Removing deprecated use of lmfit (#344)

Removed the lmfit based fitting and the old API for calling functions

closes #296

python/examples/fits.py

@@ -1,106 +1,78 @@
 # SPDX-License-Identifier: MPL-2.0
 import matplotlib.pyplot as plt
 import numpy as np
-import sys
-sys.path.insert(0, '/home/kferjaoui/sw/aare/build')
-from aare import fit_gaus, fit_pol1
-from aare import Gaussian, fit
-from aare import pol1
 
-textpm = f"±"  #
-textmu = f"μ"  #
-textsigma = f"σ"  #
+from aare import Gaussian, Pol1
 
+textpm = "±"
+textmu = "μ"
+textsigma = "σ"
 
 
 # ================================= Gauss fit =================================
-# Parameters
-mu = np.random.uniform(1, 100)  # Mean of Gaussian
-sigma = np.random.uniform(4, 20)  # Standard deviation
-num_points = 10000  # Number of points for smooth distribution
-# noise_sigma = 10
-
-# Generate Gaussian distribution
-data = np.random.normal(mu, sigma, num_points)
+mu = np.random.uniform(1, 100)
+sigma = np.random.uniform(4, 20)
+data = np.random.normal(mu, sigma, 10000)
 counts, edges = np.histogram(data, bins=100)
 
-x = 0.5 * (edges[:-1] + edges[1:])       # proper bin centers
+x = 0.5 * (edges[:-1] + edges[1:])
 y = counts.astype(np.float64)
-
-# Poisson noise
 yerr = np.sqrt(np.maximum(y, 1))
-# yerr = np.abs(np.random.normal(0, noise_sigma, len(x)))
 
-# Create subplot
 fig0, ax0 = plt.subplots(1, 1, num=0, figsize=(12, 8))
-
-# Add the errors as error bars in the step plot
 ax0.errorbar(x, y, yerr=yerr, fmt=". ", capsize=5)
 ax0.grid()
 
-# Fit with lmfit
-result_lm = fit_gaus(x, y, yerr)
-par_lm = result_lm["par"]
-err_lm = result_lm["par_err"]
-chi2_lm = result_lm["chi2"] 
-print("[lmfit] fit_gaus:            ", par_lm, err_lm, chi2_lm)
+gaussian = Gaussian(compute_errors=True)
+result = gaussian.fit(x, y, yerr)
+par = result["par"]
+err = result["par_err"]
+chi2 = result["chi2"]
+print(f"Gaussian.fit: par={par}, err={err}, chi2={chi2}")
 
-# Fit with Minuit2 + analytic gradient + Hesse errors
-gaussian = Gaussian()
-gaussian.compute_errors = True
-result_m2 = gaussian.fit(x, y, yerr)
-par_m2 = result_m2['par']
-err_m2 = result_m2['par_err']
-chi2_m2 = result_m2['chi2']
-print(f"[minuit2] gaussian.fit: par={par_m2}, err={err_m2}, chi2={chi2_m2}")
-
-x = np.linspace(x[0], x[-1], 1000)
-ax0.plot(x, gaussian(x, par_lm), marker="", label="fit_gaus")
-ax0.plot(x, gaussian(x, par_m2), marker="", linestyle=":", label="fit_gaus_minuit_grad")
+x_plot = np.linspace(x[0], x[-1], 1000)
+ax0.plot(x_plot, gaussian(x_plot, par), marker="", label="Gaussian.fit")
 ax0.legend()
-ax0.set(xlabel="x", ylabel="Counts", 
+ax0.set(
+    xlabel="x",
+    ylabel="Counts",
     title=(
-        f"fit_gaus:       A={par_lm[0]:0.2f}{textpm}{err_lm[0]:0.2f}  "
-        f"{textmu}={par_lm[1]:0.2f}{textpm}{err_lm[1]:0.2f}  "
-        f"{textsigma}={par_lm[2]:0.2f}{textpm}{err_lm[2]:0.2f}\n"
-        f"minuit_grad: A={par_m2[0]:0.2f}{textpm}{err_m2[0]:0.2f}  "
-        f"{textmu}={par_m2[1]:0.2f}{textpm}{err_m2[1]:0.2f}  "
-        f"{textsigma}={par_m2[2]:0.2f}{textpm}{err_m2[2]:0.2f}\n"
+        f"A={par[0]:0.2f}{textpm}{err[0]:0.2f}  "
+        f"{textmu}={par[1]:0.2f}{textpm}{err[1]:0.2f}  "
+        f"{textsigma}={par[2]:0.2f}{textpm}{err[2]:0.2f}\n"
         f"(truth: {textmu}={mu:0.2f}, {textsigma}={sigma:0.2f})"
     ),
 )
 fig0.tight_layout()
 
 
-
-# ================================= pol1 fit =================================
-# Parameters
+# ================================= Pol1 fit =================================
 n_points = 40
-
-# Generate random slope and intercept (origin)
-slope = np.random.uniform(-10, 10)  # Random slope between 0.5 and 2.0
-intercept = np.random.uniform(-10, 10)  # Random intercept between -10 and 10
-
-# Generate random x values
+slope = np.random.uniform(-10, 10)
+intercept = np.random.uniform(-10, 10)
 x_values = np.random.uniform(-10, 10, n_points)
-
-# Calculate y values based on the linear function y = mx + b + error
 errors = np.abs(np.random.normal(0, np.random.uniform(1, 5), n_points))
-var_points = np.random.normal(0, np.random.uniform(0.1, 2), n_points)
-y_values = slope * x_values + intercept + var_points
+y_values = slope * x_values + intercept + np.random.normal(0, 1, n_points)
 
 fig1, ax1 = plt.subplots(1, 1, num=1, figsize=(12, 8))
 ax1.errorbar(x_values, y_values, yerr=errors, fmt=". ", capsize=5)
-result_pol = fit_pol1(x_values, y_values, errors)
-par = result_pol["par"]
-err = result_pol["par_err"]
 
-x = np.linspace(np.min(x_values), np.max(x_values), 1000)
-ax1.plot(x, pol1(x, par), marker="")
-ax1.set(xlabel="x", ylabel="y", title=f"a = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
-                                      f"b = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
-                                      f"(init: {slope:0.2f}, {intercept:0.2f})")
+pol1 = Pol1(compute_errors=True)
+result = pol1.fit(x_values, y_values, errors)
+par = result["par"]
+err = result["par_err"]
+
+x_plot = np.linspace(np.min(x_values), np.max(x_values), 1000)
+ax1.plot(x_plot, pol1(x_plot, par), marker="")
+ax1.set(
+    xlabel="x",
+    ylabel="y",
+    title=(
+        f"intercept = {par[0]:0.2f}{textpm}{err[0]:0.2f}\n"
+        f"slope = {par[1]:0.2f}{textpm}{err[1]:0.2f}\n"
+        f"(truth: {intercept:0.2f}, {slope:0.2f})"
+    ),
+)
 fig1.tight_layout()
 
 plt.show()
-