semvec/pypi-packages huggingface dataset:
common-utility-pepe 0.3.1
korean pepe lover
Requires Python: >=3.6

# Pepe's Common Utility

## Before you start

```bash
python -m venv myenv
source myenv/bin/activate  # macOS/Linux
# i don't give a shit about windows
pip install -r requirements.txt
```

## 1. slack_pepe

### How to use

#### 1.1. slack_pepe_webhook

```python
from common_utility_pepe.slack_pepe import SlackPepe

slack_pepe_webhook = SlackPepe.from_webhook(
    webhook_url="your_web_hook_url"
)
slack_pepe_webhook.send_slack_message(title="go away")
```

#### 1.2. slack_pepe_bot

```python
from common_utility_pepe.slack_pepe import SlackPepe

slack_pepe_bot = SlackPepe.from_bot(
    channel="your_channel_id",
    bot_token="your_bot_token"
)
slack_pepe_bot.send_slack_message(title="hi there")

slack_pepe_bot.update_slack_message(timestamp="your_timestamp", title="it's fake")
```
incontinuity 0.0.0
Context control for LLM inference on a per-token basis
Requires Python: <4.0,>=3.8
Keywords: llm, llm agent, prompt, ai

<!-- HEADER -->
<div align="center">
   <img src="./assets/paige_mascot.png" alt="Logo" width="200" height="200">
   <h1 align="center">Incontinuity</h1>
    <p align="center">Context control for LLM inference on a per-token basis</p>
</div>

<!-- BADGES -->
<p align="center">
   <a href="https://pypi.org/project/incontinuity/"><img src="https://img.shields.io/pypi/v/incontinuity?logo=pypi&logoColor=white"/></a>
</p>

<!-- DESCRIPTION -->
Incontinuity allows developers to modify the context prompt after each token is generated by an LLM. Instead of waiting for the entire LLM output to be generated, developers can now control the prompt and outputs at any point in the generation.

For example, instead of waiting for an LLM to generate some typed XML code, you can make a `ContextController` that will backtrack and add context to the prompt if the LLM makes a mistake in the generation. 

Think of it like using an IDE. You're writing code and you made a typo, so you go back and fix it. Oh no, you used the wrong type, so you go back and fix it. Incontinuity allows you to do the same thing with LLMs.

---

I've been working on LLM agents since the inception of LLMs and I've always wanted to have this level of control. I havent seen any packages for this, so I decided to create one. I hope you find it useful!
SIGRN 1.1.0
GRN inference
Requires Python: >=3.7

# SIGRN
SIGRN: Inferring Gene Regulatory Network with Soft Introspective Variational Autoencoders

# Architecture

![SIGRN](https://github.com/lryup/SIGRN/blob/main/images/SIGRN_arc.png)


# Dependencies
- python =3.8
- torch==2.1.0
- scanpy==1.9.1
- other detailed installation packages can be found in requirements.txt
- CUDA toolkit 11.0 or later.

# Installation

1. Clone the SIGRN repository from GitHub:
```
git clone https://github.com/lryup/SIGRN.git
```
2. Create a new environment:
```
conda create -n your_env_name python=3.8.0
```
3. Activate the environment:
```
conda activate your_env_name
```
4. Install the required packages:
 ```
pip install -r requirements.txt# It is recommended to install only the missing packages
 ```

# Data Preparation
In our study, we trained our model using data from [BEENLINE](https://bcb.cs.tufts.edu/DAZZLE/BEELINE.zip).
You can download the datasets from the provided link. 
# Runing
The training command we used is as follows:
```
python run.py
```
# Example tutorial

We provide an example tutorial  in **Tutorial.ipynb**
Check out the [this tutorial](https://github.com/lryup/SIGRN/blob/main/Tutorial.ipynb) for a quick overview  of how to use SIGRN for your research!

# Usage

SIGRN accepts input data in CSV, TSV format, or H5AD format as provided by Scanpy (genes in rows and cells in columns for TSV and CSV). The output of the  GRN inference task includes an adjacency matrix and various evaluation metrics, such as AUC, EPR, and AUPRR.

# Baseline methods
- Beeline https://github.com/Murali-group/Beeline/tree/master
- DeepSEM https://github.com/HantaoShu/DeepSEM
- GRN-VAE/DAZZLE https://github.com/TuftsBCB/dazzle/tree/main

# References

- Thanks to the following authors for their papers and codes.

  [1] Pratapa, A., Jalihal, A.P., Law, J.N., Bharadwaj, A., Murali, T.: Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nature methods 17(2), 147–154 (2020)

  [2] Shu, H., Zhou, J., Lian, Q., Li, H., Zhao, D., Zeng, J., Ma, J.: Modeling gene regulatory networks using neural network architectures. Nature Computational Science 1(7), 491–501 (2021)

  [3] Zhu, H., Slonim, D.: Grn-vae: A simplified and stabilized sem model for gene regulatory network inference. bioRxiv pp. 2023–01 (2023)
mgpustat 0.1.0
A GPU statistics utility for Apple Silicon Macs.
Requires Python: >=3.7

# mgpustat

A GPU statistics utility for Apple Silicon Macs, inspired by gpustat.

## Installation

```bash
$ pip install mgpustat
```

## Usage

Run the utility with:

```bash
$ sudo mgpustat
```

You may need to grant terminal access to your IDE or terminal emulator in System Preferences > Security & Privacy > Privacy > Full Disk Access.

## Features

- Displays overall GPU usage
- Shows individual GPU engine usage (i.e. cores)
- Lists top GPU-using processes

## Requirements

- macOS with Apple Silicon (M1 or newer)
- Python 3.7 or newer

## Contributing

Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
DIYables-MicroPython-Ultrasonic-Sensor 0.1.2
MicroPython Library for Ultrasonic Sensor, created by DIYables
Requires Python: >=3.6

## MicroPython Ultrasonic Sensor Library - DIYables_MicroPython_Ultrasonic_Sensor
This MicroPython Ultrasonic Sensor library is designed for any hardware platform that supports MicroPython such as Raspberry Pi Pico, ESP32, Micro:bit... to make it easy to use with ultrasonic sensor. It is easy to use for not only beginners but also experienced users... 

It is created by DIYables to work with DIYables Ultrasonic Sensor, but also work with products from other brands. Please consider purchasing products from [DIYables Store on Amazon](https://www.amazon.com/dp/B0BDFLPZ2R) to support our work.



Features
----------------------------
* Works with any hardware platform that supports MicroPython such as Raspberry Pi Pico, ESP32, Micro:bit...
* Supports the noise filter  (filtering noise from enviroment, ultrasonic interference...)
* Supports detection threshold. If distance > threshold, the library will not consider any object detected.


Available Functions
----------------------------
* \_\_init\_\_(trig_pin, echo_pin)
* set_detection_threshold(distance)
* enable_filter(num_samples=20)
* disable_filter()
* loop()
* get_distance()


Available Examples
----------------------------
* main.py



Tutorials
----------------------------
* [Arduino MicroPython - Ultrasonic Sensor](https://newbiely.com/tutorials/arduino-micropython/arduino-micropython-ultrasonic-sensor)
* [ESP32 MicroPython - Ultrasonic Sensor](https://newbiely.com/tutorials/esp32-micropython/esp32-micropython-ultrasonic-sensor)
* [Raspberry Pi Pico - Ultrasonic Sensor](https://newbiely.com/tutorials/raspberry-pico/raspberry-pi-pico-ultrasonic-sensor)



References
----------------------------
* [MicroPython Ultrasonic Sensor Library](https://newbiely.com/tutorials/micropython/micropython-ultrasonic-sensor-library)