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 6140 CPU @ 2.30GHz, 36 cores, 251 GB RAM

RAxML-NG was called at 16-Jul-2021 23:29:55 as follows:

raxml-ng --msa /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/2_msa/A8MWE9_trimmed_msa.fasta --data-type AA --model LG4X --prefix /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9 --seed 2 --threads 1 --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), NONE/sequential

[00:00:00] Reading alignment from file: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/2_msa/A8MWE9_trimmed_msa.fasta
[00:00:00] Loaded alignment with 92 taxa and 64 sites

WARNING: Sequences tr_G3QD46_G3QD46_GORGO_9595 and tr_H2P1M7_H2P1M7_PONAB_9601 are exactly identical!
WARNING: Sequences tr_G3QD46_G3QD46_GORGO_9595 and sp_A8MWE9_EFCB8_HUMAN_9606 are exactly identical!
WARNING: Sequences tr_H2RD57_H2RD57_PANTR_9598 and tr_A0A2R9CID9_A0A2R9CID9_PANPA_9597 are exactly identical!
WARNING: Sequences tr_A0A1D5QLM4_A0A1D5QLM4_MACMU_9544 and tr_G7PGM3_G7PGM3_MACFA_9541 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0N5DV32_A0A0N5DV32_TRIMR_70415 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V0RLK8_A0A0V0RLK8_9BILA_6336 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V1CC13_A0A0V1CC13_TRIBR_45882 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V0WNN1_A0A0V0WNN1_9BILA_92179 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V0VWX6_A0A0V0VWX6_9BILA_181606 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V1LMB3_A0A0V1LMB3_9BILA_6335 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V0ZX68_A0A0V0ZX68_9BILA_990121 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V1F6T8_A0A0V1F6T8_TRIPS_6337 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V1M5J4_A0A0V1M5J4_9BILA_268474 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V1NMD6_A0A0V1NMD6_9BILA_92180 are exactly identical!
WARNING: Sequences tr_E5SMY7_E5SMY7_TRISP_6334 and tr_A0A0V0UAJ1_A0A0V0UAJ1_9BILA_144512 are exactly identical!
WARNING: Sequences tr_B3SC47_B3SC47_TRIAD_10228 and tr_A0A369RUS9_A0A369RUS9_9METZ_287889 are exactly identical!
WARNING: Sequences tr_A0A096NUX1_A0A096NUX1_PAPAN_9555 and tr_A0A2K5NUK9_A0A2K5NUK9_CERAT_9531 are exactly identical!
WARNING: Sequences tr_A0A096NUX1_A0A096NUX1_PAPAN_9555 and tr_A0A2K6CJS5_A0A2K6CJS5_MACNE_9545 are exactly identical!
WARNING: Sequences tr_A0A096NUX1_A0A096NUX1_PAPAN_9555 and tr_A0A2K6AKP3_A0A2K6AKP3_MANLE_9568 are exactly identical!
WARNING: Sequences tr_A0A151MI79_A0A151MI79_ALLMI_8496 and tr_A0A1U7S7K2_A0A1U7S7K2_ALLSI_38654 are exactly identical!
WARNING: Duplicate sequences found: 20

NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) 
NOTE: was saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9.raxml.reduced.phy

Alignment comprises 1 partitions and 64 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 64 / 64
Gaps: 4.35 %
Invariant sites: 0.00 %


NOTE: Binary MSA file created: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9.raxml.rba

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

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

[00:00:00] Generating 20 random starting tree(s) with 92 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 64 / 5120
[00:00:00] Data distribution: max. searches per worker: 20

Starting ML tree search with 20 distinct starting trees

[00:00:00 -11317.870102] Initial branch length optimization
[00:00:00 -9062.491823] Model parameter optimization (eps = 10.000000)
[00:00:02 -8996.702481] AUTODETECT spr round 1 (radius: 5)
[00:00:04 -6618.617753] AUTODETECT spr round 2 (radius: 10)
[00:00:05 -5338.309735] AUTODETECT spr round 3 (radius: 15)
[00:00:07 -5210.077560] AUTODETECT spr round 4 (radius: 20)
[00:00:09 -5194.969599] AUTODETECT spr round 5 (radius: 25)
[00:00:10 -5194.962681] SPR radius for FAST iterations: 20 (autodetect)
[00:00:10 -5194.962681] Model parameter optimization (eps = 3.000000)
[00:00:12 -5182.553870] FAST spr round 1 (radius: 20)
[00:00:13 -4846.394179] FAST spr round 2 (radius: 20)
[00:00:15 -4817.235250] FAST spr round 3 (radius: 20)
[00:00:16 -4814.975833] FAST spr round 4 (radius: 20)
[00:00:17 -4814.975654] Model parameter optimization (eps = 1.000000)
[00:00:18 -4814.217188] SLOW spr round 1 (radius: 5)
[00:00:21 -4813.237273] SLOW spr round 2 (radius: 5)
[00:00:23 -4813.236300] SLOW spr round 3 (radius: 10)
[00:00:25 -4813.236275] SLOW spr round 4 (radius: 15)
[00:00:28 -4813.236274] SLOW spr round 5 (radius: 20)
[00:00:31 -4813.236274] SLOW spr round 6 (radius: 25)
[00:00:33 -4813.236274] Model parameter optimization (eps = 0.100000)

[00:00:34] ML tree search #1, logLikelihood: -4813.217259

[00:00:34 -11375.376172] Initial branch length optimization
[00:00:34 -8908.763864] Model parameter optimization (eps = 10.000000)
[00:00:36 -8850.510018] AUTODETECT spr round 1 (radius: 5)
[00:00:38 -6376.502316] AUTODETECT spr round 2 (radius: 10)
[00:00:39 -5494.421611] AUTODETECT spr round 3 (radius: 15)
[00:00:41 -5125.437647] AUTODETECT spr round 4 (radius: 20)
[00:00:42 -5125.421082] SPR radius for FAST iterations: 15 (autodetect)
[00:00:42 -5125.421082] Model parameter optimization (eps = 3.000000)
[00:00:44 -5114.976308] FAST spr round 1 (radius: 15)
[00:00:46 -4838.112934] FAST spr round 2 (radius: 15)
[00:00:48 -4822.772210] FAST spr round 3 (radius: 15)
[00:00:49 -4815.256027] FAST spr round 4 (radius: 15)
[00:00:50 -4814.949629] FAST spr round 5 (radius: 15)
[00:00:51 -4814.949228] Model parameter optimization (eps = 1.000000)
[00:00:52 -4813.980188] SLOW spr round 1 (radius: 5)
[00:00:55 -4813.217507] SLOW spr round 2 (radius: 5)
[00:00:57 -4813.216945] SLOW spr round 3 (radius: 10)
[00:01:00 -4813.216936] SLOW spr round 4 (radius: 15)
[00:01:02 -4813.216936] SLOW spr round 5 (radius: 20)
[00:01:05 -4813.216936] SLOW spr round 6 (radius: 25)
[00:01:07 -4813.216936] Model parameter optimization (eps = 0.100000)

[00:01:08] ML tree search #2, logLikelihood: -4813.208724

[00:01:08 -11426.779173] Initial branch length optimization
[00:01:08 -8964.202726] Model parameter optimization (eps = 10.000000)
[00:01:10 -8896.559899] AUTODETECT spr round 1 (radius: 5)
[00:01:12 -6556.260497] AUTODETECT spr round 2 (radius: 10)
[00:01:13 -5154.201932] AUTODETECT spr round 3 (radius: 15)
[00:01:15 -5119.480516] AUTODETECT spr round 4 (radius: 20)
[00:01:16 -5119.470907] SPR radius for FAST iterations: 15 (autodetect)
[00:01:16 -5119.470907] Model parameter optimization (eps = 3.000000)
[00:01:19 -5108.021771] FAST spr round 1 (radius: 15)
[00:01:20 -4834.253407] FAST spr round 2 (radius: 15)
[00:01:22 -4819.254811] FAST spr round 3 (radius: 15)
[00:01:23 -4818.635587] FAST spr round 4 (radius: 15)
[00:01:24 -4817.590342] FAST spr round 5 (radius: 15)
[00:01:25 -4817.590236] Model parameter optimization (eps = 1.000000)
[00:01:26 -4816.666475] SLOW spr round 1 (radius: 5)
[00:01:29 -4815.936499] SLOW spr round 2 (radius: 5)
[00:01:31 -4815.935328] SLOW spr round 3 (radius: 10)
[00:01:33 -4815.935157] SLOW spr round 4 (radius: 15)
[00:01:36 -4815.935114] SLOW spr round 5 (radius: 20)
[00:01:39 -4815.935103] SLOW spr round 6 (radius: 25)
[00:01:41 -4815.935100] Model parameter optimization (eps = 0.100000)

[00:01:42] ML tree search #3, logLikelihood: -4815.855613

[00:01:42 -11354.206808] Initial branch length optimization
[00:01:42 -9071.118325] Model parameter optimization (eps = 10.000000)
[00:01:44 -9004.851189] AUTODETECT spr round 1 (radius: 5)
[00:01:45 -6000.494885] AUTODETECT spr round 2 (radius: 10)
[00:01:47 -4937.035267] AUTODETECT spr round 3 (radius: 15)
[00:01:48 -4918.056793] AUTODETECT spr round 4 (radius: 20)
[00:01:50 -4918.047125] SPR radius for FAST iterations: 15 (autodetect)
[00:01:50 -4918.047125] Model parameter optimization (eps = 3.000000)
[00:01:52 -4905.317108] FAST spr round 1 (radius: 15)
[00:01:53 -4823.494515] FAST spr round 2 (radius: 15)
[00:01:54 -4817.120728] FAST spr round 3 (radius: 15)
[00:01:56 -4817.120662] Model parameter optimization (eps = 1.000000)
[00:01:57 -4816.266048] SLOW spr round 1 (radius: 5)
[00:01:59 -4814.432453] SLOW spr round 2 (radius: 5)
[00:02:02 -4814.432162] SLOW spr round 3 (radius: 10)
[00:02:04 -4814.432160] SLOW spr round 4 (radius: 15)
[00:02:07 -4814.432159] SLOW spr round 5 (radius: 20)
[00:02:10 -4814.432159] SLOW spr round 6 (radius: 25)
[00:02:12 -4814.432159] Model parameter optimization (eps = 0.100000)

[00:02:12] ML tree search #4, logLikelihood: -4814.416609

[00:02:12 -10961.959303] Initial branch length optimization
[00:02:12 -8731.367028] Model parameter optimization (eps = 10.000000)
[00:02:15 -8670.543092] AUTODETECT spr round 1 (radius: 5)
[00:02:16 -6338.534705] AUTODETECT spr round 2 (radius: 10)
[00:02:18 -5397.361302] AUTODETECT spr round 3 (radius: 15)
[00:02:19 -5363.153275] AUTODETECT spr round 4 (radius: 20)
[00:02:21 -5187.742789] AUTODETECT spr round 5 (radius: 25)
[00:02:21 -5187.710789] SPR radius for FAST iterations: 20 (autodetect)
[00:02:21 -5187.710789] Model parameter optimization (eps = 3.000000)
[00:02:23 -5174.933774] FAST spr round 1 (radius: 20)
[00:02:25 -4834.150749] FAST spr round 2 (radius: 20)
[00:02:26 -4821.942200] FAST spr round 3 (radius: 20)
[00:02:27 -4820.697423] FAST spr round 4 (radius: 20)
[00:02:28 -4820.697130] Model parameter optimization (eps = 1.000000)
[00:02:30 -4819.434876] SLOW spr round 1 (radius: 5)
[00:02:33 -4818.838431] SLOW spr round 2 (radius: 5)
[00:02:35 -4818.838342] SLOW spr round 3 (radius: 10)
[00:02:38 -4817.827722] SLOW spr round 4 (radius: 5)
[00:02:42 -4815.076430] SLOW spr round 5 (radius: 5)
[00:02:45 -4814.896440] SLOW spr round 6 (radius: 5)
[00:02:47 -4814.896346] SLOW spr round 7 (radius: 10)
[00:02:50 -4814.896345] SLOW spr round 8 (radius: 15)
[00:02:52 -4814.896345] SLOW spr round 9 (radius: 20)
[00:02:55 -4814.896345] SLOW spr round 10 (radius: 25)
[00:02:58 -4814.896345] Model parameter optimization (eps = 0.100000)

[00:02:58] ML tree search #5, logLikelihood: -4814.762738

[00:02:58 -11152.540690] Initial branch length optimization
[00:02:59 -8855.331718] Model parameter optimization (eps = 10.000000)
[00:03:01 -8791.508022] AUTODETECT spr round 1 (radius: 5)
[00:03:02 -6415.234005] AUTODETECT spr round 2 (radius: 10)
[00:03:04 -5256.673888] AUTODETECT spr round 3 (radius: 15)
[00:03:06 -5153.409076] AUTODETECT spr round 4 (radius: 20)
[00:03:07 -4992.364055] AUTODETECT spr round 5 (radius: 25)
[00:03:08 -4992.359614] SPR radius for FAST iterations: 20 (autodetect)
[00:03:08 -4992.359614] Model parameter optimization (eps = 3.000000)
[00:03:10 -4987.556083] FAST spr round 1 (radius: 20)
[00:03:12 -4831.637078] FAST spr round 2 (radius: 20)
[00:03:13 -4813.747137] FAST spr round 3 (radius: 20)
[00:03:14 -4813.424104] FAST spr round 4 (radius: 20)
[00:03:15 -4811.782003] FAST spr round 5 (radius: 20)
[00:03:16 -4811.781769] Model parameter optimization (eps = 1.000000)
[00:03:17 -4811.347320] SLOW spr round 1 (radius: 5)
[00:03:20 -4810.505273] SLOW spr round 2 (radius: 5)
[00:03:22 -4810.504643] SLOW spr round 3 (radius: 10)
[00:03:24 -4810.504624] SLOW spr round 4 (radius: 15)
[00:03:27 -4810.504623] SLOW spr round 5 (radius: 20)
[00:03:30 -4810.504623] SLOW spr round 6 (radius: 25)
[00:03:33 -4810.504623] Model parameter optimization (eps = 0.100000)

[00:03:33] ML tree search #6, logLikelihood: -4810.502617

[00:03:33 -11348.070791] Initial branch length optimization
[00:03:33 -9148.310457] Model parameter optimization (eps = 10.000000)
[00:03:36 -9080.199586] AUTODETECT spr round 1 (radius: 5)
[00:03:37 -6633.057645] AUTODETECT spr round 2 (radius: 10)
[00:03:38 -5197.774734] AUTODETECT spr round 3 (radius: 15)
[00:03:40 -5158.434699] AUTODETECT spr round 4 (radius: 20)
[00:03:42 -5116.165212] AUTODETECT spr round 5 (radius: 25)
[00:03:43 -5116.163924] SPR radius for FAST iterations: 20 (autodetect)
[00:03:43 -5116.163924] Model parameter optimization (eps = 3.000000)
[00:03:45 -5101.890025] FAST spr round 1 (radius: 20)
[00:03:47 -4853.131150] FAST spr round 2 (radius: 20)
[00:03:48 -4822.017169] FAST spr round 3 (radius: 20)
[00:03:49 -4820.065969] FAST spr round 4 (radius: 20)
[00:03:50 -4820.065804] Model parameter optimization (eps = 1.000000)
[00:03:51 -4819.992988] SLOW spr round 1 (radius: 5)
[00:03:54 -4818.698364] SLOW spr round 2 (radius: 5)
[00:03:57 -4818.698187] SLOW spr round 3 (radius: 10)
[00:03:59 -4818.432477] SLOW spr round 4 (radius: 5)
[00:04:03 -4815.855958] SLOW spr round 5 (radius: 5)
[00:04:06 -4814.856531] SLOW spr round 6 (radius: 5)
[00:04:09 -4814.856513] SLOW spr round 7 (radius: 10)
[00:04:11 -4814.856512] SLOW spr round 8 (radius: 15)
[00:04:14 -4814.856512] SLOW spr round 9 (radius: 20)
[00:04:17 -4814.856512] SLOW spr round 10 (radius: 25)
[00:04:19 -4814.856512] Model parameter optimization (eps = 0.100000)

[00:04:20] ML tree search #7, logLikelihood: -4814.842520

[00:04:20 -11407.001003] Initial branch length optimization
[00:04:20 -9021.622915] Model parameter optimization (eps = 10.000000)
[00:04:22 -8950.179672] AUTODETECT spr round 1 (radius: 5)
[00:04:23 -5831.195431] AUTODETECT spr round 2 (radius: 10)
[00:04:25 -5004.199850] AUTODETECT spr round 3 (radius: 15)
[00:04:27 -4991.819222] AUTODETECT spr round 4 (radius: 20)
[00:04:28 -4991.811848] SPR radius for FAST iterations: 15 (autodetect)
[00:04:28 -4991.811848] Model parameter optimization (eps = 3.000000)
[00:04:31 -4979.157289] FAST spr round 1 (radius: 15)
[00:04:32 -4829.976471] FAST spr round 2 (radius: 15)
[00:04:34 -4820.639950] FAST spr round 3 (radius: 15)
[00:04:35 -4820.249686] FAST spr round 4 (radius: 15)
[00:04:36 -4820.249413] Model parameter optimization (eps = 1.000000)
[00:04:36 -4820.006052] SLOW spr round 1 (radius: 5)
[00:04:39 -4819.962525] SLOW spr round 2 (radius: 10)
[00:04:42 -4817.280234] SLOW spr round 3 (radius: 5)
[00:04:45 -4816.601237] SLOW spr round 4 (radius: 5)
[00:04:49 -4815.906778] SLOW spr round 5 (radius: 5)
[00:04:51 -4815.465903] SLOW spr round 6 (radius: 5)
[00:04:54 -4815.465547] SLOW spr round 7 (radius: 10)
[00:04:56 -4815.338540] SLOW spr round 8 (radius: 5)
[00:05:00 -4815.199405] SLOW spr round 9 (radius: 5)
[00:05:03 -4815.199294] SLOW spr round 10 (radius: 10)
[00:05:06 -4815.199289] SLOW spr round 11 (radius: 15)
[00:05:08 -4815.199289] SLOW spr round 12 (radius: 20)
[00:05:11 -4815.199289] SLOW spr round 13 (radius: 25)
[00:05:14 -4815.199289] Model parameter optimization (eps = 0.100000)

[00:05:15] ML tree search #8, logLikelihood: -4815.005455

[00:05:15 -11225.344155] Initial branch length optimization
[00:05:15 -8809.906357] Model parameter optimization (eps = 10.000000)
[00:05:17 -8742.755443] AUTODETECT spr round 1 (radius: 5)
[00:05:19 -6646.948464] AUTODETECT spr round 2 (radius: 10)
[00:05:20 -5543.459832] AUTODETECT spr round 3 (radius: 15)
[00:05:22 -5390.399564] AUTODETECT spr round 4 (radius: 20)
[00:05:24 -5274.143064] AUTODETECT spr round 5 (radius: 25)
[00:05:24 -5274.140751] SPR radius for FAST iterations: 20 (autodetect)
[00:05:24 -5274.140751] Model parameter optimization (eps = 3.000000)
[00:05:26 -5259.715254] FAST spr round 1 (radius: 20)
[00:05:28 -4843.154598] FAST spr round 2 (radius: 20)
[00:05:30 -4823.439404] FAST spr round 3 (radius: 20)
[00:05:31 -4823.439361] Model parameter optimization (eps = 1.000000)
[00:05:31 -4823.331083] SLOW spr round 1 (radius: 5)
[00:05:34 -4820.333963] SLOW spr round 2 (radius: 5)
[00:05:37 -4818.939869] SLOW spr round 3 (radius: 5)
[00:05:39 -4818.105825] SLOW spr round 4 (radius: 5)
[00:05:42 -4818.102772] SLOW spr round 5 (radius: 10)
[00:05:44 -4817.115985] SLOW spr round 6 (radius: 5)
[00:05:48 -4816.966591] SLOW spr round 7 (radius: 5)
[00:05:51 -4816.965802] SLOW spr round 8 (radius: 10)
[00:05:54 -4816.965774] SLOW spr round 9 (radius: 15)
[00:05:56 -4816.965772] SLOW spr round 10 (radius: 20)
[00:06:00 -4816.965772] SLOW spr round 11 (radius: 25)
[00:06:02 -4816.965772] Model parameter optimization (eps = 0.100000)

[00:06:02] ML tree search #9, logLikelihood: -4816.895005

[00:06:02 -11162.265612] Initial branch length optimization
[00:06:02 -8908.260119] Model parameter optimization (eps = 10.000000)
[00:06:05 -8855.296826] AUTODETECT spr round 1 (radius: 5)
[00:06:06 -6202.848635] AUTODETECT spr round 2 (radius: 10)
[00:06:08 -5423.781646] AUTODETECT spr round 3 (radius: 15)
[00:06:10 -5330.632383] AUTODETECT spr round 4 (radius: 20)
[00:06:11 -5312.767127] AUTODETECT spr round 5 (radius: 25)
[00:06:12 -5312.765686] SPR radius for FAST iterations: 20 (autodetect)
[00:06:12 -5312.765686] Model parameter optimization (eps = 3.000000)
[00:06:13 -5309.288078] FAST spr round 1 (radius: 20)
[00:06:15 -4837.796118] FAST spr round 2 (radius: 20)
[00:06:16 -4826.618404] FAST spr round 3 (radius: 20)
[00:06:17 -4824.560447] FAST spr round 4 (radius: 20)
[00:06:18 -4824.559918] Model parameter optimization (eps = 1.000000)
[00:06:19 -4823.903368] SLOW spr round 1 (radius: 5)
[00:06:22 -4823.722983] SLOW spr round 2 (radius: 5)
[00:06:24 -4823.721747] SLOW spr round 3 (radius: 10)
[00:06:27 -4820.845899] SLOW spr round 4 (radius: 5)
[00:06:31 -4818.334649] SLOW spr round 5 (radius: 5)
[00:06:34 -4818.326830] SLOW spr round 6 (radius: 10)
[00:06:36 -4816.964751] SLOW spr round 7 (radius: 5)
[00:06:40 -4814.472473] SLOW spr round 8 (radius: 5)
[00:06:44 -4814.096425] SLOW spr round 9 (radius: 5)
[00:06:46 -4814.096067] SLOW spr round 10 (radius: 10)
[00:06:49 -4812.532806] SLOW spr round 11 (radius: 5)
[00:06:53 -4812.532439] SLOW spr round 12 (radius: 10)
[00:06:55 -4812.532414] SLOW spr round 13 (radius: 15)
[00:06:58 -4812.532413] SLOW spr round 14 (radius: 20)
[00:07:01 -4812.532413] SLOW spr round 15 (radius: 25)
[00:07:03 -4812.532413] Model parameter optimization (eps = 0.100000)

[00:07:04] ML tree search #10, logLikelihood: -4812.045665

[00:07:04 -11338.194173] Initial branch length optimization
[00:07:04 -9106.281349] Model parameter optimization (eps = 10.000000)
[00:07:07 -9039.910531] AUTODETECT spr round 1 (radius: 5)
[00:07:09 -6219.588458] AUTODETECT spr round 2 (radius: 10)
[00:07:10 -5361.476888] AUTODETECT spr round 3 (radius: 15)
[00:07:13 -5151.503536] AUTODETECT spr round 4 (radius: 20)
[00:07:14 -5123.177779] AUTODETECT spr round 5 (radius: 25)
[00:07:15 -5123.165527] SPR radius for FAST iterations: 20 (autodetect)
[00:07:15 -5123.165527] Model parameter optimization (eps = 3.000000)
[00:07:17 -5108.969770] FAST spr round 1 (radius: 20)
[00:07:19 -4835.992840] FAST spr round 2 (radius: 20)
[00:07:20 -4821.177210] FAST spr round 3 (radius: 20)
[00:07:21 -4816.709251] FAST spr round 4 (radius: 20)
[00:07:22 -4816.708928] Model parameter optimization (eps = 1.000000)
[00:07:23 -4816.178382] SLOW spr round 1 (radius: 5)
[00:07:26 -4815.499306] SLOW spr round 2 (radius: 5)
[00:07:29 -4814.745365] SLOW spr round 3 (radius: 5)
[00:07:31 -4814.745265] SLOW spr round 4 (radius: 10)
[00:07:33 -4814.745261] SLOW spr round 5 (radius: 15)
[00:07:36 -4814.745261] SLOW spr round 6 (radius: 20)
[00:07:39 -4814.745261] SLOW spr round 7 (radius: 25)
[00:07:41 -4814.745261] Model parameter optimization (eps = 0.100000)

[00:07:42] ML tree search #11, logLikelihood: -4814.707219

[00:07:42 -11098.371858] Initial branch length optimization
[00:07:42 -8929.180842] Model parameter optimization (eps = 10.000000)
[00:07:45 -8868.893940] AUTODETECT spr round 1 (radius: 5)
[00:07:46 -6332.085443] AUTODETECT spr round 2 (radius: 10)
[00:07:48 -5577.803830] AUTODETECT spr round 3 (radius: 15)
[00:07:49 -5044.839865] AUTODETECT spr round 4 (radius: 20)
[00:07:51 -5008.103057] AUTODETECT spr round 5 (radius: 25)
[00:07:52 -5008.086242] SPR radius for FAST iterations: 20 (autodetect)
[00:07:52 -5008.086242] Model parameter optimization (eps = 3.000000)
[00:07:54 -4991.045504] FAST spr round 1 (radius: 20)
[00:07:55 -4836.930621] FAST spr round 2 (radius: 20)
[00:07:57 -4820.909706] FAST spr round 3 (radius: 20)
[00:07:58 -4819.505466] FAST spr round 4 (radius: 20)
[00:07:59 -4819.370422] FAST spr round 5 (radius: 20)
[00:08:00 -4818.993410] FAST spr round 6 (radius: 20)
[00:08:01 -4818.993384] Model parameter optimization (eps = 1.000000)
[00:08:02 -4818.210692] SLOW spr round 1 (radius: 5)
[00:08:05 -4817.979575] SLOW spr round 2 (radius: 5)
[00:08:07 -4817.979012] SLOW spr round 3 (radius: 10)
[00:08:10 -4817.978991] SLOW spr round 4 (radius: 15)
[00:08:13 -4817.978988] SLOW spr round 5 (radius: 20)
[00:08:15 -4817.978988] SLOW spr round 6 (radius: 25)
[00:08:17 -4817.978988] Model parameter optimization (eps = 0.100000)

[00:08:18] ML tree search #12, logLikelihood: -4817.969948

[00:08:18 -11157.679288] Initial branch length optimization
[00:08:18 -8866.692957] Model parameter optimization (eps = 10.000000)
[00:08:21 -8779.131080] AUTODETECT spr round 1 (radius: 5)
[00:08:22 -6472.473926] AUTODETECT spr round 2 (radius: 10)
[00:08:24 -5327.725154] AUTODETECT spr round 3 (radius: 15)
[00:08:25 -5150.921729] AUTODETECT spr round 4 (radius: 20)
[00:08:27 -5126.251983] AUTODETECT spr round 5 (radius: 25)
[00:08:28 -5126.236107] SPR radius for FAST iterations: 20 (autodetect)
[00:08:28 -5126.236107] Model parameter optimization (eps = 3.000000)
[00:08:30 -5111.634978] FAST spr round 1 (radius: 20)
[00:08:32 -4851.653190] FAST spr round 2 (radius: 20)
[00:08:33 -4823.097123] FAST spr round 3 (radius: 20)
[00:08:34 -4822.681339] FAST spr round 4 (radius: 20)
[00:08:35 -4822.679035] Model parameter optimization (eps = 1.000000)
[00:08:36 -4822.486577] SLOW spr round 1 (radius: 5)
[00:08:39 -4817.885939] SLOW spr round 2 (radius: 5)
[00:08:41 -4815.341800] SLOW spr round 3 (radius: 5)
[00:08:44 -4815.340972] SLOW spr round 4 (radius: 10)
[00:08:46 -4815.340930] SLOW spr round 5 (radius: 15)
[00:08:49 -4815.340927] SLOW spr round 6 (radius: 20)
[00:08:52 -4815.340926] SLOW spr round 7 (radius: 25)
[00:08:54 -4815.340926] Model parameter optimization (eps = 0.100000)

[00:08:54] ML tree search #13, logLikelihood: -4815.331491

[00:08:54 -11060.032896] Initial branch length optimization
[00:08:54 -8653.536101] Model parameter optimization (eps = 10.000000)
[00:08:57 -8578.567984] AUTODETECT spr round 1 (radius: 5)
[00:08:58 -6242.901370] AUTODETECT spr round 2 (radius: 10)
[00:09:00 -5449.430742] AUTODETECT spr round 3 (radius: 15)
[00:09:02 -5166.675641] AUTODETECT spr round 4 (radius: 20)
[00:09:03 -5166.656184] SPR radius for FAST iterations: 15 (autodetect)
[00:09:03 -5166.656184] Model parameter optimization (eps = 3.000000)
[00:09:05 -5154.965659] FAST spr round 1 (radius: 15)
[00:09:07 -4835.447905] FAST spr round 2 (radius: 15)
[00:09:08 -4823.993936] FAST spr round 3 (radius: 15)
[00:09:09 -4823.993681] Model parameter optimization (eps = 1.000000)
[00:09:10 -4823.133422] SLOW spr round 1 (radius: 5)
[00:09:13 -4821.672437] SLOW spr round 2 (radius: 5)
[00:09:16 -4821.672269] SLOW spr round 3 (radius: 10)
[00:09:18 -4820.100040] SLOW spr round 4 (radius: 5)
[00:09:22 -4816.569261] SLOW spr round 5 (radius: 5)
[00:09:25 -4816.118018] SLOW spr round 6 (radius: 5)
[00:09:27 -4816.117168] SLOW spr round 7 (radius: 10)
[00:09:30 -4816.117152] SLOW spr round 8 (radius: 15)
[00:09:32 -4816.117152] SLOW spr round 9 (radius: 20)
[00:09:35 -4816.117152] SLOW spr round 10 (radius: 25)
[00:09:37 -4816.117152] Model parameter optimization (eps = 0.100000)

[00:09:38] ML tree search #14, logLikelihood: -4815.886310

[00:09:38 -11166.411981] Initial branch length optimization
[00:09:38 -8725.604013] Model parameter optimization (eps = 10.000000)
[00:09:41 -8663.672452] AUTODETECT spr round 1 (radius: 5)
[00:09:42 -6700.000713] AUTODETECT spr round 2 (radius: 10)
[00:09:44 -5548.260791] AUTODETECT spr round 3 (radius: 15)
[00:09:46 -5402.278991] AUTODETECT spr round 4 (radius: 20)
[00:09:47 -5376.066785] AUTODETECT spr round 5 (radius: 25)
[00:09:48 -5376.056866] SPR radius for FAST iterations: 20 (autodetect)
[00:09:48 -5376.056866] Model parameter optimization (eps = 3.000000)
[00:09:50 -5358.574550] FAST spr round 1 (radius: 20)
[00:09:52 -4827.769117] FAST spr round 2 (radius: 20)
[00:09:53 -4819.843333] FAST spr round 3 (radius: 20)
[00:09:54 -4819.843259] Model parameter optimization (eps = 1.000000)
[00:09:55 -4819.309105] SLOW spr round 1 (radius: 5)
[00:09:58 -4816.462787] SLOW spr round 2 (radius: 5)
[00:10:01 -4816.025568] SLOW spr round 3 (radius: 5)
[00:10:03 -4816.025514] SLOW spr round 4 (radius: 10)
[00:10:05 -4816.025511] SLOW spr round 5 (radius: 15)
[00:10:08 -4816.025511] SLOW spr round 6 (radius: 20)
[00:10:11 -4816.025511] SLOW spr round 7 (radius: 25)
[00:10:13 -4816.025511] Model parameter optimization (eps = 0.100000)

[00:10:14] ML tree search #15, logLikelihood: -4815.923871

[00:10:14 -11263.665130] Initial branch length optimization
[00:10:14 -8956.682141] Model parameter optimization (eps = 10.000000)
[00:10:17 -8897.001630] AUTODETECT spr round 1 (radius: 5)
[00:10:18 -6775.552830] AUTODETECT spr round 2 (radius: 10)
[00:10:20 -5307.500743] AUTODETECT spr round 3 (radius: 15)
[00:10:22 -5057.194637] AUTODETECT spr round 4 (radius: 20)
[00:10:24 -5057.190030] SPR radius for FAST iterations: 15 (autodetect)
[00:10:24 -5057.190030] Model parameter optimization (eps = 3.000000)
[00:10:25 -5049.956154] FAST spr round 1 (radius: 15)
[00:10:27 -4893.405195] FAST spr round 2 (radius: 15)
[00:10:29 -4825.908279] FAST spr round 3 (radius: 15)
[00:10:30 -4824.954258] FAST spr round 4 (radius: 15)
[00:10:31 -4823.370588] FAST spr round 5 (radius: 15)
[00:10:32 -4823.368697] Model parameter optimization (eps = 1.000000)
[00:10:33 -4822.880616] SLOW spr round 1 (radius: 5)
[00:10:36 -4822.230482] SLOW spr round 2 (radius: 5)
[00:10:39 -4822.230015] SLOW spr round 3 (radius: 10)
[00:10:41 -4821.724768] SLOW spr round 4 (radius: 5)
[00:10:45 -4819.419561] SLOW spr round 5 (radius: 5)
[00:10:48 -4819.419549] SLOW spr round 6 (radius: 10)
[00:10:50 -4819.419548] SLOW spr round 7 (radius: 15)
[00:10:53 -4819.419548] SLOW spr round 8 (radius: 20)
[00:10:56 -4819.419548] SLOW spr round 9 (radius: 25)
[00:10:58 -4819.419548] Model parameter optimization (eps = 0.100000)

[00:10:59] ML tree search #16, logLikelihood: -4819.408032

[00:10:59 -11467.176651] Initial branch length optimization
[00:10:59 -9071.734451] Model parameter optimization (eps = 10.000000)
[00:11:01 -8993.226391] AUTODETECT spr round 1 (radius: 5)
[00:11:03 -6320.889339] AUTODETECT spr round 2 (radius: 10)
[00:11:04 -5524.893452] AUTODETECT spr round 3 (radius: 15)
[00:11:06 -5090.455923] AUTODETECT spr round 4 (radius: 20)
[00:11:08 -5090.444314] SPR radius for FAST iterations: 15 (autodetect)
[00:11:08 -5090.444314] Model parameter optimization (eps = 3.000000)
[00:11:09 -5071.244744] FAST spr round 1 (radius: 15)
[00:11:11 -4854.413779] FAST spr round 2 (radius: 15)
[00:11:13 -4824.151712] FAST spr round 3 (radius: 15)
[00:11:14 -4823.285956] FAST spr round 4 (radius: 15)
[00:11:15 -4822.874376] FAST spr round 5 (radius: 15)
[00:11:16 -4822.874322] Model parameter optimization (eps = 1.000000)
[00:11:17 -4822.426372] SLOW spr round 1 (radius: 5)
[00:11:20 -4822.340106] SLOW spr round 2 (radius: 10)
[00:11:22 -4819.527429] SLOW spr round 3 (radius: 5)
[00:11:26 -4816.471592] SLOW spr round 4 (radius: 5)
[00:11:29 -4815.109132] SLOW spr round 5 (radius: 5)
[00:11:32 -4815.014482] SLOW spr round 6 (radius: 10)
[00:11:34 -4815.013550] SLOW spr round 7 (radius: 15)
[00:11:36 -4815.013522] SLOW spr round 8 (radius: 20)
[00:11:39 -4815.013521] SLOW spr round 9 (radius: 25)
[00:11:42 -4815.013521] Model parameter optimization (eps = 0.100000)

[00:11:42] ML tree search #17, logLikelihood: -4814.923209

[00:11:42 -11564.862011] Initial branch length optimization
[00:11:42 -9085.402507] Model parameter optimization (eps = 10.000000)
[00:11:45 -8995.634900] AUTODETECT spr round 1 (radius: 5)
[00:11:46 -6981.523618] AUTODETECT spr round 2 (radius: 10)
[00:11:48 -5645.065959] AUTODETECT spr round 3 (radius: 15)
[00:11:50 -5051.516427] AUTODETECT spr round 4 (radius: 20)
[00:11:51 -4968.883139] AUTODETECT spr round 5 (radius: 25)
[00:11:52 -4968.879840] SPR radius for FAST iterations: 20 (autodetect)
[00:11:52 -4968.879840] Model parameter optimization (eps = 3.000000)
[00:11:54 -4961.896115] FAST spr round 1 (radius: 20)
[00:11:56 -4844.811423] FAST spr round 2 (radius: 20)
[00:11:57 -4836.006495] FAST spr round 3 (radius: 20)
[00:11:59 -4828.590470] FAST spr round 4 (radius: 20)
[00:12:00 -4825.107493] FAST spr round 5 (radius: 20)
[00:12:01 -4824.857048] FAST spr round 6 (radius: 20)
[00:12:02 -4820.894323] FAST spr round 7 (radius: 20)
[00:12:03 -4819.537941] FAST spr round 8 (radius: 20)
[00:12:05 -4819.536790] Model parameter optimization (eps = 1.000000)
[00:12:06 -4817.307936] SLOW spr round 1 (radius: 5)
[00:12:09 -4815.905291] SLOW spr round 2 (radius: 5)
[00:12:11 -4815.904875] SLOW spr round 3 (radius: 10)
[00:12:14 -4815.904855] SLOW spr round 4 (radius: 15)
[00:12:16 -4815.904854] SLOW spr round 5 (radius: 20)
[00:12:19 -4815.904854] SLOW spr round 6 (radius: 25)
[00:12:22 -4815.904854] Model parameter optimization (eps = 0.100000)

[00:12:22] ML tree search #18, logLikelihood: -4815.874478

[00:12:22 -11235.800775] Initial branch length optimization
[00:12:22 -8942.895019] Model parameter optimization (eps = 10.000000)
[00:12:24 -8870.042053] AUTODETECT spr round 1 (radius: 5)
[00:12:26 -6563.420045] AUTODETECT spr round 2 (radius: 10)
[00:12:28 -5417.587858] AUTODETECT spr round 3 (radius: 15)
[00:12:29 -5323.903834] AUTODETECT spr round 4 (radius: 20)
[00:12:30 -5323.894331] SPR radius for FAST iterations: 15 (autodetect)
[00:12:30 -5323.894331] Model parameter optimization (eps = 3.000000)
[00:12:33 -5312.854921] FAST spr round 1 (radius: 15)
[00:12:35 -4839.173400] FAST spr round 2 (radius: 15)
[00:12:36 -4818.083314] FAST spr round 3 (radius: 15)
[00:12:37 -4817.961018] FAST spr round 4 (radius: 15)
[00:12:38 -4817.960722] Model parameter optimization (eps = 1.000000)
[00:12:39 -4816.918021] SLOW spr round 1 (radius: 5)
[00:12:42 -4814.967673] SLOW spr round 2 (radius: 5)
[00:12:45 -4814.804790] SLOW spr round 3 (radius: 5)
[00:12:47 -4814.804487] SLOW spr round 4 (radius: 10)
[00:12:50 -4814.804480] SLOW spr round 5 (radius: 15)
[00:12:53 -4814.804480] SLOW spr round 6 (radius: 20)
[00:12:55 -4814.804480] SLOW spr round 7 (radius: 25)
[00:12:57 -4814.804480] Model parameter optimization (eps = 0.100000)

[00:12:58] ML tree search #19, logLikelihood: -4814.727269

[00:12:58 -11169.221498] Initial branch length optimization
[00:12:58 -8905.818168] Model parameter optimization (eps = 10.000000)
[00:13:00 -8834.492298] AUTODETECT spr round 1 (radius: 5)
[00:13:01 -6118.510842] AUTODETECT spr round 2 (radius: 10)
[00:13:03 -5048.840212] AUTODETECT spr round 3 (radius: 15)
[00:13:05 -5038.489511] AUTODETECT spr round 4 (radius: 20)
[00:13:07 -5038.475311] SPR radius for FAST iterations: 15 (autodetect)
[00:13:07 -5038.475311] Model parameter optimization (eps = 3.000000)
[00:13:08 -5018.848799] FAST spr round 1 (radius: 15)
[00:13:10 -4835.782162] FAST spr round 2 (radius: 15)
[00:13:11 -4819.032272] FAST spr round 3 (radius: 15)
[00:13:12 -4819.031510] Model parameter optimization (eps = 1.000000)
[00:13:13 -4818.864754] SLOW spr round 1 (radius: 5)
[00:13:16 -4818.704407] SLOW spr round 2 (radius: 5)
[00:13:18 -4818.616439] SLOW spr round 3 (radius: 10)
[00:13:21 -4818.616045] SLOW spr round 4 (radius: 15)
[00:13:24 -4818.616042] SLOW spr round 5 (radius: 20)
[00:13:27 -4818.616042] SLOW spr round 6 (radius: 25)
[00:13:28 -4818.616042] Model parameter optimization (eps = 0.100000)

[00:13:29] ML tree search #20, logLikelihood: -4818.584785


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.059860,0.605352) (0.159359,0.413141) (0.598599,0.915774) (0.182182,1.919752) 
   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: -4810.502617

AIC score: 9995.005234 / AICc score: 80307.005234 / BIC score: 10398.716371
Free parameters (model + branch lengths): 187

WARNING: Number of free parameters (K=187) is larger than alignment size (n=64).
         This might lead to overfitting and compromise tree inference results!


WARNING: Best ML tree contains 8 near-zero branches!

Best ML tree with collapsed near-zero branches saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9.raxml.bestTreeCollapsed
Best ML tree saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9.raxml.bestTree
All ML trees saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9.raxml.mlTrees
Optimized model saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9.raxml.bestModel

Execution log saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/A8MWE9/3_mltree/A8MWE9.raxml.log

Analysis started: 16-Jul-2021 23:29:55 / finished: 16-Jul-2021 23:43:24

Elapsed time: 809.074 seconds