2019-03-11 10:15:30 +00:00
|
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{
|
|
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"cells": [
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-03-18 07:24:14 +00:00
|
|
|
"execution_count": 7,
|
2019-03-11 10:15:30 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
2019-03-18 07:24:14 +00:00
|
|
|
"image/png": "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
|
2019-03-11 10:15:30 +00:00
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
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|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
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|
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},
|
|
|
|
|
"output_type": "display_data"
|
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|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"from math import cos, pi\n",
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
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|
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|
"\n",
|
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|
|
|
"def f(x):\n",
|
2019-03-18 07:24:14 +00:00
|
|
|
" return x**2 - 10*x + 20\n",
|
2019-03-11 10:15:30 +00:00
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|
"\n",
|
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|
|
"images = []\n",
|
|
|
|
|
"antecedants = []\n",
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"\n",
|
2019-03-18 07:24:14 +00:00
|
|
|
"for x in range(10):\n",
|
2019-03-11 10:15:30 +00:00
|
|
|
" images.append(f(x))\n",
|
|
|
|
|
" antecedants.append(x)\n",
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"\n",
|
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|
|
"plt.plot(antecedants, images)\n",
|
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|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2019-03-18 07:24:14 +00:00
|
|
|
"execution_count": 11,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
|
|
"image/png": "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
|
|
|
|
|
"text/plain": [
|
|
|
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"metadata": {
|
|
|
|
|
"needs_background": "light"
|
|
|
|
|
},
|
|
|
|
|
"output_type": "display_data"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"from math import cos, pi\n",
|
|
|
|
|
"import matplotlib.pyplot as plt\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"def g(x):\n",
|
|
|
|
|
" return -(x - 5)**2 + 5\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"images = []\n",
|
|
|
|
|
"antecedants = []\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"for x in range(15):\n",
|
|
|
|
|
" images.append(g(x))\n",
|
|
|
|
|
" antecedants.append(x)\n",
|
|
|
|
|
"\n",
|
|
|
|
|
"plt.plot(antecedants, images)\n",
|
|
|
|
|
"plt.show()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 2,
|
2019-03-11 10:15:30 +00:00
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"data": {
|
|
|
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"image/png": "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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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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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"from math import sin, pi\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"def f(x):\n",
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" return sin(x*pi/2)\n",
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"\n",
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"min_x = -2\n",
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"max_x = 2\n",
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"\n",
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"images = []\n",
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"antecedants = []\n",
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"\n",
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"x = min_x\n",
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"while x <= max_x:\n",
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" antecedants.append(x)\n",
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" images.append(f(x))\n",
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" x += 1\n",
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"\n",
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"plt.plot(antecedants, images)\n",
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"\n",
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"\n",
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"images = []\n",
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"antecedants = []\n",
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"\n",
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|
"x = min_x\n",
|
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|
|
|
"while x <= max_x:\n",
|
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|
|
|
" antecedants.append(x)\n",
|
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|
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" images.append(f(x))\n",
|
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|
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" x += 0.5\n",
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"\n",
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"plt.plot(antecedants, images)\n",
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"\n",
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"\n",
|
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|
"images = []\n",
|
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|
"antecedants = []\n",
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"\n",
|
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|
|
|
"x = min_x\n",
|
|
|
|
|
"while x <= max_x:\n",
|
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|
|
|
" antecedants.append(x)\n",
|
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|
|
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" images.append(f(x))\n",
|
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|
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" x += 0.1\n",
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"\n",
|
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|
|
"plt.plot(antecedants, images)\n",
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"\n",
|
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|
|
"plt.show()"
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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": null,
|
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|
|
|
"metadata": {},
|
|
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|
|
"outputs": [],
|
|
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|
|
"source": []
|
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|
}
|
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|
],
|
|
|
|
|
"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": "3.7.2"
|
|
|
|
|
}
|
|
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|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
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|
"nbformat_minor": 2
|
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}
|
