semvec/pypi-packages huggingface dataset:
django-middle-management 0.1.2
A Django app for remote execution of management commands.
Requires Python: >=3.11

# Django Middle Management

It's usually a bad idea to connect to production servers to run one-off or repetitive
maintenance commands. There may not be any auditing, commands and payloads can easily
have mistakes, and a rogue developer could get away with almost anything. With
`django-middle-management`, though, you don't have to allow shell access to your
production servers while still being able to run commands on them.

This is a small library that makes it possible to securely and remotely execute Django
management commands via `POST` requests. Commands must be merged into your code base
before they're eligible to be used. They must also be listed in `settings.py` or they
cannot be triggered. Finally, requests must be authenticated by your system before any
command can be given.

**Warning:** This project runs management commands remotely but _synchronously_.
Long-running commands will potentially block your server from responding to other requests.
Using a task queue like Celery is recommend for anything that may take more than a few
milliseconds.

## Installation

```bash
pip install django-middle-management
```


Add the package to your `INSTALLED_APPS`:

```python
INSTALLED_APPS = [
    ...,
    "middle_management",
    ...
]
```

Add the URLs to your project's `urls.py`:

```python
from middle_management.urls import manage_urls

urlpatterns = [
    ...
] + manage_urls
```

You'll need to write a new management command or select an
existing one to expose. Finally, add that command name to
an allowlist of commands in `settings.py`:

```python
MANAGE_ALLOW_LIST = ["noop"]
```

## Usage

To execute a management command, make a `POST` request to the
`/__manage__/<command name>` endpoint with a payload similar to
the following:

```json
{
    "data": {
        "arg1": "value1",
        "arg2": "value2"
    }
}
```

Your `POST` must also contain a valid `HTTP_AUTHORIZATION` header
with the value `Bearer <token>`. The final request will look
something like:

```shell
curl -XPOST \
  -H 'Authorization: Bearer not-a-real-token' \
  -H "Content-type: application/json" \
  -d '{"data": { "arg1": "value1", "arg2": "value2" }}' \
  'https://example.com/__manage__/noop'
```
spared 2.2.1
SpaRED and Spackle library
Requires Python: >=3.7

# Library_Spared_Spackle

This repository contains all the necessary files to create a PyPI library to the SPARED and SpaCKLE contributions

This is the  README file which will contain the long description of the PiPy library. Most libraries have a README file. Mean while this file will only contain this information and will be soon updated. 

## Enhancing Gene Expression Prediction from Histology Images with Spatial Transcriptomics Completion

[Gabriel Mejía](https://scholar.google.com/citations?hl=es&user=yh69hnYAAAAJ)<sup>1,2</sup>\*, [Daniela Ruiz](https://scholar.google.com/citations?hl=es&user=Zm-tYR0AAAAJ)<sup>1,2</sup>\*, Paula Cárdenas<sup>1,2</sup>, Leonardo Manrique<sup>1,2</sup>, Daniela Vega<sup>1,2</sup>, [Pablo Arbelaez](https://scholar.google.com/citations?hl=es&user=k0nZO90AAAAJ)<sup>1,2</sup>

<br/>
<font size="1"><sup>*</sup>Equal contribution.</font><br/>
<font size="1"><sup>1 </sup> Center  for  Research  and  Formation  in  Artificial  Intelligence (<a href="https://cinfonia.uniandes.edu.co">CinfonIA</a>), Bogotá, Colombia.</font><br/>
<font size="1"><sup>2 </sup> Universidad  de  los  Andes,  Bogotá, Colombia.</font><br/>

- Preprint available at arXiv
- Visit the project on our [website](https://bcv-uniandes.github.io/spared_webpage/)
- Download [SpaRED datasets](https://drive.google.com/drive/folders/15W_rZlt5PhUlslM-u5_jw9etjkGRXb-N?usp=sharing)

### Abstract

Spatial Transcriptomics is a novel technology that aligns histology images with spatially resolved gene expression profiles. Although groundbreaking, it struggles with gene capture yielding high corruption in acquired data. Given potential applications, recent efforts have focused on predicting transcriptomic profiles solely from histology images. However, differences in databases, preprocessing techniques, and training hyperparameters impact a fair comparison between methods. To address these challenges, we present a systematically curated and processed database collected from 26 public sources, representing an 8.6-fold increase compared to previous works. Additionally, we propose a state-of-the-art transformer-based completion technique for inferring gene expression, which significantly boosts the performance of transcriptomic profile predictions across all datasets. Altogether, our contributions constitute the most comprehensive benchmark of gene expression prediction from histology images to date and a stepping stone for future research.

## System Dependencies

Before installing the Python package, ensure the following system dependencies are installed:

```shell
conda create -n spared
conda activate spared
conda install pytorch torchvision torchaudio pytorch-cuda=11.8 -c pytorch -c nvidia
conda install lightning -c conda-forge
pip install torch_geometric
conda install -c conda-forge squidpy
pip install wandb
pip install wget
pip install combat
pip install opencv-python
pip install positional-encodings[pytorch]
pip install openpyxl
pip install pyzipper
pip install plotly
pip install sh
pip install sphinx
pip install -U sphinx-copybutton
pip install -U sphinx_rtd_theme
```