CMIP6 Cookbook
-
-
-
This Project Pythia Cookbook covers examples of analysis of Google Cloud CMIP6 data using Pangeo tools.
-Motivation
-From the CMIP6 website:
---The simulation data produced by models under previous phases of CMIP have been used in thousands of research papers … and the multi-model results provide some perspective on errors and uncertainty in model simulations. This information has proved invaluable in preparing high profile reports assessing our understanding of climate and climate change (e.g., the IPCC Assessment Reports).
-
With such a large amount of model output produced, moving the data around is inefficient. In this collection of notebooks, you will learn how to access cloud-optimized CMIP6 datasets, in addition to a few examples of using that data to analyze some aspects of climate change.
-Structure
- -Example workflows
-There are currently four examples of using this data to
--
-
Estimate equilibrium climate sensitivity (ECS)
-Plot global mean surface temperature under two different Shared Socioeconomic Pathways
-Plot changes in precipitation intensity under the SSP585 scenario
-Calculate changes in ocean heat uptake after regridding with xESMF
-
Running the Notebooks
-You can either run the notebook using Binder or on your local machine.
-Running on Binder
-The simplest way to interact with a Jupyter Notebook is through -Binder, which enables the execution of a -Jupyter Book in the cloud. The details of how this works are not -important for now. All you need to know is how to launch a Pythia -Cookbooks chapter via Binder. Simply navigate your mouse to -the top right corner of the book chapter you are viewing and click -on the rocket ship icon, (see figure below), and be sure to select -“launch Binder”. After a moment you should be presented with a -notebook that you can interact with. I.e. you’ll be able to execute -and even change the example programs. You’ll see that the code cells -have no output at first, until you execute them by pressing -Shift+Enter. Complete details on how to interact with -a live Jupyter notebook are described in Getting Started with -Jupyter.
-Running on Your Own Machine
-If you are interested in running this material locally on your computer, you will need to follow this workflow:
--
-
Clone the
-https://github.com/ProjectPythia/cmip6-cookbookrepository:--git clone https://github.com/ProjectPythia/cmip6-cookbook.git -
-Move into the
-cmip6-cookbookdirectory--cd cmip6-cookbook -
-Create and activate your conda environment from the
-environment.ymlfile--conda env create -f environment.yml -conda activate cmip6-cookbook-dev -
-Move into the
-notebooksdirectory and start up Jupyterlab--cd notebooks/ -jupyter lab -
-
At this point, you can interact with the notebooks! Make sure to check out the “Getting Started with Jupyter” content from the Pythia Foundations material if you are new to Jupyter or need a refresher.
-
-
-# CMIP6 Cookbook
-
-[](https://github.com/ProjectPythia/cmip6-cookbook/actions/workflows/nightly-build.yaml)
-[](https://binder.projectpythia.org/v2/gh/ProjectPythia/cmip6-cookbook.git/main)
-[](https://zenodo.org/badge/latestdoi/507993770)
-
-This Project Pythia Cookbook covers examples of analysis of Google Cloud CMIP6 data using Pangeo tools.
-
-## Motivation
-
-From the [CMIP6 website](https://esgf-node.llnl.gov/projects/cmip6/):
-
-> The simulation data produced by models under previous phases of CMIP have been used in thousands of research papers ... and the multi-model results provide some perspective on errors and uncertainty in model simulations. This information has proved invaluable in preparing high profile reports assessing our understanding of climate and climate change (e.g., the IPCC Assessment Reports).
-
-With such a large amount of model output produced, moving the data around is inefficient. In this collection of notebooks, you will learn how to access cloud-optimized CMIP6 datasets, in addition to a few examples of using that data to analyze some aspects of climate change.
-
-## Authors
-
-[Ryan Abernathey](https://github.com/rabernat), [Henri Drake](https://github.com/hdrake), [Robert Ford](https://github.com/r-ford), [Max Grover](https://github.com/mgrover1)
-
-### Contributors
-
-![\"CMIP6](\"../images/cmip6-cookbook-thumbnail.png\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Estimating Equilibrium Climate Sensitivity"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "attachments": {},
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "Equilibrium Climate Sensitivity (ECS) is defined as change in global-mean near-surface air temperature (GMST) change due to an instantaneous doubling of CO$_2$ concentrations and once the coupled ocean-atmosphere-sea ice system has acheived a statistical equilibrium (i.e. at the top-of-atmosphere, incoming solar shortwave radiation is balanced by reflected solar shortwave and outgoing thermal longwave radiation).\n",
- "\n",
- "This notebook uses the “[Gregory method]( https://doi.org/10.1029/2003GL018747)” to approximate the ECS of CMIP6 models based on the first 150 years after an abrupt doubling of CO$_2$\n",
- "concentrations. The Gregory method extrapolates the quasi-linear relationship between GMST and radiative imbalance at the top-of-atmosphere to estimate how much warming would occur if the system were in radiative balance at the top-of-atmosphere, which is by definition the equilibrium response. In particular, we extrapolate the linear relationship that occurs between 100 and 150 years after the abrupt quadrupling. \n",
- "\n",
- "Since the radiative forcing due to CO$_2$ is a logarithmic function of the CO$_2$ concentration, the GMST change from a first doubling is roughly the same as for a second doubling (to first order, we can assume feedbacks as constant), which means that the GMST change due to a quadrupling of CO$_2$ is roughly $\\Delta T_{4\\times\\mathrm{CO}_2}=2\\times\\mathrm{ECS}$. See also [Mauritsen et al. 2019](https://doi.org/10.1029/2018MS001400) for a detailed application of the Gregory method (with modifications) for the case of one specific CMIP6 model, the MPI-M Earth System Model.\n",
- "\n",
- "For another take on applying the Gregory method to estimate ECS, see [Angeline Pendergrass’ code](https://github.com/apendergrass/cmip6-ecs)."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n",
- "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
- "| Dask | Helpful | |\n",
- "| Climate sensitivity | Helpful | |\n",
- "\n",
- "- **Time to learn**: 30 minutes"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
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- "text": [
- "/tmp/ipykernel_98/1804016931.py:8: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
- " from tqdm.autonotebook import tqdm # Fancy progress bars for our loops!\n"
- ]
- }
- ],
- "source": [
- "from matplotlib import pyplot as plt\n",
- "import sys\n",
- "import numpy as np\n",
- "import pandas as pd\n",
- "import xarray as xr\n",
- "import cartopy\n",
- "import dask\n",
- "from tqdm.autonotebook import tqdm # Fancy progress bars for our loops!\n",
- "import intake\n",
- "import fsspec\n",
- "from dask_gateway import Gateway\n",
- "from dask.distributed import Client\n",
- "\n",
- "%matplotlib inline\n",
- "plt.rcParams['figure.figsize'] = 12, 6"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "tags": []
- },
- "source": [
- "## Compute Cluster"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Here we use a dask cluster to parallelize our analysis."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "execution": {
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- "source": [
- "platform = sys.platform\n",
- "\n",
- "if (platform == 'win32'):\n",
- " import multiprocessing.popen_spawn_win32\n",
- "else:\n",
- " import multiprocessing.popen_spawn_posix"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Initiate the Dask client:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
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- "outputs": [
- {
- "data": {
- "text/html": [
- "![\"ESGF](\"../images/esgf.png\")
\n",
- "![\"ARM](\"../images/arm_logo.png\"){kind=logo}
"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "b26398c8-01f4-40e7-bf6b-db9e4c466eac",
- "metadata": {},
- "source": [
- "# Compare Data from ESGF and ARM"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "dd0adfff-43d4-4874-9d6d-511693ce8613",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "This notebook details how to compare CMIP6 data hosted through the Earth System Grid Federation (ESGF) to observations collected and hosted through the Department of Energy's Atmospheric Radiation Measurement (ARM) user facility.\n",
- "\n",
- "The measurement of focus is 2 meter air temperature, collected at the Southern Great Plains (SGP) site in Northern Oklahoma. This climate observatory has collected state-of-the-art observations since 1993."
- ]
- },
- {
- "cell_type": "markdown",
- "id": "5ffd1461-464c-4e04-ad8f-e2aa1a710d59",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n",
- "| [Search and Load CMIP6 Data via ESGF/OPeNDAP](https://projectpythia.org/cmip6-cookbook/notebooks/foundations/esgf-opendap.html) | Necessary | Familiarity with data access patterns |\n",
- "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
- "| [Dask Arrays with Xarray](https://foundations.projectpythia.org/core/xarray/dask-arrays-xarray.html) | Helpful | Familiarity with lazy-loading |\n",
- "\n",
- "- **Time to learn**: 25 minutes"
- ]
- },
- {
- "cell_type": "markdown",
- "id": "a40b8cb6-ba5a-470e-90ef-3e21e0a0dc97",
- "metadata": {},
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
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- " element.appendChild(document.createTextNode(css));\n",
- " document.body.appendChild(element);\n",
- " }\n",
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- " var js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n",
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- " for (var i = 0; i < inline_js.length; i++) {\n",
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i < links.length; i++) {\n var link = links[i]\n if (link.href != null) {\n\texisting_stylesheets.push(link.href)\n }\n }\n for (var i = 0; i < css_urls.length; i++) {\n var url = css_urls[i];\n if (existing_stylesheets.indexOf(url) !== -1) {\n\ton_load()\n\tcontinue;\n }\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 } if (((window['jsPanel'] !== undefined) && (!(window['jsPanel'] instanceof HTMLElement))) || window.requirejs) {\n var urls = ['https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/jspanel.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/modal/jspanel.modal.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/tooltip/jspanel.tooltip.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/hint/jspanel.hint.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/layout/jspanel.layout.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/contextmenu/jspanel.contextmenu.js', 'https://cdn.holoviz.org/panel/1.3.1/dist/bundled/floatpanel/jspanel4@4.12.0/dist/extensions/dock/jspanel.dock.js'];\n for (var i = 0; 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i < js_modules.length; i++) {\n var url = js_modules[i];\n if (skip.indexOf(url) !== -1 || existing_scripts.indexOf(url) !== -1) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\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 element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (const name in js_exports) {\n var url = js_exports[name];\n if (skip.indexOf(url) >= 0 || root[name] != null) {\n\tif (!window.requirejs) {\n\t on_load();\n\t}\n\tcontinue;\n }\n var element = document.createElement('script');\n element.onerror = on_error;\n element.async = false;\n element.type = \"module\";\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n element.textContent = `\n import ${name} from \"${url}\"\n window.${name} = ${name}\n window._bokeh_on_load()\n `\n document.head.appendChild(element);\n }\n if (!js_urls.length && !js_modules.length) {\n on_load()\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.bokeh.org/bokeh/release/bokeh-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.1.min.js\", \"https://cdn.holoviz.org/panel/1.3.1/dist/panel.min.js\"];\n var js_modules = [];\n var js_exports = {};\n var css_urls = [];\n var inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {} // ensure no trailing comma for IE\n ];\n\n function run_inline_js() {\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 }\n // Cache old bokeh versions\n if (Bokeh != undefined && !reloading) {\n\tvar NewBokeh = root.Bokeh;\n\tif (Bokeh.versions === undefined) {\n\t Bokeh.versions = new Map();\n\t}\n\tif (NewBokeh.version !== Bokeh.version) {\n\t Bokeh.versions.set(NewBokeh.version, NewBokeh)\n\t}\n\troot.Bokeh = 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 }\n root._bokeh_is_initializing = false\n }\n\n function load_or_wait() {\n // Implement a backoff loop that tries to ensure we do not load multiple\n // versions of Bokeh and its dependencies at the same time.\n // In recent versions we use the root._bokeh_is_initializing flag\n // to determine whether there is an ongoing attempt to initialize\n // bokeh, however for backward compatibility we also try to ensure\n // that we do not start loading a newer (Panel>=1.0 and Bokeh>3) version\n // before older versions are fully initialized.\n if (root._bokeh_is_initializing && Date.now() > root._bokeh_timeout) {\n root._bokeh_is_initializing = false;\n root._bokeh_onload_callbacks = undefined;\n console.log(\"Bokeh: BokehJS was loaded multiple times but one version failed to initialize.\");\n load_or_wait();\n } else if (root._bokeh_is_initializing || (typeof root._bokeh_is_initializing === \"undefined\" && root._bokeh_onload_callbacks !== undefined)) {\n setTimeout(load_or_wait, 100);\n } else {\n Bokeh = root.Bokeh;\n bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n if (!reloading && (!bokeh_loaded || is_dev)) {\n\troot.Bokeh = undefined;\n }\n load_libs(css_urls, js_urls, js_modules, js_exports, function() {\n\tconsole.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n\trun_inline_js();\n });\n }\n }\n // Give older versions of the autoload script a head-start to ensure\n // they initialize before we start loading newer version.\n setTimeout(load_or_wait, 100)\n}(window));"
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- " var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
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- " }\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",
- "/**\n",
- " * Handle when an output is cleared or removed\n",
- " */\n",
- "function handle_clear_output(event, handle) {\n",
- " var id = handle.cell.output_area._hv_plot_id;\n",
- " var server_id = handle.cell.output_area._bokeh_server_id;\n",
- " if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
- " var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
- " if (server_id !== null) {\n",
- " comm.send({event_type: 'server_delete', 'id': server_id});\n",
- " return;\n",
- " } else if (comm !== null) {\n",
- " comm.send({event_type: 'delete', 'id': id});\n",
- " }\n",
- " delete PyViz.plot_index[id];\n",
- " if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n",
- " var doc = window.Bokeh.index[id].model.document\n",
- " doc.clear();\n",
- " const i = window.Bokeh.documents.indexOf(doc);\n",
- " if (i > -1) {\n",
- " window.Bokeh.documents.splice(i, 1);\n",
- " }\n",
- " }\n",
- "}\n",
- "\n",
- "/**\n",
- " * Handle kernel restart event\n",
- " */\n",
- "function handle_kernel_cleanup(event, handle) {\n",
- " delete PyViz.comms[\"hv-extension-comm\"];\n",
- " window.PyViz.plot_index = {}\n",
- "}\n",
- "\n",
- "/**\n",
- " * Handle update_display_data messages\n",
- " */\n",
- "function handle_update_output(event, handle) {\n",
- " handle_clear_output(event, {cell: {output_area: handle.output_area}})\n",
- " handle_add_output(event, handle)\n",
- "}\n",
- "\n",
- "function register_renderer(events, OutputArea) {\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[0]);\n",
- " element.append(toinsert);\n",
- " return toinsert\n",
- " }\n",
- "\n",
- " events.on('output_added.OutputArea', handle_add_output);\n",
- " events.on('output_updated.OutputArea', handle_update_output);\n",
- " events.on('clear_output.CodeCell', handle_clear_output);\n",
- " events.on('delete.Cell', handle_clear_output);\n",
- " events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
- "\n",
- " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
- " safe: true,\n",
- " index: 0\n",
- " });\n",
- "}\n",
- "\n",
- "if (window.Jupyter !== undefined) {\n",
- " try {\n",
- " var events = require('base/js/events');\n",
- " var OutputArea = require('notebook/js/outputarea').OutputArea;\n",
- " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
- " register_renderer(events, OutputArea);\n",
- " }\n",
- " } catch(err) {\n",
- " }\n",
- "}\n"
- ],
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comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n console.log(message)\n var content = {data: message.data, comm_id};\n var buffers = []\n for (var buffer of message.buffers || []) {\n buffers.push(new DataView(buffer))\n }\n var metadata = message.metadata || {};\n var msg = {content, buffers, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n })\n }\n }\n\n JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n if (comm_id in window.PyViz.comms) {\n return window.PyViz.comms[comm_id];\n } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n if (msg_handler) {\n comm.on_msg(msg_handler);\n }\n } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n comm.open();\n if (msg_handler) {\n comm.onMsg = msg_handler;\n }\n } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n var comm_promise = google.colab.kernel.comms.open(comm_id)\n comm_promise.then((comm) => {\n window.PyViz.comms[comm_id] = comm;\n if (msg_handler) {\n var messages = comm.messages[Symbol.asyncIterator]();\n function processIteratorResult(result) {\n var message = result.value;\n var content = {data: message.data};\n var metadata = message.metadata || {comm_id};\n var msg = {content, metadata}\n msg_handler(msg);\n return messages.next().then(processIteratorResult);\n }\n return messages.next().then(processIteratorResult);\n }\n }) \n var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n return comm_promise.then((comm) => {\n comm.send(data, metadata, buffers, disposeOnDone);\n });\n };\n var comm = {\n send: sendClosure\n };\n }\n window.PyViz.comms[comm_id] = comm;\n return comm;\n }\n window.PyViz.comm_manager = new JupyterCommManager();\n \n\n\nvar JS_MIME_TYPE = 'application/javascript';\nvar HTML_MIME_TYPE = 'text/html';\nvar EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\nvar CLASS_NAME = 'output';\n\n/**\n * Render data to the DOM node\n */\nfunction render(props, node) {\n var div = document.createElement(\"div\");\n var script = document.createElement(\"script\");\n node.appendChild(div);\n node.appendChild(script);\n}\n\n/**\n * Handle when a new output is added\n */\nfunction handle_add_output(event, handle) {\n var output_area = handle.output_area;\n var output = handle.output;\n if ((output.data == undefined) || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n return\n }\n var id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n var toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n if (id !== undefined) {\n var nchildren = toinsert.length;\n var html_node = toinsert[nchildren-1].children[0];\n html_node.innerHTML = output.data[HTML_MIME_TYPE];\n var scripts = [];\n var nodelist = html_node.querySelectorAll(\"script\");\n for (var i in nodelist) {\n if (nodelist.hasOwnProperty(i)) {\n scripts.push(nodelist[i])\n }\n }\n\n scripts.forEach( function (oldScript) {\n var newScript = document.createElement(\"script\");\n var attrs = [];\n var nodemap = oldScript.attributes;\n for (var j in nodemap) {\n if (nodemap.hasOwnProperty(j)) {\n attrs.push(nodemap[j])\n }\n }\n attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n oldScript.parentNode.replaceChild(newScript, oldScript);\n });\n if (JS_MIME_TYPE in output.data) {\n toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n }\n output_area._hv_plot_id = id;\n if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n window.PyViz.plot_index[id] = Bokeh.index[id];\n } else {\n window.PyViz.plot_index[id] = null;\n }\n } else 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].childNodes[1].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/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n var id = handle.cell.output_area._hv_plot_id;\n var server_id = handle.cell.output_area._bokeh_server_id;\n if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n if (server_id !== null) {\n comm.send({event_type: 'server_delete', 'id': server_id});\n return;\n } else if (comm !== null) {\n comm.send({event_type: 'delete', 'id': id});\n }\n delete PyViz.plot_index[id];\n if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n var doc = window.Bokeh.index[id].model.document\n doc.clear();\n const i = window.Bokeh.documents.indexOf(doc);\n if (i > -1) {\n window.Bokeh.documents.splice(i, 1);\n }\n }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n delete PyViz.comms[\"hv-extension-comm\"];\n window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\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[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n"
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