{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cobra\n",
    "from cobra.io import read_sbml_model, load_model\n",
    "from cobra import Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cobra.__path__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.24.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cobra.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_results = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaling...\n",
      " A: min|aij| =  1.000e+00  max|aij| =  1.000e+00  ratio =  1.000e+00\n",
      "Problem data seem to be well scaled\n"
     ]
    }
   ],
   "source": [
    "m = load_model('iJO1366')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 s ± 1.45 s per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 12 s ± 1.45 s per loop (mean ± std. dev. of 7 runs, 1 loop each)>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "m2 = Model()\n",
    "for r in m.reactions:\n",
    "    m2.add_reaction(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_results['add_reaction'] = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "565 ms ± 83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 565 ms ± 83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "m2 = Model()\n",
    "m2.add_reactions(m.reactions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_results['add_reactions'] = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/uridavidakavia/PycharmProjects/cobrapy/src/cobra/core/group.py:148: UserWarning: need to pass in a list\n",
      "  warn(\"need to pass in a list\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.06 s ± 368 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 7.06 s ± 368 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "model = load_model('iJO1366')\n",
    "for reaction in model.reactions:\n",
    "    model.remove_reactions([reaction])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_results['remove_reaction'] = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "291 ms ± 18.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 291 ms ± 18.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "model = load_model('e_coli_core')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_results['load_model'] = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = load_model('e_coli_core')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "133 ms ± 5.07 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 133 ms ± 5.07 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "for reaction in model.reactions:\n",
    "    model.objective = reaction\n",
    "    solution = model.optimize()\n",
    "    solution.objective_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_results['iteratete_all_reactions_set_objective_solve'] = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.9 ms ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<TimeitResult : 15.9 ms ± 1.55 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%timeit -o\n",
    "model.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "benchmark_results['model_copy'] = _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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