Defining Models

In ezfit, you define your model as a Python function.

Function Signature

Your model function must have the independent variable as the first argument, followed by the parameters:

def model(x, param1, param2, param3, ...):
    # Model calculation
    return result

Simple Example

Linear model:

def line(x, m, b):
    """Linear model: y = m*x + b"""
    return m * x + b

Using Built-in Functions

ezfit provides optimized functions:

from ezfit import gaussian, exponential, power_law

# Use directly
model, ax, _ = df.fit(gaussian, "x", "y", "yerr", ...)

# Or combine
def two_peaks(x, A1, c1, w1, A2, c2, w2):
    return gaussian(x, A1, c1, w1) + gaussian(x, A2, c2, w2)

Complex Models

You can define any mathematical relationship:

def complex_model(x, A, B, C, D):
    """Custom model"""
    return A * np.sin(x / B) * np.exp(-x / C) + D

Best Practices

1. Use descriptive parameter names

2. Include docstrings explaining the model

3. Use NumPy operations for vectorization

4. Keep it simple - complex models are harder to fit