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lunin_l
8f8173feb6
To improve codebase quality and reduce human error, this PR introduces the pre-commit framework. This ensures that all code adheres to project standards before it is even committed, maintaining a consistent style and catching common mistakes early. Key Changes: - Code Formatting: Automated C++ formatting using clang-format (based on the project's .clang-format file). - Syntax Validation: Basic checks for file integrity and syntax. - Spell Check: Automated scanning for typos in source code and comments. - CMake Formatting: Standardization of CMakeLists.txt and .cmake configuration files. - GitHub Workflow: Added a CI action that validates every Pull Request against the pre-commit configuration to ensure compliance. The configuration includes a [ci] block to handle automated fixes within the PR. Currently, this is disabled. If we want the CI to automatically commit formatting fixes back to the PR branch, this can be toggled to true in .pre-commit-config.yaml. ```yaml ci: autofix_commit_msg: [pre-commit] auto fixes from pre-commit hooks autofix_prs: false autoupdate_schedule: monthly ``` The last large commit with the fit functions, for example, was not formatted according to the clang-format rules. This PR would allow to avoid similar mistakes in the future. Python fomat with `ruff` for tests and sanitiser for `.ipynb` notebooks can be added as well.
73 KiB
73 KiB
In [1]:
import numpy as np import matplotlib.pyplot as plt import sys sys.path.insert(0, '/home/ferjao_k/sw/aare/build') from aare import Pol1, Pol2
Ground truth¶
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x = np.linspace(0, 10, 50) # Pol1: y = 3.0 + 1.5*x true_pol1 = [3.0, 1.5] y_pol1 = true_pol1[0] + true_pol1[1] * x y_pol1_noisy = y_pol1 + np.random.default_rng(42).normal(0, 0.5, x.size) # Pol2: y = 2.0 - 0.5*x + 0.3*x^2 true_pol2 = [2.0, -0.5, 0.3] y_pol2 = true_pol2[0] + true_pol2[1] * x + true_pol2[2] * x**2 y_pol2_noisy = y_pol2 + np.random.default_rng(7).normal(0, 1.0, x.size)
Fit with error estimation¶
In [11]:
m1 = Pol1() m2 = Pol2() res1 = m1.fit(x, y_pol1_noisy) res2 = m2.fit(x, y_pol2_noisy) p1 = res1['par'] print("=== Pol1 ===") print(f" True: p0={true_pol1[0]:.4f} p1={true_pol1[1]:.4f}") print(f" Fitted: p0={p1[0]:.4f} p1={p1[1]:.4f}") print(f" Chi2: {res1['chi2']}") print() p2 = res2['par'] print("=== Pol2 ===") print(f" True: p0={true_pol2[0]:.4f} p1={true_pol2[1]:.4f} p2={true_pol2[2]:.4f}") print(f" Fitted: p0={p2[0]:.4f} p1={p2[1]:.4f} p2={p2[2]:.4f}") print(f" Chi2: {res2['chi2']}")
=== Pol1 === True: p0=3.0000 p1=1.5000 Fitted: p0=2.9325 p1=1.5226 Chi2: [7.00870822] === Pol2 === True: p0=2.0000 p1=-0.5000 p2=0.3000 Fitted: p0=2.1391 p1=-0.8442 p2=0.3383 Chi2: [34.11224393]
Plot¶
In [13]:
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # Pol1 ax1.plot(x, y_pol1_noisy, 'o', ms=4, alpha=0.6, label='data') ax1.plot(x, y_pol1, 'k--', label='truth') ax1.plot(x, m1(x, res1['par']), 'r-', lw=2, label='fit') ax1.set_title('Pol1') ax1.legend() ax1.grid(alpha=0.3) # Pol2 ax2.plot(x, y_pol2_noisy, 'o', ms=4, alpha=0.6, label='data') ax2.plot(x, y_pol2, 'k--', label='truth') ax2.plot(x, m2(x, res2['par']), 'r-', lw=2, label='fit') ax2.set_title('Pol2') ax2.legend() ax2.grid(alpha=0.3) fig.tight_layout() plt.show()
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