From a5227d91137a5022448c2cff1dcc9e26004fba90 Mon Sep 17 00:00:00 2001 From: hoellinger Date: Tue, 17 Jun 2025 00:57:49 +0200 Subject: [PATCH] minor; redefinition of the bend limiter pre-factor --- notebooks/0_nonreg.ipynb | 286 +- notebooks/10_use_fit_P3M_limiter.ipynb | 4659 ++++++++++--------- notebooks/8_fit_P3M_limiter.ipynb | 4 +- notebooks/9_fit_P3M_limiter_external.ipynb | 4 +- src/wip3m/convergence_cola_p3m_only_expl.py | 70 +- src/wip3m/params.py | 4 +- src/wip3m/tools.py | 4 +- submit/actual_p3m_limiter_cola.sh | 130 +- 8 files changed, 2642 insertions(+), 2519 deletions(-) diff --git a/notebooks/0_nonreg.ipynb b/notebooks/0_nonreg.ipynb index 68e8d69..daf30a6 100644 --- a/notebooks/0_nonreg.ipynb +++ b/notebooks/0_nonreg.ipynb @@ -42,7 +42,7 @@ "workdir = ROOT_PATH + \"results/\"\n", "output_path = OUTPUT_PATH\n", "\n", - "L = 32 # Box size in Mpc/h\n", + "L = 64 # Box size in Mpc/h\n", "N = 64 # Density grid size\n", "Np = 32 # Number of dark matter particles per spatial dimension\n", "Npm = 64 # PM grid size\n", @@ -105,14 +105,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "k_max = 10.883000000000001\n" + "k_max = 5.442\n" ] } ], @@ -179,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -261,65 +261,65 @@ "name": "stdout", "output_type": "stream", "text": [ - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/example_lpt.sbmy'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/example_lpt.sbmy' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/example_lpt.sbmy\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/example_lpt.sbmy'...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/example_lpt.sbmy' done.\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/example_lpt.sbmy\n", "PM nsteps = 50:\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 50\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_pm.sbmy'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_pm.sbmy' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_pm.sbmy\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5'...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5' done.\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 50\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5'...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_pm.h5' done.\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_pm.sbmy'...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_pm.sbmy' done.\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_pm.sbmy\n", "COLA1 nsteps = 10:\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 10\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_example_cola.sbmy'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_example_cola.sbmy' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_example_cola.sbmy\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5'...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5' done.\n", + "[21:20:30|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 10\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5'...\n", + "[21:20:30|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_ts_cola.h5' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_example_cola.sbmy'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_example_cola.sbmy' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps10_example_cola.sbmy\n", "COLA2 nsteps = 3:\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5' done.\n", - "[00:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 3\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5'...\n", - "[00:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_example_cola.sbmy'...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_example_cola.sbmy' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_example_cola.sbmy\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 3\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_ts_cola.h5' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_example_cola.sbmy'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_example_cola.sbmy' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps3_example_cola.sbmy\n", "SPM nsteps = 50:\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5'...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 50\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5'...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_spm.sbmy'...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_spm.sbmy' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_spm.sbmy\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 50\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_spm.h5' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_spm.sbmy'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_spm.sbmy' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_spm.sbmy\n", "P3M nsteps = 50:\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5'...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 50\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5'...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_p3m.sbmy'...\n", - "[00:54:00|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_p3m.sbmy' done.\n", - "[00:54:00|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_p3m.sbmy\n" + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Write timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 50\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_ts_p3m.h5' done.\n", + "[21:20:31|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_p3m.sbmy'...\n", + "[21:20:31|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_p3m.sbmy' done.\n", + "[21:20:32|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook1/nsteps50_example_p3m.sbmy\n" ] }, { @@ -446,16 +446,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid...\n", - "[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid done.\n", - "[00:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook1/input_power.h5'...\n", - "]|Computing normalization of the power spectrum...\n", - "[00:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing normalization of the power spectrum done.\n", - "[00:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum...\n", - "[00:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum done.\n", - "[00:54:01|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=32, L1=32, L2=32\u001b[00m\n", - "[00:54:01|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=64, N1=64, N2=64, N2_HC=33, N_HC=135168, NUM_MODES=1914\u001b[00m\n", - "[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook1/input_power.h5' done.\n" + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid...\n", + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid done.\n", + "[21:20:32\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing normalization of the power spectrum...\n", + "[21:20:32\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing normalization of the power spectrum done.\n", + "[21:20:32\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum...\n", + "[21:20:32\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum done.\n", + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook1/input_power.h5'...\n", + "[21:20:32|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=64, L1=64, L2=64\u001b[00m\n", + "[21:20:32|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=64, N1=64, N2=64, N2_HC=33, N_HC=135168, NUM_MODES=1914\u001b[00m\n", + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook1/input_power.h5' done.\n" ] } ], @@ -475,12 +475,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid...\n", - "[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid done.\n", - "[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Write Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5'...\n", - "[00:54:01|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=32, L1=32, L2=32\u001b[00m\n", - "[00:54:01|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=64, N1=64, N2=64, N2_HC=33, N_HC=135168, NUM_MODES=51\u001b[00m\n", - "[00:54:01|\u001b[38;5;113mSTATUS \u001b[00m]|Write Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5' done.\n" + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid...\n", + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid done.\n", + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Write Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5'...\n", + "[21:20:32|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=64, L1=64, L2=64\u001b[00m\n", + "[21:20:32|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=64, N1=64, N2=64, N2_HC=33, N_HC=135168, NUM_MODES=51\u001b[00m\n", + "[21:20:32|\u001b[38;5;113mSTATUS \u001b[00m]|Write Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5' done.\n" ] } ], @@ -595,24 +595,24 @@ "name": "stdout", "output_type": "stream", "text": [ - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5'...\n", - "[00:54:53|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5' done.\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5'...\n", - "[00:54:53|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5' done.\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5'...\n", - "[00:54:53|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5' done.\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5'...\n", - "[00:54:53|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5' done.\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5'...\n", - "[00:54:53|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5' done.\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5'...\n", - "[00:54:53|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:53|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5' done.\n" + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5'...\n", + "[21:21:14|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5' done.\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5'...\n", + "[21:21:14|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5' done.\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5'...\n", + "[21:21:14|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5' done.\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5'...\n", + "[21:21:14|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5' done.\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5'...\n", + "[21:21:14|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5' done.\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5'...\n", + "[21:21:14|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:14|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5' done.\n" ] } ], @@ -637,9 +637,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "max(DELTA_PM) = 932.3659057617188, min(DELTA_PM) = -1.0\n", - "max(DELTA_P3M) = 1230.1751708984375, min(DELTA_P3M) = -1.0\n", - "max(diff) = 390.24505615234375, min(diff) = -88.38835144042969\n" + "max(DELTA_PM) = 598.9951782226562, min(DELTA_PM) = -1.0\n", + "max(DELTA_P3M) = 747.01904296875, min(DELTA_P3M) = -1.0\n", + "max(diff) = 148.02386474609375, min(diff) = -37.60849380493164\n" ] } ], @@ -656,7 +656,7 @@ "outputs": [ { "data": { - "image/png": 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" ] @@ -785,45 +785,45 @@ "name": "stdout", "output_type": "stream", "text": [ - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5'...\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=32.0, L1=32.0, L2=32.0\u001b[00m\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=64, N1=64, N2=64, N2_HC=33, N_HC=135168, NUM_MODES=51\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5' done.\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/initial_density.h5'...\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/initial_density.h5' done.\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5'...\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5' done.\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5'...\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5' done.\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5'...\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5' done.\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5'...\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5' done.\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5'...\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5' done.\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5'...\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", - "[00:54:55|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.float64(-16.0), np.float64(16.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", - "[00:54:55|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5' done.\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", - "[00:54:55\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n" + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5'...\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=64.0, L1=64.0, L2=64.0\u001b[00m\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=64, N1=64, N2=64, N2_HC=33, N_HC=135168, NUM_MODES=51\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read Fourier grid in data file '/Users/hoellinger/WIP3M/notebook1/input_ss_k_grid.h5' done.\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/initial_density.h5'...\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/initial_density.h5' done.\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5'...\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/lpt_density.h5' done.\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5'...\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_pm.h5' done.\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5'...\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps10_final_density_cola.h5' done.\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5'...\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps3_final_density_cola.h5' done.\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5'...\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_spm.h5' done.\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5'...\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n", + "[21:21:16|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mranges=[np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.float64(-32.0), np.float64(32.0), np.int32(64), np.int32(64), np.int32(64)]\u001b[00m\n", + "[21:21:16|\u001b[38;5;113mSTATUS \u001b[00m]|Read field in data file '/Users/hoellinger/WIP3M/notebook1/nsteps50_final_density_p3m.h5' done.\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores)...\n", + "[21:21:16\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Getting auto-correlation in Fourier space (using 8 cores) done.\n" ] } ], @@ -877,12 +877,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "kmin = 0.6722924113273621, kmax = 10.397425651550293\n" + "kmin = 0.33615824580192566, kmax = 5.199185848236084\n" ] }, { "data": { - "image/png": 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", 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" ] @@ -1030,17 +1030,17 @@ "name": "stdout", "output_type": "stream", "text": [ - "Newton prefactor = 7.96e-02\n", - "Nyquist: 1.00 Mpc/h\n", - "Particle length: 0.06 Mpc/h\n", - "Split scale: 0.62 Mpc/h\n", - "Short-range reach: 2.81 Mpc/h\n", + "Newton prefactor = 6.37e-01\n", + "Nyquist: 2.00 Mpc/h\n", + "Particle length: 0.12 Mpc/h\n", + "Split scale: 1.25 Mpc/h\n", + "Short-range reach: 5.62 Mpc/h\n", "Figure saved to: /Users/hoellinger/WIP3M/notebook1/force_diagnostic_comparison.png\n" ] }, { "data": { - "image/png": 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fbbsdUz1s9D0ET9XhcGwYVDsHbPiTIYcOGtv+8K/b0JBW7t+UpnktQ7U+d7gcnY82TF2XQ1rZOW6fV1q1faHPWy9ZcfOHvq7/KpTWvvd7zPxhtqRpowjllwqKuwFQ9UTWOa8REHh4BYDxURnARmcpGgWjTDlEr7anMqlzjw6VATTkctllWMDXqExZ9/6mEYHsWUP7VsGgKj1P/foWm0JEyw3RvreGbkpz0edS0fX+scp6HUO9qgynrOvJfVZQ+alKo+p5bKqbPq8NGy1nVsC2iL8O7RtdE1WfE/x8Svuh6vWp4+4fmyrX5VjO4aH21dDXjY2e5irbc9W4vWxtmVXP1yHuX13SPV7Xf5VAr0vfLTqWodhCnWA0EEJgNHHPPvvsIOt99NFHs5/6qZ+q/L1PfOITM+fE6VKdeWI+85nPzJwTp0vPPPNM5WDu9evXa80phDAVhkIVyVXmiWkylIgNH2KBzTrzmdrytO7QtoxtKCYVkFQoa9pyzp0zqO7QKu5y+pqv0FovmqrD4VjgygLiYy9wDrU/1No2FBi13nNlGzK4gUfR+6K5PNse0krnvFVuaL0KcPlp1r7UPo19nikbpqjP/R6zouGe6lbIKb9V4wO3MkzLGnv+AQB4g8oNes5QGUblg7pTL6gyuq354u3+Uye4p3KLGxCoEvD1K9SbBBdVJtG+sPuyTW1St5LbRmeYtX9tlJMpanP/aDn+82aVYJ6/Dmvw3Nax0bLqBPOaXBv++qo8H4Q+q/1ZpWwaarBeJ69RsMpG6LJ06H0MDfzaOoeH2lcxXDd+L2Z9t8q5WjRdU9XA6BD3ry5ZY4yy9YZVpiLTMu1eaVMQ9VW/hvQxlC4A1GA9Nd2CiAqAoYnCYx9KJLWhmJoGaWIYWqWulIYOGvP+YEireKQ4TFFToTypblDU7oV2bdh9kKAoAKTH5g3TkO6W11cd1t3v8dNUnecPvzxQ9h5vFfju/mgaLPEDrU3mjNN2MEz9sPunSpAi9KzQpj6vDeu51uQZwc9L1Ki/Cr/s2eRYq0HovKE8x3wOx7av+rpu9Fn//lM1zy0aGbHOM3Hf12hX7N5YJdDrX9/af7P2oeokVfZQIy2ComgTgVEAqHjTV7DFn9tTN2fNWVe1csAdSqTqvJR+cFJDifQ9TIu2WxXq6omVgraGVnEDaDa0StfaGg6nrXNqaLHtjzaHtCqriyGtxqbNYYpSoPPBP47a3jqBTC3LvxdqNIMmc58CAMZBeb2eARQgtbmvdS8pc79VY6s2KnPbut+sr6+X+pxfDmxj/f7+0r01NNx93eWhm/1TVKbW1AFl+d+vO/pTDNeGtP2cUHV5fg/IJo223RGHJKaRY9o4h4faV0NfN6HnnSrrtm3wG5SqTqxpQ4A+rtGuWH1ZlfPIb3DOvQtDYShdAJOkm3eZORQVILThMkKFcxWCVPFep4VYDEOJFJnqUEyxDK1S1RSGDhr7/pjikFYxSG2YoqZCPX/rnJfaHxo+118OQVEAmCZ7LrBygso9qoQOBflsJIqm8/b1OWqF7nt+RW7TubmLyhzaL3Wfsfz53NHd/lFdghrU6rzQMVT9QpNnpjZ7fsU+okvbQvlMk2Pt778mjRViO4eH3ldDXjd+b9a6PWV1bzt27FjecFbLqJNfp3SNaj9UrWvxGx23dT8FqiIwCmCSqhZurXWbfqrgoxt3V0HBKkG0rirvpzgUU2xDqww5HI4bVNQDxNiGK4l1f4xxSCu3ErDqkFaxSGWYoqZ0HvuVunWDmX5PUe3jWIZTB9r2zDPPzPz7ww8/XGu5f+fv/J3s9ddfL/z729/+9lrL/chHPpK9+uqrhX9/61vfWmu5P/ETP5F9+9vfLvz7m9/85lrLfeqpp7L3v//9hX9/05veVGu5P/RDP5Tt37+/8O8PPvhgreVqme9973trfXeKgVKVrUMj4lRpDBpDxXLo2bGo3F+V/zzXJAiTUmV7F9rcP1pWk56EXT5nD3EeqOddmwHEKtsQamTRZt6ixvKxaHpsh95XQ103oQbsTRp06rtNvp9SXq3R46ocl9A0Nal1uMB4EBgFMEkKnAzd88mCrX4ws+pQIu732xyCZ2qFkzaHVnHne60ztEpVUxk6aMz7Y4pDWsUgpWGKmtDDpz/8uvK8OteG8jf/XKjTKxcYi8cee6yT5b7jHe/oZLltBWt8O3fu7GS5jzzySCfLVWC5bnB5FgWW6waXp0j3GT2fhHok6T5UdcoK1+7du7O+hEYaaqt871co2zQcdZafUmV7F2LZP0WjDLWlz2ujaN/6jfHm8cuWsxq2lJnyosmx9u+jMTWQbHoOj3lfNbluQkH7IfODIa7RrlQNVvvHwkbpAoZAYBQABhTzEDxTG4oppqFVxjQcTmxi3R+xVMRMDfv9DX5vnbqNg3SP8XvSN20xDQBI/16sXkr+vWhMZcxQ47C2KnJDjRnqBkapXI5n/+j5Xs+Det7Xe/WkiynA1jY1wHMbp1ZtUOnvmyqN90L7NdW6jKbncOz7qqvrJtSBIKXg5Jj4o1BNrUMG4kJgFAAGxBA8cYhtaJUxDYcTm1j3x9SGtIrFGNPcNgUy3fOgyYgJZ86cue93fk9UAMB46X7RRSWlghxarns/atIzsm99j5pRNxDQVY/xNujYtzGP3MbGRu3vdr1/tI16FvFHr9G5r/KSng91visdaigwpsYB89i2+cNCl8lP/P2g/KJKvhC6PkO9vOuKKY9qeg7HuK/6uG5CeSoNSYbhHz/mF8WQCIxOfE6crmzfvr2TOXFiM29OnNgKRFWGI0H8uh6CJ6YHgK7FNrTKVIbDmdL+mNqQVrGYektgVTC4Q3s3HUben4dZxjYHMQCg+J6hSui7d+92UmHsN9SRsQRG+y4Pj61h4tTpvNa145adLahT1PMxxaCMglvuNCQaDatMYNSf0iY0xU1V9EKLf1/1ed2Qp8Zbh8m1iiERGE1cV3PidCXmFo59zonTFebDGZ8hh+BJ8WEtxaFVYh8Op2/sjziGtMLwdM9we3OqcqpJEFPLC51DCwsLle4rqjTjARgA4qXnjS6eA5T3+z3Kxjh/OeCWlf0ebOrdduHChUlOM6BtVgM8a5SnBnUqP87aF/qMu/9U71G1sYTyK/+5Rf+fUn3GmPbVENfN2OqaUxWaXxQYEoFRAIhMLEPwTKnwOOahVWIcDmdI7I84hrTCsHTMDxw4sPl/3VOaBrYvXrwY/H2VvFLnEOcRAMSty16cKpu45ZKxjEzil6faDCaEejKleK/UNqU0yoTOBw0B6Z4XKmuFpvWYEu0Td7QSlUeLGsVZL3X3ma1O4zmdW/7IOF018Bi7offVUNdNaPvGcv9JiV8vQ2NZDI3AKABEgiF4hpPa0CoUMLea8v6IaUgr9EMP+Trm9rBft5KpTKMD9bZPsfIWAKZMleZdlZ38e8ZY7iGhZ662ggmhyvmxN1BVOUTH1g126P8qh6ZY1hIFfVPZviZUd6F9oQbcGj5b+YkCYWoUoXxFI/mo/Kieola21O+17+rmB6FgH0Gv+PbVkNdNaASp0Khh6BbziyI224ZOAABMnQqGKhC4Lef04LC6uppXaDN8ZffGXPlA68et2B/hIa2MDWk1SxtDWmEYboWDrgXdR9qq3A4FRjkvACA9169f761yeiyNPEP30rbKl6EGmgwvGDcF/dzjr/OYoOgbQQ+VFxUQ1TWjcqheCn5pf6knqU31oX2mZxSr82hSpgwFVximO759NeR1E8rDOUfGN79o2fuu8hiVN3bu3Jmf86H6D6VH+ZE+p6lh9Aztj9qH9BEYBYAB6WasG7AbhFAgVA8IPBD3Z8zBtNBDZGo9YKtgf8x/ANaQVkVDcOthQA+tpq3ehuiHjq3uKxYUbfM+4ueJBEUBIE1qINUV/16yb9++bKzBhBs3brSybL9ynnJX3FSG9svRmhuxqjLPmhZkHMtzqQW53DKiyqL6vXrnbWxs5K+7d+/m5VSNSNNGWTV0zXTZwGPMhtpXQ183Oif9Op+28nCU4x//qs+SCmK6U8UUUX2G8hblO8pndJxV5+qWbfQZ/U7nhOo7lCedPn06P2/c4b2RPgKjADBwzx73YVitKac+L4lP+8gN1HRhzEOrhAqUY3l47gL7o3hIKxX6VflgvdR1banwrwcD/dR1YA8CemjWNTB05Vwf138qrDWsroHbt2+3Hrj0GxgQGAWAdHXVa8KtgA9VVMcqVB5ShWsXPWhSKPekXPYO9XCrM8JTmYabKtepl+VYGnkqvUNc06E57Isagda5RlPqRTbUvorhujl69Oh9+VQbvUa1nC4bFKWi6fyily5dmtuYSsdC9Ro2r7HOdTvux48fz3/q3FDg1Bpn6DM6VyyYrnM45XsYtiIwCgADYQieeIx5aBWGDtqK/RHHkFbo/36iY61jpuNXtVJKD/MaQmjWteIPOT6WymwAQHVdzS3uj5IzJu7UBG0FE/zh/XRvVRlt7NONqJJ5zFOVdNnryaRY8a7jru0aYtv8PGve1CFlpdiDbIh9FcN1o/T52ghonjlzJrhszD4H1AC6LB0nHft5DYdUp6HgqdsT3Z5Z9X2dnzpefl2HO62ZjKUxCpojMAoAAxh6KBFsNeahVRg6aCv2RxxDWqE/esDTw6LNTV0nYGnXyKxKCn+5qVZ4AgDeeH5Q46k26dnHfR7RsHVj4qdX+6hpQMFvFBvbPqk73Yg+ExqRJwVtTS2QYsNNKxuqZ1ff1NDCPxZNe3paT8CxNeKIcV/FcN1onX762uiYoH039ChLQ7G5PNXAdlYDAv/+X3UofQUz9aw7bz9fvHjxvnS461V61ZvUP//c80r3PRqITweBUQAYQAxDiYxFXwHdsQ6twtBBW7E/4hjSqi006JhNebvOST0oPv/887WPtQ3BOwsPiAAwLWpY2VYZyu+lpF6RYyufKL1+T6smleoq47j7V/dZv1dqDKo2HrWyG+WGYqFnw1nP6mNpjGbHfKhG2f712LQXn4IxMTZYmOq+auO68fNwPd83qauxnoxT7DFqI09ZnZmeSYvKDKHpwsqWAbSP9aw6byQLa6zk16u69yz9LRRctXuvPVNjOgiMAsAAYhhKpG+xD8U05qFVGDpoK/ZHHENajen6HyObG7ZJT1H3gXFeT+Fjx44l39MBALCVhpdrozzsVpiGAox1rK+vZ31T5alb6artqlvOtPnOiuZfi4VfiTxvblU7zqmOQNJGA0w9H/qV96GyugV9qpbxhrg23GOubdEwmRY06vNcdfMWmzakSa95HacUz+W+91Us143S4QfplBfXPU/13VAP3Fiv0TZZMHze86HN2+mfG2VHH9A+1vk6r7eojaDkc+/RRWUP/V6jaOn+luL1jmIERgFgADEMJdK32IdiGvPQKgwdtBX7I44hrcZ0/Y+NKhMUpLd5Y5vQfUT7ed59yM/HxnT/AQBUZ+VgzenVpGGY3bNM3REO/PtO3ftQ0+WoUt2tOD1w4EDlSnUFEdyAc2hov3lC6e5iKhC/YdS8gIbOG+2foXuMdrV/QtdClSCPnkmUNj84E0qbynhl9mMs14bbW1DfVd6xc+fOfJjNopf+rrK+Aqn6vK6NJj3V/cYLdXq+63pWoxDlU3WmO2pL19d4n/sqputG2+z2zrfgW1Xabn23zDkSyzXalqKG1/4obHZe6fzw7/1l0m5zioZ6nIaCzf555p5jDJGLoA0ASNyOHTs2lN25r7Nnzw6aJj89Bw8erLyMy5cv37ecw4cPF37u7t27hcvS3/xlzfp8W8fi3LlzMz9v6dI2lLVnz55K63Ctra3dtx+qrLto32u5Xaf96tWrW76rfd3E8vJyvpzV1dWNoYx5fywuLraS5/j7QOmoQnmLbX8X13Rs138s+71LOgeL8vs6Tpw4UXo/22e7vE8AAIah+7Ll78rvRWVYu3/rp+6rVfJ+fd69b6h8VpXS4N9/3HJ62fToc0XL0bZXvadZGUsvlRvKfr+NfaKygF9W0UvloDrLm8dfV9E67FmqyfNTG7reP355s2w5255PLA0qyxU9d1o5eFYZNMZrwz+/6760T7Xtdcua/vaUPSe1T3V8tf4hn4P7vMb72lexXDf+8729qjxbWf4/71jEeI22xU9HaF/YsbNj5OYP8+pA7Tg3uQ7d9bX17Iy0EBgFkLxQ4WHoSm6/UKhXVSoo+4XCUOHCKjlm6Ssw6qfXKl3aCiyK/wBRNTDiB5ybBJT03SoFsKZp9x9E6waFrFJj6MLjmPeHf43XzXOaBujcBz6lqcoD2Biv/1j2e1fcCmrl93Vf2j5/X5V56PQbjwzdyAgA0E1g1C0r6H2oAlmfD907dK/Q/cG9z+h9lcpN3adCjVvnBVL8+5LueXWWU/a+7+4XfW9Woy9tvxtMrbJP7P5fZ1v0ahqodMsftlw/7fYZbVff+t4/uib8ctS8gJWdK34Qx9+vOof0GbsGxnht+PlF01eVxrGzngX17DHrucI+P0RQdOhrvI99NeR1U0TfcZc1Lz1ajwWDiz43hmu0DRao1f4I1S9o/xRtl6U3dN7qvLNn8aaNANx11c1HkDYCowCSFgr4xRDwCbWkrHLT103dCnx+QShUYJn3gGo9kOqmp27QcV66rAK/Cr/wWKdg2KT1oF8IqxKEaiPtfvCp6nG0B5YYexiOaX+0lef4+6DqcorywHkPVNpma3yh/KqN/KCP6z+W/d4FvxKg7VedivMY8gkAQDssfy9q9GI9X6rei+o0orHKc6v0L/MKlRWtcrXqcuY13vL3i1/e1P+VFm27PYu5986qFbTWMKnKdrjb00aFsFtZ7W+nW0FepUFbW4baP34DAL1UftU+0TL1dzs3tO9C5Wk9h4euKX0+tC9jvjb83nFKq/aH9oH/chvshRqNt5GPiMqp/vWpbXevTzd4UuXab1MM13hf+2qI66Zqb0wtW79TWpQuN0ip97Oef2K+Rttmx8kCsnre1zG03xddt+55Zg2t9LLzq85xDHGP6RD3JsSPwCiAZPmtcmMZciK2oURSGYpp1vAidbajj6FV3LQXtayt0/pz7EMHjXl/xDhcTixDWnV5/ce439sUamHd5qtq7w53H1UZCt4qXEKNeAAAw9I9uez9QOUiqyx2e8fopzWsGno41b7pHmcVu1Z+tHusBReGLF+3eZ7o+LrHXds45VEk3OvBDYpoH6nMNO9a0Lmjz+izZT4fI7fhXN2ghr5j+yFUXm0S2Jh1fep3VYcJT1lf+yrG60bLCOVvFrgluFZ8T3CPof4/736nv7vf00/9v606SLfzB8+eKEJgFEASrMVYqNVYlUr/OnN91jXUUCIpDcVUd3iRIYdWaZr2sq1BxzR00Nj3xxiGy4llSKs2r/8x7Pe2+A1g2n7Vufe559S8CjD9zT5fJn8EAADAOLhlwjbrU0K9FwGgjCrzmWK6tmcAkIh79+5lO3bsyF91v3/nzp2sL0rn2tpatrKykp07dy67detW/jp06FB28ODBbM+ePdnS0lKeruvXr2dXrlzJf3f16tX87+5ynn/++ezAgQP5Z/U6efJk/jd9fnV1tbV91cY+WlxczNN05MiR7ObNm/ky9+7dmx0+fDj/m/5//vz5/L22dRalxbalbPr1Kktp0r4+depUniY7Plrf0aNH8+Ozvr6eb8eNGzfyZevz2r55aaqa9qrpX15ezk6cOJEdP348P3eUfr10TiiNu3fvzs8/bdO1a9fy7+jzOheHMOb9YemomnadO13vA23P2bNn820VXVd6hdbhLsfyoyLKY/R57dehrv+Y93vbul6XroOqdF4pP1T+qGOp/NC9d4muKV1P+rv2r86X06dP175PAwAAIB4q46k+Q1S+m1d+r+Ly5cv5M4c9x2hdej7RcwIAzOLmRXpmBUIWFB0N/gUA0CsV9C9evLhZ4NdLDxcq+O/bty+/mSt4MKviXBXQVgCY9/kYKL1uUFjboO3VA1DVgEsfFFDTMfLTq5eCAUp3jA9qSuelS5fyh0ul2wJBlnadKwoCTiVYMZX9oUoEt5GEtrfq+an9ozxJ+YpVSrgU+Kp7zo/t+sf8+5eOqR1LXT+6d+mYKhDuNugBAADA+KmBo8qB1miuizL8wsLC5vuu1gEgLTt37txsXKyG2XUaAiN9BEYBAAASo1581npbAam2Wm8rwKUGAqaoVzoAAACAdCnooOBDGw0mywZfhxzhCMA4qKGujWAkhL5QZFvhXwAAADA6XQ9ppQoJd12zhtwFAAAAkB5/ip1du3Z1sh6NQFK0TgDw2dRIQk9RzEJgFAAAICGaR9VoPse2+a203R6kAAAAANLnB0K7aizpLnf//v2drANAOtyG4UznglkIjAIAACQ0pJUNNSVdzTPstrzUsFkAAAAApkMj07jPBJpvvotnG7f3F0EOAPO4ecahQ4cGTQviRmAUAAAgEQxpBQAAAKAPZ8+e3XyvqTza7jXqjoSjoCjDYgKY15hCL0NjCsxCYBQAACARDGkFAAAAoA8KOiwvL2/+f+/eva09f5w6dWpzyo7FxcXs8uXLrSwXQLpu3Lix+Z6GFJiHwCgAAEAiGNIKAAAAQJ+9Rq3nqJ4TlpaWsvPnz9dengKrGv5SPVBFzzaaM1DPOQAwi+om1JCCxhQoY2FjY2Oj1CcBAAAQPQUt3bk0NAeoHgzacuTIkc3W23rwUEUFAAAAgOm6efNmPvStfoqeP9Tr8+jRo6WCmnqGOXfu3OZzhqg3qjtcLwAAbSEwCgAAkBhVQlgra1VErK6uthIcdZer5Wm5tN4GAAAAYAFOBTPdEWb03KAGlepNas8O6l26vr6eB1I1/KXNC6jPnjx5Mjtx4gTPGQCAzhAYBQAASJACmApkGrXAVgVD3SGtVEFhFRwa0kpD07TZExUAAABAOtT78/r163nwU88Td+7c2QyAKui5a9eu/Oe+ffvy+UltGEwAALpGYBQAACBRDGkFAAAAAAAAfAeBUQAAgMQxpBUAAAAAAABAYBQAAGBSGNIKAAAAAAAAU0VgFAAAAAAAAAAAAEDytg2dAAAAAAAAAAAAAADoGoFRAAAAAAAAAAAAAMkjMAoAAAAAAAAAAAAgeQRGAQAAAAAAAAAAACSPwCgAAAAAAAAAAACA5BEYBQAAAAAAAAAAAJA8AqMAAAAAAAAAAAAAkkdgFAAAAAAAAAAAAEDyCIwCAAAAAAAAAAAASB6BUQAAAAAAAAAAAADJIzAKAAAAAAAAAAAAIHkERgEAAAAAAAAAAAAkj8AoAAAAAAAAAAAAgOQRGAUAAAAAAAAAAACQPAKjAAAAAAAAAAAAAJJHYBQAAAAAAAAAAABA8giMAgAAAAAAAAAAAEgegVEAAAAAAAAAAAAAySMwCgAAAAAAAAAAACB5BEYBAAAAAAAAAAAAJI/AKAAAAAAAAAAAAIDkERgFAAAAAAAAAAAAkDwCowAAAAAAAAAAAACSR2AUAAAAAAAAAAAAQPIIjAIAAAAAAAAAAABIHoFRAAAAAAAAAAAAAMkjMAoAAAAAAAAAAAAgeQRGAQAAAAAAAAAAACSPwCgAAAAAAAAAAACA5BEYBQAAAAAAAAAAAJA8AqMAAAAAAAAAAAAAkkdgFAAAAAAAAAAAAEDyCIwCAAAAAAAAAAAASB6BUQAAAAAAAAAAAADJIzAKAAAAAAAAAAAAIHkERgEAAAAAAAAAAAAkj8AoAAAAAAAAAAAAgOQRGAUAAAAAAAAAAACQPAKjAAAAAAAAAAAAAJJHYBQAAAAAAAAAAABA8giMAgAAAAAAAAAAAEgegVEAAAAAAAAAAAAAySMwCgAAAAAAAAAAACB5BEYBAAAAAAAAAAAAJI/AKAAAAAAAAAAAAIDkERgFAAAAAAAAAAAAkDwCowAAAAAAAAAAAACSR2AUAAAAAAAAAAAAQPIIjAIAAAAAAAAAAABIHoFRAAAAAAAAAAAAAMkjMAoAAAAAAAAAAAAgeQRGAQAAAAAAAAAAACSPwCgAAAAAAAAAAACA5BEYBQAAAAAAAAAAAJA8AqMAAAA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", 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" ] diff --git a/notebooks/10_use_fit_P3M_limiter.ipynb b/notebooks/10_use_fit_P3M_limiter.ipynb index 0a59c24..ea62da8 100644 --- a/notebooks/10_use_fit_P3M_limiter.ipynb +++ b/notebooks/10_use_fit_P3M_limiter.ipynb @@ -57,7 +57,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "03aa3f4e", "metadata": {}, "outputs": [], @@ -211,25 +211,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[02:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy'...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy' done.\n", - "[02:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy\n", - "[02:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n", - "[02:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 98\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n", - "[02:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy'...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy' done.\n", - "[02:53:59|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy\n" + "[19:44:01|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[19:44:01|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy'...\n", + "[19:44:01|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy' done.\n", + "[19:44:01|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy\n", + "[19:44:01|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Time-stepping distribution file: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5\n", + "[19:44:01|\u001b[38;5;113mSTATUS \u001b[00m]|Write custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", + "[19:44:01|\u001b[38;5;113mSTATUS \u001b[00m]|Write custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n", + "[19:44:01|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m TS.ai = 0.050000, TS.af = 1.000000, TS.nsteps = 104\n", + "[19:44:01|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", + "[19:44:01|\u001b[38;5;113mSTATUS \u001b[00m]|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n", + "[19:44:02|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Generating parameter file...\n", + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy'...\n", + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Writing parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy' done.\n", + "[19:44:02|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.tools)\u001b[00m Parameter file written to /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "f5b71b98", "metadata": {}, "outputs": [ @@ -255,8 +255,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Read custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n" + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Read custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Read custom timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n" ] } ], @@ -318,16 +318,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid...\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid done.\n", - "[02:53:59\u001b[00m[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook10/input_power.h5'...\n", - "|\u001b[38;5;113mSTATUS \u001b[00m]|Computing normalization of the power spectrum...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing normalization of the power spectrum done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum done.\n", - "[02:53:59|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=64, L1=64, L2=64\u001b[00m\n", - "[02:53:59|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=32, N1=32, N2=32, N2_HC=17, N_HC=17408, NUM_MODES=464\u001b[00m\n", - "[02:53:59|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook10/input_power.h5' done.\n" + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid...\n", + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Setting up Fourier grid done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing normalization of the power spectrum...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing normalization of the power spectrum done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Computing power spectrum done.\n", + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook10/input_power.h5'...\n", + "[19:44:02|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mL0=64, L1=64, L2=64\u001b[00m\n", + "[19:44:02|\u001b[38;5;246mDIAGNOSTIC\u001b[00m]==|\u001b[38;5;246mN0=32, N1=32, N2=32, N2_HC=17, N_HC=17408, NUM_MODES=464\u001b[00m\n", + "[19:44:02|\u001b[38;5;113mSTATUS \u001b[00m]|Write power spectrum in data file '/Users/hoellinger/WIP3M/notebook10/input_power.h5' done.\n" ] } ], @@ -356,75 +356,75 @@ "name": "stdout", "output_type": "stream", "text": [ - "[02:53:59\u001b[00m|\u001b[38;5;227mCOMMAND \u001b[00m]|\u001b[38;5;227msimbelmyne /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy /Users/hoellinger/WIP3M/notebook10/logs/lpt.txt\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~~-.--.\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| : )\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .~ ~ -.\\ /.- ~~ .\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| > `. .' <\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( .- -. )\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `- -.-~ `- -' ~-.- -'\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( : ) _ _ .-: ___________________________________\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~--. : .--~ .-~ .-~ } \u001b[1;38;5;157mSIMBELMYNË\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.-^-.-~ \\_ .~ .-~ .~ (c) Florent Leclercq 2012 - SBMY_YEAR \n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| \\ ' \\ '_ _ -~ ___________________________________\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `.`. //\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| . - ~ ~-.__`.`-.//\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~ . - ~ }~ ~ ~-.~-.\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .' .-~ .-~ :/~-.~-./:\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| /_~_ _ . - ~ ~-.~-._\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.<\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|2025-06-16 02:53:59: Starting SIMBELMYNË, commit hash bcdce9c1b02682972d65f1d3d414b5774015c141\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy'...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot...\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot done.\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT snapshot initialization: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions...\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/input_white_noise.h5'...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/input_white_noise.h5' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading power spectrum...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Reading power spectrum in '/Users/hoellinger/WIP3M/notebook10/input_power.h5'...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Reading power spectrum in '/Users/hoellinger/WIP3M/notebook10/input_power.h5' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading power spectrum done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Generating Gaussian random field (using 8 cores)...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Generating Gaussian random field (using 8 cores) done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/initial_density.h5'...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/initial_density.h5' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions done.\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT initial conditions: 0.003 CPU - 0.002 wallclock seconds used.\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores)...\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores)...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores) done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores)...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores) done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores) done.\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT evolution: 0.054 CPU - 0.016 wallclock seconds used.\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Computing outputs...\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/lpt_density.h5'...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/lpt_density.h5' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3'...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3' (32768 particles)...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS '...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS ' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL '...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL ' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID '...\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID ' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3' done.\n", - "[02:53:59\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Computing outputs done.\u001b[00m\n", - "[02:53:59\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT output: 0.012 CPU - 0.004 wallclock seconds used.\n", - "[02:53:59\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|ModuleLPT: 0.069 CPU - 0.022 wallclock seconds used.\n", - "[02:53:59\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|Simbelmynë: 0.070 CPU - 0.023 wallclock seconds used.\n", - "[02:53:59\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|Everything done successfully, exiting.\n" + "[19:44:02\u001b[00m|\u001b[38;5;227mCOMMAND \u001b[00m]|\u001b[38;5;227msimbelmyne /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy /Users/hoellinger/WIP3M/notebook10/logs/lpt.txt\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~~-.--.\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| : )\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .~ ~ -.\\ /.- ~~ .\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| > `. .' <\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( .- -. )\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `- -.-~ `- -' ~-.- -'\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( : ) _ _ .-: ___________________________________\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~--. : .--~ .-~ .-~ } \u001b[1;38;5;157mSIMBELMYNË\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.-^-.-~ \\_ .~ .-~ .~ (c) Florent Leclercq 2012 - SBMY_YEAR \n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| \\ ' \\ '_ _ -~ ___________________________________\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `.`. //\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| . - ~ ~-.__`.`-.//\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~ . - ~ }~ ~ ~-.~-.\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .' .-~ .-~ :/~-.~-./:\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| /_~_ _ . - ~ ~-.~-._\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.<\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|2025-06-16 19:44:02: Starting SIMBELMYNË, commit hash 9a4b2a980c0e50bcefef663b26d1b82205d6b327\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_lpt.sbmy' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot done.\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT snapshot initialization: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/input_white_noise.h5'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/input_white_noise.h5' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading power spectrum...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Reading power spectrum in '/Users/hoellinger/WIP3M/notebook10/input_power.h5'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Reading power spectrum in '/Users/hoellinger/WIP3M/notebook10/input_power.h5' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading power spectrum done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Generating Gaussian random field (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Generating Gaussian random field (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/initial_density.h5'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/initial_density.h5' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions done.\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT initial conditions: 0.003 CPU - 0.002 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores)...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores) done.\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT evolution: 0.049 CPU - 0.015 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Computing outputs...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/lpt_density.h5'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/lpt_density.h5' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3' (32768 particles)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS '...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS ' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL '...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL ' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID '...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID ' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/lpt_particles.gadget3' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Computing outputs done.\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT output: 0.013 CPU - 0.004 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|ModuleLPT: 0.065 CPU - 0.022 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|Simbelmynë: 0.066 CPU - 0.023 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|Everything done successfully, exiting.\n" ] } ], @@ -442,2152 +442,2278 @@ "name": "stdout", "output_type": "stream", "text": [ - "[02:54:00\u001b[00m|\u001b[38;5;227mCOMMAND \u001b[00m]|\u001b[38;5;227msimbelmyne /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy /Users/hoellinger/WIP3M/notebook10/logs/p3m.txt\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~~-.--.\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| : )\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .~ ~ -.\\ /.- ~~ .\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| > `. .' <\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( .- -. )\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `- -.-~ `- -' ~-.- -'\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( : ) _ _ .-: ___________________________________\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~--. : .--~ .-~ .-~ } \u001b[1;38;5;157mSIMBELMYNË\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.-^-.-~ \\_ .~ .-~ .~ (c) Florent Leclercq 2012 - SBMY_YEAR \n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| \\ ' \\ '_ _ -~ ___________________________________\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `.`. //\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| . - ~ ~-.__`.`-.//\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~ . - ~ }~ ~ ~-.~-.\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .' .-~ .-~ :/~-.~-./:\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| /_~_ _ . - ~ ~-.~-._\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.<\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", - "[02:54:00\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|2025-06-16 02:54:00: Starting SIMBELMYNË, commit hash bcdce9c1b02682972d65f1d3d414b5774015c141\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy'...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy' done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot...\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot done.\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT snapshot initialization: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions...\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/initial_density.h5'...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/initial_density.h5' done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions done.\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT initial conditions: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores)...\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores) done.\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT evolution: 0.055 CPU - 0.018 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|ModuleLPT: 0.056 CPU - 0.018 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleP3M: Evolving with P3M...\u001b[00m\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|OutputForceDiagnostic: force_diagnostic.csv\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|OutputSnapshotsBase: particles_\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_minus operator: changing reference frame before COLA evolution...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_minus operator: changing reference frame before COLA evolution done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 1/98, time_kick:0.050000, time_drift=0.050000.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 1/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 1/98, time_kick:0.051500, time_drift=0.053000.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Accelerations (short-range): 0.264 CPU - 0.041 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/98: Total Evolution: 0.381 CPU - 0.066 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 2/98, time_kick:0.051500, time_drift=0.053000.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 2/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 2/98, time_kick:0.054590, time_drift=0.056090.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Density: 0.007 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Accelerations (short-range): 0.245 CPU - 0.037 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/98: Total Evolution: 0.357 CPU - 0.062 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 3/98, time_kick:0.054590, time_drift=0.056090.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 3/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 3/98, time_kick:0.057865, time_drift=0.059365.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Accelerations (long-range): 0.090 CPU - 0.021 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Accelerations (short-range): 0.233 CPU - 0.044 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Kick: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/98: Total Evolution: 0.344 CPU - 0.071 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 4/98, time_kick:0.057865, time_drift=0.059365.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 4/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 4/98, time_kick:0.061337, time_drift=0.062837.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Density: 0.007 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Accelerations (short-range): 0.240 CPU - 0.043 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/98: Total Evolution: 0.355 CPU - 0.067 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 5/98, time_kick:0.061337, time_drift=0.062837.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 5/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 5/98, time_kick:0.065018, time_drift=0.066518.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Accelerations (long-range): 0.092 CPU - 0.019 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Accelerations (short-range): 0.254 CPU - 0.037 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/98: Total Evolution: 0.372 CPU - 0.062 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 6/98, time_kick:0.065018, time_drift=0.066518.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 6/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 6/98, time_kick:0.068919, time_drift=0.070419.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Accelerations (long-range): 0.097 CPU - 0.018 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Accelerations (short-range): 0.259 CPU - 0.038 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/98: Total Evolution: 0.376 CPU - 0.063 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 7/98, time_kick:0.068919, time_drift=0.070419.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 7/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 7/98, time_kick:0.073054, time_drift=0.074554.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Accelerations (short-range): 0.245 CPU - 0.037 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/98: Total Evolution: 0.360 CPU - 0.063 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 8/98, time_kick:0.073054, time_drift=0.074554.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 8/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 8/98, time_kick:0.077437, time_drift=0.078937.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Density: 0.012 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Accelerations (long-range): 0.093 CPU - 0.024 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Accelerations (short-range): 0.249 CPU - 0.038 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/98: Total Evolution: 0.366 CPU - 0.069 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 9/98, time_kick:0.077437, time_drift=0.078937.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 9/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 9/98, time_kick:0.082083, time_drift=0.083583.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Accelerations (short-range): 0.254 CPU - 0.037 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/98: Total Evolution: 0.370 CPU - 0.061 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 10/98, time_kick:0.082083, time_drift=0.083583.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 10/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 10/98, time_kick:0.087008, time_drift=0.088508.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Accelerations (long-range): 0.094 CPU - 0.020 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Accelerations (short-range): 0.252 CPU - 0.037 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/98: Total Evolution: 0.370 CPU - 0.063 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 11/98, time_kick:0.087008, time_drift=0.088508.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 11/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 11/98, time_kick:0.092229, time_drift=0.093729.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Density: 0.014 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Accelerations (long-range): 0.087 CPU - 0.020 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Accelerations (short-range): 0.230 CPU - 0.038 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/98: Total Evolution: 0.343 CPU - 0.064 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 12/98, time_kick:0.092229, time_drift=0.093729.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 12/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 12/98, time_kick:0.097762, time_drift=0.099262.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Accelerations (long-range): 0.088 CPU - 0.023 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Accelerations (short-range): 0.229 CPU - 0.041 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Kick: 0.006 CPU - 0.003 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/98: Total Evolution: 0.338 CPU - 0.071 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 13/98, time_kick:0.097762, time_drift=0.099262.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 13/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 13/98, time_kick:0.103628, time_drift=0.105128.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Potential: 0.005 CPU - 0.004 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Accelerations (short-range): 0.259 CPU - 0.036 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/98: Total Evolution: 0.375 CPU - 0.063 wallclock seconds used.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 14/98, time_kick:0.103628, time_drift=0.105128.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 14/98 done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:00\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 14/98, time_kick:0.109846, time_drift=0.111346.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Density: 0.014 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Accelerations (long-range): 0.095 CPU - 0.019 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Accelerations (short-range): 0.263 CPU - 0.036 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/98: Total Evolution: 0.383 CPU - 0.060 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 15/98, time_kick:0.109846, time_drift=0.111346.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 15/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 15/98, time_kick:0.116437, time_drift=0.117937.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Accelerations (long-range): 0.092 CPU - 0.023 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Accelerations (short-range): 0.260 CPU - 0.036 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Kick: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/98: Total Evolution: 0.375 CPU - 0.065 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 16/98, time_kick:0.116437, time_drift=0.117937.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 16/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 16/98, time_kick:0.123423, time_drift=0.124923.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Accelerations (long-range): 0.088 CPU - 0.020 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Accelerations (short-range): 0.238 CPU - 0.038 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/98: Total Evolution: 0.346 CPU - 0.064 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 17/98, time_kick:0.123423, time_drift=0.124923.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 17/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 17/98, time_kick:0.130828, time_drift=0.132328.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Accelerations (long-range): 0.088 CPU - 0.019 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Accelerations (short-range): 0.240 CPU - 0.038 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/98: Total Evolution: 0.351 CPU - 0.063 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 18/98, time_kick:0.130828, time_drift=0.132328.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 18/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 18/98, time_kick:0.138678, time_drift=0.140178.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Accelerations (short-range): 0.258 CPU - 0.036 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/98: Total Evolution: 0.379 CPU - 0.059 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 19/98, time_kick:0.138678, time_drift=0.140178.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 19/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 19/98, time_kick:0.146998, time_drift=0.148498.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Accelerations (long-range): 0.086 CPU - 0.026 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Accelerations (short-range): 0.254 CPU - 0.040 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/98: Total Evolution: 0.361 CPU - 0.072 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 20/98, time_kick:0.146998, time_drift=0.148498.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 20/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 20/98, time_kick:0.155818, time_drift=0.157318.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Accelerations (short-range): 0.253 CPU - 0.039 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/98: Total Evolution: 0.368 CPU - 0.064 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 21/98, time_kick:0.155818, time_drift=0.157318.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 21/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 21/98, time_kick:0.165167, time_drift=0.166667.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Density: 0.007 CPU - 0.004 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Potential: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Accelerations (short-range): 0.274 CPU - 0.040 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/98: Total Evolution: 0.388 CPU - 0.067 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 22/98, time_kick:0.165167, time_drift=0.166667.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 22/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 22/98, time_kick:0.175078, time_drift=0.176578.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Accelerations (short-range): 0.268 CPU - 0.039 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/98: Total Evolution: 0.390 CPU - 0.063 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 23/98, time_kick:0.175078, time_drift=0.176578.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 23/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 23/98, time_kick:0.185582, time_drift=0.187082.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Accelerations (short-range): 0.270 CPU - 0.036 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/98: Total Evolution: 0.390 CPU - 0.060 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 24/98, time_kick:0.185582, time_drift=0.187082.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 24/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 24/98, time_kick:0.196717, time_drift=0.198217.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Accelerations (long-range): 0.094 CPU - 0.018 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Accelerations (short-range): 0.267 CPU - 0.036 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/98: Total Evolution: 0.388 CPU - 0.061 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 25/98, time_kick:0.196717, time_drift=0.198217.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 25/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 25/98, time_kick:0.208520, time_drift=0.210020.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Density: 0.015 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Accelerations (short-range): 0.268 CPU - 0.036 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/98: Total Evolution: 0.388 CPU - 0.061 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 26/98, time_kick:0.208520, time_drift=0.210020.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 26/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 26/98, time_kick:0.221031, time_drift=0.222531.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Accelerations (long-range): 0.086 CPU - 0.021 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Accelerations (short-range): 0.253 CPU - 0.037 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/98: Total Evolution: 0.360 CPU - 0.064 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 27/98, time_kick:0.221031, time_drift=0.222531.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 27/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 27/98, time_kick:0.234293, time_drift=0.235793.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Density: 0.014 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Accelerations (short-range): 0.273 CPU - 0.037 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/98: Total Evolution: 0.393 CPU - 0.061 wallclock seconds used.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 28/98, time_kick:0.234293, time_drift=0.235793.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 28/98 done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:01\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 28/98, time_kick:0.248351, time_drift=0.249851.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Accelerations (short-range): 0.273 CPU - 0.037 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/98: Total Evolution: 0.395 CPU - 0.061 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 29/98, time_kick:0.248351, time_drift=0.249851.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 29/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 29/98, time_kick:0.263252, time_drift=0.264752.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Accelerations (long-range): 0.097 CPU - 0.018 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Accelerations (short-range): 0.277 CPU - 0.037 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/98: Total Evolution: 0.400 CPU - 0.061 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 30/98, time_kick:0.263252, time_drift=0.264752.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 30/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 30/98, time_kick:0.279047, time_drift=0.280547.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Potential: 0.005 CPU - 0.004 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Accelerations (short-range): 0.273 CPU - 0.037 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/98: Total Evolution: 0.394 CPU - 0.063 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 31/98, time_kick:0.279047, time_drift=0.280547.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 31/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 31/98, time_kick:0.295790, time_drift=0.297290.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Accelerations (short-range): 0.277 CPU - 0.037 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/98: Total Evolution: 0.401 CPU - 0.061 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 32/98, time_kick:0.295790, time_drift=0.297290.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 32/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 32/98, time_kick:0.313442, time_drift=0.314942.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Accelerations (short-range): 0.279 CPU - 0.039 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/98: Total Evolution: 0.403 CPU - 0.063 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 33/98, time_kick:0.313442, time_drift=0.314942.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 33/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 33/98, time_kick:0.329532, time_drift=0.331032.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Accelerations (short-range): 0.280 CPU - 0.039 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/98: Total Evolution: 0.402 CPU - 0.063 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 34/98, time_kick:0.329532, time_drift=0.331032.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 34/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 34/98, time_kick:0.344469, time_drift=0.345969.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Accelerations (long-range): 0.095 CPU - 0.019 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Accelerations (short-range): 0.281 CPU - 0.038 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/98: Total Evolution: 0.402 CPU - 0.063 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 35/98, time_kick:0.344469, time_drift=0.345969.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 35/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 35/98, time_kick:0.358518, time_drift=0.360018.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Accelerations (long-range): 0.094 CPU - 0.019 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Accelerations (short-range): 0.273 CPU - 0.042 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/98: Total Evolution: 0.394 CPU - 0.066 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 36/98, time_kick:0.358518, time_drift=0.360018.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 36/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 36/98, time_kick:0.371863, time_drift=0.373363.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Accelerations (long-range): 0.094 CPU - 0.019 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Accelerations (short-range): 0.287 CPU - 0.043 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/98: Total Evolution: 0.407 CPU - 0.069 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 37/98, time_kick:0.371863, time_drift=0.373363.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 37/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 37/98, time_kick:0.384634, time_drift=0.386134.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Density: 0.011 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Accelerations (long-range): 0.094 CPU - 0.021 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Accelerations (short-range): 0.285 CPU - 0.042 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Kick: 0.006 CPU - 0.005 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/98: Total Evolution: 0.401 CPU - 0.073 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 38/98, time_kick:0.384634, time_drift=0.386134.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 38/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 38/98, time_kick:0.396928, time_drift=0.398428.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Accelerations (long-range): 0.095 CPU - 0.019 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Accelerations (short-range): 0.283 CPU - 0.042 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/98: Total Evolution: 0.403 CPU - 0.067 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 39/98, time_kick:0.396928, time_drift=0.398428.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 39/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 39/98, time_kick:0.408822, time_drift=0.410322.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Density: 0.012 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Accelerations (long-range): 0.094 CPU - 0.019 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Accelerations (short-range): 0.262 CPU - 0.043 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/98: Total Evolution: 0.380 CPU - 0.067 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 40/98, time_kick:0.408822, time_drift=0.410322.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 40/98 done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 40/98, time_kick:0.420376, time_drift=0.421876.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Accelerations (long-range): 0.094 CPU - 0.020 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Accelerations (short-range): 0.294 CPU - 0.044 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/98: Total Evolution: 0.415 CPU - 0.070 wallclock seconds used.\n", - "[02:54:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 41/98, time_kick:0.420376, time_drift=0.421876.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 41/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 41/98, time_kick:0.431637, time_drift=0.433137.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Accelerations (long-range): 0.095 CPU - 0.017 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Accelerations (short-range): 0.282 CPU - 0.042 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/98: Total Evolution: 0.402 CPU - 0.066 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 42/98, time_kick:0.431637, time_drift=0.433137.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 42/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 42/98, time_kick:0.442644, time_drift=0.444144.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Accelerations (long-range): 0.095 CPU - 0.017 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Accelerations (short-range): 0.296 CPU - 0.045 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/98: Total Evolution: 0.419 CPU - 0.068 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 43/98, time_kick:0.442644, time_drift=0.444144.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 43/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 43/98, time_kick:0.453430, time_drift=0.454930.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Accelerations (short-range): 0.298 CPU - 0.041 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/98: Total Evolution: 0.418 CPU - 0.065 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 44/98, time_kick:0.453430, time_drift=0.454930.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 44/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 44/98, time_kick:0.464023, time_drift=0.465523.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Accelerations (long-range): 0.090 CPU - 0.020 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Accelerations (short-range): 0.303 CPU - 0.044 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/98: Total Evolution: 0.417 CPU - 0.071 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 45/98, time_kick:0.464023, time_drift=0.465523.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 45/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 45/98, time_kick:0.474446, time_drift=0.475946.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Accelerations (long-range): 0.094 CPU - 0.018 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Accelerations (short-range): 0.303 CPU - 0.046 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/98: Total Evolution: 0.422 CPU - 0.071 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 46/98, time_kick:0.474446, time_drift=0.475946.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 46/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 46/98, time_kick:0.484720, time_drift=0.486220.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Accelerations (long-range): 0.084 CPU - 0.021 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Accelerations (short-range): 0.308 CPU - 0.051 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/98: Total Evolution: 0.419 CPU - 0.078 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 47/98, time_kick:0.484720, time_drift=0.486220.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 47/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 47/98, time_kick:0.494861, time_drift=0.496361.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Accelerations (long-range): 0.094 CPU - 0.021 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Accelerations (short-range): 0.288 CPU - 0.050 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Kick: 0.007 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/98: Total Evolution: 0.402 CPU - 0.077 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 48/98, time_kick:0.494861, time_drift=0.496361.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 48/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 48/98, time_kick:0.504885, time_drift=0.506385.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Accelerations (short-range): 0.312 CPU - 0.045 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/98: Total Evolution: 0.436 CPU - 0.070 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 49/98, time_kick:0.504885, time_drift=0.506385.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 49/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 49/98, time_kick:0.514806, time_drift=0.516306.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Density: 0.014 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Accelerations (short-range): 0.308 CPU - 0.045 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/98: Total Evolution: 0.429 CPU - 0.069 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 50/98, time_kick:0.514806, time_drift=0.516306.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 50/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 50/98, time_kick:0.524635, time_drift=0.526135.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Accelerations (long-range): 0.092 CPU - 0.019 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Accelerations (short-range): 0.272 CPU - 0.048 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/98: Total Evolution: 0.388 CPU - 0.074 wallclock seconds used.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 51/98, time_kick:0.524635, time_drift=0.526135.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 51/98 done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 51/98, time_kick:0.534385, time_drift=0.535885.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Accelerations (short-range): 0.317 CPU - 0.045 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/98: Total Evolution: 0.438 CPU - 0.070 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 52/98, time_kick:0.534385, time_drift=0.535885.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 52/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 52/98, time_kick:0.544063, time_drift=0.545563.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Accelerations (long-range): 0.095 CPU - 0.017 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Accelerations (short-range): 0.312 CPU - 0.045 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/98: Total Evolution: 0.434 CPU - 0.069 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 53/98, time_kick:0.544063, time_drift=0.545563.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 53/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 53/98, time_kick:0.553680, time_drift=0.555180.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Accelerations (short-range): 0.312 CPU - 0.047 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/98: Total Evolution: 0.432 CPU - 0.072 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 54/98, time_kick:0.553680, time_drift=0.555180.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 54/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 54/98, time_kick:0.563243, time_drift=0.564743.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Accelerations (long-range): 0.095 CPU - 0.020 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Accelerations (short-range): 0.316 CPU - 0.044 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/98: Total Evolution: 0.437 CPU - 0.070 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 55/98, time_kick:0.563243, time_drift=0.564743.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 55/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 55/98, time_kick:0.572760, time_drift=0.574260.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Density: 0.012 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Accelerations (long-range): 0.095 CPU - 0.022 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Accelerations (short-range): 0.317 CPU - 0.051 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/98: Total Evolution: 0.437 CPU - 0.079 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 56/98, time_kick:0.572760, time_drift=0.574260.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 56/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 56/98, time_kick:0.582238, time_drift=0.583738.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Accelerations (long-range): 0.095 CPU - 0.020 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Accelerations (short-range): 0.317 CPU - 0.051 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/98: Total Evolution: 0.439 CPU - 0.077 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 57/98, time_kick:0.582238, time_drift=0.583738.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 57/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 57/98, time_kick:0.591683, time_drift=0.593183.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Accelerations (short-range): 0.320 CPU - 0.049 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/98: Total Evolution: 0.443 CPU - 0.074 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 58/98, time_kick:0.591683, time_drift=0.593183.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 58/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 58/98, time_kick:0.601100, time_drift=0.602600.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Accelerations (long-range): 0.087 CPU - 0.020 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Accelerations (short-range): 0.315 CPU - 0.052 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/98: Total Evolution: 0.429 CPU - 0.079 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 59/98, time_kick:0.601100, time_drift=0.602600.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 59/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 59/98, time_kick:0.610497, time_drift=0.611997.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Accelerations (long-range): 0.091 CPU - 0.023 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Accelerations (short-range): 0.319 CPU - 0.048 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/98: Total Evolution: 0.432 CPU - 0.076 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 60/98, time_kick:0.610497, time_drift=0.611997.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 60/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 60/98, time_kick:0.619877, time_drift=0.621377.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Accelerations (long-range): 0.093 CPU - 0.018 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Accelerations (short-range): 0.303 CPU - 0.053 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/98: Total Evolution: 0.418 CPU - 0.077 wallclock seconds used.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 61/98, time_kick:0.619877, time_drift=0.621377.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 61/98 done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 61/98, time_kick:0.629246, time_drift=0.630746.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Accelerations (short-range): 0.323 CPU - 0.053 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/98: Total Evolution: 0.443 CPU - 0.078 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 62/98, time_kick:0.629246, time_drift=0.630746.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 62/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 62/98, time_kick:0.638608, time_drift=0.640108.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Accelerations (long-range): 0.095 CPU - 0.019 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Accelerations (short-range): 0.326 CPU - 0.047 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Kick: 0.007 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/98: Total Evolution: 0.448 CPU - 0.072 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 63/98, time_kick:0.638608, time_drift=0.640108.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 63/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 63/98, time_kick:0.647969, time_drift=0.649469.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Accelerations (long-range): 0.088 CPU - 0.020 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Accelerations (short-range): 0.324 CPU - 0.056 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Drift: 0.001 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/98: Total Evolution: 0.436 CPU - 0.085 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 64/98, time_kick:0.647969, time_drift=0.649469.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 64/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 64/98, time_kick:0.657331, time_drift=0.658831.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Accelerations (long-range): 0.095 CPU - 0.020 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Accelerations (short-range): 0.326 CPU - 0.055 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Drift: 0.001 CPU - 0.004 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/98: Total Evolution: 0.450 CPU - 0.085 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 65/98, time_kick:0.657331, time_drift=0.658831.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 65/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 65/98, time_kick:0.666701, time_drift=0.668201.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Accelerations (short-range): 0.331 CPU - 0.056 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/98: Total Evolution: 0.453 CPU - 0.080 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 66/98, time_kick:0.666701, time_drift=0.668201.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 66/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 66/98, time_kick:0.676081, time_drift=0.677581.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Accelerations (short-range): 0.333 CPU - 0.049 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/98: Total Evolution: 0.457 CPU - 0.074 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 67/98, time_kick:0.676081, time_drift=0.677581.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 67/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 67/98, time_kick:0.685476, time_drift=0.686976.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Potential: 0.005 CPU - 0.005 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Accelerations (long-range): 0.095 CPU - 0.021 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Accelerations (short-range): 0.325 CPU - 0.056 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/98: Total Evolution: 0.445 CPU - 0.086 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 68/98, time_kick:0.685476, time_drift=0.686976.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 68/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 68/98, time_kick:0.694890, time_drift=0.696390.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Accelerations (long-range): 0.094 CPU - 0.064 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Accelerations (short-range): 0.328 CPU - 0.055 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Kick: 0.006 CPU - 0.009 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/98: Total Evolution: 0.447 CPU - 0.133 wallclock seconds used.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 69/98, time_kick:0.694890, time_drift=0.696390.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 69/98 done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 69/98, time_kick:0.704326, time_drift=0.705826.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Potential: 0.005 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Accelerations (long-range): 0.094 CPU - 0.018 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Accelerations (short-range): 0.315 CPU - 0.058 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/98: Total Evolution: 0.430 CPU - 0.083 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 70/98, time_kick:0.704326, time_drift=0.705826.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 70/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 70/98, time_kick:0.713788, time_drift=0.715288.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Density: 0.012 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Potential: 0.005 CPU - 0.005 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Accelerations (long-range): 0.094 CPU - 0.019 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Accelerations (short-range): 0.329 CPU - 0.058 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/98: Total Evolution: 0.447 CPU - 0.086 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 71/98, time_kick:0.713788, time_drift=0.715288.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 71/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 71/98, time_kick:0.723280, time_drift=0.724780.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Accelerations (long-range): 0.094 CPU - 0.019 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Accelerations (short-range): 0.333 CPU - 0.057 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/98: Total Evolution: 0.456 CPU - 0.081 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 72/98, time_kick:0.723280, time_drift=0.724780.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 72/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 72/98, time_kick:0.732806, time_drift=0.734306.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Accelerations (short-range): 0.341 CPU - 0.053 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/98: Total Evolution: 0.460 CPU - 0.078 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 73/98, time_kick:0.732806, time_drift=0.734306.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 73/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 73/98, time_kick:0.742369, time_drift=0.743869.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Accelerations (long-range): 0.099 CPU - 0.021 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Accelerations (short-range): 0.352 CPU - 0.071 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/98: Total Evolution: 0.479 CPU - 0.099 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 74/98, time_kick:0.742369, time_drift=0.743869.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 74/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 74/98, time_kick:0.751972, time_drift=0.753472.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Accelerations (long-range): 0.090 CPU - 0.020 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Accelerations (short-range): 0.342 CPU - 0.055 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Kick: 0.005 CPU - 0.004 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/98: Total Evolution: 0.454 CPU - 0.084 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 75/98, time_kick:0.751972, time_drift=0.753472.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 75/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 75/98, time_kick:0.761621, time_drift=0.763121.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Accelerations (short-range): 0.334 CPU - 0.059 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/98: Total Evolution: 0.455 CPU - 0.084 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 76/98, time_kick:0.761621, time_drift=0.763121.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 76/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 76/98, time_kick:0.771318, time_drift=0.772818.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Potential: 0.006 CPU - 0.003 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Accelerations (long-range): 0.086 CPU - 0.022 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Accelerations (short-range): 0.343 CPU - 0.070 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/98: Total Evolution: 0.453 CPU - 0.100 wallclock seconds used.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 77/98, time_kick:0.771318, time_drift=0.772818.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 77/98 done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 77/98, time_kick:0.781066, time_drift=0.782566.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Accelerations (long-range): 0.092 CPU - 0.021 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Accelerations (short-range): 0.344 CPU - 0.062 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/98: Total Evolution: 0.463 CPU - 0.089 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 78/98, time_kick:0.781066, time_drift=0.782566.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 78/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 78/98, time_kick:0.790871, time_drift=0.792371.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Accelerations (long-range): 0.091 CPU - 0.019 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Accelerations (short-range): 0.336 CPU - 0.059 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/98: Total Evolution: 0.448 CPU - 0.085 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 79/98, time_kick:0.790871, time_drift=0.792371.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 79/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 79/98, time_kick:0.800736, time_drift=0.802236.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Accelerations (long-range): 0.098 CPU - 0.023 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Accelerations (short-range): 0.338 CPU - 0.069 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Kick: 0.007 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/98: Total Evolution: 0.461 CPU - 0.098 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 80/98, time_kick:0.800736, time_drift=0.802236.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 80/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 80/98, time_kick:0.810665, time_drift=0.812165.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Accelerations (long-range): 0.091 CPU - 0.019 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Accelerations (short-range): 0.347 CPU - 0.061 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/98: Total Evolution: 0.464 CPU - 0.087 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 81/98, time_kick:0.810665, time_drift=0.812165.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 81/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 81/98, time_kick:0.820662, time_drift=0.822162.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Accelerations (long-range): 0.089 CPU - 0.022 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Accelerations (short-range): 0.348 CPU - 0.061 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/98: Total Evolution: 0.461 CPU - 0.089 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 82/98, time_kick:0.820662, time_drift=0.822162.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 82/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 82/98, time_kick:0.830731, time_drift=0.832231.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Accelerations (long-range): 0.093 CPU - 0.019 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Accelerations (short-range): 0.344 CPU - 0.055 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Kick: 0.006 CPU - 0.006 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/98: Total Evolution: 0.461 CPU - 0.085 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 83/98, time_kick:0.830731, time_drift=0.832231.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 83/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 83/98, time_kick:0.840876, time_drift=0.842376.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Density: 0.012 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Accelerations (long-range): 0.097 CPU - 0.019 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Accelerations (short-range): 0.347 CPU - 0.057 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Kick: 0.006 CPU - 0.004 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/98: Total Evolution: 0.468 CPU - 0.085 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 84/98, time_kick:0.840876, time_drift=0.842376.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 84/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 84/98, time_kick:0.851102, time_drift=0.852602.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Accelerations (long-range): 0.092 CPU - 0.019 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Accelerations (short-range): 0.314 CPU - 0.066 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/98: Total Evolution: 0.434 CPU - 0.091 wallclock seconds used.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 85/98, time_kick:0.851102, time_drift=0.852602.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 85/98 done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 85/98, time_kick:0.861414, time_drift=0.862914.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Density: 0.012 CPU - 0.004 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Accelerations (long-range): 0.094 CPU - 0.018 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Accelerations (short-range): 0.340 CPU - 0.065 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/98: Total Evolution: 0.457 CPU - 0.091 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 86/98, time_kick:0.861414, time_drift=0.862914.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 86/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 86/98, time_kick:0.871816, time_drift=0.873316.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Density: 0.009 CPU - 0.003 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Accelerations (long-range): 0.096 CPU - 0.026 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Accelerations (short-range): 0.374 CPU - 0.091 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Kick: 0.007 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/98: Total Evolution: 0.492 CPU - 0.124 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 87/98, time_kick:0.871816, time_drift=0.873316.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 87/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 87/98, time_kick:0.882314, time_drift=0.883814.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Density: 0.011 CPU - 0.004 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Accelerations (short-range): 0.385 CPU - 0.075 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Kick: 0.007 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/98: Total Evolution: 0.504 CPU - 0.102 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 88/98, time_kick:0.882314, time_drift=0.883814.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 88/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 88/98, time_kick:0.892911, time_drift=0.894411.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Accelerations (short-range): 0.358 CPU - 0.063 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/98: Total Evolution: 0.478 CPU - 0.088 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 89/98, time_kick:0.892911, time_drift=0.894411.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 89/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 89/98, time_kick:0.903615, time_drift=0.905115.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Accelerations (long-range): 0.095 CPU - 0.018 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Accelerations (short-range): 0.364 CPU - 0.058 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/98: Total Evolution: 0.487 CPU - 0.083 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 90/98, time_kick:0.903615, time_drift=0.905115.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 90/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 90/98, time_kick:0.914430, time_drift=0.915930.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Accelerations (long-range): 0.094 CPU - 0.018 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Accelerations (short-range): 0.358 CPU - 0.060 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/98: Total Evolution: 0.479 CPU - 0.085 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 91/98, time_kick:0.914430, time_drift=0.915930.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 91/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 91/98, time_kick:0.925363, time_drift=0.926863.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Accelerations (long-range): 0.091 CPU - 0.019 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Accelerations (short-range): 0.363 CPU - 0.062 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/98: Total Evolution: 0.479 CPU - 0.087 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 92/98, time_kick:0.925363, time_drift=0.926863.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 92/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 92/98, time_kick:0.936419, time_drift=0.937919.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Accelerations (long-range): 0.095 CPU - 0.019 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Accelerations (short-range): 0.327 CPU - 0.067 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/98: Total Evolution: 0.448 CPU - 0.092 wallclock seconds used.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 93/98, time_kick:0.936419, time_drift=0.937919.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 93/98 done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 93/98, time_kick:0.947606, time_drift=0.949106.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Accelerations (long-range): 0.092 CPU - 0.019 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Accelerations (short-range): 0.353 CPU - 0.066 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/98: Total Evolution: 0.467 CPU - 0.091 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 94/98, time_kick:0.947606, time_drift=0.949106.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 94/98 done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 94/98, time_kick:0.958930, time_drift=0.960430.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Accelerations (long-range): 0.096 CPU - 0.018 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Accelerations (short-range): 0.371 CPU - 0.067 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/98: Total Evolution: 0.495 CPU - 0.092 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 95/98, time_kick:0.958930, time_drift=0.960430.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 95/98 done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 95/98, time_kick:0.970398, time_drift=0.971898.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Potential: 0.004 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Accelerations (long-range): 0.095 CPU - 0.017 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Accelerations (short-range): 0.361 CPU - 0.062 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/98: Total Evolution: 0.484 CPU - 0.085 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 96/98, time_kick:0.970398, time_drift=0.971898.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 96/98 done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 96/98, time_kick:0.982019, time_drift=0.983519.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Accelerations (long-range): 0.095 CPU - 0.020 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Accelerations (short-range): 0.363 CPU - 0.065 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Kick: 0.007 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/98: Total Evolution: 0.487 CPU - 0.092 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 97/98, time_kick:0.982019, time_drift=0.983519.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 97/98 done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 97/98, time_kick:0.993801, time_drift=0.995301.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Potential: 0.005 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Accelerations (long-range): 0.096 CPU - 0.025 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Accelerations (short-range): 0.399 CPU - 0.110 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/98: Total Evolution: 0.523 CPU - 0.142 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 98/98, time_kick:0.993801, time_drift=0.995301.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 98/98 done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 98/98, time_kick:1.000000, time_drift=1.000000.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Density: 0.030 CPU - 0.007 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Potential: 0.010 CPU - 0.004 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Accelerations (long-range): 0.190 CPU - 0.037 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Accelerations (short-range): 0.752 CPU - 0.147 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Kick: 0.012 CPU - 0.003 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/98: Total Evolution: 0.996 CPU - 0.199 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_plus operator: changing reference frame after COLA evolution...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_plus operator: changing reference frame after COLA evolution done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Density: 1.276 CPU - 0.261 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Potential: 0.477 CPU - 0.186 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Accelerations (long-range): 9.256 CPU - 1.963 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Accelerations (short-range): 30.085 CPU - 4.948 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Kick: 0.586 CPU - 0.165 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Drift: 0.123 CPU - 0.056 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Total Evolution: 41.804 CPU - 7.579 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleP3M: Evolving with P3M done.\u001b[00m\n", - "[02:54:09\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModulePMCOLA: Computing outputs...\u001b[00m\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/final_density_p3m.h5'...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/final_density_p3m.h5' done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3'...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3' done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3' (32768 particles)...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS '...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS ' done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL '...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL ' done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID '...\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID ' done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3' done.\n", - "[02:54:09\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModulePMCOLA: Computing outputs done.\u001b[00m\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|PMCOLA output: 0.016 CPU - 0.004 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|ModulePMCOLA: 43.932 CPU - 9.704 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|Simbelmynë: 43.989 CPU - 9.723 wallclock seconds used.\n", - "[02:54:09\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|Everything done successfully, exiting.\n" + "[19:44:02\u001b[00m|\u001b[38;5;227mCOMMAND \u001b[00m]|\u001b[38;5;227msimbelmyne /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy /Users/hoellinger/WIP3M/notebook10/logs/p3m.txt\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~~-.--.\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| : )\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .~ ~ -.\\ /.- ~~ .\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| > `. .' <\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( .- -. )\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `- -.-~ `- -' ~-.- -'\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ( : ) _ _ .-: ___________________________________\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~--. : .--~ .-~ .-~ } \u001b[1;38;5;157mSIMBELMYNË\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.-^-.-~ \\_ .~ .-~ .~ (c) Florent Leclercq 2012 - SBMY_YEAR \n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| \\ ' \\ '_ _ -~ ___________________________________\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| `.`. //\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| . - ~ ~-.__`.`-.//\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .-~ . - ~ }~ ~ ~-.~-.\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| .' .-~ .-~ :/~-.~-./:\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| /_~_ _ . - ~ ~-.~-._\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]| ~-.<\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|\n", + "[19:44:02\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|2025-06-16 19:44:02: Starting SIMBELMYNË, commit hash 9a4b2a980c0e50bcefef663b26d1b82205d6b327\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]|Reading parameter file in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/example_p3m.sbmy' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Initializing snapshot done.\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT snapshot initialization: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/initial_density.h5'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Reading field in '/Users/hoellinger/WIP3M/notebook10/initial_density.h5' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Returning initial conditions done.\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT initial conditions: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores)...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian potentials, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Computing Lagrangian displacement field (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Changing velocities of particles done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Displacing particles done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleLPT: Evolving with Lagrangian perturbation theory (using 8 cores) done.\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|LPT evolution: 0.051 CPU - 0.016 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|ModuleLPT: 0.052 CPU - 0.016 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleP3M: Evolving with P3M...\u001b[00m\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5'...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Read timestepping configuration in '/Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/ts_p3m.h5' done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|OutputForceDiagnostic: force_diagnostic.csv\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|OutputSnapshotsBase: particles_\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_minus operator: changing reference frame before COLA evolution...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_minus operator: changing reference frame before COLA evolution done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 1/104, time_kick:0.050000, time_drift=0.050000.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 1/104 done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 1/104, time_kick:0.051250, time_drift=0.052500.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Accelerations (short-range): 0.243 CPU - 0.042 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 1/104: Total Evolution: 0.329 CPU - 0.062 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 2/104, time_kick:0.051250, time_drift=0.052500.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 2/104 done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 2/104, time_kick:0.053813, time_drift=0.055063.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Accelerations (short-range): 0.252 CPU - 0.036 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 2/104: Total Evolution: 0.343 CPU - 0.056 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 3/104, time_kick:0.053813, time_drift=0.055063.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 3/104 done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 3/104, time_kick:0.056503, time_drift=0.057753.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Accelerations (long-range): 0.063 CPU - 0.014 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Accelerations (short-range): 0.251 CPU - 0.036 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 3/104: Total Evolution: 0.343 CPU - 0.057 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 4/104, time_kick:0.056503, time_drift=0.057753.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 4/104 done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 4/104, time_kick:0.059328, time_drift=0.060578.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Accelerations (long-range): 0.061 CPU - 0.019 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Accelerations (short-range): 0.262 CPU - 0.043 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Kick: 0.005 CPU - 0.002 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 4/104: Total Evolution: 0.347 CPU - 0.069 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 5/104, time_kick:0.059328, time_drift=0.060578.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 5/104 done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 5/104, time_kick:0.062295, time_drift=0.063545.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Accelerations (long-range): 0.061 CPU - 0.013 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Accelerations (short-range): 0.235 CPU - 0.040 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 5/104: Total Evolution: 0.319 CPU - 0.059 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 6/104, time_kick:0.062295, time_drift=0.063545.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 6/104 done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 6/104, time_kick:0.065409, time_drift=0.066659.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Accelerations (long-range): 0.060 CPU - 0.014 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Accelerations (short-range): 0.240 CPU - 0.038 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 6/104: Total Evolution: 0.325 CPU - 0.058 wallclock seconds used.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 7/104, time_kick:0.065409, time_drift=0.066659.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 7/104 done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:02\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 7/104, time_kick:0.068680, time_drift=0.069930.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Accelerations (long-range): 0.061 CPU - 0.020 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Accelerations (short-range): 0.245 CPU - 0.038 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Kick: 0.005 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 7/104: Total Evolution: 0.332 CPU - 0.066 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 8/104, time_kick:0.068680, time_drift=0.069930.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 8/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 8/104, time_kick:0.072114, time_drift=0.073364.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Accelerations (long-range): 0.057 CPU - 0.014 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Accelerations (short-range): 0.238 CPU - 0.036 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 8/104: Total Evolution: 0.319 CPU - 0.057 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 9/104, time_kick:0.072114, time_drift=0.073364.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 9/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 9/104, time_kick:0.075720, time_drift=0.076970.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Accelerations (long-range): 0.059 CPU - 0.014 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Accelerations (short-range): 0.225 CPU - 0.040 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 9/104: Total Evolution: 0.307 CPU - 0.060 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 10/104, time_kick:0.075720, time_drift=0.076970.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 10/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 10/104, time_kick:0.079506, time_drift=0.080756.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Accelerations (long-range): 0.059 CPU - 0.014 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Accelerations (short-range): 0.239 CPU - 0.040 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 10/104: Total Evolution: 0.321 CPU - 0.060 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 11/104, time_kick:0.079506, time_drift=0.080756.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 11/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 11/104, time_kick:0.083481, time_drift=0.084731.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Potential: 0.008 CPU - 0.005 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Accelerations (long-range): 0.059 CPU - 0.014 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Accelerations (short-range): 0.245 CPU - 0.037 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 11/104: Total Evolution: 0.326 CPU - 0.059 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 12/104, time_kick:0.083481, time_drift=0.084731.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 12/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 12/104, time_kick:0.087655, time_drift=0.088905.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Accelerations (long-range): 0.061 CPU - 0.013 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Accelerations (short-range): 0.238 CPU - 0.039 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 12/104: Total Evolution: 0.322 CPU - 0.059 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 13/104, time_kick:0.087655, time_drift=0.088905.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 13/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 13/104, time_kick:0.092038, time_drift=0.093288.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Accelerations (long-range): 0.062 CPU - 0.014 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Accelerations (short-range): 0.233 CPU - 0.044 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 13/104: Total Evolution: 0.319 CPU - 0.064 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 14/104, time_kick:0.092038, time_drift=0.093288.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 14/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 14/104, time_kick:0.096640, time_drift=0.097890.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Accelerations (long-range): 0.058 CPU - 0.017 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Accelerations (short-range): 0.250 CPU - 0.036 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 14/104: Total Evolution: 0.334 CPU - 0.060 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 15/104, time_kick:0.096640, time_drift=0.097890.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 15/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 15/104, time_kick:0.101471, time_drift=0.102721.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Accelerations (short-range): 0.253 CPU - 0.038 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 15/104: Total Evolution: 0.343 CPU - 0.058 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 16/104, time_kick:0.101471, time_drift=0.102721.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 16/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 16/104, time_kick:0.106545, time_drift=0.107795.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Accelerations (short-range): 0.257 CPU - 0.036 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 16/104: Total Evolution: 0.345 CPU - 0.056 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 17/104, time_kick:0.106545, time_drift=0.107795.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 17/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 17/104, time_kick:0.111872, time_drift=0.113122.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Accelerations (short-range): 0.259 CPU - 0.037 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 17/104: Total Evolution: 0.349 CPU - 0.056 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 18/104, time_kick:0.111872, time_drift=0.113122.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 18/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 18/104, time_kick:0.117466, time_drift=0.118716.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Density: 0.012 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Accelerations (long-range): 0.060 CPU - 0.014 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Accelerations (short-range): 0.257 CPU - 0.036 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 18/104: Total Evolution: 0.345 CPU - 0.057 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 19/104, time_kick:0.117466, time_drift=0.118716.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 19/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 19/104, time_kick:0.123339, time_drift=0.124589.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Accelerations (short-range): 0.257 CPU - 0.036 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 19/104: Total Evolution: 0.346 CPU - 0.055 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 20/104, time_kick:0.123339, time_drift=0.124589.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 20/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 20/104, time_kick:0.129506, time_drift=0.130756.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Accelerations (long-range): 0.063 CPU - 0.013 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Accelerations (short-range): 0.254 CPU - 0.037 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 20/104: Total Evolution: 0.342 CPU - 0.056 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 21/104, time_kick:0.129506, time_drift=0.130756.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 21/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 21/104, time_kick:0.135982, time_drift=0.137232.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Density: 0.014 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Accelerations (long-range): 0.065 CPU - 0.014 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Accelerations (short-range): 0.259 CPU - 0.036 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 21/104: Total Evolution: 0.352 CPU - 0.056 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 22/104, time_kick:0.135982, time_drift=0.137232.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 22/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 22/104, time_kick:0.142781, time_drift=0.144031.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Density: 0.014 CPU - 0.002 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Accelerations (short-range): 0.259 CPU - 0.036 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 22/104: Total Evolution: 0.350 CPU - 0.056 wallclock seconds used.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 23/104, time_kick:0.142781, time_drift=0.144031.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 23/104 done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:03\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 23/104, time_kick:0.149920, time_drift=0.151170.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Accelerations (short-range): 0.260 CPU - 0.037 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 23/104: Total Evolution: 0.350 CPU - 0.057 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 24/104, time_kick:0.149920, time_drift=0.151170.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 24/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 24/104, time_kick:0.157416, time_drift=0.158666.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Accelerations (long-range): 0.063 CPU - 0.014 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Accelerations (short-range): 0.257 CPU - 0.037 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 24/104: Total Evolution: 0.345 CPU - 0.057 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 25/104, time_kick:0.157416, time_drift=0.158666.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 25/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 25/104, time_kick:0.165286, time_drift=0.166536.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Accelerations (long-range): 0.064 CPU - 0.015 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Accelerations (short-range): 0.262 CPU - 0.040 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Kick: 0.006 CPU - 0.005 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 25/104: Total Evolution: 0.350 CPU - 0.065 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 26/104, time_kick:0.165286, time_drift=0.166536.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 26/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 26/104, time_kick:0.173551, time_drift=0.174801.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Density: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Accelerations (long-range): 0.063 CPU - 0.014 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Accelerations (short-range): 0.258 CPU - 0.040 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 26/104: Total Evolution: 0.346 CPU - 0.062 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 27/104, time_kick:0.173551, time_drift=0.174801.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 27/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 27/104, time_kick:0.182228, time_drift=0.183478.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Accelerations (long-range): 0.063 CPU - 0.016 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Accelerations (short-range): 0.259 CPU - 0.038 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Drift: 0.002 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 27/104: Total Evolution: 0.349 CPU - 0.064 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 28/104, time_kick:0.182228, time_drift=0.183478.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 28/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 28/104, time_kick:0.191340, time_drift=0.192590.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Density: 0.012 CPU - 0.007 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Accelerations (long-range): 0.062 CPU - 0.014 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Accelerations (short-range): 0.233 CPU - 0.041 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 28/104: Total Evolution: 0.323 CPU - 0.067 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 29/104, time_kick:0.191340, time_drift=0.192590.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 29/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 29/104, time_kick:0.200907, time_drift=0.202157.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Accelerations (long-range): 0.062 CPU - 0.014 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Accelerations (short-range): 0.263 CPU - 0.037 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 29/104: Total Evolution: 0.352 CPU - 0.057 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 30/104, time_kick:0.200907, time_drift=0.202157.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 30/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 30/104, time_kick:0.210952, time_drift=0.212202.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Accelerations (short-range): 0.262 CPU - 0.038 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 30/104: Total Evolution: 0.351 CPU - 0.057 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 31/104, time_kick:0.210952, time_drift=0.212202.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 31/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 31/104, time_kick:0.221500, time_drift=0.222750.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Density: 0.011 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Accelerations (long-range): 0.065 CPU - 0.012 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Accelerations (short-range): 0.264 CPU - 0.037 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 31/104: Total Evolution: 0.354 CPU - 0.056 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 32/104, time_kick:0.221500, time_drift=0.222750.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 32/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 32/104, time_kick:0.232575, time_drift=0.233825.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Accelerations (long-range): 0.063 CPU - 0.013 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Accelerations (short-range): 0.265 CPU - 0.037 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 32/104: Total Evolution: 0.356 CPU - 0.057 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 33/104, time_kick:0.232575, time_drift=0.233825.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 33/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 33/104, time_kick:0.244203, time_drift=0.245453.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Accelerations (long-range): 0.061 CPU - 0.013 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Accelerations (short-range): 0.268 CPU - 0.037 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 33/104: Total Evolution: 0.359 CPU - 0.057 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 34/104, time_kick:0.244203, time_drift=0.245453.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 34/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 34/104, time_kick:0.256413, time_drift=0.257663.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Density: 0.012 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Accelerations (long-range): 0.065 CPU - 0.014 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Accelerations (short-range): 0.264 CPU - 0.038 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 34/104: Total Evolution: 0.356 CPU - 0.059 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 35/104, time_kick:0.256413, time_drift=0.257663.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 35/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 35/104, time_kick:0.269234, time_drift=0.270484.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Potential: 0.010 CPU - 0.005 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Accelerations (long-range): 0.065 CPU - 0.017 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Accelerations (short-range): 0.277 CPU - 0.070 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Kick: 0.007 CPU - 0.002 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 35/104: Total Evolution: 0.371 CPU - 0.099 wallclock seconds used.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 36/104, time_kick:0.269234, time_drift=0.270484.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 36/104 done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:04\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 36/104, time_kick:0.282696, time_drift=0.283946.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Density: 0.007 CPU - 0.004 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Accelerations (long-range): 0.065 CPU - 0.097 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Accelerations (short-range): 0.263 CPU - 0.097 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Kick: 0.007 CPU - 0.006 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Drift: 0.002 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 36/104: Total Evolution: 0.352 CPU - 0.210 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 37/104, time_kick:0.282696, time_drift=0.283946.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 37/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 37/104, time_kick:0.296831, time_drift=0.298081.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Density: 0.012 CPU - 0.005 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Potential: 0.010 CPU - 0.008 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Accelerations (long-range): 0.063 CPU - 0.014 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Accelerations (short-range): 0.281 CPU - 0.074 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 37/104: Total Evolution: 0.373 CPU - 0.103 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 38/104, time_kick:0.296831, time_drift=0.298081.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 38/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 38/104, time_kick:0.311672, time_drift=0.312922.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Accelerations (long-range): 0.062 CPU - 0.015 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Accelerations (short-range): 0.267 CPU - 0.050 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 38/104: Total Evolution: 0.354 CPU - 0.073 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 39/104, time_kick:0.311672, time_drift=0.312922.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 39/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 39/104, time_kick:0.327256, time_drift=0.328506.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Density: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Potential: 0.010 CPU - 0.005 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Accelerations (long-range): 0.064 CPU - 0.015 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Accelerations (short-range): 0.281 CPU - 0.060 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 39/104: Total Evolution: 0.369 CPU - 0.084 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 40/104, time_kick:0.327256, time_drift=0.328506.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 40/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 40/104, time_kick:0.342342, time_drift=0.343592.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Accelerations (long-range): 0.065 CPU - 0.018 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Accelerations (short-range): 0.277 CPU - 0.054 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Kick: 0.006 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 40/104: Total Evolution: 0.370 CPU - 0.081 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 41/104, time_kick:0.342342, time_drift=0.343592.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 41/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 41/104, time_kick:0.356509, time_drift=0.357759.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Density: 0.007 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Accelerations (long-range): 0.066 CPU - 0.016 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Accelerations (short-range): 0.261 CPU - 0.066 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 41/104: Total Evolution: 0.349 CPU - 0.090 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 42/104, time_kick:0.356509, time_drift=0.357759.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 42/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 42/104, time_kick:0.369948, time_drift=0.371198.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Density: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Accelerations (long-range): 0.068 CPU - 0.019 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Accelerations (short-range): 0.288 CPU - 0.055 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Kick: 0.006 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 42/104: Total Evolution: 0.382 CPU - 0.083 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 43/104, time_kick:0.369948, time_drift=0.371198.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 43/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 43/104, time_kick:0.382796, time_drift=0.384046.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Density: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Accelerations (long-range): 0.061 CPU - 0.014 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Accelerations (short-range): 0.276 CPU - 0.043 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 43/104: Total Evolution: 0.360 CPU - 0.063 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 44/104, time_kick:0.382796, time_drift=0.384046.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 44/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 44/104, time_kick:0.395155, time_drift=0.396405.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Accelerations (short-range): 0.281 CPU - 0.043 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 44/104: Total Evolution: 0.373 CPU - 0.063 wallclock seconds used.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 45/104, time_kick:0.395155, time_drift=0.396405.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 45/104 done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:05\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 45/104, time_kick:0.407104, time_drift=0.408354.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Accelerations (long-range): 0.065 CPU - 0.012 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Accelerations (short-range): 0.279 CPU - 0.042 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 45/104: Total Evolution: 0.373 CPU - 0.061 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 46/104, time_kick:0.407104, time_drift=0.408354.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 46/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 46/104, time_kick:0.418704, time_drift=0.419954.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Density: 0.011 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Accelerations (short-range): 0.284 CPU - 0.042 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 46/104: Total Evolution: 0.372 CPU - 0.062 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 47/104, time_kick:0.418704, time_drift=0.419954.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 47/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 47/104, time_kick:0.430005, time_drift=0.431255.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Accelerations (long-range): 0.061 CPU - 0.016 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Accelerations (short-range): 0.282 CPU - 0.046 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 47/104: Total Evolution: 0.370 CPU - 0.070 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 48/104, time_kick:0.430005, time_drift=0.431255.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 48/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 48/104, time_kick:0.441047, time_drift=0.442297.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Accelerations (long-range): 0.058 CPU - 0.014 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Accelerations (short-range): 0.282 CPU - 0.044 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 48/104: Total Evolution: 0.369 CPU - 0.065 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 49/104, time_kick:0.441047, time_drift=0.442297.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 49/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 49/104, time_kick:0.451864, time_drift=0.453114.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Density: 0.011 CPU - 0.004 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Accelerations (long-range): 0.062 CPU - 0.014 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Accelerations (short-range): 0.284 CPU - 0.045 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 49/104: Total Evolution: 0.372 CPU - 0.068 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 50/104, time_kick:0.451864, time_drift=0.453114.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 50/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 50/104, time_kick:0.462483, time_drift=0.463733.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Accelerations (short-range): 0.268 CPU - 0.045 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 50/104: Total Evolution: 0.356 CPU - 0.065 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 51/104, time_kick:0.462483, time_drift=0.463733.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 51/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 51/104, time_kick:0.472930, time_drift=0.474180.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Accelerations (long-range): 0.066 CPU - 0.017 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Accelerations (short-range): 0.283 CPU - 0.051 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 51/104: Total Evolution: 0.377 CPU - 0.076 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 52/104, time_kick:0.472930, time_drift=0.474180.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 52/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 52/104, time_kick:0.483224, time_drift=0.484474.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Density: 0.011 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Potential: 0.010 CPU - 0.004 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Accelerations (long-range): 0.065 CPU - 0.014 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Accelerations (short-range): 0.286 CPU - 0.059 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 52/104: Total Evolution: 0.379 CPU - 0.083 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 53/104, time_kick:0.483224, time_drift=0.484474.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 53/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 53/104, time_kick:0.493384, time_drift=0.494634.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Accelerations (long-range): 0.061 CPU - 0.013 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Accelerations (short-range): 0.307 CPU - 0.049 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 53/104: Total Evolution: 0.400 CPU - 0.069 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 54/104, time_kick:0.493384, time_drift=0.494634.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 54/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 54/104, time_kick:0.503424, time_drift=0.504674.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Accelerations (short-range): 0.300 CPU - 0.044 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 54/104: Total Evolution: 0.391 CPU - 0.064 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 55/104, time_kick:0.503424, time_drift=0.504674.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 55/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 55/104, time_kick:0.513359, time_drift=0.514609.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Accelerations (short-range): 0.294 CPU - 0.044 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 55/104: Total Evolution: 0.384 CPU - 0.064 wallclock seconds used.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 56/104, time_kick:0.513359, time_drift=0.514609.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 56/104 done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:06\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 56/104, time_kick:0.523201, time_drift=0.524451.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Accelerations (long-range): 0.059 CPU - 0.013 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Accelerations (short-range): 0.300 CPU - 0.049 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Kick: 0.006 CPU - 0.005 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 56/104: Total Evolution: 0.390 CPU - 0.072 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 57/104, time_kick:0.523201, time_drift=0.524451.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 57/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 57/104, time_kick:0.532962, time_drift=0.534212.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Accelerations (long-range): 0.063 CPU - 0.013 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Accelerations (short-range): 0.298 CPU - 0.051 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 57/104: Total Evolution: 0.392 CPU - 0.071 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 58/104, time_kick:0.532962, time_drift=0.534212.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 58/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 58/104, time_kick:0.542650, time_drift=0.543900.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Accelerations (long-range): 0.059 CPU - 0.015 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Accelerations (short-range): 0.295 CPU - 0.048 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 58/104: Total Evolution: 0.385 CPU - 0.070 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 59/104, time_kick:0.542650, time_drift=0.543900.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 59/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 59/104, time_kick:0.552276, time_drift=0.553526.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Accelerations (long-range): 0.061 CPU - 0.015 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Accelerations (short-range): 0.312 CPU - 0.066 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 59/104: Total Evolution: 0.402 CPU - 0.088 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 60/104, time_kick:0.552276, time_drift=0.553526.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 60/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 60/104, time_kick:0.561846, time_drift=0.563096.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Accelerations (long-range): 0.062 CPU - 0.014 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Accelerations (short-range): 0.286 CPU - 0.057 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 60/104: Total Evolution: 0.378 CPU - 0.080 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 61/104, time_kick:0.561846, time_drift=0.563096.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 61/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 61/104, time_kick:0.571370, time_drift=0.572620.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Density: 0.011 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Accelerations (long-range): 0.062 CPU - 0.019 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Accelerations (short-range): 0.309 CPU - 0.104 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Kick: 0.005 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 61/104: Total Evolution: 0.397 CPU - 0.131 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 62/104, time_kick:0.571370, time_drift=0.572620.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 62/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 62/104, time_kick:0.580853, time_drift=0.582103.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Accelerations (long-range): 0.059 CPU - 0.014 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Accelerations (short-range): 0.302 CPU - 0.051 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 62/104: Total Evolution: 0.390 CPU - 0.073 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 63/104, time_kick:0.580853, time_drift=0.582103.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 63/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 63/104, time_kick:0.590302, time_drift=0.591552.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Accelerations (long-range): 0.064 CPU - 0.012 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Accelerations (short-range): 0.311 CPU - 0.047 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 63/104: Total Evolution: 0.404 CPU - 0.066 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 64/104, time_kick:0.590302, time_drift=0.591552.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 64/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 64/104, time_kick:0.599723, time_drift=0.600973.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Accelerations (short-range): 0.309 CPU - 0.050 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 64/104: Total Evolution: 0.400 CPU - 0.069 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 65/104, time_kick:0.599723, time_drift=0.600973.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 65/104 done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 65/104, time_kick:0.609122, time_drift=0.610372.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Accelerations (short-range): 0.309 CPU - 0.053 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 65/104: Total Evolution: 0.401 CPU - 0.074 wallclock seconds used.\n", + "[19:44:07\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 66/104, time_kick:0.609122, time_drift=0.610372.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 66/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 66/104, time_kick:0.618504, time_drift=0.619754.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Accelerations (long-range): 0.063 CPU - 0.013 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Accelerations (short-range): 0.309 CPU - 0.052 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 66/104: Total Evolution: 0.400 CPU - 0.072 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 67/104, time_kick:0.618504, time_drift=0.619754.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 67/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 67/104, time_kick:0.627874, time_drift=0.629124.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Accelerations (long-range): 0.059 CPU - 0.013 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Accelerations (short-range): 0.315 CPU - 0.055 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 67/104: Total Evolution: 0.400 CPU - 0.075 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 68/104, time_kick:0.627874, time_drift=0.629124.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 68/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 68/104, time_kick:0.637237, time_drift=0.638487.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Accelerations (long-range): 0.065 CPU - 0.012 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Accelerations (short-range): 0.314 CPU - 0.055 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 68/104: Total Evolution: 0.405 CPU - 0.074 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 69/104, time_kick:0.637237, time_drift=0.638487.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 69/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 69/104, time_kick:0.646598, time_drift=0.647848.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Accelerations (long-range): 0.057 CPU - 0.014 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Accelerations (short-range): 0.306 CPU - 0.049 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 69/104: Total Evolution: 0.391 CPU - 0.071 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 70/104, time_kick:0.646598, time_drift=0.647848.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 70/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 70/104, time_kick:0.655960, time_drift=0.657210.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Accelerations (short-range): 0.319 CPU - 0.055 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 70/104: Total Evolution: 0.411 CPU - 0.075 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 71/104, time_kick:0.655960, time_drift=0.657210.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 71/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 71/104, time_kick:0.665329, time_drift=0.666579.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Accelerations (long-range): 0.058 CPU - 0.014 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Accelerations (short-range): 0.313 CPU - 0.058 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 71/104: Total Evolution: 0.399 CPU - 0.079 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 72/104, time_kick:0.665329, time_drift=0.666579.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 72/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 72/104, time_kick:0.674707, time_drift=0.675957.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Accelerations (long-range): 0.064 CPU - 0.012 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Accelerations (short-range): 0.324 CPU - 0.050 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 72/104: Total Evolution: 0.418 CPU - 0.069 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 73/104, time_kick:0.674707, time_drift=0.675957.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 73/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 73/104, time_kick:0.684100, time_drift=0.685350.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Accelerations (long-range): 0.061 CPU - 0.014 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Accelerations (short-range): 0.325 CPU - 0.052 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 73/104: Total Evolution: 0.414 CPU - 0.073 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 74/104, time_kick:0.684100, time_drift=0.685350.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 74/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 74/104, time_kick:0.693510, time_drift=0.694760.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Accelerations (long-range): 0.063 CPU - 0.014 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Accelerations (short-range): 0.328 CPU - 0.056 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Kick: 0.006 CPU - 0.005 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 74/104: Total Evolution: 0.423 CPU - 0.081 wallclock seconds used.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 75/104, time_kick:0.693510, time_drift=0.694760.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 75/104 done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:08\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 75/104, time_kick:0.702943, time_drift=0.704193.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Density: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Accelerations (short-range): 0.313 CPU - 0.059 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 75/104: Total Evolution: 0.402 CPU - 0.079 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 76/104, time_kick:0.702943, time_drift=0.704193.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 76/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 76/104, time_kick:0.712401, time_drift=0.713651.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Accelerations (short-range): 0.333 CPU - 0.057 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 76/104: Total Evolution: 0.425 CPU - 0.076 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 77/104, time_kick:0.712401, time_drift=0.713651.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 77/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 77/104, time_kick:0.721888, time_drift=0.723138.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Accelerations (short-range): 0.326 CPU - 0.058 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 77/104: Total Evolution: 0.412 CPU - 0.078 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 78/104, time_kick:0.721888, time_drift=0.723138.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 78/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 78/104, time_kick:0.731409, time_drift=0.732659.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Density: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Accelerations (long-range): 0.057 CPU - 0.015 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Accelerations (short-range): 0.312 CPU - 0.054 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 78/104: Total Evolution: 0.394 CPU - 0.077 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 79/104, time_kick:0.731409, time_drift=0.732659.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 79/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 79/104, time_kick:0.740966, time_drift=0.742216.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Accelerations (long-range): 0.063 CPU - 0.013 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Accelerations (short-range): 0.334 CPU - 0.058 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 79/104: Total Evolution: 0.429 CPU - 0.078 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 80/104, time_kick:0.740966, time_drift=0.742216.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 80/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 80/104, time_kick:0.750563, time_drift=0.751813.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Accelerations (long-range): 0.061 CPU - 0.013 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Accelerations (short-range): 0.311 CPU - 0.062 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Kick: 0.005 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 80/104: Total Evolution: 0.400 CPU - 0.082 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 81/104, time_kick:0.750563, time_drift=0.751813.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 81/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 81/104, time_kick:0.760205, time_drift=0.761455.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Density: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Accelerations (long-range): 0.061 CPU - 0.014 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Accelerations (short-range): 0.333 CPU - 0.063 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 81/104: Total Evolution: 0.420 CPU - 0.084 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 82/104, time_kick:0.760205, time_drift=0.761455.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 82/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 82/104, time_kick:0.769895, time_drift=0.771145.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Accelerations (short-range): 0.323 CPU - 0.062 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 82/104: Total Evolution: 0.414 CPU - 0.082 wallclock seconds used.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 83/104, time_kick:0.769895, time_drift=0.771145.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 83/104 done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:09\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 83/104, time_kick:0.779636, time_drift=0.780886.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Density: 0.010 CPU - 0.002 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Accelerations (long-range): 0.060 CPU - 0.014 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Accelerations (short-range): 0.325 CPU - 0.068 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 83/104: Total Evolution: 0.410 CPU - 0.089 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 84/104, time_kick:0.779636, time_drift=0.780886.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 84/104 done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 84/104, time_kick:0.789432, time_drift=0.790682.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Accelerations (long-range): 0.062 CPU - 0.013 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Accelerations (short-range): 0.331 CPU - 0.061 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 84/104: Total Evolution: 0.426 CPU - 0.081 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 85/104, time_kick:0.789432, time_drift=0.790682.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 85/104 done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 85/104, time_kick:0.799288, time_drift=0.800538.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Density: 0.014 CPU - 0.004 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Potential: 0.009 CPU - 0.004 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Accelerations (long-range): 0.066 CPU - 0.018 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Accelerations (short-range): 0.339 CPU - 0.077 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 85/104: Total Evolution: 0.434 CPU - 0.104 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 86/104, time_kick:0.799288, time_drift=0.800538.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 86/104 done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 86/104, time_kick:0.809207, time_drift=0.810457.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Density: 0.007 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Accelerations (long-range): 0.064 CPU - 0.015 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Accelerations (short-range): 0.318 CPU - 0.076 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 86/104: Total Evolution: 0.405 CPU - 0.099 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 87/104, time_kick:0.809207, time_drift=0.810457.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 87/104 done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 87/104, time_kick:0.819194, time_drift=0.820444.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Accelerations (long-range): 0.070 CPU - 0.020 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Accelerations (short-range): 0.351 CPU - 0.131 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 87/104: Total Evolution: 0.452 CPU - 0.159 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 88/104, time_kick:0.819194, time_drift=0.820444.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 88/104 done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 88/104, time_kick:0.829252, time_drift=0.830502.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Accelerations (long-range): 0.067 CPU - 0.014 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Accelerations (short-range): 0.350 CPU - 0.070 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 88/104: Total Evolution: 0.447 CPU - 0.092 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 89/104, time_kick:0.829252, time_drift=0.830502.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 89/104 done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 89/104, time_kick:0.839386, time_drift=0.840636.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Accelerations (long-range): 0.061 CPU - 0.013 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Accelerations (short-range): 0.337 CPU - 0.063 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 89/104: Total Evolution: 0.426 CPU - 0.084 wallclock seconds used.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 90/104, time_kick:0.839386, time_drift=0.840636.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 90/104 done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:10\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 90/104, time_kick:0.849600, time_drift=0.850850.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Accelerations (long-range): 0.064 CPU - 0.016 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Accelerations (short-range): 0.340 CPU - 0.068 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 90/104: Total Evolution: 0.434 CPU - 0.092 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 91/104, time_kick:0.849600, time_drift=0.850850.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 91/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 91/104, time_kick:0.859899, time_drift=0.861149.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Density: 0.010 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Accelerations (long-range): 0.064 CPU - 0.016 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Accelerations (short-range): 0.349 CPU - 0.073 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Kick: 0.007 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 91/104: Total Evolution: 0.440 CPU - 0.098 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 92/104, time_kick:0.859899, time_drift=0.861149.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 92/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 92/104, time_kick:0.870287, time_drift=0.871537.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Accelerations (long-range): 0.068 CPU - 0.016 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Accelerations (short-range): 0.360 CPU - 0.070 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 92/104: Total Evolution: 0.459 CPU - 0.095 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 93/104, time_kick:0.870287, time_drift=0.871537.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 93/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 93/104, time_kick:0.880770, time_drift=0.882020.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Accelerations (long-range): 0.063 CPU - 0.012 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Accelerations (short-range): 0.331 CPU - 0.069 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 93/104: Total Evolution: 0.425 CPU - 0.088 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 94/104, time_kick:0.880770, time_drift=0.882020.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 94/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 94/104, time_kick:0.891353, time_drift=0.892603.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Density: 0.017 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Potential: 0.008 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Accelerations (long-range): 0.064 CPU - 0.012 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Accelerations (short-range): 0.349 CPU - 0.065 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 94/104: Total Evolution: 0.444 CPU - 0.085 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 95/104, time_kick:0.891353, time_drift=0.892603.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 95/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 95/104, time_kick:0.902041, time_drift=0.903291.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Density: 0.013 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Accelerations (long-range): 0.062 CPU - 0.014 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Accelerations (short-range): 0.346 CPU - 0.067 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 95/104: Total Evolution: 0.437 CPU - 0.088 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 96/104, time_kick:0.902041, time_drift=0.903291.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 96/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 96/104, time_kick:0.912840, time_drift=0.914090.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Density: 0.011 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Accelerations (short-range): 0.352 CPU - 0.065 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 96/104: Total Evolution: 0.443 CPU - 0.085 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 97/104, time_kick:0.912840, time_drift=0.914090.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 97/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 97/104, time_kick:0.923755, time_drift=0.925005.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Density: 0.015 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Potential: 0.009 CPU - 0.002 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Accelerations (short-range): 0.349 CPU - 0.064 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 97/104: Total Evolution: 0.444 CPU - 0.084 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 98/104, time_kick:0.923755, time_drift=0.925005.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 98/104 done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 98/104, time_kick:0.934793, time_drift=0.936043.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Density: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Accelerations (short-range): 0.351 CPU - 0.067 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 98/104: Total Evolution: 0.443 CPU - 0.087 wallclock seconds used.\n", + "[19:44:11\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 99/104, time_kick:0.934793, time_drift=0.936043.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 99/104 done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 99/104, time_kick:0.945960, time_drift=0.947210.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Density: 0.014 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Accelerations (long-range): 0.063 CPU - 0.013 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Accelerations (short-range): 0.357 CPU - 0.061 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 99/104: Total Evolution: 0.450 CPU - 0.081 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 100/104, time_kick:0.945960, time_drift=0.947210.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 100/104 done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 100/104, time_kick:0.957263, time_drift=0.958513.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Accelerations (long-range): 0.064 CPU - 0.013 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Accelerations (short-range): 0.358 CPU - 0.068 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 100/104: Total Evolution: 0.450 CPU - 0.088 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 101/104, time_kick:0.957263, time_drift=0.958513.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 101/104 done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 101/104, time_kick:0.968710, time_drift=0.969960.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Density: 0.016 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Accelerations (long-range): 0.066 CPU - 0.012 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Accelerations (short-range): 0.360 CPU - 0.068 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Kick: 0.006 CPU - 0.001 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Drift: 0.001 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 101/104: Total Evolution: 0.458 CPU - 0.087 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 102/104, time_kick:0.968710, time_drift=0.969960.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 102/104 done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 102/104, time_kick:0.980308, time_drift=0.981558.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Density: 0.007 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Potential: 0.009 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Accelerations (long-range): 0.057 CPU - 0.016 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Accelerations (short-range): 0.351 CPU - 0.070 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Kick: 0.006 CPU - 0.002 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Drift: 0.002 CPU - 0.001 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 102/104: Total Evolution: 0.432 CPU - 0.095 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 103/104, time_kick:0.980308, time_drift=0.981558.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 103/104 done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 103/104, time_kick:0.992065, time_drift=0.993315.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Density: 0.013 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Potential: 0.008 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Accelerations (long-range): 0.065 CPU - 0.016 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Accelerations (short-range): 0.359 CPU - 0.078 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Kick: 0.006 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Drift: 0.001 CPU - 0.001 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 103/104: Total Evolution: 0.452 CPU - 0.103 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: Begin COLA+P3M step 104/104, time_kick:0.992065, time_drift=0.993315.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|ModuleP3M: Compute time step limiters for step 104/104 done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Drifting particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Getting gravitational potential, periodic boundary conditions (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Kicking particles (using 8 cores) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModuleP3M: End COLA+P3M step 104/104, time_kick:1.000000, time_drift=1.000000.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Density: 0.027 CPU - 0.006 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Potential: 0.018 CPU - 0.006 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Accelerations (long-range): 0.131 CPU - 0.031 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Accelerations (short-range): 0.707 CPU - 0.171 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Kick: 0.012 CPU - 0.003 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Drift: 0.002 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Step 104/104: Total Evolution: 0.896 CPU - 0.218 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_plus operator: changing reference frame after COLA evolution...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|ModulePMCOLA: L_plus operator: changing reference frame after COLA evolution done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Density: 1.240 CPU - 0.282 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Potential: 0.888 CPU - 0.291 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Accelerations (long-range): 6.567 CPU - 1.565 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Accelerations (short-range): 30.906 CPU - 5.616 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Kick: 0.613 CPU - 0.173 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Drift: 0.134 CPU - 0.060 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Inputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Diagnostic: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Outputs: 0.000 CPU - 0.000 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]==|Box: Total Evolution: 40.349 CPU - 7.987 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModuleP3M: Evolving with P3M done.\u001b[00m\n", + "[19:44:12\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModulePMCOLA: Computing outputs...\u001b[00m\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Getting density contrast (using 8 cores and 8 arrays, parallel routine 1) done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/final_density_p3m.h5'...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing field to '/Users/hoellinger/WIP3M/notebook10/final_density_p3m.h5' done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3'...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing header in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3' done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3' (32768 particles)...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS '...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'POS ' done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL '...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'VEL ' done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID '...\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]====|Writing block: 'ID ' done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;113mSTATUS \u001b[00m]==|Writing snapshot in '/Users/hoellinger/WIP3M/notebook10/p3m_snapshot.gadget3' done.\n", + "[19:44:12\u001b[00m|\u001b[38;5;147mMODULE \u001b[00m]|\u001b[38;5;147mModulePMCOLA: Computing outputs done.\u001b[00m\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|PMCOLA output: 0.015 CPU - 0.005 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|ModulePMCOLA: 42.540 CPU - 10.196 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;159mTIMER \u001b[00m]|Simbelmynë: 42.594 CPU - 10.213 wallclock seconds used.\n", + "[19:44:12\u001b[00m|\u001b[38;5;117mINFO \u001b[00m]|Everything done successfully, exiting.\n" ] } ], @@ -2610,43 +2736,6 @@ { "cell_type": "code", "execution_count": 13, - "id": "7d24f105", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[02:54:09|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.plot_utils)\u001b[00m Plotting timestep limiters from /Users/hoellinger/WIP3M/notebook10/timesteps_log.txt...\n", - "[02:54:10|\u001b[1;36mINFO \u001b[00m]==|\u001b[38;5;147m(wip3m.plot_utils)\u001b[00m Figure saved to: /Users/hoellinger/Library/CloudStorage/Dropbox/travail/these/science/code/simbelmyne/simbelmyne2025/WIP_P3M/results/notebook10/time_step_diagnostics.pdf\n", - "[02:54:10|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.plot_utils)\u001b[00m Plotting timestep limiters from /Users/hoellinger/WIP3M/notebook10/timesteps_log.txt done.\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plot_timestepping_diagnostics(\n", - " log_path=OutputTimestepsLog,\n", - " aiDrift=aiDrift,\n", - " TimeStepDistribution=TimeStepDistribution,\n", - " nsteps=nsteps,\n", - " exact=False,\n", - " save_path=wd+\"time_step_diagnostics.pdf\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 19, "id": "c1c096bb", "metadata": {}, "outputs": [ @@ -2654,13 +2743,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[02:58:22|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.plot_utils)\u001b[00m Plotting timestep limiters from /Users/hoellinger/WIP3M/notebook10/timesteps_log.txt and /Users/hoellinger/WIP3M/notebook10/timesteps_log_custom.txt...\n", - "[02:58:22|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.plot_utils)\u001b[00m Plotting timestep limiters from /Users/hoellinger/WIP3M/notebook10/timesteps_log.txt and /Users/hoellinger/WIP3M/notebook10/timesteps_log_custom.txt done.\n" + "[19:44:12|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.plot_utils)\u001b[00m Plotting timestep limiters from /Users/hoellinger/WIP3M/notebook10/timesteps_log.txt and /Users/hoellinger/WIP3M/notebook10/timesteps_log_custom.txt...\n", + "[19:44:12|\u001b[1;36mINFO \u001b[00m]|\u001b[38;5;147m(wip3m.plot_utils)\u001b[00m Plotting timestep limiters from /Users/hoellinger/WIP3M/notebook10/timesteps_log.txt and /Users/hoellinger/WIP3M/notebook10/timesteps_log_custom.txt done.\n" ] }, { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -2687,7 +2776,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "id": "7a122521", "metadata": {}, "outputs": [ diff --git a/notebooks/8_fit_P3M_limiter.ipynb b/notebooks/8_fit_P3M_limiter.ipynb index 5003127..123bfee 100644 --- a/notebooks/8_fit_P3M_limiter.ipynb +++ b/notebooks/8_fit_P3M_limiter.ipynb @@ -15690,7 +15690,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "c1c096bb", "metadata": {}, "outputs": [ @@ -15762,7 +15762,7 @@ " ymin=3e-3,\n", " ymax=0.5,\n", " fac_hubble=fac_hubble,\n", - " fac_bend=fac_bend * (2 ** sim_idx),\n", + " fac_bend=fac_bend,\n", " da_max_early=da_early,\n", " da_max_late=DEFAULT_DA_MAX_LATE_CUSTOM,\n", " show=False,\n", diff --git a/notebooks/9_fit_P3M_limiter_external.ipynb b/notebooks/9_fit_P3M_limiter_external.ipynb index 2d6cc82..4b7ee84 100644 --- a/notebooks/9_fit_P3M_limiter_external.ipynb +++ b/notebooks/9_fit_P3M_limiter_external.ipynb @@ -108,7 +108,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "7126d1c8", "metadata": {}, "outputs": [], @@ -134,7 +134,7 @@ "\n", "# Alternatively, let the user specify the parameters directly:\n", "fac_hubble_list = [0.02, 0.02]\n", - "fac_bend_list = [0.04375/2, 0.04375]\n", + "fac_bend_list = [0.3125/2, 0.3125]\n", "da_early_list = [0.0013, 0.0013]\n" ] }, diff --git a/src/wip3m/convergence_cola_p3m_only_expl.py b/src/wip3m/convergence_cola_p3m_only_expl.py index b9699fb..80ce5d2 100644 --- a/src/wip3m/convergence_cola_p3m_only_expl.py +++ b/src/wip3m/convergence_cola_p3m_only_expl.py @@ -69,10 +69,10 @@ parser.add_argument( help="Whether to compute and plot the LPT power spectrum.", ) parser.add_argument( - "--scale_limiter", + "--rescale_limiter", type=none_bool_str, default=False, - help="Which limiter should be scaled by the scaling arguments? ", + help="Which limiter should be affected by the mod_val argument? ", ) parser.add_argument( "--use_p3m_fit", @@ -90,14 +90,14 @@ parser.add_argument( # Timestep settings per method for scheme in ["p3m1", "p3m2", "p3m3"]: parser.add_argument( - f"--scaling_{scheme}", + f"--mod_val_{scheme}", type=float, default={ - "p3m1": 1., + "p3m1": 1.0, "p3m2": 0.95, "p3m3": 0.9, }[scheme], - help=f"Relax the step limiter by this factor for {scheme.upper()}.", + help=f"Use this value value for the limiter indicated by rescale_limiter, for {scheme.upper()}.", ) args = parser.parse_args() @@ -115,10 +115,10 @@ if __name__ == "__main__": Np = args.Np Npm = args.Npm n_Tiles = args.n_Tiles - scaling_p3m1 = args.scaling_p3m1 - scaling_p3m2 = args.scaling_p3m2 - scaling_p3m3 = args.scaling_p3m3 - name_scaled = args.scale_limiter + mod_val_p3m1 = args.mod_val_p3m1 + mod_val_p3m2 = args.mod_val_p3m2 + mod_val_p3m3 = args.mod_val_p3m3 + name_scaled = args.rescale_limiter use_p3m_fit = args.use_p3m_fit if name_scaled is not False and name_scaled not in [ "fac_dyn_custom", @@ -144,9 +144,9 @@ if __name__ == "__main__": logger.info(f"Number of tiles: {n_Tiles}^3") logger.info(f"Limiter to scale: {name_scaled if name_scaled else 'None'}") if name_scaled: - logger.info(f"Limiter rescaling for P3M1: {scaling_p3m1}") - logger.info(f"Limiter rescaling for P3M2: {scaling_p3m2}") - logger.info(f"Limiter rescaling for P3M3: {scaling_p3m3}") + logger.info(f"New limiter value for P3M1: {mod_val_p3m1}") + logger.info(f"New limiter value for P3M2: {mod_val_p3m2}") + logger.info(f"New limiter value for P3M3: {mod_val_p3m3}") INDENT() lpt = args.lpt @@ -192,9 +192,9 @@ if __name__ == "__main__": "RedshiftLPT": RedshiftLPT, "RedshiftFCs": RedshiftFCs, "name_scaled": name_scaled, - "scaling_p3m1": scaling_p3m1, - "scaling_p3m2": scaling_p3m2, - "scaling_p3m3": scaling_p3m3, + "mod_val_p3m1": mod_val_p3m1, + "mod_val_p3m2": mod_val_p3m2, + "mod_val_p3m3": mod_val_p3m3, } with open(wd + "sim_params.txt", "w") as f: f.write(f"{sim_params}\n") @@ -221,9 +221,9 @@ if __name__ == "__main__": lpt_params["ICsMode"] = 1 lpt_params["InputWhiteNoise"] = input_white_noise_file - fac_dyn_custom = DEFAULT_FAC_DYN_CUSTOM + fac_dyn_custom = DEFAULT_FAC_DYN_CUSTOM_COLA da_early = DEFAULT_DA_MAX_EARLY_CUSTOM - fac_H_custom = DEFAULT_FAC_H_CUSTOM + fac_H_custom = DEFAULT_FAC_H_CUSTOM_COLA fac_bend = DEFAULT_FAC_BEND sub_bend1 = DEFAULT_SUB_BEND1_COLA sub_bend2 = DEFAULT_SUB_BEND2_COLA @@ -283,9 +283,9 @@ if __name__ == "__main__": p3m1_params["n_Tiles"] = n_Tiles p3m1_params["PrintOutputTimestepsLog"] = True p3m1_params["OutputTimestepsLog"] = simdir + "colap3m1_timestep_log.txt" - p3m1_params["relax"] = scaling_p3m1 + p3m1_params["rescale"] = mod_val_p3m1 if name_scaled: - p3m1_params[name_scaled] /= scaling_p3m1 + p3m1_params[name_scaled] = mod_val_p3m1 p3m2_params = common_params_num.copy() p3m2_params["method"] = "p3m" @@ -293,9 +293,9 @@ if __name__ == "__main__": p3m2_params["n_Tiles"] = n_Tiles p3m2_params["PrintOutputTimestepsLog"] = True p3m2_params["OutputTimestepsLog"] = simdir + "colap3m2_timestep_log.txt" - p3m2_params["relax"] = scaling_p3m2 + p3m2_params["rescale"] = mod_val_p3m2 if name_scaled: - p3m2_params[name_scaled] /= scaling_p3m2 + p3m2_params[name_scaled] = mod_val_p3m2 p3m3_params = common_params_num.copy() p3m3_params["method"] = "p3m" @@ -303,9 +303,9 @@ if __name__ == "__main__": p3m3_params["n_Tiles"] = n_Tiles p3m3_params["PrintOutputTimestepsLog"] = True p3m3_params["OutputTimestepsLog"] = simdir + "colap3m3_timestep_log.txt" - p3m3_params["relax"] = scaling_p3m3 + p3m3_params["rescale"] = mod_val_p3m3 if name_scaled: - p3m3_params[name_scaled] /= scaling_p3m3 + p3m3_params[name_scaled] = mod_val_p3m3 all_sim_params = [ ("p3m1", p3m1_params), @@ -323,9 +323,9 @@ if __name__ == "__main__": for name, parameters in all_sim_params: logger.info( - f"Generating parameters for {name.upper()} with relax = {parameters['relax']}..." + f"Generating parameters for {name.upper()} with rescale = {parameters['rescale']}..." ) - file_ext = f"{name}_relax{str(parameters['relax']).replace('.', '_')}" + file_ext = f"{name}_rescale{str(parameters['rescale']).replace('.', '_')}" generate_sim_params(parameters, ICs_path, wd, simdir, file_ext, force) logger.info(f"Generating parameters for {name.upper()} done.") @@ -398,7 +398,7 @@ if __name__ == "__main__": nsteps_dict = {} for name, parameters in all_sim_params: TimeStepDistribution = parameters["TimeStepDistribution"] - file_ext = f"{name}_relax{str(parameters['relax']).replace('.', '_')}" + file_ext = f"{name}_rescale{str(parameters['rescale']).replace('.', '_')}" method = parameters["method"] TSpath = wd + file_ext + f"_ts_{method}.h5" if file_ext else wd + f"ts_{method}.h5" if TimeStepDistribution in [0, 1, 2]: @@ -462,17 +462,17 @@ if __name__ == "__main__": DELTA = read_field(simdir + "lpt_density.h5") Pk_LPT, _ = get_autocorrelation(DELTA, G, AliasingCorr) - DELTA = read_field(simdir + f"p3m1_relax{str(p3m1_params['relax']).replace('.', '_')}_final_density_p3m.h5") + DELTA = read_field(simdir + f"p3m1_rescale{str(p3m1_params['rescale']).replace('.', '_')}_final_density_p3m.h5") if plot_fields: delta_p3m1 = DELTA.data[slice_ijk] Pk_P3M1, _ = get_autocorrelation(DELTA, G, AliasingCorr) - DELTA = read_field(simdir + f"p3m2_relax{str(p3m2_params['relax']).replace('.', '_')}_final_density_p3m.h5") + DELTA = read_field(simdir + f"p3m2_rescale{str(p3m2_params['rescale']).replace('.', '_')}_final_density_p3m.h5") if plot_fields: delta_p3m2 = DELTA.data[slice_ijk] Pk_P3M2, _ = get_autocorrelation(DELTA, G, AliasingCorr) - DELTA = read_field(simdir + f"p3m3_relax{str(p3m3_params['relax']).replace('.', '_')}_final_density_p3m.h5") + DELTA = read_field(simdir + f"p3m3_rescale{str(p3m3_params['rescale']).replace('.', '_')}_final_density_p3m.h5") if plot_fields: delta_p3m3 = DELTA.data[slice_ijk] Pk_P3M3, _ = get_autocorrelation(DELTA, G, AliasingCorr) @@ -480,9 +480,9 @@ if __name__ == "__main__": ref = "P3M1" - scaling_reference = scaling_p3m1 + mod_val_reference = mod_val_p3m1 Pk_ref = Pk_P3M1 - label_ref = f"P3M, scaling={scaling_reference}, ns={nsteps_dict['p3m1']}" + label_ref = f"P3M, mod_val={mod_val_reference}, ns={nsteps_dict['p3m1']}" logger.info(f"Plotting power spectra...") INDENT() @@ -516,7 +516,7 @@ if __name__ == "__main__": (field_name, Pk) for field_name, Pk in fields_to_plot if field_name != ref ] for field_name, Pk in fields_to_plot: - label = f"{field_name}, scaling={eval(f'scaling_{field_name.lower()}')}, ns={nsteps_dict[field_name.lower()]}" + label = f"{field_name}, mod_val={eval(f'mod_val_{field_name.lower()}')}, ns={nsteps_dict[field_name.lower()]}" linestyle = "--" zorder = 2 ax.plot(k, Pk / Pk_ref, label=label, linestyle=linestyle) @@ -627,9 +627,9 @@ if __name__ == "__main__": } titles_dict = { "lpt": "LPT", - "p3m1": f"P3M1 relax={scaling_p3m1}", - "p3m2": f"P3M2 relax={scaling_p3m2}", - "p3m3": f"P3M3 relax={scaling_p3m3}", + "p3m1": f"P3M1 rescale={mod_val_p3m1}", + "p3m2": f"P3M2 rescale={mod_val_p3m2}", + "p3m3": f"P3M3 rescale={mod_val_p3m3}", "diff_p3m2_p3m1": r"$\delta_{\rm P3M2}-\delta_{\rm P3M1}$", "diff_p3m3_p3m1": r"$\delta_{\rm P3M3}-\delta_{\rm P3M1}$", "diff_p3m1_spm": r"$\delta_{\rm P3M1}-\delta_{\rm sPM}$", diff --git a/src/wip3m/params.py b/src/wip3m/params.py index ed6bf6a..c643b3c 100644 --- a/src/wip3m/params.py +++ b/src/wip3m/params.py @@ -138,8 +138,8 @@ DEFAULT_FAC_DYN_CUSTOM = 0.0154 DEFAULT_FAC_DYN_CUSTOM_COLA = 0.03 DEFAULT_DA_MAX_EARLY_CUSTOM = 0.0013 DEFAULT_FAC_H_CUSTOM = 0.03 -DEFAULT_FAC_H_CUSTOM_COLA = 0.06 -DEFAULT_FAC_BEND = 0.04375 +DEFAULT_FAC_H_CUSTOM_COLA = 0.05 +DEFAULT_FAC_BEND = 0.3125 DEFAULT_SUB_BEND1 = 0.012 DEFAULT_SUB_BEND2 = 0.007 DEFAULT_SUB_BEND1_COLA = 0.012 diff --git a/src/wip3m/tools.py b/src/wip3m/tools.py index d25fbc7..fceda62 100644 --- a/src/wip3m/tools.py +++ b/src/wip3m/tools.py @@ -486,8 +486,8 @@ def run_simulation(name, params, wd, logdir): if params.get("nsteps") is not None: file_ext = f"{name}_nsteps{params['nsteps']}" - elif params.get("relax") is not None: - file_ext = f"{name}_relax{str(params['relax']).replace('.', '_')}" + elif params.get("rescale") is not None: + file_ext = f"{name}_rescale{str(params['rescale']).replace('.', '_')}" elif params.get("file_ext") is not None: file_ext = params["file_ext"] else: diff --git a/submit/actual_p3m_limiter_cola.sh b/submit/actual_p3m_limiter_cola.sh index c7b8df6..16a95e8 100644 --- a/submit/actual_p3m_limiter_cola.sh +++ b/submit/actual_p3m_limiter_cola.sh @@ -1,11 +1,11 @@ #!/bin/bash -#SBATCH --job-name=cola_2_1_05_L64_N64_Np64_v4 -#SBATCH --output=/data70/hoellinger/WIP3M/cola_2_1_05_L64_N64_Np64_v4/log.log -#SBATCH --error=/data70/hoellinger/WIP3M/cola_2_1_05_L64_N64_Np64_v4/err.err +#SBATCH --job-name=cola_Pf_L512_N512_Np256 +#SBATCH --output=/data70/hoellinger/WIP3M/cola_Pf_L512_N512_Np256/log.log +#SBATCH --error=/data70/hoellinger/WIP3M/cola_Pf_L512_N512_Np256/err.err #SBATCH --nodes=1 # Number of nodes (value or min-max) -#SBATCH --ntasks=64 # The number of tasks (i.e. cores) per node +#SBATCH --ntasks=128 # The number of tasks (i.e. cores) per node #SBATCH --partition=comp,pscomp # Partition name -#SBATCH --time=12:00:00 +#SBATCH --time=48:00:00 ##SBATCH --exclusive ##SBATCH --nodelist=i26 # Node name @@ -15,43 +15,26 @@ conda activate p3m -export OMP_NUM_THREADS=16 -python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ - --run_id cola_2_1_05_L64_N64_Np64_v4 \ - --L 64 \ - --N 64 \ - --Np 64 \ - --Npm 128 \ - --n_Tiles 16 \ - --z_i 19.0 \ - --z_f 0.0 \ - --plot_fields True \ - --scale_limiter "fac_p3m_fit" \ - --use_p3m_fit True \ - --scaling_p3m1 2.0 \ - --scaling_p3m2 1.0 \ - --scaling_p3m3 0.5 - -# export OMP_NUM_THREADS=32 +# export OMP_NUM_THREADS=64 # python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ -# --run_id cola_2_1_05_L128_N128_Np128 \ -# --L 128 \ -# --N 128 \ -# --Np 128 \ -# --Npm 256 \ -# --n_Tiles 32 \ +# --run_id cola_Pf_LNNp512 \ +# --L 512 \ +# --N 512 \ +# --Np 512 \ +# --Npm 1024 \ +# --n_Tiles 128 \ # --z_i 19.0 \ # --z_f 0.0 \ # --plot_fields True \ -# --scale_limiter "fac_p3m_fit" \ +# --rescale_limiter "fac_p3m_fit" \ # --use_p3m_fit True \ -# --scaling_p3m1 2.0 \ -# --scaling_p3m2 1.0 \ -# --scaling_p3m3 0.5 +# --mod_val_p3m1 0.3 \ +# --mod_val_p3m2 0.5 \ +# --mod_val_p3m3 1.1 # export OMP_NUM_THREADS=64 # python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ -# --run_id cola_432_L1024_N512_Np512 \ +# --run_id cola_Pf_L1024_N512_Np512 \ # --L 1024 \ # --N 512 \ # --Np 512 \ @@ -60,27 +43,78 @@ python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ # --z_i 19.0 \ # --z_f 0.0 \ # --plot_fields True \ -# --scale_limiter "fac_p3m_fit" \ +# --rescale_limiter "fac_p3m_fit" \ # --use_p3m_fit True \ -# --scaling_p3m1 4.0 \ -# --scaling_p3m2 3.0 \ -# --scaling_p3m3 2.0 +# --mod_val_p3m1 0.33 \ +# --mod_val_p3m2 0.4 \ +# --mod_val_p3m3 0.5 # export OMP_NUM_THREADS=64 # python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ -# --run_id cola_432_L128_N256_Np128 \ -# --L 128 \ -# --N 256 \ -# --Np 128 \ -# --Npm 256 \ -# --n_Tiles 32 \ +# --run_id cola_Pf_L1024_N1024_Np512 \ +# --L 1024 \ +# --N 1024 \ +# --Np 512 \ +# --Npm 1024 \ +# --n_Tiles 128 \ # --z_i 19.0 \ # --z_f 0.0 \ # --plot_fields True \ -# --scale_limiter "fac_p3m_fit" \ +# --rescale_limiter "fac_p3m_fit" \ # --use_p3m_fit True \ -# --scaling_p3m1 4.0 \ -# --scaling_p3m2 3.0 \ -# --scaling_p3m3 2.0 +# --mod_val_p3m1 0.33 \ +# --mod_val_p3m2 0.4 \ +# --mod_val_p3m3 0.5 + +# export OMP_NUM_THREADS=64 +# python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ +# --run_id cola_Pf_LNNp256 \ +# --L 256 \ +# --N 256 \ +# --Np 256 \ +# --Npm 512 \ +# --n_Tiles 64 \ +# --z_i 19.0 \ +# --z_f 0.0 \ +# --plot_fields True \ +# --rescale_limiter "fac_p3m_fit" \ +# --use_p3m_fit True \ +# --mod_val_p3m1 0.3 \ +# --mod_val_p3m2 0.5 \ +# --mod_val_p3m3 1.1 + +# export OMP_NUM_THREADS=64 +# python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ +# --run_id cola_Pf_L512_N256_Np256 \ +# --L 512 \ +# --N 256 \ +# --Np 256 \ +# --Npm 512 \ +# --n_Tiles 64 \ +# --z_i 19.0 \ +# --z_f 0.0 \ +# --plot_fields True \ +# --rescale_limiter "fac_p3m_fit" \ +# --use_p3m_fit True \ +# --mod_val_p3m1 0.33 \ +# --mod_val_p3m2 0.4 \ +# --mod_val_p3m3 0.5 + +export OMP_NUM_THREADS=64 +python $WIP3M_ROOT_PATH"src/wip3m/convergence_cola_p3m_only_expl.py" \ + --run_id cola_Pf_L512_N512_Np256 \ + --L 512 \ + --N 512 \ + --Np 256 \ + --Npm 512 \ + --n_Tiles 64 \ + --z_i 19.0 \ + --z_f 0.0 \ + --plot_fields True \ + --rescale_limiter "fac_p3m_fit" \ + --use_p3m_fit True \ + --mod_val_p3m1 0.33 \ + --mod_val_p3m2 0.4 \ + --mod_val_p3m3 0.5 exit 0 \ No newline at end of file