semvec/pypi-packages huggingface dataset:
sinutils 0.0.5
A Python Utitlity Modules containing various useful functions.
Keywords: utils

# sinutils

A Python Utitlity Modules containing various useful functions.

Install
===============

```
 pip install sinutils
```

Usage
===============

```python
# -*- coding: UTF-8 -*
'''
Created on 2021-04-16
'''
from __future__ import print_function
import time

import sinutils

# 进度显示
count = 80
with sinutils.Progress(total=count, title='TEST') as pg:
    for i in range(count):
        pg.feed(status='C%d' % i)
        time.sleep(.01)


# 任务分割器
def doit(datas):
    print('handle', datas)


with sinutils.Feeder(doit, 5) as fd:
    for i in range(12):
        fd.feed(i)

# watch modules
import testm

testm.test()
mw = sinutils.ModulesWatcher([testm])
while True:
    # edit testm.py to test auto_reload function
    mds = mw.auto_reload()
    if mds:
        print('changed', mds)
        testm.test()
    time.sleep(1)

```

Output:
```
TEST...
TEST...  5% Finish:4/80 Spent:35ms Speed:112/s Left:676ms Status:C3
TEST... 10% Finish:8/80 Spent:80ms Speed:100/s Left:721ms Status:C7
TEST... 15% Finish:12/80 Spent:124ms Speed:96/s Left:704ms Status:C11
TEST... 20% Finish:16/80 Spent:169ms Speed:94/s Left:679ms Status:C15
TEST... 25% Finish:20/80 Spent:218ms Speed:91/s Left:656ms Status:C19
TEST... 30% Finish:24/80 Spent:266ms Speed:90/s Left:621ms Status:C23
TEST... 35% Finish:28/80 Spent:313ms Speed:89/s Left:583ms Status:C27
TEST... 40% Finish:32/80 Spent:359ms Speed:89/s Left:539ms Status:C31
TEST... 45% Finish:36/80 Spent:405ms Speed:89/s Left:495ms Status:C35
TEST... 50% Finish:40/80 Spent:453ms Speed:88/s Left:453ms Status:C39
TEST... 55% Finish:44/80 Spent:497ms Speed:88/s Left:406ms Status:C43
TEST... 60% Finish:48/80 Spent:543ms Speed:88/s Left:362ms Status:C47
TEST... 65% Finish:52/80 Spent:588ms Speed:88/s Left:316ms Status:C51
TEST... 70% Finish:56/80 Spent:632ms Speed:89/s Left:270ms Status:C55
TEST... 75% Finish:60/80 Spent:677ms Speed:89/s Left:225ms Status:C59
TEST... 80% Finish:64/80 Spent:724ms Speed:88/s Left:181ms Status:C63
TEST... 85% Finish:68/80 Spent:773ms Speed:88/s Left:136ms Status:C67
TEST... 90% Finish:72/80 Spent:821ms Speed:88/s Left:91ms Status:C71
TEST... 95% Finish:76/80 Spent:867ms Speed:88/s Left:45ms Status:C75
TEST...100% Finish:80/80 Spent:916ms Speed:87/s Left:0ms Status:C79
TEST Finished Spent:927ms
handle [0, 1, 2, 3, 4]
handle [5, 6, 7, 8, 9]
handle [10, 11]
hi 43311
```

[Click to view more information!](https://github.com/sintrb/sinutils)


CHANGES
===============
0.0.1

- first version
image-processing-jaderjf 0.0.1
image processing package using skimage
Requires Python: >=3.0

# image_processing

Description. 
The package image_processing is used to:
	Processing:
		- Histrogram matching
		- Structural similarity
		- Resize image
	Utils:
		- Read image
		- Save image
		- Plot image
		- Plot result
		- Plot histogram 


## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install image_processing

```bash
pip install image_processing
```

## Author
Jader Jesus

## License
[MIT](https://choosealicense.com/licenses/mit/)
autoreview-ufz-VS 1.0.2
A package for automating operations on Excel files.
Requires Python: >=3.9

# Review Scripts

The following scripts are designed to facilitate the automated analysis of data exported from the Web of Science (WoS) database.

## Usage

The different scripts can be utilized according to specific analytical requirements.

```sh
python <file>.py
```

### File Concatenation

The script `concatenate_xlsx.py` merges multiple Excel spreadsheets into a single consolidated table. Please ensure that the input tables have an identical structure to enable accurate concatenation. This script is particularly useful for aggregating multiple XLSX files exported from WoS.

### Ordering

The script `ordering.py` allows for the sorting and filtering of records based on user-defined criteria, with the final output provided in XLSX format.

### Highlighting

The scripts `script_highlight.py` and `script_highlight_excel.py` are used to highlight specified keywords from an Excel file. The output is generated in different formats: DOCX for `script_highlight.py` and XLSX for `script_highlight_excel.py`.

### Full Article Analysis

The script `analyse_articles_doi.py` retrieves full-text articles from the web based on DOIs listed in an Excel file. It subsequently highlights relevant terms within the full text of these articles, similar to the functionality provided by the other highlighting scripts.
pyrosequel 0.1.2
PyroSequel, advanced SQL querying
Requires Python: >=3.10

# PyroSequel
PyroSequel is a small and asynchronous ORM / SQL query builder. PyroSequel started as a wrapper for the `aiosqlite` library, but now supports using different databases.

Right now, only `SQLiteDatabase` is implemented (using aiosqlite).

# Installation
To install PyroSequel with SQLite support, run this:

> $ pip install pyrosequel

# Example
Here's a small example, showcasing simple use case for the PyroSequel.

```py
import asyncio
from pyrosequel import *

async def main():
    db = await SQLiteDatabase.connect(":memory:")

    @db.register_model()
    class User(Model):
        id = IntegerField(PrimaryKey())
        name = TextField(NotNull())
    
    await User.create_table()

    # insert to specified column
    await User.insert(
        (User.name, "gecko")
    ).cast()

    # or just pass values in
    await User.insert(None, "zedikon").cast()

    # now fetch the user
    print(await User.select()
                    .where(User.name == "gecko")
                    .cast())
    # [(1, 'gecko')]
```

# Licensing
This project is licensed under BSD 3-clause licese. See LICENSE file for more.
Copyright 2024 GeckoNerd (geckwwo)

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.

3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
robosource-common 20241030.1
A package offering common-use functionality for RoboSource developers, making it easy to do the right thing
Requires Python: >=3.11

# RoboSource Common (Python Edition)

This package contains functionality that RoboSource developers use over and over across multiple client automations. It is public and free to use at your own convenience. We offer no official external support of this package, but drop us a line if you have a suggestion or bugfix to include! If you are using this package and don't know the values for any of the secrets, reach out and we may be able to help you out.

## CoachBot API
neca 2.1.13
ECA: Event Condition Action
Requires Python: <4,>=3.10

# NECA - the Successor of ECA (Event-Condition-Action)
NECA is a simple Python library that allows you to create event-driven applications. It is inspired by the [ECA (Event-Condition-Action) paradigm](https://en.wikipedia.org/wiki/Event_condition_action). NECA is a successor of [ECA](https://github.com/utwente-db/eca) and is built on top of [Flask](https://flask.palletsprojects.com/en/2.0.x/).

NECA is specialized for creating web dashboards. It allows you to easily create a web server with a dashboard that can be updated real-time. It also allows you to easily add new functionality to your dashboard.

## Features
- Event-driven programming
- Basic building blocks for creating web dashboards
- Easy to use
- Easy to extend
- Easy integration with external data sources

## Installation
you can install neca using pip:
```bash
pip install neca
```

## Creating a new project
use the neca commandline tool to create a new project:
```bash
neca [folder name]
```

## Running your newly created project
to run your project, simply execute the main python file in the generated project folder:
```bash
python main.py
```

## Tutorials
If you want to learn about the neca framework in an interactive way, you can check out the tutorials. 
you can load a tutorial by creating a new project with a tutorial template. Inside these templates there's a README.md file that explains the tutorial content and has some small exercises to help you learn. To open this file in visual studio code, right click on the file and select "Open Preview".

You can also view the tutorials in the Wiki:
- [Tutorial 1 - the Event System](https://github.com/NiekAukes/eca2/wiki/Tutorial-1)
- [Tutorial 2 - creating your first block](https://github.com/NiekAukes/eca2/wiki/Tutorial-2)

## Documentation
You can find the documentation [here](https://github.com/NiekAukes/eca2/wiki).

## Demos
You can instantiate a demo with the neca commandline tool and choosing an example from the list
```bash
neca [folder name]
```
neuroagent 0.3.3
Search agent for neuroscience.
Requires Python: >=3.10

# Neuroagent

LLM agent made to communicate with different neuroscience related tools. It allows to communicate in a ChatGPT like fashion to get information about brain regions, morphologies, electric traces and the scientific literature.


1. [Funding and Acknowledgement](#funding-and-acknowledgement)

## Funding and Acknowledgement

The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

Copyright (c) 2024 Blue Brain Project/EPFL

## Release workflow

Commits with a special prefix will be added to the CHANGELOG of the latest release PR.
The main prefixes can be found here:
https://www.conventionalcommits.org/en/v1.0.0/#summary

When a PR is merged into the main branch, a new release PR will be created if there is no open one. Otherwise all changes
from the merged branch will be added to the latest existing release PR.

The workflow is:
1. When merging a PR, change the squashed commit message to one that contains one of the above prefixes. This will trigger the creation of a release PR if there isnt one. The commit message will be automatically added to the changelog.
2. When the release PR is merged, a new release tag will be automatically created on github.
starst3r 0.4.0
Ultra fast 3D reconstruction and novel view synthesis.

# Starst3r

Ultra fast 3D reconstruction and novel view synthesis.

![](docs/images/demo.gif)

Starst3r combines the Mast3r 3D reconstruction paper and the
gsplat novel view synthesis project into a simple Python package.

Docs: https://starst3r.readthedocs.io/en/latest/
sacrecomet 0.1.0
Tool to guide you through reporting the use of COMET for machine translation evaluation.
Requires Python: >=3.7
Keywords: machine-translation, evaluation, quality-estimaation

# SacreCOMET

The tool has two functionalities.
First, it creates a signature with your setup and COMET model:

```
pip install sacrecomet

# Without anything will try to detect the local environment and will
# ask you questions about which COMET model you used.
# Example output: Python3.11.8|Comet2.2.2|fp32|unite-mup

sacrecomet 

# Arguments can also be specified non-interactively:

sacrecomet --model unite-mup --prec fp32
```

The other functionality is to find specific citations for COMET models that you're using:

```
sacrecomet cite Unbabel/xcomet-xl

https://arxiv.org/abs/2310.10482
@misc{guerreiro2023xcomet,
    title={xCOMET: Transparent Machine Translation Evaluation through Fine-grained Error Detection}, 
    ...
```

Please open an issue/pull request in the [repository](https://github.com/PinzhenChen/sacreCOMET) if you wish to add models/functionality.


<!-- 
Notes for maintainers:

cd python-tool
# newer version might not work
pip install 'build<0.10.0' twine

python3 -m build

# live
twine upload dist/* -u __token__
# user __token__ as username and the API token generated online
-->

## Changelog

- v0.1.0 (30 October 2024):
  - Add `list` command to list available models
  - Add references usage to the SacreCOMET usage.
  - Deprecate `sacrecomet cite model_name` positional model name specification. Citations now have to explicitly use the `--model` argument.
- v0.0.1 (7 August 2024)
  - First release
uvicorn-ssl-restarter 0.1.4
Add your description here
Requires Python: >=3.9

# Uvicorn SSL Restarter

This service is intended to be used with any uvicorn application to automatically restart it when new SSL-Certificates are available.

## Usage

```sh
pip install uvicorn-ssl-restarter
```

Import the `UvicornSSLRestarter` class into your `entrypoint.py` file. (Or wherever you start your Uvicorn server).

```
from app.uvicorn_ssl_restarter import UvicornSSLRestarter
```

Start the server using:

```python
uvicorn_ssl_restarter = UvicornSSLRestarter(
    virtual_host="example.com",
    app_path="app.main:app",
    fallback_certs_dir="/app/fallback-certs",
    real_certs_dir="/app/certs",
    renew_check_interval=60*60*2, # Seconds between checks for new certificates
    server_port=443,
)
await uvicorn_ssl_restarter.run()
```

Change variables as needed.

## Publish package

Update the version in the `pyproject.toml` and then run:

```sh
uv sync
uv build
# make sure that UV_PUBLISH_TOKEN is populated with your pypi token
uv publish
```
django-custom-commands 0.1.0
A Django package which allows specifying custom paths to load managenent commands from.
Requires Python: >=3.11
Keywords: django, commands

# Django Custom Commands

A Django package which allows specifying custom paths to load managenent commands from. This allows loading management commands which are not in Django apps.

## Installation

```bash
pip install django-custom-commands
```

## Usage

There's two things you need to do to use this package:
1. Import `call_command`, `get_commands` and `execute_from_command_line` from `django_custom_commands.management` instead of `django.core.management`.
2. Define a `CUSTOM_COMMAND_LOCATIONS` setting in your settings.

The `CUSTOM_COMMAND_LOCATIONS` should be a list of strings, where each string points to a module where commands can appear. The string should be a dotted path, similar to how apps are defined. The package will then try to load commands from `.management.commands` in those modules. For example if you have a command in `src.demo.management.commands`, but you can't/don't want to make `src.demo` a Django app, you would configure `CUSTOM_COMMAND_LOCATIONS = ["src.demo"]`.

## Running tests

```bash
poetry run pytest
```
dbnl-bear 0.4.1
A Python package to use an AI assistant for historical analysis
Requires Python: >=3.10

![My Package Logo](assets/logo.png)

This is dbnl_bear, a python package to make it easier to use AI for reading assistance in historical analaysis. 

The "bear" part of the package name is inspired by a passage from Alex Komoroske's Bits and Bobs:

"LLMs are like a trained circus bear that can make you porridge in your kitchen. It's a miracle that it's able to do it at all, but watch out because no matter how well they can act like a human on some tasks they're still a wild animal. They might ransack your kitchen, and they could kill you, accidentally or intentionally. Just because it can talk like a human doesn't mean it deserves the responsibility of a human!" (editorial changes by AvD)

The package is available on PyPi. However, currently, it's not perfect, and should be used with some caution. 

The goal of the package is to make it easier for historians to use AI in their research. It will assist in parsing texts, sending them to the OpenAI API, and viewing the results. 

See the notebook for info on how to use this.