harmory/analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of intermediate outputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import note_seq as ns\n",
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from analysis import compute_structure_statistics\n",
    "from create import load_structures\n",
    "from search import HarmonicPatternFinder\n",
    "from sonification import get_harmonic_notesequence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1792/1792 [00:01<00:00, 918.11it/s] \n"
     ]
    }
   ],
   "source": [
    "# Dictionary Segment ID (ChoCo-like) to TpsTimeSeries\n",
    "hstructures = load_structures(\"../data/structures/v1/\")\n",
    "# Dictionary from Pattern ID to Segment ID\n",
    "with open(\"../data/similarities/v1/pattern2id.pkl\", \"rb\") as handle:\n",
    "    pattern2id = pickle.load(handle)\n",
    "\n",
    "# Harmonic pattern finder for search similar patterns given a query (as index or time series)\n",
    "hfinder_model = HarmonicPatternFinder(model_ckpt=\"../data/similarities/v1/hfinder.pkl\")\n",
    "# Dataset of patterns, trivially works as map PatternID -> Pattern (time series)\n",
    "patterns = hfinder_model.get_pattern_dataset()\n",
    "# Similarity relationships (sim and same) for patterns\n",
    "simi_relations = pd.read_csv(\"../data/similarities/v1/similarities.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Harmonic structures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 25848/25848 [00:00<00:00, 651685.32it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tot. structures detected: 25848\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "segment_statistics = compute_structure_statistics(hstructures)\n",
    "# print(\", \".join(list(segment_statistics.keys())))\n",
    "\n",
    "print(f\"Tot. structures detected: {segment_statistics['glob_num_structures']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. duration of structures: 14.34 ± 9.01 seconds\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 495,
       "width": 1989
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Avg. duration of structures: \"\n",
    "      f\"{segment_statistics['glob_dur_mu']:.2f} \"\n",
    "      f\"± {segment_statistics['glob_dur_std']:.2f} seconds\")\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(10, 5))\n",
    "sax = sns.displot(segment_statistics[\"glob_durations\"], aspect=4)\n",
    "sax.fig.suptitle(\"Duration of harmonic structures (globally)\", size=14)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. number of structures per progression: 14.42 ± 5.06\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 495,
       "width": 489
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Avg. number of structures per progression: \"\n",
    "      f\"{np.mean(segment_statistics['num_structures_php']):.2f} \"\n",
    "      f\"± {np.std(segment_statistics['num_structures_php']):.2f}\")\n",
    "\n",
    "sax = sns.displot(segment_statistics['num_structures_php'])\n",
    "sax.fig.suptitle(\"Avg. number of harmonic structures per progression\", size=12)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg. duration of structures per progression: 14.97 ± 6.89 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 495,
       "width": 489
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f\"Avg. duration of structures per progression: \"\n",
    "      f\"{np.mean(segment_statistics['dur_structures_php']):.2f} \"\n",
    "      f\"± {np.std(segment_statistics['dur_structures_php']):.2f} seconds\")\n",
    "\n",
    "sax = sns.displot(segment_statistics['dur_structures_php'])\n",
    "sax.fig.suptitle(\"Avg. duration of harmonic structures per progression\", size=12)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Similarities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from analysis import compute_similarity_statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_pattern_ids = list(pattern2id.keys())\n",
    "simi_statistics = compute_similarity_statistics(simi_relations, all_pattern_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SAME relations: 13667 (0.17)\n",
      "SIMI relations: 66175 (0.83)\n",
      "Avg. SIMI distance: 1.10 ± 0.56\n"
     ]
    }
   ],
   "source": [
    "no_simi_rels, no_same_rels = simi_statistics[\"no_simi_rels\"], simi_statistics[\"no_same_rels\"], \n",
    "\n",
    "print(f\"SAME relations: {no_same_rels} ({no_same_rels / (len(simi_relations)):.2f})\\n\"\n",
    "      f\"SIMI relations: {no_simi_rels} ({no_simi_rels / (len(simi_relations)):.2f})\")\n",
    "print(f\"Avg. SIMI distance: {simi_statistics['simi_rel_avg']:.2f}\"\n",
    "      f\" ± {simi_statistics['simi_rel_std']:.2f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pattern families: 4089 (0.16)\n",
      "Pattern friendly: 13583 (0.53)\n",
      "Pattern unique: 8176 (0.32)\n"
     ]
    }
   ],
   "source": [
    "no_pattern_families, no_pattern_friendly, no_pattern_unique = \\\n",
    "    simi_statistics[\"no_pattern_families\"], \\\n",
    "        simi_statistics[\"no_pattern_friendly\"], \\\n",
    "            simi_statistics[\"no_pattern_unique\"]\n",
    "\n",
    "total_patterns = no_pattern_families + no_pattern_friendly + no_pattern_unique\n",
    "\n",
    "print(f\"Pattern families: {no_pattern_families} ({no_pattern_families / total_patterns:.2f})\\n\"\n",
    "      f\"Pattern friendly: {no_pattern_friendly} ({no_pattern_friendly / total_patterns:.2f})\\n\"\n",
    "      f\"Pattern unique: {no_pattern_unique} ({no_pattern_unique / total_patterns:.2f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No. of proper similarity relationships per pattern')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x400 with 3 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 373,
       "width": 1277
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=3, figsize=(15,4), sharey=True)\n",
    "sns.histplot(simi_statistics[\"pattern_neigh_sizes\"],\n",
    "             label=\"neighbourhood\", ax=ax[0])\n",
    "ax[0].set_title(\"No. of similarities per pattern\")\n",
    "sns.histplot(simi_statistics[\"pattern_family_sizes\"],\n",
    "             label=\"family\", ax=ax[1])\n",
    "ax[1].set_title(\"No. of family relationships per pattern\")\n",
    "sns.histplot(simi_statistics[\"pattern_simila_sizes\"],\n",
    "             label=\"neighbourhood\", ax=ax[2])\n",
    "ax[2].set_title(\"No. of proper similarity relationships per pattern\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average distance for each pattern (no same)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 373,
       "width": 1247
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(15,4), sharey=True)\n",
    "\n",
    "sns.histplot(simi_statistics[\"pattern_avg_distance_all\"], ax=ax[0])\n",
    "ax[0].set_title(\"Average distance for each pattern\")\n",
    "\n",
    "sns.histplot(simi_statistics[\"pattern_avg_distance_nosame\"], ax=ax[1])\n",
    "ax[1].set_title(\"Average distance for each pattern (no same)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "musilar",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "f94780de1f3f5aa4c9046e32c047f69f4d58290f5069cff27f2bde9c8077bb27"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}