species_correction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Species assignment of mosquitoes undetermined by PCR\n",
    "Notebook adapted from Batson, *et al.* (https://github.com/czbiohub/california-mosquito-study)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns; sns.set(color_codes=True)\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.cluster.hierarchy import fcluster\n",
    "import scipy.cluster.hierarchy as shc\n",
    "import matplotlib.patches as mpatches\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from collections import namedtuple\n",
    "from numpy import nan\n",
    "from scipy.spatial.distance import pdist, squareform\n",
    "from sklearn.metrics import silhouette_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>PCR_Subspecies</th>\n",
       "      <th>SKA_Subspecies</th>\n",
       "      <th>Species</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Location</th>\n",
       "      <th>Control</th>\n",
       "      <th>visual_genus</th>\n",
       "      <th>visual_species</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>Municipality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MEMO001</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens</td>\n",
       "      <td>female</td>\n",
       "      <td>MM</td>\n",
       "      <td>No</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>50.995.194</td>\n",
       "      <td>5.621.833</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MEMO002</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens</td>\n",
       "      <td>female</td>\n",
       "      <td>MM</td>\n",
       "      <td>No</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>50.995.194</td>\n",
       "      <td>5.621.833</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MEMO003</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens</td>\n",
       "      <td>female</td>\n",
       "      <td>MM</td>\n",
       "      <td>No</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>50.995.194</td>\n",
       "      <td>5.621.833</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MEMO004</td>\n",
       "      <td>Culex torrentium</td>\n",
       "      <td>Culex torrentium</td>\n",
       "      <td>Culex torrentium</td>\n",
       "      <td>female</td>\n",
       "      <td>MM</td>\n",
       "      <td>No</td>\n",
       "      <td>Culex</td>\n",
       "      <td>torrentium</td>\n",
       "      <td>50.995.194</td>\n",
       "      <td>5.621.833</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MEMO005</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens pipiens</td>\n",
       "      <td>Culex pipiens</td>\n",
       "      <td>female</td>\n",
       "      <td>MM</td>\n",
       "      <td>No</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>50.995.194</td>\n",
       "      <td>5.621.833</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>MEMO020-NC</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>MEMO046-NC</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>MEMO076-NC</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>MEMO114-NC</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>MEMO150-NC</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>control</td>\n",
       "      <td>Yes</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>208 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Sample         PCR_Subspecies         SKA_Subspecies  \\\n",
       "0       MEMO001  Culex pipiens pipiens  Culex pipiens pipiens   \n",
       "1       MEMO002  Culex pipiens pipiens  Culex pipiens pipiens   \n",
       "2       MEMO003  Culex pipiens pipiens  Culex pipiens pipiens   \n",
       "3       MEMO004       Culex torrentium       Culex torrentium   \n",
       "4       MEMO005  Culex pipiens pipiens  Culex pipiens pipiens   \n",
       "..          ...                    ...                    ...   \n",
       "203  MEMO020-NC                control                control   \n",
       "204  MEMO046-NC                control                control   \n",
       "205  MEMO076-NC                control                control   \n",
       "206  MEMO114-NC                control                control   \n",
       "207  MEMO150-NC                control                control   \n",
       "\n",
       "              Species      Sex Location Control visual_genus   visual_species  \\\n",
       "0       Culex pipiens   female       MM      No        Culex  pipiens pipiens   \n",
       "1       Culex pipiens   female       MM      No        Culex  pipiens pipiens   \n",
       "2       Culex pipiens   female       MM      No        Culex  pipiens pipiens   \n",
       "3    Culex torrentium   female       MM      No        Culex       torrentium   \n",
       "4       Culex pipiens   female       MM      No        Culex  pipiens pipiens   \n",
       "..                ...      ...      ...     ...          ...              ...   \n",
       "203           control  control  control     Yes          NaN              NaN   \n",
       "204           control  control  control     Yes          NaN              NaN   \n",
       "205           control  control  control     Yes          NaN              NaN   \n",
       "206           control  control  control     Yes          NaN              NaN   \n",
       "207           control  control  control     Yes          NaN              NaN   \n",
       "\n",
       "            lat       long  Municipality  \n",
       "0    50.995.194  5.621.833  Maasmechelen  \n",
       "1    50.995.194  5.621.833  Maasmechelen  \n",
       "2    50.995.194  5.621.833  Maasmechelen  \n",
       "3    50.995.194  5.621.833  Maasmechelen  \n",
       "4    50.995.194  5.621.833  Maasmechelen  \n",
       "..          ...        ...           ...  \n",
       "203         NaN        NaN           NaN  \n",
       "204         NaN        NaN           NaN  \n",
       "205         NaN        NaN           NaN  \n",
       "206         NaN        NaN           NaN  \n",
       "207         NaN        NaN           NaN  \n",
       "\n",
       "[208 rows x 12 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_metadata = pd.read_csv(\n",
    "    \"data/BEmosq_metadata.csv\",\n",
    "    header=0,\n",
    "    sep=\";\"\n",
    ")\n",
    "new_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-5-e5a99af1c840>:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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",
      "  species_id.dropna(how='all', inplace= True)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>visual_genus</th>\n",
       "      <th>visual_species</th>\n",
       "      <th>Municipality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEMO001</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO002</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO003</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO004</th>\n",
       "      <td>Culex</td>\n",
       "      <td>torrentium</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO005</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO50</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Bertem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO51</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Bertem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO52</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Bertem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO53</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Bertem</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO54</th>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Bertem</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        visual_genus   visual_species  Municipality\n",
       "Sample                                             \n",
       "MEMO001        Culex  pipiens pipiens  Maasmechelen\n",
       "MEMO002        Culex  pipiens pipiens  Maasmechelen\n",
       "MEMO003        Culex  pipiens pipiens  Maasmechelen\n",
       "MEMO004        Culex       torrentium  Maasmechelen\n",
       "MEMO005        Culex  pipiens pipiens  Maasmechelen\n",
       "...              ...              ...           ...\n",
       "NEMO50         Culex  pipiens pipiens        Bertem\n",
       "NEMO51         Culex  pipiens pipiens        Bertem\n",
       "NEMO52         Culex  pipiens pipiens        Bertem\n",
       "NEMO53         Culex  pipiens pipiens        Bertem\n",
       "NEMO54         Culex  pipiens pipiens        Bertem\n",
       "\n",
       "[198 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "species_id = new_metadata[[\n",
    "    \"Sample\",\n",
    "    \"visual_genus\",\n",
    "    \"visual_species\",\n",
    "    \"Municipality\"\n",
    "]]\n",
    "\n",
    "species_id.set_index(\"Sample\", inplace=True)\n",
    "species_id.dropna(how='all', inplace= True)\n",
    "species_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable\n",
    "from matplotlib import colorbar\n",
    "\n",
    "def clean_data(dd_tsv, metadata):\n",
    "    '''\n",
    "    remove trailing suffix and replace column name spaces with underscores\n",
    "    '''\n",
    "    \n",
    "    dd_tsv.columns = dd_tsv.columns.str.replace(\" \", \"_\")\n",
    "\n",
    "    # Add prefix columns and size\n",
    "    dd_tsv[\"Sample_1\"] = dd_tsv.apply(\n",
    "        lambda row: row[\"Sample_1\"],\n",
    "        axis=1\n",
    "    )\n",
    "    dd_tsv[\"Sample_2\"] = dd_tsv.apply(\n",
    "        lambda row: row[\"Sample_2\"],\n",
    "        axis=1\n",
    "    )\n",
    "\n",
    "    # re-organize columns\n",
    "    dd_tsv = dd_tsv[\n",
    "        [\n",
    "            \"Sample_1\",\n",
    "            \"Sample_2\",\n",
    "            \"Matches\",\n",
    "            \"Mismatches\",\n",
    "            \"Jaccard_Index\",\n",
    "            \"Mash-like_distance\",\n",
    "            \"SNPs\",\n",
    "            \"SNP_distance\"\n",
    "        ]\n",
    "    ]\n",
    "    \n",
    "    # Add row to keep last sample\n",
    "    uniques_sample_2 = [\n",
    "    y\n",
    "    for y in dd_tsv[\"Sample_2\"].unique()\n",
    "    ]\n",
    "    \n",
    "    last_sample = uniques_sample_2[-1]\n",
    "    \n",
    "    last_row = {\n",
    "        \"Sample_1\":last_sample, \n",
    "        \"Sample_2\":last_sample, \n",
    "        \"Matches\":0, \n",
    "        \"Mismatches\":0, \n",
    "        \"Jaccard_Index\":0, \n",
    "        \"Mash-like_distance\":0, \n",
    "        \"SNPs\":0, \n",
    "        \"SNP_distance\":0\n",
    "    }\n",
    "    \n",
    "    dd_tsv=dd_tsv.append(last_row, ignore_index=True)\n",
    "    return dd_tsv\n",
    "\n",
    "\n",
    "def create_pivot(df, values=\"SNP_distance\", diagonal=0, remove_water=False):\n",
    "    ''' create pivot tables with sample 1 and sample 2 correlations'''\n",
    "\n",
    "    dd_tsv_pivot = df.pivot(\n",
    "        columns=\"Sample_1\",\n",
    "        index=\"Sample_2\",\n",
    "        values=values\n",
    "    )\n",
    "    dd_tsv_pivot = dd_tsv_pivot.reindex(dd_tsv_pivot.columns)\n",
    "    np.fill_diagonal(dd_tsv_pivot.values, diagonal)\n",
    "    dd_tsv_pivot = dd_tsv_pivot.fillna(0) + dd_tsv_pivot.T.fillna(0) - np.diag(dd_tsv_pivot.values.diagonal())\n",
    "\n",
    "    # remove waters from pivot table\n",
    "    if remove_water:\n",
    "        t_raw_piv_no_waters = dd_tsv_pivot[~(dd_tsv_pivot.index.str.contains(\"ater\"))]\n",
    "        t_raw_piv_no_waters_col = t_raw_piv_no_waters.drop(\n",
    "            columns=t_raw_piv_no_waters.columns[\n",
    "                t_raw_piv_no_waters.columns.str.contains(\"ater\")\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "        return t_raw_piv_no_waters_col\n",
    "    \n",
    "    else:\n",
    "        return dd_tsv_pivot\n",
    "\n",
    "\n",
    "def add_metadata_to_pivot(df, metadata_fields, metadata):\n",
    "    \"\"\"join metadata onto pivot table for axis labeling\"\"\"\n",
    "\n",
    "    dd_tsv_pivot_w_labels = pd.merge(\n",
    "        df.reset_index(),\n",
    "        metadata[metadata_fields],\n",
    "        how='left',\n",
    "        left_on=\"Sample_1\",\n",
    "        right_index=True,\n",
    "    ).reset_index().set_index(\n",
    "        [\"Sample_1\"] + metadata_fields\n",
    "    ).drop([\"index\"], axis=1)\n",
    "\n",
    "    return dd_tsv_pivot_w_labels\n",
    "\n",
    "\n",
    "def get_linkage(pivot_df):\n",
    "    '''clustering linkage'''\n",
    "\n",
    "    cdist = scipy.spatial.distance.squareform(pivot_df)\n",
    "    return scipy.cluster.hierarchy.linkage(cdist, method=\"ward\")\n",
    "\n",
    "\n",
    "def get_cluster_map(pivot_df, linkage, num_clusters, extra=True):\n",
    "    \"\"\"cluster distance matrix and re-assign species as cluster species mode\"\"\"\n",
    "\n",
    "    clusters = fcluster(linkage, num_clusters, criterion='maxclust')\n",
    "    cluster_map = pd.DataFrame()\n",
    "    \n",
    "    if extra:\n",
    "        cluster_map['Sample'] = pivot_df.index.get_level_values(0)\n",
    "        #cluster_map['visual_genus'] = pivot_df['visual_genus'].tolist()\n",
    "        #cluster_map['visual_species'] = pivot_df['visual_species'].tolist()\n",
    "        cluster_map['visual_genus'] = pivot_df.index.get_level_values(1)\n",
    "        cluster_map['visual_species'] = pivot_df.index.get_level_values(2)\n",
    "        cluster_map['Municipality'] = pivot_df.index.get_level_values(3)\n",
    "\n",
    "    cluster_map[\"Sample_1\"] = pivot_df.columns#.drop(['visual_species', 'visual_genus'])\n",
    "    cluster_map['cluster'] = clusters\n",
    "    cluster_map.set_index(\"Sample_1\", inplace=True)\n",
    "\n",
    "    cluster_chunks = []\n",
    "    for clust in cluster_map.cluster.unique():\n",
    "        subset = cluster_map[cluster_map.cluster == clust]\n",
    "        species_mode = subset.visual_species.mode()[0]\n",
    "        genus_mode = subset[\n",
    "            subset[\"visual_species\"] == species_mode\n",
    "        ].visual_genus.values[0]\n",
    "        subset[\"ska_species\"] = species_mode\n",
    "        subset[\"ska_genus\"] = genus_mode\n",
    "        cluster_chunks.append(subset)\n",
    "\n",
    "    cluster_map_w_ska = pd.concat(cluster_chunks)\n",
    "    return cluster_map_w_ska\n",
    "\n",
    "\n",
    "def join_on_ska_labels(raw_df, metadata, k, values=\"SNP_distance\", diagonal=0):\n",
    "    '''create pivot from distance matrix with old and new species labels'''\n",
    "\n",
    "    df_cleaned = clean_data(\n",
    "        raw_df,\n",
    "        metadata=metadata\n",
    "    )\n",
    "\n",
    "    dd_pivot = create_pivot(\n",
    "        df_cleaned,\n",
    "        values=values,\n",
    "        diagonal=diagonal\n",
    "    )\n",
    "\n",
    "    metadata_fields = [\n",
    "        \"visual_genus\",\n",
    "        \"visual_species\",\n",
    "        \"Municipality\"\n",
    "    ]\n",
    "\n",
    "    dd_pivot_w_labels = add_metadata_to_pivot(\n",
    "        dd_pivot,\n",
    "        metadata_fields,\n",
    "        metadata=metadata\n",
    "    )\n",
    "\n",
    "    linkage = get_linkage(dd_pivot)\n",
    "\n",
    "    cluster_map = get_cluster_map(\n",
    "        dd_pivot_w_labels,\n",
    "        linkage,\n",
    "        k,\n",
    "        extra=True\n",
    "    )\n",
    "\n",
    "    ska_metadata_fields = metadata_fields + [\"ska_genus\", \"ska_species\"]\n",
    "\n",
    "    final_pivot_w_labels = add_metadata_to_pivot(\n",
    "        dd_pivot,\n",
    "        ska_metadata_fields,\n",
    "        metadata=cluster_map\n",
    "    )\n",
    "\n",
    "    return final_pivot_w_labels, cluster_map, linkage\n",
    "\n",
    "\n",
    "def wrap_clustermap_and_mismatches(\n",
    "    raw_df,\n",
    "    metadata,\n",
    "    k=4,\n",
    "    figsize=(35, 35),\n",
    "    values=\"SNP_distance\",\n",
    "    diagonal=0,\n",
    "    fontsize=35,\n",
    "    labelsize=25\n",
    "):\n",
    "    '''create clustermap with new and old species assigned labels'''\n",
    "\n",
    "    pivot_w_labels, cluster_map, linkage = join_on_ska_labels(\n",
    "        raw_df, \n",
    "        metadata, \n",
    "        k, \n",
    "        values=values, \n",
    "        diagonal=0\n",
    "    )\n",
    "    \n",
    "    c, leg, cbar = correlation_matrix(\n",
    "        pivot_w_labels,\n",
    "        linkage,\n",
    "        cluster_map,\n",
    "        figsize=figsize,\n",
    "        fontsize=fontsize,\n",
    "        labelsize=labelsize\n",
    "    )\n",
    "\n",
    "    return c, leg, cbar, pivot_w_labels\n",
    "\n",
    "def hierarchical_clustering(\n",
    "    raw_df,\n",
    "    metadata,\n",
    "    k,\n",
    "    values=\"SNP_distance\",\n",
    "    diagonal=0\n",
    "):\n",
    "    pivot_df, cluster_map, linkage = join_on_ska_labels(\n",
    "        raw_df, \n",
    "        metadata, \n",
    "        k, \n",
    "        values=values,\n",
    "        diagonal=diagonal\n",
    "    )\n",
    "    cdist = scipy.spatial.distance.squareform(pivot_df)\n",
    "    Z = shc.linkage(cdist, method=\"ward\")\n",
    "    fig = plt.figure(figsize=(25, 10))\n",
    "\n",
    "    dn = shc.dendrogram(\n",
    "        Z,\n",
    "        leaf_font_size=8,\n",
    "        labels=pivot_df.index.get_level_values(4),\n",
    "        color_threshold=0.008\n",
    "    )\n",
    "\n",
    "    return dn\n",
    "\n",
    "\n",
    "def get_silhouette_score(\n",
    "    raw_df,\n",
    "    metadata,\n",
    "    k,\n",
    "    values=\"SNP_distance\",\n",
    "    diagonal=0,\n",
    "    correlation=False\n",
    "):\n",
    "    '''use silhouette metric to see how compact and distinct clusters are '''\n",
    "    \n",
    "    pivot_df, cluster_map, linkage = join_on_ska_labels(\n",
    "        raw_df, \n",
    "        metadata, \n",
    "        tailing, \n",
    "        k, \n",
    "        values=values,\n",
    "        diagonal=diagonal\n",
    "    )\n",
    "    cdist = scipy.spatial.distance.squareform(pivot_df)\n",
    "    linkage = scipy.cluster.hierarchy.linkage(cdist, method=\"ward\")\n",
    "    clusters = fcluster(linkage, k, criterion='maxclust')\n",
    "\n",
    "    cluster_map = pd.DataFrame()\n",
    "\n",
    "    cluster_map[\"Sample_1\"] = pivot_df.columns.drop(['visual_species', 'visual_genus'])\n",
    "    cluster_map['cluster'] = clusters\n",
    "    cluster_map.set_index(\"Sample_1\", inplace=True)\n",
    "\n",
    "    return silhouette_score(\n",
    "        pivot_df,\n",
    "        metric=\"precomputed\",\n",
    "        labels=cluster_map[\"cluster\"]\n",
    "    )\n",
    "\n",
    "\n",
    "def correlation_matrix(\n",
    "    pivot_w_labels,\n",
    "    linkage,\n",
    "    cluster_map,\n",
    "    figsize=(35, 35),\n",
    "    fontsize=35,\n",
    "    labelsize=25\n",
    "):\n",
    "    '''correlation matrix comparing visual and ska species assignment'''\n",
    "\n",
    "    legend_labeling = {\n",
    "         \"Culex pipiens pipiens\": \"#54C568FF\",#\"#4c72a5\",\n",
    "         \"Culex pipiens molestus\": \"#1FA188FF\",#\"#48a365\",\n",
    "         \"Culex pipiens undetermined\": \"#FDE725FF\",#\"#77bedb\",\n",
    "         \"Culex torrentium\": \"#BBDF27FF\",#\"#90ce9b\",\n",
    "         \"Aedes japonicus\": \"#2A788EFF\",#\"#ccc197\",\n",
    "    }\n",
    "    \n",
    "    location_labeling = {\n",
    "        \"Bertem\": \"#0D0887FF\",      \n",
    "        \"Dilsen-Stokkem\": \"#47039FFF\",              \n",
    "        \"Eupen\": \"#7301A8FF\",\n",
    "        \"Frameries\": \"#9C179EFF\",\n",
    "        \"Kallo\": \"#BD3786FF\",\n",
    "        \"Leuven\": \"#D8576BFF\",\n",
    "        \"Maasmechelen\": \"#ED7953FF\",\n",
    "        \"Natoye\": \"#FA9E3BFF\",\n",
    "        \"Villers-Le-Bouillet\": \"#FDC926FF\",\n",
    "        \"Vrasene\": \"#F0F921FF\"                                      \n",
    "    }\n",
    "\n",
    "    pivot_index = pivot_w_labels.reset_index()\n",
    "    pivot_index['visual_species'] = pivot_index['visual_species']\n",
    "    pivot_index['ska_species'] = pivot_index['ska_species']\n",
    "    pivot_index['Municipality'] = pivot_index['Municipality']\n",
    "\n",
    "    key_colors_original = pivot_index[\"visual_species\"].map({\n",
    "        k.split(\" \", 1)[1]: v\n",
    "        for k, v in legend_labeling.items()\n",
    "    })\n",
    "    #key_colors_ska = pivot_index[\"ska_species\"].map({\n",
    "    #    k.split(\" \", 1)[1]: v\n",
    "    #    for k, v in legend_labeling.items()\n",
    "    #})\n",
    "    #key_colors_location = pivot_index[\"Municipality\"].map({\n",
    "    #    k: v\n",
    "    #    for k, v in location_labeling.items()\n",
    "    #})\n",
    "    \n",
    "    key_colors_labels = pd.DataFrame.from_dict(\n",
    "        {\n",
    "            \"index\": pivot_index[\"Sample_1\"].values,\n",
    "            #\"Location\": key_colors_location,\n",
    "            \"Species\": key_colors_original#,      \n",
    "            #\"PCR\": key_colors_original,\n",
    "            #\"SKA\": key_colors_ska\n",
    "        }\n",
    "    ).set_index(\"index\")\n",
    "    \n",
    "    c = sns.clustermap(\n",
    "            pivot_w_labels.reset_index().drop([\n",
    "                \"visual_genus\",\n",
    "                \"visual_species\",\n",
    "                \"ska_genus\",\n",
    "                \"ska_species\",\n",
    "                \"Municipality\"\n",
    "            ], axis=1).set_index(\"Sample_1\"),\n",
    "            metric=\"correlation\",\n",
    "            cmap=\"mako\",\n",
    "            row_colors=key_colors_labels,\n",
    "            col_colors=key_colors_labels,\n",
    "            figsize=figsize,\n",
    "            col_linkage=linkage,\n",
    "            row_linkage=linkage,\n",
    "            #xticklabels=pivot_w_labels.index.get_level_values(0),\n",
    "            #yticklabels=pivo_w_labels.index.get_level_values(0)\n",
    "            xticklabels=False,\n",
    "            yticklabels=False\n",
    "    )\n",
    "    \n",
    "    # make colorbar labels larger\n",
    "    c.ax_col_colors.tick_params(labelright=False, right=False)\n",
    "    c.ax_row_colors.tick_params(labelbottom=False)\n",
    "    c.ax_col_colors\n",
    "    \n",
    "    # add seperating white line between colorbars\n",
    "    c.ax_col_colors.axhline(1, linewidth=2, color=\"white\")\n",
    "    c.ax_row_colors.axvline(1, linewidth=2, color='white')\n",
    "    \n",
    "    # disable existing scale (too small)\n",
    "    c.cax.set_visible(False)\n",
    "    \n",
    "    # add colorbar legend\n",
    "    legend_patches = [\n",
    "        mpatches.Patch(\n",
    "            color=c, label=sp\n",
    "        )\n",
    "        for sp, c in sorted(legend_labeling.items())\n",
    "    ]\n",
    "    \n",
    "    #location_patches = [\n",
    "    #    mpatches.Patch(\n",
    "    #        color=c, label=sp\n",
    "    #    )\n",
    "    #    for sp, c in sorted(location_labeling.items())\n",
    "    #]\n",
    "    \n",
    "    ax = c.ax_heatmap\n",
    "    ax.set_xlabel(\"\")\n",
    "    ax.set_ylabel(\"\")\n",
    "    leg = c.ax_heatmap.legend(\n",
    "        loc='upper left',\n",
    "        bbox_to_anchor=(1.10, 1.0),\n",
    "        handles=legend_patches,\n",
    "        frameon=False,\n",
    "        fontsize=fontsize,\n",
    "        prop={'size': fontsize, 'style': 'italic'}\n",
    "    )\n",
    "    \n",
    "    #loc = c.ax_heatmap.legend(\n",
    "    #    loc='lower left',\n",
    "    #    bbox_to_anchor=(1.10, 1.0),\n",
    "    #    handles=location_patches,\n",
    "    #    frameon=False,\n",
    "    #    fontsize=35,\n",
    "    #    prop={'size': 35}\n",
    "    #)\n",
    "\n",
    "    # add in larger SNP Distance scale\n",
    "    cax = c.fig.add_axes([0.05, 0.7, 0.02, 0.15])\n",
    "    cbar = c.fig.colorbar(\n",
    "        c.ax_heatmap.get_children()[0], \n",
    "        cax=cax, \n",
    "        orientation=\"vertical\",\n",
    "    )\n",
    "    cbar.set_label(label=\"SNP Distance\", fontsize=fontsize)\n",
    "    cbar.ax.tick_params(labelsize=labelsize, width=0, length=0)\n",
    "    cax.yaxis.set_label_position('left') \n",
    "    \n",
    "    return c, leg, cbar\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample 1</th>\n",
       "      <th>Sample 2</th>\n",
       "      <th>Matches</th>\n",
       "      <th>Mismatches</th>\n",
       "      <th>Jaccard Index</th>\n",
       "      <th>Mash-like distance</th>\n",
       "      <th>SNPs</th>\n",
       "      <th>SNP distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>MEMO001</td>\n",
       "      <td>MEMO002</td>\n",
       "      <td>1782090</td>\n",
       "      <td>16914160</td>\n",
       "      <td>0.095318</td>\n",
       "      <td>0.056401</td>\n",
       "      <td>6385</td>\n",
       "      <td>0.003583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MEMO001</td>\n",
       "      <td>MEMO003</td>\n",
       "      <td>2013914</td>\n",
       "      <td>16657195</td>\n",
       "      <td>0.107863</td>\n",
       "      <td>0.052780</td>\n",
       "      <td>6349</td>\n",
       "      <td>0.003153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MEMO001</td>\n",
       "      <td>MEMO004</td>\n",
       "      <td>368863</td>\n",
       "      <td>12711345</td>\n",
       "      <td>0.0282001</td>\n",
       "      <td>0.093648</td>\n",
       "      <td>2051</td>\n",
       "      <td>0.005560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MEMO001</td>\n",
       "      <td>MEMO005</td>\n",
       "      <td>2286444</td>\n",
       "      <td>19166200</td>\n",
       "      <td>0.106581</td>\n",
       "      <td>0.053128</td>\n",
       "      <td>7339</td>\n",
       "      <td>0.003210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>MEMO001</td>\n",
       "      <td>MEMO006</td>\n",
       "      <td>2228120</td>\n",
       "      <td>19024556</td>\n",
       "      <td>0.10484</td>\n",
       "      <td>0.053609</td>\n",
       "      <td>6931</td>\n",
       "      <td>0.003111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19301</th>\n",
       "      <td>NEMO51</td>\n",
       "      <td>NEMO53</td>\n",
       "      <td>4186864</td>\n",
       "      <td>38715120</td>\n",
       "      <td>0.0975914</td>\n",
       "      <td>0.055708</td>\n",
       "      <td>18655</td>\n",
       "      <td>0.004456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19302</th>\n",
       "      <td>NEMO51</td>\n",
       "      <td>NEMO54</td>\n",
       "      <td>4315595</td>\n",
       "      <td>43678984</td>\n",
       "      <td>0.0899184</td>\n",
       "      <td>0.058123</td>\n",
       "      <td>21097</td>\n",
       "      <td>0.004889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19303</th>\n",
       "      <td>NEMO52</td>\n",
       "      <td>NEMO53</td>\n",
       "      <td>4921335</td>\n",
       "      <td>40118568</td>\n",
       "      <td>0.109266</td>\n",
       "      <td>0.052404</td>\n",
       "      <td>22807</td>\n",
       "      <td>0.004634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19304</th>\n",
       "      <td>NEMO52</td>\n",
       "      <td>NEMO54</td>\n",
       "      <td>5035638</td>\n",
       "      <td>45111288</td>\n",
       "      <td>0.100418</td>\n",
       "      <td>0.054870</td>\n",
       "      <td>24499</td>\n",
       "      <td>0.004865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19305</th>\n",
       "      <td>NEMO53</td>\n",
       "      <td>NEMO54</td>\n",
       "      <td>7905900</td>\n",
       "      <td>55487288</td>\n",
       "      <td>0.124712</td>\n",
       "      <td>0.048585</td>\n",
       "      <td>42995</td>\n",
       "      <td>0.005438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19110 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Sample 1 Sample 2  Matches  Mismatches Jaccard Index  \\\n",
       "0      MEMO001  MEMO002  1782090    16914160      0.095318   \n",
       "1      MEMO001  MEMO003  2013914    16657195      0.107863   \n",
       "2      MEMO001  MEMO004   368863    12711345     0.0282001   \n",
       "3      MEMO001  MEMO005  2286444    19166200      0.106581   \n",
       "4      MEMO001  MEMO006  2228120    19024556       0.10484   \n",
       "...        ...      ...      ...         ...           ...   \n",
       "19301   NEMO51   NEMO53  4186864    38715120     0.0975914   \n",
       "19302   NEMO51   NEMO54  4315595    43678984     0.0899184   \n",
       "19303   NEMO52   NEMO53  4921335    40118568      0.109266   \n",
       "19304   NEMO52   NEMO54  5035638    45111288      0.100418   \n",
       "19305   NEMO53   NEMO54  7905900    55487288      0.124712   \n",
       "\n",
       "       Mash-like distance   SNPs  SNP distance  \n",
       "0                0.056401   6385      0.003583  \n",
       "1                0.052780   6349      0.003153  \n",
       "2                0.093648   2051      0.005560  \n",
       "3                0.053128   7339      0.003210  \n",
       "4                0.053609   6931      0.003111  \n",
       "...                   ...    ...           ...  \n",
       "19301            0.055708  18655      0.004456  \n",
       "19302            0.058123  21097      0.004889  \n",
       "19303            0.052404  22807      0.004634  \n",
       "19304            0.054870  24499      0.004865  \n",
       "19305            0.048585  42995      0.005438  \n",
       "\n",
       "[19110 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_raw = pd.read_csv(\"data/ska_ksize_15.distances.tsv\", sep=\"\\t\")\n",
    "\n",
    "# Remove sample MEMO011 because it doesn't have enoough mosquito reads ==> skews clustering\n",
    "t_raw=t_raw[~t_raw.stack().str.contains('MEMO011').any(level=0)]\n",
    "t_raw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NEMO54'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uniques_sample_1 = [\n",
    "    y\n",
    "    for y in t_raw[\"Sample 1\"].unique()\n",
    "]\n",
    "\n",
    "uniques_sample_2 = [\n",
    "    y\n",
    "    for y in t_raw[\"Sample 2\"].unique()\n",
    "]\n",
    "#uniques_sample_1\n",
    "uniques_sample_2[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# count in both samples 1 and 2 for some non-repeated data in original raw ska df\n",
    "#[x for x in species_id.index if x not in uniques_sample_1 + uniques_sample_2]\n",
    "\n",
    "t_raw_cleaned = clean_data(t_raw, species_id)\n",
    "# Add last row to tsv file comparing last sample to itself, SNP distance 0!\n",
    "t_raw_piv = create_pivot(t_raw_cleaned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check for missing samples:\n",
    "#print([x for x in species_id.index if x not in t_raw_piv.index])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Sample_1</th>\n",
       "      <th>MEMO001</th>\n",
       "      <th>MEMO002</th>\n",
       "      <th>MEMO003</th>\n",
       "      <th>MEMO004</th>\n",
       "      <th>MEMO005</th>\n",
       "      <th>MEMO006</th>\n",
       "      <th>MEMO007</th>\n",
       "      <th>MEMO008</th>\n",
       "      <th>MEMO010</th>\n",
       "      <th>MEMO012</th>\n",
       "      <th>...</th>\n",
       "      <th>NEMO45</th>\n",
       "      <th>NEMO46</th>\n",
       "      <th>NEMO47</th>\n",
       "      <th>NEMO48</th>\n",
       "      <th>NEMO49</th>\n",
       "      <th>NEMO50</th>\n",
       "      <th>NEMO51</th>\n",
       "      <th>NEMO52</th>\n",
       "      <th>NEMO53</th>\n",
       "      <th>NEMO54</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEMO001</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.003111</td>\n",
       "      <td>0.003453</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>0.003063</td>\n",
       "      <td>0.003251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002894</td>\n",
       "      <td>0.004149</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>0.003716</td>\n",
       "      <td>0.004293</td>\n",
       "      <td>0.004694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO002</th>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.003263</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.003399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.003249</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.003695</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>0.004755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO003</th>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.002931</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.003096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.003852</td>\n",
       "      <td>0.003456</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.003182</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.004378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO004</th>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>0.007576</td>\n",
       "      <td>0.004038</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.006496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005244</td>\n",
       "      <td>0.009433</td>\n",
       "      <td>0.010171</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.009184</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.010467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO005</th>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002970</td>\n",
       "      <td>0.003105</td>\n",
       "      <td>0.002342</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.002853</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.003566</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.004296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO50</th>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>0.003522</td>\n",
       "      <td>0.002834</td>\n",
       "      <td>0.003002</td>\n",
       "      <td>0.003245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003041</td>\n",
       "      <td>0.004116</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.003893</td>\n",
       "      <td>0.003482</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>0.004322</td>\n",
       "      <td>0.004704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO51</th>\n",
       "      <td>0.003543</td>\n",
       "      <td>0.003695</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.003343</td>\n",
       "      <td>0.003579</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.003043</td>\n",
       "      <td>0.003355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002920</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.004456</td>\n",
       "      <td>0.004889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO52</th>\n",
       "      <td>0.003716</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.003788</td>\n",
       "      <td>0.002826</td>\n",
       "      <td>0.003271</td>\n",
       "      <td>0.003489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003201</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.004865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO53</th>\n",
       "      <td>0.004293</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.004039</td>\n",
       "      <td>0.004209</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.004071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.004798</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>0.004669</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.004322</td>\n",
       "      <td>0.004456</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO54</th>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.004755</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.010467</td>\n",
       "      <td>0.004296</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.004543</td>\n",
       "      <td>0.004009</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>0.004365</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.005126</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>0.005438</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Sample_1   MEMO001   MEMO002   MEMO003   MEMO004   MEMO005   MEMO006  \\\n",
       "Sample_1                                                               \n",
       "MEMO001   0.000000  0.003583  0.003153  0.005560  0.003210  0.003111   \n",
       "MEMO002   0.003583  0.000000  0.003298  0.011072  0.003293  0.003263   \n",
       "MEMO003   0.003153  0.003298  0.000000  0.005862  0.002997  0.002931   \n",
       "MEMO004   0.005560  0.011072  0.005862  0.000000  0.006731  0.007598   \n",
       "MEMO005   0.003210  0.003293  0.002997  0.006731  0.000000  0.002970   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "NEMO50    0.003500  0.003447  0.003215  0.008573  0.003233  0.003155   \n",
       "NEMO51    0.003543  0.003695  0.003241  0.005482  0.003068  0.003343   \n",
       "NEMO52    0.003716  0.003806  0.003474  0.005819  0.003327  0.003515   \n",
       "NEMO53    0.004293  0.004433  0.004111  0.009938  0.003766  0.004039   \n",
       "NEMO54    0.004694  0.004755  0.004378  0.010467  0.004296  0.004217   \n",
       "\n",
       "Sample_1   MEMO007   MEMO008   MEMO010   MEMO012  ...    NEMO45    NEMO46  \\\n",
       "Sample_1                                          ...                       \n",
       "MEMO001   0.003453  0.002710  0.003063  0.003251  ...  0.002894  0.004149   \n",
       "MEMO002   0.003607  0.002917  0.003026  0.003399  ...  0.003051  0.004263   \n",
       "MEMO003   0.003280  0.002551  0.002887  0.003096  ...  0.002770  0.003852   \n",
       "MEMO004   0.007576  0.004038  0.007815  0.006496  ...  0.005244  0.009433   \n",
       "MEMO005   0.003105  0.002342  0.002513  0.002853  ...  0.002553  0.003566   \n",
       "...            ...       ...       ...       ...  ...       ...       ...   \n",
       "NEMO50    0.003522  0.002834  0.003002  0.003245  ...  0.003041  0.004116   \n",
       "NEMO51    0.003579  0.002649  0.003043  0.003355  ...  0.002920  0.004262   \n",
       "NEMO52    0.003788  0.002826  0.003271  0.003489  ...  0.003201  0.004398   \n",
       "NEMO53    0.004209  0.003556  0.003746  0.004071  ...  0.003731  0.004798   \n",
       "NEMO54    0.004543  0.004009  0.004128  0.004365  ...  0.004052  0.005126   \n",
       "\n",
       "Sample_1    NEMO47    NEMO48    NEMO49    NEMO50    NEMO51    NEMO52  \\\n",
       "Sample_1                                                               \n",
       "MEMO001   0.003687  0.003878  0.003533  0.003500  0.003543  0.003716   \n",
       "MEMO002   0.003249  0.003897  0.003354  0.003447  0.003695  0.003806   \n",
       "MEMO003   0.003456  0.003673  0.003182  0.003215  0.003241  0.003474   \n",
       "MEMO004   0.010171  0.007418  0.009184  0.008573  0.005482  0.005819   \n",
       "MEMO005   0.003520  0.003507  0.003102  0.003233  0.003068  0.003327   \n",
       "...            ...       ...       ...       ...       ...       ...   \n",
       "NEMO50    0.003699  0.003893  0.003482  0.000000  0.003683  0.003828   \n",
       "NEMO51    0.003870  0.003959  0.003601  0.003683  0.000000  0.003834   \n",
       "NEMO52    0.004052  0.004113  0.003690  0.003828  0.003834  0.000000   \n",
       "NEMO53    0.004696  0.004669  0.004229  0.004322  0.004456  0.004634   \n",
       "NEMO54    0.005067  0.004962  0.004378  0.004704  0.004889  0.004865   \n",
       "\n",
       "Sample_1    NEMO53    NEMO54  \n",
       "Sample_1                      \n",
       "MEMO001   0.004293  0.004694  \n",
       "MEMO002   0.004433  0.004755  \n",
       "MEMO003   0.004111  0.004378  \n",
       "MEMO004   0.009938  0.010467  \n",
       "MEMO005   0.003766  0.004296  \n",
       "...            ...       ...  \n",
       "NEMO50    0.004322  0.004704  \n",
       "NEMO51    0.004456  0.004889  \n",
       "NEMO52    0.004634  0.004865  \n",
       "NEMO53    0.000000  0.005438  \n",
       "NEMO54    0.005438  0.000000  \n",
       "\n",
       "[196 rows x 196 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t_raw_piv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>MEMO001</th>\n",
       "      <th>MEMO002</th>\n",
       "      <th>MEMO003</th>\n",
       "      <th>MEMO004</th>\n",
       "      <th>MEMO005</th>\n",
       "      <th>MEMO006</th>\n",
       "      <th>MEMO007</th>\n",
       "      <th>MEMO008</th>\n",
       "      <th>MEMO010</th>\n",
       "      <th>MEMO012</th>\n",
       "      <th>...</th>\n",
       "      <th>NEMO45</th>\n",
       "      <th>NEMO46</th>\n",
       "      <th>NEMO47</th>\n",
       "      <th>NEMO48</th>\n",
       "      <th>NEMO49</th>\n",
       "      <th>NEMO50</th>\n",
       "      <th>NEMO51</th>\n",
       "      <th>NEMO52</th>\n",
       "      <th>NEMO53</th>\n",
       "      <th>NEMO54</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_1</th>\n",
       "      <th>visual_genus</th>\n",
       "      <th>visual_species</th>\n",
       "      <th>Municipality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEMO001</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.003111</td>\n",
       "      <td>0.003453</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>0.003063</td>\n",
       "      <td>0.003251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002894</td>\n",
       "      <td>0.004149</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>0.003716</td>\n",
       "      <td>0.004293</td>\n",
       "      <td>0.004694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO002</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.003263</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.003399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.003249</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.003695</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>0.004755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO003</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.002931</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.003096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.003852</td>\n",
       "      <td>0.003456</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.003182</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.004378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO004</th>\n",
       "      <th>Culex</th>\n",
       "      <th>torrentium</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>0.007576</td>\n",
       "      <td>0.004038</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.006496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005244</td>\n",
       "      <td>0.009433</td>\n",
       "      <td>0.010171</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.009184</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.010467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO005</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002970</td>\n",
       "      <td>0.003105</td>\n",
       "      <td>0.002342</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.002853</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.003566</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.004296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO50</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>0.003522</td>\n",
       "      <td>0.002834</td>\n",
       "      <td>0.003002</td>\n",
       "      <td>0.003245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003041</td>\n",
       "      <td>0.004116</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.003893</td>\n",
       "      <td>0.003482</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>0.004322</td>\n",
       "      <td>0.004704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO51</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <td>0.003543</td>\n",
       "      <td>0.003695</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.003343</td>\n",
       "      <td>0.003579</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.003043</td>\n",
       "      <td>0.003355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002920</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.004456</td>\n",
       "      <td>0.004889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO52</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <td>0.003716</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.003788</td>\n",
       "      <td>0.002826</td>\n",
       "      <td>0.003271</td>\n",
       "      <td>0.003489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003201</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.004865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO53</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <td>0.004293</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.004039</td>\n",
       "      <td>0.004209</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.004071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.004798</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>0.004669</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.004322</td>\n",
       "      <td>0.004456</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO54</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.004755</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.010467</td>\n",
       "      <td>0.004296</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.004543</td>\n",
       "      <td>0.004009</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>0.004365</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.005126</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>0.005438</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     MEMO001   MEMO002  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.000000  0.003583   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003583  0.000000   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.003153  0.003298   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.005560  0.011072   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.003210  0.003293   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003500  0.003447   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.003543  0.003695   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003716  0.003806   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.004293  0.004433   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004694  0.004755   \n",
       "\n",
       "                                                     MEMO003   MEMO004  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.003153  0.005560   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003298  0.011072   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.000000  0.005862   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.005862  0.000000   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.002997  0.006731   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003215  0.008573   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.003241  0.005482   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003474  0.005819   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.004111  0.009938   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004378  0.010467   \n",
       "\n",
       "                                                     MEMO005   MEMO006  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.003210  0.003111   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003293  0.003263   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.002997  0.002931   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.006731  0.007598   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.000000  0.002970   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003233  0.003155   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.003068  0.003343   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003327  0.003515   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.003766  0.004039   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004296  0.004217   \n",
       "\n",
       "                                                     MEMO007   MEMO008  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.003453  0.002710   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003607  0.002917   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.003280  0.002551   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.007576  0.004038   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.003105  0.002342   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003522  0.002834   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.003579  0.002649   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003788  0.002826   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.004209  0.003556   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004543  0.004009   \n",
       "\n",
       "                                                     MEMO010   MEMO012  ...  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                      ...   \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.003063  0.003251  ...   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003026  0.003399  ...   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.002887  0.003096  ...   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.007815  0.006496  ...   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.002513  0.002853  ...   \n",
       "...                                                      ...       ...  ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003002  0.003245  ...   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.003043  0.003355  ...   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003271  0.003489  ...   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.003746  0.004071  ...   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004128  0.004365  ...   \n",
       "\n",
       "                                                      NEMO45    NEMO46  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.002894  0.004149   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003051  0.004263   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.002770  0.003852   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.005244  0.009433   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.002553  0.003566   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003041  0.004116   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.002920  0.004262   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003201  0.004398   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.003731  0.004798   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004052  0.005126   \n",
       "\n",
       "                                                      NEMO47    NEMO48  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.003687  0.003878   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003249  0.003897   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.003456  0.003673   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.010171  0.007418   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.003520  0.003507   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003699  0.003893   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.003870  0.003959   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.004052  0.004113   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.004696  0.004669   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.005067  0.004962   \n",
       "\n",
       "                                                      NEMO49    NEMO50  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.003533  0.003500   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003354  0.003447   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.003182  0.003215   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.009184  0.008573   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.003102  0.003233   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003482  0.000000   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.003601  0.003683   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003690  0.003828   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.004229  0.004322   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004378  0.004704   \n",
       "\n",
       "                                                      NEMO51    NEMO52  \\\n",
       "Sample_1 visual_genus visual_species  Municipality                       \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.003543  0.003716   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.003695  0.003806   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.003241  0.003474   \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.005482  0.005819   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.003068  0.003327   \n",
       "...                                                      ...       ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.003683  0.003828   \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.000000  0.003834   \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.003834  0.000000   \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.004456  0.004634   \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.004889  0.004865   \n",
       "\n",
       "                                                      NEMO53    NEMO54  \n",
       "Sample_1 visual_genus visual_species  Municipality                      \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen  0.004293  0.004694  \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen  0.004433  0.004755  \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen  0.004111  0.004378  \n",
       "MEMO004  Culex        torrentium      Maasmechelen  0.009938  0.010467  \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen  0.003766  0.004296  \n",
       "...                                                      ...       ...  \n",
       "NEMO50   Culex        pipiens pipiens Bertem        0.004322  0.004704  \n",
       "NEMO51   Culex        pipiens pipiens Bertem        0.004456  0.004889  \n",
       "NEMO52   Culex        pipiens pipiens Bertem        0.004634  0.004865  \n",
       "NEMO53   Culex        pipiens pipiens Bertem        0.000000  0.005438  \n",
       "NEMO54   Culex        pipiens pipiens Bertem        0.005438  0.000000  \n",
       "\n",
       "[196 rows x 196 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metadata_fields = [\n",
    "    \"visual_genus\",\n",
    "    \"visual_species\",\n",
    "    \"Municipality\"\n",
    "]\n",
    "\n",
    "dd_pivot_w_labels = add_metadata_to_pivot(\n",
    "    t_raw_piv,\n",
    "    metadata_fields,\n",
    "    metadata=species_id\n",
    ")\n",
    "dd_pivot_w_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "linkage = get_linkage(t_raw_piv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-6-fb0b9749e6c5>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-6-fb0b9749e6c5>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n",
      "<ipython-input-6-fb0b9749e6c5>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-6-fb0b9749e6c5>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n",
      "<ipython-input-6-fb0b9749e6c5>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-6-fb0b9749e6c5>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n",
      "<ipython-input-6-fb0b9749e6c5>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-6-fb0b9749e6c5>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>visual_genus</th>\n",
       "      <th>visual_species</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>cluster</th>\n",
       "      <th>ska_species</th>\n",
       "      <th>ska_genus</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_1</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEMO001</th>\n",
       "      <td>MEMO001</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "      <td>2</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Culex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO002</th>\n",
       "      <td>MEMO002</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "      <td>2</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Culex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO003</th>\n",
       "      <td>MEMO003</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "      <td>2</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Culex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO005</th>\n",
       "      <td>MEMO005</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "      <td>2</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Culex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO006</th>\n",
       "      <td>MEMO006</td>\n",
       "      <td>Culex</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Maasmechelen</td>\n",
       "      <td>2</td>\n",
       "      <td>pipiens pipiens</td>\n",
       "      <td>Culex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO113</th>\n",
       "      <td>MEMO113</td>\n",
       "      <td>Aedes</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Natoye</td>\n",
       "      <td>3</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Aedes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO115</th>\n",
       "      <td>MEMO115</td>\n",
       "      <td>Aedes</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Natoye</td>\n",
       "      <td>3</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Aedes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO116</th>\n",
       "      <td>MEMO116</td>\n",
       "      <td>Aedes</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Natoye</td>\n",
       "      <td>3</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Aedes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO117</th>\n",
       "      <td>MEMO117</td>\n",
       "      <td>Aedes</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Natoye</td>\n",
       "      <td>3</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Aedes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO118</th>\n",
       "      <td>MEMO118</td>\n",
       "      <td>Aedes</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Natoye</td>\n",
       "      <td>3</td>\n",
       "      <td>japonicus</td>\n",
       "      <td>Aedes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Sample visual_genus   visual_species  Municipality  cluster  \\\n",
       "Sample_1                                                                 \n",
       "MEMO001   MEMO001        Culex  pipiens pipiens  Maasmechelen        2   \n",
       "MEMO002   MEMO002        Culex  pipiens pipiens  Maasmechelen        2   \n",
       "MEMO003   MEMO003        Culex  pipiens pipiens  Maasmechelen        2   \n",
       "MEMO005   MEMO005        Culex  pipiens pipiens  Maasmechelen        2   \n",
       "MEMO006   MEMO006        Culex  pipiens pipiens  Maasmechelen        2   \n",
       "...           ...          ...              ...           ...      ...   \n",
       "MEMO113   MEMO113        Aedes        japonicus        Natoye        3   \n",
       "MEMO115   MEMO115        Aedes        japonicus        Natoye        3   \n",
       "MEMO116   MEMO116        Aedes        japonicus        Natoye        3   \n",
       "MEMO117   MEMO117        Aedes        japonicus        Natoye        3   \n",
       "MEMO118   MEMO118        Aedes        japonicus        Natoye        3   \n",
       "\n",
       "              ska_species ska_genus  \n",
       "Sample_1                             \n",
       "MEMO001   pipiens pipiens     Culex  \n",
       "MEMO002   pipiens pipiens     Culex  \n",
       "MEMO003   pipiens pipiens     Culex  \n",
       "MEMO005   pipiens pipiens     Culex  \n",
       "MEMO006   pipiens pipiens     Culex  \n",
       "...                   ...       ...  \n",
       "MEMO113         japonicus     Aedes  \n",
       "MEMO115         japonicus     Aedes  \n",
       "MEMO116         japonicus     Aedes  \n",
       "MEMO117         japonicus     Aedes  \n",
       "MEMO118         japonicus     Aedes  \n",
       "\n",
       "[196 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_map = get_cluster_map(\n",
    "    dd_pivot_w_labels,\n",
    "    linkage,\n",
    "    4,\n",
    "    extra=True\n",
    ")\n",
    "cluster_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>MEMO001</th>\n",
       "      <th>MEMO002</th>\n",
       "      <th>MEMO003</th>\n",
       "      <th>MEMO004</th>\n",
       "      <th>MEMO005</th>\n",
       "      <th>MEMO006</th>\n",
       "      <th>MEMO007</th>\n",
       "      <th>MEMO008</th>\n",
       "      <th>MEMO010</th>\n",
       "      <th>MEMO012</th>\n",
       "      <th>...</th>\n",
       "      <th>NEMO45</th>\n",
       "      <th>NEMO46</th>\n",
       "      <th>NEMO47</th>\n",
       "      <th>NEMO48</th>\n",
       "      <th>NEMO49</th>\n",
       "      <th>NEMO50</th>\n",
       "      <th>NEMO51</th>\n",
       "      <th>NEMO52</th>\n",
       "      <th>NEMO53</th>\n",
       "      <th>NEMO54</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_1</th>\n",
       "      <th>visual_genus</th>\n",
       "      <th>visual_species</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>ska_genus</th>\n",
       "      <th>ska_species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEMO001</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.003111</td>\n",
       "      <td>0.003453</td>\n",
       "      <td>0.002710</td>\n",
       "      <td>0.003063</td>\n",
       "      <td>0.003251</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002894</td>\n",
       "      <td>0.004149</td>\n",
       "      <td>0.003687</td>\n",
       "      <td>0.003878</td>\n",
       "      <td>0.003533</td>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.003543</td>\n",
       "      <td>0.003716</td>\n",
       "      <td>0.004293</td>\n",
       "      <td>0.004694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO002</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.003263</td>\n",
       "      <td>0.003607</td>\n",
       "      <td>0.002917</td>\n",
       "      <td>0.003026</td>\n",
       "      <td>0.003399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003051</td>\n",
       "      <td>0.004263</td>\n",
       "      <td>0.003249</td>\n",
       "      <td>0.003897</td>\n",
       "      <td>0.003354</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.003695</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>0.004755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO003</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003153</td>\n",
       "      <td>0.003298</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.002931</td>\n",
       "      <td>0.003280</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>0.002887</td>\n",
       "      <td>0.003096</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.003852</td>\n",
       "      <td>0.003456</td>\n",
       "      <td>0.003673</td>\n",
       "      <td>0.003182</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.004378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO004</th>\n",
       "      <th>Culex</th>\n",
       "      <th>torrentium</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <th>Culex</th>\n",
       "      <th>torrentium</th>\n",
       "      <td>0.005560</td>\n",
       "      <td>0.011072</td>\n",
       "      <td>0.005862</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.007598</td>\n",
       "      <td>0.007576</td>\n",
       "      <td>0.004038</td>\n",
       "      <td>0.007815</td>\n",
       "      <td>0.006496</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005244</td>\n",
       "      <td>0.009433</td>\n",
       "      <td>0.010171</td>\n",
       "      <td>0.007418</td>\n",
       "      <td>0.009184</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.010467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO005</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Maasmechelen</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003210</td>\n",
       "      <td>0.003293</td>\n",
       "      <td>0.002997</td>\n",
       "      <td>0.006731</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002970</td>\n",
       "      <td>0.003105</td>\n",
       "      <td>0.002342</td>\n",
       "      <td>0.002513</td>\n",
       "      <td>0.002853</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002553</td>\n",
       "      <td>0.003566</td>\n",
       "      <td>0.003520</td>\n",
       "      <td>0.003507</td>\n",
       "      <td>0.003102</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.004296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO50</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003500</td>\n",
       "      <td>0.003447</td>\n",
       "      <td>0.003215</td>\n",
       "      <td>0.008573</td>\n",
       "      <td>0.003233</td>\n",
       "      <td>0.003155</td>\n",
       "      <td>0.003522</td>\n",
       "      <td>0.002834</td>\n",
       "      <td>0.003002</td>\n",
       "      <td>0.003245</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003041</td>\n",
       "      <td>0.004116</td>\n",
       "      <td>0.003699</td>\n",
       "      <td>0.003893</td>\n",
       "      <td>0.003482</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>0.004322</td>\n",
       "      <td>0.004704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO51</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003543</td>\n",
       "      <td>0.003695</td>\n",
       "      <td>0.003241</td>\n",
       "      <td>0.005482</td>\n",
       "      <td>0.003068</td>\n",
       "      <td>0.003343</td>\n",
       "      <td>0.003579</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.003043</td>\n",
       "      <td>0.003355</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002920</td>\n",
       "      <td>0.004262</td>\n",
       "      <td>0.003870</td>\n",
       "      <td>0.003959</td>\n",
       "      <td>0.003601</td>\n",
       "      <td>0.003683</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.004456</td>\n",
       "      <td>0.004889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO52</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003716</td>\n",
       "      <td>0.003806</td>\n",
       "      <td>0.003474</td>\n",
       "      <td>0.005819</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>0.003515</td>\n",
       "      <td>0.003788</td>\n",
       "      <td>0.002826</td>\n",
       "      <td>0.003271</td>\n",
       "      <td>0.003489</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003201</td>\n",
       "      <td>0.004398</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.004113</td>\n",
       "      <td>0.003690</td>\n",
       "      <td>0.003828</td>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.004865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO53</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.004293</td>\n",
       "      <td>0.004433</td>\n",
       "      <td>0.004111</td>\n",
       "      <td>0.009938</td>\n",
       "      <td>0.003766</td>\n",
       "      <td>0.004039</td>\n",
       "      <td>0.004209</td>\n",
       "      <td>0.003556</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.004071</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003731</td>\n",
       "      <td>0.004798</td>\n",
       "      <td>0.004696</td>\n",
       "      <td>0.004669</td>\n",
       "      <td>0.004229</td>\n",
       "      <td>0.004322</td>\n",
       "      <td>0.004456</td>\n",
       "      <td>0.004634</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.005438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NEMO54</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <th>Bertem</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.004694</td>\n",
       "      <td>0.004755</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.010467</td>\n",
       "      <td>0.004296</td>\n",
       "      <td>0.004217</td>\n",
       "      <td>0.004543</td>\n",
       "      <td>0.004009</td>\n",
       "      <td>0.004128</td>\n",
       "      <td>0.004365</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004052</td>\n",
       "      <td>0.005126</td>\n",
       "      <td>0.005067</td>\n",
       "      <td>0.004962</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>0.004704</td>\n",
       "      <td>0.004889</td>\n",
       "      <td>0.004865</td>\n",
       "      <td>0.005438</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>196 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                               MEMO001  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.000000   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003583   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003153   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.005560   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003210   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003500   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003543   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003716   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004293   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004694   \n",
       "\n",
       "                                                                               MEMO002  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003583   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.000000   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003298   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.011072   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003293   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003447   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003695   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003806   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004433   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004755   \n",
       "\n",
       "                                                                               MEMO003  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003153   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003298   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.000000   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.005862   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002997   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003215   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003241   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003474   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004111   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004378   \n",
       "\n",
       "                                                                               MEMO004  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.005560   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.011072   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.005862   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.000000   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.006731   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.008573   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.005482   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.005819   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.009938   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.010467   \n",
       "\n",
       "                                                                               MEMO005  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003210   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003293   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002997   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.006731   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.000000   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003233   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003068   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003327   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003766   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004296   \n",
       "\n",
       "                                                                               MEMO006  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003111   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003263   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002931   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.007598   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002970   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003155   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003343   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003515   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004039   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004217   \n",
       "\n",
       "                                                                               MEMO007  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003453   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003607   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003280   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.007576   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003105   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003522   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003579   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003788   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004209   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004543   \n",
       "\n",
       "                                                                               MEMO008  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002710   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002917   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002551   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.004038   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002342   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.002834   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.002649   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.002826   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003556   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004009   \n",
       "\n",
       "                                                                               MEMO010  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003063   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003026   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002887   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.007815   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002513   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003002   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003043   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003271   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003746   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004128   \n",
       "\n",
       "                                                                               MEMO012  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003251   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003399   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003096   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.006496   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002853   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003245   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003355   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003489   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004071   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004365   \n",
       "\n",
       "                                                                              ...  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species      ...   \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  ...   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  ...   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  ...   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       ...   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  ...   \n",
       "...                                                                           ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  ...   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  ...   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  ...   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  ...   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  ...   \n",
       "\n",
       "                                                                                NEMO45  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002894   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003051   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002770   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.005244   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.002553   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003041   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.002920   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003201   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003731   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004052   \n",
       "\n",
       "                                                                                NEMO46  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004149   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004263   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003852   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.009433   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003566   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004116   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004262   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004398   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004798   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.005126   \n",
       "\n",
       "                                                                                NEMO47  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003687   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003249   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003456   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.010171   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003520   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003699   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003870   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004052   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004696   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.005067   \n",
       "\n",
       "                                                                                NEMO48  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003878   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003897   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003673   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.007418   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003507   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003893   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003959   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004113   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004669   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004962   \n",
       "\n",
       "                                                                                NEMO49  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003533   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003354   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003182   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.009184   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003102   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003482   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003601   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003690   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004229   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004378   \n",
       "\n",
       "                                                                                NEMO50  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003500   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003447   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003215   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.008573   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003233   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.000000   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003683   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003828   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004322   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004704   \n",
       "\n",
       "                                                                                NEMO51  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003543   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003695   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003241   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.005482   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003068   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003683   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.000000   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003834   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004456   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004889   \n",
       "\n",
       "                                                                                NEMO52  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003716   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003806   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003474   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.005819   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003327   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003828   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.003834   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.000000   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004634   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004865   \n",
       "\n",
       "                                                                                NEMO53  \\\n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                 \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004293   \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004433   \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004111   \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.009938   \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.003766   \n",
       "...                                                                                ...   \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004322   \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004456   \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004634   \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.000000   \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.005438   \n",
       "\n",
       "                                                                                NEMO54  \n",
       "Sample_1 visual_genus visual_species  Municipality ska_genus ska_species                \n",
       "MEMO001  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004694  \n",
       "MEMO002  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004755  \n",
       "MEMO003  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004378  \n",
       "MEMO004  Culex        torrentium      Maasmechelen Culex     torrentium       0.010467  \n",
       "MEMO005  Culex        pipiens pipiens Maasmechelen Culex     pipiens pipiens  0.004296  \n",
       "...                                                                                ...  \n",
       "NEMO50   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004704  \n",
       "NEMO51   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004889  \n",
       "NEMO52   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.004865  \n",
       "NEMO53   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.005438  \n",
       "NEMO54   Culex        pipiens pipiens Bertem       Culex     pipiens pipiens  0.000000  \n",
       "\n",
       "[196 rows x 196 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ska_metadata_fields = metadata_fields + [\"ska_genus\", \"ska_species\"]\n",
    "\n",
    "final_pivot_w_labels = add_metadata_to_pivot(\n",
    "    t_raw_piv,\n",
    "    ska_metadata_fields,\n",
    "    metadata=cluster_map\n",
    ")\n",
    "final_pivot_w_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-20-e53f84085c4b>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-20-e53f84085c4b>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n",
      "<ipython-input-20-e53f84085c4b>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-20-e53f84085c4b>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n",
      "<ipython-input-20-e53f84085c4b>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-20-e53f84085c4b>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n",
      "<ipython-input-20-e53f84085c4b>:133: 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",
      "  subset[\"ska_species\"] = species_mode\n",
      "<ipython-input-20-e53f84085c4b>:134: 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",
      "  subset[\"ska_genus\"] = genus_mode\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "    .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></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>MEMO001</th>\n",
       "      <th>MEMO002</th>\n",
       "      <th>MEMO003</th>\n",
       "      <th>MEMO004</th>\n",
       "      <th>MEMO005</th>\n",
       "      <th>MEMO006</th>\n",
       "      <th>MEMO007</th>\n",
       "      <th>MEMO008</th>\n",
       "      <th>MEMO010</th>\n",
       "      <th>MEMO012</th>\n",
       "      <th>...</th>\n",
       "      <th>NEMO45</th>\n",
       "      <th>NEMO46</th>\n",
       "      <th>NEMO47</th>\n",
       "      <th>NEMO48</th>\n",
       "      <th>NEMO49</th>\n",
       "      <th>NEMO50</th>\n",
       "      <th>NEMO51</th>\n",
       "      <th>NEMO52</th>\n",
       "      <th>NEMO53</th>\n",
       "      <th>NEMO54</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sample_1</th>\n",
       "      <th>visual_genus</th>\n",
       "      <th>visual_species</th>\n",
       "      <th>Municipality</th>\n",
       "      <th>ska_genus</th>\n",
       "      <th>ska_species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MEMO019</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens undetermined</th>\n",
       "      <th>Eupen</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003265</td>\n",
       "      <td>0.003473</td>\n",
       "      <td>0.003141</td>\n",
       "      <td>0.005224</td>\n",
       "      <td>0.002982</td>\n",
       "      <td>0.003119</td>\n",
       "      <td>0.003362</td>\n",
       "      <td>0.002435</td>\n",
       "      <td>0.002928</td>\n",
       "      <td>0.003152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002739</td>\n",
       "      <td>0.003930</td>\n",
       "      <td>0.003525</td>\n",
       "      <td>0.003776</td>\n",
       "      <td>0.003420</td>\n",
       "      <td>0.003326</td>\n",
       "      <td>0.003349</td>\n",
       "      <td>0.003587</td>\n",
       "      <td>0.004214</td>\n",
       "      <td>0.004522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO045</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens undetermined</th>\n",
       "      <th>Vrasene</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens molestus</th>\n",
       "      <td>0.003834</td>\n",
       "      <td>0.003415</td>\n",
       "      <td>0.003803</td>\n",
       "      <td>0.012071</td>\n",
       "      <td>0.003714</td>\n",
       "      <td>0.003586</td>\n",
       "      <td>0.003906</td>\n",
       "      <td>0.003325</td>\n",
       "      <td>0.003588</td>\n",
       "      <td>0.003845</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003431</td>\n",
       "      <td>0.004631</td>\n",
       "      <td>0.002004</td>\n",
       "      <td>0.004555</td>\n",
       "      <td>0.003894</td>\n",
       "      <td>0.003726</td>\n",
       "      <td>0.004081</td>\n",
       "      <td>0.004349</td>\n",
       "      <td>0.004881</td>\n",
       "      <td>0.005169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO095</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens undetermined</th>\n",
       "      <th>Villers-Le-Bouillet</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens pipiens</th>\n",
       "      <td>0.003736</td>\n",
       "      <td>0.003786</td>\n",
       "      <td>0.003605</td>\n",
       "      <td>0.011289</td>\n",
       "      <td>0.003302</td>\n",
       "      <td>0.003550</td>\n",
       "      <td>0.003846</td>\n",
       "      <td>0.003140</td>\n",
       "      <td>0.003291</td>\n",
       "      <td>0.003521</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003360</td>\n",
       "      <td>0.004425</td>\n",
       "      <td>0.004103</td>\n",
       "      <td>0.004258</td>\n",
       "      <td>0.003752</td>\n",
       "      <td>0.003831</td>\n",
       "      <td>0.003914</td>\n",
       "      <td>0.004179</td>\n",
       "      <td>0.004667</td>\n",
       "      <td>0.005053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MEMO101</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens undetermined</th>\n",
       "      <th>Dilsen-Stokkem</th>\n",
       "      <th>Culex</th>\n",
       "      <th>pipiens molestus</th>\n",
       "      <td>0.003409</td>\n",
       "      <td>0.003286</td>\n",
       "      <td>0.003149</td>\n",
       "      <td>0.005424</td>\n",
       "      <td>0.003225</td>\n",
       "      <td>0.003144</td>\n",
       "      <td>0.003575</td>\n",
       "      <td>0.002702</td>\n",
       "      <td>0.003176</td>\n",
       "      <td>0.003274</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002889</td>\n",
       "      <td>0.004413</td>\n",
       "      <td>0.002170</td>\n",
       "      <td>0.004012</td>\n",
       "      <td>0.003564</td>\n",
       "      <td>0.003454</td>\n",
       "      <td>0.003535</td>\n",
       "      <td>0.003771</td>\n",
       "      <td>0.004462</td>\n",
       "      <td>0.004907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 196 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                            MEMO001  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003265   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003834   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003736   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003409   \n",
       "\n",
       "                                                                                            MEMO002  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003473   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003415   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003786   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003286   \n",
       "\n",
       "                                                                                            MEMO003  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003141   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003803   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003605   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003149   \n",
       "\n",
       "                                                                                            MEMO004  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.005224   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.012071   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.011289   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.005424   \n",
       "\n",
       "                                                                                            MEMO005  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.002982   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003714   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003302   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003225   \n",
       "\n",
       "                                                                                            MEMO006  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003119   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003586   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003550   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003144   \n",
       "\n",
       "                                                                                            MEMO007  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003362   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003906   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003846   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003575   \n",
       "\n",
       "                                                                                            MEMO008  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.002435   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003325   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003140   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.002702   \n",
       "\n",
       "                                                                                            MEMO010  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.002928   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003588   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003291   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003176   \n",
       "\n",
       "                                                                                            MEMO012  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003152   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003845   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003521   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003274   \n",
       "\n",
       "                                                                                           ...  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species       ...   \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   ...   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  ...   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   ...   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  ...   \n",
       "\n",
       "                                                                                             NEMO45  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.002739   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003431   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003360   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.002889   \n",
       "\n",
       "                                                                                             NEMO46  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003930   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.004631   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.004425   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.004413   \n",
       "\n",
       "                                                                                             NEMO47  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003525   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.002004   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.004103   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.002170   \n",
       "\n",
       "                                                                                             NEMO48  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003776   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.004555   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.004258   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.004012   \n",
       "\n",
       "                                                                                             NEMO49  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003420   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003894   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003752   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003564   \n",
       "\n",
       "                                                                                             NEMO50  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003326   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.003726   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003831   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003454   \n",
       "\n",
       "                                                                                             NEMO51  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003349   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.004081   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.003914   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003535   \n",
       "\n",
       "                                                                                             NEMO52  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.003587   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.004349   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.004179   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.003771   \n",
       "\n",
       "                                                                                             NEMO53  \\\n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                  \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.004214   \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.004881   \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.004667   \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.004462   \n",
       "\n",
       "                                                                                             NEMO54  \n",
       "Sample_1 visual_genus visual_species       Municipality        ska_genus ska_species                 \n",
       "MEMO019  Culex        pipiens undetermined Eupen               Culex     pipiens pipiens   0.004522  \n",
       "MEMO045  Culex        pipiens undetermined Vrasene             Culex     pipiens molestus  0.005169  \n",
       "MEMO095  Culex        pipiens undetermined Villers-Le-Bouillet Culex     pipiens pipiens   0.005053  \n",
       "MEMO101  Culex        pipiens undetermined Dilsen-Stokkem      Culex     pipiens molestus  0.004907  \n",
       "\n",
       "[4 rows x 196 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2520x2520 with 7 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "c, leg, cbar, final_pivot_w_labels = wrap_clustermap_and_mismatches(\n",
    "    t_raw,\n",
    "    species_id,\n",
    "    k=4,\n",
    "    figsize=(35,35),\n",
    "    values=\"SNP_distance\",\n",
    "    diagonal=0,\n",
    "    fontsize=35,\n",
    "    labelsize=25\n",
    ")\n",
    "\n",
    "final_pivot_w_labels.query(\n",
    "    'visual_species != ska_species'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "c.savefig(\n",
    "    \"figures/species_assignment_clustermap.pdf\",\n",
    "    format=\"pdf\",\n",
    "    bbox_inches='tight',\n",
    "    dpi=300\n",
    ")"
   ]
  }
 ],
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