RAxML-NG v. 1.0.2 released on 22.02.2021 by The Exelixis Lab.
Developed by: Alexey M. Kozlov and Alexandros Stamatakis.
Contributors: Diego Darriba, Tomas Flouri, Benoit Morel, Sarah Lutteropp, Ben Bettisworth.
Latest version: https://github.com/amkozlov/raxml-ng
Questions/problems/suggestions? Please visit: https://groups.google.com/forum/#!forum/raxml

System: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 40 cores, 376 GB RAM

RAxML-NG was called at 01-Jul-2021 21:12:44 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/2_msa/O15417_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417 --seed 2 --threads 7 --tree rand{20} pars{20}

Analysis options:
  run mode: ML tree search
  start tree(s): random (20)
  random seed: 2
  tip-inner: OFF
  pattern compression: ON
  per-rate scalers: OFF
  site repeats: ON
  fast spr radius: AUTO
  spr subtree cutoff: 1.000000
  branch lengths: proportional (ML estimate, algorithm: NR-FAST)
  SIMD kernels: AVX2
  parallelization: coarse-grained (auto), PTHREADS (7 threads), thread pinning: OFF

[00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/2_msa/O15417_trimmed_msa.fasta
[00:00:00] Loaded alignment with 301 taxa and 593 sites

WARNING: Sequences tr_A0A2I3T8Q0_A0A2I3T8Q0_PANTR_9598 and tr_A0A2R9A4A3_A0A2R9A4A3_PANPA_9597 are exactly identical!
WARNING: Sequences sp_O15417_TNC18_HUMAN_9606 and tr_F7HUE7_F7HUE7_MACMU_9544 are exactly identical!
WARNING: Sequences sp_O15417_TNC18_HUMAN_9606 and tr_A0A2K5M071_A0A2K5M071_CERAT_9531 are exactly identical!
WARNING: Sequences sp_O15417_TNC18_HUMAN_9606 and tr_A0A2K6BJN1_A0A2K6BJN1_MACNE_9545 are exactly identical!
WARNING: Sequences tr_A0A1S3RCI1_A0A1S3RCI1_SALSA_8030 and tr_A0A1S3S1N4_A0A1S3S1N4_SALSA_8030 are exactly identical!
WARNING: Sequences tr_A0A060Z8E2_A0A060Z8E2_ONCMY_8022 and tr_A0A061A8E4_A0A061A8E4_ONCMY_8022 are exactly identical!
WARNING: Sequences tr_A0A2D0PM61_A0A2D0PM61_ICTPU_7998 and tr_A0A2D0PNT5_A0A2D0PNT5_ICTPU_7998 are exactly identical!
WARNING: Sequences tr_A0A2D0PM61_A0A2D0PM61_ICTPU_7998 and tr_A0A2D0PNX0_A0A2D0PNX0_ICTPU_7998 are exactly identical!
WARNING: Sequences tr_A0A2D0PM61_A0A2D0PM61_ICTPU_7998 and tr_A0A2D0PNX1_A0A2D0PNX1_ICTPU_7998 are exactly identical!
WARNING: Sequences tr_A0A2D0PM61_A0A2D0PM61_ICTPU_7998 and tr_A0A2D0PQJ5_A0A2D0PQJ5_ICTPU_7998 are exactly identical!
WARNING: Sequences tr_A0A2D0PM61_A0A2D0PM61_ICTPU_7998 and tr_A0A2D0PQL1_A0A2D0PQL1_ICTPU_7998 are exactly identical!
WARNING: Sequences tr_A0A2D0PV56_A0A2D0PV56_ICTPU_7998 and tr_W5UEY6_W5UEY6_ICTPU_7998 are exactly identical!
WARNING: Sequences tr_A0A2D0S936_A0A2D0S936_ICTPU_7998 and tr_A0A2D0SAJ5_A0A2D0SAJ5_ICTPU_7998 are exactly identical!
WARNING: Duplicate sequences found: 13

NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) 
NOTE: was saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417.raxml.reduced.phy

Alignment comprises 1 partitions and 593 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 593 / 593
Gaps: 23.82 %
Invariant sites: 0.00 %


NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417.raxml.rba

Parallelization scheme autoconfig: 1 worker(s) x 7 thread(s)

Parallel reduction/worker buffer size: 1 KB  / 0 KB

[00:00:00] Generating 20 random starting tree(s) with 301 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 85 / 6800
[00:00:00] Data distribution: max. searches per worker: 20

Starting ML tree search with 20 distinct starting trees

[00:00:00 -199862.230556] Initial branch length optimization
[00:00:00 -162787.782496] Model parameter optimization (eps = 10.000000)
[00:00:09 -162158.204096] AUTODETECT spr round 1 (radius: 5)
[00:00:19 -108972.154529] AUTODETECT spr round 2 (radius: 10)
[00:00:33 -81260.744093] AUTODETECT spr round 3 (radius: 15)
[00:00:50 -72752.629545] AUTODETECT spr round 4 (radius: 20)
[00:01:15 -72560.619191] AUTODETECT spr round 5 (radius: 25)
[00:01:40 -72472.562329] SPR radius for FAST iterations: 25 (autodetect)
[00:01:40 -72472.562329] Model parameter optimization (eps = 3.000000)
[00:01:48 -72081.386267] FAST spr round 1 (radius: 25)
[00:02:07 -66846.676200] FAST spr round 2 (radius: 25)
[00:02:21 -66705.405500] FAST spr round 3 (radius: 25)
[00:02:32 -66697.743790] FAST spr round 4 (radius: 25)
[00:02:42 -66697.646211] Model parameter optimization (eps = 1.000000)
[00:02:46 -66695.180617] SLOW spr round 1 (radius: 5)
[00:03:03 -66688.226413] SLOW spr round 2 (radius: 5)
[00:03:20 -66687.786881] SLOW spr round 3 (radius: 5)
[00:03:36 -66687.785742] SLOW spr round 4 (radius: 10)
[00:03:52 -66686.909408] SLOW spr round 5 (radius: 5)
[00:04:14 -66686.682164] SLOW spr round 6 (radius: 5)
[00:04:33 -66685.040931] SLOW spr round 7 (radius: 5)
[00:04:50 -66685.039588] SLOW spr round 8 (radius: 10)
[00:05:07 -66685.039154] SLOW spr round 9 (radius: 15)
[00:05:34 -66685.038900] SLOW spr round 10 (radius: 20)
[00:06:08 -66685.038703] SLOW spr round 11 (radius: 25)
[00:06:43 -66685.038527] Model parameter optimization (eps = 0.100000)

[00:06:45] ML tree search #1, logLikelihood: -66685.003101

[00:06:45 -201063.460698] Initial branch length optimization
[00:06:45 -162751.932258] Model parameter optimization (eps = 10.000000)
[00:06:54 -162092.206700] AUTODETECT spr round 1 (radius: 5)
[00:07:04 -112826.452826] AUTODETECT spr round 2 (radius: 10)
[00:07:17 -86440.745214] AUTODETECT spr round 3 (radius: 15)
[00:07:34 -73712.116326] AUTODETECT spr round 4 (radius: 20)
[00:07:53 -73234.728984] AUTODETECT spr round 5 (radius: 25)
[00:08:14 -73216.407042] SPR radius for FAST iterations: 25 (autodetect)
[00:08:14 -73216.407042] Model parameter optimization (eps = 3.000000)
[00:08:22 -72859.362186] FAST spr round 1 (radius: 25)
[00:08:40 -67174.010710] FAST spr round 2 (radius: 25)
[00:08:54 -66732.781297] FAST spr round 3 (radius: 25)
[00:09:06 -66719.699130] FAST spr round 4 (radius: 25)
[00:09:16 -66719.231691] FAST spr round 5 (radius: 25)
[00:09:26 -66719.195097] Model parameter optimization (eps = 1.000000)
[00:09:31 -66712.280368] SLOW spr round 1 (radius: 5)
[00:09:49 -66705.074048] SLOW spr round 2 (radius: 5)
[00:10:05 -66704.909406] SLOW spr round 3 (radius: 5)
[00:10:22 -66704.907244] SLOW spr round 4 (radius: 10)
[00:10:38 -66704.817963] SLOW spr round 5 (radius: 15)
[00:11:08 -66704.816074] SLOW spr round 6 (radius: 20)
[00:11:44 -66704.755816] SLOW spr round 7 (radius: 25)
[00:12:21 -66704.755617] Model parameter optimization (eps = 0.100000)

[00:12:23] ML tree search #2, logLikelihood: -66704.743474

[00:12:23 -197389.173507] Initial branch length optimization
[00:12:24 -161177.114112] Model parameter optimization (eps = 10.000000)
[00:12:41 -160514.004380] AUTODETECT spr round 1 (radius: 5)
[00:12:51 -112409.491766] AUTODETECT spr round 2 (radius: 10)
[00:13:05 -84046.826441] AUTODETECT spr round 3 (radius: 15)
[00:13:22 -74729.370683] AUTODETECT spr round 4 (radius: 20)
[00:13:40 -72427.663430] AUTODETECT spr round 5 (radius: 25)
[00:14:03 -72412.876589] SPR radius for FAST iterations: 25 (autodetect)
[00:14:03 -72412.876589] Model parameter optimization (eps = 3.000000)
[00:14:12 -72090.718711] FAST spr round 1 (radius: 25)
[00:14:28 -66884.847029] FAST spr round 2 (radius: 25)
[00:14:41 -66715.606145] FAST spr round 3 (radius: 25)
[00:14:54 -66704.260011] FAST spr round 4 (radius: 25)
[00:15:05 -66704.096444] FAST spr round 5 (radius: 25)
[00:15:15 -66704.094145] Model parameter optimization (eps = 1.000000)
[00:15:18 -66702.577425] SLOW spr round 1 (radius: 5)
[00:15:35 -66693.939973] SLOW spr round 2 (radius: 5)
[00:15:52 -66692.926578] SLOW spr round 3 (radius: 5)
[00:16:08 -66692.924040] SLOW spr round 4 (radius: 10)
[00:16:25 -66692.361341] SLOW spr round 5 (radius: 5)
[00:16:47 -66692.358925] SLOW spr round 6 (radius: 10)
[00:17:08 -66692.025695] SLOW spr round 7 (radius: 5)
[00:17:29 -66691.982854] SLOW spr round 8 (radius: 10)
[00:17:49 -66690.389818] SLOW spr round 9 (radius: 5)
[00:18:10 -66689.960860] SLOW spr round 10 (radius: 5)
[00:18:29 -66689.959334] SLOW spr round 11 (radius: 10)
[00:18:47 -66689.958808] SLOW spr round 12 (radius: 15)
[00:19:16 -66689.958610] SLOW spr round 13 (radius: 20)
[00:19:54 -66689.958534] SLOW spr round 14 (radius: 25)
[00:20:30 -66689.958505] Model parameter optimization (eps = 0.100000)

[00:20:33] ML tree search #3, logLikelihood: -66689.782493

[00:20:33 -195217.659148] Initial branch length optimization
[00:20:33 -160838.278219] Model parameter optimization (eps = 10.000000)
[00:20:43 -160142.440351] AUTODETECT spr round 1 (radius: 5)
[00:20:54 -108757.949487] AUTODETECT spr round 2 (radius: 10)
[00:21:07 -85982.247024] AUTODETECT spr round 3 (radius: 15)
[00:21:24 -74171.107966] AUTODETECT spr round 4 (radius: 20)
[00:21:42 -72891.609806] AUTODETECT spr round 5 (radius: 25)
[00:22:03 -72674.912429] SPR radius for FAST iterations: 25 (autodetect)
[00:22:03 -72674.912429] Model parameter optimization (eps = 3.000000)
[00:22:12 -72413.398622] FAST spr round 1 (radius: 25)
[00:22:29 -67093.685899] FAST spr round 2 (radius: 25)
[00:22:43 -66723.394697] FAST spr round 3 (radius: 25)
[00:22:55 -66716.780047] FAST spr round 4 (radius: 25)
[00:23:05 -66716.779752] Model parameter optimization (eps = 1.000000)
[00:23:09 -66712.662574] SLOW spr round 1 (radius: 5)
[00:23:26 -66701.622511] SLOW spr round 2 (radius: 5)
[00:23:44 -66700.083937] SLOW spr round 3 (radius: 5)
[00:24:00 -66700.083281] SLOW spr round 4 (radius: 10)
[00:24:16 -66700.083026] SLOW spr round 5 (radius: 15)
[00:24:46 -66700.082844] SLOW spr round 6 (radius: 20)
[00:25:25 -66700.082683] SLOW spr round 7 (radius: 25)
[00:26:00 -66700.082530] Model parameter optimization (eps = 0.100000)

[00:26:02] ML tree search #4, logLikelihood: -66700.047524

[00:26:02 -198382.093148] Initial branch length optimization
[00:26:03 -160945.382611] Model parameter optimization (eps = 10.000000)
[00:26:12 -160300.329075] AUTODETECT spr round 1 (radius: 5)
[00:26:22 -109652.996070] AUTODETECT spr round 2 (radius: 10)
[00:26:35 -85778.672593] AUTODETECT spr round 3 (radius: 15)
[00:26:52 -74572.247145] AUTODETECT spr round 4 (radius: 20)
[00:27:11 -72444.966088] AUTODETECT spr round 5 (radius: 25)
[00:27:31 -72440.781564] SPR radius for FAST iterations: 25 (autodetect)
[00:27:31 -72440.781564] Model parameter optimization (eps = 3.000000)
[00:27:39 -72056.063349] FAST spr round 1 (radius: 25)
[00:27:57 -66992.513854] FAST spr round 2 (radius: 25)
[00:28:12 -66721.611715] FAST spr round 3 (radius: 25)
[00:28:25 -66707.305193] FAST spr round 4 (radius: 25)
[00:28:35 -66707.302560] Model parameter optimization (eps = 1.000000)
[00:28:39 -66702.841396] SLOW spr round 1 (radius: 5)
[00:28:57 -66695.641315] SLOW spr round 2 (radius: 5)
[00:29:14 -66691.648463] SLOW spr round 3 (radius: 5)
[00:29:30 -66690.643225] SLOW spr round 4 (radius: 5)
[00:29:47 -66690.642522] SLOW spr round 5 (radius: 10)
[00:30:04 -66690.012751] SLOW spr round 6 (radius: 5)
[00:30:25 -66690.012245] SLOW spr round 7 (radius: 10)
[00:30:45 -66689.963057] SLOW spr round 8 (radius: 15)
[00:31:14 -66689.957311] SLOW spr round 9 (radius: 20)
[00:31:51 -66689.956029] SLOW spr round 10 (radius: 25)
[00:32:27 -66689.955536] Model parameter optimization (eps = 0.100000)

[00:32:29] ML tree search #5, logLikelihood: -66689.885489

[00:32:29 -196722.052656] Initial branch length optimization
[00:32:29 -160541.688142] Model parameter optimization (eps = 10.000000)
[00:32:38 -159933.416635] AUTODETECT spr round 1 (radius: 5)
[00:32:48 -105541.573690] AUTODETECT spr round 2 (radius: 10)
[00:33:02 -85081.736748] AUTODETECT spr round 3 (radius: 15)
[00:33:17 -72323.580219] AUTODETECT spr round 4 (radius: 20)
[00:33:35 -71841.014756] AUTODETECT spr round 5 (radius: 25)
[00:33:56 -71838.013806] SPR radius for FAST iterations: 25 (autodetect)
[00:33:56 -71838.013806] Model parameter optimization (eps = 3.000000)
[00:34:04 -71499.654488] FAST spr round 1 (radius: 25)
[00:34:22 -66793.231421] FAST spr round 2 (radius: 25)
[00:34:36 -66717.722849] FAST spr round 3 (radius: 25)
[00:34:48 -66711.305426] FAST spr round 4 (radius: 25)
[00:34:58 -66709.940345] FAST spr round 5 (radius: 25)
[00:35:08 -66709.939371] Model parameter optimization (eps = 1.000000)
[00:35:11 -66707.008255] SLOW spr round 1 (radius: 5)
[00:35:28 -66694.175712] SLOW spr round 2 (radius: 5)
[00:35:44 -66694.155530] SLOW spr round 3 (radius: 10)
[00:36:00 -66692.351073] SLOW spr round 4 (radius: 5)
[00:36:23 -66690.816936] SLOW spr round 5 (radius: 5)
[00:36:41 -66690.816815] SLOW spr round 6 (radius: 10)
[00:36:59 -66689.446200] SLOW spr round 7 (radius: 5)
[00:37:21 -66689.425207] SLOW spr round 8 (radius: 10)
[00:37:40 -66689.270713] SLOW spr round 9 (radius: 5)
[00:38:02 -66688.066292] SLOW spr round 10 (radius: 5)
[00:38:20 -66688.064401] SLOW spr round 11 (radius: 10)
[00:38:37 -66688.063830] SLOW spr round 12 (radius: 15)
[00:39:05 -66688.063655] SLOW spr round 13 (radius: 20)
[00:39:42 -66688.063601] SLOW spr round 14 (radius: 25)
[00:40:17 -66688.063584] Model parameter optimization (eps = 0.100000)

[00:40:20] ML tree search #6, logLikelihood: -66687.879068

[00:40:20 -197946.488390] Initial branch length optimization
[00:40:20 -161676.040520] Model parameter optimization (eps = 10.000000)
[00:40:30 -161027.782326] AUTODETECT spr round 1 (radius: 5)
[00:40:40 -110715.886040] AUTODETECT spr round 2 (radius: 10)
[00:40:53 -82757.207357] AUTODETECT spr round 3 (radius: 15)
[00:41:09 -71443.469853] AUTODETECT spr round 4 (radius: 20)
[00:41:28 -70507.071990] AUTODETECT spr round 5 (radius: 25)
[00:41:50 -70502.752809] SPR radius for FAST iterations: 25 (autodetect)
[00:41:50 -70502.752809] Model parameter optimization (eps = 3.000000)
[00:41:59 -70177.060197] FAST spr round 1 (radius: 25)
[00:42:16 -66789.300777] FAST spr round 2 (radius: 25)
[00:42:30 -66716.734158] FAST spr round 3 (radius: 25)
[00:42:41 -66703.124279] FAST spr round 4 (radius: 25)
[00:42:51 -66703.123559] Model parameter optimization (eps = 1.000000)
[00:42:55 -66701.155418] SLOW spr round 1 (radius: 5)
[00:43:12 -66694.369911] SLOW spr round 2 (radius: 5)
[00:43:29 -66694.368920] SLOW spr round 3 (radius: 10)
[00:43:45 -66694.368617] SLOW spr round 4 (radius: 15)
[00:44:13 -66694.368417] SLOW spr round 5 (radius: 20)
[00:44:48 -66694.368242] SLOW spr round 6 (radius: 25)
[00:45:24 -66694.368073] Model parameter optimization (eps = 0.100000)

[00:45:26] ML tree search #7, logLikelihood: -66694.360534

[00:45:26 -197123.871425] Initial branch length optimization
[00:45:26 -159482.447482] Model parameter optimization (eps = 10.000000)
[00:45:34 -158829.310933] AUTODETECT spr round 1 (radius: 5)
[00:45:44 -106647.419871] AUTODETECT spr round 2 (radius: 10)
[00:45:57 -83962.997002] AUTODETECT spr round 3 (radius: 15)
[00:46:12 -73339.882584] AUTODETECT spr round 4 (radius: 20)
[00:46:30 -72972.707181] AUTODETECT spr round 5 (radius: 25)
[00:46:46 -72931.818975] SPR radius for FAST iterations: 25 (autodetect)
[00:46:46 -72931.818975] Model parameter optimization (eps = 3.000000)
[00:46:55 -72608.605549] FAST spr round 1 (radius: 25)
[00:47:14 -66976.694303] FAST spr round 2 (radius: 25)
[00:47:28 -66712.128680] FAST spr round 3 (radius: 25)
[00:47:39 -66705.443033] FAST spr round 4 (radius: 25)
[00:47:49 -66704.811018] FAST spr round 5 (radius: 25)
[00:47:59 -66704.809622] Model parameter optimization (eps = 1.000000)
[00:48:02 -66701.771184] SLOW spr round 1 (radius: 5)
[00:48:19 -66689.227240] SLOW spr round 2 (radius: 5)
[00:48:36 -66688.965170] SLOW spr round 3 (radius: 5)
[00:48:53 -66688.573533] SLOW spr round 4 (radius: 5)
[00:49:09 -66688.573105] SLOW spr round 5 (radius: 10)
[00:49:25 -66687.057534] SLOW spr round 6 (radius: 5)
[00:49:46 -66687.057437] SLOW spr round 7 (radius: 10)
[00:50:06 -66687.057340] SLOW spr round 8 (radius: 15)
[00:50:33 -66685.958896] SLOW spr round 9 (radius: 5)
[00:50:55 -66685.957984] SLOW spr round 10 (radius: 10)
[00:51:16 -66685.957881] SLOW spr round 11 (radius: 15)
[00:51:42 -66685.957851] SLOW spr round 12 (radius: 20)
[00:52:15 -66685.957839] SLOW spr round 13 (radius: 25)
[00:52:48 -66685.957834] Model parameter optimization (eps = 0.100000)

[00:52:50] ML tree search #8, logLikelihood: -66685.947672

[00:52:50 -197447.285138] Initial branch length optimization
[00:52:50 -163549.103958] Model parameter optimization (eps = 10.000000)
[00:53:02 -162801.851353] AUTODETECT spr round 1 (radius: 5)
[00:53:12 -110106.208327] AUTODETECT spr round 2 (radius: 10)
[00:53:25 -88753.903000] AUTODETECT spr round 3 (radius: 15)
[00:53:41 -74047.212441] AUTODETECT spr round 4 (radius: 20)
[00:54:00 -71516.627227] AUTODETECT spr round 5 (radius: 25)
[00:54:21 -71513.214849] SPR radius for FAST iterations: 25 (autodetect)
[00:54:21 -71513.214849] Model parameter optimization (eps = 3.000000)
[00:54:29 -71211.392279] FAST spr round 1 (radius: 25)
[00:54:47 -66980.476208] FAST spr round 2 (radius: 25)
[00:55:01 -66719.091025] FAST spr round 3 (radius: 25)
[00:55:11 -66697.517655] FAST spr round 4 (radius: 25)
[00:55:21 -66697.516771] Model parameter optimization (eps = 1.000000)
[00:55:25 -66695.287395] SLOW spr round 1 (radius: 5)
[00:55:42 -66689.340630] SLOW spr round 2 (radius: 5)
[00:55:58 -66689.338443] SLOW spr round 3 (radius: 10)
[00:56:15 -66688.616861] SLOW spr round 4 (radius: 5)
[00:56:36 -66688.612029] SLOW spr round 5 (radius: 10)
[00:56:54 -66688.610677] SLOW spr round 6 (radius: 15)
[00:57:17 -66688.610084] SLOW spr round 7 (radius: 20)
[00:57:48 -66688.609740] SLOW spr round 8 (radius: 25)
[00:58:19 -66688.609504] Model parameter optimization (eps = 0.100000)

[00:58:21] ML tree search #9, logLikelihood: -66688.598498

[00:58:21 -197784.896234] Initial branch length optimization
[00:58:21 -162312.042403] Model parameter optimization (eps = 10.000000)
[00:58:28 -161612.471315] AUTODETECT spr round 1 (radius: 5)
[00:58:37 -112129.517396] AUTODETECT spr round 2 (radius: 10)
[00:58:49 -82955.086021] AUTODETECT spr round 3 (radius: 15)
[00:59:04 -73909.303429] AUTODETECT spr round 4 (radius: 20)
[00:59:21 -72172.347217] AUTODETECT spr round 5 (radius: 25)
[00:59:40 -72164.721176] SPR radius for FAST iterations: 25 (autodetect)
[00:59:40 -72164.721176] Model parameter optimization (eps = 3.000000)
[00:59:48 -71815.228283] FAST spr round 1 (radius: 25)
[01:00:03 -67002.029108] FAST spr round 2 (radius: 25)
[01:00:16 -66712.969996] FAST spr round 3 (radius: 25)
[01:00:25 -66700.132543] FAST spr round 4 (radius: 25)
[01:00:34 -66700.128078] Model parameter optimization (eps = 1.000000)
[01:00:37 -66697.411900] SLOW spr round 1 (radius: 5)
[01:00:52 -66688.565727] SLOW spr round 2 (radius: 5)
[01:01:06 -66688.554694] SLOW spr round 3 (radius: 10)
[01:01:20 -66688.315054] SLOW spr round 4 (radius: 5)
[01:01:39 -66688.293436] SLOW spr round 5 (radius: 10)
[01:01:56 -66688.291115] SLOW spr round 6 (radius: 15)
[01:02:19 -66688.289589] SLOW spr round 7 (radius: 20)
[01:02:49 -66688.288523] SLOW spr round 8 (radius: 25)
[01:03:20 -66688.287752] Model parameter optimization (eps = 0.100000)

[01:03:21] ML tree search #10, logLikelihood: -66688.247841

[01:03:21 -197507.855409] Initial branch length optimization
[01:03:22 -161482.348144] Model parameter optimization (eps = 10.000000)
[01:03:29 -160776.169112] AUTODETECT spr round 1 (radius: 5)
[01:03:38 -109986.185222] AUTODETECT spr round 2 (radius: 10)
[01:03:49 -88694.023148] AUTODETECT spr round 3 (radius: 15)
[01:04:03 -74515.459126] AUTODETECT spr round 4 (radius: 20)
[01:04:21 -73017.177688] AUTODETECT spr round 5 (radius: 25)
[01:04:40 -73014.836058] SPR radius for FAST iterations: 25 (autodetect)
[01:04:40 -73014.836058] Model parameter optimization (eps = 3.000000)
[01:04:47 -72691.981741] FAST spr round 1 (radius: 25)
[01:05:01 -67001.553707] FAST spr round 2 (radius: 25)
[01:05:13 -66715.731580] FAST spr round 3 (radius: 25)
[01:05:23 -66709.106660] FAST spr round 4 (radius: 25)
[01:05:31 -66705.695421] FAST spr round 5 (radius: 25)
[01:05:40 -66705.694111] Model parameter optimization (eps = 1.000000)
[01:05:44 -66697.094032] SLOW spr round 1 (radius: 5)
[01:05:58 -66689.116608] SLOW spr round 2 (radius: 5)
[01:06:12 -66687.634184] SLOW spr round 3 (radius: 5)
[01:06:26 -66687.632904] SLOW spr round 4 (radius: 10)
[01:06:40 -66687.632622] SLOW spr round 5 (radius: 15)
[01:07:04 -66687.632554] SLOW spr round 6 (radius: 20)
[01:07:36 -66687.632537] SLOW spr round 7 (radius: 25)
[01:08:07 -66687.632533] Model parameter optimization (eps = 0.100000)

[01:08:08] ML tree search #11, logLikelihood: -66687.628087

[01:08:08 -196872.081234] Initial branch length optimization
[01:08:08 -160689.739039] Model parameter optimization (eps = 10.000000)
[01:08:17 -159987.652319] AUTODETECT spr round 1 (radius: 5)
[01:08:26 -110024.870276] AUTODETECT spr round 2 (radius: 10)
[01:08:38 -86999.360462] AUTODETECT spr round 3 (radius: 15)
[01:08:51 -72946.364557] AUTODETECT spr round 4 (radius: 20)
[01:09:05 -72637.078726] AUTODETECT spr round 5 (radius: 25)
[01:09:21 -72360.193113] SPR radius for FAST iterations: 25 (autodetect)
[01:09:21 -72360.193113] Model parameter optimization (eps = 3.000000)
[01:09:30 -72001.710359] FAST spr round 1 (radius: 25)
[01:09:45 -66971.982554] FAST spr round 2 (radius: 25)
[01:09:56 -66741.337886] FAST spr round 3 (radius: 25)
[01:10:06 -66711.509367] FAST spr round 4 (radius: 25)
[01:10:15 -66711.505467] Model parameter optimization (eps = 1.000000)
[01:10:18 -66709.397919] SLOW spr round 1 (radius: 5)
[01:10:33 -66698.362723] SLOW spr round 2 (radius: 5)
[01:10:47 -66697.510459] SLOW spr round 3 (radius: 5)
[01:11:01 -66697.510332] SLOW spr round 4 (radius: 10)
[01:11:15 -66697.510324] SLOW spr round 5 (radius: 15)
[01:11:39 -66697.510321] SLOW spr round 6 (radius: 20)
[01:12:11 -66696.171144] SLOW spr round 7 (radius: 5)
[01:12:31 -66693.641968] SLOW spr round 8 (radius: 5)
[01:12:48 -66693.637170] SLOW spr round 9 (radius: 10)
[01:13:04 -66693.636604] SLOW spr round 10 (radius: 15)
[01:13:28 -66693.636527] SLOW spr round 11 (radius: 20)
[01:14:00 -66693.636513] SLOW spr round 12 (radius: 25)
[01:14:32 -66693.636508] Model parameter optimization (eps = 0.100000)

[01:14:35] ML tree search #12, logLikelihood: -66693.385521

[01:14:35 -198263.608549] Initial branch length optimization
[01:14:35 -160596.180550] Model parameter optimization (eps = 10.000000)
[01:14:44 -159920.103306] AUTODETECT spr round 1 (radius: 5)
[01:14:53 -107873.175694] AUTODETECT spr round 2 (radius: 10)
[01:15:04 -81637.712974] AUTODETECT spr round 3 (radius: 15)
[01:15:19 -73989.332245] AUTODETECT spr round 4 (radius: 20)
[01:15:38 -71903.228161] AUTODETECT spr round 5 (radius: 25)
[01:15:58 -71894.185918] SPR radius for FAST iterations: 25 (autodetect)
[01:15:58 -71894.185918] Model parameter optimization (eps = 3.000000)
[01:16:06 -71532.923222] FAST spr round 1 (radius: 25)
[01:16:21 -66947.321773] FAST spr round 2 (radius: 25)
[01:16:34 -66715.187215] FAST spr round 3 (radius: 25)
[01:16:43 -66711.413415] FAST spr round 4 (radius: 25)
[01:16:52 -66711.155009] FAST spr round 5 (radius: 25)
[01:17:01 -66711.154039] Model parameter optimization (eps = 1.000000)
[01:17:03 -66709.760244] SLOW spr round 1 (radius: 5)
[01:17:18 -66693.693699] SLOW spr round 2 (radius: 5)
[01:17:33 -66690.516668] SLOW spr round 3 (radius: 5)
[01:17:47 -66690.437739] SLOW spr round 4 (radius: 10)
[01:18:01 -66689.658787] SLOW spr round 5 (radius: 5)
[01:18:20 -66689.628383] SLOW spr round 6 (radius: 10)
[01:18:36 -66689.374388] SLOW spr round 7 (radius: 5)
[01:18:55 -66687.338385] SLOW spr round 8 (radius: 5)
[01:19:11 -66687.335417] SLOW spr round 9 (radius: 10)
[01:19:26 -66687.334622] SLOW spr round 10 (radius: 15)
[01:19:50 -66687.334274] SLOW spr round 11 (radius: 20)
[01:20:21 -66687.334046] SLOW spr round 12 (radius: 25)
[01:20:52 -66687.333858] Model parameter optimization (eps = 0.100000)

[01:20:54] ML tree search #13, logLikelihood: -66686.980080

[01:20:55 -196820.391166] Initial branch length optimization
[01:20:55 -160868.554894] Model parameter optimization (eps = 10.000000)
[01:21:05 -160140.882827] AUTODETECT spr round 1 (radius: 5)
[01:21:14 -108253.882584] AUTODETECT spr round 2 (radius: 10)
[01:21:25 -85337.047331] AUTODETECT spr round 3 (radius: 15)
[01:21:39 -74107.604438] AUTODETECT spr round 4 (radius: 20)
[01:21:56 -72306.732988] AUTODETECT spr round 5 (radius: 25)
[01:22:15 -72293.491118] SPR radius for FAST iterations: 25 (autodetect)
[01:22:15 -72293.491118] Model parameter optimization (eps = 3.000000)
[01:22:22 -71994.829744] FAST spr round 1 (radius: 25)
[01:22:38 -67108.893313] FAST spr round 2 (radius: 25)
[01:22:50 -66729.301733] FAST spr round 3 (radius: 25)
[01:23:00 -66701.975145] FAST spr round 4 (radius: 25)
[01:23:08 -66701.694720] FAST spr round 5 (radius: 25)
[01:23:17 -66701.694083] Model parameter optimization (eps = 1.000000)
[01:23:20 -66696.710346] SLOW spr round 1 (radius: 5)
[01:23:34 -66689.052772] SLOW spr round 2 (radius: 5)
[01:23:49 -66687.019055] SLOW spr round 3 (radius: 5)
[01:24:03 -66687.015451] SLOW spr round 4 (radius: 10)
[01:24:17 -66686.888003] SLOW spr round 5 (radius: 5)
[01:24:35 -66686.853815] SLOW spr round 6 (radius: 10)
[01:24:53 -66686.472441] SLOW spr round 7 (radius: 5)
[01:25:11 -66686.460539] SLOW spr round 8 (radius: 10)
[01:25:27 -66686.457440] SLOW spr round 9 (radius: 15)
[01:25:51 -66686.456395] SLOW spr round 10 (radius: 20)
[01:26:22 -66686.456022] SLOW spr round 11 (radius: 25)
[01:26:53 -66686.455905] Model parameter optimization (eps = 0.100000)

[01:26:55] ML tree search #14, logLikelihood: -66686.440687

[01:26:55 -199390.596065] Initial branch length optimization
[01:26:56 -163560.001976] Model parameter optimization (eps = 10.000000)
[01:27:03 -162873.145944] AUTODETECT spr round 1 (radius: 5)
[01:27:12 -109822.603489] AUTODETECT spr round 2 (radius: 10)
[01:27:23 -88203.554224] AUTODETECT spr round 3 (radius: 15)
[01:27:38 -72579.484375] AUTODETECT spr round 4 (radius: 20)
[01:27:54 -72381.383880] AUTODETECT spr round 5 (radius: 25)
[01:28:15 -72378.516744] SPR radius for FAST iterations: 25 (autodetect)
[01:28:15 -72378.516744] Model parameter optimization (eps = 3.000000)
[01:28:23 -72079.695140] FAST spr round 1 (radius: 25)
[01:28:37 -66836.591979] FAST spr round 2 (radius: 25)
[01:28:48 -66716.927845] FAST spr round 3 (radius: 25)
[01:28:58 -66697.698573] FAST spr round 4 (radius: 25)
[01:29:06 -66697.697106] Model parameter optimization (eps = 1.000000)
[01:29:09 -66695.075925] SLOW spr round 1 (radius: 5)
[01:29:24 -66690.665473] SLOW spr round 2 (radius: 5)
[01:29:38 -66690.635615] SLOW spr round 3 (radius: 10)
[01:29:52 -66689.379876] SLOW spr round 4 (radius: 5)
[01:30:11 -66688.287614] SLOW spr round 5 (radius: 5)
[01:30:27 -66688.287291] SLOW spr round 6 (radius: 10)
[01:30:42 -66688.287129] SLOW spr round 7 (radius: 15)
[01:31:07 -66688.286978] SLOW spr round 8 (radius: 20)
[01:31:38 -66688.286942] SLOW spr round 9 (radius: 25)
[01:32:08 -66688.286939] Model parameter optimization (eps = 0.100000)

[01:32:10] ML tree search #15, logLikelihood: -66688.002877

[01:32:10 -197298.153294] Initial branch length optimization
[01:32:11 -162143.519637] Model parameter optimization (eps = 10.000000)
[01:32:17 -161489.748799] AUTODETECT spr round 1 (radius: 5)
[01:32:26 -107180.580500] AUTODETECT spr round 2 (radius: 10)
[01:32:37 -81018.557898] AUTODETECT spr round 3 (radius: 15)
[01:32:52 -72458.486623] AUTODETECT spr round 4 (radius: 20)
[01:33:13 -72376.262760] AUTODETECT spr round 5 (radius: 25)
[01:33:34 -72376.215218] SPR radius for FAST iterations: 20 (autodetect)
[01:33:34 -72376.215218] Model parameter optimization (eps = 3.000000)
[01:33:41 -72072.584995] FAST spr round 1 (radius: 20)
[01:33:55 -66899.537063] FAST spr round 2 (radius: 20)
[01:34:06 -66715.604738] FAST spr round 3 (radius: 20)
[01:34:16 -66713.200263] FAST spr round 4 (radius: 20)
[01:34:24 -66713.197081] Model parameter optimization (eps = 1.000000)
[01:34:28 -66707.262254] SLOW spr round 1 (radius: 5)
[01:34:43 -66697.794869] SLOW spr round 2 (radius: 5)
[01:34:57 -66697.775514] SLOW spr round 3 (radius: 10)
[01:35:11 -66695.572967] SLOW spr round 4 (radius: 5)
[01:35:31 -66695.191231] SLOW spr round 5 (radius: 5)
[01:35:50 -66695.190801] SLOW spr round 6 (radius: 10)
[01:36:09 -66695.190779] SLOW spr round 7 (radius: 15)
[01:36:40 -66695.190776] SLOW spr round 8 (radius: 20)
[01:37:17 -66695.190775] SLOW spr round 9 (radius: 25)
[01:37:54 -66695.190775] Model parameter optimization (eps = 0.100000)

[01:37:56] ML tree search #16, logLikelihood: -66695.176560

[01:37:56 -195760.173592] Initial branch length optimization
[01:37:57 -160413.940166] Model parameter optimization (eps = 10.000000)
[01:38:07 -159717.907555] AUTODETECT spr round 1 (radius: 5)
[01:38:18 -106807.571748] AUTODETECT spr round 2 (radius: 10)
[01:38:32 -82151.876107] AUTODETECT spr round 3 (radius: 15)
[01:38:50 -74466.311295] AUTODETECT spr round 4 (radius: 20)
[01:39:11 -74047.227144] AUTODETECT spr round 5 (radius: 25)
[01:39:31 -74044.401151] SPR radius for FAST iterations: 25 (autodetect)
[01:39:31 -74044.401151] Model parameter optimization (eps = 3.000000)
[01:39:40 -73707.765794] FAST spr round 1 (radius: 25)
[01:39:57 -66972.275179] FAST spr round 2 (radius: 25)
[01:40:12 -66725.943000] FAST spr round 3 (radius: 25)
[01:40:24 -66713.153002] FAST spr round 4 (radius: 25)
[01:40:34 -66713.129675] Model parameter optimization (eps = 1.000000)
[01:40:39 -66710.690015] SLOW spr round 1 (radius: 5)
[01:40:56 -66697.797944] SLOW spr round 2 (radius: 5)
[01:41:13 -66697.322878] SLOW spr round 3 (radius: 5)
[01:41:30 -66697.317194] SLOW spr round 4 (radius: 10)
[01:41:47 -66697.044341] SLOW spr round 5 (radius: 5)
[01:42:09 -66697.042684] SLOW spr round 6 (radius: 10)
[01:42:29 -66697.042117] SLOW spr round 7 (radius: 15)
[01:42:57 -66697.041908] SLOW spr round 8 (radius: 20)
[01:43:34 -66697.041837] SLOW spr round 9 (radius: 25)
[01:44:12 -66697.041814] Model parameter optimization (eps = 0.100000)

[01:44:14] ML tree search #17, logLikelihood: -66697.016966

[01:44:14 -198209.541521] Initial branch length optimization
[01:44:15 -162559.222234] Model parameter optimization (eps = 10.000000)
[01:44:24 -161910.466767] AUTODETECT spr round 1 (radius: 5)
[01:44:35 -109505.229948] AUTODETECT spr round 2 (radius: 10)
[01:44:50 -84951.377535] AUTODETECT spr round 3 (radius: 15)
[01:45:06 -77054.854111] AUTODETECT spr round 4 (radius: 20)
[01:45:27 -73622.805433] AUTODETECT spr round 5 (radius: 25)
[01:45:46 -73476.367005] SPR radius for FAST iterations: 25 (autodetect)
[01:45:46 -73476.367005] Model parameter optimization (eps = 3.000000)
[01:45:55 -73182.669545] FAST spr round 1 (radius: 25)
[01:46:12 -67148.441720] FAST spr round 2 (radius: 25)
[01:46:27 -66716.773765] FAST spr round 3 (radius: 25)
[01:46:38 -66714.358166] FAST spr round 4 (radius: 25)
[01:46:49 -66714.357313] Model parameter optimization (eps = 1.000000)
[01:46:56 -66699.307961] SLOW spr round 1 (radius: 5)
[01:47:14 -66694.592872] SLOW spr round 2 (radius: 5)
[01:47:31 -66694.561456] SLOW spr round 3 (radius: 10)
[01:47:48 -66692.926632] SLOW spr round 4 (radius: 5)
[01:48:12 -66691.198222] SLOW spr round 5 (radius: 5)
[01:48:31 -66691.166701] SLOW spr round 6 (radius: 10)
[01:48:49 -66690.458656] SLOW spr round 7 (radius: 5)
[01:49:12 -66690.282137] SLOW spr round 8 (radius: 5)
[01:49:30 -66690.281503] SLOW spr round 9 (radius: 10)
[01:49:49 -66690.281470] SLOW spr round 10 (radius: 15)
[01:50:19 -66690.281465] SLOW spr round 11 (radius: 20)
[01:50:56 -66690.281463] SLOW spr round 12 (radius: 25)
[01:51:33 -66690.281462] Model parameter optimization (eps = 0.100000)

[01:51:36] ML tree search #18, logLikelihood: -66690.164679

[01:51:36 -201181.902787] Initial branch length optimization
[01:51:37 -163844.621291] Model parameter optimization (eps = 10.000000)
[01:51:44 -163149.911041] AUTODETECT spr round 1 (radius: 5)
[01:51:55 -110725.403806] AUTODETECT spr round 2 (radius: 10)
[01:52:08 -89370.248726] AUTODETECT spr round 3 (radius: 15)
[01:52:25 -74670.269737] AUTODETECT spr round 4 (radius: 20)
[01:52:43 -74362.355583] AUTODETECT spr round 5 (radius: 25)
[01:53:04 -74143.038249] SPR radius for FAST iterations: 25 (autodetect)
[01:53:04 -74143.038249] Model parameter optimization (eps = 3.000000)
[01:53:12 -73778.909276] FAST spr round 1 (radius: 25)
[01:53:31 -67285.349352] FAST spr round 2 (radius: 25)
[01:53:47 -66723.465230] FAST spr round 3 (radius: 25)
[01:53:59 -66705.717852] FAST spr round 4 (radius: 25)
[01:54:09 -66705.712999] Model parameter optimization (eps = 1.000000)
[01:54:13 -66702.171028] SLOW spr round 1 (radius: 5)
[01:54:31 -66695.090536] SLOW spr round 2 (radius: 5)
[01:54:48 -66691.779447] SLOW spr round 3 (radius: 5)
[01:55:05 -66691.739217] SLOW spr round 4 (radius: 10)
[01:55:23 -66691.698062] SLOW spr round 5 (radius: 15)
[01:55:53 -66691.693804] SLOW spr round 6 (radius: 20)
[01:56:32 -66691.693197] SLOW spr round 7 (radius: 25)
[01:57:10 -66691.693091] Model parameter optimization (eps = 0.100000)

[01:57:12] ML tree search #19, logLikelihood: -66691.641746

[01:57:12 -198716.330398] Initial branch length optimization
[01:57:13 -159846.263518] Model parameter optimization (eps = 10.000000)
[01:57:22 -159220.611829] AUTODETECT spr round 1 (radius: 5)
[01:57:33 -109206.340847] AUTODETECT spr round 2 (radius: 10)
[01:57:46 -84943.157293] AUTODETECT spr round 3 (radius: 15)
[01:58:04 -72659.083199] AUTODETECT spr round 4 (radius: 20)
[01:58:21 -72233.390852] AUTODETECT spr round 5 (radius: 25)
[01:58:40 -72217.344505] SPR radius for FAST iterations: 25 (autodetect)
[01:58:40 -72217.344505] Model parameter optimization (eps = 3.000000)
[01:58:57 -71859.760394] FAST spr round 1 (radius: 25)
[01:59:14 -66970.319677] FAST spr round 2 (radius: 25)
[01:59:30 -66717.529887] FAST spr round 3 (radius: 25)
[01:59:42 -66707.924826] FAST spr round 4 (radius: 25)
[01:59:53 -66707.573315] FAST spr round 5 (radius: 25)
[02:00:03 -66707.571941] Model parameter optimization (eps = 1.000000)
[02:00:08 -66704.741110] SLOW spr round 1 (radius: 5)
[02:00:26 -66699.146894] SLOW spr round 2 (radius: 5)
[02:00:43 -66698.723154] SLOW spr round 3 (radius: 5)
[02:00:59 -66698.722323] SLOW spr round 4 (radius: 10)
[02:01:16 -66698.681559] SLOW spr round 5 (radius: 15)
[02:01:47 -66698.678822] SLOW spr round 6 (radius: 20)
[02:02:26 -66698.678332] SLOW spr round 7 (radius: 25)
[02:03:03 -66698.678114] Model parameter optimization (eps = 0.100000)

[02:03:04] ML tree search #20, logLikelihood: -66698.642751


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.169013,0.532594) (0.109144,1.347456) (0.374238,0.705823) (0.347605,1.434881) 
   Base frequencies (model): 
M0: 0.147383 0.017579 0.058208 0.017707 0.026331 0.041582 0.017494 0.027859 0.011849 0.076971 0.147823 0.019535 0.037132 0.029940 0.008059 0.088179 0.089653 0.006477 0.032308 0.097931 
M1: 0.063139 0.066357 0.011586 0.066571 0.010800 0.009276 0.053984 0.146986 0.034214 0.088822 0.098196 0.032390 0.021263 0.072697 0.016761 0.020711 0.020797 0.025463 0.045615 0.094372 
M2: 0.062457 0.066826 0.049332 0.065270 0.006513 0.041231 0.058965 0.080852 0.028024 0.037024 0.075925 0.064131 0.019620 0.028710 0.104579 0.056388 0.062027 0.008241 0.033124 0.050760 
M3: 0.106471 0.074171 0.044513 0.096390 0.002148 0.066733 0.158908 0.037625 0.020691 0.014608 0.028797 0.105352 0.007864 0.007477 0.083595 0.055726 0.047711 0.003975 0.010088 0.027159 
   Substitution rates (model): 
M 0: 0.295719 0.067388 0.253712 1.029289 0.107964 0.514644 10.868848 0.380498 0.084223 0.086976 0.188789 0.286389 0.155567 1.671061 2.132922 0.529591 0.115551 0.102453 0.916683 0.448317 0.457483 0.576016 1.741924 0.736017 0.704334 5.658311 0.123387 0.221777 93.433377 0.382175 0.235965 6.535048 0.525521 0.303537 0.641259 0.289466 0.102065 2.358429 0.251987 0.216561 0.503084 0.435271 4.873453 0.090748 0.033310 0.746537 0.128905 0.127321 0.904011 0.939733 0.435450 0.046646 0.262076 0.043986 0.189008 0.599450 109.901504 1.070052 5.229858 0.052764 0.021407 0.621146 0.081091 0.205164 5.164456 0.747330 0.308078 0.260889 0.185083 0.080708 0.029955 0.084794 1.862626 0.553477 0.151733 0.230320 0.096955 0.352526 0.590018 0.386853 1.559564 0.606648 0.587531 0.592318 0.885230 4.117654 0.246260 6.508329 0.054187 0.195703 1.669092 0.810168 0.066081 2.437439 0.165666 0.106333 0.093417 0.035149 0.072549 1.202023 1.634845 0.060194 0.069359 2.448827 0.232297 0.064822 3.537387 0.435384 0.290413 0.280695 0.105999 0.206603 0.404968 0.048984 0.069963 0.256662 0.228519 0.241077 4.320442 3.656545 0.290216 0.307466 0.096556 0.306067 0.204296 0.504221 1.991533 0.655465 6.799829 11.291065 0.961142 0.448965 6.227274 20.304886 0.205944 1.495537 0.091940 1.994320 0.754940 0.170343 0.050315 0.372166 0.206332 0.097050 5.381403 0.122332 3.256485 2.261319 0.848067 0.064441 0.102493 0.459041 0.133091 0.561215 0.457430 0.163849 5.260446 0.360946 0.389413 0.033291 0.115301 0.112593 1.559944 0.426508 0.132547 0.498634 0.559069 0.264728 0.693307 0.438856 0.306683 0.109129 18.392863 66.647302 0.400021 4.586081 2.099355 0.411347 0.476350 0.584622 3.634276 0.101797 0.148995 0.089177 0.034710 0.063603 0.755865 20.561407 0.133790 0.154902 
M 1: 0.066142 0.590377 0.069930 9.850951 1.101363 0.150375 0.568586 0.051668 0.127170 0.292429 0.071458 1.218562 0.075144 7.169085 30.139501 13.461692 0.021372 0.045779 4.270235 0.468325 0.013688 0.302287 1.353957 0.028386 0.037750 0.262130 0.016923 0.064289 0.855973 0.079621 0.011169 0.161937 0.276530 0.161053 0.081472 0.036742 0.030342 2.851667 3.932151 8.159169 0.219934 0.421974 2.468752 0.344765 0.210724 1.172204 0.763553 0.082464 0.726566 11.149790 4.782635 0.058046 0.498072 0.258487 0.146882 0.249672 0.560142 0.046719 0.106259 0.003656 0.004200 0.014189 0.009876 0.002656 0.040244 0.267322 0.053740 0.006597 0.027639 0.012745 0.582670 0.005035 0.275844 0.098208 0.445038 1.217010 0.033969 1.988516 0.681161 0.825960 18.762977 11.949233 0.286794 0.534219 4.336817 3.054085 0.129551 4.210126 0.165753 1.088704 1.889645 3.344809 0.111063 2.067758 3.547017 2.466507 0.188236 0.203493 0.281953 0.037250 0.029788 0.008541 0.014768 0.125869 0.056702 0.004186 0.110993 0.201148 0.139705 0.009201 0.012095 0.043812 0.013513 0.002533 0.005848 0.031390 0.021612 0.004854 0.129497 0.976631 0.053397 0.019475 0.004964 0.015539 0.031779 0.064558 0.065585 0.079927 0.095591 0.196886 0.408834 0.126088 0.037226 0.452302 0.016212 7.278994 0.029917 7.918203 0.450964 0.169797 0.104288 1.578530 0.015909 0.094365 16.179952 0.042762 14.799537 1.506485 0.637893 0.123793 0.641351 0.154810 0.140750 3.416059 0.259400 0.009457 0.090576 0.292108 0.297913 0.017172 0.021976 0.032578 1.375871 0.457399 0.598048 4.418398 0.239749 0.168432 2.950318 0.143327 0.328689 0.125011 0.562720 1.414883 0.227807 3.478333 2.984862 0.061299 0.077470 1.050562 13.974326 0.154326 0.224675 0.112000 0.060703 0.123480 5.294490 0.447011 0.033381 0.045528 
M 2: 0.733336 0.558955 0.503360 4.149599 1.415369 1.367574 1.263002 0.994098 0.517204 0.775054 0.763094 1.890137 0.540460 0.200122 4.972745 1.825593 0.450842 0.526135 3.839269 0.597671 0.058964 2.863355 2.872594 0.258365 0.366868 2.578946 0.358350 0.672023 5.349861 0.691594 0.063347 0.032875 0.821562 0.580847 0.661866 0.265730 0.395134 5.581680 1.279881 1.335650 0.397108 1.840061 5.739035 0.284730 0.109781 1.612642 0.466979 0.141582 0.019509 4.670980 1.967383 0.088064 0.581928 0.145401 0.225860 0.434096 2.292917 1.024707 0.821921 0.027824 0.021443 0.088850 0.060820 0.018288 0.042687 1.199607 0.420710 0.037642 0.141233 0.090101 1.043232 0.209978 0.823594 3.039380 1.463390 1.983693 0.397640 2.831098 4.102068 0.059723 5.901348 2.034980 2.600668 5.413080 4.193725 4.534772 0.377181 4.877840 0.370939 1.298542 3.509873 2.646440 0.087872 0.072299 1.139018 0.864479 0.390688 0.322761 0.625409 0.496780 0.532488 0.232460 0.169219 0.755219 0.379926 0.020447 0.023282 0.503875 0.577513 0.109318 0.153776 0.696533 0.398817 0.008940 0.043707 0.436013 0.087640 0.064863 0.036426 1.673207 0.124068 0.218118 0.039217 0.104335 0.349195 0.838324 0.888693 0.488389 1.385133 0.050226 0.962470 0.502294 1.065585 8.351808 0.377304 5.102837 0.561690 7.010411 3.054968 0.039318 0.204155 2.653232 0.564368 0.854294 15.559906 0.401070 8.929538 5.525874 0.067505 0.273372 0.437116 1.927515 0.940458 2.508169 1.357738 0.043394 0.023126 0.567639 1.048288 0.120994 0.180650 0.449074 3.135353 0.012695 0.570771 2.319555 1.856122 0.975427 3.404087 0.015631 0.458799 0.151684 4.154750 11.429924 1.457957 0.233109 0.077004 0.011074 0.026268 0.052132 8.113282 0.377578 0.429221 0.260296 0.222293 0.273138 2.903836 4.731579 0.564762 0.681215 
M 3: 0.658412 0.566269 0.854111 0.884454 1.309554 1.272639 1.874713 0.552007 0.227683 0.581512 0.695190 0.967985 0.344015 0.978992 3.427163 2.333253 0.154701 0.221089 2.088785 0.540749 0.058015 5.851132 2.294145 0.182966 0.684164 3.192521 0.528161 1.128882 3.010922 1.012866 0.227296 0.156635 0.878405 0.802754 0.830884 0.431617 0.456530 3.060574 1.279257 1.438430 0.431464 2.075952 4.840271 0.644656 0.266076 2.084975 0.720060 0.291854 0.028961 4.071574 2.258357 0.073037 1.238426 0.199728 0.160296 0.482619 2.992763 1.296206 0.841829 0.031467 0.048542 0.132774 0.133055 0.056045 0.209188 0.925172 0.360522 0.094591 0.313945 0.118104 0.992259 0.086318 2.149634 5.103188 3.775817 3.954021 0.190734 1.776095 4.495841 0.264277 7.063879 2.221150 3.017954 8.558815 4.310199 2.130054 0.571406 4.137385 0.437589 2.071689 2.498630 1.763546 0.116381 0.296578 1.033710 1.283423 0.312579 0.305772 0.681277 0.507160 0.351381 0.189152 0.217780 0.767361 0.278392 0.092075 0.177263 0.451893 0.653836 0.074620 0.181992 0.752277 0.679853 0.025780 0.082005 0.326441 0.343977 0.195877 0.217424 3.057583 0.377558 0.401252 0.072258 0.241015 0.665865 1.266791 0.680174 0.717301 4.001286 0.362942 1.189259 0.964545 1.350568 12.869737 0.531100 8.904999 0.652629 10.091413 2.671718 0.086367 0.359932 4.797423 0.336801 1.021885 23.029406 0.440178 14.013035 5.069337 0.539010 0.742569 0.780580 1.331875 1.531589 4.414850 1.082703 0.091278 0.172734 0.693405 1.422571 0.068958 0.163829 0.481711 4.643214 0.121821 0.584083 4.216178 1.677263 1.575754 5.046403 0.161015 1.531223 0.599244 5.832025 33.873091 1.914768 1.287474 0.444362 0.076328 0.079916 0.466823 5.231362 0.548763 0.831890 0.382271 0.208791 0.307846 3.717971 5.910440 0.282540 0.964421 


Final LogLikelihood: -66685.003101

AIC score: 134580.006203 / AICc score: 867840.006203 / BIC score: 137233.048814
Free parameters (model + branch lengths): 605

WARNING: Number of free parameters (K=605) is larger than alignment size (n=593).
         This might lead to overfitting and compromise tree inference results!


WARNING: Best ML tree contains 27 near-zero branches!

Best ML tree with collapsed near-zero branches saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417.raxml.bestTreeCollapsed
Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417.raxml.bestTree
All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417.raxml.mlTrees
Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417.raxml.bestModel

Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/O15417/3_mltree/O15417.raxml.log

Analysis started: 01-Jul-2021 21:12:44 / finished: 01-Jul-2021 23:15:48

Elapsed time: 7384.614 seconds

Consumed energy: 541.329 Wh (= 3 km in an electric car, or 14 km with an e-scooter!)