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Small profiling notebook for model instantation
Also tests GERG-2008
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{ | ||
"cells": [ | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 1, | ||
"id": "fdae749b", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"j1 = {\n", | ||
" \"kind\": \"vdW1\",\n", | ||
" \"model\": {\n", | ||
" \"a\": 1,\n", | ||
" \"b\": 2\n", | ||
" }\n", | ||
"}\n", | ||
"j2 = {\n", | ||
" \"kind\": \"PCSAFT\",\n", | ||
" \"model\": {\n", | ||
" \"names\": [\"Methane\", \"Ethane\"],\n", | ||
" \"kmat\": [[0.0, 0.0],[0.0,0.0]]\n", | ||
" }\n", | ||
"}\n", | ||
"j3 = {\"kind\": \"SAFT-VR-Mie\", \"model\": {\"coeffs\": [{\"name\": \"R1234YF\", \"BibTeXKey\": \"Paricaud\", \"m\": 1.3656, \"sigma_Angstrom\": 4.5307, \"epsilon_over_k\": 299.424, \"lambda_r\": 21.7779, \"lambda_a\": 6.0, \"mu_D\": 2.2814, \"nmu\": 1.0, \"Q_DA\": 1.4151, \"nQ\": 1.0}, {\"name\": \"?\", \"BibTeXKey\": \"Paricaud\", \"m\": 1.4656, \"sigma_Angstrom\": 4.7307, \"epsilon_over_k\": 289.424, \"lambda_r\": 21.7779, \"lambda_a\": 6.0, \"mu_D\": 2.2814, \"nmu\": 1.0, \"Q_DA\": 1.4151, \"nQ\": 1.0}], \"polar_model\": \"GubbinsTwu+GubbinsTwu\"}}\n", | ||
"import teqp" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 2, | ||
"id": "3efd41cd", | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"10.2 µs ± 54.7 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n", | ||
"240 µs ± 4.39 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n", | ||
"520 µs ± 1.56 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"%timeit teqp.make_model(j1)\n", | ||
"%timeit teqp.make_model(j2)\n", | ||
"%timeit teqp.make_model(j3)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 3, | ||
"id": "788e6678", | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"3.22 µs ± 38.3 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n", | ||
"9.89 µs ± 46.7 ns per loop (mean ± std. dev. of 7 runs, 100,000 loops each)\n", | ||
"28.3 µs ± 101 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"%timeit teqp.make_model(j1, validate=False)\n", | ||
"%timeit teqp.make_model(j2, validate=False)\n", | ||
"%timeit teqp.make_model(j3, validate=False)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 7, | ||
"id": "a285b19b", | ||
"metadata": {}, | ||
"outputs": [], | ||
"source": [ | ||
"names = [\"methane\", \"ethane\", \"propane\", \"n-butane\"]\n", | ||
"\n", | ||
"j1 = {\n", | ||
" \"kind\": \"GERG2004resid\",\n", | ||
" \"model\": {\n", | ||
" \"names\": names\n", | ||
" }\n", | ||
"}\n", | ||
"\n", | ||
"j2 = {\n", | ||
" \"kind\": \"GERG2008resid\",\n", | ||
" \"model\": {\n", | ||
" \"names\": names\n", | ||
" }\n", | ||
"}\n", | ||
"m1 = teqp.make_model(j1)\n", | ||
"m2 = teqp.make_model(j2)" | ||
] | ||
}, | ||
{ | ||
"cell_type": "code", | ||
"execution_count": 8, | ||
"id": "21b3242c", | ||
"metadata": {}, | ||
"outputs": [ | ||
{ | ||
"name": "stdout", | ||
"output_type": "stream", | ||
"text": [ | ||
"25.9 µs ± 168 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n", | ||
"26.2 µs ± 37.2 ns per loop (mean ± std. dev. of 7 runs, 10,000 loops each)\n" | ||
] | ||
} | ||
], | ||
"source": [ | ||
"%timeit teqp.make_model(j1, validate=False)\n", | ||
"%timeit teqp.make_model(j2, validate=False)" | ||
] | ||
} | ||
], | ||
"metadata": { | ||
"kernelspec": { | ||
"display_name": "Python 3 (ipykernel)", | ||
"language": "python", | ||
"name": "python3" | ||
}, | ||
"language_info": { | ||
"codemirror_mode": { | ||
"name": "ipython", | ||
"version": 3 | ||
}, | ||
"file_extension": ".py", | ||
"mimetype": "text/x-python", | ||
"name": "python", | ||
"nbconvert_exporter": "python", | ||
"pygments_lexer": "ipython3", | ||
"version": "3.10.6" | ||
} | ||
}, | ||
"nbformat": 4, | ||
"nbformat_minor": 5 | ||
} |