semvec/pypi-packages huggingface dataset:
ctkaddons 0.0.8a0
A package that add uneccessary widgets to customtkinter
Requires Python: >=3.12
Keywords: customtkinter, widgets, addon, calender

A package which adds extra widgets to CustomTKinter\
See the code on [github](https://github.com/TonyLovesCoding/CTkAddons).

> [!WARNING]\
> This project is **EXTREMELY** early in development\
> Proceed with caution!

# CTkCalendar
It's a calender... **duh...**\
Use `ctkaddons.CTkCalendar()`

# Date Picker
`ctkaddons.CTkDateSelect()`

# Multi Pane
Creates a side panel
pyjob-cli 0.1.2
A CLI tool for monitoring Python scripts
Requires Python: >=3.12

# pyjob-cli

A command-line tool for monitoring progress of Python scripts with real-time progress tracking.

## Features

- Run multiple Python scripts concurrently with progress monitoring
- Background execution support
- Progress tracking with UI
- Database for storing job run history

## Quick Start

1. Initialize the database:
```bash
pyjob db init
```

2. Create a simple script:
```python
# example_script.py

import time

def main(monitor):
    pages = 10
    
    monitor.add("example_job_name", pages)

    try:
        for i in range(pages):
            time.sleep(0.5) 
            monitor.update("example_job_name", i + 1)
            raise Exception("This is an example error message")
    except Exception as e:
        monitor.error("example_job_name", str(e))

    monitor.done("example_job_name")
```

3. Run your script:
```bash
pyjob run example_script.py
```

## Usage

### Basic Commands

```bash
# Initialize database
pyjob init

# Run a single script
pyjob run script.py

# Run multiple scripts concurrently
pyjob run script1.py script2.py script3.py

# Run in background mode
pyjob run --background script.py

# Show progress of running jobs in the background
pyjob show
```

## Writing Compatible Scripts

Your scripts should follow these guidelines:

1. Include a `main` function with the correct signature:
```python
def main(monitor):
    # Your code here
    pass
```

2. Use the monitor methods:
```python
# Register your job
monitor.add("job_name", total_items)

# Update progress
monitor.update("job_name", current_progress)

# Mark as complete
monitor.done("job_name")

# Report errors
monitor.error("job_name", "error message")
```

## Database

A database is created at `~/.pyjob/pyjob.db` with the following table:

- `jobs`: Individual jobs for each run

## Database Commands

```bash
# Initalize the database
pyjob db init

# Show all job history
pyjob db show  

# Show specific job by name
pyjob db show -n job_name

# Show error message by job id
pyjob db show -e job_id

# Clear all job history
pyjob db clear
```

## Requirements

- Python 3.12+

## License

MIT License
gecommon 0.2.0
A common toolkit for Grammatical Error Correction
Requires Python: >=3.11.0

# GECOMMON: A common toolkit for Grammatical Error Correcion

You can install from PyPi:
```
pip install gecommon
python -m spacy download en_core_web_sm
```

Or, from github:
```
git clone https://github.com/gotutiyan/gecommon.git
cd gecommon
pip install -e ./
python -m spacy download en_core_web_sm
```

# Features
- `gecommon.CachedERRANT`: Class to use ERRANT faster by caching.
- [gecommon.Parallel](https://github.com/gotutiyan/gecommon#gecommonparallel) ([docs](./docs/parallel.md)): Class to handle parallel and M2 format in the same interface.
- `gecommon.utils.apply_edits`: A function to apply an errant.edit.Edit sequence to a sentence.


# Use cases

### gecommon.CachedERRANT
You can replace `parse()` and `annotate()` of original ERRANT with `.extract_edits()`.  
This also caches the results of `parse()` and `annotate()`, thus it works faster when processing the same sentence or parallel sentence two or more times.

```python
from gecommon import CachedERRANT
errant = CachedERRANT()
edits = errant.extract_edits('This is a sample sentences .', 'These are sample sentences .')
print(edits)
```

### gecommon.Parallel

- The most important feature is the ability to handle both M2 and parallel formats in the same interface.

```python
from gecommon import Parallel
# If the input is M2 format
gec = Parallel.from_m2(
    m2=<a m2 file path>,
    ref_id=0
)
# If parallel format
gec = Parallel.from_parallel(
    src=<a src file path>,
    trg=<a trg file path>
)
# After that, you can handle the input data in the same interface.
assert gec.srcs is not None
assert gec.trgs is not None
assert gec.edits_list is not None
```

- To generate error detection labels
    - You can use not only binary labels but also 4-class, 25-class, 55-class like [[Yuan+ 21]](https://aclanthology.org/2021.emnlp-main.687/).
```python
from gecommon import Parallel
gec = Parallel.from_demo()
# Sentence-level labels
print(gec.ged_labels_sent()) 
# [['INCORRECT'], ['INCORRECT'], ['CORRECT']]

# Token-level labels
print(gec.ged_labels_token(mode='cat3'))
# [['CORRECT', 'R:VERB:SVA', 'R:SPELL', 'CORRECT', 'CORRECT'],
# ['CORRECT', 'CORRECT', 'U:VERB', 'CORRECT', 'R:ORTH', 'R:ORTH', 'CORRECT', 'CORRECT'],
# ['CORRECT', 'CORRECT', 'CORRECT', 'CORRECT', 'CORRECT']]
```

- To use edits information
    - This is useful for pre-processing that requires editing information, like [[Chen+ 20]](https://aclanthology.org/2020.emnlp-main.581/), [[Li+ 23]](https://aclanthology.org/2023.acl-long.380/) and [[Bout+ 23]](https://aclanthology.org/2023.emnlp-main.355/).
```python
from gecommon import Parallel
gec = Parallel.from_demo()
for edits in gec.edits_list:
    for e in edits:
        print(e.o_start, e.o_end, e.c_str)
    print('---')

# 1 2 is
# 2 2 a
# 2 3 grammatical
# ---
# 2 3 
# 4 6 grammatical
# ---
# ---
```
zfx-textutils 1.0.11
这是一个用于文本处理的 Python 工具库,欢迎各路大神反馈意见及提出需求。企鹅号：424491679,企鹅群：961483644


    免责声明:
    本模块是“按原样”提供的，没有任何明示或暗示的保证。在任何情况下，作者或版权持有者均不对因使用本模块而产生的任何索赔、损害或其他责任负责，无论是在合同、侵权或其他情况下。

    使用本模块即表示接受此免责声明。如果您不同意此免责声明的任何部分，请勿使用本模块。

    本模块仅供参考和学习用途，用户需自行承担使用本模块的风险。作者对因使用本模块而造成的任何直接或间接损害不承担任何责任。

    作者保留随时更新本免责声明的权利，恕不另行通知。最新版本的免责声明将在模块的最新版本中提供。
gen-alt-text 0.3
Generate alt-text for images from a local or remote Ollama server running LLM vision models.
Requires Python: >=3.10

# gen-alt-text

* This program generates alt-text for images using a local or remote Ollama server. 
* The Ollama server must be running before using this program.
* See [Ollama](https://github.com/ollama/ollama) for installation and usage instructions.

## Installation

``` shell
pipx install gen-alt-text
```

## Usage

``` shell
gen-alt-text -m "llama3.2-vision:11b" ~/pictures/autumn-scenery.jpg
gen-alt-text ~/pictures/winter-scenery.jpg
gen-alt-text -m "llama3.2-vision:90b" ~/pictures/coffee.jpg
```

If the model supplied to the `-m` argument is not currently available on the Ollama server, `gen-alt-text` will pull it for you, and then you must re-run the program to use it.

### Remote Ollama server

For remote Ollama servers, the server must be configured to listen on `0.0.0.0:11434` and port `11434` must be open in the firewall. Edit the systemd service file as follows:

``` shell
sudo systemctl edit ollama.service
```

Add the following:

``` shell
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"
```

Save the file and exit the editor.

I personally use a remote server that is not public-facing and is only accessible through my Tailscale network, or tailnet.

On the local machine that you're running this program on, set the `OLLAMA_HOST` environment variable to use a remote Ollama server.

``` shell
export OLLAMA_HOST="http://ollama.tailnet.ts.net:11434"
gen-alt-text ~/pictures/goth_hacker_girl.jpg
```

### Local Ollama server

The `ollama.service` systemd unit should already be configured to listen on `localhost:11434` by default, so no additional configuration is necessary.

## Example

I fed the image below to the llama3.2-vision:11b model.

![fall-leaves-cover-photo.jpg](https://files.hyperreal.coffee/images/fall-leaves-cover-photo.jpg)

This was the alt-text it generated:

> The image depicts a serene autumnal scene, with a cup of coffee placed on a stone surface amidst fallen leaves. In the foreground, a dark brown ceramic mug filled with black coffee sits atop a large, flat gray stone. The mug's handle is positioned towards the right side of the image. Surrounding the mug are vibrant red and orange fallen leaves, which have accumulated in a pile to the left of the stone. Some of these leaves appear to be scattered across the surface of the stone as well. The background of the image features more fallen leaves, creating a sense of depth and atmosphere. The overall mood of the scene is one of tranquility and coziness, evoking feelings of relaxation and comfort.
scshift 0.1.0
scaling deep identifiable models enables zero-shot characterization of single-cell biological states
Requires Python: >=3.7

# scShift

How to identify true biological differences across samples while overcoming batch effects has been a persistent challenge in single-cell RNA-seq data analysis. We found that scaling up deep identifiable models leads to a surprisingly effective solution for this challenging task. We developed scShift, a deep variational inference framework with theoretical support for disentangling batch-dependent and batch-independent variations. Through training the model with compendiums of scRNA-seq atlases, scShift shows remarkable zero-shot capabilities in revealing representations of cell types and biological states in single-cell data while overcoming batch effects. scShift incorporates four important ingredients for next-generation single-cell computation models: 1) zero-shot, 2) disentanglement, 3) scaling performance, and 4) no need for external annotation (unbiased). Please refer to our manuscript for more details.

![fig1github](fig1_illu.jpg)

Read our preprint on BioRxiv: [Scaling deep identifiable models enables zero-shot characterization of single-cell biological states](https://www.biorxiv.org/content/10.1101/2023.11.11.566161v2)

## System requirements
### OS requirements
The scShift (pertvi) package is supported for all OS in principle. The package has been tested on the following systems:
* macOS: Monterey (12.4)
* Linux: Ubantu (20.04.5)
### Dependencies
See `setup.cfg` for details.

Installation instruction, our trained models, and a tutorial will be online soon.
sallmon-wallet 1.0.1
Sallmon Wallet CLI for cryptocurrency wallet management and blockchain integration.
Requires Python: >=3.7

# Sallmon Wallet CLI

## Overview

The Sallmon Wallet CLI is a cryptocurrency wallet management tool designed for seamless blockchain integration. Developed by MinakiLabs, the project is an open-source initiative governed by principles of transparency, security, and adaptability.

## Custodianship and Philosophy

MinakiLabs reserves the right to act as the custodian of this MIT-licensed project. While the code is available to all for transparency and open-source collaboration, custodianship ensures the integrity and longevity of the project. 

This approach reflects the principle that while anyone is free to use or access parts of the code for their needs, the entirety of the project remains under the stewardship of MinakiLabs and its selected collaborators. 

MinakiLabs' philosophy aligns with the idea that the full scope of the project should be treated with the same care and discretion as the ownership of a cryptocurrency wallet. Transparency is provided on a need-to-know basis to maintain both trust and security.

## Key Features

- Create and manage cryptocurrency wallets
- Seamlessly interact with blockchain nodes
- Perform Proof of Work (PoW) mining
- View and manage transactions in the mempool
- Sync blockchain data with peers
- Fully extensible CLI with Click framework

## Installation

```bash
pip install sallmon-wallet