EDM_DJ_Timbre_Tool.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/alexjameswilliams/EDMAudioRepresentations/blob/main/EDM_DJ_Timbre_Tool.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This notebook accompanies the paper \"Deep Learning-based Audio Representations for the Analysis and Visualisation of Timbre and Rhythm in Electronic Dance Music DJ Mixes\""
      ],
      "metadata": {
        "id": "V69OR7Twx-tZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q umap-learn openl3 hdbscan==0.8.33 traja"
      ],
      "metadata": {
        "id": "5d_u9T4s0DAH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "46d4df96-bf39-47fc-d383-e642c69c2679"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/85.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m20.5/85.7 kB\u001b[0m \u001b[31m605.0 kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/85.7 kB\u001b[0m \u001b[31m606.7 kB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.7/85.7 kB\u001b[0m \u001b[31m760.7 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m91.8/91.8 kB\u001b[0m \u001b[31m11.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for hdbscan (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for openl3 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for kapre (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for resampy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import librosa as lr\n",
        "import os\n",
        "import pathlib\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.metrics.pairwise import pairwise_distances\n",
        "from sklearn.metrics import jaccard_score\n",
        "from sklearn.metrics.pairwise import pairwise_kernels\n",
        "\n",
        "import openl3\n",
        "from umap import UMAP\n",
        "import hdbscan\n",
        "import traja  # trajectory helper library\n",
        "import statistics\n",
        "\n",
        "plt.rcParams['figure.dpi'] = 300"
      ],
      "metadata": {
        "id": "7vCWgAH2HZSh"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Mount Google Drive / Assign Directories\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive/')"
      ],
      "metadata": {
        "id": "AxtgVHRoFvkM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "577e7f05-c510-4741-a179-c62b2683a294"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Parameters\n",
        "#@markdown HBDSCAN Clustering Parameter: The number of points required to create a cluster\n",
        "\n",
        "#@markdown Maxmimum Smoothness: The maximum length (in seconds) of a transition to receive a perfect smoothness score (1)\n",
        "\n",
        "#@markdown Maximum Number Of Clusters: The maximum number of clusters to receive the lowest consistency score (0)\n",
        "\n",
        "HDB_CLUSTER_SIZE = 10 #@param\n",
        "MAXIMUM_SMOOTHNESS = 90 #@param\n",
        "MAXIMUM_NUMBER_OF_CLUSTERS = 30 #@param\n",
        "UMAP_STATE = 2024 #@param"
      ],
      "metadata": {
        "id": "j6HCRY991fDo",
        "cellView": "form"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Set directory path\n",
        "DATABASE_ROOT_DIRECTORY = \"/content/drive/MyDrive/fabric_mixes\" #@param\n",
        "FABRIC_MIXES_FOLDER = \"/content/drive/MyDrive/fabric_mixes/fabric\" #@param\n",
        "FABRIC_CSV_FILE = \"/content/drive/MyDrive/fabric_mixes/fabric_mix_series_data.csv\" #@param\n",
        "\n",
        "FABRICLIVE_MIXES_FOLDER = \"/content/drive/MyDrive/fabric_mixes/fabriclive\" #@param\n",
        "FABRICLIVE_CSV_FILE = \"/content/drive/MyDrive/fabric_mixes/fabriclive_mix_series_data.csv\" #@param\n",
        "\n",
        "MFCC_REPRESENTATION_ROOT_DIRECTORY = \"/content/drive/MyDrive/fabric_mixes/mfcc\" #@param\n",
        "OPENL3_REPRESENTATION_ROOT_DIRECTORY = \"/content/drive/MyDrive/fabric_mixes/openl3\" #@param\n",
        "CAE_REPRESENTATION_ROOT_DIRECTORY = \"/content/drive/MyDrive/fabric_mixes/cae\" #@param\n",
        "JTFST_REPRESENTATION_ROOT_DIRECTORY = \"/content/drive/MyDrive/fabric_mixes/jtfs\" #@param"
      ],
      "metadata": {
        "id": "gXWPiIsMHgEy",
        "cellView": "form"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "invalid_mixes = ['2011 - FabricLive 56 - Pearson Sound & Ramadanman',\n",
        "'2007 - Fabric 32 - Luke Slater',\n",
        "'2007 - Fabric 34 - Ellen Allien',\n",
        "'2007 - Fabric 37 - Steve Bug',\n",
        "'2006 - Fabric 29 - Tiefschwarz',\n",
        "'2006 - Fabric 30 - Rub N Tug',\n",
        "'2004 - Fabric 16 - Eddie Richards',\n",
        "'2002 - Fabric 07 - Hipp-e & Halo',\n",
        "'2018 - Fabric 99 - Sasha',\n",
        "'2018 - Fabric 98 - Maceo Plex',\n",
        "'2003 - Fabric 10 - Doc Martin',\n",
        "'2009 - Fabric 49 - Magda',\n",
        "'2006 - Fabric 31 - Marco Carola',\n",
        "'2009 - Fabric 44 - John Tejada',\n",
        "'2017 - fabric 97 - Tale Of Us',\n",
        "'2006 - Fabric 26 - Global Communication',\n",
        "'2002 - Fabric 02 - Terry Francis',\n",
        "'2003 - Fabric 12 - The Amalgamation of Soundz',\n",
        "'2005 - Fabric 25 - Carl Craig',\n",
        "'2017 - Fabric 95 - Roman Flügel',\n",
        "'2005 - Fabric 24 - Rob da Bank',\n",
        "'2017 - Fabric 96 - DVS1',\n",
        "'2012 - Fabric 65 - Matthias Tanzmann',\n",
        "'2003 - Fabric 13 - Michael Mayer',\n",
        "'2006 - Fabric 27 - Matthew Dear as Audion',\n",
        "'2004 - Fabric 14 - Stacey Pullen',\n",
        "'2016 - Fabric 91 - Nina Kraviz',\n",
        "'2008 - Fabric 41 - Luciano',\n",
        "'2005 - Fabric 23 - Ivan Smagghe',\n",
        "'2006 - Fabric 28 - Wiggle',\n",
        "'2004 - Fabric 17 - Akufen',\n",
        "'2008 - Fabric 43 - Metro Area',\n",
        "'2002 - Fabric 04 - Tony Humphries',\n",
        "'2008 - Fabric 38 - M.A.N.D.Y',\n",
        "'2007 - Fabric 35 - Ewan Pearson',\n",
        "'2009 - Fabric 48 - Radio Slave',\n",
        "'2010 - Fabric 50 - Martyn',\n",
        "'2003 - Fabric 11 - Swayzak',\n",
        "'2007 - Fabric 33 - Ralph Lawson']"
      ],
      "metadata": {
        "id": "tPBurn_ukyFU"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_track_labels(t, timestamps):\n",
        "  return len(timestamps[timestamps<=t])\n",
        "\n",
        "\n",
        "def get_ssm_from_embs(embs, track_labels):\n",
        "  ssm = pairwise_distances(embs)\n",
        "  ssm = (ssm - float(np.min(ssm))) / (float(np.max(ssm)) - float(np.min(ssm)))\n",
        "  for i in range(ssm.shape[0]):\n",
        "    for j in range(i, ssm.shape[1]):\n",
        "      if (abs(track_labels[i] - track_labels[j]) > 1):\n",
        "        ssm[i][j] = 0\n",
        "        ssm[j][i] = 0\n",
        "  return ssm\n",
        "\n",
        "\n",
        "def get_mfccs(y, sr, timestamps):\n",
        "  hop_length = sr\n",
        "  n_fft = int(2 ** np.ceil(np.log2(sr)))\n",
        "  mfccs = lr.feature.mfcc(y=y, sr=sr, hop_length=hop_length, n_fft=n_fft).T\n",
        "  times = lr.frames_to_time(np.arange(mfccs.shape[0]), sr=sr, hop_length=hop_length)\n",
        "  track_labels = np.array([get_track_labels(t, timestamps) for t in times])\n",
        "  return times, mfccs, track_labels\n",
        "\n",
        "\n",
        "def get_openl3(y, sr, timestamps):\n",
        "  hop_size = 1\n",
        "  embedding_size = 512\n",
        "  openl3_path = '/content/drive/MyDrive/openl3.npy'\n",
        "  if os.path.isfile(openl3_path):\n",
        "    openl3 = np.load(openl3_path)\n",
        "  else:\n",
        "    openl3, _ = openl3.core.get_audio_embedding(y, sr, hop_size=hop_size, embedding_size=embedding_size)\n",
        "  times = lr.frames_to_time(np.arange(openl3.shape[0]), sr=sr, hop_length=hop_size*sr)\n",
        "  track_labels = np.array([get_track_labels(t, timestamps) for t in times])\n",
        "  return times, openl3, track_labels\n",
        "\n",
        "\n",
        "def plot_ssm(times, embs, track_labels, timestamps):\n",
        "  ssm = get_ssm_from_embs(embs, track_labels)\n",
        "\n",
        "  plt.matshow(ssm, cmap='Greys')\n",
        "  plt.xticks(timestamps, ['']*len(timestamps))\n",
        "  plt.yticks(timestamps, ['']*len(timestamps))\n",
        "\n",
        "  for i in range(1, len(timestamps)):\n",
        "    min_val = timestamps[i-1]\n",
        "    max_val = timestamps[i+1] if i < len(timestamps)-1 else times[-1]\n",
        "    plt.hlines(timestamps[i], min_val, max_val, linewidths=0.5, linestyles='dotted')\n",
        "    plt.vlines(timestamps[i], min_val, max_val, linewidths=0.5, linestyles='dotted')\n",
        "\n",
        "  plt.colorbar()\n",
        "  plt.show()\n",
        "\n",
        "\n",
        "def visualise_embeddings(embs, track_labels):\n",
        "  N = track_labels[-1]\n",
        "  for i in range(1, N):\n",
        "    # include label i and i+1\n",
        "    st = np.where(track_labels==i)[0][0]\n",
        "    bo = np.where(track_labels==i+1)[0][0]\n",
        "    en = np.where(track_labels==i+1)[0][-1]+1\n",
        "    colors = np.linspace(0,1,en-st)\n",
        "    s = np.ones(en-st) * (plt.rcParams['lines.markersize'] ** 2)\n",
        "    s[0] = s[0]*3\n",
        "    s[-1] = s[-1]*3\n",
        "    data = UMAP(n_components=3).fit_transform(embs[st:en])\n",
        "    print(f'from track_label {i}: {tracks[i-1]} to track_label {i+1}: {tracks[i]}')\n",
        "    fig = plt.figure(figsize=(8,8))\n",
        "    ax = fig.add_subplot(111, projection='3d')\n",
        "    cm = plt.cm.autumn # autumn is from red to yellow\n",
        "    ax.scatter(data[:bo-st,0], data[:bo-st,1], data[:bo-st,2], marker='o', c=cm(colors[:bo-st]), s=s[:bo-st])\n",
        "    ax.scatter(data[bo-st:,0], data[bo-st:,1], data[bo-st:,2], marker='+', c=cm(colors[bo-st:]), s=s[bo-st:])\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "F29vdj3ejABO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Transition smoothness heuristic for each transition, and ordering\n",
        "\n",
        "def calculate_mix_smoothness(embs, track_labels, no_increase_limit = 15, transition_length_limit = 0.5):\n",
        "\n",
        "  N = track_labels[-1] # number of tracks\n",
        "  N_transitions = N-1\n",
        "  transition_starts = np.zeros(N_transitions)\n",
        "  transition_ends = np.zeros(N_transitions)\n",
        "  transition_lengths = np.zeros(N_transitions)\n",
        "\n",
        "  # loop over each pair of tracks\n",
        "  for i in range(1,N):\n",
        "    print(\"Transition:\", i)\n",
        "    st = np.where(track_labels==i)[0][0]\n",
        "    bo = np.where(track_labels==i+1)[0][0]\n",
        "    en = np.where(track_labels==i+1)[0][-1]+1\n",
        "\n",
        "    data = embs[st:en]\n",
        "\n",
        "    transition_point_vector = data[bo-st]\n",
        "\n",
        "    print(f'from track {tracks[i-1]} to track {tracks[i]}:')\n",
        "    #print(data[bo-st]) ## This is the transition marker\n",
        "\n",
        "    ## After\n",
        "    no_increase_for = 0\n",
        "    distance_from_transition = 0\n",
        "    greatest_distance = 0\n",
        "\n",
        "    # This is how far away fro the transition you should check before stopping e.g. 50% of the length\n",
        "    distance_from_transition_to_track2_end = en-bo\n",
        "    bo_2_en_limit = round(transition_length_limit * distance_from_transition_to_track2_end)\n",
        "\n",
        "    # This is how far away fro the transition you should check before stopping e.g. 50% of the length\n",
        "    distance_from_transition_to_track1_beg = bo-st\n",
        "    bo_2_st_limit = round(transition_length_limit * distance_from_transition_to_track1_beg)\n",
        "\n",
        "\n",
        "    while (no_increase_for < no_increase_limit and bo_2_en_limit > distance_from_transition):\n",
        "\n",
        "      # Euclidian distance\n",
        "      #distance = np.linalg.norm(data[bo-st + distance_from_transition] - data[bo-st + distance_from_transition + 1])\n",
        "      distance = np.linalg.norm(transition_point_vector - data[bo-st + distance_from_transition + 1])\n",
        "\n",
        "      # Check if the distance has increased / remained the same\n",
        "      if distance >= greatest_distance:\n",
        "        no_increase_for = 0\n",
        "        greatest_distance = distance\n",
        "      else:\n",
        "        no_increase_for +=1\n",
        "\n",
        "      distance_from_transition += 1\n",
        "\n",
        "    #if (distance_from_transition < bo_2_en_distance or no_increase_for >= NO_INCREASE_LIMIT):\n",
        "    if (no_increase_for >= no_increase_limit):\n",
        "      dist_1 = distance_from_transition - no_increase_limit\n",
        "    else:\n",
        "      dist_1 = distance_from_transition\n",
        "\n",
        "\n",
        "    ## Before\n",
        "    no_increase_for = 0\n",
        "    distance_from_transition = 0\n",
        "    greatest_distance = 0\n",
        "\n",
        "    while (no_increase_for < no_increase_limit and bo_2_st_limit > distance_from_transition):\n",
        "\n",
        "      # Euclidian distance\n",
        "      distance = np.linalg.norm(transition_point_vector - data[bo-st - distance_from_transition - 1])\n",
        "\n",
        "      # Check if the distance has increased / remained the same\n",
        "      if distance >= greatest_distance:\n",
        "        no_increase_for = 0\n",
        "        greatest_distance = distance\n",
        "      else:\n",
        "        no_increase_for +=1\n",
        "\n",
        "      distance_from_transition += 1\n",
        "\n",
        "    if (no_increase_for >= no_increase_limit):\n",
        "      dist_2 = distance_from_transition - no_increase_limit\n",
        "    else:\n",
        "      dist_2 = distance_from_transition\n",
        "\n",
        "    transition_length = dist_1 + dist_2 + 1\n",
        "\n",
        "    print(\"Transition Length: \", transition_length)\n",
        "    print(\"Distance before Transition point: \", dist_2)\n",
        "    print(\"Distance after Transition point: \", dist_1)\n",
        "    transition_starts[i-1] = dist_2\n",
        "    transition_ends[i-1] = dist_1\n",
        "    transition_lengths[i-1] = transition_length\n",
        "\n",
        "  return transition_starts, transition_ends, transition_lengths\n",
        "\n",
        "# track 1 file directory\n",
        "# track 2 file directory\n",
        "# no_increase_limit = number of intervals without significant timbral change\n",
        "# limit = maximum percentage of track duration that transition can occupy e.g. 50%\n",
        "def calculate_transition_length(track_1, track_2, no_increase_limit = 15, transition_length_limit = 0.5):\n",
        "  track_1, track_1_sr = lr.load(track_1)\n",
        "  track_1_duration = lr.get_duration(y=track_1, sr=track_1_sr)\n",
        "\n",
        "  track_2, track_2_sr = lr.load(track_1)\n",
        "  track_2_duration = lr.get_duration(y=track_2, track_2_sr=track_2_sr)\n",
        "\n",
        "\n",
        "def order_by_transition_length(transition_length):\n",
        "\n",
        "  # Get the indices ordered by values\n",
        "  return np.argsort(transition_length)"
      ],
      "metadata": {
        "id": "LoTiooNbHjVs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Timbral and Rhythmic Consistency Heuristic\n",
        "\n",
        "# Helper Function for seeing which are the top two clusters a point may belong to\n",
        "def top_two_probs_diff(probs):\n",
        "    sorted_probs = np.sort(probs)\n",
        "    return sorted_probs[-1] - sorted_probs[-2]\n",
        "\n",
        "# Maximum Mean Discrepancy helper function\n",
        "def mmd(X, Y, kernel='rbf'):\n",
        "    # Compute pairwise kernel matrices\n",
        "    K_xx = pairwise_kernels(X, X, metric=kernel)\n",
        "    K_xy = pairwise_kernels(X, Y, metric=kernel)\n",
        "    K_yy = pairwise_kernels(Y, Y, metric=kernel)\n",
        "\n",
        "    # Compute MMD\n",
        "    mmd = np.mean(K_xx) - 2 * np.mean(K_xy) + np.mean(K_yy)\n",
        "\n",
        "    return mmd\n",
        "\n",
        "def calculate_consistency_by_number_of_clusters(embs):\n",
        "\n",
        "  # Initialise clustering parameters\n",
        "  hdb_clusterer = hdbscan.HDBSCAN(min_cluster_size=HDB_CLUSTER_SIZE, metric='euclidean') # prediction_data=True required for soft clustering / probabilities\n",
        "  # hdb_clusterer = hdbscan.HDBSCAN(min_cluster_size=20, metric='jaccard')\n",
        "\n",
        "  # Perform clustering\n",
        "  hdb_clusterer.fit(embs)\n",
        "\n",
        "  # cluster each point belongs to\n",
        "  point_cluster_labels = hdb_clusterer.labels_\n",
        "\n",
        "  # get number of clusters\n",
        "  number_of_clusters = hdb_clusterer.labels_.max() + 1\n",
        "\n",
        "  # return inverse i.e. low number of clusters = high consistency and vice versa\n",
        "  return number_of_clusters / 1\n",
        "\n",
        "\n",
        "def calculate_consistency_by_variance(embs):\n",
        "  return np.var(embs)\n",
        "\n",
        "\n",
        "def calculate_consistency_by_distance_between_centroids(embs):\n",
        "\n",
        "  hdb_clusterer = hdbscan.HDBSCAN(min_cluster_size=HDB_CLUSTER_SIZE, metric='euclidean', prediction_data=True) # prediction_data=True required for soft clustering / probabilities\n",
        "  # hdb_clusterer = hdbscan.HDBSCAN(min_cluster_size=20, metric='jaccard')\n",
        "\n",
        "  # Perform clustering\n",
        "  hdb_clusterer.fit(embs)\n",
        "\n",
        "  soft_clusters = hdbscan.all_points_membership_vectors(hdb_clusterer)\n",
        "\n",
        "\n",
        "  # This can be used to identify points that could be assigned to two clusters\n",
        "  ## i.e. at the end of the trajectory\n",
        "\n",
        "  # Compute the differences between the top two probabilities\n",
        "  diffs = np.array([top_two_probs_diff(x) for x in soft_clusters])\n",
        "  # Select out the indices that have a small difference, and a larger total probability\n",
        "  mixed_points = np.where((diffs < 0.001) & (np.sum(soft_clusters, axis=1) > 0.5))[0]\n",
        "\n",
        "  # This can be used to identify clustered points - yields edges for where points are connected\n",
        "\n",
        "  hdb_clusterer.condensed_tree_.to_pandas()\n",
        "  cluster_tree = tree[tree.child_size > 1]\n",
        "\n",
        "def calculate_timbral_consistency_by_MMD(embedding_series):\n",
        "\n",
        "  # Example usage\n",
        "  time_series_1 = ...\n",
        "  time_series_2 = ...\n",
        "  mmd_value = mmd(time_series_1, time_series_2, kernel='linear')\n",
        "  print(\"MMD value with linear kernel:\", mmd_value)\n",
        "\n",
        "\n",
        "def visualise_new(embs):\n",
        "  N = embs.shape[0]\n",
        "  colors = np.linspace(0,1,N)\n",
        "  fig = plt.figure(figsize=(8,8))\n",
        "  ax = fig.add_subplot(projection='3d')\n",
        "  cm = plt.cm.autumn # autumn is from red to yellow\n",
        "\n",
        "  ax.scatter(embs[:,0], embs[:,1], embs[:,2], marker='x', c=cm(colors))\n",
        "  ax.set_xticks([])\n",
        "  ax.set_yticks([])\n",
        "  ax.set_zticks([])\n",
        "  ax.set_xlabel('UMAP Component 1')\n",
        "  ax.set_ylabel('UMAP Component 2')\n",
        "  ax.set_zlabel('UMAP Component 3')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "SpvBXS8xt8cZ"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Generate statistics\n",
        "\"\"\"\n",
        "db_path: CSV file of discogs metadata\n",
        "\"\"\"\n",
        "def generate_statistics(db_path):\n",
        "\n",
        "\n",
        "  try:\n",
        "    # Load database into panda datafram\n",
        "    df = pd.read_csv(db_path)\n",
        "\n",
        "    # Initialize lists to store mix-and-track-level statistics\n",
        "    mix_statistics = []\n",
        "    track_statistics = []\n",
        "\n",
        "    # Loop through each mix in the DataFrame\n",
        "    for index, row in df.iterrows():\n",
        "        # Extract the folder path from the 'Directory' column\n",
        "        folder_path = row['Directory']\n",
        "        mix_name = os.path.basename(os.path.normpath(folder_path))\n",
        "\n",
        "        # Skip any mixes where full representation data is not available\n",
        "        if mix_name not in invalid_mixes:\n",
        "          print(f\"Processing folder: {folder_path}\")\n",
        "          mix_duration = 0.0\n",
        "          number_of_tracks = 0\n",
        "          mix_mfcc = []\n",
        "          mix_openl3 = []\n",
        "          mix_jtfst = []\n",
        "          mix_cae = []\n",
        "\n",
        "          # Loop through each file in the folder\n",
        "          for root, dirs, files in os.walk(os.path.join(DATABASE_ROOT_DIRECTORY,folder_path)):\n",
        "\n",
        "            # Sort files in mix order (os.walk returns arbitrary order)\n",
        "            for file in sorted(files):\n",
        "                audio_path = os.path.join(root, file)\n",
        "                audio_file = os.path.basename(os.path.normpath(audio_path))\n",
        "\n",
        "                # Check it is an audio file\n",
        "                if str(file).endswith('.flac'):\n",
        "                  track_name = pathlib.Path(audio_file).stem\n",
        "                  print(f\"Processing file: {track_name}\")\n",
        "\n",
        "                  # Load audio\n",
        "                  y, sr = lr.load(audio_path)\n",
        "                  track_duration = lr.get_duration(y=y, sr=sr)\n",
        "                  mix_duration = mix_duration + track_duration\n",
        "                  number_of_tracks = number_of_tracks + 1\n",
        "\n",
        "                  # Load representations\n",
        "                  mfcc_representation = np.load(os.path.join(MFCC_REPRESENTATION_ROOT_DIRECTORY, folder_path, track_name + \".npy\"))\n",
        "                  openl3_representation = np.load(os.path.join(OPENL3_REPRESENTATION_ROOT_DIRECTORY, folder_path, track_name + \".npy\"))\n",
        "                  jtfst_representation = np.load(os.path.join(JTFST_REPRESENTATION_ROOT_DIRECTORY, folder_path, track_name + \".npy\"))\n",
        "                  cae_representation = np.load(os.path.join(CAE_REPRESENTATION_ROOT_DIRECTORY, folder_path, track_name + \".npy\"))\n",
        "\n",
        "                  # Apply dimensionality reduction\n",
        "                  reduced_mfcc_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(mfcc_representation))\n",
        "                  reduced_openl3_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(openl3_representation))\n",
        "                  reduced_jtfst_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(jtfst_representation))\n",
        "                  reduced_cae_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(cae_representation))\n",
        "\n",
        "                  # Combine track-level representations for later computation\n",
        "                  mix_mfcc.append(mfcc_representation)\n",
        "                  mix_openl3.append(openl3_representation)\n",
        "                  mix_jtfst.append(jtfst_representation)\n",
        "                  mix_cae.append(cae_representation)\n",
        "\n",
        "                  # Calculate track-level statistics\n",
        "                  track_mfcc_clusters = calculate_consistency_by_number_of_clusters(reduced_mfcc_representation)\n",
        "                  track_openl3_clusters = calculate_consistency_by_number_of_clusters(reduced_openl3_representation)\n",
        "                  track_jtfst_clusters = calculate_consistency_by_number_of_clusters(reduced_jtfst_representation)\n",
        "                  track_cae_clusters = calculate_consistency_by_number_of_clusters(reduced_cae_representation)\n",
        "                  track_mfcc_variance = calculate_consistency_by_variance(mfcc_representation)\n",
        "                  track_openl3_variance = calculate_consistency_by_variance(openl3_representation)\n",
        "                  track_jtfst_variance = calculate_consistency_by_variance(jtfst_representation)\n",
        "                  track_cae_variance = calculate_consistency_by_variance(cae_representation)\n",
        "\n",
        "                  # Output track-level statistics\n",
        "                  #print(f\"Track Duration: {int(track_duration)}\")\n",
        "                  #print(f\"Track-Level Cluster Consistency: {track_mfcc_clusters}, {track_openl3_clusters}, {track_jtfst_clusters}, {track_cae_clusters}\")\n",
        "                  #print(f\"Track-Level Variance Consistency: {track_mfcc_variance}, {track_openl3_variance}, {track_jtfst_variance}, {track_cae_variance}\")\n",
        "\n",
        "                  # Append track-level statistics to the list\n",
        "                  track_statistics.append({\n",
        "                      'Mix Name': mix_name,\n",
        "                      'Track Name': track_name,\n",
        "                      'Track Duration (seconds)': track_duration,\n",
        "                      'MFCC Clusters': track_mfcc_clusters,\n",
        "                      'OpenL3 Clusters': track_openl3_clusters,\n",
        "                      'JTFST Clusters': track_jtfst_clusters,\n",
        "                      'CAE Clusters': track_cae_clusters,\n",
        "                      'MFCC Variance': track_mfcc_variance,\n",
        "                      'OpenL3 Variance': track_openl3_variance,\n",
        "                      'JTFST Variance': track_jtfst_variance,\n",
        "                      'CAE Variance': track_cae_variance\n",
        "                  })\n",
        "\n",
        "            # Convert list of numpy arrays to single numpy array\n",
        "            mix_mfcc_representation = np.concatenate(mix_mfcc, axis=1)\n",
        "            mix_openl3_representation = np.concatenate(mix_openl3, axis=1)\n",
        "            mix_jtfst_representation = np.concatenate(mix_jtfst, axis=1)\n",
        "            mix_cae_representation = np.concatenate(mix_cae, axis=1)\n",
        "\n",
        "            # Apply dimensionality reduction\n",
        "            reduced_mix_mfcc_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(mix_mfcc_representation))\n",
        "            reduced_mix_openl3_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(mix_openl3_representation))\n",
        "            reduced_mix_jtfst_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(mix_jtfst_representation))\n",
        "            reduced_mix_cae_representation = UMAP(n_components=3, random_state=UMAP_STATE).fit_transform(np.transpose(mix_cae_representation))\n",
        "\n",
        "            mix_mfcc_clusters = calculate_consistency_by_number_of_clusters(reduced_mix_mfcc_representation)\n",
        "            mix_openl3_clusters = calculate_consistency_by_number_of_clusters(reduced_mix_openl3_representation)\n",
        "            mix_jtfst_clusters = calculate_consistency_by_number_of_clusters(reduced_mix_jtfst_representation)\n",
        "            mix_cae_clusters = calculate_consistency_by_number_of_clusters(reduced_mix_cae_representation)\n",
        "            mix_mfcc_variance = calculate_consistency_by_variance(mix_mfcc_representation)\n",
        "            mix_openl3_variance = calculate_consistency_by_variance(mix_openl3_representation)\n",
        "            mix_jtfst_variance = calculate_consistency_by_variance(mix_jtfst_representation)\n",
        "            mix_cae_variance = calculate_consistency_by_variance(mix_cae_representation)\n",
        "\n",
        "            # Output mix-level statistics\n",
        "            #print(f\"Mix Duration: {int(mix_duration)} seconds\")\n",
        "            #print(f\"Mix-Level Cluster Consistency: {mix_mfcc_clusters}, {mix_openl3_clusters}, {mix_jtfst_clusters}, {mix_cae_clusters}\")\n",
        "            #print(f\"Mix-Level Variance Consistency: {mix_mfcc_variance}, {mix_openl3_variance}, {mix_jtfst_variance}, {mix_cae_variance}\")\n",
        "            #visualise_new(mix_mfcc_np)\n",
        "            #visualise_new(mix_openl3_np)\n",
        "            #visualise_new(mix_jtfst_np)\n",
        "            #visualise_new(mix_cae_np)\n",
        "\n",
        "            # Append mix-level statistics to the list\n",
        "            mix_statistics.append({\n",
        "                'Mix Name': mix_name,\n",
        "                'Mix Duration (seconds)': mix_duration,\n",
        "                'Number of Tracks': number_of_tracks,\n",
        "                'MFCC Clusters': mix_mfcc_clusters,\n",
        "                'OpenL3 Clusters': mix_openl3_clusters,\n",
        "                'JTFST Clusters': mix_jtfst_clusters,\n",
        "                'CAE Clusters': mix_cae_clusters,\n",
        "                'MFCC Variance': mix_mfcc_variance,\n",
        "                'OpenL3 Variance': mix_openl3_variance,\n",
        "                'JTFST Variance': mix_jtfst_variance,\n",
        "                'CAE Variance': mix_cae_variance\n",
        "            })\n",
        "\n",
        "          break\n",
        "\n",
        "  except:\n",
        "    print(\"Something went wrong\")\n",
        "  finally:\n",
        "    # Save mix-level statistics to a CSV file\n",
        "    mix_df = pd.DataFrame(mix_statistics)\n",
        "    mix_data_csv_name = str(os.path.basename(os.path.normpath(db_path))) + \"_mix_statistics.csv\"\n",
        "    mix_df.to_csv(mix_data_csv_name, index=False)\n",
        "\n",
        "    # Save track-level statistics to a CSV file\n",
        "    track_df = pd.DataFrame(track_statistics)\n",
        "    track_data_csv_name = str(os.path.basename(os.path.normpath(db_path))) + \"_track_statistics.csv\"\n",
        "    track_df.to_csv(track_data_csv_name, index=False)\n",
        "\n",
        "generate_statistics(FABRIC_CSV_FILE)\n",
        "generate_statistics(FABRICLIVE_CSV_FILE)"
      ],
      "metadata": {
        "id": "IbDZ7On6tVsR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69fc919f-00d2-4361-9e2f-b2ded4b4d5c4"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing folder: fabric/2001 - Fabric 01 - Craig Richards\n",
            "Processing file: 01 - Gemini - At That Cafe\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 02 - Cpen - Pirates Life\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 03 - Antonelli Electr. - The Realist\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 04 - Dub Tech Soundsystem - Sugar Rush\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 05 - Jamie Anderson - Montage\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 06 - SCSI-9 - Cozmoport\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 07 - SCSI-9 - Cologne\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 08 - Lo-kee - Sinners\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 09 - Ernst Viebeg - Nightlife\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 10 - Bushwacka! - Bluntski\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 11 - Swag - Drum Hydraulics\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 12 - Roman IV - 14x7x4\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 13 - Wavescape - Silicon Jazz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 14 - Terry Francis - Took From Me\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 15 - Helmet - Early Riser\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 16 - Schatrax - Mispent Years\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing folder: fabriclive/2001 - FabricLive 01 - James Lavelle\n",
            "Processing file: 01. James Lavelle - A Message To Our Sponsors (Intro)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 02. Rare Earth - People Get Ready\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 03. Psychonauts - Circles\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 04. Dj Shadow - Organ Donor\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 05. Divine Styler & DJ Shadow - Divine Intervention\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 06. South - Broken Head II\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 07. The Chemical Brothers - Piku\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 08. Howie B - Hey Jack\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 09. Orbital - Funny Break (One Is Enough)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 10. Bushwacka! - Feel It\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 11. Forme - Kick A Hole (Tigerstyle)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 12. Altitude - Night Stalker\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 13. Peter Dildo - Physical 2000\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 14. Green Velvet - La La Land\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 15. Animated - Grab The Rope\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 16. FC Kahuna - Mindset To Cycle\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 17. Morel - True (The Faggot Is You)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 18. Echomen - Thru 2 You\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 19. Landmine - Fairytale\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 20. Lazonby - Sacred Cycles\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 21. Medway - Release\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 22. Bushwacka! - Healer\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing file: 23. Radiohead,UNKLE - Everything In Its Right Place,Bonus Beats\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n",
            "/usr/local/lib/python3.10/dist-packages/umap/umap_.py:1945: UserWarning: n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
            "  warn(f\"n_jobs value {self.n_jobs} overridden to 1 by setting random_state. Use no seed for parallelism.\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "times, embs, track_labels = get_mfccs(y, sr, timestamps)"
      ],
      "metadata": {
        "id": "WVDDdWevDH9z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Calculate Smoothness for MFCC\n",
        "\n",
        "transition_starts, transition_ends, transition_lengths = calculate_mix_smoothness(embs, track_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s28BLqxIHtwk",
        "outputId": "34c23140-235d-437d-f7e9-eda1d093e4b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transition: 1\n",
            "from track 0:00 Fred again.. - Danielle (Smile On My Face) to track 3:30 Taylor Swift - Love Story (KH Edit):\n",
            "Transition Length:  53\n",
            "Distance before Transition point:  11\n",
            "Distance after Transition point:  41\n",
            "Transition: 2\n",
            "from track 3:30 Taylor Swift - Love Story (KH Edit) to track 7:15 Roddy Ricch - The Box (Skrillex Edit):\n",
            "Transition Length:  62\n",
            "Distance before Transition point:  36\n",
            "Distance after Transition point:  25\n",
            "Transition: 3\n",
            "from track 7:15 Roddy Ricch - The Box (Skrillex Edit) to track 10:45 Fred again.. - Bleu (Better With Time) :\n",
            "Transition Length:  62\n",
            "Distance before Transition point:  33\n",
            "Distance after Transition point:  28\n",
            "Transition: 4\n",
            "from track 10:45 Fred again.. - Bleu (Better With Time)  to track 13:00 Dog Blood - Want Some More:\n",
            "Transition Length:  93\n",
            "Distance before Transition point:  40\n",
            "Distance after Transition point:  52\n",
            "Transition: 5\n",
            "from track 13:00 Dog Blood - Want Some More to track 14:45 Skrillex & Starrah & Four Tet - Butterflies :\n",
            "Transition Length:  53\n",
            "Distance before Transition point:  27\n",
            "Distance after Transition point:  25\n",
            "Transition: 6\n",
            "from track 14:45 Skrillex & Starrah & Four Tet - Butterflies  to track 17:50 Romy & Fred again.. - Enjoy Your Life:\n",
            "Transition Length:  74\n",
            "Distance before Transition point:  48\n",
            "Distance after Transition point:  25\n",
            "Transition: 7\n",
            "from track 17:50 Romy & Fred again.. - Enjoy Your Life to track 18:40 Anna Lunoe ft. Genesis Owusu - Back Seat (Chris Lorenzo Remix) :\n",
            "Transition Length:  19\n",
            "Distance before Transition point:  5\n",
            "Distance after Transition point:  13\n",
            "Transition: 8\n",
            "from track 18:40 Anna Lunoe ft. Genesis Owusu - Back Seat (Chris Lorenzo Remix)  to track 20:30 Skrillex ft. Bobby Raps - Leave Me Like This :\n",
            "Transition Length:  48\n",
            "Distance before Transition point:  18\n",
            "Distance after Transition point:  29\n",
            "Transition: 9\n",
            "from track 20:30 Skrillex ft. Bobby Raps - Leave Me Like This  to track 23:15 Joy Anonymous - JOY (Head To The Sky) :\n",
            "Transition Length:  46\n",
            "Distance before Transition point:  43\n",
            "Distance after Transition point:  2\n",
            "Transition: 10\n",
            "from track 23:15 Joy Anonymous - JOY (Head To The Sky)  to track 24:40 PinkPantheress - Just For Me (salute Edit) :\n",
            "Transition Length:  47\n",
            "Distance before Transition point:  42\n",
            "Distance after Transition point:  4\n",
            "Transition: 11\n",
            "from track 24:40 PinkPantheress - Just For Me (salute Edit)  to track 25:31 J Balvin & Skrillex - In Da Getto (Chris Lorenzo Remix) :\n",
            "Transition Length:  16\n",
            "Distance before Transition point:  5\n",
            "Distance after Transition point:  10\n",
            "Transition: 12\n",
            "from track 25:31 J Balvin & Skrillex - In Da Getto (Chris Lorenzo Remix)  to track 27:00 J Balvin & Skrillex - In Da Getto (Andrew Lux Remix) :\n",
            "Transition Length:  45\n",
            "Distance before Transition point:  14\n",
            "Distance after Transition point:  30\n",
            "Transition: 13\n",
            "from track 27:00 J Balvin & Skrillex - In Da Getto (Andrew Lux Remix)  to track 28:00 Fred again.. & Skrillex & Four Tet ft. Lil Baby - Baby Again..  :\n",
            "Transition Length:  57\n",
            "Distance before Transition point:  30\n",
            "Distance after Transition point:  26\n",
            "Transition: 14\n",
            "from track 28:00 Fred again.. & Skrillex & Four Tet ft. Lil Baby - Baby Again..   to track 32:00 Hamdi - Skanka :\n",
            "Transition Length:  20\n",
            "Distance before Transition point:  12\n",
            "Distance after Transition point:  7\n",
            "Transition: 15\n",
            "from track 32:00 Hamdi - Skanka  to track 33:40 Skrillex & Fred again.. & Flowdan - Rumble :\n",
            "Transition Length:  53\n",
            "Distance before Transition point:  33\n",
            "Distance after Transition point:  19\n",
            "Transition: 16\n",
            "from track 33:40 Skrillex & Fred again.. & Flowdan - Rumble  to track 36:20 Mizormac - Return of The Mac (ThatOneDJGuy Edit - Soundcloud):\n",
            "Transition Length:  53\n",
            "Distance before Transition point:  32\n",
            "Distance after Transition point:  20\n",
            "Transition: 17\n",
            "from track 36:20 Mizormac - Return of The Mac (ThatOneDJGuy Edit - Soundcloud) to track 38:11 HOL! - Country Riddim :\n",
            "Transition Length:  66\n",
            "Distance before Transition point:  38\n",
            "Distance after Transition point:  27\n",
            "Transition: 18\n",
            "from track 38:11 HOL! - Country Riddim  to track 39:05 Jack Ü ft. Justin Bieber - Where Are Ü Now (Marshmello Remix / Skrillex Flip) :\n",
            "Transition Length:  7\n",
            "Distance before Transition point:  2\n",
            "Distance after Transition point:  4\n",
            "Transition: 19\n",
            "from track 39:05 Jack Ü ft. Justin Bieber - Where Are Ü Now (Marshmello Remix / Skrillex Flip)  to track 39:55 Skrillex ft. Nai Barghouti - XENA :\n",
            "Transition Length:  30\n",
            "Distance before Transition point:  25\n",
            "Distance after Transition point:  4\n",
            "Transition: 20\n",
            "from track 39:55 Skrillex ft. Nai Barghouti - XENA  to track 40:45 Skrillex & Kito ft. Aluna - Inhale Exhale :\n",
            "Transition Length:  13\n",
            "Distance before Transition point:  9\n",
            "Distance after Transition point:  3\n",
            "Transition: 21\n",
            "from track 40:45 Skrillex & Kito ft. Aluna - Inhale Exhale  to track 41:50 Fred again.. - Jungle  ATLANTIC (WARNER MUSIC):\n",
            "Transition Length:  59\n",
            "Distance before Transition point:  32\n",
            "Distance after Transition point:  26\n",
            "Transition: 22\n",
            "from track 41:50 Fred again.. - Jungle  ATLANTIC (WARNER MUSIC) to track 44:10 EPROM & G Jones - On My Mind (RamonPang Flip) :\n",
            "Transition Length:  50\n",
            "Distance before Transition point:  21\n",
            "Distance after Transition point:  28\n",
            "Transition: 23\n",
            "from track 44:10 EPROM & G Jones - On My Mind (RamonPang Flip)  to track 45:05 Skrillex ft. Starrah - Good Space :\n",
            "Transition Length:  54\n",
            "Distance before Transition point:  28\n",
            "Distance after Transition point:  25\n",
            "Transition: 24\n",
            "from track 45:05 Skrillex ft. Starrah - Good Space  to track 46:30 Skrillex & Swae Lee & Siiickbrain & Posij - Too Bizarre (Juked)  :\n",
            "Transition Length:  47\n",
            "Distance before Transition point:  18\n",
            "Distance after Transition point:  28\n",
            "Transition: 25\n",
            "from track 46:30 Skrillex & Swae Lee & Siiickbrain & Posij - Too Bizarre (Juked)   to track 49:00 Fred again.. - Delilah (Pull Me Out Of This)  :\n",
            "Transition Length:  43\n",
            "Distance before Transition point:  1\n",
            "Distance after Transition point:  41\n",
            "Transition: 26\n",
            "from track 49:00 Fred again.. - Delilah (Pull Me Out Of This)   to track 51:44 Fred again.. - Billie (Loving Arms)  :\n",
            "Transition Length:  27\n",
            "Distance before Transition point:  10\n",
            "Distance after Transition point:  16\n",
            "Transition: 27\n",
            "from track 51:44 Fred again.. - Billie (Loving Arms)   to track 52:15 Skrillex & Mr. Oizo ft. Missy Elliott - RATATA :\n",
            "Transition Length:  36\n",
            "Distance before Transition point:  16\n",
            "Distance after Transition point:  19\n",
            "Transition: 28\n",
            "from track 52:15 Skrillex & Mr. Oizo ft. Missy Elliott - RATATA  to track 55:47 Drake - Massive:\n",
            "Transition Length:  26\n",
            "Distance before Transition point:  19\n",
            "Distance after Transition point:  6\n",
            "Transition: 29\n",
            "from track 55:47 Drake - Massive to track 56:00 JAY-Z ft. Alicia Keys - Empire State Of Mind (Acappella) :\n",
            "Transition Length:  12\n",
            "Distance before Transition point:  6\n",
            "Distance after Transition point:  5\n",
            "Transition: 30\n",
            "from track 56:00 JAY-Z ft. Alicia Keys - Empire State Of Mind (Acappella)  to track 57:30 Skrillex & Mr. Oizo ft. Missy Elliott - RATATA:\n",
            "Transition Length:  59\n",
            "Distance before Transition point:  45\n",
            "Distance after Transition point:  13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Order Transitions by Normalised Smoothness\n",
        "\n",
        "# Normalization #2\n",
        "relative_smoothness = (transition_lengths - np.min(transition_lengths)) / (np.max(transition_lengths) - np.min(transition_lengths))\n",
        "\n",
        "# Reorder values_array based on order_indices\n",
        "values_array = relative_smoothness\n",
        "order_indices = order_by_transition_length(transition_lengths)\n",
        "ordered_values = values_array[order_indices]\n",
        "\n",
        "# Stack arrays horizontally to print as table\n",
        "stacked_array = np.column_stack(((order_by_transition_length(transition_lengths) +1), ordered_values))\n",
        "\n",
        "# Print the table\n",
        "print(\"Transition | Relative Smoothness\")\n",
        "print(\"---------------------------------\")\n",
        "for row in stacked_array:\n",
        "    print(f\"{row[0]:^7} | {row[1]:^7}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gTABIynEHyTa",
        "outputId": "8bb3b1a9-b38a-4eab-b1c8-7d3b6cea9988"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Transition | Relative Smoothness\n",
            "---------------------------------\n",
            " 18.0   |   0.0  \n",
            " 29.0   | 0.05813953488372093\n",
            " 20.0   | 0.06976744186046512\n",
            " 11.0   | 0.10465116279069768\n",
            "  7.0   | 0.13953488372093023\n",
            " 14.0   | 0.1511627906976744\n",
            " 28.0   | 0.22093023255813954\n",
            " 26.0   | 0.23255813953488372\n",
            " 19.0   | 0.26744186046511625\n",
            " 27.0   | 0.3372093023255814\n",
            " 25.0   | 0.4186046511627907\n",
            " 12.0   | 0.4418604651162791\n",
            "  9.0   | 0.45348837209302323\n",
            " 10.0   | 0.46511627906976744\n",
            " 24.0   | 0.46511627906976744\n",
            "  8.0   | 0.47674418604651164\n",
            " 22.0   |   0.5  \n",
            "  1.0   | 0.5348837209302325\n",
            " 15.0   | 0.5348837209302325\n",
            " 16.0   | 0.5348837209302325\n",
            "  5.0   | 0.5348837209302325\n",
            " 23.0   | 0.5465116279069767\n",
            " 13.0   | 0.5813953488372093\n",
            " 21.0   | 0.6046511627906976\n",
            " 30.0   | 0.6046511627906976\n",
            "  3.0   | 0.6395348837209303\n",
            "  2.0   | 0.6395348837209303\n",
            " 17.0   | 0.686046511627907\n",
            "  6.0   | 0.7790697674418605\n",
            "  4.0   |   1.0  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_ssm(times, embs, track_labels, timestamps)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZZV6NnE3wKT6",
        "outputId": "144f4036-a5b8-4a19-b304-d01e27f750df"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visualise_embeddings(embs, track_labels)"
      ],
      "metadata": {
        "id": "4-mAWgr0wOpN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "times, embs, track_labels = get_openl3(y, sr, timestamps)"
      ],
      "metadata": {
        "id": "n6yN_XqB4i7G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Calculate Smoothness for OpenL3\n",
        "\n",
        "transition_starts, transition_ends, transition_lengths = calculate_smoothness(embs, track_labels)"
      ],
      "metadata": {
        "id": "hLBDF6LWSeWD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#@title Order Transitions by Normalised Smoothness\n",
        "\n",
        "# Normalization #2\n",
        "relative_smoothness = (transition_lengths - np.min(transition_lengths)) / (np.max(transition_lengths) - np.min(transition_lengths))\n",
        "\n",
        "# Reorder values_array based on order_indices\n",
        "values_array = relative_smoothness\n",
        "order_indices = order_by_transition_length(transition_lengths)\n",
        "ordered_values = values_array[order_indices]\n",
        "\n",
        "# Stack arrays horizontally to print as table\n",
        "stacked_array = np.column_stack(((order_by_transition_length(transition_lengths) +1), ordered_values))\n",
        "\n",
        "# Print the table\n",
        "print(\"Transition | Relative Smoothness\")\n",
        "print(\"---------------------------------\")\n",
        "for row in stacked_array:\n",
        "    print(f\"{row[0]:^7} | {row[1]:^7}\")"
      ],
      "metadata": {
        "id": "xGYSnlTHSg_q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_ssm(times, embs, track_labels, timestamps)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tvX6za6JWcAO",
        "outputId": "4a6ec4ad-03af-48df-bc19-d1112cb9ee01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1440 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emb_viz(embs, track_labels)"
      ],
      "metadata": {
        "id": "vKLQC5DO4pNB"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}