local malaria figure, update default links

.devcontainer/scipy2024/devcontainer.json

@@ -12,7 +12,7 @@
   },
   "customizations": {
     "codespaces": {
-      "openFiles": ["workshops/scipy2024/README.md"]
+      "openFiles": ["workshops/scipy2024/index.ipynb"]
     },
     "vscode": {
       "extensions": ["ms-toolsai.jupyter", "ms-python.python"]

README.md

@@ -2,7 +2,7 @@
 
 [![CI](https://github.com/xarray-contrib/xarray-tutorial/workflows/CI/badge.svg?branch=main)](https://github.com/xarray-contrib/xarray-tutorial/actions?query=branch%3Amain)
 [![Jupyter Book Badge](https://jupyterbook.org/badge.svg)](https://tutorial.xarray.dev)
-[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD?labpath=overview/fundamental-path/index.ipynb)
+[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/xarray-contrib/xarray-tutorial/HEAD?labpath=workshops/scipy2024/index.ipynb)
 
 This is the repository for a Jupyter Book website with tutorial material for [Xarray](https://github.com/pydata/xarray), _an open source project and Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!_
 

fundamentals/01_data_structures.md

@@ -54,7 +54,7 @@ powerful and concise interface. For example:
 
 Although the Xarray library was originally developed with Earth Science datasets in mind, the datastructures work well across many other domains! For example, below is a side-by-side view of a data schematic on the left and Xarray Dataset representation on the right taken from a mosquito genetics analysis:
 
-<img src="https://vobs-resources.cog.sanger.ac.uk/training/img/workshop-4/mosquito-genotype-array.png" align="center" width="80%">
+![malaria_dataset](../images/malaria_dataset.png)
 
 The data can be stored as a 3-dimensional array, where one dimension of the array corresponds to positions (**variants**) within a reference genome, another dimension corresponds to the individual mosquitoes that were sequenced (**samples**), and a third dimension corresponds to the number of genomes within each individual (**ploidy**)."
 

fundamentals/01_datastructures.ipynb

@@ -6,14 +6,12 @@
    "source": [
     "# Xarray's Data structures\n",
     "\n",
-    "In this lesson, we cover the basics of Xarray data structures. Our\n",
-    "learning goals are as follows. By the end of the lesson, we will be able to:\n",
+    "In this lesson, we cover the basics of Xarray data structures. By the end of the lesson, we will be able to:\n",
     "\n",
     ":::{admonition} Learning Goals\n",
-    "- Understand the basic data structures (`DataArray` and `Dataset` objects) in Xarray\n",
-    "- Customize the display of Xarray objects\n",
-    "- Access variables, coordinates, and arbitrary metadata\n",
-    "- Transform to tabular Pandas data structures\n",
+    "- Understand the basic Xarray data structures `DataArray` and `Dataset` \n",
+    "- Customize the display of Xarray data structures\n",
+    "- The connection between Pandas and Xarray data structures\n",
     ":::"
    ]
   },
@@ -130,18 +128,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The output consists of:\n",
+    "☝️ From top to bottom the output consists of:\n",
     "\n",
-    "- a summary of all *dimensions* of the `Dataset` `(lat: 25, time: 2920, lon: 53)`: this tells us that the first\n",
-    "  dimension is named `lat` and has a size of `25`, the second dimension is named\n",
-    "  `time` and has a size of `2920`, and the third dimension is named `lon` and has a size\n",
-    "  of `53`. Because we will access the dimensions by name, the order doesn't matter.\n",
-    "- an unordered list of *coordinates* or dimensions with coordinates with one item\n",
-    "  per line. Each item has a name, one or more dimensions in parentheses, a dtype\n",
-    "  and a preview of the values. Also, if it is a dimension coordinate, it will be\n",
-    "  printed in **bold** font.\n",
-    "- an alphabetically sorted list of *dimensions without coordinates* (if there are any)\n",
-    "- an unordered list of *attributes*, or metadata"
+    "- **Dimensions**: summary of all *dimensions* of the `Dataset` `(lat: 25, time: 2920, lon: 53)`: this tells us that the first dimension is named `lat` and has a size of `25`, the second dimension is named `time` and has a size of `2920`, and the third dimension is named `lon` and has a size of `53`. Because we will access the dimensions by name, the order doesn't matter.\n",
+    "- **Coordinates**: an unordered list of *coordinates* or dimensions with coordinates with one item per line. Each item has a name, one or more dimensions in parentheses, a dtype and a preview of the values. Also, if it is a dimension coordinate, it will be printed in **bold** font. *dimensions without coordinates* appear in plain font (there are none in this example, but you might imagine a 'mask' coordinate that has a value assigned at every point).\n",
+    "- **Data variables**: names of each nD *measurement* in the dataset, followed by its dimensions `(time, lat, lon)`, dtype, and a preview of values.\n",
+    "- **Indexes**: Each dimension with coordinates is backed by an \"Index\". In this example, each dimension is backed by a `PandasIndex`\n",
+    "- **Attributes**: an unordered list of metadata (for example, a paragraph describing the dataset)"
    ]
   },
   {

workshops/scipy2024/index.ipynb

@@ -13,7 +13,7 @@
     "\n",
     "**Xarray**: *Friendly, Interactive, and Scalable Scientific Data Analysis*\n",
     "\n",
-    "July 8, 13:30–17:30 (US/Pacific), Room 317\n",
+    "July 8, 13:30–17:30 (US/Pacific), Tacoma Convention Center Ballroom B/C\n",
     "\n",
     "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 will review a curated set of examples that will prepare you for increasingly complex real-world data analysis tasks!\n",
     "\n",
@@ -33,13 +33,13 @@
     "| Topic | Time | Notebook Links | \n",
     "| :- | - | - | \n",
     "| Introduction and Setup | 1:30 (10 min) | --- | \n",
-    "| The Xarray Data Model | 1:40 (40 min) | [Data structures](../../fundamentals/01_datastructures.ipynb) <br> [Basic Indexing](../../fundamentals/02.1_indexing_Basic.ipynb) | \n",
+    "| The Xarray Data Model | 1:40 (40 min) | [Data structures](../../fundamentals/01_data_structures.md) <br> [Basic Indexing](../../fundamentals/02.1_indexing_Basic.ipynb) | \n",
     "| *10 minute Break*  \n",
     "| Indexing & Computational Patterns | 2:30 (50 min) |  [Advanced Indexing](../../intermediate/indexing/indexing.md) <br> [Computational Patterns](../../intermediate/01-high-level-computation-patterns.ipynb) <br>  |  \n",
     "| *10 minute Break*  | \n",
     "| Xarray Integrations and Extensions | 3:30 (50 min) | [The Xarray Ecosystem](../../intermediate/xarray_ecosystem.ipynb)  |  \n",
     "| *10 minute Break*  | \n",
-    "| Backends & Practice with your Data | 4:30 (50 min)  | TODO: [Backends](../../advanced/backends/1.Backend_without_Lazy_Loading.ipynb) or Luis' remote data notebook |\n",
+    "| Backends & Remote data| 4:30 (50 min)  | [Remote Data](../../intermediate/remote_data/remote-data.ipynb) |\n",
     "| | End 5:30 | |\n",
     "\n",
     "\n",
@@ -66,6 +66,9 @@
     "- Once you see a url to click within the terminal, simply `cmd + click` the given url.\n",
     "- This will open up another tab in your browser, leading to a [Jupyter Lab](https://jupyterlab.readthedocs.io/en/latest/) Interface.\n",
     "\n",
+    ":::{warning}\n",
+    "Consider Codespaces as ephemeral environments. You may lose your connection and any edits you make.\n",
+    ":::\n",
     "\n",
     "\n",
     "## Thanks for attending!\n",
@@ -81,14 +84,6 @@
     "- Max Jones (CarbonPlan)\n",
     "- Wietze Suijker (Space Intelligence)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {