semvec/pypi-packages huggingface dataset:
live-parquet 0.4.0
A command-line tool to view Parquet files in real-time
Requires Python: >=3.6

# Live Parquet

A command-line tool that allows you to monitor the contents of a Parquet file in real-time. The tool prints the top 5 rows of the Parquet file whenever the file size changes.

## Installation

You can install the `live-parquet` package using pip:

```bash
pip install live-parquet
translucenttbb 0.0.1
A python version TranslucentTB

### TranslucentTB
#### This is a python version TranslucentTB maybe with tkinter and ctypes
### Clear
![image](https://github.com/littlewhitecloud/TranslucentTB/assets/71159641/97763cac-2b58-4208-b98b-36d031c86880)
### Noeffect
![image](https://github.com/littlewhitecloud/TranslucentTB/assets/71159641/761f84b4-5367-40a2-81ec-e9e97b2f19f5)
### Blur
![image](https://github.com/littlewhitecloud/TranslucentTB/assets/71159641/78b2a579-4c5f-4d95-8b68-4fe2b4f2ba29)
### Acrylic
![image](https://github.com/littlewhitecloud/TranslucentTB/assets/71159641/211f4147-ce5d-4f04-9126-e274369abdfd)

### Usage
```python
from translucenttb import blur # import the blur function
blur(blurtype="Acrylic") # for example acrylic
# choose one type from ("clear", "noeffect", "blur", "acrylic")
```

#### Still working in progress
api-acceptance-tests 0.0.1
API test suite for Studentbeans
Requires Python: >=3.6

# api-acceptance-tests

## Installation
- Install Python
- Install python, black formatter and pylint vscode extensions
- Create python environment with: python3 -m venv env
- Run pip install -r requirements.txt to install all required packages

## Run tests
- Run `pytest -s --html=./reports/report.html -m regression` in terminal, the -s flag will output print console statements for passing and failing tests. The -m flag is for specifying the marker you want to use
- The -s flag will cause the report to not include the console logged details, so in the pipeline the flag is omitted

## Writing tests
### Tags
1. Tagging works differently here and is a bit more fiddly. Global tags for the whole file should be declared with: `pytestmark = tagNameHere`
2. Tagging an individual test function is done with the decorator: `@pytest.mark.tagNameHere`
3. A warning message will be shown if the marked test is not registered, to fix this add the tag name to the `pytest.ini` folder found in the root directory

### Pass variables from one test to another
1. Trying to return a value from one test to another will throw a warning as this is against the isolated nature of API unit tests
2. The correct way to do this is to create a test fixture with function name *not* beginning with test_
3. Add a decorator to the function: `@pytest.fixture(scope="scopeTypeHere")`
4. If your fixture is in a separate folder/file import it: `from accounts import test_api` and call the fixture `gql_token = test_api.retrieve_viewer_token`

### Template literals in Python
- Use f-strings: `f"I have {numberOfApple} apples"`
- If you are using triple quotes `"""` ie. for formatted JSON you will need to escape each curly bracket with a second bracket:

```   
json_string = f"""
    {{
     "I am JSON": "Who is JSON?",
        "Why is JSON?": {{
            "What is JSON?": "{stringInputHere}?"
        }}
    }}
"""
```
azuresphere-device-api 1.1.1
A library for interacting with Azure Sphere devices using the inbuilt REST server.
Requires Python: >=3.8
Keywords: azure,sphere,device,api

# Microsoft Azure Sphere Device REST APIs for Python

Microsoft Azure Sphere Device REST APIs for Python enables users to interact with an Azure Sphere device using REST APIs.

## Installation

You can find Microsoft Azure Sphere Device REST APIs for Python on [PyPi](https://pypi.org/project/azuresphere_device_api/).

1. If you haven't already, [install and/or upgrade the pip](https://pip.pypa.io/en/stable/installing/)
   of your Python environment to a recent version.
2. Run `pip install azuresphere_device_api`.

## Usage

Before using Microsoft Azure Sphere Device REST APIs for Python, you must install the Azure Sphere SDK.

- To install the Azure Sphere SDK on Windows, follow the [Windows Quickstart](https://learn.microsoft.com/azure-sphere/install/install-sdk?pivots=cli).
- To install the Azure Sphere SDK on Linux, follow the [Linux Quickstart](https://learn.microsoft.com/azure-sphere/install/install-sdk-linux?pivots=cli-linux).

### Sample

The [display_ip_deviceid sample](https://github.com/Azure/azure-sphere-tools/blob/main/Manufacturing/src/Python/device_api_sample) gets the list of attached devices, displays the device IP address, and device ID.

### Active device IP address

By default, this package will target Azure Sphere devices with IP address `192.168.35.2`. To change the active Azure Sphere device, call `set_active_device_ip_address` API as below:

```
from azuresphere_device_api import devices
devices.set_active_device_ip_address("<Device_Ip_Address")
```

## Documentation

Microsoft Azure Sphere Device REST APIs for Python documentation is available at [Azure Sphere Device REST APIs Docs](https://github.com/Azure/azure-sphere-tools/blob/main/Manufacturing/src).

## Supported host operating systems

- Windows 10 and 11
- Ubuntu 18.04, 20.04, 22.04
cmaclp 0.1.1
Corneal meta-atlas command line predictor

[![PyPI version](https://badge.fury.io/py/cmaclp.svg)](https://badge.fury.io/py/cmaclp)
[![CI/CD](https://github.com/Arts-of-coding/cmaclp/actions/workflows/ci-cd.yml/badge.svg)](https://github.com/Arts-of-coding/cmaclp/actions/workflows/ci-cd.yml)
[![Maintainability](https://api.codeclimate.com/v1/badges/598ba117b586183c46a8/maintainability)](https://codeclimate.com/github/Arts-of-coding/cmaclp/maintainability)
[![Test Coverage](https://api.codeclimate.com/v1/badges/598ba117b586183c46a8/test_coverage)](https://codeclimate.com/github/Arts-of-coding/cmaclp/test_coverage)
# cma
This repository defines a command-line tool to predict (clp) datasets according to a cell meta-atlas (cma). At the present time only the meta-atlas for the cornea has been implemented.


Install cmaclp into a conda environment and install with PyPI:
```
$ conda create -n cmaclp python=3.9 pip
$ conda activate cmaclp
$ pip install cmaclp
```
To see what each of the current functions do you can run these commands:
```
$ SVM_performance --help
$ SVM_prediction --help
$ SVM_import --help
$ SVM_pseudobulk --help
```
The documentation will be extended and improved upon in later versions.
ssh-remote-control 0.2.1
Control and monitor remote devices through SSH
Requires Python: >=3.10
Keywords: ssh,command line,remote control

# SSH Remote Control

## Initialize

```python
from ssh_remote_control import Remote

remote = Remote("192.168.0.123", ssh_user="user", ssh_password="1234")

# Will raise an error if the host is not online or the SSH connection fails
await remote.async_update_state(validate=True)

# Check the remote state
remote.state.is_online
remote.state.is_connected
```

## Initialize with a command set

```python
from ssh_remote_control.default_command_sets import linux, ServiceKey, SensorKey

remote = Remote("192.168.0.123", ssh_user="user", ssh_password="1234", command_set=linux)

await remote.async_update_state(validate=True)

# Get the value of a sensor
remote.get_sensor(SensorKey.CPU_LOAD).value

# Call a service
await remote.async_call_service(ServiceKey.RESTART)
```

## Execute a command

```python
from ssh_remote_control import Command

output = await remote.async_execute(Command("echo Hello from host"))
output.stdout
```