{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "jiggins = pd.read_csv(\"../Jiggins_Zenodo_Master.csv\", low_memory = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(49956, 25)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>CAMID</th>\n",
       "      <th>X</th>\n",
       "      <th>Image_name</th>\n",
       "      <th>Side</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>...</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Store</th>\n",
       "      <th>Eclosion.Date</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>Verbatim.Coordinates</th>\n",
       "    </tr>\n",
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       "      <td>0.sheffield.ps.nn.ikiam.batch1.csv</td>\n",
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       "      <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",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.sheffield.ps.nn.ikiam.batch1.csv</td>\n",
       "      <td>https://zenodo.org/record/4288311</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20779</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.sheffield.ps.nn.ikiam.batch1.csv</td>\n",
       "      <td>https://zenodo.org/record/4288311</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20780</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.sheffield.ps.nn.ikiam.batch1.csv</td>\n",
       "      <td>https://zenodo.org/record/4288311</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 CAMID      X Image_name Side  \\\n",
       "0           1   NaN  20776        NaN  NaN   \n",
       "1           2   NaN  20777        NaN  NaN   \n",
       "2           3   NaN  20778        NaN  NaN   \n",
       "3           4   NaN  20779        NaN  NaN   \n",
       "4           5   NaN  20780        NaN  NaN   \n",
       "\n",
       "                          zenodo_name                        zenodo_link  \\\n",
       "0  0.sheffield.ps.nn.ikiam.batch1.csv  https://zenodo.org/record/4288311   \n",
       "1  0.sheffield.ps.nn.ikiam.batch1.csv  https://zenodo.org/record/4288311   \n",
       "2  0.sheffield.ps.nn.ikiam.batch1.csv  https://zenodo.org/record/4288311   \n",
       "3  0.sheffield.ps.nn.ikiam.batch1.csv  https://zenodo.org/record/4288311   \n",
       "4  0.sheffield.ps.nn.ikiam.batch1.csv  https://zenodo.org/record/4288311   \n",
       "\n",
       "  Sequence Taxonomic.Name Locality  ... Dataset Store Eclosion.Date  Brood  \\\n",
       "0      NaN            NaN      NaN  ...     NaN   NaN           NaN    NaN   \n",
       "1      NaN            NaN      NaN  ...     NaN   NaN           NaN    NaN   \n",
       "2      NaN            NaN      NaN  ...     NaN   NaN           NaN    NaN   \n",
       "3      NaN            NaN      NaN  ...     NaN   NaN           NaN    NaN   \n",
       "4      NaN            NaN      NaN  ...     NaN   NaN           NaN    NaN   \n",
       "\n",
       "  Death.Date Cross.Type Stage  Sex Unit.Type Verbatim.Coordinates  \n",
       "0        NaN        NaN   NaN  NaN       NaN                  NaN  \n",
       "1        NaN        NaN   NaN  NaN       NaN                  NaN  \n",
       "2        NaN        NaN   NaN  NaN       NaN                  NaN  \n",
       "3        NaN        NaN   NaN  NaN       NaN                  NaN  \n",
       "4        NaN        NaN   NaN  NaN       NaN                  NaN  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'CAMID', 'X', 'Image_name', 'Side', 'zenodo_name',\n",
       "       'zenodo_link', 'Sequence', 'Taxonomic.Name', 'Locality',\n",
       "       'Sample.accession', 'Collected.by', 'Other.Id', 'Collected.By', 'Date',\n",
       "       'Dataset', 'Store', 'Eclosion.Date', 'Brood', 'Death.Date',\n",
       "       'Cross.Type', 'Stage', 'Sex', 'Unit.Type', 'Verbatim.Coordinates'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "411"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins.Image_name.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28585                  CAM036108_v.JPG\n",
       "45469                  CAM044420_v.CR2\n",
       "16097                  CAM011543_v.CR2\n",
       "22992                  CAM017525_v.JPG\n",
       "6601                   CAM000930_v.JPG\n",
       "39261                  CAM043354_v.CR2\n",
       "2577                     19N2059_d.CR2\n",
       "21344                  CAM017153_d.JPG\n",
       "48669                   CS004036_d.JPG\n",
       "19706                  CAM016699_v.JPG\n",
       "12020                  CAM009527_v.JPG\n",
       "47898                   CS000628_d.JPG\n",
       "7592                   CAM001394_d.JPG\n",
       "43523                  CAM044173_v.CR2\n",
       "12596                  CAM010226_v.JPG\n",
       "33816                  CAM041028_v.JPG\n",
       "23148    CAM017547_v_whitestandard.JPG\n",
       "44996                CAM044362_hwv.CR2\n",
       "3460                     19N2605_v.JPG\n",
       "24164    CAM017738_d_whitestandard.CR2\n",
       "Name: Image_name, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins.Image_name.sample(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <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>zenodo_name</th>\n",
       "      <th>zenodo_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22895</th>\n",
       "      <td>CAM017512</td>\n",
       "      <td>5226</td>\n",
       "      <td>CAM017512_d.JPG</td>\n",
       "      <td>CAM.coll.images.batch1_v2.csv</td>\n",
       "      <td>https://zenodo.org/record/3082688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36246</th>\n",
       "      <td>CAM041681</td>\n",
       "      <td>39690</td>\n",
       "      <td>CAM041681_d.CR2</td>\n",
       "      <td>0.gmk.broods.all.csv</td>\n",
       "      <td>https://zenodo.org/record/4291095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38630</th>\n",
       "      <td>CAM043178</td>\n",
       "      <td>29793</td>\n",
       "      <td>CAM043178_fwv.CR2</td>\n",
       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/3569598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45984</th>\n",
       "      <td>CAM044484</td>\n",
       "      <td>34635</td>\n",
       "      <td>CAM044484_hwd.CR2</td>\n",
       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4287444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15393</th>\n",
       "      <td>CAM011382</td>\n",
       "      <td>28038</td>\n",
       "      <td>CAM011382_d.JPG</td>\n",
       "      <td>2001_2.broods.batch.2.csv</td>\n",
       "      <td>https://zenodo.org/record/2550097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44443</th>\n",
       "      <td>CAM044299</td>\n",
       "      <td>33864</td>\n",
       "      <td>CAM044299_d.JPG</td>\n",
       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4287444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7117</th>\n",
       "      <td>CAM001175</td>\n",
       "      <td>7908</td>\n",
       "      <td>CAM001175_d.JPG</td>\n",
       "      <td>CAM.coll.images.batch4.csv</td>\n",
       "      <td>https://zenodo.org/record/2682669</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMID      X         Image_name  \\\n",
       "22895  CAM017512   5226    CAM017512_d.JPG   \n",
       "36246  CAM041681  39690    CAM041681_d.CR2   \n",
       "38630  CAM043178  29793  CAM043178_fwv.CR2   \n",
       "45984  CAM044484  34635  CAM044484_hwd.CR2   \n",
       "15393  CAM011382  28038    CAM011382_d.JPG   \n",
       "44443  CAM044299  33864    CAM044299_d.JPG   \n",
       "7117   CAM001175   7908    CAM001175_d.JPG   \n",
       "\n",
       "                              zenodo_name                        zenodo_link  \n",
       "22895       CAM.coll.images.batch1_v2.csv  https://zenodo.org/record/3082688  \n",
       "36246                0.gmk.broods.all.csv  https://zenodo.org/record/4291095  \n",
       "38630  batch1.Peru.image.names.Zenodo.csv  https://zenodo.org/record/3569598  \n",
       "45984  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444  \n",
       "15393           2001_2.broods.batch.2.csv  https://zenodo.org/record/2550097  \n",
       "44443  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444  \n",
       "7117           CAM.coll.images.batch4.csv  https://zenodo.org/record/2682669  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins_imgs = jiggins[['CAMID', 'X', 'Image_name', 'zenodo_name', 'zenodo_link']].dropna()\n",
    "jiggins_imgs.sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <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>zenodo_name</th>\n",
       "      <th>zenodo_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10528</th>\n",
       "      <td>CAM008821</td>\n",
       "      <td>9887</td>\n",
       "      <td>CAM008821_d.JPG</td>\n",
       "      <td>CAM.coll.images.batch6.csv</td>\n",
       "      <td>https://zenodo.org/record/2686762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19587</th>\n",
       "      <td>CAM016660</td>\n",
       "      <td>3856</td>\n",
       "      <td>CAM016660_v.JPG</td>\n",
       "      <td>CAM.coll.images.batch1_v2.csv</td>\n",
       "      <td>https://zenodo.org/record/3082688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25858</th>\n",
       "      <td>CAM017989</td>\n",
       "      <td>17211</td>\n",
       "      <td>CAM017989_v_whitestandard.JPG</td>\n",
       "      <td>CAM.coll.patricio.batch1.csv</td>\n",
       "      <td>https://zenodo.org/record/1748277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10042</th>\n",
       "      <td>CAM008668</td>\n",
       "      <td>46436</td>\n",
       "      <td>CAM008668_d.JPG</td>\n",
       "      <td>occurences_and_multimedia.csv</td>\n",
       "      <td>https://zenodo.org/record/3477891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30646</th>\n",
       "      <td>CAM040217</td>\n",
       "      <td>47915</td>\n",
       "      <td>CAM040217_v.JPG</td>\n",
       "      <td>occurences_and_multimedia.csv</td>\n",
       "      <td>https://zenodo.org/record/3477891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42889</th>\n",
       "      <td>CAM044091</td>\n",
       "      <td>33084</td>\n",
       "      <td>CAM044091_fwd.JPG</td>\n",
       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4287444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39215</th>\n",
       "      <td>CAM043344</td>\n",
       "      <td>30372</td>\n",
       "      <td>CAM043344_fwd.JPG</td>\n",
       "      <td>batch1.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/3569598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMID      X                     Image_name  \\\n",
       "10528  CAM008821   9887                CAM008821_d.JPG   \n",
       "19587  CAM016660   3856                CAM016660_v.JPG   \n",
       "25858  CAM017989  17211  CAM017989_v_whitestandard.JPG   \n",
       "10042  CAM008668  46436                CAM008668_d.JPG   \n",
       "30646  CAM040217  47915                CAM040217_v.JPG   \n",
       "42889  CAM044091  33084              CAM044091_fwd.JPG   \n",
       "39215  CAM043344  30372              CAM043344_fwd.JPG   \n",
       "\n",
       "                              zenodo_name                        zenodo_link  \n",
       "10528          CAM.coll.images.batch6.csv  https://zenodo.org/record/2686762  \n",
       "19587       CAM.coll.images.batch1_v2.csv  https://zenodo.org/record/3082688  \n",
       "25858        CAM.coll.patricio.batch1.csv  https://zenodo.org/record/1748277  \n",
       "10042       occurences_and_multimedia.csv  https://zenodo.org/record/3477891  \n",
       "30646       occurences_and_multimedia.csv  https://zenodo.org/record/3477891  \n",
       "42889  batch2.Peru.image.names.Zenodo.csv  https://zenodo.org/record/4287444  \n",
       "39215  batch1.Peru.image.names.Zenodo.csv  https://zenodo.org/record/3569598  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('JPG')].sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>zenodo_name</th>\n",
       "      <th>zenodo_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8296</th>\n",
       "      <td>CAM008147</td>\n",
       "      <td>44302</td>\n",
       "      <td>8147v.jpg</td>\n",
       "      <td>occurences_and_multimedia.csv</td>\n",
       "      <td>https://zenodo.org/record/3477891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15626</th>\n",
       "      <td>CAM011437</td>\n",
       "      <td>44275</td>\n",
       "      <td>11437v.jpg</td>\n",
       "      <td>occurences_and_multimedia.csv</td>\n",
       "      <td>https://zenodo.org/record/3477891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16975</th>\n",
       "      <td>CAM012169</td>\n",
       "      <td>25057</td>\n",
       "      <td>12169v.jpg</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
       "      <td>https://zenodo.org/record/2552371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14097</th>\n",
       "      <td>CAM010773</td>\n",
       "      <td>23942</td>\n",
       "      <td>10773v.jpg</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
       "      <td>https://zenodo.org/record/2552371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15970</th>\n",
       "      <td>CAM011513</td>\n",
       "      <td>24565</td>\n",
       "      <td>11513v.jpg</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
       "      <td>https://zenodo.org/record/2552371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13349</th>\n",
       "      <td>CAM010485</td>\n",
       "      <td>23640</td>\n",
       "      <td>10485d.jpg</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
       "      <td>https://zenodo.org/record/2552371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16919</th>\n",
       "      <td>CAM012124</td>\n",
       "      <td>25001</td>\n",
       "      <td>12124v.jpg</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part1.csv</td>\n",
       "      <td>https://zenodo.org/record/2552371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMID      X  Image_name  \\\n",
       "8296   CAM008147  44302   8147v.jpg   \n",
       "15626  CAM011437  44275  11437v.jpg   \n",
       "16975  CAM012169  25057  12169v.jpg   \n",
       "14097  CAM010773  23942  10773v.jpg   \n",
       "15970  CAM011513  24565  11513v.jpg   \n",
       "13349  CAM010485  23640  10485d.jpg   \n",
       "16919  CAM012124  25001  12124v.jpg   \n",
       "\n",
       "                                          zenodo_name  \\\n",
       "8296                    occurences_and_multimedia.csv   \n",
       "15626                   occurences_and_multimedia.csv   \n",
       "16975  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
       "14097  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
       "15970  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
       "13349  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
       "16919  Heliconius_wing_old_photos_2001_2019_part1.csv   \n",
       "\n",
       "                             zenodo_link  \n",
       "8296   https://zenodo.org/record/3477891  \n",
       "15626  https://zenodo.org/record/3477891  \n",
       "16975  https://zenodo.org/record/2552371  \n",
       "14097  https://zenodo.org/record/2552371  \n",
       "15970  https://zenodo.org/record/2552371  \n",
       "13349  https://zenodo.org/record/2552371  \n",
       "16919  https://zenodo.org/record/2552371  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('jpg')].sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>zenodo_name</th>\n",
       "      <th>zenodo_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47693</th>\n",
       "      <td>CAM050052</td>\n",
       "      <td>26152</td>\n",
       "      <td>CAM050052_M1_10_Hmr_mut_D_cut.tif</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
       "      <td>https://zenodo.org/record/2553977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47694</th>\n",
       "      <td>CAM050063</td>\n",
       "      <td>26155</td>\n",
       "      <td>CAM050063_M7_17_Hmr_mut_V_IMG_8293_wb_cut.tif</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
       "      <td>https://zenodo.org/record/2553977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47712</th>\n",
       "      <td>CAM050147</td>\n",
       "      <td>26197</td>\n",
       "      <td>CAM050147_DS1_HW_IMG_8537_cut_3.tif</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
       "      <td>https://zenodo.org/record/2553977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47637</th>\n",
       "      <td>CAM050006</td>\n",
       "      <td>26126</td>\n",
       "      <td>CAM050006_S1_17_Hsar_mut_V_8296_wb_cut.tif</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
       "      <td>https://zenodo.org/record/2553977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47626</th>\n",
       "      <td>CAM050001</td>\n",
       "      <td>26118</td>\n",
       "      <td>CAM050001_S1_5_Hs_mut_D_wb_cut.tif</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
       "      <td>https://zenodo.org/record/2553977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47692</th>\n",
       "      <td>CAM050052</td>\n",
       "      <td>26153</td>\n",
       "      <td>CAM050052_M1_10_Hmr_mut_V_cut.tif</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
       "      <td>https://zenodo.org/record/2553977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47673</th>\n",
       "      <td>CAM050026</td>\n",
       "      <td>26147</td>\n",
       "      <td>CAM050026_S4_1_Hs_mut_V_IMG_8453_wb_cut.tif</td>\n",
       "      <td>Heliconius_wing_old_photos_2001_2019_part3.csv</td>\n",
       "      <td>https://zenodo.org/record/2553977</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMID      X                                     Image_name  \\\n",
       "47693  CAM050052  26152              CAM050052_M1_10_Hmr_mut_D_cut.tif   \n",
       "47694  CAM050063  26155  CAM050063_M7_17_Hmr_mut_V_IMG_8293_wb_cut.tif   \n",
       "47712  CAM050147  26197            CAM050147_DS1_HW_IMG_8537_cut_3.tif   \n",
       "47637  CAM050006  26126     CAM050006_S1_17_Hsar_mut_V_8296_wb_cut.tif   \n",
       "47626  CAM050001  26118             CAM050001_S1_5_Hs_mut_D_wb_cut.tif   \n",
       "47692  CAM050052  26153              CAM050052_M1_10_Hmr_mut_V_cut.tif   \n",
       "47673  CAM050026  26147    CAM050026_S4_1_Hs_mut_V_IMG_8453_wb_cut.tif   \n",
       "\n",
       "                                          zenodo_name  \\\n",
       "47693  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
       "47694  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
       "47712  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
       "47637  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
       "47626  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
       "47692  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
       "47673  Heliconius_wing_old_photos_2001_2019_part3.csv   \n",
       "\n",
       "                             zenodo_link  \n",
       "47693  https://zenodo.org/record/2553977  \n",
       "47694  https://zenodo.org/record/2553977  \n",
       "47712  https://zenodo.org/record/2553977  \n",
       "47637  https://zenodo.org/record/2553977  \n",
       "47626  https://zenodo.org/record/2553977  \n",
       "47692  https://zenodo.org/record/2553977  \n",
       "47673  https://zenodo.org/record/2553977  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('tif')].sample(7)"
   ]
  },
  {
   "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></th>\n",
       "      <th>CAMID</th>\n",
       "      <th>X</th>\n",
       "      <th>Image_name</th>\n",
       "      <th>zenodo_name</th>\n",
       "      <th>zenodo_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>42543</th>\n",
       "      <td>CAM044049</td>\n",
       "      <td>32915</td>\n",
       "      <td>CAM044049_d.CR2</td>\n",
       "      <td>batch2.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4287444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44160</th>\n",
       "      <td>CAM044261</td>\n",
       "      <td>36392</td>\n",
       "      <td>CAM044261_d.JPG</td>\n",
       "      <td>batch3.Peru.image.names.Zenodo.csv</td>\n",
       "      <td>https://zenodo.org/record/4288250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14298</th>\n",
       "      <td>CAM010848</td>\n",
       "      <td>27458</td>\n",
       "      <td>CAM010848_d.JPG</td>\n",
       "      <td>2001_2.broods.batch.1.csv</td>\n",
       "      <td>https://zenodo.org/record/2549524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19141</th>\n",
       "      <td>CAM016524</td>\n",
       "      <td>759</td>\n",
       "      <td>CAM016524_d.JPG</td>\n",
       "      <td>CAM.coll.images.batch1.csv</td>\n",
       "      <td>https://zenodo.org/record/1247307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3229</th>\n",
       "      <td>19N2383</td>\n",
       "      <td>22175</td>\n",
       "      <td>19N2383_d.JPG</td>\n",
       "      <td>0.sheffield.ps.nn.ikiam.batch2.csv</td>\n",
       "      <td>https://zenodo.org/record/4288311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34948</th>\n",
       "      <td>CAM041352</td>\n",
       "      <td>14340</td>\n",
       "      <td>CAM041352_d.JPG</td>\n",
       "      <td>CAM.coll.images.batch9.csv</td>\n",
       "      <td>https://zenodo.org/record/2714333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28072</th>\n",
       "      <td>CAM021080</td>\n",
       "      <td>11587</td>\n",
       "      <td>CAM021080_d.JPG</td>\n",
       "      <td>CAM.coll.images.batch7.csv</td>\n",
       "      <td>https://zenodo.org/record/2702457</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           CAMID      X       Image_name                         zenodo_name  \\\n",
       "42543  CAM044049  32915  CAM044049_d.CR2  batch2.Peru.image.names.Zenodo.csv   \n",
       "44160  CAM044261  36392  CAM044261_d.JPG  batch3.Peru.image.names.Zenodo.csv   \n",
       "14298  CAM010848  27458  CAM010848_d.JPG           2001_2.broods.batch.1.csv   \n",
       "19141  CAM016524    759  CAM016524_d.JPG          CAM.coll.images.batch1.csv   \n",
       "3229     19N2383  22175    19N2383_d.JPG  0.sheffield.ps.nn.ikiam.batch2.csv   \n",
       "34948  CAM041352  14340  CAM041352_d.JPG          CAM.coll.images.batch9.csv   \n",
       "28072  CAM021080  11587  CAM021080_d.JPG          CAM.coll.images.batch7.csv   \n",
       "\n",
       "                             zenodo_link  \n",
       "42543  https://zenodo.org/record/4287444  \n",
       "44160  https://zenodo.org/record/4288250  \n",
       "14298  https://zenodo.org/record/2549524  \n",
       "19141  https://zenodo.org/record/1247307  \n",
       "3229   https://zenodo.org/record/4288311  \n",
       "34948  https://zenodo.org/record/2714333  \n",
       "28072  https://zenodo.org/record/2702457  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jiggins_imgs[jiggins_imgs.Image_name.str.contains('d.')].sample(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'JPG', 'CR2', 'tif', 'jpeg', 'JPG(1)', 'jpg'}\n"
     ]
    }
   ],
   "source": [
    "check_filetypes = []\n",
    "for img_name in list(jiggins_imgs.Image_name.unique()):\n",
    "    check_filetypes.append(img_name.split(\".\")[1])\n",
    "\n",
    "print(set(check_filetypes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note CR2 is raw, may be duplicates, would need conversion\n",
    "file_types = [\"JPG\", \"jpg\", \"jpeg\", \"tif\", \"JPG(1)\", \"CR2\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37821"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images = []\n",
    "\n",
    "for img_name in list(jiggins_imgs.Image_name.unique()):\n",
    "    if img_name.split(\".\")[1] in file_types:\n",
    "        images.append(img_name)\n",
    "\n",
    "len(images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49359 entries, 433 to 49791\n",
      "Data columns (total 5 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   zenodo_name  49359 non-null  object\n",
      " 4   zenodo_link  49359 non-null  object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 2.3+ MB\n"
     ]
    }
   ],
   "source": [
    "img = jiggins_imgs[jiggins_imgs.Image_name.isin(images)]\n",
    "img.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_file_type(filename):\n",
    "    jpgs = [\"JPG\", \"jpg\", \"jpeg\", \"JPG(1)\"]\n",
    "    file_type = filename.split(\".\")[1]\n",
    "    if file_type in jpgs:\n",
    "        return \"jpg\"\n",
    "    elif file_type == \"tif\":\n",
    "        return \"tif\"\n",
    "    elif file_type == \"CR2\":\n",
    "        return \"raw\"\n",
    "    else:\n",
    "        print(f\"{file_type} does not match known file types\")\n",
    "        return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49359 entries, 433 to 49791\n",
      "Data columns (total 6 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   zenodo_name  49359 non-null  object\n",
      " 4   zenodo_link  49359 non-null  object\n",
      " 5   file_type    49359 non-null  object\n",
      "dtypes: int64(1), object(5)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "img[\"file_type\"] = img[\"Image_name\"].apply(get_file_type)\n",
    "img.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "img.to_csv(\"../zendo_img_master.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unnamed: 0    49956\n",
      "CAMID         12586\n",
      "X             49956\n",
      "Image_name    37843\n",
      "dtype: int64\n",
      "\n",
      "CAMID          12586\n",
      "X              49359\n",
      "Image_name     37821\n",
      "zenodo_name       36\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(jiggins[list(jiggins.columns)[:4]].nunique())\n",
    "print()\n",
    "print(img[list(img.columns[:4])].nunique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 49956 entries, 0 to 49955\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   Unnamed: 0  49956 non-null  int64 \n",
      " 1   CAMID       49359 non-null  object\n",
      " 2   X           49956 non-null  int64 \n",
      " 3   Image_name  49545 non-null  object\n",
      "dtypes: int64(2), object(2)\n",
      "memory usage: 1.5+ MB\n",
      "None\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49359 entries, 433 to 49791\n",
      "Data columns (total 4 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   zenodo_name  49359 non-null  object\n",
      "dtypes: int64(1), object(3)\n",
      "memory usage: 1.9+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "print(jiggins[list(jiggins.columns)[:4]].info())\n",
    "print()\n",
    "print(img[list(img.columns[:4])].info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to match these up on `X`, since all entries have a value and it is unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49359 entries, 433 to 49791\n",
      "Data columns (total 25 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   Unnamed: 0            49359 non-null  int64  \n",
      " 1   CAMID                 49359 non-null  object \n",
      " 2   X                     49359 non-null  int64  \n",
      " 3   Image_name            49359 non-null  object \n",
      " 4   Side                  48288 non-null  object \n",
      " 5   zenodo_name           49359 non-null  object \n",
      " 6   zenodo_link           49359 non-null  object \n",
      " 7   Sequence              48424 non-null  object \n",
      " 8   Taxonomic.Name        45473 non-null  object \n",
      " 9   Locality              34015 non-null  object \n",
      " 10  Sample.accession      5884 non-null   object \n",
      " 11  Collected.by          5280 non-null   object \n",
      " 12  Other.Id              14382 non-null  object \n",
      " 13  Collected.By          0 non-null      float64\n",
      " 14  Date                  33718 non-null  object \n",
      " 15  Dataset               40405 non-null  object \n",
      " 16  Store                 39485 non-null  object \n",
      " 17  Eclosion.Date         97 non-null     object \n",
      " 18  Brood                 14942 non-null  object \n",
      " 19  Death.Date            318 non-null    object \n",
      " 20  Cross.Type            5133 non-null   object \n",
      " 21  Stage                 15 non-null     object \n",
      " 22  Sex                   36243 non-null  object \n",
      " 23  Unit.Type             33890 non-null  object \n",
      " 24  Verbatim.Coordinates  0 non-null      float64\n",
      "dtypes: float64(2), int64(2), object(21)\n",
      "memory usage: 9.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_img = jiggins.loc[jiggins.X.isin(list(img.X))]\n",
    "df_img.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename columns to have underscore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/3055364590.py:1: 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",
      "  df_img.rename(columns = {\"Side\": \"View\",\n"
     ]
    }
   ],
   "source": [
    "df_img.rename(columns = {\"Side\": \"View\",\n",
    "                        \"Taxonomic.Name\": \"Taxonomic_Name\",\n",
    "                        \"Cross.Type\": \"Cross_Type\",\n",
    "                        \"Sample.accession\": \"Sample_accession\",\n",
    "                        \"Collected.by\": \"Collected_by\",\n",
    "                        \"Other.Id\": \"Other_ID\",\n",
    "                        \"Death.Date\": \"Death_Date\",\n",
    "                        \"Unit.Type\": \"Unit_Type\"},\n",
    "                        inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "null_cols = [\"Unnamed: 0\", \"Collected.By\", \"Eclosion.Date\", \"Verbatim.Coordinates\"]\n",
    "non_null_cols = [col for col in list(df_img.columns) if col not in null_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_img = df_img[non_null_cols]"
   ]
  },
  {
   "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>Other_ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Dataset</th>\n",
       "      <th>Store</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>433</th>\n",
       "      <td>14N004</td>\n",
       "      <td>15131</td>\n",
       "      <td>14N004_d.JPG</td>\n",
       "      <td>dorsal</td>\n",
       "      <td>0.4.nn.requests2.csv</td>\n",
       "      <td>https://zenodo.org/record/4289223</td>\n",
       "      <td>4</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>Nadeau Sheffield</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>14N004</td>\n",
       "      <td>15132</td>\n",
       "      <td>14N004_v.JPG</td>\n",
       "      <td>ventral</td>\n",
       "      <td>0.4.nn.requests2.csv</td>\n",
       "      <td>https://zenodo.org/record/4289223</td>\n",
       "      <td>4</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>Nadeau Sheffield</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>435</th>\n",
       "      <td>14N009</td>\n",
       "      <td>15134</td>\n",
       "      <td>14N009_v.JPG</td>\n",
       "      <td>ventral</td>\n",
       "      <td>0.4.nn.requests2.csv</td>\n",
       "      <td>https://zenodo.org/record/4289223</td>\n",
       "      <td>9</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>Nadeau Sheffield</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>436</th>\n",
       "      <td>14N009</td>\n",
       "      <td>15133</td>\n",
       "      <td>14N009_d.JPG</td>\n",
       "      <td>dorsal</td>\n",
       "      <td>0.4.nn.requests2.csv</td>\n",
       "      <td>https://zenodo.org/record/4289223</td>\n",
       "      <td>9</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>Nadeau Sheffield</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>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>437</th>\n",
       "      <td>14N014</td>\n",
       "      <td>15136</td>\n",
       "      <td>14N014_v.JPG</td>\n",
       "      <td>ventral</td>\n",
       "      <td>0.4.nn.requests2.csv</td>\n",
       "      <td>https://zenodo.org/record/4289223</td>\n",
       "      <td>14</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>Nadeau Sheffield</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>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      CAMID      X    Image_name     View           zenodo_name  \\\n",
       "433  14N004  15131  14N004_d.JPG   dorsal  0.4.nn.requests2.csv   \n",
       "434  14N004  15132  14N004_v.JPG  ventral  0.4.nn.requests2.csv   \n",
       "435  14N009  15134  14N009_v.JPG  ventral  0.4.nn.requests2.csv   \n",
       "436  14N009  15133  14N009_d.JPG   dorsal  0.4.nn.requests2.csv   \n",
       "437  14N014  15136  14N014_v.JPG  ventral  0.4.nn.requests2.csv   \n",
       "\n",
       "                           zenodo_link Sequence Taxonomic_Name Locality  \\\n",
       "433  https://zenodo.org/record/4289223        4            NaN      NaN   \n",
       "434  https://zenodo.org/record/4289223        4            NaN      NaN   \n",
       "435  https://zenodo.org/record/4289223        9            NaN      NaN   \n",
       "436  https://zenodo.org/record/4289223        9            NaN      NaN   \n",
       "437  https://zenodo.org/record/4289223       14            NaN      NaN   \n",
       "\n",
       "    Sample_accession  ... Other_ID Date           Dataset Store Brood  \\\n",
       "433              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
       "434              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
       "435              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
       "436              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
       "437              NaN  ...      NaN  NaN  Nadeau Sheffield   NaN   NaN   \n",
       "\n",
       "    Death_Date Cross_Type Stage  Sex Unit_Type  \n",
       "433        NaN        NaN   NaN  NaN       NaN  \n",
       "434        NaN        NaN   NaN  NaN       NaN  \n",
       "435        NaN        NaN   NaN  NaN       NaN  \n",
       "436        NaN        NaN   NaN  NaN       NaN  \n",
       "437        NaN        NaN   NaN  NaN       NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_img.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 49359 entries, 433 to 49791\n",
      "Data columns (total 22 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",
      "dtypes: int64(1), object(21)\n",
      "memory usage: 8.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_img[\"file_type\"] = df_img[\"Image_name\"].apply(get_file_type)\n",
    "df_img.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_img.to_csv(\"../Jiggins_Zenodo_Img_Master.csv\", index = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remaining Question is just if the raw images are duplicated as jpgs or are unique. The `CAMID`'s correspond to samples (as noted in the [zenodo records](https://zenodo.org/record/4289223)), so we can check a single view for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_img.View.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['dorsal',\n",
       " 'Dorsal',\n",
       " 'Dorsal and Ventral',\n",
       " 'forewing dorsal',\n",
       " 'hindwing dorsal']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dorsal_labels = [view for view in list(df_img.View.dropna().unique()) if \"dorsal\" in view.lower()]\n",
    "dorsal_labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 24318 entries, 433 to 49790\n",
      "Data columns (total 22 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   CAMID             24318 non-null  object\n",
      " 1   X                 24318 non-null  int64 \n",
      " 2   Image_name        24318 non-null  object\n",
      " 3   View              24318 non-null  object\n",
      " 4   zenodo_name       24318 non-null  object\n",
      " 5   zenodo_link       24318 non-null  object\n",
      " 6   Sequence          23851 non-null  object\n",
      " 7   Taxonomic_Name    22511 non-null  object\n",
      " 8   Locality          16773 non-null  object\n",
      " 9   Sample_accession  2953 non-null   object\n",
      " 10  Collected_by      2656 non-null   object\n",
      " 11  Other_ID          6931 non-null   object\n",
      " 12  Date              16847 non-null  object\n",
      " 13  Dataset           19935 non-null  object\n",
      " 14  Store             19894 non-null  object\n",
      " 15  Brood             7264 non-null   object\n",
      " 16  Death_Date        107 non-null    object\n",
      " 17  Cross_Type        2572 non-null   object\n",
      " 18  Stage             8 non-null      object\n",
      " 19  Sex               17875 non-null  object\n",
      " 20  Unit_Type         16693 non-null  object\n",
      " 21  file_type         24318 non-null  object\n",
      "dtypes: int64(1), object(21)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_img_dorsal = df_img.loc[df_img[\"View\"].isin(dorsal_labels)]\n",
    "df_img_dorsal.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMID               12296\n",
       "X                   24318\n",
       "Image_name          18539\n",
       "View                    5\n",
       "zenodo_name            36\n",
       "zenodo_link            32\n",
       "Sequence            11107\n",
       "Taxonomic_Name        359\n",
       "Locality              642\n",
       "Sample_accession     1564\n",
       "Collected_by           12\n",
       "Other_ID             2897\n",
       "Date                  794\n",
       "Dataset                 8\n",
       "Store                 142\n",
       "Brood                 217\n",
       "Death_Date             64\n",
       "Cross_Type             30\n",
       "Stage                   1\n",
       "Sex                     3\n",
       "Unit_Type               6\n",
       "file_type               3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_img_dorsal.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We still have repeated `CAMID`s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/4002726096.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_img_dorsal[\"CAM_Dupe\"] = df_img_dorsal.duplicated(subset = \"CAMID\", keep = False)\n"
     ]
    }
   ],
   "source": [
    "df_img_dorsal[\"CAM_Dupe\"] = df_img_dorsal.duplicated(subset = \"CAMID\", keep = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAM_Dupe\n",
       "True     20765\n",
       "False     3553\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_img_dorsal[\"CAM_Dupe\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 20765 entries, 709 to 49692\n",
      "Data columns (total 23 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   CAMID             20765 non-null  object\n",
      " 1   X                 20765 non-null  int64 \n",
      " 2   Image_name        20765 non-null  object\n",
      " 3   View              20765 non-null  object\n",
      " 4   zenodo_name       20765 non-null  object\n",
      " 5   zenodo_link       20765 non-null  object\n",
      " 6   Sequence          20299 non-null  object\n",
      " 7   Taxonomic_Name    19368 non-null  object\n",
      " 8   Locality          15301 non-null  object\n",
      " 9   Sample_accession  2657 non-null   object\n",
      " 10  Collected_by      2653 non-null   object\n",
      " 11  Other_ID          6107 non-null   object\n",
      " 12  Date              15980 non-null  object\n",
      " 13  Dataset           16862 non-null  object\n",
      " 14  Store             17579 non-null  object\n",
      " 15  Brood             5414 non-null   object\n",
      " 16  Death_Date        26 non-null     object\n",
      " 17  Cross_Type        2572 non-null   object\n",
      " 18  Stage             0 non-null      object\n",
      " 19  Sex               14897 non-null  object\n",
      " 20  Unit_Type         13814 non-null  object\n",
      " 21  file_type         20765 non-null  object\n",
      " 22  CAM_Dupe          20765 non-null  bool  \n",
      "dtypes: bool(1), int64(1), object(21)\n",
      "memory usage: 3.7+ MB\n"
     ]
    }
   ],
   "source": [
    "duplicate_samples = df_img_dorsal.loc[df_img_dorsal[\"CAM_Dupe\"]]\n",
    "duplicate_samples.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMID                8743\n",
       "X                   20765\n",
       "Image_name          14986\n",
       "View                    5\n",
       "zenodo_name            32\n",
       "zenodo_link            31\n",
       "Sequence             8407\n",
       "Taxonomic_Name        328\n",
       "Locality              516\n",
       "Sample_accession     1268\n",
       "Collected_by           12\n",
       "Other_ID             2073\n",
       "Date                  578\n",
       "Dataset                 4\n",
       "Store                 130\n",
       "Brood                 144\n",
       "Death_Date             12\n",
       "Cross_Type             30\n",
       "Stage                   0\n",
       "Sex                     3\n",
       "Unit_Type               3\n",
       "file_type               3\n",
       "CAM_Dupe                1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_samples.nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All sources are impacted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "file_type\n",
       "jpg    14788\n",
       "raw     5956\n",
       "tif       21\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_samples.file_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will get some added duplication from `forewing dorsal` and `hindwing dorsal`, so we should filter those down to just one for a more accurate assessment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True     680\n",
       "False    132\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_samples.loc[duplicate_samples[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"])].duplicated(\"CAMID\", keep = \"first\").value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMID               132\n",
       "X                   812\n",
       "Image_name          528\n",
       "View                  2\n",
       "zenodo_name           3\n",
       "zenodo_link           3\n",
       "Sequence            132\n",
       "Taxonomic_Name        7\n",
       "Locality             59\n",
       "Sample_accession      0\n",
       "Collected_by          0\n",
       "Other_ID            127\n",
       "Date                 16\n",
       "Dataset               1\n",
       "Store                17\n",
       "Brood                 0\n",
       "Death_Date            0\n",
       "Cross_Type            0\n",
       "Stage                 0\n",
       "Sex                   0\n",
       "Unit_Type             0\n",
       "file_type             2\n",
       "CAM_Dupe              1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_samples.loc[duplicate_samples[\"View\"].isin([\"forewing dorsal\", \"hindwing dorsal\"])].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is only a couple zenodo links, so it is not contributing to the duplication across sources. It also covers two file types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 3553 entries, 433 to 49790\n",
      "Data columns (total 23 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   CAMID             3553 non-null   object\n",
      " 1   X                 3553 non-null   int64 \n",
      " 2   Image_name        3553 non-null   object\n",
      " 3   View              3553 non-null   object\n",
      " 4   zenodo_name       3553 non-null   object\n",
      " 5   zenodo_link       3553 non-null   object\n",
      " 6   Sequence          3552 non-null   object\n",
      " 7   Taxonomic_Name    3143 non-null   object\n",
      " 8   Locality          1472 non-null   object\n",
      " 9   Sample_accession  296 non-null    object\n",
      " 10  Collected_by      3 non-null      object\n",
      " 11  Other_ID          824 non-null    object\n",
      " 12  Date              867 non-null    object\n",
      " 13  Dataset           3073 non-null   object\n",
      " 14  Store             2315 non-null   object\n",
      " 15  Brood             1850 non-null   object\n",
      " 16  Death_Date        81 non-null     object\n",
      " 17  Cross_Type        0 non-null      object\n",
      " 18  Stage             8 non-null      object\n",
      " 19  Sex               2978 non-null   object\n",
      " 20  Unit_Type         2879 non-null   object\n",
      " 21  file_type         3553 non-null   object\n",
      " 22  CAM_Dupe          3553 non-null   bool  \n",
      "dtypes: bool(1), int64(1), object(21)\n",
      "memory usage: 641.9+ KB\n"
     ]
    }
   ],
   "source": [
    "unique_samples = df_img_dorsal.loc[~df_img_dorsal[\"CAM_Dupe\"]]\n",
    "unique_samples.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CAMID               3553\n",
       "X                   3553\n",
       "Image_name          3553\n",
       "View                   3\n",
       "zenodo_name           21\n",
       "zenodo_link           18\n",
       "Sequence            3016\n",
       "Taxonomic_Name       109\n",
       "Locality             160\n",
       "Sample_accession     296\n",
       "Collected_by           3\n",
       "Other_ID             824\n",
       "Date                 265\n",
       "Dataset                8\n",
       "Store                 76\n",
       "Brood                 95\n",
       "Death_Date            52\n",
       "Cross_Type             0\n",
       "Stage                  1\n",
       "Sex                    3\n",
       "Unit_Type              6\n",
       "file_type              2\n",
       "CAM_Dupe               1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_samples.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "file_type\n",
       "jpg    3535\n",
       "tif      18\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique_samples.file_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ah, well all raw images are indeed duplicated. Let's check if they're duplicated among themselves or duplicated to other file types (eg., jpg)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5753, 23)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "4944"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\")].shape)\n",
    "duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\"), \"CAMID\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Okay, so there are multiple raw images of the same sample. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "View\n",
       "dorsal             4219\n",
       "Dorsal             1331\n",
       "forewing dorsal     203\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_raw = duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"file_type\"] == \"raw\")]\n",
    "duplicate_raw.View.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "raw_dupe\n",
      "False    4240\n",
      "True     1513\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/3049709573.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  duplicate_raw[\"raw_dupe\"] = duplicate_raw.duplicated(\"CAMID\", keep = False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "View\n",
       "dorsal             1159\n",
       "Dorsal              212\n",
       "forewing dorsal     142\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_raw[\"raw_dupe\"] = duplicate_raw.duplicated(\"CAMID\", keep = False)\n",
    "print(duplicate_raw[\"raw_dupe\"].value_counts())\n",
    "print()\n",
    "duplicate_raw.loc[duplicate_raw[\"raw_dupe\"], \"View\"].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There does not seem to be an easy method of filtering these other than just keeping the first instance of a particular `CAMID`.\n",
    "\n",
    "It is interesting that non of the Cross Types are unique. Is that just because we have the forewing/hindwing duplication?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2572, 23)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "820"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna())].shape)\n",
    "duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna()), \"CAMID\"].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There still seem to be 3 images for each specimen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cross_dupe\n",
      "True    2572\n",
      "Name: count, dtype: int64\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_66533/2419376272.py:2: 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",
      "  duplicate_cross[\"cross_dupe\"] = duplicate_cross.duplicated(\"CAMID\", keep = False)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "View\n",
       "dorsal    2322\n",
       "Dorsal     250\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_cross = duplicate_samples.loc[(duplicate_samples[\"View\"] != \"hindwing dorsal\") & (duplicate_samples[\"Cross_Type\"].notna())]\n",
    "duplicate_cross[\"cross_dupe\"] = duplicate_cross.duplicated(\"CAMID\", keep = False)\n",
    "print(duplicate_cross[\"cross_dupe\"].value_counts())\n",
    "print()\n",
    "duplicate_cross.View.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, they are all duplicated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "file_type\n",
       "jpg    1753\n",
       "raw     819\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "duplicate_cross.file_type.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems there's one raw image for each sample (other than one) and then everything else is jpg. I wonder if this includes the `JPG(1)` values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = 0\n",
    "for img_name in list(duplicate_cross.Image_name.unique()):\n",
    "    if \"JPG(1)\" in img_name:\n",
    "        count = count + 1\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No, that's not the issue."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = 0\n",
    "for img_name in list(duplicate_samples.Image_name.unique()):\n",
    "    if \"JPG(1)\" in img_name:\n",
    "        count = count + 1\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = 0\n",
    "for img_name in list(df_img_dorsal.Image_name.unique()):\n",
    "    if \"JPG(1)\" in img_name:\n",
    "        count = count + 1\n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count = 0\n",
    "for img_name in list(df_img.Image_name.unique()):\n",
    "    if \"JPG(1)\" in img_name:\n",
    "        count = count + 1\n",
    "count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's only one instance of this, and it's not a dorsal image, so that's not part of the issue."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's save all the dorsal images as another CSV (with the duplicate `CAMID` indicator)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_img_dorsal.to_csv(\"../Jiggins_Zenodo_dorsal_Img_Master.csv\", index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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