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AvantiShri/yamda-test.ipynb

Created August 5, 2018 22:20
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yamda-test.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "YAMDA test.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"[View in Colaboratory](https://colab.research.google.com/gist/AvantiShri/bba262f46757295619ebdc27c0fd1958/yamda-test.ipynb)"
]
},
{
"metadata": {
"id": "STdnlCcJK67K",
"colab_type": "text"
},
"cell_type": "markdown",
"source": [
"HOMER results on the same data are [here](https://gist.github.com/AvantiShri/45cd0034f8b7f1d5b1a7ac115d3fc084)"
]
},
{
"metadata": {
"id": "uP6Knnbo1I5W",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "54316217-a21b-4443-a6a5-7b82dc5fbad0"
},
"cell_type": "code",
"source": [
"!git clone https://github.com/daquang/YAMDA.git"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"fatal: destination path 'YAMDA' already exists and is not an empty directory.\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Vk-A1Ogj2GXZ",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 139
},
"outputId": "27b48623-6c8a-4163-8ceb-af486e5bf984"
},
"cell_type": "code",
"source": [
"!pip3 install http://download.pytorch.org/whl/cu80/torch-0.4.0-cp36-cp36m-linux_x86_64.whl\n",
"!pip3 install torchvision"
],
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: torch==0.4.0 from http://download.pytorch.org/whl/cu80/torch-0.4.0-cp36-cp36m-linux_x86_64.whl in /usr/local/lib/python3.6/dist-packages (0.4.0)\n",
"Requirement already satisfied: torchvision in /usr/local/lib/python3.6/dist-packages (0.2.1)\n",
"Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.6/dist-packages (from torchvision) (5.2.0)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.11.0)\n",
"Requirement already satisfied: torch in /usr/local/lib/python3.6/dist-packages (from torchvision) (0.4.0)\n",
"Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from torchvision) (1.14.5)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "_LhxolJJ3B6F",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 88
},
"outputId": "43bebc5b-292d-4525-ad3a-5961500a4d5e"
},
"cell_type": "code",
"source": [
"!pip3 install pyfaidx"
],
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: pyfaidx in /usr/local/lib/python3.6/dist-packages (0.5.4.2)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from pyfaidx) (1.11.0)\n",
"Requirement already satisfied: setuptools>=0.7 in /usr/local/lib/python3.6/dist-packages (from pyfaidx) (39.1.0)\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "oDwahZl_3I2A",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
},
"outputId": "969967e9-97b1-4da8-d3a7-eba99e656291"
},
"cell_type": "code",
"source": [
"!pip3 install tqdm"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (4.24.0)\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "trblg4kKdm-H",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 462
},
"outputId": "7f4c0ea6-156d-4ff2-f929-3032ebb2a13a"
},
"cell_type": "code",
"source": [
"%cd ~\n",
"!ls\n",
"!wget https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/pos_seqs_concat.fa.gz -O pos_seqs_concat.fa.gz\n",
"!wget https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/neg_seqs_concat.fa.gz -O neg_seqs_concat.fa.gz"
],
"execution_count": 13,
"outputs": [
{
"output_type": "stream",
"text": [
"/content\n",
"datalab homer_out\t neg_seqs_concat.fa.fai pos_seqs_concat.fa.fai\n",
"HOMER\t neg_seqs_concat.fa pos_seqs_concat.fa YAMDA\n",
"--2018-08-05 19:38:37-- https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/pos_seqs_concat.fa.gz\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1641890 (1.6M) [application/octet-stream]\n",
"Saving to: ‘pos_seqs_concat.fa.gz’\n",
"\n",
"pos_seqs_concat.fa. 100%[===================>] 1.57M 10.4MB/s in 0.2s \n",
"\n",
"2018-08-05 19:38:38 (10.4 MB/s) - ‘pos_seqs_concat.fa.gz’ saved [1641890/1641890]\n",
"\n",
"--2018-08-05 19:38:39-- https://raw.githubusercontent.com/AvantiShri/file_hosting/master/yamda_test/neg_seqs_concat.fa.gz\n",
"Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
"Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 1573876 (1.5M) [application/octet-stream]\n",
"Saving to: ‘neg_seqs_concat.fa.gz’\n",
"\n",
"neg_seqs_concat.fa. 100%[===================>] 1.50M 7.27MB/s in 0.2s \n",
"\n",
"2018-08-05 19:38:40 (7.27 MB/s) - ‘neg_seqs_concat.fa.gz’ saved [1573876/1573876]\n",
"\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "ayWV-765lqMU",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"!zcat pos_seqs_concat.fa.gz | perl -lane 'if ($.%2==1) {print $_} else {print substr($_,300,400)}' > central400_pos_seqs_concat.fa\n",
"!zcat neg_seqs_concat.fa.gz | perl -lane 'if ($.%2==1) {print $_} else {print substr($_,300,400)}' > central400_neg_seqs_concat.fa"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "a4UNN0t6u1TC",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 870
},
"outputId": "b2074553-7e4a-4a3b-b609-96113305c925"
},
"cell_type": "code",
"source": [
"!python YAMDA/run_em.py -h"
],
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"text": [
"usage: run_em.py [-h] -i INPUT [-j INPUT2] [-b BATCHSIZE]\r\n",
" [-a {dna,rna,protein}] [-r] [-m {tcm,zoops,oops}] [-e]\r\n",
" [-f FUDGE] [-w WIDTH] [-k HALFLENGTH] [-n NMOTIFS]\r\n",
" [-mins MINSITES] [-maxs MAXSITES] [-ns NSEEDS]\r\n",
" [-maxiter MAXITER] [-t TOLERANCE] [--no_cuda] [-s SEED]\r\n",
" (-o OUTPUTDIR | -oc OUTPUTDIRC)\r\n",
"\r\n",
"Train model.\r\n",
"\r\n",
"optional arguments:\r\n",
" -h, --help show this help message and exit\r\n",
" -i INPUT, --input INPUT\r\n",
" Input FASTA file\r\n",
" -j INPUT2, --input2 INPUT2\r\n",
" Input FASTA file of negative sequences\r\n",
" -b BATCHSIZE, --batchsize BATCHSIZE\r\n",
" Input batch size for training (default: 1000)\r\n",
" -a {dna,rna,protein}, --alpha {dna,rna,protein}\r\n",
" Alphabet (default: dna)\r\n",
" -r, --revcomp Consider both the given strand and the reverse complement strand when searching for motifs in a complementable alphabet (default: consider given strand only).\r\n",
" -m {tcm,zoops,oops}, --model {tcm,zoops,oops}\r\n",
" Model (default: tcm)\r\n",
" -e, --erasewhole Erase an entire sequence if it contains a discovered motif (default: erase individual motif occurrences).\r\n",
" -f FUDGE, --fudge FUDGE\r\n",
" Fudge factor to help with extremely rare motifs. Should be >0 and <=1 (default: 0.1).\r\n",
" -w WIDTH, --width WIDTH\r\n",
" Motif width (default: 20).\r\n",
" -k HALFLENGTH, --halflength HALFLENGTH\r\n",
" k-mer half-length for gapped k-mer search seeding (default: 6).\r\n",
" -n NMOTIFS, --nmotifs NMOTIFS\r\n",
" Number of motifs to find (default: 1).\r\n",
" -mins MINSITES, --minsites MINSITES\r\n",
" Minimum number of motif occurrences (default: 100).\r\n",
" -maxs MAXSITES, --maxsites MAXSITES\r\n",
" Maximum number of motif occurrences. If left unspecified, will default to number ofsequences.\r\n",
" -ns NSEEDS, --nseeds NSEEDS\r\n",
" Number of motif seeds to try. If left unspecified, will default to 100 (for gapped k-mersearch) or 1000 (for randomly sampled subsequence method).\r\n",
" -maxiter MAXITER, --maxiter MAXITER\r\n",
" Maximum number of refining iterations of batch EM to run from any starting point. Batch EM is run for maxiter iterations or until convergence (see -tolerance, below) from each starting point for refining (default: 20)\r\n",
" -t TOLERANCE, --tolerance TOLERANCE\r\n",
" Stop iterating refining batch/on-line EM when the change in the motif probability matrix is less than tolerance. Change is defined as the euclidean distance between two successive frequency matrices (default: 1e-3).\r\n",
" --no_cuda Disable the default CUDA training.\r\n",
" -s SEED, --seed SEED Random seed for reproducibility (default: 1337).\r\n",
" -o OUTPUTDIR, --outputdir OUTPUTDIR\r\n",
" The output directory. Causes error if the directory already exists.\r\n",
" -oc OUTPUTDIRC, --outputdirc OUTPUTDIRC\r\n",
" The output directory. Will overwrite if directory already exists.\r\n",
"\r\n",
"Use `run_em.py -h` to see an auto-generated description of advanced options.\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "72BYDlrR9SY3",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 30280
},
"outputId": "d753ab77-ccac-419d-c398-5e9cc34a4520"
},
"cell_type": "code",
"source": [
"!python YAMDA/run_em.py -e -r -i central400_pos_seqs_concat.fa -j central400_neg_seqs_concat.fa -oc yamda_test_e_pos_neg"
],
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": [
"Loading sequences from FASTA\n",
"Searching positive sequences: 100%|███████████████| 9/9 [01:44<00:00, 11.59s/it]\n",
"Searching negative sequences: 100%|███████████████| 9/9 [01:49<00:00, 12.15s/it]\n",
"Converting letter sequences to tensors\n",
"On-line EM: 0%| | 0/1 [00:00<?, ?it/s]\n",
"Pass 1/1: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
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"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
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],
"name": "stdout"
},
{
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"Pass 1/1: 100%|███████████████████████████▉| 3805/3810 [00:24<00:00, 158.39it/s]\u001b[A\n",
"On-line EM: 100%|█████████████████████████████████| 1/1 [00:24<00:00, 24.06s/it]\n",
"Batch EM: 0%| | 0/1 [00:00<?, ?it/s]\n",
"Pass 1/1: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 1/1: 0%|▏ | 18/3810 [00:00<00:21, 173.49it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 1/1: 21%|██████▏ | 818/3810 [00:05<00:18, 163.41it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 1/1: 40%|███████████▏ | 1521/3810 [00:09<00:14, 160.05it/s]\u001b[A\n",
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"Pass 1/1: 42%|███████████▊ | 1604/3810 [00:10<00:13, 160.08it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 1/1: 43%|████████████ | 1638/3810 [00:10<00:13, 160.12it/s]\u001b[A\n",
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"Pass 1/1: 47%|█████████████▏ | 1790/3810 [00:11<00:12, 160.13it/s]\u001b[A\n",
"Pass 1/1: 47%|█████████████▎ | 1807/3810 [00:11<00:12, 160.09it/s]\u001b[A\n",
"Pass 1/1: 48%|█████████████▍ | 1824/3810 [00:11<00:12, 160.15it/s]\u001b[A\n",
"Pass 1/1: 48%|█████████████▌ | 1841/3810 [00:11<00:12, 160.22it/s]\u001b[A\n",
"Pass 1/1: 49%|█████████████▋ | 1858/3810 [00:11<00:12, 160.24it/s]\u001b[A\n",
"Pass 1/1: 49%|█████████████▊ | 1875/3810 [00:11<00:12, 160.31it/s]\u001b[A\n",
"Pass 1/1: 50%|█████████████▉ | 1892/3810 [00:11<00:11, 160.30it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████ | 1909/3810 [00:11<00:11, 160.28it/s]\u001b[A\n",
"Pass 1/1: 51%|██████████████▏ | 1926/3810 [00:12<00:11, 160.20it/s]\u001b[A\n",
"Pass 1/1: 51%|██████████████▎ | 1943/3810 [00:12<00:11, 160.24it/s]\u001b[A\n",
"Pass 1/1: 51%|██████████████▍ | 1960/3810 [00:12<00:11, 160.25it/s]\u001b[A\n",
"Pass 1/1: 52%|██████████████▌ | 1977/3810 [00:12<00:11, 160.28it/s]\u001b[A\n",
"Pass 1/1: 52%|██████████████▋ | 1994/3810 [00:12<00:11, 160.30it/s]\u001b[A\n",
"Pass 1/1: 53%|██████████████▊ | 2011/3810 [00:12<00:11, 160.37it/s]\u001b[A\n",
"Pass 1/1: 53%|██████████████▉ | 2028/3810 [00:12<00:11, 160.44it/s]\u001b[A\n",
"Pass 1/1: 54%|███████████████ | 2045/3810 [00:12<00:10, 160.50it/s]\u001b[A\n",
"Pass 1/1: 54%|███████████████▏ | 2062/3810 [00:12<00:10, 160.55it/s]\u001b[A\n",
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"Pass 1/1: 55%|███████████████▍ | 2096/3810 [00:13<00:10, 160.59it/s]\u001b[A\n",
"Pass 1/1: 55%|███████████████▌ | 2113/3810 [00:13<00:10, 160.61it/s]\u001b[A\n",
"Pass 1/1: 56%|███████████████▋ | 2130/3810 [00:13<00:10, 160.64it/s]\u001b[A\n",
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"Pass 1/1: 59%|████████████████▌ | 2249/3810 [00:13<00:09, 160.74it/s]\u001b[A\n",
"Pass 1/1: 59%|████████████████▋ | 2266/3810 [00:14<00:09, 160.75it/s]\u001b[A\n",
"Pass 1/1: 60%|████████████████▊ | 2283/3810 [00:14<00:09, 160.78it/s]\u001b[A\n",
"Pass 1/1: 60%|████████████████▉ | 2300/3810 [00:14<00:09, 160.83it/s]\u001b[A\n",
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"Pass 1/1: 61%|█████████████████▏ | 2334/3810 [00:14<00:09, 160.85it/s]\u001b[A\n",
"Pass 1/1: 62%|█████████████████▎ | 2351/3810 [00:14<00:09, 160.85it/s]\u001b[A\n",
"Pass 1/1: 62%|█████████████████▍ | 2368/3810 [00:14<00:08, 160.82it/s]\u001b[A\n",
"Pass 1/1: 63%|█████████████████▌ | 2385/3810 [00:14<00:08, 160.87it/s]\u001b[A\n",
"Pass 1/1: 63%|█████████████████▋ | 2402/3810 [00:14<00:08, 160.90it/s]\u001b[A\n",
"Pass 1/1: 64%|█████████████████▊ | 2420/3810 [00:15<00:08, 160.97it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 64%|█████████████████▉ | 2437/3810 [00:15<00:08, 161.02it/s]\u001b[A\n",
"Pass 1/1: 64%|██████████████████ | 2454/3810 [00:15<00:08, 161.04it/s]\u001b[A\n",
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"Pass 1/1: 66%|██████████████████▌ | 2522/3810 [00:15<00:07, 161.05it/s]\u001b[A\n",
"Pass 1/1: 67%|██████████████████▋ | 2539/3810 [00:15<00:07, 160.99it/s]\u001b[A\n",
"Pass 1/1: 67%|██████████████████▊ | 2556/3810 [00:15<00:07, 160.99it/s]\u001b[A\n",
"Pass 1/1: 68%|██████████████████▉ | 2573/3810 [00:15<00:07, 160.99it/s]\u001b[A\n",
"Pass 1/1: 68%|███████████████████ | 2590/3810 [00:16<00:07, 161.01it/s]\u001b[A\n",
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"Pass 1/1: 69%|███████████████████▎ | 2624/3810 [00:16<00:07, 161.09it/s]\u001b[A\n",
"Pass 1/1: 69%|███████████████████▍ | 2641/3810 [00:16<00:07, 161.12it/s]\u001b[A\n",
"Pass 1/1: 70%|███████████████████▌ | 2658/3810 [00:16<00:07, 161.16it/s]\u001b[A\n",
"Pass 1/1: 70%|███████████████████▋ | 2675/3810 [00:16<00:07, 161.18it/s]\u001b[A\n",
"Pass 1/1: 71%|███████████████████▊ | 2692/3810 [00:16<00:06, 161.20it/s]\u001b[A\n",
"Pass 1/1: 71%|███████████████████▉ | 2710/3810 [00:16<00:06, 161.27it/s]\u001b[A\n",
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"Pass 1/1: 84%|███████████████████████▋ | 3218/3810 [00:19<00:03, 161.35it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 85%|███████████████████████▊ | 3234/3810 [00:20<00:03, 161.34it/s]\u001b[A\n",
"Pass 1/1: 85%|███████████████████████▉ | 3250/3810 [00:20<00:03, 161.32it/s]\u001b[A\n",
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"Pass 1/1: 96%|██████████████████████████▊ | 3646/3810 [00:22<00:01, 161.00it/s]\u001b[A\n",
"Pass 1/1: 96%|██████████████████████████▉ | 3662/3810 [00:22<00:00, 160.96it/s]\u001b[A\n",
"Pass 1/1: 97%|███████████████████████████ | 3678/3810 [00:22<00:00, 160.94it/s]\u001b[A\n",
"Pass 1/1: 97%|███████████████████████████▏| 3695/3810 [00:22<00:00, 160.94it/s]\u001b[A\n",
"Pass 1/1: 97%|███████████████████████████▎| 3711/3810 [00:23<00:00, 160.93it/s]\u001b[A\n",
"Pass 1/1: 98%|███████████████████████████▍| 3727/3810 [00:23<00:00, 160.93it/s]\u001b[A\n",
"Pass 1/1: 98%|███████████████████████████▌| 3743/3810 [00:23<00:00, 160.88it/s]\u001b[A\n",
"Pass 1/1: 99%|███████████████████████████▋| 3759/3810 [00:23<00:00, 160.87it/s]\u001b[A\n",
"Pass 1/1: 99%|███████████████████████████▋| 3775/3810 [00:23<00:00, 160.86it/s]\u001b[A\n",
"Pass 1/1: 100%|███████████████████████████▊| 3791/3810 [00:23<00:00, 160.82it/s]\u001b[A\n",
"Pass 1/1: 100%|███████████████████████████▉| 3807/3810 [00:23<00:00, 160.79it/s]\u001b[A\n",
"Batch EM: 100%|███████████████████████████████████| 1/1 [00:23<00:00, 23.70s/it]\n",
"Computing log likelihood: 22%|██▊ | 836/3810 [00:01<00:04, 635.18it/s]"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Computing log likelihood: 100%|████████████| 3810/3810 [00:06<00:00, 600.12it/s]\n",
"Using seed originating from word: NNNNCGCCCCCTGCCGNNNN\n",
"Batch EM: 0%| | 0/20 [00:00<?, ?it/s]\n",
"Pass 1/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 1/20: 1%|▍ | 52/3810 [00:00<00:07, 519.12it/s]\u001b[A\n",
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"Pass 1/20: 63%|████████████████▉ | 2384/3810 [00:04<00:02, 537.84it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/20: 64%|█████████████████▎ | 2440/3810 [00:04<00:02, 538.15it/s]\u001b[A\n",
"Pass 1/20: 66%|█████████████████▋ | 2498/3810 [00:04<00:02, 538.97it/s]\u001b[A\n",
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"Pass 1/20: 74%|████████████████████ | 2836/3810 [00:05<00:01, 541.20it/s]\u001b[A\n",
"Pass 1/20: 76%|████████████████████▌ | 2894/3810 [00:05<00:01, 541.80it/s]\u001b[A\n",
"Pass 1/20: 77%|████████████████████▉ | 2951/3810 [00:05<00:01, 541.75it/s]\u001b[A\n",
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"Pass 1/20: 82%|██████████████████████ | 3119/3810 [00:05<00:01, 540.25it/s]\u001b[A\n",
"Pass 1/20: 83%|██████████████████████▌ | 3177/3810 [00:05<00:01, 540.90it/s]\u001b[A\n",
"Pass 1/20: 85%|██████████████████████▉ | 3234/3810 [00:05<00:01, 541.26it/s]\u001b[A\n",
"Pass 1/20: 86%|███████████████████████▎ | 3292/3810 [00:06<00:00, 541.79it/s]\u001b[A\n",
"Pass 1/20: 88%|███████████████████████▋ | 3348/3810 [00:06<00:00, 541.06it/s]\u001b[A\n",
"Pass 1/20: 89%|████████████████████████▏ | 3407/3810 [00:06<00:00, 541.78it/s]\u001b[A\n",
"Pass 1/20: 91%|████████████████████████▌ | 3464/3810 [00:06<00:00, 542.18it/s]\u001b[A\n",
"Pass 1/20: 92%|████████████████████████▉ | 3520/3810 [00:06<00:00, 542.37it/s]\u001b[A\n",
"Pass 1/20: 94%|█████████████████████████▎ | 3578/3810 [00:06<00:00, 542.91it/s]\u001b[A\n",
"Pass 1/20: 95%|█████████████████████████▊ | 3636/3810 [00:06<00:00, 543.45it/s]\u001b[A\n",
"Pass 1/20: 97%|██████████████████████████▏| 3696/3810 [00:06<00:00, 544.16it/s]\u001b[A\n",
"Pass 1/20: 99%|██████████████████████████▌| 3754/3810 [00:06<00:00, 543.36it/s]\u001b[A\n",
"Pass 1/20: 100%|███████████████████████████| 3810/3810 [00:07<00:00, 543.47it/s]\u001b[A\n",
"Batch EM: 5%|█▋ | 1/20 [00:07<02:13, 7.01s/it]\n",
"Pass 2/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 2/20: 2%|▍ | 58/3810 [00:00<00:06, 578.25it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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"Batch EM: 15%|█████ | 3/20 [00:19<01:52, 6.64s/it]\n",
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],
"name": "stdout"
},
{
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"text": [
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"Batch EM: 20%|██████▊ | 4/20 [00:26<01:45, 6.59s/it]\n",
"Pass 5/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
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"Pass 5/20: 32%|████████▋ | 1233/3810 [00:02<00:04, 605.13it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 5/20: 89%|████████████████████████▏ | 3406/3810 [00:05<00:00, 583.83it/s]\u001b[A\n",
"Pass 5/20: 91%|████████████████████████▌ | 3462/3810 [00:05<00:00, 583.33it/s]\u001b[A\n",
"Pass 5/20: 92%|████████████████████████▉ | 3517/3810 [00:06<00:00, 582.69it/s]\u001b[A\n",
"Pass 5/20: 94%|█████████████████████████▎ | 3572/3810 [00:06<00:00, 582.04it/s]\u001b[A\n",
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"Batch EM: 25%|████████▌ | 5/20 [00:32<01:38, 6.59s/it]\n",
"Pass 6/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
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"text": [
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"Pass 6/20: 80%|█████████████████████▌ | 3037/3810 [00:05<00:01, 570.71it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Batch EM: 30%|██████████▏ | 6/20 [00:39<01:32, 6.59s/it]\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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"Batch EM: 35%|███████████▉ | 7/20 [00:46<01:26, 6.62s/it]\n",
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],
"name": "stdout"
},
{
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"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Batch EM: 40%|█████████████▌ | 8/20 [00:53<01:19, 6.65s/it]\n",
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],
"name": "stdout"
},
{
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"text": [
"\n",
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"Pass 9/20: 100%|██████████████████████████▉| 3793/3810 [00:06<00:00, 570.69it/s]\u001b[A\n",
"Batch EM: 45%|███████████████▎ | 9/20 [00:59<01:13, 6.65s/it]\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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"Pass 10/20: 90%|███████████████████████▍ | 3428/3810 [00:05<00:00, 583.91it/s]\u001b[A\n",
"Pass 10/20: 92%|███████████████████████▊ | 3489/3810 [00:05<00:00, 584.23it/s]\u001b[A\n",
"Pass 10/20: 93%|████████████████████████▏ | 3551/3810 [00:06<00:00, 584.69it/s]\u001b[A\n",
"Pass 10/20: 95%|████████████████████████▋ | 3614/3810 [00:06<00:00, 585.38it/s]\u001b[A\n",
"Pass 10/20: 97%|█████████████████████████ | 3678/3810 [00:06<00:00, 586.22it/s]\u001b[A\n",
"Pass 10/20: 98%|█████████████████████████▌| 3743/3810 [00:06<00:00, 587.14it/s]\u001b[A\n",
"Pass 10/20: 100%|█████████████████████████▉| 3808/3810 [00:06<00:00, 588.04it/s]\u001b[A\n",
"Batch EM: 50%|████████████████▌ | 10/20 [01:06<01:06, 6.63s/it]\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 11/20: 91%|███████████████████████▌ | 3454/3810 [00:05<00:00, 599.62it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 11/20: 94%|████████████████████████▎ | 3567/3810 [00:05<00:00, 598.37it/s]\u001b[A\n",
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"Pass 11/20: 98%|█████████████████████████▌| 3738/3810 [00:06<00:00, 596.80it/s]\u001b[A\n",
"Pass 11/20: 100%|█████████████████████████▉| 3795/3810 [00:06<00:00, 596.37it/s]\u001b[A\n",
"Batch EM: 55%|██████████████████▏ | 11/20 [01:12<00:59, 6.61s/it]\n",
"Pass 12/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
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"Pass 12/20: 52%|█████████████▍ | 1963/3810 [00:03<00:03, 545.06it/s]\u001b[A\n",
"Pass 12/20: 53%|█████████████▊ | 2018/3810 [00:03<00:03, 545.15it/s]\u001b[A\n",
"Pass 12/20: 54%|██████████████▏ | 2074/3810 [00:03<00:03, 545.50it/s]\u001b[A\n",
"Pass 12/20: 56%|██████████████▌ | 2129/3810 [00:03<00:03, 545.57it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 12/20: 57%|██████████████▉ | 2184/3810 [00:04<00:02, 545.54it/s]\u001b[A\n",
"Pass 12/20: 59%|███████████████▎ | 2239/3810 [00:04<00:02, 543.55it/s]\u001b[A\n",
"Pass 12/20: 60%|███████████████▋ | 2294/3810 [00:04<00:02, 543.56it/s]\u001b[A\n",
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"Pass 12/20: 63%|████████████████▍ | 2403/3810 [00:04<00:02, 543.36it/s]\u001b[A\n",
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"Pass 12/20: 66%|█████████████████▏ | 2517/3810 [00:04<00:02, 544.40it/s]\u001b[A\n",
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"Pass 12/20: 71%|██████████████████▎ | 2689/3810 [00:04<00:02, 545.16it/s]\u001b[A\n",
"Pass 12/20: 72%|██████████████████▋ | 2745/3810 [00:05<00:01, 544.66it/s]\u001b[A\n",
"Pass 12/20: 74%|███████████████████ | 2802/3810 [00:05<00:01, 544.91it/s]\u001b[A\n",
"Pass 12/20: 75%|███████████████████▌ | 2862/3810 [00:05<00:01, 545.83it/s]\u001b[A\n",
"Pass 12/20: 77%|███████████████████▉ | 2919/3810 [00:05<00:01, 544.69it/s]\u001b[A\n",
"Pass 12/20: 78%|████████████████████▎ | 2975/3810 [00:05<00:01, 545.04it/s]\u001b[A\n",
"Pass 12/20: 80%|████████████████████▋ | 3035/3810 [00:05<00:01, 545.98it/s]\u001b[A\n",
"Pass 12/20: 81%|█████████████████████ | 3093/3810 [00:05<00:01, 546.54it/s]\u001b[A\n",
"Pass 12/20: 83%|█████████████████████▌ | 3153/3810 [00:05<00:01, 547.29it/s]\u001b[A\n",
"Pass 12/20: 84%|█████████████████████▉ | 3214/3810 [00:05<00:01, 548.24it/s]\u001b[A\n",
"Pass 12/20: 86%|██████████████████████▎ | 3275/3810 [00:05<00:00, 549.15it/s]\u001b[A\n",
"Pass 12/20: 88%|██████████████████████▊ | 3336/3810 [00:06<00:00, 550.05it/s]\u001b[A\n",
"Pass 12/20: 89%|███████████████████████▏ | 3396/3810 [00:06<00:00, 550.81it/s]\u001b[A\n",
"Pass 12/20: 91%|███████████████████████▌ | 3456/3810 [00:06<00:00, 551.58it/s]\u001b[A\n",
"Pass 12/20: 92%|███████████████████████▉ | 3516/3810 [00:06<00:00, 552.31it/s]\u001b[A\n",
"Pass 12/20: 94%|████████████████████████▍ | 3577/3810 [00:06<00:00, 553.04it/s]\u001b[A\n",
"Pass 12/20: 96%|████████████████████████▊ | 3639/3810 [00:06<00:00, 553.92it/s]\u001b[A\n",
"Pass 12/20: 97%|█████████████████████████▏| 3700/3810 [00:06<00:00, 553.77it/s]\u001b[A\n",
"Pass 12/20: 99%|█████████████████████████▋| 3761/3810 [00:06<00:00, 554.59it/s]\u001b[A\n",
"Batch EM: 60%|███████████████████▊ | 12/20 [01:19<00:53, 6.63s/it]\n",
"Pass 13/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 13/20: 2%|▍ | 60/3810 [00:00<00:06, 593.88it/s]\u001b[A\n",
"Pass 13/20: 3%|▊ | 122/3810 [00:00<00:06, 603.30it/s]\u001b[A\n",
"Pass 13/20: 5%|█▎ | 184/3810 [00:00<00:05, 607.01it/s]\u001b[A\n",
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"Pass 13/20: 9%|██▌ | 361/3810 [00:00<00:05, 596.36it/s]\u001b[A\n",
"Pass 13/20: 11%|██▉ | 419/3810 [00:00<00:05, 593.26it/s]\u001b[A\n",
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"Pass 13/20: 19%|█████ | 720/3810 [00:01<00:05, 590.46it/s]\u001b[A\n",
"Pass 13/20: 21%|█████▌ | 783/3810 [00:01<00:05, 593.13it/s]\u001b[A\n",
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"Pass 13/20: 24%|██████▍ | 908/3810 [00:01<00:04, 596.37it/s]\u001b[A\n",
"Pass 13/20: 25%|██████▊ | 970/3810 [00:01<00:04, 597.31it/s]\u001b[A\n",
"Pass 13/20: 27%|███████ | 1031/3810 [00:01<00:04, 597.71it/s]\u001b[A\n",
"Pass 13/20: 29%|███████▍ | 1092/3810 [00:01<00:04, 598.20it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 13/20: 30%|███████▊ | 1153/3810 [00:01<00:04, 598.04it/s]\u001b[A\n",
"Pass 13/20: 32%|████████▎ | 1214/3810 [00:02<00:04, 598.30it/s]\u001b[A\n",
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"Pass 13/20: 46%|███████████▉ | 1749/3810 [00:02<00:03, 592.20it/s]\u001b[A\n",
"Pass 13/20: 48%|████████████▎ | 1810/3810 [00:03<00:03, 592.52it/s]\u001b[A\n",
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"Pass 13/20: 51%|█████████████▏ | 1932/3810 [00:03<00:03, 593.36it/s]\u001b[A\n",
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"Pass 13/20: 55%|██████████████▍ | 2111/3810 [00:03<00:02, 590.05it/s]\u001b[A\n",
"Pass 13/20: 57%|██████████████▊ | 2170/3810 [00:03<00:02, 589.82it/s]\u001b[A\n",
"Pass 13/20: 59%|███████████████▏ | 2230/3810 [00:03<00:02, 589.97it/s]\u001b[A\n",
"Pass 13/20: 60%|███████████████▌ | 2289/3810 [00:03<00:02, 589.85it/s]\u001b[A\n",
"Pass 13/20: 62%|████████████████ | 2348/3810 [00:03<00:02, 589.79it/s]\u001b[A\n",
"Pass 13/20: 63%|████████████████▍ | 2407/3810 [00:04<00:02, 589.67it/s]\u001b[A\n",
"Pass 13/20: 65%|████████████████▊ | 2466/3810 [00:04<00:02, 589.43it/s]\u001b[A\n",
"Pass 13/20: 66%|█████████████████▎ | 2528/3810 [00:04<00:02, 590.03it/s]\u001b[A\n",
"Pass 13/20: 68%|█████████████████▋ | 2590/3810 [00:04<00:02, 590.62it/s]\u001b[A\n",
"Pass 13/20: 70%|██████████████████ | 2651/3810 [00:04<00:01, 590.90it/s]\u001b[A\n",
"Pass 13/20: 71%|██████████████████▌ | 2712/3810 [00:04<00:01, 590.93it/s]\u001b[A\n",
"Pass 13/20: 73%|██████████████████▉ | 2776/3810 [00:04<00:01, 591.79it/s]\u001b[A\n",
"Pass 13/20: 74%|███████████████████▎ | 2837/3810 [00:04<00:01, 590.47it/s]\u001b[A\n",
"Pass 13/20: 76%|███████████████████▊ | 2898/3810 [00:04<00:01, 590.71it/s]\u001b[A\n",
"Pass 13/20: 78%|████████████████████▏ | 2958/3810 [00:05<00:01, 590.50it/s]\u001b[A\n",
"Pass 13/20: 79%|████████████████████▌ | 3018/3810 [00:05<00:01, 590.65it/s]\u001b[A\n",
"Pass 13/20: 81%|█████████████████████ | 3079/3810 [00:05<00:01, 590.85it/s]\u001b[A\n",
"Pass 13/20: 82%|█████████████████████▍ | 3139/3810 [00:05<00:01, 590.93it/s]\u001b[A\n",
"Pass 13/20: 84%|█████████████████████▊ | 3200/3810 [00:05<00:01, 591.27it/s]\u001b[A\n",
"Pass 13/20: 86%|██████████████████████▏ | 3260/3810 [00:05<00:00, 591.07it/s]\u001b[A\n",
"Pass 13/20: 87%|██████████████████████▋ | 3320/3810 [00:05<00:00, 591.13it/s]\u001b[A\n",
"Pass 13/20: 89%|███████████████████████ | 3380/3810 [00:05<00:00, 591.07it/s]\u001b[A\n",
"Pass 13/20: 90%|███████████████████████▍ | 3440/3810 [00:05<00:00, 591.02it/s]\u001b[A\n",
"Pass 13/20: 92%|███████████████████████▉ | 3500/3810 [00:05<00:00, 591.07it/s]\u001b[A\n",
"Pass 13/20: 93%|████████████████████████▎ | 3560/3810 [00:06<00:00, 589.59it/s]\u001b[A\n",
"Pass 13/20: 95%|████████████████████████▋ | 3620/3810 [00:06<00:00, 589.71it/s]\u001b[A\n",
"Pass 13/20: 97%|█████████████████████████ | 3679/3810 [00:06<00:00, 589.66it/s]\u001b[A\n",
"Pass 13/20: 98%|█████████████████████████▌| 3740/3810 [00:06<00:00, 589.90it/s]\u001b[A\n",
"Pass 13/20: 100%|█████████████████████████▉| 3800/3810 [00:06<00:00, 589.97it/s]\u001b[A\n",
"Batch EM: 65%|█████████████████████▍ | 13/20 [01:26<00:46, 6.62s/it]\n",
"Pass 14/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 14/20: 2%|▍ | 63/3810 [00:00<00:06, 622.38it/s]\u001b[A\n",
"Pass 14/20: 3%|▉ | 129/3810 [00:00<00:05, 640.81it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 14/20: 5%|█▍ | 196/3810 [00:00<00:05, 648.16it/s]\u001b[A\n",
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"Pass 14/20: 12%|███▏ | 457/3810 [00:00<00:05, 648.84it/s]\u001b[A\n",
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"Pass 14/20: 22%|█████▉ | 841/3810 [00:01<00:04, 640.08it/s]\u001b[A\n",
"Pass 14/20: 24%|██████▍ | 905/3810 [00:01<00:04, 639.49it/s]\u001b[A\n",
"Pass 14/20: 25%|██████▉ | 971/3810 [00:01<00:04, 640.51it/s]\u001b[A\n",
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"Pass 14/20: 31%|███████▉ | 1164/3810 [00:01<00:04, 639.83it/s]\u001b[A\n",
"Pass 14/20: 32%|████████▍ | 1228/3810 [00:01<00:04, 639.55it/s]\u001b[A\n",
"Pass 14/20: 34%|████████▊ | 1292/3810 [00:02<00:03, 632.95it/s]\u001b[A\n",
"Pass 14/20: 36%|█████████▎ | 1356/3810 [00:02<00:03, 632.94it/s]\u001b[A\n",
"Pass 14/20: 37%|█████████▋ | 1420/3810 [00:02<00:03, 632.87it/s]\u001b[A\n",
"Pass 14/20: 39%|██████████▏ | 1484/3810 [00:02<00:03, 633.10it/s]\u001b[A\n",
"Pass 14/20: 41%|██████████▌ | 1548/3810 [00:02<00:03, 632.79it/s]\u001b[A\n",
"Pass 14/20: 42%|██████████▉ | 1611/3810 [00:02<00:03, 632.31it/s]\u001b[A\n",
"Pass 14/20: 44%|███████████▍ | 1674/3810 [00:02<00:03, 632.06it/s]\u001b[A\n",
"Pass 14/20: 46%|███████████▊ | 1738/3810 [00:02<00:03, 632.09it/s]\u001b[A\n",
"Pass 14/20: 47%|████████████▎ | 1801/3810 [00:02<00:03, 631.34it/s]\u001b[A\n",
"Pass 14/20: 49%|████████████▋ | 1864/3810 [00:02<00:03, 630.54it/s]\u001b[A\n",
"Pass 14/20: 51%|█████████████▏ | 1926/3810 [00:03<00:02, 629.74it/s]\u001b[A\n",
"Pass 14/20: 52%|█████████████▌ | 1989/3810 [00:03<00:02, 629.58it/s]\u001b[A\n",
"Pass 14/20: 54%|█████████████▉ | 2051/3810 [00:03<00:02, 628.43it/s]\u001b[A\n",
"Pass 14/20: 55%|██████████████▍ | 2113/3810 [00:03<00:02, 627.48it/s]\u001b[A\n",
"Pass 14/20: 57%|██████████████▊ | 2176/3810 [00:03<00:02, 627.43it/s]\u001b[A\n",
"Pass 14/20: 59%|███████████████▎ | 2239/3810 [00:03<00:02, 627.20it/s]\u001b[A\n",
"Pass 14/20: 60%|███████████████▋ | 2303/3810 [00:03<00:02, 627.43it/s]\u001b[A\n",
"Pass 14/20: 62%|████████████████▏ | 2367/3810 [00:03<00:02, 627.72it/s]\u001b[A\n",
"Pass 14/20: 64%|████████████████▌ | 2432/3810 [00:03<00:02, 628.14it/s]\u001b[A\n",
"Pass 14/20: 66%|█████████████████ | 2496/3810 [00:03<00:02, 627.28it/s]\u001b[A\n",
"Pass 14/20: 67%|█████████████████▍ | 2560/3810 [00:04<00:01, 627.55it/s]\u001b[A\n",
"Pass 14/20: 69%|█████████████████▉ | 2624/3810 [00:04<00:01, 627.62it/s]\u001b[A\n",
"Pass 14/20: 71%|██████████████████▎ | 2687/3810 [00:04<00:01, 627.46it/s]\u001b[A\n",
"Pass 14/20: 72%|██████████████████▊ | 2750/3810 [00:04<00:01, 626.80it/s]\u001b[A\n",
"Pass 14/20: 74%|███████████████████▏ | 2812/3810 [00:04<00:01, 626.29it/s]\u001b[A\n",
"Pass 14/20: 75%|███████████████████▌ | 2874/3810 [00:04<00:01, 624.37it/s]\u001b[A\n",
"Pass 14/20: 77%|████████████████████ | 2940/3810 [00:04<00:01, 625.06it/s]\u001b[A\n",
"Pass 14/20: 79%|████████████████████▌ | 3005/3810 [00:04<00:01, 625.51it/s]\u001b[A\n",
"Pass 14/20: 81%|████████████████████▉ | 3070/3810 [00:04<00:01, 625.83it/s]\u001b[A\n",
"Pass 14/20: 82%|█████████████████████▍ | 3133/3810 [00:05<00:01, 625.66it/s]\u001b[A\n",
"Pass 14/20: 84%|█████████████████████▊ | 3196/3810 [00:05<00:00, 625.23it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 14/20: 86%|██████████████████████▏ | 3260/3810 [00:05<00:00, 625.44it/s]\u001b[A\n",
"Pass 14/20: 87%|██████████████████████▋ | 3326/3810 [00:05<00:00, 626.06it/s]\u001b[A\n",
"Pass 14/20: 89%|███████████████████████▏ | 3390/3810 [00:05<00:00, 625.81it/s]\u001b[A\n",
"Pass 14/20: 91%|███████████████████████▌ | 3454/3810 [00:05<00:00, 625.98it/s]\u001b[A\n",
"Pass 14/20: 92%|████████████████████████ | 3519/3810 [00:05<00:00, 626.37it/s]\u001b[A\n",
"Pass 14/20: 94%|████████████████████████▍ | 3584/3810 [00:05<00:00, 626.69it/s]\u001b[A\n",
"Pass 14/20: 96%|████████████████████████▉ | 3648/3810 [00:05<00:00, 625.13it/s]\u001b[A\n",
"Pass 14/20: 97%|█████████████████████████▎| 3711/3810 [00:05<00:00, 625.14it/s]\u001b[A\n",
"Pass 14/20: 99%|█████████████████████████▋| 3773/3810 [00:06<00:00, 625.06it/s]\u001b[A\n",
"Batch EM: 70%|███████████████████████ | 14/20 [01:32<00:39, 6.58s/it]\n",
"Pass 15/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 15/20: 2%|▍ | 65/3810 [00:00<00:05, 645.20it/s]\u001b[A\n",
"Pass 15/20: 3%|▉ | 130/3810 [00:00<00:05, 644.90it/s]\u001b[A\n",
"Pass 15/20: 5%|█▍ | 195/3810 [00:00<00:05, 646.16it/s]\u001b[A\n",
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"Pass 15/20: 10%|██▋ | 386/3810 [00:00<00:05, 639.19it/s]\u001b[A\n",
"Pass 15/20: 12%|███▏ | 449/3810 [00:00<00:05, 637.30it/s]\u001b[A\n",
"Pass 15/20: 13%|███▋ | 513/3810 [00:00<00:05, 637.44it/s]\u001b[A\n",
"Pass 15/20: 15%|████ | 573/3810 [00:00<00:05, 631.89it/s]\u001b[A\n",
"Pass 15/20: 17%|████▍ | 633/3810 [00:01<00:05, 625.31it/s]\u001b[A\n",
"Pass 15/20: 18%|████▉ | 696/3810 [00:01<00:04, 625.37it/s]\u001b[A\n",
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"Pass 15/20: 21%|█████▊ | 818/3810 [00:01<00:04, 622.25it/s]\u001b[A\n",
"Pass 15/20: 23%|██████▏ | 880/3810 [00:01<00:04, 622.07it/s]\u001b[A\n",
"Pass 15/20: 25%|██████▋ | 941/3810 [00:01<00:04, 620.96it/s]\u001b[A\n",
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"Pass 15/20: 31%|████████ | 1186/3810 [00:01<00:04, 617.16it/s]\u001b[A\n",
"Pass 15/20: 33%|████████▌ | 1247/3810 [00:02<00:04, 616.51it/s]\u001b[A\n",
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"Pass 15/20: 38%|█████████▊ | 1430/3810 [00:02<00:03, 609.88it/s]\u001b[A\n",
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"Pass 15/20: 41%|██████████▌ | 1552/3810 [00:02<00:03, 609.61it/s]\u001b[A\n",
"Pass 15/20: 42%|███████████ | 1614/3810 [00:02<00:03, 609.75it/s]\u001b[A\n",
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"Pass 15/20: 49%|████████████▋ | 1858/3810 [00:03<00:03, 608.39it/s]\u001b[A\n",
"Pass 15/20: 50%|█████████████ | 1920/3810 [00:03<00:03, 608.57it/s]\u001b[A\n",
"Pass 15/20: 52%|█████████████▌ | 1984/3810 [00:03<00:02, 609.37it/s]\u001b[A\n",
"Pass 15/20: 54%|█████████████▉ | 2046/3810 [00:03<00:02, 609.58it/s]\u001b[A\n",
"Pass 15/20: 55%|██████████████▍ | 2108/3810 [00:03<00:02, 608.14it/s]\u001b[A\n",
"Pass 15/20: 57%|██████████████▊ | 2170/3810 [00:03<00:02, 608.18it/s]\u001b[A\n",
"Pass 15/20: 59%|███████████████▏ | 2232/3810 [00:03<00:02, 608.34it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 15/20: 60%|███████████████▋ | 2295/3810 [00:03<00:02, 608.71it/s]\u001b[A\n",
"Pass 15/20: 62%|████████████████ | 2357/3810 [00:03<00:02, 608.03it/s]\u001b[A\n",
"Pass 15/20: 63%|████████████████▌ | 2418/3810 [00:03<00:02, 607.85it/s]\u001b[A\n",
"Pass 15/20: 65%|████████████████▉ | 2480/3810 [00:04<00:02, 608.03it/s]\u001b[A\n",
"Pass 15/20: 67%|█████████████████▎ | 2541/3810 [00:04<00:02, 607.88it/s]\u001b[A\n",
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"Pass 15/20: 70%|██████████████████▏ | 2664/3810 [00:04<00:01, 607.99it/s]\u001b[A\n",
"Pass 15/20: 72%|██████████████████▌ | 2726/3810 [00:04<00:01, 608.15it/s]\u001b[A\n",
"Pass 15/20: 73%|███████████████████ | 2788/3810 [00:04<00:01, 607.81it/s]\u001b[A\n",
"Pass 15/20: 75%|███████████████████▍ | 2849/3810 [00:04<00:01, 605.26it/s]\u001b[A\n",
"Pass 15/20: 76%|███████████████████▊ | 2910/3810 [00:04<00:01, 605.28it/s]\u001b[A\n",
"Pass 15/20: 78%|████████████████████▎ | 2971/3810 [00:04<00:01, 605.30it/s]\u001b[A\n",
"Pass 15/20: 80%|████████████████████▋ | 3032/3810 [00:05<00:01, 605.38it/s]\u001b[A\n",
"Pass 15/20: 81%|█████████████████████ | 3093/3810 [00:05<00:01, 605.35it/s]\u001b[A\n",
"Pass 15/20: 83%|█████████████████████▌ | 3153/3810 [00:05<00:01, 605.16it/s]\u001b[A\n",
"Pass 15/20: 84%|█████████████████████▉ | 3213/3810 [00:05<00:00, 605.01it/s]\u001b[A\n",
"Pass 15/20: 86%|██████████████████████▎ | 3274/3810 [00:05<00:00, 605.11it/s]\u001b[A\n",
"Pass 15/20: 88%|██████████████████████▊ | 3336/3810 [00:05<00:00, 605.26it/s]\u001b[A\n",
"Pass 15/20: 89%|███████████████████████▏ | 3399/3810 [00:05<00:00, 605.54it/s]\u001b[A\n",
"Pass 15/20: 91%|███████████████████████▌ | 3460/3810 [00:05<00:00, 605.61it/s]\u001b[A\n",
"Pass 15/20: 92%|████████████████████████ | 3521/3810 [00:05<00:00, 605.36it/s]\u001b[A\n",
"Pass 15/20: 94%|████████████████████████▍ | 3582/3810 [00:05<00:00, 604.02it/s]\u001b[A\n",
"Pass 15/20: 96%|████████████████████████▊ | 3641/3810 [00:06<00:00, 602.99it/s]\u001b[A\n",
"Pass 15/20: 97%|█████████████████████████▏| 3700/3810 [00:06<00:00, 602.62it/s]\u001b[A\n",
"Pass 15/20: 99%|█████████████████████████▋| 3758/3810 [00:06<00:00, 602.18it/s]\u001b[A\n",
"Batch EM: 75%|████████████████████████▊ | 15/20 [01:38<00:32, 6.57s/it]\n",
"Pass 16/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 16/20: 2%|▍ | 60/3810 [00:00<00:06, 594.52it/s]\u001b[A\n",
"Pass 16/20: 3%|▊ | 120/3810 [00:00<00:06, 594.59it/s]\u001b[A\n",
"Pass 16/20: 5%|█▎ | 179/3810 [00:00<00:06, 591.61it/s]\u001b[A\n",
"Pass 16/20: 6%|█▋ | 239/3810 [00:00<00:06, 592.26it/s]\u001b[A\n",
"Pass 16/20: 8%|██ | 299/3810 [00:00<00:05, 592.91it/s]\u001b[A\n",
"Pass 16/20: 9%|██▌ | 359/3810 [00:00<00:05, 592.87it/s]\u001b[A\n",
"Pass 16/20: 11%|██▉ | 418/3810 [00:00<00:05, 591.58it/s]\u001b[A\n",
"Pass 16/20: 13%|███▍ | 478/3810 [00:00<00:05, 592.59it/s]\u001b[A\n",
"Pass 16/20: 14%|███▊ | 534/3810 [00:00<00:05, 585.42it/s]\u001b[A\n",
"Pass 16/20: 16%|████▏ | 595/3810 [00:01<00:05, 587.11it/s]\u001b[A\n",
"Pass 16/20: 17%|████▋ | 655/3810 [00:01<00:05, 588.19it/s]\u001b[A\n",
"Pass 16/20: 19%|█████ | 716/3810 [00:01<00:05, 589.89it/s]\u001b[A\n",
"Pass 16/20: 20%|█████▌ | 778/3810 [00:01<00:05, 591.74it/s]\u001b[A\n",
"Pass 16/20: 22%|█████▉ | 841/3810 [00:01<00:04, 594.15it/s]\u001b[A\n",
"Pass 16/20: 24%|██████▍ | 902/3810 [00:01<00:04, 593.98it/s]\u001b[A\n",
"Pass 16/20: 25%|██████▊ | 963/3810 [00:01<00:04, 594.75it/s]\u001b[A\n",
"Pass 16/20: 27%|██████▉ | 1024/3810 [00:01<00:04, 595.36it/s]\u001b[A\n",
"Pass 16/20: 29%|███████▍ | 1086/3810 [00:01<00:04, 596.66it/s]\u001b[A\n",
"Pass 16/20: 30%|███████▊ | 1147/3810 [00:01<00:04, 597.01it/s]\u001b[A\n",
"Pass 16/20: 32%|████████▏ | 1208/3810 [00:02<00:04, 597.10it/s]\u001b[A\n",
"Pass 16/20: 33%|████████▋ | 1269/3810 [00:02<00:04, 592.63it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 16/20: 35%|█████████ | 1333/3810 [00:02<00:04, 594.47it/s]\u001b[A\n",
"Pass 16/20: 37%|█████████▌ | 1398/3810 [00:02<00:04, 596.68it/s]\u001b[A\n",
"Pass 16/20: 38%|█████████▉ | 1464/3810 [00:02<00:03, 598.99it/s]\u001b[A\n",
"Pass 16/20: 40%|██████████▍ | 1528/3810 [00:02<00:03, 600.49it/s]\u001b[A\n",
"Pass 16/20: 42%|██████████▊ | 1591/3810 [00:02<00:03, 601.54it/s]\u001b[A\n",
"Pass 16/20: 44%|███████████▎ | 1660/3810 [00:02<00:03, 604.68it/s]\u001b[A\n",
"Pass 16/20: 45%|███████████▊ | 1725/3810 [00:02<00:03, 605.59it/s]\u001b[A\n",
"Pass 16/20: 47%|████████████▏ | 1792/3810 [00:02<00:03, 607.66it/s]\u001b[A\n",
"Pass 16/20: 49%|████████████▋ | 1859/3810 [00:03<00:03, 609.55it/s]\u001b[A\n",
"Pass 16/20: 51%|█████████████▏ | 1925/3810 [00:03<00:03, 609.77it/s]\u001b[A\n",
"Pass 16/20: 52%|█████████████▌ | 1990/3810 [00:03<00:02, 610.29it/s]\u001b[A\n",
"Pass 16/20: 54%|██████████████ | 2054/3810 [00:03<00:02, 607.85it/s]\u001b[A\n",
"Pass 16/20: 56%|██████████████▍ | 2115/3810 [00:03<00:02, 607.46it/s]\u001b[A\n",
"Pass 16/20: 57%|██████████████▊ | 2176/3810 [00:03<00:02, 607.22it/s]\u001b[A\n",
"Pass 16/20: 59%|███████████████▎ | 2237/3810 [00:03<00:02, 607.18it/s]\u001b[A\n",
"Pass 16/20: 60%|███████████████▋ | 2298/3810 [00:03<00:02, 606.92it/s]\u001b[A\n",
"Pass 16/20: 62%|████████████████ | 2360/3810 [00:03<00:02, 607.26it/s]\u001b[A\n",
"Pass 16/20: 64%|████████████████▌ | 2422/3810 [00:03<00:02, 607.52it/s]\u001b[A\n",
"Pass 16/20: 65%|████████████████▉ | 2485/3810 [00:04<00:02, 608.06it/s]\u001b[A\n",
"Pass 16/20: 67%|█████████████████▍ | 2547/3810 [00:04<00:02, 607.51it/s]\u001b[A\n",
"Pass 16/20: 69%|█████████████████▊ | 2611/3810 [00:04<00:01, 608.08it/s]\u001b[A\n",
"Pass 16/20: 70%|██████████████████▏ | 2674/3810 [00:04<00:01, 608.50it/s]\u001b[A\n",
"Pass 16/20: 72%|██████████████████▋ | 2736/3810 [00:04<00:01, 608.37it/s]\u001b[A\n",
"Pass 16/20: 73%|███████████████████ | 2798/3810 [00:04<00:01, 605.72it/s]\u001b[A\n",
"Pass 16/20: 75%|███████████████████▌ | 2863/3810 [00:04<00:01, 606.49it/s]\u001b[A\n",
"Pass 16/20: 77%|███████████████████▉ | 2925/3810 [00:04<00:01, 606.73it/s]\u001b[A\n",
"Pass 16/20: 78%|████████████████████▍ | 2988/3810 [00:04<00:01, 607.19it/s]\u001b[A\n",
"Pass 16/20: 80%|████████████████████▊ | 3051/3810 [00:05<00:01, 607.62it/s]\u001b[A\n",
"Pass 16/20: 82%|█████████████████████▎ | 3116/3810 [00:05<00:01, 608.40it/s]\u001b[A\n",
"Pass 16/20: 83%|█████████████████████▋ | 3179/3810 [00:05<00:01, 608.54it/s]\u001b[A\n",
"Pass 16/20: 85%|██████████████████████ | 3242/3810 [00:05<00:00, 608.79it/s]\u001b[A\n",
"Pass 16/20: 87%|██████████████████████▌ | 3305/3810 [00:05<00:00, 608.74it/s]\u001b[A\n",
"Pass 16/20: 88%|██████████████████████▉ | 3367/3810 [00:05<00:00, 608.79it/s]\u001b[A\n",
"Pass 16/20: 90%|███████████████████████▍ | 3433/3810 [00:05<00:00, 609.61it/s]\u001b[A\n",
"Pass 16/20: 92%|███████████████████████▊ | 3496/3810 [00:05<00:00, 609.88it/s]\u001b[A\n",
"Pass 16/20: 93%|████████████████████████▎ | 3559/3810 [00:05<00:00, 609.67it/s]\u001b[A\n",
"Pass 16/20: 95%|████████████████████████▋ | 3621/3810 [00:05<00:00, 608.49it/s]\u001b[A\n",
"Pass 16/20: 97%|█████████████████████████ | 3681/3810 [00:06<00:00, 608.29it/s]\u001b[A\n",
"Pass 16/20: 98%|█████████████████████████▌| 3743/3810 [00:06<00:00, 608.40it/s]\u001b[A\n",
"Pass 16/20: 100%|█████████████████████████▉| 3804/3810 [00:06<00:00, 608.28it/s]\u001b[A\n",
"Batch EM: 80%|██████████████████████████▍ | 16/20 [01:44<00:26, 6.55s/it]\n",
"Pass 17/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 17/20: 2%|▍ | 61/3810 [00:00<00:06, 603.17it/s]\u001b[A\n",
"Pass 17/20: 3%|▊ | 123/3810 [00:00<00:06, 609.45it/s]\u001b[A\n",
"Pass 17/20: 5%|█▎ | 179/3810 [00:00<00:06, 593.08it/s]\u001b[A\n",
"Pass 17/20: 6%|█▋ | 242/3810 [00:00<00:05, 601.39it/s]\u001b[A\n",
"Pass 17/20: 8%|██▏ | 300/3810 [00:00<00:05, 596.16it/s]\u001b[A\n",
"Pass 17/20: 10%|██▌ | 367/3810 [00:00<00:05, 607.59it/s]\u001b[A\n",
"Pass 17/20: 11%|███ | 434/3810 [00:00<00:05, 616.36it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 17/20: 13%|███▌ | 500/3810 [00:00<00:05, 620.66it/s]\u001b[A\n",
"Pass 17/20: 15%|███▉ | 561/3810 [00:00<00:05, 617.73it/s]\u001b[A\n",
"Pass 17/20: 16%|████▍ | 628/3810 [00:01<00:05, 622.32it/s]\u001b[A\n",
"Pass 17/20: 18%|████▉ | 695/3810 [00:01<00:04, 625.68it/s]\u001b[A\n",
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"Pass 17/20: 77%|████████████████████ | 2948/3810 [00:04<00:01, 624.58it/s]\u001b[A\n",
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"Pass 17/20: 90%|███████████████████████▎ | 3410/3810 [00:05<00:00, 626.88it/s]\u001b[A\n",
"Pass 17/20: 91%|███████████████████████▋ | 3474/3810 [00:05<00:00, 626.06it/s]\u001b[A\n",
"Pass 17/20: 93%|████████████████████████▏ | 3537/3810 [00:05<00:00, 625.15it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 17/20: 94%|████████████████████████▌ | 3598/3810 [00:05<00:00, 624.37it/s]\u001b[A\n",
"Pass 17/20: 96%|████████████████████████▉ | 3659/3810 [00:05<00:00, 621.84it/s]\u001b[A\n",
"Pass 17/20: 98%|█████████████████████████▎| 3718/3810 [00:05<00:00, 621.19it/s]\u001b[A\n",
"Pass 17/20: 99%|█████████████████████████▊| 3776/3810 [00:06<00:00, 620.11it/s]\u001b[A\n",
"Batch EM: 85%|████████████████████████████ | 17/20 [01:50<00:19, 6.52s/it]\n",
"Pass 18/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 18/20: 2%|▍ | 59/3810 [00:00<00:06, 588.97it/s]\u001b[A\n",
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"Pass 18/20: 64%|████████████████▌ | 2420/3810 [00:04<00:02, 577.33it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 18/20: 67%|█████████████████▎ | 2536/3810 [00:04<00:02, 577.40it/s]\u001b[A\n",
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"Pass 18/20: 90%|███████████████████████▎ | 3425/3810 [00:05<00:00, 574.95it/s]\u001b[A\n",
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"Pass 18/20: 96%|████████████████████████▉ | 3660/3810 [00:06<00:00, 574.67it/s]\u001b[A\n",
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"Pass 18/20: 99%|█████████████████████████▊| 3778/3810 [00:06<00:00, 574.85it/s]\u001b[A\n",
"Batch EM: 90%|█████████████████████████████▋ | 18/20 [01:57<00:13, 6.53s/it]\n",
"Pass 19/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
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"Pass 19/20: 36%|█████████▎ | 1363/3810 [00:02<00:04, 581.30it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 19/20: 62%|████████████████▏ | 2366/3810 [00:04<00:02, 579.00it/s]\u001b[A\n",
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],
"name": "stdout"
},
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"output_type": "stream",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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"Batch EM: 100%|█████████████████████████████████| 20/20 [02:10<00:00, 6.54s/it]\n",
"Removing sequences containing at least one motif occurrence\n",
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"Saving results to yamda_test_e_pos_neg\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "0unRwSMknmWh",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 578
},
"outputId": "6f1b92d2-0046-4b28-fabd-0f47390b35df"
},
"cell_type": "code",
"source": [
"!cat yamda_test_e_pos_neg/motifs.txt"
],
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": [
"MEME version 4\r\n",
"\r\n",
"ALPHABET= ACGT\r\n",
"\r\n",
"strands: + -\r\n",
"\r\n",
"Background letter frequencies (from uniform background):\r\n",
"A 0.250000 C 0.250000 G 0.250000 T 0.250000 \r\n",
"\r\n",
"MOTIF M0 N0\r\n",
"\r\n",
"letter-probability matrix: alength= 4 w= 20 nsites= 40013 E= 0\r\n",
"0.052445 0.435540 0.461143 0.050872\r\n",
"0.061709 0.453492 0.428039 0.056761\r\n",
"0.060982 0.442481 0.438933 0.057604\r\n",
"0.057580 0.444230 0.445292 0.052897\r\n",
"0.055214 0.448242 0.444655 0.051888\r\n",
"0.054037 0.449954 0.442646 0.053362\r\n",
"0.051186 0.450438 0.447251 0.051125\r\n",
"0.050030 0.459122 0.440944 0.049904\r\n",
"0.052941 0.453787 0.440664 0.052608\r\n",
"0.051222 0.450259 0.447773 0.050745\r\n",
"0.049869 0.451124 0.446848 0.052160\r\n",
"0.051689 0.443945 0.450583 0.053783\r\n",
"0.049050 0.444432 0.455758 0.050760\r\n",
"0.050360 0.450552 0.447068 0.052020\r\n",
"0.052514 0.445624 0.447005 0.054857\r\n",
"0.051096 0.447587 0.445312 0.056004\r\n",
"0.052159 0.448338 0.441158 0.058345\r\n",
"0.056824 0.441804 0.439552 0.061820\r\n",
"0.055993 0.431127 0.450516 0.062364\r\n",
"0.050381 0.464106 0.432421 0.053092\r\n",
"\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "Ix1BdJRFouts",
"colab_type": "code",
"colab": {}
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"motif_mat = np.array([[float(y) for y in x.split(\" \")] for x in\n",
"\"\"\"0.052445 0.435540 0.461143 0.050872\n",
"0.061709 0.453492 0.428039 0.056761\n",
"0.060982 0.442481 0.438933 0.057604\n",
"0.057580 0.444230 0.445292 0.052897\n",
"0.055214 0.448242 0.444655 0.051888\n",
"0.054037 0.449954 0.442646 0.053362\n",
"0.051186 0.450438 0.447251 0.051125\n",
"0.050030 0.459122 0.440944 0.049904\n",
"0.052941 0.453787 0.440664 0.052608\n",
"0.051222 0.450259 0.447773 0.050745\n",
"0.049869 0.451124 0.446848 0.052160\n",
"0.051689 0.443945 0.450583 0.053783\n",
"0.049050 0.444432 0.455758 0.050760\n",
"0.050360 0.450552 0.447068 0.052020\n",
"0.052514 0.445624 0.447005 0.054857\n",
"0.051096 0.447587 0.445312 0.056004\n",
"0.052159 0.448338 0.441158 0.058345\n",
"0.056824 0.441804 0.439552 0.061820\n",
"0.055993 0.431127 0.450516 0.062364\n",
"0.050381 0.464106 0.432421 0.053092\"\"\".split(\"\\n\")])"
],
"execution_count": 0,
"outputs": []
},
{
"metadata": {
"id": "KaIYPgXjtpLA",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 190
},
"outputId": "a10554f8-ffdb-4327-d40a-2953e09a9c80"
},
"cell_type": "code",
"source": [
"!pip install deeplift"
],
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting deeplift\n",
" Downloading https://files.pythonhosted.org/packages/06/e0/fae790dc5433d4251fca53457b422c80a76ec82866eb1f201c1bbd3a6dc0/deeplift-0.6.6.tar.gz\n",
"Requirement already satisfied: numpy>=1.9 in /usr/local/lib/python3.6/dist-packages (from deeplift) (1.14.5)\n",
"Building wheels for collected packages: deeplift\n",
" Running setup.py bdist_wheel for deeplift ... \u001b[?25l-\b \b\\\b \bdone\n",
"\u001b[?25h Stored in directory: /content/.cache/pip/wheels/72/af/a8/4ff8850b871f74d2854088cca8b4c60ea5fe2215dcf3184870\n",
"Successfully built deeplift\n",
"Installing collected packages: deeplift\n",
"Successfully installed deeplift-0.6.6\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "0rxEBaJxs4zk",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177
},
"outputId": "c685e239-f700-4222-c89d-4c7debddddb0"
},
"cell_type": "code",
"source": [
"%matplotlib inline\n",
"from deeplift.visualization import viz_sequence\n",
"viz_sequence.plot_weights(motif_mat)"
],
"execution_count": 40,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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7COkzgem/ZrvSMNvIhozfQ2W97HngrKdPfnvVKioRooVdL744OL9iYVzoi5oa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\nGQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2ca16e6780>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "IIsr974ihabo",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 27356
},
"outputId": "d8e4462c-1ea3-483d-dd00-2721e0494f8a"
},
"cell_type": "code",
"source": [
"!python YAMDA/run_em.py -r -i central400_pos_seqs_concat.fa -j central400_neg_seqs_concat.fa -oc yamda_test_no_e_pos_neg"
],
"execution_count": 44,
"outputs": [
{
"output_type": "stream",
"text": [
"Loading sequences from FASTA\n",
"Searching positive sequences: 100%|███████████████| 9/9 [01:39<00:00, 11.02s/it]\n",
"Searching negative sequences: 100%|███████████████| 9/9 [01:37<00:00, 10.85s/it]\n",
"Converting letter sequences to tensors\n",
"On-line EM: 0%| | 0/1 [00:00<?, ?it/s]\n",
"Pass 1/1: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 1/1: 0%| | 13/3810 [00:00<00:31, 120.39it/s]\u001b[A\n",
"Pass 1/1: 1%|▏ | 26/3810 [00:00<00:31, 121.41it/s]\u001b[A\n",
"Pass 1/1: 1%|▎ | 39/3810 [00:00<00:30, 122.33it/s]\u001b[A\n",
"Pass 1/1: 1%|▍ | 53/3810 [00:00<00:30, 125.10it/s]\u001b[A\n",
"Pass 1/1: 2%|▌ | 67/3810 [00:00<00:29, 126.83it/s]\u001b[A\n",
"Pass 1/1: 2%|▋ | 81/3810 [00:00<00:29, 128.20it/s]\u001b[A\n",
"Pass 1/1: 3%|▊ | 96/3810 [00:00<00:28, 131.10it/s]\u001b[A\n",
"Pass 1/1: 3%|▊ | 110/3810 [00:00<00:28, 131.32it/s]\u001b[A\n",
"Pass 1/1: 3%|▉ | 127/3810 [00:00<00:27, 134.69it/s]\u001b[A\n",
"Pass 1/1: 4%|█ | 143/3810 [00:01<00:26, 136.80it/s]\u001b[A\n",
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"Pass 1/1: 5%|█▎ | 176/3810 [00:01<00:25, 140.92it/s]\u001b[A\n",
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"Pass 1/1: 7%|██ | 275/3810 [00:01<00:24, 147.15it/s]\u001b[A\n",
"Pass 1/1: 8%|██▏ | 291/3810 [00:01<00:23, 147.62it/s]\u001b[A\n",
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"Pass 1/1: 10%|██▊ | 374/3810 [00:02<00:22, 149.46it/s]\u001b[A\n",
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"Pass 1/1: 12%|███▎ | 439/3810 [00:02<00:22, 150.07it/s]\u001b[A\n",
"Pass 1/1: 12%|███▍ | 455/3810 [00:03<00:22, 150.38it/s]\u001b[A\n",
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"Pass 1/1: 13%|███▋ | 489/3810 [00:03<00:21, 151.25it/s]\u001b[A\n",
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"Pass 1/1: 18%|█████▎ | 690/3810 [00:04<00:20, 153.59it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 19%|█████▍ | 707/3810 [00:04<00:20, 153.17it/s]\u001b[A\n",
"Pass 1/1: 19%|█████▌ | 724/3810 [00:04<00:20, 153.38it/s]\u001b[A\n",
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"Pass 1/1: 20%|█████▊ | 757/3810 [00:04<00:19, 153.65it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 774/3810 [00:05<00:19, 153.91it/s]\u001b[A\n",
"Pass 1/1: 21%|██████ | 791/3810 [00:05<00:19, 154.13it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
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],
"name": "stdout"
},
{
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"Pass 1/1: 92%|█████████████████████████▋ | 3498/3810 [00:21<00:01, 160.30it/s]\u001b[A\n",
"Pass 1/1: 92%|█████████████████████████▊ | 3515/3810 [00:21<00:01, 160.32it/s]\u001b[A\n",
"Pass 1/1: 93%|█████████████████████████▉ | 3532/3810 [00:22<00:01, 160.34it/s]\u001b[A\n",
"Pass 1/1: 93%|██████████████████████████ | 3549/3810 [00:22<00:01, 160.32it/s]\u001b[A\n",
"Pass 1/1: 94%|██████████████████████████▏ | 3566/3810 [00:22<00:01, 160.36it/s]\u001b[A\n",
"Pass 1/1: 94%|██████████████████████████▎ | 3583/3810 [00:22<00:01, 160.37it/s]\u001b[A\n",
"Pass 1/1: 94%|██████████████████████████▍ | 3600/3810 [00:22<00:01, 160.41it/s]\u001b[A\n",
"Pass 1/1: 95%|██████████████████████████▌ | 3617/3810 [00:22<00:01, 160.43it/s]\u001b[A\n",
"Pass 1/1: 95%|██████████████████████████▋ | 3634/3810 [00:22<00:01, 160.47it/s]\u001b[A\n",
"Pass 1/1: 96%|██████████████████████████▊ | 3651/3810 [00:22<00:00, 160.50it/s]\u001b[A\n",
"Pass 1/1: 96%|██████████████████████████▉ | 3668/3810 [00:22<00:00, 160.53it/s]\u001b[A\n",
"Pass 1/1: 97%|███████████████████████████ | 3685/3810 [00:22<00:00, 160.54it/s]\u001b[A\n",
"Pass 1/1: 97%|███████████████████████████▏| 3702/3810 [00:23<00:00, 160.55it/s]\u001b[A\n",
"Pass 1/1: 98%|███████████████████████████▎| 3719/3810 [00:23<00:00, 160.59it/s]\u001b[A\n",
"Pass 1/1: 98%|███████████████████████████▍| 3736/3810 [00:23<00:00, 160.62it/s]\u001b[A\n",
"Pass 1/1: 99%|███████████████████████████▌| 3753/3810 [00:23<00:00, 160.60it/s]\u001b[A\n",
"Pass 1/1: 99%|███████████████████████████▋| 3771/3810 [00:23<00:00, 160.64it/s]\u001b[A\n",
"Pass 1/1: 99%|███████████████████████████▊| 3788/3810 [00:23<00:00, 160.66it/s]\u001b[A\n",
"Pass 1/1: 100%|███████████████████████████▉| 3805/3810 [00:23<00:00, 160.67it/s]\u001b[A\n",
"On-line EM: 100%|█████████████████████████████████| 1/1 [00:23<00:00, 23.71s/it]\n",
"Batch EM: 0%| | 0/1 [00:00<?, ?it/s]\n",
"Pass 1/1: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 1/1: 0%|▏ | 19/3810 [00:00<00:20, 182.65it/s]\u001b[A\n",
"Pass 1/1: 1%|▎ | 37/3810 [00:00<00:21, 177.33it/s]\u001b[A\n",
"Pass 1/1: 1%|▍ | 55/3810 [00:00<00:21, 175.88it/s]\u001b[A\n",
"Pass 1/1: 2%|▌ | 73/3810 [00:00<00:21, 175.10it/s]\u001b[A\n",
"Pass 1/1: 2%|▋ | 90/3810 [00:00<00:21, 174.04it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 3%|▊ | 108/3810 [00:00<00:21, 173.85it/s]\u001b[A\n",
"Pass 1/1: 3%|▉ | 126/3810 [00:00<00:21, 173.95it/s]\u001b[A\n",
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"Pass 1/1: 25%|███████▏ | 950/3810 [00:05<00:16, 171.55it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 25%|███████▎ | 968/3810 [00:05<00:16, 171.56it/s]\u001b[A\n",
"Pass 1/1: 26%|███████▌ | 986/3810 [00:05<00:16, 171.59it/s]\u001b[A\n",
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"Pass 1/1: 40%|███████████▎ | 1539/3810 [00:09<00:13, 170.95it/s]\u001b[A\n",
"Pass 1/1: 41%|███████████▍ | 1557/3810 [00:09<00:13, 170.93it/s]\u001b[A\n",
"Pass 1/1: 41%|███████████▌ | 1575/3810 [00:09<00:13, 170.87it/s]\u001b[A\n",
"Pass 1/1: 42%|███████████▋ | 1593/3810 [00:09<00:12, 170.87it/s]\u001b[A\n",
"Pass 1/1: 42%|███████████▊ | 1611/3810 [00:09<00:12, 170.89it/s]\u001b[A\n",
"Pass 1/1: 43%|███████████▉ | 1629/3810 [00:09<00:12, 170.90it/s]\u001b[A\n",
"Pass 1/1: 43%|████████████ | 1647/3810 [00:09<00:12, 170.69it/s]\u001b[A\n",
"Pass 1/1: 44%|████████████▏ | 1664/3810 [00:09<00:12, 170.63it/s]\u001b[A\n",
"Pass 1/1: 44%|████████████▎ | 1682/3810 [00:09<00:12, 170.63it/s]\u001b[A\n",
"Pass 1/1: 45%|████████████▍ | 1700/3810 [00:09<00:12, 170.64it/s]\u001b[A\n",
"Pass 1/1: 45%|████████████▌ | 1717/3810 [00:10<00:12, 170.58it/s]\u001b[A\n",
"Pass 1/1: 46%|████████████▋ | 1734/3810 [00:10<00:12, 170.55it/s]\u001b[A\n",
"Pass 1/1: 46%|████████████▉ | 1752/3810 [00:10<00:12, 170.56it/s]\u001b[A\n",
"Pass 1/1: 46%|█████████████ | 1770/3810 [00:10<00:11, 170.56it/s]\u001b[A\n",
"Pass 1/1: 47%|█████████████▏ | 1787/3810 [00:10<00:11, 170.49it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 47%|█████████████▎ | 1804/3810 [00:10<00:11, 170.47it/s]\u001b[A\n",
"Pass 1/1: 48%|█████████████▍ | 1821/3810 [00:10<00:11, 170.38it/s]\u001b[A\n",
"Pass 1/1: 48%|█████████████▌ | 1838/3810 [00:10<00:11, 170.37it/s]\u001b[A\n",
"Pass 1/1: 49%|█████████████▋ | 1855/3810 [00:10<00:11, 170.15it/s]\u001b[A\n",
"Pass 1/1: 49%|█████████████▊ | 1872/3810 [00:11<00:11, 170.13it/s]\u001b[A\n",
"Pass 1/1: 50%|█████████████▉ | 1890/3810 [00:11<00:11, 170.15it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████ | 1907/3810 [00:11<00:11, 170.14it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▏ | 1924/3810 [00:11<00:11, 170.12it/s]\u001b[A\n",
"Pass 1/1: 51%|██████████████▎ | 1941/3810 [00:11<00:10, 170.08it/s]\u001b[A\n",
"Pass 1/1: 51%|██████████████▍ | 1958/3810 [00:11<00:10, 169.96it/s]\u001b[A\n",
"Pass 1/1: 52%|██████████████▌ | 1975/3810 [00:11<00:10, 169.90it/s]\u001b[A\n",
"Pass 1/1: 52%|██████████████▋ | 1992/3810 [00:11<00:10, 169.81it/s]\u001b[A\n",
"Pass 1/1: 53%|██████████████▊ | 2009/3810 [00:11<00:10, 169.75it/s]\u001b[A\n",
"Pass 1/1: 53%|██████████████▉ | 2026/3810 [00:11<00:10, 169.73it/s]\u001b[A\n",
"Pass 1/1: 54%|███████████████ | 2044/3810 [00:12<00:10, 169.73it/s]\u001b[A\n",
"Pass 1/1: 54%|███████████████▏ | 2061/3810 [00:12<00:10, 169.61it/s]\u001b[A\n",
"Pass 1/1: 55%|███████████████▎ | 2078/3810 [00:12<00:10, 169.60it/s]\u001b[A\n",
"Pass 1/1: 55%|███████████████▍ | 2095/3810 [00:12<00:10, 169.57it/s]\u001b[A\n",
"Pass 1/1: 55%|███████████████▌ | 2112/3810 [00:12<00:10, 169.47it/s]\u001b[A\n",
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"Pass 1/1: 56%|███████████████▊ | 2145/3810 [00:12<00:09, 169.19it/s]\u001b[A\n",
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"Pass 1/1: 57%|████████████████ | 2180/3810 [00:12<00:09, 169.20it/s]\u001b[A\n",
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"Pass 1/1: 59%|████████████████▌ | 2248/3810 [00:13<00:09, 169.16it/s]\u001b[A\n",
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"Pass 1/1: 60%|████████████████▊ | 2282/3810 [00:13<00:09, 168.89it/s]\u001b[A\n",
"Pass 1/1: 60%|████████████████▉ | 2299/3810 [00:13<00:08, 168.81it/s]\u001b[A\n",
"Pass 1/1: 61%|█████████████████ | 2316/3810 [00:13<00:08, 168.77it/s]\u001b[A\n",
"Pass 1/1: 61%|█████████████████▏ | 2333/3810 [00:13<00:08, 168.70it/s]\u001b[A\n",
"Pass 1/1: 62%|█████████████████▎ | 2351/3810 [00:13<00:08, 168.72it/s]\u001b[A\n",
"Pass 1/1: 62%|█████████████████▍ | 2369/3810 [00:14<00:08, 168.74it/s]\u001b[A\n",
"Pass 1/1: 63%|█████████████████▌ | 2386/3810 [00:14<00:08, 168.73it/s]\u001b[A\n",
"Pass 1/1: 63%|█████████████████▋ | 2404/3810 [00:14<00:08, 168.74it/s]\u001b[A\n",
"Pass 1/1: 64%|█████████████████▊ | 2421/3810 [00:14<00:08, 168.74it/s]\u001b[A\n",
"Pass 1/1: 64%|█████████████████▉ | 2438/3810 [00:14<00:08, 168.73it/s]\u001b[A\n",
"Pass 1/1: 64%|██████████████████ | 2455/3810 [00:14<00:08, 168.72it/s]\u001b[A\n",
"Pass 1/1: 65%|██████████████████▏ | 2472/3810 [00:14<00:07, 168.59it/s]\u001b[A\n",
"Pass 1/1: 65%|██████████████████▎ | 2489/3810 [00:14<00:07, 168.54it/s]\u001b[A\n",
"Pass 1/1: 66%|██████████████████▍ | 2506/3810 [00:14<00:07, 168.50it/s]\u001b[A\n",
"Pass 1/1: 66%|██████████████████▌ | 2523/3810 [00:14<00:07, 168.42it/s]\u001b[A\n",
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"Pass 1/1: 68%|███████████████████▏ | 2608/3810 [00:15<00:07, 168.15it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 69%|███████████████████▎ | 2625/3810 [00:15<00:07, 168.09it/s]\u001b[A\n",
"Pass 1/1: 69%|███████████████████▍ | 2641/3810 [00:15<00:06, 168.02it/s]\u001b[A\n",
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"Pass 1/1: 90%|█████████████████████████▎ | 3437/3810 [00:20<00:02, 167.79it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 91%|█████████████████████████▍ | 3454/3810 [00:20<00:02, 167.78it/s]\u001b[A\n",
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"Pass 1/1: 97%|███████████████████████████▏| 3698/3810 [00:22<00:00, 167.82it/s]\u001b[A\n",
"Pass 1/1: 98%|███████████████████████████▎| 3716/3810 [00:22<00:00, 167.84it/s]\u001b[A\n",
"Pass 1/1: 98%|███████████████████████████▍| 3734/3810 [00:22<00:00, 167.87it/s]\u001b[A\n",
"Pass 1/1: 98%|███████████████████████████▌| 3752/3810 [00:22<00:00, 167.90it/s]\u001b[A\n",
"Pass 1/1: 99%|███████████████████████████▋| 3770/3810 [00:22<00:00, 167.91it/s]\u001b[A\n",
"Pass 1/1: 99%|███████████████████████████▊| 3788/3810 [00:22<00:00, 167.94it/s]\u001b[A\n",
"Pass 1/1: 100%|███████████████████████████▉| 3806/3810 [00:22<00:00, 167.95it/s]\u001b[A\n",
"Batch EM: 100%|███████████████████████████████████| 1/1 [00:22<00:00, 22.70s/it]\n",
"Computing log likelihood: 69%|████████▎ | 2621/3810 [00:04<00:01, 639.93it/s]"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Computing log likelihood: 100%|████████████| 3810/3810 [00:05<00:00, 647.04it/s]\n",
"Using seed originating from word: NNNNCGCCCCCTGCCGNNNN\n",
"Batch EM: 0%| | 0/20 [00:00<?, ?it/s]\n",
"Pass 1/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 1/20: 2%|▌ | 70/3810 [00:00<00:05, 698.59it/s]\u001b[A\n",
"Pass 1/20: 4%|█ | 141/3810 [00:00<00:05, 700.93it/s]\u001b[A\n",
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"Pass 1/20: 77%|████████████████████▋ | 2918/3810 [00:04<00:01, 646.25it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/20: 78%|█████████████████████ | 2979/3810 [00:04<00:01, 645.17it/s]\u001b[A\n",
"Pass 1/20: 80%|█████████████████████▌ | 3040/3810 [00:04<00:01, 642.84it/s]\u001b[A\n",
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"Pass 1/20: 86%|███████████████████████▎ | 3286/3810 [00:05<00:00, 639.76it/s]\u001b[A\n",
"Pass 1/20: 88%|███████████████████████▋ | 3349/3810 [00:05<00:00, 639.55it/s]\u001b[A\n",
"Pass 1/20: 90%|████████████████████████▏ | 3411/3810 [00:05<00:00, 639.08it/s]\u001b[A\n",
"Pass 1/20: 91%|████████████████████████▌ | 3474/3810 [00:05<00:00, 638.79it/s]\u001b[A\n",
"Pass 1/20: 93%|█████████████████████████ | 3539/3810 [00:05<00:00, 638.86it/s]\u001b[A\n",
"Pass 1/20: 95%|█████████████████████████▌ | 3602/3810 [00:05<00:00, 637.48it/s]\u001b[A\n",
"Pass 1/20: 96%|█████████████████████████▉ | 3664/3810 [00:05<00:00, 636.98it/s]\u001b[A\n",
"Pass 1/20: 98%|██████████████████████████▍| 3725/3810 [00:05<00:00, 636.08it/s]\u001b[A\n",
"Pass 1/20: 99%|██████████████████████████▊| 3786/3810 [00:05<00:00, 634.97it/s]\u001b[A\n",
"Batch EM: 5%|█▋ | 1/20 [00:06<01:54, 6.02s/it]\n",
"Pass 2/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 2/20: 2%|▌ | 66/3810 [00:00<00:05, 658.07it/s]\u001b[A\n",
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"Pass 2/20: 21%|█████▉ | 811/3810 [00:01<00:04, 613.95it/s]\u001b[A\n",
"Pass 2/20: 23%|██████▍ | 871/3810 [00:01<00:04, 612.78it/s]\u001b[A\n",
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"Pass 2/20: 53%|██████████████▎ | 2023/3810 [00:03<00:02, 620.05it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 2/20: 55%|██████████████▊ | 2091/3810 [00:03<00:02, 621.65it/s]\u001b[A\n",
"Pass 2/20: 57%|███████████████▎ | 2158/3810 [00:03<00:02, 622.84it/s]\u001b[A\n",
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"Pass 2/20: 86%|███████████████████████▏ | 3271/3810 [00:05<00:00, 637.51it/s]\u001b[A\n",
"Pass 2/20: 88%|███████████████████████▋ | 3340/3810 [00:05<00:00, 638.31it/s]\u001b[A\n",
"Pass 2/20: 89%|████████████████████████▏ | 3408/3810 [00:05<00:00, 639.06it/s]\u001b[A\n",
"Pass 2/20: 91%|████████████████████████▋ | 3477/3810 [00:05<00:00, 639.82it/s]\u001b[A\n",
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"Pass 2/20: 95%|█████████████████████████▌ | 3615/3810 [00:05<00:00, 641.50it/s]\u001b[A\n",
"Pass 2/20: 97%|██████████████████████████ | 3684/3810 [00:05<00:00, 642.09it/s]\u001b[A\n",
"Pass 2/20: 99%|██████████████████████████▌| 3753/3810 [00:05<00:00, 642.80it/s]\u001b[A\n",
"Batch EM: 10%|███▍ | 2/20 [00:11<01:47, 5.97s/it]\n",
"Pass 3/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
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"Pass 3/20: 37%|█████████▉ | 1396/3810 [00:02<00:03, 653.39it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 3/20: 86%|███████████████████████▎ | 3290/3810 [00:05<00:00, 656.89it/s]\u001b[A\n",
"Pass 3/20: 88%|███████████████████████▊ | 3361/3810 [00:05<00:00, 657.82it/s]\u001b[A\n",
"Pass 3/20: 90%|████████████████████████▎ | 3432/3810 [00:05<00:00, 658.79it/s]\u001b[A\n",
"Pass 3/20: 92%|████████████████████████▊ | 3502/3810 [00:05<00:00, 656.73it/s]\u001b[A\n",
"Pass 3/20: 94%|█████████████████████████▎ | 3573/3810 [00:05<00:00, 657.68it/s]\u001b[A\n",
"Pass 3/20: 96%|█████████████████████████▊ | 3645/3810 [00:05<00:00, 658.68it/s]\u001b[A\n",
"Pass 3/20: 97%|██████████████████████████▎| 3714/3810 [00:05<00:00, 658.92it/s]\u001b[A\n",
"Pass 3/20: 99%|██████████████████████████▊| 3783/3810 [00:05<00:00, 659.06it/s]\u001b[A\n",
"Batch EM: 15%|█████ | 3/20 [00:17<01:40, 5.91s/it]\n",
"Pass 4/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 4/20: 2%|▌ | 75/3810 [00:00<00:05, 741.21it/s]\u001b[A\n",
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"Pass 4/20: 23%|██████▍ | 869/3810 [00:01<00:04, 707.22it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 4/20: 25%|██████▉ | 940/3810 [00:01<00:04, 706.81it/s]\u001b[A\n",
"Pass 4/20: 27%|███████▏ | 1010/3810 [00:01<00:03, 705.11it/s]\u001b[A\n",
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"Pass 4/20: 53%|██████████████▏ | 2006/3810 [00:02<00:02, 698.77it/s]\u001b[A\n",
"Pass 4/20: 55%|██████████████▋ | 2079/3810 [00:02<00:02, 699.56it/s]\u001b[A\n",
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"Pass 4/20: 67%|██████████████████▏ | 2562/3810 [00:03<00:01, 690.51it/s]\u001b[A\n",
"Pass 4/20: 69%|██████████████████▋ | 2632/3810 [00:03<00:01, 690.72it/s]\u001b[A\n",
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"Pass 4/20: 73%|███████████████████▋ | 2770/3810 [00:04<00:01, 690.32it/s]\u001b[A\n",
"Pass 4/20: 75%|████████████████████ | 2839/3810 [00:04<00:01, 690.31it/s]\u001b[A\n",
"Pass 4/20: 76%|████████████████████▌ | 2908/3810 [00:04<00:01, 689.72it/s]\u001b[A\n",
"Pass 4/20: 78%|█████████████████████ | 2977/3810 [00:04<00:01, 689.58it/s]\u001b[A\n",
"Pass 4/20: 80%|█████████████████████▌ | 3047/3810 [00:04<00:01, 689.78it/s]\u001b[A\n",
"Pass 4/20: 82%|██████████████████████ | 3116/3810 [00:04<00:01, 686.64it/s]\u001b[A\n",
"Pass 4/20: 84%|██████████████████████▌ | 3186/3810 [00:04<00:00, 686.89it/s]\u001b[A\n",
"Pass 4/20: 85%|███████████████████████ | 3255/3810 [00:04<00:00, 686.89it/s]\u001b[A\n",
"Pass 4/20: 87%|███████████████████████▌ | 3326/3810 [00:04<00:00, 687.36it/s]\u001b[A\n",
"Pass 4/20: 89%|████████████████████████ | 3397/3810 [00:04<00:00, 687.79it/s]\u001b[A\n",
"Pass 4/20: 91%|████████████████████████▌ | 3468/3810 [00:05<00:00, 688.13it/s]\u001b[A\n",
"Pass 4/20: 93%|█████████████████████████ | 3538/3810 [00:05<00:00, 688.14it/s]\u001b[A\n",
"Pass 4/20: 95%|█████████████████████████▌ | 3608/3810 [00:05<00:00, 687.58it/s]\u001b[A\n",
"Pass 4/20: 97%|██████████████████████████ | 3677/3810 [00:05<00:00, 686.53it/s]\u001b[A\n",
"Pass 4/20: 98%|██████████████████████████▌| 3744/3810 [00:05<00:00, 686.22it/s]\u001b[A\n",
"Batch EM: 20%|██████▊ | 4/20 [00:23<01:33, 5.82s/it]\n",
"Pass 5/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 5/20: 2%|▌ | 68/3810 [00:00<00:05, 672.82it/s]\u001b[A\n",
"Pass 5/20: 3%|▉ | 126/3810 [00:00<00:05, 621.99it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Batch EM: 35%|███████████▉ | 7/20 [00:39<01:13, 5.68s/it]\n",
"Pass 8/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 8/20: 2%|▌ | 67/3810 [00:00<00:05, 663.00it/s]\u001b[A\n",
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"Pass 8/20: 35%|█████████▌ | 1351/3810 [00:02<00:03, 661.26it/s]\u001b[A\n",
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"Pass 8/20: 46%|████████████▍ | 1757/3810 [00:02<00:03, 657.60it/s]\u001b[A\n",
"Pass 8/20: 48%|████████████▉ | 1825/3810 [00:02<00:03, 658.41it/s]\u001b[A\n",
"Pass 8/20: 50%|█████████████▍ | 1897/3810 [00:02<00:02, 660.23it/s]\u001b[A\n",
"Pass 8/20: 52%|█████████████▉ | 1967/3810 [00:02<00:02, 661.45it/s]\u001b[A\n",
"Pass 8/20: 53%|██████████████▍ | 2036/3810 [00:03<00:02, 661.91it/s]\u001b[A\n",
"Pass 8/20: 55%|██████████████▉ | 2105/3810 [00:03<00:02, 662.26it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 8/20: 57%|███████████████▍ | 2173/3810 [00:03<00:02, 662.62it/s]\u001b[A\n",
"Pass 8/20: 59%|███████████████▉ | 2243/3810 [00:03<00:02, 663.70it/s]\u001b[A\n",
"Pass 8/20: 61%|████████████████▍ | 2312/3810 [00:03<00:02, 664.11it/s]\u001b[A\n",
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"Pass 8/20: 66%|█████████████████▊ | 2518/3810 [00:03<00:01, 662.87it/s]\u001b[A\n",
"Pass 8/20: 68%|██████████████████▎ | 2588/3810 [00:03<00:01, 663.76it/s]\u001b[A\n",
"Pass 8/20: 70%|██████████████████▊ | 2658/3810 [00:03<00:01, 664.64it/s]\u001b[A\n",
"Pass 8/20: 72%|███████████████████▎ | 2728/3810 [00:04<00:01, 665.42it/s]\u001b[A\n",
"Pass 8/20: 73%|███████████████████▊ | 2797/3810 [00:04<00:01, 665.93it/s]\u001b[A\n",
"Pass 8/20: 75%|████████████████████▎ | 2866/3810 [00:04<00:01, 666.36it/s]\u001b[A\n",
"Pass 8/20: 77%|████████████████████▊ | 2935/3810 [00:04<00:01, 666.33it/s]\u001b[A\n",
"Pass 8/20: 79%|█████████████████████▎ | 3004/3810 [00:04<00:01, 666.68it/s]\u001b[A\n",
"Pass 8/20: 81%|█████████████████████▊ | 3075/3810 [00:04<00:01, 667.54it/s]\u001b[A\n",
"Pass 8/20: 83%|██████████████████████▎ | 3145/3810 [00:04<00:00, 668.23it/s]\u001b[A\n",
"Pass 8/20: 84%|██████████████████████▊ | 3215/3810 [00:04<00:00, 668.86it/s]\u001b[A\n",
"Pass 8/20: 86%|███████████████████████▎ | 3286/3810 [00:04<00:00, 669.67it/s]\u001b[A\n",
"Pass 8/20: 88%|███████████████████████▊ | 3356/3810 [00:05<00:00, 668.42it/s]\u001b[A\n",
"Pass 8/20: 90%|████████████████████████▎ | 3426/3810 [00:05<00:00, 669.04it/s]\u001b[A\n",
"Pass 8/20: 92%|████████████████████████▊ | 3495/3810 [00:05<00:00, 669.20it/s]\u001b[A\n",
"Pass 8/20: 94%|█████████████████████████▏ | 3563/3810 [00:05<00:00, 669.21it/s]\u001b[A\n",
"Pass 8/20: 95%|█████████████████████████▊ | 3634/3810 [00:05<00:00, 669.95it/s]\u001b[A\n",
"Pass 8/20: 97%|██████████████████████████▎| 3706/3810 [00:05<00:00, 670.74it/s]\u001b[A\n",
"Pass 8/20: 99%|██████████████████████████▊| 3776/3810 [00:05<00:00, 671.20it/s]\u001b[A\n",
"Batch EM: 40%|█████████████▌ | 8/20 [00:45<01:08, 5.68s/it]\n",
"Pass 9/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 9/20: 2%|▌ | 73/3810 [00:00<00:05, 725.49it/s]\u001b[A\n",
"Pass 9/20: 4%|█ | 144/3810 [00:00<00:05, 715.98it/s]\u001b[A\n",
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"Pass 9/20: 8%|██ | 287/3810 [00:00<00:04, 714.84it/s]\u001b[A\n",
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"Pass 9/20: 11%|███ | 410/3810 [00:00<00:05, 679.89it/s]\u001b[A\n",
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"Pass 9/20: 42%|███████████▎ | 1589/3810 [00:02<00:03, 711.10it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 9/20: 51%|█████████████▊ | 1955/3810 [00:02<00:02, 711.17it/s]\u001b[A\n",
"Pass 9/20: 53%|██████████████▍ | 2030/3810 [00:02<00:02, 712.38it/s]\u001b[A\n",
"Pass 9/20: 55%|██████████████▉ | 2104/3810 [00:02<00:02, 713.03it/s]\u001b[A\n",
"Pass 9/20: 57%|███████████████▍ | 2177/3810 [00:03<00:02, 710.75it/s]\u001b[A\n",
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"Pass 9/20: 71%|███████████████████ | 2696/3810 [00:03<00:01, 713.11it/s]\u001b[A\n",
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"Pass 9/20: 75%|████████████████████▏ | 2843/3810 [00:03<00:01, 713.63it/s]\u001b[A\n",
"Pass 9/20: 77%|████████████████████▋ | 2916/3810 [00:04<00:01, 713.80it/s]\u001b[A\n",
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"Pass 9/20: 80%|█████████████████████▋ | 3059/3810 [00:04<00:01, 707.00it/s]\u001b[A\n",
"Pass 9/20: 82%|██████████████████████▏ | 3125/3810 [00:04<00:00, 704.54it/s]\u001b[A\n",
"Pass 9/20: 84%|██████████████████████▌ | 3189/3810 [00:04<00:00, 702.21it/s]\u001b[A\n",
"Pass 9/20: 85%|███████████████████████ | 3252/3810 [00:04<00:00, 699.33it/s]\u001b[A\n",
"Pass 9/20: 87%|███████████████████████▍ | 3316/3810 [00:04<00:00, 697.93it/s]\u001b[A\n",
"Pass 9/20: 89%|███████████████████████▉ | 3379/3810 [00:04<00:00, 696.38it/s]\u001b[A\n",
"Pass 9/20: 90%|████████████████████████▍ | 3443/3810 [00:04<00:00, 695.06it/s]\u001b[A\n",
"Pass 9/20: 92%|████████████████████████▊ | 3507/3810 [00:05<00:00, 693.79it/s]\u001b[A\n",
"Pass 9/20: 94%|█████████████████████████▎ | 3570/3810 [00:05<00:00, 691.67it/s]\u001b[A\n",
"Pass 9/20: 95%|█████████████████████████▋ | 3632/3810 [00:05<00:00, 689.71it/s]\u001b[A\n",
"Pass 9/20: 97%|██████████████████████████▏| 3693/3810 [00:05<00:00, 688.21it/s]\u001b[A\n",
"Pass 9/20: 99%|██████████████████████████▌| 3756/3810 [00:05<00:00, 687.07it/s]\u001b[A\n",
"Batch EM: 45%|███████████████▎ | 9/20 [00:51<01:02, 5.67s/it]\n",
"Pass 10/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 10/20: 2%|▍ | 63/3810 [00:00<00:05, 626.89it/s]\u001b[A\n",
"Pass 10/20: 3%|▉ | 129/3810 [00:00<00:05, 639.68it/s]\u001b[A\n",
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"Pass 10/20: 10%|██▋ | 386/3810 [00:00<00:05, 638.24it/s]\u001b[A\n",
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"Pass 10/20: 24%|██████▌ | 919/3810 [00:01<00:04, 645.97it/s]\u001b[A\n",
"Pass 10/20: 26%|██████▉ | 987/3810 [00:01<00:04, 648.15it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 10/20: 52%|█████████████▌ | 1985/3810 [00:03<00:02, 647.67it/s]\u001b[A\n",
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"Pass 10/20: 61%|███████████████▊ | 2320/3810 [00:03<00:02, 650.58it/s]\u001b[A\n",
"Pass 10/20: 63%|████████████████▎ | 2389/3810 [00:03<00:02, 650.72it/s]\u001b[A\n",
"Pass 10/20: 64%|████████████████▊ | 2456/3810 [00:03<00:02, 649.08it/s]\u001b[A\n",
"Pass 10/20: 66%|█████████████████▏ | 2524/3810 [00:03<00:01, 649.68it/s]\u001b[A\n",
"Pass 10/20: 68%|█████████████████▋ | 2592/3810 [00:03<00:01, 650.39it/s]\u001b[A\n",
"Pass 10/20: 70%|██████████████████▏ | 2661/3810 [00:04<00:01, 651.02it/s]\u001b[A\n",
"Pass 10/20: 72%|██████████████████▋ | 2730/3810 [00:04<00:01, 651.95it/s]\u001b[A\n",
"Pass 10/20: 73%|███████████████████ | 2798/3810 [00:04<00:01, 651.89it/s]\u001b[A\n",
"Pass 10/20: 75%|███████████████████▌ | 2866/3810 [00:04<00:01, 652.42it/s]\u001b[A\n",
"Pass 10/20: 77%|████████████████████ | 2933/3810 [00:04<00:01, 652.64it/s]\u001b[A\n",
"Pass 10/20: 79%|████████████████████▍ | 3002/3810 [00:04<00:01, 653.38it/s]\u001b[A\n",
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"Pass 10/20: 82%|█████████████████████▍ | 3138/3810 [00:04<00:01, 653.52it/s]\u001b[A\n",
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"Pass 10/20: 88%|██████████████████████▊ | 3339/3810 [00:05<00:00, 650.89it/s]\u001b[A\n",
"Pass 10/20: 89%|███████████████████████▏ | 3403/3810 [00:05<00:00, 650.59it/s]\u001b[A\n",
"Pass 10/20: 91%|███████████████████████▋ | 3466/3810 [00:05<00:00, 649.95it/s]\u001b[A\n",
"Pass 10/20: 93%|████████████████████████ | 3534/3810 [00:05<00:00, 650.36it/s]\u001b[A\n",
"Pass 10/20: 95%|████████████████████████▌ | 3601/3810 [00:05<00:00, 650.69it/s]\u001b[A\n",
"Pass 10/20: 96%|█████████████████████████ | 3668/3810 [00:05<00:00, 650.89it/s]\u001b[A\n",
"Pass 10/20: 98%|█████████████████████████▍| 3734/3810 [00:05<00:00, 650.26it/s]\u001b[A\n",
"Pass 10/20: 100%|█████████████████████████▉| 3799/3810 [00:05<00:00, 648.61it/s]\u001b[A\n",
"Batch EM: 50%|████████████████▌ | 10/20 [00:56<00:56, 5.69s/it]\n",
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"Pass 11/20: 10%|██▋ | 374/3810 [00:00<00:05, 614.75it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 11/20: 68%|█████████████████▊ | 2607/3810 [00:03<00:01, 655.52it/s]\u001b[A\n",
"Pass 11/20: 70%|██████████████████▎ | 2675/3810 [00:04<00:01, 653.25it/s]\u001b[A\n",
"Pass 11/20: 72%|██████████████████▋ | 2743/3810 [00:04<00:01, 653.66it/s]\u001b[A\n",
"Pass 11/20: 74%|███████████████████▏ | 2811/3810 [00:04<00:01, 654.14it/s]\u001b[A\n",
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"Pass 11/20: 77%|████████████████████ | 2945/3810 [00:04<00:01, 654.39it/s]\u001b[A\n",
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"Pass 11/20: 89%|███████████████████████▏ | 3406/3810 [00:05<00:00, 652.87it/s]\u001b[A\n",
"Pass 11/20: 91%|███████████████████████▋ | 3472/3810 [00:05<00:00, 651.07it/s]\u001b[A\n",
"Pass 11/20: 93%|████████████████████████▏ | 3536/3810 [00:05<00:00, 650.85it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 11/20: 94%|████████████████████████▌ | 3600/3810 [00:05<00:00, 650.58it/s]\u001b[A\n",
"Pass 11/20: 96%|█████████████████████████ | 3664/3810 [00:05<00:00, 650.10it/s]\u001b[A\n",
"Pass 11/20: 98%|█████████████████████████▍| 3727/3810 [00:05<00:00, 649.37it/s]\u001b[A\n",
"Pass 11/20: 99%|█████████████████████████▊| 3790/3810 [00:05<00:00, 648.89it/s]\u001b[A\n",
"Batch EM: 55%|██████████████████▏ | 11/20 [01:02<00:51, 5.71s/it]\n",
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"Pass 12/20: 67%|█████████████████▍ | 2548/3810 [00:03<00:01, 654.85it/s]\u001b[A\n",
"Pass 12/20: 69%|█████████████████▊ | 2617/3810 [00:03<00:01, 655.51it/s]\u001b[A\n",
"Pass 12/20: 70%|██████████████████▎ | 2685/3810 [00:04<00:01, 655.97it/s]\u001b[A\n",
"Pass 12/20: 72%|██████████████████▊ | 2753/3810 [00:04<00:01, 656.46it/s]\u001b[A\n",
"Pass 12/20: 74%|███████████████████▎ | 2824/3810 [00:04<00:01, 657.51it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 12/20: 76%|███████████████████▋ | 2894/3810 [00:04<00:01, 658.25it/s]\u001b[A\n",
"Pass 12/20: 78%|████████████████████▏ | 2963/3810 [00:04<00:01, 656.32it/s]\u001b[A\n",
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"Pass 12/20: 85%|██████████████████████ | 3236/3810 [00:04<00:00, 657.91it/s]\u001b[A\n",
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"Pass 12/20: 89%|███████████████████████ | 3373/3810 [00:05<00:00, 657.41it/s]\u001b[A\n",
"Pass 12/20: 90%|███████████████████████▍ | 3439/3810 [00:05<00:00, 656.92it/s]\u001b[A\n",
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"Pass 12/20: 96%|████████████████████████▊ | 3642/3810 [00:05<00:00, 657.50it/s]\u001b[A\n",
"Pass 12/20: 97%|█████████████████████████▎| 3709/3810 [00:05<00:00, 657.67it/s]\u001b[A\n",
"Pass 12/20: 99%|█████████████████████████▊| 3776/3810 [00:05<00:00, 654.88it/s]\u001b[A\n",
"Batch EM: 60%|███████████████████▊ | 12/20 [01:08<00:45, 5.72s/it]\n",
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"Pass 13/20: 51%|█████████████▏ | 1932/3810 [00:03<00:02, 628.34it/s]\u001b[A\n",
"Pass 13/20: 52%|█████████████▌ | 1994/3810 [00:03<00:02, 627.60it/s]\u001b[A\n",
"Pass 13/20: 54%|██████████████ | 2056/3810 [00:03<00:02, 626.78it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 13/20: 56%|██████████████▍ | 2117/3810 [00:03<00:02, 626.08it/s]\u001b[A\n",
"Pass 13/20: 57%|██████████████▊ | 2179/3810 [00:03<00:02, 625.63it/s]\u001b[A\n",
"Pass 13/20: 59%|███████████████▎ | 2241/3810 [00:03<00:02, 625.35it/s]\u001b[A\n",
"Pass 13/20: 60%|███████████████▋ | 2303/3810 [00:03<00:02, 620.41it/s]\u001b[A\n",
"Pass 13/20: 62%|████████████████▏ | 2366/3810 [00:03<00:02, 620.62it/s]\u001b[A\n",
"Pass 13/20: 64%|████████████████▌ | 2426/3810 [00:03<00:02, 620.08it/s]\u001b[A\n",
"Pass 13/20: 65%|████████████████▉ | 2487/3810 [00:04<00:02, 619.67it/s]\u001b[A\n",
"Pass 13/20: 67%|█████████████████▍ | 2548/3810 [00:04<00:02, 619.27it/s]\u001b[A\n",
"Pass 13/20: 69%|█████████████████▊ | 2612/3810 [00:04<00:01, 619.54it/s]\u001b[A\n",
"Pass 13/20: 70%|██████████████████▏ | 2673/3810 [00:04<00:01, 618.12it/s]\u001b[A\n",
"Pass 13/20: 72%|██████████████████▋ | 2733/3810 [00:04<00:01, 617.36it/s]\u001b[A\n",
"Pass 13/20: 73%|███████████████████ | 2796/3810 [00:04<00:01, 617.48it/s]\u001b[A\n",
"Pass 13/20: 75%|███████████████████▍ | 2857/3810 [00:04<00:01, 617.00it/s]\u001b[A\n",
"Pass 13/20: 77%|███████████████████▉ | 2918/3810 [00:04<00:01, 616.66it/s]\u001b[A\n",
"Pass 13/20: 78%|████████████████████▎ | 2981/3810 [00:04<00:01, 616.87it/s]\u001b[A\n",
"Pass 13/20: 80%|████████████████████▊ | 3042/3810 [00:04<00:01, 613.50it/s]\u001b[A\n",
"Pass 13/20: 81%|█████████████████████▏ | 3104/3810 [00:05<00:01, 613.53it/s]\u001b[A\n",
"Pass 13/20: 83%|█████████████████████▌ | 3166/3810 [00:05<00:01, 613.49it/s]\u001b[A\n",
"Pass 13/20: 85%|██████████████████████ | 3226/3810 [00:05<00:00, 612.25it/s]\u001b[A\n",
"Pass 13/20: 86%|██████████████████████▍ | 3285/3810 [00:05<00:00, 611.80it/s]\u001b[A\n",
"Pass 13/20: 88%|██████████████████████▊ | 3344/3810 [00:05<00:00, 611.37it/s]\u001b[A\n",
"Pass 13/20: 89%|███████████████████████▏ | 3404/3810 [00:05<00:00, 611.05it/s]\u001b[A\n",
"Pass 13/20: 91%|███████████████████████▋ | 3464/3810 [00:05<00:00, 610.76it/s]\u001b[A\n",
"Pass 13/20: 92%|████████████████████████ | 3523/3810 [00:05<00:00, 610.24it/s]\u001b[A\n",
"Pass 13/20: 94%|████████████████████████▍ | 3582/3810 [00:05<00:00, 609.75it/s]\u001b[A\n",
"Pass 13/20: 96%|████████████████████████▊ | 3641/3810 [00:05<00:00, 609.07it/s]\u001b[A\n",
"Pass 13/20: 97%|█████████████████████████▎| 3704/3810 [00:06<00:00, 609.35it/s]\u001b[A\n",
"Pass 13/20: 99%|█████████████████████████▋| 3764/3810 [00:06<00:00, 607.14it/s]\u001b[A\n",
"Batch EM: 65%|█████████████████████▍ | 13/20 [01:14<00:40, 5.76s/it]\n",
"Pass 14/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 14/20: 2%|▍ | 61/3810 [00:00<00:06, 607.32it/s]\u001b[A\n",
"Pass 14/20: 3%|▊ | 115/3810 [00:00<00:06, 571.65it/s]\u001b[A\n",
"Pass 14/20: 5%|█▎ | 178/3810 [00:00<00:06, 589.06it/s]\u001b[A\n",
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"Pass 14/20: 8%|██▏ | 304/3810 [00:00<00:05, 602.89it/s]\u001b[A\n",
"Pass 14/20: 10%|██▌ | 368/3810 [00:00<00:05, 607.67it/s]\u001b[A\n",
"Pass 14/20: 11%|███ | 429/3810 [00:00<00:05, 607.49it/s]\u001b[A\n",
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"Pass 14/20: 24%|██████▌ | 923/3810 [00:01<00:04, 604.26it/s]\u001b[A\n",
"Pass 14/20: 26%|██████▉ | 987/3810 [00:01<00:04, 606.32it/s]\u001b[A\n",
"Pass 14/20: 28%|███████▏ | 1049/3810 [00:01<00:04, 604.64it/s]\u001b[A\n",
"Pass 14/20: 29%|███████▌ | 1112/3810 [00:01<00:04, 605.64it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 14/20: 31%|████████ | 1175/3810 [00:01<00:04, 606.35it/s]\u001b[A\n",
"Pass 14/20: 32%|████████▍ | 1237/3810 [00:02<00:04, 606.38it/s]\u001b[A\n",
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"Pass 14/20: 45%|███████████▊ | 1730/3810 [00:02<00:03, 603.03it/s]\u001b[A\n",
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"Pass 14/20: 54%|█████████████▉ | 2045/3810 [00:03<00:02, 606.03it/s]\u001b[A\n",
"Pass 14/20: 55%|██████████████▍ | 2111/3810 [00:03<00:02, 607.48it/s]\u001b[A\n",
"Pass 14/20: 57%|██████████████▊ | 2175/3810 [00:03<00:02, 607.69it/s]\u001b[A\n",
"Pass 14/20: 59%|███████████████▎ | 2238/3810 [00:03<00:02, 604.47it/s]\u001b[A\n",
"Pass 14/20: 60%|███████████████▋ | 2298/3810 [00:03<00:02, 604.29it/s]\u001b[A\n",
"Pass 14/20: 62%|████████████████ | 2358/3810 [00:03<00:02, 603.93it/s]\u001b[A\n",
"Pass 14/20: 64%|████████████████▌ | 2423/3810 [00:04<00:02, 604.92it/s]\u001b[A\n",
"Pass 14/20: 65%|████████████████▉ | 2488/3810 [00:04<00:02, 605.96it/s]\u001b[A\n",
"Pass 14/20: 67%|█████████████████▍ | 2555/3810 [00:04<00:02, 607.40it/s]\u001b[A\n",
"Pass 14/20: 69%|█████████████████▉ | 2625/3810 [00:04<00:01, 609.51it/s]\u001b[A\n",
"Pass 14/20: 71%|██████████████████▎ | 2691/3810 [00:04<00:01, 610.11it/s]\u001b[A\n",
"Pass 14/20: 72%|██████████████████▊ | 2756/3810 [00:04<00:01, 610.93it/s]\u001b[A\n",
"Pass 14/20: 74%|███████████████████▎ | 2823/3810 [00:04<00:01, 612.14it/s]\u001b[A\n",
"Pass 14/20: 76%|███████████████████▋ | 2891/3810 [00:04<00:01, 613.51it/s]\u001b[A\n",
"Pass 14/20: 78%|████████████████████▏ | 2958/3810 [00:04<00:01, 613.14it/s]\u001b[A\n",
"Pass 14/20: 79%|████████████████████▋ | 3023/3810 [00:04<00:01, 610.53it/s]\u001b[A\n",
"Pass 14/20: 81%|█████████████████████ | 3084/3810 [00:05<00:01, 610.42it/s]\u001b[A\n",
"Pass 14/20: 83%|█████████████████████▍ | 3145/3810 [00:05<00:01, 609.35it/s]\u001b[A\n",
"Pass 14/20: 84%|█████████████████████▉ | 3210/3810 [00:05<00:00, 610.08it/s]\u001b[A\n",
"Pass 14/20: 86%|██████████████████████▎ | 3277/3810 [00:05<00:00, 611.05it/s]\u001b[A\n",
"Pass 14/20: 88%|██████████████████████▊ | 3342/3810 [00:05<00:00, 611.65it/s]\u001b[A\n",
"Pass 14/20: 89%|███████████████████████▏ | 3405/3810 [00:05<00:00, 611.96it/s]\u001b[A\n",
"Pass 14/20: 91%|███████████████████████▋ | 3470/3810 [00:05<00:00, 612.54it/s]\u001b[A\n",
"Pass 14/20: 93%|████████████████████████ | 3534/3810 [00:05<00:00, 612.93it/s]\u001b[A\n",
"Pass 14/20: 95%|████████████████████████▌ | 3603/3810 [00:05<00:00, 614.13it/s]\u001b[A\n",
"Pass 14/20: 96%|█████████████████████████ | 3671/3810 [00:05<00:00, 615.16it/s]\u001b[A\n",
"Pass 14/20: 98%|█████████████████████████▌| 3738/3810 [00:06<00:00, 615.98it/s]\u001b[A\n",
"Pass 14/20: 100%|█████████████████████████▉| 3804/3810 [00:06<00:00, 614.95it/s]\u001b[A\n",
"Batch EM: 70%|███████████████████████ | 14/20 [01:21<00:34, 5.79s/it]\n",
"Pass 15/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 15/20: 2%|▍ | 67/3810 [00:00<00:05, 663.74it/s]\u001b[A\n",
"Pass 15/20: 4%|▉ | 134/3810 [00:00<00:05, 664.99it/s]\u001b[A\n",
"Pass 15/20: 5%|█▍ | 201/3810 [00:00<00:05, 666.27it/s]\u001b[A\n",
"Pass 15/20: 7%|█▉ | 265/3810 [00:00<00:05, 658.23it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 15/20: 9%|██▎ | 333/3810 [00:00<00:05, 661.59it/s]\u001b[A\n",
"Pass 15/20: 11%|██▊ | 401/3810 [00:00<00:05, 663.19it/s]\u001b[A\n",
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"Pass 15/20: 19%|█████▏ | 734/3810 [00:01<00:04, 663.04it/s]\u001b[A\n",
"Pass 15/20: 21%|█████▋ | 799/3810 [00:01<00:04, 653.71it/s]\u001b[A\n",
"Pass 15/20: 23%|██████ | 862/3810 [00:01<00:04, 648.49it/s]\u001b[A\n",
"Pass 15/20: 24%|██████▌ | 925/3810 [00:01<00:04, 646.72it/s]\u001b[A\n",
"Pass 15/20: 26%|███████ | 990/3810 [00:01<00:04, 646.78it/s]\u001b[A\n",
"Pass 15/20: 28%|███████▏ | 1056/3810 [00:01<00:04, 647.18it/s]\u001b[A\n",
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"Pass 15/20: 38%|█████████▉ | 1448/3810 [00:02<00:03, 647.31it/s]\u001b[A\n",
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"Pass 15/20: 43%|███████████▏ | 1647/3810 [00:02<00:03, 644.02it/s]\u001b[A\n",
"Pass 15/20: 45%|███████████▋ | 1715/3810 [00:02<00:03, 644.67it/s]\u001b[A\n",
"Pass 15/20: 47%|████████████▏ | 1783/3810 [00:02<00:03, 645.82it/s]\u001b[A\n",
"Pass 15/20: 49%|████████████▌ | 1850/3810 [00:02<00:03, 646.38it/s]\u001b[A\n",
"Pass 15/20: 50%|█████████████ | 1917/3810 [00:02<00:02, 646.89it/s]\u001b[A\n",
"Pass 15/20: 52%|█████████████▌ | 1983/3810 [00:03<00:02, 647.17it/s]\u001b[A\n",
"Pass 15/20: 54%|█████████████▉ | 2050/3810 [00:03<00:02, 647.76it/s]\u001b[A\n",
"Pass 15/20: 56%|██████████████▍ | 2116/3810 [00:03<00:02, 647.94it/s]\u001b[A\n",
"Pass 15/20: 57%|██████████████▉ | 2182/3810 [00:03<00:02, 648.09it/s]\u001b[A\n",
"Pass 15/20: 59%|███████████████▎ | 2248/3810 [00:03<00:02, 648.35it/s]\u001b[A\n",
"Pass 15/20: 61%|███████████████▊ | 2315/3810 [00:03<00:02, 648.91it/s]\u001b[A\n",
"Pass 15/20: 63%|████████████████▎ | 2382/3810 [00:03<00:02, 645.75it/s]\u001b[A\n",
"Pass 15/20: 64%|████████████████▋ | 2447/3810 [00:03<00:02, 645.78it/s]\u001b[A\n",
"Pass 15/20: 66%|█████████████████▏ | 2513/3810 [00:03<00:02, 646.01it/s]\u001b[A\n",
"Pass 15/20: 68%|█████████████████▌ | 2580/3810 [00:03<00:01, 646.56it/s]\u001b[A\n",
"Pass 15/20: 69%|██████████████████ | 2646/3810 [00:04<00:01, 646.80it/s]\u001b[A\n",
"Pass 15/20: 71%|██████████████████▌ | 2712/3810 [00:04<00:01, 646.99it/s]\u001b[A\n",
"Pass 15/20: 73%|██████████████████▉ | 2780/3810 [00:04<00:01, 647.65it/s]\u001b[A\n",
"Pass 15/20: 75%|███████████████████▍ | 2846/3810 [00:04<00:01, 647.80it/s]\u001b[A\n",
"Pass 15/20: 77%|███████████████████▉ | 2918/3810 [00:04<00:01, 649.21it/s]\u001b[A\n",
"Pass 15/20: 79%|████████████████████▍ | 2993/3810 [00:04<00:01, 651.39it/s]\u001b[A\n",
"Pass 15/20: 80%|████████████████████▉ | 3066/3810 [00:04<00:01, 652.93it/s]\u001b[A\n",
"Pass 15/20: 82%|█████████████████████▍ | 3139/3810 [00:04<00:01, 654.53it/s]\u001b[A\n",
"Pass 15/20: 84%|█████████████████████▉ | 3211/3810 [00:04<00:00, 653.26it/s]\u001b[A\n",
"Pass 15/20: 86%|██████████████████████▍ | 3286/3810 [00:05<00:00, 655.06it/s]\u001b[A\n",
"Pass 15/20: 88%|██████████████████████▉ | 3356/3810 [00:05<00:00, 655.27it/s]\u001b[A\n",
"Pass 15/20: 90%|███████████████████████▍ | 3429/3810 [00:05<00:00, 656.69it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 15/20: 92%|███████████████████████▉ | 3504/3810 [00:05<00:00, 658.31it/s]\u001b[A\n",
"Pass 15/20: 94%|████████████████████████▍ | 3579/3810 [00:05<00:00, 659.89it/s]\u001b[A\n",
"Pass 15/20: 96%|████████████████████████▉ | 3654/3810 [00:05<00:00, 661.43it/s]\u001b[A\n",
"Pass 15/20: 98%|█████████████████████████▍| 3727/3810 [00:05<00:00, 662.46it/s]\u001b[A\n",
"Pass 15/20: 100%|█████████████████████████▉| 3802/3810 [00:05<00:00, 663.96it/s]\u001b[A\n",
"Batch EM: 75%|████████████████████████▊ | 15/20 [01:26<00:28, 5.79s/it]\n",
"Pass 16/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 16/20: 2%|▌ | 73/3810 [00:00<00:05, 726.75it/s]\u001b[A\n",
"Pass 16/20: 4%|█ | 148/3810 [00:00<00:04, 738.08it/s]\u001b[A\n",
"Pass 16/20: 6%|█▌ | 221/3810 [00:00<00:04, 733.08it/s]\u001b[A\n",
"Pass 16/20: 8%|██ | 289/3810 [00:00<00:04, 715.48it/s]\u001b[A\n",
"Pass 16/20: 9%|██▌ | 357/3810 [00:00<00:04, 707.27it/s]\u001b[A\n",
"Pass 16/20: 11%|███ | 427/3810 [00:00<00:04, 705.79it/s]\u001b[A\n",
"Pass 16/20: 13%|███▌ | 502/3810 [00:00<00:04, 711.14it/s]\u001b[A\n",
"Pass 16/20: 15%|████ | 578/3810 [00:00<00:04, 717.18it/s]\u001b[A\n",
"Pass 16/20: 17%|████▋ | 654/3810 [00:00<00:04, 720.20it/s]\u001b[A\n",
"Pass 16/20: 19%|█████▏ | 730/3810 [00:01<00:04, 723.65it/s]\u001b[A\n",
"Pass 16/20: 21%|█████▋ | 804/3810 [00:01<00:04, 724.51it/s]\u001b[A\n",
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"Pass 16/20: 25%|██████▊ | 956/3810 [00:01<00:03, 729.50it/s]\u001b[A\n",
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"Pass 16/20: 70%|██████████████████ | 2653/3810 [00:03<00:01, 698.07it/s]\u001b[A\n",
"Pass 16/20: 71%|██████████████████▌ | 2723/3810 [00:03<00:01, 698.11it/s]\u001b[A\n",
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"Pass 16/20: 75%|███████████████████▌ | 2861/3810 [00:04<00:01, 696.69it/s]\u001b[A\n",
"Pass 16/20: 77%|███████████████████▉ | 2929/3810 [00:04<00:01, 692.50it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 16/20: 79%|████████████████████▍ | 2998/3810 [00:04<00:01, 692.29it/s]\u001b[A\n",
"Pass 16/20: 80%|████████████████████▉ | 3065/3810 [00:04<00:01, 691.58it/s]\u001b[A\n",
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"Pass 16/20: 88%|██████████████████████▉ | 3356/3810 [00:04<00:00, 694.11it/s]\u001b[A\n",
"Pass 16/20: 90%|███████████████████████▍ | 3428/3810 [00:04<00:00, 694.60it/s]\u001b[A\n",
"Pass 16/20: 92%|███████████████████████▉ | 3502/3810 [00:05<00:00, 695.41it/s]\u001b[A\n",
"Pass 16/20: 94%|████████████████████████▍ | 3574/3810 [00:05<00:00, 695.83it/s]\u001b[A\n",
"Pass 16/20: 96%|████████████████████████▉ | 3646/3810 [00:05<00:00, 695.97it/s]\u001b[A\n",
"Pass 16/20: 98%|█████████████████████████▎| 3718/3810 [00:05<00:00, 694.91it/s]\u001b[A\n",
"Pass 16/20: 99%|█████████████████████████▊| 3788/3810 [00:05<00:00, 693.19it/s]\u001b[A\n",
"Batch EM: 80%|██████████████████████████▍ | 16/20 [01:32<00:23, 5.77s/it]\n",
"Pass 17/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 17/20: 2%|▌ | 72/3810 [00:00<00:05, 711.86it/s]\u001b[A\n",
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"Pass 17/20: 66%|█████████████████ | 2507/3810 [00:03<00:01, 723.00it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 17/20: 68%|█████████████████▌ | 2581/3810 [00:03<00:01, 723.03it/s]\u001b[A\n",
"Pass 17/20: 70%|██████████████████ | 2654/3810 [00:03<00:01, 720.61it/s]\u001b[A\n",
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"Pass 17/20: 81%|█████████████████████ | 3094/3810 [00:04<00:00, 720.69it/s]\u001b[A\n",
"Pass 17/20: 83%|█████████████████████▋ | 3170/3810 [00:04<00:00, 721.56it/s]\u001b[A\n",
"Pass 17/20: 85%|██████████████████████▏ | 3243/3810 [00:04<00:00, 720.45it/s]\u001b[A\n",
"Pass 17/20: 87%|██████████████████████▌ | 3315/3810 [00:04<00:00, 720.39it/s]\u001b[A\n",
"Pass 17/20: 89%|███████████████████████ | 3387/3810 [00:04<00:00, 719.34it/s]\u001b[A\n",
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"Pass 17/20: 93%|████████████████████████ | 3529/3810 [00:04<00:00, 716.79it/s]\u001b[A\n",
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"Pass 17/20: 96%|█████████████████████████ | 3667/3810 [00:05<00:00, 715.56it/s]\u001b[A\n",
"Pass 17/20: 98%|█████████████████████████▌| 3737/3810 [00:05<00:00, 715.18it/s]\u001b[A\n",
"Batch EM: 85%|████████████████████████████ | 17/20 [01:37<00:17, 5.74s/it]\n",
"Pass 18/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 18/20: 2%|▌ | 70/3810 [00:00<00:05, 696.13it/s]\u001b[A\n",
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"Pass 18/20: 52%|█████████████▌ | 1981/3810 [00:02<00:02, 669.00it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 18/20: 54%|█████████████▉ | 2049/3810 [00:03<00:02, 668.77it/s]\u001b[A\n",
"Pass 18/20: 56%|██████████████▍ | 2119/3810 [00:03<00:02, 669.56it/s]\u001b[A\n",
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"Pass 18/20: 70%|██████████████████▎ | 2679/3810 [00:03<00:01, 671.58it/s]\u001b[A\n",
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"Pass 18/20: 76%|███████████████████▊ | 2897/3810 [00:04<00:01, 675.28it/s]\u001b[A\n",
"Pass 18/20: 78%|████████████████████▎ | 2971/3810 [00:04<00:01, 676.59it/s]\u001b[A\n",
"Pass 18/20: 80%|████████████████████▊ | 3044/3810 [00:04<00:01, 677.66it/s]\u001b[A\n",
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"Pass 18/20: 86%|██████████████████████▎ | 3268/3810 [00:04<00:00, 679.73it/s]\u001b[A\n",
"Pass 18/20: 88%|██████████████████████▊ | 3342/3810 [00:04<00:00, 680.91it/s]\u001b[A\n",
"Pass 18/20: 90%|███████████████████████▎ | 3416/3810 [00:05<00:00, 681.94it/s]\u001b[A\n",
"Pass 18/20: 92%|███████████████████████▊ | 3491/3810 [00:05<00:00, 683.15it/s]\u001b[A\n",
"Pass 18/20: 94%|████████████████████████▎ | 3565/3810 [00:05<00:00, 684.09it/s]\u001b[A\n",
"Pass 18/20: 96%|████████████████████████▊ | 3639/3810 [00:05<00:00, 684.64it/s]\u001b[A\n",
"Pass 18/20: 97%|█████████████████████████▎| 3714/3810 [00:05<00:00, 685.76it/s]\u001b[A\n",
"Pass 18/20: 99%|█████████████████████████▊| 3790/3810 [00:05<00:00, 686.92it/s]\u001b[A\n",
"Batch EM: 90%|█████████████████████████████▋ | 18/20 [01:43<00:11, 5.73s/it]\n",
"Pass 19/20: 0%| | 0/3810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 19/20: 2%|▌ | 76/3810 [00:00<00:04, 751.06it/s]\u001b[A\n",
"Pass 19/20: 4%|█ | 153/3810 [00:00<00:04, 759.10it/s]\u001b[A\n",
"Pass 19/20: 6%|█▌ | 219/3810 [00:00<00:04, 722.21it/s]\u001b[A\n",
"Pass 19/20: 8%|██ | 293/3810 [00:00<00:04, 724.88it/s]\u001b[A\n",
"Pass 19/20: 10%|██▌ | 366/3810 [00:00<00:04, 725.75it/s]\u001b[A\n",
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"Pass 19/20: 13%|███▋ | 513/3810 [00:00<00:04, 726.77it/s]\u001b[A\n",
"Pass 19/20: 15%|████▏ | 587/3810 [00:00<00:04, 727.59it/s]\u001b[A\n",
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"Batch EM: 100%|█████████████████████████████████| 20/20 [01:54<00:00, 5.74s/it]\n",
"Removing individual occurrences of motif occurrences\n",
"Erasing motif occurrences: 3%|▎ | 150/5000 [00:00<00:13, 369.77it/s]"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Erasing motif occurrences: 100%|███████████| 5000/5000 [00:12<00:00, 405.60it/s]\n",
"Erasing motif occurrences: 100%|███████████| 5000/5000 [00:13<00:00, 373.73it/s]\n",
"Saving results to yamda_test_no_e_pos_neg\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "ogfPyaXAwbdF",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 578
},
"outputId": "e88da52e-9854-48ec-8ee2-c0e11fd31104"
},
"cell_type": "code",
"source": [
"!cat yamda_test_no_e_pos_neg/motifs.txt"
],
"execution_count": 45,
"outputs": [
{
"output_type": "stream",
"text": [
"MEME version 4\r\n",
"\r\n",
"ALPHABET= ACGT\r\n",
"\r\n",
"strands: + -\r\n",
"\r\n",
"Background letter frequencies (from uniform background):\r\n",
"A 0.250000 C 0.250000 G 0.250000 T 0.250000 \r\n",
"\r\n",
"MOTIF M0 N0\r\n",
"\r\n",
"letter-probability matrix: alength= 4 w= 20 nsites= 40013 E= 0\r\n",
"0.052445 0.435540 0.461143 0.050872\r\n",
"0.061709 0.453492 0.428039 0.056761\r\n",
"0.060982 0.442481 0.438933 0.057604\r\n",
"0.057580 0.444230 0.445292 0.052897\r\n",
"0.055214 0.448242 0.444655 0.051888\r\n",
"0.054037 0.449954 0.442646 0.053362\r\n",
"0.051186 0.450438 0.447251 0.051125\r\n",
"0.050030 0.459122 0.440944 0.049904\r\n",
"0.052941 0.453787 0.440664 0.052608\r\n",
"0.051222 0.450259 0.447773 0.050745\r\n",
"0.049869 0.451124 0.446848 0.052160\r\n",
"0.051689 0.443945 0.450583 0.053783\r\n",
"0.049050 0.444432 0.455758 0.050760\r\n",
"0.050360 0.450552 0.447068 0.052020\r\n",
"0.052514 0.445624 0.447005 0.054857\r\n",
"0.051096 0.447587 0.445312 0.056004\r\n",
"0.052159 0.448338 0.441158 0.058345\r\n",
"0.056824 0.441804 0.439552 0.061820\r\n",
"0.055993 0.431127 0.450516 0.062364\r\n",
"0.050381 0.464106 0.432421 0.053092\r\n",
"\r\n"
],
"name": "stdout"
}
]
},
{
"metadata": {
"id": "VR3THkO77EOF",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177
},
"outputId": "0d886f91-36f4-4ebd-8e90-e5b4ad48195b"
},
"cell_type": "code",
"source": [
"import numpy as np\n",
"motif_mat = np.array([[float(y) for y in x.split(\" \")] for x in\n",
"\"\"\"0.052445 0.435540 0.461143 0.050872\n",
"0.061709 0.453492 0.428039 0.056761\n",
"0.060982 0.442481 0.438933 0.057604\n",
"0.057580 0.444230 0.445292 0.052897\n",
"0.055214 0.448242 0.444655 0.051888\n",
"0.054037 0.449954 0.442646 0.053362\n",
"0.051186 0.450438 0.447251 0.051125\n",
"0.050030 0.459122 0.440944 0.049904\n",
"0.052941 0.453787 0.440664 0.052608\n",
"0.051222 0.450259 0.447773 0.050745\n",
"0.049869 0.451124 0.446848 0.052160\n",
"0.051689 0.443945 0.450583 0.053783\n",
"0.049050 0.444432 0.455758 0.050760\n",
"0.050360 0.450552 0.447068 0.052020\n",
"0.052514 0.445624 0.447005 0.054857\n",
"0.051096 0.447587 0.445312 0.056004\n",
"0.052159 0.448338 0.441158 0.058345\n",
"0.056824 0.441804 0.439552 0.061820\n",
"0.055993 0.431127 0.450516 0.062364\n",
"0.050381 0.464106 0.432421 0.053092\"\"\".split(\"\\n\")])\n",
"from deeplift.visualization import viz_sequence\n",
"viz_sequence.plot_weights(motif_mat)"
],
"execution_count": 46,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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7COkzgem/ZrvSMNvIhozfQ2W97HngrKdPfnvVKioRooVdL744OL9iYVzoi5oa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\nGQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f2ca11ce978>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"metadata": {
"id": "R1YCvvb_CRU5",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 189587
},
"outputId": "c74b9a69-b104-4322-a6b8-3f424871014f"
},
"cell_type": "code",
"source": [
"!python YAMDA/run_em.py -e -r -i pos_seqs_concat.fa -oc yamda_test_e_posonly"
],
"execution_count": 47,
"outputs": [
{
"output_type": "stream",
"text": [
"Loading sequences from FASTA\n",
"Converting letter sequences to tensors\n",
"On-line EM: 0%| | 0/1 [00:00<?, ?it/s]\n",
"Pass 1/1: 0%| | 0/9810 [00:00<?, ?it/s]\u001b[A\n",
"Pass 1/1: 0%| | 3/9810 [00:00<08:36, 18.98it/s]\u001b[A\n",
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"Pass 1/1: 0%| | 7/9810 [00:00<09:50, 16.59it/s]\u001b[A\n",
"Pass 1/1: 0%| | 9/9810 [00:00<09:51, 16.56it/s]\u001b[A\n",
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"Pass 1/1: 1%|▎ | 91/9810 [00:04<08:29, 19.06it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 1%|▎ | 93/9810 [00:04<08:29, 19.07it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
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"text": [
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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"text": [
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
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"text": [
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],
"name": "stdout"
},
{
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"text": [
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],
"name": "stdout"
},
{
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 1/1: 19%|█████▋ | 1905/9810 [01:38<06:48, 19.35it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 1/1: 20%|█████▊ | 1971/9810 [01:41<06:44, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▊ | 1973/9810 [01:41<06:44, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▊ | 1975/9810 [01:42<06:44, 19.36it/s]\u001b[A\n",
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"Pass 1/1: 20%|█████▊ | 1983/9810 [01:42<06:44, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▊ | 1985/9810 [01:42<06:44, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▊ | 1987/9810 [01:42<06:44, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 1989/9810 [01:42<06:44, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 1991/9810 [01:42<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 1993/9810 [01:42<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 1995/9810 [01:43<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 1997/9810 [01:43<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 1999/9810 [01:43<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 2001/9810 [01:43<06:43, 19.36it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 20%|█████▉ | 2003/9810 [01:43<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 2005/9810 [01:43<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 2007/9810 [01:43<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 2009/9810 [01:43<06:43, 19.36it/s]\u001b[A\n",
"Pass 1/1: 20%|█████▉ | 2011/9810 [01:43<06:42, 19.36it/s]\u001b[A\n",
"Pass 1/1: 21%|█████▉ | 2013/9810 [01:43<06:42, 19.36it/s]\u001b[A\n",
"Pass 1/1: 21%|█████▉ | 2015/9810 [01:44<06:42, 19.36it/s]\u001b[A\n",
"Pass 1/1: 21%|█████▉ | 2017/9810 [01:44<06:42, 19.36it/s]\u001b[A\n",
"Pass 1/1: 21%|█████▉ | 2019/9810 [01:44<06:42, 19.36it/s]\u001b[A\n",
"Pass 1/1: 21%|█████▉ | 2021/9810 [01:44<06:42, 19.36it/s]\u001b[A\n",
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"Pass 1/1: 21%|██████▏ | 2095/9810 [01:48<06:38, 19.36it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 21%|██████▏ | 2097/9810 [01:48<06:38, 19.36it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
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},
{
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],
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},
{
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],
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},
{
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],
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},
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],
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},
{
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],
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},
{
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],
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},
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],
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},
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
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},
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},
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],
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},
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],
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},
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
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"Pass 1/1: 48%|█████████████▊ | 4674/9810 [04:01<04:25, 19.32it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 48%|█████████████▊ | 4676/9810 [04:02<04:25, 19.32it/s]\u001b[A\n",
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],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 49%|██████████████ | 4772/9810 [04:07<04:20, 19.31it/s]\u001b[A\n",
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"Pass 1/1: 50%|██████████████▎ | 4858/9810 [04:11<04:16, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▎ | 4860/9810 [04:11<04:16, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▎ | 4862/9810 [04:11<04:16, 19.32it/s]\u001b[A\n",
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"Pass 1/1: 50%|██████████████▍ | 4866/9810 [04:11<04:15, 19.32it/s]\u001b[A"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n",
"Pass 1/1: 50%|██████████████▍ | 4868/9810 [04:12<04:15, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▍ | 4870/9810 [04:12<04:15, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▍ | 4872/9810 [04:12<04:15, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▍ | 4874/9810 [04:12<04:15, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▍ | 4876/9810 [04:12<04:15, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▍ | 4878/9810 [04:12<04:15, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▍ | 4880/9810 [04:12<04:15, 19.32it/s]\u001b[A\n",
"Pass 1/1: 50%|██████████████▍ | 4882/9810 [04:12<04:15, 19.32it/s]\u0
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