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‎plot_axes-2.ipynb

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‎plot_axes.ipynb

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{
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"cells": [
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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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"outputs": [],
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"source": [
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"%matplotlib inline"
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]
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},
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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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"Simple axes example\n",
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"====================\n",
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"\n",
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"This example shows a couple of simple usage of axes.\n",
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"\n",
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"\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": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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\n",
33+
"text/plain": [
34+
"<Figure size 432x288 with 2 Axes>"
35+
]
36+
},
37+
"metadata": {},
38+
"output_type": "display_data"
39+
}
40+
],
41+
"source": [
42+
"import matplotlib.pyplot as plt\n",
43+
"\n",
44+
"plt.axes([.1, .1, .8, .8])\n",
45+
"plt.xticks([])\n",
46+
"plt.yticks([])\n",
47+
"plt.text(.6, .6, 'axes([0.1, 0.1, 0.8, 0.8])', ha='center', va='center',\n",
48+
" size=20, alpha=.5)\n",
49+
"\n",
50+
"plt.axes([.2, .2, .3, .3])\n",
51+
"plt.xticks([])\n",
52+
"plt.yticks([])\n",
53+
"plt.text(.5, .5, 'axes([0.2, 0.2, 0.3, 0.3])', ha='center', va='center',\n",
54+
" size=16, alpha=.5)\n",
55+
"\n",
56+
"plt.show()"
57+
]
58+
},
59+
{
60+
"cell_type": "code",
61+
"execution_count": null,
62+
"metadata": {},
63+
"outputs": [],
64+
"source": []
65+
},
66+
{
67+
"cell_type": "code",
68+
"execution_count": null,
69+
"metadata": {},
70+
"outputs": [],
71+
"source": []
72+
}
73+
],
74+
"metadata": {
75+
"kernelspec": {
76+
"display_name": "Python 3",
77+
"language": "python",
78+
"name": "python3"
79+
},
80+
"language_info": {
81+
"codemirror_mode": {
82+
"name": "ipython",
83+
"version": 3
84+
},
85+
"file_extension": ".py",
86+
"mimetype": "text/x-python",
87+
"name": "python",
88+
"nbconvert_exporter": "python",
89+
"pygments_lexer": "ipython3",
90+
"version": "3.7.3"
91+
}
92+
},
93+
"nbformat": 4,
94+
"nbformat_minor": 1
95+
}

‎plot_bad.ipynb

+91
Original file line numberDiff line numberDiff line change
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1+
{
2+
"cells": [
3+
{
4+
"cell_type": "code",
5+
"execution_count": 1,
6+
"metadata": {},
7+
"outputs": [],
8+
"source": [
9+
"%matplotlib inline"
10+
]
11+
},
12+
{
13+
"cell_type": "markdown",
14+
"metadata": {},
15+
"source": [
16+
"\n",
17+
"A simple plotting example\n",
18+
"==========================\n",
19+
"\n",
20+
"A plotting example with a few simple tweaks\n",
21+
"\n"
22+
]
23+
},
24+
{
25+
"cell_type": "code",
26+
"execution_count": 2,
27+
"metadata": {},
28+
"outputs": [
29+
{
30+
"name": "stderr",
31+
"output_type": "stream",
32+
"text": [
33+
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\figure.py:445: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
34+
" % get_backend())\n"
35+
]
36+
}
37+
],
38+
"source": [
39+
"import numpy as np\n",
40+
"import matplotlib\n",
41+
"matplotlib.use('Agg')\n",
42+
"import matplotlib.pyplot as plt\n",
43+
"\n",
44+
"fig = plt.figure(figsize=(5, 4), dpi=72)\n",
45+
"axes = fig.add_axes([0.01, 0.01, .98, 0.98])\n",
46+
"x = np.linspace(0, 2, 200)\n",
47+
"y = np.sin(2 * np.pi * x)\n",
48+
"plt.plot(x, y, lw=.25, c='k')\n",
49+
"plt.xticks(np.arange(0.0, 2.0, 0.1))\n",
50+
"plt.yticks(np.arange(-1.0, 1.0, 0.1))\n",
51+
"plt.grid()\n",
52+
"plt.show()"
53+
]
54+
},
55+
{
56+
"cell_type": "code",
57+
"execution_count": null,
58+
"metadata": {},
59+
"outputs": [],
60+
"source": []
61+
},
62+
{
63+
"cell_type": "code",
64+
"execution_count": null,
65+
"metadata": {},
66+
"outputs": [],
67+
"source": []
68+
}
69+
],
70+
"metadata": {
71+
"kernelspec": {
72+
"display_name": "Python 3",
73+
"language": "python",
74+
"name": "python3"
75+
},
76+
"language_info": {
77+
"codemirror_mode": {
78+
"name": "ipython",
79+
"version": 3
80+
},
81+
"file_extension": ".py",
82+
"mimetype": "text/x-python",
83+
"name": "python",
84+
"nbconvert_exporter": "python",
85+
"pygments_lexer": "ipython3",
86+
"version": "3.7.3"
87+
}
88+
},
89+
"nbformat": 4,
90+
"nbformat_minor": 1
91+
}

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