mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2026-04-21 05:34:37 +02:00
Khalil Ferjaoui
a6afa45b3b
## Unified Minuit2 fitting framework with FitModel API ### Models (`Models.hpp`) Consolidate all model structs (Gaussian, RisingScurve, FallingScurve) into a single header. Each model provides: `eval`, `eval_and_grad`, `is_valid`, `estimate_par`, `compute_steps`, and `param_info` metadata. No Minuit2 dependency. ### Chi2 functors (`Chi2.hpp`) Generic `Chi2Model1DGrad` (analytic gradient) templated on the model struct. Replaces the separate Chi2Gaussian, Chi2GaussianGradient, Chi2Scurves, and Chi2ScurvesGradient headers. ### FitModel (`FitModel.hpp`) Configuration object wrapping `MnUserParameters`, strategy, tolerance, and user-override tracking. User constraints (fixed parameters, start values, limits) always take precedence over automatic data-driven estimates. ### Fit functions (`Fit.hpp`) - `fit_pixel<Model, FCN>(model, x, y, y_err)` -> single-pixel, self-contained - `fit_pixel<Model, FCN>(model, upar_local, x, y, y_err)` -> pre-cloned upar for hot loops - `fit_3d<Model, FCN>(model, x, y, y_err, ..., n_threads)` -> row-parallel over pixel grid ### Python bindings - `Pol1`, `Pol2`, `Gaussian`, `RisingScurve`, `FallingScurve` model classes with `FixParameter`, `SetParLimits`, `SetParameter`, and properties for `max_calls`, `tolerance`, `compute_errors` - Single `fit(model, x, y, y_err, n_threads)` dispatch replacing the old `fit_gaus_minuit`, `fit_gaus_minuit_grad`, `fit_scurve_minuit_grad`, etc. ### Benchmarks - Updated `fit_benchmark.cpp` (Google Benchmark) to use the new FitModel API - Jupyter notebooks for 1D and 3D S-curve fitting (lmfit vs Minuit2 analytic) - ~1.8x speedup over lmfit, near-linear thread scaling up to physical core count --------- Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
107 lines
3.5 KiB
Python
107 lines
3.5 KiB
Python
# SPDX-License-Identifier: MPL-2.0
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import matplotlib.pyplot as plt
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import numpy as np
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import sys
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sys.path.insert(0, '/home/kferjaoui/sw/aare/build')
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from aare import fit_gaus, fit_pol1
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from aare import Gaussian, fit
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from aare import pol1
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textpm = f"±" #
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textmu = f"μ" #
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textsigma = f"σ" #
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# ================================= Gauss fit =================================
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# Parameters
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mu = np.random.uniform(1, 100) # Mean of Gaussian
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sigma = np.random.uniform(4, 20) # Standard deviation
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num_points = 10000 # Number of points for smooth distribution
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# noise_sigma = 10
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# Generate Gaussian distribution
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data = np.random.normal(mu, sigma, num_points)
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counts, edges = np.histogram(data, bins=100)
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x = 0.5 * (edges[:-1] + edges[1:]) # proper bin centers
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y = counts.astype(np.float64)
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# Poisson noise
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yerr = np.sqrt(np.maximum(y, 1))
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# yerr = np.abs(np.random.normal(0, noise_sigma, len(x)))
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# Create subplot
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fig0, ax0 = plt.subplots(1, 1, num=0, figsize=(12, 8))
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# Add the errors as error bars in the step plot
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ax0.errorbar(x, y, yerr=yerr, fmt=". ", capsize=5)
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ax0.grid()
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# Fit with lmfit
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result_lm = fit_gaus(x, y, yerr)
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par_lm = result_lm["par"]
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err_lm = result_lm["par_err"]
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chi2_lm = result_lm["chi2"]
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print("[lmfit] fit_gaus: ", par_lm, err_lm, chi2_lm)
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# Fit with Minuit2 + analytic gradient + Hesse errors
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gaussian = Gaussian()
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gaussian.compute_errors = True
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result_m2 = gaussian.fit(x, y, yerr)
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par_m2 = result_m2['par']
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err_m2 = result_m2['par_err']
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chi2_m2 = result_m2['chi2']
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print(f"[minuit2] gaussian.fit: par={par_m2}, err={err_m2}, chi2={chi2_m2}")
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x = np.linspace(x[0], x[-1], 1000)
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ax0.plot(x, gaussian(x, par_lm), marker="", label="fit_gaus")
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ax0.plot(x, gaussian(x, par_m2), marker="", linestyle=":", label="fit_gaus_minuit_grad")
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ax0.legend()
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ax0.set(xlabel="x", ylabel="Counts",
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title=(
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f"fit_gaus: A={par_lm[0]:0.2f}{textpm}{err_lm[0]:0.2f} "
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f"{textmu}={par_lm[1]:0.2f}{textpm}{err_lm[1]:0.2f} "
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f"{textsigma}={par_lm[2]:0.2f}{textpm}{err_lm[2]:0.2f}\n"
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f"minuit_grad: A={par_m2[0]:0.2f}{textpm}{err_m2[0]:0.2f} "
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f"{textmu}={par_m2[1]:0.2f}{textpm}{err_m2[1]:0.2f} "
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f"{textsigma}={par_m2[2]:0.2f}{textpm}{err_m2[2]:0.2f}\n"
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f"(truth: {textmu}={mu:0.2f}, {textsigma}={sigma:0.2f})"
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),
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)
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fig0.tight_layout()
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# ================================= pol1 fit =================================
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# Parameters
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n_points = 40
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# Generate random slope and intercept (origin)
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slope = np.random.uniform(-10, 10) # Random slope between 0.5 and 2.0
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intercept = np.random.uniform(-10, 10) # Random intercept between -10 and 10
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# Generate random x values
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x_values = np.random.uniform(-10, 10, n_points)
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# Calculate y values based on the linear function y = mx + b + error
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errors = np.abs(np.random.normal(0, np.random.uniform(1, 5), n_points))
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var_points = np.random.normal(0, np.random.uniform(0.1, 2), n_points)
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y_values = slope * x_values + intercept + var_points
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fig1, ax1 = plt.subplots(1, 1, num=1, figsize=(12, 8))
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ax1.errorbar(x_values, y_values, yerr=errors, fmt=". ", capsize=5)
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result_pol = fit_pol1(x_values, y_values, errors)
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par = result_pol["par"]
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err = result_pol["par_err"]
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x = np.linspace(np.min(x_values), np.max(x_values), 1000)
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ax1.plot(x, pol1(x, par), marker="")
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ax1.set(xlabel="x", ylabel="y", title=f"a = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
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f"b = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
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f"(init: {slope:0.2f}, {intercept:0.2f})")
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fig1.tight_layout()
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plt.show()
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