semvec/pypi-packages huggingface dataset:
andeplane-streamlit-custom-component 0.0.3
Streamlit component that allows you to do X
Requires Python: >=3.7

# streamlit-custom-component

Streamlit component that allows you to do X

## Installation instructions

```sh
pip install streamlit-custom-component
```

## Usage instructions

```python
import streamlit as st

from my_component import my_component

value = my_component()

st.write(value)
```
ecodev-model 0.0.164
Ecoact internal domain model
Requires Python: <4.0,>=3.11

# ecodev-model


## How to publish new version:
Do this once
* poetry config repositories.ecodev_model https://ecopypi.lcabox.com/
* run poetry config http-basic.ecodev_model <user> <password>

When you want to publish a new version:
* poetry version patch (will automatically increase version in pyproject.toml)
* poetry build
* poetry publish --repository ecodev_model
spine-segmentation 0.3.2
Anatomical labeling of the spine in small field-of-view MRI scans
Requires Python: <3.13.0,>=3.8.1

<h1 align="center">Spine</h1>

A python library including pretrained models in order to annotate vertebrae and IVDs (intevertebral discs) on small FOV (field-of-view) MRIs. This was created as part of my [master's thesis](https://brutenis.net/master-thesis) which was submitted on 9 November 2023.

## Showcase

Below three spines can be seen which were automatically segmented from MRI images using this library. **(a)** shows sacralization (lack of lumbar vertebra L5), **(c)** shows lumbarization (additional vertebra L6). **(b)** shows a normal spine.

![](media/showcase.png)


## Workflow

Initially, 162 samples annotated by experts are utilized to train a preliminary segmentation model.
This model is then employed to segment an extensive dataset of 10,833 MRI scans.
The segmented data is separated into vertebrae and IVDs, leveraging the fact that all instances can be inferred in a complete MRI scan by assuming the topmost vertebra is C2.
By cropping images from the large dataset, a substantial training set is generated.
This set is used to train an anatomical labeling segmentation model specifically for small FOV MRI images.

![](media/workflow.png)

## Installation

```sh
poetry install
```
gym-cooking-lipo 0.0.2
A variant of https://github.com/DavidRother/cooking_zoo used in LIPO

# Multi-recipe Overcooked used in LIPO

A variant of https://github.com/DavidRother/cooking_zoo used in LIPO.

### Installation
```bash
pip install gym-cooking-lipo
```
---
### Examples

#### Environment creation
```python
from gym_cooking.environment.game.game import Game
from gym_cooking.environment import cooking_zoo

n_agents = 2
max_steps = 100
render = False

level = "full_divider_salad_4"
seed = 1
record = False
max_num_timesteps = 1000
recipes = [
    "LettuceSalad",
    "TomatoSalad",
    "TomatoLettuceSalad",
    "TomatoCarrotSalad",
    "ChoppedCarrot",
    "ChoppedOnion",
]

env = parallel_env = cooking_zoo.parallel_env(
    level=level,
    num_agents=n_agents,
    record=record,
    max_steps=max_num_timesteps,
    recipes=recipes,
    obs_spaces=["dense"],
)
obs = env.reset()
print(obs)
```

#### Human-AI gameplay
```python
from gym_cooking.environment.game.game import Game
from gym_cooking.environment import cooking_zoo

n_agents = 2
num_humans = 1
max_steps = 100
render = False

level = "full_divider_salad_4"  # 'open_room_salad_easy'
seed = 1
record = False
max_num_timesteps = 1000
recipes = [
    "LettuceSalad",
    "TomatoSalad",
    "ChoppedCarrot",
    "ChoppedOnion",
    "TomatoLettuceSalad",
    "TomatoCarrotSalad",
]

parallel_env = cooking_zoo.parallel_env(
    level=level,
    num_agents=n_agents,
    record=record,
    max_steps=max_num_timesteps,
    recipes=recipes,
    obs_spaces=["dense"],
    interact_reward=0.5,
    progress_reward=1.0,
    complete_reward=10.0,
    step_cost=0.05,
)

action_spaces = parallel_env.action_spaces


class CookingAgent:

    def __init__(self, action_space):
        self.action_space = action_space

    def get_action(self, observation) -> int:
        return self.action_space.sample()


player_2_action_space = action_spaces["player_1"]
cooking_agent = CookingAgent(player_2_action_space)
game = Game(parallel_env, num_humans, [cooking_agent], max_steps, render=False)
store = game.on_execute()

print("done")
```

#### Single-player gameplay
```python
from gym_cooking.environment.game.game import Game
from gym_cooking.environment import cooking_zoo


n_agents = 1
num_humans = 1
max_steps = 100
render = False

level = "open_room_blender"
seed = 1
record = False
max_num_timesteps = 100
recipes = ["ChoppedCarrot", "LettuceSalad"]

parallel_env = cooking_zoo.parallel_env(
    level=level,
    num_agents=n_agents,
    record=record,
    max_steps=max_num_timesteps,
    recipes=recipes,
    obs_spaces=["dense"],
)

game = Game(parallel_env, num_humans, [], max_steps)
store = game.on_execute()

print("done")
```
socialmediaextractor 0.4
A library to extract social media links from websites

Usage
========================================
To use the socialmediaextractor library, you first need to install it using pip:

.. code-block:: bash

    pip install socialmediaextractor

Once the library is installed, you can extract social media links from a website using the following code:

.. code-block:: python

    from socialmediaextractor import extract_social_media_accounts

    # Example: Extract social media accounts from a website
    url = 'https://example.com'
    social_media_links = extract_social_media_accounts(url)
    print(social_media_links)
    # Output: {'Facebook': 'https://www.facebook.com/example', 'LinkedIn': 'https://www.linkedin.com/company/example/', 'Twitter': 'https://twitter.com/example'}

For more information and documentation, please visit the `GitHub repository <https://github.com/irfanalidv/socialmediaextractor>`_.
MSCSol 1.0.5
A tool for predicting the solubility of small molecule drugs.


## Using MSCSol to predict moleculer solubility

### MSCSol

[![Downloads](https://static.pepy.tech/badge/MSCSol)](https://pepy.tech/project/MSCSol)

Our trained model has been uploaded to PyPI, accessible through this link (https://pypi.org/project/MSCSol/). We've included detailed installation instructions and usage guidelines, making it easy to obtain prediction results by inputting SMILES strings.


### Installation

```
pip install MSCSol==1.0.5
```

### Package needed

see https://github.com/ZiyuFanCSU/MSCSol and you need to download the required data files for the program and place them in the directory where you are running MSCSol, including trained.pt and fingerprint.csv.

### Quick Start

```
import MSCSol

MSCSol.pred(<your_SMILES>)
```

### Note

It will take some time to calculate the molecular signatures, so please be patient for a while. Also note that dipole moment features are not used here as they cannot be obtained directly by calling the code. 

The training data was restricted to molecular weights less than or equal to 504, LogS values greater than or equal to -8, and experimental temperatures of 20-25 degrees Celsius, so if the molecule does not apply to the above conditions, the prediction results may have a large deviation. 

In addition, due to the computational requirements of the node vector feature of the GVP-GNN, the input molecule atom number must be greater than or equal to 3.

In addition, four temporary files will be generated at runtime, named img_MSCSol.png, shadow_MSCSol_1.png, shadow_MSCSol_2.png and shadow_MSCSol_3.png. Please make sure not to have the same file name as yours to avoid accidental deletion.

## Update log

`1.0.1--1.0.5` Modifying some bugs.

`1.0.0` First release.

## Contact

We thank all the researchers who contributed to this work.

If you have any questions, please contact fzychina@csu.edu.cn.
moabdb 0.1.55
MoabDB

# MoabDB
The repository for the official MoabDB Python client.
Check out our [official site](https://moabdb.com) for information about MoabDB.

## Getting Started
Install with pip:
```bash
pip install moabdb
```
Check out the [docs](https://docs.moabdb.com) and [getting started guide](https://docs.moabdb.com/tutorial/quickstart.html).

## Development
Contributions will be licensed under the MIT license unless specified otherwise.
Before submitting a pull request, make sure that pylint passes. PRs will not be merged without proper linting.
```bash
pylint moabdb
```

## License
This client library is licensed under the MIT license.
dbtcpy-xLitus 1.3.0
A small example package
Requires Python: >=3.8

<div align="center">

#    › ‎ ‎ ‎ ‎ ‎ ‎ DBTCPY

</div>

## features

- sophisticated code + PEP8
- easy functions
- outdated code

---

|  functions  |                  __init__                  |                      __del__                      |               __enter__              |                                               __exit__                                              | add                                                                               |
|:-----------:|:------------------------------------------:|:-------------------------------------------------:|:------------------------------------:|:---------------------------------------------------------------------------------------------------:|-----------------------------------------------------------------------------------|
| description |        initializes the dbtcpy class.       | Destructor to ensure database connection closure. | Enter method for context management. |                                 Exit method for context management.                                 | Adds a new user to the database.                                                  |   
|  parameters | db_name: Name of the SQLite database file. |                        None                       |                 None                 | exc_value: Value of exception. , exc_value: Value of exception. , traceback: Traceback information. | user_data: A dictionary containing user data with keys "username" and "password". |  
|   returns   |                    None                    |                        None                       |      The dbtcpy instance itself.     |                                                 None                                                | None                                                                              |  
    
---

```py
with dbtcpy("test.db") as db:
    db._create_database()
    user_data = {"username": "jhon doe", "password": "password_123"}
    db.add(user_data)
    user = db.show(1)
    if user:
        print_success(
            f"user id: {user[0]}, username: {user[1]}, password: {user[2]}"
        )
    else:
        print_warning("user not found.")
    users = db.search(username="john_doe")
    if users:
        print_success("search results:")
        for user in users:
            print_success(
                f"user id: {user[0]}, username: {user[1]}, password: {user[2]}"
            )
    else:
        print_warning("no users found matching the search criteria.")
```

<div align="center">
    
---
    
`#TLM #TokenLifesMatter #NoTerm #FreeToucan #FreeToken #FreeToucans #FreeTokens`

</div>
urltoken 1.0.0
Simplifies encoding sensitive data like emails URL-safe tokens.
Requires Python: >=3.6

# urltoken

## Description
URL Token Encoder simplifies encoding sensitive data like emails and usernames into URL-safe tokens. Unlike encryption, it preserves data integrity for easy retrieval. Ideal for securing information in URLs without encryption complexities.

Features:

* Encode sensitive content into URL-safe tokens.
* Lightweight and easy integration into Python apps.
* Simplifies data security in web apps and APIs.


### Installation
   
    pip install urltoken

    
### Usage
    
    from urltoken import UrlTokenEncoder
    
    token = UrlTokenEncoder.encode('example@domain.com')
    print(token)

    decoded = UrlTokenEncoder.decode(token)
    print(decoded)

### Issues
https://github.com/laonan/urltoken/issues
sqlite-spellfix 1.1
Loadable spellfix1 extension for sqlite

# sqlite-spellfix

This python package includes a loadable `spellfix1` extension module for sqlite. This allows other python packages to use this extension without requiring dependencies outside of the python ecosystem. For more details on the extension itself, see [the official documentation](https://www.sqlite.org/spellfix1.html).

## Installation

### Latest Release

Install the `sqlite-spellfix` package from pypi.

### Current Development Version

Install via pip

```sh
pip install git+https://github.com/karlb/sqlite-spellfix
```

or add this to you requirements.txt:

```
git+https://github.com/karlb/sqlite-spellfix
```


## Usage


```python
import sqlite3
import sqlite_spellfix

conn = sqlite3.connect(":memory:")
conn.enable_load_extension(True)
conn.load_extension(sqlite_spellfix.extension_path())
# now use as described in https://www.sqlite.org/spellfix1.html
```

## See Also
* [sqlite-icu python package](https://github.com/karlb/sqlite-icu)
nonebot-plugin-zhichang 1.0.4
有趣的职场人专属日历
Requires Python: <4.0,>=3.8

<div align="center">
  <a href="https://v2.nonebot.dev/store"><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/nbp_logo.png" width="180" height="180" alt="NoneBotPluginLogo"></a>
  <br>
  <p><img src="https://github.com/A-kirami/nonebot-plugin-template/blob/resources/NoneBotPlugin.svg" width="240" alt="NoneBotPluginText"></p>
</div>

<div align="center">

# nonebot-plugin-zhichang

_✨ 有趣的职场人专属日历 ✨_

<a href="./LICENSE">
    <img src="https://img.shields.io/github/license/A-kirami/nonebot-plugin-moyu.svg" alt="license">
</a>
<a href="https://pypi.python.org/pypi/nonebot-plugin-60s">
    <img src="https://img.shields.io/pypi/v/nonebot-plugin-60s.svg" alt="pypi">
</a>
<img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="python">

</div>

## 📖 介绍

<details>
  <summary>效果图</summary>

![example](https://github.com/Priopus/nonebot_plugin_zhichang/blob/main/images/8af5b7c09ab1dbd5a9edf66cb6d92d9d.png)

</details>

## 💿 安装

<details>
<summary>使用 nb-cli 安装</summary>
在 nonebot2 项目的根目录下打开命令行, 输入以下指令即可安装

    nb plugin install nonebot-plugin-zhichang

</details>

## 🎉 使用
### 指令表
| 指令  | 说明 |
|:-----:|:----:|
| 职场/职场日历 | 查看今天的职场人日历 |
| 职场/职场日历+设置 | 以连续对话的形式设置职场人日历的推送时间 |
| 职场/职场日历+设置 小时:分钟 | 设置职场人日历的推送时间 |
| 职场/职场日历+状态 | 查看本群的职场人日历状态 |
| 职场/职场日历+禁用 | 禁用本群的职场人日历推送 |

## 💡 鸣谢

### [A-kirami的摸鱼日历](https://github.com/A-kirami/nonebot-plugin-moyu)
### [techotaku39的60s日历](https://github.com/techotaku39/nonebot-plugin-60s)
本项目就是用以上大佬的项目改了几行代码，连说明文档也是（）