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Add SciPy2024 workshop landing page (#258)
* 2024 draft landing page * add a new codespace config, fix dates * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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‎.devcontainer/scipy2024/devcontainer.json

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{
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"build": {
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"dockerfile": "../Dockerfile",
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"context": "../../conda"
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},
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"postCreateCommand": {
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"jupyterlab": "mkdir /home/jovyan/.jupyter && cp ${containerWorkspaceFolder}/.devcontainer/scipy2024/jupyter_lab_config.py /home/jovyan/.jupyter/jupyter_lab_config.py",
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"vscode": "mkdir ${containerWorkspaceFolder}/.vscode && cp ${containerWorkspaceFolder}/.devcontainer/scipy2024/tasks.json ${containerWorkspaceFolder}/.vscode/tasks.json"
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},
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"hostRequirements": {
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"cpus": 2
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},
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"customizations": {
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"codespaces": {
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"openFiles": ["workshops/scipy2024/README.md"]
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},
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"vscode": {
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"extensions": ["ms-toolsai.jupyter", "ms-python.python"]
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}
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}
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}

‎.devcontainer/scipy2024/jupyter_lab_config.py

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# Configuration file for lab.
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c = get_config() # noqa
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## The default URL to redirect to from `/`
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# Default: '/lab'
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c.LabApp.default_url = '/lab/tree/workshops/scipy2024/index.ipynb'
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## Set the Access-Control-Allow-Origin header
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#
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# Use '*' to allow any origin to access your server.
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#
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# Takes precedence over allow_origin_pat.
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# Default: ''
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c.ServerApp.allow_origin = '*'

‎.devcontainer/scipy2024/tasks.json

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{
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"version": "2.0.0",
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"tasks": [
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{
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"label": "jupyterlab",
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"type": "shell",
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"command": "/srv/conda/envs/notebook/bin/jupyter lab --no-browser",
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"presentation": {
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"reveal": "always"
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},
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"runOptions": {
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"runOn": "folderOpen"
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}
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}
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]
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}

‎images/scipy2024.png

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‎workshops/scipy2024/README.md

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# SciPy 2024
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## Xarray: Friendly, Interactive, and Scalable Scientific Data Analysis
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Organized by:
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- Scott Henderson (Univ. Washington)
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- Jessica Scheick (Univ. New Hampshire)
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- Negin Sobhani (National Center for Atmospheric Research)
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- Tom Nicholas [C]worthy
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- Max Jones (CarbonPlan)
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- Wietze Suijker (Space Intelligence)
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## Learning Goals
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- Learn how to leverage Xarray’s powerful backend and extension capabilities to customize workflows and open a variety of scientific datasets
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- Effectively use Xarray’s multidimensional indexing operations
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- Understand how Xarray can wrap other array types in the scientific Python ecosystem
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- Understand how to apply custom functions to Xarray data structures
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- Orient yourself to Xarray resources to continue on your Xarray journey!
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## Outline
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These are links to rendered webpages if you'd just like to read. Further down this page is an 'Instructions' section that explains how to spin up an interactive computing environment.
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```{dropdown} Introduction
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{doc}`../../overview/get-started`
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{doc}`../../fundamentals/02.1_indexing_Basic`
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TODO: Domain-agnositic data model, backends/engines
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```
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```{dropdown} Indexing and Computation
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{doc}`../../intermediate/indexing/advanced-indexing`
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{doc}`../../intermediate/01-high-level-computation-patterns`
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{doc}`../../intermediate/xarray_and_dask`
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```
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```{dropdown} Extending & Customizing Xarray
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{doc}`../../advanced/01_accessor_examples.ipynb`
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{doc}`../../advanced/1.Backend_without_Lazy_Loading.ipynb
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```
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```{dropdown} Synthesis
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TODO: A Guided 'bring-your-own data exercise
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```
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## Instructions
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### Running Locally
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See instructions to set up the environment for running the tutorial material [here](get-started).
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### Github Codespaces
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This tutorial is available to run within [Github Codespaces](https://github.com/features/codespaces) - "a development environment that's hosted in the cloud" - with the conda environment specification in the [`conda-lock.yml`](../../conda/conda-lock.yml) file.
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[![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://github.com/codespaces/new/xarray-contrib/xarray-tutorial/tree/main?devcontainer_path=.devcontainer%2Fscipy2023%2Fdevcontainer.json)
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☝️ Click the button above to go to options window to launch a Github codespace.
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A codespace is a development environment that's hosted in the cloud.
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You can choose from a selection of virtual machine types: 2 cores - 4 GB RAM - 32 GB storage, and 4 cores - 8 GB RAM - 32GB storage.
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Additionally, you are able to chose from various Dev container configuration, for this specific workshop, please ensure that `Scipy2024` is selected.
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GitHub currently gives every user [120 vCPU hours per month for free](https://docs.github.com/en/billing/managing-billing-for-github-codespaces/about-billing-for-github-codespaces#monthly-included-storage-and-core-hours-for-personal-accounts), beyond that you must pay. **So be sure to explicitly stop or shut down your codespace when you are done by going to this page (https://github.com/codespaces).**
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Once your codespace is launched, the following happens:
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- [Visual Studio Code](https://code.visualstudio.com/) Interface will open up within your browser.
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- A built in terminal will open and it will execute `jupyter lab` automatically.
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- Once you see a url to click within the terminal, simply `cmd + click` the given url.
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- This will open up another tab in your browser, leading to a [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/) Interface.

‎workshops/scipy2024/index.ipynb

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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "0",
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"metadata": {},
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"source": [
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"<img src=\"../../images/scipy2024.png\" align=\"right\" width=\"20%\">\n",
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"\n",
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"\n",
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"# Welcome to the Xarray SciPy 2024 Tutorial! \n",
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"\n",
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"**Xarray**: *Friendly, Interactive, and Scalable Scientific Data Analysis*\n",
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"\n",
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"July 8, 13:30–17:30 (US/Pacific), Room 317\n",
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"\n",
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"This *4-hour* workshop will explore content from [the Xarray tutorial](https://tutorial.xarray.dev), which contains a comprehensive collection of hands-on tutorial Jupyter Notebooks. We won't cover it all today, but instead will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
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"\n",
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"## *Draft* Schedule \n",
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"\n",
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"*Times in US/Pacific Timezone (Tacoma, WA)\n",
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"\n",
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"Use the links to navigate to the right notebooks.\n",
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"\n",
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"| Topic | Time | Notebook Links | \n",
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"| :- | - | - | \n",
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"| Introduction and Setup | 1:30 (10 min) | --- | \n",
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"| Xarray Data Model, Backends, Extensions | 1:40 (40 min) | [Quick Introduction to Indexing](../../fundamentals/02.1_indexing_Basic.ipynb) <br> [Boolean Indexing & Masking](../../intermediate/indexing/boolean-masking-indexing.ipynb) | \n",
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"| *10 minute Break* \n",
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"| Computational Patterns | 2:30 (50 min) | [Computation Patterns](../../intermediate/01-high-level-computation-patterns.ipynb) | \n",
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"| *10 minute Break* | \n",
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"| Wrapping other arrays | 3:30 (50 min) | [Xarray and Dask](../../intermediate/xarray_and_dask.ipynb) | \n",
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"| *10 minute Break* | \n",
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"| Synthesis, Explore your data! | 4:30 (30 min) <br> <br> <br> 5:00 (30 min) | Apply what you've learned, let's work together with your own data |\n",
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"| | **End 5:30** | |\n",
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"\n",
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"\n",
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"## Thanks for attending!\n",
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"\n",
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"Please continue to explore the subfolders in the JupyterLab File Browser for additional tutorial notebooks to run, or read the rendered notebooks at [https://tutorial.xarray.dev](https://tutorial.xarray.dev)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "1",
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"metadata": {},
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"source": []
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}
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],
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"metadata": {
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}

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