87 lines
55 KiB
Plaintext
87 lines
55 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n",
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"# Population exercise\n",
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"\n",
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"Plot populations of hares, lynxes, and carrots\n"
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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": 1,
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"metadata": {
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"collapsed": false,
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"jupyter": {
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"outputs_hidden": false
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}
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 640x480 with 1 Axes>"
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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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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"\n",
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"# 从文本文件载入数据，默认按列分割；返回二维数组 shape=(年份数, 4)\n",
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"data = np.loadtxt(\"populations.txt\")\n",
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"\n",
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"# 将各列分别赋给 year（年份）、hares（野兔）、lynxes（猞猁）、carrots（胡萝卜）\n",
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"year, hares, lynxes, carrots = data.T\n",
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"\n",
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"# 手动创建坐标轴：left=0.2, bottom=0.1, width=0.5, height=0.8（百分比，相对整个图窗）\n",
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"plt.axes((0.2, 0.1, 0.5, 0.8))\n",
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"\n",
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"# 在同一幅图里绘制三条折线：野兔、猞猁、胡萝卜随年份的变化\n",
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"plt.plot(year, hares, year, lynxes, year, carrots)\n",
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"\n",
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"# 添加图例，标签顺序与上面绘图顺序一致；loc=(1.05, 0.5) 把图例放在坐标轴右侧居中\n",
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"plt.legend((\"Hare\", \"Lynx\", \"Carrot\"), loc=(1.05, 0.5))\n",
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"\n",
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"# 显示图形\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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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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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": "3.12.11"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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