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March 24, 2017 21:58
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{ | |
"cells": [ | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"nbpresent": { | |
"id": "6247e680-5f75-4f80-9dd0-5f494a1c564b" | |
}, | |
"slideshow": { | |
"slide_type": "slide" | |
} | |
}, | |
"source": [ | |
"# Plotting in python" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"nbpresent": { | |
"id": "33c29eae-a04b-45c2-8ef8-b015ba4ec501" | |
}, | |
"slideshow": { | |
"slide_type": "slide" | |
} | |
}, | |
"outputs": [], | |
"source": [ | |
"#Import the necessary packages and modules\n", | |
"import matplotlib.pyplot as plt\n", | |
"import numpy as np" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": { | |
"deletable": true, | |
"editable": true, | |
"nbpresent": { | |
"id": "ba821c52-38f7-4c54-b173-401bbdf5042a" | |
}, | |
"slideshow": { | |
"slide_type": "subslide" | |
} | |
}, | |
"source": [ | |
"The following equation will be plotted\n", | |
"$$f(x) = x$$\n", | |
"This is a linear equation where the input is the same as the output" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"nbpresent": { | |
"id": "a79af47f-1db3-47bb-8bea-9ca38a33aaa4" | |
}, | |
"slideshow": { | |
"slide_type": "subslide" | |
} | |
}, | |
"outputs": [], | |
"source": [ | |
"# Prepare the data\n", | |
"x = np.linspace(0, 10, 100)\n", | |
"\n", | |
"def f(x):\n", | |
" return x**2" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"nbpresent": { | |
"id": "83c20a28-219e-4436-9a2f-e41e555248d5" | |
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"scrolled": false, | |
"slideshow": { | |
"slide_type": "subslide" | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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RcPQY86YOJy1FIe+VKD/ffw/wipk1A3YBt1H3y2OhmU0F9gDj/DyGiISYr4ZQHiqp9LXk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\nAAAAAElFTkSuQmCC\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x110c834e0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"# Plot the data\n", | |
"plt.plot(x, f(x), label='linear')\n", | |
"\n", | |
"# Add a legend\n", | |
"plt.legend()\n", | |
"\n", | |
"# Show the plot\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false, | |
"deletable": true, | |
"editable": true, | |
"nbpresent": { | |
"id": "6e393d78-ded1-4879-a13c-68fa5b412e64" | |
}, | |
"slideshow": { | |
"slide_type": "slide" | |
} | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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"text/plain": [ | |
"<matplotlib.figure.Figure at 0x11475a828>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"from IPython.html.widgets import interact\n", | |
"import matplotlib.pyplot as plt\n", | |
"from numpy import pi, sin\n", | |
"\n", | |
"def pltsin(freq, amp):\n", | |
" plt.plot(x, amp*sin(2*pi*x*freq))\n", | |
" plt.ylim(-10,10)\n", | |
" plt.show()\n", | |
" \n", | |
"interact(pltsin, freq=(1,10,0.1), amp=(1,10,1));" | |
] | |
} | |
], | |
"metadata": { | |
"celltoolbar": "Slideshow", | |
"kernelspec": { | |
"display_name": "Python [conda root]", | |
"language": "python", | |
"name": "conda-root-py" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 3 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython3", | |
"version": "3.6.0" | |
}, | |
"nbpresent": { | |
"slides": { | |
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