semvec/pypi-packages huggingface dataset:
az-create-tasks 0.1.1
Cli tool to create child tasks on user stories in Azure DevOps.
Requires Python: >=3.10
Keywords: azure, cli, devops

# az-create-tasks
Cli tool to create child tasks on user stories in azure boards.

## Installation
`pip install az-create-tasks`

## Usage
```bash
az-create-tasks [-h] [--parents <parent id> [<parent id> ...]]
                       [--area <area>] [--iteration <iteration>]
                       [--set <key>=<value>] [--values <value> [<value> ...]]
                       [--update-pat] [--dry-run] [-s]
                       <template file> <organization> <project>
```

`az-create-tasks` requires a YAML template file argument. A sample has been provided [here](./sample-template.yaml).

The program expects the template file to have a single `tasks` key which will hold a list of all the tasks you wish to add to a single work item. Each task must follow this schema:

- `name: string` Required, name of the task
- `assigned: string` Email of the person assigned the task

### Variable substitution

You can specify placeholder values in your template file by prefixing a `$`. There are two types of variables, ordered and named. Ordered variables are positive integers starting at 1. Named variables are custom names that can include alphabetical characters, numbers, or underscores. Named variables cannot start with a number.

The `$` can be escaped with backslash `\$` to treat it as a literal `$`.

You can provide values for these variables through the `--values` and `--set` options.

The `--values` option accepts a list of values and will substitute in order into `$1, $2, $3, ...` placeholders.

The `--set` option is used to set define individual key value pairs. For each custom variable name add:  
`--set <name>=<value>`

Example `--set reviewer="person@example.com"`.

### Executing

It is recommended that you run your defined command with the `--dry-run` option first. This option will do two things:

- Print the values for each task with variables substituted. Useful for checking if values are as expected.
- Validate that the tasks with the given field values can be created without creating them. If the task cannot be created, an error will be thrown.

The first run of the program will prompt you to enter your Azure DevOps personal access token. Read [here](https://learn.microsoft.com/en-us/azure/devops/organizations/accounts/use-personal-access-tokens-to-authenticate?view=azure-devops&tabs=Windows#create-a-pat) for how to create them. Copy and paste it into the terminal when prompted.
If you need to change your access token, run the command with `--update-pat` to prompt for a new token.

Example:
`az-create-tasks sample-template.yaml MyOrg MyProject --values "developer@example.com" "reviewer@example.com" --parents 101 102 103`

This command uses the provided template file and creates the defined tasks as children of work items 101, 102, and 103.
All instances of `$1` in the template were replaced with `developer@example.com` and all instances of `$2` were replaced with `reviewer@example.com`.
pysio-pyemg-cometa 0.0.2
A pythonic wrapper library for Cometa sEMG Waveplus devices
Requires Python: <3.14,>=3.7
Keywords: wearables, sensors, surface electromyography, cometa, streaming, sensing, physiology

# Python Binding for Cometa WavePlus Mini Wave and Pico sEMG devices 

Python SDK to interface the [Cometa WavePlus](https://www.cometasystems.com/waveplus/) commercial Mini Wave or Pico sEMG sensors via the [OEM C# shared libraries](https://www.cometasystems.com/sdk-and-integrations/) for realtime streaming acquisition.

> [!NOTE]
> Cometa WavePlus is a system of up to 16 wearable sEMG sensors, and a receiver base station connected to a Windows PC, with a practical range of ~20 meters. Lost packets are filled by the base station with repeated most recently received values. Ensure the person stays in range or the base station is moved around for this device.

> [!NOTE]
> While [it says](https://www.cometasystems.com/sdk-and-integrations/) that Cometa has a Python integration, it's a community driven package. You can access a work-in-progress Python binding, [pyemg-cometa](https://github.com/maximyudayev/pyemg-cometa), to the C# OEM DLLs that powers this package and is suitable for out-of-the-box realtime streaming acquisition.

## Installation
The package interfaces identically the Mini Wave and the Pico sensors, whichever are on and connected to the Cometa WavePlus base station.

Install WavePlus drivers (`EmgMDrivers` and `EmgMUsbDrivers`) for Windows after registering on the [Cometa website](https://www.cometasystems.com/my-account/).

> [!NOTE]
> The latest distributed sEMG drivers marked for Windows 8.1 are compatible with Windows 10/11 and have been validated. The distributed USB drivers marked for Windows 10 are also compatible Windows 11.

### From PyPI
```bash
pip install pysio-pyemg-cometa
```

### From source
```bash
git clone https://github.com/maximyudayev/pyemg-cometa.git
pip install -e pyemg-cometa
```

## Usage
Verify in EMG Motion Tools Windows desktop application that sensors are attached, impedance is checked, and the qulity of the signal is good. Close the application.

> [!IMPORTANT]
> Only 1 connection to the base station can be done at a time, either EMG Motion Tools app, or HERMES. Else the device won't connect.

## Citation
When using any parts of this repository outside of its intended use, please cite the parent project [HERMES](https://github.com/maximyudayev/hermes).
xl-pdf 0.1.1
PDF stamp and manipulation utilities using PyMuPDF
Requires Python: >=3.7

# xl-pdf

PDF stamp and manipulation utilities using PyMuPDF.

## Installation

```bash
pip install xl-pdf
```

## Usage

```python
from xl_pdf import PDFStampAdder

# 从文件路径或bytes创建PDFStampAdder
adder = PDFStampAdder("input.pdf")
# 或
adder = PDFStampAdder(pdf_bytes)

# 添加印章
adder.add_stamp_to_pdf("stamp.png", scale_height=50, page_num=0, x=100, y=100)

# 添加文字
adder.add_text_to_pdf("Hello World", page_num=0, x=100, y=200, font_size=20)

# 添加二维码
adder.add_qrcode_to_pdf("https://example.com", size=100, page_num=0, x=100, y=300)

# 保存PDF
adder.save("output.pdf")
# 或获取bytes
pdf_bytes = adder.bytes
```

## Features

- Add stamps to PDF pages
- Add text to PDF pages
- Add QR codes to PDF pages
- Add cross-page stamps (骑缝章)
- Search and add stamps/text around specific text
- Compress images automatically
- Export to file or bytes
xl-image 1.0.1
xilong image processing utility library
Requires Python: >=3.7

# xl-image

xilong image processing utility library

## Installation

```bash
pip install xl-image
```

## Usage

```python
from xl_image import Image

# Create image from base64 string
img = Image("data:image/png;base64,...")

# Add watermark
watermarked = img.watermark("Watermark Text")

# Create thumbnail
thumb_data = img.thumbnail((128, 128))

# Trim image
trimmed = img.trim()

# Get image size
width, height = img.size

# Get image bytes
image_bytes = img.bytes
```
hollowfoot 0.5.1
Companion workflow engine for xraylarch.
Requires Python: >=3.8

# Hollowfoot


[![PyPI badge](https://img.shields.io/pypi/v/hollowfoot.svg)](https://pypi.python.org/pypi/hollowfoot)



A work-flow engine for performing analysis of experimental X-ray data
using xraylarch and other domain-specific frameworks.

A hollowfoot analysis is:

**Declarative:** A typical analysis workflow in hollowfoot is best
 thought of as a series of steps to be performed on some underlying
 data. The analysis engine is responsible for executing the steps for
 you when needed.

**Jupyter-first:** Many analysis frameworks, like *xraylarch*,
  provide tools that are used as part of some larger analysis
  application. *Hollowfoot* aims to extend these tools so that they
  can be used as first-class objects in a jupyter notebook
  environment.


## Documentation

Sphinx-generated documentation for this project can be found here:
https://spc-group.github.io/hollowfoot/


## Usage

An example workflow for XAFS analysis might look like:

```python
import hollowfoot as hf

analysis = (
    XAFSAnalysis  # What kind of analysis are we doing?
        .from_aps_20bmb("my_data_folder/")  # Load data from disk
        .to_mu("mono-energy", "It", "I0", is_transmission=True)  # Apply reference correction
        .plot_mu()  # Plot all data sets together
        .merge()  # Merge data sets into a single group
        .fit_edge_jump()  # Do some normalization, etc
        .subtract_background()  # Convert from µ(E) to χ(E)
        .plot_mu()  # Plot the single, corrected dataset
    	.summarize()  # Print out a summary of the steps that have been taken
)
```

Notice how each line of the previous code snippet describes a concise
step of analysis. These steps can be re-ordered or commented out,
making for easy comparisons between analysis strategies.


## Installation

The following will download the package and load it into the python environment.

```bash
$ pip install hollowfoot
```

## Running the Tests

```bash
$ uv run --dev pytest
```

# File Format Support

## XAS Data Interchange Format (XDI)

*Hollowfoot* includes an Xarray backend for loading `.xdi` files.

The easiest way to import XDI files is with `xarray.open_dataset()`:

```python
import xarray as xr

dataset = xr.open_dataset("example.xdi")
```

It is also possible to load XDI data from a string using ``hollowfoot.xdi.load()``:

```python
import hollowfoot as hf

with open("tests/example.xdi") as fp:
    dataset = hf.xdi.load(fp.read())
```

It is also possible to **convert a dataset back to XDI**:

```python
# This example overwrites existing files, be careful!
with open("new_file.xdi", mode='w') as fp:
    fp.write(hf.xdi.dump(dataset))
```
arkm-client 0.1.0
It is a simple Arkm API client library.
Requires Python: >=3.9

# ArkmClient

ArkmClient 是一个用于与 Arkm API 交互的 Python 客户端库。

## 项目简介

简易的 Arkm API 客户端库，支持异步和同步调用。


## 安装说明
pip install arkm-client

### 环境要求
- Python 3.9+

### 安装步骤
\`\`\`bash
pip install -r requirements.txt
\`\`\`

或者使用setup.py安装：
\`\`\`bash
python setup.py install
\`\`\`

## 使用方法

\`\`\`python
# 示例代码
import arkm

# 使用示例
client = arkm.SyncArkmClient(cookie="your_cookie")
result = client.get_user_info()
print(result)
\`\`\`

## 配置说明

描述项目的配置方式和参数设置。

## 贡献指南

如果您想为项目做贡献，请遵循以下步骤：
1. Fork本仓库
2. 创建功能分支
3. 提交更改
4. 推送分支
5. 创建Pull Request

## 许可证

本项目采用 [MIT](LICENSE) 许可证。

## 联系方式

- 作者：[byebydoggy](https://github.com/byebydoggy)
textx-gen-vscode 0.3.0
A VS Code extension generator
Requires Python: >=3.5
Keywords: VS Code, textX, language, DSL

# VS Code Extension Generator for textX Languages

A _textX_ generator which outputs simple, installable _VS Code_ extension from a registered _textX_ language project.

It is used primary by [textX-LS](https://github.com/textX/textX-LS) project when generating and installing _textX_ languages.

## CLI Examples

Generate a _VS Code_ extension for `tx_workflow` project packaged in archive:

```bash
textx generate examples/workflow/tx_workflow/workflow.tx -o . --target=vscode --project_name tx-workflow
```

Generate a _VS Code_ extension for `tx_workflow` project packaged in installable _vsix_ format:

```bash
textx generate examples/workflow/tx_workflow/workflow.tx -o . --target=vscode --project_name tx-workflow --vsix True
```

## Other Notes

- textX language project should be registrated in order generator to find it by passed _project name_
- path to the grammar (`examples/workflow/tx_workflow/workflow.tx`) is not used for now, but idea is to be able to create project from it