semvec/pypi-packages huggingface dataset:
canvas_pyr 0.1.0
A Python canvas library powered by Skia, with bindings implemented in Rust.
Requires Python: >=3.10

# canvas_pyr

A Python canvas library powered by Skia, with bindings implemented in Rust.

## 来源与致谢

本项目的核心源自 [Brooooooklyn/canvas](https://github.com/Brooooooklyn/canvas)，该项目通过 [Node-API](https://napi.rs/) 为 Node.js 提供绑定。

本项目在此基础上，使用 [PyO3](https://pyo3.rs/) 使其能够作为 Python 扩展模块使用。

## 许可证

原项目遵循 MIT 许可证。本项目完整保留了其原始版权声明（见 `LICENSE` 文件），并在此基础上新增的 Python 绑定代码同样采用 MIT 许可证发布。
societal-costs-pipeline-2026-02-13-1300 0.1.0
Societal costs pipeline CLI.
Requires Python: >=3.9

# societal_costs

Workspace for the societal costs study.

## Crates

- `crates/costs_core` — core domain types, aggregation, pricing, and validation.
- `crates/costs_models` — two-part, Tweedie, and log-normal models + selection tools.
- `crates/costs_matching` — matching + balance diagnostics.
- `crates/costs_cohort` — cohort assignment + diagnostics.
- `crates/costs_components` — register-derived component builders (LMDB volume so far).
- `crates/costs_population` — population files + public stats QC.
- `crates/costs_pipeline_common` — config, errors, tracing, and shared helpers.
- `crates/costs_pipeline_io` — report/Arrow/CSV/JSON writers.
- `crates/costs_registers` — register ingestion flows (BEF/LPR/LMDB/VNDS/DOD/etc).
- `crates/costs_pipeline_core` — orchestration + analysis/matching/modeling logic.
- `crates/costs_pipeline` — thin facade that re-exports common/core/io APIs.

## LMDB example

See `crates/costs_pipeline/README.md` for the LMDB ingestion example.

## Pipeline config

Start from `pipeline.example.toml` and update the register file paths to match
your local dataset layout. Most SAS7BDAT paths accept either a single file
or a directory (all `.sas7bdat` files are loaded, sorted by name). You can
also set `base_path` in the config to prefix all relative register paths.
File filtering uses `study.data_*` and optional `study.baseline_*` /
`study.followup_*` windows to avoid reading out-of-scope files.
Event registers (VNDS/DOD/DODSAARS/DODSAASG) are **not** year-filtered.
If you use contact-based SCD classification, set
`components.lpr.scd_inpatient_contact_types` (or flip
`components.lpr.scd_allow_outpatient = true` to allow outpatient contacts).

Pipeline outputs include consolidated QC under `qc_summary.csv/json`, plus
step-specific outputs such as `lpr_episode_qc.csv`, `matching_outcome_qc.csv`,
`matching_parent_qc.csv`, and `population_public_stats.csv`.

For terminal progress and timing, set:
- `pipeline.progress = true`
- `pipeline.log_level = "info"` (or `debug`)

## Additional docs

- `docs/README.md` — onboarding index and quick-start path.
- `crates/costs_core/README.md` — core domain and aggregation usage.
- `crates/costs_models/README.md` — model APIs and examples.
- `crates/costs_models/examples/README.md` — runnable model examples.
- `crates/costs_components/README.md` — LMDB note (missing price fields).
- `crates/costs_pipeline/README.md` — pipeline usage and LMDB example.
- `docs/notes/pipeline_contracts.md` — step-level inputs, outputs, invariants.
iflow-mcp_captainji-software_planning_mcp 0.1.0
Software planning MCP server for software development planning tools
Requires Python: >=3.11

# 软件规划MCP服务器
https://github.com/NightTrek/Software-planning-mcp 项目python版，方便使用sse接入cursor
这个MCP服务器提供了软件开发规划工具，帮助用户制定实施计划和管理待办事项。

## 功能特点

- 创建软件开发目标
- 制定详细的实施计划
- 管理待办事项列表
- 跟踪任务完成状态
- 提供结构化的思考过程

## 安装

```bash
# 克隆仓库
git clone https://github.com/yourusername/software-planning-mcp.git
cd software-planning-mcp

# 创建并激活虚拟环境
python -m venv .venv
source .venv/bin/activate  # Linux/Mac
# 或
.venv\Scripts\activate  # Windows

# 安装依赖
pip install -e .
```

## 使用方法

### 启动服务器

```bash
# 使用SSE传输协议（推荐用于Cursor集成）
software-planning-mcp --debug

# 使用自定义端口
software-planning-mcp --port 9000 --debug

# 使用自定义主机地址
software-planning-mcp --host 127.0.0.1 --debug

# 使用stdio传输协议（用于命令行测试）
software-planning-mcp --transport stdio --debug
```

### 在Cursor中配置MCP服务器

1. 打开Cursor编辑器
2. 进入设置 -> MCP Servers
3. 点击"Add new MCP server"
4. 输入服务器链接：`http://localhost:8000/sse`（如果使用了自定义端口，请相应修改）
5. 保存配置

## 可用工具

| 工具名称 | 描述 | 参数 |
|---------|------|------|
| start_planning | 开始一个新的规划会话，设置目标 | goal: 软件开发目标 |
| save_plan | 保存当前实施计划 | plan: 实施计划文本 |
| add_todo | 向当前计划添加新的待办事项 | title: 标题<br>description: 描述<br>complexity: 复杂度(0-10)<br>code_example: 代码示例(可选) |
| remove_todo | 从当前计划中移除待办事项 | todo_id: 待办事项ID |
| get_todos | 获取当前计划中的所有待办事项 | 无 |
| update_todo_status | 更新待办事项的完成状态 | todo_id: 待办事项ID<br>is_complete: 完成状态 |

## 可用资源

| 资源URI | 描述 |
|---------|------|
| planning://current-goal | 当前软件开发目标 |
| planning://implementation-plan | 当前实施计划及待办事项 |

## 环境变量

- `SOFTWARE_PLANNING_PORT`: 设置SSE服务器端口号（默认：8000）
- `SOFTWARE_PLANNING_HOST`: 设置SSE服务器主机地址（默认：0.0.0.0）
- `SOFTWARE_PLANNING_TRANSPORT`: 设置传输类型，可选值为"stdio"或"sse"（默认："sse"）
- `SOFTWARE_PLANNING_DEBUG`: 启用调试模式，可选值为"true"、"1"或"yes"（默认：禁用）

## 许可证

MIT
laboneq 26.4.0b1
Zurich Instruments LabOne Q software framework for quantum computing control
Requires Python: >=3.10
Keywords: quantum, sdk, zhinst

![LabOne Q logo](https://github.com/zhinst/laboneq/raw/main/docs/images/Logo_LabOneQ.png)


# LabOne Q

[LabOne Q](https://www.zhinst.com/quantum-computing-systems/labone-q) is Zurich
Instruments’ software framework to accelerate progress in quantum computing. Its
Python-based, high-level programming interface enables users to concentrate on
intuitive, efficient experiment design, while automatically accounting for their
instrumentation details and maximizing useful computation time. Tight system
integration between software and hardware ensures a seamless user experience
from setups with a single qubit to those with 100 and more.

## Requirements

> ⚠️ **This software requires Python 3.10 or higher.** We assume that
> `pip` and `python` use a corresponding Python version.

> 💡 To ease the maintenance of multiple installations, we recommended to
> use Python environments through e.g. **venv**, **pipenv** or **conda**.

## Installation

The following command will fetch the latest (quarterly) *stable* release of
LabOne Q from [PyPI](https://pypi.org/project/laboneq/) and make it available in
your current environment.

```sh
$ pip install --upgrade laboneq
```

*Preview* releases are typically published every two weeks and contain new
features, improvements, or bugfixes. They undergo the similar internal testing,
but do not receive backports of bugfixes. Preview releases can be installed
through:

```sh
$ pip install --upgrade --pre laboneq
```

If you instead would like to install from source, you will additionally need to install
a Rust toolchain. For this, follow the instructions on [rustup.rs](https://rustup.rs/).

## Documentation

Find the LabOne Q Manual here:
<https://docs.zhinst.com/labone_q_user_manual/>

Dive right into using LabOne Q and generate your first pulse sequence:
<https://docs.zhinst.com/labone_q_user_manual/getting_started/index.html>

The API Documentation is published here:
<https://docs.zhinst.com/labone_q_user_manual/core/reference/simple.html>

## Architecture

![Overview of the LabOne Q Software Stack](https://github.com/zhinst/laboneq/raw/main/docs/images/flowchart_QCCS.png)