R9482/Homework49303ae846ba9master
README.md
SP4E - Homework 4
General Info
This file provides a brief documentation and information related to the fourth (last) Homework of the course "Scientific Programming for Engineers", fall 2019.
This homework is done by O. Ashtari and A. Sieber.
Last update: 15.01.2020
Project Description
The first part of the Homework aims at using the Pybind11 library in order to create Python binding for the C++ particles code. The ultimate goal of this part consists in allowing the user to run this code through a Python interface.
The second part of the Homework focuses on the planet trajectory simulation module of the particles code. There, a Python optimization routine is written which main purpose is to modify the initial planets states (one planet at a time) in order to minimize the error of their computed trajectories when compared to reference trajectories.
Executable Files
Optimization routine
The optimization routine error_minimization.py is intended to minimize the error on Mercury trajectory (or any other planet of the solar system) by scaling the initial velocity of the planet of interest. The minimization is performed by computing the error between the computed planet trajectory and a reference planet trajectory. This routine calls main.py, therefore, in order to run it, the user must first make sure she/he can launch the particles code through the python interface. Once the requirement is satisfied the error_minimization.py routine can be called as follow:
$ python3 directory_comp directory_ref planet input_file scale nb_steps freq
where:
- directory_comp, the path to the directory containing the computed trajectory data.
- directory_ref, the path to the directory containing the reference trajectory data.
- planet, the planet of interest.
- input_file, the file containing the initial state of the different planets.
- scale, the initial scaling factor (initial guess of the minimization).
- nb_steps, the number of time steps of the simulation within the particles code.
- freq, the dumping frequency at which the particles code writes outputs.
As an example a minimization on Mercury trajectory over 365 days with a dumping frequency of 1 day could be run as such:
$ python3 dumps trajectories mercury init.csv 1.0 365 1
The error_minimization.py routine then prints the scaling factor minimizing the error, the value of this error and the amount of minimization iterations needed to reach an optimum. It moreover plots the evolution of the error versus the scaling factor as shown in the link below:
!Optimization on Mercury initial velocity
The above graph shows that the optimized scaling is evaluated at 0.3989. Comparing this result to the exact scaling of 0.4 (found by dividing the reference initial velocity by the one stored in init.csv) leads to a relative error of less than 0.3% in the optimization procedure.
Comment on how createSimulation function is overloaded
To comment on this function overload, let's first discuss what is the role of createComputes in the whole code.
In the ParticlesFactoryInterface class, createComputes is a Real to voind function which is to be defined later, if needed. In the constructor of MaterialPointsFactory, createComputes is defined to be the default function (i.e. to be createDefaultComputes.) In the default function, ComputeTemperature is added to the system evolution object. Similarly in the PlanetsFactory where createComputes is defined to be createDefaultComputes in which verlet is added to the system evolution object.
As a summary, by constructing an object of type MaterialPointsFactory or PlanetsFactory, createComputes takes a default definition. It is then used inside the first definition of createSimulation method whose arguments are file name and time step size. The whole idea behind overloading createSimulation and using the templated functor is to give the flexibility to the python user to tailor compute cobjects (i.e. ComputeTemperature or verlet etc.) based on his/her needs more easily. The reason is that when a C++ code is wrapped to be used through python, the C++ side is supposed to not be touched anymore. However, default settings for material point, for instance, is hard-coded inside its factory. Therefore, to change default values, the python user should manipulate the C++ files. Or assume, like the previous homework, one wants to get length, conductivity, etc. from command line arguments. Or one wants to develop the code such that these variables are read from a file. Using the overloaded createSimulation with very little insight into how the C++ code is designed, one can do all these by just writing few lines in python, with neither changing any C++ file nor editing the binding file.