84 lines
20 KiB
Plaintext
84 lines
20 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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"# 2D plotting\n",
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"\n",
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"Plot a basic 2D figure\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 2 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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"# 创建一个随机数生成器实例（NumPy 推荐的新写法，可复现）\n",
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"rng = np.random.default_rng()\n",
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"\n",
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"# 生成 30×30 的二维数组，元素为 [0, 1) 区间的均匀随机值，模拟一幅“图像”\n",
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"image = rng.random((30, 30))\n",
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"\n",
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"# 将数组可视化为热力图，颜色映射使用 \"hot\"（黑→红→黄→白）\n",
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"plt.imshow(image, cmap=\"hot\")\n",
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"\n",
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"# 为图像添加右侧的颜色标尺，直观对应数值与颜色\n",
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"plt.colorbar()\n",
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"\n",
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"# 弹出窗口显示最终图形\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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