low_esotericism

A project in modern C++ that calculates an approximate solution of the equilibrium equation in the case of a three-parameter closure.

Installation

git clone git@github.com:sermir2003/low_esotericism.git
cd low_esotericism
mkdir build-directory
cd build-directory
cmake ..
make

Use

Biological and technical data are entered using json files. To create such a file, enter

./low_esotericism newtask

to create a file with default name, or

./low_esotericism newtask research_name

if you want to specify a research name and it would be research_name.json.

Enter the task data

Input

Kernels

currently only Gauss kernels are supported, to specify such a kernel, enter The standart deviations for the death and birth kernels

{
  "kernel": {
    "type": "Gauss",
    "sigma m": 0.15,
    "sigma w": 0.15,
    "limit m": -1,
    "limit w": -1
  }
}

where sigma w and sigma m are standard deviations for the death and birth kernels, and limit w and limit m --- limits of the kernels.

The kernels in this case are the following

$$m(x)=\begin{cases} \frac{1}{\text{sigma_m}\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{x^2}{\text{sigma_m}^2}\right)} & x\leqslant \text{limit_m}, \\\\ 0, & x> \text{limit_m} \end{cases}$$

You can set the parameters to -1 if you want the kernel to have no limits and the kernel to look like this:

$$m(x)=\frac{1}{\text{sigma_m}\sqrt{2\pi}}e^{-\frac{1}{2}\left(\frac{x^2}{\text{sigma_m}^2}\right)}$$

Other parameters

Parameter	Description	Type
b	Birth rating	Real
d	Environment death rating	Real
d'	Competition rating	Real
Grid count	Number of calculation points	Positive integer
Iteration count	Number of iterations of the algorithm	Positive integer
Integration area radius	The rounding boundary of the integral [-r,r]	Real
alpha, beta, gamma	Parameters of three-parameter closure	String

To start the calculations, enter

./low_esotericism solve

to use data from a file with a default name, or

./low_esotericism solve research_name.json

if you want to specify a research name.

Output

Upon completion of calculations on the input data from the file with name research_name.json, two files will be created in the same folder with it: research_name_N.txt and research_name_C.csv. File research_name_N.txt would contain only one number: the first spatial moment. File research_name_C.csv would contain a line graph of the second moment in the form of pairs of coordinates x,y. The first line contains only the string "x,y", the subsequent ones contain the coordinates of the points.

To then build a line graph in the form of an image, you need to run a special python script in the project directory:

python3 plot_maker.py research_name

to build a graph image for the data from file research_name_C.csv.

License

The program is licensed under the MIT License

The program contains the nlohmann/json library which is licensed under the MIT License.