{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "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", " \"
\\n\"+\n", " \"

\\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", " \"

\\n\"+\n", " \"\\n\"+\n", " \"\\n\"+\n", " \"from bokeh.resources import INLINE\\n\"+\n", " \"output_notebook(resources=INLINE)\\n\"+\n", " \"\\n\"+\n", " \"
\"}};\n", "\n", " function display_loaded() {\n", " var el = document.getElementById(null);\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) {\n", " if (callback != null)\n", " callback();\n", " });\n", " } finally {\n", " delete root._bokeh_onload_callbacks\n", " }\n", " console.debug(\"Bokeh: all callbacks have finished\");\n", " }\n", "\n", " function load_libs(css_urls, js_urls, callback) {\n", " if (css_urls == null) css_urls = [];\n", " if (js_urls == null) js_urls = [];\n", "\n", " root._bokeh_onload_callbacks.push(callback);\n", " if (root._bokeh_is_loading > 0) {\n", " console.debug(\"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.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", "\n", " function on_load() {\n", " root._bokeh_is_loading--;\n", " if (root._bokeh_is_loading === 0) {\n", " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", " run_callbacks()\n", " }\n", " }\n", "\n", " function on_error() {\n", " console.error(\"failed to load \" + url);\n", " }\n", "\n", " for (var i = 0; i < css_urls.length; i++) {\n", " var url = css_urls[i];\n", " const element = document.createElement(\"link\");\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.rel = \"stylesheet\";\n", " element.type = \"text/css\";\n", " element.href = url;\n", " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", " document.body.appendChild(element);\n", " }\n", "\n", " for (var i = 0; i < js_urls.length; i++) {\n", " var url = js_urls[i];\n", " var element = document.createElement('script');\n", " element.onload = on_load;\n", " element.onerror = on_error;\n", " element.async = false;\n", " element.src = url;\n", " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", " document.head.appendChild(element);\n", " }\n", " };\n", "\n", " function inject_raw_css(css) {\n", " const element = document.createElement(\"style\");\n", " element.appendChild(document.createTextNode(css));\n", " document.body.appendChild(element);\n", " }\n", "\n", " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n", " var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n", "\n", " var inline_js = [\n", " function(Bokeh) {\n", " Bokeh.set_log_level(\"info\");\n", " },\n", " \n", " function(Bokeh) {\n", " \n", " },\n", " function(Bokeh) {} // ensure no trailing comma for IE\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", " }} 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(null)).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.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", " run_inline_js();\n", " } else {\n", " load_libs(css_urls, js_urls, function() {\n", " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", " run_inline_js();\n", " });\n", " }\n", "}(window));" ], "application/vnd.holoviews_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 \"
\\n\"+\n \"

\\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 \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n var el = document.getElementById(null);\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) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"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.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error;\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[i];\n var element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error;\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-1.1.0.min.js\"];\n var css_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-1.1.0.min.css\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-1.1.0.min.css\"];\n\n var inline_js = [\n function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\n \n function(Bokeh) {\n \n },\n function(Bokeh) {} // ensure no trailing comma for IE\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 }} 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(null)).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.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "\n", "\n", "\n" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "\n", "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import adaptive\n", "adaptive.notebook_extension(_inline_js=False)\n", "\n", "import numpy as np\n", "from functools import partial\n", "import random" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "offset = random.uniform(-0.5, 0.5)\n", "\n", "def f(x, offset=offset, wait=True):\n", " from time import sleep\n", " from random import random\n", "\n", " a = 0.01\n", " if wait:\n", " sleep(random() / 10)\n", " return x + a**2 / (a**2 + (x - offset)**2)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "learner = adaptive.Learner1D(f, bounds=(-1, 1))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# The end condition is when the \"loss\" is less than 0.1. In the context of the\n", "# 1D learner this means that we will resolve features in 'func' with width 0.1 or wider.\n", "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "await runner.task # This is not needed in a notebook environment!" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3a1c1b746b204a8c820007f568b97d25", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value='\\n
\\n
status
f…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runner.live_info()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "29c6da91d43948c6a167f4d80caafc0a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='cancel live-plot', layout=Layout(width='150px'), style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "
\n", " \n", "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "
\n", "
\n", "" ], "text/plain": [ ":DynamicMap []\n", " :Overlay\n", " .Scatter.I :Scatter [x] (y)\n", " .Path.I :Path [x,y]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "runner.live_plot(update_interval=0.1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "if not runner.task.done():\n", " raise RuntimeError('Wait for the runner to finish before executing the cells below!')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "
\n", "
\n", "" ], "text/plain": [ ":Layout\n", " .Overlay.I :Overlay\n", " .Scatter.I :Scatter [x] (y)\n", " .Path.I :Path [x,y]\n", " .Overlay.II :Overlay\n", " .Scatter.I :Scatter [x] (y)\n", " .Path.I :Path [x,y]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "learner2 = adaptive.Learner1D(f, bounds=learner.bounds)\n", "\n", "xs = np.linspace(*learner.bounds, len(learner.data))\n", "learner2.tell_many(xs, map(partial(f, wait=False), xs))\n", "\n", "learner.plot() + learner2.plot()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "random.seed(0)\n", "offsets = [random.uniform(-0.8, 0.8) for _ in range(3)]\n", "\n", "# sharp peaks at random locations in the domain\n", "def f_levels(x, offsets=offsets):\n", " a = 0.01\n", " return np.array([offset + x + a**2 / (a**2 + (x - offset)**2)\n", " for offset in offsets])" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "learner = adaptive.Learner1D(f_levels, bounds=(-1, 1))\n", "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "await runner.task # This is not needed in a notebook environment!" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "196b7b33048c4560a9d0f03e63f1a6be", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value='\\n
\\n
status
f…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runner.live_info()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a9f17c1fd90846e2b9d4bae1cb8db20f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='cancel live-plot', layout=Layout(width='150px'), style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "
\n", " \n", "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "
\n", "
\n", "" ], "text/plain": [ ":DynamicMap []\n", " :Overlay\n", " .Path.I :Path [x,y]\n", " .Scatter.I :Scatter [x] (y)" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "runner.live_plot(update_interval=0.1)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from adaptive.learner.learner1D import (curvature_loss_function,\n", " uniform_loss,\n", " default_loss)\n", "curvature_loss = curvature_loss_function()\n", "learner = adaptive.Learner1D(f, bounds=(-1, 1), loss_per_interval=curvature_loss)\n", "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "await runner.task # This is not needed in a notebook environment!" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cb37b0e936944665ab7fe01f9173bdd1", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(HTML(value='\\n
\\n
status
f…" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "runner.live_info()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "aa1daad9af05427287608ef2a407f009", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Button(description='cancel live-plot', layout=Layout(width='150px'), style=ButtonStyle())" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "text/html": [ "
\n", "
\n", " \n", "\n", "\n", "\n", "\n", "\n", "
\n", "\n", "
\n", "
\n", "" ], "text/plain": [ ":DynamicMap []\n", " :Overlay\n", " .Scatter.I :Scatter [x] (y)\n", " .Path.I :Path [x,y]" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "runner.live_plot(update_interval=0.1)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", "\n", "\n", "\n", "
\n", "
\n", "" ], "text/plain": [ ":Layout\n", " .Overlay.Homogeneous :Overlay\n", " .Scatter.I :Scatter [x] (y)\n", " .Path.I :Path [x,y]\n", " .Overlay.Euclidean_loss :Overlay\n", " .Scatter.I :Scatter [x] (y)\n", " .Path.I :Path [x,y]\n", " .Overlay.Curvature_loss :Overlay\n", " .Scatter.I :Scatter [x] (y)\n", " .Path.I :Path [x,y]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def sin_exp(x):\n", " from math import exp, sin\n", " return sin(15 * x) * exp(-x**2*2)\n", "\n", "learner_h = adaptive.Learner1D(sin_exp, (-1, 1), loss_per_interval=uniform_loss)\n", "learner_1 = adaptive.Learner1D(sin_exp, (-1, 1), loss_per_interval=default_loss)\n", "learner_2 = adaptive.Learner1D(sin_exp, (-1, 1), loss_per_interval=curvature_loss)\n", "\n", "npoints_goal = lambda l: l.npoints >= 100\n", "# adaptive.runner.simple is a non parallel blocking runner.\n", "adaptive.runner.simple(learner_h, goal=npoints_goal)\n", "adaptive.runner.simple(learner_1, 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