tax-credit_mock_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "82297225-ead8-4832-93f1-b92ccd8b01e6",
   "metadata": {},
   "source": [
    "# Testing out mock community 12-02-2024\n",
    "\n",
    "Example jupyter notebook for tax-credit mock community analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "54308893",
   "metadata": {},
   "source": [
    "## Format mock community data  \n",
    "\n",
    "Expected results are needed for each mock community. These should be organized in separate folders, and the names of the folders should match variable names provided in the code below. You need the expected taxonomies based on each reference database for each mock community.  \n",
    "\n",
    "If you just want to evaluate Taxon Detection Rates, you can provide the expected relative proportion of each taxon. If you want stats like Precision and Recall, you need to provide \"trueish\" taxonomies for each ASV sequence.  \n",
    "\n",
    "```\n",
    "mock-community/\n",
    "    mock-1/\n",
    "        feature_table.biom\n",
    "        database-1/\n",
    "            expected/\n",
    "                expected-taxonomy.tsv #tsv of expected read counts or proportions collapsed by taxonomy\n",
    "                table*biom # Read count table collapsed by taxonomy,e.g., table.L6-taxa.biom\n",
    "                trueish-taxonomies.tsv # expected taxonomy for each ASV sequence\n",
    "        database-2/\n",
    "            expected/\n",
    "                ...\n",
    "\n",
    "```\n",
    "        \n",
    "Let's look at these files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "1ef01360",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxon\tMP_ERR3712072\tMP_ERR3712077\n",
      "Eukaryota;Haptista;Haptophyta;NA;Prymnesiophyceae;Prymnesiales;NA;NA;NA\t32153\t73024\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;NA;NA;NA;NA\t22820\t39622\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;NA;NA;NA;NA\t6109\t5246\n",
      "Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;NA;NA;NA;NA\t3404\t7127\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;NA\t0\t8738\n",
      "Eukaryota;TSAR;Rhizaria;Radiolaria;Polycystinea;Spumellaria;Pyloniidae;Larcopyle;NA\t4960\t3494\n",
      "Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;NA;NA;NA\t2001\t2873\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Gymnodinium;NA\t0\t4332\n",
      "Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrichea;Oligotrichida;NA;NA;NA\t3280\t328\n"
     ]
    }
   ],
   "source": [
    "# expected-taxonomy.tsv \n",
    "! head data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/expected-taxonomy.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "8a4e0f17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id\ttaxonomy\n",
      "ASV_1\tEukaryota;Haptista;Haptophyta;NA;Prymnesiophyceae;Prymnesiales;NA;NA;NA\n",
      "ASV_2\tEukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;NA;NA;NA;NA\n",
      "ASV_3\tEukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;NA;NA;NA;NA\n",
      "ASV_4\tEukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;NA;NA;NA;NA\n",
      "ASV_5\tEukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;NA;NA;NA;NA\n",
      "ASV_6\tEukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;NA\n",
      "ASV_7\tEukaryota;TSAR;Rhizaria;Radiolaria;Polycystinea;Spumellaria;Pyloniidae;Larcopyle;NA\n",
      "ASV_8\tEukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;NA;NA;NA\n",
      "ASV_9\tEukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Gymnodinium;NA\n"
     ]
    }
   ],
   "source": [
    "# trueish-taxonomies.tsv\n",
    "! head data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/trueish-taxonomies.tsv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b0f8b41",
   "metadata": {},
   "source": [
    "### Convert .tsv read count file to biom  \n",
    "\n",
    "#### feature_table.biom  \n",
    "\n",
    "This is a biom formatted file of the ASV read counts. It does NOT include expected taxonomy. \n",
    "\n",
    "```\n",
    "mock-community/\n",
    "    mock-1/\n",
    "        feature_table.biom\n",
    "```\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b7007b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "! biom convert -i data/mock-community/Fuhrman_18Sv4_counts.tsv -o data/mock-community/fuhrman_18Sv4/feature_table.biom --to-hdf5 --table-type=\"OTU table\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7f16b5d",
   "metadata": {},
   "source": [
    "#### Collapsed taxa biom files\n",
    "Need a collapsed biom file with taxonomy in 1st column, then proportions for each sample. Put this is a folder with dataset/database/expected/expected-taxonomy.tsv \n",
    "\n",
    "Activate qiime2 conda env  \n",
    "\n",
    "```\n",
    "conda activate qiime2-amplicon-2024.10\n",
    "```  \n",
    "\n",
    "Convert tsv to biom  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "846cc631",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taxon\tMP_ERR3712072\tMP_ERR3712077\n",
      "Eukaryota;Haptista;Haptophyta;NA;Prymnesiophyceae;Prymnesiales;NA;NA;NA\t32153\t73024\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;NA;NA;NA;NA\t22820\t39622\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;NA;NA;NA;NA\t6109\t5246\n",
      "Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;NA;NA;NA;NA\t3404\t7127\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;NA\t0\t8738\n",
      "Eukaryota;TSAR;Rhizaria;Radiolaria;Polycystinea;Spumellaria;Pyloniidae;Larcopyle;NA\t4960\t3494\n",
      "Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;NA;NA;NA\t2001\t2873\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Gymnodinium;NA\t0\t4332\n",
      "Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrichea;Oligotrichida;NA;NA;NA\t3280\t328\n"
     ]
    }
   ],
   "source": [
    "# format of collapsed taxonomy file\n",
    "! head data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/expected-taxonomy.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6dc34ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# functiom to clean up taxa strings and make biom file \n",
    "def amend_biom_taxonomy_ids(biom_table,\n",
    "                            empty_taxonomy=['k__', 'p__', 'c__', 'o__',\n",
    "                                            'f__', 'g__', 's__'],\n",
    "                            clean_obs_ids=True):\n",
    "    '''Convert biom table taxonomy strings so that strings with incomplete\n",
    "    taxonomies are filled out with ambiguous labels\n",
    "    '''\n",
    "    if clean_obs_ids is True:\n",
    "        clean_taxonomy_ids(biom_table)\n",
    "\n",
    "    new_ids = {}\n",
    "    for taxa in biom_table.ids(axis='observation'):\n",
    "        old_taxonomy = taxa.split(';')\n",
    "        if len(old_taxonomy) < len(empty_taxonomy):\n",
    "            new_taxonomy = empty_taxonomy\n",
    "            for i in range(len(old_taxonomy)):\n",
    "                new_taxonomy[i] = old_taxonomy[i]\n",
    "            new_ids[taxa] = ';'.join(new_taxonomy)\n",
    "        else:\n",
    "            new_ids[taxa] = ';'.join(old_taxonomy)\n",
    "\n",
    "    return biom_table.update_ids(new_ids, axis='observation')\n",
    "\n",
    "def clean_taxonomy_ids(table, delete_chars='[]()'):\n",
    "    new_ids = {obs_id: obs_id.translate(str.maketrans('', '', delete_chars))\n",
    "               for obs_id in table.ids(axis=\"observation\")}\n",
    "    return table.update_ids(new_ids, axis='observation')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e51b81f3",
   "metadata": {},
   "source": [
    "Run this code for each mock community and each database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "5e613bca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy expected taxonomy.tsv and convert to biom\n",
    "exp_taxa_fp = \"data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/expected-taxonomy.tsv\"\n",
    "exp_biom_fp = \"data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/table.L6-taxa.biom\"\n",
    "\n",
    "newbiom = amend_biom_taxonomy_ids(load_table(exp_taxa_fp))\n",
    "# add taxonomy ids (names) as observation metadata\n",
    "metadata = {sid: {'taxonomy': sid.split(';')}\n",
    "            for sid in newbiom.ids(axis='observation')}\n",
    "newbiom.add_metadata(metadata, 'observation')\n",
    "write_biom_table(newbiom, 'hdf5', exp_biom_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8c298d1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy expected taxonomy.tsv and convert to biom\n",
    "exp_taxa_fp = \"data/mock-community/fuhrman_18Sv4/rcrux-18sv4/expected/expected-taxonomy.tsv\"\n",
    "exp_biom_fp = \"data/mock-community/fuhrman_18Sv4/rcrux-18sv4/expected/table.L6-taxa.biom\"\n",
    "\n",
    "newbiom = amend_biom_taxonomy_ids(load_table(exp_taxa_fp))\n",
    "# add taxonomy ids (names) as observation metadata\n",
    "metadata = {sid: {'taxonomy': sid.split(';')}\n",
    "            for sid in newbiom.ids(axis='observation')}\n",
    "newbiom.add_metadata(metadata, 'observation')\n",
    "write_biom_table(newbiom, 'hdf5', exp_biom_fp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33389d4f",
   "metadata": {},
   "source": [
    "### Add fasta file of ASV sequences   \n",
    "\n",
    "```\n",
    "mock-community/\n",
    "    mock-1/\n",
    "        rep_seqs.fna\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac64e194",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported data/mock-community/fuhrman_18Sv4/rep_seqs.fna as DNASequencesDirectoryFormat to data/mock-community/fuhrman_18Sv4/rep_seqs.qza\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# cp data/mock/Fuhrman_18Sv4_ASVs.fasta data/mock/fuhrman_18Sv4/rep_seqs.fna\n",
    "# convert to qiime because that's what we're using for taxa assignment\n",
    "qiime tools import \\\n",
    "--type 'FeatureData[Sequence]' \\\n",
    "--input-path data/mock-community/fuhrman_18Sv4/rep_seqs.fna \\\n",
    "    --output-path data/mock-community/fuhrman_18Sv4/rep_seqs.qza \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11c712ee",
   "metadata": {},
   "source": [
    "### Format expected taxonomy  \n",
    "\n",
    "Should have the headers Taxonomy\\tStandard Taxonomy?\n",
    "\n",
    "I think this is supposed to just be for the expected_sequences.fasta... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cf7f7de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature.ID\tTaxon\n",
      "ASV_1\tEukaryota;Haptista;Haptophyta;NA;Prymnesiophyceae;Prymnesiales;NA;NA;NA\n",
      "ASV_2\tEukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;NA;NA;NA;NA\n",
      "ASV_3\tEukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;NA;NA;NA;NA\n",
      "ASV_4\tEukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;NA;NA;NA;NA\n",
      "ASV_5\tEukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;NA;NA;NA;NA\n",
      "ASV_6\tEukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;NA\n",
      "ASV_7\tEukaryota;TSAR;Rhizaria;Radiolaria;Polycystinea;Spumellaria;Pyloniidae;Larcopyle;NA\n",
      "ASV_8\tEukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;NA;NA;NA\n",
      "ASV_9\tEukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Gymnodinium;NA\n"
     ]
    }
   ],
   "source": [
    "! head data/original_files/Furhman_18Sv4_taxa_pr2.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "293b5462",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "# do for each mock and each database\n",
    "sed '1s/.*/id\\ttaxonomy/' data/original_files/Furhman_18Sv4_taxa_pr2.tsv > data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/trueish-taxonomies.tsv\n",
    "\n",
    "sed '1s/.*/id\\ttaxonomy/' data/original_files/Furhman_18Sv4_taxa_blast.tsv > data/mock-community/fuhrman_18Sv4/rcrux-18sv4/expected/trueish-taxonomies.tsv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f915d158",
   "metadata": {},
   "source": [
    "### Format reference databases  \n",
    "\n",
    "Make sure your reference databases are organized, with a fasta file and taxonomy file. They can have whatever name you want. Make sure they are in the format needed for your taxa assignments (eg, .qza if using qiime, correct headers)\n",
    "\n",
    "Requirements:  \n",
    "**fasta file**: no lower case sequences  \n",
    "**taxonomy file**: Feature ID\\tTaxon header (required for qiime2), no quotes, no repeated accession ids  \n",
    "\n",
    "```\n",
    "ref_dbs/\n",
    "    database-1/\n",
    "        ref-seqs.fna\n",
    "        ref-tax.tsv\n",
    "        ref_seqs.qza\n",
    "        ref_tax.qza"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e11e8c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "#rcrux-18sv4\n",
    "echo -e \"Feature ID\\tTaxon\">tmp.txt\n",
    "sed 's/\\\"//g' data/ref_dbs/rcrux-18sv4/18s_V4_derep_and_clean_taxonomy.txt >> tmp.txt\n",
    "mv tmp.txt data/ref_dbs/rcrux-18sv4/18s_V4_derep_and_clean_taxonomy.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "65ba7b6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported data/ref_dbs/rcrux-18sv4/18s_V4_derep_and_clean.fasta as DNASequencesDirectoryFormat to data/ref_dbs/rcrux-18sv4/ref_seqs.qza\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# now import to qiime2 artifacts \n",
    "    qiime tools import \\\n",
    "    --type 'FeatureData[Sequence]' \\\n",
    "    --input-path data/ref_dbs/rcrux-18sv4/18s_V4_derep_and_clean.fasta \\\n",
    "    --output-path data/ref_dbs/rcrux-18sv4/ref_seqs.qza \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "20e49ad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imported data/ref_dbs/rcrux-18sv4/18s_V4_derep_and_clean_taxonomy.txt as TSVTaxonomyDirectoryFormat to data/ref_dbs/rcrux-18sv4/ref_tax.qza\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "# now import to qiime2 artifacts \n",
    "    qiime tools import \\\n",
    "    --type 'FeatureData[Taxonomy]' \\\n",
    "    --input-path data/ref_dbs/rcrux-18sv4/18s_V4_derep_and_clean_taxonomy.txt \\\n",
    "    --output-path data/ref_dbs/rcrux-18sv4/ref_tax.qza \n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba82ba9b",
   "metadata": {},
   "source": [
    "## Assign taxonomy  \n",
    "Based on [this notebook](https://github.com/caporaso-lab/tax-credit/blob/a1878884f62d60b8ba9dcc6c6ef4a9c8fdc0d917/ipynb/mock-community/taxonomy-assignment-blast%2B.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "28b4dbe6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# need the tax-credit code to be in the same directory as the notebook\n",
    "from os.path import join, expandvars \n",
    "from joblib import Parallel, delayed\n",
    "from glob import glob\n",
    "from os import system\n",
    "from tax_credit.framework_functions import (parameter_sweep,\n",
    "                                            generate_per_method_biom_tables,\n",
    "                                            move_results_to_repository)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "5c31de1f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro\n"
     ]
    }
   ],
   "source": [
    "! pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c1d58968",
   "metadata": {},
   "outputs": [],
   "source": [
    "project_dir = \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro\"\n",
    "analysis_name= \"mock-community\" # must use this name for plotting code to work\n",
    "data_dir = join(project_dir, \"data\", analysis_name)# where the mock community data is stored\n",
    "\n",
    "reference_database_dir = \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs\"\n",
    "results_dir = expandvars(\"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23df9a4e",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "source": [
    "### Preparing data to sweep  \n",
    "First, we're going to define the data sets that we'll sweep over. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "fb8841ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "# format is mock community name and reference database name\n",
    "dataset_reference_combinations = [\n",
    " ('fuhrman_18Sv4', 'pr2-ssu'), \n",
    " ('fuhrman_18Sv4', 'rcrux-18sv4')]\n",
    "\n",
    "# format is path to sequences, path to taxonomy\n",
    "\n",
    "reference_dbs = {'pr2-ssu' : (join(reference_database_dir, 'pr2-ssu/pr2_version_5.0.0_SSU_seqs.qza'),\n",
    "                            join(reference_database_dir, 'pr2-ssu/pr2_version_5.0.0_SSU_tax.qza')),\n",
    "                 'rcrux-18sv4' : (join(reference_database_dir, 'rcrux-18sv4/ref_seqs.qza'), \n",
    "                                   join(reference_database_dir, 'rcrux-18sv4/ref_tax.qza'))}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd65920d",
   "metadata": {
    "vscode": {
     "languageId": "markdown"
    }
   },
   "source": [
    "### Preparing the method/parameter combinations and generating commands\n",
    "Now we set the methods and method-specific parameters that we want to sweep. Modify to sweep other methods. Note how method_parameters_combinations feeds method/parameter combinations to parameter_sweep() in the cell below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2ea2e9a",
   "metadata": {},
   "source": [
    "Here we provide an example of taxonomy assignment using classifiers executed on the command line. To accomplish this, we must first convert commands to a string, which we then pass to bash for execution. we must also activate a separate environment in which QIIME2 has been installed if not installed in the tax-credit environment. If any environmental variables need to be set (in this example, the RDP_JAR_PATH), we must also source the .bashrc file.  \n",
    "\n",
    "Now enter the template of the command to sweep, and generate a list of commands with parameter_sweep().\n",
    "\n",
    "Fields must adhere to following format:\n",
    "\n",
    "                  {0} = output directory\n",
    "                  {1} = input data\n",
    "                  {2} = output destination\n",
    "                  {3} = reference taxonomy\n",
    "                  {4} = method name\n",
    "                  {5} = other parameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52a42ed0",
   "metadata": {},
   "source": [
    "## Blast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "389225fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_parameters_combinations = {\n",
    "              'blast+' : {'p-evalue': [0.001],\n",
    "                          'p-maxaccepts': [1, 10],\n",
    "                          'p-perc-identity': [0.80, 0.97],\n",
    "                          'p-min-consensus': [0.51, 0.75]}\n",
    "             }\n",
    "\n",
    "command_template = (\"mkdir -p {0}; \"\n",
    "                    \"qiime feature-classifier classify-consensus-blast --i-query {1} --o-classification \"\n",
    "                    \"{0}/rep_seqs_tax_assignments.qza --i-reference-reads {2} --i-reference-taxonomy {3} {5} --o-search-results {0}/top_blast_hits.qza ; \"\n",
    "                    \"qiime tools export --input-path {0}/rep_seqs_tax_assignments.qza --output-path {0}/query_tax_assignments.txt --output-format TSVTaxonomyFormat;\")\n",
    "\n",
    "\n",
    "commands = parameter_sweep(data_dir, results_dir, reference_dbs,\n",
    "                           dataset_reference_combinations,\n",
    "                           method_parameters_combinations, command_template,\n",
    "                           infile='rep_seqs.qza', output_name='rep_seqs_tax_assignments.qza')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b49a58fa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16\n",
      "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8; qiime feature-classifier classify-consensus-blast --i-query /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/rep_seqs_tax_assignments.qza --i-reference-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_seqs.qza --i-reference-taxonomy /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_tax.qza --p-evalue 0.001 --p-maxaccepts 1 --p-min-consensus 0.51 --p-perc-identity 0.8 --o-search-results /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/top_blast_hits.qza ; qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/rep_seqs_tax_assignments.qza --output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/query_tax_assignments.txt --output-format TSVTaxonomyFormat;\n"
     ]
    }
   ],
   "source": [
    "# sanity check, do commands look ok?\n",
    "print(len(commands))\n",
    "print(commands[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3f6c3768",
   "metadata": {},
   "outputs": [],
   "source": [
    "Parallel(n_jobs=4)(delayed(system)(command) for command in commands)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caa45f5a",
   "metadata": {},
   "source": [
    "Example of a full command:  \n",
    "```\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8\n",
    "qiime feature-classifier classify-consensus-blast --i-query /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza \\\n",
    "--o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/rep_seqs_tax_assignments.qza \\\n",
    "--i-reference-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_seqs.qza --i-reference-taxonomy /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_tax.qza \\\n",
    "--p-evalue 0.001 --p-maxaccepts 1 --p-min-consensus 0.51 --p-perc-identity 0.8 \\\n",
    "--o-search-results /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/top_blast_hits.qza\n",
    "qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/rep_seqs_tax_assignments.qza \\\n",
    "--output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/query_tax_assignments.txt --output-format TSVTaxonomyFormat;\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "5ce43dd4",
   "metadata": {},
   "outputs": [],
   "source": [
    "taxonomy_glob = join(results_dir, '*', '*', '*', '*', 'query_tax_assignments.txt')\n",
    "generate_per_method_biom_tables(taxonomy_glob, data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2906d139",
   "metadata": {},
   "source": [
    "## Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19a190ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "method_parameters_combinations = {\n",
    "    'naive-bayes' : {'p-feat-ext--ngram-range': \n",
    "                     ['[7,7]','[10,10]']}\n",
    "}\n",
    "\n",
    "command_template = ('mkdir -p \"{0}\"; '\n",
    "                    'qiime feature-classifier fit-classifier-naive-bayes --o-classifier '\n",
    "                    '\"{0}/classifier.qza\" --i-reference-reads {2} --i-reference-taxonomy {3} {5}; ')\n",
    "\n",
    "confidences = [0.5, 0.7, 0.9]\n",
    "command_template += ''.join(\n",
    "    'mkdir -p \"{0}:' + str(c) + '\"; '\n",
    "    'qiime feature-classifier classify-sklearn '\n",
    "    '--o-classification \"{0}:' + str(c) + '/rep_seqs_tax_assignments.qza\" '\n",
    "    '--i-classifier \"{0}/classifier.qza\" '\n",
    "    '--i-reads {1} --p-confidence ' + str(c) + '; '\n",
    "    'qiime tools export --input-path \"{0}:' + str(c) + '/rep_seqs_tax_assignments.qza\" --output-path \"{0}:' + str(c) + '/taxonomy.tsv\" --output-format TSVTaxonomyFormat; '\n",
    "    'mv \"{0}:' + str(c) + '/taxonomy.tsv\" \"{0}:' + str(c) + '/query_tax_assignments.txt\"; 'for c in confidences)\n",
    "\n",
    "command_template += 'rm \"{0}/classifier.qza\"; rmdir \"{0}\"'\n",
    "\n",
    "commands = parameter_sweep(data_dir, results_dir, reference_dbs,\n",
    "                           dataset_reference_combinations,\n",
    "                           method_parameters_combinations, command_template,\n",
    "                           infile='rep_seqs.qza', output_name='rep_seqs_tax_assignments.qza')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "349fd295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n",
      "mkdir -p \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]\"; qiime feature-classifier fit-classifier-naive-bayes --o-classifier \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\" --i-reference-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_seqs.qza --i-reference-taxonomy /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_tax.qza; mkdir -p \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5\"; qiime feature-classifier classify-sklearn --o-classification \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/rep_seqs_tax_assignments.qza\" --i-classifier \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\" --i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.5; qiime tools export --input-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/rep_seqs_tax_assignments.qza\" --output-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/taxonomy.tsv\"; mv \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/taxonomy.tsv\" \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/query_tax_assignments.txt\"; mkdir -p \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7\"; qiime feature-classifier classify-sklearn --o-classification \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza\" --i-classifier \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\" --i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.7; qiime tools export --input-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza\" --output-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/taxonomy.tsv\"; mv \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/taxonomy.tsv\" \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/query_tax_assignments.txt\"; mkdir -p \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9\"; qiime feature-classifier classify-sklearn --o-classification \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza\" --i-classifier \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\" --i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.9; qiime tools export --input-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza\" --output-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/taxonomy.tsv\"; mv \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/taxonomy.tsv\" \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/query_tax_assignments.txt\"; rm \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\"; rmdir \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]\"\n"
     ]
    }
   ],
   "source": [
    "# sanity check, do commands look ok?\n",
    "print(len(commands))\n",
    "print(commands[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2b10ea5",
   "metadata": {},
   "source": [
    "### Run commands"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc3d9854",
   "metadata": {},
   "outputs": [],
   "source": [
    "Parallel(n_jobs=5)(delayed(system)(command) for command in commands);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "e7d290cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved TaxonomicClassifier to: /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]\n",
    "\n",
    "qiime feature-classifier fit-classifier-naive-bayes \\\n",
    "--o-classifier /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza \\\n",
    "--i-reference-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_seqs.qza --i-reference-taxonomy /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs/pr2-ssu/pr2_version_5.0.0_SSU_tax.qza\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "c688b23b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved FeatureData[Taxonomy] to: /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/rep_seqs_tax_assignments.qza\n",
      "Exported /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/rep_seqs_tax_assignments.qza as TSVTaxonomyFormat to file /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/taxonomy.tsv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5;\n",
    "qiime feature-classifier classify-sklearn --o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/rep_seqs_tax_assignments.qza --p-n-jobs 5 --i-classifier /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza \\\n",
    "--i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.5\n",
    "\n",
    "qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/rep_seqs_tax_assignments.qza \\\n",
    "--output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/taxonomy.tsv --output-format TSVTaxonomyFormat\n",
    "mv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/taxonomy.tsv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.5/query_tax_assignments.txt; \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "c6d8fb68",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved FeatureData[Taxonomy] to: /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza\n",
      "Exported /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza as TSVTaxonomyFormat to file /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/taxonomy.tsv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7;\n",
    "qiime feature-classifier classify-sklearn --o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza --p-n-jobs 5 --i-classifier /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza \\\n",
    "--i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.7\n",
    "\n",
    "qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza \\\n",
    "--output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/taxonomy.tsv --output-format TSVTaxonomyFormat\n",
    "mv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/taxonomy.tsv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/query_tax_assignments.txt; \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "aae03deb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved FeatureData[Taxonomy] to: /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza\n",
      "Exported /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza as TSVTaxonomyFormat to file /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/taxonomy.tsv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9;\n",
    "qiime feature-classifier classify-sklearn --o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza --p-n-jobs 5 --i-classifier /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza \\\n",
    "--i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.9\n",
    "\n",
    "qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza \\\n",
    "--output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/taxonomy.tsv --output-format TSVTaxonomyFormat\n",
    "mv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/taxonomy.tsv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/query_tax_assignments.txt; \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "bcdb6468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved FeatureData[Taxonomy] to: /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/rep_seqs_tax_assignments.qza\n",
      "Exported /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/rep_seqs_tax_assignments.qza as TSVTaxonomyFormat to file /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/taxonomy.tsv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9;\n",
    "qiime feature-classifier classify-sklearn --o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/rep_seqs_tax_assignments.qza --p-n-jobs 5 --i-classifier /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]/classifier.qza \\\n",
    "--i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.9\n",
    "\n",
    "qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/rep_seqs_tax_assignments.qza \\\n",
    "--output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/taxonomy.tsv --output-format TSVTaxonomyFormat\n",
    "mv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/taxonomy.tsv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.9/query_tax_assignments.txt; \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "6ecbb9fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved FeatureData[Taxonomy] to: /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/rep_seqs_tax_assignments.qza\n",
      "Exported /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/rep_seqs_tax_assignments.qza as TSVTaxonomyFormat to file /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/taxonomy.tsv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7;\n",
    "qiime feature-classifier classify-sklearn --o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/rep_seqs_tax_assignments.qza --p-n-jobs 5 --i-classifier /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]/classifier.qza \\\n",
    "--i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.7\n",
    "\n",
    "qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/rep_seqs_tax_assignments.qza \\\n",
    "--output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/taxonomy.tsv --output-format TSVTaxonomyFormat\n",
    "mv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/taxonomy.tsv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.7/query_tax_assignments.txt; \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "18f2ce17",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved FeatureData[Taxonomy] to: /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/rep_seqs_tax_assignments.qza\n",
      "Exported /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/rep_seqs_tax_assignments.qza as TSVTaxonomyFormat to file /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/taxonomy.tsv\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "mkdir -p /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5;\n",
    "qiime feature-classifier classify-sklearn --o-classification /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/rep_seqs_tax_assignments.qza --p-n-jobs 5 --i-classifier /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]/classifier.qza \\\n",
    "--i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.5\n",
    "\n",
    "qiime tools export --input-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/rep_seqs_tax_assignments.qza \\\n",
    "--output-path /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/taxonomy.tsv --output-format TSVTaxonomyFormat\n",
    "mv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/taxonomy.tsv /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[10,10]:0.5/query_tax_assignments.txt; \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40547e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mkdir -p \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7\"; qiime feature-classifier classify-sklearn --o-classification \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza\" --i-classifier \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\" --i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.7; qiime tools export --input-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/rep_seqs_tax_assignments.qza\" --output-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/taxonomy.tsv\"; mv \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/taxonomy.tsv\" \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.7/query_tax_assignments.txt\"; mkdir -p \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9\"; qiime feature-classifier classify-sklearn --o-classification \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza\" --i-classifier \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\" --i-reads /Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/mock-community/fuhrman_18Sv4/rep_seqs.qza --p-confidence 0.9; qiime tools export --input-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/rep_seqs_tax_assignments.qza\" --output-path \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/taxonomy.tsv\"; mv \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/taxonomy.tsv\" \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]:0.9/query_tax_assignments.txt\"; rm \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]/classifier.qza\"; rmdir \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community/fuhrman_18Sv4/pr2-ssu/naive-bayes/[7,7]\"\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da632e71",
   "metadata": {},
   "source": [
    "## Generate per-method biom tables  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e71ce51",
   "metadata": {},
   "source": [
    "### Actual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "536904f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community'"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "babeee1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "taxonomy_glob = join(results_dir, '*', '*', '*', '*', 'query_tax_assignments.txt')\n",
    "generate_per_method_biom_tables(taxonomy_glob, data_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "260ea997",
   "metadata": {},
   "source": [
    "## Evaluate Accuracy \n",
    "Adapted from [this notebook](https://github.com/caporaso-lab/tax-credit/blob/a1878884f62d60b8ba9dcc6c6ef4a9c8fdc0d917/ipynb/mock-community/evaluate-classification-accuracy.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6da9543c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "006197ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from os.path import join, exists, expandvars\n",
    "import pandas as pd\n",
    "from IPython.display import display, Markdown\n",
    "#import seaborn.xkcd_rgb as colors\n",
    "import seaborn as sns\n",
    "from tax_credit.plotting_functions import (pointplot_from_data_frame,\n",
    "                                           boxplot_from_data_frame,\n",
    "                                           heatmap_from_data_frame,\n",
    "                                           per_level_kruskal_wallis,\n",
    "                                           beta_diversity_pcoa,\n",
    "                                           average_distance_boxplots,\n",
    "                                           rank_optimized_method_performance_by_dataset)\n",
    "from tax_credit.eval_framework import (evaluate_results,\n",
    "                                       method_by_dataset_a1,\n",
    "                                       parameter_comparisons,\n",
    "                                       merge_expected_and_observed_tables,\n",
    "                                       filter_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "3f6a91f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "## project_dir should be the directory where you've downloaded (or cloned) the \n",
    "## tax-credit repository. \n",
    "project_dir = \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro\"\n",
    "\n",
    "analysis_name= \"mock-community\" # must use this name for plotting code to work\n",
    "data_dir = join(project_dir, \"data\", analysis_name)# where the mock community data is stored\n",
    "\n",
    "reference_database_dir = \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/data/ref_dbs\"\n",
    "results_dir = \"/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/analyses/mock-community\"\n",
    "\n",
    "## expected_results_dir contains expected composition data in the structure\n",
    "## expected_results_dir/<dataset name>/<reference name>/expected/\n",
    "expected_results_dir = data_dir\n",
    "\n",
    "## mock_results_fp designates the files to which summary results are written.\n",
    "## If this file exists, it can be read in to generate results plots, instead\n",
    "## of computing new scores.\n",
    "mock_results_fp = join(results_dir, 'mock_results.tsv')\n",
    "\n",
    "## results_dirs should contain the directory or directories where\n",
    "## results can be found. By default, this is the same location as expected \n",
    "## results included with the project. If other results should be included, \n",
    "## absolute paths to those directories should be added to this list.\n",
    "results_dirs = [results_dir]\n",
    "\n",
    "## directory containing mock community data, e.g., feature table without taxonomy\n",
    "mock_dir = join(project_dir, \"data\", \"mock-community\")\n",
    "\n",
    "## Minimum number of times an OTU must be observed for it to be included in analyses. Edit this\n",
    "## to analyze the effect of the minimum count on taxonomic results.\n",
    "min_count = 1\n",
    "\n",
    "## Define the range of taxonomic levels over which to compute accuracy scores.\n",
    "## The default given below will compute order (level 2) through species (level 6)\n",
    "taxonomy_level_range = range(2,7)\n",
    "\n",
    "\n",
    "# we can save plots in this directory\n",
    "outdir = './plots'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "4e4854cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dataset_ids = ['fuhrman_18Sv4']\n",
    "method_ids = ['blast+', 'naive-bayes']\n",
    "ref_ids = ['pr2-ssu', 'rcrux-18sv4']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a21b3798",
   "metadata": {},
   "source": [
    "### Find mock community pre-computed tables, expected tables, and \"query\" tables  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "40a5a779",
   "metadata": {},
   "outputs": [],
   "source": [
    "force = False\n",
    "if force or not exists(mock_results_fp):\n",
    "    mock_results = evaluate_results(results_dirs, \n",
    "                                expected_results_dir, \n",
    "                                mock_results_fp, \n",
    "                                mock_dir,\n",
    "                                filename_pattern='table*biom',\n",
    "                                taxonomy_level_range=range(2,7), \n",
    "                                min_count=min_count,\n",
    "                                taxa_to_keep=None, \n",
    "                                md_key='taxonomy', \n",
    "                                subsample=False,\n",
    "                                per_seq_precision=True,\n",
    "                                exclude=['other'],\n",
    "                                dataset_ids=dataset_ids,\n",
    "                                reference_ids=ref_ids,\n",
    "                                method_ids=method_ids,\n",
    "                                append=False,\n",
    "                                force=False,\n",
    "                                backup=False)\n",
    "else:\n",
    "    mock_results = pd.read_csv(mock_results_fp, sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e332523",
   "metadata": {},
   "source": [
    "It worked! This creates a TSV with results, that can be used for plotting below or read into R for other plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "81c1d75c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tDataset\tLevel\tSampleID\tReference\tMethod\tParameters\tPrecision\tRecall\tF-measure\tTaxon Accuracy Rate\tTaxon Detection Rate\n",
      "0\tfuhrman_18Sv4\t2\tMP_ERR3712072\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t1.0\t1.0\t1.0\t1.0\t1.0\n",
      "1\tfuhrman_18Sv4\t2\tMP_ERR3712077\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t1.0\t1.0\t1.0\t1.0\t1.0\n",
      "2\tfuhrman_18Sv4\t3\tMP_ERR3712072\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t0.5967315097013709\t0.5967315097013709\t0.5967315097013709\t0.8571428571428571\t0.8571428571428571\n",
      "3\tfuhrman_18Sv4\t3\tMP_ERR3712077\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t0.5011272185710968\t0.5011272185710968\t0.5011272185710968\t0.8571428571428571\t0.8571428571428571\n",
      "4\tfuhrman_18Sv4\t4\tMP_ERR3712072\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t0.5342589457049328\t0.5342589457049328\t0.5342589457049328\t0.6666666666666666\t0.75\n",
      "5\tfuhrman_18Sv4\t4\tMP_ERR3712077\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t0.47129350038940276\t0.47129350038940276\t0.47129350038940276\t0.7\t0.7777777777777778\n",
      "6\tfuhrman_18Sv4\t5\tMP_ERR3712072\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t0.09871944413089012\t0.09871944413089012\t0.09871944413089011\t0.2727272727272727\t0.3333333333333333\n",
      "7\tfuhrman_18Sv4\t5\tMP_ERR3712077\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t0.1141360040443236\t0.1141360040443236\t0.1141360040443236\t0.35714285714285715\t0.45454545454545453\n",
      "8\tfuhrman_18Sv4\t6\tMP_ERR3712072\tpr2-ssu\tnaive-bayes\t[7,7]:0.5\t0.032484228217380944\t0.032484228217380944\t0.032484228217380944\t0.09090909090909091\t0.1111111111111111\n"
     ]
    }
   ],
   "source": [
    "! head analyses/mock-community/mock_results.tsv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "eb09848e",
   "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>Dataset</th>\n",
       "      <th>Level</th>\n",
       "      <th>SampleID</th>\n",
       "      <th>Reference</th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>fuhrman_18Sv4</td>\n",
       "      <td>2</td>\n",
       "      <td>MP_ERR3712072</td>\n",
       "      <td>pr2-ssu</td>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[7,7]:0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>fuhrman_18Sv4</td>\n",
       "      <td>2</td>\n",
       "      <td>MP_ERR3712077</td>\n",
       "      <td>pr2-ssu</td>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[7,7]:0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fuhrman_18Sv4</td>\n",
       "      <td>3</td>\n",
       "      <td>MP_ERR3712072</td>\n",
       "      <td>pr2-ssu</td>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[7,7]:0.5</td>\n",
       "      <td>0.596732</td>\n",
       "      <td>0.596732</td>\n",
       "      <td>0.596732</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>fuhrman_18Sv4</td>\n",
       "      <td>3</td>\n",
       "      <td>MP_ERR3712077</td>\n",
       "      <td>pr2-ssu</td>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[7,7]:0.5</td>\n",
       "      <td>0.501127</td>\n",
       "      <td>0.501127</td>\n",
       "      <td>0.501127</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>fuhrman_18Sv4</td>\n",
       "      <td>4</td>\n",
       "      <td>MP_ERR3712072</td>\n",
       "      <td>pr2-ssu</td>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[7,7]:0.5</td>\n",
       "      <td>0.534259</td>\n",
       "      <td>0.534259</td>\n",
       "      <td>0.534259</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Dataset  Level       SampleID Reference       Method Parameters  \\\n",
       "0  fuhrman_18Sv4      2  MP_ERR3712072   pr2-ssu  naive-bayes  [7,7]:0.5   \n",
       "1  fuhrman_18Sv4      2  MP_ERR3712077   pr2-ssu  naive-bayes  [7,7]:0.5   \n",
       "2  fuhrman_18Sv4      3  MP_ERR3712072   pr2-ssu  naive-bayes  [7,7]:0.5   \n",
       "3  fuhrman_18Sv4      3  MP_ERR3712077   pr2-ssu  naive-bayes  [7,7]:0.5   \n",
       "4  fuhrman_18Sv4      4  MP_ERR3712072   pr2-ssu  naive-bayes  [7,7]:0.5   \n",
       "\n",
       "   Precision    Recall  F-measure  Taxon Accuracy Rate  Taxon Detection Rate  \n",
       "0   1.000000  1.000000   1.000000             1.000000              1.000000  \n",
       "1   1.000000  1.000000   1.000000             1.000000              1.000000  \n",
       "2   0.596732  0.596732   0.596732             0.857143              0.857143  \n",
       "3   0.501127  0.501127   0.501127             0.857143              0.857143  \n",
       "4   0.534259  0.534259   0.534259             0.666667              0.750000  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mock_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "60938f73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mock_results = filter_df(mock_results, column_name='Method', values=['naive-bayes'], exclude=False)\n",
    "mock_results = mock_results.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31559807",
   "metadata": {},
   "source": [
    "Compute and summarize precision, recall, and F-measure for mock communities\n",
    "----------------------------------------------------------------------------------------\n",
    "\n",
    "In this evaluation, we compute and summarize precision, recall, and F-measure of each result (pre-computed and query) based on the known composition of the mock communities. We then summarize the results in two ways: first with boxplots, and second with a table of the top methods based on their F-measures. **Higher scores = better accuracy**\n",
    "\n",
    "As a first step, we will evaluate **average** method performance at each taxonomic level for each method within each reference dataset type.\n",
    "\n",
    "**Note that, as parameter configurations can cause results to vary widely, average results are not a good representation of the \"best\" results. See [here](#Optimized-method-performance) for results using optimized parameters for each method.**\n",
    "\n",
    "First we will define our [color palette](http://matplotlib.org/examples/color/named_colors.html) and the variables we want to plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "60eedada",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_palette={\n",
    "    'pr2-ssu': 'coral', 'rcrux-18sv4': 'green'\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "7a74d58d",
   "metadata": {},
   "outputs": [],
   "source": [
    "color_palette_method={\n",
    "    'blast+': 'coral', 'naive-bayes': 'dodgerblue',\n",
    "    'vsearch': 'firebrick','expected': 'black'\n",
    "}\n",
    "y_vars = [\"Precision\", \"Recall\", \"F-measure\", \"Taxon Accuracy Rate\", \"Taxon Detection Rate\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "752db71c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/miniconda3/envs/tax-assess/lib/python3.8/site-packages/seaborn/axisgrid.py:712: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/Users/katherine.silliman/miniconda3/envs/tax-assess/lib/python3.8/site-packages/seaborn/axisgrid.py:712: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/Users/katherine.silliman/miniconda3/envs/tax-assess/lib/python3.8/site-packages/seaborn/axisgrid.py:712: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/Users/katherine.silliman/miniconda3/envs/tax-assess/lib/python3.8/site-packages/seaborn/axisgrid.py:712: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n",
      "/Users/katherine.silliman/miniconda3/envs/tax-assess/lib/python3.8/site-packages/seaborn/axisgrid.py:712: UserWarning: Using the pointplot function without specifying `order` is likely to produce an incorrect plot.\n",
      "  warnings.warn(warning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 725.125x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 725.125x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 725.125x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 725.125x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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4ffs2QUFBL3R9IfIblfJoQpcQIh1/f3+GDx9OYGCgpUMRQgghRDaTEWMhhBBCCCGQxFgIIYQQQghAplIIIYQQQggByIixEEIIIYQQgCTGQgghhBBCAJIYCyGEEEIIARSAxFhRFPR6PTKVWgjxNNJPCCGEgAKQGBsMBk6ePInBYLB0KEKIXEr6CSGEEFAAEmMhhBBCCCEyQxJjIYQQQgghkMT4pSkGhYexCSQlJls6FCGEEEII8RKsLB1AXnX7ZgRhe7dT5dYfFFYeAnDFujxR1Vrzaht/bG2tLRyhEEIIIYQwhyTGL+DvA8eo9NscSmM6SuyRch2Pk4u5/M9eCg/8kKLFXSwToBBCCCGEMJtFp1Lcv3+f1157jSNHjjy1TXBwMAEBAXh5edG2bVt+//33HIwwvSvnrlHlt0+x5+lTJyomXyVq2SfodPocjEwIIYQQQrwMiyXGx44do0ePHty8efOpba5fv86IESMYNWoUoaGhjBgxgtGjRxMREZGDkZqK2bMeW1Ke265K0kVO7T+aAxEJIYQQQoisYJHEeNOmTbz77ruMGTPmue18fX1p2bIlVlZWtGvXDj8/P3788cccitRUVGQ0taJDTMrOacpxRvMKoRpPLqlLm9RpTuzJyfCEEEIIIcRLsMgc40aNGhEQEICVldUzk+PLly9TuXJlk7KKFSty/vx5s6+p17/8tIZ/r96gGI83AFCAooZYXJUHxrKjGk/c9RG48YBS8dez5LpCiMzRaDQvdbz8vQpRMLxsXyHyL4skxsWLF89Uu/j4eOzs7EzKbG1tSUhIMPuap0+fNvuY/7p7O4KaTzxXARpM30j99OdJRsshq+q46e9x8+TJl76uECJzfHx8Xur4rOgnhBC538v2FSL/ytWrUtjZ2ZGUlGRSlpSUhIODg9nnqlmz5kt/Qox0u4fh9FLUKMayf1XF0KKnkBJvLLMhlfq6szxQOXLn2m282rfC2lr7UtcWQmS/rOgnhBBC5F25OjGuXLkyZ8+eNSm7fPkyNWrUMPtcGo3mpd/wSpYuwUnn2njFHjOW1TRcJQYHDllVw0d3EWt0xrrCShz1zyzn1vmdRNTrQ23/BqjUqpeKQQiRfbKinxBCCJF35eqd7zp27EhISAg7duxAp9OxY8cOQkJC6NSpk8Vism0eRCqmb5yFiKe+7hx3VYUI1VRJd0wZ3R18Dn7GhTnv88/xf3IqVCGEEEIIYYZclxh7e3uzZcsWADw8PFi4cCGLFy/Gz8+PRYsWsWDBAl555RWLxefp7cmphqNJyWCwvbRyD1/9BQ5pa3LGrlr6YxMvUnXLRI5/MYuwq7dzIlwhhBBCCJFJKkVRlOc3y7v0ej0nT57Ey8srS78ivXb+Bnf3baVm5AHs/reucbimBDcqtqJG+7Y4Otrx94FQCh/8nnKp6ZNgHWpCS7WiUpceskOeEBaWXf2EEEKIvEUS45eUlJTCg6horLRWFCteON0cYp1Oz7Htv+Lx9zqKGaLTHR+nsuN0pS54de6Inb1NlscnhHg+SYyFEEKAJMY5Jj4ukVObfuHVK5sz3E46UlOU69498WnTDI2VvDELkZNySz8hhBDCsnLdHOP8ysHRjvp9Xyf+nUUccXsN/X9+9SX096gT+hU3Pn2XU3+dsFCUQgghhBAFlyTGOay4axHqvj2UsF7zOOmcfoHxCinXqbVnBqc/m8aVf65ZIEIhhBBCiIJJEmMLKV+5LF5jJ3Om7Qwu21RIV18z7m9e+XEcRxfOJyI8ygIRCiGEEEIULJIYW1iNujWpMGEOR+uN5o7GdKtsNQp+d3+n0LfDOLRiFbEx8U85ixBCCCGEeFmSGOcCarUavzZNcRn/FYeqvkGsyt6k3pYU6l//Gf2XQzm8cQepKbqnnEkIIYQQQrwoSYxzEVtba+r36IJhxNccLhOQbhORwoZY6p1awp1PR3J83yEUQ75eUEQIIYQQIkdJYpwLuRRxpt7AAdztv4BjRRqlq3fX/Uvt/Z9yYc5Ezp84b4EIhRBCCCHyH0mMc7HS5dzwGTmOC11mc84+oy2mz+P5ywcc//JTbl3/1wIRCiGEEELkH5IY5wFVXq1M1Xc/4kSz97mpLZ2uvvaDQ7iuGMHhJUt5cC/WAhEKIYQQQuR9khjnESq1Cu9mdSk5/guOeA3hnrqQSb0WPfVub8fqq3c4tPZnkhLT764nhBBCCCGeThLjPEZrbUXdzm2wGfM1hyp0IxFrk3onJYH6F1YR/dlwju74HYPBYKFIhRBCCCHyFkmM8yhHJzvqv9GLh0MWEVKiBQZUJvVu+ij8QuZzbfa7nD70t4WiFEIIIYTIOyQxzuNKlCxKnaHDufH6PP528k5X75F8jZq7p/H35zO4dv6GBSIUQgghhMgbrJ7fROQFr3iWA88pnD70N/Z/fI9H8jWT+lcfnkC/7m9CSjSnfJeelChZ1KT+zq27hF+8jKLX41SiBJVqVkKlNh2FFkIIIYTIz1SKouTrXSL0ej0nT57Ey8sLjUZj6XByhMFg4NiuYNyPrcFNH5WuPhFrTlboRM0uXYi8FUHsztXUij2Gmsf/FK5ZuxNVOxC/Ns1yMHIhLKMg9hNCCCHSk8Q4H0tKTObEL9upceFnnJSEdPX3Vc78oylLHd05tGR8k96h8l2p369PdocqhEUV5H5CCCHEYzLHOB+ztbOh/uuB6IZ/zeEy7UnF9A2/iBJLQ90Z/lUX44SmEhl9Qqp//WeO/3YoZwIWQgghhLAgSYwLgMJFnak3cCAR/RZwrHD9dPVlDZF46y/xj7ocF9Tu6ertQ3/JiTCFEEIIISxKEuMCpEz5kviMeo/znT7hnKZ8uvpqhhtUMYTxj6YcRzTViMYRAM/EC7LltBBCCCHyPVmVogDy9PYk+pe7HNdUorgSg7sh0qS+qj5tWbcUNJzQVMKACs3tCMqUL2mJcIUQQgghcoQkxgVUitqG2vpLpKLmsFU1qujDKKw8NGljjR5v/SUAHv46h5BTDXDwaUpV32qo1fJlgxBCCCHyF0mMC6gwl2qUuH8QLQbq6c7xEFsOWVXHXR9JGeVuuvZOSgJ1In6FHb8SsbsoV8s0wa1BU16pUs4C0QshhBBCZD1JjAso2/qtYftB43MnkqivO4sCnFe7E61yoorhJoWVuHTHuurv4XpjE9zYxBXr8kRWbIpH48bpNg0RQgghhMhLJDEuoKr5VCf0cBN87+03KVcBnoYwAFJR86dVLbSODtSKPoYtKenO45FyHY9z1zGcW8kZx5okVG1K1cb1cHK2z4mXIYQQQgiRZcxOjK9cucLatWu5c+cO//d//8f27dvp00c2gMhrVGoVtYYM5+gSK/zu7suwTbi2FOV7v03p8iV5GJvAqQOHsf8nmGpxp012yQNQo1Aj7hQcPUXS0cUcK1IHjVcTqtfzRmstn7+EEEIIkfuZtfPdn3/+yciRI2nWrBm///4727dvJzAwkP79+zN48ODsjPOFyY5Wz3f57FXuHdhNsQeX0Bh0RNu5YvDyp1aTOlhZpf+dRf57jysHDlDicjAeKdefee4HKiculGyES52mVKlVGZValU2vQogXJ/2EEEIIMDMx7tq1KyNHjqRp06b4+flx9OhRTp8+zejRo/ntt98yfdF79+7x4YcfEhISgkajoWPHjkyYMAErq/Qji99//z3ff/890dHRlC5dmuHDh9O6detMX0ve8LLXtfM3uHMomAq39uOqv/fMtres3Agr15QyDZvgXqFUDkUoxPNJPyGEEALMnEpx48YNmjRpAoBKlTbyV7NmTWJiYsy66OjRo3F1deXAgQNERUXxzjvvsGLFCgYOHGjSLjg4mMWLF7Nq1SoqVKjA7t27GT16NHv37qVMmTJmXVNkj1c8y/GK5xsYDH04G3qO+GPBVI38CyclIV3bMro7lLnyI1z5kQu2lXlQuSmVmzSkSLFCFohcCCGEEMKUWYlxqVKlOH78OD4+Psay06dPU7Jk5jd+uHHjBiEhIezfvx87Ozvc3d0ZOnQoc+bMSZcYX716FUVRjA+NRoNWq81wZFlYllqtpnqdGlCnBklJgzj2ZyjqU8FUjzmONbp07askXYRTF9GdWsZJZ2901ZtSvXEd7OxtLBC9EEIIIYSZifGQIUN455136NmzJ6mpqSxZsoQffviBsWPHZvocly5dwsXFBVdXV2OZh4cH4eHhxMbG4uzsbCxv3749GzdupF27dmg0GlQqFXPmzMHNzc2csEUOs7W1xqdFA2jRgOj7sZzY/xfOF4Kpmng+XVsrDHjFHoNDx4g/bMvRYvWwrd2Man410GQwv1kIIYQQIruYlRi3b98eR0dHVq9eTalSpTh8+DCTJk0ya85vfHw8dnZ2JmWPnickJJgkxqmpqXh6evLxxx/j6enJ1q1bmTRpEh4eHlSpUsWc0NHr9Wa1F1nDqZADvgGvQcBrhN2MIOyvg5S5HkzZ1PB0bR2UJPzu/gG7/+Du3sJcLt2YYnUb80qV8nLTnsiUl50fLP2EEAWD3EsgnsasxHjnzp20bduWpk2bmpT/+OOP9OjRI1PnsLe3JzEx0aTs0XMHBweT8v/7v/+jdu3a1KpVC0i7+W/btm1s2rSJ999/35zQOX36tFntRfaw8fQgsnIFzv/7AKvr53n1wTGKKunnqBc3PKB42BYI28JVqzJcKu6NjUdFnAo7ZHDWx/QGAzERMeiTU9DYWFPItRAa2b66wHhymteLkH5CiILhZfsKkX89NzFOTEzkwYMHAEycOBEvLy+eXMji4cOHzJo1K9OJcaVKlYiOjiYqKopixYoBaWsju7m54eTkZNI2PDycGjVqmAZsZYVWq83UtZ5Us2ZN+YSY6/ij0+k5GXKK1JMHqH7vCPYkp2tVQXeLCv/egn/hnH01HlZpSpXG9XAq9DhJTk5O5e8tOyh/aRd19I+3tA7XlOBGpdZ4dWqHtbX5/25EwSL9hBBCFGzPTYzj4uJo3749SUlJAPj7+6MoCiqVyvjfli1bZvqC5cuXx8fHh5kzZzJjxgwePHjAokWLCAoKStfW39+fVatW0bx5c6pWrcqePXs4cuSIWXOaH9FoNPKGlwtpNBq8GvtCY1/i4xI5evAINmf3U/3h32gwpGtfLeEcnDhHyokl/O3iC7WaUrF2DW4u+ZT68elH+0rpIyl1/gfOhJ2k4vBJ2NrJzX3i6aSfEEKIgi1T6xjfu3ePxMREAgIC2LZtm0mdjY2NceQ3s6KiopgxYwZHjhxBrVbTuXNn3n33XTQaDd7e3kyfPp2OHTui0+n4+uuv2bRpEzExMZQrV44xY8bQuHHjTF9L1ifNm6IiHnD5wEGKXgqmUvKVZ7aNUTlwXlMWJyUBe30isRpHAFwMcZRVIo3tQlxbUuedYdkat8ibpJ8QQggBZm7wYTAYUGcwX1On0+XaJdTkDS/vu345jH//3E/5m8GUfGKaREYS0WJHKgB/azx4Vf84qU5FQ/Q731LctUi2xivyHuknhBBCgJk33926dYuFCxcSERGBwZD2NXdqairXrl3j8OHD2RKgEOUrulO+Ym8Mhp78c+I8sUeD8Yz4i0JKXLq2j5JigEr6WyRhhe3/1lHWoudy8H6Kd++cU6ELIYQQIg8x63b9SZMmcfv2bZycnNDpdFSuXJlLly7Rp0+f7IpPCCO1Wk1Vn2rUffsdbCd8x4mm73PYuiYpT/l8Z08ypzSVTM/xICInQhVCCCFEHmRWYnzmzBkWLlzI0KFDcXJyYvLkycydO5dDhw5lV3xCZMjGVot387pQoiyJWHNEU5Wz6vLp2tXR/8MldWnjc0WTO6f8CCGEEMLyzEqM7ezsKFSoEGXLluXixYsANGnShKtXr2ZLcEI8j5V7FQqRQF39P1Q3XM8wOXZR4klC+7/2lXM4QiGEEELkFWYlxmXLliU4OBgHBwcMBgNhYWFERESg0+myKz4hnqlm03rcVz/eLdFJSeCM5hUSebxmcXElmiua0txXOVGzaT1LhCmEEEKIPMCsxHjw4MGMHDmSsLAwevToweuvv063bt3w9/fPrviEeCYbWy2Xa79hfF5WiaSG/hrnNBXQP/HPu7r+OmdL+2NjK5t8CCGEECJjZi3XBhAREUHRokWxsrJix44dxMXF0aVLlxfajS4nyDJMBcPhDVvxO7PCZFOQ36x8aKE7Znwep7JHM2wedsVKWCJEkYtJPyGEEALMHDEGcHV1Na5Z3K5dO7p3784PP/yQ5YEJYY56QQGEv7GAQ+4duW3lSozKkdJKFOc05Y1tHJUEIn74Egx6ywUqhBBCiFwrU4nxrVu36NOnD7Vr12bs2LEkJycDEBkZSf/+/Zk7d262BilEZrhXKEX9t/pTevI3FJr6A54ffkF8x7EkYW1sUz7mHNd3/GLBKIUQQgiRW2UqMZ4xYwapqamMGzeO69evs3TpUi5dukTXrl25ffs2a9asye44hXghfq+6s8vjTZOy0qFrSbgpK6kIIYQQwlSmEuOTJ08yb948evfuzWeffcauXbsYMGAAderUYfPmzdSqVSu74xTihbXs3pZDtt7G51p0PFw7D1KTLRiVEEIIIXKbTCXGqamplCpVCoAKFSpw9epV2rdvz+eff469vX22BijEy3K0UaHpMpz7KidjmWviLW5vlLnxQgghhHgsU4mxSqUyea7Vahk1alS2BCREdqhTpQi7qg01KSv9z3bi/zlhoYiEEEIIkduYvSoFgLW1NXZ2dlkdixDZqlOnemxzbGFSptu4ABJiLRSREEIIIXITq8w0MhgMhIaG8mjJY51OZ/IcwM/PL3siFCKLOFiDW9e3uPHDGcoZIgAolPqAyHVfU6L/e/Cfb0aEEEIIUbBkaoMPT0/PZ59EpeKff/7JsqCykizcL/7ruy3nefP4JJPNQBLajcC+juzgWFBJPyGEEAIyOWJ8/vz57I5DiBzTs60nq6925Y3on4xl6l1LoGI1KOJmwciEEEIIYUkvNMdYiLzMTgvVg7rzt6aisczWkMSDtbIrnhBCCFGQSWIsCiSfMlYcrjOaBGyMZYXvnifhj00WjEoIIYQQliSJsSiw3mxRmm+L9TMpszmwDm5ftkxAQgghhLAoSYxFgWVrBc27tmaf1sdYplH0xP34BaTIrnhCCCFEQSOJsSjQXi2p4p8Gw4lSORvLHGNvk7hjheWCEkIIIYRFmJUYHzlyhHbt2lGtWjWqVq1q8hAirxrUxIUFbsNNyuxO7kK5eMxCEQkhhBDCEjK1XNsjs2bN4tVXX2Xy5MlYWZl1qBC5lrUGugf6sXZpK3om7zGWJ2/8CtsRX4BDIcsFJ4QQQogcY1Z2e/36ddatW4eNjc3zGwuRh1QvDsFN+nHtt9O8YvgXANukaJI2LcK29/uyK54QQghRAJg1laJ8+fJERkZmVyxCWNSgenYsKDMa3RN/FraXQ1CO/2bBqIQQQgiRU8waMW7bti0DBw4kKCiI4sWLm9R17tw5K+MSIsdpNTCkY2UWLuvOqMR1xnLdjmVoy1eHoiUtGJ0QQgghsptZifG6dWnJwtq1a03KVSqVJMYiX6hSDH5rFsTxPceprb8IgFafRMqGL7AeOBM0GgtHKIQQQojsYlZivG/fvuyKQ4hcY7CfhmEXRjP36lgcSALA+t+LKAc2oGrWw7LBCSGEECLbmL2O8ZkzZ5g2bRqDBg1i4sSJhIaGmn3Re/fuMXToUHx9falbty4ff/wxOp0uw7YhISF069YNb29vmjZtyuLFi82+nhDmsFLDux1KMtNxgEm5Erwebl20UFRCCCGEyG5mJcYHDx6kV69eREdHU6VKFeLi4ujfvz+//vqrWRcdPXo09vb2HDhwgA0bNnDo0CFWrFiRrt2VK1cYPHgwvXr14vjx4yxevJjvvvuOXbt2mXU9IcxVqQiUa96S3do6xjK1YiB1wxeQnGi5wIQQQgiRbcyaSjF//nxmz55N27ZtjWU7d+5k0aJFtGzZMlPnuHHjBiEhIezfvx87Ozvc3d0ZOnQoc+bMYeDAgSZt16xZQ4sWLejSpQsAnp6erFu3DkdHR3PCFuKFvOWtYuCFoXhfukgJJRoAbfS/KLtXoOr4jmWDE0IIIUSWM2vE+Nq1a7Ru3dqkrHXr1ly/fj3T57h06RIuLi64uroayzw8PAgPDyc2Ntak7alTpyhTpgxjx46lbt26tG3blpCQkHQrYgiRHTRqmNK2EJOcTHfFUx3fAxeOWigqIYQQQmQXs0aMXVxcuHjxIp6ensay8+fPm5WoxsfHY2dnZ1L26HlCQgLOzs7G8piYGFauXMm8efP49NNPOXHiBEOGDKFQoUK0adPGnNDR6/VmtRcCoKwz1GvmzQ+729A3+fEUHv3mr+DtueDoYrngRDqal1w1RPoJIQqGl+0rRP5lVmLcrVs33nnnHYYMGUKZMmW4efMmS5YsoVevXpk+h729PYmJpnM0Hz13cHAwKbe2tqZFixY0a9YMAD8/Pzp16sTOnTvNToxPnz5tVnshHqmlwMwSXal/+zQVDbcB0CTG8mD1bK7W6S674uUiPj4+L3W89BNCFAwv21eI/MusxHjQoEEkJyezePFioqKiKF26NH369KF///6ZPkelSpWIjo4mKiqKYsWKAWk32bm5ueHk5GTS1sPDg5SUFJMyvV6PoijmhA1AzZo15ROieGFzK8D4H0ax9sEHaEkbVSwceQlvw10Un1YWjk5kFeknhBCiYDMrMVapVIwYMYIRI0a88AXLly+Pj48PM2fOZMaMGTx48IBFixYRFBSUru3rr7/OwIED+eWXX+jYsSOhoaFs3bqVzz77zOzrajQaecMTL+yVItC5eSW+2NGD8UlrHlfs+R5NhVpQrLTlghNZRvoJIYQo2FRKJoZfv/32WwYPHsxXX3311DbDhw9/at1/RUVFMWPGDI4cOYJaraZz5868++67aDQavL29mT59Oh07dgQgODiY+fPnc+3aNYoUKcLAgQN5/fXXM30tvV7PyZMn8fLykjc88VIUBd7YqGfUucn46s8/Li9VEdVbn4DGrM+ZIheRfkIIUZDcuHGDcuXKWTqMXClT7+RHjx5l8ODBHDlyJMN6lZlzLIsVK8b8+fMzrDtx4oTJ86ZNm9K0aVOzzi9EdlCpYNZrGgaEj+Lne2Nw/N+ueKrwyxC8HvwzP9deCCGEyKwqVaoAaUvkVqhQwaRu+fLlzJo1i+HDh2fqG/3Zs2fz4MEDZs2aZTz3ypUrqVu3bpbE6u/vz/DhwwkMDMyS8+W0TCXGS5YsAWDu3LkZrkBx6dKlrI1KiFyqtDP0b+7G1B2D+DxhgbFcOfAzqkq1wd3zGUcLIYQQL6Zw4cJs2rSJcePGmZRv3LjRrP0dHjx4kNWh5StmrWP83zWMIe0ryB49emRZQELkdj2qQ1Sl5uzU1jOWqRQDysYvZFc8IYQQ2SIgIIBffvkFg8FgLDt16hQpKSlUq1bNWKYoCitXrqR169b4+vrSq1cvzpw5A8DChQvZunUrW7duNU5ZBfjzzz/p1KkT3t7eBAUFcfHiRWNdaGgovXv3xtfXF39/f7744gvjwgiKovDNN9/QqFEjfH19mT17dp5f9vK5ifGNGzdo2bIlLVq0IDExkRYtWpg8mjRpQunScuORKDhUKpj9moqZhd8hQlX4cfmDCNj1nQUjE0IIkV81a9aM1NRU/vrrL2PZhg0b0i1esGbNGpYvX86XX37JoUOHCAwMpH///kRFRTFs2DACAgIICAhgy5YtxmNCQkJYtmwZhw4donDhwsyePRuAq1ev0r9/f1q1asVff/3F8uXL2bdvH59++ikAP//8M99//z2LFy/mr7/+QqvVcufOnRz4bWSf5ybG5cqVY9KkSQwbNgytVsvw4cNNHpMmTeK77yQZEAWLmyOMae7Muw7/mc914lf4J+O5+EIIIcSLsrKyIiAggE2bNgGQlJTE7t276dy5s0m71atXM2TIEDw9PdFqtQQFBeHh4WGSCP9X//79KVasGLa2trRs2ZKbN28CsHXrVqpUqcKbb76JtbU15cqVY9y4cfz0008YDAZ++eUXunfvTvXq1bG2tmbUqFEULlz4qdfJCzI1x7h58+YAlClTBk9PT2xtbbG2tubq1asULlw4z/8ShHgRXTxhx2Vvlp9pT//k7cZyZctCVGUqgVMRC0YnhBAivwkMDKRHjx7ExcXx66+/Urt27XT3ft2+fZvZs2ebLG2r0+moUaPGU8/r4uJi/Fmr1RqnQ9y7dw93d3eTtmXKlCEpKYl79+4RGRlJyZIljXUajYZSpUq9zEu0OLPmGBsMBpo2bcq5c+cA2LJlC61bt+bUqVPZEpwQuZlKBTP94evCfbmkLvO4PPEh/PJV2vpuQgghRBbx9PSkQoUK7Ny5k40bN2a4B4SbmxsfffQRoaGhxseWLVsYOXKk2dcrXbq0cfT4kZs3b2JtbU2hQoVwc3MjLCzMWKcoCpGRkea/sFzErMR4zpw5TJw4ES8vLwBGjx7NhAkTmDlzZnbEJkSuV8IBJje3YbTDaFKe/ALm8gk4utNygQkhhMiXAgMDWbFiBdeuXctwOdvu3bvz9ddfc+XKFQAOHDhA+/btOXr0KADW1tY8fPgwU9dq3749V65c4fvvvyclJYWbN28yd+5cAgICsLa2plu3bqxfv54TJ06QmprK119/zd27d7PuxVqAWYnx9evX6datm0lZYGAgly9fztKghMhLAipDWU8P5tqZbjyj7Pke7oY95SghhBDCfB06dODGjRt07NgRK6v0M2L79etH586dGTp0KN7e3nz88cdMmTKFFi1aANCuXTuOHz9Os2bNnnutMmXKsHTpUnbv3k2DBg3o1asXDRs2ZMqUKcZYRo4cyZgxY6hTpw5hYWHGNZfzqkztfPdIq1at+Oyzz6hVq5ax7PTp04wfP55du3ZlS4AvS3a0EjnhXgK0/kHPwrtTqKs797iiZAV4axZYaS0XnHgu6SeEEEJAJm++e6R3794MHjyYHj16ULp0acLDw1m/fr1Z20ELkR8VtYcZ/hrGbh3FztgxOJOQVvHvVQj+EVr0sWyAQgghhHgusxLjN998EycnJzZv3syePXsoWbIkEydOpEOHDtkVnxB5RrtKsLNqCaacHsQXCV8ay5WDm1BVrA3lqj3jaCGEEEJYmllTKfIi+YpU5KQHifDaDwpT735OQOqfjytcSsDb88DW3nLBiaeSfkIIIQSYefMdwPr16+nYsSN169YlPDyckSNHEh8fnx2xCZHnFLaDT1qqmGQ/hH9VRR9XREfCzqWWC0wIIYQQz2VWYrxixQqWLVtGnz590Ov1ODg4EBERwSeffJJd8QmR57xWAVpWd2Lcf3fF+/t3OPtXxgcJIYQQwuLMSozXrl3LokWL6N69O2q1mkKFCrFgwQJ+//337IpPiDxpalO47PIqS20CTCu2fQ2x9ywTlBBCCCGeyazE+MGDB7zyyitA2u4mAEWLFkWn02V9ZELkYYVs4NOW8KldHy6on9hOMzEONi8Ag8FywQkhhBAiQ2Ylxp6envz4448AqFQqAHbs2EGlSpWyPjIh8rhm5aFLDWtGOYwh+ckFYK7+DSE7LBaXEEIIITJmVmI8YcIE5s6dy+uvv05CQgKDBg1ixowZjB8/PrviEyJPm9wYYgu/wmd2vU0r9q6EyJsZHySEEEIIizB7ubbIyEi2bNnC7du3cXNzIyAggFKlSmVXfC9NlmESlnbwJvTZaGBN3FQa6M48rnB7BQbOll3xcgHpJ4QQL8qgwP4b8MsFuBMHdlqoXwa6VQMXW0tHJ8xl1ojxRx99RIkSJRg4cCBTp05lyJAhlCpVivfeey+74hMiz2tUFnrXUjPWYRQxqifWMb5zDX5fa7nAhBBCvJQrD6D1anjzF9h4Hv66Bb9dg48OQN1lsPJvS0eYsSlTpjBlypQsP++tW7eoUqUKt27dyvJz55Tn7nwXERHBoUOHAPjpp5+oUaOGSf3Dhw/Zu3dv9kQnRD4xsREE3yjGZN3bLIif+7jiz81QqTaUr/HUY4UQQuQ+t2Khxwa4m5BxfZIOPvwDDEC/V3MysuebMWOGpUPItZ47Yly4cGFWrVrF/PnzSUlJYf78+SaPNWvWMHz48JyIVYg8y8EaPnsNtlg35hdt4ydqFNg0HxJlkxwhhMhLPjn49KT4SR8fgLvZ0MU/Gp396aef8Pf3x8fHh/79+3Pnzh0UReHbb78lICAAX19f/Pz8GDduHElJSQC8//77vP/++8TFxeHt7c3BgweN542NjaVWrVqcOnUKgO3btxMQEICPjw+BgYEmbZ9m8+bNtGzZkgYNGjB58mTi4uIAnhlXREQE1apV4/jx48bzREVFUb16dW7evImiKKxcuZLWrVvj6+tLr169OHPm8fTE3bt30759e3x8fGjbti2LFi16od/rcxNja2trNmzYwL59+2jQoAH79u0zeezcuZO33nrrhS4uREFSrwwM8ILJ9oO5rSr2uCLmLuz41mJxCSGEME9EPOy6krm2KXr48Wz2xfLHH3+wefNmdu/eTVRUFIsWLWLnzp2sXLmSBQsWEBoayrp16zh48CBbt241OdbR0ZG2bduyadMmY9m2bdsoV64ctWrVIjg4mKlTpzJlyhRCQkIYMWIEI0aM4NKlS8+MKTQ0lPXr17NlyxYuXrzIzJkzAZ4Zl6urKw0bNuSXX34xnmfLli14e3tTtmxZ1qxZw/Lly/nyyy85dOgQgYGB9O/fn6ioKJKSkhg/fjxTpkzh2LFjfP755yxZssSY3JvDrDnGy5YtA+DcuXPs2bOHlJQU7t2TzQqEyKz3GkDRIo6McxiJAdXjitP74fQBywUmhBAi0w7fAp0Zy9EfDMu+WAYNGoSzszPFihXD39+f69ev06RJEzZs2ED58uW5f/8+Dx48wMXFhYiIiHTHd+vWjd9++804qrtp0yaCgoIAWLVqFT179sTPzw+NRkPz5s3x9/dn3bp1z4zp/fffp0iRIhQrVoyRI0eydetWDAbDc+Pq2rUru3btIiUlxRhL165dAVi9ejVDhgzB09MTrVZLUFAQHh4ebNmyBQBbW1s2bNjAoUOH8PDw4NixY9SqVcvs36dZifH9+/d5/fXX6d69OxMmTCAsLIyWLVty4sQJsy8sREFkp02bUnFYW5MlNh1NK7cvhpgoywQmhBAi0xJSs7e9OYoVe/wNpJWVFYqioCgK8+bNo06dOvTq1YvVq1eTmppKRguReXt7U6ZMGXbv3s2VK1c4f/48nTp1AuD27dusXLkSX19f42Pfvn2Eh4cTHh6Ot7e38fHkzXxlypQx/lyyZElSUlKIjo5+blz+/v4ABAcHc/bsWW7fvk3r1q2NscyePdsklvPnzxMeHo6trS1r167FYDAwbtw4/Pz8mDBhAjExMWb/Pp97892TPvroIypXrszy5ctp0qQJHh4eDB48mE8//ZS1a+XueiEyw7cUDKoNnx3rTWPd31TTX0+rSIqHzfOh7zRQm/WZVQghRA4qbv/8Ni/T/mV99tlnhIeHs2/fPhwdHQEICAh4avugoCC2bdvGjRs3aNmyJS4uLgC4ubnRuXNnBg8ebGz7KBEtUqRIuoHRR6tRREREGK9769Yt7O3tKVKkCFOnTn1mXNbW1gQEBLB9+3ZKlSpF27Ztsbe3N8YycuRI2rdvb2x/8+ZNXFxciIuLIzIyks8//xyAf/75h7Fjx/LNN98wYcIEs353Zr37HjlyhA8++AA7OzvjzncDBw7k8uXLZl1UiIJuXH1wL6JltMNoknhiHeNrp+Hw1qcfKIQQwuIalTVvjeKAytkXS0bi4uKwsbFBo9GQnJzMd999x8WLF0lNzXjounPnzpw8eZLNmzfTrVs3Y3n37t1ZuXKlca7u6dOnCQwMZNu2bc+8/pw5c4iJieHOnTt8+eWX9OjRI9NxBQUFceDAAfbu3UtgYKBJLF9//TVXrqRN7j5w4ADt27fn6NGjxMfHM2jQILZu3YqiKJQoUQK1Wk3hwoXN/t2ZNWKs1WpJSkrCzs7OOOwdHx+Pg4OD2RcWoiCztYK5raDL+nLMtuvL1MTvHlf+tgo8vEBjBTuXppW1HQjFSlskViGEEKZsraB3TVh49Plt3RyhbcXsj+lJo0eP5oMPPqBBgwbY29vj4+NDp06duHjxYobtXVxc8Pf358SJE9SvX99Y3qZNGxISEpg4cSLh4eG4uLjQr18/+vbt+8zre3t706ZNG9RqNR06dGDMmDGZjsvT05OyZcuSkJCAj4+Psbxfv34oisLQoUOJjIzE1dWVKVOm0KJFCwDmz5/PF198wZQpU7C1taVdu3b069fP7N+dWTvfzZgxg5s3bzJ58mS6d+/Ozp07+eijj3B2dmb69OlmXzwnyI5WIjf79C9YFGJgVdx0GumeuHu2uDuoVI+3jS5dKW2XPJUq4xOJlyL9hBDCXMk66L8F/nzGjXWO1rC6C3i55Vxc+cHw4cOpVauWyRSOnGLWVIpx48Zhb29PmzZtiI2NpVGjRiQmJvLuu++addF79+4xdOhQfH19qVu3Lh9//DE6ne6Zx1y8eJFXX32VI0eOmHUtIXKzUXWgSjE14xxGEK1yfFxxN+xxUgxw+xIsHAGXjuV8kEIIIdKxsYLlHWFw7bQE+L8alIGfu0lSbI6wsDD27t3LX3/9ZTKNIieZNZXCwcGB+fPnc//+fW7duoWbmxslSpQw+6KjR4/G1dWVAwcOEBUVxTvvvMOKFSsYOHBghu0TExNNFqYWIr+wsYLPWkHnH4sx0f5tFsV/lmG7VDRoo27DmpkQOBpqNs6wnRBCiJxjYwWTGsPoumlbQUfEg70W6paGikUsHV3e89VXX/Hbb78xceJEk9U2cpJZifH169c5cuQI0dHRFClShKJFi5p9wRs3bhASEsL+/fuxs7PD3d2doUOHMmfOnKcmxtOnT6dly5ZPnRsjRF5WswQM94MvjjTk59RQuqb8ka5NtMqR4koMKAbYvADKVoVCluk0hBBCmHKwho5VLB1F3jd79mxLh5C5xFhRFGbMmMG6deuwtramUKFCxMbGkpKSwptvvmnWUhiXLl3CxcUFV1dXY5mHhwfh4eHExsbi7Oxs0n7z5s3cuHGDjz/++IW394O0OYRC5FbveCvsOfIvU+0HUkd3FnfDXZN6HU/Me9WnYgjdjdLs9RyOMvd72fnB0k8IUTDIvQTiaTKVGC9dupTff/+dZcuW0aBBAyDtDeTPP/9k6tSplC9f3rgUx/PEx8djZ2dnUvboeUJCgklifOXKFebNm8fatWtf+h/x6dOnX+p4IbKTbWwk82J20cH5M8baj+LHuA9R8/i+WEcl0aR9yvHfOevimdNh5npP3sH8IqSfEKJgeNm+QuRfmUqMN2/ezKxZs6hXr56xTKPR0KRJE6ZPn878+fMznRjb29uTmGj6Jv/o+ZPLviUnJzNmzBgmTpxIqVKlMnXuZ6lZs6Z8QhS517XTaAw3GZ30I3Ps+vCNTWeGJj/eu94J078ZrS4ZLy+vHA4y/5N+QgghCrZMJcbh4eHUrVs3w7qGDRsyfvz4TF+wUqVKREdHExUVZZxYfeXKFdzc3HBycjK2O336NNevX2fSpElMmjTJWP7222/TqVMnpk2blulrQloiL294Ircy2KWtSPF20ib2aOsy164nA5O3Yk3Gq7U8VNnhIv+es5z0E0IIUbBlark2lUpl3OnuvzQazVN3UslI+fLl8fHxYebMmcTFxREWFsaiRYsICgoyaefr68upU6cIDQ01PgC++eYbs5NiIXK785pyRKpcsMLAZ/HzUaNwV+Xy1PanVBVyLjghhBCigDBrHeOsMn/+fHQ6HS1atKB79+40btyYoUOHAmm7pWzZssUSYQlhMQ9StayxaQVAZcMtxiauxfCMzTzqJh6DU/tzKjwhhBDPY9BDwkNITbZ0JOIlZGoqRUpKCl999dVT680ZMQYoVqwY8+fPz7DuxIkTTz3uwoULZl1HiLzC2QYW23amZWooNfRXGZS8hX9Vj5dDfKBywlFJQEvaqgk26GDjPIi8Af69QW2Rz7hCCCHuXIPD2+Dsn4+T4tKVoU5bqNEY8vj0rOvXr1O+fHlLh5FjMvVu6u3tzZEjR576kJuAhHg5VYtBISc7ejlOY5+VDxoMaDAY6+NUtvRynM49lelyhhzcCOtmQVJCDkcshBCCY3tg8btwcp/pSPHti7DpS1g1A5ITn378S7h16xZVqlTh1q1b6eo2btyIv7//S19j3759vPXWWy99nrwkUyPGP/zwQ3bHIUSBZqWGPjVhziEn+jtNxlN3nQ8Tl1NSdw+Ay2p3QrTV6eg0h6XxM6mqv/H44ItHYdn70PMDKFLSQq9APIuiwJ04iEuF4vbgYmvpiIQQL+1iKGz9+tltrp1KS5Bffz9nYspi0dHRKIqSrvzWrVu0aNEiX36TL9+/CpFLDKwNvv/La89bledD+8EEW3kRbOXFDPsBANzSlCDQ6RN2af+zSszdMFjyHlw9lcNRi2dJ0cP3f0OT76Hed9DyB3h1MfTZBPuuWTo6IcQLUxTYtzZzbc8fgfAr2RbK5s2badmyJQ0aNGDy5MnExcWla7NhwwYCAwOpW7cu3t7eDBkyhPv37wMQERHBwIEDqVOnDk2aNGH48OFERkZy5MgRpk6dSnh4ON7e3kRERGTba8hNJDEWIpewtYKVnaFjZVABVzWlecNpKm84TeWqpjT2VlDIBhJUdrzt8B5f2nYzPUFiHPwwHUJ2WiJ88R9xKWkJ8JQ/4GaMad2Bm9B/C8w8mPb+KoTIY/69CneuZr79sT3ZFkpoaCjr169ny5YtXLx4kZkzZ5rUnzp1io8++ohp06Zx5MgRdu7cyfXr11m5ciUAc+fOxc3NjT///JMdO3aQkJDAt99+S926dZk+fTqlSpXixIkTuLq6EhAQgK+vLx07dgTSVhDz9fVlyJAh2fb6clqmplIIIXKGgzUsaAvjG0DfTXD9fwlVlaKwqTsk6eCdHXDktpq5dr24oCnH5/HzsSMlraFigB3fpt2U1+YtsNJa7sUUcO/uhSO3036uoL/NtISlAEyzH8hVTWkAFh8Dd2foW8tSUQohXkjEdTPb33h+mxf0/vvvU6RIEQBGjhzJO++8Q+3atY31lStXZtu2bZQpU4aYmBgiIyMpUqSIcQTYxsaGo0ePsn37durXr8/SpUtRP+WG7q1btwKPp1I8Wko3P5ERYyFyobKF4LtO0KRs2uPr9mlJc1F7WNUlbT4ywHbrhgQ5zST8iRUsAAjdnTZ6HB+b88ELzkfBzsuPn09NWEZT3Uma6k4yJeE7k7ZfHQWdASFEXpKLvuopU6aM8eeSJUuSkpJCdHS0sUytVrNy5Urq169PYGAg33zzDXFxcca5w5MnT6Zdu3YsW7aMpk2bEhgYmC8T3swye8Q4MjKSmzdvppuM7efnl2VBCSHAozD80CV9ubUGPvYHz2IwLRjOWHnQ0XkOi+Nm46N/4kaIG2dhyXjoORFcy+Vc4IL1Z02fexge3zVe0RBmUncnDoJvQItXciIyIUSWKFHWvPbF3bMnDtLmCDs6pu2eeuvWLezt7Y0jyAArVqzgzz//ZOvWrcYdh99++21j/blz5+jRowcjRozg/v37LFy4kOHDh3P48OFsizk3Mysx/uGHH5g1axZ6vd6kXKVS8c8//2RpYEKIZ+tbCyoVgbe3w92kwrzu9H98nPAN3VP2PW4UHQlL34fA0VA1423dRda7/ODxz7V151H/ZyChsu4G8So7bmtKAHDlgSTGQuQppStBiXJp09Yyw+e1bAtlzpw5zJ49m8TERL788kt69OhhUh8XF4eVlRVarRadTsf27ds5cOAAbdq0AdJ2FNZqtXzyySc4OztjZ2dH4cKFgbRpFomJieh0OqysHqeMZcqUyZcrUoCZUym+//57pkyZwpkzZzh//rzxIUmxEJZRrwxseR08i0KKSst4++HMsOuP/sk/7dQk+HEWBP+Uq77+y880KlApBv4vYTGbHn6ACtPf+0Wrcrgq92mX/Ccgc9qEyHNUKmjW4/ntACrWTkuks4m3tzdt2rSha9eu+Pn5MWbMGJP6AQMGULJkSZo3b07jxo3ZsmULvXr14uLFiwDMmDEDg8FAixYt8PPz4++//+bLL78E0mYDFC1aFD8/v3ybCP+XSslogbqnqF27NqGhoU+dlJ0b6fV6Tp48iZeXF5o8vvuMEE8TnwJj98Cu/60I1DT1OAviP6eQ8p+NP6o3gk7Dwdom54PMxbK6n/j0L3AKXsk7yZsA+FdVlJJK2prUYeriNCr0LZB2U14xwwPGvl6D+mWeejohRG51eCvs+u7p9e6e0Hsy2DrkXEzipZiV4dapU4cjR45kVyxCiBfkYJ12g96Y/82WCNbWprPTp1xRlzJtePYgLJ8EMVE5H2QB0rtsFIOSf3luu6ua0lzSlMXBSkbyhciT6gXAW7OgRiNQPzE71bU8tB8Cb86QpDiPMWuOsaurK0OGDKFu3brGCdyPfPLJJ1kamBDCPGoVjK4HlYumjR5fpTSdnWbzVfznNNWdfNzw3yvw7Xh4fULaaIbIcqUv/wY8udTE48TXXklGpRhQVGnjEg/UzvTYYGBRBxXNy+domEKIrOBeJe2RmgwJD0FrA/ZOlo5KvCCzRoxTUlJo3759uqRYCJF7tKuUtuZxGWeIVTvS33EyS20CTBvFR8OKD+HkvgzPIV5S+GWTpw5KkvHnokose2NHYWNINpYl6NW8tQV+/M9qFkKIPERrA4WKSVKcx5k1xzgvkjnGoqC6nwjvbIfD/9tkolvyb3yc8A026Ewb1u8Er/UFdcH9+8jyfmLVDLh84vH5UaH5zw14USpnWjvNI0pTxKR8TF0YVTft3h4hhBA5y+y76L7//nvatWvHq6++SsuWLfnmm2/SrWkshLC8InZpm4E82lXtJ5sW9HT6P+6qCpk2PPQLrPkYEuNzPsj8qrCbydP/JsUAxZRYDsS+Q7VU021l5x2B936FVH26Q4QQQmQzs5drW758OX369GHBggX069ePdevWsWTJkuyKTwjxErQa+Kg5zPQHKzUcs/Kko/Mczmr+s2ju5ROwdAJE3bZMoPmNd4tMNbMnha3xE3jN2fT3vv4cvLU1bbURIYQQOcesxHjdunUsWrSIXr160aRJE/r06cOiRYv48ccfsys+IUQW6F0T1gSmjSKHq4vT1Wkm27QNTBvdu52WHF8+aZEY85VSHuDhnammVoqOJRHvM7CI6QTj4BvQ/WeIlIF8IYTIMWYlxpGRkXh6mt7F7unpabIntxAid6pbOm0zkKrFIFFlyzCHd/nctqdpo6R4WP1/aWtzyhSpl9N1DLhVyFRTVVIck69N45Oif5iUn4mELuvTdsYTQgiR/cxKjMuVK8fevXtNyvbu3Uu5cuWyNCghRPZwd4aN3aFtRUClYr5dd4Y4vEcCT2z4oRjSFqzfshB0qRaLNc+zd4IBH0PzXqD6T1db0iNt/VPt49+7yqCj1+UvWVtkncmHkluxELgejobnVOBCCFFwmbUqxa+//sro0aN57bXXcHd35+bNm/z222/Mnz+f5s2bZ2ecL0xWpRAiPYMCC0Jg7uG051V111gS/wnuhrumDd09occEcHTJ8RhzUrb3E/MGQ8z/frdORWDcsrSfw6+k3fgYZzokfK1sU9o8HEYSWmOZjQa+bPO/DzVCCCGyhVkjxi1btmTp0qVYW1tz9uxZnJ2dWb16da5NioUQGVOr0pYEW9we7LXwj9UrdHSawxGraqYNw86nbQby7zXLBJofPbksXikPGPQplDD91u2Vm8Ec1k6jpDrWWJasT1t+b/nJHIpTCCEKILNGjCMjIylRokS68tDQUHx9fbM0sKwiI8ZCPNv5KBi4FcJiQaukMiNhCb1STKdMobWBziOheoOMT5LH5eiIcaHiMOZb0/qkBNjwmcnaxwDJhUrS3WYyJ1NNt/Ye4gPvN0z7gCOEECLrmDVi3LlzZ/7880/jc0VRmD9/Pv369cvquIQQOcSzWNpNefXLQKpKywf27zDVbiC6J7uH1GT4aQ78vg4MhqefTLwYW3voOQl825gU28T8y8bYCXS0OWdSvvgYjNoFyf/Zq0UIIcTLMSsxHjZsGMOGDePLL78kLCyMXr16sXnzZpYtW5Zd8QkhckARO/ihM7xRC1CpWGHbnjccpxCtcjRtGPxjWoKcnGiJMPOu4mWe+Nk94zYaDbQfDK37A4+HgjVJccy/O5VRdvtNmm+5CG9shphkhBBCZBGzt4S+cOECb7/9NpGRkbRs2ZKPP/4YR0fH5x9oITKVQgjzrDkNH/4BOgOU0//LsriZVDLcMm3kWh56fgAu6adW5UXZ3k9E3YadS9N+bjsQipV+dvt/jsDPc0FnusPH5lI9GZXQzWS/6MpF4ftOUMopq4MWQoiCx6wR48TERH788Ueio6Np2LAhhw8fZv/+/c8/UAiRZ/SqCWsDoagd3NCUpIvzLPZZ+Zg2iriedlPejXMZnkP8R7HS0Hdq2uN5STFA1brQ/2NwLGxS3Dl8LZus52OtPF5G7+I96Pwj/HP3vycRQghhLrMS444dO3L06FHWr1/Pt99+y9SpU5k2bRpjxozJrviEEBZQ53+bgVQrDg9VDrzl+AFf23QxbZQQC99PhWN7Mz6JeDmlK8LA2VCirElx7cg/+EM9nUKGh8ayiHgI2gAHb+Z0kEIIkb+YlRg3aNCADRs2UKlSJQDatWvHpk2biIiIyJbghBCWU8YZfu4G7SuBQaVhlv0bjLYfZbK2LgYdbF0EO5aCXm+5YPMrl+Iw4BPw8DIpLn3/LH8a3sdD+ddYFpcC/X6BjedzOEYhhMhHzJ5jnJHU1FS0Wu3zG1qAzDEW4uUoCnx1FD47lPbcS3eRb+Nm4ar8Z5/iCq9C0Li0Hd+eZO78WgvI9f2EXgc7lsCxPSbFqbbODLB7n/1UNSl/rwEM9TWZiiyEECITzEqMb968ycKFC4mIiMDwvyWbUlNTuXbtGocPH870Re/du8eHH35ISEgIGo2Gjh07MmHCBKysrNK1Xbt2LStWrDCuofzGG2/Qu3fvTF8r17/hCZFH7LkCo3dDfCq4Gu6xJG4Wr+ovmzYqUjLtprwnV15YNePx+rwVa0OfD3Mu6EzKE/2EosBfv8DelcDjbtug0TLFZQQ/GBqbNO9TE2Y0A41Z3wsKIUTBZlaXOWnSJG7fvo2TkxM6nY7KlStz6dIl+vTpY9ZFR48ejb29PQcOHGDDhg0cOnSIFStWpGv366+/MnfuXGbPns3x48eZNWsWX3zxBbt37zbrekKIl9fKAzZ1h7KFIEJdlG5OH7HZuolpo/v/wpIJcPHY47K7T6xocTcsZ4LNj1QqaNgZuo8HK2tjsVqfykf35jJN9VNa8vw/q07DkO2QmJrBuYQQQmTIrMT4zJkzLFy4kKFDh+Lk5MTkyZOZO3cuhw4dyvQ5bty4QUhICOPHj8fOzg53d3eGDh3K6tWr07WNiIhg0KBBeHl5oVKp8Pb2pm7duhw9etScsIUQWaRKMdjSAxqUgWSVDaPsRzPLrg+GJ9bdJSUR1nwM+3+Cv34h9WG0sSo15gFs+yZteoV4MdXqQ7//A4dCJsX976/he9VXaJ9YsWLvVei5Ee4l5HSQQgiRN6Wfu/AMdnZ2FCpUCCsrKy5evAhAkyZNmDBhQqbPcenSJVxcXHB1dTWWeXh4EB4eTmxsLM7Ozsby/06ZuHfvHkePHuWDDz4wJ2wg7atSIcTLc7aG5QEw808V359S87VtVy6qyzI/fi6OJP2vlQL71gAQoypEMWIAiFY5UDx0N8qJfRgCR4Nn3SyN7WWnQeSZfqKkBwz4BPXamaiiHo/IN3uwj+1OkQSpJxCrTltf/sQd6LJeYUVHA+UKPe2EQhQsuXbKlLA4sxLjsmXLEhwcTNOmTTEYDISFhWFtbY1Ol/l9SePj47GzszMpe/Q8ISHBJDF+0t27dxkyZAg1atSgQ4cO5oQNwOnTp80+RgjxdJ2cwb5iUZZcKctv1n501sxmWdxMyhlMV6lxVuKMP+v+1+Wo9Kmofp7LhYZvkuBSKsti8vHxeX6jZ8hr/YTa93U8Qn/GOeqasazKwzPstRlPkM1UwjRuANyIUdFpnYFJ1S5TyUmGj4V42b5C5F+ZSoyPHTuGj48PgwcPZuTIkWzbto0ePXrw+uuvo9FoaNGiRaYvaG9vT2Ki6Xayj547ODhkeMzJkycZNWoUvr6+fPLJJxnepPc8NWvWlE+IQmQxL6BZuMLQnQqXEsvS0elTvo6fQwPdGWMbazIehVUb9HhGnsHQrF3OBJsJebKfqO2LYecS1Cd+Mxa5Jd9hrzKB3jYfcMzKE4DYVC1Tz3qyoLWBFq9YKlghhMjdMrUqRe3atTl+/DiQNu+3aNGiWFlZsWPHDuLi4ujcuTPW1tbPOUua69ev07p1a/7880+KFSsGwI4dO5g9ezbBwcHp2m/YsIGPPvqIkSNHMmDAAHNeG5BH7jYXIo+7HQuDtsHZu2Cl6JiauIw3knela3df5UQR5fHGFIpKjWrsEnAqkpPhppPn+wlFgT83w68rTYr1ai1jHUax2aqhsUytgo+aQ++aORyjEELkAZm6+e7J3NnV1dU4YtuuXTu6d++e6aQYoHz58vj4+DBz5kzi4uIICwtj0aJFBAUFpWu7e/dupk2bxoIFC14oKRZC5IzS/9sMJKAy6FRWfGg/hIn2Q0jFNMks9MS0CgCVYpAb8bKCSgWNukA30xUrNIZUvnz4GWN1PxtXrDAoMHEfzPnLZBELIYQQZDIxVmXxKvHz589Hp9PRokULunfvTuPGjRk6dCgA3t7ebNmyBYCvvvoKvV7PyJEj8fb2Nj6mTJmSpfEIIV6enRYWtIFRKT+jUgystmlDb8dp6Hl2/yHJWRaq3gDenJFuxYpRD1exMHUhVsrj+0G+Ogrj9kJKHrnfUAghckKmplJUrVqVUqWefYPMb7/99sx6S8nzX5EKkcccnz2N+zotIx3GEq+y43D0W5RU7gMQpSpEMSXG2FaPmusDluJRtrClwk2LI7/1Ew8iYPVH8MSKFQAn7WvRV/seserH93M0codv2oOTTU4HKYQQuU+m7mLTarUMHz48u2MRQuQDP9q+xuwHn7E5dgIDHT9Ap9IYN2pLwnTa1V6tH44UxsMCceZrhV3hrVmwfjZce7zShlfCKXbZvE8Pm8mEadKWzDwYBt03wIpO4OpoqYCFECJ3yFRibGVlRZcuXbI7FiFEPnDIsS6nYytQU3+VLQ/fIw67DNslY8VXtkFMM3+RGZEZdg7Q+0PYvhieWLGidPItdhom0Md2IietKgNwLgo6/wjfd4bKRS0UrxBC5AJm33wnhBDPUsfdigGOkzivLouLEocGg7Hu0c9JWDPM4V1uOFSkenFLRVoAWGmh4zBo0cek2Ck1hp/jP6Rdyl/GsvA46PoTHL7135MIIUTBkanEuGPHjtkdhxAin+hbCyLVRejsPJtpdgP478fq723a0tp5Hnut69KtWtpNeyIbqVTQuCsEjQPN41+2lSGFr+Pn8E7SJuMdkLHJ0HczbLtooViFEMLCMpUYT58+PbvjEELkE6+6QZ+akKiyZbltAHrV425Gh5op9oO5rilF2UIw3M+CgRY0NRqlrVhhb7q76PuJK5mT9LVxxYoUPQzbCUuOy4ohQoiCJ1OJsRBCmGNGM3jLO+3nK+oyxvLLGncAapSAH7tCkYynH4vsUtYTBs6GYqVNirsn7WV14kc4KfHGso8OwIz9oDf89yRCCJF/SWIshMhyGjVMaQL734Q5hd4i2MqLYCsvPnUawA+dYevrUMrJ0lEWUEXc0lasKF/DpLhe8t9sjf+AMvpIY9l3J9NGj5N0CCFEgSD3gwshsk05FyjqXpo3DFMBaFYOmpSzbEwCsHOEPlNg2zdwcp+x+JXUMLYZJvCm/Qf8/b8VK3ZehrvxsDQAbsbALxfgTjzYWUH9MtChMtjKO4kQIp/I1AYfeVm+W7hfiDzmygOY9kfaz9OagYdl9/LIUIHtJxQFDmyAfWtMilPU1oywG80u6/rGMlurjEeOXWxhelPo7JndwQohRPaTqRRCiGzlURh+6JL2yI1JcYGmUkGTbtB1rMmKFdaGFL6Jn8PgpM3GO/CeNp0iOglG7YYN53IgXiGEyGaSGAshREFXszG8OR3sHk/8VqEwKfF7Pk74Bo2if+4pJv0ODxKzM0ghhMh+khgLIYSAslXTVqwoWsqkuE/KHr6L+xhHJQGACvrbrHw4nZUPp9Mg5SQuhlggbUR5vYwaCyHyOEmMhRBCpClaMm3FinLVTIqb6U6wKXYCpQx3mZqwjKa6kzTVnWRI8hb6JO8mKClty+nfr1sgZiGEyEKSGAshhHjM3gn6ToNazUyKKxtusTX2XarqrxvLPAy3+MquGz/bNKe0PpLbsZAsS7sJIfIwSYyFEEKYstJCl5FcfvV1k+JiSizFlQfpmisqNbc1JbgZC/W+S9sY5OK9nApWCCGyjiTGQggh0lOpKNX0NXSoTIqffNNQZ7Da5/1EWHYCXlsFXdbDujMQn5LNsQohRBaRxFgIIUSG7MPPYcXTl7ovpURxIHoIIxN/xF0fka7++L8w4TfwWwrv/Zr2PH+vnC+EyOtkvyIhhBAZS0l6ZrUKKKtEMi5pHeOS1nFT48ZmbRN2aetyVvNK2jrJQHwq/Hg27VG5KLxeHbp4QhG7HHgNQghhBkmMhRBCZMzRxazmZfV3GKlfz8ik9dzRFGeHVR32ausQYlUNnSrt7ebivbQ5yLP+hFYVoEd1aFQW1KrnnFwIIXKAJMZCCCEy9kotsHeGhFizD3XT32WAfjsDkrcTrXLkN60ve7V1CNZ6kaCyI0UP2y6lPco4Qffq0K0alHJ6/rmFECK7qBQlf8/40uv1nDx5Ei8vLzQajaXDEULkQtJPPMO+NbD/p+e3U2vA8Pwd8pLQclD7Knu1dfhV60eU2sVYpwKalEubatGyAljL/wohRA6TEWMhhBBP16Qb3L4EV04+vY21HbwxLW3qxYWjcP4IXD8LiiFdU1tSaZkaSsvUUAyoOKapwh7ruuzR1uG6phTBNyD4BhS1g8CqaVMtKhXJrhcnhBCmZMRYCFHgST/xHLpU+H0thO6G5ATTuldqQZsB4FrOtDzhIVw6BudD4PIJSH32jXwAF9Xu7LGuwx5tHU5pKqKo0hZO8i2ZliB3qAz22qx6UUIIkZ4kxkKIAk/6iUxKSYLje+HoblCroVU/qFT7+celpsC1U2kjyReOQnzMcw+5oyrCXus67NbW5bBVdVJVWhytIaBy2lSLV12Ni14IIUSWkcRYCFHgST+Rgwx6uHUpLUk+fwTu//vcQ2Kx53dtbfZY1+UPbW3iVPZ4Fk0bRe7iCYVl2TchRBaRxFgIUeBJP2EhigJRt9KmW5w/kjaX+TlSsOIvq5rssU5bCi5aW4Q2HmlJcgN3WfZNCPFyJDEWQhR40k/kErH34UJIWqJ87TQYdM895ISmEnu0ddljXYcklzL0qKGiW1Uo+Zxl3xSDQlxcIoqi4ORkjyoPZ9RJSSkkJSTh6OyAlVXe/fer1+mJe5iAja01tnY2lg5HFFCSGAshCjzpJ3KhpPi0m/bOH4GLxyAl8bmHXFGXYq+2Dnut6+LsUZkeNdS0eAW0T/wvjYmO49zO3ZS7spdSurRtrCPVRbhSvgWV2rSjWAmXbHpBWctgMHDy9yNYH99BtfizqFFIRsvpovVwatKBKq9WtnSImXbtwg0if99OjYgDOChpN2letK1ETK02eLVsgtZaFtASOUcSYyFEgSf9RC6nS4XrZ9JGki+EwMP7zz0kUuXCr1o/jjjWodSrtQiqZY3VvVvYrJ2Bm/5uhsc8UDtzt/NkKteqlNWvIEulpKRy5ut51H5w6KltDlXpQ/2eXXMwqhcTsn0ftY8uxIr0S/sBnLWvTtm3J+LkbJ/DkYmCyiKJ8b179/jwww8JCQlBo9HQsWNHJkyYgJVV+k+FwcHBfPbZZ4SFhVGyZEnee+89mjdvnulryRueEOJ5pJ/IQwwGCL/8OEm+G/bcQ+Kx5Q+tNxc05fDQ3+K11KPYk5xh2wcqJ1IGzcW1VLGsjjzLHPnma+re2fPcdkfrjMSvXebfL3Pa6b9OUn3PDNQ8Ow056ezDq6Mn5enpLiLvUFvioqNHj8be3p4DBw6wYcMGDh06xIoVK9K1u379OiNGjGDUqFGEhoYyYsQIRo8eTURERM4HLYQQwvLUaihTGVr2gWHzYfhX8Nob4O6JQsaJkwNJtE89xNikdbRLPcTfVhXZom3EP+py6VKywspDru7alv2v4wXdvhmB3529mWpb+tg69Lrn70ZoKdoDa5+bFAN4xR7jwqmLORCREBYYMb5x4watWrVi//79uLq6ArBjxw7mzJnD77//btJ23rx5nD59mu+++85YNnDgQGrVqsXIkSMzdT0ZCRJCPI/0E/lEXDRcOIrh/BGUK6fQGFKfe0i4qih6VQb/z9W589+BYjCgykQyaWyvUqPKhQs+KwqolPRJuwEVd1RF8dZfwJrH9UeL++M3bEROhigKqByf0X7p0iVcXFyMSTGAh4cH4eHhxMbG4uzsbCy/fPkylSub3kBQsWJFzp8/b/Z19frc+6lZCJE1XjaplX4ij7NzAi//tEdKIvorf5N0JgTN5WPY6uIzPKSUco8M88yMp7yKHFCOCA5rqlFPf85Y5hJ7I0v/PuUDsHiaHE+M4+PjsbMzXY390fOEhASTxDijtra2tiQk/GdL0kw4ffr0C0QrhMhLfHx8Xup46SfyG1uo1ASde1389s7hjqootqRQRHlo6cDEc/z3s4rBYODkyZNZdv6X7StE/pXjibG9vT2JiabL7jx67uDgYFJuZ2dHUlKSSVlSUlK6dplRs2ZN+YQohHgm6Sfyrzu/uxqXZ4vFniua0jgpCRRS4jI+QGWRW3CeS1EU86dSZGM8L0VJPyxvQM1NtSveetPNXmILlaW2l1cOBSYKshxPjCtVqkR0dDRRUVEUK5Z21++VK1dwc3PDycl0RfbKlStz9uxZk7LLly9To0YNs6+r0WjkDU8I8UzST+RfNyq0pNTF1QA4k5Au8XrS4TIdqDfwrZwKzSxhV8NxXzksU20jNEUp+sHiXLnph2JQuDx7PJWSr6Src9U/SFfmUK+V/G2KHJHjH4nLly+Pj48PM2fOJC4ujrCwMBYtWkRQUFC6th07diQkJIQdO3ag0+nYsWMHISEhdOrUKafDFkIIkYdVbdOKu+rCz20Xq7KnbOuAHIjoxbhXKMXR4s0y1fZ6re65MikGUKlVxDfskam2ZxxrUbV21WyOSIg0FvmuaP78+eh0Olq0aEH37t1p3LgxQ4cOBcDb25stW7YAaTflLVy4kMWLF+Pn58eiRYtYsGABr7zyiiXCFkIIkUe5FHHmYbcpRD0jOY5V2XO7wyRKuZfIwcjMV3PA2/ztVPuZbQ5V6EadgNdyKKIX49XEjyOvDsbwjMkeF2wr4/7Wu7KGscgxsvOdEKLAk36i4IiKjObSrh1Uur6XYoZoAGJUjpwr488rrTvgVqa4ZQPMJL1Oz/E9+yl0ageVky6nlaHmVCEftA06UKNuLQtHmHkXT10iNngbte79hTU6AK5ZuxNZtS1ebVtiY6u1cISiIJHEWAhR4Ek/UfAYDAbu3Y0GBQoXK5RrpxxkRkxMPIlxiRQq7ISdvY2lw3lhSUkpRN+LxdbOGpcizs8/QIhskOM33wkhhBCWplarKe5axNJhZIlChRwoVMj81ZpyG1tba9xK596tuEXBkDvXoxFCCCGEECKH5fsR40czRWRHKyEKBrXa/C1wpZ8QouB5kb5C5H/5PjE2GNIWEJcdrYQoGF5knrD0E0IUPHJPgchIvr/5zmAwoNPp5JOhEAXEi/ytSz8hRMEjf+8iI/k+MRZCCCGEECIz5OY7IYQQQgghkMRYCCGEEEIIQBJjIYQQQgghAEmMhRBCCCGEACQxFkIIIYQQApDEWAghhBBCCEAS45dy/vx5+vfvT506dWjYsCHvvfce9+/ft3RYL+TQoUN069aN2rVr07BhQ/7v//6PpKQkS4f1wvR6PX379uX999+3dCgvbMeOHVSrVg1vb2/jY/z48ZYOy2zR0dG899571K1bFz8/P4YOHUpkZKSlw8pR+aWvkH4i95F+QoisJYnxC0pKSmLgwIF4e3tz8OBBtm3bRnR0NBMnTrR0aGa7f/8+Q4YMoWfPnoSGhrJp0yZCQkL49ttvLR3aC/vqq68IDQ21dBgv5fTp03Tq1IkTJ04YH3PmzLF0WGYbMWIECQkJ7N27l99//x2NRsOHH35o6bByTH7pK6SfyJ2knxAia+X7LaGzS3h4OJ6engwbNgyNRoO1tTU9evTgvffes3RoZitSpAh//fUXjo6OKIpCdHQ0ycnJFClSxNKhvZBDhw6xZ88eWrVqZelQXsrp06dp27atpcN4KWfOnOHvv/82/vsC+L//+z/u3r1r4chyTn7pK6SfyJ2knxAia8mI8QuqUKECS5cuNdlnfffu3VSvXt2CUb24R51R06ZNCQgIoHjx4gQGBlo4KvPdu3ePSZMm8fnnn2NnZ2fpcF6YwWDg7Nmz/PHHHzRv3pwmTZrw4YcfEhMTY+nQzHLq1CkqVqzI+vXree2112jUqBGzZ8+mePHilg4tx+SnvkL6idxF+gkhsp4kxllAURTmzZvH77//zqRJkywdzkvZs2cP+/fvR61WM3LkSEuHYxaDwcD48ePp378/np6elg7npdy/f59q1arRunVrduzYwbp167h+/XqemzsYExPDhQsXuH79Ops2bWLz5s1EREQwYcIES4dmEfmlr5B+IneQfkKIrCdTKV5SXFwcH3zwAWfPnmXVqlVUqVLF0iG9FFtbW2xtbRk/fjzdunUjJiaGQoUKWTqsTFm8eDHW1tb07dvX0qG8tGLFirF69Wrjczs7O8aPH0/37t2Ji4szjtzldtbW1gBMmjQJGxsbHB0dGT16NN27dyc+Ph4HBwcLR5hz8lNfIf1E7iD9hBBZT0aMX8LNmzfp2rUrcXFxbNiwIc++0R0/fpw2bdqQkpJiLEtJSUGr1eaprxl/+eUXQkJC8PX1xdfXl23btrFt2zZ8fX0tHZrZzp8/z2effYaiKMaylJQU1Gq18U0kL6hYsSIGg4HU1FRjmcFgADB5bfldfugrpJ/IfaSfECLrSWL8gmJiYnjzzTepXbs2y5Yty7M3oABUqVKFpKQkPv/8c1JSUrh9+zazZ88mKCgoT3Wuu3bt4vjx44SGhhIaGkqHDh3o0KFDnrzr3MXFhdWrV7N06VJ0Oh3h4eHMmTOHLl265Kn/Jw0aNMDd3Z2JEycSHx/P/fv3mTdvHi1btswzo1kvK7/0FdJP5D7STwiR9VSKfBx7IcuXL2fWrFnY2dmhUqlM6k6cOGGhqF7c5cuXmTlzJqdPn8bJyYmAgACGDRuWpzrX/3q0NumsWbMsHMmLCQkJYe7cuVy8eBEbGxvat2/P+PHjsbGxsXRoZomIiGDWrFkcPXqU5ORk/P39mTRpEs7OzpYOLUfkp75C+oncR/oJIbKWJMZCCCGEEEIgUymEEEIIIYQAJDEWQgghhBACkMRYCCGEEEIIQBJjIYQQQgghAEmMhRBCCCGEACQxFkIIIYQQApDEWAghhBBCCEASYyGEEEIIIQBJjEUuVqVKFY4cOVJgriuEMJ/0E0KIrCSJsRBCCCGEEEhiLPKw7du3ExAQgI+PD4GBgRw8eBCAQ4cOUatWLR4+fGhsGxwcTJ06dUhJSSElJYUvv/ySFi1aUKdOHQYNGsSNGzcs9TKEENlI+gkhhDkkMRZ5UnBwMFOnTmXKlCmEhIQwYsQIRowYwaVLl6hXrx6urq7s3LnT2H7Tpk107NgRa2tr5s2bxx9//MGKFSs4cOAAr776KgMGDCA5OdmCr0gIkdWknxBCmEsSY5EnrVq1ip49e+Ln54dGo6F58+b4+/uzbt06VCoVQUFBbN68GYDY2Fj27dtHUFAQiqKwbt06xo4di7u7OzY2NgwbNozU1FT++OMPi74mIUTWkn5CCGEuK0sHIMSLuH37NiEhIaxdu9ZYptfrqVevHgCBgYEsWLCAsLAwDhw4QKVKlfD09OTevXskJCQwatQo1OrHnwtTU1O5fft2jr8OIUT2kX5CCGEuSYxFnuTm5kbnzp0ZPHiwsSw8PBxbW1sAihcvTpMmTdi2bRvBwcEEBQUBULhwYWxsbPjuu+/w8vIyHnv16lVcXV1z9DUIIbKX9BNCCHPJVAqRq92/f587d+6YPHQ6Hd27d2flypWcOnUKgNOnTxMYGMi2bduMx3bv3p3169dz4cIFAgICAFCr1QQFBfH5559z584dDAYDmzZtokOHDnJjjRB5lPQTQoisIiPGIlcbPXp0urIdO3bQpk0bEhISmDhxIuHh4bi4uNCvXz/69u1rbNe4cWMMBgOtWrXC0dHRWD5hwgQWLFhAr169iI6Oxt3dnfnz51OtWrWceElCiCwm/YQQIquoFEVRLB2EEEIIIYQQliZTKYQQQgghhEASYyGEEEIIIQBJjIUQQgghhAAkMRZCCCGEEAKQxFgIIYQQQghAEmMhhBBCCCEASYyFEEIIIYQAJDEWQgghhBACkMRYCCGEEEIIQBJjIYQQQgghAEmMhRBCCCGEACQxFkIIIYQQAoD/B2yuiJ94L+bxAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 725.125x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "point = pointplot_from_data_frame(mock_results, \"Level\", y_vars, \n",
    "                                  group_by=\"Reference\", color_by=\"Method\",\n",
    "                                  color_palette=color_palette_method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "8f803c56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plots folder must already exist\n",
    "for k, v in point.items():\n",
    "    v.savefig(join(outdir, 'mock-{0}-lineplots.pdf'.format(k)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "679ed843",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x490 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Precision'}, xlabel='Reference-Level', ylabel='Method-Parameters'>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"Precision\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "20c8af51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x490 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'F-measure'}, xlabel='Reference-Level', ylabel='Method-Parameters'>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"F-measure\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "5d26c584",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x490 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'Taxon Detection Rate'}, xlabel='Reference-Level', ylabel='Method-Parameters'>"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results, metric=\"Taxon Detection Rate\", rows=[\"Method\", \"Parameters\"], cols=[\"Reference\", \"Level\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef0b6d13",
   "metadata": {},
   "source": [
    "### Focus on species level results (for genus level, change 6 to 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "a4029d78",
   "metadata": {},
   "outputs": [],
   "source": [
    "mock_results_6 = mock_results[mock_results['Level'] == 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "0698f22c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: xlabel='Method', ylabel='Taxon Detection Rate'>"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxplot_from_data_frame(mock_results_6, group_by=\"Method\", metric=\"Taxon Detection Rate\", color_palette=color_palette_method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "14d8bfa1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_f/3hfwkwps2rq9q60vkv4fnd_n9rf1vk/T/ipykernel_50497/856066177.py:1: FutureWarning: The default value of numeric_only in DataFrameGroupBy.median is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  mock_results_6.groupby(\"Method\").median()\n"
     ]
    },
    {
     "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>Level</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>blast+</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.032484</td>\n",
       "      <td>0.032484</td>\n",
       "      <td>0.032484</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.111111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.016242</td>\n",
       "      <td>0.016242</td>\n",
       "      <td>0.016242</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.055556</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Level  Precision    Recall  F-measure  Taxon Accuracy Rate  \\\n",
       "Method                                                                    \n",
       "blast+         6.0   0.032484  0.032484   0.032484             0.090909   \n",
       "naive-bayes    6.0   0.016242  0.016242   0.016242             0.045455   \n",
       "\n",
       "             Taxon Detection Rate  \n",
       "Method                             \n",
       "blast+                   0.111111  \n",
       "naive-bayes              0.055556  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mock_results_6.groupby(\"Method\").median()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17bf70fe",
   "metadata": {},
   "source": [
    "In the following heatmaps, we assess accuracy rates in each dataset X method configuration combination. This allows us to assess how evenly configurations affect performance, whether specific mock communities outperform of underperform relative to others, and generally assess how increasing/decreasing specific parameters affects accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "d29e554a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 100x490 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot: title={'center': 'F-measure'}, xlabel='Dataset', ylabel='Method-Parameters'>"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heatmap_from_data_frame(mock_results_6, \"F-measure\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55e70082",
   "metadata": {},
   "source": [
    "### Method optimization  \n",
    "Which method/parameter configuration performed \"best\" for a given score? We can rank the top-performing configuration by dataset, method, and taxonomic level.\n",
    "\n",
    "Now we can determine which parameter configuration performed best for each method. Count best values in each column indicate how many samples a given method achieved within one mean absolute deviation of the best result (which is why they may sum to more than the total number of samples)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "2b1d4528",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## naive-bayes"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>[10,10]:0.9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[7,7]:0.9</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[10,10]:0.5</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[7,7]:0.5</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>[7,7]:0.7</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Taxon Accuracy Rate  Taxon Detection Rate  Precision  Recall  \\\n",
       "[10,10]:0.9                    2                     2          2       2   \n",
       "[7,7]:0.9                      2                     2          2       2   \n",
       "[10,10]:0.5                    2                     2          1       1   \n",
       "[7,7]:0.5                      2                     2          1       1   \n",
       "[7,7]:0.7                      2                     2          1       1   \n",
       "\n",
       "             F-measure  \n",
       "[10,10]:0.9          2  \n",
       "[7,7]:0.9            2  \n",
       "[10,10]:0.5          1  \n",
       "[7,7]:0.5            1  \n",
       "[7,7]:0.7            1  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/eval_framework.py:60: FutureWarning: The 'mad' method is deprecated and will be removed in a future version. To compute the same result, you may do `(df - df.mean()).abs().mean()`.\n",
      "  mad_metric_value = m_res[metric].mad()\n"
     ]
    },
    {
     "data": {
      "text/markdown": [
       "## blast+"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.001:10:0.75:0.8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001:10:0.75:0.97</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001:10:0.51:0.97</th>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>0.001:1:0.51:0.8</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>0.001:1:0.75:0.8</th>\n",
       "      <td>2</td>\n",
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       "      <td>0.0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Taxon Accuracy Rate  Taxon Detection Rate  Precision  \\\n",
       "0.001:10:0.75:0.8                     2                     2        2.0   \n",
       "0.001:10:0.75:0.97                    2                     2        2.0   \n",
       "0.001:10:0.51:0.97                    2                     2        0.0   \n",
       "0.001:1:0.51:0.8                      2                     2        0.0   \n",
       "0.001:1:0.75:0.8                      2                     2        0.0   \n",
       "\n",
       "                    Recall  F-measure  \n",
       "0.001:10:0.75:0.8      2.0        2.0  \n",
       "0.001:10:0.75:0.97     2.0        2.0  \n",
       "0.001:10:0.51:0.97     0.0        0.0  \n",
       "0.001:1:0.51:0.8       0.0        0.0  \n",
       "0.001:1:0.75:0.8       0.0        0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for method in mock_results_6['Method'].unique():\n",
    "    top_params = parameter_comparisons(\n",
    "        mock_results_6, method, \n",
    "        metrics=['Taxon Accuracy Rate', 'Taxon Detection Rate', 'Precision', 'Recall', 'F-measure'])\n",
    "    display(Markdown('## {0}'.format(method)))\n",
    "    display(top_params[:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9fb926",
   "metadata": {},
   "source": [
    "And, finally, which method performed best at each individual taxonomic level for each reference dataset (i.e., for across all fungal and bacterial mock communities combined)?\n",
    "\n",
    "For this analysis, we rank the top-performing method/parameter combination for each method at family through species levels. Methods are ranked by top F-measure, and the average value for each metric is shown (rather than count best as above). F-measure distributions are plotted for each method, and compared using paired t-tests with FDR-corrected P-values. This cell does not need to be altered, unless if you wish to change the metric used for sorting best methods and for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "3ea579b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 4"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.51:0.97</td>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.505890</td>\n",
       "      <td>0.505890</td>\n",
       "      <td>0.505890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
       "      <td>0.683333</td>\n",
       "      <td>0.763889</td>\n",
       "      <td>0.502776</td>\n",
       "      <td>0.502776</td>\n",
       "      <td>0.502776</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97             0.763889              0.763889   \n",
       "1  naive-bayes         [10,10]:0.5             0.683333              0.763889   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
       "0   0.505890  0.505890   0.505890  \n",
       "1   0.502776  0.502776   0.502776  "
      ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>-1.092406</td>\n",
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       "      <td>0.471904</td>\n",
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      ],
      "text/plain": [
       "                          stat         P     FDR P\n",
       "Method A    Method B                              \n",
       "naive-bayes blast+   -1.092406  0.471904  0.471904"
      ]
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     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 5"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
     "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>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.75:0.8</td>\n",
       "      <td>0.314935</td>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.152626</td>\n",
       "      <td>0.152119</td>\n",
       "      <td>0.152372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.9</td>\n",
       "      <td>0.290210</td>\n",
       "      <td>0.348485</td>\n",
       "      <td>0.149695</td>\n",
       "      <td>0.147860</td>\n",
       "      <td>0.148764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method         Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.75:0.8             0.314935              0.393939   \n",
       "1  naive-bayes        [10,10]:0.9             0.290210              0.348485   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
       "0   0.152626  0.152119   0.152372  \n",
       "1   0.149695  0.147860   0.148764  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>-0.096171</td>\n",
       "      <td>0.938963</td>\n",
       "      <td>0.938963</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          stat         P     FDR P\n",
       "Method A    Method B                              \n",
       "naive-bayes blast+   -0.096171  0.938963  0.938963"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.75:0.8</td>\n",
       "      <td>0.139205</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.107043</td>\n",
       "      <td>0.106619</td>\n",
       "      <td>0.106830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.9</td>\n",
       "      <td>0.112121</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.103818</td>\n",
       "      <td>0.102360</td>\n",
       "      <td>0.103078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method         Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.75:0.8             0.139205              0.180556   \n",
       "1  naive-bayes        [10,10]:0.9             0.112121              0.138889   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
       "0   0.107043  0.106619   0.106830  \n",
       "1   0.103818  0.102360   0.103078  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>-0.099618</td>\n",
       "      <td>0.93679</td>\n",
       "      <td>0.93679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          stat        P    FDR P\n",
       "Method A    Method B                            \n",
       "naive-bayes blast+   -0.099618  0.93679  0.93679"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 4"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
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      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97                  0.0                   0.0   \n",
       "1  naive-bayes         [10,10]:0.5                  0.0                   0.0   \n",
       "\n",
       "   Precision  Recall  F-measure  \n",
       "0        0.0     0.0        0.0  \n",
       "1        0.0     0.0        0.0  "
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       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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      "text/plain": [
       "                      stat   P  FDR P\n",
       "Method A    Method B                 \n",
       "naive-bayes blast+     NaN NaN    NaN"
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     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 5"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
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       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
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       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "boxes = rank_optimized_method_performance_by_dataset(mock_results,\n",
    "                                                     dataset=\"Reference\",\n",
    "                                                     metric=\"F-measure\",\n",
    "                                                     level_range=range(4,7),\n",
    "                                                     display_fields=[\"Method\",\n",
    "                                                                     \"Parameters\",\n",
    "                                                                     \"Taxon Accuracy Rate\",\n",
    "                                                                     \"Taxon Detection Rate\",\n",
    "                                                                     \"Precision\",\n",
    "                                                                     \"Recall\",\n",
    "                                                                     \"F-measure\"],\n",
    "                                                     paired=True,\n",
    "                                                     parametric=True,\n",
    "                                                     color=None,\n",
    "                                                     color_palette=color_palette_method)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "5cc02fbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save plots\n",
    "\n",
    "for k, v in boxes.items():\n",
    "    v.get_figure().savefig(join(outdir, 'mock-fmeasure-{0}-boxplots.pdf'.format(k)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "14ed9a75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "## Taxon Accuracy Rate"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.51:0.97</td>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.060928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
       "      <td>0.139205</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.060928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97             0.150000              0.180556   \n",
       "1  naive-bayes         [10,10]:0.5             0.139205              0.180556   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
       "0   0.060928  0.060928   0.060928  \n",
       "1   0.060928  0.060928   0.060928  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>-6.333333</td>\n",
       "      <td>0.099696</td>\n",
       "      <td>0.099696</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          stat         P     FDR P\n",
       "Method A    Method B                              \n",
       "naive-bayes blast+   -6.333333  0.099696  0.099696"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
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       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
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      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97                  0.0                   0.0   \n",
       "1  naive-bayes         [10,10]:0.5                  0.0                   0.0   \n",
       "\n",
       "   Precision  Recall  F-measure  \n",
       "0        0.0     0.0        0.0  \n",
       "1        0.0     0.0        0.0  "
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      stat   P  FDR P\n",
       "Method A    Method B                 \n",
       "naive-bayes blast+     NaN NaN    NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Taxon Detection Rate"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.51:0.8</td>\n",
       "      <td>0.139205</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.061200</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.061064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
       "      <td>0.139205</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.060928</td>\n",
       "      <td>0.060928</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method         Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.8             0.139205              0.180556   \n",
       "1  naive-bayes        [10,10]:0.5             0.139205              0.180556   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
       "0   0.061200  0.060928   0.061064  \n",
       "1   0.060928  0.060928   0.060928  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      stat   P  FDR P\n",
       "Method A    Method B                 \n",
       "naive-bayes blast+     NaN NaN    NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.51:0.97</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97                  0.0                   0.0   \n",
       "1  naive-bayes         [10,10]:0.5                  0.0                   0.0   \n",
       "\n",
       "   Precision  Recall  F-measure  \n",
       "0        0.0     0.0        0.0  \n",
       "1        0.0     0.0        0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "                      stat   P  FDR P\n",
       "Method A    Method B                 \n",
       "naive-bayes blast+     NaN NaN    NaN"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Precision"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.75:0.8</td>\n",
       "      <td>0.139205</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.107043</td>\n",
       "      <td>0.106619</td>\n",
       "      <td>0.106830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.9</td>\n",
       "      <td>0.112121</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.103818</td>\n",
       "      <td>0.102360</td>\n",
       "      <td>0.103078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method         Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.75:0.8             0.139205              0.180556   \n",
       "1  naive-bayes        [10,10]:0.9             0.112121              0.138889   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
       "0   0.107043  0.106619   0.106830  \n",
       "1   0.103818  0.102360   0.103078  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>-0.086859</td>\n",
       "      <td>0.944843</td>\n",
       "      <td>0.944843</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          stat         P     FDR P\n",
       "Method A    Method B                              \n",
       "naive-bayes blast+   -0.086859  0.944843  0.944843"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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cHOXk5KikpESpqanNXRMAAECTBDxGxul06vHHH1dWVpYuueQS7dmzR2PGjNGBAweCUR8AAMAxBRxknnzySf3nP//RX//6V9lsNrVo0UIZGRmaMWNGMOoDAAA4poAvLS1btkyvvPKK0tPTZTKZFBsbqz//+c+67LLLglEfAADAMQXcI1NTU+MfF+Pb3SA6Olpmc8BvBQAAcFICTh+ZmZl68sknJcm/eeSLL76oXr16NW9lAAAAxxHwpaV77rlHEyZM0BtvvKHq6moNHTpU1dXVev7554NRHwAAwDEFHGTS0tL0zjvv6OOPP9bevXuVkZGhgQMHKj4+Phj1AQAAHFPAQWb48OFaunSphgwZEox6AAAAmuyERujW1tY2dx0AAAABC7hHJicnR+PGjdNFF12kVq1aNXru1ltvbbbCAAAAjifgIPP999+rXbt22rlzp3bu3Olv981gAgAACJWAgsyTTz6p+Ph4XXjhhRo/fnywagIAAGiSJo+ReeSRR/Tyyy/LZrNp9uzZevbZZ4NZFwAAwHE1Oci8/fbbeuGFFzR79mzNnj1by5YtC2ZdAAAAx9XkIFNZWanOnTtLkrKysrR///6gFQUAANAUTQ4yh++lZLUGPEYYAACg2TU5yPg2iAQAAIgUTe5acblcevPNN/2PnU5no8eSNGrUqGYqCwAA4PiaHGTS0tI0e/Zs/+OUlJRGj00mE0EGAACEVJODzL/+9a9g1gEAABCwE9prCQAAIBIQZAAAgGGFJcgUFxcrLy9P2dnZysnJ0YwZM+RyuX72NVu3btW5556rzz//PERVAgCASBeWIDNlyhTFxsZq9erVWrJkidasWaMFCxYc8/ja2lrdeeedqqurC12RAAAg4oV8Zbtdu3Zp7dq1WrVqlWJiYtSuXTvl5eVp1qxZmjRp0k++Zvr06br00ku1devWEz6v2+0+4dcCAIDQaurv7ZAHmW3btik5OVnp6en+tk6dOqmwsFAVFRVKTExsdPybb76pXbt2acaMGZo7d+4Jn3fTpk0n/FoAABCZQh5kqqurFRMT06jN97impqZRkMnPz9fjjz+uV155RRaL5aTO26tXr5N+DwAAEBput7tJnRAhDzKxsbGqra1t1OZ7HBcX52+rr6/X1KlTdc8996hNmzYnfV6LxUKQAQDgFBPywb6dO3dWWVmZioqK/G35+fnKyMhQQkKCv23Tpk0qKCjQvffeq+zsbGVnZ0uSfve73+n+++8PddkAACAChbxHpkOHDsrKytLMmTP1wAMPqLS0VHPnztXYsWMbHZedna2NGzc2auvataueeeYZ5eTkhLJkAAAQocIy/Xr27NlyuVwaNGiQrrzySvXv3195eXmSpN69e2vp0qXhKAsAABiMyev1esNdRDC53W5t2LBBmZmZjJEBAMAgmvr7my0KAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYRFkAACAYVnDXQDQHIqKirRgwQLV1NRIksxms4YMGaKsrKwwVwYACCaCDE4JH3zwgdatW9eorbS0lCADAKc4Li3hlPDtt99KkrolWjW2XYwk6fvvv1dFRUU4ywIABBlBBoZXU1Oj/Px8SdKvz4zRgFZRah3d8E/766+/DmdpAIAgC0uQKS4uVl5enrKzs5WTk6MZM2bI5XL95LGvvPKKBg8erN69e2vw4MFatGhRiKtFpNu4caPcbrdaRpnVIsoiSeqeZJMkrV+/PpylAQCCLCxBZsqUKYqNjdXq1au1ZMkSrVmzRgsWLDjquI8++kiPPfaYHn74Ya1fv14PPfSQnnjiCb3//vuhLxoRa+3atZKkXsk2f1vPw4LMsUIyAMD4Qj7Yd9euXVq7dq1WrVqlmJgYtWvXTnl5eZo1a5YmTZrU6Nj9+/frxhtvVGZmpiSpd+/eysnJ0bp16zR48OCAzut2u5vrW0AEqaur0xdffCFJyjwsyHSMtyjRalJFdbW++uor9enTJ1wlAgBOQFN/b4c8yGzbtk3JyclKT0/3t3Xq1EmFhYWqqKhQYmKiv338+PGNXltcXKx169bp7rvvDvi8mzZtOvGiEbE2b96s+vp6pdnN6hBn8bebTSb1SbXp4wMOLVu2TGYzw8EA4FQU8iBTXV2tmJiYRm2+xzU1NY2CzOEOHjyom2++WT179tTw4cMDPm+vXr1ksViOfyAM5a233pIk5aTZZTKZGj2X0yJKHx9waPv27frFL36hpKSkcJQIADgBbre7SZ0QIQ8ysbGxqq2tbdTmexwXF/eTr9mwYYNuv/12ZWdn689//rOs1sDLtlgsBJlTTEFBgb777juZTVK/Fvajnj8j1qIOcRYVVLv1r3/9S2PHjg1DlQCAYAp5f3vnzp1VVlamoqIif1t+fr4yMjKUkJBw1PFLlizRhAkTdP311+svf/mL7Pajf2Hh9LRs2TJJDWNjkuw//U95QKsoSQ0L5jkcjpDVBgAIjZAHmQ4dOigrK0szZ85UVVWV9uzZo7lz5/7kp+X3339f999/v+bMmaPf/va3oS4VEWz//v367LPPJEmD0qOOeVzvFJtS7SaVl5dr5cqVoSoPABAiYRkBOXv2bLlcLg0aNEhXXnml+vfvr7y8PEkNM5OWLl0qSXryySfldrt12223qXfv3v7bfffdF46yEUHefPNNeTwenZ1o1Zlxx77UaDGZNCg9WlLDeBqn0xmqEgEAIRCWvZbS0tI0e/bsn3zuq6++8t/3XToADrdv3z598sknkqRftY4+7vH90uz6cF+diouL9dFHH2nIkCHBLhEAECLMSYXhLF68WG63W90TreoYf/wsbjObdPmhwPPGG2+orq4u2CUCAEKEIAND2bFjh39szPC2x++N8Tk/za60KLPKy8vp6QOAUwhBBobh9Xr10ksvSZKyU21qF9v0K6MWk0m5bRqCz7Jly1RaWhqUGgEAoUWQgWGsX79emzdvltUk5baJOf4LjtA7xaYOcRbV19frH//4RxAqBACEGkEGhuByufy9MQNbRSk1KvB/uiaTSVec0RCAVq5cqd27dzdrjQCA0CPIwBBWrFihwsJCxVt/HLh7IjrGW5WZbJPX69WLL77YjBUCAMKBIIOIV1NTo9dee02SNKR1tGIspuO84ueNOCNaFpO0ceNGbdiwoRkqBACEC0EGEW/p0qWqrKxUqyizLmh58ltUtIyy6KKWDasBL1q0SB6P56TfEwAQHgQZRLSysjItX75ckjSibbQsppPrjfEZ3DpK0WZp9+7d+vTTT5vlPQEAoUeQQUR78803VV9frw5xFp2TbGu2942zmnVpRsNYmyVLlsjtdjfbewMAQocgg4hVWlqqjz76SJI0rE20TM3UG+MzsFWU4q0m7du3T//+97+b9b0BAKFBkEHEeuedd+R0OvWLOIu6JjT/tmBRFpMuObRztm8TSgCAsRBkEJFqamr8vTGXZzR/b4zPhS2jFGORCgsLG21YCgAwBoIMItKqVatUW1urVlFmdU8K3ibtMRaTzk9r6JV57733gnYeAEBwEGQQcbxer1asWCFJuqhVlMwB9MZ4vV55vd6AzndhS7tMalhXZv/+/QG9FgAQXgQZRJyCggLt3r1bVpN0XmrTZyp5vV49/l2VnviuKqAwkxb14xicVatWBVwvACB8CDKIOGvWrJEk9UyyKdba9H+iDo+0s9qtHdVuOQIct5vdwt7o3AAAYyDIIOKsW7dOUsNu1aFyTrJNFpO0d+9eFRYWhuy8AICTQ5BBRDlw4IAKCwtllnR2UuiCTIzFpE7xDZeX/vvf/4bsvACAk0OQQUT55ptvJEnt4ywBbQ7p8Hj1UkGN//FLBTVyeAIb9NstsSHIbN68OaDXAQDChyCDiLJ161ZJUsf4wKZcLyqo0YYyp//xhjKnFh0WbJrCd85t27YF9DoAQPgEb4EO4AQUFBRIauiRaSqnx6tNh4UYn01lTjk9XtnMTevZaRdrkUkNG1WWlZUpOTm5yTUAaDqXy6Xy8nKVlpaqtLRURUVFKi4ulsvlapb3N5lMSk5OVlpamlJTU5WSkqLk5GRFR0c3y/sjshBkEDG8Xq++//57SVLr6KYHmQqnR86fuIrk9DY81yKqae9lN5uUFmXWwXqPvv/+e4IMcBwej0e1tbWqqalRdXV1o1tVVZWqqqpUWVnpv5WXl6uiokJVVVVhqTcqKkrJyclKSEhQYmKiEhISlJCQoPj4eP8tLi7Of4uNjVVsbKysVn5VRjL+dhAxKioqVF9fL5OktKjwXPVseSjI7N+/Xz179gxLDUAweb1eOZ1O1dXVqba2VrW1tf77h7cdfqupqWl033erq6sLeAFKfx0ms9y2BHnsSXLZk+SOSpHX3DwD/E1ej8yOclkdZTI7KmRxlMvscaq+vl779+8PeOHLqKgoxcbGKiYmxh9uYmJi/LfY2FhFR0c3avM9Prw9KioqaNutnM4IMogYxcXFkqQEq0nWJl4Oam4p9oYAVVJSEpbzA0fyBY+fCx11dXWN2n/q8eH33W5389ZosshjjZXHGiOPJebH+7a4hq/W+Ib7tni5bfHy2OLlscZKpgA/sHgOXUIONPB4vTK562V2VsnirJTZWSWzq1oWZ7XMrhqZXb6vtTK5ag/dr5PZUy9Jqq+vV319vUpLSwM77xFMJpOio6P9N1/Q+anHP3X/yJAUFRUls5mhrgQZRIzKykpJUrwtfJ9Y4q2mRrUAJ8Pr9crhcKiqqsp/yeXwyzC+ng5fD4fvvi94+O43d/Dw8Zht8lqi5LVEyWOJPvQ1Sl5L9KGvUfJYYn583trQ7rHEHLofI481OvBgESCT26GU7YsVU/K1JKk2tadKz7pKXou9iW9gktcaLbc1Wu6YtKaf2OOW2V0nk7tOZlftYfcPfXXXH/pa1xCU/F/rD3u+Xia3QyY1bJ/iC5/N4fBg5OsZOryX6PAeJN+lMt9X36W0mJgYw/cSEWQQMXz/uaMDmHbd3HznrqkJbMYTTi91dXUqKipSUVGRSktLVVZWpvLycpWVlamyslIVFRWqrKxUVVWVnM6jB6KfKI/Z3ihs/Hjf3hA0zL77UYc9d+i++bD7h74G3CMSJinbFyu2eIP/se9+Sddrg3tis0Uec5xki9NJRUmvVyaP47CQ03Azexwy+cOP47Aw5JDJ8+P9H19zWDg6IhidaG+R2WxWXFycf7xQYmKikpKSlJSUpOTkZKWkpKhFixZKS0tTQkJCRIYeggwihu8HvvUk/qMMGjRIw4cP19tvv60VK1aoPIDBvpL8l7Saa/YEjM/tdmvjxo36+uuvtXPnTu3du1dlZWUBvYfXZJbH0nC5xWuNkccSfegyjK+XI7qhF8R66Ks/pEQbMng0K49TMaVfH9UcU7Kx4VJTkHuDmoXJ9GOIbI7383pl8jiP6BWqP6y3yBeCahsukbnrZHLVyex/XNNwCc3jlMfj8Q/GPp6YmBi1bt1a7du3V9euXXXeeecpPj6+Ob6jk0KQQcQ5mbw/fPhwtWnTRsOHD9eKFStUUu9RxwD+n0XeZw2Ek9fr1QMPPKAtW7Yc9ZzHEi1XVIo89kS5bYly2xPksSXIbYuTxxp3aHxInDzW2EMhhH9dJ8LiqJTJ0/DB4sgPKraqvXImdghvgeFgMslrsTf0vp3M+3icDeOBnDWyuKpldlYfGkdUJbOzUhZnhSyOClnqy2RxVqq2tlY7duzQjh07tHLlSi188UX9eeZMZWRkNNd3dkIIMogYNlvDJyvXCc6CkKS3337b/4NOklIDnP3kOrQaMNMtITX0Eubn5zdqc1tjVZ/cVY64tnJHpchtT5LbniiPLb4hsCBojvygYq0vlVMdwl2WcZlt8tiTGmaOHd7ucR8KND8GGWvdQUWXbZWt9scZXzXV1dqzZw9BBvDxLVZV5z7xILNixQqtWLHC/zjJFliQqTsUZFg4C5Jkt9s1depUvfnmm9q2bZu8Xq8srhrFFn2l2KKvjjreY7Y3zMixxcnt75XxXVKKPTQ4NsY/u+fHy0qn6WWjJnDbE+Q1WWTyuht/UDGZ5IhvF+7yIpd/XE7dYZeXag9dXqr1z9Lyz9hyVjfcd1bL4jr+GMFWrVpp4MCBysrKCsE38/MIMogYCQkJkqRq14kHmZNVdWhlvcTExLDVgMiSlZWlrKws1dXVqaCgQIWFhdq3b59/NdqSkhKVlpbK6XTK7HHIXF8i1Qc+fd/jHxNz+IyhxmNlfmpW0eFjaDyWKHnNUZK56ePCIp7ZptrUXoot3tDog0p9XLvAZiAZgddz1KDgRoODj5oNVf8zM6bqZPKe+IUns9mspKQkpaSkKDU1VS1btlSrVq3Upk0bnXnmmUpNTW3Gb/zkEGQQMXz/MSqcXrm9XlnCMKagzNnwHz8lJSXk50Zki46OVrdu3dStW7ejnvPNHvGtXOsbPOlb2fbIVW99062rq6v9A8vNh34JSeUnXavXZD1i5tKh2U7m6B/v+0OS/YiwdHSQCndvUelZV0mSYoo3SfKqLrlb8GcsNcVRs5F+HHTbuM3RKGyY3A6ZPUcGlIZZTM3NZDIdc/p1XFycfxr2kbOW4uPjDbNGDUEGESMpKUk2m01Op1PF9R61auI2BYk2s2wmHbVNgc3U8FwgDtY3BJn09PSAXofTm++XRWxsrFq3bh3Qa51O51HrydTV1fnbjlxt98gF8Q7/6gtFJq9LFpdLclU3y/fnMUfJaz28x6hhhlXj9WQabl5LjDy22EMDnWNObOG7I3gt9obgcqIL4h3zjQ8tlOdfEM93uaXhZvJdhjls1k/D8b61Y+pkUvP3IJtMpqMWxPup1YKjo6P9qwr71o3x3ffdTofVhAkyiBhms1lt27ZVQUGBfqhzNznI2Mwm9Uq2aX1p4/U6eiXbmrxhpNSw+eTBuoYg07Zt26YXDpwEm83mX7fjZLlcruOu+Hvk/Z/bosC3EJ/ZUy856mVRRcA1eWU6FGjiDlvd99AML3tiwy0qWS57iryWJgYUz8+vzWPyemRxlDfMtnGUy+KoaJiF42hY1dfiqvKPCTmZyy/+8x1amO54WxT81Eq9P7Wyr91uP+XDR3MiyCCidOjQQQUFBdpT7da5yU1/3fgOsfJ4a7Th0C7Ymck2je8QG9C599a65VHDWJ1Iuv4LNJXVavVfKjhZR26NcOTqw0feDt8s8vBLaCY1DJC2uGqkuoPN8F02P7vd3mjDSN/9w1fFPXyV3MN7QU6XXo9IRpBBROncubM+/vhj7agKbB1Nu9mkazrEasOGhvEF13SIlT3A/Zp2VDV0y5911ln8UMJpz2QyyW63y263n3Bvkdvt9o8T8o0dKi8vV3l5uX9F5JKSEpWUlKi8/OTHBh0uOjpaaWlpSk1NVUpKipKTk/09X4mJiY12v7bbm7jVASISQQYRpXv37pKkndUu1bu9igrhdgXfVbga1QDg5FgsliZfNnM4HM22p5TJZKKX5DRCkEFEad26tVq2bKmDBw9qa6VLvZJDs/y4w+PVtsqGIHPuueeG5JwAfkSvCE6UMeZW4bRhMpmUnZ0tSfqqNLCpiHaz1DHOoo5xFtkD/Je9udwpp7dhkaczzzwzsBcDAMKGIIOI07dvX0nSxjKn6gNY5ddkMmlK13hN6RofcJfyuuKG0NSvXz+6owHAQAgyiDhdu3ZVRkaG6j3S+gB7ZUwmU8BBpNzp0ebyhstKF110UUCvBQCEF0EGEcdkMumSSy6RJK064JD3JDaRbIpPDzrkkdSlSxedccYZQT0XAKB5EWQQkS6++GLZbDZ9X+vW9gCnYgfC4fHq3wfrJUlDhgwJ2nkAAMFBkEFESkxM1MCBAyVJH+6rC9p5/lPkUKXLq7S0NOXk5ATtPACA4CDIIGLl5ubKbDbr2wqXCqpdzf7+To9XHx0KSSNGjJDFcgrtGAwApwmCDCJWenq6+vfvL0l6Z2/z98p8VuRQqdOrlJQUXXzxxc3+/gCA4CPIIKKNHTtWFotFWypd+q7i5zeKC0S926v3f2gIR6NHj2YxLgAwKIIMIlqrVq106aWXSpLe2lsnTzPNYFqxv06VLq/S09P9M6QAAMZDkEHEGzNmjGJiYrSnxq0vS06+V6bc6dGK/Q0zlX7zm9/IamWnDgAwKoIMIl5SUpJGjhwpSVpWWCuH5+R6Zd7ZWyeHp2Gnbd8qwgAAYyLIwBCGDRumFi1aqNTh1ceHelNOxN4at/5zaDuCa6+9lu0IAMDgCDIwBLvdrt/85jeSGtaVqXR6An4Pr9erN7+vlVcN+zl17dq1masEAIQaQQaGccEFF6hjx46q80jv/hD4dOxvK1zaUumS1WrV1VdfHYQKAQChRpCBYZjNZl1zzTWSGvZHOlDX9K0LPF6v3tpbK0kaPHiw0tPTg1IjACC0CDIwlB49eqh3797ySHqnsOm9MutLnSqs9Sg2NlZXXHFF8AoEAIQUQQaG4xsrs77Uqb01x++VcXu9Wl7441YECQkJQa0PABA6BBkYTvv27dWvXz9J0ntNGCvzZYlTB+s9SkhIYIdrADjFEGRgSGPGjJEk/bfMqX0/M1bG4/X6d8/Ozc1VdHR0SOoDAIQGQQaG1K5dO2VnZ8sr6V8/s67M5nKX9tU1jI257LLLQlcgACAkCDIwrNzcXEnSF8UOVbt+el2Zjw80hJxBgwYpNjY2ZLUBAEKDIAPD6tq1qzp06CCnV/r80Gq9hztQ59bWSpdMJpMGDx4chgoBAMFGkIFhmUwm/87Y/ylyyHvEzti+rQgyMzPVsmXLkNcHAAg+ggwM7fzzz5fNZtMPdR7trf3x8pLX69WXJQ1BZsCAAeEqDwAQZGEJMsXFxcrLy1N2drZycnI0Y8YMuVyunzz2k08+UW5urjIzMzVkyBCtXLkyxNUiksXFxSkzM1OStKH0x8tLu2rcKnF4FRUVpaysrDBVBwAItrAEmSlTpig2NlarV6/WkiVLtGbNGi1YsOCo4woKCjR58mTdfvvt+uKLLzR58mRNmTJF+/fvD33RiFg5OTmSpE3lTn/b12UN93v37i273R6WugAAwWcN9Ql37dqltWvXatWqVYqJiVG7du2Ul5enWbNmadKkSY2OfeONN5Sdne0fBzF06FC9/vrrWrx4sW677bYmnc83bsLhcMhisTTvN4OI0KNHD0VFRanI7VWZx6JEm1nbautlt9uVmZkph+PogcAAgMjmdjesEXbk+McjhTzIbNu2TcnJyY027evUqZMKCwtVUVGhxMREf/v27dvVpUuXRq8/66yztGXLliafz+NpGDfxzTffnGTliGS33367JKny0G3swB+f27RpUzhKAgA0A9/v8WMJeZCprq5WTExMozbf45qamkZB5qeOjY6OVk1NTZPPZ7Va1atXL5nNZplMppOoHAAAhIrX65XH45HV+vNRJeRBJjY2VrW1tY3afI/j4uIatcfExKiurvFeOnV1dUcd93PMZjNjJAAAOEWFfLBv586dVVZWpqKiIn9bfn6+MjIyjtqVuEuXLtq2bVujtu3bt6tz584hqRUAAES2kAeZDh06KCsrSzNnzlRVVZX27NmjuXPnauzYsUcdO2LECK1du1bLly+Xy+XS8uXLtXbtWo0cOTLUZQMAgAhk8h5vOHAQFBUV6YEHHtDnn38us9msUaNG6Q9/+IMsFot69+6t6dOna8SIEZKk1atX69FHH9Xu3bvVtm1b3XXXXSxwBgAAJIUpyAAAADQHtigAAACGRZABAACGRZABAACGRZABAACGRZABAACGRZABAEDH35wQkYkgg9PK8TYfA3D68P08cLlcksR+fAZFkMEp7/BPWb4fVLt37w5XOQAigNvtltlsVn5+vmbMmKEbbrhBBQUFkuiZMRqCDE55JpNJ//73v/Xpp5/KZDLpmWee0bRp047akBTA6cNisWjLli269tprlZKSoqysLHXo0EGS5HQ6w1scAhLy3a+BcPj222/1l7/8RWPGjNHq1au1cOFCRUdHh7ssAGHicDj02GOP6dZbb9XVV1+tqqoqPfDAAyotLVVGRoZuvPFGpaamhrtMNAE9Mjhleb1e/zXwG2+8UQMGDNDrr7+uqVOnqkOHDnI4HGGuEEC4OBwO1dTUqLKyUgsXLtSIESO0fft2xcXF6cMPP9TBgwfDXSKaiB4ZnHIqKyuVkJAgk8nkDzJPP/20tmzZot/+9rf63//9XyUnJ+viiy8Oc6UAQsXj8chs/vGze3x8vPr376958+bpggsu0NixY5WXlydJGj9+vCorK8NVKgJEkMEp56mnnlJZWZkeeughbdq0SdHR0VqyZIlefPFFnXnmmYqJidHvf/97rVmzRklJSY1+uAE49bjdblksFu3atUvr1q1Tfn6+hg4dqhtuuEHXX3+9HA6HfyLAH//4R9XW1qp3795hrhpNxe7XOKW43W6tWbNGM2fOVGFhoUaOHKnp06ertLRUKSkpcrlcslqt2rNnj9q1axfucgEEmdfrlclk0nfffaeJEydqwIABKi4u1v79+9WlSxfdddddKiws1MSJE9W5c2c5nU69/PLLstls/gCEyEaPDE4pFotFvXv3ltPplNfr9fe2pKSkyOFwyG63S5I/xPh+yAE4NZlMJtXU1GjWrFmaOHGibrjhBklSTk6OLr74YkVFRemcc87RE088odTUVHXp0kUWi8X/oQeRjz51nBLcbrf/fnR0tO677z799a9/1bp163TPPfdIkux2e6PjJBbAAk5Vh/9fd7lcKi4u1oUXXihJGj16tAYMGKDx48fr6quv1tatW3XBBRfo7LPPlsVikcfjIcQYCEEGhudyufw/fF588UUtXLhQrVq10gUXXKCbbrpJGzZs0P/7f/9PknTgwAEWuwJOAxaLRTt37tTGjRuVkJCgpKQkbdiwQWPGjFHHjh31yCOPyG63y2QyKT4+vtFrGTdnLEROGJrX65XVapXH49GVV14pm82mVq1aaeTIkbLZbBo+fLisVqtmzZqlyy67TA6HQ0uXLlVSUlK4SwcQZK+++qpWrlypDz74QN27d9e0adM0cOBAPfroo5Kk+++/XykpKWrdunWYK8XJIMjA0HyXhqZMmaIuXbpo5syZkqRly5Zpx44dat26ta688kq1bt1aK1eu1LBhwwgxwGniqquu0rZt27Rx40b9z//8j0pLS7Vt2zZdc801io2NVVFRkRYvXuxfqoGeGGNi1hJOCdOmTVPnzp3VvXt3zZkzR9u2bdN5552n999/Xy+99JKys7PDXSKAIPIFkcMH9TudTt1yyy2Ki4vT448/Lkn65JNPVF1dLbvdrosvvpiBvacA/uZgOEdOiXS5XDKZTFqwYIFatWqlVq1aacWKFfJ4PHI6nYqKigpjtQBCwWw2a9++fbr++us1ZswYZWVlKSsrS3fddZfuuusuffzxxxo4cKAGDBjQ6HVut5sQY3D87cFQfCHG4/HomWeeUXJysrKysnTvvfdq586dMpvNatOmjWJiYvSPf/xD3377rdLS0sJdNoAgOfySUEZGhgYOHKj169frb3/7m8aOHausrCxlZmb6txw48oMQ68QYH5eWYBi+H1gej0cjR45UdHS0KioqlJ6e7l/oatu2bZo7d67Ky8u1detWPfPMM+rZs2e4SwcQBL5QUlhYqK+//lqdOnVSp06dVF1drTVr1mj+/PlKSUnRypUrdeaZZ+r5559X27Ztw102mhlBBoYzadIkpaam6pFHHtHbb7+tv//970pNTdV1112n7OxsLV68WPHx8erbt6/OPPPMcJcLIAh8H2y2bNmiSZMmKS4uTm63W7/97W+Vm5urhIQEHThwQAcOHNDLL7+szz77TBMmTNCECRMY2HuKIcgg4uXn56tTp07yer0qKCjQPffco1deeUWSNGPGDNXU1Gj//v2qrq7WNddco2HDhoW5YgChsGvXLl133XWaNGmSxo0bp1tuuUUVFRUaMWKErrjiikbrw/ztb3/TqlWr9OKLL4axYgQDkRQR7c0339To0aO1Zs0amUwm/wqd5eXl+tOf/qTNmzdrxowZuvDCC1VQUKAFCxaopKSERe+A08C7776rIUOG6Nprr1VFRYVSU1OVkJCgV199VUuXLlVJSYn/2BEjRujgwYMqKCgIX8EICoIMIlrPnj01evRoTZs2TZ9++qk6d+6sRYsW6YcfftCWLVu0YMECSVJ5ebmGDRumv/3tb0pNTWXrAeAUdfiHlB9++EHV1dVyuVy6+eab1bFjRz333HOqrq7W/Pnz/b0vLpdLy5cvV2VlpRISEsJVOoKEWUuISL51Hc466yz97ne/k8Vi0bRp0/Tggw/qggsuUGlpqWpra/Xee++psrJSr732mhYuXKjk5ORwlw4gCHwDew/f6HXixImKiorSnDlzlJGRod///veSpO7du6tfv34aP368JMlqtapTp0566aWX1KJFi7B9DwgOggwijm9dB5fLpQ8++EB2u10dO3ZUixYt/GHmvPPOU/fu3fXCCy+ourpa8+bNU8eOHcNdOoAgsVgsys/P18svvyy32620tDTdeuutkho++Pg+xNx9991yuVwaP368zGaz/0PRRRddFMbqEUwM9kVE+fDDD3XZZZfJ4/FozJgxioqKksvl0p49e5STk6P27dvrnXfe0RNPPKFzzjlHBw8elMViUWpqarhLB9DM6urqNH36dE2fPl1FRUUaNWqURowYobq6Oq1bt05xcXFavHixXnnlFS1atEhRUVEym8167bXXZLPZmJ10miDIIGJ8/fXXGjt2rH7/+9+rbdu2+uKLL/TQQw+ppKREmzZt0pQpU5Sbm6uWLVtqwYIFeuqpp9S3b99wlw0gSIqLizV06FCdddZZGjx4sGpqavS73/1OHo9HRUVFmjhxonr06KGHHnpIX3zxherq6tSvXz+2HTjNEFURMXr27KnZs2frueee07x583T22WdLkpKSkjRgwAD96U9/Un5+vvr27auxY8eyYy1wimvRooWWLl2qqqoqzZw5UzU1NZIa1pBp1aqVpkyZou+++05lZWXKzs7WhRdeKIvFwrYDpxn+phFRLr/8ctlsNt1xxx367rvvJKnR8uMej0fnnnuusrKy6DIGTgPp6el69tlnNXnyZK1Zs0aS/CGlXbt2ioqKktvtbvQath04vfCbABHn4osv1qxZs/TWW2/p+eef989Q2LNnj6xWqxwOByEGOE24XC6lp6drzpw5qqur04QJE5Sfn6+ysjLNnz9fiYmJjJE7zTFGBhHr/fff15133qn27durX79+eu+99zRv3jz16NEj3KUBCAHfOBe3260VK1bonHPO0Y033qht27Zp1KhRqqys1BNPPMHA3tMcf+uIWIMHD9ZTTz2l/Px81dTUaOnSpYQY4DRxeIgZNWqU1q9fr4yMDM2fP19t2rSR1+vVk08+KZvNJpfLRYg5jdEjg4j3ySef6IwzzlCnTp3CXQqAIPAtdudyueTxeGS32/3P9e/fXzk5OXr00Uf9bSUlJUpKSjpqgTycnggyAICw8V0S2rp1q+bOnat9+/apS5cuOuecc3TRRRdp8eLFmjx5sqQfA4/PkY9xeqIvDgAQNmazWTt27ND111+vs846S1OnTpXX69Wjjz6qgoICf4hxOp1HhRZCDCSCDAAgzD744AONGjVKt956q3JycrRhwwbl5uaqdevW2rBhgyTJZrOFt0hELIIMACCkfOu+eDweSVJBQYGqq6slSaNGjVLnzp1177336o477tDatWvDVieMgSADAAgpi8Wi3bt3a9q0aZKkrKwsHTx4UEOHDlXXrl312GOPSZKio6PVpk2bcJYKAyDIAABCzuPx6O2339aaNWvUr18/7d+/Xx6PR6NHj1ZdXZ3+8Ic/qKKiQkOGDAl3qYhwzFoCAATdkZs4OhwOPfjgg0pMTNRdd92lb775Rs8++6x27Nih5ORk2e12Pf3007LZbMxOws8iyAAAgubwAFNUVCSz2ezfUmDp0qW6//77tWTJEnXs2FFVVVWqr6+X2+1Wy5YtZTKZ2MUax0WQAQAERV1dnaZPn65JkyapXbt2GjlypFJSUtS1a1fdddddio2N1fTp0xUTE6MpU6bIZrM1WtyObQfQFAQZAEBQbN++XQUFBbrgggu0detWtWnTRqtWrdKrr76qqqoqXXrppdq/f7/MZrP+7//+j54XnBCCDAAgaDwejx544AFt2rRJd955p84//3xJ0qJFi7Rv3z4tWrRINTU1evDBBzVu3LgwVwsjIsgAAJrVkZeENm/erAULFqi0tFRjx47Vr371K/9zGzdu1H/+8x9NnDiRAb04IQQZAECz8c0w2rNnj1asWKFf/OIX6tu3r3744QfNmTNHFRUVGjdunC6//PKjXsvAXpwIggwAoFn4emK2bNmiCRMmqH379iooKNC4ceN00003qaSkRHPmzFFVVZVyc3M1fPjwcJeMUwDDwQEAzcJsNmvv3r265ZZblJeXp8WLF2vw4MH66KOPNG/ePKWlpWny5MlyuVz6+uuvw10uThH04QEATprX65XJZNJnn32mzMxMXXfddTp48KAcDocyMzO1fPlyeTwe/eY3v9GsWbOUnJwc7pJxiqBHBgBwwnwbQPq4XC45HA7V1NRo4sSJSklJ0UMPPaTWrVtr+fLlWrhwoVJTU2U2m/2bRgIngx4ZAMAJ8Q3s3bFjhxYtWqSWLVuqVatWmjNnjl588UWlpqbqj3/8oySpZcuWGjx4sK699lr/61nsDs2BIAMACJjX65XFYtG2bdv061//Wn369NF///tfWa1Wpaenq0WLFvJ6vdq5c6fmzZun4uJiXXPNNf6eGEIMmguzlgAAJ6SkpEQffPCB6urqNGHCBG3evFn//Oc/9e2336ply5aqqqpSWVmZTCaTXn31VdlsNkIMmh09MgCAgFVXV2vEiBEym826++67JUk9evSQyWTSG2+8oR07dqhXr166+uqrlZaWJovFwjoxCApiMQAgYHFxcZo2bZpqa2v15Zdf+tu7d++uK664QikpKSouLlZ6erosFovcbjchBkHBpSUAwAn78MMPNXXqVN144426/fbb/e27du1Su3btuIyEoCPIAABOygcffKA77rhDN998syZPntzoOcbEINj41wUAOCmXX365Hn/8cT311FN67bXXGj1HiEGw0SMDAGgWa9euVZ8+fRgLg5AiyAAAmhWzkxBKBBkAAGBYXLwEAACGRZABAACGRZABAACGRZABAACGRZABAACGRZABYDi7du0KdwkAIgRBBkDQde3aVV27dtWOHTuOeu75559X165dNWfOnCa918MPP6ynn3660Xt//vnnzVbrJZdcotdff73Z3g9AcBFkAIRESkqK3njjjaPaX3/9dcXHxzf5fUpLS5uzLAAGR5ABEBK5ubl666235PF4/G0bN26Uw+FQ9+7d/W1er1cLFy7U4MGDlZ2drauvvlpff/21JOmpp57SsmXLtGzZMo0YMcL/mk8//VQjR45U7969NXbsWG3dutX/3BdffKHx48crOztbl1xyiZ544gk5HA7/uZ555hldeOGFys7O1sMPPyy32x3sPwoAzYggAyAkBg4cKKfTqc8++8zftmTJEo0dO7bRcS+//LKef/55/fWvf9WaNWs0evRo3XDDDSoqKtItt9yi3Nxc5ebmaunSpf7XrF27Vn//+9+1Zs0apaSk6OGHH5Yk7dixQzfccIMuv/xyffbZZ3r++ef1r3/9S4888ogk6Z///KdeeOEFzZs3T5999plsNpv27dsXgj8NAM2FIAMgJKxWq3Jzc/2Xl+rq6vT+++9r1KhRjY5btGiRbr75ZnXr1k02m01jx45Vp06dGgWXI91www1KS0tTdHS0Lr30Uu3evVuStGzZMnXt2lXXX3+97Ha72rdvrzvvvFOvvfaaPB6P3nrrLV155ZXq0aOH7Ha7br/9dqWkpATtzwBA82NXLwAhM3r0aF111VWqqqrSRx99pD59+qhly5aNjtm7d68efvhhPfroo/42l8ulnj17HvN9k5OT/fdtNpv/8lBxcbHatWvX6NgzzjhDdXV1Ki4u1oEDB9S6dWv/cxaLRW3atDmZbxFAiBFkAIRMt27d1LFjR7377rtatmyZrr/++qOOycjI0G233aZhw4b523bv3t0orDRV27Zt9cEHHzRq2717t+x2u5KSkpSRkaE9e/b4n/N6vTpw4EDA5wEQPlxaAhBSo0eP1oIFC7Rz504NGDDgqOevvPJKPf3008rPz5ckrV69WsOGDdO6deskSXa7XZWVlU0617Bhw5Sfn68XXnhBDodDu3fv1mOPPabc3FzZ7XaNGzdO//jHP/TVV1/J6XTq6aef1sGDB5vvmwUQdPTIAAip4cOH6+GHH9b1118vq/XoH0ETJkyQ1+tVXl6eDhw4oPT0dN13330aNGiQJGno0KGaOnWqBg4cqI8//vhnz3XGGWdo/vz5euyxxzRnzhxFR0dr+PDhmjJlir+W0tJSTZ06VeXl5frVr36lrl27Nve3DCCITF6v1xvuIgAAAE4El5YAAIBhEWQAAIBhEWQAAIBhEWQAAIBhEWQAAIBhEWQAAIBhEWQAAIBhEWQAAIBhEWQAAIBhEWQAAIBhEWQAAIBh/X/wd1SE21gB1wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97                  0.0                   0.0   \n",
       "1  naive-bayes         [10,10]:0.5                  0.0                   0.0   \n",
       "\n",
       "   Precision  Recall  F-measure  \n",
       "0        0.0     0.0        0.0  \n",
       "1        0.0     0.0        0.0  "
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       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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      "text/plain": [
       "                      stat   P  FDR P\n",
       "Method A    Method B                 \n",
       "naive-bayes blast+     NaN NaN    NaN"
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     "output_type": "display_data"
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## Recall"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
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       "        Method         Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.75:0.8             0.139205              0.180556   \n",
       "1  naive-bayes        [10,10]:0.9             0.112121              0.138889   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
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       "                          stat         P     FDR P\n",
       "Method A    Method B                              \n",
       "naive-bayes blast+   -0.111538  0.929285  0.929285"
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97                  0.0                   0.0   \n",
       "1  naive-bayes         [10,10]:0.5                  0.0                   0.0   \n",
       "\n",
       "   Precision  Recall  F-measure  \n",
       "0        0.0     0.0        0.0  \n",
       "1        0.0     0.0        0.0  "
      ]
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>naive-bayes</th>\n",
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      "text/plain": [
       "                      stat   P  FDR P\n",
       "Method A    Method B                 \n",
       "naive-bayes blast+     NaN NaN    NaN"
      ]
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     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## F-measure"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## pr2-ssu level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F-measure</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>blast+</td>\n",
       "      <td>0.001:10:0.75:0.8</td>\n",
       "      <td>0.139205</td>\n",
       "      <td>0.180556</td>\n",
       "      <td>0.107043</td>\n",
       "      <td>0.106619</td>\n",
       "      <td>0.106830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.9</td>\n",
       "      <td>0.112121</td>\n",
       "      <td>0.138889</td>\n",
       "      <td>0.103818</td>\n",
       "      <td>0.102360</td>\n",
       "      <td>0.103078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Method         Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.75:0.8             0.139205              0.180556   \n",
       "1  naive-bayes        [10,10]:0.9             0.112121              0.138889   \n",
       "\n",
       "   Precision    Recall  F-measure  \n",
       "0   0.107043  0.106619   0.106830  \n",
       "1   0.103818  0.102360   0.103078  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>naive-bayes</th>\n",
       "      <th>blast+</th>\n",
       "      <td>-0.099618</td>\n",
       "      <td>0.93679</td>\n",
       "      <td>0.93679</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          stat        P    FDR P\n",
       "Method A    Method B                            \n",
       "naive-bayes blast+   -0.099618  0.93679  0.93679"
      ]
     },
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/markdown": [
       "## rcrux-18sv4 level 6"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:744: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg = subset.groupby(params).mean().reset_index()\n",
      "/Users/katherine.silliman/Projects/NOAA/rcrux_taxcredit_metro/tax_credit/plotting_functions.py:549: FutureWarning: The default value of numeric_only in DataFrameGroupBy.mean is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  avg_best = best.groupby([group_by, params]).mean().reset_index()\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>Method</th>\n",
       "      <th>Parameters</th>\n",
       "      <th>Taxon Accuracy Rate</th>\n",
       "      <th>Taxon Detection Rate</th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>naive-bayes</td>\n",
       "      <td>[10,10]:0.5</td>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "        Method          Parameters  Taxon Accuracy Rate  Taxon Detection Rate  \\\n",
       "0       blast+  0.001:10:0.51:0.97                  0.0                   0.0   \n",
       "1  naive-bayes         [10,10]:0.5                  0.0                   0.0   \n",
       "\n",
       "   Precision  Recall  F-measure  \n",
       "0        0.0     0.0        0.0  \n",
       "1        0.0     0.0        0.0  "
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>stat</th>\n",
       "      <th>P</th>\n",
       "      <th>FDR P</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Method A</th>\n",
       "      <th>Method B</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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      "text/plain": [
       "                      stat   P  FDR P\n",
       "Method A    Method B                 \n",
       "naive-bayes blast+     NaN NaN    NaN"
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     "output_type": "display_data"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for metric in [\"Taxon Accuracy Rate\", \"Taxon Detection Rate\", \"Precision\", \"Recall\", \"F-measure\"]:\n",
    "    display(Markdown('## {0}'.format(metric)))\n",
    "    boxes = rank_optimized_method_performance_by_dataset(mock_results,\n",
    "                                                         dataset=\"Reference\",\n",
    "                                                         metric=metric,\n",
    "                                                         level_range=range(6,7),\n",
    "                                                         display_fields=[\"Method\",\n",
    "                                                                         \"Parameters\",\n",
    "                                                                         \"Taxon Accuracy Rate\",\n",
    "                                                                         \"Taxon Detection Rate\",\n",
    "                                                                         \"Precision\",\n",
    "                                                                         \"Recall\",\n",
    "                                                                         \"F-measure\"],\n",
    "                                                         paired=True,\n",
    "                                                         parametric=True,\n",
    "                                                         color=None,\n",
    "                                                         color_palette=color_palette_method)\n",
    "    for k, v in boxes.items():\n",
    "        v.get_figure().savefig(join(outdir, 'mock-{0}-{1}-boxplots.pdf'.format(metric, k)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c5fa72",
   "metadata": {},
   "source": [
    "### Testing code, not needed for actiually running"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f657b406",
   "metadata": {},
   "outputs": [],
   "source": [
    "from biom import load_table, Table\n",
    "from biom.cli.util import write_biom_table\n",
    "from biom.parse import MetadataMap\n",
    "from os import makedirs, remove, system, stat\n",
    "\n",
    "def add_metadata_to_biom_table(biom_input_fp, taxonomy_map_fp, biom_output_fp):\n",
    "    '''Load biom, add metadata, write to new table'''\n",
    "    newbiom = load_table(biom_input_fp)\n",
    "    if stat(taxonomy_map_fp).st_size == 0:\n",
    "        metadata = {}\n",
    "    else:\n",
    "        metadata = MetadataMap.from_file(\n",
    "            taxonomy_map_fp, header=['Sample ID', 'taxonomy', 'c'])\n",
    "            #taxonomy_map_fp, header=['Sample ID', 'taxonomy', 'c'])\n",
    "        print(metadata)\n",
    "    newbiom.add_metadata(metadata, 'observation')\n",
    "    write_biom_table(newbiom, 'json', biom_output_fp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86f48fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "ASV_1\t32153.0\t73024.0\n",
      "ASV_2\t14255.0\t30957.0\n",
      "ASV_3\t8565.0\t4849.0\n",
      "ASV_4\t6109.0\t5246.0\n",
      "ASV_5\t3404.0\t7127.0\n",
      "ASV_6\t0.0\t8738.0\n",
      "ASV_7\t4960.0\t3494.0\n",
      "ASV_8\t2001.0\t2873.0\n",
      "ASV_9\t0.0\t4332.0\n",
      "ASV_10\t0.0\t3816.0\n",
      "ASV_11\t3280.0\t328.0\n",
      "ASV_12\t2590.0\t12.0\n",
      "ASV_13\t2393.0\t20.0\n",
      "ASV_14\t21.0\t873.0\n",
      "ASV_15\t0.0\t424.0\n",
      "ASV_16\t0.0\t265.0\n"
     ]
    }
   ],
   "source": [
    "newbiom = load_table(\"data/mock-community/fuhrman_18Sv4/feature_table.biom\")\n",
    "print(newbiom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34cbe4c3",
   "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>MP_ERR3712072</th>\n",
       "      <th>MP_ERR3712077</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASV_1</th>\n",
       "      <td>32153.0</td>\n",
       "      <td>73024.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_2</th>\n",
       "      <td>14255.0</td>\n",
       "      <td>30957.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_3</th>\n",
       "      <td>8565.0</td>\n",
       "      <td>4849.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_4</th>\n",
       "      <td>6109.0</td>\n",
       "      <td>5246.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_5</th>\n",
       "      <td>3404.0</td>\n",
       "      <td>7127.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>8738.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_7</th>\n",
       "      <td>4960.0</td>\n",
       "      <td>3494.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_8</th>\n",
       "      <td>2001.0</td>\n",
       "      <td>2873.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_9</th>\n",
       "      <td>0.0</td>\n",
       "      <td>4332.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_10</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_11</th>\n",
       "      <td>3280.0</td>\n",
       "      <td>328.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_12</th>\n",
       "      <td>2590.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_13</th>\n",
       "      <td>2393.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_14</th>\n",
       "      <td>21.0</td>\n",
       "      <td>873.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_15</th>\n",
       "      <td>0.0</td>\n",
       "      <td>424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_16</th>\n",
       "      <td>0.0</td>\n",
       "      <td>265.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        MP_ERR3712072  MP_ERR3712077\n",
       "ASV_1         32153.0        73024.0\n",
       "ASV_2         14255.0        30957.0\n",
       "ASV_3          8565.0         4849.0\n",
       "ASV_4          6109.0         5246.0\n",
       "ASV_5          3404.0         7127.0\n",
       "ASV_6             0.0         8738.0\n",
       "ASV_7          4960.0         3494.0\n",
       "ASV_8          2001.0         2873.0\n",
       "ASV_9             0.0         4332.0\n",
       "ASV_10            0.0         3816.0\n",
       "ASV_11         3280.0          328.0\n",
       "ASV_12         2590.0           12.0\n",
       "ASV_13         2393.0           20.0\n",
       "ASV_14           21.0          873.0\n",
       "ASV_15            0.0          424.0\n",
       "ASV_16            0.0          265.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newbiom = load_table(\"data/mock-community/fuhrman_18Sv4/feature_table.biom\").to_dataframe()\n",
    "newbiom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb47d036",
   "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>Taxon</th>\n",
       "      <th>Consensus</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASV_1</th>\n",
       "      <td>Eukaryota;Haptista;Haptophyta;Haptophyta_X;Pry...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_10</th>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_11</th>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrich...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_12</th>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_13</th>\n",
       "      <td>Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropo...</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                        Taxon  Consensus\n",
       "Feature ID                                                              \n",
       "ASV_1       Eukaryota;Haptista;Haptophyta;Haptophyta_X;Pry...        1.0\n",
       "ASV_10      Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...        1.0\n",
       "ASV_11      Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrich...        1.0\n",
       "ASV_12      Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...        1.0\n",
       "ASV_13      Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropo...        1.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "metadata = pd.read_csv(\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/query_tax_assignments.txt\",sep=\"\\t\",index_col=0)\n",
    "metadata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d99c2dd",
   "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>MP_ERR3712072</th>\n",
       "      <th>MP_ERR3712077</th>\n",
       "      <th>taxonomy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature ID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASV_1</th>\n",
       "      <td>32153.0</td>\n",
       "      <td>73024.0</td>\n",
       "      <td>Eukaryota;Haptista;Haptophyta;Haptophyta_X;Pry...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_2</th>\n",
       "      <td>14255.0</td>\n",
       "      <td>30957.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_3</th>\n",
       "      <td>8565.0</td>\n",
       "      <td>4849.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_4</th>\n",
       "      <td>6109.0</td>\n",
       "      <td>5246.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_5</th>\n",
       "      <td>3404.0</td>\n",
       "      <td>7127.0</td>\n",
       "      <td>Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            MP_ERR3712072  MP_ERR3712077  \\\n",
       "Feature ID                                 \n",
       "ASV_1             32153.0        73024.0   \n",
       "ASV_2             14255.0        30957.0   \n",
       "ASV_3              8565.0         4849.0   \n",
       "ASV_4              6109.0         5246.0   \n",
       "ASV_5              3404.0         7127.0   \n",
       "\n",
       "                                                     taxonomy  \n",
       "Feature ID                                                     \n",
       "ASV_1       Eukaryota;Haptista;Haptophyta;Haptophyta_X;Pry...  \n",
       "ASV_2       Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...  \n",
       "ASV_3       Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...  \n",
       "ASV_4       Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...  \n",
       "ASV_5       Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodi...  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged = newbiom.merge(metadata, left_index=True, right_on=\"Feature ID\")\n",
    "merged.rename(columns={'Taxon': 'taxonomy'}, inplace=True)\n",
    "merged.drop(columns=['Consensus'], inplace=True)\n",
    "merged.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e70a041f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_f/3hfwkwps2rq9q60vkv4fnd_n9rf1vk/T/ipykernel_22996/1534377717.py:1: FutureWarning: The behavior of .astype from SparseDtype to a non-sparse dtype is deprecated. In a future version, this will return a non-sparse array with the requested dtype. To retain the old behavior, use `obj.astype(SparseDtype(dtype))`\n",
      "  merged.to_csv(\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/feature_w_taxa.tsv\", sep=\"\\t\")\n"
     ]
    }
   ],
   "source": [
    "merged.to_csv(\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/feature_w_taxa.tsv\", sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27dfc52f",
   "metadata": {},
   "outputs": [],
   "source": [
    "! biom convert -i analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/feature_w_taxa.tsv -o analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/feature_w_taxa.biom --to-hdf5 --table-type=\"OTU table\" --process-obs-metadata taxonomy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7376f6fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "Eukaryota;Haptophyta;Prymnesiales__c;Prymnesiales;NA;NA;NA\t0.40326849\t0.498872781\n",
      "Eukaryota;Dinophyceae__p;Dinophyceae;Syndiniales;NA;NA;NA\t0.286212389\t0.270682753\n",
      "Eukaryota;Dinophyceae__p;Dinophyceae;NA;NA;NA;NA\t0.076620135\t0.035838719\n",
      "Eukaryota;Bacillariophyta;Coscinodiscophyceae;NA;NA;NA;NA\t0.042693557\t0.048689011\n",
      "Eukaryota;Dinophyceae__p;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;NA\t0.0\t0.059694763\n",
      "Eukaryota;Polycystinea__p;Polycystinea;Spumellaria;Litheliidae;Larcopyle;NA\t0.062209178\t0.023869707\n",
      "Eukaryota;Cercozoa;Thecofilosea;Cryomonadida;NA;NA;NA\t0.025096888\t0.019627266\n",
      "Eukaryota;Dinophyceae__p;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Gymnodinium;NA\t0.0\t0.029594611\n",
      "Eukaryota;Alveolata;Spirotrichea;Oligotrichida;NA;NA;NA\t0.041138328\t0.002240774\n",
      "Eukaryota;Dinophyceae__p;Dinophyceae;Gonyaulacales;Ceratiaceae;Neoceratium;NA\t0.032484228\t8.19795e-05\n",
      "Eukaryota;Arthropoda;NA;NA;NA;NA;NA\t0.03001342\t0.000136633\n",
      "Eukaryota;Bacillariophyta;Coscinodiscophyceae;;NA;NA;NA\t0.000263386\t0.005964011\n",
      "Eukaryota;Ciliophora;Phyllopharyngea;Suctoria;NA;NA;NA\t0.0\t0.00289661\n",
      "Eukaryota;Dinophyceae__p;Dinophyceae;Gymnodiniales;Gyrodiniaceae;Gyrodinium;NA\t0.0\t0.001810381\n"
     ]
    }
   ],
   "source": [
    "taxtaxbiom = load_table(\"data/mock-community/fuhrman_18Sv4/rcrux-18sv4/expected/table.L7.biom\")\n",
    "print(taxtaxbiom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83cdfa0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "ASV_1\t32153.0\t73024.0\n",
      "ASV_2\t14255.0\t30957.0\n",
      "ASV_3\t8565.0\t4849.0\n",
      "ASV_4\t6109.0\t5246.0\n",
      "ASV_5\t3404.0\t7127.0\n",
      "ASV_6\t0.0\t8738.0\n",
      "ASV_7\t4960.0\t3494.0\n",
      "ASV_8\t2001.0\t2873.0\n",
      "ASV_9\t0.0\t4332.0\n",
      "ASV_10\t0.0\t3816.0\n",
      "ASV_11\t3280.0\t328.0\n",
      "ASV_12\t2590.0\t12.0\n",
      "ASV_13\t2393.0\t20.0\n",
      "ASV_14\t21.0\t873.0\n",
      "ASV_15\t0.0\t424.0\n",
      "ASV_16\t0.0\t265.0\n"
     ]
    }
   ],
   "source": [
    "taxbiom = load_table(\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/feature_w_taxa.biom\")\n",
    "print(taxbiom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75d1b591",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_f/3hfwkwps2rq9q60vkv4fnd_n9rf1vk/T/ipykernel_22996/802251557.py:1: FutureWarning: The behavior of .astype from SparseDtype to a non-sparse dtype is deprecated. In a future version, this will return a non-sparse array with the requested dtype. To retain the old behavior, use `obj.astype(SparseDtype(dtype))`\n",
      "  merged.values\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[32153.0, 73024.0,\n",
       "        'Eukaryota;Haptista;Haptophyta;Haptophyta_X;Prymnesiophyceae;Prymnesiales;Chrysochromulinaceae;Chrysochromulina;Chrysochromulina_sp.;',\n",
       "        1.0],\n",
       "       [14255.0, 30957.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-II;Dino-Group-II-Clade-3;Dino-Group-II-Clade-3_X;Dino-Group-II-Clade-3_X_sp.;',\n",
       "        1.0],\n",
       "       [8565.0, 4849.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-I;Dino-Group-I-Clade-5;Dino-Group-I-Clade-5_X;Dino-Group-I-Clade-5_X_sp.;',\n",
       "        1.0],\n",
       "       [6109.0, 5246.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Dinophyceae_X;Dinophyceae_XX;Dinophyceae_XXX;Dinophyceae_XXX_sp.;',\n",
       "        1.0],\n",
       "       [3404.0, 7127.0,\n",
       "        'Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;Rhizosoleniales;Rhizosoleniaceae;Guinardia;Guinardia_striata;',\n",
       "        1.0],\n",
       "       [0, 8738.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;Lingulodinium_polyedra;',\n",
       "        1.0],\n",
       "       [4960.0, 3494.0,\n",
       "        'Eukaryota;TSAR;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B_X_Group-IVe;RAD-B_X_Group-IVe_X;RAD-B_X_Group-IVe_X_sp.;',\n",
       "        1.0],\n",
       "       [2001.0, 2873.0,\n",
       "        'Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;Cryothecomonas-lineage;Cryothecomonas;Cryothecomonas_sp.;',\n",
       "        1.0],\n",
       "       [0, 4332.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Barrufeta;Barrufeta_bravensis;',\n",
       "        1.0],\n",
       "       [0, 3816.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-II;Dino-Group-II-Clade-10-and-11;Dino-Group-II-Clade-10-and-11_X;Dino-Group-II-Clade-10-and-11_X_sp.;',\n",
       "        1.0],\n",
       "       [3280.0, 328.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrichea;Oligotrichida;Oligotrichida_X;Oligotrichida_XX;Oligotrichida_XX_sp.;',\n",
       "        1.0],\n",
       "       [2590.0, 12.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Ceratiaceae;Tripos;Tripos_declinatus;',\n",
       "        1.0],\n",
       "       [2393.0, 20.0,\n",
       "        'Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropoda;Crustacea;Maxillopoda;Paracalanus;Paracalanus_parvus;',\n",
       "        1.0],\n",
       "       [21.0, 873.0,\n",
       "        'Eukaryota;TSAR;Stramenopiles;Gyrista;Mediophyceae;Chaetocerotales;Leptocylindraceae;Leptocylindrus;Leptocylindrus_convexus;',\n",
       "        1.0],\n",
       "       [0, 424.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Ciliophora;Phyllopharyngea;Suctoria;Suctoria_X;Suctoria_XX;Suctoria_XX_sp.;',\n",
       "        1.0],\n",
       "       [0, 265.0,\n",
       "        'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Suessiales;Suessiaceae;Biecheleria;Biecheleria_cincta;',\n",
       "        1.0]], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged.loc[\"merged.values\"].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c59d7d41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_f/3hfwkwps2rq9q60vkv4fnd_n9rf1vk/T/ipykernel_22996/3518007311.py:1: FutureWarning: The behavior of .astype from SparseDtype to a non-sparse dtype is deprecated. In a future version, this will return a non-sparse array with the requested dtype. To retain the old behavior, use `obj.astype(SparseDtype(dtype))`\n",
      "  table = Table(merged.values, merged.index, merged.columns)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "could not convert string to float: 'Eukaryota;Haptista;Haptophyta;Haptophyta_X;Prymnesiophyceae;Prymnesiales;Chrysochromulinaceae;Chrysochromulina;Chrysochromulina_sp.;'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)\n",
      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n",
      "\u001b[0;32m----> 1\u001b[0m table \u001b[38;5;241m=\u001b[39m \u001b[43mTable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmerged\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmerged\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmerged\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcolumns\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;32m      2\u001b[0m \u001b[38;5;28mprint\u001b[39m(table)\n",
      "\n",
      "File \u001b[0;32m~/miniconda3/envs/tax-assess/lib/python3.8/site-packages/biom/table.py:468\u001b[0m, in \u001b[0;36mTable.__init__\u001b[0;34m(self, data, observation_ids, sample_ids, observation_metadata, sample_metadata, table_id, type, create_date, generated_by, observation_group_metadata, sample_group_metadata, validate, observation_index, sample_index, **kwargs)\u001b[0m\n",
      "\u001b[1;32m    466\u001b[0m     shape \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mlen\u001b[39m(observation_ids), \u001b[38;5;28mlen\u001b[39m(sample_ids))\n",
      "\u001b[1;32m    467\u001b[0m     input_is_dense \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124minput_is_dense\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "\u001b[0;32m--> 468\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data \u001b[38;5;241m=\u001b[39m \u001b[43mTable\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_to_sparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minput_is_dense\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minput_is_dense\u001b[49m\u001b[43m,\u001b[49m\n",
      "\u001b[1;32m    469\u001b[0m \u001b[43m                                  \u001b[49m\u001b[43mshape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;32m    470\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[1;32m    471\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mtocsr()\n",
      "\n",
      "File \u001b[0;32m~/miniconda3/envs/tax-assess/lib/python3.8/site-packages/biom/table.py:597\u001b[0m, in \u001b[0;36mTable._to_sparse\u001b[0;34m(values, transpose, dtype, input_is_dense, shape)\u001b[0m\n",
      "\u001b[1;32m    595\u001b[0m         mat \u001b[38;5;241m=\u001b[39m nparray_to_sparse(values\u001b[38;5;241m.\u001b[39mT, dtype)\n",
      "\u001b[1;32m    596\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[0;32m--> 597\u001b[0m         mat \u001b[38;5;241m=\u001b[39m \u001b[43mnparray_to_sparse\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvalues\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;32m    598\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m mat\n",
      "\u001b[1;32m    599\u001b[0m \u001b[38;5;66;03m# the empty list\u001b[39;00m\n",
      "\n",
      "File \u001b[0;32m~/miniconda3/envs/tax-assess/lib/python3.8/site-packages/biom/table.py:5335\u001b[0m, in \u001b[0;36mnparray_to_sparse\u001b[0;34m(data, dtype)\u001b[0m\n",
      "\u001b[1;32m   5332\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "\u001b[1;32m   5333\u001b[0m     shape \u001b[38;5;241m=\u001b[39m data\u001b[38;5;241m.\u001b[39mshape\n",
      "\u001b[0;32m-> 5335\u001b[0m matrix \u001b[38;5;241m=\u001b[39m \u001b[43mcoo_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshape\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[1;32m   5336\u001b[0m matrix \u001b[38;5;241m=\u001b[39m matrix\u001b[38;5;241m.\u001b[39mtocsr()\n",
      "\u001b[1;32m   5337\u001b[0m matrix\u001b[38;5;241m.\u001b[39meliminate_zeros()\n",
      "\n",
      "File \u001b[0;32m~/miniconda3/envs/tax-assess/lib/python3.8/site-packages/scipy/sparse/_coo.py:194\u001b[0m, in \u001b[0;36mcoo_matrix.__init__\u001b[0;34m(self, arg1, shape, dtype, copy)\u001b[0m\n",
      "\u001b[1;32m    191\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhas_canonical_format \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "\u001b[1;32m    193\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dtype \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;32m--> 194\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mastype\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcopy\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
      "\u001b[1;32m    196\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check()\n",
      "\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'Eukaryota;Haptista;Haptophyta;Haptophyta_X;Prymnesiophyceae;Prymnesiales;Chrysochromulinaceae;Chrysochromulina;Chrysochromulina_sp.;'"
     ]
    }
   ],
   "source": [
    "table = Table(merged.values, merged.index, merged.columns)\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64cdbcdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Feature ID': {'taxonomy': 'Taxon', 'c': 'Consensus'}, 'ASV_1': {'taxonomy': 'Eukaryota;Haptista;Haptophyta;Haptophyta_X;Prymnesiophyceae;Prymnesiales;Chrysochromulinaceae;Chrysochromulina;Chrysochromulina_sp.;', 'c': '1.0'}, 'ASV_10': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-II;Dino-Group-II-Clade-10-and-11;Dino-Group-II-Clade-10-and-11_X;Dino-Group-II-Clade-10-and-11_X_sp.;', 'c': '1.0'}, 'ASV_11': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrichea;Oligotrichida;Oligotrichida_X;Oligotrichida_XX;Oligotrichida_XX_sp.;', 'c': '1.0'}, 'ASV_12': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Ceratiaceae;Tripos;Tripos_declinatus;', 'c': '1.0'}, 'ASV_13': {'taxonomy': 'Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropoda;Crustacea;Maxillopoda;Paracalanus;Paracalanus_parvus;', 'c': '1.0'}, 'ASV_14': {'taxonomy': 'Eukaryota;TSAR;Stramenopiles;Gyrista;Mediophyceae;Chaetocerotales;Leptocylindraceae;Leptocylindrus;Leptocylindrus_convexus;', 'c': '1.0'}, 'ASV_15': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Ciliophora;Phyllopharyngea;Suctoria;Suctoria_X;Suctoria_XX;Suctoria_XX_sp.;', 'c': '1.0'}, 'ASV_16': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Suessiales;Suessiaceae;Biecheleria;Biecheleria_cincta;', 'c': '1.0'}, 'ASV_2': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-II;Dino-Group-II-Clade-3;Dino-Group-II-Clade-3_X;Dino-Group-II-Clade-3_X_sp.;', 'c': '1.0'}, 'ASV_3': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-I;Dino-Group-I-Clade-5;Dino-Group-I-Clade-5_X;Dino-Group-I-Clade-5_X_sp.;', 'c': '1.0'}, 'ASV_4': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Dinophyceae_X;Dinophyceae_XX;Dinophyceae_XXX;Dinophyceae_XXX_sp.;', 'c': '1.0'}, 'ASV_5': {'taxonomy': 'Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;Rhizosoleniales;Rhizosoleniaceae;Guinardia;Guinardia_striata;', 'c': '1.0'}, 'ASV_6': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;Lingulodinium_polyedra;', 'c': '1.0'}, 'ASV_7': {'taxonomy': 'Eukaryota;TSAR;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B_X_Group-IVe;RAD-B_X_Group-IVe_X;RAD-B_X_Group-IVe_X_sp.;', 'c': '1.0'}, 'ASV_8': {'taxonomy': 'Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;Cryothecomonas-lineage;Cryothecomonas;Cryothecomonas_sp.;', 'c': '1.0'}, 'ASV_9': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Barrufeta;Barrufeta_bravensis;', 'c': '1.0'}}\n"
     ]
    }
   ],
   "source": [
    "from biom import load_table, Table\n",
    "from biom.cli.util import write_biom_table\n",
    "from biom.parse import MetadataMap\n",
    "from os import makedirs, remove, system, stat\n",
    "\n",
    "newbiom = load_table(\"data/mock-community/fuhrman_18Sv4/feature_table.biom\")\n",
    "if stat(\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/query_tax_assignments.txt\").st_size == 0:\n",
    "    metadata = {}\n",
    "else:\n",
    "    metadata = MetadataMap.from_file(\n",
    "        \"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/query_tax_assignments.txt\", header=['Sample ID', 'taxonomy', 'c'])\n",
    "            #taxonomy_map_fp, header=['Sample ID', 'taxonomy', 'c'])\n",
    "    print(metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eda66854",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "ASV_1\t32153.0\t73024.0\n",
      "ASV_2\t14255.0\t30957.0\n",
      "ASV_3\t8565.0\t4849.0\n",
      "ASV_4\t6109.0\t5246.0\n",
      "ASV_5\t3404.0\t7127.0\n",
      "ASV_6\t0.0\t8738.0\n",
      "ASV_7\t4960.0\t3494.0\n",
      "ASV_8\t2001.0\t2873.0\n",
      "ASV_9\t0.0\t4332.0\n",
      "ASV_10\t0.0\t3816.0\n",
      "ASV_11\t3280.0\t328.0\n",
      "ASV_12\t2590.0\t12.0\n",
      "ASV_13\t2393.0\t20.0\n",
      "ASV_14\t21.0\t873.0\n",
      "ASV_15\t0.0\t424.0\n",
      "ASV_16\t0.0\t265.0\n"
     ]
    }
   ],
   "source": [
    "newbiom.add_metadata(metadata, 'observation')\n",
    "print(newbiom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "638179e1",
   "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>c</th>\n",
       "      <th>taxonomy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ASV_1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;Haptista;Haptophyta;Haptophyta_X;Pry...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Rhizaria;Radiolaria;RAD-B;RAD-B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_8</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecof...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_9</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_10</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_11</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrich...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_12</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_13</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_14</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Stramenopiles;Gyrista;Mediophyc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_15</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Ciliophora;Phyllophar...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASV_16</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          c                                           taxonomy\n",
       "ASV_1   1.0  Eukaryota;Haptista;Haptophyta;Haptophyta_X;Pry...\n",
       "ASV_2   1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...\n",
       "ASV_3   1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...\n",
       "ASV_4   1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...\n",
       "ASV_5   1.0  Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodi...\n",
       "ASV_6   1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...\n",
       "ASV_7   1.0  Eukaryota;TSAR;Rhizaria;Radiolaria;RAD-B;RAD-B...\n",
       "ASV_8   1.0  Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecof...\n",
       "ASV_9   1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...\n",
       "ASV_10  1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndin...\n",
       "ASV_11  1.0  Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrich...\n",
       "ASV_12  1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph...\n",
       "ASV_13  1.0  Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropo...\n",
       "ASV_14  1.0  Eukaryota;TSAR;Stramenopiles;Gyrista;Mediophyc...\n",
       "ASV_15  1.0  Eukaryota;TSAR;Alveolata;Ciliophora;Phyllophar...\n",
       "ASV_16  1.0  Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinoph..."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "newbiom = load_table(\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.97/table.biom\")\n",
    "newbiom.metadata_to_dataframe('observation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8711b26b",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'observation does not have metadata'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)\n",
      "Cell \u001b[0;32mIn[18], line 2\u001b[0m\n",
      "\u001b[1;32m      1\u001b[0m newbiom \u001b[38;5;241m=\u001b[39m load_table(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/mock-community/fuhrman_18Sv4/pr2-ssu/expected/table.L7.biom\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "\u001b[0;32m----> 2\u001b[0m \u001b[43mnewbiom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmetadata_to_dataframe\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mobservation\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "\n",
      "File \u001b[0;32m~/miniconda3/envs/tax-assess/lib/python3.8/site-packages/biom/table.py:4363\u001b[0m, in \u001b[0;36mTable.metadata_to_dataframe\u001b[0;34m(self, axis)\u001b[0m\n",
      "\u001b[1;32m   4361\u001b[0m md \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmetadata(axis\u001b[38;5;241m=\u001b[39maxis)\n",
      "\u001b[1;32m   4362\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m md \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "\u001b[0;32m-> 4363\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m does not have metadata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m axis)\n",
      "\u001b[1;32m   4365\u001b[0m test \u001b[38;5;241m=\u001b[39m md[\u001b[38;5;241m0\u001b[39m]\n",
      "\u001b[1;32m   4366\u001b[0m kv_test \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msorted\u001b[39m(\u001b[38;5;28mlist\u001b[39m(test\u001b[38;5;241m.\u001b[39mitems()))\n",
      "\n",
      "\u001b[0;31mKeyError\u001b[0m: 'observation does not have metadata'"
     ]
    }
   ],
   "source": [
    "newbiom = load_table(\"data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/table.L7.biom\")\n",
    "newbiom.metadata_to_dataframe('observation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68598457",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Feature ID': {'taxonomy': 'Taxon', 'c': 'Consensus'}, 'ASV_1': {'taxonomy': 'Eukaryota;Haptista;Haptophyta;Haptophyta_X;Prymnesiophyceae;Prymnesiales;Chrysochromulinaceae;Chrysochromulina;Chrysochromulina_sp.;', 'c': '1.0'}, 'ASV_10': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-II;Dino-Group-II-Clade-10-and-11;Dino-Group-II-Clade-10-and-11_X;Dino-Group-II-Clade-10-and-11_X_sp.;', 'c': '1.0'}, 'ASV_11': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrichea;Oligotrichida;Oligotrichida_X;Oligotrichida_XX;Oligotrichida_XX_sp.;', 'c': '1.0'}, 'ASV_12': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Ceratiaceae;Tripos;Tripos_declinatus;', 'c': '1.0'}, 'ASV_13': {'taxonomy': 'Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropoda;Crustacea;Maxillopoda;Paracalanus;Paracalanus_parvus;', 'c': '1.0'}, 'ASV_14': {'taxonomy': 'Eukaryota;TSAR;Stramenopiles;Gyrista;Mediophyceae;Chaetocerotales;Leptocylindraceae;Leptocylindrus;Leptocylindrus_convexus;', 'c': '1.0'}, 'ASV_15': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Ciliophora;Phyllopharyngea;Suctoria;Suctoria_X;Suctoria_XX;Suctoria_XX_sp.;', 'c': '1.0'}, 'ASV_16': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Suessiales;Suessiaceae;Biecheleria;Biecheleria_cincta;', 'c': '1.0'}, 'ASV_2': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-II;Dino-Group-II-Clade-3;Dino-Group-II-Clade-3_X;Dino-Group-II-Clade-3_X_sp.;', 'c': '1.0'}, 'ASV_3': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;Dino-Group-I;Dino-Group-I-Clade-5;Dino-Group-I-Clade-5_X;Dino-Group-I-Clade-5_X_sp.;', 'c': '1.0'}, 'ASV_4': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Dinophyceae_X;Dinophyceae_XX;Dinophyceae_XXX;Dinophyceae_XXX_sp.;', 'c': '1.0'}, 'ASV_5': {'taxonomy': 'Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;Rhizosoleniales;Rhizosoleniaceae;Guinardia;Guinardia_striata;', 'c': '1.0'}, 'ASV_6': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;Lingulodinium_polyedra;', 'c': '1.0'}, 'ASV_7': {'taxonomy': 'Eukaryota;TSAR;Rhizaria;Radiolaria;RAD-B;RAD-B_X;RAD-B_X_Group-IVe;RAD-B_X_Group-IVe_X;RAD-B_X_Group-IVe_X_sp.;', 'c': '1.0'}, 'ASV_8': {'taxonomy': 'Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;Cryothecomonas-lineage;Cryothecomonas;Cryothecomonas_sp.;', 'c': '1.0'}, 'ASV_9': {'taxonomy': 'Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Barrufeta;Barrufeta_bravensis;', 'c': '1.0'}}\n"
     ]
    }
   ],
   "source": [
    "add_metadata_to_biom_table(\"data/mock-community/fuhrman_18Sv4/feature_table.biom\",\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/query_tax_assignments.txt\",\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.8/table.biom\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fd03873",
   "metadata": {},
   "outputs": [],
   "source": [
    "%reload_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85cf3855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method Table.metadata of 2 x 3 <class 'biom.table.Table'> with 4 nonzero entries (66% dense)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from biom.table import Table\n",
    "data = np.asarray([[0, 0, 1], [1, 3, 42]])\n",
    "table = Table(data, ['O1', 'O2'], ['S1', 'S2', 'S3'],\n",
    "              [{'full_genome_available': True},\n",
    "               {'full_genome_available': False}],\n",
    "              [{'sample_type': 'a'}, {'sample_type': 'a'},\n",
    "               {'sample_type': 'b'}])\n",
    "table.metadata('01','sa')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c79a6d02",
   "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>taxonomy_0</th>\n",
       "      <th>taxonomy_1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>O1</th>\n",
       "      <td>Bacteria</td>\n",
       "      <td>Firmicutes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>O2</th>\n",
       "      <td>Bacteria</td>\n",
       "      <td>Bacteroidetes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   taxonomy_0     taxonomy_1\n",
       "O1   Bacteria     Firmicutes\n",
       "O2   Bacteria  Bacteroidetes"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from biom import example_table\n",
    "example_table.metadata_to_dataframe('observation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73088ad5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tS1\tS2\tS3\n",
      "O1\t0.0\t1.0\t2.0\n",
      "O2\t3.0\t4.0\t5.0\n"
     ]
    }
   ],
   "source": [
    "print(example_table)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcff6f82",
   "metadata": {},
   "source": [
    "Test mount_observbations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "398c9dd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def mount_observations(table_fp, min_count=0, taxonomy_level=6,\n",
    "                       taxa_to_keep=None, md_key='taxonomy', normalize=True,\n",
    "                       clean_obs_ids=True, filter_obs=True):\n",
    "    '''load biom table, filter by abundance, collapse taxonomy, return biom.\n",
    "\n",
    "    table_fp: path\n",
    "        Input biom table.\n",
    "    min_count: int\n",
    "        Minimum abundance threshold; features detected at lower abundance are\n",
    "        removed from table.\n",
    "    taxonomy_level: int\n",
    "        Taxonomic level at which to collapse table.\n",
    "    taxa_to_keep: list of taxonomies to retain, others are removed before\n",
    "        evaluation.\n",
    "    md_key: str\n",
    "        biom observation metadata key on which to collapse and filter.\n",
    "    normalize: bool\n",
    "        Normalize table to relative abundance across sample rows?\n",
    "    clean_obs_ids: bool\n",
    "        Remove '[]()' characters from observation ids? (these are removed from\n",
    "        the ref db during filtering/cleaning steps, and should be removed from\n",
    "        expected taxonomy files to avoid mismatches).\n",
    "    filter_obs: bool\n",
    "        Filter observations? filter_table will remove observations if taxonomy\n",
    "        strings are shorter than taxonomic_level, count is less than min_count,\n",
    "        or observation is not included in taxa_to_keep.\n",
    "    '''\n",
    "    try:\n",
    "        table = load_table(table_fp)\n",
    "    except ValueError:\n",
    "        raise ValueError(\"Couldn't parse BIOM table: {0}\".format(table_fp))\n",
    "\n",
    "    if filter_obs is True and min_count > 0 and taxa_to_keep is not None:\n",
    "        try:\n",
    "            table = filter_table(table, min_count, taxonomy_level,\n",
    "                                 taxa_to_keep, md_key=md_key)\n",
    "        except TableException:\n",
    "            # if all data is filtered out, move on to the next table\n",
    "            pass\n",
    "\n",
    "        except TypeError:\n",
    "            print(\"Missing taxonomic information in table \" + table_fp)\n",
    "\n",
    "        if table.is_empty():\n",
    "            raise ValueError(\"Table is empty after filtering at\"\n",
    "                             \" {0}\".format(table_fp))\n",
    "\n",
    "    collapse_taxonomy = get_taxonomy_collapser(taxonomy_level, md_key=md_key)\n",
    "\n",
    "    try:\n",
    "        table = table.collapse(collapse_taxonomy, axis='observation',\n",
    "                               min_group_size=1)\n",
    "    except TableException:\n",
    "        raise TableException(\"Failure to collapse taxonomy for table at:\"\n",
    "                             \" {0}\".format(table_fp))\n",
    "    except TypeError:\n",
    "        raise TypeError(\"Failure to collapse taxonomy: {0}\".format(table_fp))\n",
    "\n",
    "    if normalize is True:\n",
    "        table.norm(axis='sample')\n",
    "\n",
    "    return table\n",
    "\n",
    "def get_taxonomy_collapser(level, md_key='taxonomy',\n",
    "                           unassignable_label='unassigned'):\n",
    "    \"\"\" Returns fn to pass to table.collapse\n",
    "\n",
    "        level: the level to collapse on in the \"taxonomy\" observation\n",
    "         metdata category\n",
    "\n",
    "    \"\"\"\n",
    "    def f(id_, md):\n",
    "        try:\n",
    "            levels = [l.strip() for l in md[md_key].split(';')]\n",
    "        except AttributeError:\n",
    "            try:\n",
    "                levels = [l.strip() for l in md[md_key]]\n",
    "            # if no metadata is listed for observation, group as Unassigned\n",
    "            except TypeError:\n",
    "                levels = [unassignable_label]\n",
    "        # this happens if the table is empty\n",
    "        except TypeError:\n",
    "            levels = [unassignable_label]\n",
    "        result = ';'.join(levels[:level+1])\n",
    "        return result\n",
    "    return f\n",
    "\n",
    "\n",
    "def filter_table(table, min_count=0, taxonomy_level=None,\n",
    "                 taxa_to_keep=None, md_key='taxonomy'):\n",
    "    try:\n",
    "        _taxa_to_keep = ';'.join(taxa_to_keep)\n",
    "    except TypeError:\n",
    "        _taxa_to_keep = None\n",
    "\n",
    "    def f(data_vector, id_, metadata):\n",
    "        # if filtering based on number of taxonomy levels, and this\n",
    "        # observation has taxonomic information, and\n",
    "        # there are a sufficient number of taxonomic levels\n",
    "        # Table filtering here removed taxa that have insufficient levels\n",
    "        enough_levels = taxonomy_level is None or \\\n",
    "                        (metadata[md_key] is not None and\n",
    "                         len(metadata[md_key]) >= taxonomy_level+1)\n",
    "        # if filtering to specific taxa, this OTU is assigned to that taxonomy\n",
    "        allowed_taxa = _taxa_to_keep is None or \\\n",
    "            id_.startswith(_taxa_to_keep) or \\\n",
    "            (metadata is not None and md_key in metadata and\n",
    "             ';'.join(metadata[md_key]).startswith(_taxa_to_keep))\n",
    "        # the count of this observation is at least min_count\n",
    "        sufficient_count = data_vector.sum() >= min_count\n",
    "        return sufficient_count and allowed_taxa and enough_levels\n",
    "\n",
    "    return table.filter(f, axis='observation', inplace=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "648816a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# expected\n",
    "table_fp=\"data/mock-community/fuhrman_18Sv4/pr2-ssu/expected/table.L7.biom\"\n",
    "min_count=0\n",
    "taxonomy_level=3\n",
    "taxa_to_keep=None\n",
    "md_key='taxonomy'\n",
    "normalize=True\n",
    "clean_obs_ids=True\n",
    "filter_obs=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8830eb30",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = load_table(table_fp)\n",
    "\n",
    "if filter_obs is True and min_count > 0 and taxa_to_keep is not None:\n",
    "    print(\"yes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "546d488e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "Eukaryota;Haptista;Haptophyta;NA;Prymnesiophyceae;Prymnesiales;NA;NA;NA\t32153.0\t73024.0\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Syndiniales;NA;NA;NA;NA\t22820.0\t39622.0\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;NA;NA;NA;NA\t6109.0\t5246.0\n",
      "Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;NA;NA;NA;NA\t3404.0\t7127.0\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Lingulodiniaceae;Lingulodinium;NA\t0.0\t8738.0\n",
      "Eukaryota;TSAR;Rhizaria;Radiolaria;Polycystinea;Spumellaria;Pyloniidae;Larcopyle;NA\t4960.0\t3494.0\n",
      "Eukaryota;TSAR;Rhizaria;Cercozoa;Filosa-Thecofilosea;Cryomonadida;NA;NA;NA\t2001.0\t2873.0\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Gymnodinium;NA\t0.0\t4332.0\n",
      "Eukaryota;TSAR;Alveolata;Ciliophora;Spirotrichea;Oligotrichida;NA;NA;NA\t3280.0\t328.0\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gonyaulacales;Ceratiaceae;Neoceratium;NA\t2590.0\t12.0\n",
      "Eukaryota;Obazoa;Opisthokonta;Metazoa;Arthropoda;NA;NA;NA;NA\t2393.0\t20.0\n",
      "Eukaryota;TSAR;Stramenopiles;Gyrista;Coscinodiscophyceae;;NA;NA;NA\t21.0\t873.0\n",
      "Eukaryota;TSAR;Alveolata;Ciliophora;Phyllopharyngea;Suctoria;NA;NA;NA\t0.0\t424.0\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata;Dinophyceae;Gymnodiniales;Gymnodiniaceae;Gyrodinium;NA\t0.0\t265.0\n"
     ]
    }
   ],
   "source": [
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fd9d08d",
   "metadata": {},
   "outputs": [],
   "source": [
    "collapse_taxonomy = get_taxonomy_collapser(3, md_key=md_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d31053a",
   "metadata": {},
   "outputs": [],
   "source": [
    "table_c = table.collapse(collapse_taxonomy, axis='observation',\n",
    "                               min_group_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a41cd26c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "unassigned\t5695.071428571428\t10455.57142857143\n"
     ]
    }
   ],
   "source": [
    "print(table_c)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fedd3bad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yes\n"
     ]
    }
   ],
   "source": [
    "if normalize is True:\n",
    "    print(\"yes\")\n",
    "    table_c.norm(axis='sample')\n",
    "else:\n",
    "    print(\"no\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d8f267d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# actual\n",
    "table_fp=\"analyses/mock-community/fuhrman_18Sv4/pr2-ssu/blast+/0.001:1:0.51:0.97/table.biom\"\n",
    "min_count=1\n",
    "taxonomy_level=3\n",
    "taxa_to_keep=None\n",
    "md_key='taxonomy'\n",
    "normalize=True\n",
    "clean_obs_ids=True\n",
    "filter_obs=True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3d70073",
   "metadata": {},
   "outputs": [],
   "source": [
    "table = load_table(table_fp)\n",
    "\n",
    "if filter_obs is True and min_count > 0 and taxa_to_keep is not None:\n",
    "    print(\"yes\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3460c87c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "ASV_1\t32153.0\t73024.0\n",
      "ASV_2\t14255.0\t30957.0\n",
      "ASV_3\t8565.0\t4849.0\n",
      "ASV_4\t6109.0\t5246.0\n",
      "ASV_5\t3404.0\t7127.0\n",
      "ASV_6\t0.0\t8738.0\n",
      "ASV_7\t4960.0\t3494.0\n",
      "ASV_8\t2001.0\t2873.0\n",
      "ASV_9\t0.0\t4332.0\n",
      "ASV_10\t0.0\t3816.0\n",
      "ASV_11\t3280.0\t328.0\n",
      "ASV_12\t2590.0\t12.0\n",
      "ASV_13\t2393.0\t20.0\n",
      "ASV_14\t21.0\t873.0\n",
      "ASV_15\t0.0\t424.0\n",
      "ASV_16\t0.0\t265.0\n"
     ]
    }
   ],
   "source": [
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "171d4207",
   "metadata": {},
   "outputs": [],
   "source": [
    "table_c = table.collapse(collapse_taxonomy, axis='observation',\n",
    "                               min_group_size=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12af8d83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Constructed from biom file\n",
      "#OTU ID\tMP_ERR3712072\tMP_ERR3712077\n",
      "Eukaryota;Haptista;Haptophyta;Haptophyta_X\t32153.0\t73024.0\n",
      "Eukaryota;TSAR;Alveolata;Dinoflagellata\t3939.875\t7276.875\n",
      "Eukaryota;TSAR;Stramenopiles;Gyrista\t1712.5\t4000.0\n",
      "Eukaryota;TSAR;Rhizaria;Radiolaria\t4960.0\t3494.0\n",
      "Eukaryota;TSAR;Rhizaria;Cercozoa\t2001.0\t2873.0\n",
      "Eukaryota;TSAR;Alveolata;Ciliophora\t1640.0\t376.0\n",
      "Eukaryota;Obazoa;Opisthokonta;Metazoa\t2393.0\t20.0\n"
     ]
    }
   ],
   "source": [
    "print(table_c)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a594d2ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "241df1f5-effc-484b-9fbd-fb1cf806b570",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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