semvec/pypi-packages huggingface dataset:
tiledbsoma-ml 0.1.0
Machine learning tools for use with tiledbsoma
Requires Python: >=3.9

# TileDB-SOMA-ML
A Python package containing ML tools for use with [TileDB-SOMA].

[![tiledbsoma-ml package on PyPI](https://img.shields.io/pypi/v/tiledbsoma-ml?label=tiledbsoma-ml)][pypi]


Docs: [single-cell-data.github.io/TileDB-SOMA-ML].

**NOTE:** this is a _pre-release_ package, and may be subject to breaking API changes prior to first release.

## Description

The package contains a prototype PyTorch [`IterableDataset`], [`ExperimentDataset`], for use with the [`torch.utils.data.DataLoader`] API.

[notebooks/](notebooks) contains tutorials and examples that use this repo to train toy models. For a general introduction to PyTorch data loading, [see this tutorial][torch data tutorial]. Additional information on the DataLoader/Dataset pattern [can be found here][`torch.data`].

Defects and feature requests should be filed as [a GitHub issue][/issues] in this repo. Please include a reproducible test case in all bug reports.

## Getting Started

### Installing

Install [from PyPI][pypi]:
```bash
pip install tiledbsoma-ml
```

Developers may install editable, from source, in the usual manner -- clone the repo and execute:

```bash
pip install -e .
```

### Documentation

Documentation can be found at [single-cell-data.github.io/TileDB-SOMA-ML], and in the [notebooks] directory.

## Builds

This is a pure Python package. To build a wheel, ensure you have the `build` package installed, and then:

```bash
python -m build .
```

## Version History

See the [CHANGELOG.md] file.

## License

This project is licensed under the MIT License.

## Acknowledgements

The SOMA team is grateful to the Chan Zuckerberg Initiative Foundation [CELLxGENE Census](https://cellxgene.cziscience.com) team for their initial contribution.

[TileDB-SOMA]: https://github.com/single-cell-data/TileDB-SOMA
[`IterableDataset`]: https://pytorch.org/docs/stable/data.html#torch.utils.data.IterableDataset
[`ExperimentDataset`]: src/tiledbsoma_ml/dataset.py
[`torch.utils.data.DataLoader`]: https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader
[/issues]: https://github.com/single-cell-data/TileDB-SOMA-ML/issues/new
[pypi]: https://pypi.org/project/tiledbsoma-ml/
[torch data tutorial]: https://pytorch.org/tutorials/beginner/data_loading_tutorial.html
[`torch.data`]: https://pytorch.org/docs/stable/data.html
[single-cell-data.github.io/TileDB-SOMA-ML]: https://single-cell-data.github.io/TileDB-SOMA-ML/
[notebooks]: notebooks
[CHANGELOG.md]: CHANGELOG.md
[www]: https://single-cell-data.github.io/TileDB-SOMA-ML/
[single-cell-data.github.io/TileDB-SOMA-ML]: https://single-cell-data.github.io/TileDB-SOMA-ML/
mke-m17-v2 0.1.1
A Python/MicroPython driver for the L9110 motor controller over I2C
Requires Python: >=3.6

# Describe
A Python package to control the L9110 motor driver over I2C using smbus2.

# Installation
1. Create virtual environment: `python3 -m venv your_env`
2. Activate virtual environment: `source your_env/bin/activate`
3. install package: `pip install MKE-M17`

> **⚠️ Warning**  
> - Check hardware connection: Ensure your device is correctly connected to the I2C bus (SDA, SCL, VCC, GND) before using the package.  
> - Verify I2C address: Use the command `i2cdetect -y 1` on a Raspberry Pi to check the I2C address of the L9110 device.  
> - Resource management: The package supports context manager (`with` statement) to automatically close the I2C bus after use, preventing resource leaks. It is recommended to use this syntax whenever possible.

# How to use `MKE_M17` lib

## 1. Initialize the Driver
> Create an L9110 instance to control your device.

**Parameters:**

* `i2c_address` (int): The I2C address of the L9110 chip. Default is `0x40`.
* `i2c_bus_number` (int): The number of the I2C bus to use. Default is `1`.

**Example:**
> **⚠️ Warning:** Run in virtual environment (your_env/bin/python3.11)

```python
from MKE_M17 import L9110

# Default settings
l9110 = L9110()

# Custom address and bus
l9110 = L9110(address=0x42, bus_number=1)

# With context manager (recommended)
with L9110(address=0x42) as l9110:
    pass
```
## 2. Control Servos
> Use `control_servo()` to set servo angles.

**Args:**

* `servo_id` (int): 1 (S1) or 2 (S2).
* `degree` (int): Angle in degrees (default: 0-180).

**Returns:**
> `True` on success, `False` on failure.

**Example:**
```python
l9110.control_servo(1, 150)  # Servo 1 to 150°
l9110.control_servo(2, 90)   # Servo 2 to 90°
```

## 3. Control DC Motors
> Use `control_motor()` to set motor speed and direction.

**Args:**

* `motor_id` (int): 0 (MA) or 1 (MB).
* `percent` (float): Speed (0-100).
* `direction` (int): 0 (CW) or 1 (CCW).

**Returns:**
> `True` on success, `False` on failure.

**Example:** 
```python
l9110.control_motor(0, 50, 0)  # Motor A, 50%, clockwise
l9110.control_motor(1, 70, 1)  # Motor B, 70%, counterclockwise
```

## 4. Change I2C Address
> Update the device’s I2C address with `set_address()`.

**Args:**
* `new_address` (byte): New address (0x40-0x44).

**Returns:**
> `True` on success, `False` on failure.

**Example:**
```python
l9110.set_address(0x42)
# Output: "Set address successful. New address: 0x42"
```

## 5. Customize Servo Range
### Set new range degree.
> using `set_range_degree(min_degree, max_degree)` to set new range degree for servo motor

**Args:**
* `min_degree`(int): (0-359).
* `max_degree`(int): (1-360).

**Example:**
```python
l9110.set_range_degree(0, 270)
```
### Set new range pulse.
> using `set_range_pulse(min_pulse, max_pulse)` to set new range degree for servo motor

**Args:**
* `min_pulse`(int): (0-2814).
* `max_pulse`(int): (1-2815).

**Example:**
```python
l9110.set_range_pulse(600, 2400)
```
hightop-sdk 0.1.1
Python SDK for the Hightop API
Requires Python: >=3.7

# Hightop SDK

A Python SDK for the Hightop API.

## Installation

```bash
pip install hightop_sdk
```

## Usage

### Basic Setup

```python
from hightop_sdk import HightopSDK

# Initialize the SDK with your API key
sdk = HightopSDK(api_key="your_api_key_here")

# Make API calls using the SDK
response = sdk.your_api_endpoint()
```

### Configuration

You can configure the SDK with various options:

```python
from hightop_sdk import HightopSDK, Configuration

# Create a custom configuration
config = Configuration(
    api_key="your_api_key_here",
    host="https://api.hightop.com/api",  # Optional: customize API host
    timeout=30  # Optional: set request timeout
)

# Initialize SDK with custom configuration
sdk = HightopSDK(configuration=config)
```

### Error Handling

The SDK includes built-in error handling for common API errors:

```python
from hightop_sdk.exceptions import ApiException

try:
    response = sdk.your_api_endpoint()
except ApiException as e:
    print(f"API Error: {e.status} - {e.reason}")
except Exception as e:
    print(f"Unexpected error: {str(e)}")
```

### Response Handling

API responses are automatically deserialized into Python objects:

```python
# Example response handling
response = sdk.your_api_endpoint()
data = response.data  # Access the deserialized response data
```

## Examples

### Making API Requests

```python
# Example of making a GET request
response = sdk.get_data()

# Example of making a POST request with data
data = {
    "name": "Example",
    "value": 123
}
response = sdk.create_data(data)

# Example of making a request with query parameters
response = sdk.search_data(query="example", limit=10)
```

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
gen-lsp-tdm 2025.3.20.1
Fix compile commands for gcc/tdm-gcc for use with clangd on Windows
Requires Python: >=3.10

# gen_lsp_tdm

Fix compile commands for gcc/tdm-gcc for use with clangd on Windows

## usage

run gen_lsp_tdm after build

See [DEMO][demo]

[demo]: https://github.com/soda92/gen_lsp_tdm/blob/main/demo/README.md