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) CPU E5-2690 v4 @ 2.60GHz, 28 cores, 251 GB RAM

RAxML-NG was called at 03-Jun-2021 15:03:57 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/2_msa/Q86YV5_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5 --seed 2 --threads 9 --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 (9 threads), thread pinning: OFF

[00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/2_msa/Q86YV5_trimmed_msa.fasta
[00:00:00] Loaded alignment with 235 taxa and 1236 sites

Alignment comprises 1 partitions and 1236 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 1236 / 1236
Gaps: 21.68 %
Invariant sites: 0.40 %


NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5.raxml.rba

Parallelization scheme autoconfig: 1 worker(s) x 9 thread(s)

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

[00:00:00] Generating 20 random starting tree(s) with 235 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 138 / 11040
[00:00:00] Data distribution: max. searches per worker: 20

Starting ML tree search with 20 distinct starting trees

[00:00:00 -399539.950050] Initial branch length optimization
[00:00:01 -310273.378159] Model parameter optimization (eps = 10.000000)
[00:00:27 -309748.588179] AUTODETECT spr round 1 (radius: 5)
[00:00:51 -191394.629223] AUTODETECT spr round 2 (radius: 10)
[00:01:26 -135932.304339] AUTODETECT spr round 3 (radius: 15)
[00:02:09 -127437.917020] AUTODETECT spr round 4 (radius: 20)
[00:02:56 -125291.289139] AUTODETECT spr round 5 (radius: 25)
[00:03:36 -125250.897142] SPR radius for FAST iterations: 25 (autodetect)
[00:03:36 -125250.897142] Model parameter optimization (eps = 3.000000)
[00:04:01 -124511.474368] FAST spr round 1 (radius: 25)
[00:04:38 -116521.681593] FAST spr round 2 (radius: 25)
[00:05:06 -116267.337759] FAST spr round 3 (radius: 25)
[00:05:29 -116227.080087] FAST spr round 4 (radius: 25)
[00:05:51 -116200.336337] FAST spr round 5 (radius: 25)
[00:06:11 -116200.264558] Model parameter optimization (eps = 1.000000)
[00:06:21 -116196.866301] SLOW spr round 1 (radius: 5)
[00:07:04 -116183.953509] SLOW spr round 2 (radius: 5)
[00:07:42 -116181.999919] SLOW spr round 3 (radius: 5)
[00:08:19 -116181.998445] SLOW spr round 4 (radius: 10)
[00:09:01 -116181.997694] SLOW spr round 5 (radius: 15)
[00:10:15 -116181.997586] SLOW spr round 6 (radius: 20)
[00:11:42 -116181.997576] SLOW spr round 7 (radius: 25)

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) CPU E5-2690 v4 @ 2.60GHz, 28 cores, 251 GB RAM

RAxML-NG was called at 03-Jun-2021 15:19:19 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/2_msa/Q86YV5_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5 --seed 2 --threads 9 --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 (9 threads), thread pinning: OFF

WARNING: The model you specified on the command line (LG4X) will be ignored 
         since the binary MSA file already contains a model definition.
         If you want to change the model, please re-run RAxML-NG 
         with the original PHYLIP/FASTA alignment and --redo option.

[00:00:00] Loading binary alignment from file: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5.raxml.rba
[00:00:00] Alignment comprises 235 taxa, 1 partitions and 1236 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 1236 / 1236
Gaps: 21.68 %
Invariant sites: 0.40 %


Parallelization scheme autoconfig: 1 worker(s) x 9 thread(s)

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

[00:00:00] NOTE: Resuming execution from checkpoint (logLH: -116182.00, ML trees: 0, bootstraps: 0)
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 138 / 11040
[00:00:00] Data distribution: max. searches per worker: 20

ERROR: CPU core oversubscription detected! RAxML-NG will terminate now to avoid wasting resources.
NOTE:  Details: https://github.com/amkozlov/raxml-ng/wiki/Parallelization#core-oversubscription
NOTE:  You can use '--force perf_threads' to disable this check, but ONLY if you are 200% sure this is a false alarm!


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 6258R CPU @ 2.70GHz, 56 cores, 187 GB RAM

RAxML-NG was called at 04-Jun-2021 03:53:03 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/2_msa/Q86YV5_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5 --seed 2 --threads 9 --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 (9 threads), thread pinning: OFF

WARNING: The model you specified on the command line (LG4X) will be ignored 
         since the binary MSA file already contains a model definition.
         If you want to change the model, please re-run RAxML-NG 
         with the original PHYLIP/FASTA alignment and --redo option.

[00:00:00] Loading binary alignment from file: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5.raxml.rba
[00:00:00] Alignment comprises 235 taxa, 1 partitions and 1236 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 1236 / 1236
Gaps: 21.68 %
Invariant sites: 0.40 %


Parallelization scheme autoconfig: 1 worker(s) x 9 thread(s)

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

[00:00:00] NOTE: Resuming execution from checkpoint (logLH: -116182.00, ML trees: 0, bootstraps: 0)
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 138 / 11040
[00:00:00] Data distribution: max. searches per worker: 20

Starting ML tree search with 20 distinct starting trees

[00:00:00 -116181.997576] SPR radius for FAST iterations: 25 (autodetect)
[00:00:00 -116181.997576] SLOW spr round 7 (radius: 25)
[00:00:43 -116181.997572] Model parameter optimization (eps = 0.100000)

[00:00:45] ML tree search #1, logLikelihood: -116181.963645

[00:00:45 -396309.446305] Initial branch length optimization
[00:00:46 -302685.448016] Model parameter optimization (eps = 10.000000)
[00:01:00 -302070.242157] AUTODETECT spr round 1 (radius: 5)
[00:01:12 -191469.453822] AUTODETECT spr round 2 (radius: 10)
[00:01:26 -152305.269687] AUTODETECT spr round 3 (radius: 15)
[00:01:45 -130948.138904] AUTODETECT spr round 4 (radius: 20)
[00:02:06 -129322.434208] AUTODETECT spr round 5 (radius: 25)
[00:02:28 -128042.668676] SPR radius for FAST iterations: 25 (autodetect)
[00:02:28 -128042.668676] Model parameter optimization (eps = 3.000000)
[00:02:44 -127283.106546] FAST spr round 1 (radius: 25)
[00:03:01 -116677.974366] FAST spr round 2 (radius: 25)
[00:03:16 -116219.177402] FAST spr round 3 (radius: 25)
[00:03:28 -116213.105830] FAST spr round 4 (radius: 25)
[00:03:38 -116213.105260] Model parameter optimization (eps = 1.000000)
[00:03:44 -116208.858849] SLOW spr round 1 (radius: 5)
[00:04:06 -116191.431767] SLOW spr round 2 (radius: 5)
[00:04:27 -116186.419667] SLOW spr round 3 (radius: 5)
[00:04:45 -116186.415912] SLOW spr round 4 (radius: 10)
[00:05:07 -116186.415236] SLOW spr round 5 (radius: 15)
[00:05:44 -116186.414693] SLOW spr round 6 (radius: 20)
[00:06:29 -116186.414160] SLOW spr round 7 (radius: 25)
[00:07:09 -116186.413628] Model parameter optimization (eps = 0.100000)

[00:07:12] ML tree search #2, logLikelihood: -116186.289868

[00:07:12 -397297.710355] Initial branch length optimization
[00:07:12 -310179.740950] Model parameter optimization (eps = 10.000000)
[00:07:23 -309709.422284] AUTODETECT spr round 1 (radius: 5)
[00:07:34 -195956.147049] AUTODETECT spr round 2 (radius: 10)
[00:07:47 -152601.425971] AUTODETECT spr round 3 (radius: 15)
[00:08:04 -131478.138427] AUTODETECT spr round 4 (radius: 20)
[00:08:27 -131121.622183] AUTODETECT spr round 5 (radius: 25)
[00:08:53 -131120.976527] SPR radius for FAST iterations: 25 (autodetect)
[00:08:53 -131120.976527] Model parameter optimization (eps = 3.000000)
[00:09:08 -130406.798911] FAST spr round 1 (radius: 25)
[00:09:27 -116615.438250] FAST spr round 2 (radius: 25)
[00:09:42 -116238.408245] FAST spr round 3 (radius: 25)
[00:09:53 -116219.813420] FAST spr round 4 (radius: 25)
[00:10:03 -116218.443699] FAST spr round 5 (radius: 25)
[00:10:13 -116215.839022] FAST spr round 6 (radius: 25)
[00:10:23 -116213.548167] FAST spr round 7 (radius: 25)
[00:10:32 -116213.547914] Model parameter optimization (eps = 1.000000)
[00:10:38 -116209.902579] SLOW spr round 1 (radius: 5)
[00:10:59 -116197.914938] SLOW spr round 2 (radius: 5)
[00:11:17 -116196.520696] SLOW spr round 3 (radius: 5)
[00:11:35 -116196.520460] SLOW spr round 4 (radius: 10)
[00:11:56 -116196.520299] SLOW spr round 5 (radius: 15)
[00:12:33 -116196.520186] SLOW spr round 6 (radius: 20)
[00:13:16 -116196.520106] SLOW spr round 7 (radius: 25)
[00:13:55 -116196.520049] Model parameter optimization (eps = 0.100000)

[00:13:58] ML tree search #3, logLikelihood: -116196.383408

[00:13:58 -404026.449417] Initial branch length optimization
[00:13:59 -312384.674426] Model parameter optimization (eps = 10.000000)
[00:14:08 -311892.778841] AUTODETECT spr round 1 (radius: 5)
[00:14:19 -192272.724410] AUTODETECT spr round 2 (radius: 10)
[00:14:34 -147790.407637] AUTODETECT spr round 3 (radius: 15)
[00:14:50 -129755.020723] AUTODETECT spr round 4 (radius: 20)
[00:15:11 -129062.580674] AUTODETECT spr round 5 (radius: 25)
[00:15:31 -128926.588922] SPR radius for FAST iterations: 25 (autodetect)
[00:15:31 -128926.588922] Model parameter optimization (eps = 3.000000)
[00:15:44 -128129.797822] FAST spr round 1 (radius: 25)
[00:16:00 -116929.015451] FAST spr round 2 (radius: 25)
[00:16:15 -116259.940402] FAST spr round 3 (radius: 25)
[00:16:27 -116204.773767] FAST spr round 4 (radius: 25)
[00:16:38 -116201.281352] FAST spr round 5 (radius: 25)
[00:16:48 -116200.556924] FAST spr round 6 (radius: 25)
[00:16:57 -116200.231936] FAST spr round 7 (radius: 25)
[00:17:07 -116199.231528] FAST spr round 8 (radius: 25)
[00:17:17 -116198.961356] FAST spr round 9 (radius: 25)
[00:17:27 -116198.851434] FAST spr round 10 (radius: 25)
[00:17:37 -116198.847354] Model parameter optimization (eps = 1.000000)
[00:17:41 -116196.464063] SLOW spr round 1 (radius: 5)
[00:18:01 -116190.915888] SLOW spr round 2 (radius: 5)
[00:18:20 -116190.487381] SLOW spr round 3 (radius: 5)
[00:18:38 -116190.487039] SLOW spr round 4 (radius: 10)
[00:18:58 -116190.486941] SLOW spr round 5 (radius: 15)
[00:19:33 -116190.486906] SLOW spr round 6 (radius: 20)
[00:20:15 -116190.486893] SLOW spr round 7 (radius: 25)
[00:20:55 -116190.486888] Model parameter optimization (eps = 0.100000)

[00:20:57] ML tree search #4, logLikelihood: -116190.449062

[00:20:57 -401127.854927] Initial branch length optimization
[00:20:57 -311585.777909] Model parameter optimization (eps = 10.000000)
[00:21:08 -311022.619429] AUTODETECT spr round 1 (radius: 5)
[00:21:19 -205061.254677] AUTODETECT spr round 2 (radius: 10)
[00:21:33 -146553.348104] AUTODETECT spr round 3 (radius: 15)
[00:21:52 -133174.259321] AUTODETECT spr round 4 (radius: 20)
[00:22:14 -132354.661276] AUTODETECT spr round 5 (radius: 25)
[00:22:36 -132354.615001] SPR radius for FAST iterations: 20 (autodetect)
[00:22:36 -132354.615001] Model parameter optimization (eps = 3.000000)
[00:22:50 -131518.814562] FAST spr round 1 (radius: 20)
[00:23:06 -116527.845272] FAST spr round 2 (radius: 20)
[00:23:19 -116279.232046] FAST spr round 3 (radius: 20)
[00:23:30 -116253.820742] FAST spr round 4 (radius: 20)
[00:23:40 -116229.920648] FAST spr round 5 (radius: 20)
[00:23:50 -116229.911782] Model parameter optimization (eps = 1.000000)
[00:23:55 -116226.050510] SLOW spr round 1 (radius: 5)
[00:24:16 -116200.006305] SLOW spr round 2 (radius: 5)
[00:24:35 -116195.549376] SLOW spr round 3 (radius: 5)
[00:24:53 -116195.548306] SLOW spr round 4 (radius: 10)
[00:25:13 -116195.547679] SLOW spr round 5 (radius: 15)
[00:25:48 -116195.547106] SLOW spr round 6 (radius: 20)
[00:26:30 -116195.546560] SLOW spr round 7 (radius: 25)
[00:27:09 -116195.546028] Model parameter optimization (eps = 0.100000)

[00:27:13] ML tree search #5, logLikelihood: -116195.263495

[00:27:13 -400214.864304] Initial branch length optimization
[00:27:14 -309747.857164] Model parameter optimization (eps = 10.000000)
[00:27:30 -309123.770099] AUTODETECT spr round 1 (radius: 5)
[00:27:41 -192031.074352] AUTODETECT spr round 2 (radius: 10)
[00:27:55 -135043.858620] AUTODETECT spr round 3 (radius: 15)
[00:28:13 -128618.545190] AUTODETECT spr round 4 (radius: 20)
[00:28:32 -126806.147508] AUTODETECT spr round 5 (radius: 25)
[00:28:51 -125696.241889] SPR radius for FAST iterations: 25 (autodetect)
[00:28:51 -125696.241889] Model parameter optimization (eps = 3.000000)
[00:29:05 -124954.351920] FAST spr round 1 (radius: 25)
[00:29:22 -116387.866705] FAST spr round 2 (radius: 25)
[00:29:35 -116230.029279] FAST spr round 3 (radius: 25)
[00:29:45 -116226.919921] FAST spr round 4 (radius: 25)
[00:29:55 -116224.892926] FAST spr round 5 (radius: 25)
[00:30:04 -116224.644930] FAST spr round 6 (radius: 25)
[00:30:14 -116224.305217] FAST spr round 7 (radius: 25)
[00:30:24 -116209.793969] FAST spr round 8 (radius: 25)
[00:30:34 -116209.793947] Model parameter optimization (eps = 1.000000)
[00:30:39 -116204.182455] SLOW spr round 1 (radius: 5)
[00:31:00 -116188.466692] SLOW spr round 2 (radius: 5)
[00:31:17 -116187.451517] SLOW spr round 3 (radius: 5)
[00:31:35 -116185.704208] SLOW spr round 4 (radius: 5)
[00:31:53 -116185.701270] SLOW spr round 5 (radius: 10)
[00:32:13 -116185.699811] SLOW spr round 6 (radius: 15)
[00:32:49 -116185.699107] SLOW spr round 7 (radius: 20)
[00:33:32 -116185.698777] SLOW spr round 8 (radius: 25)
[00:34:12 -116185.698623] Model parameter optimization (eps = 0.100000)

[00:34:14] ML tree search #6, logLikelihood: -116185.686495

[00:34:14 -395217.188327] Initial branch length optimization
[00:34:15 -303295.200110] Model parameter optimization (eps = 10.000000)
[00:34:24 -302813.937288] AUTODETECT spr round 1 (radius: 5)
[00:34:35 -196569.029015] AUTODETECT spr round 2 (radius: 10)
[00:34:49 -137036.330608] AUTODETECT spr round 3 (radius: 15)
[00:35:06 -126625.309761] AUTODETECT spr round 4 (radius: 20)
[00:35:28 -126414.546543] AUTODETECT spr round 5 (radius: 25)
[00:35:51 -126414.364246] SPR radius for FAST iterations: 25 (autodetect)
[00:35:51 -126414.364246] Model parameter optimization (eps = 3.000000)
[00:36:04 -125690.748372] FAST spr round 1 (radius: 25)
[00:36:21 -117031.618150] FAST spr round 2 (radius: 25)
[00:36:36 -116245.446411] FAST spr round 3 (radius: 25)
[00:36:48 -116226.910021] FAST spr round 4 (radius: 25)
[00:36:59 -116215.461670] FAST spr round 5 (radius: 25)
[00:37:09 -116213.132479] FAST spr round 6 (radius: 25)
[00:37:18 -116212.267872] FAST spr round 7 (radius: 25)
[00:37:28 -116211.469132] FAST spr round 8 (radius: 25)
[00:37:38 -116209.659700] FAST spr round 9 (radius: 25)
[00:37:48 -116209.239896] FAST spr round 10 (radius: 25)
[00:37:59 -116209.189072] Model parameter optimization (eps = 1.000000)
[00:38:04 -116204.558977] SLOW spr round 1 (radius: 5)
[00:38:25 -116188.062081] SLOW spr round 2 (radius: 5)
[00:38:44 -116186.702247] SLOW spr round 3 (radius: 5)
[00:39:02 -116186.698294] SLOW spr round 4 (radius: 10)
[00:39:22 -116186.698122] SLOW spr round 5 (radius: 15)
[00:39:58 -116186.698114] SLOW spr round 6 (radius: 20)
[00:40:41 -116186.698112] SLOW spr round 7 (radius: 25)
[00:41:20 -116186.698111] Model parameter optimization (eps = 0.100000)

[00:41:23] ML tree search #7, logLikelihood: -116186.578522

[00:41:23 -395716.593239] Initial branch length optimization
[00:41:24 -312915.952016] Model parameter optimization (eps = 10.000000)
[00:41:34 -312458.513636] AUTODETECT spr round 1 (radius: 5)
[00:41:45 -187872.533479] AUTODETECT spr round 2 (radius: 10)
[00:42:01 -134588.497520] AUTODETECT spr round 3 (radius: 15)
[00:42:19 -125230.073890] AUTODETECT spr round 4 (radius: 20)
[00:42:42 -124978.791820] AUTODETECT spr round 5 (radius: 25)
[00:43:00 -124874.227324] SPR radius for FAST iterations: 25 (autodetect)
[00:43:00 -124874.227324] Model parameter optimization (eps = 3.000000)
[00:43:13 -124133.359462] FAST spr round 1 (radius: 25)
[00:43:32 -116369.086878] FAST spr round 2 (radius: 25)
[00:43:46 -116215.721269] FAST spr round 3 (radius: 25)
[00:43:57 -116211.590037] FAST spr round 4 (radius: 25)
[00:44:07 -116208.697296] FAST spr round 5 (radius: 25)
[00:44:17 -116208.359277] FAST spr round 6 (radius: 25)
[00:44:27 -116207.036191] FAST spr round 7 (radius: 25)
[00:44:37 -116206.753261] FAST spr round 8 (radius: 25)
[00:44:46 -116206.515499] FAST spr round 9 (radius: 25)
[00:44:56 -116206.205930] FAST spr round 10 (radius: 25)
[00:45:06 -116206.204661] Model parameter optimization (eps = 1.000000)
[00:45:10 -116203.436421] SLOW spr round 1 (radius: 5)
[00:45:31 -116191.893997] SLOW spr round 2 (radius: 5)
[00:45:49 -116190.746250] SLOW spr round 3 (radius: 5)
[00:46:07 -116190.745576] SLOW spr round 4 (radius: 10)
[00:46:27 -116190.744984] SLOW spr round 5 (radius: 15)
[00:47:02 -116190.744391] SLOW spr round 6 (radius: 20)
[00:47:44 -116190.743797] SLOW spr round 7 (radius: 25)
[00:48:25 -116190.743200] Model parameter optimization (eps = 0.100000)

[00:48:27] ML tree search #8, logLikelihood: -116190.677480

[00:48:27 -392271.658803] Initial branch length optimization
[00:48:27 -304078.015461] Model parameter optimization (eps = 10.000000)
[00:48:38 -303621.176406] AUTODETECT spr round 1 (radius: 5)
[00:48:49 -184811.540756] AUTODETECT spr round 2 (radius: 10)
[00:49:03 -152464.863923] AUTODETECT spr round 3 (radius: 15)
[00:49:21 -131452.127911] AUTODETECT spr round 4 (radius: 20)
[00:49:45 -129548.771236] AUTODETECT spr round 5 (radius: 25)
[00:50:09 -129548.715532] SPR radius for FAST iterations: 20 (autodetect)
[00:50:09 -129548.715532] Model parameter optimization (eps = 3.000000)
[00:50:22 -128666.193334] FAST spr round 1 (radius: 20)
[00:50:41 -116791.205650] FAST spr round 2 (radius: 20)
[00:50:55 -116244.168379] FAST spr round 3 (radius: 20)
[00:51:05 -116244.059399] FAST spr round 4 (radius: 20)
[00:51:15 -116244.058861] Model parameter optimization (eps = 1.000000)
[00:51:19 -116241.271267] SLOW spr round 1 (radius: 5)
[00:51:40 -116206.011999] SLOW spr round 2 (radius: 5)
[00:52:00 -116194.379539] SLOW spr round 3 (radius: 5)
[00:52:19 -116189.020980] SLOW spr round 4 (radius: 5)
[00:52:38 -116188.555862] SLOW spr round 5 (radius: 5)
[00:52:56 -116188.555243] SLOW spr round 6 (radius: 10)
[00:53:17 -116188.554684] SLOW spr round 7 (radius: 15)
[00:53:54 -116188.554126] SLOW spr round 8 (radius: 20)
[00:54:37 -116188.553565] SLOW spr round 9 (radius: 25)
[00:55:16 -116188.553004] Model parameter optimization (eps = 0.100000)

[00:55:19] ML tree search #9, logLikelihood: -116188.303480

[00:55:19 -380977.648233] Initial branch length optimization
[00:55:20 -297122.278633] Model parameter optimization (eps = 10.000000)
[00:55:31 -296590.773400] AUTODETECT spr round 1 (radius: 5)
[00:55:42 -190758.604664] AUTODETECT spr round 2 (radius: 10)
[00:55:56 -144943.490930] AUTODETECT spr round 3 (radius: 15)
[00:56:13 -130616.060049] AUTODETECT spr round 4 (radius: 20)
[00:56:34 -129799.404052] AUTODETECT spr round 5 (radius: 25)
[00:56:54 -129797.494892] SPR radius for FAST iterations: 25 (autodetect)
[00:56:54 -129797.494892] Model parameter optimization (eps = 3.000000)
[00:57:07 -129156.388120] FAST spr round 1 (radius: 25)
[00:57:24 -116794.043793] FAST spr round 2 (radius: 25)
[00:57:40 -116250.172277] FAST spr round 3 (radius: 25)
[00:57:53 -116205.048702] FAST spr round 4 (radius: 25)
[00:58:03 -116204.350729] FAST spr round 5 (radius: 25)
[00:58:13 -116204.028262] FAST spr round 6 (radius: 25)
[00:58:23 -116204.013612] Model parameter optimization (eps = 1.000000)
[00:58:30 -116197.160342] SLOW spr round 1 (radius: 5)
[00:58:50 -116182.805794] SLOW spr round 2 (radius: 5)
[00:59:09 -116181.676289] SLOW spr round 3 (radius: 5)
[00:59:27 -116181.674221] SLOW spr round 4 (radius: 10)
[00:59:47 -116181.673773] SLOW spr round 5 (radius: 15)
[01:00:22 -116181.673687] SLOW spr round 6 (radius: 20)
[01:01:03 -116181.673669] SLOW spr round 7 (radius: 25)
[01:01:44 -116181.673664] Model parameter optimization (eps = 0.100000)

[01:01:46] ML tree search #10, logLikelihood: -116181.603367

[01:01:46 -390458.009771] Initial branch length optimization
[01:01:47 -307427.966705] Model parameter optimization (eps = 10.000000)
[01:01:57 -306948.548220] AUTODETECT spr round 1 (radius: 5)
[01:02:08 -191039.394894] AUTODETECT spr round 2 (radius: 10)
[01:02:22 -148067.554261] AUTODETECT spr round 3 (radius: 15)
[01:02:38 -132181.841236] AUTODETECT spr round 4 (radius: 20)
[01:02:58 -128018.496927] AUTODETECT spr round 5 (radius: 25)
[01:03:18 -128010.649556] SPR radius for FAST iterations: 25 (autodetect)
[01:03:18 -128010.649556] Model parameter optimization (eps = 3.000000)
[01:03:30 -127241.013954] FAST spr round 1 (radius: 25)
[01:03:46 -116684.777507] FAST spr round 2 (radius: 25)
[01:04:01 -116307.826097] FAST spr round 3 (radius: 25)
[01:04:14 -116233.401130] FAST spr round 4 (radius: 25)
[01:04:24 -116230.484097] FAST spr round 5 (radius: 25)
[01:04:35 -116229.708792] FAST spr round 6 (radius: 25)
[01:04:45 -116227.443333] FAST spr round 7 (radius: 25)
[01:04:55 -116227.119209] FAST spr round 8 (radius: 25)
[01:05:06 -116226.129418] FAST spr round 9 (radius: 25)
[01:05:16 -116225.857882] FAST spr round 10 (radius: 25)
[01:05:26 -116225.720539] FAST spr round 11 (radius: 25)
[01:05:36 -116223.926658] FAST spr round 12 (radius: 25)
[01:05:46 -116223.854015] Model parameter optimization (eps = 1.000000)
[01:05:51 -116219.247912] SLOW spr round 1 (radius: 5)
[01:06:13 -116193.600029] SLOW spr round 2 (radius: 5)
[01:06:31 -116186.310736] SLOW spr round 3 (radius: 5)
[01:06:49 -116186.310235] SLOW spr round 4 (radius: 10)
[01:07:10 -116186.310187] SLOW spr round 5 (radius: 15)
[01:07:45 -116186.310179] SLOW spr round 6 (radius: 20)
[01:08:25 -116186.310177] SLOW spr round 7 (radius: 25)
[01:09:05 -116186.310176] Model parameter optimization (eps = 0.100000)

[01:09:07] ML tree search #11, logLikelihood: -116186.236058

[01:09:07 -391039.797823] Initial branch length optimization
[01:09:08 -307353.201345] Model parameter optimization (eps = 10.000000)
[01:09:21 -306873.169877] AUTODETECT spr round 1 (radius: 5)
[01:09:32 -184412.760979] AUTODETECT spr round 2 (radius: 10)
[01:09:46 -138395.634825] AUTODETECT spr round 3 (radius: 15)
[01:10:04 -125227.897776] AUTODETECT spr round 4 (radius: 20)
[01:10:28 -124908.984446] AUTODETECT spr round 5 (radius: 25)
[01:10:48 -124869.495287] SPR radius for FAST iterations: 25 (autodetect)
[01:10:48 -124869.495287] Model parameter optimization (eps = 3.000000)
[01:11:01 -124198.735626] FAST spr round 1 (radius: 25)
[01:11:19 -116576.036670] FAST spr round 2 (radius: 25)
[01:11:32 -116224.102508] FAST spr round 3 (radius: 25)
[01:11:44 -116212.237350] FAST spr round 4 (radius: 25)
[01:11:54 -116212.236637] Model parameter optimization (eps = 1.000000)
[01:11:59 -116208.121449] SLOW spr round 1 (radius: 5)
[01:12:21 -116187.872511] SLOW spr round 2 (radius: 5)
[01:12:40 -116184.270978] SLOW spr round 3 (radius: 5)
[01:12:59 -116182.328409] SLOW spr round 4 (radius: 5)
[01:13:16 -116182.324284] SLOW spr round 5 (radius: 10)
[01:13:36 -116182.322865] SLOW spr round 6 (radius: 15)
[01:14:13 -116182.322210] SLOW spr round 7 (radius: 20)
[01:14:56 -116182.321909] SLOW spr round 8 (radius: 25)
[01:15:35 -116182.321772] Model parameter optimization (eps = 0.100000)

[01:15:38] ML tree search #12, logLikelihood: -116182.079934

[01:15:38 -391810.993378] Initial branch length optimization
[01:15:39 -310120.154049] Model parameter optimization (eps = 10.000000)
[01:15:54 -309466.517084] AUTODETECT spr round 1 (radius: 5)
[01:16:05 -189439.080723] AUTODETECT spr round 2 (radius: 10)
[01:16:19 -147168.362064] AUTODETECT spr round 3 (radius: 15)
[01:16:37 -125454.724954] AUTODETECT spr round 4 (radius: 20)
[01:17:00 -125290.928755] AUTODETECT spr round 5 (radius: 25)
[01:17:23 -125289.348491] SPR radius for FAST iterations: 25 (autodetect)
[01:17:23 -125289.348491] Model parameter optimization (eps = 3.000000)
[01:17:36 -124610.765552] FAST spr round 1 (radius: 25)
[01:17:53 -116511.167778] FAST spr round 2 (radius: 25)
[01:18:07 -116229.658578] FAST spr round 3 (radius: 25)
[01:18:19 -116218.569371] FAST spr round 4 (radius: 25)
[01:18:30 -116218.009583] FAST spr round 5 (radius: 25)
[01:18:40 -116215.485349] FAST spr round 6 (radius: 25)
[01:18:50 -116215.484675] Model parameter optimization (eps = 1.000000)
[01:18:55 -116212.654562] SLOW spr round 1 (radius: 5)
[01:19:16 -116188.328949] SLOW spr round 2 (radius: 5)
[01:19:35 -116186.563409] SLOW spr round 3 (radius: 5)
[01:19:53 -116186.560856] SLOW spr round 4 (radius: 10)
[01:20:14 -116186.559836] SLOW spr round 5 (radius: 15)
[01:20:49 -116186.559364] SLOW spr round 6 (radius: 20)
[01:21:31 -116186.559128] SLOW spr round 7 (radius: 25)
[01:22:11 -116186.559005] Model parameter optimization (eps = 0.100000)

[01:22:14] ML tree search #13, logLikelihood: -116186.456865

[01:22:14 -400655.499231] Initial branch length optimization
[01:22:15 -314671.592773] Model parameter optimization (eps = 10.000000)
[01:22:30 -314140.537331] AUTODETECT spr round 1 (radius: 5)
[01:22:42 -188319.858978] AUTODETECT spr round 2 (radius: 10)
[01:22:56 -146471.024969] AUTODETECT spr round 3 (radius: 15)
[01:23:15 -134297.687316] AUTODETECT spr round 4 (radius: 20)
[01:23:34 -133705.858060] AUTODETECT spr round 5 (radius: 25)
[01:23:52 -132388.820721] SPR radius for FAST iterations: 25 (autodetect)
[01:23:52 -132388.820721] Model parameter optimization (eps = 3.000000)
[01:24:08 -131696.143793] FAST spr round 1 (radius: 25)
[01:24:26 -117011.691723] FAST spr round 2 (radius: 25)
[01:24:41 -116295.696847] FAST spr round 3 (radius: 25)
[01:24:54 -116224.381210] FAST spr round 4 (radius: 25)
[01:25:05 -116220.853804] FAST spr round 5 (radius: 25)
[01:25:15 -116220.509102] FAST spr round 6 (radius: 25)
[01:25:25 -116220.346732] FAST spr round 7 (radius: 25)
[01:25:34 -116220.338945] Model parameter optimization (eps = 1.000000)
[01:25:40 -116209.630069] SLOW spr round 1 (radius: 5)
[01:26:01 -116185.069325] SLOW spr round 2 (radius: 5)
[01:26:20 -116182.563379] SLOW spr round 3 (radius: 5)
[01:26:39 -116180.832504] SLOW spr round 4 (radius: 5)
[01:26:57 -116180.826450] SLOW spr round 5 (radius: 10)
[01:27:17 -116180.823641] SLOW spr round 6 (radius: 15)
[01:27:51 -116180.822261] SLOW spr round 7 (radius: 20)
[01:28:32 -116180.821607] SLOW spr round 8 (radius: 25)
[01:29:13 -116180.821306] Model parameter optimization (eps = 0.100000)

[01:29:15] ML tree search #14, logLikelihood: -116180.684337

[01:29:15 -395002.104840] Initial branch length optimization
[01:29:16 -306383.072964] Model parameter optimization (eps = 10.000000)
[01:29:27 -305848.481183] AUTODETECT spr round 1 (radius: 5)
[01:29:37 -197445.901921] AUTODETECT spr round 2 (radius: 10)
[01:29:51 -144231.868592] AUTODETECT spr round 3 (radius: 15)
[01:30:08 -127390.854251] AUTODETECT spr round 4 (radius: 20)
[01:30:24 -126393.360197] AUTODETECT spr round 5 (radius: 25)
[01:30:45 -126393.346093] SPR radius for FAST iterations: 20 (autodetect)
[01:30:45 -126393.346093] Model parameter optimization (eps = 3.000000)
[01:30:57 -125655.326750] FAST spr round 1 (radius: 20)
[01:31:13 -116450.057424] FAST spr round 2 (radius: 20)
[01:31:26 -116295.243092] FAST spr round 3 (radius: 20)
[01:31:37 -116229.764312] FAST spr round 4 (radius: 20)
[01:31:46 -116225.092944] FAST spr round 5 (radius: 20)
[01:31:55 -116224.377043] FAST spr round 6 (radius: 20)
[01:32:04 -116224.350368] Model parameter optimization (eps = 1.000000)
[01:32:08 -116222.223442] SLOW spr round 1 (radius: 5)
[01:32:28 -116197.068029] SLOW spr round 2 (radius: 5)
[01:32:47 -116194.292684] SLOW spr round 3 (radius: 5)
[01:33:05 -116194.291430] SLOW spr round 4 (radius: 10)
[01:33:25 -116194.290748] SLOW spr round 5 (radius: 15)
[01:34:00 -116194.290121] SLOW spr round 6 (radius: 20)
[01:34:41 -116194.289519] SLOW spr round 7 (radius: 25)
[01:35:20 -116194.288928] Model parameter optimization (eps = 0.100000)

[01:35:22] ML tree search #15, logLikelihood: -116194.265762

[01:35:22 -407437.331234] Initial branch length optimization
[01:35:23 -320526.966286] Model parameter optimization (eps = 10.000000)
[01:35:37 -319954.131630] AUTODETECT spr round 1 (radius: 5)
[01:35:48 -192399.359468] AUTODETECT spr round 2 (radius: 10)
[01:36:02 -139840.439881] AUTODETECT spr round 3 (radius: 15)
[01:36:18 -125681.158229] AUTODETECT spr round 4 (radius: 20)
[01:36:38 -125129.199131] AUTODETECT spr round 5 (radius: 25)
[01:36:59 -125129.177207] SPR radius for FAST iterations: 20 (autodetect)
[01:36:59 -125129.177207] Model parameter optimization (eps = 3.000000)
[01:37:11 -124368.837205] FAST spr round 1 (radius: 20)
[01:37:28 -116831.949386] FAST spr round 2 (radius: 20)
[01:37:43 -116269.914890] FAST spr round 3 (radius: 20)
[01:37:56 -116201.196022] FAST spr round 4 (radius: 20)
[01:38:06 -116199.138865] FAST spr round 5 (radius: 20)
[01:38:16 -116198.169599] FAST spr round 6 (radius: 20)
[01:38:26 -116197.439310] FAST spr round 7 (radius: 20)
[01:38:36 -116196.728727] FAST spr round 8 (radius: 20)
[01:38:45 -116196.728135] Model parameter optimization (eps = 1.000000)
[01:38:51 -116192.370773] SLOW spr round 1 (radius: 5)
[01:39:11 -116182.273412] SLOW spr round 2 (radius: 5)
[01:39:29 -116181.831758] SLOW spr round 3 (radius: 5)
[01:39:47 -116181.830299] SLOW spr round 4 (radius: 10)
[01:40:08 -116181.829304] SLOW spr round 5 (radius: 15)
[01:40:44 -116181.828510] SLOW spr round 6 (radius: 20)
[01:41:27 -116181.827814] SLOW spr round 7 (radius: 25)
[01:42:06 -116181.827165] Model parameter optimization (eps = 0.100000)

[01:42:08] ML tree search #16, logLikelihood: -116181.776861

[01:42:08 -387753.194993] Initial branch length optimization
[01:42:08 -299653.997497] Model parameter optimization (eps = 10.000000)
[01:42:20 -299202.513599] AUTODETECT spr round 1 (radius: 5)
[01:42:31 -199658.320573] AUTODETECT spr round 2 (radius: 10)
[01:42:44 -146457.715279] AUTODETECT spr round 3 (radius: 15)
[01:43:01 -133551.678929] AUTODETECT spr round 4 (radius: 20)
[01:43:20 -130937.934362] AUTODETECT spr round 5 (radius: 25)
[01:43:37 -130892.202702] SPR radius for FAST iterations: 25 (autodetect)
[01:43:38 -130892.202702] Model parameter optimization (eps = 3.000000)
[01:43:51 -130202.706745] FAST spr round 1 (radius: 25)
[01:44:09 -117017.559126] FAST spr round 2 (radius: 25)
[01:44:25 -116239.631507] FAST spr round 3 (radius: 25)
[01:44:36 -116218.253175] FAST spr round 4 (radius: 25)
[01:44:47 -116213.095485] FAST spr round 5 (radius: 25)
[01:44:57 -116212.627198] FAST spr round 6 (radius: 25)
[01:45:07 -116212.258273] FAST spr round 7 (radius: 25)
[01:45:17 -116211.395324] FAST spr round 8 (radius: 25)
[01:45:27 -116211.085948] FAST spr round 9 (radius: 25)
[01:45:37 -116210.786904] FAST spr round 10 (radius: 25)
[01:45:47 -116210.505312] FAST spr round 11 (radius: 25)
[01:45:57 -116210.000340] FAST spr round 12 (radius: 25)
[01:46:07 -116209.882352] FAST spr round 13 (radius: 25)
[01:46:17 -116209.791213] Model parameter optimization (eps = 1.000000)
[01:46:21 -116206.080264] SLOW spr round 1 (radius: 5)
[01:46:42 -116187.510936] SLOW spr round 2 (radius: 5)
[01:47:01 -116184.594117] SLOW spr round 3 (radius: 5)
[01:47:19 -116184.233499] SLOW spr round 4 (radius: 5)
[01:47:37 -116184.231542] SLOW spr round 5 (radius: 10)
[01:47:57 -116184.230828] SLOW spr round 6 (radius: 15)
[01:48:31 -116184.230560] SLOW spr round 7 (radius: 20)
[01:49:12 -116184.230460] SLOW spr round 8 (radius: 25)
[01:49:51 -116184.230422] Model parameter optimization (eps = 0.100000)

[01:49:53] ML tree search #17, logLikelihood: -116184.061435

[01:49:53 -400491.962412] Initial branch length optimization
[01:49:54 -309313.462557] Model parameter optimization (eps = 10.000000)
[01:50:04 -308847.179787] AUTODETECT spr round 1 (radius: 5)
[01:50:14 -197496.357082] AUTODETECT spr round 2 (radius: 10)
[01:50:28 -150047.683513] AUTODETECT spr round 3 (radius: 15)
[01:50:46 -130284.473524] AUTODETECT spr round 4 (radius: 20)
[01:51:04 -126347.386447] AUTODETECT spr round 5 (radius: 25)
[01:51:24 -126347.351938] SPR radius for FAST iterations: 20 (autodetect)
[01:51:24 -126347.351938] Model parameter optimization (eps = 3.000000)
[01:51:37 -125628.584221] FAST spr round 1 (radius: 20)
[01:51:53 -116644.012395] FAST spr round 2 (radius: 20)
[01:52:06 -116219.207647] FAST spr round 3 (radius: 20)
[01:52:17 -116213.613722] FAST spr round 4 (radius: 20)
[01:52:27 -116212.875843] FAST spr round 5 (radius: 20)
[01:52:37 -116211.148425] FAST spr round 6 (radius: 20)
[01:52:47 -116210.744521] FAST spr round 7 (radius: 20)
[01:52:57 -116210.577728] FAST spr round 8 (radius: 20)
[01:53:07 -116208.100053] FAST spr round 9 (radius: 20)
[01:53:16 -116205.744852] FAST spr round 10 (radius: 20)
[01:53:26 -116205.744694] Model parameter optimization (eps = 1.000000)
[01:53:31 -116201.317009] SLOW spr round 1 (radius: 5)
[01:53:52 -116191.515813] SLOW spr round 2 (radius: 5)
[01:54:10 -116190.155544] SLOW spr round 3 (radius: 5)
[01:54:28 -116190.151657] SLOW spr round 4 (radius: 10)
[01:54:48 -116190.151492] SLOW spr round 5 (radius: 15)
[01:55:25 -116190.151485] SLOW spr round 6 (radius: 20)
[01:56:08 -116190.151485] SLOW spr round 7 (radius: 25)
[01:56:47 -116190.151484] Model parameter optimization (eps = 0.100000)

[01:56:49] ML tree search #18, logLikelihood: -116190.059823

[01:56:49 -399149.641217] Initial branch length optimization
[01:56:50 -307980.567531] Model parameter optimization (eps = 10.000000)
[01:57:02 -307505.397371] AUTODETECT spr round 1 (radius: 5)
[01:57:13 -190302.973205] AUTODETECT spr round 2 (radius: 10)
[01:57:27 -142705.208254] AUTODETECT spr round 3 (radius: 15)
[01:57:44 -129057.075030] AUTODETECT spr round 4 (radius: 20)
[01:58:07 -127908.540264] AUTODETECT spr round 5 (radius: 25)
[01:58:31 -127908.524312] SPR radius for FAST iterations: 20 (autodetect)
[01:58:31 -127908.524312] Model parameter optimization (eps = 3.000000)
[01:58:44 -127198.447960] FAST spr round 1 (radius: 20)
[01:59:01 -116544.619583] FAST spr round 2 (radius: 20)
[01:59:15 -116220.778944] FAST spr round 3 (radius: 20)
[01:59:25 -116207.883379] FAST spr round 4 (radius: 20)
[01:59:35 -116207.160334] FAST spr round 5 (radius: 20)
[01:59:44 -116207.159764] Model parameter optimization (eps = 1.000000)
[01:59:50 -116202.438899] SLOW spr round 1 (radius: 5)
[02:00:11 -116183.603151] SLOW spr round 2 (radius: 5)
[02:00:29 -116183.601172] SLOW spr round 3 (radius: 10)
[02:00:49 -116183.600402] SLOW spr round 4 (radius: 15)
[02:01:23 -116183.599757] SLOW spr round 5 (radius: 20)
[02:02:04 -116183.599135] SLOW spr round 6 (radius: 25)
[02:02:44 -116183.598528] Model parameter optimization (eps = 0.100000)

[02:02:47] ML tree search #19, logLikelihood: -116183.385271

[02:02:47 -387631.081341] Initial branch length optimization
[02:02:48 -297124.952836] Model parameter optimization (eps = 10.000000)
[02:02:59 -296643.256163] AUTODETECT spr round 1 (radius: 5)
[02:03:09 -200014.305908] AUTODETECT spr round 2 (radius: 10)
[02:03:23 -142423.327376] AUTODETECT spr round 3 (radius: 15)
[02:03:41 -128203.242197] AUTODETECT spr round 4 (radius: 20)
[02:04:02 -125855.501865] AUTODETECT spr round 5 (radius: 25)
[02:04:24 -125814.157421] SPR radius for FAST iterations: 25 (autodetect)
[02:04:24 -125814.157421] Model parameter optimization (eps = 3.000000)
[02:04:37 -125045.444726] FAST spr round 1 (radius: 25)
[02:04:53 -116428.808944] FAST spr round 2 (radius: 25)
[02:05:06 -116244.216133] FAST spr round 3 (radius: 25)
[02:05:16 -116202.123770] FAST spr round 4 (radius: 25)
[02:05:25 -116201.939430] FAST spr round 5 (radius: 25)
[02:05:34 -116201.936202] Model parameter optimization (eps = 1.000000)
[02:05:39 -116200.674167] SLOW spr round 1 (radius: 5)
[02:05:58 -116183.726545] SLOW spr round 2 (radius: 5)
[02:06:16 -116182.997510] SLOW spr round 3 (radius: 5)
[02:06:33 -116182.994227] SLOW spr round 4 (radius: 10)
[02:06:53 -116182.993670] SLOW spr round 5 (radius: 15)
[02:07:27 -116182.993578] SLOW spr round 6 (radius: 20)
[02:08:07 -116182.993563] SLOW spr round 7 (radius: 25)
[02:08:47 -116182.993560] Model parameter optimization (eps = 0.100000)

[02:08:48] ML tree search #20, logLikelihood: -116182.943276


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.232142,0.452889) (0.133087,1.083679) (0.268431,0.646359) (0.366341,1.575417) 
   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: -116180.684337

AIC score: 233307.368674 / AICc score: 233895.825367 / BIC score: 235728.956331
Free parameters (model + branch lengths): 473

Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5.raxml.bestTree
All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5.raxml.mlTrees
Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5.raxml.bestModel

Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/020621_run/phylogeny-snakemake/results/Q86YV5/3_mltree/Q86YV5.raxml.log

Analysis started: 04-Jun-2021 03:53:03 / finished: 04-Jun-2021 06:01:52

Elapsed time: 7729.040 seconds (this run) / 8431.586 seconds (total with restarts)

Consumed energy: 978.628 Wh (= 5 km in an electric car, or 24 km with an e-scooter!)