Animations Using clear_output.ipynb
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r6463 | { | ||
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r18669 | "cells": [ | ||
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r6463 | { | ||
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r18669 | "cell_type": "markdown", | ||
"metadata": {}, | ||||
"source": [ | ||||
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r20547 | "# Simple Animations Using clear_output" | ||
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r18669 | ] | ||
}, | ||||
{ | ||||
"cell_type": "markdown", | ||||
"metadata": {}, | ||||
"source": [ | ||||
"Sometimes you want to clear the output area in the middle of a calculation. This can be useful for doing simple animations. In terminals, there is the carriage-return (`'\\r'`) for overwriting a single line, but the notebook frontend can clear the whole output area, not just a single line.\n", | ||||
"\n", | ||||
"To clear output in the Notebook you can use the `clear_output()` function. If you are clearing the output every frame of an animation, calling `clear_output()` will create noticeable flickering. You can use `clear_output(wait=True)` to add the *clear_output* call to a queue. When data becomes available to replace the existing output, the *clear_output* will be called immediately before the new data is added. This avoids the flickering by not rendering the cleared output to the screen." | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "markdown", | ||||
"metadata": {}, | ||||
"source": [ | ||||
"## Simple example" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "markdown", | ||||
"metadata": {}, | ||||
"source": [ | ||||
"Here we show our progress iterating through a list:" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "code", | ||||
"execution_count": 1, | ||||
"metadata": { | ||||
"collapsed": true | ||||
}, | ||||
"outputs": [], | ||||
"source": [ | ||||
"import sys\n", | ||||
"import time" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "code", | ||||
"execution_count": 2, | ||||
"metadata": { | ||||
"collapsed": false | ||||
}, | ||||
"outputs": [ | ||||
Brian Granger
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r9193 | { | ||
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r18669 | "name": "stdout", | ||
"output_type": "stream", | ||||
"text": [ | ||||
"9\n" | ||||
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r9193 | ] | ||
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r18669 | } | ||
], | ||||
"source": [ | ||||
"from IPython.display import display, clear_output\n", | ||||
"for i in range(10):\n", | ||||
" time.sleep(0.25)\n", | ||||
" clear_output(wait=True)\n", | ||||
" print(i)\n", | ||||
" sys.stdout.flush()" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "markdown", | ||||
"metadata": {}, | ||||
"source": [ | ||||
"## AsyncResult.wait_interactive" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "markdown", | ||||
"metadata": {}, | ||||
"source": [ | ||||
"The AsyncResult object has a special `wait_interactive()` method, which prints its progress interactively,\n", | ||||
"so you can watch as your parallel computation completes.\n", | ||||
"\n", | ||||
"**This example assumes you have an IPython cluster running, which you can start from the [cluster panel](/#clusters)**" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "code", | ||||
"execution_count": 3, | ||||
"metadata": { | ||||
"collapsed": false | ||||
}, | ||||
"outputs": [ | ||||
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r6463 | { | ||
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r18669 | "name": "stdout", | ||
"output_type": "stream", | ||||
"text": [ | ||||
" 100/100 tasks finished after 6 s\n", | ||||
"done\n" | ||||
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r6463 | ] | ||
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r18669 | } | ||
], | ||||
"source": [ | ||||
"from IPython import parallel\n", | ||||
"rc = parallel.Client()\n", | ||||
"view = rc.load_balanced_view()\n", | ||||
"\n", | ||||
"amr = view.map_async(time.sleep, [0.5]*100)\n", | ||||
"\n", | ||||
"amr.wait_interactive()" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "markdown", | ||||
"metadata": {}, | ||||
"source": [ | ||||
"## Matplotlib example" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "markdown", | ||||
"metadata": {}, | ||||
"source": [ | ||||
"You can also use `clear_output()` to clear figures and plots." | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "code", | ||||
"execution_count": 4, | ||||
"metadata": { | ||||
"collapsed": false | ||||
}, | ||||
"outputs": [], | ||||
"source": [ | ||||
"%matplotlib inline\n", | ||||
"import numpy as np\n", | ||||
"import matplotlib.pyplot as plt" | ||||
] | ||||
}, | ||||
{ | ||||
"cell_type": "code", | ||||
"execution_count": 5, | ||||
"metadata": { | ||||
"collapsed": false | ||||
}, | ||||
"outputs": [ | ||||
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r6463 | { | ||
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r18669 | "data": { | ||
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], | ||||
"text/plain": [ | ||||
"<matplotlib.figure.Figure at 0x10f01b310>" | ||||
] | ||||
}, | ||||
MinRK
|
r7739 | "metadata": {}, | ||
Min RK
|
r18669 | "output_type": "display_data" | ||
MinRK
|
r6463 | } | ||
MinRK
|
r7739 | ], | ||
Min RK
|
r18669 | "source": [ | ||
"from scipy.special import jn\n", | ||||
"x = np.linspace(0,5)\n", | ||||
"f, ax = plt.subplots()\n", | ||||
"ax.set_title(\"Bessel functions\")\n", | ||||
"\n", | ||||
"for n in range(1,10):\n", | ||||
" time.sleep(1)\n", | ||||
" ax.plot(x, jn(x,n))\n", | ||||
" clear_output(wait=True)\n", | ||||
" display(f)\n", | ||||
"\n", | ||||
"# close the figure at the end, so we don't get a duplicate\n", | ||||
"# of the last plot\n", | ||||
"plt.close()" | ||||
] | ||||
MinRK
|
r6463 | } | ||
Min RK
|
r18669 | ], | ||
Min RK
|
r20278 | "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": "3.4.2" | ||||
} | ||||
}, | ||||
Min RK
|
r18669 | "nbformat": 4, | ||
"nbformat_minor": 0 | ||||
Min RK
|
r20278 | } | ||