diff --git a/CokeOvenServiceSimulator.ipynb b/CokeOvenServiceSimulator.ipynb index d9a93f5..02445fc 100644 --- a/CokeOvenServiceSimulator.ipynb +++ b/CokeOvenServiceSimulator.ipynb @@ -7,12 +7,274 @@ "metadata": {}, "outputs": [], "source": [ + "from functools import reduce\n", + "\n", "import numpy as np\n", "\n", + "import matplotlib.ticker \n", "from matplotlib import pyplot as plt\n", "%matplotlib widget" ] }, + { + "cell_type": "markdown", + "id": "fd1817d3", + "metadata": {}, + "source": [ + "# 탄화실 나이 분포" + ] + }, + { + "cell_type": "markdown", + "id": "9c64324b", + "metadata": {}, + "source": [ + "## 장입-추출 시퀀스\n", + "\n", + "$n$ 번 건류실 추출/장입 후 $n+5$ 번 건류실에 추출/장입\n", + "* 1 번에서 시작시 66번까지 진행 이후 3 번에서 시작. $ 1, 6, 11, \\ldots , 66 $\n", + "* 3 번에서 63번까지 진행 이후 5 번에서 시작. $ 3, 6, 11, \\ldots , 63 $\n", + "* 5 번에서 65번까지 진행 이후 2 번에서 시작. $ 5, 6, 11, \\ldots , 65 $\n", + "* 2 번에서 62번까지 진행 이후 4 번에서 시작. $ 2, 6, 11, \\ldots , 62 $\n", + "* 4 번에서 64번까지 진행 이후 다시 1 번에서 시작. $ 4, 9, 14, \\ldots , 64 $\n" + ] + }, + { + "cell_type": "markdown", + "id": "de03e1d6", + "metadata": {}, + "source": [ + "### 탄화실 별 장입 시기" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "99dd258f", + "metadata": {}, + "outputs": [], + "source": [ + "n_oven = 66\n", + "\n", + "oven_idx_sequence = np.zeros(n_oven)\n", + "\n", + "start_indices = [1, 3, 5, 2, 4]\n", + "\n", + "i_order = 0\n", + "for idx0 in np.array(start_indices)-1:\n", + " for idx in range(idx0, n_oven, 5):\n", + " oven_idx_sequence[idx] = i_order\n", + " i_order += 1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "125f87b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "10a1cd2fcc854c1fa22f5ec555903a44", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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itsd+Eet/uf8zHzfvsV/EvD4+pyylkaXU8shSvllKLY8saWbhs1MAB4HDR7K499k9kZ1g7Oi2e5/dE4eP9Nzj0xx3Iid7TlmKm6XU8shSvllKLY8saWbh81EAB4GX9/3uuJ+GPi6LiP0dH8T237z9mY77JJ/3nLIUP0up5ZGlfLOUWh5Z0sjy8r7ffa7j6UkBHATauz7dk+X/dnV/ruP645yyFC9LqeWRpXyzlFoeWdLI0hfnQAEcFEZXDPtU+51Vkf9cx/XHOWUpXpZSyyNL+WYptTyypJGlL86BAjgoXFQ/Kmoqh0VvH4DPxUefkJo09guf6bhP8nnPKUvxs5RaHlnKN0up5ZEljSwX1Y/6HEdzLAVwEDj1lFzho/HHPmmOrt8ze8Jx90j6NMf1xzllKW6WUssjS/lmKbU8sqSZhc+nbAtgc3NzXHjhhVFRURGjR4+Oa665Jl599dUe+9xyyy2Ry+V6LBdffHGREn+yqxpqYuVNF8TokT0vp1dXDouVN13Q672RPum4H9x0Qfygj88pS2lkKbU8spRvllLLI0uaWfjsyvZG0C0tLTF//vy48MIL48MPP4wlS5bErFmzYs+ePXH66acX9rvqqqti1apVhfWhQ4cWI+6nclVDTXz17DPjvKYXIiLiR//nwvjauLN+709Dv++4/jinLMXPUmp5ZCnfLKWWR5Y0s/DZlG0BXL9+fY/1VatWxejRo2P79u1x6aWXFrbn8/morq4e6Hif28efAJ/lV+N80nH9cU5ZSiNLqeWRpXyzlFoeWdLMwqdXti8BH6ujoyMiIkaN6vnm0U2bNsXo0aNj/Pjxceutt0Z7e3uv5+ju7o7Ozs4eCwDAYJNEAcyyLBYuXBiXXHJJNDQ0FLY3NjbG448/Hi+++GI8+OCDsXXr1rj88suju/v4e11FfPS+wsrKysJSV1c3UH8FAIA+U7YvAX/cHXfcEa+88kr8/Oc/77H9+uuvL/y5oaEhJk+eHGPHjo3nnnsu5syZc9x5Fi1aFAsXLiysd3Z2KoEAwKBT9gXwzjvvjGeeeSY2b94cY8aM+cR9a2pqYuzYsbF3794Tjufz+cjnj78RKgDAYFK2BTDLsrjzzjvjqaeeik2bNkV9ff3vPebAgQPR2toaNTU+Zg4AlK+yfQ/g/Pnz47HHHou1a9dGRUVFtLW1RVtbW7z//vsREfHuu+/Gt7/97fjP//zPeP3112PTpk0xe/bsOPPMM+Paa68tcnoAgP5TtlcAV65cGRER06dP77F91apVccstt8Spp54au3btijVr1sQ777wTNTU1MWPGjHjiiSeioqKiCIkBAAZG2RbALMs+cXz48OGxYcOGAUoDAFA6yvYlYAAATkwBBABIjAIIAJAYBRAAIDEKIABAYhRAAIDEKIAAAIlRAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBiFEAAgMQogAAAiVEAAQASowACACRGAQQASIwCCACQGAUQACAxCiAAQGIUQACAxCiAAACJUQABABKjAAIAJEYBBABIjAIIAJAYBRAAIDEKIABAYhRAAIDEKIAAAIkp2wLY3NwcF154YVRUVMTo0aPjmmuuiVdffbXHPlmWRVNTU9TW1sbw4cNj+vTpsXv37iIlBgAYGGVbAFtaWmL+/PmxZcuW2LhxY3z44Ycxa9asOHjwYGGfBx54IJYtWxYrVqyIrVu3RnV1dcycOTO6urqKmBwAoH8NKXaA/rJ+/foe66tWrYrRo0fH9u3b49JLL40sy2L58uWxZMmSmDNnTkRErF69OqqqqmLt2rXxrW99qxixAQD6XdleATxWR0dHRESMGjUqIiL27dsXbW1tMWvWrMI++Xw+LrvssnjppZdOeI7u7u7o7OzssQAADDZJFMAsy2LhwoVxySWXRENDQ0REtLW1RUREVVVVj32rqqoKY8dqbm6OysrKwlJXV9e/wQEA+kESBfCOO+6IV155Jf71X//1uLFcLtdjPcuy47YdtWjRoujo6Cgsra2t/ZIXAKA/le17AI+6884745lnnonNmzfHmDFjCturq6sj4qMrgTU1NYXt7e3tx10VPCqfz0c+n+/fwAAA/axsrwBmWRZ33HFHPPnkk/Hiiy9GfX19j/H6+vqorq6OjRs3FrYdOnQoWlpaYtq0aQMdFwBgwJTtFcD58+fH2rVr49///d+joqKi8L6+ysrKGD58eORyuViwYEEsXbo0xo0bF+PGjYulS5fGiBEj4oYbbihyegCA/lO2BXDlypURETF9+vQe21etWhW33HJLRETcfffd8f7778ftt98eb7/9dkyZMiVeeOGFqKioGOC0AAADp2wLYJZlv3efXC4XTU1N0dTU1P+BAABKRNm+BxAAgBNTAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBiFEAAgMQogAAAiVEAAQASowACACRGAQQASIwCCACQGAUQACAxCiAAQGIUQACAxCiAAACJUQABABKjAAIAJEYBBABIjAIIAJAYBRAAIDEKIABAYhRAAIDEKIAAAIlRAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBiyrYAbt68OWbPnh21tbWRy+Xi6aef7jF+yy23RC6X67FcfPHFxQkLADCAyrYAHjx4MM4///xYsWJFr/tcddVVsX///sLyk5/8ZAATAgAUx5BiB+gvjY2N0djY+In75PP5qK6uHqBEAACloWyvAH4amzZtitGjR8f48ePj1ltvjfb29k/cv7u7Ozo7O3ssAACDTbIFsLGxMR5//PF48cUX48EHH4ytW7fG5ZdfHt3d3b0e09zcHJWVlYWlrq5uABMDAPSNsn0J+Pe5/vrrC39uaGiIyZMnx9ixY+O5556LOXPmnPCYRYsWxcKFCwvrnZ2dSiAAMOgkWwCPVVNTE2PHjo29e/f2uk8+n498Pj+AqQAA+l6yLwEf68CBA9Ha2ho1NTXFjgIA0K/K9grgu+++G7/+9a8L6/v27YudO3fGqFGjYtSoUdHU1BTXXXdd1NTUxOuvvx6LFy+OM888M6699toipgYA6H9lWwC3bdsWM2bMKKwffe/e3LlzY+XKlbFr165Ys2ZNvPPOO1FTUxMzZsyIJ554IioqKooVGQBgQJRtAZw+fXpkWdbr+IYNGwYwDQBA6fAeQACAxCiAAACJUQABABKjAAIAJEYBBABIjAIIAJAYBRAAIDEKIABAYhRAAIDEKIAAAIlRAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBiFEAAgMQogAAAiVEAAQASowACACRGAQQASIwCCACQGAUQACAxCiAAQGIUQACAxCiAAACJUQABABKjAAIAJEYBBABIjAIIAJAYBRAAIDFlWwA3b94cs2fPjtra2sjlcvH000/3GM+yLJqamqK2tjaGDx8e06dPj927dxcnLADAACrbAnjw4ME4//zzY8WKFSccf+CBB2LZsmWxYsWK2Lp1a1RXV8fMmTOjq6trgJMCAAysIcUO0F8aGxujsbHxhGNZlsXy5ctjyZIlMWfOnIiIWL16dVRVVcXatWvjW9/61kBGBQAYUGV7BfCT7Nu3L9ra2mLWrFmFbfl8Pi677LJ46aWXej2uu7s7Ojs7eywAAINNkgWwra0tIiKqqqp6bK+qqiqMnUhzc3NUVlYWlrq6un7NCQDQH5IsgEflcrke61mWHbft4xYtWhQdHR2FpbW1tb8jAgD0ubJ9D+Anqa6ujoiPrgTW1NQUtre3tx93VfDj8vl85PP5fs8HANCfkrwCWF9fH9XV1bFx48bCtkOHDkVLS0tMmzatiMkAAPpf2V4BfPfdd+PXv/51YX3fvn2xc+fOGDVqVPzRH/1RLFiwIJYuXRrjxo2LcePGxdKlS2PEiBFxww03FDE1AED/K9sCuG3btpgxY0ZhfeHChRERMXfu3PjRj34Ud999d7z//vtx++23x9tvvx1TpkyJF154ISoqKooVGQBgQJRtAZw+fXpkWdbreC6Xi6ampmhqahq4UAAAJSDJ9wACAKRMAQQASIwCCACQGAUQACAxCiAAQGIUQACAxCiAAACJUQABABKjAAIAJEYBBABIjAIIAJAYBRAAIDEKIABAYhRAAIDEKIAAAIlRAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBiFEAAgMQogAAAiVEAAQASowACACRGAQQASIwCCACQGAUQACAxCiAAQGIUQACAxCiAAACJSbYANjU1RS6X67FUV1cXOxYAQL8bUuwAxXTuuefGf/zHfxTWTz311CKmAQAYGEkXwCFDhrjqBwAkJ9mXgCMi9u7dG7W1tVFfXx/f+MY34rXXXvvE/bu7u6Ozs7PHAgAw2CRbAKdMmRJr1qyJDRs2xCOPPBJtbW0xbdq0OHDgQK/HNDc3R2VlZWGpq6sbwMQAAH0j2QLY2NgY1113XZx33nlx5ZVXxnPPPRcREatXr+71mEWLFkVHR0dhaW1tHai4AAB9Jun3AH7c6aefHuedd17s3bu3133y+Xzk8/kBTAUA0PeSvQJ4rO7u7vjVr34VNTU1xY4CANCvki2A3/72t6OlpSX27dsX//Vf/xV//ud/Hp2dnTF37txiRwMA6FfJvgT8P//zP/HNb34z3nrrrTjrrLPi4osvji1btsTYsWOLHQ0AoF8lWwDXrVtX7AgAAEWR7EvAAACpUgABABKjAAIAJEYBBABIjAIIAJAYBRAAIDEKIABAYhRAAIDEKIAAAIlRAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBiFEAAgMQogAAAiVEAAQASowACACRGAQQASIwCCACQGAUQACAxCiAAQGIUQACAxCiAAACJUQABABKjAAIAJEYBBABIjAIIAJAYBRAAIDEKIABAYpIvgN///vejvr4+hg0bFpMmTYqf/exnxY4EANCvki6ATzzxRCxYsCCWLFkSO3bsiK997WvR2NgYb7zxRrGjAQD0myHFDlBMy5Yti7/5m7+Jv/3bv42IiOXLl8eGDRti5cqV0dzcXLRcWZZF/sPuiIg48t57ceTD//+f6cihD40Z+9RjpZbHWPmOlVoeY+UzlmVZ0PdyWaIze+jQoRgxYkT827/9W1x77bWF7XfddVfs3LkzWlpajjumu7s7uru7C+udnZ1RV1cXHR0dMXLkyD7L9u47ndF68ZQ+Ox8ADFZ1W/4rzviDvvseG/HR9+/Kyso+//49mCT7EvBbb70Vhw8fjqqqqh7bq6qqoq2t7YTHNDc3R2VlZWGpq6vrl2wjhiZ9YRYACnxP7B/Jz2oul+uxnmXZcduOWrRoUSxcuLCwfvQKYJ9nGj48zvnF9j4/LwAMNrnhw4sdoSwlWwDPPPPMOPXUU4+72tfe3n7cVcGj8vl85PP5fs+Wy+UiN2JEvz8OAJCmZF8CHjp0aEyaNCk2btzYY/vGjRtj2rRpRUoFAND/kr0CGBGxcOHCuPnmm2Py5MkxderUePjhh+ONN96IefPmFTsaAEC/SboAXn/99XHgwIH4x3/8x9i/f380NDTET37ykxg7dmyxowEA9JtkbwPTF3yMHAAGH9+/E34PIABAqhRAAIDEKIAAAIlRAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBikv5VcCfr6C9R6ezsLHISAODTOvp9O+VfhqYAnoSurq6IiKirqytyEgDgs+rq6orKyspixygKvwv4JBw5ciTefPPNqKioiFwu16fn7uzsjLq6umhtbU329xSeiHk5MfPSO3NzYuald+bmxMppXrIsi66urqitrY1TTknz3XCuAJ6EU045JcaMGdOvjzFy5MhB/0TrD+blxMxL78zNiZmX3pmbEyuXeUn1yt9RadZeAICEKYAAAIlRAEtUPp+Pe+65J/L5fLGjlBTzcmLmpXfm5sTMS+/MzYmZl/LiQyAAAIlxBRAAIDEKIABAYhRAAIDEKIAAAIlRAEvQ97///aivr49hw4bFpEmT4mc/+1mxIw24zZs3x+zZs6O2tjZyuVw8/fTTPcazLIumpqaora2N4cOHx/Tp02P37t3FCTuAmpub48ILL4yKiooYPXp0XHPNNfHqq6/22CfFuVm5cmVMnDixcIPaqVOnxvPPP18YT3FOTqS5uTlyuVwsWLCgsC3VuWlqaopcLtdjqa6uLoynOi8REf/7v/8bN910U/zhH/5hjBgxIr785S/H9u3bC+Mpz005UQBLzBNPPBELFiyIJUuWxI4dO+JrX/taNDY2xhtvvFHsaAPq4MGDcf7558eKFStOOP7AAw/EsmXLYsWKFbF169aorq6OmTNnFn4/c7lqaWmJ+fPnx5YtW2Ljxo3x4YcfxqxZs+LgwYOFfVKcmzFjxsT9998f27Zti23btsXll18eV199deGbUopzcqytW7fGww8/HBMnTuyxPeW5Offcc2P//v2FZdeuXYWxVOfl7bffjq9+9atx2mmnxfPPPx979uyJBx98MP7gD/6gsE+qc1N2MkrKRRddlM2bN6/Hti996UvZP/zDPxQpUfFFRPbUU08V1o8cOZJVV1dn999/f2HbBx98kFVWVmY/+MEPipCweNrb27OIyFpaWrIsMzcf94UvfCH7l3/5F3OSZVlXV1c2bty4bOPGjdlll12W3XXXXVmWpf31cs8992Tnn3/+CcdSnpfvfOc72SWXXNLreMpzU25cASwhhw4diu3bt8esWbN6bJ81a1a89NJLRUpVevbt2xdtbW095imfz8dll12W3Dx1dHRERMSoUaMiwtxERBw+fDjWrVsXBw8ejKlTp5qTiJg/f358/etfjyuvvLLH9tTnZu/evVFbWxv19fXxjW98I1577bWISHtennnmmZg8eXL8xV/8RYwePTq+8pWvxCOPPFIYT3luyo0CWELeeuutOHz4cFRVVfXYXlVVFW1tbUVKVXqOzkXq85RlWSxcuDAuueSSaGhoiIi052bXrl1xxhlnRD6fj3nz5sVTTz0VEyZMSHpOIiLWrVsX27dvj+bm5uPGUp6bKVOmxJo1a2LDhg3xyCOPRFtbW0ybNi0OHDiQ9Ly89tprsXLlyhg3blxs2LAh5s2bF3/3d38Xa9asiYi0v2bKzZBiB+B4uVyux3qWZcdtwzzdcccd8corr8TPf/7z48ZSnJtzzjkndu7cGe+88078+Mc/jrlz50ZLS0thPMU5aW1tjbvuuiteeOGFGDZsWK/7pTg3jY2NhT+fd955MXXq1PjjP/7jWL16dVx88cURkea8HDlyJCZPnhxLly6NiIivfOUrsXv37li5cmX81V/9VWG/FOem3LgCWELOPPPMOPXUU4/7Kaq9vf24n7ZSdvSTeinP05133hnPPPNM/PSnP40xY8YUtqc8N0OHDo2zzz47Jk+eHM3NzXH++efHQw89lPScbN++Pdrb22PSpEkxZMiQGDJkSLS0tMR3v/vdGDJkSOHvn+LcHOv000+P8847L/bu3Zv010xNTU1MmDChx7Y/+ZM/KXwQMeW5KTcKYAkZOnRoTJo0KTZu3Nhj+8aNG2PatGlFSlV66uvro7q6usc8HTp0KFpaWsp+nrIsizvuuCOefPLJePHFF6O+vr7HeMpzc6wsy6K7uzvpObniiiti165dsXPnzsIyefLkuPHGG2Pnzp3xxS9+Mdm5OVZ3d3f86le/ipqamqS/Zr761a8ed2up//7v/46xY8dGhP9jykqxPn3Cia1bty477bTTskcffTTbs2dPtmDBguz000/PXn/99WJHG1BdXV3Zjh07sh07dmQRkS1btizbsWNH9pvf/CbLsiy7//77s8rKyuzJJ5/Mdu3alX3zm9/Mampqss7OziIn71+33XZbVllZmW3atCnbv39/YXnvvfcK+6Q4N4sWLco2b96c7du3L3vllVeyxYsXZ6ecckr2wgsvZFmW5pz05uOfAs6ydOfm7//+77NNmzZlr732WrZly5bsz/7sz7KKiorC/7WpzsvLL7+cDRkyJLvvvvuyvXv3Zo8//ng2YsSI7LHHHivsk+rclBsFsAR973vfy8aOHZsNHTo0u+CCCwq3+EjJT3/60ywijlvmzp2bZdlHtyK45557surq6iyfz2eXXnpptmvXruKGHgAnmpOIyFatWlXYJ8W5+eu//uvCc+ass87KrrjiikL5y7I056Q3xxbAVOfm+uuvz2pqarLTTjstq62tzebMmZPt3r27MJ7qvGRZlj377LNZQ0NDls/nsy996UvZww8/3GM85bkpJ7ksy7LiXHsEAKAYvAcQACAxCiAAQGIUQACAxCiAAACJUQABABKjAAIAJEYBBABIjAIIAJAYBRAAIDEKIABAYhRAAIDEKIAAAIlRAAEAEqMAAgAkRgEEAEiMAggAkBgFEAAgMQogAEBiFEAAgMQogAAAiVEAAQASowACACRGAQQASIwCCACQGAUQACAxCiAAQGIUQACAxCiAAACJUQABABKjAAIAJEYBBABIzP8LOoxfwvi80eUAAAAASUVORK5CYII=", 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure()\n", + "plt.stem(((66 - oven_idx_sequence)[:-1] + (66 - oven_idx_sequence)[1:])/2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6b18807d", + "metadata": {}, + "outputs": [], + "source": [ + "n_oven = 66\n", + "\n", + "start_indices = [1, 3, 5, 2, 4]\n", + "\n", + "oven_idx_order = np.concatenate([np.array(range(i0 - 1, n_oven, 5)) for i0 in start_indices])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1007d82d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "66" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oven_idx_order.size" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3fc5bccd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 2, 7, 12,\n", + " 17, 22, 27, 32, 37, 42, 47, 52, 57, 62, 4, 9, 14, 19, 24, 29, 34,\n", + " 39, 44, 49, 54, 59, 64, 1, 6, 11, 16, 21, 26, 31, 36, 41, 46, 51,\n", + " 56, 61, 3, 8, 13, 18, 23, 28, 33, 38, 43, 48, 53, 58, 63])" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "oven_idx_order" + ] + }, { "cell_type": "markdown", "id": "9fb0a4f4", @@ -23,7 +285,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "id": "37a3a13f", "metadata": {}, "outputs": [], @@ -51,9 +313,9 @@ " self.t = - normal_period * self.size\n", " self.t_last = self.t\n", " \n", - " for i in range(self.size * 2):\n", + " for i in range(int(np.ceil(self.size * 11.))):\n", " \"\"\" Fill battety with normal charge\"\"\"\n", - " self.update(normal_period / 2.)\n", + " self.update(normal_period / 11.)\n", "\n", " def next_oven (self):\n", " next_oven_id = self.oven_idx_order[self.sequence_idx % self.size]\n", @@ -61,7 +323,7 @@ " return next_oven_id\n", "\n", " def bake (self, dt):\n", - " dQ = self.dQ(dt)\n", + " dQ = self.dQ(dt) # array, dQ to all ovens\n", " for cc in self.processing:\n", " cc.bake(dQ)\n", "\n", @@ -74,29 +336,46 @@ " coke = self.processing.pop(0)\n", " coke.end_baking(self.t)\n", " self.product.append(coke)\n", - " \n", + "\n", " def charge (self, coke_charge):\n", " self.processing.append(coke_charge)\n", - " \n", + "\n", " def dQ (self, dt):\n", " return self.heat_program.dQ(self.t, self.t+dt)\n", + " \n", + " def is_pc_time (self, dt):\n", + " return self.t + dt >= period + self.t_last \n", "\n", " def update (self, dt):\n", - " dQ = self.heat_program.dQ(self.t, self.t+dt)\n", + " # dQ = self.heat_program.dQ(self.t, self.t+dt) # t, t+dt 사이 공급하는 열량, array 로 대체 필요\n", + " \n", + " # t 에서 t+dt 까지 탄화실 가열\n", " self.bake(dt)\n", - " if self.t+dt > self.charge_program.period(self.t) + self.t_last :\n", - " print(\"Push timing within this time step.\", \n", - " self.t+dt - self.t_last, \"since last P/C. \", \n", + " \n", + " period = self.charge_program.period(self.t) # 현재 장입 시간 간격\n", + " \n", + " # t_last + period 가 t, t + dt 사이에 들어오는 것 검사\n", + " # t + dt 가 다음 추출/장입 시각 이후일 때 => 이번 time step 에 추출/장입 시기가 포함될 수 있음\n", + " if self.t + dt >= period + self.t_last :\n", + " print(\"Push timing within [ {} , {} ].\".format(self.t, self.t + dt), \n", + " self.t + dt - self.t_last, \"since last P/C. \", \n", " \"P/C interval = \", self.charge_program.period(self.t))\n", " \n", - " if self.t_last + self.charge_program.period(self.t) > self.t:\n", - " self.t_last += self.charge_program.period(self.t)\n", + " # 마지막 장입 시각 + 장입 시간 간격 이 이번 time step 에 포함됨\n", + " # 일정한 간격으로 장입 진행 중\n", + " if self.t < self.t_last + period:\n", + " self.t_last += period\n", + " # 마지막 장입 이후 현재 장입 간격보다 긴 시간이 경과함\n", + " # 이번 time step 끝을 마지막 장입 시각으로 업데이트\n", " else:\n", - " self.t_last = self.t\n", + " self.t_last = self.t + dt\n", " \n", + " # 추출/장입 실행\n", " self.push_and_charge(CokeCharge(self.t_last, self.next_oven()))\n", + " \n", + " # 시뮬레이션 시간 업데이트\n", " self.t += dt\n", - " \n", + "\n", "\n", "class CokeCharge:\n", " \n", @@ -123,7 +402,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "id": "070094b4", "metadata": {}, "outputs": [], @@ -166,17 +445,66 @@ "'''.split(), dtype=np.double).reshape((-1,2))" ] }, + { + "cell_type": "code", + "execution_count": 11, + "id": "bc714fba", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3aba5035779f4edb9cc793de15c9e4ca", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_time = np.linspace(-9, 72, 1000)\n", + "plt.figure()\n", + "plt.plot(sample_time / 60., np.interp(sample_time, *sample_program.T), '-o')" + ] + }, { "cell_type": "markdown", "id": "a2665c72", "metadata": {}, "source": [ - "## 이전 정수 계획 중 가져옴" + "## 이전 정수 계획 중 가져옴\n", + "정수 감/증급 단계와 추가감산 감/증급 단계의 중첩 있음" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, "id": "9713bb3a", "metadata": {}, "outputs": [], @@ -231,72 +559,24 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "bc714fba", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6ff3705eb05d48289039ee2d15e4937c", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", - "text/html": [ - "\n", - "
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P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.4444444444444464 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962962976 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962962994 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629630116 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629630294 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963047 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963065 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629630827 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629631005 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963118 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963136 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963154 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629631715 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629631893 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963207 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963225 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629632426 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629632603 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963278 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963296 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629633136 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629633314 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963349 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963367 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629633847 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629634024 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963438 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963447 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629634424 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963438 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629634335 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963429 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629634247 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296342 since last P/C. P/C interval = 0.2962962962962963\n", - "1395.1111111111131 -19.259259259259256\n", - "1373.4814814814833 -18.96296296296296\n", - "1351.8518518518536 -18.66666666666666\n", - "1330.2222222222238 -18.370370370370363\n", - "1308.592592592594 -18.074074074074066\n", - "1286.9629629629642 -17.777777777777768\n", - "1265.3333333333344 -17.48148148148147\n", - "1243.7037037037046 -17.185185185185173\n", - "1222.0740740740748 -16.888888888888875\n", - "1200.444444444445 -16.592592592592577\n", - "1178.8148148148152 -16.29629629629628\n", - "1168.0000000000005 -15.999999999999984\n", - "1146.3703703703707 -15.703703703703688\n", - "1124.7407407407409 -15.407407407407392\n", - "1103.111111111111 -15.111111111111097\n", - "1081.4814814814813 -14.8148148148148\n", - "1059.8518518518517 -14.518518518518505\n", - "1038.222222222222 -14.222222222222209\n", - "1016.5925925925923 -13.925925925925913\n", - "994.9629629629626 -13.629629629629617\n", - "973.3333333333329 -13.333333333333321\n", - "951.7037037037032 -13.037037037037026\n", - "930.0740740740736 -12.74074074074073\n", - "908.4444444444439 -12.444444444444434\n", - "886.8148148148142 -12.148148148148138\n", - "865.1851851851845 -11.851851851851842\n", - "843.5555555555549 -11.555555555555546\n", - "821.9259259259252 -11.25925925925925\n", - "800.2962962962955 -10.962962962962955\n", - "778.6666666666658 -10.666666666666659\n", - "757.0370370370362 -10.370370370370363\n", - "735.4074074074065 -10.074074074074067\n", - "713.7777777777768 -9.777777777777771\n", - "692.1481481481471 -9.481481481481476\n", - "670.5185185185175 -9.18518518518518\n", - "648.8888888888878 -8.888888888888884\n", - "627.2592592592582 -8.592592592592588\n", - "605.6296296296287 -8.296296296296292\n", - "583.9999999999991 -7.9999999999999964\n", - "562.3703703703696 -7.703703703703701\n", - "540.7407407407402 -7.407407407407405\n", - "519.1111111111107 -7.111111111111109\n", - "497.48148148148124 -6.814814814814813\n", - "475.8518518518517 -6.518518518518517\n", - "454.2222222222221 -6.222222222222221\n", - "432.59259259259255 -5.925925925925926\n", - "410.962962962963 -5.62962962962963\n", - "389.33333333333337 -5.333333333333334\n", - "367.7037037037037 -5.037037037037038\n", - "346.074074074074 -4.740740740740742\n", - "324.44444444444434 -4.444444444444446\n", - "302.81481481481467 -4.148148148148151\n", - "281.185185185185 -3.8518518518518543\n", - "259.5555555555553 -3.555555555555558\n", - "237.92592592592572 -3.2592592592592617\n", - "216.29629629629613 -2.9629629629629655\n", - "194.66666666666654 -2.666666666666669\n", - "173.03703703703695 -2.370370370370373\n", - "151.4074074074074 -2.0740740740740766\n", - "129.77777777777777 -1.7777777777777803\n", - "108.14814814814812 -1.481481481481484\n", - "86.51851851851849 -1.1851851851851878\n", - "64.88888888888887 -0.8888888888888915\n", - "43.25925925925925 -0.5925925925925952\n", - "21.629629629629626 -0.29629629629629894\n", - "0 -2.6645352591003757e-15\n" + "Push timing within [ -19.286195286195277 , -19.25925925925925 ]. 0.2962962962963047 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -18.989898989898972 , -18.962962962962944 ]. 0.2962962962963118 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -18.693602693602667 , -18.66666666666664 ]. 0.29629629629631893 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -18.397306397306362 , -18.370370370370335 ]. 0.29629629629632603 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -18.101010101010058 , -18.07407407407403 ]. 0.29629629629633314 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -17.804713804713753 , -17.777777777777725 ]. 0.29629629629634024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -17.50841750841745 , -17.48148148148142 ]. 0.29629629629634735 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -17.212121212121144 , -17.185185185185116 ]. 0.29629629629635446 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -16.91582491582484 , -16.88888888888881 ]. 0.29629629629636156 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -16.619528619528534 , -16.592592592592506 ]. 0.29629629629636867 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -16.32323232323223 , -16.2962962962962 ]. 0.29629629629637577 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -16.026936026935925 , -15.999999999999897 ]. 0.2962962962963829 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -15.73063973063962 , -15.703703703703592 ]. 0.29629629629639176 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -15.434343434343315 , -15.407407407407288 ]. 0.29629629629640064 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -15.13804713804701 , -15.111111111110983 ]. 0.2962962962964095 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -14.841750841750706 , -14.814814814814678 ]. 0.2962962962964184 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -14.545454545454401 , -14.518518518518373 ]. 0.2962962962964273 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -14.249158249158096 , -14.222222222222069 ]. 0.29629629629643617 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -13.952861952861792 , -13.925925925925764 ]. 0.29629629629644505 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -13.656565656565487 , -13.62962962962946 ]. 0.29629629629645393 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -13.360269360269182 , -13.333333333333155 ]. 0.2962962962964628 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -13.063973063972877 , -13.03703703703685 ]. 0.2962962962964717 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -12.767676767676573 , -12.740740740740545 ]. 0.2962962962964806 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -12.471380471380268 , -12.44444444444424 ]. 0.29629629629648946 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -12.175084175083963 , -12.148148148147936 ]. 0.29629629629649834 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -11.878787878787659 , -11.851851851851631 ]. 0.2962962962965072 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -11.582491582491354 , -11.555555555555326 ]. 0.2962962962965161 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -11.28619528619505 , -11.259259259259021 ]. 0.296296296296525 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -10.989898989898744 , -10.962962962962717 ]. 0.29629629629653387 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -10.69360269360244 , -10.666666666666412 ]. 0.29629629629654275 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -10.397306397306135 , -10.370370370370107 ]. 0.29629629629655163 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -10.10101010100983 , -10.074074074073803 ]. 0.2962962962965605 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -9.804713804713526 , -9.777777777777498 ]. 0.2962962962965694 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -9.50841750841722 , -9.481481481481193 ]. 0.2962962962965783 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -9.212121212120916 , -9.185185185184888 ]. 0.29629629629658716 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -8.915824915824611 , -8.888888888888584 ]. 0.29629629629659604 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -8.619528619528307 , -8.592592592592279 ]. 0.2962962962966049 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -8.323232323232002 , -8.296296296295974 ]. 0.2962962962966138 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -8.026936026935697 , -7.9999999999996705 ]. 0.2962962962966218 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -7.730639730639402 , -7.7037037037033755 ]. 0.2962962962966209 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -7.434343434343107 , -7.407407407407081 ]. 0.29629629629662 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -7.1380471380468125 , -7.111111111110786 ]. 0.29629629629661913 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -6.8417508417505175 , -6.814814814814491 ]. 0.29629629629661824 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -6.545454545454223 , -6.518518518518196 ]. 0.29629629629661736 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -6.249158249157928 , -6.222222222221901 ]. 0.29629629629661647 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -5.952861952861633 , -5.925925925925606 ]. 0.2962962962966156 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -5.656565656565338 , -5.629629629629311 ]. 0.2962962962966147 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -5.360269360269043 , -5.333333333333016 ]. 0.2962962962966138 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -5.063973063972748 , -5.037037037036721 ]. 0.2962962962966129 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -4.767676767676453 , -4.740740740740426 ]. 0.296296296296612 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -4.471380471380158 , -4.444444444444131 ]. 0.29629629629661114 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -4.175084175083863 , -4.148148148147836 ]. 0.29629629629661025 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -3.878787878787568 , -3.851851851851541 ]. 0.29629629629660936 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -3.582491582491273 , -3.5555555555552463 ]. 0.29629629629660803 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -3.286195286194978 , -3.2592592592589513 ]. 0.2962962962966067 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -2.989898989898683 , -2.9629629629626564 ]. 0.29629629629660537 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -2.6936026936023882 , -2.6666666666663614 ]. 0.29629629629660403 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -2.3973063973060933 , -2.3703703703700665 ]. 0.2962962962966027 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -2.1010101010097983 , -2.0740740740737715 ]. 0.29629629629660137 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -1.8047138047135016 , -1.7777777777774746 ]. 0.29629629629660204 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -1.5084175084172042 , -1.4814814814811772 ]. 0.29629629629660315 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -1.2121212121209068 , -1.1851851851848798 ]. 0.29629629629660426 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -0.9158249158246099 , -0.888888888888583 ]. 0.2962962962966048 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -0.6195286195283137 , -0.5925925925922868 ]. 0.2962962962966047 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -0.32323232323201756 , -0.29629629629599064 ]. 0.2962962962966046 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ -0.02693602693572134 , 3.0559582642197824e-13 ]. 0.29629629629660453 since last P/C. P/C interval = 0.2962962962962963\n", + "1405.925925925954 -19.259259259259256 0\n", + "1384.2962962963227 -18.96296296296296 5\n", + "1362.666666666691 -18.66666666666666 10\n", + "1341.0370370370597 -18.370370370370363 15\n", + "1319.407407407428 -18.074074074074066 20\n", + "1297.7777777777967 -17.777777777777768 25\n", + "1276.148148148165 -17.48148148148147 30\n", + "1254.5185185185337 -17.185185185185173 35\n", + "1232.888888888902 -16.888888888888875 40\n", + "1211.2592592592707 -16.592592592592577 45\n", + "1189.629629629639 -16.29629629629628 50\n", + "1168.0000000000077 -15.999999999999984 55\n", + "1146.3703703703775 -15.703703703703688 60\n", + "1124.7407407407472 -15.407407407407392 65\n", + "1103.111111111117 -15.111111111111097 2\n", + "1081.4814814814863 -14.8148148148148 7\n", + "1059.8518518518556 -14.518518518518505 12\n", + "1038.2222222222247 -14.222222222222209 17\n", + "1016.5925925925949 -13.925925925925913 22\n", + "994.9629629629651 -13.629629629629617 27\n", + "973.3333333333355 -13.333333333333321 32\n", + "951.703703703706 -13.037037037037026 37\n", + "930.0740740740764 -12.74074074074073 42\n", + "908.4444444444466 -12.444444444444434 47\n", + "886.814814814817 -12.148148148148138 52\n", + "865.1851851851875 -11.851851851851842 57\n", + "843.5555555555579 -11.555555555555546 62\n", + "821.9259259259281 -11.25925925925925 4\n", + "800.2962962962986 -10.962962962962955 9\n", + "778.666666666669 -10.666666666666659 14\n", + "757.0370370370395 -10.370370370370363 19\n", + "735.4074074074097 -10.074074074074067 24\n", + "713.7777777777801 -9.777777777777771 29\n", + "692.1481481481503 -9.481481481481476 34\n", + "670.5185185185205 -9.18518518518518 39\n", + "648.88888888889 -8.888888888888884 44\n", + "627.2592592592591 -8.592592592592588 49\n", + "605.6296296296282 -8.296296296296292 54\n", + "583.9999999999975 -7.9999999999999964 59\n", + "562.3703703703685 -7.703703703703701 64\n", + "540.7407407407395 -7.407407407407405 1\n", + "519.1111111111103 -7.111111111111109 6\n", + "497.4814814814805 -6.814814814814813 11\n", + "475.8518518518509 -6.518518518518517 16\n", + "454.22222222222126 -6.222222222222221 21\n", + "432.59259259259164 -5.925925925925926 26\n", + "410.962962962962 -5.62962962962963 31\n", + "389.33333333333246 -5.333333333333334 36\n", + "367.70370370370284 -5.037037037037038 41\n", + "346.0740740740732 -4.740740740740742 46\n", + "324.44444444444355 -4.444444444444446 51\n", + "302.81481481481393 -4.148148148148151 56\n", + "281.1851851851843 -3.8518518518518543 61\n", + "259.555555555555 -3.555555555555558 3\n", + "237.92592592592567 -3.2592592592592617 8\n", + "216.29629629629636 -2.9629629629629655 13\n", + "194.66666666666686 -2.666666666666669 18\n", + "173.03703703703724 -2.370370370370373 23\n", + "151.40740740740762 -2.0740740740740766 28\n", + "129.77777777777794 -1.7777777777777803 33\n", + "108.14814814814822 -1.481481481481484 38\n", + "86.51851851851852 -1.1851851851851878 43\n", + "64.88888888888886 -0.8888888888888915 48\n", + "43.25925925925923 -0.5925925925925952 53\n", + "21.62962962962962 -0.29629629629629894 58\n", + "0 -2.6645352591003757e-15 63\n" ] } ], "source": [ "bat3A = Battery(\"3A\", 66, sample_schedule2, sample_charging, 1220) \n", + "\n", "for cc in bat3A.processing:\n", - " print (cc.Q, cc.t_charge)" + " print (cc.Q, cc.t_charge, cc.idx_oven)" + ] + }, + { + "cell_type": "markdown", + "id": "41951d5c", + "metadata": {}, + "source": [ + "## 시뮬레이션 실행" ] }, { "cell_type": "code", - "execution_count": 14, - "id": "99d40063", + "execution_count": 21, + "id": "c38bbf3e", "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.2962962962962963" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bat3A.charge_program.period(6)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "99d40063", + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Push timing within this time step. 0.30000000000004573 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370374977 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074074544 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111111115775 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481481938 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185189645 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555555993 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.309259259259302 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.296296296296338 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3000000000000407 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370374333 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.307407407407446 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111111114864 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814818513 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518518878 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555555909 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.309259259259294 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2962962962963305 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3000000000000336 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370373667 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740743977 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111111114287 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814817936 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185188246 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555558556 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925928865 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29629629629632603 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3000000000000451 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3037037037037642 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074074833 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 9.127777777778093 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29999999999998295 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3037037037036683 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074073536 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111111103895 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814805857 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518517439 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555542924 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925911457 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629627999 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999998195 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370350486 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074071902 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111108755 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814789514 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518515805 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555552658 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925895115 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629626365 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999996561 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370334143 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740702677 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111107121 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481477317 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185141705 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555551024 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592587877 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296247306 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999994927 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.303703703703178 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740686334 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111105487 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481475683 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518512536 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555493896 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592586243 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296230963 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29999999999932925 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3037037037030146 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074066999 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111111038525 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814740487 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518510902 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555477554 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592584609 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629621462 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999991658 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370285116 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074065365 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111102218 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814724144 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518509268 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555546121 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925829745 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629619828 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999990024 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370268773 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740637307 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111100584 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.298148148147078 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185076335 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555544487 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.309259259258134 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296181936 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.299999999998839 since last P/C. P/C interval = 0.2962962962962963\n" + "Push timing within [ 0.2833333333336389 , 0.3000000000003056 ]. 0.30000000000030824 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 0.5833333333336391 , 0.6000000000003058 ]. 0.30370370370401223 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 0.8833333333336401 , 0.9000000000003068 ]. 0.3074074074077169 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 1.1833333333336398 , 1.2000000000003064 ]. 0.3111111111114202 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 1.466666666666972 , 1.4833333333336387 ]. 0.29814814814845625 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 1.766666666666971 , 1.7833333333336376 ]. 0.3018518518521589 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 2.06666666666697 , 2.083333333333637 ]. 0.3055555555558618 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 2.366666666666969 , 2.3833333333336357 ]. 0.30925925925956443 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 2.6500000000003014 , 2.666666666666968 ]. 0.2962962962966005 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 2.9500000000003004 , 2.966666666666967 ]. 0.30000000000030314 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 3.2500000000002993 , 3.266666666666966 ]. 0.3037037037040058 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 3.5500000000002983 , 3.566666666666965 ]. 0.30740740740770844 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 3.850000000000297 , 3.866666666666964 ]. 0.3111111111114111 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 4.1333333333336295 , 4.150000000000296 ]. 0.29814814814844715 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 4.4333333333336284 , 4.450000000000295 ]. 0.3018518518521498 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 4.733333333333627 , 4.750000000000294 ]. 0.3055555555558529 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 5.033333333333626 , 5.050000000000293 ]. 0.309259259259556 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 5.316666666666959 , 5.333333333333625 ]. 0.2962962962965925 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 5.616666666666958 , 5.633333333333624 ]. 0.3000000000002956 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 5.9166666666669565 , 5.933333333333623 ]. 0.3037037037039987 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 6.216666666666955 , 6.233333333333622 ]. 0.3074074074077018 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 6.516666666666954 , 6.533333333333621 ]. 0.3111111111114049 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 6.800000000000287 , 6.816666666666953 ]. 0.29814814814844137 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 7.100000000000286 , 7.116666666666952 ]. 0.30185185185214447 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 7.400000000000285 , 7.416666666666951 ]. 0.30555555555584757 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 7.7000000000002835 , 7.71666666666695 ]. 0.30925925925955067 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 7.983333333333616 , 8.000000000000282 ]. 0.29629629629658716 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 8.28333333333363 , 8.300000000000297 ]. 0.30000000000030624 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 8.583333333333645 , 8.600000000000312 ]. 0.30370370370402533 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 8.88333333333366 , 8.900000000000327 ]. 0.3074074074077444 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 18.000000000000565 , 18.01666666666723 ]. 9.127777777778352 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 18.300000000000548 , 18.316666666667214 ]. 0.29999999999998295 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 18.60000000000053 , 18.616666666667196 ]. 0.3037037037036683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 18.900000000000514 , 18.91666666666718 ]. 0.3074074074073536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 19.200000000000497 , 19.216666666667162 ]. 0.31111111111103895 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 19.483333333333814 , 19.50000000000048 ]. 0.29814814814805857 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 19.783333333333797 , 19.800000000000463 ]. 0.3018518518517439 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 20.08333333333378 , 20.100000000000446 ]. 0.30555555555542924 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 20.383333333333763 , 20.40000000000043 ]. 0.30925925925911457 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 20.683333333333746 , 20.70000000000041 ]. 0.3129629629627999 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 20.966666666667063 , 20.98333333333373 ]. 0.2999999999998195 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 21.266666666667046 , 21.28333333333371 ]. 0.30370370370350486 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 21.56666666666703 , 21.583333333333695 ]. 0.3074074074071902 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 21.86666666666701 , 21.883333333333677 ]. 0.3111111111108755 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 22.15000000000033 , 22.166666666666995 ]. 0.29814814814789514 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 22.450000000000312 , 22.466666666666978 ]. 0.3018518518515805 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 22.750000000000295 , 22.76666666666696 ]. 0.3055555555552658 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 23.050000000000278 , 23.066666666666944 ]. 0.30925925925895115 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 23.35000000000026 , 23.366666666666926 ]. 0.3129629629626365 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 23.633333333333578 , 23.650000000000244 ]. 0.2999999999996561 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 23.93333333333356 , 23.950000000000227 ]. 0.30370370370334143 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 24.233333333333544 , 24.25000000000021 ]. 0.30740740740702677 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 24.533333333333527 , 24.550000000000193 ]. 0.3111111111107121 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 24.816666666666844 , 24.83333333333351 ]. 0.2981481481477317 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 25.116666666666827 , 25.133333333333493 ]. 0.30185185185141705 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 25.41666666666681 , 25.433333333333476 ]. 0.3055555555551024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 25.716666666666793 , 25.73333333333346 ]. 0.3092592592587877 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 26.016666666666776 , 26.03333333333344 ]. 0.31296296296247306 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 26.300000000000093 , 26.31666666666676 ]. 0.2999999999994927 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 26.600000000000076 , 26.61666666666674 ]. 0.303703703703178 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 26.90000000000006 , 26.916666666666725 ]. 0.30740740740686334 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 27.200000000000042 , 27.216666666666708 ]. 0.3111111111105487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 27.48333333333336 , 27.500000000000025 ]. 0.2981481481475683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 27.783333333333342 , 27.800000000000008 ]. 0.3018518518512536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 28.083333333333325 , 28.09999999999999 ]. 0.30555555555493896 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 28.383333333333308 , 28.399999999999974 ]. 0.3092592592586243 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 28.68333333333329 , 28.699999999999957 ]. 0.31296296296230963 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 28.966666666666608 , 28.983333333333274 ]. 0.29999999999932925 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 29.26666666666659 , 29.283333333333257 ]. 0.3037037037030146 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 29.566666666666574 , 29.58333333333324 ]. 0.3074074074066999 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 29.866666666666557 , 29.883333333333223 ]. 0.31111111111038525 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 30.149999999999874 , 30.16666666666654 ]. 0.29814814814740487 since last P/C. P/C interval = 0.2962962962962963\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Push timing within this time step. 2.0703703703690906 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29999999999998295 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3037037037036683 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074073536 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111111103895 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814805857 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518517439 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555542924 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925911457 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629627999 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999998195 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370350486 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074071902 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111108755 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814789514 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518515805 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555552658 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925895115 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629626365 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999996561 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370334143 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740702677 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111107121 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481477317 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185141705 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555551024 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592587877 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296247306 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999994927 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.303703703703178 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740686334 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111105487 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481475683 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518512536 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555493896 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592586243 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296230963 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29999999999932925 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3037037037030146 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074066999 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111111038525 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814740487 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518510902 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555477554 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592584609 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629621462 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999991658 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370285116 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074065365 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111102218 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814724144 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518509268 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555546121 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925829745 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629619828 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999990024 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370268773 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740637307 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111100584 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.298148148147078 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185076335 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555544487 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.309259259258134 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296181936 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.299999999998839 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3037037037025243 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740620964 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.311111111109895 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481469146 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185059993 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555428526 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592579706 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296165593 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29999999999867555 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3037037037023609 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074060462 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111110973155 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814675117 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518504365 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555412184 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592578072 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629614925 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2999999999985121 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370219746 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074058828 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111110957523 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29814814814659485 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518502873 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555397973 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30925925925767217 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3129629629613646 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29999999999839133 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30370370370208377 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3074074074057762 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31111111110946865 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481464954 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3018518518501878 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30555555555388025 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592575727 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.31296296296126513 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.29999999999829186 since last P/C. P/C interval = 0.2962962962962963\n" + "Push timing within [ 30.449999999999857 , 30.466666666666523 ]. 0.3018518518510902 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 30.74999999999984 , 30.766666666666506 ]. 0.30555555555477554 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 31.049999999999823 , 31.06666666666649 ]. 0.3092592592584609 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 31.349999999999806 , 31.36666666666647 ]. 0.3129629629621462 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 31.633333333333123 , 31.64999999999979 ]. 0.2999999999991658 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 31.933333333333106 , 31.949999999999772 ]. 0.30370370370285116 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 32.23333333333309 , 32.24999999999976 ]. 0.30740740740654005 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 32.533333333333076 , 32.54999999999974 ]. 0.31111111111022893 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 32.81666666666639 , 32.83333333333306 ]. 0.29814814814724855 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 33.116666666666376 , 33.13333333333304 ]. 0.3018518518509339 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 33.41666666666636 , 33.433333333333024 ]. 0.3055555555546192 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 33.71666666666634 , 33.73333333333301 ]. 0.30925925925830455 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 34.016666666666325 , 34.03333333333299 ]. 0.3129629629619899 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 34.29999999999964 , 34.31666666666631 ]. 0.2999999999990095 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 34.599999999999625 , 34.61666666666629 ]. 0.30370370370269484 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 34.89999999999961 , 34.91666666666627 ]. 0.3074074074063802 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 35.19999999999959 , 35.216666666666256 ]. 0.3111111111100655 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 35.48333333333291 , 35.499999999999574 ]. 0.2981481481470851 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 35.78333333333289 , 35.79999999999956 ]. 0.30185185185077046 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 36.083333333332874 , 36.09999999999954 ]. 0.3055555555544558 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 36.38333333333286 , 36.39999999999952 ]. 0.30925925925814113 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 36.68333333333284 , 36.699999999999505 ]. 0.31296296296182646 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 36.96666666666616 , 36.98333333333282 ]. 0.2999999999988461 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 39.01666666666604 , 39.033333333332706 ]. 2.053703703702432 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 39.31666666666602 , 39.33333333333269 ]. 0.29999999999998295 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 39.616666666666006 , 39.63333333333267 ]. 0.3037037037036683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 39.91666666666599 , 39.933333333332655 ]. 0.3074074074073536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 40.21666666666597 , 40.23333333333264 ]. 0.31111111111103895 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 40.49999999999929 , 40.516666666665955 ]. 0.29814814814805857 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 40.79999999999927 , 40.81666666666594 ]. 0.3018518518517439 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 41.099999999999255 , 41.11666666666592 ]. 0.30555555555542924 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 41.39999999999924 , 41.416666666665904 ]. 0.30925925925911457 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 41.69999999999922 , 41.71666666666589 ]. 0.3129629629627999 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 41.98333333333254 , 41.999999999999204 ]. 0.2999999999998195 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 42.28333333333252 , 42.29999999999919 ]. 0.30370370370350486 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 42.583333333332504 , 42.59999999999917 ]. 0.3074074074071902 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 42.88333333333249 , 42.89999999999915 ]. 0.3111111111108755 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 43.166666666665805 , 43.18333333333247 ]. 0.29814814814789514 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 43.46666666666579 , 43.48333333333245 ]. 0.3018518518515805 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 43.76666666666577 , 43.783333333332436 ]. 0.3055555555552658 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 44.06666666666575 , 44.08333333333242 ]. 0.30925925925895115 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 44.366666666665736 , 44.3833333333324 ]. 0.3129629629626365 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 44.649999999999054 , 44.66666666666572 ]. 0.2999999999996561 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 44.94999999999904 , 44.9666666666657 ]. 0.30370370370334143 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 45.24999999999902 , 45.266666666665685 ]. 0.30740740740702677 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 45.549999999999 , 45.56666666666567 ]. 0.3111111111107121 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 45.83333333333232 , 45.849999999998985 ]. 0.2981481481477317 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 46.1333333333323 , 46.14999999999897 ]. 0.30185185185141705 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 46.433333333332286 , 46.44999999999895 ]. 0.3055555555551024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 46.73333333333227 , 46.749999999998934 ]. 0.3092592592587877 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 47.03333333333225 , 47.04999999999892 ]. 0.31296296296247306 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 47.31666666666557 , 47.333333333332234 ]. 0.2999999999994927 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 47.61666666666555 , 47.63333333333222 ]. 0.303703703703178 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 47.916666666665535 , 47.9333333333322 ]. 0.30740740740686334 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 48.21666666666552 , 48.23333333333218 ]. 0.3111111111105487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 48.499999999998835 , 48.5166666666655 ]. 0.2981481481475683 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 48.79999999999882 , 48.81666666666548 ]. 0.3018518518512536 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 49.0999999999988 , 49.116666666665466 ]. 0.30555555555493896 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 49.39999999999878 , 49.41666666666545 ]. 0.3092592592586243 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 49.69999999999877 , 49.71666666666543 ]. 0.31296296296230963 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 49.983333333332084 , 49.99999999999875 ]. 0.29999999999932925 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 50.28333333333207 , 50.29999999999873 ]. 0.3037037037030146 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 50.58333333333205 , 50.599999999998715 ]. 0.3074074074066999 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 50.88333333333203 , 50.8999999999987 ]. 0.31111111111038525 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 51.16666666666535 , 51.183333333332016 ]. 0.29814814814740487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 51.46666666666533 , 51.483333333332 ]. 0.3018518518510902 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 51.766666666665316 , 51.78333333333198 ]. 0.30555555555477554 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 52.0666666666653 , 52.083333333331964 ]. 0.3092592592584609 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 52.36666666666528 , 52.38333333333195 ]. 0.3129629629621462 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 52.6499999999986 , 52.666666666665265 ]. 0.2999999999991658 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 52.94999999999858 , 52.96666666666525 ]. 0.30370370370285116 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 53.249999999998565 , 53.26666666666523 ]. 0.3074074074065365 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 53.54999999999855 , 53.56666666666521 ]. 0.3111111111102218 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 53.833333333331865 , 53.84999999999853 ]. 0.29814814814724144 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 54.13333333333185 , 54.14999999999851 ]. 0.3018518518509268 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 54.43333333333183 , 54.4499999999985 ]. 0.3055555555546121 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 54.733333333331814 , 54.74999999999848 ]. 0.30925925925829745 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 55.0333333333318 , 55.04999999999846 ]. 0.3129629629619828 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 55.316666666665114 , 55.33333333333178 ]. 0.2999999999990024 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 55.6166666666651 , 55.63333333333176 ]. 0.30370370370268773 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 55.91666666666508 , 55.933333333331746 ]. 0.30740740740637307 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 56.21666666666506 , 56.23333333333173 ]. 0.3111111111100584 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 56.49999999999838 , 56.516666666665046 ]. 0.298148148147078 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 56.79999999999836 , 56.81666666666503 ]. 0.30185185185076335 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 57.099999999998346 , 57.11666666666501 ]. 0.3055555555544487 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 57.39999999999833 , 57.416666666664995 ]. 0.309259259258134 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 57.69999999999831 , 57.71666666666498 ]. 0.31296296296181936 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 57.98333333333163 , 57.999999999998295 ]. 0.299999999998839 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 58.28333333333161 , 58.29999999999828 ]. 0.3037037037025243 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 58.583333333331595 , 58.59999999999826 ]. 0.30740740740620964 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 58.88333333333158 , 58.89999999999824 ]. 0.311111111109895 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 59.166666666664895 , 59.18333333333156 ]. 0.2981481481469146 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 59.46666666666488 , 59.483333333331544 ]. 0.30185185185059993 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 59.76666666666486 , 59.78333333333153 ]. 0.30555555555428526 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 60.066666666664844 , 60.08333333333151 ]. 0.3092592592579706 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 60.36666666666483 , 60.38333333333149 ]. 0.31296296296165593 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 60.649999999998144 , 60.66666666666481 ]. 0.29999999999867555 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 60.94999999999813 , 60.96666666666479 ]. 0.3037037037023609 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 61.24999999999811 , 61.266666666664776 ]. 0.3074074074060462 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 61.54999999999809 , 61.56666666666476 ]. 0.31111111110973155 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 61.83333333333141 , 61.849999999998076 ]. 0.29814814814675117 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 62.13333333333139 , 62.14999999999806 ]. 0.3018518518504365 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 62.433333333331376 , 62.44999999999804 ]. 0.30555555555412184 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 62.73333333333136 , 62.749999999998025 ]. 0.3092592592578072 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 63.03333333333134 , 63.04999999999801 ]. 0.3129629629614925 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 63.31666666666466 , 63.333333333331325 ]. 0.2999999999985121 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 63.61666666666464 , 63.63333333333131 ]. 0.30370370370219746 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 63.916666666664625 , 63.93333333333129 ]. 0.3074074074058828 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 64.21666666666461 , 64.23333333333127 ]. 0.31111111110956813 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 64.49999999999793 , 64.51666666666459 ]. 0.29814814814659485 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 64.79999999999791 , 64.81666666666457 ]. 0.3018518518502873 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 65.09999999999789 , 65.11666666666456 ]. 0.30555555555397973 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 65.39999999999787 , 65.41666666666454 ]. 0.30925925925767217 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 65.69999999999786 , 65.71666666666452 ]. 0.3129629629613646 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 65.98333333333117 , 65.99999999999784 ]. 0.29999999999839133 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 66.28333333333116 , 66.29999999999782 ]. 0.30370370370208377 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 66.58333333333114 , 66.5999999999978 ]. 0.3074074074057762 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 66.88333333333112 , 66.89999999999779 ]. 0.31111111110946865 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 67.16666666666444 , 67.1833333333311 ]. 0.2981481481464954 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 67.46666666666442 , 67.48333333333109 ]. 0.3018518518501878 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 67.7666666666644 , 67.78333333333107 ]. 0.30555555555388025 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 68.06666666666439 , 68.08333333333105 ]. 0.3092592592575727 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 68.36666666666437 , 68.38333333333104 ]. 0.31296296296126513 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 68.64999999999769 , 68.66666666666436 ]. 0.29999999999829186 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 68.94999999999767 , 68.96666666666434 ]. 0.3037037037019843 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 69.24999999999766 , 69.26666666666432 ]. 0.30740740740567674 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 69.54999999999764 , 69.5666666666643 ]. 0.3111111111093692 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 69.83333333333096 , 69.84999999999762 ]. 0.2981481481463959 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 70.13333333333094 , 70.1499999999976 ]. 0.30185185185008834 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 70.43333333333092 , 70.44999999999759 ]. 0.3055555555537808 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 70.7333333333309 , 70.74999999999757 ]. 0.3092592592574732 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 71.03333333333089 , 71.04999999999755 ]. 0.31296296296116566 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 71.3166666666642 , 71.33333333333087 ]. 0.2999999999981924 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 71.61666666666419 , 71.63333333333085 ]. 0.3037037037018848 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 71.91666666666417 , 71.93333333333084 ]. 0.30740740740557726 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 72.21666666666415 , 72.23333333333082 ]. 0.3111111111092697 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 72.49999999999747 , 72.51666666666414 ]. 0.2981481481462964 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 72.79999999999745 , 72.81666666666412 ]. 0.30185185184998886 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 73.09999999999744 , 73.1166666666641 ]. 0.3055555555536813 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 73.39999999999742 , 73.41666666666409 ]. 0.30925925925737374 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 73.6999999999974 , 73.71666666666407 ]. 0.3129629629610662 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 73.98333333333072 , 73.99999999999739 ]. 0.2999999999980929 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 74.2833333333307 , 74.29999999999737 ]. 0.30370370370178534 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 74.58333333333069 , 74.59999999999735 ]. 0.3074074074054778 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 74.88333333333067 , 74.89999999999733 ]. 0.3111111111091702 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 75.16666666666399 , 75.18333333333065 ]. 0.29814814814619695 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 75.46666666666397 , 75.48333333333063 ]. 0.3018518518498894 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 75.76666666666395 , 75.78333333333062 ]. 0.3055555555535818 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 76.06666666666393 , 76.0833333333306 ]. 0.30925925925727427 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 76.36666666666392 , 76.38333333333058 ]. 0.3129629629609667 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 76.64999999999723 , 76.6666666666639 ]. 0.2999999999979934 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 76.94999999999722 , 76.96666666666388 ]. 0.30370370370168587 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 77.2499999999972 , 77.26666666666387 ]. 0.3074074074053783 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 77.54999999999718 , 77.56666666666385 ]. 0.31111111110907075 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 77.8333333333305 , 77.84999999999717 ]. 0.29814814814609747 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 78.13333333333048 , 78.14999999999715 ]. 0.3018518518497899 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 78.43333333333047 , 78.44999999999713 ]. 0.30555555555348235 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 78.73333333333045 , 78.74999999999712 ]. 0.3092592592571748 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 79.03333333333043 , 79.0499999999971 ]. 0.31296296296086723 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 79.31666666666375 , 79.33333333333042 ]. 0.29999999999789395 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 79.61666666666373 , 79.6333333333304 ]. 0.3037037037015864 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 79.91666666666372 , 79.93333333333038 ]. 0.30740740740527883 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 80.2166666666637 , 80.23333333333036 ]. 0.31111111110897127 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 80.49999999999702 , 80.51666666666368 ]. 0.298148148145998 since last P/C. P/C interval = 0.2962962962962963\n", + "Push timing within [ 80.799999999997 , 80.81666666666366 ]. 0.30185185184969043 since last P/C. P/C interval = 0.2962962962962963\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "Push timing within this time step. 0.3037037037019843 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30740740740567674 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3111111111093692 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.2981481481463959 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.30185185185008834 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3055555555537808 since last P/C. P/C interval = 0.2962962962962963\n", - "Push timing within this time step. 0.3092592592574732 since last P/C. 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P/C interval = 0.2962962962962963\n" ] } ], @@ -909,277 +1256,278 @@ "# meanQ = [np.mean([cc.Q for cc in bat3A.processing])]\n", "meanQ = []\n", "for it in range (5000):\n", - " t += dt\n", " bat3A.update(dt)\n", " meanQ.append(np.mean([cc.Q for cc in bat3A.processing]))" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 44, "id": "a86acbef", - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-1155.5555555555554 1417.0111111111137\n", - "-1137.7777777777776 1417.2814814814844\n", - "-1119.9999999999995 1417.5518518518552\n", - "-1102.2222222222217 1417.822222222226\n", - "-1084.444444444444 1416.87592592593\n", - "-1066.666666666666 1417.1462962963008\n", - "-1048.8888888888882 1417.4166666666715\n", - "-1031.1111111111104 1417.6870370370423\n", - "-1013.3333333333325 1416.7407407407463\n", - "-995.5555555555546 1417.011111111117\n", - "-977.7777777777768 1415.4662592592636\n", - 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1428.3666666666081 42\n", + "3408.666666666634 1428.3666666666081 47\n", + "3426.4444444444116 1427.1499999999414 52\n", + "3444.2222222221894 1427.1499999999414 57\n", + "3461.9999999999673 1427.1499999999414 62\n", + "3479.777777777745 1428.3666666666081 4\n", + "3497.555555555523 1427.1499999999414 9\n", + "3515.3333333333007 1427.1499999999414 14\n", + "3533.1111111110786 1427.1499999999414 19\n", + "3550.888888888857 1428.3666666666081 24\n", + "3568.6666666666347 1428.3666666666081 29\n", + "3586.4444444444125 1427.1499999999414 34\n", + "3604.2222222221903 1427.1499999999414 39\n", + "3621.999999999968 1427.1499999999414 44\n", + "3639.777777777746 1428.3666666666081 49\n", + "3657.555555555524 1427.1499999999414 54\n", + "3675.3333333333017 1427.1499999999414 59\n", + "3693.1111111110795 1427.1499999999414 64\n", + "3710.8888888888573 1428.3666666666081 1\n", + "3728.666666666635 1428.3666666666081 6\n", + "3746.444444444413 1427.1499999999414 11\n", + "3764.222222222191 1427.1499999999414 16\n", + "3781.9999999999686 1427.1499999999414 21\n", + "3799.7777777777465 1428.3666666666081 26\n", + "3817.5555555555243 1427.1499999999414 31\n" ] } ], "source": [ "for cc in bat3A.product:\n", - " print(cc.t_charge * 60, cc.Q)" + " print(cc.t_charge * 60, cc.Q, cc.idx_oven)" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 24, "id": "1179dfeb", "metadata": {}, "outputs": [ @@ -1204,7 +1552,7 @@ " 62.29 , 67.54 , 67.54 , 73. , 73. ]])" ] }, - "execution_count": 16, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1215,7 +1563,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 25, "id": "3ed23b12", "metadata": { "scrolled": false @@ -1224,18 +1572,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8950e306f2b64631be0bdcafc0129056", + "model_id": "424e28a4583344f28258eb8a64f1afc6", "version_major": 2, "version_minor": 0 }, - "image/png": 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"f729effef0e942dcb9e4200c7a1d6812", "version_major": 2, "version_minor": 0 }, @@ -1425,7 +1773,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 30, "id": "1ea1b3e5", "metadata": {}, "outputs": [], @@ -1441,7 +1789,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 31, "id": "245eff1f", "metadata": {}, "outputs": [], @@ -1473,7 +1821,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 32, "id": "d79e655e", "metadata": {}, "outputs": [ @@ -1492,7 +1840,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 33, "id": "1a04c477", "metadata": {}, "outputs": [ @@ -1502,7 +1850,7 @@ "(5, 3)" ] }, - "execution_count": 25, + "execution_count": 33, "metadata": {}, "output_type": "execute_result" } @@ -1517,7 +1865,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 34, "id": "51040443", "metadata": {}, "outputs": [ @@ -1542,7 +1890,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "id": "37535ff4", "metadata": { "scrolled": false @@ -1551,7 +1899,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "eeb5f29cbfc345a79623104061cd0ad5", + "model_id": "7f9afee1aefd4bbdbddfcfcfb119f852", "version_major": 2, "version_minor": 0 }, @@ -1593,7 +1941,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 36, "id": "32f3b457", "metadata": {}, "outputs": [ @@ -1623,7 +1971,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Combustion\\AppData\\Local\\Temp\\ipykernel_33276\\1486307578.py:134: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_89912\\1486307578.py:134: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", " plt.grid(b=True, which='minor', color='r', linestyle='--')\n" ] }, @@ -1633,14 +1981,14 @@ "(1120.0, 1320.0)" ] }, - "execution_count": 28, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "82a9416cc7d04c76ae2e9a1c42d83965", + "model_id": "608f9e6e064c44968ea374cec51d72b0", "version_major": 2, "version_minor": 0 }, @@ -1820,7 +2168,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 37, "id": "35f7f4af", "metadata": {}, "outputs": [ @@ -1850,7 +2198,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Combustion\\AppData\\Local\\Temp\\ipykernel_33276\\1371208751.py:132: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_89912\\1371208751.py:132: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", " plt.grid(b=True, which='minor', color='r', linestyle='--')\n" ] }, @@ -1860,14 +2208,14 @@ "(1120.0, 1320.0)" ] }, - "execution_count": 29, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d12970af28fb4afcad0da181d82b655a", + "model_id": "20961405d6014c2b8432079015766cc4", "version_major": 2, "version_minor": 0 }, @@ -2037,7 +2385,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 38, "id": "01cba2bf", "metadata": { "scrolled": false @@ -2049,7 +2397,7 @@ "160.4006440677966" ] }, - "execution_count": 30, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -2060,7 +2408,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 39, "id": "417a4faf", "metadata": { "scrolled": false @@ -2072,7 +2420,7 @@ "159.0" ] }, - "execution_count": 31, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" } @@ -2083,7 +2431,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 40, "id": "4ac4926b", "metadata": {}, "outputs": [ @@ -2094,7 +2442,7 @@ " 15., 9., 9.])" ] }, - "execution_count": 32, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -2105,7 +2453,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 41, "id": "58fb00cb", "metadata": {}, "outputs": [ @@ -2130,7 +2478,7 @@ " [ 9.00000000e+00, 9.00000000e+00, 0.00000000e+00]])" ] }, - "execution_count": 33, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -2143,7 +2491,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 42, "id": "2d1a860e", "metadata": {}, "outputs": [ @@ -2151,7 +2499,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Combustion\\AppData\\Local\\Temp\\ipykernel_33276\\1001734892.py:1: RuntimeWarning: invalid value encountered in true_divide\n", + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_89912\\1001734892.py:1: RuntimeWarning: invalid value encountered in true_divide\n", " 100 * (np.array(cosp.step_sizes) * 60 / 20 - np.round(np.array(cosp.step_sizes) * 60 / 20)) / (np.array(cosp.step_sizes) * 60 / 20)\n" ] }, @@ -2164,7 +2512,7 @@ " 0. ])" ] }, - "execution_count": 34, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } @@ -2183,7 +2531,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 43, "id": "0fa97f83", "metadata": {}, "outputs": [ @@ -2213,7 +2561,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\Combustion\\AppData\\Local\\Temp\\ipykernel_33276\\4051397401.py:170: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", + "C:\\Users\\RIST\\AppData\\Local\\Temp\\ipykernel_89912\\4051397401.py:170: MatplotlibDeprecationWarning: The 'b' parameter of grid() has been renamed 'visible' since Matplotlib 3.5; support for the old name will be dropped two minor releases later.\n", " plt.grid(b=True, which='minor', color='r', linestyle='--')\n" ] }, @@ -2223,489 +2571,14 @@ "(1120.0, 1320.0)" ] }, - "execution_count": 35, + "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "305b7c7f68c04b69848cc330c12a0654", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "class CokeOvenServiceProgram:\n", - " \"\"\" as implemented in excel sheet provided \"\"\"\n", - " def __init__ (self, duration, start, load, T0, q0):\n", - " self.start = start\n", - " self.end = start + duration\n", - " self.duration = duration\n", - " self.load = load\n", - " self.T0 = T0\n", - " self.q0 = q0\n", - " \n", - " self.a = 0.6 - (self.duration - 4) * 0.01 # 최소 열량 / 최대 열량 비율\n", - " self.b = 6 + self.duration / 2 # 감급 시작에서 감급 완료까지 시간, 정수 -6 에서 감급 시작 정수 중간 시각에 감급 완료\n", - "\n", - " t5 = self.lowest_heat_duration()\n", - " q5 = self.q0 * self.a\n", - "\n", - " t1 = self.b / 4\n", - " t2 = self.b / 4\n", - " t3 = self.b / 4\n", - " t4 = self.b / 3.5\n", - "\n", - " t40 = 13 / 4\n", - " t30 = t40 * 0.8\n", - " t20 = t30 * 0.8\n", - " t10 = t20 * 0.8\n", - " t00 = self.b / 5.9\n", - "\n", - " tt1 = 3\n", - " tt2 = 3\n", - " tt3 = 5\n", - " tt20 = 3\n", - " tt10 = 3\n", - "\n", - " q1 = q0 - 1 * (q0 - q5) / 3.7 \n", - " q2 = q0 - 2 * (q0 - q5) / 3.8\n", - " q3 = q0 - 3 * (q0 - q5) / 3.9\n", - " q4 = q0 - 3.7 * (q0 - q5) / 4\n", - "\n", - " q40 = q4\n", - " q30 = q3\n", - " q20 = q2\n", - " q10 = q1\n", - " q00 = q0\n", - " \n", - " qq1 = q00 * 0.9\n", - " qq2 = q00 * 0.83\n", - " qq3 = q00 * 0.77\n", - " qq20 = qq2\n", - " qq10 = qq1\n", - " \n", - " T1 = T0 - (q0 - q1) / (0.07 * 4.184) * 0.6\n", - " T2 = T0 - (q0 - q2) / (0.07 * 4.184) * 0.6\n", - " T3 = T0 - (q0 - q3) / (0.07 * 4.184) * 0.6 \n", - " T4 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", - " \n", - " T5 = T0 - (q0 - q5) / (0.07 * 4.184) * 0.6\n", - " \n", - " T40 = T0 - (q0 - q4) / (0.07 * 4.184) * 0.6\n", - " T30 = T0 - (q0 - q30) / (0.07 * 4.184) * 0.6\n", - " T20 = T0 - (q0 - q20) / (0.07 * 4.184) * 0.6\n", - " T10 = T0 - (q0 - q10) / (0.07 * 4.184) * 0.6\n", - " T00 = T0 - (q0 - q00) / (0.07 * 4.184) * 0.6\n", - " \n", - " TT1 = T0 - (q0 - qq1) / (0.07 * 4.184) * 0.6\n", - " TT2 = T0 - (q0 - qq2) / (0.07 * 4.184) * 0.6\n", - " TT3 = T0 - (q0 - qq3) / (0.07 * 4.184) * 0.6\n", - " \n", - " TT20 = T0 - (q0 - qq20) / (0.07 * 4.184) * 0.6\n", - " TT10 = T0 - (q0 - qq10) / (0.07 * 4.184) * 0.6\n", - " \n", - " self.step_sizes = [0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10]\n", - " \n", - " self.step_sizes = np.round(np.array(self.step_sizes) * 60 / 20) / 3\n", - " \n", - " self.step_times = np.cumsum([0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10])\n", - " self.step_times = np.cumsum(self.step_sizes)\n", - " \n", - " self.step_qs = np.array([q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, q0])\n", - " \n", - " self.step_temperatures = [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", - " \n", - " if False: # use_old_temperature:\n", - " self.step_temperatures = self.calculate_temperatues_old()\n", - " \n", - " self.step_q_from_to = list(zip(self.step_qs[:-1], self.step_qs[1:]))\n", - " \n", - " for pair in zip(self.step_times, self.step_q_from_to):\n", - " print(pair)\n", - " \n", - " self.program = list(zip(self.step_times, self.step_q_from_to))\n", - " \n", - " self.drying_time = 24. / load\n", - " \n", - " self.aux_duration = (q0 * self.step_times[9] - (self.step_sizes.ravel()[:10] * self.step_qs[:10]).sum()) / 2 / qq3\n", - "\n", - " def lowest_heat_duration (self):\n", - "\n", - " t = self.duration\n", - " t5 = t\n", - "\n", - " if t > 14 :\n", - " t5 = t - 6\n", - " elif t > 12 :\n", - " t5 = t - 5\n", - " elif t > 10 :\n", - " t5 = t - 4\n", - " elif t > 8 :\n", - " t5 = t - 3\n", - " elif t > 7 :\n", - " t5 = t - 2\n", - " elif t > 6 :\n", - " t5 = t - 1\n", - " elif t < 5 :\n", - " t5 = t + 1\n", - "\n", - " return t5\n", - " \n", - " def calculate_temperatues_old (self):\n", - " t0, t1, t2, t3, t4, t5, t40, t30, t20, t10, t00, tt1, tt2, tt3, tt20, tt10 = self.step_sizes\n", - " q0, q1, q2, q3, q4, q5, q40, q30, q20, q10, q00, qq1, qq2, qq3, qq20, qq10, qq00 = self.step_qs\n", - " \n", - " T0 = self.T0\n", - " \n", - " T1 = T0 - (q0 - q1) * t1 * 3 / (0.4 * 4.18) * 0.33\n", - " T2 = T0 - (q0 - q2) * t2 * 3 / (0.4 * 4.18) * 0.33\n", - " T3 = T0 - (q0 - q3) * t3 * 3 / (0.4 * 4.18) * 0.3\n", - " T4 = T0 - (q0 - q4) * t4 * 3 / (0.4 * 4.18) * 0.25\n", - " \n", - " T5 = T4 + 2 * t5\n", - " \n", - " T40 = T5 + (q40 - q5 ) * t40 * 3 / (0.4 * 4.18) * 0.33\n", - " T30 = T40 + (q30 - q40) * t30 * 3 / (0.4 * 4.18) * 0.33\n", - " T20 = T30 + (q20 - q30) * t20 * 3 / (0.4 * 4.18) * 0.33\n", - " T10 = T20 + (q10 - q20) * t10 * 3 / (0.4 * 4.18) * 0.33\n", - " T00 = T10 + (q00 - q10) * t00 * 3 / (0.4 * 4.18) * 0.33\n", - " \n", - " TT1 = T00 + (qq1 - q00) * tt1 * 3 / (0.4 * 4.18) * 0.33\n", - " TT2 = TT1 + (qq2 - qq1) * tt2 * 3 / (0.4 * 4.18) * 0.33\n", - " TT3 = TT2 + (qq3 - qq2) * tt3 * 3 / (0.4 * 4.18) * 0.33\n", - " \n", - " TT20 = TT3 + (qq20 - qq3) * tt20 * 3 / (0.4 * 4.18) * 0.33\n", - " TT10 = TT20 + (qq10 - qq20) * tt10 * 3 / (0.4 * 4.18) * 0.33\n", - " \n", - " return [T0, T1 , T2 , T3 , T4 , T5 , T40, T30, T20, T10, T00, TT1, TT2, TT3, TT2, TT1,]\n", - "\n", - "duration = 16\n", - "start = 6\n", - "load = 81 / 66\n", - "T0 = 1212\n", - "q0 = 81\n", - "\n", - "cosp = CokeOvenServiceProgram(duration, start, load, T0, q0)\n", - "cosp.step_temperatures = cosp.calculate_temperatues_old()\n", - "\n", - "from functools import reduce\n", - "\n", - "program_arr = np.array(reduce(lambda x, y: x+y,[ [(t, q0), (t, q1)] for t, (q0, q1) in cosp.program]))\n", - "\n", - "import matplotlib.ticker \n", - "\n", - "plt.figure(figsize=(10, 4))\n", - "plt.plot(program_arr.T[0] * 60, program_arr.T[1])\n", - "ax = plt.gca()\n", - "ax.xaxis.set_major_locator(matplotlib.ticker.MultipleLocator(60))\n", - "ax.xaxis.set_minor_locator(matplotlib.ticker.MultipleLocator(20))\n", - "plt.xticks(np.arange(0, 60)*60, np.arange(0, 60), rotation=90)\n", - "# plt.xticks(rotation=90)\n", - "# plt.minorticks_on()\n", - "plt.grid()\n", - "plt.grid(b=True, which='minor', color='r', linestyle='--')\n", - "\n", - "plt.axvline((start)*60)\n", - "plt.axvline((start+duration)*60)\n", - "\n", - "plt.axvline((start+duration+cosp.drying_time)*60)\n", - "plt.axvline((start+duration+cosp.drying_time+cosp.aux_duration)*60)\n", - "\n", - "twin = plt.gca().twinx()\n", - "twin.plot(cosp.step_times * 60, cosp.step_temperatures, 'o:')\n", - "twin.set_ylim(1120, 1320)" - ] - }, - { - "cell_type": "markdown", - "id": "fd1817d3", - "metadata": {}, - "source": [ - "# 탄화실 나이 분포" - ] - }, - { - "cell_type": "markdown", - "id": "9c64324b", - "metadata": {}, - "source": [ - "## 장입-추출 시퀀스\n", - "\n", - "$n$ 번 건류실 추출/장입 후 $n+5$ 번 건류실에 추출/장입\n", - "* 1 번에서 시작시 66번까지 진행 이후 3 번에서 시작. $ 1, 6, 11, \\ldots , 66 $\n", - "* 3 번에서 63번까지 진행 이후 5 번에서 시작. $ 3, 6, 11, \\ldots , 63 $\n", - "* 5 번에서 65번까지 진행 이후 2 번에서 시작. $ 5, 6, 11, \\ldots , 65 $\n", - "* 2 번에서 62번까지 진행 이후 4 번에서 시작. $ 2, 6, 11, \\ldots , 62 $\n", - "* 4 번에서 64번까지 진행 이후 다시 1 번에서 시작. $ 4, 9, 14, \\ldots , 64 $\n" - ] - }, - { - "cell_type": "markdown", - "id": "de03e1d6", - "metadata": {}, - "source": [ - "### 탄화실 별 장입 시기" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "99dd258f", - "metadata": {}, - "outputs": [], - "source": [ - "n_oven = 66\n", - "\n", - "oven_idx_sequence = np.zeros(n_oven)\n", - "\n", - "start_indices = [1, 3, 5, 2, 4]\n", - "\n", - "i_order = 0\n", - "for idx0 in np.array(start_indices)-1:\n", - " for idx in range(idx0, n_oven, 5):\n", - " oven_idx_sequence[idx] = i_order\n", - " i_order += 1" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "125f87b0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4bf81a2ed1b34b6ab4e21bc192a8a9b8", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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