semvec/pypi-packages huggingface dataset:
mianalyzer 0.3.1
MIA, deep learning based Microscopic Image Analyzer
Requires Python: >=3.6

# MIA - Microscopic Image Analyzer
![MIA](https://github.com/MIAnalyzer/MIA/blob/master/docs/source/gettingstarted/images/user_interface.PNG?raw=true)

MIA is a software for deep learning based image analysis. It covers image labeling, neural network training and inference. It can be used for image classification, object detection, semantic segmentation and tracking.

## Installation
The easiest way to install MIA is via conda (see https://docs.conda.io/en/latest/miniconda.html for installation options).

After installation of conda, download the [environment](https://github.com/MIAnalyzer/MIA/releases/download/weights/mia_environment.yaml) file. 

Then, open an anaconda prompt and type:
- ```cd /path/to/mia_environment.yaml```  (change ```/path/to/``` to the path of the folder with the environment file)
- ```conda env create -f mia_environment.yaml```
- wait and follow instructions
  
### to start the software 
type in an anaconda prompt:
  - ```conda activate mia_environment```
  - ```mianalyzer```

## How to get help?

A quickstart guide can be found [here](https://mianalyzer.github.io/gettingstarted/quickstart.html) and the complete user manual [here](https://mianalyzer.github.io/).

Please use [image.sc](https://forum.image.sc/tag/mia) with the ```mia```-tag for general discussion, questions about how to use the software or feature requests. Bugs can be reported directly in the [issues](https://github.com/MIAnalyzer/MIA/issues) panel on github.

## Reference
If you use this code for your research, please cite: 

https://www.cell.com/cell-reports-methods/pdf/S2667-2375(23)00146-7.pdf

Körber, MIA is an open-source standalone deep learning application for microscopic image analysis, Cell Reports Methods (2023)


## Requirements

In general, MIA should run on any system with Linux or windows. You can use the cpu only, but it is highly recommended to have a system with a [cuda-compatible](https://developer.nvidia.com/cuda-gpus) gpu (from NVIDIA) to accelerate neural network training.
Brian2GeNN 1.7.0
An interface to use the GeNN framework as a device in Brian 2
Requires Python: >=3.9

Brian2GeNN
==========
Brian2GeNN is an interface between Brian 2 and GeNN. Brian2 is a simulator for spiking neural networks available on a variety of platforms. It is the successor of Brian1 and shares its approach of being highly flexible and easily extensible. It is based on a code generation framework that allows to execute simulations using other programming languages and/or on different
devices. 

GeNN (GPU enhanced Neuronal Networks, https://github.com/genn-team/genn) is a framework that uses code generation methods to allow using GPU accelerators without in-depth knowledge of the CUDA programming interface.

Brian2Genn provides an interface to use GeNN as a backend device in Brian2. This allows users to run their Brian 2 scripts on NVIDIA GPU accelerators without any further necessary programming.

We currently consider this software to be in the beta status, please report
issues to the github issue tracker (https://github.com/brian-team/brian2genn/issues).

Documentation for Brian2GeNN can be found at http://brian2genn.readthedocs.org

[![PyPI package](https://img.shields.io/pypi/v/Brian2GeNN.svg)](https://pypi.python.org/pypi/Brian2GeNN)
[![Documentation Status](https://readthedocs.org/projects/brian2genn/badge/?version=stable)](https://brian2genn.readthedocs.io/en/stable/?badge=stable)
[![Build Status](https://github.com/brian-team/brian2genn/actions/workflows/testsuite.yml/badge.svg)](https://github.com/brian-team/brian2genn/actions/workflows/testsuite.yml)

If you use BrianGeNN for your published research, we kindly ask you to cite our article:  
Marcel Stimberg, Dan F. M. Goodman, and Thomas Nowotny. “Brian2GeNN: Accelerating Spiking Neural Network Simulations with Graphics Hardware.” Sci Rep 10 (January 2020): 410. [doi: 10.1038/s41598-019-54957-7](https://doi.org/10.1038/s41598-019-54957-7).
tootstagram 0.0.7
Duplicate posted images from Mastodon to Instagram
Requires Python: >=3.8

# Tootstagram

https://cvs.sonologic.net/gmc/tootstagram

## Installation

```
pip install tootstagram
```

## Usage

Create a configuration file name `tootstragram.toml` based on the
example `tootstragram.toml.example` and place it in any of these
locations:

* In your home or user directory
* In a subdirectory `.tootstagram` under your home or user directory
* In the current working directory

The tool will search each of these locations in that order.

Then simply invoke the tool with:

```
tootstagram
```

This will fetch the feed from the Mastodon account and post any
images that were not already posted to the Instagram account.

## Security

Make sure you keep the configuration file as well as the `.tootstagram`
directory in your home or user dir private. The configuration file 
contains your instagram username and password, and the `.tootstagram`
directory contains a file `session.json` which should be treated as
equivalent to your username and password.

## Limitations

* Only posts toots with images, not videos (I don't do videos myself)
* Any html tags in the toot are crudely removed by a simple regexp
* If a toot has multiple images, but not all have alt text, none of the
  instagram posts will have alt text (because it is impossible to derive
  from the feed which alt text belongs to which image)
* Editing a toot will not alter on or repost to instagram
* Only posts listed toots, as those are the only ones available from the
  feed

## Planned features

* Hashtag translation

## Releasing

```
python -m build
python3 -m twine upload dist/*
```