pop_rock_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import musif\n",
    "# from musif.config import ExtractConfiguration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib.request\n",
    "import zipfile\n",
    "from pathlib import Path\n",
    "\n",
    "data_dir = Path(\"data_poprock\")\n",
    "dataset_path = \"dataset.zip\"\n",
    "urllib.request.urlretrieve(\"https://figshare.com/ndownloader/articles/5436031/versions/1\", dataset_path)\n",
    "with zipfile.ZipFile(dataset_path, 'r') as zip_ref:\n",
    "    zip_ref.extractall(data_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# musif.extract.constants contains various constants useful to access the `data` dictionary\n",
    "import musif.extract.constants as C\n",
    "\n",
    "# the following list is specific to this dataset\n",
    "drums_list = ['drumset', 'tambourine', 'drum', 'concert snare drum', 'hi-hat', 'automobile brake drums']\n",
    "\n",
    "# a hook is any object that contains a `execute(cfg, data)` method\n",
    "class RemoveDrums:\n",
    "    def execute(cfg, data):\n",
    "        # let's get the list of parts\n",
    "        parts = list(data[C.DATA_FILTERED_PARTS])\n",
    "        # C.DATA_SCORE is a music21.stream.Score object\n",
    "        drums = [i for i in data[C.DATA_SCORE].parts if i._partName.split(',')[0].lower() in drums_list]\n",
    "        # remove all the drums from the score\n",
    "        data[C.DATA_SCORE].remove(drums)\n",
    "        # reset the list of parts in the dictionary\n",
    "        data[C.DATA_FILTERED_PARTS] = tuple([p for p in parts if p not in drums])\n",
    "        # return the data\n",
    "        return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RenameSimilarParts:\n",
    "    def execute(cfg, data):\n",
    "        parts = list(data[C.DATA_FILTERED_PARTS])\n",
    "        part_names = set()\n",
    "        counter = 1\n",
    "        for part in data[C.DATA_SCORE].parts:\n",
    "            \n",
    "            # If the part is already in part_names set, we must change its partName, Abbreviation and Id\n",
    "            if part.partName in part_names:\n",
    "                part.partName = part.partName + f'({counter})'\n",
    "                part.partAbbreviation = part.partAbbreviation + f'({counter})'\n",
    "                part.id = part.id+ f'({counter})'\n",
    "                counter += 1\n",
    "                part_names.add(part.partName)\n",
    "            else:\n",
    "                part_names.add(part.partName)\n",
    "        # By altering the part object of music21, the change its already reflected also in the parts list.       \n",
    "        data[C.DATA_FILTERED_PARTS] = tuple(parts)\n",
    "        return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ntpath import basename\n",
    "from musif.musicxml.tempo import get_number_of_beats\n",
    "import musif.extract.constants as C\n",
    "from musif.extract.features.core.constants import DATA_NOTES_AND_RESTS, DATA_MEASURES\n",
    "from musif.extract.features.tempo.constants import TIME_SIGNATURE\n",
    "from musif.extract.features.prefix import get_part_feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "VOICE_SILENCE_BEATS = 'voice_silence_beats'\n",
    "\n",
    "class custom_feature_package:\n",
    "    class voice_silence_beats:\n",
    "        \"\"\"\n",
    "        This feature calculates the number of beats the voice is in silence over the total of the song\n",
    "        \"\"\"\n",
    "        class handler:\n",
    "            def update_part_objects(\n",
    "                score_data: dict = None,\n",
    "                part_data: list = None,\n",
    "                cfg: object = None,\n",
    "                parts_features: list = None,\n",
    "            ):\n",
    "                # We will extract the number of beats that voice parts are in silence and \n",
    "                # divide it by the total number of beats. First we need to filter only voice/singer parts. \n",
    "                if part_data[C.DATA_FAMILY] == 'voice':\n",
    "                    rests = [i for i in part_data[DATA_NOTES_AND_RESTS] if i.isRest]\n",
    "                    rests_duration = sum([i.quarterLength for i in rests])\n",
    "                    number_of_measures = score_data[DATA_MEASURES]\n",
    "                    total_beats = get_number_of_beats(score_data[TIME_SIGNATURE]) * number_of_measures\n",
    "                    voice_silence = rests_duration / total_beats\n",
    "                    parts_features[get_part_feature(part_data[C.DATA_PART_ABBREVIATION], VOICE_SILENCE_BEATS)] = voice_silence\n",
    "                else:\n",
    "                    pass\n",
    "            def update_score_objects(\n",
    "                score_data: dict = None,\n",
    "                parts_data: list = None,\n",
    "                cfg: object = None,\n",
    "                parts_features: list = None,\n",
    "                score_features: dict = None,\n",
    "            ):\n",
    "                # Finally, we need to add the data to score_features, \n",
    "                # the dictionary where all final info is stored. \n",
    "                # Otherwise it will not be reflected in the final dataframe.\n",
    "                features = {}\n",
    "                for i, part_data in enumerate(parts_data):\n",
    "                    if part_data[C.DATA_FAMILY] == 'voice':\n",
    "                        part_abbreviation = part_data[C.DATA_PART_ABBREVIATION]\n",
    "                        feature_name = get_part_feature(part_abbreviation, VOICE_SILENCE_BEATS)\n",
    "                        features[feature_name]=parts_features[i][get_part_feature(part_abbreviation, VOICE_SILENCE_BEATS)]\n",
    "                score_features.update(features)\n",
    "\n",
    "class custom_basic_module:\n",
    "    class custom_file_name:\n",
    "        \"set up artist and title from the file name\"\n",
    "        class handler:\n",
    "            \n",
    "            def update_score_objects(\n",
    "                score_data,\n",
    "                parts_data,\n",
    "                cfg,\n",
    "                parts_features,\n",
    "                score_features\n",
    "            ):\n",
    "                chunks = basename(score_data[C.DATA_FILE]).split('.')\n",
    "                artist = chunks[0]\n",
    "                title = chunks[-1]\n",
    "\n",
    "                score_features.update(\n",
    "                    {\n",
    "                        'Artist': artist,\n",
    "                        'Title': title,\n",
    "                    }\n",
    "                )\n",
    "\n",
    "            def update_part_objects(\n",
    "                score_data, part_data, cfg, part_features\n",
    "            ):\n",
    "                pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from musif.config import ExtractConfiguration\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = ExtractConfiguration(\n",
    "    None,\n",
    "    musescore_dir=\"data_poprock\",\n",
    "    # First, we tell musif where the features are\n",
    "    # We can pass our custom features as python variables or as a strings that is importable by __import__\n",
    "    feature_modules_addresses=[\"musif.extract.features\", custom_feature_package], \n",
    "    basic_modules_addresses=[\"musif.extract.basic_modules\", custom_basic_module],\n",
    "    # Now, we list the features that we want to extract \n",
    "    basic_modules=['scoring', \"custom_file_name\"],\n",
    "    features=[\"core\", \"ambitus\", \"melody\", \"tempo\", \n",
    "            \"density\", \"texture\", \"lyrics\", \"scale\", \n",
    "            \"key\", \"dynamics\", \"rhythm\", \n",
    "            \"voice_silence_beats\"],\n",
    "    # As for features, hooks can be expressed as variables or as strings that can imported by __import__\n",
    "    precache_hooks=[RemoveDrums, RenameSimilarParts], \n",
    "    # window size is 8 measures with overlap of 50%\n",
    "    window_size=8, overlap=4,\n",
    "    # we also activate the cache: in this directory musif will put some pickled objects \n",
    "    # that it will reload at the next extraction\n",
    "    cache_dir='__tutorial_cache'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;37mCache activated!\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from musif.extract.extract import FeaturesExtractor\n",
    "\n",
    "extractor = FeaturesExtractor(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "import musif.musescore.constants as musescore_c\n",
    "musescore_c.MUSESCORE_FILE_EXTENSION = '.mid'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extrac = FeaturesExtractor(\n",
    "#     ExtractConfiguration('/Users/ivansimo/Desktop/ICCMU/ICCMU_CODE/musicXXI/MUSIF/config_extraction_example.yml'),\n",
    "#     xml_dir = '/Users/ivansimo/Desktop/ICCMU/ICCMU_CODE/musicXXI/MUSIF/data/AnatomyComposerAttributionMIDIFilesAndFeatureData_1_0/MIDI',\n",
    "#     musescore_dir = '/Users/ivansimo/Desktop/ICCMU/ICCMU_CODE/musicXXI/MUSIF/data/AnatomyComposerAttributionMIDIFilesAndFeatureData_1_0/MIDI/Ib_noPen',\n",
    "# ).extract()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/25 [00:00<?, ?it/s]/Users/ivansimo/Documents/MUSIF_rep/musif/musif/extract/features/melody/handler.py:592: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  skew(absolute_numeric_intervals, bias=False)\n",
      "/Users/ivansimo/Documents/MUSIF_rep/musif/musif/extract/features/melody/handler.py:597: RuntimeWarning: Precision loss occurred in moment calculation due to catastrophic cancellation. This occurs when the data are nearly identical. Results may be unreliable.\n",
      "  kurtosis(absolute_numeric_intervals, bias=False)\n",
      "  4%|▍         | 1/25 [00:10<04:01, 10.08s/it]"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:462\u001b[0m, in \u001b[0;36mDeepHash._prep_tuple\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    461\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 462\u001b[0m     \u001b[43mobj\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_asdict\u001b[49m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;66;03m# It must be a normal tuple\u001b[39;00m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute '_asdict'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mextractor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/extract/extract.py:316\u001b[0m, in \u001b[0;36mFeaturesExtractor.extract\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(filenames) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m    314\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mFileNotFoundError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo file found for extracting features!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 316\u001b[0m score_df \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_corpus\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilenames\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    318\u001b[0m \u001b[38;5;66;03m# fix dtypes\u001b[39;00m\n\u001b[1;32m    319\u001b[0m score_df \u001b[38;5;241m=\u001b[39m score_df\u001b[38;5;241m.\u001b[39mconvert_dtypes()\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/extract/extract.py:343\u001b[0m, in \u001b[0;36mFeaturesExtractor._process_corpus\u001b[0;34m(self, filenames)\u001b[0m\n\u001b[1;32m    340\u001b[0m             \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    341\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m score_features\n\u001b[0;32m--> 343\u001b[0m scores_features \u001b[38;5;241m=\u001b[39m \u001b[43mParallel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_jobs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparallel\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    344\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdelayed\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocess_corpus_par\u001b[49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    345\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfname\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43menumerate\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtqdm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilenames\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    346\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    348\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cfg\u001b[38;5;241m.\u001b[39mwindow_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    349\u001b[0m     all_dfs \u001b[38;5;241m=\u001b[39m []\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/joblib/parallel.py:1863\u001b[0m, in \u001b[0;36mParallel.__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1861\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_sequential_output(iterable)\n\u001b[1;32m   1862\u001b[0m     \u001b[38;5;28mnext\u001b[39m(output)\n\u001b[0;32m-> 1863\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m output \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreturn_generator \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43moutput\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1865\u001b[0m \u001b[38;5;66;03m# Let's create an ID that uniquely identifies the current call. If the\u001b[39;00m\n\u001b[1;32m   1866\u001b[0m \u001b[38;5;66;03m# call is interrupted early and that the same instance is immediately\u001b[39;00m\n\u001b[1;32m   1867\u001b[0m \u001b[38;5;66;03m# re-used, this id will be used to prevent workers that were\u001b[39;00m\n\u001b[1;32m   1868\u001b[0m \u001b[38;5;66;03m# concurrently finalizing a task from the previous call to run the\u001b[39;00m\n\u001b[1;32m   1869\u001b[0m \u001b[38;5;66;03m# callback.\u001b[39;00m\n\u001b[1;32m   1870\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/joblib/parallel.py:1792\u001b[0m, in \u001b[0;36mParallel._get_sequential_output\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m   1790\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_batches \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1791\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_dispatched_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m-> 1792\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1793\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_completed_tasks \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m   1794\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprint_progress()\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/extract/extract.py:330\u001b[0m, in \u001b[0;36mFeaturesExtractor._process_corpus.<locals>.process_corpus_par\u001b[0;34m(idx, filename)\u001b[0m\n\u001b[1;32m    328\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    329\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cfg\u001b[38;5;241m.\u001b[39mwindow_size \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 330\u001b[0m         score_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_score_windows\u001b[49m\u001b[43m(\u001b[49m\u001b[43midx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    331\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    332\u001b[0m         score_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_score(idx, filename)\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/extract/extract.py:431\u001b[0m, in \u001b[0;36mFeaturesExtractor._process_score_windows\u001b[0;34m(self, idx, filename)\u001b[0m\n\u001b[1;32m    424\u001b[0m window_data, window_parts_data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_window_data(\n\u001b[1;32m    425\u001b[0m     score_data, parts_data, first_window_measure, last_window_measure\n\u001b[1;32m    426\u001b[0m )\n\u001b[1;32m    427\u001b[0m window_data\u001b[38;5;241m.\u001b[39mupdate(\n\u001b[1;32m    428\u001b[0m     {k: v \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m score_data\u001b[38;5;241m.\u001b[39mitems() \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m window_data}\n\u001b[1;32m    429\u001b[0m )\n\u001b[0;32m--> 431\u001b[0m window_features \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mextract_modules\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    432\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cfg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfeature_modules_addresses\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    433\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwindow_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    434\u001b[0m \u001b[43m    \u001b[49m\u001b[43mwindow_parts_data\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    435\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbasic\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m    436\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    438\u001b[0m window_features[\n\u001b[1;32m    439\u001b[0m     C\u001b[38;5;241m.\u001b[39mWINDOW_RANGE\n\u001b[1;32m    440\u001b[0m ] \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfirst_window_measure\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m - \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlast_window_measure\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    442\u001b[0m window_features[C\u001b[38;5;241m.\u001b[39mWINDOW_ID] \u001b[38;5;241m=\u001b[39m idx\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/extract/extract.py:492\u001b[0m, in \u001b[0;36mFeaturesExtractor.extract_modules\u001b[0;34m(self, packages, data, parts_data, basic)\u001b[0m\n\u001b[1;32m    490\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m package \u001b[38;5;129;01min\u001b[39;00m packages:\n\u001b[1;32m    491\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m module \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_find_modules(package, basic):\n\u001b[0;32m--> 492\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_parts_module_features\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    493\u001b[0m \u001b[43m            \u001b[49m\u001b[43mmodule\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparts_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparts_features\u001b[49m\n\u001b[1;32m    494\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    495\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_score_module_features(\n\u001b[1;32m    496\u001b[0m             module, data, parts_data, parts_features, score_features\n\u001b[1;32m    497\u001b[0m         )\n\u001b[1;32m    498\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m score_features\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/extract/extract.py:718\u001b[0m, in \u001b[0;36mFeaturesExtractor._update_parts_module_features\u001b[0;34m(self, module, score_data, parts_data, parts_features)\u001b[0m\n\u001b[1;32m    714\u001b[0m ldebug(\n\u001b[1;32m    715\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mExtracting part \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mpart_data[C\u001b[38;5;241m.\u001b[39mDATA_PART_ABBREVIATION]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodule_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m features.\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    716\u001b[0m )\n\u001b[1;32m    717\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 718\u001b[0m     \u001b[43mmodule\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mupdate_part_objects\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    719\u001b[0m \u001b[43m        \u001b[49m\u001b[43mscore_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpart_data\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cfg\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpart_features\u001b[49m\n\u001b[1;32m    720\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    721\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    722\u001b[0m     score_name \u001b[38;5;241m=\u001b[39m score_data[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfile\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/extract/features/core/handler.py:50\u001b[0m, in \u001b[0;36mupdate_part_objects\u001b[0;34m(score_data, part_data, cfg, part_features)\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mupdate_part_objects\u001b[39m(\n\u001b[1;32m     42\u001b[0m     score_data: \u001b[38;5;28mdict\u001b[39m, part_data: \u001b[38;5;28mdict\u001b[39m, cfg: ExtractConfiguration, part_features: \u001b[38;5;28mdict\u001b[39m\n\u001b[1;32m     43\u001b[0m ):\n\u001b[1;32m     44\u001b[0m     part \u001b[38;5;241m=\u001b[39m part_data[DATA_PART]\n\u001b[1;32m     45\u001b[0m     (\n\u001b[1;32m     46\u001b[0m         notes,\n\u001b[1;32m     47\u001b[0m         measures,\n\u001b[1;32m     48\u001b[0m         sounding_measures,\n\u001b[1;32m     49\u001b[0m         notes_and_rests,\n\u001b[0;32m---> 50\u001b[0m     ) \u001b[38;5;241m=\u001b[39m \u001b[43mget_notes_and_measures\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpart\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     51\u001b[0m     lyrics \u001b[38;5;241m=\u001b[39m _get_lyrics_in_notes(notes)\n\u001b[1;32m     52\u001b[0m     intervals \u001b[38;5;241m=\u001b[39m _get_intervals(notes)\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/musicxml/common.py:118\u001b[0m, in \u001b[0;36mget_notes_and_measures\u001b[0;34m(part)\u001b[0m\n\u001b[1;32m    104\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_notes_and_measures\u001b[39m(\n\u001b[1;32m    105\u001b[0m     part: Part,\n\u001b[1;32m    106\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tuple[List[Note], List[Note], List[Measure], List[Measure]]:\n\u001b[1;32m    107\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    108\u001b[0m \u001b[38;5;124;03m    Obtains lists of notes, tied notes, measures, measures that containg notes, and notes and rests.\u001b[39;00m\n\u001b[1;32m    109\u001b[0m \u001b[38;5;124;03m    Information that is useful in the subsequent process of extraction.\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    115\u001b[0m \n\u001b[1;32m    116\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 118\u001b[0m     measures \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mpart\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetElementsByClass\u001b[49m\u001b[43m(\u001b[49m\u001b[43mMeasure\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m    119\u001b[0m     sounding_measures \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    120\u001b[0m         idx \u001b[38;5;28;01mfor\u001b[39;00m idx, measure \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(measures) \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(measure\u001b[38;5;241m.\u001b[39mnotes) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m    121\u001b[0m     ]\n\u001b[1;32m    122\u001b[0m     original_notes \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m    123\u001b[0m         note \u001b[38;5;28;01mfor\u001b[39;00m measure \u001b[38;5;129;01min\u001b[39;00m measures \u001b[38;5;28;01mfor\u001b[39;00m note \u001b[38;5;129;01min\u001b[39;00m measure\u001b[38;5;241m.\u001b[39mnotes \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(note, Note)\n\u001b[1;32m    124\u001b[0m     ]\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/cache/cache.py:489\u001b[0m, in \u001b[0;36mMethodCache.__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    487\u001b[0m args \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wmo, args))\n\u001b[1;32m    488\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m {k: \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_wmo(v) \u001b[38;5;28;01mfor\u001b[39;00m k, v \u001b[38;5;129;01min\u001b[39;00m kwargs\u001b[38;5;241m.\u001b[39mitems()}\n\u001b[0;32m--> 489\u001b[0m call_args \u001b[38;5;241m=\u001b[39m \u001b[43mCallableArguments\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    490\u001b[0m cached_res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcache\u001b[38;5;241m.\u001b[39mget(call_args)\n\u001b[1;32m    491\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cached_res \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
      "File \u001b[0;32m~/Documents/MUSIF_rep/musif/musif/cache/cache.py:422\u001b[0m, in \u001b[0;36mCallableArguments.__init__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    420\u001b[0m \u001b[38;5;66;03m# hashing other object types by value\u001b[39;00m\n\u001b[1;32m    421\u001b[0m all_args \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwargs)\n\u001b[0;32m--> 422\u001b[0m h \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[43mDeepHash\u001b[49m\u001b[43m(\u001b[49m\u001b[43mall_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mexclude_types\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mSmartModuleCache\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m[all_args]\n\u001b[1;32m    424\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_hash \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(h, \u001b[38;5;241m16\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:196\u001b[0m, in \u001b[0;36mDeepHash.__init__\u001b[0;34m(self, obj, hashes, exclude_types, exclude_paths, include_paths, exclude_regex_paths, hasher, ignore_repetition, significant_digits, truncate_datetime, number_format_notation, apply_hash, ignore_type_in_groups, ignore_string_type_changes, ignore_numeric_type_changes, ignore_type_subclasses, ignore_string_case, exclude_obj_callback, number_to_string_func, ignore_private_variables, parent, encodings, ignore_encoding_errors, ignore_iterable_order, **kwargs)\u001b[0m\n\u001b[1;32m    193\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mignore_encoding_errors \u001b[38;5;241m=\u001b[39m ignore_encoding_errors\n\u001b[1;32m    194\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mignore_iterable_order \u001b[38;5;241m=\u001b[39m ignore_iterable_order\n\u001b[0;32m--> 196\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_hash\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mfrozenset\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[43mget_id\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    198\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhashes[UNPROCESSED_KEY]:\n\u001b[1;32m    199\u001b[0m     logger\u001b[38;5;241m.\u001b[39mwarning(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCan not hash the following items: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhashes[UNPROCESSED_KEY]))\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:515\u001b[0m, in \u001b[0;36mDeepHash._hash\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    512\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prep_dict(obj\u001b[38;5;241m=\u001b[39mobj, parent\u001b[38;5;241m=\u001b[39mparent, parents_ids\u001b[38;5;241m=\u001b[39mparents_ids)\n\u001b[1;32m    514\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[0;32m--> 515\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prep_tuple\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    517\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, Iterable):\n\u001b[1;32m    518\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prep_iterable(obj\u001b[38;5;241m=\u001b[39mobj, parent\u001b[38;5;241m=\u001b[39mparent, parents_ids\u001b[38;5;241m=\u001b[39mparents_ids)\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:465\u001b[0m, in \u001b[0;36mDeepHash._prep_tuple\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    463\u001b[0m \u001b[38;5;66;03m# It must be a normal tuple\u001b[39;00m\n\u001b[1;32m    464\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m:\n\u001b[0;32m--> 465\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prep_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    466\u001b[0m \u001b[38;5;66;03m# We assume it is a namedtuple then\u001b[39;00m\n\u001b[1;32m    467\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    468\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prep_obj(obj, parent, parents_ids\u001b[38;5;241m=\u001b[39mparents_ids, is_namedtuple\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:417\u001b[0m, in \u001b[0;36mDeepHash._prep_iterable\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    416\u001b[0m parents_ids_added \u001b[38;5;241m=\u001b[39m add_to_frozen_set(parents_ids, item_id)\n\u001b[0;32m--> 417\u001b[0m hashed, count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_hash\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_parent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids_added\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    418\u001b[0m \u001b[38;5;66;03m# counting repetitions\u001b[39;00m\n\u001b[1;32m    419\u001b[0m result[hashed] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:518\u001b[0m, in \u001b[0;36mDeepHash._hash\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    515\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prep_tuple(obj\u001b[38;5;241m=\u001b[39mobj, parent\u001b[38;5;241m=\u001b[39mparent, parents_ids\u001b[38;5;241m=\u001b[39mparents_ids)\n\u001b[1;32m    517\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, Iterable):\n\u001b[0;32m--> 518\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prep_iterable\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    520\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m obj \u001b[38;5;241m==\u001b[39m BoolObj\u001b[38;5;241m.\u001b[39mTRUE \u001b[38;5;129;01mor\u001b[39;00m obj \u001b[38;5;241m==\u001b[39m BoolObj\u001b[38;5;241m.\u001b[39mFALSE:\n\u001b[1;32m    521\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbool:true\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m BoolObj\u001b[38;5;241m.\u001b[39mTRUE \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbool:false\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:417\u001b[0m, in \u001b[0;36mDeepHash._prep_iterable\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    414\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    416\u001b[0m parents_ids_added \u001b[38;5;241m=\u001b[39m add_to_frozen_set(parents_ids, item_id)\n\u001b[0;32m--> 417\u001b[0m hashed, count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_hash\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_parent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids_added\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    418\u001b[0m \u001b[38;5;66;03m# counting repetitions\u001b[39;00m\n\u001b[1;32m    419\u001b[0m result[hashed] \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:523\u001b[0m, in \u001b[0;36mDeepHash._hash\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    521\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbool:true\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m obj \u001b[38;5;129;01mis\u001b[39;00m BoolObj\u001b[38;5;241m.\u001b[39mTRUE \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbool:false\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    522\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 523\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prep_obj\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    525\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m result \u001b[38;5;129;01mis\u001b[39;00m not_hashed:  \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[1;32m    526\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhashes[UNPROCESSED_KEY]\u001b[38;5;241m.\u001b[39mappend(obj)\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:338\u001b[0m, in \u001b[0;36mDeepHash._prep_obj\u001b[0;34m(self, obj, parent, parents_ids, is_namedtuple)\u001b[0m\n\u001b[1;32m    335\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m (unprocessed, \u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m    336\u001b[0m obj \u001b[38;5;241m=\u001b[39m d\n\u001b[0;32m--> 338\u001b[0m result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prep_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    339\u001b[0m \u001b[43m                                 \u001b[49m\u001b[43mprint_as_attribute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43moriginal_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43moriginal_type\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    340\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnt\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(result) \u001b[38;5;28;01mif\u001b[39;00m is_namedtuple \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mobj\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(result)\n\u001b[1;32m    341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result, counts\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:384\u001b[0m, in \u001b[0;36mDeepHash._prep_dict\u001b[0;34m(self, obj, parent, parents_ids, print_as_attribute, original_type)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    383\u001b[0m parents_ids_added \u001b[38;5;241m=\u001b[39m add_to_frozen_set(parents_ids, item_id)\n\u001b[0;32m--> 384\u001b[0m hashed, count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_hash\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey_in_report\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids_added\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    385\u001b[0m hashed \u001b[38;5;241m=\u001b[39m KEY_TO_VAL_STR\u001b[38;5;241m.\u001b[39mformat(key_hash, hashed)\n\u001b[1;32m    386\u001b[0m result\u001b[38;5;241m.\u001b[39mappend(hashed)\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:512\u001b[0m, in \u001b[0;36mDeepHash._hash\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    509\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prep_number(obj)\n\u001b[1;32m    511\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, MutableMapping):\n\u001b[0;32m--> 512\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_prep_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mobj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    514\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, \u001b[38;5;28mtuple\u001b[39m):\n\u001b[1;32m    515\u001b[0m     result, counts \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_prep_tuple(obj\u001b[38;5;241m=\u001b[39mobj, parent\u001b[38;5;241m=\u001b[39mparent, parents_ids\u001b[38;5;241m=\u001b[39mparents_ids)\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:384\u001b[0m, in \u001b[0;36mDeepHash._prep_dict\u001b[0;34m(self, obj, parent, parents_ids, print_as_attribute, original_type)\u001b[0m\n\u001b[1;32m    382\u001b[0m     \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m    383\u001b[0m parents_ids_added \u001b[38;5;241m=\u001b[39m add_to_frozen_set(parents_ids, item_id)\n\u001b[0;32m--> 384\u001b[0m hashed, count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_hash\u001b[49m\u001b[43m(\u001b[49m\u001b[43mitem\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparent\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mkey_in_report\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparents_ids\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparents_ids_added\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    385\u001b[0m hashed \u001b[38;5;241m=\u001b[39m KEY_TO_VAL_STR\u001b[38;5;241m.\u001b[39mformat(key_hash, hashed)\n\u001b[1;32m    386\u001b[0m result\u001b[38;5;241m.\u001b[39mappend(hashed)\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:538\u001b[0m, in \u001b[0;36mDeepHash._hash\u001b[0;34m(self, obj, parent, parents_ids)\u001b[0m\n\u001b[1;32m    534\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    535\u001b[0m         result_cleaned \u001b[38;5;241m=\u001b[39m prepare_string_for_hashing(\n\u001b[1;32m    536\u001b[0m             result, ignore_string_type_changes\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mignore_string_type_changes,\n\u001b[1;32m    537\u001b[0m             ignore_string_case\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mignore_string_case)\n\u001b[0;32m--> 538\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhasher\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresult_cleaned\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    540\u001b[0m \u001b[38;5;66;03m# It is important to keep the hash of all objects.\u001b[39;00m\n\u001b[1;32m    541\u001b[0m \u001b[38;5;66;03m# The hashes will be later used for comparing the objects.\u001b[39;00m\n\u001b[1;32m    542\u001b[0m \u001b[38;5;66;03m# Object to hash when possible otherwise ObjectID to hash\u001b[39;00m\n\u001b[1;32m    543\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/envs/MUSIFV2/lib/python3.10/site-packages/deepdiff/deephash.py:32\u001b[0m, in \u001b[0;36msha256hex\u001b[0;34m(obj)\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(obj, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m     31\u001b[0m     obj \u001b[38;5;241m=\u001b[39m obj\u001b[38;5;241m.\u001b[39mencode(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 32\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msha256\u001b[49m\u001b[43m(\u001b[49m\u001b[43mobj\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhexdigest\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "df = extractor.extract()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Enya</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>...</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Enya</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.429688</td>\n",
       "      <td>...</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Enya</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.296875</td>\n",
       "      <td>...</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Enya</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.203125</td>\n",
       "      <td>...</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">24</th>\n",
       "      <th>6</th>\n",
       "      <td>TheBeatles</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.65625</td>\n",
       "      <td>...</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TheBeatles</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>...</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.125</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1</td>\n",
       "      <td>0.041667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TheBeatles</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.539062</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>4</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TheBeatles</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.632812</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>3</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TheBeatles</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>0.617188</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>2</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>1</td>\n",
       "      <td>0.047619</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>610 rows × 26056 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Artist  FamilyElectric_Density  FamilyElectric_Notes  \\\n",
       "0  0         Enya                     0.0                     0   \n",
       "   1         Enya                     0.0                     0   \n",
       "   2         Enya                     0.0                     0   \n",
       "   3         Enya                     0.0                     0   \n",
       "   4         Enya                     0.0                     0   \n",
       "...           ...                     ...                   ...   \n",
       "24 6   TheBeatles                    <NA>                  <NA>   \n",
       "   7   TheBeatles                    <NA>                  <NA>   \n",
       "   8   TheBeatles                    <NA>                  <NA>   \n",
       "   9   TheBeatles                    <NA>                  <NA>   \n",
       "   10  TheBeatles                    <NA>                  <NA>   \n",
       "\n",
       "       FamilyElectric_NotesMean  FamilyElectric_NumberOfFilteredParts  \\\n",
       "0  0                        0.0                                     1   \n",
       "   1                        0.0                                     1   \n",
       "   2                        0.0                                     1   \n",
       "   3                        0.0                                     1   \n",
       "   4                        0.0                                     1   \n",
       "...                         ...                                   ...   \n",
       "24 6                       <NA>                                  <NA>   \n",
       "   7                       <NA>                                  <NA>   \n",
       "   8                       <NA>                                  <NA>   \n",
       "   9                       <NA>                                  <NA>   \n",
       "   10                      <NA>                                  <NA>   \n",
       "\n",
       "       FamilyElectric_NumberOfParts  FamilyElectric_SoundingDensity  \\\n",
       "0  0                              1                             0.0   \n",
       "   1                              1                             0.0   \n",
       "   2                              1                             0.0   \n",
       "   3                              1                             0.0   \n",
       "   4                              1                             0.0   \n",
       "...                             ...                             ...   \n",
       "24 6                           <NA>                            <NA>   \n",
       "   7                           <NA>                            <NA>   \n",
       "   8                           <NA>                            <NA>   \n",
       "   9                           <NA>                            <NA>   \n",
       "   10                          <NA>                            <NA>   \n",
       "\n",
       "       FamilyElectric_SoundingMeasures  FamilyElectric_SoundingMeasuresMean  \\\n",
       "0  0                                 0                                  0.0   \n",
       "   1                                 0                                  0.0   \n",
       "   2                                 0                                  0.0   \n",
       "   3                                 0                                  0.0   \n",
       "   4                                 0                                  0.0   \n",
       "...                                ...                                  ...   \n",
       "24 6                              <NA>                                 <NA>   \n",
       "   7                              <NA>                                 <NA>   \n",
       "   8                              <NA>                                 <NA>   \n",
       "   9                              <NA>                                 <NA>   \n",
       "   10                             <NA>                                 <NA>   \n",
       "\n",
       "       FamilyGen_Density  ...  SoundVc_IntervalP5_Count  \\\n",
       "0  0              0.0625  ...                      <NA>   \n",
       "   1            0.296875  ...                      <NA>   \n",
       "   2            0.429688  ...                      <NA>   \n",
       "   3            0.296875  ...                      <NA>   \n",
       "   4            0.203125  ...                      <NA>   \n",
       "...                  ...  ...                       ...   \n",
       "24 6             0.65625  ...                      <NA>   \n",
       "   7            0.554688  ...                      <NA>   \n",
       "   8            0.539062  ...                         2   \n",
       "   9            0.632812  ...                         2   \n",
       "   10           0.617188  ...                         1   \n",
       "\n",
       "       SoundVc_IntervalP5_Per  SoundVc_Intervald1_Count  \\\n",
       "0  0                     <NA>                      <NA>   \n",
       "   1                     <NA>                      <NA>   \n",
       "   2                     <NA>                      <NA>   \n",
       "   3                     <NA>                      <NA>   \n",
       "   4                     <NA>                      <NA>   \n",
       "...                       ...                       ...   \n",
       "24 6                     <NA>                      <NA>   \n",
       "   7                     <NA>                      <NA>   \n",
       "   8                 0.083333                      <NA>   \n",
       "   9                 0.095238                      <NA>   \n",
       "   10                0.047619                         1   \n",
       "\n",
       "       SoundVc_Intervald1_Per  SoundVc_Intervalm-2_Count  \\\n",
       "0  0                     <NA>                       <NA>   \n",
       "   1                     <NA>                       <NA>   \n",
       "   2                     <NA>                       <NA>   \n",
       "   3                     <NA>                       <NA>   \n",
       "   4                     <NA>                       <NA>   \n",
       "...                       ...                        ...   \n",
       "24 6                     <NA>                          2   \n",
       "   7                     <NA>                          3   \n",
       "   8                     <NA>                          4   \n",
       "   9                     <NA>                          3   \n",
       "   10                0.047619                          2   \n",
       "\n",
       "       SoundVc_Intervalm-2_Per  SoundVc_Intervalm2_Count  \\\n",
       "0  0                      <NA>                      <NA>   \n",
       "   1                      <NA>                      <NA>   \n",
       "   2                      <NA>                      <NA>   \n",
       "   3                      <NA>                      <NA>   \n",
       "   4                      <NA>                      <NA>   \n",
       "...                        ...                       ...   \n",
       "24 6                  0.095238                      <NA>   \n",
       "   7                     0.125                      <NA>   \n",
       "   8                  0.166667                         1   \n",
       "   9                  0.142857                         1   \n",
       "   10                 0.095238                      <NA>   \n",
       "\n",
       "      SoundVc_Intervalm2_Per  SoundVc_Intervalm3_Count  SoundVc_Intervalm3_Per  \n",
       "0  0                    <NA>                      <NA>                    <NA>  \n",
       "   1                    <NA>                      <NA>                    <NA>  \n",
       "   2                    <NA>                      <NA>                    <NA>  \n",
       "   3                    <NA>                      <NA>                    <NA>  \n",
       "   4                    <NA>                      <NA>                    <NA>  \n",
       "...                      ...                       ...                     ...  \n",
       "24 6                    <NA>                         1                0.047619  \n",
       "   7                    <NA>                         1                0.041667  \n",
       "   8                0.041667                      <NA>                    <NA>  \n",
       "   9                0.047619                      <NA>                    <NA>  \n",
       "   10                   <NA>                         1                0.047619  \n",
       "\n",
       "[610 rows x 26056 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1;37m\n",
      "Post-processing data...\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(610, 165)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from musif.process.processor import DataProcessor\n",
    "\n",
    "processed_df = DataProcessor(df, None).process().data\n",
    "\n",
    "# with `.shape` you can see the number of rows and columns of the DataFrame.\n",
    "processed_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(610, 165)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_df.dropna(axis=1, inplace=True)\n",
    "processed_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: scikit-learn in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (1.2.2)\n",
      "Requirement already satisfied: seaborn in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (0.12.2)\n",
      "Requirement already satisfied: numpy>=1.17.3 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from scikit-learn) (1.24.3)\n",
      "Requirement already satisfied: scipy>=1.3.2 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from scikit-learn) (1.10.1)\n",
      "Requirement already satisfied: joblib>=1.1.1 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from scikit-learn) (1.2.0)\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from scikit-learn) (2.2.0)\n",
      "Requirement already satisfied: pandas>=0.25 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from seaborn) (1.5.3)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.1 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from seaborn) (3.7.1)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.0.5)\n",
      "Requirement already satisfied: cycler>=0.10 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (4.25.0)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (1.4.4)\n",
      "Requirement already satisfied: packaging>=20.0 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (23.1)\n",
      "Requirement already satisfied: pillow>=6.2.0 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (9.5.0)\n",
      "Requirement already satisfied: pyparsing>=2.3.1 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (3.0.9)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.1->seaborn) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2020.1 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from pandas>=0.25->seaborn) (2023.3)\n",
      "Requirement already satisfied: six>=1.5 in /Users/ivansimo/anaconda3/envs/ICCMU/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.1->seaborn) (1.16.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install scikit-learn seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 415,
       "width": 546
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA\n",
    "from sklearn.pipeline import make_pipeline\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.decomposition import PCA\n",
    "import seaborn as sns\n",
    "\n",
    "target = processed_df['Artist']\n",
    "data = processed_df.drop(columns=['Id', 'WindowId']).select_dtypes([int, float])\n",
    "data = make_pipeline(StandardScaler(), \n",
    "                     VarianceThreshold(0.25),\n",
    "                     LDA(n_components=2, solver='svd')).fit_transform(data, target)\n",
    "sns.scatterplot(x=data[:, 0], y=data[:, 1], hue=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 546
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target = processed_df['Artist'].groupby(level=0).nth(0) # 'Artist' of each window number 0\n",
    "data = processed_df.drop(columns=['Id', 'WindowId']).select_dtypes([int, float])\n",
    "data = data.groupby(level=0).mean() # taking the average of the features across the windows here!\n",
    "data = make_pipeline(StandardScaler(),\n",
    "                     VarianceThreshold(0.25),\n",
    "                     LDA(n_components=2, solver='svd')).fit_transform(data, target)\n",
    "sns.scatterplot(x=data[:, 0], y=data[:, 1], hue=target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 413,
       "width": 546
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.fft import fft\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "\n",
    "# data selection\n",
    "target = processed_df.groupby(level=0).nth(0)['Artist']  # 'Artist' of each window number 0\n",
    "score_groups = processed_df['Id'].to_numpy().astype(str) # groups of samples used for the DCT\n",
    "data = processed_df.select_dtypes([float, int])          # features\n",
    "\n",
    "# function to compute the FFT of a score\n",
    "def compute_fft(x):\n",
    "    groups = pd.DataFrame(x).groupby(score_groups)\n",
    "    ret = groups.apply(lambda df: fft(df, n=8, axis=0).mean(axis=0))\n",
    "    return np.absolute(np.stack(ret.to_list()))\n",
    "\n",
    "# training model\n",
    "model = make_pipeline(StandardScaler(), \n",
    "                      VarianceThreshold(0.2),\n",
    "                      FunctionTransformer(compute_fft),\n",
    "                      LDA(n_components=2, solver='svd'))\n",
    "data = model.fit_transform(data, target)\n",
    "sns.scatterplot(x=data[:, 0], y=data[:, 1], hue=target, legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}