Rework Packaging lecture slides and exercise (#203)

* Rework Python packaging slides and update the exercise

* Rework Packaging introduction slides

* Further reworking of the Python packaging slides

* Remove loading bar symbols from PyPI uoload snap because the PDF creator throws warnings

Author: IshaanDesai

03_building_and_packaging/intro_slides.md

@@ -29,21 +29,24 @@ slideOptions:
 
 ## Learning goals of chapter
 
-- What is code packaging and which technologies are available to package code
-- What are the various ways in which a Python code can be packaged
-- How is the Python Package Index (PyPI) used to distribute Python packages
+- Explain why software is packaged.
+- Create Python packages, publish on PyPI, and install with pip.
+- Understand the difference between static and dynamic libraries and common ways of installation on Linux.
+- Build C++ software and handle dependencies with Make and CMake.
+- Package C++ software with CPack and create Debian packages.
+- Create Spack packages, e.g., for high-performance computing systems with restricted access rights.
 
 ---
 
 ## What is packaging?
 
-- Bare code is often hard to understand for everyone except the developer.
+- Bare code is often hard to understand for everyone except the developer(s).
 - Packaging is a workflow to convert a code into a standardized distributable software.
-- Broadly code can be standardized in various ways. Some examples are
-    - creating a package according to some standardization.
-    - providing an installation recipe, for example, using CMake / make (`make install`).
+- A code can be standardized in various ways. Some examples are
+    - creating a compact form by following a standardization.
+    - providing an installation recipe, for example, using CMake / make.
     - bundling code into an app or software with some UI.
-- In this first lecture you will learn about **creating a package according to some standardization**.
+- We discuss **creating a compact form by following a standardization**.
 
 ---
 
@@ -52,24 +55,25 @@ slideOptions:
 - A bare code with many files typically has difficulties like
     - multiple dependencies and requirements of specific versions of dependencies.
     - intricate compilation / installation steps which are hard to get right.
-    - missing or limited starting information / documentation which means a high entry barrier.
+    - missing or limited starting information / documentation, which means a high entry barrier.
 
 ---
 
 ## Why should we package code? 2/2
 
-- Create a package of bare code to
+- Create a package to
     - benefit from a package index or package manager which is familiar for a broad audience.
     - benefit from automated handling of dependencies of package managers.
     - have ease of distribution and maintenance due to standardization.
+    - increase overall usability and sustainability of your code.
 
 ---
 
 ## How to package code?
 
-- First step is finding the right type of packaging standard that fits best for your code.
-- There are several (overlapping) options:
-    - One of the many Linux package managers: apt, dpkg, yum, RPM Package Manager and many more ...
+- First step is finding the right standard for your code.
+- There are several options:
+    - One of the many Linux package managers: apt, dpkg, yum, RPM and many more ...
     - [CMake](https://cmake.org/) <span>: building / installation / packaging tool mostly for C, C++ projects<!-- .element: class="fragment" data-fragment-index="1" --></span>
     - [Spack](https://spack.io/) <span>: a package management tool mostly for supercomputing centers<!-- .element: class="fragment" data-fragment-index="1" --></span>
     - [Conan](https://conan.io/) <span>: open-source package manager for C and C++ development<!-- .element: class="fragment" data-fragment-index="1" --></span>
@@ -78,11 +82,11 @@ slideOptions:
 
 ---
 
-## Why do we particularly look at packaging a Python code?
+## Why do we look at packaging a Python code?
 
 - Python is easy to understand and widely used in research software.
-- A finetuned packaging workflow is already well established in the Python community.
-- Various examples of packaged codes already exist: [NumPy](https://pypi.org/project/numpy/), [PyMOR](https://pypi.org/project/pymor/), [fenicsprecice](https://pypi.org/project/fenicsprecice/) and more ...
+- A well established packaging workflow already exists in the Python community.
+- Various examples of packaged codes already exist: [NumPy](https://pypi.org/project/numpy/), [SciPy](https://pypi.org/project/scipy/), [PyTorch](https://pypi.org/project/torch/) and more ...
 
 ---
 

03_building_and_packaging/pip_demo.md

@@ -1,6 +1,6 @@
 # `pip`: Python Packaging Tool Demo Notes
 
-**Note**: Maybe run everything in a fresh Ubuntu Docker container. Launch such a container using `docker run --rm -it ubuntu:jammy`. The other option is to work in a Python virtual environment. An environment can be created as follows
+**Note**: It is recommended run everything in a Ubuntu Docker container or a virtual environment. Launch a container using `docker run --rm -it ubuntu:jammy`. A virtual environment can be created as follows
 
 ```bash
 python -m venv env-name
@@ -9,7 +9,7 @@ source env-name/bin/activate
 
 ## 0. Getting `pip`
 
-If Python has been installed using a system package manager like `apt`, it will not come with `pip`. `pip`can be installed in several ways
+A clean Docker container may not have Python installed. Install Python using `apt`: `apt install python3`. If Python has been installed using a package manager like `apt`, it will not come with `pip`. `pip`can be installed in several ways
 
 ```bash
 python -m ensurepip --upgrade
@@ -18,7 +18,7 @@ python -m ensurepip --upgrade
 or
 
 ```bash
-apt install pip
+apt install python3-pip
 ```
 
 - If installed with Python, the Python version is used to determine which `pip` is installed. So Python 3.8 will install a `pip` by the name of `pip3.8` which will be compatible with Python 3.8.
@@ -27,7 +27,7 @@ apt install pip
 
 ## 1. Installing packages with `pip`
 
-Let us install the finite element library [Nutils](https://nutils.org/)
+Let us install the finite element library [Nutils](https://nutils.org/).
 
 ```bash
 pip install nutils
@@ -36,7 +36,7 @@ pip install nutils
 Install a specific version of a package
 
 ```bash
-pip install nutils==6.0.0
+pip install nutils==7.0.0
 ```
 
 Uninstalling a package
@@ -56,9 +56,6 @@ cd nutils
 pip install --user .
 ```
 
-- A `build` folder is created and the package files are copied into it.
-- But now `pip uninstall package-name` does not work as `pip` looks in the home path. Removal of package can be done by deleting the `build` folder or using manually using `setup.py`.
-
 ## 2. Getting information of currently installed packages
 
 ```bash

03_building_and_packaging/pypi_exercise.md

@@ -2,8 +2,8 @@
 
 ## Starting remarks
 
-- [Exercise repository link](https://github.com/Simulation-Software-Engineering/diffusion2d)
-- Deadline for submitting this exercise is **Thursday 3rd November 09:00**.
+- [Exercise repository link](https://github.com/Simulation-Software-Engineering/diffusion2D)
+- Deadline for submitting this exercise is **Wednesday 20th November 09:00**.
 - The code in this exercise produces plots and in order to view them you need to use a GUI-based operating system or environment.
 
 ## Brief idea of the exercise
@@ -24,7 +24,7 @@ In this exercise you will convert a raw Python code into a packaged code which i
 
 ## Step 1 - Acquiring the raw code and getting familiar with it
 
-- Fork the [exercise repository](https://github.com/Simulation-Software-Engineering/diffusion2d).
+- Fork the [exercise repository](https://github.com/Simulation-Software-Engineering/diffusion2D).
 - Open the file `diffusion2d.py` and go through the file and try to understand the code components.
 - Check if your system has Python version >= 3.6 and update it if it is older than 3.6.
 - Install pip, build, and Twine.