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aare/python/tests/test_Minuit2_gauss.ipynb
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refactor: hide Minuit2 from aare's public API (#331)
- Move Chi2.hpp from include/aare/ to src/ (private)
- Pimpl on FitModel<Model>: MnUserParameters/MnStrategy behind opaque
src/FitModelImpl.hpp, no Minuit2 includes in public headers
- Move fit_pixel/fit_3d bodies to Fit.cpp with explicit instantiations
for all 8 models; drop FCN template param from public API
- CMake: aare::Minuit2 wrapped in $<BUILD_INTERFACE:...> (hidden from
exported targets, same pattern as lmfit), MINUIT2_INSTALL OFF, Chi2.hpp
removed from PUBLICHEADERS
- Update python bindings and benchmark callsites accordingly

---------

Co-authored-by: Erik Fröjdh <erik.frojdh@psi.ch>
Co-authored-by: Alice <alice.mazzoleni@psi.ch>
2026-07-02 16:04:22 +02:00

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{
"cells":
[
{
"cell_type": "code",
"execution_count": 2,
"id": "efef8e20-6571-4561-8f0b-6048b57907de",
"metadata": {},
"outputs": [],
"source": [
"import time\n",
"import random\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.gridspec import GridSpec\n",
"import sys\n",
"sys.path.insert(0, '/home/ferjao_k/aare/build')\n",
"from aare import fit_gaus # lmfit\n",
"from aare import Gaussian, fit # minuit2"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "35772e2c-37c6-4986-a9c0-7dca4a034827",
"metadata": {},
"outputs": [],
"source": [
"ROWS = 100\n",
"COLS = 100\n",
"N_SCAN = 100\n",
"NOISE_FRAC = 0.05\n",
"SEED = 42\n",
"N_THREADS = 4\n",
"\n",
"N_REPEATS = 7\n",
"N_WARMUP = 3 # untimed iterations (icache + branch predictor warmup)\n",
"COOLDOWN = 2.0 # seconds between (method, thread_count) pairs"
]
},
{
"cell_type": "markdown",
"id": "be455445-7df8-47cc-9f39-d2a38856e0bd",
"metadata": {},
"source": [
"## Data generator"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "06c8fddb-56d9-4f84-8b21-4ffd8b7bd26f",
"metadata": {},
"outputs": [],
"source": [
"def generate_3d_data(rows, cols, n_scan, noise_frac, seed):\n",
" \"\"\"\n",
" Generate a synthetic detector image stack where each pixel has a\n",
" Gaussian response curve with per-pixel variation in A, mu, sigma.\n",
"\n",
" Returns x (n_scan,), y (rows, cols, n_scan), y_err (rows, cols, n_scan),\n",
" and the ground-truth parameter arrays.\n",
" \"\"\"\n",
" rng = np.random.default_rng(seed)\n",
"\n",
" # Per-pixel true params each of shape: [rows, cols, 1]\n",
" A_true = rng.uniform(200, 1000, size=(rows, cols))\n",
" mu_true = rng.uniform(20, 80, size=(rows, cols))\n",
" sig_true = rng.uniform(3, 12, size=(rows, cols))\n",
" \n",
" # One common binned energy array\n",
" x = np.linspace(0, 100, n_scan) # shape [1, 1, nscan]\n",
"\n",
" # Build ground truth signals per-pixel\n",
" exponent = -0.5 * ((x[None, None, :] - mu_true[:, :, None]) / sig_true[:,:, None])**2 # shape [rows, cols, nscan]\n",
" y_clean = A_true[:, :, None] * np.exp(exponent)\n",
"\n",
" # Perturb with noise\n",
" noise_sigma = noise_frac * A_true[:, :, None] * np.ones_like(y_clean) # shape [rows, cols, nscan]\n",
" noise = rng.normal(0, noise_sigma)\n",
" y = y_clean + noise\n",
"\n",
" y_err = noise_sigma.copy()\n",
"\n",
" return x, y, y_err, A_true, mu_true, sig_true "
]
},
{
"cell_type": "markdown",
"id": "30ed6bb8-4798-497f-9990-4c518f885855",
"metadata": {},
"source": [
"## Profiling function"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "dc0b52b6-9fc3-453d-b6eb-e325a2b8342e",
"metadata": {},
"outputs": [],
"source": [
"def bench(fn, n_warmup=N_WARMUP, n_repeats=N_REPEATS):\n",
" \"\"\"\n",
" Warmup then time `fn` over `n_repeats` calls.\n",
" Returns (last_result, list_of_walltimes_in_seconds).\n",
" \"\"\"\n",
" # warmup: primes icache, branch predictor, and lets CPU ramp to boost clock\n",
" for _ in range(n_warmup):\n",
" res = fn()\n",
"\n",
" times = []\n",
" for _ in range(n_repeats):\n",
" t0 = time.perf_counter()\n",
" res = fn()\n",
" t1 = time.perf_counter()\n",
" times.append(t1 - t0)\n",
" return res, times"
]
},
{
"cell_type": "markdown",
"id": "377fc820-95b2-48aa-b104-a272c50e4103",
"metadata": {},
"source": [
"# Quick check on small (2x2) frame"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "9cc999b9-e0d8-4deb-ae29-e8bd0141534b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"== True Gaussian params == \n",
"A_true = \n",
" [[819.16483884 551.1027518 ]\n",
" [886.87833593 757.89442325]]\n",
"mu_true = \n",
" [[25.65064087 78.5373411 ]\n",
" [65.66838212 67.16385832]]\n",
"sig_true = \n",
" [[ 4.15302269 7.05347344]\n",
" [ 6.33718222 11.3408849 ]]\n",
"\n",
"\n",
"== Fit results ==\n",
"A_fit = \n",
" [[812.09277132 559.04069721]\n",
" [899.09335849 759.24481682]]\n",
"mu_fit = \n",
" [[25.6598209 78.40461782]\n",
" [65.52261318 66.84540995]]\n",
"sig_fit = \n",
" [[ 4.2778026 7.041045 ]\n",
" [ 6.29190225 11.34233504]]\n"
]
}
],
"source": [
"# Generate 2 x 2 dataset of Gaussian-like profiles for each pixel\n",
"x2, y2, yerr2, true_A2, true_mu2, true_sig2 = generate_3d_data(\n",
" 2, 2, N_SCAN, NOISE_FRAC, SEED\n",
")\n",
"model_g = Gaussian()\n",
"model_g.compute_errors = True\n",
"result = model_g.fit(x2, y2, yerr2)\n",
"\n",
"from pprint import pprint\n",
"print(\"== True Gaussian params == \")\n",
"print(\"A_true = \\n\", true_A2)\n",
"print(\"mu_true = \\n\", true_mu2)\n",
"print(\"sig_true = \\n\",true_sig2)\n",
"print(\"\\n\")\n",
"\n",
"print(\"== Fit results ==\")\n",
"par = result['par']\n",
"# print(par)\n",
"A_fit = par[:, :, 0]\n",
"mu_fit = par[:, :, 1]\n",
"sig_fit = par[:, :, 2]\n",
"print(\"A_fit = \\n\", A_fit)\n",
"print(\"mu_fit = \\n\", mu_fit)\n",
"print(\"sig_fit = \\n\", sig_fit)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "a98a41f3-fd2e-4bfc-9ec6-0d23dd38e896",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1200x800 with 4 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(2, 2, figsize=(12,8))\n",
"\n",
"# Gaussians in 2x2 frame: True vs Fit\n",
"for row in range(2):\n",
" for col in range(2):\n",
" ax[row, col].plot(x2, y2[row, col,:], label=\"data\")\n",
" ax[row, col].plot(x2, model_g(x2, result['par'][row, col,:]), linewidth=1, color=\"green\", label=\"minuit\")\n",
" ax[row, col].set_title(f\"Gaussian Fit to data in pixel [{row}, {col}]\")\n",
" ax[row, col].legend()"
]
},
{
"cell_type": "markdown",
"id": "fcf52481-0278-4f95-8676-829a6d61eff8",
"metadata": {},
"source": [
"## Fit data with different backends"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "1f6bc651-80c1-41dd-8a05-7f15aba006aa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Generating synthetic data: 100x100 pixels, 100 scan points, noise_frac=0.05\n",
"\n",
"model.max_calls = 100\n",
"model.tolerance = 0.5\n",
"model.compute_errors = False\n"
]
}
],
"source": [
"# ===============\n",
"# DATA GENERATION\n",
"# ===============\n",
"print(f\"Generating synthetic data: {ROWS}x{COLS} pixels, \"\n",
" f\"{N_SCAN} scan points, noise_frac={NOISE_FRAC}\\n\")\n",
"\n",
"x, y, yerr, true_A, true_mu, true_sig = generate_3d_data(\n",
" ROWS, COLS, N_SCAN, NOISE_FRAC, SEED\n",
")\n",
"\n",
"model = Gaussian()\n",
"print(f\"model.max_calls = {model.max_calls}\")\n",
"print(f\"model.tolerance = {model.tolerance}\")\n",
"print(\"model.compute_errors =\", model.compute_errors)\n",
"METHOD_DEFS = [\n",
" (\"lmfit (LM)\",\n",
" lambda nt: lambda: fit_gaus(x, y, n_threads=nt),\n",
" \"#2196F3\", {\"linewidth\": 3.0, \"linestyle\": \"-\"}),\n",
"\n",
" (\"Minuit2 (obj API)\",\n",
" lambda nt: lambda: model.fit(x, y, n_threads=nt),\n",
" \"#FF9800\", {\"linewidth\": 2.5, \"linestyle\": \":\"}),\n",
"]\n",
"\n",
"colors = {label: c for label, _, c, _ in METHOD_DEFS}\n",
"styles = {label: s for label, _, _, s in METHOD_DEFS}"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "5a417145-ce42-4c3a-a7bf-05ff97ba0450",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Method time (ms) med|dA| med|dMu| med|dSig|\n",
"--------------------------------------------------------------------------------\n",
"[lmfit (LM) ] 90.30 ms 6.272 0.0940 0.0949\n",
"[Minuit2 (obj API) ] 59.60 ms 6.272 0.0940 0.0949 chi2/ndf=880.9946\n"
]
}
],
"source":
[
"# ====================================\n",
"# SINGLE-CALL BENCHMARK (at N_THREADS)\n",
"# ====================================\n",
"def extract_result(label, res):\n",
" \"\"\"Normalize return values across fitters into a common dict.\"\"\"\n",
" if isinstance(res, dict):\n",
" out = {\"par\": res[\"par\"]}\n",
" if \"par_err\" in res:\n",
" out[\"par_err\"] = res[\"par_err\"]\n",
" if \"chi2\" in res:\n",
" out[\"chi2\"] = res[\"chi2\"]\n",
" return out\n",
" # fit_gaus without y_err returns a raw array\n",
" return {\"par\": res}\n",
" \n",
"methods = {}\n",
"for label, factory, _, _ in METHOD_DEFS:\n",
" time.sleep(COOLDOWN)\n",
" res, times = bench(factory(N_THREADS))\n",
" entry = extract_result(label, res)\n",
" entry[\"times\"] = times\n",
" methods[label] = entry\n",
"\n",
"# ---- Print summary ----\n",
"ndf = N_SCAN - 3\n",
"print(f\"{'Method':24s} {'time (ms)':>10s} {'med|dA|':>10s} {'med|dMu|':>10s} {'med|dSig|':>10s}\")\n",
"print(\"-\" * 80)\n",
"for name, m in methods.items():\n",
" par = m[\"par\"]\n",
" med_t = np.median(m[\"times\"]) * 1e3\n",
" dA = np.median(np.abs(par[:,:,0] - true_A))\n",
" dMu = np.median(np.abs(par[:,:,1] - true_mu))\n",
" dSig = np.median(np.abs(par[:,:,2] - true_sig))\n",
"\n",
" chi2_str = \"\"\n",
" if \"chi2\" in m:\n",
" chi2_str = f\" chi2/ndf={np.median(m['chi2'] / ndf):.4f}\"\n",
"\n",
" print(f\"[{name:22s}] {med_t:8.2f} ms \"\n",
" f\"{dA:10.3f} {dMu:10.4f} {dSig:10.4f}{chi2_str}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "55ecc77c-0823-408d-a811-8e4f4900f332",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# ===============\n",
"# THREAD SCALING\n",
"# ===============\n",
"thread_counts = [1, 2, 4, 8, 16]\n",
"\n",
"thread_times = {label: [] for label, _, _, _ in METHOD_DEFS}\n",
"ttimes_stddev = {label: [] for label, _, _, _ in METHOD_DEFS}\n",
"\n",
"for nt in thread_counts:\n",
" # shuffle method order per thread count to decorrelate thermal bias\n",
" run_order = list(METHOD_DEFS)\n",
" random.shuffle(run_order)\n",
"\n",
" for label, factory, _, _ in run_order:\n",
" time.sleep(COOLDOWN)\n",
" _, times = bench(factory(nt))\n",
"\n",
" med = np.median(times) * 1e3\n",
" std = np.std(times) * 1e3\n",
" thread_times[label].append(med)\n",
" ttimes_stddev[label].append(std)\n",
"\n",
" per_px = med / (ROWS * COLS) * 1e3\n",
" per_px_std = std / (ROWS * COLS) * 1e3\n",
" print(f\" {label:22s} n_threads={nt:2d} \"\n",
" f\"{med:8.2f} ± {std:6.2f} ms \"\n",
" f\"({per_px:.4f} ± {per_px_std:.4f} μs/pixel)\")\n",
" print(\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5f3152d-be84-420e-8045-9c42ac5c24cb",
"metadata": {},
"outputs": [],
"source": [
"# =============================\n",
"# FIGURE 1: Residual histograms\n",
"# =============================\n",
"param_names = [\"A\", \"μ\", \"σ\"]\n",
"param_truths = [true_A, true_mu, true_sig]\n",
"\n",
"fig1, axes1 = plt.subplots(1, 3, figsize=(15, 5))\n",
"fig1.suptitle(f\"Parameter Residuals — {ROWS}×{COLS} pixels, {N_SCAN} scan points\",\n",
" fontsize=14, fontweight=\"bold\")\n",
"\n",
"for col, (pname, truth) in enumerate(zip(param_names, param_truths)):\n",
" ax = axes1[col]\n",
"\n",
" # collect residuals across all methods for shared bin edges\n",
" res_by_method = {}\n",
" all_res = []\n",
" for mname, m in methods.items():\n",
" residual = (m[\"par\"][:, :, col] - truth).ravel()\n",
" res_by_method[mname] = residual\n",
" all_res.append(residual)\n",
" all_res = np.concatenate(all_res)\n",
"\n",
" lo, hi = np.percentile(all_res, [0.5, 99.5])\n",
" edges = np.linspace(lo, hi, 101)\n",
"\n",
" for mname, r in res_by_method.items():\n",
" ax.hist(r, bins=edges, histtype=\"step\", label=mname,\n",
" color=colors[mname],\n",
" linewidth=styles[mname][\"linewidth\"],\n",
" linestyle=styles[mname][\"linestyle\"])\n",
"\n",
" ax.axvline(0, color=\"k\", linestyle=\"--\", linewidth=1, alpha=0.7)\n",
" ax.set_xlabel(f\"Fitted {pname} True {pname}\")\n",
" ax.set_ylabel(\"Pixel count\")\n",
" ax.set_title(f\"Δ{pname}\")\n",
" ax.legend(fontsize=8)\n",
" ax.grid(alpha=0.3)\n",
"\n",
"fig1.tight_layout()\n",
"# fig1.savefig(\"fig1_residual_histograms.png\", dpi=150, bbox_inches=\"tight\")\n",
"# print(\"\\nSaved fig1_residual_histograms.png\")\n",
"\n",
"# ====================================================\n",
"# FIGURE 2: Performance — bar chart + thread scaling\n",
"# ====================================================\n",
"fig2 = plt.figure(figsize=(14, 5))\n",
"gs = GridSpec(1, 2, figure=fig2, width_ratios=[1, 1.3])\n",
"\n",
"# -- Left: bar chart at N_THREADS --\n",
"ax2a = fig2.add_subplot(gs[0])\n",
"names = list(methods.keys())\n",
"medians = [np.median(methods[n][\"times\"]) * 1e3 for n in names]\n",
"bars = ax2a.barh(names, medians,\n",
" color=[colors[n] for n in names],\n",
" edgecolor=\"white\", height=0.5)\n",
"ax2a.set_xlabel(\"Median wall time (ms)\")\n",
"ax2a.set_title(f\"Single call — {ROWS}×{COLS} px, {N_THREADS} threads\")\n",
"for bar, val in zip(bars, medians):\n",
" ax2a.text(bar.get_width() + max(medians) * 0.02,\n",
" bar.get_y() + bar.get_height() / 2,\n",
" f\"{val:.1f} ms\", va=\"center\", fontsize=10)\n",
"ax2a.grid(axis=\"x\", alpha=0.3)\n",
"ax2a.set_xlim(0, max(medians) * 1.25)\n",
"\n",
"# -- Right: thread scaling with error bars --\n",
"ax2b = fig2.add_subplot(gs[1])\n",
"for label, _, _, _ in METHOD_DEFS:\n",
" tt = thread_times[label]\n",
" sd = ttimes_stddev[label]\n",
" speedup = [tt[0] / t for t in tt]\n",
" # propagate uncertainty: S = t0/t → δS/S = sqrt((δt0/t0)² + (δt/t)²)\n",
" speedup_err = [\n",
" s * np.sqrt((sd[0] / tt[0])**2 + (sd[i] / tt[i])**2)\n",
" for i, s in enumerate(speedup)\n",
" ]\n",
" ax2b.errorbar(thread_counts, speedup, yerr=speedup_err,\n",
" fmt=\"o-\", label=label, color=colors[label],\n",
" linewidth=2, markersize=7, capsize=4)\n",
"\n",
"ax2b.plot(thread_counts, thread_counts, \"k--\", alpha=0.4, label=\"Ideal linear\")\n",
"ax2b.set_xlabel(\"Number of threads\")\n",
"ax2b.set_ylabel(\"Speedup vs 1 thread\")\n",
"ax2b.set_title(\"Thread scaling\")\n",
"ax2b.set_xticks(thread_counts)\n",
"ax2b.legend(fontsize=9)\n",
"ax2b.grid(alpha=0.3)\n",
"\n",
"fig2.tight_layout()\n",
"# fig2.savefig(\"fig2_performance.png\", dpi=150, bbox_inches=\"tight\")\n",
"# print(\"Saved fig2_performance.png\")\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92a8c724-e139-45d4-a354-1b8408637ead",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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