semvec/pypi-packages huggingface dataset:
MirKusAnalytics 1.1.0
Private analytics tools

# MirKusAnalytics

**MirKusAnalytics** is a private Python package providing internal analytics tools, designed to simplify data processing using `pandas`, `SQLAlchemy`, and `.env` configuration.

## 📦 Installation: From PyPi (use upgrade option to get the latest version)
```bash
pip install mirkusanalytics --upgrade
```
stepfun-mcp 0.0.2
StepFun MCP Server
Requires Python: >=3.11
Keywords: stepfun, mcp, text-generation, vision, image-generation, speech

# 阶跃星辰 MCP server

## 简介

**本项目仅供个人学习交流使用。**

根据阶跃星辰开放平台[StepFun](https://platform.stepfun.com/docs/overview/concept)提供的各种模型能力，仿照[MiniMax MCP](https://github.com/MiniMax-AI/MiniMax-MCP/tree/main)的实现，开发了一个基于stepfun api 的 MCP server。

## 功能特性

- 支持文本大模型调用
- 支持视觉理解大模型调用
- 支持文生图模型调用
- 支持语音模型调用

## 快速开始

1. **安装**

```bash
pip install stepfun-mcp
# 或者
# git clone git@github.com:weidafeng/StepFunMCP.git
# pip install .
```

2. **配置mcp server config**

可参考：[mcp_server_config_uvx_demo.json](mcp_server_config_uvx_demo.json) 或者 [mcp_server_config_demo.json](mcp_server_config_demo.json)

```json
{
  "mcpServers": {
    "StepFun": {
      "command": "stepfun-mcp",
      "args": [
      ],
      "env": {
        "STEPFUN_API_KEY": "YOUR_STEPFUN_API_KEY_HERE",
        "STEPFUN_API_HOST": "https://api.stepfun.com",
        "STEPFUN_MCP_BASE_PATH": "YOUR_OUTPUT_DIR", 
        "STEPFUN_API_RESOURCE_MODE": "local"
      }
    }
  }
}
```

## 交流

本文持续关注VLM、LLM在具身智能、Agent中的应用，欢迎交流。
- 公众号：[特里斯丹](公众号-特里斯丹.png)
- 知乎：[特里斯丹](https://www.zhihu.com/people/wwdafg)


![](公众号-特里斯丹.png)
cbtope-2 1.6
CBTope2: A tool for predicting interactivity of B-cell epitopes
Requires Python: >=3.7

# CBTOPE2: Prediction of interactivity of antigens in protein sequence

This repository contains the standalone code for CBTOPE2 prediction tool. CBTOPE2 is an antibody interacting residues predictor. To support the scientific community, we have developed a standalone software and web server for the tool. For a user-friendly access to CBTOPE2, please use https://webs.iiitd.edu.in/raghava/cbtope2/.

## Installation (dependencies)

## 1. Standalone

Download standalone zip file from https://webs.iiitd.edu.in/raghava/cbtope2/standalone.html

1. Unzip the zip file you just downloaded
   
2. ```bash
   pip install Bio joblib sklearn openpyxl
   ```
   
3. **Execute**:
   ```bash
   python standalone.py [-i INPUT] [filename.fasta] [-t probability threshold = 0.5] [-m {1,2}]
   ```

   **Optional arguments:**

- `-i INPUT`, `--input INPUT`  
  **Input:** Protein or peptide sequence(s) in FASTA format or a single sequence per line in single-letter code.  
  *Input File:* Allows users to provide input in FASTA format.

- `-t THRESHOLD`, `--threshold THRESHOLD`  
  **Threshold:** Value between 0 to 1 (default is 0.5).  
  *Threshold:* User should provide a threshold between 0 and 1; by default, it is 0.5.

- `-m {1,2}`, `--model {1,2}`  
  **Model Type:**  
  `1`: PSSM-based model  
  `2`: RSA + PSSM ensemble model (Best Model).  
  *Model:* User can choose between two models: `1` for PSSM-based, and `2` for RSA + PSSM ensemble.

### OR

## 2. PIP package

To install the dependency - SPOT-1D-Single, use the following commands **in the same order:**

1. Clone the repository:
    ```bash
    git clone https://github.com/jas-preet/SPOT-1D-Single.git
    ```

2. Move into the directory:
    ```bash
    cd SPOT-1D-Single
    ```

3. Download the model checkpoints:
    ```bash
    wget https://apisz.sparks-lab.org:8443/downloads/Resource/Protein/2_Protein_local_structure_prediction/jits.tar.xz
    ```

4. Extract the files:
    ```bash
    tar -xvf jits.tar.xz
    ```

5. Create a conda environment:
    ```bash
    conda create -n cbtope2_env python=3.7
    ```

6. Activate the environment:
    ```bash
    conda activate cbtope2_env
    ```

7. Install PyTorch:

    - **If using GPU:**
      ```bash
      conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.1 -c pytorch
      ```

    - **For CPU only:**
      ```bash
      conda install pytorch==1.6.0 torchvision==0.7.0 cpuonly -c pytorch
      ```

8. Install pandas:
    ```bash
    conda install pandas=1.1.1
    ```

*(Refer to https://github.com/jas-preet/SPOT-1D-Single/tree/master for details.)*

9. Install PSSM dependency:
    You can install system-specific ncbi psi-blast files from https://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/

10. Install CBTOPE2 package:
    ```bash
    pip install cbtope-2
    ```
uynab 0.1.5
Micro YNAB - a small SDK for YNAB budgeting app
Requires Python: >=3.11
Keywords: API, SDK, YNAB, budget, money

# Micro YNAB

Micro YNAB is a small SDK for the YNAB budgeting app. It provides a simple interface to interact with the YNAB API, 
allowing you to manage budgets, categories, transactions, and more.

## Features

- Manage budgets and budget settings
- Handle categories and category groups
- Work with payees and transactions
- Simple and easy-to-use interface

## Installation

To install the package, use pip:

```sh
pip install uynab
```

## Usage

### Code

Here's a basic example of how to use the SDK:

```python
from uynab.client import YNABClient

client = YNABClient(api_token="YOUR_YNAB_API_TOKEN")

# Get all budgets
budgets = client.budget.get_all_budgets()
print(budgets)
```

### Standalone

```sh
python -m uynab budget list
```

## Storing API Token

You can store YNAB API token in an environmental variable: `YNAB_API_TOKEN`.

For example:

```bash
export YNAB_API_TOKEN='<your token>'
```

For security reasons you can also store API token in a `.env` file. The application auto-loads the `.env` file from the root directory if it exists. Format is the same: `YNAB_API_TOKEN='<your token>'`.

Note: Environment variables defined in your system will override the entries from the `.env` file.

## Documentation

For detailed documentation, visit the [docs](https://ajwalkiewicz.github.io/uynab/).

## Contributing

Contributions are welcome! Please see the [contribution guidelines](https://ajwalkiewicz.github.io/uynab/contribution/) for more information.

## License

This project is licensed under the MIT License. See the [LICENSE](https://ajwalkiewicz.github.io/uynab/license/) file for details.
codetree-cli 0.2.2
A tool for counting lines of code in a project.
Requires Python: >=3.6

# Codetree Cli

Codetree is a Python tool that helps you count the lines of code in your project directory. It supports multiple programming languages and provides a detailed breakdown of the number of lines in your project files. Perfect for analyzing and tracking the size of your codebase in an easy-to-read format.

## Features

- Counts lines of code for a wide variety of programming languages.
- Skips hidden files and directories (those starting with a \.dot).
- Provides a summary of total lines of code and file count.
- Supports command-line interface (CLI) for easy integration.

## Installation

### Using pip

Install Codetree directly from PyPI using pip:

```bash
pip install codetree-cli
```

### Manual Installation

To install Codetree manually, clone the repository and use pip:

```bash
git clone https://github.com/rezamardaniDev/codetree.git
cd codetree
pip install .
```

## Usage

Use Codetree via the command-line interface (CLI). Run the following command in your terminal:

```bash
codetree
```

This scans the specified directory (or current directory by default) and outputs the number of lines of code in each file, plus a summary of total lines.

### Example

```bash
$ codetree .
Scanning directory: .
|-- src/
|    â””â”€â”€ main.py -> 150 lines of code
|    â””â”€â”€ utils.py -> 85 lines of code
|
|-- test/
|    â””â”€â”€ test_main.py -> 25 lines of code
|
Total files checked: 3
Total lines of code: 260
Total folders: 2
```

## Options

- **Path**: Specify the project directory as an argument. If no path is provided, the current directory (\.) is used by default.

```bash
codetree /path/to/project
```

## Contributing

We welcome contributions to Codetree! To contribute:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-name`).
3. Make your changes.
4. Commit your changes (`git commit -m 'Add new feature'`).
5. Push to the branch (`git push origin feature-name`).
6. Create a new pull request.

If you find a bug or want to add a feature, feel free to open an issue or submit a pull request!