Currently, the color palette is hardcoded in visuals.py:
https://github.com/MLWave/kepler-mapper/blob/0dfa7f736bd4218e8996888785b9d2adc63d5344/kmapper/visuals.py#L8-L15

and again in kmapper.js

https://github.com/MLWave/kepler-mapper/blob/0dfa7f736bd4218e8996888785b9d2adc63d5344/kmapper/static/kmapper.js#L25-L32

I'd like to see this

• duplication fixed and
• allow the palette

Test code:

from sklearn.datasets import load_digits
from gtda.homology import CubicalPersistence
from gtda.diagrams import PairwiseDistance

X, _ = load_digits(return_X_y=True) 
nb_samples = 100
CP = CubicalPersistence(homology_dimensions=(0, 1))
X_diag = CP.fit_transform(X[:nb_samples].reshape(nb_samples, 8, 8))
PD = PairwiseDistance(
    metric='bottleneck', metric_params={'delta'

Each project has a set of example scripts, it looks like Sphinx-gallery makes displaying rendering these examples look very nice.

In Representative Cocycles tutorial, this code block

result = ripser(x, coeff=17, do_cocycles=True)
diagrams = result['dgms']
cocycles = result['cocycles']
D = result['dm']

raises a KeyError: 'dm' since returned object doesn't have the key. The available keys are:

result.keys()
# ['dgms', 'cocycles', 'num_edges', 'dperm2all', 'idx_perm', 'r_cover']

It could be useful to have a way to clone a simplex tree, more efficiently than a loop on get_filtration doing inserts (we have a copy constructor in C++).

Need to adjust documentation to reflect above fact. Potentially add parameter in permutation test function to allow user to pick their own distance function (takes persistent homology of two datasets as parameters, returns numeric).

Thanks to @kisungyou for bringing this to my attention.