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rebuild docs
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‎2.1.0/_downloads/close_event.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Close Event\n\n\n\n"
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"\n# Close Event\n\n\nExample to show connecting events that occur when the figure closes.\n\n"
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]
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},
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{

‎2.1.0/_downloads/close_event.py‎

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Close Event
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===========
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Example to show connecting events that occur when the figure closes.
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"""
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from __future__ import print_function
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import matplotlib.pyplot as plt

‎2.1.0/_downloads/data_browser.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Data Browser\n\n\n\n"
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"\n# Data Browser\n\n\nConnecting data between multiple canvases.\n\nThis example covers how to interact data with multiple canvases. This\nlet's you select and highlight a point on one axis, and generating the\ndata of that point on the other axis.\n\n"
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]
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},
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{

‎2.1.0/_downloads/data_browser.py‎

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Data Browser
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============
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Connecting data between multiple canvases.
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This example covers how to interact data with multiple canvases. This
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let's you select and highlight a point on one axis, and generating the
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data of that point on the other axis.
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"""
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import numpy as np
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‎2.1.0/_downloads/gallery_jupyter.zip‎

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‎2.1.0/_downloads/gallery_python.zip‎

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‎2.1.0/_downloads/looking_glass.ipynb‎

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n# Looking Glass\n\n\n\n"
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"\n# Looking Glass\n\n\nExample using mouse events to simulate a looking glass for inspecting data.\n\n"
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]
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},
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{

‎2.1.0/_downloads/looking_glass.py‎

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Looking Glass
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=============
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Example using mouse events to simulate a looking glass for inspecting data.
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"""
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import numpy as np
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import matplotlib.pyplot as plt

‎2.1.0/_downloads/movie_demo_sgskip.ipynb‎

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},
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"outputs": [],
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"source": [
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"from __future__ import print_function\n\nimport subprocess\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n\nfiles = []\n\nfig, ax = plt.subplots(figsize=(5, 5))\nfor i in range(50): # 50 frames\n plt.cla()\n plt.imshow(np.random.rand(5, 5), interpolation='nearest')\n fname = '_tmp%03d.png' % i\n print('Saving frame', fname)\n plt.savefig(fname)\n files.append(fname)\n\nprint('Making movie animation.mpg - this may take a while')\nsubprocess.call(\"mencoder 'mf://_tmp*.png' -mf type=png:fps=10 -ovc lavc \"\n \"-lavcopts vcodec=wmv2 -oac copy -o animation.mpg\", shell=True)\n\n# cleanup\nfor fname in files:\n os.remove(fname)"
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"from __future__ import print_function\n\nimport os\nimport subprocess\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Fixing random state for reproducibility\nnp.random.seed(19680801)\n\n\nfiles = []\n\nfig, ax = plt.subplots(figsize=(5, 5))\nfor i in range(50): # 50 frames\n plt.cla()\n plt.imshow(np.random.rand(5, 5), interpolation='nearest')\n fname = '_tmp%03d.png' % i\n print('Saving frame', fname)\n plt.savefig(fname)\n files.append(fname)\n\nprint('Making movie animation.mpg - this may take a while')\nsubprocess.call(\"mencoder 'mf://_tmp*.png' -mf type=png:fps=10 -ovc lavc \"\n \"-lavcopts vcodec=wmv2 -oac copy -o animation.mpg\", shell=True)\n\n# cleanup\nfor fname in files:\n os.remove(fname)"
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}
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‎2.1.0/_downloads/movie_demo_sgskip.py‎

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from __future__ import print_function
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import os
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import subprocess
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import matplotlib.pyplot as plt
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import numpy as np

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