Initial meta changes

This will require further changes on review when the course has been written

README.md

@@ -1,3 +1,31 @@
+# Building the Site Locally
+
+Both of these steps should be followed within `rterm`.
+
+## Setup
+
+```r
+# Setup mirrors
+options(repos = c(
+  carpentries = "https://carpentries.r-universe.dev/", 
+  CRAN = "https://cran.rstudio.com/"
+))
+# Setup install from github
+install.packages("devtools")
+library(devtools)
+# Install Uni of Shef Varnish theme
+install_github("RSE-Sheffield/uos-varnish")
+# Install remaining official carpentries packages
+install.packages(c("sandpaper", "tinkr", "pegboard"))
+```
+## Development Server
+
+```r
+sandpaper::serve()
+```
+
+--------------
+
 # The Carpentries Workbench Template Markdown Lesson
 
 This lesson is a template lesson that uses [The Carpentries Workbench][workbench].

config.yaml

@@ -60,6 +60,9 @@ contact: '[email protected]'
 # Order of episodes in your lesson
 episodes: 
 - introduction.md
+- profiling-introduction.md
+- profiling-functions.md
+- profiling-lines.md
 
 # Information for Learners
 learners: 

episodes/profiling-functions.md

@@ -0,0 +1,17 @@
+---
+title: "Function Level Profiling"
+teaching: 0
+exercises: 0
+---
+
+:::::::::::::::::::::::::::::::::::::: questions
+
+- TODO
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: objectives
+
+- TODO
+
+::::::::::::::::::::::::::::::::::::::::::::::::

episodes/profiling-introduction.md

@@ -0,0 +1,17 @@
+---
+title: "Introduction to Profiling"
+teaching: 0
+exercises: 0
+---
+
+:::::::::::::::::::::::::::::::::::::: questions
+
+- TODO
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: objectives
+
+- TODO
+
+::::::::::::::::::::::::::::::::::::::::::::::::

episodes/profiling-lines.md

@@ -0,0 +1,17 @@
+---
+title: "Line Level Profiling"
+teaching: 0
+exercises: 0
+---
+
+:::::::::::::::::::::::::::::::::::::: questions
+
+- TODO
+
+::::::::::::::::::::::::::::::::::::::::::::::::
+
+::::::::::::::::::::::::::::::::::::: objectives
+
+- TODO
+
+::::::::::::::::::::::::::::::::::::::::::::::::

index.md

@@ -2,8 +2,45 @@
 site: sandpaper::sandpaper_site
 ---
 
-This is a new lesson built with [The Carpentries Workbench][workbench]. 
+![Welcome to Performance Profiling & Optimisation (Python) Training!
+](episodes/fig/pando-python-hex-sticker.png){
+alt='Performance Profiling & Optimisation (Python) Training'
+style='padding: 2%'}
 
+The training curriculum for this course is designed for researchers that are writing Python and lack formal training. The curriculum covers how to assess where time is being spent during execution of a Python program, it also provides a high level understanding of how code executes and how this maps to the limiting factors of performance.
 
-[workbench]: https://carpentries.github.io/sandpaper-docs
+If you are now comfortable using Python, this course may be of interest to supplement and advance your programming knowledge. This course is particularly relevant if you are writing research code and desire greater confidence that your code is both performant and suitable for publication.
 
+<!-- TODO: course duration? -->
+<!-- TODO: confident code syllabus? -->
+
+
+## Learning Objectives
+<!-- Aim for 3-4 objectives for every 6 hours of training -->
+<!-- SMART Objectives
+    - Specific
+    - Measureable
+    - Attainable (within the span of the course)
+    - Relevant
+    - Time-bound (implicitly the length of the course)
+-->
+<!-- Evaluation tool: https://web.cs.manchester.ac.uk/iloadvisor/ -->
+After attending this training, participants will be able to:
+
+- identify the most expensive functions and lines of code using `cprofile` and `line_profiler`.
+- evaluate code to determine the limiting factors of it's performance.
+- recognise and implement optimisations for common limiting factors of performance.
+
+::::::::::::::::::::::::::::::::::::::::::  prereq
+
+## Prerequisites
+
+Before joining Performance Profiling & Optimisation (Python) Training, participants should be able to:
+
+- implement basic algorithms in Python
+- follow the control flow of Python code, and dry run the execution in their head or on paper.
+
+See the [Research Computing Training Hub](https://sites.google.com/sheffield.ac.uk/research-training/research-training) for other courses to help with learning these skills.
+<!-- TODO: could make a dedicated page (like https://carpentries.github.io/lesson-development-training/markdown-github-primer.html) that highlights specific courses/resources. -->
+
+::::::::::::::::::::::::::::::::::::::::::::::::::

learners/setup.md

@@ -2,53 +2,30 @@
 title: Setup
 ---
 
-FIXME: Setup instructions live in this document. Please specify the tools and
-the data sets the Learner needs to have installed.
-
+<!--
 ## Data Sets
 
-<!--
 FIXME: place any data you want learners to use in `episodes/data` and then use
        a relative link ( [data zip file](data/lesson-data.zip) ) to provide a
        link to it, replacing the example.com link.
--->
+       
 Download the [data zip file](https://example.com/FIXME) and unzip it to your Desktop
+-->
 
 ## Software Setup
 
 ::::::::::::::::::::::::::::::::::::::: discussion
 
 ### Details
 
-Setup for different systems can be presented in dropdown menus via a `solution`
-tag. They will join to this discussion block, so you can give a general overview
-of the software used in this lesson here and fill out the individual operating
-systems (and potentially add more, e.g. online setup) in the solutions blocks.
-
-:::::::::::::::::::::::::::::::::::::::::::::::::::
-
-:::::::::::::::: solution
-
-### Windows
-
-Use PuTTY
+This course uses Python and was developed using Python 3.11, therefore it is recommended that you have a Python 3.11 or newer environment.
 
-:::::::::::::::::::::::::
+<!-- Todo suggest using a venv?-->
 
-:::::::::::::::: solution
-
-### MacOS
-
-Use Terminal.app
-
-:::::::::::::::::::::::::
-
-
-:::::::::::::::: solution
-
-### Linux
-
-Use Terminal
-
-:::::::::::::::::::::::::
+The non-core Python packages required by the course are `snakeviz` and `line_profiler` and can be installed via `pip`.
+
+```input
+pip install snakeviz line_profiler[all]
+```
 
+:::::::::::::::::::::::::::::::::::::::::::::::::::