lumafit 0.2.3

Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Tests
5. Roadmap
6. Contributing
7. License
8. Contact
9. Acknowledgments

About The Project

This library provides a high-performance implementation of the Levenberg-Marquardt (LM) algorithm for non-linear least squares fitting, accelerated using Numba.

The primary motivation for lumafit is to efficiently perform fitting tasks on large multi-dimensional datasets, such as fitting a curve along the third dimension for every pixel in a 3D image stack. Numba's Just-In-Time (JIT) compilation and parallel processing capabilities (numba.prange) are leveraged to drastically reduce computation time compared to pure Python implementations.

Key features:

• Core Levenberg-Marquardt algorithm (levenberg_marquardt_core).
• Specialized function for fitting curves pixel-wise on 3D data (levenberg_marquardt_pixelwise) with parallel execution.
• Support for weighted least squares.
• Numerical Jacobian calculation via finite differences (Numba-accelerated).
• Implementation based on standard LM algorithm formulations.

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Built With

• 
• 
• 
• (Used in tests for comparison)

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Getting Started

To get a local copy of lumafit up and running, follow these simple steps.

Prerequisites

You need Python 3.9+ installed. Using a virtual environment is recommended. You will also need standard Python build tools, typically included with pip.

python -m venv venv
source venv/bin/activate # On Linux/macOS
# venv\Scripts\activate # On Windows

Installation

1. Clone the repository:

   git clone https://github.com/arunoruto/lumafit.git
   cd lumafit
   

2. Install the package using pip, which will use the pyproject.toml file:

   pip install .
   

   If you want to install dependencies required for running tests, use the [test] extra:

   pip install .[test]
   

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Usage

The library provides two main functions: levenberg_marquardt_core for fitting a single curve and levenberg_marquardt_pixelwise for fitting a 3D data stack.

First, you need to define your model function. Your model function must be compatible with Numba's nopython=True mode. This means it should primarily use NumPy functions and basic Python constructs supported by Numba.

# Example model function (exponential decay + another exponential decay)
import numpy as np
from numba import jit

@jit(nopython=True, cache=True)
def my_model(t, p):
    """
    My example non-linear model.

    Args:
        t (np.ndarray): Independent variable (1D array).
        p (np.ndarray): Parameters (1D array), e.g., [A1, tau1, A2, tau2].

    Returns:
        np.ndarray: Model output evaluated at t.
    """
    # Add checks for potential zero divisors if parameters are in denominators
    term1 = np.zeros_like(t, dtype=np.float64)
    if np.abs(p[1]) > 1e-12: # Avoid division by zero or near-zero
        term1 = p[0] * np.exp(-t / p[1])

    term2 = np.zeros_like(t, dtype=np.float64)
    if np.abs(p[3]) > 1e-12:
         term2 = p[2] * np.exp(-t / p[3])

    return term1 + term2

# Or the polarization model:
# @jit(nopython=True, cache=True)
# def polarization_model(t, p):
#     # ... (your polarization model code from tests/lmba.py)
#     t_rad = t * np.pi / 180.0
#     p3_rad = p[3] * np.pi / 180.0
#     sin_t_rad_arr = np.sin(t_rad)
#     term1_base = sin_t_rad_arr.copy()
#     mask_problematic_base = (np.abs(term1_base) < 1e-15) & (p[1] < 0.0) # Using a small epsilon
#     term1_base[mask_problematic_base] = 1e-15 # Replace near-zero with tiny number
#     term1 = np.power(term1_base, p[1])
#     term2 = np.power(np.cos(t_rad / 2.0), p[2])
#     term3 = np.sin(t_rad - p3_rad)
#     return p[0] * term1 * term2 * term3

Fitting a single curve

If you have a single 1D array of data (y_data) corresponding to independent variable values (t_data), you can use levenberg_marquardt_core. This function is the core engine for minimizing the difference between your model and the data.

import numpy as np
from lmba import levenberg_marquardt_core

# Assume my_model is defined and JIT-compiled above

# Generate some synthetic data (replace with your actual data)
t_data = np.linspace(0.1, 25, 100, dtype=np.float64)
p_true = np.array([5.0, 2.0, 2.0, 10.0], dtype=np.float64)
y_clean = my_model(t_data, p_true)
noise = np.random.default_rng(42).normal(0, 0.1, size=t_data.shape).astype(np.float64)
y_data = (y_clean + noise).astype(np.float64)

# Initial guess for parameters
p_initial = np.array([4.0, 1.5, 1.5, 8.0], dtype=np.float64)

# Optional: weights (e.g., inverse variance if noise std is known)
weights = 1.0 / (0.1**2 + np.finfo(float).eps) # Assuming noise_std = 0.1

# Run the fit
p_fit, cov, chi2, iters, conv = levenberg_marquardt_core(
    my_model,       # Your Numba-compiled model function
    t_data,         # Independent variable data (1D array)
    y_data,         # Dependent variable data (1D array)
    p_initial,      # Initial guess (1D array)
    weights=weights, # Optional weights (1D array or None)
    max_iter=1000,  # Max iterations
    tol_g=1e-7,     # Gradient tolerance
    tol_p=1e-7,     # Parameter change tolerance
    tol_c=1e-7,     # Chi-squared change tolerance
    # ... other optional parameters
)

print(f"Fit converged: {conv}")
print(f"Iterations: {iters}")
print(f"Final Chi-squared: {chi2}")
print(f"Fitted parameters: {p_fit}")
# print(f"Covariance matrix: {cov}") # Covariance can be large

Fitting pixel-wise on 3D data

For a 3D NumPy array where each (row, col) location has a curve along the third dimension (data_cube[row, col, :]), use levenberg_marquardt_pixelwise. This function parallelizes the fitting process across the row and col dimensions using numba.prange.

import numpy as np
from lmba import levenberg_marquardt_pixelwise

# Assume my_model is defined and JIT-compiled above

# Generate some synthetic 3D data (replace with your actual data)
rows, cols, depth = 100, 100, 50 # Example dimensions
t_data = np.linspace(0.1, 25, depth, dtype=np.float64)
data_cube = np.empty((rows, cols, depth), dtype=np.float64)

p_true_base = np.array([5.0, 2.0, 2.0, 10.0], dtype=np.float64)
rng = np.random.default_rng(42)

for r_idx in range(rows):
    for c_idx in range(cols):
        # Vary true params slightly per pixel
        p_pixel_true = p_true_base * (1 + rng.uniform(-0.05, 0.05, size=p_true_base.shape))
        y_clean_pixel = my_model(t_data, p_pixel_true)
        noise_pixel = rng.normal(0, 0.1, size=depth).astype(np.float64)
        data_cube[r_idx,c_idx,:] = (y_clean_pixel + noise_pixel).astype(np.float64)

# Global initial guess for all pixels
p0_global = np.array([4.0, 1.5, 1.5, 8.0], dtype=np.float64)

# Optional weights (applied to each pixel identically)
# weights_1d = 1.0 / (0.1**2 + np.finfo(float).eps) # Assuming noise_std = 0.1

# Run the pixel-wise fit (this is parallelized)
p_results, cov_results, chi2_results, n_iter_results, conv_results = levenberg_marquardt_pixelwise(
    my_model,        # Your Numba-compiled model function
    t_data,          # Independent variable (1D array, common for all pixels)
    data_cube,       # 3D data array (rows x cols x depth)
    p0_global,       # Global initial guess (1D array)
    # Optional parameters for the core LM algorithm, passed to each pixel fit
    # weights_1d=weights_1d,
    max_iter=500,
    tol_g=1e-6,
    tol_p=1e-6,
    tol_c=1e-6,
    # ... other optional parameters
)

print(f"Pixel-wise fitting finished.")
print(f"Shape of fitted parameters: {p_results.shape}") # (rows x cols x n_params)
print(f"Shape of convergence flags: {conv_results.shape}") # (rows x cols)
print(f"Percentage converged: {np.sum(conv_results) / (rows*cols) * 100.0:.2f}%")

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Tests

The library includes a test suite using pytest to verify the correctness of the core LM algorithm and the pixel-wise function against known solutions (for noiseless data) and against scipy.optimize.least_squares (for noisy data).

To run the tests:

1. Ensure you have installed the test dependencies: pip install .[test]
2. Navigate to the project root directory in your terminal.
3. Run pytest:

   pytest
   

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Roadmap

• Add support for analytical Jacobian functions (instead of only finite differences).
• Implement parameter bounds.
• Investigate alternative damping strategies (e.g., Nielsen's method).
• Improve robustness for ill-conditioned problems.
• Add more detailed documentation and examples.
• Potentially publish on PyPI.

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Contributing

Contributions are welcome! If you have suggestions or find bugs, please open an issue or submit a pull request.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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License

Distributed under the MIT License. See LICENSE for more information.

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Contact

Mirza Arnaut - mirza.arnaut@tu-dortmund.de

Project Link: https://github.com/arunoruto/lumafit

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Acknowledgments

• The original Levenberg-Marquardt algorithm (see references in lmba/__init__.py).
• Numba for providing the acceleration capabilities.
• NumPy and SciPy for fundamental numerical computing tools.
• pytest for the testing framework.
• othneildrew/Best-README-Template for the README structure template.

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