Course PM

MVE080/MMG640 course in scientific visualisation.

Your student representatives:

Jakob Bergman   jakob@svardby.se
Andreas Helgesson  andreas.helgesson@hotmail.com
Jacob Ljungmark  jacobljungmark@icloud.com
Lean Tong Ng  leanto@student.chalmers.se 

Contact details:

Sviatlana Shashkova (sviatlana.shashkova@physics.gu.se)

Linde Viaene (linde.viaene@physics.gu.se)

Program

In this course you will learn about various concepts, techniques, and tools for visualisation of scientific data in 2 dimensions. The GU course code is MMG640. The Chalmers course code is MVE080. The schedule of the course is in TimeEdit - just search for the course code. Monday lectures will be given in the Euler lecture room; Wednesday computer labs and lectures will take place in the computer rooms MV:F22, 24-25.

The course is based on the online book Fundamentals of Data Visualization by Claus O. Wilke, the on-line ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham and a review article The science of visual data communication: What works by Franconeri et al. We will use the grammar of graphics Python library plotnine, which is based on ggplot2. Hence when googling for help, you can search for both ggplot and plotnine.

After each lecture, the slides, code used to produce some visuals, and relevant datasets will be uploaded.

Weekly homework should be completed within two weeks (the last one within one week) which includes both attempts. I suggest to aim to hand the first attempt in ASAP, so that there is enough time to check your file and for you to correct it. If you need more time, let Linde (and me) know. The final version of the final homework should be submitted by December 11, 23:59.

Preliminary course outline (modified throughout the course):

*Computer lab: each Wednesday 08:00-09:45
It is not allowed to use ChatGPT or any other large language model to directly answer any of the questions. Large language models can be excellent tools, e.g. for editing text (which you are allowed to do), but using them to directly answer a question is bad practice, as i) you do not learn anything, and ii) these models are often incorrect - and confidently reporting an incorrect answer is unprofessional. Any direct usage of ChatGPT to answer questions will be considered cheating and reported. In summary, you are allowed to use ChatGPT or similar models for text editing or to help with coding, but, you are responsible to ensure correctness of your answers.

• Monday 2024-11-04  13:15 - 15:00 : Course introduction ()
  Relevant links:
  Python basics tutorial
  NumPy for MATLAB users
  The Anaconda Python distribution - I recommend you install Anaconda for Python3.11. For plotnine to work you must use Python 3.11.
  Jupyter Notebook basics
  Download VS Code - A good editor with support for notebooks
  The correct environment (Python version and package version) can be recreated from this yml-file: MVE080.yml. If you have correctly installed Jupyter Notebooks and plotnine you will be able to run the following notebook :  Hello_MVE080-1.ipynb
  Lecture 1 slides Lecture 1 Introduction.pdf
   
• Wednesday 2024-11-06 10:00-11:45 : Visualising amounts and distributions ()
  Lecture 2 slides Lecture 2 Amounts.pdf
  Lecture2-2024.ipynb
  Datasets:
  Operating system dataset : mobile_os_market_share.csv
  Median lifespan data : Life_tidy.csv Life_tidy-1.csv  
  Weather data for Gothenburg : Weather_tidy-1.csv 
  The non summary winter dataset : Winter_vås.csv 
  Weather data for Västerås : Winter_vås_summary.csv 
  Data on Schizophrenics born :  Aus_schz.csv
  
  
• Monday 2024-11-11 13:15-15:00: Colours and proportions () 
  Lecture 3 slides Lecture 3 Colours.pdf
  Lecture3-2024.ipynb
  Datasets: 
  Dataset used to compare colormaps : Ex_color_maps-1.csv 
  Circles dataset : Circles.csv 
  Data on life-happiness for 2018 : 2018.csv 
  Data with Simpsons's paradox (testing categorical colors) : Ex_colors.csv 
  GDP data : GDP_tidy.csv 
  Swedish education data : Education_plot_format.csv 
  Titanic data : Titanic.csv 
  Spain employment data : Span_unemployment.csv 
  Data for using rainbow desaturated palette : Rainbow_desaturated (2).csv (in the notebook there is code for reading and using this palette)
   
• Wednesday 2024-11-13 10:00-11:45: Time series and associations ()
  Lecture 4 slides Lecture 4 Time_ Associations.pdf
  Lecture4-2024.ipynb
  Datasets:
  Spain employment data : Spain_tidy.csv 
  Inflation data : Inflation.csv 
  Sweden population data : Sweden_pop.csv 
  France population data : France_pop.csv 
  Bacterial growth data : Growth_rate.csv 
  Apartment prices data : Housing_sweden.csv 
  World happiness data : World_2018.csv 
  Salary data : Salary_sweden.csv 
  Lifespan data for horizon plot : Life_tidy-1.csv 
  R-code for horizon plot : Horizon_plot.R  
   
• Monday 2024-11-18 13:15-15:00: Geospatial ()
  Lecture 5 slides Lecture 5 Geospatial.pdf
  Geospatial_data 2024.ipynb
  Datasets:
  Forest dataset : Forest_tidy-3.csv  
  Sweden map region Kommun_Sweref99TM_region-2.zip  and municipal Kommun_Sweref99TM_region-3.zip 
  Sweden population data : Swe_pop_län_2021-1.csv and Swe_pop_2021-1.csv 
  USA map : cb_2018_us_state_500k-1.zip 
  Starbucks data : Startbucks_location-1.csv
   
• Wednesday 2024-11-20 10:00-11:45: Uncertainty ()
  Lecture 6 slides Lecture 6 Uncertainty.pdf
  Datasets:
  Chocolate datasets : Chocolate_tidy-1.csv and Chocolate_sum-1.csv 
  Curve fit data : Traces_fit-1.csv, Sum_curve_fit-1.csv, Sum_curve_fit-2.csv
  Probability : Plot_uncertainity-1.csv 
  
  
• Monday 2024-11-25 13:15-15:00 
• Wednesday 2024-11-27 08:00-11:45 COMPUTER LAB
• Monday 2024-12-02 13:15-15:00 
• Wednesday 2024-12-04 10:00-11:45 
• Monday 2024-12-09 13:15-15:00 
• Wednesday 2024-12-11 10:00-11:45

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