semvec/pypi-packages huggingface dataset:
fshredder 0.1.0
Advanced file shredding tool that deletes files with no data left using encryption techniques
Requires Python: >=3.6

# File Shredder

Advanced file shredding tool that deletes files with no data left using encryption techniques.

## Features

- Delete any file securely and completely with no data left
- Using Encryption and AI techniques
- Shred all files in a directory

## Installation

pip install fshredder

## Usage

fshredder or fshredder.py
LinkToFabricHelper 0.1.0
A small package to help developers make exported data from dataverse via Link to Microsoft Fabric functionality easieer accessible.
Requires Python: >=3.8

# LinkToFabricHelper

[![PyPI Latest Release](https://img.shields.io/pypi/v/linkToFabricHelper.svg)](https://pypi.org/project/LinkToFabricHelper/) 

Disclaimer: Since the package is in an early stage of development it is not recommended to use it in production-environments. 

This package is designed to help developers of solutions im Microsoft Fabric that use dataverse's functionality "Link to Microsoft Fabric" to handle the cdm-like structure of the data. 

### Common Data Model 

For more information on the "Common Data Model" (CDM) see for example the official documentation of Microsoft on [Microsoft Learn](https://learn.microsoft.com/en-us/common-data-model/). 

## How to use LinkToFabricHelper

### Utilize CDM-Schema

The exported tables from dataverse come with referenced optionssets for lots of columns. This comes with the need to dereference this columns somewhen to present the data to business users. 

This package provides the functionality to read the metadata provided by the "Link to Microsoft Fabric" functionality within the lakehouse and utilize it in views so that business users get an easy interface to access their data in a raw format if required and give data engineers the possibility to easily transform the data in later stages. 

Turn this: 
![Table with options](docs/img/001_TableWithOptions.png)
Into that: 
![View with labels](docs/img/002_ViewWithLabels.png)

The package is designed to work when called from within a Fabric notebook so that such an update of views can run e.g. after a dataverse release and thus a possible schema change. But be aware that there is currently no possibility to create views in the sql endpoint directly out of a notebook (inofficially announced in a blog for later in 2024) so currently the created script needs to be executed manually via the sql endpoint. 

For a detailed explanation of how to use the package to create these views see [the detailed documentation here.](docs/CreateConsumptionViews.md)

#### Local execution

It is also possible to connect to the sql endpoint directly and retrieve the metadata from there. Since the vision of this package is a direct execution within a Fabric notebook and needs someone executing it on a local machine this is not the recommended approach. 
Nevertheless it is nice to have this possibility for debugging and development so an exemplary call is given in [localexecution.py](localexecution.py) which needs SQL_ENDPOINT (sql endpoint connection string from Fabric lakehouse) and DATABASE_NAME (name of the lakehouse in Fabric) defined in a seperate script.
button_beaver 0.2.0.dev0
Helper for all your 88x31 buttoning needs!
Requires Python: >=3.10

# button_beaver

![Button with a beaver and text saying Button Beaver!](https://tildegit.org/MinekPo1/button_beaver/raw/branch/main/button.png)

Button beaver is a little helper for all your 88x31 buttoning needs! It can be told various [`.well-known/button.json`](https://codeberg.org/LunarEclipse/well-known-button) enabled button sources (or you can add buttons manualy)
and it will handle the rest!

## instalation

```bash
$ pip install button_beaver
```

## usage

TODO
gh-issue-exporter 0.0.2
Easy way to export github issues
Requires Python: >=3.8

# Github Issue Exporter

Do you want a backup of your Github export?

This is your chance to do it!

## Installation

### Dependencies
Only dependency is the python library `requests`

Either clone the repo and install with `pip install .` or wait for me to publish it on pypi.

### Local install

1. Clone repo
2. `python3 -m virtualenv venv` + `source venv/bin/activate`
3. pip install .

## Usage

Use the handy CLI provided by GH Issue Export.

### Exporting issues from a Github repository

`gh-issue-exporter export <REPO_URL>`

ex.

`gh-issue-exporter export https://github.com/mal-lang/mal-toolbox/`

This creates a JSON file with issues called 'mal-toolbox.json' in the same directory as it was run.

To select outfile name, use the o-flag: `-o <outfile>`

To export pull requests as well:

`gh-issue-exporter export https://github.com/mal-lang/mal-toolbox/ --pull_requests [-p]`


### Importing issues from an issue file to a repository

#### Preparation

- For gh-issue-exporter to be able to create issues, you need to use a Github Token.
    - Visit: https://github.com/settings/tokens
    - Click 'Generate New Token'
    - Authenticate if needed
    - Give the token access to the repository you want to import TO
    - Give read and write access to 'Issues'

To import issues from an existing issues file, run:

`gh-issue-exporter import <REPO_URL> <ISSUE_FILE> <TOKEN>`

ex.

`gh-issue-exporter import https://github.com/mrkickling/github-issue-exporter mal-toolbox.json github_[redacted]`
IMPACTBIOINFORMATICS 4.6
IMPACT: Interpretable Microbial Phenotype Analysis via microbial Characteristic Traits
Requires Python: >=3.6

We present a framework that combines a feature engineering step to transform tabular ASV abundance data to sample wised image-format using functional microbial annotation databases, with a residual spatial attention transformer block architecture for phenotype classification. 

We find that our model delivers improved predictive accuracy performance across multiclass classification compared to similar methods. More importantly, our approach provides interpretable feature importance through image classification saliency methods. This enables the extraction of taxa markers (features) associated with a disease outcome but also their associated functional microbial traits and metabolites.
pycaan 0.1.2
Python calcium imaging analysis (PyCaAn)
Requires Python: >=3.10

<p align="center">
  <img width="60%" src="docs/logo.png">
</p>

PyCaAn stands for  **Python Calcium imaging Analysis**
This repository contains tools to analyze large datasets of calcium imaging data, plot, and extract statistics.

Features:
- Tuning curves
- Unbiased information theoretic metrics
- Low-dimensional embeddings (manifolds)
- Data simulator
- Dataframe support for quick plotting/stats
- and more!

# Installation
To use internal functions in other projects, you need to install PyCaAn in your preferred environment.
Currently, this is done with a developer install:
`
pip install -e .
`

# Run analyses
PyCaAn provides both high- and low-level access to analytical functions.

## Low level
Any function can be called using:
`
import pycaan
`

Example: to binarize calcium transients, use:
`
from pycaan import binarize_ca_traces
binarized_traces, neural_data = binarize_ca_traces(ca_traces, z_threshold, sampling_frequency)
`

## High level
First define your paths (input dataset, output result folders) and other parameters in params.yaml,
you can analyze a single session from the terminal using:
`
% python3 pycaan//analysis/extract_tuning_data.py --session_path ../../example_dataset/example_region/example_subject/example_mouse
`

you can also perform a specific type of analysis on selected sessions (batch process) after running run_dataset_curation.py:
`
% python3 run_analysis.py --extract_tuning
`

Finally, for a fully automatized dataset analysis, you can run:
`
sh runAll.sh
`
This function will curate your dataset with desired threshold (e.g. minimum number of neurons per recording) and run all analyses.

# Dataset naming convention
Dataset path has to be specified in params.yaml
The naming convention should follow these principles:
`
region/subject/subject_task_condition1_condition2_..._date
`
For example:
`
amygdala/F173/F173_OF_darkness_20230804
`