CRIM_07a_HeatMaps_Notes_Ngrams.ipynb

{
 "cells": [
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "42a20163",
   "metadata": {},
   "source": [
    "# Heatmaps \n",
    "\n",
    "### What can you do with this Notebook?\n",
    "\n",
    "* Heatmaps are a helpful way to see the big picture. This notebook allows you to plot notes and ngrams across an entire piece, with identical items represented in the same color. \n",
    "* Results can be limited in various ways:  clicking to highlight, passing a 'top ten', or even a single pattern to the mapping tool.\n",
    "* It is also possible to map patterns according to their **similarlity score** using Levenshtein edit-distance calculations. \n",
    "\n",
    "![screenshot_353-2.png](attachment:screenshot_353-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17af2718",
   "metadata": {},
   "source": [
    "## A.1.  Import Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "98bcb269",
   "metadata": {},
   "outputs": [],
   "source": [
    "# python imports\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import requests\n",
    "import numpy as np\n",
    "\n",
    "# our imports\n",
    "import intervals\n",
    "from intervals import importScore \n",
    "import intervals.visualizations as viz\n",
    "from ipywidgets import interact\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04438b70",
   "metadata": {},
   "source": [
    "## A. Heatmap of Notes \n",
    "\n",
    "* Here we get all notes, then fill the Rests and NaN's with \"-\" (so they are ignored).\n",
    "* We also get the duration of each note, which is also rendered on the heatmap.\n",
    "* Clik on any combination of notes in the top of the chart to limit the heatmap to just those pitches.\n",
    "\n",
    "Get the piece:\n",
    "\n",
    "```\n",
    "prefix = 'https://crimproject.org/mei/'    \n",
    "mei_file = 'CRIM_Model_0008.mei'\n",
    "url = prefix + mei_file\n",
    "piece = importScore(url)\n",
    "print(piece.metadata)\n",
    "```\n",
    "\n",
    "Get the notes and durations:\n",
    "\n",
    "```\n",
    "notes = piece.notes()\n",
    "notes_durs = piece.durations(df=notes, mask_df=notes)\n",
    "notes.replace(\"Rest\", np.nan, inplace=True)\n",
    "```\n",
    "\n",
    "Then create the map:\n",
    "\n",
    "```\n",
    "viz.plot_ngrams_heatmap(notes, notes_durs, selected_patterns=[], voices=[])\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48f47eea",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading remote score...\n",
      "Successfully imported https://crimproject.org/mei/CRIM_Model_0008.mei\n",
      "{'title': 'Ave Maria', 'composer': 'Josquin Des Prés'}\n"
     ]
    }
   ],
   "source": [
    "prefix = 'https://crimproject.org/mei/'    \n",
    "# prefix = 'Music_Files/'\n",
    "mei_file = 'CRIM_Model_0008.mei'\n",
    "url = prefix + mei_file\n",
    "piece = importScore(url)\n",
    "print(piece.metadata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7eb5afef",
   "metadata": {},
   "outputs": [],
   "source": [
    "notes = piece.notes()\n",
    "notes_durs = piece.durations(df=notes, mask_df=notes)\n",
    "notes.replace(\"Rest\", np.nan, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e93d202a",
   "metadata": {},
   "outputs": [
    {
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{\"start\": 992.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 998.0}, {\"start\": 998.0, \"voice\": \"Bassus\", \"pattern\": \"F, #, 3\", \"end\": 999.0}, {\"start\": 999.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1000.0}, {\"start\": 1000.0, \"voice\": \"Bassus\", \"pattern\": \"F, #, 3\", \"end\": 1002.0}, {\"start\": 1002.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1006.0}, {\"start\": 1006.0, \"voice\": \"Bassus\", \"pattern\": \"F, #, 3\", \"end\": 1008.0}, {\"start\": 1008.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1016.0}, {\"start\": 1036.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1040.0}, {\"start\": 1040.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1044.0}, {\"start\": 1044.0, \"voice\": \"Bassus\", \"pattern\": \"F, 3\", \"end\": 1048.0}, {\"start\": 1048.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1052.0}, {\"start\": 1052.0, \"voice\": \"Bassus\", \"pattern\": \"C, 3\", \"end\": 1056.0}, {\"start\": 1056.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1062.0}, {\"start\": 1062.0, \"voice\": \"Bassus\", \"pattern\": \"F, #, 3\", \"end\": 1063.0}, {\"start\": 1063.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1064.0}, {\"start\": 1064.0, \"voice\": \"Bassus\", \"pattern\": \"F, #, 3\", \"end\": 1066.0}, {\"start\": 1066.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1070.0}, {\"start\": 1070.0, \"voice\": \"Bassus\", \"pattern\": \"F, #, 3\", \"end\": 1072.0}, {\"start\": 1072.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1080.0}, {\"start\": 1092.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1096.0}, {\"start\": 1096.0, \"voice\": \"Bassus\", \"pattern\": \"F, 3\", \"end\": 1099.0}, {\"start\": 1099.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1100.0}, {\"start\": 1100.0, \"voice\": \"Bassus\", \"pattern\": \"D, 3\", \"end\": 1104.0}, {\"start\": 1104.0, \"voice\": \"Bassus\", \"pattern\": \"C, 3\", \"end\": 1107.0}, {\"start\": 1107.0, \"voice\": \"Bassus\", \"pattern\": \"D, 3\", \"end\": 1108.0}, {\"start\": 1108.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1109.0}, {\"start\": 1109.0, \"voice\": \"Bassus\", \"pattern\": \"F, 3\", \"end\": 1110.0}, {\"start\": 1110.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1112.0}, {\"start\": 1112.0, \"voice\": \"Bassus\", \"pattern\": \"A, 3\", \"end\": 1114.0}, {\"start\": 1114.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1116.0}, {\"start\": 1116.0, \"voice\": \"Bassus\", \"pattern\": \"F, 3\", \"end\": 1120.0}, {\"start\": 1120.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1128.0}, {\"start\": 1156.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1160.0}, {\"start\": 1160.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1162.0}, {\"start\": 1162.0, \"voice\": \"Bassus\", \"pattern\": \"A, 3\", \"end\": 1165.0}, {\"start\": 1165.0, \"voice\": 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\"voice\": \"Bassus\", \"pattern\": \"C, 3\", \"end\": 1208.0}, {\"start\": 1208.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1212.0}, {\"start\": 1212.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1216.0}, {\"start\": 1216.0, \"voice\": \"Bassus\", \"pattern\": \"F, 3\", \"end\": 1222.0}, {\"start\": 1222.0, \"voice\": \"Bassus\", \"pattern\": \"E, 3\", \"end\": 1223.0}, {\"start\": 1223.0, \"voice\": \"Bassus\", \"pattern\": \"D, 3\", \"end\": 1224.0}, {\"start\": 1224.0, \"voice\": \"Bassus\", \"pattern\": \"C, 3\", \"end\": 1232.0}, {\"start\": 1236.0, \"voice\": \"Bassus\", \"pattern\": \"C, 3\", \"end\": 1240.0}, {\"start\": 1240.0, \"voice\": \"Bassus\", \"pattern\": \"C, 4\", \"end\": 1244.0}, {\"start\": 1244.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1248.0}, {\"start\": 1248.0, \"voice\": \"Bassus\", \"pattern\": \"A, 3\", \"end\": 1256.0}, {\"start\": 1256.0, \"voice\": \"Bassus\", \"pattern\": \"G, 3\", \"end\": 1272.0}, {\"start\": 1272.0, \"voice\": \"Bassus\", \"pattern\": \"C, 3\", \"end\": 1288.0}, {\"start\": 1288.0, \"voice\": \"Bassus\", \"pattern\": \"C, 3\", \"end\": 1304.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.plot_ngrams_heatmap(notes, notes_durs, selected_patterns=[], voices=[], includeCount=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f9f7d31",
   "metadata": {},
   "outputs": [],
   "source": [
    "## barchart of counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca2f46fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "      outputDiv = document.getElementById(\"altair-viz-36cace330e1f43b0a38127eee0593f64\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
       "    };\n",
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       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
       "      return (VEGA_DEBUG[key] == version) ?\n",
       "        Promise.resolve(paths[lib]) :\n",
       "        new Promise(function(resolve, reject) {\n",
       "          var s = document.createElement('script');\n",
       "          document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "          s.async = true;\n",
       "          s.onload = () => {\n",
       "            VEGA_DEBUG[key] = version;\n",
       "            return resolve(paths[lib]);\n",
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       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
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       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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       "\n",
       "    function displayChart(vegaEmbed) {\n",
       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
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{\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.plot_ngrams_barchart(notes, notes_durs, selected_patterns=[], voices=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ffb28aa",
   "metadata": {},
   "source": [
    "## B. nGram Heatmap\n",
    "\n",
    "\n",
    "* The heatmap tool can plot ngrams in various ways:\n",
    "\n",
    "    * **All ngrams** (you can click on one or more to limit the results)\n",
    "    * **Limit to Entries** (which shows only ngrams that occur after a rest or section break). Default is True\n",
    "    * **Combine Unisons** (note that ngrams in effect become shorter when this is True).  Default is False\n",
    "    * **Interval Type** (diatonic, chromatic, etc).  Default is diatonic.\n",
    "    * **Compound or Simple melodic intervals**. Default is False\n",
    "    * **Length of ngrams**.  Default is 5\n",
    "    * **Find ngrams by some regular duration**.  `0` is without regularization (default_.  `2` regularizes to half-note, `4` to the whole note, etc.  Note that this does NOT work with Limit to Entries!\n",
    "    * **Select Most Frequent Patterns**.  \"All\" will return all the patterns for the given selection.  \"5\", \"10\", etc, will return only the most 5 or 10 most frequent ngrams (according to the other criteria already selected)\n",
    "\n",
    "* Read the documentation with `help(viz_demo.plot_ngrams_heatmap)`\n",
    "\n",
    "```\n",
    "@interact\n",
    "def get_plot(entries_only = [True, False],\n",
    "             combine_unisons=[False, True], \n",
    "             kind=[\"d\", \"q\", \"c\", \"z\"], \n",
    "             compound=[True, False], \n",
    "             length=[5, 2, 3, 4, 6],\n",
    "            regularize=[0, 1, 2, 4],\n",
    "            top_patterns=[\"all\", 5, 10, 20]):\n",
    "    if entries_only == True:\n",
    "        nr = piece.notes(combineUnisons=combine_unisons)\n",
    "        # unit MUST be 0 for \"limit to entries\" \n",
    "        mel = piece.melodic(df=nr, kind=kind, compound=compound, unit=0, end=False)\n",
    "        mel_ngrams = piece.ngrams(df=mel, n=length)\n",
    "        entry_ngrams = piece.entries(df=mel, n=length)\n",
    "        mel_ngrams_duration = piece.durations(df=mel, n=length, mask_df=entry_ngrams)\n",
    "        if top_patterns == \"all\":\n",
    "            return viz.plot_ngrams_heatmap(entry_ngrams, mel_ngrams_duration, selected_patterns=[], voices=[])\n",
    "        else:\n",
    "            mel_ngrams_top_patterns_df = entry_ngrams.stack().value_counts().to_frame().head(top_patterns)\n",
    "            mel_ngrams_top_patterns_list = mel_ngrams_top_patterns_df.index.to_list()\n",
    "            return viz.plot_ngrams_heatmap(entry_ngrams, mel_ngrams_duration, selected_patterns=mel_ngrams_top_patterns_list, voices=[])\n",
    "    else:\n",
    "        if top_patterns == \"all\":\n",
    "            nr = piece.notes(combineUnisons=combine_unisons)\n",
    "            mel = piece.melodic(df=nr, kind=kind, compound=False, unit=regularize, end=False)\n",
    "            mel_ngrams = piece.ngrams(df=mel, n=length)\n",
    "            mel_ngrams_duration = piece.durations(df=mel, n=5, mask_df=mel_ngrams)\n",
    "            return viz.plot_ngrams_heatmap(mel_ngrams, mel_ngrams_duration, selected_patterns=[], voices=[])           \n",
    "        else:\n",
    "            mel = piece.melodic(kind='d', compound=False, unit=regularize)\n",
    "            mel_ngrams = piece.ngrams(df=mel, n=5)\n",
    "            mel_ngrams_duration = piece.durations(df=mel, n=5, mask_df=mel_ngrams)\n",
    "            mel_ngrams_top_patterns_df = mel_ngrams.stack().value_counts().to_frame().head(top_patterns)\n",
    "            mel_ngrams_top_patterns_list = mel_ngrams_top_patterns_df.index.to_list()\n",
    "            return viz.plot_ngrams_heatmap(mel_ngrams, mel_ngrams_duration, selected_patterns=mel_ngrams_top_patterns_list, voices=[])\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "30b192a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Previously imported piece detected.\n",
      "{'title': 'Ave Maria', 'composer': 'Josquin Des Prés'}\n"
     ]
    }
   ],
   "source": [
    "prefix = 'https://crimproject.org/mei/' \n",
    "# prefix = 'Music_Files/'\n",
    "mei_file = 'CRIM_Model_0008.mei'\n",
    "url = prefix + mei_file\n",
    "piece = importScore(url)\n",
    "print(piece.metadata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71a3919b",
   "metadata": {},
   "source": [
    "#### Don't edit the Cell below.  \n",
    "\n",
    "Run it, then select your preferred settings with the Interact boxes\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d55c77d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a21001845f4b471ab4b97549b0e8f908",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='entries_only', options=(True, False), value=True), Dropdown(descri…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "@interact\n",
    "def get_plot(entries_only = [True, False],\n",
    "             combine_unisons=[False, True], \n",
    "             kind=[\"d\", \"q\", \"c\", \"z\"], \n",
    "             compound=[True, False], \n",
    "             length=[5, 2, 3, 4, 6],\n",
    "            regularize=[0, 1, 2, 4],\n",
    "            top_patterns=[\"all\", 5, 10, 20]):\n",
    "    if entries_only == True:\n",
    "        nr = piece.notes(combineUnisons=combine_unisons)\n",
    "        # unit MUST be 0 for \"limit to entries\" \n",
    "        mel = piece.melodic(df=nr, kind=kind, compound=compound, unit=0, end=False)\n",
    "        mel_ngrams = piece.ngrams(df=mel, n=length)\n",
    "        entry_ngrams = piece.entries(df=mel, n=length)\n",
    "        mel_ngrams_duration = piece.durations(df=mel, n=length, mask_df=entry_ngrams)\n",
    "        if top_patterns == \"all\":\n",
    "            return viz.plot_ngrams_heatmap(entry_ngrams, mel_ngrams_duration, selected_patterns=[], voices=[])\n",
    "        else:\n",
    "            mel_ngrams_top_patterns_df = entry_ngrams.stack().value_counts().to_frame().head(top_patterns)\n",
    "            mel_ngrams_top_patterns_list = mel_ngrams_top_patterns_df.index.to_list()\n",
    "#             return mel_ngrams_top_patterns_list\n",
    "            return viz.plot_ngrams_heatmap(entry_ngrams, mel_ngrams_duration, selected_patterns=mel_ngrams_top_patterns_list, voices=[])\n",
    "    else:\n",
    "        if top_patterns == \"all\":\n",
    "            nr = piece.notes(combineUnisons=combine_unisons)\n",
    "            mel = piece.melodic(df=nr, kind=kind, compound=False, unit=regularize, end=False)\n",
    "            mel_ngrams = piece.ngrams(df=mel, n=length)\n",
    "            mel_ngrams_duration = piece.durations(df=mel, n=5, mask_df=mel_ngrams)\n",
    "            return viz.plot_ngrams_heatmap(mel_ngrams, mel_ngrams_duration, selected_patterns=[], voices=[])           \n",
    "        else:\n",
    "            mel = piece.melodic(kind='d', compound=False, unit=regularize)\n",
    "            mel_ngrams = piece.ngrams(df=mel, n=5)\n",
    "            mel_ngrams_duration = piece.durations(df=mel, n=5, mask_df=mel_ngrams)\n",
    "            mel_ngrams_top_patterns_df = mel_ngrams.stack().value_counts().to_frame().head(top_patterns)\n",
    "            mel_ngrams_top_patterns_list = mel_ngrams_top_patterns_df.index.to_list()\n",
    "            return viz.plot_ngrams_heatmap(mel_ngrams, \n",
    "                                           mel_ngrams_duration, \n",
    "                                           selected_patterns=mel_ngrams_top_patterns_list, \n",
    "                                           voices=[], \n",
    "                                           includeCount=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b44b749",
   "metadata": {},
   "source": [
    "### Selecting Individual nGrams and Voices\n",
    "\n",
    "* Finally, it is possible select an individual ngram (and voice) explicitly.\n",
    "\n",
    "* Note that in the plot tool below each **selected pattern** must be **list** consisting of **strings** in a **tuple**:\n",
    "\n",
    "This: `selected_pattterns[('4', '1', '2', '2', '-3')]`\n",
    "\n",
    "not this: `selected_patterns=[(4, 1, 2, 2, -3)]`\n",
    "\n",
    "For more than one pattern: `selected_patterns=[('1', '2', '-2', '-2', '-2'), ('-2', '-2', '-2', '2', '-2')`\n",
    "\n",
    "The complete code will in turn be:\n",
    "\n",
    "```\n",
    "viz.plot_ngrams_heatmap(mel_ngrams, mel_ngrams_duration, selected_patterns=\n",
    "                                            [('1', '2', '-2', '-2', '-2'), ('-2', '-2', '-2', '2', '-2')], \n",
    "                                            voices=[])\n",
    "```\n",
    "                                            \n",
    "If the `voices` field is left blank, the heatmap will include all voices.  But it also possible to limit the plot to one or more voices.  Note that the voice name must match exactly the one in your score!\n",
    "\n",
    "```\n",
    "viz.plot_ngrams_heatmap(mel_ngrams, mel_ngrams_duration, selected_patterns=\n",
    "                                            [('1', '2', '-2', '-2', '-2'), \n",
    "                                            ('-2', '-2', '-2', '2', '-2')], \n",
    "                                            voices=['[Superius]', 'Tenor'])\n",
    "```\n",
    "                                            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dbbd5cb5",
   "metadata": {},
   "outputs": [
    {
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       "  var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
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       "    let outputDiv = document.currentScript.previousElementSibling;\n",
       "    if (outputDiv.id !== \"altair-viz-046d29ed5f494649821fba8a9ebb310b\") {\n",
       "      outputDiv = document.getElementById(\"altair-viz-046d29ed5f494649821fba8a9ebb310b\");\n",
       "    }\n",
       "    const paths = {\n",
       "      \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
       "      \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
       "      \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
       "      \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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       "    function maybeLoadScript(lib, version) {\n",
       "      var key = `${lib.replace(\"-\", \"\")}_version`;\n",
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       "            VEGA_DEBUG[key] = version;\n",
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       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
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       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-78355b80b687054df4ba615f27319925\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"pattern\", \"type\": \"nominal\"}, \"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector017\"}, \"value\": 0.2}, \"tooltip\": [{\"field\": \"start\", \"type\": \"quantitative\"}, {\"field\": \"end\", \"type\": \"quantitative\"}, {\"field\": \"pattern\", \"type\": \"nominal\"}], \"x\": {\"field\": \"start\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"end\"}, \"y\": {\"field\": \"voice\", \"sort\": null, \"type\": \"nominal\"}}, \"height\": 300, \"selection\": {\"selector017\": {\"type\": \"multi\", \"fields\": [\"pattern\"]}}, \"width\": 800, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-78355b80b687054df4ba615f27319925\": [{\"start\": 0.0, \"voice\": \"[Superius]\", \"pattern\": \"4, 2, 2, -3\", \"end\": 24.0}, {\"start\": 16.0, \"voice\": \"Altus\", \"pattern\": \"4, 2, 2, -3\", \"end\": 40.0}, {\"start\": 32.0, \"voice\": \"Tenor\", \"pattern\": \"4, 2, 2, -3\", \"end\": 56.0}, {\"start\": 48.0, \"voice\": \"Bassus\", \"pattern\": \"4, 2, 2, -3\", \"end\": 72.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.Chart(...)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nr = piece.notes(combineUnisons=True)\n",
    "mel = piece.melodic(df=nr, kind=\"d\", compound=True, end=False)\n",
    "mel_ngrams = piece.ngrams(df=mel, n=4)\n",
    "entry_ngrams = piece.entries(df=mel, n=4, exclude=[\"Rest\"])\n",
    "entry_ngrams_durations = piece.durations(df=mel, n=4, mask_df=entry_ngrams)\n",
    "mel_ngrams_duration = piece.durations(df=mel, n=4, mask_df=mel_ngrams)\n",
    "\n",
    "viz.plot_ngrams_heatmap(entry_ngrams, entry_ngrams_durations, selected_patterns=[('4', '2', '2', '-3')], voices=[])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e966abba",
   "metadata": {},
   "outputs": [
    {
     "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>[Superius]</th>\n",
       "      <th>Altus</th>\n",
       "      <th>Tenor</th>\n",
       "      <th>Bassus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.0</th>\n",
       "      <td>(4, 2, 2, -3)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>(4, 2, 2, -3)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(4, 2, 2, -3)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(4, 2, 2, -3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56.0</th>\n",
       "      <td>(-2, -2, -2, 2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1148.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(-2, -2, 2, -2)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1156.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(-3, 4, -2, -2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1158.0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>(-2, -2, -2, 2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1200.0</th>\n",
       "      <td>(2, 2, 2, -2)</td>\n",
       "      <td>(2, -2, Rest, Rest)</td>\n",
       "      <td>(-2, -2, 2, 2)</td>\n",
       "      <td>(5, -2, -2, -2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236.0</th>\n",
       "      <td>(-2, -2, -2, 2)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(-2, 2, 2, -2)</td>\n",
       "      <td>(8, -4, 2, -2)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             [Superius]                Altus            Tenor           Bassus\n",
       "0.0       (4, 2, 2, -3)                  NaN              NaN              NaN\n",
       "16.0                NaN        (4, 2, 2, -3)              NaN              NaN\n",
       "32.0                NaN                  NaN    (4, 2, 2, -3)              NaN\n",
       "48.0                NaN                  NaN              NaN    (4, 2, 2, -3)\n",
       "56.0    (-2, -2, -2, 2)                  NaN              NaN              NaN\n",
       "...                 ...                  ...              ...              ...\n",
       "1148.0              NaN                  NaN  (-2, -2, 2, -2)              NaN\n",
       "1156.0              NaN                  NaN              NaN  (-3, 4, -2, -2)\n",
       "1158.0              NaN      (-2, -2, -2, 2)              NaN              NaN\n",
       "1200.0    (2, 2, 2, -2)  (2, -2, Rest, Rest)   (-2, -2, 2, 2)  (5, -2, -2, -2)\n",
       "1236.0  (-2, -2, -2, 2)                  NaN   (-2, 2, 2, -2)   (8, -4, 2, -2)\n",
       "\n",
       "[62 rows x 4 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entry_ngrams"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cf4c433",
   "metadata": {},
   "source": [
    "## C. Mapping Similar nGrams with Levenshtein Distance\n",
    "\n",
    "* The `plot_close_match_heatmap` tool takes a single ngram and allows you to see a sliding scale of ngrams that are similar to it.  The 'distance' between two any pair of ngrams is measured accoring to their **Levenshtein distance**. \n",
    "\n",
    "* Levenshtein distance takes into account **how many changes we need to make to turn a pattern into another**. For example, pattern '2, 2, 1, 2' and pattern '2, 2, 1, 3' would have the same Levenshtein score (25) when compared to pattern '2, 2, 1, 1' because we need to make **one change at the fourth character of the two strings** to turn it into '2, 2, 1, 1'.  Note that these distances therefore **do not** consider the size of the intervallic difference at any given position, simply the fact that one character needs to be changed.\n",
    "\n",
    "* As we change the cutoff with the slider, the ngrams with scores lower than the cutoff would be hidden away.  A lower number on the slider allows more matches.  A higher number restricts the map to strict or exact matches.\n",
    "\n",
    "* You could real more about Levenshtein distance on [Wikipedia](https://en.wikipedia.org/wiki/Levenshtein_distance#:~:text=Informally%2C%20the%20Levenshtein%20distance%20between,considered%20this%20distance%20in%201965.).\n",
    "\n",
    "* Read more about our heatmap tool for close matches:  `help(viz.plot_close_match_heatmap)`\n",
    "\n",
    "* Note that in this tool the 'reference' nGram is presented as a **string of integers**, not a tuple or list:  \n",
    "\n",
    ">`'4, 1, 2, 2, -3'`\n",
    "\n",
    "Build the map:\n",
    "\n",
    ">`viz.plot_close_match_heatmap(mel_ngrams, '4, 1, 2, 2, -3', mel_ngrams_duration, voices=[])`\n",
    "\n",
    "It is also possible to limit the map to specific voices. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5aeeec4",
   "metadata": {},
   "source": [
    "### Close Match Map of All Mel Ngrams\n",
    "\n",
    "With selected ngram:\n",
    "\n",
    "```\n",
    "viz.plot_close_match_heatmap(mel_ngrams, '4, 1, 2, 2, -3', mel_ngrams_duration, voices=[])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "debf8234",
   "metadata": {},
   "outputs": [
    {
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       "      vegaEmbed(outputDiv, spec, embedOpt)\n",
       "        .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
       "    }\n",
       "\n",
       "    if(typeof define === \"function\" && define.amd) {\n",
       "      requirejs.config({paths});\n",
       "      require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-08effc39ad23a491f213ca7d73b55fe0\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"score\", \"type\": \"quantitative\"}, \"opacity\": {\"condition\": {\"value\": 1, \"test\": \"(datum.score > SelectorName.cutoff)\"}, \"value\": 0.2}, \"tooltip\": [{\"field\": \"start\", \"type\": \"quantitative\"}, {\"field\": \"end\", \"type\": \"quantitative\"}, {\"field\": \"pattern\", \"type\": \"nominal\"}, {\"field\": \"score\", \"type\": \"quantitative\"}], \"x\": {\"field\": \"start\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"end\"}, \"y\": {\"field\": \"voice\", \"sort\": null, \"type\": \"nominal\"}}, \"height\": 300, \"selection\": {\"SelectorName\": {\"type\": \"single\", \"fields\": [\"cutoff\"], \"bind\": {\"input\": \"range\", \"max\": 100, \"min\": 0, \"name\": \"cutoff:\", \"step\": 1}, \"init\": {\"cutoff\": 50}}}, \"width\": 800, \"$schema\": 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       "</script>"
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      "text/plain": [
       "alt.Chart(...)"
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "viz.plot_close_match_heatmap(mel_ngrams, '4, 1, 2, 2, -3', mel_ngrams_duration, voices=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01d6830a",
   "metadata": {},
   "source": [
    "### Close Plot of All Entry Ngrams\n",
    "\n",
    "With selected ngram:\n",
    "\n",
    "```\n",
    "viz.plot_close_match_heatmap(entry_ngrams, '4, 1, 2, 2, -3', entry_ngrams_duration, voices=[])\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "5238faa6",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 34,
     "metadata": {},
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   ],
   "source": [
    "viz.plot_close_match_heatmap(entry_ngrams, '4, 1, 2, 2, -3', entry_ngrams_durations, voices=[])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5caa117",
   "metadata": {},
   "source": [
    "### More about Edit Distances\n",
    "\n",
    "* How are these 'distances\" measured?  See separate notebook"
   ]
  }
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