V1.0.0

Final release with commented and stream-lined code to accompany the publication of the material for

[196] [PlumX]
"The percolating cluster is invisible to image recognition with deep learning"
D. Bayo, A. Honecker, R. A. Römer
New J. Phys. 25(11), 113041 (2023)
[PDF]

on the Warwick Research repository https://wrap.warwick.ac.uk/175178/

v0.1.0: Merge pull request #15 from DisQS/devel

Set of python routines to compute percolation clusters, identify them and the largest cluster, together with information whether the clusters are spanning top to bottom, left to right, etc. Further routines to compute correlation functions for each configuration, the estimate correlation lengths and to average said correlation length. The resulting .pkl, .cor, .cl and .acl files (not on github) are meant to be used as input to machine learning codes.

Deep learning routines are not yet fully implemented.

Code at this stage was used to create L100 system size data.