This folder has 3 subfolders, code, figures and latex. What the folder contains is quite self explanatory. A description of what the different files in each folder does is explained below. All of the code is commented to explain what each step does.

All of the code for this project was run with the following specs:

Computer : 2,7 GHz Intel Core i5Processor
Version : MacOS Sierra 10.12.6
Processor : 2,7 GHz Intel Core i5
Memory : 8 GB 1867 MHz DDR3
Graphics : Intel Iris Graphics 6100 1536 MB

The data is not added to GitHub:octocat:, to reduce the amount of storage used by GitHub:octocat:, since some of the data files are quite large. The data is saved in the subdirectory data. The code we did not end up needing is saved in the subdirectory code/trash_code.

The file functions.cpp include all of the functions we use in this project. The functions does the following: write results to file, calculate the largest non diagonal element, rotate in jacobis method, find the analytical eigenvalues for the test function, create a tridiagonal matrix with scalar input, create tridiagonal matrix with vector input, to compute the whole of jacobis method, and to find the eigenvectors of the input matrix using jacobis method. These functions are included in other programs through the headerfile functions.hpp.

The file count_iterations.cpp runs jacobis method for different matrix sizes and calculated the iterations needed to reach a given accuracy, and writes the results to file. The file takes three input arguments; the exponent of the lowest size matrix, the exponent of the highest size matrix, and the step factor for each run.

The file size_for_leading_digits.cpp calculates the needed matrix size to reach a specific accuracy in the lowest eigenvalues. It takes three command line arguments: the start size of the matrix, the maximal position value, and tolerance.

The file test_all.cpp runs all of the four test functions we have implemented for our system. The code takes no command line arguments. It tests the following: are the analytical eigenvalues the same as the numerical ones using the armadillo eigenvalue finder and the algorithm using jacobis method which we have implemented, tests eigenvectors for a 3x3 matrix versus analytical solution, test jacobis method eigenvalues vs analytical, and lastly test if the off function chooses the largest non-diag element.

The file 2_d.cpp find the eigenvalues and eigenvectors, and writes them to file, for the single electron in a 3D harmonic oscillator potential for the lowest energy eigenstates.

The file 2_e.cpp find the eigenvalues and eigenvectors, and writes them to file, for the two interacting electrons in a 3D harmonic oscillator potential for the lowest energy eigenstates.

The data generated in C++ is plotted in python through the two files plot_data.py and plot_3D_data.py. The 3D plot for finding ideal value of rho_max is made in plot_3D_data.py, while all other plots are made from plot_data.py. The date for the 3D plot is calculated by try_3D_plot.cpp, which takes three inputarguments from the command line: the size of the matrix, rho_max and the tolerance. This program is run for different command line arguments through the python file data_3D_plot.py.

In the directory figures we save the figures which is plotted by the programs plot_data.py and plot_3D_data.py in the directory code. Most of the figures are not pushed to GitHub:octocat: to save space.

In the directory latex we have all the code used for LaTeX. For our references we use BibTeX.