DCGAN [arxiv](https://arxiv.org/abs/1511.06434)
 * Conditional GANs [arxiv](https://arxiv.org/abs/1411.1784)
 * LSGAN [arxiv](https://arxiv.org/abs/1611.04076)
 * SAGAN [arxiv](https://arxiv.org/abs/1805.08318)
 * Pix2Pix [arxiv](https://arxiv.org/abs/1611.07004)
 * Pix2Pix-HD [arxiv](https://arxiv.org/abs/1711.11585) [PyTorch imp.](https://github.com/NVIDIA/pix2pixHD)
 * CycleGAN [arxiv](https://arxiv.org/abs/1703.10593) [code](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix)
 * StarGAN [arxiv](https://arxiv.org/abs/1711.09020) [PyTorch imp.](https://github.com/yunjey/stargan)
 * Geometric GAN [arxiv](https://arxiv.org/abs/1705.02894)
 * InfoGAN [arxiv](https://arxiv.org/abs/1606.03657)
 * BigGAN [arxiv](https://arxiv.org/abs/1809.11096)
 * ConSinGAN [arxiv](https://arxiv.org/abs/2003.11512) [code](https://github.com/tohinz/ConSinGAN)
 * OT-GAN [arxiv](https://arxiv.org/abs/1803.05573)

## References - part 2
 
 * Improved Techniques for Training GANs [arxiv](https://arxiv.org/abs/1606.03498)
 * On Convergence and Stability of GANs [arxiv](https://arxiv.org/abs/1705.07215)
 * Which Training Methods for GANs do actually Converge? [arxiv](https://arxiv.org/abs/1801.04406)
 * Is generator conditioning causally related to GAN performance? [arxiv](https://arxiv.org/abs/1802.08768)
 * The unusual effectiveness of averaging in gan training [arxiv](https://arxiv.org/abs/1806.04498)
 * cGANs with projection discriminator [arxiv](https://arxiv.org/abs/1802.05637)