diff --git a/README.rst b/README.rst index 91154dc5..fa659df0 100644 --- a/README.rst +++ b/README.rst @@ -1,7 +1,7 @@ .. raw:: html

- xCDAT logo + xCDAT logo

.. container:: @@ -15,7 +15,7 @@ +--------------------+------------------------------------------------------+ | | Badges | +====================+======================================================+ - | Distribution | |conda| |conda-forge| |platforms| |conda-downloads| | + | Distribution | |conda-forge| |platforms| |conda-downloads| | +--------------------+------------------------------------------------------+ | Citation | |zenodo-doi| | +--------------------+------------------------------------------------------+ @@ -28,8 +28,6 @@ -.. |conda| image:: https://anaconda.org/conda-forge/xcdat/badges/installer/conda.svg - :target: https://anaconda.org/conda-forge/xcdat .. |conda-forge| image:: https://img.shields.io/conda/vn/conda-forge/xcdat.svg :target: https://anaconda.org/conda-forge/xcdat .. |platforms| image:: https://img.shields.io/conda/pn/conda-forge/xcdat.svg @@ -54,15 +52,14 @@ :target: http://mypy-lang.org/ xCDAT is an extension of `xarray`_ for climate data analysis on structured grids. It -serves as the spiritual successor to the Community Data Analysis Tools (`CDAT`_) -library. +serves as a modern successor to the Community Data Analysis Tools (`CDAT`_) library. -The goal of xCDAT is to provide generalizable climate domain features and utilities -that streamline the developer experience for data analysis code. xCDAT's design -philosophy is to reduce the complexity and overhead required by the user to accomplish -specific tasks in xarray. Some xCDAT features are inspired by or ported from core CDAT -functionalities, while others leverage powerful libraries in the xarray ecosystem -(e.g., `xESMF`_ and `cf_xarray`_) to deliver robust APIs. +The goal of xCDAT is to provide generalizable features and utilities for simple and +robust analysis of climate data. xCDAT's design philosophy is focused on reducing the +overhead required to accomplish certain tasks in xarray. Some key xCDAT features are +inspired by or ported from the core CDAT library, while others leverage powerful +libraries in the xarray ecosystem (e.g., `xESMF`_ and `cf_xarray`_) to deliver +robust APIs. The xCDAT core team's mission is to provide a maintainable and extensible package that serves the needs of the climate community in the long-term. We are excited @@ -194,13 +191,14 @@ This software is jointly developed by scientists and developers from the Energy Earth System Model (`E3SM`_) Project and Program for Climate Model Diagnosis and Intercomparison (`PCMDI`_). The work is performed for the E3SM project, which is sponsored by Earth System Model Development (`ESMD`_) program, and the Simplifying ESM -Analysis Through Standards (SEATS) project, which is sponsored by the Regional and +Analysis Through Standards (`SEATS`_) project, which is sponsored by the Regional and Global Model Analysis (`RGMA`_) program. ESMD and RGMA are programs for the Earth and Environmental Systems Sciences Division (`EESSD`_) in the Office of Biological and Environmental Research (`BER`_) within the `Department of Energy`_'s `Office of Science`_. .. _E3SM: https://e3sm.org/ .. _PCMDI: https://pcmdi.llnl.gov/ +.. _SEATS: https://www.seatstandards.org/ .. _ESMD: https://climatemodeling.science.energy.gov/program/earth-system-model-development .. _RGMA: https://climatemodeling.science.energy.gov/program/regional-global-model-analysis .. _EESSD: https://science.osti.gov/ber/Research/eessd diff --git a/docs/_static/CF-xarray.png b/docs/_static/CF-xarray.png new file mode 100644 index 00000000..8b0b18c5 Binary files /dev/null and b/docs/_static/CF-xarray.png differ diff --git a/docs/_static/CMIP6-logo.png b/docs/_static/CMIP6-logo.png new file mode 100644 index 00000000..d8ef5a24 Binary files /dev/null and b/docs/_static/CMIP6-logo.png differ diff --git a/docs/_static/NumFocus-logo.png b/docs/_static/NumFocus-logo.png new file mode 100644 index 00000000..a1daebf5 Binary files /dev/null and b/docs/_static/NumFocus-logo.png differ diff --git a/docs/_static/dask-logo.svg b/docs/_static/dask-logo.svg new file mode 100644 index 00000000..868fcfa3 --- /dev/null +++ b/docs/_static/dask-logo.svg @@ -0,0 +1,13 @@ + + + + + + + + + + + + + diff --git a/docs/_static/dataset-diagram.webp b/docs/_static/dataset-diagram.webp new file mode 100644 index 00000000..bbef3025 Binary files /dev/null and b/docs/_static/dataset-diagram.webp differ diff --git a/docs/_static/e3sm-logo.jpg b/docs/_static/e3sm-logo.jpg new file mode 100644 index 00000000..3db1b923 Binary files /dev/null and b/docs/_static/e3sm-logo.jpg differ diff --git a/docs/_static/e3sm-logo.png b/docs/_static/e3sm-logo.png new file mode 100644 index 00000000..f8c17ca2 Binary files /dev/null and b/docs/_static/e3sm-logo.png differ diff --git a/docs/_static/numpy-logo.svg b/docs/_static/numpy-logo.svg new file mode 100644 index 00000000..63d61c50 --- /dev/null +++ b/docs/_static/numpy-logo.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/_static/pandas-logo.svg b/docs/_static/pandas-logo.svg new file mode 100644 index 00000000..a7af4e4d --- /dev/null +++ b/docs/_static/pandas-logo.svg @@ -0,0 +1 @@ +Artboard 63 \ No newline at end of file diff --git a/docs/_static/pcmdi-logo.png b/docs/_static/pcmdi-logo.png new file mode 100644 index 00000000..f92058f4 Binary files /dev/null and b/docs/_static/pcmdi-logo.png differ diff --git a/docs/_static/seats-logo.png b/docs/_static/seats-logo.png new file mode 100644 index 00000000..93c061f5 Binary files /dev/null and b/docs/_static/seats-logo.png differ diff --git a/docs/_static/xarray_logo.png b/docs/_static/xarray-logo.png similarity index 100% rename from docs/_static/xarray_logo.png rename to docs/_static/xarray-logo.png diff --git a/docs/_static/xcdat_logo.png b/docs/_static/xcdat-logo.png similarity index 100% rename from docs/_static/xcdat_logo.png rename to docs/_static/xcdat-logo.png diff --git a/docs/conf.py b/docs/conf.py index aeb0caf1..f9d07f6a 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -122,7 +122,7 @@ # # sphinx_book_theme configurations # https://sphinx-book-theme.readthedocs.io/en/latest/configure.html -html_logo = "_static/xcdat_logo.png" +html_logo = "_static/xcdat-logo.png" html_title = "xCDAT Documentation" html_theme_options = { "repository_url": "https://github.com/xCDAT/xcdat", diff --git a/docs/examples/introduction-to-xcdat.ipynb b/docs/examples/introduction-to-xcdat.ipynb new file mode 100644 index 00000000..91879fe5 --- /dev/null +++ b/docs/examples/introduction-to-xcdat.ipynb @@ -0,0 +1,4619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "cell_style": "center", + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "
\n", + " \"xCDAT\n", + "
\n", + "\n", + "\n", + "# A Gentle Introduction to xCDAT (Xarray Climate Data Analysis Tools)\n", + "\n", + "

\n", + " \"A Python package for simple and robust climate data analysis.\"

\n", + "

Core Developers: Tom Vo, Stephen Po-Chedley, Jason Boutte, Jill Zhang, Jiwoo Lee

\n", + "\n", + "

With thanks to Peter Gleckler, Paul Durack, Karl Taylor, and Chris Golaz

\n", + "\n", + "\n", + "_This work is performed under the auspices of the U. S. DOE by Lawrence Livermore National Laboratory under contract No. DE-AC52-07NA27344._" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Presentation Overview\n", + "\n", + "Intended audience: Some or no familiarity with `xarray` and `xcdat`\n", + "\n", + "* What is xCDAT?\n", + "* An overview of Xarray\n", + "* How does xCDAT fit in the Xarray ecosystem?\n", + "* The API design of xCDAT\n", + "* Demo of xCDAT capabilities and Dask parallelism\n", + "* Wrap up and resources\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## What is xCDAT?\n", + "\n", + "* xCDAT is an **extension of xarray** for **climate data analysis on structured grids**\n", + "* The goal is to provide **generalizable features and utilities for simple and robust analysis of climate data**\n", + "* Jointly developed by scientists and developers from **E3SM** and **PCMDI** at **Lawrence Livermore National Lab**\n", + " * In collaboration with external users and organizations through GitHub\n", + "* Performed for the E3SM and **SEATS** (Simplifying ESM Analysis Through Standards) projects\n", + "\n", + "
\n", + "\"E3SM\n", + "\"PCMDI\n", + "\"SEATS\n", + "\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "* Some key xCDAT features are inspired by or ported from the core **CDAT** library\n", + " * Examples: spatial/temporal averaging, regrid2 for horizontal regridding\n", + "* Other features **leverage powerful libraries** in the **xarray ecosystem** \n", + " * Examples: `xESMF` and `CF-xarray`\n", + "* xCDAT strives to support **CF compliant datasets** and datasets with **common non-CF compliant metadata**\n", + " * Example common non-CF metadata: time units in “months since …” or “years since …”\n", + "\n", + "
\n", + " \"CMIP6\n", + " \"cf-xarray\n", + "\n", + "
\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## First, Let's Dive into Xarray\n", + "\n", + "* Xarray is an evolution of an internal tool developed at The Climate Corporation\n", + "* Released as open source ina May 2014\n", + "* __NumFocus__ fiscally sponsored project since August 2018\n", + "\n", + "\n", + "
\n", + " \"xarray\n", + " \"NumFOCUS\n", + "
\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Key Features and Capabilities in Xarray\n", + "\n", + "* __“N-D labeled arrays and datasets in Python”__\n", + " * Built upon and extends NumPy and pandas\n", + "* __Interoperable with scientific Python ecosystem__ including NumPy, Dask, Pandas, and Matplotlib\n", + "* Supports file I/O, indexing and selecting, interpolating, grouping, aggregating, parallelism (Dask), plotting (matplotlib wrapper)\n", + " * Supports I/O for netCDF, Iris, OPeNDAP, Zarr, and GRIB\n", + "\n", + "
\n", + "\"NumPy\n", + "\"Pandas\n", + " \"Dask\n", + "
\n", + "\n", + "Source: https://xarray.dev/#features\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Why Xarray?\n", + "\n", + "> \"Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like \n", + "> multidimensional arrays, which allows for a more intuitive, more concise, and less error-prone developer \n", + "> experience.\"\n", + ">\n", + "> — https://xarray.pydata.org/en/v2022.10.0/getting-started-guide/why-xarray.html\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Xarray uses labels on arrays to provide a powerful and concise interface\n", + "\n", + " * __Apply operations over dimensions by name__\n", + " * `x.sum('time')`\n", + " * __Select values by label__ (or logical location) instead of integer location\n", + " * `x.loc['2014-01-01']` or `x.sel(time='2014-01-01')`\n", + " * __Mathematical operations vectorize across multiple dimensions__ (array broadcasting) based on __dimension names__, not shape\n", + " * `x - y`\n", + " * Easily use the __split-apply-combine paradigm__ with groupby\n", + " * `x.groupby('time.dayofyear').mean()`.\n", + " * __Database-like alignment__ based on coordinate labels that __smoothly handles missing values__\n", + " * `x, y = xr.align(x, y, join='outer')`\n", + " * Keep track of __arbitrary metadata in__ the form of a __Python dictionary__\n", + " * `x.attrs`\n", + "\n", + "Source: https://docs.xarray.dev/en/v2022.10.0/getting-started-guide/why-xarray.html#what-labels-enable" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## The Xarray Core Data Structures\n", + "\n", + "> \"Xarray data models are borrowed from netCDF file format, which provides xarray with a natural and portable\n", + "> serialization format.\"\n", + ">\n", + "> — https://docs.xarray.dev/en/v2022.10.0/getting-started-guide/why-xarray.html\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "Xarray has two core data structures:\n", + "\n", + "1. `xarray.DataArray`\n", + " * A class that attaches __dimension names__, __coordinates__, and __attributes__ to __multi-dimensional arrays__ (aka \"labeled arrays\")\n", + " * An N-D generalization of a `pandas.Series`\n", + "2. `xarray.Dataset`\n", + " * A __dictionary-like container__ of DataArray objects with __aligned dimensions__ \n", + " * DataArray objects are classified as \"coordinate variables\" or \"data variables\"\n", + " * All data variables have a shared __union__ of coordinates\n", + " * Serves a similar purpose to a `pandas.DataFrame`\n", + "\n", + "
\n", + " \"xarray\n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Dissecting Xarray Data Structures in a Real-World Dataset\n", + "\n", + "This example netCDF4 dataset is opened directly from ESGF using xarray's OPeNDAP support.\n", + "\n", + "It contains the `tas` variable, which represents near-surface air temperature.\n", + "`tas` is recorded on a monthly frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "slideshow": { + "slide_type": "skip" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# This style import is necessary to properly render Xarray's HTML output with\n", + "# the Jupyer RISE extension.\n", + "# GitHub Issue: https://github.com/damianavila/RISE/issues/594\n", + "# Source: https://github.com/smartass101/xarray-pydata-prague-2020/blob/main/rise.css\n", + "\n", + "from IPython.core.display import HTML\n", + "\n", + "style = \"\"\"\n", + "\n", + "\"\"\"\n", + "\n", + "\n", + "HTML(style)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "cell_style": "center", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "\n", + "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", + "\n", + "ds = xr.open_dataset(filepath)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_style": "split", + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### The `Dataset` Model\n", + "\n", + "A dictionary-like container of labeled arrays (DataArray objects) with aligned dimensions. \n", + "\n", + "Key properties:\n", + "\n", + "* `dims`: a dictionary mapping from dimension names to the fixed length of each dimension (e.g., {'x': 6, 'y': 6, 'time': 8})\n", + "* `coords`: another dict-like container of DataArrays intended to label points used in data_vars (e.g., arrays of numbers, datetime objects or strings)\n", + "* `data_vars`: a dict-like container of DataArrays corresponding to variables\n", + "* `attrs`: dict to hold arbitrary metadata\n", + "\n", + "Source: https://docs.xarray.dev/en/stable/user-guide/data-structures.html#dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "cell_style": "split", + "scrolled": false, + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + "\n", + "[55123200 values with dtype=float32]\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n", + " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", + " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n", + " height float64 2.0\n", + "Attributes:\n", + " standard_name: air_temperature\n", + " long_name: Near-Surface Air Temperature\n", + " comment: near-surface (usually, 2 meter) air temperature\n", + " units: K\n", + " cell_methods: area: time: mean\n", + " cell_measures: area: areacella\n", + " history: 2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n", + " _ChunkSizes: [ 1 145 192]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.tas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Resources for Learning Xarray\n", + "\n", + "* Now that you have a general sense of xarray data models, give xarray a shot if you haven't already!\n", + "* Here are some highly recommended resources:\n", + " * [Xarray Tutorial](https://tutorial.xarray.dev/intro.html)\n", + " * [\"Xarray in 45 minutes\"](https://tutorial.xarray.dev/overview/xarray-in-45-min.html#) \n", + " * [Xarray Documentation](https://docs.xarray.dev/en/stable/index.html)\n", + " * [Xarray API Reference](https://docs.xarray.dev/en/stable/api.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "cell_style": "center", + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Jumping Forward to xCDAT, an Extension of Xarray\n", + "\n", + "> \"Xarray is designed as a general purpose library, and hence tries to avoid including overly domain specific\n", + "> functionality. But inevitably, the need for more domain specific logic arises.\"\n", + ">\n", + "> — https://docs.xarray.dev/en/v2022.10.0/internals/extending-xarray.html#extending-xarray\n", + "\n", + "* xCDAT aims to provide **generalizable features and utilities for simple and robust analysis of climate data**.\n", + "* xCDAT's design philosophy is focused on **reducing the overhead required to accomplish certain tasks in xarray**." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Available xCDAT Features\n", + "\n", + "* Extension of xarray's ``open_dataset()`` and ``open_mfdataset()`` with post-processing options\n", + " * Generate bounds that don't exist\n", + " * Keep a single data variable in the Dataset\n", + " * Optional decoding of time coordinates, centering of time coordinates, swapping longitudinal axis orientation between [0, 360) and [-180, 180)\n", + "* Temporal averaging\n", + " * Time series averages (single snapshot and grouped), climatologies, and departures\n", + " * Weighted or unweighted\n", + " * Optional seasonal configuration (e.g., DJF vs. JFD, custom seasons)\n", + "* Geospatial weighted averaging (rectilinear grid)\n", + " * Optional specification of regional domain\n", + "* Horizontal structured regridding (rectilinear and curvilinear grids)\n", + " * Python implementation of `regrid2`_ for handling cartesian latitude longitude grids\n", + " * API that wraps `xESMF`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## xCDAT's API Design\n", + "\n", + "xCDAT provides public APIs in two ways:\n", + "\n", + "1. Top-level APIs functions\n", + " * Example: `xcdat.open_dataset()`, `xcdat.center_times()`\n", + "2. Accessor classes\n", + " * xcdat provides `Dataset` accessors, which are __implicit namespaces for custom functionality__.\n", + " * Accessor __namespaces__ clearly identifies __separation from built-in xarray methods__. \n", + " * Example: `ds.spatial`, `ds.temporal`, `ds.regridder`\n", + "\n", + "
\n", + "
\n", + " \"xcdat\n", + "
xcdat spatial functionality is exposed by chaining the .spatial accessor attribute to the xr.Dataset object.
\n", + "
\n", + "
\n", + "\n", + "Source: https://xcdat.readthedocs.io/en/latest/api.html" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## A Demo of xCDAT Capabilities\n", + "\n", + "* Prerequisites\n", + " * Installing `xcdat`\n", + " * Import `xcdat`\n", + " * Open a dataset and apply postprocessing operations\n", + "* Scenario 1 - Calculate the spatial averages over the tropical region\n", + "* Scenario 2 - Calculate the annual anomalies\n", + "* Scenario 3 - Horizontal regridding (bilinear, gaussian grid)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Installing `xcdat`\n", + "\n", + "xCDAT is available on Anaconda under the `conda-forge` channel (https://anaconda.org/conda-forge/xcdat)\n", + "\n", + "Two ways to install `xcdat` with recommended dependencies (`xesmf`):\n", + "\n", + "1. Create a conda environment from scratch (`conda create`)\n", + "\n", + " ```bash\n", + " conda create -n -c conda-forge xcdat xesmf\n", + " conda activate \n", + " ```\n", + "\n", + "2. Install `xcdat` in an existing conda environment (`conda install`)\n", + "\n", + " ```bash\n", + " conda activate \n", + " conda install -c conda-forge xcdat xesmf\n", + " ```\n", + "\n", + "_Source_: https://xcdat.readthedocs.io/en/latest/getting-started.html" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Opening a dataset\n", + "\n", + "This example netCDF4 dataset is opened directly from ESGF using xarray's OPeNDAP support.\n", + "\n", + "It contains the `tas` variable, which represents near-surface air temperature.\n", + "`tas` is recorded on a monthly frequency." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# This gives access to all xcdat public top-level APIs and accessor classes.\n", + "import xcdat as xc\n", + "\n", + "# We import these packages specifically for plotting. It is not required to use xcdat.\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "cell_style": "center", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", + "\n", + "ds = xc.open_dataset(\n", + " filepath,\n", + " add_bounds=True,\n", + " decode_times=True,\n", + " center_times=True\n", + ")\n", + "\n", + "# Unit adjustment from Kelvin to Celcius.\n", + "ds[\"tas\"] = ds.tas - 273.15" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "cell_style": "center", + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n",
+       "  * lat        (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
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+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) datetime64[ns] 1850-01-01 1850-02-01 ... 2015-01-01\n",
+       "    lat_bnds   (lat, bnds) float64 -90.0 -89.38 -89.38 ... 89.38 89.38 90.0\n",
+       "    lon_bnds   (lon, bnds) float64 -0.9375 0.9375 0.9375 ... 357.2 357.2 359.1\n",
+       "    tas        (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n",
+       "Attributes: (12/48)\n",
+       "    Conventions:                     CF-1.7 CMIP-6.2\n",
+       "    activity_id:                     CMIP\n",
+       "    branch_method:                   standard\n",
+       "    branch_time_in_child:            0.0\n",
+       "    branch_time_in_parent:           87658.0\n",
+       "    creation_date:                   2020-06-05T04:06:11Z\n",
+       "    ...                              ...\n",
+       "    variant_label:                   r10i1p1f1\n",
+       "    version:                         v20200605\n",
+       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
+       "    cmor_version:                    3.4.0\n",
+       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 1980, bnds: 2, lat: 145, lon: 192)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n", + " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", + " * lon (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n", + " height float64 2.0\n", + "Dimensions without coordinates: bnds\n", + "Data variables:\n", + " time_bnds (time, bnds) datetime64[ns] ...\n", + " lat_bnds (lat, bnds) float64 ...\n", + " lon_bnds (lon, bnds) float64 ...\n", + " tas (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n", + "Attributes: (12/48)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: CMIP\n", + " branch_method: standard\n", + " branch_time_in_child: 0.0\n", + " branch_time_in_parent: 87658.0\n", + " creation_date: 2020-06-05T04:06:11Z\n", + " ... ...\n", + " variant_label: r10i1p1f1\n", + " version: v20200605\n", + " license: CMIP6 model data produced by CSIRO is li...\n", + " cmor_version: 3.4.0\n", + " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", + " DODS_EXTRA.Unlimited_Dimension: time" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Scenario 1: Spatial Averaging\n", + "\n", + "Related accessor: `ds.spatial`\n", + "\n", + "In this example, we calculate the spatial average of `tas` over the tropical region." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "ds_trop_avg = ds.spatial.average(\"tas\", axis=[\"X\",\"Y\"], lat_bounds=(-25,25))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'tas' (time: 1980)>\n",
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" + ], + "text/plain": [ + "\n", + "array([25.24722608, 25.61795924, 25.96516235, ..., 26.79536823,\n", + " 26.67771602, 26.27182383])\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n", + " height float64 2.0" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_trop_avg.tas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Plot the first 100 time steps" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_trop_avg.tas.isel(time=slice(0, 100)).plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Scenario 2: Calculate temporal average \n", + "\n", + "Related accessor: `ds.temporal`\n", + "\n", + "In this example, we calculate the temporal average of `tas` as a single snapshot. The time dimension is removed after averaging." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'tas' (lat: 145, lon: 192)>\n",
+       "array([[-48.01481628, -48.01481628, -48.01481628, ..., -48.01481628,\n",
+       "        -48.01481628, -48.01481628],\n",
+       "       [-44.94085363, -44.97948214, -45.01815398, ..., -44.82408252,\n",
+       "        -44.86273067, -44.9009281 ],\n",
+       "       [-44.11875274, -44.23060624, -44.33960158, ..., -43.76766492,\n",
+       "        -43.88593717, -44.00303006],\n",
+       "       ...,\n",
+       "       [-18.21076615, -18.17513373, -18.13957458, ..., -18.32720478,\n",
+       "        -18.28428828, -18.2486193 ],\n",
+       "       [-18.50778243, -18.49301854, -18.47902819, ..., -18.55410851,\n",
+       "        -18.5406963 , -18.52413098],\n",
+       "       [-19.07366375, -19.07366375, -19.07366375, ..., -19.07366375,\n",
+       "        -19.07366375, -19.07366375]])\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
+       "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
+       "    height   float64 2.0\n",
+       "Attributes:\n",
+       "    operation:  temporal_avg\n",
+       "    mode:       average\n",
+       "    freq:       month\n",
+       "    weighted:   True
" + ], + "text/plain": [ + "\n", + "array([[-48.01481628, -48.01481628, -48.01481628, ..., -48.01481628,\n", + " -48.01481628, -48.01481628],\n", + " [-44.94085363, -44.97948214, -45.01815398, ..., -44.82408252,\n", + " -44.86273067, -44.9009281 ],\n", + " [-44.11875274, -44.23060624, -44.33960158, ..., -43.76766492,\n", + " -43.88593717, -44.00303006],\n", + " ...,\n", + " [-18.21076615, -18.17513373, -18.13957458, ..., -18.32720478,\n", + " -18.28428828, -18.2486193 ],\n", + " [-18.50778243, -18.49301854, -18.47902819, ..., -18.55410851,\n", + " -18.5406963 , -18.52413098],\n", + " [-19.07366375, -19.07366375, -19.07366375, ..., -19.07366375,\n", + " -19.07366375, -19.07366375]])\n", + "Coordinates:\n", + " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", + " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n", + " height float64 2.0\n", + "Attributes:\n", + " operation: temporal_avg\n", + " mode: average\n", + " freq: month\n", + " weighted: True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds_avg = ds.temporal.average(\"tas\", weighted=True)\n", + "ds_avg.tas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Plot the temporal average" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds_avg.tas.plot(label=\"weighted\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Scenario 3: Horizontal Regridding\n", + "\n", + "Related accessor: `ds.regridder`\n", + "\n", + "In this example, we will generate a gaussian grid with 32 latitudes to regrid our input data to." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Create the output grid" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "output_grid = xc.create_gaussian_grid(32)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(ncols=2, figsize=(16, 4))\n", + "\n", + "ds.regridder.grid.plot.scatter('lon', 'lat', s=0.01, ax=axes[0])\n", + "axes[0].set_title('Input Grid')\n", + "\n", + "output_grid.plot.scatter('lon', 'lat', s=0.1, ax=axes[1])\n", + "axes[1].set_title('Output Grid')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "#### Regrid the data\n", + "\n", + "xCDAT offers horizontal regridding with `xESMF` (default) and a Python port of `regrid2`.\n", + "We will be using `xESMF` to regrid.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "# xesmf supports \"bilinear\", \"conservative\", \"nearest_s2d\", \"nearest_d2s\", and \"patch\"\n", + "output = ds.regridder.horizontal('tas', output_grid, tool='xesmf', method='bilinear')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, axes = plt.subplots(ncols=2, figsize=(16, 4))\n", + "\n", + "ds.tas.isel(time=0).plot(ax=axes[0])\n", + "axes[0].set_title('Input data')\n", + "\n", + "output.tas.isel(time=0).plot(ax=axes[1])\n", + "axes[1].set_title('Output data')\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Parallelism with Dask\n", + "\n", + "> Nearly all existing xarray methods have been extended to work automatically with Dask arrays for parallelism\n", + "— https://docs.xarray.dev/en/stable/user-guide/dask.html#using-dask-with-xarray\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "* Parallelized xarray methods include __indexing, computation, concatenating and grouped operations__\n", + "* xCDAT APIs that build upon xarray methods inherently support Dask parallelism\n", + " * Dask arrays are loaded into memory only when absolutely required (e.g., decoding time, handling bounds)\n", + "\n", + "
\n", + " \"Dask\n", + "
" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### High-level Overview of Dask Mechanics\n", + "\n", + "* __Dask divides arrays__ into many small pieces, called __\"chunks\"__ (each presumed to be small enough to fit into memory)\n", + "* Dask arrays __operations are lazy__\n", + " * Operations __queue__ up a series of tasks mapped over blocks\n", + " * No computation is performed until values need to be computed (lazy)\n", + " * Data is loaded into memory and __computation__ is performed in __streaming fashion__, __block-by-block__\n", + "* Computation is controlled by multi-processing or thread pool\n", + "\n", + "\n", + "Source: https://docs.xarray.dev/en/stable/user-guide/dask.html" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### How do I activate Dask with Xarray/xCDAT?\n", + "\n", + "* The usual way to create a Dataset filled with Dask arrays is to load the data from a netCDF file or files\n", + "* You can do this by supplying a `chunks` argument to `open_dataset()` or using the `open_mfdataset()` function\n", + " * By default, `open_mfdataset()` will chunk each netCDF file into a single Dask array\n", + " * Supply the `chunks` argument to control the size of the resulting Dask arrays\n", + " * Xarray maintains a Dask array until it is not possible (raises an exception instead of loading into memory)\n", + "\n", + "Source: https://docs.xarray.dev/en/stable/user-guide/dask.html#reading-and-writing-data" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [], + "source": [ + "filepath = \"http://esgf.nci.org.au/thredds/dodsC/master/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", + "\n", + "# Use .chunk() to activate Dask arrays\n", + "# NOTE: `open_mfdataset()` automatically chunks by the number of files, which\n", + "# might not be optimal.\n", + "ds = xc.open_dataset(\n", + " filepath,\n", + " chunks={\"time\": \"auto\"}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:    (time: 1980, bnds: 2, lat: 145, lon: 192)\n",
+       "Coordinates:\n",
+       "  * time       (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n",
+       "  * lat        (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
+       "  * lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
+       "    height     float64 ...\n",
+       "Dimensions without coordinates: bnds\n",
+       "Data variables:\n",
+       "    time_bnds  (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
+       "    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(145, 2), meta=np.ndarray>\n",
+       "    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
+       "    tas        (time, lat, lon) float32 dask.array<chunksize=(990, 145, 192), meta=np.ndarray>\n",
+       "Attributes: (12/49)\n",
+       "    Conventions:                     CF-1.7 CMIP-6.2\n",
+       "    activity_id:                     CMIP\n",
+       "    branch_method:                   standard\n",
+       "    branch_time_in_child:            0.0\n",
+       "    branch_time_in_parent:           87658.0\n",
+       "    creation_date:                   2020-06-05T04:06:11Z\n",
+       "    ...                              ...\n",
+       "    version:                         v20200605\n",
+       "    license:                         CMIP6 model data produced by CSIRO is li...\n",
+       "    cmor_version:                    3.4.0\n",
+       "    _NCProperties:                   version=2,netcdf=4.6.2,hdf5=1.10.5\n",
+       "    tracking_id:                     hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n",
+       "    DODS_EXTRA.Unlimited_Dimension:  time
" + ], + "text/plain": [ + "\n", + "Dimensions: (time: 1980, bnds: 2, lat: 145, lon: 192)\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n", + " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", + " * lon (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n", + " height float64 ...\n", + "Dimensions without coordinates: bnds\n", + "Data variables:\n", + " time_bnds (time, bnds) datetime64[ns] dask.array\n", + " lat_bnds (lat, bnds) float64 dask.array\n", + " lon_bnds (lon, bnds) float64 dask.array\n", + " tas (time, lat, lon) float32 dask.array\n", + "Attributes: (12/49)\n", + " Conventions: CF-1.7 CMIP-6.2\n", + " activity_id: CMIP\n", + " branch_method: standard\n", + " branch_time_in_child: 0.0\n", + " branch_time_in_parent: 87658.0\n", + " creation_date: 2020-06-05T04:06:11Z\n", + " ... ...\n", + " version: v20200605\n", + " license: CMIP6 model data produced by CSIRO is li...\n", + " cmor_version: 3.4.0\n", + " _NCProperties: version=2,netcdf=4.6.2,hdf5=1.10.5\n", + " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", + " DODS_EXTRA.Unlimited_Dimension: time" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Example of Parallelism in xCDAT" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'tas' (time: 1980)>\n",
+       "dask.array<truediv, shape=(1980,), dtype=float64, chunksize=(990,), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time     (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n",
+       "    height   float64 ...\n",
+       "Attributes:\n",
+       "    standard_name:  air_temperature\n",
+       "    long_name:      Near-Surface Air Temperature\n",
+       "    comment:        near-surface (usually, 2 meter) air temperature\n",
+       "    units:          K\n",
+       "    cell_methods:   area: time: mean\n",
+       "    cell_measures:  area: areacella\n",
+       "    history:        2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n",
+       "    _ChunkSizes:    [  1 145 192]
" + ], + "text/plain": [ + "\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n", + " height float64 ...\n", + "Attributes:\n", + " standard_name: air_temperature\n", + " long_name: Near-Surface Air Temperature\n", + " comment: near-surface (usually, 2 meter) air temperature\n", + " units: K\n", + " cell_methods: area: time: mean\n", + " cell_measures: area: areacella\n", + " history: 2020-06-05T04:06:10Z altered by CMOR: Treated scalar dime...\n", + " _ChunkSizes: [ 1 145 192]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tas_global = ds.spatial.average(\"tas\", axis=[\"X\", \"Y\"], weights=\"generate\")[\"tas\"]\n", + "tas_global" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "### Further Dask Guidance\n", + "\n", + "Visit these pages for more guidance (e.g., when to parallelize):\n", + "\n", + "* Parallel computing with Dask: https://docs.xarray.dev/en/stable/user-guide/dask.html\n", + "* Xarray with Dask Arrays: https://examples.dask.org/xarray.html\n", + "\n", + "[//]: # \"TODO: Add link to xCDAT Dask guidance\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Wrapping Things Up\n", + "* \"Xarray introduces labels in the form of dimensions, coordinates and attributes on top of raw NumPy-like multidimensional arrays, which allows for a more intuitive, more concise, and less error-prone developer \n", + "experience\"\n", + "* xCDAT is an extension of xarray for climate data analysis on structured grids. \n", + "* xCDAT aims to make analyzing climate data simple and robust with xarray" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### We'd love your support!\n", + "\n", + "* The xCDAT core team's mission is to provide a __maintainable and extensible package that serves the needs of the climate community in the long-term__. \n", + "* Please check out the repository and give it a star to increase xCDAT's visibility!\n", + "* We're always open to contributions, whether through GitHub issues, pull requests, discussions, etc.\n", + "\n", + "\n", + "Repository: https://github.com/xCDAT/xcdat" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Resources\n", + "\n", + "__If you comments or questions, reach out to us on the GitHub discussions forum!__\n", + "\n", + "* GitHub repository: https://github.com/xCDAT/xcdat\n", + "* GitHub Discussions forum: https://github.com/xCDAT/xcdat/discussions\n", + "* Documentation: https://xcdat.readthedocs.io/en/latest/\n", + "* Anaconda page: https://anaconda.org/conda-forge/xcdat\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "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": "3.9.13" + }, + "rise": { + "scroll": true, + "theme": "simple" + }, + "vscode": { + "interpreter": { + "hash": "937205ea97caa5d37934716e0a0c6b9e4ffcdaf6e0f20ca1ff82361fb532cef2" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/spatial-average.ipynb b/docs/examples/spatial-average.ipynb index c688024c..79694139 100644 --- a/docs/examples/spatial-average.ipynb +++ b/docs/examples/spatial-average.ipynb @@ -104,6 +104,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -437,7 +438,7 @@ " lat_bnds (lat, bnds) float64 -90.0 -89.38 -89.38 ... 89.38 89.38 90.0\n", " lon_bnds (lon, bnds) float64 -0.9375 0.9375 0.9375 ... 357.2 357.2 359.1\n", " tas (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n", - "Attributes: (12/49)\n", + "Attributes: (12/48)\n", " Conventions: CF-1.7 CMIP-6.2\n", " activity_id: CMIP\n", " branch_method: standard\n", @@ -445,15 +446,15 @@ " branch_time_in_parent: 87658.0\n", " creation_date: 2020-06-05T04:06:11Z\n", " ... ...\n", + " variant_label: r10i1p1f1\n", " version: v20200605\n", " license: CMIP6 model data produced by CSIRO is li...\n", " cmor_version: 3.4.0\n", - " _NCProperties: version=2,netcdf=4.6.2,hdf5=1.10.5\n", " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", - " DODS_EXTRA.Unlimited_Dimension: time" + "seaIce: CICE4.1 (same grid as ocean)
source_id :
ACCESS-ESM1-5
source_type :
AOGCM
sub_experiment :
none
sub_experiment_id :
none
table_id :
Amon
table_info :
Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4
title :
ACCESS-ESM1-5 output prepared for CMIP6
variable_id :
tas
variant_label :
r10i1p1f1
version :
v20200605
license :
CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
cmor_version :
3.4.0
tracking_id :
hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1
DODS_EXTRA.Unlimited_Dimension :
time
" ], "text/plain": [ "\n", @@ -575,7 +576,7 @@ " lat_bnds (lat, bnds) float64 ...\n", " lon_bnds (lon, bnds) float64 ...\n", " tas (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n", - "Attributes: (12/49)\n", + "Attributes: (12/48)\n", " Conventions: CF-1.7 CMIP-6.2\n", " activity_id: CMIP\n", " branch_method: standard\n", @@ -583,10 +584,10 @@ " branch_time_in_parent: 87658.0\n", " creation_date: 2020-06-05T04:06:11Z\n", " ... ...\n", + " variant_label: r10i1p1f1\n", " version: v20200605\n", " license: CMIP6 model data produced by CSIRO is li...\n", " cmor_version: 3.4.0\n", - " _NCProperties: version=2,netcdf=4.6.2,hdf5=1.10.5\n", " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", " DODS_EXTRA.Unlimited_Dimension: time" ] @@ -597,7 +598,7 @@ } ], "source": [ - "filepath = \"http://esgf.nci.org.au/thredds/dodsC/master/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", + "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", "ds = xcdat.open_dataset(filepath)\n", "\n", "# Unit adjust (-273.15, K to C)\n", @@ -661,6 +662,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -986,11 +988,11 @@ " 14.65664621, 13.84951678])\n", "Coordinates:\n", " * time (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12-16T12:00:00\n", - " height float64 2.0
    • " ], "text/plain": [ "\n", @@ -1018,7 +1020,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 5, @@ -1027,7 +1029,7 @@ }, { "data": { - "image/png": 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", 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", + "image/png": 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", "text/plain": [ "
    " ] diff --git a/docs/examples/temporal-average.ipynb b/docs/examples/temporal-average.ipynb index 7a06bb57..7e097b01 100644 --- a/docs/examples/temporal-average.ipynb +++ b/docs/examples/temporal-average.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2018-11-28T20:51:35.958210Z", @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -129,6 +129,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -462,7 +463,7 @@ " lat_bnds (lat, bnds) float64 -90.0 -89.38 -89.38 ... 89.38 89.38 90.0\n", " lon_bnds (lon, bnds) float64 -0.9375 0.9375 0.9375 ... 357.2 357.2 359.1\n", " tas (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n", - "Attributes: (12/49)\n", + "Attributes: (12/48)\n", " Conventions: CF-1.7 CMIP-6.2\n", " activity_id: CMIP\n", " branch_method: standard\n", @@ -470,15 +471,15 @@ " branch_time_in_parent: 87658.0\n", " creation_date: 2020-06-05T04:06:11Z\n", " ... ...\n", + " variant_label: r10i1p1f1\n", " version: v20200605\n", " license: CMIP6 model data produced by CSIRO is li...\n", " cmor_version: 3.4.0\n", - " _NCProperties: version=2,netcdf=4.6.2,hdf5=1.10.5\n", " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", - " DODS_EXTRA.Unlimited_Dimension: time" + "seaIce: CICE4.1 (same grid as ocean)
    source_id :
    ACCESS-ESM1-5
    source_type :
    AOGCM
    sub_experiment :
    none
    sub_experiment_id :
    none
    table_id :
    Amon
    table_info :
    Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4
    title :
    ACCESS-ESM1-5 output prepared for CMIP6
    variable_id :
    tas
    variant_label :
    r10i1p1f1
    version :
    v20200605
    license :
    CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
    cmor_version :
    3.4.0
    tracking_id :
    hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1
    DODS_EXTRA.Unlimited_Dimension :
    time
    " ], "text/plain": [ "\n", @@ -600,7 +601,7 @@ " lat_bnds (lat, bnds) float64 ...\n", " lon_bnds (lon, bnds) float64 ...\n", " tas (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n", - "Attributes: (12/49)\n", + "Attributes: (12/48)\n", " Conventions: CF-1.7 CMIP-6.2\n", " activity_id: CMIP\n", " branch_method: standard\n", @@ -608,33 +609,32 @@ " branch_time_in_parent: 87658.0\n", " creation_date: 2020-06-05T04:06:11Z\n", " ... ...\n", + " variant_label: r10i1p1f1\n", " version: v20200605\n", " license: CMIP6 model data produced by CSIRO is li...\n", " cmor_version: 3.4.0\n", - " _NCProperties: version=2,netcdf=4.6.2,hdf5=1.10.5\n", " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", " DODS_EXTRA.Unlimited_Dimension: time" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "filepath = \"http://esgf.nci.org.au/thredds/dodsC/master/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", - "\n", + "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", "ds = xcdat.open_dataset(filepath)\n", "\n", "# Unit adjust (-273.15, K to C)\n", "ds[\"tas\"] = ds.tas - 273.15\n", "\n", - "ds\n" + "ds" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -643,7 +643,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -680,6 +680,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -1022,7 +1023,7 @@ " operation: temporal_avg\n", " mode: average\n", " freq: month\n", - " weighted: True
  • operation :
    temporal_avg
    mode :
    average
    freq :
    month
    weighted :
    True
    • " ], "text/plain": [ "\n", @@ -1101,7 +1102,7 @@ " weighted: True" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1112,16 +1113,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, @@ -1186,7 +1187,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -1223,6 +1224,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -1556,7 +1558,7 @@ " lat_bnds (lat, bnds) float64 -90.0 -89.38 -89.38 ... 89.38 89.38 90.0\n", " lon_bnds (lon, bnds) float64 -0.9375 0.9375 0.9375 ... 357.2 357.2 359.1\n", " tas (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n", - "Attributes: (12/49)\n", + "Attributes: (12/48)\n", " Conventions: CF-1.7 CMIP-6.2\n", " activity_id: CMIP\n", " branch_method: standard\n", @@ -1564,15 +1566,15 @@ " branch_time_in_parent: 87658.0\n", " creation_date: 2020-06-05T04:06:11Z\n", " ... ...\n", + " variant_label: r10i1p1f1\n", " version: v20200605\n", " license: CMIP6 model data produced by CSIRO is li...\n", " cmor_version: 3.4.0\n", - " _NCProperties: version=2,netcdf=4.6.2,hdf5=1.10.5\n", " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", - " DODS_EXTRA.Unlimited_Dimension: time" + "seaIce: CICE4.1 (same grid as ocean)
    source_id :
    ACCESS-ESM1-5
    source_type :
    AOGCM
    sub_experiment :
    none
    sub_experiment_id :
    none
    table_id :
    Amon
    table_info :
    Creation Date:(30 April 2019) MD5:5bd755e94c2173cb3050a0f3480f60c4
    title :
    ACCESS-ESM1-5 output prepared for CMIP6
    variable_id :
    tas
    variant_label :
    r10i1p1f1
    version :
    v20200605
    license :
    CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/). Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment. Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file). The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.
    cmor_version :
    3.4.0
    tracking_id :
    hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f29eb467cd1
    DODS_EXTRA.Unlimited_Dimension :
    time
    " ], "text/plain": [ "\n", @@ -1694,7 +1696,7 @@ " lat_bnds (lat, bnds) float64 ...\n", " lon_bnds (lon, bnds) float64 ...\n", " tas (time, lat, lon) float32 -27.19 -27.19 -27.19 ... -25.29 -25.29\n", - "Attributes: (12/49)\n", + "Attributes: (12/48)\n", " Conventions: CF-1.7 CMIP-6.2\n", " activity_id: CMIP\n", " branch_method: standard\n", @@ -1702,28 +1704,27 @@ " branch_time_in_parent: 87658.0\n", " creation_date: 2020-06-05T04:06:11Z\n", " ... ...\n", + " variant_label: r10i1p1f1\n", " version: v20200605\n", " license: CMIP6 model data produced by CSIRO is li...\n", " cmor_version: 3.4.0\n", - " _NCProperties: version=2,netcdf=4.6.2,hdf5=1.10.5\n", " tracking_id: hdl:21.14100/af78ae5e-f3a6-4e99-8cfe-5f2...\n", " DODS_EXTRA.Unlimited_Dimension: time" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "filepath = \"http://esgf.nci.org.au/thredds/dodsC/master/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", - "\n", + "filepath = \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/Amon/tas/gn/v20200605/tas_Amon_ACCESS-ESM1-5_historical_r10i1p1f1_gn_185001-201412.nc\"\n", "ds = xcdat.open_dataset(filepath)\n", "\n", "# Unit adjust (-273.15, K to C)\n", "ds[\"tas\"] = ds.tas - 273.15\n", "\n", - "ds\n" + "ds" ] }, { @@ -1737,7 +1738,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -1746,7 +1747,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1783,6 +1784,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -2104,47 +2106,47 @@ " fill: currentColor;\n", "}\n", "
    <xarray.DataArray 'tas' (time: 165, lat: 145, lon: 192)>\n",
-       "array([[[-48.755733, -48.755733, -48.755733, ..., -48.755733,\n",
-       "         -48.755733, -48.755733],\n",
-       "        [-45.652065, -45.693024, -45.73506 , ..., -45.52128 ,\n",
-       "         -45.563866, -45.60669 ],\n",
-       "        [-44.775234, -44.905838, -45.03297 , ..., -44.37118 ,\n",
-       "         -44.50631 , -44.640503],\n",
+       "array([[[-48.75573349, -48.75573349, -48.75573349, ..., -48.75573349,\n",
+       "         -48.75573349, -48.75573349],\n",
+       "        [-45.65206528, -45.69302368, -45.73506165, ..., -45.52127838,\n",
+       "         -45.56386566, -45.60668945],\n",
+       "        [-44.77523422, -44.90583801, -45.03297043, ..., -44.37118149,\n",
+       "         -44.50630951, -44.64050293],\n",
        "        ...,\n",
-       "        [-20.505976, -20.481321, -20.454565, ..., -20.588959,\n",
-       "         -20.557522, -20.530872],\n",
-       "        [-20.797592, -20.784252, -20.775455, ..., -20.83268 ,\n",
-       "         -20.823357, -20.807684],\n",
-       "        [-21.201149, -21.201149, -21.201149, ..., -21.201149,\n",
-       "         -21.201149, -21.201149]],\n",
-       "\n",
-       "       [[-48.95255 , -48.95255 , -48.95255 , ..., -48.95255 ,\n",
-       "         -48.95255 , -48.95255 ],\n",
-       "        [-45.83191 , -45.864902, -45.89875 , ..., -45.73217 ,\n",
-       "         -45.76544 , -45.798595],\n",
-       "        [-44.935368, -45.037956, -45.13801 , ..., -44.61143 ,\n",
-       "         -44.71986 , -44.829372],\n",
+       "        [-20.50597572, -20.48132133, -20.45456505, ..., -20.58895874,\n",
+       "         -20.55752182, -20.53087234],\n",
+       "        [-20.79759216, -20.78425217, -20.77545547, ..., -20.83267975,\n",
+       "         -20.82335663, -20.80768394],\n",
+       "        [-21.20114899, -21.20114899, -21.20114899, ..., -21.20114899,\n",
+       "         -21.20114899, -21.20114899]],\n",
+       "\n",
+       "       [[-48.95254898, -48.95254898, -48.95254898, ..., -48.95254898,\n",
+       "         -48.95254898, -48.95254898],\n",
+       "        [-45.83190918, -45.8649025 , -45.89875031, ..., -45.7321701 ,\n",
+       "         -45.76544189, -45.79859543],\n",
+       "        [-44.93536758, -45.03795624, -45.13800812, ..., -44.61143112,\n",
+       "         -44.71986008, -44.82937241],\n",
        "...\n",
-       "        [-14.916271, -14.899261, -14.88381 , ..., -14.99543 ,\n",
-       "         -14.965137, -14.938532],\n",
-       "        [-15.405922, -15.396681, -15.385955, ..., -15.432463,\n",
-       "         -15.426056, -15.413568],\n",
-       "        [-15.945   , -15.945   , -15.945   , ..., -15.945   ,\n",
-       "         -15.945   , -15.945   ]],\n",
-       "\n",
-       "       [[-47.59732 , -47.59732 , -47.59732 , ..., -47.59732 ,\n",
-       "         -47.59732 , -47.59732 ],\n",
-       "        [-44.721367, -44.763428, -44.803505, ..., -44.592392,\n",
-       "         -44.634445, -44.678226],\n",
-       "        [-43.85032 , -43.969563, -44.08714 , ..., -43.4709  ,\n",
-       "         -43.596764, -43.72408 ],\n",
+       "        [-14.91627121, -14.89926147, -14.88381004, ..., -14.99542999,\n",
+       "         -14.96513653, -14.93853188],\n",
+       "        [-15.40592194, -15.39668083, -15.38595486, ..., -15.43246269,\n",
+       "         -15.42605591, -15.41356754],\n",
+       "        [-15.94499969, -15.94499969, -15.94499969, ..., -15.94499969,\n",
+       "         -15.94499969, -15.94499969]],\n",
+       "\n",
+       "       [[-47.59732056, -47.59732056, -47.59732056, ..., -47.59732056,\n",
+       "         -47.59732056, -47.59732056],\n",
+       "        [-44.72136688, -44.76342773, -44.80350494, ..., -44.59239197,\n",
+       "         -44.63444519, -44.67822647],\n",
+       "        [-43.85031891, -43.96956253, -44.08713913, ..., -43.47090149,\n",
+       "         -43.59676361, -43.72407913],\n",
        "        ...,\n",
-       "        [-14.52023 , -14.474079, -14.432307, ..., -14.675514,\n",
-       "         -14.620932, -14.567368],\n",
-       "        [-14.911236, -14.892309, -14.869016, ..., -14.982012,\n",
-       "         -14.962668, -14.938723],\n",
-       "        [-15.618406, -15.618406, -15.618406, ..., -15.618406,\n",
-       "         -15.618406, -15.618406]]], dtype=float32)\n",
+       "        [-14.52023029, -14.47407913, -14.43230724, ..., -14.67551422,\n",
+       "         -14.62093163, -14.56736755],\n",
+       "        [-14.91123581, -14.89230919, -14.86901569, ..., -14.9820118 ,\n",
+       "         -14.96266842, -14.93872261],\n",
+       "        [-15.6184063 , -15.6184063 , -15.6184063 , ..., -15.6184063 ,\n",
+       "         -15.6184063 , -15.6184063 ]]])\n",
        "Coordinates:\n",
        "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
        "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
@@ -2154,47 +2156,47 @@
        "    operation:  temporal_avg\n",
        "    mode:       group_average\n",
        "    freq:       year\n",
-       "    weighted:   True
  • operation :
    temporal_avg
    mode :
    group_average
    freq :
    year
    weighted :
    True
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@@ -2909,47 +2912,47 @@
        "    freq:                 season\n",
        "    weighted:             True\n",
        "    dec_mode:             DJF\n",
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    weighted :
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    axis :
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    standard_name :
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    _ChunkSizes :
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    • " ], "text/plain": [ "\n", @@ -3490,7 +3494,7 @@ " _ChunkSizes: 1" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3529,7 +3533,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -3566,6 +3570,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -3887,35 +3892,35 @@ " fill: currentColor;\n", "}\n", "
    <xarray.Dataset>\n",
-       "Dimensions:    (time: 18262, bnds: 2, lat: 145, lon: 192)\n",
+       "Dimensions:    (time: 14608, lat: 145, bnds: 2, lon: 192)\n",
        "Coordinates:\n",
-       "  * time       (time) datetime64[ns] 1850-01-01T12:00:00 ... 1899-12-31T12:00:00\n",
+       "  * time       (time) datetime64[ns] 2010-01-01T03:00:00 ... 2015-01-01\n",
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        "  * lon        (lon) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
        "    height     float64 ...\n",
        "Dimensions without coordinates: bnds\n",
        "Data variables:\n",
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        "    lat_bnds   (lat, bnds) float64 dask.array<chunksize=(145, 2), meta=np.ndarray>\n",
        "    lon_bnds   (lon, bnds) float64 dask.array<chunksize=(192, 2), meta=np.ndarray>\n",
-       "    tas        (time, lat, lon) float32 dask.array<chunksize=(794, 145, 192), meta=np.ndarray>\n",
+       "    tas        (time, lat, lon) float32 dask.array<chunksize=(913, 145, 192), meta=np.ndarray>\n",
+       "    time_bnds  (time, bnds) datetime64[ns] 2010-01-01T01:30:00 ... 2015-01-01...\n",
        "Attributes: (12/48)\n",
        "    Conventions:                     CF-1.7 CMIP-6.2\n",
        "    activity_id:                     CMIP\n",
        "    branch_method:                   standard\n",
        "    branch_time_in_child:            0.0\n",
-       "    branch_time_in_parent:           21915.0\n",
-       "    creation_date:                   2019-11-15T17:30:04Z\n",
+       "    branch_time_in_parent:           87658.0\n",
+       "    creation_date:                   2020-06-05T04:54:56Z\n",
        "    ...                              ...\n",
-       "    variant_label:                   r1i1p1f1\n",
-       "    version:                         v20191115\n",
-       "    cmor_version:                    3.4.0\n",
-       "    tracking_id:                     hdl:21.14100/a9d8ba3a-bcbf-4d54-9970-cfc...\n",
+       "    variant_label:                   r10i1p1f1\n",
+       "    version:                         v20200605\n",
        "    license:                         CMIP6 model data produced by CSIRO is li...\n",
-       "    DODS_EXTRA.Unlimited_Dimension:  time
  • operation :
    temporal_avg
    mode :
    group_average
    freq :
    month
    weighted :
    True
    • " ], "text/plain": [ - "\n", - "dask.array\n", + "\n", + "dask.array\n", "Coordinates:\n", " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n", " height float64 ...\n", - " * time (time) object 1850-01-01 00:00:00 ... 1899-12-01 00:00:00\n", + " * time (time) object 2010-01-01 00:00:00 ... 2015-01-01 00:00:00\n", "Attributes:\n", " operation: temporal_avg\n", " mode: group_average\n", @@ -4859,7 +4863,7 @@ " weighted: True" ] }, - "execution_count": 15, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -4881,7 +4885,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -4889,8 +4893,8 @@ "# request using Dask to avoid hitting the OPeNDAP file size request limit for\n", "# this ESGF node.\n", "ds3 = xcdat.open_dataset(\n", - " \"http://esgf.nci.org.au/thredds/dodsC/master/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/3hr/tas/gn/v20200605/tas_3hr_ACCESS-ESM1-5_historical_r10i1p1f1_gn_201001010300-201501010000.nc\",\n", - " chunks={\"time\": \"auto\"}\n", + " \"https://esgf-data1.llnl.gov/thredds/dodsC/css03_data/CMIP6/CMIP/CSIRO/ACCESS-ESM1-5/historical/r10i1p1f1/3hr/tas/gn/v20200605/tas_3hr_ACCESS-ESM1-5_historical_r10i1p1f1_gn_201001010300-201501010000.nc\",\n", + " chunks={\"time\": \"auto\"},\n", ")\n", "\n", "# Unit adjust (-273.15, K to C)\n", @@ -4899,7 +4903,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -4936,6 +4940,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -5262,7 +5267,7 @@ " * time (time) datetime64[ns] 2010-01-01T03:00:00 ... 2015-01-01\n", " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n", - " height float64 ..." + " 345. , 346.875, 348.75 , 350.625, 352.5 , 354.375, 356.25 , 358.125])
  • height
    ()
    float64
    ...
    units :
    m
    axis :
    Z
    positive :
    up
    long_name :
    height
    standard_name :
    height
    array(2.)
    • " ], "text/plain": [ "\n", @@ -5426,7 +5431,7 @@ " height float64 ..." ] }, - "execution_count": 17, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -5437,7 +5442,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -5446,7 +5451,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -5483,6 +5488,7 @@ "}\n", "\n", "html[theme=dark],\n", + "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", @@ -5804,7 +5810,7 @@ " fill: currentColor;\n", "}\n", "
    <xarray.DataArray 'tas' (time: 1827, lat: 145, lon: 192)>\n",
-       "dask.array<stack, shape=(1827, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray>\n",
+       "dask.array<truediv, shape=(1827, 145, 192), dtype=float64, chunksize=(1, 145, 192), chunktype=numpy.ndarray>\n",
        "Coordinates:\n",
        "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
        "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
@@ -5814,7 +5820,7 @@
        "    operation:  temporal_avg\n",
        "    mode:       group_average\n",
        "    freq:       day\n",
-       "    weighted:   True
    " + " dtype=object)
  • operation :
    temporal_avg
    mode :
    group_average
    freq :
    day
    weighted :
    True
    • " ], "text/plain": [ "\n", - "dask.array\n", + "dask.array\n", "Coordinates:\n", " * lat (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n", " * lon (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n", @@ -5993,7 +5999,7 @@ " weighted: True" ] }, - "execution_count": 19, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } diff --git a/docs/gallery.rst b/docs/gallery.rst index d2b5af83..d4158a4d 100644 --- a/docs/gallery.rst +++ b/docs/gallery.rst @@ -9,7 +9,8 @@ Please checkout the :doc:`contributing` guide. :maxdepth: 2 :hidden: - examples/general-utilities + examples/introduction-to-xcdat.ipynb + examples/general-utilities.ipynb examples/spatial-average.ipynb examples/temporal-average.ipynb examples/climatology-and-departures.ipynb diff --git a/docs/index.rst b/docs/index.rst index 62d0c40a..dda302eb 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -2,15 +2,14 @@ xCDAT: Xarray Climate Data Analysis Tools ========================================= xCDAT is an extension of `xarray`_ for climate data analysis on structured grids. It -serves as the spiritual successor to the Community Data Analysis Tools (`CDAT`_) -library. +serves as a modern successor to the Community Data Analysis Tools (`CDAT`_) library. -The goal of xCDAT is to provide generalizable climate domain features and utilities -that streamline the developer experience for data analysis code. xCDAT's design -philosophy is to reduce the complexity and overhead required by the user to accomplish -specific tasks in xarray. Some xCDAT features are inspired by or ported from core CDAT -functionalities, while others leverage powerful libraries in the xarray ecosystem -(e.g., `xESMF`_ and `cf_xarray`_) to deliver robust APIs. +The goal of xCDAT is to provide generalizable features and utilities for simple and +robust analysis of climate data. xCDAT's design philosophy is focused on reducing the +overhead required to accomplish certain tasks in xarray. Some key xCDAT features are +inspired by or ported from the core CDAT library, while others leverage powerful +libraries in the xarray ecosystem (e.g., `xESMF`_ and `cf_xarray`_) to deliver +robust APIs. The xCDAT core team's mission is to provide a maintainable and extensible package that serves the needs of the climate community in the long-term. We are excited @@ -141,13 +140,14 @@ This software is jointly developed by scientists and developers from the Energy Earth System Model (`E3SM`_) Project and Program for Climate Model Diagnosis and Intercomparison (`PCMDI`_). The work is performed for the E3SM project, which is sponsored by Earth System Model Development (`ESMD`_) program, and the Simplifying ESM -Analysis Through Standards (SEATS) project, which is sponsored by the Regional and +Analysis Through Standards (`SEATS`_) project, which is sponsored by the Regional and Global Model Analysis (`RGMA`_) program. ESMD and RGMA are programs for the Earth and Environmental Systems Sciences Division (`EESSD`_) in the Office of Biological and Environmental Research (`BER`_) within the `Department of Energy`_'s `Office of Science`_. .. _E3SM: https://e3sm.org/ .. _PCMDI: https://pcmdi.llnl.gov/ +.. _SEATS: https://www.seatstandards.org/ .. _ESMD: https://climatemodeling.science.energy.gov/program/earth-system-model-development .. _RGMA: https://climatemodeling.science.energy.gov/program/regional-global-model-analysis .. _EESSD: https://science.osti.gov/ber/Research/eessd