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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../Jiggins_Zenodo_Img_Master.csv\", low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMID               12586\n",
       "X                   49359\n",
       "Image_name          37821\n",
       "View                   10\n",
       "zenodo_name            36\n",
       "zenodo_link            32\n",
       "Sequence            11301\n",
       "Taxonomic_Name        363\n",
       "Locality              645\n",
       "Sample_accession     1571\n",
       "Collected_by           12\n",
       "Other_ID             3088\n",
       "Date                  810\n",
       "Dataset                 8\n",
       "Store                 142\n",
       "Brood                 226\n",
       "Death_Date             82\n",
       "Cross_Type             30\n",
       "Stage                   1\n",
       "Sex                     3\n",
       "Unit_Type               6\n",
       "file_type               3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "file_type\n",
       "jpg    37072\n",
       "raw    12226\n",
       "tif       61\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.file_type.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View\n",
       "dorsal                15128\n",
       "ventral               13424\n",
       "Dorsal                 8360\n",
       "Ventral                8090\n",
       "ventral                1644\n",
       "forewing dorsal         406\n",
       "hindwing dorsal         406\n",
       "forewing ventral        406\n",
       "hindwing ventral        406\n",
       "Dorsal and Ventral       18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.View.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Not great that `ventral` gets listed twice as lowercase and _again_ as `Ventral`.\n",
    "\n",
    "### Standardize `View` Column\n",
    "Let's standardize `View` so that there isn't a discrepancy based on case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View\n",
       "dorsal                23488\n",
       "ventral               21514\n",
       "ventral                1644\n",
       "forewing dorsal         406\n",
       "hindwing dorsal         406\n",
       "forewing ventral        406\n",
       "hindwing ventral        406\n",
       "dorsal and ventral       18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"View\"] = df.View.str.lower()\n",
    "df.View.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dorsal' 'ventral' nan 'dorsal and ventral' 'ventral ' 'forewing dorsal'\n",
      " 'hindwing dorsal' 'forewing ventral' 'hindwing ventral']\n"
     ]
    }
   ],
   "source": [
    "print(df.View.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, one has a space after it, so we'll replace that."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View\n",
       "dorsal                23488\n",
       "ventral               23158\n",
       "forewing dorsal         406\n",
       "hindwing dorsal         406\n",
       "forewing ventral        406\n",
       "hindwing ventral        406\n",
       "dorsal and ventral       18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"View\"] == \"ventral \", \"View\"] = \"ventral\"\n",
    "df.View.value_counts() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Record Number Column\n",
    "We'll add a `record_number` column for easier matching to the license/citation file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_record_number(url):\n",
    "    num = url.split(sep = \"/\")[-1]\n",
    "    return num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"record_number\"] = df.zenodo_link.apply(get_record_number)\n",
    "df.record_number.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have 32 unique records represented in the full dataset. When we reduce down to just the Heliconius images, this will probably be less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add `species` and `subspecies` Columns\n",
    "This will make some analysis easier and allow for easy viewing on the [Data Dashboard](https://huggingface.co/spaces/imageomics/dashboard-prototype)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_species(taxa_name):\n",
    "    if type(taxa_name) != float: #taxa name not null\n",
    "        species = taxa_name.split(sep = \" ssp\")[0]\n",
    "        return species\n",
    "    else:\n",
    "        return taxa_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_subspecies(taxa_name):\n",
    "    if type(taxa_name) != float:\n",
    "        if \"ssp.\" in taxa_name:\n",
    "            subspecies = taxa_name.split(sep = \"ssp. \")[1]\n",
    "        elif \"ssp \" in taxa_name:\n",
    "            subspecies = taxa_name.split(sep = \"ssp \")[1]\n",
    "        else:\n",
    "            subspecies = None\n",
    "    else:\n",
    "        subspecies = None\n",
    "    return subspecies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "246"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"species\"] = df.Taxonomic_Name.apply(get_species)\n",
    "df.species.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "139"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"subspecies\"] = df.Taxonomic_Name.apply(get_subspecies)\n",
    "df.subspecies.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cross Types are labeled differently:\n",
    "They are all abbreviations, we have `malleti (mal), plesseni (ple), notabilis (not), lativitta (lat)`, and Neil would guess that `latRo` refers to lativitta with a rounded apical band (e.g., a phenotypic variant of lativitta), but he couldn't say for sure without some more digging, so that will have to stay as-is. We will leave the `Test cross...` ones, but there is not much more to do with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mal', 'mal x ple', 'ple', 'ple x mal', 'latRo x not',\n",
       "       '(latRo x not) x not', '(mal x ple) x mal', '(mal x ple) x ple',\n",
       "       'ple x (mal x ple)', '(ple x mal) x (mal x ple)', 'lat x not',\n",
       "       '(ple x mal) x ple', '(mal x ple) x (mal x ple)',\n",
       "       '(ple x mal) x mal', '(ple x mal) x (ple x mal)',\n",
       "       '(mal x ple) x (ple x mal)', 'hybrid', 'mal x (ple x mal)',\n",
       "       '(lat x not) x lat', '(lat x not) x not', 'Ac heterozygote',\n",
       "       'ple x (ple x mal)', '2 banded', 'lat',\n",
       "       'Test cross (2 banded F2 x 2 banded F2)',\n",
       "       'Test cross (4 spots x 2 banded)', 'Test cross (N heterozygozity)',\n",
       "       'Test cross (short HW bar)', 'Test cross (4 spots x 4 spots)',\n",
       "       'Test cross (N heterozygocity - NBNN x mal - thin)'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Cross_Type.dropna().unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clean_cross_types(cross_type):\n",
    "    if type(cross_type) != float:\n",
    "        cross_type = cross_type.replace(\"mal\", \"malleti\")\n",
    "        cross_type = cross_type.replace(\"ple\", \"plesseni\")\n",
    "        cross_type = cross_type.replace(\"not\", \"notabilis\")\n",
    "        if \"latRo\" not in cross_type:\n",
    "            #latRo does not cross with lativitta, so only apply when latRo isn't present\n",
    "            cross_type = cross_type.replace(\"lat\", \"lativitta\")\n",
    "    return cross_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Cross_Type\"] = df[\"Cross_Type\"].apply(clean_cross_types)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can fill these cross types in for the `subspecies` column (all Cross Types are just labeled to the spceies level in `Taxonomic_Name`, so they did not get processed previously)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "156"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_type_subspecies = [ct for ct in list(df.Cross_Type.dropna().unique()) if \"Test\" not in ct and \"banded\" not in ct]\n",
    "cross_type_subspecies.remove(\"hybrid\")\n",
    "cross_type_subspecies.remove(\"Ac heterozygote\")\n",
    "\n",
    "for ct in cross_type_subspecies:\n",
    "    df.loc[df[\"Cross_Type\"] == ct, \"subspecies\"] = ct\n",
    "\n",
    "df.subspecies.nunique()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(cross_type_subspecies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subspecies\n",
       "(malleti x plesseni) x malleti                 1204\n",
       "plesseni x (malleti x plesseni)                 600\n",
       "malleti x (plesseni x malleti)                  370\n",
       "(plesseni x malleti) x plesseni                 363\n",
       "(plesseni x malleti) x (malleti x plesseni)     354\n",
       "(plesseni x malleti) x (plesseni x malleti)     286\n",
       "(malleti x plesseni) x plesseni                 278\n",
       "plesseni x malleti                              234\n",
       "malleti x plesseni                              192\n",
       "lativitta x notabilis                           136\n",
       "(lativitta x notabilis) x lativitta             110\n",
       "plesseni x (plesseni x malleti)                 106\n",
       "(lativitta x notabilis) x notabilis             106\n",
       "(malleti x plesseni) x (malleti x plesseni)      98\n",
       "(plesseni x malleti) x malleti                   80\n",
       "(malleti x plesseni) x (plesseni x malleti)      56\n",
       "malleti                                          28\n",
       "plesseni                                         28\n",
       "(latRo x notabilis) x notabilis                  16\n",
       "latRo x notabilis                                 4\n",
       "lativitta                                         4\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['malleti', 'plesseni', 'plesseni x malleti', 'lativitta']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "already_present_subspecies = []\n",
    "\n",
    "for subspecies in list(df.loc[df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
    "    if subspecies in list(df.loc[~df[\"Cross_Type\"].notna(), \"subspecies\"].dropna().unique()):\n",
    "        already_present_subspecies.append(subspecies)\n",
    "\n",
    "print(len(already_present_subspecies))\n",
    "already_present_subspecies"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perfect, this adds 17 more subspecies (`lativitta`, `plessani`, `maletti`, and `plesseni x malleti` were already represented). Note, this is based on _exact_ duplicates. `notabilis x lativitta` is also already in the dataset, but the order (where the cross types are concerned) general goes `maternal x paternal`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>CAMID</th>\n",
       "      <th>X</th>\n",
       "      <th>Image_name</th>\n",
       "      <th>View</th>\n",
       "      <th>zenodo_name</th>\n",
       "      <th>zenodo_link</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Taxonomic_Name</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Sample_accession</th>\n",
       "      <th>...</th>\n",
       "      <th>Brood</th>\n",
       "      <th>Death_Date</th>\n",
       "      <th>Cross_Type</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Unit_Type</th>\n",
       "      <th>file_type</th>\n",
       "      <th>record_number</th>\n",
       "      <th>species</th>\n",
       "      <th>subspecies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986</th>\n",
       "      <td>19N1989</td>\n",
       "      <td>21369</td>\n",
       "      <td>19N1989_v.JPG</td>\n",
       "      <td>ventral</td>\n",
       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
       "      <td>https://zenodo.org/record/4288311</td>\n",
       "      <td>1,989</td>\n",
       "      <td>Heliconius melpomene ssp. malleti</td>\n",
       "      <td>Ikiam Mariposario</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>IKIAM.P44</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "      <td>reared</td>\n",
       "      <td>jpg</td>\n",
       "      <td>4288311</td>\n",
       "      <td>Heliconius melpomene</td>\n",
       "      <td>malleti</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45062</th>\n",
       "      <td>CAM044423</td>\n",
       "      <td>34391</td>\n",
       "      <td>CAM044423_d.CR2</td>\n",
       "      <td>dorsal</td>\n",
       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4287444</td>\n",
       "      <td>44,423</td>\n",
       "      <td>Taygetis cleopatra</td>\n",
       "      <td>B6old6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>4287444</td>\n",
       "      <td>Taygetis cleopatra</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48534</th>\n",
       "      <td>E23</td>\n",
       "      <td>37555</td>\n",
       "      <td>E23_d.CR2</td>\n",
       "      <td>dorsal</td>\n",
       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
       "      <td>https://zenodo.org/record/2554218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>2554218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45206</th>\n",
       "      <td>CAM044445</td>\n",
       "      <td>37132</td>\n",
       "      <td>CAM044445_d.JPG</td>\n",
       "      <td>dorsal</td>\n",
       "      <td>batch3.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4288250</td>\n",
       "      <td>44,445</td>\n",
       "      <td>Taygetis cleopatra</td>\n",
       "      <td>B4old2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>4288250</td>\n",
       "      <td>Taygetis cleopatra</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12212</th>\n",
       "      <td>CAM010238</td>\n",
       "      <td>23307</td>\n",
       "      <td>10238v.jpg</td>\n",
       "      <td>ventral</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
       "      <td>https://zenodo.org/record/2552371</td>\n",
       "      <td>10,238</td>\n",
       "      <td>Heliconius sp.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>B043</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Female</td>\n",
       "      <td>reared</td>\n",
       "      <td>jpg</td>\n",
       "      <td>2552371</td>\n",
       "      <td>Heliconius sp.</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39059</th>\n",
       "      <td>CAM043418</td>\n",
       "      <td>30654</td>\n",
       "      <td>CAM043418_v.JPG</td>\n",
       "      <td>ventral</td>\n",
       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/3569598</td>\n",
       "      <td>43,418</td>\n",
       "      <td>Archaeoprepona licomedes</td>\n",
       "      <td>B6rec6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>3569598</td>\n",
       "      <td>Archaeoprepona licomedes</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38163</th>\n",
       "      <td>CAM043170</td>\n",
       "      <td>29755</td>\n",
       "      <td>CAM043170_d.CR2</td>\n",
       "      <td>dorsal</td>\n",
       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/3569598</td>\n",
       "      <td>43,170</td>\n",
       "      <td>Adelpha mesentina</td>\n",
       "      <td>F3rec2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>3569598</td>\n",
       "      <td>Adelpha mesentina</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMID      X       Image_name     View  \\\n",
       "1986     19N1989  21369    19N1989_v.JPG  ventral   \n",
       "45062  CAM044423  34391  CAM044423_d.CR2   dorsal   \n",
       "48534        E23  37555        E23_d.CR2   dorsal   \n",
       "45206  CAM044445  37132  CAM044445_d.JPG   dorsal   \n",
       "12212  CAM010238  23307       10238v.jpg  ventral   \n",
       "39059  CAM043418  30654  CAM043418_v.JPG  ventral   \n",
       "38163  CAM043170  29755  CAM043170_d.CR2   dorsal   \n",
       "\n",
       "                                          zenodo_name  \\\n",
       "1986               0.sheffield.ps.nn.ikiam.batch2.csv   \n",
       "45062              batch2.Peru.image.names.Zenodo.csv   \n",
       "48534              Anniina.Matilla.Field.Caught.E.csv   \n",
       "45206              batch3.Peru.image.names.Zenodo.csv   \n",
       "12212  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
       "39059              batch1.Peru.image.names.Zenodo.csv   \n",
       "38163              batch1.Peru.image.names.Zenodo.csv   \n",
       "\n",
       "                             zenodo_link Sequence  \\\n",
       "1986   https://zenodo.org/record/4288311    1,989   \n",
       "45062  https://zenodo.org/record/4287444   44,423   \n",
       "48534  https://zenodo.org/record/2554218      NaN   \n",
       "45206  https://zenodo.org/record/4288250   44,445   \n",
       "12212  https://zenodo.org/record/2552371   10,238   \n",
       "39059  https://zenodo.org/record/3569598   43,418   \n",
       "38163  https://zenodo.org/record/3569598   43,170   \n",
       "\n",
       "                          Taxonomic_Name           Locality Sample_accession  \\\n",
       "1986   Heliconius melpomene ssp. malleti  Ikiam Mariposario              NaN   \n",
       "45062                 Taygetis cleopatra             B6old6              NaN   \n",
       "48534                                NaN                NaN              NaN   \n",
       "45206                 Taygetis cleopatra             B4old2              NaN   \n",
       "12212                     Heliconius sp.                NaN              NaN   \n",
       "39059           Archaeoprepona licomedes             B6rec6              NaN   \n",
       "38163                  Adelpha mesentina             F3rec2              NaN   \n",
       "\n",
       "       ...      Brood Death_Date Cross_Type Stage     Sex Unit_Type file_type  \\\n",
       "1986   ...  IKIAM.P44        NaN        NaN   NaN    Male    reared       jpg   \n",
       "45062  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
       "48534  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
       "45206  ...        NaN        NaN        NaN   NaN     NaN       NaN       jpg   \n",
       "12212  ...       B043        NaN        NaN   NaN  Female    reared       jpg   \n",
       "39059  ...        NaN        NaN        NaN   NaN     NaN       NaN       jpg   \n",
       "38163  ...        NaN        NaN        NaN   NaN     NaN       NaN       raw   \n",
       "\n",
       "      record_number                   species subspecies  \n",
       "1986        4288311      Heliconius melpomene    malleti  \n",
       "45062       4287444        Taygetis cleopatra       None  \n",
       "48534       2554218                       NaN       None  \n",
       "45206       4288250        Taygetis cleopatra       None  \n",
       "12212       2552371            Heliconius sp.       None  \n",
       "39059       3569598  Archaeoprepona licomedes       None  \n",
       "38163       3569598         Adelpha mesentina       None  \n",
       "\n",
       "[7 rows x 25 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Genus Column\n",
    "\n",
    "This willl allow us to easily remove all non Heliconius samples, and make some image stats easier to see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_genus(species):\n",
    "    if type(species) != float: #taxa name not null\n",
    "        return species.split(sep = \" \")[0]\n",
    "    return species"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"genus\"] = df[\"species\"].apply(get_genus)\n",
    "df.genus.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final stats for all data summarized here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMID               12586\n",
       "X                   49359\n",
       "Image_name          37821\n",
       "View                    7\n",
       "zenodo_name            36\n",
       "zenodo_link            32\n",
       "Sequence            11301\n",
       "Taxonomic_Name        363\n",
       "Locality              645\n",
       "Sample_accession     1571\n",
       "Collected_by           12\n",
       "Other_ID             3088\n",
       "Date                  810\n",
       "Dataset                 8\n",
       "Store                 142\n",
       "Brood                 226\n",
       "Death_Date             82\n",
       "Cross_Type             30\n",
       "Stage                   1\n",
       "Sex                     3\n",
       "Unit_Type               6\n",
       "file_type               3\n",
       "record_number          32\n",
       "species               246\n",
       "subspecies            156\n",
       "genus                  94\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49359 entries, 0 to 49358\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   CAMID             49359 non-null  object\n",
      " 1   X                 49359 non-null  int64 \n",
      " 2   Image_name        49359 non-null  object\n",
      " 3   View              48288 non-null  object\n",
      " 4   zenodo_name       49359 non-null  object\n",
      " 5   zenodo_link       49359 non-null  object\n",
      " 6   Sequence          48424 non-null  object\n",
      " 7   Taxonomic_Name    45473 non-null  object\n",
      " 8   Locality          34015 non-null  object\n",
      " 9   Sample_accession  5884 non-null   object\n",
      " 10  Collected_by      5280 non-null   object\n",
      " 11  Other_ID          14382 non-null  object\n",
      " 12  Date              33718 non-null  object\n",
      " 13  Dataset           40405 non-null  object\n",
      " 14  Store             39485 non-null  object\n",
      " 15  Brood             14942 non-null  object\n",
      " 16  Death_Date        318 non-null    object\n",
      " 17  Cross_Type        5133 non-null   object\n",
      " 18  Stage             15 non-null     object\n",
      " 19  Sex               36243 non-null  object\n",
      " 20  Unit_Type         33890 non-null  object\n",
      " 21  file_type         49359 non-null  object\n",
      " 22  record_number     49359 non-null  object\n",
      " 23  species           45473 non-null  object\n",
      " 24  subspecies        25715 non-null  object\n",
      " 25  genus             45473 non-null  object\n",
      "dtypes: int64(1), object(25)\n",
      "memory usage: 9.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observe that not all images have a species label."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>CAMID</th>\n",
       "      <th>X</th>\n",
       "      <th>Image_name</th>\n",
       "      <th>View</th>\n",
       "      <th>zenodo_name</th>\n",
       "      <th>zenodo_link</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Taxonomic_Name</th>\n",
       "      <th>Locality</th>\n",
       "      <th>Sample_accession</th>\n",
       "      <th>...</th>\n",
       "      <th>Death_Date</th>\n",
       "      <th>Cross_Type</th>\n",
       "      <th>Stage</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Unit_Type</th>\n",
       "      <th>file_type</th>\n",
       "      <th>record_number</th>\n",
       "      <th>species</th>\n",
       "      <th>subspecies</th>\n",
       "      <th>genus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48538</th>\n",
       "      <td>E24</td>\n",
       "      <td>37559</td>\n",
       "      <td>E24_d.CR2</td>\n",
       "      <td>dorsal</td>\n",
       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
       "      <td>https://zenodo.org/record/2554218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>2554218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37246</th>\n",
       "      <td>CAM042045</td>\n",
       "      <td>43973</td>\n",
       "      <td>CAM042045_v.JPG</td>\n",
       "      <td>ventral</td>\n",
       "      <td>Collection_August2019.csv</td>\n",
       "      <td>https://zenodo.org/record/5731587</td>\n",
       "      <td>42,045</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>5731587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37484</th>\n",
       "      <td>CAM042166</td>\n",
       "      <td>44211</td>\n",
       "      <td>CAM042166_v.JPG</td>\n",
       "      <td>ventral</td>\n",
       "      <td>Collection_August2019.csv</td>\n",
       "      <td>https://zenodo.org/record/5731587</td>\n",
       "      <td>42,166</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jpg</td>\n",
       "      <td>5731587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48780</th>\n",
       "      <td>E83</td>\n",
       "      <td>37777</td>\n",
       "      <td>E83_v.CR2</td>\n",
       "      <td>ventral</td>\n",
       "      <td>Anniina.Matilla.Field.Caught.E.csv</td>\n",
       "      <td>https://zenodo.org/record/2554218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>2554218</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3118</th>\n",
       "      <td>19N2627</td>\n",
       "      <td>22498</td>\n",
       "      <td>19N2627_v.CR2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
       "      <td>https://zenodo.org/record/4288311</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>4288311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46111</th>\n",
       "      <td>CAM045060</td>\n",
       "      <td>42806</td>\n",
       "      <td>CAM045060_v.CR2</td>\n",
       "      <td>ventral</td>\n",
       "      <td>image.names.cook.island.erato.csv</td>\n",
       "      <td>https://zenodo.org/record/5526257</td>\n",
       "      <td>45,060</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>5526257</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39502</th>\n",
       "      <td>CAM043576</td>\n",
       "      <td>31097</td>\n",
       "      <td>CAM043576_v.CR2</td>\n",
       "      <td>ventral</td>\n",
       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4287444</td>\n",
       "      <td>43,576</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>raw</td>\n",
       "      <td>4287444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMID      X       Image_name     View  \\\n",
       "48538        E24  37559        E24_d.CR2   dorsal   \n",
       "37246  CAM042045  43973  CAM042045_v.JPG  ventral   \n",
       "37484  CAM042166  44211  CAM042166_v.JPG  ventral   \n",
       "48780        E83  37777        E83_v.CR2  ventral   \n",
       "3118     19N2627  22498    19N2627_v.CR2      NaN   \n",
       "46111  CAM045060  42806  CAM045060_v.CR2  ventral   \n",
       "39502  CAM043576  31097  CAM043576_v.CR2  ventral   \n",
       "\n",
       "                              zenodo_name                        zenodo_link  \\\n",
       "48538  Anniina.Matilla.Field.Caught.E.csv  https://zenodo.org/record/2554218   \n",
       "37246           Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
       "37484           Collection_August2019.csv  https://zenodo.org/record/5731587   \n",
       "48780  Anniina.Matilla.Field.Caught.E.csv  https://zenodo.org/record/2554218   \n",
       "3118   0.sheffield.ps.nn.ikiam.batch2.csv  https://zenodo.org/record/4288311   \n",
       "46111   image.names.cook.island.erato.csv  https://zenodo.org/record/5526257   \n",
       "39502  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444   \n",
       "\n",
       "      Sequence Taxonomic_Name Locality Sample_accession  ... Death_Date  \\\n",
       "48538      NaN            NaN      NaN              NaN  ...        NaN   \n",
       "37246   42,045            NaN      NaN              NaN  ...        NaN   \n",
       "37484   42,166            NaN      NaN              NaN  ...        NaN   \n",
       "48780      NaN            NaN      NaN              NaN  ...        NaN   \n",
       "3118         0            NaN      NaN              NaN  ...        NaN   \n",
       "46111   45,060            NaN      NaN              NaN  ...        NaN   \n",
       "39502   43,576            NaN      NaN              NaN  ...        NaN   \n",
       "\n",
       "      Cross_Type Stage  Sex Unit_Type file_type record_number species  \\\n",
       "48538        NaN   NaN  NaN       NaN       raw       2554218     NaN   \n",
       "37246        NaN   NaN  NaN       NaN       jpg       5731587     NaN   \n",
       "37484        NaN   NaN  NaN       NaN       jpg       5731587     NaN   \n",
       "48780        NaN   NaN  NaN       NaN       raw       2554218     NaN   \n",
       "3118         NaN   NaN  NaN       NaN       raw       4288311     NaN   \n",
       "46111        NaN   NaN  NaN       NaN       raw       5526257     NaN   \n",
       "39502        NaN   NaN  NaN       NaN       raw       4287444     NaN   \n",
       "\n",
       "      subspecies genus  \n",
       "48538       None   NaN  \n",
       "37246       None   NaN  \n",
       "37484       None   NaN  \n",
       "48780       None   NaN  \n",
       "3118        None   NaN  \n",
       "46111       None   NaN  \n",
       "39502       None   NaN  \n",
       "\n",
       "[7 rows x 26 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.species.isna()].sample(7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Update Master File with Genus through Subspecies Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make Heliconius Subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 34929 entries, 6 to 49358\n",
      "Data columns (total 26 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   CAMID             34929 non-null  object\n",
      " 1   X                 34929 non-null  int64 \n",
      " 2   Image_name        34929 non-null  object\n",
      " 3   View              34150 non-null  object\n",
      " 4   zenodo_name       34929 non-null  object\n",
      " 5   zenodo_link       34929 non-null  object\n",
      " 6   Sequence          34929 non-null  object\n",
      " 7   Taxonomic_Name    34929 non-null  object\n",
      " 8   Locality          23417 non-null  object\n",
      " 9   Sample_accession  5860 non-null   object\n",
      " 10  Collected_by      5280 non-null   object\n",
      " 11  Other_ID          6404 non-null   object\n",
      " 12  Date              23162 non-null  object\n",
      " 13  Dataset           32846 non-null  object\n",
      " 14  Store             29446 non-null  object\n",
      " 15  Brood             14921 non-null  object\n",
      " 16  Death_Date        316 non-null    object\n",
      " 17  Cross_Type        5133 non-null   object\n",
      " 18  Stage             6 non-null      object\n",
      " 19  Sex               33880 non-null  object\n",
      " 20  Unit_Type         31975 non-null  object\n",
      " 21  file_type         34929 non-null  object\n",
      " 22  record_number     34929 non-null  object\n",
      " 23  species           34929 non-null  object\n",
      " 24  subspecies        24953 non-null  object\n",
      " 25  genus             34929 non-null  object\n",
      "dtypes: int64(1), object(25)\n",
      "memory usage: 7.2+ MB\n"
     ]
    }
   ],
   "source": [
    "heliconius_subset = df.loc[df.genus.str.lower() == \"heliconius\"]\n",
    "\n",
    "heliconius_subset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMID                9546\n",
       "X                   34929\n",
       "Image_name          26946\n",
       "View                    3\n",
       "zenodo_name            31\n",
       "zenodo_link            28\n",
       "Sequence             8701\n",
       "Taxonomic_Name        129\n",
       "Locality              472\n",
       "Sample_accession     1559\n",
       "Collected_by           12\n",
       "Other_ID             1865\n",
       "Date                  776\n",
       "Dataset                 8\n",
       "Store                 121\n",
       "Brood                 224\n",
       "Death_Date             81\n",
       "Cross_Type             30\n",
       "Stage                   1\n",
       "Sex                     3\n",
       "Unit_Type               4\n",
       "file_type               3\n",
       "record_number          28\n",
       "species                37\n",
       "subspecies            110\n",
       "genus                   1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heliconius_subset.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View\n",
       "dorsal                17218\n",
       "ventral               16914\n",
       "dorsal and ventral       18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heliconius_subset.View.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that this subset is distributed across 28 Zenodo records from the [Butterfly Genetics Group](https://zenodo.org/communities/butterfly?q=&l=list&p=1&s=10&sort=newest)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the Heliconius Subset to CSV\n",
    "We'll drop the `genus` column, since they're all `Heliconius`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "heliconius_subset[list(heliconius_subset.columns)[:-1]].to_csv(\"../Jiggins_Heliconius_Master.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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