doc/sourmash-collections.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sourmash: working with private collections of signatures\n",
    "\n",
    "### Running this notebook.\n",
    "\n",
    "You can run this notebook interactively via mybinder; click on this button:\n",
    "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/dib-lab/sourmash/latest?labpath=doc%2Fsourmash-collections.ipynb)\n",
    "\n",
    "A rendered version of this notebook is available at [sourmash.readthedocs.io](https://sourmash.readthedocs.io) under \"Tutorials and notebooks\".\n",
    "\n",
    "You can also get this notebook from the [doc/ subdirectory of the sourmash github repository](https://github.com/dib-lab/sourmash/tree/latest/doc). See [binder/environment.yaml](https://github.com/dib-lab/sourmash/blob/latest/binder/environment.yml) for installation dependencies.\n",
    "\n",
    "### What is this?\n",
    "\n",
    "This is a Jupyter Notebook using Python 3. If you are running this via [binder](https://mybinder.org), you can use Shift-ENTER to run cells, and double click on code cells to edit them.\n",
    "\n",
    "Contact: C. Titus Brown, ctbrown@ucdavis.edu. Please [file issues on GitHub](https://github.com/dib-lab/sourmash/issues/) if you have any questions or comments!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## download a bunch of genomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/t/dev/sourmash/doc/big_genomes\n",
      "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
      "                                 Dload  Upload   Total   Spent    Left  Speed\n",
      "100   459  100   459    0     0   1017      0 --:--:-- --:--:-- --:--:--  1017\n",
      "100 61.1M  100 61.1M    0     0  2932k      0  0:00:21  0:00:21 --:--:-- 3468k\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p big_genomes\n",
    "!curl -L https://osf.io/8uxj9/?action=download | (cd big_genomes && tar xzf -)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## compute signatures for each file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/t/dev/sourmash/doc/big_genomes\n",
      "\u001b[K\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kcomputing signatures for files: 0.fa, 1.fa, 10.fa, 11.fa, 12.fa, 13.fa, 14.fa, 15.fa, 16.fa, 17.fa, 18.fa, 19.fa, 2.fa, 20.fa, 21.fa, 22.fa, 23.fa, 24.fa, 25.fa, 26.fa, 27.fa, 28.fa, 29.fa, 3.fa, 30.fa, 31.fa, 32.fa, 33.fa, 34.fa, 35.fa, 36.fa, 37.fa, 38.fa, 39.fa, 4.fa, 40.fa, 41.fa, 42.fa, 43.fa, 44.fa, 45.fa, 46.fa, 47.fa, 48.fa, 49.fa, 5.fa, 50.fa, 51.fa, 52.fa, 53.fa, 54.fa, 55.fa, 56.fa, 57.fa, 58.fa, 59.fa, 6.fa, 60.fa, 61.fa, 62.fa, 63.fa, 7.fa, 8.fa, 9.fa\n",
      "\u001b[KComputing a total of 1 signature(s).\n",
      "\u001b[K... reading sequences from 0.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 0.fa\n",
      "\u001b[Ksaved signature(s) to 0.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 1.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 1.fa\n",
      "\u001b[Ksaved signature(s) to 1.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 10.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 10.fa\n",
      "\u001b[Ksaved signature(s) to 10.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 11.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 11.fa\n",
      "\u001b[Ksaved signature(s) to 11.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 12.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 12.fa\n",
      "\u001b[Ksaved signature(s) to 12.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 13.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 13.fa\n",
      "\u001b[Ksaved signature(s) to 13.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 14.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 14.fa\n",
      "\u001b[Ksaved signature(s) to 14.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 15.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 15.fa\n",
      "\u001b[Ksaved signature(s) to 15.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 16.fa\n",
      "\u001b[Kcalculated 1 signatures for 4 sequences in 16.fa\n",
      "\u001b[Ksaved signature(s) to 16.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 17.fa\n",
      "\u001b[Kcalculated 1 signatures for 2 sequences in 17.fa\n",
      "\u001b[Ksaved signature(s) to 17.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 18.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 18.fa\n",
      "\u001b[Ksaved signature(s) to 18.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 19.fa\n",
      "\u001b[Kcalculated 1 signatures for 9 sequences in 19.fa\n",
      "\u001b[Ksaved signature(s) to 19.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 2.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 2.fa\n",
      "\u001b[Ksaved signature(s) to 2.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 20.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 20.fa\n",
      "\u001b[Ksaved signature(s) to 20.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 21.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 21.fa\n",
      "\u001b[Ksaved signature(s) to 21.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 22.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 22.fa\n",
      "\u001b[Ksaved signature(s) to 22.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 23.fa\n",
      "\u001b[Kcalculated 1 signatures for 5 sequences in 23.fa\n",
      "\u001b[Ksaved signature(s) to 23.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 24.fa\n",
      "\u001b[Kcalculated 1 signatures for 3 sequences in 24.fa\n",
      "\u001b[Ksaved signature(s) to 24.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 25.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 25.fa\n",
      "\u001b[Ksaved signature(s) to 25.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 26.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 26.fa\n",
      "\u001b[Ksaved signature(s) to 26.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 27.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 27.fa\n",
      "\u001b[Ksaved signature(s) to 27.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 28.fa\n",
      "\u001b[Kcalculated 1 signatures for 3 sequences in 28.fa\n",
      "\u001b[Ksaved signature(s) to 28.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 29.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 29.fa\n",
      "\u001b[Ksaved signature(s) to 29.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 3.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 3.fa\n",
      "\u001b[Ksaved signature(s) to 3.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 30.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 30.fa\n",
      "\u001b[Ksaved signature(s) to 30.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 31.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 31.fa\n",
      "\u001b[Ksaved signature(s) to 31.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 32.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 32.fa\n",
      "\u001b[Ksaved signature(s) to 32.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 33.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 33.fa\n",
      "\u001b[Ksaved signature(s) to 33.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 34.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 34.fa\n",
      "\u001b[Ksaved signature(s) to 34.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 35.fa\n",
      "\u001b[Kcalculated 1 signatures for 7 sequences in 35.fa\n",
      "\u001b[Ksaved signature(s) to 35.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 36.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 36.fa\n",
      "\u001b[Ksaved signature(s) to 36.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 37.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 37.fa\n",
      "\u001b[Ksaved signature(s) to 37.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 38.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 38.fa\n",
      "\u001b[Ksaved signature(s) to 38.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 39.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 39.fa\n",
      "\u001b[Ksaved signature(s) to 39.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 4.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 4.fa\n",
      "\u001b[Ksaved signature(s) to 4.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 40.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 40.fa\n",
      "\u001b[Ksaved signature(s) to 40.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 41.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 41.fa\n",
      "\u001b[Ksaved signature(s) to 41.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 42.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 42.fa\n",
      "\u001b[Ksaved signature(s) to 42.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 43.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 43.fa\n",
      "\u001b[Ksaved signature(s) to 43.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 44.fa\n",
      "\u001b[Kcalculated 1 signatures for 2 sequences in 44.fa\n",
      "\u001b[Ksaved signature(s) to 44.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 45.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 45.fa\n",
      "\u001b[Ksaved signature(s) to 45.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 46.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 46.fa\n",
      "\u001b[Ksaved signature(s) to 46.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 47.fa\n",
      "\u001b[Kcalculated 1 signatures for 2 sequences in 47.fa\n",
      "\u001b[Ksaved signature(s) to 47.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 48.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 48.fa\n",
      "\u001b[Ksaved signature(s) to 48.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 49.fa\n",
      "\u001b[Kcalculated 1 signatures for 228 sequences in 49.fa\n",
      "\u001b[Ksaved signature(s) to 49.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 5.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 5.fa\n",
      "\u001b[Ksaved signature(s) to 5.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 50.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 50.fa\n",
      "\u001b[Ksaved signature(s) to 50.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 51.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 51.fa\n",
      "\u001b[Ksaved signature(s) to 51.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 52.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 52.fa\n",
      "\u001b[Ksaved signature(s) to 52.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 53.fa\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[Kcalculated 1 signatures for 1 sequences in 53.fa\n",
      "\u001b[Ksaved signature(s) to 53.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 54.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 54.fa\n",
      "\u001b[Ksaved signature(s) to 54.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 55.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 55.fa\n",
      "\u001b[Ksaved signature(s) to 55.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 56.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 56.fa\n",
      "\u001b[Ksaved signature(s) to 56.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 57.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 57.fa\n",
      "\u001b[Ksaved signature(s) to 57.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 58.fa\n",
      "\u001b[Kcalculated 1 signatures for 30 sequences in 58.fa\n",
      "\u001b[Ksaved signature(s) to 58.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 59.fa\n",
      "\u001b[Kcalculated 1 signatures for 5 sequences in 59.fa\n",
      "\u001b[Ksaved signature(s) to 59.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 6.fa\n",
      "\u001b[Kcalculated 1 signatures for 76 sequences in 6.fa\n",
      "\u001b[Ksaved signature(s) to 6.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 60.fa\n",
      "\u001b[Kcalculated 1 signatures for 11 sequences in 60.fa\n",
      "\u001b[Ksaved signature(s) to 60.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 61.fa\n",
      "\u001b[Kcalculated 1 signatures for 47 sequences in 61.fa\n",
      "\u001b[Ksaved signature(s) to 61.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 62.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 62.fa\n",
      "\u001b[Ksaved signature(s) to 62.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 63.fa\n",
      "\u001b[Kcalculated 1 signatures for 4 sequences in 63.fa\n",
      "\u001b[Ksaved signature(s) to 63.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 7.fa\n",
      "\u001b[Kcalculated 1 signatures for 3 sequences in 7.fa\n",
      "\u001b[Ksaved signature(s) to 7.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 8.fa\n",
      "\u001b[Kcalculated 1 signatures for 1 sequences in 8.fa\n",
      "\u001b[Ksaved signature(s) to 8.fa.sig. Note: signature license is CC0.\n",
      "\u001b[K... reading sequences from 9.fa\n",
      "\u001b[Kcalculated 1 signatures for 3 sequences in 9.fa\n",
      "\u001b[Ksaved signature(s) to 9.fa.sig. Note: signature license is CC0.\n"
     ]
    }
   ],
   "source": [
    "!cd big_genomes/ && sourmash sketch dna -p k=31,scaled=1000 --name-from-first *.fa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compare them all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/0.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/1.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/10.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/11.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/12.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/13.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/14.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/15.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/16.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/17.fa.sig'10 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/18.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/19.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/2.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/20.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/21.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/22.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/23.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/24.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/25.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/26.fa.sig'20 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/27.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/28.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/29.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/3.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/30.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/31.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/32.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/33.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/34.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/35.fa.sig'30 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/36.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/37.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/38.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/39.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/4.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/40.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/41.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/42.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/43.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/44.fa.sig'40 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/45.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/46.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/47.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/48.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/49.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/5.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/50.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/51.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/52.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/53.fa.sig'50 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/54.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/55.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/56.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/57.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/58.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/59.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/6.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/60.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/61.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/62.fa.sig'60 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/63.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/7.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/8.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/9.fa.sig'g'\n",
      "\u001b[Kloaded 64 signatures total.                                                    \n",
      "\u001b[K\n",
      "min similarity in matrix: 0.000\n",
      "\u001b[Ksaving labels to: compare_all.mat.labels.txt\n",
      "\u001b[Ksaving comparison matrix to: compare_all.mat\n",
      "\u001b[K\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloading comparison matrix from compare_all.mat...\n",
      "\u001b[K...got 64 x 64 matrix.\n",
      "\u001b[Kloading labels from compare_all.mat.labels.txt\n",
      "\u001b[Ksaving histogram of matrix values => compare_all.mat.hist.png\n",
      "\u001b[Kwrote dendrogram to: compare_all.mat.dendro.png\n",
      "\u001b[Kwrote numpy distance matrix to: compare_all.mat.matrix.png\n"
     ]
    }
   ],
   "source": [
    "!sourmash compare big_genomes/*.sig -o compare_all.mat\n",
    "!sourmash plot compare_all.mat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AxLzqirIr+vxvv/1WzZo1q/k4EAhcaSQAAAAAMYbCxAaSXAlWRwAalauuuko+n6/m44QErkEAABo79jAB7IfCxAbKK3/46/bOyXerRbLHwjRA7PP5fErLu/zP79Spkw4cOCC/3689e/bo5ptvjlg2AAAAALGBwsRmktwJ8rr5tgKhVF3GNWIeKzx+/Hjl5OQoMTFRK1asiEJKAAAQTwxFeIUJe5gAluOVNQDUw+bNmy947KGHHrIgCQAAAICGQGECAHUoLS1V79699emnn+rDDz9Uhw4d9POf/1xnzpxRQkKCli1bpvbt21sdEwAA20lqOy3k8+UlMxooST0YRkRXmAQNI2LnAnB5KExshrHCQN3CvUaSkpK0adMmTZo0SdLZMcPLli1Tenq6tm7dqnnz5mnhwoXRiAoAAADAIhQmNsNYYaBu4Y4VdjqdSk1NrfnY4/EoPT1dkuRyueR08qMUAIDGzvj+P5E8HwBr8Vu+DTBWGLBGZWWlXnjhBb322mtWRwEAAAAQYRQmNsBYYSA8VzpW+JwnnnhCo0ePVseOHa/8ZAAAIK4ZRmSn5ER24g6Ay0FhYjOMFQbqdjljhc1mzpypDh06aNiwYRFIBAAAACDW8MoaAOphwIAB2r9/vz777DMNGjRIM2bMUFZWlt5//3316NFDs2fPtjoiAACwECtMAPuhMAGAOpSWlurrr7+Wz+fTH/7wB3Xv3l2PPPKIxowZI5/Pp8TERKsjAgAQl+JqbHAdKEwA+6EwAYA6mMcKS9KkSZO0ePHimmk5AAAAAOyFwsRmyv3VKvNXWR0DiGnhXiPmscKVlZU6cuSInn76aZ08eVIzZ85Uz549Ix0TAADEFcf3t0ieD4CVKExsJnvudqsjADGvuqLsij7/q6++0scff6y1a9fK6XRq8ODB+uijjyKUDgAAAEAsoDCxgWAwaHUEoFFp3ry5OnfurNatW0s6uwKlqqpKTic/UgEAaKzYwwSwH367twHDMGru75x8t1okeyxMA8Q+n8+ntLzL//ykpCQ1b95c3333nZxOp/x+P2UJAAAAYDP8hm8zSe4Eed18W4FQqi7jGjl/rPCoUaM0a9YsDRw4UJWVlcrNzY1CSgAAEE9YYQLYD6+sAaAeNm/efMFjO3futCAJAAAAgIZAYQIAdSgtLVXv3r316aef6sMPP1T37t01f/58vfXWW0pOTtby5ct13XXXWR0TAICYlNR22iWfKy+Z0YBJosuQQ0YEJ9tE8lwALg+Fic0wVhioW7jXSFJSkjZt2qRJkyZJkk6cOKG//OUvKioq0p49e5Sbm6vFixdHIyoAAAAAi1CY2AxjhYG6hTtW2Ol0KjU1tebjo0ePKiMjQ4Zh6Ec/+pEeffTRSEcEAABxxjCMCO9hYtR9EICoojCxAcYKAw2rY8eO2rNnjyoqKrR9+3Z98803VkcCAAAAEGEUJjbAWGEgPFc6Vviaa67R6NGj1bdvX91yyy3q0qVLxLIBAID4dHaFSeRWhbDCBLAehYnNMFYYqNvljBU2GzlypEaOHKmCggJde+21EUgFAAAAIJbwyhoA6mHAgAHav3+/PvvsM40aNUrvvvuuTp06pXbt2mnhwoVWxwMAABYzDEeE9zBhSg5gNQoTAAhh3759Gj9+vBwOh+6880698cYb+slPfiKHwyG/368JEyYoKSnJ6pgAAFgm1NhgyV6jg0NhrDBgP1yFABBCenq6tmzZoh07dqhTp07asGGD3nvvPRUUFGj27NlasGCB1REBAAAARAErTGym3F+tMn+V1TGAmBbONdKqVaua+y6XS06nUy6XS9LZzWNvuummiOcDAADxh7fkAPZDYWIz2XO3Wx0BiHnVFWVhf05JSYm2bdumqVOn6tSpU/rZz36mkpISvfPOO1FICAAAAMBq1JY2EAwGrY4A2JrP59OIESO0bNkyuVwupaamqqioSG+99ZamTJlidTwAABADzq0wieQNgLVYYWID589o3zn5brVI9liYBoh9Pp9PaXn1OzYQCOjhhx/W888/r86dO6uqqkoOh0MOh0MpKSlq0qRJVLMCAAAAsAaFic0kuRPkdfNtBUKpCuMaWbNmjXbt2qXS0lLl5ubqV7/6lZYuXVpTmjBSGAAASEzJAeyIV9YAEMLw4cM1fPjwWo/98pe/tCgNAAAAgIZCYQIAIezbt0/jx4+Xw+FQy5Yt9cYbb+jLL7/UmDFj5PP5dM8992jatGlWxwQAIGqS2ob+37nykhkNlCTGGY6zt0ieD4ClKExshrHCQN3CuUbS09O1ZcsWeb1eTZkyRRs2bNDbb7+txYsXKz09PYopAQAAAFiJwsRmGCsM1C2cscKtWrWque9yuSRJR44c0dNPP62TJ09q5syZ6tmzZ8QzAgCA+BLpyTZMyQGsR2FiA4wVBqKvpKRE27Zt02OPPaaPP/5Ya9euldPp1ODBg/XRRx9ZHQ8AAABAhFGY2ABjhYHwhDNW+NzxI0aM0LJly3TNNdeoc+fOat26tSTJ6XSqqqpKTic/TgEAaMwMw6j1e3kkzgfAWvyGbzOMFQbqFs5Y4UAgoIcffljPP/+8OnfuLElq3ry5vvvuOzmdTvn9fsoSAAAAwIb4LR8AQlizZo127dql0tJS5ebm6sknn9SsWbM0cOBAVVZWKjc31+qIAAAgBhgyZCiCe5iIFSaA1ShMACCEzp07q1u3bgoGg2rZsqV+9rOf6d5771VCQoL8fr+mTJmi/v37Wx0TAIDLxtjgCInwpq+MFQasR2ECACGYxwpv3LhRBQUFkqSVK1fq8OHD1gYEAAAAEBUUJjZT7q9Wmb/K6hhATAvnGjGPFT5/v5K1a9dq9uzZEc0GAADilGGcvUXyfAAsRWFiM9lzt1sdAYh51RVlYX/OubHCU6dOlSSVlpbq2LFj6tatW6TjAQAAAIgBFCY2EAwGrY4A2Nr5Y4VdLpckaePGjRo8eLDFyQAAQMwwpAju+SpVR/BcAC4LhYkNnD+jfefku9Ui2WNhGiD2+Xw+peXV79iLjRWWzr4dZ9asWdEJCAAAAMByFCY2k+ROkNfNtxUIpSqMa+RiY4UHDBigkpISZWRkRDElAACIK+xhAtgOr6wBIIThw4dr+PDhFzz+t7/9zYI0AAAAABoKhQkAhPDJJ59o1KhRcjqdSk5O1urVq7V8+XKtWLFChmFo6tSpGjRokNUxAQAIKanttEs+V14yowGT2BgrTADboTCxGcYKA3UL5xrp0qWLioqKJEkzZszQ+vXrtWjRIn388ccqKyvTvffeS2ECAAAA2BCFic0wVhioWzhjhc9NxZGksrIy3XjjjerUqZPKy8tVWlqqFi1aRCMiAACINw5FdkpOJM8F4LJwGdoAY4WB6MrPz9ett96q7du3q2PHjurXr5+6deum22+/XePGjbM6HgAAAIAoYIWJDTBWGAhPOGOFJalPnz4qLi7W3Llz9fLLL+vtt9/WwYMH5ff71atXL/Xp06fWdQgAABohw1CQPUwAW6EwsRnGCgN1C2escEVFhTyesyVkSkqKkpKSlJiYKI/HI6fTqYqKCgWDQQoTAAAAwGZ4ZQ0AIeTn52vevHlyOBxKTU3V8uXLVVFRoR49eigQCGjs2LFyOHh3IwAAjZ7x/S2S5wNgKQoTAAhh4MCBGjhwoFatWqVx48bJ6/XqoYceUmFhoXw+n7788kurIwIAEHJssMTo4AbhMM7eInk+AJaiMAGAOlRXV2vdunVq06aNJGnSpElavHix0tPTLU4GAAAAIFooTGym3F+tMn+V1TGAmBbuNfLmm29q6NChmj9/viorK3XkyBE9/fTTOnnypGbOnKmePXtGKSkAAIgbhhHZjVrZHw2wHIWJzWTP3W51BCDmVVeU1fvYQCCgNWvWaMOGDZo/f76++uorffzxx1q7dq2cTqcGDx6sjz76KIppAQAAAFiBwsQGgsGg1REA21q5cqUefPDBmo1dmzdvrs6dO6t169aSJKfTqaqqKjmd/DgFAKBRY9NXwHb4Dd8Gzh9nunPy3WqR7LEwDRD7fD6f0vLqd+yBAwdUXFyslStX6uDBg5oyZYqaN2+u7777Tk6nU36/n7IEAAAAsCF+y7eZJHeCvG6+rUAoVWFcI3PmzKm5n5mZqQULFmjXrl0aOHCgKisrlZubG42IAAAg3jAlB7AdXlkDQD3t3btXktSzZ0/t3LnT4jQAAAAAoonCBABCOHLkiG6//XZlZGRIktauXashQ4bI4XDI7/dryZIl6t69u8UpAQB2l9R2Wsjny0tmNFASXBJTcgDboTCxGcYKA3UL9xrJycnRunXraj5+77335HK5tGPHDi1YsECvv/56pCMCAAAAsBiFic0wVhioWzhjhSWpqKhI2dnZys7O1qxZs+RyuSSd3Tz2pptuikZEAAAQb5iSA9iOw+oAuHKMFQaiJy0tTYcOHVJhYaFOnjyp9evX69SpU8rKytKYMWN01113WR0RAAAAQBSwwsQGGCsMhCecscIej0cez9lrasiQIdq9e7fuv/9+FRUV6aOPPtKUKVP07rvvRi8sAACID0zJAWyHwsRmGCsM1C2cscKlpaVq2rSpJKmwsFBdu3ZVdXW1HA6HUlJS1KRJk2jFBAAAAGAhXlkDQAgffPCBpk6dKq/Xqw4dOmj06NG655575HA45HA4tHDhQqsjAgCAWMAeJoDtUJgAQAhdu3ZVSUmJMjIyVFJSIq/Xq8GDB+utt95ScnKymjVrZnVEAIANMDY4/gVlKBjBUcBBGhPAcmz6CgB1yMnJUUFBgQoKChQIBPSXv/xFRUVFmjlzpnJzc62OBwAAACAKWGFiM+X+apX5q6yOAcS0cK+R88cK33fffcrIyJBhGPrRj36kRx99NEopAQBAXImBTV8XLVqkefPm6fjx48rIyFBeXp6ys7Mvefwbb7yhuXPn6uDBg0pJSVG/fv30u9/9Ti1atLiS5IBtUJjYTPbc7VZHAGJedUVZvY89N1bY6/Xq8ccf19GjR7Vnzx5VVFRo+/bt+uabb6KYFAAAoH5Wr16t8ePHa9GiRcrKytKrr76q/v3768CBA2rbtu0Fx3/wwQd65JFHtGDBAg0aNEhffPGFRo8erccee0zr16+34F8AxB7ekmMDSa4EqyMAtuXxeNSkSRMZhqEhQ4bok08+0ejRo9W3b1/99a9/VZcuXayOCAAAYoERhVsYXnrpJT366KN67LHH1LVrV+Xl5alNmzZavHjxRY//8MMP1b59e40bN04dOnTQnXfeqVGjRmnv3r1h/sMB+2KFiQ0Y520utXfqTxgrDNTB5/MpLa9+x15srPAjjzyikSNHqqCgQNdee230ggIAgEbP5/PV+tjj8cjj8dR6zO/3a9++fXrmmWdqPd63b1/t2rXrouft2bOnnnvuOW3evFn9+/fXyZMntW7dOv30pz+N7D8AiGO8srYZr9tJYQLUoSqMa8Q8Vjg3N1cPPfSQTp06pXbt2jFWGAAAnGUYZ2+RPJ+kNm3a1Hp42rRpmj59eq3HvvrqKwUCAbVs2bLW4y1bttSJEycuevqePXvqjTfe0LBhw3TmzBlVVVVp8ODB+j//5/9E7t8AxDleWQNACP3791f//v1rPfanP/3JojQAAKCxOXbsmJo1a1bzsXl1yfkMU2ETDAYveOycAwcOaNy4cXr++ed177336vjx45o0aZJGjx6t119/PTLhgThHYQIAIRw5ckS33367MjIyJElr165VRUWFxowZI5/Pp3vuuUfTpk2zOCUAIB4ktb30/16Ul8xowCSIiihNyWnWrFmtwuRirrnmGiUkJFywmuTkyZMXrDo5Z/bs2crKytKkSZMkSTfffLOaNGmi7OxszZw5U2lpaRH4RwDxjcLEZhgpDNQt3OskJydH69atq/l4+PDhWrx4sdLT0yMdDQAAIGxut1u33Xab8vPz9fOf/7zm8fz8fN13330X/ZyysjI5nbVfDiYknB0mEQwGoxcWiCMUJjZw/g+0zJnvWZgEiA/hjBWWpKKiImVnZys7O1szZszQkSNH9PTTT+vkyZOaOXOmevbsGaWkAAAgblzGZJs6zxeGCRMmaMSIEcrMzFSPHj20ZMkSlZSUaPTo0ZKkZ599Vl988YVWrFghSRo0aJAef/xxLV68uOYtOePHj9cdd9yh6667LoL/ECB+UZjYQHllwOoIgG2lpaXp0KFD8nq9evzxx7VmzRp9/PHHWrt2rZxOpwYPHqyPPvrI6pgAAKCRGzZsmL7++mu98MILOn78uLp3767NmzerXbt2kqTjx4+rpKSk5viRI0eqtLRUL7/8sp5++mk1b95cvXr10pw5c6z6JwAxh8LEZnZOvkctkt1WxwBiWjhjhc8f3TdkyBC9//776ty5s1q3bi1JcjqdqqqqumBJKwAAaGSiNCUnHGPGjNGYMWMu+tzy5csveOxf/uVf9C//8i9hfx2gseA3fJtJcjsYKwzUIZyxwqWlpWratKkkqbCwUDfddJP27t2r7777Tk6nU36/n7IEAAAAsCF+yweAED744ANNnTpVXq9XHTp0UG5urjp16qSBAweqsrJSubm5VkcEAACxIAZWmACILAoTGyj3By56H8CVu/baa+X1euVwOHTmzBkFg0EdO3ZMVVVV8nq96t69u9URAQAxItTYYInRwbbn+P4WyfMBsBSXIQCEkJ6eri1btmjHjh3q1KmTNmzYoJdeekk7duxQbm4uK0wAAAAAm2KFic2U+6tV5q+yOgYQ08K5Rlq1alVz3+Vy6T/+4z+UkZEht9utrKwsTZw4MRoRAQBAvDEU4bfkRO5UAC4PhYnN9M0rtDoCEPOqK8rC/pySkhJt27ZNL774ok6dOlXzeCDA2+AAAAAAO6IwsYFEF++sAqLJ5/NpxIgRWrZsmQKBgHw+X81zCQkJFiYDAAAxw1BkV4WwwgSwHIWJDRjnLf3bOflutUj2WJgGiH0+n09pefU7NhAI6OGHH9bzzz+vzp07q7KyUgcOHJDf79eePXt08803RzUrAAAAAGtQmNiAeTKO1823FQilKoxrZM2aNdq1a5dKS0uVm5urJ598UuPHj1dOTo4SExO1YsWKKCYFAADxIugwFHREbllIJM8F4PLwyhoAQhg+fLiGDx9+weMPPfSQBWkAAAAANBQKExs4f4WJebUJgCtXUFCg3NxcVVVVacKECdqwYYM2bdqkadOm6Te/+Y3V8QAADSSp7bSQz5eXzGigJIhJhhHhKTmsMAGsRmFiM+WVjBUG6hLONXLmzBnNnz9ff/3rX+V2uyVJd9xxh3JycnT69OloRQQAAABgMQoTm7lvYZHVEYCYF85Y4V27dikpKUmDBg2S1+vV4sWLlZaWFsV0AAAgLjElB7Ad5tHaAGOFgej58ssv9fnnn+vPf/6znnjiCU2fPt3qSAAAAAAaACtMbOBMZXXN/XfG9tQNLZtamAaIfeGMFW7evLnuvPNOud1u9erVS7Nnz45qNgAAEKcMQ4rkZBv2MAEsR2FiM0muBMYKA3UIZ6zwHXfcoby8PElScXGxrr/++iilAgAAABBLeGUNACG0aNFCgwcP1l133SWHw6GlS5fq2Wef1caNGxUIBHT48GEtWLDA6pgAAMBqTMkBbIfCBABCqK6u1kcffaRgMKjq6moFg0HNnj2bt+YAgA0xNhhXhE1fAdtht1AACGH//v2qqKjQzp079T//5//Uyy+/bHUkAAAAAA2AFSY2U15ZrTJ/ldUxgJgWzjXSunVrSVIwGNS3336r1NTUaMUCAADxzBHhTV8jeS4Al4XCxGbuW1hkdQQg5lVXlNX72GuuuUYOh0Ndu3ZVRUWFioq4xgAAAIDGgLfk2EAwGLQ6AmBbW7ZsUVJSkv7v//2/euuttzRhwgSrIwEAgFh0boVJJG8ALMUKExswzttB+52xPXVDy6YWpgFin8/nU1pe/Y+/6qqrJEnNmzfXt99+G5VMAAAAAGILhYnNJLkS5HXzbQVCqQrjGunbt6/++Mc/KicnRxUVFXrppZeimAwAAMSroHH2FsnzAbAWr6wBIISEhAS9+eabVscAAAAA0MAoTAAghE8++USjRo2S0+lUcnKyVq9erVdffVVvvfWWkpOTtXz5cl133XVWxwQA1FNS22mXfK68ZEYDJoHtMCUHsB0KE5thrDBQt3CukS5dutRMxpkxY4bWr1+vv/zlLyoqKtKePXuUm5urxYsXRysqAAAAAItQmNgMY4WBuoUzVtjlctXcLysrU+fOnZWRkSHDMPSjH/1Ijz76aDQiAgCAeGMYZ2+RPB8ASzFW2AYYKwxEV35+vm699VZt375dHTp00J49e1RRUaFt27bpm2++sToeAAAAgChghYkNMFYYCE+4Y4X79Omj4uJizZ07V0uXLtXo0aPVt29f3XLLLerSpUvUcgIAgDjCHiaA7VCY2AxjhYG6hTNWuKKiQh6PR5KUkpIiv9+vkSNHauTIkSooKNC1114brZgAAAAALMQrawAIIT8/X/PmzZPD4VBqaqqWL1+uhx56SKdOnVK7du20cOFCqyMCAIBY4FBkNzxg8wTAchQmNlBeGbjofQBXbuDAgUpOTlZubq6+/PJL5efn609/+pPVsQAAlxBqbLDE6GBEEZu+ArZDYQIAIZw5c0bz58/XX//6V7ndbqvjAAAAAGggFCY2c6ayWmX+KqtjADEtnGtk165dSkpK0qBBg+T1erV48WK1atUqiukAAEBcYtNXwHYoTGxm2JIPrY4AxLzqirJ6H/vll1/q888/V1FRkd577z1Nnz5dr7zyShTTAQAAAIgFbCVkA1d5XVZHAGyrefPmuvPOO+V2u9WrVy8dOHDA6kgAACAGBWUoaETwJlaYAFZjhYkNGOdtCLV1fLZaX+21MA0Q+3w+n9Ly6nfsHXfcoby8swcXFxfr+uuvj1ouAAAAALGDwsRmktwJ8rr5tgKhVIVxjbRo0UKDBw/WXXfdJYfDoaVLl0YxGQAAiFuMFQZsh1fWAFCHsWPHauzYsVbHAAAAANCAKEwAoA6lpaXq3bu3Pv30U3344Yfq3Lmz+vbtK0kqLy+X3+9XcXGxxSkBoHFIajst5PPlJTMaKAlgwpQcwHYoTGym3M9YYaAu4V4jSUlJ2rRpkyZNmiRJcrvdKigokCStXLlShw8fjnREAAAAABajMLGZvnmFVkcAYl44Y4Ulyel0KjU19aLPrV27VrNnz45ELAAAEM8M4+wtkucDYCm2ErKBRBffRsAKpaWlOnbsmLp162Z1FAAAAAARxgoTGzh/rPDOyXerRbLHwjRA7AtnrHAoGzdu1ODBg6/8RAAAIP6xhwlgOxQmNsNYYaBu4YwVDmXt2rWaNWtWRM4FAAAAILbwyhoA6mHAgAHav3+/PvvsM40aNUpDhgxRSUmJMjIyrI4GAABigfH9LZLnA2ApChMAqENpaam+/vpr+Xw+/eEPf1D37t0lSRs2bJDH49G+fftqHgMAXLlQo4MZG4xYFXQYCkbwbTSRPBeAy8NuoQBQh3NjhYcOHVrr8Tlz5igrK8uiVAAAAACiiRUmNlPuD6jMX2V1DCCmhXuNXGys8Oeffy7DMNS2bdtIRgMAAPGKTV8B26EwsYFgMFhzP3tugXVBgDhRXVF2xeeYM2eOnnnmGU2fPv3KAwEAAACIORQmNnCmstrqCECjcvjwYUlS+/btrQ0CAABih2GcvUXyfAAsRWFiM1vH36XWVydZHQOIaT6fT2l5l//5//7v/65PP/1U/fr109///ncdOnRI27dvl8vlilhGAAAAANaiMLGZJLdDXjffViCUqsu4RsxjhXfu3ClJGjlypCZOnEhZAgBAY+dQZEdqMJ4DsByvrAGgHjZv3nzRx5cvX96wQQAAAAA0CAoTG0hyJ1z0PoDImDhxov7t3/5Nbdu21bJly+R2u62OBABxLanttJDPl5fMaKAkQAQZivAeJpE7FYDLQ2FiM+X+asYKA3UI5xopLi7WiRMntHPnTs2aNUvr1q3TL37xiyimAwAAABALKExsJnvudqsjADEvnLHCu3fvVt++fSVJ/fr107JlyyhMAADAhQxDcjAlB7ATthKygSQXb8MBouXbb79Vs2bNJEkpKSn67//+b4sTAQAAAGgIrDCxAeO89nnv1J8wJQeoQzhjha+66ir5fD5JZ8uTq6++OnrBAABA/HJEeIVJJM8F4LLwytpmvG4nhQlQh3DGCv/4xz/W/Pnz9cgjj2jLli3KysqKYjIAAAAAsYK35ABACLfeeqtatWql7OxsHThwQEOGDLE6EgAAiEFBw4j4DYC1WIoAACF88skn2r17t5xOp7799lv5/X7GCgNoFILB6sv+XG+70GOBGRsMW3Iosn+O5k/bgOW4DAEghC5duqioqEg7duzQHXfcofXr11sdCQAAAEADYIWJzZT5q6yOAMS8cK4Tl8v1w+eVlenGG2+MRiQAABDvDCOyo4B5Sw5gOQoTm8mc+Z7VEYCYV11RFtbx+fn5mjx5slwul377299GKRUAAACAWMJbcgCgDn369FFxcbGGDh2qJUuWWB0HAADEonNjhSN5A2ApVpjY0N6pveV1J1gdA4hZPp9PaXn1O7aiokIej0eSlJKSIr/fH71gAAAAAGIGhYkNed0J8rr51gKXUhXG9ZGfn6958+bJ4XAoNTVVy5cvj14wAAAQvyK9KoQVJoDleFUNACEMHDhQAwcOtDoGAAAAgAZGYWJDZf4qVpgAEbJv3z6NHz9eDodDLVu21BtvvFFrcg4A2JVhXHqru6S200J+btnR0M8DtmR8f4vk+QBYilfVNlTur2a8MBBCONdHenq6tmzZIq/XqylTpmjDhg164IEHopgOAAAAQCygMLGh7LnbrY4AxLRwxgq3atWq5r7L5ZLTyY9NAABwoaDDUDCC+45E8lwALg9jhQGgHkpKSrRt2zb2MwEAAAAaCf5UagPBYLDWx3un/oQ9TIAQwhkrfO74ESNGaNmyZexfAgAALs4wzt4ieT4AluJVtQ2UVwZqfex1OylMgBDCGSscCAT08MMP6/nnn1fnzp2jmAoAAABALOEtOQAQwpo1a7Rr1y7l5ubq7rvv1urVq62OBAAAYpHDiPwNgKVYhgAAIdx000268cYbFQwGlZycrJ/+9KdavXq18vLylJSUpH/9139VmzZtrI4JABEXanRwecmMkJ8bDFZHOg4Q+xgrDNgOK0wAIIQuXbqoqKhIO3bs0B133KH169frpZde0o4dO5Sbm6vc3FyrIwIAAACIAlaY2EC5v/YeJmWmjwHUVuavqvex52/yWlZWprZt2yojI0Nut1tZWVmaOHFiNCICAIA44zAkRyT/HM0KE8ByFCY2lDlzm9URgJhWXVEW1vH5+fmaPHmyXC6X7rvvPjVr1qzmuUCAghIAAACwI96SAwB16NOnj4qLizV06FDt2LFDPp+v5rmEhAQLkwEAgFhxbqpwJG8ArMUKExs4/y0574ztqRtaNrUwDRD7fD6f0vLqd2xFRYU8Ho8kKSUlRX6/XwcOHJDf79eePXt08803Ry8oAAAAAMtQmNhMkitBXjffViCUqjCukfz8fM2bN08Oh0Opqalavny5UlNTlZOTo8TERK1YsSKKSQEAQLyI9KoQVpgA1uMtOQAQwsCBA7Vjxw5t375da9askdfr1UMPPaTdu3dr+/btjBQGAAAxY9GiRerQoYMSExN12223aefOnSGPr6io0HPPPad27drJ4/GoY8eOWrp0aQOlBWIfSxEAoB5WrVqlcePG6dSpU1ZHAYCISGo7LeTz5SUzLvvchsHf5ND4GIYhI4LLQsI91+rVqzV+/HgtWrRIWVlZevXVV9W/f38dOHBAbdu2vejnPPjgg/ryyy/1+uuvq1OnTjp58qSqquo/TRCwOwoTmymvrA5rZCrQGIV7jVRXV2vdunWsJgEAADHrpZde0qOPPqrHHntMkpSXl6ctW7Zo8eLFmj179gXHv/vuu9qxY4f+8z//U1dffbUkqX379g0ZGYh5FCY2c9/CIqsjADEv3LHCb775poYOHar58+dHKREAAIh3Vu5h4vf7tW/fPj3zzDO1Hu/bt6927dp10c/ZuHGjMjMzNXfuXP3xj39UkyZNNHjwYOXm5iopKelKogO2QWFiA4kulr0C0RIIBLRmzRpt2LCBwgQAADQ4n89X62OPx1Mzwe+cr776SoFAQC1btqz1eMuWLXXixImLnvc///M/9cEHHygxMVHr16/XV199pTFjxui///u/2ccE+B6FiQ2cqayuuc9YYaBu4YwVXrlypR588EE5HBSTAADg0qK1wsT8luBp06Zp+vTpl/ic2gGCweAl90Kprq6WYRh64403lJKSIuns23qGDh2qhQsXssoEEIWJ7TBWGKhbOGOFDxw4oOLiYq1cuVIHDx7UU089pQULFkQxHQAAwA+OHTumZs2a1XxsXl0iSddcc40SEhIuWE1y8uTJC1adnJOWlqb09PSaskSSunbtqmAwqP/3//6fbrjhhgj9C4D4xStrAAhhzpw5NfczMzMpSwAAwMU5pIgOiPr+XM2aNatVmFyM2+3Wbbfdpvz8fP385z+veTw/P1/33XffRT8nKytLa9eu1enTp5WcnCxJ+o//+A85HA61bt06Mv8GIM5RmABACEeOHNHtt9+ujIwMJScn69SpU0pNTbU6FgDUKZpjgwFcyMpNXyVpwoQJGjFihDIzM9WjRw8tWbJEJSUlGj16tCTp2Wef1RdffKEVK1ZIkn7xi18oNzdXv/rVrzRjxgx99dVXmjRpkn7961/zdhzgexQmAFCHnJwcrVu3zuoYAAAAlzRs2DB9/fXXeuGFF3T8+HF1795dmzdvVrt27SRJx48fV0lJSc3xycnJys/P17/8y78oMzNTLVq00IMPPqiZM2da9U8AYg6Fic2UV1arzF9ldQwgpoV7jRQVFSk7O1vZ2dmaNWvWJTdPAwAAjZfDOHuLlOBlnGvMmDEaM2bMRZ9bvnz5BY/deOONys/PD/8LAY0EhYnN3LewyOoIQMyrriir97FpaWk6dOiQvF6vHn/8ca1fv173339/FNMBAAAAiAXMybSBYDBodQTAtjwej5o0aSLDMDRkyBDt37/f6kgAACAGndvDJJI3ANZihYkNnP/2gHfG9tQNLZtamAaIfT6fT2l59Tu2tLRUTZuevaYKCwvVtWvX6AUDAAAAEDMoTGwmyZUgr5tvKxBKVRjXyAcffKCpU6fK6/WqQ4cOys3NjWIyAAAQr6yekgMg8nhlDQAh9O/fX/3797c6BgAAAIAGRmECACEcOXJEt99+uzIyMiRJa9euVWpqqkpKSnTDDTdo37596t69u8UpATRGSW2nhXy+vGRGAyUBIJ19m3wkJ+kxlQ+wHoWJzTBWGKhbuNdITk6O1q1bV+uxOXPmKCsrK5KxAAAAAMQQChObYawwULdwxgpLUlFRkbKzs5Wdna1Zs2bpyJEjMgxDbdu2jVJCAAAQbwzH2VskzwfAWlyGNsBYYSB60tLSdOjQIRUWFurkyZNav3695syZo4kTJ1odDQAAAEAUscLEBhgrDIQnnLHCHo9HHo9HkjRkyBC9+eabatKkidq3bx+1fAAAIP4wJQewHwoTm2GsMFC3cMYKl5aWqmnTsyVkYWGh+vTpoz/84Q/q16+f/v73v+vQoUPavn27XC5XtOICAAAAsABvyQGAED744APddtttys7O1hdffKFf/OIX2rlzp95991316dNHr7zyCmUJAACoWWESyRsAa7EUAQBC6N+/v+6880717t1bb7/9tiZPnqzu3bvrrrvuksPh0BNPPKElS5YwWhhAVIQaHczYYCC2GIrwW3IidyoAl4kVJgBQh6SkJG3atElDhw6teey9995TQUGBZs+erQULFliYDgAAAEA0sMLEZsorq1Xmr7I6BhDTwr1GnE6nUlNTaz127m04Pp9PN910U8SyAQCA+OQwzt4iJcgSE8ByFCY2c9/CIqsjADGvuqLsis9x6tQp/exnP1NJSYneeeedCKQCAAAAEEt4S44NBINBqyMAjU5qaqqKior01ltvacqUKVbHAQAAFmPTV8B+WGFiA8Z5P03fGdtTN7RsamEaIPb5fD6l5V3+51dVVcnhcMjhcCglJUVNmjSJWDYAAAAAsYHCxGaSXAnyuvm2AqFUXcY1MmDAAO3fv1+fffaZBg4cqK1bt9aUJgsXLoxCSgAAEE8ivSqEFSaA9XhlDQD1sHnz5lofP/fccxYlAQAAANAQKEwAIIR9+/Zp/Pjxcjgcatmypd544w29+uqrWrFihQzD0NSpUzVo0CCrYwKIU0ltp4V8vrxkRgMlAXClDIchI4JjciJ5LgCXh8LEZhgrDNQtnGskPT1dW7Zskdfr1ZQpU7RhwwYtWrRIH3/8scrKynTvvfdSmAAAAAA2RGFiM4wVBuoWzljhVq1a1dx3uVxyOp3q1KmTysvLVVpaqhYtWkQjIgAAiDPsYQLYD4WJDTBWGIi+kpISbdu2TVOnTtXx48fVrVs3BQIBLV++3OpoAAAAAKKAwsQGGCsMhCfcscI+n08jRozQsmXLVF5eriVLlujgwYPy+/3q1auX+vTpU+s6BAAAjQ8rTAD7oTCxgfLKQK2PGSsMhBbOWOFAIKCHH35Yzz//vDp37qzTp08rMTFRHo9HTqdTFRUVCgaDFCYAAACAzfDKGgBCWLNmjXbt2qXS0lLl5ubqySef1NChQ9WjRw8FAgGNHTtWDofD6pgAAMBqEV5hIv4WA1iOwgQAQhg+fLgGDhyo3r1769NPP1VGRoaGDRumTz/9VJs2bVJVFVOpAFwaY4OBxsNhnL1F8nwArEVhAgB1SEpK0qZNmzRp0qSax1588UXl5OTo9OnTFiYDAAAAEC0UJjZzprJaZX7+4g2EEu414nQ6lZqaWuuxtLS0SEYCAABxjk1fAfuhMLGZYUs+tDoCEPOqK8qsjgAAAAAgxlGY2MBVXpfVEQAAAIBGzXCcvUXyfACsRWFiA+ePM906Plutr/ZamAaIfT6fT2l5VqcAAAAAEMsoTGwmyZ0gr5tvKxBK1WVcIwMGDND+/fv12WefadSoUfrss8+0ceNGBQIBHT58WAsWLIhCUgAAEC/YwwSwH15ZA0A9bN68+YLHZs+ebUESAAAAAA2BwgQAQti3b5/Gjx8vh8Ohli1b6o033tDvf/97vfXWW0pOTtby5ct13XXXWR0TgEWS2k4L+Xx5yYwGSgLAaoZh1HqrfCTOB8BaFCY2U+5nrDBQl3CukfT0dG3ZskVer1dTpkzRn/70J/3lL39RUVGR9uzZo9zcXC1evDiKaQEAAABYgcLEZvrmFVodAYh54YwVbtWqVc19l8ul//qv/1JGRoYMw9CPfvQjPfroo9GICAAA4gx7mAD2w7AqG0h08W0Eoq2kpETbtm3Tr3/9a+3Zs0cVFRXatm2bvvnmG6ujAQAAAIgCVpjYwPnvb9w5+W61SPZYmAaIfeGOFfb5fBoxYoSWLVum1NRUjR49Wn379tUtt9yiLl26RC0nAACIH6wwAeyHwsRmGCsM1C2cscKBQEAPP/ywnn/+eXXu3FmSNHLkSI0cOVIFBQW69tproxUTAAAAgIV4ZQ0AIaxZs0a7du1SaWmpcnNz9eSTT2r9+vU6deqU2rVrp4ULF1odEQAAxABWmAD2Q2ECACH06NFDDscP+wT9+Mc/1uuvv67Kykp9/vnn+vLLL9W+fXvrAgKIulCjgxkbDOAch3H2FsnzAbAWhQkA1CEnJ0fr1q2TJFVUVGjZsmVKT0/X1q1bNW/ePFaZAAAAADZEYWIz5f6AyvxVVscAYlq410hRUZGys7OVnZ2tWbNmKT09XdLZMcNOJz9GAQDA2fGjEV1hErlTAbhM/KZvA8FgsOZ+9twC64IAcaK6oqzex6alpenQoUPyer16/PHHtX79et1///2qrKzUCy+8oNdeey2KSQEAAABYheLSBs5UVlsdAbAtj8ejJk2ayDAMDRkyRPv375ckPfHEExo9erQ6duxobUAAABATHEYw4jcA1mKFic1sHX+XWl+dZHUMIKb5fD6l5dXv2NLSUjVt2lSSVFhYqK5du2rmzJnq0KGDhg0bFr2QAAAAACxFYWIzSW6HvG6+rUAoVWFcIx988IGmTp0qr9erDh066Mknn1THjh2VlZWl999/Xz169NDs2bOjmBYAAMQDpuQA9sMrawAIoX///urfv3+txyorKy1KAwAAAKChUJjYQLk/cNH7AK7ckSNHdPvttysjI0OStHbtWk2ePFmbNm3StGnT9Jvf/MbihACuVFLbaSGfLy+Z0UBJAMQzQ5HdIJIFJoD1KExspryymrHCQB3CvUZycnK0bt26mo9ffPFF5eTk6PTp05GOBgAAACBGUJjYzH0Li6yOAMS8cMYKS1JRUZGys7OVnZ2tWbNmKS0tLUrJAABAvIr0ZBum5ADWY6ywDSS6+DYC0ZKWlqZDhw6psLBQJ0+e1Pr1662OBAAAAKABsMLEBgzjh3c4bh2frdZXey1MA8S+cMYKezweeTweSdKQIUO0e/du3X///dELBwAA4hJTcgD7oTCxmSR3AmOFgTqEM1a4tLRUTZs2lSQVFhaqa9eu0YoFAAAAIIbwyhoAQvjggw80depUeb1edejQQbm5uXr22We1ceNGBQIBHT58WAsWLLA6JgAAsJhDkd3vgDfdA9ajMLEBxgoD0dO1a1eVlJQoIyNDJSUl+uabb5Senq4mTZrIMAz16tXL6ogA6sDYYAANgbfkAPZDYQIAdTCPFV60aJE+/vhjlZWV6d5779WgQYMsTAcAAAAgGihMbKa8MqAyf5XVMYCYFu41Yh4r3KlTJ5WXl6u0tFQtWrSIUkoAABBPDCMoI4KjgCN5LgCXh8LEBoLBH36Y3rdwl4VJgPhQXVFW72PPjRX2er16/PHHtX79evXr10/dunVTIBDQ8uXLoxcUAAAAgGUoTGzgTFW11REA2zKPFc7Pz9f777+vgwcPyu/3q1evXurTp0+t8d4AAKDxYQ8TwH4oTGxm9RM/1k2tU6yOAcQ0n8+ntLz6HWseK3zLLbdo165d8ng8cjqdqqioUDAYpDABAAAAbIbCxGYSXQ553XxbgVCqwrhGLjZW+OTJk+rRo4cCgYDGjh0rh4PBfwAANHaMFQbsh1fWABBC//791b9//1qPTZw4URMnTrQoEQAAAICGQGFiA0muhIveB3DlAoGAHnnkEX3xxRdq3769/vCHP8jlclkdC4BJUttpl3yuvGRGAyYB0Fg5jKAcEZxsE8lzAbg8FCY2U15ZzVhhoA7hXCNvv/22rr/+er3xxhuaO3eu3n77bQ0bNiyK6QAAAADEAgoTm7lvYZHVEYCYF85Y4f/8z//ULbfcIkn60Y9+pA0bNlCYAACACzAlB7Af9hKygUQX30YgWrp27ar3339fkrRt2zZ9++231gYCAAAA0CBYYWID548z3To+W62v9lqYBoh94YwVHjhwoLZv36577rlH3bt3V6tWraKaDQAAxCdDkf1rNAtMAOtRmNhMkjuBscJAHcIZK+xwOLRgwQJJ0vTp09W7d+9oxQIAAAAQQ3gvBwCEcOLECd19993q3bu3EhMTdeedd1odCQAAxKBze5hE8gbAWixFAIAQWrVqpT//+c/q3bu3Zs6cqYEDB6p79+664YYblJ6eLkl67rnn1KdPH4uTAvYWamywxOhgANZjrDBgPxQmAFCHpKQkbdq0SZMmTap5LCUlRQUFBdaFAgAAABBVFCY2U+6vVpm/yuoYQEwL9xpxOp1KTU2t9djp06eVk5Oj9PR0vfzyy7r66qsjGREAAMQZxgoD9kNhYjN98wqtjgDEvOqKsis+R1FRkVq0aKEVK1Zo+vTp+v3vfx+BZAAAAABiBZu+2kCii28j0NBatGghSXrggQe0f/9+a8MAAADLOaJwA2AtVpjYgGH8sF5v5+S71SLZY2EaIPb5fD6l5V3+5/v9fgWDQXk8HhUWFqpTp04RywYAAAAgNlCY2EySO0FeN99WIJSqy7hGBgwYoP379+uzzz7Tz372M61Zs0ZNmjSRx+PR0qVLo5ASAADEE6bkAPbDK2sAqIfNmzfX+vi3v/2tRUkAAAAANAQKEwAI4ZNPPtGoUaPkdDqVnJys1atXa8CAAXI4HPL7/VqyZIm6d+9udUwg7iW1nRby+fKSGQ2UBAAuD1NyAPuhMLGZcn+AscJAHcK5Rrp06aKioiJJ0owZM7R+/Xq99957crlc2rFjhxYsWKDXX389WlEBAAAAWITCxAaCwR/e35g9t8C6IECcCGessMvlqrlfVlamG2+8seYxn8+nm266KeL5AABA/GGFCWA/TKuygTOV1VZHAGwtPz9ft956q7Zv366OHTvq1KlTysrK0pgxY3TXXXdZHQ8AAABAFLDCxGa2jr9Lra9OsjoGENPCHSvcp08fFRcXa+7cuVqyZImeeeYZFRUV6aOPPtKUKVP07rvvRi0rAACIDw5F9q/R/GUbsB6Fic0kuR2MFQbqEM5Y4YqKCnk8HklSSkqK/H6/qqur5XA4lJKSoiZNmkQrJgAAAAALUVwCQAj5+fnKycnRPffco/fee0/333+/7rnnHt1zzz0aM2aMZs2aZXVEAAAQAwwjKEcEb4YRrPuLmixatEgdOnRQYmKibrvtNu3cubNen1dUVCSn06lbbrkl7K8J2BlLEWyg3B+46H0AV659+/aqqqqS0+nUP//5TyUnJ8vj8ejMmTOSpMTERIsTAvGBscEA7M7qTV9Xr16t8ePHa9GiRcrKytKrr76q/v3768CBA2rbtu0lP++7777TI488op/85Cf68ssvrzA1YC+sMAGAEM6NFd6xY4fuuOMObdiwQcuWLVNhYaGeffZZzZs3z+qIAAAAeumll/Too4/qscceU9euXZWXl6c2bdpo8eLFIT9v1KhR+sUvfqEePXo0UFIgfrDCxGbKK6tV5q+yOgYQ08K5Rsxjhbt166b09PSa55xOfowCAIDobfrq8/lqPe7xeGr2VzvH7/dr3759euaZZ2o93rdvX+3ateuSX2PZsmU6fPiwVq5cqZkzZ0YkN2An/KZvM/ctLLI6AhDzqivKwjo+Pz9fkydPlsvl0m9/+1tJUmVlpV544QW99tpr0YgIAAAgSWrTpk2tj6dNm6bp06fXeuyrr75SIBBQy5Ytaz3esmVLnThx4qLnPXjwoJ555hnt3LmTPwABl8CVYQOJLt5ZBUTTxcYKP/HEExo9erQ6duxodTwAABADHIrwHibf/99jx46pWbNmNY+bV5eczzBqBwgGgxc8JkmBQEC/+MUvNGPGDHXu3DkieQE7ojCxgfN/CG4dn63WV3stTAPEPp/Pp7S8+h17sbHCM2fOVIcOHTRs2LDohQQAAJDUrFmzWoXJxVxzzTVKSEi4YDXJyZMnL1h1IkmlpaXau3eviouL9Zvf/EaSVF1drWAwKKfTqa1bt6pXr16R+0cAcYrCxAbMk3G8br6tQChVYVwj+fn5mjdvnhwOh1JTU5WXl6d27dopKytL77//vnr06KHZs2dHMS0AAIgHxmWOAg51vvpyu9267bbblJ+fr5///Oc1j+fn5+u+++674PhmzZrp73//e63HFi1apPfff1/r1q1Thw4dLj84YCO8sgaAEAYOHKiBAwfWeqyystKiNAAAABc3YcIEjRgxQpmZmerRo4eWLFmikpISjR49WpL07LPP6osvvtCKFSvkcDjUvXv3Wp9/7bXXKjEx8YLHgcaMwsQGyisDF70P4Mp98sknGjVqlJxOp5KTk7V69WrdeuutNZNynnvuOfXp08filEBsSGo77ZLPlZfMaMAkANDwHEaE9zAJ81zDhg3T119/rRdeeEHHjx9X9+7dtXnzZrVr106SdPz4cZWUlEQuINAIUJjYzBnGCgN1Cuca6dKli4qKzk6fmjFjhtavX6+UlBQVFBREKR0AAMDlGTNmjMaMGXPR55YvXx7yc6dPn37B9B2gsaMwsZlhSz60OgIQ88IZK+xyuWrul5WV6cYbb9Tp06eVk5Oj9PR0vfzyy7r66qujERMAAMQRh36YbBOp8wGwFtehDSQ6+TYC0ZSfn69bb71V27dvV8eOHVVUVKQdO3aoX79+/CUGAAAAsClWmNjAmarqmvurn/j/dFPr5taFAeJAOGOFJalPnz4qLi7W3LlztWTJEj3zzDOSpAceeECvvfZadEICAIC44jCCckRwSk4kzwXg8lCY2EyiK4GxwkAdwhkrXFFRIY/HI0lKSUnRmTNnah4rLCxUp06dohUTAAAAgIV4ZQ0AIeTn52vevHlyOBxKTU3VvHnz1LNnTzVp0kQej0dLly61OiIAAIgBVk/JARB5FCYAEEJaWpqqq39429t1112nm2++WZs2bdK0adPUpk0bC9MBDSvU2GCJ0cEAGjcjwoWJQWECWI7CBABCSE9P15YtW+T1ejVlyhRt2LBBL774onJycnT69Gmr4wEAAACIEgoTmzlTWa0yf5XVMYCYFs410qpVq5r7LpdLTqdTaWlp0YgFAADiWML3t0ieD4C1KExsZtiSD62OAMS86oqysD+npKRE27Zt09SpU6OQCAAAAECsoTCxgau8LqsjALbm8/k0YsQILVu2TC4X1xsAALgQY4UB+6EwsQHjvB2hto7PVuurvRamAWKfz+dTWl79jg0EAnr44Yf1/PPPq3PnzlHNBQAAACB2UJjYTJI7QV4331YglKowrpE1a9Zo165dKi0tVW5urp588knt379fGzduVCAQ0OHDh7VgwYIopgUAAPGAscKA/fDKGgBCGD58uIYPH17rsWHDhmn27NkWJQIAAADQEChMACCEffv2afz48XI4HGrZsqXeeOMNdevWTenp6ZKk5557Tn369LE4JRAZSW2nhXy+vGRGAyUBgPjDChPAfihMbKbcz1hhoC7hXCPp6enasmWLvF6vpkyZog0bNiglJUUFBQXRCwgAAADAchQmNtM3r9DqCEDMC2escKtWrWruu1wuOZ1OnT59Wjk5OUpPT9fLL7+sq6++OhoxAQBAHEkwzt4ieT4A1nJYHQBXLtHFtxGItpKSEm3btk0DBw5UUVGRduzYoX79+mn69OlWRwMAAAAQBawwsYHzxwrvnHy3WiR7LEwDxL5wxgqfO37EiBFatmyZXC6XWrRoIUl64IEH9Nprr0UnJAAAiCvsYQLYD4WJzTBWGKhbOGOFA4GAHn74YT3//PPq3Lmz/H6/gsGgPB6PCgsL1alTpygmBQAAAGAVXlkDQAhr1qzRrl27VFpaqtzcXD355JOaO3eumjRpIo/Ho6VLl1odEQAAxACHEZTDCEb0fACsRWECACH06NFDDscP+wT16tVLkpSXlyenkx+hiC+MDQaA6DEi/JYcg7fkAJbjt30AqENOTo7WrVsnSaqsrNRLL72knTt3as+ePcrNzdWSJUssTggAAAAg0ihMbKbcH1CZv8rqGEBMC/caKSoqUnZ2trKzs/Xwww8rIyNDbrdbWVlZmjhxYpRSAgCAeJLw/S2S5wNgLQoTGyir+OHFX/bcAuuCAHGiuqKs3sempaXp0KFD8nq9evzxx/XOO++oWbNmNc8HAoFoRAQAAABgMUfdhyDWnamstjoCYFsej0dNmjSRYRgaMmSIiouL5fP5ap5PSODvPwAA4IexwpG8AbAWK0xs5p2xWbqhZbLVMYCY5vP5lJZXv2NLS0vVtGlTSVJhYaEGDhyoxYsXy+/3a8+ePbr55pujFxQAAACAZShMbCbJ5ZDXzbcVCKUqjGvkgw8+0NSpU+X1etWhQwfl5ubK4/EoJydHiYmJWrFiRRSTAgCAeMFYYcB+eGUNACH0799f/fv3r/XYQw89pIceesiiRAAAAAAaAoWJDSS5Ey56H0BklJaWqnfv3vr000/14Ycfqnv37nr55Ze1YsUKGYahqVOnatCgQVbHBCRJSW2nXfK58pIZDZgEABqXBOPsLZLnA2AtChObKfdXM1YYqEO410hSUpI2bdqkSZMm1Ty2aNEiffzxxyorK9O9995LYQIAAADYDIWJzfTNK7Q6AhDzwhkrLElOp1Opqam1HuvUqZPKy8tVWlqqFi1aRDIeAACIQ5GebMOUHMB6FCY2kOhiOjTQ0Pr166du3bopEAho+fLlVscBAAAAEGEUJjZgGD/Uzzsn360WyR4L0wCxL5yxwpf6/CVLlujgwYPy+/3q1auX+vTpU+taBAAAjQsrTAD7oTCxmSR3AmOFgTqEM1b4YhwOhxITE+XxeOR0OlVRUaFgMEhhAgAAANgIr6wBoB4GDBig/fv367PPPtOoUaM0dOhQ9ejRQ4FAQGPHjpXDwVvjAABozByK8AqTyJ0KwGWiMAGAehgxYoT27Nmj3bt364YbblB6eroSExP13HPPqU+fPlbHg40Eg9Uhn/e2Cz0amNHBAGANhxFUghGM6PkAWIvCBADqUF1drXXr1qlNmzaSpJSUFBUUFFgbCgAAAEBUUZjYTLk/oDJ/ldUxgJgW7jXy5ptvaujQoZo/f74k6fTp08rJyVF6erpefvllXX311dGICQAA4ohDkX0bDW/JAaxHYWIDweAPy/Wy5xZYFwSIE9UVZfU+NhAIaM2aNdqwYUNNYVJUVKQWLVpoxYoVmj59un7/+99HKyoAAAAAi1Bc2sCZytDvdwdw+VauXKkHH3yw1qauLVq0kCQ98MAD2r9/v0XJAABALDk3VjiSNwDWYoWJzWwdf5daX51kdQwgpvl8PqXl1e/YAwcOqLi4WCtXrtTBgwf11FNP6X/9r/8lj8ejwsJCderUKapZAQAAAFiDwsRmktwOed18W4FQqsK4RubMmVNzPzMzU88884x69uypJk2ayOPxaOnSpdGICAAA4kykV4WwwgSwHq+sAaCe9u7dK0nat2+fxUkAAAAARBuFiQ2U+wMXvQ/gyn3yyScaNWqUnE6nkpOTtXr1aiUnJ6ukpEQ33HCD9u3bp+7du1sdEzbibTcj5PNlR6c1UBIAQDgSDCnBCNZ9YBjnA2AtChMbKK/8oST5pqySscJAHcK5Rrp06aKioiJJ0owZM7R+/XqNGDFCc+bMUVZWVrQiAgAAALAYhYnNDFvyodURgJgXzlhhl8tVc7+srEw33nijPv/8cxmGobZt20YjHgAAiEPsYQLYD2OFbSDRybcRiKb8/Hzdeuut2r59uzp27Kg5c+Zo4sSJVscCAAAAEEWsMLEBw/ihfn5nbE/d0LKphWmA2BfOWGFJ6tOnj4qLizV37lwtXrxYktS+ffuoZAMAAPGJFSaA/VCY2ECSO6Hm/tVN3IwVBuoQzljhiooKeTweSVJKSopOnDihTz/9VP369dPf//53HTp0SNu3b6/11h0AAAAA8Y9X1gAQQn5+vubNmyeHw6HU1FQtX75c06adnVIycuRITZw4kbIEAACwwgSwIQoTAAihffv2qqqqktPp1D//+U9VV1frV7/6lTZt2qRp06YxUhhhS2obeixweUnoscIAgNjkMCI7CpjCBLAehQkAhHCxscIvvviicnJydPr0aYvTAQAAAIgWChObKfdXq8xfZXUMIKaFc41cbKxwWlpaNGIBAIA45jCCchjBiJ4PgLUoTGymb16h1RGAmFddURbW8fn5+Zo8ebJcLpd++9vfRikVAAAAgFjisDoArlyii28jEE3nxgoPHTpUS5YssToOAACIQY4o3ABYixUmNmAYP+wItXPy3WqR7LEwDRD7fD6f0vLqd6x5rLDf749eMAAAAAAxg8LEZpLcCfK6+bYCoVSFcY1cbKzws88+q40bNyoQCOjw4cNasGBBFNMCAIB4wFhhwH54ZQ0AIQwcOFADBw6s9djs2bM1e/ZsixIBAAAAaAgUJgAQwieffKJRo0bJ6XQqOTlZq1at0uDBgyVJ5eXl8vv9Ki4utjglYk1S22mXfK68ZEYDJgEANJQE4+wtkucDYC0KE5sp9wcYKwzUIZxrpEuXLioqKpIkzZgxQ++8844KCgokSStXrtThw4ejEREAAACAxShMbCAY/GFGe/bcAuuCAHEinLHCLper5n5ZWZluvPHGmo/Xrl3LW3MAAIAkyWEE5TCCdR8YxvkAWItpVTZwprLa6giAreXn5+vWW2/V9u3b1bFjR0lSaWmpjh07pm7dulmcDgAAAEA0sMLEBsr9gZr774zN0g0tky1MA8S+cMYKS1KfPn1UXFysuXPnasmSJXrmmWe0cePGmr1MAAAAmJID2A+Fic0kuRyMFQbqEM5Y4YqKCnk8HklSSkqK/H6/pLNvx5k1a1ZU8gEAAACwHq+sASCE/Px8zZs3Tw6HQ6mpqVq+fLlKS0tVUlKijIwMq+MBAIAYwQoTwH4oTGwgyZ1w0fsArlz79u1VVVUlp9Opf/7zn6qurtZ3332n1q1b6+6779Y999yjadMuPUIW9hRqbLDE6GAAaIwciuwGkWw2CViPwgQAQjCPFV6/fr02b96sxYsXKz093eJ0AAAAAKKFwsRmyv3VKvNXWR0DiGnhXCMXGyu8aNEiPf300zp58qRmzpypnj17RiMmAACIJ4ZkRPJtNLwlB7AchYnN9M0rtDoCEPOqK8rCOj4/P1+TJ0+Wy+XSuHHj9PHHH2vt2rVyOp0aPHiwPvrooyglBQAAAGAV3hpnA4kuvo1ANJ0bKzx06FCtWLFCnTt3VuvWrdWqVSs5nU5VVbGqCwCAxs6Iwg2AtVhhYgPGeWv/dk6+Wy2SPRamAWKfz+dTWl79jr3YWOHmzZvru+++k9PplN/vl9PJj1IAAADAbvgt32aS3Anyuvm2AqFUhXGNXGys8G233aaBAweqsrJSubm5UUwKAADihRHhPUwiuh8KgMvCK2sACGHgwIEaOHBgrcd69uypnTt3WpQIAAAAQEOgMAGAeli1apXGjRunU6dO6Ve/+pU2bdqkadOm6Te/+Y3V0XCZqqrPXPK5pu1nh/zc8pIZkY4DAIhzDkV2g0h2KQSsR2FiY8FgUOWVAatjADEn3NHb1dXVWrdundq0aSNJevHFF5WTk6PTp09HIx4AAACAGEBhYlPBYFBDX9mtfUe/sToKEHPCHSv85ptvaujQoZo/f74kKS0tLRqxAABAHDOMoAwjGNHzAbAWK71sqrwyQFkCREAgENCaNWs0bNgwq6MAAAAAaECsMGkE9k7tLa87weoYQMwIZ6zwypUr9eCDD8rhoF8GAACXZnx/i+T5AFiLwsRmyv3VKvNX1dqjwcuoYaCWcMYKHzhwQMXFxVq5cqUOHjyop556SgsWLIhiOgAAAACxgFfRNlDu/2Fj1+y52y1MAtjPnDlzau5nZmZqwYIFevbZZ7Vx40YFAgEdPnyYAgUAAJxdYRLBZSGsMAGsR2FiA4ku3ioARNuqVat09OhRSdItt9yigoICJSUlacKECRYnw+UKNTq49MizDZgEAGAHvCUHsB8KExswzquyd06+Wy2SPSrzVylz5nsWpgLs4/yxwpWVlXrppZe0c+dO7dmzR7m5uVqyZInVEQEAAABEGIWJzSRdZL+SsvPesgNAtfb4qY/zxwofPHhQGRkZcrvdysrK0sSJE6OUEgAAxBOHcfYWyfMBsBaFSSOQOXOb1RGAmFJdUVbvY8+NFd6wYYPmz5+vb7/9Vs2aNav1PAAAAAD7YfMLm0pyJSiz3VVWxwDinnms8FVXXSWfz1fzfEICI7sBAMAPe5hE8gbAWqwwsSnDMLR2dA+VV/LXb8DM5/MpLa9+x5rHCi9ZskQHDhyQ3+/Xnj17dPPNN0c1KwAAQH0tWrRI8+bN0/Hjx5WRkaG8vDxlZ2df9Ni3335bixcv1v79+1VRUaGMjAxNnz5d9957bwOnBmIXhYmNGYZxwX4mAKSqMK6Li40V/tOf/qScnBwlJiZqxYoV0YgIAADijGFEeKxwmOdavXq1xo8fr0WLFikrK0uvvvqq+vfvrwMHDqht27YXHF9YWKg+ffroxRdfVPPmzbVs2TINGjRI//Zv/6Zbb701Qv8KIL7xahoA6mnv3r2SpIceekgPPfSQxWkAAAB+8NJLL+nRRx/VY489JknKy8vTli1btHjxYs2ePfuC4/Py8mp9/OKLL+qdd97Rn//8ZwoT4HsUJgBQh4KCAuXm5qqqqkoTJkzQ/Pnz5XA45Pf7tWTJEnXv3t3qiI1SVfWZkM83bX/hL4fnKy+ZEck4AIBGLtL7jpw71/l7p0mSx+ORx+Op9Zjf79e+ffv0zDPP1Hq8b9++2rVrV72+XnV1tUpLS3X11VdfdmbAbihMbCwYDLKHCXAR4YwVPnPmjObPn6+//vWvcrvdkqQBAwbI5XJpx44dWrBggV5//fVoRQUAAI1cmzZtan08bdo0TZ8+vdZjX331lQKBgFq2bFnr8ZYtW+rEiRP1+jrz58/XP//5Tz344INXlBewEwoTmwoGgxr6ym7tO/qN1VGAmBPOWOFdu3YpKSlJgwYNktfr1eLFi9WqVStJZ//ic9NNN0UrJgAAiCPRWmFy7NgxNWvWrOZx8+qSWp9j2vgkGAxe8NjFrFq1StOnT9c777yja6+99rLyAnZEYWJT5ZUByhIgAr788kt9/vnnKioq0nvvvafp06crNzdXP/vZz1RSUqJ33nnH6ogAAMDGmjVrVqswuZhrrrlGCQkJF6wmOXny5AWrTsxWr16tRx99VGvXrlXv3r2vOC9gJxQmjcDeqb3ldSdYHQOIGeGMFW7evLnuvPNOud1u9erVS7Nnz1ZqaqqKior00UcfacqUKXr33XejmhcAAMQ+h3H2Fsnz1Zfb7dZtt92m/Px8/fznP695PD8/X/fdd98lP2/VqlX69a9/rVWrVumnP/3plcQFbInCxGbK/dUq81fV2qPB605gvDBwnnDGCt9xxx01u8gXFxerbdu2qq6ulsPhUEpKipo0aRKllAAAAPU3YcIEjRgxQpmZmerRo4eWLFmikpISjR49WpL07LPP6osvvtCKFSsknS1LHnnkEf3v//2/9eMf/7hmdUpSUpJSUlIs+3cAsYRX0TZQ7v9hY9fsudstTALYT4sWLTR48GDdddddcjgcmjVrlu655x45HA45HA4tXLjQ6ogAACAGRGsPk/oaNmyYvv76a73wwgs6fvy4unfvrs2bN6tdu3aSpOPHj6ukpKTm+FdffVVVVVUaO3asxo4dW/P4L3/5Sy1fvjwC/wIg/lGY2ECiy2F1BMDWMjIy5HK5VFVVpa+++koej0dnzpwdaZuYmGhxusarrrHBpUeebaAkAABIMoIyjGBEzxeuMWPGaMyYMRd9zlyCFBQUXEYooHGhMLGB83e+3jn5brVI9qjMX6XMme9ZmAqwB/NY4YqKCmVmZio9PV1bt27VvHnzWGUCAAAA2BCFic0kXWS/krLz3rIDQLX2+KnLxcYKp6enS5JcLpecTn6MAgAA69+SAyDy+E2/Ecicuc3qCEBMqa4oq/exFxsr/Morr6iyslIvvPCCXnvttSgmBQAAAGAVNr+wqSRXgjLbXWV1DCDumccKHzhwQJL0xBNPaPTo0erYsaPFCQEAQCwwjMjfAFiLFSY2ZRiG1o7uofJK3o4DmPl8PqXl1e9Y81jh66+/XjNnzlSHDh00bNiwqGUEAAAAYC0KExszDOOC/UwASFVhXBfmscIvvviicnJylJWVpffff189evTQ7Nmhp7UAAAD7cyiyy/d5KwBgPV5NA0Adxo4dq7Fjx9Z8XFlZaWEaAAAAAA2BwgQAQti3b5/Gjx8vh8Ohli1b6o033pDL5VJJSYluuOEG7du3T927d7c6ZtzyV5++5HMp7eeF/NzykhmRjgMAwGWL9L4j7GECWI/CpBEIBoPsZQKcJ5yxwunp6dqyZYu8Xq+mTJmiDRs26IEHHtCcOXOUlZUVxZQAAAAArERhYnPBYFBDX9mtfUe/sToKEDPCGSvcqlWrmvsul0tOp1Off/65DMNQ27ZtoxEPAADEIeP7WyTPB8Ba7CVkc+WVAcoSIAJKSkq0bds2DRw4UHPmzNHEiROtjgQAAAAgilhh0ojsndpbXneC1TEAy4UzVvjc8SNGjNCyZctUUlIiSWrfvn1UsgEAgPjEHiaA/VCY2Ey5v7rW/gzn3/e6ExgzDCi8scKBQEAPP/ywnn/+eXXu3Flvv/22Pv30U/Xr109///vfdejQIW3fvl0ulyuKiQEAAAA0NF4920z23O1WRwBsZc2aNdq1a5dKS0uVm5urJ598Ujt37pQkjRw5UhMnTqQsAQAA7GEC2BCFiQ0kOtmKBoiWzp07q1u3bgoGg2rZsqX69eunvn376syZM0pISFBycrLVEeNaqNHB3x2Z1IBJAAC4Mg7j7C2S5wNgLQqTOBcMBjXi9T01H++d+pNab7sp81cpc+Z7VkQDbME8Vvjdd9/VsmXLlJ6erq1bt2revHlauHCh1TEBAAAARBiFSZwrrwyo+Ni3NR973c5L7lNS5g80UCogtp2/t09dzGOF3W630tPTaz52OvkxCgAAeEsOYEf8pt+IZM7cZnUEICZUV5SF/TnnxgpPnTpVklRZWakXXnhBr732WqTjAQAAAIgBbH5hc0muBGW2u8rqGEBcO3+s8LkNXp944gmNHj1aHTt2tDgdAACIBYYRjPgNgLVYYWJzhmFo7egeKq/k7TjAOT6fT2l59TvWPFZYkmbOnKkOHTpo2LBh0QsJAAAAwFIUJo2AYRiX3NcEaIyqwrgezGOFf/WrX2nGjBnKysrS+++/rx49emj27NlRTAsAAOIBe5gA9sOraAAIYfjw4Ro+fHitx375y19alAYAAABAQ6EwAYB6WLVqlcaNG6dTp05p/vz5euutt5ScnKzly5fruuuuszpe1FQEfCGff6H425DPv3T/6yGfLy+ZEW4kAABikmGcvUXyfACsRWGCRi0YDKq8qtzqGGhgZZXhTcmprq7WunXr1KZNG504cUJ/+ctfVFRUpD179ig3N1eLFy+OUlIAAAAAVqEwQaMVDAb1yF8f0f5T+62OggYWKA9vE+Q333xTQ4cO1fz583X06FFlZGTIMAz96Ec/0qOPPhqllAAAIJ6whwlgP4wVRqNVXlVOWYI6BQIBrVmzpmYiTseOHbVnzx5VVFRo27Zt+uabbyxOCAAAACAaWGECSCp4sEBJziSrY6CB+Hw+pT2ZVq9jV65cqQcffFAOx9l++ZprrtHo0aPVt29f3XLLLerSpUs0owIAgDjhUGT/Gs1ftgHrUZjYUDAYVHlleG85aIzKKgMKVrtkOCqV5EyS1+W1OhIaSJWrqt7HHjhwQMXFxVq5cqUOHjyop556SgsWLNDIkSNVUFCga6+9NopJAQAAAFiFwsRmgsGghr6yW/uO8jaB+slV067PWB0CMWzOnDk19zMzM7VgwQI99NBDOnXqlNq1a6eFCxdamA4AAMSMCE/JYRMTwHoUJjZTXhmgLAGiYNWqVTp69Kgk6c4779SKFSv06aefatu2bRo0aJDF6aLHYSSEfL6uscET3mZTXABAY8G2r4DdUJjY2N6pveV1h36x05iVVZbr7jU5VsdAHDh/rLAkLVq0SB9//LHKysp077332rowAQAAABorChMb87oT5HXzLb4kI0GGo1LS2Yk5aDzKKsvCOv78scKS1KlTJ5WXl6u0tFQtWrSIRkQAABBnjO//E8nzAbAWr6bj1LmNXcv89d+8Epd295q7rY6ABhQor/+myOfGCm/YsKGmMOnXr5+6deumQCCg5cuXRyklAAAAACtRmMShUBu7lvuZjlNfSc4k3XrtrSo+WWx1FMQw81hhn8+nJUuW6ODBg/L7/erVq5f69OkjI6K7vAEAgHhjGA4ZRuSGAUfyXAAuD4VJHAq1sWv23IKGDRPHDMPQv/b7V96O0wj5fD6lPZlWr2PNY4WnTZumxMREeTweOZ1OVVRUKBgMUpgAAAAANkNhEud2Tr6bkuQKGIYhr8trdQw0sCpX/d/KdrGxwr/73e/Uo0cPBQIBjR07tmb1CQAAaMyYkgPYDYVJnEs6bwrO3qk/kSRlznzPqjiAre3du1eSNHHiRE2cONHiNAAAAACiicLERpiIA0TPqlWrNG7cOH3xxRfq27evJKm8vFx+v1/FxfbdB6dZ+7khn/cdmRzy+eog+yoBABqHs+tLIjklB4DVeIVtY2VsAAtcVLjTpaqrq7Vu3Tq1adNGbrdbBQUFks5uCHv48OEoJAQAAABgNQoTG8ucuc3qCEBMqq4oC+v4N998U0OHDq0ZK3zO2rVrNXv27EhGAwAAcYs9TAC7YadCm0lyJSiz3VVWxwBsIxAIaM2aNRo2bFitx0tLS3Xs2DF169bNomQAAAAAookVJjZjGIbWju6h8krejgNcis/nU1pe/Y5duXKlHnzwwQsm4WzcuFGDBw+OfDgAABCXDMMhw4jc36MjeS4Al4fCxIYMw2ADWCCEqjCujwMHDqi4uFgrV67UwYMH9dRTT2nBggVau3atZs2aFcWUAAAAAKzEq2oACGHOnDk19zMzM7VgwQKVlpaqpKREGRkZFiYDAACxhT1MALuhMAGAeli1apWOHj0qSWratKk2bNggj8ejffv2qXv37hanC+3L8v8b8vn2XVZd8rnykhmRjgMAgC0Z3/8nkucDYC3eGAcAdTh/rPA5c+bMUVZWloWpAAAAAEQTK0xw2YLBIJvLIi6V+avCOt48Vvjzzz+XYRhq27ZtNOIBAIA4xAoTwH4oTHBZgsGghr6yW/uOfmN1FCBs1RVl9T723FjhDRs21BQmc+bM0TPPPKPp06dHKSEAAAAAq1GYxJlgMBj2X8ejobwyQFmCRsE8Vvjw4cOSpPbt21uYCgAAxB6HIrvjAbsnAFajMIkjsbqqY+/U3vK6E6yOAdSbz+dTWl79jjWPFe7du7dat26tfv366e9//7sOHTqk7du3y+VyRTUzAAAAgIZFYRJH6lrVUeZvuP1Ezl/l4nUnyOvmv0qIH1Vh/PfVPFZ47969NR+PHDlSEydOpCwBAAAyDEOGEcE9TCJ4LgCXh1e5NpI5c5vVEQBbO78skaTly5dbEwQAAABA1FGYxLkkV4Iy212lvTH2Nh3AblatWqVx48bp1KlTuuuuu+RwOOT3+7VkyRJ1797d6nghte+yKuTzRz4b3kBJAACwM+P7WyTPB8BKFCZxzjAMrR3do8HH+5b5q5Q5873v7zNaGPEl3I2Tq6urtW7dOrVp00aS9N5778nlcmnHjh1asGCBXn/99WjEBAAAAGAhChMbMAzD0j1EeCsQ4k04Y4Ul6c0339TQoUNrxgqf27PE5/Pppptuing+AAAQf4zv/xPJ8wGwFrOqcFnOvRUIsLtAIKA1a9Zo2LBhNY+dOnVKWVlZGjNmjO666y4L0wEAAACIFlaY4LJY9VYgIBLCGSu8cuVKPfjgg3I4fuiXU1NTVVRUpI8++khTpkzRu+++G52gAAAgjjgU2b9H87dtwGoUJrhsVr8VCLhc4YwVPnDggIqLi7Vy5UodPHhQTz31lObPny+Hw6GUlBQ1adIkikkBAAAAWIVXuwAQwpw5c2ruZ2ZmauLEibrnnnvkcDjkcDi0cOFCC9MBAIBYwR4mgP1QmABACEeOHNHtt9+ujIwMJScny+l0yjAMBYNBnT59WsOHD1dxcXFUM2z74lDI5wf1+GPI58tLZkQyDgAAuAjDMGQYESxMInguAJeHwgQA6pCTk6N169bVfFxQUCDp7P4mhw8ftigVAAAAgGiiMEHMCwaDbC6LiCrzV4V1fFFRkbKzs5Wdna1Zs2bV/MVn7dq1mj17djQiAgCAuGN8f4vk+QBYicIEMS0YDGroK7u17+g3VkeBjVRXlNX72LS0NB06dEher1ePP/641q9fr/vvv1+lpaU6duyYunXrFsWkAAAAAKzCrCrEtPLKAGUJLOXxeNSkSRMZhqEhQ4Zo//79kqSNGzdq8ODB1oYDAAAx4+yWr44I3lhhAliNFSaIG3un9pbXnWB1DNiAz+dTWl79ji0tLVXTpk0lSYWFheratauks2/HmTVrVpQSAgAAALAahUmcK/Pbe2+P8/ea8LoT5HXzX1lcuaow/nv0wQcfaOrUqfJ6verQoYNyc3NVWlqqkpISZWRkRDElAACIL+xhAtgNrz7jXObMbVZHAGytf//+6t+/f63HmjZtqr/97W8WJQIAAADQEChM4tStbZqr+Ni3VscAbO/IkSO6/fbba1aTrF27Vj179lR6erok6bnnnlOfPn2immFQjz+GfP7Pu0dE9esDAIC6GYZRM0kvUucDYC0Kkzi18rE7GsUP0TJ/lTJnvvf9fXu//QgNJ9yxwjk5OVq3bl3NxykpKSooKIhwKgAAAACxhMIkThmG0ej28+DtR4iUcMYKS1JRUZGys7OVnZ2tWbNm6fTp08rJyVF6erpefvllXX311VFKCgAA4gd7mAB2w1hhxLQkV4Iy211ldQw0YmlpaTp06JAKCwt18uRJrV+/XkVFRdqxY4f69eun6dOnWx0RAAAAQBQ0riUKiDuGYWjt6B4qr+TtOIiccMYKezweeTweSdKQIUO0e/du3X///ZKkBx54QK+99lqUUgIAgHhiyCEjgn+PjuS5AFweChPEvMb49iNEVzhjhUtLS9W0aVNJUmFhobp27aqKigp5PB4VFhaqU6dO0YoJAAAAwEK8CgWAED744ANNnTpVXq9XHTp00Pjx49WzZ081adJEHo9HS5cutToiAACICexhAtgNhQkAhNC/f38lJSUpNzdXR48e1YcffqjJkycrLy9PTmd4P0L/ePCIkpKbXPS5sX2WX/LzyktmhPV1AABAwzO+/08kzwfAWhQmABDCmTNnNH/+fP31r3+V2+1WZWWl7rzzTu3cuVN79uxRbm6ulixZYnVMAAAAABFGYQL7CAalyvDGxaKR8v+z3ofu2rVLSUlJGjRokLxeryZNmqSMjAy53W5lZWVp4sSJUQwKAADihWEYMowIrjCJ4LkAXB4KE9hDMCgtvVc69m9WJ0E8qAjW+9Avv/xSn3/+uYqKivTee+9p+vTp6tatW83zgQATnAAAAAA7YlYV7KGyjLIEUdG8eXPdeeedcrvd6tWrl4qLi+Xz+WqeT0hIsDAdAACIHY4o3ABYiRUmsJ+JhyS31+oUiGU+n/S/rqvXoXfccYfy8vIkScXFxerbt68OHDggv9+vPXv26Oabb45iUAAAAABWoTCJU2V+3gZQiz8gBT3yGhVnyxL3xSeRAJIkd/2vnxYtWmjw4MG666675HA4tHTpUn300UfKyclRYmKiVqxYEcWgAAAgXjAlB7AfCpM4lTlzm9URYtAyHUn8hdUhYENjx47V2LFjaz6+/vrr9dBDD1mYCAAAAEC0UZjEkSRXgjLbXaW9R7+xOgrQ6KxatUrjxo3TkSNH9POf/1xnzpxRQkKCli1bpvbt29frHE/f96YMh+uizy3MHxm5sAAAwALG97dIng+AlShM4ohhGFo7uofKK3k7zgX8ZdK8Tj/cB0IJY6ywJFVXV2vdunVq06aNnE6nli1bpvT0dG3dulXz5s3TwoULoxQUAAAAgFUoTOKMYRjyuvm2XShBMirO3v1dJ2ujIPaFMVZYkt58800NHTpU8+fPl8fjUXp6uiTJ5XLJ6eR6BAAAZ39PN4wI7mESwXMBuDzMqoI9uLxSmx9bnQI2FAgEtGbNGg0bNqzW45WVlXrhhRc0btw4i5IBAADUtmjRInXo0EGJiYm67bbbtHPnzpDH79ixQ7fddpsSExN1/fXX65VXXmmgpEB84E+jsAfDkH79rlTJ23FQD2GMFV65cqUefPBBORy1++UnnnhCo0ePVseOHaOREAAAxB2HIvv36PDOtXr1ao0fP16LFi1SVlaWXn31VfXv318HDhxQ27ZtLzj+888/14ABA/T4449r5cqVKioq0pgxY5SamqohQ4ZE6h8BxDUKE9iHYTBOGPUTxljhAwcOqLi4WCtXrtTBgwf11FNPqUWLFurQocMFq04AAACs8tJLL+nRRx/VY489JknKy8vTli1btHjxYs2ePfuC41955RW1bdtWeXl5kqSuXbtq7969+t3vfkdhAnyPwiRGBINn91Tw+XwWJwHs79x1du66C2XOnDk19zMzMzVp0iS1a9dOWVlZev/999WjR4+L/hJyvnNfJ1hdecljyk9feiNan+90nTkBAEDknPvf3vr8rnBOqe+fMiI42abU98/vs9R+feDxeOTxeGo95vf7tW/fPj3zzDO1Hu/bt6927dp10fPv3r1bffv2rfXYvffeq9dff12VlZVyuS4+2Q9oTChMYkRpaakkqU2bNhYnARqP0tJSpaSk1Pv4vXv3Sjq7f0m4X0eS/Mc3XvKY32S+dennwvpqAAAgUurzu4Lb7VarVq3Upk1OxL9+cnLyBa8Ppk2bpunTp9d67KuvvlIgEFDLli1rPd6yZUudOHHiouc+ceLERY+vqqrSV199pbS0tCv/BwBxjsIkRlx33XU6duyYmjZtyo7YQJQFg0GVlpbquuvqt4/JleL6BgAgvoTzu0JiYqI+//xz+f3+qOQw/+5gXl1yPvOxF/v8uo6/2ONAY0VhEiMcDodat25tdQyg0QhnZcmV4voGACD+hPO7QmJiohITE6OYJrRrrrlGCQkJF6wmOXny5AWrSM5p1arVRY93Op1q0aJF1LIC8YSxwgAAAAAQx9xut2677Tbl5+fXejw/P189e/a86Of06NHjguO3bt2qzMxM9i8BvkdhAgAAAABxbsKECXrttde0dOlS/eMf/9BTTz2lkpISjR49WpL07LPP6pFHHqk5fvTo0Tp69KgmTJigf/zjH1q6dKlef/11TZw40ap/AhBzeEsOAAAAAMS5YcOG6euvv9YLL7yg48ePq3v37tq8ebPatWsnSTp+/LhKSkpqju/QoYM2b96sp556SgsXLtR1112n3//+94wUBs5jBMOZlQUAAAAAANAI8JYcAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAAAAAAAAEwoTAMD/344dCwAAAAAM8rfeP4bCCAAAGGECAAAAMMIEAAAAYIQJAAAAwAgTAAAAgBEmAAAAACNMAAAAAEaYAAAAAIwwAQAAABhhAgAAADDCBAAAAGCECQAAAMAIEwAAAIARJgAAAAAjTAAAAABGmAAAAACMMAEAAAAYYQIAAAAwwgQAAABghAkAAADACBMAAACAESYAAAAAE4n7B8TDq+k/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "\n",
    "Image(filename=\"compare_all.mat.matrix.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make a fast(er) search database for all of them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kloading 64 files into SBT\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/0.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/1.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/10.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/11.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/12.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/13.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/14.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/15.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/16.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/17.fa.sig'10 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/18.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/19.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/2.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/20.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/21.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/22.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/23.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/24.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/25.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/26.fa.sig'20 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/27.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/28.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/29.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/3.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/30.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/31.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/32.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/33.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/34.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/35.fa.sig'30 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/36.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/37.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/38.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/39.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/4.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/40.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/41.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/42.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/43.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/44.fa.sig'40 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/45.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/46.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/47.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/48.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/49.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/5.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/50.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/51.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/52.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/53.fa.sig'50 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/54.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/55.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/56.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/57.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/58.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/59.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/6.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/60.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/61.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/62.fa.sig'60 sigs total\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/63.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/7.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/8.fa.sig'g'\n",
      "\u001b[Kloaded 1 sigs from 'big_genomes/9.fa.sig'g'\n",
      "\u001b[K\n",
      "\u001b[Kloaded 64 sigs; saving SBT under \"all-genomes\"\n",
      "\u001b[KFinished saving nodes, now saving SBT index file.\n",
      "\u001b[KFinished saving SBT index, available at /Users/t/dev/sourmash/doc/all-genomes.sbt.zip\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!sourmash index -k 31 all-genomes big_genomes/*.sig"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can now use this to search, and gather."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[K\r\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n",
      "\r",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n",
      "\r\n",
      "\r",
      "\u001b[KCannot open file 'shew_os185.fa.sig'\r\n"
     ]
    }
   ],
   "source": [
    "!sourmash search shew_os185.fa.sig all-genomes --threshold=0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kcomputing signatures for files: fake-metagenome.fa\n",
      "\u001b[KComputing a total of 1 signature(s).\n",
      "\u001b[K... reading sequences from fake-metagenome.fa\n",
      "\u001b[Kcalculated 1 signatures for 3 sequences in fake-metagenome.fa\n",
      "\u001b[Ksaved signature(s) to fake-metagenome.fa.sig. Note: signature license is CC0.\n"
     ]
    }
   ],
   "source": [
    "# (make fake metagenome again, just in case)\n",
    "!cat genomes/*.fa > fake-metagenome.fa\n",
    "!rm -f fake-metagenome.fa.sig\n",
    "!sourmash sketch dna -p k=31,scaled=1000 fake-metagenome.fa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[Kselect query k=31 automatically.\n",
      "\u001b[Kloaded query: fake-metagenome.fa... (k=31, DNA)\n",
      "\u001b[Kloaded 1 databases.                                                            \n",
      "\n",
      "\n",
      "overlap     p_query p_match\n",
      "---------   ------- -------\n",
      "0.5 Mbp       42.2%   10.5%    NC_011663.1 Shewanella baltica OS223,...\n",
      "499.0 kbp     38.4%   18.5%    CP001071.1 Akkermansia muciniphila AT...\n",
      "0.5 Mbp       19.4%    4.9%    NC_009665.1 Shewanella baltica OS185,...\n",
      "\n",
      "found 3 matches total;\n",
      "the recovered matches hit 100.0% of the query\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!sourmash gather fake-metagenome.fa.sig all-genomes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## build a database with taxonomic information --\n",
    "\n",
    "for this, we need to provide a metadata file that contains accession => tax information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>accession</th>\n",
       "      <th>taxid</th>\n",
       "      <th>superkingdom</th>\n",
       "      <th>phylum</th>\n",
       "      <th>class</th>\n",
       "      <th>order</th>\n",
       "      <th>family</th>\n",
       "      <th>genus</th>\n",
       "      <th>species</th>\n",
       "      <th>strain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE000782</td>\n",
       "      <td>224325</td>\n",
       "      <td>Archaea</td>\n",
       "      <td>Euryarchaeota</td>\n",
       "      <td>Archaeoglobi</td>\n",
       "      <td>Archaeoglobales</td>\n",
       "      <td>Archaeoglobaceae</td>\n",
       "      <td>Archaeoglobus</td>\n",
       "      <td>Archaeoglobus fulgidus</td>\n",
       "      <td>Archaeoglobus fulgidus DSM 4304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NC_000909</td>\n",
       "      <td>243232</td>\n",
       "      <td>Archaea</td>\n",
       "      <td>Euryarchaeota</td>\n",
       "      <td>Methanococci</td>\n",
       "      <td>Methanococcales</td>\n",
       "      <td>Methanocaldococcaceae</td>\n",
       "      <td>Methanocaldococcus</td>\n",
       "      <td>Methanocaldococcus jannaschii</td>\n",
       "      <td>Methanocaldococcus jannaschii DSM 2661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NC_003272</td>\n",
       "      <td>103690</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>Cyanobacteria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nostocales</td>\n",
       "      <td>Nostocaceae</td>\n",
       "      <td>Nostoc</td>\n",
       "      <td>Nostoc sp. PCC 7120</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AE009441</td>\n",
       "      <td>178306</td>\n",
       "      <td>Archaea</td>\n",
       "      <td>Crenarchaeota</td>\n",
       "      <td>Thermoprotei</td>\n",
       "      <td>Thermoproteales</td>\n",
       "      <td>Thermoproteaceae</td>\n",
       "      <td>Pyrobaculum</td>\n",
       "      <td>Pyrobaculum aerophilum</td>\n",
       "      <td>Pyrobaculum aerophilum str. IM2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AE009950</td>\n",
       "      <td>186497</td>\n",
       "      <td>Archaea</td>\n",
       "      <td>Euryarchaeota</td>\n",
       "      <td>Thermococci</td>\n",
       "      <td>Thermococcales</td>\n",
       "      <td>Thermococcaceae</td>\n",
       "      <td>Pyrococcus</td>\n",
       "      <td>Pyrococcus furiosus</td>\n",
       "      <td>Pyrococcus furiosus DSM 3638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>NZ_ABZS01000228</td>\n",
       "      <td>432331</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>Aquificae</td>\n",
       "      <td>Aquificae</td>\n",
       "      <td>Aquificales</td>\n",
       "      <td>Hydrogenothermaceae</td>\n",
       "      <td>Sulfurihydrogenibium</td>\n",
       "      <td>Sulfurihydrogenibium yellowstonense</td>\n",
       "      <td>Sulfurihydrogenibium yellowstonense SS-5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>NZ_JGWU01000001</td>\n",
       "      <td>1458259</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>Proteobacteria</td>\n",
       "      <td>Betaproteobacteria</td>\n",
       "      <td>Burkholderiales</td>\n",
       "      <td>Alcaligenaceae</td>\n",
       "      <td>Bordetella</td>\n",
       "      <td>Bordetella bronchiseptica</td>\n",
       "      <td>Bordetella bronchiseptica D989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>NZ_FWDH01000003</td>\n",
       "      <td>31899</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>Firmicutes</td>\n",
       "      <td>Clostridia</td>\n",
       "      <td>Thermoanaerobacterales</td>\n",
       "      <td>Thermoanaerobacterales Family III. Incertae Sedis</td>\n",
       "      <td>Caldicellulosiruptor</td>\n",
       "      <td>Caldicellulosiruptor bescii</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>NC_009972</td>\n",
       "      <td>316274</td>\n",
       "      <td>Bacteria</td>\n",
       "      <td>Chloroflexi</td>\n",
       "      <td>Chloroflexia</td>\n",
       "      <td>Herpetosiphonales</td>\n",
       "      <td>Herpetosiphonaceae</td>\n",
       "      <td>Herpetosiphon</td>\n",
       "      <td>Herpetosiphon aurantiacus</td>\n",
       "      <td>Herpetosiphon aurantiacus DSM 785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>NC_005213</td>\n",
       "      <td>228908</td>\n",
       "      <td>Archaea</td>\n",
       "      <td>Nanoarchaeota</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nanoarchaeales</td>\n",
       "      <td>Nanoarchaeaceae</td>\n",
       "      <td>Nanoarchaeum</td>\n",
       "      <td>Nanoarchaeum equitans</td>\n",
       "      <td>Nanoarchaeum equitans Kin4-M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          accession    taxid superkingdom          phylum               class  \\\n",
       "0          AE000782   224325      Archaea   Euryarchaeota        Archaeoglobi   \n",
       "1         NC_000909   243232      Archaea   Euryarchaeota        Methanococci   \n",
       "2         NC_003272   103690     Bacteria   Cyanobacteria                 NaN   \n",
       "3          AE009441   178306      Archaea   Crenarchaeota        Thermoprotei   \n",
       "4          AE009950   186497      Archaea   Euryarchaeota         Thermococci   \n",
       "..              ...      ...          ...             ...                 ...   \n",
       "59  NZ_ABZS01000228   432331     Bacteria       Aquificae           Aquificae   \n",
       "60  NZ_JGWU01000001  1458259     Bacteria  Proteobacteria  Betaproteobacteria   \n",
       "61  NZ_FWDH01000003    31899     Bacteria      Firmicutes          Clostridia   \n",
       "62        NC_009972   316274     Bacteria     Chloroflexi        Chloroflexia   \n",
       "63        NC_005213   228908      Archaea   Nanoarchaeota                 NaN   \n",
       "\n",
       "                     order                                             family  \\\n",
       "0          Archaeoglobales                                   Archaeoglobaceae   \n",
       "1          Methanococcales                              Methanocaldococcaceae   \n",
       "2               Nostocales                                        Nostocaceae   \n",
       "3          Thermoproteales                                   Thermoproteaceae   \n",
       "4           Thermococcales                                    Thermococcaceae   \n",
       "..                     ...                                                ...   \n",
       "59             Aquificales                                Hydrogenothermaceae   \n",
       "60         Burkholderiales                                     Alcaligenaceae   \n",
       "61  Thermoanaerobacterales  Thermoanaerobacterales Family III. Incertae Sedis   \n",
       "62       Herpetosiphonales                                 Herpetosiphonaceae   \n",
       "63          Nanoarchaeales                                    Nanoarchaeaceae   \n",
       "\n",
       "                   genus                              species  \\\n",
       "0          Archaeoglobus               Archaeoglobus fulgidus   \n",
       "1     Methanocaldococcus        Methanocaldococcus jannaschii   \n",
       "2                 Nostoc                  Nostoc sp. PCC 7120   \n",
       "3            Pyrobaculum               Pyrobaculum aerophilum   \n",
       "4             Pyrococcus                  Pyrococcus furiosus   \n",
       "..                   ...                                  ...   \n",
       "59  Sulfurihydrogenibium  Sulfurihydrogenibium yellowstonense   \n",
       "60            Bordetella            Bordetella bronchiseptica   \n",
       "61  Caldicellulosiruptor          Caldicellulosiruptor bescii   \n",
       "62         Herpetosiphon            Herpetosiphon aurantiacus   \n",
       "63          Nanoarchaeum                Nanoarchaeum equitans   \n",
       "\n",
       "                                      strain  \n",
       "0            Archaeoglobus fulgidus DSM 4304  \n",
       "1     Methanocaldococcus jannaschii DSM 2661  \n",
       "2                                        NaN  \n",
       "3            Pyrobaculum aerophilum str. IM2  \n",
       "4               Pyrococcus furiosus DSM 3638  \n",
       "..                                       ...  \n",
       "59  Sulfurihydrogenibium yellowstonense SS-5  \n",
       "60            Bordetella bronchiseptica D989  \n",
       "61                                       NaN  \n",
       "62         Herpetosiphon aurantiacus DSM 785  \n",
       "63              Nanoarchaeum equitans Kin4-M  \n",
       "\n",
       "[64 rows x 10 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "df = pandas.read_csv(\"podar-lineage.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[K\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\n",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\n",
      "\n",
      "\u001b[KBuilding LCA database with ksize=31 scaled=10000 moltype=DNA.\n",
      "\u001b[Kexamining spreadsheet headers...\n",
      "\u001b[K** assuming column 'accession' is identifiers in spreadsheet\n",
      "\u001b[K64 distinct identities in spreadsheet out of 64 rows.\n",
      "\u001b[K64 distinct lineages in spreadsheet out of 64 rows.\n",
      "\u001b[K... loaded 64 signatures.H01000003.1 Caldicellulo (64 of 64); skipped 0 so far\n",
      "\u001b[Kloaded 19993 hashes at ksize=31 scaled=10000\n",
      "\u001b[K64 assigned lineages out of 64 distinct lineages in spreadsheet.\n",
      "\u001b[K64 identifiers used out of 64 distinct identifiers in spreadsheet.\n",
      "\u001b[Ksaving to LCA DB: taxdb.lca.json\n"
     ]
    }
   ],
   "source": [
    "!sourmash lca index podar-lineage.csv taxdb big_genomes/*.sig  -C 3 --split-identifiers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This database 'taxdb.lca.json' can be used for search and gather as above:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[K\r\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n",
      "\r",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n",
      "\r\n",
      "\r",
      "\u001b[Kselect query k=31 automatically.\r\n",
      "\r",
      "\u001b[Kloaded query: fake-metagenome.fa... (k=31, DNA)\r\n",
      "\r",
      "\u001b[Kloading from taxdb.lca.json...\r",
      "\r",
      "\u001b[Kloaded LCA taxdb.lca.json\r",
      "\r",
      "\u001b[K                                                                               \r",
      "\r",
      "\u001b[Kloaded 1 databases.\r\n",
      "\r\n",
      "\r\n",
      "overlap     p_query p_match\r\n",
      "---------   ------- -------\r\n",
      "0.6 Mbp       46.7%   11.6%    NC_011663.1 Shewanella baltica OS223,...\r\n",
      "0.5 Mbp       38.7%   19.3%    CP001071.1 Akkermansia muciniphila AT...\r\n",
      "0.5 Mbp       14.6%    3.9%    NC_009665.1 Shewanella baltica OS185,...\r\n",
      "\r\n",
      "found 3 matches total;\r\n",
      "the recovered matches hit 100.0% of the query\r\n",
      "\r\n"
     ]
    }
   ],
   "source": [
    "!sourmash gather fake-metagenome.fa.sig taxdb.lca.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "...but can also be used for taxonomic summarization:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r",
      "\u001b[K\r\n",
      "== This is sourmash version 4.0.0a4.dev12+g31c5eda2. ==\r\n",
      "\r",
      "\u001b[K== Please cite Brown and Irber (2016), doi:10.21105/joss.00027. ==\r\n",
      "\r\n",
      "\r",
      "\u001b[K\r",
      "\u001b[K\r",
      "\u001b[K... loading database taxdb.lca.json\r",
      "\r",
      "\u001b[K\r",
      "\u001b[K\r",
      "\u001b[Kloaded 1 LCA databases. ksize=31, scaled=10000 moltype=DNA\r\n",
      "\r",
      "\u001b[Kfinding query signatures...\r\n",
      "\r",
      "\u001b[K\r",
      "\u001b[K\r",
      "\u001b[K... loading fake-metagenome.fa (file 1 of 1)\r",
      "38.7%    53   Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia;Akkermansia muciniphila;Akkermansia muciniphila ATCC BAA-835   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "38.7%    53   Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia;Akkermansia muciniphila   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "38.7%    53   Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae;Akkermansia   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "38.7%    53   Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales;Akkermansiaceae   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "38.7%    53   Bacteria;Verrucomicrobia;Verrucomicrobiae;Verrucomicrobiales   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "38.7%    53   Bacteria;Verrucomicrobia;Verrucomicrobiae   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "38.7%    53   Bacteria;Verrucomicrobia   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "100.0%   137   Bacteria   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "61.3%    84   Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "61.3%    84   Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "61.3%    84   Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "61.3%    84   Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "61.3%    84   Bacteria;Proteobacteria;Gammaproteobacteria   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "61.3%    84   Bacteria;Proteobacteria   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "22.6%    31   Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS223   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "14.6%    20   Bacteria;Proteobacteria;Gammaproteobacteria;Alteromonadales;Shewanellaceae;Shewanella;Shewanella baltica;Shewanella baltica OS185   fake-metagenome.fa.sig:4e1ac0cf fake-metagenome.fa\r\n",
      "\r",
      "\u001b[K\r",
      "\u001b[K\r",
      "\u001b[Kloaded 1 signatures from 1 files total.\r\n"
     ]
    }
   ],
   "source": [
    "!sourmash lca summarize --query fake-metagenome.fa.sig --db taxdb.lca.json"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Other pointers\n",
    "\n",
    "[Sourmash: a practical guide](https://sourmash.readthedocs.io/en/latest/using-sourmash-a-guide.html)\n",
    "\n",
    "[Classifying signatures taxonomically](https://sourmash.readthedocs.io/en/latest/classifying-signatures.html)\n",
    "\n",
    "[Pre-built search databases](https://sourmash.readthedocs.io/en/latest/databases.html)\n",
    "\n",
    "## A full list of notebooks\n",
    "\n",
    "[An introduction to k-mers for genome comparison and analysis](kmers-and-minhash.ipynb)\n",
    "\n",
    "[Some sourmash command line examples!](sourmash-examples.ipynb)\n",
    "\n",
    "[Working with private collections of signatures.](sourmash-collections.ipynb)\n",
    "\n",
    "[Using the LCA_Database API.](using-LCA-database-API.ipynb)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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