predictor-dl-outputs.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "# Clasificación de vocalizaciones de aves"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:28:52.647686Z",
     "start_time": "2024-06-18T13:28:52.641982Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#Imports Globales\n",
    "\n",
    "import os\n",
    "import h5py\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf\n",
    "from sklearn.metrics import f1_score, confusion_matrix\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from tensorflow.keras import layers, models\n",
    "\n",
    "# Funciones Globales.\n",
    "def count_files_in_subfolders(main_folder):\n",
    "    sfcounts = {}\n",
    "    # root is the current directory path\n",
    "    # dirs is a list of subdirectories in the current directory\n",
    "    # files is a list of files in the current directory\n",
    "    for root, dirs, files in os.walk(main_folder):\n",
    "        # Count only in subfolders, not in the main folder itself\n",
    "        if root != main_folder:\n",
    "            h5_files = [file for file in files if file.endswith(\".h5\")]\n",
    "            subfolder_name = os.path.basename(root)\n",
    "            sfcounts[subfolder_name] = len(h5_files)\n",
    "    return sfcounts\n",
    "\n",
    "def plot_histogram(sub_folder_counts):\n",
    "    # Extract counts and subfolder names\n",
    "    sorted_subfolders = sorted(sub_folder_counts.items(), key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    # Extract counts and subfolder names after sorting\n",
    "    counts = [count for subfolder, count in sorted_subfolders]\n",
    "    subfolders = [subfolder for subfolder, count in sorted_subfolders]\n",
    "\n",
    "    # Plot histogram\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    bars = plt.bar(subfolders, counts, color='mediumturquoise', edgecolor='lightseagreen')\n",
    "    plt.xlabel('Bird Names')\n",
    "    plt.ylabel('Number of Spectograms')\n",
    "    plt.title('Number of Spectograms of Birds', weight='bold')\n",
    "    plt.xticks(rotation=45, ha='right')\n",
    "\n",
    "    # Add counts above each bar\n",
    "    for bar, count in zip(bars, counts):\n",
    "        plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height(), str(count),\n",
    "                 ha='center', va='bottom')\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ],
   "outputs": [],
   "execution_count": 20
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Conteo de espectrogramas por ave"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:28:57.448259Z",
     "start_time": "2024-06-18T13:28:57.243083Z"
    }
   },
   "source": [
    "# Ruta a los espectrogramas convertidos a hdf5\n",
    "main_folder_path = os.path.abspath(\"./data/spects_h5\")\n",
    "\n",
    "# Conteo de archivos por tipo de pájaro\n",
    "subfolder_counts = count_files_in_subfolders(main_folder_path)\n",
    "\n",
    "# Histograma de distribución de muestras\n",
    "plot_histogram(subfolder_counts)"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:29:15.303707Z",
     "start_time": "2024-06-18T13:29:02.490501Z"
    }
   },
   "source": [
    "# Cargar los espectrogramas & labels\n",
    "spectrograms_folder = os.path.abspath(\"./data/spectrograms\")\n",
    "bird_folders = [folder for folder in os.listdir(spectrograms_folder) if not os.path.isfile(folder)]\n",
    "print(bird_folders)\n",
    "\n",
    "# Crear un archivo h5 para agrupar los espectrogramas por nombre de audio\n",
    "with h5py.File(os.path.abspath(\"./data/spectrograms.h5\"), \"w\") as hf:\n",
    "    for bird_folder in bird_folders:\n",
    "        bird_name = bird_folder.split(\"_\")[0]  # Obtener el nombre del pájaro\n",
    "        spectogram_files = os.listdir(os.path.join(spectrograms_folder, bird_folder))\n",
    "        for spectogram_file in spectogram_files:\n",
    "            if spectogram_file.endswith(\".npy\"):\n",
    "                audio_name = spectogram_file.split(\"_\")[0]\n",
    "                if audio_name not in hf:\n",
    "                    audio_group = hf.create_group(audio_name)\n",
    "                    # Agregar el nombre del pájaro al grupo del audio\n",
    "                    audio_group.attrs[\"bird\"] = bird_name\n",
    "                spectogram_path = os.path.join(spectrograms_folder, bird_folder, spectogram_file)\n",
    "                spectogram = np.load(spectogram_path)\n",
    "                hf[audio_name].create_dataset(spectogram_file, data=spectogram)\n",
    "                \n",
    "# Open the h5 file\n",
    "audio_names = []\n",
    "with h5py.File(os.path.abspath(\"./data/spectrograms.h5\")) as hf:\n",
    "    # Get the keys of the groups (audio names)\n",
    "    audio_names = list(hf.keys())\n",
    "\n",
    "print(audio_names)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Acrocephalus scirpaceus', 'Anas (Mareca) strepera', 'Porphyrio porphyrio', 'Circus aeruginosus', 'Motacilla flava', 'Acrocephalus arundinaceus', 'Fulica atra', 'Botaurus stellaris', 'Himantopus himantopus', 'Acrocephalus melanopogon', 'Tachybaptus ruficollis', 'Ardea purpurea', 'Charadrius alexandrinus', 'Ixobrychus minutus', 'Anas platyrhynchos', 'Coracias garrulus', 'Dendrocopos minor', 'Alcedo atthis', 'Ciconia ciconia', 'Gallinula chloropus']\n",
      "['XC100296', 'XC100619', 'XC100621', 'XC100622', 'XC102793', 'XC102794', 'XC103243', 'XC103696', 'XC106948', 'XC106971', 'XC109629', 'XC112488', 'XC113587', 'XC113968', 'XC118195', 'XC120332', 'XC120378', 'XC120411', 'XC120730', 'XC121174', 'XC127375', 'XC129156', 'XC129188', 'XC129193', 'XC129377', 'XC131532', 'XC132077', 'XC132435', 'XC133548', 'XC134051', 'XC134073', 'XC134081', 'XC134219', 'XC134221', 'XC134240', 'XC134257', 'XC134457', 'XC134761', 'XC134762', 'XC134843', 'XC135278', 'XC135429', 'XC135579', 'XC135658', 'XC141019', 'XC141170', 'XC141171', 'XC141571', 'XC142515', 'XC143954', 'XC144531', 'XC144533', 'XC145121', 'XC145159', 'XC146712', 'XC149045', 'XC149069', 'XC149070', 'XC149071', 'XC149072', 'XC149153', 'XC150591', 'XC150699', 'XC153193', 'XC153870', 'XC154289', 'XC154554', 'XC154555', 'XC154563', 'XC154909', 'XC154931', 'XC155092', 'XC155665', 'XC155710', 'XC156081', 'XC161931', 'XC161966', 'XC163297', 'XC164172', 'XC164817', 'XC166246', 'XC167717', 'XC169478', 'XC171088', 'XC171122', 'XC171445', 'XC172478', 'XC172480', 'XC172692', 'XC172946', 'XC180287', 'XC180605', 'XC180756', 'XC181349', 'XC182877', 'XC183432', 'XC183433', 'XC183440', 'XC184313', 'XC184314', 'XC184422', 'XC184429', 'XC184433', 'XC184434', 'XC184842', 'XC185101', 'XC185103', 'XC185104', 'XC185268', 'XC185273', 'XC185305', 'XC186806', 'XC188143', 'XC188144', 'XC188145', 'XC188146', 'XC188147', 'XC188148', 'XC188149', 'XC188150', 'XC189879', 'XC189882', 'XC189883', 'XC190968', 'XC191096', 'XC196976', 'XC196991', 'XC197138', 'XC205050', 'XC205076', 'XC206253', 'XC206255', 'XC207216', 'XC208527', 'XC208705', 'XC209951', 'XC210379', 'XC213784', 'XC214099', 'XC215129', 'XC215785', 'XC216709', 'XC216710', 'XC223168', 'XC225374', 'XC227072', 'XC232846', 'XC234105', 'XC235176', 'XC235721', 'XC236456', 'XC236458', 'XC237486', 'XC237893', 'XC240134', 'XC244232', 'XC245725', 'XC252756', 'XC252757', 'XC253188', 'XC253191', 'XC253273', 'XC25468', 'XC25558', 'XC262846', 'XC263296', 'XC263298', 'XC265127', 'XC267202', 'XC26929', 'XC26930', 'XC26959', 'XC26960', 'XC269744', 'XC269745', 'XC270056', 'XC270373', 'XC270427', 'XC270428', 'XC27569', 'XC276851', 'XC277204', 'XC277591', 'XC279853', 'XC279854', 'XC280353', 'XC280719', 'XC280964', 'XC280965', 'XC280966', 'XC280978', 'XC280980', 'XC280981', 'XC280982', 'XC280983', 'XC280984', 'XC280997', 'XC280998', 'XC281044', 'XC281045', 'XC281357', 'XC281358', 'XC281359', 'XC281360', 'XC287835', 'XC288186', 'XC292201', 'XC293293', 'XC295996', 'XC296053', 'XC296061', 'XC29798', 'XC298591', 'XC299191', 'XC299330', 'XC299495', 'XC301648', 'XC302642', 'XC303341', 'XC303987', 'XC303989', 'XC304006', 'XC306130', 'XC307038', 'XC308991', 'XC309607', 'XC310507', 'XC310528', 'XC310529', 'XC310709', 'XC311152', 'XC311153', 'XC312051', 'XC312572', 'XC312823', 'XC313565', 'XC314267', 'XC314269', 'XC314610', 'XC314697', 'XC315733', 'XC31615', 'XC316371', 'XC317323', 'XC317421', 'XC317692', 'XC318091', 'XC318414', 'XC318416', 'XC318417', 'XC318418', 'XC318427', 'XC324343', 'XC326162', 'XC327581', 'XC328055', 'XC329900', 'XC329910', 'XC329937', 'XC332043', 'XC332533', 'XC333813', 'XC333823', 'XC336861', 'XC336862', 'XC336866', 'XC337592', 'XC337594', 'XC338275', 'XC338276', 'XC338277', 'XC338279', 'XC338280', 'XC338283', 'XC339477', 'XC339478', 'XC339480', 'XC340598', 'XC340916', 'XC340917', 'XC340922', 'XC341211', 'XC341212', 'XC341264', 'XC341986', 'XC343737', 'XC345884', 'XC345889', 'XC345951', 'XC347269', 'XC347981', 'XC348396', 'XC354109', 'XC354111', 'XC356385', 'XC356656', 'XC356695', 'XC356696', 'XC356856', 'XC357023', 'XC357198', 'XC357368', 'XC357371', 'XC357387', 'XC357495', 'XC357630', 'XC357687', 'XC357688', 'XC357885', 'XC357888', 'XC357910', 'XC357911', 'XC357912', 'XC357913', 'XC357914', 'XC358495', 'XC358560', 'XC358561', 'XC358628', 'XC358629', 'XC358639', 'XC358986', 'XC359026', 'XC35968', 'XC35969', 'XC360704', 'XC360705', 'XC361803', 'XC361804', 'XC361806', 'XC362363', 'XC362697', 'XC363294', 'XC364346', 'XC364458', 'XC365601', 'XC366752', 'XC367038', 'XC367080', 'XC367081', 'XC367088', 'XC367219', 'XC367685', 'XC369056', 'XC370227', 'XC370744', 'XC371000', 'XC371001', 'XC371002', 'XC371003', 'XC371004', 'XC371155', 'XC372105', 'XC372401', 'XC372950', 'XC373560', 'XC373853', 'XC374911', 'XC374912', 'XC374913', 'XC37543', 'XC376813', 'XC376954', 'XC376955', 'XC376956', 'XC376958', 'XC376959', 'XC377102', 'XC377189', 'XC379596', 'XC379836', 'XC379837', 'XC379838', 'XC379892', 'XC379953', 'XC380536', 'XC380537', 'XC380967', 'XC381727', 'XC381813', 'XC381860', 'XC381861', 'XC381862', 'XC383514', 'XC385622', 'XC386307', 'XC388290', 'XC388291', 'XC395186', 'XC396412', 'XC396413', 'XC396414', 'XC396416', 'XC399454', 'XC399455', 'XC400197', 'XC401830', 'XC40189', 'XC402382', 'XC405096', 'XC406576', 'XC407138', 'XC407139', 'XC407219', 'XC408868', 'XC410111', 'XC410121', 'XC410140', 'XC411002', 'XC412265', 'XC41249', 'XC412840', 'XC413271', 'XC413273', 'XC413305', 'XC414856', 'XC416671', 'XC416749', 'XC416949', 'XC417021', 'XC417089', 'XC417119', 'XC417157', 'XC417158', 'XC417425', 'XC417722', 'XC418140', 'XC418401', 'XC418412', 'XC419196', 'XC419606', 'XC420352', 'XC420542', 'XC421398', 'XC421400', 'XC421435', 'XC421439', 'XC421556', 'XC421578', 'XC422018', 'XC422019', 'XC422341', 'XC422437', 'XC422660', 'XC422797', 'XC423144', 'XC424110', 'XC42540', 'XC42541', 'XC42542', 'XC42554', 'XC425999', 'XC428404', 'XC428706', 'XC428707', 'XC428861', 'XC428977', 'XC429210', 'XC429604', 'XC429605', 'XC429606', 'XC429607', 'XC429608', 'XC429780', 'XC431154', 'XC431155', 'XC431157', 'XC431158', 'XC431217', 'XC431789', 'XC432145', 'XC432223', 'XC432232', 'XC432572', 'XC434438', 'XC435064', 'XC436382', 'XC436465', 'XC436938', 'XC437194', 'XC437805', 'XC437807', 'XC437808', 'XC439336', 'XC440102', 'XC440123', 'XC440159', 'XC440361', 'XC440482', 'XC441909', 'XC442159', 'XC442556', 'XC443794', 'XC445285', 'XC44631', 'XC447310', 'XC447433', 'XC447442', 'XC448652', 'XC448653', 'XC448948', 'XC449187', 'XC454200', 'XC454603', 'XC454995', 'XC456255', 'XC456725', 'XC457036', 'XC458081', 'XC458082', 'XC458084', 'XC459391', 'XC459392', 'XC459393', 'XC459394', 'XC459669', 'XC459686', 'XC459694', 'XC459832', 'XC459840', 'XC459861', 'XC459889', 'XC459964', 'XC460172', 'XC460174', 'XC460195', 'XC461324', 'XC461762', 'XC462116', 'XC462742', 'XC462870', 'XC463076', 'XC463078', 'XC463089', 'XC463296', 'XC463331', 'XC463509', 'XC463520', 'XC463603', 'XC464578', 'XC464633', 'XC465331', 'XC465333', 'XC465476', 'XC466132', 'XC466157', 'XC466742', 'XC466744', 'XC466746', 'XC466747', 'XC466748', 'XC466765', 'XC466805', 'XC466883', 'XC466994', 'XC467260', 'XC467261', 'XC467263', 'XC467373', 'XC467402', 'XC467403', 'XC467596', 'XC467851', 'XC468672', 'XC469295', 'XC469410', 'XC469570', 'XC469783', 'XC469794', 'XC469957', 'XC469959', 'XC469976', 'XC470195', 'XC470261', 'XC470347', 'XC470421', 'XC470470', 'XC470564', 'XC470657', 'XC470740', 'XC470769', 'XC470995', 'XC471249', 'XC471545', 'XC471554', 'XC471558', 'XC471731', 'XC471768', 'XC471769', 'XC471968', 'XC472589', 'XC473249', 'XC473571', 'XC473833', 'XC473930', 'XC473954', 'XC474017', 'XC474019', 'XC474176', 'XC474931', 'XC475474', 'XC475479', 'XC475965', 'XC476146', 'XC476409', 'XC476854', 'XC477007', 'XC477329', 'XC477330', 'XC477331', 'XC477444', 'XC477688', 'XC477749', 'XC477945', 'XC478070', 'XC478415', 'XC478424', 'XC478425', 'XC478498', 'XC478568', 'XC478850', 'XC478876', 'XC479839', 'XC479924', 'XC479946', 'XC479978', 'XC480053', 'XC480061', 'XC480203', 'XC480979', 'XC481289', 'XC481399', 'XC481404', 'XC481405', 'XC481567', 'XC482370', 'XC483334', 'XC483554', 'XC483557', 'XC483692', 'XC483694', 'XC483697', 'XC483906', 'XC484626', 'XC484717', 'XC485245', 'XC485312', 'XC485349', 'XC485350', 'XC485377', 'XC485380', 'XC485381', 'XC486202', 'XC486755', 'XC487692', 'XC487693', 'XC487843', 'XC487848', 'XC488300', 'XC488301', 'XC488409', 'XC489350', 'XC489445', 'XC490614', 'XC490838', 'XC491787', 'XC493312', 'XC494524', 'XC494536', 'XC494537', 'XC495092', 'XC495533', 'XC495852', 'XC495975', 'XC496271', 'XC496380', 'XC496443', 'XC497345', 'XC497799', 'XC498608', 'XC499043', 'XC499081', 'XC499150', 'XC499646', 'XC499652', 'XC500306', 'XC500406', 'XC500624', 'XC500750', 'XC500751', 'XC501144', 'XC501772', 'XC501872', 'XC502956', 'XC502961', 'XC503769', 'XC503772', 'XC504105', 'XC504113', 'XC505719', 'XC505960', 'XC509421', 'XC509443', 'XC509674', 'XC510178', 'XC510499', 'XC510569', 'XC510571', 'XC510585', 'XC510716', 'XC510720', 'XC511182', 'XC511183', 'XC511184', 'XC511190', 'XC511192', 'XC511193', 'XC511641', 'XC511642', 'XC511647', 'XC511664', 'XC511665', 'XC511666', 'XC511667', 'XC511668', 'XC511669', 'XC511670', 'XC511671', 'XC511672', 'XC511673', 'XC511674', 'XC511677', 'XC511697', 'XC512909', 'XC513304', 'XC514029', 'XC514030', 'XC514729', 'XC514731', 'XC515865', 'XC516174', 'XC516379', 'XC518345', 'XC518346', 'XC518347', 'XC518348', 'XC518349', 'XC518351', 'XC518425', 'XC518432', 'XC518527', 'XC519437', 'XC519587', 'XC519898', 'XC519899', 'XC519927', 'XC519985', 'XC519994', 'XC520116', 'XC520709', 'XC521464', 'XC521562', 'XC521563', 'XC521611', 'XC521814', 'XC522273', 'XC522340', 'XC522491', 'XC522819', 'XC522864', 'XC522990', 'XC522991', 'XC524288', 'XC524524', 'XC526231', 'XC526232', 'XC526238', 'XC526276', 'XC527477', 'XC527806', 'XC528227', 'XC528507', 'XC528508', 'XC529190', 'XC529774', 'XC531395', 'XC533927', 'XC534240', 'XC534416', 'XC534607', 'XC534750', 'XC534754', 'XC534756', 'XC536892', 'XC536895', 'XC538130', 'XC538132', 'XC538144', 'XC538213', 'XC538422', 'XC538423', 'XC538467', 'XC538478', 'XC538710', 'XC538750', 'XC538941', 'XC538943', 'XC539273', 'XC539614', 'XC539686', 'XC540348', 'XC540475', 'XC540685', 'XC540742', 'XC540804', 'XC540808', 'XC542331', 'XC542351', 'XC542356', 'XC542357', 'XC542359', 'XC542543', 'XC542651', 'XC543233', 'XC543411', 'XC543454', 'XC543771', 'XC543793', 'XC544319', 'XC544658', 'XC544680', 'XC544685', 'XC544686', 'XC544846', 'XC544857', 'XC544935', 'XC545123', 'XC545223', 'XC545614', 'XC545755', 'XC545994', 'XC546757', 'XC547095', 'XC547368', 'XC548338', 'XC548458', 'XC548542', 'XC548742', 'XC548770', 'XC548772', 'XC548774', 'XC548775', 'XC548777', 'XC548872', 'XC550266', 'XC550870', 'XC552628', 'XC552636', 'XC67347', 'XC67350', 'XC67351', 'XC81916', 'XC88896', 'XC90128', 'XC90713', 'XC91179', 'XC93331', 'XC97974']\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:29:21.530456Z",
     "start_time": "2024-06-18T13:29:21.492431Z"
    }
   },
   "source": [
    "# Empty list to store the name of each group and bird name\n",
    "bird_info = []\n",
    "# Leer el archivo H5 existente\n",
    "with h5py.File(os.path.abspath(\"./data/spectrograms.h5\")) as hf:\n",
    "    for audio_name in hf.keys():\n",
    "        bird_name = hf[audio_name].attrs[\"bird\"]\n",
    "        bird_info.append([audio_name, bird_name])\n",
    "\n",
    "print(bird_info)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['XC100296', 'Botaurus stellaris'], ['XC100619', 'Circus aeruginosus'], ['XC100621', 'Circus aeruginosus'], ['XC100622', 'Circus aeruginosus'], ['XC102793', 'Motacilla flava'], ['XC102794', 'Motacilla flava'], ['XC103243', 'Ixobrychus minutus'], ['XC103696', 'Motacilla flava'], ['XC106948', 'Acrocephalus melanopogon'], ['XC106971', 'Ixobrychus minutus'], ['XC109629', 'Anas (Mareca) strepera'], ['XC112488', 'Circus aeruginosus'], ['XC113587', 'Anas platyrhynchos'], ['XC113968', 'Anas platyrhynchos'], ['XC118195', 'Himantopus himantopus'], ['XC120332', 'Charadrius alexandrinus'], ['XC120378', 'Botaurus stellaris'], ['XC120411', 'Dendrocopos minor'], ['XC120730', 'Coracias garrulus'], ['XC121174', 'Coracias garrulus'], ['XC127375', 'Anas platyrhynchos'], ['XC129156', 'Dendrocopos minor'], ['XC129188', 'Ciconia ciconia'], ['XC129193', 'Dendrocopos minor'], ['XC129377', 'Dendrocopos minor'], ['XC131532', 'Circus aeruginosus'], ['XC132077', 'Himantopus himantopus'], ['XC132435', 'Himantopus himantopus'], ['XC133548', 'Dendrocopos minor'], ['XC134051', 'Coracias garrulus'], ['XC134073', 'Ciconia ciconia'], ['XC134081', 'Himantopus himantopus'], ['XC134219', 'Ciconia ciconia'], ['XC134221', 'Ciconia ciconia'], ['XC134240', 'Tachybaptus ruficollis'], ['XC134257', 'Motacilla flava'], ['XC134457', 'Acrocephalus melanopogon'], ['XC134761', 'Ixobrychus minutus'], ['XC134762', 'Ixobrychus minutus'], ['XC134843', 'Circus aeruginosus'], ['XC135278', 'Dendrocopos minor'], ['XC135429', 'Ardea purpurea'], ['XC135579', 'Ciconia ciconia'], ['XC135658', 'Charadrius alexandrinus'], ['XC141019', 'Acrocephalus melanopogon'], ['XC141170', 'Coracias garrulus'], ['XC141171', 'Ciconia ciconia'], ['XC141571', 'Coracias garrulus'], ['XC142515', 'Dendrocopos minor'], ['XC143954', 'Circus aeruginosus'], ['XC144531', 'Charadrius alexandrinus'], ['XC144533', 'Himantopus himantopus'], ['XC145121', 'Circus aeruginosus'], ['XC145159', 'Acrocephalus melanopogon'], ['XC146712', 'Ixobrychus minutus'], ['XC149045', 'Ciconia ciconia'], ['XC149069', 'Ciconia ciconia'], ['XC149070', 'Ciconia ciconia'], ['XC149071', 'Ciconia ciconia'], ['XC149072', 'Ciconia ciconia'], ['XC149153', 'Ixobrychus minutus'], ['XC150591', 'Dendrocopos minor'], ['XC150699', 'Ardea purpurea'], ['XC153193', 'Dendrocopos minor'], ['XC153870', 'Tachybaptus ruficollis'], ['XC154289', 'Himantopus himantopus'], ['XC154554', 'Ciconia ciconia'], ['XC154555', 'Ciconia ciconia'], ['XC154563', 'Motacilla flava'], ['XC154909', 'Motacilla flava'], ['XC154931', 'Himantopus himantopus'], ['XC155092', 'Himantopus himantopus'], ['XC155665', 'Charadrius alexandrinus'], ['XC155710', 'Motacilla flava'], ['XC156081', 'Ixobrychus minutus'], ['XC161931', 'Tachybaptus ruficollis'], ['XC161966', 'Circus aeruginosus'], ['XC163297', 'Acrocephalus melanopogon'], ['XC164172', 'Motacilla flava'], ['XC164817', 'Circus aeruginosus'], ['XC166246', 'Ardea purpurea'], ['XC167717', 'Ciconia ciconia'], ['XC169478', 'Ciconia ciconia'], ['XC171088', 'Ciconia ciconia'], ['XC171122', 'Dendrocopos minor'], ['XC171445', 'Botaurus stellaris'], ['XC172478', 'Acrocephalus melanopogon'], ['XC172480', 'Acrocephalus melanopogon'], ['XC172692', 'Botaurus stellaris'], ['XC172946', 'Ciconia ciconia'], ['XC180287', 'Ixobrychus minutus'], ['XC180605', 'Ixobrychus minutus'], ['XC180756', 'Botaurus stellaris'], ['XC181349', 'Ciconia ciconia'], ['XC182877', 'Charadrius alexandrinus'], ['XC183432', 'Tachybaptus ruficollis'], ['XC183433', 'Tachybaptus ruficollis'], ['XC183440', 'Tachybaptus ruficollis'], ['XC184313', 'Botaurus stellaris'], ['XC184314', 'Botaurus stellaris'], ['XC184422', 'Himantopus himantopus'], ['XC184429', 'Botaurus stellaris'], ['XC184433', 'Motacilla flava'], ['XC184434', 'Motacilla flava'], ['XC184842', 'Ardea purpurea'], ['XC185101', 'Charadrius alexandrinus'], ['XC185103', 'Charadrius alexandrinus'], ['XC185104', 'Charadrius alexandrinus'], ['XC185268', 'Charadrius alexandrinus'], ['XC185273', 'Charadrius alexandrinus'], ['XC185305', 'Coracias garrulus'], ['XC186806', 'Ixobrychus minutus'], ['XC188143', 'Coracias garrulus'], ['XC188144', 'Coracias garrulus'], ['XC188145', 'Coracias garrulus'], ['XC188146', 'Coracias garrulus'], ['XC188147', 'Coracias garrulus'], ['XC188148', 'Coracias garrulus'], ['XC188149', 'Coracias garrulus'], ['XC188150', 'Coracias garrulus'], ['XC189879', 'Circus aeruginosus'], ['XC189882', 'Circus aeruginosus'], ['XC189883', 'Circus aeruginosus'], ['XC190968', 'Himantopus himantopus'], ['XC191096', 'Ardea purpurea'], ['XC196976', 'Circus aeruginosus'], ['XC196991', 'Anas platyrhynchos'], ['XC197138', 'Dendrocopos minor'], ['XC205050', 'Ardea purpurea'], ['XC205076', 'Circus aeruginosus'], ['XC206253', 'Himantopus himantopus'], ['XC206255', 'Himantopus himantopus'], ['XC207216', 'Anas platyrhynchos'], ['XC208527', 'Anas (Mareca) strepera'], ['XC208705', 'Motacilla flava'], ['XC209951', 'Anas (Mareca) strepera'], ['XC210379', 'Anas (Mareca) strepera'], ['XC213784', 'Acrocephalus melanopogon'], ['XC214099', 'Botaurus stellaris'], ['XC215129', 'Tachybaptus ruficollis'], ['XC215785', 'Botaurus stellaris'], ['XC216709', 'Botaurus stellaris'], ['XC216710', 'Botaurus stellaris'], ['XC223168', 'Motacilla flava'], ['XC225374', 'Tachybaptus ruficollis'], ['XC227072', 'Ardea purpurea'], ['XC232846', 'Tachybaptus ruficollis'], ['XC234105', 'Botaurus stellaris'], ['XC235176', 'Circus aeruginosus'], ['XC235721', 'Ciconia ciconia'], ['XC236456', 'Dendrocopos minor'], ['XC236458', 'Dendrocopos minor'], ['XC237486', 'Botaurus stellaris'], ['XC237893', 'Ardea purpurea'], ['XC240134', 'Coracias garrulus'], ['XC244232', 'Ciconia ciconia'], ['XC245725', 'Ixobrychus minutus'], ['XC252756', 'Motacilla flava'], ['XC252757', 'Motacilla flava'], ['XC253188', 'Ixobrychus minutus'], ['XC253191', 'Ciconia ciconia'], ['XC253273', 'Acrocephalus melanopogon'], ['XC25468', 'Ciconia ciconia'], ['XC25558', 'Botaurus stellaris'], ['XC262846', 'Circus aeruginosus'], ['XC263296', 'Himantopus himantopus'], ['XC263298', 'Himantopus himantopus'], ['XC265127', 'Ardea purpurea'], ['XC267202', 'Porphyrio porphyrio'], ['XC26929', 'Tachybaptus ruficollis'], ['XC26930', 'Tachybaptus ruficollis'], ['XC26959', 'Anas (Mareca) strepera'], ['XC26960', 'Anas (Mareca) strepera'], ['XC269744', 'Circus aeruginosus'], ['XC269745', 'Circus aeruginosus'], ['XC270056', 'Motacilla flava'], ['XC270373', 'Ardea purpurea'], ['XC270427', 'Ixobrychus minutus'], ['XC270428', 'Ixobrychus minutus'], ['XC27569', 'Ciconia ciconia'], ['XC276851', 'Ardea purpurea'], ['XC277204', 'Ardea purpurea'], ['XC277591', 'Himantopus himantopus'], ['XC279853', 'Ardea purpurea'], ['XC279854', 'Ardea purpurea'], ['XC280353', 'Himantopus himantopus'], ['XC280719', 'Tachybaptus ruficollis'], ['XC280964', 'Botaurus stellaris'], ['XC280965', 'Botaurus stellaris'], ['XC280966', 'Botaurus stellaris'], ['XC280978', 'Ardea purpurea'], ['XC280980', 'Tachybaptus ruficollis'], ['XC280981', 'Tachybaptus ruficollis'], ['XC280982', 'Tachybaptus ruficollis'], ['XC280983', 'Tachybaptus ruficollis'], ['XC280984', 'Tachybaptus ruficollis'], ['XC280997', 'Circus aeruginosus'], ['XC280998', 'Circus aeruginosus'], ['XC281044', 'Himantopus himantopus'], ['XC281045', 'Himantopus himantopus'], ['XC281357', 'Motacilla flava'], ['XC281358', 'Motacilla flava'], ['XC281359', 'Motacilla flava'], ['XC281360', 'Motacilla flava'], ['XC287835', 'Anas platyrhynchos'], ['XC288186', 'Ixobrychus minutus'], ['XC292201', 'Porphyrio porphyrio'], ['XC293293', 'Botaurus stellaris'], ['XC295996', 'Ciconia ciconia'], ['XC296053', 'Motacilla flava'], ['XC296061', 'Circus aeruginosus'], ['XC29798', 'Himantopus himantopus'], ['XC298591', 'Ardea purpurea'], ['XC299191', 'Botaurus stellaris'], ['XC299330', 'Porphyrio porphyrio'], ['XC299495', 'Ardea purpurea'], ['XC301648', 'Tachybaptus ruficollis'], ['XC302642', 'Porphyrio porphyrio'], ['XC303341', 'Porphyrio porphyrio'], ['XC303987', 'Anas platyrhynchos'], ['XC303989', 'Anas platyrhynchos'], ['XC304006', 'Anas platyrhynchos'], ['XC306130', 'Anas platyrhynchos'], ['XC307038', 'Motacilla flava'], ['XC308991', 'Dendrocopos minor'], ['XC309607', 'Botaurus stellaris'], ['XC310507', 'Botaurus stellaris'], ['XC310528', 'Acrocephalus melanopogon'], ['XC310529', 'Acrocephalus melanopogon'], ['XC310709', 'Dendrocopos minor'], ['XC311152', 'Ardea purpurea'], ['XC311153', 'Ardea purpurea'], ['XC312051', 'Coracias garrulus'], ['XC312572', 'Himantopus himantopus'], ['XC312823', 'Dendrocopos minor'], ['XC313565', 'Himantopus himantopus'], ['XC314267', 'Motacilla flava'], ['XC314269', 'Motacilla flava'], ['XC314610', 'Dendrocopos minor'], ['XC314697', 'Botaurus stellaris'], ['XC315733', 'Botaurus stellaris'], ['XC31615', 'Coracias garrulus'], ['XC316371', 'Motacilla flava'], ['XC317323', 'Botaurus stellaris'], ['XC317421', 'Botaurus stellaris'], ['XC317692', 'Tachybaptus ruficollis'], ['XC318091', 'Ardea purpurea'], ['XC318414', 'Charadrius alexandrinus'], ['XC318416', 'Charadrius alexandrinus'], ['XC318417', 'Charadrius alexandrinus'], ['XC318418', 'Charadrius alexandrinus'], ['XC318427', 'Coracias garrulus'], ['XC324343', 'Motacilla flava'], ['XC326162', 'Botaurus stellaris'], ['XC327581', 'Ciconia ciconia'], ['XC328055', 'Himantopus himantopus'], ['XC329900', 'Acrocephalus melanopogon'], ['XC329910', 'Acrocephalus melanopogon'], ['XC329937', 'Dendrocopos minor'], ['XC332043', 'Himantopus himantopus'], ['XC332533', 'Himantopus himantopus'], ['XC333813', 'Charadrius alexandrinus'], ['XC333823', 'Charadrius alexandrinus'], ['XC336861', 'Porphyrio porphyrio'], ['XC336862', 'Porphyrio porphyrio'], ['XC336866', 'Porphyrio porphyrio'], ['XC337592', 'Porphyrio porphyrio'], ['XC337594', 'Porphyrio porphyrio'], ['XC338275', 'Charadrius alexandrinus'], ['XC338276', 'Charadrius alexandrinus'], ['XC338277', 'Charadrius alexandrinus'], ['XC338279', 'Himantopus himantopus'], ['XC338280', 'Himantopus himantopus'], ['XC338283', 'Himantopus himantopus'], ['XC339477', 'Charadrius alexandrinus'], ['XC339478', 'Charadrius alexandrinus'], ['XC339480', 'Charadrius alexandrinus'], ['XC340598', 'Anas platyrhynchos'], ['XC340916', 'Botaurus stellaris'], ['XC340917', 'Ixobrychus minutus'], ['XC340922', 'Ciconia ciconia'], ['XC341211', 'Charadrius alexandrinus'], ['XC341212', 'Charadrius alexandrinus'], ['XC341264', 'Circus aeruginosus'], ['XC341986', 'Charadrius alexandrinus'], ['XC343737', 'Ardea purpurea'], ['XC345884', 'Motacilla flava'], ['XC345889', 'Motacilla flava'], ['XC345951', 'Motacilla flava'], ['XC347269', 'Acrocephalus melanopogon'], ['XC347981', 'Himantopus himantopus'], ['XC348396', 'Anas platyrhynchos'], ['XC354109', 'Botaurus stellaris'], ['XC354111', 'Botaurus stellaris'], ['XC356385', 'Tachybaptus ruficollis'], ['XC356656', 'Ardea purpurea'], ['XC356695', 'Acrocephalus melanopogon'], ['XC356696', 'Acrocephalus melanopogon'], ['XC356856', 'Dendrocopos minor'], ['XC357023', 'Anas platyrhynchos'], ['XC357198', 'Acrocephalus melanopogon'], ['XC357368', 'Acrocephalus melanopogon'], ['XC357371', 'Circus aeruginosus'], ['XC357387', 'Acrocephalus melanopogon'], ['XC357495', 'Porphyrio porphyrio'], ['XC357630', 'Acrocephalus melanopogon'], ['XC357687', 'Tachybaptus ruficollis'], ['XC357688', 'Tachybaptus ruficollis'], ['XC357885', 'Tachybaptus ruficollis'], ['XC357888', 'Acrocephalus melanopogon'], ['XC357910', 'Porphyrio porphyrio'], ['XC357911', 'Porphyrio porphyrio'], ['XC357912', 'Porphyrio porphyrio'], ['XC357913', 'Porphyrio porphyrio'], ['XC357914', 'Porphyrio porphyrio'], ['XC358495', 'Porphyrio porphyrio'], ['XC358560', 'Acrocephalus melanopogon'], ['XC358561', 'Porphyrio porphyrio'], ['XC358628', 'Acrocephalus melanopogon'], ['XC358629', 'Acrocephalus melanopogon'], ['XC358639', 'Porphyrio porphyrio'], ['XC358986', 'Charadrius alexandrinus'], ['XC359026', 'Charadrius alexandrinus'], ['XC35968', 'Ixobrychus minutus'], ['XC35969', 'Ixobrychus minutus'], ['XC360704', 'Acrocephalus melanopogon'], ['XC360705', 'Acrocephalus melanopogon'], ['XC361803', 'Botaurus stellaris'], ['XC361804', 'Botaurus stellaris'], ['XC361806', 'Botaurus stellaris'], ['XC362363', 'Ardea purpurea'], ['XC362697', 'Botaurus stellaris'], ['XC363294', 'Botaurus stellaris'], ['XC364346', 'Dendrocopos minor'], ['XC364458', 'Anas (Mareca) strepera'], ['XC365601', 'Circus aeruginosus'], ['XC366752', 'Charadrius alexandrinus'], ['XC367038', 'Anas platyrhynchos'], ['XC367080', 'Porphyrio porphyrio'], ['XC367081', 'Porphyrio porphyrio'], ['XC367088', 'Charadrius alexandrinus'], ['XC367219', 'Botaurus stellaris'], ['XC367685', 'Tachybaptus ruficollis'], ['XC369056', 'Ardea purpurea'], ['XC370227', 'Himantopus himantopus'], ['XC370744', 'Himantopus himantopus'], ['XC371000', 'Charadrius alexandrinus'], ['XC371001', 'Charadrius alexandrinus'], ['XC371002', 'Charadrius alexandrinus'], ['XC371003', 'Charadrius alexandrinus'], ['XC371004', 'Charadrius alexandrinus'], ['XC371155', 'Charadrius alexandrinus'], ['XC372105', 'Botaurus stellaris'], ['XC372401', 'Motacilla flava'], ['XC372950', 'Ciconia ciconia'], ['XC373560', 'Tachybaptus ruficollis'], ['XC373853', 'Ixobrychus minutus'], ['XC374911', 'Ardea purpurea'], ['XC374912', 'Himantopus himantopus'], ['XC374913', 'Himantopus himantopus'], ['XC37543', 'Botaurus stellaris'], ['XC376813', 'Ixobrychus minutus'], ['XC376954', 'Charadrius alexandrinus'], ['XC376955', 'Charadrius alexandrinus'], ['XC376956', 'Charadrius alexandrinus'], ['XC376958', 'Charadrius alexandrinus'], ['XC376959', 'Charadrius alexandrinus'], ['XC377102', 'Ixobrychus minutus'], ['XC377189', 'Tachybaptus ruficollis'], ['XC379596', 'Ciconia ciconia'], ['XC379836', 'Ixobrychus minutus'], ['XC379837', 'Ixobrychus minutus'], ['XC379838', 'Ixobrychus minutus'], ['XC379892', 'Himantopus himantopus'], ['XC379953', 'Motacilla flava'], ['XC380536', 'Ardea purpurea'], ['XC380537', 'Ardea purpurea'], ['XC380967', 'Porphyrio porphyrio'], ['XC381727', 'Porphyrio porphyrio'], ['XC381813', 'Ardea purpurea'], ['XC381860', 'Porphyrio porphyrio'], ['XC381861', 'Porphyrio porphyrio'], ['XC381862', 'Porphyrio porphyrio'], ['XC383514', 'Dendrocopos minor'], ['XC385622', 'Himantopus himantopus'], ['XC386307', 'Porphyrio porphyrio'], ['XC388290', 'Tachybaptus ruficollis'], ['XC388291', 'Tachybaptus ruficollis'], ['XC395186', 'Alcedo atthis'], ['XC396412', 'Botaurus stellaris'], ['XC396413', 'Botaurus stellaris'], ['XC396414', 'Botaurus stellaris'], ['XC396416', 'Botaurus stellaris'], ['XC399454', 'Porphyrio porphyrio'], ['XC399455', 'Porphyrio porphyrio'], ['XC400197', 'Dendrocopos minor'], ['XC401830', 'Dendrocopos minor'], ['XC40189', 'Tachybaptus ruficollis'], ['XC402382', 'Alcedo atthis'], ['XC405096', 'Dendrocopos minor'], ['XC406576', 'Acrocephalus melanopogon'], ['XC407138', 'Tachybaptus ruficollis'], ['XC407139', 'Tachybaptus ruficollis'], ['XC407219', 'Dendrocopos minor'], ['XC408868', 'Acrocephalus melanopogon'], ['XC410111', 'Porphyrio porphyrio'], ['XC410121', 'Porphyrio porphyrio'], ['XC410140', 'Porphyrio porphyrio'], ['XC411002', 'Charadrius alexandrinus'], ['XC412265', 'Porphyrio porphyrio'], ['XC41249', 'Anas (Mareca) strepera'], ['XC412840', 'Tachybaptus ruficollis'], ['XC413271', 'Himantopus himantopus'], ['XC413273', 'Himantopus himantopus'], ['XC413305', 'Himantopus himantopus'], ['XC414856', 'Botaurus stellaris'], ['XC416671', 'Ciconia ciconia'], ['XC416749', 'Acrocephalus scirpaceus'], ['XC416949', 'Ixobrychus minutus'], ['XC417021', 'Himantopus himantopus'], ['XC417089', 'Botaurus stellaris'], ['XC417119', 'Acrocephalus scirpaceus'], ['XC417157', 'Acrocephalus arundinaceus'], ['XC417158', 'Acrocephalus arundinaceus'], ['XC417425', 'Acrocephalus arundinaceus'], ['XC417722', 'Acrocephalus arundinaceus'], ['XC418140', 'Acrocephalus scirpaceus'], ['XC418401', 'Acrocephalus melanopogon'], ['XC418412', 'Acrocephalus scirpaceus'], ['XC419196', 'Tachybaptus ruficollis'], ['XC419606', 'Acrocephalus arundinaceus'], ['XC420352', 'Acrocephalus arundinaceus'], ['XC420542', 'Acrocephalus arundinaceus'], ['XC421398', 'Acrocephalus scirpaceus'], ['XC421400', 'Acrocephalus scirpaceus'], ['XC421435', 'Acrocephalus scirpaceus'], ['XC421439', 'Acrocephalus scirpaceus'], ['XC421556', 'Acrocephalus scirpaceus'], ['XC421578', 'Acrocephalus scirpaceus'], ['XC422018', 'Acrocephalus arundinaceus'], ['XC422019', 'Acrocephalus arundinaceus'], ['XC422341', 'Tachybaptus ruficollis'], ['XC422437', 'Motacilla flava'], ['XC422660', 'Acrocephalus melanopogon'], ['XC422797', 'Acrocephalus arundinaceus'], ['XC423144', 'Ixobrychus minutus'], ['XC424110', 'Acrocephalus scirpaceus'], ['XC42540', 'Botaurus stellaris'], ['XC42541', 'Botaurus stellaris'], ['XC42542', 'Botaurus stellaris'], ['XC42554', 'Tachybaptus ruficollis'], ['XC425999', 'Alcedo atthis'], ['XC428404', 'Alcedo atthis'], ['XC428706', 'Motacilla flava'], ['XC428707', 'Himantopus himantopus'], ['XC428861', 'Ciconia ciconia'], ['XC428977', 'Acrocephalus arundinaceus'], ['XC429210', 'Acrocephalus scirpaceus'], ['XC429604', 'Porphyrio porphyrio'], ['XC429605', 'Porphyrio porphyrio'], ['XC429606', 'Porphyrio porphyrio'], ['XC429607', 'Porphyrio porphyrio'], ['XC429608', 'Porphyrio porphyrio'], ['XC429780', 'Acrocephalus melanopogon'], ['XC431154', 'Ardea purpurea'], ['XC431155', 'Ixobrychus minutus'], ['XC431157', 'Ixobrychus minutus'], ['XC431158', 'Ixobrychus minutus'], ['XC431217', 'Motacilla flava'], ['XC431789', 'Himantopus himantopus'], ['XC432145', 'Ardea purpurea'], ['XC432223', 'Ardea purpurea'], ['XC432232', 'Charadrius alexandrinus'], ['XC432572', 'Coracias garrulus'], ['XC434438', 'Ixobrychus minutus'], ['XC435064', 'Charadrius alexandrinus'], ['XC436382', 'Fulica atra'], ['XC436465', 'Fulica atra'], ['XC436938', 'Gallinula chloropus'], ['XC437194', 'Alcedo atthis'], ['XC437805', 'Acrocephalus scirpaceus'], ['XC437807', 'Acrocephalus scirpaceus'], ['XC437808', 'Acrocephalus scirpaceus'], ['XC439336', 'Fulica atra'], ['XC440102', 'Porphyrio porphyrio'], ['XC440123', 'Ciconia ciconia'], ['XC440159', 'Himantopus himantopus'], ['XC440361', 'Acrocephalus scirpaceus'], ['XC440482', 'Gallinula chloropus'], ['XC441909', 'Porphyrio porphyrio'], ['XC442159', 'Alcedo atthis'], ['XC442556', 'Acrocephalus melanopogon'], ['XC443794', 'Acrocephalus melanopogon'], ['XC445285', 'Acrocephalus scirpaceus'], ['XC44631', 'Ardea purpurea'], ['XC447310', 'Acrocephalus scirpaceus'], ['XC447433', 'Alcedo atthis'], ['XC447442', 'Circus aeruginosus'], ['XC448652', 'Fulica atra'], ['XC448653', 'Fulica atra'], ['XC448948', 'Himantopus himantopus'], ['XC449187', 'Acrocephalus arundinaceus'], ['XC454200', 'Gallinula chloropus'], ['XC454603', 'Circus aeruginosus'], ['XC454995', 'Himantopus himantopus'], ['XC456255', 'Acrocephalus melanopogon'], ['XC456725', 'Charadrius alexandrinus'], ['XC457036', 'Fulica atra'], ['XC458081', 'Fulica atra'], ['XC458082', 'Fulica atra'], ['XC458084', 'Fulica atra'], ['XC459391', 'Tachybaptus ruficollis'], ['XC459392', 'Tachybaptus ruficollis'], ['XC459393', 'Tachybaptus ruficollis'], ['XC459394', 'Tachybaptus ruficollis'], ['XC459669', 'Fulica atra'], ['XC459686', 'Fulica atra'], ['XC459694', 'Dendrocopos minor'], ['XC459832', 'Ardea purpurea'], ['XC459840', 'Alcedo atthis'], ['XC459861', 'Himantopus himantopus'], ['XC459889', 'Alcedo atthis'], ['XC459964', 'Gallinula chloropus'], ['XC460172', 'Acrocephalus melanopogon'], ['XC460174', 'Acrocephalus melanopogon'], ['XC460195', 'Alcedo atthis'], ['XC461324', 'Fulica atra'], ['XC461762', 'Porphyrio porphyrio'], ['XC462116', 'Fulica atra'], ['XC462742', 'Tachybaptus ruficollis'], ['XC462870', 'Alcedo atthis'], ['XC463076', 'Alcedo atthis'], ['XC463078', 'Alcedo atthis'], ['XC463089', 'Alcedo atthis'], ['XC463296', 'Himantopus himantopus'], ['XC463331', 'Botaurus stellaris'], ['XC463509', 'Alcedo atthis'], ['XC463520', 'Acrocephalus scirpaceus'], ['XC463603', 'Fulica atra'], ['XC464578', 'Gallinula chloropus'], ['XC464633', 'Charadrius alexandrinus'], ['XC465331', 'Acrocephalus scirpaceus'], ['XC465333', 'Acrocephalus scirpaceus'], ['XC465476', 'Gallinula chloropus'], ['XC466132', 'Acrocephalus scirpaceus'], ['XC466157', 'Gallinula chloropus'], ['XC466742', 'Dendrocopos minor'], ['XC466744', 'Acrocephalus melanopogon'], ['XC466746', 'Acrocephalus melanopogon'], ['XC466747', 'Acrocephalus melanopogon'], ['XC466748', 'Acrocephalus melanopogon'], ['XC466765', 'Alcedo atthis'], ['XC466805', 'Fulica atra'], ['XC466883', 'Botaurus stellaris'], ['XC466994', 'Gallinula chloropus'], ['XC467260', 'Gallinula chloropus'], ['XC467261', 'Fulica atra'], ['XC467263', 'Gallinula chloropus'], ['XC467373', 'Ardea purpurea'], ['XC467402', 'Acrocephalus melanopogon'], ['XC467403', 'Acrocephalus melanopogon'], ['XC467596', 'Fulica atra'], ['XC467851', 'Acrocephalus arundinaceus'], ['XC468672', 'Fulica atra'], ['XC469295', 'Acrocephalus scirpaceus'], ['XC469410', 'Alcedo atthis'], ['XC469570', 'Circus aeruginosus'], ['XC469783', 'Fulica atra'], ['XC469794', 'Himantopus himantopus'], ['XC469957', 'Motacilla flava'], ['XC469959', 'Motacilla flava'], ['XC469976', 'Ardea purpurea'], ['XC470195', 'Gallinula chloropus'], ['XC470261', 'Himantopus himantopus'], ['XC470347', 'Acrocephalus scirpaceus'], ['XC470421', 'Ciconia ciconia'], ['XC470470', 'Porphyrio porphyrio'], ['XC470564', 'Anas (Mareca) strepera'], ['XC470657', 'Dendrocopos minor'], ['XC470740', 'Porphyrio porphyrio'], ['XC470769', 'Alcedo atthis'], ['XC470995', 'Gallinula chloropus'], ['XC471249', 'Gallinula chloropus'], ['XC471545', 'Ardea purpurea'], ['XC471554', 'Motacilla flava'], ['XC471558', 'Motacilla flava'], ['XC471731', 'Tachybaptus ruficollis'], ['XC471768', 'Tachybaptus ruficollis'], ['XC471769', 'Tachybaptus ruficollis'], ['XC471968', 'Acrocephalus scirpaceus'], ['XC472589', 'Ardea purpurea'], ['XC473249', 'Acrocephalus arundinaceus'], ['XC473571', 'Acrocephalus arundinaceus'], ['XC473833', 'Acrocephalus scirpaceus'], ['XC473930', 'Acrocephalus scirpaceus'], ['XC473954', 'Circus aeruginosus'], ['XC474017', 'Circus aeruginosus'], ['XC474019', 'Circus aeruginosus'], ['XC474176', 'Alcedo atthis'], ['XC474931', 'Acrocephalus scirpaceus'], ['XC475474', 'Acrocephalus arundinaceus'], ['XC475479', 'Acrocephalus arundinaceus'], ['XC475965', 'Acrocephalus arundinaceus'], ['XC476146', 'Acrocephalus arundinaceus'], ['XC476409', 'Acrocephalus scirpaceus'], ['XC476854', 'Motacilla flava'], ['XC477007', 'Coracias garrulus'], ['XC477329', 'Coracias garrulus'], ['XC477330', 'Coracias garrulus'], ['XC477331', 'Coracias garrulus'], ['XC477444', 'Motacilla flava'], ['XC477688', 'Acrocephalus arundinaceus'], ['XC477749', 'Gallinula chloropus'], ['XC477945', 'Acrocephalus arundinaceus'], ['XC478070', 'Gallinula chloropus'], ['XC478415', 'Acrocephalus arundinaceus'], ['XC478424', 'Himantopus himantopus'], ['XC478425', 'Himantopus himantopus'], ['XC478498', 'Ixobrychus minutus'], ['XC478568', 'Gallinula chloropus'], ['XC478850', 'Acrocephalus arundinaceus'], ['XC478876', 'Gallinula chloropus'], ['XC479839', 'Acrocephalus arundinaceus'], ['XC479924', 'Tachybaptus ruficollis'], ['XC479946', 'Himantopus himantopus'], ['XC479978', 'Acrocephalus arundinaceus'], ['XC480053', 'Acrocephalus melanopogon'], ['XC480061', 'Acrocephalus arundinaceus'], ['XC480203', 'Ardea purpurea'], ['XC480979', 'Fulica atra'], ['XC481289', 'Ardea purpurea'], ['XC481399', 'Himantopus himantopus'], ['XC481404', 'Himantopus himantopus'], ['XC481405', 'Himantopus himantopus'], ['XC481567', 'Acrocephalus scirpaceus'], ['XC482370', 'Fulica atra'], ['XC483334', 'Fulica atra'], ['XC483554', 'Fulica atra'], ['XC483557', 'Gallinula chloropus'], ['XC483692', 'Fulica atra'], ['XC483694', 'Fulica atra'], ['XC483697', 'Fulica atra'], ['XC483906', 'Acrocephalus arundinaceus'], ['XC484626', 'Dendrocopos minor'], ['XC484717', 'Motacilla flava'], ['XC485245', 'Ardea purpurea'], ['XC485312', 'Fulica atra'], ['XC485349', 'Charadrius alexandrinus'], ['XC485350', 'Charadrius alexandrinus'], ['XC485377', 'Ardea purpurea'], ['XC485380', 'Acrocephalus scirpaceus'], ['XC485381', 'Tachybaptus ruficollis'], ['XC486202', 'Alcedo atthis'], ['XC486755', 'Gallinula chloropus'], ['XC487692', 'Gallinula chloropus'], ['XC487693', 'Gallinula chloropus'], ['XC487843', 'Himantopus himantopus'], ['XC487848', 'Himantopus himantopus'], ['XC488300', 'Acrocephalus arundinaceus'], ['XC488301', 'Acrocephalus arundinaceus'], ['XC488409', 'Acrocephalus arundinaceus'], ['XC489350', 'Alcedo atthis'], ['XC489445', 'Ixobrychus minutus'], ['XC490614', 'Ixobrychus minutus'], ['XC490838', 'Alcedo atthis'], ['XC491787', 'Ixobrychus minutus'], ['XC493312', 'Alcedo atthis'], ['XC494524', 'Charadrius alexandrinus'], ['XC494536', 'Charadrius alexandrinus'], ['XC494537', 'Charadrius alexandrinus'], ['XC495092', 'Himantopus himantopus'], ['XC495533', 'Fulica atra'], ['XC495852', 'Fulica atra'], ['XC495975', 'Alcedo atthis'], ['XC496271', 'Fulica atra'], ['XC496380', 'Himantopus himantopus'], ['XC496443', 'Coracias garrulus'], ['XC497345', 'Porphyrio porphyrio'], ['XC497799', 'Tachybaptus ruficollis'], ['XC498608', 'Alcedo atthis'], ['XC499043', 'Alcedo atthis'], ['XC499081', 'Alcedo atthis'], ['XC499150', 'Porphyrio porphyrio'], ['XC499646', 'Alcedo atthis'], ['XC499652', 'Alcedo atthis'], ['XC500306', 'Alcedo atthis'], ['XC500406', 'Ciconia ciconia'], ['XC500624', 'Himantopus himantopus'], ['XC500750', 'Dendrocopos minor'], ['XC500751', 'Dendrocopos minor'], ['XC501144', 'Alcedo atthis'], ['XC501772', 'Circus aeruginosus'], ['XC501872', 'Fulica atra'], ['XC502956', 'Tachybaptus ruficollis'], ['XC502961', 'Alcedo atthis'], ['XC503769', 'Alcedo atthis'], ['XC503772', 'Alcedo atthis'], ['XC504105', 'Fulica atra'], ['XC504113', 'Gallinula chloropus'], ['XC505719', 'Alcedo atthis'], ['XC505960', 'Porphyrio porphyrio'], ['XC509421', 'Tachybaptus ruficollis'], ['XC509443', 'Alcedo atthis'], ['XC509674', 'Alcedo atthis'], ['XC510178', 'Himantopus himantopus'], ['XC510499', 'Fulica atra'], ['XC510569', 'Fulica atra'], ['XC510571', 'Fulica atra'], ['XC510585', 'Gallinula chloropus'], ['XC510716', 'Tachybaptus ruficollis'], ['XC510720', 'Tachybaptus ruficollis'], ['XC511182', 'Acrocephalus arundinaceus'], ['XC511183', 'Acrocephalus arundinaceus'], ['XC511184', 'Acrocephalus scirpaceus'], ['XC511190', 'Acrocephalus scirpaceus'], ['XC511192', 'Acrocephalus scirpaceus'], ['XC511193', 'Acrocephalus scirpaceus'], ['XC511641', 'Alcedo atthis'], ['XC511642', 'Alcedo atthis'], ['XC511647', 'Alcedo atthis'], ['XC511664', 'Alcedo atthis'], ['XC511665', 'Alcedo atthis'], ['XC511666', 'Alcedo atthis'], ['XC511667', 'Alcedo atthis'], ['XC511668', 'Alcedo atthis'], ['XC511669', 'Alcedo atthis'], ['XC511670', 'Alcedo atthis'], ['XC511671', 'Alcedo atthis'], ['XC511672', 'Alcedo atthis'], ['XC511673', 'Alcedo atthis'], ['XC511674', 'Alcedo atthis'], ['XC511677', 'Alcedo atthis'], ['XC511697', 'Acrocephalus scirpaceus'], ['XC512909', 'Anas platyrhynchos'], ['XC513304', 'Fulica atra'], ['XC514029', 'Himantopus himantopus'], ['XC514030', 'Himantopus himantopus'], ['XC514729', 'Himantopus himantopus'], ['XC514731', 'Himantopus himantopus'], ['XC515865', 'Ardea purpurea'], ['XC516174', 'Acrocephalus melanopogon'], ['XC516379', 'Tachybaptus ruficollis'], ['XC518345', 'Porphyrio porphyrio'], ['XC518346', 'Porphyrio porphyrio'], ['XC518347', 'Porphyrio porphyrio'], ['XC518348', 'Porphyrio porphyrio'], ['XC518349', 'Porphyrio porphyrio'], ['XC518351', 'Circus aeruginosus'], ['XC518425', 'Ciconia ciconia'], ['XC518432', 'Tachybaptus ruficollis'], ['XC518527', 'Tachybaptus ruficollis'], ['XC519437', 'Fulica atra'], ['XC519587', 'Motacilla flava'], ['XC519898', 'Botaurus stellaris'], ['XC519899', 'Botaurus stellaris'], ['XC519927', 'Acrocephalus arundinaceus'], ['XC519985', 'Dendrocopos minor'], ['XC519994', 'Acrocephalus scirpaceus'], ['XC520116', 'Tachybaptus ruficollis'], ['XC520709', 'Alcedo atthis'], ['XC521464', 'Porphyrio porphyrio'], ['XC521562', 'Anas platyrhynchos'], ['XC521563', 'Anas platyrhynchos'], ['XC521611', 'Ciconia ciconia'], ['XC521814', 'Porphyrio porphyrio'], ['XC522273', 'Charadrius alexandrinus'], ['XC522340', 'Acrocephalus melanopogon'], ['XC522491', 'Fulica atra'], ['XC522819', 'Alcedo atthis'], ['XC522864', 'Acrocephalus arundinaceus'], ['XC522990', 'Alcedo atthis'], ['XC522991', 'Alcedo atthis'], ['XC524288', 'Botaurus stellaris'], ['XC524524', 'Motacilla flava'], ['XC526231', 'Charadrius alexandrinus'], ['XC526232', 'Charadrius alexandrinus'], ['XC526238', 'Alcedo atthis'], ['XC526276', 'Alcedo atthis'], ['XC527477', 'Ciconia ciconia'], ['XC527806', 'Gallinula chloropus'], ['XC528227', 'Dendrocopos minor'], ['XC528507', 'Gallinula chloropus'], ['XC528508', 'Gallinula chloropus'], ['XC529190', 'Gallinula chloropus'], ['XC529774', 'Acrocephalus melanopogon'], ['XC531395', 'Himantopus himantopus'], ['XC533927', 'Gallinula chloropus'], ['XC534240', 'Dendrocopos minor'], ['XC534416', 'Fulica atra'], ['XC534607', 'Alcedo atthis'], ['XC534750', 'Gallinula chloropus'], ['XC534754', 'Gallinula chloropus'], ['XC534756', 'Gallinula chloropus'], ['XC536892', 'Anas platyrhynchos'], ['XC536895', 'Anas platyrhynchos'], ['XC538130', 'Alcedo atthis'], ['XC538132', 'Alcedo atthis'], ['XC538144', 'Fulica atra'], ['XC538213', 'Fulica atra'], ['XC538422', 'Ciconia ciconia'], ['XC538423', 'Ciconia ciconia'], ['XC538467', 'Ixobrychus minutus'], ['XC538478', 'Ixobrychus minutus'], ['XC538710', 'Gallinula chloropus'], ['XC538750', 'Fulica atra'], ['XC538941', 'Alcedo atthis'], ['XC538943', 'Alcedo atthis'], ['XC539273', 'Gallinula chloropus'], ['XC539614', 'Dendrocopos minor'], ['XC539686', 'Ixobrychus minutus'], ['XC540348', 'Fulica atra'], ['XC540475', 'Fulica atra'], ['XC540685', 'Fulica atra'], ['XC540742', 'Fulica atra'], ['XC540804', 'Gallinula chloropus'], ['XC540808', 'Gallinula chloropus'], ['XC542331', 'Gallinula chloropus'], ['XC542351', 'Acrocephalus melanopogon'], ['XC542356', 'Acrocephalus melanopogon'], ['XC542357', 'Acrocephalus melanopogon'], ['XC542359', 'Gallinula chloropus'], ['XC542543', 'Gallinula chloropus'], ['XC542651', 'Gallinula chloropus'], ['XC543233', 'Fulica atra'], ['XC543411', 'Gallinula chloropus'], ['XC543454', 'Fulica atra'], ['XC543771', 'Fulica atra'], ['XC543793', 'Gallinula chloropus'], ['XC544319', 'Fulica atra'], ['XC544658', 'Alcedo atthis'], ['XC544680', 'Circus aeruginosus'], ['XC544685', 'Botaurus stellaris'], ['XC544686', 'Circus aeruginosus'], ['XC544846', 'Charadrius alexandrinus'], ['XC544857', 'Dendrocopos minor'], ['XC544935', 'Gallinula chloropus'], ['XC545123', 'Gallinula chloropus'], ['XC545223', 'Fulica atra'], ['XC545614', 'Gallinula chloropus'], ['XC545755', 'Gallinula chloropus'], ['XC545994', 'Gallinula chloropus'], ['XC546757', 'Gallinula chloropus'], ['XC547095', 'Circus aeruginosus'], ['XC547368', 'Gallinula chloropus'], ['XC548338', 'Gallinula chloropus'], ['XC548458', 'Fulica atra'], ['XC548542', 'Botaurus stellaris'], ['XC548742', 'Gallinula chloropus'], ['XC548770', 'Fulica atra'], ['XC548772', 'Gallinula chloropus'], ['XC548774', 'Gallinula chloropus'], ['XC548775', 'Gallinula chloropus'], ['XC548777', 'Gallinula chloropus'], ['XC548872', 'Fulica atra'], ['XC550266', 'Fulica atra'], ['XC550870', 'Dendrocopos minor'], ['XC552628', 'Dendrocopos minor'], ['XC552636', 'Dendrocopos minor'], ['XC67347', 'Ixobrychus minutus'], ['XC67350', 'Himantopus himantopus'], ['XC67351', 'Himantopus himantopus'], ['XC81916', 'Circus aeruginosus'], ['XC88896', 'Botaurus stellaris'], ['XC90128', 'Himantopus himantopus'], ['XC90713', 'Botaurus stellaris'], ['XC91179', 'Ciconia ciconia'], ['XC93331', 'Ciconia ciconia'], ['XC97974', 'Tachybaptus ruficollis']]\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:29:32.760177Z",
     "start_time": "2024-06-18T13:29:32.756319Z"
    }
   },
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "audios_per_bird = defaultdict(list)\n",
    "for audio in bird_info:\n",
    "    bird_name = audio[1]\n",
    "    audios_per_bird[bird_name].append(audio[0])\n",
    "\n",
    "train_audio_names = []\n",
    "test_audio_names = []\n",
    "\n",
    "for bird, audios in audios_per_bird.items():\n",
    "    num_audios = len(audios)\n",
    "    num_train = int(0.7 * num_audios)\n",
    "\n",
    "    train_audio_names.extend([audio for audio in audios[:num_train]])\n",
    "    test_audio_names.extend([audio for audio in audios[num_train:]])\n",
    "\n",
    "print(\"Training list:\")\n",
    "print(train_audio_names)\n",
    "\n",
    "print(\"Testing list:\")\n",
    "print(test_audio_names)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training list:\n",
      "['XC100296', 'XC120378', 'XC171445', 'XC172692', 'XC180756', 'XC184313', 'XC184314', 'XC184429', 'XC214099', 'XC215785', 'XC216709', 'XC216710', 'XC234105', 'XC237486', 'XC25558', 'XC280964', 'XC280965', 'XC280966', 'XC293293', 'XC299191', 'XC309607', 'XC310507', 'XC314697', 'XC315733', 'XC317323', 'XC317421', 'XC326162', 'XC340916', 'XC354109', 'XC354111', 'XC361803', 'XC361804', 'XC361806', 'XC362697', 'XC363294', 'XC367219', 'XC372105', 'XC37543', 'XC396412', 'XC100619', 'XC100621', 'XC100622', 'XC112488', 'XC131532', 'XC134843', 'XC143954', 'XC145121', 'XC161966', 'XC164817', 'XC189879', 'XC189882', 'XC189883', 'XC196976', 'XC205076', 'XC235176', 'XC262846', 'XC269744', 'XC269745', 'XC280997', 'XC280998', 'XC296061', 'XC341264', 'XC357371', 'XC365601', 'XC102793', 'XC102794', 'XC103696', 'XC134257', 'XC154563', 'XC154909', 'XC155710', 'XC164172', 'XC184433', 'XC184434', 'XC208705', 'XC223168', 'XC252756', 'XC252757', 'XC270056', 'XC281357', 'XC281358', 'XC281359', 'XC281360', 'XC296053', 'XC307038', 'XC314267', 'XC314269', 'XC316371', 'XC324343', 'XC345884', 'XC345889', 'XC345951', 'XC372401', 'XC103243', 'XC106971', 'XC134761', 'XC134762', 'XC146712', 'XC149153', 'XC156081', 'XC180287', 'XC180605', 'XC186806', 'XC245725', 'XC253188', 'XC270427', 'XC270428', 'XC288186', 'XC340917', 'XC35968', 'XC35969', 'XC373853', 'XC376813', 'XC377102', 'XC379836', 'XC379837', 'XC379838', 'XC416949', 'XC423144', 'XC106948', 'XC134457', 'XC141019', 'XC145159', 'XC163297', 'XC172478', 'XC172480', 'XC213784', 'XC253273', 'XC310528', 'XC310529', 'XC329900', 'XC329910', 'XC347269', 'XC356695', 'XC356696', 'XC357198', 'XC357368', 'XC357387', 'XC357630', 'XC357888', 'XC358560', 'XC358628', 'XC358629', 'XC360704', 'XC360705', 'XC406576', 'XC408868', 'XC418401', 'XC422660', 'XC429780', 'XC442556', 'XC443794', 'XC456255', 'XC109629', 'XC208527', 'XC209951', 'XC210379', 'XC26959', 'XC26960', 'XC113587', 'XC113968', 'XC127375', 'XC196991', 'XC207216', 'XC287835', 'XC303987', 'XC303989', 'XC304006', 'XC306130', 'XC340598', 'XC348396', 'XC357023', 'XC118195', 'XC132077', 'XC132435', 'XC134081', 'XC144533', 'XC154289', 'XC154931', 'XC155092', 'XC184422', 'XC190968', 'XC206253', 'XC206255', 'XC263296', 'XC263298', 'XC277591', 'XC280353', 'XC281044', 'XC281045', 'XC29798', 'XC312572', 'XC313565', 'XC328055', 'XC332043', 'XC332533', 'XC338279', 'XC338280', 'XC338283', 'XC347981', 'XC370227', 'XC370744', 'XC374912', 'XC374913', 'XC379892', 'XC385622', 'XC413271', 'XC413273', 'XC413305', 'XC417021', 'XC428707', 'XC431789', 'XC440159', 'XC448948', 'XC454995', 'XC459861', 'XC463296', 'XC469794', 'XC120332', 'XC135658', 'XC144531', 'XC155665', 'XC182877', 'XC185101', 'XC185103', 'XC185104', 'XC185268', 'XC185273', 'XC318414', 'XC318416', 'XC318417', 'XC318418', 'XC333813', 'XC333823', 'XC338275', 'XC338276', 'XC338277', 'XC339477', 'XC339478', 'XC339480', 'XC341211', 'XC341212', 'XC341986', 'XC358986', 'XC359026', 'XC366752', 'XC367088', 'XC371000', 'XC371001', 'XC371002', 'XC371003', 'XC371004', 'XC371155', 'XC376954', 'XC376955', 'XC120411', 'XC129156', 'XC129193', 'XC129377', 'XC133548', 'XC135278', 'XC142515', 'XC150591', 'XC153193', 'XC171122', 'XC197138', 'XC236456', 'XC236458', 'XC308991', 'XC310709', 'XC312823', 'XC314610', 'XC329937', 'XC356856', 'XC364346', 'XC383514', 'XC400197', 'XC401830', 'XC405096', 'XC407219', 'XC459694', 'XC466742', 'XC120730', 'XC121174', 'XC134051', 'XC141170', 'XC141571', 'XC185305', 'XC188143', 'XC188144', 'XC188145', 'XC188146', 'XC188147', 'XC188148', 'XC188149', 'XC188150', 'XC240134', 'XC312051', 'XC129188', 'XC134073', 'XC134219', 'XC134221', 'XC135579', 'XC141171', 'XC149045', 'XC149069', 'XC149070', 'XC149071', 'XC149072', 'XC154554', 'XC154555', 'XC167717', 'XC169478', 'XC171088', 'XC172946', 'XC181349', 'XC235721', 'XC244232', 'XC253191', 'XC25468', 'XC27569', 'XC295996', 'XC327581', 'XC340922', 'XC372950', 'XC379596', 'XC134240', 'XC153870', 'XC161931', 'XC183432', 'XC183433', 'XC183440', 'XC215129', 'XC225374', 'XC232846', 'XC26929', 'XC26930', 'XC280719', 'XC280980', 'XC280981', 'XC280982', 'XC280983', 'XC280984', 'XC301648', 'XC317692', 'XC356385', 'XC357687', 'XC357688', 'XC357885', 'XC367685', 'XC373560', 'XC377189', 'XC388290', 'XC388291', 'XC40189', 'XC407138', 'XC407139', 'XC412840', 'XC419196', 'XC422341', 'XC42554', 'XC459391', 'XC459392', 'XC459393', 'XC135429', 'XC150699', 'XC166246', 'XC184842', 'XC191096', 'XC205050', 'XC227072', 'XC237893', 'XC265127', 'XC270373', 'XC276851', 'XC277204', 'XC279853', 'XC279854', 'XC280978', 'XC298591', 'XC299495', 'XC311152', 'XC311153', 'XC318091', 'XC343737', 'XC356656', 'XC362363', 'XC369056', 'XC374911', 'XC380536', 'XC380537', 'XC381813', 'XC431154', 'XC267202', 'XC292201', 'XC299330', 'XC302642', 'XC303341', 'XC336861', 'XC336862', 'XC336866', 'XC337592', 'XC337594', 'XC357495', 'XC357910', 'XC357911', 'XC357912', 'XC357913', 'XC357914', 'XC358495', 'XC358561', 'XC358639', 'XC367080', 'XC367081', 'XC380967', 'XC381727', 'XC381860', 'XC381861', 'XC381862', 'XC386307', 'XC399454', 'XC399455', 'XC410111', 'XC410121', 'XC410140', 'XC412265', 'XC429604', 'XC429605', 'XC429606', 'XC429607', 'XC395186', 'XC402382', 'XC425999', 'XC428404', 'XC437194', 'XC442159', 'XC447433', 'XC459840', 'XC459889', 'XC460195', 'XC462870', 'XC463076', 'XC463078', 'XC463089', 'XC463509', 'XC466765', 'XC469410', 'XC470769', 'XC474176', 'XC486202', 'XC489350', 'XC490838', 'XC493312', 'XC495975', 'XC498608', 'XC499043', 'XC499081', 'XC499646', 'XC499652', 'XC500306', 'XC501144', 'XC502961', 'XC503769', 'XC503772', 'XC505719', 'XC509443', 'XC509674', 'XC511641', 'XC511642', 'XC511647', 'XC511664', 'XC511665', 'XC511666', 'XC511667', 'XC416749', 'XC417119', 'XC418140', 'XC418412', 'XC421398', 'XC421400', 'XC421435', 'XC421439', 'XC421556', 'XC421578', 'XC424110', 'XC429210', 'XC437805', 'XC437807', 'XC437808', 'XC440361', 'XC445285', 'XC447310', 'XC463520', 'XC465331', 'XC465333', 'XC466132', 'XC469295', 'XC470347', 'XC471968', 'XC417157', 'XC417158', 'XC417425', 'XC417722', 'XC419606', 'XC420352', 'XC420542', 'XC422018', 'XC422019', 'XC422797', 'XC428977', 'XC449187', 'XC467851', 'XC473249', 'XC473571', 'XC475474', 'XC475479', 'XC475965', 'XC476146', 'XC477688', 'XC477945', 'XC478415', 'XC478850', 'XC436382', 'XC436465', 'XC439336', 'XC448652', 'XC448653', 'XC457036', 'XC458081', 'XC458082', 'XC458084', 'XC459669', 'XC459686', 'XC461324', 'XC462116', 'XC463603', 'XC466805', 'XC467261', 'XC467596', 'XC468672', 'XC469783', 'XC480979', 'XC482370', 'XC483334', 'XC483554', 'XC483692', 'XC483694', 'XC483697', 'XC485312', 'XC495533', 'XC495852', 'XC496271', 'XC501872', 'XC504105', 'XC510499', 'XC510569', 'XC510571', 'XC513304', 'XC519437', 'XC522491', 'XC436938', 'XC440482', 'XC454200', 'XC459964', 'XC464578', 'XC465476', 'XC466157', 'XC466994', 'XC467260', 'XC467263', 'XC470195', 'XC470995', 'XC471249', 'XC477749', 'XC478070', 'XC478568', 'XC478876', 'XC483557', 'XC486755', 'XC487692', 'XC487693', 'XC504113', 'XC510585', 'XC527806', 'XC528507', 'XC528508', 'XC529190', 'XC533927', 'XC534750', 'XC534754', 'XC534756', 'XC538710', 'XC539273', 'XC540804', 'XC540808', 'XC542331', 'XC542359']\n",
      "Testing list:\n",
      "['XC396413', 'XC396414', 'XC396416', 'XC414856', 'XC417089', 'XC42540', 'XC42541', 'XC42542', 'XC463331', 'XC466883', 'XC519898', 'XC519899', 'XC524288', 'XC544685', 'XC548542', 'XC88896', 'XC90713', 'XC447442', 'XC454603', 'XC469570', 'XC473954', 'XC474017', 'XC474019', 'XC501772', 'XC518351', 'XC544680', 'XC544686', 'XC547095', 'XC81916', 'XC379953', 'XC422437', 'XC428706', 'XC431217', 'XC469957', 'XC469959', 'XC471554', 'XC471558', 'XC476854', 'XC477444', 'XC484717', 'XC519587', 'XC524524', 'XC431155', 'XC431157', 'XC431158', 'XC434438', 'XC478498', 'XC489445', 'XC490614', 'XC491787', 'XC538467', 'XC538478', 'XC539686', 'XC67347', 'XC460172', 'XC460174', 'XC466744', 'XC466746', 'XC466747', 'XC466748', 'XC467402', 'XC467403', 'XC480053', 'XC516174', 'XC522340', 'XC529774', 'XC542351', 'XC542356', 'XC542357', 'XC364458', 'XC41249', 'XC470564', 'XC367038', 'XC512909', 'XC521562', 'XC521563', 'XC536892', 'XC536895', 'XC470261', 'XC478424', 'XC478425', 'XC479946', 'XC481399', 'XC481404', 'XC481405', 'XC487843', 'XC487848', 'XC495092', 'XC496380', 'XC500624', 'XC510178', 'XC514029', 'XC514030', 'XC514729', 'XC514731', 'XC531395', 'XC67350', 'XC67351', 'XC90128', 'XC376956', 'XC376958', 'XC376959', 'XC411002', 'XC432232', 'XC435064', 'XC456725', 'XC464633', 'XC485349', 'XC485350', 'XC494524', 'XC494536', 'XC494537', 'XC522273', 'XC526231', 'XC526232', 'XC544846', 'XC470657', 'XC484626', 'XC500750', 'XC500751', 'XC519985', 'XC528227', 'XC534240', 'XC539614', 'XC544857', 'XC550870', 'XC552628', 'XC552636', 'XC31615', 'XC318427', 'XC432572', 'XC477007', 'XC477329', 'XC477330', 'XC477331', 'XC496443', 'XC416671', 'XC428861', 'XC440123', 'XC470421', 'XC500406', 'XC518425', 'XC521611', 'XC527477', 'XC538422', 'XC538423', 'XC91179', 'XC93331', 'XC459394', 'XC462742', 'XC471731', 'XC471768', 'XC471769', 'XC479924', 'XC485381', 'XC497799', 'XC502956', 'XC509421', 'XC510716', 'XC510720', 'XC516379', 'XC518432', 'XC518527', 'XC520116', 'XC97974', 'XC432145', 'XC432223', 'XC44631', 'XC459832', 'XC467373', 'XC469976', 'XC471545', 'XC472589', 'XC480203', 'XC481289', 'XC485245', 'XC485377', 'XC515865', 'XC429608', 'XC440102', 'XC441909', 'XC461762', 'XC470470', 'XC470740', 'XC497345', 'XC499150', 'XC505960', 'XC518345', 'XC518346', 'XC518347', 'XC518348', 'XC518349', 'XC521464', 'XC521814', 'XC511668', 'XC511669', 'XC511670', 'XC511671', 'XC511672', 'XC511673', 'XC511674', 'XC511677', 'XC520709', 'XC522819', 'XC522990', 'XC522991', 'XC526238', 'XC526276', 'XC534607', 'XC538130', 'XC538132', 'XC538941', 'XC538943', 'XC544658', 'XC473833', 'XC473930', 'XC474931', 'XC476409', 'XC481567', 'XC485380', 'XC511184', 'XC511190', 'XC511192', 'XC511193', 'XC511697', 'XC519994', 'XC479839', 'XC479978', 'XC480061', 'XC483906', 'XC488300', 'XC488301', 'XC488409', 'XC511182', 'XC511183', 'XC519927', 'XC522864', 'XC534416', 'XC538144', 'XC538213', 'XC538750', 'XC540348', 'XC540475', 'XC540685', 'XC540742', 'XC543233', 'XC543454', 'XC543771', 'XC544319', 'XC545223', 'XC548458', 'XC548770', 'XC548872', 'XC550266', 'XC542543', 'XC542651', 'XC543411', 'XC543793', 'XC544935', 'XC545123', 'XC545614', 'XC545755', 'XC545994', 'XC546757', 'XC547368', 'XC548338', 'XC548742', 'XC548772', 'XC548774', 'XC548775', 'XC548777']\n"
     ]
    }
   ],
   "execution_count": 24
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:29:54.192419Z",
     "start_time": "2024-06-18T13:29:41.278341Z"
    }
   },
   "source": [
    "# Create a new file h5 to store the data for training the model\n",
    "with (  h5py.File(os.path.abspath(\"./data/data_train.h5\"), \"w\") as hf_train,\n",
    "        h5py.File(os.path.abspath(\"./data/spectrograms.h5\")) as hf ):\n",
    "    # Create group \"X\" to store the spectograms\n",
    "    X_group = hf_train.create_group(\"X\")\n",
    "\n",
    "    # Create group \"y\" to store the names of the birds (labels)\n",
    "    y_group = hf_train.create_group(\"y\")\n",
    "\n",
    "    for audio_name in train_audio_names:\n",
    "        # get the name of the corresponding bird\n",
    "        bird_name = hf[audio_name].attrs[\"bird\"]\n",
    "\n",
    "        # read the spectograms of the audio\n",
    "        spectrogram_names = list(hf[audio_name].keys())\n",
    "\n",
    "        for spectrogram_name in spectrogram_names:\n",
    "            # Read the espectogram\n",
    "            spectrogram_data = hf[audio_name][spectrogram_name][...]\n",
    "            # Save the espectogram in group X\n",
    "            X_group.create_dataset(spectrogram_name, data=spectrogram_data)\n",
    "            # Save the name of the corresponding bird in th group Y\n",
    "            y_group.create_dataset(spectrogram_name, data=bird_name)\n",
    "            \n",
    "# create the LabelEncoder\n",
    "le = LabelEncoder()\n",
    "X_train = []\n",
    "y_train = []\n",
    "\n",
    "# Agrupamos espectrogramas\n",
    "with h5py.File(os.path.abspath(\"./data/data_train.h5\")) as hf_train:\n",
    "    # Access to a specific group\n",
    "    group_Xtrain = 'X'\n",
    "    group_ytrain = 'y'\n",
    "    groupXtrain = hf_train[group_Xtrain]\n",
    "    groupytrain = hf_train[group_ytrain]\n",
    "\n",
    "    # Apilamos espectrogramas y labels\n",
    "    for key in groupXtrain.keys():\n",
    "        spectogram = groupXtrain[key]\n",
    "        X_train.append(spectogram[()])\n",
    "\n",
    "    for key in groupytrain.keys():\n",
    "        bird = groupytrain[key]\n",
    "        y_train.append(bird[()])\n"
   ],
   "outputs": [],
   "execution_count": 25
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:29:58.919242Z",
     "start_time": "2024-06-18T13:29:58.911398Z"
    }
   },
   "source": [
    "y_train = [data.decode('utf-8') for data in y_train]\n",
    "y_train"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Motacilla flava',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Acrocephalus melanopogon',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Ixobrychus minutus',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Anas (Mareca) strepera',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Charadrius alexandrinus',\n",
       " 'Charadrius alexandrinus',\n",
       " 'Charadrius alexandrinus',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Botaurus stellaris',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Anas platyrhynchos',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Circus aeruginosus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Dendrocopos minor',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Coracias garrulus',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Himantopus himantopus',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Ciconia ciconia',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " 'Tachybaptus ruficollis',\n",
       " ...]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 26
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:30:03.833944Z",
     "start_time": "2024-06-18T13:30:03.826641Z"
    }
   },
   "source": [
    "print(len(X_train))\n",
    "print(X_train[0])\n",
    "print(len(y_train))\n",
    "print(y_train[0])\n",
    "\n",
    "# Encode the labels\n",
    "y_train = le.fit_transform(y_train)\n",
    "print(y_train)\n",
    "# Print the labels of the numeric values\n",
    "print(le.classes_)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12401\n",
      "[[-21.927137  -15.885366  -19.596134  ... -18.738977  -23.22766\n",
      "  -17.782948 ]\n",
      " [-12.636152  -11.506808  -11.128322  ... -10.50968   -10.998185\n",
      "  -10.78977  ]\n",
      " [-12.187175  -11.1988535  -9.465328  ... -12.4952135  -8.489763\n",
      "   -6.875704 ]\n",
      " ...\n",
      " [-30.68443   -33.02369   -44.774403  ... -43.156998  -44.312088\n",
      "  -45.63829  ]\n",
      " [-31.113174  -33.508293  -51.244854  ... -46.52222   -44.80766\n",
      "  -45.12627  ]\n",
      " [-31.21983   -33.517517  -46.67358   ... -47.840645  -51.671494\n",
      "  -52.546104 ]]\n",
      "12401\n",
      "Botaurus stellaris\n",
      "[ 7  7  7 ... 14 14 14]\n",
      "['Acrocephalus arundinaceus' 'Acrocephalus melanopogon'\n",
      " 'Acrocephalus scirpaceus' 'Alcedo atthis' 'Anas (Mareca) strepera'\n",
      " 'Anas platyrhynchos' 'Ardea purpurea' 'Botaurus stellaris'\n",
      " 'Charadrius alexandrinus' 'Ciconia ciconia' 'Circus aeruginosus'\n",
      " 'Coracias garrulus' 'Dendrocopos minor' 'Fulica atra'\n",
      " 'Gallinula chloropus' 'Himantopus himantopus' 'Ixobrychus minutus'\n",
      " 'Motacilla flava' 'Porphyrio porphyrio' 'Tachybaptus ruficollis']\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:30:10.451757Z",
     "start_time": "2024-06-18T13:30:08.603415Z"
    }
   },
   "source": [
    "X_train = np.array(X_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "X_train = X_train / 255.0\n",
    "\n",
    "X_train = X_train.reshape((-1, 224, 224, 1))\n",
    "print(X_train.shape)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12401, 224, 224, 1)\n"
     ]
    }
   ],
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T13:30:13.388811Z",
     "start_time": "2024-06-18T13:30:13.386809Z"
    }
   },
   "source": [
    "print(y_train.shape)\n",
    "print(len(y_train))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(12401,)\n",
      "12401\n"
     ]
    }
   ],
   "execution_count": 29
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:05:54.080673Z",
     "start_time": "2024-06-18T13:30:17.748479Z"
    }
   },
   "source": [
    "from keras.src.losses import SparseCategoricalCrossentropy\n",
    "\n",
    "# Si el modelo no existe, entrenarlo y guardarlo,\n",
    "if not os.path.exists(os.path.abspath(\"./data/DL_model_birds.h5\")):\n",
    "    # CNN model\n",
    "    model = models.Sequential([\n",
    "        layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 1)),\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        layers.Conv2D(64, (3, 3), activation='relu'),\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        layers.Conv2D(128, (3, 3), activation='relu'),\n",
    "        layers.MaxPooling2D((2, 2)),\n",
    "        layers.Conv2D(256, (3, 3), activation='relu'),\n",
    "        layers.Dropout(0.5),\n",
    "        layers.Flatten(),\n",
    "        layers.Dense(64, activation='relu'),\n",
    "        layers.Dropout(0.5),\n",
    "        layers.Dense(20)    \n",
    "    ])\n",
    "    \n",
    "    # Compilar el modelo\n",
    "    model.compile(optimizer='adam', loss=SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])\n",
    "    \n",
    "    # Entrenar el modelo\n",
    "    model.fit(X_train, y_train, epochs=35)\n",
    "    \n",
    "    # Guardar el modelo\n",
    "    model.save(os.path.abspath(\"./data/DL_model_birds.h5\"))\n",
    "    print(f\"Modelo guardado exitosamente en {os.path.abspath('./data/DL_model_birds.h5')}\")\n",
    "else:\n",
    "    from tensorflow.keras.models import load_model\n",
    "    # cargar el modelo\n",
    "    model = load_model(os.path.abspath(\"./data/DL_model_birds.h5\"))\n",
    "    print(f\"Cargamos el modelo desde el archivo {os.path.abspath('./data/DL_model_birds.h5')}\")"
   ],
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/edwinlopezariaz/UNI/MINERIA-DATOS/PRACTICA/mbd-datamining-p01/.venv/lib/python3.11/site-packages/keras/src/layers/convolutional/base_conv.py:99: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m163s\u001B[0m 417ms/step - accuracy: 0.2622 - loss: 2.3925\n",
      "Epoch 2/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 422ms/step - accuracy: 0.5450 - loss: 1.4102\n",
      "Epoch 3/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m171s\u001B[0m 442ms/step - accuracy: 0.6518 - loss: 1.0621\n",
      "Epoch 4/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 422ms/step - accuracy: 0.7017 - loss: 0.8910\n",
      "Epoch 5/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m160s\u001B[0m 412ms/step - accuracy: 0.7339 - loss: 0.7690\n",
      "Epoch 6/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 422ms/step - accuracy: 0.7680 - loss: 0.6717\n",
      "Epoch 7/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 423ms/step - accuracy: 0.7908 - loss: 0.6088\n",
      "Epoch 8/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m163s\u001B[0m 419ms/step - accuracy: 0.8104 - loss: 0.5489\n",
      "Epoch 9/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m163s\u001B[0m 419ms/step - accuracy: 0.8315 - loss: 0.4898\n",
      "Epoch 10/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m163s\u001B[0m 420ms/step - accuracy: 0.8389 - loss: 0.4692\n",
      "Epoch 11/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 424ms/step - accuracy: 0.8497 - loss: 0.4251\n",
      "Epoch 12/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 424ms/step - accuracy: 0.8582 - loss: 0.3992\n",
      "Epoch 13/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 422ms/step - accuracy: 0.8695 - loss: 0.3705\n",
      "Epoch 14/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 422ms/step - accuracy: 0.8683 - loss: 0.3592\n",
      "Epoch 15/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m163s\u001B[0m 419ms/step - accuracy: 0.8811 - loss: 0.3392\n",
      "Epoch 16/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 423ms/step - accuracy: 0.8878 - loss: 0.3139\n",
      "Epoch 17/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 423ms/step - accuracy: 0.8926 - loss: 0.2971\n",
      "Epoch 18/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m162s\u001B[0m 418ms/step - accuracy: 0.8980 - loss: 0.2895\n",
      "Epoch 19/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m167s\u001B[0m 430ms/step - accuracy: 0.9029 - loss: 0.2707\n",
      "Epoch 20/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 423ms/step - accuracy: 0.9098 - loss: 0.2588\n",
      "Epoch 21/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m164s\u001B[0m 422ms/step - accuracy: 0.9098 - loss: 0.2600\n",
      "Epoch 22/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m162s\u001B[0m 418ms/step - accuracy: 0.9112 - loss: 0.2503\n",
      "Epoch 23/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m162s\u001B[0m 418ms/step - accuracy: 0.9151 - loss: 0.2342\n",
      "Epoch 24/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m162s\u001B[0m 417ms/step - accuracy: 0.9152 - loss: 0.2437\n",
      "Epoch 25/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m161s\u001B[0m 415ms/step - accuracy: 0.9212 - loss: 0.2301\n",
      "Epoch 26/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m163s\u001B[0m 421ms/step - accuracy: 0.9189 - loss: 0.2184\n",
      "Epoch 27/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m160s\u001B[0m 412ms/step - accuracy: 0.9134 - loss: 0.2416\n",
      "Epoch 28/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m160s\u001B[0m 413ms/step - accuracy: 0.9134 - loss: 0.2363\n",
      "Epoch 29/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m161s\u001B[0m 415ms/step - accuracy: 0.9236 - loss: 0.2118\n",
      "Epoch 30/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m173s\u001B[0m 447ms/step - accuracy: 0.9304 - loss: 0.1944\n",
      "Epoch 31/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m165s\u001B[0m 424ms/step - accuracy: 0.9286 - loss: 0.1915\n",
      "Epoch 32/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m162s\u001B[0m 416ms/step - accuracy: 0.9310 - loss: 0.1904\n",
      "Epoch 33/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m165s\u001B[0m 426ms/step - accuracy: 0.9339 - loss: 0.1861\n",
      "Epoch 34/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m175s\u001B[0m 452ms/step - accuracy: 0.9353 - loss: 0.1786\n",
      "Epoch 35/35\n",
      "\u001B[1m388/388\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m162s\u001B[0m 417ms/step - accuracy: 0.9313 - loss: 0.1955\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Modelo guardado exitosamente en /Users/edwinlopezariaz/UNI/MINERIA-DATOS/PRACTICA/mbd-datamining-p01/data/DL_model_birds.h5\n"
     ]
    }
   ],
   "execution_count": 30
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "  ## Testing part: X_test, y_test"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:06:31.568369Z",
     "start_time": "2024-06-18T15:06:28.574403Z"
    }
   },
   "source": [
    "# H5 file to store the testing data\n",
    "with (  h5py.File(os.path.abspath(\"./data/data_test.h5\"), \"w\") as hf_test, \n",
    "        h5py.File(os.path.abspath(\"./data/spectrograms.h5\")) as hf ):\n",
    "    # Group \"X\" to store the spectograms for testing\n",
    "    X_group = hf_test.create_group(\"X\")\n",
    "\n",
    "    #  Group \"X\" to store the labels for testing\n",
    "    y_group = hf_test.create_group(\"y\")\n",
    "\n",
    "    for audio_name in test_audio_names:\n",
    "        # Get the name of the corresponding bird of the audio\n",
    "        bird_name = hf[audio_name].attrs[\"bird\"]\n",
    "\n",
    "        # read the spectograms\n",
    "        spectrogram_names = list(hf[audio_name].keys())\n",
    "\n",
    "        for spectrogram_name in spectrogram_names:\n",
    "            # Read the spectograms\n",
    "            spectrogram_data = hf[audio_name][spectrogram_name][...]\n",
    "            # Save the spectogram in group \"X\"\n",
    "            X_group.create_dataset(spectrogram_name, data=spectrogram_data)\n",
    "            # Save the name of the corresponding bird in group \"y\"\n",
    "            y_group.create_dataset(spectrogram_name, data=bird_name)"
   ],
   "outputs": [],
   "execution_count": 31
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:06:35.981022Z",
     "start_time": "2024-06-18T15:06:35.929115Z"
    }
   },
   "source": [
    "# LabelEncoder\n",
    "le = LabelEncoder()\n",
    "X_test = []\n",
    "y_test = []"
   ],
   "outputs": [],
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:06:41.014469Z",
     "start_time": "2024-06-18T15:06:40.349828Z"
    }
   },
   "source": [
    "with h5py.File(os.path.abspath(\"./data/data_test.h5\")) as hf_test:\n",
    "    # Access to a specific group\n",
    "    group_Xtest = 'X'\n",
    "    group_ytest = 'y'\n",
    "    groupXtest = hf_test[group_Xtest]\n",
    "    groupytest = hf_test[group_ytest]\n",
    "\n",
    "    # Save spectograms and labels\n",
    "    for key in groupXtest.keys():\n",
    "        spectogram = groupXtest[key]\n",
    "        X_test.append(spectogram[()])\n",
    "\n",
    "    for key in groupytest.keys():\n",
    "        bird = groupytest[key]\n",
    "        y_test.append(bird[()])"
   ],
   "outputs": [],
   "execution_count": 33
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:06:44.386990Z",
     "start_time": "2024-06-18T15:06:44.383327Z"
    }
   },
   "source": [
    "y_test = le.fit_transform(y_test)\n",
    "print(y_test)\n",
    "\n",
    "print(len(X_test))\n",
    "print(X_test[0])\n",
    "print(len(y_test))\n",
    "print(y_test[0])"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11 11 11 ... 19 19 19]\n",
      "5005\n",
      "[[-20.10495  -22.45483  -34.465416 ... -31.30346  -23.764065 -20.10495 ]\n",
      " [-20.191296 -22.451729 -34.652542 ... -31.30468  -23.671677 -20.191296]\n",
      " [-20.099775 -22.468739 -34.67266  ... -31.017902 -23.719769 -20.099775]\n",
      " ...\n",
      " [-36.612804 -38.671574 -49.253773 ... -39.029205 -36.009964 -36.612804]\n",
      " [-36.426376 -38.79451  -50.17266  ... -43.316032 -35.622826 -36.426376]\n",
      " [-36.450287 -38.73686  -51.529774 ... -44.752293 -36.199436 -36.450287]]\n",
      "5005\n",
      "11\n"
     ]
    }
   ],
   "execution_count": 34
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:06:51.431627Z",
     "start_time": "2024-06-18T15:06:51.145036Z"
    }
   },
   "source": [
    "X_test = np.array(X_test)\n",
    "y_test = np.array(y_test)\n",
    "\n",
    "X_test = X_test / 255.0\n",
    "X_test = X_test.reshape((-1, 224, 224, 1))"
   ],
   "outputs": [],
   "execution_count": 35
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:07:12.842169Z",
     "start_time": "2024-06-18T15:06:55.475519Z"
    }
   },
   "source": [
    "# Evaluation of the model with the testing data\n",
    "loss, accuracy = model.evaluate(X_test, y_test)\n",
    "\n",
    "print(\"Pérdida:\", loss)\n",
    "print(\"Precisión:\", accuracy)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m157/157\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m17s\u001B[0m 109ms/step - accuracy: 0.7417 - loss: 1.8398\n",
      "Pérdida: 1.3286551237106323\n",
      "Precisión: 0.7746253609657288\n"
     ]
    }
   ],
   "execution_count": 36
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "## Predictions of the models and confusion matrix"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:07:37.208513Z",
     "start_time": "2024-06-18T15:07:20.080745Z"
    }
   },
   "source": [
    "# Prediction of the model with testing data\n",
    "y_pred = model.predict(X_test)\n",
    "y_pred = np.argmax(y_pred, axis=1)\n",
    "\n",
    "# F1-score\n",
    "f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "print(\"F1 Score:\", f1)"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001B[1m157/157\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m17s\u001B[0m 108ms/step\n",
      "F1 Score: 0.776846434692808\n"
     ]
    }
   ],
   "execution_count": 37
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-06-18T15:07:44.552721Z",
     "start_time": "2024-06-18T15:07:44.062846Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from utils import CLASS_NAMES\n",
    "\n",
    "# Confusion matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "conf_matrix_df = pd.DataFrame(cm, index=CLASS_NAMES, columns=CLASS_NAMES)\n",
    "\n",
    "# Plot\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.heatmap(conf_matrix_df, annot=True, fmt=\"d\", cmap=\"Blues\", cbar=False)\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title(\"Confusion Matrix - Deep Learning Model\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 38
  },
  {
   "metadata": {},
   "cell_type": "code",
   "outputs": [],
   "execution_count": null,
   "source": ""
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "birds",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}