characterizing_intonation_in_turkish_makam_music.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Characterizing intonation in Turkish Makam music\n",
    "\n",
    "In this notebook we provide code to download some relevant files from the [Makam Dunya corpora](https://dunya.compmusic.upf.edu/makam/) given a set of makamler, and then compute the difference in cents between the performed pitches and the theoretical pitches. This will allow us to visualize and compare these differences between Makamler. \n",
    "\n",
    "## Setting up the Docker image\n",
    "\n",
    "This notebook uses CompMusic and other tools provided in the [mir-toolbox](https://hub.docker.com/r/mtgupf/mir-toolbox). Please be sure to have Docker Desktop installed and running, open this directory in a cmdline and run `docker-compose up`. Then you can run this notebook from `http://localhost:8888` using the password \"**mir**\".\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\r\n",
    "## Getting the files from Dunya\r\n",
    "\r\n",
    "In order to download these files you will need a Dunya API token. You can apply for one on your account page once you have created one (you can do so [here](https://dunya.compmusic.upf.edu/user/register/)). We provide a `dataset.json` file with links to a selection of recordings' data in MusicBrainz, feel free to add more makamler or recording links. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports - If this is already failing check the Docker image is running\r\n",
    "import json, os, urllib, urllib.request, shutil\r\n",
    "import numpy as np\r\n",
    "import pandas as pd\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import compmusic.dunya.conn\r\n",
    "from compmusic.dunya.docserver import get_document_as_json, get_mp3\r\n",
    "from music21 import *\r\n",
    "import xml.etree.ElementTree as ET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting Dunya token\r\n",
    "token = 'insertYourTokenHere'\r\n",
    "compmusic.dunya.conn.set_token(token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available makamler: \n",
      "['Acemasiran', 'Acemkurdi', 'Bestenigar', 'Beyati', 'Hicaz', 'Hicazkar', 'Huseyni', 'Huzzam', 'Karcigar', 'Kurdilihicazkar', 'Mahur', 'Muhayyer', 'Neva', 'Nihavent', 'Rast', 'Saba', 'Segah', 'Sultaniyegah', 'Suzinak', 'Ussak']\n"
     ]
    }
   ],
   "source": [
    "# Setting path where the files will be saved to\n",
    "data_folder = './data/'\n",
    "\n",
    "# Getting the list of recordings from dataset.json\n",
    "makam_recordings_list_file = './dataset.json'\n",
    "makam_recordings_list = json.load(open(makam_recordings_list_file))\n",
    "\n",
    "# Available makamler as fetched from dataset.json\n",
    "makamler = list(makam_recordings_list.keys())\n",
    "print(\"Available makamler: \")\n",
    "print(makamler)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# From the available makamler printed above, select those you want to compare.\r\n",
    "# We selected Neva and Ussak since they have many notes in common (including the karar).\r\n",
    "selected_makamler = ['Neva', 'Ussak']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterating over the recording lists of the selected makamler\r\n",
    "for i in np.arange(len(selected_makamler)):\r\n",
    "\r\n",
    "    # Path to makam dir and creating it if it doesn't exist\r\n",
    "    makam_folder = os.path.join(data_folder, selected_makamler[i])\r\n",
    "    if not os.path.exists(makam_folder):\r\n",
    "        os.makedirs(makam_folder)\r\n",
    "\r\n",
    "    for recording_url in makam_recordings_list[selected_makamler[i]]:\r\n",
    "\r\n",
    "        # Getting MusicBrainz ID for the recording from the url\r\n",
    "        recording_mbid = recording_url.split('/')[-1]\r\n",
    "\r\n",
    "        # Trying to get metadata from recording ID, if not, ignore this recording entry\r\n",
    "        try:\r\n",
    "            metadata = get_document_as_json(recording_mbid, 'audioanalysis', 'metadata')\r\n",
    "        except:\r\n",
    "            print(\"Failed to get metadata from recording with ID \" + recording_mbid + \r\n",
    "            \", makam \" + selected_makamler[i])\r\n",
    "            continue\r\n",
    "\r\n",
    "        # Downloading note_models with information about the performed pitches and theoretical pitches\r\n",
    "        # for each recording, skipping empty/corrupted files. These note_models come from the JointAnalyzer\r\n",
    "        # from tomato (https://github.com/sertansenturk/tomato), trough the compmusic dunya API.\r\n",
    "        recording_folder = os.path.join(makam_folder, recording_mbid)\r\n",
    "        notemodels_file_path = os.path.join(recording_folder, 'note_models.json') \r\n",
    "        metadata_file_path = os.path.join(recording_folder, 'metadata.json') \r\n",
    "        if not os.path.exists(notemodels_file_path):\r\n",
    "            try:\r\n",
    "                note_models = get_document_as_json(recording_mbid, 'jointanalysis',\"note_models\")\r\n",
    "                if len(note_models) > 0 and list(note_models.values())[0] != None:\r\n",
    "                    if not os.path.exists(recording_folder):\r\n",
    "                        os.makedirs(recording_folder)\r\n",
    "                    json.dump(note_models, open(notemodels_file_path, 'w'))\r\n",
    "                    json.dump(metadata, open(metadata_file_path, 'w'))\r\n",
    "                    print(\"Fetched note_models for recording with ID \" + recording_mbid + \", makam \" + selected_makamler[i] + \": OK\")\r\n",
    "                #else:\r\n",
    "                    #print(\"Fetched note_models for recording with ID \" + recording_mbid + \", makam \" + selected_makamler[i] + \": EMPTY\")\r\n",
    "            except:\r\n",
    "                #print(\"Fetched note_models for recording with ID \" + recording_mbid + \", makam \" + selected_makamler[i] + \": FAILED\")\r\n",
    "                continue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Notes as represented in western score notation\r\n",
    "\r\n",
    "We download the available scores corresponding to the recordings listed in `dataset.json` to visualize the note distributions in the given makamler.  \r\n",
    "  \r\n",
    "For the makamler we selected, the scales are:  \r\n",
    "Neva  \r\n",
    "![neva-scale](img/neva.png)  \r\n",
    "Uşşâk  \r\n",
    "![ussak-scale](img/ussak.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting list with MBIDs for works (not recordings) from SymbTr repository\r\n",
    "symbtr_mbid_url = 'https://raw.githubusercontent.com/MTG/SymbTr/master/symbTr_mbid.json'\r\n",
    "symbtr_mbid = json.loads(urllib.request.urlopen(symbtr_mbid_url).read())\r\n",
    "symbtr_xml_folder = './MusicXML/'\r\n",
    "\r\n",
    "# For each of the downloaded metadata files, we check if a score is available in the symbTr dataset, and\r\n",
    "# if so we copy it to the recording folder.\r\n",
    "for root, dirs, files in os.walk(data_folder):\r\n",
    "\r\n",
    "    for f in files:\r\n",
    "        if f == 'metadata.json':\r\n",
    "            metadata_path = os.path.join(root, f)\r\n",
    "            # Getting recording mbid and makam name from directory path\r\n",
    "            recording_mbid = metadata_path.split('/')[len(metadata_path.split('/'))-2]\r\n",
    "            makam = metadata_path.split('/')[len(metadata_path.split('/'))-3]\r\n",
    "\r\n",
    "            # Loading metadata from file\r\n",
    "            metadata = json.load(open(metadata_path))\r\n",
    "\r\n",
    "            # If the recording has a work associated, search for the score and copy it to the\r\n",
    "            # recording folder\r\n",
    "            if 'works' in metadata.keys() and len(metadata['works']) == 1:\r\n",
    "                work_mbid = metadata['works'][0]['mbid']\r\n",
    "                symbtr_name = None\r\n",
    "                for sm in symbtr_mbid:\r\n",
    "                    if work_mbid in sm['uuid']:\r\n",
    "                        symbtr_name = sm['name']\r\n",
    "                if symbtr_name != None and not os.path.exists(os.path.join(root, symbtr_name + '.xml')):\r\n",
    "                    shutil.copy(symbtr_xml_folder + symbtr_name + '.xml', root)\r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# With this dict we can map the name of the accidentals as shown in Music21 to the names of the notes as they\r\n",
    "# appear in note_models.json (so it is easier to compare)\r\n",
    "makam_accidentals = {'double-slash-flat':'b8' , 'flat':'b5', 'slash-flat':'b4',\r\n",
    "'half-flat':'b4', 'quarter-flat':'b1', 'quarter-sharp':'#1', 'half-sharp':'#1', 'sharp':'#4',\r\n",
    "'slash-quarter-sharp':'#5', 'slash-sharp':'#8'}\r\n",
    "\r\n",
    "# Function that gets the path to a score (.xml) and returns an array of notes formatted to match note\r\n",
    "# names as they appear in note_models.json\r\n",
    "def get_note_array_from_score(makam_score_path):\r\n",
    "\r\n",
    "    # Bypass key signature as it causes issues when going through the score with Music21\r\n",
    "    tree = ET.parse(makam_score_path)\r\n",
    "    score_root = tree.getroot()\r\n",
    "    for attribute in score_root.iter('attributes'):\r\n",
    "        if attribute.find('key'):\r\n",
    "            attribute.remove(attribute.find('key'))\r\n",
    "    new_makam_score = makam_score_path[:-4] + '--noKeySignature.xml'\r\n",
    "    tree.write(new_makam_score)\r\n",
    "\r\n",
    "    # Get note stream from score\r\n",
    "    score = converter.parse(new_makam_score)\r\n",
    "    all_notes = score.flat.notes.stream()\r\n",
    "\r\n",
    "    # Go through all notes in score and store re-formatted name in list\r\n",
    "    note_array = []\r\n",
    "    for note in all_notes:\r\n",
    "        note_name = str(note.pitch)\r\n",
    "        if note.pitch.accidental:\r\n",
    "            if len(note_name) == 3:\r\n",
    "                note_name = note_name[0] + note_name[2]\r\n",
    "            note_name = note_name + makam_accidentals[str(note.pitch.accidental.name)]\r\n",
    "        note_array.append(note_name)\r\n",
    "            \r\n",
    "    # Clean up\r\n",
    "    os.remove(new_makam_score)\r\n",
    "\r\n",
    "    # Return note list\r\n",
    "    return note_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Auxiliary function to help sort pandas DataFrames' notes so they appear\r\n",
    "# in order in plots\r\n",
    "def sort_note_list(unique_note_list):\r\n",
    "\r\n",
    "    def note_order(note):\r\n",
    "        note_ord = ['C', 'D', 'E', 'F', 'G', 'A', 'B']\r\n",
    "        return note_ord.index(note)\r\n",
    "\r\n",
    "    def accidental_order(full_note):\r\n",
    "        if len(full_note) == 2:\r\n",
    "            full_note += '00'\r\n",
    "        accidental_order = ['b8', 'b5', 'b4', 'b1', '00', '#1', '#4', '#5', '#8']\r\n",
    "        return accidental_order.index(full_note[2:])\r\n",
    "\r\n",
    "    sorted_note_list = unique_note_list\r\n",
    "    \r\n",
    "    sorted_note_list.sort(key=lambda x:accidental_order(x))\r\n",
    "    sorted_note_list.sort(key=lambda x:note_order(x[0]))\r\n",
    "    sorted_note_list.sort(key=lambda x:x[1])\r\n",
    "\r\n",
    "    return sorted_note_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": "<Figure size 360x936 with 1 Axes>"
     },
     "metadata": {
      "image/png": {
       "height": 927,
       "width": 352
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Going through the scores and storing the note lists in a DataFrame\r\n",
    "note_counts_df = pd.DataFrame()\r\n",
    "for makam in selected_makamler:\r\n",
    "    notes_in_makam_from_scores = []\r\n",
    "    for root, dirs, files in os.walk(os.path.join(data_folder, makam)):\r\n",
    "        for f in files:\r\n",
    "            if f.endswith('xml'):\r\n",
    "                filepath = os.path.join(root, f)\r\n",
    "                makam = filepath.split('/')[len(filepath.split('/'))-3]\r\n",
    "                notes_in_makam_from_scores.extend(get_note_array_from_score(filepath))\r\n",
    "\r\n",
    "    note_counts_df_makam = pd.DataFrame(pd.Series(notes_in_makam_from_scores).value_counts()).rename(columns={0: makam})\r\n",
    "    note_counts_df[makam] = note_counts_df_makam[makam]\r\n",
    "\r\n",
    "# Sort DataFrame by notes and normalize to obtain distribution from note counts\r\n",
    "note_counts_df = note_counts_df.transpose()\r\n",
    "note_counts_df = note_counts_df[sort_note_list(list(note_counts_df.columns.to_list()))].transpose()\r\n",
    "note_counts_df_normalized = note_counts_df[note_counts_df.columns.to_list()]/note_counts_df[note_counts_df.columns.to_list()].sum()\r\n",
    "\r\n",
    "# Plot distributions\r\n",
    "ax1 = note_counts_df_normalized.plot(kind='barh', figsize=(5,13), width=0.8,\r\n",
    "    color=greyscale_colors[0:len(list(note_counts_df_normalized.columns.to_list()))], edgecolor=\"black\")\r\n",
    "ax1.set_xlabel('Distribution of notes in available scores'), ax1.set_ylabel('Notes in given makamler'), \r\n",
    "ax1.legend(bbox_to_anchor=(0, 1, 1, 0), loc=\"lower left\", mode=\"expand\", ncol=2)\r\n",
    "ax1.set_axisbelow(True)\r\n",
    "ax1.grid(ls='--')\r\n",
    "fig = ax1.get_figure()\r\n",
    "fig.tight_layout()\r\n",
    "fig.savefig('score_notes.png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting differences between performed (stable) pitch and theoretical pitch\r\n",
    "\r\n",
    "From the previously downloaded note models we can plot the differences between the performed pitches and, taking the karar as reference, the pitch according to the theoretical interval. This will allow us to see how much the same pitches in notation differ in intonation when performed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we want to retrieve the information from note_models and store it in DataFrames per recording\r\n",
    "\r\n",
    "# Format DataFrame to display uniform number of decimals\r\n",
    "pd.options.display.float_format = \"{:,.2f}\".format\r\n",
    "\r\n",
    "\r\n",
    "# Iterate through the selected makamler\r\n",
    "makamler_dfs = {} \r\n",
    "for makam in selected_makamler:\r\n",
    "    makam_path = os.path.join(data_folder,makam)    \r\n",
    "    makamler_dfs[makam] = []\r\n",
    "\r\n",
    "    # Get mbids\r\n",
    "    mbids = os.listdir(makam_path)\r\n",
    "\r\n",
    "    # For each recording, load note_models.json\r\n",
    "    for mbid in mbids:\r\n",
    "        path = os.path.join(makam_path,mbid)\r\n",
    "        note_models_file = os.path.join(path,'note_models.json')\r\n",
    "\r\n",
    "        if os.path.exists(note_models_file):\r\n",
    "\r\n",
    "            with open(note_models_file) as f:\r\n",
    "                note_models_data = json.load(f)\r\n",
    "                new_dict = {}\r\n",
    "\r\n",
    "                # Some note_models.json are formatted a bit differently (more/less node levels) these lines bypass that\r\n",
    "                if len(note_models_data) == 1:\r\n",
    "                    if list(note_models_data.values())[0] == None:\r\n",
    "                        continue\r\n",
    "                    note_models_data = list(note_models_data.values())[0]\r\n",
    "\r\n",
    "                # Retrieve values and add to DataFrame\r\n",
    "                for note in note_models_data:\r\n",
    "                    new_dict[note] = { \"stable_pitch\":note_models_data[note][\"stable_pitch\"][\"value\"], \"theoretical_pitch\":note_models_data[note][\"theoretical_pitch\"][\"value\"]}\r\n",
    "                df = pd.DataFrame.from_dict(new_dict)\r\n",
    "                makamler_dfs[makam].append({mbid: df})\r\n",
    "\r\n",
    "# Display DataFrame for each recording with stable pitch and theoretical pitch. Commented because it's very long,\r\n",
    "# uncomment it if you want to check them out.\r\n",
    "#for mk in makamler_dfs:\r\n",
    "#    for rec in makamler_dfs[mk]:\r\n",
    "#        rec_name = list(rec.keys())[0]\r\n",
    "#        print(rec_name)\r\n",
    "#        display(rec[rec_name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Auxiliary function to calculate the cents between two given frequencies\r\n",
    "def hz_to_cents(f1, f2):\r\n",
    "    cents = np.round(1200.0 * np.log2(f2 / f1), 1)\r\n",
    "    if cents == 0:\r\n",
    "        cents = abs(cents)\r\n",
    "    return cents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_______________________________________________ NEVA _______________________________________________\n"
     ]
    },
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>C4#4</th>\n      <th>D4</th>\n      <th>E4</th>\n      <th>F4#1</th>\n      <th>F4#4</th>\n      <th>G4</th>\n      <th>G4#4</th>\n      <th>A4</th>\n      <th>B4b5</th>\n      <th>B4b4</th>\n      <th>B4b1</th>\n      <th>B4</th>\n      <th>C5</th>\n      <th>C5#4</th>\n      <th>D5b4</th>\n      <th>D5</th>\n      <th>E5b4</th>\n      <th>E5b1</th>\n      <th>E5</th>\n      <th>F5</th>\n      <th>F5#1</th>\n      <th>F5#4</th>\n      <th>G5</th>\n      <th>G5#4</th>\n      <th>A5</th>\n      <th>B5b5</th>\n      <th>B5b1</th>\n      <th>B5</th>\n      <th>C6</th>\n      <th>D6</th>\n    </tr>\n  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<td>7.0</td>\n      <td>29.0</td>\n      <td>-2.5</td>\n      <td>-5.0</td>\n      <td>-</td>\n      <td>-10.5</td>\n      <td>-2.5</td>\n      <td>7.0</td>\n      <td>-7.5</td>\n      <td>-</td>\n      <td>-4.5</td>\n      <td>-12.5</td>\n      <td>-7.5</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      <th>a2e650dc-8822-4647-9f4c-c41c0f81b601</th>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-2.5</td>\n      <td>-</td>\n      <td>0.0</td>\n      <td>39.0</td>\n      <td>-</td>\n      <td>15.5</td>\n      <td>-5.0</td>\n      <td>-7.5</td>\n      <td>-6.5</td>\n      <td>-</td>\n      <td>2.5</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-2.5</td>\n      <td>-5.0</td>\n      <td>-</td>\n      <td>-10.5</td>\n      <td>-2.5</td>\n      <td>-</td>\n      <td>15.0</td>\n      <td>11.5</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      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      "______________________________________________ USSAK _______________________________________________\n"
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<td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n    </tr>\n    <tr>\n      <th>f970f1e0-0be9-4914-8302-709a0eac088e</th>\n      <td>14.0</td>\n      <td>-17.5</td>\n      <td>0.0</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-</td>\n      <td>-22.0</td>\n      <td>-15.0</td>\n      <td>-</td>\n      <td>2.5</td>\n      <td>-15.5</td>\n      <td>-</td>\n      <td>12.5</td>\n      <td>-5.0</td>\n      <td>-10.5</td>\n      <td>-10.0</td>\n      <td>0.0</td>\n      <td>26.5</td>\n      <td>-</td>\n      <td>3.0</td>\n      <td>15.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "                                      F4#4    G4  A4 A4#4 B4b5 B4b4  B4b1  \\\n03faad43-689e-4814-93ad-b7f678531e5f   6.5   5.0 0.0    -    -    - -14.5   \n32e029de-8550-4bc6-ac8b-df55e8ba456e -16.0 -10.0 0.0    -    - 39.5 -37.0   \n64ab7fb3-e754-4121-b25f-02bedf331380     -  -2.5 0.0    -    -    - -37.0   \n6e220f2d-fbcf-4f8b-a961-9c7f73c623d7  44.0  -2.5 0.0    -    -    - -29.5   \n80b42df4-e113-4084-8b25-e33eb7ee4aa6     -   5.0 0.0 32.0    -    -  -7.0   \n92ef6776-09fa-41be-8661-025f9b33be4f     -     - 0.0    -    -    - -14.5   \n995cbbb4-2bf1-4f1c-8e35-f4467b22afac     - -10.0 0.0    -    - 17.0 -22.0   \n99d17fb7-9ff6-4d16-8742-4244a870cdae -23.5  -2.5 0.0    -  9.0    - -44.5   \nc4b8b080-dbe0-45d3-b12c-798a57a2813f     - -10.0 0.0    -    - 17.0  -7.0   \ncca7ba0a-9614-449d-b73e-ac761f270c63  -1.0 -17.5 0.0    -    -    - -37.0   \ne72db0ad-2ed9-467b-88ae-1f91edcd2c59     -  -2.5 0.0    -    -    - -14.5   \nead1da37-00bc-48de-96b3-5aa2617cd53d     -     - 0.0    -    -    - -22.0   \ned187560-ae33-455a-98a3-224f72b0e127     -     - 0.0    -    -    -  -7.0   \nf970f1e0-0be9-4914-8302-709a0eac088e  14.0 -17.5 0.0    -    -    - -22.0   \n\n                                        C5  C5#4   D5  E5b4 E5b1   E5    F5  \\\n03faad43-689e-4814-93ad-b7f678531e5f   0.0     -  2.5  44.5    - 12.5   2.5   \n32e029de-8550-4bc6-ac8b-df55e8ba456e  -7.5     - -5.0  14.5    -  5.0 -12.5   \n64ab7fb3-e754-4121-b25f-02bedf331380  -7.5     - -5.0     -    - 12.5 -12.5   \n6e220f2d-fbcf-4f8b-a961-9c7f73c623d7  -7.5     - -5.0     -    - -2.5     -   \n80b42df4-e113-4084-8b25-e33eb7ee4aa6 -15.0 -36.5  2.5  -0.5  6.5 -2.5   2.5   \n92ef6776-09fa-41be-8661-025f9b33be4f -22.5     - -5.0     -    - -2.5 -20.0   \n995cbbb4-2bf1-4f1c-8e35-f4467b22afac   0.0  -6.5  2.5     -    - 12.5   2.5   \n99d17fb7-9ff6-4d16-8742-4244a870cdae  -7.5     - 10.0     -    -  5.0 -20.0   \nc4b8b080-dbe0-45d3-b12c-798a57a2813f  -7.5  -6.5  2.5     -    -  5.0  -5.0   \ncca7ba0a-9614-449d-b73e-ac761f270c63 -15.0     -  2.5     -    - 12.5  -5.0   \ne72db0ad-2ed9-467b-88ae-1f91edcd2c59  -7.5     -  2.5     -    - 12.5  -5.0   \nead1da37-00bc-48de-96b3-5aa2617cd53d   0.0     -  2.5     -    -    -     -   \ned187560-ae33-455a-98a3-224f72b0e127  -7.5     -    -     -    -    -     -   \nf970f1e0-0be9-4914-8302-709a0eac088e -15.0     -  2.5 -15.5    - 12.5  -5.0   \n\n                                      F5#4    G5    A5 B5b5 B5b4  B5b1    C6  \n03faad43-689e-4814-93ad-b7f678531e5f -18.0  12.5  15.0  4.0    -     -     -  \n32e029de-8550-4bc6-ac8b-df55e8ba456e  -3.0  -2.5   0.0    -    -     -     -  \n64ab7fb3-e754-4121-b25f-02bedf331380  -3.0 -10.0   0.0    -    -  10.5 -15.0  \n6e220f2d-fbcf-4f8b-a961-9c7f73c623d7     -  -2.5     -    -    -     -     -  \n80b42df4-e113-4084-8b25-e33eb7ee4aa6 -33.0 -10.0   0.0    - 41.0 -34.5 -30.0  \n92ef6776-09fa-41be-8661-025f9b33be4f -10.5  -2.5  -7.5    -    - -19.5 -37.5  \n995cbbb4-2bf1-4f1c-8e35-f4467b22afac     - -10.0   0.0    -    -     -     -  \n99d17fb7-9ff6-4d16-8742-4244a870cdae -25.5  -2.5   0.0    -    -     -     -  \nc4b8b080-dbe0-45d3-b12c-798a57a2813f     - -17.5 -15.0    -    -     -     -  \ncca7ba0a-9614-449d-b73e-ac761f270c63     -  -2.5   0.0    -    -     -     -  \ne72db0ad-2ed9-467b-88ae-1f91edcd2c59  -3.0  -2.5   0.0    -    -  33.0   0.0  \nead1da37-00bc-48de-96b3-5aa2617cd53d     -     -     -    -    -     -     -  \ned187560-ae33-455a-98a3-224f72b0e127     -     -     -    -    -     -     -  \nf970f1e0-0be9-4914-8302-709a0eac088e -10.5 -10.0   0.0 26.5    -   3.0  15.0  "
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Computing the difference in cents from stable pitch to theoretical pitch and\r\n",
    "# joining all recordings in one big beautiful DataFrame per Makam\r\n",
    "\r\n",
    "# Format display to 1 decimal\r\n",
    "pd.options.display.float_format = \"{:,.1f}\".format\r\n",
    "\r\n",
    "# Get a dict the makamles as keys and a set of unique performed note labels for each\r\n",
    "unique_notes_per_makam = {}\r\n",
    "for makam_key in makamler_dfs:\r\n",
    "    unique_notes_per_makam[makam_key] = []\r\n",
    "    for recording_dict in makamler_dfs[makam_key]:\r\n",
    "        notes = recording_dict[list(recording_dict.keys())[0]].columns.tolist()\r\n",
    "        unique_notes_per_makam[makam_key].extend(notes)\r\n",
    "    aux_set = set(unique_notes_per_makam[makam_key])\r\n",
    "    unique_notes_per_makam[makam_key] = aux_set\r\n",
    "\r\n",
    "\r\n",
    "# Calculating cents for each recording (f1 = theoretical pitch, f2 = stable pitch)\r\n",
    "makamler_dfs_diff_pitch = {}\r\n",
    "for makam_key in makamler_dfs:\r\n",
    "    makamler_dfs_diff_pitch[makam_key] = []\r\n",
    "    for recording in makamler_dfs[makam_key]:\r\n",
    "        \r\n",
    "        # Building up data structure\r\n",
    "        recording_name = list(recording.keys())[0]\r\n",
    "        new_dict = {recording_name: recording[recording_name].copy()}\r\n",
    "        notes_in_makam = new_dict[recording_name].columns.tolist()\r\n",
    "        ser = pd.Series(data=np.zeros(len(notes_in_makam)), index=notes_in_makam)\r\n",
    "\r\n",
    "        # Calculating the difference in cents between theoretical and stable pitches\r\n",
    "        for note_name in new_dict[recording_name].loc['stable_pitch'].keys():\r\n",
    "            f1 = new_dict[recording_name].loc['theoretical_pitch'][note_name]\r\n",
    "            f2 = new_dict[recording_name].loc['stable_pitch'][note_name]\r\n",
    "            ser[note_name] = hz_to_cents(f1, f2)\r\n",
    "\r\n",
    "        # Appending to data structure\r\n",
    "        new_dict[recording_name].loc['cents'] = ser\r\n",
    "        new_dict[recording_name] = new_dict[recording_name].drop([\"stable_pitch\", \"theoretical_pitch\"])\r\n",
    "        makamler_dfs_diff_pitch[makam_key].append(new_dict)\r\n",
    "\r\n",
    "\r\n",
    "# Moving from 1 DataFrame per recording to 1 DataFrame per Makam, keeping MBID as index\r\n",
    "makamler_cents_dfs = {}\r\n",
    "for makam_key in makamler_dfs_diff_pitch:\r\n",
    "    makamler_cents_dfs[makam_key] = pd.DataFrame(columns=unique_notes_per_makam[makam_key])\r\n",
    "    for i in range(len(makamler_dfs_diff_pitch[makam_key])):\r\n",
    "        recording_name = list(makamler_dfs_diff_pitch[makam_key][i].keys())[0]\r\n",
    "\r\n",
    "        # Creating auxiliary DataFrame to change labels and reformat a bit\r\n",
    "        df_aux = makamler_dfs_diff_pitch[makam_key][i][recording_name].copy()\r\n",
    "        df_aux = df_aux.rename(index={'cents': recording_name})\r\n",
    "        cols = df_aux.columns.tolist()\r\n",
    "        for unique_note in unique_notes_per_makam[makam_key]:\r\n",
    "            if unique_note not in cols:\r\n",
    "                df_aux.insert(0, unique_note, '-')\r\n",
    "        \r\n",
    "        # Appending auxiliary DataFrame to data structure\r\n",
    "        makamler_cents_dfs[makam_key] = pd.concat([makamler_cents_dfs[makam_key], df_aux], sort=True)\r\n",
    "\r\n",
    "# Display all columns in DataFrame when printing\r\n",
    "pd.set_option('display.max_columns', None)\r\n",
    "\r\n",
    "# Sorting note columns and displaying DataFrames\r\n",
    "for mk in makamler_cents_dfs:\r\n",
    "    makamler_cents_dfs[mk] = makamler_cents_dfs[mk][sort_note_list(list(unique_notes_per_makam[mk]))]\r\n",
    "    makamler_cents_dfs[mk] = makamler_cents_dfs[mk].replace(np.nan, '-').round(1)\r\n",
    "    print((' ' + str.upper(mk) + ' ').center(100, '_'))\r\n",
    "    display(makamler_cents_dfs[mk])       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_______________________________________________ NEVA _______________________________________________\n"
     ]
    },
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>G4</th>\n      <th>G4#4</th>\n      <th>A4</th>\n      <th>B4b4</th>\n      <th>B4b1</th>\n      <th>B4</th>\n      <th>C5</th>\n      <th>C5#4</th>\n      <th>D5</th>\n      <th>E5</th>\n      <th>F5</th>\n      <th>F5#4</th>\n      <th>G5</th>\n      <th>G5#4</th>\n      <th>A5</th>\n      <th>B5b1</th>\n      <th>B5</th>\n      <th>C6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>mean</th>\n      <td>-10.5</td>\n      <td>18.3</td>\n      <td>0.0</td>\n      <td>0.9</td>\n      <td>-0.8</td>\n      <td>-3.9</td>\n      <td>-7.5</td>\n      <td>5.0</td>\n      <td>0.1</td>\n      <td>2.5</td>\n      <td>-2.2</td>\n      <td>-6.3</td>\n      <td>-5.4</td>\n      <td>14.5</td>\n      <td>3.8</td>\n      <td>-4.5</td>\n      <td>7.9</td>\n      <td>-5.0</td>\n    </tr>\n    <tr>\n      <th>std_dev</th>\n      <td>6.9</td>\n      <td>16.7</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>9.0</td>\n      <td>5.2</td>\n      <td>7.0</td>\n      <td>13.6</td>\n      <td>7.9</td>\n      <td>8.1</td>\n      <td>9.4</td>\n      <td>9.0</td>\n      <td>6.4</td>\n      <td>19.4</td>\n      <td>9.0</td>\n      <td>15.0</td>\n      <td>16.0</td>\n      <td>16.3</td>\n    </tr>\n    <tr>\n      <th>n_notes</th>\n      <td>14.0</td>\n      <td>9.0</td>\n      <td>19.0</td>\n      <td>7.0</td>\n      <td>18.0</td>\n      <td>7.0</td>\n      <td>17.0</td>\n      <td>17.0</td>\n      <td>19.0</td>\n      <td>18.0</td>\n      <td>16.0</td>\n      <td>18.0</td>\n      <td>18.0</td>\n      <td>7.0</td>\n      <td>18.0</td>\n      <td>10.0</td>\n      <td>7.0</td>\n      <td>9.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "           G4  G4#4   A4  B4b4  B4b1   B4   C5  C5#4   D5   E5   F5  F5#4  \\\nmean    -10.5  18.3  0.0   0.9  -0.8 -3.9 -7.5   5.0  0.1  2.5 -2.2  -6.3   \nstd_dev   6.9  16.7  0.0   8.0   9.0  5.2  7.0  13.6  7.9  8.1  9.4   9.0   \nn_notes  14.0   9.0 19.0   7.0  18.0  7.0 17.0  17.0 19.0 18.0 16.0  18.0   \n\n          G5  G5#4   A5  B5b1   B5   C6  \nmean    -5.4  14.5  3.8  -4.5  7.9 -5.0  \nstd_dev  6.4  19.4  9.0  15.0 16.0 16.3  \nn_notes 18.0   7.0 18.0  10.0  7.0  9.0  "
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "______________________________________________ USSAK _______________________________________________\n"
     ]
    },
    {
     "data": {
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>F4#4</th>\n      <th>G4</th>\n      <th>A4</th>\n      <th>B4b1</th>\n      <th>C5</th>\n      <th>D5</th>\n      <th>E5</th>\n      <th>F5</th>\n      <th>F5#4</th>\n      <th>G5</th>\n      <th>A5</th>\n      <th>B5b1</th>\n      <th>C6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>mean</th>\n      <td>4.0</td>\n      <td>-5.9</td>\n      <td>0.0</td>\n      <td>-22.5</td>\n      <td>-8.6</td>\n      <td>0.8</td>\n      <td>6.9</td>\n      <td>-7.0</td>\n      <td>-13.3</td>\n      <td>-5.0</td>\n      <td>-0.7</td>\n      <td>-1.5</td>\n      <td>-13.5</td>\n    </tr>\n    <tr>\n      <th>std_dev</th>\n      <td>24.0</td>\n      <td>7.8</td>\n      <td>0.0</td>\n      <td>12.6</td>\n      <td>6.5</td>\n      <td>4.5</td>\n      <td>6.5</td>\n      <td>8.3</td>\n      <td>11.3</td>\n      <td>7.4</td>\n      <td>7.1</td>\n      <td>26.3</td>\n      <td>21.5</td>\n    </tr>\n    <tr>\n      <th>n_notes</th>\n      <td>6.0</td>\n      <td>11.0</td>\n      <td>14.0</td>\n      <td>14.0</td>\n      <td>14.0</td>\n      <td>13.0</td>\n      <td>12.0</td>\n      <td>11.0</td>\n      <td>8.0</td>\n      <td>12.0</td>\n      <td>11.0</td>\n      <td>5.0</td>\n      <td>5.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
      "text/plain": "         F4#4   G4   A4  B4b1   C5   D5   E5   F5  F5#4   G5   A5  B5b1    C6\nmean      4.0 -5.9  0.0 -22.5 -8.6  0.8  6.9 -7.0 -13.3 -5.0 -0.7  -1.5 -13.5\nstd_dev  24.0  7.8  0.0  12.6  6.5  4.5  6.5  8.3  11.3  7.4  7.1  26.3  21.5\nn_notes   6.0 11.0 14.0  14.0 14.0 13.0 12.0 11.0   8.0 12.0 11.0   5.0   5.0"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Since these differences also vary per Makam, an easier way to visualize them could be to calculate the mean of the difference\r\n",
    "# per note and the standard deviation.\r\n",
    "\r\n",
    "# Changing format to display 1 decimal\r\n",
    "pd.options.display.float_format = \"{:,.1f}\".format\r\n",
    "\r\n",
    "# Iterate through makamler\r\n",
    "mean_std_dev = {}\r\n",
    "for mk in makamler_cents_dfs:\r\n",
    "\r\n",
    "    mean_std_dev[mk] = pd.DataFrame(columns=makamler_cents_dfs[mk].columns.tolist())\r\n",
    "\r\n",
    "    # Calculating mean\r\n",
    "    mean_df = pd.DataFrame(makamler_cents_dfs[mk].replace('-', np.nan).mean()).transpose().rename(index={0: 'mean'})\r\n",
    "\r\n",
    "    # Calculating standard deviation\r\n",
    "    std_dev_df = pd.DataFrame(makamler_cents_dfs[mk].replace('-', np.nan).std()).transpose().replace(np.nan, '-').rename(index={0: 'std_dev'})\r\n",
    "    \r\n",
    "    # Number of notes used in these calculations (since e.g., the standar deviation for a note that only has one occurence\r\n",
    "    # will not be meaningful)\r\n",
    "    n_notes_df = pd.DataFrame(makamler_cents_dfs[mk].replace('-', np.nan).notna().sum()).transpose().rename(index={0: 'n_notes'}).replace(True, 1.0)\r\n",
    "    uninteresting_columns = n_notes_df.columns[(n_notes_df < 5.0).iloc[0]].to_list()\r\n",
    "\r\n",
    "    mean_df = mean_df.drop(uninteresting_columns, axis=1)\r\n",
    "    std_dev_df = std_dev_df.drop(uninteresting_columns, axis=1)\r\n",
    "    n_notes_df = n_notes_df.drop(uninteresting_columns, axis=1)\r\n",
    "\r\n",
    "    # Append data\r\n",
    "    mean_std_dev[mk] = pd.concat([mean_df.round(1), std_dev_df.round(1), n_notes_df.round(0)])\r\n",
    "\r\n",
    "    # Display\r\n",
    "    print((' ' + str.upper(mk) + ' ').center(100, '_'))\r\n",
    "    display(mean_std_dev[mk])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": "<Figure size 360x864 with 1 Axes>"
     },
     "metadata": {
      "image/png": {
       "height": 855,
       "width": 352
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's plot!\r\n",
    "\r\n",
    "# Reformatting data structure to make plotting easier\r\n",
    "mean_std_dev_comb_df = pd.DataFrame()\r\n",
    "for mk in mean_std_dev:\r\n",
    "    mean_std_dev_comb_df[mk] = mean_std_dev[mk].loc['mean',:]\r\n",
    "for mk in mean_std_dev:\r\n",
    "    mean_std_dev_comb_df['std_dev ' + mk] = mean_std_dev[mk].loc['std_dev',:]\r\n",
    "\r\n",
    "# Plotting\r\n",
    "n_makams = len(mean_std_dev)\r\n",
    "greyscale_colors=[\"#fafafa\", \"#37474f\", \"#cfd8dc\", \"#455a64\"]\r\n",
    "mean_std_dev_comb_df = mean_std_dev_comb_df.dropna().replace('-', 0.0)\r\n",
    "ax = mean_std_dev_comb_df.plot(kind='barh', y=mean_std_dev_comb_df.iloc[:,0:n_makams].columns.to_list(), xerr=mean_std_dev_comb_df.iloc[:,n_makams:].values.T,\r\n",
    "    capsize=3, figsize=(5,12), width=0.8, color=greyscale_colors[0:n_makams], edgecolor=\"black\")\r\n",
    "ax.set_xlabel('Intonation deviation [cents]'), ax.set_ylabel('Notes in common in given makamler'), \r\n",
    "ax.legend(bbox_to_anchor=(0, 1, 1, 0), loc=\"lower left\", mode=\"expand\", ncol=2)\r\n",
    "ax.set_axisbelow(True)\r\n",
    "ax.axvline(0, color=greyscale_colors[1], zorder=1), ax.set_xlim(-40, 40)\r\n",
    "ax.grid(ls='--', zorder=0)\r\n",
    "fig = ax.get_figure()\r\n",
    "fig.tight_layout()\r\n",
    "fig.savefig('mean_stddev_cents.png', dpi=300)\r\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thank you for trying out our code!\r\n",
    "  \r\n",
    "![horon-kemence](img/horon_kemence.gif)  \r\n",
    "  \r\n",
    "  Please feel free to contact us on GitHub or open an issue if something is not working as it should.  \r\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}