diff --git a/.isort.cfg b/.isort.cfg index 0fc010f..cd544e6 100644 --- a/.isort.cfg +++ b/.isort.cfg @@ -1,2 +1,2 @@ [settings] -known_third_party = +known_third_party =holoviews,hvplot,panel,xarray diff --git a/Containerfile b/Containerfile new file mode 100644 index 0000000..1537553 --- /dev/null +++ b/Containerfile @@ -0,0 +1,27 @@ +# Use an official Python runtime as a base image +FROM docker.io/mambaorg/micromamba:latest + +USER root + +RUN apt-get update && apt-get install -y git-all + +# Set the working directory in the container to /app +WORKDIR /home/mambauser/app + +# Copy the current directory contents into the container at /usr/src/app +RUN git clone https://github.com/NicholasCote/ERA5_interactive-cookbook-ncote.git + +# Install any needed packages specified in requirements.yml +RUN micromamba env create -f ERA5_interactive-cookbook-ncote/environment.yml + +RUN mv ERA5_interactive-cookbook-ncote/notebooks/04_dashboard.ipynb . + +RUN rm -r ERA5_interactive-cookbook-ncote/ + +# Activate the environment by providing ENV_NAME as an environment variable at runtime +# Make port bokeh application port to the world outside this container +EXPOSE 5006 + +USER mambauser + +CMD ["panel", "serve", "04_dashboard.ipynb", "--allow-websocket-origin=*", "--autoreload"] \ No newline at end of file diff --git a/README.md b/README.md index 7c9762c..5b548c2 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ In the notebooks which comprise this Cookbook, we demonstrate the following: ## Authors -[Kevin Tyle](https://github.com/ktyle), [Michael Barletta](https://github.com/Michael-Barletta) +[Kevin Tyle](https://github.com/ktyle), [Michael Barletta](https://github.com/Michael-Barletta), [Negin Sobhani](https://github.com/negin513), [Nicholas Cote](https://github.com/ncote) , [Harsha Hampapura](https://github.com/hrhampapura) , and [Philip Chmielowiec](https://github.com/philip2c) We also gratefully acknowledge the Google Cloud Research team for making an ARCO-friendly version of the ERA-5 available. Citations for their effort and the ERA-5 reanalysis are below: @@ -65,7 +65,7 @@ We also gratefully acknowledge the Google Cloud Research team for making an ARCO ## Structure -This cookbook currently consists of two notebooks that access, regrid, and visualize the ARCO ERA-5 repository. +This cookbook currently consists of multiple notebooks that access, regrid, and visualize the ARCO ERA-5 repository. Additionally we cover a section on how to preprocess and create ARCO files. Additional notebooks will follow. @@ -73,10 +73,24 @@ Additional notebooks will follow. This notebook reads in a sea-level pressure ERA-5 grid, regrids from Gaussian to Cartesian coordinates, and visualizes the data with Matplotlib and Cartopy. -### Section 2 ( "Interactive Visualization 1" ) +### Section 2 ( "Interactive Visualization Part 1: `GeoViews`" ) This notebook reads in sea-level pressure and 2-meter temperature ERA-5 grids, regrids as in the first notebook, and visualizes the data in an interactive manner using [Geoviews](https://geoviews.org). +### Section 3 ("Interactive Visualization Part 2: `hvPlot`") +This notebook reads in annual average 2-m temperature from RDA's Zarr store and visualizes the data using `hvPlot`. The notebook also demonstrates how to create a simple interactive plot that allows the user to select a specific year and visualize the 2-m and how to create animations. + +### Section 4 ("Creating an Interactive Dashboard with `Panel`") +This notebook demonstrates how to create an interactive dashboard using `Panel` that allows the user to select a specific year and visualize the 2-m temperature. + +## Preprocessing Notebooks for NCAR RDA +### Section 5 ( "Generate annual/yearly Zarr stores from hourly ERA5 NetCDF files on NCAR’s Research Data Archive") +This notebook demonstrates how to preprocess hourly ERA5 NetCDF files from NCAR's Research Data Archive (RDA) and generate annual/yearly Zarr stores. + +### Section 6 ( "Calculate Temperature Anomalies") +This notebook demonstrates how to calculate temperature anomalies from the annual 2-m temperature Zarr store generated in Section 5. + + ## Running the Notebooks You can either run the notebook using [Binder](https://binder.projectpythia.org/) or on your local machine. diff --git a/_config.yml b/_config.yml index 305e45c..e032d0f 100644 --- a/_config.yml +++ b/_config.yml @@ -2,13 +2,16 @@ # Learn more at https://jupyterbook.org/customize/config.html title: ARCO ERA-5 Interactive Visualization -author: Michael Barletta and Kevin Tyle +author: Michael Barletta, Kevin Tyle, Negin Sobhani, Nicholas Cote, Harshah Hampapura, Philip Chmielowiec logo: notebooks/images/logos/pythia_logo-white-rtext.svg copyright: "2024" execute: # To execute notebooks via a Binder instead, replace 'cache' with 'binder' execute_notebooks: cache + exclude_patterns: + - '*era5_anomaly*' + - '*data_preprocessing*' timeout: 600 allow_errors: False # cells with expected failures must set the `raises-exception` cell tag @@ -28,7 +31,7 @@ parse: sphinx: config: linkcheck_ignore: ["https://doi.org/*", "https://zenodo.org/badge/*"] # don't run link checker on DOI links since they are immutable - nb_execution_raise_on_error: true # raise exception in build if there are notebook errors (this flag is ignored if building on binder) + nb_execution_raise_on_error: false # raise exception in build if there are notebook errors (this flag is ignored if building on binder) html_favicon: notebooks/images/icons/favicon.ico html_last_updated_fmt: "%-d %B %Y" html_theme: sphinx_pythia_theme diff --git a/_toc.yml b/_toc.yml index 932d1f1..226bb24 100644 --- a/_toc.yml +++ b/_toc.yml @@ -4,12 +4,14 @@ parts: - caption: Preamble chapters: - file: notebooks/how-to-cite - - caption: Visualization notebooks + - caption: Visualization Notebooks chapters: - file: notebooks/01BasicVisualization - file: notebooks/02InteractiveVisualization - - file: notebooks/03_intro_to_interactive_viz.ipynb + - file: notebooks/03_hvplot + - file: notebooks/04_dashboard - caption: Preprocessing Notebooks for NCAR RDA chapters: - - file: notebooks/01_data_preprocessing.ipynb + - file: notebooks/05_data_preprocessing + - file: notebooks/06_era5_anomaly \ No newline at end of file diff --git a/environment.yml b/environment.yml index 952bd6e..da6e9e0 100644 --- a/environment.yml +++ b/environment.yml @@ -22,3 +22,6 @@ dependencies: - gcsfs - cf_xarray - sphinx-pythia-theme + - hvplot + - spatialpandas + - watchfiles \ No newline at end of file diff --git a/notebooks/01BasicVisualization.ipynb b/notebooks/01BasicVisualization.ipynb index da24bd0..14e4567 100644 --- a/notebooks/01BasicVisualization.ipynb +++ b/notebooks/01BasicVisualization.ipynb @@ -11,19 +11,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 01_BasicVisualization" + "# Basic Visualization using `matplotlib` and `Cartopy`" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -6291,7 +6281,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.6 64-bit", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -6305,7 +6295,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.13" }, "nbdime-conflicts": { "local_diff": [ diff --git a/notebooks/02InteractiveVisualization.ipynb b/notebooks/02InteractiveVisualization.ipynb index 7ce75e5..9e49d15 100644 --- a/notebooks/02InteractiveVisualization.ipynb +++ b/notebooks/02InteractiveVisualization.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# 02_InteractiveVisualization Part 1: Geoviews" + "# Interactive Visualization using `GeoViews`" ] }, { @@ -536,7 +536,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.9.6 64-bit", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -550,7 +550,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.6" + "version": "3.10.13" }, "nbdime-conflicts": { "local_diff": [ diff --git a/notebooks/03_hvplot.ipynb b/notebooks/03_hvplot.ipynb new file mode 100644 index 0000000..6f2a640 --- /dev/null +++ b/notebooks/03_hvplot.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e5b8ce18-19f4-4535-b764-684402d18275", + "metadata": {}, + "source": [ + "\"Project \n", + "\n", + "\n", + "# Interactive Visualuzation using `hvPlot`" + ] + }, + { + "cell_type": "markdown", + "id": "c22d2a99-73ed-48f2-a42e-a5b2d3d29ab0", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "id": "80641766-1b70-4e80-b684-047d99c052b5", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "ERA-5 Dataset is available from NCAR RDA in netcdf format. A subset of this dataset is processed into Zarr format and available from NCAR RDA endpoints. To learn how you can create Zarr files from NCAR RDA netcdf files, please see [this notebook](./05_data_preprocessing.ipynb).\n", + "\n", + "\n", + "By the end of this notebook, you should be able to:\n", + "* Understand the importance for interactive plots and the challenges associated with them\n", + "* Use `hvPlot` to generate basic interactive plots with `Xarray`" + ] + }, + { + "cell_type": "markdown", + "id": "babd7594-e441-4405-9199-d04e2999259f", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray.html) | Necessary | |\n", + "\n", + "- **Time to learn**: 30 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "2cad7118-9932-4d79-8634-82c3004c73b7", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "59989a91-6889-4150-a7ba-ab86130c5f59", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import holoviews as hv\n", + "import xarray as xr\n", + "from holoviews import opts\n", + "\n", + "hv.extension(\"bokeh\")" + ] + }, + { + "cell_type": "markdown", + "id": "079cf1a1-b66d-495b-a609-cca87ed55828", + "metadata": {}, + "source": [ + "## Data\n", + "\n", + "As we mentioned above a subset of NCAR RDA data is available in Zarr format. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d2f20c5-1193-4958-9388-94847b90cf52", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "rda_url = \"https://data.rda.ucar.edu/\"\n", + "annual_means = rda_url + \"pythia_era5_24/annual_means/\"\n", + "xrds = xr.open_dataset(annual_means + \"temp_2m_annual_1940_2023.zarr\", engine=\"zarr\")\n", + "xrds = xrds.isel(time=slice(0, 5))\n", + "xrds.load()" + ] + }, + { + "cell_type": "markdown", + "id": "ab405068-8ad2-4f5c-bfdd-a39210a3db77", + "metadata": {}, + "source": [ + "## Considerations for Interactive Plots\n", + "\n", + "Add some markdown text on some of the following ideas:\n", + "* What are some reasons we want to make data visualuzation interactive?" + ] + }, + { + "cell_type": "markdown", + "id": "30179753-dff3-45eb-8814-781fc38514b5", + "metadata": {}, + "source": [ + "## Baisc Interactivity using `hvPlot`\n", + "\n", + "The `hvPlot` package is a familiar and high level API for data exploration and visualuzation.\n", + "\n", + "\n", + "\n", + "
\n", + " \n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "id": "406ef3ff-90d5-4322-86d8-0dcce428ccbd", + "metadata": {}, + "source": [ + "One of the most powerfull features of `hvPlot` is that it provides an alternative plotting API that directly attaches to existing Python objects through the `.hvplot()` attribute. For the case of `Xarray`, importing `hvplot.xarray` adds a brand new set of plotting routines accessible either through `xr.DataArray.hvplot()` or `xr.Dataset.hvplot()`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d580ec83-3619-4d14-97f7-41f6abd4360e", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import hvplot.xarray" + ] + }, + { + "cell_type": "markdown", + "id": "8608ad85-559f-4712-92b5-051fde020066", + "metadata": {}, + "source": [ + "Before using `hvPlot`, let's take a look at the default `Xarray` plotting methods." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "984ad7f2-b8f3-425d-955e-1c0cd93fddc7", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].plot()" + ] + }, + { + "cell_type": "markdown", + "id": "c5aaa818-d5b1-4f00-9aaa-04ebbde55ade", + "metadata": {}, + "source": [ + "We can replace the `.plot()` function call with `.hvplot()`. By default, `hvPlot` uses the `Bokeh` backend, which has naitive interactive tools, such as :\n", + "* Panning\n", + "* Box Select\n", + "* Scroll Zoom\n", + "* Saving\n", + "* Resetting" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9237fe13-a42b-459d-835f-f6e6b2668b14", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].hvplot()" + ] + }, + { + "cell_type": "markdown", + "id": "6a05552d-80b9-4a64-900f-ede652640719", + "metadata": {}, + "source": [ + "If we wanted to plot ..." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2ec04385-3eb7-422b-8969-9a2d433e1da0", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].isel(time=0).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "72a72dd8-e0da-4768-bbfa-13c0c14ccdf1", + "metadata": {}, + "source": [ + "Switching" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d58771bf-0917-4f52-b691-c55ae1b7d9eb", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].isel(time=0).hvplot()" + ] + }, + { + "cell_type": "markdown", + "id": "7cfa6367-0b18-4b66-a4fd-99bc467b141a", + "metadata": {}, + "source": [ + "### Time Widget" + ] + }, + { + "cell_type": "markdown", + "id": "2f0d07e2-27bd-4c23-955a-a4b89ee360fa", + "metadata": {}, + "source": [ + "Climate data typically comes with multiple timesteps. We can create a basic widget that allows us to seek through time by setting the `groupby='time'` parameter in our `.hvplot()` call. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "56c46384-a431-4576-a62f-332ff878b873", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].hvplot(groupby=\"time\", widget_location=\"bottom\")" + ] + }, + { + "cell_type": "markdown", + "id": "7e2a3740-f71e-4275-acd6-19df39c56fde", + "metadata": {}, + "source": [ + "You may notice that our colorbar is dynamically changing as we change our time steps. We can fix the colorbar by setting a `clim` value, which is a tuple of the minimum and maximum desired colorbar range.\n", + "\n", + "One suggestion is to use the minimum and maximum of the data variable you are visualuzing across time." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e831c93b-234a-4684-b0bb-f5bc1bbc908a", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "clim = (xrds[\"VAR_2T\"].values.min(), xrds[\"VAR_2T\"].values.max())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a93199b-9328-4c88-80ca-3d645e4d7e63", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].hvplot(clim=clim, groupby=\"time\", widget_location=\"bottom\")" + ] + }, + { + "cell_type": "markdown", + "id": "e96aab84-1f1f-4f6c-8cd6-cabec345a748", + "metadata": {}, + "source": [ + "You may have noticed that there is a slight lag when switching time steps. This is due to `hvPlot` plotting the full resolution of our dataset. We can instead rasterize the output by setting `rasterize=True`, which will significantly improve the perfromance of our interactive plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8928f979-f6c4-4160-b4c5-ba928688c9ba", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].hvplot(\n", + " rasterize=True, clim=clim, groupby=\"time\", widget_location=\"bottom\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "599b4d58-b9dc-4e0c-ba48-ad45f2df2e24", + "metadata": {}, + "source": [ + "### Animation Widget" + ] + }, + { + "cell_type": "markdown", + "id": "0769b0ec-074d-4d27-b30f-3b7f768b37a8", + "metadata": {}, + "source": [ + "Another usefull interactive feature is animations. Instead of manually scrolling through time, we can set up a widget that lets us animate our data across time. This can be achieved by adding a Scrubber widget to our plot by setting `widget_type=\"scrubber\"`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b7b48f1-0a07-4b73-8f9c-8880dd25e5ab", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "xrds[\"VAR_2T\"].hvplot(\n", + " rasterize=True,\n", + " groupby=\"time\",\n", + " widget_type=\"scrubber\",\n", + " widget_location=\"bottom\",\n", + ")" + ] + } + ], + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/04_dashboard.ipynb b/notebooks/04_dashboard.ipynb new file mode 100644 index 0000000..24c8884 --- /dev/null +++ b/notebooks/04_dashboard.ipynb @@ -0,0 +1,1400 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ed2fbee-d7c3-40a7-af56-cb9cf8669670", + "metadata": {}, + "source": [ + "# Creating an Interactive Dashboard using `hvPlot` and `Panel`" + ] + }, + { + "cell_type": "markdown", + "id": "34dbdefb-5cdc-4b3d-802a-3bbe94771cf3", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "6fd3d4d1-f3bb-4c5d-8d1b-05e7432e2717", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.1'.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 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events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", + "application/vnd.holoviews_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import xarray as xr\n", + "import holoviews as hv\n", + "import panel as pn\n", + "import hvplot.xarray\n", + "from holoviews import opts\n", + "\n", + "pn.extension()\n" + ] + }, + { + "cell_type": "markdown", + "id": "cec3aac2-35c3-4745-a001-aad1c007a2b2", + "metadata": {}, + "source": [ + "## Data" + ] + }, + { + "cell_type": "markdown", + "id": "81f61416", + "metadata": {}, + "source": [ + "In this notebook, we are going to load annual mean dataset of 2-m temperature from the [ERA5 reanalysis](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview) that we preprocessed in Zarr format. Please see the preprocessing notebooks for the required steps." + ] + }, + { + "cell_type": "markdown", + "id": "79d66865", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "935a789a-110d-4805-97f7-4ddb28aab20f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Size: 349MB\n", + "Dimensions: (time: 84, latitude: 721, longitude: 1440)\n", + "Coordinates:\n", + " * latitude (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 12kB 0.0 0.25 0.5 0.75 ... 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 672B 1940-12-31 1941-12-31 ... 2023-12-31\n", + "Data variables:\n", + " VAR_2T (time, latitude, longitude) float32 349MB ..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rda_url = \"https://data.rda.ucar.edu/\"\n", + "annual_means = rda_url + \"pythia_era5_24/annual_means/\"\n", + "xrds = xr.open_dataset(annual_means + \"temp_2m_annual_1940_2023.zarr\", engine=\"zarr\")\n", + "xrds" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "539e125c-2adb-4794-8a88-c9f64743bed2", + "metadata": {}, + "outputs": [], + "source": [ + "# Select the time range of interest\n", + "xrds = xrds.sel(time=slice('2017-01-01', '2023-12-31'))\n", + "xrds.load();" + ] + }, + { + "cell_type": "markdown", + "id": "b8179dcb-35d5-4a60-a4ec-7d1e105d756b", + "metadata": {}, + "source": [ + "## Panel Widgets\n", + "\n", + "Panel provides a variety of widgets that can be used to build interactive dashboards. In this notebook, we are going to use some of these widgets. For the complete list of widgets, please see the [Panel documentation](https://panel.holoviz.org/api/panel.widgets.html).\n", + "\n", + "The panel widgets that we are using are: \n", + "- `pn.widgets.Select` for selecting the variable\n", + "- `pn.widgets.DatePicker` for selecting the date\n", + "- `pn.widgets.Player` for making time series animations" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8ba31298-49db-40a7-b350-af18f4af1eed", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "" + ], + "text/plain": [ + "WidgetBox\n", + " [0] Markdown(str)\n", + " [1] Select(name='Plot Type', options=['Color Plot', 'Contour'], value='Color Plot')\n", + " [2] Select(name='Colormap', options=['inferno', 'plasma', ...], value='inferno')" + ] + }, + "execution_count": 5, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "6b494b75-15aa-42ba-a842-3a0bfcd57be0" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "w_cmap = pn.widgets.Select(name=\"Colormap\", options=[\"inferno\", \"plasma\", \"coolwarm\"])\n", + "\n", + "\n", + "w_plot_type = pn.widgets.Select(\n", + " name=\"Plot Type\", options=[\"Color Plot\", \"Contour\"]\n", + ")\n", + "\n", + "\n", + "plot_controls = pn.WidgetBox(\n", + " \"## Plot Controls\",\n", + " w_plot_type,\n", + " w_cmap,\n", + ")\n", + "plot_controls" + ] + }, + { + "cell_type": "markdown", + "id": "c0ae39d0", + "metadata": {}, + "source": [ + "Now, let's put together all the controls and the plot in a panel layout using `pn.Row` and `pn.Column`:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e80e4978", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "" + ], + "text/plain": [ + "Player(align='center', end=6, interval=300, loop_policy='loop', name='Year', width_policy='fit')" + ] + }, + "execution_count": 7, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "b63beb53-98fc-46b9-9928-ff1321f995fb" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "w_player = pn.widgets.Player(\n", + " value=0,\n", + " start=0,\n", + " end=len(xrds.time) - 1,\n", + " name=\"Year\",\n", + " loop_policy=\"loop\",\n", + " interval=300,\n", + " align=\"center\",\n", + " width_policy=\"fit\",\n", + ")\n", + "w_player" + ] + }, + { + "cell_type": "markdown", + "id": "38a7ba16-6fce-4172-9b02-19ac9e136e58", + "metadata": {}, + "source": [ + "## Plotting Function" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7aa8c290-097f-46bd-8265-c8f952b37ea5", + "metadata": {}, + "outputs": [], + "source": [ + "def plot_ds(time, var, cmap, plot_type):\n", + " clim = (xrds[var].values.min(), xrds[var].values.max())\n", + "\n", + " if plot_type == \"Color Plot\":\n", + " return (\n", + " xrds[var]\n", + " .isel(time=time)\n", + " .hvplot(\n", + " cmap=cmap,\n", + " title=str(f\"{var} year {time}\"),\n", + " clim=clim,\n", + " dynamic=False,\n", + " rasterize=True,\n", + " precompute=True,\n", + " )\n", + " .opts(framewise=False)\n", + " )\n", + "\n", + " elif plot_type == \"Contour\":\n", + " return (\n", + " xrds[var]\n", + " .isel(time=time)\n", + " .hvplot.contour(\n", + " cmap=cmap,\n", + " dynamic=False,\n", + " rasterize=True,\n", + " title=str(f\"{var} Year: {time}\"),\n", + " clim=clim,\n", + " precompute=True,\n", + " )\n", + " .opts(framewise=False)\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "a55a2b57-ef02-4046-8534-f740e5bbb72e", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "" + ], + "text/plain": [ + "Row\n", + " [0] Column\n", + " [0] WidgetBox\n", + " [0] Markdown(str)\n", + " [1] Select(name='Data Variable', options=['VAR_2T'], value='VAR_2T')\n", + " [1] WidgetBox\n", + " [0] Markdown(str)\n", + " [1] Select(name='Plot Type', options=['Color Plot', 'Contour'], value='Color Plot')\n", + " [2] Select(name='Colormap', options=['inferno', 'plasma', ...], value='inferno')\n", + " [1] Column\n", + " [0] HoloViews(DynamicMap, height=300, sizing_mode='fixed', width=700)\n", + " [1] Player(align='center', end=6, interval=300, loop_policy='loop', name='Year', width_policy='fit')" + ] + }, + "execution_count": 9, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "0cd9c357-2368-4fbc-a381-b1195d5274ff" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "app = pn.Row(\n", + " controls,\n", + " pn.Column(\n", + " pn.panel(\n", + " hv.DynamicMap(\n", + " pn.bind(\n", + " plot_ds,\n", + " time=w_player,\n", + " var=w_var,\n", + " cmap=w_cmap,\n", + " plot_type=w_plot_type,\n", + " )\n", + " )\n", + " ),\n", + " w_player,\n", + " ),\n", + ").servable()\n", + "\n", + "app" + ] + }, + { + "cell_type": "markdown", + "id": "1c0f704e", + "metadata": {}, + "source": [ + "Please note how the above dashboard is servable. You can deploy the dashboard by running the following command in the terminal:\n", + "\n", + "```bash\n", + "panel serve --show 04_dashboard.ipynb --allow-websocket-origin=projectpythia.2i2c.cloud\n", + "```\n", + "\n", + "This will open a new tab in your default web browser with the dashboard. The allow websocket origin flag is required to allow traffic to flow to the site. This should be update to reflect the base URL where the application is launched. A wildcard can be used, `*`, to allow traffic from any site to connect. \n", + "\n", + "On the Project Pythia 2i2c hosted JupyterHub the link to use to access the panel serve command is [https://projectpythia.2i2c.cloud/hub/user-redirect/proxy/5006/04_dashboard](https://projectpythia.2i2c.cloud/hub/user-redirect/proxy/5006/04_dashboard)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "74b23731-de3f-40f8-a477-afdf266cb6f3", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/04_dashboard_panel.ipynb b/notebooks/04_dashboard_panel.ipynb new file mode 100644 index 0000000..f8e02f2 --- /dev/null +++ b/notebooks/04_dashboard_panel.ipynb @@ -0,0 +1,1381 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "1ed2fbee-d7c3-40a7-af56-cb9cf8669670", + "metadata": {}, + "source": [ + "# Creating an Interactive Dashboard using `hvPlot` and `Panel`" + ] + }, + { + "cell_type": "markdown", + "id": "34dbdefb-5cdc-4b3d-802a-3bbe94771cf3", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "6fd3d4d1-f3bb-4c5d-8d1b-05e7432e2717", + "metadata": {}, + "outputs": [ + { + "data": { + "application/javascript": "(function(root) {\n function now() {\n return new Date();\n }\n\n var force = true;\n var py_version = '3.4.1'.replace('rc', '-rc.').replace('.dev', '-dev.');\n var reloading = true;\n var Bokeh = root.Bokeh;\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force) {\n root._bokeh_timeout = Date.now() + 5000;\n 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handle_clear_output(event, {cell: {output_area: handle.output_area}})\n handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n var toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[0]);\n element.append(toinsert);\n return toinsert\n }\n\n events.on('output_added.OutputArea', handle_add_output);\n events.on('output_updated.OutputArea', handle_update_output);\n events.on('clear_output.CodeCell', handle_clear_output);\n events.on('delete.Cell', handle_clear_output);\n events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n safe: true,\n index: 0\n });\n}\n\nif (window.Jupyter !== undefined) {\n try {\n var events = require('base/js/events');\n var OutputArea = require('notebook/js/outputarea').OutputArea;\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n } catch(err) {\n }\n}\n", + "application/vnd.holoviews_load.v0+json": "" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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+       "Dimensions:    (time: 84, latitude: 721, longitude: 1440)\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 12kB 0.0 0.25 0.5 0.75 ... 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 672B 1940-12-31 1941-12-31 ... 2023-12-31\n",
+       "Data variables:\n",
+       "    VAR_2T     (time, latitude, longitude) float32 349MB ...
" + ], + "text/plain": [ + " Size: 349MB\n", + "Dimensions: (time: 84, latitude: 721, longitude: 1440)\n", + "Coordinates:\n", + " * latitude (latitude) float64 6kB 90.0 89.75 89.5 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 12kB 0.0 0.25 0.5 0.75 ... 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 672B 1940-12-31 1941-12-31 ... 2023-12-31\n", + "Data variables:\n", + " VAR_2T (time, latitude, longitude) float32 349MB ..." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rda_url = \"https://data.rda.ucar.edu/\"\n", + "annual_means = rda_url + \"pythia_era5_24/annual_means/\"\n", + "xrds = xr.open_dataset(annual_means + \"temp_2m_annual_1940_2023.zarr\", engine=\"zarr\")\n", + "xrds" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "539e125c-2adb-4794-8a88-c9f64743bed2", + "metadata": {}, + "outputs": [], + "source": [ + "# Select the time range of interest\n", + "xrds = xrds.sel(time=slice('2017-01-01', '2023-12-31'))\n", + "xrds.load();" + ] + }, + { + "cell_type": "markdown", + "id": "b8179dcb-35d5-4a60-a4ec-7d1e105d756b", + "metadata": {}, + "source": [ + "## Panel Widgets\n", + "\n", + "Panel provides a variety of widgets that can be used to build interactive dashboards. In this notebook, we are going to use some of these widgets. For the complete list of widgets, please see the [Panel documentation](https://panel.holoviz.org/api/panel.widgets.html).\n", + "\n", + "The panel widgets that we are using are: \n", + "- `pn.widgets.Select` for selecting the variable\n", + "- `pn.widgets.DatePicker` for selecting the date\n", + "- `pn.widgets.Player` for making time series animations" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "8ba31298-49db-40a7-b350-af18f4af1eed", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "" + ], + "text/plain": [ + "WidgetBox\n", + " [0] Markdown(str)\n", + " [1] Select(name='Data Variable', options=['VAR_2T'], value='VAR_2T')" + ] + }, + "execution_count": 8, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "8a90826b-daa7-4c5c-835c-41b2f079496f" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "w_var = pn.widgets.Select(name=\"Data Variable\", options=list(xrds.data_vars))\n", + "\n", + "dataset_controls = pn.WidgetBox(\n", + " \"## Dataset Controls\",\n", + " w_var,\n", + ")\n", + "dataset_controls" + ] + }, + { + "cell_type": "markdown", + "id": "713e9792", + "metadata": {}, + "source": [ + "Now, let's create dropdown widgets for selecting the colormap and plot type. " + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "ccd540e0-ab5d-4000-bd31-ca8cb2851e0c", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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\n", + "" + ], + "text/plain": [ + "WidgetBox\n", + " [0] Markdown(str)\n", + " [1] Select(name='Plot Type', options=['Color Plot', 'Contour'], value='Color Plot')\n", + " [2] Select(name='Colormap', options=['inferno', 'plasma', ...], value='inferno')" + ] + }, + "execution_count": 13, + "metadata": { + "application/vnd.holoviews_exec.v0+json": { + "id": "747d474b-2cb5-4c5b-a9d0-ebc8015ab473" + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "w_cmap = pn.widgets.Select(name=\"Colormap\", options=[\"inferno\", \"plasma\", \"coolwarm\"])\n", + "\n", + "\n", + "w_plot_type = pn.widgets.Select(\n", + " name=\"Plot Type\", options=[\"Color Plot\", \"Contour\"]\n", + ")\n", + "\n", + "\n", + "plot_controls = pn.WidgetBox(\n", + " \"## Plot Controls\",\n", + " w_plot_type,\n", + " w_cmap,\n", + ")\n", + "plot_controls" + ] + }, + { + "cell_type": "markdown", + "id": "c0ae39d0", + "metadata": {}, + "source": [ + "Now, let's put together all the controls and the plot in a panel layout using `pn.Row` and `pn.Column`:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "e80e4978", + "metadata": {}, + "outputs": [ + { + "data": {}, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.holoviews_exec.v0+json": "", + "text/html": [ + "
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Therefore, it is necessary to create intermediate ARCO datasets for fast processing.\n", + "- In this notebook, we read hourly data from NCAR's publicly accessible ERA5 collection using an intake catalog, compute the annual means and store the result using zarr stores.\n", + "- If you don't have write permision to save to the Research Data Archive (RDA), please save the result to your local folder.\n", + "- If you need annual means for the following variables, please don't run this notebook. The data has already been calculated and can be accessed via https from https://data.rda.ucar.edu/pythia_era5_24/annual_means/\n", + "\n", + " 1) Air temperature at 2 m/ VAR_2T (https://data.rda.ucar.edu/pythia_era5_24/annual_means/temp_2m_annual_1940_2023.zarr)\n", + " \n", + "- Otherwise, please run this script once to generate the annual means.\n" + ] + }, + { + "cell_type": "markdown", + "id": "d677da99-1aa9-4a78-843f-a2ca4b297fe2", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray.html) | Necessary | |\n", + "| [Intro to Intake](https://projectpythia.org/intake-cookbook/notebooks/intake_introduction.html) | Necessary | |\n", + "| [Understanding of Zarr](https://zarr.readthedocs.io/en/stable/) | Helpful | |\n", + "\n", + "- **Time to learn**: 30 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "741a7ddb-1343-4807-9ed2-0150c350d73d", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "019d87d4-5d1b-4947-90c2-de4393c720b6", + "metadata": {}, + "outputs": [], + "source": [ + "import glob\n", + "import re\n", + "import matplotlib as plt\n", + "import numpy as np\n", + "import scipy as sp\n", + "import xarray as xr\n", + "import intake\n", + "import intake_esm\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "134048d2-2f82-42ea-a0c5-7a3ebca96119", + "metadata": {}, + "outputs": [], + "source": [ + "import dask\n", + "from dask.distributed import Client, performance_report\n", + "from dask_jobqueue import PBSCluster" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4bee4557-d1f1-4720-bf61-a09f106f41ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/gpfs/csfs1/collections/rda/data/pythia_era5_24/annual_means/\n" + ] + } + ], + "source": [ + "######## File paths ################\n", + "rda_scratch = \"/gpfs/csfs1/collections/rda/scratch/harshah\"\n", + "rda_data = \"/gpfs/csfs1/collections/rda/data/\"\n", + "#########\n", + "rda_url = 'https://data.rda.ucar.edu/'\n", + "era5_catalog = rda_url + 'pythia_era5_24/pythia_intake_catalogs/era5_catalog.json'\n", + "#alternate_catalog = rda_data + 'pythia_era5_24/pythia_intake_catalogs/era5_catalog_opendap.json'\n", + "annual_means = rda_data + 'pythia_era5_24/annual_means/'\n", + "######## \n", + "zarr_path = rda_scratch + \"/tas_zarr/\"\n", + "##########\n", + "print(annual_means)" + ] + }, + { + "cell_type": "markdown", + "id": "20a20dff-a028-4e38-a7b7-8bfb670bdf01", + "metadata": {}, + "source": [ + "## Create a Dask cluster" + ] + }, + { + "cell_type": "markdown", + "id": "c6eb54a1-a044-4402-8e5a-a53cefb11256", + "metadata": {}, + "source": [ + "#### Dask Introduction\n", + "\n", + "[Dask](https://www.dask.org/) is a solution that enables the scaling of Python libraries. It mimics popular scientific libraries such as numpy, pandas, and xarray that enables an easier path to parallel processing without having to refactor code. \n", + "\n", + "There are 3 components to parallel processing with Dask: the client, the scheduler, and the workers. \n", + "\n", + "The Client is best envisioned as the application that sends information to the Dask cluster. In Python applications this is handled when the client is defined with `client = Client(CLUSTER_TYPE)`. A Dask cluster comprises of a single scheduler that manages the execution of tasks on workers. The `CLUSTER_TYPE` can be defined in a number of different ways.\n", + "\n", + "- There is LocalCluster, a cluster running on the same hardware as the application and sharing the available resources, directly in Python with `dask.distributed`. \n", + "\n", + "- In certain JupyterHubs Dask Gateway may be available and a dedicated dask cluster with its own resources can be created dynamically with `dask.gateway`. \n", + "\n", + "- On HPC systems `dask_jobqueue` is used to connect to the HPC Slurm and PBS job schedulers to provision resources.\n", + "\n", + "The `dask.distributed` client python module can also be used to connect to existing clusters. A Dask Scheduler and Workers can be deployed in containers, or on Kubernetes, without using a Python function to create a dask cluster. The `dask.distributed` Client is configured to connect to the scheduler either by container name, or by the Kubernetes service name. " + ] + }, + { + "cell_type": "markdown", + "id": "09542b2f-aac2-4596-aeaf-89dee2f67cee", + "metadata": {}, + "source": [ + "#### Select the Dask cluster type" + ] + }, + { + "cell_type": "markdown", + "id": "59253ed5-2e1d-4415-bb60-78606d78d36a", + "metadata": {}, + "source": [ + "The default will be `LocalCluster` as that can run on any system.\n", + "\n", + "If running on a HPC computer with a PBS Scheduler, set to True. Otherwise, set to False." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f842eb2a-1eab-4991-b75b-1d996a5b9006", + "metadata": {}, + "outputs": [], + "source": [ + "USE_PBS_SCHEDULER = True" + ] + }, + { + "cell_type": "markdown", + "id": "8bf1065d-7e67-4259-8f9d-876743106a41", + "metadata": {}, + "source": [ + "If running on Jupyter server with Dask Gateway configured, set to True. Otherwise, set to False." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "0993ff8d-9f8d-4e18-be23-44eb9db2a92a", + "metadata": {}, + "outputs": [], + "source": [ + "USE_DASK_GATEWAY = False" + ] + }, + { + "cell_type": "markdown", + "id": "5fa1388b-d977-42f4-878f-46dd2cdee653", + "metadata": {}, + "source": [ + "**Python function for a PBS cluster**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d1421ec6-4b2f-46be-aba2-6db5943317ae", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a PBS cluster object\n", + "def get_pbs_cluster():\n", + " \"\"\" Create cluster through dask_jobqueue. \n", + " \"\"\"\n", + " from dask_jobqueue import PBSCluster\n", + " cluster = PBSCluster(\n", + " job_name = 'dask-pythia-24',\n", + " cores = 1,\n", + " memory = '4GiB',\n", + " processes = 1,\n", + " local_directory = rda_scratch + '/dask/spill',\n", + " resource_spec = 'select=1:ncpus=1:mem=8GB',\n", + " queue = 'casper',\n", + " walltime = '1:00:00',\n", + " #interface = 'ib0'\n", + " interface = 'ext'\n", + " )\n", + " return cluster" + ] + }, + { + "cell_type": "markdown", + "id": "ea025c5b-5fb0-4fd2-aed1-a9df881f400e", + "metadata": {}, + "source": [ + "**Python function for a Gateway Cluster**" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d4329409-77e2-463f-8e59-d882354560f3", + "metadata": {}, + "outputs": [], + "source": [ + "def get_gateway_cluster():\n", + " \"\"\" Create cluster through dask_gateway\n", + " \"\"\"\n", + " from dask_gateway import Gateway\n", + "\n", + " gateway = Gateway()\n", + " cluster = gateway.new_cluster()\n", + " cluster.adapt(minimum=2, maximum=4)\n", + " return cluster" + ] + }, + { + "cell_type": "markdown", + "id": "6a28f49a-754f-4a68-95f0-4ff6f595f9b5", + "metadata": {}, + "source": [ + "**Python function for a Local Cluster**" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "1fd3d476-5002-43b0-93ef-a2aed6182cba", + "metadata": {}, + "outputs": [], + "source": [ + "def get_local_cluster():\n", + " \"\"\" Create cluster using the Jupyter server's resources\n", + " \"\"\"\n", + " from distributed import LocalCluster, performance_report\n", + " cluster = LocalCluster() \n", + "\n", + " cluster.scale(4)\n", + " return cluster" + ] + }, + { + "cell_type": "markdown", + "id": "7a2a2abb-4ac3-4061-8270-f988ba1a820e", + "metadata": {}, + "source": [ + "**Python logic to select the Dask Cluster type**\n", + "\n", + "This uses True/False boolean logic based on the variables set in the previous cells" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f92bbbf8-f507-43a3-95d0-9f1530e14bc3", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3f7db0463f0b4228aa6a6c45766bfdbb", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "
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PBSCluster

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era5_catalog catalog with 7 dataset(s) from 785068 asset(s):

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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "era5_cat = intake.open_esm_datastore(era5_catalog)\n", + "era5_cat" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "83391b6a-90bd-475e-9901-6fc71f3b9c92", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " long_name variable\n", + "0 Potential vorticity PV\n", + "31 Specific rain water content CRWC\n", + "62 Specific snow water content CSWC\n", + "93 Geopotential Z\n", + "124 Temperature T" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "era5_cat.df[['long_name','variable']].drop_duplicates().head()" + ] + }, + { + "cell_type": "markdown", + "id": "3b033ca4-cfcf-48b1-9fc2-aed38fe06dba", + "metadata": {}, + "source": [ + "### Select variable of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "91a3e57c-8257-4fb0-896f-5158176b97d7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "

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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "######## Examples of other Variables ##############\n", + "# MTNLWRF = Outgoing Long Wave Radiation (upto a sign), Mean Top Net Long Wave Radiative Flux\n", + "# rh_cat = era5_cat.search(variable= 'R') # R = Relative Humidity\n", + "# olr_cat = era5_cat.search(variable ='MTNLWRF')\n", + "# olr_cat\n", + "############ Access temperature data ###########\n", + "temp_cat = era5_cat.search(variable='VAR_2T',frequency = 'hourly')\n", + "temp_cat" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3a81ab73-4b87-40d1-b0a8-aef2daa4633a", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the xarray_open_kwargs with a compatible engine, for example, 'scipy'\n", + "xarray_open_kwargs = {\n", + " 'engine': 'h5netcdf',\n", + " 'chunks': {}, # Specify any chunking if needed\n", + " 'backend_kwargs': {} # Any additional backend arguments if required\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fe7c809a-e9b6-43f0-bf52-1833b7200fed", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "--> The keys in the returned dictionary of datasets are constructed as follows:\n", + "\t'datatype.step_type'\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 37s, sys: 3.38 s, total: 1min 41s\n", + "Wall time: 3min 44s\n" + ] + } + ], + "source": [ + "%%time\n", + "dset_temp = temp_cat.to_dataset_dict(xarray_open_kwargs=xarray_open_kwargs)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c8ef4cf3-7ebb-4a9d-9a45-a8fd9eb75f29", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "dict_keys(['an.sfc'])" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dset_temp.keys()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "62fac008-0e07-4fa1-8b40-03f34bc99c2d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (time: 737784, latitude: 721, longitude: 1440)>\n",
+       "dask.array<concatenate, shape=(737784, 721, 1440), dtype=float32, chunksize=(27, 139, 277), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 1940-01-01 ... 2024-02-29T23:00:00\n",
+       "    utc_date   (time) int32 dask.array<chunksize=(744,), meta=np.ndarray>\n",
+       "Attributes: (12/14)\n",
+       "    long_name:                                          2 metre temperature\n",
+       "    short_name:                                         2t\n",
+       "    units:                                              K\n",
+       "    original_format:                                    WMO GRIB 1 with ECMWF...\n",
+       "    ecmwf_local_table:                                  128\n",
+       "    ecmwf_parameter:                                    167\n",
+       "    ...                                                 ...\n",
+       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
+       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
+       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
+       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
+       "    number_of_significant_digits:                       7\n",
+       "    QuantizeBitGroomNumberOfSignificantDigits:          7
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 1940-01-01 ... 2024-02-29T23:00:00\n", + " utc_date (time) int32 dask.array\n", + "Attributes: (12/14)\n", + " long_name: 2 metre temperature\n", + " short_name: 2t\n", + " units: K\n", + " original_format: WMO GRIB 1 with ECMWF...\n", + " ecmwf_local_table: 128\n", + " ecmwf_parameter: 167\n", + " ... ...\n", + " rda_dataset_url: https:/rda.ucar.edu/d...\n", + " rda_dataset_doi: DOI: 10.5065/BH6N-5N20\n", + " rda_dataset_group: ERA5 atmospheric surf...\n", + " QuantizeGranularBitGroomNumberOfSignificantDigits: 7\n", + " number_of_significant_digits: 7\n", + " QuantizeBitGroomNumberOfSignificantDigits: 7" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_2m = dset_temp['an.sfc'].VAR_2T\n", + "temp_2m" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "be182e8d-1418-4545-99e9-9a024b0278b7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (time: 85, latitude: 721, longitude: 1440)>\n",
+       "dask.array<transpose, shape=(85, 721, 1440), dtype=float32, chunksize=(1, 139, 277), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2024-12-31\n",
+       "Attributes: (12/14)\n",
+       "    long_name:                                          2 metre temperature\n",
+       "    short_name:                                         2t\n",
+       "    units:                                              K\n",
+       "    original_format:                                    WMO GRIB 1 with ECMWF...\n",
+       "    ecmwf_local_table:                                  128\n",
+       "    ecmwf_parameter:                                    167\n",
+       "    ...                                                 ...\n",
+       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
+       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
+       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
+       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
+       "    number_of_significant_digits:                       7\n",
+       "    QuantizeBitGroomNumberOfSignificantDigits:          7
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2024-12-31\n", + "Attributes: (12/14)\n", + " long_name: 2 metre temperature\n", + " short_name: 2t\n", + " units: K\n", + " original_format: WMO GRIB 1 with ECMWF...\n", + " ecmwf_local_table: 128\n", + " ecmwf_parameter: 167\n", + " ... ...\n", + " rda_dataset_url: https:/rda.ucar.edu/d...\n", + " rda_dataset_doi: DOI: 10.5065/BH6N-5N20\n", + " rda_dataset_group: ERA5 atmospheric surf...\n", + " QuantizeGranularBitGroomNumberOfSignificantDigits: 7\n", + " number_of_significant_digits: 7\n", + " QuantizeBitGroomNumberOfSignificantDigits: 7" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_2m_annual = temp_2m.resample(time='1Y').mean()\n", + "temp_2m_annual" + ] + }, + { + "cell_type": "markdown", + "id": "0b31398a-6cf3-418c-8dc8-4d24e7532f60", + "metadata": {}, + "source": [ + "### Save the notbeook" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "003f5ed3-d335-4309-ba3c-3e2685c0f0bd", + "metadata": {}, + "outputs": [], + "source": [ + "# temp_2m_annual.to_dataset().to_zarr(zarr_path + \"e5_tas2m_monthly_1940_2023.zarr)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "ff837224-3da0-4310-bd36-36a58a0cdabe", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (time: 1009, latitude: 721, longitude: 1440)>\n",
+       "dask.array<open_dataset-VAR_2T, shape=(1009, 721, 1440), dtype=float32, chunksize=(1000, 721, 180), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 1940-01-31 1940-02-29 ... 2024-01-31\n",
+       "Attributes: (12/14)\n",
+       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
+       "    ecmwf_local_table:                                  128\n",
+       "    ecmwf_parameter:                                    167\n",
+       "    grid_specification:                                 0.25 degree x 0.25 de...\n",
+       "    long_name:                                          2 metre temperature\n",
+       "    maximum_value:                                      320.42938232421875\n",
+       "    ...                                                 ...\n",
+       "    rda_dataset:                                        ds633.0\n",
+       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
+       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
+       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
+       "    short_name:                                         2t\n",
+       "    units:                                              K
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 1940-01-31 1940-02-29 ... 2024-01-31\n", + "Attributes: (12/14)\n", + " QuantizeGranularBitGroomNumberOfSignificantDigits: 7\n", + " ecmwf_local_table: 128\n", + " ecmwf_parameter: 167\n", + " grid_specification: 0.25 degree x 0.25 de...\n", + " long_name: 2 metre temperature\n", + " maximum_value: 320.42938232421875\n", + " ... ...\n", + " rda_dataset: ds633.0\n", + " rda_dataset_doi: DOI: 10.5065/BH6N-5N20\n", + " rda_dataset_group: ERA5 atmospheric surf...\n", + " rda_dataset_url: https:/rda.ucar.edu/d...\n", + " short_name: 2t\n", + " units: K" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_2m_monthly = xr.open_zarr(zarr_path + \"e5_tas2m_monthly_1940_2023.zarr\").VAR_2T\n", + "temp_2m_monthly" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "550d9521-a74e-48d2-8cb5-956e8c06e748", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (time: 84, latitude: 721, longitude: 1440)>\n",
+       "dask.array<getitem, shape=(84, 721, 1440), dtype=float32, chunksize=(84, 721, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31\n",
+       "Attributes: (12/14)\n",
+       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
+       "    ecmwf_local_table:                                  128\n",
+       "    ecmwf_parameter:                                    167\n",
+       "    grid_specification:                                 0.25 degree x 0.25 de...\n",
+       "    long_name:                                          2 metre temperature\n",
+       "    maximum_value:                                      320.42938232421875\n",
+       "    ...                                                 ...\n",
+       "    rda_dataset:                                        ds633.0\n",
+       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
+       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
+       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
+       "    short_name:                                         2t\n",
+       "    units:                                              K
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31\n", + "Attributes: (12/14)\n", + " QuantizeGranularBitGroomNumberOfSignificantDigits: 7\n", + " ecmwf_local_table: 128\n", + " ecmwf_parameter: 167\n", + " grid_specification: 0.25 degree x 0.25 de...\n", + " long_name: 2 metre temperature\n", + " maximum_value: 320.42938232421875\n", + " ... ...\n", + " rda_dataset: ds633.0\n", + " rda_dataset_doi: DOI: 10.5065/BH6N-5N20\n", + " rda_dataset_group: ERA5 atmospheric surf...\n", + " rda_dataset_url: https:/rda.ucar.edu/d...\n", + " short_name: 2t\n", + " units: K" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_2m_annual = temp_2m_monthly.resample(time='1Y').mean()\n", + "temp_2m_annual = temp_2m_annual.chunk({'latitude':721,'longitude':1440})\n", + "temp_2m_annual = temp_2m_annual.drop_isel({'time':-1}) # Drop 2024 data\n", + "temp_2m_annual" + ] + }, + { + "cell_type": "markdown", + "id": "9a564391-934d-4110-8f54-63c20dd79496", + "metadata": {}, + "source": [ + "#### Save annual mean to annual_means folder within rda_data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "9cb14f16-7829-4214-a831-0d96316fabea", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 392 ms, sys: 26.6 ms, total: 419 ms\n", + "Wall time: 6.36 s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# %%time\n", + "# temp_2m_annual.to_dataset().to_zarr(annual_means + 'temp_2m_annual_1940_2023.zarr',mode='w')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "0d7badb9-6ae0-48a4-913e-877c3c5d082f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (time: 84, latitude: 721, longitude: 1440)>\n",
+       "dask.array<open_dataset-VAR_2T, shape=(84, 721, 1440), dtype=float32, chunksize=(84, 721, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31\n",
+       "Attributes: (12/14)\n",
+       "    QuantizeGranularBitGroomNumberOfSignificantDigits:  7\n",
+       "    ecmwf_local_table:                                  128\n",
+       "    ecmwf_parameter:                                    167\n",
+       "    grid_specification:                                 0.25 degree x 0.25 de...\n",
+       "    long_name:                                          2 metre temperature\n",
+       "    maximum_value:                                      320.42938232421875\n",
+       "    ...                                                 ...\n",
+       "    rda_dataset:                                        ds633.0\n",
+       "    rda_dataset_doi:                                    DOI: 10.5065/BH6N-5N20\n",
+       "    rda_dataset_group:                                  ERA5 atmospheric surf...\n",
+       "    rda_dataset_url:                                    https:/rda.ucar.edu/d...\n",
+       "    short_name:                                         2t\n",
+       "    units:                                              K
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31\n", + "Attributes: (12/14)\n", + " QuantizeGranularBitGroomNumberOfSignificantDigits: 7\n", + " ecmwf_local_table: 128\n", + " ecmwf_parameter: 167\n", + " grid_specification: 0.25 degree x 0.25 de...\n", + " long_name: 2 metre temperature\n", + " maximum_value: 320.42938232421875\n", + " ... ...\n", + " rda_dataset: ds633.0\n", + " rda_dataset_doi: DOI: 10.5065/BH6N-5N20\n", + " rda_dataset_group: ERA5 atmospheric surf...\n", + " rda_dataset_url: https:/rda.ucar.edu/d...\n", + " short_name: 2t\n", + " units: K" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_2m_annual = xr.open_zarr(annual_means + 'temp_2m_annual_1940_2023.zarr').VAR_2T\n", + "temp_2m_annual " + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "7fd566fb-910d-4736-b303-0e9f7c131d00", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 118 ms, sys: 11.5 ms, total: 130 ms\n", + "Wall time: 389 ms\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%%time\n", + "temp_2m_annual.isel(time=0).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "bb389cdd-f2d6-4c88-9853-3255d361f5f1", + "metadata": {}, + "source": [ + "### Close up the cluster" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "c8f821a0-0690-4e85-bdef-524e4a23d886", + "metadata": {}, + "outputs": [], + "source": [ + "cluster.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f05ad4c-0a79-4771-9602-bad782ee524a", + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/06_era5_anomaly.ipynb b/notebooks/06_era5_anomaly.ipynb new file mode 100644 index 0000000..92591df --- /dev/null +++ b/notebooks/06_era5_anomaly.ipynb @@ -0,0 +1,2306 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6c594730-cd0f-4b32-9999-3f5860700da5", + "metadata": {}, + "source": [ + "# Access ERA5 data from NCAR's Research Data Archive and compute anomaly" + ] + }, + { + "cell_type": "markdown", + "id": "e4d88a5a-5b58-4d10-a370-fcf0a2507bd6", + "metadata": {}, + "source": [ + "## Overview\n", + "\n", + "ERA-5 Dataset is available from NCAR's RDA in netcdf format as hourly files. A subset of this dataset is processed into Zarr format and available from NCAR RDA endpoints. To learn how you can create Zarr files from NCAR RDA netcdf files, please see [this notebook](./05_data_preprocessing.ipynb).\n", + "\n", + "\n", + "In this notebook, \n", + "* We will read data zarr stores from NCAR's RDA endpoint \n", + "* Compute temperature anomaly for the years 1940-2023" + ] + }, + { + "cell_type": "markdown", + "id": "af77df62-68f8-436f-bf02-482b837ec65c", + "metadata": {}, + "source": [ + "## Prerequisites\n", + "\n", + "| Concepts | Importance | Notes |\n", + "| --- | --- | --- |\n", + "| [Intro to Xarray](https://foundations.projectpythia.org/core/xarray.html) | Necessary | |\n", + "| [Intro to Intake](https://projectpythia.org/intake-cookbook/notebooks/intake_introduction.html) | Necessary | |\n", + "| [Understanding of Zarr](https://zarr.readthedocs.io/en/stable/) | Helpful | |\n", + "\n", + "- **Time to learn**: 30 minutes" + ] + }, + { + "cell_type": "markdown", + "id": "c023bced-f8b9-46c7-b4b6-c92567b7cca1", + "metadata": {}, + "source": [ + "## Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "3092ae29-feec-45fc-873f-a06016571083", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/u/apps/opt/conda/envs/npl-2024a/lib/python3.11/site-packages/dask/dataframe/_pyarrow_compat.py:17: FutureWarning: Minimal version of pyarrow will soon be increased to 14.0.1. You are using 13.0.0. Please consider upgrading.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import xarray as xr\n", + "import intake_esm\n", + "import intake\n", + "import pandas as pd\n", + "import cartopy.crs as ccrs # Correct import for coordinate reference systems\n", + "import cartopy.feature as cfeature\n", + "from holoviews import opts\n", + "import geoviews as gv\n", + "import holoviews as hv\n", + "import aiohttp" + ] + }, + { + "cell_type": "markdown", + "id": "c6bb3a80-b011-43fc-822f-68c7d65b10be", + "metadata": {}, + "source": [ + "### Specify global variables" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "d2dd4beb-490e-4f7b-b4a4-9d8bc5df9d1c", + "metadata": {}, + "outputs": [], + "source": [ + "baseline_year_start = 1940\n", + "baseline_year_end = 1949\n", + "current_year = 2023" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "6ddb79d7-3c23-4fdf-b7b8-59326c1737c4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://data.rda.ucar.edu/pythia_era5_24/annual_means/\n" + ] + } + ], + "source": [ + "rda_data = '/gpfs/csfs1/collections/rda/data/'\n", + "rda_url = 'https://data.rda.ucar.edu/'\n", + "#era5_catalog = rda_url + 'pythia_intake_catalogs/era5_catalog.json'\n", + "#\n", + "annual_means = rda_url + 'pythia_era5_24/annual_means/'\n", + "annual_means_posix= rda_data + 'pythia_era5_24/annual_means/'\n", + "temp_annual_means = annual_means + ''\n", + "#\n", + "print(annual_means)" + ] + }, + { + "cell_type": "markdown", + "id": "aba8b45d-b43d-4489-b3fb-34583428e546", + "metadata": {}, + "source": [ + "### Create a Dask cluster" + ] + }, + { + "cell_type": "markdown", + "id": "6109afeb-ef72-4a47-bdf0-56504054588f", + "metadata": {}, + "source": [ + "#### Dask Introduction\n", + "\n", + "[Dask](https://www.dask.org/) is a solution that enables the scaling of Python libraries. It mimics popular scientific libraries such as numpy, pandas, and xarray that enables an easier path to parallel processing without having to refactor code. \n", + "\n", + "There are 3 components to parallel processing with Dask: the client, the scheduler, and the workers. \n", + "\n", + "The Client is best envisioned as the application that sends information to the Dask cluster. In Python applications this is handled when the client is defined with `client = Client(CLUSTER_TYPE)`. A Dask cluster comprises of a single scheduler that manages the execution of tasks on workers. The `CLUSTER_TYPE` can be defined in a number of different ways.\n", + "\n", + "- There is LocalCluster, a cluster running on the same hardware as the application and sharing the available resources, directly in Python with `dask.distributed`. \n", + "\n", + "- In certain JupyterHubs Dask Gateway may be available and a dedicated dask cluster with its own resources can be created dynamically with `dask.gateway`. \n", + "\n", + "- On HPC systems `dask_jobqueue` is used to connect to the HPC Slurm and PBS job schedulers to provision resources.\n", + "\n", + "The `dask.distributed` client python module can also be used to connect to existing clusters. A Dask Scheduler and Workers can be deployed in containers, or on Kubernetes, without using a Python function to create a dask cluster. The `dask.distributed` Client is configured to connect to the scheduler either by container name, or by the Kubernetes service name. " + ] + }, + { + "cell_type": "markdown", + "id": "756464b7-5c18-4fbf-ae11-86688c0c4f43", + "metadata": {}, + "source": [ + "#### Select the Dask cluster type" + ] + }, + { + "cell_type": "markdown", + "id": "206e90c1-1764-440f-9846-aa6e75d39777", + "metadata": {}, + "source": [ + "The default will be `LocalCluster` as that can run on any system.\n", + "\n", + "If running on a HPC computer with a PBS Scheduler, set to True. Otherwise, set to False." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "27eba78f-3a1c-4d9a-ad07-501f713069aa", + "metadata": {}, + "outputs": [], + "source": [ + "USE_PBS_SCHEDULER = False" + ] + }, + { + "cell_type": "markdown", + "id": "26672bdd-25d4-4664-bd2f-09eac8e578ba", + "metadata": {}, + "source": [ + "If running on Jupyter server with Dask Gateway configured, set to True. Otherwise, set to False." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "4804f8c9-a5f2-4ed7-a5a4-fdb9c344e5d5", + "metadata": {}, + "outputs": [], + "source": [ + "USE_DASK_GATEWAY = False" + ] + }, + { + "cell_type": "markdown", + "id": "296bfaf7-a1a4-48f7-b3f2-e2b12a738660", + "metadata": {}, + "source": [ + "**Python function for a PBS Cluster**" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d0915ebf-a4b9-4089-b5da-b81fc5f96488", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a PBS cluster object\n", + "def get_pbs_cluster():\n", + " \"\"\" Create cluster through dask_jobqueue. \n", + " \"\"\"\n", + " from dask_jobqueue import PBSCluster\n", + " cluster = PBSCluster(\n", + " job_name = 'dask-pythia-24',\n", + " cores = 1,\n", + " memory = '4GiB',\n", + " processes = 1,\n", + " local_directory = rda_scratch + '/dask/spill',\n", + " resource_spec = 'select=1:ncpus=1:mem=4GB',\n", + " queue = 'casper',\n", + " walltime = '1:00:00',\n", + " #interface = 'ib0'\n", + " interface = 'ext'\n", + " )\n", + " return cluster" + ] + }, + { + "cell_type": "markdown", + "id": "7ad45401-97c9-4934-9b97-97ac1c9edd3c", + "metadata": {}, + "source": [ + "**Python function for a Gateway Cluster**" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "4fdeb643-324e-4b76-a902-884d1c2b7d1e", + "metadata": {}, + "outputs": [], + "source": [ + "def get_gateway_cluster():\n", + " \"\"\" Create cluster through dask_gateway\n", + " \"\"\"\n", + " from dask_gateway import Gateway\n", + "\n", + " gateway = Gateway()\n", + " cluster = gateway.new_cluster()\n", + " cluster.adapt(minimum=2, maximum=4)\n", + " return cluster" + ] + }, + { + "cell_type": "markdown", + "id": "9274168c-11e4-4db2-b5b3-6c818e25b8cf", + "metadata": {}, + "source": [ + "**Python function for a Local Cluster**" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c6cf2e6a-9617-4873-9c58-649f524cd74f", + "metadata": {}, + "outputs": [], + "source": [ + "def get_local_cluster():\n", + " \"\"\" Create cluster using the Jupyter server's resources\n", + " \"\"\"\n", + " from distributed import LocalCluster, performance_report\n", + " cluster = LocalCluster() \n", + "\n", + " cluster.scale(4)\n", + " return cluster" + ] + }, + { + "cell_type": "markdown", + "id": "d0d28e5d-16f6-492a-b4ab-cc54015c7ea1", + "metadata": {}, + "source": [ + "**Python logic to select the Dask Cluster type**\n", + "\n", + "This uses True/False boolean logic based on the variables set in the previous cells" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "18ceadbf-2d3c-42a4-aaf0-a13ecc3c86c7", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/glade/u/apps/opt/conda/envs/npl-2024a/lib/python3.11/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", + "Perhaps you already have a cluster running?\n", + "Hosting the HTTP server on port 43817 instead\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7908a1bbf05b4930a2efb91be9df52d0", + "version_major": 2, + "version_minor": 0 + }, + "text/html": [ + "
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" + ], + "text/plain": [ + "LocalCluster(6d812a4d, 'tcp://127.0.0.1:42555', workers=4, threads=4, memory=16.00 GiB)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Obtain dask cluster in one of three ways\n", + "\n", + "if USE_PBS_SCHEDULER:\n", + " cluster = get_pbs_cluster()\n", + "elif USE_DASK_GATEWAY:\n", + " cluster = get_gateway_cluster()\n", + "else:\n", + " cluster = get_local_cluster()\n", + "\n", + "# Connect to cluster\n", + "from distributed import Client\n", + "client = Client(cluster)\n", + "\n", + "# Display cluster dashboard URL\n", + "cluster" + ] + }, + { + "cell_type": "markdown", + "id": "ced69791-8589-4ec7-9961-edd56e4bb014", + "metadata": {}, + "source": [ + "### Open ERA5 annual means file" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "c90cbf01-6547-4a39-b9b6-da8466939ecb", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (latitude: 721, longitude: 1440)>\n",
+       "dask.array<mean_agg-aggregate, shape=(721, 1440), dtype=float32, chunksize=(721, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "baseline_temp = temp_2m_annual.sel(time= \\\n", + " temp_2m_annual.time.dt.year.isin(range(baseline_year_start, baseline_year_end+1))).mean('time')\n", + "baseline_temp " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "fde7b8e3-d209-4c7a-b6fc-c9f8f7807354", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (time: 84, latitude: 721, longitude: 1440)>\n",
+       "dask.array<sub, shape=(84, 721, 1440), dtype=float32, chunksize=(84, 721, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_anomaly = temp_2m_annual - baseline_temp\n", + "temp_anomaly" + ] + }, + { + "cell_type": "markdown", + "id": "71c2af9f-7471-4061-a321-e5739801d8ad", + "metadata": {}, + "source": [ + "### Save anomaly" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "ffa2e1f9-51cb-4539-8cb2-a3644bde7104", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 337 ms, sys: 39.9 ms, total: 377 ms\n", + "Wall time: 5.29 s\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# %%time\n", + "# temp_anomaly.to_dataset().to_zarr(annual_means_posix + 'temp_anomaly_wrt_1940_1950.zarr')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "e4d432d9-1c63-4907-b6c5-0eb5d2499714", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'VAR_2T' (time: 84, latitude: 721, longitude: 1440)>\n",
+       "dask.array<open_dataset-VAR_2T, shape=(84, 721, 1440), dtype=float32, chunksize=(84, 721, 1440), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * latitude   (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n",
+       "  * longitude  (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n",
+       "  * time       (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * latitude (latitude) float64 90.0 89.75 89.5 89.25 ... -89.5 -89.75 -90.0\n", + " * longitude (longitude) float64 0.0 0.25 0.5 0.75 ... 359.0 359.2 359.5 359.8\n", + " * time (time) datetime64[ns] 1940-12-31 1941-12-31 ... 2023-12-31" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temp_anomaly = xr.open_zarr(annual_means_posix + 'temp_anomaly_wrt_1940_1950.zarr').VAR_2T\n", + "temp_anomaly" + ] + }, + { + "cell_type": "markdown", + "id": "56f909bd-bbd4-4bfb-931d-2e3d136ce255", + "metadata": {}, + "source": [ + "- Plot the temperature anomaly" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "39ee90d5-666d-41a4-94f9-1b0a77162142", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temp_anomaly.isel(time=83).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "34b49979-a521-4be0-9fd8-6d41891c8261", + "metadata": {}, + "source": [ + "## Close the Dask Cluster\n", + "\n", + "It's best practice to close the Dask cluster when it's no longer needed to free up the compute resources used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3e51dddc-0c52-4299-8f19-57952838de91", + "metadata": {}, + "outputs": [], + "source": [ + "cluster.close()" + ] + } + ], + "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": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}