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flutefreak7/bokeh_interactive.ipynb

Last active April 4, 2019 20:07
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Interactive bokeh plot with ipywidget slider and variable number of curves
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bokeh_interactive.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" - Gist Location: https://gist.github.com/flutefreak7/113d2e0f484ce11aadc684266e2f93ca\n",
" - nbviewer link: https://nbviewer.jupyter.org/gist/flutefreak7/113d2e0f484ce11aadc684266e2f93ca?flush_cache=True\n",
" - Binder link: https://mybinder.org/v2/gist/flutefreak7/113d2e0f484ce11aadc684266e2f93ca/master?filepath=bokeh_interactive.ipynb"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# If running in Binder, uncomment and run the line below to install Bokeh in the Binder environment, then restart your kernal and re-comment the line\n",
"# %pip install bokeh"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"from bokeh.io import output_notebook, show, push_notebook\n",
"from bokeh.models import Legend, LegendItem, GlyphRenderer\n",
"from bokeh.palettes import inferno\n",
"from bokeh.plotting import figure\n",
"from bokeh.plotting.helpers import _update_legend\n",
"\n",
"from ipywidgets import interact, fixed, widgets\n",
"\n",
"import numpy as np\n",
"import time"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div class=\"bk-root\">\n",
" <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></a>\n",
" <span id=\"1930\">Loading BokehJS ...</span>\n",
" </div>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/javascript": [
"\n",
"(function(root) {\n",
" function now() {\n",
" return new Date();\n",
" }\n",
"\n",
" var force = true;\n",
"\n",
" if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n",
" root._bokeh_onload_callbacks = [];\n",
" root._bokeh_is_loading = undefined;\n",
" }\n",
"\n",
" var JS_MIME_TYPE = 'application/javascript';\n",
" var HTML_MIME_TYPE = 'text/html';\n",
" var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
" var CLASS_NAME = 'output_bokeh rendered_html';\n",
"\n",
" /**\n",
" * Render data to the DOM node\n",
" */\n",
" function render(props, node) {\n",
" var script = document.createElement(\"script\");\n",
" node.appendChild(script);\n",
" }\n",
"\n",
" /**\n",
" * Handle when an output is cleared or removed\n",
" */\n",
" function handleClearOutput(event, handle) {\n",
" var cell = handle.cell;\n",
"\n",
" var id = cell.output_area._bokeh_element_id;\n",
" var server_id = cell.output_area._bokeh_server_id;\n",
" // Clean up Bokeh references\n",
" if (id != null && id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
"\n",
" if (server_id !== undefined) {\n",
" // Clean up Bokeh references\n",
" var cmd = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
" cell.notebook.kernel.execute(cmd, {\n",
" iopub: {\n",
" output: function(msg) {\n",
" var id = msg.content.text.trim();\n",
" if (id in Bokeh.index) {\n",
" Bokeh.index[id].model.document.clear();\n",
" delete Bokeh.index[id];\n",
" }\n",
" }\n",
" }\n",
" });\n",
" // Destroy server and session\n",
" var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
" cell.notebook.kernel.execute(cmd);\n",
" }\n",
" }\n",
"\n",
" /**\n",
" * Handle when a new output is added\n",
" */\n",
" function handleAddOutput(event, handle) {\n",
" var output_area = handle.output_area;\n",
" var output = handle.output;\n",
"\n",
" // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
" if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n",
" return\n",
" }\n",
"\n",
" var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
"\n",
" if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
" toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
" // store reference to embed id on output_area\n",
" output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
" }\n",
" if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
" var bk_div = document.createElement(\"div\");\n",
" bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
" var script_attrs = bk_div.children[0].attributes;\n",
" for (var i = 0; i < script_attrs.length; i++) {\n",
" toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
" }\n",
" // store reference to server id on output_area\n",
" output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
" }\n",
" }\n",
"\n",
" function register_renderer(events, OutputArea) {\n",
"\n",
" function append_mime(data, metadata, element) {\n",
" // create a DOM node to render to\n",
" var toinsert = this.create_output_subarea(\n",
" metadata,\n",
" CLASS_NAME,\n",
" EXEC_MIME_TYPE\n",
" );\n",
" this.keyboard_manager.register_events(toinsert);\n",
" // Render to node\n",
" var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
" render(props, toinsert[toinsert.length - 1]);\n",
" element.append(toinsert);\n",
" return toinsert\n",
" }\n",
"\n",
" /* Handle when an output is cleared or removed */\n",
" events.on('clear_output.CodeCell', handleClearOutput);\n",
" events.on('delete.Cell', handleClearOutput);\n",
"\n",
" /* Handle when a new output is added */\n",
" events.on('output_added.OutputArea', handleAddOutput);\n",
"\n",
" /**\n",
" * Register the mime type and append_mime function with output_area\n",
" */\n",
" OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
" /* Is output safe? */\n",
" safe: true,\n",
" /* Index of renderer in `output_area.display_order` */\n",
" index: 0\n",
" });\n",
" }\n",
"\n",
" // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
" if (root.Jupyter !== undefined) {\n",
" var events = require('base/js/events');\n",
" var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
"\n",
" if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
" register_renderer(events, OutputArea);\n",
" }\n",
" }\n",
"\n",
" \n",
" if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
" root._bokeh_timeout = Date.now() + 5000;\n",
" root._bokeh_failed_load = false;\n",
" }\n",
"\n",
" var NB_LOAD_WARNING = {'data': {'text/html':\n",
" \"<div style='background-color: #fdd'>\\n\"+\n",
" \"<p>\\n\"+\n",
" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
" \"</code>\\n\"+\n",
" \"</div>\"}};\n",
"\n",
" function display_loaded() {\n",
" var el = document.getElementById(\"1930\");\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS is loading...\";\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" if (el != null) {\n",
" el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
" }\n",
" } else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(display_loaded, 100)\n",
" }\n",
" }\n",
"\n",
"\n",
" function run_callbacks() {\n",
" try {\n",
" root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n",
" }\n",
" finally {\n",
" delete root._bokeh_onload_callbacks\n",
" }\n",
" console.info(\"Bokeh: all callbacks have finished\");\n",
" }\n",
"\n",
" function load_libs(js_urls, callback) {\n",
" root._bokeh_onload_callbacks.push(callback);\n",
" if (root._bokeh_is_loading > 0) {\n",
" console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
" return null;\n",
" }\n",
" if (js_urls == null || js_urls.length === 0) {\n",
" run_callbacks();\n",
" return null;\n",
" }\n",
" console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
" root._bokeh_is_loading = js_urls.length;\n",
" for (var i = 0; i < js_urls.length; i++) {\n",
" var url = js_urls[i];\n",
" var s = document.createElement('script');\n",
" s.src = url;\n",
" s.async = false;\n",
" s.onreadystatechange = s.onload = function() {\n",
" root._bokeh_is_loading--;\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: all BokehJS libraries loaded\");\n",
" run_callbacks()\n",
" }\n",
" };\n",
" s.onerror = function() {\n",
" console.warn(\"failed to load library \" + url);\n",
" };\n",
" console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" }\n",
" };var element = document.getElementById(\"1930\");\n",
" if (element == null) {\n",
" console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1930' but no matching script tag was found. \")\n",
" return false;\n",
" }\n",
"\n",
" var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n",
"\n",
" var inline_js = [\n",
" function(Bokeh) {\n",
" Bokeh.set_log_level(\"info\");\n",
" },\n",
" \n",
" function(Bokeh) {\n",
" \n",
" },\n",
" function(Bokeh) {\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n",
" console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n",
" }\n",
" ];\n",
"\n",
" function run_inline_js() {\n",
" \n",
" if ((root.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i].call(root, root.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < root._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!root._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" root._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"1930\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
"\n",
" }\n",
"\n",
" if (root._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
"}(window));"
],
"application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n\n if (typeof (root._bokeh_onload_callbacks) === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n \n\n \n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n var NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n var el = document.getElementById(\"1930\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n }\n finally {\n delete root._bokeh_onload_callbacks\n }\n console.info(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(js_urls, callback) {\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = js_urls.length;\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var s = document.createElement('script');\n s.src = url;\n s.async = false;\n s.onreadystatechange = s.onload = function() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: all BokehJS libraries loaded\");\n run_callbacks()\n }\n };\n s.onerror = function() {\n console.warn(\"failed to load library \" + url);\n };\n console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.getElementsByTagName(\"head\")[0].appendChild(s);\n }\n };var element = document.getElementById(\"1930\");\n if (element == null) {\n console.log(\"Bokeh: ERROR: autoload.js configured with elementid '1930' but no matching script tag was found. \")\n return false;\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.0.4.min.js\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.0.4.min.css\");\n console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.0.4.min.css\");\n }\n ];\n\n function run_inline_js() {\n \n if ((root.Bokeh !== undefined) || (force === true)) {\n for (var i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }if (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n var cell = $(document.getElementById(\"1930\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n\n }\n\n if (root._bokeh_is_loading === 0) {\n console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(js_urls, function() {\n console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"output_notebook()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Generate Data\n",
" - Need multiple chunks of data to plot where each chunk has a variable number of lines\n",
" - Knowing the maximum possible number of lines enables pre-making all the bokeh renderers"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"max_num_curves = 10"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"def make_data():\n",
" \n",
" n_sections = 10\n",
" sections = []\n",
" \n",
" for i in range(n_sections):\n",
" n_curves = np.random.randint(5, max_num_curves)\n",
" amplitudes = np.random.rand(n_curves)\n",
" \n",
" lines = []\n",
"\n",
" x = np.linspace(0, 2*np.pi, 1000)\n",
"\n",
" for a in amplitudes:\n",
" y = a * np.sin(x)\n",
"\n",
" lines.append((x, y))\n",
" \n",
" sections.append(lines)\n",
" \n",
" return sections"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting Function\n",
" - Same function works for creating the first plot and as a callback for updates\n",
" - First pass will show a bokeh plot and return a interactive widget which displays below the plot\n",
" - The widget will trigger additional calls of the function, passing the bokeh handle back in and the existing already-loaded data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Fast Legend Building\n",
" - I noticed that when attempting to update the legend that legend creation was around 10X faster than legend updating\n",
" - plot_stuff() builds LegendItems and the Legend manually and replaces the existing legend on each update\n",
" - While acceptable overall at about 10 Hz, it is odd that the legend takes about 90% of the update time."
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"def plot_stuff(index=0, bokeh=True, handle=None, existing_data=None):\n",
"\n",
" t1 = time.time()\n",
"\n",
" from bokeh.palettes import inferno\n",
" from bokeh.models import Legend, LegendItem\n",
"\n",
" if existing_data is None:\n",
" data = make_data()\n",
" else:\n",
" data = existing_data\n",
"\n",
" lines = data[index]\n",
"\n",
" if bokeh:\n",
" if handle:\n",
" f = handle.doc.roots[0]\n",
"\n",
" # Get all lines from the plot\n",
" bokeh_lines = [r for r in f.renderers if isinstance(r, GlyphRenderer)]\n",
" # Empty out the glyphs we won't be using\n",
" for bokeh_line in bokeh_lines[len(lines):]:\n",
" bokeh_line.data_source.data = {'x': [], 'y': []}\n",
" bokeh_line.visible = False\n",
" else:\n",
" f = figure(width=1000, match_aspect=False, title='Cool Plot')\n",
"\n",
" # Make 6 lines\n",
" bokeh_lines = []\n",
" for i in range(max_num_curves):\n",
" bokeh_lines.append(f.line(x=[], y=[]))\n",
"\n",
" f.title.text = f'Cool Plot @ {index}'\n",
"\n",
" legend_items = []\n",
"\n",
" line1_x, line1_y = lines[0]\n",
" bokeh_lines[0].data_source.data = {'x': line1_x, 'y': line1_y}\n",
" bokeh_lines[0].glyph.line_color = 'black'\n",
" bokeh_lines[0].visible = True\n",
" \n",
" legend_items.append(LegendItem(label='Special Line', renderers=[bokeh_lines[0]]))\n",
"\n",
" n_curves = len(lines) - 1\n",
" # Omit the edges of the palette because they don't work well\n",
" colors = inferno(n_curves + 4)[2:-2]\n",
"\n",
" # traverse the lines\n",
" for i, (x, y) in enumerate(lines[1:]):\n",
"\n",
" label = f'Line {i}'\n",
"\n",
" color = colors[i]\n",
" t11 = time.time()\n",
" bokeh_lines[i+1].data_source.data = {'x': x, 'y': y}\n",
" bokeh_lines[i+1].glyph.line_color = color\n",
" bokeh_lines[i+1].visible = True\n",
" t12 = time.time()\n",
" legend_items.append(LegendItem(label=label, renderers=[bokeh_lines[i+1]]))\n",
" t13 = time.time()\n",
" print(f'Data Update: {t12-t11:1.6}s | Points: {len(x)} | LegendItem: {t13-t12:1.6} s')\n",
"\n",
" t21 = time.time()\n",
" legend = Legend(items=legend_items, location='center_right')\n",
" if handle is not None:\n",
" legends = [r for r in f.renderers if isinstance(r, Legend)]\n",
" f.renderers.remove(legends[0])\n",
" \n",
" f.add_layout(legend)\n",
"\n",
" t22 = time.time()\n",
"\n",
" t2 = time.time()\n",
" print(f'Total: {t2-t1} | Legend: {t22-t21}')\n",
"\n",
" if handle is not None:\n",
" push_notebook(handle=handle)\n",
" return None\n",
" else:\n",
" h = show(f, notebook_handle=True)\n",
"\n",
" index_slider = widgets.IntSlider(value=index, min=0, max=len(data)-1)\n",
"\n",
" i = interact(plot_stuff, bokeh=fixed(True), handle=fixed(h), index=index_slider, existing_data=fixed(data))\n",
" return i\n",
"\n",
" else:\n",
" # Matplotlib version\n",
" import matplotlib.pyplot as plt\n",
" fig, ax = plt.subplots()\n",
"\n",
" plt.plot(*lines[0], color='black', label='Case')\n",
"\n",
" for i, (x, y) in enumerate(lines[1:]):\n",
" web = webs[i]\n",
" i_time = np.interp(web, out.web, out.time)\n",
" plt.plot(x, y, label=f'Web: {web} | Time: {i_time:1.4g}s')\n",
"\n",
" plt.legend()\n",
"\n",
" return ax"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data Update: 0.0s | Points: 1000 | LegendItem: 0.000986338 s\n",
"Data Update: 0.00100255s | Points: 1000 | LegendItem: 0.0 s\n",
"Data Update: 0.00100279s | Points: 1000 | LegendItem: 0.00100303 s\n",
"Data Update: 0.00100303s | Points: 1000 | LegendItem: 0.0 s\n",
"Data Update: 0.00200534s | Points: 1000 | LegendItem: 0.0 s\n",
"Total: 0.01905059814453125 | Legend: 0.0\n"
]
},
{
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},
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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1000]}},\"selected\":{\"id\":\"159180\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"159179\",\"type\":\"UnionRenderers\"}},\"id\":\"158832\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1000]}},\"selected\":{\"id\":\"159184\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"159183\",\"type\":\"UnionRenderers\"}},\"id\":\"158842\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"159186\",\"type\":\"Selection\"},{\"attributes\":{\"active_drag\":\"auto\",\"active_inspect\":\"auto\",\"active_multi\":null,\"active_scroll\":\"auto\",\"active_tap\":\"auto\",\"tools\":[{\"id\":\"158807\",\"type\":\"PanTool\"},{\"id\":\"158808\",\"type\":\"WheelZoomTool\"},{\"id\":\"158809\",\"type\":\"BoxZoomTool\"},{\"id\":\"158810\",\"type\":\"SaveTool\"},{\"id\":\"158811\",\"type\":\"ResetTool\"},{\"id\":\"158812\",\"type\":\"HelpTool\"}]},\"id\":\"158813\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"159185\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"line_color\":\"#BA3655\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"158838\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"159176\",\"type\":\"Selection\"},{\"attributes\":{\"line_color\":\"#550F6D\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"158828\",\"type\":\"Line\"},{\"attributes\":{\"source\":{\"id\":\"158837\",\"type\":\"ColumnDataSource\"}},\"id\":\"158841\",\"type\":\"CDSView\"},{\"attributes\":{\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"158858\",\"type\":\"Line\"},{\"attributes\":{\"data_source\":{\"id\":\"158847\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"158848\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"158849\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"158851\",\"type\":\"CDSView\"}},\"id\":\"158850\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":[],\"y\":[]},\"selected\":{\"id\":\"159190\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"159189\",\"type\":\"UnionRenderers\"}},\"id\":\"158857\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"data_source\":{\"id\":\"158822\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"158823\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"158824\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"158826\",\"type\":\"CDSView\"}},\"id\":\"158825\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"158829\",\"type\":\"Line\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":[],\"y\":[]},\"selected\":{\"id\":\"159194\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"159193\",\"type\":\"UnionRenderers\"}},\"id\":\"158867\",\"type\":\"ColumnDataSource\"},{\"attributes\":{},\"id\":\"159190\",\"type\":\"Selection\"},{\"attributes\":{\"line_color\":\"#88216A\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"158833\",\"type\":\"Line\"},{\"attributes\":{\"data_source\":{\"id\":\"158827\",\"type\":\"ColumnDataSource\"},\"glyph\":{\"id\":\"158828\",\"type\":\"Line\"},\"hover_glyph\":null,\"muted_glyph\":null,\"nonselection_glyph\":{\"id\":\"158829\",\"type\":\"Line\"},\"selection_glyph\":null,\"view\":{\"id\":\"158831\",\"type\":\"CDSView\"}},\"id\":\"158830\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"source\":{\"id\":\"158857\",\"type\":\"ColumnDataSource\"}},\"id\":\"158861\",\"type\":\"CDSView\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"#1f77b4\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"158859\",\"type\":\"Line\"},{\"attributes\":{},\"id\":\"159175\",\"type\":\"UnionRenderers\"},{\"attributes\":{\"bottom_units\":\"screen\",\"fill_alpha\":{\"value\":0.5},\"fill_color\":{\"value\":\"lightgrey\"},\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":{\"value\":1.0},\"line_color\":{\"value\":\"black\"},\"line_dash\":[4,4],\"line_width\":{\"value\":2},\"plot\":null,\"render_mode\":\"css\",\"right_units\":\"screen\",\"top_units\":\"screen\"},\"id\":\"158815\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"158795\",\"type\":\"LinearScale\"},{\"attributes\":{},\"id\":\"159193\",\"type\":\"UnionRenderers\"}],\"root_ids\":[\"158787\"]},\"title\":\"Bokeh Application\",\"version\":\"1.0.4\"}};\n",
" var render_items = [{\"docid\":\"a9379082-5c24-4930-b1f4-79b25f4e9cb3\",\"notebook_comms_target\":\"159195\",\"roots\":{\"158787\":\"d569bf90-c488-4aef-a234-9dcdca859882\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "158787"
}
},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "b48641651afb41ab9ab43fbd8181e275",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=0, description='index', max=9), Output()), _dom_classes=('widget-interac…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.plot_stuff(index=0, bokeh=True, handle=None, existing_data=None)>"
]
},
"execution_count": 66,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_stuff()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Slower Legend Updating\n",
" - When you use figure.line( ... , legend='Curvy') bokeh uses a helper called `_update_legend` which we can use\n",
" - `_update_legend` searches for existing legend entries which it will update or add renderers to, this is why its slow I think\n",
" - Legend updates (and thus plot updates) with this method are about 10X slower - up to 0.7s for this example"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [],
"source": [
"def plot_stuff_slowly(index=0, bokeh=True, handle=None, existing_data=None):\n",
"\n",
" t1 = time.time()\n",
"\n",
" from bokeh.palettes import inferno\n",
" from bokeh.models import Legend, LegendItem\n",
"\n",
" if existing_data is None:\n",
" data = make_data()\n",
" else:\n",
" data = existing_data\n",
"\n",
" lines = data[index]\n",
"\n",
" if bokeh:\n",
" if handle:\n",
" f = handle.doc.roots[0]\n",
"\n",
" # Get all lines from the plot\n",
" bokeh_lines = [r for r in f.renderers if isinstance(r, GlyphRenderer)]\n",
" # Empty out the glyphs we won't be using\n",
" for bokeh_line in bokeh_lines[len(lines):]:\n",
" bokeh_line.data_source.data = {'x': [], 'y': []}\n",
" bokeh_line.visible = False\n",
" else:\n",
" f = figure(width=1000, match_aspect=False, title='Cool Plot')\n",
"\n",
" # Make 6 lines\n",
" bokeh_lines = []\n",
" for i in range(max_num_curves):\n",
" bokeh_lines.append(f.line(x=[], y=[]))\n",
"\n",
" f.title.text = f'Cool Plot @ {index}'\n",
"\n",
" line1_x, line1_y = lines[0]\n",
" bokeh_lines[0].data_source.data = {'x': line1_x, 'y': line1_y}\n",
" bokeh_lines[0].glyph.line_color = 'black'\n",
" bokeh_lines[0].visible = True\n",
" \n",
" legend = f.select(Legend)\n",
" if legend:\n",
" legend[0].items = []\n",
" _update_legend(f, 'Special Line', bokeh_lines[0])\n",
"\n",
" n_curves = len(lines) - 1\n",
" # Omit the edges of the palette because they don't work well\n",
" colors = inferno(n_curves + 4)[2:-2]\n",
"\n",
" # traverse the lines\n",
" for i, (x, y) in enumerate(lines[1:]):\n",
"\n",
" label = f'Line {i}'\n",
"\n",
" color = colors[i]\n",
" t11 = time.time()\n",
" bokeh_lines[i+1].data_source.data = {'x': x, 'y': y}\n",
" bokeh_lines[i+1].glyph.line_color = color\n",
" bokeh_lines[i+1].visible = True\n",
" t12 = time.time()\n",
" _update_legend(f, label, bokeh_lines[i+1])\n",
" t13 = time.time()\n",
" print(f'Data Update: {t12-t11:1.6}s | Points: {len(x)} | LegendItem: {t13-t12:1.6} s')\n",
" \n",
" t2 = time.time()\n",
" print(f'Total: {t2-t1}')\n",
"\n",
" if handle is not None:\n",
" push_notebook(handle=handle)\n",
" return None\n",
" else:\n",
" h = show(f, notebook_handle=True)\n",
"\n",
" index_slider = widgets.IntSlider(value=index, min=0, max=len(data)-1)\n",
"\n",
" i = interact(plot_stuff_slowly, bokeh=fixed(True), handle=fixed(h), index=index_slider, existing_data=fixed(data))\n",
" return i\n",
"\n",
" else:\n",
" # Matplotlib version\n",
" import matplotlib.pyplot as plt\n",
" fig, ax = plt.subplots()\n",
"\n",
" plt.plot(*lines[0], color='black', label='Case')\n",
"\n",
" for i, (x, y) in enumerate(lines[1:]):\n",
" plt.plot(x, y, label=f'Line: {i} | Time: {i_time:1.4g}s')\n",
"\n",
" plt.legend()\n",
"\n",
" return ax"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data Update: 0.00198102s | Points: 1000 | LegendItem: 0.00501466 s\n",
"Data Update: 0.00103235s | Points: 1000 | LegendItem: 0.00498152 s\n",
"Data Update: 0.00100207s | Points: 1000 | LegendItem: 0.00401115 s\n",
"Data Update: 0.00100279s | Points: 1000 | LegendItem: 0.00501299 s\n",
"Data Update: 0.00100279s | Points: 1000 | LegendItem: 0.00501299 s\n",
"Total: 0.05113506317138672\n"
]
},
{
"data": {
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},
"metadata": {},
"output_type": "display_data"
},
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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1000]}},\"selected\":{\"id\":\"160152\",\"type\":\"Selection\"},\"selection_policy\":{\"id\":\"160151\",\"type\":\"UnionRenderers\"}},\"id\":\"160101\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"source\":{\"id\":\"160101\",\"type\":\"ColumnDataSource\"}},\"id\":\"160105\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"160096\",\"type\":\"ColumnDataSource\"}},\"id\":\"160100\",\"type\":\"CDSView\"},{\"attributes\":{\"callback\":null,\"data\":{\"x\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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1eXIHQLnQQrRafwdAsctGMzyMB0CoxkqyHZkHQJ/BTjH/pQdAlrxSsOCyB0CNt1Yvwr8HQISyWq6jzAdAe61eLYXZB0ByqGKsZuYHQGmjZitI8wdAYJ5qqikACEBXmW4pCw0IQE6UcqjsGQhARo92J84mCEA9inqmrzMIQDSFfiWRQAhAK4CCpHJNCEAie4YjVFoIQBl2iqI1ZwhAEHGOIRd0CEAHbJKg+IAIQP5mlh/ajQhA9WGanruaCEDsXJ4dnacIQONXopx+tAhA2lKmG2DBCEDSTaqaQc4IQMlIrhkj2whAwEOymAToCEC3PrYX5vQIQK45upbHAQlApTS+FakOCUCcL8KUihsJQJMqxhNsKAlAiiXKkk01CUCBIM4RL0IJQHgb0pAQTwlAbxbWD/JbCUBmEdqO02gJQF4M3g21dQlAVQfijJaCCUBMAuYLeI8JQEP96YpZnAlAOvjtCTupCUAx8/GIHLYJQCju9Qf+wglAH+n5ht/PCUAW5P0FwdwJQA3fAYWi6QlABNoFBIT2CUD71AmDZQMKQPLPDQJHEApA6soRgSgdCkDhxRUACioKQNjAGX/rNgpAz7sd/sxDCkDGtiF9rlAKQL2xJfyPXQpAtKwpe3FqCkCrpy36UncKQKKiMXk0hApAmZ01+BWRCkCQmDl3950KQIeTPfbYqgpAfo5Bdbq3CkB2iUX0m8QKQG2ESXN90QpAZH9N8l7eCkBbelFxQOsKQFJ1VfAh+ApASXBZbwMFC0BAa13u5BELQDdmYW3GHgtALmFl7KcrC0AlXGlriTgLQBxXbepqRQtAE1JxaUxSC0AKTXXoLV8LQAJIeWcPbAtA+UJ95vB4C0DwPYFl0oULQOc4heSzkgtA3jOJY5WfC0DVLo3idqwLQMwpkWFYuQtAwySV4DnGC0C6H5lfG9MLQLEand783wtAqBWhXd7sC0CfEKXcv/kLQJcLqVuhBgxAjgat2oITDECFAbFZZCAMQHz8tNhFLQxAc/e4Vyc6DEBq8rzWCEcMQGHtwFXqUwxAWOjE1MtgDEBP48hTrW0MQEbezNKOegxAPdnQUXCHDEA01NTQUZQMQCvP2E8zoQxAI8rczhSuDEAaxeBN9roMQBHA5MzXxwxACLvoS7nUDED/tezKmuEMQPaw8El87gxA7av0yF37DEDkpvhHPwgNQNuh/MYgFQ1A0pwARgIiDUDJlwTF4y4NQMCSCETFOw1At40Mw6ZIDUCviBBCiFUNQKaDFMFpYg1AnX4YQEtvDUCUeRy/LHwNQIt0ID4OiQ1Agm8kve+VDUB5aig80aINQHBlLLuyrw1AZ2AwOpS8DUBeWzS5dckNQFVWODhX1g1ATFE8tzjjDUBDTEA2GvANQDtHRLX7/A1AMkJINN0JDkApPUyzvhYOQCA4UDKgIw5AFzNUsYEwDkAOLlgwYz0OQAUpXK9ESg5A/CNgLiZXDkDzHmStB2QOQOoZaCzpcA5A4RRsq8p9DkDYD3AqrIoOQNAKdKmNlw5AxwV4KG+kDkC+AHynULEOQLX7fyYyvg5ArPaDpRPLDkCj8Yck9dcOQJrsi6PW5A5AkeePIrjxDkCI4pOhmf4OQH/dlyB7Cw9Adtibn1wYD0Bt058ePiUPQGTOo50fMg9AXMmnHAE/D0BTxKub4ksPQEq/rxrEWA9AQbqzmaVlD0A4tbcYh3IPQC+wu5dofw9AJqu/FkqMD0AdpsOVK5kPQBShxxQNpg9AC5zLk+6yD0ACl88S0L8PQPmR05GxzA9A8IzXEJPZD0Doh9uPdOYPQN+C3w5W8w9A677xxhsAEEBmvHOGjAYQQOK59UX9DBBAXbd3BW4TEEDZtPnE3hkQQFWye4RPIBBA0K/9Q8AmEEBMrX8DMS0QQMeqAcOhMxBAQ6iDghI6EEC+pQVCg0AQQDqjhwH0RhBAtaAJwWRNEEAxnouA1VMQQKybDUBGWhBAKJmP/7ZgEECjlhG/J2cQQB+Uk36YbRBAm5EVPgl0EEAWj5f9eXoQQJKMGb3qgBBADYqbfFuHEECJhx08zI0QQASFn/s8lBBAgIIhu62aEED7f6N6HqEQQHd9JTqPpxBA8nqn+f+tEEBueCm5cLQQQOl1q3jhuhBAZXMtOFLBEEDhcK/3wscQQFxuMbczzhBA2GuzdqTUEEBTaTU2FdsQQM9mt/WF4RBASmQ5tfbnEEDGYbt0Z+4QQEFfPTTY9BBAvVy/80j7EEA4WkGzuQERQLRXw3IqCBFAL1VFMpsOEUCrUsfxCxURQCdQSbF8GxFAok3LcO0hEUAeS00wXigRQJlIz+/OLhFAFUZRrz81EUCQQ9NusDsRQAxBVS4hQhFAhz7X7ZFIEUADPFmtAk8RQH4522xzVRFA+jZdLORbEUB1NN/rVGIRQPExYavFaBFAbS/jajZvEUDoLGUqp3URQGQq5+kXfBFA3ydpqYiCEUBbJeto+YgRQNYibShqjxFAUiDv59qVEUDNHXGnS5wRQEkb82a8ohFAxBh1Ji2pEUBAFvflna8RQLwTeaUOthFANxH7ZH+8EUCzDn0k8MIRQC4M/+NgyRFAqgmBo9HPEUAlBwNjQtYRQKEEhSKz3BFAHAIH4iPjEUCY/4ihlOkRQBP9CmEF8BFAj/qMIHb2EUAK+A7g5vwRQIb1kJ9XAxJAAvMSX8gJEkB98JQeORASQPntFt6pFhJAdOuYnRodEkDw6BpdiyMSQGvmnBz8KRJA5+Me3GwwEkBi4aCb3TYSQN7eIltOPRJAWdykGr9DEkDV2SbaL0oSQFDXqJmgUBJAzNQqWRFXEkBI0qwYgl0SQMPPLtjyYxJAP82wl2NqEkC6yjJX1HASQDbItBZFdxJAscU21rV9EkAtw7iVJoQSQKjAOlWXihJAJL68FAiREkCfuz7UeJcSQBu5wJPpnRJAlrZCU1qkEkAStMQSy6oSQI6xRtI7sRJACa/Ikay3EkCFrEpRHb4SQACqzBCOxBJAfKdO0P7KEkD3pNCPb9ESQHOiUk/g1xJA7p/UDlHeEkBqnVbOweQSQOWa2I0y6xJAYZhaTaPxEkDcldwMFPgSQFiTXsyE/hJA1JDgi/UEE0BPjmJLZgsTQMuL5ArXERNARolmykcYE0DChuiJuB4TQD2EakkpJRNAuYHsCJorE0A0f27ICjITQLB88Id7OBNAK3pyR+w+E0Cnd/QGXUUTQCJ1dsbNSxNAnnL4hT5SE0AacHpFr1gTQJVt/AQgXxNAEWt+xJBlE0CMaACEAWwTQAhmgkNychNAg2MEA+N4E0D/YIbCU38TQHpeCILEhRNA9luKQTWME0BxWQwBppITQO1WjsAWmRNAaFQQgIefE0DkUZI/+KUTQGBPFP9orBNA20yWvtmyE0BXShh+SrkTQNJHmj27vxNATkUc/SvGE0DJQp68nMwTQEVAIHwN0xNAwD2iO37ZE0A8OyT77t8TQLc4prpf5hNAMzYoetDsE0CuM6o5QfMTQCoxLPmx+RNApi6uuCIAFEAhLDB4kwYUQJ0psjcEDRRAGCc093QTFECUJLa25RkUQA8iOHZWIBRAix+6NccmFEAGHTz1Ny0UQIIavrSoMxRA/RdAdBk6FEB5FcIzikAUQPQSRPP6RhRAcBDGsmtNFEDsDUhy3FMUQGcLyjFNWhRA4whM8b1gFEBeBs6wLmcUQNoDUHCfbRRAVQHSLxB0FEDR/lPvgHoUQEz81a7xgBRAyPlXbmKHFEBD99kt040UQL/0W+1DlBRAOvLdrLSaFEC2719sJaEUQDLt4SuWpxRArepj6wauFEAp6OWqd7QUQKTlZ2rouhRAIOPpKVnBFECb4GvpyccUQBfe7ag6zhRAkttvaKvUFEAO2fEnHNsUQInWc+eM4RRABdT1pv3nFECA0Xdmbu4UQPzO+SXf9BRAeMx75U/7FEDzyf2kwAEVQG/Hf2QxCBVA6sQBJKIOFUBmwoPjEhUVQOG/BaODGxVAXb2HYvQhFUDYugkiZSgVQFS4i+HVLhVAz7UNoUY1FUBLs49gtzsVQMewESAoQhVAQq6T35hIFUC+qxWfCU8VQDmpl156VRVAtaYZHutbFUAwpJvdW2IVQKyhHZ3MaBVAJ5+fXD1vFUCjnCEcrnUVQB6ao9sefBVAmpclm4+CFUAVladaAIkVQJGSKRpxjxVADZCr2eGVFUCIjS2ZUpwVQASLr1jDohVAf4gxGDSpFUD7hbPXpK8VQHaDNZcVthVA8oC3Voa8FUBtfjkW98IVQOl7u9VnyRVAZHk9ldjPFUDgdr9USdYVQFt0QRS63BVA13HD0yrjFUBTb0WTm+kVQM5sx1IM8BVASmpJEn32FUDFZ8vR7fwVQEFlTZFeAxZAvGLPUM8JFkA4YFEQQBAWQLNd08+wFhZAL1tVjyEdFkCqWNdOkiMWQCZWWQ4DKhZAoVPbzXMwFkAdUV2N5DYWQJlO30xVPRZAFExhDMZDFkCQSePLNkoWQAtHZYunUBZAh0TnShhXFkACQmkKiV0WQH4/68n5YxZA+TxtiWpqFkB1Ou9I23AWQPA3cQhMdxZAbDXzx7x9FkDnMnWHLYQWQGMw90aeihZA3y15Bg+RFkBaK/vFf5cWQNYofYXwnRZAUSb/RGGkFkDNI4EE0qoWQEghA8RCsRZAxB6Fg7O3FkA/HAdDJL4WQLsZiQKVxBZANhcLwgXLFkCyFI2BdtEWQC0SD0Hn1xZAqQ+RAFjeFkAlDRPAyOQWQKAKlX856xZAHAgXP6rxFkCXBZn+GvgWQBMDG76L/hZAjgCdffwEF0AK/h49bQsXQIX7oPzdERdAAfkivE4YF0B89qR7vx4XQPjzJjswJRdAc/Go+qArF0Dv7iq6ETIXQGvsrHmCOBdA5ukuOfM+F0Bi57D4Y0UXQN3kMrjUSxdAWeK0d0VSF0DU3zY3tlgXQFDduPYmXxdAy9o6tpdlF0BH2Lx1CGwXQMLVPjV5chdAPtPA9Ol4F0C50EK0Wn8XQDXOxHPLhRdAsctGMzyMF0AsycjyrJIXQKjGSrIdmRdAI8TMcY6fF0CfwU4x/6UXQBq/0PBvrBdAlrxSsOCyF0ARutRvUbkXQI23Vi/CvxdACLXY7jLGF0CEslquo8wXQP+v3G0U0xdAe61eLYXZF0D3quDs9d8XQHKoYqxm5hdA7qXka9fsF0Bpo2YrSPMXQOWg6Oq4+RdAYJ5qqikAGEDcm+xpmgYYQFeZbikLDRhA05bw6HsTGEBOlHKo7BkYQMqR9GddIBhARo92J84mGEDBjPjmPi0YQD2KeqavMxhAuIf8ZSA6GEA0hX4lkUAYQK+CAOUBRxhAK4CCpHJNGECmfQRk41MYQCJ7hiNUWhhAnXgI48RgGEAZdoqiNWcYQJRzDGKmbRhAEHGOIRd0GECMbhDhh3oYQAdskqD4gBhAg2kUYGmHGED+ZpYf2o0YQHpkGN9KlBhA9WGanruaGEBxXxxeLKEYQOxcnh2dpxhAaFog3Q2uGEDjV6KcfrQYQF9VJFzvuhhA2lKmG2DBGEBWUCjb0McYQNJNqppBzhhATUssWrLUGEDJSK4ZI9sYQERGMNmT4RhAwEOymAToGEA7QTRYde4YQLc+thfm9BhAMjw411b7GECuObqWxwEZQCk3PFY4CBlApTS+FakOGUAgMkDVGRUZQJwvwpSKGxlAGC1EVPshGUA=\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"shape\":[1000]},\"y\":{\"__ndarray__\":\"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Application\",\"version\":\"1.0.4\"}};\n",
" var render_items = [{\"docid\":\"50322c4c-bf54-4685-a8a9-b71c1772f961\",\"notebook_comms_target\":\"160672\",\"roots\":{\"160056\":\"4ad31327-cd3c-467e-9c65-c3f14ca17f1f\"}}];\n",
" root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
"\n",
" }\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" } else {\n",
" var attempts = 0;\n",
" var timer = setInterval(function(root) {\n",
" if (root.Bokeh !== undefined) {\n",
" embed_document(root);\n",
" clearInterval(timer);\n",
" }\n",
" attempts++;\n",
" if (attempts > 100) {\n",
" console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
" clearInterval(timer);\n",
" }\n",
" }, 10, root)\n",
" }\n",
"})(window);"
],
"application/vnd.bokehjs_exec.v0+json": ""
},
"metadata": {
"application/vnd.bokehjs_exec.v0+json": {
"id": "160056"
}
},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f7d29aea0bb34a33849a95251d47bda7",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"interactive(children=(IntSlider(value=0, description='index', max=9), Output()), _dom_classes=('widget-interac…"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"<function __main__.plot_stuff_slowly(index=0, bokeh=True, handle=None, existing_data=None)>"
]
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"plot_stuff_slowly()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"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": "3.7.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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