Daniel Edler

[standard-version](https://github.com/conventional-changelog/standard-version) recommends [release-please](https://github.com/googleapis/release-please).

Fix print correct input type in readAl

1

Datatype 'unknown' printed as 'amino-acids_degenerate_codes' due to missing parentheses.

Running `-T1 -L 1` with `--cluster-data` doesn't run any optimization. Unless `--no-infomap`, continue with optimization after initial partition before consolidation of first level.

To replicate: ```py edges = [ [1,2], [1,3], [2,3], [3,4], [4,5], ] G = nx.Graph() G.add_edges_from(edges) im = infomap.Infomap(silent=True, no_infomap=True) im.add_networkx_graph(G) im.run() for node in im.nodes: print(node.node_id, node.data.flow) ``` Output...

Supports general state networks and both multilayer formats.

Replicate in python: ```py edges = [ [1,2], [2,3], [3,1], [3,4], [4,5], [5,6], [6,4], ] G = nx.DiGraph() G.add_edges_from(edges) py.find_communities(G, recorded_teleportation=True, num_trials=1) # gives index codelength 0.102 py.find_communities(G, recorded_teleportation=True, num_trials=2)...

Fixes #28

The two datasets have "node_1,node_2" as header instead of the expected "id_1,id_2".

Infomap uses the Mersenne Twister pseudo-random generator that is included in the standard library (`std::mt19937`), however the implementation of that differ on different platforms. This makes it harder to replicate...