notebooks/insdc_per_package_column_usage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "52a8e242",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:20.453017Z",
     "start_time": "2021-09-28T12:23:18.506469Z"
    }
   },
   "outputs": [],
   "source": [
    "from linkml_runtime.utils.schemaview import SchemaView\n",
    "from matplotlib import cm\n",
    "from pandas_profiling import ProfileReport\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecebe933",
   "metadata": {},
   "source": [
    "These queries get data from only those rows with env_package values\n",
    "\n",
    "```\n",
    "% basex /Users/MAM/Documents/gitrepos/biosample-analysis/xqueries/get_harmonized-values_all_has_env_pack.xq > /Users/MAM/Documents/gitrepos/biosample-analysis/target/get_harmonized-values_all_has_env_pack.tsv\n",
    "\n",
    "% basex /Users/MAM/Documents/gitrepos/biosample-analysis/xqueries/non-bsattribute-columns_has_env_pack.xq > /Users/MAM/Documents/gitrepos/biosample-analysis/target/non-bsattribute-columns_has_env_pack.tsv\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "32bfbd0b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:20.458864Z",
     "start_time": "2021-09-28T12:23:20.454883Z"
    }
   },
   "outputs": [],
   "source": [
    "biosample_query_results_root = \"../../biosample-analysis/\"\n",
    "\n",
    "hn_long_file = \"target/get_harmonized-values_all_has_env_pack.tsv\"\n",
    "hn_long_file = os.path.join(biosample_query_results_root, hn_long_file)\n",
    "\n",
    "bs_non_attribute_file = \"target/non-bsattribute-columns_has_env_pack.tsv\"\n",
    "bs_non_attribute_file = os.path.join(\n",
    "    biosample_query_results_root, bs_non_attribute_file\n",
    ")\n",
    "\n",
    "env_package_mapping_file = \"raw_env_package_mappings_only_20210927.tsv\"\n",
    "\n",
    "mixs_model_file = \"../../mixs-source/model/schema/mixs.yaml\"\n",
    "\n",
    "# output\n",
    "\n",
    "insdc_mixs_merged_file = \"insdc_per_package_column_usage.tsv\"\n",
    "pca_plot_file = \"insdc_per_package_column_usage.pdf\"\n",
    "pandas_profile_json_file = \"insdc_per_package_column_usage_profile.json\"\n",
    "pandas_profile_html_file = \"insdc_per_package_column_usage_profile.html\"\n",
    "\n",
    "# ---\n",
    "\n",
    "which_tidied_package_column = 'alternative'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26713062",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:20.464669Z",
     "start_time": "2021-09-28T12:23:20.461563Z"
    }
   },
   "outputs": [],
   "source": [
    "def get_mixs_slots_by_class(mixs_class):\n",
    "    class_slots = list(\n",
    "        mixs_package_slots[\"slot\"].loc[mixs_package_slots[\"class\"].eq(mixs_class)]\n",
    "    )\n",
    "    class_slots.sort()\n",
    "    #     print(class_slots)\n",
    "    return class_slots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8c5dd41",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:20.500565Z",
     "start_time": "2021-09-28T12:23:20.466518Z"
    }
   },
   "outputs": [],
   "source": [
    "mixs_view = SchemaView(mixs_model_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fc4da021",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:23.535162Z",
     "start_time": "2021-09-28T12:23:20.502204Z"
    }
   },
   "outputs": [],
   "source": [
    "soil_subclasses = mixs_view.class_children(\"soil\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9b7ca691",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:23.544749Z",
     "start_time": "2021-09-28T12:23:23.538015Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'soil'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixs_view.get_class(\"soil ME\").is_a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "64551668",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:23.551648Z",
     "start_time": "2021-09-28T12:23:23.547185Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ME']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mixs_view.get_class(\"soil ME\").mixins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b7fb9d1e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:23.556551Z",
     "start_time": "2021-09-28T12:23:23.553489Z"
    }
   },
   "outputs": [],
   "source": [
    "mixs_classes = mixs_view.all_classes()\n",
    "mixs_classes_keys = mixs_classes.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "517be8f8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:25.639955Z",
     "start_time": "2021-09-28T12:23:23.561003Z"
    }
   },
   "outputs": [],
   "source": [
    "outer_list = []\n",
    "for current_class in mixs_classes_keys:\n",
    "    current_induceds = mixs_view.class_induced_slots(current_class)\n",
    "    for i in current_induceds:\n",
    "        outer_list.append([current_class, i.name, i.required, i.recommended])\n",
    "\n",
    "mixs_package_slots = pd.DataFrame(outer_list)\n",
    "mixs_package_slots.columns = [\"class\", \"slot\", \"required\", \"recommended\"]\n",
    "# mixs_package_slots.to_clipboard(index=False)\n",
    "mixs_package_slots.to_csv(insdc_mixs_merged_file, sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75869d22",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4eec5ed1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:25.667209Z",
     "start_time": "2021-09-28T12:23:25.641587Z"
    }
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>raw_env_package</th>\n",
       "      <th>env_package</th>\n",
       "      <th>alternative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>air</td>\n",
       "      <td>air</td>\n",
       "      <td>air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MIGS/MIMS/MIMARKS.air</td>\n",
       "      <td>air</td>\n",
       "      <td>air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>built environment</td>\n",
       "      <td>built</td>\n",
       "      <td>built environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>built</td>\n",
       "      <td>built</td>\n",
       "      <td>built environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>host-associated</td>\n",
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       "      <td>host-associated</td>\n",
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       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>MIGS/MIMS/MIMARKS.air|||MIGS/MIMS/MIMARKS.micr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>ENVO:00000016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>MIMARKS_16S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>not applicable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>saliva</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       raw_env_package      env_package  \\\n",
       "0                                                  air              air   \n",
       "1                                MIGS/MIMS/MIMARKS.air              air   \n",
       "2                                    built environment            built   \n",
       "3                                                built            built   \n",
       "4                                      host-associated  host-associated   \n",
       "..                                                 ...              ...   \n",
       "97   MIGS/MIMS/MIMARKS.air|||MIGS/MIMS/MIMARKS.micr...              NaN   \n",
       "98                                       ENVO:00000016              NaN   \n",
       "99                                         MIMARKS_16S              NaN   \n",
       "100                                     not applicable              NaN   \n",
       "101                                             saliva              NaN   \n",
       "\n",
       "           alternative  \n",
       "0                  air  \n",
       "1                  air  \n",
       "2    built environment  \n",
       "3    built environment  \n",
       "4      host-associated  \n",
       "..                 ...  \n",
       "97                 NaN  \n",
       "98                 NaN  \n",
       "99                 NaN  \n",
       "100                NaN  \n",
       "101                NaN  \n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env_package_mapping = pd.read_csv(env_package_mapping_file, sep=\"\\t\")\n",
    "env_package_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63cb02a9",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:26.704063Z",
     "start_time": "2021-09-28T12:23:25.669038Z"
    }
   },
   "outputs": [],
   "source": [
    "bs_non_attribute = pd.read_csv(bs_non_attribute_file, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13a498f7",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:26.710431Z",
     "start_time": "2021-09-28T12:23:26.706265Z"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(250815, 15)"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "bs_non_attribute.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b019a1e0",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:26.734605Z",
     "start_time": "2021-09-28T12:23:26.712371Z"
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   },
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>accession</th>\n",
       "      <th>primary_id</th>\n",
       "      <th>title</th>\n",
       "      <th>dna_source</th>\n",
       "      <th>doi</th>\n",
       "      <th>entrez_links</th>\n",
       "      <th>model</th>\n",
       "      <th>package</th>\n",
       "      <th>package_name</th>\n",
       "      <th>status</th>\n",
       "      <th>status_date</th>\n",
       "      <th>taxonomy_id</th>\n",
       "      <th>taxonomy_name</th>\n",
       "      <th>paragraph</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>25530</td>\n",
       "      <td>SAMEA1706070</td>\n",
       "      <td>SAMEA1706070</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2015-08-07T13:22:21</td>\n",
       "      <td>449393</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>Neuston of Lake Llebreta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>25531</td>\n",
       "      <td>SAMEA1706069</td>\n",
       "      <td>SAMEA1706069</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2015-08-07T13:22:21</td>\n",
       "      <td>449393</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>1 m depth of Lake Llebreta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>25532</td>\n",
       "      <td>SAMEA1706067</td>\n",
       "      <td>SAMEA1706067</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2015-08-07T13:22:21</td>\n",
       "      <td>449393</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>Neuston of Lake Llebreta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25533</td>\n",
       "      <td>SAMEA1706066</td>\n",
       "      <td>SAMEA1706066</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2015-08-07T13:22:21</td>\n",
       "      <td>449393</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>1 m depth of Lake Llebreta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>25534</td>\n",
       "      <td>SAMEA1706068</td>\n",
       "      <td>SAMEA1706068</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2015-08-07T13:22:21</td>\n",
       "      <td>449393</td>\n",
       "      <td>freshwater metagenome</td>\n",
       "      <td>Neuston of Lake Llebreta</td>\n",
       "    </tr>\n",
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       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <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>250810</th>\n",
       "      <td>21289891</td>\n",
       "      <td>SAMEA9831465</td>\n",
       "      <td>SAMEA9831465</td>\n",
       "      <td>12159.THS2.DREADD9.Post1; Activation of TH and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2021-09-07T10:27:53.038</td>\n",
       "      <td>410661</td>\n",
       "      <td>mouse gut metagenome</td>\n",
       "      <td>Post-injection D1- fecal pellet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250811</th>\n",
       "      <td>21289892</td>\n",
       "      <td>SAMEA9831466</td>\n",
       "      <td>SAMEA9831466</td>\n",
       "      <td>12159.THS2.DREADD9.Post5; Activation of TH and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2021-09-07T10:27:53.059</td>\n",
       "      <td>410661</td>\n",
       "      <td>mouse gut metagenome</td>\n",
       "      <td>Post-injection D5- fecal pellet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250812</th>\n",
       "      <td>21289893</td>\n",
       "      <td>SAMEA9831467</td>\n",
       "      <td>SAMEA9831467</td>\n",
       "      <td>Activation of TH and ChAT Neurons by DREADDs E...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2021-09-07T10:27:53.079</td>\n",
       "      <td>410661</td>\n",
       "      <td>mouse gut metagenome</td>\n",
       "      <td>Post-injection D9- fecal pellet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250813</th>\n",
       "      <td>21289894</td>\n",
       "      <td>SAMEA9831468</td>\n",
       "      <td>SAMEA9831468</td>\n",
       "      <td>Activation of TH and ChAT Neurons by DREADDs E...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2021-09-07T10:27:53.099</td>\n",
       "      <td>410661</td>\n",
       "      <td>mouse gut metagenome</td>\n",
       "      <td>Pre-injection D1- fecal pellet</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250814</th>\n",
       "      <td>21289895</td>\n",
       "      <td>SAMEA9831469</td>\n",
       "      <td>SAMEA9831469</td>\n",
       "      <td>12159.THS2.DREADD9.cecum; Activation of TH and...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Generic</td>\n",
       "      <td>Generic.1.0</td>\n",
       "      <td>Generic</td>\n",
       "      <td>live</td>\n",
       "      <td>2021-09-07T10:27:53.121</td>\n",
       "      <td>410661</td>\n",
       "      <td>mouse gut metagenome</td>\n",
       "      <td>Post-injection D10 - cecal content</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250815 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id     accession    primary_id  \\\n",
       "0          25530  SAMEA1706070  SAMEA1706070   \n",
       "1          25531  SAMEA1706069  SAMEA1706069   \n",
       "2          25532  SAMEA1706067  SAMEA1706067   \n",
       "3          25533  SAMEA1706066  SAMEA1706066   \n",
       "4          25534  SAMEA1706068  SAMEA1706068   \n",
       "...          ...           ...           ...   \n",
       "250810  21289891  SAMEA9831465  SAMEA9831465   \n",
       "250811  21289892  SAMEA9831466  SAMEA9831466   \n",
       "250812  21289893  SAMEA9831467  SAMEA9831467   \n",
       "250813  21289894  SAMEA9831468  SAMEA9831468   \n",
       "250814  21289895  SAMEA9831469  SAMEA9831469   \n",
       "\n",
       "                                                    title  dna_source  doi  \\\n",
       "0                                   freshwater metagenome         NaN  NaN   \n",
       "1                                   freshwater metagenome         NaN  NaN   \n",
       "2                                   freshwater metagenome         NaN  NaN   \n",
       "3                                   freshwater metagenome         NaN  NaN   \n",
       "4                                   freshwater metagenome         NaN  NaN   \n",
       "...                                                   ...         ...  ...   \n",
       "250810  12159.THS2.DREADD9.Post1; Activation of TH and...         NaN  NaN   \n",
       "250811  12159.THS2.DREADD9.Post5; Activation of TH and...         NaN  NaN   \n",
       "250812  Activation of TH and ChAT Neurons by DREADDs E...         NaN  NaN   \n",
       "250813  Activation of TH and ChAT Neurons by DREADDs E...         NaN  NaN   \n",
       "250814  12159.THS2.DREADD9.cecum; Activation of TH and...         NaN  NaN   \n",
       "\n",
       "       entrez_links    model      package package_name status  \\\n",
       "0               NaN  Generic  Generic.1.0      Generic   live   \n",
       "1               NaN  Generic  Generic.1.0      Generic   live   \n",
       "2               NaN  Generic  Generic.1.0      Generic   live   \n",
       "3               NaN  Generic  Generic.1.0      Generic   live   \n",
       "4               NaN  Generic  Generic.1.0      Generic   live   \n",
       "...             ...      ...          ...          ...    ...   \n",
       "250810          NaN  Generic  Generic.1.0      Generic   live   \n",
       "250811          NaN  Generic  Generic.1.0      Generic   live   \n",
       "250812          NaN  Generic  Generic.1.0      Generic   live   \n",
       "250813          NaN  Generic  Generic.1.0      Generic   live   \n",
       "250814          NaN  Generic  Generic.1.0      Generic   live   \n",
       "\n",
       "                    status_date  taxonomy_id          taxonomy_name  \\\n",
       "0           2015-08-07T13:22:21       449393  freshwater metagenome   \n",
       "1           2015-08-07T13:22:21       449393  freshwater metagenome   \n",
       "2           2015-08-07T13:22:21       449393  freshwater metagenome   \n",
       "3           2015-08-07T13:22:21       449393  freshwater metagenome   \n",
       "4           2015-08-07T13:22:21       449393  freshwater metagenome   \n",
       "...                         ...          ...                    ...   \n",
       "250810  2021-09-07T10:27:53.038       410661   mouse gut metagenome   \n",
       "250811  2021-09-07T10:27:53.059       410661   mouse gut metagenome   \n",
       "250812  2021-09-07T10:27:53.079       410661   mouse gut metagenome   \n",
       "250813  2021-09-07T10:27:53.099       410661   mouse gut metagenome   \n",
       "250814  2021-09-07T10:27:53.121       410661   mouse gut metagenome   \n",
       "\n",
       "                                 paragraph  \n",
       "0                 Neuston of Lake Llebreta  \n",
       "1               1 m depth of Lake Llebreta  \n",
       "2                 Neuston of Lake Llebreta  \n",
       "3               1 m depth of Lake Llebreta  \n",
       "4                 Neuston of Lake Llebreta  \n",
       "...                                    ...  \n",
       "250810     Post-injection D1- fecal pellet  \n",
       "250811     Post-injection D5- fecal pellet  \n",
       "250812     Post-injection D9- fecal pellet  \n",
       "250813      Pre-injection D1- fecal pellet  \n",
       "250814  Post-injection D10 - cecal content  \n",
       "\n",
       "[250815 rows x 15 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs_non_attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ad86c69",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:28.270391Z",
     "start_time": "2021-09-28T12:23:26.736456Z"
    }
   },
   "outputs": [],
   "source": [
    "hn_long = pd.read_csv(hn_long_file, sep=\"\\t\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "119e5cf8",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:28.275746Z",
     "start_time": "2021-09-28T12:23:28.272078Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3963432, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hn_long.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d32263ff",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:40.060324Z",
     "start_time": "2021-09-28T12:23:28.277426Z"
    }
   },
   "outputs": [],
   "source": [
    "# 12 sec for 3963432 rows\n",
    "hn_wide = hn_long.pivot(index=\"id\", columns=\"attribute\", values=\"value\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "88b3e35c",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:40.145402Z",
     "start_time": "2021-09-28T12:23:40.062278Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>age</th>\n",
       "      <th>agrochem_addition</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>air_temp_regm</th>\n",
       "      <th>al_sat</th>\n",
       "      <th>al_sat_meth</th>\n",
       "      <th>alkalinity</th>\n",
       "      <th>altitude</th>\n",
       "      <th>ammonium</th>\n",
       "      <th>annual_season_precpt</th>\n",
       "      <th>...</th>\n",
       "      <th>variety</th>\n",
       "      <th>ventilation_type</th>\n",
       "      <th>wastewater_type</th>\n",
       "      <th>water_content</th>\n",
       "      <th>water_content_soil</th>\n",
       "      <th>water_content_soil_meth</th>\n",
       "      <th>water_current</th>\n",
       "      <th>watering_regm</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_speed</th>\n",
       "    </tr>\n",
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       "      <th>id</th>\n",
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       "    <tr>\n",
       "      <th>21289892</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <th>21289895</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>250815 rows × 295 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "attribute  age agrochem_addition air_temp air_temp_regm al_sat al_sat_meth  \\\n",
       "id                                                                           \n",
       "25530      NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "25531      NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "25532      NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "25533      NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "25534      NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "...        ...               ...      ...           ...    ...         ...   \n",
       "21289891   NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "21289892   NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "21289893   NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "21289894   NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "21289895   NaN               NaN      NaN           NaN    NaN         NaN   \n",
       "\n",
       "attribute alkalinity altitude     ammonium annual_season_precpt  ... variety  \\\n",
       "id                                                               ...           \n",
       "25530            NaN      NaN  0.700670111                 42.8  ...     NaN   \n",
       "25531            NaN      NaN  0.700670111                 42.8  ...     NaN   \n",
       "25532            NaN      NaN         0.47                 16.7  ...     NaN   \n",
       "25533            NaN      NaN         0.47                 16.7  ...     NaN   \n",
       "25534            NaN      NaN          0.5                 61.3  ...     NaN   \n",
       "...              ...      ...          ...                  ...  ...     ...   \n",
       "21289891         NaN      NaN          NaN                  NaN  ...     NaN   \n",
       "21289892         NaN      NaN          NaN                  NaN  ...     NaN   \n",
       "21289893         NaN      NaN          NaN                  NaN  ...     NaN   \n",
       "21289894         NaN      NaN          NaN                  NaN  ...     NaN   \n",
       "21289895         NaN      NaN          NaN                  NaN  ...     NaN   \n",
       "\n",
       "attribute ventilation_type wastewater_type water_content water_content_soil  \\\n",
       "id                                                                            \n",
       "25530                  NaN             NaN           NaN                NaN   \n",
       "25531                  NaN             NaN           NaN                NaN   \n",
       "25532                  NaN             NaN           NaN                NaN   \n",
       "25533                  NaN             NaN           NaN                NaN   \n",
       "25534                  NaN             NaN           NaN                NaN   \n",
       "...                    ...             ...           ...                ...   \n",
       "21289891               NaN             NaN           NaN                NaN   \n",
       "21289892               NaN             NaN           NaN                NaN   \n",
       "21289893               NaN             NaN           NaN                NaN   \n",
       "21289894               NaN             NaN           NaN                NaN   \n",
       "21289895               NaN             NaN           NaN                NaN   \n",
       "\n",
       "attribute water_content_soil_meth water_current watering_regm wind_direction  \\\n",
       "id                                                                             \n",
       "25530                         NaN           NaN           NaN            NaN   \n",
       "25531                         NaN           NaN           NaN            NaN   \n",
       "25532                         NaN           NaN           NaN            NaN   \n",
       "25533                         NaN           NaN           NaN            NaN   \n",
       "25534                         NaN           NaN           NaN            NaN   \n",
       "...                           ...           ...           ...            ...   \n",
       "21289891                      NaN           NaN           NaN            NaN   \n",
       "21289892                      NaN           NaN           NaN            NaN   \n",
       "21289893                      NaN           NaN           NaN            NaN   \n",
       "21289894                      NaN           NaN           NaN            NaN   \n",
       "21289895                      NaN           NaN           NaN            NaN   \n",
       "\n",
       "attribute wind_speed  \n",
       "id                    \n",
       "25530            NaN  \n",
       "25531            NaN  \n",
       "25532            NaN  \n",
       "25533            NaN  \n",
       "25534            NaN  \n",
       "...              ...  \n",
       "21289891         NaN  \n",
       "21289892         NaN  \n",
       "21289893         NaN  \n",
       "21289894         NaN  \n",
       "21289895         NaN  \n",
       "\n",
       "[250815 rows x 295 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hn_wide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b14bcec1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:40.156095Z",
     "start_time": "2021-09-28T12:23:40.147197Z"
    }
   },
   "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>raw_env_package</th>\n",
       "      <th>env_package</th>\n",
       "      <th>alternative</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>air</td>\n",
       "      <td>air</td>\n",
       "      <td>air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MIGS/MIMS/MIMARKS.air</td>\n",
       "      <td>air</td>\n",
       "      <td>air</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>built environment</td>\n",
       "      <td>built</td>\n",
       "      <td>built environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>built</td>\n",
       "      <td>built</td>\n",
       "      <td>built environment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>host-associated</td>\n",
       "      <td>host-associated</td>\n",
       "      <td>host-associated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>MIGS/MIMS/MIMARKS.air|||MIGS/MIMS/MIMARKS.micr...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>ENVO:00000016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>MIMARKS_16S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>not applicable</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>saliva</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>102 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       raw_env_package      env_package  \\\n",
       "0                                                  air              air   \n",
       "1                                MIGS/MIMS/MIMARKS.air              air   \n",
       "2                                    built environment            built   \n",
       "3                                                built            built   \n",
       "4                                      host-associated  host-associated   \n",
       "..                                                 ...              ...   \n",
       "97   MIGS/MIMS/MIMARKS.air|||MIGS/MIMS/MIMARKS.micr...              NaN   \n",
       "98                                       ENVO:00000016              NaN   \n",
       "99                                         MIMARKS_16S              NaN   \n",
       "100                                     not applicable              NaN   \n",
       "101                                             saliva              NaN   \n",
       "\n",
       "           alternative  \n",
       "0                  air  \n",
       "1                  air  \n",
       "2    built environment  \n",
       "3    built environment  \n",
       "4      host-associated  \n",
       "..                 ...  \n",
       "97                 NaN  \n",
       "98                 NaN  \n",
       "99                 NaN  \n",
       "100                NaN  \n",
       "101                NaN  \n",
       "\n",
       "[102 rows x 3 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "env_package_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "cdc65958",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:40.869915Z",
     "start_time": "2021-09-28T12:23:40.157843Z"
    }
   },
   "outputs": [],
   "source": [
    "hn_wide = hn_wide.merge(\n",
    "    env_package_mapping,\n",
    "    how=\"left\",\n",
    "    left_on=\"env_package\",\n",
    "    right_on=\"raw_env_package\",\n",
    "    suffixes=[\"_original\", \"_mapped\"],\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5ffcb010",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:40.873448Z",
     "start_time": "2021-09-28T12:23:40.871302Z"
    }
   },
   "outputs": [],
   "source": [
    "# hn_wide[[\"raw_env_package\", which_tidied_package_column]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7011014b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.921560Z",
     "start_time": "2021-09-28T12:23:40.874701Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>agrochem_addition</th>\n",
       "      <th>air_temp</th>\n",
       "      <th>air_temp_regm</th>\n",
       "      <th>al_sat</th>\n",
       "      <th>al_sat_meth</th>\n",
       "      <th>alkalinity</th>\n",
       "      <th>altitude</th>\n",
       "      <th>ammonium</th>\n",
       "      <th>annual_season_precpt</th>\n",
       "      <th>...</th>\n",
       "      <th>water_content</th>\n",
       "      <th>water_content_soil</th>\n",
       "      <th>water_content_soil_meth</th>\n",
       "      <th>water_current</th>\n",
       "      <th>watering_regm</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind_speed</th>\n",
       "      <th>raw_env_package</th>\n",
       "      <th>env_package_mapped</th>\n",
       "      <th>alternative</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alternative</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>air</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.018223</td>\n",
       "      <td>0.034169</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>built environment</th>\n",
       "      <td>0.060753</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host-associated</th>\n",
       "      <td>0.166557</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.163275</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000013</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-gut</th>\n",
       "      <td>0.169663</td>\n",
       "      <td>0.00000</td>\n",
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       "      <td>0.000000</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-oral</th>\n",
       "      <td>0.158244</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-skin</th>\n",
       "      <td>0.324530</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-vaginal</th>\n",
       "      <td>0.050712</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034698</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>microbial mat_biofilm</th>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.000658</td>\n",
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       "      <td>0.044093</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031589</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miscellaneous natural or artificial environment</th>\n",
       "      <td>0.008209</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.102126</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plant-associated</th>\n",
       "      <td>0.013801</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.146012</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.007452</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009661</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sediment</th>\n",
       "      <td>0.000135</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.064699</td>\n",
       "      <td>0.046233</td>\n",
       "      <td>0.062812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050411</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soil</th>\n",
       "      <td>0.300495</td>\n",
       "      <td>0.03849</td>\n",
       "      <td>0.000928</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006683</td>\n",
       "      <td>0.006683</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.184282</td>\n",
       "      <td>0.032488</td>\n",
       "      <td>0.100371</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035705</td>\n",
       "      <td>0.154950</td>\n",
       "      <td>0.101176</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wastewater_sludge</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water</th>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.012310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004974</td>\n",
       "      <td>0.128480</td>\n",
       "      <td>0.034368</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.018390</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>14 rows × 298 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                      age  agrochem_addition  \\\n",
       "alternative                                                                    \n",
       "air                                              0.000000            0.00000   \n",
       "built environment                                0.060753            0.00000   \n",
       "host-associated                                  0.166557            0.00000   \n",
       "human-gut                                        0.169663            0.00000   \n",
       "human-oral                                       0.158244            0.00000   \n",
       "human-skin                                       0.324530            0.00000   \n",
       "human-vaginal                                    0.050712            0.00000   \n",
       "microbial mat_biofilm                            0.000000            0.00000   \n",
       "miscellaneous natural or artificial environment  0.008209            0.00000   \n",
       "plant-associated                                 0.013801            0.00000   \n",
       "sediment                                         0.000135            0.00000   \n",
       "soil                                             0.300495            0.03849   \n",
       "wastewater_sludge                                0.000000            0.00000   \n",
       "water                                            0.000151            0.00000   \n",
       "\n",
       "                                                 air_temp  air_temp_regm  \\\n",
       "alternative                                                                \n",
       "air                                              0.000000       0.000000   \n",
       "built environment                                0.004178       0.000000   \n",
       "host-associated                                  0.000000       0.000000   \n",
       "human-gut                                        0.000000       0.000000   \n",
       "human-oral                                       0.000000       0.000000   \n",
       "human-skin                                       0.001916       0.000000   \n",
       "human-vaginal                                    0.000000       0.000000   \n",
       "microbial mat_biofilm                            0.000658       0.000000   \n",
       "miscellaneous natural or artificial environment  0.000000       0.000000   \n",
       "plant-associated                                 0.000000       0.009661   \n",
       "sediment                                         0.000000       0.000000   \n",
       "soil                                             0.000928       0.000000   \n",
       "wastewater_sludge                                0.000000       0.000000   \n",
       "water                                            0.012310       0.000000   \n",
       "\n",
       "                                                   al_sat  al_sat_meth  \\\n",
       "alternative                                                              \n",
       "air                                              0.000000     0.000000   \n",
       "built environment                                0.000000     0.000000   \n",
       "host-associated                                  0.000000     0.000000   \n",
       "human-gut                                        0.000000     0.000000   \n",
       "human-oral                                       0.000000     0.000000   \n",
       "human-skin                                       0.000000     0.000000   \n",
       "human-vaginal                                    0.000000     0.000000   \n",
       "microbial mat_biofilm                            0.000000     0.000000   \n",
       "miscellaneous natural or artificial environment  0.000000     0.000000   \n",
       "plant-associated                                 0.000000     0.000000   \n",
       "sediment                                         0.000000     0.000000   \n",
       "soil                                             0.006683     0.006683   \n",
       "wastewater_sludge                                0.000000     0.000000   \n",
       "water                                            0.000000     0.000000   \n",
       "\n",
       "                                                 alkalinity  altitude  \\\n",
       "alternative                                                             \n",
       "air                                                0.000000  0.466970   \n",
       "built environment                                  0.000000  0.000205   \n",
       "host-associated                                    0.000000  0.163275   \n",
       "human-gut                                          0.000000  0.066561   \n",
       "human-oral                                         0.000000  0.026759   \n",
       "human-skin                                         0.000000  0.017448   \n",
       "human-vaginal                                      0.000000  0.034698   \n",
       "microbial mat_biofilm                              0.031589  0.044093   \n",
       "miscellaneous natural or artificial environment    0.000000  0.102126   \n",
       "plant-associated                                   0.000000  0.146012   \n",
       "sediment                                           0.064699  0.046233   \n",
       "soil                                               0.000000  0.184282   \n",
       "wastewater_sludge                                  0.000000  0.000000   \n",
       "water                                              0.004974  0.128480   \n",
       "\n",
       "                                                 ammonium  \\\n",
       "alternative                                                 \n",
       "air                                              0.000000   \n",
       "built environment                                0.062877   \n",
       "host-associated                                  0.000000   \n",
       "human-gut                                        0.000000   \n",
       "human-oral                                       0.000000   \n",
       "human-skin                                       0.000000   \n",
       "human-vaginal                                    0.000000   \n",
       "microbial mat_biofilm                            0.000000   \n",
       "miscellaneous natural or artificial environment  0.000000   \n",
       "plant-associated                                 0.000000   \n",
       "sediment                                         0.062812   \n",
       "soil                                             0.032488   \n",
       "wastewater_sludge                                0.000000   \n",
       "water                                            0.034368   \n",
       "\n",
       "                                                 annual_season_precpt  ...  \\\n",
       "alternative                                                            ...   \n",
       "air                                                          0.000000  ...   \n",
       "built environment                                            0.000000  ...   \n",
       "host-associated                                              0.000013  ...   \n",
       "human-gut                                                    0.000000  ...   \n",
       "human-oral                                                   0.000000  ...   \n",
       "human-skin                                                   0.000000  ...   \n",
       "human-vaginal                                                0.000000  ...   \n",
       "microbial mat_biofilm                                        0.000000  ...   \n",
       "miscellaneous natural or artificial environment              0.000000  ...   \n",
       "plant-associated                                             0.000000  ...   \n",
       "sediment                                                     0.000000  ...   \n",
       "soil                                                         0.100371  ...   \n",
       "wastewater_sludge                                            0.000000  ...   \n",
       "water                                                        0.000000  ...   \n",
       "\n",
       "                                                 water_content  \\\n",
       "alternative                                                      \n",
       "air                                                   0.000000   \n",
       "built environment                                     0.000000   \n",
       "host-associated                                       0.000000   \n",
       "human-gut                                             0.000000   \n",
       "human-oral                                            0.000000   \n",
       "human-skin                                            0.000000   \n",
       "human-vaginal                                         0.000000   \n",
       "microbial mat_biofilm                                 0.031589   \n",
       "miscellaneous natural or artificial environment       0.000000   \n",
       "plant-associated                                      0.000000   \n",
       "sediment                                              0.050411   \n",
       "soil                                                  0.035705   \n",
       "wastewater_sludge                                     0.000000   \n",
       "water                                                 0.000000   \n",
       "\n",
       "                                                 water_content_soil  \\\n",
       "alternative                                                           \n",
       "air                                                        0.000000   \n",
       "built environment                                          0.000000   \n",
       "host-associated                                            0.000027   \n",
       "human-gut                                                  0.000000   \n",
       "human-oral                                                 0.000000   \n",
       "human-skin                                                 0.000000   \n",
       "human-vaginal                                              0.000000   \n",
       "microbial mat_biofilm                                      0.011846   \n",
       "miscellaneous natural or artificial environment            0.000123   \n",
       "plant-associated                                           0.007452   \n",
       "sediment                                                   0.000000   \n",
       "soil                                                       0.154950   \n",
       "wastewater_sludge                                          0.000000   \n",
       "water                                                      0.000000   \n",
       "\n",
       "                                                 water_content_soil_meth  \\\n",
       "alternative                                                                \n",
       "air                                                             0.000000   \n",
       "built environment                                               0.000000   \n",
       "host-associated                                                 0.000000   \n",
       "human-gut                                                       0.000000   \n",
       "human-oral                                                      0.000000   \n",
       "human-skin                                                      0.000000   \n",
       "human-vaginal                                                   0.000000   \n",
       "microbial mat_biofilm                                           0.000000   \n",
       "miscellaneous natural or artificial environment                 0.000123   \n",
       "plant-associated                                                0.000000   \n",
       "sediment                                                        0.000000   \n",
       "soil                                                            0.101176   \n",
       "wastewater_sludge                                               0.000000   \n",
       "water                                                           0.000000   \n",
       "\n",
       "                                                 water_current  watering_regm  \\\n",
       "alternative                                                                     \n",
       "air                                                   0.000000       0.000000   \n",
       "built environment                                     0.000000       0.000000   \n",
       "host-associated                                       0.000000       0.000000   \n",
       "human-gut                                             0.000000       0.000000   \n",
       "human-oral                                            0.000000       0.000000   \n",
       "human-skin                                            0.000000       0.000000   \n",
       "human-vaginal                                         0.000000       0.000000   \n",
       "microbial mat_biofilm                                 0.000000       0.000000   \n",
       "miscellaneous natural or artificial environment       0.000245       0.000000   \n",
       "plant-associated                                      0.000000       0.009661   \n",
       "sediment                                              0.000000       0.000000   \n",
       "soil                                                  0.000000       0.000000   \n",
       "wastewater_sludge                                     0.000000       0.000000   \n",
       "water                                                 0.000000       0.000000   \n",
       "\n",
       "                                                 wind_direction  wind_speed  \\\n",
       "alternative                                                                   \n",
       "air                                                    0.018223    0.034169   \n",
       "built environment                                      0.000000    0.000000   \n",
       "host-associated                                        0.000000    0.000000   \n",
       "human-gut                                              0.000000    0.000000   \n",
       "human-oral                                             0.000000    0.000000   \n",
       "human-skin                                             0.000000    0.000000   \n",
       "human-vaginal                                          0.000000    0.000000   \n",
       "microbial mat_biofilm                                  0.000000    0.000000   \n",
       "miscellaneous natural or artificial environment        0.000000    0.000000   \n",
       "plant-associated                                       0.000000    0.000000   \n",
       "sediment                                               0.000000    0.000000   \n",
       "soil                                                   0.000000    0.000000   \n",
       "wastewater_sludge                                      0.000000    0.000000   \n",
       "water                                                  0.000000    0.018390   \n",
       "\n",
       "                                                 raw_env_package  \\\n",
       "alternative                                                        \n",
       "air                                                          1.0   \n",
       "built environment                                            1.0   \n",
       "host-associated                                              1.0   \n",
       "human-gut                                                    1.0   \n",
       "human-oral                                                   1.0   \n",
       "human-skin                                                   1.0   \n",
       "human-vaginal                                                1.0   \n",
       "microbial mat_biofilm                                        1.0   \n",
       "miscellaneous natural or artificial environment              1.0   \n",
       "plant-associated                                             1.0   \n",
       "sediment                                                     1.0   \n",
       "soil                                                         1.0   \n",
       "wastewater_sludge                                            1.0   \n",
       "water                                                        1.0   \n",
       "\n",
       "                                                 env_package_mapped  \\\n",
       "alternative                                                           \n",
       "air                                                             1.0   \n",
       "built environment                                               1.0   \n",
       "host-associated                                                 1.0   \n",
       "human-gut                                                       1.0   \n",
       "human-oral                                                      1.0   \n",
       "human-skin                                                      1.0   \n",
       "human-vaginal                                                   1.0   \n",
       "microbial mat_biofilm                                           1.0   \n",
       "miscellaneous natural or artificial environment                 1.0   \n",
       "plant-associated                                                1.0   \n",
       "sediment                                                        1.0   \n",
       "soil                                                            1.0   \n",
       "wastewater_sludge                                               1.0   \n",
       "water                                                           1.0   \n",
       "\n",
       "                                                 alternative  \n",
       "alternative                                                   \n",
       "air                                                      1.0  \n",
       "built environment                                        1.0  \n",
       "host-associated                                          1.0  \n",
       "human-gut                                                1.0  \n",
       "human-oral                                               1.0  \n",
       "human-skin                                               1.0  \n",
       "human-vaginal                                            1.0  \n",
       "microbial mat_biofilm                                    1.0  \n",
       "miscellaneous natural or artificial environment          1.0  \n",
       "plant-associated                                         1.0  \n",
       "sediment                                                 1.0  \n",
       "soil                                                     1.0  \n",
       "wastewater_sludge                                        1.0  \n",
       "water                                                    1.0  \n",
       "\n",
       "[14 rows x 298 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_na_fraction = hn_wide.groupby(which_tidied_package_column).apply(lambda x: x.notnull().mean())\n",
    "column_na_fraction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0d945d1d",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.927895Z",
     "start_time": "2021-09-28T12:23:44.922962Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "set()"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "column_na_fraction_packages = set(column_na_fraction.index)\n",
    "mixs_package_slots_classes = set(mixs_package_slots[\"class\"])\n",
    "\n",
    "not_again = column_na_fraction_packages - mixs_package_slots_classes\n",
    "\n",
    "not_again\n",
    "\n",
    "# was:\n",
    "# {'built', 'microbial mat/biofilm', 'miscellaneous', 'wastewater/sludge'}\n",
    "\n",
    "# mixs_package_slots_classes\n",
    "# 'built environment',\n",
    "# 'microbial mat_biofilm',\n",
    "# 'miscellaneous natural or artificial environment',\n",
    "# 'wastewater_sludge',"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "65f600df",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.932240Z",
     "start_time": "2021-09-28T12:23:44.929145Z"
    }
   },
   "outputs": [],
   "source": [
    "mixs_for_merge = mixs_package_slots.loc[\n",
    "    mixs_package_slots[\"class\"].isin(column_na_fraction_packages)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "a2380ea5",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.936128Z",
     "start_time": "2021-09-28T12:23:44.933501Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/vt/f297tpjn6n1b6dwkcwhhnmxm0000gp/T/ipykernel_61138/1066536151.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  mixs_for_merge[\"associated\"] = True\n"
     ]
    }
   ],
   "source": [
    "mixs_for_merge[\"associated\"] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "23cf508e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.939849Z",
     "start_time": "2021-09-28T12:23:44.937488Z"
    }
   },
   "outputs": [],
   "source": [
    "all_defined_slots = list(set(mixs_for_merge[\"slot\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "be95dd83",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.948332Z",
     "start_time": "2021-09-28T12:23:44.941169Z"
    }
   },
   "outputs": [],
   "source": [
    "insdc_for_merge = column_na_fraction.copy()\n",
    "insdc_for_merge[\"package\"] = insdc_for_merge.index\n",
    "insdc_for_merge.drop(\n",
    "    labels=[\n",
    "        which_tidied_package_column,\n",
    "        \"env_package_mapped\",\n",
    "        \"env_package_original\",\n",
    "        \"raw_env_package\",\n",
    "    ],\n",
    "    inplace=True,\n",
    "    axis=1,\n",
    ")\n",
    "insdc_for_merge = pd.melt(insdc_for_merge, id_vars=\"package\")\n",
    "insdc_for_merge.columns = [\"class\", \"slot\", \"usage\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cadfb60e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.971265Z",
     "start_time": "2021-09-28T12:23:44.955118Z"
    }
   },
   "outputs": [],
   "source": [
    "insdc_mixs_merged = insdc_for_merge.merge(\n",
    "    mixs_for_merge, how=\"outer\", on=[\"slot\", \"class\"]\n",
    ")\n",
    "\n",
    "insdc_mixs_merged.drop(\n",
    "    labels=[\n",
    "        \"recommended\",\n",
    "    ],\n",
    "    inplace=True,\n",
    "    axis=1,\n",
    ")\n",
    "\n",
    "insdc_mixs_merged[\"mixs_defined\"] = insdc_mixs_merged[\"slot\"].isin(all_defined_slots)\n",
    "\n",
    "insdc_mixs_merged.sort_values(by=[\"class\", \"slot\"], inplace=True)\n",
    "\n",
    "insdc_mixs_merged[\"usage\"] = insdc_mixs_merged[\"usage\"].fillna(0)\n",
    "insdc_mixs_merged[\"required\"] = insdc_mixs_merged[\"required\"].fillna(False)\n",
    "insdc_mixs_merged[\"associated\"] = insdc_mixs_merged[\"associated\"].fillna(False)\n",
    "\n",
    "insdc_mixs_merged = insdc_mixs_merged.loc[\n",
    "    ~(~insdc_mixs_merged[\"associated\"] & insdc_mixs_merged[\"usage\"].eq(0))\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22eb1812",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:44.982433Z",
     "start_time": "2021-09-28T12:23:44.972691Z"
    }
   },
   "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>class</th>\n",
       "      <th>slot</th>\n",
       "      <th>usage</th>\n",
       "      <th>required</th>\n",
       "      <th>associated</th>\n",
       "      <th>mixs_defined</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4415</th>\n",
       "      <td>air</td>\n",
       "      <td>alt</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>air</td>\n",
       "      <td>altitude</td>\n",
       "      <td>0.466970</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4416</th>\n",
       "      <td>air</td>\n",
       "      <td>barometric_press</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>air</td>\n",
       "      <td>body_habitat</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>air</td>\n",
       "      <td>body_product</td>\n",
       "      <td>0.002278</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3933</th>\n",
       "      <td>water</td>\n",
       "      <td>treatment</td>\n",
       "      <td>0.010300</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3947</th>\n",
       "      <td>water</td>\n",
       "      <td>trophic_level</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3961</th>\n",
       "      <td>water</td>\n",
       "      <td>turbidity</td>\n",
       "      <td>0.004221</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4073</th>\n",
       "      <td>water</td>\n",
       "      <td>water_current</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4115</th>\n",
       "      <td>water</td>\n",
       "      <td>wind_speed</td>\n",
       "      <td>0.018390</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1635 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      class              slot     usage  required  associated  mixs_defined\n",
       "4415    air               alt  0.000000      True        True          True\n",
       "98      air          altitude  0.466970     False       False         False\n",
       "4416    air  barometric_press  0.000000     False        True          True\n",
       "280     air      body_habitat  0.002278     False       False         False\n",
       "308     air      body_product  0.002278     False       False         False\n",
       "...     ...               ...       ...       ...         ...           ...\n",
       "3933  water         treatment  0.010300     False       False         False\n",
       "3947  water     trophic_level  0.000653     False       False         False\n",
       "3961  water         turbidity  0.004221     False        True          True\n",
       "4073  water     water_current  0.000000     False        True          True\n",
       "4115  water        wind_speed  0.018390     False       False          True\n",
       "\n",
       "[1635 rows x 6 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "insdc_mixs_merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "554385ae",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:45.000225Z",
     "start_time": "2021-09-28T12:23:44.984001Z"
    }
   },
   "outputs": [],
   "source": [
    "insdc_mixs_merged.to_csv(insdc_mixs_merged_file, sep=\"\\t\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38f26284",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6a6114b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-27T20:22:06.891056Z",
     "start_time": "2021-09-27T20:22:06.891044Z"
    }
   },
   "source": [
    "## PCA on INSDC slot usage by package alone\n",
    "\n",
    "Could slot usage help classify samples with no `env_package` annotation?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "cdf2f6bf",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:45.009624Z",
     "start_time": "2021-09-28T12:23:45.001619Z"
    }
   },
   "outputs": [],
   "source": [
    "scaled = StandardScaler().fit_transform(column_na_fraction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f1706686",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:45.019410Z",
     "start_time": "2021-09-28T12:23:45.010747Z"
    }
   },
   "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>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>alternative</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>air</th>\n",
       "      <td>-0.859739</td>\n",
       "      <td>0.547959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>built environment</th>\n",
       "      <td>-1.931728</td>\n",
       "      <td>0.102143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>host-associated</th>\n",
       "      <td>-7.387143</td>\n",
       "      <td>-6.676377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-gut</th>\n",
       "      <td>-6.103909</td>\n",
       "      <td>-5.244542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-oral</th>\n",
       "      <td>-4.836190</td>\n",
       "      <td>-4.046849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-skin</th>\n",
       "      <td>-4.999585</td>\n",
       "      <td>-4.717088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human-vaginal</th>\n",
       "      <td>-4.434745</td>\n",
       "      <td>-3.846849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>microbial mat_biofilm</th>\n",
       "      <td>2.827510</td>\n",
       "      <td>8.814809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>miscellaneous natural or artificial environment</th>\n",
       "      <td>-1.844145</td>\n",
       "      <td>0.015033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plant-associated</th>\n",
       "      <td>-1.668038</td>\n",
       "      <td>-0.050991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sediment</th>\n",
       "      <td>4.640994</td>\n",
       "      <td>9.009230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>soil</th>\n",
       "      <td>23.255965</td>\n",
       "      <td>-11.385253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wastewater_sludge</th>\n",
       "      <td>-0.856340</td>\n",
       "      <td>3.216783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>water</th>\n",
       "      <td>4.197091</td>\n",
       "      <td>14.261994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                 principal component 1  \\\n",
       "alternative                                                              \n",
       "air                                                          -0.859739   \n",
       "built environment                                            -1.931728   \n",
       "host-associated                                              -7.387143   \n",
       "human-gut                                                    -6.103909   \n",
       "human-oral                                                   -4.836190   \n",
       "human-skin                                                   -4.999585   \n",
       "human-vaginal                                                -4.434745   \n",
       "microbial mat_biofilm                                         2.827510   \n",
       "miscellaneous natural or artificial environment              -1.844145   \n",
       "plant-associated                                             -1.668038   \n",
       "sediment                                                      4.640994   \n",
       "soil                                                         23.255965   \n",
       "wastewater_sludge                                            -0.856340   \n",
       "water                                                         4.197091   \n",
       "\n",
       "                                                 principal component 2  \n",
       "alternative                                                             \n",
       "air                                                           0.547959  \n",
       "built environment                                             0.102143  \n",
       "host-associated                                              -6.676377  \n",
       "human-gut                                                    -5.244542  \n",
       "human-oral                                                   -4.046849  \n",
       "human-skin                                                   -4.717088  \n",
       "human-vaginal                                                -3.846849  \n",
       "microbial mat_biofilm                                         8.814809  \n",
       "miscellaneous natural or artificial environment               0.015033  \n",
       "plant-associated                                             -0.050991  \n",
       "sediment                                                      9.009230  \n",
       "soil                                                        -11.385253  \n",
       "wastewater_sludge                                             3.216783  \n",
       "water                                                        14.261994  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(scaled)\n",
    "principalDf = pd.DataFrame(\n",
    "    data=principalComponents, columns=[\"principal component 1\", \"principal component 2\"]\n",
    ")\n",
    "principalDf.index = column_na_fraction.index\n",
    "principalDf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3beb63bd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:45.388896Z",
     "start_time": "2021-09-28T12:23:45.021005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = [7, 7]\n",
    "\n",
    "cmap = cm.get_cmap(\"Spectral\")\n",
    "fig, ax3 = plt.subplots()\n",
    "principalDf.plot(\n",
    "    \"principal component 1\",\n",
    "    \"principal component 2\",\n",
    "    kind=\"scatter\",\n",
    "    ax=ax3,\n",
    "    s=120,\n",
    "    linewidth=0,\n",
    "    c=range(len(principalDf)),\n",
    "    colormap=cmap,\n",
    ")\n",
    "\n",
    "for k, v in principalDf.iterrows():\n",
    "    ax3.annotate(\n",
    "        k,\n",
    "        v,\n",
    "        xytext=(10, -5),\n",
    "        textcoords=\"offset points\",\n",
    "        family=\"sans-serif\",\n",
    "        fontsize=12,\n",
    "        #         color=\"darkslategrey\",\n",
    "        color=\"black\",\n",
    "    )\n",
    "\n",
    "plt.savefig(pca_plot_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6817ba24",
   "metadata": {},
   "source": [
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04f2bc5b",
   "metadata": {},
   "source": [
    "## INSDC values ProfileReport\n",
    "- inline widgets\n",
    "- saved HTML file\n",
    "- saved JSON file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "06154b65",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:23:45.426948Z",
     "start_time": "2021-09-28T12:23:45.390266Z"
    }
   },
   "outputs": [],
   "source": [
    "profile = ProfileReport(\n",
    "    hn_wide, title=\"Pandas Profiling Report\", minimal=True, vars={\"cat\": {\"n_obs\": 20}}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dfaf3d73",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:24:02.467460Z",
     "start_time": "2021-09-28T12:23:45.428233Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "edd51c8233024159bf4a2de744bd30cc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Summarize dataset:   0%|          | 0/306 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df3f6eb196a043cd9b278f95733abcf2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render JSON:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "a = profile.to_json()\n",
    "textfile = open(pandas_profile_json_file, \"w\")\n",
    "textfile.write(a)\n",
    "textfile.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "78b7ab9e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:24:02.472162Z",
     "start_time": "2021-09-28T12:24:02.469589Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# profile.to_widgets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d5c183d6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-09-28T12:24:40.210103Z",
     "start_time": "2021-09-28T12:24:02.474421Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "25eb47095e734734ad3798b4bcc1c6d6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6dd4954f4976411cb0218c892128f552",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d2a3f0354644fb8871e946668c65288",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "profile.to_file(pandas_profile_html_file)"
   ]
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