decent_setup_parse_scrape (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3ef58de6-119f-4ce1-baab-15c40332d648",
   "metadata": {},
   "outputs": [],
   "source": [
    "import utils as u\n",
    "import discogs_api as d\n",
    "import audio_processing as ap\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4d6cbe42-21b8-49d3-980c-0a45f2836837",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58aeaa8a-fd45-4f95-bd30-16e0135a814f",
   "metadata": {},
   "source": [
    "## there are two main issues with this process\n",
    "### 1. conforming mp3 tags so they can query a single api\n",
    "### 1. a. Releases that aren't on discogs (more of an issue with UTTU and its sublabels)\n",
    "### 1. b. It's very clear that rules are hard to define for names and i'm not great at fuzzy search, some titles include things like 'EP and other titles don't in discogs/whatever database but are frequently named as such\n",
    "### 2. Basically every source lists genre as a per-release feature when I, at least, conceptualize genre as something that can be \"per song\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "89c7fb33-a54a-4770-982c-f95feaa9483f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# start discogs api client\n",
    "dc = d.get_discogs_client()\n",
    "# verify it's working\n",
    "dc.identity()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e153a980-a8e7-4387-a8c1-baf23f66faca",
   "metadata": {},
   "outputs": [],
   "source": [
    "hessle_dir = \"/mnt/d/MIR_music/Hessle/\"\n",
    "# hessle_dir = /mir_final_proj/Hessle #rough drive directory, i re-uploaded bc I retagged some mp3s to work with discogs better"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "e0779020-8095-4177-8b97-04c475dedff4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# parse mp3s by tags into a dataframe and autofill 'label' column bc this label has no sublabels\n",
    "df_hessle = u.process_files(hessle_dir, \"Hessle Audio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "b1b149a1-4a2e-4bca-b9fe-26d4853881ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# query discogs for hessle's label id number and download discography to avoid\n",
    "# querying genre and style info for each release individually\n",
    "lbl_id = d.find_label_id(dc, \"Hessle Audio\")\n",
    "# store release information\n",
    "discog = d.get_label_discography(dc, lbl_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "7e29dc55-aa6b-4142-9fca-2781db85db61",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set a new column for ints to relate df entries to discog release numbers\n",
    "df_hessle['discogs_id'] = int('nan')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "abe565ab-626a-4b78-addb-1a7f1c019704",
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize a dictionary to hold the selected ID for each album\n",
    "selected_ids = {}\n",
    "\n",
    "# iterate over the discography list\n",
    "for entry in discog:\n",
    "    album_title = entry['title']\n",
    "    # If this is the first time we see this album add it, works for hessle\n",
    "    if album_title not in selected_ids:\n",
    "        selected_ids[album_title] = entry['id']\n",
    "\n",
    "# Now update the DataFrame to pair release nos to releases\n",
    "for album, discogs_id in selected_ids.items():\n",
    "    df_hessle.loc[df_hessle['album'] == album, 'discogs_id'] = discogs_id\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "4e4867b3-c2c7-4469-ba1b-48c241be395d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#test df against downloaded discography from discogs\n",
    "for index, row in df_hessle.iterrows():\n",
    "    # Check if the 'album' value in discog matches the 'album' in the DataFrame\n",
    "    discog_entry = next((item for item in discog if item['title'] == row['album']), None)\n",
    "    \n",
    "    # if there's a matching entry in discog\n",
    "    if discog_entry:\n",
    "        expected_id = discog_entry['id']\n",
    "        # check if the discogs_id in the DataFrame matches the expected ID\n",
    "        if row['discogs_id'] != expected_id:\n",
    "            print(f\"Mismatch at index {index}: Album '{row['album']}' has discogs_id {row['discogs_id']}, expected {expected_id}\")\n",
    "    else:\n",
    "        print(f\"No discog entry found for album '{row['album']}' at index {index}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "652774ee-88bf-4e2c-91c6-3d65e3bb0312",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "41"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_hessle['album'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "f3976167-dcab-4c1b-b511-364c85acaba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use function in other file to get info per release only once\n",
    "# discogs and essentially every other source only does per release genre info\n",
    "hessle_with_discogs_info = d.process_dataframe(df_hessle, dc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "4fa8ae8f-1d53-49de-999d-4064da8880b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# save just in case\n",
    "hessle_with_discogs_info.to_csv(\"good_hessle_df.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7394f37a-73ec-4c33-b0d5-205a0e858d25",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get file paths to pass to pooling for processing\n",
    "file_paths = hessle_with_discogs_info['file_path'].tolist()\n",
    "\n",
    "hessle_results = ap.process_files_parallel(file_paths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "50c076da-c73b-4cfd-bbab-596ed7769eb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#update df with corresponding analysis results to tracks or update with error\n",
    "for i, features in enumerate(hessle_results):\n",
    "    if features is not None:\n",
    "        for key, value in features.items():\n",
    "            hessle_with_discogs_info.loc[i, key] = value\n",
    "    else:\n",
    "        hessle_with_discogs_info.loc[i, 'processing_error'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "f0c1712a-1d27-4aaa-b7d0-33550fcc3615",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      135.999178\n",
       "1      135.999178\n",
       "2       92.285156\n",
       "3       92.285156\n",
       "4      135.999178\n",
       "          ...    \n",
       "162    107.666016\n",
       "163    135.999178\n",
       "164     89.102909\n",
       "165    135.999178\n",
       "166     80.749512\n",
       "Name: tempo, Length: 167, dtype: float64"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hessle_with_discogs_info['tempo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "3ff0b18e-0074-42b0-82c9-e0c0dd8722c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['title', 'artist', 'album', 'year', 'genre', 'label', 'file_path',\n",
       "       'discogs_id', 'genres', 'styles', 'tempo', 'centroid_mean',\n",
       "       'centroid_std', 'spread_mean', 'spread_std', 'rolloff_mean',\n",
       "       'rolloff_std', 'chroma_stft_mean', 'chroma_stft_var', 'rms_mean',\n",
       "       'rms_std', 'harmony_mean', 'harmony_var', 'percu_mean', 'percu_var',\n",
       "       'mfcc_1_mean', 'mfcc_2_mean', 'mfcc_3_mean', 'mfcc_4_mean',\n",
       "       'mfcc_5_mean', 'mfcc_6_mean', 'mfcc_7_mean', 'mfcc_8_mean',\n",
       "       'mfcc_9_mean', 'mfcc_10_mean', 'mfcc_11_mean', 'mfcc_12_mean',\n",
       "       'mfcc_13_mean', 'mfcc_1_var', 'mfcc_2_var', 'mfcc_3_var', 'mfcc_4_var',\n",
       "       'mfcc_5_var', 'mfcc_6_var', 'mfcc_7_var', 'mfcc_8_var', 'mfcc_9_var',\n",
       "       'mfcc_10_var', 'mfcc_11_var', 'mfcc_12_var', 'mfcc_13_var'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hessle_with_discogs_info.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5fe3931e-94ce-4744-b5a7-68f866d1f4d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "hessle_with_discogs_info.to_csv('hessle_with_features.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b58c2a7-224b-4c2f-9d2b-0afd8de9ddbb",
   "metadata": {},
   "source": [
    "## uttu data isn't quite there yet, below here isn't functional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9f2a17b9-2f60-4516-870a-44297d409d0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "uttu_id = d.find_label_id('Unknown To The Unknown')\n",
    "hothaus_id = 565738 # can't name search via discogs api\n",
    "dancetrax_id = d.find_label_id('Dance Trax') #dance trax (4) on discogs..\n",
    "soft_comp_id = d.find_label_id(\"Soft Computing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "30c9ace2-3240-4bec-bd16-0926d9d41e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "hhrecs = d.find_label_id('Hot Haus Recs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "d2175e9e-4d1e-4432-b23d-51ea9d09eb52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "565738"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hhrecs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "bdea2670-e5d7-49ab-9927-468fa6ef0471",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Request failed with status code 404. Response: {\"message\": \"The requested resource was not found.\"}\n"
     ]
    }
   ],
   "source": [
    "uttu_discog = d.get_label_discography(uttu_id)\n",
    "hothaus_discog = d.get_label_discography(hothaus_id)\n",
    "dancetrax_discog = d.get_label_discography(dancetrax_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "9418b618-f8a2-4099-b1ea-2f66116d21ce",
   "metadata": {},
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "404: The requested resource was not found.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[167], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m hothaus_discog \u001b[38;5;241m=\u001b[39m \u001b[43md\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_label_discography\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhothaus_id\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28mlen\u001b[39m(hothaus_discog)\n",
      "File \u001b[0;32m~/solo_final_project/discogs_api.py:50\u001b[0m, in \u001b[0;36mget_label_discography\u001b[0;34m(discogs_client, label_id)\u001b[0m\n\u001b[1;32m     47\u001b[0m all_releases \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     49\u001b[0m \u001b[38;5;66;03m# Process the first page\u001b[39;00m\n\u001b[0;32m---> 50\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m release \u001b[38;5;129;01min\u001b[39;00m \u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreleases\u001b[49m\u001b[38;5;241m.\u001b[39mpage(\u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m     51\u001b[0m     all_releases\u001b[38;5;241m.\u001b[39mappend({\n\u001b[1;32m     52\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mid\u001b[39m\u001b[38;5;124m'\u001b[39m: release\u001b[38;5;241m.\u001b[39mid,\n\u001b[1;32m     53\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtitle\u001b[39m\u001b[38;5;124m'\u001b[39m: release\u001b[38;5;241m.\u001b[39mtitle,\n\u001b[1;32m     54\u001b[0m         \u001b[38;5;124m'\u001b[39m\u001b[38;5;124myear\u001b[39m\u001b[38;5;124m'\u001b[39m: release\u001b[38;5;241m.\u001b[39myear\n\u001b[1;32m     55\u001b[0m     })\n\u001b[1;32m     57\u001b[0m \u001b[38;5;66;03m# Handling pagination - check if there are more pages\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/envs/finalproj/lib/python3.8/site-packages/discogs_client/models.py:130\u001b[0m, in \u001b[0;36mObjectCollectionDescriptor.__get__\u001b[0;34m(self, instance, owner)\u001b[0m\n\u001b[1;32m    128\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\n\u001b[1;32m    129\u001b[0m wrapper_class \u001b[38;5;241m=\u001b[39m CLASS_MAP[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mclass_name\u001b[38;5;241m.\u001b[39mlower()]\n\u001b[0;32m--> 130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlist_class(instance\u001b[38;5;241m.\u001b[39mclient, \u001b[43minstance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfetch\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43murl_key\u001b[49m\u001b[43m)\u001b[49m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname, wrapper_class)\n",
      "File \u001b[0;32m~/anaconda3/envs/finalproj/lib/python3.8/site-packages/discogs_client/models.py:245\u001b[0m, in \u001b[0;36mPrimaryAPIObject.fetch\u001b[0;34m(self, key, default)\u001b[0m\n\u001b[1;32m    241\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m default\n\u001b[1;32m    243\u001b[0m \u001b[38;5;66;03m# Now refresh the object from its resource_url.\u001b[39;00m\n\u001b[1;32m    244\u001b[0m \u001b[38;5;66;03m# The key might exist but not be in our cache.\u001b[39;00m\n\u001b[0;32m--> 245\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrefresh\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    247\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    248\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata[key]\n",
      "File \u001b[0;32m~/anaconda3/envs/finalproj/lib/python3.8/site-packages/discogs_client/models.py:204\u001b[0m, in \u001b[0;36mPrimaryAPIObject.refresh\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    202\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrefresh\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    203\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mresource_url\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[0;32m--> 204\u001b[0m         data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mresource_url\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    205\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata\u001b[38;5;241m.\u001b[39mupdate(data)\n\u001b[1;32m    206\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mchanges \u001b[38;5;241m=\u001b[39m {}\n",
      "File \u001b[0;32m~/anaconda3/envs/finalproj/lib/python3.8/site-packages/discogs_client/client.py:114\u001b[0m, in \u001b[0;36mClient._get\u001b[0;34m(self, url)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_get\u001b[39m(\u001b[38;5;28mself\u001b[39m, url):\n\u001b[0;32m--> 114\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_request\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mGET\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43murl\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/finalproj/lib/python3.8/site-packages/discogs_client/client.py:111\u001b[0m, in \u001b[0;36mClient._request\u001b[0;34m(self, method, url, data)\u001b[0m\n\u001b[1;32m    109\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m body\n\u001b[1;32m    110\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 111\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(body[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmessage\u001b[39m\u001b[38;5;124m'\u001b[39m], status_code)\n",
      "\u001b[0;31mHTTPError\u001b[0m: 404: The requested resource was not found."
     ]
    }
   ],
   "source": [
    "hothaus_discog = d.get_label_discography(dc, hothaus_id)\n",
    "len(hothaus_discog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "2c97875c-8f9a-4910-87df-2d8e1d3b8004",
   "metadata": {},
   "outputs": [],
   "source": [
    "softcomp_discog = d.get_label_discography(soft_comp_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "45a3f577-9c61-4b49-8cee-98606cb17af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "221"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(uttu_discog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "30355451-ee7e-443a-9805-88aaa515dbf1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(hothaus_discog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "c30ad8b1-3bb7-4810-bc77-2f110e3a6496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "68"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(dancetrax_discog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "e0e449bb-a208-4286-8ba8-54b0c02da7b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(softcomp_discog)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "d7d451f9-c87f-44f1-a557-832b58e3d4e6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UTTU093\n",
      "UTTU093\n",
      "UTTU093\n",
      "UTTU093\n",
      "WeMe313.20\n",
      "WeMe313.20\n",
      "DATARMX\n",
      "DRUIDSDREAM1\n",
      "ETU002 \n",
      "ETU002 \n",
      "ETU002BONUS\n",
      "MINDGAME\n",
      "none\n",
      "none\n",
      "none\n",
      "none\n",
      "none\n",
      "none\n",
      "none\n",
      "none\n",
      "TRIBAL_DATA_\n",
      "TRIBALTRAX001\n",
      "UTTU 003\n",
      "UTTU 005\n",
      "UTTU 007\n",
      "UTTU 012\n",
      "UTTU 014B\n",
      "UTTU 018\n",
      "UTTU 028\n",
      "UTTU 054\n",
      "UTTU 059\n",
      "UTTU 062\n",
      "UTTU 072 \n",
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      "UTTU 090\n",
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      "UTTU 096 \n",
      "UTTU 096 \n",
      "UTTU 38 \n",
      "UTTU 38 \n",
      "UTTU FACT\n",
      "UTTU LEGO RAVE \n",
      "UTTU_004\n",
      "UTTU_005\n",
      "UTTU_005\n",
      "UTTU_006\n",
      "UTTU_008\n",
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      "UTTU_020\n",
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      "UTTU_021\n",
      "UTTU_022\n",
      "UTTU_023\n",
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      "UTTU_025\n",
      "UTTU_025\n",
      "UTTU_025\n",
      "UTTU_026\n",
      "UTTU_030\n",
      "UTTU_031\n",
      "UTTU_032\n",
      "UTTU_034\n",
      "UTTU_034\n",
      "UTTU_035\n",
      " UTTU_036\n",
      "UTTU_037\n",
      "UTTU_037\n",
      "UTTU_039\n",
      "UTTU_039\n",
      "UTTU_040\n",
      "UTTU_041\n",
      "UTTU_042\n",
      "UTTU_042\n",
      "UTTU_043\n",
      "UTTU_045\n",
      "UTTU_046\n",
      "UTTU_048\n",
      "UTTU_049_RMX\n",
      "UTTU_051\n",
      "UTTU_053\n",
      "UTTU_15_B\n",
      "UTTU_1UP\n",
      "UTTU_GOGGLE\n",
      "UTTU_GOGGLE\n",
      "UTTU_GOGGLE\n",
      "UTTU_LEGO\n",
      "UTTU_LEGO\n",
      "UTTU_LEGO\n",
      "UTTU_LEGO\n",
      "UTTU_MK\n",
      "UTTU.CLUB_002\n",
      "UTTU007\n",
      "UTTU009\n",
      "uttu012\n",
      "UTTU041\n",
      "UTTU044\n",
      "UTTU047\n",
      "UTTU049\n",
      "UTTU050\n",
      "UTTU050\n",
      "UTTU052\n",
      "UTTU055\n",
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      "UTTU060\n",
      "UTTU060\n",
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      "UTTU061\n",
      "UTTU063\n",
      "UTTU064\n",
      "UTTU065\n",
      "UTTU066\n",
      " UTTU067 \n",
      "UTTU067 \n",
      "UTTU068 \n",
      "UTTU069\n",
      "UTTU069\n",
      "UTTU069\n",
      "UTTU071\n",
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      "UTTU134\n",
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      "UTTU140\n",
      "UTTU70\n",
      "UTTU70\n",
      "UTTU76\n",
      "UTTUBONUS002\n",
      "UTTUBONUS003\n",
      "UTTUBONUS003\n",
      "UTTUDREAMS\n",
      "UTTUDREAMS\n",
      "UTTUENDORFINS\n",
      "UTTUETU\n",
      "UTTUFACT\n",
      "UTTUICEAGE\n",
      "UTTUSPOOKY\n",
      "UTTUTODDIES\n",
      "UTUU 081\n",
      "VIVOD/UTTU 001\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf311b4a-1bd3-471f-9de3-90b4a21476ea",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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