{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "681a0856",
   "metadata": {},
   "source": [
    "# OAK diff-via-mappings command\n",
    "\n",
    "This notebook is intended as a supplement to the [main OAK CLI docs](https://incatools.github.io/ontology-access-kit/cli.html).\n",
    "\n",
    "This notebook provides examples for the `diff-via-mappings` command, which can be used to **find differences between two ontologies based on mappings between them**\n",
    "\n",
    "For more on mappings, see [Mappings](https://incatools.github.io/ontology-access-kit/guide/mappings.html) in the OAK guide.\n",
    "\n",
    "\n",
    "## Help Option\n",
    "\n",
    "You can get help on any OAK command using `--help`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "460639a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage: runoak diff-via-mappings [OPTIONS] [TERMS]...\r\n",
      "\r\n",
      "  Calculates cross-ontology diff using mappings\r\n",
      "\r\n",
      "  Given a pair of ontologies, and mappings that connect terms in both\r\n",
      "  ontologies, this command will perform a structural comparison of all mapped\r\n",
      "  pairs of terms\r\n",
      "\r\n",
      "  Example:\r\n",
      "\r\n",
      "      runoak -i sqlite:obo:uberon diff-via-mappings --other-input\r\n",
      "      sqlite:obo:zfa  --source UBERON --source ZFA -O csv\r\n",
      "\r\n",
      "  Note the above command does not have any mapping file specified; the\r\n",
      "  mappings that are distributed within each ontology is used (in this case,\r\n",
      "  Uberon contains mappings to ZFA)\r\n",
      "\r\n",
      "  If the mappings are provided externally:\r\n",
      "\r\n",
      "      runoak -i ont1.obo diff-via-mappings --other-input ont2.obo --mapping-\r\n",
      "      input mappings.sssom.tsv\r\n",
      "\r\n",
      "  (in the above example, --source is not passed, so all mappings are tested)\r\n",
      "\r\n",
      "  If there are no existing mappings, you can use the lexmatch command to\r\n",
      "  generate them:\r\n",
      "\r\n",
      "      runoak -i ont1.obo diff-via-mappings -a ont2.obo lexmatch -o\r\n",
      "      mappings.sssom.tsv\r\n",
      "\r\n",
      "      runoak -i ont1.obo diff-via-mappings --other-input ont2.obo --mapping-\r\n",
      "      input mappings.sssom.tsv\r\n",
      "\r\n",
      "  The output from this command follows the cross-ontology-diff data model\r\n",
      "  (https://incatools.github.io/ontology-access-kit/datamodels/cross-ontology-\r\n",
      "  diff/index.html)\r\n",
      "\r\n",
      "  This can be serialized in YAML or TSV form\r\n",
      "\r\n",
      "Options:\r\n",
      "  -S, --source TEXT               ontology prefixes  e.g. HP, MP\r\n",
      "  --mapping-input TEXT            File of mappings in SSSOM format. If not\r\n",
      "                                  provided then mappings in ontology(ies) are\r\n",
      "                                  used\r\n",
      "  -X, --other-input TEXT          Additional input file\r\n",
      "  --other-input-type TEXT         Type of additional input file\r\n",
      "  --intra / --no-intra            If true, then all sources are in the main\r\n",
      "                                  input ontology  [default: no-intra]\r\n",
      "  --autolabel / --no-autolabel    If set, results will automatically have\r\n",
      "                                  labels assigned  [default: autolabel]\r\n",
      "  --include-identity-mappings / --no-include-identity-mappings\r\n",
      "                                  Use identity relation as mapping; use this\r\n",
      "                                  for two versions of the same ontology\r\n",
      "                                  [default: no-include-identity-mappings]\r\n",
      "  --filter-category-identical / --no-filter-category-identical\r\n",
      "                                  Do not report cases where a relationship has\r\n",
      "                                  not changed  [default: no-filter-category-\r\n",
      "                                  identical]\r\n",
      "  --bidirectional / --no-bidirectional\r\n",
      "                                  Show diff from both left and right\r\n",
      "                                  perspectives  [default: bidirectional]\r\n",
      "  -p, --predicates TEXT           A comma-separated list of predicates. This\r\n",
      "                                  may be a shorthand (i, p) or CURIE\r\n",
      "  -o, --output FILENAME           Output file, e.g. obo file\r\n",
      "  -O, --output-type TEXT          Desired output type\r\n",
      "  --help                          Show this message and exit.\r\n"
     ]
    }
   ],
   "source": [
    "!runoak diff-via-mappings --help"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "672f0f62",
   "metadata": {},
   "source": [
    "## Example: Diff between two anatomy ontologies\n",
    "\n",
    "To illustrate usage, we will calculate the diff between [UBERON](https://obofoundry.org/ontology/uberon) (a multi-species anatomy ontology) and [ZFA](https://obofoundry.org/ontology/zfa) (an anatomy ontology for zebrafish).\n",
    "\n",
    "Note that rather than provide a set of external mappings, we will use the mappings present in both ontologies (in this case, UBERON has xrefs to ZFA).\n",
    "\n",
    "To simplify the comparison, we will only consider [is_a](https://incatools.github.io/ontology-access-kit/glossary.html#term-IS_A) and [part_of](https://incatools.github.io/ontology-access-kit/glossary.html#term-Part-Of) relationships. This is specified using the `--predicates` (`-p`) option"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ef4a124b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!runoak -i sqlite:obo:uberon diff-via-mappings --other-input \\\n",
    "      sqlite:obo:zfa  --source UBERON --source ZFA -p i,p -O csv -o output/uberon-zfa-diff.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb6e5491",
   "metadata": {},
   "source": [
    "## Analyzing the results using Pandas\n",
    "\n",
    "Note that we asked for the output as a tabular file (`-O csv`), rather than native YAML. You could take the tabular output, analyze it in a spreadsheet, etc. Here we will use the Python pandas library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "26c079cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"output/uberon-zfa-diff.csv\", sep=\"\\t\").fillna(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1ad732b4",
   "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>left_subject_id</th>\n",
       "      <th>left_object_id</th>\n",
       "      <th>left_predicate_id</th>\n",
       "      <th>category</th>\n",
       "      <th>left_subject_label</th>\n",
       "      <th>left_object_label</th>\n",
       "      <th>left_predicate_label</th>\n",
       "      <th>right_subject_id</th>\n",
       "      <th>right_object_id</th>\n",
       "      <th>right_predicate_ids</th>\n",
       "      <th>right_subject_label</th>\n",
       "      <th>right_object_label</th>\n",
       "      <th>right_predicate_labels</th>\n",
       "      <th>left_subject_is_functional</th>\n",
       "      <th>left_object_is_functional</th>\n",
       "      <th>subject_mapping_predicate</th>\n",
       "      <th>object_mapping_predicate</th>\n",
       "      <th>right_intermediate_ids</th>\n",
       "      <th>subject_mapping_cardinality</th>\n",
       "      <th>object_mapping_cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>UBERON:0000995</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>uterine cervix</td>\n",
       "      <td>uterus</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>UBERON:0001560</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>uterine cervix</td>\n",
       "      <td>neck of organ</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UBERON:0000002</td>\n",
       "      <td>UBERON:0005156</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>uterine cervix</td>\n",
       "      <td>reproductive structure</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UBERON:0000003</td>\n",
       "      <td>UBERON:0000033</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>naris</td>\n",
       "      <td>head</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>UBERON:0000003</td>\n",
       "      <td>UBERON:0005725</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>naris</td>\n",
       "      <td>olfactory system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36336</th>\n",
       "      <td>ZFA:0009401</td>\n",
       "      <td>ZFA:0009000</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>lens fiber cell</td>\n",
       "      <td>cell</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36337</th>\n",
       "      <td>ZFA:0009402</td>\n",
       "      <td>ZFA:0005065</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>heart valve cell</td>\n",
       "      <td>heart valve</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36338</th>\n",
       "      <td>ZFA:0009402</td>\n",
       "      <td>ZFA:0009000</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>heart valve cell</td>\n",
       "      <td>cell</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36339</th>\n",
       "      <td>ZFA:0009403</td>\n",
       "      <td>ZFA:0009402</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>heart valve interstitial cell</td>\n",
       "      <td>heart valve cell</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36340</th>\n",
       "      <td>ZFA:0009404</td>\n",
       "      <td>ZFA:0009402</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>MissingMapping</td>\n",
       "      <td>heart valve endothelial cell</td>\n",
       "      <td>heart valve cell</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1:0</td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>36341 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      left_subject_id  left_object_id left_predicate_id        category  \\\n",
       "0      UBERON:0000002  UBERON:0000995       BFO:0000050  MissingMapping   \n",
       "1      UBERON:0000002  UBERON:0001560   rdfs:subClassOf  MissingMapping   \n",
       "2      UBERON:0000002  UBERON:0005156   rdfs:subClassOf  MissingMapping   \n",
       "3      UBERON:0000003  UBERON:0000033       BFO:0000050  MissingMapping   \n",
       "4      UBERON:0000003  UBERON:0005725       BFO:0000050  MissingMapping   \n",
       "...               ...             ...               ...             ...   \n",
       "36336     ZFA:0009401     ZFA:0009000   rdfs:subClassOf  MissingMapping   \n",
       "36337     ZFA:0009402     ZFA:0005065       BFO:0000050  MissingMapping   \n",
       "36338     ZFA:0009402     ZFA:0009000   rdfs:subClassOf  MissingMapping   \n",
       "36339     ZFA:0009403     ZFA:0009402   rdfs:subClassOf  MissingMapping   \n",
       "36340     ZFA:0009404     ZFA:0009402   rdfs:subClassOf  MissingMapping   \n",
       "\n",
       "                  left_subject_label       left_object_label  \\\n",
       "0                     uterine cervix                  uterus   \n",
       "1                     uterine cervix           neck of organ   \n",
       "2                     uterine cervix  reproductive structure   \n",
       "3                              naris                    head   \n",
       "4                              naris        olfactory system   \n",
       "...                              ...                     ...   \n",
       "36336                lens fiber cell                    cell   \n",
       "36337               heart valve cell             heart valve   \n",
       "36338               heart valve cell                    cell   \n",
       "36339  heart valve interstitial cell        heart valve cell   \n",
       "36340   heart valve endothelial cell        heart valve cell   \n",
       "\n",
       "      left_predicate_label right_subject_id right_object_id  \\\n",
       "0                                                             \n",
       "1                                                             \n",
       "2                                                             \n",
       "3                                                             \n",
       "4                                                             \n",
       "...                    ...              ...             ...   \n",
       "36336                                                         \n",
       "36337                                                         \n",
       "36338                                                         \n",
       "36339                                                         \n",
       "36340                                                         \n",
       "\n",
       "      right_predicate_ids right_subject_label right_object_label  \\\n",
       "0                                                                  \n",
       "1                                                                  \n",
       "2                                                                  \n",
       "3                                                                  \n",
       "4                                                                  \n",
       "...                   ...                 ...                ...   \n",
       "36336                                                              \n",
       "36337                                                              \n",
       "36338                                                              \n",
       "36339                                                              \n",
       "36340                                                              \n",
       "\n",
       "      right_predicate_labels left_subject_is_functional  \\\n",
       "0                                                         \n",
       "1                                                         \n",
       "2                                                         \n",
       "3                                                         \n",
       "4                                                         \n",
       "...                      ...                        ...   \n",
       "36336                                                     \n",
       "36337                                                     \n",
       "36338                                                     \n",
       "36339                                                     \n",
       "36340                                                     \n",
       "\n",
       "      left_object_is_functional subject_mapping_predicate  \\\n",
       "0                                                           \n",
       "1                                                           \n",
       "2                                                           \n",
       "3                                                           \n",
       "4                                                           \n",
       "...                         ...                       ...   \n",
       "36336                                                       \n",
       "36337                                                       \n",
       "36338                                                       \n",
       "36339                                                       \n",
       "36340                                                       \n",
       "\n",
       "      object_mapping_predicate right_intermediate_ids  \\\n",
       "0                                                       \n",
       "1                                                       \n",
       "2                                                       \n",
       "3                                                       \n",
       "4                                                       \n",
       "...                        ...                    ...   \n",
       "36336                                                   \n",
       "36337                                                   \n",
       "36338                                                   \n",
       "36339                                                   \n",
       "36340                                                   \n",
       "\n",
       "      subject_mapping_cardinality object_mapping_cardinality  \n",
       "0                             1:0                             \n",
       "1                             1:0                             \n",
       "2                             1:0                             \n",
       "3                             1:0                             \n",
       "4                             1:0                             \n",
       "...                           ...                        ...  \n",
       "36336                         1:0                             \n",
       "36337                         1:0                             \n",
       "36338                         1:0                             \n",
       "36339                         1:0                             \n",
       "36340                         1:0                             \n",
       "\n",
       "[36341 rows x 20 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6b959761",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"left_source\"] = df.apply(lambda x: x.left_subject_id.split(\":\")[0], axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b0f2f50",
   "metadata": {},
   "source": [
    "## Plotting mapping diff categories\n",
    "\n",
    "The diff tool works by taking each relationship/edge in the \"left\" ontology (here all UBERON and all ZFA edges\n",
    "are considered) and trying to map it to a relationship in the \"right\" ontology.\n",
    "\n",
    "The mapping is assigned a category from the [cross-ontology-diff:DiffCategory](https://w3id.org/oak/cross-ontology-diff/DiffCategory) enumeration.\n",
    "\n",
    "(note that like many OAK operations, the output conforms to a data model that makes its semantics explicit)\n",
    "\n",
    "We will use seaborn/matplotlib to plot the category counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "20be04b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set plot style: grey grid in the background:\n",
    "sns.set(style=\"darkgrid\")\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "sns.countplot(data=df, x='category')\n",
    "plt.title('Count of each category')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d619fecb",
   "metadata": {},
   "source": [
    "the dominant category is MissingMapping. This means that either/both subject and object of the left edge could not be matched in the right ontology. It is not surprising there are so many, because Uberon covers more species."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26fb6e19",
   "metadata": {},
   "source": [
    "### Breaking down by predicate, and using log scale\n",
    "\n",
    "We can get a more detailed view by breaking down the counts by the predicate of the left edge.\n",
    "\n",
    "We will also use a log scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ab65e2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df, x='category', hue=\"left_predicate_id\")\n",
    "plt.title('Count of each category')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Count')\n",
    "plt.yscale('log')\n",
    "plt.xticks(rotation=45) # Make x-axis labels diagonal\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed630003",
   "metadata": {},
   "source": [
    "## Exploring categories\n",
    "\n",
    "### Non-entailed relationships\n",
    "\n",
    "i.e. this is an edge in UBERON that corresponds to a different kind of edge in ZFA (or vice versa), where the former\n",
    "doesn't subsume the latter.\n",
    "\n",
    "For example, in UBERON PNS *part_of* NS; in ZFA PNS *is_a* NS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "866cd714",
   "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>left_subject_id</th>\n",
       "      <th>left_object_id</th>\n",
       "      <th>left_predicate_id</th>\n",
       "      <th>category</th>\n",
       "      <th>left_subject_label</th>\n",
       "      <th>left_object_label</th>\n",
       "      <th>left_predicate_label</th>\n",
       "      <th>right_subject_id</th>\n",
       "      <th>right_object_id</th>\n",
       "      <th>right_predicate_ids</th>\n",
       "      <th>right_subject_label</th>\n",
       "      <th>right_object_label</th>\n",
       "      <th>right_predicate_labels</th>\n",
       "      <th>left_subject_is_functional</th>\n",
       "      <th>left_object_is_functional</th>\n",
       "      <th>subject_mapping_predicate</th>\n",
       "      <th>object_mapping_predicate</th>\n",
       "      <th>right_intermediate_ids</th>\n",
       "      <th>subject_mapping_cardinality</th>\n",
       "      <th>object_mapping_cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>UBERON:0000010</td>\n",
       "      <td>UBERON:0001016</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>peripheral nervous system</td>\n",
       "      <td>nervous system</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0000142</td>\n",
       "      <td>ZFA:0000396</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>peripheral nervous system</td>\n",
       "      <td>nervous system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>UBERON:0000011</td>\n",
       "      <td>UBERON:0002410</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>parasympathetic nervous system</td>\n",
       "      <td>autonomic nervous system</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001575</td>\n",
       "      <td>ZFA:0001574</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>parasympathetic nervous system</td>\n",
       "      <td>autonomic nervous system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>UBERON:0000013</td>\n",
       "      <td>UBERON:0002410</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>sympathetic nervous system</td>\n",
       "      <td>autonomic nervous system</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001576</td>\n",
       "      <td>ZFA:0001574</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>sympathetic nervous system</td>\n",
       "      <td>autonomic nervous system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>UBERON:0000095</td>\n",
       "      <td>UBERON:0002342</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>cardiac neural crest</td>\n",
       "      <td>neural crest</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001648</td>\n",
       "      <td>ZFA:0000045</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>cardiac neural crest</td>\n",
       "      <td>neural crest</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>725</th>\n",
       "      <td>UBERON:0000936</td>\n",
       "      <td>UBERON:0003931</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>posterior commissure</td>\n",
       "      <td>diencephalic white matter</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0000320</td>\n",
       "      <td>ZFA:0000338</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>caudal commissure</td>\n",
       "      <td>diencephalic white matter</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35155</th>\n",
       "      <td>ZFA:0005661</td>\n",
       "      <td>ZFA:0001488</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>olfactory bulb glomerulus</td>\n",
       "      <td>multi-tissue structure</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0005387</td>\n",
       "      <td>UBERON:0000481</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002323</td>\n",
       "      <td>olfactory glomerulus</td>\n",
       "      <td>multi-tissue structure</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35458</th>\n",
       "      <td>ZFA:0005829</td>\n",
       "      <td>ZFA:0005249</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>Schlemm's canal</td>\n",
       "      <td>vasculature</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0004029</td>\n",
       "      <td>UBERON:0002049</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002323</td>\n",
       "      <td>canal of Schlemm</td>\n",
       "      <td>vasculature</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35680</th>\n",
       "      <td>ZFA:0007017</td>\n",
       "      <td>ZFA:0001477</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>posterior neural plate</td>\n",
       "      <td>portion of tissue</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0003057</td>\n",
       "      <td>UBERON:0000479</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002323</td>\n",
       "      <td>chordal neural plate</td>\n",
       "      <td>tissue</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35701</th>\n",
       "      <td>ZFA:0007037</td>\n",
       "      <td>ZFA:0001477</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>posterior neural tube</td>\n",
       "      <td>portion of tissue</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0003076</td>\n",
       "      <td>UBERON:0000479</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...</td>\n",
       "      <td>posterior neural tube</td>\n",
       "      <td>tissue</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35702</th>\n",
       "      <td>ZFA:0007038</td>\n",
       "      <td>ZFA:0001477</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NonEntailedRelationship</td>\n",
       "      <td>anterior neural tube</td>\n",
       "      <td>portion of tissue</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0003080</td>\n",
       "      <td>UBERON:0000479</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...</td>\n",
       "      <td>anterior neural tube</td>\n",
       "      <td>tissue</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>205 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      left_subject_id  left_object_id left_predicate_id  \\\n",
       "22     UBERON:0000010  UBERON:0001016       BFO:0000050   \n",
       "24     UBERON:0000011  UBERON:0002410       BFO:0000050   \n",
       "29     UBERON:0000013  UBERON:0002410       BFO:0000050   \n",
       "196    UBERON:0000095  UBERON:0002342       BFO:0000050   \n",
       "725    UBERON:0000936  UBERON:0003931       BFO:0000050   \n",
       "...               ...             ...               ...   \n",
       "35155     ZFA:0005661     ZFA:0001488   rdfs:subClassOf   \n",
       "35458     ZFA:0005829     ZFA:0005249   rdfs:subClassOf   \n",
       "35680     ZFA:0007017     ZFA:0001477   rdfs:subClassOf   \n",
       "35701     ZFA:0007037     ZFA:0001477   rdfs:subClassOf   \n",
       "35702     ZFA:0007038     ZFA:0001477   rdfs:subClassOf   \n",
       "\n",
       "                      category              left_subject_label  \\\n",
       "22     NonEntailedRelationship       peripheral nervous system   \n",
       "24     NonEntailedRelationship  parasympathetic nervous system   \n",
       "29     NonEntailedRelationship      sympathetic nervous system   \n",
       "196    NonEntailedRelationship            cardiac neural crest   \n",
       "725    NonEntailedRelationship            posterior commissure   \n",
       "...                        ...                             ...   \n",
       "35155  NonEntailedRelationship       olfactory bulb glomerulus   \n",
       "35458  NonEntailedRelationship                 Schlemm's canal   \n",
       "35680  NonEntailedRelationship          posterior neural plate   \n",
       "35701  NonEntailedRelationship           posterior neural tube   \n",
       "35702  NonEntailedRelationship            anterior neural tube   \n",
       "\n",
       "               left_object_label left_predicate_label right_subject_id  \\\n",
       "22                nervous system                           ZFA:0000142   \n",
       "24      autonomic nervous system                           ZFA:0001575   \n",
       "29      autonomic nervous system                           ZFA:0001576   \n",
       "196                 neural crest                           ZFA:0001648   \n",
       "725    diencephalic white matter                           ZFA:0000320   \n",
       "...                          ...                  ...              ...   \n",
       "35155     multi-tissue structure                        UBERON:0005387   \n",
       "35458                vasculature                        UBERON:0004029   \n",
       "35680          portion of tissue                        UBERON:0003057   \n",
       "35701          portion of tissue                        UBERON:0003076   \n",
       "35702          portion of tissue                        UBERON:0003080   \n",
       "\n",
       "      right_object_id                                right_predicate_ids  \\\n",
       "22        ZFA:0000396                                    rdfs:subClassOf   \n",
       "24        ZFA:0001574                                    rdfs:subClassOf   \n",
       "29        ZFA:0001574                                    rdfs:subClassOf   \n",
       "196       ZFA:0000045                                    rdfs:subClassOf   \n",
       "725       ZFA:0000338                                    rdfs:subClassOf   \n",
       "...               ...                                                ...   \n",
       "35155  UBERON:0000481                  BFO:0000050|RO:0002131|RO:0002323   \n",
       "35458  UBERON:0002049                  BFO:0000050|RO:0002131|RO:0002323   \n",
       "35680  UBERON:0000479                  BFO:0000050|RO:0002131|RO:0002323   \n",
       "35701  UBERON:0000479  BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...   \n",
       "35702  UBERON:0000479  BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...   \n",
       "\n",
       "                  right_subject_label         right_object_label  \\\n",
       "22          peripheral nervous system             nervous system   \n",
       "24     parasympathetic nervous system   autonomic nervous system   \n",
       "29         sympathetic nervous system   autonomic nervous system   \n",
       "196              cardiac neural crest               neural crest   \n",
       "725                 caudal commissure  diencephalic white matter   \n",
       "...                               ...                        ...   \n",
       "35155            olfactory glomerulus     multi-tissue structure   \n",
       "35458                canal of Schlemm                vasculature   \n",
       "35680            chordal neural plate                     tissue   \n",
       "35701           posterior neural tube                     tissue   \n",
       "35702            anterior neural tube                     tissue   \n",
       "\n",
       "      right_predicate_labels left_subject_is_functional  \\\n",
       "22                                                        \n",
       "24                                                        \n",
       "29                                                        \n",
       "196                                                       \n",
       "725                                                       \n",
       "...                      ...                        ...   \n",
       "35155                                                     \n",
       "35458                                                     \n",
       "35680                                                     \n",
       "35701                                                     \n",
       "35702                                                     \n",
       "\n",
       "      left_object_is_functional subject_mapping_predicate  \\\n",
       "22                                                          \n",
       "24                                                          \n",
       "29                                                          \n",
       "196                                                         \n",
       "725                                                         \n",
       "...                         ...                       ...   \n",
       "35155                                                       \n",
       "35458                                                       \n",
       "35680                                                       \n",
       "35701                                                       \n",
       "35702                                                       \n",
       "\n",
       "      object_mapping_predicate right_intermediate_ids  \\\n",
       "22                                                      \n",
       "24                                                      \n",
       "29                                                      \n",
       "196                                                     \n",
       "725                                                     \n",
       "...                        ...                    ...   \n",
       "35155                                                   \n",
       "35458                                                   \n",
       "35680                                                   \n",
       "35701                                                   \n",
       "35702                                                   \n",
       "\n",
       "      subject_mapping_cardinality object_mapping_cardinality  \n",
       "22                                                            \n",
       "24                                                            \n",
       "29                                                            \n",
       "196                                                           \n",
       "725                                                           \n",
       "...                           ...                        ...  \n",
       "35155                                                         \n",
       "35458                                                         \n",
       "35680                                                         \n",
       "35701                                                         \n",
       "35702                                                         \n",
       "\n",
       "[205 rows x 20 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"category=='NonEntailedRelationship'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c88189b1",
   "metadata": {},
   "source": [
    "### No relationship\n",
    "\n",
    "In this case, the edge in UBERON has no corresponding edge in ZFA (or vice versa), entailed or otherwise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a6807bcf",
   "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>left_subject_id</th>\n",
       "      <th>left_object_id</th>\n",
       "      <th>left_predicate_id</th>\n",
       "      <th>category</th>\n",
       "      <th>left_subject_label</th>\n",
       "      <th>left_object_label</th>\n",
       "      <th>left_predicate_label</th>\n",
       "      <th>right_subject_id</th>\n",
       "      <th>right_object_id</th>\n",
       "      <th>right_predicate_ids</th>\n",
       "      <th>right_subject_label</th>\n",
       "      <th>right_object_label</th>\n",
       "      <th>right_predicate_labels</th>\n",
       "      <th>left_subject_is_functional</th>\n",
       "      <th>left_object_is_functional</th>\n",
       "      <th>subject_mapping_predicate</th>\n",
       "      <th>object_mapping_predicate</th>\n",
       "      <th>right_intermediate_ids</th>\n",
       "      <th>subject_mapping_cardinality</th>\n",
       "      <th>object_mapping_cardinality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>UBERON:0000004</td>\n",
       "      <td>UBERON:0000475</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>nose</td>\n",
       "      <td>organism subdivision</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0000047</td>\n",
       "      <td>ZFA:0001308</td>\n",
       "      <td></td>\n",
       "      <td>peripheral olfactory organ</td>\n",
       "      <td>organism subdivision</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>UBERON:0000089</td>\n",
       "      <td>UBERON:0004750</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>hypoblast (generic)</td>\n",
       "      <td>blastoderm</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0000117</td>\n",
       "      <td>ZFA:0001176</td>\n",
       "      <td></td>\n",
       "      <td>hypoblast</td>\n",
       "      <td>blastoderm</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>UBERON:0000471</td>\n",
       "      <td>UBERON:0003103</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>compound organ component</td>\n",
       "      <td>compound organ</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001489</td>\n",
       "      <td>ZFA:0000496</td>\n",
       "      <td></td>\n",
       "      <td>compound organ component</td>\n",
       "      <td>compound organ</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>UBERON:0000479</td>\n",
       "      <td>UBERON:0000468</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>tissue</td>\n",
       "      <td>multicellular organism</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001477</td>\n",
       "      <td>ZFA:0001094</td>\n",
       "      <td></td>\n",
       "      <td>portion of tissue</td>\n",
       "      <td>whole organism</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>UBERON:0000480</td>\n",
       "      <td>UBERON:0000468</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>anatomical group</td>\n",
       "      <td>multicellular organism</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001512</td>\n",
       "      <td>ZFA:0001094</td>\n",
       "      <td></td>\n",
       "      <td>anatomical group</td>\n",
       "      <td>whole organism</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35714</th>\n",
       "      <td>ZFA:0007057</td>\n",
       "      <td>ZFA:0005297</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>ocular blood vessel</td>\n",
       "      <td>cranial blood vessel</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0002203</td>\n",
       "      <td>UBERON:0011362</td>\n",
       "      <td></td>\n",
       "      <td>vasculature of eye</td>\n",
       "      <td>cranial blood vasculature</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35715</th>\n",
       "      <td>ZFA:0007058</td>\n",
       "      <td>ZFA:0000012</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>roof plate</td>\n",
       "      <td>central nervous system</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0003054</td>\n",
       "      <td>UBERON:0001017</td>\n",
       "      <td></td>\n",
       "      <td>roof plate</td>\n",
       "      <td>central nervous system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35716</th>\n",
       "      <td>ZFA:0007058</td>\n",
       "      <td>ZFA:0001488</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>roof plate</td>\n",
       "      <td>multi-tissue structure</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0003054</td>\n",
       "      <td>UBERON:0000481</td>\n",
       "      <td></td>\n",
       "      <td>roof plate</td>\n",
       "      <td>multi-tissue structure</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35720</th>\n",
       "      <td>ZFA:0007071</td>\n",
       "      <td>ZFA:0001477</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>flexural organ</td>\n",
       "      <td>portion of tissue</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0011577</td>\n",
       "      <td>UBERON:0000479</td>\n",
       "      <td></td>\n",
       "      <td>flexural organ</td>\n",
       "      <td>tissue</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35722</th>\n",
       "      <td>ZFA:0007072</td>\n",
       "      <td>ZFA:0001488</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>NoRelationship</td>\n",
       "      <td>blood sinus</td>\n",
       "      <td>multi-tissue structure</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0006615</td>\n",
       "      <td>UBERON:0000481</td>\n",
       "      <td></td>\n",
       "      <td>venous sinus</td>\n",
       "      <td>multi-tissue structure</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1375 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      left_subject_id  left_object_id left_predicate_id        category  \\\n",
       "7      UBERON:0000004  UBERON:0000475   rdfs:subClassOf  NoRelationship   \n",
       "188    UBERON:0000089  UBERON:0004750       BFO:0000050  NoRelationship   \n",
       "647    UBERON:0000471  UBERON:0003103       BFO:0000050  NoRelationship   \n",
       "661    UBERON:0000479  UBERON:0000468       BFO:0000050  NoRelationship   \n",
       "663    UBERON:0000480  UBERON:0000468       BFO:0000050  NoRelationship   \n",
       "...               ...             ...               ...             ...   \n",
       "35714     ZFA:0007057     ZFA:0005297   rdfs:subClassOf  NoRelationship   \n",
       "35715     ZFA:0007058     ZFA:0000012       BFO:0000050  NoRelationship   \n",
       "35716     ZFA:0007058     ZFA:0001488   rdfs:subClassOf  NoRelationship   \n",
       "35720     ZFA:0007071     ZFA:0001477   rdfs:subClassOf  NoRelationship   \n",
       "35722     ZFA:0007072     ZFA:0001488   rdfs:subClassOf  NoRelationship   \n",
       "\n",
       "             left_subject_label       left_object_label left_predicate_label  \\\n",
       "7                          nose    organism subdivision                        \n",
       "188         hypoblast (generic)              blastoderm                        \n",
       "647    compound organ component          compound organ                        \n",
       "661                      tissue  multicellular organism                        \n",
       "663            anatomical group  multicellular organism                        \n",
       "...                         ...                     ...                  ...   \n",
       "35714       ocular blood vessel    cranial blood vessel                        \n",
       "35715                roof plate  central nervous system                        \n",
       "35716                roof plate  multi-tissue structure                        \n",
       "35720            flexural organ       portion of tissue                        \n",
       "35722               blood sinus  multi-tissue structure                        \n",
       "\n",
       "      right_subject_id right_object_id right_predicate_ids  \\\n",
       "7          ZFA:0000047     ZFA:0001308                       \n",
       "188        ZFA:0000117     ZFA:0001176                       \n",
       "647        ZFA:0001489     ZFA:0000496                       \n",
       "661        ZFA:0001477     ZFA:0001094                       \n",
       "663        ZFA:0001512     ZFA:0001094                       \n",
       "...                ...             ...                 ...   \n",
       "35714   UBERON:0002203  UBERON:0011362                       \n",
       "35715   UBERON:0003054  UBERON:0001017                       \n",
       "35716   UBERON:0003054  UBERON:0000481                       \n",
       "35720   UBERON:0011577  UBERON:0000479                       \n",
       "35722   UBERON:0006615  UBERON:0000481                       \n",
       "\n",
       "              right_subject_label         right_object_label  \\\n",
       "7      peripheral olfactory organ       organism subdivision   \n",
       "188                     hypoblast                 blastoderm   \n",
       "647      compound organ component             compound organ   \n",
       "661             portion of tissue             whole organism   \n",
       "663              anatomical group             whole organism   \n",
       "...                           ...                        ...   \n",
       "35714          vasculature of eye  cranial blood vasculature   \n",
       "35715                  roof plate     central nervous system   \n",
       "35716                  roof plate     multi-tissue structure   \n",
       "35720              flexural organ                     tissue   \n",
       "35722                venous sinus     multi-tissue structure   \n",
       "\n",
       "      right_predicate_labels left_subject_is_functional  \\\n",
       "7                                                         \n",
       "188                                                       \n",
       "647                                                       \n",
       "661                                                       \n",
       "663                                                       \n",
       "...                      ...                        ...   \n",
       "35714                                                     \n",
       "35715                                                     \n",
       "35716                                                     \n",
       "35720                                                     \n",
       "35722                                                     \n",
       "\n",
       "      left_object_is_functional subject_mapping_predicate  \\\n",
       "7                                                           \n",
       "188                                                         \n",
       "647                                                         \n",
       "661                                                         \n",
       "663                                                         \n",
       "...                         ...                       ...   \n",
       "35714                                                       \n",
       "35715                                                       \n",
       "35716                                                       \n",
       "35720                                                       \n",
       "35722                                                       \n",
       "\n",
       "      object_mapping_predicate right_intermediate_ids  \\\n",
       "7                                                       \n",
       "188                                                     \n",
       "647                                                     \n",
       "661                                                     \n",
       "663                                                     \n",
       "...                        ...                    ...   \n",
       "35714                                                   \n",
       "35715                                                   \n",
       "35716                                                   \n",
       "35720                                                   \n",
       "35722                                                   \n",
       "\n",
       "      subject_mapping_cardinality object_mapping_cardinality  \n",
       "7                                                             \n",
       "188                                                           \n",
       "647                                                           \n",
       "661                                                           \n",
       "663                                                           \n",
       "...                           ...                        ...  \n",
       "35714                                                         \n",
       "35715                                                         \n",
       "35716                                                         \n",
       "35720                                                         \n",
       "35722                                                         \n",
       "\n",
       "[1375 rows x 20 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"category=='NoRelationship'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a931a547",
   "metadata": {},
   "source": [
    "### LeftEntailedByRight\n",
    "\n",
    "in this case UBERON has a direct edge that corresponds to two or more direct edges in ZFA (or vice versa)\n",
    "\n",
    "In this case the edges must chain together via OWL semantics.\n",
    "\n",
    "for example, UBERON has `ganglion part-of nervous system`, ZFA has `ganglion part-of PNS is-a nervous system`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "188a5c06",
   "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>left_subject_id</th>\n",
       "      <th>left_object_id</th>\n",
       "      <th>left_predicate_id</th>\n",
       "      <th>category</th>\n",
       "      <th>left_subject_label</th>\n",
       "      <th>left_object_label</th>\n",
       "      <th>left_predicate_label</th>\n",
       "      <th>right_subject_id</th>\n",
       "      <th>right_object_id</th>\n",
       "      <th>right_predicate_ids</th>\n",
       "      <th>...</th>\n",
       "      <th>right_object_label</th>\n",
       "      <th>right_predicate_labels</th>\n",
       "      <th>left_subject_is_functional</th>\n",
       "      <th>left_object_is_functional</th>\n",
       "      <th>subject_mapping_predicate</th>\n",
       "      <th>object_mapping_predicate</th>\n",
       "      <th>right_intermediate_ids</th>\n",
       "      <th>subject_mapping_cardinality</th>\n",
       "      <th>object_mapping_cardinality</th>\n",
       "      <th>left_source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>UBERON:0000016</td>\n",
       "      <td>UBERON:0000949</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>endocrine pancreas</td>\n",
       "      <td>endocrine system</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001260</td>\n",
       "      <td>ZFA:0001158</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002202</td>\n",
       "      <td>...</td>\n",
       "      <td>endocrine system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>UBERON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>UBERON:0000045</td>\n",
       "      <td>UBERON:0001016</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>ganglion</td>\n",
       "      <td>nervous system</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0000190</td>\n",
       "      <td>ZFA:0000396</td>\n",
       "      <td>BFO:0000050|RO:0002131</td>\n",
       "      <td>...</td>\n",
       "      <td>nervous system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>UBERON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>UBERON:0000086</td>\n",
       "      <td>UBERON:0000992</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>zona pellucida</td>\n",
       "      <td>ovary</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001111</td>\n",
       "      <td>ZFA:0000403</td>\n",
       "      <td>BFO:0000050|RO:0002131</td>\n",
       "      <td>...</td>\n",
       "      <td>ovary</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>UBERON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>645</th>\n",
       "      <td>UBERON:0000467</td>\n",
       "      <td>UBERON:0000061</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>anatomical system</td>\n",
       "      <td>anatomical structure</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001439</td>\n",
       "      <td>ZFA:0000037</td>\n",
       "      <td>BFO:0000050|RO:0002131|rdfs:subClassOf</td>\n",
       "      <td>...</td>\n",
       "      <td>anatomical structure</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>UBERON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>658</th>\n",
       "      <td>UBERON:0000477</td>\n",
       "      <td>UBERON:0001062</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>anatomical cluster</td>\n",
       "      <td>anatomical entity</td>\n",
       "      <td></td>\n",
       "      <td>ZFA:0001478</td>\n",
       "      <td>ZFA:0100000</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>...</td>\n",
       "      <td>zebrafish anatomical entity</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>UBERON</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35706</th>\n",
       "      <td>ZFA:0007043</td>\n",
       "      <td>ZFA:0001477</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>hindbrain neural tube</td>\n",
       "      <td>portion of tissue</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:2007043</td>\n",
       "      <td>UBERON:0000479</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...</td>\n",
       "      <td>...</td>\n",
       "      <td>tissue</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZFA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35711</th>\n",
       "      <td>ZFA:0007048</td>\n",
       "      <td>ZFA:0005145</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>ventral intermandibularis posterior</td>\n",
       "      <td>muscle</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:2007048</td>\n",
       "      <td>UBERON:0001630</td>\n",
       "      <td>RO:0002131|RO:0002202|RO:0002254|RO:0002258|RO...</td>\n",
       "      <td>...</td>\n",
       "      <td>muscle organ</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZFA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35717</th>\n",
       "      <td>ZFA:0007068</td>\n",
       "      <td>ZFA:0001486</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>otic epithelium</td>\n",
       "      <td>epithelium</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0003249</td>\n",
       "      <td>UBERON:0000483</td>\n",
       "      <td>RO:0002131|RO:0002323|rdfs:subClassOf</td>\n",
       "      <td>...</td>\n",
       "      <td>epithelium</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZFA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35721</th>\n",
       "      <td>ZFA:0007072</td>\n",
       "      <td>ZFA:0000010</td>\n",
       "      <td>BFO:0000050</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>blood sinus</td>\n",
       "      <td>cardiovascular system</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0006615</td>\n",
       "      <td>UBERON:0004535</td>\n",
       "      <td>BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...</td>\n",
       "      <td>...</td>\n",
       "      <td>cardiovascular system</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZFA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35723</th>\n",
       "      <td>ZFA:0007073</td>\n",
       "      <td>ZFA:0001643</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>LeftEntailedByRight</td>\n",
       "      <td>blood sinus cavity</td>\n",
       "      <td>anatomical space</td>\n",
       "      <td></td>\n",
       "      <td>UBERON:0034940</td>\n",
       "      <td>UBERON:0000464</td>\n",
       "      <td>rdfs:subClassOf</td>\n",
       "      <td>...</td>\n",
       "      <td>anatomical space</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZFA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>883 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      left_subject_id  left_object_id left_predicate_id             category  \\\n",
       "35     UBERON:0000016  UBERON:0000949       BFO:0000050  LeftEntailedByRight   \n",
       "91     UBERON:0000045  UBERON:0001016       BFO:0000050  LeftEntailedByRight   \n",
       "182    UBERON:0000086  UBERON:0000992       BFO:0000050  LeftEntailedByRight   \n",
       "645    UBERON:0000467  UBERON:0000061   rdfs:subClassOf  LeftEntailedByRight   \n",
       "658    UBERON:0000477  UBERON:0001062   rdfs:subClassOf  LeftEntailedByRight   \n",
       "...               ...             ...               ...                  ...   \n",
       "35706     ZFA:0007043     ZFA:0001477   rdfs:subClassOf  LeftEntailedByRight   \n",
       "35711     ZFA:0007048     ZFA:0005145   rdfs:subClassOf  LeftEntailedByRight   \n",
       "35717     ZFA:0007068     ZFA:0001486   rdfs:subClassOf  LeftEntailedByRight   \n",
       "35721     ZFA:0007072     ZFA:0000010       BFO:0000050  LeftEntailedByRight   \n",
       "35723     ZFA:0007073     ZFA:0001643   rdfs:subClassOf  LeftEntailedByRight   \n",
       "\n",
       "                        left_subject_label      left_object_label  \\\n",
       "35                      endocrine pancreas       endocrine system   \n",
       "91                                ganglion         nervous system   \n",
       "182                         zona pellucida                  ovary   \n",
       "645                      anatomical system   anatomical structure   \n",
       "658                     anatomical cluster      anatomical entity   \n",
       "...                                    ...                    ...   \n",
       "35706                hindbrain neural tube      portion of tissue   \n",
       "35711  ventral intermandibularis posterior                 muscle   \n",
       "35717                      otic epithelium             epithelium   \n",
       "35721                          blood sinus  cardiovascular system   \n",
       "35723                   blood sinus cavity       anatomical space   \n",
       "\n",
       "      left_predicate_label right_subject_id right_object_id  \\\n",
       "35                              ZFA:0001260     ZFA:0001158   \n",
       "91                              ZFA:0000190     ZFA:0000396   \n",
       "182                             ZFA:0001111     ZFA:0000403   \n",
       "645                             ZFA:0001439     ZFA:0000037   \n",
       "658                             ZFA:0001478     ZFA:0100000   \n",
       "...                    ...              ...             ...   \n",
       "35706                        UBERON:2007043  UBERON:0000479   \n",
       "35711                        UBERON:2007048  UBERON:0001630   \n",
       "35717                        UBERON:0003249  UBERON:0000483   \n",
       "35721                        UBERON:0006615  UBERON:0004535   \n",
       "35723                        UBERON:0034940  UBERON:0000464   \n",
       "\n",
       "                                     right_predicate_ids  ...  \\\n",
       "35                     BFO:0000050|RO:0002131|RO:0002202  ...   \n",
       "91                                BFO:0000050|RO:0002131  ...   \n",
       "182                               BFO:0000050|RO:0002131  ...   \n",
       "645               BFO:0000050|RO:0002131|rdfs:subClassOf  ...   \n",
       "658                                      rdfs:subClassOf  ...   \n",
       "...                                                  ...  ...   \n",
       "35706  BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...  ...   \n",
       "35711  RO:0002131|RO:0002202|RO:0002254|RO:0002258|RO...  ...   \n",
       "35717              RO:0002131|RO:0002323|rdfs:subClassOf  ...   \n",
       "35721  BFO:0000050|RO:0002131|RO:0002202|RO:0002254|R...  ...   \n",
       "35723                                    rdfs:subClassOf  ...   \n",
       "\n",
       "                right_object_label right_predicate_labels  \\\n",
       "35                endocrine system                          \n",
       "91                  nervous system                          \n",
       "182                          ovary                          \n",
       "645           anatomical structure                          \n",
       "658    zebrafish anatomical entity                          \n",
       "...                            ...                    ...   \n",
       "35706                       tissue                          \n",
       "35711                 muscle organ                          \n",
       "35717                   epithelium                          \n",
       "35721        cardiovascular system                          \n",
       "35723             anatomical space                          \n",
       "\n",
       "      left_subject_is_functional left_object_is_functional  \\\n",
       "35                                                           \n",
       "91                                                           \n",
       "182                                                          \n",
       "645                                                          \n",
       "658                                                          \n",
       "...                          ...                       ...   \n",
       "35706                                                        \n",
       "35711                                                        \n",
       "35717                                                        \n",
       "35721                                                        \n",
       "35723                                                        \n",
       "\n",
       "      subject_mapping_predicate object_mapping_predicate  \\\n",
       "35                                                         \n",
       "91                                                         \n",
       "182                                                        \n",
       "645                                                        \n",
       "658                                                        \n",
       "...                         ...                      ...   \n",
       "35706                                                      \n",
       "35711                                                      \n",
       "35717                                                      \n",
       "35721                                                      \n",
       "35723                                                      \n",
       "\n",
       "      right_intermediate_ids subject_mapping_cardinality  \\\n",
       "35                                                         \n",
       "91                                                         \n",
       "182                                                        \n",
       "645                                                        \n",
       "658                                                        \n",
       "...                      ...                         ...   \n",
       "35706                                                      \n",
       "35711                                                      \n",
       "35717                                                      \n",
       "35721                                                      \n",
       "35723                                                      \n",
       "\n",
       "      object_mapping_cardinality left_source  \n",
       "35                                    UBERON  \n",
       "91                                    UBERON  \n",
       "182                                   UBERON  \n",
       "645                                   UBERON  \n",
       "658                                   UBERON  \n",
       "...                          ...         ...  \n",
       "35706                                    ZFA  \n",
       "35711                                    ZFA  \n",
       "35717                                    ZFA  \n",
       "35721                                    ZFA  \n",
       "35723                                    ZFA  \n",
       "\n",
       "[883 rows x 21 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query(\"category=='LeftEntailedByRight'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "06b37850",
   "metadata": {},
   "source": [
    "## Breaking things down by direction\n",
    "\n",
    "By default, the command will do diffs in both directions, unless `--no-bidirectional` is passed.\n",
    "\n",
    "We can post-hoc break the summary statistics down based on the source of the left term"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e07d6097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_srcs = df['left_source'].unique()\n",
    "n_unique = len(unique_srcs)\n",
    "fig, axs = plt.subplots(nrows=n_unique, figsize=(10, 6*n_unique))\n",
    "\n",
    "for ax, left_source in zip(axs, unique_srcs):\n",
    "    sub_df = df[df['left_source'] == left_source]\n",
    "    sns.countplot(data=sub_df, x='category', hue='left_predicate_id', ax=ax)\n",
    "    ax.set_title(f'Count of each type broken down by left_source = {left_source}')\n",
    "    ax.set_xlabel('Type')\n",
    "    ax.set_ylabel('Count')\n",
    "    ax.set_yscale('log') # Make y-axis logarithmic\n",
    "    ax.tick_params(axis='x', rotation=45) # Make x-axis labels diagonal\n",
    "\n",
    "plt.tight_layout() # Ensure layout is tight so labels don't get cut off\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2b0bc2",
   "metadata": {},
   "source": [
    "As expected `MissingMapping` dominates the Uberon->ZFA direction. Note this is not actually \"missing\" in the sense of incomplete, we expect most edges in uberon to be non-mappable to ZFA due to difference in taxonomic scope."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cae729ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}