100 lines
22 KiB
Plaintext
100 lines
22 KiB
Plaintext
{
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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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"# GridSpec\n",
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"\n",
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"An example demoing gridspec\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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",
|
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"text/plain": [
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"<Figure size 600x400 with 5 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"import matplotlib.pyplot as plt\n",
|
|
"from matplotlib import gridspec\n",
|
|
"\n",
|
|
"plt.figure(figsize=(6, 4))\n",
|
|
"G = gridspec.GridSpec(3, 3)\n",
|
|
"\n",
|
|
"axes_1 = plt.subplot(G[0, :])\n",
|
|
"plt.xticks([])\n",
|
|
"plt.yticks([])\n",
|
|
"plt.text(0.5, 0.5, \"Axes 1\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n",
|
|
"\n",
|
|
"axes_2 = plt.subplot(G[1, :-1])\n",
|
|
"plt.xticks([])\n",
|
|
"plt.yticks([])\n",
|
|
"plt.text(0.5, 0.5, \"Axes 2\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n",
|
|
"\n",
|
|
"axes_3 = plt.subplot(G[1:, -1])\n",
|
|
"plt.xticks([])\n",
|
|
"plt.yticks([])\n",
|
|
"plt.text(0.5, 0.5, \"Axes 3\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n",
|
|
"\n",
|
|
"axes_4 = plt.subplot(G[-1, 0])\n",
|
|
"plt.xticks([])\n",
|
|
"plt.yticks([])\n",
|
|
"plt.text(0.5, 0.5, \"Axes 4\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n",
|
|
"\n",
|
|
"axes_5 = plt.subplot(G[-1, -2])\n",
|
|
"plt.xticks([])\n",
|
|
"plt.yticks([])\n",
|
|
"plt.text(0.5, 0.5, \"Axes 5\", ha=\"center\", va=\"center\", size=24, alpha=0.5)\n",
|
|
"\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()"
|
|
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{
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