142 lines
12 KiB
Text
142 lines
12 KiB
Text
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{
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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": 45,
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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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"array([ 2.71828183e+00, 4.00584723e+00, 5.90329225e+00,\n",
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" 8.69949785e+00, 1.28201789e+01, 1.88926982e+01,\n",
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" 2.78415807e+01, 4.10292700e+01, 6.04635568e+01,\n",
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" 8.91032597e+01, 1.31308697e+02, 1.93505536e+02,\n",
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" 2.85163079e+02, 4.20235945e+02, 6.19288618e+02,\n",
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" 9.12626340e+02, 1.34490900e+03, 1.98195049e+03,\n",
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" 2.92073868e+03, 4.30420159e+03, 6.34296777e+03,\n",
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" 9.34743397e+03, 1.37750222e+04, 2.02998210e+04,\n",
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" 2.99152137e+04, 4.40851183e+04, 6.49668652e+04,\n",
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" 9.57396450e+04, 1.41088532e+05, 2.07917773e+05,\n",
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" 3.06401944e+05, 4.51534998e+05, 6.65413057e+05,\n",
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" 9.80598489e+05, 1.44507744e+06, 2.12956560e+06,\n",
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" 3.13827447e+06, 4.62477730e+06, 6.81539020e+06,\n",
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" 1.00436282e+07, 1.48009819e+07, 2.18117459e+07,\n",
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" 3.21432903e+07, 4.73685653e+07, 6.98055788e+07,\n",
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" 1.02870306e+08, 1.51596765e+08, 2.23403430e+08,\n",
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" 3.29222673e+08, 4.85165195e+08])"
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]
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},
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"execution_count": 45,
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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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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAEDCAYAAAAVyO4LAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAGDJJREFUeJzt3X90XGd95/H3V79tSfEvyY4T25Ht\nhoDTJY5RnRRC+JHihLQlW3raYw5NoaS47IFd2G2Xw273tGz3H9g95XS3y7Z1IIeQZQmwlJZSQh1o\n2iSncTZ2sFLbMdiJ5ciJrRnJljWWrJFm5rt/zIwtKzPSSJq5PzSf1zlzZnTnGc1X11cfP3rmufcx\nd0dEROKjIewCRERkfhTcIiIxo+AWEYkZBbeISMwouEVEYkbBLSISMzULbjN7yMwSZna4grabzOwJ\nM/uxmb1gZvfWqi4RkbirZY/7K8A9Fbb9T8A33f1WYDfwv2pVlIhI3NUsuN39SeDc9G1mttXMfmBm\nB83sKTN7Y7E5cE3h8QrgtVrVJSISd00Bv99e4GPuftzMbiPfs3438Flgn5n9a6Ad+IWA6xIRiY3A\ngtvMOoC3At8ys+Lm1sL9B4CvuPsfm9nPA4+Y2c+6ey6o+kRE4iLIHncDMOLu20s89wCF8XB3f8bM\n2oAuIBFgfSIisRDYdEB3HwVOmtmvAVjeLYWnXwHuKmx/E9AGJIOqTUQkTqxWVwc0s68D7yTfcx4E\n/hD4e+DPgPVAM/Cou/+RmW0DHgQ6yH9Q+Wl331eTwkREYq5mwS0iIrWhMydFRGKmJh9OdnV1eU9P\nTy2+tYjIknTw4MEhd++upG1FwW1m/UAKyAIZd++drX1PTw8HDhyo5FuLiAhgZqcqbTufHve73H1o\nAfWIiEgVaYxbRCRmKg1uJ39K+kEz21OqgZntMbMDZnYgmdQUbBGRWqk0uO9w9x3Ae4GPm9mdMxu4\n+15373X33u7uisbXRURkASoKbnd/tXCfAL4D7KxlUSIiUt6cwW1m7WbWWXwM7ALmXBxBRERqo5JZ\nJeuA7xSu6NcE/B93/0FNqxIRkbLmDG53fxm4Za52IiL17PGjg7yUvMjH3rG15u+l6YAiIlXwg8Nn\nefif+gN5LwW3iEgVJFITrO1snbthFSi4RUSqIJlK093ZFsh7KbhFRKogkUqz9hr1uEVEYmEyk+Pc\n2KSGSkRE4mLoYhqAtRoqERGJh0SqGNzqcYuIxEJidAJAY9wiInFxpcetoRIRkVhIpNKYQVdHSyDv\np+AWEVmkZGqCNe2tNDUGE6kKbhGRRUqMpgP7YBIU3CIiixbkyTeg4BYRWbQgr1MCCm4RkUXJ5pyh\ni5OBzSgBBbeIyKKcG5skm3MNlYiIxEUiVTj5RkMlIiLxkBjNn3wT1CVdQcEtIrIo6nGLiMTMlR63\ngltEJBYSqTQrljXT1twY2HsquEVEFiHoOdyg4BYRWZSgz5oEBbeIyKLkr1MS3IwSUHCLiCyYu5NM\nBXuBKVBwi4gs2IVLU0xmc4HOKAEFt4jIgl1e+eYaDZWIiMRCcQ63hkpERGIijLMmQcEtIrJgGioR\nEYmZxGia5S2NdLQ2Bfq+Cm4RkQUK46xJmEdwm1mjmf3YzL5Xy4JEROIikQr+5BuYX4/7k8CLtSpE\nRCRukqk03QGf7g4VBreZbQB+EfhSbcsREYmPxGi0h0r+BPg0kCvXwMz2mNkBMzuQTCarUpyISFSN\npTOMTWajOVRiZr8EJNz94Gzt3H2vu/e6e293d3fVChQRiaLLUwEj2uN+G/A+M+sHHgXebWb/u6ZV\niYhEXGK0cPJNFMe43f0/uPsGd+8BdgN/7+6/UfPKREQi7EqPO4JDJSIi8nphDpXM63Qfd/8H4B9q\nUomISIwkUhO0NDawcnlz4O+tHreIyAIkR9N0d7ZiZoG/t4JbRGQBEql04AsoFCm4RUQWIKzrlICC\nW0RkQcJY3b1IwS0iMk8TU1lGxqdCmQoICm4RkXlLhjgVEBTcIiLzdmXlGwW3iEgsJC+vNamhEhGR\nWAjzrElQcIuIzFtiNE2DwZoOBbeISCwkUhOs6WilsSH4syZBwS0iMm/5tSbD6W2DgltEZN4Sowpu\nEZFYSaTSrLsmnBkloOAWEZmXTDbH8Jh63CIisTE8Nok7dKvHLSISD4nRcOdwg4JbRGReEpfPmlRw\ni4jEwpXrlGioREQkFopDJd0hnTUJCm4RkXlJpCZYtbyZlqbw4lPBLSIyD/mzJsMbJgEFt4jIvIS5\nZFmRgltEZB5eGR5jw6plodag4BYRqdCF8SnOj0/Rs6Y91DoU3CIiFeofHgOgp0vBLSISC8Xg3qzg\nFhGJh5NDY5jBptXLQ61DwS0iUqH+oTGuW7GMtubGUOtQcIuIVOjk8Dg9XeH2tkHBLSJSsf6hMW4I\neUYJKLhFRCpyfmySC5em2ByH4DazNjP7f2bWZ2ZHzOw/B1GYiEiUnIzIVECApgrapIF3u/tFM2sG\nnjazx9x9f41rExGJjFOXpwKGP8Y9Z3C7uwMXC182F25ey6JERKLm5NA4DQYbQ54KCBWOcZtZo5kd\nAhLA4+7+bIk2e8zsgJkdSCaT1a5TRCRU/UNjXLdyGa1N4U4FhAqD292z7r4d2ADsNLOfLdFmr7v3\nuntvd3d3tesUEQlV//BY6GdMFs1rVom7jwBPAPfUphwRkehxd04OjYV+camiSmaVdJvZysLjZcB7\ngGO1LkxEJCrOjU2SmshEYkYJVDarZD3wsJk1kg/6b7r792pblohIdFy+KuCa8D+YhMpmlbwA3BpA\nLSIikXRyaByIxhxu0JmTIiJzOjU8lp8KuCoaPW4Ft4jIHE4OjbFh1fJQV3afLhpViIhEWP/wWGSG\nSUDBLSIyK3enf2iczRH5YBIU3CIisxq6OMnFdHSmAoKCW0RkVlFZIHg6BbeIyCxODhWuChiRsyZB\nwS0iMqtTw2M0NhjXr1oWdimXKbhFRGbRPzTOxlXLaG6MTlxGpxIRkQg6ORStqYCg4BYRKcvd83O4\nIzS+DQpuEZGykqk045PZyFyHu0jBLSJSRnFGiYZKRERiojiHO0pTAUHBLSJS1smhcZobjetWtoVd\nylUU3CIiZZwaHmPj6uU0RWgqICi4RUTKitI6k9MpuEVESnB3Tg2PK7hFROJicDTNpaksm7uicznX\nIgW3iEgJUZ0KCApuEZGSrqzsruAWEYmF/qExWhobuG5ldK4KWKTgFhEp4eTQGJvWLKexwcIu5XUU\n3CIiJUR1RgkouEVEXieXy18VMIozSkDBLSLyOi8lL5LO5HjDus6wSylJwS0iMsOhgREAbt20MuRK\nSlNwi4jMcGhghM7WJrZ0dYRdSkkKbhGRGfpOj/DmjStoiOCMElBwi4hcZWIqy7EzKW7ZEM1hElBw\ni4hc5chrF8jknO0bYxzcZrbRzJ4ws6NmdsTMPhlEYSIiYTg0cAEg0sHdVEGbDPC77v68mXUCB83s\ncXc/WuPaREQC1zcwwvoVbay9Jlqr3kw3Z4/b3c+4+/OFxyngReD6WhcmIhKGQwMjke5twzzHuM2s\nB7gVeLbEc3vM7ICZHUgmk9WpTkQkQOfGJnnl3Di3LJXgNrMO4NvAp9x9dObz7r7X3Xvdvbe7u7ua\nNYqIBKLvdP7EmyjPKIEKg9vMmsmH9tfc/S9rW5KISDgOvTJCg8GbN6wIu5RZVTKrxIAvAy+6+xdq\nX5KISDj6To9w49pO2lsrmbcRnkp63G8D7gfebWaHCrd7a1yXiEig3J2+gRFu2Rjt3jZUMB3Q3Z8G\nonnep4hIlbxybpzz41Ns37gq7FLmpDMnRUS4ckXAOPS4FdwiIkDfwAXamhu4KaLX4J5OwS0iAhwa\nOM+/uH4FTY3Rj8XoVygiUmNT2RyHXxuN/PztIgW3iNS9n5xNMZnJsT2iK97MpOAWkbr344F4nDFZ\npOAWkbrXNzDCmvYWNqx
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"text/plain": [
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"<matplotlib.figure.Figure at 0x7ff60c7a3588>"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"x = np.linspace(1, 20)\n",
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"y = []\n",
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"\n",
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"w = 500\n",
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"for _x in x:\n",
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" d = 500/1.2**_x\n",
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" d = 10 if d < 10 else d\n",
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" \n",
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" if w > d:\n",
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" w -= d\n",
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" y.append(w)\n",
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" else: \n",
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" y.append(0)\n",
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" \n",
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"y = np.exp\n",
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"\n",
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"plt.plot(x,y)\n",
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"y"
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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": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Help on class map in module builtins:\n",
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"\n",
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"class map(object)\n",
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" | map(func, *iterables) --> map object\n",
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" | \n",
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" | Make an iterator that computes the function using arguments from\n",
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" | each of the iterables. Stops when the shortest iterable is exhausted.\n",
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" | \n",
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" | Methods defined here:\n",
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" | \n",
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" | __getattribute__(self, name, /)\n",
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" | Return getattr(self, name).\n",
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" | \n",
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" | __iter__(self, /)\n",
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" | Implement iter(self).\n",
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" | \n",
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" | __new__(*args, **kwargs) from builtins.type\n",
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" | Create and return a new object. See help(type) for accurate signature.\n",
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" | \n",
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" | __next__(self, /)\n",
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" | Implement next(self).\n",
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" | \n",
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" | __reduce__(...)\n",
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" | Return state information for pickling.\n",
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"\n"
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]
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}
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],
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"source": [
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"help(map)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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