semvec/pypi-packages huggingface dataset:
pxplore 0.2.0
Various tools to explore hidden details of the Python code.
Requires Python: >=3.6


::

  pip install pxplore

pxplore_ is a Python package providing a set of useful tools to explore
hidden details of the Python code.

.. _pxplore: https://github.com/pacesm/pxplore                                         

.. code-block:: python

 import pxplore

 # print names (variables, functions, classes ... etc.) in the current frame
 pxplore.print_names()

 # print names (variables, functions, classes ... etc.) contained by a module
 pxplore.print_module_names(pxplore)
pydiverse.pipetest 0.0.1
An adaption layer for pydiverse.pipedag that simplyfies execution of pipedag steps as unit tests with cache invalidation awareness.
Requires Python: >=3.8

# pydiverse.pipetest

[![CI](https://github.com/pydiverse/pydiverse.pipetest/actions/workflows/ci.yml/badge.svg)](https://github.com/pydiverse/pydiverse.pipetest/actions/workflows/ci.yml)

An adaption layer for pydiverse.pipedag that simplyfies execution of pipedag steps as unit tests with cache invalidation awareness.

## Installation

You can install the package in development mode using:

```bash
git clone https://github.com/pydiverse/pydiverse.pipetest.git
cd pydiverse.pipetest

# create and activate a fresh environment named pydiverse.pipetest
# see environment.yml for details
mamba env create
conda activate pydiverse.pipetest

pre-commit install
pip install --no-build-isolation -e .
```
pydiverse.pipedag 0.0.1
A pipeline orchestration layer built on top of prefect for caching and cache invalidation to SQL and blob store targets.
Requires Python: >=3.8

# pydiverse.pipedag

[![CI](https://github.com/pydiverse/pydiverse.pipedag/actions/workflows/ci.yml/badge.svg)](https://github.com/pydiverse/pydiverse.pipedag/actions/workflows/ci.yml)

A pipeline orchestration layer built on top of prefect for caching and cache invalidation to SQL and blob store targets.

## Installation

You can install the package in development mode using:

```bash
git clone https://github.com/pydiverse/pydiverse.pipedag.git
cd pydiverse.pipedag

# create and activate a fresh environment named pydiverse.pipedag
# see environment.yml for details
mamba env create
conda activate pydiverse.pipedag

pre-commit install
pip install --no-build-isolation -e .
```
torchest 0.0.6
Toolbelt for pytorch framework
Keywords: python,ai,machine learning,neural network,pytorch,trainer


# Torchest
Pytorch tools and utilities (Trainers, data generators, functions, and more...)

# Trainers

One of the most common tasks you will do in pytorch is define training loops, which is a hassle because they are almost always the same. Torchest has trainers you can use for your projects

```python
from torchest.trainer import SimpleTrainer

# previous definition of model and data preparation
# train_dataloader = DataLoader(train_data)
# dev_dataloader = DataLoader(dev_data)
# test_dataloader = DataLoader(test_data)
# model = nn.Sequential(...)
...

"""
Prepare Trainer
"""
loss_fn = nn.CrossEntropyLoss()
learning_rate= 1e-2
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

trainer = SimpleTrainer(model, loss_fn, optimizer)

"""
Train
"""
epochs = 500
trainer.train(data_train=train_dataloader, data_dev=dev_dataloader, data_test=test_dataloader, epochs=epochs)
```

Training loop progress

```
31%|██████████████████▍      | 156/500 [00:02<00:05, 58.01epoch/s, train_accuracy=76.2, train_cost=0.238290]
```

Trainer also saves the cost for the train, dev and test passes. You can simply call `trainer.plot_costs()` to display the graph with the costs

<img src="readme_images/trainer_costs.png" alt="Trainer costs" style="width: 400px; display:block; margin-left:auto; margin-right:auto"/>


## Wandb visualization

 Trainers support [wandb](https://wandb.ai/) to visualize loss and acurracy as well as to save model versions. if you want to enable you have to set 2 parameters in your trainer initialization, `name` and `wandb`

 ```python
 trainer = SimpleTrainer(model, loss_fn, optimizer, wandb_project_name="MyProject", wandb=True)
 ```

 On the first run you will have to enter your API key, which you can obtain at https://wandb.ai/settings

# Data generators

## Spiral data

This will create a 2d matrix with points in a dataplot. Ideal for testing non-linearity in your network

```python
from torchest.datagen import spiral_datagen

class_num =  3
X, Y = spiral_datagen(450, class_num) # 450 elements per class
```

<img src="readme_images/spiral_2d.png" alt="Spiral data screenshot" style="width: 400px; display:block; margin-left:auto; margin-right:auto"/>
cell-machine-levels 0.7.1
A library for manipulating modded cell machine level codes.
Requires Python: >=3.6

## This library is currently in beta and can have major API changes

# Cell Machine Levels

A library for manipulating modded cell machine level codes.

To install do:

```bash
pip3 install cell_machine_levels
```

Or just `pip` on some OSes

## Level class

Currently supports the main 3 types (V1, V2, V3), support is going to be added for more than just these

Level creation example:

```py
from cell_machine_levels import level
test = level.Level(10, 10, "test", level.WallEffect.wrap)
print(test.save("V3"))
```

Level importing example:

```py
from cell_machine_levels import level
test = level.open("V3;a;a;;;test;2")
print(f"{test.name}: {test.size}, {test.wall_effect}")
```
dis-uniter-Stupid-Genius 0.0.1
A keep-alive server for discord.py on replit
Requires Python: >=3.8

# dis-uniter
This creates a simple website to go with your discord.py bot on https://replit.com so it can be kept alive. If your bot does not have intents that reveal private data (message content, presences, members), it also includes a logger.
## Usage 
```
from disuniter import keepAlive
from discord.ext import commands
bot = commands.Bot()
keepAlive(bot)
bot.run()
```
## Why is it called dis-uniter?
I am keeping Discord bots alive. Discord = disagreement. I am dis-uniting by keeping disagreements alive.