Project marking¶

Below are further details of what you must submit for the project and guideline marking grids.

Code¶

You should submit a zip file or tarball containing all your Python scripts, which should be submitted via Moodle. This zip file or tarball should also contain a plain text file called README or README.txt that briefly describes (in a single sentence) the contents of each Python file and how to run your simulation(s), e.g., which file to run to reproduce simulations. If you produced large data files you do not need to include these in the submission. In the vast majority of cases we will not be attempting to run your code - the evidence for it working should be in your report - but don't use this as an excuse to submit code that fails.

Some general advice is:

Back up your code somewhere (e.g., OneDrive, Google Drive, Box, Dropbox, github).
Try to make your code modular. Have classes and functions in their own files. Have scripts that do certain tests or simulations in their own files too. If possible don't just edit or append every test run or simulation into one long file.
Keep notes of the tests you're doing. Make sure that you document the initial conditions of each test, length of run, numerical approximant that your used, etc. Again, have different tests/simulations in different appropriately named and commented files.
For long simulations output the final data to a file(s) and have any plotting scripts as separate files that read in that data. You don't want to have to re-run a long simulation just to change the marker style or font size on a plot!
If you're not sure how to create a zip file then please look up how to do this well before the deadline!
Back up your code, data files and plots somewhere (it's worth repeating!)

You should try and generate results at each stage as you develop your simulation rather than waiting until everything is complete. Having a complete Solar System is a possible (but challenging) goal. You may not reach this stage, or you may go beyond it. However, it is more important that your code works and you can show it works than simulating the full system.

If you have a working simulation it is more important to get it tested rather than making fancy plots or interfaces. While having a fancy GUI interface or movies of the simulations are nice, do not sacrifice spending time doing these things when tests or good documentation or writing your report are more important.

If you are struggling with where to start it is useful for you to make use of the two project exercises ("Final Project Part 1" and "Final Project Part 2" on Moodle) as a basis for your simulation. If you look at the marking grid below, code similar to that from the Final Project Part 1 and 2 exercises would already enable you to get a grade D with some small additional work. Also look at, and experiment with, the example test file accompanying the Week 6 exercises on Moodle.

Code marking grid¶

Below is a marking grid giving a guide as to how the code will be marked. The code will be marked based on a set of four attributes: functionality, style, algorithms used, and testing. The weights of marks between the different attributes are given in brackets. Note that some of the evidence of code testing is taken from the report (as well as from the code written to enable those tests).

Grade	Functionality (30%)	Style (20%)	Algorithms (30%)	Testing (20%)
A+	Code accurately evolves an \(N\)-body gravitationally interacting system using advanced numerical methods with the ability naturally to take in arbitrary bodies, e.g., by downloading ephemerides from external websites. Relevant additional physics is simulated correctly. There is an exceptional user interface.	Object oriented features including classes with appropriate methods are used to set up and evolve the simulation. Code is exceptionally well laid out and easy to understand. Code is exceptionally well documented with docstrings and comments throughout. Data and outputs are stored in easily understandable and accessible objects and file types.	Three or more different numerical approximations are used for evolving the system and are implemented in a correct and exceptionally efficient manner. There are no bugs.	Detailed unit tests of the key algorithms are provided. Exceptionally detailed tests comparing the simulation to simplified analytical situations are provided. There are extensive tests of the extent to which all conserved properties of the system are indeed conserved to a good approximation.
A	Code accurately evolves an \(N\)-body gravitationally interacting system. The inclusion of additional bodies is easy. There is an effective user interface.	Some use of classes to define the main objects in the simulation, which provide appropriate methods to evolve the simulation. Good use of functions with proper docstrings and comments. Code uses modularity, e.g., with classes in different files where appropriate. Data and outputs are stored in an easily usable form.	At least three different numerical approximations are used for evolving the system and are implemented correctly in an efficient manner. There are one or two bugs of minor importance.	Comparisons to simplified analytical situations are provided. Tests show that conserved properties of the system are indeed conserved to a good approximation.
B	The simulation is able to evolve a system of three or more particles based on their mutual gravitational attraction. Adding new bodies to the system is possible, but would require code alterations.	Code uses functions and basic classes. Functions have basic but informative docstrings. Code comments are widely but inconsistently used. Data are stored, but the format is not easy to use.	At least two different numerical approximations are correctly implemented and used for evolving the system (e.g., Euler and Euler-Cromer), but the methods could be implemented more efficiently. There is a small number of bugs.	Some quantitative tests (e.g., of conservation laws) are provided.
C	The simulation is able to evolve a system consisting of two particles based on their mutual gravitational attraction.	Code has some very basic docstrings and non-descriptive comments. Data are produced and an attempt is made to store it.	At least one numerical approximation is correctly implemented. There are several bugs in the code.	Qualitative, rather than quantitative, tests are provided in the report to show the simulation "looks" correct.
D	The simulation is able to evolve a system consisting of a single particle in a gravitational field.	Code lacks comments and docstrings. Code is provided in a single script with no modular aspects. There is minimal use of functions and no use of classes. No attempt is made to store results in a usable format.	Only basic numerical approximations are used. There are serious bugs in the code.	Only very basic qualitative testing is performed.
Fail	No attempt is made to evolve the motion of objects.	Code is incoherent.	No recognisable numerical methods.	No testing of the code is provided.

Note

You can write your project without making use of Python classes at all. In terms of marking the code "style", to get the highest grades without using classes the other aspects of the code style must be shown to be at a higher level, i.e., the code documentation must be present and informative, the structure of the code must be sensible and well thought out, the data must be stored within the code in a clear and sensible way.

Report¶

The report should be no more than 10 pages long including pictures (but excluding title page, abstract and references). You do not need to include code within the report. The report can be written in whatever software you prefer (e.g., LaTeX or Word), but you must submit a PDF file for marking on Moodle. If using LaTeX an example template file is available on Moodle and you may want to make use of online services such as Overleaf or Authorea for writing your report. Writing a report about a programming project may seem a challenge, but the report is not meant to be a description of your code. It is meant to describe the physical processes that you are simulating, how you determined if the simulation worked, how different methods (i.e., numerical approximations) compared, and any insights that you gained.

Make sure figures you include are legible, i.e., they have large enough label and legend fonts to be readable and they use distinguishable lines or markers. Within the report figures should be captioned to briefly describe what they show and they should be numbered and referred to in the text (do not include figures if you are not going to describe and refer to them in the text of the report). If using LaTeX it is good to save your figures using Matplotlib directly as PDFs rather than converting between png/jpg and pdf. If saving as png/jpg use a dpi equal to 150 or more to give good quality images.

The report should contain:

An abstract briefly outlining the aims of the project and any outcomes. If you have quantifiable results you can summarise them here.
An introduction describing the physics behind the simulation and motivations for the numerical approximation methods used, including equations as appropriate. You do not need to include a long historic background discussion of, e.g., planetary motion.
A description of how you tested your code and any outcomes of tests, e.g., simplified situations or tests against analytical results.
A description of global tests of your simulation, quantified if possible and with some explanation of features observed, e.g.,
- it reproduces stable orbits
- it conserves total linear momentum or angular momentum
- comparisons against the JPL ephemeris
- comparisons of different numerical approximation methods for the above cases and using different time steps (in what situations did certain setups work well or fail?).
A conclusion/discussion summarising your findings, describing limitations of your simulation, and further work you could do to enhance you simulation.
Relevant references to the literature; try not to just refer to websites or course notes if a published primary source (book or paper) is available (some references to websites are acceptable, e.g., for software or Solar System ephemerides).

If you extend or produce a simulation with additional/different physics (e.g., electrostatics) or of alternative systems to the Solar System then that is also perfectly acceptable.

Please do try and make your report legible and well laid out and formatted. If it is easy for us to see that you have fulfilled certain goals it is easier for us to award marks. If we have to try and infer things based on incomplete information or an incoherent layout then awarding marks becomes more difficult. In particular, please ensure that figures have easily readable axis labels, legends, etc. The size of the text in the figures should be about the same as the size of the text in the surrounding document, which should be at least 10 point.

The report should not contain:

Lengthy descriptions of your code (you are submitting your full code, so we do not need to see it twice);
Descriptions of the process of debugging your code (instead you should provide evidence that your final code works);
Too many figures; you only have 10 pages, so think about what you want to show and therefore produce figures that provide useful summary information.
References purely to websites (unless to software without an associated publication).

Report marking grid¶

Below is a marking grid giving a guide to how the report will be marked. The report will be marked based on a set of five attributes: abstract, introduction, results (including testing), conclusions, and presentation. The weights of marks between the different attributes are given in brackets.

Grade	Abstract (10%)	Introduction (20%)	Results (40%)	Conclusions (20%)	Presentation (10%)
A+	Abstract succinctly summarises the project and enthuses the reader. It includes key quantitative results from the simulations and brief explanations of the effects seen.	An exceptional description of the background theory behind the simulations and algorithms, including numerical approximations beyond the Euler and Euler-Cromer methods.	Thorough and coherent descriptions of the outcomes of simulations along with extensive evidence validating those simulations are provided. In-depth comparisons between different numerical approximations and time steps are described, including detailed quantitative and qualitative assessments of the differences.	A thorough critical examination of the simulation and results is provided. Detailed discussions on the limitations of the methods used are given. Conclusions show high levels of insight into features of the observed results and describe areas for improvement.	The report is superbly laid out, with a highly readable writing style. All figures and tables are exceptionally clear, with self-contained and informative captions.
A	Abstract provides a readable and succinct summary of the project. It includes relevant quantitative results from the simulations.	An excellent description of the background theory behind the simulations and algorithms, including numerical approximations beyond the Euler and Euler-Cromer methods.	A coherent description of the outcomes of simulations is provided, along with evidence validating those simulations. Comparisons between different numerical approximations and different time steps are described, including quantitative and qualitative assessments of the differences. Any errors are minor and do not affect the interpretation of the results.	A critical examination of the simulations and results is provided. Discussions show insight into features of the observed results and the limitations of the methods and describe areas for improvement.	The report is very well laid out, with clearly defined sections that are ordered in an appropriate manner. All figures and tables are legible, of high quality and have informative captions.
B	Abstract provides a good and understandable summary of the project. It includes some qualitative description of results from the simulations.	A good description of the background theory behind the simulation and algorithms, including at least two numerical approximations such as the Euler and Euler-Cromer methods.	Results include a qualitative description of the outcomes of a suite of tests validating the simulation. Comparisons between different numerical approximations are described, including a qualitative assessment of the differences. There may be some minor errors in the results that affect their interpretation.	A good summary of the results is provided, including some qualitative interpretation. Limitations of the simulation and areas for improvement are acknowledged, but lack detail.	The report has a good structure with relevant sections included. The writing style is good and generally flows in a coherent manner between sections. Figures are generally legible, with minor exceptions. Captions are included, but provide more limited information.
C	Abstract provides a satisfactory summary of the project. It includes some qualitative description of results from simulations.	A satisfactory description of the background theory behind the simulation and algorithms, including at least one numerical approximation such as the Euler or Euler-Cromer methods.	Results include a qualitative description of the outcomes of more than one test validating the simulation. There are several errors in the results that lead to misinterpretations of the results.	A satisfactory summary of the results is provided, including some qualitative interpretation. Limitations of the simulation and areas for improvement are not mentioned.	The report has a satisfactory structure, with most relevant sections included. The writing style is mostly clear, but has areas of inconsistency and incoherence. Tables and figures exist and have captions, but are hard to interpret with the information given.
D	Abstract is present and contains some information about the project scope, but no discussion of the results obtained.	The introduction describes some background, although the description of how this relates to the simulation is unclear. Numerical approximations are not described or are inadequately described.	Results lack any qualitative or quantitative description of the simulation output. There are many errors in the results that lead to misinterpretations of the results.	Results are summarised, but not in a clear manner, giving the reader only limited understanding of the outcomes.	Poor report structure with missing or undifferentiated sections. Writing style is unclear and can be incoherent. Figures are poorly formatted, with missing or illegible material. Captions are missing or inadequate.
Fail	Abstract is absent or provides no meaningful description of the project.	Introduction is missing or gives no indication of the necessary background material relevant to the project.	Results are missing. Data are either not presented or not included in a way that is understandable. No description of the process by which results were obtained is given. There are very many errors in the results and their interpretation.	The conclusion provides no coherent discussion of the outcomes of the work and no mention of the limitations of the work.	Very poor report structure with important sections missing. Figures are missing or illegible.