Curve-Fitting Plugins

What Curve-Fitting Plugins Do

Curve-fitting plugins define mathematical models that can be fitted to the active plot’s X and Y data. In the configuration window, the user chooses a label mode and then selects a fit type from the visible curve-fit list.

PhysPlot supports two plugin kinds:

poly: Polynomial fits using numpy.polyfit.
callable: Custom model functions fitted using scipy.optimize.curve_fit.

Layman Example

If your plotted data looks like a line, choose a linear fit. If your plotted data follows exponential decay, choose an exponential model. A curve-fitting plugin is simply a small file that tells PhysPlot what equation to use.

Required File Location

Put new curve-fitting files in:

curvefitting/

Use numbered filenames to control display order:

curvefitting/12_gaussian.py

Polynomial Template

"""Quadratic curve fit plugin for PhysPlot."""

DISPLAY_NAME = "Quadratic"
DEFAULT_LABEL = "Quadratic"
KIND = "poly"
DEGREE = 2
LABEL_MODES = ["Off", "Equation", "Custom"]

For polynomial fits, PhysPlot calculates the model with:

coefficients = numpy.polyfit(x, y, DEGREE)

Callable Template

"""Gaussian curve fit plugin for PhysPlot."""

import numpy as np

DISPLAY_NAME = "Gaussian"
DEFAULT_LABEL = "Gaussian"
KIND = "callable"
INITIAL_GUESS = [1.0, 0.0, 1.0]
LABEL_MODES = ["Off", "Equation", "Custom"]


def function(x, amplitude, center, width):
    """function(x, amplitude, center, width) -> numpy.ndarray

    Evaluate a Gaussian model.

    Parameters:
        x (numpy.ndarray): One-dimensional X data array.
        amplitude (float): Peak height.
        center (float): Peak center position.
        width (float): Peak width.

    Returns:
        numpy.ndarray: Model Y values for the input X array.
    """
    return amplitude * np.exp(-((x - center) / width) ** 2)

Required Fields

DISPLAY_NAME: Text shown in the curve-fit list.
DEFAULT_LABEL: Default custom-label text.
KIND: Either "poly" or "callable".
LABEL_MODES: Label choices shown in the configuration window, usually ["Off", "Equation", "Custom"].
DEGREE: Required only for KIND = "poly".
function(x, ...): Required only for KIND = "callable".
INITIAL_GUESS: Optional but strongly recommended for callable fits. It gives SciPy a starting point for the unknown parameters.

Practical Rules

Use polynomial plugins for simple polynomial equations.
Use callable plugins for physical models such as Gaussian, Lorentzian, exponential decay, or instrument response functions.
Keep the function vectorized: it should accept a NumPy array x and return a NumPy array.
If fitting fails, try better INITIAL_GUESS values.

Existing Examples

See:

curvefitting/01_linear.py
curvefitting/10_tenth_degree.py
curvefitting/11_exponential_decay.py