semvec/pypi-packages huggingface dataset:
briefs 0.1.0
Add your description here
Requires Python: >=3.11

# briefs

Initial PyPI package scaffold for `briefs`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```
hana-cloud-interface 0.3.4
Runs a SQL command on SAP HANA Cloud using OAuth single sign on and returns a pandas or polars dataframe
Requires Python: >=3.12

# Usage of the `hana_cloud_interface` package 

This package provides a simple interface to connect to SAP HANA Cloud databases and execute SQL queries. Below are some examples of how to use the package.


## initialising class
when initialising the class it has two optional args. 

- config_file : Path to the configuration file (JSON format) containing OAuth credentials and other settings. the config file only needs to be added on the first time running the class on a computer. 

- data_frame_type : Default data frame type for SQL query results. Options are 'pandas' or 'polars'. Default is 'pandas'.
if the data type is not added here it will need to be added for every time you run hana_sql function

```python
Hana_cloud_interface(config_file = 'location of configuration file',  df = 'pandas')
```

## hana_sql() function
Execute a SQL command on the HANA Cloud database and return the result as a DataFrame.

Args:
    sql_command (str, optional): The SQL command to execute. Defaults to 'validate', which only checks the connection.
    DF_type_local_override (str, optional): DataFrame type for this query ('pandas' or 'polars'). Overrides the global setting if provided. 

Returns:
    str: If sql_command is 'validate', returns a validation message.
    pandas.DataFrame or polars.DataFrame: The query result as a DataFrame, depending on the specified or default DataFrame type.

Raises:
    ValueError: If the DataFrame type is not specified or is invalid.

## hana_upload() function
Upload a DataFrame to the HANA Cloud database.

Args:
    data (pandas.DataFrame or polars.DataFrame): The data to upload. If a polars DataFrame is provided, it will be converted to pandas.
    data_name (str): The name of the table to create or replace in HANA Cloud.
    SCHEMA (str): The schema in which to create the table.

Returns:
    bool: True if the upload is successful.

Raises:
    Exception: If the upload fails due to connection or data issues.


## configuration file
the configuration file is a .json file
```python
{
    "CLIENT_ID": "",
    "CLIENT_SECRET": "",
    "AUTH_URL": "",
    "TOKEN_URL": "",
    "protected_url": "",
    "REDIRECT_URI": "",
    "SCOPE": "",
	"HC_prod_URL": ""
}
```

## example
the main function is very simple It takes a SQL command as a string and returns the data
```python
import hana_cloud_interface 

# initialise class
hci = Hana_cloud_interface(df='polars')

sql_command = """
SELECT top 10
    "data1"
    "data2"
FROM "table1"
"""
    
data = hci.hana_sql(sql_command)

```
syndesi 0.5.2
Syndesi
Requires Python: >=3.10
Keywords: python, syndesi, interface, ethernet, serial, visa, protocol, driver, adpater

# Syndesi Python Implementation

Syndesi description is available [here](https://github.com/syndesi-project/Syndesi/README.md)

Syndesi is a modular Python framework designed to streamline communication and control of a wide range of electronic instruments and devices. By providing a unified abstraction layer for adapters, protocols, and device drivers, Syndesi enables seamless integration with test equipment such as multimeters, oscilloscopes, power supplies, UART/USB devices, and more. Its flexible architecture supports both high-level and low-level operations, making it ideal for automation, data acquisition, and custom device interfacing in laboratory, industrial, and research environments.

## Installation

The syndesi Python package can be installed through pip

``pip install syndesi``

The package can also be installed locally by cloning this repository

```bash
git clone https://github.com/syndesi-project/Syndesi
cd Syndesi
pip install .
```

## Usage

The user can work with any of the three following layers :

- Adapters : low-level communication (IP, UART, ...)
- Protocols : Encapsulated protocols (Delimited, Modbus, ...)
- Drivers : Device or application specific commands

### Adapters

The adapter allows the user to read and write raw data through IP, serial and VISA

```python
from syndesi import IP

my_adapter = IP('192.168.1.12', port=5025)

my_adapter.write(b'ping\n')

my_adapter.read() # -> b'pong'
```

```python
from syndesi import SerialPort

arduino = SerialPort('/dev/ttyUSB0', baudrate=115200) # COMx on Windows
arduino.query(b'get_temperature\n') # -> 20.5
```

### Protocols

Protocols encapsulate and format data

```python
from syndesi import IP, Delimited

my_server = Delimited(IP('test.server.local', port=1234))

my_server.query('Hello world\n') # -> Hello world (\n is removed by Delimited)

```

### Drivers

A driver only requires an adapter, the protocol (if used) is instanciated internally

```python
from syndesi_drivers.instruments.mutlimeters.siglent.SDM3055 import SDM3055
from syndesi.adapters import IP

mm = SDM3055(IP("192.168.1.123"))

voltage = mm.measure_dc_voltage()
```
workers-py 1.7.3
A set of libraries and tools for Python Workers
Requires Python: >=3.12

# workers-py

A set of libraries and tools for Python Workers.


## Pywrangler

A CLI tool for managing vendored packages in a Python Workers project.

### Installation

On Linux, you may be able to install the tool globally by running:

```
uv tool install workers-py
```

Alternatively, you can add `workers-py` to your pyproject.toml:

```
[dependency-groups]
dev = ["workers-py"]
```

Then run via `uv run pywrangler`.

### Usage

```bash
uv run pywrangler --help
uv run pywrangler sync
```

### Development

To run the CLI tool while developing it, install it globally:

```
uv tool install -e .
```

Then run it via `pywrangler`.

Alternatively, you can add `workers-py` to your pyproject.toml:

```
[dependency-groups]
dev = ["workers-py"]

[tool.uv.sources]
workers-py = { path = "../workers-py" }
```

Then run via `uv run pywrangler`.

#### Lint

```
uv run ruff check --fix
uv run ruff format
```

#### Tests

```
$ uv cache clean
$ uv run pytest
$ uv run pytest tests/test_cli.py::test_sync_command_handles_missing_pyproject -v # Specific test
```
hiveminds 0.1.0
Add your description here
Requires Python: >=3.11

# hiveminds

Initial PyPI package scaffold for `hiveminds`, using `uv` for build and publish.

## Local workflow

```bash
uv build
uv publish
```

`uv publish` reads `UV_PUBLISH_TOKEN` automatically.

If your token is stored as `PYPI_TOKEN`, run:

```bash
export UV_PUBLISH_TOKEN="$PYPI_TOKEN"
uv publish
```

## TestPyPI

```bash
uv publish --publish-url https://test.pypi.org/legacy/
```