172 lines
13 KiB
Text
172 lines
13 KiB
Text
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"from matplotlib import pyplot as plt\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[333.33333333333337,\n",
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" 224.47979543556363,\n",
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" 153.3852391583965,\n",
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" 106.95187530214611,\n",
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" 76.625259708662398,\n",
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" 56.818303309764161,\n",
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" 43.881959635722509,\n",
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" 33.881959635722509,\n",
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" 23.881959635722509,\n",
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" 13.881959635722509,\n",
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" 3.8819596357225095,\n",
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]
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},
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"execution_count": 38,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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MbEBUfNDPmzaGto5g7dZX0y7FzKwkKj7oz52anDjl4Rszy6iKD/qx9cOYmatn9Ys+ojez\nbKr4oIf8OP3ql/YSEWmXYmY24Bz05MfpXzl4jM17DqVdipnZgHPQ4wucmVm2OeiB03OjaBhe4+ve\nmFkmOeiBqioxb9oY/l/Lbo/Tm1nm9Br0ku6UtEvSuoK2sZKWS3o+eR6TtEvSrZJaJK2VNLeUxQ+k\ny2afwuY9h1j/sm9EYmbZUswR/beBBZ3abgZWRMQsYEXyGuByYFbyWAzcNjBllt6C2adQUyV+/OTL\naZdiZjageg36iPgV8Eqn5oXA0mR5KXBVQft3Im8l0Chp4kAVW0pj6ofx9lnj+Mna7XR0ePjGzLLj\nRMfoJ0TE9mR5BzAhWZ4MbCnotzVpGxKunDOJba8e5okt/lLWzLKj31/GRv7byz4fAktaLKlZUnNr\na2t/yxgQl5x1CnU1VSxb4+EbM8uOEw36nceHZJLnXUn7NmBKQb9Tk7Y3iYglEdEUEU25XO4EyxhY\no+pquPgt4/npU9tpa+9IuxwzswFxokG/DFiULC8C7i9ovy6ZfXM+sK9giGdI+IO3TmL3gWOs3NT5\nawkzs6GpmOmVdwOPAGdI2irpeuDvgEskPQ+8K3kN8ACwCWgBbgf+a0mqLqHfP3M8o+pqPPvGzDKj\nprcOEXFtN6su7qJvADf2t6g0Da+t5tKzJvDguu38zVWzqaupTrskM7N+8ZmxXfiDcyax/0gb//e5\n3WmXYmbWbw76Llw4axyNI2v58VoP35jZ0Oeg70JtdRWXnz2R5U/v9L1kzWzIc9B348pzJnHoWDsr\nnt2ZdilmZv3ioO/G/BljGd9Q55OnzGzIc9B3o7pKvPutE/nFhlb2H3k97XLMzE6Yg74HV54ziWPt\nHTy0bkfapZiZnTAHfQ/mTGlkytgR/HjtkDq518zsDRz0PZDEledM4t9bdvPC7oNpl2NmdkIc9L34\n0NtmMKy6iluWP5d2KWZmJ8RB34tcQx0fuXA6P37yZda/vC/tcszM+sxBX4TF7ziNk0bU8uWHNqRd\niplZnznoi3DSiFr+5KLT+MWGVh7dtCftcszM+sRBX6RFF0xnwug6/v6hDeQv0mlmNjQ46Is0Ylg1\n/+3iWax6cS8/f3ZX7xuYmQ0SDvo+uLppCtNPHsmXH9pAR4eP6s1saHDQ90FtdRV/dukZPLvjNZb5\nDlRmNkQ46PvoPb87kbMmjuaW5c9xrM03EDezwc9B30dVVeIvLjuDl145xPcefyntcszMeuWgPwEX\nnZFj/vSx3PrzFg4ebUu7HDOzHjnoT4AkPn35mew5cJRP/WCtp1ua2aDWr6CXtFnSU5LWSGpO2sZK\nWi7p+eR5zMCUOrjMmzaGTy84k58+tZ2v/2Jj2uWYmXVrII7ofz8i5kREU/L6ZmBFRMwCViSvM2nx\nO2Zy5TmT+IefbeDnvuWgmQ1SpRi6WQgsTZaXAleV4D0GBUl86Q/fylkTR/Pxu9fQsutA2iWZmb1J\nf4M+gJ9JWiVpcdI2ISKO36ljBzChqw0lLZbULKm5tbW1n2WkZ8SwapZc18SwmioWf6eZfYd920Ez\nG1z6G/QXRsRc4HLgRknvKFwZ+W8pu/ymMiKWRERTRDTlcrl+lpGuyY0juO0D83jplUN84p4naPdZ\ns2Y2iPQr6CNiW/K8C7gPmA/slDQRIHmuiAvDzJ8xlr++cjYPb2jlKz/z5YzNbPA44aCXVC+p4fgy\ncCmwDlgGLEq6LQLu72+RQ8UHzpvKtfOn8PVfbOTex7ekXY6ZGQA1/dh2AnCfpOM/518j4v9Iehy4\nV9L1wIvA1f0vc2iQxBeuPJutew/zqR+u5WhbOx+8YHraZZlZhTvhoI+ITcA5XbTvAS7uT1FD2bCa\nKm6/romb/nU1f3X/eg4da+eG/3ha2mWZWQXzmbElMLy2mts+MI/3vHUi//PBZ7ll+XM+e9bMUtOf\noRvrQW11Ff94zbmMqK3m1hXPc+hoG59991tIhrrMzMrGQV9C1VX5E6pGDqvmW79+gUOvt/O3C8+m\nqsphb2bl46Avsaoq8fkrZzNiWA3f+OVGDh9r58t/9FZqqj1qZmbl4aAvA0l8esEZ1A+r5ivLn+PI\n6+384zXnMqzGYW9mpeekKRNJ/OnFs/jv734LD67bweK7mjnyenvaZZlZBXDQl9lH3z6T//He3+WX\nz7XyoX9+jAO+cYmZlZiDPgXvP28qt1x9Do9v3ssH73jUF0Izs5Jy0KfkveeeytfeP5d12/Zx7ZKV\n7DlwNO2SzCyjHPQpWnD2Kdx+XRMbWw/wviUr2bn/SNolmVkGOehTdtEZ41n6kflsf/UwV3/zEbbu\nPZR2SWaWMQ76QeD8mSdz10fPY+/BY1z9jUd4YffBtEsyswxx0A8Sc6eO4e7F53OkrYM//sYjbNjx\nWtolmVlGOOgHkdmTTuLeG86nSvC+JY/w1NZ9aZdkZhngoB9kTh/fwPc/dgH1w2p4/+0rad78Stol\nmdkQ56AfhKadXM/3P3YBuYY6PnjHY/x7y+60SzKzIcxBP0hNahzB9264gGknj+TD336cFc/sTLsk\nMxuiHPSDWK6hjrv/y/mceUoDN9y1ip+u3Z52SWY2BDnoB7kx9cP4l4+ex7lTG/nTu1fzg1Vb0y7J\nzIYYB/0QMHp4LUs/Mp+3nTaOT37/Se56ZHPaJZnZEOKgHyJGDqvhW4uaeNdbxvNX96/nm7/c6PvQ\nmllRShb0khZI2iCpRdLNpXqfStL5puOX/a9f8c1fbvQ1csysRyrFUaGkauA54BJgK/A4cG1EPN1V\n/6ampmhubh7wOrKqvSO4t3kL32/ewuqXXqVKcOGsHH84dzKXzT6F4bXVaZdoZmUgaVVENPXWr1S3\nEpwPtETEpqSYe4CFQJdBb31TXSWunT+Va+dPZVPrAX60ehv3PbGNj9+zhlF1NUw8aXjaJZpZkd73\nH6bw0bfPLOl7lCroJwNbCl5vBc4r7CBpMbAYYOrUqSUqI/tm5kbxycvO4M8u+R1WbtrDj9duZ9/h\nY2mXZWZFGjeqruTvkdrNwSNiCbAE8kM3adWRFVVV4m2nj+Ntp49LuxQzG2RK9WXsNmBKwetTkzYz\nMyuzUgX948AsSTMkDQOuAZaV6L3MzKwHJRm6iYg2STcBDwHVwJ0Rsb4U72VmZj0r2Rh9RDwAPFCq\nn29mZsXxmbFmZhnnoDczyzgHvZlZxjnozcwyriTXuulzEVIr8GLadXRjHDCY7+U32OuDwV+j6+sf\n19c//alvWkTkeus0KIJ+MJPUXMxFg9Iy2OuDwV+j6+sf19c/5ajPQzdmZhnnoDczyzgHfe+WpF1A\nLwZ7fTD4a3R9/eP6+qfk9XmM3sws43xEb2aWcQ56M7OMc9ADkqZIeljS05LWS/p4F30ukrRP0prk\n8bky17hZ0lPJe7/pBrvKuzW5GftaSXPLWNsZBftljaT9kj7RqU/Z95+kOyXtkrSuoG2spOWSnk+e\nx3Sz7aKkz/OSFpWxvi9Lejb5b3ifpMZutu3x81DC+j4vaVvBf8crutl2gaQNyefx5jLW972C2jZL\nWtPNtiXdf91lSmqfv4io+AcwEZibLDeQv7H5WZ36XAT8JMUaNwPjelh/BfAgIOB84NGU6qwGdpA/\nkSPV/Qe8A5gLrCto+3vg5mT5ZuBLXWw3FtiUPI9JlseUqb5LgZpk+Utd1VfM56GE9X0e+GQRn4GN\nwExgGPBk5/+fSlVfp/VfAT6Xxv7rLlPS+vz5iB6IiO0RsTpZfg14hvx9b4eShcB3Im8l0ChpYgp1\nXAxsjIjUz3SOiF8Br3RqXggsTZaXAld1sellwPKIeCUi9gLLgQXlqC8ifhYRbcnLleTvzpaKbvZf\nMeYDLRGxKSKOAfeQ3+8Dqqf6JAm4Grh7oN+3GD1kSiqfPwd9J5KmA+cCj3ax+gJJT0p6UNLsshYG\nAfxM0qrkxuqddXVD9jT+sbqG7v/nSnP/HTchIrYnyzuACV30GSz78iPk/0rrSm+fh1K6KRlaurOb\noYfBsP/eDuyMiOe7WV+2/dcpU1L5/DnoC0gaBfwQ+ERE7O+0ejX54YhzgH8C/neZy7swIuYClwM3\nSnpHmd+/V8ltI68Evt/F6rT335tE/u/kQTm/WNJngTbgu910SevzcBtwGjAH2E5+eGQwupaej+bL\nsv96ypRyfv4c9AlJteT/g3w3In7UeX1E7I+IA8nyA0CtpHHlqi8itiXPu4D7yP95XGgw3JD9cmB1\nROzsvCLt/Vdg5/EhreR5Vxd9Ut2Xkj4EvAf4z0kYvEkRn4eSiIidEdEeER3A7d28b9r7rwb4T8D3\nuutTjv3XTaak8vlz0POb8bw7gGci4pZu+pyS9EPSfPL7bk+Z6quX1HB8mfwXdus6dVsGXJfMvjkf\n2FfwJ2K5dHsUleb+62QZcHwWwyLg/i76PARcKmlMMjRxadJWcpIWAJ8CroyIQ930KebzUKr6Cr/3\neW837/s4MEvSjOSvvGvI7/dyeRfwbERs7WplOfZfD5mSzuevVN86D6UHcCH5P6HWAmuSxxXAx4CP\nJX1uAtaTn0GwEnhbGeubmbzvk0kNn03aC+sT8DXysx2eAprKvA/ryQf3SQVtqe4/8v/obAdeJz/O\neT1wMrACeB74N2Bs0rcJ+FbBth8BWpLHh8tYXwv58dnjn8NvJH0nAQ/09HkoU313JZ+vteRDa2Ln\n+pLXV5CfabKxnPUl7d8+/rkr6FvW/ddDpqTy+fMlEMzMMs5DN2ZmGeegNzPLOAe9mVnGOejNzDLO\nQW9mlnEOejOzjHPQm5ll3P8Hj3Wm+Asa3qIAAAAASUVORK5CYII=\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7ff60ef169e8>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"x = np.linspace(1, 20)\n",
|
|
"y = []\n",
|
|
"\n",
|
|
"w = 500\n",
|
|
"for _x in x:\n",
|
|
" d = 500/3**_x\n",
|
|
" d = 10 if d < 10 else d\n",
|
|
" \n",
|
|
" if w > d:\n",
|
|
" w -= d\n",
|
|
" y.append(w)\n",
|
|
" else: \n",
|
|
" y.append(0)\n",
|
|
"\n",
|
|
"plt.plot(x,y)\n",
|
|
"y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 13,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Help on class map in module builtins:\n",
|
|
"\n",
|
|
"class map(object)\n",
|
|
" | map(func, *iterables) --> map object\n",
|
|
" | \n",
|
|
" | Make an iterator that computes the function using arguments from\n",
|
|
" | each of the iterables. Stops when the shortest iterable is exhausted.\n",
|
|
" | \n",
|
|
" | Methods defined here:\n",
|
|
" | \n",
|
|
" | __getattribute__(self, name, /)\n",
|
|
" | Return getattr(self, name).\n",
|
|
" | \n",
|
|
" | __iter__(self, /)\n",
|
|
" | Implement iter(self).\n",
|
|
" | \n",
|
|
" | __new__(*args, **kwargs) from builtins.type\n",
|
|
" | Create and return a new object. See help(type) for accurate signature.\n",
|
|
" | \n",
|
|
" | __next__(self, /)\n",
|
|
" | Implement next(self).\n",
|
|
" | \n",
|
|
" | __reduce__(...)\n",
|
|
" | Return state information for pickling.\n",
|
|
"\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"help(map)"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.6.2"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|