robintw/Example Periodic Callback.ipynb

## Example Periodic Callback.ipynb
{
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  {
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    "trusted": true
   },
   "cell_type": "code",
   "source": "import numpy as np\nfrom matplotlib.figure import Figure\nimport panel as pn\nimport panel.widgets as pnw",
   "execution_count": 3,
   "outputs": []
  },
  {
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   "source": "pn.extension()",
   "execution_count": 7,
   "outputs": [
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     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "def random_imshow(magnitude=5, sigma=2, colormap='viridis'):\n    arr = np.random.randn(10,10)\n    arr *= sigma\n    arr += magnitude\n    \n    ax = Figure().add_subplot()\n    im = ax.imshow(arr, vmin=0, vmax=10, cmap=colormap)\n    c = ax.figure.colorbar(im)\n    \n    return ax.figure",
   "execution_count": 4,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "magnitude = pnw.IntSlider(end=10, name='value', value=5, value_throttled=5)\ncmap = pnw.RadioButtonGroup(name='cmap', value='viridis', \n                                 options=['viridis', 'plasma', 'inferno', 'magma', 'cividis'])\n\nboundfn = pn.bind(random_imshow, magnitude=magnitude, sigma=3, colormap=cmap)\n\npane = pn.Row(pn.Column(magnitude, cmap), boundfn)\n\npn.state.add_periodic_callback(boundfn, period=100)\n\npane",
   "execution_count": 9,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {},
     "metadata": {}
    },
    {
     "output_type": "execute_result",
     "execution_count": 9,
     "data": {
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&quot; width=&quot;460px&quot; height=&quot;345px&quot; alt=&quot;&quot;&gt;&lt;/img&gt;\",\"width\":460},\"id\":\"1018\",\"type\":\"panel.models.markup.HTML\"}],\"root_ids\":[\"1013\",\"1019\"]},\"title\":\"Bokeh Application\",\"version\":\"2.2.3\"}};\n    var render_items = [{\"docid\":\"07c80f36-aa92-4309-8c60-44c724165ea1\",\"root_ids\":[\"1013\"],\"roots\":{\"1013\":\"e4e3f8e6-db58-4449-a25e-57ee01aaa145\"}}];\n    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n    embed_document(root);\n  } else {\n    var attempts = 0;\n    var timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else if (document.readyState == \"complete\") {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);</script>",
      "application/vnd.holoviews_exec.v0+json": "",
      "text/plain": "Row\n    [0] Column\n        [0] IntSlider(end=10, name='value', value=5, value_throttled=5)\n        [1] RadioButtonGroup(name='cmap', options=['viridis', 'plasma', ...], value='viridis')\n    [1] ParamFunction(function)"
     },
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1013"
      }
     }
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3",
   "language": "python"
  },
  "hide_input": false,
  "language_info": {
   "name": "python",
   "version": "3.7.8",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}