semvec/pypi-packages huggingface dataset:
pyv-file-running 1.0.2
A module for '.pyv' file
License: MIT
Requires Python: >=3.7

# PYV

## Install

```sh
pip install pyv_file_running
```

## Usage

There is a new file extension `.pyv` which is used to write python code with conditional compilation.

```pyv
print("start")

$ version <= 3.7; platform == "win32"
print("old windows")

$ version == 3.8
print("python 3.8")

$ platform == "win32"
print("windows")

$ _
print("other")

$

s = f"""{
$ version <= 9.99
"this is contain"
$
}
"""

ss = """
$ version == 9.99
this is still here
$
"""

print("end")

```

The special syntax `$` is used to write conditional compilation code.

The syntax is as follows:

```pyv
$ condition
code
$
```

The condition only supports "version", "platform", and "implement" three keywords, and supports the following operators: `==`, `!=`, `<=`, `>=`, `<`, `>`. For "platform" and "implement", it only support "==" or "!=". The version must contain the major and minor version number (e.g. 3.7).

The "$ _" means that if the condition above are not satisfied, the code below will be analyse.

The "$" means the end of the analyse.

If the syntan appeared in string, it will be ignored.

## Comment

You can also use "\#" to write comments.

```pyv
$ version <= 3.7; platform == "win32" # this is a comment
code
$
```

## Exec the file

You can use this module to exec it:

```python
import pyv
global_dict = {}
local_dict = {}
pyv.exec_pyv_file("test.pyv", global_dict, local_dict)
```

## Set a new environment

You can set a new environment:

```python
import pyv
pyv.set_new_env(version=(3, 8), platform="win32", implement="cpython")
```

Then you can use this environment in the `.pyv` file:

```python
import pyv
new_env = pyv.set_new_env(version=(3, 8), platform="win32", implement="cpython")
with open("test.pyv", "r") as f:
    code = f.read()
final_code = pyv.preprocess_pyv(code, new_env)
with open("test.py", "w") as f:
    f.write(final_code)
```

## Write the file to python

You can use this module to write the `.pyv` file to python file:

```python
import pyv
pyv.witer_pyv_file_to_py("test.pyv", "test.py")
```

If you have the code string, you can also write it to python file:

```python
import pyv
code = """
$ version <= 3.7; platform == "win32"
print("old_python")
$ _
print("other")
$
"""
pyv.witer_pyv_to_py(code, "test.py")
```

## All apis

```python
pyv.set_new_env(version: tuple[int, int] | None=None, platform: str | None=None, implement: str | None=None) -> dict[str, Any]: ...
pyv.preprocess_pyv(source: str, env=None) -> str: ...
pyv.exec_pyv_source(source: str, filename="<pyv>", globals=None, locals=None, env=None) -> None: ...
pyv.exec_pyv_file(path: str, globals=None, locals=None, env=None) -> None: ...
pyv.witer_pyv_to_py(source: str, filename: str, env=None) -> None: ...
pyv.witer_pyv_file_to_py(path: str, file_name: str, env=None) -> None: ...
```

## License

MIT
BOS-HA 1.1.0
基于深度学习的手语识别系统
License: MIT
Requires Python: >=3.7
Keywords: sign-language-recognition, deep-learning, computer-vision, openvino

# BOS-HA - 基于深度学习的手语识别系统

BOS-HA是一个完整的手语识别系统，包括模型训练、实时识别、模型管理和Web客户端界面等功能。

## 功能特点

- **实时手语识别**: 支持通过摄像头进行实时手语识别
- **多种模型支持**: 支持PyTorch和OpenVINO模型
- **模型训练与转换**: 内置训练模块，支持模型转换为OpenVINO格式
- **Web客户端界面**: 提供直观的Web界面，支持实时显示和历史记录
- **WebSocket通信**: 基于WebSocket的实时通信架构
- **可扩展的模型库**: 支持多种模型的添加和切换

## 安装

```bash
pip install BOS-HA
```

## 快速开始

### 启动服务器

```bash
bosha-server
```

### 启动Web客户端

```bash
bosha-client
```

### 训练模型

```bash
bosha-train --config config.json
```

## 模型管理

### 列出可用模型

```bash
# 通过API获取
curl http://localhost:8000/models
```

### 切换模型

```bash
# 通过API切换
curl -X POST "http://localhost:8000/models/switch?model_name=hand_sign_model&model_type=pytorch"
```

## 技术架构

- **后端**: FastAPI + WebSocket
- **前端**: Flask + SocketIO + HTML/CSS/JavaScript
- **模型**: PyTorch + OpenVINO
- **视频处理**: OpenCV

## 目录结构

```
bosha/
├── server/         # 后端服务
│   ├── models/     # 模型文件
│   ├── services/   # 服务模块
│   └── main.py     # 主入口
├── client/         # 客户端
│   └── web_client/ # Web客户端
└── training/       # 训练模块
```

## 许可证

MIT License

## 贡献

欢迎提交Issue和Pull Request！

## 联系方式

- 项目主页: https://github.com/bos-ha/BOS-HA
- 联系邮箱: contact@bos-ha.com
mayim 1.3.1
The NOT ORM hydrator
License: MIT
Keywords: orm

# Mayim

> The *NOT* ORM Python hydrator

**What is Mayim?**

The simplest way to describe it would be to call it a **one-way ORM**. That is to say that it does *not* craft SQL statements for you. Think of it as **BYOQ** (Bring Your Own Query).

**Why?**

I have nothing against ORMs, truthfully. They serve a great purpose and can be the right tool for the job in many situations. I just prefer not to use them where possible. Instead, I would rather have control of my SQL statements.

The typical tradeoff though is that there is more work needed to hydrate from SQL queries to objects. Mayim aims to solve that.

## Getting Started

```
pip install mayim[postgres]
```

```python
import asyncio
from typing import List
from mayim import Mayim, PostgresExecutor, sql
from dataclasses import dataclass

@dataclass
class Person:
    name: str

class PersonExecutor(PostgresExecutor):
    @sql("SELECT * FROM people LIMIT $limit OFFSET $offset")
    async def select_all_people(
        self, limit: int = 4, offset: int = 0
    ) -> List[Person]:
        ...

async def run():
    executor = PersonExecutor()
    Mayim(dsn="postgres://...")
    print(await executor.select_all_people())


asyncio.run(run())
```

## Documentation

The docs: [ahopkins.github.io/mayim](https://ahopkins.github.io/mayim/guide/)

## Framework support

Out of the box, Mayim comes with extensions to support Quart, Sanic, and Starlette applications. Checkout the docs for more info.
npc-mvr 0.1.15
Tools for reading raw video data from MindScope Neuropixels experiments, compatible with data in the cloud.
License: MIT
Requires Python: >=3.9

# npc_mvr

Tools for reading raw video data from MindScope Neuropixels experiments, compatible with data in the cloud.

[![PyPI](https://img.shields.io/pypi/v/npc_mvr.svg?label=PyPI&color=blue)](https://pypi.org/project/npc_mvr/)
[![Python version](https://img.shields.io/pypi/pyversions/npc_mvr)](https://pypi.org/project/npc_mvr/)

[![Coverage](https://img.shields.io/codecov/c/github/AllenInstitute/npc_mvr?logo=codecov)](https://app.codecov.io/github/AllenInstitute/npc_mvr)
[![CI/CD](https://img.shields.io/github/actions/workflow/status/AllenInstitute/npc_mvr/publish.yml?label=CI/CD&logo=github)](https://github.com/AllenInstitute/npc_mvr/actions/workflows/publish.yml)
[![GitHub issues](https://img.shields.io/github/issues/AllenInstitute/npc_mvr?logo=github)](https://github.com/AllenInstitute/npc_mvr/issues)

# Usage
```bash
conda create -n npc_mvr python>=3.9
conda activate npc_mvr
pip install npc_mvr
```

## Python
```python
>>> import npc_mvr

>>> d = npc_mvr.MVRDataset('s3://aind-ephys-data/ecephys_670248_2023-08-03_12-04-15/behavior')

# get paths
>>> d.video_paths['behavior']
S3Path('s3://aind-ephys-data/ecephys_670248_2023-08-03_12-04-15/behavior/Behavior_20230803T120430.mp4')
>>> d.info_paths['behavior']
S3Path('s3://aind-ephys-data/ecephys_670248_2023-08-03_12-04-15/behavior/Behavior_20230803T120430.json')

# get data
>>> type(d.video_data['behavior'])
<class 'cv2.VideoCapture'>
>>> type(d.info_data['behavior'])
<class 'dict'>
>>> type(d.sync_data)
<class 'npc_sync.sync.SyncDataset'>

# get frame times for each camera on sync clock
# - nans correspond to frames not recorded on sync
# - first nan is metadata frame 
>>> d.frame_times['behavior']
array([       nan,   14.08409,   14.10075, ..., 5084.4582 , 5084.47487, 5084.49153]) 

>>> d.validate()
```

# Development
See instructions in https://github.com/AllenInstitute/npc_mvr/CONTRIBUTING.md and the original template: https://github.com/AllenInstitute/copier-pdm-npc/blob/main/README.md