+ESGF Cookbook
+
+
+
This Project Pythia Cookbook covers how to access and analyze datasets that can be accessed from Earth System Grid Federation (ESGF) cyberinfrastructure.
+Motivation
+This cookbook focuses on highlighting analysis recipes, as well as data acccess methods, all accesible within the Python programming language. This cookbook also spans beyond the scope of a single Climate Model Intercomparison Project (ex. CMIP6), expanding to other experiments/datasets such as CMIP5 and obs4MIPs.
+Authors
+Max Grover, Nathan Collier, Carsten Ehbrecht, Jacqueline Nugent, Gerardo Rivera Tello
+Contributors
+ +Structure
+ + +Running the Notebooks
+You can either run the notebook using the NIMBUS Binder or on your local machine.
+Running on Binder
+The simplest way to interact with a Jupyter Notebook is through +the NIMBUS Binder, which enables the execution of a +Jupyter Book in the cloud-like infrastructure. 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:
+(Replace “cookbook-example” with the title of your cookbooks)
+-
+
Clone the
+https://github.com/esgf2-us/esgf-cookbookrepository:++git clone https://github.com/esgf2-us/esgf-cookbook.git +
+Move into the
+cookbook-exampledirectory++cd esgf-cookbook +
+Create and activate your conda environment from the
+environment.ymlfile++conda env create -f environment.yml +conda activate esgf-cookbook-dev +
+Move into the
+notebooksdirectory and start up Jupyterlab++cd notebooks/ +jupyter lab +
+
+
+# ESGF Cookbook
+
+[](https://github.com/ProjectPythia/cookbook-template/actions/workflows/nightly-build.yaml)
+[](https://binder-nimbus.llnl.gov/v2/gh/esgf2-us/esgf-cookbook/main?labpath=notebooks)
+[](https://zenodo.org/badge/latestdoi/475509405)
+
+This Project Pythia Cookbook covers how to access and analyze datasets that can be accessed from Earth System Grid Federation (ESGF) cyberinfrastructure.
+
+## Motivation
+
+This cookbook focuses on highlighting analysis recipes, as well as data acccess methods, all accesible within the Python programming language. This cookbook also spans beyond the scope of a single Climate Model Intercomparison Project (ex. CMIP6), expanding to other experiments/datasets such as CMIP5 and obs4MIPs.
+
+## Authors
+
+[Max Grover](@mgrover1), [Nathan Collier](@nocollier), [Carsten Ehbrecht](@cehbrecht), [Jacqueline Nugent](@jacnugent), [Gerardo Rivera Tello](@griverat)
+
+### Contributors
+
+![\"ESGF](\"images/logos/esgf2-us.png\")
"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Complex Searching with `intake-esgf`\n",
+ "\n",
+ "## Overview\n",
+ "\n",
+ "In this tutorial we will present an interface under design to facilitate complex searching using [intake-esgf](https://github.com/esgf2-us/intake-esgf). `intake-esgf` is a small `intake` and `intake-esm` *inspired* package under development in ESGF2. Please note that there is a name collison with an existing package in PyPI and conda. You will need to install the package from [source](https://github.com/esgf2-us/intake-esgf).\n",
+ "\n",
+ "## Prerequisites\n",
+ "\n",
+ "| Concepts | Importance | Notes |\n",
+ "| --- | --- | --- |\n",
+ "| [Install Package](https://github.com/esgf2-us/intake-esgf) | Necessary | |\n",
+ "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
+ "| Familiar with [intake-esm](https://intake-esm.readthedocs.io/en/stable/) | Helpful | Similar interface |\n",
+ "| [Transient climate response](https://doi.org/10.1029/2008JD010405) | Background | |\n",
+ "- **Time to learn**: 30 minutes\n",
+ "\n",
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from intake_esgf import ESGFCatalog"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Initializing the Catalog\n",
+ "\n",
+ "As with `intake-esm` we first instantiate the catalog. However, since we will populate the catalog with search results, the catalog starts empty. Internally, we query different ESGF index nodes for information about what datasets you wish to include in your analysis. As ESGF2 is actively working on an index redesign, our catlogs by default point to a Globus (ElasticSearch) based index at ALCF (Argonne Leadership Computing Facility)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Perform a search() to populate the catalog.\n",
+ "GlobusESGFIndex('anl-dev')\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat = ESGFCatalog()\n",
+ "print(cat)\n",
+ "for ind in cat.indices: # Which indices are included?\n",
+ " print(ind)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We also provide support for connecting to the ESGF1 Solr-based indices. You may specify a server or list or just include `True` to choose all the federated index nodes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "GlobusESGFIndex('anl-dev')\n",
+ "SolrESGFIndex('esgf.ceda.ac.uk')\n",
+ "SolrESGFIndex('esgf-data.dkrz.de')\n",
+ "SolrESGFIndex('esgf-node.ipsl.upmc.fr')\n",
+ "SolrESGFIndex('esg-dn1.nsc.liu.se')\n",
+ "SolrESGFIndex('esgf-node.llnl.gov')\n",
+ "SolrESGFIndex('esgf.nci.org.au')\n",
+ "SolrESGFIndex('esgf-node.ornl.gov')\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat = ESGFCatalog(esgf1_indices=\"esgf-node.llnl.gov\") # include LLNL\n",
+ "cat = ESGFCatalog(esgf1_indices=[\"esgf-node.ornl.gov\", \"esgf.ceda.ac.uk\"]) # ORNL & CEDA\n",
+ "cat = ESGFCatalog(esgf1_indices=True) # all federated indices\n",
+ "for ind in cat.indices:\n",
+ " print(ind)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Populate the catalog\n",
+ "\n",
+ "Many times, an analysis will require several variables across multiple experiments. For example, if one were to compute the transient climate response (TCRE), you would need tempererature (`tas`) and carbon emissions from land (`nbp`) and ocean (`fgco2`) for a 1% CO2 increase experiment (`1pctCO2`) as well as the control experiment (`piControl`). If TCRE is not in your particular science, that is ok for this notebook. It is a motivating example and the specifics are less important than the search concepts. First, we perform a search in a familiar syntax."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ " Searching indices: 100%|███████████████████████████████|8/8 [ 1.36s/index]\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Summary information for 399 results:\n",
+ "mip_era [CMIP6]\n",
+ "activity_id [CMIP]\n",
+ "institution_id [CNRM-CERFACS, IPSL, MOHC, MRI, MPI-M, NCAR, N...\n",
+ "source_id [CNRM-ESM2-1, CNRM-CM6-1, IPSL-CM6A-LR, CNRM-C...\n",
+ "experiment_id [piControl, 1pctCO2]\n",
+ "member_id [r1i1p1f2, r2i1p1f2, r3i1p1f2, r1i1p1f1, r4i1p...\n",
+ "table_id [Omon, Amon, Lmon]\n",
+ "variable_id [fgco2, tas, nbp]\n",
+ "grid_label [gn, gr, gr1]\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat.search(\n",
+ " experiment_id=[\"piControl\", \"1pctCO2\"],\n",
+ " variable_id=[\"tas\", \"fgco2\", \"nbp\"],\n",
+ " table_id=[\"Amon\", \"Omon\", \"Lmon\"],\n",
+ ")\n",
+ "print(cat)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Internally, this launches simultaneous searches that are combined locally to provide a global view of what datasets are available. While the Solr indices themselves can be searched in distributed fashion, they will not report if an index has failed to return a response. As index nodes go down from time to time, this can leave you with a false impression that you have found all the datasets of interest. By managing the searches locally, `intake-esgf` can report back to you that an index has failed and that your results may be incomplete.\n",
+ "\n",
+ "If you would like details about what `intake-esgf` is doing, look in the local cache directory (`${HOME}/.esgf/`) for a `esgf.log` file. This is a full history of everything that `intake-esgf` has searched, downloaded, or accessed. You can also look at just this session by calling `session_log()`. In this case you will see how long each index took to return a response and if any failed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[36;20m2023-12-07 09:37:01 \u001b[0m\u001b[36;32msearch begin\u001b[0m experiment_id=['piControl', '1pctCO2'], variable_id=['tas', 'fgco2', 'nbp'], table_id=['Amon', 'Omon', 'Lmon']\n",
+ "\u001b[36;20m2023-12-07 09:37:02 \u001b[0m└─SolrESGFIndex('esgf-node.ipsl.upmc.fr') response_time=1.42 total_time=1.89\n",
+ "\u001b[36;20m2023-12-07 09:37:03 \u001b[0m└─GlobusESGFIndex('anl-dev') results=329 response_time=2.19 total_time=2.19\n",
+ "\u001b[36;20m2023-12-07 09:37:03 \u001b[0m└─SolrESGFIndex('esg-dn1.nsc.liu.se') response_time=1.90 total_time=2.47\n",
+ "\u001b[36;20m2023-12-07 09:37:06 \u001b[0m└─SolrESGFIndex('esgf.ceda.ac.uk') response_time=2.40 total_time=5.31\n",
+ "\u001b[36;20m2023-12-07 09:37:07 \u001b[0m└─SolrESGFIndex('esgf.nci.org.au') response_time=3.26 total_time=6.56\n",
+ "\u001b[36;20m2023-12-07 09:37:08 \u001b[0m└─SolrESGFIndex('esgf-node.ornl.gov') response_time=1.51 total_time=6.92\n",
+ "\u001b[36;20m2023-12-07 09:37:08 \u001b[0m└─SolrESGFIndex('esgf-data.dkrz.de') response_time=2.80 total_time=7.63\n",
+ "\u001b[36;20m2023-12-07 09:37:11 \u001b[0m└─SolrESGFIndex('esgf-node.llnl.gov') response_time=1.49 total_time=10.89\n",
+ "\u001b[36;20m2023-12-07 09:37:12 \u001b[0m\u001b[36;32msearch end\u001b[0m total_time=11.41\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(cat.session_log())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "At this stage of the search you have a catalog full of possibly relevant datasets for your analysis, stored in a `pandas` dataframe. You are free to view and manipulate this dataframe to help hone these results down. It is available to you as the `df` member of the `ESGFCatalog`. You should be careful to only remove rows as internally we could use any column in the downloading of the data. Also note that we have removed the user-facing notion of *where* the data is hosted. The `id` column of this dataframe is a list of full `dataset_ids` which includes the location information. At the point when you are ready to download data, we will choose locations automatically that are fastest for you."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "![\"Globus](\"images/globus-logo.png\"){kind=logo}
\n",
+ "![\"ESGF](\"images/esgf.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "483dcdb6-e125-4a52-a21f-55cfe1000dea",
+ "metadata": {},
+ "source": [
+ "# ENSO Calculations using Globus Flows"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4a415308-0e9a-470c-bb68-da75b349c006",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "\n",
+ "In this workflow, we combine topics covered in previous Pythia Foundations and CMIP6 Cookbook content to compute the [Niño 3.4 Index](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni) to multiple datasets, with the primary computations occuring on a remote machine. As a refresher of what the ENSO 3.4 index is, please see the following text, which is also included in the [ENSO Xarray](https://foundations.projectpythia.org/core/xarray/enso-xarray.html) content in the Pythia Foundations content.\n",
+ "\n",
+ "> Niño 3.4 (5N-5S, 170W-120W): The Niño 3.4 anomalies may be thought of as representing the average equatorial SSTs across the Pacific from about the dateline to the South American coast. The Niño 3.4 index typically uses a 5-month running mean, and El Niño or La Niña events are defined when the Niño 3.4 SSTs exceed +/- 0.4C for a period of six months or more.\n",
+ "\n",
+ "> Niño X Index computation: a) Compute area averaged total SST from Niño X region; b) Compute monthly climatology (e.g., 1950-1979) for area averaged total SST from Niño X region, and subtract climatology from area averaged total SST time series to obtain anomalies; c) Smooth the anomalies with a 5-month running mean; d) Normalize the smoothed values by its standard deviation over the climatological period.\n",
+ "\n",
+ "\n",
+ "\n",
+ "The previous cookbook, we ran this in a single notebook locally. In this example, we aim to execute the workflow on a remote machine, with only the visualizion of the dataset occuring locally.\n",
+ "\n",
+ "The overall goal of this tutorial is to introduce the idea of functions as a service with Globus, and how this can be used to calculate ENSO indices."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2d4c6aed-a9c5-4d29-bfa3-c8e8be230567",
+ "metadata": {},
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "| Concepts | Importance | Notes |\n",
+ "| --- | --- | --- |\n",
+ "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | |\n",
+ "| [hvPlot Basics](https://hvplot.holoviz.org/getting_started/hvplot.html) | Necessary | Interactive Visualization with hvPlot |\n",
+ "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
+ "| [Calculating ENSO with Xarray](https://foundations.projectpythia.org/core/xarray/enso-xarray.html) | Neccessary | Understanding of Masking and Xarray Functions |\n",
+ "| Dask | Helpful | |\n",
+ "\n",
+ "- **Time to learn**: 30 minutes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7ff38f37-8f14-443f-b0c7-188baf75d1be",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "52bcfa1a-3907-446d-b384-29e97b5c8cb9",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
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+ " var js_modules = [];\n",
+ " var js_exports = {};\n",
+ " var css_urls = [];\n",
+ " var inline_js = [ function(Bokeh) {\n",
+ " Bokeh.set_log_level(\"info\");\n",
+ " },\n",
+ "function(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",
+ "\ttry {\n",
+ " inline_js[i].call(root, root.Bokeh);\n",
+ "\t} catch(e) {\n",
+ "\t if (!reloading) {\n",
+ "\t throw e;\n",
+ "\t }\n",
+ "\t}\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",
+ " root._bokeh_is_initializing = true\n",
+ " root._bokeh_onload_callbacks = []\n",
+ " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
+ " if (!reloading && !bokeh_loaded) {\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));"
+ ],
+ "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\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 window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; 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 } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[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 console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; 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-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/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\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\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 root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\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));"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "\n",
+ "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+ " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+ "}\n",
+ "\n",
+ "\n",
+ " function JupyterCommManager() {\n",
+ " }\n",
+ "\n",
+ " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+ " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+ " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+ " comm_manager.register_target(comm_id, function(comm) {\n",
+ " comm.on_msg(msg_handler);\n",
+ " });\n",
+ " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+ " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+ " comm.onMsg = msg_handler;\n",
+ " });\n",
+ " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+ " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+ " var messages = 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",
+ "\n",
+ "var JS_MIME_TYPE = 'application/javascript';\n",
+ "var HTML_MIME_TYPE = 'text/html';\n",
+ "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+ "var CLASS_NAME = 'output';\n",
+ "\n",
+ "/**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
+ " */\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",
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\n",
+ "\n",
+ "\n",
+ " 
\n",
+ " \n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "![\"Rooki](\"images/logos/rooki_logo.png\"){kind=logo}
\n",
+ "![\"Cartopy](\"images/logos/cartopy_logo.png\"){kind=logo}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50b5d7e7-df4e-4992-a29b-8804b081a320",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Demo: Regridding and Plotting with Rooki and Cartopy \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "abd4b497-cdbf-4c29-857c-3017abf9e927",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f79862d-7181-4f04-966c-19b5e03a22a5",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "In this notebook, we demonstrate how to use Rooki to regrid CMIP model data and plot it in Cartopy for two examples:\n",
+ "\n",
+ "1. Regrid two CMIP models onto the same grid \n",
+ "1. Coarsen the output for one model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f1db315-fb2d-466d-bd6e-8a4ef18b6cf1",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "| Concepts | Importance | Notes |\n",
+ "| --- | --- | --- |\n",
+ "| [Intro to intake-esgf](https://projectpythia.org/esgf-cookbook/notebooks/intro-search.html) | Necessary | |\n",
+ "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
+ "| [Using Rooki to access CMIP6 data](https://projectpythia.org/esgf-cookbook/notebooks/rooki.html) | Helpful | Familiarity with rooki |\n",
+ "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
+ "\n",
+ "- **Time to learn**: 15 minutes\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7cbc5d91-db3f-4afd-9093-c3abc7dec82b",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2582d535-9b99-4115-b0ee-7459acd76ec0",
+ "metadata": {},
+ "source": [
+ "## Imports\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ad4562c9-f5eb-496e-9e17-6453f426e910",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "import rooki.operators as ops\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.colors as mcolors\n",
+ "import cartopy.crs as ccrs\n",
+ "import cartopy.feature as cfeature\n",
+ "\n",
+ "from intake_esgf import ESGFCatalog\n",
+ "from rooki import rooki\n",
+ "from matplotlib.gridspec import GridSpec\n",
+ "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2b47b1d-db2d-4074-8c92-bb71fa0459a7",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Example 1: Regrid two CMIP6 models onto the same grid"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc1d512a-68d3-43cf-aac7-6ca233d9ef73",
+ "metadata": {},
+ "source": [
+ "In this example, we want to compare the historical precipitation output between two CMIP models, CESM2 and CanESM5. Here will will look at the annual mean precipitation for 2010. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46f5fba3-7410-465c-abdf-4e338855284c",
+ "metadata": {},
+ "source": [
+ "### Access the desired datasets using intake-esgf and rooki"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c5f4dc65-0dff-4023-880c-f511cbc58666",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "The function and workflow to read in CMPI6 data using `intake-esgf` and `rooki` in the next few cells are adapted from [intake-esgf-with-rooki.ipynb](https://github.com/ProjectPythia/esgf-cookbook/blob/cf69015a464b68ee28cfdd4a27cee4e9d6ca2ca9/notebooks/use-intake-esgf-with-rooki.ipynb). Essentially, we use `intake-esgf` to find the dataset IDs we want and then subset and average them using `rooki`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d053a676-2a27-4be0-93c0-eafb9671c0bc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def separate_dataset_id(full_dataset):\n",
+ " return full_dataset[0].split(\"|\")[0]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "095db615-275a-4dbc-8467-833fd7992aed",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d94f8c4ed5c24cd8b123fac457ee6d00",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " Searching indices: 0%| |0/2 [ ?index/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['CMIP6.CMIP.CCCma.CanESM5.historical.r1i1p1f1.Amon.pr.gn.v20190429',\n",
+ " 'CMIP6.CMIP.NCAR.CESM2.historical.r1i1p1f1.Amon.pr.gn.v20190401']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cat = ESGFCatalog()\n",
+ "cat.search(\n",
+ " activity_id='CMIP',\n",
+ " experiment_id=[\"historical\",],\n",
+ " variable_id=[\"pr\"],\n",
+ " member_id='r1i1p1f1',\n",
+ " grid_label='gn',\n",
+ " table_id=\"Amon\",\n",
+ " source_id = [ \"CESM2\", \"CanESM5\"]\n",
+ " )\n",
+ "\n",
+ "dsets = [separate_dataset_id(dataset) for dataset in list(cat.df.id.values)]\n",
+ "dsets\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "777f6bc4-f3a8-4110-bc2a-82cbf227ec4e",
+ "metadata": {},
+ "source": [
+ "Subset the data to get the precipitation variable for 2010 and then average by time:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bf653879-96b5-48e0-be9b-0f0cc08152e2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading to /tmp/metalink_6_iqo1_6/pr_Amon_CanESM5_historical_r1i1p1f1_gn_20100101-20100101_avg-year.nc.\n",
+ "Downloading to /tmp/metalink_d7uge4gt/pr_Amon_CESM2_historical_r1i1p1f1_gn_20100101-20100101_avg-year.nc.\n"
+ ]
+ }
+ ],
+ "source": [
+ "dset_list = [[]]*len(dsets)\n",
+ "\n",
+ "for i, dset_id in enumerate(dsets):\n",
+ " wf = ops.AverageByTime(\n",
+ " ops.Subset(\n",
+ " ops.Input('pr', [dset_id]),\n",
+ " time='2010/2010'\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " resp = wf.orchestrate()\n",
+ "\n",
+ " # if it worked, add the dataset to our list\n",
+ " if resp.ok:\n",
+ " dset_list[i] = resp.datasets()[0]\n",
+ " \n",
+ " # if it failed, tell us why\n",
+ " else:\n",
+ " print(resp.status)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e040d078-3981-4246-a10b-c50cf104d8ed",
+ "metadata": {},
+ "source": [
+ "Print the dataset list to get an overview of the metadata structure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b2ed096a-2cfc-4e51-9b2a-43b9ee4f103e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[![\"Project](\"images/ProjectPythia_Logo_Final-01-Blue.svg\"){kind=logo}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Project Pythia Notebook Template\n",
+ "\n",
+ "Next, title your notebook appropriately with a top-level Markdown header, `#`. Do not use this level header anywhere else in the notebook. Our book build process will use this title in the navbar, table of contents, etc. Keep it short, keep it descriptive. Follow this with a `---` cell to visually distinguish the transition to the prerequisites section."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,\n",
+ "\n",
+ "1. This is a numbered list of the specific topics\n",
+ "1. These should map approximately to your main sections of content\n",
+ "1. Or each second-level, `##`, header in your notebook\n",
+ "1. Keep the size and scope of your notebook in check\n",
+ "1. And be sure to let the reader know up front the important concepts they'll be leaving with"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Prerequisites\n",
+ "This section was inspired by [this template](https://github.com/alan-turing-institute/the-turing-way/blob/master/book/templates/chapter-template/chapter-landing-page.md) of the wonderful [The Turing Way](https://the-turing-way.netlify.app) Jupyter Book.\n",
+ "\n",
+ "Following your overview, tell your reader what concepts, packages, or other background information they'll **need** before learning your material. Tie this explicitly with links to other pages here in Foundations or to relevant external resources. Remove this body text, then populate the Markdown table, denoted in this cell with `|` vertical brackets, below, and fill out the information following. In this table, lay out prerequisite concepts by explicitly linking to other Foundations material or external resources, or describe generally helpful concepts.\n",
+ "\n",
+ "Label the importance of each concept explicitly as **helpful/necessary**.\n",
+ "\n",
+ "| Concepts | Importance | Notes |\n",
+ "| --- | --- | --- |\n",
+ "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
+ "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
+ "| Project management | Helpful | |\n",
+ "\n",
+ "- **Time to learn**: estimate in minutes. For a rough idea, use 5 mins per subsection, 10 if longer; add these up for a total. Safer to round up and overestimate.\n",
+ "- **System requirements**:\n",
+ " - Populate with any system, version, or non-Python software requirements if necessary\n",
+ " - Otherwise use the concepts table above and the Imports section below to describe required packages as necessary\n",
+ " - If no extra requirements, remove the **System requirements** point altogether"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Imports\n",
+ "Begin your body of content with another `---` divider before continuing into this section, then remove this body text and populate the following code cell with all necessary Python imports **up-front**:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import sys"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Your first content section"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This is where you begin your first section of material, loosely tied to your objectives stated up front. Tie together your notebook as a narrative, with interspersed Markdown text, images, and more as necessary,"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# as well as any and all of your code cells\n",
+ "print(\"Hello world!\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### A content subsection\n",
+ "Divide and conquer your objectives with Markdown subsections, which will populate the helpful navbar in Jupyter Lab and here on the Jupyter Book!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# some subsection code\n",
+ "new = \"helpful information\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Another content subsection\n",
+ "Keep up the good work! A note, *try to avoid using code comments as narrative*, and instead let them only exist as brief clarifications where necessary."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Your second content section\n",
+ "Here we can move on to our second objective, and we can demonstrate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Subsection to the second section\n",
+ "\n",
+ "#### a quick demonstration\n",
+ "\n",
+ "##### of further and further\n",
+ "\n",
+ "###### header levels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "as well $m = a * t / h$ text! Similarly, you have access to other $\\LaTeX$ equation [**functionality**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Typesetting%20Equations.html) via MathJax (demo below from link),\n",
+ "\n",
+ "\\begin{align}\n",
+ "\\dot{x} & = \\sigma(y-x) \\\\\n",
+ "\\dot{y} & = \\rho x - y - xz \\\\\n",
+ "\\dot{z} & = -\\beta z + xy\n",
+ "\\end{align}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Check out [**any number of helpful Markdown resources**](https://www.markdownguide.org/basic-syntax/) for further customizing your notebooks and the [**Jupyter docs**](https://jupyter-notebook.readthedocs.io/en/stable/examples/Notebook/Working%20With%20Markdown%20Cells.html) for Jupyter-specific formatting information. Don't hesitate to ask questions if you have problems getting it to look *just right*."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Last Section\n",
+ "\n",
+ "If you're comfortable, and as we briefly used for our embedded logo up top, you can embed raw html into Jupyter Markdown cells (edit to see):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "![\"Alpha](\"images/alpha_output.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cffd29c8",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "81f6c01b-1e08-463d-90d5-b9e7be5a61ac",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Overview\n",
+ "\n",
+ "In this demo we combine multiple multiple tools described in previous cookbooks to subset, regrid and process CMIP6 data. We will be computing a measure of ENSO nonlinearity by computing the EOFs of the pacific sea surface temperature anomalies. This measure is particularly useful for characterizing models by their ability to represent different ENSO extremes (Karamperidou et al., 2017).\n",
+ "\n",
+ "The process we are going to follow in this demo is:\n",
+ "\n",
+ "1. Find the CMIP6 data we need using intake-esgf\n",
+ "2. Subset the data and regrid it to a common grid using Rooki\n",
+ "3. Load the datasets into xarray and perform the computations\n",
+ "4. Plot the results\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "31d3693d-4e01-4982-b1d0-dffcd2a13157",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "| Concepts | Importance | Notes |\n",
+ "| --- | --- | --- |\n",
+ "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray/xarray-intro.html) | Necessary | How to use xarray to work with NetCDF data |\n",
+ "| [Intro to Intake-ESGF](intro-search) | Necessary | How to configure a search and use output |\n",
+ "| [Intro to Rooki](rooki) | Helpful | How to initialize and run rooki |\n",
+ "| [Intro to EOFs](https://projectpythia.org/eofs-cookbook/notebooks/eof-intro.html) | Helpful | Understanding of EOFs |\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "- **Time to learn**: 20 minutes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "288086a4",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "a2339b90",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "os.environ[\"ROOK_URL\"] = \"http://rook.dkrz.de/wps\"\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import numpy.polynomial.polynomial as poly\n",
+ "import xarray as xr\n",
+ "import xeofs as xe\n",
+ "from intake_esgf import ESGFCatalog\n",
+ "from rooki import operators as ops\n",
+ "from rooki import rooki"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d6ed87c2",
+ "metadata": {},
+ "source": [
+ "## Retrieve subset of CMIP6 data\n",
+ "\n",
+ "The CMIP6 dataset is identified by a dataset-id. Using intake-esgf we can query the ESGF database for the variables and models we are interested in. For this demo we are interested in the tos (sea surface temperature) variable for the historical runs. Also, for sake of simplicity we will only query a subset of the models available."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "66b3b3c0-6aa0-465b-bc17-86ae2ce5f25b",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "2e14f0415b3142848615c8e9dfd26be9",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " Searching indices: 0%| |0/2 [ ?index/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Summary information for 11 results:\n",
+ "activity_drs [CMIP]\n",
+ "variable_id [tos]\n",
+ "member_id [r1i1p1f1, r1i1p1f2]\n",
+ "mip_era [CMIP6, nan]\n",
+ "source_id [FGOALS-g3, CAMS-CSM1-0, EC-Earth3-Veg, CMCC-C...\n",
+ "grid_label [gn]\n",
+ "datetime_start [1848-10-25T13:00:00Z, 1850-01-16T12:00:00Z, 1...\n",
+ "datetime_stop [nan, 2014-12-16T12:00:00Z, 2014-12-15T12:00:00Z]\n",
+ "institution_id [CAS, CAMS, EC-Earth-Consortium, CMCC, CNRM-CE...\n",
+ "experiment_id [historical]\n",
+ "table_id [Omon]\n",
+ "project [CMIP6]\n",
+ "dtype: object\n"
+ ]
+ }
+ ],
+ "source": [
+ "cat = ESGFCatalog()\n",
+ "cat.search(\n",
+ " experiment_id=[\"historical\"],\n",
+ " variable_id=[\"tos\"],\n",
+ " table_id=[\"Omon\"],\n",
+ " project=[\"CMIP6\"],\n",
+ " grid_label=[\"gn\"],\n",
+ " source_id=[\n",
+ " \"CAMS-CSM1-0\",\n",
+ " \"FGOALS-g3\",\n",
+ " \"CMCC-CM2-SR5\",\n",
+ " \"CNRM-CM6-1\",\n",
+ " \"CNRM-ESM2-1\",\n",
+ " \"EC-Earth3-Veg\",\n",
+ " \"CESM2\",\n",
+ " ],\n",
+ ")\n",
+ "cat.remove_ensembles()\n",
+ "print(cat)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4aea426b",
+ "metadata": {},
+ "source": [
+ "Once the catalog has been queried, we have to do some manipulation in pandas to keep only the dataset_id. This has to be done because the same data has multiple locations online, and these get appended at the end of the dataset_id. Rookie only accepts the dataset_id without the online location, so we get rid of it in the next step."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "9482b7d7",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def keep_ds_id(ds):\n",
+ " return ds[0].split(\"|\")[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "46726e56-030d-4e54-a1a4-5e2f2ca11b43",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['CMIP6.CMIP.CAS.FGOALS-g3.historical.r1i1p1f1.Omon.tos.gn.v20191107',\n",
+ " 'CMIP6.CMIP.CAMS.CAMS-CSM1-0.historical.r1i1p1f1.Omon.tos.gn.v20190708',\n",
+ " 'CMIP6.CMIP.EC-Earth-Consortium.EC-Earth3-Veg.historical.r1i1p1f1.Omon.tos.gn.v20211207',\n",
+ " 'CMIP6.CMIP.CMCC.CMCC-CM2-SR5.historical.r1i1p1f1.Omon.tos.gn.v20200616',\n",
+ " 'CMIP6.CMIP.CNRM-CERFACS.CNRM-ESM2-1.historical.r1i1p1f2.Omon.tos.gn.v20181206',\n",
+ " 'CMIP6.CMIP.CNRM-CERFACS.CNRM-CM6-1.historical.r1i1p1f2.Omon.tos.gn.v20180917',\n",
+ " 'CMIP6.CMIP.NCAR.CESM2.historical.r1i1p1f1.Omon.tos.gn.v20190308',\n",
+ " 'CMIP6.CMIP.CAS.FGOALS-g3.historical.r1i1p1f1.Omon.tos.gn.v20191107',\n",
+ " 'CMIP6.CMIP.CMCC.CMCC-CM2-SR5.historical.r1i1p1f1.Omon.tos.gn.v20200616',\n",
+ " 'CMIP6.CMIP.CNRM-CERFACS.CNRM-CM6-1.historical.r1i1p1f2.Omon.tos.gn.v20180917',\n",
+ " 'CMIP6.CMIP.EC-Earth-Consortium.EC-Earth3-Veg.historical.r1i1p1f1.Omon.tos.gn.v20211207']"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "collections = cat.df.id.apply(keep_ds_id).to_list()\n",
+ "collections"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "513d3941",
+ "metadata": {},
+ "source": [
+ "We are left with a list of dataset_ids that Rookie can accept as input for the next step."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "674a3b8b",
+ "metadata": {},
+ "source": [
+ "## Subset and regrid the data\n",
+ "\n",
+ "We define a function that will do the subset and regridding for us for each of the dataset_ids we have. The function will take the dataset_id as input and then use Rookie functions to select 100 years of data for the tos variable in the Pacific Ocean region. We don't need high resolution data for this particular use, so 2.5 degree resolution is enough."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "30e8c66b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_pacific_ocean(dataset_id):\n",
+ " wf = ops.Regrid(\n",
+ " ops.Subset(\n",
+ " ops.Input(\"tos\", [dataset_id]),\n",
+ " time=\"1900-01-01/2000-01-31\",\n",
+ " area=\"100,-20,280,20\",\n",
+ " ),\n",
+ " method=\"nearest_s2d\",\n",
+ " grid=\"2pt5deg\",\n",
+ " )\n",
+ " resp = wf.orchestrate()\n",
+ " if resp.ok:\n",
+ " print(f\"{resp.size_in_mb=}\")\n",
+ " ds = resp.datasets()[0]\n",
+ " else:\n",
+ " ds = xr.Dataset()\n",
+ " return ds"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "eacbecbd",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "resp.size_in_mb=47.61813259124756\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_fq1_rikt/tos_Omon_FGOALS-g3_historical_r1i1p1f1_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.61836910247803\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_78zekaf5/tos_Omon_CAMS-CSM1-0_historical_r1i1p1f1_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.622283935546875\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_btxkrzn3/tos_Omon_CMCC-CM2-SR5_historical_r1i1p1f1_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.62028503417969\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_guxi9kpa/tos_Omon_CNRM-ESM2-1_historical_r1i1p1f2_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.621718406677246\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_qw22qb9h/tos_Omon_CNRM-CM6-1_historical_r1i1p1f2_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.61574363708496\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_fusqog81/tos_Omon_CESM2_historical_r1i1p1f1_gr_19000115-20000115_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.61813259124756\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_6d4b6nu0/tos_Omon_FGOALS-g3_historical_r1i1p1f1_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.622283935546875\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_15dtyr8e/tos_Omon_CMCC-CM2-SR5_historical_r1i1p1f1_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n",
+ "resp.size_in_mb=47.621886253356934\n",
+ "Downloading to /var/folders/zr/06cgf3250qb0fzy5t_79rc340000gn/T/metalink_yugmj4nu/tos_Omon_CNRM-CM6-1_historical_r1i1p1f2_gr_19000116-20000116_regrid-nearest_s2d-72x144_cells_grid.nc.\n"
+ ]
+ }
+ ],
+ "source": [
+ "sst_data = {dset: get_pacific_ocean(dset) for dset in collections}"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46301d38",
+ "metadata": {},
+ "source": [
+ "## ENSO nonlinearity measure: `alpha` value"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "788b135d",
+ "metadata": {},
+ "source": [
+ "This part of the demo is computation heavy. You can refer to Takahashi et al. (2011) and Karamperidou et al. (2017) for more details on the usefulness and computation of the `alpha` parameter.\n",
+ "\n",
+ "The `alpha` parameter is computed by doing a quadratic fit to the first two EOFs for the DJF season of the SST anomalies in the Pacific region. We are looking to obtain two EOFs modes that represent the Eastern and central pacific SST patterns, which is why we include a correction factor to account for the fact the sometimes the EOFs come with the opposite sign.\n",
+ "\n",
+ "The higher the value of `alpha`, the more nonlinear (or extreme) ENSO events can be represented by the model. Likewise, a model with lower `alpha` values will have a harder time representing extreme ENSO events, making it not suitable for climate studies of ENSO in a warming climate (Cai et al., 2018, 2021)."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "99631cca",
+ "metadata": {},
+ "source": [
+ "We are looking to obtain data that can reproduce a figure similar to the one below (taken from Karamperiou et al., 2017):"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a4266bc6",
+ "metadata": {},
+ "source": [
+ "![\"Alpha](\"images/alpha_example.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "31e2e06a",
+ "metadata": {},
+ "source": [
+ "Each of the \"wings\" of this boomerang-shaped distribution represents a different ENSO extreme, with the left (right) wing representing the extreme central (eastern) pacific El Niño events. More details on Takahashi et al. (2011)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "f43be532-c565-45e7-84d8-21be6e4e351e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def compute_alpha(pc1, pc2):\n",
+ " coefs = poly.polyfit(pc1, pc2, deg=2)\n",
+ " xfit = np.arange(pc1.min(), pc1.max() + 0.1, 0.1)\n",
+ " fit = poly.polyval(xfit, coefs)\n",
+ " return coefs[-1], xfit, fit\n",
+ "\n",
+ "\n",
+ "def correction_factor(model):\n",
+ " _eofs = model.components()\n",
+ " _subset = dict(lat=slice(-5, 5), lon=slice(140, 180))\n",
+ " corr_factor = np.zeros(2)\n",
+ " corr_factor[0] = 1 if _eofs.sel(mode=1, **_subset).mean() > 0 else -1\n",
+ " corr_factor[1] = 1 if _eofs.sel(mode=2, **_subset).mean() > 0 else -1\n",
+ " return xr.DataArray(corr_factor, coords=[(\"mode\", [1, 2])])\n",
+ "\n",
+ "\n",
+ "def compute_index(ds):\n",
+ " tos = ds.tos.sel(lat=slice(-20, 20), lon=slice(100, 280))\n",
+ " tos_anom = tos.groupby(\"time.month\").apply(lambda x: x - x.mean(\"time\"))\n",
+ "\n",
+ " # Compute Eofs\n",
+ " model = xe.models.EOF(n_modes=2, use_coslat=True)\n",
+ " model.fit(tos_anom, dim=\"time\")\n",
+ " corr_factor = correction_factor(model)\n",
+ " # eofs = s_model.components()\n",
+ " scale_factor = model.singular_values() / np.sqrt(model.explained_variance())\n",
+ " pcs = (\n",
+ " model.scores().convert_calendar(\"standard\", align_on=\"date\")\n",
+ " * scale_factor\n",
+ " * corr_factor\n",
+ " )\n",
+ "\n",
+ " pc1 = pcs.sel(mode=1)\n",
+ " pc1 = pc1.sel(time=pc1.time.dt.month.isin([12, 1, 2]))\n",
+ " pc1 = pc1.resample(time=\"QS-DEC\").mean().dropna(\"time\")\n",
+ "\n",
+ " pc2 = pcs.sel(mode=2)\n",
+ " pc2 = pc2.sel(time=pc2.time.dt.month.isin([12, 1, 2]))\n",
+ " pc2 = pc2.resample(time=\"QS-DEC\").mean().dropna(\"time\")\n",
+ "\n",
+ " alpha, xfit, fit = compute_alpha(pc1, pc2)\n",
+ "\n",
+ " return pc1, pc2, alpha, xfit, fit"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2334677a",
+ "metadata": {},
+ "source": [
+ "Now we can compute the `alpha` parameter for each of the models we have selected."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "140ee71c-01ad-4df5-a4af-d3f7f28c622a",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "alpha_fits = {}\n",
+ "for key, item in sst_data.items():\n",
+ " if len(item.variables) == 0:\n",
+ " continue\n",
+ " alpha_fits[key] = compute_index(item)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b8714f85",
+ "metadata": {},
+ "source": [
+ "## Plot the results\n",
+ "\n",
+ "Finally, we can plot the results of the `alpha` parameter for each of the models we have selected. This will give us an idea of how well the models represent different ENSO extremes."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "6b327ec5-f261-4ae8-9ee8-19fff28b62dc",
+ "metadata": {
+ "tags": [],
+ "trusted": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "![\"Intake](\"images/intake-rooki-demo.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fa96801d-4d1a-4264-94f9-9bf12a77421a",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "545103ee-abac-4da8-af3b-c8877a3d2d6c",
+ "metadata": {},
+ "source": [
+ "# Using intake-esgf with rooki\n",
+ "Here we dig into using intake-esgf to search for data, then rooki to do server-side computing!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7f90a23b-fd23-4339-924f-d665f1d36472",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "d57fa890-a874-4282-bdf2-dfb905678467",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "If you have an introductory paragraph, lead with it here! Keep it short and tied to your material, then be sure to continue into the required list of topics below,\n",
+ "\n",
+ "1. Search and find data using intake-esgf, returning the dataset ids\n",
+ "1. Feed the dataset ids to rooki to subset and average the data remotely\n",
+ "1. Visualize the results on the end-user side"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0b633dab-4c9d-482a-b148-8f9b09102e78",
+ "metadata": {},
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "| Concepts | Importance | Notes |\n",
+ "| --- | --- | --- |\n",
+ "| [Intro to Intake-ESGF](intro-search) | Necessary | How to configure a search and use output |\n",
+ "| [Intro to Rooki](rooki) | Helpful | How to initialize and run rooki |\n",
+ "| [Intro to hvPlot](https://hvplot.holoviz.org/user_guide/Geographic_Data.html) | Necessary | How to plot interactive visualizations |\n",
+ "\n",
+ "- **Time to learn**: 30 minutes"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64a5b6f9-eeee-4726-abdc-686e96dfc3cf",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f7571ba4-43cf-4f61-b74a-a51cdee373aa",
+ "metadata": {},
+ "source": [
+ "## Imports"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 89,
+ "id": "c12d0875-9794-4101-b74f-346148cb36c0",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/javascript": [
+ "(function(root) {\n",
+ " function now() {\n",
+ " return new Date();\n",
+ " }\n",
+ "\n",
+ " var force = true;\n",
+ " var py_version = '3.4.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n",
+ " var reloading = false;\n",
+ " var Bokeh = root.Bokeh;\n",
+ "\n",
+ " if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n",
+ " root._bokeh_timeout = Date.now() + 5000;\n",
+ " root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n",
+ " if (css_urls == null) css_urls = [];\n",
+ " if (js_urls == null) js_urls = [];\n",
+ " if (js_modules == null) js_modules = [];\n",
+ " if (js_exports == null) js_exports = {};\n",
+ "\n",
+ " root._bokeh_onload_callbacks.push(callback);\n",
+ "\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n",
+ " run_callbacks();\n",
+ " return null;\n",
+ " }\n",
+ " if (!reloading) {\n",
+ " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+ " }\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",
+ " window._bokeh_on_load = on_load\n",
+ "\n",
+ " function on_error() {\n",
+ " console.error(\"failed to load \" + url);\n",
+ " }\n",
+ "\n",
+ " var skip = [];\n",
+ " if (window.requirejs) {\n",
+ " window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n",
+ " root._bokeh_is_loading = css_urls.length + 0;\n",
+ " } else {\n",
+ " root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n",
+ " }\n",
+ "\n",
+ " var existing_stylesheets = []\n",
+ " var links = document.getElementsByTagName('link')\n",
+ " for (var i = 0; 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",
+ " } var existing_scripts = []\n",
+ " var scripts = document.getElementsByTagName('script')\n",
+ " for (var i = 0; i < scripts.length; i++) {\n",
+ " var script = scripts[i]\n",
+ " if (script.src != null) {\n",
+ "\texisting_scripts.push(script.src)\n",
+ " }\n",
+ " }\n",
+ " for (var i = 0; i < js_urls.length; i++) {\n",
+ " var url = js_urls[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",
+ " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+ " document.head.appendChild(element);\n",
+ " }\n",
+ " for (var i = 0; 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-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.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",
+ " },\n",
+ "function(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",
+ "\ttry {\n",
+ " inline_js[i].call(root, root.Bokeh);\n",
+ "\t} catch(e) {\n",
+ "\t if (!reloading) {\n",
+ "\t throw e;\n",
+ "\t }\n",
+ "\t}\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",
+ " root._bokeh_is_initializing = true\n",
+ " root._bokeh_onload_callbacks = []\n",
+ " var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n",
+ " if (!reloading && !bokeh_loaded) {\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));"
+ ],
+ "application/vnd.holoviews_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.0'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = false;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\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, js_modules, js_exports, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n if (js_modules == null) js_modules = [];\n if (js_exports == null) js_exports = {};\n\n root._bokeh_onload_callbacks.push(callback);\n\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.length === 0 && js_modules.length === 0 && Object.keys(js_exports).length === 0) {\n run_callbacks();\n return null;\n }\n if (!reloading) {\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n }\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 window._bokeh_on_load = on_load\n\n function on_error() {\n console.error(\"failed to load \" + url);\n }\n\n var skip = [];\n if (window.requirejs) {\n window.requirejs.config({'packages': {}, 'paths': {}, 'shim': {}});\n root._bokeh_is_loading = css_urls.length + 0;\n } else {\n root._bokeh_is_loading = css_urls.length + js_urls.length + js_modules.length + Object.keys(js_exports).length;\n }\n\n var existing_stylesheets = []\n var links = document.getElementsByTagName('link')\n for (var i = 0; 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 } var existing_scripts = []\n var scripts = document.getElementsByTagName('script')\n for (var i = 0; i < scripts.length; i++) {\n var script = scripts[i]\n if (script.src != null) {\n\texisting_scripts.push(script.src)\n }\n }\n for (var i = 0; i < js_urls.length; i++) {\n var url = js_urls[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 console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n for (var i = 0; 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-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.4.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.4.0.min.js\", \"https://cdn.holoviz.org/panel/1.4.1/dist/panel.min.js\", \"https://cdn.jsdelivr.net/npm/@holoviz/geoviews@1.12.0/dist/geoviews.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\ttry {\n inline_js[i].call(root, root.Bokeh);\n\t} catch(e) {\n\t if (!reloading) {\n\t throw e;\n\t }\n\t}\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 root._bokeh_is_initializing = true\n root._bokeh_onload_callbacks = []\n var bokeh_loaded = Bokeh != null && (Bokeh.version === py_version || (Bokeh.versions !== undefined && Bokeh.versions.has(py_version)));\n if (!reloading && !bokeh_loaded) {\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));"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "application/javascript": [
+ "\n",
+ "if ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n",
+ " window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n",
+ "}\n",
+ "\n",
+ "\n",
+ " function JupyterCommManager() {\n",
+ " }\n",
+ "\n",
+ " JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n",
+ " if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
+ " var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
+ " comm_manager.register_target(comm_id, function(comm) {\n",
+ " comm.on_msg(msg_handler);\n",
+ " });\n",
+ " } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
+ " window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
+ " comm.onMsg = msg_handler;\n",
+ " });\n",
+ " } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
+ " google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
+ " var messages = 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",
+ "\n",
+ "var JS_MIME_TYPE = 'application/javascript';\n",
+ "var HTML_MIME_TYPE = 'text/html';\n",
+ "var EXEC_MIME_TYPE = 'application/vnd.holoviews_exec.v0+json';\n",
+ "var CLASS_NAME = 'output';\n",
+ "\n",
+ "/**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ "function 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",
+ " */\n",
+ "function 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",
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+ "execution_count": 27,
+ "id": "937299ba-5838-494d-a77f-06cb5b4bc666",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
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+ "data": {
+ "text/html": [
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+ ],
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+ ":Overlay\n",
+ " .Land.I :Feature [Longitude,Latitude]\n",
+ " .Ocean.I :Feature [Longitude,Latitude]\n",
+ " .Image.I :Image [lon,lat] (tas)\n",
+ " .Borders.I :Feature [Longitude,Latitude]\n",
+ " .Lakes.I :Feature [Longitude,Latitude]"
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {
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+ ],
+ "source": [
+ "subset_location(ds).tas.isel(time=-1).hvplot(x='lon',\n",
+ " y='lat',\n",
+ " features=[\"land\", \"lakes\", \"ocean\", \"borders\"],\n",
+ " cmap='Reds',\n",
+ " geo=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "aaf3c9a5-29bb-4145-b10e-3742d9b46779",
+ "metadata": {},
+ "source": [
+ "### Calculate a yearly average"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "99861710-a1b3-4b94-bfe5-39919917c419",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def yearly_average(ds):\n",
+ " from clisops.ops.average import average_time\n",
+ " return average_time(ds, \"year\", output_type=\"xarray\")[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "id": "92c6bb5b-cd66-44ca-bfbc-bb25dec72eab",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ":Overlay\n",
+ " .Land.I :Feature [Longitude,Latitude]\n",
+ " .Ocean.I :Feature [Longitude,Latitude]\n",
+ " .Image.I :Image [lon,lat] (tas)\n",
+ " .Borders.I :Feature [Longitude,Latitude]\n",
+ " .Lakes.I :Feature [Longitude,Latitude]"
+ ]
+ },
+ "execution_count": 40,
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {}
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "yearly_average(subset_location(ds)).isel(time=0).tas.hvplot(x='lon',\n",
+ " y='lat',\n",
+ " features=[\"land\", \"lakes\", \"ocean\", \"borders\"],\n",
+ " cmap='Reds',\n",
+ " geo=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "4103dcaa-9fe0-4129-b62f-64921dec0658",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
"
+ ],
+ "text/plain": [
+ ":Overlay\n",
+ " .Land.I :Feature [Longitude,Latitude]\n",
+ " .Ocean.I :Feature [Longitude,Latitude]\n",
+ " .Image.I :Image [lon,lat] (tas)\n",
+ " .Borders.I :Feature [Longitude,Latitude]\n",
+ " .Lakes.I :Feature [Longitude,Latitude]"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {
+ "application/vnd.holoviews_exec.v0+json": {}
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "yearly_average(subset_location(ds)).isel(time=-1).tas.hvplot(x='lon',\n",
+ " y='lat',\n",
+ " features=[\"land\", \"lakes\", \"ocean\", \"borders\"],\n",
+ " cmap='Reds',\n",
+ " geo=True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2111d7bc-b451-4d92-9794-46ac087ca248",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "In this notebook, we applied data reduction functions from the ESGF stack to data accessed through intake-esgf.\n",
+ "\n",
+ "### What's next?\n",
+ "We will see some more advanced examples of using these functions, including full task orchestration using Globus-Flows.\n",
+ "\n",
+ "## Resources and references\n",
+ "- [Intake-ESGF Documentation](https://github.com/nocollier/intake-esgf)\n",
+ "- [Globus Compute Documentation](https://www.globus.org/compute)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "b2166127-1d65-4757-8953-fff73e71384f",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.13"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/_preview/32/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css b/_preview/32/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
new file mode 100644
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+++ b/_preview/32/_sphinx_design_static/design-style.1e8bd061cd6da7fc9cf755528e8ffc24.min.css
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+ font-size: 90%;
+}
+
+div.related h3 {
+ display: none;
+}
+
+div.related ul {
+ margin: 0;
+ padding: 0 0 0 10px;
+ list-style: none;
+}
+
+div.related li {
+ display: inline;
+}
+
+div.related li.right {
+ float: right;
+ margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+ padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+ float: left;
+ width: 270px;
+ margin-left: -100%;
+ font-size: 90%;
+ word-wrap: break-word;
+ overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+ list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+ margin-left: 20px;
+ list-style: square;
+}
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+div.sphinxsidebar ul ul {
+ margin-top: 0;
+ margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+ margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+ border: 1px solid #98dbcc;
+ font-family: sans-serif;
+ font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+ overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+ float: left;
+ width: 80%;
+ padding: 0.25em;
+ box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+ float: left;
+ width: 20%;
+ border-left: none;
+ padding: 0.25em;
+ box-sizing: border-box;
+}
+
+
+img {
+ border: 0;
+ max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+ margin: 10px 0 0 20px;
+ padding: 0;
+}
+
+ul.search li {
+ padding: 5px 0 5px 20px;
+ background-image: url(file.png);
+ background-repeat: no-repeat;
+ background-position: 0 7px;
+}
+
+ul.search li a {
+ font-weight: bold;
+}
+
+ul.search li p.context {
+ color: #888;
+ margin: 2px 0 0 30px;
+ text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+ font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+ width: 90%;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table.contentstable p.biglink {
+ line-height: 150%;
+}
+
+a.biglink {
+ font-size: 1.3em;
+}
+
+span.linkdescr {
+ font-style: italic;
+ padding-top: 5px;
+ font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+ width: 100%;
+}
+
+table.indextable td {
+ text-align: left;
+ vertical-align: top;
+}
+
+table.indextable ul {
+ margin-top: 0;
+ margin-bottom: 0;
+ list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+ padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+ height: 10px;
+}
+
+table.indextable tr.cap {
+ margin-top: 10px;
+ background-color: #f2f2f2;
+}
+
+img.toggler {
+ margin-right: 3px;
+ margin-top: 3px;
+ cursor: pointer;
+}
+
+div.modindex-jumpbox {
+ border-top: 1px solid #ddd;
+ border-bottom: 1px solid #ddd;
+ margin: 1em 0 1em 0;
+ padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+ border-top: 1px solid #ddd;
+ border-bottom: 1px solid #ddd;
+ margin: 1em 0 1em 0;
+ padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+ padding: 2px;
+ border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+ min-width: 450px;
+ max-width: 800px;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+ -moz-hyphens: auto;
+ -ms-hyphens: auto;
+ -webkit-hyphens: auto;
+ hyphens: auto;
+}
+
+a.headerlink {
+ visibility: hidden;
+}
+
+a.brackets:before,
+span.brackets > a:before{
+ content: "[";
+}
+
+a.brackets:after,
+span.brackets > a:after {
+ content: "]";
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+ visibility: visible;
+}
+
+div.body p.caption {
+ text-align: inherit;
+}
+
+div.body td {
+ text-align: left;
+}
+
+.first {
+ margin-top: 0 !important;
+}
+
+p.rubric {
+ margin-top: 30px;
+ font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+ clear: left;
+ float: left;
+ margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+ clear: right;
+ float: right;
+ margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+.align-left {
+ text-align: left;
+}
+
+.align-center {
+ text-align: center;
+}
+
+.align-default {
+ text-align: center;
+}
+
+.align-right {
+ text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+ margin: 0 0 0.5em 1em;
+ border: 1px solid #ddb;
+ padding: 7px;
+ background-color: #ffe;
+ width: 40%;
+ float: right;
+ clear: right;
+ overflow-x: auto;
+}
+
+p.sidebar-title {
+ font-weight: bold;
+}
+
+div.admonition, div.topic, blockquote {
+ clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+div.topic {
+ border: 1px solid #ccc;
+ padding: 7px;
+ margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+ font-size: 1.1em;
+ font-weight: bold;
+ margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+ margin-top: 10px;
+ margin-bottom: 10px;
+ padding: 7px;
+}
+
+div.admonition dt {
+ font-weight: bold;
+}
+
+p.admonition-title {
+ margin: 0px 10px 5px 0px;
+ font-weight: bold;
+}
+
+div.body p.centered {
+ text-align: center;
+ margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+ display: block;
+ content: '';
+ clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
+ border: 0;
+ border-collapse: collapse;
+}
+
+table.align-center {
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table.align-default {
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table caption span.caption-number {
+ font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+ padding: 1px 8px 1px 5px;
+ border-top: 0;
+ border-left: 0;
+ border-right: 0;
+ border-bottom: 1px solid #aaa;
+}
+
+table.footnote td, table.footnote th {
+ border: 0 !important;
+}
+
+th {
+ text-align: left;
+ padding-right: 5px;
+}
+
+table.citation {
+ border-left: solid 1px gray;
+ margin-left: 1px;
+}
+
+table.citation td {
+ border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+ margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+ margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+ margin: 0.5em;
+ padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+ padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+ font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+ border: 0 !important;
+}
+
+.field-list ul {
+ margin: 0;
+ padding-left: 1em;
+}
+
+.field-list p {
+ margin: 0;
+}
+
+.field-name {
+ -moz-hyphens: manual;
+ -ms-hyphens: manual;
+ -webkit-hyphens: manual;
+ hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+ margin: 1em 0;
+}
+
+table.hlist td {
+ vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+ font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+ background-color: transparent;
+ font-weight: bold;
+}
+
+.sig-name {
+ font-size: 1.1em;
+}
+
+code.descname {
+ font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+ background-color: transparent;
+}
+
+.optional {
+ font-size: 1.3em;
+}
+
+.sig-paren {
+ font-size: larger;
+}
+
+.sig-param.n {
+ font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+ font-family: unset;
+}
+
+.sig.c .k, .sig.c .kt,
+.sig.cpp .k, .sig.cpp .kt {
+ color: #0033B3;
+}
+
+.sig.c .m,
+.sig.cpp .m {
+ color: #1750EB;
+}
+
+.sig.c .s, .sig.c .sc,
+.sig.cpp .s, .sig.cpp .sc {
+ color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+ list-style: decimal;
+}
+
+ol.loweralpha {
+ list-style: lower-alpha;
+}
+
+ol.upperalpha {
+ list-style: upper-alpha;
+}
+
+ol.lowerroman {
+ list-style: lower-roman;
+}
+
+ol.upperroman {
+ list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+ margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+ margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+ margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+ margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+ margin-bottom: 0;
+}
+
+dl.footnote > dt,
+dl.citation > dt {
+ float: left;
+ margin-right: 0.5em;
+}
+
+dl.footnote > dd,
+dl.citation > dd {
+ margin-bottom: 0em;
+}
+
+dl.footnote > dd:after,
+dl.citation > dd:after {
+ content: "";
+ clear: both;
+}
+
+dl.field-list {
+ display: grid;
+ grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+ font-weight: bold;
+ word-break: break-word;
+ padding-left: 0.5em;
+ padding-right: 5px;
+}
+
+dl.field-list > dt:after {
+ content: ":";
+}
+
+dl.field-list > dd {
+ padding-left: 0.5em;
+ margin-top: 0em;
+ margin-left: 0em;
+ margin-bottom: 0em;
+}
+
+dl {
+ margin-bottom: 15px;
+}
+
+dd > :first-child {
+ margin-top: 0px;
+}
+
+dd ul, dd table {
+ margin-bottom: 10px;
+}
+
+dd {
+ margin-top: 3px;
+ margin-bottom: 10px;
+ margin-left: 30px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+ background-color: #fbe54e;
+}
+
+rect.highlighted {
+ fill: #fbe54e;
+}
+
+dl.glossary dt {
+ font-weight: bold;
+ font-size: 1.1em;
+}
+
+.versionmodified {
+ font-style: italic;
+}
+
+.system-message {
+ background-color: #fda;
+ padding: 5px;
+ border: 3px solid red;
+}
+
+.footnote:target {
+ background-color: #ffa;
+}
+
+.line-block {
+ display: block;
+ margin-top: 1em;
+ margin-bottom: 1em;
+}
+
+.line-block .line-block {
+ margin-top: 0;
+ margin-bottom: 0;
+ margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+ font-family: sans-serif;
+}
+
+.accelerator {
+ text-decoration: underline;
+}
+
+.classifier {
+ font-style: oblique;
+}
+
+.classifier:before {
+ font-style: normal;
+ margin: 0 0.5em;
+ content: ":";
+ display: inline-block;
+}
+
+abbr, acronym {
+ border-bottom: dotted 1px;
+ cursor: help;
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+ overflow: auto;
+ overflow-y: hidden; /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+ clear: both;
+}
+
+span.pre {
+ -moz-hyphens: none;
+ -ms-hyphens: none;
+ -webkit-hyphens: none;
+ hyphens: none;
+ white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+ margin: 1em 0;
+}
+
+td.linenos pre {
+ border: 0;
+ background-color: transparent;
+ color: #aaa;
+}
+
+table.highlighttable {
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
+}
+
+table.highlighttable td {
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
+}
+
+div.code-block-caption {
+ margin-top: 1em;
+ padding: 2px 5px;
+ font-size: small;
+}
+
+div.code-block-caption code {
+ background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
+ -webkit-user-select: text; /* Safari fallback only */
+ -webkit-user-select: none; /* Chrome/Safari */
+ -moz-user-select: none; /* Firefox */
+ -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+ padding: 0.1em 0.3em;
+ font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+ margin: 1em 0;
+}
+
+code.xref, a code {
+ background-color: transparent;
+ font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+ background-color: transparent;
+}
+
+.viewcode-link {
+ float: right;
+}
+
+.viewcode-back {
+ float: right;
+ font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+ margin: -1px -10px;
+ padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+ vertical-align: middle;
+}
+
+div.body div.math p {
+ text-align: center;
+}
+
+span.eqno {
+ float: right;
+}
+
+span.eqno a.headerlink {
+ position: absolute;
+ z-index: 1;
+}
+
+div.math:hover a.headerlink {
+ visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+ div.document,
+ div.documentwrapper,
+ div.bodywrapper {
+ margin: 0 !important;
+ width: 100%;
+ }
+
+ div.sphinxsidebar,
+ div.related,
+ div.footer,
+ #top-link {
+ display: none;
+ }
+}
\ No newline at end of file
diff --git a/_preview/32/_static/check-solid.svg b/_preview/32/_static/check-solid.svg
new file mode 100644
index 0000000..92fad4b
--- /dev/null
+++ b/_preview/32/_static/check-solid.svg
@@ -0,0 +1,4 @@
+
diff --git a/_preview/32/_static/clipboard.min.js b/_preview/32/_static/clipboard.min.js
new file mode 100644
index 0000000..54b3c46
--- /dev/null
+++ b/_preview/32/_static/clipboard.min.js
@@ -0,0 +1,7 @@
+/*!
+ * clipboard.js v2.0.8
+ * https://clipboardjs.com/
+ *
+ * Licensed MIT © Zeno Rocha
+ */
+!function(t,e){"object"==typeof exports&&"object"==typeof module?module.exports=e():"function"==typeof define&&define.amd?define([],e):"object"==typeof exports?exports.ClipboardJS=e():t.ClipboardJS=e()}(this,function(){return n={686:function(t,e,n){"use strict";n.d(e,{default:function(){return o}});var e=n(279),i=n.n(e),e=n(370),u=n.n(e),e=n(817),c=n.n(e);function a(t){try{return document.execCommand(t)}catch(t){return}}var f=function(t){t=c()(t);return a("cut"),t};var l=function(t){var e,n,o,r=1