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 26-Jul-2021 00:10:20 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/2_msa/A0A0B4J262_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/3_mltree/A0A0B4J262 --seed 2 --threads 2 --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 (2 threads), thread pinning: OFF

[00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/2_msa/A0A0B4J262_trimmed_msa.fasta
[00:00:00] Loaded alignment with 664 taxa and 115 sites

WARNING: Sequences tr_A0A075B6A6_A0A075B6A6_MOUSE_10090 and tr_A0A075B6B0_A0A075B6B0_MOUSE_10090 are exactly identical!
WARNING: Sequences tr_G1PZU6_G1PZU6_MYOLU_59463 and tr_G1Q8A4_G1Q8A4_MYOLU_59463 are exactly identical!
WARNING: Sequences tr_H2NKP1_H2NKP1_PONAB_9601 and sp_A0JD32_TV382_HUMAN_9606 are exactly identical!
WARNING: Sequences tr_A0A2I3RHR8_A0A2I3RHR8_PANTR_9598 and tr_A0A2R9AS68_A0A2R9AS68_PANPA_9597 are exactly identical!
WARNING: Sequences tr_A0A2I3RMN8_A0A2I3RMN8_PANTR_9598 and tr_A0A2R9ANY6_A0A2R9ANY6_PANPA_9597 are exactly identical!
WARNING: Sequences tr_A0A2I3SSM1_A0A2I3SSM1_PANTR_9598 and tr_A0A2R8ZKS1_A0A2R8ZKS1_PANPA_9597 are exactly identical!
WARNING: Sequences tr_A0A2I3TAG7_A0A2I3TAG7_PANTR_9598 and tr_A0A2R8ZBR7_A0A2R8ZBR7_PANPA_9597 are exactly identical!
WARNING: Sequences tr_A0A2I3TQV9_A0A2I3TQV9_PANTR_9598 and tr_A0A2R9AWE8_A0A2R9AWE8_PANPA_9597 are exactly identical!
WARNING: Sequences tr_W5NSB4_W5NSB4_SHEEP_9940 and tr_W5NXD2_W5NXD2_SHEEP_9940 are exactly identical!
WARNING: Sequences tr_W5NSB4_W5NSB4_SHEEP_9940 and tr_W5QE47_W5QE47_SHEEP_9940 are exactly identical!
WARNING: Sequences tr_D3ZAJ0_D3ZAJ0_RAT_10116 and tr_M0RE10_M0RE10_RAT_10116 are exactly identical!
WARNING: Sequences tr_A0A1D5R6S8_A0A1D5R6S8_MACMU_9544 and tr_G7P9Q9_G7P9Q9_MACFA_9541 are exactly identical!
WARNING: Sequences tr_A0A096MV46_A0A096MV46_PAPAN_9555 and tr_A0A2K6ALT7_A0A2K6ALT7_MACNE_9545 are exactly identical!
WARNING: Sequences tr_A0A2K6CZI6_A0A2K6CZI6_MACNE_9545 and tr_A0A2K5XU33_A0A2K5XU33_MANLE_9568 are exactly identical!
WARNING: Duplicate sequences found: 14

NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) 
NOTE: was saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/3_mltree/A0A0B4J262.raxml.reduced.phy

Alignment comprises 1 partitions and 115 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 115 / 115
Gaps: 7.88 %
Invariant sites: 0.00 %


NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/3_mltree/A0A0B4J262.raxml.rba

Parallelization scheme autoconfig: 2 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 664 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 115 / 9200
[00:00:00] Data distribution: max. searches per worker: 10

Starting ML tree search with 20 distinct starting trees

[00:00:00 -141943.416524] Initial branch length optimization
[00:00:03 -123359.253221] Model parameter optimization (eps = 10.000000)
[00:00:38 -122728.458723] AUTODETECT spr round 1 (radius: 5)
[00:02:28 -94564.066876] AUTODETECT spr round 2 (radius: 10)
[00:04:30 -68518.695032] AUTODETECT spr round 3 (radius: 15)
[00:06:58 -59398.122129] AUTODETECT spr round 4 (radius: 20)
[00:09:48 -56297.512155] AUTODETECT spr round 5 (radius: 25)
[00:13:25 -56102.372692] SPR radius for FAST iterations: 25 (autodetect)
[00:13:25 -56102.372692] Model parameter optimization (eps = 3.000000)
[00:13:53 -56043.667796] FAST spr round 1 (radius: 25)
[00:16:07 -49783.577022] FAST spr round 2 (radius: 25)
[00:17:53 -49494.364650] FAST spr round 3 (radius: 25)
[00:19:28 -49483.412367] FAST spr round 4 (radius: 25)
[00:20:53 -49482.251707] FAST spr round 5 (radius: 25)
[00:22:17 -49479.332161] FAST spr round 6 (radius: 25)
[00:23:39 -49479.332124] Model parameter optimization (eps = 1.000000)
[00:24:06 -49452.414272] SLOW spr round 1 (radius: 5)
[00:26:10 -49433.160960] SLOW spr round 2 (radius: 5)
[00:28:06 -49432.481747] SLOW spr round 3 (radius: 5)
[00:29:57 -49432.481716] SLOW spr round 4 (radius: 10)
[00:31:53 -49432.481715] SLOW spr round 5 (radius: 15)
[00:35:32 -49432.481715] SLOW spr round 6 (radius: 20)
[00:40:19 -49432.481715] SLOW spr round 7 (radius: 25)
[00:45:57 -49432.481715] Model parameter optimization (eps = 0.100000)

[00:46:04] [worker #0] ML tree search #1, logLikelihood: -49432.427000

[00:46:05 -141161.023314] Initial branch length optimization
[00:46:07 -122284.388664] Model parameter optimization (eps = 10.000000)
[00:46:50 -121693.600059] AUTODETECT spr round 1 (radius: 5)
[00:48:41 -91544.301891] AUTODETECT spr round 2 (radius: 10)
[00:49:54] [worker #1] ML tree search #2, logLikelihood: -49440.007321
[00:50:41 -68085.136851] AUTODETECT spr round 3 (radius: 15)
[00:53:00 -59823.146124] AUTODETECT spr round 4 (radius: 20)
[00:55:49 -57089.589783] AUTODETECT spr round 5 (radius: 25)
[00:59:21 -56743.860979] SPR radius for FAST iterations: 25 (autodetect)
[00:59:21 -56743.860979] Model parameter optimization (eps = 3.000000)
[00:59:45 -56685.293929] FAST spr round 1 (radius: 25)
[01:02:02 -50024.368636] FAST spr round 2 (radius: 25)
[01:03:51 -49531.750300] FAST spr round 3 (radius: 25)
[01:05:30 -49499.104069] FAST spr round 4 (radius: 25)
[01:07:01 -49492.685413] FAST spr round 5 (radius: 25)
[01:08:24 -49491.651435] FAST spr round 6 (radius: 25)
[01:09:46 -49491.649564] Model parameter optimization (eps = 1.000000)
[01:10:06 -49471.306702] SLOW spr round 1 (radius: 5)
[01:12:15 -49451.984698] SLOW spr round 2 (radius: 5)
[01:14:12 -49450.670012] SLOW spr round 3 (radius: 5)
[01:16:05 -49450.640894] SLOW spr round 4 (radius: 10)
[01:18:05 -49449.836272] SLOW spr round 5 (radius: 5)
[01:20:37 -49449.806039] SLOW spr round 6 (radius: 10)
[01:22:50 -49449.387339] SLOW spr round 7 (radius: 5)
[01:25:17 -49449.298550] SLOW spr round 8 (radius: 10)
[01:27:26 -49449.062546] SLOW spr round 9 (radius: 5)
[01:29:56 -49448.139326] SLOW spr round 10 (radius: 5)
[01:32:01 -49448.139320] SLOW spr round 11 (radius: 10)
[01:34:03 -49448.139320] SLOW spr round 12 (radius: 15)
[01:37:37 -49448.139320] SLOW spr round 13 (radius: 20)
[01:42:33 -49448.139320] SLOW spr round 14 (radius: 25)
[01:46:52] [worker #1] ML tree search #4, logLikelihood: -49401.864107
[01:48:02 -49448.139320] Model parameter optimization (eps = 0.100000)

[01:48:09] [worker #0] ML tree search #3, logLikelihood: -49448.130047

[01:48:09 -141733.904462] Initial branch length optimization
[01:48:12 -123055.205789] Model parameter optimization (eps = 10.000000)
[01:48:48 -122463.482564] AUTODETECT spr round 1 (radius: 5)
[01:50:38 -92591.989620] AUTODETECT spr round 2 (radius: 10)
[01:52:41 -66410.335370] AUTODETECT spr round 3 (radius: 15)
[01:55:00 -60114.143431] AUTODETECT spr round 4 (radius: 20)
[01:57:33 -57867.678443] AUTODETECT spr round 5 (radius: 25)
[02:01:03 -57350.834642] SPR radius for FAST iterations: 25 (autodetect)
[02:01:03 -57350.834642] Model parameter optimization (eps = 3.000000)
[02:01:30 -57283.840454] FAST spr round 1 (radius: 25)
[02:03:44 -49945.514234] FAST spr round 2 (radius: 25)
[02:05:32 -49523.081539] FAST spr round 3 (radius: 25)
[02:07:05 -49484.620399] FAST spr round 4 (radius: 25)
[02:08:34 -49476.550186] FAST spr round 5 (radius: 25)
[02:09:58 -49475.791511] FAST spr round 6 (radius: 25)
[02:11:20 -49475.791510] Model parameter optimization (eps = 1.000000)
[02:11:43 -49462.434782] SLOW spr round 1 (radius: 5)
[02:13:45 -49455.679175] SLOW spr round 2 (radius: 5)
[02:15:44 -49451.968439] SLOW spr round 3 (radius: 5)
[02:17:38 -49451.490355] SLOW spr round 4 (radius: 5)
[02:19:29 -49451.490321] SLOW spr round 5 (radius: 10)
[02:21:28 -49450.374245] SLOW spr round 6 (radius: 5)
[02:24:00 -49449.366784] SLOW spr round 7 (radius: 5)
[02:26:09 -49448.155782] SLOW spr round 8 (radius: 5)
[02:28:07 -49448.155782] SLOW spr round 9 (radius: 10)
[02:30:07 -49448.155782] SLOW spr round 10 (radius: 15)
[02:33:51 -49445.616951] SLOW spr round 11 (radius: 5)
[02:36:26 -49445.559267] SLOW spr round 12 (radius: 10)
[02:38:43 -49445.468204] SLOW spr round 13 (radius: 15)
[02:42:06 -49445.418501] SLOW spr round 14 (radius: 20)
[02:42:33] [worker #1] ML tree search #6, logLikelihood: -49434.983241
[02:47:32 -49445.397554] SLOW spr round 15 (radius: 25)
[02:53:47 -49445.394798] Model parameter optimization (eps = 0.100000)

[02:54:05] [worker #0] ML tree search #5, logLikelihood: -49444.772284

[02:54:05 -142113.602760] Initial branch length optimization
[02:54:08 -123256.838320] Model parameter optimization (eps = 10.000000)
[02:54:44 -122587.200216] AUTODETECT spr round 1 (radius: 5)
[02:56:33 -93391.963880] AUTODETECT spr round 2 (radius: 10)
[02:58:32 -70875.479150] AUTODETECT spr round 3 (radius: 15)
[03:01:01 -58715.373187] AUTODETECT spr round 4 (radius: 20)
[03:04:24 -56616.843872] AUTODETECT spr round 5 (radius: 25)
[03:08:04 -56405.552644] SPR radius for FAST iterations: 25 (autodetect)
[03:08:05 -56405.552644] Model parameter optimization (eps = 3.000000)
[03:08:32 -56352.554257] FAST spr round 1 (radius: 25)
[03:10:43 -49842.214803] FAST spr round 2 (radius: 25)
[03:12:29 -49509.156647] FAST spr round 3 (radius: 25)
[03:14:08 -49478.056031] FAST spr round 4 (radius: 25)
[03:15:32 -49474.615184] FAST spr round 5 (radius: 25)
[03:16:55 -49470.727204] FAST spr round 6 (radius: 25)
[03:18:17 -49470.727156] Model parameter optimization (eps = 1.000000)
[03:18:34 -49458.069337] SLOW spr round 1 (radius: 5)
[03:20:39 -49442.664927] SLOW spr round 2 (radius: 5)
[03:22:41 -49432.005959] SLOW spr round 3 (radius: 5)
[03:24:33 -49431.253210] SLOW spr round 4 (radius: 5)
[03:26:22 -49431.252854] SLOW spr round 5 (radius: 10)
[03:28:19 -49430.103427] SLOW spr round 6 (radius: 5)
[03:30:50 -49429.367027] SLOW spr round 7 (radius: 5)
[03:32:57 -49429.367013] SLOW spr round 8 (radius: 10)
[03:34:56 -49429.367007] SLOW spr round 9 (radius: 15)
[03:38:41 -49429.367001] SLOW spr round 10 (radius: 20)
[03:43:49] [worker #1] ML tree search #8, logLikelihood: -49435.384912
[03:43:56 -49429.366997] SLOW spr round 11 (radius: 25)
[03:50:11 -49429.366993] Model parameter optimization (eps = 0.100000)

[03:50:17] [worker #0] ML tree search #7, logLikelihood: -49429.335261

[03:50:17 -142179.266955] Initial branch length optimization
[03:50:20 -123250.849758] Model parameter optimization (eps = 10.000000)
[03:50:54 -122630.841549] AUTODETECT spr round 1 (radius: 5)
[03:52:44 -90970.775393] AUTODETECT spr round 2 (radius: 10)
[03:54:44 -70434.752932] AUTODETECT spr round 3 (radius: 15)
[03:57:01 -63000.903776] AUTODETECT spr round 4 (radius: 20)
[04:00:00 -58361.258772] AUTODETECT spr round 5 (radius: 25)
[04:04:02 -58198.505654] SPR radius for FAST iterations: 25 (autodetect)
[04:04:02 -58198.505654] Model parameter optimization (eps = 3.000000)
[04:04:29 -58146.951750] FAST spr round 1 (radius: 25)
[04:06:44 -49886.763210] FAST spr round 2 (radius: 25)
[04:08:29 -49501.209223] FAST spr round 3 (radius: 25)
[04:10:00 -49489.452922] FAST spr round 4 (radius: 25)
[04:11:24 -49488.709336] FAST spr round 5 (radius: 25)
[04:12:47 -49488.708867] Model parameter optimization (eps = 1.000000)
[04:13:11 -49465.914752] SLOW spr round 1 (radius: 5)
[04:15:16 -49442.364579] SLOW spr round 2 (radius: 5)
[04:17:12 -49440.608103] SLOW spr round 3 (radius: 5)
[04:19:03 -49440.608090] SLOW spr round 4 (radius: 10)
[04:21:02 -49436.347940] SLOW spr round 5 (radius: 5)
[04:23:31 -49436.347937] SLOW spr round 6 (radius: 10)
[04:25:42 -49436.347937] SLOW spr round 7 (radius: 15)
[04:29:06 -49436.347937] SLOW spr round 8 (radius: 20)
[04:34:09 -49436.347937] SLOW spr round 9 (radius: 25)
[04:39:57 -49436.347937] Model parameter optimization (eps = 0.100000)

[04:40:03] [worker #0] ML tree search #9, logLikelihood: -49436.338791

[04:40:03 -142283.523296] Initial branch length optimization
[04:40:06 -123120.048032] Model parameter optimization (eps = 10.000000)
[04:41:04 -122478.147165] AUTODETECT spr round 1 (radius: 5)
[04:42:54 -90833.891349] AUTODETECT spr round 2 (radius: 10)
[04:44:58 -69356.804267] AUTODETECT spr round 3 (radius: 15)
[04:47:14] [worker #1] ML tree search #10, logLikelihood: -49449.466512
[04:47:27 -61270.716528] AUTODETECT spr round 4 (radius: 20)
[04:50:41 -57587.008507] AUTODETECT spr round 5 (radius: 25)
[04:54:23 -57131.249413] SPR radius for FAST iterations: 25 (autodetect)
[04:54:23 -57131.249413] Model parameter optimization (eps = 3.000000)
[04:54:55 -57049.225444] FAST spr round 1 (radius: 25)
[04:57:07 -49905.185128] FAST spr round 2 (radius: 25)
[04:58:52 -49533.137320] FAST spr round 3 (radius: 25)
[05:00:28 -49498.294894] FAST spr round 4 (radius: 25)
[05:01:54 -49495.102548] FAST spr round 5 (radius: 25)
[05:03:17 -49494.191647] FAST spr round 6 (radius: 25)
[05:04:39 -49494.191575] Model parameter optimization (eps = 1.000000)
[05:04:57 -49475.478652] SLOW spr round 1 (radius: 5)
[05:07:02 -49463.150942] SLOW spr round 2 (radius: 5)
[05:09:00 -49455.109700] SLOW spr round 3 (radius: 5)
[05:10:56 -49452.304498] SLOW spr round 4 (radius: 5)
[05:12:46 -49452.304362] SLOW spr round 5 (radius: 10)
[05:14:46 -49447.933557] SLOW spr round 6 (radius: 5)
[05:17:16 -49447.933521] SLOW spr round 7 (radius: 10)
[05:19:29 -49446.938470] SLOW spr round 8 (radius: 5)
[05:22:01 -49444.027107] SLOW spr round 9 (radius: 5)
[05:24:08 -49442.949841] SLOW spr round 10 (radius: 5)
[05:26:11 -49441.532302] SLOW spr round 11 (radius: 5)
[05:28:04 -49440.904180] SLOW spr round 12 (radius: 5)
[05:29:55 -49440.904163] SLOW spr round 13 (radius: 10)
[05:31:53 -49440.904162] SLOW spr round 14 (radius: 15)
[05:34:23] [worker #1] ML tree search #12, logLikelihood: -49449.953064
[05:35:32 -49440.904161] SLOW spr round 15 (radius: 20)
[05:40:09 -49440.904160] SLOW spr round 16 (radius: 25)
[05:45:25 -49440.904159] Model parameter optimization (eps = 0.100000)

[05:45:44] [worker #0] ML tree search #11, logLikelihood: -49439.098574

[05:45:44 -143519.498045] Initial branch length optimization
[05:45:46 -124138.095167] Model parameter optimization (eps = 10.000000)
[05:46:29 -123492.003420] AUTODETECT spr round 1 (radius: 5)
[05:48:21 -90803.873233] AUTODETECT spr round 2 (radius: 10)
[05:50:21 -68785.105395] AUTODETECT spr round 3 (radius: 15)
[05:52:43 -58810.285878] AUTODETECT spr round 4 (radius: 20)
[05:55:32 -56652.045241] AUTODETECT spr round 5 (radius: 25)
[05:58:48 -56458.815382] SPR radius for FAST iterations: 25 (autodetect)
[05:58:48 -56458.815382] Model parameter optimization (eps = 3.000000)
[05:59:20 -56345.439628] FAST spr round 1 (radius: 25)
[06:01:25 -49809.970330] FAST spr round 2 (radius: 25)
[06:03:11 -49473.443572] FAST spr round 3 (radius: 25)
[06:04:45 -49450.648099] FAST spr round 4 (radius: 25)
[06:06:09 -49450.647824] Model parameter optimization (eps = 1.000000)
[06:06:27 -49442.739670] SLOW spr round 1 (radius: 5)
[06:08:33 -49429.546580] SLOW spr round 2 (radius: 5)
[06:10:32 -49427.029467] SLOW spr round 3 (radius: 5)
[06:12:24 -49426.710436] SLOW spr round 4 (radius: 5)
[06:14:15 -49426.710063] SLOW spr round 5 (radius: 10)
[06:16:13 -49425.142904] SLOW spr round 6 (radius: 5)
[06:18:43 -49424.859788] SLOW spr round 7 (radius: 5)
[06:20:50 -49424.859786] SLOW spr round 8 (radius: 10)
[06:22:50 -49424.674379] SLOW spr round 9 (radius: 5)
[06:25:01] [worker #1] ML tree search #14, logLikelihood: -49436.861487
[06:25:20 -49421.826955] SLOW spr round 10 (radius: 5)
[06:27:26 -49421.826949] SLOW spr round 11 (radius: 10)
[06:29:25 -49421.826949] SLOW spr round 12 (radius: 15)
[06:32:57 -49421.826949] SLOW spr round 13 (radius: 20)
[06:37:56 -49421.826949] SLOW spr round 14 (radius: 25)
[06:43:34 -49421.826949] Model parameter optimization (eps = 0.100000)

[06:43:40] [worker #0] ML tree search #13, logLikelihood: -49421.807862

[06:43:40 -142249.236452] Initial branch length optimization
[06:43:42 -123426.781489] Model parameter optimization (eps = 10.000000)
[06:44:22 -122835.049281] AUTODETECT spr round 1 (radius: 5)
[06:46:13 -92614.023835] AUTODETECT spr round 2 (radius: 10)
[06:48:16 -70188.297010] AUTODETECT spr round 3 (radius: 15)
[06:50:36 -62144.567135] AUTODETECT spr round 4 (radius: 20)
[06:53:24 -56442.796628] AUTODETECT spr round 5 (radius: 25)
[06:56:36 -56277.871041] SPR radius for FAST iterations: 25 (autodetect)
[06:56:36 -56277.871041] Model parameter optimization (eps = 3.000000)
[06:57:22 -56212.405106] FAST spr round 1 (radius: 25)
[06:59:36 -49700.557159] FAST spr round 2 (radius: 25)
[07:01:24 -49526.485494] FAST spr round 3 (radius: 25)
[07:03:02 -49498.578670] FAST spr round 4 (radius: 25)
[07:04:34 -49491.316012] FAST spr round 5 (radius: 25)
[07:05:56 -49491.313305] Model parameter optimization (eps = 1.000000)
[07:06:15 -49490.164974] SLOW spr round 1 (radius: 5)
[07:08:22 -49454.181028] SLOW spr round 2 (radius: 5)
[07:10:21 -49448.848757] SLOW spr round 3 (radius: 5)
[07:12:16 -49447.088984] SLOW spr round 4 (radius: 5)
[07:14:06 -49447.088934] SLOW spr round 5 (radius: 10)
[07:16:05 -49446.106179] SLOW spr round 6 (radius: 5)
[07:18:34 -49446.106168] SLOW spr round 7 (radius: 10)
[07:19:41] [worker #1] ML tree search #16, logLikelihood: -49428.559047
[07:20:45 -49445.373458] SLOW spr round 8 (radius: 5)
[07:23:11 -49445.373349] SLOW spr round 9 (radius: 10)
[07:25:20 -49444.256323] SLOW spr round 10 (radius: 5)
[07:27:47 -49442.582992] SLOW spr round 11 (radius: 5)
[07:29:52 -49442.582990] SLOW spr round 12 (radius: 10)
[07:31:53 -49442.582989] SLOW spr round 13 (radius: 15)
[07:35:25 -49442.582989] SLOW spr round 14 (radius: 20)
[07:40:13 -49442.582989] SLOW spr round 15 (radius: 25)
[07:45:41 -49442.582989] Model parameter optimization (eps = 0.100000)

[07:45:58] [worker #0] ML tree search #15, logLikelihood: -49442.134237

[07:45:58 -143435.940348] Initial branch length optimization
[07:46:00 -124167.664069] Model parameter optimization (eps = 10.000000)
[07:46:39 -123546.809413] AUTODETECT spr round 1 (radius: 5)
[07:48:31 -90917.841906] AUTODETECT spr round 2 (radius: 10)
[07:50:33 -68278.269332] AUTODETECT spr round 3 (radius: 15)
[07:52:56 -59572.197129] AUTODETECT spr round 4 (radius: 20)
[07:55:43 -56727.458594] AUTODETECT spr round 5 (radius: 25)
[07:58:51 -56638.019533] SPR radius for FAST iterations: 25 (autodetect)
[07:58:51 -56638.019533] Model parameter optimization (eps = 3.000000)
[07:59:24 -56513.171053] FAST spr round 1 (radius: 25)
[08:01:31 -49767.014657] FAST spr round 2 (radius: 25)
[08:03:10 -49480.494944] FAST spr round 3 (radius: 25)
[08:04:43 -49437.977737] FAST spr round 4 (radius: 25)
[08:06:13 -49430.624110] FAST spr round 5 (radius: 25)
[08:07:36 -49425.516552] FAST spr round 6 (radius: 25)
[08:08:59 -49425.516332] Model parameter optimization (eps = 1.000000)
[08:09:10 -49425.182730] SLOW spr round 1 (radius: 5)
[08:11:15 -49415.935443] SLOW spr round 2 (radius: 5)
[08:13:13 -49414.534215] SLOW spr round 3 (radius: 5)
[08:15:05 -49414.532683] SLOW spr round 4 (radius: 10)
[08:17:02 -49411.631018] SLOW spr round 5 (radius: 5)
[08:19:16] [worker #1] ML tree search #18, logLikelihood: -49435.833524
[08:19:32 -49411.630995] SLOW spr round 6 (radius: 10)
[08:21:39 -49409.959415] SLOW spr round 7 (radius: 5)
[08:24:05 -49409.959398] SLOW spr round 8 (radius: 10)
[08:26:11 -49407.680793] SLOW spr round 9 (radius: 5)
[08:28:38 -49407.680786] SLOW spr round 10 (radius: 10)
[08:30:43 -49407.680786] SLOW spr round 11 (radius: 15)
[08:34:07 -49407.680786] SLOW spr round 12 (radius: 20)
[08:39:05 -49407.680786] SLOW spr round 13 (radius: 25)
[08:44:43 -49407.680786] Model parameter optimization (eps = 0.100000)

[08:44:53] [worker #0] ML tree search #17, logLikelihood: -49407.576471

[08:44:53 -142130.026440] Initial branch length optimization
[08:44:56 -123058.237614] Model parameter optimization (eps = 10.000000)
[08:45:33 -122462.536596] AUTODETECT spr round 1 (radius: 5)
[08:47:23 -91242.063288] AUTODETECT spr round 2 (radius: 10)
[08:49:26 -71141.620823] AUTODETECT spr round 3 (radius: 15)
[08:51:55 -62244.313668] AUTODETECT spr round 4 (radius: 20)
[08:54:57 -57892.360456] AUTODETECT spr round 5 (radius: 25)
[08:58:38 -57886.917886] SPR radius for FAST iterations: 25 (autodetect)
[08:58:38 -57886.917886] Model parameter optimization (eps = 3.000000)
[08:59:12 -57820.265594] FAST spr round 1 (radius: 25)
[09:01:26 -49887.158952] FAST spr round 2 (radius: 25)
[09:03:15 -49523.858844] FAST spr round 3 (radius: 25)
[09:04:53 -49493.888307] FAST spr round 4 (radius: 25)
[09:06:19 -49491.021582] FAST spr round 5 (radius: 25)
[09:07:43 -49489.108207] FAST spr round 6 (radius: 25)
[09:09:05 -49489.108191] Model parameter optimization (eps = 1.000000)
[09:09:34 -49470.349968] SLOW spr round 1 (radius: 5)
[09:11:37 -49443.587683] SLOW spr round 2 (radius: 5)
[09:13:37 -49434.871246] SLOW spr round 3 (radius: 5)
[09:15:32 -49434.478507] SLOW spr round 4 (radius: 5)
[09:17:22 -49434.478418] SLOW spr round 5 (radius: 10)
[09:19:20 -49434.227232] SLOW spr round 6 (radius: 5)
[09:21:49 -49434.218378] SLOW spr round 7 (radius: 10)
[09:24:02 -49434.217487] SLOW spr round 8 (radius: 15)
[09:24:31] [worker #1] ML tree search #20, logLikelihood: -49437.056097
[09:27:29 -49434.217400] SLOW spr round 9 (radius: 20)
[09:32:34 -49434.217391] SLOW spr round 10 (radius: 25)
[09:38:14 -49434.217390] Model parameter optimization (eps = 0.100000)

[09:38:27] [worker #0] ML tree search #19, logLikelihood: -49434.057845


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.104985,0.321502) (0.156015,0.417705) (0.355226,0.916591) (0.383775,1.499532) 
   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: -49401.864107

AIC score: 101465.728214 / AICc score: 3647249.728214 / BIC score: 105119.232877
Free parameters (model + branch lengths): 1331

WARNING: Number of free parameters (K=1331) is larger than alignment size (n=115).
         This might lead to overfitting and compromise tree inference results!


Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/3_mltree/A0A0B4J262.raxml.bestTree
All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/3_mltree/A0A0B4J262.raxml.mlTrees
Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/3_mltree/A0A0B4J262.raxml.bestModel

Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/250721_run/phylogeny-snakemake/results/A0A0B4J262/3_mltree/A0A0B4J262.raxml.log

Analysis started: 26-Jul-2021 00:10:20 / finished: 26-Jul-2021 09:48:48

Elapsed time: 34707.594 seconds

Consumed energy: 1800.468 Wh (= 9 km in an electric car, or 45 km with an e-scooter!)