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129 lines
19 KiB
129 lines
19 KiB
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt"
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 0 Axes>"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 0 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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"plt.figure()"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x206a7258d08>]"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plt.plot([1,2],[3,4])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 7,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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\n",
|
|
"text/plain": [
|
|
"<Figure size 432x288 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {
|
|
"needs_background": "light"
|
|
},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Create Figure (empty canvas)\n",
|
|
"fig = plt.figure()\n",
|
|
"\n",
|
|
"# Add set of axes to figure\n",
|
|
"axes = fig.add_axes([0, 0, 1, 1])"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|