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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 文字も書けるよMarkdown"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$$ F = \\int ma $$\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- import系は道具箱、asはその名前付け\n",
"- numpyは行列演算、matplotlibはグラフの描画(plot)\n",
"- %matplotlib inlineはJupyter上でplotするためにいるヤツ"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 7 10]\n",
" [15 22]]\n"
]
}
],
"source": [
"A = np.array([[1,2],[3,4]])\n",
"print(np.dot(A,A))"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x11ce621d0>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"x = np.linspace(-100,100,10000)\n",
"f = lambda a, b, x: a * (x**2) + b\n",
"y = f(3,-1,x)\n",
"fig = plt.figure(figsize=(6, 4))\n",
"ax = fig.add_subplot(1, 1, 1)\n",
"ax.plot(x, y, label=\"function\")\n",
"ax.set_title(\"test\")\n",
"ax.set_xlabel(\"x axis\")\n",
"ax.set_ylabel(\"y axis\")\n",
"plt.legend()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [conda env:py35]",
"language": "python",
"name": "conda-env-py35-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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