I really hesitated to include any printed output that wasn't a warning for an invalid setting. However, maybe even more valuable than the ultimate factorization output, with FACTOR_FINDER method, is the running list of smooth numbers successfully identified in the sieve. Based on empirical tests (or theory that isn't yet apparent to me), the script wheel_tuner.py in the project root can be used with this intermediate output, of a list of smooth number, to better tune the application of wheel factorization.
Full Changelog: v6.6.0...v6.7.0
sha1sum results:
c8fcbc1c2d0c3cf011961a2383b1d03ce2f74a8b FindAFactor-6.7.0-cp310-cp310-manylinux_2_35_x86_64.whl
f960cb97c6bdd3da066f87251986a2fbfed895d5 FindAFactor-6.7.0-cp312-cp312-manylinux_2_39_x86_64.whl
cb6d29e7ec2495f860fc0ceda65fc688ca15ce47 FindAFactor-6.7.0-cp312-cp312-win_amd64.whl
80072f46be02a1a32192d455dc35d0be01b3f876 FindAFactor-6.7.0-cp313-cp313-macosx_14_0_arm64.whl
1bbc58a9d9f2b8cce344793a8f15e4fbc967415d FindAFactor-6.7.0-cp313-cp313-macosx_15_0_arm64.whl
017942d818f58ab56331e17764ae5eb34d4683a6 FindAFactor-6.7.0-cp38-cp38-manylinux_2_31_x86_64.whl
4c4d85167e4d8ed1299135948808162c4f155035 findafactor-6.7.0.tar.gz