semvec/pypi-packages huggingface dataset:
neuromeka-clients3 3.0.5
Neuromeka client protocols for Indy, IndyEye, Moby, Ecat, and Motor
Requires Python: >=3.6

# Neuromeka Clients
![PyPI](https://img.shields.io/pypi/v/neuromeka-clients)

This package provides client protocols for users to interact with Neuromeka's products, including Indy, Moby, Ecat, and Motor.

## Installation

You can install the package from PyPI:

```bash
pip install neuromeka-clients
```

## Usage
The package contatins the following client classes:

* IndyClient in indy.py
* IndyClient3 in indy_client3.py
* MobyClient in moby.py
* EcatClient in ecat.py
* MotorClient in motor.py

To use a client class, simply import it and create an instance:

```python
from neuromeka import EcatClient, MobyClient, IndyClient, IndyClient3, MotorClient

ecat = EcatClient("192.168.214.20")
moby = MobyClient("192.168.214.20")
indy = IndyClient("192.168.0.11")
indyclient3 = IndyClient3("192.168.0.11")
motor = MotorClient("192.168.0.20")
```

Replace EcatClient with the desired client class and the IP address with the appropriate address for your device.

## Dependencies
This package requires the following dependencies:

* grpcio
* grpcio-tools
* protobuf

These dependencies will be automatically installed when you install the package using pip.

## Examples
Please refer to the 'example.py' file in the package for usage examples.

## Support
If you encounter any issues or need help, please open an issue on the project's repository.

## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
sstpy 0.1.6
A python wrapper project for sstable
Requires Python: >=3.6

## 项目名称
> 请介绍一下你的项目吧  



## 运行条件
> 列出运行该项目所必须的条件和相关依赖  
* 条件一
* 条件二
* 条件三



## 运行说明
> 说明如何运行和使用你的项目，建议给出具体的步骤说明
* 操作一
* 操作二
* 操作三  



## 测试说明
> 如果有测试相关内容需要说明，请填写在这里  



## 技术架构
> 使用的技术框架或系统架构图等相关说明，请填写在这里  


## 协作者
> 高效的协作会激发无尽的创造力，将他们的名字记录在这里吧
sortByTanvir 1.0.3
A simple sorting algorithms education package


# sortByTanvir

sortByTanvir is a Python package that provides various sorting algorithms for educational purposes. It includes the following sorting algorithms:

- Bubble Sort
- Counting Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Radix Sort
- Selection Sort

## Table of Contents
- [sortByTanvir](#sortbytanvir)
  - [Table of Contents](#table-of-contents)
  - [Installation](#installation)
  - [Usage](#usage)
  - [Sorting Algorithms](#sorting-algorithms)
  - [Contributing](#contributing)
  - [License](#license)

## Installation
You can install sortByTanvir via pip:

```
pip install sortByTanvir
```

## Usage
Here's an example of how to use the bubble sort algorithm:

```
from sortByTanvir import bubble_sort

arr = [12, 11, 13, 5, 6, 7]
bubble_sort(arr)
print(arr)  # sorted array
```
Here's an example of how to use the Counting sort algorithm:
```
from sortByTanvir import Counting_sort

arr = [12, 11, 13, 5, 6, 7]
Counting_sort(arr)
print(arr)  # sorted array
```

## Sorting Algorithms
Here's a brief description of each sorting algorithm:

- **Bubble Sort**: A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order.
- **Counting Sort**: A sorting algorithm that sorts a collection of objects according to the count of a particular key value.
- **Insertion Sort**: A simple sorting algorithm that builds the final sorted array one item at a time.
- **Merge Sort**: An external algorithm based on divide and conquer strategy.
- **Quick Sort**: A divide and conquer algorithm.
- **Radix Sort**: A sorting algorithm that sorts data with integer keys by grouping keys by the individual digits that share the same significant position and value.
- **Selection Sort**: A simple sorting algorithm that sorts an array by repeatedly finding the minimum element from unsorted part and putting it at the beginning.

## Contributing
We welcome contributions from the community. Please submit a pull request if you have any improvements or new features to add.

## License
sortByTanvir is released under the MIT License.
with-cloudflared 0.1.0
Context manger for Cloudflared
Requires Python: >=3.8

# with-cloudflared

With-cloudflared provides a context manager to start and stop cloudflared. Cloudflare Tunnel provides you with a secure way to connect your resources to Cloudflare without a publicly routable IP address. This is useful for development and testing, as well as for production use cases. See the [Cloudflare Tunnel documentation](https://developers.cloudflare.com/cloudflare-one/connections/connect-networks/) for more information.

## Installation

```bash
pip install with-cloudflared
```

## Usage

```python
import uvicorn
from fastapi import FastAPI
from with_cloudflared import cloudflared

app = FastAPI()

@app.get("/")
def read_root():
    return {"Hello": "World"}

if __name__ == "__main__":
    port = 8000
    with cloudflared(port=port) as cloudflared_address:
        print(f" * Running on {cloudflared_address}")
        uvicorn.run(app, port=port)
```

This will start cloudflared and then start the FastAPI server. The app will be publicly available at the cloudflared address e.g. something like `https://rp-son-configured-army.trycloudflare.com`.

The first time you run this it will take a few seconds to download and start cloudflared. Subsequent runs will be much faster.
hashable-df 0.0.8
Makes unhashable values in a pandas DataFrame hashable

# hashable_df

If you have ever tried to use native python objects in Pandas DataFrames,
you may have run into an issue similar to this:

```python
df = pd.DataFrame({"A": [1, 2, 3, 4],
                   "B": ["a", "b", "c", "d"],
                   "C": [[1, 2, 3], [1, 2], [1, 2, 3], 4],
                   "D": [{1: 1, 2: 2}, {1: 1, 3: 3}, {1: 1, 4: 4}, {1: 1, 2: 2}],
                   "E": [[{1: {2: 2}}, {2: {3: 3}}], [{1: {2: 2}}, {2: {3: 3}}],
                         [{1: {2: 2}}, {2: {3: 3}}], [{1: {2: 2}}, {2: {3: 3}}]]
                   })
df['C'].unique()
```

`TypeError: unhashable type: 'list'`

This is caused by unhashable values in the DataFrame cells.

This small library helps to resolve that making this possible:

```python
from hashable_df import hashable_df
hashable_df(df)['E'].unique()
```

returning
```
array([[{1: {2: 2}}, {2: {3: 3}}]], dtype=object)
```