semvec/pypi-packages huggingface dataset:
system-helpers 0.0.3.post4
Helper scripts for Linux systems.
Requires Python: >=3.10

[![PyPI - Version](https://img.shields.io/pypi/v/system-helpers?color=blue)](https://pypi.org/project/system-helpers/)
![GitHub Actions Workflow Status](https://img.shields.io/github/actions/workflow/status/uliegecsm/system-helpers/test.yml)

# System helpers

This repository contains useful standalone helper scripts for `Linux` systems.

## `apt`

Useful for dealing with `apt` related tasks.

For instance, the following command
```bash
apt-helpers install-packages --update --upgrade --clean --packages git wget
```
will perform:
1. `apt update`
2. `apt upgrade`
3. `apt --yes --no-install-recommends install git wget`
4. `apt clean && rm -rf /var/lib/apt/lists`

## `update-alternatives`

Useful for dealing with `update-alternatives` tasks.
code-ninja 0.1.20
Code-ninja is the error handler library and help to detect the error and after detect the error give the solution of error.
Requires Python: >=3.11

# ErrorHandler-AI

Python error handler library with UV and Rich, provides AI-powered suggestions for fixing errors in user code. Uses OpenAI for error cause analysis and solution output. Styled suggestions are shown in the terminal using Rich.

---

## Table of Contents

- [Overview](#overview)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [Decorator Usage](#decorator-usage)


---

## Overview

ErrorHandler-AI provides a decorator that lets you catch and explain Python errors in your functions automatically. If an error occurs, the library uses OpenAI to analyze the cause and suggest a fix and Correct code. Output is neatly formatted in your terminal using Rich panels for easy readability.

**Main features:**

- Catches Python errors in user code (decorator-based)
- AI-powered error cause and fix suggestions and Correct code.
- Rich terminal output with styled panels.
- Customizable with environment variables.

---

## Installation

Install via pip:

```
pip install code-ninja==0.1.18
```

Set your OpenAI API key and other environment variables in a `.env` file:
```
API_KEY=your-api-key
BASE_URL=your-openai-base-url # optional
INSTRUCTION=Explain the error in more detail with examples. # optional
MODEL_NAME=Your-model # optional
```


---

## Quickstart

Add the decorator to your function:

```
from code_ninja import catch

@catch
def divide(x, y):
return x / y

divide(10, 0)
```



When an error is raised, you'll see:

- Error type and info.
- AI-analyzed cause.
- AI fix suggestion.
- Correct code.

---

## Decorator Usage

Use the `@catch` decorator to wrap any function you want to monitor for errors:
hydromt-fiat 1.0.0rc2
HydroMT plugin for Delft-FIAT.
Requires Python: >=3.11
Keywords: HydroMT, FIAT, Impact

.. _readme:

========================================
HydroMT-FIAT: Building Delft-FIAT models
========================================

|pypi| |conda forge| |quality| |coverage| |license|

What is HydroMT-FIAT?
---------------------
**HydroMT-FIAT** is an open-source python package for building for **Delft-FIAT** models.
It is meant to make model building easier on the user by streamlining and generalizing
data processing. For this purpose HydroMT-FIAT relies on the excellent **HydroMT**
python library.

Documentation
-------------
Important documentation in regard to HydroMT-FIAT.

- `HydroMT-FIAT <HydroMT_FIAT_>`_
- `Delft-FIAT <Delft_FIAT_>`_
- `HydroMT <HydroMT_>`_

Attribution
-----------
HydroMT-FIAT relies on these excellent libraries:

- barril
- geopandas
- hydromt
- numpy
- osmnx
- pooch
- pyproj
- requests
- shapely
- tomlkit
- xarray

.. |pypi| image:: https://img.shields.io/pypi/v/hydromt-fiat
    :alt: PyPI
    :target: https://pypi.org/project/hydromt_fiat/

.. |conda forge| image:: https://img.shields.io/conda/v/conda-forge/hydromt_fiat
    :alt: Conda-Forge
    :target: https://anaconda.org/conda-forge/hydromt_fiat

.. |quality| image:: https://sonarcloud.io/api/project_badges/measure?project=Deltares_hydromt_fiat&metric=alert_status
    :alt: SonarQube status
    :target: https://sonarcloud.io/summary/new_code?id=Deltares_hydromt_fiat

.. |coverage| image:: https://sonarcloud.io/api/project_badges/measure?project=Deltares_hydromt_fiat&metric=coverage
    :alt: Coverage
    :target: https://sonarcloud.io/summary/new_code?id=Deltares_hydromt_fiat

.. |license| image:: https://img.shields.io/github/license/Deltares/hydromt_fiat
    :alt: License
    :target: https://github.com/Deltares/hydromt_fiat/blob/main/LICENSE

.. _Delft_FIAT: https://deltares.github.io/Delft-FIAT/stable
.. _HydroMT_FIAT: https://deltares.github.io/hydromt_fiat/latest
.. _HydroMT: https://deltares.github.io/hydromt/stable
mdup 0.3.0
Keep markdown files up-to-date.
Requires Python: >=3.8

# `mdup`

[![Tests](https://github.com/alexandru-dinu/mdup/actions/workflows/main.yml/badge.svg)](https://github.com/alexandru-dinu/mdup/actions/workflows/main.yml)
[![PyPI version](https://badge.fury.io/py/mdup.svg)](https://pypi.org/project/mdup/)

`mdup` is a command-line tool for keeping markdown files up-to-date by injecting code, script or command output between special blocks. One immediate use case is keeping documentation up-to-date without having to manually update markdown files with info from code snippets, scripts or command outputs [^1].

`mdup` does not depend on anything apart from Python stdlib.

## Install
```
pip install mdup
```
or easier usage via `uv`:
```
uv tool run mdup
```

## Usage
<!-- MDUP:BEG uv run mdup --help -->
```
usage: mdup [-h] -i INPUT [-o OUTPUT] [-v]

options:
  -h, --help           show this help message and exit
  -i, --input INPUT    input markdown file
  -o, --output OUTPUT  output markdown file; if not specified, the input file
                       will be edited in place
  -v, --version        show program's version number and exit
```
<!-- MDUP:END -->

`mdup` will replace the contents between each **block** with the command output (see details below).
You can keep files up-to-date by simply rerunning `mdup`.

Omitting the `-o` option will edit the file in-place.

For example, the command-line usage block above is automatically generated by defining the following block in the README.md:

    <!-- MDUP:BEG uv run mdup --help -->
    <!-- MDUP:END -->

then running `mdup -i README.md` which executes `uv run mdup --help` and injects the output inside the block.

## Block definition

The blocks are defined using HTML comments.

**Begin**

    <!-- MDUP:BEG (command) (; pre) -->

Examples:

    <!-- MDUP:BEG cat ./mdup.py ; py -->
    <!-- MDUP:BEG ls -lh -->

If you want to add a markdown language tag (`pre`, like `py` in the first example), then it must follow the command, separated by a semicolon.

Paths are relative to the markdown file containing the blocks.

**End**

    <!-- MDUP:END -->

The blocks must be defined as the sole contents of the line, i.e. matching `^` and `$` anchors.

## Examples

For examples, see [tests](./tests/).

One _real-world_ use case is my [Advent of Code](https://adventofcode.com/)
[README.md](https://github.com/alexandru-dinu/programming-challenges/blob/main/advent-of-code/README.md),
in which I use `mdup` to automatically update year progress badges.

## ⚠️ Disclaimer

I created this tool primarily to meet my own needs -- it's very simple and ad-hoc. While I don't anticipate it gaining too much adoption, always beware when running `mdup` on markdown files of unknown origin (which can be malicious), e.g.:

    <!-- MDUP:BEG rm -rf / -->
    <!-- MDUP:END -->

This tool should only be used for **simple** tasks, e.g. keeping **simple** documentation up-to-date.

[^1]: Inspired by [DavidWells/markdown-magic](https://github.com/DavidWells/markdown-magic).
jsontas 1.4.2
JSONTas is a tool for generating dynamic JSON structures.
Requires Python: >=3.9

=======
JSONTas
=======

JSONTas is a tool for generating dynamic JSON data.

Description
===========

JSONTas adds conditionals and logic to JSON files in order to create dynamic JSON data depending on which dataset you supply.

It opens up the possibility to create generic tools where most of the operations are done by executing JSONTas on the data.

Documentation: https://jsontas.readthedocs.io/en/latest


Features
========

- Simple yet powerful syntax.
- HTTP requests on parse
- Separation of environments by providing different datasets.

Installation
============

Install the project by running:

   pip install jsontas

Examples
========

First we create two datasets. One for our 'dev' environment and one for our 'prod' environment.

Dataset 'dev.json'
------------------

.. code-block:: JSON

   {
      "mode": "dev",
      "database": "dev_db"
   }


Dataset 'prod.json'
-------------------

.. code-block:: JSON

   {
      "mode": "prod",
      "database": "prod_db"
   }

JSONTas JSON file
-----------------

Next up, let's create our JSONTas file.

.. code-block:: JSON

   {
      "database": {
         "host": "myawesomedb.example.com",
         "database": "$database"
      },
      "message": {
         "$condition": {
            "if": {
               "key": "$mode",
               "operator": "$eq",
               "value": "dev"
            },
            "then": "This is the DEV server.",
            "else": "This is the PROD server."
         }
      }
   }

JSONTas execute with 'dev' dataset
----------------------------------

.. code-block:: bash

   jsontas -d dev.json data.json

.. code-block:: JSON

   {
      "database": {
         "host": "myawesomedb.example.com",
         "database": "dev_db"
      },
      "message": "This is the DEV server."
   }

JSONTas execute with 'prod' dataset
-----------------------------------

.. code-block:: bash

   jsontas -d prod.json data.json

.. code-block:: JSON

   {
      "database": {
         "host": "myawesomedb.example.com",
         "database": "prod_db"
      },
      "message": "This is the PROD server."
   }

These examples only show the bare minimum.
For more examples look at our documentation at: https://jsontas.readthedocs.io/en/latest

Contribute
==========

- Issue Tracker: https://github.com/AxisCommunications/jsontas/issues
- Source Code: https://github.com/AxisCommunications/jsontas

Support
=======

If you are having issues, please let us know.
Email tobias.persson@axis.com or just write an issue.
pydpack 0.0.2
This is a pydpack.
Requires Python: >=3.11

# pydpack

## What is this?

- by: axiner
- pydpack
- This is a pydpack.（pyd打包，可将py目录或文件打包成pyd(so)）

## Installation

This package can be installed using pip (Python>=3.11):
> pip install pydpack

## Usage

- 1）help
    - `pydpack -h`
- 2）pack
    - `pydpack -s <py目录或文件> -e main.py`
    - 注：
        - 自动跳过：__init__.py，空文件，只存在注释的文件，当然还有非py文件
        - 排除编译：正则表达式
            - 文件夹加正斜杠'/'即可，如：tests/, tests/a/
            - 多个则用'|'隔开，如：main.py|tests/
            - 项目的入口文件一般是不编译的，排除即可
        - 若编译不成功或编译后执行不成功：
            - 规范代码，确保代码的正确性与严谨性
            - 编译失败，目录下会保留对应的源文件
            - 执行环境，需要与编译时的环境一致
        - 输出：目录（源+扩展后缀），该目录与src结构一致
magnes-artifact-removal 0.1.0
Magnes utility for the removal of artifacts from timeseries.
Requires Python: >=3.12

# Magnes Artifact Removal
Magnes utility for the removal of artifacts from time series.


## Installation
Install the package using
```bash
    pip install artrem 
```
*Note*, might need to be adapted to the final location of the package.

## Quick Start

``` python
    import numpy as np
    from martrem import clean_with_adaptive_shape_correlation
    from martrem.aux import templating

    # Prepare your signal data
    fs = 250.0  # Sampling frequency
    signal_data = np.array([...])  # Your time series data

    # Create a search template
    psi_n = 25
    psi = templating.mexican(psi_n, fs=fs, bound=3)

    # Clean the signal
    corrected, artifact = clean_with_adaptive_shape_correlation(
        signal_data,
        fs=fs,
        psi=psi,
        tau_n=50,
        fco_hp=0.1,
        xcorr_min_peak_distance=80
    )
```
## Submodules
### Autoenc
Autoencoder-based signal filtering/artifact detection.

### Auxiliary Tools
Collection of submodules for filtering, scaling, and generating signal templates (shapelets/wavelets).

### Cleaning
Main interface of the package. Collection of functions cleaning corrupted time series using a specific method.

## Scripts
The package is evaluated using a number of scripts. Scripts can be seen as studies of a specific
strategy or a comparison thereof. For example, scripts to evaluate the search-template-based ECG artifact
removal strategy from blurred-peak waves are provided, including a single-experiment run `scripts/singlerun.py`
and a parametric sweep `scripts/sweep.py`.

Due to the folder hierarchy, scripts are to be run from root as modules, i.e. to run the
script `scripts/foo.py` call
```bash
uv run -m scripts.foo [ARGS]
```

**Note** The scripts are not included in package, but are only available in the full source.

## Testing
Testing is performed using `pytest`. Package tests are defined in `tests/`, replicating the
To run the package unittests, call
```bash
uv run -m pytest tests [OPTIONS]
```
To run the scripts' tests call
```bash
uv run -m pytest scripts [OPTIONS]
```
To run the all tests call
```bash
uv run -m pytest [OPTIONS]
```

All calls are assumed to be made from the project root.