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Access to parameter names and fast erf approx (#298)
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Browse Source 
- Set parameter starting values, limits or fix through name as well as
index
- Updated parameter names for the scurve
- Fast approximation to erf function (~10% speedup of fitting)

---------

Co-authored-by: Khalil Ferjaoui <khalilferjaoui@yahoo.fr>
This commit is contained in:
Erik FröjdhandKhalil Ferjaoui authored and GitHub committed 2026-04-02 13:33:37 +02:00
1 parent a6afa45b3b
commit a25f5d2344
4 files changed
+126 -51
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python/tests/test_Minuit2_scurve_1d.ipynb
+34 -25
View File
@@ -16,8 +16,7 @@
"from scipy.special import erf\n",
"\n",
"import sys\n",
"# sys.path.insert(0, '/home/ferjao_k/sw/aare/build')\n",
"sys.path.insert(0, '/home/kferjaoui/sw/aare/build')\n",
"sys.path.insert(0, '/home/ferjao_k/sw/aare/build')\n",
"\n",
"from aare import fit_scurve, fit_scurve2 # lmfit\n",
"from aare import RisingScurve, FallingScurve, fit # minuit2 (object based API)\n",
@@ -134,7 +133,7 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 18,
"id": "37d2fda4-40b8-4ea2-8f2c-6c3f2384d158",
"metadata": {},
"outputs": [
@@ -142,6 +141,10 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Paramter list of the Scurve:\n",
"['p0', 'p1', 'mu', 'sigma', 'A', 'C']\n",
"6 \n",
"\n",
"== Tuned fit settings ==\n",
"max_calls : 500\n",
"tolerance : 0.01\n",
@@ -152,10 +155,10 @@
"True rising params: [100. 0.25 60. 6. 120. 1. ]\n",
"lmfit rising result: [ 99.467831 0.287017 60.078405 5.715461 117.257939 0.967234]\n",
"minuit_grad (obj api ) rising result:\n",
"{'par': array([ 99.288864, 0.291575, 60.091759, 5.675642, 117.08836 ,\n",
" 0.963865]),\n",
" 'par_err': array([2.853807, 0.104648, 0.764892, 0.930086, 8.309872, 0.191738]),\n",
" 'chi2': array([7.28966])}\n"
"{'par': array([100. , 0.269137, 60.050193, 5.729932, 117.777635,\n",
" 0.984073]),\n",
" 'par_err': array([1. , 0.054406, 0.747753, 0.909864, 7.887499, 0.174058]),\n",
" 'chi2': array([7.351743])}\n"
]
}
],
@@ -163,8 +166,14 @@
"res_r_lmfit = fit_scurve(x, y_rising)\n",
"\n",
"model_r = RisingScurve()\n",
"# model_r.FixParameter(0, 100) # Fixed p0 = 100 (optimizer does not touch the value)\n",
"model_r.SetParameter(4, 100) # Start value p4 = 100 \n",
"print(\"Paramter list of the Scurve:\")\n",
"# print(model_r.GetParNames())\n",
"print(model_r.par_names)\n",
"print(model_r.n_par, '\\n')\n",
"\n",
"model_r.FixParameter('p0', 100) # Fixed p0 = 100 (optimizer does not touch the value)\n",
"model_r.SetParameter('A', 100) # Start value A = 100\n",
"\n",
"print(\"== Tuned fit settings ==\")\n",
"model_r.compute_errors = True\n",
"model_r.max_calls = 500\n",
@@ -192,7 +201,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 19,
"id": "5ce22d2d-1adb-47d9-bf36-6cb535fa8b77",
"metadata": {},
"outputs": [
@@ -201,11 +210,11 @@
"output_type": "stream",
"text": [
"True falling params: [100. 0.25 60. 6. 120. -1. ]\n",
"lmfit falling result: [101.734479 0.225786 60.251971 6.092915 118.476735 -0.998677]\n",
"lmfit falling result: [101.73448 0.225786 60.251971 6.092915 118.476735 -0.998677]\n",
"minuit_grad (obj api ) falling result\n",
"{'par': array([101.734403, 0.225778, 60.251667, 6.093245, 118.479457,\n",
" -0.998603]),\n",
" 'chi2': array([1865.728851])}\n"
"{'par': array([101.741754, 0.225723, 60.251604, 6.092887, 118.473268,\n",
" -0.998565]),\n",
" 'chi2': array([1865.728797])}\n"
]
}
],
@@ -227,13 +236,13 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": 20,
"id": "fe72503a-fc77-4b17-9b17-b98055e2fc87",
"metadata": {},
"outputs": [
{
"data": {
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truncated
"image/png": "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 truncated
"text/plain": [
"<Figure size 1200x400 with 2 Axes>"
]
@@ -274,7 +283,7 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 21,
"id": "3ba40e9a-0c64-4ef6-ba90-b1116f99b862",
"metadata": {},
"outputs": [
@@ -283,11 +292,11 @@
"output_type": "stream",
"text": [
"Rising abs error lmfit : [0.532169 0.037017 0.078405 0.284539 2.742061 0.032766]\n",
"Rising abs error minuit_grad: [0.711136 0.041575 0.091759 0.324358 2.91164 0.036135]\n",
"Rising abs error minuit_grad: [0. 0.019137 0.050193 0.270068 2.222365 0.015927]\n",
"\n",
"\n",
"Falling abs error lmfit : [1.734479 0.024214 0.251971 0.092915 1.523265 0.001323]\n",
"Falling abs error minuit_grad: [1.734403 0.024222 0.251667 0.093245 1.520543 0.001397]\n"
"Falling abs error lmfit : [1.73448 0.024214 0.251971 0.092915 1.523265 0.001323]\n",
"Falling abs error minuit_grad: [1.741754 0.024277 0.251604 0.092887 1.526732 0.001435]\n"
]
}
],
@@ -309,7 +318,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 22,
"id": "dc0b52b6-9fc3-453d-b6eb-e325a2b8342e",
"metadata": {},
"outputs": [
@@ -317,11 +326,11 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Rising lmfit : 0.445 ms\n",
"Rising minuit2 : 0.322 ms\n",
"Rising lmfit : 0.334 ms\n",
"Rising minuit2 : 0.239 ms\n",
"\n",
"Falling lmfit : 0.392 ms\n",
"Falling minuit2 : 0.309 ms\n"
"Falling lmfit : 0.294 ms\n",
"Falling minuit2 : 0.214 ms\n"
]
}
],