semvec/pypi-packages huggingface dataset:
pynlopol 1.1.0
A Python library for nonlinear polarimetry.
Requires Python: >=3.6

# Nonlinear polarimetry in Python â€“ pynlopol

## Overview
pynlopol is a Python library for nonlinear microscopy and polarimetry.

## Quick start
pynlopol requires Python v3.6 or later. To run the scripts from the command
line, Python has to be included in the system path.

Install pynlopol:
`python -m pip install pynlopol`

Your Python folder should now contain a PIPO analysis script under Scripts:
`fit_pipo_img.py`

and some test data under test_data:
`RTT v1_4 PIPO - 11_51_23_.221_.dat.zip`

Dragging the ZIP archive should start an example fitting routine.

## Demo
A video showing pynlopol installation and a demonstration of a rat-tail tendon SHG PIPO dataset fit. The video is slightly outdated, the package is called _pynolpol_ and is hosted on _test.pypi_.

https://github.com/lukaskontenis/pynlopol/assets/3004611/1e09f3dd-66de-4fac-b040-85d6ce02e57c

## Contact

Contact: Lukas Kontenis, dse.ssd@gmail.com

Copyright 2015-2023 Lukas Kontenis
pixelyai-serve 0.0.8
serve utilises of pixelyai in jax and torch
Requires Python: >=3.8
Keywords: machine learning,deep learning,pytorch,jax,flax

# PixelyAI

PixelyAI main AI Engine Python Package

# Installation


### Password

Password ON PYPI : `PixelyAIServeJAXTORCH`

## Linux

```bash
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip3 install jax pixelyai_serve
```

## Windows

```sh
pip3 install torch torchvision torchaudio
pip3 install jax pixelyai_serve
```

## Mac

```sh
# MPS acceleration is available on MacOS 12.~+
pip3 install torch torchvision torchaudio
pip3 install jax pixelyai_serve
```
pymajic 1.0.1
PyMajic is a Python package that simplifies working with Django projects by providing convenient commands for environment management, custom commands, and more.
Keywords: Django,project management,virtual environment,Django development,utility,command-line tool,workflow optimization,automation

# PyMajic: Crafting Django Projects, One Command at a Time!
![PyMajic Logo](https://github.com/Codewithshagbaor/PyMajic/assets/67190277/9a12d4a9-07c5-446e-a392-55e1457f3292)
PyMajic is a Python package that simplifies working with Django projects by providing convenient commands for environment management, custom commands, and more.

## Installation

```bash
pip install pymajic
```

## Usage

### Store Data

To store environment and project directory information:

```bash
pymajic store data
```

### Run Custom Command(In Dev Mode)

To run a custom Django management command:

```bash
pymajic run command <your_custom_command>
```

### Django Shell Access

To open a Django shell session:

```bash
pymajic shell
```

### Environment Verification(Dev Mode)

Ensure the correctness of your project environment:

```bash
pymajic verify environment
```

### Project Sharing(Dev Mode)

Share your Django project easily with PyMajic:

```bash
pymajic share project
```

This command creates a new virtual environment, installs the project dependencies, and runs the project.

### Database Backup and Restore

Backup your Django project database:

```bash
pymajic backup database
```

Restore your Django project database:

```bash
pymajic restore database <backup_filename>
```

### Project Configuration Viewer

View the configuration of your Django project:

```bash
pymajic project info
```

### Update Check

Check for updates to the PyMajic package:

```bash
pymajic check update
```

### Config File Editor

Edit the PyMajic configuration file:

```bash
pymajic edit config
```

### Interactive Mode

Enter interactive mode to execute commands dynamically:

```bash
pymajic interactive mode
```

### Set Command Alias

Set aliases for your custom commands:

```bash
pymajic set alias <alias_name> <your_custom_command>
```

## Contributing

Contributions are welcome! If you have any ideas, bug reports, or feature requests, please open an issue or submit a pull request.

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
vsddcolor 1.0.0
DDColor function for VapourSynth
Requires Python: >=3.11
Keywords: DDColor,VapourSynth

# DDColor
Towards Photo-Realistic Image Colorization via Dual Decoders, based on https://github.com/piddnad/DDColor.


## Dependencies
- [PyTorch](https://pytorch.org/get-started) 2.1.1 or later
- [VapourSynth](http://www.vapoursynth.com/) R62 or later


## Installation
```
pip install -U vsddcolor
python -m vsddcolor
```


## Usage
```python
from vsddcolor import ddcolor

ret = ddcolor(clip)
```

See `__init__.py` for the description of the parameters.
AutoBET 0.2.1
Automatic Brain Extraction Tools

# Automatic Brain Extraction Tools
```shell
Copyright Jiameng Liu, IDEA Lab, School of Biomedical Engineering, ShanghaiTech University.

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Repo for Automatic Brain Extraction Tools (AutoBET)
Contact: JiamengLiu.PRC@gmail.com
```

## Install
`pip install AutoBET`

### GPU
Currently, our brain extraction tools works on GPU with at least 14G GPU memories. We are working on reduce the GPU capacity and will release it in the next version.

Due to the size limitation of PYPI we cannot include our model with the released AutoBET, please feel free to contact us to obtain our pretrained model (JiamengLiu.PRC@gmail.com)

## Usase

```python
from AutoBET.BET import Auto_BET, _model_init

source_img_path = '/path/to/your/T1/data'
target_img_path = '/path/to/your/skull/stripped/data'
target_seg_path = '/path/to/your/brain/mask'

model_path = '/path/to/pretrained/model'

model = _model_init(model_path)
Auto_BET(source_img_path, target_img_path, target_seg_path, model)
```
mlax-nn 0.2.6
A pure functional machine learning library build on top of Google JAX.

# MLAX: Functional NN library built on top of Google JAX

[**Overview**](#overview)
| [**Installation**](#installation)
| [**Quickstart**](#quickstart)
| [**Examples**](https://github.com/zongyf02/mlax/tree/main/examples)
| [**Documentation**](https://mlax.readthedocs.io/en/latest/)

## What is MLAX?<a id="overview"></a>
MLAX is a purely functional neural network library built on top of Google [JAX](https://github.com/google/jax).

MLAX follows object-oriented semantics like Keras and PyTorch but remains fully
compatible with native JAX transformations.

Learn more about MLAX on [Read the Docs](https://mlax.readthedocs.io/en/latest/overview.html).

## Installation<a id="installation"></a>
[Install JAX](https://github.com/google/jax#installation) first if you have not
already.

```pip install mlax-nn```

## Quickstart<a id="quickstart"></a>
This is a simple lazy linear layer defined in MLAX.

``` Python
import jax
from jax import (
    numpy as jnp,
    nn,
    random
)
from mlax import Module, Parameter, Variable

class Linear(Module):
    def __init__(self, rng, out_features):
        super().__init__()
        self.rng = Variable(data=rng)
        self.out_features = out_features
        
        self.kernel_weight = Parameter()
        self.bias_weight = Parameter()
    
    # Define a ``set_up`` method for lazy initialziation of parameters
    def set_up(self, x):
        rng1, rng2 = random.split(self.rng.data)
        self.kernel_weight.data = nn.initializers.lecun_normal()(
            rng1, (x.shape[-1], self.out_features)
        )
        self.bias_weight.data=nn.initializers.zeros(rng2, (self.out_features,))

    # Define an ``forward`` method for the forward pass
    def forward(
        self, x, rng = None, inference_mode = False, batch_axis_name = ()
    ):
        return x @ self.kernel_weight.data + self.bias_weight.data
```

It is fully compatible with native JAX transformations:

``` Python
def loss_fn(x, y, model):
    pred, model = model(x, rng=None, inference_mode=True)
    return jnp.mean(y - pred) ** 2, model

x = jnp.ones((4, 3), dtype=jnp.float32)
y = jnp.ones((4, 4), dtype=jnp.float32)
model = Linear(random.PRNGKey(0), 4)

loss, updated_model = loss_fn(x, y, model)
print(loss)

# Now let's apply `jax.jit` and `jax.value_and_grad`
(loss, updated_model), grads = jax.jit(
    jax.value_and_grad(
        loss_fn,
        has_aux=True
    )
)(x, y, model)

print(loss)
print(grads)
```

For end-to-end examples with reference PyTorch implementations, visit MLAX's
[GitHub](https://github.com/zongyf02/mlax/tree/main/examples).

View the full documentation on [Read the Docs](https://mlax.readthedocs.io/en/latest/).

## Bugs and Feature Requests
Please [create an issue](https://github.com/zongyf02/mlax/issues) on MLAX's
Github repository.

## Contribution
If you wish to contribute, thank you and please contact me by email:
y22zong@uwaterloo.ca.
adventkit 0.2.0
Solutions to Advent of Code puzzles.
Requires Python: >= 3.6.1
Keywords: Advent of Code,puzzles

Adventkit -- Advent of Code in Python
=====================================

These are my solutions in Python to some of the [Advent of Code
puzzles](https://adventofcode.com). My goal is to make the code readable
while also making it reasonably fast.

Installation
------------

You can install Adventkit using pip:

    pip install adventkit

Alternatively, after cloning the Adventkit repository, the code can be
run as is.

Requirements
------------

-   Python 3.6.1+
-   Puzzle input files placed in the right locations: the input for day
    1 of Advent of Code 2019 is read from `input/year2019/day01.txt`
    (relative to the current working directory).

Optional, to run `src/run.sh` after cloning the repository:

-   A POSIX-compliant shell (`/bin/sh`)
-   A shell command `python3` to run Python 3.6.1+ (any implementation)
-   If extra speed is desired on selected puzzles: A shell command
    `pypy3` to invoke PyPy 3.6+

How to run
----------

After installing Adventkit, you can run a single day's solver like this:

    adventkit 2019 10

Cloning the repository gives you more options:

-   Run a single day's solver: `src/run.sh 2019 10` (or
    `python3 src/run.py 2019 10`)
-   Run all solvers: `src/run.sh`
-   Run all solvers from one year: `src/run.sh 2019`

When invoked with the optional argument `--time`, `src/run.sh` prints
the execution time for each day. If the command `pypy3` invokes a
suitable version of PyPy, `src/run.sh` uses it for any solvers that are
expected to run faster under PyPy than CPython.

Testing
-------

To run the tests, first install
[`pytest`](https://pypi.org/project/pytest/) and
[`pytest-subtests`](https://pypi.org/project/pytest-subtests/), as well
as your local copy of `adventkit`. Then run the command `pytest`.