A parser for the output of the C++ library Ripser that calculates the Vietoris–Rips persistence barcodes. This makes it easier to be used with the TDA library in R.
Download the Files
On the RipserOnR GitHub project page you should select the option “Clone or Download” and then “Download Zip” to get all the necessary files for using this R code. Unzip all the files and put them on your working folder.
Note : For an advanced user, you should just clone the repo to your working folder.
You should put all the files from the Zip in your working folder. The most important files are :
For an easier setup experience we can use Linux ( the R code should work on Windows too, but we need also a compiled .exe from ripser.cpp which would need to setup a compiler like MinGW ). For this to work we need this apps installed, which come easily with most Linux distros :
Being this a simple C++ development setup.
Finally, we import the library to our current environment ( with our files on the root of the folder ) using the next line
source( 'Ripser.R' )
An example of usage can be seen in the “useRipser.R” file which shows the next code
# Import library source( 'Ripser.R' ) # Load matrix of points of size n x 3 m = torus # Point cloud diaG = ripserDiag( m , dimension = 2 , threshold = 2 ) plot.diagram( diaG )
We obtain a function named ripserDiag which follows the same usage as ripsDiag from the R TDA library. This meaning that we provide the data to which we want to apply Persistent Homology, some parameters and this fucntion will return an object that contains all the information of the cycles of the filtration ( as big list of intervals ).
To plot the diagrams and barcodes resulting from the persisten homology of the data we use the function plot.diagram from the R TDA library
plot.diagram( diAG ) plot.diagram( diAG , barcode = TRUE )
The parameters for the fucntion ripserDiag follow the names of the Ripser library parameters :
- format : use the specified file format for the input. Options are:
- lower-distance (lower triangular distance matrix; default)
- upper-distance (upper triangular distance matrix)
- distance (full distance matrix)
- point-cloud (point cloud in Euclidean space)
- dipha (distance matrix in DIPHA file format)
- dim : compute persistent homology up to dimension
- threshold : compute Rips complexes up to diameter