semvec/pypi-packages huggingface dataset:
azure-ai-travel-agents-client 1.0.2
test
Keywords: test


# anupamas01
 <img src="[https://s3.amazonaws.com/nrjio/interactive.gif](https://compote.slate.com/images/697b023b-64a5-49a0-8059-27b963453fb1.gif?crop=780%2C520%2Cx0%2Cy0&width=1280)https://compote.slate.com/images/697b023b-64a5-49a0-8059-27b963453fb1.gif?crop=780%2C520%2Cx0%2Cy0&width=1280" alt="Interactive notifications example" width=350/>
mfd-base-tool 2.7.0
Module that contains abstraction of tool in Modular Framework Design (MFD).
Requires Python: <3.14,>=3.10

> [!IMPORTANT]  
> This project is under development. All source code and features on the main branch are for the purpose of testing or evaluation and not production ready.

# MFD Base Tool
Module for abstraction of tool (wrappers).

ToolTemplate has got 3 main methods:

* `_get_tool_exec_factory(self) -> str` - responsible to return correct tool execute name according to e.g. OS. Should use `tool_executable_name` structure as store for names.
* `check_if_available(self) -> None` - responsible for checking, if tool is available and executable in system. Should raise exception if not
* `get_version(self) -> str` - responsible for getting version of tool.

All methods and `tool_executable_name` variable in class must be implemented in developed tool.
Arguments in public methods must be forced as named arguments:
`__init__(self, *, arg1, arg2)` etc.

## Usage
Example implementation using ToolTemplate:

```python
from mfd_base_tool import ToolTemplate


class MyTool(ToolTemplate):
    tool_executable_name = "my tool name"
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

    def _get_tool_exec_factory(self) -> str:
        return self.tool_executable_name

    def check_if_available(self) -> None:
        if not "if statement for check":
            raise MyToolNotAvailable()

    def get_version(self) -> str:
        return "my read tool version"
    
    def my_tool_method(self):
        pass
```
## OS supported:
* LNX
* WINDOWS
* ESXI
* FREEBSD
* EFI shell support

## Issue reporting

If you encounter any bugs or have suggestions for improvements, you're welcome to contribute directly or open an issue [here](https://github.com/intel/mfd-base-tool/issues).
tapd-mcp-server 0.0.7
TAPD MCP Server
Requires Python: >=3.10
Keywords: mcp, tapd

# TAPD MCP Server

TAPD 是腾讯敏捷研发管理平台，覆盖需求、计划、研发、测试、发布研发全生命周期。支持用自然语言与 TAPD 对话，实现需求、缺陷、任务、迭代等管理。

* 与 TAPD API 无缝集成，提升开发效率

## 系统要求

* uv
* TAPD Access Token（推荐）或 TAPD API 账号密码

## 快速开始
### Install uv
```
brew install uv
# OR
curl -LsSf https://astral.sh/uv/install.sh | sh
```

### 参数
- TAPD_ACCESS_TOKEN: 个人令牌（推荐）
- TAPD_API_USER: API账号 
- TAPD_API_PASSWORD: API密钥 
- BOT_URL: 企业微信机器人 webhook 地址，如果需要发送消息到企业微信群才需要填（选填）

注意 TAPD_API_USER/TAPD_API_PASSWORD（TAPD API 账号密码） 与 TAPD_ACCESS_TOKEN（TAPD 个人授权令牌）为两种调用 MCP 的凭据，选一种即可。


### 获取 TAPD Access Token（推荐）
TAPD_ACCESS_TOKEN 为 TAPD 个人令牌，在 [我的设置-个人访问令牌](https://tapd.woa.com/platform/myhome?not_direct=1&amp;from=left_tree_v2#tab=tab-mytoken)  点“创建个人访问令牌”，令牌只显示一次，注意保存下来。

### 获取 API 账号密码（兼容）
[TAPD开发者后台](https://o.tapd.woa.com/admin/myapps)

## IDE 配置
### Claude Desktop / Cursor
#### 本地 stdio 运行，环境变量配置示例（推荐个人 Token）
```json
{
  "mcpServers": {
    "mcp-server-tapd": {
      "command": "uvx",
      "args": ["mcp-server-tapd"],
      "env": {
        "TAPD_ACCESS_TOKEN": "您的个人Token",
        // 兼容账号密码方式（如未配置 Token 时生效）
        "TAPD_API_USER": "",
        "TAPD_API_PASSWORD": "",
        "TAPD_API_BASE_URL": "http://oss.apiv2.tapd.woa.com",
        "TAPD_BASE_URL": "https://tapd.woa.com",
        "BOT_URL": ""
      }
    }
  }
}
```


### Streamable HTTP 配置
使用 Streamable HTTP 替代 stdio
1. 在终端手动启动服务
```
git clone https://cnb.cool/tapd_mcp/mcp-server-tapd.git

cd mcp-server-tapd/src/mcp_server_tapd

uv venv && source .venv/bin/activate

uv pip install requests markdown mcp mcp_server_tapd

python server.py --mode=streamable-http --host="0.0.0.0" --port=8000 --path="/mcp" --api-user=your_api_user --api-password=your_api_password --api-base-url=https://api.tapd.cn --tapd-base-url=https://www.tapd.cn  --bot-url="https://qyapi.weixin.qq.com/cgi-bin/webhook/send?key=XXX"

# 如果使用个人 Token，则将 --api-user=your_api_user --api-password=your_api_password 替换为 --access-token=您的个人Token

```

2. 通过如下配置连接到已启动的服务
```json
{
  "mcpServers": {
    "tapd_mcp_http": {
      "url": "http://localhost:8000/mcp/"
    }
  }
}
```
steven 0.3.3
Sample Things EVENly
Requires Python: >=3.8

# Steven

**Steven (Sample Things EVENly)** helps you sample your data in nice easy ways, evenly across the range of the data!

Steven is available on PyPI: `pip install steven`.

## How to use Steven

The main method of `steven` is `sample_data_evenly`. This takes as input a sequence-liked object such as a `list`, `tuple`, `np.ndarray` or `pd.Series`, and samples it in such a way that the items returned represent a balanced distribution across the data range.

This is useful for balancing both continuous and discrete data for machine learning applications, among other things!

Let's set up an example and plot the distribution.
```
import numpy as np
import matplotlib.pyplot as plt

from steven.sampling import sample_data_evenly

# Seed for reproducibility
seed = 8675309
np.random.seed(seed)

# Create some data...
data = np.exp(np.random.rand(100_000))
plt.hist(data, bins=50, range=[data.min(), data.max()], label='All data')

# Now sample the data...
data_sampled = sample_data_evenly(data, n_bins=50, sample_size=20_000, random_state=seed)
plt.hist(data_sampled, bins=50, range=[data.min(), data.max()], label='Sampled')

plt.title('Sample data evenly, example')
plt.xlabel('Value')
plt.ylabel('Frequency')
plt.legend()
plt.show()
```

The result should look like this:

<img width="377" alt="image" src="https://github.com/user-attachments/assets/051b34ff-66eb-4f8b-953e-aef1289832b3" />

## Keeping track of sampled indices

Optionally, `subset_data_evenly` accepts a `return_ixs` argument, which allows us to keep track of which indexes have been sampled from the inputted data. Continuing with the above example, we can do:

```
sampled_data, ixs = subset_data_evenly(data, n_bins=50, sample_size=10, random_state=seed, return_ixs=True)
```

This will return the sampled data and the ixs as a tuple:

```
>>> sampled_data, ixs 
(array([2.29744662, 1.56124329, 1.75257412, 1.39012692, 1.04761057,
        1.32016874, 1.98088368, 1.84552982, 2.6627304 , 1.5303134 ]),
 [49023, 44730, 83142, 98395, 37441, 81177, 9769, 38017, 3088, 59028])
```