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 03-Jul-2021 11:32:30 as follows:

raxml-ng --msa /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/2_msa/Q8WVS4_trimmed_msa.fasta --data-type AA --model LG4X --prefix /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/3_mltree/Q8WVS4 --seed 2 --threads 8 --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 (8 threads), thread pinning: OFF

[00:00:00] Reading alignment from file: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/2_msa/Q8WVS4_trimmed_msa.fasta
[00:00:00] Loaded alignment with 122 taxa and 1003 sites

Alignment comprises 1 partitions and 1003 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 1003 / 1003
Gaps: 18.77 %
Invariant sites: 0.90 %


NOTE: Binary MSA file created: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/3_mltree/Q8WVS4.raxml.rba

Parallelization scheme autoconfig: 2 worker(s) x 4 thread(s)

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

[00:00:00] Generating 20 random starting tree(s) with 122 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 251 / 20080
[00:00:00] Data distribution: max. searches per worker: 10

Starting ML tree search with 20 distinct starting trees

[00:00:00 -169393.022944] Initial branch length optimization
[00:00:00 -136499.830237] Model parameter optimization (eps = 10.000000)
[00:00:12 -135969.839209] AUTODETECT spr round 1 (radius: 5)
[00:00:17 -108850.002719] AUTODETECT spr round 2 (radius: 10)
[00:00:26 -98093.962572] AUTODETECT spr round 3 (radius: 15)
[00:00:35 -92363.254272] AUTODETECT spr round 4 (radius: 20)
[00:00:45 -91565.431308] AUTODETECT spr round 5 (radius: 25)
[00:00:53 -91565.397695] SPR radius for FAST iterations: 20 (autodetect)
[00:00:53 -91565.397695] Model parameter optimization (eps = 3.000000)
[00:00:59 -91390.824943] FAST spr round 1 (radius: 20)
[00:01:10 -89568.038834] FAST spr round 2 (radius: 20)
[00:01:18 -89477.050724] FAST spr round 3 (radius: 20)
[00:01:26 -89469.050945] FAST spr round 4 (radius: 20)
[00:01:34 -89467.156137] FAST spr round 5 (radius: 20)
[00:01:41 -89466.482856] FAST spr round 6 (radius: 20)
[00:01:48 -89465.368848] FAST spr round 7 (radius: 20)
[00:01:56 -89462.884537] FAST spr round 8 (radius: 20)
[00:02:03 -89462.831128] Model parameter optimization (eps = 1.000000)
[00:02:06 -89457.089086] SLOW spr round 1 (radius: 5)
[00:02:20 -89451.321434] SLOW spr round 2 (radius: 5)
[00:02:34 -89450.173942] SLOW spr round 3 (radius: 5)
[00:02:47 -89450.068213] SLOW spr round 4 (radius: 5)
[00:03:01 -89450.008884] SLOW spr round 5 (radius: 10)
[00:03:16 -89450.004362] SLOW spr round 6 (radius: 15)
[00:03:40 -89450.003345] SLOW spr round 7 (radius: 20)
[00:04:03] [worker #1] ML tree search #2, logLikelihood: -89449.886886
[00:04:04 -89450.003107] SLOW spr round 8 (radius: 25)
[00:04:16 -89450.003051] Model parameter optimization (eps = 0.100000)

[00:04:18] [worker #0] ML tree search #1, logLikelihood: -89449.911558

[00:04:19 -170994.709286] Initial branch length optimization
[00:04:19 -138502.625272] Model parameter optimization (eps = 10.000000)
[00:04:30 -137859.401695] AUTODETECT spr round 1 (radius: 5)
[00:04:35 -109031.317857] AUTODETECT spr round 2 (radius: 10)
[00:04:43 -97472.452711] AUTODETECT spr round 3 (radius: 15)
[00:04:54 -95953.951345] AUTODETECT spr round 4 (radius: 20)
[00:05:04 -95953.894773] SPR radius for FAST iterations: 15 (autodetect)
[00:05:04 -95953.894773] Model parameter optimization (eps = 3.000000)
[00:05:11 -95730.969108] FAST spr round 1 (radius: 15)
[00:05:23 -89635.745793] FAST spr round 2 (radius: 15)
[00:05:34 -89490.822165] FAST spr round 3 (radius: 15)
[00:05:42 -89475.795085] FAST spr round 4 (radius: 15)
[00:05:49 -89473.870280] FAST spr round 5 (radius: 15)
[00:05:57 -89472.520748] FAST spr round 6 (radius: 15)
[00:06:04 -89471.370884] FAST spr round 7 (radius: 15)
[00:06:11 -89462.661107] FAST spr round 8 (radius: 15)
[00:06:18 -89461.994645] FAST spr round 9 (radius: 15)
[00:06:24 -89461.384577] FAST spr round 10 (radius: 15)
[00:06:31 -89461.276842] FAST spr round 11 (radius: 15)
[00:06:38 -89461.265639] Model parameter optimization (eps = 1.000000)
[00:06:43 -89451.998957] SLOW spr round 1 (radius: 5)
[00:06:56 -89450.796939] SLOW spr round 2 (radius: 5)
[00:07:10 -89450.282423] SLOW spr round 3 (radius: 5)
[00:07:23 -89450.211023] SLOW spr round 4 (radius: 10)
[00:07:39 -89450.211014] SLOW spr round 5 (radius: 15)
[00:07:49] [worker #1] ML tree search #4, logLikelihood: -89449.373284
[00:08:02 -89450.211014] SLOW spr round 6 (radius: 20)
[00:08:26 -89450.211014] SLOW spr round 7 (radius: 25)
[00:08:38 -89450.211014] Model parameter optimization (eps = 0.100000)

[00:08:39] [worker #0] ML tree search #3, logLikelihood: -89450.145901

[00:08:40 -169194.819648] Initial branch length optimization
[00:08:40 -138229.156982] Model parameter optimization (eps = 10.000000)
[00:08:53 -137683.273384] AUTODETECT spr round 1 (radius: 5)
[00:08:59 -108878.259518] AUTODETECT spr round 2 (radius: 10)
[00:09:06 -102043.814965] AUTODETECT spr round 3 (radius: 15)
[00:09:16 -95961.590467] AUTODETECT spr round 4 (radius: 20)
[00:09:25 -95565.006704] AUTODETECT spr round 5 (radius: 25)
[00:09:33 -95560.118406] SPR radius for FAST iterations: 25 (autodetect)
[00:09:33 -95560.118406] Model parameter optimization (eps = 3.000000)
[00:09:39 -95349.598429] FAST spr round 1 (radius: 25)
[00:09:50 -89571.622641] FAST spr round 2 (radius: 25)
[00:09:59 -89491.734584] FAST spr round 3 (radius: 25)
[00:10:07 -89479.139593] FAST spr round 4 (radius: 25)
[00:10:15 -89474.080667] FAST spr round 5 (radius: 25)
[00:10:22 -89469.835061] FAST spr round 6 (radius: 25)
[00:10:30 -89465.865890] FAST spr round 7 (radius: 25)
[00:10:37 -89465.749089] FAST spr round 8 (radius: 25)
[00:10:44 -89465.702799] Model parameter optimization (eps = 1.000000)
[00:10:48 -89461.125380] SLOW spr round 1 (radius: 5)
[00:11:01 -89450.970451] SLOW spr round 2 (radius: 5)
[00:11:15 -89450.369065] SLOW spr round 3 (radius: 5)
[00:11:28 -89450.042561] SLOW spr round 4 (radius: 5)
[00:11:41 -89449.625695] SLOW spr round 5 (radius: 5)
[00:11:54 -89449.624942] SLOW spr round 6 (radius: 10)
[00:11:58] [worker #1] ML tree search #6, logLikelihood: -89449.886797
[00:12:10 -89449.624926] SLOW spr round 7 (radius: 15)
[00:12:35 -89449.624925] SLOW spr round 8 (radius: 20)
[00:12:57 -89449.624925] SLOW spr round 9 (radius: 25)
[00:13:07 -89449.624925] Model parameter optimization (eps = 0.100000)

[00:13:10] [worker #0] ML tree search #5, logLikelihood: -89449.266726

[00:13:10 -167475.531665] Initial branch length optimization
[00:13:11 -138012.938351] Model parameter optimization (eps = 10.000000)
[00:13:23 -137440.955259] AUTODETECT spr round 1 (radius: 5)
[00:13:29 -108748.285857] AUTODETECT spr round 2 (radius: 10)
[00:13:36 -95884.567568] AUTODETECT spr round 3 (radius: 15)
[00:13:45 -93285.553799] AUTODETECT spr round 4 (radius: 20)
[00:13:56 -93285.513711] SPR radius for FAST iterations: 15 (autodetect)
[00:13:56 -93285.513711] Model parameter optimization (eps = 3.000000)
[00:14:01 -93110.095434] FAST spr round 1 (radius: 15)
[00:14:12 -90658.201015] FAST spr round 2 (radius: 15)
[00:14:21 -89466.165500] FAST spr round 3 (radius: 15)
[00:14:29 -89456.814159] FAST spr round 4 (radius: 15)
[00:14:36 -89455.271660] FAST spr round 5 (radius: 15)
[00:14:43 -89454.727145] FAST spr round 6 (radius: 15)
[00:14:50 -89454.631266] Model parameter optimization (eps = 1.000000)
[00:14:54 -89450.359190] SLOW spr round 1 (radius: 5)
[00:15:07 -89449.042238] SLOW spr round 2 (radius: 5)
[00:15:20 -89448.799415] SLOW spr round 3 (radius: 5)
[00:15:33 -89448.798597] SLOW spr round 4 (radius: 10)
[00:15:37] [worker #1] ML tree search #8, logLikelihood: -89448.610846
[00:15:49 -89448.798581] SLOW spr round 5 (radius: 15)
[00:16:15 -89448.798580] SLOW spr round 6 (radius: 20)
[00:16:37 -89448.798580] SLOW spr round 7 (radius: 25)
[00:16:48 -89448.798580] Model parameter optimization (eps = 0.100000)

[00:16:49] [worker #0] ML tree search #7, logLikelihood: -89448.756664

[00:16:49 -166107.470511] Initial branch length optimization
[00:16:49 -136098.332587] Model parameter optimization (eps = 10.000000)
[00:17:00 -135553.134879] AUTODETECT spr round 1 (radius: 5)
[00:17:06 -108294.397834] AUTODETECT spr round 2 (radius: 10)
[00:17:14 -97718.485389] AUTODETECT spr round 3 (radius: 15)
[00:17:23 -95793.812273] AUTODETECT spr round 4 (radius: 20)
[00:17:33 -95754.256031] AUTODETECT spr round 5 (radius: 25)
[00:17:42 -95752.063776] SPR radius for FAST iterations: 25 (autodetect)
[00:17:42 -95752.063776] Model parameter optimization (eps = 3.000000)
[00:17:48 -95573.047758] FAST spr round 1 (radius: 25)
[00:18:00 -89757.850634] FAST spr round 2 (radius: 25)
[00:18:10 -89484.695632] FAST spr round 3 (radius: 25)
[00:18:19 -89473.374435] FAST spr round 4 (radius: 25)
[00:18:26 -89470.191851] FAST spr round 5 (radius: 25)
[00:18:34 -89467.489804] FAST spr round 6 (radius: 25)
[00:18:41 -89465.617868] FAST spr round 7 (radius: 25)
[00:18:49 -89465.144279] FAST spr round 8 (radius: 25)
[00:18:56 -89465.010347] FAST spr round 9 (radius: 25)
[00:19:03 -89464.901348] FAST spr round 10 (radius: 25)
[00:19:11 -89464.858910] Model parameter optimization (eps = 1.000000)
[00:19:15 -89457.976010] SLOW spr round 1 (radius: 5)
[00:19:29 -89448.880096] SLOW spr round 2 (radius: 5)
[00:19:43 -89448.605592] SLOW spr round 3 (radius: 5)
[00:19:56 -89448.467518] SLOW spr round 4 (radius: 5)
[00:20:06] [worker #1] ML tree search #10, logLikelihood: -89449.886874
[00:20:10 -89448.467495] SLOW spr round 5 (radius: 10)
[00:20:26 -89448.467491] SLOW spr round 6 (radius: 15)
[00:20:50 -89448.467490] SLOW spr round 7 (radius: 20)
[00:21:13 -89448.467490] SLOW spr round 8 (radius: 25)
[00:21:24 -89448.467490] Model parameter optimization (eps = 0.100000)

[00:21:25] [worker #0] ML tree search #9, logLikelihood: -89448.428981

[00:21:25 -166906.190967] Initial branch length optimization
[00:21:26 -136670.185718] Model parameter optimization (eps = 10.000000)
[00:21:38 -136084.933038] AUTODETECT spr round 1 (radius: 5)
[00:21:43 -110554.157626] AUTODETECT spr round 2 (radius: 10)
[00:21:52 -92941.452459] AUTODETECT spr round 3 (radius: 15)
[00:22:00 -91832.268640] AUTODETECT spr round 4 (radius: 20)
[00:22:10 -91832.247069] SPR radius for FAST iterations: 15 (autodetect)
[00:22:10 -91832.247069] Model parameter optimization (eps = 3.000000)
[00:22:16 -91624.303286] FAST spr round 1 (radius: 15)
[00:22:25 -89563.723657] FAST spr round 2 (radius: 15)
[00:22:33 -89496.649695] FAST spr round 3 (radius: 15)
[00:22:41 -89490.098162] FAST spr round 4 (radius: 15)
[00:22:48 -89488.328773] FAST spr round 5 (radius: 15)
[00:22:56 -89486.658362] FAST spr round 6 (radius: 15)
[00:23:03 -89486.327456] FAST spr round 7 (radius: 15)
[00:23:10 -89485.572930] FAST spr round 8 (radius: 15)
[00:23:18 -89485.448744] FAST spr round 9 (radius: 15)
[00:23:25 -89484.308444] FAST spr round 10 (radius: 15)
[00:23:32 -89479.622233] FAST spr round 11 (radius: 15)
[00:23:39 -89478.336666] FAST spr round 12 (radius: 15)
[00:23:46 -89476.585744] FAST spr round 13 (radius: 15)
[00:23:54 -89475.093260] FAST spr round 14 (radius: 15)
[00:24:01 -89472.634017] FAST spr round 15 (radius: 15)
[00:24:08 -89470.662570] FAST spr round 16 (radius: 15)
[00:24:16 -89469.109571] FAST spr round 17 (radius: 15)
[00:24:23 -89468.786886] FAST spr round 18 (radius: 15)
[00:24:30 -89468.353086] FAST spr round 19 (radius: 15)
[00:24:37 -89464.901457] FAST spr round 20 (radius: 15)
[00:24:44 -89464.483942] FAST spr round 21 (radius: 15)
[00:24:51 -89464.173618] FAST spr round 22 (radius: 15)
[00:24:58 -89464.173465] Model parameter optimization (eps = 1.000000)
[00:25:02 -89460.871351] SLOW spr round 1 (radius: 5)
[00:25:15 -89451.774916] SLOW spr round 2 (radius: 5)
[00:25:29 -89450.551894] SLOW spr round 3 (radius: 5)
[00:25:42 -89449.228093] SLOW spr round 4 (radius: 5)
[00:25:47] [worker #1] ML tree search #12, logLikelihood: -89449.373242
[00:25:55 -89449.029371] SLOW spr round 5 (radius: 5)
[00:26:08 -89449.028590] SLOW spr round 6 (radius: 10)
[00:26:24 -89449.028576] SLOW spr round 7 (radius: 15)
[00:26:49 -89449.028575] SLOW spr round 8 (radius: 20)
[00:27:11 -89449.028575] SLOW spr round 9 (radius: 25)
[00:27:22 -89449.028575] Model parameter optimization (eps = 0.100000)

[00:27:25] [worker #0] ML tree search #11, logLikelihood: -89448.756360

[00:27:25 -167173.061571] Initial branch length optimization
[00:27:26 -136038.170068] Model parameter optimization (eps = 10.000000)
[00:27:37 -135475.282856] AUTODETECT spr round 1 (radius: 5)
[00:27:42 -111359.766134] AUTODETECT spr round 2 (radius: 10)
[00:27:50 -95915.013449] AUTODETECT spr round 3 (radius: 15)
[00:27:59 -93326.826421] AUTODETECT spr round 4 (radius: 20)
[00:28:08 -93261.042916] AUTODETECT spr round 5 (radius: 25)
[00:28:16 -93261.027404] SPR radius for FAST iterations: 20 (autodetect)
[00:28:16 -93261.027404] Model parameter optimization (eps = 3.000000)
[00:28:22 -93103.537898] FAST spr round 1 (radius: 20)
[00:28:33 -89521.109878] FAST spr round 2 (radius: 20)
[00:28:42 -89475.181968] FAST spr round 3 (radius: 20)
[00:28:50 -89463.958913] FAST spr round 4 (radius: 20)
[00:28:57 -89463.424313] FAST spr round 5 (radius: 20)
[00:29:04 -89463.310167] FAST spr round 6 (radius: 20)
[00:29:11 -89463.209741] FAST spr round 7 (radius: 20)
[00:29:19 -89463.202719] Model parameter optimization (eps = 1.000000)
[00:29:22 -89459.237988] SLOW spr round 1 (radius: 5)
[00:29:36 -89455.938456] SLOW spr round 2 (radius: 5)
[00:29:49 -89455.750276] SLOW spr round 3 (radius: 5)
[00:30:03 -89453.580051] SLOW spr round 4 (radius: 5)
[00:30:16 -89453.580027] SLOW spr round 5 (radius: 10)
[00:30:32 -89453.580024] SLOW spr round 6 (radius: 15)
[00:30:56 -89453.580024] SLOW spr round 7 (radius: 20)
[00:31:19 -89453.580024] SLOW spr round 8 (radius: 25)
[00:31:31 -89453.580024] Model parameter optimization (eps = 0.100000)

[00:31:33] [worker #0] ML tree search #13, logLikelihood: -89453.394988

[00:31:33 -168002.201424] Initial branch length optimization
[00:31:34 -137127.773304] Model parameter optimization (eps = 10.000000)
[00:31:39] [worker #1] ML tree search #14, logLikelihood: -89449.911102
[00:31:45 -136562.228357] AUTODETECT spr round 1 (radius: 5)
[00:31:51 -111423.378045] AUTODETECT spr round 2 (radius: 10)
[00:31:59 -98703.581191] AUTODETECT spr round 3 (radius: 15)
[00:32:08 -96123.215121] AUTODETECT spr round 4 (radius: 20)
[00:32:18 -94545.215963] AUTODETECT spr round 5 (radius: 25)
[00:32:25 -94545.202979] SPR radius for FAST iterations: 20 (autodetect)
[00:32:25 -94545.202979] Model parameter optimization (eps = 3.000000)
[00:32:31 -94343.031039] FAST spr round 1 (radius: 20)
[00:32:42 -89556.528663] FAST spr round 2 (radius: 20)
[00:32:51 -89489.594006] FAST spr round 3 (radius: 20)
[00:32:58 -89472.662654] FAST spr round 4 (radius: 20)
[00:33:06 -89466.853353] FAST spr round 5 (radius: 20)
[00:33:13 -89466.017997] FAST spr round 6 (radius: 20)
[00:33:21 -89465.506704] FAST spr round 7 (radius: 20)
[00:33:28 -89465.116758] FAST spr round 8 (radius: 20)
[00:33:35 -89464.742921] FAST spr round 9 (radius: 20)
[00:33:42 -89464.338786] FAST spr round 10 (radius: 20)
[00:33:49 -89464.042073] FAST spr round 11 (radius: 20)
[00:33:57 -89464.040495] Model parameter optimization (eps = 1.000000)
[00:34:01 -89457.725047] SLOW spr round 1 (radius: 5)
[00:34:16 -89448.761493] SLOW spr round 2 (radius: 5)
[00:34:29 -89448.455420] SLOW spr round 3 (radius: 5)
[00:34:43 -89448.453279] SLOW spr round 4 (radius: 10)
[00:34:59 -89448.453117] SLOW spr round 5 (radius: 15)
[00:35:23 -89448.453093] SLOW spr round 6 (radius: 20)
[00:35:37] [worker #1] ML tree search #16, logLikelihood: -89448.428590
[00:35:45 -89448.453087] SLOW spr round 7 (radius: 25)
[00:35:57 -89448.453086] Model parameter optimization (eps = 0.100000)

[00:35:58] [worker #0] ML tree search #15, logLikelihood: -89448.428759

[00:35:58 -169714.502045] Initial branch length optimization
[00:35:58 -137035.234386] Model parameter optimization (eps = 10.000000)
[00:36:11 -136495.383543] AUTODETECT spr round 1 (radius: 5)
[00:36:16 -113196.186366] AUTODETECT spr round 2 (radius: 10)
[00:36:24 -95250.579585] AUTODETECT spr round 3 (radius: 15)
[00:36:34 -93887.683991] AUTODETECT spr round 4 (radius: 20)
[00:36:43 -93887.641372] SPR radius for FAST iterations: 15 (autodetect)
[00:36:43 -93887.641372] Model parameter optimization (eps = 3.000000)
[00:36:49 -93700.632352] FAST spr round 1 (radius: 15)
[00:37:00 -89707.890876] FAST spr round 2 (radius: 15)
[00:37:09 -89472.868739] FAST spr round 3 (radius: 15)
[00:37:16 -89470.005415] FAST spr round 4 (radius: 15)
[00:37:23 -89468.299255] FAST spr round 5 (radius: 15)
[00:37:30 -89467.044579] FAST spr round 6 (radius: 15)
[00:37:37 -89466.343328] FAST spr round 7 (radius: 15)
[00:37:45 -89465.952986] FAST spr round 8 (radius: 15)
[00:37:52 -89465.684700] FAST spr round 9 (radius: 15)
[00:37:59 -89465.576715] FAST spr round 10 (radius: 15)
[00:38:06 -89465.515997] Model parameter optimization (eps = 1.000000)
[00:38:10 -89458.771874] SLOW spr round 1 (radius: 5)
[00:38:24 -89451.415943] SLOW spr round 2 (radius: 5)
[00:38:37 -89450.173822] SLOW spr round 3 (radius: 5)
[00:38:50 -89450.173786] SLOW spr round 4 (radius: 10)
[00:39:06 -89450.035847] SLOW spr round 5 (radius: 5)
[00:39:25 -89449.561777] SLOW spr round 6 (radius: 5)
[00:39:41] [worker #1] ML tree search #18, logLikelihood: -89449.910803
[00:39:41 -89449.558954] SLOW spr round 7 (radius: 10)
[00:39:58 -89449.558534] SLOW spr round 8 (radius: 15)
[00:40:21 -89449.558443] SLOW spr round 9 (radius: 20)
[00:40:44 -89449.558423] SLOW spr round 10 (radius: 25)
[00:40:56 -89449.558419] Model parameter optimization (eps = 0.100000)

[00:40:58] [worker #0] ML tree search #17, logLikelihood: -89449.373234

[00:40:58 -168881.691354] Initial branch length optimization
[00:40:59 -137544.992599] Model parameter optimization (eps = 10.000000)
[00:41:08 -136950.988025] AUTODETECT spr round 1 (radius: 5)
[00:41:14 -108006.220433] AUTODETECT spr round 2 (radius: 10)
[00:41:22 -93995.730267] AUTODETECT spr round 3 (radius: 15)
[00:41:31 -92750.359782] AUTODETECT spr round 4 (radius: 20)
[00:41:40 -92750.333677] SPR radius for FAST iterations: 15 (autodetect)
[00:41:40 -92750.333677] Model parameter optimization (eps = 3.000000)
[00:41:46 -92520.375278] FAST spr round 1 (radius: 15)
[00:41:56 -89532.675469] FAST spr round 2 (radius: 15)
[00:42:04 -89504.835172] FAST spr round 3 (radius: 15)
[00:42:11 -89499.137036] FAST spr round 4 (radius: 15)
[00:42:19 -89495.106048] FAST spr round 5 (radius: 15)
[00:42:26 -89492.500839] FAST spr round 6 (radius: 15)
[00:42:33 -89491.339688] FAST spr round 7 (radius: 15)
[00:42:40 -89490.213617] FAST spr round 8 (radius: 15)
[00:42:47 -89484.832600] FAST spr round 9 (radius: 15)
[00:42:54 -89483.378264] FAST spr round 10 (radius: 15)
[00:43:01 -89483.270120] FAST spr round 11 (radius: 15)
[00:43:08 -89483.188807] Model parameter optimization (eps = 1.000000)
[00:43:11 -89481.809831] SLOW spr round 1 (radius: 5)
[00:43:26 -89457.268794] SLOW spr round 2 (radius: 5)
[00:43:40 -89450.605812] SLOW spr round 3 (radius: 5)
[00:43:53 -89449.525218] SLOW spr round 4 (radius: 5)
[00:44:06 -89449.524084] SLOW spr round 5 (radius: 10)
[00:44:22 -89449.524055] SLOW spr round 6 (radius: 15)
[00:44:31] [worker #1] ML tree search #20, logLikelihood: -89449.910802
[00:44:47 -89449.524053] SLOW spr round 7 (radius: 20)
[00:45:09 -89449.524052] SLOW spr round 8 (radius: 25)
[00:45:20 -89449.524052] Model parameter optimization (eps = 0.100000)

[00:45:22] [worker #0] ML tree search #19, logLikelihood: -89449.266639


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.179172,0.428846) (0.126734,0.577963) (0.306327,0.762692) (0.387768,1.589308) 
   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: -89448.428590

AIC score: 179390.857180 / AICc score: 179553.124729 / BIC score: 180603.812624
Free parameters (model + branch lengths): 247

Best ML tree saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/3_mltree/Q8WVS4.raxml.bestTree
All ML trees saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/3_mltree/Q8WVS4.raxml.mlTrees
Optimized model saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/3_mltree/Q8WVS4.raxml.bestModel

Execution log saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/Q8WVS4/3_mltree/Q8WVS4.raxml.log

Analysis started: 03-Jul-2021 11:32:30 / finished: 03-Jul-2021 12:17:53

Elapsed time: 2722.877 seconds