Add Day 3 notebook

.github/workflows/deploy.yml

@@ -27,16 +27,17 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest]
-        python-version: [3.8]
+        python-version: [3.12]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v4
 
     # Install dependencies
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v1
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
+
     - name: Install dependencies
       run: |
         pip install -r requirements.txt
@@ -46,19 +47,21 @@ jobs:
       run: |
         jupyter-book build site
 
-    - name: Create deployment artifact
-      uses: actions/upload-pages-artifact@v1
+    # Upload the book's HTML as an artifact
+    - name: Upload deployment artifact
+      uses: actions/upload-pages-artifact@v3
       with:
-        path: sitio/_build/html
+        path: site/_build/html
 
+  # Deploy the book's HTML to GitHub Pages
   deploy:
     environment:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
     runs-on: ubuntu-latest
     needs: build-book
-    if: github.ref == 'refs/heads/main' && github.repository == 'emiliom/dinosip-python'
+    if: github.ref == 'refs/heads/main' && github.repository == 'UW-APL-SURP/aplsurp-python'
     steps:
-      - name: Deploy to Github Pages
+      - name: Deploy to GitHub Pages
         id: deployment
-        uses: actions/deploy-pages@v1
+        uses: actions/deploy-pages@v4

README.md

@@ -1,3 +1,45 @@
 # README
 
-Stub.
+**(NONE OF THIS IS ON THE PUBLIC-REPO README. DELETE, ADAPT)**
+
+- ~~Rename the base folder to aplsurp-python~~
+- ~~Conda env (previously `dinosip-python-web`, now renamed to `aplsurp-python-web`)~~
+- Conda env: I currently just use `environment.yml`. But maybe `requirements.txt` will be needed later for CI-based builds on GH
+- To build the site: `jb build site` (or `jb clean site` first)
+- To preview it: open the file `site/_build/html/index.html`
+- To publish: `ghp-import -n -p -f --remote origin site/_build/html`
+- Integrating Ethan's notebooks:
+    - See my local clone of Ethan's repo: `/home/mayorga/Desktop/DEI/APL/2021_DON_APL-DID-NOW/Python-crashcourse/NCAT_MATE_floats_teaching/`
+    - Use [url encoding](https://meyerweb.com/eric/tools/dencoder/) to transform the names of the notebook files if they have spaces and other unsafe characters
+- Say something about notebooks: Jupyter notebooks vs notebooks in Colab
+- Weave the individual lessons (from Carpentry & Pythia) into the notebooks, or as additional notes here next to each notebook
+
+
+## Stuff in Spanish about Jupyter Book that I'm going to cull and translate
+
+## From Hackaton OHW en español
+
+Con el [Jupyter Book](https://jupyterbook.org) (y muchos sistemas parecidos), uno modifica los archivos "fuente", típicamente en markdown, y luego "genera" los archivos del sitio en sí. Pueden leer sobre esto [aquí](https://jupyterbook.org/en/stable/basics/building/index.html). 
+
+Los archivos fuente (el contenido) se almacenan en el branch principal, `main`, bajo la carpeta [`sitio`](sitio). Los archivos generados del sitio son colocados en el branch `gh-pages`. En la gran mayoría de los casos no es recomendable editar los archivos en `gh-pages` manualmente, directamente. Entonces, hay dos workflows recomendados:
+- Se corre (`build`) jupyter book localmente, para confirmar que todo se vea bien. Una vez que esté listo, se empujan los archivos del build al repositorio usando el programa ghp-import, corrido con algo parecido a esto: `ghp-import -n -p -f --remote upstream sitio/_build/html`. Los archivos fuente en el repositorio son actualizados usando idealmente un pull request. Esto es lo que he estado haciendo hasta ahora.
+- Activando un sistema en GitHub que genera (build) el sitio automáticamente cada vez que se envía un pull request. Este sistema también incluye un preview de los cambios propuestos, que es muy útil. Este automatización hace futuras modificaciones mucho más sencillas, especialmente cuando se trata de modificaciones de páginas existentes.
+
+Este mecanismo automático ya está activo. Ahora, al crear un pull request (PR), después de un minuto (mas o menos) verán un enlace como este bajo "Deploy Preview for sage-puppy-e64764 ready!":
+![image](https://user-images.githubusercontent.com/742403/220565139-f8aea2b4-9801-4ef0-81e8-9a5e318d3a4d.png)
+
+Este enlace los llevará a una versión temporal del sitio donde pueden confirmar los cambios propuestos en el PR. Una vez que le hagan "merge" al PR, el sitio será reconstruido automáticamente en `gh-pages` con los cambios en el PR. Esto toma un par de minutos.
+
+Pueden ver un ejemplo de esto en un PR reciente, como este: #3
+
+Es sencillo editar una página directamente desde el sitio web: en los íconos arriba a la derecha de cada página, presionen el ícono de gihub y luego seleccionen "suggest edit". Esto los lleva a github, con el interfaz de modificación del markdown. 
+![image](https://user-images.githubusercontent.com/742403/220717342-8afb20b4-5ee4-4547-9c92-c5011633431b.png)
+
+Cuando estén listos a enviar los cambios, por favor seleccionen "Create a new branch for this commit and start a pull request" antes de presionar "Commit changes". Esto nos permitir a otros revisar los cambios antes de aprobarlos.
+
+![image](https://user-images.githubusercontent.com/742403/220717826-e1062654-4757-4bcb-9d85-aa8300dcf432.png)
+
+
+Hay dos archivos importantes de configuración del sitio:
+- [`_config.yml`](sitio/_config.yml). [Configuraciones básicas del sitio](https://jupyterbook.org/en/stable/customize/config.html). Raremente tendremos que modificar esto.
+- [`_toc.yml`](sitio/_toc.yml). "Table of Content". [Organización de las páginas en el sitio.](https://jupyterbook.org/en/stable/basics/organize.html)

environment.yml

@@ -1,20 +1,20 @@
-name: dinosip-python-web
+name: aplsurp-python-web
 channels:
   - conda-forge
   - nodefaults
 dependencies:
-  - python=3.9
+  - python=3.12
   - pip
   - jupyter-book
   - ipykernel
   - ghp-import
+  - folium
   - matplotlib
   - numpy
   - pandas
   - seabird
   # - geopandas
   # - xarray
   # - seaborn
-  # - folium
   # - cartopy
 

requirements.txt

@@ -1,5 +1,5 @@
 jupyter-book
-attrs<22
-nbclient<0.6
+attrs
+nbclient
 matplotlib
 numpy

site/_config.yml

@@ -1,13 +1,14 @@
 # Book settings
 # Learn more at https://jupyterbook.org/customize/config.html
 
-title: DINOSIP Python workshop
-author: Emilio Mayorga
-logo: img/dinosip-logo.png
+title: APL SURP Python Course
+author: Emilio Mayorga, Ethan Campbell
+copyright: "2025"
+logo: img/surp_logo_115x168.png
 # HMM, where do these show up?
-announcement: Python tour workshop for DINO SIP
+announcement: Python course for APL SURP program
 description: >-
-  Python tour workshop for DINO SIP
+  Python course for APL SURP program
 
 # Force re-execution of notebooks on each build.
 # See https://jupyterbook.org/content/execute.html
@@ -25,21 +26,24 @@ parse:
 
 # Information about where the book exists on the web
 repository:
-  url: https://github.com/emiliom/dinosip-python  # Online location of your book
+  url: https://github.com/UW-APL-SURP/aplsurp-python  # Online location of your book
   path_to_book: site  # Optional path to your book, relative to the repository root
   branch: main  # Which branch of the repository should be used when creating links (optional)
 
 # Add GitHub buttons to your book
 # See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
 html:
-  #baseurl: https://emiliom.github.io/nica-ecocomputacion/
-  #favicon: imagenes/favicon1.ico
-  home_page_in_navbar: false
+  #baseurl: https://UW-APL-SURP.github.io/aplsurp-python/
+  #favicon: img/favicon1.ico
+  home_page_in_navbar: true
   #extra_navbar: ""
   use_issues_button: true
   use_repository_button: true
   use_edit_page_button: true
 
+launch_buttons:
+  colab_url: "https://colab.research.google.com"
+
 sphinx:
   config:
     language: en

site/_toc.yml

@@ -2,15 +2,28 @@
 # Learn more at https://jupyterbook.org/customize/toc.html
 
 format: jb-book
-root: index 
-
-chapters:
-- file: install-run
-  title: Installation, execution
-- file: overview
-  title: Quick overview
-- file: lessons
-  title: Detailed lessons
-- file: advancedexamples
-  title: Advanced examples
-- file: cheatsheets
+root: index
+parts:
+- caption: 
+  chapters:
+  - file: python
+  - file: overview
+- caption: Main lessons
+  chapters:
+  - file: lessons/index
+    title: Overview
+  - file: "lessons/APL_SURP_Python_course_Notebook_1.ipynb"
+    title: Day 1
+  - file: "lessons/APL_SURP_Python_course_Notebook_2.ipynb"
+    title: Day 2
+  - file: "lessons/APL_SURP_Python_course_Notebook_3_blank_copy.ipynb"
+    title: Day 3
+- caption: Other resources
+  chapters:
+  - file: install-run
+    title: Running Python
+  - file: complementary_lessons
+    title: Complementary lessons
+  - file: cheatsheets
+  - file: advancedexamples
+    title: Advanced examples

site/advancedexamples.md

@@ -4,7 +4,7 @@
 
 [SeaBird](https://www.seabird.com/) CTD instruments are widely used in Oceanography. Reading SeaBird CTD `.cnv` data files is therefore a common task. The files are text based and one could write code from scratch to read and parse them, but then lots of people would be reinventing the wheel, imperfectly.
 
-We will use the existing `ctd` open-source Python package, https://pyoceans.github.io/python-ctd/. We'll go through a notebook that demonstrates its use. I copied the notebook to [notebooks/ctd-quick_intro.ipynb](./notebooks/ctd-quick_intro.ipynb). It can be run by installing the `ctd` package in your conda environment. If at the terminal, you'd do this, where `my_conda_env` is the name of your conda environment:
+We will use the existing `ctd` open-source Python package, https://pyoceans.github.io/python-ctd/. We'll go through a [notebook found on that site](https://pyoceans.github.io/python-ctd/quick_intro-output.html#Reading-and-plotting) that demonstrates its use; I copied the notebook to [notebooks/ctd-quick_intro.ipynb](./notebooks/ctd-quick_intro.ipynb). It can be run by installing the `ctd` package in your conda environment. If at the terminal, you'd do this, where `my_conda_env` is the name of your conda environment:
 ```bash
 conda activate my_conda_env
 conda install -c conda-forge ctd
@@ -18,10 +18,3 @@ Here's a depth profile image created by the `ctd` example notebook:
 ```{note}
 It's not uncommon for different people or groups to create open-source packages with overlapping capabilities. There is at least one other package that reads SeaBird `cnv` files: the `seabird` package, https://seabird.readthedocs.io. Often in these cases, each package may have some features that the other one doesn't.
 ```
-
-## microSWIFT data file
-
-Prospects for reading [microSWIFT](https://apl.uw.edu/project/project.php?id=swift) data files in Python:
-
-- https://github.com/jacobrdavis
-- https://github.com/edwinrainville/microSWIFT-io

site/cheatsheets.md

@@ -3,7 +3,6 @@
 It's hard to remember the details of syntax and function calls without using it actively and regularly. Compact "cheat sheets" can be a convenient resource for looking up specific details and capabilities. Here are a set of Python cheat sheets that should be useful.
 
 - General: https://www.pythoncheatsheet.org/
-- General and Matplotlib: https://ehmatthes.github.io/pcc_2e/cheat_sheets/cheat_sheets/
 - Numpy: https://www.datacamp.com/cheat-sheet/numpy-cheat-sheet-data-analysis-in-python
-- Matplotlib and Pandas: https://python-graph-gallery.com/cheat-sheets/
-- Pandas: https://www.datacamp.com/cheat-sheet/pandas-cheat-sheet-for-data-science-in-python
+- Matplotlib: https://matplotlib.org/cheatsheets/
+- Pandas: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf

site/lessons.md → site/complementary_lessons.md

@@ -1,22 +1,22 @@
-# Detailed lessons
+# Complementary, in-depth lessons from other groups
 
-There are **many** free Python tutorials online; a quick search will produce a very long list. For example, [Python Basics: Introduction to Python](https://realpython.com/learning-paths/python-basics/) features many detailed lessons from [https://realpython.com](https://realpython.com), which I find to be a great source for Python in-depth learning. [Project Pythia Foundations](https://foundations.projectpythia.org/landing-page.html) has a great collection of more advanced tutorials especially geared for the earth sciences, including Oceanography.
+There are **many** free Python tutorials online; a quick search will produce a very long list. For example, [Python Basics: Introduction to Python](https://realpython.com/learning-paths/python-basics/) features many detailed lessons from [https://realpython.com](https://realpython.com), which are a great source for Python in-depth learning. [Project Pythia Foundations](https://foundations.projectpythia.org/landing-page.html) has a great collection of more advanced tutorials especially geared for the earth sciences, including Oceanography.
 
-Instead of creating learning materials from scratch, we'll take advantage of [The Carpentries, https://carpentries.org](https://carpentries.org), "a global community teaching foundational computational and data science skills to researchers in academia, industry and government." Carpentries tutorials and lessons are openly accessible.
+The [lessons we created for the APL SURP course](lessons/index) cover each topic relatively briefly. Rather than creating new, in-depth learning materials from scratch, we'll take advantage of [The Carpentries](https://carpentries.org), "a global community teaching foundational computational and data science skills to researchers in academia, industry and government." Carpentries tutorials and lessons are openly accessible.
 
-Specifically, **we'll use the [Plotting and Programming in Python](https://swcarpentry.github.io/python-novice-gapminder/) lesson from The Carpentries**: "This lesson is an introduction to programming in Python 3 for people with little or no previous programming experience." It's also referred to as the "gapminder" lesson, based on the dataset it uses.
+Specifically, **we'll focus on the [Plotting and Programming in Python](https://swcarpentry.github.io/python-novice-gapminder/) lesson from The Carpentries**: "This lesson is an introduction to programming in Python 3 for people with little or no previous programming experience." It's also referred to as the "gapminder" lesson, based on the dataset it uses.
 
 ```{note}
 Another, similar Carpentries lesson that you may find helpful is [Programming with Python](https://swcarpentry.github.io/python-novice-inflammation/): "The best way to learn how to program is to do something useful, so this introduction to Python is built around a common scientific task: data analysis."
 ```
 
 ## Getting the Data
 
-The data we will use is taken from the [gapminder](https://en.wikipedia.org/wiki/Gapminder_Foundation) dataset. To obtain it, download and unzip the file [python-novice-gapminder-data.zip](https://swcarpentry.github.io/python-novice-gapminder/files/python-novice-gapminder-data.zip). In order to follow the presented material, you should launch JupyterLab in the folder that contains the unzipped ddadta file. (see [Starting JupyterLab](https://swcarpentry.github.io/python-novice-gapminder/01-run-quit.html#starting-jupyterlab)).
+The data used in this subset of Software Carpentry lessons is taken from the [gapminder](https://en.wikipedia.org/wiki/Gapminder_Foundation) dataset. To obtain it, download and unzip the file [python-novice-gapminder-data.zip](https://swcarpentry.github.io/python-novice-gapminder/files/python-novice-gapminder-data.zip). To execute the lesson materials, launch JupyterLab in the folder that contains the unzipped data file. (see [Starting JupyterLab](https://swcarpentry.github.io/python-novice-gapminder/01-run-quit.html#starting-jupyterlab)).
 
 ## Lesson episodes
 
-The Plotting and Programming in Python lesson is intended as a workshop that takes a full day. We will go through a *subset* of the materials (referred to as "episodes") and will only highlight some specific points within each episode. The goal is to examine in more detail most of the Python and programming elements we saw in the previous "Quick overview".
+The *Plotting and Programming in Python* lesson is intended as a workshop that takes a full day. The *subset* of materials (referred to as "episodes") presented here expands on the lessons presented directly in the APL SURP course.
 
 ### The basics
 
@@ -29,7 +29,6 @@ The Plotting and Programming in Python lesson is intended as a workshop that tak
 - [12. For Loops](https://swcarpentry.github.io/python-novice-gapminder/12-for-loops.html)
 - [13. Conditionals](https://swcarpentry.github.io/python-novice-gapminder/13-conditionals.html)
 
-### -- Break and Q/A
 ### More advanced topics
 
 - [Numpy -- from Project Pythia](https://foundations.projectpythia.org/core/numpy/numpy-basics.html)
@@ -40,5 +39,3 @@ The Plotting and Programming in Python lesson is intended as a workshop that tak
 - [16. Writing Functions](https://swcarpentry.github.io/python-novice-gapminder/16-writing-functions.html)
 
 The key points from each episode are [summarized here](https://swcarpentry.github.io/python-novice-gapminder/instructor/key-points.html)
-
-### -- Break and Q/A