From 97e12eab25fe75a6743f70fd94be13e6ff6745a0 Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Wed, 22 Oct 2025 17:07:38 -0600 Subject: [PATCH 01/10] made it much easier to just hit play on onboarding.ipynb, then specifying files in clustering_plots.ipynb THEN hitting play once again. also added some extra comments for clarity on variables, as well as adding more tuning parameters to parameter cell --- analysis/clustering_plots.ipynb | 145 ++- sims/Onboarding.ipynb | 1604 ++++++++++++++++++++++++++++++- 2 files changed, 1697 insertions(+), 52 deletions(-) diff --git a/analysis/clustering_plots.ipynb b/analysis/clustering_plots.ipynb index b9c7131..759ef16 100644 --- a/analysis/clustering_plots.ipynb +++ b/analysis/clustering_plots.ipynb @@ -134,38 +134,61 @@ }, { "cell_type": "markdown", - "id": "12554e81-0c42-4703-80ef-d480340cf722", + "id": "133b5518-c37d-4744-9685-b1d58a7f6158", "metadata": {}, "source": [ - "## Adjust to whichever file you're trying to analyze, then hit run until the end" + "# Adjust to whichever file you're trying to analyze, then hit run until the end" ] }, { "cell_type": "code", "execution_count": 5, + "id": "a02546b6-1ec3-46cb-b8d7-f680e694fb22", + "metadata": {}, + "outputs": [], + "source": [ + "start_file = \"20_10mer5f_0.005dt_start.txt\"\n", + "traj_file = \"20_10mer5f_0.005dt.gsd\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f4d20f72-efca-47f4-8dd5-6f8048de1c93", + "metadata": {}, + "outputs": [], + "source": [ + "with open(start_file) as f:\n", + " start = int(f.readline().strip())" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "id": "330a659a-e793-441f-8467-ece43451d4a8", "metadata": {}, "outputs": [], "source": [ - "traj = gsd.hoomd.open(\"60_10mer10f_0.005dt_Sept24.gsd\")\n", + "with open(start_file) as f:\n", + " start = int(f.readline().strip()) # extracting post-shrink sim beginning frame\n", + "traj = gsd.hoomd.open(traj_file)\n", "times, n_clusters, avg_sizes = analyze_clusters_over_time(\n", " traj,\n", - " start=5000, # post-shrink\n", - " step=1,\n", - " cutoff=2.5,\n", - " beads_per_flake=50\n", - ")" + " start=start, # post-shrink\n", + " step=1, # analyze every \"x\" frame, leave this at 1\n", + " cutoff=2.5\n", + ") # this function is used to initially calculate the decorrelation time, so that we know every how many certain amount of frames to analyze. " ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "7217fadb-1e36-4144-856e-5e0ed85d6a1d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -194,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "c4c508c3-f7ad-459f-a824-3efa5d91e7b2", "metadata": {}, "outputs": [], @@ -213,9 +236,17 @@ "n, dt = autocorr1D(avg_sizes)" ] }, + { + "cell_type": "markdown", + "id": "fc9d7575-7794-4b3d-af76-3effed81d4fd", + "metadata": {}, + "source": [ + "# Decorrelation time is now calculated; now we can plot all the independent samples present within this simulation. Lower independent samples means more average aggregation over time is occurring, should occur as kT is increased. " + ] + }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "791c5fa4-2804-45ca-bbb2-274f7e51a8c7", "metadata": {}, "outputs": [ @@ -223,7 +254,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Independent samples and decorrelation time: 25 40\n" + "Independent samples and decorrelation time: 143 7\n" ] } ], @@ -233,11 +264,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "2f0afd4b-ae71-486b-9900-600aa341a006", "metadata": {}, "outputs": [], "source": [ + "# now we can plot every \"dt\"th frame, whose length should be ~= independent samples\n", "decorrelated_times = times[::dt]\n", "decorrelated_sizes = avg_sizes[::dt]\n", "decorrelated_clusters = n_clusters[::dt]" @@ -245,7 +277,25 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, + "id": "12c865ec-ca92-4738-aa53-d258b776cd29", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "143\n" + ] + } + ], + "source": [ + "print(len(decorrelated_sizes)) # verifying it is equal to calculated independent samples" + ] + }, + { + "cell_type": "code", + "execution_count": 13, "id": "614f5d74-3842-4289-944d-5134ba76a6e0", "metadata": {}, "outputs": [ @@ -255,13 +305,13 @@ "Text(0, 0.5, 'Avg Cluster Size')" ] }, - "execution_count": 10, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -271,41 +321,84 @@ } ], "source": [ - "plt.plot(decorrelated_times, decorrelated_sizes)\n", - "plt.title(\"Average Cluster Size (Decorrelated), kT = 5\")\n", + "plt.plot(decorrelated_times, decorrelated_sizes) # plot of every independent sample in simulation\n", + "plt.title(\"Average Cluster Size (Decorrelated)\")\n", "plt.xlabel(\"Frame\")\n", "plt.ylabel(\"Avg Cluster Size\")" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "a95d53e0-3cd4-4f94-8b6b-1a1ea37ed17a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.4755244755244755" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(decorrelated_sizes)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "03ca0f50-bbe1-40f8-b70f-8a56826ab11e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.23776223776223776" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(decorrelated_clusters)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "06022fb2-060b-48d2-bf13-aa71836a49a4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '# Clusters')" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(decorrelated_times, decorrelated_clusters)\n", - "plt.title(\"Number of Clusters (Decorrelated), kT = 3\")\n", + "plt.title(\"Number of Clusters (Decorrelated)\")\n", "plt.xlabel(\"Frame\")\n", "plt.ylabel(\"# Clusters\")" ] diff --git a/sims/Onboarding.ipynb b/sims/Onboarding.ipynb index 45c85e5..cd7af7d 100644 --- a/sims/Onboarding.ipynb +++ b/sims/Onboarding.ipynb @@ -18,10 +18,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "8c3308c6-8bc7-4368-bbf6-5c53489c0a18", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/eridanirojas/miniconda3/envs/flowermd-dev/lib/python3.12/site-packages/gmso/core/element.py:10: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", + " from pkg_resources import resource_filename\n" + ] + } + ], "source": [ "import flowermd # wrapper we use on top of hoomd\n", "import gsd # to work with simulation data files\n", @@ -50,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "2dce7cdb-61d5-4f40-b1d5-2b69849f7d10", "metadata": {}, "outputs": [], @@ -98,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "4bca3b72-54b5-4e7b-bf1b-4d3b41050ddb", "metadata": {}, "outputs": [], @@ -134,7 +143,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "f74ebf6d-79e1-488f-8921-a188ebe92673", "metadata": {}, "outputs": [], @@ -145,7 +154,11 @@ "N_flakes = 5 # number of flakes\n", "chain_length = 10 # length of polymer chains\n", "dt = 0.005 # step size of simulation\n", - "temp = 3.0 # kT, temperature of simulation" + "temp = 3.0 # kT, temperature of simulation\n", + "write_frequency = 1000 # write frequency of simulation, the lower the more frames captured\n", + "shrink_steps = 5e5 # amount of steps to run shrink\n", + "sim_start = int(shrink_steps/write_frequency) # for use in analysis notebook, post-shrink simulation starting point\n", + "steps = 1e6 # amount of steps to run simulation for" ] }, { @@ -158,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "b06e0517-6747-4eff-a7db-ac8ab0c99b48", "metadata": {}, "outputs": [], @@ -176,7 +189,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "21378af5-3fba-491f-9360-c069360d46b5", "metadata": {}, "outputs": [], @@ -198,53 +211,1592 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "00a6af7b-f424-4a27-a8be-f80a20c8ca03", "metadata": {}, "outputs": [], "source": [ "gsd = f\"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.gsd\" # name of output gsd files\n", - "log = f\"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.txt\" # name of output log files" + "log = f\"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.txt\" # name of output log files\n", + "start_file = f\"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt_start.txt\" # name of output start of sim" ] }, { "cell_type": "markdown", - "id": "5a16ce5f-f237-400f-b333-91292c6f9d8c", + "id": "d9b22575-1805-4cbc-a310-d6f30aedd81a", "metadata": {}, "source": [ - "### Step 8: Initializing simulation, then running shrink -> simulation" + "### Step 7.5: Saving simulation startpoint" ] }, { "cell_type": "code", - "execution_count": null, - "id": "64f9fae9-a3b9-418e-a03b-681b754e1b03", + "execution_count": 8, + "id": "6c81ac75-e729-4ce1-bab2-abc2d4156c28", "metadata": {}, "outputs": [], "source": [ - "sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=device, dt = dt, gsd_write_freq=int(1000), log_file_name = log, gsd_file_name = gsd) # initializing simulation\n", - "sim.run_update_volume(final_box_lengths=target_box, kT=6.0, n_steps=5e5,tau_kt=100*sim.dt,period=10,thermalize_particles=True) # shrink simulation run\n", - "sim.run_NVT(n_steps=1e6, kT=temp, tau_kt=dt*100) # simulation run\n", - "sim.flush_writers() # updating data files" + "with open(start_file, \"w\") as f:\n", + " f.write(str(sim_start))" + ] + }, + { + "cell_type": "markdown", + "id": "5a16ce5f-f237-400f-b333-91292c6f9d8c", + "metadata": {}, + "source": [ + "### Step 8: Initializing simulation, then running shrink -> simulation" ] }, { "cell_type": "code", - "execution_count": null, - "id": "a8af76ae-efe0-4912-8417-612048485a77", + "execution_count": 9, + "id": "64f9fae9-a3b9-418e-a03b-681b754e1b03", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "*Warning*: dihedral.harmonic: specified K <= 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing simulation state from a gsd.hoomd.Frame.\n", + "Step 1000 of 500000; TPS: 1245.42; ETA: 6.7 minutes\n", + "Step 2000 of 500000; TPS: 2035.58; ETA: 4.1 minutes\n", + "Step 3000 of 500000; TPS: 2590.96; ETA: 3.2 minutes\n", + "Step 4000 of 500000; TPS: 2757.65; ETA: 3.0 minutes\n", + "Step 5000 of 500000; TPS: 3066.49; ETA: 2.7 minutes\n", + "Step 6000 of 500000; TPS: 3325.67; ETA: 2.5 minutes\n", + "Step 7000 of 500000; TPS: 3535.74; ETA: 2.3 minutes\n", + "Step 8000 of 500000; TPS: 3698.35; ETA: 2.2 minutes\n", + "Step 9000 of 500000; TPS: 3845.91; ETA: 2.1 minutes\n", + "Step 10000 of 500000; TPS: 3830.63; ETA: 2.1 minutes\n", + "Step 11000 of 500000; TPS: 3837.42; ETA: 2.1 minutes\n", + "Step 12000 of 500000; TPS: 3960.79; ETA: 2.1 minutes\n", + "Step 13000 of 500000; TPS: 4074.39; ETA: 2.0 minutes\n", + "Step 14000 of 500000; TPS: 4173.35; ETA: 1.9 minutes\n", + "Step 15000 of 500000; TPS: 4263.12; ETA: 1.9 minutes\n", + "Step 16000 of 500000; TPS: 4338.67; ETA: 1.9 minutes\n", + "Step 17000 of 500000; 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215000 of 500000; TPS: 5299.78; ETA: 0.9 minutes\n", + "Step 216000 of 500000; TPS: 5296.03; ETA: 0.9 minutes\n", + "Step 217000 of 500000; TPS: 5294.32; ETA: 0.9 minutes\n", + "Step 218000 of 500000; TPS: 5292.97; ETA: 0.9 minutes\n", + "Step 219000 of 500000; TPS: 5291.63; ETA: 0.9 minutes\n", + "Step 220000 of 500000; TPS: 5512.32; ETA: 0.8 minutes\n", + "Step 221000 of 500000; TPS: 5509.8; ETA: 0.8 minutes\n", + "Step 222000 of 500000; TPS: 5506.91; ETA: 0.8 minutes\n", + "Step 223000 of 500000; TPS: 5501.62; ETA: 0.8 minutes\n", + "Step 224000 of 500000; TPS: 5499.12; ETA: 0.8 minutes\n", + "Step 225000 of 500000; TPS: 5496.43; ETA: 0.8 minutes\n", + "Step 226000 of 500000; TPS: 5494.16; ETA: 0.8 minutes\n", + "Step 227000 of 500000; TPS: 5492.2; ETA: 0.8 minutes\n", + "Step 228000 of 500000; TPS: 5490.43; ETA: 0.8 minutes\n", + "Step 229000 of 500000; TPS: 5487.92; ETA: 0.8 minutes\n", + "Step 230000 of 500000; TPS: 5482.0; ETA: 0.8 minutes\n", + "Step 231000 of 500000; TPS: 5480.87; ETA: 0.8 minutes\n", + "Step 232000 of 500000; TPS: 5480.4; ETA: 0.8 minutes\n", + "Step 233000 of 500000; TPS: 5480.07; ETA: 0.8 minutes\n", + "Step 234000 of 500000; TPS: 5479.49; ETA: 0.8 minutes\n", + "Step 235000 of 500000; TPS: 5478.61; ETA: 0.8 minutes\n", + "Step 236000 of 500000; TPS: 5477.67; ETA: 0.8 minutes\n", + "Step 237000 of 500000; TPS: 5475.31; ETA: 0.8 minutes\n", + "Step 238000 of 500000; TPS: 5474.16; ETA: 0.8 minutes\n", + "Step 239000 of 500000; TPS: 5473.43; ETA: 0.8 minutes\n", + "Step 240000 of 500000; TPS: 5473.0; ETA: 0.8 minutes\n", + "Step 241000 of 500000; TPS: 5472.02; ETA: 0.8 minutes\n", + "Step 242000 of 500000; TPS: 5471.73; ETA: 0.8 minutes\n", + "Step 243000 of 500000; TPS: 5469.76; ETA: 0.8 minutes\n", + "Step 244000 of 500000; TPS: 5466.29; ETA: 0.8 minutes\n", + "Step 245000 of 500000; TPS: 5463.79; ETA: 0.8 minutes\n", + "Step 246000 of 500000; TPS: 5461.27; ETA: 0.8 minutes\n", + "Step 247000 of 500000; TPS: 5459.16; ETA: 0.8 minutes\n", + "Step 248000 of 500000; TPS: 5456.6; ETA: 0.8 minutes\n", + "Step 249000 of 500000; TPS: 5454.42; ETA: 0.8 minutes\n", + "Step 250000 of 500000; TPS: 5452.19; ETA: 0.8 minutes\n", + "Step 251000 of 500000; TPS: 5448.55; ETA: 0.8 minutes\n", + "Step 252000 of 500000; TPS: 5446.72; ETA: 0.8 minutes\n", + "Step 253000 of 500000; TPS: 5444.59; ETA: 0.8 minutes\n", + "Step 254000 of 500000; TPS: 5442.49; ETA: 0.8 minutes\n", + "Step 255000 of 500000; TPS: 5440.57; ETA: 0.8 minutes\n", + "Step 256000 of 500000; TPS: 5438.82; ETA: 0.7 minutes\n", + "Step 257000 of 500000; TPS: 5436.63; ETA: 0.7 minutes\n", + "Step 258000 of 500000; TPS: 5433.54; ETA: 0.7 minutes\n", + "Step 259000 of 500000; TPS: 5431.83; ETA: 0.7 minutes\n", + "Step 260000 of 500000; TPS: 5429.52; ETA: 0.7 minutes\n", + "Step 261000 of 500000; TPS: 5427.54; ETA: 0.7 minutes\n", + "Step 262000 of 500000; TPS: 5425.22; ETA: 0.7 minutes\n", + "Step 263000 of 500000; TPS: 5423.56; ETA: 0.7 minutes\n", + "Step 264000 of 500000; TPS: 5421.59; ETA: 0.7 minutes\n", + "Step 265000 of 500000; TPS: 5418.32; ETA: 0.7 minutes\n", + "Step 266000 of 500000; TPS: 5416.49; ETA: 0.7 minutes\n", + "Step 267000 of 500000; TPS: 5411.88; ETA: 0.7 minutes\n", + "Step 268000 of 500000; TPS: 5409.65; ETA: 0.7 minutes\n", + "Step 269000 of 500000; TPS: 5407.76; ETA: 0.7 minutes\n", + "Step 270000 of 500000; TPS: 5406.2; ETA: 0.7 minutes\n", + "Step 271000 of 500000; TPS: 5404.38; ETA: 0.7 minutes\n", + "Step 272000 of 500000; TPS: 5402.65; ETA: 0.7 minutes\n", + "Step 273000 of 500000; TPS: 5400.09; ETA: 0.7 minutes\n", + "Step 274000 of 500000; TPS: 5398.69; ETA: 0.7 minutes\n", + "Step 275000 of 500000; TPS: 5396.9; ETA: 0.7 minutes\n", + "Step 276000 of 500000; TPS: 5397.01; ETA: 0.7 minutes\n", + "Step 277000 of 500000; TPS: 5397.2; ETA: 0.7 minutes\n", + "Step 278000 of 500000; TPS: 5397.12; ETA: 0.7 minutes\n", + "Step 279000 of 500000; TPS: 5397.36; ETA: 0.7 minutes\n", + "Step 280000 of 500000; TPS: 5396.41; ETA: 0.7 minutes\n", + "Step 281000 of 500000; TPS: 5396.17; ETA: 0.7 minutes\n", + "Step 282000 of 500000; TPS: 5396.14; ETA: 0.7 minutes\n", + "Step 283000 of 500000; TPS: 5396.11; ETA: 0.7 minutes\n", + "Step 284000 of 500000; TPS: 5395.96; ETA: 0.7 minutes\n", + "Step 285000 of 500000; TPS: 5394.13; ETA: 0.7 minutes\n", + "Step 286000 of 500000; TPS: 5392.11; ETA: 0.7 minutes\n", + "Step 287000 of 500000; TPS: 5389.8; ETA: 0.7 minutes\n", + "Step 288000 of 500000; TPS: 5388.23; ETA: 0.7 minutes\n", + "Step 289000 of 500000; TPS: 5386.36; ETA: 0.7 minutes\n", + "Step 290000 of 500000; TPS: 5384.88; ETA: 0.6 minutes\n", + "Step 291000 of 500000; TPS: 5383.42; ETA: 0.6 minutes\n", + "Step 292000 of 500000; TPS: 5381.89; ETA: 0.6 minutes\n", + "Step 293000 of 500000; TPS: 5380.44; ETA: 0.6 minutes\n", + "Step 294000 of 500000; TPS: 5378.37; ETA: 0.6 minutes\n", + "Step 295000 of 500000; TPS: 5376.89; ETA: 0.6 minutes\n", + "Step 296000 of 500000; TPS: 5375.04; ETA: 0.6 minutes\n", + "Step 297000 of 500000; TPS: 5373.75; ETA: 0.6 minutes\n", + "Step 298000 of 500000; TPS: 5372.46; ETA: 0.6 minutes\n", + "Step 299000 of 500000; TPS: 5371.19; ETA: 0.6 minutes\n", + "Step 300000 of 500000; TPS: 5370.06; ETA: 0.6 minutes\n", + "Step 301000 of 500000; TPS: 5368.14; ETA: 0.6 minutes\n", + "Step 302000 of 500000; TPS: 5366.68; ETA: 0.6 minutes\n", + "Step 303000 of 500000; TPS: 5365.45; ETA: 0.6 minutes\n", + "Step 304000 of 500000; TPS: 5364.21; ETA: 0.6 minutes\n", + "Step 305000 of 500000; TPS: 5362.87; ETA: 0.6 minutes\n", + "Step 306000 of 500000; TPS: 5361.71; ETA: 0.6 minutes\n", + "Step 307000 of 500000; TPS: 5360.46; ETA: 0.6 minutes\n", + "Step 308000 of 500000; TPS: 5358.32; ETA: 0.6 minutes\n", + "Step 309000 of 500000; TPS: 5356.49; ETA: 0.6 minutes\n", + "Step 310000 of 500000; TPS: 5355.15; ETA: 0.6 minutes\n", + "Step 311000 of 500000; TPS: 5353.84; ETA: 0.6 minutes\n", + "Step 312000 of 500000; TPS: 5354.02; ETA: 0.6 minutes\n", + "Step 313000 of 500000; TPS: 5353.94; ETA: 0.6 minutes\n", + "Step 314000 of 500000; TPS: 5353.2; ETA: 0.6 minutes\n", + "Step 315000 of 500000; TPS: 5352.23; ETA: 0.6 minutes\n", + "Step 316000 of 500000; TPS: 5351.74; ETA: 0.6 minutes\n", + "Step 317000 of 500000; TPS: 5351.39; ETA: 0.6 minutes\n", + "Step 318000 of 500000; TPS: 5349.5; ETA: 0.6 minutes\n", + "Step 319000 of 500000; TPS: 5348.63; ETA: 0.6 minutes\n", + "Step 320000 of 500000; TPS: 5347.95; ETA: 0.6 minutes\n", + "Step 321000 of 500000; TPS: 5347.41; ETA: 0.6 minutes\n", + "Step 322000 of 500000; TPS: 5346.53; ETA: 0.6 minutes\n", + "Step 323000 of 500000; TPS: 5346.3; ETA: 0.6 minutes\n", + "Step 324000 of 500000; TPS: 5345.86; ETA: 0.5 minutes\n", + "Step 325000 of 500000; TPS: 5344.75; ETA: 0.5 minutes\n", + "Step 326000 of 500000; TPS: 5342.88; ETA: 0.5 minutes\n", + "Step 327000 of 500000; TPS: 5339.98; ETA: 0.5 minutes\n", + "Step 328000 of 500000; TPS: 5338.52; ETA: 0.5 minutes\n", + "Step 329000 of 500000; TPS: 5336.59; ETA: 0.5 minutes\n", + "Step 330000 of 500000; TPS: 5334.82; ETA: 0.5 minutes\n", + "Step 331000 of 500000; TPS: 5332.86; ETA: 0.5 minutes\n", + "Step 332000 of 500000; TPS: 5331.4; ETA: 0.5 minutes\n", + "Step 333000 of 500000; TPS: 5329.87; ETA: 0.5 minutes\n", + "Step 334000 of 500000; TPS: 5328.46; ETA: 0.5 minutes\n", + "Step 335000 of 500000; TPS: 5326.7; ETA: 0.5 minutes\n", + "Step 336000 of 500000; TPS: 5324.54; ETA: 0.5 minutes\n", + "Step 337000 of 500000; TPS: 5323.06; ETA: 0.5 minutes\n", + "Step 338000 of 500000; TPS: 5321.29; ETA: 0.5 minutes\n", + "Step 339000 of 500000; TPS: 5319.61; ETA: 0.5 minutes\n", + "Step 340000 of 500000; TPS: 5318.04; ETA: 0.5 minutes\n", + "Step 341000 of 500000; TPS: 5316.56; ETA: 0.5 minutes\n", + "Step 342000 of 500000; TPS: 5315.21; ETA: 0.5 minutes\n", + "Step 343000 of 500000; TPS: 5313.73; ETA: 0.5 minutes\n", + "Step 344000 of 500000; TPS: 5312.33; ETA: 0.5 minutes\n", + "Step 345000 of 500000; TPS: 5310.93; ETA: 0.5 minutes\n", + "Step 346000 of 500000; TPS: 5309.44; ETA: 0.5 minutes\n", + "Step 347000 of 500000; TPS: 5307.26; ETA: 0.5 minutes\n", + "Step 348000 of 500000; TPS: 5305.65; ETA: 0.5 minutes\n", + "Step 349000 of 500000; TPS: 5305.4; ETA: 0.5 minutes\n", + "Step 350000 of 500000; TPS: 5305.36; ETA: 0.5 minutes\n", + "Step 351000 of 500000; TPS: 5305.85; ETA: 0.5 minutes\n", + "Step 352000 of 500000; TPS: 5305.71; ETA: 0.5 minutes\n", + "Step 353000 of 500000; TPS: 5306.1; ETA: 0.5 minutes\n", + "Step 354000 of 500000; TPS: 5306.44; ETA: 0.5 minutes\n", + "Step 355000 of 500000; TPS: 5306.78; ETA: 0.5 minutes\n", + "Step 356000 of 500000; TPS: 5306.61; ETA: 0.5 minutes\n", + "Step 357000 of 500000; TPS: 5306.03; ETA: 0.4 minutes\n", + "Step 358000 of 500000; TPS: 5305.98; ETA: 0.4 minutes\n", + "Step 359000 of 500000; TPS: 5305.87; ETA: 0.4 minutes\n", + "Step 360000 of 500000; TPS: 5305.96; ETA: 0.4 minutes\n", + "Step 361000 of 500000; TPS: 5306.63; ETA: 0.4 minutes\n", + "Step 362000 of 500000; TPS: 5307.01; ETA: 0.4 minutes\n", + "Step 363000 of 500000; TPS: 5307.55; ETA: 0.4 minutes\n", + "Step 364000 of 500000; TPS: 5307.49; ETA: 0.4 minutes\n", + "Step 365000 of 500000; TPS: 5307.79; ETA: 0.4 minutes\n", + "Step 366000 of 500000; TPS: 5307.46; ETA: 0.4 minutes\n", + "Step 367000 of 500000; TPS: 5306.76; ETA: 0.4 minutes\n", + "Step 368000 of 500000; TPS: 5305.81; ETA: 0.4 minutes\n", + "Step 369000 of 500000; TPS: 5301.25; ETA: 0.4 minutes\n", + "Step 370000 of 500000; TPS: 5300.69; ETA: 0.4 minutes\n", + "Step 371000 of 500000; TPS: 5300.06; ETA: 0.4 minutes\n", + "Step 372000 of 500000; TPS: 5299.71; ETA: 0.4 minutes\n", + "Step 373000 of 500000; TPS: 5299.24; ETA: 0.4 minutes\n", + "Step 374000 of 500000; TPS: 5298.97; ETA: 0.4 minutes\n", + "Step 375000 of 500000; TPS: 5298.72; ETA: 0.4 minutes\n", + "Step 376000 of 500000; TPS: 5298.23; ETA: 0.4 minutes\n", + "Step 377000 of 500000; TPS: 5297.92; ETA: 0.4 minutes\n", + "Step 378000 of 500000; TPS: 5297.04; ETA: 0.4 minutes\n", + "Step 379000 of 500000; TPS: 5296.55; ETA: 0.4 minutes\n", + "Step 380000 of 500000; TPS: 5296.04; ETA: 0.4 minutes\n", + "Step 381000 of 500000; TPS: 5295.82; ETA: 0.4 minutes\n", + "Step 382000 of 500000; TPS: 5412.79; ETA: 0.4 minutes\n", + "Step 383000 of 500000; TPS: 5411.9; ETA: 0.4 minutes\n", + "Step 384000 of 500000; TPS: 5411.2; ETA: 0.4 minutes\n", + "Step 385000 of 500000; TPS: 5410.65; ETA: 0.4 minutes\n", + "Step 386000 of 500000; TPS: 5410.02; ETA: 0.4 minutes\n", + "Step 387000 of 500000; TPS: 5409.46; ETA: 0.3 minutes\n", + "Step 388000 of 500000; TPS: 5408.8; ETA: 0.3 minutes\n", + "Step 389000 of 500000; TPS: 5408.07; ETA: 0.3 minutes\n", + "Step 390000 of 500000; TPS: 5407.27; ETA: 0.3 minutes\n", + "Step 391000 of 500000; TPS: 5406.35; ETA: 0.3 minutes\n", + "Step 392000 of 500000; TPS: 5405.88; ETA: 0.3 minutes\n", + "Step 393000 of 500000; TPS: 5405.07; ETA: 0.3 minutes\n", + "Step 394000 of 500000; TPS: 5404.5; ETA: 0.3 minutes\n", + "Step 395000 of 500000; TPS: 5403.79; ETA: 0.3 minutes\n", + "Step 396000 of 500000; TPS: 5403.32; ETA: 0.3 minutes\n", + "Step 397000 of 500000; TPS: 5402.7; ETA: 0.3 minutes\n", + "Step 398000 of 500000; TPS: 5402.27; ETA: 0.3 minutes\n", + "Step 399000 of 500000; TPS: 5401.82; ETA: 0.3 minutes\n", + "Step 400000 of 500000; TPS: 5400.73; ETA: 0.3 minutes\n", + "Step 401000 of 500000; TPS: 5400.13; ETA: 0.3 minutes\n", + "Step 402000 of 500000; TPS: 5399.46; ETA: 0.3 minutes\n", + "Step 403000 of 500000; TPS: 5398.58; ETA: 0.3 minutes\n", + "Step 404000 of 500000; TPS: 5397.84; ETA: 0.3 minutes\n", + "Step 405000 of 500000; TPS: 5397.49; ETA: 0.3 minutes\n", + "Step 406000 of 500000; TPS: 5397.47; ETA: 0.3 minutes\n", + "Step 407000 of 500000; TPS: 5397.24; ETA: 0.3 minutes\n", + "Step 408000 of 500000; TPS: 5396.98; ETA: 0.3 minutes\n", + "Step 409000 of 500000; TPS: 5396.93; ETA: 0.3 minutes\n", + "Step 410000 of 500000; TPS: 5396.86; ETA: 0.3 minutes\n", + "Step 411000 of 500000; TPS: 5396.74; ETA: 0.3 minutes\n", + "Step 412000 of 500000; TPS: 5396.21; ETA: 0.3 minutes\n", + "Step 413000 of 500000; TPS: 5396.23; ETA: 0.3 minutes\n", + "Step 414000 of 500000; TPS: 5396.15; ETA: 0.3 minutes\n", + "Step 415000 of 500000; TPS: 5396.04; ETA: 0.3 minutes\n", + "Step 416000 of 500000; TPS: 5395.68; ETA: 0.3 minutes\n", + "Step 417000 of 500000; TPS: 5394.56; ETA: 0.3 minutes\n", + "Step 418000 of 500000; TPS: 5393.54; ETA: 0.3 minutes\n", + "Step 419000 of 500000; TPS: 5392.37; ETA: 0.3 minutes\n", + "Step 420000 of 500000; TPS: 5391.21; ETA: 0.2 minutes\n", + "Step 421000 of 500000; TPS: 5389.98; ETA: 0.2 minutes\n", + "Step 422000 of 500000; TPS: 5389.01; ETA: 0.2 minutes\n", + "Step 423000 of 500000; TPS: 5387.68; ETA: 0.2 minutes\n", + "Step 424000 of 500000; TPS: 5386.59; ETA: 0.2 minutes\n", + "Step 425000 of 500000; TPS: 5385.62; ETA: 0.2 minutes\n", + "Step 426000 of 500000; TPS: 5384.24; ETA: 0.2 minutes\n", + "Step 427000 of 500000; TPS: 5383.24; ETA: 0.2 minutes\n", + "Step 428000 of 500000; TPS: 5382.19; ETA: 0.2 minutes\n", + "Step 429000 of 500000; TPS: 5379.95; ETA: 0.2 minutes\n", + "Step 430000 of 500000; TPS: 5378.75; ETA: 0.2 minutes\n", + "Step 431000 of 500000; TPS: 5377.38; ETA: 0.2 minutes\n", + "Step 432000 of 500000; TPS: 5375.58; ETA: 0.2 minutes\n", + "Step 433000 of 500000; TPS: 5374.1; ETA: 0.2 minutes\n", + "Step 434000 of 500000; TPS: 5372.75; ETA: 0.2 minutes\n", + "Step 435000 of 500000; TPS: 5371.12; ETA: 0.2 minutes\n", + "Step 436000 of 500000; TPS: 5369.71; ETA: 0.2 minutes\n", + "Step 437000 of 500000; TPS: 5368.36; ETA: 0.2 minutes\n", + "Step 438000 of 500000; TPS: 5366.72; ETA: 0.2 minutes\n", + "Step 439000 of 500000; TPS: 5365.23; ETA: 0.2 minutes\n", + "Step 440000 of 500000; TPS: 5363.96; ETA: 0.2 minutes\n", + "Step 441000 of 500000; TPS: 5362.71; ETA: 0.2 minutes\n", + "Step 442000 of 500000; TPS: 5361.27; ETA: 0.2 minutes\n", + "Step 443000 of 500000; TPS: 5359.7; ETA: 0.2 minutes\n", + "Step 444000 of 500000; TPS: 5358.21; ETA: 0.2 minutes\n", + "Step 445000 of 500000; TPS: 5356.77; ETA: 0.2 minutes\n", + "Step 446000 of 500000; TPS: 5355.56; ETA: 0.2 minutes\n", + "Step 447000 of 500000; TPS: 5354.19; ETA: 0.2 minutes\n", + "Step 448000 of 500000; TPS: 5352.75; ETA: 0.2 minutes\n", + "Step 449000 of 500000; TPS: 5352.05; ETA: 0.2 minutes\n", + "Step 450000 of 500000; TPS: 5351.82; ETA: 0.2 minutes\n", + "Step 451000 of 500000; TPS: 5351.53; ETA: 0.2 minutes\n", + "Step 452000 of 500000; TPS: 5351.07; ETA: 0.1 minutes\n", + "Step 453000 of 500000; TPS: 5350.61; ETA: 0.1 minutes\n", + "Step 454000 of 500000; TPS: 5349.25; ETA: 0.1 minutes\n", + "Step 455000 of 500000; TPS: 5348.91; ETA: 0.1 minutes\n", + "Step 456000 of 500000; TPS: 5347.78; ETA: 0.1 minutes\n", + "Step 457000 of 500000; TPS: 5346.33; ETA: 0.1 minutes\n", + "Step 458000 of 500000; TPS: 5345.02; ETA: 0.1 minutes\n", + "Step 459000 of 500000; TPS: 5343.56; ETA: 0.1 minutes\n", + "Step 460000 of 500000; TPS: 5342.18; ETA: 0.1 minutes\n", + "Step 461000 of 500000; TPS: 5340.47; ETA: 0.1 minutes\n", + "Step 462000 of 500000; TPS: 5338.82; ETA: 0.1 minutes\n", + "Step 463000 of 500000; TPS: 5337.29; ETA: 0.1 minutes\n", + "Step 464000 of 500000; TPS: 5335.56; ETA: 0.1 minutes\n", + "Step 465000 of 500000; TPS: 5333.97; ETA: 0.1 minutes\n", + "Step 466000 of 500000; TPS: 5332.7; ETA: 0.1 minutes\n", + "Step 467000 of 500000; TPS: 5331.3; ETA: 0.1 minutes\n", + "Step 468000 of 500000; TPS: 5329.79; ETA: 0.1 minutes\n", + "Step 469000 of 500000; TPS: 5328.09; ETA: 0.1 minutes\n", + "Step 470000 of 500000; TPS: 5326.8; ETA: 0.1 minutes\n", + "Step 471000 of 500000; TPS: 5325.08; ETA: 0.1 minutes\n", + "Step 472000 of 500000; TPS: 5323.27; ETA: 0.1 minutes\n", + "Step 473000 of 500000; TPS: 5321.94; ETA: 0.1 minutes\n", + "Step 474000 of 500000; TPS: 5320.34; ETA: 0.1 minutes\n", + "Step 475000 of 500000; TPS: 5318.83; ETA: 0.1 minutes\n", + "Step 476000 of 500000; TPS: 5317.62; ETA: 0.1 minutes\n", + "Step 477000 of 500000; TPS: 5313.7; ETA: 0.1 minutes\n", + "Step 478000 of 500000; TPS: 5311.72; ETA: 0.1 minutes\n", + "Step 479000 of 500000; TPS: 5309.25; ETA: 0.1 minutes\n", + "Step 480000 of 500000; TPS: 5307.73; ETA: 0.1 minutes\n", + "Step 481000 of 500000; TPS: 5306.76; ETA: 0.1 minutes\n", + "Step 482000 of 500000; TPS: 5305.79; ETA: 0.1 minutes\n", + "Step 483000 of 500000; TPS: 5304.76; ETA: 0.1 minutes\n", + "Step 484000 of 500000; TPS: 5303.61; ETA: 0.1 minutes\n", + "Step 485000 of 500000; TPS: 5302.46; ETA: 0.0 minutes\n", + "Step 486000 of 500000; TPS: 5301.22; ETA: 0.0 minutes\n", + "Step 487000 of 500000; TPS: 5299.99; ETA: 0.0 minutes\n", + "Step 488000 of 500000; TPS: 5298.55; ETA: 0.0 minutes\n", + "Step 489000 of 500000; TPS: 5297.15; ETA: 0.0 minutes\n", + "Step 490000 of 500000; TPS: 5296.1; ETA: 0.0 minutes\n", + "Step 491000 of 500000; TPS: 5294.95; ETA: 0.0 minutes\n", + "Step 492000 of 500000; TPS: 5293.81; ETA: 0.0 minutes\n", + "Step 493000 of 500000; TPS: 5292.31; ETA: 0.0 minutes\n", + "Step 494000 of 500000; TPS: 5290.2; ETA: 0.0 minutes\n", + "Step 495000 of 500000; TPS: 5287.86; ETA: 0.0 minutes\n", + "Step 496000 of 500000; TPS: 5285.44; ETA: 0.0 minutes\n", + "Step 497000 of 500000; TPS: 5283.07; ETA: 0.0 minutes\n", + "Step 498000 of 500000; TPS: 5280.86; ETA: 0.0 minutes\n", + "Step 499000 of 500000; TPS: 5277.76; ETA: 0.0 minutes\n", + "Step 500000 of 500000; TPS: 5275.34; ETA: 0.0 minutes\n", + "Step 999 of 1000000; TPS: 4590.68; ETA: 3.6 minutes\n", + "Step 1999 of 1000000; TPS: 4636.99; ETA: 3.6 minutes\n", + "Step 2999 of 1000000; TPS: 4645.88; ETA: 3.6 minutes\n", + "Step 3999 of 1000000; TPS: 4653.18; ETA: 3.6 minutes\n", + "Step 4999 of 1000000; TPS: 4654.25; ETA: 3.6 minutes\n", + "Step 5999 of 1000000; TPS: 4645.71; ETA: 3.6 minutes\n", + "Step 6999 of 1000000; TPS: 4646.66; ETA: 3.6 minutes\n", + "Step 7999 of 1000000; TPS: 4643.04; ETA: 3.6 minutes\n", + "Step 8999 of 1000000; TPS: 4634.2; ETA: 3.6 minutes\n", + "Step 9999 of 1000000; TPS: 4623.95; ETA: 3.6 minutes\n", + "Step 10999 of 1000000; TPS: 4622.58; ETA: 3.6 minutes\n", + "Step 11999 of 1000000; TPS: 4632.2; ETA: 3.6 minutes\n", + "Step 12999 of 1000000; TPS: 4643.12; ETA: 3.5 minutes\n", + "Step 13999 of 1000000; TPS: 4650.46; ETA: 3.5 minutes\n", + "Step 14999 of 1000000; TPS: 4652.47; ETA: 3.5 minutes\n", + "Step 15999 of 1000000; TPS: 4676.62; ETA: 3.5 minutes\n", + "Step 16999 of 1000000; TPS: 4694.5; ETA: 3.5 minutes\n", + "Step 17999 of 1000000; TPS: 4707.96; ETA: 3.5 minutes\n", + "Step 18999 of 1000000; TPS: 4719.55; ETA: 3.5 minutes\n", + "Step 19999 of 1000000; TPS: 4728.56; ETA: 3.5 minutes\n", + "Step 20999 of 1000000; TPS: 4737.7; ETA: 3.4 minutes\n", + "Step 21999 of 1000000; TPS: 4746.69; ETA: 3.4 minutes\n", + "Step 22999 of 1000000; TPS: 4755.66; ETA: 3.4 minutes\n", + "Step 23999 of 1000000; TPS: 4742.93; ETA: 3.4 minutes\n", + "Step 24999 of 1000000; TPS: 4753.91; ETA: 3.4 minutes\n", + "Step 25999 of 1000000; TPS: 4763.4; ETA: 3.4 minutes\n", + "Step 26999 of 1000000; TPS: 4772.3; ETA: 3.4 minutes\n", + "Step 27999 of 1000000; TPS: 4779.94; ETA: 3.4 minutes\n", + "Step 28999 of 1000000; TPS: 4786.18; ETA: 3.4 minutes\n", + "Step 29999 of 1000000; TPS: 4793.68; ETA: 3.4 minutes\n", + "Step 30999 of 1000000; TPS: 4802.22; ETA: 3.4 minutes\n", + "Step 31999 of 1000000; TPS: 4807.44; ETA: 3.4 minutes\n", + "Step 32999 of 1000000; TPS: 4807.04; ETA: 3.4 minutes\n", + "Step 33999 of 1000000; TPS: 4805.86; ETA: 3.4 minutes\n", + "Step 34999 of 1000000; TPS: 4805.15; ETA: 3.3 minutes\n", + "Step 35999 of 1000000; TPS: 4804.88; ETA: 3.3 minutes\n", + "Step 36999 of 1000000; TPS: 4805.94; ETA: 3.3 minutes\n", + "Step 37999 of 1000000; TPS: 4802.61; ETA: 3.3 minutes\n", + "Step 38999 of 1000000; TPS: 5919.56; ETA: 2.7 minutes\n", + "Step 39999 of 1000000; TPS: 5885.07; ETA: 2.7 minutes\n", + "Step 40999 of 1000000; TPS: 5853.86; ETA: 2.7 minutes\n", + "Step 41999 of 1000000; TPS: 5826.38; ETA: 2.7 minutes\n", + "Step 42999 of 1000000; TPS: 5796.43; ETA: 2.8 minutes\n", + "Step 43999 of 1000000; TPS: 5771.75; ETA: 2.8 minutes\n", + "Step 44999 of 1000000; TPS: 5748.28; ETA: 2.8 minutes\n", + "Step 45999 of 1000000; TPS: 5723.78; ETA: 2.8 minutes\n", + "Step 46999 of 1000000; TPS: 5699.15; ETA: 2.8 minutes\n", + "Step 47999 of 1000000; TPS: 5675.14; ETA: 2.8 minutes\n", + "Step 48999 of 1000000; TPS: 5652.28; ETA: 2.8 minutes\n", + "Step 49999 of 1000000; TPS: 5633.58; ETA: 2.8 minutes\n", + "Step 50999 of 1000000; TPS: 5613.22; ETA: 2.8 minutes\n", + "Step 51999 of 1000000; TPS: 5596.87; ETA: 2.8 minutes\n", + "Step 52999 of 1000000; TPS: 5580.55; ETA: 2.8 minutes\n", + "Step 53999 of 1000000; TPS: 5565.37; ETA: 2.8 minutes\n", + "Step 54999 of 1000000; TPS: 5548.77; ETA: 2.8 minutes\n", + "Step 55999 of 1000000; TPS: 5532.91; ETA: 2.8 minutes\n", + "Step 56999 of 1000000; TPS: 5518.48; ETA: 2.8 minutes\n", + "Step 57999 of 1000000; TPS: 5503.23; ETA: 2.9 minutes\n", + "Step 58999 of 1000000; TPS: 5488.65; ETA: 2.9 minutes\n", + "Step 59999 of 1000000; TPS: 5473.39; ETA: 2.9 minutes\n", + "Step 60999 of 1000000; TPS: 5460.28; ETA: 2.9 minutes\n", + "Step 61999 of 1000000; TPS: 5450.2; ETA: 2.9 minutes\n", + "Step 62999 of 1000000; TPS: 5435.7; ETA: 2.9 minutes\n", + "Step 63999 of 1000000; TPS: 5422.24; ETA: 2.9 minutes\n", + "Step 64999 of 1000000; TPS: 5411.59; ETA: 2.9 minutes\n", + "Step 65999 of 1000000; TPS: 5400.13; ETA: 2.9 minutes\n", + "Step 66999 of 1000000; TPS: 5389.34; ETA: 2.9 minutes\n", + "Step 67999 of 1000000; TPS: 5379.18; ETA: 2.9 minutes\n", + "Step 68999 of 1000000; TPS: 5372.71; ETA: 2.9 minutes\n", + "Step 69999 of 1000000; TPS: 5366.83; ETA: 2.9 minutes\n", + "Step 70999 of 1000000; TPS: 5359.33; ETA: 2.9 minutes\n", + "Step 71999 of 1000000; TPS: 5353.39; ETA: 2.9 minutes\n", + "Step 72999 of 1000000; TPS: 5347.76; ETA: 2.9 minutes\n", + "Step 73999 of 1000000; TPS: 5342.61; ETA: 2.9 minutes\n", + "Step 74999 of 1000000; TPS: 5339.03; ETA: 2.9 minutes\n", + "Step 75999 of 1000000; TPS: 5334.22; ETA: 2.9 minutes\n", + "Step 76999 of 1000000; TPS: 5329.07; ETA: 2.9 minutes\n", + "Step 77999 of 1000000; TPS: 5323.96; ETA: 2.9 minutes\n", + "Step 78999 of 1000000; TPS: 5314.28; ETA: 2.9 minutes\n", + "Step 79999 of 1000000; TPS: 5296.74; ETA: 2.9 minutes\n", + "Step 80999 of 1000000; TPS: 5286.59; ETA: 2.9 minutes\n", + "Step 81999 of 1000000; TPS: 5277.76; ETA: 2.9 minutes\n", + "Step 82999 of 1000000; TPS: 5269.0; ETA: 2.9 minutes\n", + "Step 83999 of 1000000; TPS: 5258.9; ETA: 2.9 minutes\n", + "Step 84999 of 1000000; TPS: 5249.74; ETA: 2.9 minutes\n", + "Step 85999 of 1000000; TPS: 5240.36; ETA: 2.9 minutes\n", + "Step 86999 of 1000000; TPS: 5232.2; ETA: 2.9 minutes\n", + "Step 87999 of 1000000; TPS: 5225.33; ETA: 2.9 minutes\n", + "Step 88999 of 1000000; TPS: 5217.25; ETA: 2.9 minutes\n", + "Step 89999 of 1000000; TPS: 5209.76; ETA: 2.9 minutes\n", + "Step 90999 of 1000000; TPS: 5202.33; ETA: 2.9 minutes\n", + "Step 91999 of 1000000; TPS: 5195.58; ETA: 2.9 minutes\n", + "Step 92999 of 1000000; TPS: 5187.17; ETA: 2.9 minutes\n", + "Step 93999 of 1000000; TPS: 5177.98; ETA: 2.9 minutes\n", + "Step 94999 of 1000000; TPS: 5170.56; ETA: 2.9 minutes\n", + "Step 95999 of 1000000; TPS: 5163.69; ETA: 2.9 minutes\n", + "Step 96999 of 1000000; TPS: 5156.49; ETA: 2.9 minutes\n", + "Step 97999 of 1000000; TPS: 5149.45; ETA: 2.9 minutes\n", + "Step 98999 of 1000000; TPS: 5141.57; ETA: 2.9 minutes\n", + "Step 99999 of 1000000; TPS: 5135.69; ETA: 2.9 minutes\n", + "Step 100999 of 1000000; TPS: 5130.01; ETA: 2.9 minutes\n", + "Step 101999 of 1000000; TPS: 5125.38; ETA: 2.9 minutes\n", + "Step 102999 of 1000000; TPS: 5120.63; ETA: 2.9 minutes\n", + "Step 103999 of 1000000; TPS: 5114.14; ETA: 2.9 minutes\n", + "Step 104999 of 1000000; TPS: 5108.8; ETA: 2.9 minutes\n", + "Step 105999 of 1000000; TPS: 5104.28; ETA: 2.9 minutes\n", + "Step 106999 of 1000000; TPS: 5099.84; ETA: 2.9 minutes\n", + "Step 107999 of 1000000; TPS: 5094.8; ETA: 2.9 minutes\n", + "Step 108999 of 1000000; TPS: 5090.78; ETA: 2.9 minutes\n", + "Step 109999 of 1000000; TPS: 5090.19; ETA: 2.9 minutes\n", + "Step 110999 of 1000000; TPS: 5090.03; ETA: 2.9 minutes\n", + "Step 111999 of 1000000; TPS: 5089.8; ETA: 2.9 minutes\n", + "Step 112999 of 1000000; TPS: 5089.01; ETA: 2.9 minutes\n", + "Step 113999 of 1000000; TPS: 5088.47; ETA: 2.9 minutes\n", + "Step 114999 of 1000000; TPS: 5088.63; ETA: 2.9 minutes\n", + "Step 115999 of 1000000; TPS: 5086.59; ETA: 2.9 minutes\n", + "Step 116999 of 1000000; TPS: 5086.13; ETA: 2.9 minutes\n", + "Step 117999 of 1000000; TPS: 5082.2; ETA: 2.9 minutes\n", + "Step 118999 of 1000000; TPS: 5077.56; ETA: 2.9 minutes\n", + "Step 119999 of 1000000; TPS: 5072.45; ETA: 2.9 minutes\n", + "Step 120999 of 1000000; TPS: 5068.08; ETA: 2.9 minutes\n", + "Step 121999 of 1000000; TPS: 5064.35; ETA: 2.9 minutes\n", + "Step 122999 of 1000000; TPS: 5056.91; ETA: 2.9 minutes\n", + "Step 123999 of 1000000; TPS: 5050.91; ETA: 2.9 minutes\n", + "Step 124999 of 1000000; TPS: 5045.56; ETA: 2.9 minutes\n", + "Step 125999 of 1000000; TPS: 5040.53; ETA: 2.9 minutes\n", + "Step 126999 of 1000000; TPS: 5031.14; ETA: 2.9 minutes\n", + "Step 127999 of 1000000; TPS: 5025.88; ETA: 2.9 minutes\n", + "Step 128999 of 1000000; TPS: 5019.91; ETA: 2.9 minutes\n", + "Step 129999 of 1000000; TPS: 5014.92; ETA: 2.9 minutes\n", + "Step 130999 of 1000000; TPS: 5009.03; ETA: 2.9 minutes\n", + "Step 131999 of 1000000; TPS: 5004.51; ETA: 2.9 minutes\n", + "Step 132999 of 1000000; TPS: 4998.21; ETA: 2.9 minutes\n", + "Step 133999 of 1000000; TPS: 4992.93; ETA: 2.9 minutes\n", + "Step 134999 of 1000000; TPS: 4988.06; ETA: 2.9 minutes\n", + "Step 135999 of 1000000; TPS: 4983.69; ETA: 2.9 minutes\n", + "Step 136999 of 1000000; TPS: 4979.22; ETA: 2.9 minutes\n", + "Step 137999 of 1000000; TPS: 4973.18; ETA: 2.9 minutes\n", + "Step 138999 of 1000000; TPS: 4968.63; ETA: 2.9 minutes\n", + "Step 139999 of 1000000; TPS: 4964.0; ETA: 2.9 minutes\n", + "Step 140999 of 1000000; TPS: 4960.62; ETA: 2.9 minutes\n", + "Step 141999 of 1000000; TPS: 4958.91; ETA: 2.9 minutes\n", + "Step 142999 of 1000000; TPS: 4956.73; ETA: 2.9 minutes\n", + "Step 143999 of 1000000; TPS: 4950.78; ETA: 2.9 minutes\n", + "Step 144999 of 1000000; TPS: 4949.03; ETA: 2.9 minutes\n", + "Step 145999 of 1000000; TPS: 4947.64; ETA: 2.9 minutes\n", + "Step 146999 of 1000000; TPS: 4946.66; ETA: 2.9 minutes\n", + "Step 147999 of 1000000; TPS: 4945.42; ETA: 2.9 minutes\n", + "Step 148999 of 1000000; TPS: 4943.39; ETA: 2.9 minutes\n", + "Step 149999 of 1000000; TPS: 4941.71; ETA: 2.9 minutes\n", + "Step 150999 of 1000000; TPS: 4940.36; ETA: 2.9 minutes\n", + "Step 151999 of 1000000; TPS: 4938.93; ETA: 2.9 minutes\n", + "Step 152999 of 1000000; TPS: 4938.13; ETA: 2.9 minutes\n", + "Step 153999 of 1000000; TPS: 4935.34; ETA: 2.9 minutes\n", + "Step 154999 of 1000000; TPS: 4930.86; ETA: 2.9 minutes\n", + "Step 155999 of 1000000; TPS: 4926.59; ETA: 2.9 minutes\n", + "Step 156999 of 1000000; TPS: 4923.27; ETA: 2.9 minutes\n", + "Step 157999 of 1000000; TPS: 4919.58; ETA: 2.9 minutes\n", + "Step 158999 of 1000000; TPS: 4916.88; ETA: 2.9 minutes\n", + "Step 159999 of 1000000; TPS: 4914.1; ETA: 2.8 minutes\n", + "Step 160999 of 1000000; TPS: 4909.46; ETA: 2.8 minutes\n", + "Step 161999 of 1000000; TPS: 4905.24; ETA: 2.8 minutes\n", + "Step 162999 of 1000000; TPS: 4900.91; ETA: 2.8 minutes\n", + "Step 163999 of 1000000; TPS: 4896.3; ETA: 2.8 minutes\n", + "Step 164999 of 1000000; TPS: 4892.29; ETA: 2.8 minutes\n", + "Step 165999 of 1000000; TPS: 4887.74; ETA: 2.8 minutes\n", + "Step 166999 of 1000000; TPS: 4883.9; ETA: 2.8 minutes\n", + "Step 167999 of 1000000; TPS: 4878.91; ETA: 2.8 minutes\n", + "Step 168999 of 1000000; TPS: 4872.9; ETA: 2.8 minutes\n", + "Step 169999 of 1000000; TPS: 4868.27; ETA: 2.8 minutes\n", + "Step 170999 of 1000000; TPS: 4863.72; ETA: 2.8 minutes\n", + "Step 171999 of 1000000; TPS: 4857.39; ETA: 2.8 minutes\n", + "Step 172999 of 1000000; TPS: 4853.14; ETA: 2.8 minutes\n", + "Step 173999 of 1000000; TPS: 4850.54; ETA: 2.8 minutes\n", + "Step 174999 of 1000000; TPS: 4848.13; ETA: 2.8 minutes\n", + "Step 175999 of 1000000; TPS: 4846.58; ETA: 2.8 minutes\n", + "Step 176999 of 1000000; TPS: 4845.25; ETA: 2.8 minutes\n", + "Step 177999 of 1000000; TPS: 4843.82; ETA: 2.8 minutes\n", + "Step 178999 of 1000000; TPS: 4842.34; ETA: 2.8 minutes\n", + "Step 179999 of 1000000; TPS: 4840.89; ETA: 2.8 minutes\n", + "Step 180999 of 1000000; TPS: 4839.32; ETA: 2.8 minutes\n", + "Step 181999 of 1000000; TPS: 4837.63; ETA: 2.8 minutes\n", + "Step 182999 of 1000000; TPS: 4835.14; ETA: 2.8 minutes\n", + "Step 183999 of 1000000; TPS: 4833.69; ETA: 2.8 minutes\n", + "Step 184999 of 1000000; TPS: 4831.93; ETA: 2.8 minutes\n", + "Step 185999 of 1000000; TPS: 4830.84; ETA: 2.8 minutes\n", + "Step 186999 of 1000000; TPS: 4828.8; ETA: 2.8 minutes\n", + "Step 187999 of 1000000; TPS: 4828.6; ETA: 2.8 minutes\n", + "Step 188999 of 1000000; TPS: 5019.38; ETA: 2.7 minutes\n", + "Step 189999 of 1000000; TPS: 4999.01; ETA: 2.7 minutes\n", + "Step 190999 of 1000000; TPS: 4992.99; ETA: 2.7 minutes\n", + "Step 191999 of 1000000; TPS: 4990.43; ETA: 2.7 minutes\n", + "Step 192999 of 1000000; TPS: 4985.43; ETA: 2.7 minutes\n", + "Step 193999 of 1000000; TPS: 4983.13; ETA: 2.7 minutes\n", + "Step 194999 of 1000000; TPS: 4981.41; ETA: 2.7 minutes\n", + "Step 195999 of 1000000; TPS: 4978.43; ETA: 2.7 minutes\n", + "Step 196999 of 1000000; TPS: 4974.43; ETA: 2.7 minutes\n", + "Step 197999 of 1000000; TPS: 4972.83; ETA: 2.7 minutes\n", + "Step 198999 of 1000000; TPS: 4970.3; ETA: 2.7 minutes\n", + "Step 199999 of 1000000; TPS: 4968.2; ETA: 2.7 minutes\n", + "Step 200999 of 1000000; TPS: 4966.06; ETA: 2.7 minutes\n", + "Step 201999 of 1000000; TPS: 4965.24; ETA: 2.7 minutes\n", + "Step 202999 of 1000000; TPS: 4963.7; ETA: 2.7 minutes\n", + "Step 203999 of 1000000; TPS: 4963.37; ETA: 2.7 minutes\n", + "Step 204999 of 1000000; TPS: 4962.26; ETA: 2.7 minutes\n", + "Step 205999 of 1000000; TPS: 4961.44; ETA: 2.7 minutes\n", + "Step 206999 of 1000000; TPS: 4960.26; ETA: 2.7 minutes\n", + "Step 207999 of 1000000; TPS: 4959.47; ETA: 2.7 minutes\n", + "Step 208999 of 1000000; TPS: 4958.81; ETA: 2.7 minutes\n", + "Step 209999 of 1000000; TPS: 4958.13; ETA: 2.7 minutes\n", + "Step 210999 of 1000000; TPS: 4957.51; ETA: 2.7 minutes\n", + "Step 211999 of 1000000; TPS: 4956.7; ETA: 2.6 minutes\n", + "Step 212999 of 1000000; TPS: 4956.03; ETA: 2.6 minutes\n", + "Step 213999 of 1000000; TPS: 4954.88; ETA: 2.6 minutes\n", + "Step 214999 of 1000000; TPS: 4953.84; ETA: 2.6 minutes\n", + "Step 215999 of 1000000; TPS: 4953.08; ETA: 2.6 minutes\n", + "Step 216999 of 1000000; TPS: 4951.83; ETA: 2.6 minutes\n", + "Step 217999 of 1000000; TPS: 4950.1; ETA: 2.6 minutes\n", + "Step 218999 of 1000000; TPS: 4948.19; ETA: 2.6 minutes\n", + "Step 219999 of 1000000; TPS: 4946.26; ETA: 2.6 minutes\n", + "Step 220999 of 1000000; TPS: 4944.85; ETA: 2.6 minutes\n", + "Step 221999 of 1000000; TPS: 4942.95; ETA: 2.6 minutes\n", + "Step 222999 of 1000000; TPS: 4941.07; ETA: 2.6 minutes\n", + "Step 223999 of 1000000; TPS: 4939.7; ETA: 2.6 minutes\n", + "Step 224999 of 1000000; TPS: 4934.13; ETA: 2.6 minutes\n", + "Step 225999 of 1000000; TPS: 4932.82; ETA: 2.6 minutes\n", + "Step 226999 of 1000000; TPS: 4930.76; ETA: 2.6 minutes\n", + "Step 227999 of 1000000; TPS: 4928.7; ETA: 2.6 minutes\n", + "Step 228999 of 1000000; TPS: 4927.23; ETA: 2.6 minutes\n", + "Step 229999 of 1000000; TPS: 4925.71; ETA: 2.6 minutes\n", + "Step 230999 of 1000000; TPS: 4925.16; ETA: 2.6 minutes\n", + "Step 231999 of 1000000; TPS: 4924.86; ETA: 2.6 minutes\n", + "Step 232999 of 1000000; TPS: 4924.38; ETA: 2.6 minutes\n", + "Step 233999 of 1000000; TPS: 4922.42; ETA: 2.6 minutes\n", + "Step 234999 of 1000000; TPS: 4920.17; ETA: 2.6 minutes\n", + "Step 235999 of 1000000; TPS: 4917.92; ETA: 2.6 minutes\n", + "Step 236999 of 1000000; TPS: 4915.45; ETA: 2.6 minutes\n", + "Step 237999 of 1000000; TPS: 4912.48; ETA: 2.6 minutes\n", + "Step 238999 of 1000000; TPS: 4910.05; ETA: 2.6 minutes\n", + "Step 239999 of 1000000; TPS: 4907.65; ETA: 2.6 minutes\n", + "Step 240999 of 1000000; TPS: 4905.2; ETA: 2.6 minutes\n", + "Step 241999 of 1000000; TPS: 4901.75; ETA: 2.6 minutes\n", + "Step 242999 of 1000000; TPS: 4899.73; ETA: 2.6 minutes\n", + "Step 243999 of 1000000; TPS: 4897.33; ETA: 2.6 minutes\n", + "Step 244999 of 1000000; TPS: 4895.5; ETA: 2.6 minutes\n", + "Step 245999 of 1000000; TPS: 4893.06; ETA: 2.6 minutes\n", + "Step 246999 of 1000000; TPS: 4891.05; ETA: 2.6 minutes\n", + "Step 247999 of 1000000; TPS: 4889.04; ETA: 2.6 minutes\n", + "Step 248999 of 1000000; TPS: 4886.74; ETA: 2.6 minutes\n", + "Step 249999 of 1000000; TPS: 4884.9; ETA: 2.6 minutes\n", + "Step 250999 of 1000000; TPS: 4883.12; ETA: 2.6 minutes\n", + "Step 251999 of 1000000; TPS: 4880.28; ETA: 2.6 minutes\n", + "Step 252999 of 1000000; TPS: 4878.3; ETA: 2.6 minutes\n", + "Step 253999 of 1000000; TPS: 4876.24; ETA: 2.5 minutes\n", + "Step 254999 of 1000000; TPS: 4874.52; ETA: 2.5 minutes\n", + "Step 255999 of 1000000; TPS: 4872.7; ETA: 2.5 minutes\n", + "Step 256999 of 1000000; TPS: 4870.89; ETA: 2.5 minutes\n", + "Step 257999 of 1000000; TPS: 4869.9; ETA: 2.5 minutes\n", + "Step 258999 of 1000000; TPS: 4869.67; ETA: 2.5 minutes\n", + "Step 259999 of 1000000; TPS: 4869.78; ETA: 2.5 minutes\n", + "Step 260999 of 1000000; TPS: 4869.79; ETA: 2.5 minutes\n", + "Step 261999 of 1000000; TPS: 4869.17; ETA: 2.5 minutes\n", + "Step 262999 of 1000000; TPS: 4869.1; ETA: 2.5 minutes\n", + "Step 263999 of 1000000; TPS: 4868.79; ETA: 2.5 minutes\n", + "Step 264999 of 1000000; TPS: 4868.3; ETA: 2.5 minutes\n", + "Step 265999 of 1000000; TPS: 4867.53; ETA: 2.5 minutes\n", + "Step 266999 of 1000000; TPS: 4866.78; ETA: 2.5 minutes\n", + "Step 267999 of 1000000; TPS: 4865.53; ETA: 2.5 minutes\n", + "Step 268999 of 1000000; TPS: 4864.24; ETA: 2.5 minutes\n", + "Step 269999 of 1000000; TPS: 4863.44; ETA: 2.5 minutes\n", + "Step 270999 of 1000000; TPS: 4861.06; ETA: 2.5 minutes\n", + "Step 271999 of 1000000; TPS: 4858.25; ETA: 2.5 minutes\n", + "Step 272999 of 1000000; TPS: 4855.87; ETA: 2.5 minutes\n", + "Step 273999 of 1000000; TPS: 4853.43; ETA: 2.5 minutes\n", + "Step 274999 of 1000000; TPS: 4851.03; ETA: 2.5 minutes\n", + "Step 275999 of 1000000; TPS: 4848.37; ETA: 2.5 minutes\n", + "Step 276999 of 1000000; TPS: 4845.8; ETA: 2.5 minutes\n", + "Step 277999 of 1000000; TPS: 4843.6; ETA: 2.5 minutes\n", + "Step 278999 of 1000000; TPS: 4840.79; ETA: 2.5 minutes\n", + "Step 279999 of 1000000; TPS: 4838.34; ETA: 2.5 minutes\n", + "Step 280999 of 1000000; TPS: 4835.11; ETA: 2.5 minutes\n", + "Step 281999 of 1000000; TPS: 4832.87; ETA: 2.5 minutes\n", + "Step 282999 of 1000000; TPS: 4830.83; ETA: 2.5 minutes\n", + "Step 283999 of 1000000; TPS: 4828.64; ETA: 2.5 minutes\n", + "Step 284999 of 1000000; TPS: 4826.62; ETA: 2.5 minutes\n", + "Step 285999 of 1000000; TPS: 4824.27; ETA: 2.5 minutes\n", + "Step 286999 of 1000000; TPS: 4822.18; ETA: 2.5 minutes\n", + "Step 287999 of 1000000; TPS: 4820.05; ETA: 2.5 minutes\n", + "Step 288999 of 1000000; TPS: 4817.79; ETA: 2.5 minutes\n", + "Step 289999 of 1000000; TPS: 4815.93; ETA: 2.5 minutes\n", + "Step 290999 of 1000000; TPS: 4813.98; ETA: 2.5 minutes\n", + "Step 291999 of 1000000; TPS: 4812.25; ETA: 2.5 minutes\n", + "Step 292999 of 1000000; TPS: 4810.28; ETA: 2.4 minutes\n", + "Step 293999 of 1000000; TPS: 4808.3; ETA: 2.4 minutes\n", + "Step 294999 of 1000000; TPS: 4806.51; ETA: 2.4 minutes\n", + "Step 295999 of 1000000; TPS: 4806.16; ETA: 2.4 minutes\n", + "Step 296999 of 1000000; TPS: 4805.39; ETA: 2.4 minutes\n", + "Step 297999 of 1000000; TPS: 4804.36; ETA: 2.4 minutes\n", + "Step 298999 of 1000000; TPS: 4803.83; ETA: 2.4 minutes\n", + "Step 299999 of 1000000; TPS: 4803.26; ETA: 2.4 minutes\n", + "Step 300999 of 1000000; TPS: 4802.66; ETA: 2.4 minutes\n", + "Step 301999 of 1000000; TPS: 4802.02; ETA: 2.4 minutes\n", + "Step 302999 of 1000000; TPS: 4801.29; ETA: 2.4 minutes\n", + "Step 303999 of 1000000; TPS: 4800.71; ETA: 2.4 minutes\n", + "Step 304999 of 1000000; TPS: 4799.96; ETA: 2.4 minutes\n", + "Step 305999 of 1000000; TPS: 4799.53; ETA: 2.4 minutes\n", + "Step 306999 of 1000000; TPS: 4798.45; ETA: 2.4 minutes\n", + "Step 307999 of 1000000; TPS: 4795.74; ETA: 2.4 minutes\n", + "Step 308999 of 1000000; TPS: 4793.15; ETA: 2.4 minutes\n", + "Step 309999 of 1000000; TPS: 4790.41; ETA: 2.4 minutes\n", + "Step 310999 of 1000000; TPS: 4787.93; ETA: 2.4 minutes\n", + "Step 311999 of 1000000; TPS: 4785.66; ETA: 2.4 minutes\n", + "Step 312999 of 1000000; TPS: 4783.29; ETA: 2.4 minutes\n", + "Step 313999 of 1000000; TPS: 4781.07; ETA: 2.4 minutes\n", + "Step 314999 of 1000000; TPS: 4777.24; ETA: 2.4 minutes\n", + "Step 315999 of 1000000; TPS: 4774.68; ETA: 2.4 minutes\n", + "Step 316999 of 1000000; TPS: 4772.42; ETA: 2.4 minutes\n", + "Step 317999 of 1000000; TPS: 4770.37; ETA: 2.4 minutes\n", + "Step 318999 of 1000000; TPS: 4768.15; ETA: 2.4 minutes\n", + "Step 319999 of 1000000; TPS: 4766.02; ETA: 2.4 minutes\n", + "Step 320999 of 1000000; TPS: 4763.76; ETA: 2.4 minutes\n", + "Step 321999 of 1000000; TPS: 4761.71; ETA: 2.4 minutes\n", + "Step 322999 of 1000000; TPS: 4759.69; ETA: 2.4 minutes\n", + "Step 323999 of 1000000; TPS: 4757.6; ETA: 2.4 minutes\n", + "Step 324999 of 1000000; TPS: 4755.76; ETA: 2.4 minutes\n", + "Step 325999 of 1000000; TPS: 4754.33; ETA: 2.4 minutes\n", + "Step 326999 of 1000000; TPS: 4753.71; ETA: 2.4 minutes\n", + "Step 327999 of 1000000; TPS: 4753.02; ETA: 2.4 minutes\n", + "Step 328999 of 1000000; TPS: 4752.46; ETA: 2.4 minutes\n", + "Step 329999 of 1000000; TPS: 4752.04; ETA: 2.3 minutes\n", + "Step 330999 of 1000000; TPS: 4751.19; ETA: 2.3 minutes\n", + "Step 331999 of 1000000; TPS: 4852.15; ETA: 2.3 minutes\n", + "Step 332999 of 1000000; TPS: 4850.59; ETA: 2.3 minutes\n", + "Step 333999 of 1000000; TPS: 4849.62; ETA: 2.3 minutes\n", + "Step 334999 of 1000000; TPS: 4848.57; ETA: 2.3 minutes\n", + "Step 335999 of 1000000; TPS: 4847.76; ETA: 2.3 minutes\n", + "Step 336999 of 1000000; TPS: 4846.78; ETA: 2.3 minutes\n", + "Step 337999 of 1000000; TPS: 4845.77; ETA: 2.3 minutes\n", + "Step 338999 of 1000000; TPS: 4844.64; ETA: 2.3 minutes\n", + "Step 339999 of 1000000; TPS: 4843.81; ETA: 2.3 minutes\n", + "Step 340999 of 1000000; TPS: 4842.85; ETA: 2.3 minutes\n", + "Step 341999 of 1000000; TPS: 4841.93; ETA: 2.3 minutes\n", + "Step 342999 of 1000000; TPS: 4840.92; ETA: 2.3 minutes\n", + "Step 343999 of 1000000; TPS: 4839.45; ETA: 2.3 minutes\n", + "Step 344999 of 1000000; TPS: 4837.4; ETA: 2.3 minutes\n", + "Step 345999 of 1000000; TPS: 4836.6; ETA: 2.3 minutes\n", + "Step 346999 of 1000000; TPS: 4835.53; ETA: 2.3 minutes\n", + "Step 347999 of 1000000; TPS: 4834.58; ETA: 2.2 minutes\n", + "Step 348999 of 1000000; TPS: 4833.58; ETA: 2.2 minutes\n", + "Step 349999 of 1000000; TPS: 4831.7; ETA: 2.2 minutes\n", + "Step 350999 of 1000000; TPS: 4829.56; ETA: 2.2 minutes\n", + "Step 351999 of 1000000; TPS: 4827.31; ETA: 2.2 minutes\n", + "Step 352999 of 1000000; TPS: 4824.33; ETA: 2.2 minutes\n", + "Step 353999 of 1000000; TPS: 4821.07; ETA: 2.2 minutes\n", + "Step 354999 of 1000000; TPS: 4818.84; ETA: 2.2 minutes\n", + "Step 355999 of 1000000; TPS: 4816.5; ETA: 2.2 minutes\n", + "Step 356999 of 1000000; TPS: 4814.17; ETA: 2.2 minutes\n", + "Step 357999 of 1000000; TPS: 4811.88; ETA: 2.2 minutes\n", + "Step 358999 of 1000000; TPS: 4809.94; ETA: 2.2 minutes\n", + "Step 359999 of 1000000; TPS: 4807.53; ETA: 2.2 minutes\n", + "Step 360999 of 1000000; TPS: 4805.33; ETA: 2.2 minutes\n", + "Step 361999 of 1000000; TPS: 4803.01; ETA: 2.2 minutes\n", + "Step 362999 of 1000000; TPS: 4800.89; ETA: 2.2 minutes\n", + "Step 363999 of 1000000; TPS: 4798.69; ETA: 2.2 minutes\n", + "Step 364999 of 1000000; TPS: 4795.63; ETA: 2.2 minutes\n", + "Step 365999 of 1000000; TPS: 4794.02; ETA: 2.2 minutes\n", + "Step 366999 of 1000000; TPS: 4792.48; ETA: 2.2 minutes\n", + "Step 367999 of 1000000; TPS: 4790.77; ETA: 2.2 minutes\n", + "Step 368999 of 1000000; TPS: 4788.99; ETA: 2.2 minutes\n", + "Step 369999 of 1000000; TPS: 4787.49; ETA: 2.2 minutes\n", + "Step 370999 of 1000000; TPS: 4786.53; ETA: 2.2 minutes\n", + "Step 371999 of 1000000; TPS: 4785.74; ETA: 2.2 minutes\n", + "Step 372999 of 1000000; TPS: 4785.31; ETA: 2.2 minutes\n", + "Step 373999 of 1000000; TPS: 4785.09; ETA: 2.2 minutes\n", + "Step 374999 of 1000000; TPS: 4784.77; ETA: 2.2 minutes\n", + "Step 375999 of 1000000; TPS: 4784.17; ETA: 2.2 minutes\n", + "Step 376999 of 1000000; TPS: 4783.62; ETA: 2.2 minutes\n", + "Step 377999 of 1000000; TPS: 4783.03; ETA: 2.2 minutes\n", + "Step 378999 of 1000000; TPS: 4782.42; ETA: 2.2 minutes\n", + "Step 379999 of 1000000; TPS: 4782.17; ETA: 2.2 minutes\n", + "Step 380999 of 1000000; TPS: 4781.77; ETA: 2.2 minutes\n", + "Step 381999 of 1000000; TPS: 4780.99; ETA: 2.2 minutes\n", + "Step 382999 of 1000000; TPS: 4780.49; ETA: 2.2 minutes\n", + "Step 383999 of 1000000; TPS: 4780.12; ETA: 2.1 minutes\n", + "Step 384999 of 1000000; TPS: 4779.77; ETA: 2.1 minutes\n", + "Step 385999 of 1000000; TPS: 4778.27; ETA: 2.1 minutes\n", + "Step 386999 of 1000000; TPS: 4777.06; ETA: 2.1 minutes\n", + "Step 387999 of 1000000; TPS: 4775.45; ETA: 2.1 minutes\n", + "Step 388999 of 1000000; TPS: 4774.1; ETA: 2.1 minutes\n", + "Step 389999 of 1000000; TPS: 4772.85; ETA: 2.1 minutes\n", + "Step 390999 of 1000000; TPS: 4770.58; ETA: 2.1 minutes\n", + "Step 391999 of 1000000; TPS: 4768.9; ETA: 2.1 minutes\n", + "Step 392999 of 1000000; TPS: 4767.27; ETA: 2.1 minutes\n", + "Step 393999 of 1000000; TPS: 4765.04; ETA: 2.1 minutes\n", + "Step 394999 of 1000000; TPS: 4763.58; ETA: 2.1 minutes\n", + "Step 395999 of 1000000; TPS: 4762.18; ETA: 2.1 minutes\n", + "Step 396999 of 1000000; TPS: 4760.56; ETA: 2.1 minutes\n", + "Step 397999 of 1000000; TPS: 4758.94; ETA: 2.1 minutes\n", + "Step 398999 of 1000000; TPS: 4757.41; ETA: 2.1 minutes\n", + "Step 399999 of 1000000; TPS: 4755.97; ETA: 2.1 minutes\n", + "Step 400999 of 1000000; TPS: 4754.4; ETA: 2.1 minutes\n", + "Step 401999 of 1000000; TPS: 4752.88; ETA: 2.1 minutes\n", + "Step 402999 of 1000000; TPS: 4751.2; ETA: 2.1 minutes\n", + "Step 403999 of 1000000; TPS: 4749.56; ETA: 2.1 minutes\n", + "Step 404999 of 1000000; TPS: 4748.03; ETA: 2.1 minutes\n", + "Step 405999 of 1000000; TPS: 4746.44; ETA: 2.1 minutes\n", + "Step 406999 of 1000000; TPS: 4744.8; ETA: 2.1 minutes\n", + "Step 407999 of 1000000; TPS: 4743.32; ETA: 2.1 minutes\n", + "Step 408999 of 1000000; TPS: 4739.08; ETA: 2.1 minutes\n", + "Step 409999 of 1000000; TPS: 4738.55; ETA: 2.1 minutes\n", + "Step 410999 of 1000000; TPS: 4737.95; ETA: 2.1 minutes\n", + "Step 411999 of 1000000; TPS: 4737.43; ETA: 2.1 minutes\n", + "Step 412999 of 1000000; TPS: 4736.85; ETA: 2.1 minutes\n", + "Step 413999 of 1000000; TPS: 4736.38; ETA: 2.1 minutes\n", + "Step 414999 of 1000000; TPS: 4735.93; ETA: 2.1 minutes\n", + "Step 415999 of 1000000; TPS: 4734.76; ETA: 2.1 minutes\n", + "Step 416999 of 1000000; TPS: 4734.34; ETA: 2.1 minutes\n", + "Step 417999 of 1000000; TPS: 4733.89; ETA: 2.0 minutes\n", + "Step 418999 of 1000000; TPS: 4732.88; ETA: 2.0 minutes\n", + "Step 419999 of 1000000; TPS: 4731.82; ETA: 2.0 minutes\n", + "Step 420999 of 1000000; TPS: 4730.48; ETA: 2.0 minutes\n", + "Step 421999 of 1000000; TPS: 4728.27; ETA: 2.0 minutes\n", + "Step 422999 of 1000000; TPS: 4725.61; ETA: 2.0 minutes\n", + "Step 423999 of 1000000; TPS: 4723.64; ETA: 2.0 minutes\n", + "Step 424999 of 1000000; TPS: 4721.69; ETA: 2.0 minutes\n", + "Step 425999 of 1000000; TPS: 4719.66; ETA: 2.0 minutes\n", + "Step 426999 of 1000000; TPS: 4717.57; ETA: 2.0 minutes\n", + "Step 427999 of 1000000; TPS: 4715.36; ETA: 2.0 minutes\n", + "Step 428999 of 1000000; TPS: 4713.29; ETA: 2.0 minutes\n", + "Step 429999 of 1000000; TPS: 4711.28; ETA: 2.0 minutes\n", + "Step 430999 of 1000000; TPS: 4709.08; ETA: 2.0 minutes\n", + "Step 431999 of 1000000; TPS: 4706.94; ETA: 2.0 minutes\n", + "Step 432999 of 1000000; TPS: 4705.04; ETA: 2.0 minutes\n", + "Step 433999 of 1000000; TPS: 4701.83; ETA: 2.0 minutes\n", + "Step 434999 of 1000000; TPS: 4699.69; ETA: 2.0 minutes\n", + "Step 435999 of 1000000; TPS: 4697.81; ETA: 2.0 minutes\n", + "Step 436999 of 1000000; TPS: 4695.97; ETA: 2.0 minutes\n", + "Step 437999 of 1000000; TPS: 4694.11; ETA: 2.0 minutes\n", + "Step 438999 of 1000000; TPS: 4691.98; ETA: 2.0 minutes\n", + "Step 439999 of 1000000; TPS: 4689.88; ETA: 2.0 minutes\n", + "Step 440999 of 1000000; TPS: 4687.84; ETA: 2.0 minutes\n", + "Step 441999 of 1000000; TPS: 4686.68; ETA: 2.0 minutes\n", + "Step 442999 of 1000000; TPS: 4685.71; ETA: 2.0 minutes\n", + "Step 443999 of 1000000; TPS: 4684.5; ETA: 2.0 minutes\n", + "Step 444999 of 1000000; TPS: 4683.52; ETA: 2.0 minutes\n", + "Step 445999 of 1000000; TPS: 4682.62; ETA: 2.0 minutes\n", + "Step 446999 of 1000000; TPS: 4681.6; ETA: 2.0 minutes\n", + "Step 447999 of 1000000; TPS: 4680.62; ETA: 2.0 minutes\n", + "Step 448999 of 1000000; TPS: 4679.64; ETA: 2.0 minutes\n", + "Step 449999 of 1000000; TPS: 4678.89; ETA: 2.0 minutes\n", + "Step 450999 of 1000000; TPS: 4677.19; ETA: 2.0 minutes\n", + "Step 451999 of 1000000; TPS: 4676.33; ETA: 2.0 minutes\n", + "Step 452999 of 1000000; TPS: 4675.54; ETA: 1.9 minutes\n", + "Step 453999 of 1000000; TPS: 4674.21; ETA: 1.9 minutes\n", + "Step 454999 of 1000000; TPS: 4672.23; ETA: 1.9 minutes\n", + "Step 455999 of 1000000; TPS: 4670.31; ETA: 1.9 minutes\n", + "Step 456999 of 1000000; TPS: 4668.22; ETA: 1.9 minutes\n", + "Step 457999 of 1000000; TPS: 4666.21; ETA: 1.9 minutes\n", + "Step 458999 of 1000000; TPS: 4664.49; ETA: 1.9 minutes\n", + "Step 459999 of 1000000; TPS: 4662.41; ETA: 1.9 minutes\n", + "Step 460999 of 1000000; TPS: 4660.28; ETA: 1.9 minutes\n", + "Step 461999 of 1000000; TPS: 4658.26; ETA: 1.9 minutes\n", + "Step 462999 of 1000000; TPS: 4656.48; ETA: 1.9 minutes\n", + "Step 463999 of 1000000; TPS: 4654.63; ETA: 1.9 minutes\n", + "Step 464999 of 1000000; TPS: 4652.69; ETA: 1.9 minutes\n", + "Step 465999 of 1000000; TPS: 4651.1; ETA: 1.9 minutes\n", + "Step 466999 of 1000000; TPS: 4649.36; ETA: 1.9 minutes\n", + "Step 467999 of 1000000; TPS: 4717.02; ETA: 1.9 minutes\n", + "Step 468999 of 1000000; TPS: 4714.11; ETA: 1.9 minutes\n", + "Step 469999 of 1000000; TPS: 4711.97; ETA: 1.9 minutes\n", + "Step 470999 of 1000000; TPS: 4709.7; ETA: 1.9 minutes\n", + "Step 471999 of 1000000; TPS: 4708.13; ETA: 1.9 minutes\n", + "Step 472999 of 1000000; TPS: 4706.28; ETA: 1.9 minutes\n", + "Step 473999 of 1000000; TPS: 4704.66; ETA: 1.9 minutes\n", + "Step 474999 of 1000000; TPS: 4702.92; ETA: 1.9 minutes\n", + "Step 475999 of 1000000; TPS: 4701.16; ETA: 1.9 minutes\n", + "Step 476999 of 1000000; TPS: 4699.54; ETA: 1.9 minutes\n", + "Step 477999 of 1000000; TPS: 4697.05; ETA: 1.9 minutes\n", + "Step 478999 of 1000000; TPS: 4695.84; ETA: 1.8 minutes\n", + "Step 479999 of 1000000; TPS: 4695.09; ETA: 1.8 minutes\n", + "Step 480999 of 1000000; TPS: 4694.27; ETA: 1.8 minutes\n", + "Step 481999 of 1000000; TPS: 4693.48; ETA: 1.8 minutes\n", + "Step 482999 of 1000000; TPS: 4692.61; ETA: 1.8 minutes\n", + "Step 483999 of 1000000; TPS: 4691.77; ETA: 1.8 minutes\n", + "Step 484999 of 1000000; TPS: 4691.14; ETA: 1.8 minutes\n", + "Step 485999 of 1000000; TPS: 4690.44; ETA: 1.8 minutes\n", + "Step 486999 of 1000000; TPS: 4689.8; ETA: 1.8 minutes\n", + "Step 487999 of 1000000; TPS: 4689.02; ETA: 1.8 minutes\n", + "Step 488999 of 1000000; TPS: 4688.28; ETA: 1.8 minutes\n", + "Step 489999 of 1000000; TPS: 4687.57; ETA: 1.8 minutes\n", + "Step 490999 of 1000000; TPS: 4686.76; ETA: 1.8 minutes\n", + "Step 491999 of 1000000; TPS: 4686.05; ETA: 1.8 minutes\n", + "Step 492999 of 1000000; TPS: 4685.42; ETA: 1.8 minutes\n", + "Step 493999 of 1000000; TPS: 4683.97; ETA: 1.8 minutes\n", + "Step 494999 of 1000000; TPS: 4682.45; ETA: 1.8 minutes\n", + "Step 495999 of 1000000; TPS: 4680.87; ETA: 1.8 minutes\n", + "Step 496999 of 1000000; TPS: 4679.25; ETA: 1.8 minutes\n", + "Step 497999 of 1000000; TPS: 4677.09; ETA: 1.8 minutes\n", + "Step 498999 of 1000000; TPS: 4675.65; ETA: 1.8 minutes\n", + "Step 499999 of 1000000; TPS: 4674.06; ETA: 1.8 minutes\n", + "Step 500999 of 1000000; TPS: 4672.41; ETA: 1.8 minutes\n", + "Step 501999 of 1000000; TPS: 4670.97; ETA: 1.8 minutes\n", + "Step 502999 of 1000000; TPS: 4669.5; ETA: 1.8 minutes\n", + "Step 503999 of 1000000; TPS: 4667.51; ETA: 1.8 minutes\n", + "Step 504999 of 1000000; TPS: 4665.9; ETA: 1.8 minutes\n", + "Step 505999 of 1000000; TPS: 4664.47; ETA: 1.8 minutes\n", + "Step 506999 of 1000000; TPS: 4662.87; ETA: 1.8 minutes\n", + "Step 507999 of 1000000; TPS: 4661.47; ETA: 1.8 minutes\n", + "Step 508999 of 1000000; TPS: 4660.01; ETA: 1.8 minutes\n", + "Step 509999 of 1000000; TPS: 4658.49; ETA: 1.8 minutes\n", + "Step 510999 of 1000000; TPS: 4657.19; ETA: 1.7 minutes\n", + "Step 511999 of 1000000; TPS: 4655.92; ETA: 1.7 minutes\n", + "Step 512999 of 1000000; TPS: 4654.51; ETA: 1.7 minutes\n", + "Step 513999 of 1000000; TPS: 4652.99; ETA: 1.7 minutes\n", + "Step 514999 of 1000000; TPS: 4651.54; ETA: 1.7 minutes\n", + "Step 515999 of 1000000; TPS: 4650.25; ETA: 1.7 minutes\n", + "Step 516999 of 1000000; TPS: 4648.87; ETA: 1.7 minutes\n", + "Step 517999 of 1000000; TPS: 4648.14; ETA: 1.7 minutes\n", + "Step 518999 of 1000000; TPS: 4647.53; ETA: 1.7 minutes\n", + "Step 519999 of 1000000; TPS: 4646.95; ETA: 1.7 minutes\n", + "Step 520999 of 1000000; TPS: 4646.43; ETA: 1.7 minutes\n", + "Step 521999 of 1000000; TPS: 4645.93; ETA: 1.7 minutes\n", + "Step 522999 of 1000000; TPS: 4645.36; ETA: 1.7 minutes\n", + "Step 523999 of 1000000; TPS: 4644.8; ETA: 1.7 minutes\n", + "Step 524999 of 1000000; TPS: 4644.24; ETA: 1.7 minutes\n", + "Step 525999 of 1000000; TPS: 4643.76; ETA: 1.7 minutes\n", + "Step 526999 of 1000000; TPS: 4643.13; ETA: 1.7 minutes\n", + "Step 527999 of 1000000; TPS: 4641.8; ETA: 1.7 minutes\n", + "Step 528999 of 1000000; TPS: 4640.47; ETA: 1.7 minutes\n", + "Step 529999 of 1000000; TPS: 4638.51; ETA: 1.7 minutes\n", + "Step 530999 of 1000000; TPS: 4637.03; ETA: 1.7 minutes\n", + "Step 531999 of 1000000; TPS: 4635.8; ETA: 1.7 minutes\n", + "Step 532999 of 1000000; TPS: 4634.57; ETA: 1.7 minutes\n", + "Step 533999 of 1000000; TPS: 4633.31; ETA: 1.7 minutes\n", + "Step 534999 of 1000000; TPS: 4632.2; ETA: 1.7 minutes\n", + "Step 535999 of 1000000; TPS: 4630.82; ETA: 1.7 minutes\n", + "Step 536999 of 1000000; TPS: 4629.61; ETA: 1.7 minutes\n", + "Step 537999 of 1000000; TPS: 4628.35; ETA: 1.7 minutes\n", + "Step 538999 of 1000000; TPS: 4626.56; ETA: 1.7 minutes\n", + "Step 539999 of 1000000; TPS: 4624.42; ETA: 1.7 minutes\n", + "Step 540999 of 1000000; TPS: 4622.94; ETA: 1.7 minutes\n", + "Step 541999 of 1000000; TPS: 4621.77; ETA: 1.7 minutes\n", + "Step 542999 of 1000000; TPS: 4620.57; ETA: 1.6 minutes\n", + "Step 543999 of 1000000; TPS: 4619.12; ETA: 1.6 minutes\n", + "Step 544999 of 1000000; TPS: 4617.06; ETA: 1.6 minutes\n", + "Step 545999 of 1000000; TPS: 4615.87; ETA: 1.6 minutes\n", + "Step 546999 of 1000000; TPS: 4614.76; ETA: 1.6 minutes\n", + "Step 547999 of 1000000; TPS: 4613.38; ETA: 1.6 minutes\n", + "Step 548999 of 1000000; TPS: 4612.89; ETA: 1.6 minutes\n", + "Step 549999 of 1000000; TPS: 4612.4; ETA: 1.6 minutes\n", + "Step 550999 of 1000000; TPS: 4611.96; ETA: 1.6 minutes\n", + "Step 551999 of 1000000; TPS: 4611.44; ETA: 1.6 minutes\n", + "Step 552999 of 1000000; TPS: 4610.95; ETA: 1.6 minutes\n", + "Step 553999 of 1000000; TPS: 4610.35; ETA: 1.6 minutes\n", + "Step 554999 of 1000000; TPS: 4609.67; ETA: 1.6 minutes\n", + "Step 555999 of 1000000; TPS: 4609.16; ETA: 1.6 minutes\n", + "Step 556999 of 1000000; TPS: 4608.7; ETA: 1.6 minutes\n", + "Step 557999 of 1000000; TPS: 4608.25; ETA: 1.6 minutes\n", + "Step 558999 of 1000000; TPS: 4607.76; ETA: 1.6 minutes\n", + "Step 559999 of 1000000; TPS: 4607.31; ETA: 1.6 minutes\n", + "Step 560999 of 1000000; TPS: 4606.86; ETA: 1.6 minutes\n", + "Step 561999 of 1000000; TPS: 4605.72; ETA: 1.6 minutes\n", + "Step 562999 of 1000000; TPS: 4604.44; ETA: 1.6 minutes\n", + "Step 563999 of 1000000; TPS: 4603.24; ETA: 1.6 minutes\n", + "Step 564999 of 1000000; TPS: 4601.97; ETA: 1.6 minutes\n", + "Step 565999 of 1000000; TPS: 4600.58; ETA: 1.6 minutes\n", + "Step 566999 of 1000000; TPS: 4599.33; ETA: 1.6 minutes\n", + "Step 567999 of 1000000; TPS: 4598.19; ETA: 1.6 minutes\n", + "Step 568999 of 1000000; TPS: 4597.17; ETA: 1.6 minutes\n", + "Step 569999 of 1000000; TPS: 4596.12; ETA: 1.6 minutes\n", + "Step 570999 of 1000000; TPS: 4594.92; ETA: 1.6 minutes\n", + "Step 571999 of 1000000; TPS: 4593.8; ETA: 1.6 minutes\n", + "Step 572999 of 1000000; TPS: 4592.7; ETA: 1.5 minutes\n", + "Step 573999 of 1000000; TPS: 4591.46; ETA: 1.5 minutes\n", + "Step 574999 of 1000000; TPS: 4589.78; ETA: 1.5 minutes\n", + "Step 575999 of 1000000; TPS: 4588.28; ETA: 1.5 minutes\n", + "Step 576999 of 1000000; TPS: 4587.01; ETA: 1.5 minutes\n", + "Step 577999 of 1000000; TPS: 4586.01; ETA: 1.5 minutes\n", + "Step 578999 of 1000000; TPS: 4584.97; ETA: 1.5 minutes\n", + "Step 579999 of 1000000; TPS: 4583.42; ETA: 1.5 minutes\n", + "Step 580999 of 1000000; TPS: 4582.78; ETA: 1.5 minutes\n", + "Step 581999 of 1000000; TPS: 4582.45; ETA: 1.5 minutes\n", + "Step 582999 of 1000000; TPS: 4582.17; ETA: 1.5 minutes\n", + "Step 583999 of 1000000; TPS: 4581.82; ETA: 1.5 minutes\n", + "Step 584999 of 1000000; TPS: 4581.43; ETA: 1.5 minutes\n", + "Step 585999 of 1000000; TPS: 4580.97; ETA: 1.5 minutes\n", + "Step 586999 of 1000000; TPS: 4580.65; ETA: 1.5 minutes\n", + "Step 587999 of 1000000; TPS: 4580.16; ETA: 1.5 minutes\n", + "Step 588999 of 1000000; TPS: 4579.96; ETA: 1.5 minutes\n", + "Step 589999 of 1000000; TPS: 4579.75; ETA: 1.5 minutes\n", + "Step 590999 of 1000000; TPS: 4579.54; ETA: 1.5 minutes\n", + "Step 591999 of 1000000; TPS: 4579.27; ETA: 1.5 minutes\n", + "Step 592999 of 1000000; TPS: 4578.88; ETA: 1.5 minutes\n", + "Step 593999 of 1000000; TPS: 4578.54; ETA: 1.5 minutes\n", + "Step 594999 of 1000000; TPS: 4578.05; ETA: 1.5 minutes\n", + "Step 595999 of 1000000; TPS: 4577.47; ETA: 1.5 minutes\n", + "Step 596999 of 1000000; TPS: 4576.63; ETA: 1.5 minutes\n", + "Step 597999 of 1000000; TPS: 4575.72; ETA: 1.5 minutes\n", + "Step 598999 of 1000000; TPS: 4574.71; ETA: 1.5 minutes\n", + "Step 599999 of 1000000; TPS: 4574.02; ETA: 1.5 minutes\n", + "Step 600999 of 1000000; TPS: 4628.26; ETA: 1.4 minutes\n", + "Step 601999 of 1000000; TPS: 4627.6; ETA: 1.4 minutes\n", + "Step 602999 of 1000000; TPS: 4626.86; ETA: 1.4 minutes\n", + "Step 603999 of 1000000; TPS: 4626.18; ETA: 1.4 minutes\n", + "Step 604999 of 1000000; TPS: 4625.46; ETA: 1.4 minutes\n", + "Step 605999 of 1000000; TPS: 4624.52; ETA: 1.4 minutes\n", + "Step 606999 of 1000000; TPS: 4623.8; ETA: 1.4 minutes\n", + "Step 607999 of 1000000; TPS: 4623.02; ETA: 1.4 minutes\n", + "Step 608999 of 1000000; TPS: 4622.23; ETA: 1.4 minutes\n", + "Step 609999 of 1000000; TPS: 4621.44; ETA: 1.4 minutes\n", + "Step 610999 of 1000000; TPS: 4620.66; ETA: 1.4 minutes\n", + "Step 611999 of 1000000; TPS: 4619.86; ETA: 1.4 minutes\n", + "Step 612999 of 1000000; TPS: 4618.86; ETA: 1.4 minutes\n", + "Step 613999 of 1000000; TPS: 4618.16; ETA: 1.4 minutes\n", + "Step 614999 of 1000000; TPS: 4617.3; ETA: 1.4 minutes\n", + "Step 615999 of 1000000; TPS: 4616.57; ETA: 1.4 minutes\n", + "Step 616999 of 1000000; TPS: 4615.58; ETA: 1.4 minutes\n", + "Step 617999 of 1000000; TPS: 4614.79; ETA: 1.4 minutes\n", + "Step 618999 of 1000000; TPS: 4614.0; ETA: 1.4 minutes\n", + "Step 619999 of 1000000; TPS: 4613.31; ETA: 1.4 minutes\n", + "Step 620999 of 1000000; TPS: 4613.01; ETA: 1.4 minutes\n", + "Step 621999 of 1000000; TPS: 4612.9; ETA: 1.4 minutes\n", + "Step 622999 of 1000000; TPS: 4612.18; ETA: 1.4 minutes\n", + "Step 623999 of 1000000; TPS: 4611.97; ETA: 1.4 minutes\n", + "Step 624999 of 1000000; TPS: 4611.78; ETA: 1.4 minutes\n", + "Step 625999 of 1000000; TPS: 4611.51; ETA: 1.4 minutes\n", + "Step 626999 of 1000000; TPS: 4611.31; ETA: 1.3 minutes\n", + "Step 627999 of 1000000; TPS: 4610.83; ETA: 1.3 minutes\n", + "Step 628999 of 1000000; TPS: 4610.76; ETA: 1.3 minutes\n", + "Step 629999 of 1000000; TPS: 4609.99; ETA: 1.3 minutes\n", + "Step 630999 of 1000000; TPS: 4609.84; ETA: 1.3 minutes\n", + "Step 631999 of 1000000; TPS: 4609.74; ETA: 1.3 minutes\n", + "Step 632999 of 1000000; TPS: 4609.7; ETA: 1.3 minutes\n", + "Step 633999 of 1000000; TPS: 4609.67; ETA: 1.3 minutes\n", + "Step 634999 of 1000000; TPS: 4609.59; ETA: 1.3 minutes\n", + "Step 635999 of 1000000; TPS: 4609.51; ETA: 1.3 minutes\n", + "Step 636999 of 1000000; TPS: 4609.31; ETA: 1.3 minutes\n", + "Step 637999 of 1000000; TPS: 4608.55; ETA: 1.3 minutes\n", + "Step 638999 of 1000000; TPS: 4607.75; ETA: 1.3 minutes\n", + "Step 639999 of 1000000; TPS: 4606.98; ETA: 1.3 minutes\n", + "Step 640999 of 1000000; TPS: 4606.28; ETA: 1.3 minutes\n", + "Step 641999 of 1000000; TPS: 4605.49; ETA: 1.3 minutes\n", + "Step 642999 of 1000000; TPS: 4604.64; ETA: 1.3 minutes\n", + "Step 643999 of 1000000; TPS: 4603.93; ETA: 1.3 minutes\n", + "Step 644999 of 1000000; TPS: 4603.16; ETA: 1.3 minutes\n", + "Step 645999 of 1000000; TPS: 4602.43; ETA: 1.3 minutes\n", + "Step 646999 of 1000000; TPS: 4601.73; ETA: 1.3 minutes\n", + "Step 647999 of 1000000; TPS: 4600.97; ETA: 1.3 minutes\n", + "Step 648999 of 1000000; TPS: 4600.19; ETA: 1.3 minutes\n", + "Step 649999 of 1000000; TPS: 4599.42; ETA: 1.3 minutes\n", + "Step 650999 of 1000000; TPS: 4598.63; ETA: 1.3 minutes\n", + "Step 651999 of 1000000; TPS: 4597.99; ETA: 1.3 minutes\n", + "Step 652999 of 1000000; TPS: 4596.99; ETA: 1.3 minutes\n", + "Step 653999 of 1000000; TPS: 4596.25; ETA: 1.3 minutes\n", + "Step 654999 of 1000000; TPS: 4595.55; ETA: 1.3 minutes\n", + "Step 655999 of 1000000; TPS: 4594.79; ETA: 1.2 minutes\n", + "Step 656999 of 1000000; TPS: 4594.07; ETA: 1.2 minutes\n", + "Step 657999 of 1000000; TPS: 4593.41; ETA: 1.2 minutes\n", + "Step 658999 of 1000000; TPS: 4592.82; ETA: 1.2 minutes\n", + "Step 659999 of 1000000; TPS: 4592.09; ETA: 1.2 minutes\n", + "Step 660999 of 1000000; TPS: 4591.37; ETA: 1.2 minutes\n", + "Step 661999 of 1000000; TPS: 4590.81; ETA: 1.2 minutes\n", + "Step 662999 of 1000000; TPS: 4590.49; ETA: 1.2 minutes\n", + "Step 663999 of 1000000; TPS: 4590.3; ETA: 1.2 minutes\n", + "Step 664999 of 1000000; TPS: 4590.14; ETA: 1.2 minutes\n", + "Step 665999 of 1000000; TPS: 4590.04; ETA: 1.2 minutes\n", + "Step 666999 of 1000000; TPS: 4589.44; ETA: 1.2 minutes\n", + "Step 667999 of 1000000; TPS: 4589.32; ETA: 1.2 minutes\n", + "Step 668999 of 1000000; TPS: 4589.23; ETA: 1.2 minutes\n", + "Step 669999 of 1000000; TPS: 4589.31; ETA: 1.2 minutes\n", + "Step 670999 of 1000000; TPS: 4589.28; ETA: 1.2 minutes\n", + "Step 671999 of 1000000; TPS: 4589.08; ETA: 1.2 minutes\n", + "Step 672999 of 1000000; TPS: 4587.7; ETA: 1.2 minutes\n", + "Step 673999 of 1000000; TPS: 4587.06; ETA: 1.2 minutes\n", + "Step 674999 of 1000000; TPS: 4586.35; ETA: 1.2 minutes\n", + "Step 675999 of 1000000; TPS: 4585.59; ETA: 1.2 minutes\n", + "Step 676999 of 1000000; TPS: 4584.84; ETA: 1.2 minutes\n", + "Step 677999 of 1000000; TPS: 4584.22; ETA: 1.2 minutes\n", + "Step 678999 of 1000000; TPS: 4583.52; ETA: 1.2 minutes\n", + "Step 679999 of 1000000; TPS: 4582.89; ETA: 1.2 minutes\n", + "Step 680999 of 1000000; TPS: 4582.13; ETA: 1.2 minutes\n", + "Step 681999 of 1000000; TPS: 4581.53; ETA: 1.2 minutes\n", + "Step 682999 of 1000000; TPS: 4580.86; ETA: 1.2 minutes\n", + "Step 683999 of 1000000; TPS: 4580.27; ETA: 1.1 minutes\n", + "Step 684999 of 1000000; TPS: 4579.0; ETA: 1.1 minutes\n", + "Step 685999 of 1000000; TPS: 4578.25; ETA: 1.1 minutes\n", + "Step 686999 of 1000000; TPS: 4577.53; ETA: 1.1 minutes\n", + "Step 687999 of 1000000; TPS: 4576.87; ETA: 1.1 minutes\n", + "Step 688999 of 1000000; TPS: 4576.18; ETA: 1.1 minutes\n", + "Step 689999 of 1000000; TPS: 4575.46; ETA: 1.1 minutes\n", + "Step 690999 of 1000000; TPS: 4574.9; ETA: 1.1 minutes\n", + "Step 691999 of 1000000; TPS: 4574.49; ETA: 1.1 minutes\n", + "Step 692999 of 1000000; TPS: 4574.11; ETA: 1.1 minutes\n", + "Step 693999 of 1000000; TPS: 4574.11; ETA: 1.1 minutes\n", + "Step 694999 of 1000000; TPS: 4574.23; ETA: 1.1 minutes\n", + "Step 695999 of 1000000; TPS: 4574.21; ETA: 1.1 minutes\n", + "Step 696999 of 1000000; TPS: 4574.26; ETA: 1.1 minutes\n", + "Step 697999 of 1000000; TPS: 4574.13; ETA: 1.1 minutes\n", + "Step 698999 of 1000000; TPS: 4573.84; ETA: 1.1 minutes\n", + "Step 699999 of 1000000; TPS: 4573.84; ETA: 1.1 minutes\n", + "Step 700999 of 1000000; TPS: 4573.95; ETA: 1.1 minutes\n", + "Step 701999 of 1000000; TPS: 4574.09; ETA: 1.1 minutes\n", + "Step 702999 of 1000000; TPS: 4574.11; ETA: 1.1 minutes\n", + "Step 703999 of 1000000; TPS: 4574.19; ETA: 1.1 minutes\n", + "Step 704999 of 1000000; TPS: 4574.03; ETA: 1.1 minutes\n", + "Step 705999 of 1000000; TPS: 4574.02; ETA: 1.1 minutes\n", + "Step 706999 of 1000000; TPS: 4574.02; ETA: 1.1 minutes\n", + "Step 707999 of 1000000; TPS: 4573.57; ETA: 1.1 minutes\n", + "Step 708999 of 1000000; TPS: 4573.14; ETA: 1.1 minutes\n", + "Step 709999 of 1000000; TPS: 4572.81; ETA: 1.1 minutes\n", + "Step 710999 of 1000000; TPS: 4572.5; ETA: 1.1 minutes\n", + "Step 711999 of 1000000; TPS: 4572.23; ETA: 1.0 minutes\n", + "Step 712999 of 1000000; TPS: 4571.64; ETA: 1.0 minutes\n", + "Step 713999 of 1000000; TPS: 4571.12; ETA: 1.0 minutes\n", + "Step 714999 of 1000000; TPS: 4570.55; ETA: 1.0 minutes\n", + "Step 715999 of 1000000; TPS: 4570.01; ETA: 1.0 minutes\n", + "Step 716999 of 1000000; TPS: 4569.17; ETA: 1.0 minutes\n", + "Step 717999 of 1000000; TPS: 4568.86; ETA: 1.0 minutes\n", + "Step 718999 of 1000000; TPS: 4568.62; ETA: 1.0 minutes\n", + "Step 719999 of 1000000; TPS: 4568.43; ETA: 1.0 minutes\n", + "Step 720999 of 1000000; TPS: 4568.18; ETA: 1.0 minutes\n", + "Step 721999 of 1000000; TPS: 4568.1; ETA: 1.0 minutes\n", + "Step 722999 of 1000000; TPS: 4567.95; ETA: 1.0 minutes\n", + "Step 723999 of 1000000; TPS: 4567.71; ETA: 1.0 minutes\n", + "Step 724999 of 1000000; TPS: 4567.33; ETA: 1.0 minutes\n", + "Step 725999 of 1000000; TPS: 4567.22; ETA: 1.0 minutes\n", + "Step 726999 of 1000000; TPS: 4567.15; ETA: 1.0 minutes\n", + "Step 727999 of 1000000; TPS: 4567.4; ETA: 1.0 minutes\n", + "Step 728999 of 1000000; TPS: 4567.66; ETA: 1.0 minutes\n", + "Step 729999 of 1000000; TPS: 4567.83; ETA: 1.0 minutes\n", + "Step 730999 of 1000000; TPS: 4567.9; ETA: 1.0 minutes\n", + "Step 731999 of 1000000; TPS: 4568.16; ETA: 1.0 minutes\n", + "Step 732999 of 1000000; TPS: 4568.48; ETA: 1.0 minutes\n", + "Step 733999 of 1000000; TPS: 4568.75; ETA: 1.0 minutes\n", + "Step 734999 of 1000000; TPS: 4569.11; ETA: 1.0 minutes\n", + "Step 735999 of 1000000; TPS: 4569.44; ETA: 1.0 minutes\n", + "Step 736999 of 1000000; TPS: 4569.71; ETA: 1.0 minutes\n", + "Step 737999 of 1000000; TPS: 4570.16; ETA: 1.0 minutes\n", + "Step 738999 of 1000000; TPS: 4570.79; ETA: 1.0 minutes\n", + "Step 739999 of 1000000; TPS: 4571.15; ETA: 0.9 minutes\n", + "Step 740999 of 1000000; TPS: 4571.48; ETA: 0.9 minutes\n", + "Step 741999 of 1000000; TPS: 4616.25; ETA: 0.9 minutes\n", + "Step 742999 of 1000000; TPS: 4616.66; ETA: 0.9 minutes\n", + "Step 743999 of 1000000; TPS: 4617.01; ETA: 0.9 minutes\n", + "Step 744999 of 1000000; TPS: 4617.55; ETA: 0.9 minutes\n", + "Step 745999 of 1000000; TPS: 4617.95; ETA: 0.9 minutes\n", + "Step 746999 of 1000000; TPS: 4618.28; ETA: 0.9 minutes\n", + "Step 747999 of 1000000; TPS: 4618.38; ETA: 0.9 minutes\n", + "Step 748999 of 1000000; TPS: 4618.43; ETA: 0.9 minutes\n", + "Step 749999 of 1000000; TPS: 4618.61; ETA: 0.9 minutes\n", + "Step 750999 of 1000000; TPS: 4618.83; ETA: 0.9 minutes\n", + "Step 751999 of 1000000; TPS: 4618.87; ETA: 0.9 minutes\n", + "Step 752999 of 1000000; TPS: 4618.73; ETA: 0.9 minutes\n", + "Step 753999 of 1000000; TPS: 4618.54; ETA: 0.9 minutes\n", + "Step 754999 of 1000000; TPS: 4618.37; ETA: 0.9 minutes\n", + "Step 755999 of 1000000; TPS: 4618.31; ETA: 0.9 minutes\n", + "Step 756999 of 1000000; TPS: 4618.16; ETA: 0.9 minutes\n", + "Step 757999 of 1000000; TPS: 4618.16; ETA: 0.9 minutes\n", + "Step 758999 of 1000000; TPS: 4618.18; ETA: 0.9 minutes\n", + "Step 759999 of 1000000; TPS: 4618.18; ETA: 0.9 minutes\n", + "Step 760999 of 1000000; TPS: 4618.23; ETA: 0.9 minutes\n", + "Step 761999 of 1000000; TPS: 4618.36; ETA: 0.9 minutes\n", + "Step 762999 of 1000000; TPS: 4618.45; ETA: 0.9 minutes\n", + "Step 763999 of 1000000; TPS: 4618.58; ETA: 0.9 minutes\n", + "Step 764999 of 1000000; TPS: 4618.42; ETA: 0.8 minutes\n", + "Step 765999 of 1000000; TPS: 4618.4; ETA: 0.8 minutes\n", + "Step 766999 of 1000000; TPS: 4618.44; ETA: 0.8 minutes\n", + "Step 767999 of 1000000; TPS: 4618.4; ETA: 0.8 minutes\n", + "Step 768999 of 1000000; TPS: 4618.27; ETA: 0.8 minutes\n", + "Step 769999 of 1000000; TPS: 4618.22; ETA: 0.8 minutes\n", + "Step 770999 of 1000000; TPS: 4617.24; ETA: 0.8 minutes\n", + "Step 771999 of 1000000; TPS: 4617.2; ETA: 0.8 minutes\n", + "Step 772999 of 1000000; TPS: 4616.98; ETA: 0.8 minutes\n", + "Step 773999 of 1000000; TPS: 4616.82; ETA: 0.8 minutes\n", + "Step 774999 of 1000000; TPS: 4616.82; ETA: 0.8 minutes\n", + "Step 775999 of 1000000; TPS: 4616.77; ETA: 0.8 minutes\n", + "Step 776999 of 1000000; TPS: 4616.91; ETA: 0.8 minutes\n", + "Step 777999 of 1000000; TPS: 4617.21; ETA: 0.8 minutes\n", + "Step 778999 of 1000000; TPS: 4617.58; ETA: 0.8 minutes\n", + "Step 779999 of 1000000; TPS: 4617.86; ETA: 0.8 minutes\n", + "Step 780999 of 1000000; TPS: 4618.06; ETA: 0.8 minutes\n", + "Step 781999 of 1000000; TPS: 4618.36; ETA: 0.8 minutes\n", + "Step 782999 of 1000000; TPS: 4618.87; ETA: 0.8 minutes\n", + "Step 783999 of 1000000; TPS: 4619.38; ETA: 0.8 minutes\n", + "Step 784999 of 1000000; TPS: 4619.78; ETA: 0.8 minutes\n", + "Step 785999 of 1000000; TPS: 4620.21; ETA: 0.8 minutes\n", + "Step 786999 of 1000000; TPS: 4620.47; ETA: 0.8 minutes\n", + "Step 787999 of 1000000; TPS: 4620.9; ETA: 0.8 minutes\n", + "Step 788999 of 1000000; TPS: 4621.3; ETA: 0.8 minutes\n", + "Step 789999 of 1000000; TPS: 4621.43; ETA: 0.8 minutes\n", + "Step 790999 of 1000000; TPS: 4621.37; ETA: 0.8 minutes\n", + "Step 791999 of 1000000; TPS: 4621.44; ETA: 0.8 minutes\n", + "Step 792999 of 1000000; TPS: 4621.51; ETA: 0.7 minutes\n", + "Step 793999 of 1000000; TPS: 4621.56; ETA: 0.7 minutes\n", + "Step 794999 of 1000000; TPS: 4621.57; ETA: 0.7 minutes\n", + "Step 795999 of 1000000; TPS: 4621.5; ETA: 0.7 minutes\n", + "Step 796999 of 1000000; TPS: 4621.51; ETA: 0.7 minutes\n", + "Step 797999 of 1000000; TPS: 4621.56; ETA: 0.7 minutes\n", + "Step 798999 of 1000000; TPS: 4621.57; ETA: 0.7 minutes\n", + "Step 799999 of 1000000; TPS: 4621.54; ETA: 0.7 minutes\n", + "Step 800999 of 1000000; TPS: 4621.52; ETA: 0.7 minutes\n", + "Step 801999 of 1000000; TPS: 4621.49; ETA: 0.7 minutes\n", + "Step 802999 of 1000000; TPS: 4621.37; ETA: 0.7 minutes\n", + "Step 803999 of 1000000; TPS: 4621.38; ETA: 0.7 minutes\n", + "Step 804999 of 1000000; TPS: 4621.37; ETA: 0.7 minutes\n", + "Step 805999 of 1000000; TPS: 4621.19; ETA: 0.7 minutes\n", + "Step 806999 of 1000000; TPS: 4621.11; ETA: 0.7 minutes\n", + "Step 807999 of 1000000; TPS: 4620.97; ETA: 0.7 minutes\n", + "Step 808999 of 1000000; TPS: 4620.92; ETA: 0.7 minutes\n", + "Step 809999 of 1000000; TPS: 4620.93; ETA: 0.7 minutes\n", + "Step 810999 of 1000000; TPS: 4620.91; ETA: 0.7 minutes\n", + "Step 811999 of 1000000; TPS: 4620.87; ETA: 0.7 minutes\n", + "Step 812999 of 1000000; TPS: 4620.97; ETA: 0.7 minutes\n", + "Step 813999 of 1000000; TPS: 4621.14; ETA: 0.7 minutes\n", + "Step 814999 of 1000000; TPS: 4621.28; ETA: 0.7 minutes\n", + "Step 815999 of 1000000; TPS: 4621.45; ETA: 0.7 minutes\n", + "Step 816999 of 1000000; TPS: 4621.6; ETA: 0.7 minutes\n", + "Step 817999 of 1000000; TPS: 4621.02; ETA: 0.7 minutes\n", + "Step 818999 of 1000000; TPS: 4621.41; ETA: 0.7 minutes\n", + "Step 819999 of 1000000; TPS: 4621.78; ETA: 0.6 minutes\n", + "Step 820999 of 1000000; TPS: 4622.13; ETA: 0.6 minutes\n", + "Step 821999 of 1000000; TPS: 4622.5; ETA: 0.6 minutes\n", + "Step 822999 of 1000000; TPS: 4623.05; ETA: 0.6 minutes\n", + "Step 823999 of 1000000; TPS: 4623.61; ETA: 0.6 minutes\n", + "Step 824999 of 1000000; TPS: 4624.14; ETA: 0.6 minutes\n", + "Step 825999 of 1000000; TPS: 4624.66; ETA: 0.6 minutes\n", + "Step 826999 of 1000000; TPS: 4625.14; ETA: 0.6 minutes\n", + "Step 827999 of 1000000; TPS: 4625.6; ETA: 0.6 minutes\n", + "Step 828999 of 1000000; TPS: 4625.8; ETA: 0.6 minutes\n", + "Step 829999 of 1000000; TPS: 4626.03; ETA: 0.6 minutes\n", + "Step 830999 of 1000000; TPS: 4625.88; ETA: 0.6 minutes\n", + "Step 831999 of 1000000; TPS: 4626.08; ETA: 0.6 minutes\n", + "Step 832999 of 1000000; TPS: 4626.14; ETA: 0.6 minutes\n", + "Step 833999 of 1000000; TPS: 4626.27; ETA: 0.6 minutes\n", + "Step 834999 of 1000000; TPS: 4626.36; ETA: 0.6 minutes\n", + "Step 835999 of 1000000; TPS: 4626.51; ETA: 0.6 minutes\n", + "Step 836999 of 1000000; TPS: 4626.64; ETA: 0.6 minutes\n", + "Step 837999 of 1000000; TPS: 4626.77; ETA: 0.6 minutes\n", + "Step 838999 of 1000000; TPS: 4626.95; ETA: 0.6 minutes\n", + "Step 839999 of 1000000; TPS: 4627.0; ETA: 0.6 minutes\n", + "Step 840999 of 1000000; TPS: 4627.14; ETA: 0.6 minutes\n", + "Step 841999 of 1000000; TPS: 4627.33; ETA: 0.6 minutes\n", + "Step 842999 of 1000000; TPS: 4627.52; ETA: 0.6 minutes\n", + "Step 843999 of 1000000; TPS: 4627.7; ETA: 0.6 minutes\n", + "Step 844999 of 1000000; TPS: 4627.9; ETA: 0.6 minutes\n", + "Step 845999 of 1000000; TPS: 4628.11; ETA: 0.6 minutes\n", + "Step 846999 of 1000000; TPS: 4628.1; ETA: 0.6 minutes\n", + "Step 847999 of 1000000; TPS: 4628.16; ETA: 0.5 minutes\n", + "Step 848999 of 1000000; TPS: 4628.31; ETA: 0.5 minutes\n", + "Step 849999 of 1000000; TPS: 4628.51; ETA: 0.5 minutes\n", + "Step 850999 of 1000000; TPS: 4628.73; ETA: 0.5 minutes\n", + "Step 851999 of 1000000; TPS: 4628.78; ETA: 0.5 minutes\n", + "Step 852999 of 1000000; TPS: 4629.09; ETA: 0.5 minutes\n", + "Step 853999 of 1000000; TPS: 4629.62; ETA: 0.5 minutes\n", + "Step 854999 of 1000000; TPS: 4630.23; ETA: 0.5 minutes\n", + "Step 855999 of 1000000; TPS: 4630.76; ETA: 0.5 minutes\n", + "Step 856999 of 1000000; TPS: 4631.33; ETA: 0.5 minutes\n", + "Step 857999 of 1000000; TPS: 4631.84; ETA: 0.5 minutes\n", + "Step 858999 of 1000000; TPS: 4632.38; ETA: 0.5 minutes\n", + "Step 859999 of 1000000; TPS: 4632.92; ETA: 0.5 minutes\n", + "Step 860999 of 1000000; TPS: 4633.42; ETA: 0.5 minutes\n", + "Step 861999 of 1000000; TPS: 4633.9; ETA: 0.5 minutes\n", + "Step 862999 of 1000000; TPS: 4634.4; ETA: 0.5 minutes\n", + "Step 863999 of 1000000; TPS: 4634.99; ETA: 0.5 minutes\n", + "Step 864999 of 1000000; TPS: 4635.62; ETA: 0.5 minutes\n", + "Step 865999 of 1000000; TPS: 4636.32; ETA: 0.5 minutes\n", + "Step 866999 of 1000000; TPS: 4636.49; ETA: 0.5 minutes\n", + "Step 867999 of 1000000; TPS: 4637.03; ETA: 0.5 minutes\n", + "Step 868999 of 1000000; TPS: 4637.26; ETA: 0.5 minutes\n", + "Step 869999 of 1000000; TPS: 4637.59; ETA: 0.5 minutes\n", + "Step 870999 of 1000000; TPS: 4637.89; ETA: 0.5 minutes\n", + "Step 871999 of 1000000; TPS: 4638.17; ETA: 0.5 minutes\n", + "Step 872999 of 1000000; TPS: 4638.37; ETA: 0.5 minutes\n", + "Step 873999 of 1000000; TPS: 4638.67; ETA: 0.5 minutes\n", + "Step 874999 of 1000000; TPS: 4638.97; ETA: 0.4 minutes\n", + "Step 875999 of 1000000; TPS: 4639.32; ETA: 0.4 minutes\n", + "Step 876999 of 1000000; TPS: 4639.53; ETA: 0.4 minutes\n", + "Step 877999 of 1000000; TPS: 4639.87; ETA: 0.4 minutes\n", + "Step 878999 of 1000000; TPS: 4640.2; ETA: 0.4 minutes\n", + "Step 879999 of 1000000; TPS: 4640.35; ETA: 0.4 minutes\n", + "Step 880999 of 1000000; TPS: 4640.72; ETA: 0.4 minutes\n", + "Step 881999 of 1000000; TPS: 4641.06; ETA: 0.4 minutes\n", + "Step 882999 of 1000000; TPS: 4641.36; ETA: 0.4 minutes\n", + "Step 883999 of 1000000; TPS: 4641.58; ETA: 0.4 minutes\n", + "Step 884999 of 1000000; TPS: 4641.9; ETA: 0.4 minutes\n", + "Step 885999 of 1000000; TPS: 4641.97; ETA: 0.4 minutes\n", + "Step 886999 of 1000000; TPS: 4642.23; ETA: 0.4 minutes\n", + "Step 887999 of 1000000; TPS: 4642.59; ETA: 0.4 minutes\n", + "Step 888999 of 1000000; TPS: 4642.82; ETA: 0.4 minutes\n", + "Step 889999 of 1000000; TPS: 4643.14; ETA: 0.4 minutes\n", + "Step 890999 of 1000000; TPS: 4643.72; ETA: 0.4 minutes\n", + "Step 891999 of 1000000; TPS: 4644.37; ETA: 0.4 minutes\n", + "Step 892999 of 1000000; TPS: 4644.99; ETA: 0.4 minutes\n", + "Step 893999 of 1000000; TPS: 4645.69; ETA: 0.4 minutes\n", + "Step 894999 of 1000000; TPS: 4646.36; ETA: 0.4 minutes\n", + "Step 895999 of 1000000; TPS: 4647.05; ETA: 0.4 minutes\n", + "Step 896999 of 1000000; TPS: 4684.36; ETA: 0.4 minutes\n", + "Step 897999 of 1000000; TPS: 4684.98; ETA: 0.4 minutes\n", + "Step 898999 of 1000000; TPS: 4685.65; ETA: 0.4 minutes\n", + "Step 899999 of 1000000; TPS: 4686.28; ETA: 0.4 minutes\n", + "Step 900999 of 1000000; TPS: 4686.86; ETA: 0.4 minutes\n", + "Step 901999 of 1000000; TPS: 4687.54; ETA: 0.3 minutes\n", + "Step 902999 of 1000000; TPS: 4688.17; ETA: 0.3 minutes\n", + "Step 903999 of 1000000; TPS: 4688.81; ETA: 0.3 minutes\n", + "Step 904999 of 1000000; TPS: 4689.39; ETA: 0.3 minutes\n", + "Step 905999 of 1000000; TPS: 4690.04; ETA: 0.3 minutes\n", + "Step 906999 of 1000000; TPS: 4690.65; ETA: 0.3 minutes\n", + "Step 907999 of 1000000; TPS: 4691.31; ETA: 0.3 minutes\n", + "Step 908999 of 1000000; TPS: 4691.91; ETA: 0.3 minutes\n", + "Step 909999 of 1000000; TPS: 4692.45; ETA: 0.3 minutes\n", + "Step 910999 of 1000000; TPS: 4693.0; ETA: 0.3 minutes\n", + "Step 911999 of 1000000; TPS: 4693.49; ETA: 0.3 minutes\n", + "Step 912999 of 1000000; TPS: 4694.0; ETA: 0.3 minutes\n", + "Step 913999 of 1000000; TPS: 4694.51; ETA: 0.3 minutes\n", + "Step 914999 of 1000000; TPS: 4695.08; ETA: 0.3 minutes\n", + "Step 915999 of 1000000; TPS: 4695.65; ETA: 0.3 minutes\n", + "Step 916999 of 1000000; TPS: 4696.19; ETA: 0.3 minutes\n", + "Step 917999 of 1000000; TPS: 4696.47; ETA: 0.3 minutes\n", + "Step 918999 of 1000000; TPS: 4696.84; ETA: 0.3 minutes\n", + "Step 919999 of 1000000; TPS: 4697.13; ETA: 0.3 minutes\n", + "Step 920999 of 1000000; TPS: 4697.4; ETA: 0.3 minutes\n", + "Step 921999 of 1000000; TPS: 4697.77; ETA: 0.3 minutes\n", + "Step 922999 of 1000000; TPS: 4698.06; ETA: 0.3 minutes\n", + "Step 923999 of 1000000; TPS: 4698.4; ETA: 0.3 minutes\n", + "Step 924999 of 1000000; TPS: 4698.78; ETA: 0.3 minutes\n", + "Step 925999 of 1000000; TPS: 4698.49; ETA: 0.3 minutes\n", + "Step 926999 of 1000000; TPS: 4698.83; ETA: 0.3 minutes\n", + "Step 927999 of 1000000; TPS: 4699.19; ETA: 0.3 minutes\n", + "Step 928999 of 1000000; TPS: 4699.45; ETA: 0.3 minutes\n", + "Step 929999 of 1000000; TPS: 4699.69; ETA: 0.2 minutes\n", + "Step 930999 of 1000000; TPS: 4700.08; ETA: 0.2 minutes\n", + "Step 931999 of 1000000; TPS: 4700.38; ETA: 0.2 minutes\n", + "Step 932999 of 1000000; TPS: 4700.66; ETA: 0.2 minutes\n", + "Step 933999 of 1000000; TPS: 4700.94; ETA: 0.2 minutes\n", + "Step 934999 of 1000000; TPS: 4701.2; ETA: 0.2 minutes\n", + "Step 935999 of 1000000; TPS: 4701.53; ETA: 0.2 minutes\n", + "Step 936999 of 1000000; TPS: 4701.88; ETA: 0.2 minutes\n", + "Step 937999 of 1000000; TPS: 4702.24; ETA: 0.2 minutes\n", + "Step 938999 of 1000000; TPS: 4702.34; ETA: 0.2 minutes\n", + "Step 939999 of 1000000; TPS: 4702.61; ETA: 0.2 minutes\n", + "Step 940999 of 1000000; TPS: 4702.99; ETA: 0.2 minutes\n", + "Step 941999 of 1000000; TPS: 4703.36; ETA: 0.2 minutes\n", + "Step 942999 of 1000000; TPS: 4703.96; ETA: 0.2 minutes\n", + "Step 943999 of 1000000; TPS: 4704.69; ETA: 0.2 minutes\n", + "Step 944999 of 1000000; TPS: 4705.33; ETA: 0.2 minutes\n", + "Step 945999 of 1000000; TPS: 4706.08; ETA: 0.2 minutes\n", + "Step 946999 of 1000000; TPS: 4706.68; ETA: 0.2 minutes\n", + "Step 947999 of 1000000; TPS: 4707.3; ETA: 0.2 minutes\n", + "Step 948999 of 1000000; TPS: 4707.95; ETA: 0.2 minutes\n", + "Step 949999 of 1000000; TPS: 4708.64; ETA: 0.2 minutes\n", + "Step 950999 of 1000000; TPS: 4709.3; ETA: 0.2 minutes\n", + "Step 951999 of 1000000; TPS: 4709.99; ETA: 0.2 minutes\n", + "Step 952999 of 1000000; TPS: 4710.71; ETA: 0.2 minutes\n", + "Step 953999 of 1000000; TPS: 4711.35; ETA: 0.2 minutes\n", + "Step 954999 of 1000000; TPS: 4712.04; ETA: 0.2 minutes\n", + "Step 955999 of 1000000; TPS: 4712.67; ETA: 0.2 minutes\n", + "Step 956999 of 1000000; TPS: 4713.39; ETA: 0.2 minutes\n", + "Step 957999 of 1000000; TPS: 4714.05; ETA: 0.1 minutes\n", + "Step 958999 of 1000000; TPS: 4714.68; ETA: 0.1 minutes\n", + "Step 959999 of 1000000; TPS: 4715.0; ETA: 0.1 minutes\n", + "Step 960999 of 1000000; TPS: 4715.34; ETA: 0.1 minutes\n", + "Step 961999 of 1000000; TPS: 4715.64; ETA: 0.1 minutes\n", + "Step 962999 of 1000000; TPS: 4716.0; ETA: 0.1 minutes\n", + "Step 963999 of 1000000; TPS: 4716.33; ETA: 0.1 minutes\n", + "Step 964999 of 1000000; TPS: 4716.7; ETA: 0.1 minutes\n", + "Step 965999 of 1000000; TPS: 4717.09; ETA: 0.1 minutes\n", + "Step 966999 of 1000000; TPS: 4717.44; ETA: 0.1 minutes\n", + "Step 967999 of 1000000; TPS: 4717.86; ETA: 0.1 minutes\n", + "Step 968999 of 1000000; TPS: 4718.26; ETA: 0.1 minutes\n", + "Step 969999 of 1000000; TPS: 4718.48; ETA: 0.1 minutes\n", + "Step 970999 of 1000000; TPS: 4718.83; ETA: 0.1 minutes\n", + "Step 971999 of 1000000; TPS: 4719.18; ETA: 0.1 minutes\n", + "Step 972999 of 1000000; TPS: 4719.52; ETA: 0.1 minutes\n", + "Step 973999 of 1000000; TPS: 4719.89; ETA: 0.1 minutes\n", + "Step 974999 of 1000000; TPS: 4720.16; ETA: 0.1 minutes\n", + "Step 975999 of 1000000; TPS: 4720.44; ETA: 0.1 minutes\n", + "Step 976999 of 1000000; TPS: 4720.75; ETA: 0.1 minutes\n", + "Step 977999 of 1000000; TPS: 4720.75; ETA: 0.1 minutes\n", + "Step 978999 of 1000000; TPS: 4720.93; ETA: 0.1 minutes\n", + "Step 979999 of 1000000; TPS: 4721.12; ETA: 0.1 minutes\n", + "Step 980999 of 1000000; TPS: 4721.26; ETA: 0.1 minutes\n", + "Step 981999 of 1000000; TPS: 4721.36; ETA: 0.1 minutes\n", + "Step 982999 of 1000000; TPS: 4721.47; ETA: 0.1 minutes\n", + "Step 983999 of 1000000; TPS: 4721.6; ETA: 0.1 minutes\n", + "Step 984999 of 1000000; TPS: 4721.44; ETA: 0.1 minutes\n", + "Step 985999 of 1000000; TPS: 4721.77; ETA: 0.0 minutes\n", + "Step 986999 of 1000000; TPS: 4722.26; ETA: 0.0 minutes\n", + "Step 987999 of 1000000; TPS: 4722.68; ETA: 0.0 minutes\n", + "Step 988999 of 1000000; TPS: 4723.19; ETA: 0.0 minutes\n", + "Step 989999 of 1000000; TPS: 4723.78; ETA: 0.0 minutes\n", + "Step 990999 of 1000000; TPS: 4724.38; ETA: 0.0 minutes\n", + "Step 991999 of 1000000; TPS: 4725.03; ETA: 0.0 minutes\n", + "Step 992999 of 1000000; TPS: 4725.61; ETA: 0.0 minutes\n", + "Step 993999 of 1000000; TPS: 4726.22; ETA: 0.0 minutes\n", + "Step 994999 of 1000000; TPS: 4726.86; ETA: 0.0 minutes\n", + "Step 995999 of 1000000; TPS: 4727.52; ETA: 0.0 minutes\n", + "Step 996999 of 1000000; TPS: 4728.1; ETA: 0.0 minutes\n", + "Step 997999 of 1000000; TPS: 4728.78; ETA: 0.0 minutes\n", + "Step 998999 of 1000000; TPS: 4729.41; ETA: 0.0 minutes\n", + "Step 999999 of 1000000; TPS: 4730.02; ETA: 0.0 minutes\n" + ] + } + ], "source": [ - "!mv $gsd $log ../analysis/" + "sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=device, dt = dt, \n", + " gsd_write_freq=int(write_frequency), log_file_name = log, gsd_file_name = gsd) # initializing simulation\n", + "sim.run_update_volume(final_box_lengths=target_box, kT=6.0, n_steps=shrink_steps,tau_kt=100*sim.dt,period=10,thermalize_particles=True) # shrink simulation run\n", + "sim.run_NVT(n_steps=steps, kT=temp, tau_kt=dt*100) # simulation run\n", + "sim.flush_writers() # updating data files\n", + "del sim # drop references so files are closed" ] }, { "cell_type": "code", - "execution_count": null, - "id": "08523f6e-ef2f-4c46-bdc2-6995b73a6266", + "execution_count": 12, + "id": "a8af76ae-efe0-4912-8417-612048485a77", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mv: cannot stat '20_10mer5f_0.005dt.txt': No such file or directory\n", + "mv: cannot stat '20_10mer5f_0.005dt.gsd': No such file or directory\n", + "mv: cannot stat '20_10mer5f_0.005dt_start.txt': No such file or directory\n" + ] + } + ], + "source": [ + "!mv \"{log}\" \"{gsd}\" \"{start_file}\" ../analysis/" + ] } ], "metadata": { From 9f497fc6bc4ff300ace1f17669328e066ac3300e Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Wed, 29 Oct 2025 14:53:27 -0600 Subject: [PATCH 02/10] time logging implementedw --- sims/Onboarding.ipynb | 3029 +++++++++++++++++++++-------------------- 1 file changed, 1523 insertions(+), 1506 deletions(-) diff --git a/sims/Onboarding.ipynb b/sims/Onboarding.ipynb index cd7af7d..040b59f 100644 --- a/sims/Onboarding.ipynb +++ b/sims/Onboarding.ipynb @@ -33,6 +33,7 @@ ], "source": [ "import flowermd # wrapper we use on top of hoomd\n", + "import time # for measuring simulation time\n", "import gsd # to work with simulation data files\n", "import gsd.hoomd # to work with simulation data files directly coming from hoomd sims\n", "import hoomd # MD simulation engine used for initializing and running sims\n", @@ -250,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "id": "64f9fae9-a3b9-418e-a03b-681b754e1b03", "metadata": {}, "outputs": [ @@ -266,1536 +267,1552 @@ "output_type": "stream", "text": [ "Initializing simulation state from a gsd.hoomd.Frame.\n", - "Step 1000 of 500000; TPS: 1245.42; ETA: 6.7 minutes\n", - "Step 2000 of 500000; TPS: 2035.58; ETA: 4.1 minutes\n", - "Step 3000 of 500000; TPS: 2590.96; ETA: 3.2 minutes\n", - "Step 4000 of 500000; TPS: 2757.65; ETA: 3.0 minutes\n", - "Step 5000 of 500000; TPS: 3066.49; ETA: 2.7 minutes\n", - "Step 6000 of 500000; TPS: 3325.67; ETA: 2.5 minutes\n", - "Step 7000 of 500000; TPS: 3535.74; ETA: 2.3 minutes\n", - "Step 8000 of 500000; TPS: 3698.35; ETA: 2.2 minutes\n", - "Step 9000 of 500000; TPS: 3845.91; ETA: 2.1 minutes\n", - "Step 10000 of 500000; TPS: 3830.63; ETA: 2.1 minutes\n", - "Step 11000 of 500000; TPS: 3837.42; ETA: 2.1 minutes\n", - "Step 12000 of 500000; TPS: 3960.79; ETA: 2.1 minutes\n", - "Step 13000 of 500000; TPS: 4074.39; ETA: 2.0 minutes\n", - "Step 14000 of 500000; TPS: 4173.35; ETA: 1.9 minutes\n", - "Step 15000 of 500000; TPS: 4263.12; ETA: 1.9 minutes\n", - "Step 16000 of 500000; TPS: 4338.67; ETA: 1.9 minutes\n", - "Step 17000 of 500000; TPS: 4402.25; ETA: 1.8 minutes\n", - "Step 18000 of 500000; TPS: 4333.33; ETA: 1.9 minutes\n", - "Step 19000 of 500000; TPS: 4381.38; ETA: 1.8 minutes\n", - "Step 20000 of 500000; TPS: 4433.22; ETA: 1.8 minutes\n", - "Step 21000 of 500000; TPS: 4480.43; ETA: 1.8 minutes\n", - "Step 22000 of 500000; TPS: 4522.22; ETA: 1.8 minutes\n", - "Step 23000 of 500000; TPS: 4563.82; ETA: 1.7 minutes\n", - "Step 24000 of 500000; TPS: 4595.5; ETA: 1.7 minutes\n", - "Step 25000 of 500000; TPS: 4534.11; ETA: 1.7 minutes\n", - "Step 26000 of 500000; TPS: 4566.83; ETA: 1.7 minutes\n", - "Step 27000 of 500000; TPS: 4601.97; ETA: 1.7 minutes\n", - "Step 28000 of 500000; TPS: 4637.4; ETA: 1.7 minutes\n", - "Step 29000 of 500000; TPS: 4666.36; ETA: 1.7 minutes\n", - "Step 30000 of 500000; TPS: 4696.06; ETA: 1.7 minutes\n", - "Step 31000 of 500000; TPS: 4717.61; ETA: 1.7 minutes\n", - "Step 32000 of 500000; TPS: 4739.03; ETA: 1.6 minutes\n", - "Step 33000 of 500000; TPS: 4693.77; ETA: 1.7 minutes\n", - "Step 34000 of 500000; TPS: 4716.94; ETA: 1.6 minutes\n", - "Step 35000 of 500000; TPS: 4742.27; ETA: 1.6 minutes\n", - "Step 36000 of 500000; TPS: 4766.65; ETA: 1.6 minutes\n", - "Step 37000 of 500000; TPS: 4790.01; ETA: 1.6 minutes\n", - "Step 38000 of 500000; TPS: 4814.91; ETA: 1.6 minutes\n", - "Step 39000 of 500000; TPS: 4840.21; ETA: 1.6 minutes\n", - "Step 40000 of 500000; TPS: 4810.98; ETA: 1.6 minutes\n", - "Step 41000 of 500000; TPS: 4832.69; ETA: 1.6 minutes\n", - "Step 42000 of 500000; TPS: 4855.4; ETA: 1.6 minutes\n", - "Step 43000 of 500000; TPS: 4873.41; ETA: 1.6 minutes\n", - "Step 44000 of 500000; TPS: 4877.66; ETA: 1.6 minutes\n", - "Step 45000 of 500000; TPS: 4899.02; ETA: 1.5 minutes\n", - "Step 46000 of 500000; TPS: 4914.96; ETA: 1.5 minutes\n", - "Step 47000 of 500000; TPS: 4890.04; ETA: 1.5 minutes\n", - "Step 48000 of 500000; TPS: 4912.67; ETA: 1.5 minutes\n", - "Step 49000 of 500000; TPS: 4935.38; ETA: 1.5 minutes\n", - "Step 50000 of 500000; TPS: 4956.56; ETA: 1.5 minutes\n", - "Step 51000 of 500000; TPS: 4977.65; ETA: 1.5 minutes\n", - "Step 52000 of 500000; TPS: 4998.59; ETA: 1.5 minutes\n", - "Step 53000 of 500000; TPS: 5016.34; ETA: 1.5 minutes\n", - "Step 54000 of 500000; TPS: 5000.38; ETA: 1.5 minutes\n", - "Step 55000 of 500000; TPS: 5017.03; ETA: 1.5 minutes\n", - "Step 56000 of 500000; TPS: 5944.48; ETA: 1.2 minutes\n", - "Step 57000 of 500000; TPS: 5947.9; ETA: 1.2 minutes\n", - "Step 58000 of 500000; TPS: 5950.03; ETA: 1.2 minutes\n", - "Step 59000 of 500000; TPS: 5951.43; ETA: 1.2 minutes\n", - "Step 60000 of 500000; TPS: 5952.72; ETA: 1.2 minutes\n", - "Step 61000 of 500000; TPS: 5905.36; ETA: 1.2 minutes\n", - "Step 62000 of 500000; TPS: 5908.09; ETA: 1.2 minutes\n", - "Step 63000 of 500000; TPS: 5911.71; ETA: 1.2 minutes\n", - "Step 64000 of 500000; TPS: 5915.1; ETA: 1.2 minutes\n", - "Step 65000 of 500000; TPS: 5917.88; ETA: 1.2 minutes\n", - "Step 66000 of 500000; TPS: 5919.92; ETA: 1.2 minutes\n", - "Step 67000 of 500000; TPS: 5921.83; ETA: 1.2 minutes\n", - "Step 68000 of 500000; TPS: 5888.18; ETA: 1.2 minutes\n", - "Step 69000 of 500000; TPS: 5890.7; ETA: 1.2 minutes\n", - "Step 70000 of 500000; TPS: 5888.63; ETA: 1.2 minutes\n", - "Step 71000 of 500000; TPS: 5881.78; ETA: 1.2 minutes\n", - "Step 72000 of 500000; TPS: 5877.82; ETA: 1.2 minutes\n", - "Step 73000 of 500000; TPS: 5875.05; ETA: 1.2 minutes\n", - "Step 74000 of 500000; TPS: 5871.93; ETA: 1.2 minutes\n", - "Step 75000 of 500000; TPS: 5830.85; ETA: 1.2 minutes\n", - "Step 76000 of 500000; TPS: 5826.8; ETA: 1.2 minutes\n", - "Step 77000 of 500000; TPS: 5821.56; ETA: 1.2 minutes\n", - "Step 78000 of 500000; TPS: 5817.14; ETA: 1.2 minutes\n", - "Step 79000 of 500000; TPS: 5813.46; ETA: 1.2 minutes\n", - "Step 80000 of 500000; TPS: 5809.98; ETA: 1.2 minutes\n", - "Step 81000 of 500000; TPS: 5806.17; ETA: 1.2 minutes\n", - "Step 82000 of 500000; TPS: 5775.45; ETA: 1.2 minutes\n", - "Step 83000 of 500000; TPS: 5772.28; ETA: 1.2 minutes\n", - "Step 84000 of 500000; TPS: 5770.3; ETA: 1.2 minutes\n", - "Step 85000 of 500000; TPS: 5765.45; ETA: 1.2 minutes\n", - "Step 86000 of 500000; TPS: 5761.85; ETA: 1.2 minutes\n", - "Step 87000 of 500000; TPS: 5758.41; ETA: 1.2 minutes\n", - "Step 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1.2 minutes\n", - "Step 105000 of 500000; TPS: 5624.73; ETA: 1.2 minutes\n", - "Step 106000 of 500000; TPS: 5622.69; ETA: 1.2 minutes\n", - "Step 107000 of 500000; TPS: 5613.14; ETA: 1.2 minutes\n", - "Step 108000 of 500000; TPS: 5611.01; ETA: 1.2 minutes\n", - "Step 109000 of 500000; TPS: 5607.54; ETA: 1.2 minutes\n", - "Step 110000 of 500000; TPS: 5592.94; ETA: 1.2 minutes\n", - "Step 111000 of 500000; TPS: 5593.31; ETA: 1.2 minutes\n", - "Step 112000 of 500000; TPS: 5591.27; ETA: 1.2 minutes\n", - "Step 113000 of 500000; TPS: 5588.18; ETA: 1.2 minutes\n", - "Step 114000 of 500000; TPS: 5585.64; ETA: 1.2 minutes\n", - "Step 115000 of 500000; TPS: 5582.68; ETA: 1.1 minutes\n", - "Step 116000 of 500000; TPS: 5579.64; ETA: 1.1 minutes\n", - "Step 117000 of 500000; TPS: 5560.58; ETA: 1.1 minutes\n", - "Step 118000 of 500000; TPS: 5558.36; ETA: 1.1 minutes\n", - "Step 119000 of 500000; TPS: 5555.48; ETA: 1.1 minutes\n", - "Step 120000 of 500000; TPS: 5551.5; ETA: 1.1 minutes\n", - "Step 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5471.75; ETA: 1.1 minutes\n", - "Step 138000 of 500000; TPS: 5459.19; ETA: 1.1 minutes\n", - "Step 139000 of 500000; TPS: 5455.61; ETA: 1.1 minutes\n", - "Step 140000 of 500000; TPS: 5452.71; ETA: 1.1 minutes\n", - "Step 141000 of 500000; TPS: 5449.45; ETA: 1.1 minutes\n", - "Step 142000 of 500000; TPS: 5446.68; ETA: 1.1 minutes\n", - "Step 143000 of 500000; TPS: 5443.95; ETA: 1.1 minutes\n", - "Step 144000 of 500000; TPS: 5440.92; ETA: 1.1 minutes\n", - "Step 145000 of 500000; TPS: 5427.16; ETA: 1.1 minutes\n", - "Step 146000 of 500000; TPS: 5424.82; ETA: 1.1 minutes\n", - "Step 147000 of 500000; TPS: 5422.68; ETA: 1.1 minutes\n", - "Step 148000 of 500000; TPS: 5417.83; ETA: 1.1 minutes\n", - "Step 149000 of 500000; TPS: 5415.33; ETA: 1.1 minutes\n", - "Step 150000 of 500000; TPS: 5413.87; ETA: 1.1 minutes\n", - "Step 151000 of 500000; TPS: 5414.22; ETA: 1.1 minutes\n", - "Step 152000 of 500000; TPS: 5414.32; ETA: 1.1 minutes\n", - "Step 153000 of 500000; TPS: 5409.11; ETA: 1.1 minutes\n", - "Step 154000 of 500000; TPS: 5406.1; ETA: 1.1 minutes\n", - "Step 155000 of 500000; TPS: 5403.72; ETA: 1.1 minutes\n", - "Step 156000 of 500000; TPS: 5400.74; ETA: 1.1 minutes\n", - "Step 157000 of 500000; TPS: 5385.95; ETA: 1.1 minutes\n", - "Step 158000 of 500000; TPS: 5383.46; ETA: 1.1 minutes\n", - "Step 159000 of 500000; TPS: 5381.34; ETA: 1.1 minutes\n", - "Step 160000 of 500000; TPS: 5368.91; ETA: 1.1 minutes\n", - "Step 161000 of 500000; TPS: 5366.24; ETA: 1.1 minutes\n", - "Step 162000 of 500000; TPS: 5364.34; ETA: 1.1 minutes\n", - "Step 163000 of 500000; TPS: 5362.51; ETA: 1.0 minutes\n", - "Step 164000 of 500000; TPS: 5360.45; ETA: 1.0 minutes\n", - "Step 165000 of 500000; TPS: 5358.49; ETA: 1.0 minutes\n", - "Step 166000 of 500000; TPS: 5356.96; ETA: 1.0 minutes\n", - "Step 167000 of 500000; TPS: 5350.19; ETA: 1.0 minutes\n", - "Step 168000 of 500000; TPS: 5348.78; ETA: 1.0 minutes\n", - "Step 169000 of 500000; TPS: 5347.11; ETA: 1.0 minutes\n", - "Step 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5325.61; ETA: 1.0 minutes\n", - "Step 187000 of 500000; TPS: 5326.75; ETA: 1.0 minutes\n", - "Step 188000 of 500000; TPS: 5324.22; ETA: 1.0 minutes\n", - "Step 189000 of 500000; TPS: 5324.47; ETA: 1.0 minutes\n", - "Step 190000 of 500000; TPS: 5325.19; ETA: 1.0 minutes\n", - "Step 191000 of 500000; TPS: 5326.21; ETA: 1.0 minutes\n", - "Step 192000 of 500000; TPS: 5327.18; ETA: 1.0 minutes\n", - "Step 193000 of 500000; TPS: 5328.23; ETA: 1.0 minutes\n", - "Step 194000 of 500000; TPS: 5329.14; ETA: 1.0 minutes\n", - "Step 195000 of 500000; TPS: 5325.53; ETA: 1.0 minutes\n", - "Step 196000 of 500000; TPS: 5325.32; ETA: 1.0 minutes\n", - "Step 197000 of 500000; TPS: 5324.32; ETA: 0.9 minutes\n", - "Step 198000 of 500000; TPS: 5323.61; ETA: 0.9 minutes\n", - "Step 199000 of 500000; TPS: 5322.94; ETA: 0.9 minutes\n", - "Step 200000 of 500000; TPS: 5322.45; ETA: 0.9 minutes\n", - "Step 201000 of 500000; TPS: 5321.73; ETA: 0.9 minutes\n", - "Step 202000 of 500000; TPS: 5318.42; ETA: 0.9 minutes\n", - "Step 203000 of 500000; TPS: 5317.67; ETA: 0.9 minutes\n", - "Step 204000 of 500000; TPS: 5317.34; ETA: 0.9 minutes\n", - "Step 205000 of 500000; TPS: 5316.57; ETA: 0.9 minutes\n", - "Step 206000 of 500000; TPS: 5315.47; ETA: 0.9 minutes\n", - "Step 207000 of 500000; TPS: 5315.11; ETA: 0.9 minutes\n", - "Step 208000 of 500000; TPS: 5310.85; ETA: 0.9 minutes\n", - "Step 209000 of 500000; TPS: 5307.51; ETA: 0.9 minutes\n", - "Step 210000 of 500000; TPS: 5306.68; ETA: 0.9 minutes\n", - "Step 211000 of 500000; TPS: 5305.57; ETA: 0.9 minutes\n", - "Step 212000 of 500000; TPS: 5304.16; ETA: 0.9 minutes\n", - "Step 213000 of 500000; TPS: 5302.91; ETA: 0.9 minutes\n", - "Step 214000 of 500000; TPS: 5301.46; ETA: 0.9 minutes\n", - "Step 215000 of 500000; TPS: 5299.78; ETA: 0.9 minutes\n", - "Step 216000 of 500000; TPS: 5296.03; ETA: 0.9 minutes\n", - "Step 217000 of 500000; TPS: 5294.32; ETA: 0.9 minutes\n", - "Step 218000 of 500000; TPS: 5292.97; ETA: 0.9 minutes\n", - "Step 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minutes\n", - "Step 252000 of 500000; TPS: 5446.72; ETA: 0.8 minutes\n", - "Step 253000 of 500000; TPS: 5444.59; ETA: 0.8 minutes\n", - "Step 254000 of 500000; TPS: 5442.49; ETA: 0.8 minutes\n", - "Step 255000 of 500000; TPS: 5440.57; ETA: 0.8 minutes\n", - "Step 256000 of 500000; TPS: 5438.82; ETA: 0.7 minutes\n", - "Step 257000 of 500000; TPS: 5436.63; ETA: 0.7 minutes\n", - "Step 258000 of 500000; TPS: 5433.54; ETA: 0.7 minutes\n", - "Step 259000 of 500000; TPS: 5431.83; ETA: 0.7 minutes\n", - "Step 260000 of 500000; TPS: 5429.52; ETA: 0.7 minutes\n", - "Step 261000 of 500000; TPS: 5427.54; ETA: 0.7 minutes\n", - "Step 262000 of 500000; TPS: 5425.22; ETA: 0.7 minutes\n", - "Step 263000 of 500000; TPS: 5423.56; ETA: 0.7 minutes\n", - "Step 264000 of 500000; TPS: 5421.59; ETA: 0.7 minutes\n", - "Step 265000 of 500000; TPS: 5418.32; ETA: 0.7 minutes\n", - "Step 266000 of 500000; TPS: 5416.49; ETA: 0.7 minutes\n", - "Step 267000 of 500000; TPS: 5411.88; ETA: 0.7 minutes\n", - "Step 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minutes\n", - "Step 301000 of 500000; TPS: 5368.14; ETA: 0.6 minutes\n", - "Step 302000 of 500000; TPS: 5366.68; ETA: 0.6 minutes\n", - "Step 303000 of 500000; TPS: 5365.45; ETA: 0.6 minutes\n", - "Step 304000 of 500000; TPS: 5364.21; ETA: 0.6 minutes\n", - "Step 305000 of 500000; TPS: 5362.87; ETA: 0.6 minutes\n", - "Step 306000 of 500000; TPS: 5361.71; ETA: 0.6 minutes\n", - "Step 307000 of 500000; TPS: 5360.46; ETA: 0.6 minutes\n", - "Step 308000 of 500000; TPS: 5358.32; ETA: 0.6 minutes\n", - "Step 309000 of 500000; TPS: 5356.49; ETA: 0.6 minutes\n", - "Step 310000 of 500000; TPS: 5355.15; ETA: 0.6 minutes\n", - "Step 311000 of 500000; TPS: 5353.84; ETA: 0.6 minutes\n", - "Step 312000 of 500000; TPS: 5354.02; ETA: 0.6 minutes\n", - "Step 313000 of 500000; TPS: 5353.94; ETA: 0.6 minutes\n", - "Step 314000 of 500000; TPS: 5353.2; ETA: 0.6 minutes\n", - "Step 315000 of 500000; TPS: 5352.23; ETA: 0.6 minutes\n", - "Step 316000 of 500000; TPS: 5351.74; ETA: 0.6 minutes\n", - "Step 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5329.87; ETA: 0.5 minutes\n", - "Step 334000 of 500000; TPS: 5328.46; ETA: 0.5 minutes\n", - "Step 335000 of 500000; TPS: 5326.7; ETA: 0.5 minutes\n", - "Step 336000 of 500000; TPS: 5324.54; ETA: 0.5 minutes\n", - "Step 337000 of 500000; TPS: 5323.06; ETA: 0.5 minutes\n", - "Step 338000 of 500000; TPS: 5321.29; ETA: 0.5 minutes\n", - "Step 339000 of 500000; TPS: 5319.61; ETA: 0.5 minutes\n", - "Step 340000 of 500000; TPS: 5318.04; ETA: 0.5 minutes\n", - "Step 341000 of 500000; TPS: 5316.56; ETA: 0.5 minutes\n", - "Step 342000 of 500000; TPS: 5315.21; ETA: 0.5 minutes\n", - "Step 343000 of 500000; TPS: 5313.73; ETA: 0.5 minutes\n", - "Step 344000 of 500000; TPS: 5312.33; ETA: 0.5 minutes\n", - "Step 345000 of 500000; TPS: 5310.93; ETA: 0.5 minutes\n", - "Step 346000 of 500000; TPS: 5309.44; ETA: 0.5 minutes\n", - "Step 347000 of 500000; TPS: 5307.26; ETA: 0.5 minutes\n", - "Step 348000 of 500000; TPS: 5305.65; ETA: 0.5 minutes\n", - "Step 349000 of 500000; TPS: 5305.4; ETA: 0.5 minutes\n", - "Step 350000 of 500000; TPS: 5305.36; ETA: 0.5 minutes\n", - "Step 351000 of 500000; TPS: 5305.85; ETA: 0.5 minutes\n", - "Step 352000 of 500000; TPS: 5305.71; ETA: 0.5 minutes\n", - "Step 353000 of 500000; TPS: 5306.1; ETA: 0.5 minutes\n", - "Step 354000 of 500000; TPS: 5306.44; ETA: 0.5 minutes\n", - "Step 355000 of 500000; TPS: 5306.78; ETA: 0.5 minutes\n", - "Step 356000 of 500000; TPS: 5306.61; ETA: 0.5 minutes\n", - "Step 357000 of 500000; TPS: 5306.03; ETA: 0.4 minutes\n", - "Step 358000 of 500000; TPS: 5305.98; ETA: 0.4 minutes\n", - "Step 359000 of 500000; TPS: 5305.87; ETA: 0.4 minutes\n", - "Step 360000 of 500000; TPS: 5305.96; ETA: 0.4 minutes\n", - "Step 361000 of 500000; TPS: 5306.63; ETA: 0.4 minutes\n", - "Step 362000 of 500000; TPS: 5307.01; ETA: 0.4 minutes\n", - "Step 363000 of 500000; TPS: 5307.55; ETA: 0.4 minutes\n", - "Step 364000 of 500000; TPS: 5307.49; ETA: 0.4 minutes\n", - "Step 365000 of 500000; TPS: 5307.79; ETA: 0.4 minutes\n", - "Step 366000 of 500000; TPS: 5307.46; ETA: 0.4 minutes\n", - "Step 367000 of 500000; TPS: 5306.76; ETA: 0.4 minutes\n", - "Step 368000 of 500000; TPS: 5305.81; ETA: 0.4 minutes\n", - "Step 369000 of 500000; TPS: 5301.25; ETA: 0.4 minutes\n", - "Step 370000 of 500000; TPS: 5300.69; ETA: 0.4 minutes\n", - "Step 371000 of 500000; TPS: 5300.06; ETA: 0.4 minutes\n", - "Step 372000 of 500000; TPS: 5299.71; ETA: 0.4 minutes\n", - "Step 373000 of 500000; TPS: 5299.24; ETA: 0.4 minutes\n", - "Step 374000 of 500000; TPS: 5298.97; ETA: 0.4 minutes\n", - "Step 375000 of 500000; TPS: 5298.72; ETA: 0.4 minutes\n", - "Step 376000 of 500000; TPS: 5298.23; ETA: 0.4 minutes\n", - "Step 377000 of 500000; TPS: 5297.92; ETA: 0.4 minutes\n", - "Step 378000 of 500000; TPS: 5297.04; ETA: 0.4 minutes\n", - "Step 379000 of 500000; TPS: 5296.55; ETA: 0.4 minutes\n", - "Step 380000 of 500000; TPS: 5296.04; ETA: 0.4 minutes\n", - "Step 381000 of 500000; TPS: 5295.82; ETA: 0.4 minutes\n", - "Step 382000 of 500000; TPS: 5412.79; ETA: 0.4 minutes\n", - "Step 383000 of 500000; TPS: 5411.9; ETA: 0.4 minutes\n", - "Step 384000 of 500000; TPS: 5411.2; ETA: 0.4 minutes\n", - "Step 385000 of 500000; TPS: 5410.65; ETA: 0.4 minutes\n", - "Step 386000 of 500000; TPS: 5410.02; ETA: 0.4 minutes\n", - "Step 387000 of 500000; TPS: 5409.46; ETA: 0.3 minutes\n", - "Step 388000 of 500000; TPS: 5408.8; ETA: 0.3 minutes\n", - "Step 389000 of 500000; TPS: 5408.07; ETA: 0.3 minutes\n", - "Step 390000 of 500000; TPS: 5407.27; ETA: 0.3 minutes\n", - "Step 391000 of 500000; TPS: 5406.35; ETA: 0.3 minutes\n", - "Step 392000 of 500000; TPS: 5405.88; ETA: 0.3 minutes\n", - "Step 393000 of 500000; TPS: 5405.07; ETA: 0.3 minutes\n", - "Step 394000 of 500000; TPS: 5404.5; ETA: 0.3 minutes\n", - "Step 395000 of 500000; TPS: 5403.79; ETA: 0.3 minutes\n", - "Step 396000 of 500000; TPS: 5403.32; ETA: 0.3 minutes\n", - "Step 397000 of 500000; TPS: 5402.7; ETA: 0.3 minutes\n", - "Step 398000 of 500000; TPS: 5402.27; ETA: 0.3 minutes\n", - "Step 399000 of 500000; TPS: 5401.82; ETA: 0.3 minutes\n", - "Step 400000 of 500000; TPS: 5400.73; ETA: 0.3 minutes\n", - "Step 401000 of 500000; TPS: 5400.13; ETA: 0.3 minutes\n", - "Step 402000 of 500000; TPS: 5399.46; ETA: 0.3 minutes\n", - "Step 403000 of 500000; TPS: 5398.58; ETA: 0.3 minutes\n", - "Step 404000 of 500000; TPS: 5397.84; ETA: 0.3 minutes\n", - "Step 405000 of 500000; TPS: 5397.49; ETA: 0.3 minutes\n", - "Step 406000 of 500000; TPS: 5397.47; ETA: 0.3 minutes\n", - "Step 407000 of 500000; TPS: 5397.24; ETA: 0.3 minutes\n", - "Step 408000 of 500000; TPS: 5396.98; ETA: 0.3 minutes\n", - "Step 409000 of 500000; TPS: 5396.93; ETA: 0.3 minutes\n", - "Step 410000 of 500000; TPS: 5396.86; ETA: 0.3 minutes\n", - "Step 411000 of 500000; TPS: 5396.74; ETA: 0.3 minutes\n", - "Step 412000 of 500000; TPS: 5396.21; ETA: 0.3 minutes\n", - "Step 413000 of 500000; TPS: 5396.23; ETA: 0.3 minutes\n", - "Step 414000 of 500000; TPS: 5396.15; ETA: 0.3 minutes\n", - "Step 415000 of 500000; TPS: 5396.04; ETA: 0.3 minutes\n", - "Step 416000 of 500000; TPS: 5395.68; ETA: 0.3 minutes\n", - "Step 417000 of 500000; TPS: 5394.56; ETA: 0.3 minutes\n", - "Step 418000 of 500000; TPS: 5393.54; ETA: 0.3 minutes\n", - "Step 419000 of 500000; TPS: 5392.37; ETA: 0.3 minutes\n", - "Step 420000 of 500000; TPS: 5391.21; ETA: 0.2 minutes\n", - "Step 421000 of 500000; TPS: 5389.98; ETA: 0.2 minutes\n", - "Step 422000 of 500000; TPS: 5389.01; ETA: 0.2 minutes\n", - "Step 423000 of 500000; TPS: 5387.68; ETA: 0.2 minutes\n", - "Step 424000 of 500000; TPS: 5386.59; ETA: 0.2 minutes\n", - "Step 425000 of 500000; TPS: 5385.62; ETA: 0.2 minutes\n", - "Step 426000 of 500000; TPS: 5384.24; ETA: 0.2 minutes\n", - "Step 427000 of 500000; TPS: 5383.24; ETA: 0.2 minutes\n", - "Step 428000 of 500000; TPS: 5382.19; ETA: 0.2 minutes\n", - "Step 429000 of 500000; TPS: 5379.95; ETA: 0.2 minutes\n", - "Step 430000 of 500000; TPS: 5378.75; ETA: 0.2 minutes\n", - "Step 431000 of 500000; TPS: 5377.38; ETA: 0.2 minutes\n", - "Step 432000 of 500000; TPS: 5375.58; ETA: 0.2 minutes\n", - "Step 433000 of 500000; TPS: 5374.1; ETA: 0.2 minutes\n", - "Step 434000 of 500000; TPS: 5372.75; ETA: 0.2 minutes\n", - "Step 435000 of 500000; TPS: 5371.12; ETA: 0.2 minutes\n", - "Step 436000 of 500000; TPS: 5369.71; ETA: 0.2 minutes\n", - "Step 437000 of 500000; TPS: 5368.36; ETA: 0.2 minutes\n", - "Step 438000 of 500000; TPS: 5366.72; ETA: 0.2 minutes\n", - "Step 439000 of 500000; TPS: 5365.23; ETA: 0.2 minutes\n", - "Step 440000 of 500000; TPS: 5363.96; ETA: 0.2 minutes\n", - "Step 441000 of 500000; TPS: 5362.71; ETA: 0.2 minutes\n", - "Step 442000 of 500000; TPS: 5361.27; ETA: 0.2 minutes\n", - "Step 443000 of 500000; TPS: 5359.7; ETA: 0.2 minutes\n", - "Step 444000 of 500000; TPS: 5358.21; ETA: 0.2 minutes\n", - "Step 445000 of 500000; TPS: 5356.77; ETA: 0.2 minutes\n", - "Step 446000 of 500000; TPS: 5355.56; ETA: 0.2 minutes\n", - "Step 447000 of 500000; TPS: 5354.19; ETA: 0.2 minutes\n", - "Step 448000 of 500000; TPS: 5352.75; ETA: 0.2 minutes\n", - "Step 449000 of 500000; TPS: 5352.05; ETA: 0.2 minutes\n", - "Step 450000 of 500000; TPS: 5351.82; ETA: 0.2 minutes\n", - "Step 451000 of 500000; TPS: 5351.53; ETA: 0.2 minutes\n", - "Step 452000 of 500000; TPS: 5351.07; ETA: 0.1 minutes\n", - "Step 453000 of 500000; TPS: 5350.61; ETA: 0.1 minutes\n", - "Step 454000 of 500000; TPS: 5349.25; ETA: 0.1 minutes\n", - "Step 455000 of 500000; TPS: 5348.91; ETA: 0.1 minutes\n", - "Step 456000 of 500000; TPS: 5347.78; ETA: 0.1 minutes\n", - "Step 457000 of 500000; TPS: 5346.33; ETA: 0.1 minutes\n", - "Step 458000 of 500000; TPS: 5345.02; ETA: 0.1 minutes\n", - "Step 459000 of 500000; TPS: 5343.56; ETA: 0.1 minutes\n", - "Step 460000 of 500000; TPS: 5342.18; ETA: 0.1 minutes\n", - "Step 461000 of 500000; TPS: 5340.47; ETA: 0.1 minutes\n", - "Step 462000 of 500000; TPS: 5338.82; ETA: 0.1 minutes\n", - "Step 463000 of 500000; TPS: 5337.29; ETA: 0.1 minutes\n", - "Step 464000 of 500000; TPS: 5335.56; ETA: 0.1 minutes\n", - "Step 465000 of 500000; TPS: 5333.97; ETA: 0.1 minutes\n", - "Step 466000 of 500000; TPS: 5332.7; ETA: 0.1 minutes\n", - "Step 467000 of 500000; TPS: 5331.3; ETA: 0.1 minutes\n", - "Step 468000 of 500000; TPS: 5329.79; ETA: 0.1 minutes\n", - "Step 469000 of 500000; TPS: 5328.09; ETA: 0.1 minutes\n", - "Step 470000 of 500000; TPS: 5326.8; ETA: 0.1 minutes\n", - "Step 471000 of 500000; TPS: 5325.08; ETA: 0.1 minutes\n", - "Step 472000 of 500000; TPS: 5323.27; ETA: 0.1 minutes\n", - "Step 473000 of 500000; TPS: 5321.94; ETA: 0.1 minutes\n", - "Step 474000 of 500000; TPS: 5320.34; ETA: 0.1 minutes\n", - "Step 475000 of 500000; TPS: 5318.83; ETA: 0.1 minutes\n", - "Step 476000 of 500000; TPS: 5317.62; ETA: 0.1 minutes\n", - "Step 477000 of 500000; TPS: 5313.7; ETA: 0.1 minutes\n", - "Step 478000 of 500000; TPS: 5311.72; ETA: 0.1 minutes\n", - "Step 479000 of 500000; TPS: 5309.25; ETA: 0.1 minutes\n", - "Step 480000 of 500000; TPS: 5307.73; ETA: 0.1 minutes\n", - "Step 481000 of 500000; TPS: 5306.76; ETA: 0.1 minutes\n", - "Step 482000 of 500000; TPS: 5305.79; ETA: 0.1 minutes\n", - "Step 483000 of 500000; TPS: 5304.76; ETA: 0.1 minutes\n", - "Step 484000 of 500000; TPS: 5303.61; ETA: 0.1 minutes\n", - "Step 485000 of 500000; TPS: 5302.46; ETA: 0.0 minutes\n", - "Step 486000 of 500000; TPS: 5301.22; ETA: 0.0 minutes\n", - "Step 487000 of 500000; TPS: 5299.99; ETA: 0.0 minutes\n", - "Step 488000 of 500000; TPS: 5298.55; ETA: 0.0 minutes\n", - "Step 489000 of 500000; TPS: 5297.15; ETA: 0.0 minutes\n", - "Step 490000 of 500000; TPS: 5296.1; ETA: 0.0 minutes\n", - "Step 491000 of 500000; TPS: 5294.95; ETA: 0.0 minutes\n", - "Step 492000 of 500000; TPS: 5293.81; ETA: 0.0 minutes\n", - "Step 493000 of 500000; TPS: 5292.31; ETA: 0.0 minutes\n", - "Step 494000 of 500000; TPS: 5290.2; ETA: 0.0 minutes\n", - "Step 495000 of 500000; TPS: 5287.86; ETA: 0.0 minutes\n", - "Step 496000 of 500000; TPS: 5285.44; ETA: 0.0 minutes\n", - "Step 497000 of 500000; TPS: 5283.07; ETA: 0.0 minutes\n", - "Step 498000 of 500000; TPS: 5280.86; ETA: 0.0 minutes\n", - "Step 499000 of 500000; TPS: 5277.76; ETA: 0.0 minutes\n", - "Step 500000 of 500000; TPS: 5275.34; ETA: 0.0 minutes\n", - "Step 999 of 1000000; TPS: 4590.68; ETA: 3.6 minutes\n", - "Step 1999 of 1000000; TPS: 4636.99; ETA: 3.6 minutes\n", - "Step 2999 of 1000000; TPS: 4645.88; ETA: 3.6 minutes\n", - "Step 3999 of 1000000; TPS: 4653.18; ETA: 3.6 minutes\n", - "Step 4999 of 1000000; TPS: 4654.25; ETA: 3.6 minutes\n", - "Step 5999 of 1000000; TPS: 4645.71; ETA: 3.6 minutes\n", - "Step 6999 of 1000000; TPS: 4646.66; ETA: 3.6 minutes\n", - "Step 7999 of 1000000; TPS: 4643.04; ETA: 3.6 minutes\n", - "Step 8999 of 1000000; TPS: 4634.2; ETA: 3.6 minutes\n", - "Step 9999 of 1000000; TPS: 4623.95; ETA: 3.6 minutes\n", - "Step 10999 of 1000000; TPS: 4622.58; ETA: 3.6 minutes\n", - "Step 11999 of 1000000; TPS: 4632.2; ETA: 3.6 minutes\n", - "Step 12999 of 1000000; TPS: 4643.12; ETA: 3.5 minutes\n", - "Step 13999 of 1000000; TPS: 4650.46; ETA: 3.5 minutes\n", - "Step 14999 of 1000000; TPS: 4652.47; ETA: 3.5 minutes\n", - "Step 15999 of 1000000; TPS: 4676.62; ETA: 3.5 minutes\n", - "Step 16999 of 1000000; TPS: 4694.5; ETA: 3.5 minutes\n", - "Step 17999 of 1000000; TPS: 4707.96; ETA: 3.5 minutes\n", - "Step 18999 of 1000000; TPS: 4719.55; ETA: 3.5 minutes\n", - "Step 19999 of 1000000; TPS: 4728.56; ETA: 3.5 minutes\n", - "Step 20999 of 1000000; TPS: 4737.7; ETA: 3.4 minutes\n", - "Step 21999 of 1000000; TPS: 4746.69; ETA: 3.4 minutes\n", - "Step 22999 of 1000000; TPS: 4755.66; ETA: 3.4 minutes\n", - "Step 23999 of 1000000; TPS: 4742.93; ETA: 3.4 minutes\n", - "Step 24999 of 1000000; TPS: 4753.91; ETA: 3.4 minutes\n", - "Step 25999 of 1000000; TPS: 4763.4; ETA: 3.4 minutes\n", - "Step 26999 of 1000000; TPS: 4772.3; ETA: 3.4 minutes\n", - "Step 27999 of 1000000; TPS: 4779.94; ETA: 3.4 minutes\n", - "Step 28999 of 1000000; TPS: 4786.18; ETA: 3.4 minutes\n", - "Step 29999 of 1000000; TPS: 4793.68; ETA: 3.4 minutes\n", - "Step 30999 of 1000000; TPS: 4802.22; ETA: 3.4 minutes\n", - "Step 31999 of 1000000; TPS: 4807.44; ETA: 3.4 minutes\n", - "Step 32999 of 1000000; TPS: 4807.04; ETA: 3.4 minutes\n", - "Step 33999 of 1000000; TPS: 4805.86; ETA: 3.4 minutes\n", - "Step 34999 of 1000000; TPS: 4805.15; ETA: 3.3 minutes\n", - "Step 35999 of 1000000; TPS: 4804.88; ETA: 3.3 minutes\n", - "Step 36999 of 1000000; TPS: 4805.94; ETA: 3.3 minutes\n", - "Step 37999 of 1000000; TPS: 4802.61; ETA: 3.3 minutes\n", - "Step 38999 of 1000000; TPS: 5919.56; ETA: 2.7 minutes\n", - "Step 39999 of 1000000; TPS: 5885.07; ETA: 2.7 minutes\n", - "Step 40999 of 1000000; TPS: 5853.86; ETA: 2.7 minutes\n", - "Step 41999 of 1000000; TPS: 5826.38; ETA: 2.7 minutes\n", - "Step 42999 of 1000000; TPS: 5796.43; ETA: 2.8 minutes\n", - "Step 43999 of 1000000; TPS: 5771.75; ETA: 2.8 minutes\n", - "Step 44999 of 1000000; TPS: 5748.28; ETA: 2.8 minutes\n", - "Step 45999 of 1000000; TPS: 5723.78; ETA: 2.8 minutes\n", - "Step 46999 of 1000000; TPS: 5699.15; ETA: 2.8 minutes\n", - "Step 47999 of 1000000; TPS: 5675.14; ETA: 2.8 minutes\n", - "Step 48999 of 1000000; TPS: 5652.28; ETA: 2.8 minutes\n", - "Step 49999 of 1000000; TPS: 5633.58; ETA: 2.8 minutes\n", - "Step 50999 of 1000000; TPS: 5613.22; ETA: 2.8 minutes\n", - "Step 51999 of 1000000; TPS: 5596.87; ETA: 2.8 minutes\n", - "Step 52999 of 1000000; TPS: 5580.55; ETA: 2.8 minutes\n", - "Step 53999 of 1000000; TPS: 5565.37; ETA: 2.8 minutes\n", - "Step 54999 of 1000000; TPS: 5548.77; ETA: 2.8 minutes\n", - "Step 55999 of 1000000; TPS: 5532.91; ETA: 2.8 minutes\n", - "Step 56999 of 1000000; TPS: 5518.48; ETA: 2.8 minutes\n", - "Step 57999 of 1000000; TPS: 5503.23; ETA: 2.9 minutes\n", - "Step 58999 of 1000000; TPS: 5488.65; ETA: 2.9 minutes\n", - "Step 59999 of 1000000; TPS: 5473.39; ETA: 2.9 minutes\n", - "Step 60999 of 1000000; TPS: 5460.28; ETA: 2.9 minutes\n", - "Step 61999 of 1000000; TPS: 5450.2; ETA: 2.9 minutes\n", - "Step 62999 of 1000000; TPS: 5435.7; ETA: 2.9 minutes\n", - "Step 63999 of 1000000; TPS: 5422.24; ETA: 2.9 minutes\n", - "Step 64999 of 1000000; TPS: 5411.59; ETA: 2.9 minutes\n", - "Step 65999 of 1000000; TPS: 5400.13; ETA: 2.9 minutes\n", - "Step 66999 of 1000000; TPS: 5389.34; ETA: 2.9 minutes\n", - "Step 67999 of 1000000; TPS: 5379.18; ETA: 2.9 minutes\n", - "Step 68999 of 1000000; TPS: 5372.71; ETA: 2.9 minutes\n", - "Step 69999 of 1000000; TPS: 5366.83; ETA: 2.9 minutes\n", - "Step 70999 of 1000000; TPS: 5359.33; ETA: 2.9 minutes\n", - "Step 71999 of 1000000; TPS: 5353.39; ETA: 2.9 minutes\n", - "Step 72999 of 1000000; TPS: 5347.76; ETA: 2.9 minutes\n", - "Step 73999 of 1000000; TPS: 5342.61; ETA: 2.9 minutes\n", - "Step 74999 of 1000000; TPS: 5339.03; ETA: 2.9 minutes\n", - "Step 75999 of 1000000; TPS: 5334.22; ETA: 2.9 minutes\n", - "Step 76999 of 1000000; TPS: 5329.07; ETA: 2.9 minutes\n", - "Step 77999 of 1000000; TPS: 5323.96; ETA: 2.9 minutes\n", - "Step 78999 of 1000000; TPS: 5314.28; ETA: 2.9 minutes\n", - "Step 79999 of 1000000; TPS: 5296.74; ETA: 2.9 minutes\n", - "Step 80999 of 1000000; TPS: 5286.59; ETA: 2.9 minutes\n", - "Step 81999 of 1000000; TPS: 5277.76; ETA: 2.9 minutes\n", - "Step 82999 of 1000000; TPS: 5269.0; ETA: 2.9 minutes\n", - "Step 83999 of 1000000; TPS: 5258.9; ETA: 2.9 minutes\n", - "Step 84999 of 1000000; TPS: 5249.74; ETA: 2.9 minutes\n", - "Step 85999 of 1000000; TPS: 5240.36; ETA: 2.9 minutes\n", - "Step 86999 of 1000000; TPS: 5232.2; ETA: 2.9 minutes\n", - "Step 87999 of 1000000; TPS: 5225.33; ETA: 2.9 minutes\n", - "Step 88999 of 1000000; TPS: 5217.25; ETA: 2.9 minutes\n", - "Step 89999 of 1000000; TPS: 5209.76; ETA: 2.9 minutes\n", - "Step 90999 of 1000000; TPS: 5202.33; ETA: 2.9 minutes\n", - "Step 91999 of 1000000; TPS: 5195.58; ETA: 2.9 minutes\n", - "Step 92999 of 1000000; TPS: 5187.17; ETA: 2.9 minutes\n", - "Step 93999 of 1000000; TPS: 5177.98; ETA: 2.9 minutes\n", - "Step 94999 of 1000000; TPS: 5170.56; ETA: 2.9 minutes\n", - "Step 95999 of 1000000; TPS: 5163.69; ETA: 2.9 minutes\n", - "Step 96999 of 1000000; TPS: 5156.49; ETA: 2.9 minutes\n", - "Step 97999 of 1000000; TPS: 5149.45; ETA: 2.9 minutes\n", - "Step 98999 of 1000000; TPS: 5141.57; ETA: 2.9 minutes\n", - "Step 99999 of 1000000; TPS: 5135.69; ETA: 2.9 minutes\n", - "Step 100999 of 1000000; TPS: 5130.01; ETA: 2.9 minutes\n", - "Step 101999 of 1000000; TPS: 5125.38; ETA: 2.9 minutes\n", - "Step 102999 of 1000000; TPS: 5120.63; ETA: 2.9 minutes\n", - "Step 103999 of 1000000; TPS: 5114.14; ETA: 2.9 minutes\n", - "Step 104999 of 1000000; TPS: 5108.8; ETA: 2.9 minutes\n", - "Step 105999 of 1000000; TPS: 5104.28; ETA: 2.9 minutes\n", - "Step 106999 of 1000000; TPS: 5099.84; ETA: 2.9 minutes\n", - "Step 107999 of 1000000; TPS: 5094.8; ETA: 2.9 minutes\n", - "Step 108999 of 1000000; TPS: 5090.78; ETA: 2.9 minutes\n", - "Step 109999 of 1000000; TPS: 5090.19; ETA: 2.9 minutes\n", - "Step 110999 of 1000000; TPS: 5090.03; ETA: 2.9 minutes\n", - "Step 111999 of 1000000; TPS: 5089.8; ETA: 2.9 minutes\n", - "Step 112999 of 1000000; TPS: 5089.01; ETA: 2.9 minutes\n", - "Step 113999 of 1000000; TPS: 5088.47; ETA: 2.9 minutes\n", - "Step 114999 of 1000000; TPS: 5088.63; ETA: 2.9 minutes\n", - "Step 115999 of 1000000; TPS: 5086.59; ETA: 2.9 minutes\n", - "Step 116999 of 1000000; TPS: 5086.13; ETA: 2.9 minutes\n", - "Step 117999 of 1000000; TPS: 5082.2; ETA: 2.9 minutes\n", - "Step 118999 of 1000000; TPS: 5077.56; ETA: 2.9 minutes\n", - "Step 119999 of 1000000; TPS: 5072.45; ETA: 2.9 minutes\n", - "Step 120999 of 1000000; TPS: 5068.08; ETA: 2.9 minutes\n", - "Step 121999 of 1000000; TPS: 5064.35; ETA: 2.9 minutes\n", - "Step 122999 of 1000000; TPS: 5056.91; ETA: 2.9 minutes\n", - "Step 123999 of 1000000; TPS: 5050.91; ETA: 2.9 minutes\n", - "Step 124999 of 1000000; TPS: 5045.56; ETA: 2.9 minutes\n", - "Step 125999 of 1000000; TPS: 5040.53; ETA: 2.9 minutes\n", - "Step 126999 of 1000000; TPS: 5031.14; ETA: 2.9 minutes\n", - "Step 127999 of 1000000; TPS: 5025.88; ETA: 2.9 minutes\n", - "Step 128999 of 1000000; TPS: 5019.91; ETA: 2.9 minutes\n", - "Step 129999 of 1000000; TPS: 5014.92; ETA: 2.9 minutes\n", - "Step 130999 of 1000000; TPS: 5009.03; ETA: 2.9 minutes\n", - "Step 131999 of 1000000; TPS: 5004.51; ETA: 2.9 minutes\n", - "Step 132999 of 1000000; TPS: 4998.21; ETA: 2.9 minutes\n", - "Step 133999 of 1000000; TPS: 4992.93; ETA: 2.9 minutes\n", - "Step 134999 of 1000000; TPS: 4988.06; ETA: 2.9 minutes\n", - "Step 135999 of 1000000; TPS: 4983.69; ETA: 2.9 minutes\n", - "Step 136999 of 1000000; TPS: 4979.22; ETA: 2.9 minutes\n", - "Step 137999 of 1000000; TPS: 4973.18; ETA: 2.9 minutes\n", - "Step 138999 of 1000000; TPS: 4968.63; ETA: 2.9 minutes\n", - "Step 139999 of 1000000; TPS: 4964.0; ETA: 2.9 minutes\n", - "Step 140999 of 1000000; TPS: 4960.62; ETA: 2.9 minutes\n", - "Step 141999 of 1000000; TPS: 4958.91; ETA: 2.9 minutes\n", - "Step 142999 of 1000000; TPS: 4956.73; ETA: 2.9 minutes\n", - "Step 143999 of 1000000; TPS: 4950.78; ETA: 2.9 minutes\n", - "Step 144999 of 1000000; TPS: 4949.03; ETA: 2.9 minutes\n", - "Step 145999 of 1000000; TPS: 4947.64; ETA: 2.9 minutes\n", - "Step 146999 of 1000000; TPS: 4946.66; ETA: 2.9 minutes\n", - "Step 147999 of 1000000; TPS: 4945.42; ETA: 2.9 minutes\n", - "Step 148999 of 1000000; TPS: 4943.39; ETA: 2.9 minutes\n", - "Step 149999 of 1000000; TPS: 4941.71; ETA: 2.9 minutes\n", - "Step 150999 of 1000000; TPS: 4940.36; ETA: 2.9 minutes\n", - "Step 151999 of 1000000; TPS: 4938.93; ETA: 2.9 minutes\n", - "Step 152999 of 1000000; TPS: 4938.13; ETA: 2.9 minutes\n", - "Step 153999 of 1000000; TPS: 4935.34; ETA: 2.9 minutes\n", - "Step 154999 of 1000000; TPS: 4930.86; ETA: 2.9 minutes\n", - "Step 155999 of 1000000; TPS: 4926.59; ETA: 2.9 minutes\n", - "Step 156999 of 1000000; TPS: 4923.27; ETA: 2.9 minutes\n", - "Step 157999 of 1000000; TPS: 4919.58; ETA: 2.9 minutes\n", - "Step 158999 of 1000000; TPS: 4916.88; ETA: 2.9 minutes\n", - "Step 159999 of 1000000; TPS: 4914.1; ETA: 2.8 minutes\n", - "Step 160999 of 1000000; TPS: 4909.46; ETA: 2.8 minutes\n", - "Step 161999 of 1000000; TPS: 4905.24; ETA: 2.8 minutes\n", - "Step 162999 of 1000000; TPS: 4900.91; ETA: 2.8 minutes\n", - "Step 163999 of 1000000; TPS: 4896.3; ETA: 2.8 minutes\n", - "Step 164999 of 1000000; TPS: 4892.29; ETA: 2.8 minutes\n", - "Step 165999 of 1000000; TPS: 4887.74; ETA: 2.8 minutes\n", - "Step 166999 of 1000000; TPS: 4883.9; ETA: 2.8 minutes\n", - "Step 167999 of 1000000; TPS: 4878.91; ETA: 2.8 minutes\n", - "Step 168999 of 1000000; TPS: 4872.9; ETA: 2.8 minutes\n", - "Step 169999 of 1000000; TPS: 4868.27; ETA: 2.8 minutes\n", - "Step 170999 of 1000000; TPS: 4863.72; ETA: 2.8 minutes\n", - "Step 171999 of 1000000; TPS: 4857.39; ETA: 2.8 minutes\n", - "Step 172999 of 1000000; TPS: 4853.14; ETA: 2.8 minutes\n", - "Step 173999 of 1000000; TPS: 4850.54; ETA: 2.8 minutes\n", - "Step 174999 of 1000000; TPS: 4848.13; ETA: 2.8 minutes\n", - "Step 175999 of 1000000; TPS: 4846.58; ETA: 2.8 minutes\n", - "Step 176999 of 1000000; TPS: 4845.25; ETA: 2.8 minutes\n", - "Step 177999 of 1000000; TPS: 4843.82; ETA: 2.8 minutes\n", - "Step 178999 of 1000000; TPS: 4842.34; ETA: 2.8 minutes\n", - "Step 179999 of 1000000; TPS: 4840.89; ETA: 2.8 minutes\n", - "Step 180999 of 1000000; TPS: 4839.32; ETA: 2.8 minutes\n", - "Step 181999 of 1000000; TPS: 4837.63; ETA: 2.8 minutes\n", - "Step 182999 of 1000000; TPS: 4835.14; ETA: 2.8 minutes\n", - "Step 183999 of 1000000; TPS: 4833.69; ETA: 2.8 minutes\n", - "Step 184999 of 1000000; TPS: 4831.93; ETA: 2.8 minutes\n", - "Step 185999 of 1000000; TPS: 4830.84; ETA: 2.8 minutes\n", - "Step 186999 of 1000000; TPS: 4828.8; ETA: 2.8 minutes\n", - "Step 187999 of 1000000; TPS: 4828.6; ETA: 2.8 minutes\n", - "Step 188999 of 1000000; TPS: 5019.38; ETA: 2.7 minutes\n", - "Step 189999 of 1000000; TPS: 4999.01; ETA: 2.7 minutes\n", - "Step 190999 of 1000000; TPS: 4992.99; ETA: 2.7 minutes\n", - "Step 191999 of 1000000; TPS: 4990.43; ETA: 2.7 minutes\n", - "Step 192999 of 1000000; TPS: 4985.43; ETA: 2.7 minutes\n", - "Step 193999 of 1000000; TPS: 4983.13; ETA: 2.7 minutes\n", - "Step 194999 of 1000000; TPS: 4981.41; ETA: 2.7 minutes\n", - "Step 195999 of 1000000; TPS: 4978.43; ETA: 2.7 minutes\n", - "Step 196999 of 1000000; TPS: 4974.43; ETA: 2.7 minutes\n", - "Step 197999 of 1000000; TPS: 4972.83; ETA: 2.7 minutes\n", - "Step 198999 of 1000000; TPS: 4970.3; ETA: 2.7 minutes\n", - "Step 199999 of 1000000; TPS: 4968.2; ETA: 2.7 minutes\n", - "Step 200999 of 1000000; TPS: 4966.06; ETA: 2.7 minutes\n", - "Step 201999 of 1000000; TPS: 4965.24; ETA: 2.7 minutes\n", - "Step 202999 of 1000000; TPS: 4963.7; ETA: 2.7 minutes\n", - "Step 203999 of 1000000; TPS: 4963.37; ETA: 2.7 minutes\n", - "Step 204999 of 1000000; TPS: 4962.26; ETA: 2.7 minutes\n", - "Step 205999 of 1000000; TPS: 4961.44; ETA: 2.7 minutes\n", - "Step 206999 of 1000000; TPS: 4960.26; ETA: 2.7 minutes\n", - "Step 207999 of 1000000; TPS: 4959.47; ETA: 2.7 minutes\n", - "Step 208999 of 1000000; TPS: 4958.81; ETA: 2.7 minutes\n", - "Step 209999 of 1000000; TPS: 4958.13; ETA: 2.7 minutes\n", - "Step 210999 of 1000000; TPS: 4957.51; ETA: 2.7 minutes\n", - "Step 211999 of 1000000; TPS: 4956.7; ETA: 2.6 minutes\n", - "Step 212999 of 1000000; TPS: 4956.03; ETA: 2.6 minutes\n", - "Step 213999 of 1000000; TPS: 4954.88; ETA: 2.6 minutes\n", - "Step 214999 of 1000000; TPS: 4953.84; ETA: 2.6 minutes\n", - "Step 215999 of 1000000; TPS: 4953.08; ETA: 2.6 minutes\n", - "Step 216999 of 1000000; TPS: 4951.83; ETA: 2.6 minutes\n", - "Step 217999 of 1000000; TPS: 4950.1; ETA: 2.6 minutes\n", - "Step 218999 of 1000000; TPS: 4948.19; ETA: 2.6 minutes\n", - "Step 219999 of 1000000; TPS: 4946.26; ETA: 2.6 minutes\n", - "Step 220999 of 1000000; TPS: 4944.85; ETA: 2.6 minutes\n", - "Step 221999 of 1000000; TPS: 4942.95; ETA: 2.6 minutes\n", - "Step 222999 of 1000000; TPS: 4941.07; ETA: 2.6 minutes\n", - "Step 223999 of 1000000; TPS: 4939.7; ETA: 2.6 minutes\n", - "Step 224999 of 1000000; TPS: 4934.13; ETA: 2.6 minutes\n", - "Step 225999 of 1000000; TPS: 4932.82; ETA: 2.6 minutes\n", - "Step 226999 of 1000000; TPS: 4930.76; ETA: 2.6 minutes\n", - "Step 227999 of 1000000; TPS: 4928.7; ETA: 2.6 minutes\n", - "Step 228999 of 1000000; TPS: 4927.23; ETA: 2.6 minutes\n", - "Step 229999 of 1000000; TPS: 4925.71; ETA: 2.6 minutes\n", - "Step 230999 of 1000000; TPS: 4925.16; ETA: 2.6 minutes\n", - "Step 231999 of 1000000; TPS: 4924.86; ETA: 2.6 minutes\n", - "Step 232999 of 1000000; TPS: 4924.38; ETA: 2.6 minutes\n", - "Step 233999 of 1000000; TPS: 4922.42; ETA: 2.6 minutes\n", - "Step 234999 of 1000000; TPS: 4920.17; ETA: 2.6 minutes\n", - "Step 235999 of 1000000; TPS: 4917.92; ETA: 2.6 minutes\n", - "Step 236999 of 1000000; TPS: 4915.45; ETA: 2.6 minutes\n", - "Step 237999 of 1000000; TPS: 4912.48; ETA: 2.6 minutes\n", - "Step 238999 of 1000000; TPS: 4910.05; ETA: 2.6 minutes\n", - "Step 239999 of 1000000; TPS: 4907.65; ETA: 2.6 minutes\n", - "Step 240999 of 1000000; TPS: 4905.2; ETA: 2.6 minutes\n", - "Step 241999 of 1000000; TPS: 4901.75; ETA: 2.6 minutes\n", - "Step 242999 of 1000000; TPS: 4899.73; ETA: 2.6 minutes\n", - "Step 243999 of 1000000; TPS: 4897.33; ETA: 2.6 minutes\n", - "Step 244999 of 1000000; TPS: 4895.5; ETA: 2.6 minutes\n", - "Step 245999 of 1000000; TPS: 4893.06; ETA: 2.6 minutes\n", - "Step 246999 of 1000000; TPS: 4891.05; ETA: 2.6 minutes\n", - "Step 247999 of 1000000; TPS: 4889.04; ETA: 2.6 minutes\n", - "Step 248999 of 1000000; TPS: 4886.74; ETA: 2.6 minutes\n", - "Step 249999 of 1000000; TPS: 4884.9; ETA: 2.6 minutes\n", - "Step 250999 of 1000000; TPS: 4883.12; ETA: 2.6 minutes\n", - "Step 251999 of 1000000; TPS: 4880.28; ETA: 2.6 minutes\n", - "Step 252999 of 1000000; TPS: 4878.3; ETA: 2.6 minutes\n", - "Step 253999 of 1000000; TPS: 4876.24; ETA: 2.5 minutes\n", - "Step 254999 of 1000000; TPS: 4874.52; ETA: 2.5 minutes\n", - "Step 255999 of 1000000; TPS: 4872.7; ETA: 2.5 minutes\n", - "Step 256999 of 1000000; TPS: 4870.89; ETA: 2.5 minutes\n", - "Step 257999 of 1000000; TPS: 4869.9; ETA: 2.5 minutes\n", - "Step 258999 of 1000000; TPS: 4869.67; ETA: 2.5 minutes\n", - "Step 259999 of 1000000; TPS: 4869.78; ETA: 2.5 minutes\n", - "Step 260999 of 1000000; TPS: 4869.79; ETA: 2.5 minutes\n", - "Step 261999 of 1000000; TPS: 4869.17; ETA: 2.5 minutes\n", - "Step 262999 of 1000000; TPS: 4869.1; ETA: 2.5 minutes\n", - "Step 263999 of 1000000; TPS: 4868.79; ETA: 2.5 minutes\n", - "Step 264999 of 1000000; TPS: 4868.3; ETA: 2.5 minutes\n", - "Step 265999 of 1000000; TPS: 4867.53; ETA: 2.5 minutes\n", - "Step 266999 of 1000000; TPS: 4866.78; ETA: 2.5 minutes\n", - "Step 267999 of 1000000; TPS: 4865.53; ETA: 2.5 minutes\n", - "Step 268999 of 1000000; TPS: 4864.24; ETA: 2.5 minutes\n", - "Step 269999 of 1000000; TPS: 4863.44; ETA: 2.5 minutes\n", - "Step 270999 of 1000000; TPS: 4861.06; ETA: 2.5 minutes\n", - "Step 271999 of 1000000; TPS: 4858.25; ETA: 2.5 minutes\n", - "Step 272999 of 1000000; TPS: 4855.87; ETA: 2.5 minutes\n", - "Step 273999 of 1000000; TPS: 4853.43; ETA: 2.5 minutes\n", - "Step 274999 of 1000000; TPS: 4851.03; ETA: 2.5 minutes\n", - "Step 275999 of 1000000; TPS: 4848.37; ETA: 2.5 minutes\n", - "Step 276999 of 1000000; TPS: 4845.8; ETA: 2.5 minutes\n", - "Step 277999 of 1000000; TPS: 4843.6; ETA: 2.5 minutes\n", - "Step 278999 of 1000000; TPS: 4840.79; ETA: 2.5 minutes\n", - "Step 279999 of 1000000; TPS: 4838.34; ETA: 2.5 minutes\n", - "Step 280999 of 1000000; TPS: 4835.11; ETA: 2.5 minutes\n", - "Step 281999 of 1000000; TPS: 4832.87; ETA: 2.5 minutes\n", - "Step 282999 of 1000000; TPS: 4830.83; ETA: 2.5 minutes\n", - "Step 283999 of 1000000; TPS: 4828.64; ETA: 2.5 minutes\n", - "Step 284999 of 1000000; TPS: 4826.62; ETA: 2.5 minutes\n", - "Step 285999 of 1000000; TPS: 4824.27; ETA: 2.5 minutes\n", - "Step 286999 of 1000000; TPS: 4822.18; ETA: 2.5 minutes\n", - "Step 287999 of 1000000; TPS: 4820.05; ETA: 2.5 minutes\n", - "Step 288999 of 1000000; TPS: 4817.79; ETA: 2.5 minutes\n", - "Step 289999 of 1000000; TPS: 4815.93; ETA: 2.5 minutes\n", - "Step 290999 of 1000000; TPS: 4813.98; ETA: 2.5 minutes\n", - "Step 291999 of 1000000; TPS: 4812.25; ETA: 2.5 minutes\n", - "Step 292999 of 1000000; TPS: 4810.28; ETA: 2.4 minutes\n", - "Step 293999 of 1000000; TPS: 4808.3; ETA: 2.4 minutes\n", - "Step 294999 of 1000000; TPS: 4806.51; ETA: 2.4 minutes\n", - "Step 295999 of 1000000; TPS: 4806.16; ETA: 2.4 minutes\n", - "Step 296999 of 1000000; TPS: 4805.39; ETA: 2.4 minutes\n", - "Step 297999 of 1000000; TPS: 4804.36; ETA: 2.4 minutes\n", - "Step 298999 of 1000000; TPS: 4803.83; ETA: 2.4 minutes\n", - "Step 299999 of 1000000; TPS: 4803.26; ETA: 2.4 minutes\n", - "Step 300999 of 1000000; TPS: 4802.66; ETA: 2.4 minutes\n", - "Step 301999 of 1000000; TPS: 4802.02; ETA: 2.4 minutes\n", - "Step 302999 of 1000000; TPS: 4801.29; ETA: 2.4 minutes\n", - "Step 303999 of 1000000; TPS: 4800.71; ETA: 2.4 minutes\n", - "Step 304999 of 1000000; TPS: 4799.96; ETA: 2.4 minutes\n", - "Step 305999 of 1000000; TPS: 4799.53; ETA: 2.4 minutes\n", - "Step 306999 of 1000000; TPS: 4798.45; ETA: 2.4 minutes\n", - "Step 307999 of 1000000; TPS: 4795.74; ETA: 2.4 minutes\n", - "Step 308999 of 1000000; TPS: 4793.15; ETA: 2.4 minutes\n", - "Step 309999 of 1000000; TPS: 4790.41; ETA: 2.4 minutes\n", - "Step 310999 of 1000000; TPS: 4787.93; ETA: 2.4 minutes\n", - "Step 311999 of 1000000; TPS: 4785.66; ETA: 2.4 minutes\n", - "Step 312999 of 1000000; TPS: 4783.29; ETA: 2.4 minutes\n", - "Step 313999 of 1000000; TPS: 4781.07; ETA: 2.4 minutes\n", - "Step 314999 of 1000000; TPS: 4777.24; ETA: 2.4 minutes\n", - "Step 315999 of 1000000; TPS: 4774.68; ETA: 2.4 minutes\n", - "Step 316999 of 1000000; TPS: 4772.42; ETA: 2.4 minutes\n", - "Step 317999 of 1000000; TPS: 4770.37; ETA: 2.4 minutes\n", - "Step 318999 of 1000000; TPS: 4768.15; ETA: 2.4 minutes\n", - "Step 319999 of 1000000; TPS: 4766.02; ETA: 2.4 minutes\n", - "Step 320999 of 1000000; TPS: 4763.76; ETA: 2.4 minutes\n", - "Step 321999 of 1000000; TPS: 4761.71; ETA: 2.4 minutes\n", - "Step 322999 of 1000000; TPS: 4759.69; ETA: 2.4 minutes\n", - "Step 323999 of 1000000; TPS: 4757.6; ETA: 2.4 minutes\n", - "Step 324999 of 1000000; TPS: 4755.76; ETA: 2.4 minutes\n", - "Step 325999 of 1000000; TPS: 4754.33; ETA: 2.4 minutes\n", - "Step 326999 of 1000000; TPS: 4753.71; ETA: 2.4 minutes\n", - "Step 327999 of 1000000; TPS: 4753.02; ETA: 2.4 minutes\n", - "Step 328999 of 1000000; TPS: 4752.46; ETA: 2.4 minutes\n", - "Step 329999 of 1000000; TPS: 4752.04; ETA: 2.3 minutes\n", - "Step 330999 of 1000000; TPS: 4751.19; ETA: 2.3 minutes\n", - "Step 331999 of 1000000; TPS: 4852.15; ETA: 2.3 minutes\n", - "Step 332999 of 1000000; TPS: 4850.59; ETA: 2.3 minutes\n", - "Step 333999 of 1000000; TPS: 4849.62; ETA: 2.3 minutes\n", - "Step 334999 of 1000000; TPS: 4848.57; ETA: 2.3 minutes\n", - "Step 335999 of 1000000; TPS: 4847.76; ETA: 2.3 minutes\n", - "Step 336999 of 1000000; TPS: 4846.78; ETA: 2.3 minutes\n", - "Step 337999 of 1000000; TPS: 4845.77; ETA: 2.3 minutes\n", - "Step 338999 of 1000000; TPS: 4844.64; ETA: 2.3 minutes\n", - "Step 339999 of 1000000; TPS: 4843.81; ETA: 2.3 minutes\n", - "Step 340999 of 1000000; TPS: 4842.85; ETA: 2.3 minutes\n", - "Step 341999 of 1000000; TPS: 4841.93; ETA: 2.3 minutes\n", - "Step 342999 of 1000000; TPS: 4840.92; ETA: 2.3 minutes\n", - "Step 343999 of 1000000; TPS: 4839.45; ETA: 2.3 minutes\n", - "Step 344999 of 1000000; TPS: 4837.4; ETA: 2.3 minutes\n", - "Step 345999 of 1000000; TPS: 4836.6; ETA: 2.3 minutes\n", - "Step 346999 of 1000000; TPS: 4835.53; ETA: 2.3 minutes\n", - "Step 347999 of 1000000; TPS: 4834.58; ETA: 2.2 minutes\n", - "Step 348999 of 1000000; TPS: 4833.58; ETA: 2.2 minutes\n", - "Step 349999 of 1000000; TPS: 4831.7; ETA: 2.2 minutes\n", - "Step 350999 of 1000000; TPS: 4829.56; ETA: 2.2 minutes\n", - "Step 351999 of 1000000; TPS: 4827.31; ETA: 2.2 minutes\n", - "Step 352999 of 1000000; TPS: 4824.33; ETA: 2.2 minutes\n", - "Step 353999 of 1000000; TPS: 4821.07; ETA: 2.2 minutes\n", - "Step 354999 of 1000000; TPS: 4818.84; ETA: 2.2 minutes\n", - "Step 355999 of 1000000; TPS: 4816.5; ETA: 2.2 minutes\n", - "Step 356999 of 1000000; TPS: 4814.17; ETA: 2.2 minutes\n", - "Step 357999 of 1000000; TPS: 4811.88; ETA: 2.2 minutes\n", - "Step 358999 of 1000000; TPS: 4809.94; ETA: 2.2 minutes\n", - "Step 359999 of 1000000; TPS: 4807.53; ETA: 2.2 minutes\n", - "Step 360999 of 1000000; TPS: 4805.33; ETA: 2.2 minutes\n", - "Step 361999 of 1000000; TPS: 4803.01; ETA: 2.2 minutes\n", - "Step 362999 of 1000000; TPS: 4800.89; ETA: 2.2 minutes\n", - "Step 363999 of 1000000; TPS: 4798.69; ETA: 2.2 minutes\n", - "Step 364999 of 1000000; TPS: 4795.63; ETA: 2.2 minutes\n", - "Step 365999 of 1000000; TPS: 4794.02; ETA: 2.2 minutes\n", - "Step 366999 of 1000000; TPS: 4792.48; ETA: 2.2 minutes\n", - "Step 367999 of 1000000; TPS: 4790.77; ETA: 2.2 minutes\n", - "Step 368999 of 1000000; TPS: 4788.99; ETA: 2.2 minutes\n", - "Step 369999 of 1000000; TPS: 4787.49; ETA: 2.2 minutes\n", - "Step 370999 of 1000000; TPS: 4786.53; ETA: 2.2 minutes\n", - "Step 371999 of 1000000; TPS: 4785.74; ETA: 2.2 minutes\n", - "Step 372999 of 1000000; TPS: 4785.31; ETA: 2.2 minutes\n", - "Step 373999 of 1000000; TPS: 4785.09; ETA: 2.2 minutes\n", - "Step 374999 of 1000000; TPS: 4784.77; ETA: 2.2 minutes\n", - "Step 375999 of 1000000; TPS: 4784.17; ETA: 2.2 minutes\n", - "Step 376999 of 1000000; TPS: 4783.62; ETA: 2.2 minutes\n", - "Step 377999 of 1000000; TPS: 4783.03; ETA: 2.2 minutes\n", - "Step 378999 of 1000000; TPS: 4782.42; ETA: 2.2 minutes\n", - "Step 379999 of 1000000; TPS: 4782.17; ETA: 2.2 minutes\n", - "Step 380999 of 1000000; TPS: 4781.77; ETA: 2.2 minutes\n", - "Step 381999 of 1000000; TPS: 4780.99; ETA: 2.2 minutes\n", - "Step 382999 of 1000000; TPS: 4780.49; ETA: 2.2 minutes\n", - "Step 383999 of 1000000; TPS: 4780.12; ETA: 2.1 minutes\n", - "Step 384999 of 1000000; TPS: 4779.77; ETA: 2.1 minutes\n", - "Step 385999 of 1000000; TPS: 4778.27; ETA: 2.1 minutes\n", - "Step 386999 of 1000000; TPS: 4777.06; ETA: 2.1 minutes\n", - "Step 387999 of 1000000; TPS: 4775.45; ETA: 2.1 minutes\n", - "Step 388999 of 1000000; TPS: 4774.1; ETA: 2.1 minutes\n", - "Step 389999 of 1000000; TPS: 4772.85; ETA: 2.1 minutes\n", - "Step 390999 of 1000000; TPS: 4770.58; ETA: 2.1 minutes\n", - "Step 391999 of 1000000; TPS: 4768.9; ETA: 2.1 minutes\n", - "Step 392999 of 1000000; TPS: 4767.27; ETA: 2.1 minutes\n", - "Step 393999 of 1000000; TPS: 4765.04; ETA: 2.1 minutes\n", - "Step 394999 of 1000000; TPS: 4763.58; ETA: 2.1 minutes\n", - "Step 395999 of 1000000; TPS: 4762.18; ETA: 2.1 minutes\n", - "Step 396999 of 1000000; TPS: 4760.56; ETA: 2.1 minutes\n", - "Step 397999 of 1000000; TPS: 4758.94; ETA: 2.1 minutes\n", - "Step 398999 of 1000000; TPS: 4757.41; ETA: 2.1 minutes\n", - "Step 399999 of 1000000; TPS: 4755.97; ETA: 2.1 minutes\n", - "Step 400999 of 1000000; TPS: 4754.4; ETA: 2.1 minutes\n", - "Step 401999 of 1000000; TPS: 4752.88; ETA: 2.1 minutes\n", - "Step 402999 of 1000000; TPS: 4751.2; ETA: 2.1 minutes\n", - "Step 403999 of 1000000; TPS: 4749.56; ETA: 2.1 minutes\n", - "Step 404999 of 1000000; TPS: 4748.03; ETA: 2.1 minutes\n", - "Step 405999 of 1000000; TPS: 4746.44; ETA: 2.1 minutes\n", - "Step 406999 of 1000000; TPS: 4744.8; ETA: 2.1 minutes\n", - "Step 407999 of 1000000; TPS: 4743.32; ETA: 2.1 minutes\n", - "Step 408999 of 1000000; TPS: 4739.08; ETA: 2.1 minutes\n", - "Step 409999 of 1000000; TPS: 4738.55; ETA: 2.1 minutes\n", - "Step 410999 of 1000000; TPS: 4737.95; ETA: 2.1 minutes\n", - "Step 411999 of 1000000; TPS: 4737.43; ETA: 2.1 minutes\n", - "Step 412999 of 1000000; TPS: 4736.85; ETA: 2.1 minutes\n", - "Step 413999 of 1000000; TPS: 4736.38; ETA: 2.1 minutes\n", - "Step 414999 of 1000000; TPS: 4735.93; ETA: 2.1 minutes\n", - "Step 415999 of 1000000; TPS: 4734.76; ETA: 2.1 minutes\n", - "Step 416999 of 1000000; TPS: 4734.34; ETA: 2.1 minutes\n", - "Step 417999 of 1000000; TPS: 4733.89; ETA: 2.0 minutes\n", - "Step 418999 of 1000000; TPS: 4732.88; ETA: 2.0 minutes\n", - "Step 419999 of 1000000; TPS: 4731.82; ETA: 2.0 minutes\n", - "Step 420999 of 1000000; TPS: 4730.48; ETA: 2.0 minutes\n", - "Step 421999 of 1000000; TPS: 4728.27; ETA: 2.0 minutes\n", - "Step 422999 of 1000000; TPS: 4725.61; ETA: 2.0 minutes\n", - "Step 423999 of 1000000; TPS: 4723.64; ETA: 2.0 minutes\n", - "Step 424999 of 1000000; TPS: 4721.69; ETA: 2.0 minutes\n", - "Step 425999 of 1000000; TPS: 4719.66; ETA: 2.0 minutes\n", - "Step 426999 of 1000000; TPS: 4717.57; ETA: 2.0 minutes\n", - "Step 427999 of 1000000; TPS: 4715.36; ETA: 2.0 minutes\n", - "Step 428999 of 1000000; TPS: 4713.29; ETA: 2.0 minutes\n", - "Step 429999 of 1000000; TPS: 4711.28; ETA: 2.0 minutes\n", - "Step 430999 of 1000000; TPS: 4709.08; ETA: 2.0 minutes\n", - "Step 431999 of 1000000; TPS: 4706.94; ETA: 2.0 minutes\n", - "Step 432999 of 1000000; TPS: 4705.04; ETA: 2.0 minutes\n", - "Step 433999 of 1000000; TPS: 4701.83; ETA: 2.0 minutes\n", - "Step 434999 of 1000000; TPS: 4699.69; ETA: 2.0 minutes\n", - "Step 435999 of 1000000; TPS: 4697.81; ETA: 2.0 minutes\n", - "Step 436999 of 1000000; TPS: 4695.97; ETA: 2.0 minutes\n", - "Step 437999 of 1000000; TPS: 4694.11; ETA: 2.0 minutes\n", - "Step 438999 of 1000000; TPS: 4691.98; ETA: 2.0 minutes\n", - "Step 439999 of 1000000; TPS: 4689.88; ETA: 2.0 minutes\n", - "Step 440999 of 1000000; TPS: 4687.84; ETA: 2.0 minutes\n", - "Step 441999 of 1000000; TPS: 4686.68; ETA: 2.0 minutes\n", - "Step 442999 of 1000000; TPS: 4685.71; ETA: 2.0 minutes\n", - "Step 443999 of 1000000; TPS: 4684.5; ETA: 2.0 minutes\n", - "Step 444999 of 1000000; TPS: 4683.52; ETA: 2.0 minutes\n", - "Step 445999 of 1000000; TPS: 4682.62; ETA: 2.0 minutes\n", - "Step 446999 of 1000000; TPS: 4681.6; ETA: 2.0 minutes\n", - "Step 447999 of 1000000; TPS: 4680.62; ETA: 2.0 minutes\n", - "Step 448999 of 1000000; TPS: 4679.64; ETA: 2.0 minutes\n", - "Step 449999 of 1000000; TPS: 4678.89; ETA: 2.0 minutes\n", - "Step 450999 of 1000000; TPS: 4677.19; ETA: 2.0 minutes\n", - "Step 451999 of 1000000; TPS: 4676.33; ETA: 2.0 minutes\n", - "Step 452999 of 1000000; TPS: 4675.54; ETA: 1.9 minutes\n", - "Step 453999 of 1000000; TPS: 4674.21; ETA: 1.9 minutes\n", - "Step 454999 of 1000000; TPS: 4672.23; ETA: 1.9 minutes\n", - "Step 455999 of 1000000; TPS: 4670.31; ETA: 1.9 minutes\n", - "Step 456999 of 1000000; TPS: 4668.22; ETA: 1.9 minutes\n", - "Step 457999 of 1000000; TPS: 4666.21; ETA: 1.9 minutes\n", - "Step 458999 of 1000000; TPS: 4664.49; ETA: 1.9 minutes\n", - "Step 459999 of 1000000; TPS: 4662.41; ETA: 1.9 minutes\n", - "Step 460999 of 1000000; TPS: 4660.28; ETA: 1.9 minutes\n", - "Step 461999 of 1000000; TPS: 4658.26; ETA: 1.9 minutes\n", - "Step 462999 of 1000000; TPS: 4656.48; ETA: 1.9 minutes\n", - "Step 463999 of 1000000; TPS: 4654.63; ETA: 1.9 minutes\n", - "Step 464999 of 1000000; TPS: 4652.69; ETA: 1.9 minutes\n", - "Step 465999 of 1000000; TPS: 4651.1; ETA: 1.9 minutes\n", - "Step 466999 of 1000000; TPS: 4649.36; ETA: 1.9 minutes\n", - "Step 467999 of 1000000; TPS: 4717.02; ETA: 1.9 minutes\n", - "Step 468999 of 1000000; TPS: 4714.11; ETA: 1.9 minutes\n", - "Step 469999 of 1000000; TPS: 4711.97; ETA: 1.9 minutes\n", - "Step 470999 of 1000000; TPS: 4709.7; ETA: 1.9 minutes\n", - "Step 471999 of 1000000; TPS: 4708.13; ETA: 1.9 minutes\n", - "Step 472999 of 1000000; TPS: 4706.28; ETA: 1.9 minutes\n", - "Step 473999 of 1000000; TPS: 4704.66; ETA: 1.9 minutes\n", - "Step 474999 of 1000000; TPS: 4702.92; ETA: 1.9 minutes\n", - "Step 475999 of 1000000; TPS: 4701.16; ETA: 1.9 minutes\n", - "Step 476999 of 1000000; TPS: 4699.54; ETA: 1.9 minutes\n", - "Step 477999 of 1000000; TPS: 4697.05; ETA: 1.9 minutes\n", - "Step 478999 of 1000000; TPS: 4695.84; ETA: 1.8 minutes\n", - "Step 479999 of 1000000; TPS: 4695.09; ETA: 1.8 minutes\n", - "Step 480999 of 1000000; TPS: 4694.27; ETA: 1.8 minutes\n", - "Step 481999 of 1000000; TPS: 4693.48; ETA: 1.8 minutes\n", - "Step 482999 of 1000000; TPS: 4692.61; ETA: 1.8 minutes\n", - "Step 483999 of 1000000; TPS: 4691.77; ETA: 1.8 minutes\n", - "Step 484999 of 1000000; TPS: 4691.14; ETA: 1.8 minutes\n", - "Step 485999 of 1000000; TPS: 4690.44; ETA: 1.8 minutes\n", - "Step 486999 of 1000000; TPS: 4689.8; ETA: 1.8 minutes\n", - "Step 487999 of 1000000; TPS: 4689.02; ETA: 1.8 minutes\n", - "Step 488999 of 1000000; TPS: 4688.28; ETA: 1.8 minutes\n", - "Step 489999 of 1000000; TPS: 4687.57; ETA: 1.8 minutes\n", - "Step 490999 of 1000000; TPS: 4686.76; ETA: 1.8 minutes\n", - "Step 491999 of 1000000; TPS: 4686.05; ETA: 1.8 minutes\n", - "Step 492999 of 1000000; TPS: 4685.42; ETA: 1.8 minutes\n", - "Step 493999 of 1000000; TPS: 4683.97; ETA: 1.8 minutes\n", - "Step 494999 of 1000000; TPS: 4682.45; ETA: 1.8 minutes\n", - "Step 495999 of 1000000; TPS: 4680.87; ETA: 1.8 minutes\n", - "Step 496999 of 1000000; TPS: 4679.25; ETA: 1.8 minutes\n", - "Step 497999 of 1000000; TPS: 4677.09; ETA: 1.8 minutes\n", - "Step 498999 of 1000000; TPS: 4675.65; ETA: 1.8 minutes\n", - "Step 499999 of 1000000; TPS: 4674.06; ETA: 1.8 minutes\n", - "Step 500999 of 1000000; TPS: 4672.41; ETA: 1.8 minutes\n", - "Step 501999 of 1000000; TPS: 4670.97; ETA: 1.8 minutes\n", - "Step 502999 of 1000000; TPS: 4669.5; ETA: 1.8 minutes\n", - "Step 503999 of 1000000; TPS: 4667.51; ETA: 1.8 minutes\n", - "Step 504999 of 1000000; TPS: 4665.9; ETA: 1.8 minutes\n", - "Step 505999 of 1000000; TPS: 4664.47; ETA: 1.8 minutes\n", - "Step 506999 of 1000000; TPS: 4662.87; ETA: 1.8 minutes\n", - "Step 507999 of 1000000; TPS: 4661.47; ETA: 1.8 minutes\n", - "Step 508999 of 1000000; TPS: 4660.01; ETA: 1.8 minutes\n", - "Step 509999 of 1000000; TPS: 4658.49; ETA: 1.8 minutes\n", - "Step 510999 of 1000000; TPS: 4657.19; ETA: 1.7 minutes\n", - "Step 511999 of 1000000; TPS: 4655.92; ETA: 1.7 minutes\n", - "Step 512999 of 1000000; TPS: 4654.51; ETA: 1.7 minutes\n", - "Step 513999 of 1000000; TPS: 4652.99; ETA: 1.7 minutes\n", - "Step 514999 of 1000000; TPS: 4651.54; ETA: 1.7 minutes\n", - "Step 515999 of 1000000; TPS: 4650.25; ETA: 1.7 minutes\n", - "Step 516999 of 1000000; TPS: 4648.87; ETA: 1.7 minutes\n", - "Step 517999 of 1000000; TPS: 4648.14; ETA: 1.7 minutes\n", - "Step 518999 of 1000000; TPS: 4647.53; ETA: 1.7 minutes\n", - "Step 519999 of 1000000; TPS: 4646.95; ETA: 1.7 minutes\n", - "Step 520999 of 1000000; TPS: 4646.43; ETA: 1.7 minutes\n", - "Step 521999 of 1000000; TPS: 4645.93; ETA: 1.7 minutes\n", - "Step 522999 of 1000000; TPS: 4645.36; ETA: 1.7 minutes\n", - "Step 523999 of 1000000; TPS: 4644.8; ETA: 1.7 minutes\n", - "Step 524999 of 1000000; TPS: 4644.24; ETA: 1.7 minutes\n", - "Step 525999 of 1000000; TPS: 4643.76; ETA: 1.7 minutes\n", - "Step 526999 of 1000000; TPS: 4643.13; ETA: 1.7 minutes\n", - "Step 527999 of 1000000; TPS: 4641.8; ETA: 1.7 minutes\n", - "Step 528999 of 1000000; TPS: 4640.47; ETA: 1.7 minutes\n", - "Step 529999 of 1000000; TPS: 4638.51; ETA: 1.7 minutes\n", - "Step 530999 of 1000000; TPS: 4637.03; ETA: 1.7 minutes\n", - "Step 531999 of 1000000; TPS: 4635.8; ETA: 1.7 minutes\n", - "Step 532999 of 1000000; TPS: 4634.57; ETA: 1.7 minutes\n", - "Step 533999 of 1000000; TPS: 4633.31; ETA: 1.7 minutes\n", - "Step 534999 of 1000000; TPS: 4632.2; ETA: 1.7 minutes\n", - "Step 535999 of 1000000; TPS: 4630.82; ETA: 1.7 minutes\n", - "Step 536999 of 1000000; TPS: 4629.61; ETA: 1.7 minutes\n", - "Step 537999 of 1000000; TPS: 4628.35; ETA: 1.7 minutes\n", - "Step 538999 of 1000000; TPS: 4626.56; ETA: 1.7 minutes\n", - "Step 539999 of 1000000; TPS: 4624.42; ETA: 1.7 minutes\n", - "Step 540999 of 1000000; TPS: 4622.94; ETA: 1.7 minutes\n", - "Step 541999 of 1000000; TPS: 4621.77; ETA: 1.7 minutes\n", - "Step 542999 of 1000000; TPS: 4620.57; ETA: 1.6 minutes\n", - "Step 543999 of 1000000; TPS: 4619.12; ETA: 1.6 minutes\n", - "Step 544999 of 1000000; TPS: 4617.06; ETA: 1.6 minutes\n", - "Step 545999 of 1000000; TPS: 4615.87; ETA: 1.6 minutes\n", - "Step 546999 of 1000000; TPS: 4614.76; ETA: 1.6 minutes\n", - "Step 547999 of 1000000; TPS: 4613.38; ETA: 1.6 minutes\n", - "Step 548999 of 1000000; TPS: 4612.89; ETA: 1.6 minutes\n", - "Step 549999 of 1000000; TPS: 4612.4; ETA: 1.6 minutes\n", - "Step 550999 of 1000000; TPS: 4611.96; ETA: 1.6 minutes\n", - "Step 551999 of 1000000; TPS: 4611.44; ETA: 1.6 minutes\n", - "Step 552999 of 1000000; TPS: 4610.95; ETA: 1.6 minutes\n", - "Step 553999 of 1000000; TPS: 4610.35; ETA: 1.6 minutes\n", - "Step 554999 of 1000000; TPS: 4609.67; ETA: 1.6 minutes\n", - "Step 555999 of 1000000; TPS: 4609.16; ETA: 1.6 minutes\n", - "Step 556999 of 1000000; TPS: 4608.7; ETA: 1.6 minutes\n", - "Step 557999 of 1000000; TPS: 4608.25; ETA: 1.6 minutes\n", - "Step 558999 of 1000000; TPS: 4607.76; ETA: 1.6 minutes\n", - "Step 559999 of 1000000; TPS: 4607.31; ETA: 1.6 minutes\n", - "Step 560999 of 1000000; TPS: 4606.86; ETA: 1.6 minutes\n", - "Step 561999 of 1000000; TPS: 4605.72; ETA: 1.6 minutes\n", - "Step 562999 of 1000000; TPS: 4604.44; ETA: 1.6 minutes\n", - "Step 563999 of 1000000; TPS: 4603.24; ETA: 1.6 minutes\n", - "Step 564999 of 1000000; TPS: 4601.97; ETA: 1.6 minutes\n", - "Step 565999 of 1000000; TPS: 4600.58; ETA: 1.6 minutes\n", - "Step 566999 of 1000000; TPS: 4599.33; ETA: 1.6 minutes\n", - "Step 567999 of 1000000; TPS: 4598.19; ETA: 1.6 minutes\n", - "Step 568999 of 1000000; TPS: 4597.17; ETA: 1.6 minutes\n", - "Step 569999 of 1000000; TPS: 4596.12; ETA: 1.6 minutes\n", - "Step 570999 of 1000000; TPS: 4594.92; ETA: 1.6 minutes\n", - "Step 571999 of 1000000; TPS: 4593.8; ETA: 1.6 minutes\n", - "Step 572999 of 1000000; TPS: 4592.7; ETA: 1.5 minutes\n", - "Step 573999 of 1000000; TPS: 4591.46; ETA: 1.5 minutes\n", - "Step 574999 of 1000000; TPS: 4589.78; ETA: 1.5 minutes\n", - "Step 575999 of 1000000; TPS: 4588.28; ETA: 1.5 minutes\n", - "Step 576999 of 1000000; TPS: 4587.01; ETA: 1.5 minutes\n", - "Step 577999 of 1000000; TPS: 4586.01; ETA: 1.5 minutes\n", - "Step 578999 of 1000000; TPS: 4584.97; ETA: 1.5 minutes\n", - "Step 579999 of 1000000; TPS: 4583.42; ETA: 1.5 minutes\n", - "Step 580999 of 1000000; TPS: 4582.78; ETA: 1.5 minutes\n", - "Step 581999 of 1000000; TPS: 4582.45; ETA: 1.5 minutes\n", - "Step 582999 of 1000000; TPS: 4582.17; ETA: 1.5 minutes\n", - "Step 583999 of 1000000; TPS: 4581.82; ETA: 1.5 minutes\n", - "Step 584999 of 1000000; TPS: 4581.43; ETA: 1.5 minutes\n", - "Step 585999 of 1000000; TPS: 4580.97; ETA: 1.5 minutes\n", - "Step 586999 of 1000000; TPS: 4580.65; ETA: 1.5 minutes\n", - "Step 587999 of 1000000; TPS: 4580.16; ETA: 1.5 minutes\n", - "Step 588999 of 1000000; TPS: 4579.96; ETA: 1.5 minutes\n", - "Step 589999 of 1000000; TPS: 4579.75; ETA: 1.5 minutes\n", - "Step 590999 of 1000000; TPS: 4579.54; ETA: 1.5 minutes\n", - "Step 591999 of 1000000; TPS: 4579.27; ETA: 1.5 minutes\n", - "Step 592999 of 1000000; TPS: 4578.88; ETA: 1.5 minutes\n", - "Step 593999 of 1000000; TPS: 4578.54; ETA: 1.5 minutes\n", - "Step 594999 of 1000000; TPS: 4578.05; ETA: 1.5 minutes\n", - "Step 595999 of 1000000; TPS: 4577.47; ETA: 1.5 minutes\n", - "Step 596999 of 1000000; TPS: 4576.63; ETA: 1.5 minutes\n", - "Step 597999 of 1000000; TPS: 4575.72; ETA: 1.5 minutes\n", - "Step 598999 of 1000000; TPS: 4574.71; ETA: 1.5 minutes\n", - "Step 599999 of 1000000; TPS: 4574.02; ETA: 1.5 minutes\n", - "Step 600999 of 1000000; TPS: 4628.26; ETA: 1.4 minutes\n", - "Step 601999 of 1000000; TPS: 4627.6; ETA: 1.4 minutes\n", - "Step 602999 of 1000000; TPS: 4626.86; ETA: 1.4 minutes\n", - "Step 603999 of 1000000; TPS: 4626.18; ETA: 1.4 minutes\n", - "Step 604999 of 1000000; TPS: 4625.46; ETA: 1.4 minutes\n", - "Step 605999 of 1000000; TPS: 4624.52; ETA: 1.4 minutes\n", - "Step 606999 of 1000000; TPS: 4623.8; ETA: 1.4 minutes\n", - "Step 607999 of 1000000; TPS: 4623.02; ETA: 1.4 minutes\n", - "Step 608999 of 1000000; TPS: 4622.23; ETA: 1.4 minutes\n", - "Step 609999 of 1000000; TPS: 4621.44; ETA: 1.4 minutes\n", - "Step 610999 of 1000000; TPS: 4620.66; ETA: 1.4 minutes\n", - "Step 611999 of 1000000; TPS: 4619.86; ETA: 1.4 minutes\n", - "Step 612999 of 1000000; TPS: 4618.86; ETA: 1.4 minutes\n", - "Step 613999 of 1000000; TPS: 4618.16; ETA: 1.4 minutes\n", - "Step 614999 of 1000000; TPS: 4617.3; ETA: 1.4 minutes\n", - "Step 615999 of 1000000; TPS: 4616.57; ETA: 1.4 minutes\n", - "Step 616999 of 1000000; TPS: 4615.58; ETA: 1.4 minutes\n", - "Step 617999 of 1000000; TPS: 4614.79; ETA: 1.4 minutes\n", - "Step 618999 of 1000000; TPS: 4614.0; ETA: 1.4 minutes\n", - "Step 619999 of 1000000; TPS: 4613.31; ETA: 1.4 minutes\n", - "Step 620999 of 1000000; TPS: 4613.01; ETA: 1.4 minutes\n", - "Step 621999 of 1000000; TPS: 4612.9; ETA: 1.4 minutes\n", - "Step 622999 of 1000000; TPS: 4612.18; ETA: 1.4 minutes\n", - "Step 623999 of 1000000; TPS: 4611.97; ETA: 1.4 minutes\n", - "Step 624999 of 1000000; TPS: 4611.78; ETA: 1.4 minutes\n", - "Step 625999 of 1000000; TPS: 4611.51; ETA: 1.4 minutes\n", - "Step 626999 of 1000000; TPS: 4611.31; ETA: 1.3 minutes\n", - "Step 627999 of 1000000; TPS: 4610.83; ETA: 1.3 minutes\n", - "Step 628999 of 1000000; TPS: 4610.76; ETA: 1.3 minutes\n", - "Step 629999 of 1000000; TPS: 4609.99; ETA: 1.3 minutes\n", - "Step 630999 of 1000000; TPS: 4609.84; ETA: 1.3 minutes\n", - "Step 631999 of 1000000; TPS: 4609.74; ETA: 1.3 minutes\n", - "Step 632999 of 1000000; TPS: 4609.7; ETA: 1.3 minutes\n", - "Step 633999 of 1000000; TPS: 4609.67; ETA: 1.3 minutes\n", - "Step 634999 of 1000000; TPS: 4609.59; ETA: 1.3 minutes\n", - "Step 635999 of 1000000; TPS: 4609.51; ETA: 1.3 minutes\n", - "Step 636999 of 1000000; TPS: 4609.31; ETA: 1.3 minutes\n", - "Step 637999 of 1000000; TPS: 4608.55; ETA: 1.3 minutes\n", - "Step 638999 of 1000000; TPS: 4607.75; ETA: 1.3 minutes\n", - "Step 639999 of 1000000; TPS: 4606.98; ETA: 1.3 minutes\n", - "Step 640999 of 1000000; TPS: 4606.28; ETA: 1.3 minutes\n", - "Step 641999 of 1000000; TPS: 4605.49; ETA: 1.3 minutes\n", - "Step 642999 of 1000000; TPS: 4604.64; ETA: 1.3 minutes\n", - "Step 643999 of 1000000; TPS: 4603.93; ETA: 1.3 minutes\n", - "Step 644999 of 1000000; TPS: 4603.16; ETA: 1.3 minutes\n", - "Step 645999 of 1000000; TPS: 4602.43; ETA: 1.3 minutes\n", - "Step 646999 of 1000000; TPS: 4601.73; ETA: 1.3 minutes\n", - "Step 647999 of 1000000; TPS: 4600.97; ETA: 1.3 minutes\n", - "Step 648999 of 1000000; TPS: 4600.19; ETA: 1.3 minutes\n", - "Step 649999 of 1000000; TPS: 4599.42; ETA: 1.3 minutes\n", - "Step 650999 of 1000000; TPS: 4598.63; ETA: 1.3 minutes\n", - "Step 651999 of 1000000; TPS: 4597.99; ETA: 1.3 minutes\n", - "Step 652999 of 1000000; TPS: 4596.99; ETA: 1.3 minutes\n", - "Step 653999 of 1000000; TPS: 4596.25; ETA: 1.3 minutes\n", - "Step 654999 of 1000000; TPS: 4595.55; ETA: 1.3 minutes\n", - "Step 655999 of 1000000; TPS: 4594.79; ETA: 1.2 minutes\n", - "Step 656999 of 1000000; TPS: 4594.07; ETA: 1.2 minutes\n", - "Step 657999 of 1000000; TPS: 4593.41; ETA: 1.2 minutes\n", - "Step 658999 of 1000000; TPS: 4592.82; ETA: 1.2 minutes\n", - "Step 659999 of 1000000; TPS: 4592.09; ETA: 1.2 minutes\n", - "Step 660999 of 1000000; TPS: 4591.37; ETA: 1.2 minutes\n", - "Step 661999 of 1000000; TPS: 4590.81; ETA: 1.2 minutes\n", - "Step 662999 of 1000000; TPS: 4590.49; ETA: 1.2 minutes\n", - "Step 663999 of 1000000; TPS: 4590.3; ETA: 1.2 minutes\n", - "Step 664999 of 1000000; TPS: 4590.14; ETA: 1.2 minutes\n", - "Step 665999 of 1000000; TPS: 4590.04; ETA: 1.2 minutes\n", - "Step 666999 of 1000000; TPS: 4589.44; ETA: 1.2 minutes\n", - "Step 667999 of 1000000; TPS: 4589.32; ETA: 1.2 minutes\n", - "Step 668999 of 1000000; TPS: 4589.23; ETA: 1.2 minutes\n", - "Step 669999 of 1000000; TPS: 4589.31; ETA: 1.2 minutes\n", - "Step 670999 of 1000000; TPS: 4589.28; ETA: 1.2 minutes\n", - "Step 671999 of 1000000; TPS: 4589.08; ETA: 1.2 minutes\n", - "Step 672999 of 1000000; TPS: 4587.7; ETA: 1.2 minutes\n", - "Step 673999 of 1000000; TPS: 4587.06; ETA: 1.2 minutes\n", - "Step 674999 of 1000000; TPS: 4586.35; ETA: 1.2 minutes\n", - "Step 675999 of 1000000; TPS: 4585.59; ETA: 1.2 minutes\n", - "Step 676999 of 1000000; TPS: 4584.84; ETA: 1.2 minutes\n", - "Step 677999 of 1000000; TPS: 4584.22; ETA: 1.2 minutes\n", - "Step 678999 of 1000000; TPS: 4583.52; ETA: 1.2 minutes\n", - "Step 679999 of 1000000; TPS: 4582.89; ETA: 1.2 minutes\n", - "Step 680999 of 1000000; TPS: 4582.13; ETA: 1.2 minutes\n", - "Step 681999 of 1000000; TPS: 4581.53; ETA: 1.2 minutes\n", - "Step 682999 of 1000000; TPS: 4580.86; ETA: 1.2 minutes\n", - "Step 683999 of 1000000; TPS: 4580.27; ETA: 1.1 minutes\n", - "Step 684999 of 1000000; TPS: 4579.0; ETA: 1.1 minutes\n", - "Step 685999 of 1000000; TPS: 4578.25; ETA: 1.1 minutes\n", - "Step 686999 of 1000000; TPS: 4577.53; ETA: 1.1 minutes\n", - "Step 687999 of 1000000; TPS: 4576.87; ETA: 1.1 minutes\n", - "Step 688999 of 1000000; TPS: 4576.18; ETA: 1.1 minutes\n", - "Step 689999 of 1000000; TPS: 4575.46; ETA: 1.1 minutes\n", - "Step 690999 of 1000000; TPS: 4574.9; ETA: 1.1 minutes\n", - "Step 691999 of 1000000; TPS: 4574.49; ETA: 1.1 minutes\n", - "Step 692999 of 1000000; TPS: 4574.11; ETA: 1.1 minutes\n", - "Step 693999 of 1000000; TPS: 4574.11; ETA: 1.1 minutes\n", - "Step 694999 of 1000000; TPS: 4574.23; ETA: 1.1 minutes\n", - "Step 695999 of 1000000; TPS: 4574.21; ETA: 1.1 minutes\n", - "Step 696999 of 1000000; TPS: 4574.26; ETA: 1.1 minutes\n", - "Step 697999 of 1000000; TPS: 4574.13; ETA: 1.1 minutes\n", - "Step 698999 of 1000000; TPS: 4573.84; ETA: 1.1 minutes\n", - "Step 699999 of 1000000; TPS: 4573.84; ETA: 1.1 minutes\n", - "Step 700999 of 1000000; TPS: 4573.95; ETA: 1.1 minutes\n", - "Step 701999 of 1000000; TPS: 4574.09; ETA: 1.1 minutes\n", - "Step 702999 of 1000000; TPS: 4574.11; ETA: 1.1 minutes\n", - "Step 703999 of 1000000; TPS: 4574.19; ETA: 1.1 minutes\n", - "Step 704999 of 1000000; TPS: 4574.03; ETA: 1.1 minutes\n", - "Step 705999 of 1000000; TPS: 4574.02; ETA: 1.1 minutes\n", - "Step 706999 of 1000000; TPS: 4574.02; ETA: 1.1 minutes\n", - "Step 707999 of 1000000; TPS: 4573.57; ETA: 1.1 minutes\n", - "Step 708999 of 1000000; TPS: 4573.14; ETA: 1.1 minutes\n", - "Step 709999 of 1000000; TPS: 4572.81; ETA: 1.1 minutes\n", - "Step 710999 of 1000000; TPS: 4572.5; ETA: 1.1 minutes\n", - "Step 711999 of 1000000; TPS: 4572.23; ETA: 1.0 minutes\n", - "Step 712999 of 1000000; TPS: 4571.64; ETA: 1.0 minutes\n", - "Step 713999 of 1000000; TPS: 4571.12; ETA: 1.0 minutes\n", - "Step 714999 of 1000000; TPS: 4570.55; ETA: 1.0 minutes\n", - "Step 715999 of 1000000; TPS: 4570.01; ETA: 1.0 minutes\n", - "Step 716999 of 1000000; TPS: 4569.17; ETA: 1.0 minutes\n", - "Step 717999 of 1000000; TPS: 4568.86; ETA: 1.0 minutes\n", - "Step 718999 of 1000000; TPS: 4568.62; ETA: 1.0 minutes\n", - "Step 719999 of 1000000; TPS: 4568.43; ETA: 1.0 minutes\n", - "Step 720999 of 1000000; TPS: 4568.18; ETA: 1.0 minutes\n", - "Step 721999 of 1000000; TPS: 4568.1; ETA: 1.0 minutes\n", - "Step 722999 of 1000000; TPS: 4567.95; ETA: 1.0 minutes\n", - "Step 723999 of 1000000; TPS: 4567.71; ETA: 1.0 minutes\n", - "Step 724999 of 1000000; TPS: 4567.33; ETA: 1.0 minutes\n", - "Step 725999 of 1000000; TPS: 4567.22; ETA: 1.0 minutes\n", - "Step 726999 of 1000000; TPS: 4567.15; ETA: 1.0 minutes\n", - "Step 727999 of 1000000; TPS: 4567.4; ETA: 1.0 minutes\n", - "Step 728999 of 1000000; TPS: 4567.66; ETA: 1.0 minutes\n", - "Step 729999 of 1000000; TPS: 4567.83; ETA: 1.0 minutes\n", - "Step 730999 of 1000000; TPS: 4567.9; ETA: 1.0 minutes\n", - "Step 731999 of 1000000; TPS: 4568.16; ETA: 1.0 minutes\n", - "Step 732999 of 1000000; TPS: 4568.48; ETA: 1.0 minutes\n", - "Step 733999 of 1000000; TPS: 4568.75; ETA: 1.0 minutes\n", - "Step 734999 of 1000000; TPS: 4569.11; ETA: 1.0 minutes\n", - "Step 735999 of 1000000; TPS: 4569.44; ETA: 1.0 minutes\n", - "Step 736999 of 1000000; TPS: 4569.71; ETA: 1.0 minutes\n", - "Step 737999 of 1000000; TPS: 4570.16; ETA: 1.0 minutes\n", - "Step 738999 of 1000000; TPS: 4570.79; ETA: 1.0 minutes\n", - "Step 739999 of 1000000; TPS: 4571.15; ETA: 0.9 minutes\n", - "Step 740999 of 1000000; TPS: 4571.48; ETA: 0.9 minutes\n", - "Step 741999 of 1000000; TPS: 4616.25; ETA: 0.9 minutes\n", - "Step 742999 of 1000000; TPS: 4616.66; ETA: 0.9 minutes\n", - "Step 743999 of 1000000; TPS: 4617.01; ETA: 0.9 minutes\n", - "Step 744999 of 1000000; TPS: 4617.55; ETA: 0.9 minutes\n", - "Step 745999 of 1000000; TPS: 4617.95; ETA: 0.9 minutes\n", - "Step 746999 of 1000000; TPS: 4618.28; ETA: 0.9 minutes\n", - "Step 747999 of 1000000; TPS: 4618.38; ETA: 0.9 minutes\n", - "Step 748999 of 1000000; TPS: 4618.43; ETA: 0.9 minutes\n", - "Step 749999 of 1000000; TPS: 4618.61; ETA: 0.9 minutes\n", - "Step 750999 of 1000000; TPS: 4618.83; ETA: 0.9 minutes\n", - "Step 751999 of 1000000; TPS: 4618.87; ETA: 0.9 minutes\n", - "Step 752999 of 1000000; TPS: 4618.73; ETA: 0.9 minutes\n", - "Step 753999 of 1000000; TPS: 4618.54; ETA: 0.9 minutes\n", - "Step 754999 of 1000000; TPS: 4618.37; ETA: 0.9 minutes\n", - "Step 755999 of 1000000; TPS: 4618.31; ETA: 0.9 minutes\n", - "Step 756999 of 1000000; TPS: 4618.16; ETA: 0.9 minutes\n", - "Step 757999 of 1000000; TPS: 4618.16; ETA: 0.9 minutes\n", - "Step 758999 of 1000000; TPS: 4618.18; ETA: 0.9 minutes\n", - "Step 759999 of 1000000; TPS: 4618.18; ETA: 0.9 minutes\n", - "Step 760999 of 1000000; TPS: 4618.23; ETA: 0.9 minutes\n", - "Step 761999 of 1000000; TPS: 4618.36; ETA: 0.9 minutes\n", - "Step 762999 of 1000000; TPS: 4618.45; ETA: 0.9 minutes\n", - "Step 763999 of 1000000; TPS: 4618.58; ETA: 0.9 minutes\n", - "Step 764999 of 1000000; TPS: 4618.42; ETA: 0.8 minutes\n", - "Step 765999 of 1000000; TPS: 4618.4; ETA: 0.8 minutes\n", - "Step 766999 of 1000000; TPS: 4618.44; ETA: 0.8 minutes\n", - "Step 767999 of 1000000; TPS: 4618.4; ETA: 0.8 minutes\n", - "Step 768999 of 1000000; TPS: 4618.27; ETA: 0.8 minutes\n", - "Step 769999 of 1000000; TPS: 4618.22; ETA: 0.8 minutes\n", - "Step 770999 of 1000000; TPS: 4617.24; ETA: 0.8 minutes\n", - "Step 771999 of 1000000; TPS: 4617.2; ETA: 0.8 minutes\n", - "Step 772999 of 1000000; TPS: 4616.98; ETA: 0.8 minutes\n", - "Step 773999 of 1000000; TPS: 4616.82; ETA: 0.8 minutes\n", - "Step 774999 of 1000000; TPS: 4616.82; ETA: 0.8 minutes\n", - "Step 775999 of 1000000; TPS: 4616.77; ETA: 0.8 minutes\n", - "Step 776999 of 1000000; TPS: 4616.91; ETA: 0.8 minutes\n", - "Step 777999 of 1000000; TPS: 4617.21; ETA: 0.8 minutes\n", - "Step 778999 of 1000000; TPS: 4617.58; ETA: 0.8 minutes\n", - "Step 779999 of 1000000; TPS: 4617.86; ETA: 0.8 minutes\n", - "Step 780999 of 1000000; TPS: 4618.06; ETA: 0.8 minutes\n", - "Step 781999 of 1000000; TPS: 4618.36; ETA: 0.8 minutes\n", - "Step 782999 of 1000000; TPS: 4618.87; ETA: 0.8 minutes\n", - "Step 783999 of 1000000; TPS: 4619.38; ETA: 0.8 minutes\n", - "Step 784999 of 1000000; TPS: 4619.78; ETA: 0.8 minutes\n", - "Step 785999 of 1000000; TPS: 4620.21; ETA: 0.8 minutes\n", - "Step 786999 of 1000000; TPS: 4620.47; ETA: 0.8 minutes\n", - "Step 787999 of 1000000; TPS: 4620.9; ETA: 0.8 minutes\n", - "Step 788999 of 1000000; TPS: 4621.3; ETA: 0.8 minutes\n", - "Step 789999 of 1000000; TPS: 4621.43; ETA: 0.8 minutes\n", - "Step 790999 of 1000000; TPS: 4621.37; ETA: 0.8 minutes\n", - "Step 791999 of 1000000; TPS: 4621.44; ETA: 0.8 minutes\n", - "Step 792999 of 1000000; TPS: 4621.51; ETA: 0.7 minutes\n", - "Step 793999 of 1000000; TPS: 4621.56; ETA: 0.7 minutes\n", - "Step 794999 of 1000000; TPS: 4621.57; ETA: 0.7 minutes\n", - "Step 795999 of 1000000; TPS: 4621.5; ETA: 0.7 minutes\n", - "Step 796999 of 1000000; TPS: 4621.51; ETA: 0.7 minutes\n", - "Step 797999 of 1000000; TPS: 4621.56; ETA: 0.7 minutes\n", - "Step 798999 of 1000000; TPS: 4621.57; ETA: 0.7 minutes\n", - "Step 799999 of 1000000; TPS: 4621.54; ETA: 0.7 minutes\n", - "Step 800999 of 1000000; TPS: 4621.52; ETA: 0.7 minutes\n", - "Step 801999 of 1000000; TPS: 4621.49; ETA: 0.7 minutes\n", - "Step 802999 of 1000000; TPS: 4621.37; ETA: 0.7 minutes\n", - "Step 803999 of 1000000; TPS: 4621.38; ETA: 0.7 minutes\n", - "Step 804999 of 1000000; TPS: 4621.37; ETA: 0.7 minutes\n", - "Step 805999 of 1000000; TPS: 4621.19; ETA: 0.7 minutes\n", - "Step 806999 of 1000000; TPS: 4621.11; ETA: 0.7 minutes\n", - "Step 807999 of 1000000; TPS: 4620.97; ETA: 0.7 minutes\n", - "Step 808999 of 1000000; TPS: 4620.92; ETA: 0.7 minutes\n", - "Step 809999 of 1000000; TPS: 4620.93; ETA: 0.7 minutes\n", - "Step 810999 of 1000000; TPS: 4620.91; ETA: 0.7 minutes\n", - "Step 811999 of 1000000; TPS: 4620.87; ETA: 0.7 minutes\n", - "Step 812999 of 1000000; TPS: 4620.97; ETA: 0.7 minutes\n", - "Step 813999 of 1000000; TPS: 4621.14; ETA: 0.7 minutes\n", - "Step 814999 of 1000000; TPS: 4621.28; ETA: 0.7 minutes\n", - "Step 815999 of 1000000; TPS: 4621.45; ETA: 0.7 minutes\n", - "Step 816999 of 1000000; TPS: 4621.6; ETA: 0.7 minutes\n", - "Step 817999 of 1000000; TPS: 4621.02; ETA: 0.7 minutes\n", - "Step 818999 of 1000000; TPS: 4621.41; ETA: 0.7 minutes\n", - "Step 819999 of 1000000; TPS: 4621.78; ETA: 0.6 minutes\n", - "Step 820999 of 1000000; TPS: 4622.13; ETA: 0.6 minutes\n", - "Step 821999 of 1000000; TPS: 4622.5; ETA: 0.6 minutes\n", - "Step 822999 of 1000000; TPS: 4623.05; ETA: 0.6 minutes\n", - "Step 823999 of 1000000; TPS: 4623.61; ETA: 0.6 minutes\n", - "Step 824999 of 1000000; TPS: 4624.14; ETA: 0.6 minutes\n", - "Step 825999 of 1000000; TPS: 4624.66; ETA: 0.6 minutes\n", - "Step 826999 of 1000000; TPS: 4625.14; ETA: 0.6 minutes\n", - "Step 827999 of 1000000; TPS: 4625.6; ETA: 0.6 minutes\n", - "Step 828999 of 1000000; TPS: 4625.8; ETA: 0.6 minutes\n", - "Step 829999 of 1000000; TPS: 4626.03; ETA: 0.6 minutes\n", - "Step 830999 of 1000000; TPS: 4625.88; ETA: 0.6 minutes\n", - "Step 831999 of 1000000; TPS: 4626.08; ETA: 0.6 minutes\n", - "Step 832999 of 1000000; TPS: 4626.14; ETA: 0.6 minutes\n", - "Step 833999 of 1000000; TPS: 4626.27; ETA: 0.6 minutes\n", - "Step 834999 of 1000000; TPS: 4626.36; ETA: 0.6 minutes\n", - "Step 835999 of 1000000; TPS: 4626.51; ETA: 0.6 minutes\n", - "Step 836999 of 1000000; TPS: 4626.64; ETA: 0.6 minutes\n", - "Step 837999 of 1000000; TPS: 4626.77; ETA: 0.6 minutes\n", - "Step 838999 of 1000000; TPS: 4626.95; ETA: 0.6 minutes\n", - "Step 839999 of 1000000; TPS: 4627.0; ETA: 0.6 minutes\n", - "Step 840999 of 1000000; TPS: 4627.14; ETA: 0.6 minutes\n", - "Step 841999 of 1000000; TPS: 4627.33; ETA: 0.6 minutes\n", - "Step 842999 of 1000000; TPS: 4627.52; ETA: 0.6 minutes\n", - "Step 843999 of 1000000; TPS: 4627.7; ETA: 0.6 minutes\n", - "Step 844999 of 1000000; TPS: 4627.9; ETA: 0.6 minutes\n", - "Step 845999 of 1000000; TPS: 4628.11; ETA: 0.6 minutes\n", - "Step 846999 of 1000000; TPS: 4628.1; ETA: 0.6 minutes\n", - "Step 847999 of 1000000; TPS: 4628.16; ETA: 0.5 minutes\n", - "Step 848999 of 1000000; TPS: 4628.31; ETA: 0.5 minutes\n", - "Step 849999 of 1000000; TPS: 4628.51; ETA: 0.5 minutes\n", - "Step 850999 of 1000000; TPS: 4628.73; ETA: 0.5 minutes\n", - "Step 851999 of 1000000; TPS: 4628.78; ETA: 0.5 minutes\n", - "Step 852999 of 1000000; TPS: 4629.09; ETA: 0.5 minutes\n", - "Step 853999 of 1000000; TPS: 4629.62; ETA: 0.5 minutes\n", - "Step 854999 of 1000000; TPS: 4630.23; ETA: 0.5 minutes\n", - "Step 855999 of 1000000; TPS: 4630.76; ETA: 0.5 minutes\n", - "Step 856999 of 1000000; TPS: 4631.33; ETA: 0.5 minutes\n", - "Step 857999 of 1000000; TPS: 4631.84; ETA: 0.5 minutes\n", - "Step 858999 of 1000000; TPS: 4632.38; ETA: 0.5 minutes\n", - "Step 859999 of 1000000; TPS: 4632.92; ETA: 0.5 minutes\n", - "Step 860999 of 1000000; TPS: 4633.42; ETA: 0.5 minutes\n", - "Step 861999 of 1000000; TPS: 4633.9; ETA: 0.5 minutes\n", - "Step 862999 of 1000000; TPS: 4634.4; ETA: 0.5 minutes\n", - "Step 863999 of 1000000; TPS: 4634.99; ETA: 0.5 minutes\n", - "Step 864999 of 1000000; TPS: 4635.62; ETA: 0.5 minutes\n", - "Step 865999 of 1000000; TPS: 4636.32; ETA: 0.5 minutes\n", - "Step 866999 of 1000000; TPS: 4636.49; ETA: 0.5 minutes\n", - "Step 867999 of 1000000; TPS: 4637.03; ETA: 0.5 minutes\n", - "Step 868999 of 1000000; TPS: 4637.26; ETA: 0.5 minutes\n", - "Step 869999 of 1000000; TPS: 4637.59; ETA: 0.5 minutes\n", - "Step 870999 of 1000000; TPS: 4637.89; ETA: 0.5 minutes\n", - "Step 871999 of 1000000; TPS: 4638.17; ETA: 0.5 minutes\n", - "Step 872999 of 1000000; TPS: 4638.37; ETA: 0.5 minutes\n", - "Step 873999 of 1000000; TPS: 4638.67; ETA: 0.5 minutes\n", - "Step 874999 of 1000000; TPS: 4638.97; ETA: 0.4 minutes\n", - "Step 875999 of 1000000; TPS: 4639.32; ETA: 0.4 minutes\n", - "Step 876999 of 1000000; TPS: 4639.53; ETA: 0.4 minutes\n", - "Step 877999 of 1000000; TPS: 4639.87; ETA: 0.4 minutes\n", - "Step 878999 of 1000000; TPS: 4640.2; ETA: 0.4 minutes\n", - "Step 879999 of 1000000; TPS: 4640.35; ETA: 0.4 minutes\n", - "Step 880999 of 1000000; TPS: 4640.72; ETA: 0.4 minutes\n", - "Step 881999 of 1000000; TPS: 4641.06; ETA: 0.4 minutes\n", - "Step 882999 of 1000000; TPS: 4641.36; ETA: 0.4 minutes\n", - "Step 883999 of 1000000; TPS: 4641.58; ETA: 0.4 minutes\n", - "Step 884999 of 1000000; TPS: 4641.9; ETA: 0.4 minutes\n", - "Step 885999 of 1000000; TPS: 4641.97; ETA: 0.4 minutes\n", - "Step 886999 of 1000000; TPS: 4642.23; ETA: 0.4 minutes\n", - "Step 887999 of 1000000; TPS: 4642.59; ETA: 0.4 minutes\n", - "Step 888999 of 1000000; TPS: 4642.82; ETA: 0.4 minutes\n", - "Step 889999 of 1000000; TPS: 4643.14; ETA: 0.4 minutes\n", - "Step 890999 of 1000000; TPS: 4643.72; ETA: 0.4 minutes\n", - "Step 891999 of 1000000; TPS: 4644.37; ETA: 0.4 minutes\n", - "Step 892999 of 1000000; TPS: 4644.99; ETA: 0.4 minutes\n", - "Step 893999 of 1000000; TPS: 4645.69; ETA: 0.4 minutes\n", - "Step 894999 of 1000000; TPS: 4646.36; ETA: 0.4 minutes\n", - "Step 895999 of 1000000; TPS: 4647.05; ETA: 0.4 minutes\n", - "Step 896999 of 1000000; TPS: 4684.36; ETA: 0.4 minutes\n", - "Step 897999 of 1000000; TPS: 4684.98; ETA: 0.4 minutes\n", - "Step 898999 of 1000000; TPS: 4685.65; ETA: 0.4 minutes\n", - "Step 899999 of 1000000; TPS: 4686.28; ETA: 0.4 minutes\n", - "Step 900999 of 1000000; TPS: 4686.86; ETA: 0.4 minutes\n", - "Step 901999 of 1000000; TPS: 4687.54; ETA: 0.3 minutes\n", - "Step 902999 of 1000000; TPS: 4688.17; ETA: 0.3 minutes\n", - "Step 903999 of 1000000; TPS: 4688.81; ETA: 0.3 minutes\n", - "Step 904999 of 1000000; TPS: 4689.39; ETA: 0.3 minutes\n", - "Step 905999 of 1000000; TPS: 4690.04; ETA: 0.3 minutes\n", - "Step 906999 of 1000000; TPS: 4690.65; ETA: 0.3 minutes\n", - "Step 907999 of 1000000; TPS: 4691.31; ETA: 0.3 minutes\n", - "Step 908999 of 1000000; TPS: 4691.91; ETA: 0.3 minutes\n", - "Step 909999 of 1000000; TPS: 4692.45; ETA: 0.3 minutes\n", - "Step 910999 of 1000000; TPS: 4693.0; ETA: 0.3 minutes\n", - "Step 911999 of 1000000; TPS: 4693.49; ETA: 0.3 minutes\n", - "Step 912999 of 1000000; TPS: 4694.0; ETA: 0.3 minutes\n", - "Step 913999 of 1000000; TPS: 4694.51; ETA: 0.3 minutes\n", - "Step 914999 of 1000000; TPS: 4695.08; ETA: 0.3 minutes\n", - "Step 915999 of 1000000; TPS: 4695.65; ETA: 0.3 minutes\n", - "Step 916999 of 1000000; TPS: 4696.19; ETA: 0.3 minutes\n", - "Step 917999 of 1000000; TPS: 4696.47; ETA: 0.3 minutes\n", - "Step 918999 of 1000000; TPS: 4696.84; ETA: 0.3 minutes\n", - "Step 919999 of 1000000; TPS: 4697.13; ETA: 0.3 minutes\n", - "Step 920999 of 1000000; TPS: 4697.4; ETA: 0.3 minutes\n", - "Step 921999 of 1000000; TPS: 4697.77; ETA: 0.3 minutes\n", - "Step 922999 of 1000000; TPS: 4698.06; ETA: 0.3 minutes\n", - "Step 923999 of 1000000; TPS: 4698.4; ETA: 0.3 minutes\n", - "Step 924999 of 1000000; TPS: 4698.78; ETA: 0.3 minutes\n", - "Step 925999 of 1000000; TPS: 4698.49; ETA: 0.3 minutes\n", - "Step 926999 of 1000000; TPS: 4698.83; ETA: 0.3 minutes\n", - "Step 927999 of 1000000; TPS: 4699.19; ETA: 0.3 minutes\n", - "Step 928999 of 1000000; TPS: 4699.45; ETA: 0.3 minutes\n", - "Step 929999 of 1000000; TPS: 4699.69; ETA: 0.2 minutes\n", - "Step 930999 of 1000000; TPS: 4700.08; ETA: 0.2 minutes\n", - "Step 931999 of 1000000; TPS: 4700.38; ETA: 0.2 minutes\n", - "Step 932999 of 1000000; TPS: 4700.66; ETA: 0.2 minutes\n", - "Step 933999 of 1000000; TPS: 4700.94; ETA: 0.2 minutes\n", - "Step 934999 of 1000000; TPS: 4701.2; ETA: 0.2 minutes\n", - "Step 935999 of 1000000; TPS: 4701.53; ETA: 0.2 minutes\n", - "Step 936999 of 1000000; TPS: 4701.88; ETA: 0.2 minutes\n", - "Step 937999 of 1000000; TPS: 4702.24; ETA: 0.2 minutes\n", - "Step 938999 of 1000000; TPS: 4702.34; ETA: 0.2 minutes\n", - "Step 939999 of 1000000; TPS: 4702.61; ETA: 0.2 minutes\n", - "Step 940999 of 1000000; TPS: 4702.99; ETA: 0.2 minutes\n", - "Step 941999 of 1000000; TPS: 4703.36; ETA: 0.2 minutes\n", - "Step 942999 of 1000000; TPS: 4703.96; ETA: 0.2 minutes\n", - "Step 943999 of 1000000; TPS: 4704.69; ETA: 0.2 minutes\n", - "Step 944999 of 1000000; TPS: 4705.33; ETA: 0.2 minutes\n", - "Step 945999 of 1000000; TPS: 4706.08; ETA: 0.2 minutes\n", - "Step 946999 of 1000000; TPS: 4706.68; ETA: 0.2 minutes\n", - "Step 947999 of 1000000; TPS: 4707.3; ETA: 0.2 minutes\n", - "Step 948999 of 1000000; TPS: 4707.95; ETA: 0.2 minutes\n", - "Step 949999 of 1000000; TPS: 4708.64; ETA: 0.2 minutes\n", - "Step 950999 of 1000000; TPS: 4709.3; ETA: 0.2 minutes\n", - "Step 951999 of 1000000; TPS: 4709.99; ETA: 0.2 minutes\n", - "Step 952999 of 1000000; TPS: 4710.71; ETA: 0.2 minutes\n", - "Step 953999 of 1000000; TPS: 4711.35; ETA: 0.2 minutes\n", - "Step 954999 of 1000000; TPS: 4712.04; ETA: 0.2 minutes\n", - "Step 955999 of 1000000; TPS: 4712.67; ETA: 0.2 minutes\n", - "Step 956999 of 1000000; TPS: 4713.39; ETA: 0.2 minutes\n", - "Step 957999 of 1000000; TPS: 4714.05; ETA: 0.1 minutes\n", - "Step 958999 of 1000000; TPS: 4714.68; ETA: 0.1 minutes\n", - "Step 959999 of 1000000; TPS: 4715.0; ETA: 0.1 minutes\n", - "Step 960999 of 1000000; TPS: 4715.34; ETA: 0.1 minutes\n", - "Step 961999 of 1000000; TPS: 4715.64; ETA: 0.1 minutes\n", - "Step 962999 of 1000000; TPS: 4716.0; ETA: 0.1 minutes\n", - "Step 963999 of 1000000; TPS: 4716.33; ETA: 0.1 minutes\n", - "Step 964999 of 1000000; TPS: 4716.7; ETA: 0.1 minutes\n", - "Step 965999 of 1000000; TPS: 4717.09; ETA: 0.1 minutes\n", - "Step 966999 of 1000000; TPS: 4717.44; ETA: 0.1 minutes\n", - "Step 967999 of 1000000; TPS: 4717.86; ETA: 0.1 minutes\n", - "Step 968999 of 1000000; TPS: 4718.26; ETA: 0.1 minutes\n", - "Step 969999 of 1000000; TPS: 4718.48; ETA: 0.1 minutes\n", - "Step 970999 of 1000000; TPS: 4718.83; ETA: 0.1 minutes\n", - "Step 971999 of 1000000; TPS: 4719.18; ETA: 0.1 minutes\n", - "Step 972999 of 1000000; TPS: 4719.52; ETA: 0.1 minutes\n", - "Step 973999 of 1000000; TPS: 4719.89; ETA: 0.1 minutes\n", - "Step 974999 of 1000000; TPS: 4720.16; ETA: 0.1 minutes\n", - "Step 975999 of 1000000; TPS: 4720.44; ETA: 0.1 minutes\n", - "Step 976999 of 1000000; TPS: 4720.75; ETA: 0.1 minutes\n", - "Step 977999 of 1000000; TPS: 4720.75; ETA: 0.1 minutes\n", - "Step 978999 of 1000000; TPS: 4720.93; ETA: 0.1 minutes\n", - "Step 979999 of 1000000; TPS: 4721.12; ETA: 0.1 minutes\n", - "Step 980999 of 1000000; TPS: 4721.26; ETA: 0.1 minutes\n", - "Step 981999 of 1000000; TPS: 4721.36; ETA: 0.1 minutes\n", - "Step 982999 of 1000000; TPS: 4721.47; ETA: 0.1 minutes\n", - "Step 983999 of 1000000; TPS: 4721.6; ETA: 0.1 minutes\n", - "Step 984999 of 1000000; TPS: 4721.44; ETA: 0.1 minutes\n", - "Step 985999 of 1000000; TPS: 4721.77; ETA: 0.0 minutes\n", - "Step 986999 of 1000000; TPS: 4722.26; ETA: 0.0 minutes\n", - "Step 987999 of 1000000; TPS: 4722.68; ETA: 0.0 minutes\n", - "Step 988999 of 1000000; TPS: 4723.19; ETA: 0.0 minutes\n", - "Step 989999 of 1000000; TPS: 4723.78; ETA: 0.0 minutes\n", - "Step 990999 of 1000000; TPS: 4724.38; ETA: 0.0 minutes\n", - "Step 991999 of 1000000; TPS: 4725.03; ETA: 0.0 minutes\n", - "Step 992999 of 1000000; TPS: 4725.61; ETA: 0.0 minutes\n", - "Step 993999 of 1000000; TPS: 4726.22; ETA: 0.0 minutes\n", - "Step 994999 of 1000000; TPS: 4726.86; ETA: 0.0 minutes\n", - "Step 995999 of 1000000; TPS: 4727.52; ETA: 0.0 minutes\n", - "Step 996999 of 1000000; TPS: 4728.1; ETA: 0.0 minutes\n", - "Step 997999 of 1000000; TPS: 4728.78; ETA: 0.0 minutes\n", - "Step 998999 of 1000000; TPS: 4729.41; ETA: 0.0 minutes\n", - "Step 999999 of 1000000; TPS: 4730.02; ETA: 0.0 minutes\n" + "Step 1000 of 500000; TPS: 982.15; ETA: 8.5 minutes\n", + "Step 2000 of 500000; TPS: 1620.09; ETA: 5.1 minutes\n", + "Step 3000 of 500000; TPS: 2064.47; ETA: 4.0 minutes\n", + "Step 4000 of 500000; TPS: 2185.47; ETA: 3.8 minutes\n", + "Step 5000 of 500000; TPS: 2433.0; ETA: 3.4 minutes\n", + "Step 6000 of 500000; TPS: 2633.55; ETA: 3.1 minutes\n", + "Step 7000 of 500000; TPS: 2796.2; ETA: 2.9 minutes\n", + "Step 8000 of 500000; TPS: 2924.7; ETA: 2.8 minutes\n", + "Step 9000 of 500000; TPS: 3046.9; ETA: 2.7 minutes\n", + "Step 10000 of 500000; TPS: 2980.6; ETA: 2.7 minutes\n", + "Step 11000 of 500000; TPS: 2946.47; ETA: 2.8 minutes\n", + "Step 12000 of 500000; TPS: 3025.82; ETA: 2.7 minutes\n", + "Step 13000 of 500000; TPS: 3095.79; ETA: 2.6 minutes\n", + "Step 14000 of 500000; TPS: 3150.38; ETA: 2.6 minutes\n", + "Step 15000 of 500000; TPS: 3216.79; ETA: 2.5 minutes\n", + "Step 16000 of 500000; TPS: 3279.66; ETA: 2.5 minutes\n", + "Step 17000 of 500000; TPS: 3325.15; ETA: 2.4 minutes\n", + "Step 18000 of 500000; TPS: 3266.75; ETA: 2.5 minutes\n", + "Step 19000 of 500000; TPS: 3295.47; ETA: 2.4 minutes\n", + "Step 20000 of 500000; TPS: 3342.52; ETA: 2.4 minutes\n", + "Step 21000 of 500000; TPS: 3385.41; ETA: 2.4 minutes\n", + "Step 22000 of 500000; TPS: 3420.73; ETA: 2.3 minutes\n", + "Step 23000 of 500000; TPS: 3451.31; ETA: 2.3 minutes\n", + "Step 24000 of 500000; TPS: 3486.91; ETA: 2.3 minutes\n", + "Step 25000 of 500000; TPS: 3439.4; ETA: 2.3 minutes\n", + "Step 26000 of 500000; TPS: 3458.14; ETA: 2.3 minutes\n", + "Step 27000 of 500000; TPS: 3491.31; ETA: 2.3 minutes\n", + "Step 28000 of 500000; TPS: 3520.36; ETA: 2.2 minutes\n", + "Step 29000 of 500000; TPS: 3548.72; ETA: 2.2 minutes\n", + "Step 30000 of 500000; TPS: 3574.45; ETA: 2.2 minutes\n", + "Step 31000 of 500000; TPS: 3601.82; ETA: 2.2 minutes\n", + "Step 32000 of 500000; TPS: 3619.09; ETA: 2.2 minutes\n", + "Step 33000 of 500000; TPS: 3589.59; ETA: 2.2 minutes\n", + "Step 34000 of 500000; TPS: 3618.07; ETA: 2.1 minutes\n", + "Step 35000 of 500000; TPS: 3640.88; ETA: 2.1 minutes\n", + "Step 36000 of 500000; TPS: 3663.73; ETA: 2.1 minutes\n", + "Step 37000 of 500000; TPS: 3688.06; ETA: 2.1 minutes\n", + "Step 38000 of 500000; TPS: 3709.78; ETA: 2.1 minutes\n", + "Step 39000 of 500000; TPS: 3714.65; ETA: 2.1 minutes\n", + "Step 40000 of 500000; TPS: 3689.05; ETA: 2.1 minutes\n", + "Step 41000 of 500000; TPS: 3706.4; ETA: 2.1 minutes\n", + "Step 42000 of 500000; TPS: 3725.26; ETA: 2.0 minutes\n", + "Step 43000 of 500000; TPS: 3740.14; ETA: 2.0 minutes\n", + "Step 44000 of 500000; TPS: 3758.21; ETA: 2.0 minutes\n", + "Step 45000 of 500000; TPS: 3773.13; ETA: 2.0 minutes\n", + "Step 46000 of 500000; TPS: 3789.68; ETA: 2.0 minutes\n", + "Step 47000 of 500000; TPS: 3767.39; ETA: 2.0 minutes\n", + "Step 48000 of 500000; TPS: 3785.08; ETA: 2.0 minutes\n", + "Step 49000 of 500000; TPS: 3801.01; ETA: 2.0 minutes\n", + "Step 50000 of 500000; TPS: 3817.67; ETA: 2.0 minutes\n", + "Step 51000 of 500000; TPS: 3832.94; ETA: 2.0 minutes\n", + "Step 52000 of 500000; TPS: 3850.9; ETA: 1.9 minutes\n", + "Step 53000 of 500000; TPS: 3865.95; ETA: 1.9 minutes\n", + "Step 54000 of 500000; TPS: 3853.63; ETA: 1.9 minutes\n", + "Step 55000 of 500000; TPS: 3868.24; ETA: 1.9 minutes\n", + "Step 56000 of 500000; TPS: 3881.37; ETA: 1.9 minutes\n", + "Step 57000 of 500000; TPS: 3893.63; ETA: 1.9 minutes\n", + "Step 58000 of 500000; TPS: 3910.06; ETA: 1.9 minutes\n", + "Step 59000 of 500000; TPS: 3923.07; ETA: 1.9 minutes\n", + "Step 60000 of 500000; TPS: 3936.16; ETA: 1.9 minutes\n", + "Step 61000 of 500000; TPS: 3923.35; ETA: 1.9 minutes\n", + "Step 62000 of 500000; TPS: 3935.73; ETA: 1.9 minutes\n", + "Step 63000 of 500000; TPS: 3950.82; ETA: 1.8 minutes\n", + "Step 64000 of 500000; TPS: 3963.19; ETA: 1.8 minutes\n", + "Step 65000 of 500000; TPS: 3975.12; ETA: 1.8 minutes\n", + "Step 66000 of 500000; TPS: 3987.89; ETA: 1.8 minutes\n", + "Step 67000 of 500000; TPS: 3998.56; ETA: 1.8 minutes\n", + "Step 68000 of 500000; TPS: 3984.66; ETA: 1.8 minutes\n", + "Step 69000 of 500000; TPS: 3995.21; ETA: 1.8 minutes\n", + "Step 70000 of 500000; TPS: 4005.22; ETA: 1.8 minutes\n", + "Step 71000 of 500000; TPS: 4015.15; ETA: 1.8 minutes\n", + "Step 72000 of 500000; TPS: 4025.36; ETA: 1.8 minutes\n", + "Step 73000 of 500000; TPS: 4035.73; ETA: 1.8 minutes\n", + "Step 74000 of 500000; TPS: 4044.32; ETA: 1.8 minutes\n", + "Step 75000 of 500000; TPS: 4035.54; ETA: 1.8 minutes\n", + "Step 76000 of 500000; TPS: 4045.1; ETA: 1.7 minutes\n", + "Step 77000 of 500000; TPS: 4055.07; ETA: 1.7 minutes\n", + "Step 78000 of 500000; TPS: 4061.63; ETA: 1.7 minutes\n", + "Step 79000 of 500000; TPS: 3604.65; ETA: 1.9 minutes\n", + "Step 80000 of 500000; TPS: 3617.45; ETA: 1.9 minutes\n", + "Step 81000 of 500000; TPS: 3631.79; ETA: 1.9 minutes\n", + "Step 82000 of 500000; TPS: 3627.99; ETA: 1.9 minutes\n", + "Step 83000 of 500000; TPS: 3640.02; ETA: 1.9 minutes\n", + "Step 84000 of 500000; TPS: 3651.77; ETA: 1.9 minutes\n", + "Step 85000 of 500000; TPS: 3660.08; ETA: 1.9 minutes\n", + "Step 86000 of 500000; TPS: 3660.85; ETA: 1.9 minutes\n", + "Step 87000 of 500000; TPS: 3669.09; ETA: 1.9 minutes\n", + "Step 88000 of 500000; TPS: 3677.92; ETA: 1.9 minutes\n", + "Step 89000 of 500000; TPS: 3666.69; ETA: 1.9 minutes\n", + "Step 90000 of 500000; TPS: 3676.1; ETA: 1.9 minutes\n", + "Step 91000 of 500000; TPS: 3686.62; ETA: 1.8 minutes\n", + "Step 92000 of 500000; TPS: 3696.12; ETA: 1.8 minutes\n", + "Step 93000 of 500000; TPS: 3704.78; ETA: 1.8 minutes\n", + "Step 94000 of 500000; TPS: 3708.9; ETA: 1.8 minutes\n", + "Step 95000 of 500000; TPS: 3713.39; ETA: 1.8 minutes\n", + "Step 96000 of 500000; TPS: 3706.78; ETA: 1.8 minutes\n", + "Step 97000 of 500000; TPS: 3713.28; ETA: 1.8 minutes\n", + "Step 98000 of 500000; TPS: 3719.1; ETA: 1.8 minutes\n", + "Step 99000 of 500000; TPS: 3726.17; ETA: 1.8 minutes\n", + "Step 100000 of 500000; TPS: 3733.19; ETA: 1.8 minutes\n", + "Step 101000 of 500000; TPS: 3740.33; ETA: 1.8 minutes\n", + "Step 102000 of 500000; TPS: 3748.45; ETA: 1.8 minutes\n", + "Step 103000 of 500000; TPS: 3742.08; ETA: 1.8 minutes\n", + "Step 104000 of 500000; TPS: 3746.64; ETA: 1.8 minutes\n", + "Step 105000 of 500000; TPS: 3751.29; ETA: 1.8 minutes\n", + "Step 106000 of 500000; TPS: 3753.04; ETA: 1.7 minutes\n", + "Step 107000 of 500000; TPS: 3758.58; ETA: 1.7 minutes\n", + "Step 108000 of 500000; TPS: 3766.26; ETA: 1.7 minutes\n", + "Step 109000 of 500000; TPS: 3773.68; ETA: 1.7 minutes\n", + "Step 110000 of 500000; TPS: 3771.33; ETA: 1.7 minutes\n", + "Step 111000 of 500000; TPS: 3778.66; ETA: 1.7 minutes\n", + "Step 112000 of 500000; TPS: 3784.6; ETA: 1.7 minutes\n", + "Step 113000 of 500000; TPS: 3790.91; ETA: 1.7 minutes\n", + "Step 114000 of 500000; TPS: 3795.74; ETA: 1.7 minutes\n", + "Step 115000 of 500000; TPS: 3800.12; ETA: 1.7 minutes\n", + "Step 116000 of 500000; TPS: 3806.93; ETA: 1.7 minutes\n", + "Step 117000 of 500000; TPS: 3801.08; ETA: 1.7 minutes\n", + "Step 118000 of 500000; TPS: 3807.65; ETA: 1.7 minutes\n", + "Step 119000 of 500000; TPS: 3814.85; ETA: 1.7 minutes\n", + "Step 120000 of 500000; TPS: 3820.76; ETA: 1.7 minutes\n", + "Step 121000 of 500000; TPS: 3827.55; ETA: 1.7 minutes\n", + "Step 122000 of 500000; TPS: 3833.83; ETA: 1.6 minutes\n", + "Step 123000 of 500000; TPS: 3834.62; ETA: 1.6 minutes\n", + "Step 124000 of 500000; TPS: 3827.88; ETA: 1.6 minutes\n", + "Step 125000 of 500000; TPS: 3833.52; ETA: 1.6 minutes\n", + "Step 126000 of 500000; TPS: 3838.56; ETA: 1.6 minutes\n", + "Step 127000 of 500000; TPS: 3845.0; ETA: 1.6 minutes\n", + "Step 128000 of 500000; TPS: 3851.68; ETA: 1.6 minutes\n", + "Step 129000 of 500000; TPS: 3858.63; ETA: 1.6 minutes\n", + "Step 130000 of 500000; TPS: 3865.69; ETA: 1.6 minutes\n", + "Step 131000 of 500000; TPS: 3861.13; ETA: 1.6 minutes\n", + "Step 132000 of 500000; TPS: 3865.61; ETA: 1.6 minutes\n", + "Step 133000 of 500000; TPS: 3867.03; ETA: 1.6 minutes\n", + "Step 134000 of 500000; TPS: 3871.3; ETA: 1.6 minutes\n", + "Step 135000 of 500000; TPS: 3874.59; ETA: 1.6 minutes\n", + "Step 136000 of 500000; TPS: 3879.5; ETA: 1.6 minutes\n", + "Step 137000 of 500000; TPS: 3884.75; ETA: 1.6 minutes\n", + "Step 138000 of 500000; TPS: 3884.9; ETA: 1.6 minutes\n", + "Step 139000 of 500000; TPS: 3889.33; ETA: 1.5 minutes\n", + "Step 140000 of 500000; TPS: 3895.61; ETA: 1.5 minutes\n", + "Step 141000 of 500000; TPS: 3900.51; ETA: 1.5 minutes\n", + "Step 142000 of 500000; TPS: 3905.9; ETA: 1.5 minutes\n", + "Step 143000 of 500000; TPS: 3910.27; ETA: 1.5 minutes\n", + "Step 144000 of 500000; TPS: 3915.52; ETA: 1.5 minutes\n", + "Step 145000 of 500000; TPS: 3912.61; ETA: 1.5 minutes\n", + "Step 146000 of 500000; TPS: 3917.75; ETA: 1.5 minutes\n", + "Step 147000 of 500000; TPS: 3922.23; ETA: 1.5 minutes\n", + "Step 148000 of 500000; TPS: 3928.28; ETA: 1.5 minutes\n", + "Step 149000 of 500000; TPS: 3932.45; ETA: 1.5 minutes\n", + "Step 150000 of 500000; TPS: 3937.78; ETA: 1.5 minutes\n", + "Step 151000 of 500000; TPS: 3943.77; ETA: 1.5 minutes\n", + "Step 152000 of 500000; TPS: 3947.68; ETA: 1.5 minutes\n", + "Step 153000 of 500000; TPS: 3948.25; ETA: 1.5 minutes\n", + "Step 154000 of 500000; TPS: 3952.22; ETA: 1.5 minutes\n", + "Step 155000 of 500000; TPS: 3957.32; ETA: 1.5 minutes\n", + "Step 156000 of 500000; TPS: 3962.11; ETA: 1.4 minutes\n", + "Step 157000 of 500000; TPS: 3966.53; ETA: 1.4 minutes\n", + "Step 158000 of 500000; TPS: 3970.94; ETA: 1.4 minutes\n", + "Step 159000 of 500000; TPS: 3976.18; ETA: 1.4 minutes\n", + "Step 160000 of 500000; TPS: 3975.83; ETA: 1.4 minutes\n", + "Step 161000 of 500000; TPS: 3980.0; ETA: 1.4 minutes\n", + "Step 162000 of 500000; TPS: 3984.37; ETA: 1.4 minutes\n", + "Step 163000 of 500000; TPS: 3988.82; ETA: 1.4 minutes\n", + "Step 164000 of 500000; TPS: 3993.49; ETA: 1.4 minutes\n", + "Step 165000 of 500000; TPS: 3998.16; ETA: 1.4 minutes\n", + "Step 166000 of 500000; TPS: 4002.95; ETA: 1.4 minutes\n", + "Step 167000 of 500000; TPS: 4003.84; ETA: 1.4 minutes\n", + "Step 168000 of 500000; TPS: 4008.02; ETA: 1.4 minutes\n", + "Step 169000 of 500000; TPS: 4011.34; ETA: 1.4 minutes\n", + "Step 170000 of 500000; TPS: 4015.22; ETA: 1.4 minutes\n", + "Step 171000 of 500000; TPS: 4018.4; ETA: 1.4 minutes\n", + "Step 172000 of 500000; TPS: 4021.84; ETA: 1.4 minutes\n", + "Step 173000 of 500000; TPS: 4025.06; ETA: 1.4 minutes\n", + "Step 174000 of 500000; TPS: 4025.08; ETA: 1.3 minutes\n", + "Step 175000 of 500000; TPS: 4028.75; ETA: 1.3 minutes\n", + "Step 176000 of 500000; TPS: 4032.5; ETA: 1.3 minutes\n", + "Step 177000 of 500000; TPS: 4035.96; ETA: 1.3 minutes\n", + "Step 178000 of 500000; TPS: 4040.18; ETA: 1.3 minutes\n", + "Step 179000 of 500000; TPS: 4044.2; ETA: 1.3 minutes\n", + "Step 180000 of 500000; TPS: 4048.23; ETA: 1.3 minutes\n", + "Step 181000 of 500000; TPS: 4048.76; ETA: 1.3 minutes\n", + "Step 182000 of 500000; TPS: 4052.68; ETA: 1.3 minutes\n", + "Step 183000 of 500000; TPS: 4054.53; ETA: 1.3 minutes\n", + "Step 184000 of 500000; TPS: 4057.38; ETA: 1.3 minutes\n", + "Step 185000 of 500000; TPS: 4061.41; ETA: 1.3 minutes\n", + "Step 186000 of 500000; TPS: 4065.77; ETA: 1.3 minutes\n", + "Step 187000 of 500000; TPS: 4070.62; ETA: 1.3 minutes\n", + "Step 188000 of 500000; TPS: 4072.3; ETA: 1.3 minutes\n", + "Step 189000 of 500000; TPS: 4075.96; ETA: 1.3 minutes\n", + "Step 190000 of 500000; TPS: 4080.55; ETA: 1.3 minutes\n", + "Step 191000 of 500000; TPS: 4084.65; ETA: 1.3 minutes\n", + "Step 192000 of 500000; TPS: 4089.46; ETA: 1.3 minutes\n", + "Step 193000 of 500000; TPS: 4093.54; ETA: 1.2 minutes\n", + "Step 194000 of 500000; TPS: 4097.38; ETA: 1.2 minutes\n", + "Step 195000 of 500000; TPS: 4099.83; ETA: 1.2 minutes\n", + "Step 196000 of 500000; TPS: 4103.96; ETA: 1.2 minutes\n", + "Step 197000 of 500000; TPS: 4107.26; ETA: 1.2 minutes\n", + "Step 198000 of 500000; TPS: 4110.22; ETA: 1.2 minutes\n", + "Step 199000 of 500000; TPS: 4113.32; ETA: 1.2 minutes\n", + "Step 200000 of 500000; TPS: 4116.6; ETA: 1.2 minutes\n", + "Step 201000 of 500000; TPS: 4119.85; ETA: 1.2 minutes\n", + "Step 202000 of 500000; TPS: 4119.93; ETA: 1.2 minutes\n", + "Step 203000 of 500000; TPS: 4122.82; ETA: 1.2 minutes\n", + "Step 204000 of 500000; TPS: 4125.02; ETA: 1.2 minutes\n", + "Step 205000 of 500000; TPS: 4127.38; ETA: 1.2 minutes\n", + "Step 206000 of 500000; TPS: 4129.07; ETA: 1.2 minutes\n", + "Step 207000 of 500000; TPS: 4130.63; ETA: 1.2 minutes\n", + "Step 208000 of 500000; TPS: 4130.91; ETA: 1.2 minutes\n", + "Step 209000 of 500000; TPS: 4131.6; ETA: 1.2 minutes\n", + "Step 210000 of 500000; TPS: 4131.93; ETA: 1.2 minutes\n", + "Step 211000 of 500000; TPS: 4132.61; ETA: 1.2 minutes\n", + "Step 212000 of 500000; TPS: 4133.1; ETA: 1.2 minutes\n", + "Step 213000 of 500000; TPS: 4134.75; ETA: 1.2 minutes\n", + "Step 214000 of 500000; TPS: 4136.53; ETA: 1.2 minutes\n", + "Step 215000 of 500000; TPS: 4137.86; ETA: 1.1 minutes\n", + "Step 216000 of 500000; TPS: 4136.87; ETA: 1.1 minutes\n", + "Step 217000 of 500000; TPS: 4138.88; ETA: 1.1 minutes\n", + "Step 218000 of 500000; TPS: 4141.22; ETA: 1.1 minutes\n", + "Step 219000 of 500000; TPS: 4143.19; ETA: 1.1 minutes\n", + "Step 220000 of 500000; TPS: 4144.88; ETA: 1.1 minutes\n", + "Step 221000 of 500000; TPS: 4146.75; ETA: 1.1 minutes\n", + "Step 222000 of 500000; TPS: 4148.26; ETA: 1.1 minutes\n", + "Step 223000 of 500000; TPS: 4147.68; ETA: 1.1 minutes\n", + "Step 224000 of 500000; TPS: 4148.94; ETA: 1.1 minutes\n", + "Step 225000 of 500000; TPS: 3955.16; ETA: 1.2 minutes\n", + "Step 226000 of 500000; TPS: 3958.19; ETA: 1.2 minutes\n", + "Step 227000 of 500000; TPS: 3961.01; ETA: 1.1 minutes\n", + "Step 228000 of 500000; TPS: 3963.52; ETA: 1.1 minutes\n", + "Step 229000 of 500000; TPS: 3966.09; ETA: 1.1 minutes\n", + "Step 230000 of 500000; TPS: 3965.91; ETA: 1.1 minutes\n", + "Step 231000 of 500000; TPS: 3968.07; ETA: 1.1 minutes\n", + "Step 232000 of 500000; TPS: 3969.48; ETA: 1.1 minutes\n", + "Step 233000 of 500000; TPS: 3972.07; ETA: 1.1 minutes\n", + "Step 234000 of 500000; TPS: 3973.23; ETA: 1.1 minutes\n", + "Step 235000 of 500000; TPS: 3975.33; ETA: 1.1 minutes\n", + "Step 236000 of 500000; TPS: 3977.4; ETA: 1.1 minutes\n", + "Step 237000 of 500000; TPS: 3977.68; ETA: 1.1 minutes\n", + "Step 238000 of 500000; TPS: 3979.19; ETA: 1.1 minutes\n", + "Step 239000 of 500000; TPS: 3981.28; ETA: 1.1 minutes\n", + "Step 240000 of 500000; TPS: 3982.42; ETA: 1.1 minutes\n", + "Step 241000 of 500000; TPS: 3985.04; ETA: 1.1 minutes\n", + "Step 242000 of 500000; TPS: 3985.93; ETA: 1.1 minutes\n", + "Step 243000 of 500000; TPS: 3988.18; ETA: 1.1 minutes\n", + "Step 244000 of 500000; TPS: 3989.09; ETA: 1.1 minutes\n", + "Step 245000 of 500000; TPS: 3992.21; ETA: 1.1 minutes\n", + "Step 246000 of 500000; TPS: 3994.89; ETA: 1.1 minutes\n", + "Step 247000 of 500000; TPS: 3997.9; ETA: 1.1 minutes\n", + "Step 248000 of 500000; TPS: 4000.92; ETA: 1.0 minutes\n", + "Step 249000 of 500000; TPS: 4005.13; ETA: 1.0 minutes\n", + "Step 250000 of 500000; TPS: 4007.43; ETA: 1.0 minutes\n", + "Step 251000 of 500000; TPS: 4010.17; ETA: 1.0 minutes\n", + "Step 252000 of 500000; TPS: 4013.64; ETA: 1.0 minutes\n", + "Step 253000 of 500000; TPS: 4017.72; ETA: 1.0 minutes\n", + "Step 254000 of 500000; TPS: 4019.88; ETA: 1.0 minutes\n", + "Step 255000 of 500000; TPS: 4023.2; ETA: 1.0 minutes\n", + "Step 256000 of 500000; TPS: 4026.28; ETA: 1.0 minutes\n", + "Step 257000 of 500000; TPS: 4028.85; ETA: 1.0 minutes\n", + "Step 258000 of 500000; TPS: 4031.49; ETA: 1.0 minutes\n", + "Step 259000 of 500000; TPS: 4034.47; ETA: 1.0 minutes\n", + "Step 260000 of 500000; TPS: 4037.6; ETA: 1.0 minutes\n", + "Step 261000 of 500000; TPS: 4040.1; ETA: 1.0 minutes\n", + "Step 262000 of 500000; TPS: 4042.76; ETA: 1.0 minutes\n", + "Step 263000 of 500000; TPS: 4044.83; ETA: 1.0 minutes\n", + "Step 264000 of 500000; TPS: 4047.47; ETA: 1.0 minutes\n", + "Step 265000 of 500000; TPS: 4049.77; ETA: 1.0 minutes\n", + "Step 266000 of 500000; TPS: 4052.32; ETA: 1.0 minutes\n", + "Step 267000 of 500000; TPS: 4054.97; ETA: 1.0 minutes\n", + "Step 268000 of 500000; TPS: 4058.05; ETA: 1.0 minutes\n", + "Step 269000 of 500000; TPS: 4060.34; ETA: 0.9 minutes\n", + "Step 270000 of 500000; TPS: 4062.6; ETA: 0.9 minutes\n", + "Step 271000 of 500000; TPS: 4065.15; ETA: 0.9 minutes\n", + "Step 272000 of 500000; TPS: 4067.91; ETA: 0.9 minutes\n", + "Step 273000 of 500000; TPS: 4069.72; ETA: 0.9 minutes\n", + "Step 274000 of 500000; TPS: 4071.99; ETA: 0.9 minutes\n", + "Step 275000 of 500000; TPS: 4074.75; ETA: 0.9 minutes\n", + "Step 276000 of 500000; TPS: 4077.65; ETA: 0.9 minutes\n", + "Step 277000 of 500000; TPS: 4080.48; ETA: 0.9 minutes\n", + "Step 278000 of 500000; TPS: 4082.9; ETA: 0.9 minutes\n", + "Step 279000 of 500000; TPS: 4085.65; ETA: 0.9 minutes\n", + "Step 280000 of 500000; TPS: 4087.78; ETA: 0.9 minutes\n", + "Step 281000 of 500000; TPS: 4090.96; ETA: 0.9 minutes\n", + "Step 282000 of 500000; TPS: 4094.22; ETA: 0.9 minutes\n", + "Step 283000 of 500000; TPS: 4096.59; ETA: 0.9 minutes\n", + "Step 284000 of 500000; TPS: 4099.81; ETA: 0.9 minutes\n", + "Step 285000 of 500000; TPS: 4102.08; ETA: 0.9 minutes\n", + "Step 286000 of 500000; TPS: 4103.73; ETA: 0.9 minutes\n", + "Step 287000 of 500000; TPS: 4104.79; ETA: 0.9 minutes\n", + "Step 288000 of 500000; TPS: 4107.81; ETA: 0.9 minutes\n", + "Step 289000 of 500000; TPS: 4110.67; ETA: 0.9 minutes\n", + "Step 290000 of 500000; TPS: 4113.53; ETA: 0.9 minutes\n", + "Step 291000 of 500000; TPS: 4116.32; ETA: 0.8 minutes\n", + "Step 292000 of 500000; TPS: 4119.19; ETA: 0.8 minutes\n", + "Step 293000 of 500000; TPS: 4121.85; ETA: 0.8 minutes\n", + "Step 294000 of 500000; TPS: 4122.86; ETA: 0.8 minutes\n", + "Step 295000 of 500000; TPS: 4125.05; ETA: 0.8 minutes\n", + "Step 296000 of 500000; TPS: 4127.09; ETA: 0.8 minutes\n", + "Step 297000 of 500000; TPS: 4129.74; ETA: 0.8 minutes\n", + "Step 298000 of 500000; TPS: 4132.61; ETA: 0.8 minutes\n", + "Step 299000 of 500000; TPS: 4135.77; ETA: 0.8 minutes\n", + "Step 300000 of 500000; TPS: 4137.31; ETA: 0.8 minutes\n", + "Step 301000 of 500000; TPS: 4137.53; ETA: 0.8 minutes\n", + "Step 302000 of 500000; TPS: 4137.05; ETA: 0.8 minutes\n", + "Step 303000 of 500000; TPS: 4136.2; ETA: 0.8 minutes\n", + "Step 304000 of 500000; TPS: 4135.88; ETA: 0.8 minutes\n", + "Step 305000 of 500000; TPS: 4137.44; ETA: 0.8 minutes\n", + "Step 306000 of 500000; TPS: 4139.61; ETA: 0.8 minutes\n", + "Step 307000 of 500000; TPS: 4141.22; ETA: 0.8 minutes\n", + "Step 308000 of 500000; TPS: 4142.21; ETA: 0.8 minutes\n", + "Step 309000 of 500000; TPS: 4142.76; ETA: 0.8 minutes\n", + "Step 310000 of 500000; TPS: 4142.16; ETA: 0.8 minutes\n", + "Step 311000 of 500000; TPS: 4143.62; ETA: 0.8 minutes\n", + "Step 312000 of 500000; TPS: 4145.34; ETA: 0.8 minutes\n", + "Step 313000 of 500000; TPS: 4147.3; ETA: 0.8 minutes\n", + "Step 314000 of 500000; TPS: 4150.2; ETA: 0.7 minutes\n", + "Step 315000 of 500000; TPS: 4151.12; ETA: 0.7 minutes\n", + "Step 316000 of 500000; TPS: 4152.64; ETA: 0.7 minutes\n", + "Step 317000 of 500000; TPS: 4154.31; ETA: 0.7 minutes\n", + "Step 318000 of 500000; TPS: 4156.46; ETA: 0.7 minutes\n", + "Step 319000 of 500000; TPS: 4158.85; ETA: 0.7 minutes\n", + "Step 320000 of 500000; TPS: 4161.01; ETA: 0.7 minutes\n", + "Step 321000 of 500000; TPS: 4163.14; ETA: 0.7 minutes\n", + "Step 322000 of 500000; TPS: 4164.56; ETA: 0.7 minutes\n", + "Step 323000 of 500000; TPS: 4166.59; ETA: 0.7 minutes\n", + "Step 324000 of 500000; TPS: 4169.03; ETA: 0.7 minutes\n", + "Step 325000 of 500000; TPS: 4170.88; ETA: 0.7 minutes\n", + "Step 326000 of 500000; TPS: 4171.66; ETA: 0.7 minutes\n", + "Step 327000 of 500000; TPS: 4173.6; ETA: 0.7 minutes\n", + "Step 328000 of 500000; TPS: 4175.36; ETA: 0.7 minutes\n", + "Step 329000 of 500000; TPS: 4176.19; ETA: 0.7 minutes\n", + "Step 330000 of 500000; TPS: 4178.18; ETA: 0.7 minutes\n", + "Step 331000 of 500000; TPS: 4179.91; ETA: 0.7 minutes\n", + "Step 332000 of 500000; TPS: 4181.61; ETA: 0.7 minutes\n", + "Step 333000 of 500000; TPS: 4183.56; ETA: 0.7 minutes\n", + "Step 334000 of 500000; TPS: 4184.14; ETA: 0.7 minutes\n", + "Step 335000 of 500000; TPS: 4185.95; ETA: 0.7 minutes\n", + "Step 336000 of 500000; TPS: 4186.43; ETA: 0.7 minutes\n", + "Step 337000 of 500000; TPS: 4187.04; ETA: 0.6 minutes\n", + "Step 338000 of 500000; TPS: 4189.1; ETA: 0.6 minutes\n", + "Step 339000 of 500000; TPS: 4190.75; ETA: 0.6 minutes\n", + "Step 340000 of 500000; TPS: 4192.78; ETA: 0.6 minutes\n", + "Step 341000 of 500000; TPS: 4195.23; ETA: 0.6 minutes\n", + "Step 342000 of 500000; TPS: 4197.76; ETA: 0.6 minutes\n", + "Step 343000 of 500000; TPS: 4199.66; ETA: 0.6 minutes\n", + "Step 344000 of 500000; TPS: 4201.33; ETA: 0.6 minutes\n", + "Step 345000 of 500000; TPS: 4203.85; ETA: 0.6 minutes\n", + "Step 346000 of 500000; TPS: 4204.27; ETA: 0.6 minutes\n", + "Step 347000 of 500000; TPS: 4205.13; ETA: 0.6 minutes\n", + "Step 348000 of 500000; TPS: 4207.05; ETA: 0.6 minutes\n", + "Step 349000 of 500000; TPS: 4207.59; ETA: 0.6 minutes\n", + "Step 350000 of 500000; TPS: 4208.91; ETA: 0.6 minutes\n", + "Step 351000 of 500000; TPS: 4211.32; ETA: 0.6 minutes\n", + "Step 352000 of 500000; TPS: 4213.4; ETA: 0.6 minutes\n", + "Step 353000 of 500000; TPS: 4214.93; ETA: 0.6 minutes\n", + "Step 354000 of 500000; TPS: 4215.82; ETA: 0.6 minutes\n", + "Step 355000 of 500000; TPS: 4217.73; ETA: 0.6 minutes\n", + "Step 356000 of 500000; TPS: 4219.4; ETA: 0.6 minutes\n", + "Step 357000 of 500000; TPS: 4220.98; ETA: 0.6 minutes\n", + "Step 358000 of 500000; TPS: 4223.16; ETA: 0.6 minutes\n", + "Step 359000 of 500000; TPS: 4224.69; ETA: 0.6 minutes\n", + "Step 360000 of 500000; TPS: 4227.23; ETA: 0.6 minutes\n", + "Step 361000 of 500000; TPS: 4229.34; ETA: 0.5 minutes\n", + "Step 362000 of 500000; TPS: 4231.52; ETA: 0.5 minutes\n", + "Step 363000 of 500000; TPS: 4234.3; ETA: 0.5 minutes\n", + "Step 364000 of 500000; TPS: 4236.55; ETA: 0.5 minutes\n", + "Step 365000 of 500000; TPS: 4238.53; ETA: 0.5 minutes\n", + "Step 366000 of 500000; TPS: 4240.51; ETA: 0.5 minutes\n", + "Step 367000 of 500000; TPS: 4242.16; ETA: 0.5 minutes\n", + "Step 368000 of 500000; TPS: 4242.62; ETA: 0.5 minutes\n", + "Step 369000 of 500000; TPS: 4244.29; ETA: 0.5 minutes\n", + "Step 370000 of 500000; TPS: 4246.45; ETA: 0.5 minutes\n", + "Step 371000 of 500000; TPS: 4248.42; ETA: 0.5 minutes\n", + "Step 372000 of 500000; TPS: 4250.49; ETA: 0.5 minutes\n", + "Step 373000 of 500000; TPS: 4252.42; ETA: 0.5 minutes\n", + "Step 374000 of 500000; TPS: 4254.38; ETA: 0.5 minutes\n", + "Step 375000 of 500000; TPS: 4256.41; ETA: 0.5 minutes\n", + "Step 376000 of 500000; TPS: 4257.15; ETA: 0.5 minutes\n", + "Step 377000 of 500000; TPS: 4256.91; ETA: 0.5 minutes\n", + "Step 378000 of 500000; TPS: 4257.28; ETA: 0.5 minutes\n", + "Step 379000 of 500000; TPS: 4139.23; ETA: 0.5 minutes\n", + "Step 380000 of 500000; TPS: 4140.54; ETA: 0.5 minutes\n", + "Step 381000 of 500000; TPS: 4140.8; ETA: 0.5 minutes\n", + "Step 382000 of 500000; TPS: 4141.85; ETA: 0.5 minutes\n", + "Step 383000 of 500000; TPS: 4143.01; ETA: 0.5 minutes\n", + "Step 384000 of 500000; TPS: 4144.12; ETA: 0.5 minutes\n", + "Step 385000 of 500000; TPS: 4145.44; ETA: 0.5 minutes\n", + "Step 386000 of 500000; TPS: 4146.09; ETA: 0.5 minutes\n", + "Step 387000 of 500000; TPS: 4146.96; ETA: 0.5 minutes\n", + "Step 388000 of 500000; TPS: 4147.8; ETA: 0.5 minutes\n", + "Step 389000 of 500000; TPS: 4148.34; ETA: 0.4 minutes\n", + "Step 390000 of 500000; TPS: 4149.46; ETA: 0.4 minutes\n", + "Step 391000 of 500000; TPS: 4150.81; ETA: 0.4 minutes\n", + "Step 392000 of 500000; TPS: 4151.65; ETA: 0.4 minutes\n", + "Step 393000 of 500000; TPS: 4152.37; ETA: 0.4 minutes\n", + "Step 394000 of 500000; TPS: 4153.16; ETA: 0.4 minutes\n", + "Step 395000 of 500000; TPS: 4153.22; ETA: 0.4 minutes\n", + "Step 396000 of 500000; TPS: 4154.16; ETA: 0.4 minutes\n", + "Step 397000 of 500000; TPS: 4155.4; ETA: 0.4 minutes\n", + "Step 398000 of 500000; TPS: 4156.77; ETA: 0.4 minutes\n", + "Step 399000 of 500000; TPS: 4158.45; ETA: 0.4 minutes\n", + "Step 400000 of 500000; TPS: 4159.6; ETA: 0.4 minutes\n", + "Step 401000 of 500000; TPS: 4160.06; ETA: 0.4 minutes\n", + "Step 402000 of 500000; TPS: 4160.18; ETA: 0.4 minutes\n", + "Step 403000 of 500000; TPS: 4161.15; ETA: 0.4 minutes\n", + "Step 404000 of 500000; TPS: 4162.32; ETA: 0.4 minutes\n", + "Step 405000 of 500000; TPS: 4163.75; ETA: 0.4 minutes\n", + "Step 406000 of 500000; TPS: 4164.96; ETA: 0.4 minutes\n", + "Step 407000 of 500000; TPS: 4165.05; ETA: 0.4 minutes\n", + "Step 408000 of 500000; TPS: 4166.01; ETA: 0.4 minutes\n", + "Step 409000 of 500000; TPS: 4167.3; ETA: 0.4 minutes\n", + "Step 410000 of 500000; TPS: 4168.19; ETA: 0.4 minutes\n", + "Step 411000 of 500000; TPS: 4168.2; ETA: 0.4 minutes\n", + "Step 412000 of 500000; TPS: 4169.73; ETA: 0.4 minutes\n", + "Step 413000 of 500000; TPS: 4170.72; ETA: 0.3 minutes\n", + "Step 414000 of 500000; TPS: 4170.39; ETA: 0.3 minutes\n", + "Step 415000 of 500000; TPS: 4171.35; ETA: 0.3 minutes\n", + "Step 416000 of 500000; TPS: 4172.01; ETA: 0.3 minutes\n", + "Step 417000 of 500000; TPS: 4172.74; ETA: 0.3 minutes\n", + "Step 418000 of 500000; TPS: 4174.18; ETA: 0.3 minutes\n", + "Step 419000 of 500000; TPS: 4175.14; ETA: 0.3 minutes\n", + "Step 420000 of 500000; TPS: 4175.39; ETA: 0.3 minutes\n", + "Step 421000 of 500000; TPS: 4175.59; ETA: 0.3 minutes\n", + "Step 422000 of 500000; TPS: 4176.39; ETA: 0.3 minutes\n", + "Step 423000 of 500000; TPS: 4177.52; ETA: 0.3 minutes\n", + "Step 424000 of 500000; TPS: 4178.87; ETA: 0.3 minutes\n", + "Step 425000 of 500000; TPS: 4178.71; ETA: 0.3 minutes\n", + "Step 426000 of 500000; TPS: 4178.93; ETA: 0.3 minutes\n", + "Step 427000 of 500000; TPS: 4179.51; ETA: 0.3 minutes\n", + "Step 428000 of 500000; TPS: 4179.91; ETA: 0.3 minutes\n", + "Step 429000 of 500000; TPS: 4180.58; ETA: 0.3 minutes\n", + "Step 430000 of 500000; TPS: 4180.26; ETA: 0.3 minutes\n", + "Step 431000 of 500000; TPS: 4180.9; ETA: 0.3 minutes\n", + "Step 432000 of 500000; TPS: 4181.51; ETA: 0.3 minutes\n", + "Step 433000 of 500000; TPS: 4182.78; ETA: 0.3 minutes\n", + "Step 434000 of 500000; TPS: 4183.73; ETA: 0.3 minutes\n", + "Step 435000 of 500000; TPS: 4184.27; ETA: 0.3 minutes\n", + "Step 436000 of 500000; TPS: 4185.2; ETA: 0.3 minutes\n", + "Step 437000 of 500000; TPS: 4185.77; ETA: 0.3 minutes\n", + "Step 438000 of 500000; TPS: 4187.25; ETA: 0.2 minutes\n", + "Step 439000 of 500000; TPS: 4188.63; ETA: 0.2 minutes\n", + "Step 440000 of 500000; TPS: 4190.31; ETA: 0.2 minutes\n", + "Step 441000 of 500000; TPS: 4191.61; ETA: 0.2 minutes\n", + "Step 442000 of 500000; TPS: 4192.23; ETA: 0.2 minutes\n", + "Step 443000 of 500000; TPS: 4194.02; ETA: 0.2 minutes\n", + "Step 444000 of 500000; TPS: 4195.41; ETA: 0.2 minutes\n", + "Step 445000 of 500000; TPS: 4197.08; ETA: 0.2 minutes\n", + "Step 446000 of 500000; TPS: 4198.93; ETA: 0.2 minutes\n", + "Step 447000 of 500000; TPS: 4200.51; ETA: 0.2 minutes\n", + "Step 448000 of 500000; TPS: 4202.09; ETA: 0.2 minutes\n", + "Step 449000 of 500000; TPS: 4203.91; ETA: 0.2 minutes\n", + "Step 450000 of 500000; TPS: 4205.54; ETA: 0.2 minutes\n", + "Step 451000 of 500000; TPS: 4207.35; ETA: 0.2 minutes\n", + "Step 452000 of 500000; TPS: 4209.25; ETA: 0.2 minutes\n", + "Step 453000 of 500000; TPS: 4211.02; ETA: 0.2 minutes\n", + "Step 454000 of 500000; TPS: 4212.87; ETA: 0.2 minutes\n", + "Step 455000 of 500000; TPS: 4214.3; ETA: 0.2 minutes\n", + "Step 456000 of 500000; TPS: 4215.73; ETA: 0.2 minutes\n", + "Step 457000 of 500000; TPS: 4217.42; ETA: 0.2 minutes\n", + "Step 458000 of 500000; TPS: 4219.3; ETA: 0.2 minutes\n", + "Step 459000 of 500000; TPS: 4221.27; ETA: 0.2 minutes\n", + "Step 460000 of 500000; TPS: 4222.96; ETA: 0.2 minutes\n", + "Step 461000 of 500000; TPS: 4224.5; ETA: 0.2 minutes\n", + "Step 462000 of 500000; TPS: 4226.26; ETA: 0.1 minutes\n", + "Step 463000 of 500000; TPS: 4228.22; ETA: 0.1 minutes\n", + "Step 464000 of 500000; TPS: 4229.47; ETA: 0.1 minutes\n", + "Step 465000 of 500000; TPS: 4231.17; ETA: 0.1 minutes\n", + "Step 466000 of 500000; TPS: 4232.6; ETA: 0.1 minutes\n", + "Step 467000 of 500000; TPS: 4232.62; ETA: 0.1 minutes\n", + "Step 468000 of 500000; TPS: 4234.19; ETA: 0.1 minutes\n", + "Step 469000 of 500000; TPS: 4235.39; ETA: 0.1 minutes\n", + "Step 470000 of 500000; TPS: 4236.6; ETA: 0.1 minutes\n", + "Step 471000 of 500000; TPS: 4237.73; ETA: 0.1 minutes\n", + "Step 472000 of 500000; TPS: 4238.7; ETA: 0.1 minutes\n", + "Step 473000 of 500000; TPS: 4239.79; ETA: 0.1 minutes\n", + "Step 474000 of 500000; TPS: 4240.99; ETA: 0.1 minutes\n", + "Step 475000 of 500000; TPS: 4242.52; ETA: 0.1 minutes\n", + "Step 476000 of 500000; TPS: 4244.02; ETA: 0.1 minutes\n", + "Step 477000 of 500000; TPS: 4245.27; ETA: 0.1 minutes\n", + "Step 478000 of 500000; TPS: 4246.63; ETA: 0.1 minutes\n", + "Step 479000 of 500000; TPS: 4247.9; ETA: 0.1 minutes\n", + "Step 480000 of 500000; TPS: 4247.49; ETA: 0.1 minutes\n", + "Step 481000 of 500000; TPS: 4247.91; ETA: 0.1 minutes\n", + "Step 482000 of 500000; TPS: 4248.93; ETA: 0.1 minutes\n", + "Step 483000 of 500000; TPS: 4249.1; ETA: 0.1 minutes\n", + "Step 484000 of 500000; TPS: 4250.18; ETA: 0.1 minutes\n", + "Step 485000 of 500000; TPS: 4251.03; ETA: 0.1 minutes\n", + "Step 486000 of 500000; TPS: 4251.86; ETA: 0.1 minutes\n", + "Step 487000 of 500000; TPS: 4251.22; ETA: 0.1 minutes\n", + "Step 488000 of 500000; TPS: 4250.26; ETA: 0.0 minutes\n", + "Step 489000 of 500000; TPS: 4249.58; ETA: 0.0 minutes\n", + "Step 490000 of 500000; TPS: 4248.03; ETA: 0.0 minutes\n", + "Step 491000 of 500000; TPS: 4247.87; ETA: 0.0 minutes\n", + "Step 492000 of 500000; TPS: 4248.46; ETA: 0.0 minutes\n", + "Step 493000 of 500000; TPS: 4248.42; ETA: 0.0 minutes\n", + "Step 494000 of 500000; TPS: 4248.54; ETA: 0.0 minutes\n", + "Step 495000 of 500000; TPS: 4248.16; ETA: 0.0 minutes\n", + "Step 496000 of 500000; TPS: 4248.22; ETA: 0.0 minutes\n", + "Step 497000 of 500000; TPS: 4248.16; ETA: 0.0 minutes\n", + "Step 498000 of 500000; TPS: 4247.9; ETA: 0.0 minutes\n", + "Step 499000 of 500000; TPS: 4248.33; ETA: 0.0 minutes\n", + "Step 500000 of 500000; TPS: 4248.12; ETA: 0.0 minutes\n", + "Step 999 of 1000000; TPS: 4576.94; ETA: 3.6 minutes\n", + "Step 1999 of 1000000; TPS: 4825.46; ETA: 3.4 minutes\n", + "Step 2999 of 1000000; TPS: 4944.62; ETA: 3.4 minutes\n", + "Step 3999 of 1000000; TPS: 4923.02; ETA: 3.4 minutes\n", + "Step 4999 of 1000000; TPS: 4929.32; ETA: 3.4 minutes\n", + "Step 5999 of 1000000; TPS: 4912.68; ETA: 3.4 minutes\n", + "Step 6999 of 1000000; TPS: 4842.95; ETA: 3.4 minutes\n", + "Step 7999 of 1000000; TPS: 4827.23; ETA: 3.4 minutes\n", + "Step 8999 of 1000000; TPS: 4870.43; ETA: 3.4 minutes\n", + "Step 9999 of 1000000; TPS: 4876.09; ETA: 3.4 minutes\n", + "Step 10999 of 1000000; TPS: 4883.6; ETA: 3.4 minutes\n", + "Step 11999 of 1000000; TPS: 4888.22; ETA: 3.4 minutes\n", + "Step 12999 of 1000000; TPS: 4900.37; ETA: 3.4 minutes\n", + "Step 13999 of 1000000; TPS: 4911.84; ETA: 3.3 minutes\n", + "Step 14999 of 1000000; TPS: 4897.3; ETA: 3.4 minutes\n", + "Step 15999 of 1000000; TPS: 4899.47; ETA: 3.3 minutes\n", + "Step 16999 of 1000000; TPS: 4896.3; ETA: 3.3 minutes\n", + "Step 17999 of 1000000; TPS: 4890.36; ETA: 3.3 minutes\n", + "Step 18999 of 1000000; TPS: 4892.73; ETA: 3.3 minutes\n", + "Step 19999 of 1000000; TPS: 4878.62; ETA: 3.3 minutes\n", + "Step 20999 of 1000000; TPS: 4873.47; ETA: 3.3 minutes\n", + "Step 21999 of 1000000; TPS: 4858.24; ETA: 3.4 minutes\n", + "Step 22999 of 1000000; TPS: 4848.96; ETA: 3.4 minutes\n", + "Step 23999 of 1000000; TPS: 4847.38; ETA: 3.4 minutes\n", + "Step 24999 of 1000000; TPS: 4844.15; ETA: 3.4 minutes\n", + "Step 25999 of 1000000; TPS: 4839.95; ETA: 3.4 minutes\n", + "Step 26999 of 1000000; TPS: 4854.8; ETA: 3.3 minutes\n", + "Step 27999 of 1000000; TPS: 4871.53; ETA: 3.3 minutes\n", + "Step 28999 of 1000000; TPS: 4882.54; ETA: 3.3 minutes\n", + "Step 29999 of 1000000; TPS: 3416.14; ETA: 4.7 minutes\n", + "Step 30999 of 1000000; TPS: 3443.83; ETA: 4.7 minutes\n", + "Step 31999 of 1000000; TPS: 3470.5; ETA: 4.6 minutes\n", + "Step 32999 of 1000000; TPS: 3503.66; ETA: 4.6 minutes\n", + "Step 33999 of 1000000; TPS: 3536.14; ETA: 4.6 minutes\n", + "Step 34999 of 1000000; TPS: 3564.7; ETA: 4.5 minutes\n", + "Step 35999 of 1000000; TPS: 3591.21; ETA: 4.5 minutes\n", + "Step 36999 of 1000000; TPS: 3616.05; ETA: 4.4 minutes\n", + "Step 37999 of 1000000; TPS: 3638.64; ETA: 4.4 minutes\n", + "Step 38999 of 1000000; TPS: 3665.06; ETA: 4.4 minutes\n", + "Step 39999 of 1000000; TPS: 3691.88; ETA: 4.3 minutes\n", + "Step 40999 of 1000000; TPS: 3717.24; ETA: 4.3 minutes\n", + "Step 41999 of 1000000; TPS: 3741.24; ETA: 4.3 minutes\n", + "Step 42999 of 1000000; TPS: 3757.89; ETA: 4.2 minutes\n", + "Step 43999 of 1000000; TPS: 3770.27; ETA: 4.2 minutes\n", + "Step 44999 of 1000000; TPS: 3785.18; ETA: 4.2 minutes\n", + "Step 45999 of 1000000; TPS: 3799.93; ETA: 4.2 minutes\n", + "Step 46999 of 1000000; TPS: 3810.2; ETA: 4.2 minutes\n", + "Step 47999 of 1000000; TPS: 3825.72; ETA: 4.1 minutes\n", + "Step 48999 of 1000000; TPS: 3845.03; ETA: 4.1 minutes\n", + "Step 49999 of 1000000; TPS: 3867.43; ETA: 4.1 minutes\n", + "Step 50999 of 1000000; TPS: 3891.4; ETA: 4.1 minutes\n", + "Step 51999 of 1000000; TPS: 3915.23; ETA: 4.0 minutes\n", + "Step 52999 of 1000000; TPS: 3932.61; ETA: 4.0 minutes\n", + "Step 53999 of 1000000; TPS: 3952.98; ETA: 4.0 minutes\n", + "Step 54999 of 1000000; TPS: 3971.17; ETA: 4.0 minutes\n", + "Step 55999 of 1000000; TPS: 3988.73; ETA: 3.9 minutes\n", + "Step 56999 of 1000000; TPS: 4005.56; ETA: 3.9 minutes\n", + "Step 57999 of 1000000; TPS: 4021.51; ETA: 3.9 minutes\n", + "Step 58999 of 1000000; TPS: 4040.66; ETA: 3.9 minutes\n", + "Step 59999 of 1000000; TPS: 4056.18; ETA: 3.9 minutes\n", + "Step 60999 of 1000000; TPS: 4070.55; ETA: 3.8 minutes\n", + "Step 61999 of 1000000; TPS: 4087.52; ETA: 3.8 minutes\n", + "Step 62999 of 1000000; TPS: 4104.02; ETA: 3.8 minutes\n", + "Step 63999 of 1000000; TPS: 4113.54; ETA: 3.8 minutes\n", + "Step 64999 of 1000000; TPS: 4123.21; ETA: 3.8 minutes\n", + "Step 65999 of 1000000; TPS: 4134.49; ETA: 3.8 minutes\n", + "Step 66999 of 1000000; TPS: 4146.52; ETA: 3.8 minutes\n", + "Step 67999 of 1000000; TPS: 4156.18; ETA: 3.7 minutes\n", + "Step 68999 of 1000000; TPS: 4169.7; ETA: 3.7 minutes\n", + "Step 69999 of 1000000; TPS: 4182.98; ETA: 3.7 minutes\n", + "Step 70999 of 1000000; TPS: 4188.96; ETA: 3.7 minutes\n", + "Step 71999 of 1000000; TPS: 4198.57; ETA: 3.7 minutes\n", + "Step 72999 of 1000000; TPS: 4207.57; ETA: 3.7 minutes\n", + "Step 73999 of 1000000; TPS: 4212.28; ETA: 3.7 minutes\n", + "Step 74999 of 1000000; TPS: 4222.53; ETA: 3.7 minutes\n", + "Step 75999 of 1000000; TPS: 4232.38; ETA: 3.6 minutes\n", + "Step 76999 of 1000000; TPS: 4242.76; ETA: 3.6 minutes\n", + "Step 77999 of 1000000; TPS: 4255.46; ETA: 3.6 minutes\n", + "Step 78999 of 1000000; TPS: 4265.73; ETA: 3.6 minutes\n", + "Step 79999 of 1000000; TPS: 4274.45; ETA: 3.6 minutes\n", + "Step 80999 of 1000000; TPS: 4283.63; ETA: 3.6 minutes\n", + "Step 81999 of 1000000; TPS: 4293.31; ETA: 3.6 minutes\n", + "Step 82999 of 1000000; TPS: 4301.88; ETA: 3.6 minutes\n", + "Step 83999 of 1000000; TPS: 4309.31; ETA: 3.5 minutes\n", + "Step 84999 of 1000000; TPS: 4316.53; ETA: 3.5 minutes\n", + "Step 85999 of 1000000; TPS: 4323.92; ETA: 3.5 minutes\n", + "Step 86999 of 1000000; TPS: 4333.13; ETA: 3.5 minutes\n", + "Step 87999 of 1000000; TPS: 4338.45; ETA: 3.5 minutes\n", + "Step 88999 of 1000000; TPS: 4343.43; ETA: 3.5 minutes\n", + "Step 89999 of 1000000; TPS: 4350.49; ETA: 3.5 minutes\n", + "Step 90999 of 1000000; TPS: 4357.0; ETA: 3.5 minutes\n", + "Step 91999 of 1000000; TPS: 4364.72; ETA: 3.5 minutes\n", + "Step 92999 of 1000000; TPS: 4371.94; ETA: 3.5 minutes\n", + "Step 93999 of 1000000; TPS: 4379.38; ETA: 3.4 minutes\n", + "Step 94999 of 1000000; TPS: 4385.94; ETA: 3.4 minutes\n", + "Step 95999 of 1000000; TPS: 4391.72; ETA: 3.4 minutes\n", + "Step 96999 of 1000000; TPS: 4397.62; ETA: 3.4 minutes\n", + "Step 97999 of 1000000; TPS: 4404.55; ETA: 3.4 minutes\n", + "Step 98999 of 1000000; TPS: 4411.51; ETA: 3.4 minutes\n", + "Step 99999 of 1000000; TPS: 4419.08; ETA: 3.4 minutes\n", + "Step 100999 of 1000000; TPS: 4425.54; ETA: 3.4 minutes\n", + "Step 101999 of 1000000; TPS: 4432.97; ETA: 3.4 minutes\n", + "Step 102999 of 1000000; TPS: 4439.2; ETA: 3.4 minutes\n", + "Step 103999 of 1000000; TPS: 4444.73; ETA: 3.4 minutes\n", + "Step 104999 of 1000000; TPS: 4450.03; ETA: 3.4 minutes\n", + "Step 105999 of 1000000; TPS: 4455.57; ETA: 3.3 minutes\n", + "Step 106999 of 1000000; TPS: 4460.26; ETA: 3.3 minutes\n", + "Step 107999 of 1000000; TPS: 4464.85; ETA: 3.3 minutes\n", + "Step 108999 of 1000000; TPS: 4468.13; ETA: 3.3 minutes\n", + "Step 109999 of 1000000; TPS: 4471.29; ETA: 3.3 minutes\n", + "Step 110999 of 1000000; TPS: 4477.95; ETA: 3.3 minutes\n", + "Step 111999 of 1000000; TPS: 4481.74; ETA: 3.3 minutes\n", + "Step 112999 of 1000000; TPS: 4485.31; ETA: 3.3 minutes\n", + "Step 113999 of 1000000; TPS: 4490.39; ETA: 3.3 minutes\n", + "Step 114999 of 1000000; TPS: 4492.9; ETA: 3.3 minutes\n", + "Step 115999 of 1000000; TPS: 4497.51; ETA: 3.3 minutes\n", + "Step 116999 of 1000000; TPS: 4502.06; ETA: 3.3 minutes\n", + "Step 117999 of 1000000; TPS: 4504.67; ETA: 3.3 minutes\n", + "Step 118999 of 1000000; TPS: 4506.16; ETA: 3.3 minutes\n", + "Step 119999 of 1000000; TPS: 4507.7; ETA: 3.3 minutes\n", + "Step 120999 of 1000000; TPS: 4509.86; ETA: 3.2 minutes\n", + "Step 121999 of 1000000; TPS: 4513.48; ETA: 3.2 minutes\n", + "Step 122999 of 1000000; TPS: 4516.36; ETA: 3.2 minutes\n", + "Step 123999 of 1000000; TPS: 4519.43; ETA: 3.2 minutes\n", + "Step 124999 of 1000000; TPS: 4520.95; ETA: 3.2 minutes\n", + "Step 125999 of 1000000; TPS: 4525.02; ETA: 3.2 minutes\n", + "Step 126999 of 1000000; TPS: 4527.82; ETA: 3.2 minutes\n", + "Step 127999 of 1000000; TPS: 4530.61; ETA: 3.2 minutes\n", + "Step 128999 of 1000000; TPS: 4533.7; ETA: 3.2 minutes\n", + "Step 129999 of 1000000; TPS: 4534.85; ETA: 3.2 minutes\n", + "Step 130999 of 1000000; TPS: 4537.25; ETA: 3.2 minutes\n", + "Step 131999 of 1000000; TPS: 4539.82; ETA: 3.2 minutes\n", + "Step 132999 of 1000000; TPS: 4543.99; ETA: 3.2 minutes\n", + "Step 133999 of 1000000; TPS: 4546.93; ETA: 3.2 minutes\n", + "Step 134999 of 1000000; TPS: 4548.61; ETA: 3.2 minutes\n", + "Step 135999 of 1000000; TPS: 4550.57; ETA: 3.2 minutes\n", + "Step 136999 of 1000000; TPS: 4553.93; ETA: 3.2 minutes\n", + "Step 137999 of 1000000; TPS: 4557.95; ETA: 3.2 minutes\n", + "Step 138999 of 1000000; TPS: 4561.51; ETA: 3.1 minutes\n", + "Step 139999 of 1000000; TPS: 4566.09; ETA: 3.1 minutes\n", + "Step 140999 of 1000000; TPS: 4570.08; ETA: 3.1 minutes\n", + "Step 141999 of 1000000; TPS: 4574.56; ETA: 3.1 minutes\n", + "Step 142999 of 1000000; TPS: 4578.76; ETA: 3.1 minutes\n", + "Step 143999 of 1000000; TPS: 4583.32; ETA: 3.1 minutes\n", + "Step 144999 of 1000000; TPS: 4587.75; ETA: 3.1 minutes\n", + "Step 145999 of 1000000; TPS: 4593.08; ETA: 3.1 minutes\n", + "Step 146999 of 1000000; TPS: 4595.3; ETA: 3.1 minutes\n", + "Step 147999 of 1000000; TPS: 4597.09; ETA: 3.1 minutes\n", + "Step 148999 of 1000000; TPS: 4598.63; ETA: 3.1 minutes\n", + "Step 149999 of 1000000; TPS: 4601.85; ETA: 3.1 minutes\n", + "Step 150999 of 1000000; TPS: 4604.96; ETA: 3.1 minutes\n", + "Step 151999 of 1000000; TPS: 4607.53; ETA: 3.1 minutes\n", + "Step 152999 of 1000000; TPS: 4610.94; ETA: 3.1 minutes\n", + "Step 153999 of 1000000; TPS: 4614.67; ETA: 3.1 minutes\n", + "Step 154999 of 1000000; TPS: 4617.87; ETA: 3.0 minutes\n", + "Step 155999 of 1000000; TPS: 4619.54; ETA: 3.0 minutes\n", + "Step 156999 of 1000000; TPS: 4621.77; ETA: 3.0 minutes\n", + "Step 157999 of 1000000; TPS: 4624.26; ETA: 3.0 minutes\n", + "Step 158999 of 1000000; TPS: 4627.99; ETA: 3.0 minutes\n", + "Step 159999 of 1000000; TPS: 4631.18; ETA: 3.0 minutes\n", + "Step 160999 of 1000000; TPS: 4633.18; ETA: 3.0 minutes\n", + "Step 161999 of 1000000; TPS: 4636.16; ETA: 3.0 minutes\n", + "Step 162999 of 1000000; TPS: 4639.89; ETA: 3.0 minutes\n", + "Step 163999 of 1000000; TPS: 4641.64; ETA: 3.0 minutes\n", + "Step 164999 of 1000000; TPS: 4644.03; ETA: 3.0 minutes\n", + "Step 165999 of 1000000; TPS: 4643.77; ETA: 3.0 minutes\n", + "Step 166999 of 1000000; TPS: 4645.88; ETA: 3.0 minutes\n", + "Step 167999 of 1000000; TPS: 4646.74; ETA: 3.0 minutes\n", + "Step 168999 of 1000000; TPS: 4647.81; ETA: 3.0 minutes\n", + "Step 169999 of 1000000; TPS: 4648.87; ETA: 3.0 minutes\n", + "Step 170999 of 1000000; TPS: 4649.94; ETA: 3.0 minutes\n", + "Step 171999 of 1000000; TPS: 4649.73; ETA: 3.0 minutes\n", + "Step 172999 of 1000000; TPS: 4650.97; ETA: 3.0 minutes\n", + "Step 173999 of 1000000; TPS: 4651.92; ETA: 3.0 minutes\n", + "Step 174999 of 1000000; TPS: 4652.71; ETA: 3.0 minutes\n", + "Step 175999 of 1000000; TPS: 4653.92; ETA: 3.0 minutes\n", + "Step 176999 of 1000000; TPS: 4656.43; ETA: 2.9 minutes\n", + "Step 177999 of 1000000; TPS: 4657.84; ETA: 2.9 minutes\n", + "Step 178999 of 1000000; TPS: 4659.64; ETA: 2.9 minutes\n", + "Step 179999 of 1000000; TPS: 4659.45; ETA: 2.9 minutes\n", + "Step 180999 of 1000000; TPS: 4661.48; ETA: 2.9 minutes\n", + "Step 181999 of 1000000; TPS: 4663.14; ETA: 2.9 minutes\n", + "Step 182999 of 1000000; TPS: 4665.05; ETA: 2.9 minutes\n", + "Step 183999 of 1000000; TPS: 4666.15; ETA: 2.9 minutes\n", + "Step 184999 of 1000000; TPS: 4666.4; ETA: 2.9 minutes\n", + "Step 185999 of 1000000; TPS: 4668.03; ETA: 2.9 minutes\n", + "Step 186999 of 1000000; TPS: 4669.03; ETA: 2.9 minutes\n", + "Step 187999 of 1000000; TPS: 4670.67; ETA: 2.9 minutes\n", + "Step 188999 of 1000000; TPS: 4672.76; ETA: 2.9 minutes\n", + "Step 189999 of 1000000; TPS: 4673.79; ETA: 2.9 minutes\n", + "Step 190999 of 1000000; TPS: 4675.52; ETA: 2.9 minutes\n", + "Step 191999 of 1000000; TPS: 4405.62; ETA: 3.1 minutes\n", + "Step 192999 of 1000000; TPS: 4406.9; ETA: 3.1 minutes\n", + "Step 193999 of 1000000; TPS: 4407.91; ETA: 3.0 minutes\n", + "Step 194999 of 1000000; TPS: 4408.88; ETA: 3.0 minutes\n", + "Step 195999 of 1000000; TPS: 4409.63; ETA: 3.0 minutes\n", + "Step 196999 of 1000000; TPS: 4410.62; ETA: 3.0 minutes\n", + "Step 197999 of 1000000; TPS: 4409.42; ETA: 3.0 minutes\n", + "Step 198999 of 1000000; TPS: 4408.52; ETA: 3.0 minutes\n", + "Step 199999 of 1000000; TPS: 4407.98; ETA: 3.0 minutes\n", + "Step 200999 of 1000000; TPS: 4407.82; ETA: 3.0 minutes\n", + "Step 201999 of 1000000; TPS: 4405.78; ETA: 3.0 minutes\n", + "Step 202999 of 1000000; TPS: 4406.55; ETA: 3.0 minutes\n", + "Step 203999 of 1000000; TPS: 4407.13; ETA: 3.0 minutes\n", + "Step 204999 of 1000000; TPS: 4407.99; ETA: 3.0 minutes\n", + "Step 205999 of 1000000; TPS: 4408.92; ETA: 3.0 minutes\n", + "Step 206999 of 1000000; TPS: 4411.23; ETA: 3.0 minutes\n", + "Step 207999 of 1000000; TPS: 4413.2; ETA: 3.0 minutes\n", + "Step 208999 of 1000000; TPS: 4414.54; ETA: 3.0 minutes\n", + "Step 209999 of 1000000; TPS: 4416.99; ETA: 3.0 minutes\n", + "Step 210999 of 1000000; TPS: 4420.04; ETA: 3.0 minutes\n", + "Step 211999 of 1000000; TPS: 4423.86; ETA: 3.0 minutes\n", + "Step 212999 of 1000000; TPS: 4427.67; ETA: 3.0 minutes\n", + "Step 213999 of 1000000; TPS: 4430.57; ETA: 3.0 minutes\n", + "Step 214999 of 1000000; TPS: 4433.58; ETA: 3.0 minutes\n", + "Step 215999 of 1000000; TPS: 4435.46; ETA: 2.9 minutes\n", + "Step 216999 of 1000000; TPS: 4438.84; ETA: 2.9 minutes\n", + "Step 217999 of 1000000; TPS: 4441.78; ETA: 2.9 minutes\n", + "Step 218999 of 1000000; TPS: 4442.25; ETA: 2.9 minutes\n", + "Step 219999 of 1000000; TPS: 4444.77; ETA: 2.9 minutes\n", + "Step 220999 of 1000000; TPS: 4447.93; ETA: 2.9 minutes\n", + "Step 221999 of 1000000; TPS: 4450.36; ETA: 2.9 minutes\n", + "Step 222999 of 1000000; TPS: 4453.25; ETA: 2.9 minutes\n", + "Step 223999 of 1000000; TPS: 4455.88; ETA: 2.9 minutes\n", + "Step 224999 of 1000000; TPS: 4458.05; ETA: 2.9 minutes\n", + "Step 225999 of 1000000; TPS: 4460.28; ETA: 2.9 minutes\n", + "Step 226999 of 1000000; TPS: 4461.43; ETA: 2.9 minutes\n", + "Step 227999 of 1000000; TPS: 4462.83; ETA: 2.9 minutes\n", + "Step 228999 of 1000000; TPS: 4464.89; ETA: 2.9 minutes\n", + "Step 229999 of 1000000; TPS: 4467.22; ETA: 2.9 minutes\n", + "Step 230999 of 1000000; TPS: 4469.18; ETA: 2.9 minutes\n", + "Step 231999 of 1000000; TPS: 4471.18; ETA: 2.9 minutes\n", + "Step 232999 of 1000000; TPS: 4472.88; ETA: 2.9 minutes\n", + "Step 233999 of 1000000; TPS: 4474.43; ETA: 2.9 minutes\n", + "Step 234999 of 1000000; TPS: 4476.07; ETA: 2.8 minutes\n", + "Step 235999 of 1000000; TPS: 4478.06; ETA: 2.8 minutes\n", + "Step 236999 of 1000000; TPS: 4479.81; ETA: 2.8 minutes\n", + "Step 237999 of 1000000; TPS: 4482.04; ETA: 2.8 minutes\n", + "Step 238999 of 1000000; TPS: 4483.42; ETA: 2.8 minutes\n", + "Step 239999 of 1000000; TPS: 4485.27; ETA: 2.8 minutes\n", + "Step 240999 of 1000000; TPS: 4488.22; ETA: 2.8 minutes\n", + "Step 241999 of 1000000; TPS: 4490.48; ETA: 2.8 minutes\n", + "Step 242999 of 1000000; TPS: 4491.66; ETA: 2.8 minutes\n", + "Step 243999 of 1000000; TPS: 4493.47; ETA: 2.8 minutes\n", + "Step 244999 of 1000000; TPS: 4495.34; ETA: 2.8 minutes\n", + "Step 245999 of 1000000; TPS: 4498.06; ETA: 2.8 minutes\n", + "Step 246999 of 1000000; TPS: 4499.88; ETA: 2.8 minutes\n", + "Step 247999 of 1000000; TPS: 4501.84; ETA: 2.8 minutes\n", + "Step 248999 of 1000000; TPS: 4504.41; ETA: 2.8 minutes\n", + "Step 249999 of 1000000; TPS: 4506.59; ETA: 2.8 minutes\n", + "Step 250999 of 1000000; TPS: 4509.56; ETA: 2.8 minutes\n", + "Step 251999 of 1000000; TPS: 4510.45; ETA: 2.8 minutes\n", + "Step 252999 of 1000000; TPS: 4512.14; ETA: 2.8 minutes\n", + "Step 253999 of 1000000; TPS: 4513.63; ETA: 2.8 minutes\n", + "Step 254999 of 1000000; TPS: 4515.54; ETA: 2.7 minutes\n", + "Step 255999 of 1000000; TPS: 4518.14; ETA: 2.7 minutes\n", + "Step 256999 of 1000000; TPS: 4520.62; ETA: 2.7 minutes\n", + "Step 257999 of 1000000; TPS: 4522.9; ETA: 2.7 minutes\n", + "Step 258999 of 1000000; TPS: 4525.21; ETA: 2.7 minutes\n", + "Step 259999 of 1000000; TPS: 4527.4; ETA: 2.7 minutes\n", + "Step 260999 of 1000000; TPS: 4529.59; ETA: 2.7 minutes\n", + "Step 261999 of 1000000; TPS: 4531.14; ETA: 2.7 minutes\n", + "Step 262999 of 1000000; TPS: 4532.99; ETA: 2.7 minutes\n", + "Step 263999 of 1000000; TPS: 4534.53; ETA: 2.7 minutes\n", + "Step 264999 of 1000000; TPS: 4537.35; ETA: 2.7 minutes\n", + "Step 265999 of 1000000; TPS: 4538.83; ETA: 2.7 minutes\n", + "Step 266999 of 1000000; TPS: 4539.42; ETA: 2.7 minutes\n", + "Step 267999 of 1000000; TPS: 4539.13; ETA: 2.7 minutes\n", + "Step 268999 of 1000000; TPS: 4540.05; ETA: 2.7 minutes\n", + "Step 269999 of 1000000; TPS: 4540.91; ETA: 2.7 minutes\n", + "Step 270999 of 1000000; TPS: 4541.87; ETA: 2.7 minutes\n", + "Step 271999 of 1000000; TPS: 4542.73; ETA: 2.7 minutes\n", + "Step 272999 of 1000000; TPS: 4543.62; ETA: 2.7 minutes\n", + "Step 273999 of 1000000; TPS: 4544.81; ETA: 2.7 minutes\n", + "Step 274999 of 1000000; TPS: 4542.79; ETA: 2.7 minutes\n", + "Step 275999 of 1000000; TPS: 4541.96; ETA: 2.7 minutes\n", + "Step 276999 of 1000000; TPS: 4541.29; ETA: 2.7 minutes\n", + "Step 277999 of 1000000; TPS: 4538.58; ETA: 2.7 minutes\n", + "Step 278999 of 1000000; TPS: 4538.1; ETA: 2.6 minutes\n", + "Step 279999 of 1000000; TPS: 4538.29; ETA: 2.6 minutes\n", + "Step 280999 of 1000000; TPS: 4538.52; ETA: 2.6 minutes\n", + "Step 281999 of 1000000; TPS: 4539.81; ETA: 2.6 minutes\n", + "Step 282999 of 1000000; TPS: 4539.75; ETA: 2.6 minutes\n", + "Step 283999 of 1000000; TPS: 4539.84; ETA: 2.6 minutes\n", + "Step 284999 of 1000000; TPS: 4540.34; ETA: 2.6 minutes\n", + "Step 285999 of 1000000; TPS: 4539.9; ETA: 2.6 minutes\n", + "Step 286999 of 1000000; TPS: 4541.07; ETA: 2.6 minutes\n", + "Step 287999 of 1000000; TPS: 4541.16; ETA: 2.6 minutes\n", + "Step 288999 of 1000000; TPS: 4540.86; ETA: 2.6 minutes\n", + "Step 289999 of 1000000; TPS: 4540.6; ETA: 2.6 minutes\n", + "Step 290999 of 1000000; TPS: 4540.18; ETA: 2.6 minutes\n", + "Step 291999 of 1000000; TPS: 4541.2; ETA: 2.6 minutes\n", + "Step 292999 of 1000000; TPS: 4541.84; ETA: 2.6 minutes\n", + "Step 293999 of 1000000; TPS: 4541.34; ETA: 2.6 minutes\n", + "Step 294999 of 1000000; TPS: 4541.92; ETA: 2.6 minutes\n", + "Step 295999 of 1000000; TPS: 4542.62; ETA: 2.6 minutes\n", + "Step 296999 of 1000000; TPS: 4538.55; ETA: 2.6 minutes\n", + "Step 297999 of 1000000; TPS: 4535.84; ETA: 2.6 minutes\n", + "Step 298999 of 1000000; TPS: 4533.9; ETA: 2.6 minutes\n", + "Step 299999 of 1000000; TPS: 4534.76; ETA: 2.6 minutes\n", + "Step 300999 of 1000000; TPS: 4535.77; ETA: 2.6 minutes\n", + "Step 301999 of 1000000; TPS: 4537.61; ETA: 2.6 minutes\n", + "Step 302999 of 1000000; TPS: 4538.93; ETA: 2.6 minutes\n", + "Step 303999 of 1000000; TPS: 4539.65; ETA: 2.6 minutes\n", + "Step 304999 of 1000000; TPS: 4541.28; ETA: 2.6 minutes\n", + "Step 305999 of 1000000; TPS: 4542.25; ETA: 2.5 minutes\n", + "Step 306999 of 1000000; TPS: 4541.99; ETA: 2.5 minutes\n", + "Step 307999 of 1000000; TPS: 4543.97; ETA: 2.5 minutes\n", + "Step 308999 of 1000000; TPS: 4545.2; ETA: 2.5 minutes\n", + "Step 309999 of 1000000; TPS: 4546.41; ETA: 2.5 minutes\n", + "Step 310999 of 1000000; TPS: 4547.56; ETA: 2.5 minutes\n", + "Step 311999 of 1000000; TPS: 4549.56; ETA: 2.5 minutes\n", + "Step 312999 of 1000000; TPS: 4550.86; ETA: 2.5 minutes\n", + "Step 313999 of 1000000; TPS: 4552.1; ETA: 2.5 minutes\n", + "Step 314999 of 1000000; TPS: 4553.18; ETA: 2.5 minutes\n", + "Step 315999 of 1000000; TPS: 4553.92; ETA: 2.5 minutes\n", + "Step 316999 of 1000000; TPS: 4554.8; ETA: 2.5 minutes\n", + "Step 317999 of 1000000; TPS: 4556.21; ETA: 2.5 minutes\n", + "Step 318999 of 1000000; TPS: 4557.49; ETA: 2.5 minutes\n", + "Step 319999 of 1000000; TPS: 4559.2; ETA: 2.5 minutes\n", + "Step 320999 of 1000000; TPS: 4560.84; ETA: 2.5 minutes\n", + "Step 321999 of 1000000; TPS: 4562.65; ETA: 2.5 minutes\n", + "Step 322999 of 1000000; TPS: 4562.88; ETA: 2.5 minutes\n", + "Step 323999 of 1000000; TPS: 4564.03; ETA: 2.5 minutes\n", + "Step 324999 of 1000000; TPS: 4565.01; ETA: 2.5 minutes\n", + "Step 325999 of 1000000; TPS: 4563.32; ETA: 2.5 minutes\n", + "Step 326999 of 1000000; TPS: 4564.13; ETA: 2.5 minutes\n", + "Step 327999 of 1000000; TPS: 4564.85; ETA: 2.5 minutes\n", + "Step 328999 of 1000000; TPS: 4566.32; ETA: 2.4 minutes\n", + "Step 329999 of 1000000; TPS: 4566.61; ETA: 2.4 minutes\n", + "Step 330999 of 1000000; TPS: 4567.14; ETA: 2.4 minutes\n", + "Step 331999 of 1000000; TPS: 4568.03; ETA: 2.4 minutes\n", + "Step 332999 of 1000000; TPS: 4569.42; ETA: 2.4 minutes\n", + "Step 333999 of 1000000; TPS: 4570.65; ETA: 2.4 minutes\n", + "Step 334999 of 1000000; TPS: 4572.63; ETA: 2.4 minutes\n", + "Step 335999 of 1000000; TPS: 4573.29; ETA: 2.4 minutes\n", + "Step 336999 of 1000000; TPS: 4573.91; ETA: 2.4 minutes\n", + "Step 337999 of 1000000; TPS: 4575.4; ETA: 2.4 minutes\n", + "Step 338999 of 1000000; TPS: 4577.02; ETA: 2.4 minutes\n", + "Step 339999 of 1000000; TPS: 4578.14; ETA: 2.4 minutes\n", + "Step 340999 of 1000000; TPS: 4579.37; ETA: 2.4 minutes\n", + "Step 341999 of 1000000; TPS: 4580.45; ETA: 2.4 minutes\n", + "Step 342999 of 1000000; TPS: 4581.41; ETA: 2.4 minutes\n", + "Step 343999 of 1000000; TPS: 4582.49; ETA: 2.4 minutes\n", + "Step 344999 of 1000000; TPS: 4583.33; ETA: 2.4 minutes\n", + "Step 345999 of 1000000; TPS: 4584.43; ETA: 2.4 minutes\n", + "Step 346999 of 1000000; TPS: 4585.22; ETA: 2.4 minutes\n", + "Step 347999 of 1000000; TPS: 4439.47; ETA: 2.4 minutes\n", + "Step 348999 of 1000000; TPS: 4440.87; ETA: 2.4 minutes\n", + "Step 349999 of 1000000; TPS: 4441.19; ETA: 2.4 minutes\n", + "Step 350999 of 1000000; TPS: 4439.74; ETA: 2.4 minutes\n", + "Step 351999 of 1000000; TPS: 4439.61; ETA: 2.4 minutes\n", + "Step 352999 of 1000000; TPS: 4440.08; ETA: 2.4 minutes\n", + "Step 353999 of 1000000; TPS: 4440.64; ETA: 2.4 minutes\n", + "Step 354999 of 1000000; TPS: 4441.41; ETA: 2.4 minutes\n", + "Step 355999 of 1000000; TPS: 4442.17; ETA: 2.4 minutes\n", + "Step 356999 of 1000000; TPS: 4443.06; ETA: 2.4 minutes\n", + "Step 357999 of 1000000; TPS: 4443.83; ETA: 2.4 minutes\n", + "Step 358999 of 1000000; TPS: 4444.16; ETA: 2.4 minutes\n", + "Step 359999 of 1000000; TPS: 4444.93; ETA: 2.4 minutes\n", + "Step 360999 of 1000000; TPS: 4445.44; ETA: 2.4 minutes\n", + "Step 361999 of 1000000; TPS: 4445.58; ETA: 2.4 minutes\n", + "Step 362999 of 1000000; TPS: 4446.9; ETA: 2.4 minutes\n", + "Step 363999 of 1000000; TPS: 4447.32; ETA: 2.4 minutes\n", + "Step 364999 of 1000000; TPS: 4448.63; ETA: 2.4 minutes\n", + "Step 365999 of 1000000; TPS: 4450.2; ETA: 2.4 minutes\n", + "Step 366999 of 1000000; TPS: 4451.99; ETA: 2.4 minutes\n", + "Step 367999 of 1000000; TPS: 4453.89; ETA: 2.4 minutes\n", + "Step 368999 of 1000000; TPS: 4455.71; ETA: 2.4 minutes\n", + "Step 369999 of 1000000; TPS: 4456.7; ETA: 2.4 minutes\n", + "Step 370999 of 1000000; TPS: 4458.18; ETA: 2.4 minutes\n", + "Step 371999 of 1000000; TPS: 4459.24; ETA: 2.3 minutes\n", + "Step 372999 of 1000000; TPS: 4460.25; ETA: 2.3 minutes\n", + "Step 373999 of 1000000; TPS: 4461.99; ETA: 2.3 minutes\n", + "Step 374999 of 1000000; TPS: 4463.26; ETA: 2.3 minutes\n", + "Step 375999 of 1000000; TPS: 4465.05; ETA: 2.3 minutes\n", + "Step 376999 of 1000000; TPS: 4466.79; ETA: 2.3 minutes\n", + "Step 377999 of 1000000; TPS: 4468.0; ETA: 2.3 minutes\n", + "Step 378999 of 1000000; TPS: 4468.94; ETA: 2.3 minutes\n", + "Step 379999 of 1000000; TPS: 4470.7; ETA: 2.3 minutes\n", + "Step 380999 of 1000000; TPS: 4471.82; ETA: 2.3 minutes\n", + "Step 381999 of 1000000; TPS: 4472.46; ETA: 2.3 minutes\n", + "Step 382999 of 1000000; TPS: 4473.74; ETA: 2.3 minutes\n", + "Step 383999 of 1000000; TPS: 4474.16; ETA: 2.3 minutes\n", + "Step 384999 of 1000000; TPS: 4475.47; ETA: 2.3 minutes\n", + "Step 385999 of 1000000; TPS: 4477.24; ETA: 2.3 minutes\n", + "Step 386999 of 1000000; TPS: 4478.88; ETA: 2.3 minutes\n", + "Step 387999 of 1000000; TPS: 4480.28; ETA: 2.3 minutes\n", + "Step 388999 of 1000000; TPS: 4482.49; ETA: 2.3 minutes\n", + "Step 389999 of 1000000; TPS: 4484.37; ETA: 2.3 minutes\n", + "Step 390999 of 1000000; TPS: 4485.73; ETA: 2.3 minutes\n", + "Step 391999 of 1000000; TPS: 4487.31; ETA: 2.3 minutes\n", + "Step 392999 of 1000000; TPS: 4489.0; ETA: 2.3 minutes\n", + "Step 393999 of 1000000; TPS: 4490.68; ETA: 2.2 minutes\n", + "Step 394999 of 1000000; TPS: 4492.51; ETA: 2.2 minutes\n", + "Step 395999 of 1000000; TPS: 4494.3; ETA: 2.2 minutes\n", + "Step 396999 of 1000000; TPS: 4495.88; ETA: 2.2 minutes\n", + "Step 397999 of 1000000; TPS: 4496.9; ETA: 2.2 minutes\n", + "Step 398999 of 1000000; TPS: 4499.09; ETA: 2.2 minutes\n", + "Step 399999 of 1000000; TPS: 4500.4; ETA: 2.2 minutes\n", + "Step 400999 of 1000000; TPS: 4501.34; ETA: 2.2 minutes\n", + "Step 401999 of 1000000; TPS: 4502.1; ETA: 2.2 minutes\n", + "Step 402999 of 1000000; TPS: 4503.45; ETA: 2.2 minutes\n", + "Step 403999 of 1000000; TPS: 4504.52; ETA: 2.2 minutes\n", + "Step 404999 of 1000000; TPS: 4504.1; ETA: 2.2 minutes\n", + "Step 405999 of 1000000; TPS: 4505.1; ETA: 2.2 minutes\n", + "Step 406999 of 1000000; TPS: 4505.86; ETA: 2.2 minutes\n", + "Step 407999 of 1000000; TPS: 4507.08; ETA: 2.2 minutes\n", + "Step 408999 of 1000000; TPS: 4508.04; ETA: 2.2 minutes\n", + "Step 409999 of 1000000; TPS: 4508.95; ETA: 2.2 minutes\n", + "Step 410999 of 1000000; TPS: 4509.71; ETA: 2.2 minutes\n", + "Step 411999 of 1000000; TPS: 4510.66; ETA: 2.2 minutes\n", + "Step 412999 of 1000000; TPS: 4511.7; ETA: 2.2 minutes\n", + "Step 413999 of 1000000; TPS: 4512.59; ETA: 2.2 minutes\n", + "Step 414999 of 1000000; TPS: 4513.14; ETA: 2.2 minutes\n", + "Step 415999 of 1000000; TPS: 4514.2; ETA: 2.2 minutes\n", + "Step 416999 of 1000000; TPS: 4515.12; ETA: 2.2 minutes\n", + "Step 417999 of 1000000; TPS: 4516.06; ETA: 2.1 minutes\n", + "Step 418999 of 1000000; TPS: 4516.19; ETA: 2.1 minutes\n", + "Step 419999 of 1000000; TPS: 4517.3; ETA: 2.1 minutes\n", + "Step 420999 of 1000000; TPS: 4518.57; ETA: 2.1 minutes\n", + "Step 421999 of 1000000; TPS: 4520.16; ETA: 2.1 minutes\n", + "Step 422999 of 1000000; TPS: 4520.52; ETA: 2.1 minutes\n", + "Step 423999 of 1000000; TPS: 4521.38; ETA: 2.1 minutes\n", + "Step 424999 of 1000000; TPS: 4522.85; ETA: 2.1 minutes\n", + "Step 425999 of 1000000; TPS: 4524.08; ETA: 2.1 minutes\n", + "Step 426999 of 1000000; TPS: 4525.72; ETA: 2.1 minutes\n", + "Step 427999 of 1000000; TPS: 4526.93; ETA: 2.1 minutes\n", + "Step 428999 of 1000000; TPS: 4527.92; ETA: 2.1 minutes\n", + "Step 429999 of 1000000; TPS: 4528.78; ETA: 2.1 minutes\n", + "Step 430999 of 1000000; TPS: 4530.1; ETA: 2.1 minutes\n", + "Step 431999 of 1000000; TPS: 4531.43; ETA: 2.1 minutes\n", + "Step 432999 of 1000000; TPS: 4532.47; ETA: 2.1 minutes\n", + "Step 433999 of 1000000; TPS: 4533.77; ETA: 2.1 minutes\n", + "Step 434999 of 1000000; TPS: 4535.02; ETA: 2.1 minutes\n", + "Step 435999 of 1000000; TPS: 4536.22; ETA: 2.1 minutes\n", + "Step 436999 of 1000000; TPS: 4537.33; ETA: 2.1 minutes\n", + "Step 437999 of 1000000; TPS: 4538.39; ETA: 2.1 minutes\n", + "Step 438999 of 1000000; TPS: 4539.9; ETA: 2.1 minutes\n", + "Step 439999 of 1000000; TPS: 4541.04; ETA: 2.1 minutes\n", + "Step 440999 of 1000000; TPS: 4541.98; ETA: 2.1 minutes\n", + "Step 441999 of 1000000; TPS: 4542.08; ETA: 2.0 minutes\n", + "Step 442999 of 1000000; TPS: 4542.68; ETA: 2.0 minutes\n", + "Step 443999 of 1000000; TPS: 4543.97; ETA: 2.0 minutes\n", + "Step 444999 of 1000000; TPS: 4544.76; ETA: 2.0 minutes\n", + "Step 445999 of 1000000; TPS: 4545.97; ETA: 2.0 minutes\n", + "Step 446999 of 1000000; TPS: 4546.23; ETA: 2.0 minutes\n", + "Step 447999 of 1000000; TPS: 4545.56; ETA: 2.0 minutes\n", + "Step 448999 of 1000000; TPS: 4546.85; ETA: 2.0 minutes\n", + "Step 449999 of 1000000; TPS: 4548.21; ETA: 2.0 minutes\n", + "Step 450999 of 1000000; TPS: 4549.46; ETA: 2.0 minutes\n", + "Step 451999 of 1000000; TPS: 4550.71; ETA: 2.0 minutes\n", + "Step 452999 of 1000000; TPS: 4551.87; ETA: 2.0 minutes\n", + "Step 453999 of 1000000; TPS: 4553.1; ETA: 2.0 minutes\n", + "Step 454999 of 1000000; TPS: 4554.62; ETA: 2.0 minutes\n", + "Step 455999 of 1000000; TPS: 4555.83; ETA: 2.0 minutes\n", + "Step 456999 of 1000000; TPS: 4556.7; ETA: 2.0 minutes\n", + "Step 457999 of 1000000; TPS: 4557.92; ETA: 2.0 minutes\n", + "Step 458999 of 1000000; TPS: 4558.13; ETA: 2.0 minutes\n", + "Step 459999 of 1000000; TPS: 4559.15; ETA: 2.0 minutes\n", + "Step 460999 of 1000000; TPS: 4559.75; ETA: 2.0 minutes\n", + "Step 461999 of 1000000; TPS: 4560.49; ETA: 2.0 minutes\n", + "Step 462999 of 1000000; TPS: 4561.25; ETA: 2.0 minutes\n", + "Step 463999 of 1000000; TPS: 4561.62; ETA: 2.0 minutes\n", + "Step 464999 of 1000000; TPS: 4562.0; ETA: 2.0 minutes\n", + "Step 465999 of 1000000; TPS: 4562.59; ETA: 2.0 minutes\n", + "Step 466999 of 1000000; TPS: 4563.48; ETA: 1.9 minutes\n", + "Step 467999 of 1000000; TPS: 4564.71; ETA: 1.9 minutes\n", + "Step 468999 of 1000000; TPS: 4565.22; ETA: 1.9 minutes\n", + "Step 469999 of 1000000; TPS: 4565.77; ETA: 1.9 minutes\n", + "Step 470999 of 1000000; TPS: 4565.82; ETA: 1.9 minutes\n", + "Step 471999 of 1000000; TPS: 4565.92; ETA: 1.9 minutes\n", + "Step 472999 of 1000000; TPS: 4567.06; ETA: 1.9 minutes\n", + "Step 473999 of 1000000; TPS: 4567.96; ETA: 1.9 minutes\n", + "Step 474999 of 1000000; TPS: 4569.0; ETA: 1.9 minutes\n", + "Step 475999 of 1000000; TPS: 4569.83; ETA: 1.9 minutes\n", + "Step 476999 of 1000000; TPS: 4570.62; ETA: 1.9 minutes\n", + "Step 477999 of 1000000; TPS: 4571.86; ETA: 1.9 minutes\n", + "Step 478999 of 1000000; TPS: 4573.02; ETA: 1.9 minutes\n", + "Step 479999 of 1000000; TPS: 4573.8; ETA: 1.9 minutes\n", + "Step 480999 of 1000000; TPS: 4574.7; ETA: 1.9 minutes\n", + "Step 481999 of 1000000; TPS: 4575.8; ETA: 1.9 minutes\n", + "Step 482999 of 1000000; TPS: 4577.05; ETA: 1.9 minutes\n", + "Step 483999 of 1000000; TPS: 4578.51; ETA: 1.9 minutes\n", + "Step 484999 of 1000000; TPS: 4579.83; ETA: 1.9 minutes\n", + "Step 485999 of 1000000; TPS: 4580.85; ETA: 1.9 minutes\n", + "Step 486999 of 1000000; TPS: 4581.34; ETA: 1.9 minutes\n", + "Step 487999 of 1000000; TPS: 4582.18; ETA: 1.9 minutes\n", + "Step 488999 of 1000000; TPS: 4582.49; ETA: 1.9 minutes\n", + "Step 489999 of 1000000; TPS: 4581.61; ETA: 1.9 minutes\n", + "Step 490999 of 1000000; TPS: 4581.9; ETA: 1.9 minutes\n", + "Step 491999 of 1000000; TPS: 4582.89; ETA: 1.8 minutes\n", + "Step 492999 of 1000000; TPS: 4583.7; ETA: 1.8 minutes\n", + "Step 493999 of 1000000; TPS: 4584.15; ETA: 1.8 minutes\n", + "Step 494999 of 1000000; TPS: 4584.27; ETA: 1.8 minutes\n", + "Step 495999 of 1000000; TPS: 4584.94; ETA: 1.8 minutes\n", + "Step 496999 of 1000000; TPS: 4585.86; ETA: 1.8 minutes\n", + "Step 497999 of 1000000; TPS: 4586.13; ETA: 1.8 minutes\n", + "Step 498999 of 1000000; TPS: 4586.89; ETA: 1.8 minutes\n", + "Step 499999 of 1000000; TPS: 4588.22; ETA: 1.8 minutes\n", + "Step 500999 of 1000000; TPS: 4589.03; ETA: 1.8 minutes\n", + "Step 501999 of 1000000; TPS: 4589.9; ETA: 1.8 minutes\n", + "Step 502999 of 1000000; TPS: 4590.93; ETA: 1.8 minutes\n", + "Step 503999 of 1000000; TPS: 4591.36; ETA: 1.8 minutes\n", + "Step 504999 of 1000000; TPS: 4592.26; ETA: 1.8 minutes\n", + "Step 505999 of 1000000; TPS: 4593.36; ETA: 1.8 minutes\n", + "Step 506999 of 1000000; TPS: 4594.19; ETA: 1.8 minutes\n", + "Step 507999 of 1000000; TPS: 4496.53; ETA: 1.8 minutes\n", + "Step 508999 of 1000000; TPS: 4497.72; ETA: 1.8 minutes\n", + "Step 509999 of 1000000; TPS: 4498.25; ETA: 1.8 minutes\n", + "Step 510999 of 1000000; TPS: 4498.72; ETA: 1.8 minutes\n", + "Step 511999 of 1000000; TPS: 4499.66; ETA: 1.8 minutes\n", + "Step 512999 of 1000000; TPS: 4500.74; ETA: 1.8 minutes\n", + "Step 513999 of 1000000; TPS: 4501.52; ETA: 1.8 minutes\n", + "Step 514999 of 1000000; TPS: 4502.3; ETA: 1.8 minutes\n", + "Step 515999 of 1000000; TPS: 4502.74; ETA: 1.8 minutes\n", + "Step 516999 of 1000000; TPS: 4503.13; ETA: 1.8 minutes\n", + "Step 517999 of 1000000; TPS: 4503.64; ETA: 1.8 minutes\n", + "Step 518999 of 1000000; TPS: 4503.97; ETA: 1.8 minutes\n", + "Step 519999 of 1000000; TPS: 4504.62; ETA: 1.8 minutes\n", + "Step 520999 of 1000000; TPS: 4504.86; ETA: 1.8 minutes\n", + "Step 521999 of 1000000; TPS: 4505.16; ETA: 1.8 minutes\n", + "Step 522999 of 1000000; TPS: 4505.28; ETA: 1.8 minutes\n", + "Step 523999 of 1000000; TPS: 4505.93; ETA: 1.8 minutes\n", + "Step 524999 of 1000000; TPS: 4507.25; ETA: 1.8 minutes\n", + "Step 525999 of 1000000; TPS: 4508.11; ETA: 1.8 minutes\n", + "Step 526999 of 1000000; TPS: 4509.15; ETA: 1.7 minutes\n", + "Step 527999 of 1000000; TPS: 4510.35; ETA: 1.7 minutes\n", + "Step 528999 of 1000000; TPS: 4511.11; ETA: 1.7 minutes\n", + "Step 529999 of 1000000; TPS: 4512.3; ETA: 1.7 minutes\n", + "Step 530999 of 1000000; TPS: 4513.18; ETA: 1.7 minutes\n", + "Step 531999 of 1000000; TPS: 4513.99; ETA: 1.7 minutes\n", + "Step 532999 of 1000000; TPS: 4514.99; ETA: 1.7 minutes\n", + "Step 533999 of 1000000; TPS: 4516.29; ETA: 1.7 minutes\n", + "Step 534999 of 1000000; TPS: 4516.36; ETA: 1.7 minutes\n", + "Step 535999 of 1000000; TPS: 4516.87; ETA: 1.7 minutes\n", + "Step 536999 of 1000000; TPS: 4517.98; ETA: 1.7 minutes\n", + "Step 537999 of 1000000; TPS: 4519.03; ETA: 1.7 minutes\n", + "Step 538999 of 1000000; TPS: 4520.34; ETA: 1.7 minutes\n", + "Step 539999 of 1000000; TPS: 4521.32; ETA: 1.7 minutes\n", + "Step 540999 of 1000000; TPS: 4522.44; ETA: 1.7 minutes\n", + "Step 541999 of 1000000; TPS: 4523.59; ETA: 1.7 minutes\n", + "Step 542999 of 1000000; TPS: 4524.9; ETA: 1.7 minutes\n", + "Step 543999 of 1000000; TPS: 4526.13; ETA: 1.7 minutes\n", + "Step 544999 of 1000000; TPS: 4527.57; ETA: 1.7 minutes\n", + "Step 545999 of 1000000; TPS: 4528.75; ETA: 1.7 minutes\n", + "Step 546999 of 1000000; TPS: 4529.82; ETA: 1.7 minutes\n", + "Step 547999 of 1000000; TPS: 4531.24; ETA: 1.7 minutes\n", + "Step 548999 of 1000000; TPS: 4532.51; ETA: 1.7 minutes\n", + "Step 549999 of 1000000; TPS: 4533.66; ETA: 1.7 minutes\n", + "Step 550999 of 1000000; TPS: 4534.8; ETA: 1.7 minutes\n", + "Step 551999 of 1000000; TPS: 4535.85; ETA: 1.6 minutes\n", + "Step 552999 of 1000000; TPS: 4536.8; ETA: 1.6 minutes\n", + "Step 553999 of 1000000; TPS: 4538.15; ETA: 1.6 minutes\n", + "Step 554999 of 1000000; TPS: 4539.31; ETA: 1.6 minutes\n", + "Step 555999 of 1000000; TPS: 4539.95; ETA: 1.6 minutes\n", + "Step 556999 of 1000000; TPS: 4540.05; ETA: 1.6 minutes\n", + "Step 557999 of 1000000; TPS: 4539.64; ETA: 1.6 minutes\n", + "Step 558999 of 1000000; TPS: 4540.69; ETA: 1.6 minutes\n", + "Step 559999 of 1000000; TPS: 4541.89; ETA: 1.6 minutes\n", + "Step 560999 of 1000000; TPS: 4542.9; ETA: 1.6 minutes\n", + "Step 561999 of 1000000; TPS: 4543.84; ETA: 1.6 minutes\n", + "Step 562999 of 1000000; TPS: 4544.79; ETA: 1.6 minutes\n", + "Step 563999 of 1000000; TPS: 4545.78; ETA: 1.6 minutes\n", + "Step 564999 of 1000000; TPS: 4545.5; ETA: 1.6 minutes\n", + "Step 565999 of 1000000; TPS: 4546.17; ETA: 1.6 minutes\n", + "Step 566999 of 1000000; TPS: 4547.05; ETA: 1.6 minutes\n", + "Step 567999 of 1000000; TPS: 4547.98; ETA: 1.6 minutes\n", + "Step 568999 of 1000000; TPS: 4548.67; ETA: 1.6 minutes\n", + "Step 569999 of 1000000; TPS: 4549.87; ETA: 1.6 minutes\n", + "Step 570999 of 1000000; TPS: 4550.81; ETA: 1.6 minutes\n", + "Step 571999 of 1000000; TPS: 4551.62; ETA: 1.6 minutes\n", + "Step 572999 of 1000000; TPS: 4552.31; ETA: 1.6 minutes\n", + "Step 573999 of 1000000; TPS: 4551.97; ETA: 1.6 minutes\n", + "Step 574999 of 1000000; TPS: 4552.61; ETA: 1.6 minutes\n", + "Step 575999 of 1000000; TPS: 4553.51; ETA: 1.6 minutes\n", + "Step 576999 of 1000000; TPS: 4554.29; ETA: 1.5 minutes\n", + "Step 577999 of 1000000; TPS: 4555.16; ETA: 1.5 minutes\n", + "Step 578999 of 1000000; TPS: 4555.89; ETA: 1.5 minutes\n", + "Step 579999 of 1000000; TPS: 4556.94; ETA: 1.5 minutes\n", + "Step 580999 of 1000000; TPS: 4557.94; ETA: 1.5 minutes\n", + "Step 581999 of 1000000; TPS: 4558.84; ETA: 1.5 minutes\n", + "Step 582999 of 1000000; TPS: 4560.03; ETA: 1.5 minutes\n", + "Step 583999 of 1000000; TPS: 4560.37; ETA: 1.5 minutes\n", + "Step 584999 of 1000000; TPS: 4560.83; ETA: 1.5 minutes\n", + "Step 585999 of 1000000; TPS: 4561.18; ETA: 1.5 minutes\n", + "Step 586999 of 1000000; TPS: 4561.07; ETA: 1.5 minutes\n", + "Step 587999 of 1000000; TPS: 4560.67; ETA: 1.5 minutes\n", + "Step 588999 of 1000000; TPS: 4561.15; ETA: 1.5 minutes\n", + "Step 589999 of 1000000; TPS: 4562.18; ETA: 1.5 minutes\n", + "Step 590999 of 1000000; TPS: 4562.58; ETA: 1.5 minutes\n", + "Step 591999 of 1000000; TPS: 4562.66; ETA: 1.5 minutes\n", + "Step 592999 of 1000000; TPS: 4563.1; ETA: 1.5 minutes\n", + "Step 593999 of 1000000; TPS: 4563.99; ETA: 1.5 minutes\n", + "Step 594999 of 1000000; TPS: 4564.75; ETA: 1.5 minutes\n", + "Step 595999 of 1000000; TPS: 4565.69; ETA: 1.5 minutes\n", + "Step 596999 of 1000000; TPS: 4566.33; ETA: 1.5 minutes\n", + "Step 597999 of 1000000; TPS: 4566.62; ETA: 1.5 minutes\n", + "Step 598999 of 1000000; TPS: 4567.02; ETA: 1.5 minutes\n", + "Step 599999 of 1000000; TPS: 4567.67; ETA: 1.5 minutes\n", + "Step 600999 of 1000000; TPS: 4568.61; ETA: 1.5 minutes\n", + "Step 601999 of 1000000; TPS: 4569.46; ETA: 1.5 minutes\n", + "Step 602999 of 1000000; TPS: 4570.08; ETA: 1.4 minutes\n", + "Step 603999 of 1000000; TPS: 4570.67; ETA: 1.4 minutes\n", + "Step 604999 of 1000000; TPS: 4571.11; ETA: 1.4 minutes\n", + "Step 605999 of 1000000; TPS: 4571.76; ETA: 1.4 minutes\n", + "Step 606999 of 1000000; TPS: 4572.4; ETA: 1.4 minutes\n", + "Step 607999 of 1000000; TPS: 4572.9; ETA: 1.4 minutes\n", + "Step 608999 of 1000000; TPS: 4573.69; ETA: 1.4 minutes\n", + "Step 609999 of 1000000; TPS: 4574.54; ETA: 1.4 minutes\n", + "Step 610999 of 1000000; TPS: 4574.36; ETA: 1.4 minutes\n", + "Step 611999 of 1000000; TPS: 4574.53; ETA: 1.4 minutes\n", + "Step 612999 of 1000000; TPS: 4574.88; ETA: 1.4 minutes\n", + "Step 613999 of 1000000; TPS: 4574.89; ETA: 1.4 minutes\n", + "Step 614999 of 1000000; TPS: 4575.63; ETA: 1.4 minutes\n", + "Step 615999 of 1000000; TPS: 4575.64; ETA: 1.4 minutes\n", + "Step 616999 of 1000000; TPS: 4576.1; ETA: 1.4 minutes\n", + "Step 617999 of 1000000; TPS: 4576.95; ETA: 1.4 minutes\n", + "Step 618999 of 1000000; TPS: 4577.84; ETA: 1.4 minutes\n", + "Step 619999 of 1000000; TPS: 4578.66; ETA: 1.4 minutes\n", + "Step 620999 of 1000000; TPS: 4579.45; ETA: 1.4 minutes\n", + "Step 621999 of 1000000; TPS: 4579.98; ETA: 1.4 minutes\n", + "Step 622999 of 1000000; TPS: 4580.87; ETA: 1.4 minutes\n", + "Step 623999 of 1000000; TPS: 4581.49; ETA: 1.4 minutes\n", + "Step 624999 of 1000000; TPS: 4582.15; ETA: 1.4 minutes\n", + "Step 625999 of 1000000; TPS: 4582.69; ETA: 1.4 minutes\n", + "Step 626999 of 1000000; TPS: 4583.37; ETA: 1.4 minutes\n", + "Step 627999 of 1000000; TPS: 4583.93; ETA: 1.4 minutes\n", + "Step 628999 of 1000000; TPS: 4584.5; ETA: 1.3 minutes\n", + "Step 629999 of 1000000; TPS: 4585.43; ETA: 1.3 minutes\n", + "Step 630999 of 1000000; TPS: 4586.25; ETA: 1.3 minutes\n", + "Step 631999 of 1000000; TPS: 4586.81; ETA: 1.3 minutes\n", + "Step 632999 of 1000000; TPS: 4587.55; ETA: 1.3 minutes\n", + "Step 633999 of 1000000; TPS: 4587.24; ETA: 1.3 minutes\n", + "Step 634999 of 1000000; TPS: 4588.05; ETA: 1.3 minutes\n", + "Step 635999 of 1000000; TPS: 4588.17; ETA: 1.3 minutes\n", + "Step 636999 of 1000000; TPS: 4588.4; ETA: 1.3 minutes\n", + "Step 637999 of 1000000; TPS: 4589.26; ETA: 1.3 minutes\n", + "Step 638999 of 1000000; TPS: 4590.12; ETA: 1.3 minutes\n", + "Step 639999 of 1000000; TPS: 4590.73; ETA: 1.3 minutes\n", + "Step 640999 of 1000000; TPS: 4591.47; ETA: 1.3 minutes\n", + "Step 641999 of 1000000; TPS: 4592.15; ETA: 1.3 minutes\n", + "Step 642999 of 1000000; TPS: 4592.73; ETA: 1.3 minutes\n", + "Step 643999 of 1000000; TPS: 4593.36; ETA: 1.3 minutes\n", + "Step 644999 of 1000000; TPS: 4593.98; ETA: 1.3 minutes\n", + "Step 645999 of 1000000; TPS: 4593.99; ETA: 1.3 minutes\n", + "Step 646999 of 1000000; TPS: 4593.99; ETA: 1.3 minutes\n", + "Step 647999 of 1000000; TPS: 4593.8; ETA: 1.3 minutes\n", + "Step 648999 of 1000000; TPS: 4593.93; ETA: 1.3 minutes\n", + "Step 649999 of 1000000; TPS: 4594.07; ETA: 1.3 minutes\n", + "Step 650999 of 1000000; TPS: 4594.27; ETA: 1.3 minutes\n", + "Step 651999 of 1000000; TPS: 4594.72; ETA: 1.3 minutes\n", + "Step 652999 of 1000000; TPS: 4594.98; ETA: 1.3 minutes\n", + "Step 653999 of 1000000; TPS: 4594.82; ETA: 1.3 minutes\n", + "Step 654999 of 1000000; TPS: 4595.06; ETA: 1.3 minutes\n", + "Step 655999 of 1000000; TPS: 4595.29; ETA: 1.2 minutes\n", + "Step 656999 of 1000000; TPS: 4595.52; ETA: 1.2 minutes\n", + "Step 657999 of 1000000; TPS: 4596.02; ETA: 1.2 minutes\n", + "Step 658999 of 1000000; TPS: 4596.13; ETA: 1.2 minutes\n", + "Step 659999 of 1000000; TPS: 4596.13; ETA: 1.2 minutes\n", + "Step 660999 of 1000000; TPS: 4596.93; ETA: 1.2 minutes\n", + "Step 661999 of 1000000; TPS: 4597.61; ETA: 1.2 minutes\n", + "Step 662999 of 1000000; TPS: 4598.07; ETA: 1.2 minutes\n", + "Step 663999 of 1000000; TPS: 4598.46; ETA: 1.2 minutes\n", + "Step 664999 of 1000000; TPS: 4598.52; ETA: 1.2 minutes\n", + "Step 665999 of 1000000; TPS: 4598.74; ETA: 1.2 minutes\n", + "Step 666999 of 1000000; TPS: 4598.94; ETA: 1.2 minutes\n", + "Step 667999 of 1000000; TPS: 4527.6; ETA: 1.2 minutes\n", + "Step 668999 of 1000000; TPS: 4528.01; ETA: 1.2 minutes\n", + "Step 669999 of 1000000; TPS: 4528.57; ETA: 1.2 minutes\n", + "Step 670999 of 1000000; TPS: 4528.96; ETA: 1.2 minutes\n", + "Step 671999 of 1000000; TPS: 4528.26; ETA: 1.2 minutes\n", + "Step 672999 of 1000000; TPS: 4528.6; ETA: 1.2 minutes\n", + "Step 673999 of 1000000; TPS: 4529.2; ETA: 1.2 minutes\n", + "Step 674999 of 1000000; TPS: 4528.46; ETA: 1.2 minutes\n", + "Step 675999 of 1000000; TPS: 4528.59; ETA: 1.2 minutes\n", + "Step 676999 of 1000000; TPS: 4529.36; ETA: 1.2 minutes\n", + "Step 677999 of 1000000; TPS: 4529.85; ETA: 1.2 minutes\n", + "Step 678999 of 1000000; TPS: 4530.4; ETA: 1.2 minutes\n", + "Step 679999 of 1000000; TPS: 4530.81; ETA: 1.2 minutes\n", + "Step 680999 of 1000000; TPS: 4531.19; ETA: 1.2 minutes\n", + "Step 681999 of 1000000; TPS: 4531.58; ETA: 1.2 minutes\n", + "Step 682999 of 1000000; TPS: 4531.98; ETA: 1.2 minutes\n", + "Step 683999 of 1000000; TPS: 4532.61; ETA: 1.2 minutes\n", + "Step 684999 of 1000000; TPS: 4533.31; ETA: 1.2 minutes\n", + "Step 685999 of 1000000; TPS: 4533.87; ETA: 1.2 minutes\n", + "Step 686999 of 1000000; TPS: 4534.47; ETA: 1.2 minutes\n", + "Step 687999 of 1000000; TPS: 4534.73; ETA: 1.1 minutes\n", + "Step 688999 of 1000000; TPS: 4534.34; ETA: 1.1 minutes\n", + "Step 689999 of 1000000; TPS: 4534.88; ETA: 1.1 minutes\n", + "Step 690999 of 1000000; TPS: 4535.02; ETA: 1.1 minutes\n", + "Step 691999 of 1000000; TPS: 4534.98; ETA: 1.1 minutes\n", + "Step 692999 of 1000000; TPS: 4534.99; ETA: 1.1 minutes\n", + "Step 693999 of 1000000; TPS: 4535.1; ETA: 1.1 minutes\n", + "Step 694999 of 1000000; TPS: 4535.38; ETA: 1.1 minutes\n", + "Step 695999 of 1000000; TPS: 4535.47; ETA: 1.1 minutes\n", + "Step 696999 of 1000000; TPS: 4535.96; ETA: 1.1 minutes\n", + "Step 697999 of 1000000; TPS: 4536.39; ETA: 1.1 minutes\n", + "Step 698999 of 1000000; TPS: 4537.07; ETA: 1.1 minutes\n", + "Step 699999 of 1000000; TPS: 4537.49; ETA: 1.1 minutes\n", + "Step 700999 of 1000000; TPS: 4537.98; ETA: 1.1 minutes\n", + "Step 701999 of 1000000; TPS: 4538.55; ETA: 1.1 minutes\n", + "Step 702999 of 1000000; TPS: 4538.79; ETA: 1.1 minutes\n", + "Step 703999 of 1000000; TPS: 4538.05; ETA: 1.1 minutes\n", + "Step 704999 of 1000000; TPS: 4538.27; ETA: 1.1 minutes\n", + "Step 705999 of 1000000; TPS: 4538.41; ETA: 1.1 minutes\n", + "Step 706999 of 1000000; TPS: 4538.64; ETA: 1.1 minutes\n", + "Step 707999 of 1000000; TPS: 4538.59; ETA: 1.1 minutes\n", + "Step 708999 of 1000000; TPS: 4539.01; ETA: 1.1 minutes\n", + "Step 709999 of 1000000; TPS: 4539.6; ETA: 1.1 minutes\n", + "Step 710999 of 1000000; TPS: 4540.05; ETA: 1.1 minutes\n", + "Step 711999 of 1000000; TPS: 4539.96; ETA: 1.1 minutes\n", + "Step 712999 of 1000000; TPS: 4540.33; ETA: 1.1 minutes\n", + "Step 713999 of 1000000; TPS: 4540.88; ETA: 1.0 minutes\n", + "Step 714999 of 1000000; TPS: 4541.4; ETA: 1.0 minutes\n", + "Step 715999 of 1000000; TPS: 4541.86; ETA: 1.0 minutes\n", + "Step 716999 of 1000000; TPS: 4542.42; ETA: 1.0 minutes\n", + "Step 717999 of 1000000; TPS: 4543.18; ETA: 1.0 minutes\n", + "Step 718999 of 1000000; TPS: 4543.82; ETA: 1.0 minutes\n", + "Step 719999 of 1000000; TPS: 4544.53; ETA: 1.0 minutes\n", + "Step 720999 of 1000000; TPS: 4545.18; ETA: 1.0 minutes\n", + "Step 721999 of 1000000; TPS: 4545.1; ETA: 1.0 minutes\n", + "Step 722999 of 1000000; TPS: 4544.6; ETA: 1.0 minutes\n", + "Step 723999 of 1000000; TPS: 4544.12; ETA: 1.0 minutes\n", + "Step 724999 of 1000000; TPS: 4543.72; ETA: 1.0 minutes\n", + "Step 725999 of 1000000; TPS: 4543.63; ETA: 1.0 minutes\n", + "Step 726999 of 1000000; TPS: 4543.37; ETA: 1.0 minutes\n", + "Step 727999 of 1000000; TPS: 4542.83; ETA: 1.0 minutes\n", + "Step 728999 of 1000000; TPS: 4542.4; ETA: 1.0 minutes\n", + "Step 729999 of 1000000; TPS: 4541.87; ETA: 1.0 minutes\n", + "Step 730999 of 1000000; TPS: 4541.47; ETA: 1.0 minutes\n", + "Step 731999 of 1000000; TPS: 4541.18; ETA: 1.0 minutes\n", + "Step 732999 of 1000000; TPS: 4540.89; ETA: 1.0 minutes\n", + "Step 733999 of 1000000; TPS: 4540.86; ETA: 1.0 minutes\n", + "Step 734999 of 1000000; TPS: 4540.35; ETA: 1.0 minutes\n", + "Step 735999 of 1000000; TPS: 4540.29; ETA: 1.0 minutes\n", + "Step 736999 of 1000000; TPS: 4540.11; ETA: 1.0 minutes\n", + "Step 737999 of 1000000; TPS: 4540.1; ETA: 1.0 minutes\n", + "Step 738999 of 1000000; TPS: 4539.31; ETA: 1.0 minutes\n", + "Step 739999 of 1000000; TPS: 4539.38; ETA: 1.0 minutes\n", + "Step 740999 of 1000000; TPS: 4539.23; ETA: 1.0 minutes\n", + "Step 741999 of 1000000; TPS: 4539.07; ETA: 0.9 minutes\n", + "Step 742999 of 1000000; TPS: 4538.83; ETA: 0.9 minutes\n", + "Step 743999 of 1000000; TPS: 4538.77; ETA: 0.9 minutes\n", + "Step 744999 of 1000000; TPS: 4538.67; ETA: 0.9 minutes\n", + "Step 745999 of 1000000; TPS: 4538.54; ETA: 0.9 minutes\n", + "Step 746999 of 1000000; TPS: 4538.29; ETA: 0.9 minutes\n", + "Step 747999 of 1000000; TPS: 4538.28; ETA: 0.9 minutes\n", + "Step 748999 of 1000000; TPS: 4538.21; ETA: 0.9 minutes\n", + "Step 749999 of 1000000; TPS: 4538.14; ETA: 0.9 minutes\n", + "Step 750999 of 1000000; TPS: 4538.08; ETA: 0.9 minutes\n", + "Step 751999 of 1000000; TPS: 4538.06; ETA: 0.9 minutes\n", + "Step 752999 of 1000000; TPS: 4538.1; ETA: 0.9 minutes\n", + "Step 753999 of 1000000; TPS: 4538.0; ETA: 0.9 minutes\n", + "Step 754999 of 1000000; TPS: 4537.99; ETA: 0.9 minutes\n", + "Step 755999 of 1000000; TPS: 4538.11; ETA: 0.9 minutes\n", + "Step 756999 of 1000000; TPS: 4538.27; ETA: 0.9 minutes\n", + "Step 757999 of 1000000; TPS: 4538.14; ETA: 0.9 minutes\n", + "Step 758999 of 1000000; TPS: 4537.99; ETA: 0.9 minutes\n", + "Step 759999 of 1000000; TPS: 4537.7; ETA: 0.9 minutes\n", + "Step 760999 of 1000000; TPS: 4538.01; ETA: 0.9 minutes\n", + "Step 761999 of 1000000; TPS: 4538.03; ETA: 0.9 minutes\n", + "Step 762999 of 1000000; TPS: 4537.9; ETA: 0.9 minutes\n", + "Step 763999 of 1000000; TPS: 4538.11; ETA: 0.9 minutes\n", + "Step 764999 of 1000000; TPS: 4538.29; ETA: 0.9 minutes\n", + "Step 765999 of 1000000; TPS: 4538.33; ETA: 0.9 minutes\n", + "Step 766999 of 1000000; TPS: 4538.55; ETA: 0.9 minutes\n", + "Step 767999 of 1000000; TPS: 4538.42; ETA: 0.9 minutes\n", + "Step 768999 of 1000000; TPS: 4538.74; ETA: 0.8 minutes\n", + "Step 769999 of 1000000; TPS: 4539.33; ETA: 0.8 minutes\n", + "Step 770999 of 1000000; TPS: 4539.61; ETA: 0.8 minutes\n", + "Step 771999 of 1000000; TPS: 4540.11; ETA: 0.8 minutes\n", + "Step 772999 of 1000000; TPS: 4540.35; ETA: 0.8 minutes\n", + "Step 773999 of 1000000; TPS: 4540.66; ETA: 0.8 minutes\n", + "Step 774999 of 1000000; TPS: 4540.63; ETA: 0.8 minutes\n", + "Step 775999 of 1000000; TPS: 4540.9; ETA: 0.8 minutes\n", + "Step 776999 of 1000000; TPS: 4541.26; ETA: 0.8 minutes\n", + "Step 777999 of 1000000; TPS: 4541.79; ETA: 0.8 minutes\n", + "Step 778999 of 1000000; TPS: 4542.15; ETA: 0.8 minutes\n", + "Step 779999 of 1000000; TPS: 4542.21; ETA: 0.8 minutes\n", + "Step 780999 of 1000000; TPS: 4542.59; ETA: 0.8 minutes\n", + "Step 781999 of 1000000; TPS: 4542.79; ETA: 0.8 minutes\n", + "Step 782999 of 1000000; TPS: 4543.2; ETA: 0.8 minutes\n", + "Step 783999 of 1000000; TPS: 4543.69; ETA: 0.8 minutes\n", + "Step 784999 of 1000000; TPS: 4544.15; ETA: 0.8 minutes\n", + "Step 785999 of 1000000; TPS: 4544.38; ETA: 0.8 minutes\n", + "Step 786999 of 1000000; TPS: 4544.6; ETA: 0.8 minutes\n", + "Step 787999 of 1000000; TPS: 4544.96; ETA: 0.8 minutes\n", + "Step 788999 of 1000000; TPS: 4545.34; ETA: 0.8 minutes\n", + "Step 789999 of 1000000; TPS: 4545.52; ETA: 0.8 minutes\n", + "Step 790999 of 1000000; TPS: 4545.47; ETA: 0.8 minutes\n", + "Step 791999 of 1000000; TPS: 4545.87; ETA: 0.8 minutes\n", + "Step 792999 of 1000000; TPS: 4546.2; ETA: 0.8 minutes\n", + "Step 793999 of 1000000; TPS: 4546.64; ETA: 0.8 minutes\n", + "Step 794999 of 1000000; TPS: 4546.94; ETA: 0.8 minutes\n", + "Step 795999 of 1000000; TPS: 4547.54; ETA: 0.7 minutes\n", + "Step 796999 of 1000000; TPS: 4547.81; ETA: 0.7 minutes\n", + "Step 797999 of 1000000; TPS: 4547.88; ETA: 0.7 minutes\n", + "Step 798999 of 1000000; TPS: 4548.33; ETA: 0.7 minutes\n", + "Step 799999 of 1000000; TPS: 4548.68; ETA: 0.7 minutes\n", + "Step 800999 of 1000000; TPS: 4548.98; ETA: 0.7 minutes\n", + "Step 801999 of 1000000; TPS: 4549.41; ETA: 0.7 minutes\n", + "Step 802999 of 1000000; TPS: 4549.52; ETA: 0.7 minutes\n", + "Step 803999 of 1000000; TPS: 4549.21; ETA: 0.7 minutes\n", + "Step 804999 of 1000000; TPS: 4549.55; ETA: 0.7 minutes\n", + "Step 805999 of 1000000; TPS: 4550.0; ETA: 0.7 minutes\n", + "Step 806999 of 1000000; TPS: 4550.32; ETA: 0.7 minutes\n", + "Step 807999 of 1000000; TPS: 4550.6; ETA: 0.7 minutes\n", + "Step 808999 of 1000000; TPS: 4550.89; ETA: 0.7 minutes\n", + "Step 809999 of 1000000; TPS: 4551.24; ETA: 0.7 minutes\n", + "Step 810999 of 1000000; TPS: 4551.4; ETA: 0.7 minutes\n", + "Step 811999 of 1000000; TPS: 4551.43; ETA: 0.7 minutes\n", + "Step 812999 of 1000000; TPS: 4551.65; ETA: 0.7 minutes\n", + "Step 813999 of 1000000; TPS: 4551.94; ETA: 0.7 minutes\n", + "Step 814999 of 1000000; TPS: 4551.82; ETA: 0.7 minutes\n", + "Step 815999 of 1000000; TPS: 4551.6; ETA: 0.7 minutes\n", + "Step 816999 of 1000000; TPS: 4551.6; ETA: 0.7 minutes\n", + "Step 817999 of 1000000; TPS: 4551.93; ETA: 0.7 minutes\n", + "Step 818999 of 1000000; TPS: 4552.22; ETA: 0.7 minutes\n", + "Step 819999 of 1000000; TPS: 4552.41; ETA: 0.7 minutes\n", + "Step 820999 of 1000000; TPS: 4552.68; ETA: 0.7 minutes\n", + "Step 821999 of 1000000; TPS: 4552.61; ETA: 0.7 minutes\n", + "Step 822999 of 1000000; TPS: 4552.64; ETA: 0.6 minutes\n", + "Step 823999 of 1000000; TPS: 4552.71; ETA: 0.6 minutes\n", + "Step 824999 of 1000000; TPS: 4552.85; ETA: 0.6 minutes\n", + "Step 825999 of 1000000; TPS: 4552.83; ETA: 0.6 minutes\n", + "Step 826999 of 1000000; TPS: 4553.1; ETA: 0.6 minutes\n", + "Step 827999 of 1000000; TPS: 4553.27; ETA: 0.6 minutes\n", + "Step 828999 of 1000000; TPS: 4553.47; ETA: 0.6 minutes\n", + "Step 829999 of 1000000; TPS: 4553.76; ETA: 0.6 minutes\n", + "Step 830999 of 1000000; TPS: 4553.84; ETA: 0.6 minutes\n", + "Step 831999 of 1000000; TPS: 4554.14; ETA: 0.6 minutes\n", + "Step 832999 of 1000000; TPS: 4554.49; ETA: 0.6 minutes\n", + "Step 833999 of 1000000; TPS: 4555.06; ETA: 0.6 minutes\n", + "Step 834999 of 1000000; TPS: 4555.74; ETA: 0.6 minutes\n", + "Step 835999 of 1000000; TPS: 4556.5; ETA: 0.6 minutes\n", + "Step 836999 of 1000000; TPS: 4557.21; ETA: 0.6 minutes\n", + "Step 837999 of 1000000; TPS: 4557.9; ETA: 0.6 minutes\n", + "Step 838999 of 1000000; TPS: 4558.59; ETA: 0.6 minutes\n", + "Step 839999 of 1000000; TPS: 4559.53; ETA: 0.6 minutes\n", + "Step 840999 of 1000000; TPS: 4560.1; ETA: 0.6 minutes\n", + "Step 841999 of 1000000; TPS: 4560.58; ETA: 0.6 minutes\n", + "Step 842999 of 1000000; TPS: 4561.18; ETA: 0.6 minutes\n", + "Step 843999 of 1000000; TPS: 4561.96; ETA: 0.6 minutes\n", + "Step 844999 of 1000000; TPS: 4562.41; ETA: 0.6 minutes\n", + "Step 845999 of 1000000; TPS: 4563.21; ETA: 0.6 minutes\n", + "Step 846999 of 1000000; TPS: 4563.55; ETA: 0.6 minutes\n", + "Step 847999 of 1000000; TPS: 4564.18; ETA: 0.6 minutes\n", + "Step 848999 of 1000000; TPS: 4564.84; ETA: 0.6 minutes\n", + "Step 849999 of 1000000; TPS: 4565.57; ETA: 0.5 minutes\n", + "Step 850999 of 1000000; TPS: 4566.32; ETA: 0.5 minutes\n", + "Step 851999 of 1000000; TPS: 4566.89; ETA: 0.5 minutes\n", + "Step 852999 of 1000000; TPS: 4567.32; ETA: 0.5 minutes\n", + "Step 853999 of 1000000; TPS: 4567.94; ETA: 0.5 minutes\n", + "Step 854999 of 1000000; TPS: 4568.5; ETA: 0.5 minutes\n", + "Step 855999 of 1000000; TPS: 4569.22; ETA: 0.5 minutes\n", + "Step 856999 of 1000000; TPS: 4569.74; ETA: 0.5 minutes\n", + "Step 857999 of 1000000; TPS: 4570.52; ETA: 0.5 minutes\n", + "Step 858999 of 1000000; TPS: 4571.15; ETA: 0.5 minutes\n", + "Step 859999 of 1000000; TPS: 4571.96; ETA: 0.5 minutes\n", + "Step 860999 of 1000000; TPS: 4572.74; ETA: 0.5 minutes\n", + "Step 861999 of 1000000; TPS: 4573.41; ETA: 0.5 minutes\n", + "Step 862999 of 1000000; TPS: 4574.34; ETA: 0.5 minutes\n", + "Step 863999 of 1000000; TPS: 4574.51; ETA: 0.5 minutes\n", + "Step 864999 of 1000000; TPS: 4575.12; ETA: 0.5 minutes\n", + "Step 865999 of 1000000; TPS: 4575.93; ETA: 0.5 minutes\n", + "Step 866999 of 1000000; TPS: 4576.23; ETA: 0.5 minutes\n", + "Step 867999 of 1000000; TPS: 4576.63; ETA: 0.5 minutes\n", + "Step 868999 of 1000000; TPS: 4577.1; ETA: 0.5 minutes\n", + "Step 869999 of 1000000; TPS: 4577.74; ETA: 0.5 minutes\n", + "Step 870999 of 1000000; TPS: 4578.35; ETA: 0.5 minutes\n", + "Step 871999 of 1000000; TPS: 4578.9; ETA: 0.5 minutes\n", + "Step 872999 of 1000000; TPS: 4579.64; ETA: 0.5 minutes\n", + "Step 873999 of 1000000; TPS: 4580.26; ETA: 0.5 minutes\n", + "Step 874999 of 1000000; TPS: 4580.95; ETA: 0.5 minutes\n", + "Step 875999 of 1000000; TPS: 4581.51; ETA: 0.5 minutes\n", + "Step 876999 of 1000000; TPS: 4581.93; ETA: 0.4 minutes\n", + "Step 877999 of 1000000; TPS: 4582.53; ETA: 0.4 minutes\n", + "Step 878999 of 1000000; TPS: 4583.05; ETA: 0.4 minutes\n", + "Step 879999 of 1000000; TPS: 4583.46; ETA: 0.4 minutes\n", + "Step 880999 of 1000000; TPS: 4583.69; ETA: 0.4 minutes\n", + "Step 881999 of 1000000; TPS: 4583.8; ETA: 0.4 minutes\n", + "Step 882999 of 1000000; TPS: 4583.67; ETA: 0.4 minutes\n", + "Step 883999 of 1000000; TPS: 4583.68; ETA: 0.4 minutes\n", + "Step 884999 of 1000000; TPS: 4584.07; ETA: 0.4 minutes\n", + "Step 885999 of 1000000; TPS: 4584.7; ETA: 0.4 minutes\n", + "Step 886999 of 1000000; TPS: 4585.07; ETA: 0.4 minutes\n", + "Step 887999 of 1000000; TPS: 4585.7; ETA: 0.4 minutes\n", + "Step 888999 of 1000000; TPS: 4586.29; ETA: 0.4 minutes\n", + "Step 889999 of 1000000; TPS: 4586.61; ETA: 0.4 minutes\n", + "Step 890999 of 1000000; TPS: 4587.07; ETA: 0.4 minutes\n", + "Step 891999 of 1000000; TPS: 4587.64; ETA: 0.4 minutes\n", + "Step 892999 of 1000000; TPS: 4588.05; ETA: 0.4 minutes\n", + "Step 893999 of 1000000; TPS: 4588.71; ETA: 0.4 minutes\n", + "Step 894999 of 1000000; TPS: 4589.28; ETA: 0.4 minutes\n", + "Step 895999 of 1000000; TPS: 4590.0; ETA: 0.4 minutes\n", + "Step 896999 of 1000000; TPS: 4590.52; ETA: 0.4 minutes\n", + "Step 897999 of 1000000; TPS: 4591.14; ETA: 0.4 minutes\n", + "Step 898999 of 1000000; TPS: 4591.72; ETA: 0.4 minutes\n", + "Step 899999 of 1000000; TPS: 4592.51; ETA: 0.4 minutes\n", + "Step 900999 of 1000000; TPS: 4593.23; ETA: 0.4 minutes\n", + "Step 901999 of 1000000; TPS: 4593.27; ETA: 0.4 minutes\n", + "Step 902999 of 1000000; TPS: 4593.75; ETA: 0.4 minutes\n", + "Step 903999 of 1000000; TPS: 4594.42; ETA: 0.3 minutes\n", + "Step 904999 of 1000000; TPS: 4594.66; ETA: 0.3 minutes\n", + "Step 905999 of 1000000; TPS: 4595.24; ETA: 0.3 minutes\n", + "Step 906999 of 1000000; TPS: 4595.87; ETA: 0.3 minutes\n", + "Step 907999 of 1000000; TPS: 4596.38; ETA: 0.3 minutes\n", + "Step 908999 of 1000000; TPS: 4596.98; ETA: 0.3 minutes\n", + "Step 909999 of 1000000; TPS: 4597.64; ETA: 0.3 minutes\n", + "Step 910999 of 1000000; TPS: 4598.45; ETA: 0.3 minutes\n", + "Step 911999 of 1000000; TPS: 4599.08; ETA: 0.3 minutes\n", + "Step 912999 of 1000000; TPS: 4599.65; ETA: 0.3 minutes\n", + "Step 913999 of 1000000; TPS: 4600.34; ETA: 0.3 minutes\n", + "Step 914999 of 1000000; TPS: 4600.98; ETA: 0.3 minutes\n", + "Step 915999 of 1000000; TPS: 4601.47; ETA: 0.3 minutes\n", + "Step 916999 of 1000000; TPS: 4602.01; ETA: 0.3 minutes\n", + "Step 917999 of 1000000; TPS: 4602.29; ETA: 0.3 minutes\n", + "Step 918999 of 1000000; TPS: 4602.94; ETA: 0.3 minutes\n", + "Step 919999 of 1000000; TPS: 4603.28; ETA: 0.3 minutes\n", + "Step 920999 of 1000000; TPS: 4603.84; ETA: 0.3 minutes\n", + "Step 921999 of 1000000; TPS: 4604.27; ETA: 0.3 minutes\n", + "Step 922999 of 1000000; TPS: 4604.93; ETA: 0.3 minutes\n", + "Step 923999 of 1000000; TPS: 4605.53; ETA: 0.3 minutes\n", + "Step 924999 of 1000000; TPS: 4606.06; ETA: 0.3 minutes\n", + "Step 925999 of 1000000; TPS: 4606.67; ETA: 0.3 minutes\n", + "Step 926999 of 1000000; TPS: 4607.04; ETA: 0.3 minutes\n", + "Step 927999 of 1000000; TPS: 4607.49; ETA: 0.3 minutes\n", + "Step 928999 of 1000000; TPS: 4608.02; ETA: 0.3 minutes\n", + "Step 929999 of 1000000; TPS: 4608.62; ETA: 0.3 minutes\n", + "Step 930999 of 1000000; TPS: 4609.23; ETA: 0.2 minutes\n", + "Step 931999 of 1000000; TPS: 4609.62; ETA: 0.2 minutes\n", + "Step 932999 of 1000000; TPS: 4609.78; ETA: 0.2 minutes\n", + "Step 933999 of 1000000; TPS: 4610.14; ETA: 0.2 minutes\n", + "Step 934999 of 1000000; TPS: 4610.18; ETA: 0.2 minutes\n", + "Step 935999 of 1000000; TPS: 4610.6; ETA: 0.2 minutes\n", + "Step 936999 of 1000000; TPS: 4611.12; ETA: 0.2 minutes\n", + "Step 937999 of 1000000; TPS: 4611.47; ETA: 0.2 minutes\n", + "Step 938999 of 1000000; TPS: 4611.89; ETA: 0.2 minutes\n", + "Step 939999 of 1000000; TPS: 4612.25; ETA: 0.2 minutes\n", + "Step 940999 of 1000000; TPS: 4612.66; ETA: 0.2 minutes\n", + "Step 941999 of 1000000; TPS: 4612.96; ETA: 0.2 minutes\n", + "Step 942999 of 1000000; TPS: 4613.3; ETA: 0.2 minutes\n", + "Step 943999 of 1000000; TPS: 4613.51; ETA: 0.2 minutes\n", + "Step 944999 of 1000000; TPS: 4613.87; ETA: 0.2 minutes\n", + "Step 945999 of 1000000; TPS: 4614.11; ETA: 0.2 minutes\n", + "Step 946999 of 1000000; TPS: 4614.5; ETA: 0.2 minutes\n", + "Step 947999 of 1000000; TPS: 4614.84; ETA: 0.2 minutes\n", + "Step 948999 of 1000000; TPS: 4615.21; ETA: 0.2 minutes\n", + "Step 949999 of 1000000; TPS: 4615.64; ETA: 0.2 minutes\n", + "Step 950999 of 1000000; TPS: 4616.23; ETA: 0.2 minutes\n", + "Step 951999 of 1000000; TPS: 4616.86; ETA: 0.2 minutes\n", + "Step 952999 of 1000000; TPS: 4617.39; ETA: 0.2 minutes\n", + "Step 953999 of 1000000; TPS: 4617.74; ETA: 0.2 minutes\n", + "Step 954999 of 1000000; TPS: 4617.88; ETA: 0.2 minutes\n", + "Step 955999 of 1000000; TPS: 4618.28; ETA: 0.2 minutes\n", + "Step 956999 of 1000000; TPS: 4618.96; ETA: 0.2 minutes\n", + "Step 957999 of 1000000; TPS: 4619.36; ETA: 0.2 minutes\n", + "Step 958999 of 1000000; TPS: 4619.91; ETA: 0.1 minutes\n", + "Step 959999 of 1000000; TPS: 4620.58; ETA: 0.1 minutes\n", + "Step 960999 of 1000000; TPS: 4621.3; ETA: 0.1 minutes\n", + "Step 961999 of 1000000; TPS: 4621.89; ETA: 0.1 minutes\n", + "Step 962999 of 1000000; TPS: 4622.54; ETA: 0.1 minutes\n", + "Step 963999 of 1000000; TPS: 4623.1; ETA: 0.1 minutes\n", + "Step 964999 of 1000000; TPS: 4623.53; ETA: 0.1 minutes\n", + "Step 965999 of 1000000; TPS: 4623.72; ETA: 0.1 minutes\n", + "Step 966999 of 1000000; TPS: 4623.93; ETA: 0.1 minutes\n", + "Step 967999 of 1000000; TPS: 4624.09; ETA: 0.1 minutes\n", + "Step 968999 of 1000000; TPS: 4623.96; ETA: 0.1 minutes\n", + "Step 969999 of 1000000; TPS: 4624.33; ETA: 0.1 minutes\n", + "Step 970999 of 1000000; TPS: 4624.69; ETA: 0.1 minutes\n", + "Step 971999 of 1000000; TPS: 4624.63; ETA: 0.1 minutes\n", + "Step 972999 of 1000000; TPS: 4624.85; ETA: 0.1 minutes\n", + "Step 973999 of 1000000; TPS: 4625.04; ETA: 0.1 minutes\n", + "Step 974999 of 1000000; TPS: 4625.27; ETA: 0.1 minutes\n", + "Step 975999 of 1000000; TPS: 4625.46; ETA: 0.1 minutes\n", + "Step 976999 of 1000000; TPS: 4625.69; ETA: 0.1 minutes\n", + "Step 977999 of 1000000; TPS: 4625.84; ETA: 0.1 minutes\n", + "Step 978999 of 1000000; TPS: 4626.11; ETA: 0.1 minutes\n", + "Step 979999 of 1000000; TPS: 4626.19; ETA: 0.1 minutes\n", + "Step 980999 of 1000000; TPS: 4578.79; ETA: 0.1 minutes\n", + "Step 981999 of 1000000; TPS: 4578.87; ETA: 0.1 minutes\n", + "Step 982999 of 1000000; TPS: 4578.49; ETA: 0.1 minutes\n", + "Step 983999 of 1000000; TPS: 4578.67; ETA: 0.1 minutes\n", + "Step 984999 of 1000000; TPS: 4578.97; ETA: 0.1 minutes\n", + "Step 985999 of 1000000; TPS: 4579.15; ETA: 0.1 minutes\n", + "Step 986999 of 1000000; TPS: 4579.35; ETA: 0.0 minutes\n", + "Step 987999 of 1000000; TPS: 4579.6; ETA: 0.0 minutes\n", + "Step 988999 of 1000000; TPS: 4578.96; ETA: 0.0 minutes\n", + "Step 989999 of 1000000; TPS: 4578.93; ETA: 0.0 minutes\n", + "Step 990999 of 1000000; TPS: 4579.09; ETA: 0.0 minutes\n", + "Step 991999 of 1000000; TPS: 4579.29; ETA: 0.0 minutes\n", + "Step 992999 of 1000000; TPS: 4579.3; ETA: 0.0 minutes\n", + "Step 993999 of 1000000; TPS: 4579.35; ETA: 0.0 minutes\n", + "Step 994999 of 1000000; TPS: 4579.34; ETA: 0.0 minutes\n", + "Step 995999 of 1000000; TPS: 4579.31; ETA: 0.0 minutes\n", + "Step 996999 of 1000000; TPS: 4579.36; ETA: 0.0 minutes\n", + "Step 997999 of 1000000; TPS: 4579.38; ETA: 0.0 minutes\n", + "Step 998999 of 1000000; TPS: 4579.5; ETA: 0.0 minutes\n", + "Step 999999 of 1000000; TPS: 4579.49; ETA: 0.0 minutes\n" ] } ], "source": [ "sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=device, dt = dt, \n", " gsd_write_freq=int(write_frequency), log_file_name = log, gsd_file_name = gsd) # initializing simulation\n", + "start_shrink = time.time()\n", "sim.run_update_volume(final_box_lengths=target_box, kT=6.0, n_steps=shrink_steps,tau_kt=100*sim.dt,period=10,thermalize_particles=True) # shrink simulation run\n", + "end_shrink = time.time()\n", + "start_run = time.time()\n", "sim.run_NVT(n_steps=steps, kT=temp, tau_kt=dt*100) # simulation run\n", + "end_run = time.time()\n", "sim.flush_writers() # updating data files\n", "del sim # drop references so files are closed" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "id": "a8af76ae-efe0-4912-8417-612048485a77", "metadata": {}, + "outputs": [], + "source": [ + "!mv \"{log}\" \"{gsd}\" \"{start_file}\" ../analysis/" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "de590eda-bbb3-42a7-a87f-c278705455c8", + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "mv: cannot stat '20_10mer5f_0.005dt.txt': No such file or directory\n", - "mv: cannot stat '20_10mer5f_0.005dt.gsd': No such file or directory\n", - "mv: cannot stat '20_10mer5f_0.005dt_start.txt': No such file or directory\n" + "Shrink phase completed in 117.72965431213379 seconds\n", + "Run phase completed in 218.36900329589844 seconds\n", + "Total time: 5.60 minutes\n" ] } ], "source": [ - "!mv \"{log}\" \"{gsd}\" \"{start_file}\" ../analysis/" + "print(f\"Shrink phase completed in {(end_shrink - start_shrink)} seconds\")\n", + "print(f\"Run phase completed in {(end_run - start_run)} seconds\")\n", + "print(f\"Total time: {((end_run - start_run)+(end_shrink - start_shrink))/60:.2f} minutes\")" ] } ], From 1b5938feed776b4ea4b6d1415fee9d58c42e836c Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Wed, 29 Oct 2025 15:02:10 -0600 Subject: [PATCH 03/10] updated readme --- README.md | 2 +- sims/clean_sim.ipynb | 3878 ------------------------------------------ 2 files changed, 1 insertion(+), 3879 deletions(-) delete mode 100644 sims/clean_sim.ipynb diff --git a/README.md b/README.md index d682e66..9e1c28d 100644 --- a/README.md +++ b/README.md @@ -27,7 +27,7 @@ Run the following in your terminal: Then, in order to run a small scale simulation, navigate to the sims/ directory: -- clean_sim.ipynb = current working simulation you should use, shrink step is included allowing system to be packed loosely then shrunk to be dense, causing more interactions to occur +- Onboarding.ipynb = current working simulation you should use, shrink step is included allowing system to be packed loosely then shrunk to be dense, causing more interactions to occur - clean_sim_with_randomwalk.ipynb = a future notebook, has a shrink step but also manipulates the geometry of the chains is randomly set so that we may set a dihedral to be something other than zero To analyze results, navigate to working_example/: diff --git a/sims/clean_sim.ipynb b/sims/clean_sim.ipynb deleted file mode 100644 index 89cf5b6..0000000 --- a/sims/clean_sim.ipynb +++ /dev/null @@ -1,3878 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "d1241ede-f8d9-438c-be59-da4f41137625", - "metadata": {}, - "source": [ - "## Importing necessary libraries" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6cef10db-1cf2-4d9a-a56d-9f4eec7f1e5a", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "import flowermd\n", - "import gsd\n", - "import gsd.hoomd\n", - "import hoomd\n", - "import matplotlib.pyplot as plt\n", - "import mbuild as mb\n", - "import numpy as np\n", - "import warnings\n", - "from cmeutils.visualize import FresnelGSD\n", - "from flowermd.base import Pack, Simulation, System, Molecule\n", - "from flowermd.base.forcefield import BaseHOOMDForcefield\n", - "from flowermd.library import KremerGrestBeadSpring, LJChain\n", - "from flowermd.library.forcefields import BeadSpring\n", - "from flowermd.utils import get_target_box_number_density\n", - "from mbuild.compound import Compound\n", - "from mbuild.lattice import Lattice\n", - "import unyt as u\n", - "warnings.filterwarnings('ignore')" - ] - }, - { - "cell_type": "markdown", - "id": "cc52258a-df3f-42a7-a085-6b65531df103", - "metadata": {}, - "source": [ - "## Switch to .CPU() if running on CPU, GPU if on GPU" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "edcecf0b-e70b-4a18-ba3a-4b09b8b47137", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "cpu = hoomd.device.GPU()" - ] - }, - { - "cell_type": "markdown", - "id": "4577acb4-a6e4-4d60-8d3e-40381c215496", - "metadata": {}, - "source": [ - "## Class defining flake geometry" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4bb641a0-5cb8-4843-8f3d-b247cd75a81c", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "class Graphene(System):\n", - " def __init__(\n", - " self,\n", - " x_repeat,\n", - " y_repeat,\n", - " n_layers,\n", - " base_units=dict(),\n", - " periodicity=(True, True, False),\n", - " ):\n", - " surface = mb.Compound(periodicity=periodicity)\n", - " a = 3**.5\n", - " lattice = Lattice(\n", - " lattice_spacing=[a,a,a],\n", - " lattice_vectors= [[a,0,0],[a/2,3/2,0],[0,0,1]],\n", - " lattice_points={\"A\": [[1/3,1/3,0], [2/3, 2/3, 0]]},\n", - " ) \n", - " Flakium = Compound(name=\"F\", element=\"F\") # defines a carbon atom that will be used to populate lattice points\n", - " layers = lattice.populate(\n", - " compound_dict={\"A\": Flakium}, x=x_repeat, y=y_repeat, z=n_layers\n", - " ) # populates the lattice using the previously defined carbon atom for every \"A\" site, repeated in all x,y, and z directions\n", - " surface.add(layers) # adds populated carbon lattice layers to the 'surface' compound, which represents our graphene structure \n", - " surface.freud_generate_bonds(\"F\", \"F\", dmin=0.9, dmax=1.1) # generates bonds depending on input distance range, scales with lattice\n", - " surface_mol = Molecule(num_mols=1, compound=surface) # wraps into a Molecule object, creating \"1\" instance of this molecule\n", - "\n", - " super(Graphene, self).__init__(\n", - " molecules=[surface_mol],\n", - " base_units=base_units,\n", - " )\n", - "\n", - " def _build_system(self):\n", - " return self.all_molecules[0]\n", - "#OK, so we want to initialize a system with some chains and some flakes" - ] - }, - { - "cell_type": "markdown", - "id": "3f88e122-6c5f-4c04-b547-2511f9012344", - "metadata": {}, - "source": [ - "## Forcefield parameters defining WCA interactions, essentially zero attraction, pure repulsion" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f0a777f9-bcbd-41b4-8687-0dc45705e2d4", - "metadata": {}, - "outputs": [], - "source": [ - "ff = BeadSpring(\n", - " r_cut=2**(1/6), \n", - " beads={\n", - " \"A\": dict(epsilon=1.0, sigma=1.0), # chains\n", - " \"F\": dict(epsilon=1.0, sigma=1.0), # flakes\n", - " },\n", - " bonds={\n", - " \"F-F\": dict(r0=1.0, k=1000),\n", - " \"A-A\": dict(r0=1.0, k=1000.0), # increased k to avoid chain collapse\n", - " },\n", - " angles={\n", - " \"A-A-A\": dict(t0=2* np.pi / 3., k=100.0), # moderate stiffness for chains\n", - " \"F-F-F\": dict(t0=2 * np.pi / 3., k=5000),\n", - " },\n", - " dihedrals={\n", - " \"A-A-A-A\": dict(phi0=0.0, k=0, d=-1, n=2), #need to turn this on later, messed up with straight chains\n", - " \"F-F-F-F\": dict(phi0=0.0, k=500, d=-1, n=2),\n", - " }\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "066b2946-1b32-4b6c-939a-09de31f23411", - "metadata": {}, - "source": [ - "## Simulation parameters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d58821d9-adbb-4e9c-bd10-2751d1eeb77b", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "N_chains = 100\n", - "initial_dens = 0.001\n", - "final_dens = 0.3\n", - "N_flakes = 10\n", - "chain_length = 10\n", - "dt = 0.005\n", - "temp = 3" - ] - }, - { - "cell_type": "markdown", - "id": "53c7ad4a-1e7d-40ef-9148-06612a286a6d", - "metadata": {}, - "source": [ - "## Initializing chains, flakes, and system parameters (like initial density -> final density)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "87ba678c-6699-409a-8926-9c3ffc4c5782", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "application/3dmoljs_load.v0": "
\n

3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n
\n", - "text/html": [ - "
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3Dmol.js failed to load for some reason. Please check your browser console for error messages.

\n", - "
\n", - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "kg_chain = LJChain(lengths=chain_length,num_mols=N_chains)\n", - "sheet = Graphene(x_repeat=5, y_repeat=5, n_layers=1, periodicity=(False, False, False))\n", - "system = Pack(molecules=[Molecule(compound=sheet.all_molecules[0], num_mols=N_flakes), kg_chain], \n", - " density=initial_dens, packing_expand_factor = 6, seed=2)\n", - "target_box = get_target_box_number_density(density=final_dens*u.Unit(\"nm**-3\"),n_beads=(500+(N_chains*10)))" - ] - }, - { - "cell_type": "markdown", - "id": "818aff5c-23a4-4785-8c79-b7cf3c1e0269", - "metadata": { - "scrolled": true - }, - "source": [ - "## Output files" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8ae5c06-86e5-4e0b-9d4c-19374f9865e5", - "metadata": { - "scrolled": true - }, - "outputs": [], - "source": [ - "gsd = f\"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.gsd\"\n", - "log = f\"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.txt\"" - ] - }, - { - "cell_type": "markdown", - "id": "ccbf8beb-31e2-4536-9939-8cd20f6ba255", - "metadata": {}, - "source": [ - "## Initializing simulation, shrinking, running, then flushing data into output files." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a8e068b0-b092-4819-bf7a-e98fc8488670", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "*Warning*: dihedral.harmonic: specified K <= 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing simulation state from a gsd.hoomd.Frame.\n", - "Step 1000 of 5000000; TPS: 2275.44; ETA: 36.6 minutes\n", - "Step 2000 of 5000000; TPS: 2988.88; ETA: 27.9 minutes\n", - "Step 3000 of 5000000; TPS: 2329.69; ETA: 35.7 minutes\n", - "Step 4000 of 5000000; TPS: 2783.88; ETA: 29.9 minutes\n", - "Step 5000 of 5000000; TPS: 3155.88; ETA: 26.4 minutes\n", - "Step 6000 of 5000000; TPS: 3474.78; ETA: 24.0 minutes\n", - "Step 7000 of 5000000; TPS: 3743.7; ETA: 22.2 minutes\n", - 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"Step 93000 of 5000000; TPS: 6414.64; ETA: 12.7 minutes\n", - "Step 94000 of 5000000; TPS: 6418.58; ETA: 12.7 minutes\n", - "Step 95000 of 5000000; TPS: 6412.53; ETA: 12.7 minutes\n", - "Step 96000 of 5000000; TPS: 6416.35; ETA: 12.7 minutes\n", - "Step 97000 of 5000000; TPS: 6420.15; ETA: 12.7 minutes\n", - "Step 98000 of 5000000; TPS: 6423.77; ETA: 12.7 minutes\n", - "Step 99000 of 5000000; TPS: 6427.18; ETA: 12.7 minutes\n", - "Step 100000 of 5000000; TPS: 6430.51; ETA: 12.7 minutes\n", - "Step 101000 of 5000000; TPS: 6433.69; ETA: 12.7 minutes\n", - "Step 102000 of 5000000; TPS: 6435.69; ETA: 12.7 minutes\n", - "Step 103000 of 5000000; TPS: 6439.22; ETA: 12.7 minutes\n", - "Step 104000 of 5000000; TPS: 6441.9; ETA: 12.7 minutes\n", - "Step 105000 of 5000000; TPS: 6444.87; ETA: 12.7 minutes\n", - "Step 106000 of 5000000; TPS: 6412.43; ETA: 12.7 minutes\n", - "Step 107000 of 5000000; TPS: 6415.07; ETA: 12.7 minutes\n", - "Step 108000 of 5000000; TPS: 6417.99; ETA: 12.7 minutes\n", - "Step 109000 of 5000000; TPS: 6420.99; ETA: 12.7 minutes\n", - "Step 110000 of 5000000; TPS: 6423.5; ETA: 12.7 minutes\n", - "Step 111000 of 5000000; TPS: 6425.69; ETA: 12.7 minutes\n", - "Step 112000 of 5000000; TPS: 6429.1; ETA: 12.7 minutes\n", - "Step 113000 of 5000000; TPS: 6431.95; ETA: 12.7 minutes\n", - "Step 114000 of 5000000; TPS: 6434.64; ETA: 12.7 minutes\n", - "Step 115000 of 5000000; TPS: 6437.2; ETA: 12.6 minutes\n", - "Step 116000 of 5000000; TPS: 6439.84; ETA: 12.6 minutes\n", - "Step 117000 of 5000000; TPS: 6441.78; ETA: 12.6 minutes\n", - "Step 118000 of 5000000; TPS: 6444.88; ETA: 12.6 minutes\n", - "Step 119000 of 5000000; TPS: 6447.3; ETA: 12.6 minutes\n", - "Step 120000 of 5000000; TPS: 6449.68; ETA: 12.6 minutes\n", - "Step 121000 of 5000000; TPS: 6452.15; ETA: 12.6 minutes\n", - "Step 122000 of 5000000; TPS: 6454.41; ETA: 12.6 minutes\n", - "Step 123000 of 5000000; TPS: 6456.66; ETA: 12.6 minutes\n", - "Step 124000 of 5000000; TPS: 6458.68; ETA: 12.6 minutes\n", - "Step 125000 of 5000000; TPS: 6460.85; ETA: 12.6 minutes\n", - "Step 126000 of 5000000; TPS: 6462.39; ETA: 12.6 minutes\n", - "Step 127000 of 5000000; TPS: 6464.79; ETA: 12.6 minutes\n", - "Step 128000 of 5000000; TPS: 6466.71; ETA: 12.6 minutes\n", - "Step 129000 of 5000000; TPS: 6468.55; ETA: 12.6 minutes\n", - "Step 130000 of 5000000; TPS: 6470.31; ETA: 12.5 minutes\n", - "Step 131000 of 5000000; TPS: 6472.28; ETA: 12.5 minutes\n", - "Step 132000 of 5000000; TPS: 6474.11; ETA: 12.5 minutes\n", - "Step 133000 of 5000000; TPS: 6475.9; ETA: 12.5 minutes\n", - "Step 134000 of 5000000; TPS: 6477.62; ETA: 12.5 minutes\n", - "Step 135000 of 5000000; TPS: 6479.27; ETA: 12.5 minutes\n", - "Step 136000 of 5000000; TPS: 6480.79; ETA: 12.5 minutes\n", - "Step 137000 of 5000000; TPS: 6482.28; ETA: 12.5 minutes\n", - "Step 138000 of 5000000; TPS: 6484.02; ETA: 12.5 minutes\n", - "Step 139000 of 5000000; TPS: 6485.6; ETA: 12.5 minutes\n", - "Step 140000 of 5000000; TPS: 6487.16; ETA: 12.5 minutes\n", - "Step 141000 of 5000000; TPS: 6488.63; ETA: 12.5 minutes\n", - "Step 142000 of 5000000; TPS: 6490.18; ETA: 12.5 minutes\n", - "Step 143000 of 5000000; TPS: 6491.64; ETA: 12.5 minutes\n", - "Step 144000 of 5000000; TPS: 6493.15; ETA: 12.5 minutes\n", - "Step 145000 of 5000000; TPS: 6494.53; ETA: 12.5 minutes\n", - "Step 146000 of 5000000; TPS: 6495.76; ETA: 12.5 minutes\n", - "Step 147000 of 5000000; TPS: 6497.0; ETA: 12.4 minutes\n", - "Step 148000 of 5000000; TPS: 6498.42; ETA: 12.4 minutes\n", - "Step 149000 of 5000000; TPS: 6499.69; ETA: 12.4 minutes\n", - "Step 150000 of 5000000; TPS: 6500.95; ETA: 12.4 minutes\n", - "Step 151000 of 5000000; TPS: 6502.23; ETA: 12.4 minutes\n", - "Step 152000 of 5000000; TPS: 6503.49; ETA: 12.4 minutes\n", - "Step 153000 of 5000000; TPS: 6504.66; ETA: 12.4 minutes\n", - "Step 154000 of 5000000; TPS: 6505.85; ETA: 12.4 minutes\n", - "Step 155000 of 5000000; TPS: 6506.86; ETA: 12.4 minutes\n", - "Step 156000 of 5000000; TPS: 6507.72; ETA: 12.4 minutes\n", - "Step 157000 of 5000000; TPS: 6508.79; ETA: 12.4 minutes\n", - "Step 158000 of 5000000; TPS: 6509.93; ETA: 12.4 minutes\n", - "Step 159000 of 5000000; TPS: 6510.98; ETA: 12.4 minutes\n", - "Step 160000 of 5000000; TPS: 6512.0; ETA: 12.4 minutes\n", - "Step 161000 of 5000000; TPS: 6485.0; ETA: 12.4 minutes\n", - "Step 162000 of 5000000; TPS: 6486.21; ETA: 12.4 minutes\n", - "Step 163000 of 5000000; TPS: 6487.26; ETA: 12.4 minutes\n", - "Step 164000 of 5000000; TPS: 6488.28; ETA: 12.4 minutes\n", - "Step 165000 of 5000000; TPS: 6489.57; ETA: 12.4 minutes\n", - "Step 166000 of 5000000; TPS: 6490.71; ETA: 12.4 minutes\n", - "Step 167000 of 5000000; TPS: 6491.77; ETA: 12.4 minutes\n", - "Step 168000 of 5000000; TPS: 6492.37; ETA: 12.4 minutes\n", - "Step 169000 of 5000000; TPS: 6493.64; ETA: 12.4 minutes\n", - "Step 170000 of 5000000; TPS: 6494.76; ETA: 12.4 minutes\n", - "Step 171000 of 5000000; TPS: 6495.77; ETA: 12.4 minutes\n", - "Step 172000 of 5000000; TPS: 6496.86; ETA: 12.4 minutes\n", - "Step 173000 of 5000000; TPS: 6497.96; ETA: 12.4 minutes\n", - "Step 174000 of 5000000; TPS: 6499.0; ETA: 12.4 minutes\n", - "Step 175000 of 5000000; TPS: 6499.97; ETA: 12.4 minutes\n", - "Step 176000 of 5000000; TPS: 6500.92; ETA: 12.4 minutes\n", - "Step 177000 of 5000000; TPS: 6502.0; ETA: 12.4 minutes\n", - "Step 178000 of 5000000; TPS: 6503.04; ETA: 12.4 minutes\n", - "Step 179000 of 5000000; TPS: 6504.07; ETA: 12.4 minutes\n", - "Step 180000 of 5000000; TPS: 6505.15; ETA: 12.3 minutes\n", - "Step 181000 of 5000000; TPS: 6505.95; ETA: 12.3 minutes\n", - "Step 182000 of 5000000; TPS: 6507.18; ETA: 12.3 minutes\n", - "Step 183000 of 5000000; TPS: 6508.08; ETA: 12.3 minutes\n", - "Step 184000 of 5000000; TPS: 6508.98; ETA: 12.3 minutes\n", - "Step 185000 of 5000000; TPS: 6509.91; ETA: 12.3 minutes\n", - "Step 186000 of 5000000; TPS: 6510.76; ETA: 12.3 minutes\n", - "Step 187000 of 5000000; TPS: 6511.7; ETA: 12.3 minutes\n", - "Step 188000 of 5000000; TPS: 6512.58; ETA: 12.3 minutes\n", - "Step 189000 of 5000000; TPS: 6513.48; ETA: 12.3 minutes\n", - "Step 190000 of 5000000; TPS: 6514.26; ETA: 12.3 minutes\n", - "Step 191000 of 5000000; TPS: 6515.12; ETA: 12.3 minutes\n", - "Step 192000 of 5000000; TPS: 6515.9; ETA: 12.3 minutes\n", - "Step 193000 of 5000000; TPS: 6516.69; ETA: 12.3 minutes\n", - "Step 194000 of 5000000; TPS: 6517.46; ETA: 12.3 minutes\n", - "Step 195000 of 5000000; TPS: 6518.21; ETA: 12.3 minutes\n", - "Step 196000 of 5000000; TPS: 6518.97; ETA: 12.3 minutes\n", - "Step 197000 of 5000000; TPS: 6519.78; ETA: 12.3 minutes\n", - "Step 198000 of 5000000; TPS: 6520.52; ETA: 12.3 minutes\n", - "Step 199000 of 5000000; TPS: 6521.33; ETA: 12.3 minutes\n", - "Step 200000 of 5000000; TPS: 6522.08; ETA: 12.3 minutes\n", - "Step 201000 of 5000000; TPS: 6522.75; ETA: 12.3 minutes\n", - "Step 202000 of 5000000; TPS: 6523.48; ETA: 12.3 minutes\n", - "Step 203000 of 5000000; TPS: 6524.33; ETA: 12.3 minutes\n", - "Step 204000 of 5000000; TPS: 6525.03; ETA: 12.3 minutes\n", - "Step 205000 of 5000000; TPS: 6525.76; ETA: 12.2 minutes\n", - "Step 206000 of 5000000; TPS: 6526.55; ETA: 12.2 minutes\n", - "Step 207000 of 5000000; TPS: 6527.3; ETA: 12.2 minutes\n", - "Step 208000 of 5000000; TPS: 6528.05; ETA: 12.2 minutes\n", - "Step 209000 of 5000000; TPS: 6528.82; ETA: 12.2 minutes\n", - "Step 210000 of 5000000; TPS: 6529.59; ETA: 12.2 minutes\n", - "Step 211000 of 5000000; TPS: 6530.26; ETA: 12.2 minutes\n", - "Step 212000 of 5000000; TPS: 6530.91; ETA: 12.2 minutes\n", - "Step 213000 of 5000000; TPS: 6531.58; ETA: 12.2 minutes\n", - "Step 214000 of 5000000; TPS: 6532.27; ETA: 12.2 minutes\n", - "Step 215000 of 5000000; TPS: 6532.94; ETA: 12.2 minutes\n", - "Step 216000 of 5000000; TPS: 6517.01; ETA: 12.2 minutes\n", - "Step 217000 of 5000000; TPS: 6517.68; ETA: 12.2 minutes\n", - "Step 218000 of 5000000; TPS: 6518.4; ETA: 12.2 minutes\n", - "Step 219000 of 5000000; TPS: 6519.11; ETA: 12.2 minutes\n", - "Step 220000 of 5000000; TPS: 6519.85; ETA: 12.2 minutes\n", - "Step 221000 of 5000000; TPS: 6520.6; ETA: 12.2 minutes\n", - "Step 222000 of 5000000; TPS: 6521.33; ETA: 12.2 minutes\n", - "Step 223000 of 5000000; TPS: 6522.02; ETA: 12.2 minutes\n", - "Step 224000 of 5000000; TPS: 6522.7; ETA: 12.2 minutes\n", - "Step 225000 of 5000000; TPS: 6523.45; ETA: 12.2 minutes\n", - "Step 226000 of 5000000; TPS: 6524.16; ETA: 12.2 minutes\n", - "Step 227000 of 5000000; TPS: 6524.73; ETA: 12.2 minutes\n", - "Step 228000 of 5000000; TPS: 6521.41; ETA: 12.2 minutes\n", - "Step 229000 of 5000000; TPS: 6522.0; ETA: 12.2 minutes\n", - "Step 230000 of 5000000; TPS: 6522.65; ETA: 12.2 minutes\n", - "Step 231000 of 5000000; TPS: 6523.28; ETA: 12.2 minutes\n", - "Step 232000 of 5000000; TPS: 6523.94; ETA: 12.2 minutes\n", - "Step 233000 of 5000000; TPS: 6524.59; ETA: 12.2 minutes\n", - "Step 234000 of 5000000; TPS: 6525.27; ETA: 12.2 minutes\n", - "Step 235000 of 5000000; TPS: 6525.6; ETA: 12.2 minutes\n", - "Step 236000 of 5000000; TPS: 6526.13; ETA: 12.2 minutes\n", - "Step 237000 of 5000000; TPS: 6526.85; ETA: 12.2 minutes\n", - "Step 238000 of 5000000; TPS: 6527.46; ETA: 12.2 minutes\n", - "Step 239000 of 5000000; TPS: 6528.1; ETA: 12.2 minutes\n", - "Step 240000 of 5000000; TPS: 6528.66; ETA: 12.2 minutes\n", - "Step 241000 of 5000000; TPS: 6529.31; ETA: 12.1 minutes\n", - "Step 242000 of 5000000; TPS: 6529.84; ETA: 12.1 minutes\n", - "Step 243000 of 5000000; TPS: 6530.44; ETA: 12.1 minutes\n", - "Step 244000 of 5000000; TPS: 6531.13; ETA: 12.1 minutes\n", - "Step 245000 of 5000000; TPS: 6531.74; ETA: 12.1 minutes\n", - "Step 246000 of 5000000; TPS: 6532.3; ETA: 12.1 minutes\n", - "Step 247000 of 5000000; TPS: 6532.84; ETA: 12.1 minutes\n", - "Step 248000 of 5000000; TPS: 6533.4; ETA: 12.1 minutes\n", - "Step 249000 of 5000000; TPS: 6533.76; ETA: 12.1 minutes\n", - "Step 250000 of 5000000; TPS: 6534.31; ETA: 12.1 minutes\n", - "Step 251000 of 5000000; TPS: 6534.85; ETA: 12.1 minutes\n", - "Step 252000 of 5000000; TPS: 6535.2; ETA: 12.1 minutes\n", - "Step 253000 of 5000000; TPS: 6535.67; ETA: 12.1 minutes\n", - "Step 254000 of 5000000; TPS: 6536.18; ETA: 12.1 minutes\n", - "Step 255000 of 5000000; TPS: 6536.67; ETA: 12.1 minutes\n", - "Step 256000 of 5000000; TPS: 6537.03; ETA: 12.1 minutes\n", - "Step 257000 of 5000000; TPS: 6537.49; ETA: 12.1 minutes\n", - "Step 258000 of 5000000; TPS: 6538.02; ETA: 12.1 minutes\n", - "Step 259000 of 5000000; TPS: 6538.55; ETA: 12.1 minutes\n", - "Step 260000 of 5000000; TPS: 6539.05; ETA: 12.1 minutes\n", - "Step 261000 of 5000000; TPS: 6539.56; ETA: 12.1 minutes\n", - "Step 262000 of 5000000; TPS: 6540.1; ETA: 12.1 minutes\n", - "Step 263000 of 5000000; TPS: 6540.62; ETA: 12.1 minutes\n", - "Step 264000 of 5000000; TPS: 6540.92; ETA: 12.1 minutes\n", - "Step 265000 of 5000000; TPS: 6541.4; ETA: 12.1 minutes\n", - "Step 266000 of 5000000; TPS: 6541.57; ETA: 12.1 minutes\n", - "Step 267000 of 5000000; TPS: 6542.01; ETA: 12.1 minutes\n", - "Step 268000 of 5000000; TPS: 6542.46; ETA: 12.1 minutes\n", - "Step 269000 of 5000000; TPS: 6542.75; ETA: 12.1 minutes\n", - "Step 270000 of 5000000; TPS: 6542.96; ETA: 12.0 minutes\n", - "Step 271000 of 5000000; TPS: 6530.07; ETA: 12.1 minutes\n", - "Step 272000 of 5000000; TPS: 6530.54; ETA: 12.1 minutes\n", - "Step 273000 of 5000000; TPS: 6531.02; ETA: 12.1 minutes\n", - "Step 274000 of 5000000; TPS: 6531.44; ETA: 12.1 minutes\n", - "Step 275000 of 5000000; TPS: 6532.18; ETA: 12.1 minutes\n", - "Step 276000 of 5000000; TPS: 6532.58; ETA: 12.1 minutes\n", - "Step 277000 of 5000000; TPS: 6533.09; ETA: 12.0 minutes\n", - "Step 278000 of 5000000; TPS: 6533.39; ETA: 12.0 minutes\n", - "Step 279000 of 5000000; TPS: 6533.74; ETA: 12.0 minutes\n", - "Step 280000 of 5000000; TPS: 6534.17; ETA: 12.0 minutes\n", - "Step 281000 of 5000000; TPS: 6534.61; ETA: 12.0 minutes\n", - "Step 282000 of 5000000; TPS: 6535.13; ETA: 12.0 minutes\n", - "Step 283000 of 5000000; TPS: 6535.51; ETA: 12.0 minutes\n", - "Step 284000 of 5000000; TPS: 6535.94; ETA: 12.0 minutes\n", - "Step 285000 of 5000000; TPS: 6536.35; ETA: 12.0 minutes\n", - "Step 286000 of 5000000; TPS: 6536.87; ETA: 12.0 minutes\n", - "Step 287000 of 5000000; TPS: 6537.35; ETA: 12.0 minutes\n", - "Step 288000 of 5000000; TPS: 6537.68; ETA: 12.0 minutes\n", - "Step 289000 of 5000000; TPS: 6538.13; ETA: 12.0 minutes\n", - "Step 290000 of 5000000; TPS: 6538.58; ETA: 12.0 minutes\n", - "Step 291000 of 5000000; TPS: 6539.0; ETA: 12.0 minutes\n", - "Step 292000 of 5000000; TPS: 6539.47; ETA: 12.0 minutes\n", - "Step 293000 of 5000000; TPS: 6539.94; ETA: 12.0 minutes\n", - "Step 294000 of 5000000; TPS: 6538.73; ETA: 12.0 minutes\n", - "Step 295000 of 5000000; TPS: 6539.15; ETA: 12.0 minutes\n", - "Step 296000 of 5000000; TPS: 6539.42; ETA: 12.0 minutes\n", - "Step 297000 of 5000000; TPS: 6539.59; ETA: 12.0 minutes\n", - "Step 298000 of 5000000; TPS: 6539.77; ETA: 12.0 minutes\n", - "Step 299000 of 5000000; TPS: 6540.14; ETA: 12.0 minutes\n", - "Step 300000 of 5000000; TPS: 6540.6; ETA: 12.0 minutes\n", - "Step 301000 of 5000000; TPS: 6540.83; ETA: 12.0 minutes\n", - "Step 302000 of 5000000; TPS: 6541.27; ETA: 12.0 minutes\n", - "Step 303000 of 5000000; TPS: 6541.52; ETA: 12.0 minutes\n", - "Step 304000 of 5000000; TPS: 6541.94; ETA: 12.0 minutes\n", - "Step 305000 of 5000000; TPS: 6542.37; ETA: 12.0 minutes\n", - "Step 306000 of 5000000; TPS: 6542.79; ETA: 12.0 minutes\n", - "Step 307000 of 5000000; TPS: 6543.21; ETA: 12.0 minutes\n", - "Step 308000 of 5000000; TPS: 6543.66; ETA: 12.0 minutes\n", - "Step 309000 of 5000000; TPS: 6544.07; ETA: 11.9 minutes\n", - "Step 310000 of 5000000; TPS: 6544.51; ETA: 11.9 minutes\n", - "Step 311000 of 5000000; TPS: 6544.93; ETA: 11.9 minutes\n", - "Step 312000 of 5000000; TPS: 6545.33; ETA: 11.9 minutes\n", - "Step 313000 of 5000000; TPS: 6545.75; ETA: 11.9 minutes\n", - "Step 314000 of 5000000; TPS: 6546.14; ETA: 11.9 minutes\n", - "Step 315000 of 5000000; TPS: 6546.6; ETA: 11.9 minutes\n", - "Step 316000 of 5000000; TPS: 6546.99; ETA: 11.9 minutes\n", - "Step 317000 of 5000000; TPS: 6547.35; ETA: 11.9 minutes\n", - "Step 318000 of 5000000; TPS: 6547.73; ETA: 11.9 minutes\n", - "Step 319000 of 5000000; TPS: 6548.15; ETA: 11.9 minutes\n", - "Step 320000 of 5000000; TPS: 6548.51; ETA: 11.9 minutes\n", - "Step 321000 of 5000000; TPS: 6548.87; ETA: 11.9 minutes\n", - "Step 322000 of 5000000; TPS: 6549.19; ETA: 11.9 minutes\n", - "Step 323000 of 5000000; TPS: 6549.34; ETA: 11.9 minutes\n", - "Step 324000 of 5000000; TPS: 6549.45; ETA: 11.9 minutes\n", - "Step 325000 of 5000000; TPS: 6549.71; ETA: 11.9 minutes\n", - "Step 326000 of 5000000; TPS: 6527.67; ETA: 11.9 minutes\n", - "Step 327000 of 5000000; TPS: 6528.07; ETA: 11.9 minutes\n", - "Step 328000 of 5000000; TPS: 6528.5; ETA: 11.9 minutes\n", - "Step 329000 of 5000000; TPS: 6528.93; ETA: 11.9 minutes\n", - "Step 330000 of 5000000; TPS: 6529.3; ETA: 11.9 minutes\n", - "Step 331000 of 5000000; TPS: 6529.68; ETA: 11.9 minutes\n", - "Step 332000 of 5000000; TPS: 6530.13; ETA: 11.9 minutes\n", - "Step 333000 of 5000000; TPS: 6530.54; ETA: 11.9 minutes\n", - "Step 334000 of 5000000; TPS: 6530.95; ETA: 11.9 minutes\n", - "Step 335000 of 5000000; TPS: 6531.39; ETA: 11.9 minutes\n", - "Step 336000 of 5000000; TPS: 6531.81; ETA: 11.9 minutes\n", - "Step 337000 of 5000000; TPS: 6532.13; ETA: 11.9 minutes\n", - "Step 338000 of 5000000; TPS: 6532.56; ETA: 11.9 minutes\n", - "Step 339000 of 5000000; TPS: 6532.95; ETA: 11.9 minutes\n", - "Step 340000 of 5000000; TPS: 6533.35; ETA: 11.9 minutes\n", - "Step 341000 of 5000000; TPS: 6533.75; ETA: 11.9 minutes\n", - "Step 342000 of 5000000; TPS: 6534.18; ETA: 11.9 minutes\n", - "Step 343000 of 5000000; TPS: 6534.58; ETA: 11.9 minutes\n", - "Step 344000 of 5000000; TPS: 6534.97; ETA: 11.9 minutes\n", - "Step 345000 of 5000000; TPS: 6535.37; ETA: 11.9 minutes\n", - "Step 346000 of 5000000; TPS: 6535.73; ETA: 11.9 minutes\n", - "Step 347000 of 5000000; TPS: 6536.08; ETA: 11.9 minutes\n", - "Step 348000 of 5000000; TPS: 6536.45; ETA: 11.9 minutes\n", - "Step 349000 of 5000000; TPS: 6536.82; ETA: 11.9 minutes\n", - "Step 350000 of 5000000; TPS: 6537.21; ETA: 11.9 minutes\n", - "Step 351000 of 5000000; TPS: 6537.61; ETA: 11.9 minutes\n", - "Step 352000 of 5000000; TPS: 6537.98; ETA: 11.8 minutes\n", - "Step 353000 of 5000000; TPS: 6538.34; ETA: 11.8 minutes\n", - "Step 354000 of 5000000; TPS: 6538.69; ETA: 11.8 minutes\n", - "Step 355000 of 5000000; TPS: 6539.04; ETA: 11.8 minutes\n", - "Step 356000 of 5000000; TPS: 6539.43; ETA: 11.8 minutes\n", - "Step 357000 of 5000000; TPS: 6539.8; ETA: 11.8 minutes\n", - "Step 358000 of 5000000; TPS: 6540.17; ETA: 11.8 minutes\n", - "Step 359000 of 5000000; TPS: 6540.54; ETA: 11.8 minutes\n", - "Step 360000 of 5000000; TPS: 6539.54; ETA: 11.8 minutes\n", - "Step 361000 of 5000000; TPS: 6539.9; ETA: 11.8 minutes\n", - "Step 362000 of 5000000; TPS: 6540.27; ETA: 11.8 minutes\n", - "Step 363000 of 5000000; TPS: 6540.62; ETA: 11.8 minutes\n", - "Step 364000 of 5000000; TPS: 6540.89; ETA: 11.8 minutes\n", - "Step 365000 of 5000000; TPS: 6541.31; ETA: 11.8 minutes\n", - "Step 366000 of 5000000; TPS: 6541.64; ETA: 11.8 minutes\n", - "Step 367000 of 5000000; TPS: 6541.79; ETA: 11.8 minutes\n", - "Step 368000 of 5000000; TPS: 6542.12; ETA: 11.8 minutes\n", - "Step 369000 of 5000000; TPS: 6542.5; ETA: 11.8 minutes\n", - "Step 370000 of 5000000; TPS: 6542.83; ETA: 11.8 minutes\n", - "Step 371000 of 5000000; TPS: 6543.15; ETA: 11.8 minutes\n", - "Step 372000 of 5000000; TPS: 6543.46; ETA: 11.8 minutes\n", - "Step 373000 of 5000000; TPS: 6543.75; ETA: 11.8 minutes\n", - "Step 374000 of 5000000; TPS: 6544.04; ETA: 11.8 minutes\n", - "Step 375000 of 5000000; TPS: 6544.35; ETA: 11.8 minutes\n", - "Step 376000 of 5000000; TPS: 6544.72; ETA: 11.8 minutes\n", - "Step 377000 of 5000000; TPS: 6545.04; ETA: 11.8 minutes\n", - "Step 378000 of 5000000; TPS: 6545.24; ETA: 11.8 minutes\n", - "Step 379000 of 5000000; TPS: 6545.59; ETA: 11.8 minutes\n", - "Step 380000 of 5000000; TPS: 6545.92; ETA: 11.8 minutes\n", - "Step 381000 of 5000000; TPS: 6536.47; ETA: 11.8 minutes\n", - "Step 382000 of 5000000; TPS: 6536.79; ETA: 11.8 minutes\n", - "Step 383000 of 5000000; TPS: 6537.1; ETA: 11.8 minutes\n", - "Step 384000 of 5000000; TPS: 6537.44; ETA: 11.8 minutes\n", - "Step 385000 of 5000000; TPS: 6537.79; ETA: 11.8 minutes\n", - "Step 386000 of 5000000; TPS: 6538.13; ETA: 11.8 minutes\n", - "Step 387000 of 5000000; TPS: 6538.46; ETA: 11.8 minutes\n", - "Step 388000 of 5000000; TPS: 6538.8; ETA: 11.8 minutes\n", - "Step 389000 of 5000000; TPS: 6539.13; ETA: 11.8 minutes\n", - "Step 390000 of 5000000; TPS: 6539.46; ETA: 11.7 minutes\n", - "Step 391000 of 5000000; TPS: 6539.81; ETA: 11.7 minutes\n", - "Step 392000 of 5000000; TPS: 6540.13; ETA: 11.7 minutes\n", - "Step 393000 of 5000000; TPS: 6540.44; ETA: 11.7 minutes\n", - "Step 394000 of 5000000; TPS: 6540.78; ETA: 11.7 minutes\n", - "Step 395000 of 5000000; TPS: 6541.07; ETA: 11.7 minutes\n", - "Step 396000 of 5000000; TPS: 6541.34; ETA: 11.7 minutes\n", - "Step 397000 of 5000000; TPS: 6541.71; ETA: 11.7 minutes\n", - "Step 398000 of 5000000; TPS: 6542.03; ETA: 11.7 minutes\n", - "Step 399000 of 5000000; TPS: 6542.37; ETA: 11.7 minutes\n", - "Step 400000 of 5000000; TPS: 6542.71; ETA: 11.7 minutes\n", - "Step 401000 of 5000000; TPS: 6543.05; ETA: 11.7 minutes\n", - "Step 402000 of 5000000; TPS: 6543.38; ETA: 11.7 minutes\n", - "Step 403000 of 5000000; TPS: 6543.68; ETA: 11.7 minutes\n", - "Step 404000 of 5000000; TPS: 6544.0; ETA: 11.7 minutes\n", - "Step 405000 of 5000000; TPS: 6544.3; ETA: 11.7 minutes\n", - "Step 406000 of 5000000; TPS: 6544.62; ETA: 11.7 minutes\n", - "Step 407000 of 5000000; TPS: 6544.65; ETA: 11.7 minutes\n", - "Step 408000 of 5000000; TPS: 6544.94; ETA: 11.7 minutes\n", - "Step 409000 of 5000000; TPS: 6545.25; ETA: 11.7 minutes\n", - "Step 410000 of 5000000; TPS: 6545.55; ETA: 11.7 minutes\n", - "Step 411000 of 5000000; TPS: 6545.83; ETA: 11.7 minutes\n", - "Step 412000 of 5000000; TPS: 6546.13; ETA: 11.7 minutes\n", - "Step 413000 of 5000000; TPS: 6546.45; ETA: 11.7 minutes\n", - "Step 414000 of 5000000; TPS: 6546.77; ETA: 11.7 minutes\n", - "Step 415000 of 5000000; TPS: 6547.07; ETA: 11.7 minutes\n", - "Step 416000 of 5000000; TPS: 6547.38; ETA: 11.7 minutes\n", - "Step 417000 of 5000000; TPS: 6547.67; ETA: 11.7 minutes\n", - "Step 418000 of 5000000; TPS: 6547.95; ETA: 11.7 minutes\n", - "Step 419000 of 5000000; TPS: 6548.23; ETA: 11.7 minutes\n", - "Step 420000 of 5000000; TPS: 6548.53; ETA: 11.7 minutes\n", - "Step 421000 of 5000000; TPS: 6548.82; ETA: 11.7 minutes\n", - "Step 422000 of 5000000; TPS: 6549.1; ETA: 11.7 minutes\n", - "Step 423000 of 5000000; TPS: 6549.39; ETA: 11.6 minutes\n", - "Step 424000 of 5000000; TPS: 6549.69; ETA: 11.6 minutes\n", - "Step 425000 of 5000000; TPS: 6549.95; ETA: 11.6 minutes\n", - "Step 426000 of 5000000; TPS: 6550.23; ETA: 11.6 minutes\n", - "Step 427000 of 5000000; TPS: 6548.85; ETA: 11.6 minutes\n", - "Step 428000 of 5000000; TPS: 6549.15; ETA: 11.6 minutes\n", - "Step 429000 of 5000000; TPS: 6549.38; ETA: 11.6 minutes\n", - "Step 430000 of 5000000; TPS: 6549.64; ETA: 11.6 minutes\n", - "Step 431000 of 5000000; TPS: 6549.92; ETA: 11.6 minutes\n", - "Step 432000 of 5000000; TPS: 6550.2; ETA: 11.6 minutes\n", - "Step 433000 of 5000000; TPS: 6550.46; ETA: 11.6 minutes\n", - "Step 434000 of 5000000; TPS: 6550.72; ETA: 11.6 minutes\n", - "Step 435000 of 5000000; TPS: 6551.0; ETA: 11.6 minutes\n", - "Step 436000 of 5000000; TPS: 6543.04; ETA: 11.6 minutes\n", - "Step 437000 of 5000000; TPS: 6543.27; ETA: 11.6 minutes\n", - "Step 438000 of 5000000; TPS: 6543.55; ETA: 11.6 minutes\n", - "Step 439000 of 5000000; TPS: 6543.83; ETA: 11.6 minutes\n", - "Step 440000 of 5000000; TPS: 6544.06; ETA: 11.6 minutes\n", - "Step 441000 of 5000000; TPS: 6544.32; ETA: 11.6 minutes\n", - "Step 442000 of 5000000; TPS: 6544.61; ETA: 11.6 minutes\n", - "Step 443000 of 5000000; TPS: 6544.79; ETA: 11.6 minutes\n", - "Step 444000 of 5000000; TPS: 6544.94; ETA: 11.6 minutes\n", - "Step 445000 of 5000000; TPS: 6545.21; ETA: 11.6 minutes\n", - "Step 446000 of 5000000; TPS: 6545.49; ETA: 11.6 minutes\n", - "Step 447000 of 5000000; TPS: 6545.77; ETA: 11.6 minutes\n", - "Step 448000 of 5000000; TPS: 6546.02; ETA: 11.6 minutes\n", - "Step 449000 of 5000000; TPS: 6546.28; ETA: 11.6 minutes\n", - "Step 450000 of 5000000; TPS: 6546.55; ETA: 11.6 minutes\n", - "Step 451000 of 5000000; TPS: 6546.82; ETA: 11.6 minutes\n", - "Step 452000 of 5000000; TPS: 6547.08; ETA: 11.6 minutes\n", - "Step 453000 of 5000000; TPS: 6547.34; ETA: 11.6 minutes\n", - "Step 454000 of 5000000; TPS: 6547.59; ETA: 11.6 minutes\n", - "Step 455000 of 5000000; TPS: 6547.85; ETA: 11.6 minutes\n", - "Step 456000 of 5000000; TPS: 6548.12; ETA: 11.6 minutes\n", - "Step 457000 of 5000000; TPS: 6548.37; ETA: 11.6 minutes\n", - "Step 458000 of 5000000; TPS: 6548.64; ETA: 11.6 minutes\n", - "Step 459000 of 5000000; TPS: 6548.88; ETA: 11.6 minutes\n", - "Step 460000 of 5000000; TPS: 6549.13; ETA: 11.6 minutes\n", - "Step 461000 of 5000000; TPS: 6549.38; ETA: 11.6 minutes\n", - "Step 462000 of 5000000; TPS: 6549.64; ETA: 11.5 minutes\n", - "Step 463000 of 5000000; TPS: 6549.91; ETA: 11.5 minutes\n", - "Step 464000 of 5000000; TPS: 6550.15; ETA: 11.5 minutes\n", - "Step 465000 of 5000000; TPS: 6550.41; ETA: 11.5 minutes\n", - "Step 466000 of 5000000; TPS: 6550.66; ETA: 11.5 minutes\n", - "Step 467000 of 5000000; TPS: 6550.89; ETA: 11.5 minutes\n", - "Step 468000 of 5000000; TPS: 6551.13; ETA: 11.5 minutes\n", - "Step 469000 of 5000000; TPS: 6551.38; ETA: 11.5 minutes\n", - "Step 470000 of 5000000; TPS: 6551.65; ETA: 11.5 minutes\n", - "Step 471000 of 5000000; TPS: 6551.89; ETA: 11.5 minutes\n", - "Step 472000 of 5000000; TPS: 6552.14; ETA: 11.5 minutes\n", - "Step 473000 of 5000000; TPS: 6552.37; ETA: 11.5 minutes\n", - "Step 474000 of 5000000; TPS: 6552.48; ETA: 11.5 minutes\n", - "Step 475000 of 5000000; TPS: 6552.69; ETA: 11.5 minutes\n", - "Step 476000 of 5000000; TPS: 6552.94; ETA: 11.5 minutes\n", - "Step 477000 of 5000000; TPS: 6552.89; ETA: 11.5 minutes\n", - "Step 478000 of 5000000; TPS: 6553.08; ETA: 11.5 minutes\n", - "Step 479000 of 5000000; TPS: 6553.33; ETA: 11.5 minutes\n", - "Step 480000 of 5000000; TPS: 6553.53; ETA: 11.5 minutes\n", - "Step 481000 of 5000000; TPS: 6553.75; ETA: 11.5 minutes\n", - "Step 482000 of 5000000; TPS: 6553.97; ETA: 11.5 minutes\n", - "Step 483000 of 5000000; TPS: 6554.22; ETA: 11.5 minutes\n", - "Step 484000 of 5000000; TPS: 6554.41; ETA: 11.5 minutes\n", - "Step 485000 of 5000000; TPS: 6554.62; ETA: 11.5 minutes\n", - "Step 486000 of 5000000; TPS: 6554.85; ETA: 11.5 minutes\n", - "Step 487000 of 5000000; TPS: 6555.05; ETA: 11.5 minutes\n", - "Step 488000 of 5000000; TPS: 6555.28; ETA: 11.5 minutes\n", - "Step 489000 of 5000000; TPS: 6555.5; ETA: 11.5 minutes\n", - "Step 490000 of 5000000; TPS: 6555.71; ETA: 11.5 minutes\n", - "Step 491000 of 5000000; TPS: 6548.51; ETA: 11.5 minutes\n", - "Step 492000 of 5000000; TPS: 6548.72; ETA: 11.5 minutes\n", - "Step 493000 of 5000000; TPS: 6547.25; ETA: 11.5 minutes\n", - "Step 494000 of 5000000; TPS: 6547.48; ETA: 11.5 minutes\n", - "Step 495000 of 5000000; TPS: 6547.72; ETA: 11.5 minutes\n", - "Step 496000 of 5000000; TPS: 6547.95; ETA: 11.5 minutes\n", - "Step 497000 of 5000000; TPS: 6548.18; ETA: 11.5 minutes\n", - "Step 498000 of 5000000; TPS: 6548.39; ETA: 11.5 minutes\n", - "Step 499000 of 5000000; TPS: 6548.52; ETA: 11.5 minutes\n", - "Step 500000 of 5000000; TPS: 6548.6; ETA: 11.5 minutes\n", - "Step 501000 of 5000000; TPS: 6548.84; ETA: 11.4 minutes\n", - "Step 502000 of 5000000; TPS: 6549.07; ETA: 11.4 minutes\n", - "Step 503000 of 5000000; TPS: 6549.31; ETA: 11.4 minutes\n", - "Step 504000 of 5000000; TPS: 6549.54; ETA: 11.4 minutes\n", - "Step 505000 of 5000000; TPS: 6549.77; ETA: 11.4 minutes\n", - "Step 506000 of 5000000; TPS: 6550.0; ETA: 11.4 minutes\n", - "Step 507000 of 5000000; TPS: 6550.25; ETA: 11.4 minutes\n", - "Step 508000 of 5000000; TPS: 6550.47; ETA: 11.4 minutes\n", - "Step 509000 of 5000000; TPS: 6550.7; ETA: 11.4 minutes\n", - "Step 510000 of 5000000; TPS: 6550.91; ETA: 11.4 minutes\n", - "Step 511000 of 5000000; TPS: 6551.13; ETA: 11.4 minutes\n", - "Step 512000 of 5000000; TPS: 6551.36; ETA: 11.4 minutes\n", - "Step 513000 of 5000000; TPS: 6551.57; ETA: 11.4 minutes\n", - "Step 514000 of 5000000; TPS: 6551.82; ETA: 11.4 minutes\n", - "Step 515000 of 5000000; TPS: 6552.05; ETA: 11.4 minutes\n", - "Step 516000 of 5000000; TPS: 6552.26; ETA: 11.4 minutes\n", - "Step 517000 of 5000000; TPS: 6552.47; ETA: 11.4 minutes\n", - "Step 518000 of 5000000; TPS: 6552.7; ETA: 11.4 minutes\n", - "Step 519000 of 5000000; TPS: 6552.89; ETA: 11.4 minutes\n", - "Step 520000 of 5000000; TPS: 6553.1; ETA: 11.4 minutes\n", - "Step 521000 of 5000000; TPS: 6553.32; ETA: 11.4 minutes\n", - "Step 522000 of 5000000; TPS: 6553.54; ETA: 11.4 minutes\n", - "Step 523000 of 5000000; TPS: 6553.77; ETA: 11.4 minutes\n", - "Step 524000 of 5000000; TPS: 6553.99; ETA: 11.4 minutes\n", - "Step 525000 of 5000000; TPS: 6554.19; ETA: 11.4 minutes\n", - "Step 526000 of 5000000; TPS: 6554.3; ETA: 11.4 minutes\n", - "Step 527000 of 5000000; TPS: 6554.48; ETA: 11.4 minutes\n", - "Step 528000 of 5000000; TPS: 6554.69; ETA: 11.4 minutes\n", - "Step 529000 of 5000000; TPS: 6554.9; ETA: 11.4 minutes\n", - "Step 530000 of 5000000; TPS: 6555.12; ETA: 11.4 minutes\n", - "Step 531000 of 5000000; TPS: 6555.34; ETA: 11.4 minutes\n", - "Step 532000 of 5000000; TPS: 6555.53; ETA: 11.4 minutes\n", - "Step 533000 of 5000000; TPS: 6555.72; ETA: 11.4 minutes\n", - "Step 534000 of 5000000; TPS: 6555.91; ETA: 11.4 minutes\n", - "Step 535000 of 5000000; TPS: 6556.1; ETA: 11.4 minutes\n", - "Step 536000 of 5000000; TPS: 6556.28; ETA: 11.3 minutes\n", - "Step 537000 of 5000000; TPS: 6556.49; ETA: 11.3 minutes\n", - "Step 538000 of 5000000; TPS: 6556.68; ETA: 11.3 minutes\n", - "Step 539000 of 5000000; TPS: 6556.85; ETA: 11.3 minutes\n", - "Step 540000 of 5000000; TPS: 6556.87; ETA: 11.3 minutes\n", - "Step 541000 of 5000000; TPS: 6557.05; ETA: 11.3 minutes\n", - "Step 542000 of 5000000; TPS: 6557.22; ETA: 11.3 minutes\n", - "Step 543000 of 5000000; TPS: 6557.41; ETA: 11.3 minutes\n", - "Step 544000 of 5000000; TPS: 6557.59; ETA: 11.3 minutes\n", - "Step 545000 of 5000000; TPS: 6557.76; ETA: 11.3 minutes\n", - "Step 546000 of 5000000; TPS: 6557.95; ETA: 11.3 minutes\n", - "Step 547000 of 5000000; TPS: 6551.77; ETA: 11.3 minutes\n", - "Step 548000 of 5000000; TPS: 6551.88; ETA: 11.3 minutes\n", - "Step 549000 of 5000000; TPS: 6552.02; ETA: 11.3 minutes\n", - "Step 550000 of 5000000; TPS: 6552.19; ETA: 11.3 minutes\n", - "Step 551000 of 5000000; TPS: 6552.37; ETA: 11.3 minutes\n", - "Step 552000 of 5000000; TPS: 6552.55; ETA: 11.3 minutes\n", - "Step 553000 of 5000000; TPS: 6552.72; ETA: 11.3 minutes\n", - "Step 554000 of 5000000; TPS: 6552.92; ETA: 11.3 minutes\n", - "Step 555000 of 5000000; TPS: 6553.11; ETA: 11.3 minutes\n", - "Step 556000 of 5000000; TPS: 6553.28; ETA: 11.3 minutes\n", - "Step 557000 of 5000000; TPS: 6553.45; ETA: 11.3 minutes\n", - "Step 558000 of 5000000; TPS: 6553.65; ETA: 11.3 minutes\n", - "Step 559000 of 5000000; TPS: 6552.89; ETA: 11.3 minutes\n", - "Step 560000 of 5000000; TPS: 6553.06; ETA: 11.3 minutes\n", - "Step 561000 of 5000000; TPS: 6553.23; ETA: 11.3 minutes\n", - "Step 562000 of 5000000; TPS: 6553.42; ETA: 11.3 minutes\n", - "Step 563000 of 5000000; TPS: 6553.59; ETA: 11.3 minutes\n", - "Step 564000 of 5000000; TPS: 6553.8; ETA: 11.3 minutes\n", - "Step 565000 of 5000000; TPS: 6553.87; ETA: 11.3 minutes\n", - "Step 566000 of 5000000; TPS: 6553.89; ETA: 11.3 minutes\n", - "Step 567000 of 5000000; TPS: 6554.04; ETA: 11.3 minutes\n", - "Step 568000 of 5000000; TPS: 6554.21; ETA: 11.3 minutes\n", - "Step 569000 of 5000000; TPS: 6554.36; ETA: 11.3 minutes\n", - "Step 570000 of 5000000; TPS: 6554.53; ETA: 11.3 minutes\n", - "Step 571000 of 5000000; TPS: 6554.69; ETA: 11.3 minutes\n", - "Step 572000 of 5000000; TPS: 6554.68; ETA: 11.3 minutes\n", - "Step 573000 of 5000000; TPS: 6554.85; ETA: 11.3 minutes\n", - "Step 574000 of 5000000; TPS: 6555.01; ETA: 11.3 minutes\n", - "Step 575000 of 5000000; TPS: 6555.19; ETA: 11.3 minutes\n", - "Step 576000 of 5000000; TPS: 6555.33; ETA: 11.2 minutes\n", - "Step 577000 of 5000000; TPS: 6555.51; ETA: 11.2 minutes\n", - "Step 578000 of 5000000; TPS: 6555.7; ETA: 11.2 minutes\n", - "Step 579000 of 5000000; TPS: 6555.86; ETA: 11.2 minutes\n", - "Step 580000 of 5000000; TPS: 6556.03; ETA: 11.2 minutes\n", - "Step 581000 of 5000000; TPS: 6556.21; ETA: 11.2 minutes\n", - "Step 582000 of 5000000; TPS: 6556.33; ETA: 11.2 minutes\n", - "Step 583000 of 5000000; TPS: 6556.46; ETA: 11.2 minutes\n", - "Step 584000 of 5000000; TPS: 6556.59; ETA: 11.2 minutes\n", - "Step 585000 of 5000000; TPS: 6556.7; ETA: 11.2 minutes\n", - "Step 586000 of 5000000; TPS: 6556.77; ETA: 11.2 minutes\n", - "Step 587000 of 5000000; TPS: 6556.92; ETA: 11.2 minutes\n", - "Step 588000 of 5000000; TPS: 6556.99; ETA: 11.2 minutes\n", - "Step 589000 of 5000000; TPS: 6557.07; ETA: 11.2 minutes\n", - "Step 590000 of 5000000; TPS: 6557.23; ETA: 11.2 minutes\n", - "Step 591000 of 5000000; TPS: 6557.32; ETA: 11.2 minutes\n", - "Step 592000 of 5000000; TPS: 6557.41; ETA: 11.2 minutes\n", - "Step 593000 of 5000000; TPS: 6557.56; ETA: 11.2 minutes\n", - "Step 594000 of 5000000; TPS: 6557.73; ETA: 11.2 minutes\n", - "Step 595000 of 5000000; TPS: 6557.87; ETA: 11.2 minutes\n", - "Step 596000 of 5000000; TPS: 6557.91; ETA: 11.2 minutes\n", - "Step 597000 of 5000000; TPS: 6558.0; ETA: 11.2 minutes\n", - "Step 598000 of 5000000; TPS: 6557.98; ETA: 11.2 minutes\n", - "Step 599000 of 5000000; TPS: 6558.13; ETA: 11.2 minutes\n", - "Step 600000 of 5000000; TPS: 6558.23; ETA: 11.2 minutes\n", - "Step 601000 of 5000000; TPS: 6558.32; ETA: 11.2 minutes\n", - "Step 602000 of 5000000; TPS: 6552.9; ETA: 11.2 minutes\n", - "Step 603000 of 5000000; TPS: 6553.06; ETA: 11.2 minutes\n", - "Step 604000 of 5000000; TPS: 6553.2; ETA: 11.2 minutes\n", - "Step 605000 of 5000000; TPS: 6553.2; ETA: 11.2 minutes\n", - "Step 606000 of 5000000; TPS: 6553.3; ETA: 11.2 minutes\n", - "Step 607000 of 5000000; TPS: 6553.3; ETA: 11.2 minutes\n", - "Step 608000 of 5000000; TPS: 6553.19; ETA: 11.2 minutes\n", - "Step 609000 of 5000000; TPS: 6553.37; ETA: 11.2 minutes\n", - "Step 610000 of 5000000; TPS: 6553.54; ETA: 11.2 minutes\n", - "Step 611000 of 5000000; TPS: 6553.7; ETA: 11.2 minutes\n", - "Step 612000 of 5000000; TPS: 6553.8; ETA: 11.2 minutes\n", - "Step 613000 of 5000000; TPS: 6553.92; ETA: 11.2 minutes\n", - "Step 614000 of 5000000; TPS: 6554.07; ETA: 11.2 minutes\n", - "Step 615000 of 5000000; TPS: 6554.21; ETA: 11.2 minutes\n", - "Step 616000 of 5000000; TPS: 6554.38; ETA: 11.1 minutes\n", - "Step 617000 of 5000000; TPS: 6554.54; ETA: 11.1 minutes\n", - "Step 618000 of 5000000; TPS: 6554.7; ETA: 11.1 minutes\n", - "Step 619000 of 5000000; TPS: 6554.77; ETA: 11.1 minutes\n", - "Step 620000 of 5000000; TPS: 6554.92; ETA: 11.1 minutes\n", - "Step 621000 of 5000000; TPS: 6555.09; ETA: 11.1 minutes\n", - "Step 622000 of 5000000; TPS: 6555.26; ETA: 11.1 minutes\n", - "Step 623000 of 5000000; TPS: 6555.38; ETA: 11.1 minutes\n", - "Step 624000 of 5000000; TPS: 6555.54; ETA: 11.1 minutes\n", - "Step 625000 of 5000000; TPS: 6554.9; ETA: 11.1 minutes\n", - "Step 626000 of 5000000; TPS: 6555.06; ETA: 11.1 minutes\n", - "Step 627000 of 5000000; TPS: 6555.24; ETA: 11.1 minutes\n", - "Step 628000 of 5000000; TPS: 6555.4; ETA: 11.1 minutes\n", - "Step 629000 of 5000000; TPS: 6555.57; ETA: 11.1 minutes\n", - "Step 630000 of 5000000; TPS: 6555.74; ETA: 11.1 minutes\n", - "Step 631000 of 5000000; TPS: 6555.91; ETA: 11.1 minutes\n", - "Step 632000 of 5000000; TPS: 6556.07; ETA: 11.1 minutes\n", - "Step 633000 of 5000000; TPS: 6556.24; ETA: 11.1 minutes\n", - "Step 634000 of 5000000; TPS: 6556.41; ETA: 11.1 minutes\n", - "Step 635000 of 5000000; TPS: 6556.49; ETA: 11.1 minutes\n", - "Step 636000 of 5000000; TPS: 6556.66; ETA: 11.1 minutes\n", - "Step 637000 of 5000000; TPS: 6556.82; ETA: 11.1 minutes\n", - "Step 638000 of 5000000; TPS: 6556.99; ETA: 11.1 minutes\n", - "Step 639000 of 5000000; TPS: 6557.16; ETA: 11.1 minutes\n", - "Step 640000 of 5000000; TPS: 6557.32; ETA: 11.1 minutes\n", - "Step 641000 of 5000000; TPS: 6557.5; ETA: 11.1 minutes\n", - "Step 642000 of 5000000; TPS: 6557.66; ETA: 11.1 minutes\n", - "Step 643000 of 5000000; TPS: 6557.82; ETA: 11.1 minutes\n", - "Step 644000 of 5000000; TPS: 6557.94; ETA: 11.1 minutes\n", - "Step 645000 of 5000000; TPS: 6558.05; ETA: 11.1 minutes\n", - "Step 646000 of 5000000; TPS: 6558.2; ETA: 11.1 minutes\n", - "Step 647000 of 5000000; TPS: 6558.34; ETA: 11.1 minutes\n", - "Step 648000 of 5000000; TPS: 6558.5; ETA: 11.1 minutes\n", - "Step 649000 of 5000000; TPS: 6558.63; ETA: 11.1 minutes\n", - "Step 650000 of 5000000; TPS: 6558.77; ETA: 11.1 minutes\n", - "Step 651000 of 5000000; TPS: 6558.93; ETA: 11.1 minutes\n", - "Step 652000 of 5000000; TPS: 6559.07; ETA: 11.0 minutes\n", - "Step 653000 of 5000000; TPS: 6559.19; ETA: 11.0 minutes\n", - "Step 654000 of 5000000; TPS: 6559.32; ETA: 11.0 minutes\n", - "Step 655000 of 5000000; TPS: 6559.44; ETA: 11.0 minutes\n", - "Step 656000 of 5000000; TPS: 6559.59; ETA: 11.0 minutes\n", - "Step 657000 of 5000000; TPS: 6554.82; ETA: 11.0 minutes\n", - "Step 658000 of 5000000; TPS: 6554.92; ETA: 11.0 minutes\n", - "Step 659000 of 5000000; TPS: 6555.02; ETA: 11.0 minutes\n", - "Step 660000 of 5000000; TPS: 6555.14; ETA: 11.0 minutes\n", - "Step 661000 of 5000000; TPS: 6555.29; ETA: 11.0 minutes\n", - "Step 662000 of 5000000; TPS: 6555.45; ETA: 11.0 minutes\n", - "Step 663000 of 5000000; TPS: 6555.6; ETA: 11.0 minutes\n", - "Step 664000 of 5000000; TPS: 6555.64; ETA: 11.0 minutes\n", - "Step 665000 of 5000000; TPS: 6555.46; ETA: 11.0 minutes\n", - "Step 666000 of 5000000; TPS: 6555.31; ETA: 11.0 minutes\n", - "Step 667000 of 5000000; TPS: 6555.37; ETA: 11.0 minutes\n", - "Step 668000 of 5000000; TPS: 6555.49; ETA: 11.0 minutes\n", - "Step 669000 of 5000000; TPS: 6555.62; ETA: 11.0 minutes\n", - "Step 670000 of 5000000; TPS: 6555.76; ETA: 11.0 minutes\n", - "Step 671000 of 5000000; TPS: 6555.93; ETA: 11.0 minutes\n", - "Step 672000 of 5000000; TPS: 6556.08; ETA: 11.0 minutes\n", - "Step 673000 of 5000000; TPS: 6556.22; ETA: 11.0 minutes\n", - "Step 674000 of 5000000; TPS: 6556.36; ETA: 11.0 minutes\n", - "Step 675000 of 5000000; TPS: 6556.48; ETA: 11.0 minutes\n", - "Step 676000 of 5000000; TPS: 6556.61; ETA: 11.0 minutes\n", - "Step 677000 of 5000000; TPS: 6556.75; ETA: 11.0 minutes\n", - "Step 678000 of 5000000; TPS: 6556.86; ETA: 11.0 minutes\n", - "Step 679000 of 5000000; TPS: 6556.97; ETA: 11.0 minutes\n", - "Step 680000 of 5000000; TPS: 6557.1; ETA: 11.0 minutes\n", - "Step 681000 of 5000000; TPS: 6557.23; ETA: 11.0 minutes\n", - "Step 682000 of 5000000; TPS: 6557.38; ETA: 11.0 minutes\n", - "Step 683000 of 5000000; TPS: 6557.54; ETA: 11.0 minutes\n", - "Step 684000 of 5000000; TPS: 6557.69; ETA: 11.0 minutes\n", - "Step 685000 of 5000000; TPS: 6557.83; ETA: 11.0 minutes\n", - "Step 686000 of 5000000; TPS: 6557.97; ETA: 11.0 minutes\n", - "Step 687000 of 5000000; TPS: 6558.11; ETA: 11.0 minutes\n", - "Step 688000 of 5000000; TPS: 6558.25; ETA: 11.0 minutes\n", - "Step 689000 of 5000000; TPS: 6558.38; ETA: 11.0 minutes\n", - "Step 690000 of 5000000; TPS: 6558.51; ETA: 11.0 minutes\n", - "Step 691000 of 5000000; TPS: 6557.94; ETA: 11.0 minutes\n", - "Step 692000 of 5000000; TPS: 6558.11; ETA: 10.9 minutes\n", - "Step 693000 of 5000000; TPS: 6558.25; ETA: 10.9 minutes\n", - "Step 694000 of 5000000; TPS: 6558.37; ETA: 10.9 minutes\n", - "Step 695000 of 5000000; TPS: 6558.5; ETA: 10.9 minutes\n", - "Step 696000 of 5000000; TPS: 6558.63; ETA: 10.9 minutes\n", - "Step 697000 of 5000000; TPS: 6558.78; ETA: 10.9 minutes\n", - "Step 698000 of 5000000; TPS: 6558.94; ETA: 10.9 minutes\n", - "Step 699000 of 5000000; TPS: 6559.08; ETA: 10.9 minutes\n", - "Step 700000 of 5000000; TPS: 6559.21; ETA: 10.9 minutes\n", - "Step 701000 of 5000000; TPS: 6559.36; ETA: 10.9 minutes\n", - "Step 702000 of 5000000; TPS: 6559.49; ETA: 10.9 minutes\n", - "Step 703000 of 5000000; TPS: 6559.63; ETA: 10.9 minutes\n", - "Step 704000 of 5000000; TPS: 6559.75; ETA: 10.9 minutes\n", - "Step 705000 of 5000000; TPS: 6559.86; ETA: 10.9 minutes\n", - "Step 706000 of 5000000; TPS: 6560.0; ETA: 10.9 minutes\n", - "Step 707000 of 5000000; TPS: 6560.11; ETA: 10.9 minutes\n", - "Step 708000 of 5000000; TPS: 6560.23; ETA: 10.9 minutes\n", - "Step 709000 of 5000000; TPS: 6560.38; ETA: 10.9 minutes\n", - "Step 710000 of 5000000; TPS: 6560.53; ETA: 10.9 minutes\n", - "Step 711000 of 5000000; TPS: 6560.67; ETA: 10.9 minutes\n", - "Step 712000 of 5000000; TPS: 6556.46; ETA: 10.9 minutes\n", - "Step 713000 of 5000000; TPS: 6556.6; ETA: 10.9 minutes\n", - "Step 714000 of 5000000; TPS: 6556.74; ETA: 10.9 minutes\n", - "Step 715000 of 5000000; TPS: 6556.85; ETA: 10.9 minutes\n", - "Step 716000 of 5000000; TPS: 6556.99; ETA: 10.9 minutes\n", - "Step 717000 of 5000000; TPS: 6557.14; ETA: 10.9 minutes\n", - "Step 718000 of 5000000; TPS: 6557.28; ETA: 10.9 minutes\n", - "Step 719000 of 5000000; TPS: 6557.42; ETA: 10.9 minutes\n", - "Step 720000 of 5000000; TPS: 6557.56; ETA: 10.9 minutes\n", - "Step 721000 of 5000000; TPS: 6557.7; ETA: 10.9 minutes\n", - "Step 722000 of 5000000; TPS: 6557.83; ETA: 10.9 minutes\n", - "Step 723000 of 5000000; TPS: 6557.87; ETA: 10.9 minutes\n", - "Step 724000 of 5000000; TPS: 6558.01; ETA: 10.9 minutes\n", - "Step 725000 of 5000000; TPS: 6558.14; ETA: 10.9 minutes\n", - "Step 726000 of 5000000; TPS: 6558.27; ETA: 10.9 minutes\n", - "Step 727000 of 5000000; TPS: 6558.42; ETA: 10.9 minutes\n", - "Step 728000 of 5000000; TPS: 6558.55; ETA: 10.9 minutes\n", - "Step 729000 of 5000000; TPS: 6558.68; ETA: 10.9 minutes\n", - "Step 730000 of 5000000; TPS: 6558.82; ETA: 10.9 minutes\n", - "Step 731000 of 5000000; TPS: 6558.96; ETA: 10.8 minutes\n", - "Step 732000 of 5000000; TPS: 6559.03; ETA: 10.8 minutes\n", - "Step 733000 of 5000000; TPS: 6559.16; ETA: 10.8 minutes\n", - "Step 734000 of 5000000; TPS: 6559.29; ETA: 10.8 minutes\n", - "Step 735000 of 5000000; TPS: 6559.44; ETA: 10.8 minutes\n", - "Step 736000 of 5000000; TPS: 6559.57; ETA: 10.8 minutes\n", - "Step 737000 of 5000000; TPS: 6559.71; ETA: 10.8 minutes\n", - "Step 738000 of 5000000; TPS: 6559.84; ETA: 10.8 minutes\n", - "Step 739000 of 5000000; TPS: 6559.97; ETA: 10.8 minutes\n", - "Step 740000 of 5000000; TPS: 6560.11; ETA: 10.8 minutes\n", - "Step 741000 of 5000000; TPS: 6560.24; ETA: 10.8 minutes\n", - "Step 742000 of 5000000; TPS: 6560.38; ETA: 10.8 minutes\n", - "Step 743000 of 5000000; TPS: 6560.52; ETA: 10.8 minutes\n", - "Step 744000 of 5000000; TPS: 6560.65; ETA: 10.8 minutes\n", - "Step 745000 of 5000000; TPS: 6560.78; ETA: 10.8 minutes\n", - "Step 746000 of 5000000; TPS: 6560.91; ETA: 10.8 minutes\n", - "Step 747000 of 5000000; TPS: 6561.05; ETA: 10.8 minutes\n", - "Step 748000 of 5000000; TPS: 6561.18; ETA: 10.8 minutes\n", - "Step 749000 of 5000000; TPS: 6561.32; ETA: 10.8 minutes\n", - "Step 750000 of 5000000; TPS: 6561.44; ETA: 10.8 minutes\n", - "Step 751000 of 5000000; TPS: 6561.57; ETA: 10.8 minutes\n", - "Step 752000 of 5000000; TPS: 6561.71; ETA: 10.8 minutes\n", - "Step 753000 of 5000000; TPS: 6561.83; ETA: 10.8 minutes\n", - "Step 754000 of 5000000; TPS: 6561.96; ETA: 10.8 minutes\n", - "Step 755000 of 5000000; TPS: 6562.09; ETA: 10.8 minutes\n", - "Step 756000 of 5000000; TPS: 6562.22; ETA: 10.8 minutes\n", - "Step 757000 of 5000000; TPS: 6562.33; ETA: 10.8 minutes\n", - "Step 758000 of 5000000; TPS: 6561.79; ETA: 10.8 minutes\n", - "Step 759000 of 5000000; TPS: 6561.92; ETA: 10.8 minutes\n", - "Step 760000 of 5000000; TPS: 6562.05; ETA: 10.8 minutes\n", - "Step 761000 of 5000000; TPS: 6562.18; ETA: 10.8 minutes\n", - "Step 762000 of 5000000; TPS: 6562.3; ETA: 10.8 minutes\n", - "Step 763000 of 5000000; TPS: 6562.41; ETA: 10.8 minutes\n", - "Step 764000 of 5000000; TPS: 6562.53; ETA: 10.8 minutes\n", - "Step 765000 of 5000000; TPS: 6562.65; ETA: 10.8 minutes\n", - "Step 766000 of 5000000; TPS: 6562.77; ETA: 10.8 minutes\n", - "Step 767000 of 5000000; TPS: 6558.9; ETA: 10.8 minutes\n", - "Step 768000 of 5000000; TPS: 6559.0; ETA: 10.8 minutes\n", - "Step 769000 of 5000000; TPS: 6559.12; ETA: 10.8 minutes\n", - "Step 770000 of 5000000; TPS: 6559.26; ETA: 10.7 minutes\n", - "Step 771000 of 5000000; TPS: 6559.38; ETA: 10.7 minutes\n", - "Step 772000 of 5000000; TPS: 6559.5; ETA: 10.7 minutes\n", - "Step 773000 of 5000000; TPS: 6559.64; ETA: 10.7 minutes\n", - "Step 774000 of 5000000; TPS: 6559.77; ETA: 10.7 minutes\n", - "Step 775000 of 5000000; TPS: 6559.9; ETA: 10.7 minutes\n", - "Step 776000 of 5000000; TPS: 6560.02; ETA: 10.7 minutes\n", - "Step 777000 of 5000000; TPS: 6560.14; ETA: 10.7 minutes\n", - "Step 778000 of 5000000; TPS: 6560.28; ETA: 10.7 minutes\n", - "Step 779000 of 5000000; TPS: 6560.4; ETA: 10.7 minutes\n", - "Step 780000 of 5000000; TPS: 6560.52; ETA: 10.7 minutes\n", - "Step 781000 of 5000000; TPS: 6560.65; ETA: 10.7 minutes\n", - "Step 782000 of 5000000; TPS: 6560.76; ETA: 10.7 minutes\n", - "Step 783000 of 5000000; TPS: 6560.88; ETA: 10.7 minutes\n", - "Step 784000 of 5000000; TPS: 6561.0; ETA: 10.7 minutes\n", - "Step 785000 of 5000000; TPS: 6561.12; ETA: 10.7 minutes\n", - "Step 786000 of 5000000; TPS: 6561.24; ETA: 10.7 minutes\n", - "Step 787000 of 5000000; TPS: 6561.37; ETA: 10.7 minutes\n", - "Step 788000 of 5000000; TPS: 6561.49; ETA: 10.7 minutes\n", - "Step 789000 of 5000000; TPS: 6561.62; ETA: 10.7 minutes\n", - "Step 790000 of 5000000; TPS: 6561.75; ETA: 10.7 minutes\n", - "Step 791000 of 5000000; TPS: 6561.87; ETA: 10.7 minutes\n", - "Step 792000 of 5000000; TPS: 6562.0; ETA: 10.7 minutes\n", - "Step 793000 of 5000000; TPS: 6562.12; ETA: 10.7 minutes\n", - "Step 794000 of 5000000; TPS: 6562.24; ETA: 10.7 minutes\n", - "Step 795000 of 5000000; TPS: 6562.36; ETA: 10.7 minutes\n", - "Step 796000 of 5000000; TPS: 6562.47; ETA: 10.7 minutes\n", - "Step 797000 of 5000000; TPS: 6562.59; ETA: 10.7 minutes\n", - "Step 798000 of 5000000; TPS: 6562.7; ETA: 10.7 minutes\n", - "Step 799000 of 5000000; TPS: 6562.82; ETA: 10.7 minutes\n", - "Step 800000 of 5000000; TPS: 6562.94; ETA: 10.7 minutes\n", - "Step 801000 of 5000000; TPS: 6563.06; ETA: 10.7 minutes\n", - "Step 802000 of 5000000; TPS: 6563.17; ETA: 10.7 minutes\n", - "Step 803000 of 5000000; TPS: 6563.26; ETA: 10.7 minutes\n", - "Step 804000 of 5000000; TPS: 6563.38; ETA: 10.7 minutes\n", - "Step 805000 of 5000000; TPS: 6563.49; ETA: 10.7 minutes\n", - "Step 806000 of 5000000; TPS: 6563.59; ETA: 10.6 minutes\n", - "Step 807000 of 5000000; TPS: 6562.06; ETA: 10.6 minutes\n", - "Step 808000 of 5000000; TPS: 6562.14; ETA: 10.6 minutes\n", - "Step 809000 of 5000000; TPS: 6562.23; ETA: 10.6 minutes\n", - "Step 810000 of 5000000; TPS: 6562.35; ETA: 10.6 minutes\n", - "Step 811000 of 5000000; TPS: 6562.46; ETA: 10.6 minutes\n", - "Step 812000 of 5000000; TPS: 6562.56; ETA: 10.6 minutes\n", - "Step 813000 of 5000000; TPS: 6562.68; ETA: 10.6 minutes\n", - "Step 814000 of 5000000; TPS: 6562.79; ETA: 10.6 minutes\n", - "Step 815000 of 5000000; TPS: 6562.91; ETA: 10.6 minutes\n", - "Step 816000 of 5000000; TPS: 6563.02; ETA: 10.6 minutes\n", - "Step 817000 of 5000000; TPS: 6563.14; ETA: 10.6 minutes\n", - "Step 818000 of 5000000; TPS: 6563.25; ETA: 10.6 minutes\n", - "Step 819000 of 5000000; TPS: 6563.36; ETA: 10.6 minutes\n", - "Step 820000 of 5000000; TPS: 6563.48; ETA: 10.6 minutes\n", - "Step 821000 of 5000000; TPS: 6563.59; ETA: 10.6 minutes\n", - "Step 822000 of 5000000; TPS: 6560.1; ETA: 10.6 minutes\n", - "Step 823000 of 5000000; TPS: 6560.2; ETA: 10.6 minutes\n", - "Step 824000 of 5000000; TPS: 6559.72; ETA: 10.6 minutes\n", - "Step 825000 of 5000000; TPS: 6559.84; ETA: 10.6 minutes\n", - "Step 826000 of 5000000; TPS: 6559.96; ETA: 10.6 minutes\n", - "Step 827000 of 5000000; TPS: 6560.07; ETA: 10.6 minutes\n", - "Step 828000 of 5000000; TPS: 6560.13; ETA: 10.6 minutes\n", - "Step 829000 of 5000000; TPS: 6560.25; ETA: 10.6 minutes\n", - "Step 830000 of 5000000; TPS: 6560.37; ETA: 10.6 minutes\n", - "Step 831000 of 5000000; TPS: 6560.49; ETA: 10.6 minutes\n", - "Step 832000 of 5000000; TPS: 6560.61; ETA: 10.6 minutes\n", - "Step 833000 of 5000000; TPS: 6560.72; ETA: 10.6 minutes\n", - "Step 834000 of 5000000; TPS: 6560.81; ETA: 10.6 minutes\n", - "Step 835000 of 5000000; TPS: 6560.94; ETA: 10.6 minutes\n", - "Step 836000 of 5000000; TPS: 6561.05; ETA: 10.6 minutes\n", - "Step 837000 of 5000000; TPS: 6561.16; ETA: 10.6 minutes\n", - "Step 838000 of 5000000; TPS: 6561.28; ETA: 10.6 minutes\n", - "Step 839000 of 5000000; TPS: 6561.4; ETA: 10.6 minutes\n", - "Step 840000 of 5000000; TPS: 6561.5; ETA: 10.6 minutes\n", - "Step 841000 of 5000000; TPS: 6561.63; ETA: 10.6 minutes\n", - "Step 842000 of 5000000; TPS: 6561.73; ETA: 10.6 minutes\n", - "Step 843000 of 5000000; TPS: 6561.84; ETA: 10.6 minutes\n", - "Step 844000 of 5000000; TPS: 6561.96; ETA: 10.6 minutes\n", - "Step 845000 of 5000000; TPS: 6562.08; ETA: 10.6 minutes\n", - "Step 846000 of 5000000; TPS: 6562.19; ETA: 10.6 minutes\n", - "Step 847000 of 5000000; TPS: 6562.29; ETA: 10.5 minutes\n", - "Step 848000 of 5000000; TPS: 6562.41; ETA: 10.5 minutes\n", - "Step 849000 of 5000000; TPS: 6562.51; ETA: 10.5 minutes\n", - "Step 850000 of 5000000; TPS: 6562.63; ETA: 10.5 minutes\n", - "Step 851000 of 5000000; TPS: 6562.73; ETA: 10.5 minutes\n", - "Step 852000 of 5000000; TPS: 6562.85; ETA: 10.5 minutes\n", - "Step 853000 of 5000000; TPS: 6562.96; ETA: 10.5 minutes\n", - "Step 854000 of 5000000; TPS: 6563.06; ETA: 10.5 minutes\n", - "Step 855000 of 5000000; TPS: 6563.18; ETA: 10.5 minutes\n", - "Step 856000 of 5000000; TPS: 6563.28; ETA: 10.5 minutes\n", - "Step 857000 of 5000000; TPS: 6563.39; ETA: 10.5 minutes\n", - "Step 858000 of 5000000; TPS: 6563.49; ETA: 10.5 minutes\n", - "Step 859000 of 5000000; TPS: 6563.59; ETA: 10.5 minutes\n", - "Step 860000 of 5000000; TPS: 6563.7; ETA: 10.5 minutes\n", - "Step 861000 of 5000000; TPS: 6563.81; ETA: 10.5 minutes\n", - "Step 862000 of 5000000; TPS: 6563.92; ETA: 10.5 minutes\n", - "Step 863000 of 5000000; TPS: 6564.03; ETA: 10.5 minutes\n", - "Step 864000 of 5000000; TPS: 6564.11; ETA: 10.5 minutes\n", - "Step 865000 of 5000000; TPS: 6564.22; ETA: 10.5 minutes\n", - "Step 866000 of 5000000; TPS: 6564.32; ETA: 10.5 minutes\n", - "Step 867000 of 5000000; TPS: 6564.43; ETA: 10.5 minutes\n", - "Step 868000 of 5000000; TPS: 6564.53; ETA: 10.5 minutes\n", - "Step 869000 of 5000000; TPS: 6564.63; ETA: 10.5 minutes\n", - "Step 870000 of 5000000; TPS: 6564.73; ETA: 10.5 minutes\n", - "Step 871000 of 5000000; TPS: 6564.83; ETA: 10.5 minutes\n", - "Step 872000 of 5000000; TPS: 6564.87; ETA: 10.5 minutes\n", - "Step 873000 of 5000000; TPS: 6564.91; ETA: 10.5 minutes\n", - "Step 874000 of 5000000; TPS: 6565.0; ETA: 10.5 minutes\n", - "Step 875000 of 5000000; TPS: 6565.09; ETA: 10.5 minutes\n", - "Step 876000 of 5000000; TPS: 6565.19; ETA: 10.5 minutes\n", - "Step 877000 of 5000000; TPS: 6562.03; ETA: 10.5 minutes\n", - "Step 878000 of 5000000; TPS: 6562.13; ETA: 10.5 minutes\n", - "Step 879000 of 5000000; TPS: 6562.23; ETA: 10.5 minutes\n", - "Step 880000 of 5000000; TPS: 6562.32; ETA: 10.5 minutes\n", - "Step 881000 of 5000000; TPS: 6562.41; ETA: 10.5 minutes\n", - "Step 882000 of 5000000; TPS: 6562.52; ETA: 10.5 minutes\n", - "Step 883000 of 5000000; TPS: 6562.61; ETA: 10.5 minutes\n", - "Step 884000 of 5000000; TPS: 6562.71; ETA: 10.5 minutes\n", - "Step 885000 of 5000000; TPS: 6562.82; ETA: 10.5 minutes\n", - "Step 886000 of 5000000; TPS: 6562.92; ETA: 10.4 minutes\n", - "Step 887000 of 5000000; TPS: 6563.02; ETA: 10.4 minutes\n", - "Step 888000 of 5000000; TPS: 6563.13; ETA: 10.4 minutes\n", - "Step 889000 of 5000000; TPS: 6563.23; ETA: 10.4 minutes\n", - "Step 890000 of 5000000; TPS: 6563.33; ETA: 10.4 minutes\n", - "Step 891000 of 5000000; TPS: 6562.78; ETA: 10.4 minutes\n", - "Step 892000 of 5000000; TPS: 6562.89; ETA: 10.4 minutes\n", - "Step 893000 of 5000000; TPS: 6562.99; ETA: 10.4 minutes\n", - "Step 894000 of 5000000; TPS: 6563.1; ETA: 10.4 minutes\n", - "Step 895000 of 5000000; TPS: 6563.2; ETA: 10.4 minutes\n", - "Step 896000 of 5000000; TPS: 6563.3; ETA: 10.4 minutes\n", - "Step 897000 of 5000000; TPS: 6563.39; ETA: 10.4 minutes\n", - "Step 898000 of 5000000; TPS: 6563.48; ETA: 10.4 minutes\n", - "Step 899000 of 5000000; TPS: 6563.58; ETA: 10.4 minutes\n", - "Step 900000 of 5000000; TPS: 6563.68; ETA: 10.4 minutes\n", - "Step 901000 of 5000000; TPS: 6563.78; ETA: 10.4 minutes\n", - "Step 902000 of 5000000; TPS: 6563.87; ETA: 10.4 minutes\n", - "Step 903000 of 5000000; TPS: 6563.97; ETA: 10.4 minutes\n", - "Step 904000 of 5000000; TPS: 6564.07; ETA: 10.4 minutes\n", - "Step 905000 of 5000000; TPS: 6564.11; ETA: 10.4 minutes\n", - "Step 906000 of 5000000; TPS: 6564.2; ETA: 10.4 minutes\n", - "Step 907000 of 5000000; TPS: 6564.22; ETA: 10.4 minutes\n", - "Step 908000 of 5000000; TPS: 6564.22; ETA: 10.4 minutes\n", - "Step 909000 of 5000000; TPS: 6564.31; ETA: 10.4 minutes\n", - "Step 910000 of 5000000; TPS: 6564.21; ETA: 10.4 minutes\n", - "Step 911000 of 5000000; TPS: 6564.13; ETA: 10.4 minutes\n", - "Step 912000 of 5000000; TPS: 6564.17; ETA: 10.4 minutes\n", - "Step 913000 of 5000000; TPS: 6564.24; ETA: 10.4 minutes\n", - "Step 914000 of 5000000; TPS: 6564.31; ETA: 10.4 minutes\n", - "Step 915000 of 5000000; TPS: 6564.33; ETA: 10.4 minutes\n", - "Step 916000 of 5000000; TPS: 6564.3; ETA: 10.4 minutes\n", - "Step 917000 of 5000000; TPS: 6564.37; ETA: 10.4 minutes\n", - "Step 918000 of 5000000; TPS: 6564.45; ETA: 10.4 minutes\n", - "Step 919000 of 5000000; TPS: 6564.54; ETA: 10.4 minutes\n", - "Step 920000 of 5000000; TPS: 6564.64; ETA: 10.4 minutes\n", - "Step 921000 of 5000000; TPS: 6564.7; ETA: 10.4 minutes\n", - "Step 922000 of 5000000; TPS: 6564.77; ETA: 10.4 minutes\n", - "Step 923000 of 5000000; TPS: 6564.82; ETA: 10.4 minutes\n", - "Step 924000 of 5000000; TPS: 6564.88; ETA: 10.3 minutes\n", - "Step 925000 of 5000000; TPS: 6564.96; ETA: 10.3 minutes\n", - "Step 926000 of 5000000; TPS: 6565.05; ETA: 10.3 minutes\n", - "Step 927000 of 5000000; TPS: 6565.13; ETA: 10.3 minutes\n", - "Step 928000 of 5000000; TPS: 6565.21; ETA: 10.3 minutes\n", - "Step 929000 of 5000000; TPS: 6565.29; ETA: 10.3 minutes\n", - "Step 930000 of 5000000; TPS: 6565.37; ETA: 10.3 minutes\n", - "Step 931000 of 5000000; TPS: 6565.45; ETA: 10.3 minutes\n", - "Step 932000 of 5000000; TPS: 6562.53; ETA: 10.3 minutes\n", - "Step 933000 of 5000000; TPS: 6562.6; ETA: 10.3 minutes\n", - "Step 934000 of 5000000; TPS: 6562.69; ETA: 10.3 minutes\n", - "Step 935000 of 5000000; TPS: 6562.77; ETA: 10.3 minutes\n", - "Step 936000 of 5000000; TPS: 6562.85; ETA: 10.3 minutes\n", - "Step 937000 of 5000000; TPS: 6562.94; ETA: 10.3 minutes\n", - "Step 938000 of 5000000; TPS: 6563.04; ETA: 10.3 minutes\n", - "Step 939000 of 5000000; TPS: 6563.12; ETA: 10.3 minutes\n", - "Step 940000 of 5000000; TPS: 6563.1; ETA: 10.3 minutes\n", - "Step 941000 of 5000000; TPS: 6563.19; ETA: 10.3 minutes\n", - "Step 942000 of 5000000; TPS: 6563.29; ETA: 10.3 minutes\n", - "Step 943000 of 5000000; TPS: 6563.36; ETA: 10.3 minutes\n", - "Step 944000 of 5000000; TPS: 6563.45; ETA: 10.3 minutes\n", - "Step 945000 of 5000000; TPS: 6563.54; ETA: 10.3 minutes\n", - "Step 946000 of 5000000; TPS: 6563.63; ETA: 10.3 minutes\n", - "Step 947000 of 5000000; TPS: 6563.71; ETA: 10.3 minutes\n", - "Step 948000 of 5000000; TPS: 6563.8; ETA: 10.3 minutes\n", - "Step 949000 of 5000000; TPS: 6563.9; ETA: 10.3 minutes\n", - "Step 950000 of 5000000; TPS: 6563.98; ETA: 10.3 minutes\n", - "Step 951000 of 5000000; TPS: 6564.07; ETA: 10.3 minutes\n", - "Step 952000 of 5000000; TPS: 6564.16; ETA: 10.3 minutes\n", - "Step 953000 of 5000000; TPS: 6564.25; ETA: 10.3 minutes\n", - "Step 954000 of 5000000; TPS: 6564.33; ETA: 10.3 minutes\n", - "Step 955000 of 5000000; TPS: 6564.42; ETA: 10.3 minutes\n", - "Step 956000 of 5000000; TPS: 6564.5; ETA: 10.3 minutes\n", - "Step 957000 of 5000000; TPS: 6563.66; ETA: 10.3 minutes\n", - "Step 958000 of 5000000; TPS: 6563.75; ETA: 10.3 minutes\n", - "Step 959000 of 5000000; TPS: 6563.84; ETA: 10.3 minutes\n", - "Step 960000 of 5000000; TPS: 6563.93; ETA: 10.3 minutes\n", - "Step 961000 of 5000000; TPS: 6563.99; ETA: 10.3 minutes\n", - "Step 962000 of 5000000; TPS: 6564.08; ETA: 10.3 minutes\n", - "Step 963000 of 5000000; TPS: 6564.16; ETA: 10.3 minutes\n", - "Step 964000 of 5000000; TPS: 6564.22; ETA: 10.2 minutes\n", - "Step 965000 of 5000000; TPS: 6564.31; ETA: 10.2 minutes\n", - "Step 966000 of 5000000; TPS: 6564.39; ETA: 10.2 minutes\n", - "Step 967000 of 5000000; TPS: 6564.46; ETA: 10.2 minutes\n", - "Step 968000 of 5000000; TPS: 6564.56; ETA: 10.2 minutes\n", - "Step 969000 of 5000000; TPS: 6564.65; ETA: 10.2 minutes\n", - "Step 970000 of 5000000; TPS: 6564.73; ETA: 10.2 minutes\n", - "Step 971000 of 5000000; TPS: 6564.82; ETA: 10.2 minutes\n", - "Step 972000 of 5000000; TPS: 6564.9; ETA: 10.2 minutes\n", - "Step 973000 of 5000000; TPS: 6564.98; ETA: 10.2 minutes\n", - "Step 974000 of 5000000; TPS: 6565.06; ETA: 10.2 minutes\n", - "Step 975000 of 5000000; TPS: 6565.15; ETA: 10.2 minutes\n", - "Step 976000 of 5000000; TPS: 6565.24; ETA: 10.2 minutes\n", - "Step 977000 of 5000000; TPS: 6565.32; ETA: 10.2 minutes\n", - "Step 978000 of 5000000; TPS: 6565.41; ETA: 10.2 minutes\n", - "Step 979000 of 5000000; TPS: 6565.5; ETA: 10.2 minutes\n", - "Step 980000 of 5000000; TPS: 6565.57; ETA: 10.2 minutes\n", - "Step 981000 of 5000000; TPS: 6565.64; ETA: 10.2 minutes\n", - "Step 982000 of 5000000; TPS: 6565.71; ETA: 10.2 minutes\n", - "Step 983000 of 5000000; TPS: 6565.78; ETA: 10.2 minutes\n", - "Step 984000 of 5000000; TPS: 6565.86; ETA: 10.2 minutes\n", - "Step 985000 of 5000000; TPS: 6565.93; ETA: 10.2 minutes\n", - "Step 986000 of 5000000; TPS: 6566.0; ETA: 10.2 minutes\n", - "Step 987000 of 5000000; TPS: 6563.36; ETA: 10.2 minutes\n", - "Step 988000 of 5000000; TPS: 6563.42; ETA: 10.2 minutes\n", - "Step 989000 of 5000000; TPS: 6563.5; ETA: 10.2 minutes\n", - "Step 990000 of 5000000; TPS: 6563.58; ETA: 10.2 minutes\n", - "Step 991000 of 5000000; TPS: 6563.67; ETA: 10.2 minutes\n", - "Step 992000 of 5000000; TPS: 6563.74; ETA: 10.2 minutes\n", - "Step 993000 of 5000000; TPS: 6563.76; ETA: 10.2 minutes\n", - "Step 994000 of 5000000; TPS: 6563.85; ETA: 10.2 minutes\n", - "Step 995000 of 5000000; TPS: 6563.93; ETA: 10.2 minutes\n", - "Step 996000 of 5000000; TPS: 6564.0; ETA: 10.2 minutes\n", - "Step 997000 of 5000000; TPS: 6564.09; ETA: 10.2 minutes\n", - "Step 998000 of 5000000; TPS: 6564.16; ETA: 10.2 minutes\n", - "Step 999000 of 5000000; TPS: 6564.24; ETA: 10.2 minutes\n", - "Step 1000000 of 5000000; TPS: 6564.32; ETA: 10.2 minutes\n", - "Step 1001000 of 5000000; TPS: 6564.4; ETA: 10.2 minutes\n", - "Step 1002000 of 5000000; TPS: 6564.48; ETA: 10.2 minutes\n", - "Step 1003000 of 5000000; TPS: 6564.56; ETA: 10.1 minutes\n", - "Step 1004000 of 5000000; TPS: 6564.64; ETA: 10.1 minutes\n", - "Step 1005000 of 5000000; TPS: 6564.72; ETA: 10.1 minutes\n", - "Step 1006000 of 5000000; TPS: 6564.73; ETA: 10.1 minutes\n", - "Step 1007000 of 5000000; TPS: 6564.77; ETA: 10.1 minutes\n", - "Step 1008000 of 5000000; TPS: 6564.85; ETA: 10.1 minutes\n", - "Step 1009000 of 5000000; TPS: 6564.93; ETA: 10.1 minutes\n", - "Step 1010000 of 5000000; TPS: 6564.99; ETA: 10.1 minutes\n", - "Step 1011000 of 5000000; TPS: 6565.07; ETA: 10.1 minutes\n", - "Step 1012000 of 5000000; TPS: 6565.15; ETA: 10.1 minutes\n", - "Step 1013000 of 5000000; TPS: 6565.22; ETA: 10.1 minutes\n", - "Step 1014000 of 5000000; TPS: 6565.3; ETA: 10.1 minutes\n", - "Step 1015000 of 5000000; TPS: 6565.37; ETA: 10.1 minutes\n", - "Step 1016000 of 5000000; TPS: 6565.45; ETA: 10.1 minutes\n", - "Step 1017000 of 5000000; TPS: 6565.52; ETA: 10.1 minutes\n", - "Step 1018000 of 5000000; TPS: 6565.59; ETA: 10.1 minutes\n", - "Step 1019000 of 5000000; TPS: 6565.66; ETA: 10.1 minutes\n", - "Step 1020000 of 5000000; TPS: 6565.73; ETA: 10.1 minutes\n", - "Step 1021000 of 5000000; TPS: 6565.81; ETA: 10.1 minutes\n", - "Step 1022000 of 5000000; TPS: 6565.89; ETA: 10.1 minutes\n", - "Step 1023000 of 5000000; TPS: 6565.56; ETA: 10.1 minutes\n", - "Step 1024000 of 5000000; TPS: 6565.64; ETA: 10.1 minutes\n", - "Step 1025000 of 5000000; TPS: 6565.72; ETA: 10.1 minutes\n", - "Step 1026000 of 5000000; TPS: 6565.8; ETA: 10.1 minutes\n", - "Step 1027000 of 5000000; TPS: 6565.87; ETA: 10.1 minutes\n", - "Step 1028000 of 5000000; TPS: 6565.94; ETA: 10.1 minutes\n", - "Step 1029000 of 5000000; TPS: 6566.01; ETA: 10.1 minutes\n", - "Step 1030000 of 5000000; TPS: 6566.08; ETA: 10.1 minutes\n", - "Step 1031000 of 5000000; TPS: 6566.14; ETA: 10.1 minutes\n", - "Step 1032000 of 5000000; TPS: 6566.22; ETA: 10.1 minutes\n", - "Step 1033000 of 5000000; TPS: 6566.28; ETA: 10.1 minutes\n", - "Step 1034000 of 5000000; TPS: 6566.36; ETA: 10.1 minutes\n", - "Step 1035000 of 5000000; TPS: 6566.43; ETA: 10.1 minutes\n", - "Step 1036000 of 5000000; TPS: 6566.51; ETA: 10.1 minutes\n", - "Step 1037000 of 5000000; TPS: 6566.58; ETA: 10.1 minutes\n", - "Step 1038000 of 5000000; TPS: 6566.63; ETA: 10.1 minutes\n", - "Step 1039000 of 5000000; TPS: 6566.68; ETA: 10.1 minutes\n", - "Step 1040000 of 5000000; TPS: 6566.76; ETA: 10.1 minutes\n", - "Step 1041000 of 5000000; TPS: 6566.84; ETA: 10.0 minutes\n", - "Step 1042000 of 5000000; TPS: 6564.45; ETA: 10.0 minutes\n", - "Step 1043000 of 5000000; TPS: 6564.52; ETA: 10.0 minutes\n", - "Step 1044000 of 5000000; TPS: 6564.6; ETA: 10.0 minutes\n", - "Step 1045000 of 5000000; TPS: 6564.64; ETA: 10.0 minutes\n", - "Step 1046000 of 5000000; TPS: 6564.72; ETA: 10.0 minutes\n", - "Step 1047000 of 5000000; TPS: 6564.8; ETA: 10.0 minutes\n", - "Step 1048000 of 5000000; TPS: 6564.88; ETA: 10.0 minutes\n", - "Step 1049000 of 5000000; TPS: 6564.95; ETA: 10.0 minutes\n", - "Step 1050000 of 5000000; TPS: 6565.03; ETA: 10.0 minutes\n", - "Step 1051000 of 5000000; TPS: 6565.08; ETA: 10.0 minutes\n", - "Step 1052000 of 5000000; TPS: 6565.14; ETA: 10.0 minutes\n", - "Step 1053000 of 5000000; TPS: 6565.21; ETA: 10.0 minutes\n", - "Step 1054000 of 5000000; TPS: 6565.28; ETA: 10.0 minutes\n", - "Step 1055000 of 5000000; TPS: 6565.37; ETA: 10.0 minutes\n", - "Step 1056000 of 5000000; TPS: 6565.45; ETA: 10.0 minutes\n", - "Step 1057000 of 5000000; TPS: 6565.52; ETA: 10.0 minutes\n", - "Step 1058000 of 5000000; TPS: 6565.55; ETA: 10.0 minutes\n", - "Step 1059000 of 5000000; TPS: 6565.62; ETA: 10.0 minutes\n", - "Step 1060000 of 5000000; TPS: 6565.7; ETA: 10.0 minutes\n", - "Step 1061000 of 5000000; TPS: 6565.78; ETA: 10.0 minutes\n", - "Step 1062000 of 5000000; TPS: 6565.85; ETA: 10.0 minutes\n", - "Step 1063000 of 5000000; TPS: 6565.92; ETA: 10.0 minutes\n", - "Step 1064000 of 5000000; TPS: 6565.99; ETA: 10.0 minutes\n", - "Step 1065000 of 5000000; TPS: 6566.06; ETA: 10.0 minutes\n", - "Step 1066000 of 5000000; TPS: 6566.13; ETA: 10.0 minutes\n", - "Step 1067000 of 5000000; TPS: 6566.21; ETA: 10.0 minutes\n", - "Step 1068000 of 5000000; TPS: 6566.29; ETA: 10.0 minutes\n", - "Step 1069000 of 5000000; TPS: 6566.36; ETA: 10.0 minutes\n", - "Step 1070000 of 5000000; TPS: 6566.43; ETA: 10.0 minutes\n", - "Step 1071000 of 5000000; TPS: 6566.51; ETA: 10.0 minutes\n", - "Step 1072000 of 5000000; TPS: 6566.59; ETA: 10.0 minutes\n", - "Step 1073000 of 5000000; TPS: 6566.56; ETA: 10.0 minutes\n", - "Step 1074000 of 5000000; TPS: 6566.61; ETA: 10.0 minutes\n", - "Step 1075000 of 5000000; TPS: 6566.67; ETA: 10.0 minutes\n", - "Step 1076000 of 5000000; TPS: 6566.73; ETA: 10.0 minutes\n", - "Step 1077000 of 5000000; TPS: 6566.78; ETA: 10.0 minutes\n", - "Step 1078000 of 5000000; TPS: 6566.85; ETA: 10.0 minutes\n", - "Step 1079000 of 5000000; TPS: 6566.92; ETA: 10.0 minutes\n", - "Step 1080000 of 5000000; TPS: 6566.97; ETA: 9.9 minutes\n", - "Step 1081000 of 5000000; TPS: 6567.03; ETA: 9.9 minutes\n", - "Step 1082000 of 5000000; TPS: 6567.08; ETA: 9.9 minutes\n", - "Step 1083000 of 5000000; TPS: 6567.15; ETA: 9.9 minutes\n", - "Step 1084000 of 5000000; TPS: 6567.22; ETA: 9.9 minutes\n", - "Step 1085000 of 5000000; TPS: 6567.28; ETA: 9.9 minutes\n", - "Step 1086000 of 5000000; TPS: 6567.35; ETA: 9.9 minutes\n", - "Step 1087000 of 5000000; TPS: 6567.42; ETA: 9.9 minutes\n", - "Step 1088000 of 5000000; TPS: 6567.5; ETA: 9.9 minutes\n", - "Step 1089000 of 5000000; TPS: 6567.14; ETA: 9.9 minutes\n", - "Step 1090000 of 5000000; TPS: 6567.2; ETA: 9.9 minutes\n", - "Step 1091000 of 5000000; TPS: 6567.28; ETA: 9.9 minutes\n", - "Step 1092000 of 5000000; TPS: 6567.34; ETA: 9.9 minutes\n", - "Step 1093000 of 5000000; TPS: 6567.41; ETA: 9.9 minutes\n", - "Step 1094000 of 5000000; TPS: 6567.49; ETA: 9.9 minutes\n", - "Step 1095000 of 5000000; TPS: 6567.56; ETA: 9.9 minutes\n", - "Step 1096000 of 5000000; TPS: 6567.63; ETA: 9.9 minutes\n", - "Step 1097000 of 5000000; TPS: 6567.69; ETA: 9.9 minutes\n", - "Step 1098000 of 5000000; TPS: 6565.4; ETA: 9.9 minutes\n", - "Step 1099000 of 5000000; TPS: 6565.45; ETA: 9.9 minutes\n", - "Step 1100000 of 5000000; TPS: 6565.52; ETA: 9.9 minutes\n", - "Step 1101000 of 5000000; TPS: 6565.58; ETA: 9.9 minutes\n", - "Step 1102000 of 5000000; TPS: 6565.65; ETA: 9.9 minutes\n", - "Step 1103000 of 5000000; TPS: 6565.71; ETA: 9.9 minutes\n", - "Step 1104000 of 5000000; TPS: 6565.79; ETA: 9.9 minutes\n", - "Step 1105000 of 5000000; TPS: 6565.86; ETA: 9.9 minutes\n", - "Step 1106000 of 5000000; TPS: 6565.93; ETA: 9.9 minutes\n", - "Step 1107000 of 5000000; TPS: 6566.0; ETA: 9.9 minutes\n", - "Step 1108000 of 5000000; TPS: 6566.07; ETA: 9.9 minutes\n", - "Step 1109000 of 5000000; TPS: 6566.14; ETA: 9.9 minutes\n", - "Step 1110000 of 5000000; TPS: 6566.21; ETA: 9.9 minutes\n", - "Step 1111000 of 5000000; TPS: 6566.26; ETA: 9.9 minutes\n", - "Step 1112000 of 5000000; TPS: 6566.32; ETA: 9.9 minutes\n", - "Step 1113000 of 5000000; TPS: 6566.39; ETA: 9.9 minutes\n", - "Step 1114000 of 5000000; TPS: 6566.45; ETA: 9.9 minutes\n", - "Step 1115000 of 5000000; TPS: 6566.53; ETA: 9.9 minutes\n", - "Step 1116000 of 5000000; TPS: 6566.6; ETA: 9.9 minutes\n", - "Step 1117000 of 5000000; TPS: 6566.67; ETA: 9.9 minutes\n", - "Step 1118000 of 5000000; TPS: 6566.73; ETA: 9.9 minutes\n", - "Step 1119000 of 5000000; TPS: 6566.8; ETA: 9.9 minutes\n", - "Step 1120000 of 5000000; TPS: 6566.87; ETA: 9.8 minutes\n", - "Step 1121000 of 5000000; TPS: 6566.93; ETA: 9.8 minutes\n", - "Step 1122000 of 5000000; TPS: 6567.0; ETA: 9.8 minutes\n", - "Step 1123000 of 5000000; TPS: 6567.06; ETA: 9.8 minutes\n", - "Step 1124000 of 5000000; TPS: 6567.13; ETA: 9.8 minutes\n", - "Step 1125000 of 5000000; TPS: 6567.19; ETA: 9.8 minutes\n", - "Step 1126000 of 5000000; TPS: 6567.26; ETA: 9.8 minutes\n", - "Step 1127000 of 5000000; TPS: 6567.26; ETA: 9.8 minutes\n", - "Step 1128000 of 5000000; TPS: 6567.32; ETA: 9.8 minutes\n", - "Step 1129000 of 5000000; TPS: 6567.4; ETA: 9.8 minutes\n", - "Step 1130000 of 5000000; TPS: 6567.47; ETA: 9.8 minutes\n", - "Step 1131000 of 5000000; TPS: 6567.54; ETA: 9.8 minutes\n", - "Step 1132000 of 5000000; TPS: 6567.61; ETA: 9.8 minutes\n", - "Step 1133000 of 5000000; TPS: 6567.67; ETA: 9.8 minutes\n", - "Step 1134000 of 5000000; TPS: 6567.73; ETA: 9.8 minutes\n", - "Step 1135000 of 5000000; TPS: 6567.79; ETA: 9.8 minutes\n", - "Step 1136000 of 5000000; TPS: 6567.85; ETA: 9.8 minutes\n", - "Step 1137000 of 5000000; TPS: 6567.91; ETA: 9.8 minutes\n", - "Step 1138000 of 5000000; TPS: 6567.98; ETA: 9.8 minutes\n", - "Step 1139000 of 5000000; TPS: 6568.03; ETA: 9.8 minutes\n", - "Step 1140000 of 5000000; TPS: 6568.02; ETA: 9.8 minutes\n", - "Step 1141000 of 5000000; TPS: 6568.09; ETA: 9.8 minutes\n", - "Step 1142000 of 5000000; TPS: 6568.14; ETA: 9.8 minutes\n", - "Step 1143000 of 5000000; TPS: 6568.21; ETA: 9.8 minutes\n", - "Step 1144000 of 5000000; TPS: 6568.27; ETA: 9.8 minutes\n", - "Step 1145000 of 5000000; TPS: 6568.34; ETA: 9.8 minutes\n", - "Step 1146000 of 5000000; TPS: 6568.4; ETA: 9.8 minutes\n", - "Step 1147000 of 5000000; TPS: 6568.46; ETA: 9.8 minutes\n", - "Step 1148000 of 5000000; TPS: 6568.52; ETA: 9.8 minutes\n", - "Step 1149000 of 5000000; TPS: 6568.56; ETA: 9.8 minutes\n", - "Step 1150000 of 5000000; TPS: 6568.62; ETA: 9.8 minutes\n", - "Step 1151000 of 5000000; TPS: 6568.68; ETA: 9.8 minutes\n", - "Step 1152000 of 5000000; TPS: 6568.74; ETA: 9.8 minutes\n", - "Step 1153000 of 5000000; TPS: 6566.71; ETA: 9.8 minutes\n", - "Step 1154000 of 5000000; TPS: 6566.76; ETA: 9.8 minutes\n", - "Step 1155000 of 5000000; TPS: 6566.42; ETA: 9.8 minutes\n", - "Step 1156000 of 5000000; TPS: 6566.49; ETA: 9.8 minutes\n", - "Step 1157000 of 5000000; TPS: 6566.55; ETA: 9.8 minutes\n", - "Step 1158000 of 5000000; TPS: 6566.6; ETA: 9.8 minutes\n", - "Step 1159000 of 5000000; TPS: 6566.67; ETA: 9.7 minutes\n", - "Step 1160000 of 5000000; TPS: 6566.73; ETA: 9.7 minutes\n", - "Step 1161000 of 5000000; TPS: 6566.79; ETA: 9.7 minutes\n", - "Step 1162000 of 5000000; TPS: 6566.85; ETA: 9.7 minutes\n", - "Step 1163000 of 5000000; TPS: 6566.92; ETA: 9.7 minutes\n", - "Step 1164000 of 5000000; TPS: 6566.98; ETA: 9.7 minutes\n", - "Step 1165000 of 5000000; TPS: 6567.04; ETA: 9.7 minutes\n", - "Step 1166000 of 5000000; TPS: 6567.1; ETA: 9.7 minutes\n", - "Step 1167000 of 5000000; TPS: 6567.16; ETA: 9.7 minutes\n", - "Step 1168000 of 5000000; TPS: 6567.23; ETA: 9.7 minutes\n", - "Step 1169000 of 5000000; TPS: 6567.29; ETA: 9.7 minutes\n", - "Step 1170000 of 5000000; TPS: 6567.35; ETA: 9.7 minutes\n", - "Step 1171000 of 5000000; TPS: 6567.42; ETA: 9.7 minutes\n", - "Step 1172000 of 5000000; TPS: 6567.48; ETA: 9.7 minutes\n", - "Step 1173000 of 5000000; TPS: 6567.41; ETA: 9.7 minutes\n", - "Step 1174000 of 5000000; TPS: 6567.45; ETA: 9.7 minutes\n", - "Step 1175000 of 5000000; TPS: 6567.49; ETA: 9.7 minutes\n", - "Step 1176000 of 5000000; TPS: 6567.54; ETA: 9.7 minutes\n", - "Step 1177000 of 5000000; TPS: 6567.5; ETA: 9.7 minutes\n", - "Step 1178000 of 5000000; TPS: 6567.54; ETA: 9.7 minutes\n", - "Step 1179000 of 5000000; TPS: 6567.59; ETA: 9.7 minutes\n", - "Step 1180000 of 5000000; TPS: 6567.56; ETA: 9.7 minutes\n", - "Step 1181000 of 5000000; TPS: 6567.6; ETA: 9.7 minutes\n", - "Step 1182000 of 5000000; TPS: 6567.64; ETA: 9.7 minutes\n", - "Step 1183000 of 5000000; TPS: 6567.69; ETA: 9.7 minutes\n", - "Step 1184000 of 5000000; TPS: 6567.75; ETA: 9.7 minutes\n", - "Step 1185000 of 5000000; TPS: 6567.78; ETA: 9.7 minutes\n", - "Step 1186000 of 5000000; TPS: 6567.82; ETA: 9.7 minutes\n", - "Step 1187000 of 5000000; TPS: 6567.86; ETA: 9.7 minutes\n", - "Step 1188000 of 5000000; TPS: 6567.91; ETA: 9.7 minutes\n", - "Step 1189000 of 5000000; TPS: 6567.95; ETA: 9.7 minutes\n", - "Step 1190000 of 5000000; TPS: 6568.01; ETA: 9.7 minutes\n", - "Step 1191000 of 5000000; TPS: 6568.06; ETA: 9.7 minutes\n", - "Step 1192000 of 5000000; TPS: 6568.13; ETA: 9.7 minutes\n", - "Step 1193000 of 5000000; TPS: 6568.18; ETA: 9.7 minutes\n", - "Step 1194000 of 5000000; TPS: 6568.25; ETA: 9.7 minutes\n", - "Step 1195000 of 5000000; TPS: 6568.3; ETA: 9.7 minutes\n", - "Step 1196000 of 5000000; TPS: 6568.36; ETA: 9.7 minutes\n", - "Step 1197000 of 5000000; TPS: 6568.32; ETA: 9.6 minutes\n", - "Step 1198000 of 5000000; TPS: 6568.27; ETA: 9.6 minutes\n", - "Step 1199000 of 5000000; TPS: 6568.25; ETA: 9.6 minutes\n", - "Step 1200000 of 5000000; TPS: 6568.3; ETA: 9.6 minutes\n", - "Step 1201000 of 5000000; TPS: 6568.36; ETA: 9.6 minutes\n", - "Step 1202000 of 5000000; TPS: 6568.4; ETA: 9.6 minutes\n", - "Step 1203000 of 5000000; TPS: 6568.46; ETA: 9.6 minutes\n", - "Step 1204000 of 5000000; TPS: 6568.5; ETA: 9.6 minutes\n", - "Step 1205000 of 5000000; TPS: 6568.55; ETA: 9.6 minutes\n", - "Step 1206000 of 5000000; TPS: 6568.6; ETA: 9.6 minutes\n", - "Step 1207000 of 5000000; TPS: 6568.65; ETA: 9.6 minutes\n", - "Step 1208000 of 5000000; TPS: 6566.79; ETA: 9.6 minutes\n", - "Step 1209000 of 5000000; TPS: 6566.84; ETA: 9.6 minutes\n", - "Step 1210000 of 5000000; TPS: 6566.9; ETA: 9.6 minutes\n", - "Step 1211000 of 5000000; TPS: 6566.96; ETA: 9.6 minutes\n", - "Step 1212000 of 5000000; TPS: 6567.01; ETA: 9.6 minutes\n", - "Step 1213000 of 5000000; TPS: 6567.07; ETA: 9.6 minutes\n", - "Step 1214000 of 5000000; TPS: 6567.12; ETA: 9.6 minutes\n", - "Step 1215000 of 5000000; TPS: 6567.18; ETA: 9.6 minutes\n", - "Step 1216000 of 5000000; TPS: 6567.23; ETA: 9.6 minutes\n", - "Step 1217000 of 5000000; TPS: 6567.29; ETA: 9.6 minutes\n", - "Step 1218000 of 5000000; TPS: 6567.34; ETA: 9.6 minutes\n", - "Step 1219000 of 5000000; TPS: 6567.4; ETA: 9.6 minutes\n", - "Step 1220000 of 5000000; TPS: 6567.45; ETA: 9.6 minutes\n", - "Step 1221000 of 5000000; TPS: 6567.02; ETA: 9.6 minutes\n", - "Step 1222000 of 5000000; TPS: 6567.07; ETA: 9.6 minutes\n", - "Step 1223000 of 5000000; TPS: 6567.13; ETA: 9.6 minutes\n", - "Step 1224000 of 5000000; TPS: 6567.18; ETA: 9.6 minutes\n", - "Step 1225000 of 5000000; TPS: 6567.21; ETA: 9.6 minutes\n", - "Step 1226000 of 5000000; TPS: 6567.25; ETA: 9.6 minutes\n", - "Step 1227000 of 5000000; TPS: 6567.28; ETA: 9.6 minutes\n", - "Step 1228000 of 5000000; TPS: 6567.33; ETA: 9.6 minutes\n", - "Step 1229000 of 5000000; TPS: 6567.39; ETA: 9.6 minutes\n", - "Step 1230000 of 5000000; TPS: 6567.44; ETA: 9.6 minutes\n", - "Step 1231000 of 5000000; TPS: 6567.49; ETA: 9.6 minutes\n", - "Step 1232000 of 5000000; TPS: 6567.53; ETA: 9.6 minutes\n", - "Step 1233000 of 5000000; TPS: 6567.58; ETA: 9.6 minutes\n", - "Step 1234000 of 5000000; TPS: 6567.63; ETA: 9.6 minutes\n", - "Step 1235000 of 5000000; TPS: 6567.68; ETA: 9.6 minutes\n", - "Step 1236000 of 5000000; TPS: 6567.72; ETA: 9.6 minutes\n", - "Step 1237000 of 5000000; TPS: 6567.77; ETA: 9.5 minutes\n", - "Step 1238000 of 5000000; TPS: 6567.83; ETA: 9.5 minutes\n", - "Step 1239000 of 5000000; TPS: 6567.89; ETA: 9.5 minutes\n", - "Step 1240000 of 5000000; TPS: 6567.94; ETA: 9.5 minutes\n", - "Step 1241000 of 5000000; TPS: 6567.98; ETA: 9.5 minutes\n", - "Step 1242000 of 5000000; TPS: 6568.03; ETA: 9.5 minutes\n", - "Step 1243000 of 5000000; TPS: 6568.0; ETA: 9.5 minutes\n", - "Step 1244000 of 5000000; TPS: 6568.05; ETA: 9.5 minutes\n", - "Step 1245000 of 5000000; TPS: 6568.1; ETA: 9.5 minutes\n", - "Step 1246000 of 5000000; TPS: 6568.14; ETA: 9.5 minutes\n", - "Step 1247000 of 5000000; TPS: 6568.18; ETA: 9.5 minutes\n", - "Step 1248000 of 5000000; TPS: 6568.21; ETA: 9.5 minutes\n", - "Step 1249000 of 5000000; TPS: 6568.26; ETA: 9.5 minutes\n", - "Step 1250000 of 5000000; TPS: 6568.3; ETA: 9.5 minutes\n", - "Step 1251000 of 5000000; TPS: 6568.35; ETA: 9.5 minutes\n", - "Step 1252000 of 5000000; TPS: 6568.41; ETA: 9.5 minutes\n", - "Step 1253000 of 5000000; TPS: 6568.42; ETA: 9.5 minutes\n", - "Step 1254000 of 5000000; TPS: 6568.44; ETA: 9.5 minutes\n", - "Step 1255000 of 5000000; TPS: 6568.49; ETA: 9.5 minutes\n", - "Step 1256000 of 5000000; TPS: 6568.52; ETA: 9.5 minutes\n", - "Step 1257000 of 5000000; TPS: 6568.55; ETA: 9.5 minutes\n", - "Step 1258000 of 5000000; TPS: 6568.59; ETA: 9.5 minutes\n", - "Step 1259000 of 5000000; TPS: 6568.64; ETA: 9.5 minutes\n", - "Step 1260000 of 5000000; TPS: 6568.69; ETA: 9.5 minutes\n", - "Step 1261000 of 5000000; TPS: 6568.74; ETA: 9.5 minutes\n", - "Step 1262000 of 5000000; TPS: 6568.79; ETA: 9.5 minutes\n", - "Step 1263000 of 5000000; TPS: 6566.99; ETA: 9.5 minutes\n", - "Step 1264000 of 5000000; TPS: 6567.02; ETA: 9.5 minutes\n", - "Step 1265000 of 5000000; TPS: 6567.02; ETA: 9.5 minutes\n", - "Step 1266000 of 5000000; TPS: 6567.05; ETA: 9.5 minutes\n", - "Step 1267000 of 5000000; TPS: 6567.01; ETA: 9.5 minutes\n", - "Step 1268000 of 5000000; TPS: 6567.02; ETA: 9.5 minutes\n", - "Step 1269000 of 5000000; TPS: 6567.06; ETA: 9.5 minutes\n", - "Step 1270000 of 5000000; TPS: 6567.09; ETA: 9.5 minutes\n", - "Step 1271000 of 5000000; TPS: 6567.14; ETA: 9.5 minutes\n", - "Step 1272000 of 5000000; TPS: 6567.19; ETA: 9.5 minutes\n", - "Step 1273000 of 5000000; TPS: 6566.37; ETA: 9.5 minutes\n", - "Step 1274000 of 5000000; TPS: 6566.37; ETA: 9.5 minutes\n", - "Step 1275000 of 5000000; TPS: 6566.37; ETA: 9.5 minutes\n", - "Step 1276000 of 5000000; TPS: 6566.37; ETA: 9.5 minutes\n", - "Step 1277000 of 5000000; TPS: 6566.4; ETA: 9.4 minutes\n", - "Step 1278000 of 5000000; TPS: 6566.44; ETA: 9.4 minutes\n", - "Step 1279000 of 5000000; TPS: 6566.48; ETA: 9.4 minutes\n", - "Step 1280000 of 5000000; TPS: 6566.54; ETA: 9.4 minutes\n", - "Step 1281000 of 5000000; TPS: 6566.58; ETA: 9.4 minutes\n", - "Step 1282000 of 5000000; TPS: 6566.63; ETA: 9.4 minutes\n", - "Step 1283000 of 5000000; TPS: 6566.68; ETA: 9.4 minutes\n", - "Step 1284000 of 5000000; TPS: 6566.73; ETA: 9.4 minutes\n", - "Step 1285000 of 5000000; TPS: 6566.79; ETA: 9.4 minutes\n", - "Step 1286000 of 5000000; TPS: 6566.84; ETA: 9.4 minutes\n", - "Step 1287000 of 5000000; TPS: 6566.44; ETA: 9.4 minutes\n", - "Step 1288000 of 5000000; TPS: 6566.48; ETA: 9.4 minutes\n", - "Step 1289000 of 5000000; TPS: 6566.52; ETA: 9.4 minutes\n", - "Step 1290000 of 5000000; TPS: 6566.57; ETA: 9.4 minutes\n", - "Step 1291000 of 5000000; TPS: 6566.61; ETA: 9.4 minutes\n", - "Step 1292000 of 5000000; TPS: 6566.67; ETA: 9.4 minutes\n", - "Step 1293000 of 5000000; TPS: 6566.71; ETA: 9.4 minutes\n", - "Step 1294000 of 5000000; TPS: 6566.76; ETA: 9.4 minutes\n", - "Step 1295000 of 5000000; TPS: 6566.8; ETA: 9.4 minutes\n", - "Step 1296000 of 5000000; TPS: 6566.84; ETA: 9.4 minutes\n", - "Step 1297000 of 5000000; TPS: 6566.89; ETA: 9.4 minutes\n", - "Step 1298000 of 5000000; TPS: 6566.93; ETA: 9.4 minutes\n", - "Step 1299000 of 5000000; TPS: 6566.98; ETA: 9.4 minutes\n", - "Step 1300000 of 5000000; TPS: 6567.03; ETA: 9.4 minutes\n", - "Step 1301000 of 5000000; TPS: 6567.08; ETA: 9.4 minutes\n", - "Step 1302000 of 5000000; TPS: 6567.13; ETA: 9.4 minutes\n", - "Step 1303000 of 5000000; TPS: 6567.18; ETA: 9.4 minutes\n", - "Step 1304000 of 5000000; TPS: 6567.24; ETA: 9.4 minutes\n", - "Step 1305000 of 5000000; TPS: 6567.29; ETA: 9.4 minutes\n", - "Step 1306000 of 5000000; TPS: 6567.34; ETA: 9.4 minutes\n", - "Step 1307000 of 5000000; TPS: 6567.39; ETA: 9.4 minutes\n", - "Step 1308000 of 5000000; TPS: 6567.43; ETA: 9.4 minutes\n", - "Step 1309000 of 5000000; TPS: 6567.48; ETA: 9.4 minutes\n", - "Step 1310000 of 5000000; TPS: 6567.53; ETA: 9.4 minutes\n", - "Step 1311000 of 5000000; TPS: 6567.57; ETA: 9.4 minutes\n", - "Step 1312000 of 5000000; TPS: 6567.62; ETA: 9.4 minutes\n", - "Step 1313000 of 5000000; TPS: 6567.67; ETA: 9.4 minutes\n", - "Step 1314000 of 5000000; TPS: 6567.72; ETA: 9.4 minutes\n", - "Step 1315000 of 5000000; TPS: 6567.75; ETA: 9.4 minutes\n", - "Step 1316000 of 5000000; TPS: 6567.8; ETA: 9.3 minutes\n", - "Step 1317000 of 5000000; TPS: 6567.85; ETA: 9.3 minutes\n", - "Step 1318000 of 5000000; TPS: 6564.6; ETA: 9.3 minutes\n", - "Step 1319000 of 5000000; TPS: 6564.65; ETA: 9.3 minutes\n", - "Step 1320000 of 5000000; TPS: 6564.7; ETA: 9.3 minutes\n", - "Step 1321000 of 5000000; TPS: 6564.74; ETA: 9.3 minutes\n", - "Step 1322000 of 5000000; TPS: 6564.77; ETA: 9.3 minutes\n", - "Step 1323000 of 5000000; TPS: 6564.83; ETA: 9.3 minutes\n", - "Step 1324000 of 5000000; TPS: 6564.88; ETA: 9.3 minutes\n", - "Step 1325000 of 5000000; TPS: 6564.93; ETA: 9.3 minutes\n", - "Step 1326000 of 5000000; TPS: 6564.97; ETA: 9.3 minutes\n", - "Step 1327000 of 5000000; TPS: 6565.02; ETA: 9.3 minutes\n", - "Step 1328000 of 5000000; TPS: 6565.01; ETA: 9.3 minutes\n", - "Step 1329000 of 5000000; TPS: 6565.06; ETA: 9.3 minutes\n", - "Step 1330000 of 5000000; TPS: 6565.11; ETA: 9.3 minutes\n", - "Step 1331000 of 5000000; TPS: 6565.16; ETA: 9.3 minutes\n", - "Step 1332000 of 5000000; TPS: 6565.2; ETA: 9.3 minutes\n", - "Step 1333000 of 5000000; TPS: 6565.25; ETA: 9.3 minutes\n", - "Step 1334000 of 5000000; TPS: 6565.3; ETA: 9.3 minutes\n", - "Step 1335000 of 5000000; TPS: 6565.35; ETA: 9.3 minutes\n", - "Step 1336000 of 5000000; TPS: 6565.38; ETA: 9.3 minutes\n", - "Step 1337000 of 5000000; TPS: 6565.42; ETA: 9.3 minutes\n", - "Step 1338000 of 5000000; TPS: 6565.45; ETA: 9.3 minutes\n", - "Step 1339000 of 5000000; TPS: 6565.49; ETA: 9.3 minutes\n", - "Step 1340000 of 5000000; TPS: 6565.55; ETA: 9.3 minutes\n", - "Step 1341000 of 5000000; TPS: 6565.59; ETA: 9.3 minutes\n", - "Step 1342000 of 5000000; TPS: 6565.64; ETA: 9.3 minutes\n", - "Step 1343000 of 5000000; TPS: 6565.69; ETA: 9.3 minutes\n", - "Step 1344000 of 5000000; TPS: 6565.73; ETA: 9.3 minutes\n", - "Step 1345000 of 5000000; TPS: 6565.79; ETA: 9.3 minutes\n", - "Step 1346000 of 5000000; TPS: 6565.83; ETA: 9.3 minutes\n", - "Step 1347000 of 5000000; TPS: 6565.87; ETA: 9.3 minutes\n", - "Step 1348000 of 5000000; TPS: 6565.93; ETA: 9.3 minutes\n", - "Step 1349000 of 5000000; TPS: 6565.98; ETA: 9.3 minutes\n", - "Step 1350000 of 5000000; TPS: 6566.02; ETA: 9.3 minutes\n", - "Step 1351000 of 5000000; TPS: 6566.07; ETA: 9.3 minutes\n", - "Step 1352000 of 5000000; TPS: 6566.12; ETA: 9.3 minutes\n", - "Step 1353000 of 5000000; TPS: 6565.8; ETA: 9.3 minutes\n", - "Step 1354000 of 5000000; TPS: 6565.84; ETA: 9.3 minutes\n", - "Step 1355000 of 5000000; TPS: 6565.89; ETA: 9.3 minutes\n", - "Step 1356000 of 5000000; TPS: 6565.93; ETA: 9.2 minutes\n", - "Step 1357000 of 5000000; TPS: 6565.98; ETA: 9.2 minutes\n", - "Step 1358000 of 5000000; TPS: 6566.02; ETA: 9.2 minutes\n", - "Step 1359000 of 5000000; TPS: 6566.06; ETA: 9.2 minutes\n", - "Step 1360000 of 5000000; TPS: 6566.11; ETA: 9.2 minutes\n", - "Step 1361000 of 5000000; TPS: 6566.15; ETA: 9.2 minutes\n", - "Step 1362000 of 5000000; TPS: 6566.2; ETA: 9.2 minutes\n", - "Step 1363000 of 5000000; TPS: 6566.24; ETA: 9.2 minutes\n", - "Step 1364000 of 5000000; TPS: 6566.29; ETA: 9.2 minutes\n", - "Step 1365000 of 5000000; TPS: 6566.34; ETA: 9.2 minutes\n", - "Step 1366000 of 5000000; TPS: 6566.38; ETA: 9.2 minutes\n", - "Step 1367000 of 5000000; TPS: 6566.42; ETA: 9.2 minutes\n", - "Step 1368000 of 5000000; TPS: 6566.46; ETA: 9.2 minutes\n", - "Step 1369000 of 5000000; TPS: 6566.5; ETA: 9.2 minutes\n", - "Step 1370000 of 5000000; TPS: 6566.54; ETA: 9.2 minutes\n", - "Step 1371000 of 5000000; TPS: 6566.57; ETA: 9.2 minutes\n", - "Step 1372000 of 5000000; TPS: 6566.6; ETA: 9.2 minutes\n", - "Step 1373000 of 5000000; TPS: 6565.01; ETA: 9.2 minutes\n", - "Step 1374000 of 5000000; TPS: 6565.04; ETA: 9.2 minutes\n", - "Step 1375000 of 5000000; TPS: 6565.03; ETA: 9.2 minutes\n", - "Step 1376000 of 5000000; TPS: 6565.07; ETA: 9.2 minutes\n", - "Step 1377000 of 5000000; TPS: 6565.11; ETA: 9.2 minutes\n", - "Step 1378000 of 5000000; TPS: 6565.15; ETA: 9.2 minutes\n", - "Step 1379000 of 5000000; TPS: 6565.18; ETA: 9.2 minutes\n", - "Step 1380000 of 5000000; TPS: 6565.22; ETA: 9.2 minutes\n", - "Step 1381000 of 5000000; TPS: 6565.26; ETA: 9.2 minutes\n", - "Step 1382000 of 5000000; TPS: 6565.31; ETA: 9.2 minutes\n", - "Step 1383000 of 5000000; TPS: 6565.34; ETA: 9.2 minutes\n", - "Step 1384000 of 5000000; TPS: 6565.39; ETA: 9.2 minutes\n", - "Step 1385000 of 5000000; TPS: 6565.43; ETA: 9.2 minutes\n", - "Step 1386000 of 5000000; TPS: 6565.47; ETA: 9.2 minutes\n", - "Step 1387000 of 5000000; TPS: 6565.5; ETA: 9.2 minutes\n", - "Step 1388000 of 5000000; TPS: 6565.55; ETA: 9.2 minutes\n", - "Step 1389000 of 5000000; TPS: 6565.59; ETA: 9.2 minutes\n", - "Step 1390000 of 5000000; TPS: 6565.62; ETA: 9.2 minutes\n", - "Step 1391000 of 5000000; TPS: 6565.66; ETA: 9.2 minutes\n", - "Step 1392000 of 5000000; TPS: 6565.7; ETA: 9.2 minutes\n", - "Step 1393000 of 5000000; TPS: 6565.74; ETA: 9.2 minutes\n", - "Step 1394000 of 5000000; TPS: 6565.71; ETA: 9.2 minutes\n", - "Step 1395000 of 5000000; TPS: 6565.74; ETA: 9.2 minutes\n", - "Step 1396000 of 5000000; TPS: 6565.74; ETA: 9.1 minutes\n", - "Step 1397000 of 5000000; TPS: 6565.78; ETA: 9.1 minutes\n", - "Step 1398000 of 5000000; TPS: 6565.81; ETA: 9.1 minutes\n", - "Step 1399000 of 5000000; TPS: 6565.82; ETA: 9.1 minutes\n", - "Step 1400000 of 5000000; TPS: 6565.87; ETA: 9.1 minutes\n", - "Step 1401000 of 5000000; TPS: 6565.9; ETA: 9.1 minutes\n", - "Step 1402000 of 5000000; TPS: 6565.94; ETA: 9.1 minutes\n", - "Step 1403000 of 5000000; TPS: 6565.98; ETA: 9.1 minutes\n", - "Step 1404000 of 5000000; TPS: 6566.0; ETA: 9.1 minutes\n", - "Step 1405000 of 5000000; TPS: 6566.04; ETA: 9.1 minutes\n", - "Step 1406000 of 5000000; TPS: 6566.08; ETA: 9.1 minutes\n", - "Step 1407000 of 5000000; TPS: 6566.11; ETA: 9.1 minutes\n", - "Step 1408000 of 5000000; TPS: 6566.15; ETA: 9.1 minutes\n", - "Step 1409000 of 5000000; TPS: 6566.19; ETA: 9.1 minutes\n", - "Step 1410000 of 5000000; TPS: 6566.23; ETA: 9.1 minutes\n", - "Step 1411000 of 5000000; TPS: 6566.28; ETA: 9.1 minutes\n", - "Step 1412000 of 5000000; TPS: 6566.32; ETA: 9.1 minutes\n", - "Step 1413000 of 5000000; TPS: 6566.35; ETA: 9.1 minutes\n", - "Step 1414000 of 5000000; TPS: 6566.39; ETA: 9.1 minutes\n", - "Step 1415000 of 5000000; TPS: 6566.43; ETA: 9.1 minutes\n", - "Step 1416000 of 5000000; TPS: 6566.46; ETA: 9.1 minutes\n", - "Step 1417000 of 5000000; TPS: 6566.49; ETA: 9.1 minutes\n", - "Step 1418000 of 5000000; TPS: 6566.53; ETA: 9.1 minutes\n", - "Step 1419000 of 5000000; TPS: 6566.25; ETA: 9.1 minutes\n", - "Step 1420000 of 5000000; TPS: 6566.28; ETA: 9.1 minutes\n", - "Step 1421000 of 5000000; TPS: 6566.31; ETA: 9.1 minutes\n", - "Step 1422000 of 5000000; TPS: 6566.34; ETA: 9.1 minutes\n", - "Step 1423000 of 5000000; TPS: 6566.36; ETA: 9.1 minutes\n", - "Step 1424000 of 5000000; TPS: 6566.4; ETA: 9.1 minutes\n", - "Step 1425000 of 5000000; TPS: 6566.44; ETA: 9.1 minutes\n", - "Step 1426000 of 5000000; TPS: 6566.48; ETA: 9.1 minutes\n", - "Step 1427000 of 5000000; TPS: 6566.52; ETA: 9.1 minutes\n", - "Step 1428000 of 5000000; TPS: 6565.04; ETA: 9.1 minutes\n", - "Step 1429000 of 5000000; TPS: 6565.07; ETA: 9.1 minutes\n", - "Step 1430000 of 5000000; TPS: 6565.1; ETA: 9.1 minutes\n", - "Step 1431000 of 5000000; TPS: 6565.14; ETA: 9.1 minutes\n", - "Step 1432000 of 5000000; TPS: 6565.18; ETA: 9.1 minutes\n", - "Step 1433000 of 5000000; TPS: 6565.21; ETA: 9.1 minutes\n", - "Step 1434000 of 5000000; TPS: 6565.25; ETA: 9.1 minutes\n", - "Step 1435000 of 5000000; TPS: 6565.29; ETA: 9.1 minutes\n", - "Step 1436000 of 5000000; TPS: 6565.33; ETA: 9.0 minutes\n", - "Step 1437000 of 5000000; TPS: 6565.36; ETA: 9.0 minutes\n", - "Step 1438000 of 5000000; TPS: 6565.39; ETA: 9.0 minutes\n", - "Step 1439000 of 5000000; TPS: 6565.43; ETA: 9.0 minutes\n", - "Step 1440000 of 5000000; TPS: 6565.47; ETA: 9.0 minutes\n", - "Step 1441000 of 5000000; TPS: 6565.51; ETA: 9.0 minutes\n", - "Step 1442000 of 5000000; TPS: 6565.54; ETA: 9.0 minutes\n", - "Step 1443000 of 5000000; TPS: 6565.58; ETA: 9.0 minutes\n", - "Step 1444000 of 5000000; TPS: 6565.62; ETA: 9.0 minutes\n", - "Step 1445000 of 5000000; TPS: 6565.65; ETA: 9.0 minutes\n", - "Step 1446000 of 5000000; TPS: 6565.69; ETA: 9.0 minutes\n", - "Step 1447000 of 5000000; TPS: 6565.73; ETA: 9.0 minutes\n", - "Step 1448000 of 5000000; TPS: 6565.76; ETA: 9.0 minutes\n", - "Step 1449000 of 5000000; TPS: 6565.8; ETA: 9.0 minutes\n", - "Step 1450000 of 5000000; TPS: 6565.84; ETA: 9.0 minutes\n", - "Step 1451000 of 5000000; TPS: 6565.87; ETA: 9.0 minutes\n", - "Step 1452000 of 5000000; TPS: 6565.9; ETA: 9.0 minutes\n", - "Step 1453000 of 5000000; TPS: 6565.94; ETA: 9.0 minutes\n", - "Step 1454000 of 5000000; TPS: 6565.97; ETA: 9.0 minutes\n", - "Step 1455000 of 5000000; TPS: 6566.01; ETA: 9.0 minutes\n", - "Step 1456000 of 5000000; TPS: 6566.04; ETA: 9.0 minutes\n", - "Step 1457000 of 5000000; TPS: 6566.08; ETA: 9.0 minutes\n", - "Step 1458000 of 5000000; TPS: 6566.11; ETA: 9.0 minutes\n", - "Step 1459000 of 5000000; TPS: 6566.15; ETA: 9.0 minutes\n", - "Step 1460000 of 5000000; TPS: 6566.12; ETA: 9.0 minutes\n", - "Step 1461000 of 5000000; TPS: 6566.13; ETA: 9.0 minutes\n", - "Step 1462000 of 5000000; TPS: 6566.16; ETA: 9.0 minutes\n", - "Step 1463000 of 5000000; TPS: 6566.18; ETA: 9.0 minutes\n", - "Step 1464000 of 5000000; TPS: 6566.2; ETA: 9.0 minutes\n", - "Step 1465000 of 5000000; TPS: 6566.24; ETA: 9.0 minutes\n", - "Step 1466000 of 5000000; TPS: 6566.28; ETA: 9.0 minutes\n", - "Step 1467000 of 5000000; TPS: 6566.3; ETA: 9.0 minutes\n", - "Step 1468000 of 5000000; TPS: 6566.33; ETA: 9.0 minutes\n", - "Step 1469000 of 5000000; TPS: 6566.36; ETA: 9.0 minutes\n", - "Step 1470000 of 5000000; TPS: 6566.38; ETA: 9.0 minutes\n", - "Step 1471000 of 5000000; TPS: 6566.42; ETA: 9.0 minutes\n", - "Step 1472000 of 5000000; TPS: 6566.45; ETA: 9.0 minutes\n", - "Step 1473000 of 5000000; TPS: 6566.47; ETA: 9.0 minutes\n", - "Step 1474000 of 5000000; TPS: 6566.5; ETA: 8.9 minutes\n", - "Step 1475000 of 5000000; TPS: 6566.53; ETA: 8.9 minutes\n", - "Step 1476000 of 5000000; TPS: 6566.56; ETA: 8.9 minutes\n", - "Step 1477000 of 5000000; TPS: 6566.59; ETA: 8.9 minutes\n", - "Step 1478000 of 5000000; TPS: 6566.63; ETA: 8.9 minutes\n", - "Step 1479000 of 5000000; TPS: 6566.66; ETA: 8.9 minutes\n", - "Step 1480000 of 5000000; TPS: 6566.69; ETA: 8.9 minutes\n", - "Step 1481000 of 5000000; TPS: 6566.72; ETA: 8.9 minutes\n", - "Step 1482000 of 5000000; TPS: 6566.76; ETA: 8.9 minutes\n", - "Step 1483000 of 5000000; TPS: 6565.38; ETA: 8.9 minutes\n", - "Step 1484000 of 5000000; TPS: 6565.39; ETA: 8.9 minutes\n", - "Step 1485000 of 5000000; TPS: 6565.11; ETA: 8.9 minutes\n", - "Step 1486000 of 5000000; TPS: 6565.12; ETA: 8.9 minutes\n", - "Step 1487000 of 5000000; TPS: 6565.15; ETA: 8.9 minutes\n", - "Step 1488000 of 5000000; TPS: 6565.18; ETA: 8.9 minutes\n", - "Step 1489000 of 5000000; TPS: 6565.16; ETA: 8.9 minutes\n", - "Step 1490000 of 5000000; TPS: 6565.18; ETA: 8.9 minutes\n", - "Step 1491000 of 5000000; TPS: 6565.15; ETA: 8.9 minutes\n", - "Step 1492000 of 5000000; TPS: 6565.17; ETA: 8.9 minutes\n", - "Step 1493000 of 5000000; TPS: 6565.17; ETA: 8.9 minutes\n", - "Step 1494000 of 5000000; TPS: 6565.19; ETA: 8.9 minutes\n", - "Step 1495000 of 5000000; TPS: 6565.2; ETA: 8.9 minutes\n", - "Step 1496000 of 5000000; TPS: 6565.24; ETA: 8.9 minutes\n", - "Step 1497000 of 5000000; TPS: 6565.27; ETA: 8.9 minutes\n", - "Step 1498000 of 5000000; TPS: 6565.3; ETA: 8.9 minutes\n", - "Step 1499000 of 5000000; TPS: 6565.33; ETA: 8.9 minutes\n", - "Step 1500000 of 5000000; TPS: 6565.36; ETA: 8.9 minutes\n", - "Step 1501000 of 5000000; TPS: 6565.39; ETA: 8.9 minutes\n", - "Step 1502000 of 5000000; TPS: 6565.42; ETA: 8.9 minutes\n", - "Step 1503000 of 5000000; TPS: 6565.45; ETA: 8.9 minutes\n", - "Step 1504000 of 5000000; TPS: 6565.48; ETA: 8.9 minutes\n", - "Step 1505000 of 5000000; TPS: 6565.45; ETA: 8.9 minutes\n", - "Step 1506000 of 5000000; TPS: 6565.47; ETA: 8.9 minutes\n", - "Step 1507000 of 5000000; TPS: 6565.47; ETA: 8.9 minutes\n", - "Step 1508000 of 5000000; TPS: 6565.48; ETA: 8.9 minutes\n", - "Step 1509000 of 5000000; TPS: 6565.51; ETA: 8.9 minutes\n", - "Step 1510000 of 5000000; TPS: 6565.52; ETA: 8.9 minutes\n", - "Step 1511000 of 5000000; TPS: 6565.54; ETA: 8.9 minutes\n", - "Step 1512000 of 5000000; TPS: 6565.55; ETA: 8.9 minutes\n", - "Step 1513000 of 5000000; TPS: 6565.56; ETA: 8.9 minutes\n", - "Step 1514000 of 5000000; TPS: 6565.53; ETA: 8.8 minutes\n", - "Step 1515000 of 5000000; TPS: 6565.54; ETA: 8.8 minutes\n", - "Step 1516000 of 5000000; TPS: 6565.56; ETA: 8.8 minutes\n", - "Step 1517000 of 5000000; TPS: 6565.58; ETA: 8.8 minutes\n", - "Step 1518000 of 5000000; TPS: 6565.59; ETA: 8.8 minutes\n", - "Step 1519000 of 5000000; TPS: 6565.62; ETA: 8.8 minutes\n", - "Step 1520000 of 5000000; TPS: 6565.57; ETA: 8.8 minutes\n", - "Step 1521000 of 5000000; TPS: 6565.6; ETA: 8.8 minutes\n", - "Step 1522000 of 5000000; TPS: 6565.6; ETA: 8.8 minutes\n", - "Step 1523000 of 5000000; TPS: 6565.6; ETA: 8.8 minutes\n", - "Step 1524000 of 5000000; TPS: 6565.63; ETA: 8.8 minutes\n", - "Step 1525000 of 5000000; TPS: 6565.65; ETA: 8.8 minutes\n", - "Step 1526000 of 5000000; TPS: 6565.64; ETA: 8.8 minutes\n", - "Step 1527000 of 5000000; TPS: 6565.63; ETA: 8.8 minutes\n", - "Step 1528000 of 5000000; TPS: 6565.66; ETA: 8.8 minutes\n", - "Step 1529000 of 5000000; TPS: 6565.68; ETA: 8.8 minutes\n", - "Step 1530000 of 5000000; TPS: 6565.7; ETA: 8.8 minutes\n", - "Step 1531000 of 5000000; TPS: 6565.74; ETA: 8.8 minutes\n", - "Step 1532000 of 5000000; TPS: 6565.76; ETA: 8.8 minutes\n", - "Step 1533000 of 5000000; TPS: 6565.79; ETA: 8.8 minutes\n", - "Step 1534000 of 5000000; TPS: 6565.8; ETA: 8.8 minutes\n", - "Step 1535000 of 5000000; TPS: 6565.82; ETA: 8.8 minutes\n", - "Step 1536000 of 5000000; TPS: 6565.84; ETA: 8.8 minutes\n", - "Step 1537000 of 5000000; TPS: 6565.87; ETA: 8.8 minutes\n", - "Step 1538000 of 5000000; TPS: 6564.58; ETA: 8.8 minutes\n", - "Step 1539000 of 5000000; TPS: 6564.6; ETA: 8.8 minutes\n", - "Step 1540000 of 5000000; TPS: 6564.63; ETA: 8.8 minutes\n", - "Step 1541000 of 5000000; TPS: 6564.61; ETA: 8.8 minutes\n", - "Step 1542000 of 5000000; TPS: 6564.6; ETA: 8.8 minutes\n", - "Step 1543000 of 5000000; TPS: 6564.62; ETA: 8.8 minutes\n", - "Step 1544000 of 5000000; TPS: 6564.63; ETA: 8.8 minutes\n", - "Step 1545000 of 5000000; TPS: 6564.65; ETA: 8.8 minutes\n", - "Step 1546000 of 5000000; TPS: 6564.68; ETA: 8.8 minutes\n", - "Step 1547000 of 5000000; TPS: 6564.7; ETA: 8.8 minutes\n", - "Step 1548000 of 5000000; TPS: 6564.72; ETA: 8.8 minutes\n", - "Step 1549000 of 5000000; TPS: 6564.72; ETA: 8.8 minutes\n", - "Step 1550000 of 5000000; TPS: 6564.75; ETA: 8.8 minutes\n", - "Step 1551000 of 5000000; TPS: 6564.48; ETA: 8.8 minutes\n", - "Step 1552000 of 5000000; TPS: 6564.48; ETA: 8.8 minutes\n", - "Step 1553000 of 5000000; TPS: 6564.49; ETA: 8.8 minutes\n", - "Step 1554000 of 5000000; TPS: 6564.52; ETA: 8.7 minutes\n", - "Step 1555000 of 5000000; TPS: 6564.54; ETA: 8.7 minutes\n", - "Step 1556000 of 5000000; TPS: 6564.56; ETA: 8.7 minutes\n", - "Step 1557000 of 5000000; TPS: 6564.58; ETA: 8.7 minutes\n", - "Step 1558000 of 5000000; TPS: 6564.58; ETA: 8.7 minutes\n", - "Step 1559000 of 5000000; TPS: 6564.61; ETA: 8.7 minutes\n", - "Step 1560000 of 5000000; TPS: 6564.6; ETA: 8.7 minutes\n", - "Step 1561000 of 5000000; TPS: 6564.58; ETA: 8.7 minutes\n", - "Step 1562000 of 5000000; TPS: 6564.59; ETA: 8.7 minutes\n", - "Step 1563000 of 5000000; TPS: 6564.53; ETA: 8.7 minutes\n", - "Step 1564000 of 5000000; TPS: 6564.54; ETA: 8.7 minutes\n", - "Step 1565000 of 5000000; TPS: 6564.53; ETA: 8.7 minutes\n", - "Step 1566000 of 5000000; TPS: 6564.52; ETA: 8.7 minutes\n", - "Step 1567000 of 5000000; TPS: 6564.52; ETA: 8.7 minutes\n", - "Step 1568000 of 5000000; TPS: 6564.53; ETA: 8.7 minutes\n", - "Step 1569000 of 5000000; TPS: 6564.49; ETA: 8.7 minutes\n", - "Step 1570000 of 5000000; TPS: 6564.5; ETA: 8.7 minutes\n", - "Step 1571000 of 5000000; TPS: 6564.45; ETA: 8.7 minutes\n", - "Step 1572000 of 5000000; TPS: 6564.42; ETA: 8.7 minutes\n", - "Step 1573000 of 5000000; TPS: 6564.37; ETA: 8.7 minutes\n", - "Step 1574000 of 5000000; TPS: 6564.38; ETA: 8.7 minutes\n", - "Step 1575000 of 5000000; TPS: 6564.37; ETA: 8.7 minutes\n", - "Step 1576000 of 5000000; TPS: 6564.38; ETA: 8.7 minutes\n", - "Step 1577000 of 5000000; TPS: 6564.4; ETA: 8.7 minutes\n", - "Step 1578000 of 5000000; TPS: 6564.43; ETA: 8.7 minutes\n", - "Step 1579000 of 5000000; TPS: 6564.45; ETA: 8.7 minutes\n", - "Step 1580000 of 5000000; TPS: 6564.47; ETA: 8.7 minutes\n", - "Step 1581000 of 5000000; TPS: 6564.49; ETA: 8.7 minutes\n", - "Step 1582000 of 5000000; TPS: 6564.52; ETA: 8.7 minutes\n", - "Step 1583000 of 5000000; TPS: 6564.55; ETA: 8.7 minutes\n", - "Step 1584000 of 5000000; TPS: 6564.57; ETA: 8.7 minutes\n", - "Step 1585000 of 5000000; TPS: 6564.6; ETA: 8.7 minutes\n", - "Step 1586000 of 5000000; TPS: 6564.62; ETA: 8.7 minutes\n", - "Step 1587000 of 5000000; TPS: 6564.63; ETA: 8.7 minutes\n", - "Step 1588000 of 5000000; TPS: 6564.64; ETA: 8.7 minutes\n", - "Step 1589000 of 5000000; TPS: 6564.66; ETA: 8.7 minutes\n", - "Step 1590000 of 5000000; TPS: 6564.69; ETA: 8.7 minutes\n", - "Step 1591000 of 5000000; TPS: 6564.71; ETA: 8.7 minutes\n", - "Step 1592000 of 5000000; TPS: 6564.69; ETA: 8.7 minutes\n", - "Step 1593000 of 5000000; TPS: 6563.5; ETA: 8.7 minutes\n", - "Step 1594000 of 5000000; TPS: 6563.51; ETA: 8.6 minutes\n", - "Step 1595000 of 5000000; TPS: 6563.54; ETA: 8.6 minutes\n", - "Step 1596000 of 5000000; TPS: 6563.54; ETA: 8.6 minutes\n", - "Step 1597000 of 5000000; TPS: 6563.55; ETA: 8.6 minutes\n", - "Step 1598000 of 5000000; TPS: 6563.57; ETA: 8.6 minutes\n", - "Step 1599000 of 5000000; TPS: 6563.58; ETA: 8.6 minutes\n", - "Step 1600000 of 5000000; TPS: 6563.6; ETA: 8.6 minutes\n", - "Step 1601000 of 5000000; TPS: 6563.61; ETA: 8.6 minutes\n", - "Step 1602000 of 5000000; TPS: 6563.64; ETA: 8.6 minutes\n", - "Step 1603000 of 5000000; TPS: 6563.65; ETA: 8.6 minutes\n", - "Step 1604000 of 5000000; TPS: 6563.67; ETA: 8.6 minutes\n", - "Step 1605000 of 5000000; TPS: 6563.69; ETA: 8.6 minutes\n", - "Step 1606000 of 5000000; TPS: 6563.71; ETA: 8.6 minutes\n", - "Step 1607000 of 5000000; TPS: 6563.74; ETA: 8.6 minutes\n", - "Step 1608000 of 5000000; TPS: 6563.76; ETA: 8.6 minutes\n", - "Step 1609000 of 5000000; TPS: 6563.78; ETA: 8.6 minutes\n", - "Step 1610000 of 5000000; TPS: 6563.81; ETA: 8.6 minutes\n", - "Step 1611000 of 5000000; TPS: 6563.83; ETA: 8.6 minutes\n", - "Step 1612000 of 5000000; TPS: 6563.85; ETA: 8.6 minutes\n", - "Step 1613000 of 5000000; TPS: 6563.87; ETA: 8.6 minutes\n", - "Step 1614000 of 5000000; TPS: 6563.89; ETA: 8.6 minutes\n", - "Step 1615000 of 5000000; TPS: 6563.92; ETA: 8.6 minutes\n", - "Step 1616000 of 5000000; TPS: 6563.94; ETA: 8.6 minutes\n", - "Step 1617000 of 5000000; TPS: 6563.68; ETA: 8.6 minutes\n", - "Step 1618000 of 5000000; TPS: 6563.7; ETA: 8.6 minutes\n", - "Step 1619000 of 5000000; TPS: 6563.72; ETA: 8.6 minutes\n", - "Step 1620000 of 5000000; TPS: 6563.74; ETA: 8.6 minutes\n", - "Step 1621000 of 5000000; TPS: 6563.76; ETA: 8.6 minutes\n", - "Step 1622000 of 5000000; TPS: 6563.79; ETA: 8.6 minutes\n", - "Step 1623000 of 5000000; TPS: 6563.81; ETA: 8.6 minutes\n", - "Step 1624000 of 5000000; TPS: 6563.84; ETA: 8.6 minutes\n", - "Step 1625000 of 5000000; TPS: 6563.86; ETA: 8.6 minutes\n", - "Step 1626000 of 5000000; TPS: 6563.88; ETA: 8.6 minutes\n", - "Step 1627000 of 5000000; TPS: 6563.9; ETA: 8.6 minutes\n", - "Step 1628000 of 5000000; TPS: 6563.92; ETA: 8.6 minutes\n", - "Step 1629000 of 5000000; TPS: 6563.95; ETA: 8.6 minutes\n", - "Step 1630000 of 5000000; TPS: 6563.97; ETA: 8.6 minutes\n", - "Step 1631000 of 5000000; TPS: 6563.98; ETA: 8.6 minutes\n", - "Step 1632000 of 5000000; TPS: 6564.01; ETA: 8.6 minutes\n", - "Step 1633000 of 5000000; TPS: 6564.04; ETA: 8.5 minutes\n", - "Step 1634000 of 5000000; TPS: 6564.05; ETA: 8.5 minutes\n", - "Step 1635000 of 5000000; TPS: 6564.08; ETA: 8.5 minutes\n", - "Step 1636000 of 5000000; TPS: 6564.1; ETA: 8.5 minutes\n", - "Step 1637000 of 5000000; TPS: 6564.1; ETA: 8.5 minutes\n", - "Step 1638000 of 5000000; TPS: 6564.12; ETA: 8.5 minutes\n", - "Step 1639000 of 5000000; TPS: 6564.13; ETA: 8.5 minutes\n", - "Step 1640000 of 5000000; TPS: 6564.16; ETA: 8.5 minutes\n", - "Step 1641000 of 5000000; TPS: 6564.18; ETA: 8.5 minutes\n", - "Step 1642000 of 5000000; TPS: 6564.2; ETA: 8.5 minutes\n", - "Step 1643000 of 5000000; TPS: 6564.21; ETA: 8.5 minutes\n", - "Step 1644000 of 5000000; TPS: 6564.23; ETA: 8.5 minutes\n", - "Step 1645000 of 5000000; TPS: 6564.26; ETA: 8.5 minutes\n", - "Step 1646000 of 5000000; TPS: 6564.27; ETA: 8.5 minutes\n", - "Step 1647000 of 5000000; TPS: 6564.28; ETA: 8.5 minutes\n", - "Step 1648000 of 5000000; TPS: 6564.3; ETA: 8.5 minutes\n", - "Step 1649000 of 5000000; TPS: 6563.2; ETA: 8.5 minutes\n", - "Step 1650000 of 5000000; TPS: 6563.21; ETA: 8.5 minutes\n", - "Step 1651000 of 5000000; TPS: 6563.22; ETA: 8.5 minutes\n", - "Step 1652000 of 5000000; TPS: 6563.23; ETA: 8.5 minutes\n", - "Step 1653000 of 5000000; TPS: 6563.24; ETA: 8.5 minutes\n", - "Step 1654000 of 5000000; TPS: 6563.26; ETA: 8.5 minutes\n", - "Step 1655000 of 5000000; TPS: 6563.28; ETA: 8.5 minutes\n", - "Step 1656000 of 5000000; TPS: 6563.29; ETA: 8.5 minutes\n", - "Step 1657000 of 5000000; TPS: 6563.3; ETA: 8.5 minutes\n", - "Step 1658000 of 5000000; TPS: 6563.27; ETA: 8.5 minutes\n", - "Step 1659000 of 5000000; TPS: 6563.28; ETA: 8.5 minutes\n", - "Step 1660000 of 5000000; TPS: 6563.29; ETA: 8.5 minutes\n", - "Step 1661000 of 5000000; TPS: 6563.26; ETA: 8.5 minutes\n", - "Step 1662000 of 5000000; TPS: 6563.27; ETA: 8.5 minutes\n", - "Step 1663000 of 5000000; TPS: 6563.27; ETA: 8.5 minutes\n", - "Step 1664000 of 5000000; TPS: 6563.29; ETA: 8.5 minutes\n", - "Step 1665000 of 5000000; TPS: 6563.3; ETA: 8.5 minutes\n", - "Step 1666000 of 5000000; TPS: 6563.31; ETA: 8.5 minutes\n", - "Step 1667000 of 5000000; TPS: 6563.32; ETA: 8.5 minutes\n", - "Step 1668000 of 5000000; TPS: 6563.34; ETA: 8.5 minutes\n", - "Step 1669000 of 5000000; TPS: 6563.35; ETA: 8.5 minutes\n", - "Step 1670000 of 5000000; TPS: 6563.36; ETA: 8.5 minutes\n", - "Step 1671000 of 5000000; TPS: 6563.36; ETA: 8.5 minutes\n", - "Step 1672000 of 5000000; TPS: 6563.37; ETA: 8.5 minutes\n", - "Step 1673000 of 5000000; TPS: 6563.38; ETA: 8.4 minutes\n", - "Step 1674000 of 5000000; TPS: 6563.39; ETA: 8.4 minutes\n", - "Step 1675000 of 5000000; TPS: 6563.42; ETA: 8.4 minutes\n", - "Step 1676000 of 5000000; TPS: 6563.44; ETA: 8.4 minutes\n", - "Step 1677000 of 5000000; TPS: 6563.46; ETA: 8.4 minutes\n", - "Step 1678000 of 5000000; TPS: 6563.48; ETA: 8.4 minutes\n", - "Step 1679000 of 5000000; TPS: 6563.5; ETA: 8.4 minutes\n", - "Step 1680000 of 5000000; TPS: 6563.53; ETA: 8.4 minutes\n", - "Step 1681000 of 5000000; TPS: 6563.55; ETA: 8.4 minutes\n", - "Step 1682000 of 5000000; TPS: 6563.57; ETA: 8.4 minutes\n", - "Step 1683000 of 5000000; TPS: 6563.31; ETA: 8.4 minutes\n", - "Step 1684000 of 5000000; TPS: 6563.34; ETA: 8.4 minutes\n", - "Step 1685000 of 5000000; TPS: 6563.36; ETA: 8.4 minutes\n", - "Step 1686000 of 5000000; TPS: 6563.37; ETA: 8.4 minutes\n", - "Step 1687000 of 5000000; TPS: 6563.38; ETA: 8.4 minutes\n", - "Step 1688000 of 5000000; TPS: 6563.4; ETA: 8.4 minutes\n", - "Step 1689000 of 5000000; TPS: 6563.42; ETA: 8.4 minutes\n", - "Step 1690000 of 5000000; TPS: 6563.44; ETA: 8.4 minutes\n", - "Step 1691000 of 5000000; TPS: 6563.46; ETA: 8.4 minutes\n", - "Step 1692000 of 5000000; TPS: 6563.47; ETA: 8.4 minutes\n", - "Step 1693000 of 5000000; TPS: 6563.49; ETA: 8.4 minutes\n", - "Step 1694000 of 5000000; TPS: 6563.51; ETA: 8.4 minutes\n", - "Step 1695000 of 5000000; TPS: 6563.52; ETA: 8.4 minutes\n", - "Step 1696000 of 5000000; TPS: 6563.53; ETA: 8.4 minutes\n", - "Step 1697000 of 5000000; TPS: 6563.55; ETA: 8.4 minutes\n", - "Step 1698000 of 5000000; TPS: 6563.57; ETA: 8.4 minutes\n", - "Step 1699000 of 5000000; TPS: 6563.58; ETA: 8.4 minutes\n", - "Step 1700000 of 5000000; TPS: 6563.61; ETA: 8.4 minutes\n", - "Step 1701000 of 5000000; TPS: 6563.63; ETA: 8.4 minutes\n", - "Step 1702000 of 5000000; TPS: 6563.65; ETA: 8.4 minutes\n", - "Step 1703000 of 5000000; TPS: 6563.66; ETA: 8.4 minutes\n", - "Step 1704000 of 5000000; TPS: 6562.55; ETA: 8.4 minutes\n", - "Step 1705000 of 5000000; TPS: 6562.57; ETA: 8.4 minutes\n", - "Step 1706000 of 5000000; TPS: 6562.58; ETA: 8.4 minutes\n", - "Step 1707000 of 5000000; TPS: 6562.6; ETA: 8.4 minutes\n", - "Step 1708000 of 5000000; TPS: 6562.62; ETA: 8.4 minutes\n", - "Step 1709000 of 5000000; TPS: 6562.62; ETA: 8.4 minutes\n", - "Step 1710000 of 5000000; TPS: 6562.64; ETA: 8.4 minutes\n", - "Step 1711000 of 5000000; TPS: 6562.63; ETA: 8.4 minutes\n", - "Step 1712000 of 5000000; TPS: 6562.64; ETA: 8.4 minutes\n", - "Step 1713000 of 5000000; TPS: 6562.65; ETA: 8.3 minutes\n", - "Step 1714000 of 5000000; TPS: 6562.67; ETA: 8.3 minutes\n", - "Step 1715000 of 5000000; TPS: 6562.68; ETA: 8.3 minutes\n", - "Step 1716000 of 5000000; TPS: 6562.69; ETA: 8.3 minutes\n", - "Step 1717000 of 5000000; TPS: 6562.71; ETA: 8.3 minutes\n", - "Step 1718000 of 5000000; TPS: 6562.72; ETA: 8.3 minutes\n", - "Step 1719000 of 5000000; TPS: 6562.73; ETA: 8.3 minutes\n", - "Step 1720000 of 5000000; TPS: 6562.75; ETA: 8.3 minutes\n", - "Step 1721000 of 5000000; TPS: 6562.78; ETA: 8.3 minutes\n", - "Step 1722000 of 5000000; TPS: 6562.79; ETA: 8.3 minutes\n", - "Step 1723000 of 5000000; TPS: 6562.81; ETA: 8.3 minutes\n", - "Step 1724000 of 5000000; TPS: 6562.8; ETA: 8.3 minutes\n", - "Step 1725000 of 5000000; TPS: 6562.81; ETA: 8.3 minutes\n", - "Step 1726000 of 5000000; TPS: 6562.83; ETA: 8.3 minutes\n", - "Step 1727000 of 5000000; TPS: 6562.85; ETA: 8.3 minutes\n", - "Step 1728000 of 5000000; TPS: 6562.87; ETA: 8.3 minutes\n", - "Step 1729000 of 5000000; TPS: 6562.88; ETA: 8.3 minutes\n", - "Step 1730000 of 5000000; TPS: 6562.9; ETA: 8.3 minutes\n", - "Step 1731000 of 5000000; TPS: 6562.91; ETA: 8.3 minutes\n", - "Step 1732000 of 5000000; TPS: 6562.93; ETA: 8.3 minutes\n", - "Step 1733000 of 5000000; TPS: 6562.94; ETA: 8.3 minutes\n", - "Step 1734000 of 5000000; TPS: 6562.96; ETA: 8.3 minutes\n", - "Step 1735000 of 5000000; TPS: 6562.98; ETA: 8.3 minutes\n", - "Step 1736000 of 5000000; TPS: 6562.99; ETA: 8.3 minutes\n", - "Step 1737000 of 5000000; TPS: 6563.0; ETA: 8.3 minutes\n", - "Step 1738000 of 5000000; TPS: 6563.02; ETA: 8.3 minutes\n", - "Step 1739000 of 5000000; TPS: 6563.04; ETA: 8.3 minutes\n", - "Step 1740000 of 5000000; TPS: 6563.05; ETA: 8.3 minutes\n", - "Step 1741000 of 5000000; TPS: 6563.07; ETA: 8.3 minutes\n", - "Step 1742000 of 5000000; TPS: 6563.08; ETA: 8.3 minutes\n", - "Step 1743000 of 5000000; TPS: 6563.1; ETA: 8.3 minutes\n", - "Step 1744000 of 5000000; TPS: 6563.11; ETA: 8.3 minutes\n", - "Step 1745000 of 5000000; TPS: 6563.13; ETA: 8.3 minutes\n", - "Step 1746000 of 5000000; TPS: 6563.14; ETA: 8.3 minutes\n", - "Step 1747000 of 5000000; TPS: 6563.16; ETA: 8.3 minutes\n", - "Step 1748000 of 5000000; TPS: 6563.18; ETA: 8.3 minutes\n", - "Step 1749000 of 5000000; TPS: 6562.9; ETA: 8.3 minutes\n", - "Step 1750000 of 5000000; TPS: 6562.88; ETA: 8.3 minutes\n", - "Step 1751000 of 5000000; TPS: 6562.86; ETA: 8.3 minutes\n", - "Step 1752000 of 5000000; TPS: 6562.87; ETA: 8.2 minutes\n", - "Step 1753000 of 5000000; TPS: 6562.85; ETA: 8.2 minutes\n", - "Step 1754000 of 5000000; TPS: 6562.83; ETA: 8.2 minutes\n", - "Step 1755000 of 5000000; TPS: 6562.82; ETA: 8.2 minutes\n", - "Step 1756000 of 5000000; TPS: 6562.82; ETA: 8.2 minutes\n", - "Step 1757000 of 5000000; TPS: 6562.83; ETA: 8.2 minutes\n", - "Step 1758000 of 5000000; TPS: 6562.83; ETA: 8.2 minutes\n", - "Step 1759000 of 5000000; TPS: 6561.89; ETA: 8.2 minutes\n", - "Step 1760000 of 5000000; TPS: 6561.91; ETA: 8.2 minutes\n", - "Step 1761000 of 5000000; TPS: 6561.9; ETA: 8.2 minutes\n", - "Step 1762000 of 5000000; TPS: 6561.9; ETA: 8.2 minutes\n", - "Step 1763000 of 5000000; TPS: 6561.92; ETA: 8.2 minutes\n", - "Step 1764000 of 5000000; TPS: 6561.9; ETA: 8.2 minutes\n", - "Step 1765000 of 5000000; TPS: 6561.9; ETA: 8.2 minutes\n", - "Step 1766000 of 5000000; TPS: 6561.92; ETA: 8.2 minutes\n", - "Step 1767000 of 5000000; TPS: 6561.93; ETA: 8.2 minutes\n", - "Step 1768000 of 5000000; TPS: 6561.94; ETA: 8.2 minutes\n", - "Step 1769000 of 5000000; TPS: 6561.94; ETA: 8.2 minutes\n", - "Step 1770000 of 5000000; TPS: 6561.96; ETA: 8.2 minutes\n", - "Step 1771000 of 5000000; TPS: 6561.97; ETA: 8.2 minutes\n", - "Step 1772000 of 5000000; TPS: 6561.99; ETA: 8.2 minutes\n", - "Step 1773000 of 5000000; TPS: 6562.0; ETA: 8.2 minutes\n", - "Step 1774000 of 5000000; TPS: 6562.01; ETA: 8.2 minutes\n", - "Step 1775000 of 5000000; TPS: 6562.03; ETA: 8.2 minutes\n", - "Step 1776000 of 5000000; TPS: 6562.05; ETA: 8.2 minutes\n", - "Step 1777000 of 5000000; TPS: 6562.07; ETA: 8.2 minutes\n", - "Step 1778000 of 5000000; TPS: 6562.08; ETA: 8.2 minutes\n", - "Step 1779000 of 5000000; TPS: 6562.1; ETA: 8.2 minutes\n", - "Step 1780000 of 5000000; TPS: 6562.1; ETA: 8.2 minutes\n", - "Step 1781000 of 5000000; TPS: 6562.12; ETA: 8.2 minutes\n", - "Step 1782000 of 5000000; TPS: 6562.13; ETA: 8.2 minutes\n", - "Step 1783000 of 5000000; TPS: 6562.14; ETA: 8.2 minutes\n", - "Step 1784000 of 5000000; TPS: 6562.14; ETA: 8.2 minutes\n", - "Step 1785000 of 5000000; TPS: 6562.15; ETA: 8.2 minutes\n", - "Step 1786000 of 5000000; TPS: 6562.16; ETA: 8.2 minutes\n", - "Step 1787000 of 5000000; TPS: 6562.17; ETA: 8.2 minutes\n", - "Step 1788000 of 5000000; TPS: 6562.19; ETA: 8.2 minutes\n", - "Step 1789000 of 5000000; TPS: 6562.2; ETA: 8.2 minutes\n", - "Step 1790000 of 5000000; TPS: 6562.19; ETA: 8.2 minutes\n", - "Step 1791000 of 5000000; TPS: 6562.2; ETA: 8.2 minutes\n", - "Step 1792000 of 5000000; TPS: 6562.22; ETA: 8.1 minutes\n", - "Step 1793000 of 5000000; TPS: 6562.23; ETA: 8.1 minutes\n", - "Step 1794000 of 5000000; TPS: 6562.24; ETA: 8.1 minutes\n", - "Step 1795000 of 5000000; TPS: 6562.25; ETA: 8.1 minutes\n", - "Step 1796000 of 5000000; TPS: 6562.27; ETA: 8.1 minutes\n", - "Step 1797000 of 5000000; TPS: 6562.28; ETA: 8.1 minutes\n", - "Step 1798000 of 5000000; TPS: 6562.3; ETA: 8.1 minutes\n", - "Step 1799000 of 5000000; TPS: 6562.31; ETA: 8.1 minutes\n", - "Step 1800000 of 5000000; TPS: 6562.33; ETA: 8.1 minutes\n", - "Step 1801000 of 5000000; TPS: 6562.34; ETA: 8.1 minutes\n", - "Step 1802000 of 5000000; TPS: 6562.36; ETA: 8.1 minutes\n", - "Step 1803000 of 5000000; TPS: 6562.37; ETA: 8.1 minutes\n", - "Step 1804000 of 5000000; TPS: 6562.38; ETA: 8.1 minutes\n", - "Step 1805000 of 5000000; TPS: 6562.4; ETA: 8.1 minutes\n", - "Step 1806000 of 5000000; TPS: 6562.41; ETA: 8.1 minutes\n", - "Step 1807000 of 5000000; TPS: 6562.42; ETA: 8.1 minutes\n", - "Step 1808000 of 5000000; TPS: 6562.44; ETA: 8.1 minutes\n", - "Step 1809000 of 5000000; TPS: 6562.45; ETA: 8.1 minutes\n", - "Step 1810000 of 5000000; TPS: 6562.46; ETA: 8.1 minutes\n", - "Step 1811000 of 5000000; TPS: 6562.47; ETA: 8.1 minutes\n", - "Step 1812000 of 5000000; TPS: 6562.48; ETA: 8.1 minutes\n", - "Step 1813000 of 5000000; TPS: 6562.5; ETA: 8.1 minutes\n", - "Step 1814000 of 5000000; TPS: 6561.62; ETA: 8.1 minutes\n", - "Step 1815000 of 5000000; TPS: 6561.4; ETA: 8.1 minutes\n", - "Step 1816000 of 5000000; TPS: 6561.41; ETA: 8.1 minutes\n", - "Step 1817000 of 5000000; TPS: 6561.42; ETA: 8.1 minutes\n", - "Step 1818000 of 5000000; TPS: 6561.43; ETA: 8.1 minutes\n", - "Step 1819000 of 5000000; TPS: 6561.44; ETA: 8.1 minutes\n", - "Step 1820000 of 5000000; TPS: 6561.45; ETA: 8.1 minutes\n", - "Step 1821000 of 5000000; TPS: 6561.46; ETA: 8.1 minutes\n", - "Step 1822000 of 5000000; TPS: 6561.44; ETA: 8.1 minutes\n", - "Step 1823000 of 5000000; TPS: 6561.35; ETA: 8.1 minutes\n", - "Step 1824000 of 5000000; TPS: 6561.32; ETA: 8.1 minutes\n", - "Step 1825000 of 5000000; TPS: 6561.32; ETA: 8.1 minutes\n", - "Step 1826000 of 5000000; TPS: 6561.32; ETA: 8.1 minutes\n", - "Step 1827000 of 5000000; TPS: 6561.31; ETA: 8.1 minutes\n", - "Step 1828000 of 5000000; TPS: 6561.29; ETA: 8.1 minutes\n", - "Step 1829000 of 5000000; TPS: 6561.26; ETA: 8.1 minutes\n", - "Step 1830000 of 5000000; TPS: 6561.23; ETA: 8.1 minutes\n", - "Step 1831000 of 5000000; TPS: 6561.22; ETA: 8.0 minutes\n", - "Step 1832000 of 5000000; TPS: 6561.22; ETA: 8.0 minutes\n", - "Step 1833000 of 5000000; TPS: 6561.23; ETA: 8.0 minutes\n", - "Step 1834000 of 5000000; TPS: 6561.24; ETA: 8.0 minutes\n", - "Step 1835000 of 5000000; TPS: 6561.2; ETA: 8.0 minutes\n", - "Step 1836000 of 5000000; TPS: 6561.21; ETA: 8.0 minutes\n", - "Step 1837000 of 5000000; TPS: 6561.21; ETA: 8.0 minutes\n", - "Step 1838000 of 5000000; TPS: 6561.22; ETA: 8.0 minutes\n", - "Step 1839000 of 5000000; TPS: 6561.23; ETA: 8.0 minutes\n", - "Step 1840000 of 5000000; TPS: 6561.24; ETA: 8.0 minutes\n", - "Step 1841000 of 5000000; TPS: 6561.22; ETA: 8.0 minutes\n", - "Step 1842000 of 5000000; TPS: 6561.23; ETA: 8.0 minutes\n", - "Step 1843000 of 5000000; TPS: 6561.24; ETA: 8.0 minutes\n", - "Step 1844000 of 5000000; TPS: 6561.24; ETA: 8.0 minutes\n", - "Step 1845000 of 5000000; TPS: 6561.25; ETA: 8.0 minutes\n", - "Step 1846000 of 5000000; TPS: 6561.25; ETA: 8.0 minutes\n", - "Step 1847000 of 5000000; TPS: 6561.24; ETA: 8.0 minutes\n", - "Step 1848000 of 5000000; TPS: 6561.25; ETA: 8.0 minutes\n", - "Step 1849000 of 5000000; TPS: 6561.26; ETA: 8.0 minutes\n", - "Step 1850000 of 5000000; TPS: 6561.23; ETA: 8.0 minutes\n", - "Step 1851000 of 5000000; TPS: 6561.23; ETA: 8.0 minutes\n", - "Step 1852000 of 5000000; TPS: 6561.23; ETA: 8.0 minutes\n", - "Step 1853000 of 5000000; TPS: 6561.23; ETA: 8.0 minutes\n", - "Step 1854000 of 5000000; TPS: 6561.24; ETA: 8.0 minutes\n", - "Step 1855000 of 5000000; TPS: 6561.24; ETA: 8.0 minutes\n", - "Step 1856000 of 5000000; TPS: 6561.18; ETA: 8.0 minutes\n", - "Step 1857000 of 5000000; TPS: 6561.12; ETA: 8.0 minutes\n", - "Step 1858000 of 5000000; TPS: 6561.11; ETA: 8.0 minutes\n", - "Step 1859000 of 5000000; TPS: 6561.02; ETA: 8.0 minutes\n", - "Step 1860000 of 5000000; TPS: 6561.01; ETA: 8.0 minutes\n", - "Step 1861000 of 5000000; TPS: 6561.02; ETA: 8.0 minutes\n", - "Step 1862000 of 5000000; TPS: 6561.03; ETA: 8.0 minutes\n", - "Step 1863000 of 5000000; TPS: 6561.01; ETA: 8.0 minutes\n", - "Step 1864000 of 5000000; TPS: 6561.0; ETA: 8.0 minutes\n", - "Step 1865000 of 5000000; TPS: 6561.0; ETA: 8.0 minutes\n", - "Step 1866000 of 5000000; TPS: 6560.98; ETA: 8.0 minutes\n", - "Step 1867000 of 5000000; TPS: 6560.98; ETA: 8.0 minutes\n", - "Step 1868000 of 5000000; TPS: 6560.95; ETA: 8.0 minutes\n", - "Step 1869000 of 5000000; TPS: 6560.11; ETA: 8.0 minutes\n", - "Step 1870000 of 5000000; TPS: 6560.11; ETA: 8.0 minutes\n", - "Step 1871000 of 5000000; TPS: 6560.09; ETA: 7.9 minutes\n", - "Step 1872000 of 5000000; TPS: 6560.08; ETA: 7.9 minutes\n", - "Step 1873000 of 5000000; TPS: 6560.07; ETA: 7.9 minutes\n", - "Step 1874000 of 5000000; TPS: 6560.07; ETA: 7.9 minutes\n", - "Step 1875000 of 5000000; TPS: 6560.07; ETA: 7.9 minutes\n", - "Step 1876000 of 5000000; TPS: 6560.06; ETA: 7.9 minutes\n", - "Step 1877000 of 5000000; TPS: 6560.06; ETA: 7.9 minutes\n", - "Step 1878000 of 5000000; TPS: 6560.06; ETA: 7.9 minutes\n", - "Step 1879000 of 5000000; TPS: 6560.06; ETA: 7.9 minutes\n", - "Step 1880000 of 5000000; TPS: 6560.06; ETA: 7.9 minutes\n", - "Step 1881000 of 5000000; TPS: 6559.44; ETA: 7.9 minutes\n", - "Step 1882000 of 5000000; TPS: 6559.42; ETA: 7.9 minutes\n", - "Step 1883000 of 5000000; TPS: 6559.41; ETA: 7.9 minutes\n", - "Step 1884000 of 5000000; TPS: 6559.4; ETA: 7.9 minutes\n", - "Step 1885000 of 5000000; TPS: 6559.39; ETA: 7.9 minutes\n", - "Step 1886000 of 5000000; TPS: 6559.36; ETA: 7.9 minutes\n", - "Step 1887000 of 5000000; TPS: 6559.36; ETA: 7.9 minutes\n", - "Step 1888000 of 5000000; TPS: 6559.36; ETA: 7.9 minutes\n", - "Step 1889000 of 5000000; TPS: 6559.36; ETA: 7.9 minutes\n", - "Step 1890000 of 5000000; TPS: 6559.36; ETA: 7.9 minutes\n", - "Step 1891000 of 5000000; TPS: 6559.33; ETA: 7.9 minutes\n", - "Step 1892000 of 5000000; TPS: 6559.33; ETA: 7.9 minutes\n", - "Step 1893000 of 5000000; TPS: 6559.32; ETA: 7.9 minutes\n", - "Step 1894000 of 5000000; TPS: 6559.33; ETA: 7.9 minutes\n", - "Step 1895000 of 5000000; TPS: 6559.33; ETA: 7.9 minutes\n", - "Step 1896000 of 5000000; TPS: 6559.34; ETA: 7.9 minutes\n", - "Step 1897000 of 5000000; TPS: 6559.35; ETA: 7.9 minutes\n", - "Step 1898000 of 5000000; TPS: 6559.36; ETA: 7.9 minutes\n", - "Step 1899000 of 5000000; TPS: 6559.36; ETA: 7.9 minutes\n", - "Step 1900000 of 5000000; TPS: 6559.37; ETA: 7.9 minutes\n", - "Step 1901000 of 5000000; TPS: 6559.37; ETA: 7.9 minutes\n", - "Step 1902000 of 5000000; TPS: 6559.37; ETA: 7.9 minutes\n", - "Step 1903000 of 5000000; TPS: 6559.38; ETA: 7.9 minutes\n", - "Step 1904000 of 5000000; TPS: 6559.38; ETA: 7.9 minutes\n", - "Step 1905000 of 5000000; TPS: 6559.39; ETA: 7.9 minutes\n", - "Step 1906000 of 5000000; TPS: 6559.39; ETA: 7.9 minutes\n", - "Step 1907000 of 5000000; TPS: 6559.37; ETA: 7.9 minutes\n", - "Step 1908000 of 5000000; TPS: 6559.38; ETA: 7.9 minutes\n", - "Step 1909000 of 5000000; TPS: 6559.38; ETA: 7.9 minutes\n", - "Step 1910000 of 5000000; TPS: 6559.37; ETA: 7.9 minutes\n", - "Step 1911000 of 5000000; TPS: 6559.37; ETA: 7.8 minutes\n", - "Step 1912000 of 5000000; TPS: 6559.34; ETA: 7.8 minutes\n", - "Step 1913000 of 5000000; TPS: 6559.34; ETA: 7.8 minutes\n", - "Step 1914000 of 5000000; TPS: 6559.23; ETA: 7.8 minutes\n", - "Step 1915000 of 5000000; TPS: 6559.2; ETA: 7.8 minutes\n", - "Step 1916000 of 5000000; TPS: 6559.2; ETA: 7.8 minutes\n", - "Step 1917000 of 5000000; TPS: 6559.2; ETA: 7.8 minutes\n", - "Step 1918000 of 5000000; TPS: 6559.16; ETA: 7.8 minutes\n", - "Step 1919000 of 5000000; TPS: 6559.14; ETA: 7.8 minutes\n", - "Step 1920000 of 5000000; TPS: 6559.14; ETA: 7.8 minutes\n", - "Step 1921000 of 5000000; TPS: 6559.09; ETA: 7.8 minutes\n", - "Step 1922000 of 5000000; TPS: 6559.07; ETA: 7.8 minutes\n", - "Step 1923000 of 5000000; TPS: 6559.07; ETA: 7.8 minutes\n", - "Step 1924000 of 5000000; TPS: 6558.26; ETA: 7.8 minutes\n", - "Step 1925000 of 5000000; TPS: 6558.26; ETA: 7.8 minutes\n", - "Step 1926000 of 5000000; TPS: 6558.26; ETA: 7.8 minutes\n", - "Step 1927000 of 5000000; TPS: 6558.23; ETA: 7.8 minutes\n", - "Step 1928000 of 5000000; TPS: 6558.23; ETA: 7.8 minutes\n", - "Step 1929000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1930000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1931000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1932000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1933000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1934000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1935000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1936000 of 5000000; TPS: 6558.23; ETA: 7.8 minutes\n", - "Step 1937000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1938000 of 5000000; TPS: 6558.23; ETA: 7.8 minutes\n", - "Step 1939000 of 5000000; TPS: 6558.23; ETA: 7.8 minutes\n", - "Step 1940000 of 5000000; TPS: 6558.22; ETA: 7.8 minutes\n", - "Step 1941000 of 5000000; TPS: 6558.21; ETA: 7.8 minutes\n", - "Step 1942000 of 5000000; TPS: 6558.2; ETA: 7.8 minutes\n", - "Step 1943000 of 5000000; TPS: 6558.19; ETA: 7.8 minutes\n", - "Step 1944000 of 5000000; TPS: 6558.2; ETA: 7.8 minutes\n", - "Step 1945000 of 5000000; TPS: 6558.2; ETA: 7.8 minutes\n", - "Step 1946000 of 5000000; TPS: 6558.2; ETA: 7.8 minutes\n", - "Step 1947000 of 5000000; TPS: 6557.96; ETA: 7.8 minutes\n", - "Step 1948000 of 5000000; TPS: 6557.95; ETA: 7.8 minutes\n", - "Step 1949000 of 5000000; TPS: 6557.95; ETA: 7.8 minutes\n", - "Step 1950000 of 5000000; TPS: 6557.94; ETA: 7.8 minutes\n", - "Step 1951000 of 5000000; TPS: 6557.94; ETA: 7.7 minutes\n", - "Step 1952000 of 5000000; TPS: 6557.94; ETA: 7.7 minutes\n", - "Step 1953000 of 5000000; TPS: 6557.95; ETA: 7.7 minutes\n", - "Step 1954000 of 5000000; TPS: 6557.94; ETA: 7.7 minutes\n", - "Step 1955000 of 5000000; TPS: 6557.94; ETA: 7.7 minutes\n", - "Step 1956000 of 5000000; TPS: 6557.94; ETA: 7.7 minutes\n", - "Step 1957000 of 5000000; TPS: 6557.93; ETA: 7.7 minutes\n", - "Step 1958000 of 5000000; TPS: 6557.93; ETA: 7.7 minutes\n", - "Step 1959000 of 5000000; TPS: 6557.92; ETA: 7.7 minutes\n", - "Step 1960000 of 5000000; TPS: 6557.89; ETA: 7.7 minutes\n", - "Step 1961000 of 5000000; TPS: 6557.88; ETA: 7.7 minutes\n", - "Step 1962000 of 5000000; TPS: 6557.87; ETA: 7.7 minutes\n", - "Step 1963000 of 5000000; TPS: 6557.85; ETA: 7.7 minutes\n", - "Step 1964000 of 5000000; TPS: 6557.85; ETA: 7.7 minutes\n", - "Step 1965000 of 5000000; TPS: 6557.84; ETA: 7.7 minutes\n", - "Step 1966000 of 5000000; TPS: 6557.84; ETA: 7.7 minutes\n", - "Step 1967000 of 5000000; TPS: 6557.84; ETA: 7.7 minutes\n", - "Step 1968000 of 5000000; TPS: 6557.84; ETA: 7.7 minutes\n", - "Step 1969000 of 5000000; TPS: 6557.83; ETA: 7.7 minutes\n", - "Step 1970000 of 5000000; TPS: 6557.83; ETA: 7.7 minutes\n", - "Step 1971000 of 5000000; TPS: 6557.83; ETA: 7.7 minutes\n", - "Step 1972000 of 5000000; TPS: 6557.83; ETA: 7.7 minutes\n", - "Step 1973000 of 5000000; TPS: 6557.83; ETA: 7.7 minutes\n", - "Step 1974000 of 5000000; TPS: 6557.83; ETA: 7.7 minutes\n", - "Step 1975000 of 5000000; TPS: 6557.8; ETA: 7.7 minutes\n", - "Step 1976000 of 5000000; TPS: 6557.8; ETA: 7.7 minutes\n", - "Step 1977000 of 5000000; TPS: 6557.8; ETA: 7.7 minutes\n", - "Step 1978000 of 5000000; TPS: 6557.79; ETA: 7.7 minutes\n", - "Step 1979000 of 5000000; TPS: 6557.07; ETA: 7.7 minutes\n", - "Step 1980000 of 5000000; TPS: 6557.06; ETA: 7.7 minutes\n", - "Step 1981000 of 5000000; TPS: 6557.03; ETA: 7.7 minutes\n", - "Step 1982000 of 5000000; TPS: 6557.02; ETA: 7.7 minutes\n", - "Step 1983000 of 5000000; TPS: 6557.01; ETA: 7.7 minutes\n", - "Step 1984000 of 5000000; TPS: 6557.0; ETA: 7.7 minutes\n", - "Step 1985000 of 5000000; TPS: 6557.0; ETA: 7.7 minutes\n", - "Step 1986000 of 5000000; TPS: 6556.94; ETA: 7.7 minutes\n", - "Step 1987000 of 5000000; TPS: 6556.94; ETA: 7.7 minutes\n", - "Step 1988000 of 5000000; TPS: 6556.94; ETA: 7.7 minutes\n", - "Step 1989000 of 5000000; TPS: 6556.94; ETA: 7.7 minutes\n", - "Step 1990000 of 5000000; TPS: 6556.92; ETA: 7.7 minutes\n", - "Step 1991000 of 5000000; TPS: 6556.92; ETA: 7.6 minutes\n", - "Step 1992000 of 5000000; TPS: 6556.91; ETA: 7.6 minutes\n", - "Step 1993000 of 5000000; TPS: 6556.91; ETA: 7.6 minutes\n", - "Step 1994000 of 5000000; TPS: 6556.91; ETA: 7.6 minutes\n", - "Step 1995000 of 5000000; TPS: 6556.91; ETA: 7.6 minutes\n", - "Step 1996000 of 5000000; TPS: 6556.91; ETA: 7.6 minutes\n", - "Step 1997000 of 5000000; TPS: 6556.91; ETA: 7.6 minutes\n", - "Step 1998000 of 5000000; TPS: 6556.91; ETA: 7.6 minutes\n", - "Step 1999000 of 5000000; TPS: 6556.9; ETA: 7.6 minutes\n", - "Step 2000000 of 5000000; TPS: 6556.9; ETA: 7.6 minutes\n", - "Step 2001000 of 5000000; TPS: 6556.89; ETA: 7.6 minutes\n", - "Step 2002000 of 5000000; TPS: 6556.88; ETA: 7.6 minutes\n", - "Step 2003000 of 5000000; TPS: 6556.88; ETA: 7.6 minutes\n", - "Step 2004000 of 5000000; TPS: 6556.88; ETA: 7.6 minutes\n", - "Step 2005000 of 5000000; TPS: 6556.87; ETA: 7.6 minutes\n", - "Step 2006000 of 5000000; TPS: 6556.85; ETA: 7.6 minutes\n", - "Step 2007000 of 5000000; TPS: 6556.84; ETA: 7.6 minutes\n", - "Step 2008000 of 5000000; TPS: 6556.83; ETA: 7.6 minutes\n", - "Step 2009000 of 5000000; TPS: 6556.82; ETA: 7.6 minutes\n", - "Step 2010000 of 5000000; TPS: 6556.81; ETA: 7.6 minutes\n", - "Step 2011000 of 5000000; TPS: 6556.81; ETA: 7.6 minutes\n", - "Step 2012000 of 5000000; TPS: 6556.8; ETA: 7.6 minutes\n", - "Step 2013000 of 5000000; TPS: 6556.57; ETA: 7.6 minutes\n", - "Step 2014000 of 5000000; TPS: 6556.56; ETA: 7.6 minutes\n", - "Step 2015000 of 5000000; TPS: 6556.55; ETA: 7.6 minutes\n", - "Step 2016000 of 5000000; TPS: 6556.55; ETA: 7.6 minutes\n", - "Step 2017000 of 5000000; TPS: 6556.54; ETA: 7.6 minutes\n", - "Step 2018000 of 5000000; TPS: 6556.54; ETA: 7.6 minutes\n", - "Step 2019000 of 5000000; TPS: 6556.53; ETA: 7.6 minutes\n", - "Step 2020000 of 5000000; TPS: 6556.52; ETA: 7.6 minutes\n", - "Step 2021000 of 5000000; TPS: 6556.51; ETA: 7.6 minutes\n", - "Step 2022000 of 5000000; TPS: 6556.5; ETA: 7.6 minutes\n", - "Step 2023000 of 5000000; TPS: 6556.48; ETA: 7.6 minutes\n", - "Step 2024000 of 5000000; TPS: 6556.48; ETA: 7.6 minutes\n", - "Step 2025000 of 5000000; TPS: 6556.47; ETA: 7.6 minutes\n", - "Step 2026000 of 5000000; TPS: 6556.46; ETA: 7.6 minutes\n", - "Step 2027000 of 5000000; TPS: 6556.45; ETA: 7.6 minutes\n", - "Step 2028000 of 5000000; TPS: 6556.44; ETA: 7.6 minutes\n", - "Step 2029000 of 5000000; TPS: 6556.43; ETA: 7.6 minutes\n", - "Step 2030000 of 5000000; TPS: 6556.43; ETA: 7.5 minutes\n", - "Step 2031000 of 5000000; TPS: 6556.43; ETA: 7.5 minutes\n", - "Step 2032000 of 5000000; TPS: 6556.42; ETA: 7.5 minutes\n", - "Step 2033000 of 5000000; TPS: 6556.41; ETA: 7.5 minutes\n", - "Step 2034000 of 5000000; TPS: 6555.73; ETA: 7.5 minutes\n", - "Step 2035000 of 5000000; TPS: 6555.71; ETA: 7.5 minutes\n", - "Step 2036000 of 5000000; TPS: 6555.68; ETA: 7.5 minutes\n", - "Step 2037000 of 5000000; TPS: 6555.66; ETA: 7.5 minutes\n", - "Step 2038000 of 5000000; TPS: 6555.63; ETA: 7.5 minutes\n", - "Step 2039000 of 5000000; TPS: 6555.62; ETA: 7.5 minutes\n", - "Step 2040000 of 5000000; TPS: 6555.62; ETA: 7.5 minutes\n", - "Step 2041000 of 5000000; TPS: 6555.62; ETA: 7.5 minutes\n", - "Step 2042000 of 5000000; TPS: 6555.59; ETA: 7.5 minutes\n", - "Step 2043000 of 5000000; TPS: 6555.57; ETA: 7.5 minutes\n", - "Step 2044000 of 5000000; TPS: 6555.56; ETA: 7.5 minutes\n", - "Step 2045000 of 5000000; TPS: 6555.56; ETA: 7.5 minutes\n", - "Step 2046000 of 5000000; TPS: 6555.55; ETA: 7.5 minutes\n", - "Step 2047000 of 5000000; TPS: 6555.55; ETA: 7.5 minutes\n", - "Step 2048000 of 5000000; TPS: 6555.54; ETA: 7.5 minutes\n", - "Step 2049000 of 5000000; TPS: 6555.53; ETA: 7.5 minutes\n", - "Step 2050000 of 5000000; TPS: 6555.53; ETA: 7.5 minutes\n", - "Step 2051000 of 5000000; TPS: 6555.49; ETA: 7.5 minutes\n", - "Step 2052000 of 5000000; TPS: 6555.43; ETA: 7.5 minutes\n", - "Step 2053000 of 5000000; TPS: 6555.4; ETA: 7.5 minutes\n", - "Step 2054000 of 5000000; TPS: 6555.38; ETA: 7.5 minutes\n", - "Step 2055000 of 5000000; TPS: 6555.37; ETA: 7.5 minutes\n", - "Step 2056000 of 5000000; TPS: 6555.36; ETA: 7.5 minutes\n", - "Step 2057000 of 5000000; TPS: 6555.34; ETA: 7.5 minutes\n", - "Step 2058000 of 5000000; TPS: 6555.31; ETA: 7.5 minutes\n", - "Step 2059000 of 5000000; TPS: 6555.31; ETA: 7.5 minutes\n", - "Step 2060000 of 5000000; TPS: 6555.3; ETA: 7.5 minutes\n", - "Step 2061000 of 5000000; TPS: 6555.29; ETA: 7.5 minutes\n", - "Step 2062000 of 5000000; TPS: 6555.29; ETA: 7.5 minutes\n", - "Step 2063000 of 5000000; TPS: 6555.28; ETA: 7.5 minutes\n", - "Step 2064000 of 5000000; TPS: 6555.27; ETA: 7.5 minutes\n", - "Step 2065000 of 5000000; TPS: 6555.26; ETA: 7.5 minutes\n", - "Step 2066000 of 5000000; TPS: 6555.25; ETA: 7.5 minutes\n", - "Step 2067000 of 5000000; TPS: 6555.25; ETA: 7.5 minutes\n", - "Step 2068000 of 5000000; TPS: 6555.24; ETA: 7.5 minutes\n", - "Step 2069000 of 5000000; TPS: 6555.23; ETA: 7.5 minutes\n", - "Step 2070000 of 5000000; TPS: 6555.23; ETA: 7.4 minutes\n", - "Step 2071000 of 5000000; TPS: 6555.22; ETA: 7.4 minutes\n", - "Step 2072000 of 5000000; TPS: 6555.21; ETA: 7.4 minutes\n", - "Step 2073000 of 5000000; TPS: 6555.21; ETA: 7.4 minutes\n", - "Step 2074000 of 5000000; TPS: 6555.2; ETA: 7.4 minutes\n", - "Step 2075000 of 5000000; TPS: 6555.2; ETA: 7.4 minutes\n", - "Step 2076000 of 5000000; TPS: 6555.19; ETA: 7.4 minutes\n", - "Step 2077000 of 5000000; TPS: 6555.19; ETA: 7.4 minutes\n", - "Step 2078000 of 5000000; TPS: 6555.19; ETA: 7.4 minutes\n", - "Step 2079000 of 5000000; TPS: 6554.98; ETA: 7.4 minutes\n", - "Step 2080000 of 5000000; TPS: 6554.96; ETA: 7.4 minutes\n", - "Step 2081000 of 5000000; TPS: 6554.95; ETA: 7.4 minutes\n", - "Step 2082000 of 5000000; TPS: 6554.95; ETA: 7.4 minutes\n", - "Step 2083000 of 5000000; TPS: 6554.94; ETA: 7.4 minutes\n", - "Step 2084000 of 5000000; TPS: 6554.94; ETA: 7.4 minutes\n", - "Step 2085000 of 5000000; TPS: 6554.94; ETA: 7.4 minutes\n", - "Step 2086000 of 5000000; TPS: 6554.93; ETA: 7.4 minutes\n", - "Step 2087000 of 5000000; TPS: 6554.93; ETA: 7.4 minutes\n", - "Step 2088000 of 5000000; TPS: 6554.92; ETA: 7.4 minutes\n", - "Step 2089000 of 5000000; TPS: 6554.3; ETA: 7.4 minutes\n", - "Step 2090000 of 5000000; TPS: 6554.29; ETA: 7.4 minutes\n", - "Step 2091000 of 5000000; TPS: 6554.25; ETA: 7.4 minutes\n", - "Step 2092000 of 5000000; TPS: 6554.23; ETA: 7.4 minutes\n", - "Step 2093000 of 5000000; TPS: 6554.2; ETA: 7.4 minutes\n", - "Step 2094000 of 5000000; TPS: 6554.16; ETA: 7.4 minutes\n", - "Step 2095000 of 5000000; TPS: 6554.14; ETA: 7.4 minutes\n", - "Step 2096000 of 5000000; TPS: 6554.13; ETA: 7.4 minutes\n", - "Step 2097000 of 5000000; TPS: 6554.11; ETA: 7.4 minutes\n", - "Step 2098000 of 5000000; TPS: 6554.1; ETA: 7.4 minutes\n", - "Step 2099000 of 5000000; TPS: 6554.09; ETA: 7.4 minutes\n", - "Step 2100000 of 5000000; TPS: 6554.07; ETA: 7.4 minutes\n", - "Step 2101000 of 5000000; TPS: 6554.06; ETA: 7.4 minutes\n", - "Step 2102000 of 5000000; TPS: 6554.04; ETA: 7.4 minutes\n", - "Step 2103000 of 5000000; TPS: 6554.03; ETA: 7.4 minutes\n", - "Step 2104000 of 5000000; TPS: 6554.02; ETA: 7.4 minutes\n", - "Step 2105000 of 5000000; TPS: 6554.01; ETA: 7.4 minutes\n", - "Step 2106000 of 5000000; TPS: 6554.0; ETA: 7.4 minutes\n", - "Step 2107000 of 5000000; TPS: 6553.99; ETA: 7.4 minutes\n", - "Step 2108000 of 5000000; TPS: 6553.98; ETA: 7.4 minutes\n", - "Step 2109000 of 5000000; TPS: 6553.97; ETA: 7.4 minutes\n", - "Step 2110000 of 5000000; TPS: 6553.96; ETA: 7.3 minutes\n", - "Step 2111000 of 5000000; TPS: 6553.95; ETA: 7.3 minutes\n", - "Step 2112000 of 5000000; TPS: 6553.94; ETA: 7.3 minutes\n", - "Step 2113000 of 5000000; TPS: 6553.92; ETA: 7.3 minutes\n", - "Step 2114000 of 5000000; TPS: 6553.9; ETA: 7.3 minutes\n", - "Step 2115000 of 5000000; TPS: 6553.89; ETA: 7.3 minutes\n", - "Step 2116000 of 5000000; TPS: 6553.87; ETA: 7.3 minutes\n", - "Step 2117000 of 5000000; TPS: 6553.81; ETA: 7.3 minutes\n", - "Step 2118000 of 5000000; TPS: 6553.8; ETA: 7.3 minutes\n", - "Step 2119000 of 5000000; TPS: 6553.78; ETA: 7.3 minutes\n", - "Step 2120000 of 5000000; TPS: 6553.77; ETA: 7.3 minutes\n", - "Step 2121000 of 5000000; TPS: 6553.75; ETA: 7.3 minutes\n", - "Step 2122000 of 5000000; TPS: 6553.73; ETA: 7.3 minutes\n", - "Step 2123000 of 5000000; TPS: 6553.72; ETA: 7.3 minutes\n", - "Step 2124000 of 5000000; TPS: 6552.97; ETA: 7.3 minutes\n", - "Step 2125000 of 5000000; TPS: 6552.95; ETA: 7.3 minutes\n", - "Step 2126000 of 5000000; TPS: 6552.92; ETA: 7.3 minutes\n", - "Step 2127000 of 5000000; TPS: 6552.91; ETA: 7.3 minutes\n", - "Step 2128000 of 5000000; TPS: 6552.9; ETA: 7.3 minutes\n", - "Step 2129000 of 5000000; TPS: 6552.88; ETA: 7.3 minutes\n", - "Step 2130000 of 5000000; TPS: 6552.84; ETA: 7.3 minutes\n", - "Step 2131000 of 5000000; TPS: 6552.83; ETA: 7.3 minutes\n", - "Step 2132000 of 5000000; TPS: 6552.8; ETA: 7.3 minutes\n", - "Step 2133000 of 5000000; TPS: 6552.78; ETA: 7.3 minutes\n", - "Step 2134000 of 5000000; TPS: 6552.75; ETA: 7.3 minutes\n", - "Step 2135000 of 5000000; TPS: 6552.74; ETA: 7.3 minutes\n", - "Step 2136000 of 5000000; TPS: 6552.72; ETA: 7.3 minutes\n", - "Step 2137000 of 5000000; TPS: 6552.71; ETA: 7.3 minutes\n", - "Step 2138000 of 5000000; TPS: 6552.7; ETA: 7.3 minutes\n", - "Step 2139000 of 5000000; TPS: 6552.68; ETA: 7.3 minutes\n", - "Step 2140000 of 5000000; TPS: 6552.66; ETA: 7.3 minutes\n", - "Step 2141000 of 5000000; TPS: 6552.65; ETA: 7.3 minutes\n", - "Step 2142000 of 5000000; TPS: 6552.64; ETA: 7.3 minutes\n", - "Step 2143000 of 5000000; TPS: 6552.6; ETA: 7.3 minutes\n", - "Step 2144000 of 5000000; TPS: 6551.73; ETA: 7.3 minutes\n", - "Step 2145000 of 5000000; TPS: 6551.71; ETA: 7.3 minutes\n", - "Step 2146000 of 5000000; TPS: 6551.69; ETA: 7.3 minutes\n", - "Step 2147000 of 5000000; TPS: 6551.68; ETA: 7.3 minutes\n", - "Step 2148000 of 5000000; TPS: 6551.67; ETA: 7.3 minutes\n", - "Step 2149000 of 5000000; TPS: 6551.65; ETA: 7.3 minutes\n", - "Step 2150000 of 5000000; TPS: 6551.62; ETA: 7.3 minutes\n", - "Step 2151000 of 5000000; TPS: 6551.6; ETA: 7.2 minutes\n", - "Step 2152000 of 5000000; TPS: 6551.59; ETA: 7.2 minutes\n", - "Step 2153000 of 5000000; TPS: 6551.58; ETA: 7.2 minutes\n", - "Step 2154000 of 5000000; TPS: 6551.56; ETA: 7.2 minutes\n", - "Step 2155000 of 5000000; TPS: 6551.55; ETA: 7.2 minutes\n", - "Step 2156000 of 5000000; TPS: 6551.54; ETA: 7.2 minutes\n", - "Step 2157000 of 5000000; TPS: 6551.51; ETA: 7.2 minutes\n", - "Step 2158000 of 5000000; TPS: 6551.5; ETA: 7.2 minutes\n", - "Step 2159000 of 5000000; TPS: 6551.48; ETA: 7.2 minutes\n", - "Step 2160000 of 5000000; TPS: 6551.47; ETA: 7.2 minutes\n", - "Step 2161000 of 5000000; TPS: 6551.45; ETA: 7.2 minutes\n", - "Step 2162000 of 5000000; TPS: 6551.44; ETA: 7.2 minutes\n", - "Step 2163000 of 5000000; TPS: 6551.43; ETA: 7.2 minutes\n", - "Step 2164000 of 5000000; TPS: 6551.42; ETA: 7.2 minutes\n", - "Step 2165000 of 5000000; TPS: 6551.41; ETA: 7.2 minutes\n", - "Step 2166000 of 5000000; TPS: 6551.4; ETA: 7.2 minutes\n", - "Step 2167000 of 5000000; TPS: 6551.39; ETA: 7.2 minutes\n", - "Step 2168000 of 5000000; TPS: 6551.37; ETA: 7.2 minutes\n", - "Step 2169000 of 5000000; TPS: 6551.36; ETA: 7.2 minutes\n", - "Step 2170000 of 5000000; TPS: 6551.35; ETA: 7.2 minutes\n", - "Step 2171000 of 5000000; TPS: 6551.33; ETA: 7.2 minutes\n", - "Step 2172000 of 5000000; TPS: 6551.32; ETA: 7.2 minutes\n", - "Step 2173000 of 5000000; TPS: 6551.3; ETA: 7.2 minutes\n", - "Step 2174000 of 5000000; TPS: 6551.29; ETA: 7.2 minutes\n", - "Step 2175000 of 5000000; TPS: 6551.27; ETA: 7.2 minutes\n", - "Step 2176000 of 5000000; TPS: 6551.26; ETA: 7.2 minutes\n", - "Step 2177000 of 5000000; TPS: 6551.24; ETA: 7.2 minutes\n", - "Step 2178000 of 5000000; TPS: 6551.23; ETA: 7.2 minutes\n", - "Step 2179000 of 5000000; TPS: 6551.21; ETA: 7.2 minutes\n", - "Step 2180000 of 5000000; TPS: 6551.2; ETA: 7.2 minutes\n", - "Step 2181000 of 5000000; TPS: 6551.19; ETA: 7.2 minutes\n", - "Step 2182000 of 5000000; TPS: 6551.17; ETA: 7.2 minutes\n", - "Step 2183000 of 5000000; TPS: 6551.14; ETA: 7.2 minutes\n", - "Step 2184000 of 5000000; TPS: 6551.11; ETA: 7.2 minutes\n", - "Step 2185000 of 5000000; TPS: 6551.09; ETA: 7.2 minutes\n", - "Step 2186000 of 5000000; TPS: 6551.07; ETA: 7.2 minutes\n", - "Step 2187000 of 5000000; TPS: 6551.04; ETA: 7.2 minutes\n", - "Step 2188000 of 5000000; TPS: 6551.03; ETA: 7.2 minutes\n", - "Step 2189000 of 5000000; TPS: 6551.01; ETA: 7.2 minutes\n", - "Step 2190000 of 5000000; TPS: 6551.0; ETA: 7.1 minutes\n", - "Step 2191000 of 5000000; TPS: 6550.98; ETA: 7.1 minutes\n", - "Step 2192000 of 5000000; TPS: 6550.94; ETA: 7.1 minutes\n", - "Step 2193000 of 5000000; TPS: 6550.92; ETA: 7.1 minutes\n", - "Step 2194000 of 5000000; TPS: 6550.9; ETA: 7.1 minutes\n", - "Step 2195000 of 5000000; TPS: 6550.86; ETA: 7.1 minutes\n", - "Step 2196000 of 5000000; TPS: 6550.84; ETA: 7.1 minutes\n", - "Step 2197000 of 5000000; TPS: 6550.82; ETA: 7.1 minutes\n", - "Step 2198000 of 5000000; TPS: 6550.8; ETA: 7.1 minutes\n", - "Step 2199000 of 5000000; TPS: 6550.78; ETA: 7.1 minutes\n", - "Step 2200000 of 5000000; TPS: 6550.23; ETA: 7.1 minutes\n", - "Step 2201000 of 5000000; TPS: 6550.19; ETA: 7.1 minutes\n", - "Step 2202000 of 5000000; TPS: 6550.15; ETA: 7.1 minutes\n", - "Step 2203000 of 5000000; TPS: 6550.13; ETA: 7.1 minutes\n", - "Step 2204000 of 5000000; TPS: 6550.12; ETA: 7.1 minutes\n", - "Step 2205000 of 5000000; TPS: 6550.1; ETA: 7.1 minutes\n", - "Step 2206000 of 5000000; TPS: 6550.08; ETA: 7.1 minutes\n", - "Step 2207000 of 5000000; TPS: 6550.05; ETA: 7.1 minutes\n", - "Step 2208000 of 5000000; TPS: 6550.04; ETA: 7.1 minutes\n", - "Step 2209000 of 5000000; TPS: 6549.82; ETA: 7.1 minutes\n", - "Step 2210000 of 5000000; TPS: 6549.79; ETA: 7.1 minutes\n", - "Step 2211000 of 5000000; TPS: 6549.77; ETA: 7.1 minutes\n", - "Step 2212000 of 5000000; TPS: 6549.74; ETA: 7.1 minutes\n", - "Step 2213000 of 5000000; TPS: 6549.72; ETA: 7.1 minutes\n", - "Step 2214000 of 5000000; TPS: 6549.69; ETA: 7.1 minutes\n", - "Step 2215000 of 5000000; TPS: 6549.67; ETA: 7.1 minutes\n", - "Step 2216000 of 5000000; TPS: 6549.64; ETA: 7.1 minutes\n", - "Step 2217000 of 5000000; TPS: 6549.62; ETA: 7.1 minutes\n", - "Step 2218000 of 5000000; TPS: 6549.6; ETA: 7.1 minutes\n", - "Step 2219000 of 5000000; TPS: 6549.58; ETA: 7.1 minutes\n", - "Step 2220000 of 5000000; TPS: 6549.56; ETA: 7.1 minutes\n", - "Step 2221000 of 5000000; TPS: 6549.54; ETA: 7.1 minutes\n", - "Step 2222000 of 5000000; TPS: 6549.52; ETA: 7.1 minutes\n", - "Step 2223000 of 5000000; TPS: 6549.51; ETA: 7.1 minutes\n", - "Step 2224000 of 5000000; TPS: 6549.49; ETA: 7.1 minutes\n", - "Step 2225000 of 5000000; TPS: 6549.48; ETA: 7.1 minutes\n", - "Step 2226000 of 5000000; TPS: 6549.45; ETA: 7.1 minutes\n", - "Step 2227000 of 5000000; TPS: 6549.43; ETA: 7.1 minutes\n", - "Step 2228000 of 5000000; TPS: 6549.4; ETA: 7.1 minutes\n", - "Step 2229000 of 5000000; TPS: 6549.38; ETA: 7.1 minutes\n", - "Step 2230000 of 5000000; TPS: 6549.36; ETA: 7.0 minutes\n", - "Step 2231000 of 5000000; TPS: 6544.27; ETA: 7.1 minutes\n", - "Step 2232000 of 5000000; TPS: 6544.04; ETA: 7.0 minutes\n", - "Step 2233000 of 5000000; TPS: 6543.99; ETA: 7.0 minutes\n", - "Step 2234000 of 5000000; TPS: 6543.97; ETA: 7.0 minutes\n", - "Step 2235000 of 5000000; TPS: 6543.95; ETA: 7.0 minutes\n", - "Step 2236000 of 5000000; TPS: 6543.94; ETA: 7.0 minutes\n", - "Step 2237000 of 5000000; TPS: 6543.92; ETA: 7.0 minutes\n", - "Step 2238000 of 5000000; TPS: 6543.9; ETA: 7.0 minutes\n", - "Step 2239000 of 5000000; TPS: 6543.87; ETA: 7.0 minutes\n", - "Step 2240000 of 5000000; TPS: 6543.85; ETA: 7.0 minutes\n", - "Step 2241000 of 5000000; TPS: 6543.82; ETA: 7.0 minutes\n", - "Step 2242000 of 5000000; TPS: 6543.79; ETA: 7.0 minutes\n", - "Step 2243000 of 5000000; TPS: 6543.77; ETA: 7.0 minutes\n", - "Step 2244000 of 5000000; TPS: 6543.75; ETA: 7.0 minutes\n", - "Step 2245000 of 5000000; TPS: 6543.72; ETA: 7.0 minutes\n", - "Step 2246000 of 5000000; TPS: 6543.66; ETA: 7.0 minutes\n", - "Step 2247000 of 5000000; TPS: 6543.63; ETA: 7.0 minutes\n", - "Step 2248000 of 5000000; TPS: 6543.61; ETA: 7.0 minutes\n", - "Step 2249000 of 5000000; TPS: 6543.59; ETA: 7.0 minutes\n", - "Step 2250000 of 5000000; TPS: 6543.57; ETA: 7.0 minutes\n", - "Step 2251000 of 5000000; TPS: 6543.55; ETA: 7.0 minutes\n", - "Step 2252000 of 5000000; TPS: 6543.53; ETA: 7.0 minutes\n", - "Step 2253000 of 5000000; TPS: 6543.51; ETA: 7.0 minutes\n", - "Step 2254000 of 5000000; TPS: 6543.49; ETA: 7.0 minutes\n", - "Step 2255000 of 5000000; TPS: 6542.5; ETA: 7.0 minutes\n", - "Step 2256000 of 5000000; TPS: 6542.45; ETA: 7.0 minutes\n", - "Step 2257000 of 5000000; TPS: 6542.41; ETA: 7.0 minutes\n", - "Step 2258000 of 5000000; TPS: 6542.37; ETA: 7.0 minutes\n", - "Step 2259000 of 5000000; TPS: 6542.34; ETA: 7.0 minutes\n", - "Step 2260000 of 5000000; TPS: 6542.32; ETA: 7.0 minutes\n", - "Step 2261000 of 5000000; TPS: 6542.3; ETA: 7.0 minutes\n", - "Step 2262000 of 5000000; TPS: 6542.28; ETA: 7.0 minutes\n", - "Step 2263000 of 5000000; TPS: 6542.26; ETA: 7.0 minutes\n", - "Step 2264000 of 5000000; TPS: 6542.23; ETA: 7.0 minutes\n", - "Step 2265000 of 5000000; TPS: 6542.21; ETA: 7.0 minutes\n", - "Step 2266000 of 5000000; TPS: 6542.18; ETA: 7.0 minutes\n", - "Step 2267000 of 5000000; TPS: 6542.17; ETA: 7.0 minutes\n", - "Step 2268000 of 5000000; TPS: 6542.15; ETA: 7.0 minutes\n", - "Step 2269000 of 5000000; TPS: 6542.13; ETA: 7.0 minutes\n", - "Step 2270000 of 5000000; TPS: 6542.11; ETA: 7.0 minutes\n", - "Step 2271000 of 5000000; TPS: 6542.07; ETA: 7.0 minutes\n", - "Step 2272000 of 5000000; TPS: 5373.35; ETA: 8.5 minutes\n", - "Step 2273000 of 5000000; TPS: 5373.82; ETA: 8.5 minutes\n", - "Step 2274000 of 5000000; TPS: 5374.28; ETA: 8.5 minutes\n", - "Step 2275000 of 5000000; TPS: 5374.73; ETA: 8.5 minutes\n", - "Step 2276000 of 5000000; TPS: 5375.19; ETA: 8.4 minutes\n", - "Step 2277000 of 5000000; TPS: 5375.63; ETA: 8.4 minutes\n", - "Step 2278000 of 5000000; TPS: 5376.07; ETA: 8.4 minutes\n", - "Step 2279000 of 5000000; TPS: 5376.5; ETA: 8.4 minutes\n", - "Step 2280000 of 5000000; TPS: 5376.92; ETA: 8.4 minutes\n", - "Step 2281000 of 5000000; TPS: 5377.36; ETA: 8.4 minutes\n", - "Step 2282000 of 5000000; TPS: 5377.46; ETA: 8.4 minutes\n", - "Step 2283000 of 5000000; TPS: 5377.89; ETA: 8.4 minutes\n", - "Step 2284000 of 5000000; TPS: 5378.31; ETA: 8.4 minutes\n", - "Step 2285000 of 5000000; TPS: 5378.77; ETA: 8.4 minutes\n", - "Step 2286000 of 5000000; TPS: 5379.21; ETA: 8.4 minutes\n", - "Step 2287000 of 5000000; TPS: 5379.65; ETA: 8.4 minutes\n", - "Step 2288000 of 5000000; TPS: 5380.08; ETA: 8.4 minutes\n", - "Step 2289000 of 5000000; TPS: 5380.52; ETA: 8.4 minutes\n", - "Step 2290000 of 5000000; TPS: 5380.96; ETA: 8.4 minutes\n", - "Step 2291000 of 5000000; TPS: 5381.39; ETA: 8.4 minutes\n", - "Step 2292000 of 5000000; TPS: 5381.81; ETA: 8.4 minutes\n", - "Step 2293000 of 5000000; TPS: 5382.23; ETA: 8.4 minutes\n", - "Step 2294000 of 5000000; TPS: 5382.65; ETA: 8.4 minutes\n", - "Step 2295000 of 5000000; TPS: 5383.07; ETA: 8.4 minutes\n", - "Step 2296000 of 5000000; TPS: 5383.49; ETA: 8.4 minutes\n", - "Step 2297000 of 5000000; TPS: 5383.92; ETA: 8.4 minutes\n", - "Step 2298000 of 5000000; TPS: 5384.35; ETA: 8.4 minutes\n", - "Step 2299000 of 5000000; TPS: 5384.78; ETA: 8.4 minutes\n", - "Step 2300000 of 5000000; TPS: 5385.2; ETA: 8.4 minutes\n", - "Step 2301000 of 5000000; TPS: 5385.6; ETA: 8.4 minutes\n", - "Step 2302000 of 5000000; TPS: 5386.02; ETA: 8.3 minutes\n", - "Step 2303000 of 5000000; TPS: 5386.42; ETA: 8.3 minutes\n", - "Step 2304000 of 5000000; TPS: 5386.83; ETA: 8.3 minutes\n", - "Step 2305000 of 5000000; TPS: 5387.23; ETA: 8.3 minutes\n", - "Step 2306000 of 5000000; TPS: 5387.63; ETA: 8.3 minutes\n", - "Step 2307000 of 5000000; TPS: 5388.03; ETA: 8.3 minutes\n", - "Step 2308000 of 5000000; TPS: 5388.4; ETA: 8.3 minutes\n", - "Step 2309000 of 5000000; TPS: 5388.8; ETA: 8.3 minutes\n", - "Step 2310000 of 5000000; TPS: 5388.86; ETA: 8.3 minutes\n", - "Step 2311000 of 5000000; TPS: 5389.25; ETA: 8.3 minutes\n", - "Step 2312000 of 5000000; TPS: 5389.65; ETA: 8.3 minutes\n", - "Step 2313000 of 5000000; TPS: 5390.05; ETA: 8.3 minutes\n", - "Step 2314000 of 5000000; TPS: 5390.44; ETA: 8.3 minutes\n", - "Step 2315000 of 5000000; TPS: 5390.84; ETA: 8.3 minutes\n", - "Step 2316000 of 5000000; TPS: 5391.26; ETA: 8.3 minutes\n", - "Step 2317000 of 5000000; TPS: 5391.65; ETA: 8.3 minutes\n", - "Step 2318000 of 5000000; TPS: 5392.04; ETA: 8.3 minutes\n", - "Step 2319000 of 5000000; TPS: 5392.43; ETA: 8.3 minutes\n", - "Step 2320000 of 5000000; TPS: 5392.83; ETA: 8.3 minutes\n", - "Step 2321000 of 5000000; TPS: 5393.23; ETA: 8.3 minutes\n", - "Step 2322000 of 5000000; TPS: 5393.61; ETA: 8.3 minutes\n", - "Step 2323000 of 5000000; TPS: 5394.02; ETA: 8.3 minutes\n", - "Step 2324000 of 5000000; TPS: 5394.41; ETA: 8.3 minutes\n", - "Step 2325000 of 5000000; TPS: 5394.81; ETA: 8.3 minutes\n", - "Step 2326000 of 5000000; TPS: 5395.2; ETA: 8.3 minutes\n", - "Step 2327000 of 5000000; TPS: 5395.59; ETA: 8.3 minutes\n", - "Step 2328000 of 5000000; TPS: 5395.98; ETA: 8.3 minutes\n", - "Step 2329000 of 5000000; TPS: 5396.35; ETA: 8.2 minutes\n", - "Step 2330000 of 5000000; TPS: 5396.73; ETA: 8.2 minutes\n", - "Step 2331000 of 5000000; TPS: 5397.11; ETA: 8.2 minutes\n", - "Step 2332000 of 5000000; TPS: 5397.49; ETA: 8.2 minutes\n", - "Step 2333000 of 5000000; TPS: 5397.87; ETA: 8.2 minutes\n", - "Step 2334000 of 5000000; TPS: 5398.25; ETA: 8.2 minutes\n", - "Step 2335000 of 5000000; TPS: 5398.33; ETA: 8.2 minutes\n", - "Step 2336000 of 5000000; TPS: 5398.7; ETA: 8.2 minutes\n", - "Step 2337000 of 5000000; TPS: 5398.88; ETA: 8.2 minutes\n", - "Step 2338000 of 5000000; TPS: 5399.27; ETA: 8.2 minutes\n", - "Step 2339000 of 5000000; TPS: 5399.39; ETA: 8.2 minutes\n", - "Step 2340000 of 5000000; TPS: 5399.78; ETA: 8.2 minutes\n", - "Step 2341000 of 5000000; TPS: 5400.17; ETA: 8.2 minutes\n", - "Step 2342000 of 5000000; TPS: 5400.55; ETA: 8.2 minutes\n", - "Step 2343000 of 5000000; TPS: 5400.93; ETA: 8.2 minutes\n", - "Step 2344000 of 5000000; TPS: 5401.32; ETA: 8.2 minutes\n", - "Step 2345000 of 5000000; TPS: 5401.71; ETA: 8.2 minutes\n", - "Step 2346000 of 5000000; TPS: 5402.1; ETA: 8.2 minutes\n", - "Step 2347000 of 5000000; TPS: 5402.49; ETA: 8.2 minutes\n", - "Step 2348000 of 5000000; TPS: 5402.87; ETA: 8.2 minutes\n", - "Step 2349000 of 5000000; TPS: 5403.26; ETA: 8.2 minutes\n", - "Step 2350000 of 5000000; TPS: 5403.65; ETA: 8.2 minutes\n", - "Step 2351000 of 5000000; TPS: 5404.03; ETA: 8.2 minutes\n", - "Step 2352000 of 5000000; TPS: 5404.4; ETA: 8.2 minutes\n", - "Step 2353000 of 5000000; TPS: 5404.78; ETA: 8.2 minutes\n", - "Step 2354000 of 5000000; TPS: 5405.16; ETA: 8.2 minutes\n", - "Step 2355000 of 5000000; TPS: 5405.54; ETA: 8.2 minutes\n", - "Step 2356000 of 5000000; TPS: 5405.93; ETA: 8.2 minutes\n", - "Step 2357000 of 5000000; TPS: 5406.31; ETA: 8.1 minutes\n", - "Step 2358000 of 5000000; TPS: 5406.68; ETA: 8.1 minutes\n", - "Step 2359000 of 5000000; TPS: 5407.05; ETA: 8.1 minutes\n", - "Step 2360000 of 5000000; TPS: 5407.44; ETA: 8.1 minutes\n", - "Step 2361000 of 5000000; TPS: 5407.82; ETA: 8.1 minutes\n", - "Step 2362000 of 5000000; TPS: 5408.2; ETA: 8.1 minutes\n", - "Step 2363000 of 5000000; TPS: 5408.57; ETA: 8.1 minutes\n", - "Step 2364000 of 5000000; TPS: 5408.95; ETA: 8.1 minutes\n", - "Step 2365000 of 5000000; TPS: 5409.02; ETA: 8.1 minutes\n", - "Step 2366000 of 5000000; TPS: 5409.4; ETA: 8.1 minutes\n", - "Step 2367000 of 5000000; TPS: 5409.77; ETA: 8.1 minutes\n", - "Step 2368000 of 5000000; TPS: 5263.33; ETA: 8.3 minutes\n", - "Step 2369000 of 5000000; TPS: 5263.75; ETA: 8.3 minutes\n", - "Step 2370000 of 5000000; TPS: 5264.18; ETA: 8.3 minutes\n", - "Step 2371000 of 5000000; TPS: 5264.6; ETA: 8.3 minutes\n", - "Step 2372000 of 5000000; TPS: 5264.98; ETA: 8.3 minutes\n", - "Step 2373000 of 5000000; TPS: 5265.38; ETA: 8.3 minutes\n", - "Step 2374000 of 5000000; TPS: 5265.79; ETA: 8.3 minutes\n", - "Step 2375000 of 5000000; TPS: 5265.87; ETA: 8.3 minutes\n", - "Step 2376000 of 5000000; TPS: 5266.28; ETA: 8.3 minutes\n", - "Step 2377000 of 5000000; TPS: 5266.71; ETA: 8.3 minutes\n", - "Step 2378000 of 5000000; TPS: 5267.12; ETA: 8.3 minutes\n", - "Step 2379000 of 5000000; TPS: 5267.54; ETA: 8.3 minutes\n", - "Step 2380000 of 5000000; TPS: 5267.95; ETA: 8.3 minutes\n", - "Step 2381000 of 5000000; TPS: 5268.36; ETA: 8.3 minutes\n", - "Step 2382000 of 5000000; TPS: 5268.76; ETA: 8.3 minutes\n", - "Step 2383000 of 5000000; TPS: 5269.17; ETA: 8.3 minutes\n", - "Step 2384000 of 5000000; TPS: 5269.59; ETA: 8.3 minutes\n", - "Step 2385000 of 5000000; TPS: 5270.01; ETA: 8.3 minutes\n", - "Step 2386000 of 5000000; TPS: 5270.42; ETA: 8.3 minutes\n", - "Step 2387000 of 5000000; TPS: 5270.83; ETA: 8.3 minutes\n", - "Step 2388000 of 5000000; TPS: 5271.24; ETA: 8.3 minutes\n", - "Step 2389000 of 5000000; TPS: 5271.66; ETA: 8.3 minutes\n", - "Step 2390000 of 5000000; TPS: 5272.06; ETA: 8.3 minutes\n", - "Step 2391000 of 5000000; TPS: 5272.47; ETA: 8.2 minutes\n", - "Step 2392000 of 5000000; TPS: 5272.88; ETA: 8.2 minutes\n", - "Step 2393000 of 5000000; TPS: 5273.28; ETA: 8.2 minutes\n", - "Step 2394000 of 5000000; TPS: 5273.67; ETA: 8.2 minutes\n", - "Step 2395000 of 5000000; TPS: 5274.06; ETA: 8.2 minutes\n", - "Step 2396000 of 5000000; TPS: 5274.46; ETA: 8.2 minutes\n", - "Step 2397000 of 5000000; TPS: 5274.85; ETA: 8.2 minutes\n", - "Step 2398000 of 5000000; TPS: 5275.26; ETA: 8.2 minutes\n", - "Step 2399000 of 5000000; TPS: 5275.66; ETA: 8.2 minutes\n", - "Step 2400000 of 5000000; TPS: 5276.06; ETA: 8.2 minutes\n", - "Step 2401000 of 5000000; TPS: 5276.47; ETA: 8.2 minutes\n", - "Step 2402000 of 5000000; TPS: 5276.88; ETA: 8.2 minutes\n", - "Step 2403000 of 5000000; TPS: 5277.29; ETA: 8.2 minutes\n", - "Step 2404000 of 5000000; TPS: 5277.69; ETA: 8.2 minutes\n", - "Step 2405000 of 5000000; TPS: 5278.1; ETA: 8.2 minutes\n", - "Step 2406000 of 5000000; TPS: 5278.5; ETA: 8.2 minutes\n", - "Step 2407000 of 5000000; TPS: 5278.91; ETA: 8.2 minutes\n", - "Step 2408000 of 5000000; TPS: 5279.31; ETA: 8.2 minutes\n", - "Step 2409000 of 5000000; TPS: 5279.7; ETA: 8.2 minutes\n", - "Step 2410000 of 5000000; TPS: 5280.11; ETA: 8.2 minutes\n", - "Step 2411000 of 5000000; TPS: 5280.52; ETA: 8.2 minutes\n", - "Step 2412000 of 5000000; TPS: 5280.92; ETA: 8.2 minutes\n", - "Step 2413000 of 5000000; TPS: 5281.32; ETA: 8.2 minutes\n", - "Step 2414000 of 5000000; TPS: 5281.73; ETA: 8.2 minutes\n", - "Step 2415000 of 5000000; TPS: 5282.12; ETA: 8.2 minutes\n", - "Step 2416000 of 5000000; TPS: 5282.52; ETA: 8.2 minutes\n", - "Step 2417000 of 5000000; TPS: 5282.92; ETA: 8.1 minutes\n", - "Step 2418000 of 5000000; TPS: 5283.32; ETA: 8.1 minutes\n", - "Step 2419000 of 5000000; TPS: 5283.72; ETA: 8.1 minutes\n", - "Step 2420000 of 5000000; TPS: 5283.84; ETA: 8.1 minutes\n", - "Step 2421000 of 5000000; TPS: 5284.24; ETA: 8.1 minutes\n", - "Step 2422000 of 5000000; TPS: 5284.64; ETA: 8.1 minutes\n", - "Step 2423000 of 5000000; TPS: 5285.04; ETA: 8.1 minutes\n", - "Step 2424000 of 5000000; TPS: 5285.44; ETA: 8.1 minutes\n", - "Step 2425000 of 5000000; TPS: 5285.84; ETA: 8.1 minutes\n", - "Step 2426000 of 5000000; TPS: 5286.23; ETA: 8.1 minutes\n", - "Step 2427000 of 5000000; TPS: 5286.63; ETA: 8.1 minutes\n", - "Step 2428000 of 5000000; TPS: 5287.03; ETA: 8.1 minutes\n", - "Step 2429000 of 5000000; TPS: 5287.43; ETA: 8.1 minutes\n", - "Step 2430000 of 5000000; TPS: 5287.83; ETA: 8.1 minutes\n", - "Step 2431000 of 5000000; TPS: 5288.23; ETA: 8.1 minutes\n", - "Step 2432000 of 5000000; TPS: 5288.63; ETA: 8.1 minutes\n", - "Step 2433000 of 5000000; TPS: 5288.89; ETA: 8.1 minutes\n", - "Step 2434000 of 5000000; TPS: 5289.29; ETA: 8.1 minutes\n", - "Step 2435000 of 5000000; TPS: 5289.66; ETA: 8.1 minutes\n", - "Step 2436000 of 5000000; TPS: 5290.05; ETA: 8.1 minutes\n", - "Step 2437000 of 5000000; TPS: 5290.45; ETA: 8.1 minutes\n", - "Step 2438000 of 5000000; TPS: 5290.81; ETA: 8.1 minutes\n", - "Step 2439000 of 5000000; TPS: 5291.2; ETA: 8.1 minutes\n", - "Step 2440000 of 5000000; TPS: 5291.59; ETA: 8.1 minutes\n", - "Step 2441000 of 5000000; TPS: 5291.98; ETA: 8.1 minutes\n", - "Step 2442000 of 5000000; TPS: 5292.37; ETA: 8.1 minutes\n", - "Step 2443000 of 5000000; TPS: 5292.75; ETA: 8.1 minutes\n", - "Step 2444000 of 5000000; TPS: 5293.14; ETA: 8.0 minutes\n", - "Step 2445000 of 5000000; TPS: 5293.54; ETA: 8.0 minutes\n", - "Step 2446000 of 5000000; TPS: 5293.93; ETA: 8.0 minutes\n", - "Step 2447000 of 5000000; TPS: 5294.31; ETA: 8.0 minutes\n", - "Step 2448000 of 5000000; TPS: 5061.65; ETA: 8.4 minutes\n", - "Step 2449000 of 5000000; TPS: 5062.15; ETA: 8.4 minutes\n", - "Step 2450000 of 5000000; TPS: 5062.65; ETA: 8.4 minutes\n", - "Step 2451000 of 5000000; TPS: 5063.12; ETA: 8.4 minutes\n", - "Step 2452000 of 5000000; TPS: 5063.59; ETA: 8.4 minutes\n", - "Step 2453000 of 5000000; TPS: 5064.05; ETA: 8.4 minutes\n", - "Step 2454000 of 5000000; TPS: 5064.52; ETA: 8.4 minutes\n", - "Step 2455000 of 5000000; TPS: 5064.98; ETA: 8.4 minutes\n", - "Step 2456000 of 5000000; TPS: 5065.44; ETA: 8.4 minutes\n", - "Step 2457000 of 5000000; TPS: 5065.89; ETA: 8.4 minutes\n", - "Step 2458000 of 5000000; TPS: 5066.35; ETA: 8.4 minutes\n", - "Step 2459000 of 5000000; TPS: 5066.81; ETA: 8.4 minutes\n", - "Step 2460000 of 5000000; TPS: 5067.27; ETA: 8.4 minutes\n", - "Step 2461000 of 5000000; TPS: 5067.73; ETA: 8.4 minutes\n", - "Step 2462000 of 5000000; TPS: 5068.19; ETA: 8.3 minutes\n", - "Step 2463000 of 5000000; TPS: 5068.64; ETA: 8.3 minutes\n", - "Step 2464000 of 5000000; TPS: 5069.1; ETA: 8.3 minutes\n", - "Step 2465000 of 5000000; TPS: 5069.55; ETA: 8.3 minutes\n", - "Step 2466000 of 5000000; TPS: 5070.0; ETA: 8.3 minutes\n", - "Step 2467000 of 5000000; TPS: 5070.45; ETA: 8.3 minutes\n", - "Step 2468000 of 5000000; TPS: 5070.89; ETA: 8.3 minutes\n", - "Step 2469000 of 5000000; TPS: 5071.32; ETA: 8.3 minutes\n", - "Step 2470000 of 5000000; TPS: 5071.76; ETA: 8.3 minutes\n", - "Step 2471000 of 5000000; TPS: 5072.22; ETA: 8.3 minutes\n", - "Step 2472000 of 5000000; TPS: 5072.66; ETA: 8.3 minutes\n", - "Step 2473000 of 5000000; TPS: 5073.1; ETA: 8.3 minutes\n", - "Step 2474000 of 5000000; TPS: 5073.54; ETA: 8.3 minutes\n", - "Step 2475000 of 5000000; TPS: 5073.74; ETA: 8.3 minutes\n", - "Step 2476000 of 5000000; TPS: 5074.18; ETA: 8.3 minutes\n", - "Step 2477000 of 5000000; TPS: 5074.62; ETA: 8.3 minutes\n", - "Step 2478000 of 5000000; TPS: 5075.05; ETA: 8.3 minutes\n", - "Step 2479000 of 5000000; TPS: 5075.49; ETA: 8.3 minutes\n", - "Step 2480000 of 5000000; TPS: 5075.92; ETA: 8.3 minutes\n", - "Step 2481000 of 5000000; TPS: 5076.35; ETA: 8.3 minutes\n", - "Step 2482000 of 5000000; TPS: 5076.79; ETA: 8.3 minutes\n", - "Step 2483000 of 5000000; TPS: 5077.22; ETA: 8.3 minutes\n", - "Step 2484000 of 5000000; TPS: 5077.54; ETA: 8.3 minutes\n", - "Step 2485000 of 5000000; TPS: 5077.98; ETA: 8.3 minutes\n", - "Step 2486000 of 5000000; TPS: 5078.41; ETA: 8.3 minutes\n", - "Step 2487000 of 5000000; TPS: 5078.84; ETA: 8.2 minutes\n", - "Step 2488000 of 5000000; TPS: 5079.27; ETA: 8.2 minutes\n", - "Step 2489000 of 5000000; TPS: 5079.7; ETA: 8.2 minutes\n", - "Step 2490000 of 5000000; TPS: 5080.12; ETA: 8.2 minutes\n", - "Step 2491000 of 5000000; TPS: 5080.56; ETA: 8.2 minutes\n", - "Step 2492000 of 5000000; TPS: 5080.99; ETA: 8.2 minutes\n", - "Step 2493000 of 5000000; TPS: 5081.42; ETA: 8.2 minutes\n", - "Step 2494000 of 5000000; TPS: 5081.83; ETA: 8.2 minutes\n", - "Step 2495000 of 5000000; TPS: 5082.25; ETA: 8.2 minutes\n", - "Step 2496000 of 5000000; TPS: 5082.69; ETA: 8.2 minutes\n", - "Step 2497000 of 5000000; TPS: 5083.11; ETA: 8.2 minutes\n", - "Step 2498000 of 5000000; TPS: 5083.53; ETA: 8.2 minutes\n", - "Step 2499000 of 5000000; TPS: 5083.95; ETA: 8.2 minutes\n", - "Step 2500000 of 5000000; TPS: 5084.38; ETA: 8.2 minutes\n", - "Step 2501000 of 5000000; TPS: 5084.81; ETA: 8.2 minutes\n", - "Step 2502000 of 5000000; TPS: 5085.24; ETA: 8.2 minutes\n", - "Step 2503000 of 5000000; TPS: 5085.66; ETA: 8.2 minutes\n", - "Step 2504000 of 5000000; TPS: 5086.09; ETA: 8.2 minutes\n", - "Step 2505000 of 5000000; TPS: 5086.5; ETA: 8.2 minutes\n", - "Step 2506000 of 5000000; TPS: 5086.93; ETA: 8.2 minutes\n", - "Step 2507000 of 5000000; TPS: 5087.35; ETA: 8.2 minutes\n", - "Step 2508000 of 5000000; TPS: 5087.77; ETA: 8.2 minutes\n", - "Step 2509000 of 5000000; TPS: 5088.2; ETA: 8.2 minutes\n", - "Step 2510000 of 5000000; TPS: 5088.63; ETA: 8.2 minutes\n", - "Step 2511000 of 5000000; TPS: 5089.06; ETA: 8.2 minutes\n", - "Step 2512000 of 5000000; TPS: 5089.49; ETA: 8.1 minutes\n", - "Step 2513000 of 5000000; TPS: 5089.78; ETA: 8.1 minutes\n", - "Step 2514000 of 5000000; TPS: 5090.21; ETA: 8.1 minutes\n", - "Step 2515000 of 5000000; TPS: 5090.63; ETA: 8.1 minutes\n", - "Step 2516000 of 5000000; TPS: 5091.06; ETA: 8.1 minutes\n", - "Step 2517000 of 5000000; TPS: 5091.48; ETA: 8.1 minutes\n", - "Step 2518000 of 5000000; TPS: 5091.91; ETA: 8.1 minutes\n", - "Step 2519000 of 5000000; TPS: 5092.33; ETA: 8.1 minutes\n", - "Step 2520000 of 5000000; TPS: 5092.75; ETA: 8.1 minutes\n", - "Step 2521000 of 5000000; TPS: 5093.17; ETA: 8.1 minutes\n", - "Step 2522000 of 5000000; TPS: 5093.59; ETA: 8.1 minutes\n", - "Step 2523000 of 5000000; TPS: 5094.01; ETA: 8.1 minutes\n", - "Step 2524000 of 5000000; TPS: 5094.44; ETA: 8.1 minutes\n", - "Step 2525000 of 5000000; TPS: 5094.86; ETA: 8.1 minutes\n", - "Step 2526000 of 5000000; TPS: 5095.28; ETA: 8.1 minutes\n", - "Step 2527000 of 5000000; TPS: 5095.7; ETA: 8.1 minutes\n", - "Step 2528000 of 5000000; TPS: 5096.12; ETA: 8.1 minutes\n", - "Step 2529000 of 5000000; TPS: 5096.53; ETA: 8.1 minutes\n", - "Step 2530000 of 5000000; TPS: 5019.56; ETA: 8.2 minutes\n", - "Step 2531000 of 5000000; TPS: 5020.01; ETA: 8.2 minutes\n", - "Step 2532000 of 5000000; TPS: 5020.45; ETA: 8.2 minutes\n", - "Step 2533000 of 5000000; TPS: 5020.9; ETA: 8.2 minutes\n", - "Step 2534000 of 5000000; TPS: 5021.33; ETA: 8.2 minutes\n", - "Step 2535000 of 5000000; TPS: 5021.77; ETA: 8.2 minutes\n", - "Step 2536000 of 5000000; TPS: 5022.21; ETA: 8.2 minutes\n", - "Step 2537000 of 5000000; TPS: 5022.65; ETA: 8.2 minutes\n", - "Step 2538000 of 5000000; TPS: 5023.08; ETA: 8.2 minutes\n", - "Step 2539000 of 5000000; TPS: 5023.51; ETA: 8.2 minutes\n", - "Step 2540000 of 5000000; TPS: 5023.95; ETA: 8.2 minutes\n", - "Step 2541000 of 5000000; TPS: 5024.38; ETA: 8.2 minutes\n", - "Step 2542000 of 5000000; TPS: 5024.8; ETA: 8.2 minutes\n", - "Step 2543000 of 5000000; TPS: 5025.18; ETA: 8.1 minutes\n", - "Step 2544000 of 5000000; TPS: 5025.6; ETA: 8.1 minutes\n", - "Step 2545000 of 5000000; TPS: 5026.03; ETA: 8.1 minutes\n", - "Step 2546000 of 5000000; TPS: 5026.46; ETA: 8.1 minutes\n", - "Step 2547000 of 5000000; TPS: 5026.89; ETA: 8.1 minutes\n", - "Step 2548000 of 5000000; TPS: 5027.32; ETA: 8.1 minutes\n", - "Step 2549000 of 5000000; TPS: 5027.75; ETA: 8.1 minutes\n", - "Step 2550000 of 5000000; TPS: 5028.18; ETA: 8.1 minutes\n", - "Step 2551000 of 5000000; TPS: 5028.61; ETA: 8.1 minutes\n", - "Step 2552000 of 5000000; TPS: 5029.04; ETA: 8.1 minutes\n", - "Step 2553000 of 5000000; TPS: 5029.47; ETA: 8.1 minutes\n", - "Step 2554000 of 5000000; TPS: 5029.9; ETA: 8.1 minutes\n", - "Step 2555000 of 5000000; TPS: 5030.33; ETA: 8.1 minutes\n", - "Step 2556000 of 5000000; TPS: 5030.76; ETA: 8.1 minutes\n", - "Step 2557000 of 5000000; TPS: 5031.18; ETA: 8.1 minutes\n", - "Step 2558000 of 5000000; TPS: 5031.61; ETA: 8.1 minutes\n", - "Step 2559000 of 5000000; TPS: 5032.04; ETA: 8.1 minutes\n", - "Step 2560000 of 5000000; TPS: 5032.46; ETA: 8.1 minutes\n", - "Step 2561000 of 5000000; TPS: 5032.89; ETA: 8.1 minutes\n", - "Step 2562000 of 5000000; TPS: 5033.31; ETA: 8.1 minutes\n", - "Step 2563000 of 5000000; TPS: 5033.74; ETA: 8.1 minutes\n", - "Step 2564000 of 5000000; TPS: 5034.17; ETA: 8.1 minutes\n", - "Step 2565000 of 5000000; TPS: 5034.59; ETA: 8.1 minutes\n", - "Step 2566000 of 5000000; TPS: 5035.01; ETA: 8.1 minutes\n", - "Step 2567000 of 5000000; TPS: 5035.44; ETA: 8.1 minutes\n", - "Step 2568000 of 5000000; TPS: 5035.86; ETA: 8.0 minutes\n", - "Step 2569000 of 5000000; TPS: 5036.28; ETA: 8.0 minutes\n", - "Step 2570000 of 5000000; TPS: 5036.7; ETA: 8.0 minutes\n", - "Step 2571000 of 5000000; TPS: 5037.13; ETA: 8.0 minutes\n", - "Step 2572000 of 5000000; TPS: 5037.56; ETA: 8.0 minutes\n", - "Step 2573000 of 5000000; TPS: 5037.98; ETA: 8.0 minutes\n", - "Step 2574000 of 5000000; TPS: 5038.39; ETA: 8.0 minutes\n", - "Step 2575000 of 5000000; TPS: 5038.81; ETA: 8.0 minutes\n", - "Step 2576000 of 5000000; TPS: 5039.22; ETA: 8.0 minutes\n", - "Step 2577000 of 5000000; TPS: 5039.63; ETA: 8.0 minutes\n", - "Step 2578000 of 5000000; TPS: 5040.04; ETA: 8.0 minutes\n", - "Step 2579000 of 5000000; TPS: 5040.46; ETA: 8.0 minutes\n", - "Step 2580000 of 5000000; TPS: 5040.87; ETA: 8.0 minutes\n", - "Step 2581000 of 5000000; TPS: 5041.28; ETA: 8.0 minutes\n", - "Step 2582000 of 5000000; TPS: 5041.7; ETA: 8.0 minutes\n", - "Step 2583000 of 5000000; TPS: 5042.11; ETA: 8.0 minutes\n", - "Step 2584000 of 5000000; TPS: 5042.53; ETA: 8.0 minutes\n", - "Step 2585000 of 5000000; TPS: 5042.74; ETA: 8.0 minutes\n", - "Step 2586000 of 5000000; TPS: 5043.16; ETA: 8.0 minutes\n", - "Step 2587000 of 5000000; TPS: 5043.58; ETA: 8.0 minutes\n", - "Step 2588000 of 5000000; TPS: 5043.99; ETA: 8.0 minutes\n", - "Step 2589000 of 5000000; TPS: 5044.41; ETA: 8.0 minutes\n", - "Step 2590000 of 5000000; TPS: 5044.83; ETA: 8.0 minutes\n", - "Step 2591000 of 5000000; TPS: 5045.24; ETA: 8.0 minutes\n", - "Step 2592000 of 5000000; TPS: 5045.65; ETA: 8.0 minutes\n", - "Step 2593000 of 5000000; TPS: 5046.06; ETA: 8.0 minutes\n", - "Step 2594000 of 5000000; TPS: 5046.38; ETA: 7.9 minutes\n", - "Step 2595000 of 5000000; TPS: 5046.8; ETA: 7.9 minutes\n", - "Step 2596000 of 5000000; TPS: 5047.21; ETA: 7.9 minutes\n", - "Step 2597000 of 5000000; TPS: 5047.63; ETA: 7.9 minutes\n", - "Step 2598000 of 5000000; TPS: 5048.04; ETA: 7.9 minutes\n", - "Step 2599000 of 5000000; TPS: 5048.45; ETA: 7.9 minutes\n", - "Step 2600000 of 5000000; TPS: 5048.86; ETA: 7.9 minutes\n", - "Step 2601000 of 5000000; TPS: 5049.27; ETA: 7.9 minutes\n", - "Step 2602000 of 5000000; TPS: 5049.69; ETA: 7.9 minutes\n", - "Step 2603000 of 5000000; TPS: 5050.1; ETA: 7.9 minutes\n", - "Step 2604000 of 5000000; TPS: 5050.51; ETA: 7.9 minutes\n", - "Step 2605000 of 5000000; TPS: 5050.92; ETA: 7.9 minutes\n", - "Step 2606000 of 5000000; TPS: 5051.33; ETA: 7.9 minutes\n", - "Step 2607000 of 5000000; TPS: 5051.74; ETA: 7.9 minutes\n", - "Step 2608000 of 5000000; TPS: 5052.14; ETA: 7.9 minutes\n", - "Step 2609000 of 5000000; TPS: 5052.55; ETA: 7.9 minutes\n", - "Step 2610000 of 5000000; TPS: 5052.96; ETA: 7.9 minutes\n", - "Step 2611000 of 5000000; TPS: 5053.37; ETA: 7.9 minutes\n", - "Step 2612000 of 5000000; TPS: 5053.78; ETA: 7.9 minutes\n", - "Step 2613000 of 5000000; TPS: 5054.19; ETA: 7.9 minutes\n", - "Step 2614000 of 5000000; TPS: 5054.6; ETA: 7.9 minutes\n", - "Step 2615000 of 5000000; TPS: 5054.99; ETA: 7.9 minutes\n", - "Step 2616000 of 5000000; TPS: 5055.4; ETA: 7.9 minutes\n", - "Step 2617000 of 5000000; TPS: 5055.81; ETA: 7.9 minutes\n", - "Step 2618000 of 5000000; TPS: 5056.21; ETA: 7.9 minutes\n", - "Step 2619000 of 5000000; TPS: 5056.61; ETA: 7.8 minutes\n", - "Step 2620000 of 5000000; TPS: 5057.02; ETA: 7.8 minutes\n", - "Step 2621000 of 5000000; TPS: 5057.43; ETA: 7.8 minutes\n", - "Step 2622000 of 5000000; TPS: 5057.84; ETA: 7.8 minutes\n", - "Step 2623000 of 5000000; TPS: 5058.24; ETA: 7.8 minutes\n", - "Step 2624000 of 5000000; TPS: 5058.65; ETA: 7.8 minutes\n", - "Step 2625000 of 5000000; TPS: 4822.72; ETA: 8.2 minutes\n", - "Step 2626000 of 5000000; TPS: 4823.19; ETA: 8.2 minutes\n", - "Step 2627000 of 5000000; TPS: 4823.68; ETA: 8.2 minutes\n", - "Step 2628000 of 5000000; TPS: 4824.17; ETA: 8.2 minutes\n", - "Step 2629000 of 5000000; TPS: 4824.64; ETA: 8.2 minutes\n", - "Step 2630000 of 5000000; TPS: 4825.09; ETA: 8.2 minutes\n", - "Step 2631000 of 5000000; TPS: 4825.57; ETA: 8.2 minutes\n", - "Step 2632000 of 5000000; TPS: 4826.05; ETA: 8.2 minutes\n", - "Step 2633000 of 5000000; TPS: 4826.52; ETA: 8.2 minutes\n", - "Step 2634000 of 5000000; TPS: 4826.99; ETA: 8.2 minutes\n", - "Step 2635000 of 5000000; TPS: 4827.46; ETA: 8.2 minutes\n", - "Step 2636000 of 5000000; TPS: 4827.93; ETA: 8.2 minutes\n", - "Step 2637000 of 5000000; TPS: 4828.33; ETA: 8.2 minutes\n", - "Step 2638000 of 5000000; TPS: 4828.78; ETA: 8.2 minutes\n", - "Step 2639000 of 5000000; TPS: 4829.22; ETA: 8.1 minutes\n", - "Step 2640000 of 5000000; TPS: 4829.49; ETA: 8.1 minutes\n", - "Step 2641000 of 5000000; TPS: 4829.95; ETA: 8.1 minutes\n", - "Step 2642000 of 5000000; TPS: 4830.42; ETA: 8.1 minutes\n", - "Step 2643000 of 5000000; TPS: 4830.88; ETA: 8.1 minutes\n", - "Step 2644000 of 5000000; TPS: 4831.34; ETA: 8.1 minutes\n", - "Step 2645000 of 5000000; TPS: 4831.79; ETA: 8.1 minutes\n", - "Step 2646000 of 5000000; TPS: 4832.25; ETA: 8.1 minutes\n", - "Step 2647000 of 5000000; TPS: 4832.71; ETA: 8.1 minutes\n", - "Step 2648000 of 5000000; TPS: 4833.16; ETA: 8.1 minutes\n", - "Step 2649000 of 5000000; TPS: 4833.61; ETA: 8.1 minutes\n", - "Step 2650000 of 5000000; TPS: 4834.06; ETA: 8.1 minutes\n", - "Step 2651000 of 5000000; TPS: 4834.51; ETA: 8.1 minutes\n", - "Step 2652000 of 5000000; TPS: 4834.96; ETA: 8.1 minutes\n", - "Step 2653000 of 5000000; TPS: 4835.41; ETA: 8.1 minutes\n", - "Step 2654000 of 5000000; TPS: 4835.86; ETA: 8.1 minutes\n", - "Step 2655000 of 5000000; TPS: 4836.32; ETA: 8.1 minutes\n", - "Step 2656000 of 5000000; TPS: 4836.77; ETA: 8.1 minutes\n", - "Step 2657000 of 5000000; TPS: 4837.22; ETA: 8.1 minutes\n", - "Step 2658000 of 5000000; TPS: 4837.66; ETA: 8.1 minutes\n", - "Step 2659000 of 5000000; TPS: 4838.11; ETA: 8.1 minutes\n", - "Step 2660000 of 5000000; TPS: 4838.56; ETA: 8.1 minutes\n", - "Step 2661000 of 5000000; TPS: 4839.0; ETA: 8.1 minutes\n", - "Step 2662000 of 5000000; TPS: 4839.44; ETA: 8.1 minutes\n", - "Step 2663000 of 5000000; TPS: 4839.88; ETA: 8.0 minutes\n", - "Step 2664000 of 5000000; TPS: 4840.33; ETA: 8.0 minutes\n", - "Step 2665000 of 5000000; TPS: 4840.77; ETA: 8.0 minutes\n", - "Step 2666000 of 5000000; TPS: 4841.21; ETA: 8.0 minutes\n", - "Step 2667000 of 5000000; TPS: 4841.65; ETA: 8.0 minutes\n", - "Step 2668000 of 5000000; TPS: 4842.1; ETA: 8.0 minutes\n", - "Step 2669000 of 5000000; TPS: 4842.55; ETA: 8.0 minutes\n", - "Step 2670000 of 5000000; TPS: 4842.99; ETA: 8.0 minutes\n", - "Step 2671000 of 5000000; TPS: 4843.42; ETA: 8.0 minutes\n", - "Step 2672000 of 5000000; TPS: 4843.86; ETA: 8.0 minutes\n", - "Step 2673000 of 5000000; TPS: 4844.3; ETA: 8.0 minutes\n", - "Step 2674000 of 5000000; TPS: 4844.73; ETA: 8.0 minutes\n", - "Step 2675000 of 5000000; TPS: 4845.17; ETA: 8.0 minutes\n", - "Step 2676000 of 5000000; TPS: 4845.61; ETA: 8.0 minutes\n", - "Step 2677000 of 5000000; TPS: 4846.04; ETA: 8.0 minutes\n", - "Step 2678000 of 5000000; TPS: 4846.47; ETA: 8.0 minutes\n", - "Step 2679000 of 5000000; TPS: 4846.91; ETA: 8.0 minutes\n", - "Step 2680000 of 5000000; TPS: 4847.35; ETA: 8.0 minutes\n", - "Step 2681000 of 5000000; TPS: 4847.78; ETA: 8.0 minutes\n", - "Step 2682000 of 5000000; TPS: 4848.22; ETA: 8.0 minutes\n", - "Step 2683000 of 5000000; TPS: 4848.65; ETA: 8.0 minutes\n", - "Step 2684000 of 5000000; TPS: 4849.08; ETA: 8.0 minutes\n", - "Step 2685000 of 5000000; TPS: 4849.52; ETA: 8.0 minutes\n", - "Step 2686000 of 5000000; TPS: 4849.96; ETA: 8.0 minutes\n", - "Step 2687000 of 5000000; TPS: 4850.4; ETA: 7.9 minutes\n", - "Step 2688000 of 5000000; TPS: 4850.84; ETA: 7.9 minutes\n", - "Step 2689000 of 5000000; TPS: 4851.13; ETA: 7.9 minutes\n", - "Step 2690000 of 5000000; TPS: 4851.57; ETA: 7.9 minutes\n", - "Step 2691000 of 5000000; TPS: 4852.0; ETA: 7.9 minutes\n", - "Step 2692000 of 5000000; TPS: 4852.44; ETA: 7.9 minutes\n", - "Step 2693000 of 5000000; TPS: 4852.88; ETA: 7.9 minutes\n", - "Step 2694000 of 5000000; TPS: 4853.31; ETA: 7.9 minutes\n", - "Step 2695000 of 5000000; TPS: 4853.62; ETA: 7.9 minutes\n", - "Step 2696000 of 5000000; TPS: 4854.05; ETA: 7.9 minutes\n", - "Step 2697000 of 5000000; TPS: 4854.48; ETA: 7.9 minutes\n", - "Step 2698000 of 5000000; TPS: 4854.91; ETA: 7.9 minutes\n", - "Step 2699000 of 5000000; TPS: 4855.35; ETA: 7.9 minutes\n", - "Step 2700000 of 5000000; TPS: 4855.78; ETA: 7.9 minutes\n", - "Step 2701000 of 5000000; TPS: 4856.21; ETA: 7.9 minutes\n", - "Step 2702000 of 5000000; TPS: 4856.63; ETA: 7.9 minutes\n", - "Step 2703000 of 5000000; TPS: 4857.06; ETA: 7.9 minutes\n", - "Step 2704000 of 5000000; TPS: 4857.49; ETA: 7.9 minutes\n", - "Step 2705000 of 5000000; TPS: 4857.92; ETA: 7.9 minutes\n", - "Step 2706000 of 5000000; TPS: 4858.35; ETA: 7.9 minutes\n", - "Step 2707000 of 5000000; TPS: 4858.79; ETA: 7.9 minutes\n", - "Step 2708000 of 5000000; TPS: 4859.22; ETA: 7.9 minutes\n", - "Step 2709000 of 5000000; TPS: 4859.65; ETA: 7.9 minutes\n", - "Step 2710000 of 5000000; TPS: 4860.08; ETA: 7.9 minutes\n", - "Step 2711000 of 5000000; TPS: 4860.5; ETA: 7.8 minutes\n", - "Step 2712000 of 5000000; TPS: 4860.92; ETA: 7.8 minutes\n", - "Step 2713000 of 5000000; TPS: 4861.35; ETA: 7.8 minutes\n", - "Step 2714000 of 5000000; TPS: 4861.78; ETA: 7.8 minutes\n", - "Step 2715000 of 5000000; TPS: 4862.2; ETA: 7.8 minutes\n", - "Step 2716000 of 5000000; TPS: 4862.63; ETA: 7.8 minutes\n", - "Step 2717000 of 5000000; TPS: 4863.06; ETA: 7.8 minutes\n", - "Step 2718000 of 5000000; TPS: 4863.49; ETA: 7.8 minutes\n", - "Step 2719000 of 5000000; TPS: 4863.92; ETA: 7.8 minutes\n", - "Step 2720000 of 5000000; TPS: 4864.34; ETA: 7.8 minutes\n", - "Step 2721000 of 5000000; TPS: 4864.77; ETA: 7.8 minutes\n", - "Step 2722000 of 5000000; TPS: 4775.43; ETA: 8.0 minutes\n", - "Step 2723000 of 5000000; TPS: 4775.88; ETA: 7.9 minutes\n", - "Step 2724000 of 5000000; TPS: 4776.33; ETA: 7.9 minutes\n", - "Step 2725000 of 5000000; TPS: 4776.77; ETA: 7.9 minutes\n", - "Step 2726000 of 5000000; TPS: 4777.2; ETA: 7.9 minutes\n", - "Step 2727000 of 5000000; TPS: 4777.64; ETA: 7.9 minutes\n", - "Step 2728000 of 5000000; TPS: 4778.09; ETA: 7.9 minutes\n", - "Step 2729000 of 5000000; TPS: 4778.54; ETA: 7.9 minutes\n", - "Step 2730000 of 5000000; TPS: 4778.97; ETA: 7.9 minutes\n", - "Step 2731000 of 5000000; TPS: 4779.36; ETA: 7.9 minutes\n", - "Step 2732000 of 5000000; TPS: 4779.79; ETA: 7.9 minutes\n", - "Step 2733000 of 5000000; TPS: 4780.23; ETA: 7.9 minutes\n", - "Step 2734000 of 5000000; TPS: 4780.67; ETA: 7.9 minutes\n", - "Step 2735000 of 5000000; TPS: 4781.11; ETA: 7.9 minutes\n", - "Step 2736000 of 5000000; TPS: 4781.55; ETA: 7.9 minutes\n", - "Step 2737000 of 5000000; TPS: 4781.99; ETA: 7.9 minutes\n", - "Step 2738000 of 5000000; TPS: 4782.43; ETA: 7.9 minutes\n", - "Step 2739000 of 5000000; TPS: 4782.87; ETA: 7.9 minutes\n", - "Step 2740000 of 5000000; TPS: 4783.31; ETA: 7.9 minutes\n", - "Step 2741000 of 5000000; TPS: 4783.75; ETA: 7.9 minutes\n", - "Step 2742000 of 5000000; TPS: 4784.18; ETA: 7.9 minutes\n", - "Step 2743000 of 5000000; TPS: 4784.63; ETA: 7.9 minutes\n", - "Step 2744000 of 5000000; TPS: 4785.07; ETA: 7.9 minutes\n", - "Step 2745000 of 5000000; TPS: 4785.5; ETA: 7.9 minutes\n", - "Step 2746000 of 5000000; TPS: 4785.94; ETA: 7.8 minutes\n", - "Step 2747000 of 5000000; TPS: 4786.37; ETA: 7.8 minutes\n", - "Step 2748000 of 5000000; TPS: 4786.8; ETA: 7.8 minutes\n", - "Step 2749000 of 5000000; TPS: 4787.23; ETA: 7.8 minutes\n", - "Step 2750000 of 5000000; TPS: 4787.55; ETA: 7.8 minutes\n", - "Step 2751000 of 5000000; TPS: 4787.98; ETA: 7.8 minutes\n", - "Step 2752000 of 5000000; TPS: 4788.42; ETA: 7.8 minutes\n", - "Step 2753000 of 5000000; TPS: 4788.85; ETA: 7.8 minutes\n", - "Step 2754000 of 5000000; TPS: 4789.29; ETA: 7.8 minutes\n", - "Step 2755000 of 5000000; TPS: 4789.72; ETA: 7.8 minutes\n", - "Step 2756000 of 5000000; TPS: 4790.16; ETA: 7.8 minutes\n", - "Step 2757000 of 5000000; TPS: 4790.6; ETA: 7.8 minutes\n", - "Step 2758000 of 5000000; TPS: 4791.03; ETA: 7.8 minutes\n", - "Step 2759000 of 5000000; TPS: 4791.46; ETA: 7.8 minutes\n", - "Step 2760000 of 5000000; TPS: 4791.89; ETA: 7.8 minutes\n", - "Step 2761000 of 5000000; TPS: 4792.32; ETA: 7.8 minutes\n", - "Step 2762000 of 5000000; TPS: 4792.75; ETA: 7.8 minutes\n", - "Step 2763000 of 5000000; TPS: 4793.19; ETA: 7.8 minutes\n", - "Step 2764000 of 5000000; TPS: 4793.62; ETA: 7.8 minutes\n", - "Step 2765000 of 5000000; TPS: 4794.05; ETA: 7.8 minutes\n", - "Step 2766000 of 5000000; TPS: 4794.48; ETA: 7.8 minutes\n", - "Step 2767000 of 5000000; TPS: 4794.9; ETA: 7.8 minutes\n", - "Step 2768000 of 5000000; TPS: 4795.33; ETA: 7.8 minutes\n", - "Step 2769000 of 5000000; TPS: 4795.75; ETA: 7.8 minutes\n", - "Step 2770000 of 5000000; TPS: 4796.17; ETA: 7.7 minutes\n", - "Step 2771000 of 5000000; TPS: 4796.59; ETA: 7.7 minutes\n", - "Step 2772000 of 5000000; TPS: 4797.02; ETA: 7.7 minutes\n", - "Step 2773000 of 5000000; TPS: 4797.44; ETA: 7.7 minutes\n", - "Step 2774000 of 5000000; TPS: 4797.87; ETA: 7.7 minutes\n", - "Step 2775000 of 5000000; TPS: 4798.3; ETA: 7.7 minutes\n", - "Step 2776000 of 5000000; TPS: 4798.72; ETA: 7.7 minutes\n", - "Step 2777000 of 5000000; TPS: 4799.15; ETA: 7.7 minutes\n", - "Step 2778000 of 5000000; TPS: 4799.58; ETA: 7.7 minutes\n", - "Step 2779000 of 5000000; TPS: 4800.01; ETA: 7.7 minutes\n", - "Step 2780000 of 5000000; TPS: 4800.44; ETA: 7.7 minutes\n", - "Step 2781000 of 5000000; TPS: 4800.86; ETA: 7.7 minutes\n", - "Step 2782000 of 5000000; TPS: 4801.28; ETA: 7.7 minutes\n", - "Step 2783000 of 5000000; TPS: 4801.71; ETA: 7.7 minutes\n", - "Step 2784000 of 5000000; TPS: 4802.12; ETA: 7.7 minutes\n", - "Step 2785000 of 5000000; TPS: 4802.54; ETA: 7.7 minutes\n", - "Step 2786000 of 5000000; TPS: 4802.86; ETA: 7.7 minutes\n", - "Step 2787000 of 5000000; TPS: 4803.28; ETA: 7.7 minutes\n", - "Step 2788000 of 5000000; TPS: 4803.7; ETA: 7.7 minutes\n", - "Step 2789000 of 5000000; TPS: 4804.13; ETA: 7.7 minutes\n", - "Step 2790000 of 5000000; TPS: 4804.55; ETA: 7.7 minutes\n", - "Step 2791000 of 5000000; TPS: 4804.97; ETA: 7.7 minutes\n", - "Step 2792000 of 5000000; TPS: 4805.38; ETA: 7.7 minutes\n", - "Step 2793000 of 5000000; TPS: 4805.8; ETA: 7.7 minutes\n", - "Step 2794000 of 5000000; TPS: 4806.21; ETA: 7.6 minutes\n", - "Step 2795000 of 5000000; TPS: 4806.64; ETA: 7.6 minutes\n", - "Step 2796000 of 5000000; TPS: 4807.06; ETA: 7.6 minutes\n", - "Step 2797000 of 5000000; TPS: 4807.48; ETA: 7.6 minutes\n", - "Step 2798000 of 5000000; TPS: 4807.91; ETA: 7.6 minutes\n", - "Step 2799000 of 5000000; TPS: 4808.33; ETA: 7.6 minutes\n", - "Step 2800000 of 5000000; TPS: 4808.75; ETA: 7.6 minutes\n", - "Step 2801000 of 5000000; TPS: 4809.17; ETA: 7.6 minutes\n", - "Step 2802000 of 5000000; TPS: 4639.61; ETA: 7.9 minutes\n", - "Step 2803000 of 5000000; TPS: 4640.07; ETA: 7.9 minutes\n", - "Step 2804000 of 5000000; TPS: 4640.53; ETA: 7.9 minutes\n", - "Step 2805000 of 5000000; TPS: 4641.01; ETA: 7.9 minutes\n", - "Step 2806000 of 5000000; TPS: 4641.24; ETA: 7.9 minutes\n", - "Step 2807000 of 5000000; TPS: 4641.71; ETA: 7.9 minutes\n", - "Step 2808000 of 5000000; TPS: 4642.17; ETA: 7.9 minutes\n", - "Step 2809000 of 5000000; TPS: 4642.62; ETA: 7.9 minutes\n", - "Step 2810000 of 5000000; TPS: 4643.06; ETA: 7.9 minutes\n", - "Step 2811000 of 5000000; TPS: 4643.47; ETA: 7.9 minutes\n", - "Step 2812000 of 5000000; TPS: 4643.91; ETA: 7.9 minutes\n", - "Step 2813000 of 5000000; TPS: 4644.35; ETA: 7.8 minutes\n", - "Step 2814000 of 5000000; TPS: 4644.8; ETA: 7.8 minutes\n", - "Step 2815000 of 5000000; TPS: 4645.24; ETA: 7.8 minutes\n", - "Step 2816000 of 5000000; TPS: 4645.68; ETA: 7.8 minutes\n", - "Step 2817000 of 5000000; TPS: 4646.13; ETA: 7.8 minutes\n", - "Step 2818000 of 5000000; TPS: 4646.59; ETA: 7.8 minutes\n", - "Step 2819000 of 5000000; TPS: 4647.05; ETA: 7.8 minutes\n", - "Step 2820000 of 5000000; TPS: 4647.5; ETA: 7.8 minutes\n", - "Step 2821000 of 5000000; TPS: 4647.95; ETA: 7.8 minutes\n", - "Step 2822000 of 5000000; TPS: 4648.4; ETA: 7.8 minutes\n", - "Step 2823000 of 5000000; TPS: 4648.85; ETA: 7.8 minutes\n", - "Step 2824000 of 5000000; TPS: 4649.3; ETA: 7.8 minutes\n", - "Step 2825000 of 5000000; TPS: 4649.74; ETA: 7.8 minutes\n", - "Step 2826000 of 5000000; TPS: 4650.19; ETA: 7.8 minutes\n", - "Step 2827000 of 5000000; TPS: 4650.63; ETA: 7.8 minutes\n", - "Step 2828000 of 5000000; TPS: 4651.07; ETA: 7.8 minutes\n", - "Step 2829000 of 5000000; TPS: 4651.52; ETA: 7.8 minutes\n", - "Step 2830000 of 5000000; TPS: 4651.96; ETA: 7.8 minutes\n", - "Step 2831000 of 5000000; TPS: 4652.4; ETA: 7.8 minutes\n", - "Step 2832000 of 5000000; TPS: 4652.84; ETA: 7.8 minutes\n", - "Step 2833000 of 5000000; TPS: 4653.29; ETA: 7.8 minutes\n", - "Step 2834000 of 5000000; TPS: 4653.73; ETA: 7.8 minutes\n", - "Step 2835000 of 5000000; TPS: 4654.17; ETA: 7.8 minutes\n", - "Step 2836000 of 5000000; TPS: 4654.61; ETA: 7.7 minutes\n", - "Step 2837000 of 5000000; TPS: 4655.05; ETA: 7.7 minutes\n", - "Step 2838000 of 5000000; TPS: 4655.49; ETA: 7.7 minutes\n", - "Step 2839000 of 5000000; TPS: 4655.93; ETA: 7.7 minutes\n", - "Step 2840000 of 5000000; TPS: 4656.37; ETA: 7.7 minutes\n", - "Step 2841000 of 5000000; TPS: 4656.8; ETA: 7.7 minutes\n", - "Step 2842000 of 5000000; TPS: 4657.24; ETA: 7.7 minutes\n", - "Step 2843000 of 5000000; TPS: 4657.68; ETA: 7.7 minutes\n", - "Step 2844000 of 5000000; TPS: 4658.12; ETA: 7.7 minutes\n", - "Step 2845000 of 5000000; TPS: 4658.56; ETA: 7.7 minutes\n", - "Step 2846000 of 5000000; TPS: 4658.99; ETA: 7.7 minutes\n", - "Step 2847000 of 5000000; TPS: 4659.43; ETA: 7.7 minutes\n", - "Step 2848000 of 5000000; TPS: 4659.87; ETA: 7.7 minutes\n", - "Step 2849000 of 5000000; TPS: 4660.3; ETA: 7.7 minutes\n", - "Step 2850000 of 5000000; TPS: 4660.73; ETA: 7.7 minutes\n", - "Step 2851000 of 5000000; TPS: 4661.17; ETA: 7.7 minutes\n", - "Step 2852000 of 5000000; TPS: 4661.61; ETA: 7.7 minutes\n", - "Step 2853000 of 5000000; TPS: 4662.04; ETA: 7.7 minutes\n", - "Step 2854000 of 5000000; TPS: 4662.48; ETA: 7.7 minutes\n", - "Step 2855000 of 5000000; TPS: 4662.91; ETA: 7.7 minutes\n", - "Step 2856000 of 5000000; TPS: 4663.34; ETA: 7.7 minutes\n", - "Step 2857000 of 5000000; TPS: 4663.77; ETA: 7.7 minutes\n", - "Step 2858000 of 5000000; TPS: 4664.21; ETA: 7.7 minutes\n", - "Step 2859000 of 5000000; TPS: 4664.64; ETA: 7.6 minutes\n", - "Step 2860000 of 5000000; TPS: 4665.08; ETA: 7.6 minutes\n", - "Step 2861000 of 5000000; TPS: 4665.4; ETA: 7.6 minutes\n", - "Step 2862000 of 5000000; TPS: 4665.83; ETA: 7.6 minutes\n", - "Step 2863000 of 5000000; TPS: 4666.26; ETA: 7.6 minutes\n", - "Step 2864000 of 5000000; TPS: 4666.69; ETA: 7.6 minutes\n", - "Step 2865000 of 5000000; TPS: 4667.13; ETA: 7.6 minutes\n", - "Step 2866000 of 5000000; TPS: 4667.44; ETA: 7.6 minutes\n", - "Step 2867000 of 5000000; TPS: 4667.87; ETA: 7.6 minutes\n", - "Step 2868000 of 5000000; TPS: 4668.31; ETA: 7.6 minutes\n", - "Step 2869000 of 5000000; TPS: 4668.73; ETA: 7.6 minutes\n", - "Step 2870000 of 5000000; TPS: 4669.16; ETA: 7.6 minutes\n", - "Step 2871000 of 5000000; TPS: 4669.59; ETA: 7.6 minutes\n", - "Step 2872000 of 5000000; TPS: 4670.02; ETA: 7.6 minutes\n", - "Step 2873000 of 5000000; TPS: 4670.45; ETA: 7.6 minutes\n", - "Step 2874000 of 5000000; TPS: 4670.89; ETA: 7.6 minutes\n", - "Step 2875000 of 5000000; TPS: 4671.31; ETA: 7.6 minutes\n", - "Step 2876000 of 5000000; TPS: 4671.74; ETA: 7.6 minutes\n", - "Step 2877000 of 5000000; TPS: 4672.17; ETA: 7.6 minutes\n", - "Step 2878000 of 5000000; TPS: 4672.6; ETA: 7.6 minutes\n", - "Step 2879000 of 5000000; TPS: 4673.02; ETA: 7.6 minutes\n", - "Step 2880000 of 5000000; TPS: 4673.45; ETA: 7.6 minutes\n", - "Step 2881000 of 5000000; TPS: 4673.87; ETA: 7.6 minutes\n", - "Step 2882000 of 5000000; TPS: 4674.29; ETA: 7.6 minutes\n", - "Step 2883000 of 5000000; TPS: 4674.72; ETA: 7.5 minutes\n", - "Step 2884000 of 5000000; TPS: 4675.15; ETA: 7.5 minutes\n", - "Step 2885000 of 5000000; TPS: 4675.58; ETA: 7.5 minutes\n", - "Step 2886000 of 5000000; TPS: 4676.0; ETA: 7.5 minutes\n", - "Step 2887000 of 5000000; TPS: 4676.43; ETA: 7.5 minutes\n", - "Step 2888000 of 5000000; TPS: 4676.86; ETA: 7.5 minutes\n", - "Step 2889000 of 5000000; TPS: 4677.28; ETA: 7.5 minutes\n", - "Step 2890000 of 5000000; TPS: 4677.7; ETA: 7.5 minutes\n", - "Step 2891000 of 5000000; TPS: 4678.13; ETA: 7.5 minutes\n", - "Step 2892000 of 5000000; TPS: 4678.55; ETA: 7.5 minutes\n", - "Step 2893000 of 5000000; TPS: 4678.97; ETA: 7.5 minutes\n", - "Step 2894000 of 5000000; TPS: 4679.38; ETA: 7.5 minutes\n", - "Step 2895000 of 5000000; TPS: 4679.79; ETA: 7.5 minutes\n", - "Step 2896000 of 5000000; TPS: 4680.2; ETA: 7.5 minutes\n", - "Step 2897000 of 5000000; TPS: 4680.61; ETA: 7.5 minutes\n", - "Step 2898000 of 5000000; TPS: 4681.02; ETA: 7.5 minutes\n", - "Step 2899000 of 5000000; TPS: 4681.43; ETA: 7.5 minutes\n", - "Step 2900000 of 5000000; TPS: 4681.85; ETA: 7.5 minutes\n", - "Step 2901000 of 5000000; TPS: 4682.27; ETA: 7.5 minutes\n", - "Step 2902000 of 5000000; TPS: 4682.69; ETA: 7.5 minutes\n", - "Step 2903000 of 5000000; TPS: 4683.11; ETA: 7.5 minutes\n", - "Step 2904000 of 5000000; TPS: 4683.53; ETA: 7.5 minutes\n", - "Step 2905000 of 5000000; TPS: 4683.95; ETA: 7.5 minutes\n", - "Step 2906000 of 5000000; TPS: 4684.37; ETA: 7.5 minutes\n", - "Step 2907000 of 5000000; TPS: 4684.79; ETA: 7.4 minutes\n", - "Step 2908000 of 5000000; TPS: 4685.21; ETA: 7.4 minutes\n", - "Step 2909000 of 5000000; TPS: 4685.63; ETA: 7.4 minutes\n", - "Step 2910000 of 5000000; TPS: 4686.05; ETA: 7.4 minutes\n", - "Step 2911000 of 5000000; TPS: 4686.47; ETA: 7.4 minutes\n", - "Step 2912000 of 5000000; TPS: 521.96; ETA: 1.0 hours, 7.0 minutes\n", - "Step 2913000 of 5000000; TPS: 522.12; ETA: 1.0 hours, 7.0 minutes\n", - "Step 2914000 of 5000000; TPS: 522.29; ETA: 1.0 hours, 7.0 minutes\n", - "Step 2915000 of 5000000; TPS: 522.45; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2916000 of 5000000; TPS: 522.62; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2917000 of 5000000; TPS: 522.78; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2918000 of 5000000; TPS: 522.95; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2919000 of 5000000; TPS: 523.11; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2920000 of 5000000; TPS: 523.28; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2921000 of 5000000; TPS: 523.44; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2922000 of 5000000; TPS: 523.61; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2923000 of 5000000; TPS: 523.77; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2924000 of 5000000; TPS: 523.94; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2925000 of 5000000; TPS: 524.1; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2926000 of 5000000; TPS: 524.27; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2927000 of 5000000; TPS: 524.43; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2928000 of 5000000; TPS: 524.6; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2929000 of 5000000; TPS: 524.77; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2930000 of 5000000; TPS: 524.93; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2931000 of 5000000; TPS: 525.1; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2932000 of 5000000; TPS: 525.26; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2933000 of 5000000; TPS: 525.43; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2934000 of 5000000; TPS: 525.59; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2935000 of 5000000; TPS: 525.76; ETA: 1.0 hours, 6.0 minutes\n", - "Step 2936000 of 5000000; TPS: 525.92; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2937000 of 5000000; TPS: 526.09; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2938000 of 5000000; TPS: 526.25; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2939000 of 5000000; TPS: 526.42; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2940000 of 5000000; TPS: 526.58; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2941000 of 5000000; TPS: 526.75; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2942000 of 5000000; TPS: 526.91; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2943000 of 5000000; TPS: 527.08; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2944000 of 5000000; TPS: 527.24; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2945000 of 5000000; TPS: 527.41; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2946000 of 5000000; TPS: 527.57; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2947000 of 5000000; TPS: 527.74; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2948000 of 5000000; TPS: 527.9; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2949000 of 5000000; TPS: 528.07; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2950000 of 5000000; TPS: 528.23; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2951000 of 5000000; TPS: 528.4; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2952000 of 5000000; TPS: 528.56; ETA: 1.0 hours, 5.0 minutes\n", - "Step 2953000 of 5000000; TPS: 528.73; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2954000 of 5000000; TPS: 528.89; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2955000 of 5000000; TPS: 529.06; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2956000 of 5000000; TPS: 529.22; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2957000 of 5000000; TPS: 529.39; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2958000 of 5000000; TPS: 529.55; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2959000 of 5000000; TPS: 529.72; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2960000 of 5000000; TPS: 529.88; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2961000 of 5000000; TPS: 530.05; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2962000 of 5000000; TPS: 530.21; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2963000 of 5000000; TPS: 530.38; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2964000 of 5000000; TPS: 530.54; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2965000 of 5000000; TPS: 530.71; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2966000 of 5000000; TPS: 530.87; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2967000 of 5000000; TPS: 531.04; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2968000 of 5000000; TPS: 531.2; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2969000 of 5000000; TPS: 531.37; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2970000 of 5000000; TPS: 531.53; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2971000 of 5000000; TPS: 531.7; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2972000 of 5000000; TPS: 531.86; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2973000 of 5000000; TPS: 532.03; ETA: 1.0 hours, 4.0 minutes\n", - "Step 2974000 of 5000000; TPS: 532.19; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2975000 of 5000000; TPS: 532.35; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2976000 of 5000000; TPS: 532.52; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2977000 of 5000000; TPS: 532.68; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2978000 of 5000000; TPS: 532.85; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2979000 of 5000000; TPS: 533.01; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2980000 of 5000000; TPS: 533.18; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2981000 of 5000000; TPS: 533.34; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2982000 of 5000000; TPS: 533.51; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2983000 of 5000000; TPS: 533.67; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2984000 of 5000000; TPS: 533.84; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2985000 of 5000000; TPS: 534.0; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2986000 of 5000000; TPS: 534.17; ETA: 1.0 hours, 3.0 minutes\n", - "Step 2987000 of 5000000; TPS: 453.01; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2988000 of 5000000; TPS: 453.15; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2989000 of 5000000; TPS: 453.29; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2990000 of 5000000; TPS: 453.44; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2991000 of 5000000; TPS: 453.58; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2992000 of 5000000; TPS: 453.72; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2993000 of 5000000; TPS: 453.86; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2994000 of 5000000; TPS: 454.0; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2995000 of 5000000; TPS: 454.14; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2996000 of 5000000; TPS: 454.28; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2997000 of 5000000; TPS: 454.43; ETA: 1.0 hours, 14.0 minutes\n", - "Step 2998000 of 5000000; TPS: 454.57; ETA: 1.0 hours, 13.0 minutes\n", - "Step 2999000 of 5000000; TPS: 454.71; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3000000 of 5000000; TPS: 454.85; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3001000 of 5000000; TPS: 454.99; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3002000 of 5000000; TPS: 455.13; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3003000 of 5000000; TPS: 455.27; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3004000 of 5000000; TPS: 455.42; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3005000 of 5000000; TPS: 455.56; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3006000 of 5000000; TPS: 455.7; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3007000 of 5000000; TPS: 455.84; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3008000 of 5000000; TPS: 455.98; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3009000 of 5000000; TPS: 456.12; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3010000 of 5000000; TPS: 456.26; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3011000 of 5000000; TPS: 456.4; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3012000 of 5000000; TPS: 456.55; ETA: 1.0 hours, 13.0 minutes\n", - "Step 3013000 of 5000000; TPS: 456.69; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3014000 of 5000000; TPS: 456.83; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3015000 of 5000000; TPS: 456.97; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3016000 of 5000000; TPS: 457.11; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3017000 of 5000000; TPS: 457.25; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3018000 of 5000000; TPS: 457.39; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3019000 of 5000000; TPS: 457.54; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3020000 of 5000000; TPS: 457.68; ETA: 1.0 hours, 12.0 minutes\n", - "Step 3021000 of 5000000; TPS: 397.41; ETA: 1.0 hours, 23.0 minutes\n", - "Step 3022000 of 5000000; TPS: 397.54; ETA: 1.0 hours, 23.0 minutes\n", - "Step 3023000 of 5000000; TPS: 397.66; ETA: 1.0 hours, 23.0 minutes\n", - "Step 3024000 of 5000000; TPS: 397.78; ETA: 1.0 hours, 23.0 minutes\n", - "Step 3025000 of 5000000; TPS: 397.91; ETA: 1.0 hours, 23.0 minutes\n", - "Step 3026000 of 5000000; TPS: 398.03; ETA: 1.0 hours, 23.0 minutes\n", - "Step 3027000 of 5000000; TPS: 398.15; ETA: 1.0 hours, 23.0 minutes\n", - "Step 3028000 of 5000000; TPS: 398.28; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3029000 of 5000000; TPS: 398.4; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3030000 of 5000000; TPS: 398.53; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3031000 of 5000000; TPS: 398.65; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3032000 of 5000000; TPS: 398.77; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3033000 of 5000000; TPS: 398.9; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3034000 of 5000000; TPS: 399.02; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3035000 of 5000000; TPS: 399.14; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3036000 of 5000000; TPS: 399.27; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3037000 of 5000000; TPS: 399.39; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3038000 of 5000000; TPS: 399.52; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3039000 of 5000000; TPS: 399.64; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3040000 of 5000000; TPS: 399.76; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3041000 of 5000000; TPS: 399.89; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3042000 of 5000000; TPS: 400.01; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3043000 of 5000000; TPS: 400.13; ETA: 1.0 hours, 22.0 minutes\n", - "Step 3044000 of 5000000; TPS: 400.26; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3045000 of 5000000; TPS: 400.38; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3046000 of 5000000; TPS: 400.51; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3047000 of 5000000; TPS: 400.63; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3048000 of 5000000; TPS: 400.75; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3049000 of 5000000; TPS: 400.88; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3050000 of 5000000; TPS: 401.0; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3051000 of 5000000; TPS: 401.12; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3052000 of 5000000; TPS: 401.25; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3053000 of 5000000; TPS: 401.37; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3054000 of 5000000; TPS: 401.49; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3055000 of 5000000; TPS: 401.62; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3056000 of 5000000; TPS: 401.74; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3057000 of 5000000; TPS: 401.87; ETA: 1.0 hours, 21.0 minutes\n", - "Step 3058000 of 5000000; TPS: 401.99; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3059000 of 5000000; TPS: 402.11; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3060000 of 5000000; TPS: 402.24; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3061000 of 5000000; TPS: 402.36; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3062000 of 5000000; TPS: 402.48; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3063000 of 5000000; TPS: 402.61; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3064000 of 5000000; TPS: 402.73; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3065000 of 5000000; TPS: 402.85; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3066000 of 5000000; TPS: 402.98; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3067000 of 5000000; TPS: 403.1; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3068000 of 5000000; TPS: 403.22; ETA: 1.0 hours, 20.0 minutes\n", - "Step 3069000 of 5000000; TPS: 272.49; ETA: 1.0 hours, 58.0 minutes\n", - "Step 3070000 of 5000000; TPS: 272.58; ETA: 1.0 hours, 58.0 minutes\n", - "Step 3071000 of 5000000; TPS: 272.66; ETA: 1.0 hours, 58.0 minutes\n", - "Step 3072000 of 5000000; TPS: 272.75; ETA: 1.0 hours, 58.0 minutes\n", - "Step 3073000 of 5000000; TPS: 272.84; ETA: 1.0 hours, 58.0 minutes\n", - "Step 3074000 of 5000000; TPS: 272.92; ETA: 1.0 hours, 58.0 minutes\n", - "Step 3075000 of 5000000; TPS: 273.01; ETA: 1.0 hours, 58.0 minutes\n", - "Step 3076000 of 5000000; TPS: 273.09; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3077000 of 5000000; TPS: 273.18; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3078000 of 5000000; TPS: 273.26; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3079000 of 5000000; TPS: 273.35; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3080000 of 5000000; TPS: 273.43; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3081000 of 5000000; TPS: 273.52; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3082000 of 5000000; TPS: 273.6; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3083000 of 5000000; TPS: 273.69; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3084000 of 5000000; TPS: 273.77; ETA: 1.0 hours, 57.0 minutes\n", - "Step 3085000 of 5000000; TPS: 273.86; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3086000 of 5000000; TPS: 273.94; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3087000 of 5000000; TPS: 274.03; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3088000 of 5000000; TPS: 274.11; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3089000 of 5000000; TPS: 274.2; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3090000 of 5000000; TPS: 274.28; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3091000 of 5000000; TPS: 274.37; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3092000 of 5000000; TPS: 274.45; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3093000 of 5000000; TPS: 274.54; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3094000 of 5000000; TPS: 274.62; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3095000 of 5000000; TPS: 274.71; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3096000 of 5000000; TPS: 274.79; ETA: 1.0 hours, 56.0 minutes\n", - "Step 3097000 of 5000000; TPS: 274.88; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3098000 of 5000000; TPS: 274.96; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3099000 of 5000000; TPS: 275.05; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3100000 of 5000000; TPS: 275.13; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3101000 of 5000000; TPS: 275.22; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3102000 of 5000000; TPS: 275.3; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3103000 of 5000000; TPS: 275.39; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3104000 of 5000000; TPS: 275.47; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3105000 of 5000000; TPS: 275.56; ETA: 1.0 hours, 55.0 minutes\n", - "Step 3106000 of 5000000; TPS: 275.64; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3107000 of 5000000; TPS: 275.73; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3108000 of 5000000; TPS: 275.81; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3109000 of 5000000; TPS: 275.9; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3110000 of 5000000; TPS: 275.99; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3111000 of 5000000; TPS: 276.07; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3112000 of 5000000; TPS: 276.16; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3113000 of 5000000; TPS: 276.24; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3114000 of 5000000; TPS: 276.33; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3115000 of 5000000; TPS: 276.41; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3116000 of 5000000; TPS: 276.5; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3117000 of 5000000; TPS: 276.58; ETA: 1.0 hours, 54.0 minutes\n", - "Step 3118000 of 5000000; TPS: 276.67; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3119000 of 5000000; TPS: 276.75; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3120000 of 5000000; TPS: 276.84; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3121000 of 5000000; TPS: 276.92; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3122000 of 5000000; TPS: 277.01; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3123000 of 5000000; TPS: 277.09; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3124000 of 5000000; TPS: 277.18; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3125000 of 5000000; TPS: 277.26; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3126000 of 5000000; TPS: 277.35; ETA: 1.0 hours, 53.0 minutes\n", - "Step 3127000 of 5000000; TPS: 277.43; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3128000 of 5000000; TPS: 277.52; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3129000 of 5000000; TPS: 277.6; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3130000 of 5000000; TPS: 277.69; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3131000 of 5000000; TPS: 277.77; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3132000 of 5000000; TPS: 277.86; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3133000 of 5000000; TPS: 277.94; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3134000 of 5000000; TPS: 278.03; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3135000 of 5000000; TPS: 278.11; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3136000 of 5000000; TPS: 278.2; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3137000 of 5000000; TPS: 278.28; ETA: 1.0 hours, 52.0 minutes\n", - "Step 3138000 of 5000000; TPS: 210.27; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3139000 of 5000000; TPS: 210.34; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3140000 of 5000000; TPS: 210.4; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3141000 of 5000000; TPS: 210.46; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3142000 of 5000000; TPS: 210.53; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3143000 of 5000000; TPS: 210.59; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3144000 of 5000000; TPS: 210.66; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3145000 of 5000000; TPS: 210.72; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3146000 of 5000000; TPS: 210.79; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3147000 of 5000000; TPS: 210.85; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3148000 of 5000000; TPS: 210.92; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3149000 of 5000000; TPS: 210.98; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3150000 of 5000000; TPS: 211.05; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3151000 of 5000000; TPS: 211.11; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3152000 of 5000000; TPS: 211.18; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3153000 of 5000000; TPS: 211.24; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3154000 of 5000000; TPS: 211.31; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3155000 of 5000000; TPS: 211.37; ETA: 2.0 hours, 26.0 minutes\n", - "Step 3156000 of 5000000; TPS: 211.44; ETA: 2.0 hours, 25.0 minutes\n", - "Step 3157000 of 5000000; TPS: 211.5; ETA: 2.0 hours, 25.0 minutes\n", - "Step 3158000 of 5000000; TPS: 211.57; ETA: 2.0 hours, 25.0 minutes\n", - "Step 3159000 of 5000000; TPS: 211.63; ETA: 2.0 hours, 25.0 minutes\n", - "Step 3160000 of 5000000; TPS: 211.7; ETA: 2.0 hours, 25.0 minutes\n", - "Step 3161000 of 5000000; TPS: 211.76; ETA: 2.0 hours, 25.0 minutes\n", - "Step 3162000 of 5000000; TPS: 211.83; ETA: 2.0 hours, 25.0 minutes\n", - "Step 3163000 of 5000000; TPS: 211.89; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3164000 of 5000000; TPS: 211.96; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3165000 of 5000000; TPS: 212.02; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3166000 of 5000000; TPS: 212.09; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3167000 of 5000000; TPS: 212.15; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3168000 of 5000000; TPS: 212.22; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3169000 of 5000000; TPS: 212.28; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3170000 of 5000000; TPS: 212.35; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3171000 of 5000000; TPS: 212.41; ETA: 2.0 hours, 24.0 minutes\n", - "Step 3172000 of 5000000; TPS: 212.48; ETA: 2.0 hours, 23.0 minutes\n", - "Step 3173000 of 5000000; TPS: 212.54; ETA: 2.0 hours, 23.0 minutes\n", - "Step 3174000 of 5000000; TPS: 212.61; ETA: 2.0 hours, 23.0 minutes\n", - "Step 3175000 of 5000000; TPS: 212.67; ETA: 2.0 hours, 23.0 minutes\n", - "Step 3176000 of 5000000; TPS: 212.74; ETA: 2.0 hours, 23.0 minutes\n", - "Step 3177000 of 5000000; TPS: 212.8; ETA: 2.0 hours, 23.0 minutes\n", - "Step 3178000 of 5000000; TPS: 212.86; ETA: 2.0 hours, 23.0 minutes\n", - "Step 3179000 of 5000000; TPS: 212.93; ETA: 2.0 hours, 22.0 minutes\n", - "Step 3180000 of 5000000; TPS: 212.99; ETA: 2.0 hours, 22.0 minutes\n", - "Step 3181000 of 5000000; TPS: 213.06; ETA: 2.0 hours, 22.0 minutes\n", - "Step 3182000 of 5000000; TPS: 213.12; ETA: 2.0 hours, 22.0 minutes\n", - "Step 3183000 of 5000000; TPS: 213.19; ETA: 2.0 hours, 22.0 minutes\n", - "Step 3184000 of 5000000; TPS: 213.25; ETA: 2.0 hours, 22.0 minutes\n", - "Step 3185000 of 5000000; TPS: 213.32; ETA: 2.0 hours, 22.0 minutes\n", - "Step 3186000 of 5000000; TPS: 200.07; ETA: 2.0 hours, 31.0 minutes\n", - "Step 3187000 of 5000000; TPS: 200.13; ETA: 2.0 hours, 31.0 minutes\n", - "Step 3188000 of 5000000; TPS: 200.19; ETA: 2.0 hours, 31.0 minutes\n", - "Step 3189000 of 5000000; TPS: 200.25; ETA: 2.0 hours, 31.0 minutes\n", - "Step 3190000 of 5000000; TPS: 200.31; ETA: 2.0 hours, 31.0 minutes\n", - "Step 3191000 of 5000000; TPS: 200.37; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3192000 of 5000000; TPS: 200.43; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3193000 of 5000000; TPS: 200.49; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3194000 of 5000000; TPS: 200.55; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3195000 of 5000000; TPS: 200.61; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3196000 of 5000000; TPS: 200.68; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3197000 of 5000000; TPS: 200.74; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3198000 of 5000000; TPS: 200.8; ETA: 2.0 hours, 30.0 minutes\n", - "Step 3199000 of 5000000; TPS: 200.86; ETA: 2.0 hours, 29.0 minutes\n", - "Step 3200000 of 5000000; TPS: 200.92; ETA: 2.0 hours, 29.0 minutes\n", - "Step 3201000 of 5000000; TPS: 200.98; ETA: 2.0 hours, 29.0 minutes\n", - "Step 3202000 of 5000000; TPS: 201.04; ETA: 2.0 hours, 29.0 minutes\n", - "Step 3203000 of 5000000; TPS: 201.1; ETA: 2.0 hours, 29.0 minutes\n", - "Step 3204000 of 5000000; TPS: 201.16; ETA: 2.0 hours, 29.0 minutes\n", - "Step 3205000 of 5000000; TPS: 201.22; ETA: 2.0 hours, 29.0 minutes\n", - "Step 3206000 of 5000000; TPS: 201.28; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3207000 of 5000000; TPS: 201.35; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3208000 of 5000000; TPS: 201.41; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3209000 of 5000000; TPS: 201.47; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3210000 of 5000000; TPS: 201.53; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3211000 of 5000000; TPS: 201.59; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3212000 of 5000000; TPS: 201.65; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3213000 of 5000000; TPS: 201.71; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3214000 of 5000000; TPS: 201.77; ETA: 2.0 hours, 28.0 minutes\n", - "Step 3215000 of 5000000; TPS: 201.83; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3216000 of 5000000; TPS: 201.89; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3217000 of 5000000; TPS: 201.95; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3218000 of 5000000; TPS: 202.02; ETA: 2.0 hours, 27.0 minutes\n", - "Step 3219000 of 5000000; TPS: 173.21; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3220000 of 5000000; TPS: 173.26; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3221000 of 5000000; TPS: 173.31; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3222000 of 5000000; TPS: 173.37; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3223000 of 5000000; TPS: 173.42; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3224000 of 5000000; TPS: 173.47; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3225000 of 5000000; TPS: 173.52; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3226000 of 5000000; TPS: 173.57; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3227000 of 5000000; TPS: 173.63; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3228000 of 5000000; TPS: 173.68; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3229000 of 5000000; TPS: 173.73; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3230000 of 5000000; TPS: 173.78; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3231000 of 5000000; TPS: 173.84; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3232000 of 5000000; TPS: 173.89; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3233000 of 5000000; TPS: 173.94; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3234000 of 5000000; TPS: 173.99; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3235000 of 5000000; TPS: 174.05; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3236000 of 5000000; TPS: 174.1; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3237000 of 5000000; TPS: 174.15; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3238000 of 5000000; TPS: 174.2; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3239000 of 5000000; TPS: 174.26; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3240000 of 5000000; TPS: 174.31; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3241000 of 5000000; TPS: 174.36; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3242000 of 5000000; TPS: 174.41; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3243000 of 5000000; TPS: 174.47; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3244000 of 5000000; TPS: 174.52; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3245000 of 5000000; TPS: 174.57; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3246000 of 5000000; TPS: 174.62; ETA: 2.0 hours, 47.0 minutes\n", - "Step 3247000 of 5000000; TPS: 174.68; ETA: 2.0 hours, 47.0 minutes\n", - "Step 3248000 of 5000000; TPS: 174.73; ETA: 2.0 hours, 47.0 minutes\n", - "Step 3249000 of 5000000; TPS: 174.78; ETA: 2.0 hours, 47.0 minutes\n", - "Step 3250000 of 5000000; TPS: 174.83; ETA: 2.0 hours, 47.0 minutes\n", - "Step 3251000 of 5000000; TPS: 174.88; ETA: 2.0 hours, 47.0 minutes\n", - "Step 3252000 of 5000000; TPS: 174.94; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3253000 of 5000000; TPS: 174.99; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3254000 of 5000000; TPS: 175.04; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3255000 of 5000000; TPS: 175.09; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3256000 of 5000000; TPS: 175.15; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3257000 of 5000000; TPS: 175.2; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3258000 of 5000000; TPS: 175.25; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3259000 of 5000000; TPS: 175.3; ETA: 2.0 hours, 46.0 minutes\n", - "Step 3260000 of 5000000; TPS: 175.36; ETA: 2.0 hours, 45.0 minutes\n", - "Step 3261000 of 5000000; TPS: 175.41; ETA: 2.0 hours, 45.0 minutes\n", - "Step 3262000 of 5000000; TPS: 175.46; ETA: 2.0 hours, 45.0 minutes\n", - "Step 3263000 of 5000000; TPS: 175.51; ETA: 2.0 hours, 45.0 minutes\n", - "Step 3264000 of 5000000; TPS: 175.57; ETA: 2.0 hours, 45.0 minutes\n", - "Step 3265000 of 5000000; TPS: 175.62; ETA: 2.0 hours, 45.0 minutes\n", - "Step 3266000 of 5000000; TPS: 175.67; ETA: 2.0 hours, 44.0 minutes\n", - "Step 3267000 of 5000000; TPS: 175.72; ETA: 2.0 hours, 44.0 minutes\n", - "Step 3268000 of 5000000; TPS: 166.71; ETA: 2.0 hours, 53.0 minutes\n", - "Step 3269000 of 5000000; TPS: 166.76; ETA: 2.0 hours, 53.0 minutes\n", - "Step 3270000 of 5000000; TPS: 166.81; ETA: 2.0 hours, 53.0 minutes\n", - "Step 3271000 of 5000000; TPS: 166.86; ETA: 2.0 hours, 53.0 minutes\n", - "Step 3272000 of 5000000; TPS: 166.91; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3273000 of 5000000; TPS: 166.96; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3274000 of 5000000; TPS: 167.01; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3275000 of 5000000; TPS: 167.06; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3276000 of 5000000; TPS: 167.11; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3277000 of 5000000; TPS: 167.16; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3278000 of 5000000; TPS: 167.21; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3279000 of 5000000; TPS: 167.26; ETA: 2.0 hours, 52.0 minutes\n", - "Step 3280000 of 5000000; TPS: 167.31; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3281000 of 5000000; TPS: 167.36; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3282000 of 5000000; TPS: 167.41; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3283000 of 5000000; TPS: 167.46; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3284000 of 5000000; TPS: 167.51; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3285000 of 5000000; TPS: 167.56; ETA: 2.0 hours, 51.0 minutes\n", - "Step 3286000 of 5000000; TPS: 167.61; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3287000 of 5000000; TPS: 167.66; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3288000 of 5000000; TPS: 167.71; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3289000 of 5000000; TPS: 167.76; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3290000 of 5000000; TPS: 167.81; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3291000 of 5000000; TPS: 167.86; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3292000 of 5000000; TPS: 167.91; ETA: 2.0 hours, 50.0 minutes\n", - "Step 3293000 of 5000000; TPS: 167.96; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3294000 of 5000000; TPS: 168.01; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3295000 of 5000000; TPS: 168.06; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3296000 of 5000000; TPS: 168.11; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3297000 of 5000000; TPS: 168.16; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3298000 of 5000000; TPS: 168.2; ETA: 2.0 hours, 49.0 minutes\n", - "Step 3299000 of 5000000; TPS: 168.25; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3300000 of 5000000; TPS: 168.3; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3301000 of 5000000; TPS: 168.35; ETA: 2.0 hours, 48.0 minutes\n", - "Step 3302000 of 5000000; TPS: 134.08; ETA: 3.0 hours, 31.0 minutes\n", - "Step 3303000 of 5000000; TPS: 134.12; ETA: 3.0 hours, 31.0 minutes\n", - "Step 3304000 of 5000000; TPS: 134.16; ETA: 3.0 hours, 31.0 minutes\n", - "Step 3305000 of 5000000; TPS: 134.2; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3306000 of 5000000; TPS: 134.24; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3307000 of 5000000; TPS: 134.28; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3308000 of 5000000; TPS: 134.31; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3309000 of 5000000; TPS: 134.35; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3310000 of 5000000; TPS: 134.39; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3311000 of 5000000; TPS: 134.43; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3312000 of 5000000; TPS: 134.47; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3313000 of 5000000; TPS: 134.51; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3314000 of 5000000; TPS: 134.55; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3315000 of 5000000; TPS: 134.59; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3316000 of 5000000; TPS: 134.63; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3317000 of 5000000; TPS: 134.67; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3318000 of 5000000; TPS: 134.71; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3319000 of 5000000; TPS: 134.75; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3320000 of 5000000; TPS: 134.79; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3321000 of 5000000; TPS: 134.83; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3322000 of 5000000; TPS: 134.87; ETA: 3.0 hours, 27.0 minutes\n", - "Step 3323000 of 5000000; TPS: 134.91; ETA: 3.0 hours, 27.0 minutes\n", - "Step 3324000 of 5000000; TPS: 134.95; ETA: 3.0 hours, 27.0 minutes\n", - "Step 3325000 of 5000000; TPS: 134.99; ETA: 3.0 hours, 27.0 minutes\n", - "Step 3326000 of 5000000; TPS: 135.03; ETA: 3.0 hours, 27.0 minutes\n", - "Step 3327000 of 5000000; TPS: 135.07; ETA: 3.0 hours, 26.0 minutes\n", - "Step 3328000 of 5000000; TPS: 135.11; ETA: 3.0 hours, 26.0 minutes\n", - "Step 3329000 of 5000000; TPS: 135.15; ETA: 3.0 hours, 26.0 minutes\n", - "Step 3330000 of 5000000; TPS: 135.19; ETA: 3.0 hours, 26.0 minutes\n", - "Step 3331000 of 5000000; TPS: 135.23; ETA: 3.0 hours, 26.0 minutes\n", - "Step 3332000 of 5000000; TPS: 135.27; ETA: 3.0 hours, 26.0 minutes\n", - "Step 3333000 of 5000000; TPS: 135.31; ETA: 3.0 hours, 25.0 minutes\n", - "Step 3334000 of 5000000; TPS: 135.35; ETA: 3.0 hours, 25.0 minutes\n", - "Step 3335000 of 5000000; TPS: 135.39; ETA: 3.0 hours, 25.0 minutes\n", - "Step 3336000 of 5000000; TPS: 135.43; ETA: 3.0 hours, 25.0 minutes\n", - "Step 3337000 of 5000000; TPS: 135.47; ETA: 3.0 hours, 25.0 minutes\n", - "Step 3338000 of 5000000; TPS: 135.51; ETA: 3.0 hours, 24.0 minutes\n", - "Step 3339000 of 5000000; TPS: 135.55; ETA: 3.0 hours, 24.0 minutes\n", - "Step 3340000 of 5000000; TPS: 135.59; ETA: 3.0 hours, 24.0 minutes\n", - "Step 3341000 of 5000000; TPS: 135.63; ETA: 3.0 hours, 24.0 minutes\n", - "Step 3342000 of 5000000; TPS: 135.67; ETA: 3.0 hours, 24.0 minutes\n", - "Step 3343000 of 5000000; TPS: 135.71; ETA: 3.0 hours, 24.0 minutes\n", - "Step 3344000 of 5000000; TPS: 135.75; ETA: 3.0 hours, 23.0 minutes\n", - "Step 3345000 of 5000000; TPS: 135.79; ETA: 3.0 hours, 23.0 minutes\n", - "Step 3346000 of 5000000; TPS: 135.83; ETA: 3.0 hours, 23.0 minutes\n", - "Step 3347000 of 5000000; TPS: 135.87; ETA: 3.0 hours, 23.0 minutes\n", - "Step 3348000 of 5000000; TPS: 135.91; ETA: 3.0 hours, 23.0 minutes\n", - "Step 3349000 of 5000000; TPS: 135.95; ETA: 3.0 hours, 22.0 minutes\n", - "Step 3350000 of 5000000; TPS: 135.99; ETA: 3.0 hours, 22.0 minutes\n", - "Step 3351000 of 5000000; TPS: 136.03; ETA: 3.0 hours, 22.0 minutes\n", - "Step 3352000 of 5000000; TPS: 136.07; ETA: 3.0 hours, 22.0 minutes\n", - "Step 3353000 of 5000000; TPS: 136.11; ETA: 3.0 hours, 22.0 minutes\n", - "Step 3354000 of 5000000; TPS: 136.15; ETA: 3.0 hours, 22.0 minutes\n", - "Step 3355000 of 5000000; TPS: 136.19; ETA: 3.0 hours, 21.0 minutes\n", - "Step 3356000 of 5000000; TPS: 136.23; ETA: 3.0 hours, 21.0 minutes\n", - "Step 3357000 of 5000000; TPS: 136.27; ETA: 3.0 hours, 21.0 minutes\n", - "Step 3358000 of 5000000; TPS: 136.31; ETA: 3.0 hours, 21.0 minutes\n", - "Step 3359000 of 5000000; TPS: 136.35; ETA: 3.0 hours, 21.0 minutes\n", - "Step 3360000 of 5000000; TPS: 136.39; ETA: 3.0 hours, 20.0 minutes\n", - "Step 3361000 of 5000000; TPS: 136.42; ETA: 3.0 hours, 20.0 minutes\n", - "Step 3362000 of 5000000; TPS: 136.46; ETA: 3.0 hours, 20.0 minutes\n", - "Step 3363000 of 5000000; TPS: 136.5; ETA: 3.0 hours, 20.0 minutes\n", - "Step 3364000 of 5000000; TPS: 136.54; ETA: 3.0 hours, 20.0 minutes\n", - "Step 3365000 of 5000000; TPS: 136.58; ETA: 3.0 hours, 20.0 minutes\n", - "Step 3366000 of 5000000; TPS: 136.62; ETA: 3.0 hours, 19.0 minutes\n", - "Step 3367000 of 5000000; TPS: 136.66; ETA: 3.0 hours, 19.0 minutes\n", - "Step 3368000 of 5000000; TPS: 136.7; ETA: 3.0 hours, 19.0 minutes\n", - "Step 3369000 of 5000000; TPS: 136.74; ETA: 3.0 hours, 19.0 minutes\n", - "Step 3370000 of 5000000; TPS: 136.78; ETA: 3.0 hours, 19.0 minutes\n", - "Step 3371000 of 5000000; TPS: 136.82; ETA: 3.0 hours, 18.0 minutes\n", - "Step 3372000 of 5000000; TPS: 136.86; ETA: 3.0 hours, 18.0 minutes\n", - "Step 3373000 of 5000000; TPS: 136.9; ETA: 3.0 hours, 18.0 minutes\n", - "Step 3374000 of 5000000; TPS: 124.79; ETA: 3.0 hours, 37.0 minutes\n", - "Step 3375000 of 5000000; TPS: 124.83; ETA: 3.0 hours, 37.0 minutes\n", - "Step 3376000 of 5000000; TPS: 124.86; ETA: 3.0 hours, 37.0 minutes\n", - "Step 3377000 of 5000000; TPS: 124.9; ETA: 3.0 hours, 37.0 minutes\n", - "Step 3378000 of 5000000; TPS: 124.93; ETA: 3.0 hours, 36.0 minutes\n", - "Step 3379000 of 5000000; TPS: 124.97; ETA: 3.0 hours, 36.0 minutes\n", - "Step 3380000 of 5000000; TPS: 125.01; ETA: 3.0 hours, 36.0 minutes\n", - "Step 3381000 of 5000000; TPS: 125.04; ETA: 3.0 hours, 36.0 minutes\n", - "Step 3382000 of 5000000; TPS: 125.08; ETA: 3.0 hours, 36.0 minutes\n", - "Step 3383000 of 5000000; TPS: 125.12; ETA: 3.0 hours, 35.0 minutes\n", - "Step 3384000 of 5000000; TPS: 125.15; ETA: 3.0 hours, 35.0 minutes\n", - "Step 3385000 of 5000000; TPS: 125.19; ETA: 3.0 hours, 35.0 minutes\n", - "Step 3386000 of 5000000; TPS: 125.22; ETA: 3.0 hours, 35.0 minutes\n", - "Step 3387000 of 5000000; TPS: 125.26; ETA: 3.0 hours, 35.0 minutes\n", - "Step 3388000 of 5000000; TPS: 125.3; ETA: 3.0 hours, 34.0 minutes\n", - "Step 3389000 of 5000000; TPS: 125.33; ETA: 3.0 hours, 34.0 minutes\n", - "Step 3390000 of 5000000; TPS: 125.37; ETA: 3.0 hours, 34.0 minutes\n", - "Step 3391000 of 5000000; TPS: 125.41; ETA: 3.0 hours, 34.0 minutes\n", - "Step 3392000 of 5000000; TPS: 125.44; ETA: 3.0 hours, 34.0 minutes\n", - "Step 3393000 of 5000000; TPS: 125.48; ETA: 3.0 hours, 33.0 minutes\n", - "Step 3394000 of 5000000; TPS: 125.52; ETA: 3.0 hours, 33.0 minutes\n", - "Step 3395000 of 5000000; TPS: 125.55; ETA: 3.0 hours, 33.0 minutes\n", - "Step 3396000 of 5000000; TPS: 125.59; ETA: 3.0 hours, 33.0 minutes\n", - "Step 3397000 of 5000000; TPS: 125.62; ETA: 3.0 hours, 33.0 minutes\n", - "Step 3398000 of 5000000; TPS: 125.66; ETA: 3.0 hours, 32.0 minutes\n", - "Step 3399000 of 5000000; TPS: 125.7; ETA: 3.0 hours, 32.0 minutes\n", - "Step 3400000 of 5000000; TPS: 125.73; ETA: 3.0 hours, 32.0 minutes\n", - "Step 3401000 of 5000000; TPS: 125.77; ETA: 3.0 hours, 32.0 minutes\n", - "Step 3402000 of 5000000; TPS: 125.81; ETA: 3.0 hours, 32.0 minutes\n", - "Step 3403000 of 5000000; TPS: 125.84; ETA: 3.0 hours, 32.0 minutes\n", - "Step 3404000 of 5000000; TPS: 125.88; ETA: 3.0 hours, 31.0 minutes\n", - "Step 3405000 of 5000000; TPS: 125.91; ETA: 3.0 hours, 31.0 minutes\n", - "Step 3406000 of 5000000; TPS: 125.95; ETA: 3.0 hours, 31.0 minutes\n", - "Step 3407000 of 5000000; TPS: 125.99; ETA: 3.0 hours, 31.0 minutes\n", - "Step 3408000 of 5000000; TPS: 126.02; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3409000 of 5000000; TPS: 126.06; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3410000 of 5000000; TPS: 126.1; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3411000 of 5000000; TPS: 126.13; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3412000 of 5000000; TPS: 126.17; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3413000 of 5000000; TPS: 126.2; ETA: 3.0 hours, 30.0 minutes\n", - "Step 3414000 of 5000000; TPS: 126.24; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3415000 of 5000000; TPS: 126.28; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3416000 of 5000000; TPS: 126.31; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3417000 of 5000000; TPS: 126.35; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3418000 of 5000000; TPS: 126.39; ETA: 3.0 hours, 29.0 minutes\n", - "Step 3419000 of 5000000; TPS: 126.42; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3420000 of 5000000; TPS: 126.46; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3421000 of 5000000; TPS: 126.49; ETA: 3.0 hours, 28.0 minutes\n", - "Step 3422000 of 5000000; TPS: 106.66; ETA: 4.0 hours, 7.0 minutes\n", - "Step 3423000 of 5000000; TPS: 106.69; ETA: 4.0 hours, 6.0 minutes\n", - "Step 3424000 of 5000000; TPS: 106.72; ETA: 4.0 hours, 6.0 minutes\n", - "Step 3425000 of 5000000; TPS: 106.76; ETA: 4.0 hours, 6.0 minutes\n", - "Step 3426000 of 5000000; TPS: 106.79; ETA: 4.0 hours, 6.0 minutes\n", - "Step 3427000 of 5000000; TPS: 106.82; ETA: 4.0 hours, 5.0 minutes\n", - "Step 3428000 of 5000000; TPS: 106.85; ETA: 4.0 hours, 5.0 minutes\n", - "Step 3429000 of 5000000; TPS: 106.88; ETA: 4.0 hours, 5.0 minutes\n", - "Step 3430000 of 5000000; TPS: 106.91; ETA: 4.0 hours, 5.0 minutes\n", - "Step 3431000 of 5000000; TPS: 106.94; ETA: 4.0 hours, 4.0 minutes\n", - "Step 3432000 of 5000000; TPS: 106.97; ETA: 4.0 hours, 4.0 minutes\n", - "Step 3433000 of 5000000; TPS: 107.0; ETA: 4.0 hours, 4.0 minutes\n", - "Step 3434000 of 5000000; TPS: 107.03; ETA: 4.0 hours, 4.0 minutes\n", - "Step 3435000 of 5000000; TPS: 107.06; ETA: 4.0 hours, 4.0 minutes\n", - "Step 3436000 of 5000000; TPS: 107.09; ETA: 4.0 hours, 3.0 minutes\n", - "Step 3437000 of 5000000; TPS: 107.12; ETA: 4.0 hours, 3.0 minutes\n", - "Step 3438000 of 5000000; TPS: 107.15; ETA: 4.0 hours, 3.0 minutes\n", - "Step 3439000 of 5000000; TPS: 107.18; ETA: 4.0 hours, 3.0 minutes\n", - "Step 3440000 of 5000000; TPS: 107.22; ETA: 4.0 hours, 2.0 minutes\n", - "Step 3441000 of 5000000; TPS: 107.25; ETA: 4.0 hours, 2.0 minutes\n", - "Step 3442000 of 5000000; TPS: 107.28; ETA: 4.0 hours, 2.0 minutes\n", - "Step 3443000 of 5000000; TPS: 107.31; ETA: 4.0 hours, 2.0 minutes\n", - "Step 3444000 of 5000000; TPS: 107.34; ETA: 4.0 hours, 2.0 minutes\n", - "Step 3445000 of 5000000; TPS: 107.37; ETA: 4.0 hours, 1.0 minutes\n", - "Step 3446000 of 5000000; TPS: 107.4; ETA: 4.0 hours, 1.0 minutes\n", - "Step 3447000 of 5000000; TPS: 107.43; ETA: 4.0 hours, 1.0 minutes\n", - "Step 3448000 of 5000000; TPS: 107.46; ETA: 4.0 hours, 1.0 minutes\n", - "Step 3449000 of 5000000; TPS: 107.49; ETA: 4.0 hours, 0.0 minutes\n", - "Step 3450000 of 5000000; TPS: 107.52; ETA: 4.0 hours, 0.0 minutes\n", - "Step 3451000 of 5000000; TPS: 107.55; ETA: 4.0 hours, 0.0 minutes\n", - "Step 3452000 of 5000000; TPS: 107.58; ETA: 3.0 hours, 60.0 minutes\n", - "Step 3453000 of 5000000; TPS: 107.61; ETA: 3.0 hours, 60.0 minutes\n", - "Step 3454000 of 5000000; TPS: 107.64; ETA: 3.0 hours, 59.0 minutes\n", - "Step 3455000 of 5000000; TPS: 107.68; ETA: 3.0 hours, 59.0 minutes\n", - "Step 3456000 of 5000000; TPS: 107.71; ETA: 3.0 hours, 59.0 minutes\n", - "Step 3457000 of 5000000; TPS: 107.74; ETA: 3.0 hours, 59.0 minutes\n", - "Step 3458000 of 5000000; TPS: 107.77; ETA: 3.0 hours, 58.0 minutes\n", - "Step 3459000 of 5000000; TPS: 107.8; ETA: 3.0 hours, 58.0 minutes\n", - "Step 3460000 of 5000000; TPS: 107.83; ETA: 3.0 hours, 58.0 minutes\n", - "Step 3461000 of 5000000; TPS: 107.86; ETA: 3.0 hours, 58.0 minutes\n", - "Step 3462000 of 5000000; TPS: 107.89; ETA: 3.0 hours, 58.0 minutes\n", - "Step 3463000 of 5000000; TPS: 107.92; ETA: 3.0 hours, 57.0 minutes\n", - "Step 3464000 of 5000000; TPS: 107.95; ETA: 3.0 hours, 57.0 minutes\n", - "Step 3465000 of 5000000; TPS: 107.98; ETA: 3.0 hours, 57.0 minutes\n", - "Step 3466000 of 5000000; TPS: 108.01; ETA: 3.0 hours, 57.0 minutes\n", - "Step 3467000 of 5000000; TPS: 108.04; ETA: 3.0 hours, 56.0 minutes\n", - "Step 3468000 of 5000000; TPS: 108.07; ETA: 3.0 hours, 56.0 minutes\n", - "Step 3469000 of 5000000; TPS: 93.42; ETA: 4.0 hours, 33.0 minutes\n", - "Step 3470000 of 5000000; TPS: 93.45; ETA: 4.0 hours, 33.0 minutes\n", - "Step 3471000 of 5000000; TPS: 93.47; ETA: 4.0 hours, 33.0 minutes\n", - "Step 3472000 of 5000000; TPS: 93.5; ETA: 4.0 hours, 32.0 minutes\n", - "Step 3473000 of 5000000; TPS: 93.53; ETA: 4.0 hours, 32.0 minutes\n", - "Step 3474000 of 5000000; TPS: 93.55; ETA: 4.0 hours, 32.0 minutes\n", - "Step 3475000 of 5000000; TPS: 93.58; ETA: 4.0 hours, 32.0 minutes\n", - "Step 3476000 of 5000000; TPS: 93.61; ETA: 4.0 hours, 31.0 minutes\n", - "Step 3477000 of 5000000; TPS: 93.63; ETA: 4.0 hours, 31.0 minutes\n", - "Step 3478000 of 5000000; TPS: 93.66; ETA: 4.0 hours, 31.0 minutes\n", - "Step 3479000 of 5000000; TPS: 93.69; ETA: 4.0 hours, 31.0 minutes\n", - "Step 3480000 of 5000000; TPS: 93.71; ETA: 4.0 hours, 30.0 minutes\n", - "Step 3481000 of 5000000; TPS: 93.74; ETA: 4.0 hours, 30.0 minutes\n", - "Step 3482000 of 5000000; TPS: 93.77; ETA: 4.0 hours, 30.0 minutes\n", - "Step 3483000 of 5000000; TPS: 93.79; ETA: 4.0 hours, 30.0 minutes\n", - "Step 3484000 of 5000000; TPS: 93.82; ETA: 4.0 hours, 29.0 minutes\n", - "Step 3485000 of 5000000; TPS: 93.85; ETA: 4.0 hours, 29.0 minutes\n", - "Step 3486000 of 5000000; TPS: 93.87; ETA: 4.0 hours, 29.0 minutes\n", - "Step 3487000 of 5000000; TPS: 93.9; ETA: 4.0 hours, 28.0 minutes\n", - "Step 3488000 of 5000000; TPS: 93.92; ETA: 4.0 hours, 28.0 minutes\n", - "Step 3489000 of 5000000; TPS: 93.95; ETA: 4.0 hours, 28.0 minutes\n", - "Step 3490000 of 5000000; TPS: 93.98; ETA: 4.0 hours, 28.0 minutes\n", - "Step 3491000 of 5000000; TPS: 94.0; ETA: 4.0 hours, 28.0 minutes\n", - "Step 3492000 of 5000000; TPS: 94.03; ETA: 4.0 hours, 27.0 minutes\n", - "Step 3493000 of 5000000; TPS: 94.06; ETA: 4.0 hours, 27.0 minutes\n", - "Step 3494000 of 5000000; TPS: 94.08; ETA: 4.0 hours, 27.0 minutes\n", - "Step 3495000 of 5000000; TPS: 94.11; ETA: 4.0 hours, 26.0 minutes\n", - "Step 3496000 of 5000000; TPS: 94.14; ETA: 4.0 hours, 26.0 minutes\n", - "Step 3497000 of 5000000; TPS: 94.16; ETA: 4.0 hours, 26.0 minutes\n", - "Step 3498000 of 5000000; TPS: 94.19; ETA: 4.0 hours, 26.0 minutes\n", - "Step 3499000 of 5000000; TPS: 94.22; ETA: 4.0 hours, 26.0 minutes\n", - "Step 3500000 of 5000000; TPS: 94.24; ETA: 4.0 hours, 25.0 minutes\n", - "Step 3501000 of 5000000; TPS: 94.27; ETA: 4.0 hours, 25.0 minutes\n", - "Step 3502000 of 5000000; TPS: 94.3; ETA: 4.0 hours, 25.0 minutes\n", - "Step 3503000 of 5000000; TPS: 94.32; ETA: 4.0 hours, 24.0 minutes\n", - "Step 3504000 of 5000000; TPS: 94.35; ETA: 4.0 hours, 24.0 minutes\n", - "Step 3505000 of 5000000; TPS: 94.38; ETA: 4.0 hours, 24.0 minutes\n", - "Step 3506000 of 5000000; TPS: 94.4; ETA: 4.0 hours, 24.0 minutes\n", - "Step 3507000 of 5000000; TPS: 94.43; ETA: 4.0 hours, 24.0 minutes\n", - "Step 3508000 of 5000000; TPS: 94.46; ETA: 4.0 hours, 23.0 minutes\n", - "Step 3509000 of 5000000; TPS: 94.48; ETA: 4.0 hours, 23.0 minutes\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "Particle with unique tag 905 is no longer in the simulation box.\n\nCartesian coordinates: \nx: 121.375 y: -187.093 z: 332.759\nFractional coordinates: \nf.x: 2.64227 f.y: -2.8022 f.z: 6.37321\nLocal box lo: (-28.3285, -28.3285, -28.3285)\n hi: (28.3285, 28.3285, 28.3285)\n", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mRuntimeError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[20]\u001b[39m\u001b[32m, line 27\u001b[39m\n\u001b[32m 25\u001b[39m sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=cpu, dt = dt, gsd_write_freq=\u001b[38;5;28mint\u001b[39m(\u001b[32m1000\u001b[39m), log_file_name = log, gsd_file_name = gsd)\n\u001b[32m 26\u001b[39m \u001b[38;5;66;03m#sim.run_update_volume(final_box_lengths=[(1500/0.8)**1/3, (1500/0.8)**1/3, (1500/0.8)**1/3], kT=6.0, n_steps=5e6,tau_kt=100*sim.dt,period=10,thermalize_particles=True)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m27\u001b[39m \u001b[43msim\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun_update_volume\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfinal_box_lengths\u001b[49m\u001b[43m=\u001b[49m\u001b[43mtarget_box\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkT\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m6.0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mn_steps\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m5e6\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mtau_kt\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m100\u001b[39;49m\u001b[43m*\u001b[49m\u001b[43msim\u001b[49m\u001b[43m.\u001b[49m\u001b[43mdt\u001b[49m\u001b[43m,\u001b[49m\u001b[43mperiod\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mthermalize_particles\u001b[49m\u001b[43m=\u001b[49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[32m 28\u001b[39m sim.run_NVT(n_steps=\u001b[32m1e6\u001b[39m, kT=\u001b[32m5\u001b[39m, tau_kt=dt*\u001b[32m100\u001b[39m)\n\u001b[32m 29\u001b[39m sim.flush_writers()\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/lab/flowerMD/flowermd/base/simulation.py:783\u001b[39m, in \u001b[36mSimulation.run_update_volume\u001b[39m\u001b[34m(self, final_box_lengths, n_steps, period, kT, tau_kt, thermalize_particles, write_at_start)\u001b[39m\n\u001b[32m 778\u001b[39m std_out_logger_printer = hoomd.update.CustomUpdater(\n\u001b[32m 779\u001b[39m trigger=hoomd.trigger.Periodic(\u001b[38;5;28mself\u001b[39m._std_out_freq),\n\u001b[32m 780\u001b[39m action=std_out_logger,\n\u001b[32m 781\u001b[39m )\n\u001b[32m 782\u001b[39m \u001b[38;5;28mself\u001b[39m.operations.updaters.append(std_out_logger_printer)\n\u001b[32m--> \u001b[39m\u001b[32m783\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43msteps\u001b[49m\u001b[43m=\u001b[49m\u001b[43mn_steps\u001b[49m\u001b[43m \u001b[49m\u001b[43m+\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwrite_at_start\u001b[49m\u001b[43m=\u001b[49m\u001b[43mwrite_at_start\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m 784\u001b[39m \u001b[38;5;28mself\u001b[39m.operations.updaters.remove(std_out_logger_printer)\n\u001b[32m 785\u001b[39m \u001b[38;5;28mself\u001b[39m.operations.updaters.remove(box_resizer)\n", - "\u001b[36mFile \u001b[39m\u001b[32m~/miniconda3/envs/flowermd-dev/lib/python3.12/site-packages/hoomd/simulation.py:561\u001b[39m, in \u001b[36mSimulation.run\u001b[39m\u001b[34m(self, steps, write_at_start)\u001b[39m\n\u001b[32m 558\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m steps_int < \u001b[32m0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m steps_int > TIMESTEP_MAX - \u001b[32m1\u001b[39m:\n\u001b[32m 559\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33msteps must be in the range [0, \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mTIMESTEP_MAX\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39m\u001b[32m1\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m--> \u001b[39m\u001b[32m561\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_cpp_sys\u001b[49m\u001b[43m.\u001b[49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[43msteps_int\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwrite_at_start\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[31mRuntimeError\u001b[39m: Particle with unique tag 905 is no longer in the simulation box.\n\nCartesian coordinates: \nx: 121.375 y: -187.093 z: 332.759\nFractional coordinates: \nf.x: 2.64227 f.y: -2.8022 f.z: 6.37321\nLocal box lo: (-28.3285, -28.3285, -28.3285)\n hi: (28.3285, 28.3285, 28.3285)\n" - ] - } - ], - "source": [ - "sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=cpu, dt = dt, gsd_write_freq=int(1000), log_file_name = log, gsd_file_name = gsd)\n", - "#sim.run_update_volume(final_box_lengths=[(1500/0.8)**1/3, (1500/0.8)**1/3, (1500/0.8)**1/3], kT=6.0, n_steps=5e6,tau_kt=100*sim.dt,period=10,thermalize_particles=True)\n", - "sim.run_update_volume(final_box_lengths=target_box, kT=6.0, n_steps=5e6,tau_kt=100*sim.dt,period=10,thermalize_particles=True)\n", - "sim.run_NVT(n_steps=5e6, kT=temp, tau_kt=dt*100)\n", - "sim.flush_writers()" - ] - }, - { - "cell_type": "markdown", - "id": "c437da77-218f-46b4-9888-b38ca2b4cd89", - "metadata": {}, - "source": [ - "## Visualization of final frame. Can adjust to whatever frame if you'd like" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c6cd379a-22a7-442c-bff6-ba2c8eb9910d", - "metadata": {}, - "outputs": [], - "source": [ - "sim_visualizer = FresnelGSD(gsd_file=gsd, frame=-1, view_axis=(1, 1, 1))\n", - "sim_visualizer.view()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} From 1634cf045c2fd41fe993557c20c7c712648612c0 Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Wed, 29 Oct 2025 16:31:14 -0600 Subject: [PATCH 04/10] updated documentation for TPSvN and readme to reflect --- README.md | 2 +- analysis/TPSvN.ipynb | 10 +++++++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9e1c28d..5f6935f 100644 --- a/README.md +++ b/README.md @@ -33,7 +33,7 @@ Then, in order to run a small scale simulation, navigate to the sims/ directory: To analyze results, navigate to working_example/: - clustering_plots.ipynb = we use this to determine the magnitude of clustering over time in a simulation. this means both size and amount of clusters, also using independent samples -- TPSvsN.ipynb = a notebook to graph TPS v. N +- TPSvsN.ipynb = useful notebook for determining relationship between system size and timesteps per second, not included in the main workflow at the moment In order to run a large scale simulation, navigate to signac/: diff --git a/analysis/TPSvN.ipynb b/analysis/TPSvN.ipynb index 6363157..f3b8cf8 100644 --- a/analysis/TPSvN.ipynb +++ b/analysis/TPSvN.ipynb @@ -1,5 +1,13 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "0c42c94f-5f28-488a-b25e-d6e6c8987830", + "metadata": {}, + "source": [ + "# This notebook is only for use for evaluating the how system size affects timesteps per second" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -141,7 +149,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.11" + "version": "3.12.0" } }, "nbformat": 4, From d6515b710e2ff054ce04300ed5d638246b8d198f Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Mon, 3 Nov 2025 21:27:31 -0700 Subject: [PATCH 05/10] counting singletons for clusters in analysis --- analysis/clustering_plots.ipynb | 106 ++++++++++++++++++++------------ 1 file changed, 67 insertions(+), 39 deletions(-) diff --git a/analysis/clustering_plots.ipynb b/analysis/clustering_plots.ipynb index 759ef16..2f80f85 100644 --- a/analysis/clustering_plots.ipynb +++ b/analysis/clustering_plots.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 31, "id": "2eadee4b-e3fc-4dd6-9709-a92a0d91f5a9", "metadata": {}, "outputs": [], @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 32, "id": "d07d2bcc-875d-4a60-82d4-2cda752b53ef", "metadata": {}, "outputs": [], @@ -58,51 +58,49 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 33, "id": "44a4b5b1-3a0a-499c-a4f8-a2b0fdf2eaaa", "metadata": {}, "outputs": [], "source": [ - "def count_clusters(flake_coms, box_lengths, cutoff):\n", + "def count_clusters(flake_coms, box_lengths, cutoff, include_singletons=True):\n", " Lx, Ly, Lz = box_lengths\n", " n = len(flake_coms)\n", " adjacency = [set() for _ in range(n)]\n", "\n", - " # Build adjacency list using MIC\n", " for i in range(n):\n", " for j in range(i + 1, n):\n", " dr = flake_coms[j] - flake_coms[i]\n", " dr[0] -= Lx * np.round(dr[0] / Lx)\n", " dr[1] -= Ly * np.round(dr[1] / Ly)\n", " dr[2] -= Lz * np.round(dr[2] / Lz)\n", - " dist = np.linalg.norm(dr)\n", - " if dist < cutoff:\n", + " if np.linalg.norm(dr) < cutoff:\n", " adjacency[i].add(j)\n", " adjacency[j].add(i)\n", "\n", " visited = set()\n", " clusters = []\n", "\n", - " # DFS to build clusters\n", " for i in range(n):\n", - " if i not in visited and adjacency[i]:\n", + " if i not in visited:\n", " cluster = []\n", " stack = [i]\n", " while stack:\n", - " current = stack.pop()\n", - " if current not in visited:\n", - " visited.add(current)\n", - " cluster.append(current)\n", - " stack.extend(adjacency[current] - visited)\n", - " if len(cluster) > 1: # Only count as cluster if size > 1\n", + " cur = stack.pop()\n", + " if cur not in visited:\n", + " visited.add(cur)\n", + " cluster.append(cur)\n", + " stack.extend(adjacency[cur] - visited)\n", + "\n", + " if include_singletons or len(cluster) > 1:\n", " clusters.append(cluster)\n", "\n", - " return len(clusters), [len(c) for c in clusters]\n" + " return len(clusters), [len(c) for c in clusters], clusters" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 34, "id": "1e2a8371-8fc0-44f7-ae7f-c004eb1771a3", "metadata": {}, "outputs": [], @@ -123,7 +121,7 @@ " flake_positions = positions[typeids == flake_typeid]\n", "\n", " flake_coms = compute_all_flake_coms(flake_positions, box_lengths, beads_per_flake)\n", - " n_clusters, sizes = count_clusters(flake_coms, box_lengths, cutoff)\n", + " n_clusters, sizes, _ = count_clusters(flake_coms, box_lengths, cutoff)\n", "\n", " times.append(frame_idx)\n", " n_clusters_list.append(n_clusters)\n", @@ -142,7 +140,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 35, "id": "a02546b6-1ec3-46cb-b8d7-f680e694fb22", "metadata": {}, "outputs": [], @@ -153,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 36, "id": "f4d20f72-efca-47f4-8dd5-6f8048de1c93", "metadata": {}, "outputs": [], @@ -164,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 37, "id": "330a659a-e793-441f-8467-ece43451d4a8", "metadata": {}, "outputs": [], @@ -182,13 +180,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 38, "id": "7217fadb-1e36-4144-856e-5e0ed85d6a1d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -217,7 +215,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 39, "id": "c4c508c3-f7ad-459f-a824-3efa5d91e7b2", "metadata": {}, "outputs": [], @@ -246,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 40, "id": "791c5fa4-2804-45ca-bbb2-274f7e51a8c7", "metadata": {}, "outputs": [ @@ -264,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 41, "id": "2f0afd4b-ae71-486b-9900-600aa341a006", "metadata": {}, "outputs": [], @@ -277,7 +275,37 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 42, + "id": "a94631f6-fcdf-4473-8fa6-29c2737c3fdd", + "metadata": {}, + "outputs": [], + "source": [ + "sd_times = np.std(decorrelated_times)\n", + "sd_sizes = np.std(decorrelated_sizes)\n", + "sd_clusters = np.std(decorrelated_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "b769806c-5847-44c5-9e90-c439fb83e099", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "288.95674416770413 0.13424782563160698 0.47992615207758016\n" + ] + } + ], + "source": [ + "print(sd_times, sd_sizes, sd_clusters)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, "id": "12c865ec-ca92-4738-aa53-d258b776cd29", "metadata": {}, "outputs": [ @@ -295,7 +323,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 45, "id": "614f5d74-3842-4289-944d-5134ba76a6e0", "metadata": {}, "outputs": [ @@ -305,13 +333,13 @@ "Text(0, 0.5, 'Avg Cluster Size')" ] }, - "execution_count": 13, + "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -329,17 +357,17 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 46, "id": "a95d53e0-3cd4-4f94-8b6b-1a1ea37ed17a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.4755244755244755" + "1.0664335664335665" ] }, - "execution_count": 14, + "execution_count": 46, "metadata": {}, "output_type": "execute_result" } @@ -350,17 +378,17 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 47, "id": "03ca0f50-bbe1-40f8-b70f-8a56826ab11e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "0.23776223776223776" + "4.748251748251748" ] }, - "execution_count": 15, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -371,7 +399,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 48, "id": "06022fb2-060b-48d2-bf13-aa71836a49a4", "metadata": {}, "outputs": [ @@ -381,13 +409,13 @@ "Text(0, 0.5, '# Clusters')" ] }, - "execution_count": 16, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] From b15397236e7fd4003addf2562b1ebac3b1c72e64 Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Wed, 26 Nov 2025 14:39:39 -0700 Subject: [PATCH 06/10] fixed how density is calculated, added time information, added bead information, added more parameters for scaling flake size, and fixed functions in analysis notebook to reflect changes as well as adding an assertion for a no flake sim --- analysis/clustering_plots.ipynb | 272 ++-- sims/Onboarding.ipynb | 2702 +++++++++++++------------------ 2 files changed, 1285 insertions(+), 1689 deletions(-) diff --git a/analysis/clustering_plots.ipynb b/analysis/clustering_plots.ipynb index 2f80f85..53efb4b 100644 --- a/analysis/clustering_plots.ipynb +++ b/analysis/clustering_plots.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 33, "id": "2eadee4b-e3fc-4dd6-9709-a92a0d91f5a9", "metadata": {}, "outputs": [], @@ -20,16 +20,70 @@ "import matplotlib.pyplot as plt" ] }, + { + "cell_type": "markdown", + "id": "133b5518-c37d-4744-9685-b1d58a7f6158", + "metadata": {}, + "source": [ + "# Adjust to whichever file you're trying to analyze, then hit run until the end, and adjust cutoff distance for the distance clusters should be from one another" + ] + }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 35, + "id": "a541891b-9388-4009-9d9b-0e5a126c428d", + "metadata": {}, + "outputs": [], + "source": [ + "cutoff = 2.5\n", + "start_file = \"5_10mer1f_0.0005dt_start.txt\"\n", + "traj_file = \"5_10mer1f_0.0005dt.gsd\"\n", + "with open(start_file) as f:\n", + " lines = f.read().strip().splitlines()\n", + "\n", + "start_frame = int(lines[0])\n", + "beads_per_flake = int(lines[1])\n", + "sim_time = float(lines[2])" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "115e7a96-6347-4740-be4b-0606736160dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "94.89741086959839 seconds\n" + ] + } + ], + "source": [ + "print(sim_time, \"seconds\")" + ] + }, + { + "cell_type": "code", + "execution_count": 26, "id": "d07d2bcc-875d-4a60-82d4-2cda752b53ef", "metadata": {}, "outputs": [], "source": [ - "def compute_all_flake_coms(flake_positions, box_lengths, beads_per_flake=50):\n", + "def compute_all_flake_coms(flake_positions, box_lengths, beads_per_flake):\n", " Lx, Ly, Lz = box_lengths\n", - " n_flakes = len(flake_positions) // beads_per_flake\n", + "\n", + " n_beads = len(flake_positions)\n", + " if n_beads == 0:\n", + " return np.zeros((0, 3))\n", + "\n", + " assert n_beads % beads_per_flake == 0, (\n", + " f\"len(flake_positions)={n_beads} is not divisible by \"\n", + " f\"beads_per_flake={beads_per_flake}\"\n", + " )\n", + "\n", + " n_flakes = n_beads // beads_per_flake\n", " flake_coms = []\n", "\n", " for i in range(n_flakes):\n", @@ -37,7 +91,7 @@ " ref = flake[0]\n", " delta = flake - ref\n", "\n", - " # Minimum image convention\n", + " # minimum image\n", " delta[:, 0] -= Lx * np.round(delta[:, 0] / Lx)\n", " delta[:, 1] -= Ly * np.round(delta[:, 1] / Ly)\n", " delta[:, 2] -= Lz * np.round(delta[:, 2] / Lz)\n", @@ -45,20 +99,19 @@ " unwrapped = ref + delta\n", " com = np.mean(unwrapped, axis=0)\n", "\n", - " # Wrap CoM into [-L/2, L/2]\n", + " # wrap COM into [-L/2, L/2]\n", " com[0] = (com[0] + Lx/2) % Lx - Lx/2\n", " com[1] = (com[1] + Ly/2) % Ly - Ly/2\n", " com[2] = (com[2] + Lz/2) % Lz - Lz/2\n", "\n", " flake_coms.append(com)\n", "\n", - " return np.array(flake_coms)\n", - " " + " return np.array(flake_coms)" ] }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 27, "id": "44a4b5b1-3a0a-499c-a4f8-a2b0fdf2eaaa", "metadata": {}, "outputs": [], @@ -100,12 +153,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 28, "id": "1e2a8371-8fc0-44f7-ae7f-c004eb1771a3", "metadata": {}, "outputs": [], "source": [ - "def analyze_clusters_over_time(traj, start=0, step=1000, cutoff=2.5, beads_per_flake=50):\n", + "def analyze_clusters_over_time(traj, start=start_frame, step=1, cutoff=cutoff, beads_per_flake=beads_per_flake):\n", " times = []\n", " n_clusters_list = []\n", " avg_cluster_sizes = []\n", @@ -130,63 +183,31 @@ " return times, n_clusters_list, avg_cluster_sizes" ] }, - { - "cell_type": "markdown", - "id": "133b5518-c37d-4744-9685-b1d58a7f6158", - "metadata": {}, - "source": [ - "# Adjust to whichever file you're trying to analyze, then hit run until the end" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "id": "a02546b6-1ec3-46cb-b8d7-f680e694fb22", - "metadata": {}, - "outputs": [], - "source": [ - "start_file = \"20_10mer5f_0.005dt_start.txt\"\n", - "traj_file = \"20_10mer5f_0.005dt.gsd\"" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "id": "f4d20f72-efca-47f4-8dd5-6f8048de1c93", - "metadata": {}, - "outputs": [], - "source": [ - "with open(start_file) as f:\n", - " start = int(f.readline().strip())" - ] - }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 29, "id": "330a659a-e793-441f-8467-ece43451d4a8", "metadata": {}, "outputs": [], "source": [ - "with open(start_file) as f:\n", - " start = int(f.readline().strip()) # extracting post-shrink sim beginning frame\n", "traj = gsd.hoomd.open(traj_file)\n", "times, n_clusters, avg_sizes = analyze_clusters_over_time(\n", " traj,\n", - " start=start, # post-shrink\n", + " start=start_frame, # post-shrink\n", " step=1, # analyze every \"x\" frame, leave this at 1\n", - " cutoff=2.5\n", + " cutoff=cutoff\n", ") # this function is used to initially calculate the decorrelation time, so that we know every how many certain amount of frames to analyze. " ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 30, "id": "7217fadb-1e36-4144-856e-5e0ed85d6a1d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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"text/plain": [ "
" ] @@ -215,10 +236,31 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 31, "id": "c4c508c3-f7ad-459f-a824-3efa5d91e7b2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_2794/682740719.py:4: RuntimeWarning: invalid value encountered in divide\n", + " acorr /= (len(array) * np.var(array))\n" + ] + }, + { + "ename": "IndexError", + "evalue": "index 0 is out of bounds for axis 0 with size 0", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mIndexError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[31]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m 9\u001b[39m acorr = acorr[dt:nsamples] \n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m nsamples, dt\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m n, dt = \u001b[43mautocorr1D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mavg_sizes\u001b[49m\u001b[43m)\u001b[49m\n", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[31]\u001b[39m\u001b[32m, line 6\u001b[39m, in \u001b[36mautocorr1D\u001b[39m\u001b[34m(array)\u001b[39m\n\u001b[32m 3\u001b[39m acorr = np.fft.irfft(ft * np.conjugate(ft)) \n\u001b[32m 4\u001b[39m acorr /= (\u001b[38;5;28mlen\u001b[39m(array) * np.var(array)) \n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m dt = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43macorr\u001b[49m\u001b[43m \u001b[49m\u001b[43m<\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m \n\u001b[32m 7\u001b[39m nsamples = \u001b[38;5;28mlen\u001b[39m(array) // dt \n\u001b[32m 9\u001b[39m acorr = acorr[dt:nsamples] \n", + "\u001b[31mIndexError\u001b[39m: index 0 is out of bounds for axis 0 with size 0" + ] + } + ], "source": [ "def autocorr1D(array):\n", " ft = np.fft.rfft(array - np.average(array)) \n", @@ -244,15 +286,19 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 32, "id": "791c5fa4-2804-45ca-bbb2-274f7e51a8c7", "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Independent samples and decorrelation time: 143 7\n" + "ename": "NameError", + "evalue": "name 'n' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[31m---------------------------------------------------------------------------\u001b[39m", + "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", + "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[32]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mIndependent samples and decorrelation time:\u001b[39m\u001b[33m\"\u001b[39m, \u001b[43mn\u001b[49m, dt)\n", + "\u001b[31mNameError\u001b[39m: name 'n' is not defined" ] } ], @@ -262,7 +308,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "id": "2f0afd4b-ae71-486b-9900-600aa341a006", "metadata": {}, "outputs": [], @@ -275,7 +321,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "id": "a94631f6-fcdf-4473-8fa6-29c2737c3fdd", "metadata": {}, "outputs": [], @@ -287,67 +333,30 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "id": "b769806c-5847-44c5-9e90-c439fb83e099", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "288.95674416770413 0.13424782563160698 0.47992615207758016\n" - ] - } - ], + "outputs": [], "source": [ "print(sd_times, sd_sizes, sd_clusters)" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "12c865ec-ca92-4738-aa53-d258b776cd29", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "143\n" - ] - } - ], + "outputs": [], "source": [ "print(len(decorrelated_sizes)) # verifying it is equal to calculated independent samples" ] }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "614f5d74-3842-4289-944d-5134ba76a6e0", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, 'Avg Cluster Size')" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(decorrelated_times, decorrelated_sizes) # plot of every independent sample in simulation\n", "plt.title(\"Average Cluster Size (Decorrelated)\")\n", @@ -357,79 +366,44 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "id": "a95d53e0-3cd4-4f94-8b6b-1a1ea37ed17a", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1.0664335664335665" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.mean(decorrelated_sizes)" ] }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "03ca0f50-bbe1-40f8-b70f-8a56826ab11e", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4.748251748251748" - ] - }, - "execution_count": 47, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.mean(decorrelated_clusters)" ] }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "id": "06022fb2-060b-48d2-bf13-aa71836a49a4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '# Clusters')" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.plot(decorrelated_times, decorrelated_clusters)\n", "plt.title(\"Number of Clusters (Decorrelated)\")\n", "plt.xlabel(\"Frame\")\n", "plt.ylabel(\"# Clusters\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a1bcf0e5-836a-4a78-b143-ae90eb3aaf43", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/sims/Onboarding.ipynb b/sims/Onboarding.ipynb index 040b59f..2f5e524 100644 --- a/sims/Onboarding.ipynb +++ b/sims/Onboarding.ipynb @@ -149,17 +149,18 @@ "metadata": {}, "outputs": [], "source": [ - "N_chains = 20 # number of polymer chains\n", + "N_chains = 5 # number of polymer chains\n", "initial_dens = 0.001 # initial packing density to initialize system\n", "final_dens = 0.3 # final packing density for shrinking\n", - "N_flakes = 5 # number of flakes\n", + "N_flakes = 1 # number of flakes\n", "chain_length = 10 # length of polymer chains\n", - "dt = 0.005 # step size of simulation\n", - "temp = 3.0 # kT, temperature of simulation\n", - "write_frequency = 1000 # write frequency of simulation, the lower the more frames captured\n", - "shrink_steps = 5e5 # amount of steps to run shrink\n", + "dt = 0.0005 # step size of simulation\n", + "temp = 0.6 # kT, temperature of simulation\n", + "write_frequency = 10000 # write frequency of simulation, the lower the more frames captured\n", + "shrink_steps = 2e6 # amount of steps to run shrink\n", "sim_start = int(shrink_steps/write_frequency) # for use in analysis notebook, post-shrink simulation starting point\n", - "steps = 1e6 # amount of steps to run simulation for" + "steps = 4e6 # amount of steps to run simulation for\n", + "flake_repeat = 4 # amount of repeated rows flake has of particles" ] }, { @@ -196,10 +197,12 @@ "outputs": [], "source": [ "kg_chain = LJChain(lengths=chain_length,num_mols=N_chains) # initializing polymer chains\n", - "sheet = Flake(x_repeat=5, y_repeat=5, n_layers=1, periodicity=(False, False, False)) # initializing flakes\n", + "sheet = Flake(x_repeat=flake_repeat, y_repeat=flake_repeat, n_layers=1, periodicity=(False, False, False)) # initializing flakes\n", "system = Pack(molecules=[Molecule(compound=sheet.all_molecules[0], num_mols=N_flakes), kg_chain], \n", " density=initial_dens, packing_expand_factor = 6, seed=2) # packing chains and flakes into system\n", - "target_box = get_target_box_number_density(density=final_dens*u.Unit(\"nm**-3\"),n_beads=(500+(N_chains*10))) # acquiring final density scaling with number density" + "snapshot = system.hoomd_snapshot # snapshot to extract bead count\n", + "n_beads = snapshot.particles.N # beads\n", + "target_box = get_target_box_number_density(density=final_dens*u.Unit(\"nm**-3\"),n_beads=n_beads) # acquiring final density scaling with number density" ] }, { @@ -222,25 +225,6 @@ "start_file = f\"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt_start.txt\" # name of output start of sim" ] }, - { - "cell_type": "markdown", - "id": "d9b22575-1805-4cbc-a310-d6f30aedd81a", - "metadata": {}, - "source": [ - "### Step 7.5: Saving simulation startpoint" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "6c81ac75-e729-4ce1-bab2-abc2d4156c28", - "metadata": {}, - "outputs": [], - "source": [ - "with open(start_file, \"w\") as f:\n", - " f.write(str(sim_start))" - ] - }, { "cell_type": "markdown", "id": "5a16ce5f-f237-400f-b333-91292c6f9d8c", @@ -251,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "id": "64f9fae9-a3b9-418e-a03b-681b754e1b03", "metadata": {}, "outputs": [ @@ -267,1506 +251,1096 @@ "output_type": "stream", "text": [ "Initializing simulation state from a gsd.hoomd.Frame.\n", - "Step 1000 of 500000; TPS: 982.15; ETA: 8.5 minutes\n", - "Step 2000 of 500000; TPS: 1620.09; ETA: 5.1 minutes\n", - "Step 3000 of 500000; TPS: 2064.47; ETA: 4.0 minutes\n", - "Step 4000 of 500000; TPS: 2185.47; ETA: 3.8 minutes\n", - "Step 5000 of 500000; TPS: 2433.0; ETA: 3.4 minutes\n", - "Step 6000 of 500000; TPS: 2633.55; ETA: 3.1 minutes\n", - "Step 7000 of 500000; TPS: 2796.2; ETA: 2.9 minutes\n", - "Step 8000 of 500000; TPS: 2924.7; ETA: 2.8 minutes\n", - "Step 9000 of 500000; TPS: 3046.9; ETA: 2.7 minutes\n", - "Step 10000 of 500000; TPS: 2980.6; ETA: 2.7 minutes\n", - "Step 11000 of 500000; TPS: 2946.47; ETA: 2.8 minutes\n", - "Step 12000 of 500000; TPS: 3025.82; ETA: 2.7 minutes\n", - "Step 13000 of 500000; TPS: 3095.79; ETA: 2.6 minutes\n", - "Step 14000 of 500000; TPS: 3150.38; ETA: 2.6 minutes\n", - "Step 15000 of 500000; TPS: 3216.79; ETA: 2.5 minutes\n", - "Step 16000 of 500000; TPS: 3279.66; ETA: 2.5 minutes\n", - "Step 17000 of 500000; TPS: 3325.15; ETA: 2.4 minutes\n", - "Step 18000 of 500000; TPS: 3266.75; ETA: 2.5 minutes\n", - "Step 19000 of 500000; TPS: 3295.47; ETA: 2.4 minutes\n", - "Step 20000 of 500000; TPS: 3342.52; ETA: 2.4 minutes\n", - "Step 21000 of 500000; TPS: 3385.41; ETA: 2.4 minutes\n", - "Step 22000 of 500000; TPS: 3420.73; ETA: 2.3 minutes\n", - "Step 23000 of 500000; TPS: 3451.31; ETA: 2.3 minutes\n", - "Step 24000 of 500000; TPS: 3486.91; ETA: 2.3 minutes\n", - "Step 25000 of 500000; TPS: 3439.4; ETA: 2.3 minutes\n", - "Step 26000 of 500000; TPS: 3458.14; ETA: 2.3 minutes\n", - "Step 27000 of 500000; TPS: 3491.31; ETA: 2.3 minutes\n", - "Step 28000 of 500000; TPS: 3520.36; ETA: 2.2 minutes\n", - "Step 29000 of 500000; TPS: 3548.72; ETA: 2.2 minutes\n", - "Step 30000 of 500000; TPS: 3574.45; ETA: 2.2 minutes\n", - "Step 31000 of 500000; TPS: 3601.82; ETA: 2.2 minutes\n", - "Step 32000 of 500000; TPS: 3619.09; ETA: 2.2 minutes\n", - "Step 33000 of 500000; TPS: 3589.59; ETA: 2.2 minutes\n", - "Step 34000 of 500000; TPS: 3618.07; ETA: 2.1 minutes\n", - "Step 35000 of 500000; TPS: 3640.88; ETA: 2.1 minutes\n", - "Step 36000 of 500000; TPS: 3663.73; ETA: 2.1 minutes\n", - "Step 37000 of 500000; TPS: 3688.06; ETA: 2.1 minutes\n", - "Step 38000 of 500000; TPS: 3709.78; ETA: 2.1 minutes\n", - "Step 39000 of 500000; TPS: 3714.65; ETA: 2.1 minutes\n", - "Step 40000 of 500000; TPS: 3689.05; ETA: 2.1 minutes\n", - "Step 41000 of 500000; TPS: 3706.4; ETA: 2.1 minutes\n", - "Step 42000 of 500000; TPS: 3725.26; ETA: 2.0 minutes\n", - "Step 43000 of 500000; TPS: 3740.14; ETA: 2.0 minutes\n", - "Step 44000 of 500000; TPS: 3758.21; ETA: 2.0 minutes\n", - "Step 45000 of 500000; TPS: 3773.13; ETA: 2.0 minutes\n", - "Step 46000 of 500000; TPS: 3789.68; ETA: 2.0 minutes\n", - "Step 47000 of 500000; TPS: 3767.39; ETA: 2.0 minutes\n", - "Step 48000 of 500000; TPS: 3785.08; ETA: 2.0 minutes\n", - "Step 49000 of 500000; TPS: 3801.01; ETA: 2.0 minutes\n", - "Step 50000 of 500000; TPS: 3817.67; ETA: 2.0 minutes\n", - "Step 51000 of 500000; TPS: 3832.94; ETA: 2.0 minutes\n", - "Step 52000 of 500000; TPS: 3850.9; ETA: 1.9 minutes\n", - "Step 53000 of 500000; TPS: 3865.95; ETA: 1.9 minutes\n", - "Step 54000 of 500000; TPS: 3853.63; ETA: 1.9 minutes\n", - "Step 55000 of 500000; TPS: 3868.24; ETA: 1.9 minutes\n", - "Step 56000 of 500000; TPS: 3881.37; ETA: 1.9 minutes\n", - "Step 57000 of 500000; TPS: 3893.63; ETA: 1.9 minutes\n", - "Step 58000 of 500000; TPS: 3910.06; ETA: 1.9 minutes\n", - "Step 59000 of 500000; TPS: 3923.07; ETA: 1.9 minutes\n", - "Step 60000 of 500000; TPS: 3936.16; ETA: 1.9 minutes\n", - "Step 61000 of 500000; TPS: 3923.35; ETA: 1.9 minutes\n", - "Step 62000 of 500000; TPS: 3935.73; ETA: 1.9 minutes\n", - "Step 63000 of 500000; TPS: 3950.82; ETA: 1.8 minutes\n", - "Step 64000 of 500000; TPS: 3963.19; ETA: 1.8 minutes\n", - "Step 65000 of 500000; TPS: 3975.12; ETA: 1.8 minutes\n", - "Step 66000 of 500000; TPS: 3987.89; ETA: 1.8 minutes\n", - "Step 67000 of 500000; TPS: 3998.56; ETA: 1.8 minutes\n", - "Step 68000 of 500000; TPS: 3984.66; ETA: 1.8 minutes\n", - "Step 69000 of 500000; TPS: 3995.21; ETA: 1.8 minutes\n", - "Step 70000 of 500000; TPS: 4005.22; ETA: 1.8 minutes\n", - "Step 71000 of 500000; TPS: 4015.15; ETA: 1.8 minutes\n", - "Step 72000 of 500000; TPS: 4025.36; ETA: 1.8 minutes\n", - "Step 73000 of 500000; TPS: 4035.73; ETA: 1.8 minutes\n", - "Step 74000 of 500000; TPS: 4044.32; ETA: 1.8 minutes\n", - "Step 75000 of 500000; TPS: 4035.54; ETA: 1.8 minutes\n", - "Step 76000 of 500000; TPS: 4045.1; ETA: 1.7 minutes\n", - "Step 77000 of 500000; TPS: 4055.07; ETA: 1.7 minutes\n", - "Step 78000 of 500000; TPS: 4061.63; ETA: 1.7 minutes\n", - "Step 79000 of 500000; TPS: 3604.65; ETA: 1.9 minutes\n", - "Step 80000 of 500000; TPS: 3617.45; ETA: 1.9 minutes\n", - "Step 81000 of 500000; TPS: 3631.79; ETA: 1.9 minutes\n", - "Step 82000 of 500000; TPS: 3627.99; ETA: 1.9 minutes\n", - "Step 83000 of 500000; TPS: 3640.02; ETA: 1.9 minutes\n", - "Step 84000 of 500000; TPS: 3651.77; ETA: 1.9 minutes\n", - "Step 85000 of 500000; TPS: 3660.08; ETA: 1.9 minutes\n", - "Step 86000 of 500000; TPS: 3660.85; ETA: 1.9 minutes\n", - "Step 87000 of 500000; TPS: 3669.09; ETA: 1.9 minutes\n", - "Step 88000 of 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minutes\n", - "Step 105000 of 500000; TPS: 3751.29; ETA: 1.8 minutes\n", - "Step 106000 of 500000; TPS: 3753.04; ETA: 1.7 minutes\n", - "Step 107000 of 500000; TPS: 3758.58; ETA: 1.7 minutes\n", - "Step 108000 of 500000; TPS: 3766.26; ETA: 1.7 minutes\n", - "Step 109000 of 500000; TPS: 3773.68; ETA: 1.7 minutes\n", - "Step 110000 of 500000; TPS: 3771.33; ETA: 1.7 minutes\n", - "Step 111000 of 500000; TPS: 3778.66; ETA: 1.7 minutes\n", - "Step 112000 of 500000; TPS: 3784.6; ETA: 1.7 minutes\n", - "Step 113000 of 500000; TPS: 3790.91; ETA: 1.7 minutes\n", - "Step 114000 of 500000; TPS: 3795.74; ETA: 1.7 minutes\n", - "Step 115000 of 500000; TPS: 3800.12; ETA: 1.7 minutes\n", - "Step 116000 of 500000; TPS: 3806.93; ETA: 1.7 minutes\n", - "Step 117000 of 500000; TPS: 3801.08; ETA: 1.7 minutes\n", - "Step 118000 of 500000; TPS: 3807.65; ETA: 1.7 minutes\n", - "Step 119000 of 500000; TPS: 3814.85; ETA: 1.7 minutes\n", - "Step 120000 of 500000; TPS: 3820.76; ETA: 1.7 minutes\n", - "Step 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3884.75; ETA: 1.6 minutes\n", - "Step 138000 of 500000; TPS: 3884.9; ETA: 1.6 minutes\n", - "Step 139000 of 500000; TPS: 3889.33; ETA: 1.5 minutes\n", - "Step 140000 of 500000; TPS: 3895.61; ETA: 1.5 minutes\n", - "Step 141000 of 500000; TPS: 3900.51; ETA: 1.5 minutes\n", - "Step 142000 of 500000; TPS: 3905.9; ETA: 1.5 minutes\n", - "Step 143000 of 500000; TPS: 3910.27; ETA: 1.5 minutes\n", - "Step 144000 of 500000; TPS: 3915.52; ETA: 1.5 minutes\n", - "Step 145000 of 500000; TPS: 3912.61; ETA: 1.5 minutes\n", - "Step 146000 of 500000; TPS: 3917.75; ETA: 1.5 minutes\n", - "Step 147000 of 500000; TPS: 3922.23; ETA: 1.5 minutes\n", - "Step 148000 of 500000; TPS: 3928.28; ETA: 1.5 minutes\n", - "Step 149000 of 500000; TPS: 3932.45; ETA: 1.5 minutes\n", - "Step 150000 of 500000; TPS: 3937.78; ETA: 1.5 minutes\n", - "Step 151000 of 500000; TPS: 3943.77; ETA: 1.5 minutes\n", - "Step 152000 of 500000; TPS: 3947.68; ETA: 1.5 minutes\n", - "Step 153000 of 500000; TPS: 3948.25; ETA: 1.5 minutes\n", - "Step 154000 of 500000; TPS: 3952.22; ETA: 1.5 minutes\n", - "Step 155000 of 500000; TPS: 3957.32; ETA: 1.5 minutes\n", - "Step 156000 of 500000; TPS: 3962.11; ETA: 1.4 minutes\n", - "Step 157000 of 500000; TPS: 3966.53; ETA: 1.4 minutes\n", - "Step 158000 of 500000; TPS: 3970.94; ETA: 1.4 minutes\n", - "Step 159000 of 500000; TPS: 3976.18; ETA: 1.4 minutes\n", - "Step 160000 of 500000; TPS: 3975.83; ETA: 1.4 minutes\n", - "Step 161000 of 500000; TPS: 3980.0; ETA: 1.4 minutes\n", - "Step 162000 of 500000; TPS: 3984.37; ETA: 1.4 minutes\n", - "Step 163000 of 500000; TPS: 3988.82; ETA: 1.4 minutes\n", - "Step 164000 of 500000; TPS: 3993.49; ETA: 1.4 minutes\n", - "Step 165000 of 500000; TPS: 3998.16; ETA: 1.4 minutes\n", - "Step 166000 of 500000; TPS: 4002.95; ETA: 1.4 minutes\n", - "Step 167000 of 500000; TPS: 4003.84; ETA: 1.4 minutes\n", - "Step 168000 of 500000; TPS: 4008.02; ETA: 1.4 minutes\n", - "Step 169000 of 500000; TPS: 4011.34; ETA: 1.4 minutes\n", - "Step 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4065.77; ETA: 1.3 minutes\n", - "Step 187000 of 500000; TPS: 4070.62; ETA: 1.3 minutes\n", - "Step 188000 of 500000; TPS: 4072.3; ETA: 1.3 minutes\n", - "Step 189000 of 500000; TPS: 4075.96; ETA: 1.3 minutes\n", - "Step 190000 of 500000; TPS: 4080.55; ETA: 1.3 minutes\n", - "Step 191000 of 500000; TPS: 4084.65; ETA: 1.3 minutes\n", - "Step 192000 of 500000; TPS: 4089.46; ETA: 1.3 minutes\n", - "Step 193000 of 500000; TPS: 4093.54; ETA: 1.2 minutes\n", - "Step 194000 of 500000; TPS: 4097.38; ETA: 1.2 minutes\n", - "Step 195000 of 500000; TPS: 4099.83; ETA: 1.2 minutes\n", - "Step 196000 of 500000; TPS: 4103.96; ETA: 1.2 minutes\n", - "Step 197000 of 500000; TPS: 4107.26; ETA: 1.2 minutes\n", - "Step 198000 of 500000; TPS: 4110.22; ETA: 1.2 minutes\n", - "Step 199000 of 500000; TPS: 4113.32; ETA: 1.2 minutes\n", - "Step 200000 of 500000; TPS: 4116.6; ETA: 1.2 minutes\n", - "Step 201000 of 500000; TPS: 4119.85; ETA: 1.2 minutes\n", - "Step 202000 of 500000; TPS: 4119.93; ETA: 1.2 minutes\n", - "Step 203000 of 500000; TPS: 4122.82; ETA: 1.2 minutes\n", - "Step 204000 of 500000; TPS: 4125.02; ETA: 1.2 minutes\n", - "Step 205000 of 500000; TPS: 4127.38; ETA: 1.2 minutes\n", - "Step 206000 of 500000; TPS: 4129.07; ETA: 1.2 minutes\n", - "Step 207000 of 500000; TPS: 4130.63; ETA: 1.2 minutes\n", - "Step 208000 of 500000; TPS: 4130.91; ETA: 1.2 minutes\n", - "Step 209000 of 500000; TPS: 4131.6; ETA: 1.2 minutes\n", - "Step 210000 of 500000; TPS: 4131.93; ETA: 1.2 minutes\n", - "Step 211000 of 500000; TPS: 4132.61; ETA: 1.2 minutes\n", - "Step 212000 of 500000; TPS: 4133.1; ETA: 1.2 minutes\n", - "Step 213000 of 500000; TPS: 4134.75; ETA: 1.2 minutes\n", - "Step 214000 of 500000; TPS: 4136.53; ETA: 1.2 minutes\n", - "Step 215000 of 500000; TPS: 4137.86; ETA: 1.1 minutes\n", - "Step 216000 of 500000; TPS: 4136.87; ETA: 1.1 minutes\n", - "Step 217000 of 500000; TPS: 4138.88; ETA: 1.1 minutes\n", - "Step 218000 of 500000; TPS: 4141.22; ETA: 1.1 minutes\n", - "Step 219000 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ETA: 1.1 minutes\n", - "Step 236000 of 500000; TPS: 3977.4; ETA: 1.1 minutes\n", - "Step 237000 of 500000; TPS: 3977.68; ETA: 1.1 minutes\n", - "Step 238000 of 500000; TPS: 3979.19; ETA: 1.1 minutes\n", - "Step 239000 of 500000; TPS: 3981.28; ETA: 1.1 minutes\n", - "Step 240000 of 500000; TPS: 3982.42; ETA: 1.1 minutes\n", - "Step 241000 of 500000; TPS: 3985.04; ETA: 1.1 minutes\n", - "Step 242000 of 500000; TPS: 3985.93; ETA: 1.1 minutes\n", - "Step 243000 of 500000; TPS: 3988.18; ETA: 1.1 minutes\n", - "Step 244000 of 500000; TPS: 3989.09; ETA: 1.1 minutes\n", - "Step 245000 of 500000; TPS: 3992.21; ETA: 1.1 minutes\n", - "Step 246000 of 500000; TPS: 3994.89; ETA: 1.1 minutes\n", - "Step 247000 of 500000; TPS: 3997.9; ETA: 1.1 minutes\n", - "Step 248000 of 500000; TPS: 4000.92; ETA: 1.0 minutes\n", - "Step 249000 of 500000; TPS: 4005.13; ETA: 1.0 minutes\n", - "Step 250000 of 500000; TPS: 4007.43; ETA: 1.0 minutes\n", - "Step 251000 of 500000; TPS: 4010.17; ETA: 1.0 minutes\n", - "Step 252000 of 500000; TPS: 4013.64; ETA: 1.0 minutes\n", - "Step 253000 of 500000; TPS: 4017.72; ETA: 1.0 minutes\n", - "Step 254000 of 500000; TPS: 4019.88; ETA: 1.0 minutes\n", - "Step 255000 of 500000; TPS: 4023.2; ETA: 1.0 minutes\n", - "Step 256000 of 500000; TPS: 4026.28; ETA: 1.0 minutes\n", - "Step 257000 of 500000; TPS: 4028.85; ETA: 1.0 minutes\n", - "Step 258000 of 500000; TPS: 4031.49; ETA: 1.0 minutes\n", - "Step 259000 of 500000; TPS: 4034.47; ETA: 1.0 minutes\n", - "Step 260000 of 500000; TPS: 4037.6; ETA: 1.0 minutes\n", - "Step 261000 of 500000; TPS: 4040.1; ETA: 1.0 minutes\n", - "Step 262000 of 500000; TPS: 4042.76; ETA: 1.0 minutes\n", - "Step 263000 of 500000; TPS: 4044.83; ETA: 1.0 minutes\n", - "Step 264000 of 500000; TPS: 4047.47; ETA: 1.0 minutes\n", - "Step 265000 of 500000; TPS: 4049.77; ETA: 1.0 minutes\n", - "Step 266000 of 500000; TPS: 4052.32; ETA: 1.0 minutes\n", - "Step 267000 of 500000; TPS: 4054.97; ETA: 1.0 minutes\n", - "Step 268000 of 500000; TPS: 4058.05; ETA: 1.0 minutes\n", - "Step 269000 of 500000; TPS: 4060.34; ETA: 0.9 minutes\n", - "Step 270000 of 500000; TPS: 4062.6; ETA: 0.9 minutes\n", - "Step 271000 of 500000; TPS: 4065.15; ETA: 0.9 minutes\n", - "Step 272000 of 500000; TPS: 4067.91; ETA: 0.9 minutes\n", - "Step 273000 of 500000; TPS: 4069.72; ETA: 0.9 minutes\n", - "Step 274000 of 500000; TPS: 4071.99; ETA: 0.9 minutes\n", - "Step 275000 of 500000; TPS: 4074.75; ETA: 0.9 minutes\n", - "Step 276000 of 500000; TPS: 4077.65; ETA: 0.9 minutes\n", - "Step 277000 of 500000; TPS: 4080.48; ETA: 0.9 minutes\n", - "Step 278000 of 500000; TPS: 4082.9; ETA: 0.9 minutes\n", - "Step 279000 of 500000; TPS: 4085.65; ETA: 0.9 minutes\n", - "Step 280000 of 500000; TPS: 4087.78; ETA: 0.9 minutes\n", - "Step 281000 of 500000; TPS: 4090.96; ETA: 0.9 minutes\n", - "Step 282000 of 500000; TPS: 4094.22; ETA: 0.9 minutes\n", - "Step 283000 of 500000; TPS: 4096.59; ETA: 0.9 minutes\n", - "Step 284000 of 500000; TPS: 4099.81; ETA: 0.9 minutes\n", - "Step 285000 of 500000; TPS: 4102.08; ETA: 0.9 minutes\n", - "Step 286000 of 500000; TPS: 4103.73; ETA: 0.9 minutes\n", - "Step 287000 of 500000; TPS: 4104.79; ETA: 0.9 minutes\n", - "Step 288000 of 500000; TPS: 4107.81; ETA: 0.9 minutes\n", - "Step 289000 of 500000; TPS: 4110.67; ETA: 0.9 minutes\n", - "Step 290000 of 500000; TPS: 4113.53; ETA: 0.9 minutes\n", - "Step 291000 of 500000; TPS: 4116.32; ETA: 0.8 minutes\n", - "Step 292000 of 500000; TPS: 4119.19; ETA: 0.8 minutes\n", - "Step 293000 of 500000; TPS: 4121.85; ETA: 0.8 minutes\n", - "Step 294000 of 500000; TPS: 4122.86; ETA: 0.8 minutes\n", - "Step 295000 of 500000; TPS: 4125.05; ETA: 0.8 minutes\n", - "Step 296000 of 500000; TPS: 4127.09; ETA: 0.8 minutes\n", - "Step 297000 of 500000; TPS: 4129.74; ETA: 0.8 minutes\n", - "Step 298000 of 500000; TPS: 4132.61; ETA: 0.8 minutes\n", - "Step 299000 of 500000; TPS: 4135.77; ETA: 0.8 minutes\n", - "Step 300000 of 500000; TPS: 4137.31; ETA: 0.8 minutes\n", - "Step 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4154.31; ETA: 0.7 minutes\n", - "Step 318000 of 500000; TPS: 4156.46; ETA: 0.7 minutes\n", - "Step 319000 of 500000; TPS: 4158.85; ETA: 0.7 minutes\n", - "Step 320000 of 500000; TPS: 4161.01; ETA: 0.7 minutes\n", - "Step 321000 of 500000; TPS: 4163.14; ETA: 0.7 minutes\n", - "Step 322000 of 500000; TPS: 4164.56; ETA: 0.7 minutes\n", - "Step 323000 of 500000; TPS: 4166.59; ETA: 0.7 minutes\n", - "Step 324000 of 500000; TPS: 4169.03; ETA: 0.7 minutes\n", - "Step 325000 of 500000; TPS: 4170.88; ETA: 0.7 minutes\n", - "Step 326000 of 500000; TPS: 4171.66; ETA: 0.7 minutes\n", - "Step 327000 of 500000; TPS: 4173.6; ETA: 0.7 minutes\n", - "Step 328000 of 500000; TPS: 4175.36; ETA: 0.7 minutes\n", - "Step 329000 of 500000; TPS: 4176.19; ETA: 0.7 minutes\n", - "Step 330000 of 500000; TPS: 4178.18; ETA: 0.7 minutes\n", - "Step 331000 of 500000; TPS: 4179.91; ETA: 0.7 minutes\n", - "Step 332000 of 500000; TPS: 4181.61; ETA: 0.7 minutes\n", - "Step 333000 of 500000; TPS: 4183.56; ETA: 0.7 minutes\n", - "Step 334000 of 500000; TPS: 4184.14; ETA: 0.7 minutes\n", - "Step 335000 of 500000; TPS: 4185.95; ETA: 0.7 minutes\n", - "Step 336000 of 500000; TPS: 4186.43; ETA: 0.7 minutes\n", - "Step 337000 of 500000; TPS: 4187.04; ETA: 0.6 minutes\n", - "Step 338000 of 500000; TPS: 4189.1; ETA: 0.6 minutes\n", - "Step 339000 of 500000; TPS: 4190.75; ETA: 0.6 minutes\n", - "Step 340000 of 500000; TPS: 4192.78; ETA: 0.6 minutes\n", - "Step 341000 of 500000; TPS: 4195.23; ETA: 0.6 minutes\n", - "Step 342000 of 500000; TPS: 4197.76; ETA: 0.6 minutes\n", - "Step 343000 of 500000; TPS: 4199.66; ETA: 0.6 minutes\n", - "Step 344000 of 500000; TPS: 4201.33; ETA: 0.6 minutes\n", - "Step 345000 of 500000; TPS: 4203.85; ETA: 0.6 minutes\n", - "Step 346000 of 500000; TPS: 4204.27; ETA: 0.6 minutes\n", - "Step 347000 of 500000; TPS: 4205.13; ETA: 0.6 minutes\n", - "Step 348000 of 500000; TPS: 4207.05; ETA: 0.6 minutes\n", - "Step 349000 of 500000; TPS: 4207.59; ETA: 0.6 minutes\n", - "Step 350000 of 500000; TPS: 4208.91; ETA: 0.6 minutes\n", - "Step 351000 of 500000; TPS: 4211.32; ETA: 0.6 minutes\n", - "Step 352000 of 500000; TPS: 4213.4; ETA: 0.6 minutes\n", - "Step 353000 of 500000; TPS: 4214.93; ETA: 0.6 minutes\n", - "Step 354000 of 500000; TPS: 4215.82; ETA: 0.6 minutes\n", - "Step 355000 of 500000; TPS: 4217.73; ETA: 0.6 minutes\n", - "Step 356000 of 500000; TPS: 4219.4; ETA: 0.6 minutes\n", - "Step 357000 of 500000; TPS: 4220.98; ETA: 0.6 minutes\n", - "Step 358000 of 500000; TPS: 4223.16; ETA: 0.6 minutes\n", - "Step 359000 of 500000; TPS: 4224.69; ETA: 0.6 minutes\n", - "Step 360000 of 500000; TPS: 4227.23; ETA: 0.6 minutes\n", - "Step 361000 of 500000; TPS: 4229.34; ETA: 0.5 minutes\n", - "Step 362000 of 500000; TPS: 4231.52; ETA: 0.5 minutes\n", - "Step 363000 of 500000; TPS: 4234.3; ETA: 0.5 minutes\n", - "Step 364000 of 500000; TPS: 4236.55; ETA: 0.5 minutes\n", - "Step 365000 of 500000; TPS: 4238.53; ETA: 0.5 minutes\n", - "Step 366000 of 500000; TPS: 4240.51; ETA: 0.5 minutes\n", - "Step 367000 of 500000; TPS: 4242.16; ETA: 0.5 minutes\n", - "Step 368000 of 500000; TPS: 4242.62; ETA: 0.5 minutes\n", - "Step 369000 of 500000; TPS: 4244.29; ETA: 0.5 minutes\n", - "Step 370000 of 500000; TPS: 4246.45; ETA: 0.5 minutes\n", - "Step 371000 of 500000; TPS: 4248.42; ETA: 0.5 minutes\n", - "Step 372000 of 500000; TPS: 4250.49; ETA: 0.5 minutes\n", - "Step 373000 of 500000; TPS: 4252.42; ETA: 0.5 minutes\n", - "Step 374000 of 500000; TPS: 4254.38; ETA: 0.5 minutes\n", - "Step 375000 of 500000; TPS: 4256.41; ETA: 0.5 minutes\n", - "Step 376000 of 500000; TPS: 4257.15; ETA: 0.5 minutes\n", - "Step 377000 of 500000; TPS: 4256.91; ETA: 0.5 minutes\n", - "Step 378000 of 500000; TPS: 4257.28; ETA: 0.5 minutes\n", - "Step 379000 of 500000; TPS: 4139.23; ETA: 0.5 minutes\n", - "Step 380000 of 500000; TPS: 4140.54; ETA: 0.5 minutes\n", - "Step 381000 of 500000; TPS: 4140.8; ETA: 0.5 minutes\n", - "Step 382000 of 500000; TPS: 4141.85; ETA: 0.5 minutes\n", - "Step 383000 of 500000; TPS: 4143.01; ETA: 0.5 minutes\n", - "Step 384000 of 500000; TPS: 4144.12; ETA: 0.5 minutes\n", - "Step 385000 of 500000; TPS: 4145.44; ETA: 0.5 minutes\n", - "Step 386000 of 500000; TPS: 4146.09; ETA: 0.5 minutes\n", - "Step 387000 of 500000; TPS: 4146.96; ETA: 0.5 minutes\n", - "Step 388000 of 500000; TPS: 4147.8; ETA: 0.5 minutes\n", - "Step 389000 of 500000; TPS: 4148.34; ETA: 0.4 minutes\n", - "Step 390000 of 500000; TPS: 4149.46; ETA: 0.4 minutes\n", - "Step 391000 of 500000; TPS: 4150.81; ETA: 0.4 minutes\n", - "Step 392000 of 500000; TPS: 4151.65; ETA: 0.4 minutes\n", - "Step 393000 of 500000; TPS: 4152.37; ETA: 0.4 minutes\n", - "Step 394000 of 500000; TPS: 4153.16; ETA: 0.4 minutes\n", - "Step 395000 of 500000; TPS: 4153.22; ETA: 0.4 minutes\n", - "Step 396000 of 500000; TPS: 4154.16; ETA: 0.4 minutes\n", - "Step 397000 of 500000; TPS: 4155.4; ETA: 0.4 minutes\n", - "Step 398000 of 500000; TPS: 4156.77; ETA: 0.4 minutes\n", - "Step 399000 of 500000; TPS: 4158.45; ETA: 0.4 minutes\n", - "Step 400000 of 500000; TPS: 4159.6; ETA: 0.4 minutes\n", - "Step 401000 of 500000; TPS: 4160.06; ETA: 0.4 minutes\n", - "Step 402000 of 500000; TPS: 4160.18; ETA: 0.4 minutes\n", - "Step 403000 of 500000; TPS: 4161.15; ETA: 0.4 minutes\n", - "Step 404000 of 500000; TPS: 4162.32; ETA: 0.4 minutes\n", - "Step 405000 of 500000; TPS: 4163.75; ETA: 0.4 minutes\n", - "Step 406000 of 500000; TPS: 4164.96; ETA: 0.4 minutes\n", - "Step 407000 of 500000; TPS: 4165.05; ETA: 0.4 minutes\n", - "Step 408000 of 500000; TPS: 4166.01; ETA: 0.4 minutes\n", - "Step 409000 of 500000; TPS: 4167.3; ETA: 0.4 minutes\n", - "Step 410000 of 500000; TPS: 4168.19; ETA: 0.4 minutes\n", - "Step 411000 of 500000; TPS: 4168.2; ETA: 0.4 minutes\n", - "Step 412000 of 500000; TPS: 4169.73; ETA: 0.4 minutes\n", - "Step 413000 of 500000; TPS: 4170.72; ETA: 0.3 minutes\n", - "Step 414000 of 500000; TPS: 4170.39; ETA: 0.3 minutes\n", - "Step 415000 of 500000; TPS: 4171.35; ETA: 0.3 minutes\n", - "Step 416000 of 500000; TPS: 4172.01; ETA: 0.3 minutes\n", - "Step 417000 of 500000; TPS: 4172.74; ETA: 0.3 minutes\n", - "Step 418000 of 500000; TPS: 4174.18; ETA: 0.3 minutes\n", - "Step 419000 of 500000; TPS: 4175.14; ETA: 0.3 minutes\n", - "Step 420000 of 500000; TPS: 4175.39; ETA: 0.3 minutes\n", - "Step 421000 of 500000; TPS: 4175.59; ETA: 0.3 minutes\n", - "Step 422000 of 500000; TPS: 4176.39; ETA: 0.3 minutes\n", - "Step 423000 of 500000; TPS: 4177.52; ETA: 0.3 minutes\n", - "Step 424000 of 500000; TPS: 4178.87; ETA: 0.3 minutes\n", - "Step 425000 of 500000; TPS: 4178.71; ETA: 0.3 minutes\n", - "Step 426000 of 500000; TPS: 4178.93; ETA: 0.3 minutes\n", - "Step 427000 of 500000; TPS: 4179.51; ETA: 0.3 minutes\n", - "Step 428000 of 500000; TPS: 4179.91; ETA: 0.3 minutes\n", - "Step 429000 of 500000; TPS: 4180.58; ETA: 0.3 minutes\n", - "Step 430000 of 500000; TPS: 4180.26; ETA: 0.3 minutes\n", - "Step 431000 of 500000; TPS: 4180.9; ETA: 0.3 minutes\n", - "Step 432000 of 500000; TPS: 4181.51; ETA: 0.3 minutes\n", - "Step 433000 of 500000; TPS: 4182.78; ETA: 0.3 minutes\n", - "Step 434000 of 500000; TPS: 4183.73; ETA: 0.3 minutes\n", - "Step 435000 of 500000; TPS: 4184.27; ETA: 0.3 minutes\n", - "Step 436000 of 500000; TPS: 4185.2; ETA: 0.3 minutes\n", - "Step 437000 of 500000; TPS: 4185.77; ETA: 0.3 minutes\n", - "Step 438000 of 500000; TPS: 4187.25; ETA: 0.2 minutes\n", - "Step 439000 of 500000; TPS: 4188.63; ETA: 0.2 minutes\n", - "Step 440000 of 500000; TPS: 4190.31; ETA: 0.2 minutes\n", - "Step 441000 of 500000; TPS: 4191.61; ETA: 0.2 minutes\n", - "Step 442000 of 500000; TPS: 4192.23; ETA: 0.2 minutes\n", - "Step 443000 of 500000; TPS: 4194.02; ETA: 0.2 minutes\n", - "Step 444000 of 500000; TPS: 4195.41; ETA: 0.2 minutes\n", - "Step 445000 of 500000; TPS: 4197.08; ETA: 0.2 minutes\n", - "Step 446000 of 500000; TPS: 4198.93; ETA: 0.2 minutes\n", - "Step 447000 of 500000; TPS: 4200.51; ETA: 0.2 minutes\n", - "Step 448000 of 500000; TPS: 4202.09; ETA: 0.2 minutes\n", - "Step 449000 of 500000; TPS: 4203.91; ETA: 0.2 minutes\n", - "Step 450000 of 500000; TPS: 4205.54; ETA: 0.2 minutes\n", - "Step 451000 of 500000; TPS: 4207.35; ETA: 0.2 minutes\n", - "Step 452000 of 500000; TPS: 4209.25; ETA: 0.2 minutes\n", - "Step 453000 of 500000; TPS: 4211.02; ETA: 0.2 minutes\n", - "Step 454000 of 500000; TPS: 4212.87; ETA: 0.2 minutes\n", - "Step 455000 of 500000; TPS: 4214.3; ETA: 0.2 minutes\n", - "Step 456000 of 500000; TPS: 4215.73; ETA: 0.2 minutes\n", - "Step 457000 of 500000; TPS: 4217.42; ETA: 0.2 minutes\n", - "Step 458000 of 500000; TPS: 4219.3; ETA: 0.2 minutes\n", - "Step 459000 of 500000; TPS: 4221.27; ETA: 0.2 minutes\n", - "Step 460000 of 500000; TPS: 4222.96; ETA: 0.2 minutes\n", - "Step 461000 of 500000; TPS: 4224.5; ETA: 0.2 minutes\n", - "Step 462000 of 500000; TPS: 4226.26; ETA: 0.1 minutes\n", - "Step 463000 of 500000; TPS: 4228.22; ETA: 0.1 minutes\n", - "Step 464000 of 500000; TPS: 4229.47; ETA: 0.1 minutes\n", - "Step 465000 of 500000; TPS: 4231.17; ETA: 0.1 minutes\n", - "Step 466000 of 500000; TPS: 4232.6; ETA: 0.1 minutes\n", - "Step 467000 of 500000; TPS: 4232.62; ETA: 0.1 minutes\n", - "Step 468000 of 500000; TPS: 4234.19; ETA: 0.1 minutes\n", - "Step 469000 of 500000; TPS: 4235.39; ETA: 0.1 minutes\n", - "Step 470000 of 500000; TPS: 4236.6; ETA: 0.1 minutes\n", - "Step 471000 of 500000; TPS: 4237.73; ETA: 0.1 minutes\n", - "Step 472000 of 500000; TPS: 4238.7; ETA: 0.1 minutes\n", - "Step 473000 of 500000; TPS: 4239.79; ETA: 0.1 minutes\n", - "Step 474000 of 500000; TPS: 4240.99; ETA: 0.1 minutes\n", - "Step 475000 of 500000; TPS: 4242.52; ETA: 0.1 minutes\n", - "Step 476000 of 500000; TPS: 4244.02; ETA: 0.1 minutes\n", - "Step 477000 of 500000; TPS: 4245.27; ETA: 0.1 minutes\n", - "Step 478000 of 500000; TPS: 4246.63; ETA: 0.1 minutes\n", - "Step 479000 of 500000; TPS: 4247.9; ETA: 0.1 minutes\n", - "Step 480000 of 500000; TPS: 4247.49; ETA: 0.1 minutes\n", - "Step 481000 of 500000; TPS: 4247.91; ETA: 0.1 minutes\n", - "Step 482000 of 500000; TPS: 4248.93; ETA: 0.1 minutes\n", - "Step 483000 of 500000; TPS: 4249.1; ETA: 0.1 minutes\n", - "Step 484000 of 500000; TPS: 4250.18; ETA: 0.1 minutes\n", - "Step 485000 of 500000; TPS: 4251.03; ETA: 0.1 minutes\n", - "Step 486000 of 500000; TPS: 4251.86; ETA: 0.1 minutes\n", - "Step 487000 of 500000; TPS: 4251.22; ETA: 0.1 minutes\n", - "Step 488000 of 500000; TPS: 4250.26; ETA: 0.0 minutes\n", - "Step 489000 of 500000; TPS: 4249.58; ETA: 0.0 minutes\n", - "Step 490000 of 500000; TPS: 4248.03; ETA: 0.0 minutes\n", - "Step 491000 of 500000; TPS: 4247.87; ETA: 0.0 minutes\n", - "Step 492000 of 500000; TPS: 4248.46; ETA: 0.0 minutes\n", - "Step 493000 of 500000; TPS: 4248.42; ETA: 0.0 minutes\n", - "Step 494000 of 500000; TPS: 4248.54; ETA: 0.0 minutes\n", - "Step 495000 of 500000; TPS: 4248.16; ETA: 0.0 minutes\n", - "Step 496000 of 500000; TPS: 4248.22; ETA: 0.0 minutes\n", - "Step 497000 of 500000; TPS: 4248.16; ETA: 0.0 minutes\n", - "Step 498000 of 500000; TPS: 4247.9; ETA: 0.0 minutes\n", - "Step 499000 of 500000; TPS: 4248.33; ETA: 0.0 minutes\n", - "Step 500000 of 500000; TPS: 4248.12; ETA: 0.0 minutes\n", - "Step 999 of 1000000; TPS: 4576.94; ETA: 3.6 minutes\n", - "Step 1999 of 1000000; TPS: 4825.46; ETA: 3.4 minutes\n", - "Step 2999 of 1000000; TPS: 4944.62; ETA: 3.4 minutes\n", - "Step 3999 of 1000000; TPS: 4923.02; ETA: 3.4 minutes\n", - "Step 4999 of 1000000; TPS: 4929.32; ETA: 3.4 minutes\n", - "Step 5999 of 1000000; TPS: 4912.68; ETA: 3.4 minutes\n", - "Step 6999 of 1000000; TPS: 4842.95; ETA: 3.4 minutes\n", - "Step 7999 of 1000000; TPS: 4827.23; ETA: 3.4 minutes\n", - "Step 8999 of 1000000; TPS: 4870.43; ETA: 3.4 minutes\n", - "Step 9999 of 1000000; TPS: 4876.09; ETA: 3.4 minutes\n", - "Step 10999 of 1000000; TPS: 4883.6; ETA: 3.4 minutes\n", - "Step 11999 of 1000000; TPS: 4888.22; ETA: 3.4 minutes\n", - "Step 12999 of 1000000; TPS: 4900.37; ETA: 3.4 minutes\n", - "Step 13999 of 1000000; TPS: 4911.84; ETA: 3.3 minutes\n", - "Step 14999 of 1000000; TPS: 4897.3; ETA: 3.4 minutes\n", - "Step 15999 of 1000000; TPS: 4899.47; ETA: 3.3 minutes\n", - "Step 16999 of 1000000; TPS: 4896.3; ETA: 3.3 minutes\n", - "Step 17999 of 1000000; TPS: 4890.36; ETA: 3.3 minutes\n", - "Step 18999 of 1000000; TPS: 4892.73; ETA: 3.3 minutes\n", - "Step 19999 of 1000000; TPS: 4878.62; ETA: 3.3 minutes\n", - "Step 20999 of 1000000; TPS: 4873.47; ETA: 3.3 minutes\n", - "Step 21999 of 1000000; TPS: 4858.24; ETA: 3.4 minutes\n", - "Step 22999 of 1000000; TPS: 4848.96; ETA: 3.4 minutes\n", - "Step 23999 of 1000000; TPS: 4847.38; ETA: 3.4 minutes\n", - "Step 24999 of 1000000; TPS: 4844.15; ETA: 3.4 minutes\n", - "Step 25999 of 1000000; TPS: 4839.95; ETA: 3.4 minutes\n", - "Step 26999 of 1000000; TPS: 4854.8; ETA: 3.3 minutes\n", - "Step 27999 of 1000000; TPS: 4871.53; ETA: 3.3 minutes\n", - "Step 28999 of 1000000; TPS: 4882.54; ETA: 3.3 minutes\n", - "Step 29999 of 1000000; TPS: 3416.14; ETA: 4.7 minutes\n", - "Step 30999 of 1000000; TPS: 3443.83; ETA: 4.7 minutes\n", - "Step 31999 of 1000000; TPS: 3470.5; ETA: 4.6 minutes\n", - "Step 32999 of 1000000; TPS: 3503.66; ETA: 4.6 minutes\n", - "Step 33999 of 1000000; TPS: 3536.14; ETA: 4.6 minutes\n", - "Step 34999 of 1000000; TPS: 3564.7; ETA: 4.5 minutes\n", - "Step 35999 of 1000000; TPS: 3591.21; ETA: 4.5 minutes\n", - "Step 36999 of 1000000; TPS: 3616.05; ETA: 4.4 minutes\n", - "Step 37999 of 1000000; TPS: 3638.64; ETA: 4.4 minutes\n", - "Step 38999 of 1000000; TPS: 3665.06; ETA: 4.4 minutes\n", - "Step 39999 of 1000000; TPS: 3691.88; ETA: 4.3 minutes\n", - "Step 40999 of 1000000; TPS: 3717.24; ETA: 4.3 minutes\n", - "Step 41999 of 1000000; TPS: 3741.24; ETA: 4.3 minutes\n", - "Step 42999 of 1000000; TPS: 3757.89; ETA: 4.2 minutes\n", - "Step 43999 of 1000000; TPS: 3770.27; ETA: 4.2 minutes\n", - "Step 44999 of 1000000; TPS: 3785.18; ETA: 4.2 minutes\n", - "Step 45999 of 1000000; TPS: 3799.93; ETA: 4.2 minutes\n", - "Step 46999 of 1000000; TPS: 3810.2; ETA: 4.2 minutes\n", - "Step 47999 of 1000000; TPS: 3825.72; ETA: 4.1 minutes\n", - "Step 48999 of 1000000; TPS: 3845.03; ETA: 4.1 minutes\n", - "Step 49999 of 1000000; TPS: 3867.43; ETA: 4.1 minutes\n", - "Step 50999 of 1000000; TPS: 3891.4; ETA: 4.1 minutes\n", - "Step 51999 of 1000000; TPS: 3915.23; ETA: 4.0 minutes\n", - "Step 52999 of 1000000; TPS: 3932.61; ETA: 4.0 minutes\n", - "Step 53999 of 1000000; TPS: 3952.98; ETA: 4.0 minutes\n", - "Step 54999 of 1000000; TPS: 3971.17; ETA: 4.0 minutes\n", - "Step 55999 of 1000000; TPS: 3988.73; ETA: 3.9 minutes\n", - "Step 56999 of 1000000; TPS: 4005.56; ETA: 3.9 minutes\n", - "Step 57999 of 1000000; TPS: 4021.51; ETA: 3.9 minutes\n", - "Step 58999 of 1000000; TPS: 4040.66; ETA: 3.9 minutes\n", - "Step 59999 of 1000000; TPS: 4056.18; ETA: 3.9 minutes\n", - "Step 60999 of 1000000; TPS: 4070.55; ETA: 3.8 minutes\n", - "Step 61999 of 1000000; TPS: 4087.52; ETA: 3.8 minutes\n", - "Step 62999 of 1000000; TPS: 4104.02; ETA: 3.8 minutes\n", - "Step 63999 of 1000000; TPS: 4113.54; ETA: 3.8 minutes\n", - "Step 64999 of 1000000; TPS: 4123.21; ETA: 3.8 minutes\n", - "Step 65999 of 1000000; TPS: 4134.49; ETA: 3.8 minutes\n", - "Step 66999 of 1000000; TPS: 4146.52; ETA: 3.8 minutes\n", - "Step 67999 of 1000000; TPS: 4156.18; ETA: 3.7 minutes\n", - "Step 68999 of 1000000; TPS: 4169.7; ETA: 3.7 minutes\n", - "Step 69999 of 1000000; TPS: 4182.98; ETA: 3.7 minutes\n", - "Step 70999 of 1000000; TPS: 4188.96; ETA: 3.7 minutes\n", - "Step 71999 of 1000000; TPS: 4198.57; ETA: 3.7 minutes\n", - "Step 72999 of 1000000; TPS: 4207.57; ETA: 3.7 minutes\n", - "Step 73999 of 1000000; TPS: 4212.28; ETA: 3.7 minutes\n", - "Step 74999 of 1000000; TPS: 4222.53; ETA: 3.7 minutes\n", - "Step 75999 of 1000000; TPS: 4232.38; ETA: 3.6 minutes\n", - "Step 76999 of 1000000; TPS: 4242.76; ETA: 3.6 minutes\n", - "Step 77999 of 1000000; TPS: 4255.46; ETA: 3.6 minutes\n", - "Step 78999 of 1000000; TPS: 4265.73; ETA: 3.6 minutes\n", - "Step 79999 of 1000000; TPS: 4274.45; ETA: 3.6 minutes\n", - "Step 80999 of 1000000; TPS: 4283.63; ETA: 3.6 minutes\n", - "Step 81999 of 1000000; TPS: 4293.31; ETA: 3.6 minutes\n", - "Step 82999 of 1000000; TPS: 4301.88; ETA: 3.6 minutes\n", - "Step 83999 of 1000000; TPS: 4309.31; ETA: 3.5 minutes\n", - "Step 84999 of 1000000; TPS: 4316.53; ETA: 3.5 minutes\n", - "Step 85999 of 1000000; TPS: 4323.92; ETA: 3.5 minutes\n", - "Step 86999 of 1000000; TPS: 4333.13; ETA: 3.5 minutes\n", - "Step 87999 of 1000000; TPS: 4338.45; ETA: 3.5 minutes\n", - "Step 88999 of 1000000; TPS: 4343.43; ETA: 3.5 minutes\n", - "Step 89999 of 1000000; TPS: 4350.49; ETA: 3.5 minutes\n", - "Step 90999 of 1000000; TPS: 4357.0; ETA: 3.5 minutes\n", - "Step 91999 of 1000000; TPS: 4364.72; ETA: 3.5 minutes\n", - "Step 92999 of 1000000; TPS: 4371.94; ETA: 3.5 minutes\n", - "Step 93999 of 1000000; TPS: 4379.38; ETA: 3.4 minutes\n", - "Step 94999 of 1000000; TPS: 4385.94; ETA: 3.4 minutes\n", - "Step 95999 of 1000000; TPS: 4391.72; ETA: 3.4 minutes\n", - "Step 96999 of 1000000; TPS: 4397.62; ETA: 3.4 minutes\n", - "Step 97999 of 1000000; TPS: 4404.55; ETA: 3.4 minutes\n", - "Step 98999 of 1000000; TPS: 4411.51; ETA: 3.4 minutes\n", - "Step 99999 of 1000000; TPS: 4419.08; ETA: 3.4 minutes\n", - "Step 100999 of 1000000; TPS: 4425.54; ETA: 3.4 minutes\n", - "Step 101999 of 1000000; TPS: 4432.97; ETA: 3.4 minutes\n", - "Step 102999 of 1000000; TPS: 4439.2; ETA: 3.4 minutes\n", - "Step 103999 of 1000000; TPS: 4444.73; ETA: 3.4 minutes\n", - "Step 104999 of 1000000; TPS: 4450.03; ETA: 3.4 minutes\n", - "Step 105999 of 1000000; TPS: 4455.57; ETA: 3.3 minutes\n", - "Step 106999 of 1000000; TPS: 4460.26; ETA: 3.3 minutes\n", - "Step 107999 of 1000000; TPS: 4464.85; ETA: 3.3 minutes\n", - "Step 108999 of 1000000; TPS: 4468.13; ETA: 3.3 minutes\n", - "Step 109999 of 1000000; TPS: 4471.29; ETA: 3.3 minutes\n", - "Step 110999 of 1000000; TPS: 4477.95; ETA: 3.3 minutes\n", - "Step 111999 of 1000000; TPS: 4481.74; ETA: 3.3 minutes\n", - "Step 112999 of 1000000; TPS: 4485.31; ETA: 3.3 minutes\n", - "Step 113999 of 1000000; TPS: 4490.39; ETA: 3.3 minutes\n", - "Step 114999 of 1000000; TPS: 4492.9; ETA: 3.3 minutes\n", - "Step 115999 of 1000000; TPS: 4497.51; ETA: 3.3 minutes\n", - "Step 116999 of 1000000; TPS: 4502.06; ETA: 3.3 minutes\n", - "Step 117999 of 1000000; TPS: 4504.67; ETA: 3.3 minutes\n", - "Step 118999 of 1000000; TPS: 4506.16; ETA: 3.3 minutes\n", - "Step 119999 of 1000000; TPS: 4507.7; ETA: 3.3 minutes\n", - "Step 120999 of 1000000; TPS: 4509.86; ETA: 3.2 minutes\n", - "Step 121999 of 1000000; TPS: 4513.48; ETA: 3.2 minutes\n", - "Step 122999 of 1000000; TPS: 4516.36; ETA: 3.2 minutes\n", - "Step 123999 of 1000000; TPS: 4519.43; ETA: 3.2 minutes\n", - "Step 124999 of 1000000; TPS: 4520.95; ETA: 3.2 minutes\n", - "Step 125999 of 1000000; TPS: 4525.02; ETA: 3.2 minutes\n", - "Step 126999 of 1000000; TPS: 4527.82; ETA: 3.2 minutes\n", - "Step 127999 of 1000000; TPS: 4530.61; ETA: 3.2 minutes\n", - "Step 128999 of 1000000; TPS: 4533.7; ETA: 3.2 minutes\n", - "Step 129999 of 1000000; TPS: 4534.85; ETA: 3.2 minutes\n", - "Step 130999 of 1000000; TPS: 4537.25; ETA: 3.2 minutes\n", - "Step 131999 of 1000000; TPS: 4539.82; ETA: 3.2 minutes\n", - "Step 132999 of 1000000; TPS: 4543.99; ETA: 3.2 minutes\n", - "Step 133999 of 1000000; TPS: 4546.93; ETA: 3.2 minutes\n", - "Step 134999 of 1000000; TPS: 4548.61; ETA: 3.2 minutes\n", - "Step 135999 of 1000000; TPS: 4550.57; ETA: 3.2 minutes\n", - "Step 136999 of 1000000; TPS: 4553.93; ETA: 3.2 minutes\n", - "Step 137999 of 1000000; TPS: 4557.95; ETA: 3.2 minutes\n", - "Step 138999 of 1000000; TPS: 4561.51; ETA: 3.1 minutes\n", - "Step 139999 of 1000000; TPS: 4566.09; ETA: 3.1 minutes\n", - "Step 140999 of 1000000; TPS: 4570.08; ETA: 3.1 minutes\n", - "Step 141999 of 1000000; TPS: 4574.56; ETA: 3.1 minutes\n", - "Step 142999 of 1000000; TPS: 4578.76; ETA: 3.1 minutes\n", - "Step 143999 of 1000000; TPS: 4583.32; ETA: 3.1 minutes\n", - "Step 144999 of 1000000; TPS: 4587.75; ETA: 3.1 minutes\n", - "Step 145999 of 1000000; TPS: 4593.08; ETA: 3.1 minutes\n", - "Step 146999 of 1000000; TPS: 4595.3; ETA: 3.1 minutes\n", - "Step 147999 of 1000000; TPS: 4597.09; ETA: 3.1 minutes\n", - "Step 148999 of 1000000; TPS: 4598.63; ETA: 3.1 minutes\n", - "Step 149999 of 1000000; TPS: 4601.85; ETA: 3.1 minutes\n", - "Step 150999 of 1000000; TPS: 4604.96; ETA: 3.1 minutes\n", - "Step 151999 of 1000000; TPS: 4607.53; ETA: 3.1 minutes\n", - "Step 152999 of 1000000; TPS: 4610.94; ETA: 3.1 minutes\n", - "Step 153999 of 1000000; TPS: 4614.67; ETA: 3.1 minutes\n", - "Step 154999 of 1000000; TPS: 4617.87; ETA: 3.0 minutes\n", - "Step 155999 of 1000000; TPS: 4619.54; ETA: 3.0 minutes\n", - "Step 156999 of 1000000; TPS: 4621.77; ETA: 3.0 minutes\n", - "Step 157999 of 1000000; TPS: 4624.26; ETA: 3.0 minutes\n", - "Step 158999 of 1000000; TPS: 4627.99; ETA: 3.0 minutes\n", - "Step 159999 of 1000000; TPS: 4631.18; ETA: 3.0 minutes\n", - "Step 160999 of 1000000; TPS: 4633.18; ETA: 3.0 minutes\n", - "Step 161999 of 1000000; TPS: 4636.16; ETA: 3.0 minutes\n", - "Step 162999 of 1000000; TPS: 4639.89; ETA: 3.0 minutes\n", - "Step 163999 of 1000000; TPS: 4641.64; ETA: 3.0 minutes\n", - "Step 164999 of 1000000; TPS: 4644.03; ETA: 3.0 minutes\n", - "Step 165999 of 1000000; TPS: 4643.77; ETA: 3.0 minutes\n", - "Step 166999 of 1000000; TPS: 4645.88; ETA: 3.0 minutes\n", - "Step 167999 of 1000000; TPS: 4646.74; ETA: 3.0 minutes\n", - "Step 168999 of 1000000; TPS: 4647.81; ETA: 3.0 minutes\n", - "Step 169999 of 1000000; TPS: 4648.87; ETA: 3.0 minutes\n", - "Step 170999 of 1000000; TPS: 4649.94; ETA: 3.0 minutes\n", - "Step 171999 of 1000000; TPS: 4649.73; ETA: 3.0 minutes\n", - "Step 172999 of 1000000; TPS: 4650.97; ETA: 3.0 minutes\n", - "Step 173999 of 1000000; TPS: 4651.92; ETA: 3.0 minutes\n", - "Step 174999 of 1000000; TPS: 4652.71; ETA: 3.0 minutes\n", - "Step 175999 of 1000000; TPS: 4653.92; ETA: 3.0 minutes\n", - "Step 176999 of 1000000; TPS: 4656.43; ETA: 2.9 minutes\n", - "Step 177999 of 1000000; TPS: 4657.84; ETA: 2.9 minutes\n", - "Step 178999 of 1000000; TPS: 4659.64; ETA: 2.9 minutes\n", - "Step 179999 of 1000000; TPS: 4659.45; ETA: 2.9 minutes\n", - "Step 180999 of 1000000; TPS: 4661.48; ETA: 2.9 minutes\n", - "Step 181999 of 1000000; TPS: 4663.14; ETA: 2.9 minutes\n", - "Step 182999 of 1000000; TPS: 4665.05; ETA: 2.9 minutes\n", - "Step 183999 of 1000000; TPS: 4666.15; ETA: 2.9 minutes\n", - "Step 184999 of 1000000; TPS: 4666.4; ETA: 2.9 minutes\n", - "Step 185999 of 1000000; TPS: 4668.03; ETA: 2.9 minutes\n", - "Step 186999 of 1000000; TPS: 4669.03; ETA: 2.9 minutes\n", - "Step 187999 of 1000000; TPS: 4670.67; ETA: 2.9 minutes\n", - "Step 188999 of 1000000; TPS: 4672.76; ETA: 2.9 minutes\n", - "Step 189999 of 1000000; TPS: 4673.79; ETA: 2.9 minutes\n", - "Step 190999 of 1000000; TPS: 4675.52; ETA: 2.9 minutes\n", - "Step 191999 of 1000000; TPS: 4405.62; ETA: 3.1 minutes\n", - "Step 192999 of 1000000; TPS: 4406.9; ETA: 3.1 minutes\n", - "Step 193999 of 1000000; TPS: 4407.91; ETA: 3.0 minutes\n", - "Step 194999 of 1000000; TPS: 4408.88; ETA: 3.0 minutes\n", - "Step 195999 of 1000000; TPS: 4409.63; ETA: 3.0 minutes\n", - "Step 196999 of 1000000; TPS: 4410.62; ETA: 3.0 minutes\n", - "Step 197999 of 1000000; TPS: 4409.42; ETA: 3.0 minutes\n", - "Step 198999 of 1000000; TPS: 4408.52; ETA: 3.0 minutes\n", - "Step 199999 of 1000000; TPS: 4407.98; ETA: 3.0 minutes\n", - "Step 200999 of 1000000; TPS: 4407.82; ETA: 3.0 minutes\n", - "Step 201999 of 1000000; TPS: 4405.78; ETA: 3.0 minutes\n", - "Step 202999 of 1000000; TPS: 4406.55; ETA: 3.0 minutes\n", - "Step 203999 of 1000000; TPS: 4407.13; ETA: 3.0 minutes\n", - "Step 204999 of 1000000; TPS: 4407.99; ETA: 3.0 minutes\n", - "Step 205999 of 1000000; TPS: 4408.92; ETA: 3.0 minutes\n", - "Step 206999 of 1000000; TPS: 4411.23; ETA: 3.0 minutes\n", - "Step 207999 of 1000000; TPS: 4413.2; ETA: 3.0 minutes\n", - "Step 208999 of 1000000; TPS: 4414.54; ETA: 3.0 minutes\n", - "Step 209999 of 1000000; TPS: 4416.99; ETA: 3.0 minutes\n", - "Step 210999 of 1000000; TPS: 4420.04; ETA: 3.0 minutes\n", - "Step 211999 of 1000000; TPS: 4423.86; ETA: 3.0 minutes\n", - "Step 212999 of 1000000; TPS: 4427.67; ETA: 3.0 minutes\n", - "Step 213999 of 1000000; TPS: 4430.57; ETA: 3.0 minutes\n", - "Step 214999 of 1000000; TPS: 4433.58; ETA: 3.0 minutes\n", - "Step 215999 of 1000000; TPS: 4435.46; ETA: 2.9 minutes\n", - "Step 216999 of 1000000; TPS: 4438.84; ETA: 2.9 minutes\n", - "Step 217999 of 1000000; TPS: 4441.78; ETA: 2.9 minutes\n", - "Step 218999 of 1000000; TPS: 4442.25; ETA: 2.9 minutes\n", - "Step 219999 of 1000000; TPS: 4444.77; ETA: 2.9 minutes\n", - "Step 220999 of 1000000; TPS: 4447.93; ETA: 2.9 minutes\n", - "Step 221999 of 1000000; TPS: 4450.36; ETA: 2.9 minutes\n", - "Step 222999 of 1000000; TPS: 4453.25; ETA: 2.9 minutes\n", - "Step 223999 of 1000000; TPS: 4455.88; ETA: 2.9 minutes\n", - "Step 224999 of 1000000; TPS: 4458.05; ETA: 2.9 minutes\n", - "Step 225999 of 1000000; TPS: 4460.28; ETA: 2.9 minutes\n", - "Step 226999 of 1000000; TPS: 4461.43; ETA: 2.9 minutes\n", - "Step 227999 of 1000000; TPS: 4462.83; ETA: 2.9 minutes\n", - "Step 228999 of 1000000; TPS: 4464.89; ETA: 2.9 minutes\n", - "Step 229999 of 1000000; TPS: 4467.22; ETA: 2.9 minutes\n", - "Step 230999 of 1000000; TPS: 4469.18; ETA: 2.9 minutes\n", - "Step 231999 of 1000000; TPS: 4471.18; ETA: 2.9 minutes\n", - "Step 232999 of 1000000; TPS: 4472.88; ETA: 2.9 minutes\n", - "Step 233999 of 1000000; TPS: 4474.43; ETA: 2.9 minutes\n", - "Step 234999 of 1000000; TPS: 4476.07; ETA: 2.8 minutes\n", - "Step 235999 of 1000000; TPS: 4478.06; ETA: 2.8 minutes\n", - "Step 236999 of 1000000; TPS: 4479.81; ETA: 2.8 minutes\n", - "Step 237999 of 1000000; TPS: 4482.04; ETA: 2.8 minutes\n", - "Step 238999 of 1000000; TPS: 4483.42; ETA: 2.8 minutes\n", - "Step 239999 of 1000000; TPS: 4485.27; ETA: 2.8 minutes\n", - "Step 240999 of 1000000; TPS: 4488.22; ETA: 2.8 minutes\n", - "Step 241999 of 1000000; TPS: 4490.48; ETA: 2.8 minutes\n", - "Step 242999 of 1000000; TPS: 4491.66; ETA: 2.8 minutes\n", - "Step 243999 of 1000000; TPS: 4493.47; ETA: 2.8 minutes\n", - "Step 244999 of 1000000; TPS: 4495.34; ETA: 2.8 minutes\n", - "Step 245999 of 1000000; TPS: 4498.06; ETA: 2.8 minutes\n", - "Step 246999 of 1000000; TPS: 4499.88; ETA: 2.8 minutes\n", - "Step 247999 of 1000000; TPS: 4501.84; ETA: 2.8 minutes\n", - "Step 248999 of 1000000; TPS: 4504.41; ETA: 2.8 minutes\n", - "Step 249999 of 1000000; TPS: 4506.59; ETA: 2.8 minutes\n", - "Step 250999 of 1000000; TPS: 4509.56; ETA: 2.8 minutes\n", - "Step 251999 of 1000000; TPS: 4510.45; ETA: 2.8 minutes\n", - "Step 252999 of 1000000; TPS: 4512.14; ETA: 2.8 minutes\n", - "Step 253999 of 1000000; TPS: 4513.63; ETA: 2.8 minutes\n", - "Step 254999 of 1000000; TPS: 4515.54; ETA: 2.7 minutes\n", - "Step 255999 of 1000000; TPS: 4518.14; ETA: 2.7 minutes\n", - "Step 256999 of 1000000; TPS: 4520.62; ETA: 2.7 minutes\n", - "Step 257999 of 1000000; TPS: 4522.9; ETA: 2.7 minutes\n", - "Step 258999 of 1000000; TPS: 4525.21; ETA: 2.7 minutes\n", - "Step 259999 of 1000000; TPS: 4527.4; ETA: 2.7 minutes\n", - "Step 260999 of 1000000; TPS: 4529.59; ETA: 2.7 minutes\n", - "Step 261999 of 1000000; TPS: 4531.14; ETA: 2.7 minutes\n", - "Step 262999 of 1000000; TPS: 4532.99; ETA: 2.7 minutes\n", - "Step 263999 of 1000000; TPS: 4534.53; ETA: 2.7 minutes\n", - "Step 264999 of 1000000; TPS: 4537.35; ETA: 2.7 minutes\n", - "Step 265999 of 1000000; TPS: 4538.83; ETA: 2.7 minutes\n", - "Step 266999 of 1000000; TPS: 4539.42; ETA: 2.7 minutes\n", - "Step 267999 of 1000000; TPS: 4539.13; ETA: 2.7 minutes\n", - "Step 268999 of 1000000; TPS: 4540.05; ETA: 2.7 minutes\n", - "Step 269999 of 1000000; TPS: 4540.91; ETA: 2.7 minutes\n", - "Step 270999 of 1000000; TPS: 4541.87; ETA: 2.7 minutes\n", - "Step 271999 of 1000000; TPS: 4542.73; ETA: 2.7 minutes\n", - "Step 272999 of 1000000; TPS: 4543.62; ETA: 2.7 minutes\n", - "Step 273999 of 1000000; TPS: 4544.81; ETA: 2.7 minutes\n", - "Step 274999 of 1000000; TPS: 4542.79; ETA: 2.7 minutes\n", - "Step 275999 of 1000000; TPS: 4541.96; ETA: 2.7 minutes\n", - "Step 276999 of 1000000; TPS: 4541.29; ETA: 2.7 minutes\n", - "Step 277999 of 1000000; TPS: 4538.58; ETA: 2.7 minutes\n", - "Step 278999 of 1000000; TPS: 4538.1; ETA: 2.6 minutes\n", - "Step 279999 of 1000000; TPS: 4538.29; ETA: 2.6 minutes\n", - "Step 280999 of 1000000; TPS: 4538.52; ETA: 2.6 minutes\n", - "Step 281999 of 1000000; TPS: 4539.81; ETA: 2.6 minutes\n", - "Step 282999 of 1000000; TPS: 4539.75; ETA: 2.6 minutes\n", - "Step 283999 of 1000000; TPS: 4539.84; ETA: 2.6 minutes\n", - "Step 284999 of 1000000; TPS: 4540.34; ETA: 2.6 minutes\n", - "Step 285999 of 1000000; TPS: 4539.9; ETA: 2.6 minutes\n", - "Step 286999 of 1000000; TPS: 4541.07; ETA: 2.6 minutes\n", - "Step 287999 of 1000000; TPS: 4541.16; ETA: 2.6 minutes\n", - "Step 288999 of 1000000; TPS: 4540.86; ETA: 2.6 minutes\n", - "Step 289999 of 1000000; TPS: 4540.6; ETA: 2.6 minutes\n", - "Step 290999 of 1000000; TPS: 4540.18; ETA: 2.6 minutes\n", - "Step 291999 of 1000000; TPS: 4541.2; ETA: 2.6 minutes\n", - "Step 292999 of 1000000; TPS: 4541.84; ETA: 2.6 minutes\n", - "Step 293999 of 1000000; TPS: 4541.34; ETA: 2.6 minutes\n", - "Step 294999 of 1000000; TPS: 4541.92; ETA: 2.6 minutes\n", - "Step 295999 of 1000000; TPS: 4542.62; ETA: 2.6 minutes\n", - "Step 296999 of 1000000; TPS: 4538.55; ETA: 2.6 minutes\n", - "Step 297999 of 1000000; TPS: 4535.84; ETA: 2.6 minutes\n", - "Step 298999 of 1000000; TPS: 4533.9; ETA: 2.6 minutes\n", - "Step 299999 of 1000000; TPS: 4534.76; ETA: 2.6 minutes\n", - "Step 300999 of 1000000; TPS: 4535.77; ETA: 2.6 minutes\n", - "Step 301999 of 1000000; TPS: 4537.61; ETA: 2.6 minutes\n", - "Step 302999 of 1000000; TPS: 4538.93; ETA: 2.6 minutes\n", - "Step 303999 of 1000000; TPS: 4539.65; ETA: 2.6 minutes\n", - "Step 304999 of 1000000; TPS: 4541.28; ETA: 2.6 minutes\n", - "Step 305999 of 1000000; TPS: 4542.25; ETA: 2.5 minutes\n", - "Step 306999 of 1000000; TPS: 4541.99; ETA: 2.5 minutes\n", - "Step 307999 of 1000000; TPS: 4543.97; ETA: 2.5 minutes\n", - "Step 308999 of 1000000; TPS: 4545.2; ETA: 2.5 minutes\n", - "Step 309999 of 1000000; TPS: 4546.41; ETA: 2.5 minutes\n", - "Step 310999 of 1000000; TPS: 4547.56; ETA: 2.5 minutes\n", - "Step 311999 of 1000000; TPS: 4549.56; ETA: 2.5 minutes\n", - "Step 312999 of 1000000; TPS: 4550.86; ETA: 2.5 minutes\n", - "Step 313999 of 1000000; TPS: 4552.1; ETA: 2.5 minutes\n", - "Step 314999 of 1000000; TPS: 4553.18; ETA: 2.5 minutes\n", - "Step 315999 of 1000000; TPS: 4553.92; ETA: 2.5 minutes\n", - "Step 316999 of 1000000; TPS: 4554.8; ETA: 2.5 minutes\n", - "Step 317999 of 1000000; TPS: 4556.21; ETA: 2.5 minutes\n", - "Step 318999 of 1000000; TPS: 4557.49; ETA: 2.5 minutes\n", - "Step 319999 of 1000000; TPS: 4559.2; ETA: 2.5 minutes\n", - "Step 320999 of 1000000; TPS: 4560.84; ETA: 2.5 minutes\n", - "Step 321999 of 1000000; TPS: 4562.65; ETA: 2.5 minutes\n", - "Step 322999 of 1000000; TPS: 4562.88; ETA: 2.5 minutes\n", - "Step 323999 of 1000000; TPS: 4564.03; ETA: 2.5 minutes\n", - "Step 324999 of 1000000; TPS: 4565.01; ETA: 2.5 minutes\n", - "Step 325999 of 1000000; TPS: 4563.32; ETA: 2.5 minutes\n", - "Step 326999 of 1000000; TPS: 4564.13; ETA: 2.5 minutes\n", - "Step 327999 of 1000000; TPS: 4564.85; ETA: 2.5 minutes\n", - "Step 328999 of 1000000; TPS: 4566.32; ETA: 2.4 minutes\n", - "Step 329999 of 1000000; TPS: 4566.61; ETA: 2.4 minutes\n", - "Step 330999 of 1000000; TPS: 4567.14; ETA: 2.4 minutes\n", - "Step 331999 of 1000000; TPS: 4568.03; ETA: 2.4 minutes\n", - "Step 332999 of 1000000; TPS: 4569.42; ETA: 2.4 minutes\n", - "Step 333999 of 1000000; TPS: 4570.65; ETA: 2.4 minutes\n", - "Step 334999 of 1000000; TPS: 4572.63; ETA: 2.4 minutes\n", - "Step 335999 of 1000000; TPS: 4573.29; ETA: 2.4 minutes\n", - "Step 336999 of 1000000; TPS: 4573.91; ETA: 2.4 minutes\n", - "Step 337999 of 1000000; TPS: 4575.4; ETA: 2.4 minutes\n", - "Step 338999 of 1000000; TPS: 4577.02; ETA: 2.4 minutes\n", - "Step 339999 of 1000000; TPS: 4578.14; ETA: 2.4 minutes\n", - "Step 340999 of 1000000; TPS: 4579.37; ETA: 2.4 minutes\n", - "Step 341999 of 1000000; TPS: 4580.45; ETA: 2.4 minutes\n", - "Step 342999 of 1000000; TPS: 4581.41; ETA: 2.4 minutes\n", - "Step 343999 of 1000000; TPS: 4582.49; ETA: 2.4 minutes\n", - "Step 344999 of 1000000; TPS: 4583.33; ETA: 2.4 minutes\n", - "Step 345999 of 1000000; TPS: 4584.43; ETA: 2.4 minutes\n", - "Step 346999 of 1000000; TPS: 4585.22; ETA: 2.4 minutes\n", - "Step 347999 of 1000000; TPS: 4439.47; ETA: 2.4 minutes\n", - "Step 348999 of 1000000; TPS: 4440.87; ETA: 2.4 minutes\n", - "Step 349999 of 1000000; TPS: 4441.19; ETA: 2.4 minutes\n", - "Step 350999 of 1000000; TPS: 4439.74; ETA: 2.4 minutes\n", - "Step 351999 of 1000000; TPS: 4439.61; ETA: 2.4 minutes\n", - "Step 352999 of 1000000; TPS: 4440.08; ETA: 2.4 minutes\n", - "Step 353999 of 1000000; TPS: 4440.64; ETA: 2.4 minutes\n", - "Step 354999 of 1000000; TPS: 4441.41; ETA: 2.4 minutes\n", - "Step 355999 of 1000000; TPS: 4442.17; ETA: 2.4 minutes\n", - "Step 356999 of 1000000; TPS: 4443.06; ETA: 2.4 minutes\n", - "Step 357999 of 1000000; TPS: 4443.83; ETA: 2.4 minutes\n", - "Step 358999 of 1000000; TPS: 4444.16; ETA: 2.4 minutes\n", - "Step 359999 of 1000000; TPS: 4444.93; ETA: 2.4 minutes\n", - "Step 360999 of 1000000; TPS: 4445.44; ETA: 2.4 minutes\n", - "Step 361999 of 1000000; TPS: 4445.58; ETA: 2.4 minutes\n", - "Step 362999 of 1000000; TPS: 4446.9; ETA: 2.4 minutes\n", - "Step 363999 of 1000000; TPS: 4447.32; ETA: 2.4 minutes\n", - "Step 364999 of 1000000; TPS: 4448.63; ETA: 2.4 minutes\n", - "Step 365999 of 1000000; TPS: 4450.2; ETA: 2.4 minutes\n", - "Step 366999 of 1000000; TPS: 4451.99; ETA: 2.4 minutes\n", - "Step 367999 of 1000000; TPS: 4453.89; ETA: 2.4 minutes\n", - "Step 368999 of 1000000; TPS: 4455.71; ETA: 2.4 minutes\n", - "Step 369999 of 1000000; TPS: 4456.7; ETA: 2.4 minutes\n", - "Step 370999 of 1000000; TPS: 4458.18; ETA: 2.4 minutes\n", - "Step 371999 of 1000000; TPS: 4459.24; ETA: 2.3 minutes\n", - "Step 372999 of 1000000; TPS: 4460.25; ETA: 2.3 minutes\n", - "Step 373999 of 1000000; TPS: 4461.99; ETA: 2.3 minutes\n", - "Step 374999 of 1000000; TPS: 4463.26; ETA: 2.3 minutes\n", - "Step 375999 of 1000000; TPS: 4465.05; ETA: 2.3 minutes\n", - "Step 376999 of 1000000; TPS: 4466.79; ETA: 2.3 minutes\n", - "Step 377999 of 1000000; TPS: 4468.0; ETA: 2.3 minutes\n", - "Step 378999 of 1000000; TPS: 4468.94; ETA: 2.3 minutes\n", - "Step 379999 of 1000000; TPS: 4470.7; ETA: 2.3 minutes\n", - "Step 380999 of 1000000; TPS: 4471.82; ETA: 2.3 minutes\n", - "Step 381999 of 1000000; TPS: 4472.46; ETA: 2.3 minutes\n", - "Step 382999 of 1000000; TPS: 4473.74; ETA: 2.3 minutes\n", - "Step 383999 of 1000000; TPS: 4474.16; ETA: 2.3 minutes\n", - "Step 384999 of 1000000; TPS: 4475.47; ETA: 2.3 minutes\n", - "Step 385999 of 1000000; TPS: 4477.24; ETA: 2.3 minutes\n", - "Step 386999 of 1000000; TPS: 4478.88; ETA: 2.3 minutes\n", - "Step 387999 of 1000000; TPS: 4480.28; ETA: 2.3 minutes\n", - "Step 388999 of 1000000; TPS: 4482.49; ETA: 2.3 minutes\n", - "Step 389999 of 1000000; TPS: 4484.37; ETA: 2.3 minutes\n", - "Step 390999 of 1000000; TPS: 4485.73; ETA: 2.3 minutes\n", - "Step 391999 of 1000000; TPS: 4487.31; ETA: 2.3 minutes\n", - "Step 392999 of 1000000; TPS: 4489.0; ETA: 2.3 minutes\n", - "Step 393999 of 1000000; TPS: 4490.68; ETA: 2.2 minutes\n", - "Step 394999 of 1000000; TPS: 4492.51; ETA: 2.2 minutes\n", - "Step 395999 of 1000000; TPS: 4494.3; ETA: 2.2 minutes\n", - "Step 396999 of 1000000; TPS: 4495.88; ETA: 2.2 minutes\n", - "Step 397999 of 1000000; TPS: 4496.9; ETA: 2.2 minutes\n", - "Step 398999 of 1000000; TPS: 4499.09; ETA: 2.2 minutes\n", - "Step 399999 of 1000000; TPS: 4500.4; ETA: 2.2 minutes\n", - "Step 400999 of 1000000; TPS: 4501.34; ETA: 2.2 minutes\n", - "Step 401999 of 1000000; TPS: 4502.1; ETA: 2.2 minutes\n", - "Step 402999 of 1000000; TPS: 4503.45; ETA: 2.2 minutes\n", - "Step 403999 of 1000000; TPS: 4504.52; ETA: 2.2 minutes\n", - "Step 404999 of 1000000; TPS: 4504.1; ETA: 2.2 minutes\n", - "Step 405999 of 1000000; TPS: 4505.1; ETA: 2.2 minutes\n", - "Step 406999 of 1000000; TPS: 4505.86; ETA: 2.2 minutes\n", - "Step 407999 of 1000000; TPS: 4507.08; ETA: 2.2 minutes\n", - "Step 408999 of 1000000; TPS: 4508.04; ETA: 2.2 minutes\n", - "Step 409999 of 1000000; TPS: 4508.95; ETA: 2.2 minutes\n", - "Step 410999 of 1000000; TPS: 4509.71; ETA: 2.2 minutes\n", - "Step 411999 of 1000000; TPS: 4510.66; ETA: 2.2 minutes\n", - "Step 412999 of 1000000; TPS: 4511.7; ETA: 2.2 minutes\n", - "Step 413999 of 1000000; TPS: 4512.59; ETA: 2.2 minutes\n", - "Step 414999 of 1000000; TPS: 4513.14; ETA: 2.2 minutes\n", - "Step 415999 of 1000000; TPS: 4514.2; ETA: 2.2 minutes\n", - "Step 416999 of 1000000; TPS: 4515.12; ETA: 2.2 minutes\n", - "Step 417999 of 1000000; TPS: 4516.06; ETA: 2.1 minutes\n", - "Step 418999 of 1000000; TPS: 4516.19; ETA: 2.1 minutes\n", - "Step 419999 of 1000000; TPS: 4517.3; ETA: 2.1 minutes\n", - "Step 420999 of 1000000; TPS: 4518.57; ETA: 2.1 minutes\n", - "Step 421999 of 1000000; TPS: 4520.16; ETA: 2.1 minutes\n", - "Step 422999 of 1000000; TPS: 4520.52; ETA: 2.1 minutes\n", - "Step 423999 of 1000000; TPS: 4521.38; ETA: 2.1 minutes\n", - "Step 424999 of 1000000; TPS: 4522.85; ETA: 2.1 minutes\n", - "Step 425999 of 1000000; TPS: 4524.08; ETA: 2.1 minutes\n", - "Step 426999 of 1000000; TPS: 4525.72; ETA: 2.1 minutes\n", - "Step 427999 of 1000000; TPS: 4526.93; ETA: 2.1 minutes\n", - "Step 428999 of 1000000; TPS: 4527.92; ETA: 2.1 minutes\n", - "Step 429999 of 1000000; TPS: 4528.78; ETA: 2.1 minutes\n", - "Step 430999 of 1000000; TPS: 4530.1; ETA: 2.1 minutes\n", - "Step 431999 of 1000000; TPS: 4531.43; ETA: 2.1 minutes\n", - "Step 432999 of 1000000; TPS: 4532.47; ETA: 2.1 minutes\n", - "Step 433999 of 1000000; TPS: 4533.77; ETA: 2.1 minutes\n", - "Step 434999 of 1000000; TPS: 4535.02; ETA: 2.1 minutes\n", - "Step 435999 of 1000000; TPS: 4536.22; ETA: 2.1 minutes\n", - "Step 436999 of 1000000; TPS: 4537.33; ETA: 2.1 minutes\n", - "Step 437999 of 1000000; TPS: 4538.39; ETA: 2.1 minutes\n", - "Step 438999 of 1000000; TPS: 4539.9; ETA: 2.1 minutes\n", - "Step 439999 of 1000000; TPS: 4541.04; ETA: 2.1 minutes\n", - "Step 440999 of 1000000; TPS: 4541.98; ETA: 2.1 minutes\n", - "Step 441999 of 1000000; TPS: 4542.08; ETA: 2.0 minutes\n", - "Step 442999 of 1000000; TPS: 4542.68; ETA: 2.0 minutes\n", - "Step 443999 of 1000000; TPS: 4543.97; ETA: 2.0 minutes\n", - "Step 444999 of 1000000; TPS: 4544.76; ETA: 2.0 minutes\n", - "Step 445999 of 1000000; TPS: 4545.97; ETA: 2.0 minutes\n", - "Step 446999 of 1000000; TPS: 4546.23; ETA: 2.0 minutes\n", - "Step 447999 of 1000000; TPS: 4545.56; ETA: 2.0 minutes\n", - "Step 448999 of 1000000; TPS: 4546.85; ETA: 2.0 minutes\n", - "Step 449999 of 1000000; TPS: 4548.21; ETA: 2.0 minutes\n", - "Step 450999 of 1000000; TPS: 4549.46; ETA: 2.0 minutes\n", - "Step 451999 of 1000000; TPS: 4550.71; ETA: 2.0 minutes\n", - "Step 452999 of 1000000; TPS: 4551.87; ETA: 2.0 minutes\n", - "Step 453999 of 1000000; TPS: 4553.1; ETA: 2.0 minutes\n", - "Step 454999 of 1000000; TPS: 4554.62; ETA: 2.0 minutes\n", - "Step 455999 of 1000000; TPS: 4555.83; ETA: 2.0 minutes\n", - "Step 456999 of 1000000; TPS: 4556.7; ETA: 2.0 minutes\n", - "Step 457999 of 1000000; TPS: 4557.92; ETA: 2.0 minutes\n", - "Step 458999 of 1000000; TPS: 4558.13; ETA: 2.0 minutes\n", - "Step 459999 of 1000000; TPS: 4559.15; ETA: 2.0 minutes\n", - "Step 460999 of 1000000; TPS: 4559.75; ETA: 2.0 minutes\n", - "Step 461999 of 1000000; TPS: 4560.49; ETA: 2.0 minutes\n", - "Step 462999 of 1000000; TPS: 4561.25; ETA: 2.0 minutes\n", - "Step 463999 of 1000000; TPS: 4561.62; ETA: 2.0 minutes\n", - "Step 464999 of 1000000; TPS: 4562.0; ETA: 2.0 minutes\n", - "Step 465999 of 1000000; TPS: 4562.59; ETA: 2.0 minutes\n", - "Step 466999 of 1000000; TPS: 4563.48; ETA: 1.9 minutes\n", - "Step 467999 of 1000000; TPS: 4564.71; ETA: 1.9 minutes\n", - "Step 468999 of 1000000; TPS: 4565.22; ETA: 1.9 minutes\n", - "Step 469999 of 1000000; TPS: 4565.77; ETA: 1.9 minutes\n", - "Step 470999 of 1000000; TPS: 4565.82; ETA: 1.9 minutes\n", - "Step 471999 of 1000000; TPS: 4565.92; ETA: 1.9 minutes\n", - "Step 472999 of 1000000; TPS: 4567.06; ETA: 1.9 minutes\n", - "Step 473999 of 1000000; TPS: 4567.96; ETA: 1.9 minutes\n", - "Step 474999 of 1000000; TPS: 4569.0; ETA: 1.9 minutes\n", - "Step 475999 of 1000000; TPS: 4569.83; ETA: 1.9 minutes\n", - "Step 476999 of 1000000; TPS: 4570.62; ETA: 1.9 minutes\n", - "Step 477999 of 1000000; TPS: 4571.86; ETA: 1.9 minutes\n", - "Step 478999 of 1000000; TPS: 4573.02; ETA: 1.9 minutes\n", - "Step 479999 of 1000000; TPS: 4573.8; ETA: 1.9 minutes\n", - "Step 480999 of 1000000; TPS: 4574.7; ETA: 1.9 minutes\n", - "Step 481999 of 1000000; TPS: 4575.8; ETA: 1.9 minutes\n", - "Step 482999 of 1000000; TPS: 4577.05; ETA: 1.9 minutes\n", - "Step 483999 of 1000000; TPS: 4578.51; ETA: 1.9 minutes\n", - "Step 484999 of 1000000; TPS: 4579.83; ETA: 1.9 minutes\n", - "Step 485999 of 1000000; TPS: 4580.85; ETA: 1.9 minutes\n", - "Step 486999 of 1000000; TPS: 4581.34; ETA: 1.9 minutes\n", - "Step 487999 of 1000000; TPS: 4582.18; ETA: 1.9 minutes\n", - "Step 488999 of 1000000; TPS: 4582.49; ETA: 1.9 minutes\n", - "Step 489999 of 1000000; TPS: 4581.61; ETA: 1.9 minutes\n", - "Step 490999 of 1000000; TPS: 4581.9; ETA: 1.9 minutes\n", - "Step 491999 of 1000000; TPS: 4582.89; ETA: 1.8 minutes\n", - "Step 492999 of 1000000; TPS: 4583.7; ETA: 1.8 minutes\n", - "Step 493999 of 1000000; TPS: 4584.15; ETA: 1.8 minutes\n", - "Step 494999 of 1000000; TPS: 4584.27; ETA: 1.8 minutes\n", - "Step 495999 of 1000000; TPS: 4584.94; ETA: 1.8 minutes\n", - "Step 496999 of 1000000; TPS: 4585.86; ETA: 1.8 minutes\n", - "Step 497999 of 1000000; TPS: 4586.13; ETA: 1.8 minutes\n", - "Step 498999 of 1000000; TPS: 4586.89; ETA: 1.8 minutes\n", - "Step 499999 of 1000000; TPS: 4588.22; ETA: 1.8 minutes\n", - "Step 500999 of 1000000; TPS: 4589.03; ETA: 1.8 minutes\n", - "Step 501999 of 1000000; TPS: 4589.9; ETA: 1.8 minutes\n", - "Step 502999 of 1000000; TPS: 4590.93; ETA: 1.8 minutes\n", - "Step 503999 of 1000000; TPS: 4591.36; ETA: 1.8 minutes\n", - "Step 504999 of 1000000; TPS: 4592.26; ETA: 1.8 minutes\n", - "Step 505999 of 1000000; TPS: 4593.36; ETA: 1.8 minutes\n", - "Step 506999 of 1000000; TPS: 4594.19; ETA: 1.8 minutes\n", - "Step 507999 of 1000000; TPS: 4496.53; ETA: 1.8 minutes\n", - "Step 508999 of 1000000; TPS: 4497.72; ETA: 1.8 minutes\n", - "Step 509999 of 1000000; TPS: 4498.25; ETA: 1.8 minutes\n", - "Step 510999 of 1000000; TPS: 4498.72; ETA: 1.8 minutes\n", - "Step 511999 of 1000000; TPS: 4499.66; ETA: 1.8 minutes\n", - "Step 512999 of 1000000; TPS: 4500.74; ETA: 1.8 minutes\n", - "Step 513999 of 1000000; TPS: 4501.52; ETA: 1.8 minutes\n", - "Step 514999 of 1000000; TPS: 4502.3; ETA: 1.8 minutes\n", - "Step 515999 of 1000000; TPS: 4502.74; ETA: 1.8 minutes\n", - "Step 516999 of 1000000; TPS: 4503.13; ETA: 1.8 minutes\n", - "Step 517999 of 1000000; TPS: 4503.64; ETA: 1.8 minutes\n", - "Step 518999 of 1000000; TPS: 4503.97; ETA: 1.8 minutes\n", - "Step 519999 of 1000000; TPS: 4504.62; ETA: 1.8 minutes\n", - "Step 520999 of 1000000; TPS: 4504.86; ETA: 1.8 minutes\n", - "Step 521999 of 1000000; TPS: 4505.16; ETA: 1.8 minutes\n", - "Step 522999 of 1000000; TPS: 4505.28; ETA: 1.8 minutes\n", - "Step 523999 of 1000000; TPS: 4505.93; ETA: 1.8 minutes\n", - "Step 524999 of 1000000; TPS: 4507.25; ETA: 1.8 minutes\n", - "Step 525999 of 1000000; TPS: 4508.11; ETA: 1.8 minutes\n", - "Step 526999 of 1000000; TPS: 4509.15; ETA: 1.7 minutes\n", - "Step 527999 of 1000000; TPS: 4510.35; ETA: 1.7 minutes\n", - "Step 528999 of 1000000; TPS: 4511.11; ETA: 1.7 minutes\n", - "Step 529999 of 1000000; TPS: 4512.3; ETA: 1.7 minutes\n", - "Step 530999 of 1000000; TPS: 4513.18; ETA: 1.7 minutes\n", - "Step 531999 of 1000000; TPS: 4513.99; ETA: 1.7 minutes\n", - "Step 532999 of 1000000; TPS: 4514.99; ETA: 1.7 minutes\n", - "Step 533999 of 1000000; TPS: 4516.29; ETA: 1.7 minutes\n", - "Step 534999 of 1000000; TPS: 4516.36; ETA: 1.7 minutes\n", - "Step 535999 of 1000000; TPS: 4516.87; ETA: 1.7 minutes\n", - "Step 536999 of 1000000; TPS: 4517.98; ETA: 1.7 minutes\n", - "Step 537999 of 1000000; TPS: 4519.03; ETA: 1.7 minutes\n", - "Step 538999 of 1000000; TPS: 4520.34; ETA: 1.7 minutes\n", - "Step 539999 of 1000000; TPS: 4521.32; ETA: 1.7 minutes\n", - "Step 540999 of 1000000; TPS: 4522.44; ETA: 1.7 minutes\n", - "Step 541999 of 1000000; TPS: 4523.59; ETA: 1.7 minutes\n", - "Step 542999 of 1000000; TPS: 4524.9; ETA: 1.7 minutes\n", - "Step 543999 of 1000000; TPS: 4526.13; ETA: 1.7 minutes\n", - "Step 544999 of 1000000; TPS: 4527.57; ETA: 1.7 minutes\n", - "Step 545999 of 1000000; TPS: 4528.75; ETA: 1.7 minutes\n", - "Step 546999 of 1000000; TPS: 4529.82; ETA: 1.7 minutes\n", - "Step 547999 of 1000000; TPS: 4531.24; ETA: 1.7 minutes\n", - "Step 548999 of 1000000; TPS: 4532.51; ETA: 1.7 minutes\n", - "Step 549999 of 1000000; TPS: 4533.66; ETA: 1.7 minutes\n", - "Step 550999 of 1000000; TPS: 4534.8; ETA: 1.7 minutes\n", - "Step 551999 of 1000000; TPS: 4535.85; ETA: 1.6 minutes\n", - "Step 552999 of 1000000; TPS: 4536.8; ETA: 1.6 minutes\n", - "Step 553999 of 1000000; TPS: 4538.15; ETA: 1.6 minutes\n", - "Step 554999 of 1000000; TPS: 4539.31; ETA: 1.6 minutes\n", - "Step 555999 of 1000000; TPS: 4539.95; ETA: 1.6 minutes\n", - "Step 556999 of 1000000; TPS: 4540.05; ETA: 1.6 minutes\n", - "Step 557999 of 1000000; TPS: 4539.64; ETA: 1.6 minutes\n", - "Step 558999 of 1000000; TPS: 4540.69; ETA: 1.6 minutes\n", - "Step 559999 of 1000000; TPS: 4541.89; ETA: 1.6 minutes\n", - "Step 560999 of 1000000; TPS: 4542.9; ETA: 1.6 minutes\n", - "Step 561999 of 1000000; TPS: 4543.84; ETA: 1.6 minutes\n", - "Step 562999 of 1000000; TPS: 4544.79; ETA: 1.6 minutes\n", - "Step 563999 of 1000000; TPS: 4545.78; ETA: 1.6 minutes\n", - "Step 564999 of 1000000; TPS: 4545.5; ETA: 1.6 minutes\n", - "Step 565999 of 1000000; TPS: 4546.17; ETA: 1.6 minutes\n", - "Step 566999 of 1000000; TPS: 4547.05; ETA: 1.6 minutes\n", - "Step 567999 of 1000000; TPS: 4547.98; ETA: 1.6 minutes\n", - "Step 568999 of 1000000; TPS: 4548.67; ETA: 1.6 minutes\n", - "Step 569999 of 1000000; TPS: 4549.87; ETA: 1.6 minutes\n", - "Step 570999 of 1000000; TPS: 4550.81; ETA: 1.6 minutes\n", - "Step 571999 of 1000000; TPS: 4551.62; ETA: 1.6 minutes\n", - "Step 572999 of 1000000; TPS: 4552.31; ETA: 1.6 minutes\n", - "Step 573999 of 1000000; TPS: 4551.97; ETA: 1.6 minutes\n", - "Step 574999 of 1000000; TPS: 4552.61; ETA: 1.6 minutes\n", - "Step 575999 of 1000000; TPS: 4553.51; ETA: 1.6 minutes\n", - "Step 576999 of 1000000; TPS: 4554.29; ETA: 1.5 minutes\n", - "Step 577999 of 1000000; TPS: 4555.16; ETA: 1.5 minutes\n", - "Step 578999 of 1000000; TPS: 4555.89; ETA: 1.5 minutes\n", - "Step 579999 of 1000000; TPS: 4556.94; ETA: 1.5 minutes\n", - "Step 580999 of 1000000; TPS: 4557.94; ETA: 1.5 minutes\n", - "Step 581999 of 1000000; TPS: 4558.84; ETA: 1.5 minutes\n", - "Step 582999 of 1000000; TPS: 4560.03; ETA: 1.5 minutes\n", - "Step 583999 of 1000000; TPS: 4560.37; ETA: 1.5 minutes\n", - "Step 584999 of 1000000; TPS: 4560.83; ETA: 1.5 minutes\n", - "Step 585999 of 1000000; TPS: 4561.18; ETA: 1.5 minutes\n", - "Step 586999 of 1000000; TPS: 4561.07; ETA: 1.5 minutes\n", - "Step 587999 of 1000000; TPS: 4560.67; ETA: 1.5 minutes\n", - "Step 588999 of 1000000; TPS: 4561.15; ETA: 1.5 minutes\n", - "Step 589999 of 1000000; TPS: 4562.18; ETA: 1.5 minutes\n", - "Step 590999 of 1000000; TPS: 4562.58; ETA: 1.5 minutes\n", - "Step 591999 of 1000000; TPS: 4562.66; ETA: 1.5 minutes\n", - "Step 592999 of 1000000; TPS: 4563.1; ETA: 1.5 minutes\n", - "Step 593999 of 1000000; TPS: 4563.99; ETA: 1.5 minutes\n", - "Step 594999 of 1000000; TPS: 4564.75; ETA: 1.5 minutes\n", - "Step 595999 of 1000000; TPS: 4565.69; ETA: 1.5 minutes\n", - "Step 596999 of 1000000; TPS: 4566.33; ETA: 1.5 minutes\n", - "Step 597999 of 1000000; TPS: 4566.62; ETA: 1.5 minutes\n", - "Step 598999 of 1000000; TPS: 4567.02; ETA: 1.5 minutes\n", - "Step 599999 of 1000000; TPS: 4567.67; ETA: 1.5 minutes\n", - "Step 600999 of 1000000; TPS: 4568.61; ETA: 1.5 minutes\n", - "Step 601999 of 1000000; TPS: 4569.46; ETA: 1.5 minutes\n", - "Step 602999 of 1000000; TPS: 4570.08; ETA: 1.4 minutes\n", - "Step 603999 of 1000000; TPS: 4570.67; ETA: 1.4 minutes\n", - "Step 604999 of 1000000; TPS: 4571.11; ETA: 1.4 minutes\n", - "Step 605999 of 1000000; TPS: 4571.76; ETA: 1.4 minutes\n", - "Step 606999 of 1000000; TPS: 4572.4; ETA: 1.4 minutes\n", - "Step 607999 of 1000000; TPS: 4572.9; ETA: 1.4 minutes\n", - "Step 608999 of 1000000; TPS: 4573.69; ETA: 1.4 minutes\n", - "Step 609999 of 1000000; TPS: 4574.54; ETA: 1.4 minutes\n", - "Step 610999 of 1000000; TPS: 4574.36; ETA: 1.4 minutes\n", - "Step 611999 of 1000000; TPS: 4574.53; ETA: 1.4 minutes\n", - "Step 612999 of 1000000; TPS: 4574.88; ETA: 1.4 minutes\n", - "Step 613999 of 1000000; TPS: 4574.89; ETA: 1.4 minutes\n", - "Step 614999 of 1000000; TPS: 4575.63; ETA: 1.4 minutes\n", - "Step 615999 of 1000000; TPS: 4575.64; ETA: 1.4 minutes\n", - "Step 616999 of 1000000; TPS: 4576.1; ETA: 1.4 minutes\n", - "Step 617999 of 1000000; TPS: 4576.95; ETA: 1.4 minutes\n", - "Step 618999 of 1000000; TPS: 4577.84; ETA: 1.4 minutes\n", - "Step 619999 of 1000000; TPS: 4578.66; ETA: 1.4 minutes\n", - "Step 620999 of 1000000; TPS: 4579.45; ETA: 1.4 minutes\n", - "Step 621999 of 1000000; TPS: 4579.98; ETA: 1.4 minutes\n", - "Step 622999 of 1000000; TPS: 4580.87; ETA: 1.4 minutes\n", - "Step 623999 of 1000000; TPS: 4581.49; ETA: 1.4 minutes\n", - "Step 624999 of 1000000; TPS: 4582.15; ETA: 1.4 minutes\n", - "Step 625999 of 1000000; TPS: 4582.69; ETA: 1.4 minutes\n", - "Step 626999 of 1000000; TPS: 4583.37; ETA: 1.4 minutes\n", - "Step 627999 of 1000000; TPS: 4583.93; ETA: 1.4 minutes\n", - "Step 628999 of 1000000; TPS: 4584.5; ETA: 1.3 minutes\n", - "Step 629999 of 1000000; TPS: 4585.43; ETA: 1.3 minutes\n", - "Step 630999 of 1000000; TPS: 4586.25; ETA: 1.3 minutes\n", - "Step 631999 of 1000000; TPS: 4586.81; ETA: 1.3 minutes\n", - "Step 632999 of 1000000; TPS: 4587.55; ETA: 1.3 minutes\n", - "Step 633999 of 1000000; TPS: 4587.24; ETA: 1.3 minutes\n", - "Step 634999 of 1000000; TPS: 4588.05; ETA: 1.3 minutes\n", - "Step 635999 of 1000000; TPS: 4588.17; ETA: 1.3 minutes\n", - "Step 636999 of 1000000; TPS: 4588.4; ETA: 1.3 minutes\n", - "Step 637999 of 1000000; TPS: 4589.26; ETA: 1.3 minutes\n", - "Step 638999 of 1000000; TPS: 4590.12; ETA: 1.3 minutes\n", - "Step 639999 of 1000000; TPS: 4590.73; ETA: 1.3 minutes\n", - "Step 640999 of 1000000; TPS: 4591.47; ETA: 1.3 minutes\n", - "Step 641999 of 1000000; TPS: 4592.15; ETA: 1.3 minutes\n", - "Step 642999 of 1000000; TPS: 4592.73; ETA: 1.3 minutes\n", - "Step 643999 of 1000000; TPS: 4593.36; ETA: 1.3 minutes\n", - "Step 644999 of 1000000; TPS: 4593.98; ETA: 1.3 minutes\n", - "Step 645999 of 1000000; TPS: 4593.99; ETA: 1.3 minutes\n", - "Step 646999 of 1000000; TPS: 4593.99; ETA: 1.3 minutes\n", - "Step 647999 of 1000000; TPS: 4593.8; ETA: 1.3 minutes\n", - "Step 648999 of 1000000; TPS: 4593.93; ETA: 1.3 minutes\n", - "Step 649999 of 1000000; TPS: 4594.07; ETA: 1.3 minutes\n", - "Step 650999 of 1000000; TPS: 4594.27; ETA: 1.3 minutes\n", - "Step 651999 of 1000000; TPS: 4594.72; ETA: 1.3 minutes\n", - "Step 652999 of 1000000; TPS: 4594.98; ETA: 1.3 minutes\n", - "Step 653999 of 1000000; TPS: 4594.82; ETA: 1.3 minutes\n", - "Step 654999 of 1000000; TPS: 4595.06; ETA: 1.3 minutes\n", - "Step 655999 of 1000000; TPS: 4595.29; ETA: 1.2 minutes\n", - "Step 656999 of 1000000; TPS: 4595.52; ETA: 1.2 minutes\n", - "Step 657999 of 1000000; TPS: 4596.02; ETA: 1.2 minutes\n", - "Step 658999 of 1000000; TPS: 4596.13; ETA: 1.2 minutes\n", - "Step 659999 of 1000000; TPS: 4596.13; ETA: 1.2 minutes\n", - "Step 660999 of 1000000; TPS: 4596.93; ETA: 1.2 minutes\n", - "Step 661999 of 1000000; TPS: 4597.61; ETA: 1.2 minutes\n", - "Step 662999 of 1000000; TPS: 4598.07; ETA: 1.2 minutes\n", - "Step 663999 of 1000000; TPS: 4598.46; ETA: 1.2 minutes\n", - "Step 664999 of 1000000; TPS: 4598.52; ETA: 1.2 minutes\n", - "Step 665999 of 1000000; TPS: 4598.74; ETA: 1.2 minutes\n", - "Step 666999 of 1000000; TPS: 4598.94; ETA: 1.2 minutes\n", - "Step 667999 of 1000000; TPS: 4527.6; ETA: 1.2 minutes\n", - "Step 668999 of 1000000; TPS: 4528.01; ETA: 1.2 minutes\n", - "Step 669999 of 1000000; TPS: 4528.57; ETA: 1.2 minutes\n", - "Step 670999 of 1000000; TPS: 4528.96; ETA: 1.2 minutes\n", - "Step 671999 of 1000000; TPS: 4528.26; ETA: 1.2 minutes\n", - "Step 672999 of 1000000; TPS: 4528.6; ETA: 1.2 minutes\n", - "Step 673999 of 1000000; TPS: 4529.2; ETA: 1.2 minutes\n", - "Step 674999 of 1000000; TPS: 4528.46; ETA: 1.2 minutes\n", - "Step 675999 of 1000000; TPS: 4528.59; ETA: 1.2 minutes\n", - "Step 676999 of 1000000; TPS: 4529.36; ETA: 1.2 minutes\n", - "Step 677999 of 1000000; TPS: 4529.85; ETA: 1.2 minutes\n", - "Step 678999 of 1000000; TPS: 4530.4; ETA: 1.2 minutes\n", - "Step 679999 of 1000000; TPS: 4530.81; ETA: 1.2 minutes\n", - "Step 680999 of 1000000; TPS: 4531.19; ETA: 1.2 minutes\n", - "Step 681999 of 1000000; TPS: 4531.58; ETA: 1.2 minutes\n", - "Step 682999 of 1000000; TPS: 4531.98; ETA: 1.2 minutes\n", - "Step 683999 of 1000000; TPS: 4532.61; ETA: 1.2 minutes\n", - "Step 684999 of 1000000; TPS: 4533.31; ETA: 1.2 minutes\n", - "Step 685999 of 1000000; TPS: 4533.87; ETA: 1.2 minutes\n", - "Step 686999 of 1000000; TPS: 4534.47; ETA: 1.2 minutes\n", - "Step 687999 of 1000000; TPS: 4534.73; ETA: 1.1 minutes\n", - "Step 688999 of 1000000; TPS: 4534.34; ETA: 1.1 minutes\n", - "Step 689999 of 1000000; TPS: 4534.88; ETA: 1.1 minutes\n", - "Step 690999 of 1000000; TPS: 4535.02; ETA: 1.1 minutes\n", - "Step 691999 of 1000000; TPS: 4534.98; ETA: 1.1 minutes\n", - "Step 692999 of 1000000; TPS: 4534.99; ETA: 1.1 minutes\n", - "Step 693999 of 1000000; TPS: 4535.1; ETA: 1.1 minutes\n", - "Step 694999 of 1000000; TPS: 4535.38; ETA: 1.1 minutes\n", - "Step 695999 of 1000000; TPS: 4535.47; ETA: 1.1 minutes\n", - "Step 696999 of 1000000; TPS: 4535.96; ETA: 1.1 minutes\n", - "Step 697999 of 1000000; TPS: 4536.39; ETA: 1.1 minutes\n", - "Step 698999 of 1000000; TPS: 4537.07; ETA: 1.1 minutes\n", - "Step 699999 of 1000000; TPS: 4537.49; ETA: 1.1 minutes\n", - "Step 700999 of 1000000; TPS: 4537.98; ETA: 1.1 minutes\n", - "Step 701999 of 1000000; TPS: 4538.55; ETA: 1.1 minutes\n", - "Step 702999 of 1000000; TPS: 4538.79; ETA: 1.1 minutes\n", - "Step 703999 of 1000000; TPS: 4538.05; ETA: 1.1 minutes\n", - "Step 704999 of 1000000; TPS: 4538.27; ETA: 1.1 minutes\n", - "Step 705999 of 1000000; TPS: 4538.41; ETA: 1.1 minutes\n", - "Step 706999 of 1000000; TPS: 4538.64; ETA: 1.1 minutes\n", - "Step 707999 of 1000000; TPS: 4538.59; ETA: 1.1 minutes\n", - "Step 708999 of 1000000; TPS: 4539.01; ETA: 1.1 minutes\n", - "Step 709999 of 1000000; TPS: 4539.6; ETA: 1.1 minutes\n", - "Step 710999 of 1000000; TPS: 4540.05; ETA: 1.1 minutes\n", - "Step 711999 of 1000000; TPS: 4539.96; ETA: 1.1 minutes\n", - "Step 712999 of 1000000; TPS: 4540.33; ETA: 1.1 minutes\n", - "Step 713999 of 1000000; TPS: 4540.88; ETA: 1.0 minutes\n", - "Step 714999 of 1000000; TPS: 4541.4; ETA: 1.0 minutes\n", - "Step 715999 of 1000000; TPS: 4541.86; ETA: 1.0 minutes\n", - "Step 716999 of 1000000; TPS: 4542.42; ETA: 1.0 minutes\n", - "Step 717999 of 1000000; TPS: 4543.18; ETA: 1.0 minutes\n", - "Step 718999 of 1000000; TPS: 4543.82; ETA: 1.0 minutes\n", - "Step 719999 of 1000000; TPS: 4544.53; ETA: 1.0 minutes\n", - "Step 720999 of 1000000; TPS: 4545.18; ETA: 1.0 minutes\n", - "Step 721999 of 1000000; TPS: 4545.1; ETA: 1.0 minutes\n", - "Step 722999 of 1000000; TPS: 4544.6; ETA: 1.0 minutes\n", - "Step 723999 of 1000000; TPS: 4544.12; ETA: 1.0 minutes\n", - "Step 724999 of 1000000; TPS: 4543.72; ETA: 1.0 minutes\n", - "Step 725999 of 1000000; TPS: 4543.63; ETA: 1.0 minutes\n", - "Step 726999 of 1000000; TPS: 4543.37; ETA: 1.0 minutes\n", - "Step 727999 of 1000000; TPS: 4542.83; ETA: 1.0 minutes\n", - "Step 728999 of 1000000; TPS: 4542.4; ETA: 1.0 minutes\n", - "Step 729999 of 1000000; TPS: 4541.87; ETA: 1.0 minutes\n", - "Step 730999 of 1000000; TPS: 4541.47; ETA: 1.0 minutes\n", - "Step 731999 of 1000000; TPS: 4541.18; ETA: 1.0 minutes\n", - "Step 732999 of 1000000; TPS: 4540.89; ETA: 1.0 minutes\n", - "Step 733999 of 1000000; TPS: 4540.86; ETA: 1.0 minutes\n", - "Step 734999 of 1000000; TPS: 4540.35; ETA: 1.0 minutes\n", - "Step 735999 of 1000000; TPS: 4540.29; ETA: 1.0 minutes\n", - "Step 736999 of 1000000; TPS: 4540.11; ETA: 1.0 minutes\n", - "Step 737999 of 1000000; TPS: 4540.1; ETA: 1.0 minutes\n", - "Step 738999 of 1000000; TPS: 4539.31; ETA: 1.0 minutes\n", - "Step 739999 of 1000000; TPS: 4539.38; ETA: 1.0 minutes\n", - "Step 740999 of 1000000; TPS: 4539.23; ETA: 1.0 minutes\n", - "Step 741999 of 1000000; TPS: 4539.07; ETA: 0.9 minutes\n", - "Step 742999 of 1000000; TPS: 4538.83; ETA: 0.9 minutes\n", - "Step 743999 of 1000000; TPS: 4538.77; ETA: 0.9 minutes\n", - "Step 744999 of 1000000; TPS: 4538.67; ETA: 0.9 minutes\n", - "Step 745999 of 1000000; TPS: 4538.54; ETA: 0.9 minutes\n", - "Step 746999 of 1000000; TPS: 4538.29; ETA: 0.9 minutes\n", - "Step 747999 of 1000000; TPS: 4538.28; ETA: 0.9 minutes\n", - "Step 748999 of 1000000; TPS: 4538.21; ETA: 0.9 minutes\n", - "Step 749999 of 1000000; TPS: 4538.14; ETA: 0.9 minutes\n", - "Step 750999 of 1000000; TPS: 4538.08; ETA: 0.9 minutes\n", - "Step 751999 of 1000000; TPS: 4538.06; ETA: 0.9 minutes\n", - "Step 752999 of 1000000; TPS: 4538.1; ETA: 0.9 minutes\n", - "Step 753999 of 1000000; TPS: 4538.0; ETA: 0.9 minutes\n", - "Step 754999 of 1000000; TPS: 4537.99; ETA: 0.9 minutes\n", - "Step 755999 of 1000000; TPS: 4538.11; ETA: 0.9 minutes\n", - "Step 756999 of 1000000; TPS: 4538.27; ETA: 0.9 minutes\n", - "Step 757999 of 1000000; TPS: 4538.14; ETA: 0.9 minutes\n", - "Step 758999 of 1000000; TPS: 4537.99; ETA: 0.9 minutes\n", - "Step 759999 of 1000000; TPS: 4537.7; ETA: 0.9 minutes\n", - "Step 760999 of 1000000; TPS: 4538.01; ETA: 0.9 minutes\n", - "Step 761999 of 1000000; TPS: 4538.03; ETA: 0.9 minutes\n", - "Step 762999 of 1000000; TPS: 4537.9; ETA: 0.9 minutes\n", - "Step 763999 of 1000000; TPS: 4538.11; ETA: 0.9 minutes\n", - "Step 764999 of 1000000; TPS: 4538.29; ETA: 0.9 minutes\n", - "Step 765999 of 1000000; TPS: 4538.33; ETA: 0.9 minutes\n", - "Step 766999 of 1000000; TPS: 4538.55; ETA: 0.9 minutes\n", - "Step 767999 of 1000000; TPS: 4538.42; ETA: 0.9 minutes\n", - "Step 768999 of 1000000; TPS: 4538.74; ETA: 0.8 minutes\n", - "Step 769999 of 1000000; TPS: 4539.33; ETA: 0.8 minutes\n", - "Step 770999 of 1000000; TPS: 4539.61; ETA: 0.8 minutes\n", - "Step 771999 of 1000000; TPS: 4540.11; ETA: 0.8 minutes\n", - "Step 772999 of 1000000; TPS: 4540.35; ETA: 0.8 minutes\n", - "Step 773999 of 1000000; TPS: 4540.66; ETA: 0.8 minutes\n", - "Step 774999 of 1000000; TPS: 4540.63; ETA: 0.8 minutes\n", - "Step 775999 of 1000000; TPS: 4540.9; ETA: 0.8 minutes\n", - "Step 776999 of 1000000; TPS: 4541.26; ETA: 0.8 minutes\n", - "Step 777999 of 1000000; TPS: 4541.79; ETA: 0.8 minutes\n", - "Step 778999 of 1000000; TPS: 4542.15; ETA: 0.8 minutes\n", - "Step 779999 of 1000000; TPS: 4542.21; ETA: 0.8 minutes\n", - "Step 780999 of 1000000; TPS: 4542.59; ETA: 0.8 minutes\n", - "Step 781999 of 1000000; TPS: 4542.79; ETA: 0.8 minutes\n", - "Step 782999 of 1000000; TPS: 4543.2; ETA: 0.8 minutes\n", - "Step 783999 of 1000000; TPS: 4543.69; ETA: 0.8 minutes\n", - "Step 784999 of 1000000; TPS: 4544.15; ETA: 0.8 minutes\n", - "Step 785999 of 1000000; TPS: 4544.38; ETA: 0.8 minutes\n", - "Step 786999 of 1000000; TPS: 4544.6; ETA: 0.8 minutes\n", - "Step 787999 of 1000000; TPS: 4544.96; ETA: 0.8 minutes\n", - "Step 788999 of 1000000; TPS: 4545.34; ETA: 0.8 minutes\n", - "Step 789999 of 1000000; TPS: 4545.52; ETA: 0.8 minutes\n", - "Step 790999 of 1000000; TPS: 4545.47; ETA: 0.8 minutes\n", - "Step 791999 of 1000000; TPS: 4545.87; ETA: 0.8 minutes\n", - "Step 792999 of 1000000; TPS: 4546.2; ETA: 0.8 minutes\n", - "Step 793999 of 1000000; TPS: 4546.64; ETA: 0.8 minutes\n", - "Step 794999 of 1000000; TPS: 4546.94; ETA: 0.8 minutes\n", - "Step 795999 of 1000000; TPS: 4547.54; ETA: 0.7 minutes\n", - "Step 796999 of 1000000; TPS: 4547.81; ETA: 0.7 minutes\n", - "Step 797999 of 1000000; TPS: 4547.88; ETA: 0.7 minutes\n", - "Step 798999 of 1000000; TPS: 4548.33; ETA: 0.7 minutes\n", - "Step 799999 of 1000000; TPS: 4548.68; ETA: 0.7 minutes\n", - "Step 800999 of 1000000; TPS: 4548.98; ETA: 0.7 minutes\n", - "Step 801999 of 1000000; TPS: 4549.41; ETA: 0.7 minutes\n", - "Step 802999 of 1000000; TPS: 4549.52; ETA: 0.7 minutes\n", - "Step 803999 of 1000000; TPS: 4549.21; ETA: 0.7 minutes\n", - "Step 804999 of 1000000; TPS: 4549.55; ETA: 0.7 minutes\n", - "Step 805999 of 1000000; TPS: 4550.0; ETA: 0.7 minutes\n", - "Step 806999 of 1000000; TPS: 4550.32; ETA: 0.7 minutes\n", - "Step 807999 of 1000000; TPS: 4550.6; ETA: 0.7 minutes\n", - "Step 808999 of 1000000; TPS: 4550.89; ETA: 0.7 minutes\n", - "Step 809999 of 1000000; TPS: 4551.24; ETA: 0.7 minutes\n", - "Step 810999 of 1000000; TPS: 4551.4; ETA: 0.7 minutes\n", - "Step 811999 of 1000000; TPS: 4551.43; ETA: 0.7 minutes\n", - "Step 812999 of 1000000; TPS: 4551.65; ETA: 0.7 minutes\n", - "Step 813999 of 1000000; TPS: 4551.94; ETA: 0.7 minutes\n", - "Step 814999 of 1000000; TPS: 4551.82; ETA: 0.7 minutes\n", - "Step 815999 of 1000000; TPS: 4551.6; ETA: 0.7 minutes\n", - "Step 816999 of 1000000; TPS: 4551.6; ETA: 0.7 minutes\n", - "Step 817999 of 1000000; TPS: 4551.93; ETA: 0.7 minutes\n", - "Step 818999 of 1000000; TPS: 4552.22; ETA: 0.7 minutes\n", - "Step 819999 of 1000000; TPS: 4552.41; ETA: 0.7 minutes\n", - "Step 820999 of 1000000; TPS: 4552.68; ETA: 0.7 minutes\n", - "Step 821999 of 1000000; TPS: 4552.61; ETA: 0.7 minutes\n", - "Step 822999 of 1000000; TPS: 4552.64; ETA: 0.6 minutes\n", - "Step 823999 of 1000000; TPS: 4552.71; ETA: 0.6 minutes\n", - "Step 824999 of 1000000; TPS: 4552.85; ETA: 0.6 minutes\n", - "Step 825999 of 1000000; TPS: 4552.83; ETA: 0.6 minutes\n", - "Step 826999 of 1000000; TPS: 4553.1; ETA: 0.6 minutes\n", - "Step 827999 of 1000000; TPS: 4553.27; ETA: 0.6 minutes\n", - "Step 828999 of 1000000; TPS: 4553.47; ETA: 0.6 minutes\n", - "Step 829999 of 1000000; TPS: 4553.76; ETA: 0.6 minutes\n", - "Step 830999 of 1000000; TPS: 4553.84; ETA: 0.6 minutes\n", - "Step 831999 of 1000000; TPS: 4554.14; ETA: 0.6 minutes\n", - "Step 832999 of 1000000; TPS: 4554.49; ETA: 0.6 minutes\n", - "Step 833999 of 1000000; TPS: 4555.06; ETA: 0.6 minutes\n", - "Step 834999 of 1000000; TPS: 4555.74; ETA: 0.6 minutes\n", - "Step 835999 of 1000000; TPS: 4556.5; ETA: 0.6 minutes\n", - "Step 836999 of 1000000; TPS: 4557.21; ETA: 0.6 minutes\n", - "Step 837999 of 1000000; TPS: 4557.9; ETA: 0.6 minutes\n", - "Step 838999 of 1000000; TPS: 4558.59; ETA: 0.6 minutes\n", - "Step 839999 of 1000000; TPS: 4559.53; ETA: 0.6 minutes\n", - "Step 840999 of 1000000; TPS: 4560.1; ETA: 0.6 minutes\n", - "Step 841999 of 1000000; TPS: 4560.58; ETA: 0.6 minutes\n", - "Step 842999 of 1000000; TPS: 4561.18; ETA: 0.6 minutes\n", - "Step 843999 of 1000000; TPS: 4561.96; ETA: 0.6 minutes\n", - "Step 844999 of 1000000; TPS: 4562.41; ETA: 0.6 minutes\n", - "Step 845999 of 1000000; TPS: 4563.21; ETA: 0.6 minutes\n", - "Step 846999 of 1000000; TPS: 4563.55; ETA: 0.6 minutes\n", - "Step 847999 of 1000000; TPS: 4564.18; ETA: 0.6 minutes\n", - "Step 848999 of 1000000; TPS: 4564.84; ETA: 0.6 minutes\n", - "Step 849999 of 1000000; TPS: 4565.57; ETA: 0.5 minutes\n", - "Step 850999 of 1000000; TPS: 4566.32; ETA: 0.5 minutes\n", - "Step 851999 of 1000000; TPS: 4566.89; ETA: 0.5 minutes\n", - "Step 852999 of 1000000; TPS: 4567.32; ETA: 0.5 minutes\n", - "Step 853999 of 1000000; TPS: 4567.94; ETA: 0.5 minutes\n", - "Step 854999 of 1000000; TPS: 4568.5; ETA: 0.5 minutes\n", - "Step 855999 of 1000000; TPS: 4569.22; ETA: 0.5 minutes\n", - "Step 856999 of 1000000; TPS: 4569.74; ETA: 0.5 minutes\n", - "Step 857999 of 1000000; TPS: 4570.52; ETA: 0.5 minutes\n", - "Step 858999 of 1000000; TPS: 4571.15; ETA: 0.5 minutes\n", - "Step 859999 of 1000000; TPS: 4571.96; ETA: 0.5 minutes\n", - "Step 860999 of 1000000; TPS: 4572.74; ETA: 0.5 minutes\n", - "Step 861999 of 1000000; TPS: 4573.41; ETA: 0.5 minutes\n", - "Step 862999 of 1000000; TPS: 4574.34; ETA: 0.5 minutes\n", - "Step 863999 of 1000000; TPS: 4574.51; ETA: 0.5 minutes\n", - "Step 864999 of 1000000; TPS: 4575.12; ETA: 0.5 minutes\n", - "Step 865999 of 1000000; TPS: 4575.93; ETA: 0.5 minutes\n", - "Step 866999 of 1000000; TPS: 4576.23; ETA: 0.5 minutes\n", - "Step 867999 of 1000000; TPS: 4576.63; ETA: 0.5 minutes\n", - "Step 868999 of 1000000; TPS: 4577.1; ETA: 0.5 minutes\n", - "Step 869999 of 1000000; TPS: 4577.74; ETA: 0.5 minutes\n", - "Step 870999 of 1000000; TPS: 4578.35; ETA: 0.5 minutes\n", - "Step 871999 of 1000000; TPS: 4578.9; ETA: 0.5 minutes\n", - "Step 872999 of 1000000; TPS: 4579.64; ETA: 0.5 minutes\n", - "Step 873999 of 1000000; TPS: 4580.26; ETA: 0.5 minutes\n", - "Step 874999 of 1000000; TPS: 4580.95; ETA: 0.5 minutes\n", - "Step 875999 of 1000000; TPS: 4581.51; ETA: 0.5 minutes\n", - "Step 876999 of 1000000; TPS: 4581.93; ETA: 0.4 minutes\n", - "Step 877999 of 1000000; TPS: 4582.53; ETA: 0.4 minutes\n", - "Step 878999 of 1000000; TPS: 4583.05; ETA: 0.4 minutes\n", - "Step 879999 of 1000000; TPS: 4583.46; ETA: 0.4 minutes\n", - "Step 880999 of 1000000; TPS: 4583.69; ETA: 0.4 minutes\n", - "Step 881999 of 1000000; TPS: 4583.8; ETA: 0.4 minutes\n", - "Step 882999 of 1000000; TPS: 4583.67; ETA: 0.4 minutes\n", - "Step 883999 of 1000000; TPS: 4583.68; ETA: 0.4 minutes\n", - "Step 884999 of 1000000; TPS: 4584.07; ETA: 0.4 minutes\n", - "Step 885999 of 1000000; TPS: 4584.7; ETA: 0.4 minutes\n", - "Step 886999 of 1000000; TPS: 4585.07; ETA: 0.4 minutes\n", - "Step 887999 of 1000000; TPS: 4585.7; ETA: 0.4 minutes\n", - "Step 888999 of 1000000; TPS: 4586.29; ETA: 0.4 minutes\n", - "Step 889999 of 1000000; TPS: 4586.61; ETA: 0.4 minutes\n", - "Step 890999 of 1000000; TPS: 4587.07; ETA: 0.4 minutes\n", - "Step 891999 of 1000000; TPS: 4587.64; ETA: 0.4 minutes\n", - "Step 892999 of 1000000; TPS: 4588.05; ETA: 0.4 minutes\n", - "Step 893999 of 1000000; TPS: 4588.71; ETA: 0.4 minutes\n", - "Step 894999 of 1000000; TPS: 4589.28; ETA: 0.4 minutes\n", - "Step 895999 of 1000000; TPS: 4590.0; ETA: 0.4 minutes\n", - "Step 896999 of 1000000; TPS: 4590.52; ETA: 0.4 minutes\n", - "Step 897999 of 1000000; TPS: 4591.14; ETA: 0.4 minutes\n", - "Step 898999 of 1000000; TPS: 4591.72; ETA: 0.4 minutes\n", - "Step 899999 of 1000000; TPS: 4592.51; ETA: 0.4 minutes\n", - "Step 900999 of 1000000; TPS: 4593.23; ETA: 0.4 minutes\n", - "Step 901999 of 1000000; TPS: 4593.27; ETA: 0.4 minutes\n", - "Step 902999 of 1000000; TPS: 4593.75; ETA: 0.4 minutes\n", - "Step 903999 of 1000000; TPS: 4594.42; ETA: 0.3 minutes\n", - "Step 904999 of 1000000; TPS: 4594.66; ETA: 0.3 minutes\n", - "Step 905999 of 1000000; TPS: 4595.24; ETA: 0.3 minutes\n", - "Step 906999 of 1000000; TPS: 4595.87; ETA: 0.3 minutes\n", - "Step 907999 of 1000000; TPS: 4596.38; ETA: 0.3 minutes\n", - "Step 908999 of 1000000; TPS: 4596.98; ETA: 0.3 minutes\n", - "Step 909999 of 1000000; TPS: 4597.64; ETA: 0.3 minutes\n", - "Step 910999 of 1000000; TPS: 4598.45; ETA: 0.3 minutes\n", - "Step 911999 of 1000000; TPS: 4599.08; ETA: 0.3 minutes\n", - "Step 912999 of 1000000; TPS: 4599.65; ETA: 0.3 minutes\n", - "Step 913999 of 1000000; TPS: 4600.34; ETA: 0.3 minutes\n", - "Step 914999 of 1000000; TPS: 4600.98; ETA: 0.3 minutes\n", - "Step 915999 of 1000000; TPS: 4601.47; ETA: 0.3 minutes\n", - "Step 916999 of 1000000; TPS: 4602.01; ETA: 0.3 minutes\n", - "Step 917999 of 1000000; TPS: 4602.29; ETA: 0.3 minutes\n", - "Step 918999 of 1000000; TPS: 4602.94; ETA: 0.3 minutes\n", - "Step 919999 of 1000000; TPS: 4603.28; ETA: 0.3 minutes\n", - "Step 920999 of 1000000; TPS: 4603.84; ETA: 0.3 minutes\n", - "Step 921999 of 1000000; TPS: 4604.27; ETA: 0.3 minutes\n", - "Step 922999 of 1000000; TPS: 4604.93; ETA: 0.3 minutes\n", - "Step 923999 of 1000000; TPS: 4605.53; ETA: 0.3 minutes\n", - "Step 924999 of 1000000; TPS: 4606.06; ETA: 0.3 minutes\n", - "Step 925999 of 1000000; TPS: 4606.67; ETA: 0.3 minutes\n", - "Step 926999 of 1000000; TPS: 4607.04; ETA: 0.3 minutes\n", - "Step 927999 of 1000000; TPS: 4607.49; ETA: 0.3 minutes\n", - "Step 928999 of 1000000; TPS: 4608.02; ETA: 0.3 minutes\n", - "Step 929999 of 1000000; TPS: 4608.62; ETA: 0.3 minutes\n", - "Step 930999 of 1000000; TPS: 4609.23; ETA: 0.2 minutes\n", - "Step 931999 of 1000000; TPS: 4609.62; ETA: 0.2 minutes\n", - "Step 932999 of 1000000; TPS: 4609.78; ETA: 0.2 minutes\n", - "Step 933999 of 1000000; TPS: 4610.14; ETA: 0.2 minutes\n", - "Step 934999 of 1000000; TPS: 4610.18; ETA: 0.2 minutes\n", - "Step 935999 of 1000000; TPS: 4610.6; ETA: 0.2 minutes\n", - "Step 936999 of 1000000; TPS: 4611.12; ETA: 0.2 minutes\n", - "Step 937999 of 1000000; TPS: 4611.47; ETA: 0.2 minutes\n", - "Step 938999 of 1000000; TPS: 4611.89; ETA: 0.2 minutes\n", - "Step 939999 of 1000000; TPS: 4612.25; ETA: 0.2 minutes\n", - "Step 940999 of 1000000; TPS: 4612.66; ETA: 0.2 minutes\n", - "Step 941999 of 1000000; TPS: 4612.96; ETA: 0.2 minutes\n", - "Step 942999 of 1000000; TPS: 4613.3; ETA: 0.2 minutes\n", - "Step 943999 of 1000000; TPS: 4613.51; ETA: 0.2 minutes\n", - "Step 944999 of 1000000; TPS: 4613.87; ETA: 0.2 minutes\n", - "Step 945999 of 1000000; TPS: 4614.11; ETA: 0.2 minutes\n", - "Step 946999 of 1000000; TPS: 4614.5; ETA: 0.2 minutes\n", - "Step 947999 of 1000000; TPS: 4614.84; ETA: 0.2 minutes\n", - "Step 948999 of 1000000; TPS: 4615.21; ETA: 0.2 minutes\n", - "Step 949999 of 1000000; TPS: 4615.64; ETA: 0.2 minutes\n", - "Step 950999 of 1000000; TPS: 4616.23; ETA: 0.2 minutes\n", - "Step 951999 of 1000000; TPS: 4616.86; ETA: 0.2 minutes\n", - "Step 952999 of 1000000; TPS: 4617.39; ETA: 0.2 minutes\n", - "Step 953999 of 1000000; TPS: 4617.74; ETA: 0.2 minutes\n", - "Step 954999 of 1000000; TPS: 4617.88; ETA: 0.2 minutes\n", - "Step 955999 of 1000000; TPS: 4618.28; ETA: 0.2 minutes\n", - "Step 956999 of 1000000; TPS: 4618.96; ETA: 0.2 minutes\n", - "Step 957999 of 1000000; TPS: 4619.36; ETA: 0.2 minutes\n", - "Step 958999 of 1000000; TPS: 4619.91; ETA: 0.1 minutes\n", - "Step 959999 of 1000000; TPS: 4620.58; ETA: 0.1 minutes\n", - "Step 960999 of 1000000; TPS: 4621.3; ETA: 0.1 minutes\n", - "Step 961999 of 1000000; TPS: 4621.89; ETA: 0.1 minutes\n", - "Step 962999 of 1000000; TPS: 4622.54; ETA: 0.1 minutes\n", - "Step 963999 of 1000000; TPS: 4623.1; ETA: 0.1 minutes\n", - "Step 964999 of 1000000; TPS: 4623.53; ETA: 0.1 minutes\n", - "Step 965999 of 1000000; TPS: 4623.72; ETA: 0.1 minutes\n", - "Step 966999 of 1000000; TPS: 4623.93; ETA: 0.1 minutes\n", - "Step 967999 of 1000000; TPS: 4624.09; ETA: 0.1 minutes\n", - "Step 968999 of 1000000; TPS: 4623.96; ETA: 0.1 minutes\n", - "Step 969999 of 1000000; TPS: 4624.33; ETA: 0.1 minutes\n", - "Step 970999 of 1000000; TPS: 4624.69; ETA: 0.1 minutes\n", - "Step 971999 of 1000000; TPS: 4624.63; ETA: 0.1 minutes\n", - "Step 972999 of 1000000; TPS: 4624.85; ETA: 0.1 minutes\n", - "Step 973999 of 1000000; TPS: 4625.04; ETA: 0.1 minutes\n", - "Step 974999 of 1000000; TPS: 4625.27; ETA: 0.1 minutes\n", - "Step 975999 of 1000000; TPS: 4625.46; ETA: 0.1 minutes\n", - "Step 976999 of 1000000; TPS: 4625.69; ETA: 0.1 minutes\n", - "Step 977999 of 1000000; TPS: 4625.84; ETA: 0.1 minutes\n", - "Step 978999 of 1000000; TPS: 4626.11; ETA: 0.1 minutes\n", - "Step 979999 of 1000000; TPS: 4626.19; ETA: 0.1 minutes\n", - "Step 980999 of 1000000; TPS: 4578.79; ETA: 0.1 minutes\n", - "Step 981999 of 1000000; TPS: 4578.87; ETA: 0.1 minutes\n", - "Step 982999 of 1000000; TPS: 4578.49; ETA: 0.1 minutes\n", - "Step 983999 of 1000000; TPS: 4578.67; ETA: 0.1 minutes\n", - "Step 984999 of 1000000; TPS: 4578.97; ETA: 0.1 minutes\n", - "Step 985999 of 1000000; TPS: 4579.15; ETA: 0.1 minutes\n", - "Step 986999 of 1000000; TPS: 4579.35; ETA: 0.0 minutes\n", - "Step 987999 of 1000000; TPS: 4579.6; ETA: 0.0 minutes\n", - "Step 988999 of 1000000; TPS: 4578.96; ETA: 0.0 minutes\n", - "Step 989999 of 1000000; TPS: 4578.93; ETA: 0.0 minutes\n", - "Step 990999 of 1000000; TPS: 4579.09; ETA: 0.0 minutes\n", - "Step 991999 of 1000000; TPS: 4579.29; ETA: 0.0 minutes\n", - "Step 992999 of 1000000; TPS: 4579.3; ETA: 0.0 minutes\n", - "Step 993999 of 1000000; TPS: 4579.35; ETA: 0.0 minutes\n", - "Step 994999 of 1000000; TPS: 4579.34; ETA: 0.0 minutes\n", - "Step 995999 of 1000000; TPS: 4579.31; ETA: 0.0 minutes\n", - "Step 996999 of 1000000; TPS: 4579.36; ETA: 0.0 minutes\n", - "Step 997999 of 1000000; TPS: 4579.38; ETA: 0.0 minutes\n", - "Step 998999 of 1000000; TPS: 4579.5; ETA: 0.0 minutes\n", - "Step 999999 of 1000000; TPS: 4579.49; ETA: 0.0 minutes\n" + "Step 5500 of 2000000; TPS: 21335.61; ETA: 1.6 minutes\n", + "Step 11000 of 2000000; TPS: 32820.34; ETA: 1.0 minutes\n", + "Step 16500 of 2000000; TPS: 40080.94; ETA: 0.8 minutes\n", + "Step 22000 of 2000000; TPS: 44614.25; ETA: 0.7 minutes\n", + "Step 27500 of 2000000; TPS: 47982.72; ETA: 0.7 minutes\n", + "Step 33000 of 2000000; TPS: 50260.21; ETA: 0.7 minutes\n", + "Step 38500 of 2000000; TPS: 52491.0; ETA: 0.6 minutes\n", + "Step 44000 of 2000000; TPS: 54190.6; ETA: 0.6 minutes\n", + "Step 49500 of 2000000; TPS: 55246.83; ETA: 0.6 minutes\n", + "Step 55000 of 2000000; TPS: 56408.29; ETA: 0.6 minutes\n", + "Step 60500 of 2000000; TPS: 57534.83; ETA: 0.6 minutes\n", + "Step 66000 of 2000000; TPS: 58426.06; ETA: 0.6 minutes\n", + "Step 71500 of 2000000; TPS: 59293.98; ETA: 0.5 minutes\n", + "Step 77000 of 2000000; TPS: 60035.71; ETA: 0.5 minutes\n", + "Step 82500 of 2000000; TPS: 60679.61; ETA: 0.5 minutes\n", + "Step 88000 of 2000000; TPS: 60898.39; ETA: 0.5 minutes\n", + "Step 93500 of 2000000; TPS: 61419.33; ETA: 0.5 minutes\n", + "Step 99000 of 2000000; TPS: 61717.08; ETA: 0.5 minutes\n", + "Step 104500 of 2000000; TPS: 62094.23; ETA: 0.5 minutes\n", + "Step 110000 of 2000000; TPS: 62446.71; ETA: 0.5 minutes\n", + "Step 115500 of 2000000; TPS: 62814.82; ETA: 0.5 minutes\n", + "Step 121000 of 2000000; TPS: 63080.3; ETA: 0.5 minutes\n", + "Step 126500 of 2000000; TPS: 63419.46; ETA: 0.5 minutes\n", + "Step 132000 of 2000000; TPS: 63694.08; ETA: 0.5 minutes\n", + "Step 137500 of 2000000; TPS: 63994.3; ETA: 0.5 minutes\n", + "Step 143000 of 2000000; TPS: 64002.09; ETA: 0.5 minutes\n", + "Step 148500 of 2000000; TPS: 64234.41; ETA: 0.5 minutes\n", + "Step 154000 of 2000000; TPS: 64416.09; ETA: 0.5 minutes\n", + "Step 159500 of 2000000; TPS: 64647.71; ETA: 0.5 minutes\n", + "Step 165000 of 2000000; TPS: 64869.15; ETA: 0.5 minutes\n", + "Step 170500 of 2000000; TPS: 64880.38; ETA: 0.5 minutes\n", + "Step 176000 of 2000000; TPS: 65019.34; ETA: 0.5 minutes\n", + "Step 181500 of 2000000; TPS: 65192.77; ETA: 0.5 minutes\n", + "Step 187000 of 2000000; TPS: 65308.12; ETA: 0.5 minutes\n", + "Step 192500 of 2000000; TPS: 65455.55; ETA: 0.5 minutes\n", + "Step 198000 of 2000000; TPS: 65460.28; ETA: 0.5 minutes\n", + "Step 203500 of 2000000; TPS: 65572.97; ETA: 0.5 minutes\n", + "Step 209000 of 2000000; TPS: 65668.92; ETA: 0.5 minutes\n", + "Step 214500 of 2000000; TPS: 37334.2; ETA: 0.8 minutes\n", + "Step 220000 of 2000000; TPS: 37770.44; ETA: 0.8 minutes\n", + "Step 225500 of 2000000; TPS: 38215.83; ETA: 0.8 minutes\n", + "Step 231000 of 2000000; TPS: 38644.28; ETA: 0.8 minutes\n", + "Step 236500 of 2000000; TPS: 39023.78; ETA: 0.8 minutes\n", + "Step 242000 of 2000000; TPS: 39435.74; ETA: 0.7 minutes\n", + "Step 247500 of 2000000; TPS: 39818.4; ETA: 0.7 minutes\n", + "Step 253000 of 2000000; TPS: 40140.6; ETA: 0.7 minutes\n", + "Step 258500 of 2000000; TPS: 40515.22; ETA: 0.7 minutes\n", + "Step 264000 of 2000000; TPS: 40855.59; ETA: 0.7 minutes\n", + "Step 269500 of 2000000; TPS: 41198.06; ETA: 0.7 minutes\n", + "Step 275000 of 2000000; TPS: 41544.13; ETA: 0.7 minutes\n", + "Step 280500 of 2000000; TPS: 41885.61; ETA: 0.7 minutes\n", + "Step 286000 of 2000000; TPS: 42196.84; ETA: 0.7 minutes\n", + "Step 291500 of 2000000; TPS: 42521.79; ETA: 0.7 minutes\n", + "Step 297000 of 2000000; TPS: 42825.34; ETA: 0.7 minutes\n", + "Step 302500 of 2000000; TPS: 43098.62; ETA: 0.7 minutes\n", + "Step 308000 of 2000000; TPS: 43366.65; ETA: 0.7 minutes\n", + "Step 313500 of 2000000; TPS: 43645.0; ETA: 0.6 minutes\n", + "Step 319000 of 2000000; TPS: 43922.51; ETA: 0.6 minutes\n", + "Step 324500 of 2000000; TPS: 44208.76; ETA: 0.6 minutes\n", + "Step 330000 of 2000000; TPS: 44474.26; ETA: 0.6 minutes\n", + "Step 335500 of 2000000; TPS: 44738.09; ETA: 0.6 minutes\n", + "Step 341000 of 2000000; TPS: 45004.6; ETA: 0.6 minutes\n", + "Step 346500 of 2000000; TPS: 45278.0; ETA: 0.6 minutes\n", + "Step 352000 of 2000000; TPS: 45510.58; ETA: 0.6 minutes\n", + "Step 357500 of 2000000; TPS: 45757.82; ETA: 0.6 minutes\n", + "Step 363000 of 2000000; TPS: 45966.23; ETA: 0.6 minutes\n", + "Step 368500 of 2000000; TPS: 46197.44; ETA: 0.6 minutes\n", + "Step 374000 of 2000000; TPS: 46425.73; ETA: 0.6 minutes\n", + "Step 379500 of 2000000; TPS: 46659.56; ETA: 0.6 minutes\n", + "Step 385000 of 2000000; TPS: 46883.55; ETA: 0.6 minutes\n", + "Step 390500 of 2000000; TPS: 47093.15; ETA: 0.6 minutes\n", + "Step 396000 of 2000000; TPS: 47283.28; ETA: 0.6 minutes\n", + "Step 401500 of 2000000; TPS: 47485.13; ETA: 0.6 minutes\n", + "Step 407000 of 2000000; TPS: 47696.76; ETA: 0.6 minutes\n", + "Step 412500 of 2000000; TPS: 47910.42; ETA: 0.6 minutes\n", + "Step 418000 of 2000000; TPS: 48064.66; ETA: 0.5 minutes\n", + "Step 423500 of 2000000; TPS: 48247.19; ETA: 0.5 minutes\n", + "Step 429000 of 2000000; TPS: 48415.5; ETA: 0.5 minutes\n", + "Step 434500 of 2000000; TPS: 48594.31; ETA: 0.5 minutes\n", + "Step 440000 of 2000000; TPS: 48765.66; ETA: 0.5 minutes\n", + "Step 445500 of 2000000; TPS: 48952.47; ETA: 0.5 minutes\n", + "Step 451000 of 2000000; TPS: 49131.56; ETA: 0.5 minutes\n", + "Step 456500 of 2000000; TPS: 49312.56; ETA: 0.5 minutes\n", + "Step 462000 of 2000000; TPS: 49469.0; ETA: 0.5 minutes\n", + "Step 467500 of 2000000; TPS: 49637.63; ETA: 0.5 minutes\n", + "Step 473000 of 2000000; TPS: 49790.4; ETA: 0.5 minutes\n", + "Step 478500 of 2000000; TPS: 49949.81; ETA: 0.5 minutes\n", + "Step 484000 of 2000000; TPS: 50088.79; ETA: 0.5 minutes\n", + "Step 489500 of 2000000; TPS: 50244.33; ETA: 0.5 minutes\n", + "Step 495000 of 2000000; TPS: 50381.41; ETA: 0.5 minutes\n", + "Step 500500 of 2000000; TPS: 50451.73; ETA: 0.5 minutes\n", + "Step 506000 of 2000000; TPS: 50596.07; ETA: 0.5 minutes\n", + "Step 511500 of 2000000; TPS: 50733.99; ETA: 0.5 minutes\n", + "Step 517000 of 2000000; TPS: 50871.44; ETA: 0.5 minutes\n", + "Step 522500 of 2000000; TPS: 51015.68; ETA: 0.5 minutes\n", + "Step 528000 of 2000000; TPS: 51150.61; ETA: 0.5 minutes\n", + "Step 533500 of 2000000; TPS: 51264.9; ETA: 0.5 minutes\n", + "Step 539000 of 2000000; TPS: 51397.79; ETA: 0.5 minutes\n", + "Step 544500 of 2000000; TPS: 51533.12; ETA: 0.5 minutes\n", + "Step 550000 of 2000000; TPS: 51631.37; ETA: 0.5 minutes\n", + "Step 555500 of 2000000; TPS: 51746.42; ETA: 0.5 minutes\n", + "Step 561000 of 2000000; TPS: 51874.14; ETA: 0.5 minutes\n", + "Step 566500 of 2000000; TPS: 52008.32; ETA: 0.5 minutes\n", + "Step 572000 of 2000000; TPS: 52136.53; ETA: 0.5 minutes\n", + "Step 577500 of 2000000; TPS: 52256.8; ETA: 0.5 minutes\n", + "Step 583000 of 2000000; TPS: 52374.83; ETA: 0.5 minutes\n", + "Step 588500 of 2000000; TPS: 52482.88; ETA: 0.4 minutes\n", + "Step 594000 of 2000000; TPS: 52597.34; ETA: 0.4 minutes\n", + "Step 599500 of 2000000; TPS: 52727.07; ETA: 0.4 minutes\n", + "Step 605000 of 2000000; TPS: 52845.17; ETA: 0.4 minutes\n", + "Step 610500 of 2000000; TPS: 52963.67; ETA: 0.4 minutes\n", + "Step 616000 of 2000000; TPS: 53069.33; ETA: 0.4 minutes\n", + "Step 621500 of 2000000; TPS: 53187.94; ETA: 0.4 minutes\n", + "Step 627000 of 2000000; TPS: 53277.27; ETA: 0.4 minutes\n", + "Step 632500 of 2000000; TPS: 53378.78; ETA: 0.4 minutes\n", + "Step 638000 of 2000000; TPS: 53477.45; ETA: 0.4 minutes\n", + "Step 643500 of 2000000; TPS: 53554.81; ETA: 0.4 minutes\n", + "Step 649000 of 2000000; TPS: 53659.7; ETA: 0.4 minutes\n", + "Step 654500 of 2000000; TPS: 53765.4; ETA: 0.4 minutes\n", + "Step 660000 of 2000000; TPS: 53866.12; ETA: 0.4 minutes\n", + "Step 665500 of 2000000; TPS: 53964.93; ETA: 0.4 minutes\n", + "Step 671000 of 2000000; TPS: 54058.79; ETA: 0.4 minutes\n", + "Step 676500 of 2000000; TPS: 54161.6; ETA: 0.4 minutes\n", + "Step 682000 of 2000000; TPS: 54261.35; ETA: 0.4 minutes\n", + "Step 687500 of 2000000; TPS: 54364.37; ETA: 0.4 minutes\n", + "Step 693000 of 2000000; TPS: 54459.77; ETA: 0.4 minutes\n", + "Step 698500 of 2000000; TPS: 54551.59; ETA: 0.4 minutes\n", + "Step 704000 of 2000000; TPS: 54648.59; ETA: 0.4 minutes\n", + "Step 709500 of 2000000; TPS: 54725.08; ETA: 0.4 minutes\n", + "Step 715000 of 2000000; TPS: 54816.34; ETA: 0.4 minutes\n", + "Step 720500 of 2000000; TPS: 54904.32; ETA: 0.4 minutes\n", + "Step 726000 of 2000000; TPS: 54980.96; ETA: 0.4 minutes\n", + "Step 731500 of 2000000; TPS: 55053.88; ETA: 0.4 minutes\n", + "Step 737000 of 2000000; TPS: 55142.17; ETA: 0.4 minutes\n", + "Step 742500 of 2000000; TPS: 55231.65; ETA: 0.4 minutes\n", + "Step 748000 of 2000000; TPS: 55313.59; ETA: 0.4 minutes\n", + "Step 753500 of 2000000; TPS: 55381.99; ETA: 0.4 minutes\n", + "Step 759000 of 2000000; TPS: 55471.05; ETA: 0.4 minutes\n", + "Step 764500 of 2000000; TPS: 55553.42; ETA: 0.4 minutes\n", + "Step 770000 of 2000000; TPS: 55641.7; ETA: 0.4 minutes\n", + "Step 775500 of 2000000; TPS: 55726.33; ETA: 0.4 minutes\n", + "Step 781000 of 2000000; TPS: 55805.01; ETA: 0.4 minutes\n", + "Step 786500 of 2000000; TPS: 55881.68; ETA: 0.4 minutes\n", + "Step 792000 of 2000000; TPS: 55964.09; ETA: 0.4 minutes\n", + "Step 797500 of 2000000; TPS: 56043.63; ETA: 0.4 minutes\n", + "Step 803000 of 2000000; TPS: 56121.04; ETA: 0.4 minutes\n", + "Step 808500 of 2000000; TPS: 56190.53; ETA: 0.4 minutes\n", + "Step 814000 of 2000000; TPS: 56266.31; ETA: 0.4 minutes\n", + "Step 819500 of 2000000; TPS: 56333.42; ETA: 0.3 minutes\n", + "Step 825000 of 2000000; TPS: 56402.73; ETA: 0.3 minutes\n", + "Step 830500 of 2000000; TPS: 56456.86; ETA: 0.3 minutes\n", + "Step 836000 of 2000000; TPS: 56524.51; ETA: 0.3 minutes\n", + "Step 841500 of 2000000; TPS: 56596.07; ETA: 0.3 minutes\n", + "Step 847000 of 2000000; TPS: 56669.16; ETA: 0.3 minutes\n", + "Step 852500 of 2000000; TPS: 56728.97; ETA: 0.3 minutes\n", + "Step 858000 of 2000000; TPS: 56804.7; ETA: 0.3 minutes\n", + "Step 863500 of 2000000; TPS: 56854.54; ETA: 0.3 minutes\n", + "Step 869000 of 2000000; TPS: 56917.56; ETA: 0.3 minutes\n", + "Step 874500 of 2000000; TPS: 56982.09; ETA: 0.3 minutes\n", + "Step 880000 of 2000000; TPS: 57046.77; ETA: 0.3 minutes\n", + "Step 885500 of 2000000; TPS: 57106.57; ETA: 0.3 minutes\n", + "Step 891000 of 2000000; TPS: 57171.55; ETA: 0.3 minutes\n", + "Step 896500 of 2000000; TPS: 57236.61; ETA: 0.3 minutes\n", + "Step 902000 of 2000000; TPS: 57297.14; ETA: 0.3 minutes\n", + "Step 907500 of 2000000; TPS: 57367.02; ETA: 0.3 minutes\n", + "Step 913000 of 2000000; TPS: 57428.76; ETA: 0.3 minutes\n", + "Step 918500 of 2000000; TPS: 57484.37; ETA: 0.3 minutes\n", + "Step 924000 of 2000000; TPS: 57550.73; ETA: 0.3 minutes\n", + "Step 929500 of 2000000; TPS: 57612.66; ETA: 0.3 minutes\n", + "Step 935000 of 2000000; TPS: 57643.65; ETA: 0.3 minutes\n", + "Step 940500 of 2000000; TPS: 57706.68; ETA: 0.3 minutes\n", + "Step 946000 of 2000000; TPS: 57755.46; ETA: 0.3 minutes\n", + "Step 951500 of 2000000; TPS: 57819.81; ETA: 0.3 minutes\n", + "Step 957000 of 2000000; TPS: 57885.04; ETA: 0.3 minutes\n", + "Step 962500 of 2000000; TPS: 57944.9; ETA: 0.3 minutes\n", + "Step 968000 of 2000000; TPS: 58007.33; ETA: 0.3 minutes\n", + "Step 973500 of 2000000; TPS: 58065.27; ETA: 0.3 minutes\n", + "Step 979000 of 2000000; TPS: 58122.16; ETA: 0.3 minutes\n", + "Step 984500 of 2000000; TPS: 58181.94; ETA: 0.3 minutes\n", + "Step 990000 of 2000000; TPS: 58247.41; ETA: 0.3 minutes\n", + "Step 995500 of 2000000; TPS: 58305.42; ETA: 0.3 minutes\n", + "Step 1001000 of 2000000; TPS: 58366.93; ETA: 0.3 minutes\n", + "Step 1006500 of 2000000; TPS: 58425.34; ETA: 0.3 minutes\n", + "Step 1012000 of 2000000; TPS: 58459.79; ETA: 0.3 minutes\n", + "Step 1017500 of 2000000; TPS: 58516.88; ETA: 0.3 minutes\n", + "Step 1023000 of 2000000; TPS: 58575.44; ETA: 0.3 minutes\n", + "Step 1028500 of 2000000; TPS: 58630.27; ETA: 0.3 minutes\n", + "Step 1034000 of 2000000; TPS: 58678.82; ETA: 0.3 minutes\n", + "Step 1039500 of 2000000; TPS: 58739.35; ETA: 0.3 minutes\n", + "Step 1045000 of 2000000; TPS: 58788.37; ETA: 0.3 minutes\n", + "Step 1050500 of 2000000; TPS: 58843.8; ETA: 0.3 minutes\n", + "Step 1056000 of 2000000; TPS: 58888.69; ETA: 0.3 minutes\n", + "Step 1061500 of 2000000; TPS: 58938.63; ETA: 0.3 minutes\n", + "Step 1067000 of 2000000; TPS: 58983.47; ETA: 0.3 minutes\n", + "Step 1072500 of 2000000; TPS: 59037.1; ETA: 0.3 minutes\n", + "Step 1078000 of 2000000; TPS: 59080.8; ETA: 0.3 minutes\n", + "Step 1083500 of 2000000; TPS: 59113.35; ETA: 0.3 minutes\n", + "Step 1089000 of 2000000; TPS: 59153.53; ETA: 0.3 minutes\n", + "Step 1094500 of 2000000; TPS: 59202.74; ETA: 0.3 minutes\n", + "Step 1100000 of 2000000; TPS: 59245.58; ETA: 0.3 minutes\n", + "Step 1105500 of 2000000; TPS: 59291.21; ETA: 0.3 minutes\n", + "Step 1111000 of 2000000; TPS: 59330.87; ETA: 0.2 minutes\n", + "Step 1116500 of 2000000; TPS: 59382.84; ETA: 0.2 minutes\n", + "Step 1122000 of 2000000; TPS: 59426.71; ETA: 0.2 minutes\n", + "Step 1127500 of 2000000; TPS: 59472.92; ETA: 0.2 minutes\n", + "Step 1133000 of 2000000; TPS: 59514.21; ETA: 0.2 minutes\n", + "Step 1138500 of 2000000; TPS: 59559.46; ETA: 0.2 minutes\n", + "Step 1144000 of 2000000; TPS: 59600.55; ETA: 0.2 minutes\n", + "Step 1149500 of 2000000; TPS: 59650.78; ETA: 0.2 minutes\n", + "Step 1155000 of 2000000; TPS: 59704.16; ETA: 0.2 minutes\n", + "Step 1160500 of 2000000; TPS: 59751.93; ETA: 0.2 minutes\n", + "Step 1166000 of 2000000; TPS: 59803.26; ETA: 0.2 minutes\n", + "Step 1171500 of 2000000; TPS: 59848.96; ETA: 0.2 minutes\n", + "Step 1177000 of 2000000; TPS: 59897.3; ETA: 0.2 minutes\n", + "Step 1182500 of 2000000; TPS: 59941.18; ETA: 0.2 minutes\n", + "Step 1188000 of 2000000; TPS: 59980.81; ETA: 0.2 minutes\n", + "Step 1193500 of 2000000; TPS: 60018.07; ETA: 0.2 minutes\n", + "Step 1199000 of 2000000; TPS: 60062.67; ETA: 0.2 minutes\n", + "Step 1204500 of 2000000; TPS: 60000.69; ETA: 0.2 minutes\n", + "Step 1210000 of 2000000; TPS: 60040.14; ETA: 0.2 minutes\n", + "Step 1215500 of 2000000; TPS: 60084.51; ETA: 0.2 minutes\n", + "Step 1221000 of 2000000; TPS: 60122.57; ETA: 0.2 minutes\n", + "Step 1226500 of 2000000; TPS: 60165.74; ETA: 0.2 minutes\n", + "Step 1232000 of 2000000; TPS: 60201.37; ETA: 0.2 minutes\n", + "Step 1237500 of 2000000; TPS: 60244.42; ETA: 0.2 minutes\n", + "Step 1243000 of 2000000; TPS: 60281.91; ETA: 0.2 minutes\n", + "Step 1248500 of 2000000; TPS: 60320.45; ETA: 0.2 minutes\n", + "Step 1254000 of 2000000; TPS: 60355.01; ETA: 0.2 minutes\n", + "Step 1259500 of 2000000; TPS: 60393.14; ETA: 0.2 minutes\n", + "Step 1265000 of 2000000; TPS: 60425.77; ETA: 0.2 minutes\n", + "Step 1270500 of 2000000; TPS: 60459.46; ETA: 0.2 minutes\n", + "Step 1276000 of 2000000; TPS: 60485.98; ETA: 0.2 minutes\n", + "Step 1281500 of 2000000; TPS: 60528.36; ETA: 0.2 minutes\n", + "Step 1287000 of 2000000; TPS: 60564.59; ETA: 0.2 minutes\n", + "Step 1292500 of 2000000; TPS: 60581.08; ETA: 0.2 minutes\n", + "Step 1298000 of 2000000; TPS: 60616.39; ETA: 0.2 minutes\n", + "Step 1303500 of 2000000; TPS: 60649.89; ETA: 0.2 minutes\n", + "Step 1309000 of 2000000; TPS: 60679.13; ETA: 0.2 minutes\n", + "Step 1314500 of 2000000; TPS: 60715.17; ETA: 0.2 minutes\n", + "Step 1320000 of 2000000; TPS: 60741.39; ETA: 0.2 minutes\n", + "Step 1325500 of 2000000; TPS: 60777.22; ETA: 0.2 minutes\n", + "Step 1331000 of 2000000; TPS: 60808.83; ETA: 0.2 minutes\n", + "Step 1336500 of 2000000; TPS: 60847.11; ETA: 0.2 minutes\n", + "Step 1342000 of 2000000; TPS: 60877.53; ETA: 0.2 minutes\n", + "Step 1347500 of 2000000; TPS: 60916.29; ETA: 0.2 minutes\n", + "Step 1353000 of 2000000; TPS: 60949.6; ETA: 0.2 minutes\n", + "Step 1358500 of 2000000; TPS: 60987.48; ETA: 0.2 minutes\n", + "Step 1364000 of 2000000; TPS: 61019.04; ETA: 0.2 minutes\n", + "Step 1369500 of 2000000; TPS: 61058.65; ETA: 0.2 minutes\n", + "Step 1375000 of 2000000; TPS: 61086.69; ETA: 0.2 minutes\n", + "Step 1380500 of 2000000; TPS: 61119.76; ETA: 0.2 minutes\n", + "Step 1386000 of 2000000; TPS: 61148.77; ETA: 0.2 minutes\n", + "Step 1391500 of 2000000; TPS: 61178.8; ETA: 0.2 minutes\n", + "Step 1397000 of 2000000; TPS: 61209.13; ETA: 0.2 minutes\n", + "Step 1402500 of 2000000; TPS: 61238.44; ETA: 0.2 minutes\n", + "Step 1408000 of 2000000; TPS: 61257.46; ETA: 0.2 minutes\n", + "Step 1413500 of 2000000; TPS: 61285.05; ETA: 0.2 minutes\n", + "Step 1419000 of 2000000; TPS: 61317.42; ETA: 0.2 minutes\n", + "Step 1424500 of 2000000; TPS: 61350.46; ETA: 0.2 minutes\n", + "Step 1430000 of 2000000; TPS: 61380.47; ETA: 0.2 minutes\n", + "Step 1435500 of 2000000; TPS: 61416.41; ETA: 0.2 minutes\n", + "Step 1441000 of 2000000; TPS: 61421.66; ETA: 0.2 minutes\n", + "Step 1446500 of 2000000; TPS: 61448.93; ETA: 0.2 minutes\n", + "Step 1452000 of 2000000; TPS: 61458.82; ETA: 0.1 minutes\n", + "Step 1457500 of 2000000; TPS: 61487.39; ETA: 0.1 minutes\n", + "Step 1463000 of 2000000; TPS: 61515.35; ETA: 0.1 minutes\n", + "Step 1468500 of 2000000; TPS: 61541.18; ETA: 0.1 minutes\n", + "Step 1474000 of 2000000; TPS: 61533.22; ETA: 0.1 minutes\n", + "Step 1479500 of 2000000; TPS: 61565.16; ETA: 0.1 minutes\n", + "Step 1485000 of 2000000; TPS: 61589.84; ETA: 0.1 minutes\n", + "Step 1490500 of 2000000; TPS: 61619.79; ETA: 0.1 minutes\n", + "Step 1496000 of 2000000; TPS: 61648.35; ETA: 0.1 minutes\n", + "Step 1501500 of 2000000; TPS: 61668.55; ETA: 0.1 minutes\n", + "Step 1507000 of 2000000; TPS: 61685.56; ETA: 0.1 minutes\n", + "Step 1512500 of 2000000; TPS: 61715.14; ETA: 0.1 minutes\n", + "Step 1518000 of 2000000; TPS: 61739.73; ETA: 0.1 minutes\n", + "Step 1523500 of 2000000; TPS: 61766.56; ETA: 0.1 minutes\n", + "Step 1529000 of 2000000; TPS: 61794.58; ETA: 0.1 minutes\n", + "Step 1534500 of 2000000; TPS: 61822.92; ETA: 0.1 minutes\n", + "Step 1540000 of 2000000; TPS: 61848.3; ETA: 0.1 minutes\n", + "Step 1545500 of 2000000; TPS: 61876.79; ETA: 0.1 minutes\n", + "Step 1551000 of 2000000; TPS: 61905.06; ETA: 0.1 minutes\n", + "Step 1556500 of 2000000; TPS: 61936.04; ETA: 0.1 minutes\n", + "Step 1562000 of 2000000; TPS: 61963.31; ETA: 0.1 minutes\n", + "Step 1567500 of 2000000; TPS: 61995.56; ETA: 0.1 minutes\n", + "Step 1573000 of 2000000; TPS: 62018.58; ETA: 0.1 minutes\n", + "Step 1578500 of 2000000; TPS: 62044.45; ETA: 0.1 minutes\n", + "Step 1584000 of 2000000; TPS: 62067.04; ETA: 0.1 minutes\n", + "Step 1589500 of 2000000; TPS: 62094.15; ETA: 0.1 minutes\n", + "Step 1595000 of 2000000; TPS: 62124.28; ETA: 0.1 minutes\n", + "Step 1600500 of 2000000; TPS: 62149.64; ETA: 0.1 minutes\n", + "Step 1606000 of 2000000; TPS: 62166.6; ETA: 0.1 minutes\n", + "Step 1611500 of 2000000; TPS: 62190.99; ETA: 0.1 minutes\n", + "Step 1617000 of 2000000; TPS: 62212.41; ETA: 0.1 minutes\n", + "Step 1622500 of 2000000; TPS: 62234.32; ETA: 0.1 minutes\n", + "Step 1628000 of 2000000; TPS: 62261.19; ETA: 0.1 minutes\n", + "Step 1633500 of 2000000; TPS: 62279.34; ETA: 0.1 minutes\n", + "Step 1639000 of 2000000; TPS: 62300.03; ETA: 0.1 minutes\n", + "Step 1644500 of 2000000; TPS: 62324.68; ETA: 0.1 minutes\n", + "Step 1650000 of 2000000; TPS: 62342.9; ETA: 0.1 minutes\n", + "Step 1655500 of 2000000; TPS: 62357.56; ETA: 0.1 minutes\n", + "Step 1661000 of 2000000; TPS: 62367.44; ETA: 0.1 minutes\n", + "Step 1666500 of 2000000; TPS: 62392.38; ETA: 0.1 minutes\n", + "Step 1672000 of 2000000; TPS: 62411.86; ETA: 0.1 minutes\n", + "Step 1677500 of 2000000; TPS: 62432.91; ETA: 0.1 minutes\n", + "Step 1683000 of 2000000; TPS: 62449.65; ETA: 0.1 minutes\n", + "Step 1688500 of 2000000; TPS: 62469.02; ETA: 0.1 minutes\n", + "Step 1694000 of 2000000; TPS: 62493.34; ETA: 0.1 minutes\n", + "Step 1699500 of 2000000; TPS: 62521.58; ETA: 0.1 minutes\n", + "Step 1705000 of 2000000; TPS: 62531.77; ETA: 0.1 minutes\n", + "Step 1710500 of 2000000; TPS: 62551.67; ETA: 0.1 minutes\n", + "Step 1716000 of 2000000; TPS: 62570.65; ETA: 0.1 minutes\n", + "Step 1721500 of 2000000; TPS: 62588.65; ETA: 0.1 minutes\n", + "Step 1727000 of 2000000; TPS: 62606.41; ETA: 0.1 minutes\n", + "Step 1732500 of 2000000; TPS: 62630.01; ETA: 0.1 minutes\n", + "Step 1738000 of 2000000; TPS: 62647.44; ETA: 0.1 minutes\n", + "Step 1743500 of 2000000; TPS: 62670.27; ETA: 0.1 minutes\n", + "Step 1749000 of 2000000; TPS: 62692.95; ETA: 0.1 minutes\n", + "Step 1754500 of 2000000; TPS: 62716.46; ETA: 0.1 minutes\n", + "Step 1760000 of 2000000; TPS: 62734.16; ETA: 0.1 minutes\n", + "Step 1765500 of 2000000; TPS: 62752.55; ETA: 0.1 minutes\n", + "Step 1771000 of 2000000; TPS: 62773.04; ETA: 0.1 minutes\n", + "Step 1776500 of 2000000; TPS: 62796.73; ETA: 0.1 minutes\n", + "Step 1782000 of 2000000; TPS: 62819.91; ETA: 0.1 minutes\n", + "Step 1787500 of 2000000; TPS: 62837.63; ETA: 0.1 minutes\n", + "Step 1793000 of 2000000; TPS: 62855.24; ETA: 0.1 minutes\n", + "Step 1798500 of 2000000; TPS: 62878.63; ETA: 0.1 minutes\n", + "Step 1804000 of 2000000; TPS: 62894.59; ETA: 0.1 minutes\n", + "Step 1809500 of 2000000; TPS: 62914.06; ETA: 0.1 minutes\n", + "Step 1815000 of 2000000; TPS: 62932.69; ETA: 0.0 minutes\n", + "Step 1820500 of 2000000; TPS: 62950.04; ETA: 0.0 minutes\n", + "Step 1826000 of 2000000; TPS: 62970.98; ETA: 0.0 minutes\n", + "Step 1831500 of 2000000; TPS: 62994.0; ETA: 0.0 minutes\n", + "Step 1837000 of 2000000; TPS: 62999.27; ETA: 0.0 minutes\n", + "Step 1842500 of 2000000; TPS: 63012.8; ETA: 0.0 minutes\n", + "Step 1848000 of 2000000; TPS: 63028.28; ETA: 0.0 minutes\n", + "Step 1853500 of 2000000; TPS: 63050.08; ETA: 0.0 minutes\n", + "Step 1859000 of 2000000; TPS: 63069.62; ETA: 0.0 minutes\n", + "Step 1864500 of 2000000; TPS: 63072.48; ETA: 0.0 minutes\n", + "Step 1870000 of 2000000; TPS: 63082.05; ETA: 0.0 minutes\n", + "Step 1875500 of 2000000; TPS: 63098.51; ETA: 0.0 minutes\n", + "Step 1881000 of 2000000; TPS: 63115.99; ETA: 0.0 minutes\n", + "Step 1886500 of 2000000; TPS: 63128.42; ETA: 0.0 minutes\n", + "Step 1892000 of 2000000; TPS: 63145.08; ETA: 0.0 minutes\n", + "Step 1897500 of 2000000; TPS: 63164.85; ETA: 0.0 minutes\n", + "Step 1903000 of 2000000; TPS: 63151.26; ETA: 0.0 minutes\n", + "Step 1908500 of 2000000; TPS: 63164.8; ETA: 0.0 minutes\n", + "Step 1914000 of 2000000; TPS: 63175.35; ETA: 0.0 minutes\n", + "Step 1919500 of 2000000; TPS: 63188.78; ETA: 0.0 minutes\n", + "Step 1925000 of 2000000; TPS: 63196.82; ETA: 0.0 minutes\n", + "Step 1930500 of 2000000; TPS: 63213.9; ETA: 0.0 minutes\n", + "Step 1936000 of 2000000; TPS: 63226.57; ETA: 0.0 minutes\n", + "Step 1941500 of 2000000; TPS: 63238.08; ETA: 0.0 minutes\n", + "Step 1947000 of 2000000; TPS: 63255.53; ETA: 0.0 minutes\n", + "Step 1952500 of 2000000; TPS: 63271.54; ETA: 0.0 minutes\n", + "Step 1958000 of 2000000; TPS: 63289.49; ETA: 0.0 minutes\n", + "Step 1963500 of 2000000; TPS: 63304.41; ETA: 0.0 minutes\n", + "Step 1969000 of 2000000; TPS: 63302.13; ETA: 0.0 minutes\n", + "Step 1974500 of 2000000; TPS: 63299.9; ETA: 0.0 minutes\n", + "Step 1980000 of 2000000; TPS: 63302.16; ETA: 0.0 minutes\n", + "Step 1985500 of 2000000; TPS: 63307.91; ETA: 0.0 minutes\n", + "Step 1991000 of 2000000; TPS: 63313.01; ETA: 0.0 minutes\n", + "Step 1996500 of 2000000; TPS: 63323.31; ETA: 0.0 minutes\n", + "Step 1999 of 4000000; TPS: 66914.37; ETA: 1.0 minutes\n", + "Step 7499 of 4000000; TPS: 67897.43; ETA: 1.0 minutes\n", + "Step 12999 of 4000000; TPS: 68623.47; ETA: 1.0 minutes\n", + "Step 18499 of 4000000; TPS: 68777.19; ETA: 1.0 minutes\n", + "Step 23999 of 4000000; TPS: 69110.0; ETA: 1.0 minutes\n", + "Step 29499 of 4000000; TPS: 68764.32; ETA: 1.0 minutes\n", + "Step 34999 of 4000000; TPS: 68794.24; ETA: 1.0 minutes\n", + "Step 40499 of 4000000; TPS: 68800.96; ETA: 1.0 minutes\n", + "Step 45999 of 4000000; TPS: 69231.81; ETA: 1.0 minutes\n", + "Step 51499 of 4000000; TPS: 69453.49; ETA: 0.9 minutes\n", + "Step 56999 of 4000000; TPS: 69538.45; ETA: 0.9 minutes\n", + "Step 62499 of 4000000; TPS: 69619.55; ETA: 0.9 minutes\n", + "Step 67999 of 4000000; TPS: 69614.26; ETA: 0.9 minutes\n", + "Step 73499 of 4000000; TPS: 69617.54; ETA: 0.9 minutes\n", + "Step 78999 of 4000000; TPS: 69802.33; ETA: 0.9 minutes\n", + "Step 84499 of 4000000; TPS: 69791.09; ETA: 0.9 minutes\n", + "Step 89999 of 4000000; TPS: 69766.72; ETA: 0.9 minutes\n", + "Step 95499 of 4000000; TPS: 69705.98; ETA: 0.9 minutes\n", + "Step 100999 of 4000000; TPS: 69649.54; ETA: 0.9 minutes\n", + "Step 106499 of 4000000; TPS: 69560.18; ETA: 0.9 minutes\n", + "Step 111999 of 4000000; TPS: 69547.36; ETA: 0.9 minutes\n", + "Step 117499 of 4000000; TPS: 69529.12; ETA: 0.9 minutes\n", + "Step 122999 of 4000000; TPS: 69519.79; ETA: 0.9 minutes\n", + "Step 128499 of 4000000; TPS: 69548.66; ETA: 0.9 minutes\n", + "Step 133999 of 4000000; TPS: 69507.4; ETA: 0.9 minutes\n", + "Step 139499 of 4000000; TPS: 69492.52; ETA: 0.9 minutes\n", + "Step 144999 of 4000000; TPS: 69450.32; ETA: 0.9 minutes\n", + "Step 150499 of 4000000; TPS: 69385.81; ETA: 0.9 minutes\n", + "Step 155999 of 4000000; TPS: 69367.6; ETA: 0.9 minutes\n", + "Step 161499 of 4000000; TPS: 69340.11; ETA: 0.9 minutes\n", + "Step 166999 of 4000000; TPS: 69327.36; ETA: 0.9 minutes\n", + "Step 172499 of 4000000; TPS: 69275.32; ETA: 0.9 minutes\n", + "Step 177999 of 4000000; TPS: 69262.09; ETA: 0.9 minutes\n", + "Step 183499 of 4000000; TPS: 69281.22; ETA: 0.9 minutes\n", + "Step 188999 of 4000000; TPS: 69208.73; ETA: 0.9 minutes\n", + "Step 194499 of 4000000; TPS: 69226.09; ETA: 0.9 minutes\n", + "Step 199999 of 4000000; TPS: 69143.3; ETA: 0.9 minutes\n", + "Step 205499 of 4000000; TPS: 69171.55; ETA: 0.9 minutes\n", + "Step 210999 of 4000000; TPS: 69179.98; ETA: 0.9 minutes\n", + "Step 216499 of 4000000; TPS: 69188.84; ETA: 0.9 minutes\n", + "Step 221999 of 4000000; TPS: 69172.49; ETA: 0.9 minutes\n", + "Step 227499 of 4000000; TPS: 69191.49; ETA: 0.9 minutes\n", + "Step 232999 of 4000000; TPS: 69206.05; ETA: 0.9 minutes\n", + "Step 238499 of 4000000; TPS: 69189.67; ETA: 0.9 minutes\n", + "Step 243999 of 4000000; TPS: 69220.48; ETA: 0.9 minutes\n", + "Step 249499 of 4000000; TPS: 69248.88; ETA: 0.9 minutes\n", + "Step 254999 of 4000000; TPS: 69262.34; ETA: 0.9 minutes\n", + "Step 260499 of 4000000; TPS: 69284.21; ETA: 0.9 minutes\n", + "Step 265999 of 4000000; TPS: 69213.81; ETA: 0.9 minutes\n", + "Step 271499 of 4000000; TPS: 69199.36; ETA: 0.9 minutes\n", + "Step 276999 of 4000000; TPS: 69129.03; ETA: 0.9 minutes\n", + "Step 282499 of 4000000; TPS: 69142.76; ETA: 0.9 minutes\n", + "Step 287999 of 4000000; TPS: 69125.11; ETA: 0.9 minutes\n", + "Step 293499 of 4000000; TPS: 69097.28; ETA: 0.9 minutes\n", + "Step 298999 of 4000000; TPS: 69096.26; ETA: 0.9 minutes\n", + "Step 304499 of 4000000; TPS: 69069.13; ETA: 0.9 minutes\n", + "Step 309999 of 4000000; TPS: 68993.09; ETA: 0.9 minutes\n", + "Step 315499 of 4000000; TPS: 68978.4; ETA: 0.9 minutes\n", + "Step 320999 of 4000000; TPS: 68966.55; ETA: 0.9 minutes\n", + "Step 326499 of 4000000; TPS: 68946.29; ETA: 0.9 minutes\n", + "Step 331999 of 4000000; TPS: 68916.92; ETA: 0.9 minutes\n", + "Step 337499 of 4000000; TPS: 68939.36; ETA: 0.9 minutes\n", + "Step 342999 of 4000000; TPS: 68928.7; ETA: 0.9 minutes\n", + "Step 348499 of 4000000; TPS: 68908.31; ETA: 0.9 minutes\n", + "Step 353999 of 4000000; TPS: 68873.04; ETA: 0.9 minutes\n", + "Step 359499 of 4000000; TPS: 68892.56; ETA: 0.9 minutes\n", + "Step 364999 of 4000000; TPS: 68880.68; ETA: 0.9 minutes\n", + "Step 370499 of 4000000; TPS: 68842.89; ETA: 0.9 minutes\n", + "Step 375999 of 4000000; TPS: 68812.1; ETA: 0.9 minutes\n", + "Step 381499 of 4000000; TPS: 68781.15; ETA: 0.9 minutes\n", + "Step 386999 of 4000000; TPS: 68789.13; ETA: 0.9 minutes\n", + "Step 392499 of 4000000; TPS: 68805.09; ETA: 0.9 minutes\n", + "Step 397999 of 4000000; TPS: 68760.08; ETA: 0.9 minutes\n", + "Step 403499 of 4000000; TPS: 68794.37; ETA: 0.9 minutes\n", + "Step 408999 of 4000000; TPS: 68782.38; ETA: 0.9 minutes\n", + "Step 414499 of 4000000; TPS: 68775.32; ETA: 0.9 minutes\n", + "Step 419999 of 4000000; TPS: 68765.65; ETA: 0.9 minutes\n", + "Step 425499 of 4000000; TPS: 68778.85; ETA: 0.9 minutes\n", + "Step 430999 of 4000000; TPS: 68765.9; ETA: 0.9 minutes\n", + "Step 436499 of 4000000; TPS: 68696.91; ETA: 0.9 minutes\n", + "Step 441999 of 4000000; TPS: 49403.24; ETA: 1.2 minutes\n", + "Step 447499 of 4000000; TPS: 49576.69; ETA: 1.2 minutes\n", + "Step 452999 of 4000000; TPS: 49741.81; ETA: 1.2 minutes\n", + "Step 458499 of 4000000; TPS: 49910.17; ETA: 1.2 minutes\n", + "Step 463999 of 4000000; TPS: 50070.47; ETA: 1.2 minutes\n", + "Step 469499 of 4000000; TPS: 50232.91; ETA: 1.2 minutes\n", + "Step 474999 of 4000000; TPS: 50391.36; ETA: 1.2 minutes\n", + "Step 480499 of 4000000; TPS: 50541.05; ETA: 1.2 minutes\n", + "Step 485999 of 4000000; TPS: 50684.29; ETA: 1.2 minutes\n", + "Step 491499 of 4000000; TPS: 50836.76; ETA: 1.2 minutes\n", + "Step 496999 of 4000000; TPS: 50969.02; ETA: 1.1 minutes\n", + "Step 502499 of 4000000; TPS: 51112.86; ETA: 1.1 minutes\n", + "Step 507999 of 4000000; TPS: 51256.64; ETA: 1.1 minutes\n", + "Step 513499 of 4000000; TPS: 51392.5; ETA: 1.1 minutes\n", + "Step 518999 of 4000000; TPS: 51529.1; ETA: 1.1 minutes\n", + "Step 524499 of 4000000; TPS: 51663.66; ETA: 1.1 minutes\n", + "Step 529999 of 4000000; TPS: 51798.55; ETA: 1.1 minutes\n", + "Step 535499 of 4000000; TPS: 51927.33; ETA: 1.1 minutes\n", + "Step 540999 of 4000000; TPS: 52052.34; ETA: 1.1 minutes\n", + "Step 546499 of 4000000; TPS: 52178.83; ETA: 1.1 minutes\n", + "Step 551999 of 4000000; TPS: 52299.25; ETA: 1.1 minutes\n", + "Step 557499 of 4000000; TPS: 52430.93; ETA: 1.1 minutes\n", + "Step 562999 of 4000000; TPS: 52546.95; ETA: 1.1 minutes\n", + "Step 568499 of 4000000; TPS: 52633.43; ETA: 1.1 minutes\n", + "Step 573999 of 4000000; TPS: 52763.46; ETA: 1.1 minutes\n", + "Step 579499 of 4000000; TPS: 52884.54; ETA: 1.1 minutes\n", + "Step 584999 of 4000000; TPS: 52992.04; ETA: 1.1 minutes\n", + "Step 590499 of 4000000; TPS: 53104.2; ETA: 1.1 minutes\n", + "Step 595999 of 4000000; TPS: 53226.5; ETA: 1.1 minutes\n", + "Step 601499 of 4000000; TPS: 53336.75; ETA: 1.1 minutes\n", + "Step 606999 of 4000000; TPS: 53453.69; ETA: 1.1 minutes\n", + "Step 612499 of 4000000; TPS: 53558.18; ETA: 1.1 minutes\n", + "Step 617999 of 4000000; TPS: 53669.54; ETA: 1.1 minutes\n", + "Step 623499 of 4000000; TPS: 53763.46; ETA: 1.0 minutes\n", + "Step 628999 of 4000000; TPS: 53836.34; ETA: 1.0 minutes\n", + "Step 634499 of 4000000; TPS: 53948.4; ETA: 1.0 minutes\n", + "Step 639999 of 4000000; TPS: 54054.13; ETA: 1.0 minutes\n", + "Step 645499 of 4000000; TPS: 54157.99; ETA: 1.0 minutes\n", + "Step 650999 of 4000000; TPS: 54244.35; ETA: 1.0 minutes\n", + "Step 656499 of 4000000; TPS: 54339.72; ETA: 1.0 minutes\n", + "Step 661999 of 4000000; TPS: 54424.77; ETA: 1.0 minutes\n", + "Step 667499 of 4000000; TPS: 54512.57; ETA: 1.0 minutes\n", + "Step 672999 of 4000000; TPS: 54594.02; ETA: 1.0 minutes\n", + "Step 678499 of 4000000; TPS: 54685.26; ETA: 1.0 minutes\n", + "Step 683999 of 4000000; TPS: 54777.59; ETA: 1.0 minutes\n", + "Step 689499 of 4000000; TPS: 54864.05; ETA: 1.0 minutes\n", + "Step 694999 of 4000000; TPS: 54915.63; ETA: 1.0 minutes\n", + "Step 700499 of 4000000; TPS: 54924.35; ETA: 1.0 minutes\n", + "Step 705999 of 4000000; TPS: 54808.12; ETA: 1.0 minutes\n", + "Step 711499 of 4000000; TPS: 54682.79; ETA: 1.0 minutes\n", + "Step 716999 of 4000000; TPS: 54598.51; ETA: 1.0 minutes\n", + "Step 722499 of 4000000; TPS: 54695.46; ETA: 1.0 minutes\n", + "Step 727999 of 4000000; TPS: 54791.01; ETA: 1.0 minutes\n", + "Step 733499 of 4000000; TPS: 54872.26; ETA: 1.0 minutes\n", + "Step 738999 of 4000000; TPS: 54958.14; ETA: 1.0 minutes\n", + "Step 744499 of 4000000; TPS: 55042.78; ETA: 1.0 minutes\n", + "Step 749999 of 4000000; TPS: 55109.86; ETA: 1.0 minutes\n", + "Step 755499 of 4000000; TPS: 55190.29; ETA: 1.0 minutes\n", + "Step 760999 of 4000000; TPS: 55270.76; ETA: 1.0 minutes\n", + "Step 766499 of 4000000; TPS: 55341.32; ETA: 1.0 minutes\n", + "Step 771999 of 4000000; TPS: 55420.7; ETA: 1.0 minutes\n", + "Step 777499 of 4000000; TPS: 55504.2; ETA: 1.0 minutes\n", + "Step 782999 of 4000000; TPS: 55575.62; ETA: 1.0 minutes\n", + "Step 788499 of 4000000; TPS: 55653.35; ETA: 1.0 minutes\n", + "Step 793999 of 4000000; TPS: 55724.52; ETA: 1.0 minutes\n", + "Step 799499 of 4000000; TPS: 55787.51; ETA: 1.0 minutes\n", + "Step 804999 of 4000000; TPS: 55857.42; ETA: 1.0 minutes\n", + "Step 810499 of 4000000; TPS: 55934.85; ETA: 1.0 minutes\n", + "Step 815999 of 4000000; TPS: 55970.66; ETA: 0.9 minutes\n", + "Step 821499 of 4000000; TPS: 56032.75; ETA: 0.9 minutes\n", + "Step 826999 of 4000000; TPS: 56104.74; ETA: 0.9 minutes\n", + "Step 832499 of 4000000; TPS: 56175.42; ETA: 0.9 minutes\n", + "Step 837999 of 4000000; TPS: 56237.84; ETA: 0.9 minutes\n", + "Step 843499 of 4000000; TPS: 56296.63; ETA: 0.9 minutes\n", + "Step 848999 of 4000000; TPS: 56355.07; ETA: 0.9 minutes\n", + "Step 854499 of 4000000; TPS: 56429.57; ETA: 0.9 minutes\n", + "Step 859999 of 4000000; TPS: 56495.34; ETA: 0.9 minutes\n", + "Step 865499 of 4000000; TPS: 56560.24; ETA: 0.9 minutes\n", + "Step 870999 of 4000000; TPS: 56613.8; ETA: 0.9 minutes\n", + "Step 876499 of 4000000; TPS: 56680.93; ETA: 0.9 minutes\n", + "Step 881999 of 4000000; TPS: 56728.94; ETA: 0.9 minutes\n", + "Step 887499 of 4000000; TPS: 56786.39; ETA: 0.9 minutes\n", + "Step 892999 of 4000000; TPS: 56847.71; ETA: 0.9 minutes\n", + "Step 898499 of 4000000; TPS: 56906.06; ETA: 0.9 minutes\n", + "Step 903999 of 4000000; TPS: 56969.12; ETA: 0.9 minutes\n", + "Step 909499 of 4000000; TPS: 57030.74; ETA: 0.9 minutes\n", + "Step 914999 of 4000000; TPS: 57078.65; ETA: 0.9 minutes\n", + "Step 920499 of 4000000; TPS: 57142.17; ETA: 0.9 minutes\n", + "Step 925999 of 4000000; TPS: 57204.09; ETA: 0.9 minutes\n", + "Step 931499 of 4000000; TPS: 57267.06; ETA: 0.9 minutes\n", + "Step 936999 of 4000000; TPS: 57324.26; ETA: 0.9 minutes\n", + "Step 942499 of 4000000; TPS: 57389.97; ETA: 0.9 minutes\n", + "Step 947999 of 4000000; TPS: 57440.69; ETA: 0.9 minutes\n", + "Step 953499 of 4000000; TPS: 57492.21; ETA: 0.9 minutes\n", + "Step 958999 of 4000000; TPS: 57548.91; ETA: 0.9 minutes\n", + "Step 964499 of 4000000; TPS: 57590.99; ETA: 0.9 minutes\n", + "Step 969999 of 4000000; TPS: 57644.73; ETA: 0.9 minutes\n", + "Step 975499 of 4000000; TPS: 57694.01; ETA: 0.9 minutes\n", + "Step 980999 of 4000000; TPS: 57746.8; ETA: 0.9 minutes\n", + "Step 986499 of 4000000; TPS: 57788.24; ETA: 0.9 minutes\n", + "Step 991999 of 4000000; TPS: 57843.19; ETA: 0.9 minutes\n", + "Step 997499 of 4000000; TPS: 57892.76; ETA: 0.9 minutes\n", + "Step 1002999 of 4000000; TPS: 57939.89; ETA: 0.9 minutes\n", + "Step 1008499 of 4000000; TPS: 57992.09; ETA: 0.9 minutes\n", + "Step 1013999 of 4000000; TPS: 58035.24; ETA: 0.9 minutes\n", + "Step 1019499 of 4000000; TPS: 58086.02; ETA: 0.9 minutes\n", + "Step 1024999 of 4000000; TPS: 58128.94; ETA: 0.9 minutes\n", + "Step 1030499 of 4000000; TPS: 58177.67; ETA: 0.9 minutes\n", + "Step 1035999 of 4000000; TPS: 58221.33; ETA: 0.8 minutes\n", + "Step 1041499 of 4000000; TPS: 58268.27; ETA: 0.8 minutes\n", + "Step 1046999 of 4000000; TPS: 58312.17; ETA: 0.8 minutes\n", + "Step 1052499 of 4000000; TPS: 58355.86; ETA: 0.8 minutes\n", + "Step 1057999 of 4000000; TPS: 58399.47; ETA: 0.8 minutes\n", + "Step 1063499 of 4000000; TPS: 58442.68; ETA: 0.8 minutes\n", + "Step 1068999 of 4000000; TPS: 58482.14; ETA: 0.8 minutes\n", + "Step 1074499 of 4000000; TPS: 58514.48; ETA: 0.8 minutes\n", + "Step 1079999 of 4000000; TPS: 58547.31; ETA: 0.8 minutes\n", + "Step 1085499 of 4000000; TPS: 58592.93; ETA: 0.8 minutes\n", + "Step 1090999 of 4000000; TPS: 58633.97; ETA: 0.8 minutes\n", + "Step 1096499 of 4000000; TPS: 58674.16; ETA: 0.8 minutes\n", + "Step 1101999 of 4000000; TPS: 58714.04; ETA: 0.8 minutes\n", + "Step 1107499 of 4000000; TPS: 58751.58; ETA: 0.8 minutes\n", + "Step 1112999 of 4000000; TPS: 58739.46; ETA: 0.8 minutes\n", + "Step 1118499 of 4000000; TPS: 58786.73; ETA: 0.8 minutes\n", + "Step 1123999 of 4000000; TPS: 58822.23; ETA: 0.8 minutes\n", + "Step 1129499 of 4000000; TPS: 58859.71; ETA: 0.8 minutes\n", + "Step 1134999 of 4000000; TPS: 58884.86; ETA: 0.8 minutes\n", + "Step 1140499 of 4000000; TPS: 58927.22; ETA: 0.8 minutes\n", + "Step 1145999 of 4000000; TPS: 58927.38; ETA: 0.8 minutes\n", + "Step 1151499 of 4000000; TPS: 58967.92; ETA: 0.8 minutes\n", + "Step 1156999 of 4000000; TPS: 59006.36; ETA: 0.8 minutes\n", + "Step 1162499 of 4000000; TPS: 59044.41; ETA: 0.8 minutes\n", + "Step 1167999 of 4000000; TPS: 59076.4; ETA: 0.8 minutes\n", + "Step 1173499 of 4000000; TPS: 59115.59; ETA: 0.8 minutes\n", + "Step 1178999 of 4000000; TPS: 59147.84; ETA: 0.8 minutes\n", + "Step 1184499 of 4000000; TPS: 59185.99; ETA: 0.8 minutes\n", + "Step 1189999 of 4000000; TPS: 59223.74; ETA: 0.8 minutes\n", + "Step 1195499 of 4000000; TPS: 59257.33; ETA: 0.8 minutes\n", + "Step 1200999 of 4000000; TPS: 59287.16; ETA: 0.8 minutes\n", + "Step 1206499 of 4000000; TPS: 59324.22; ETA: 0.8 minutes\n", + "Step 1211999 of 4000000; TPS: 59361.62; ETA: 0.8 minutes\n", + "Step 1217499 of 4000000; TPS: 59392.53; ETA: 0.8 minutes\n", + "Step 1222999 of 4000000; TPS: 59430.79; ETA: 0.8 minutes\n", + "Step 1228499 of 4000000; TPS: 59468.69; ETA: 0.8 minutes\n", + "Step 1233999 of 4000000; TPS: 59503.96; ETA: 0.8 minutes\n", + "Step 1239499 of 4000000; TPS: 59542.16; ETA: 0.8 minutes\n", + "Step 1244999 of 4000000; TPS: 59580.47; ETA: 0.8 minutes\n", + "Step 1250499 of 4000000; TPS: 59618.01; ETA: 0.8 minutes\n", + "Step 1255999 of 4000000; TPS: 59652.74; ETA: 0.8 minutes\n", + "Step 1261499 of 4000000; TPS: 59683.65; ETA: 0.8 minutes\n", + "Step 1266999 of 4000000; TPS: 59717.98; ETA: 0.8 minutes\n", + "Step 1272499 of 4000000; TPS: 59755.53; ETA: 0.8 minutes\n", + "Step 1277999 of 4000000; TPS: 59790.88; ETA: 0.8 minutes\n", + "Step 1283499 of 4000000; TPS: 59825.31; ETA: 0.8 minutes\n", + "Step 1288999 of 4000000; TPS: 59861.97; ETA: 0.8 minutes\n", + "Step 1294499 of 4000000; TPS: 59891.72; ETA: 0.8 minutes\n", + "Step 1299999 of 4000000; TPS: 59921.55; ETA: 0.8 minutes\n", + "Step 1305499 of 4000000; TPS: 59961.01; ETA: 0.7 minutes\n", + "Step 1310999 of 4000000; TPS: 59987.76; ETA: 0.7 minutes\n", + "Step 1316499 of 4000000; TPS: 60026.13; ETA: 0.7 minutes\n", + "Step 1321999 of 4000000; TPS: 60057.76; ETA: 0.7 minutes\n", + "Step 1327499 of 4000000; TPS: 60090.71; ETA: 0.7 minutes\n", + "Step 1332999 of 4000000; TPS: 60121.76; ETA: 0.7 minutes\n", + "Step 1338499 of 4000000; TPS: 60150.5; ETA: 0.7 minutes\n", + "Step 1343999 of 4000000; TPS: 60182.16; ETA: 0.7 minutes\n", + "Step 1349499 of 4000000; TPS: 60218.47; ETA: 0.7 minutes\n", + "Step 1354999 of 4000000; TPS: 60247.3; ETA: 0.7 minutes\n", + "Step 1360499 of 4000000; TPS: 60275.18; ETA: 0.7 minutes\n", + "Step 1365999 of 4000000; TPS: 60301.86; ETA: 0.7 minutes\n", + "Step 1371499 of 4000000; TPS: 60332.61; ETA: 0.7 minutes\n", + "Step 1376999 of 4000000; TPS: 60348.2; ETA: 0.7 minutes\n", + "Step 1382499 of 4000000; TPS: 60379.71; ETA: 0.7 minutes\n", + "Step 1387999 of 4000000; TPS: 60397.31; ETA: 0.7 minutes\n", + "Step 1393499 of 4000000; TPS: 60425.0; ETA: 0.7 minutes\n", + "Step 1398999 of 4000000; TPS: 60453.02; ETA: 0.7 minutes\n", + "Step 1404499 of 4000000; TPS: 60485.88; ETA: 0.7 minutes\n", + "Step 1409999 of 4000000; TPS: 60509.57; ETA: 0.7 minutes\n", + "Step 1415499 of 4000000; TPS: 60546.33; ETA: 0.7 minutes\n", + "Step 1420999 of 4000000; TPS: 60575.24; ETA: 0.7 minutes\n", + "Step 1426499 of 4000000; TPS: 60602.97; ETA: 0.7 minutes\n", + "Step 1431999 of 4000000; TPS: 60633.31; ETA: 0.7 minutes\n", + "Step 1437499 of 4000000; TPS: 60669.74; ETA: 0.7 minutes\n", + "Step 1442999 of 4000000; TPS: 60696.55; ETA: 0.7 minutes\n", + "Step 1448499 of 4000000; TPS: 60728.1; ETA: 0.7 minutes\n", + "Step 1453999 of 4000000; TPS: 60748.54; ETA: 0.7 minutes\n", + "Step 1459499 of 4000000; TPS: 60779.07; ETA: 0.7 minutes\n", + "Step 1464999 of 4000000; TPS: 60812.35; ETA: 0.7 minutes\n", + "Step 1470499 of 4000000; TPS: 60847.57; ETA: 0.7 minutes\n", + "Step 1475999 of 4000000; TPS: 60873.83; ETA: 0.7 minutes\n", + "Step 1481499 of 4000000; TPS: 60900.29; ETA: 0.7 minutes\n", + "Step 1486999 of 4000000; TPS: 60925.01; ETA: 0.7 minutes\n", + "Step 1492499 of 4000000; TPS: 60952.78; ETA: 0.7 minutes\n", + "Step 1497999 of 4000000; TPS: 60977.83; ETA: 0.7 minutes\n", + "Step 1503499 of 4000000; TPS: 61003.74; ETA: 0.7 minutes\n", + "Step 1508999 of 4000000; TPS: 61031.79; ETA: 0.7 minutes\n", + "Step 1514499 of 4000000; TPS: 61058.45; ETA: 0.7 minutes\n", + "Step 1519999 of 4000000; TPS: 61085.45; ETA: 0.7 minutes\n", + "Step 1525499 of 4000000; TPS: 61113.77; ETA: 0.7 minutes\n", + "Step 1530999 of 4000000; TPS: 61138.16; ETA: 0.7 minutes\n", + "Step 1536499 of 4000000; TPS: 61163.77; ETA: 0.7 minutes\n", + "Step 1541999 of 4000000; TPS: 61186.32; ETA: 0.7 minutes\n", + "Step 1547499 of 4000000; TPS: 61208.86; ETA: 0.7 minutes\n", + "Step 1552999 of 4000000; TPS: 61230.46; ETA: 0.7 minutes\n", + "Step 1558499 of 4000000; TPS: 61251.73; ETA: 0.7 minutes\n", + "Step 1563999 of 4000000; TPS: 61277.45; ETA: 0.7 minutes\n", + "Step 1569499 of 4000000; TPS: 61301.68; ETA: 0.7 minutes\n", + "Step 1574999 of 4000000; TPS: 61326.37; ETA: 0.7 minutes\n", + "Step 1580499 of 4000000; TPS: 61345.26; ETA: 0.7 minutes\n", + "Step 1585999 of 4000000; TPS: 61369.7; ETA: 0.7 minutes\n", + "Step 1591499 of 4000000; TPS: 61395.41; ETA: 0.7 minutes\n", + "Step 1596999 of 4000000; TPS: 61401.12; ETA: 0.7 minutes\n", + "Step 1602499 of 4000000; TPS: 61430.12; ETA: 0.7 minutes\n", + "Step 1607999 of 4000000; TPS: 61449.76; ETA: 0.6 minutes\n", + "Step 1613499 of 4000000; TPS: 61474.48; ETA: 0.6 minutes\n", + "Step 1618999 of 4000000; TPS: 61478.52; ETA: 0.6 minutes\n", + "Step 1624499 of 4000000; TPS: 61497.2; ETA: 0.6 minutes\n", + "Step 1629999 of 4000000; TPS: 61520.51; ETA: 0.6 minutes\n", + "Step 1635499 of 4000000; TPS: 61533.87; ETA: 0.6 minutes\n", + "Step 1640999 of 4000000; TPS: 61549.05; ETA: 0.6 minutes\n", + "Step 1646499 of 4000000; TPS: 61569.47; ETA: 0.6 minutes\n", + "Step 1651999 of 4000000; TPS: 61590.01; ETA: 0.6 minutes\n", + "Step 1657499 of 4000000; TPS: 61615.46; ETA: 0.6 minutes\n", + "Step 1662999 of 4000000; TPS: 61639.05; ETA: 0.6 minutes\n", + "Step 1668499 of 4000000; TPS: 61660.51; ETA: 0.6 minutes\n", + "Step 1673999 of 4000000; TPS: 61681.01; ETA: 0.6 minutes\n", + "Step 1679499 of 4000000; TPS: 61707.13; ETA: 0.6 minutes\n", + "Step 1684999 of 4000000; TPS: 61734.83; ETA: 0.6 minutes\n", + "Step 1690499 of 4000000; TPS: 61761.55; ETA: 0.6 minutes\n", + "Step 1695999 of 4000000; TPS: 61788.47; ETA: 0.6 minutes\n", + "Step 1701499 of 4000000; TPS: 61813.98; ETA: 0.6 minutes\n", + "Step 1706999 of 4000000; TPS: 61838.04; ETA: 0.6 minutes\n", + "Step 1712499 of 4000000; TPS: 61864.86; ETA: 0.6 minutes\n", + "Step 1717999 of 4000000; TPS: 61891.29; ETA: 0.6 minutes\n", + "Step 1723499 of 4000000; TPS: 61909.92; ETA: 0.6 minutes\n", + "Step 1728999 of 4000000; TPS: 61926.57; ETA: 0.6 minutes\n", + "Step 1734499 of 4000000; TPS: 61950.44; ETA: 0.6 minutes\n", + "Step 1739999 of 4000000; TPS: 61965.54; ETA: 0.6 minutes\n", + "Step 1745499 of 4000000; TPS: 61988.34; ETA: 0.6 minutes\n", + "Step 1750999 of 4000000; TPS: 62009.47; ETA: 0.6 minutes\n", + "Step 1756499 of 4000000; TPS: 62032.9; ETA: 0.6 minutes\n", + "Step 1761999 of 4000000; TPS: 62057.34; ETA: 0.6 minutes\n", + "Step 1767499 of 4000000; TPS: 62083.33; ETA: 0.6 minutes\n", + "Step 1772999 of 4000000; TPS: 62105.16; ETA: 0.6 minutes\n", + "Step 1778499 of 4000000; TPS: 62125.3; ETA: 0.6 minutes\n", + "Step 1783999 of 4000000; TPS: 62148.55; ETA: 0.6 minutes\n", + "Step 1789499 of 4000000; TPS: 62171.48; ETA: 0.6 minutes\n", + "Step 1794999 of 4000000; TPS: 62190.14; ETA: 0.6 minutes\n", + "Step 1800499 of 4000000; TPS: 62190.88; ETA: 0.6 minutes\n", + "Step 1805999 of 4000000; TPS: 62207.95; ETA: 0.6 minutes\n", + "Step 1811499 of 4000000; TPS: 62224.55; ETA: 0.6 minutes\n", + "Step 1816999 of 4000000; TPS: 62246.4; ETA: 0.6 minutes\n", + "Step 1822499 of 4000000; TPS: 62264.01; ETA: 0.6 minutes\n", + "Step 1827999 of 4000000; TPS: 62285.52; ETA: 0.6 minutes\n", + "Step 1833499 of 4000000; TPS: 62305.59; ETA: 0.6 minutes\n", + "Step 1838999 of 4000000; TPS: 62322.48; ETA: 0.6 minutes\n", + "Step 1844499 of 4000000; TPS: 62346.73; ETA: 0.6 minutes\n", + "Step 1849999 of 4000000; TPS: 62366.63; ETA: 0.6 minutes\n", + "Step 1855499 of 4000000; TPS: 62384.85; ETA: 0.6 minutes\n", + "Step 1860999 of 4000000; TPS: 62400.33; ETA: 0.6 minutes\n", + "Step 1866499 of 4000000; TPS: 62421.43; ETA: 0.6 minutes\n", + "Step 1871999 of 4000000; TPS: 62435.13; ETA: 0.6 minutes\n", + "Step 1877499 of 4000000; TPS: 62456.59; ETA: 0.6 minutes\n", + "Step 1882999 of 4000000; TPS: 62474.21; ETA: 0.6 minutes\n", + "Step 1888499 of 4000000; TPS: 62494.37; ETA: 0.6 minutes\n", + "Step 1893999 of 4000000; TPS: 62510.36; ETA: 0.6 minutes\n", + "Step 1899499 of 4000000; TPS: 62531.03; ETA: 0.6 minutes\n", + "Step 1904999 of 4000000; TPS: 62548.73; ETA: 0.6 minutes\n", + "Step 1910499 of 4000000; TPS: 62564.47; ETA: 0.6 minutes\n", + "Step 1915999 of 4000000; TPS: 62583.88; ETA: 0.6 minutes\n", + "Step 1921499 of 4000000; TPS: 62586.11; ETA: 0.6 minutes\n", + "Step 1926999 of 4000000; TPS: 62602.58; ETA: 0.6 minutes\n", + "Step 1932499 of 4000000; TPS: 62624.69; ETA: 0.6 minutes\n", + "Step 1937999 of 4000000; TPS: 62640.0; ETA: 0.5 minutes\n", + "Step 1943499 of 4000000; TPS: 62658.48; ETA: 0.5 minutes\n", + "Step 1948999 of 4000000; TPS: 62677.51; ETA: 0.5 minutes\n", + "Step 1954499 of 4000000; TPS: 62692.11; ETA: 0.5 minutes\n", + "Step 1959999 of 4000000; TPS: 62708.6; ETA: 0.5 minutes\n", + "Step 1965499 of 4000000; TPS: 62728.55; ETA: 0.5 minutes\n", + "Step 1970999 of 4000000; TPS: 62742.07; ETA: 0.5 minutes\n", + "Step 1976499 of 4000000; TPS: 62758.48; ETA: 0.5 minutes\n", + "Step 1981999 of 4000000; TPS: 62774.42; ETA: 0.5 minutes\n", + "Step 1987499 of 4000000; TPS: 62790.96; ETA: 0.5 minutes\n", + "Step 1992999 of 4000000; TPS: 62810.96; ETA: 0.5 minutes\n", + "Step 1998499 of 4000000; TPS: 62826.31; ETA: 0.5 minutes\n", + "Step 2003999 of 4000000; TPS: 62841.5; ETA: 0.5 minutes\n", + "Step 2009499 of 4000000; TPS: 62861.57; ETA: 0.5 minutes\n", + "Step 2014999 of 4000000; TPS: 62879.09; ETA: 0.5 minutes\n", + "Step 2020499 of 4000000; TPS: 62900.54; ETA: 0.5 minutes\n", + "Step 2025999 of 4000000; TPS: 62919.55; ETA: 0.5 minutes\n", + "Step 2031499 of 4000000; TPS: 62936.47; ETA: 0.5 minutes\n", + "Step 2036999 of 4000000; TPS: 62950.79; ETA: 0.5 minutes\n", + "Step 2042499 of 4000000; TPS: 62965.75; ETA: 0.5 minutes\n", + "Step 2047999 of 4000000; TPS: 62981.64; ETA: 0.5 minutes\n", + "Step 2053499 of 4000000; TPS: 62998.07; ETA: 0.5 minutes\n", + "Step 2058999 of 4000000; TPS: 63014.0; ETA: 0.5 minutes\n", + "Step 2064499 of 4000000; TPS: 63029.69; ETA: 0.5 minutes\n", + "Step 2069999 of 4000000; TPS: 63045.67; ETA: 0.5 minutes\n", + "Step 2075499 of 4000000; TPS: 63062.08; ETA: 0.5 minutes\n", + "Step 2080999 of 4000000; TPS: 63073.45; ETA: 0.5 minutes\n", + "Step 2086499 of 4000000; TPS: 63084.4; ETA: 0.5 minutes\n", + "Step 2091999 of 4000000; TPS: 63103.2; ETA: 0.5 minutes\n", + "Step 2097499 of 4000000; TPS: 63121.0; ETA: 0.5 minutes\n", + "Step 2102999 of 4000000; TPS: 63131.92; ETA: 0.5 minutes\n", + "Step 2108499 of 4000000; TPS: 63137.19; ETA: 0.5 minutes\n", + "Step 2113999 of 4000000; TPS: 63148.57; ETA: 0.5 minutes\n", + "Step 2119499 of 4000000; TPS: 63161.57; ETA: 0.5 minutes\n", + "Step 2124999 of 4000000; TPS: 63175.73; ETA: 0.5 minutes\n", + "Step 2130499 of 4000000; TPS: 63190.29; ETA: 0.5 minutes\n", + "Step 2135999 of 4000000; TPS: 63202.63; ETA: 0.5 minutes\n", + "Step 2141499 of 4000000; TPS: 63217.46; ETA: 0.5 minutes\n", + "Step 2146999 of 4000000; TPS: 63233.49; ETA: 0.5 minutes\n", + "Step 2152499 of 4000000; TPS: 63248.54; ETA: 0.5 minutes\n", + "Step 2157999 of 4000000; TPS: 63263.92; ETA: 0.5 minutes\n", + "Step 2163499 of 4000000; TPS: 63280.5; ETA: 0.5 minutes\n", + "Step 2168999 of 4000000; TPS: 63295.21; ETA: 0.5 minutes\n", + "Step 2174499 of 4000000; TPS: 63312.24; ETA: 0.5 minutes\n", + "Step 2179999 of 4000000; TPS: 63323.81; ETA: 0.5 minutes\n", + "Step 2185499 of 4000000; TPS: 63336.81; ETA: 0.5 minutes\n", + "Step 2190999 of 4000000; TPS: 63347.45; ETA: 0.5 minutes\n", + "Step 2196499 of 4000000; TPS: 63363.09; ETA: 0.5 minutes\n", + "Step 2201999 of 4000000; TPS: 63377.36; ETA: 0.5 minutes\n", + "Step 2207499 of 4000000; TPS: 63396.1; ETA: 0.5 minutes\n", + "Step 2212999 of 4000000; TPS: 63412.4; ETA: 0.5 minutes\n", + "Step 2218499 of 4000000; TPS: 63427.66; ETA: 0.5 minutes\n", + "Step 2223999 of 4000000; TPS: 63441.27; ETA: 0.5 minutes\n", + "Step 2229499 of 4000000; TPS: 63460.19; ETA: 0.5 minutes\n", + "Step 2234999 of 4000000; TPS: 63463.25; ETA: 0.5 minutes\n", + "Step 2240499 of 4000000; TPS: 63478.62; ETA: 0.5 minutes\n", + "Step 2245999 of 4000000; TPS: 63491.11; ETA: 0.5 minutes\n", + "Step 2251499 of 4000000; TPS: 63504.16; ETA: 0.5 minutes\n", + "Step 2256999 of 4000000; TPS: 63514.56; ETA: 0.5 minutes\n", + "Step 2262499 of 4000000; TPS: 63527.89; ETA: 0.5 minutes\n", + "Step 2267999 of 4000000; TPS: 63539.64; ETA: 0.5 minutes\n", + "Step 2273499 of 4000000; TPS: 63555.81; ETA: 0.5 minutes\n", + "Step 2278999 of 4000000; TPS: 63557.47; ETA: 0.5 minutes\n", + "Step 2284499 of 4000000; TPS: 63565.06; ETA: 0.4 minutes\n", + "Step 2289999 of 4000000; TPS: 63576.51; ETA: 0.4 minutes\n", + "Step 2295499 of 4000000; TPS: 63591.51; ETA: 0.4 minutes\n", + "Step 2300999 of 4000000; TPS: 63601.46; ETA: 0.4 minutes\n", + "Step 2306499 of 4000000; TPS: 63610.87; ETA: 0.4 minutes\n", + "Step 2311999 of 4000000; TPS: 63624.37; ETA: 0.4 minutes\n", + "Step 2317499 of 4000000; TPS: 63636.55; ETA: 0.4 minutes\n", + "Step 2322999 of 4000000; TPS: 63648.76; ETA: 0.4 minutes\n", + "Step 2328499 of 4000000; TPS: 63665.7; ETA: 0.4 minutes\n", + "Step 2333999 of 4000000; TPS: 63671.24; ETA: 0.4 minutes\n", + "Step 2339499 of 4000000; TPS: 63684.85; ETA: 0.4 minutes\n", + "Step 2344999 of 4000000; TPS: 63695.33; ETA: 0.4 minutes\n", + "Step 2350499 of 4000000; TPS: 63695.97; ETA: 0.4 minutes\n", + "Step 2355999 of 4000000; TPS: 63709.14; ETA: 0.4 minutes\n", + "Step 2361499 of 4000000; TPS: 63720.1; ETA: 0.4 minutes\n", + "Step 2366999 of 4000000; TPS: 63731.51; ETA: 0.4 minutes\n", + "Step 2372499 of 4000000; TPS: 63741.24; ETA: 0.4 minutes\n", + "Step 2377999 of 4000000; TPS: 63750.43; ETA: 0.4 minutes\n", + "Step 2383499 of 4000000; TPS: 63762.11; ETA: 0.4 minutes\n", + "Step 2388999 of 4000000; TPS: 63767.56; ETA: 0.4 minutes\n", + "Step 2394499 of 4000000; TPS: 63782.8; ETA: 0.4 minutes\n", + "Step 2399999 of 4000000; TPS: 63793.78; ETA: 0.4 minutes\n", + "Step 2405499 of 4000000; TPS: 63807.38; ETA: 0.4 minutes\n", + "Step 2410999 of 4000000; TPS: 63816.35; ETA: 0.4 minutes\n", + "Step 2416499 of 4000000; TPS: 63827.83; ETA: 0.4 minutes\n", + "Step 2421999 of 4000000; TPS: 63838.21; ETA: 0.4 minutes\n", + "Step 2427499 of 4000000; TPS: 63848.24; ETA: 0.4 minutes\n", + "Step 2432999 of 4000000; TPS: 63850.59; ETA: 0.4 minutes\n", + "Step 2438499 of 4000000; TPS: 63855.54; ETA: 0.4 minutes\n", + "Step 2443999 of 4000000; TPS: 63867.3; ETA: 0.4 minutes\n", + "Step 2449499 of 4000000; TPS: 63878.61; ETA: 0.4 minutes\n", + "Step 2454999 of 4000000; TPS: 63887.42; ETA: 0.4 minutes\n", + "Step 2460499 of 4000000; TPS: 63903.85; ETA: 0.4 minutes\n", + "Step 2465999 of 4000000; TPS: 63912.64; ETA: 0.4 minutes\n", + "Step 2471499 of 4000000; TPS: 63919.39; ETA: 0.4 minutes\n", + "Step 2476999 of 4000000; TPS: 63924.99; ETA: 0.4 minutes\n", + "Step 2482499 of 4000000; TPS: 63927.95; ETA: 0.4 minutes\n", + "Step 2487999 of 4000000; TPS: 63941.01; ETA: 0.4 minutes\n", + "Step 2493499 of 4000000; TPS: 63905.21; ETA: 0.4 minutes\n", + "Step 2498999 of 4000000; TPS: 63914.39; ETA: 0.4 minutes\n", + "Step 2504499 of 4000000; TPS: 63918.61; ETA: 0.4 minutes\n", + "Step 2509999 of 4000000; TPS: 63931.55; ETA: 0.4 minutes\n", + "Step 2515499 of 4000000; TPS: 63944.09; ETA: 0.4 minutes\n", + "Step 2520999 of 4000000; TPS: 63957.12; ETA: 0.4 minutes\n", + "Step 2526499 of 4000000; TPS: 63966.18; ETA: 0.4 minutes\n", + "Step 2531999 of 4000000; TPS: 63978.06; ETA: 0.4 minutes\n", + "Step 2537499 of 4000000; TPS: 63992.72; ETA: 0.4 minutes\n", + "Step 2542999 of 4000000; TPS: 63994.09; ETA: 0.4 minutes\n", + "Step 2548499 of 4000000; TPS: 64002.13; ETA: 0.4 minutes\n", + "Step 2553999 of 4000000; TPS: 64012.33; ETA: 0.4 minutes\n", + "Step 2559499 of 4000000; TPS: 64026.44; ETA: 0.4 minutes\n", + "Step 2564999 of 4000000; TPS: 64037.96; ETA: 0.4 minutes\n", + "Step 2570499 of 4000000; TPS: 64049.07; ETA: 0.4 minutes\n", + "Step 2575999 of 4000000; TPS: 64060.17; ETA: 0.4 minutes\n", + "Step 2581499 of 4000000; TPS: 64067.4; ETA: 0.4 minutes\n", + "Step 2586999 of 4000000; TPS: 64079.56; ETA: 0.4 minutes\n", + "Step 2592499 of 4000000; TPS: 64089.1; ETA: 0.4 minutes\n", + "Step 2597999 of 4000000; TPS: 64100.06; ETA: 0.4 minutes\n", + "Step 2603499 of 4000000; TPS: 64108.92; ETA: 0.4 minutes\n", + "Step 2608999 of 4000000; TPS: 64116.55; ETA: 0.4 minutes\n", + "Step 2614499 of 4000000; TPS: 64124.19; ETA: 0.4 minutes\n", + "Step 2619999 of 4000000; TPS: 64133.35; ETA: 0.4 minutes\n", + "Step 2625499 of 4000000; TPS: 64144.63; ETA: 0.4 minutes\n", + "Step 2630999 of 4000000; TPS: 64153.99; ETA: 0.4 minutes\n", + "Step 2636499 of 4000000; TPS: 60501.11; ETA: 0.4 minutes\n", + "Step 2641999 of 4000000; TPS: 60517.21; ETA: 0.4 minutes\n", + "Step 2647499 of 4000000; TPS: 60529.56; ETA: 0.4 minutes\n", + "Step 2652999 of 4000000; TPS: 60544.2; ETA: 0.4 minutes\n", + "Step 2658499 of 4000000; TPS: 60558.63; ETA: 0.4 minutes\n", + "Step 2663999 of 4000000; TPS: 60571.44; ETA: 0.4 minutes\n", + "Step 2669499 of 4000000; TPS: 60586.95; ETA: 0.4 minutes\n", + "Step 2674999 of 4000000; TPS: 60600.89; ETA: 0.4 minutes\n", + "Step 2680499 of 4000000; TPS: 60619.29; ETA: 0.4 minutes\n", + "Step 2685999 of 4000000; TPS: 60635.04; ETA: 0.4 minutes\n", + "Step 2691499 of 4000000; TPS: 60654.83; ETA: 0.4 minutes\n", + "Step 2696999 of 4000000; TPS: 60665.96; ETA: 0.4 minutes\n", + "Step 2702499 of 4000000; TPS: 60678.69; ETA: 0.4 minutes\n", + "Step 2707999 of 4000000; TPS: 60693.88; ETA: 0.4 minutes\n", + "Step 2713499 of 4000000; TPS: 60704.05; ETA: 0.4 minutes\n", + "Step 2718999 of 4000000; TPS: 60720.88; ETA: 0.4 minutes\n", + "Step 2724499 of 4000000; TPS: 60737.4; ETA: 0.4 minutes\n", + "Step 2729999 of 4000000; TPS: 60751.25; ETA: 0.3 minutes\n", + "Step 2735499 of 4000000; TPS: 60768.07; ETA: 0.3 minutes\n", + "Step 2740999 of 4000000; TPS: 60782.87; ETA: 0.3 minutes\n", + "Step 2746499 of 4000000; TPS: 60796.63; ETA: 0.3 minutes\n", + "Step 2751999 of 4000000; TPS: 60810.79; ETA: 0.3 minutes\n", + "Step 2757499 of 4000000; TPS: 60827.52; ETA: 0.3 minutes\n", + "Step 2762999 of 4000000; TPS: 60839.22; ETA: 0.3 minutes\n", + "Step 2768499 of 4000000; TPS: 60853.91; ETA: 0.3 minutes\n", + "Step 2773999 of 4000000; TPS: 60870.7; ETA: 0.3 minutes\n", + "Step 2779499 of 4000000; TPS: 60886.14; ETA: 0.3 minutes\n", + "Step 2784999 of 4000000; TPS: 60901.68; ETA: 0.3 minutes\n", + "Step 2790499 of 4000000; TPS: 60918.35; ETA: 0.3 minutes\n", + "Step 2795999 of 4000000; TPS: 60924.52; ETA: 0.3 minutes\n", + "Step 2801499 of 4000000; TPS: 60929.09; ETA: 0.3 minutes\n", + "Step 2806999 of 4000000; TPS: 60941.58; ETA: 0.3 minutes\n", + "Step 2812499 of 4000000; TPS: 60955.51; ETA: 0.3 minutes\n", + "Step 2817999 of 4000000; TPS: 60971.21; ETA: 0.3 minutes\n", + "Step 2823499 of 4000000; TPS: 60986.07; ETA: 0.3 minutes\n", + "Step 2828999 of 4000000; TPS: 60997.08; ETA: 0.3 minutes\n", + "Step 2834499 of 4000000; TPS: 61012.73; ETA: 0.3 minutes\n", + "Step 2839999 of 4000000; TPS: 61027.16; ETA: 0.3 minutes\n", + "Step 2845499 of 4000000; TPS: 61035.9; ETA: 0.3 minutes\n", + "Step 2850999 of 4000000; TPS: 61049.33; ETA: 0.3 minutes\n", + "Step 2856499 of 4000000; TPS: 61064.77; ETA: 0.3 minutes\n", + "Step 2861999 of 4000000; TPS: 61073.33; ETA: 0.3 minutes\n", + "Step 2867499 of 4000000; TPS: 61088.39; ETA: 0.3 minutes\n", + "Step 2872999 of 4000000; TPS: 61099.51; ETA: 0.3 minutes\n", + "Step 2878499 of 4000000; TPS: 61112.8; ETA: 0.3 minutes\n", + "Step 2883999 of 4000000; TPS: 61126.76; ETA: 0.3 minutes\n", + "Step 2889499 of 4000000; TPS: 61143.4; ETA: 0.3 minutes\n", + "Step 2894999 of 4000000; TPS: 61156.93; ETA: 0.3 minutes\n", + "Step 2900499 of 4000000; TPS: 61166.03; ETA: 0.3 minutes\n", + "Step 2905999 of 4000000; TPS: 61178.68; ETA: 0.3 minutes\n", + "Step 2911499 of 4000000; TPS: 61192.54; ETA: 0.3 minutes\n", + "Step 2916999 of 4000000; TPS: 61207.39; ETA: 0.3 minutes\n", + "Step 2922499 of 4000000; TPS: 61223.81; ETA: 0.3 minutes\n", + "Step 2927999 of 4000000; TPS: 61239.68; ETA: 0.3 minutes\n", + "Step 2933499 of 4000000; TPS: 61256.3; ETA: 0.3 minutes\n", + "Step 2938999 of 4000000; TPS: 61268.05; ETA: 0.3 minutes\n", + "Step 2944499 of 4000000; TPS: 61277.73; ETA: 0.3 minutes\n", + "Step 2949999 of 4000000; TPS: 61288.57; ETA: 0.3 minutes\n", + "Step 2955499 of 4000000; TPS: 61304.92; ETA: 0.3 minutes\n", + "Step 2960999 of 4000000; TPS: 61317.99; ETA: 0.3 minutes\n", + "Step 2966499 of 4000000; TPS: 61333.23; ETA: 0.3 minutes\n", + "Step 2971999 of 4000000; TPS: 61346.25; ETA: 0.3 minutes\n", + "Step 2977499 of 4000000; TPS: 61355.85; ETA: 0.3 minutes\n", + "Step 2982999 of 4000000; TPS: 61367.52; ETA: 0.3 minutes\n", + "Step 2988499 of 4000000; TPS: 61383.41; ETA: 0.3 minutes\n", + "Step 2993999 of 4000000; TPS: 61394.72; ETA: 0.3 minutes\n", + "Step 2999499 of 4000000; TPS: 61408.42; ETA: 0.3 minutes\n", + "Step 3004999 of 4000000; TPS: 61418.62; ETA: 0.3 minutes\n", + "Step 3010499 of 4000000; TPS: 61379.84; ETA: 0.3 minutes\n", + "Step 3015999 of 4000000; TPS: 61391.91; ETA: 0.3 minutes\n", + "Step 3021499 of 4000000; TPS: 61405.06; ETA: 0.3 minutes\n", + "Step 3026999 of 4000000; TPS: 61417.61; ETA: 0.3 minutes\n", + "Step 3032499 of 4000000; TPS: 61431.8; ETA: 0.3 minutes\n", + "Step 3037999 of 4000000; TPS: 61445.71; ETA: 0.3 minutes\n", + "Step 3043499 of 4000000; TPS: 61460.73; ETA: 0.3 minutes\n", + "Step 3048999 of 4000000; TPS: 61474.15; ETA: 0.3 minutes\n", + "Step 3054499 of 4000000; TPS: 61488.7; ETA: 0.3 minutes\n", + "Step 3059999 of 4000000; TPS: 61500.68; ETA: 0.3 minutes\n", + "Step 3065499 of 4000000; TPS: 61513.96; ETA: 0.3 minutes\n", + "Step 3070999 of 4000000; TPS: 61525.6; ETA: 0.3 minutes\n", + "Step 3076499 of 4000000; TPS: 61540.13; ETA: 0.3 minutes\n", + "Step 3081999 of 4000000; TPS: 61550.85; ETA: 0.2 minutes\n", + "Step 3087499 of 4000000; TPS: 61564.27; ETA: 0.2 minutes\n", + "Step 3092999 of 4000000; TPS: 61575.71; ETA: 0.2 minutes\n", + "Step 3098499 of 4000000; TPS: 61589.38; ETA: 0.2 minutes\n", + "Step 3103999 of 4000000; TPS: 61601.23; ETA: 0.2 minutes\n", + "Step 3109499 of 4000000; TPS: 61616.22; ETA: 0.2 minutes\n", + "Step 3114999 of 4000000; TPS: 61628.49; ETA: 0.2 minutes\n", + "Step 3120499 of 4000000; TPS: 61643.49; ETA: 0.2 minutes\n", + "Step 3125999 of 4000000; TPS: 61654.99; ETA: 0.2 minutes\n", + "Step 3131499 of 4000000; TPS: 61666.22; ETA: 0.2 minutes\n", + "Step 3136999 of 4000000; TPS: 61677.62; ETA: 0.2 minutes\n", + "Step 3142499 of 4000000; TPS: 61690.11; ETA: 0.2 minutes\n", + "Step 3147999 of 4000000; TPS: 61701.53; ETA: 0.2 minutes\n", + "Step 3153499 of 4000000; TPS: 61711.95; ETA: 0.2 minutes\n", + "Step 3158999 of 4000000; TPS: 61716.94; ETA: 0.2 minutes\n", + "Step 3164499 of 4000000; TPS: 61729.18; ETA: 0.2 minutes\n", + "Step 3169999 of 4000000; TPS: 61742.22; ETA: 0.2 minutes\n", + "Step 3175499 of 4000000; TPS: 61746.47; ETA: 0.2 minutes\n", + "Step 3180999 of 4000000; TPS: 61759.46; ETA: 0.2 minutes\n", + "Step 3186499 of 4000000; TPS: 61772.44; ETA: 0.2 minutes\n", + "Step 3191999 of 4000000; TPS: 61784.46; ETA: 0.2 minutes\n", + "Step 3197499 of 4000000; TPS: 61797.6; ETA: 0.2 minutes\n", + "Step 3202999 of 4000000; TPS: 61801.21; ETA: 0.2 minutes\n", + "Step 3208499 of 4000000; TPS: 61801.91; ETA: 0.2 minutes\n", + "Step 3213999 of 4000000; TPS: 61809.62; ETA: 0.2 minutes\n", + "Step 3219499 of 4000000; TPS: 61825.17; ETA: 0.2 minutes\n", + "Step 3224999 of 4000000; TPS: 61835.42; ETA: 0.2 minutes\n", + "Step 3230499 of 4000000; TPS: 61847.24; ETA: 0.2 minutes\n", + "Step 3235999 of 4000000; TPS: 61854.88; ETA: 0.2 minutes\n", + "Step 3241499 of 4000000; TPS: 61866.53; ETA: 0.2 minutes\n", + "Step 3246999 of 4000000; TPS: 61880.47; ETA: 0.2 minutes\n", + "Step 3252499 of 4000000; TPS: 61894.49; ETA: 0.2 minutes\n", + 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ETA: 0.2 minutes\n", + "Step 3345999 of 4000000; TPS: 62065.03; ETA: 0.2 minutes\n", + "Step 3351499 of 4000000; TPS: 62076.8; ETA: 0.2 minutes\n", + "Step 3356999 of 4000000; TPS: 62088.98; ETA: 0.2 minutes\n", + "Step 3362499 of 4000000; TPS: 62102.09; ETA: 0.2 minutes\n", + "Step 3367999 of 4000000; TPS: 62114.08; ETA: 0.2 minutes\n", + "Step 3373499 of 4000000; TPS: 62123.67; ETA: 0.2 minutes\n", + "Step 3378999 of 4000000; TPS: 62135.2; ETA: 0.2 minutes\n", + "Step 3384499 of 4000000; TPS: 62146.29; ETA: 0.2 minutes\n", + "Step 3389999 of 4000000; TPS: 62156.69; ETA: 0.2 minutes\n", + "Step 3395499 of 4000000; TPS: 62168.46; ETA: 0.2 minutes\n", + "Step 3400999 of 4000000; TPS: 62180.92; ETA: 0.2 minutes\n", + "Step 3406499 of 4000000; TPS: 62193.63; ETA: 0.2 minutes\n", + "Step 3411999 of 4000000; TPS: 62206.88; ETA: 0.2 minutes\n", + "Step 3417499 of 4000000; TPS: 62218.59; ETA: 0.2 minutes\n", + "Step 3422999 of 4000000; TPS: 62224.61; ETA: 0.2 minutes\n", + "Step 3428499 of 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minutes\n", + "Step 3516499 of 4000000; TPS: 62397.25; ETA: 0.1 minutes\n", + "Step 3521999 of 4000000; TPS: 62404.61; ETA: 0.1 minutes\n", + "Step 3527499 of 4000000; TPS: 62410.96; ETA: 0.1 minutes\n", + "Step 3532999 of 4000000; TPS: 62422.22; ETA: 0.1 minutes\n", + "Step 3538499 of 4000000; TPS: 62429.78; ETA: 0.1 minutes\n", + "Step 3543999 of 4000000; TPS: 62438.39; ETA: 0.1 minutes\n", + "Step 3549499 of 4000000; TPS: 62449.9; ETA: 0.1 minutes\n", + "Step 3554999 of 4000000; TPS: 62460.85; ETA: 0.1 minutes\n", + "Step 3560499 of 4000000; TPS: 62471.64; ETA: 0.1 minutes\n", + "Step 3565999 of 4000000; TPS: 62480.95; ETA: 0.1 minutes\n", + "Step 3571499 of 4000000; TPS: 62491.56; ETA: 0.1 minutes\n", + "Step 3576999 of 4000000; TPS: 62500.01; ETA: 0.1 minutes\n", + "Step 3582499 of 4000000; TPS: 62512.99; ETA: 0.1 minutes\n", + "Step 3587999 of 4000000; TPS: 62522.39; ETA: 0.1 minutes\n", + "Step 3593499 of 4000000; TPS: 62520.22; ETA: 0.1 minutes\n", + "Step 3598999 of 4000000; TPS: 62530.65; ETA: 0.1 minutes\n", + "Step 3604499 of 4000000; TPS: 62539.77; ETA: 0.1 minutes\n", + "Step 3609999 of 4000000; TPS: 62550.71; ETA: 0.1 minutes\n", + "Step 3615499 of 4000000; TPS: 62561.02; ETA: 0.1 minutes\n", + "Step 3620999 of 4000000; TPS: 62563.65; ETA: 0.1 minutes\n", + "Step 3626499 of 4000000; TPS: 62573.96; ETA: 0.1 minutes\n", + "Step 3631999 of 4000000; TPS: 62585.79; ETA: 0.1 minutes\n", + "Step 3637499 of 4000000; TPS: 62594.14; ETA: 0.1 minutes\n", + "Step 3642999 of 4000000; TPS: 62603.28; ETA: 0.1 minutes\n", + "Step 3648499 of 4000000; TPS: 62614.54; ETA: 0.1 minutes\n", + "Step 3653999 of 4000000; TPS: 62624.7; ETA: 0.1 minutes\n", + "Step 3659499 of 4000000; TPS: 62633.54; ETA: 0.1 minutes\n", + "Step 3664999 of 4000000; TPS: 62642.78; ETA: 0.1 minutes\n", + "Step 3670499 of 4000000; TPS: 62651.96; ETA: 0.1 minutes\n", + "Step 3675999 of 4000000; TPS: 62660.54; ETA: 0.1 minutes\n", + "Step 3681499 of 4000000; TPS: 62672.75; ETA: 0.1 minutes\n", + "Step 3686999 of 4000000; TPS: 62684.57; ETA: 0.1 minutes\n", + "Step 3692499 of 4000000; TPS: 62694.64; ETA: 0.1 minutes\n", + "Step 3697999 of 4000000; TPS: 62703.8; ETA: 0.1 minutes\n", + "Step 3703499 of 4000000; TPS: 62705.17; ETA: 0.1 minutes\n", + "Step 3708999 of 4000000; TPS: 62716.88; ETA: 0.1 minutes\n", + "Step 3714499 of 4000000; TPS: 62727.57; ETA: 0.1 minutes\n", + "Step 3719999 of 4000000; TPS: 62735.21; ETA: 0.1 minutes\n", + "Step 3725499 of 4000000; TPS: 62744.52; ETA: 0.1 minutes\n", + "Step 3730999 of 4000000; TPS: 62754.72; ETA: 0.1 minutes\n", + "Step 3736499 of 4000000; TPS: 62763.0; ETA: 0.1 minutes\n", + "Step 3741999 of 4000000; TPS: 62771.01; ETA: 0.1 minutes\n", + "Step 3747499 of 4000000; TPS: 62782.79; ETA: 0.1 minutes\n", + "Step 3752999 of 4000000; TPS: 62792.75; ETA: 0.1 minutes\n", + "Step 3758499 of 4000000; TPS: 62804.69; ETA: 0.1 minutes\n", + "Step 3763999 of 4000000; TPS: 62815.21; ETA: 0.1 minutes\n", + "Step 3769499 of 4000000; TPS: 62827.54; ETA: 0.1 minutes\n", + "Step 3774999 of 4000000; TPS: 62834.82; ETA: 0.1 minutes\n", + "Step 3780499 of 4000000; TPS: 62842.49; ETA: 0.1 minutes\n", + "Step 3785999 of 4000000; TPS: 62851.19; ETA: 0.1 minutes\n", + "Step 3791499 of 4000000; TPS: 62860.09; ETA: 0.1 minutes\n", + "Step 3796999 of 4000000; TPS: 62868.04; ETA: 0.1 minutes\n", + "Step 3802499 of 4000000; TPS: 62872.8; ETA: 0.1 minutes\n", + "Step 3807999 of 4000000; TPS: 62884.48; ETA: 0.1 minutes\n", + "Step 3813499 of 4000000; TPS: 62896.54; ETA: 0.0 minutes\n", + "Step 3818999 of 4000000; TPS: 62908.59; ETA: 0.0 minutes\n", + "Step 3824499 of 4000000; TPS: 62920.16; ETA: 0.0 minutes\n", + "Step 3829999 of 4000000; TPS: 62932.33; ETA: 0.0 minutes\n", + "Step 3835499 of 4000000; TPS: 62942.23; ETA: 0.0 minutes\n", + "Step 3840999 of 4000000; TPS: 62945.63; ETA: 0.0 minutes\n", + "Step 3846499 of 4000000; TPS: 62958.59; ETA: 0.0 minutes\n", + "Step 3851999 of 4000000; TPS: 62966.78; ETA: 0.0 minutes\n", + "Step 3857499 of 4000000; TPS: 62978.62; ETA: 0.0 minutes\n", + "Step 3862999 of 4000000; TPS: 62989.83; ETA: 0.0 minutes\n", + "Step 3868499 of 4000000; TPS: 63000.89; ETA: 0.0 minutes\n", + "Step 3873999 of 4000000; TPS: 63011.1; ETA: 0.0 minutes\n", + "Step 3879499 of 4000000; TPS: 63022.52; ETA: 0.0 minutes\n", + "Step 3884999 of 4000000; TPS: 63033.41; ETA: 0.0 minutes\n", + "Step 3890499 of 4000000; TPS: 63043.6; ETA: 0.0 minutes\n", + "Step 3895999 of 4000000; TPS: 63053.4; ETA: 0.0 minutes\n", + "Step 3901499 of 4000000; TPS: 63058.59; ETA: 0.0 minutes\n", + "Step 3906999 of 4000000; TPS: 63053.91; ETA: 0.0 minutes\n", + "Step 3912499 of 4000000; TPS: 63063.34; ETA: 0.0 minutes\n", + "Step 3917999 of 4000000; TPS: 63065.63; ETA: 0.0 minutes\n", + "Step 3923499 of 4000000; TPS: 63076.52; ETA: 0.0 minutes\n", + "Step 3928999 of 4000000; TPS: 63086.4; ETA: 0.0 minutes\n", + "Step 3934499 of 4000000; TPS: 63094.29; ETA: 0.0 minutes\n", + "Step 3939999 of 4000000; TPS: 63103.2; ETA: 0.0 minutes\n", + "Step 3945499 of 4000000; TPS: 63112.73; ETA: 0.0 minutes\n", + "Step 3950999 of 4000000; TPS: 63121.99; ETA: 0.0 minutes\n", + "Step 3956499 of 4000000; TPS: 63131.33; ETA: 0.0 minutes\n", + "Step 3961999 of 4000000; TPS: 63139.37; ETA: 0.0 minutes\n", + "Step 3967499 of 4000000; TPS: 63147.26; ETA: 0.0 minutes\n", + "Step 3972999 of 4000000; TPS: 63155.54; ETA: 0.0 minutes\n", + "Step 3978499 of 4000000; TPS: 63163.38; ETA: 0.0 minutes\n", + "Step 3983999 of 4000000; TPS: 63171.19; ETA: 0.0 minutes\n", + "Step 3989499 of 4000000; TPS: 63179.47; ETA: 0.0 minutes\n", + "Step 3994999 of 4000000; TPS: 63184.3; ETA: 0.0 minutes\n" ] } ], @@ -1785,35 +1359,83 @@ }, { "cell_type": "code", - "execution_count": 11, - "id": "a8af76ae-efe0-4912-8417-612048485a77", + "execution_count": 12, + "id": "de590eda-bbb3-42a7-a87f-c278705455c8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shrink phase completed in 31.597290515899658 seconds\n", + "Run phase completed in 63.30012035369873 seconds\n", + "Total time: 1.58 minutes\n" + ] + } + ], + "source": [ + "print(f\"Shrink phase completed in {(end_shrink - start_shrink)} seconds\")\n", + "print(f\"Run phase completed in {(end_run - start_run)} seconds\")\n", + "print(f\"Total time: {((end_run - start_run)+(end_shrink - start_shrink))/60:.2f} minutes\")" + ] + }, + { + "cell_type": "markdown", + "id": "d9b22575-1805-4cbc-a310-d6f30aedd81a", + "metadata": {}, + "source": [ + "### Step 8.5: Saving useful simulation stuff" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7de87f9d-da23-46a4-8033-7e5019f9d37c", "metadata": {}, "outputs": [], "source": [ - "!mv \"{log}\" \"{gsd}\" \"{start_file}\" ../analysis/" + "flake_mol = sheet.all_molecules[0]\n", + "beads_per_flake = flake_mol.n_particles\n", + "with open(start_file, \"w\") as f:\n", + " f.write(f\"{sim_start}\\n\")\n", + " f.write(f\"{beads_per_flake}\\n\")\n", + " f.write(f\"{((end_run - start_run)+(end_shrink - start_shrink))}\\n\")" ] }, { "cell_type": "code", - "execution_count": 20, - "id": "de590eda-bbb3-42a7-a87f-c278705455c8", + "execution_count": 14, + "id": "a8af76ae-efe0-4912-8417-612048485a77", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Shrink phase completed in 117.72965431213379 seconds\n", - "Run phase completed in 218.36900329589844 seconds\n", - "Total time: 5.60 minutes\n" + "mv: cannot stat '5_10mer1f_0.0005dt.txt': No such file or directory\n", + "mv: cannot stat '5_10mer1f_0.0005dt.gsd': No such file or directory\n" ] } ], "source": [ - "print(f\"Shrink phase completed in {(end_shrink - start_shrink)} seconds\")\n", - "print(f\"Run phase completed in {(end_run - start_run)} seconds\")\n", - "print(f\"Total time: {((end_run - start_run)+(end_shrink - start_shrink))/60:.2f} minutes\")" + "!mv \"{log}\" \"{gsd}\" \"{start_file}\" ../analysis/" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a03941e8-d760-4de3-b2b7-48e1d33e4c24", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51d8156d-8f75-46c2-822a-e748d11c1325", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { From 271b22cb8bd7459eb366f3931bcb25670805bf69 Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Wed, 26 Nov 2025 14:56:00 -0700 Subject: [PATCH 07/10] oh and adding jupyter to environment --- environment.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/environment.yml b/environment.yml index 07e3821..82a3aaf 100644 --- a/environment.yml +++ b/environment.yml @@ -6,3 +6,4 @@ dependencies: - python=3.12 - flowermd - ovito + - jupyter From d1426d57d8209f773eee564ab3426f395c27650b Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Tue, 9 Dec 2025 21:38:16 -0700 Subject: [PATCH 08/10] borah functionality updated --- analysis/clustering_plots.ipynb | 179 +++-- signac/sim_with_shrink.py | 88 --- signac/simulation.py | 224 ++++++ signac/submit.sh | 2 +- sims/Onboarding.ipynb | 1197 +------------------------------ 5 files changed, 362 insertions(+), 1328 deletions(-) delete mode 100644 signac/sim_with_shrink.py create mode 100644 signac/simulation.py diff --git a/analysis/clustering_plots.ipynb b/analysis/clustering_plots.ipynb index 53efb4b..84e6c45 100644 --- a/analysis/clustering_plots.ipynb +++ b/analysis/clustering_plots.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 1, "id": "2eadee4b-e3fc-4dd6-9709-a92a0d91f5a9", "metadata": {}, "outputs": [], @@ -30,14 +30,14 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 2, "id": "a541891b-9388-4009-9d9b-0e5a126c428d", "metadata": {}, "outputs": [], "source": [ "cutoff = 2.5\n", - "start_file = \"5_10mer1f_0.0005dt_start.txt\"\n", - "traj_file = \"5_10mer1f_0.0005dt.gsd\"\n", + "start_file = \"100_20mer10f_0.0003dt_start.txt\"\n", + "traj_file = \"100_20mer10f_0.0003dt.gsd\"\n", "with open(start_file) as f:\n", " lines = f.read().strip().splitlines()\n", "\n", @@ -48,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 3, "id": "115e7a96-6347-4740-be4b-0606736160dc", "metadata": {}, "outputs": [ @@ -56,7 +56,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "94.89741086959839 seconds\n" + "5982.745808601379 seconds\n" ] } ], @@ -66,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 4, "id": "d07d2bcc-875d-4a60-82d4-2cda752b53ef", "metadata": {}, "outputs": [], @@ -111,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 5, "id": "44a4b5b1-3a0a-499c-a4f8-a2b0fdf2eaaa", "metadata": {}, "outputs": [], @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 6, "id": "1e2a8371-8fc0-44f7-ae7f-c004eb1771a3", "metadata": {}, "outputs": [], @@ -185,7 +185,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 7, "id": "330a659a-e793-441f-8467-ece43451d4a8", "metadata": {}, "outputs": [], @@ -201,13 +201,13 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 8, "id": "7217fadb-1e36-4144-856e-5e0ed85d6a1d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -236,31 +236,10 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 9, "id": "c4c508c3-f7ad-459f-a824-3efa5d91e7b2", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2794/682740719.py:4: RuntimeWarning: invalid value encountered in divide\n", - " acorr /= (len(array) * np.var(array))\n" - ] - }, - { - "ename": "IndexError", - "evalue": "index 0 is out of bounds for axis 0 with size 0", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mIndexError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[31]\u001b[39m\u001b[32m, line 12\u001b[39m\n\u001b[32m 9\u001b[39m acorr = acorr[dt:nsamples] \n\u001b[32m 11\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m nsamples, dt\n\u001b[32m---> \u001b[39m\u001b[32m12\u001b[39m n, dt = \u001b[43mautocorr1D\u001b[49m\u001b[43m(\u001b[49m\u001b[43mavg_sizes\u001b[49m\u001b[43m)\u001b[49m\n", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[31]\u001b[39m\u001b[32m, line 6\u001b[39m, in \u001b[36mautocorr1D\u001b[39m\u001b[34m(array)\u001b[39m\n\u001b[32m 3\u001b[39m acorr = np.fft.irfft(ft * np.conjugate(ft)) \n\u001b[32m 4\u001b[39m acorr /= (\u001b[38;5;28mlen\u001b[39m(array) * np.var(array)) \n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m dt = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m(\u001b[49m\u001b[43macorr\u001b[49m\u001b[43m \u001b[49m\u001b[43m<\u001b[49m\u001b[43m \u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[32;43m0\u001b[39;49m\u001b[43m]\u001b[49m \n\u001b[32m 7\u001b[39m nsamples = \u001b[38;5;28mlen\u001b[39m(array) // dt \n\u001b[32m 9\u001b[39m acorr = acorr[dt:nsamples] \n", - "\u001b[31mIndexError\u001b[39m: index 0 is out of bounds for axis 0 with size 0" - ] - } - ], + "outputs": [], "source": [ "def autocorr1D(array):\n", " ft = np.fft.rfft(array - np.average(array)) \n", @@ -286,19 +265,15 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 10, "id": "791c5fa4-2804-45ca-bbb2-274f7e51a8c7", "metadata": {}, "outputs": [ { - "ename": "NameError", - "evalue": "name 'n' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[31m---------------------------------------------------------------------------\u001b[39m", - "\u001b[31mNameError\u001b[39m Traceback (most recent call last)", - "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[32]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28mprint\u001b[39m(\u001b[33m\"\u001b[39m\u001b[33mIndependent samples and decorrelation time:\u001b[39m\u001b[33m\"\u001b[39m, \u001b[43mn\u001b[49m, dt)\n", - "\u001b[31mNameError\u001b[39m: name 'n' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Independent samples and decorrelation time: 4 60\n" ] } ], @@ -308,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "2f0afd4b-ae71-486b-9900-600aa341a006", "metadata": {}, "outputs": [], @@ -321,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "a94631f6-fcdf-4473-8fa6-29c2737c3fdd", "metadata": {}, "outputs": [], @@ -333,30 +308,67 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "b769806c-5847-44c5-9e90-c439fb83e099", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "84.8528137423857 0.17050751980751333 1.16619037896906\n" + ] + } + ], "source": [ "print(sd_times, sd_sizes, sd_clusters)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "12c865ec-ca92-4738-aa53-d258b776cd29", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], "source": [ "print(len(decorrelated_sizes)) # verifying it is equal to calculated independent samples" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "614f5d74-3842-4289-944d-5134ba76a6e0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Avg Cluster Size')" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(decorrelated_times, decorrelated_sizes) # plot of every independent sample in simulation\n", "plt.title(\"Average Cluster Size (Decorrelated)\")\n", @@ -366,44 +378,79 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "a95d53e0-3cd4-4f94-8b6b-1a1ea37ed17a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.2436507936507937" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(decorrelated_sizes)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "03ca0f50-bbe1-40f8-b70f-8a56826ab11e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "8.2" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "np.mean(decorrelated_clusters)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "06022fb2-060b-48d2-bf13-aa71836a49a4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '# Clusters')" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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aly5dyne/GhkZFfp5A0CNGjUwY8YM1KtX740BVLNmTTx+/FhtDObn5cuX8Pf3R0ZGBqZOnapa3qNHD1y4cAFVq1ZV+zxyIqlr165ISUlR++nNHDknTm/evDnX8h07diA5ObnAE6vfpDj7PCe4/n3SOAD8+OOPedbN72feuXNnGBoa4ubNm/k+PpD9O+fs7Jzv76Y6t27dAvC/E8Sp/OCRJCpzFi5cCG9vbyQkJKBOnTqq5ZIkYciQIVi7di2qVq0KLy8vnDx5Elu3bi2xWRISEvD+++9j1KhRSExMxMyZM2FsbIzAwEDVOj/++CO6du2Kzp07Y/jw4ahYsSKePHmCy5cv4/Tp0/j555/f6rEnT56MjRs3onv37pgzZw7c3NywZ88efP/99xg3bly+55q8ycaNG9GlSxd07doVI0eORNeuXWFjY4O4uDj8/vvv2LZtG06dOpXv1wAMHToUP/74I4YMGYJRo0bh8ePHWLRoESwtLXOtN2rUKJiYmKBFixZwdnZGfHw8FixYACsrK9URj5xvVA8ODoaFhQWMjY3h4eEBOzs7rFixAsOGDcOTJ0/wwQcfwMHBAQ8fPsTZs2fx8OFD/PDDDwU+z3PnzmHixIno27cvqlevDiMjI/z99984d+4cPv/88wK3bdOmDYQQCA8PR6dOnfLcHhsbi7CwMGRlZSExMRFRUVFYu3YtYmJisGTJklzbzJkzBwcPHkTz5s3x8ccfw9PTEykpKbh9+zb+/PNPBAUFoVKlShg4cCDWrVuHsWPH4urVq2jbti2ysrIQHh6OWrVqYcCAAejYsSM6d+6MadOmQalUokWLFqpPtzVs2LBIX1nwOnNz87fe5zVr1kTVqlXx+eefQwgBW1tb/P777zh48GCedevVqwcA+M9//oNhw4ZBLpfD09MT7u7umDNnDr744gvcunULXbp0gY2NDR48eICTJ0/CzMwMs2fPhkwmw9dffw1/f3/V7+azZ88wa9YstW/BAUBYWBjs7OxUj03liBZPGicqln9/uu11gwYNEgByfbpNCCESExOFv7+/cHR0FGZmZqJnz57i9u3b+X667eHDh7m2HzZsmDAzM8vzeK9/ki7nE0CbNm0SH3/8sbC3txcKhUK0atVKREZG5tn+7Nmzol+/fsLBwUHI5XLh5OQk2rVrJ4KCggr1fPMTExMjBg0aJOzs7IRcLheenp5i8eLFqk/M5Sjsp9tyvHz5Uixfvlz4+voKS0tLYWhoKFxcXETv3r3Fnj178uyH1z99tmHDBlGrVi1hbGwsateuLbZv357n020bNmwQbdu2FY6OjsLIyEi4uLiIfv36iXPnzuW6r2XLlgkPDw9hYGCQ59OLR48eFd27dxe2trZCLpeLihUriu7du4uff/5ZtU5+P+sHDx6I4cOHi5o1awozMzNhbm4u6tevL/7v//5PZGRkFLh/MjMzhbu7uxg/fnyu5Tmfbsu5GBgYCBsbG+Ht7S0mTZqk+qTa6x4+fCg+/vhj4eHhIeRyubC1tRXe3t7iiy++EM+fP8/1c/nqq69E9erVhZGRkbCzsxPt2rUTJ06cyLXOtGnThJubm5DL5cLZ2VmMGzdOPH36NNdj5veayPmZ/nsf/lth9rm6T7ddunRJdOzYUVhYWAgbGxvRt29fERsbm+d3UwghAgMDhYuLi5DJZHleX7t37xZt27YVlpaWQqFQCDc3N/HBBx+IQ4cO5bqP1atXq/ZTjRo1xNq1a/O8BoXI/qSdm5tbnk/NUfkgCfHaRymIiKjYlixZgnnz5uHevXtqv6Wa9MNff/2FTp064eLFi6hZs6a2x6FSxkgiIioBKSkpqFWrFiZMmJDvJxdJ97Vt2xbVqlXDqlWrtD0KaQFP3CYiKgHGxsbYtGlTnpORSX88ffoUrVu3zveThVT28UgSERERkRo8kkRERESkBiOJiIiISA1GEhEREZEa/DJJNbKysnD//n1YWFi81R+oJCIiotInhEBSUtIb/45hYTGS1Lh//z5cXV21PQYRERG9hTt37qBSpUrFvh9Gkho5fyvrzp07ef5UAhEREekmpVIJV1fXt/6bl69jJKmR8xabpaUlI4mIiEjPaOpUGZ64TURERKQGI4mIiIhIDUYSERERkRqMJCIiIiI1GElEREREajCSiIiIiNRgJBERERGpwUgiIiIiUoORRERERKQGI4mIiIhIDa1G0rFjx9CzZ0+4uLhAkiTs3r071+1CCMyaNQsuLi4wMTFBmzZtcPHixTfe744dO1C7dm0oFArUrl0bu3btKqFnQERERGWVViMpOTkZXl5eWLlypdrbFy1ahKVLl2LlypWIiIiAk5MTOnbsiKSkpHzvMzQ0FP3798fQoUNx9uxZDB06FP369UN4eHhJPQ0iIiIqgyQhhND2EED2H6PbtWsX3nvvPQDZR5FcXFwwadIkTJs2DQCQmpoKR0dHLFy4EGPGjFF7P/3794dSqcTevXtVy7p06QIbGxts27atULMolUpYWVkhMTFR43/g9mT0E9RwNIe1qZFG75eIiKi80/S/3zp7TlJ0dDTi4+PRqVMn1TKFQoHWrVvjxIkT+W4XGhqaaxsA6Ny5c4HbpKamQqlU5rqUhH9uPMLQNeEYtCocT5PTSuQxiIiISDN0NpLi4+MBAI6OjrmWOzo6qm7Lb7uibrNgwQJYWVmpLq6ursWYPH8OFgpYGBviUpwSg1aH4wlDiYiISGfpbCTlkCQp13UhRJ5lxd0mMDAQiYmJqsudO3fefuACVHe0wLZRPqhgrsDlOCUGrQpjKBEREekonY0kJycnAMhzBCghISHPkaLXtyvqNgqFApaWlrkuJaW6owVCRvvA3kKBK/FJGLQqDI+fp5bY4xEREdHb0dlI8vDwgJOTEw4ePKhalpaWhqNHj6J58+b5bufr65trGwA4cOBAgduUtmoO5tg26t+hFI5HDCUiIiKdotVIev78Oc6cOYMzZ84AyD5Z+8yZM4iNjYUkSZg0aRLmz5+PXbt24cKFCxg+fDhMTU0xaNAg1X34+fkhMDBQdf2TTz7BgQMHsHDhQly5cgULFy7EoUOHMGnSpFJ+dgWr5mCOkNE+cLBQ4OqD7CNKDCUiIiLdodVIioyMRMOGDdGwYUMAQEBAABo2bIivvvoKADB16lRMmjQJ48ePR+PGjXHv3j0cOHAAFhYWqvuIjY1FXFyc6nrz5s0REhKCdevWoX79+li/fj22b9+OZs2ale6TK4Sq9tmh5GipwLUHzzEwOAwPkxhKREREukBnvidJl5Tk9ySpE/0oGQODwxCvTEE1B3NsHdUMDhbGJf64REREZUm5+Z6k8sSjghlCRvvA2coYNxKyjyglJKVoeywiIqJyjZGkI9xfhZKLlTFuPsw+spSgZCgRERFpCyNJh7jZmSFktK8qlAYEh+EBQ4mIiEgrGEk6prKdKUJG+6KitQluvTpXiaFERERU+hhJOig7lHxUoTQgOAzxiQwlIiKi0sRI0lGuttmhVMnGBNGPkjEgOBRxiS+1PRYREVG5wUjSYf8OpduPX2BAcBjuP2MoERERlQZGko6rZGOK7WN84WprgphXoXSPoURERFTiGEl6oKK1CbaP9kVlW1PEPnmBAcGhuPv0hbbHIiIiKtMYSXrCxdoEIaN94GZnijtPXmJAcBhDiYiIqAQxkvRITii525ni7tPsULrzhKFERERUEhhJesbZygQho33hUcGMoURERFSCGEl6yMnKGNtG+aBKBTPce5YdSrGPGUpERESaxEjSU05Wxtg2+t+hFMpQIiIi0iBGkh5ztDRGyGgfVLE3w/3EFPQPDkXM42Rtj0VERFQmMJL0nMOrUKpqb4a4xBT0/zEMtx8xlIiIiIqLkVQGOFgYI2S0L6o5mCNemYIBwWGIZigREREVCyOpjLC3UGDbKB9UV4VSKG49fK7tsYiIiPQWI6kMsbdQYNtoH9RwNMcDZSoGBIfhJkOJiIjorTCSypgK5tlHlDwdLZCQlIqBwWG4kcBQIiIiKipGUhlkZ67A1lHNUNPpVSitCsONhCRtj0VERKRXGEllVHYo+aCmkwUeJqViQHA4rj9gKBERERUWI6kMszUzwrZRPqjlbIlHz7OPKDGUiIiICoeRVMbZmBlhq38z1Ha2xKPnaRgQHIZrDCUiIqI3YiSVAzZmRtg6qhnquFjicXIaBgaH4Wo8Q4mIiKggjKRywtrUCFv8m6FuxVehtCoMV+KV2h6LiIhIZzGSyhFrUyNs+dAH9Spa4cmrI0qX4xhKRERE6jCSyhkrUzk2+zeDVyUrPH2RjkGrwnDpPkOJiIjodYykcsjKRI6NHzaDl6t1diitDsPF+4naHouIiEinMJLKKSsTOTZ92BQNXK3x7EU6Bq0Kx4V7DCUiIqIcjKRyzNJYjo0fNkXDytZIfJmOwasZSkRERDkYSeWcpbEcG0c2RaNXoTRoVRjO32UoERERMZIIFsZybBjZFN5uNlCmZGDw6jCcu/tM22MRERFpFSOJAPwvlBqrQikcZ+880/ZYREREWsNIIhVzhSHWj2yKJu42SErJwJDV4YiKfartsYiIiLSCkUS5mCsMsX5EUzR1t0VSagb81pzEaYYSERGVQ4wkysNMYYh1I5qgmcf/QulUDEOJiIjKF0YSqZUTSj5VbPE8NQPD1p7EqZgn2h6LiIio1DCSKF+mRoZYO7wJfKvY4fmrI0qRtxlKRERUPjCSqEA5odS8qh2S0zIxbO1JRDCUiIioHGAk0RuZGBlgzbAmaFmtgiqUTkYzlIiIqGxjJFGhmBgZYPWwxmhVvQJepGVi+LqTCL/1WNtjERERlRhGEhWasdwAq/z+HUoRCGMoERFRGcVIoiLJCaV3atjjZXomRqyLwImbj7Q9FhERkcYxkqjIjOUGCB7qjdavQmnk+gicuMFQIiKiskXnIykpKQmTJk2Cm5sbTExM0Lx5c0REROS7/pEjRyBJUp7LlStXSnHqss9YboAfh3qjrac9UtKzMHJDBP5hKBERURmi85Hk7++PgwcPYtOmTTh//jw6deqEDh064N69ewVud/XqVcTFxaku1atXL6WJyw9juQGChnqjXU2H7FBaH4Hj1xlKRERUNuh0JL18+RI7duzAokWL8M4776BatWqYNWsWPDw88MMPPxS4rYODA5ycnFQXAwODUpq6fFEYGuCHIY3QvqYDUjOy8OGGCBy79lDbYxERERWbTkdSRkYGMjMzYWxsnGu5iYkJjh8/XuC2DRs2hLOzM9q3b4/Dhw8XuG5qaiqUSmWuCxWewtAA3w9phA61HJGakQX/jZE4ylAiIiI9p9ORZGFhAV9fX3z99de4f/8+MjMzsXnzZoSHhyMuLk7tNs7OzggODsaOHTuwc+dOeHp6on379jh27Fi+j7NgwQJYWVmpLq6uriX1lMoshaEBvh/cCB1rOyItIwujNkbiyNUEbY9FRET01iQhhND2EAW5efMmRo4ciWPHjsHAwACNGjVCjRo1cPr0aVy6dKlQ99GzZ09IkoTffvtN7e2pqalITU1VXVcqlXB1dUViYiIsLS018jzKi7SMLEzcehoHLj2AkYEMP/p5o62ng7bHIiKickCpVMLKykpj/37r9JEkAKhatSqOHj2K58+f486dOzh58iTS09Ph4eFR6Pvw8fHB9evX871doVDA0tIy14XejpGhDN8NboTOdRyRlpmFMRtP4fAVHlEiIiL9o/ORlMPMzAzOzs54+vQp9u/fj169ehV626ioKDg7O5fgdPRvcgMZVg5qhK51nbJDadMp/HX5gbbHIiIiKhKdj6T9+/dj3759iI6OxsGDB9G2bVt4enpixIgRAIDAwED4+fmp1l+2bBl2796N69ev4+LFiwgMDMSOHTswceJEbT2FckluIMPygQ3RrV52KI3dfAqHLjGUiIhIf+h8JCUmJmLChAmoWbMm/Pz80LJlSxw4cAByuRwAEBcXh9jYWNX6aWlpmDJlCurXr49WrVrh+PHj2LNnD3r37q2tp1BuyQ1k+M+AhuhezxnpmQLjtpzCQYYSERHpCZ0/cVsbNH3iV3mXkZmFSdvP4I9zcZAbSFg5qBE613HS9lhERFTGlLsTt0n/GRrIsKx/A/T0ckF6psCELaex70K8tsciIiIqECOJSoWhgQz/188LvRq4ICNLYOLW09h3Qf13XREREekCRhKVGkMDGZb09cJ7r0JpwtYo/HmeoURERLqJkUSlytBAhiX9GqB3w4rIzBL4aFsU9pxjKBERke4x1PYAVP4YyCQs7usFSMDO0/fwcUgUBAR61HfR9mhEREQqjCTSCgOZhMUfeEGChB2n7+KTkDPIEsC7XgwlIiLSDXy7jbTGQCZh0Qf10de7EjKzBCaFROHXM/e0PRYREREAHkkiLTOQSVjYpz4kCfgp8i4mbz8DAOjVoKJ2ByMionKPR5JI62QyCd/0ro8BTVyRJYDJ289gV9RdbY9FRETlHCOJdIJMJmH++/UwsGl2KAX8dBY7TzOUiIhIexhJpDNkMgnz3quHQc0qQwjg05/P4pdTDCUiItIORhLpFJlMwtxedTH4VSh99stZ/Bx5R9tjERFROcRIIp0jk0mY+15dDPVxgxDA1B3n8BNDiYiIShkjiXSSJEmY06sO/HyzQ2najnPYHhGr7bGIiKgcYSSRzpIkCbPfrYPhzd1fhdJ5hJxkKBERUelgJJFOkyQJM3vWxogW7gCAz3eex9ZwhhIREZU8RhLpPEmS8FWP2hjZwgMAMH3XeWwJj9HyVEREVNYxkkgvSJKEL3vUwocts0Ppi10XsCmMoURERCWHkUR6Q5IkzOheC6NaZYfSl7svYFPobe0ORUREZRYjifSKJEmY3q0WxrxTBQDw5a8XseHEbe0ORUREZRIjifSOJEn4vGtNjGmdHUozf7uI9f9Ea3kqIiIqaxhJpJckScLnXWpiXJuqAIBZv1/C2uMMJSIi0hxGEuktSZIwtbMnJrTNDqU5f1zC6v/e0vJURERUVjCSSK9JkoQpnTwxsW01AMDcPZcZSkREpBGMJNJ7kiTh00418HG7/4XSqmMMJSIiKh5GEpUJkiQhoJMnPmlfHQAw78/L+PHoTS1PRURE+oyRRGXK5I41MKlDdigt2HsFPxxhKBER0dthJFGZM6lDDUzuUAMAsHDfFXx/5IaWJyIiIn3ESKIy6ZMO1fFpx+xQWrTvKr47zFAiIqKiYSRRmfVR++qY0ik7lBbvv4oVf13X8kRERKRPGElUpk1sVx2fdfYEACw5eA3LGUpERFRIjCQq8ya0rYZpXWoCAJYevIZlh65peSIiItIHjCQqF8a1qYrPu2aH0rJD1/F/BxlKRERUMEYSlRtjW1fF9G7ZofSfv65j6cFrEEJoeSoiItJVjCQqV0a/UxUzutcCACxnKBERUQEYSVTu+LeqogqlFX/fwLcHrjKUiIgoD0YSlUv+rargyx61AQDfHb6JxfsZSkRElBsjicqtD1t6YGbP7FD6/shNLNzHUCIiov9hJFG5NqKFB2a/WwcAEHT0Jr7Ze4WhREREABhJRBjW3B1zemWH0o/HbmEBQ4mIiMBIIgIA+Pm64+tXoRR87Bbm7bnMUCIiKucYSUSvDPV1x9z36gIAVh+Pxtd/MJSIiMozRhLRvwzxccP89+sBANb+E405f1xiKBERlVOMJKLXDGpWGQt6Z4fSun9uY/bvDCUiovKIkUSkxsCmlbGwTz1IErD+xG3M+u0iQ4mIqJzR+UhKSkrCpEmT4ObmBhMTEzRv3hwREREFbnP06FF4e3vD2NgYVapUQVBQUClNS2VJ/yaVsbB3fUgSsCE0Bl/9ylAiIipPdD6S/P39cfDgQWzatAnnz59Hp06d0KFDB9y7d0/t+tHR0ejWrRtatWqFqKgoTJ8+HR9//DF27NhRypNTWdCviSsW9skOpU1hMfjy1wvIymIoERGVB5LQ4f9r/PLlS1hYWODXX39F9+7dVcsbNGiAHj16YO7cuXm2mTZtGn777TdcvnxZtWzs2LE4e/YsQkNDC/W4SqUSVlZWSExMhKWlZfGfCOm9X07dxWe/nIUQ2ecsze1VFzKZpO2xiIjoXzT977dOH0nKyMhAZmYmjI2Ncy03MTHB8ePH1W4TGhqKTp065VrWuXNnREZGIj09Xe02qampUCqVuS5E//aBdyV8+4EXJAnYGh6LL3bziBIRUVmn05FkYWEBX19ffP3117h//z4yMzOxefNmhIeHIy4uTu028fHxcHR0zLXM0dERGRkZePTokdptFixYACsrK9XF1dVV48+F9F8f70pY2s8LMgnYdjIW03edZygREZVhOh1JALBp0yYIIVCxYkUoFAosX74cgwYNgoGBQb7bSFLut0Fy3lF8fXmOwMBAJCYmqi537tzR3BOgMuX9hpWwtF8DyCQgJOIOAncylIiIyipDbQ/wJlWrVsXRo0eRnJwMpVIJZ2dn9O/fHx4eHmrXd3JyQnx8fK5lCQkJMDQ0hJ2dndptFAoFFAqFxmensum9hhUhScDk7WewPfIOsoTAwj71eY4SEVEZo/NHknKYmZnB2dkZT58+xf79+9GrVy+16/n6+uLgwYO5lh04cACNGzeGXC4vjVGpHOjVoCKWDWgImQT8fOoupu44h0weUSIiKlN0PpL279+Pffv2ITo6GgcPHkTbtm3h6emJESNGAMh+q8zPz0+1/tixYxETE4OAgABcvnwZa9euxZo1azBlyhRtPQUqo971csF/BjSEgUzCL6fuYuovDCUiorJE5yMpMTEREyZMQM2aNeHn54eWLVviwIEDqqNCcXFxiI2NVa3v4eGBP//8E0eOHEGDBg3w9ddfY/ny5ejTp4+2ngKVYT29XLD8VSjtOH0Xn/18lqFERFRG6PT3JGkLvyeJiurP83H4aFsUMrME3m9YEd/29YIBz1EiIipVmv73W+dP3CbSB93qOUMC8NG2KOyKuocsIbCkrxcMDXT+YC0REeWD/wtOpCFd6zlj5aBGMJRJ+PXMfQT8dBYZmVnaHouIiN4SI4lIg7rUdcJ3g7ND6bez9zGZoUREpLcYSUQa1rmOE74f3AhyAwm/n72PT7afYSgREekhRhJRCehUxwk/DPaG3EDCnnNx+CTkDNIZSkREeoWRRFRCOtR2RNAQbxgZyLDnfBw+3hbFUCIi0iOMJKIS1L6WI4KGNoKRgQx7L8Tjo60MJSIifcFIIiph7Wo64seh2UeU9l2Mx8Stp5GWwVAiItJ1jCSiUtC2pgOC/bxhZCjD/osPMIGhRESk8xhJRKWkjacDVvk1hpGhDAcvPcD4LQwlIiJdxkgiKkWta9hjtV9jKAxlOHT5AcZvOYXUjExtj0VERGowkohK2Ts17LF6WE4oJWDc5tMMJSIiHcRIItKCVtXtsXZ4ExjLZfj7SgLGbjqFlHSGEhGRLmEkEWlJi2oVsHZYdigdvvoQYzczlIiIdEmRI2nfvn04fvy46vp3332HBg0aYNCgQXj69KlGhyMq65pXq6A6onTk6kOM4RElIiKdUeRI+uyzz6BUKgEA58+fx6effopu3brh1q1bCAgI0PiARGVd86oVsG54U5jIDXD02kOM2hjJUCIi0gFFjqTo6GjUrl0bALBjxw706NED8+fPx/fff4+9e/dqfECi8sC3qh3WjWgCE7kB/nv9EUOJiEgHFDmSjIyM8OLFCwDAoUOH0KlTJwCAra2t6ggTERWdTxU7rB/RBKZG2aHkvyESL9MYSkRE2lLkSGrRogUCAgLw9ddf4+TJk+jevTsA4Nq1a6hUqZLGByQqT5pVscOGkU1hZmSA4zcewX9jBEOJiEhLihxJ3333HeRyOX755Rf88MMPqFixIgBg79696NKli8YHJCpvmrjbqkLpnxuPMXJ9BF6kZWh7LCKickcSQojCrpyRkYEtW7agU6dOcHZ2Lsm5tEqpVMLKygqJiYmwtLTU9jhUTp2KeYJhayPwPDUDPlVssXZ4E5gaGWp7LCIinaXpf7+LdCTJ0NAQ48aNQ1paWrEfmIgK5u2WfUTJXGGIsFtPMGIdjygREZWmIr/d1qxZM0RFRZXELET0Gm83G2z8sCksFIYIj36C4esikJzKUCIiKg1FPnY/fvx4fPrpp7h79y68vb1hZmaW6/b69etrbDgiAhpVzg4lvzUncTI6+4jSuhFNYKbgW29ERCWpSOckAYBMlvfgkyRJEEJAkiRkZur/J3F4ThLpojN3nmHomnAkpWSgibsN1o3IfiuOiIiyafrf7yJHUkxMTIG3u7m5FWsgXcBIIl119s4zDHkVSt5uNlg/ogksjOXaHouISCdoPZLKA0YS6bJzd59hyOpwKFMy0KiyNTaMbMpQIiKClj/dlmPTpk1o0aIFXFxcVEeWli1bhl9//bXYAxFRwepXssbWUT6wMpHjdOwz+K09CWVKurbHIiIqc4ocST/88AMCAgLQrVs3PHv2THUOkrW1NZYtW6bp+YhIjboVrbDFvxmsTOSIin0GvzUMJSIiTStyJK1YsQKrVq3CF198AQMDA9Xyxo0b4/z58xodjojylxNK1qbyVyd1n0TiS4YSEZGmFDmSoqOj0bBhwzzLFQoFkpOTNTIUERVO3YpW2OrvAxtTOc7eeQa/NeEMJSIiDSlyJHl4eODMmTN5lu/duxe1a9fWxExEVAS1XSyxJSeU7iZi6JpwJL5gKBERFVeRI+mzzz7DhAkTsH37dgghcPLkScybNw/Tp0/HZ599VhIzEtEb1HaxxNZRPrA1M8K5u4kYwlAiIiq2t/oKgFWrVmHu3Lm4c+cOAKBixYqYNWsWPvzwQ40PqA38CgDSV1filRi0KhxPktNQt6IlNn/YDNamRtoei4ioVOjU9yQ9evQIWVlZcHBwKPYguoSRRPrsanwSBq0Kw+PkNNR2tsQW/2awMWMoEVHZp/XvSWrXrh2ePXsGAKhQoYIqkJRKJdq1a1fsgYioeDydLLBttA8qmBvhUpwSg1eH42lymrbHIiLSO0WOpCNHjiAtLe//4KakpOC///2vRoYiouKp4WiBbaN8UMFcgUtxSgxanf0WHBERFV6h/zrmuXPnVP996dIlxMfHq65nZmZi3759qFixomanI6K3Vt3RAiGjm2FAcDguxykxaFUYtvg3g525QtujERHphUKfkySTySBJEgBA3SYmJiZYsWIFRo4cqdkJtYDnJFFZciPhOQauCsPDpFTUdLJgKBFRmaW1E7djYmIghECVKlVw8uRJ2Nvbq24zMjKCg4NDrm/g1meMJCprbj58joHBYUhISoWnowW2jGqGCgwlIipjdOrTbWUVI4nKolsPs48oPVCmorqDObaO8oG9BUOJiMoOrX+6bcOGDdizZ4/q+tSpU2FtbY3mzZsjJiam2AMRUcmoYm+OkNG+cLI0xvWE5xj06i04IiJSr8iRNH/+fJiYmAAAQkNDsXLlSixatAgVKlTA5MmTNT4gEWmORwUzhIz2UYXSwFVhSEhK0fZYREQ6qciRdOfOHVSrVg0AsHv3bnzwwQcYPXo0FixYwK8AINID7q9CydnKOPuk7uAwJCgZSkRErytyJJmbm+Px48cAgAMHDqBDhw4AAGNjY7x8+VKz0xFRicgJJRcrY9x8mIwBqxhKRESvK3IkdezYEf7+/vD398e1a9fQvXt3AMDFixfh7u6u0eEyMjIwY8YMeHh4wMTEBFWqVMGcOXOQlZWV7zZHjhyBJEl5LleuXNHobET6zs3ODCGjfVHR2gS3HiZjQHAYHjCUiIhUihxJ3333HXx9ffHw4UPs2LEDdnZ2AIBTp05h4MCBGh1u4cKFCAoKwsqVK3H58mUsWrQIixcvxooVK9647dWrVxEXF6e6VK9eXaOzEZUFle1METLaJzuUHmWHUnwiQ4mICNDxrwDo0aMHHB0dsWbNGtWyPn36wNTUFJs2bVK7zZEjR9C2bVs8ffoU1tbWb/W4/AoAKm/uPHmBAcFhuPfsJdztTLFttA+crUy0PRYRUZFo+t/vQv9ZkhzHjh0r8PZ33nnnrYd5XcuWLREUFIRr166hRo0aOHv2LI4fP45ly5a9cduGDRsiJSUFtWvXxowZM9C2bdt8101NTUVq6v8+Cq1UKjUxPpHecLU1xfYxPhgQHIbbj7ODadsoH7hYM5SIqPwq8pEkmSzvO3Q5f64EyP47bpoihMD06dOxcOFCGBgYIDMzE/PmzUNgYGC+21y9ehXHjh2Dt7c3UlNTsWnTJgQFBeHIkSP5BtysWbMwe/bsPMt5JInKm7tPX2DgqjDcefISlW2zjyhVZCgRkZ7Q+jduJyYm5rqenp6OqKgofPnll5g3bx7at29f7KFyhISE4LPPPsPixYtRp04dnDlzBpMmTcLSpUsxbNiwQt9Pz549IUkSfvvtN7W3qzuS5Orqykiicunes5cYGByG2Ccv4Gprojq5m4hI12k9kvJz7NgxTJ48GadOndLE3QEAXF1d8fnnn2PChAmqZXPnzsXmzZuL9Gm1efPmYfPmzbh8+XKh1uc5SVTe3X/2EgNXhSHmcXYobRvlg0o2ptoei4ioQFr/syT5sbe3x9WrVzV1dwCAFy9e5Hl7z8DAoMCvAFAnKioKzs7OmhyNqExzsTZByGgfuNmZ4s6TlxgQHIY7T15oeywiolJV5BO3z507l+u6EAJxcXH45ptv4OXlpbHBgOy3yebNm4fKlSujTp06iIqKwtKlSzFy5EjVOoGBgbh37x42btwIAFi2bBnc3d1Rp04dpKWlYfPmzdixYwd27Nih0dmIyjpnKxNsH+2LgavCEP3q6wFCRvvA1ZZHlIiofChyJDVo0ACSJOH1d+l8fHywdu1ajQ0GACtWrMCXX36J8ePHIyEhAS4uLhgzZgy++uor1TpxcXGIjY1VXU9LS8OUKVNw7949mJiYoE6dOtizZw+6deum0dmIygMnK2NsG+XDUCKicqnI5yTFxMTkui6TyWBvbw9jY2ONDqZNPCeJKLcHyhQMDA7DrUfJcLEyRshoX1S2YygRkW7R2RO3yxJGElFeCcoUDFgVhlsPs0Np22gfuNmZaXssIiIVrUTS8uXLC32HH3/8cbEG0gWMJCL1EpKyjyjdfJgM51dvxblXYCgRkW7QSiR5eHgU7s4kCbdu3Sr2UNrGSCLKX0JSCgatCseNhOdwssw+ouTBUCIiHcC320oBI4moYA+TUjFoVRiuJzyHo6UCIaN9GUpEpHVa/Z4kpVKp9juKsrKy+PfOiMoRewsFto32QQ1HczxQpqL/j6G4+fC5tsciItKoQkfSrl270LhxY6SkpOS5LSUlBU2aNMHvv/+u0eGISHdVMFdg6ygfeDpaICEp9dW5SgwlIio7Ch1JP/zwA6ZOnQpT07wf+zU1NcW0adOwcuVKjQ5HRLotO5SaoaZTdigNCA7DjQSGEhGVDYWOpAsXLqBNmzb53v7OO+/g/PnzmpiJiPSI3asjSjWdLPBQFUpJ2h6LiKjYCh1JT58+RUZGRr63p6en4+nTpxoZioj0i62ZEbaO8kEtZ0s8ep4dStcfMJSISL8VOpLc3d0RGRmZ7+2RkZFwc3PTyFBEpH9szYyw1b8Zajtb4tHzNAxcFYZrDCUi0mOFjqTevXvjiy++wIMHD/LcFh8fjxkzZqBPnz4aHY6I9IuNmRG2jmqGOi6vQik4DFfjGUpEpJ8K/T1JSUlJ8PX1RWxsLIYMGQJPT09IkoTLly9jy5YtcHV1RVhYGCwsLEp65hLH70kiKp5nL9IwZE04LtxTvnorrhlqOvF3iYhKlla/TDIxMRGBgYHYvn276vwjGxsb9O/fH/Pnz4e1tXWxB9IFjCSi4kt8kY4ha8Jx/l4ibM2MsMW/GWo58/eJiEqOTnzjthACjx49ghAC9vb2kCSp2IPoEkYSkWYkvkzH0DXhOHc3ETamcmzx90FtF/5OEVHJ0Oo3bueQJAn29vZwcHAoc4FERJpjZSLHpg+bwauSFZ6+SMeg1WG4eD9R22MRERXKW0USEVFhWZnIscm/GRq4WuPZi3QMXh2OC/cYSkSk+xhJRFTiLI3l2PhhUzSszFAiIv3BSCKiUmFpLMfGkU3RqLI1El+mY9CqMJy/y1AiIt3FSCKiUmNhLMeGkU3h7WYDZUoGBq8Ow7m7z7Q9FhGRWm8VSRMnTsSTJ080PQsRlQM5odRYFUrhOHvnmbbHIiLKo9CRdPfuXdV/b926Fc+fZ/+l73r16uHOnTuan4yIyixzhSHWj2yKJu42SErJwJDV4TjDUCIiHVPoSKpZsybc3NwwaNAgpKSkqMLo9u3bSE9PL7EBiahsMlcYYv2Ipmjqbouk1AwMXR2OqFj+kWwi0h2FjqTExET8/PPP8Pb2RlZWFrp164YaNWogNTUV+/fvR3x8fEnOSURlkJnCEOtGNEFTj1ehtOYkTsUwlIhINxT6G7dTUlJgbGwMIPtPkZw6dQpxcXHo0KED6tati0uXLqFSpUq4evVqiQ5cGviN20Sl60VaBkauj0DYrScwVxhiw8gm8Haz1fZYRKRntPaN25aWlmjWrBkCAgKQlpaGFy9eoEWLFjA0NFT9Lbc1a9YUeyAiKn9MjQyxdngT+Faxw/PUDPitOYnI2/xwCBFpV6Ej6f79+5gxYwYUCgUyMjLQuHFjtGrVCmlpaTh9+jQkSULLli1LclYiKsNyQql5VTskp2Vi2NqTiGAoEZEWvdUfuLWxscGxY8dw+fJl+Pn5wcnJCQ8ePEDTpk1x9OjRkpizVPHtNiLteZmWCf+NEfjnxmOYGhlkn9ztwbfeiOjNdOIP3AKAlZUV+vXrB7lcjr///hvR0dEYP358sQciovLNxMgAa4Y1QavqFfAiLRPD151E+K3H2h6LiMqht4qkc+fOoVKlSgAANzc3yOVyODk5oX///hodjojKJ2O5AVb5Nf5XKEUgjKFERKXsrSLJ1dUVMln2phcuXICrq6tGhyIiygmld2rY42V6Jkasi0DoTYYSEZUe/u02ItJZxnIDBA/1RuucUFp/EiduPNL2WERUTjCSiEinGcsN8ONQb7T1tEdKehZGbojAPwwlIioFjCQi0nnGcgMEDfVGu5oO2aG0PgLHrzOUiKhkMZKISC8oDA3ww5BGaF/TAakZWfhwQwT+e/2htsciojKMkUREekNhaIDvhzRCh1o5oRSJo9cYSkRUMhhJRKRXFIYG+H6wNzrWdkRaRhZGbYzEkasJ2h6LiMogRhIR6R0jQxm+G9QInV6F0uhNp3CYoUREGsZIIiK9ZGQow8pBjdC5TnYojdl4CoevMJSISHMYSUSkt3JCqWtdJ6RlZmHMplP46/IDbY9FRGUEI4mI9JrcQIblAxuiW73sUBq7+RQOXWIoEVHxMZKISO/JDWT4z4CG6F7PGemZAuO2nMJBhhIRFRMjiYjKhOxQaoAe9bNDafyWUzhwMV7bYxGRHmMkEVGZYWggw7L+DdDTy+VVKJ3GvgsMJSJ6O4wkIipTDA1k+L9+XnjXywUZWQITt57Gvgtx2h6LiPQQI4mIyhxDAxmW9vPCew2yQ2nC1ijsPc9QIqKi0elIysjIwIwZM+Dh4QETExNUqVIFc+bMQVZWVoHbHT16FN7e3jA2NkaVKlUQFBRUShMTka4wNJBhSb8GeL9hRWRmCUzcFoU95xhKRFR4htoeoCALFy5EUFAQNmzYgDp16iAyMhIjRoyAlZUVPvnkE7XbREdHo1u3bhg1ahQ2b96Mf/75B+PHj4e9vT369OlTys+AiLTJQCbh275ekADsjLqHj0OiICDQo76LtkcjIj2g05EUGhqKXr16oXv37gAAd3d3bNu2DZGRkfluExQUhMqVK2PZsmUAgFq1aiEyMhLffvstI4moHDKQSVjc1wuSJGHH6bv4JOQMhAB6ejGUiKhgOv12W8uWLfHXX3/h2rVrAICzZ8/i+PHj6NatW77bhIaGolOnTrmWde7cGZGRkUhPT1e7TWpqKpRKZa4LEZUdBjIJiz6oj77elZCZJfBJSBR+PXNP22MRkY7T6SNJ06ZNQ2JiImrWrAkDAwNkZmZi3rx5GDhwYL7bxMfHw9HRMdcyR0dHZGRk4NGjR3B2ds6zzYIFCzB79myNz09EusNAJmFhn/qQJOCnyLuYvP0MAKBXg4raHYyIdJZOH0navn07Nm/ejK1bt+L06dPYsGEDvv32W2zYsKHA7SRJynVdCKF2eY7AwEAkJiaqLnfu3NHMEyAinSKTSfimd30MaOKKLAFM3n4Gu6LuanssItJROn0k6bPPPsPnn3+OAQMGAADq1auHmJgYLFiwAMOGDVO7jZOTE+Ljc395XEJCAgwNDWFnZ6d2G4VCAYVCodnhiUgnyWQS5r9fD5IEbDt5B5/+dBZCAL0bVdL2aESkY3T6SNKLFy8gk+Ue0cDAoMCvAPD19cXBgwdzLTtw4AAaN24MuVxeInMSkX6RySTMe68eBjatjCwBfPrzWfxyikeUiCg3nY6knj17Yt68edizZw9u376NXbt2YenSpXj//fdV6wQGBsLPz091fezYsYiJiUFAQAAuX76MtWvXYs2aNZgyZYo2ngIR6ajsUKqLwc0qQwjgs1/O4udIvtVORP+j02+3rVixAl9++SXGjx+PhIQEuLi4YMyYMfjqq69U68TFxSE2NlZ13cPDA3/++ScmT56M7777Di4uLli+fDk//k9EechkEua+VxeSBGwOi8XUHecgAPRr7Krt0YhIB0gi56xmUlEqlbCyskJiYiIsLS21PQ4RlTAhBGb+dhEbQ2MgScA3veuhf5PK2h6LiIpI0/9+6/TbbUREpUGSJMx+tw6GN3eHEMC0HecRcjL2zRsSUZnGSCIiQnYozexZG8ObuwMAPt95HlvDGUpE5RkjiYjolZxQGtHCHQAwfdd5bAmP0e5QRKQ1jCQion+RJAlf9aiND1t6AAC+2HUBm8IYSkTlESOJiOg1kiRhRvdaGNUqO5S+3H0Bm0Jva3coIip1jCQiIjUkScL0brUw+p0qAIAvf72IDSdua3coIipVjCQionxIkoTArjUxpnV2KM387SLW/xOt5amIqLQwkoiICiBJEj7vUhPj2lQFAMz6/RLWHmcoEZUHjCQiojeQJAlTO3ti/KtQmvPHJaz+7y0tT0VEJY2RRERUCJIk4bPOnpjYthoAYO6eywwlojKOkUREVEiSJOHTTjXwcbv/hdKqYwwlorKKkUREVASSJGFyxxr4uH11AMC8Py/jx6M3tTwVEZUERhIRURFJkoSAjjUwqUN2KC3YewVBDCWiMoeRRET0liZ1qIHJHWoAAL7ZewXfH7mh5YmISJMYSURExfBJh+oI6JgdSov2XcV3hxlKRGUFI4mIqJg+bl8dUzplh9Li/Vex8u/rWp6IiDSBkUREpAET21XHZ509AQDfHriG5X8xlIj0HSOJiEhDJrSthqldskNp6cFrWHbompYnIqLiYCQREWnQ+DbV8HnXmgCAZYeu4/8OMpSI9BUjiYhIw8a2rorp3bJD6T9/XcfSg9cghNDyVERUVIwkIqISMPqdqviiWy0AwHKGEpFeYiQREZWQUe9UwYzu2aG04u8bWHKAoUSkTxhJREQlyL9VFXzZozYAYOXhG1i8/ypDiUhPMJKIiErYhy09MLNndih9f+QmFu5jKBHpA0YSEVEpGNHCA7PfrQMACDp6E9/su8JQItJxjCQiolIyrLk75vTKDqUfj97Cgr0MJSJdxkgiIipFfr7u+PpVKAUfu4V5ey4zlIh0FCOJiKiUDfV1x9z36gIAVh+PxlyGEpFOYiQREWnBEB83zH+/HgBgzfFozPnjEkOJSMcwkoiItGRQs8pY0Ds7lNb9cxuzf2coEekSRhIRkRYNbFoZC/vUgyQB60/cxqzfLjKUiHQEI4mISMv6N6mMhb3rQ5KADaEx+OpXhhKRLmAkERHpgH5NXLGwT3YobQqLwZe/XkBWFkOJSJsYSUREOqJfY1cs/sALkgRsDotlKBFpGSOJiEiHfOBdCd++CqUt4bH4YjdDiUhbGElERDqmj3clLO3nBZkEbDsZi+m7zjOUiLSAkUREpIPeb1gJS/s1gEwCQiLuIHAnQ4motDGSiIh01HsNK+L/+meH0vbIO5i24xxDiagUMZKIiHRYrwYVsWxAQ8gk4OdTdzF1xzlkMpSISoWhtgcgIqKCvevlAgnApO1n8MupuxACWPRBfRjIJG2PRlSmMZKIiPRATy8XSBLwScgZ7Dh9F0IILO7rxVAiKkGMJCIiPdGjvgtkkoSPtkVhZ9Q9CADfMpSISgwjiYhIj3Sr5wwJwEfborAr6h6yhMCSvl4wNOAppkSaxt8qIiI907WeM1YOaghDmYRfz9xHwE9nkZGZpe2xiMocRhIRkR7qUtcZ3w1uBEOZhN/O3sdkhhKRxul8JLm7u0OSpDyXCRMmqF3/yJEjate/cuVKKU9ORFSyOtdxwveDG0FuIOH3s/fxyfYzDCUiDdL5c5IiIiKQmZmpun7hwgV07NgRffv2LXC7q1evwtLSUnXd3t6+xGYkItKWTnWc8P1gb4zfcgp7zsUBAlg2oAHkPEeJqNh0PpJej5tvvvkGVatWRevWrQvczsHBAdbW1iU4GRGRbuhY2xFBQ7wxbvNp7DkfhywhsHxgQ4YSUTHp1W9QWloaNm/ejJEjR0KSCv7Ia8OGDeHs7Iz27dvj8OHDBa6bmpoKpVKZ60JEpE/a13JE0NBGMDKQYe+FeHy0NQrpfOuNqFj0KpJ2796NZ8+eYfjw4fmu4+zsjODgYOzYsQM7d+6Ep6cn2rdvj2PHjuW7zYIFC2BlZaW6uLq6lsD0REQlq11NR/w41BtGBjLsuxiPiVtPIy2DoUT0tiQhhN78EaDOnTvDyMgIv//+e5G269mzJyRJwm+//ab29tTUVKSmpqquK5VKuLq6IjExMdd5TURE+uDI1QSM3nQKaRlZ6FjbEd8NagQjQ736/8REb0WpVMLKykpj/37rzW9NTEwMDh06BH9//yJv6+Pjg+vXr+d7u0KhgKWlZa4LEZG+auPpgFV+jWFkKMPBSw8wfguPKBG9Db2JpHXr1sHBwQHdu3cv8rZRUVFwdnYugamIiHRT6xr2WO3XGApDGQ5dfoDxW04hNSPzzRsSkYpeRFJWVhbWrVuHYcOGwdAw9wfyAgMD4efnp7q+bNky7N69G9evX8fFixcRGBiIHTt2YOLEiaU9NhGRVr1Twx6rh+WEUgLGbT7NUCIqAr2IpEOHDiE2NhYjR47Mc1tcXBxiY2NV19PS0jBlyhTUr18frVq1wvHjx7Fnzx707t27NEcmItIJrarbY82wJlAYyvD3lQSM3XQKKekMJaLC0KsTt0uLpk/8IiLStn9uPMKHGyKQkp6FNp72CBriDWO5gbbHItKocnviNhERvb0W1Spg7fAmMJbLcOTqQ4zhESWiN2IkERGVE82rVsC64U1hIjfA0WsPMWpjJEOJqACMJCKicsS3qh3WjWgCE7kB/nv9EUOJqACMJCKicsanih3Wj2gCU6PsUPLfEImXaQwlotcxkoiIyqFmVeywfkRTmBoZ4PiNR/DfGMFQInoNI4mIqJxq6mGLDSObwszIAP/ceIyR6yPwIi1D22MR6QxGEhFROdbE3RYbP2wKc4UhQm8xlIj+jZFERFTOebtlH1EyVxgi7NYTjFjHUCICGElERATA280GGz9sCguFIcKjn2D4uggkpzKUqHxjJBEREQCgUeX/hdLJ6OwjSgwlKs8YSUREpNKwsg02+TeDhbEhTt5+guHrTuI5Q4nKKUYSERHl0sDVGps/zA6liNtPMWztSSSlpGt7LKJSx0giIqI8vFytscW/GSyNDXEqhqFE5RMjiYiI1KpfyRpb/H1gZSLH6dhn8Ft7EkqGEpUjjCQiIspXvUpW2OLfDFYmckTFPoPfGoYSlR+MJCIiKlDditmhZG0qx5k7zzB0zUkkvmQoUdnHSCIiojf6dyidvfMMfmvCGUpU5jGSiIioUOq4WGGrvw9sTOU4ezcRQ9eEI/EFQ4nKLkYSEREVWm0XS2wd5QNbMyOcu5uIIQwlKsMYSUREVCS1nC2xdVQz2JoZ4fy9RAxeE4ZnL9K0PRaRxjGSiIioyGo6WWLbKB/YmRnhwj0lBq0Kx9NkhhKVLYwkIiJ6K55OFtg22gcVzI1wKU6JwasZSlS2MJKIiOit1XC0wLZRPqhgrsClOCUGrQ7HE4YSlRGMJCIiKpbqjhYIGd0MFcwVuBynxKBVYXj8PFXbYxEVGyOJiIiKrZqDBUJG+8DeQoEr8UkYvDqcoUR6j5FEREQaUc3BHCGjfeDwKpQGrQrHI4YS6TFGEhERaUxV++xQcrRU4OqDJAwMDsPDJIYS6SdGEhERaVQVe3OEjPaFk6Uxric8x6BVDCXST4wkIiLSOI8KZggZ7aMKpYGrwpCQlKLtsYiKhJFEREQlwv1VKDlbGeNGwnMMDA5DgpKhRPqDkURERCUmJ5RcrIxx82EyBqxiKJH+YCQREVGJcrMzQ8hoX1S0NsGth8kYEByGBwwl0gOMJCIiKnGV7UwRMtonO5QeZYdSfCJDiXQbI4mIiEqFq+3/Qin6UTIGBIciLvGltsciyhcjiYiISk1OKFWyMcHtxy8wIDgM958xlEg3MZKIiKhU5YSSq60JYl6F0j2GEukgRhIREZW6SjamCBnti8q2poh98gIDgkMZSqRzGElERKQVFa1NEDLaB5VtTXHnyUsMCA7F3acvtD0WkQojiYiItMbF2gTbx/jAzS4nlMJw5wlDiXQDI4mIiLTK2coE20f7wt3OFHefMpRIdzCSiIhI65ysjBEy2hceFcxw7xlDiXQDI4mIiHRCdij5oMqrUOr/YyhiHzOUSHsYSUREpDMcLV+Fkr0Z7iemoH9wKGIeJ2t7LCqnGElERKRTHCyNETLKB1XtzRCXmIIBwWG4/YihRKWPkURERDrHwdIY20b7oJqDuSqUohlKVMp0PpLc3d0hSVKey4QJE/Ld5ujRo/D29oaxsTGqVKmCoKCgUpyYiIg0wcHCGNtG+aC6gznilSkYEByKWw+fa3ssKkd0PpIiIiIQFxenuhw8eBAA0LdvX7XrR0dHo1u3bmjVqhWioqIwffp0fPzxx9ixY0dpjk1ERBpgb6HA1lE+qOFojgfKVAwIDsNNhhKVEkkIIbQ9RFFMmjQJf/zxB65fvw5JkvLcPm3aNPz222+4fPmyatnYsWNx9uxZhIaGFuoxlEolrKyskJiYCEtLS43NTkREb+fR81QMXhWOqw+SYG+hwI9DveFgodD2WFQMRoYyOFgYa/Q+Nf3vt6EGZio1aWlp2Lx5MwICAtQGEgCEhoaiU6dOuZZ17twZa9asQXp6OuRyeZ5tUlNTkZqaqrquVCo1OzgRERVLBXMFto5qhsGrw3ElPgm9vz+h7ZGomBpVtsbO8S20PUaB9CqSdu/ejWfPnmH48OH5rhMfHw9HR8dcyxwdHZGRkYFHjx7B2dk5zzYLFizA7NmzNT0uERFpkJ159ltvH207jcjbT7U9DhWT3EDnz/jRr0has2YNunbtChcXlwLXe/0oU847ivkdfQoMDERAQIDqulKphKurazGnJSIiTbM1M8IWfx9tj0HlhN5EUkxMDA4dOoSdO3cWuJ6TkxPi4+NzLUtISIChoSHs7OzUbqNQKKBQ8L1tIiIi+h/dP9b1yrp16+Dg4IDu3bsXuJ6vr6/qE3A5Dhw4gMaNG6s9H4mIiIhIHb2IpKysLKxbtw7Dhg2DoWHug1+BgYHw8/NTXR87dixiYmIQEBCAy5cvY+3atVizZg2mTJlS2mMTERGRHtOLSDp06BBiY2MxcuTIPLfFxcUhNjZWdd3DwwN//vknjhw5ggYNGuDrr7/G8uXL0adPn9IcmYiIiPSc3n1PUmng9yQRERHpH03/+60XR5KIiIiIShsjiYiIiEgNRhIRERGRGowkIiIiIjUYSURERERqMJKIiIiI1GAkEREREanBSCIiIiJSg5FEREREpIbhm1cpf3K+hFypVGp5EiIiIiqsnH+3NfXHRBhJaiQlJQEAXF1dtTwJERERFVVSUhKsrKyKfT/8221qZGVl4f79+7CwsIAkSRq9b6VSCVdXV9y5c4d/F64Ucb9rB/e7dnC/awf3u3b8e79bWFggKSkJLi4ukMmKf0YRjySpIZPJUKlSpRJ9DEtLS/4SaQH3u3Zwv2sH97t2cL9rR85+18QRpBw8cZuIiIhIDUYSERERkRqMpFKmUCgwc+ZMKBQKbY9SrnC/awf3u3Zwv2sH97t2lOR+54nbRERERGrwSBIRERGRGowkIiIiIjUYSURERERqMJKIiIiI1GAklYBZs2ZBkqRcFycnJ9XtQgjMmjULLi4uMDExQZs2bXDx4kUtTqyfjh07hp49e8LFxQWSJGH37t25bi/Mfk5NTcVHH32EChUqwMzMDO+++y7u3r1bis9C/7xpvw8fPjzP69/HxyfXOtzvRbNgwQI0adIEFhYWcHBwwHvvvYerV6/mWoevd80rzH7n613zfvjhB9SvX1/15ZC+vr7Yu3ev6vbSfK0zkkpInTp1EBcXp7qcP39edduiRYuwdOlSrFy5EhEREXByckLHjh1VfzOOCic5ORleXl5YuXKl2tsLs58nTZqEXbt2ISQkBMePH8fz58/Ro0cPZGZmltbT0Dtv2u8A0KVLl1yv/z///DPX7dzvRXP06FFMmDABYWFhOHjwIDIyMtCpUyckJyer1uHrXfMKs98Bvt41rVKlSvjmm28QGRmJyMhItGvXDr169VKFUKm+1gVp3MyZM4WXl5fa27KysoSTk5P45ptvVMtSUlKElZWVCAoKKqUJyx4AYteuXarrhdnPz549E3K5XISEhKjWuXfvnpDJZGLfvn2lNrs+e32/CyHEsGHDRK9evfLdhvu9+BISEgQAcfToUSEEX++l5fX9LgRf76XFxsZGrF69utRf6zySVEKuX78OFxcXeHh4YMCAAbh16xYAIDo6GvHx8ejUqZNqXYVCgdatW+PEiRPaGrfMKcx+PnXqFNLT03Ot4+Ligrp16/JnUUxHjhyBg4MDatSogVGjRiEhIUF1G/d78SUmJgIAbG1tAfD1Xlpe3+85+HovOZmZmQgJCUFycjJ8fX1L/bXOSCoBzZo1w8aNG7F//36sWrUK8fHxaN68OR4/foz4+HgAgKOjY65tHB0dVbdR8RVmP8fHx8PIyAg2Njb5rkNF17VrV2zZsgV///03lixZgoiICLRr1w6pqakAuN+LSwiBgIAAtGzZEnXr1gXA13tpULffAb7eS8r58+dhbm4OhUKBsWPHYteuXahdu3apv9YNi/EcKB9du3ZV/Xe9evXg6+uLqlWrYsOGDaoT+iRJyrWNECLPMiq+t9nP/FkUT//+/VX/XbduXTRu3Bhubm7Ys2cPevfune923O+FM3HiRJw7dw7Hjx/Pcxtf7yUnv/3O13vJ8PT0xJkzZ/Ds2TPs2LEDw4YNw9GjR1W3l9ZrnUeSSoGZmRnq1auH69evqz7l9nrNJiQk5CljenuF2c9OTk5IS0vD06dP812His/Z2Rlubm64fv06AO734vjoo4/w22+/4fDhw6hUqZJqOV/vJSu//a4OX++aYWRkhGrVqqFx48ZYsGABvLy88J///KfUX+uMpFKQmpqKy5cvw9nZGR4eHnBycsLBgwdVt6elpeHo0aNo3ry5FqcsWwqzn729vSGXy3OtExcXhwsXLvBnoUGPHz/GnTt34OzsDID7/W0IITBx4kTs3LkTf//9Nzw8PHLdztd7yXjTfleHr/eSIYRAampq6b/W3/ZMc8rfp59+Ko4cOSJu3bolwsLCRI8ePYSFhYW4ffu2EEKIb775RlhZWYmdO3eK8+fPi4EDBwpnZ2ehVCq1PLl+SUpKElFRUSIqKkoAEEuXLhVRUVEiJiZGCFG4/Tx27FhRqVIlcejQIXH69GnRrl074eXlJTIyMrT1tHReQfs9KSlJfPrpp+LEiRMiOjpaHD58WPj6+oqKFStyvxfDuHHjhJWVlThy5IiIi4tTXV68eKFah693zXvTfufrvWQEBgaKY8eOiejoaHHu3Dkxffp0IZPJxIEDB4QQpftaZySVgP79+wtnZ2chl8uFi4uL6N27t7h48aLq9qysLDFz5kzh5OQkFAqFeOedd8T58+e1OLF+Onz4sACQ5zJs2DAhROH288uXL8XEiROFra2tMDExET169BCxsbFaeDb6o6D9/uLFC9GpUydhb28v5HK5qFy5shg2bFiefcr9XjTq9jcAsW7dOtU6fL1r3pv2O1/vJWPkyJHCzc1NGBkZCXt7e9G+fXtVIAlRuq91SQghinbsiYiIiKjs4zlJRERERGowkoiIiIjUYCQRERERqcFIIiIiIlKDkURERESkBiOJiIiISA1GEhEREZEajCQiIiIiNRhJRKTzhg8fDkmS8lxu3Lih7dGIqAwz1PYARESF0aVLF6xbty7XMnt7+1zX09LSYGRkVJpjEVEZxiNJRKQXFAoFnJyccl3at2+PiRMnIiAgABUqVEDHjh0BAEuXLkW9evVgZmYGV1dXjB8/Hs+fP1fd1/r162FtbY0//vgDnp6eMDU1xQcffIDk5GRs2LAB7u7usLGxwUcffYTMzEzVdmlpaZg6dSoqVqwIMzMzNGvWDEeOHCntXUFEpYRHkohIr23YsAHjxo3DP//8g5w/RSmTybB8+XK4u7sjOjoa48ePx9SpU/H999+rtnvx4gWWL1+OkJAQJCUloXfv3ujduzesra3x559/4tatW+jTpw9atmyJ/v37AwBGjBiB27dvIyQkBC4uLti1axe6dOmC8+fPo3r16lp5/kRUcvgHbolI5w0fPhybN2+GsbGxalnXrl3x8OFDJCYmIioqqsDtf/75Z4wbNw6PHj0CkH0kacSIEbhx4waqVq0KABg7diw2bdqEBw8ewNzcHED2W3zu7u4ICgrCzZs3Ub16ddy9excuLi6q++7QoQOaNm2K+fPna/ppE5GW8UgSEemFtm3b4ocfflBdNzMzw8CBA9G4ceM86x4+fBjz58/HpUuXoFQqkZGRgZSUFCQnJ8PMzAwAYGpqqgokAHB0dIS7u7sqkHKWJSQkAABOnz4NIQRq1KiR67FSU1NhZ2en0edKRLqBkUREesHMzAzVqlVTu/zfYmJi0K1bN4wdOxZff/01bG1tcfz4cXz44YdIT09XrSeXy3NtJ0mS2mVZWVkAgKysLBgYGODUqVMwMDDItd6/w4qIyg5GEhGVKZGRkcjIyMCSJUsgk2V/NuWnn34q9v02bNgQmZmZSEhIQKtWrYp9f0Sk+/jpNiIqU6pWrYqMjAysWLECt27dwqZNmxAUFFTs+61RowYGDx4MPz8/7Ny5E9HR0YiIiMDChQvx559/amByItI1jCQiKlMaNGiApUuXYuHChahbty62bNmCBQsWaOS+161bBz8/P3z66afw9PTEu+++i/DwcLi6umrk/olIt/DTbURERERq8EgSERERkRqMJCIiIiI1GElEREREajCSiIiIiNRgJBERERGpwUgiIiIiUoORRERERKQGI4mIiIhIDUYSERERkRqMJCIiIiI1GElEREREajCSiIiIiNT4f8i/4SvnFkPTAAAAAElFTkSuQmCC", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plt.plot(decorrelated_times, decorrelated_clusters)\n", "plt.title(\"Number of Clusters (Decorrelated)\")\n", "plt.xlabel(\"Frame\")\n", "plt.ylabel(\"# Clusters\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a1bcf0e5-836a-4a78-b143-ae90eb3aaf43", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/signac/sim_with_shrink.py b/signac/sim_with_shrink.py deleted file mode 100644 index 3b44b15..0000000 --- a/signac/sim_with_shrink.py +++ /dev/null @@ -1,88 +0,0 @@ -import flowermd -import gsd -import gsd.hoomd -import hoomd -import matplotlib.pyplot as plt -import mbuild as mb -import numpy as np -import warnings -from cmeutils.visualize import FresnelGSD -from flowermd.base import Pack, Simulation, System, Molecule -from flowermd.base.forcefield import BaseHOOMDForcefield -from flowermd.library import KremerGrestBeadSpring, LJChain -from flowermd.library.forcefields import BeadSpring -from flowermd.utils import get_target_box_number_density -from mbuild.compound import Compound -from mbuild.lattice import Lattice -import unyt as u -warnings.filterwarnings('ignore') -dt = 0.0005 -N_chains = 200 -initial_dens = 0.0005 -final_dens = 0.3 -cpu = hoomd.device.GPU() -class Graphene(System): - def __init__( - self, - x_repeat, - y_repeat, - n_layers, - base_units=dict(), - periodicity=(True, True, False), - ): - surface = mb.Compound(periodicity=periodicity) - a = 3**.5 - lattice = Lattice( - lattice_spacing=[a,a,a], - lattice_vectors= [[a,0,0],[a/2,3/2,0],[0,0,1]], - lattice_points={"A": [[1/3,1/3,0], [2/3, 2/3, 0]]}, - ) - Flakium = Compound(name="F", element="F") # defines a carbon atom that will be used to populate lattice points - layers = lattice.populate( - compound_dict={"A": Flakium}, x=x_repeat, y=y_repeat, z=n_layers - ) # populates the lattice using the previously defined carbon atom for every "A" site, repeated in all x,y, and z directions - surface.add(layers) # adds populated carbon lattice layers to the 'surface' compound, which represents our graphene structure - surface.freud_generate_bonds("F", "F", dmin=0.9, dmax=1.1) # generates bonds depending on input distance range, scales with lattice - surface_mol = Molecule(num_mols=1, compound=surface) # wraps into a Molecule object, creating "1" instance of this molecule - - super(Graphene, self).__init__( - molecules=[surface_mol], - base_units=base_units, - ) - - def _build_system(self): - return self.all_molecules[0] -#OK, so we want to initialize a system with some chains and some flakes -kg_chain = LJChain(lengths=10,num_mols=N_chains) -sheet = Graphene(x_repeat=5, y_repeat=5, n_layers=1, periodicity=(False, False, False)) -system = Pack(molecules=[Molecule(compound=sheet.all_molecules[0], num_mols=5), kg_chain], - density=initial_dens, packing_expand_factor = 6, seed=2) -target_box = get_target_box_number_density(density=final_dens*u.Unit("nm**-3"),n_beads=(500+(N_chains*10))) - -# this FF is for research question -# WCA = 2.5 **1/6, kT = 3.0, needs thousand chains, maybe 10-12 flakes. large system, need lots of steps. 5e6 probably good. -ff = BeadSpring( - r_cut=2**(1/6), - beads={ - "A": dict(epsilon=1.0, sigma=1.0), # chains - "F": dict(epsilon=1.0, sigma=1.0), # flakes - }, - bonds={ - "F-F": dict(r0=1.0, k=1000), - "A-A": dict(r0=1.0, k=1000.0), # increased k to avoid chain collapse - }, - angles={ - "A-A-A": dict(t0=2* np.pi / 3., k=100.0), # moderate stiffness for chains - "F-F-F": dict(t0=2 * np.pi / 3., k=5000), - }, - dihedrals={ - "A-A-A-A": dict(phi0=0.0, k=0, d=-1, n=2), #need to turn this on later, messed up with straight chains - "F-F-F-F": dict(phi0=0.0, k=500, d=-1, n=2), - } -) -gsd = f"{N_chains}_10mer5f_{dt}dt_5kT_large.gsd" -log = f"{N_chains}_10mer5f_{dt}dt_5kT_large.txt" -sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=cpu, dt = dt, gsd_write_freq=int(15000), log_file_name = log, gsd_file_name = gsd) -sim.run_update_volume(final_box_lengths=target_box, kT=6.0, n_steps=5e6,tau_kt=100*sim.dt,period=10,thermalize_particles=True) -sim.run_NVT(n_steps=1e8, kT=5, tau_kt=dt*100) -sim.flush_writers() diff --git a/signac/simulation.py b/signac/simulation.py new file mode 100644 index 0000000..2440844 --- /dev/null +++ b/signac/simulation.py @@ -0,0 +1,224 @@ +import time +import warnings +import flowermd +import gsd +import gsd.hoomd +import hoomd +import mbuild as mb +import numpy as np +import unyt as u +from flowermd.base import Pack, Simulation, System, Molecule +from flowermd.library import LJChain +from flowermd.library.forcefields import BeadSpring +from flowermd.utils import get_target_box_number_density +from mbuild.compound import Compound +from mbuild.lattice import Lattice +warnings.filterwarnings("ignore") + + +# --------------------------------------------------------------------------- +# Simulation parameters +# --------------------------------------------------------------------------- + +N_chains = 100 # number of polymer chains +initial_dens = 0.001 # initial packing density +final_dens = 0.15 # final packing density after shrink +N_flakes = 10 # number of flakes +chain_length = 20 # chain length +dt = 0.0005 # time step +temp = 1.0 # kT for production run + +write_frequency = 20000 # trajectory/log write frequency +shrink_steps = int(1e6) # steps for shrink phase +sim_start = int(shrink_steps / write_frequency) # for analysis notebook +steps = int(5e6) # steps for production NVT run +flake_repeat = 5 # number of lattice repeats in x,y for flakes + + +# Device: change to hoomd.device.GPU() if desired +device = hoomd.device.GPU() + + +# --------------------------------------------------------------------------- +# Flake geometry +# --------------------------------------------------------------------------- + + +class Flake(System): + def __init__( + self, + x_repeat, + y_repeat, + n_layers, + base_units=dict(), + periodicity=(True, True, False), + ): + surface = mb.Compound(periodicity=periodicity) + a = 3 ** 0.5 + + lattice = Lattice( + lattice_spacing=[a, a, a], + lattice_vectors=[[a, 0, 0], [a / 2, 3 / 2, 0], [0, 0, 1]], + lattice_points={"A": [[1 / 3, 1 / 3, 0], [2 / 3, 2 / 3, 0]]}, + ) # define lattice vectors, points, and spacings for flakes + + Flakium = Compound( + name="F", element="F" + ) # defines an atom that will be used to populate lattice points + + layers = lattice.populate( + compound_dict={"A": Flakium}, + x=x_repeat, + y=y_repeat, + z=n_layers, + ) # populates lattice for every "A" site, repeated in x, y, z + + surface.add( + layers + ) # adds populated flake lattice layers to the 'surface' compound + + surface.freud_generate_bonds( + "F", "F", dmin=0.9, dmax=1.1 + ) # generates bonds based on distance range + + surface_mol = Molecule( + num_mols=1, + compound=surface, + ) # wraps into a Molecule object (one flake) + + super(Flake, self).__init__( + molecules=[surface_mol], + base_units=base_units, + ) + + def _build_system(self): + return self.all_molecules[0] + + +# --------------------------------------------------------------------------- +# Forcefield: Weeks–Chandler–Andersen-like BeadSpring +# --------------------------------------------------------------------------- + +ff = BeadSpring( + r_cut=2 ** (1 / 6), # r_cut defines the radius within which particles interact + beads={ + "A": dict( + epsilon=1.0, + sigma=1.0, + ), # chains, epsilon = well depth, strength of attraction + "F": dict( + epsilon=1.0, + sigma=1.0, + ), # flakes, sigma = distance where PE is zero + }, + bonds={ + "F-F": dict(r0=1.0, k=1000), + "A-A": dict( + r0=1.0, + k=1000.0, + ), # r0 = equilibrium distance, k = stiffness + }, + angles={ + "A-A-A": dict(t0=2 * np.pi / 3.0, k=100.0), + "F-F-F": dict(t0=2 * np.pi / 3.0, k=5000), + }, + dihedrals={ + # do not worry about dihedrals for chains here + "A-A-A-A": dict(phi0=0.0, k=0, d=-1, n=2), + "F-F-F-F": dict(phi0=0.0, k=500, d=-1, n=2), + }, +) + +# --------------------------------------------------------------------------- +# Build system +# --------------------------------------------------------------------------- + +kg_chain = LJChain( + lengths=chain_length, + num_mols=N_chains, +) # polymer chains + +sheet = Flake( + x_repeat=flake_repeat, + y_repeat=flake_repeat, + n_layers=1, + periodicity=(False, False, False), +) # flakes (non-periodic in all directions) + +system = Pack( + molecules=[ + Molecule(compound=sheet.all_molecules[0], num_mols=N_flakes), + kg_chain, + ], + density=initial_dens, + packing_expand_factor=6, + seed=2, +) # pack chains + flakes into initial box + +snapshot = system.hoomd_snapshot # extract bead count +n_beads = snapshot.particles.N + +target_box = get_target_box_number_density( + density=final_dens * u.Unit("nm**-3"), + n_beads=n_beads, +) # final box size for target number density + + +# --------------------------------------------------------------------------- +# Output filenames +# --------------------------------------------------------------------------- + +gsd_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.gsd" +log_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.txt" +start_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt_start.txt" + + +# --------------------------------------------------------------------------- +# Run simulation: shrink -> NVT +# --------------------------------------------------------------------------- + +sim = Simulation( + initial_state=system.hoomd_snapshot, + forcefield=ff.hoomd_forces, + device=device, + dt=dt, + gsd_write_freq=int(write_frequency), + log_file_name=log_file, + gsd_file_name=gsd_file, +) + +start_shrink = time.time() +sim.run_update_volume( + final_box_lengths=target_box, + kT=6.0, + n_steps=shrink_steps, + tau_kt=100 * sim.dt, + period=10, + thermalize_particles=True, +) +end_shrink = time.time() + +start_run = time.time() +sim.run_NVT( + n_steps=steps, + kT=temp, + tau_kt=dt * 100, +) +end_run = time.time() + +sim.flush_writers() +# allow files to fully close +del sim + + +# --------------------------------------------------------------------------- +# Save helpful metadata to a text file +# --------------------------------------------------------------------------- + +flake_mol = sheet.all_molecules[0] +beads_per_flake = flake_mol.n_particles + +with open(start_file, "w") as f: + f.write(f"{sim_start}\n") + f.write(f"{beads_per_flake}\n") + f.write(f"{(end_run - start_run) + (end_shrink - start_shrink)}\n") diff --git a/signac/submit.sh b/signac/submit.sh index 2171050..9c1aa30 100644 --- a/signac/submit.sh +++ b/signac/submit.sh @@ -15,6 +15,6 @@ source ~/miniconda3/etc/profile.d/conda.sh conda activate flowermd-dev # Replace with your environment name # Run the Python script -python sim_with_shrink.py +python simulation.py diff --git a/sims/Onboarding.ipynb b/sims/Onboarding.ipynb index 2f5e524..291963e 100644 --- a/sims/Onboarding.ipynb +++ b/sims/Onboarding.ipynb @@ -18,19 +18,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "8c3308c6-8bc7-4368-bbf6-5c53489c0a18", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/eridanirojas/miniconda3/envs/flowermd-dev/lib/python3.12/site-packages/gmso/core/element.py:10: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n", - " from pkg_resources import resource_filename\n" - ] - } - ], + "outputs": [], "source": [ "import flowermd # wrapper we use on top of hoomd\n", "import time # for measuring simulation time\n", @@ -60,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "2dce7cdb-61d5-4f40-b1d5-2b69849f7d10", "metadata": {}, "outputs": [], @@ -108,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "4bca3b72-54b5-4e7b-bf1b-4d3b41050ddb", "metadata": {}, "outputs": [], @@ -144,23 +135,23 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "f74ebf6d-79e1-488f-8921-a188ebe92673", "metadata": {}, "outputs": [], "source": [ - "N_chains = 5 # number of polymer chains\n", + "N_chains = 100 # number of polymer chains\n", "initial_dens = 0.001 # initial packing density to initialize system\n", - "final_dens = 0.3 # final packing density for shrinking\n", - "N_flakes = 1 # number of flakes\n", - "chain_length = 10 # length of polymer chains\n", + "final_dens = 0.15 # final packing density for shrinking\n", + "N_flakes = 10 # number of flakes\n", + "chain_length = 20 # length of polymer chains\n", "dt = 0.0005 # step size of simulation\n", - "temp = 0.6 # kT, temperature of simulation\n", - "write_frequency = 10000 # write frequency of simulation, the lower the more frames captured\n", - "shrink_steps = 2e6 # amount of steps to run shrink\n", + "temp = 1.0 # kT, temperature of simulation\n", + "write_frequency = 20000 # write frequency of simulation, the lower the more frames captured\n", + "shrink_steps = 1e6 # amount of steps to run shrink\n", "sim_start = int(shrink_steps/write_frequency) # for use in analysis notebook, post-shrink simulation starting point\n", - "steps = 4e6 # amount of steps to run simulation for\n", - "flake_repeat = 4 # amount of repeated rows flake has of particles" + "steps = 5e6 # amount of steps to run simulation for\n", + "flake_repeat = 5 # amount of repeated rows flake has of particles" ] }, { @@ -173,7 +164,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "b06e0517-6747-4eff-a7db-ac8ab0c99b48", "metadata": {}, "outputs": [], @@ -191,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "21378af5-3fba-491f-9360-c069360d46b5", "metadata": {}, "outputs": [], @@ -215,7 +206,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "00a6af7b-f424-4a27-a8be-f80a20c8ca03", "metadata": {}, "outputs": [], @@ -235,1115 +226,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "64f9fae9-a3b9-418e-a03b-681b754e1b03", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "*Warning*: dihedral.harmonic: specified K <= 0\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Initializing simulation state from a gsd.hoomd.Frame.\n", - "Step 5500 of 2000000; TPS: 21335.61; ETA: 1.6 minutes\n", - "Step 11000 of 2000000; TPS: 32820.34; ETA: 1.0 minutes\n", - "Step 16500 of 2000000; TPS: 40080.94; ETA: 0.8 minutes\n", - "Step 22000 of 2000000; TPS: 44614.25; ETA: 0.7 minutes\n", - "Step 27500 of 2000000; TPS: 47982.72; ETA: 0.7 minutes\n", - "Step 33000 of 2000000; TPS: 50260.21; ETA: 0.7 minutes\n", - "Step 38500 of 2000000; TPS: 52491.0; ETA: 0.6 minutes\n", - "Step 44000 of 2000000; TPS: 54190.6; ETA: 0.6 minutes\n", - "Step 49500 of 2000000; TPS: 55246.83; ETA: 0.6 minutes\n", - "Step 55000 of 2000000; TPS: 56408.29; ETA: 0.6 minutes\n", - "Step 60500 of 2000000; TPS: 57534.83; ETA: 0.6 minutes\n", - "Step 66000 of 2000000; TPS: 58426.06; ETA: 0.6 minutes\n", - "Step 71500 of 2000000; TPS: 59293.98; ETA: 0.5 minutes\n", - "Step 77000 of 2000000; TPS: 60035.71; ETA: 0.5 minutes\n", - "Step 82500 of 2000000; TPS: 60679.61; ETA: 0.5 minutes\n", - 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0.6 minutes\n", - "Step 352000 of 2000000; TPS: 45510.58; ETA: 0.6 minutes\n", - "Step 357500 of 2000000; TPS: 45757.82; ETA: 0.6 minutes\n", - "Step 363000 of 2000000; TPS: 45966.23; ETA: 0.6 minutes\n", - "Step 368500 of 2000000; TPS: 46197.44; ETA: 0.6 minutes\n", - "Step 374000 of 2000000; TPS: 46425.73; ETA: 0.6 minutes\n", - "Step 379500 of 2000000; TPS: 46659.56; ETA: 0.6 minutes\n", - "Step 385000 of 2000000; TPS: 46883.55; ETA: 0.6 minutes\n", - "Step 390500 of 2000000; TPS: 47093.15; ETA: 0.6 minutes\n", - "Step 396000 of 2000000; TPS: 47283.28; ETA: 0.6 minutes\n", - "Step 401500 of 2000000; TPS: 47485.13; ETA: 0.6 minutes\n", - "Step 407000 of 2000000; TPS: 47696.76; ETA: 0.6 minutes\n", - "Step 412500 of 2000000; TPS: 47910.42; ETA: 0.6 minutes\n", - "Step 418000 of 2000000; TPS: 48064.66; ETA: 0.5 minutes\n", - "Step 423500 of 2000000; TPS: 48247.19; ETA: 0.5 minutes\n", - "Step 429000 of 2000000; TPS: 48415.5; ETA: 0.5 minutes\n", - "Step 434500 of 2000000; TPS: 48594.31; ETA: 0.5 minutes\n", - "Step 440000 of 2000000; TPS: 48765.66; ETA: 0.5 minutes\n", - "Step 445500 of 2000000; TPS: 48952.47; ETA: 0.5 minutes\n", - "Step 451000 of 2000000; TPS: 49131.56; ETA: 0.5 minutes\n", - "Step 456500 of 2000000; TPS: 49312.56; ETA: 0.5 minutes\n", - "Step 462000 of 2000000; TPS: 49469.0; ETA: 0.5 minutes\n", - "Step 467500 of 2000000; TPS: 49637.63; ETA: 0.5 minutes\n", - "Step 473000 of 2000000; TPS: 49790.4; ETA: 0.5 minutes\n", - "Step 478500 of 2000000; TPS: 49949.81; ETA: 0.5 minutes\n", - "Step 484000 of 2000000; TPS: 50088.79; ETA: 0.5 minutes\n", - "Step 489500 of 2000000; TPS: 50244.33; ETA: 0.5 minutes\n", - "Step 495000 of 2000000; TPS: 50381.41; ETA: 0.5 minutes\n", - "Step 500500 of 2000000; TPS: 50451.73; ETA: 0.5 minutes\n", - "Step 506000 of 2000000; TPS: 50596.07; ETA: 0.5 minutes\n", - "Step 511500 of 2000000; TPS: 50733.99; ETA: 0.5 minutes\n", - "Step 517000 of 2000000; TPS: 50871.44; ETA: 0.5 minutes\n", - "Step 522500 of 2000000; TPS: 51015.68; ETA: 0.5 minutes\n", - "Step 528000 of 2000000; TPS: 51150.61; ETA: 0.5 minutes\n", - "Step 533500 of 2000000; TPS: 51264.9; ETA: 0.5 minutes\n", - "Step 539000 of 2000000; TPS: 51397.79; ETA: 0.5 minutes\n", - "Step 544500 of 2000000; TPS: 51533.12; ETA: 0.5 minutes\n", - "Step 550000 of 2000000; TPS: 51631.37; ETA: 0.5 minutes\n", - "Step 555500 of 2000000; TPS: 51746.42; ETA: 0.5 minutes\n", - "Step 561000 of 2000000; TPS: 51874.14; ETA: 0.5 minutes\n", - "Step 566500 of 2000000; TPS: 52008.32; ETA: 0.5 minutes\n", - "Step 572000 of 2000000; TPS: 52136.53; ETA: 0.5 minutes\n", - "Step 577500 of 2000000; TPS: 52256.8; ETA: 0.5 minutes\n", - "Step 583000 of 2000000; TPS: 52374.83; ETA: 0.5 minutes\n", - "Step 588500 of 2000000; TPS: 52482.88; ETA: 0.4 minutes\n", - "Step 594000 of 2000000; TPS: 52597.34; ETA: 0.4 minutes\n", - "Step 599500 of 2000000; TPS: 52727.07; ETA: 0.4 minutes\n", - "Step 605000 of 2000000; TPS: 52845.17; ETA: 0.4 minutes\n", - "Step 610500 of 2000000; TPS: 52963.67; ETA: 0.4 minutes\n", - "Step 616000 of 2000000; TPS: 53069.33; ETA: 0.4 minutes\n", - "Step 621500 of 2000000; TPS: 53187.94; ETA: 0.4 minutes\n", - "Step 627000 of 2000000; TPS: 53277.27; ETA: 0.4 minutes\n", - "Step 632500 of 2000000; TPS: 53378.78; ETA: 0.4 minutes\n", - "Step 638000 of 2000000; TPS: 53477.45; ETA: 0.4 minutes\n", - "Step 643500 of 2000000; TPS: 53554.81; ETA: 0.4 minutes\n", - "Step 649000 of 2000000; TPS: 53659.7; ETA: 0.4 minutes\n", - "Step 654500 of 2000000; TPS: 53765.4; ETA: 0.4 minutes\n", - "Step 660000 of 2000000; TPS: 53866.12; ETA: 0.4 minutes\n", - "Step 665500 of 2000000; TPS: 53964.93; ETA: 0.4 minutes\n", - "Step 671000 of 2000000; TPS: 54058.79; ETA: 0.4 minutes\n", - "Step 676500 of 2000000; TPS: 54161.6; ETA: 0.4 minutes\n", - "Step 682000 of 2000000; TPS: 54261.35; ETA: 0.4 minutes\n", - "Step 687500 of 2000000; TPS: 54364.37; ETA: 0.4 minutes\n", - "Step 693000 of 2000000; TPS: 54459.77; ETA: 0.4 minutes\n", - "Step 698500 of 2000000; TPS: 54551.59; ETA: 0.4 minutes\n", - "Step 704000 of 2000000; TPS: 54648.59; ETA: 0.4 minutes\n", - "Step 709500 of 2000000; TPS: 54725.08; ETA: 0.4 minutes\n", - "Step 715000 of 2000000; TPS: 54816.34; ETA: 0.4 minutes\n", - "Step 720500 of 2000000; TPS: 54904.32; ETA: 0.4 minutes\n", - "Step 726000 of 2000000; TPS: 54980.96; ETA: 0.4 minutes\n", - "Step 731500 of 2000000; TPS: 55053.88; ETA: 0.4 minutes\n", - "Step 737000 of 2000000; TPS: 55142.17; ETA: 0.4 minutes\n", - "Step 742500 of 2000000; TPS: 55231.65; ETA: 0.4 minutes\n", - "Step 748000 of 2000000; TPS: 55313.59; ETA: 0.4 minutes\n", - "Step 753500 of 2000000; TPS: 55381.99; ETA: 0.4 minutes\n", - "Step 759000 of 2000000; TPS: 55471.05; ETA: 0.4 minutes\n", - "Step 764500 of 2000000; TPS: 55553.42; ETA: 0.4 minutes\n", - "Step 770000 of 2000000; TPS: 55641.7; ETA: 0.4 minutes\n", - "Step 775500 of 2000000; TPS: 55726.33; ETA: 0.4 minutes\n", - "Step 781000 of 2000000; TPS: 55805.01; ETA: 0.4 minutes\n", - "Step 786500 of 2000000; TPS: 55881.68; ETA: 0.4 minutes\n", - "Step 792000 of 2000000; TPS: 55964.09; ETA: 0.4 minutes\n", - "Step 797500 of 2000000; TPS: 56043.63; ETA: 0.4 minutes\n", - "Step 803000 of 2000000; TPS: 56121.04; ETA: 0.4 minutes\n", - "Step 808500 of 2000000; TPS: 56190.53; ETA: 0.4 minutes\n", - "Step 814000 of 2000000; TPS: 56266.31; ETA: 0.4 minutes\n", - "Step 819500 of 2000000; TPS: 56333.42; ETA: 0.3 minutes\n", - "Step 825000 of 2000000; TPS: 56402.73; ETA: 0.3 minutes\n", - "Step 830500 of 2000000; TPS: 56456.86; ETA: 0.3 minutes\n", - "Step 836000 of 2000000; TPS: 56524.51; ETA: 0.3 minutes\n", - "Step 841500 of 2000000; TPS: 56596.07; ETA: 0.3 minutes\n", - "Step 847000 of 2000000; TPS: 56669.16; ETA: 0.3 minutes\n", - "Step 852500 of 2000000; TPS: 56728.97; ETA: 0.3 minutes\n", - "Step 858000 of 2000000; TPS: 56804.7; ETA: 0.3 minutes\n", - "Step 863500 of 2000000; TPS: 56854.54; ETA: 0.3 minutes\n", - "Step 869000 of 2000000; TPS: 56917.56; ETA: 0.3 minutes\n", - "Step 874500 of 2000000; TPS: 56982.09; ETA: 0.3 minutes\n", - "Step 880000 of 2000000; TPS: 57046.77; ETA: 0.3 minutes\n", - "Step 885500 of 2000000; TPS: 57106.57; ETA: 0.3 minutes\n", - "Step 891000 of 2000000; TPS: 57171.55; ETA: 0.3 minutes\n", - "Step 896500 of 2000000; TPS: 57236.61; ETA: 0.3 minutes\n", - "Step 902000 of 2000000; TPS: 57297.14; ETA: 0.3 minutes\n", - "Step 907500 of 2000000; TPS: 57367.02; ETA: 0.3 minutes\n", - "Step 913000 of 2000000; TPS: 57428.76; ETA: 0.3 minutes\n", - "Step 918500 of 2000000; TPS: 57484.37; ETA: 0.3 minutes\n", - "Step 924000 of 2000000; TPS: 57550.73; ETA: 0.3 minutes\n", - "Step 929500 of 2000000; TPS: 57612.66; ETA: 0.3 minutes\n", - "Step 935000 of 2000000; TPS: 57643.65; ETA: 0.3 minutes\n", - "Step 940500 of 2000000; TPS: 57706.68; ETA: 0.3 minutes\n", - "Step 946000 of 2000000; TPS: 57755.46; ETA: 0.3 minutes\n", - "Step 951500 of 2000000; TPS: 57819.81; ETA: 0.3 minutes\n", - "Step 957000 of 2000000; TPS: 57885.04; ETA: 0.3 minutes\n", - "Step 962500 of 2000000; TPS: 57944.9; ETA: 0.3 minutes\n", - "Step 968000 of 2000000; TPS: 58007.33; ETA: 0.3 minutes\n", - "Step 973500 of 2000000; TPS: 58065.27; ETA: 0.3 minutes\n", - "Step 979000 of 2000000; TPS: 58122.16; ETA: 0.3 minutes\n", - "Step 984500 of 2000000; TPS: 58181.94; ETA: 0.3 minutes\n", - "Step 990000 of 2000000; TPS: 58247.41; ETA: 0.3 minutes\n", - "Step 995500 of 2000000; TPS: 58305.42; ETA: 0.3 minutes\n", - "Step 1001000 of 2000000; TPS: 58366.93; ETA: 0.3 minutes\n", - "Step 1006500 of 2000000; TPS: 58425.34; ETA: 0.3 minutes\n", - "Step 1012000 of 2000000; TPS: 58459.79; ETA: 0.3 minutes\n", - "Step 1017500 of 2000000; TPS: 58516.88; ETA: 0.3 minutes\n", - "Step 1023000 of 2000000; TPS: 58575.44; ETA: 0.3 minutes\n", - "Step 1028500 of 2000000; TPS: 58630.27; ETA: 0.3 minutes\n", - "Step 1034000 of 2000000; TPS: 58678.82; ETA: 0.3 minutes\n", - "Step 1039500 of 2000000; TPS: 58739.35; ETA: 0.3 minutes\n", - "Step 1045000 of 2000000; TPS: 58788.37; ETA: 0.3 minutes\n", - "Step 1050500 of 2000000; TPS: 58843.8; ETA: 0.3 minutes\n", - "Step 1056000 of 2000000; TPS: 58888.69; ETA: 0.3 minutes\n", - "Step 1061500 of 2000000; TPS: 58938.63; ETA: 0.3 minutes\n", - "Step 1067000 of 2000000; TPS: 58983.47; ETA: 0.3 minutes\n", - "Step 1072500 of 2000000; TPS: 59037.1; ETA: 0.3 minutes\n", - "Step 1078000 of 2000000; TPS: 59080.8; ETA: 0.3 minutes\n", - "Step 1083500 of 2000000; TPS: 59113.35; ETA: 0.3 minutes\n", - "Step 1089000 of 2000000; TPS: 59153.53; ETA: 0.3 minutes\n", - "Step 1094500 of 2000000; TPS: 59202.74; ETA: 0.3 minutes\n", - "Step 1100000 of 2000000; TPS: 59245.58; ETA: 0.3 minutes\n", - "Step 1105500 of 2000000; TPS: 59291.21; ETA: 0.3 minutes\n", - "Step 1111000 of 2000000; TPS: 59330.87; ETA: 0.2 minutes\n", - "Step 1116500 of 2000000; TPS: 59382.84; ETA: 0.2 minutes\n", - "Step 1122000 of 2000000; TPS: 59426.71; ETA: 0.2 minutes\n", - "Step 1127500 of 2000000; TPS: 59472.92; ETA: 0.2 minutes\n", - "Step 1133000 of 2000000; TPS: 59514.21; ETA: 0.2 minutes\n", - "Step 1138500 of 2000000; TPS: 59559.46; ETA: 0.2 minutes\n", - "Step 1144000 of 2000000; TPS: 59600.55; ETA: 0.2 minutes\n", - "Step 1149500 of 2000000; TPS: 59650.78; ETA: 0.2 minutes\n", - "Step 1155000 of 2000000; TPS: 59704.16; ETA: 0.2 minutes\n", - "Step 1160500 of 2000000; TPS: 59751.93; ETA: 0.2 minutes\n", - "Step 1166000 of 2000000; TPS: 59803.26; ETA: 0.2 minutes\n", - "Step 1171500 of 2000000; TPS: 59848.96; ETA: 0.2 minutes\n", - "Step 1177000 of 2000000; TPS: 59897.3; ETA: 0.2 minutes\n", - "Step 1182500 of 2000000; TPS: 59941.18; ETA: 0.2 minutes\n", - "Step 1188000 of 2000000; TPS: 59980.81; ETA: 0.2 minutes\n", - "Step 1193500 of 2000000; TPS: 60018.07; ETA: 0.2 minutes\n", - "Step 1199000 of 2000000; TPS: 60062.67; ETA: 0.2 minutes\n", - "Step 1204500 of 2000000; TPS: 60000.69; ETA: 0.2 minutes\n", - "Step 1210000 of 2000000; TPS: 60040.14; ETA: 0.2 minutes\n", - "Step 1215500 of 2000000; TPS: 60084.51; ETA: 0.2 minutes\n", - "Step 1221000 of 2000000; TPS: 60122.57; ETA: 0.2 minutes\n", - "Step 1226500 of 2000000; TPS: 60165.74; ETA: 0.2 minutes\n", - "Step 1232000 of 2000000; TPS: 60201.37; ETA: 0.2 minutes\n", - "Step 1237500 of 2000000; TPS: 60244.42; ETA: 0.2 minutes\n", - "Step 1243000 of 2000000; TPS: 60281.91; ETA: 0.2 minutes\n", - "Step 1248500 of 2000000; TPS: 60320.45; ETA: 0.2 minutes\n", - "Step 1254000 of 2000000; TPS: 60355.01; ETA: 0.2 minutes\n", - "Step 1259500 of 2000000; TPS: 60393.14; ETA: 0.2 minutes\n", - "Step 1265000 of 2000000; TPS: 60425.77; ETA: 0.2 minutes\n", - "Step 1270500 of 2000000; TPS: 60459.46; ETA: 0.2 minutes\n", - "Step 1276000 of 2000000; TPS: 60485.98; ETA: 0.2 minutes\n", - "Step 1281500 of 2000000; TPS: 60528.36; ETA: 0.2 minutes\n", - "Step 1287000 of 2000000; TPS: 60564.59; ETA: 0.2 minutes\n", - "Step 1292500 of 2000000; TPS: 60581.08; ETA: 0.2 minutes\n", - "Step 1298000 of 2000000; TPS: 60616.39; ETA: 0.2 minutes\n", - "Step 1303500 of 2000000; TPS: 60649.89; ETA: 0.2 minutes\n", - "Step 1309000 of 2000000; TPS: 60679.13; ETA: 0.2 minutes\n", - "Step 1314500 of 2000000; TPS: 60715.17; ETA: 0.2 minutes\n", - "Step 1320000 of 2000000; TPS: 60741.39; ETA: 0.2 minutes\n", - "Step 1325500 of 2000000; TPS: 60777.22; ETA: 0.2 minutes\n", - "Step 1331000 of 2000000; TPS: 60808.83; ETA: 0.2 minutes\n", - "Step 1336500 of 2000000; TPS: 60847.11; ETA: 0.2 minutes\n", - "Step 1342000 of 2000000; TPS: 60877.53; ETA: 0.2 minutes\n", - "Step 1347500 of 2000000; TPS: 60916.29; ETA: 0.2 minutes\n", - "Step 1353000 of 2000000; TPS: 60949.6; ETA: 0.2 minutes\n", - "Step 1358500 of 2000000; TPS: 60987.48; ETA: 0.2 minutes\n", - "Step 1364000 of 2000000; TPS: 61019.04; ETA: 0.2 minutes\n", - "Step 1369500 of 2000000; TPS: 61058.65; ETA: 0.2 minutes\n", - "Step 1375000 of 2000000; TPS: 61086.69; ETA: 0.2 minutes\n", - "Step 1380500 of 2000000; TPS: 61119.76; ETA: 0.2 minutes\n", - "Step 1386000 of 2000000; TPS: 61148.77; ETA: 0.2 minutes\n", - "Step 1391500 of 2000000; TPS: 61178.8; ETA: 0.2 minutes\n", - "Step 1397000 of 2000000; TPS: 61209.13; ETA: 0.2 minutes\n", - "Step 1402500 of 2000000; TPS: 61238.44; ETA: 0.2 minutes\n", - "Step 1408000 of 2000000; TPS: 61257.46; ETA: 0.2 minutes\n", - "Step 1413500 of 2000000; TPS: 61285.05; ETA: 0.2 minutes\n", - "Step 1419000 of 2000000; TPS: 61317.42; ETA: 0.2 minutes\n", - "Step 1424500 of 2000000; TPS: 61350.46; ETA: 0.2 minutes\n", - "Step 1430000 of 2000000; TPS: 61380.47; ETA: 0.2 minutes\n", - "Step 1435500 of 2000000; TPS: 61416.41; ETA: 0.2 minutes\n", - "Step 1441000 of 2000000; TPS: 61421.66; ETA: 0.2 minutes\n", - "Step 1446500 of 2000000; TPS: 61448.93; ETA: 0.2 minutes\n", - "Step 1452000 of 2000000; TPS: 61458.82; ETA: 0.1 minutes\n", - "Step 1457500 of 2000000; TPS: 61487.39; ETA: 0.1 minutes\n", - "Step 1463000 of 2000000; TPS: 61515.35; ETA: 0.1 minutes\n", - "Step 1468500 of 2000000; TPS: 61541.18; ETA: 0.1 minutes\n", - "Step 1474000 of 2000000; TPS: 61533.22; ETA: 0.1 minutes\n", - "Step 1479500 of 2000000; TPS: 61565.16; ETA: 0.1 minutes\n", - "Step 1485000 of 2000000; TPS: 61589.84; ETA: 0.1 minutes\n", - "Step 1490500 of 2000000; TPS: 61619.79; ETA: 0.1 minutes\n", - "Step 1496000 of 2000000; TPS: 61648.35; ETA: 0.1 minutes\n", - "Step 1501500 of 2000000; TPS: 61668.55; ETA: 0.1 minutes\n", - "Step 1507000 of 2000000; TPS: 61685.56; ETA: 0.1 minutes\n", - "Step 1512500 of 2000000; TPS: 61715.14; ETA: 0.1 minutes\n", - "Step 1518000 of 2000000; TPS: 61739.73; ETA: 0.1 minutes\n", - "Step 1523500 of 2000000; TPS: 61766.56; ETA: 0.1 minutes\n", - "Step 1529000 of 2000000; TPS: 61794.58; ETA: 0.1 minutes\n", - "Step 1534500 of 2000000; TPS: 61822.92; ETA: 0.1 minutes\n", - "Step 1540000 of 2000000; TPS: 61848.3; ETA: 0.1 minutes\n", - "Step 1545500 of 2000000; TPS: 61876.79; ETA: 0.1 minutes\n", - "Step 1551000 of 2000000; TPS: 61905.06; ETA: 0.1 minutes\n", - "Step 1556500 of 2000000; TPS: 61936.04; ETA: 0.1 minutes\n", - "Step 1562000 of 2000000; TPS: 61963.31; ETA: 0.1 minutes\n", - "Step 1567500 of 2000000; TPS: 61995.56; ETA: 0.1 minutes\n", - "Step 1573000 of 2000000; TPS: 62018.58; ETA: 0.1 minutes\n", - "Step 1578500 of 2000000; TPS: 62044.45; ETA: 0.1 minutes\n", - "Step 1584000 of 2000000; TPS: 62067.04; ETA: 0.1 minutes\n", - "Step 1589500 of 2000000; TPS: 62094.15; ETA: 0.1 minutes\n", - "Step 1595000 of 2000000; TPS: 62124.28; ETA: 0.1 minutes\n", - "Step 1600500 of 2000000; TPS: 62149.64; ETA: 0.1 minutes\n", - "Step 1606000 of 2000000; TPS: 62166.6; ETA: 0.1 minutes\n", - "Step 1611500 of 2000000; TPS: 62190.99; ETA: 0.1 minutes\n", - "Step 1617000 of 2000000; TPS: 62212.41; ETA: 0.1 minutes\n", - "Step 1622500 of 2000000; TPS: 62234.32; ETA: 0.1 minutes\n", - "Step 1628000 of 2000000; TPS: 62261.19; ETA: 0.1 minutes\n", - "Step 1633500 of 2000000; TPS: 62279.34; ETA: 0.1 minutes\n", - "Step 1639000 of 2000000; TPS: 62300.03; ETA: 0.1 minutes\n", - "Step 1644500 of 2000000; TPS: 62324.68; ETA: 0.1 minutes\n", - "Step 1650000 of 2000000; TPS: 62342.9; ETA: 0.1 minutes\n", - "Step 1655500 of 2000000; TPS: 62357.56; ETA: 0.1 minutes\n", - "Step 1661000 of 2000000; TPS: 62367.44; ETA: 0.1 minutes\n", - "Step 1666500 of 2000000; TPS: 62392.38; ETA: 0.1 minutes\n", - "Step 1672000 of 2000000; TPS: 62411.86; ETA: 0.1 minutes\n", - "Step 1677500 of 2000000; TPS: 62432.91; ETA: 0.1 minutes\n", - "Step 1683000 of 2000000; TPS: 62449.65; ETA: 0.1 minutes\n", - "Step 1688500 of 2000000; TPS: 62469.02; ETA: 0.1 minutes\n", - "Step 1694000 of 2000000; TPS: 62493.34; ETA: 0.1 minutes\n", - "Step 1699500 of 2000000; TPS: 62521.58; ETA: 0.1 minutes\n", - "Step 1705000 of 2000000; TPS: 62531.77; ETA: 0.1 minutes\n", - "Step 1710500 of 2000000; TPS: 62551.67; ETA: 0.1 minutes\n", - "Step 1716000 of 2000000; TPS: 62570.65; ETA: 0.1 minutes\n", - "Step 1721500 of 2000000; TPS: 62588.65; ETA: 0.1 minutes\n", - "Step 1727000 of 2000000; TPS: 62606.41; ETA: 0.1 minutes\n", - "Step 1732500 of 2000000; TPS: 62630.01; ETA: 0.1 minutes\n", - "Step 1738000 of 2000000; TPS: 62647.44; ETA: 0.1 minutes\n", - "Step 1743500 of 2000000; TPS: 62670.27; ETA: 0.1 minutes\n", - "Step 1749000 of 2000000; TPS: 62692.95; ETA: 0.1 minutes\n", - "Step 1754500 of 2000000; TPS: 62716.46; ETA: 0.1 minutes\n", - "Step 1760000 of 2000000; TPS: 62734.16; ETA: 0.1 minutes\n", - "Step 1765500 of 2000000; TPS: 62752.55; ETA: 0.1 minutes\n", - "Step 1771000 of 2000000; TPS: 62773.04; ETA: 0.1 minutes\n", - "Step 1776500 of 2000000; TPS: 62796.73; ETA: 0.1 minutes\n", - "Step 1782000 of 2000000; TPS: 62819.91; ETA: 0.1 minutes\n", - "Step 1787500 of 2000000; TPS: 62837.63; ETA: 0.1 minutes\n", - "Step 1793000 of 2000000; TPS: 62855.24; ETA: 0.1 minutes\n", - "Step 1798500 of 2000000; TPS: 62878.63; ETA: 0.1 minutes\n", - "Step 1804000 of 2000000; TPS: 62894.59; ETA: 0.1 minutes\n", - "Step 1809500 of 2000000; TPS: 62914.06; ETA: 0.1 minutes\n", - "Step 1815000 of 2000000; TPS: 62932.69; ETA: 0.0 minutes\n", - "Step 1820500 of 2000000; TPS: 62950.04; ETA: 0.0 minutes\n", - "Step 1826000 of 2000000; TPS: 62970.98; ETA: 0.0 minutes\n", - "Step 1831500 of 2000000; TPS: 62994.0; ETA: 0.0 minutes\n", - "Step 1837000 of 2000000; TPS: 62999.27; ETA: 0.0 minutes\n", - "Step 1842500 of 2000000; TPS: 63012.8; ETA: 0.0 minutes\n", - "Step 1848000 of 2000000; TPS: 63028.28; ETA: 0.0 minutes\n", - "Step 1853500 of 2000000; TPS: 63050.08; ETA: 0.0 minutes\n", - "Step 1859000 of 2000000; TPS: 63069.62; ETA: 0.0 minutes\n", - "Step 1864500 of 2000000; TPS: 63072.48; ETA: 0.0 minutes\n", - "Step 1870000 of 2000000; TPS: 63082.05; ETA: 0.0 minutes\n", - "Step 1875500 of 2000000; TPS: 63098.51; ETA: 0.0 minutes\n", - "Step 1881000 of 2000000; TPS: 63115.99; ETA: 0.0 minutes\n", - "Step 1886500 of 2000000; TPS: 63128.42; ETA: 0.0 minutes\n", - "Step 1892000 of 2000000; TPS: 63145.08; ETA: 0.0 minutes\n", - "Step 1897500 of 2000000; TPS: 63164.85; ETA: 0.0 minutes\n", - "Step 1903000 of 2000000; TPS: 63151.26; ETA: 0.0 minutes\n", - "Step 1908500 of 2000000; TPS: 63164.8; ETA: 0.0 minutes\n", - "Step 1914000 of 2000000; TPS: 63175.35; ETA: 0.0 minutes\n", - "Step 1919500 of 2000000; TPS: 63188.78; ETA: 0.0 minutes\n", - "Step 1925000 of 2000000; TPS: 63196.82; ETA: 0.0 minutes\n", - "Step 1930500 of 2000000; TPS: 63213.9; ETA: 0.0 minutes\n", - "Step 1936000 of 2000000; TPS: 63226.57; ETA: 0.0 minutes\n", - "Step 1941500 of 2000000; TPS: 63238.08; ETA: 0.0 minutes\n", - "Step 1947000 of 2000000; TPS: 63255.53; ETA: 0.0 minutes\n", - "Step 1952500 of 2000000; TPS: 63271.54; ETA: 0.0 minutes\n", - "Step 1958000 of 2000000; TPS: 63289.49; ETA: 0.0 minutes\n", - "Step 1963500 of 2000000; TPS: 63304.41; ETA: 0.0 minutes\n", - "Step 1969000 of 2000000; TPS: 63302.13; ETA: 0.0 minutes\n", - "Step 1974500 of 2000000; TPS: 63299.9; ETA: 0.0 minutes\n", - "Step 1980000 of 2000000; TPS: 63302.16; ETA: 0.0 minutes\n", - "Step 1985500 of 2000000; TPS: 63307.91; ETA: 0.0 minutes\n", - "Step 1991000 of 2000000; TPS: 63313.01; ETA: 0.0 minutes\n", - "Step 1996500 of 2000000; TPS: 63323.31; ETA: 0.0 minutes\n", - "Step 1999 of 4000000; TPS: 66914.37; ETA: 1.0 minutes\n", - "Step 7499 of 4000000; TPS: 67897.43; ETA: 1.0 minutes\n", - "Step 12999 of 4000000; TPS: 68623.47; ETA: 1.0 minutes\n", - "Step 18499 of 4000000; TPS: 68777.19; ETA: 1.0 minutes\n", - "Step 23999 of 4000000; TPS: 69110.0; ETA: 1.0 minutes\n", - "Step 29499 of 4000000; TPS: 68764.32; ETA: 1.0 minutes\n", - "Step 34999 of 4000000; TPS: 68794.24; ETA: 1.0 minutes\n", - "Step 40499 of 4000000; TPS: 68800.96; ETA: 1.0 minutes\n", - "Step 45999 of 4000000; TPS: 69231.81; ETA: 1.0 minutes\n", - "Step 51499 of 4000000; TPS: 69453.49; ETA: 0.9 minutes\n", - "Step 56999 of 4000000; TPS: 69538.45; ETA: 0.9 minutes\n", - "Step 62499 of 4000000; TPS: 69619.55; ETA: 0.9 minutes\n", - "Step 67999 of 4000000; TPS: 69614.26; ETA: 0.9 minutes\n", - "Step 73499 of 4000000; TPS: 69617.54; ETA: 0.9 minutes\n", - "Step 78999 of 4000000; TPS: 69802.33; ETA: 0.9 minutes\n", - "Step 84499 of 4000000; TPS: 69791.09; ETA: 0.9 minutes\n", - "Step 89999 of 4000000; TPS: 69766.72; ETA: 0.9 minutes\n", - "Step 95499 of 4000000; TPS: 69705.98; ETA: 0.9 minutes\n", - "Step 100999 of 4000000; TPS: 69649.54; ETA: 0.9 minutes\n", - "Step 106499 of 4000000; TPS: 69560.18; ETA: 0.9 minutes\n", - "Step 111999 of 4000000; TPS: 69547.36; ETA: 0.9 minutes\n", - "Step 117499 of 4000000; TPS: 69529.12; ETA: 0.9 minutes\n", - "Step 122999 of 4000000; TPS: 69519.79; ETA: 0.9 minutes\n", - "Step 128499 of 4000000; TPS: 69548.66; ETA: 0.9 minutes\n", - "Step 133999 of 4000000; TPS: 69507.4; ETA: 0.9 minutes\n", - "Step 139499 of 4000000; TPS: 69492.52; ETA: 0.9 minutes\n", - "Step 144999 of 4000000; TPS: 69450.32; ETA: 0.9 minutes\n", - "Step 150499 of 4000000; TPS: 69385.81; ETA: 0.9 minutes\n", - "Step 155999 of 4000000; TPS: 69367.6; ETA: 0.9 minutes\n", - "Step 161499 of 4000000; TPS: 69340.11; ETA: 0.9 minutes\n", - "Step 166999 of 4000000; TPS: 69327.36; ETA: 0.9 minutes\n", - "Step 172499 of 4000000; TPS: 69275.32; ETA: 0.9 minutes\n", - "Step 177999 of 4000000; TPS: 69262.09; ETA: 0.9 minutes\n", - "Step 183499 of 4000000; TPS: 69281.22; ETA: 0.9 minutes\n", - "Step 188999 of 4000000; TPS: 69208.73; ETA: 0.9 minutes\n", - "Step 194499 of 4000000; TPS: 69226.09; ETA: 0.9 minutes\n", - "Step 199999 of 4000000; TPS: 69143.3; ETA: 0.9 minutes\n", - "Step 205499 of 4000000; TPS: 69171.55; ETA: 0.9 minutes\n", - "Step 210999 of 4000000; TPS: 69179.98; ETA: 0.9 minutes\n", - "Step 216499 of 4000000; TPS: 69188.84; ETA: 0.9 minutes\n", - "Step 221999 of 4000000; TPS: 69172.49; ETA: 0.9 minutes\n", - "Step 227499 of 4000000; TPS: 69191.49; ETA: 0.9 minutes\n", - "Step 232999 of 4000000; TPS: 69206.05; ETA: 0.9 minutes\n", - "Step 238499 of 4000000; TPS: 69189.67; ETA: 0.9 minutes\n", - "Step 243999 of 4000000; TPS: 69220.48; ETA: 0.9 minutes\n", - "Step 249499 of 4000000; TPS: 69248.88; ETA: 0.9 minutes\n", - "Step 254999 of 4000000; TPS: 69262.34; ETA: 0.9 minutes\n", - "Step 260499 of 4000000; TPS: 69284.21; ETA: 0.9 minutes\n", - "Step 265999 of 4000000; TPS: 69213.81; ETA: 0.9 minutes\n", - "Step 271499 of 4000000; TPS: 69199.36; ETA: 0.9 minutes\n", - "Step 276999 of 4000000; TPS: 69129.03; ETA: 0.9 minutes\n", - "Step 282499 of 4000000; TPS: 69142.76; ETA: 0.9 minutes\n", - "Step 287999 of 4000000; TPS: 69125.11; ETA: 0.9 minutes\n", - "Step 293499 of 4000000; TPS: 69097.28; ETA: 0.9 minutes\n", - "Step 298999 of 4000000; TPS: 69096.26; ETA: 0.9 minutes\n", - "Step 304499 of 4000000; TPS: 69069.13; ETA: 0.9 minutes\n", - "Step 309999 of 4000000; TPS: 68993.09; ETA: 0.9 minutes\n", - "Step 315499 of 4000000; TPS: 68978.4; ETA: 0.9 minutes\n", - "Step 320999 of 4000000; TPS: 68966.55; ETA: 0.9 minutes\n", - "Step 326499 of 4000000; TPS: 68946.29; ETA: 0.9 minutes\n", - "Step 331999 of 4000000; TPS: 68916.92; ETA: 0.9 minutes\n", - "Step 337499 of 4000000; TPS: 68939.36; ETA: 0.9 minutes\n", - "Step 342999 of 4000000; TPS: 68928.7; ETA: 0.9 minutes\n", - "Step 348499 of 4000000; TPS: 68908.31; ETA: 0.9 minutes\n", - "Step 353999 of 4000000; TPS: 68873.04; ETA: 0.9 minutes\n", - "Step 359499 of 4000000; TPS: 68892.56; ETA: 0.9 minutes\n", - "Step 364999 of 4000000; TPS: 68880.68; ETA: 0.9 minutes\n", - "Step 370499 of 4000000; TPS: 68842.89; ETA: 0.9 minutes\n", - "Step 375999 of 4000000; TPS: 68812.1; ETA: 0.9 minutes\n", - "Step 381499 of 4000000; TPS: 68781.15; ETA: 0.9 minutes\n", - "Step 386999 of 4000000; TPS: 68789.13; ETA: 0.9 minutes\n", - "Step 392499 of 4000000; TPS: 68805.09; ETA: 0.9 minutes\n", - "Step 397999 of 4000000; TPS: 68760.08; ETA: 0.9 minutes\n", - "Step 403499 of 4000000; TPS: 68794.37; ETA: 0.9 minutes\n", - "Step 408999 of 4000000; TPS: 68782.38; ETA: 0.9 minutes\n", - "Step 414499 of 4000000; TPS: 68775.32; ETA: 0.9 minutes\n", - "Step 419999 of 4000000; TPS: 68765.65; ETA: 0.9 minutes\n", - "Step 425499 of 4000000; TPS: 68778.85; ETA: 0.9 minutes\n", - "Step 430999 of 4000000; TPS: 68765.9; ETA: 0.9 minutes\n", - "Step 436499 of 4000000; TPS: 68696.91; ETA: 0.9 minutes\n", - "Step 441999 of 4000000; TPS: 49403.24; ETA: 1.2 minutes\n", - "Step 447499 of 4000000; TPS: 49576.69; ETA: 1.2 minutes\n", - "Step 452999 of 4000000; TPS: 49741.81; ETA: 1.2 minutes\n", - "Step 458499 of 4000000; TPS: 49910.17; ETA: 1.2 minutes\n", - "Step 463999 of 4000000; TPS: 50070.47; ETA: 1.2 minutes\n", - "Step 469499 of 4000000; TPS: 50232.91; ETA: 1.2 minutes\n", - "Step 474999 of 4000000; TPS: 50391.36; ETA: 1.2 minutes\n", - "Step 480499 of 4000000; TPS: 50541.05; ETA: 1.2 minutes\n", - "Step 485999 of 4000000; TPS: 50684.29; ETA: 1.2 minutes\n", - "Step 491499 of 4000000; TPS: 50836.76; ETA: 1.2 minutes\n", - "Step 496999 of 4000000; TPS: 50969.02; ETA: 1.1 minutes\n", - "Step 502499 of 4000000; TPS: 51112.86; ETA: 1.1 minutes\n", - "Step 507999 of 4000000; TPS: 51256.64; ETA: 1.1 minutes\n", - "Step 513499 of 4000000; TPS: 51392.5; ETA: 1.1 minutes\n", - "Step 518999 of 4000000; TPS: 51529.1; ETA: 1.1 minutes\n", - "Step 524499 of 4000000; TPS: 51663.66; ETA: 1.1 minutes\n", - "Step 529999 of 4000000; TPS: 51798.55; ETA: 1.1 minutes\n", - "Step 535499 of 4000000; TPS: 51927.33; ETA: 1.1 minutes\n", - "Step 540999 of 4000000; TPS: 52052.34; ETA: 1.1 minutes\n", - "Step 546499 of 4000000; TPS: 52178.83; ETA: 1.1 minutes\n", - "Step 551999 of 4000000; TPS: 52299.25; ETA: 1.1 minutes\n", - "Step 557499 of 4000000; TPS: 52430.93; ETA: 1.1 minutes\n", - "Step 562999 of 4000000; TPS: 52546.95; ETA: 1.1 minutes\n", - "Step 568499 of 4000000; TPS: 52633.43; ETA: 1.1 minutes\n", - "Step 573999 of 4000000; TPS: 52763.46; ETA: 1.1 minutes\n", - "Step 579499 of 4000000; TPS: 52884.54; ETA: 1.1 minutes\n", - "Step 584999 of 4000000; TPS: 52992.04; ETA: 1.1 minutes\n", - "Step 590499 of 4000000; TPS: 53104.2; ETA: 1.1 minutes\n", - "Step 595999 of 4000000; TPS: 53226.5; ETA: 1.1 minutes\n", - "Step 601499 of 4000000; TPS: 53336.75; ETA: 1.1 minutes\n", - "Step 606999 of 4000000; TPS: 53453.69; ETA: 1.1 minutes\n", - "Step 612499 of 4000000; TPS: 53558.18; ETA: 1.1 minutes\n", - "Step 617999 of 4000000; TPS: 53669.54; ETA: 1.1 minutes\n", - "Step 623499 of 4000000; TPS: 53763.46; ETA: 1.0 minutes\n", - "Step 628999 of 4000000; TPS: 53836.34; ETA: 1.0 minutes\n", - "Step 634499 of 4000000; TPS: 53948.4; ETA: 1.0 minutes\n", - "Step 639999 of 4000000; TPS: 54054.13; ETA: 1.0 minutes\n", - "Step 645499 of 4000000; TPS: 54157.99; ETA: 1.0 minutes\n", - "Step 650999 of 4000000; TPS: 54244.35; ETA: 1.0 minutes\n", - "Step 656499 of 4000000; TPS: 54339.72; ETA: 1.0 minutes\n", - "Step 661999 of 4000000; TPS: 54424.77; ETA: 1.0 minutes\n", - "Step 667499 of 4000000; TPS: 54512.57; ETA: 1.0 minutes\n", - "Step 672999 of 4000000; TPS: 54594.02; ETA: 1.0 minutes\n", - "Step 678499 of 4000000; TPS: 54685.26; ETA: 1.0 minutes\n", - "Step 683999 of 4000000; TPS: 54777.59; ETA: 1.0 minutes\n", - "Step 689499 of 4000000; TPS: 54864.05; ETA: 1.0 minutes\n", - "Step 694999 of 4000000; TPS: 54915.63; ETA: 1.0 minutes\n", - "Step 700499 of 4000000; TPS: 54924.35; ETA: 1.0 minutes\n", - "Step 705999 of 4000000; TPS: 54808.12; ETA: 1.0 minutes\n", - "Step 711499 of 4000000; TPS: 54682.79; ETA: 1.0 minutes\n", - "Step 716999 of 4000000; TPS: 54598.51; ETA: 1.0 minutes\n", - "Step 722499 of 4000000; TPS: 54695.46; ETA: 1.0 minutes\n", - "Step 727999 of 4000000; TPS: 54791.01; ETA: 1.0 minutes\n", - "Step 733499 of 4000000; TPS: 54872.26; ETA: 1.0 minutes\n", - "Step 738999 of 4000000; TPS: 54958.14; ETA: 1.0 minutes\n", - "Step 744499 of 4000000; TPS: 55042.78; ETA: 1.0 minutes\n", - "Step 749999 of 4000000; TPS: 55109.86; ETA: 1.0 minutes\n", - "Step 755499 of 4000000; TPS: 55190.29; ETA: 1.0 minutes\n", - "Step 760999 of 4000000; TPS: 55270.76; ETA: 1.0 minutes\n", - "Step 766499 of 4000000; TPS: 55341.32; ETA: 1.0 minutes\n", - "Step 771999 of 4000000; TPS: 55420.7; ETA: 1.0 minutes\n", - "Step 777499 of 4000000; TPS: 55504.2; ETA: 1.0 minutes\n", - "Step 782999 of 4000000; TPS: 55575.62; ETA: 1.0 minutes\n", - "Step 788499 of 4000000; TPS: 55653.35; ETA: 1.0 minutes\n", - "Step 793999 of 4000000; TPS: 55724.52; ETA: 1.0 minutes\n", - "Step 799499 of 4000000; TPS: 55787.51; ETA: 1.0 minutes\n", - "Step 804999 of 4000000; TPS: 55857.42; ETA: 1.0 minutes\n", - "Step 810499 of 4000000; TPS: 55934.85; ETA: 1.0 minutes\n", - "Step 815999 of 4000000; TPS: 55970.66; ETA: 0.9 minutes\n", - "Step 821499 of 4000000; TPS: 56032.75; ETA: 0.9 minutes\n", - "Step 826999 of 4000000; TPS: 56104.74; ETA: 0.9 minutes\n", - "Step 832499 of 4000000; TPS: 56175.42; ETA: 0.9 minutes\n", - "Step 837999 of 4000000; TPS: 56237.84; ETA: 0.9 minutes\n", - "Step 843499 of 4000000; TPS: 56296.63; ETA: 0.9 minutes\n", - "Step 848999 of 4000000; TPS: 56355.07; ETA: 0.9 minutes\n", - "Step 854499 of 4000000; TPS: 56429.57; ETA: 0.9 minutes\n", - "Step 859999 of 4000000; TPS: 56495.34; ETA: 0.9 minutes\n", - "Step 865499 of 4000000; TPS: 56560.24; ETA: 0.9 minutes\n", - "Step 870999 of 4000000; TPS: 56613.8; ETA: 0.9 minutes\n", - "Step 876499 of 4000000; TPS: 56680.93; ETA: 0.9 minutes\n", - "Step 881999 of 4000000; TPS: 56728.94; ETA: 0.9 minutes\n", - "Step 887499 of 4000000; TPS: 56786.39; ETA: 0.9 minutes\n", - "Step 892999 of 4000000; TPS: 56847.71; ETA: 0.9 minutes\n", - "Step 898499 of 4000000; TPS: 56906.06; ETA: 0.9 minutes\n", - "Step 903999 of 4000000; TPS: 56969.12; ETA: 0.9 minutes\n", - "Step 909499 of 4000000; TPS: 57030.74; ETA: 0.9 minutes\n", - "Step 914999 of 4000000; TPS: 57078.65; ETA: 0.9 minutes\n", - "Step 920499 of 4000000; TPS: 57142.17; ETA: 0.9 minutes\n", - "Step 925999 of 4000000; TPS: 57204.09; ETA: 0.9 minutes\n", - "Step 931499 of 4000000; TPS: 57267.06; ETA: 0.9 minutes\n", - "Step 936999 of 4000000; TPS: 57324.26; ETA: 0.9 minutes\n", - "Step 942499 of 4000000; TPS: 57389.97; ETA: 0.9 minutes\n", - "Step 947999 of 4000000; TPS: 57440.69; ETA: 0.9 minutes\n", - "Step 953499 of 4000000; TPS: 57492.21; ETA: 0.9 minutes\n", - "Step 958999 of 4000000; TPS: 57548.91; ETA: 0.9 minutes\n", - "Step 964499 of 4000000; TPS: 57590.99; ETA: 0.9 minutes\n", - "Step 969999 of 4000000; TPS: 57644.73; ETA: 0.9 minutes\n", - "Step 975499 of 4000000; TPS: 57694.01; ETA: 0.9 minutes\n", - "Step 980999 of 4000000; TPS: 57746.8; ETA: 0.9 minutes\n", - "Step 986499 of 4000000; TPS: 57788.24; ETA: 0.9 minutes\n", - "Step 991999 of 4000000; TPS: 57843.19; ETA: 0.9 minutes\n", - "Step 997499 of 4000000; TPS: 57892.76; ETA: 0.9 minutes\n", - "Step 1002999 of 4000000; TPS: 57939.89; ETA: 0.9 minutes\n", - "Step 1008499 of 4000000; TPS: 57992.09; ETA: 0.9 minutes\n", - "Step 1013999 of 4000000; TPS: 58035.24; ETA: 0.9 minutes\n", - "Step 1019499 of 4000000; TPS: 58086.02; ETA: 0.9 minutes\n", - "Step 1024999 of 4000000; TPS: 58128.94; ETA: 0.9 minutes\n", - "Step 1030499 of 4000000; TPS: 58177.67; ETA: 0.9 minutes\n", - "Step 1035999 of 4000000; TPS: 58221.33; ETA: 0.8 minutes\n", - "Step 1041499 of 4000000; TPS: 58268.27; ETA: 0.8 minutes\n", - "Step 1046999 of 4000000; TPS: 58312.17; ETA: 0.8 minutes\n", - "Step 1052499 of 4000000; TPS: 58355.86; ETA: 0.8 minutes\n", - "Step 1057999 of 4000000; TPS: 58399.47; ETA: 0.8 minutes\n", - "Step 1063499 of 4000000; TPS: 58442.68; ETA: 0.8 minutes\n", - "Step 1068999 of 4000000; TPS: 58482.14; ETA: 0.8 minutes\n", - "Step 1074499 of 4000000; TPS: 58514.48; ETA: 0.8 minutes\n", - "Step 1079999 of 4000000; TPS: 58547.31; ETA: 0.8 minutes\n", - "Step 1085499 of 4000000; TPS: 58592.93; ETA: 0.8 minutes\n", - "Step 1090999 of 4000000; TPS: 58633.97; ETA: 0.8 minutes\n", - "Step 1096499 of 4000000; TPS: 58674.16; ETA: 0.8 minutes\n", - "Step 1101999 of 4000000; TPS: 58714.04; ETA: 0.8 minutes\n", - "Step 1107499 of 4000000; TPS: 58751.58; ETA: 0.8 minutes\n", - "Step 1112999 of 4000000; TPS: 58739.46; ETA: 0.8 minutes\n", - "Step 1118499 of 4000000; TPS: 58786.73; ETA: 0.8 minutes\n", - "Step 1123999 of 4000000; TPS: 58822.23; ETA: 0.8 minutes\n", - "Step 1129499 of 4000000; TPS: 58859.71; ETA: 0.8 minutes\n", - "Step 1134999 of 4000000; TPS: 58884.86; ETA: 0.8 minutes\n", - "Step 1140499 of 4000000; TPS: 58927.22; ETA: 0.8 minutes\n", - "Step 1145999 of 4000000; TPS: 58927.38; ETA: 0.8 minutes\n", - "Step 1151499 of 4000000; TPS: 58967.92; ETA: 0.8 minutes\n", - "Step 1156999 of 4000000; TPS: 59006.36; ETA: 0.8 minutes\n", - "Step 1162499 of 4000000; TPS: 59044.41; ETA: 0.8 minutes\n", - "Step 1167999 of 4000000; TPS: 59076.4; ETA: 0.8 minutes\n", - "Step 1173499 of 4000000; TPS: 59115.59; ETA: 0.8 minutes\n", - "Step 1178999 of 4000000; TPS: 59147.84; ETA: 0.8 minutes\n", - "Step 1184499 of 4000000; TPS: 59185.99; ETA: 0.8 minutes\n", - "Step 1189999 of 4000000; TPS: 59223.74; ETA: 0.8 minutes\n", - "Step 1195499 of 4000000; TPS: 59257.33; ETA: 0.8 minutes\n", - "Step 1200999 of 4000000; TPS: 59287.16; ETA: 0.8 minutes\n", - "Step 1206499 of 4000000; TPS: 59324.22; ETA: 0.8 minutes\n", - "Step 1211999 of 4000000; TPS: 59361.62; ETA: 0.8 minutes\n", - "Step 1217499 of 4000000; TPS: 59392.53; ETA: 0.8 minutes\n", - "Step 1222999 of 4000000; TPS: 59430.79; ETA: 0.8 minutes\n", - "Step 1228499 of 4000000; TPS: 59468.69; ETA: 0.8 minutes\n", - "Step 1233999 of 4000000; TPS: 59503.96; ETA: 0.8 minutes\n", - "Step 1239499 of 4000000; TPS: 59542.16; ETA: 0.8 minutes\n", - "Step 1244999 of 4000000; TPS: 59580.47; ETA: 0.8 minutes\n", - "Step 1250499 of 4000000; TPS: 59618.01; ETA: 0.8 minutes\n", - "Step 1255999 of 4000000; TPS: 59652.74; ETA: 0.8 minutes\n", - "Step 1261499 of 4000000; TPS: 59683.65; ETA: 0.8 minutes\n", - "Step 1266999 of 4000000; TPS: 59717.98; ETA: 0.8 minutes\n", - "Step 1272499 of 4000000; TPS: 59755.53; ETA: 0.8 minutes\n", - "Step 1277999 of 4000000; TPS: 59790.88; ETA: 0.8 minutes\n", - "Step 1283499 of 4000000; TPS: 59825.31; ETA: 0.8 minutes\n", - "Step 1288999 of 4000000; TPS: 59861.97; ETA: 0.8 minutes\n", - "Step 1294499 of 4000000; TPS: 59891.72; ETA: 0.8 minutes\n", - "Step 1299999 of 4000000; TPS: 59921.55; ETA: 0.8 minutes\n", - "Step 1305499 of 4000000; TPS: 59961.01; ETA: 0.7 minutes\n", - "Step 1310999 of 4000000; TPS: 59987.76; ETA: 0.7 minutes\n", - "Step 1316499 of 4000000; TPS: 60026.13; ETA: 0.7 minutes\n", - "Step 1321999 of 4000000; TPS: 60057.76; ETA: 0.7 minutes\n", - "Step 1327499 of 4000000; TPS: 60090.71; ETA: 0.7 minutes\n", - "Step 1332999 of 4000000; TPS: 60121.76; ETA: 0.7 minutes\n", - "Step 1338499 of 4000000; TPS: 60150.5; ETA: 0.7 minutes\n", - "Step 1343999 of 4000000; TPS: 60182.16; ETA: 0.7 minutes\n", - "Step 1349499 of 4000000; TPS: 60218.47; ETA: 0.7 minutes\n", - "Step 1354999 of 4000000; TPS: 60247.3; ETA: 0.7 minutes\n", - "Step 1360499 of 4000000; TPS: 60275.18; ETA: 0.7 minutes\n", - "Step 1365999 of 4000000; TPS: 60301.86; ETA: 0.7 minutes\n", - "Step 1371499 of 4000000; TPS: 60332.61; ETA: 0.7 minutes\n", - "Step 1376999 of 4000000; TPS: 60348.2; ETA: 0.7 minutes\n", - "Step 1382499 of 4000000; TPS: 60379.71; ETA: 0.7 minutes\n", - "Step 1387999 of 4000000; TPS: 60397.31; ETA: 0.7 minutes\n", - "Step 1393499 of 4000000; TPS: 60425.0; ETA: 0.7 minutes\n", - "Step 1398999 of 4000000; TPS: 60453.02; ETA: 0.7 minutes\n", - "Step 1404499 of 4000000; TPS: 60485.88; ETA: 0.7 minutes\n", - "Step 1409999 of 4000000; TPS: 60509.57; ETA: 0.7 minutes\n", - "Step 1415499 of 4000000; TPS: 60546.33; ETA: 0.7 minutes\n", - "Step 1420999 of 4000000; TPS: 60575.24; ETA: 0.7 minutes\n", - "Step 1426499 of 4000000; TPS: 60602.97; ETA: 0.7 minutes\n", - "Step 1431999 of 4000000; TPS: 60633.31; ETA: 0.7 minutes\n", - "Step 1437499 of 4000000; TPS: 60669.74; ETA: 0.7 minutes\n", - "Step 1442999 of 4000000; TPS: 60696.55; ETA: 0.7 minutes\n", - "Step 1448499 of 4000000; TPS: 60728.1; ETA: 0.7 minutes\n", - "Step 1453999 of 4000000; TPS: 60748.54; ETA: 0.7 minutes\n", - "Step 1459499 of 4000000; TPS: 60779.07; ETA: 0.7 minutes\n", - "Step 1464999 of 4000000; TPS: 60812.35; ETA: 0.7 minutes\n", - "Step 1470499 of 4000000; TPS: 60847.57; ETA: 0.7 minutes\n", - "Step 1475999 of 4000000; TPS: 60873.83; ETA: 0.7 minutes\n", - "Step 1481499 of 4000000; TPS: 60900.29; ETA: 0.7 minutes\n", - "Step 1486999 of 4000000; TPS: 60925.01; ETA: 0.7 minutes\n", - "Step 1492499 of 4000000; TPS: 60952.78; ETA: 0.7 minutes\n", - "Step 1497999 of 4000000; TPS: 60977.83; ETA: 0.7 minutes\n", - "Step 1503499 of 4000000; TPS: 61003.74; ETA: 0.7 minutes\n", - "Step 1508999 of 4000000; TPS: 61031.79; ETA: 0.7 minutes\n", - "Step 1514499 of 4000000; TPS: 61058.45; ETA: 0.7 minutes\n", - "Step 1519999 of 4000000; TPS: 61085.45; ETA: 0.7 minutes\n", - "Step 1525499 of 4000000; TPS: 61113.77; ETA: 0.7 minutes\n", - "Step 1530999 of 4000000; TPS: 61138.16; ETA: 0.7 minutes\n", - "Step 1536499 of 4000000; TPS: 61163.77; ETA: 0.7 minutes\n", - "Step 1541999 of 4000000; TPS: 61186.32; ETA: 0.7 minutes\n", - "Step 1547499 of 4000000; TPS: 61208.86; ETA: 0.7 minutes\n", - "Step 1552999 of 4000000; TPS: 61230.46; ETA: 0.7 minutes\n", - "Step 1558499 of 4000000; TPS: 61251.73; ETA: 0.7 minutes\n", - "Step 1563999 of 4000000; TPS: 61277.45; ETA: 0.7 minutes\n", - "Step 1569499 of 4000000; TPS: 61301.68; ETA: 0.7 minutes\n", - "Step 1574999 of 4000000; TPS: 61326.37; ETA: 0.7 minutes\n", - "Step 1580499 of 4000000; TPS: 61345.26; ETA: 0.7 minutes\n", - "Step 1585999 of 4000000; TPS: 61369.7; ETA: 0.7 minutes\n", - "Step 1591499 of 4000000; TPS: 61395.41; ETA: 0.7 minutes\n", - "Step 1596999 of 4000000; TPS: 61401.12; ETA: 0.7 minutes\n", - "Step 1602499 of 4000000; TPS: 61430.12; ETA: 0.7 minutes\n", - "Step 1607999 of 4000000; TPS: 61449.76; ETA: 0.6 minutes\n", - "Step 1613499 of 4000000; TPS: 61474.48; ETA: 0.6 minutes\n", - "Step 1618999 of 4000000; TPS: 61478.52; ETA: 0.6 minutes\n", - "Step 1624499 of 4000000; TPS: 61497.2; ETA: 0.6 minutes\n", - "Step 1629999 of 4000000; TPS: 61520.51; ETA: 0.6 minutes\n", - "Step 1635499 of 4000000; TPS: 61533.87; ETA: 0.6 minutes\n", - "Step 1640999 of 4000000; TPS: 61549.05; ETA: 0.6 minutes\n", - "Step 1646499 of 4000000; TPS: 61569.47; ETA: 0.6 minutes\n", - "Step 1651999 of 4000000; TPS: 61590.01; ETA: 0.6 minutes\n", - "Step 1657499 of 4000000; TPS: 61615.46; ETA: 0.6 minutes\n", - "Step 1662999 of 4000000; TPS: 61639.05; ETA: 0.6 minutes\n", - "Step 1668499 of 4000000; TPS: 61660.51; ETA: 0.6 minutes\n", - "Step 1673999 of 4000000; TPS: 61681.01; ETA: 0.6 minutes\n", - "Step 1679499 of 4000000; TPS: 61707.13; ETA: 0.6 minutes\n", - "Step 1684999 of 4000000; TPS: 61734.83; ETA: 0.6 minutes\n", - "Step 1690499 of 4000000; TPS: 61761.55; ETA: 0.6 minutes\n", - "Step 1695999 of 4000000; TPS: 61788.47; ETA: 0.6 minutes\n", - "Step 1701499 of 4000000; TPS: 61813.98; ETA: 0.6 minutes\n", - "Step 1706999 of 4000000; TPS: 61838.04; ETA: 0.6 minutes\n", - "Step 1712499 of 4000000; TPS: 61864.86; ETA: 0.6 minutes\n", - "Step 1717999 of 4000000; TPS: 61891.29; ETA: 0.6 minutes\n", - "Step 1723499 of 4000000; TPS: 61909.92; ETA: 0.6 minutes\n", - "Step 1728999 of 4000000; TPS: 61926.57; ETA: 0.6 minutes\n", - "Step 1734499 of 4000000; TPS: 61950.44; ETA: 0.6 minutes\n", - "Step 1739999 of 4000000; TPS: 61965.54; ETA: 0.6 minutes\n", - "Step 1745499 of 4000000; TPS: 61988.34; ETA: 0.6 minutes\n", - "Step 1750999 of 4000000; TPS: 62009.47; ETA: 0.6 minutes\n", - "Step 1756499 of 4000000; TPS: 62032.9; ETA: 0.6 minutes\n", - "Step 1761999 of 4000000; TPS: 62057.34; ETA: 0.6 minutes\n", - "Step 1767499 of 4000000; TPS: 62083.33; ETA: 0.6 minutes\n", - "Step 1772999 of 4000000; TPS: 62105.16; ETA: 0.6 minutes\n", - "Step 1778499 of 4000000; TPS: 62125.3; ETA: 0.6 minutes\n", - "Step 1783999 of 4000000; TPS: 62148.55; ETA: 0.6 minutes\n", - "Step 1789499 of 4000000; TPS: 62171.48; ETA: 0.6 minutes\n", - "Step 1794999 of 4000000; TPS: 62190.14; ETA: 0.6 minutes\n", - "Step 1800499 of 4000000; TPS: 62190.88; ETA: 0.6 minutes\n", - "Step 1805999 of 4000000; TPS: 62207.95; ETA: 0.6 minutes\n", - "Step 1811499 of 4000000; TPS: 62224.55; ETA: 0.6 minutes\n", - "Step 1816999 of 4000000; TPS: 62246.4; ETA: 0.6 minutes\n", - "Step 1822499 of 4000000; TPS: 62264.01; ETA: 0.6 minutes\n", - "Step 1827999 of 4000000; TPS: 62285.52; ETA: 0.6 minutes\n", - "Step 1833499 of 4000000; TPS: 62305.59; ETA: 0.6 minutes\n", - "Step 1838999 of 4000000; TPS: 62322.48; ETA: 0.6 minutes\n", - "Step 1844499 of 4000000; TPS: 62346.73; ETA: 0.6 minutes\n", - "Step 1849999 of 4000000; TPS: 62366.63; ETA: 0.6 minutes\n", - "Step 1855499 of 4000000; TPS: 62384.85; ETA: 0.6 minutes\n", - "Step 1860999 of 4000000; TPS: 62400.33; ETA: 0.6 minutes\n", - "Step 1866499 of 4000000; TPS: 62421.43; ETA: 0.6 minutes\n", - "Step 1871999 of 4000000; TPS: 62435.13; ETA: 0.6 minutes\n", - "Step 1877499 of 4000000; TPS: 62456.59; ETA: 0.6 minutes\n", - "Step 1882999 of 4000000; TPS: 62474.21; ETA: 0.6 minutes\n", - "Step 1888499 of 4000000; TPS: 62494.37; ETA: 0.6 minutes\n", - "Step 1893999 of 4000000; TPS: 62510.36; ETA: 0.6 minutes\n", - "Step 1899499 of 4000000; TPS: 62531.03; ETA: 0.6 minutes\n", - "Step 1904999 of 4000000; TPS: 62548.73; ETA: 0.6 minutes\n", - "Step 1910499 of 4000000; TPS: 62564.47; ETA: 0.6 minutes\n", - "Step 1915999 of 4000000; TPS: 62583.88; ETA: 0.6 minutes\n", - "Step 1921499 of 4000000; TPS: 62586.11; ETA: 0.6 minutes\n", - "Step 1926999 of 4000000; TPS: 62602.58; ETA: 0.6 minutes\n", - "Step 1932499 of 4000000; TPS: 62624.69; ETA: 0.6 minutes\n", - "Step 1937999 of 4000000; TPS: 62640.0; ETA: 0.5 minutes\n", - "Step 1943499 of 4000000; TPS: 62658.48; ETA: 0.5 minutes\n", - "Step 1948999 of 4000000; TPS: 62677.51; ETA: 0.5 minutes\n", - "Step 1954499 of 4000000; TPS: 62692.11; ETA: 0.5 minutes\n", - "Step 1959999 of 4000000; TPS: 62708.6; ETA: 0.5 minutes\n", - "Step 1965499 of 4000000; TPS: 62728.55; ETA: 0.5 minutes\n", - "Step 1970999 of 4000000; TPS: 62742.07; ETA: 0.5 minutes\n", - "Step 1976499 of 4000000; TPS: 62758.48; ETA: 0.5 minutes\n", - "Step 1981999 of 4000000; TPS: 62774.42; ETA: 0.5 minutes\n", - "Step 1987499 of 4000000; TPS: 62790.96; ETA: 0.5 minutes\n", - "Step 1992999 of 4000000; TPS: 62810.96; ETA: 0.5 minutes\n", - "Step 1998499 of 4000000; TPS: 62826.31; ETA: 0.5 minutes\n", - "Step 2003999 of 4000000; TPS: 62841.5; ETA: 0.5 minutes\n", - "Step 2009499 of 4000000; TPS: 62861.57; ETA: 0.5 minutes\n", - "Step 2014999 of 4000000; TPS: 62879.09; ETA: 0.5 minutes\n", - "Step 2020499 of 4000000; TPS: 62900.54; ETA: 0.5 minutes\n", - "Step 2025999 of 4000000; TPS: 62919.55; ETA: 0.5 minutes\n", - "Step 2031499 of 4000000; TPS: 62936.47; ETA: 0.5 minutes\n", - "Step 2036999 of 4000000; TPS: 62950.79; ETA: 0.5 minutes\n", - "Step 2042499 of 4000000; TPS: 62965.75; ETA: 0.5 minutes\n", - "Step 2047999 of 4000000; TPS: 62981.64; ETA: 0.5 minutes\n", - "Step 2053499 of 4000000; TPS: 62998.07; ETA: 0.5 minutes\n", - "Step 2058999 of 4000000; TPS: 63014.0; ETA: 0.5 minutes\n", - "Step 2064499 of 4000000; TPS: 63029.69; ETA: 0.5 minutes\n", - "Step 2069999 of 4000000; TPS: 63045.67; ETA: 0.5 minutes\n", - "Step 2075499 of 4000000; TPS: 63062.08; ETA: 0.5 minutes\n", - "Step 2080999 of 4000000; TPS: 63073.45; ETA: 0.5 minutes\n", - "Step 2086499 of 4000000; TPS: 63084.4; ETA: 0.5 minutes\n", - "Step 2091999 of 4000000; TPS: 63103.2; ETA: 0.5 minutes\n", - "Step 2097499 of 4000000; TPS: 63121.0; ETA: 0.5 minutes\n", - "Step 2102999 of 4000000; TPS: 63131.92; ETA: 0.5 minutes\n", - "Step 2108499 of 4000000; TPS: 63137.19; ETA: 0.5 minutes\n", - "Step 2113999 of 4000000; TPS: 63148.57; ETA: 0.5 minutes\n", - "Step 2119499 of 4000000; TPS: 63161.57; ETA: 0.5 minutes\n", - "Step 2124999 of 4000000; TPS: 63175.73; ETA: 0.5 minutes\n", - "Step 2130499 of 4000000; TPS: 63190.29; ETA: 0.5 minutes\n", - "Step 2135999 of 4000000; TPS: 63202.63; ETA: 0.5 minutes\n", - "Step 2141499 of 4000000; TPS: 63217.46; ETA: 0.5 minutes\n", - "Step 2146999 of 4000000; TPS: 63233.49; ETA: 0.5 minutes\n", - "Step 2152499 of 4000000; TPS: 63248.54; ETA: 0.5 minutes\n", - "Step 2157999 of 4000000; TPS: 63263.92; ETA: 0.5 minutes\n", - "Step 2163499 of 4000000; TPS: 63280.5; ETA: 0.5 minutes\n", - "Step 2168999 of 4000000; TPS: 63295.21; ETA: 0.5 minutes\n", - "Step 2174499 of 4000000; TPS: 63312.24; ETA: 0.5 minutes\n", - "Step 2179999 of 4000000; TPS: 63323.81; ETA: 0.5 minutes\n", - "Step 2185499 of 4000000; TPS: 63336.81; ETA: 0.5 minutes\n", - "Step 2190999 of 4000000; TPS: 63347.45; ETA: 0.5 minutes\n", - "Step 2196499 of 4000000; TPS: 63363.09; ETA: 0.5 minutes\n", - "Step 2201999 of 4000000; TPS: 63377.36; ETA: 0.5 minutes\n", - "Step 2207499 of 4000000; TPS: 63396.1; ETA: 0.5 minutes\n", - "Step 2212999 of 4000000; TPS: 63412.4; ETA: 0.5 minutes\n", - "Step 2218499 of 4000000; TPS: 63427.66; ETA: 0.5 minutes\n", - "Step 2223999 of 4000000; TPS: 63441.27; ETA: 0.5 minutes\n", - "Step 2229499 of 4000000; TPS: 63460.19; ETA: 0.5 minutes\n", - "Step 2234999 of 4000000; TPS: 63463.25; ETA: 0.5 minutes\n", - "Step 2240499 of 4000000; TPS: 63478.62; ETA: 0.5 minutes\n", - "Step 2245999 of 4000000; TPS: 63491.11; ETA: 0.5 minutes\n", - "Step 2251499 of 4000000; TPS: 63504.16; ETA: 0.5 minutes\n", - "Step 2256999 of 4000000; TPS: 63514.56; ETA: 0.5 minutes\n", - "Step 2262499 of 4000000; TPS: 63527.89; ETA: 0.5 minutes\n", - "Step 2267999 of 4000000; TPS: 63539.64; ETA: 0.5 minutes\n", - "Step 2273499 of 4000000; TPS: 63555.81; ETA: 0.5 minutes\n", - "Step 2278999 of 4000000; TPS: 63557.47; ETA: 0.5 minutes\n", - "Step 2284499 of 4000000; TPS: 63565.06; ETA: 0.4 minutes\n", - "Step 2289999 of 4000000; TPS: 63576.51; ETA: 0.4 minutes\n", - "Step 2295499 of 4000000; TPS: 63591.51; ETA: 0.4 minutes\n", - "Step 2300999 of 4000000; TPS: 63601.46; ETA: 0.4 minutes\n", - "Step 2306499 of 4000000; TPS: 63610.87; ETA: 0.4 minutes\n", - "Step 2311999 of 4000000; TPS: 63624.37; ETA: 0.4 minutes\n", - "Step 2317499 of 4000000; TPS: 63636.55; ETA: 0.4 minutes\n", - "Step 2322999 of 4000000; TPS: 63648.76; ETA: 0.4 minutes\n", - "Step 2328499 of 4000000; TPS: 63665.7; ETA: 0.4 minutes\n", - "Step 2333999 of 4000000; TPS: 63671.24; ETA: 0.4 minutes\n", - "Step 2339499 of 4000000; TPS: 63684.85; ETA: 0.4 minutes\n", - "Step 2344999 of 4000000; TPS: 63695.33; ETA: 0.4 minutes\n", - "Step 2350499 of 4000000; TPS: 63695.97; ETA: 0.4 minutes\n", - "Step 2355999 of 4000000; TPS: 63709.14; ETA: 0.4 minutes\n", - "Step 2361499 of 4000000; TPS: 63720.1; ETA: 0.4 minutes\n", - "Step 2366999 of 4000000; TPS: 63731.51; ETA: 0.4 minutes\n", - "Step 2372499 of 4000000; TPS: 63741.24; ETA: 0.4 minutes\n", - "Step 2377999 of 4000000; TPS: 63750.43; ETA: 0.4 minutes\n", - "Step 2383499 of 4000000; TPS: 63762.11; ETA: 0.4 minutes\n", - "Step 2388999 of 4000000; TPS: 63767.56; ETA: 0.4 minutes\n", - "Step 2394499 of 4000000; TPS: 63782.8; ETA: 0.4 minutes\n", - "Step 2399999 of 4000000; TPS: 63793.78; ETA: 0.4 minutes\n", - "Step 2405499 of 4000000; TPS: 63807.38; ETA: 0.4 minutes\n", - "Step 2410999 of 4000000; TPS: 63816.35; ETA: 0.4 minutes\n", - "Step 2416499 of 4000000; TPS: 63827.83; ETA: 0.4 minutes\n", - "Step 2421999 of 4000000; TPS: 63838.21; ETA: 0.4 minutes\n", - "Step 2427499 of 4000000; TPS: 63848.24; ETA: 0.4 minutes\n", - "Step 2432999 of 4000000; TPS: 63850.59; ETA: 0.4 minutes\n", - "Step 2438499 of 4000000; TPS: 63855.54; ETA: 0.4 minutes\n", - "Step 2443999 of 4000000; TPS: 63867.3; ETA: 0.4 minutes\n", - "Step 2449499 of 4000000; TPS: 63878.61; ETA: 0.4 minutes\n", - "Step 2454999 of 4000000; TPS: 63887.42; ETA: 0.4 minutes\n", - "Step 2460499 of 4000000; TPS: 63903.85; ETA: 0.4 minutes\n", - "Step 2465999 of 4000000; TPS: 63912.64; ETA: 0.4 minutes\n", - "Step 2471499 of 4000000; TPS: 63919.39; ETA: 0.4 minutes\n", - "Step 2476999 of 4000000; TPS: 63924.99; ETA: 0.4 minutes\n", - "Step 2482499 of 4000000; TPS: 63927.95; ETA: 0.4 minutes\n", - "Step 2487999 of 4000000; TPS: 63941.01; ETA: 0.4 minutes\n", - "Step 2493499 of 4000000; TPS: 63905.21; ETA: 0.4 minutes\n", - "Step 2498999 of 4000000; TPS: 63914.39; ETA: 0.4 minutes\n", - "Step 2504499 of 4000000; TPS: 63918.61; ETA: 0.4 minutes\n", - "Step 2509999 of 4000000; TPS: 63931.55; ETA: 0.4 minutes\n", - "Step 2515499 of 4000000; TPS: 63944.09; ETA: 0.4 minutes\n", - "Step 2520999 of 4000000; TPS: 63957.12; ETA: 0.4 minutes\n", - "Step 2526499 of 4000000; TPS: 63966.18; ETA: 0.4 minutes\n", - "Step 2531999 of 4000000; TPS: 63978.06; ETA: 0.4 minutes\n", - "Step 2537499 of 4000000; TPS: 63992.72; ETA: 0.4 minutes\n", - "Step 2542999 of 4000000; TPS: 63994.09; ETA: 0.4 minutes\n", - "Step 2548499 of 4000000; TPS: 64002.13; ETA: 0.4 minutes\n", - "Step 2553999 of 4000000; TPS: 64012.33; ETA: 0.4 minutes\n", - "Step 2559499 of 4000000; TPS: 64026.44; ETA: 0.4 minutes\n", - "Step 2564999 of 4000000; TPS: 64037.96; ETA: 0.4 minutes\n", - "Step 2570499 of 4000000; TPS: 64049.07; ETA: 0.4 minutes\n", - "Step 2575999 of 4000000; TPS: 64060.17; ETA: 0.4 minutes\n", - "Step 2581499 of 4000000; TPS: 64067.4; ETA: 0.4 minutes\n", - "Step 2586999 of 4000000; TPS: 64079.56; ETA: 0.4 minutes\n", - "Step 2592499 of 4000000; TPS: 64089.1; ETA: 0.4 minutes\n", - "Step 2597999 of 4000000; TPS: 64100.06; ETA: 0.4 minutes\n", - "Step 2603499 of 4000000; TPS: 64108.92; ETA: 0.4 minutes\n", - "Step 2608999 of 4000000; TPS: 64116.55; ETA: 0.4 minutes\n", - "Step 2614499 of 4000000; TPS: 64124.19; ETA: 0.4 minutes\n", - "Step 2619999 of 4000000; TPS: 64133.35; ETA: 0.4 minutes\n", - "Step 2625499 of 4000000; TPS: 64144.63; ETA: 0.4 minutes\n", - "Step 2630999 of 4000000; TPS: 64153.99; ETA: 0.4 minutes\n", - "Step 2636499 of 4000000; TPS: 60501.11; ETA: 0.4 minutes\n", - "Step 2641999 of 4000000; TPS: 60517.21; ETA: 0.4 minutes\n", - "Step 2647499 of 4000000; TPS: 60529.56; ETA: 0.4 minutes\n", - "Step 2652999 of 4000000; TPS: 60544.2; ETA: 0.4 minutes\n", - "Step 2658499 of 4000000; TPS: 60558.63; ETA: 0.4 minutes\n", - "Step 2663999 of 4000000; TPS: 60571.44; ETA: 0.4 minutes\n", - "Step 2669499 of 4000000; TPS: 60586.95; ETA: 0.4 minutes\n", - "Step 2674999 of 4000000; TPS: 60600.89; ETA: 0.4 minutes\n", - "Step 2680499 of 4000000; TPS: 60619.29; ETA: 0.4 minutes\n", - "Step 2685999 of 4000000; TPS: 60635.04; ETA: 0.4 minutes\n", - "Step 2691499 of 4000000; TPS: 60654.83; ETA: 0.4 minutes\n", - "Step 2696999 of 4000000; TPS: 60665.96; ETA: 0.4 minutes\n", - "Step 2702499 of 4000000; TPS: 60678.69; ETA: 0.4 minutes\n", - "Step 2707999 of 4000000; TPS: 60693.88; ETA: 0.4 minutes\n", - "Step 2713499 of 4000000; TPS: 60704.05; ETA: 0.4 minutes\n", - "Step 2718999 of 4000000; TPS: 60720.88; ETA: 0.4 minutes\n", - "Step 2724499 of 4000000; TPS: 60737.4; ETA: 0.4 minutes\n", - "Step 2729999 of 4000000; TPS: 60751.25; ETA: 0.3 minutes\n", - "Step 2735499 of 4000000; TPS: 60768.07; ETA: 0.3 minutes\n", - "Step 2740999 of 4000000; TPS: 60782.87; ETA: 0.3 minutes\n", - "Step 2746499 of 4000000; TPS: 60796.63; ETA: 0.3 minutes\n", - "Step 2751999 of 4000000; TPS: 60810.79; ETA: 0.3 minutes\n", - "Step 2757499 of 4000000; TPS: 60827.52; ETA: 0.3 minutes\n", - "Step 2762999 of 4000000; TPS: 60839.22; ETA: 0.3 minutes\n", - "Step 2768499 of 4000000; TPS: 60853.91; ETA: 0.3 minutes\n", - "Step 2773999 of 4000000; TPS: 60870.7; ETA: 0.3 minutes\n", - "Step 2779499 of 4000000; TPS: 60886.14; ETA: 0.3 minutes\n", - "Step 2784999 of 4000000; TPS: 60901.68; ETA: 0.3 minutes\n", - "Step 2790499 of 4000000; TPS: 60918.35; ETA: 0.3 minutes\n", - "Step 2795999 of 4000000; TPS: 60924.52; ETA: 0.3 minutes\n", - "Step 2801499 of 4000000; TPS: 60929.09; ETA: 0.3 minutes\n", - "Step 2806999 of 4000000; TPS: 60941.58; ETA: 0.3 minutes\n", - "Step 2812499 of 4000000; TPS: 60955.51; ETA: 0.3 minutes\n", - "Step 2817999 of 4000000; TPS: 60971.21; ETA: 0.3 minutes\n", - "Step 2823499 of 4000000; TPS: 60986.07; ETA: 0.3 minutes\n", - "Step 2828999 of 4000000; TPS: 60997.08; ETA: 0.3 minutes\n", - "Step 2834499 of 4000000; TPS: 61012.73; ETA: 0.3 minutes\n", - "Step 2839999 of 4000000; TPS: 61027.16; ETA: 0.3 minutes\n", - "Step 2845499 of 4000000; TPS: 61035.9; ETA: 0.3 minutes\n", - "Step 2850999 of 4000000; TPS: 61049.33; ETA: 0.3 minutes\n", - "Step 2856499 of 4000000; TPS: 61064.77; ETA: 0.3 minutes\n", - "Step 2861999 of 4000000; TPS: 61073.33; ETA: 0.3 minutes\n", - "Step 2867499 of 4000000; TPS: 61088.39; ETA: 0.3 minutes\n", - "Step 2872999 of 4000000; TPS: 61099.51; ETA: 0.3 minutes\n", - "Step 2878499 of 4000000; TPS: 61112.8; ETA: 0.3 minutes\n", - "Step 2883999 of 4000000; TPS: 61126.76; ETA: 0.3 minutes\n", - "Step 2889499 of 4000000; TPS: 61143.4; ETA: 0.3 minutes\n", - "Step 2894999 of 4000000; TPS: 61156.93; ETA: 0.3 minutes\n", - "Step 2900499 of 4000000; TPS: 61166.03; ETA: 0.3 minutes\n", - "Step 2905999 of 4000000; TPS: 61178.68; ETA: 0.3 minutes\n", - "Step 2911499 of 4000000; TPS: 61192.54; ETA: 0.3 minutes\n", - "Step 2916999 of 4000000; TPS: 61207.39; ETA: 0.3 minutes\n", - "Step 2922499 of 4000000; TPS: 61223.81; ETA: 0.3 minutes\n", - "Step 2927999 of 4000000; TPS: 61239.68; ETA: 0.3 minutes\n", - "Step 2933499 of 4000000; TPS: 61256.3; ETA: 0.3 minutes\n", - "Step 2938999 of 4000000; TPS: 61268.05; ETA: 0.3 minutes\n", - "Step 2944499 of 4000000; TPS: 61277.73; ETA: 0.3 minutes\n", - "Step 2949999 of 4000000; TPS: 61288.57; ETA: 0.3 minutes\n", - "Step 2955499 of 4000000; TPS: 61304.92; ETA: 0.3 minutes\n", - "Step 2960999 of 4000000; TPS: 61317.99; ETA: 0.3 minutes\n", - "Step 2966499 of 4000000; TPS: 61333.23; ETA: 0.3 minutes\n", - "Step 2971999 of 4000000; TPS: 61346.25; ETA: 0.3 minutes\n", - "Step 2977499 of 4000000; TPS: 61355.85; ETA: 0.3 minutes\n", - "Step 2982999 of 4000000; TPS: 61367.52; ETA: 0.3 minutes\n", - "Step 2988499 of 4000000; TPS: 61383.41; ETA: 0.3 minutes\n", - "Step 2993999 of 4000000; TPS: 61394.72; ETA: 0.3 minutes\n", - "Step 2999499 of 4000000; TPS: 61408.42; ETA: 0.3 minutes\n", - "Step 3004999 of 4000000; TPS: 61418.62; ETA: 0.3 minutes\n", - "Step 3010499 of 4000000; TPS: 61379.84; ETA: 0.3 minutes\n", - "Step 3015999 of 4000000; TPS: 61391.91; ETA: 0.3 minutes\n", - "Step 3021499 of 4000000; TPS: 61405.06; ETA: 0.3 minutes\n", - "Step 3026999 of 4000000; TPS: 61417.61; ETA: 0.3 minutes\n", - "Step 3032499 of 4000000; TPS: 61431.8; ETA: 0.3 minutes\n", - "Step 3037999 of 4000000; TPS: 61445.71; ETA: 0.3 minutes\n", - "Step 3043499 of 4000000; TPS: 61460.73; ETA: 0.3 minutes\n", - "Step 3048999 of 4000000; TPS: 61474.15; ETA: 0.3 minutes\n", - "Step 3054499 of 4000000; TPS: 61488.7; ETA: 0.3 minutes\n", - "Step 3059999 of 4000000; TPS: 61500.68; ETA: 0.3 minutes\n", - "Step 3065499 of 4000000; TPS: 61513.96; ETA: 0.3 minutes\n", - "Step 3070999 of 4000000; TPS: 61525.6; ETA: 0.3 minutes\n", - "Step 3076499 of 4000000; TPS: 61540.13; ETA: 0.3 minutes\n", - "Step 3081999 of 4000000; TPS: 61550.85; ETA: 0.2 minutes\n", - "Step 3087499 of 4000000; TPS: 61564.27; ETA: 0.2 minutes\n", - "Step 3092999 of 4000000; TPS: 61575.71; ETA: 0.2 minutes\n", - "Step 3098499 of 4000000; TPS: 61589.38; ETA: 0.2 minutes\n", - "Step 3103999 of 4000000; TPS: 61601.23; ETA: 0.2 minutes\n", - "Step 3109499 of 4000000; TPS: 61616.22; ETA: 0.2 minutes\n", - "Step 3114999 of 4000000; TPS: 61628.49; ETA: 0.2 minutes\n", - "Step 3120499 of 4000000; TPS: 61643.49; ETA: 0.2 minutes\n", - "Step 3125999 of 4000000; TPS: 61654.99; ETA: 0.2 minutes\n", - "Step 3131499 of 4000000; TPS: 61666.22; ETA: 0.2 minutes\n", - "Step 3136999 of 4000000; TPS: 61677.62; ETA: 0.2 minutes\n", - "Step 3142499 of 4000000; TPS: 61690.11; ETA: 0.2 minutes\n", - "Step 3147999 of 4000000; TPS: 61701.53; ETA: 0.2 minutes\n", - "Step 3153499 of 4000000; TPS: 61711.95; ETA: 0.2 minutes\n", - "Step 3158999 of 4000000; TPS: 61716.94; ETA: 0.2 minutes\n", - "Step 3164499 of 4000000; TPS: 61729.18; ETA: 0.2 minutes\n", - "Step 3169999 of 4000000; TPS: 61742.22; ETA: 0.2 minutes\n", - "Step 3175499 of 4000000; TPS: 61746.47; ETA: 0.2 minutes\n", - "Step 3180999 of 4000000; TPS: 61759.46; ETA: 0.2 minutes\n", - "Step 3186499 of 4000000; TPS: 61772.44; ETA: 0.2 minutes\n", - "Step 3191999 of 4000000; TPS: 61784.46; ETA: 0.2 minutes\n", - "Step 3197499 of 4000000; TPS: 61797.6; ETA: 0.2 minutes\n", - "Step 3202999 of 4000000; TPS: 61801.21; ETA: 0.2 minutes\n", - "Step 3208499 of 4000000; TPS: 61801.91; ETA: 0.2 minutes\n", - "Step 3213999 of 4000000; TPS: 61809.62; ETA: 0.2 minutes\n", - "Step 3219499 of 4000000; TPS: 61825.17; ETA: 0.2 minutes\n", - "Step 3224999 of 4000000; TPS: 61835.42; ETA: 0.2 minutes\n", - "Step 3230499 of 4000000; TPS: 61847.24; ETA: 0.2 minutes\n", - "Step 3235999 of 4000000; TPS: 61854.88; ETA: 0.2 minutes\n", - "Step 3241499 of 4000000; TPS: 61866.53; ETA: 0.2 minutes\n", - "Step 3246999 of 4000000; TPS: 61880.47; ETA: 0.2 minutes\n", - "Step 3252499 of 4000000; TPS: 61894.49; ETA: 0.2 minutes\n", - "Step 3257999 of 4000000; TPS: 61904.9; ETA: 0.2 minutes\n", - "Step 3263499 of 4000000; TPS: 61917.81; ETA: 0.2 minutes\n", - "Step 3268999 of 4000000; TPS: 61928.23; ETA: 0.2 minutes\n", - "Step 3274499 of 4000000; TPS: 61939.28; ETA: 0.2 minutes\n", - "Step 3279999 of 4000000; TPS: 61949.16; ETA: 0.2 minutes\n", - "Step 3285499 of 4000000; TPS: 61961.85; ETA: 0.2 minutes\n", - "Step 3290999 of 4000000; TPS: 61973.66; ETA: 0.2 minutes\n", - "Step 3296499 of 4000000; TPS: 61981.45; ETA: 0.2 minutes\n", - "Step 3301999 of 4000000; TPS: 61989.71; ETA: 0.2 minutes\n", - "Step 3307499 of 4000000; TPS: 61999.24; ETA: 0.2 minutes\n", - "Step 3312999 of 4000000; TPS: 62011.95; ETA: 0.2 minutes\n", - "Step 3318499 of 4000000; TPS: 62026.57; ETA: 0.2 minutes\n", - "Step 3323999 of 4000000; TPS: 62035.88; ETA: 0.2 minutes\n", - "Step 3329499 of 4000000; TPS: 62042.28; ETA: 0.2 minutes\n", - "Step 3334999 of 4000000; TPS: 62047.24; ETA: 0.2 minutes\n", - "Step 3340499 of 4000000; TPS: 62056.16; ETA: 0.2 minutes\n", - "Step 3345999 of 4000000; TPS: 62065.03; ETA: 0.2 minutes\n", - "Step 3351499 of 4000000; TPS: 62076.8; ETA: 0.2 minutes\n", - "Step 3356999 of 4000000; TPS: 62088.98; ETA: 0.2 minutes\n", - "Step 3362499 of 4000000; TPS: 62102.09; ETA: 0.2 minutes\n", - "Step 3367999 of 4000000; TPS: 62114.08; ETA: 0.2 minutes\n", - "Step 3373499 of 4000000; TPS: 62123.67; ETA: 0.2 minutes\n", - "Step 3378999 of 4000000; TPS: 62135.2; ETA: 0.2 minutes\n", - "Step 3384499 of 4000000; TPS: 62146.29; ETA: 0.2 minutes\n", - "Step 3389999 of 4000000; TPS: 62156.69; ETA: 0.2 minutes\n", - "Step 3395499 of 4000000; TPS: 62168.46; ETA: 0.2 minutes\n", - "Step 3400999 of 4000000; TPS: 62180.92; ETA: 0.2 minutes\n", - "Step 3406499 of 4000000; TPS: 62193.63; ETA: 0.2 minutes\n", - "Step 3411999 of 4000000; TPS: 62206.88; ETA: 0.2 minutes\n", - "Step 3417499 of 4000000; TPS: 62218.59; ETA: 0.2 minutes\n", - "Step 3422999 of 4000000; TPS: 62224.61; ETA: 0.2 minutes\n", - "Step 3428499 of 4000000; TPS: 62235.26; ETA: 0.2 minutes\n", - "Step 3433999 of 4000000; TPS: 62246.02; ETA: 0.2 minutes\n", - "Step 3439499 of 4000000; TPS: 62256.35; ETA: 0.2 minutes\n", - "Step 3444999 of 4000000; TPS: 62268.65; ETA: 0.1 minutes\n", - "Step 3450499 of 4000000; TPS: 62281.29; ETA: 0.1 minutes\n", - "Step 3455999 of 4000000; TPS: 62291.51; ETA: 0.1 minutes\n", - "Step 3461499 of 4000000; TPS: 62298.87; ETA: 0.1 minutes\n", - "Step 3466999 of 4000000; TPS: 62307.17; ETA: 0.1 minutes\n", - "Step 3472499 of 4000000; TPS: 62318.65; ETA: 0.1 minutes\n", - "Step 3477999 of 4000000; TPS: 62328.05; ETA: 0.1 minutes\n", - "Step 3483499 of 4000000; TPS: 62335.18; ETA: 0.1 minutes\n", - "Step 3488999 of 4000000; TPS: 62344.18; ETA: 0.1 minutes\n", - "Step 3494499 of 4000000; TPS: 62356.17; ETA: 0.1 minutes\n", - "Step 3499999 of 4000000; TPS: 62365.81; ETA: 0.1 minutes\n", - "Step 3505499 of 4000000; TPS: 62374.12; ETA: 0.1 minutes\n", - "Step 3510999 of 4000000; TPS: 62385.04; ETA: 0.1 minutes\n", - "Step 3516499 of 4000000; TPS: 62397.25; ETA: 0.1 minutes\n", - "Step 3521999 of 4000000; TPS: 62404.61; ETA: 0.1 minutes\n", - "Step 3527499 of 4000000; TPS: 62410.96; ETA: 0.1 minutes\n", - "Step 3532999 of 4000000; TPS: 62422.22; ETA: 0.1 minutes\n", - "Step 3538499 of 4000000; TPS: 62429.78; ETA: 0.1 minutes\n", - "Step 3543999 of 4000000; TPS: 62438.39; ETA: 0.1 minutes\n", - "Step 3549499 of 4000000; TPS: 62449.9; ETA: 0.1 minutes\n", - "Step 3554999 of 4000000; TPS: 62460.85; ETA: 0.1 minutes\n", - "Step 3560499 of 4000000; TPS: 62471.64; ETA: 0.1 minutes\n", - "Step 3565999 of 4000000; TPS: 62480.95; ETA: 0.1 minutes\n", - "Step 3571499 of 4000000; TPS: 62491.56; ETA: 0.1 minutes\n", - "Step 3576999 of 4000000; TPS: 62500.01; ETA: 0.1 minutes\n", - "Step 3582499 of 4000000; TPS: 62512.99; ETA: 0.1 minutes\n", - "Step 3587999 of 4000000; TPS: 62522.39; ETA: 0.1 minutes\n", - "Step 3593499 of 4000000; TPS: 62520.22; ETA: 0.1 minutes\n", - "Step 3598999 of 4000000; TPS: 62530.65; ETA: 0.1 minutes\n", - "Step 3604499 of 4000000; TPS: 62539.77; ETA: 0.1 minutes\n", - "Step 3609999 of 4000000; TPS: 62550.71; ETA: 0.1 minutes\n", - "Step 3615499 of 4000000; TPS: 62561.02; ETA: 0.1 minutes\n", - "Step 3620999 of 4000000; TPS: 62563.65; ETA: 0.1 minutes\n", - "Step 3626499 of 4000000; TPS: 62573.96; ETA: 0.1 minutes\n", - "Step 3631999 of 4000000; TPS: 62585.79; ETA: 0.1 minutes\n", - "Step 3637499 of 4000000; TPS: 62594.14; ETA: 0.1 minutes\n", - "Step 3642999 of 4000000; TPS: 62603.28; ETA: 0.1 minutes\n", - "Step 3648499 of 4000000; TPS: 62614.54; ETA: 0.1 minutes\n", - "Step 3653999 of 4000000; TPS: 62624.7; ETA: 0.1 minutes\n", - "Step 3659499 of 4000000; TPS: 62633.54; ETA: 0.1 minutes\n", - "Step 3664999 of 4000000; TPS: 62642.78; ETA: 0.1 minutes\n", - "Step 3670499 of 4000000; TPS: 62651.96; ETA: 0.1 minutes\n", - "Step 3675999 of 4000000; TPS: 62660.54; ETA: 0.1 minutes\n", - "Step 3681499 of 4000000; TPS: 62672.75; ETA: 0.1 minutes\n", - "Step 3686999 of 4000000; TPS: 62684.57; ETA: 0.1 minutes\n", - "Step 3692499 of 4000000; TPS: 62694.64; ETA: 0.1 minutes\n", - "Step 3697999 of 4000000; TPS: 62703.8; ETA: 0.1 minutes\n", - "Step 3703499 of 4000000; TPS: 62705.17; ETA: 0.1 minutes\n", - "Step 3708999 of 4000000; TPS: 62716.88; ETA: 0.1 minutes\n", - "Step 3714499 of 4000000; TPS: 62727.57; ETA: 0.1 minutes\n", - "Step 3719999 of 4000000; TPS: 62735.21; ETA: 0.1 minutes\n", - "Step 3725499 of 4000000; TPS: 62744.52; ETA: 0.1 minutes\n", - "Step 3730999 of 4000000; TPS: 62754.72; ETA: 0.1 minutes\n", - "Step 3736499 of 4000000; TPS: 62763.0; ETA: 0.1 minutes\n", - "Step 3741999 of 4000000; TPS: 62771.01; ETA: 0.1 minutes\n", - "Step 3747499 of 4000000; TPS: 62782.79; ETA: 0.1 minutes\n", - "Step 3752999 of 4000000; TPS: 62792.75; ETA: 0.1 minutes\n", - "Step 3758499 of 4000000; TPS: 62804.69; ETA: 0.1 minutes\n", - "Step 3763999 of 4000000; TPS: 62815.21; ETA: 0.1 minutes\n", - "Step 3769499 of 4000000; TPS: 62827.54; ETA: 0.1 minutes\n", - "Step 3774999 of 4000000; TPS: 62834.82; ETA: 0.1 minutes\n", - "Step 3780499 of 4000000; TPS: 62842.49; ETA: 0.1 minutes\n", - "Step 3785999 of 4000000; TPS: 62851.19; ETA: 0.1 minutes\n", - "Step 3791499 of 4000000; TPS: 62860.09; ETA: 0.1 minutes\n", - "Step 3796999 of 4000000; TPS: 62868.04; ETA: 0.1 minutes\n", - "Step 3802499 of 4000000; TPS: 62872.8; ETA: 0.1 minutes\n", - "Step 3807999 of 4000000; TPS: 62884.48; ETA: 0.1 minutes\n", - "Step 3813499 of 4000000; TPS: 62896.54; ETA: 0.0 minutes\n", - "Step 3818999 of 4000000; TPS: 62908.59; ETA: 0.0 minutes\n", - "Step 3824499 of 4000000; TPS: 62920.16; ETA: 0.0 minutes\n", - "Step 3829999 of 4000000; TPS: 62932.33; ETA: 0.0 minutes\n", - "Step 3835499 of 4000000; TPS: 62942.23; ETA: 0.0 minutes\n", - "Step 3840999 of 4000000; TPS: 62945.63; ETA: 0.0 minutes\n", - "Step 3846499 of 4000000; TPS: 62958.59; ETA: 0.0 minutes\n", - "Step 3851999 of 4000000; TPS: 62966.78; ETA: 0.0 minutes\n", - "Step 3857499 of 4000000; TPS: 62978.62; ETA: 0.0 minutes\n", - "Step 3862999 of 4000000; TPS: 62989.83; ETA: 0.0 minutes\n", - "Step 3868499 of 4000000; TPS: 63000.89; ETA: 0.0 minutes\n", - "Step 3873999 of 4000000; TPS: 63011.1; ETA: 0.0 minutes\n", - "Step 3879499 of 4000000; TPS: 63022.52; ETA: 0.0 minutes\n", - "Step 3884999 of 4000000; TPS: 63033.41; ETA: 0.0 minutes\n", - "Step 3890499 of 4000000; TPS: 63043.6; ETA: 0.0 minutes\n", - "Step 3895999 of 4000000; TPS: 63053.4; ETA: 0.0 minutes\n", - "Step 3901499 of 4000000; TPS: 63058.59; ETA: 0.0 minutes\n", - "Step 3906999 of 4000000; TPS: 63053.91; ETA: 0.0 minutes\n", - "Step 3912499 of 4000000; TPS: 63063.34; ETA: 0.0 minutes\n", - "Step 3917999 of 4000000; TPS: 63065.63; ETA: 0.0 minutes\n", - "Step 3923499 of 4000000; TPS: 63076.52; ETA: 0.0 minutes\n", - "Step 3928999 of 4000000; TPS: 63086.4; ETA: 0.0 minutes\n", - "Step 3934499 of 4000000; TPS: 63094.29; ETA: 0.0 minutes\n", - "Step 3939999 of 4000000; TPS: 63103.2; ETA: 0.0 minutes\n", - "Step 3945499 of 4000000; TPS: 63112.73; ETA: 0.0 minutes\n", - "Step 3950999 of 4000000; TPS: 63121.99; ETA: 0.0 minutes\n", - "Step 3956499 of 4000000; TPS: 63131.33; ETA: 0.0 minutes\n", - "Step 3961999 of 4000000; TPS: 63139.37; ETA: 0.0 minutes\n", - "Step 3967499 of 4000000; TPS: 63147.26; ETA: 0.0 minutes\n", - "Step 3972999 of 4000000; TPS: 63155.54; ETA: 0.0 minutes\n", - "Step 3978499 of 4000000; TPS: 63163.38; ETA: 0.0 minutes\n", - "Step 3983999 of 4000000; TPS: 63171.19; ETA: 0.0 minutes\n", - "Step 3989499 of 4000000; TPS: 63179.47; ETA: 0.0 minutes\n", - "Step 3994999 of 4000000; TPS: 63184.3; ETA: 0.0 minutes\n" - ] - } - ], + "outputs": [], "source": [ "sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=device, dt = dt, \n", " gsd_write_freq=int(write_frequency), log_file_name = log, gsd_file_name = gsd) # initializing simulation\n", @@ -1359,20 +245,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "de590eda-bbb3-42a7-a87f-c278705455c8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Shrink phase completed in 31.597290515899658 seconds\n", - "Run phase completed in 63.30012035369873 seconds\n", - "Total time: 1.58 minutes\n" - ] - } - ], + "outputs": [], "source": [ "print(f\"Shrink phase completed in {(end_shrink - start_shrink)} seconds\")\n", "print(f\"Run phase completed in {(end_run - start_run)} seconds\")\n", @@ -1389,7 +265,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "7de87f9d-da23-46a4-8033-7e5019f9d37c", "metadata": {}, "outputs": [], @@ -1404,38 +280,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "a8af76ae-efe0-4912-8417-612048485a77", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mv: cannot stat '5_10mer1f_0.0005dt.txt': No such file or directory\n", - "mv: cannot stat '5_10mer1f_0.0005dt.gsd': No such file or directory\n" - ] - } - ], + "outputs": [], "source": [ "!mv \"{log}\" \"{gsd}\" \"{start_file}\" ../analysis/" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a03941e8-d760-4de3-b2b7-48e1d33e4c24", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "51d8156d-8f75-46c2-822a-e748d11c1325", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { From 3659a0219b3438aff2b78c592109fe4795c1ab35 Mon Sep 17 00:00:00 2001 From: Eridani Rojas Date: Tue, 9 Dec 2025 23:05:38 -0700 Subject: [PATCH 09/10] fixed borah script --- signac/simulation.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/signac/simulation.py b/signac/simulation.py index 2440844..59e8d97 100644 --- a/signac/simulation.py +++ b/signac/simulation.py @@ -22,16 +22,16 @@ N_chains = 100 # number of polymer chains initial_dens = 0.001 # initial packing density -final_dens = 0.15 # final packing density after shrink +final_dens = 0.3 # final packing density after shrink N_flakes = 10 # number of flakes -chain_length = 20 # chain length +chain_length = 10 # chain length dt = 0.0005 # time step -temp = 1.0 # kT for production run +temp = 5.0 # kT for production run -write_frequency = 20000 # trajectory/log write frequency +write_frequency = 50000 # trajectory/log write frequency shrink_steps = int(1e6) # steps for shrink phase sim_start = int(shrink_steps / write_frequency) # for analysis notebook -steps = int(5e6) # steps for production NVT run +steps = int(25e6) # steps for production NVT run flake_repeat = 5 # number of lattice repeats in x,y for flakes @@ -168,9 +168,9 @@ def _build_system(self): # Output filenames # --------------------------------------------------------------------------- -gsd_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.gsd" -log_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt.txt" -start_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt_start.txt" +gsd_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt_{final_dens}_dens.gsd" +log_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt_{final_dens}_dens.txt" +start_file = f"{N_chains}_{chain_length}mer{N_flakes}f_{dt}dt_{final_dens}_dens_start.txt" # --------------------------------------------------------------------------- From 891aaa90534c1867f9902ff3d22dbb59726d2146 Mon Sep 17 00:00:00 2001 From: eridanirojas Date: Mon, 23 Mar 2026 13:46:28 -0600 Subject: [PATCH 10/10] RoG calculations at end of onboarding.ipynb --- sims/Onboarding.ipynb | 693 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 677 insertions(+), 16 deletions(-) diff --git a/sims/Onboarding.ipynb b/sims/Onboarding.ipynb index 291963e..e1cbccf 100644 --- a/sims/Onboarding.ipynb +++ b/sims/Onboarding.ipynb @@ -18,7 +18,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "8c3308c6-8bc7-4368-bbf6-5c53489c0a18", "metadata": {}, "outputs": [], @@ -38,6 +38,7 @@ "from mbuild.compound import Compound # base compound object to create molecule geometry\n", "from mbuild.lattice import Lattice # lattice object to initialize lattice spacing and points\n", "import unyt as u # module used for unit definitions\n", + "import math\n", "warnings.filterwarnings('ignore') # ignore warnings" ] }, @@ -51,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "id": "2dce7cdb-61d5-4f40-b1d5-2b69849f7d10", "metadata": {}, "outputs": [], @@ -99,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "4bca3b72-54b5-4e7b-bf1b-4d3b41050ddb", "metadata": {}, "outputs": [], @@ -135,16 +136,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "id": "f74ebf6d-79e1-488f-8921-a188ebe92673", "metadata": {}, "outputs": [], "source": [ "N_chains = 100 # number of polymer chains\n", "initial_dens = 0.001 # initial packing density to initialize system\n", - "final_dens = 0.15 # final packing density for shrinking\n", - "N_flakes = 10 # number of flakes\n", - "chain_length = 20 # length of polymer chains\n", + "final_dens = 0.35 # final packing density for shrinking\n", + "N_flakes = 5 # number of flakes\n", + "chain_length = 10 # length of polymer chains\n", "dt = 0.0005 # step size of simulation\n", "temp = 1.0 # kT, temperature of simulation\n", "write_frequency = 20000 # write frequency of simulation, the lower the more frames captured\n", @@ -164,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 114, "id": "b06e0517-6747-4eff-a7db-ac8ab0c99b48", "metadata": {}, "outputs": [], @@ -182,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "id": "21378af5-3fba-491f-9360-c069360d46b5", "metadata": {}, "outputs": [], @@ -206,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "id": "00a6af7b-f424-4a27-a8be-f80a20c8ca03", "metadata": {}, "outputs": [], @@ -226,10 +227,596 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "id": "64f9fae9-a3b9-418e-a03b-681b754e1b03", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "*Warning*: dihedral.harmonic: specified K <= 0\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Initializing simulation state from a gsd.hoomd.Frame.\n", + "Step 10500 of 1000000; TPS: 3849.49; ETA: 4.3 minutes\n", + "Step 21000 of 1000000; TPS: 4237.15; ETA: 3.9 minutes\n", + "Step 31500 of 1000000; TPS: 4365.73; ETA: 3.7 minutes\n", + "Step 42000 of 1000000; TPS: 4457.47; ETA: 3.6 minutes\n", + "Step 52500 of 1000000; TPS: 4456.35; ETA: 3.5 minutes\n", + "Step 63000 of 1000000; TPS: 4494.18; ETA: 3.5 minutes\n", + "Step 73500 of 1000000; TPS: 4537.11; ETA: 3.4 minutes\n", + "Step 84000 of 1000000; TPS: 4497.96; ETA: 3.4 minutes\n", + "Step 94500 of 1000000; TPS: 4515.82; ETA: 3.3 minutes\n", + "Step 105000 of 1000000; TPS: 4541.06; ETA: 3.3 minutes\n", + "Step 115500 of 1000000; TPS: 4581.32; ETA: 3.2 minutes\n", + "Step 126000 of 1000000; TPS: 4601.6; ETA: 3.2 minutes\n", + "Step 136500 of 1000000; TPS: 4616.38; ETA: 3.1 minutes\n", + "Step 147000 of 1000000; TPS: 4626.77; ETA: 3.1 minutes\n", + "Step 157500 of 1000000; TPS: 4640.21; ETA: 3.0 minutes\n", + "Step 168000 of 1000000; TPS: 4647.74; ETA: 3.0 minutes\n", + "Step 178500 of 1000000; TPS: 4660.76; ETA: 2.9 minutes\n", + "Step 189000 of 1000000; TPS: 4671.45; ETA: 2.9 minutes\n", + "Step 199500 of 1000000; TPS: 4683.3; ETA: 2.8 minutes\n", + "Step 210000 of 1000000; TPS: 4699.96; ETA: 2.8 minutes\n", + "Step 220500 of 1000000; TPS: 4705.91; ETA: 2.8 minutes\n", + "Step 231000 of 1000000; TPS: 4713.34; ETA: 2.7 minutes\n", + "Step 241500 of 1000000; TPS: 4721.99; ETA: 2.7 minutes\n", + "Step 252000 of 1000000; TPS: 4732.62; ETA: 2.6 minutes\n", + "Step 262500 of 1000000; TPS: 4738.25; ETA: 2.6 minutes\n", + "Step 273000 of 1000000; TPS: 4752.04; ETA: 2.5 minutes\n", + "Step 283500 of 1000000; TPS: 4762.36; ETA: 2.5 minutes\n", + "Step 294000 of 1000000; TPS: 4774.57; ETA: 2.5 minutes\n", + "Step 304500 of 1000000; TPS: 4785.97; ETA: 2.4 minutes\n", + "Step 315000 of 1000000; TPS: 4794.32; ETA: 2.4 minutes\n", + "Step 325500 of 1000000; TPS: 4801.9; ETA: 2.3 minutes\n", + "Step 336000 of 1000000; TPS: 4808.19; ETA: 2.3 minutes\n", + "Step 346500 of 1000000; TPS: 4815.02; ETA: 2.3 minutes\n", + "Step 357000 of 1000000; TPS: 4820.63; ETA: 2.2 minutes\n", + "Step 367500 of 1000000; TPS: 4825.4; ETA: 2.2 minutes\n", + "Step 378000 of 1000000; TPS: 4829.19; ETA: 2.1 minutes\n", + "Step 388500 of 1000000; TPS: 4834.36; ETA: 2.1 minutes\n", + "Step 399000 of 1000000; TPS: 4838.61; ETA: 2.1 minutes\n", + "Step 409500 of 1000000; TPS: 4842.65; ETA: 2.0 minutes\n", + "Step 420000 of 1000000; TPS: 4847.24; ETA: 2.0 minutes\n", + "Step 430500 of 1000000; TPS: 4854.81; ETA: 2.0 minutes\n", + "Step 441000 of 1000000; TPS: 4863.15; ETA: 1.9 minutes\n", + "Step 451500 of 1000000; TPS: 4870.07; ETA: 1.9 minutes\n", + "Step 462000 of 1000000; TPS: 4875.58; ETA: 1.8 minutes\n", + "Step 472500 of 1000000; TPS: 4880.2; ETA: 1.8 minutes\n", + "Step 483000 of 1000000; TPS: 4884.73; ETA: 1.8 minutes\n", + "Step 493500 of 1000000; TPS: 4886.2; ETA: 1.7 minutes\n", + "Step 504000 of 1000000; TPS: 4889.27; ETA: 1.7 minutes\n", + "Step 514500 of 1000000; TPS: 4892.82; ETA: 1.7 minutes\n", + "Step 525000 of 1000000; TPS: 4895.85; ETA: 1.6 minutes\n", + "Step 535500 of 1000000; TPS: 4896.64; ETA: 1.6 minutes\n", + "Step 546000 of 1000000; TPS: 4897.35; ETA: 1.5 minutes\n", + "Step 556500 of 1000000; TPS: 4896.71; ETA: 1.5 minutes\n", + "Step 567000 of 1000000; TPS: 4899.01; ETA: 1.5 minutes\n", + "Step 577500 of 1000000; TPS: 4901.02; ETA: 1.4 minutes\n", + "Step 588000 of 1000000; TPS: 4902.56; ETA: 1.4 minutes\n", + "Step 598500 of 1000000; TPS: 4908.18; ETA: 1.4 minutes\n", + "Step 609000 of 1000000; 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4486.98; ETA: 16.3 minutes\n", + "Step 627499 of 5000000; TPS: 4477.22; ETA: 16.3 minutes\n", + "Step 637999 of 5000000; TPS: 4469.83; ETA: 16.3 minutes\n", + "Step 648499 of 5000000; TPS: 4462.39; ETA: 16.3 minutes\n", + "Step 658999 of 5000000; TPS: 4457.49; ETA: 16.2 minutes\n", + "Step 669499 of 5000000; TPS: 4453.13; ETA: 16.2 minutes\n", + "Step 679999 of 5000000; TPS: 4449.25; ETA: 16.2 minutes\n", + "Step 690499 of 5000000; TPS: 4445.84; ETA: 16.2 minutes\n", + "Step 700999 of 5000000; TPS: 4441.46; ETA: 16.1 minutes\n", + "Step 711499 of 5000000; TPS: 4434.99; ETA: 16.1 minutes\n", + "Step 721999 of 5000000; TPS: 4430.47; ETA: 16.1 minutes\n", + "Step 732499 of 5000000; TPS: 4427.5; ETA: 16.1 minutes\n", + "Step 742999 of 5000000; TPS: 4426.51; ETA: 16.0 minutes\n", + "Step 753499 of 5000000; TPS: 4424.11; ETA: 16.0 minutes\n", + "Step 763999 of 5000000; TPS: 4420.57; ETA: 16.0 minutes\n", + "Step 774499 of 5000000; TPS: 4414.99; ETA: 16.0 minutes\n", + "Step 784999 of 5000000; TPS: 4408.25; ETA: 15.9 minutes\n", + "Step 795499 of 5000000; TPS: 4402.4; ETA: 15.9 minutes\n", + "Step 805999 of 5000000; TPS: 4396.34; ETA: 15.9 minutes\n", + "Step 816499 of 5000000; TPS: 4391.79; ETA: 15.9 minutes\n", + "Step 826999 of 5000000; TPS: 4388.78; ETA: 15.8 minutes\n", + "Step 837499 of 5000000; TPS: 4387.44; ETA: 15.8 minutes\n", + "Step 847999 of 5000000; TPS: 4383.24; ETA: 15.8 minutes\n", + "Step 858499 of 5000000; TPS: 4381.14; ETA: 15.8 minutes\n", + "Step 868999 of 5000000; TPS: 4380.38; ETA: 15.7 minutes\n", + "Step 879499 of 5000000; TPS: 4376.9; ETA: 15.7 minutes\n", + "Step 889999 of 5000000; TPS: 4364.33; ETA: 15.7 minutes\n", + "Step 900499 of 5000000; TPS: 4353.14; ETA: 15.7 minutes\n", + "Step 910999 of 5000000; TPS: 4344.92; ETA: 15.7 minutes\n", + "Step 921499 of 5000000; TPS: 4335.03; ETA: 15.7 minutes\n", + "Step 931999 of 5000000; TPS: 4322.85; ETA: 15.7 minutes\n", + "Step 942499 of 5000000; TPS: 4312.31; ETA: 15.7 minutes\n", + "Step 952999 of 5000000; TPS: 4301.28; ETA: 15.7 minutes\n", + "Step 963499 of 5000000; TPS: 4289.9; ETA: 15.7 minutes\n", + "Step 973999 of 5000000; TPS: 4277.54; ETA: 15.7 minutes\n", + "Step 984499 of 5000000; TPS: 4267.34; ETA: 15.7 minutes\n", + "Step 994999 of 5000000; TPS: 4258.22; ETA: 15.7 minutes\n", + "Step 1005499 of 5000000; TPS: 4249.28; ETA: 15.7 minutes\n", + "Step 1015999 of 5000000; TPS: 4239.33; ETA: 15.7 minutes\n", + "Step 1026499 of 5000000; TPS: 4229.16; ETA: 15.7 minutes\n", + "Step 1036999 of 5000000; TPS: 4219.49; ETA: 15.7 minutes\n", + "Step 1047499 of 5000000; TPS: 4208.61; ETA: 15.7 minutes\n", + "Step 1057999 of 5000000; TPS: 4198.75; ETA: 15.6 minutes\n", + "Step 1068499 of 5000000; TPS: 4188.18; ETA: 15.6 minutes\n", + "Step 1078999 of 5000000; TPS: 4178.73; ETA: 15.6 minutes\n", + "Step 1089499 of 5000000; TPS: 4170.24; ETA: 15.6 minutes\n", + "Step 1099999 of 5000000; TPS: 4163.09; ETA: 15.6 minutes\n", + "Step 1110499 of 5000000; TPS: 4156.86; ETA: 15.6 minutes\n", + "Step 1120999 of 5000000; TPS: 4149.97; ETA: 15.6 minutes\n", + "Step 1131499 of 5000000; TPS: 4143.92; ETA: 15.6 minutes\n", + "Step 1141999 of 5000000; TPS: 4137.02; ETA: 15.5 minutes\n", + "Step 1152499 of 5000000; TPS: 4128.52; ETA: 15.5 minutes\n", + "Step 1162999 of 5000000; TPS: 4118.71; ETA: 15.5 minutes\n", + "Step 1173499 of 5000000; TPS: 4109.77; ETA: 15.5 minutes\n", + "Step 1183999 of 5000000; TPS: 4100.18; ETA: 15.5 minutes\n", + "Step 1194499 of 5000000; TPS: 4091.86; ETA: 15.5 minutes\n", + "Step 1204999 of 5000000; TPS: 4084.16; ETA: 15.5 minutes\n", + "Step 1215499 of 5000000; TPS: 4074.69; ETA: 15.5 minutes\n", + "Step 1225999 of 5000000; TPS: 4065.61; ETA: 15.5 minutes\n", + "Step 1236499 of 5000000; TPS: 4058.62; ETA: 15.5 minutes\n", + "Step 1246999 of 5000000; TPS: 4050.41; ETA: 15.4 minutes\n", + "Step 1257499 of 5000000; TPS: 4042.57; ETA: 15.4 minutes\n", + "Step 1267999 of 5000000; TPS: 4033.8; ETA: 15.4 minutes\n", + "Step 1278499 of 5000000; TPS: 4024.86; ETA: 15.4 minutes\n", + "Step 1288999 of 5000000; TPS: 4015.78; ETA: 15.4 minutes\n", + "Step 1299499 of 5000000; TPS: 4012.91; ETA: 15.4 minutes\n", + "Step 1309999 of 5000000; TPS: 4008.67; ETA: 15.3 minutes\n", + "Step 1320499 of 5000000; TPS: 4007.88; ETA: 15.3 minutes\n", + "Step 1330999 of 5000000; TPS: 4009.56; ETA: 15.3 minutes\n", + "Step 1341499 of 5000000; TPS: 4011.48; ETA: 15.2 minutes\n", + "Step 1351999 of 5000000; TPS: 4011.36; ETA: 15.2 minutes\n", + "Step 1362499 of 5000000; TPS: 4007.98; ETA: 15.1 minutes\n", + "Step 1372999 of 5000000; TPS: 4004.67; ETA: 15.1 minutes\n", + "Step 1383499 of 5000000; TPS: 4000.98; ETA: 15.1 minutes\n", + "Step 1393999 of 5000000; TPS: 3997.35; ETA: 15.0 minutes\n", + "Step 1404499 of 5000000; TPS: 3993.74; ETA: 15.0 minutes\n", + "Step 1414999 of 5000000; TPS: 3989.52; ETA: 15.0 minutes\n", + "Step 1425499 of 5000000; TPS: 3985.48; ETA: 14.9 minutes\n", + "Step 1435999 of 5000000; TPS: 3982.03; ETA: 14.9 minutes\n", + "Step 1446499 of 5000000; TPS: 3979.6; ETA: 14.9 minutes\n", + "Step 1456999 of 5000000; TPS: 3976.45; ETA: 14.8 minutes\n", + "Step 1467499 of 5000000; TPS: 3972.99; ETA: 14.8 minutes\n", + "Step 1477999 of 5000000; TPS: 3969.41; ETA: 14.8 minutes\n", + "Step 1488499 of 5000000; TPS: 3965.23; ETA: 14.8 minutes\n", + "Step 1498999 of 5000000; TPS: 3961.7; ETA: 14.7 minutes\n", + "Step 1509499 of 5000000; TPS: 3957.32; ETA: 14.7 minutes\n", + "Step 1519999 of 5000000; TPS: 3953.4; ETA: 14.7 minutes\n", + "Step 1530499 of 5000000; TPS: 3949.52; ETA: 14.6 minutes\n", + "Step 1540999 of 5000000; TPS: 3945.29; ETA: 14.6 minutes\n", + "Step 1551499 of 5000000; TPS: 3942.45; ETA: 14.6 minutes\n", + "Step 1561999 of 5000000; TPS: 3939.31; ETA: 14.5 minutes\n", + "Step 1572499 of 5000000; TPS: 3935.95; ETA: 14.5 minutes\n", + "Step 1582999 of 5000000; TPS: 3931.88; ETA: 14.5 minutes\n", + "Step 1593499 of 5000000; TPS: 3928.08; ETA: 14.5 minutes\n", + "Step 1603999 of 5000000; TPS: 3923.95; ETA: 14.4 minutes\n", + "Step 1614499 of 5000000; TPS: 3920.35; ETA: 14.4 minutes\n", + "Step 1624999 of 5000000; TPS: 3916.33; ETA: 14.4 minutes\n", + "Step 1635499 of 5000000; TPS: 3912.97; ETA: 14.3 minutes\n", + "Step 1645999 of 5000000; TPS: 3909.49; ETA: 14.3 minutes\n", + "Step 1656499 of 5000000; TPS: 3906.55; ETA: 14.3 minutes\n", + "Step 1666999 of 5000000; TPS: 3902.68; ETA: 14.2 minutes\n", + "Step 1677499 of 5000000; TPS: 3898.3; ETA: 14.2 minutes\n", + "Step 1687999 of 5000000; TPS: 3893.83; ETA: 14.2 minutes\n", + "Step 1698499 of 5000000; TPS: 3889.24; ETA: 14.1 minutes\n", + "Step 1708999 of 5000000; TPS: 3884.32; ETA: 14.1 minutes\n", + "Step 1719499 of 5000000; TPS: 3876.62; ETA: 14.1 minutes\n", + "Step 1729999 of 5000000; TPS: 3867.92; ETA: 14.1 minutes\n", + "Step 1740499 of 5000000; TPS: 3862.54; ETA: 14.1 minutes\n", + "Step 1750999 of 5000000; TPS: 3856.15; ETA: 14.0 minutes\n", + "Step 1761499 of 5000000; TPS: 3851.76; ETA: 14.0 minutes\n", + "Step 1771999 of 5000000; TPS: 3845.16; ETA: 14.0 minutes\n", + "Step 1782499 of 5000000; TPS: 3840.14; ETA: 14.0 minutes\n", + "Step 1792999 of 5000000; TPS: 3835.83; ETA: 13.9 minutes\n", + "Step 1803499 of 5000000; TPS: 3830.67; ETA: 13.9 minutes\n", + "Step 1813999 of 5000000; TPS: 3825.96; ETA: 13.9 minutes\n", + "Step 1824499 of 5000000; TPS: 3820.9; ETA: 13.9 minutes\n", + "Step 1834999 of 5000000; TPS: 3814.6; ETA: 13.8 minutes\n", + "Step 1845499 of 5000000; TPS: 3808.58; ETA: 13.8 minutes\n", + "Step 1855999 of 5000000; TPS: 3801.38; ETA: 13.8 minutes\n", + "Step 1866499 of 5000000; TPS: 3790.05; ETA: 13.8 minutes\n", + "Step 1876999 of 5000000; TPS: 3778.96; ETA: 13.8 minutes\n", + "Step 1887499 of 5000000; TPS: 3767.31; ETA: 13.8 minutes\n", + "Step 1897999 of 5000000; TPS: 3755.93; ETA: 13.8 minutes\n", + "Step 1908499 of 5000000; TPS: 3744.87; ETA: 13.8 minutes\n", + "Step 1918999 of 5000000; TPS: 3733.88; ETA: 13.8 minutes\n", + "Step 1929499 of 5000000; TPS: 3724.06; ETA: 13.7 minutes\n", + "Step 1939999 of 5000000; TPS: 3714.61; ETA: 13.7 minutes\n", + "Step 1950499 of 5000000; TPS: 3706.46; ETA: 13.7 minutes\n", + "Step 1960999 of 5000000; TPS: 3699.21; ETA: 13.7 minutes\n", + "Step 1971499 of 5000000; TPS: 3690.64; ETA: 13.7 minutes\n", + "Step 1981999 of 5000000; TPS: 3681.75; ETA: 13.7 minutes\n", + "Step 1992499 of 5000000; TPS: 3672.27; ETA: 13.6 minutes\n", + "Step 2002999 of 5000000; TPS: 3663.4; ETA: 13.6 minutes\n", + "Step 2013499 of 5000000; TPS: 3653.57; ETA: 13.6 minutes\n", + "Step 2023999 of 5000000; TPS: 3644.31; ETA: 13.6 minutes\n", + "Step 2034499 of 5000000; TPS: 3635.58; ETA: 13.6 minutes\n", + "Step 2044999 of 5000000; TPS: 3629.85; ETA: 13.6 minutes\n", + "Step 2055499 of 5000000; TPS: 3623.29; ETA: 13.5 minutes\n", + "Step 2065999 of 5000000; TPS: 3616.24; ETA: 13.5 minutes\n", + "Step 2076499 of 5000000; TPS: 3610.08; ETA: 13.5 minutes\n", + "Step 2086999 of 5000000; TPS: 3602.88; ETA: 13.5 minutes\n", + "Step 2097499 of 5000000; TPS: 3595.09; ETA: 13.5 minutes\n", + "Step 2107999 of 5000000; TPS: 3588.63; ETA: 13.4 minutes\n", + "Step 2118499 of 5000000; TPS: 3581.38; ETA: 13.4 minutes\n", + "Step 2128999 of 5000000; TPS: 3574.79; ETA: 13.4 minutes\n", + "Step 2139499 of 5000000; TPS: 3568.23; ETA: 13.4 minutes\n", + "Step 2149999 of 5000000; TPS: 3561.25; ETA: 13.3 minutes\n", + "Step 2160499 of 5000000; TPS: 3554.02; ETA: 13.3 minutes\n", + "Step 2170999 of 5000000; TPS: 3546.49; ETA: 13.3 minutes\n", + "Step 2181499 of 5000000; TPS: 3539.02; ETA: 13.3 minutes\n", + "Step 2191999 of 5000000; TPS: 3532.16; ETA: 13.2 minutes\n", + "Step 2202499 of 5000000; TPS: 3526.19; ETA: 13.2 minutes\n", + "Step 2212999 of 5000000; TPS: 3519.59; ETA: 13.2 minutes\n", + "Step 2223499 of 5000000; TPS: 3512.34; ETA: 13.2 minutes\n", + "Step 2233999 of 5000000; TPS: 3504.91; ETA: 13.2 minutes\n", + "Step 2244499 of 5000000; TPS: 3498.73; ETA: 13.1 minutes\n", + "Step 2254999 of 5000000; TPS: 3492.86; ETA: 13.1 minutes\n", + "Step 2265499 of 5000000; TPS: 3487.76; ETA: 13.1 minutes\n", + "Step 2275999 of 5000000; TPS: 3483.02; ETA: 13.0 minutes\n", + "Step 2286499 of 5000000; TPS: 3478.27; ETA: 13.0 minutes\n", + "Step 2296999 of 5000000; TPS: 3473.96; ETA: 13.0 minutes\n", + "Step 2307499 of 5000000; TPS: 3468.13; ETA: 12.9 minutes\n", + "Step 2317999 of 5000000; TPS: 3462.0; ETA: 12.9 minutes\n", + "Step 2328499 of 5000000; TPS: 3455.36; ETA: 12.9 minutes\n", + "Step 2338999 of 5000000; TPS: 3449.49; ETA: 12.9 minutes\n", + "Step 2349499 of 5000000; TPS: 3444.02; ETA: 12.8 minutes\n", + "Step 2359999 of 5000000; TPS: 3438.66; ETA: 12.8 minutes\n", + "Step 2370499 of 5000000; TPS: 3433.37; ETA: 12.8 minutes\n", + "Step 2380999 of 5000000; TPS: 3427.8; ETA: 12.7 minutes\n", + "Step 2391499 of 5000000; TPS: 3421.45; ETA: 12.7 minutes\n", + "Step 2401999 of 5000000; TPS: 3414.82; ETA: 12.7 minutes\n", + "Step 2412499 of 5000000; TPS: 3409.54; ETA: 12.6 minutes\n", + "Step 2422999 of 5000000; TPS: 3405.86; ETA: 12.6 minutes\n", + "Step 2433499 of 5000000; TPS: 3401.39; ETA: 12.6 minutes\n", + "Step 2443999 of 5000000; TPS: 3396.96; ETA: 12.5 minutes\n", + "Step 2454499 of 5000000; TPS: 3392.2; ETA: 12.5 minutes\n", + "Step 2464999 of 5000000; TPS: 3386.19; ETA: 12.5 minutes\n", + "Step 2475499 of 5000000; TPS: 3382.16; ETA: 12.4 minutes\n", + "Step 2485999 of 5000000; TPS: 3376.96; ETA: 12.4 minutes\n", + "Step 2496499 of 5000000; TPS: 3371.32; ETA: 12.4 minutes\n", + "Step 2506999 of 5000000; TPS: 3366.38; ETA: 12.3 minutes\n", + "Step 2517499 of 5000000; TPS: 3361.97; ETA: 12.3 minutes\n", + "Step 2527999 of 5000000; TPS: 3360.47; ETA: 12.3 minutes\n", + "Step 2538499 of 5000000; TPS: 3358.6; ETA: 12.2 minutes\n", + "Step 2548999 of 5000000; TPS: 3356.89; ETA: 12.2 minutes\n", + "Step 2559499 of 5000000; TPS: 3355.13; ETA: 12.1 minutes\n", + "Step 2569999 of 5000000; TPS: 3353.4; ETA: 12.1 minutes\n", + "Step 2580499 of 5000000; TPS: 3351.65; ETA: 12.0 minutes\n", + "Step 2590999 of 5000000; TPS: 3350.05; ETA: 12.0 minutes\n", + "Step 2601499 of 5000000; TPS: 3348.39; ETA: 11.9 minutes\n", + "Step 2611999 of 5000000; TPS: 3348.55; ETA: 11.9 minutes\n", + "Step 2622499 of 5000000; TPS: 3346.77; ETA: 11.8 minutes\n", + "Step 2632999 of 5000000; TPS: 3345.06; ETA: 11.8 minutes\n", + "Step 2643499 of 5000000; TPS: 3343.0; ETA: 11.7 minutes\n", + "Step 2653999 of 5000000; TPS: 3340.16; ETA: 11.7 minutes\n", + "Step 2664499 of 5000000; TPS: 3337.97; ETA: 11.7 minutes\n", + "Step 2674999 of 5000000; TPS: 3334.64; ETA: 11.6 minutes\n", + "Step 2685499 of 5000000; TPS: 3329.98; ETA: 11.6 minutes\n", + "Step 2695999 of 5000000; TPS: 3326.45; ETA: 11.5 minutes\n", + "Step 2706499 of 5000000; TPS: 3323.2; ETA: 11.5 minutes\n", + "Step 2716999 of 5000000; TPS: 3319.13; ETA: 11.5 minutes\n", + "Step 2727499 of 5000000; TPS: 3316.29; ETA: 11.4 minutes\n", + "Step 2737999 of 5000000; TPS: 3313.67; ETA: 11.4 minutes\n", + "Step 2748499 of 5000000; TPS: 3310.7; ETA: 11.3 minutes\n", + "Step 2758999 of 5000000; TPS: 3306.7; ETA: 11.3 minutes\n", + "Step 2769499 of 5000000; TPS: 3302.47; ETA: 11.3 minutes\n", + "Step 2779999 of 5000000; TPS: 3298.18; ETA: 11.2 minutes\n", + "Step 2790499 of 5000000; TPS: 3294.39; ETA: 11.2 minutes\n", + "Step 2800999 of 5000000; TPS: 3290.43; ETA: 11.1 minutes\n", + "Step 2811499 of 5000000; TPS: 3286.11; ETA: 11.1 minutes\n", + "Step 2821999 of 5000000; TPS: 3281.83; ETA: 11.1 minutes\n", + "Step 2832499 of 5000000; TPS: 3277.39; ETA: 11.0 minutes\n", + "Step 2842999 of 5000000; TPS: 3272.92; ETA: 11.0 minutes\n", + "Step 2853499 of 5000000; TPS: 3268.29; ETA: 10.9 minutes\n", + "Step 2863999 of 5000000; TPS: 3264.6; ETA: 10.9 minutes\n", + "Step 2874499 of 5000000; TPS: 3260.57; ETA: 10.9 minutes\n", + "Step 2884999 of 5000000; TPS: 3257.39; ETA: 10.8 minutes\n", + "Step 2895499 of 5000000; TPS: 3253.47; ETA: 10.8 minutes\n", + "Step 2905999 of 5000000; TPS: 3249.28; ETA: 10.7 minutes\n", + "Step 2916499 of 5000000; TPS: 3245.22; ETA: 10.7 minutes\n", + "Step 2926999 of 5000000; TPS: 3240.97; ETA: 10.7 minutes\n", + "Step 2937499 of 5000000; TPS: 3236.89; ETA: 10.6 minutes\n", + "Step 2947999 of 5000000; TPS: 3233.46; ETA: 10.6 minutes\n", + "Step 2958499 of 5000000; TPS: 3230.07; ETA: 10.5 minutes\n", + "Step 2968999 of 5000000; TPS: 3226.23; ETA: 10.5 minutes\n", + "Step 2979499 of 5000000; TPS: 3221.76; ETA: 10.5 minutes\n", + "Step 2989999 of 5000000; TPS: 3218.23; ETA: 10.4 minutes\n", + "Step 3000499 of 5000000; TPS: 3215.51; ETA: 10.4 minutes\n", + "Step 3010999 of 5000000; TPS: 3212.39; ETA: 10.3 minutes\n", + "Step 3021499 of 5000000; TPS: 3209.06; ETA: 10.3 minutes\n", + "Step 3031999 of 5000000; TPS: 3206.46; ETA: 10.2 minutes\n", + "Step 3042499 of 5000000; TPS: 3204.09; ETA: 10.2 minutes\n", + "Step 3052999 of 5000000; TPS: 3201.26; ETA: 10.1 minutes\n", + "Step 3063499 of 5000000; TPS: 3198.05; ETA: 10.1 minutes\n", + "Step 3073999 of 5000000; TPS: 3194.74; ETA: 10.0 minutes\n", + "Step 3084499 of 5000000; TPS: 3191.58; ETA: 10.0 minutes\n", + "Step 3094999 of 5000000; TPS: 3188.26; ETA: 10.0 minutes\n", + "Step 3105499 of 5000000; TPS: 3185.88; ETA: 9.9 minutes\n", + "Step 3115999 of 5000000; TPS: 3183.28; ETA: 9.9 minutes\n", + "Step 3126499 of 5000000; TPS: 3180.35; ETA: 9.8 minutes\n", + "Step 3136999 of 5000000; TPS: 3177.61; ETA: 9.8 minutes\n", + "Step 3147499 of 5000000; TPS: 3174.67; ETA: 9.7 minutes\n", + "Step 3157999 of 5000000; TPS: 3171.64; ETA: 9.7 minutes\n", + "Step 3168499 of 5000000; TPS: 3168.54; ETA: 9.6 minutes\n", + "Step 3178999 of 5000000; TPS: 3165.34; ETA: 9.6 minutes\n", + "Step 3189499 of 5000000; TPS: 3162.78; ETA: 9.5 minutes\n", + "Step 3199999 of 5000000; TPS: 3159.79; ETA: 9.5 minutes\n", + "Step 3210499 of 5000000; TPS: 3157.3; ETA: 9.4 minutes\n", + "Step 3220999 of 5000000; TPS: 3154.46; ETA: 9.4 minutes\n", + "Step 3231499 of 5000000; TPS: 3150.96; ETA: 9.4 minutes\n", + "Step 3241999 of 5000000; TPS: 3148.07; ETA: 9.3 minutes\n", + "Step 3252499 of 5000000; TPS: 3146.53; ETA: 9.3 minutes\n", + "Step 3262999 of 5000000; TPS: 3143.58; ETA: 9.2 minutes\n", + "Step 3273499 of 5000000; TPS: 3140.97; ETA: 9.2 minutes\n", + "Step 3283999 of 5000000; TPS: 3138.14; ETA: 9.1 minutes\n", + "Step 3294499 of 5000000; TPS: 3135.56; ETA: 9.1 minutes\n", + "Step 3304999 of 5000000; TPS: 3134.0; ETA: 9.0 minutes\n", + "Step 3315499 of 5000000; TPS: 3132.63; ETA: 9.0 minutes\n", + "Step 3325999 of 5000000; TPS: 3132.85; ETA: 8.9 minutes\n", + "Step 3336499 of 5000000; TPS: 3133.25; ETA: 8.8 minutes\n", + "Step 3346999 of 5000000; TPS: 3133.59; ETA: 8.8 minutes\n", + "Step 3357499 of 5000000; TPS: 3133.87; ETA: 8.7 minutes\n", + "Step 3367999 of 5000000; TPS: 3134.24; ETA: 8.7 minutes\n", + "Step 3378499 of 5000000; TPS: 3134.64; ETA: 8.6 minutes\n", + "Step 3388999 of 5000000; TPS: 3134.82; ETA: 8.6 minutes\n", + "Step 3399499 of 5000000; TPS: 3135.03; ETA: 8.5 minutes\n", + "Step 3409999 of 5000000; TPS: 3135.15; ETA: 8.5 minutes\n", + "Step 3420499 of 5000000; TPS: 3135.29; ETA: 8.4 minutes\n", + "Step 3430999 of 5000000; TPS: 3135.42; ETA: 8.3 minutes\n", + "Step 3441499 of 5000000; TPS: 3135.46; ETA: 8.3 minutes\n", + "Step 3451999 of 5000000; TPS: 3135.63; ETA: 8.2 minutes\n", + "Step 3462499 of 5000000; TPS: 3136.17; ETA: 8.2 minutes\n", + "Step 3472999 of 5000000; TPS: 3136.53; ETA: 8.1 minutes\n", + "Step 3483499 of 5000000; TPS: 3136.85; ETA: 8.1 minutes\n", + "Step 3493999 of 5000000; TPS: 3137.06; ETA: 8.0 minutes\n", + "Step 3504499 of 5000000; TPS: 3137.23; ETA: 7.9 minutes\n", + "Step 3514999 of 5000000; TPS: 3137.1; ETA: 7.9 minutes\n", + "Step 3525499 of 5000000; TPS: 3137.25; ETA: 7.8 minutes\n", + "Step 3535999 of 5000000; TPS: 3137.03; ETA: 7.8 minutes\n", + "Step 3546499 of 5000000; TPS: 3136.89; ETA: 7.7 minutes\n", + "Step 3556999 of 5000000; TPS: 3137.2; ETA: 7.7 minutes\n", + "Step 3567499 of 5000000; TPS: 3137.7; ETA: 7.6 minutes\n", + "Step 3577999 of 5000000; TPS: 3138.09; ETA: 7.6 minutes\n", + "Step 3588499 of 5000000; TPS: 3138.44; ETA: 7.5 minutes\n", + "Step 3598999 of 5000000; TPS: 3138.49; ETA: 7.4 minutes\n", + "Step 3609499 of 5000000; TPS: 3138.65; ETA: 7.4 minutes\n", + "Step 3619999 of 5000000; TPS: 3138.89; ETA: 7.3 minutes\n", + "Step 3630499 of 5000000; TPS: 3139.13; ETA: 7.3 minutes\n", + "Step 3640999 of 5000000; TPS: 3139.07; ETA: 7.2 minutes\n", + "Step 3651499 of 5000000; TPS: 3139.23; ETA: 7.2 minutes\n", + "Step 3661999 of 5000000; TPS: 3139.48; ETA: 7.1 minutes\n", + "Step 3672499 of 5000000; TPS: 3139.91; ETA: 7.0 minutes\n", + "Step 3682999 of 5000000; TPS: 3140.15; ETA: 7.0 minutes\n", + "Step 3693499 of 5000000; TPS: 3140.56; ETA: 6.9 minutes\n", + "Step 3703999 of 5000000; TPS: 3140.81; ETA: 6.9 minutes\n", + "Step 3714499 of 5000000; TPS: 3140.86; ETA: 6.8 minutes\n", + "Step 3724999 of 5000000; TPS: 3141.07; ETA: 6.8 minutes\n", + "Step 3735499 of 5000000; TPS: 3141.18; ETA: 6.7 minutes\n", + "Step 3745999 of 5000000; TPS: 3141.3; ETA: 6.7 minutes\n", + "Step 3756499 of 5000000; TPS: 3141.46; ETA: 6.6 minutes\n", + "Step 3766999 of 5000000; TPS: 3141.94; ETA: 6.5 minutes\n", + "Step 3777499 of 5000000; TPS: 3142.44; ETA: 6.5 minutes\n", + "Step 3787999 of 5000000; TPS: 3142.8; ETA: 6.4 minutes\n", + "Step 3798499 of 5000000; TPS: 3143.06; ETA: 6.4 minutes\n", + "Step 3808999 of 5000000; TPS: 3143.2; ETA: 6.3 minutes\n", + "Step 3819499 of 5000000; TPS: 3143.3; ETA: 6.3 minutes\n", + "Step 3829999 of 5000000; TPS: 3143.33; ETA: 6.2 minutes\n", + "Step 3840499 of 5000000; TPS: 3143.43; ETA: 6.1 minutes\n", + "Step 3850999 of 5000000; TPS: 3143.56; ETA: 6.1 minutes\n", + "Step 3861499 of 5000000; TPS: 3143.76; ETA: 6.0 minutes\n", + "Step 3871999 of 5000000; TPS: 3144.17; ETA: 6.0 minutes\n", + "Step 3882499 of 5000000; TPS: 3144.59; ETA: 5.9 minutes\n", + "Step 3892999 of 5000000; TPS: 3144.92; ETA: 5.9 minutes\n", + "Step 3903499 of 5000000; TPS: 3144.99; ETA: 5.8 minutes\n", + "Step 3913999 of 5000000; TPS: 3145.22; ETA: 5.8 minutes\n", + "Step 3924499 of 5000000; TPS: 3145.32; ETA: 5.7 minutes\n", + "Step 3934999 of 5000000; TPS: 3145.48; ETA: 5.6 minutes\n", + "Step 3945499 of 5000000; TPS: 3145.69; ETA: 5.6 minutes\n", + "Step 3955999 of 5000000; TPS: 3145.88; ETA: 5.5 minutes\n", + "Step 3966499 of 5000000; TPS: 3145.99; ETA: 5.5 minutes\n", + "Step 3976999 of 5000000; TPS: 3146.54; ETA: 5.4 minutes\n", + "Step 3987499 of 5000000; TPS: 3147.01; ETA: 5.4 minutes\n", + "Step 3997999 of 5000000; TPS: 3147.24; ETA: 5.3 minutes\n", + "Step 4008499 of 5000000; TPS: 3147.43; ETA: 5.3 minutes\n", + "Step 4018999 of 5000000; TPS: 3147.61; ETA: 5.2 minutes\n", + "Step 4029499 of 5000000; TPS: 3147.73; ETA: 5.1 minutes\n", + "Step 4039999 of 5000000; TPS: 3147.89; ETA: 5.1 minutes\n", + "Step 4050499 of 5000000; TPS: 3147.95; ETA: 5.0 minutes\n", + "Step 4060999 of 5000000; TPS: 3148.11; ETA: 5.0 minutes\n", + "Step 4071499 of 5000000; TPS: 3148.26; ETA: 4.9 minutes\n", + "Step 4081999 of 5000000; TPS: 3148.62; ETA: 4.9 minutes\n", + "Step 4092499 of 5000000; TPS: 3148.97; ETA: 4.8 minutes\n", + "Step 4102999 of 5000000; TPS: 3149.05; ETA: 4.7 minutes\n", + "Step 4113499 of 5000000; TPS: 3149.29; ETA: 4.7 minutes\n", + "Step 4123999 of 5000000; TPS: 3149.37; ETA: 4.6 minutes\n", + "Step 4134499 of 5000000; TPS: 3149.46; ETA: 4.6 minutes\n", + "Step 4144999 of 5000000; TPS: 3149.67; ETA: 4.5 minutes\n", + "Step 4155499 of 5000000; TPS: 3149.17; ETA: 4.5 minutes\n", + "Step 4165999 of 5000000; TPS: 3148.64; ETA: 4.4 minutes\n", + "Step 4176499 of 5000000; TPS: 3148.36; ETA: 4.4 minutes\n", + "Step 4186999 of 5000000; TPS: 3148.31; ETA: 4.3 minutes\n", + "Step 4197499 of 5000000; TPS: 3148.39; ETA: 4.2 minutes\n", + "Step 4207999 of 5000000; TPS: 3148.09; ETA: 4.2 minutes\n", + "Step 4218499 of 5000000; TPS: 3147.88; ETA: 4.1 minutes\n", + "Step 4228999 of 5000000; TPS: 3147.92; ETA: 4.1 minutes\n", + "Step 4239499 of 5000000; TPS: 3148.02; ETA: 4.0 minutes\n", + "Step 4249999 of 5000000; TPS: 3147.71; ETA: 4.0 minutes\n", + "Step 4260499 of 5000000; TPS: 3147.76; ETA: 3.9 minutes\n", + "Step 4270999 of 5000000; TPS: 3147.84; ETA: 3.9 minutes\n", + "Step 4281499 of 5000000; TPS: 3147.83; ETA: 3.8 minutes\n", + "Step 4291999 of 5000000; TPS: 3148.13; ETA: 3.7 minutes\n", + "Step 4302499 of 5000000; TPS: 3148.27; ETA: 3.7 minutes\n", + "Step 4312999 of 5000000; TPS: 3148.11; ETA: 3.6 minutes\n", + "Step 4323499 of 5000000; TPS: 3148.23; ETA: 3.6 minutes\n", + "Step 4333999 of 5000000; TPS: 3148.32; ETA: 3.5 minutes\n", + "Step 4344499 of 5000000; TPS: 3148.07; ETA: 3.5 minutes\n", + "Step 4354999 of 5000000; TPS: 3147.73; ETA: 3.4 minutes\n", + "Step 4365499 of 5000000; TPS: 3147.73; ETA: 3.4 minutes\n", + "Step 4375999 of 5000000; TPS: 3147.68; ETA: 3.3 minutes\n", + "Step 4386499 of 5000000; TPS: 3147.99; ETA: 3.2 minutes\n", + "Step 4396999 of 5000000; TPS: 3148.25; ETA: 3.2 minutes\n", + "Step 4407499 of 5000000; TPS: 3148.23; ETA: 3.1 minutes\n", + "Step 4417999 of 5000000; TPS: 3148.29; ETA: 3.1 minutes\n", + "Step 4428499 of 5000000; TPS: 3148.41; ETA: 3.0 minutes\n", + "Step 4438999 of 5000000; TPS: 3148.45; ETA: 3.0 minutes\n", + "Step 4449499 of 5000000; TPS: 3148.48; ETA: 2.9 minutes\n", + "Step 4459999 of 5000000; TPS: 3148.55; ETA: 2.9 minutes\n", + "Step 4470499 of 5000000; TPS: 3148.6; ETA: 2.8 minutes\n", + "Step 4480999 of 5000000; TPS: 3148.39; ETA: 2.7 minutes\n", + "Step 4491499 of 5000000; TPS: 3148.72; ETA: 2.7 minutes\n", + "Step 4501999 of 5000000; TPS: 3148.73; ETA: 2.6 minutes\n", + "Step 4512499 of 5000000; TPS: 3148.71; ETA: 2.6 minutes\n", + "Step 4522999 of 5000000; TPS: 3148.76; ETA: 2.5 minutes\n", + "Step 4533499 of 5000000; TPS: 3148.84; ETA: 2.5 minutes\n", + "Step 4543999 of 5000000; TPS: 3148.89; ETA: 2.4 minutes\n", + "Step 4554499 of 5000000; TPS: 3148.81; ETA: 2.4 minutes\n", + "Step 4564999 of 5000000; TPS: 3148.76; ETA: 2.3 minutes\n", + "Step 4575499 of 5000000; TPS: 3148.83; ETA: 2.2 minutes\n", + "Step 4585999 of 5000000; TPS: 3149.02; ETA: 2.2 minutes\n", + "Step 4596499 of 5000000; TPS: 3149.26; ETA: 2.1 minutes\n", + "Step 4606999 of 5000000; TPS: 3149.33; ETA: 2.1 minutes\n", + "Step 4617499 of 5000000; TPS: 3149.49; ETA: 2.0 minutes\n", + "Step 4627999 of 5000000; TPS: 3149.55; ETA: 2.0 minutes\n", + "Step 4638499 of 5000000; TPS: 3149.64; ETA: 1.9 minutes\n", + "Step 4648999 of 5000000; TPS: 3149.66; ETA: 1.9 minutes\n", + "Step 4659499 of 5000000; TPS: 3149.67; ETA: 1.8 minutes\n", + "Step 4669999 of 5000000; TPS: 3149.55; ETA: 1.7 minutes\n", + "Step 4680499 of 5000000; TPS: 3149.6; ETA: 1.7 minutes\n", + "Step 4690999 of 5000000; TPS: 3149.8; ETA: 1.6 minutes\n", + "Step 4701499 of 5000000; TPS: 3150.03; ETA: 1.6 minutes\n", + "Step 4711999 of 5000000; TPS: 3150.26; ETA: 1.5 minutes\n", + "Step 4722499 of 5000000; TPS: 3150.37; ETA: 1.5 minutes\n", + "Step 4732999 of 5000000; TPS: 3150.46; ETA: 1.4 minutes\n", + "Step 4743499 of 5000000; TPS: 3150.44; ETA: 1.4 minutes\n", + "Step 4753999 of 5000000; TPS: 3150.4; ETA: 1.3 minutes\n", + "Step 4764499 of 5000000; TPS: 3150.41; ETA: 1.2 minutes\n", + "Step 4774999 of 5000000; TPS: 3150.47; ETA: 1.2 minutes\n", + "Step 4785499 of 5000000; TPS: 3150.5; ETA: 1.1 minutes\n", + "Step 4795999 of 5000000; TPS: 3151.28; ETA: 1.1 minutes\n", + "Step 4806499 of 5000000; TPS: 3151.26; ETA: 1.0 minutes\n", + "Step 4816999 of 5000000; TPS: 3151.3; ETA: 1.0 minutes\n", + "Step 4827499 of 5000000; TPS: 3151.28; ETA: 0.9 minutes\n", + "Step 4837999 of 5000000; TPS: 3151.29; ETA: 0.9 minutes\n", + "Step 4848499 of 5000000; TPS: 3151.32; ETA: 0.8 minutes\n", + "Step 4858999 of 5000000; TPS: 3151.19; ETA: 0.7 minutes\n", + "Step 4869499 of 5000000; TPS: 3151.23; ETA: 0.7 minutes\n", + "Step 4879999 of 5000000; TPS: 3151.26; ETA: 0.6 minutes\n", + "Step 4890499 of 5000000; TPS: 3151.26; ETA: 0.6 minutes\n", + "Step 4900999 of 5000000; TPS: 3151.39; ETA: 0.5 minutes\n", + "Step 4911499 of 5000000; TPS: 3151.52; ETA: 0.5 minutes\n", + "Step 4921999 of 5000000; TPS: 3151.57; ETA: 0.4 minutes\n", + "Step 4932499 of 5000000; TPS: 3151.66; ETA: 0.4 minutes\n", + "Step 4942999 of 5000000; TPS: 3151.75; ETA: 0.3 minutes\n", + "Step 4953499 of 5000000; TPS: 3151.88; ETA: 0.2 minutes\n", + "Step 4963999 of 5000000; TPS: 3151.94; ETA: 0.2 minutes\n", + "Step 4974499 of 5000000; TPS: 3151.98; ETA: 0.1 minutes\n", + "Step 4984999 of 5000000; TPS: 3151.74; ETA: 0.1 minutes\n", + "Step 4995499 of 5000000; TPS: 3151.58; ETA: 0.0 minutes\n" + ] + } + ], "source": [ "sim = Simulation(initial_state=system.hoomd_snapshot, forcefield=ff.hoomd_forces, device=device, dt = dt, \n", " gsd_write_freq=int(write_frequency), log_file_name = log, gsd_file_name = gsd) # initializing simulation\n", @@ -245,10 +832,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 118, "id": "de590eda-bbb3-42a7-a87f-c278705455c8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shrink phase completed in 212.03293919563293 seconds\n", + "Run phase completed in 1586.5579686164856 seconds\n", + "Total time: 29.98 minutes\n" + ] + } + ], "source": [ "print(f\"Shrink phase completed in {(end_shrink - start_shrink)} seconds\")\n", "print(f\"Run phase completed in {(end_run - start_run)} seconds\")\n", @@ -265,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 119, "id": "7de87f9d-da23-46a4-8033-7e5019f9d37c", "metadata": {}, "outputs": [], @@ -280,13 +877,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 120, "id": "a8af76ae-efe0-4912-8417-612048485a77", "metadata": {}, "outputs": [], "source": [ "!mv \"{log}\" \"{gsd}\" \"{start_file}\" ../analysis/" ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "62c55ad3-c32f-4b2b-80ee-2aed584119ec", + "metadata": {}, + "outputs": [], + "source": [ + "rg = math.sqrt(chain_length/6)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "2758b641-237e-45f5-8c0f-f88aaaaddbdf", + "metadata": {}, + "outputs": [], + "source": [ + "q = rg/flake_repeat" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "f734af70-e218-4da5-b9db-da4f7c5f6640", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.2581988897471611\n" + ] + } + ], + "source": [ + "print(q)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "b6540c41-122c-4854-ba30-24c6d8303302", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.2909944487358056\n" + ] + } + ], + "source": [ + "print(rg)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51ae94a2-ab21-4f59-8280-5683b167ea64", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": {