scietex.hal.analog-sensor 1.0.1

Analog sensor signal conversion

scietex.hal.analog_sensor

The scietex.hal.analog_sensor package is a Python library for interfacing with and processing data from analog sensors. It provides an abstract base class (AnalogSensorInterface) and concrete implementations for converting analog voltage readings into physical quantities (e.g., temperature, pressure) using linear transformations and advanced interpolation techniques.

Features

• Abstract base class (AnalogSensorInterface) for consistent sensor implementations.
• Support for single float values and NumPy arrays (NDArray[np.float64]) for batch processing.
• Linear sensor models:
  • LinearSensor: Basic linear transformation (gain * voltage + offset).
  • LinearLimitedSensor: Linear transformation with voltage clamping.
• Interpolation-based sensors for non-linear data:
  • LinearInterpolatorSensor: Linear interpolation using NumPy.
  • CubicSplineInterpolatorSensor: Cubic spline interpolation with configurable boundary conditions.
  • AkimaInterpolatorSensor: Akima interpolation for smooth curves with less overshoot.
  • PchipInterpolatorSensor: PCHIP interpolation for shape-preserving curves.
• Extensible design for custom sensor models.
• Built on NumPy and SciPy for efficient numerical and interpolation operations.

Installation

Install the package via pip (assuming it’s published to PyPI):

pip install scietex.hal.analog_sensor

Alternatively, clone the repository and install locally:

git clone https://github.com/bond-anton/scietex.hal.analog_sensor.git
cd scietex.hal.analog_sensor
pip install .

Requirements

• Python 3.9 or higher.
• numpy (for array operations and linear interpolation).
• scipy (for cubic spline, Akima, and PCHIP interpolation).

Usage

The package uses AnalogSensorInterface as a base class, requiring subclasses to implement convert_voltage. Linear sensors use a simple gain-offset model, while interpolation-based sensors require calibration data.

Basic example with a linear sensor:

from scietex.hal.analog_sensor import LinearSensor

# Linear sensor: output = gain * voltage + offset
sensor = LinearSensor(model_name="PressureSensor", gain=0.5, offset=10)

# Convert a voltage
value = sensor.convert_voltage(100)
print(f"Physical value: {value}")  # Output: 60.0

Using cubic spline interpolation:

import numpy as np
from scietex.hal.analog_sensor import CubicSplineInterpolatorSensor

# Calibration data: [voltage, physical value]
data = np.array([[0, 0], [50, 25], [100, 50]])
sensor = CubicSplineInterpolatorSensor(
    model_name="LightSensor", data=data, bc="natural"
)

# Convert an array of voltages
voltages = np.array([25, 75])
values = sensor.convert_voltage(voltages)
print(f"Physical values: {values}")  # Output depends on spline fit

Modules

• AnalogSensorInterface (in interface.py):
  • Abstract base class requiring convert_voltage implementation.
  • Attributes: model_name (str) for sensor identification.
  • Supports single float or NumPy array inputs/outputs.
• LinearSensor (in linear.py):
  • Linear transformation: gain * voltage + offset.
  • Args: model_name, gain, offset (optional, default 0.0).
• LinearLimitedSensor (in linear.py):
  • Linear transformation with voltage clamping between v_min and v_max.
  • Args: v_min, v_max, model_name, gain, offset (optional, default 0.0).
• LinearInterpolatorSensor (in interpolation.py):
  • Linear interpolation using np.interp.
  • Args: model_name, data (2D array: voltage, physical value), extrapolate (optional).
• CubicSplineInterpolatorSensor (in interpolation.py):
  • Cubic spline interpolation via scipy.interpolate.CubicSpline.
  • Args: model_name, data, bc (e.g., "natural", "clamped"), extrapolate (optional).
• AkimaInterpolatorSensor (in interpolation.py):
  • Akima interpolation via scipy.interpolate.Akima1DInterpolator.
  • Args: model_name, data, extrapolate (optional, SciPy 1.14+).
• PchipInterpolatorSensor (in interpolation.py):
  • PCHIP interpolation via scipy.interpolate.PchipInterpolator.
  • Args: model_name, data, extrapolate (optional).

Contributing

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/YourFeature).
3. Commit your changes (git commit -m "Add your message").
4. Push to the branch (git push origin feature/YourFeature).
5. Open a Pull Request.

Please include tests (if applicable) and follow PEP 8 style guidelines.

License

This project is licensed under the MIT License - see the LICENSE file for details.