CRIM_02d_Melodic_nGram_Maps.ipynb

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    "# CRIM Melodic nGram Maps\n",
    "\n",
    "## What Can You Do with this Notebook?\n",
    "\n",
    "This NB explains the Melodic nGram Maps, through which you can display all of the nGrams in one or more pieces.\n",
    "\n",
    "The method takes two dataframes as basic inputs:  a df of ngrams (all of then, or just the entries, if you prefer), and a df of the total durations of those ngrams (if none is specified, you will just have a small line for the start of each ngram instead of a color block spanning its total duration)\n",
    "\n",
    "\n",
    "```\n",
    "display(viz.plot_ngrams_heatmap(ngrams_df, \n",
    "                                ngrams_duration=None,  \n",
    "                                selected_patterns=[], \n",
    "                                voices=[], \n",
    "                                heatmap_width=800,\n",
    "                                heatmap_height=300, \n",
    "                                includeCount=False))\n",
    "```\n",
    "\n"
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      "saved_csv folder already exists.\n",
      "Music_Files folder already exists.\n"
     ]
    }
   ],
   "source": [
    "# python imports\n",
    "from intervals import * \n",
    "from intervals import importScore \n",
    "from intervals import main_objs\n",
    "from IPython.display import SVG\n",
    "from ipywidgets import interact\n",
    "import altair as alt\n",
    "import glob as glob\n",
    "import intervals\n",
    "import intervals.visualizations as viz\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import re\n",
    "import requests\n",
    "\n",
    "MYDIR = (\"saved_csv\")\n",
    "CHECK_FOLDER = os.path.isdir(MYDIR)\n",
    "\n",
    "# If folder doesn't exist, then create it.\n",
    "if not CHECK_FOLDER:\n",
    "    os.makedirs(MYDIR)\n",
    "    print(\"created folder : \", MYDIR)\n",
    "\n",
    "else:\n",
    "    print(MYDIR, \"folder already exists.\")\n",
    "    \n",
    "MUSDIR = (\"Music_Files\")\n",
    "CHECK_FOLDER = os.path.isdir(MUSDIR)\n",
    "\n",
    "# If folder doesn't exist, then create it.\n",
    "if not CHECK_FOLDER:\n",
    "    os.makedirs(MUSDIR)\n",
    "    print(\"created folder : \", MUSDIR)\n",
    "\n",
    "else:\n",
    "    print(MUSDIR, \"folder already exists.\")\n",
    "    "
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    "piece_list = ['CRIM_Model_0032.mei',\n",
    "                     'CRIM_Mass_0025_1.mei',\n",
    "                     'CRIM_Mass_0025_2.mei',\n",
    "                     'CRIM_Mass_0025_3.mei',\n",
    "                     'CRIM_Mass_0025_4.mei',\n",
    "                     'CRIM_Mass_0025_5.mei']"
   ]
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     "data": {
      "text/plain": [
       "'https://raw.githubusercontent.com/Elenacrod/Buch1/main/Gloriose_confesor_Guerrero.xml'"
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    "piece_list_2 = ['https://raw.githubusercontent.com/Elenacrod/Buch1/main/Gloriose_confesor_Guerrero.xml',\n",
    "                     'https://crimproject.org/mei/CRIM_Mass_0001_2.mei'\n",
    "                     ]\n",
    "piece_list_2[0]"
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    "for piece in piece_list:\n",
    "    prefix = 'https://crimproject.org/mei/' \n",
    "# prefix = 'Music_Files/'\n",
    "    mei_file = piece\n",
    "    url = prefix + mei_file\n",
    "    piece = importScore(url)\n",
    "    "
   ]
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   "source": [
    "# settings for ngrams, unisons and kind\n",
    "\n",
    "n=4\n",
    "combineUnisons=False\n",
    "kind='d'"
   ]
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   "source": [
    "## an initial view of the Model, with all entries"
   ]
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     "text": [
      "{'title': 'Gloriose confesor Domini', 'composer': 'Francisco Guerrero\\nMaría Elena Cuenca (ed.)', 'date': None}\n"
     ]
    },
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       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   "source": [
    "\n",
    "\n",
    "# select the model from the list\n",
    "# model = piece_list[0]\n",
    "# prefix = 'https://crimproject.org/mei/' \n",
    "# # prefix = 'Music_Files/'\n",
    "# url = prefix + model\n",
    "model = importScore(piece_list_2[0])\n",
    "\n",
    "# find entries for model\n",
    "nr = model.notes(combineUnisons=combineUnisons)\n",
    "mel = model.melodic(df=nr, kind=kind, compound=False, unit=0, end=False)\n",
    "mod_mel_ngrams = model.ngrams(df=mel, n=n)\n",
    "\n",
    "# pass the following ngrams to the plot below as first df\n",
    "mod_entry_ngrams = model.entries(df=mel, n=n, thematic=True, anywhere=True)\n",
    "\n",
    "# pass the ngram durations below to the plot as second df\n",
    "mod_mel_ngrams_duration = model.durations(df=mel, n=n, mask_df=mod_entry_ngrams)\n",
    "mod_entries_stack = list(mod_entry_ngrams.stack().unique())\n",
    "\n",
    "print(model.metadata)\n",
    "\n",
    "display(viz.plot_ngrams_heatmap(mod_entry_ngrams, \n",
    "                                mod_mel_ngrams_duration, \n",
    "                                selected_patterns=[], \n",
    "                                voices=[], \n",
    "                                heatmap_width=800,\n",
    "                                heatmap_height=300))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "6df4ca83",
   "metadata": {},
   "source": [
    "## Now the Model Paired with Each Mass Movement\n",
    "\n",
    "Showing only the 'shared' entries in each pair"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "510f6e5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'title': 'Sancta et immaculata virginitas', 'composer': 'Cristóbal de Morales', 'date': 1546}\n"
     ]
    },
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     "text": [
      "{'title': 'Missa Sancta et immaculata virginitas: Kyrie', 'composer': 'Francisco Guerrero', 'date': 1566}\n"
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       "alt.VConcatChart(...)"
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     "text": [
      "{'title': 'Sancta et immaculata virginitas', 'composer': 'Cristóbal de Morales', 'date': 1546}\n"
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      "{'title': 'Missa Sancta et immaculata virginitas: Gloria', 'composer': 'Francisco Guerrero', 'date': 1566}\n"
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      "{'title': 'Sancta et immaculata virginitas', 'composer': 'Cristóbal de Morales', 'date': 1546}\n"
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       "          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",
       "          };\n",
       "          s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
       "          s.src = paths[lib];\n",
       "        });\n",
       "    }\n",
       "\n",
       "    function showError(err) {\n",
       "      outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
       "      throw err;\n",
       "    }\n",
       "\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: ${err.message}`));\n",
       "    } else {\n",
       "      maybeLoadScript(\"vega\", \"5\")\n",
       "        .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
       "        .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
       "        .catch(showError)\n",
       "        .then(() => displayChart(vegaEmbed));\n",
       "    }\n",
       "  })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"vconcat\": [{\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"pattern\", \"type\": \"nominal\"}, \"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector013\"}, \"value\": 0.2}, \"x\": {\"axis\": {\"labelAngle\": -45}, \"field\": \"pattern\", \"type\": \"nominal\"}, \"y\": {\"aggregate\": \"count\", \"field\": \"pattern\", \"type\": \"nominal\"}}, \"selection\": {\"selector013\": {\"type\": \"multi\", \"fields\": [\"pattern\"]}}}, {\"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"pattern\", \"type\": \"nominal\"}, \"opacity\": {\"condition\": {\"value\": 1, \"selection\": \"selector013\"}, \"value\": 0.2}, \"tooltip\": [{\"field\": \"start\", \"type\": \"quantitative\"}, {\"field\": \"end\", \"type\": \"quantitative\"}, {\"field\": \"pattern\", \"type\": \"nominal\"}], \"x\": {\"field\": \"start\", \"type\": \"quantitative\"}, \"x2\": {\"field\": \"end\"}, \"y\": {\"field\": \"voice\", \"sort\": null, \"type\": \"nominal\"}}, \"height\": 300, \"selection\": {\"selector013\": {\"type\": \"multi\", \"fields\": [\"pattern\"]}}, \"width\": 800}], \"data\": {\"name\": \"data-ebfca695a41544a26ddde7da5d40e232\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-ebfca695a41544a26ddde7da5d40e232\": [{\"start\": 11.0, \"voice\": \"Superius 1\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 15.0}, {\"start\": 12.0, \"voice\": \"Superius 1\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 16.0}, {\"start\": 94.0, \"voice\": \"Superius 1\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 100.0}, {\"start\": 107.0, \"voice\": \"Superius 1\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 111.0}, {\"start\": 152.0, \"voice\": \"Superius 1\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 160.0}, {\"start\": 194.0, \"voice\": \"Superius 1\\u00ba\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 204.0}, 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\"end\": 216.0}, {\"start\": 215.0, \"voice\": \"Superius 2\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 222.0}, {\"start\": 226.0, \"voice\": \"Superius 2\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 231.0}, {\"start\": 234.0, \"voice\": \"Superius 2\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 239.0}, {\"start\": 272.0, \"voice\": \"Superius 2\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 278.0}, {\"start\": 274.0, \"voice\": \"Superius 2\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 279.0}, {\"start\": 42.0, \"voice\": \"Altus\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 48.0}, {\"start\": 59.0, \"voice\": \"Altus\", \"pattern\": \"2, -2, -2, -2\", \"end\": 63.0}, {\"start\": 98.0, \"voice\": \"Altus\", \"pattern\": \"2, -2, -2, -2\", \"end\": 103.0}, {\"start\": 126.0, \"voice\": \"Altus\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 131.0}, {\"start\": 133.0, \"voice\": \"Altus\", \"pattern\": \"2, -2, -2, -2\", \"end\": 142.0}, {\"start\": 218.0, \"voice\": \"Altus\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 228.0}, {\"start\": 226.0, \"voice\": \"Altus\", \"pattern\": \"2, -2, -2, -2\", \"end\": 231.0}, {\"start\": 228.0, \"voice\": \"Altus\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 232.0}, {\"start\": 229.0, \"voice\": \"Altus\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 233.0}, {\"start\": 230.0, \"voice\": \"Altus\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 234.0}, {\"start\": 356.0, \"voice\": \"Altus\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 365.0}, {\"start\": 436.0, \"voice\": \"Altus\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 440.0}, {\"start\": 39.0, \"voice\": \"Tenor\", \"pattern\": \"2, -2, -2, -2\", \"end\": 43.0}, {\"start\": 40.0, \"voice\": \"Tenor\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 44.0}, {\"start\": 210.0, \"voice\": \"Tenor\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 216.0}, {\"start\": 215.0, \"voice\": \"Tenor\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 222.0}, {\"start\": 232.0, \"voice\": \"Tenor\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 240.0}, {\"start\": 234.0, \"voice\": \"Tenor\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 243.0}, {\"start\": 402.0, \"voice\": \"Tenor\", \"pattern\": \"2, -2, -2, -2\", \"end\": 407.0}, {\"start\": 404.0, \"voice\": \"Tenor\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 408.0}, {\"start\": 420.0, \"voice\": \"Tenor\", \"pattern\": \"2, -2, -2, -2\", \"end\": 432.0}, {\"start\": 217.0, \"voice\": \"Bassus 1\\u00ba\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 224.0}, {\"start\": 312.0, \"voice\": \"Bassus 1\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 316.0}, {\"start\": 51.0, \"voice\": \"Bassus 2\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 58.0}, {\"start\": 52.0, \"voice\": \"Bassus 2\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 59.0}, {\"start\": 54.0, \"voice\": \"Bassus 2\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 60.0}, {\"start\": 196.0, \"voice\": \"Bassus 2\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 204.0}, {\"start\": 245.0, \"voice\": \"Bassus 2\\u00ba\", \"pattern\": \"-2, 2, 2, 2\", \"end\": 252.0}, {\"start\": 256.0, \"voice\": \"Bassus 2\\u00ba\", \"pattern\": \"2, -2, -2, -2\", \"end\": 276.0}, {\"start\": 296.0, \"voice\": \"Bassus 2\\u00ba\", \"pattern\": \"-2, -2, -2, -2\", \"end\": 300.0}]}}, {\"mode\": \"vega-lite\"});\n",
       "</script>"
      ],
      "text/plain": [
       "alt.VConcatChart(...)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# set parameters:\n",
    "\n",
    "n=4\n",
    "combineUnisons=False\n",
    "kind='d'\n",
    "\n",
    "# select the model from the list\n",
    "model = piece_list[0]\n",
    "prefix = 'https://crimproject.org/mei/' \n",
    "# prefix = 'Music_Files/'\n",
    "url = prefix + model\n",
    "model = importScore(url)\n",
    "\n",
    "# find entries for model\n",
    "nr = model.notes(combineUnisons=combineUnisons)\n",
    "mel = model.melodic(df=nr, kind=kind, compound=False, unit=0, end=False)\n",
    "mod_mel_ngrams = model.ngrams(df=mel, n=n)\n",
    "mod_entry_ngrams = model.entries(df=mel, n=n, thematic=True, anywhere=True)\n",
    "mod_mel_ngrams_duration = model.durations(df=mel, n=n, mask_df=mod_entry_ngrams)\n",
    "mod_entries_stack = list(mod_entry_ngrams.stack().unique())\n",
    "\n",
    "# find entries mass movements:\n",
    "mass_movements = piece_list[1:6]\n",
    "\n",
    "for movement in mass_movements:\n",
    "    \n",
    "    prefix = 'https://crimproject.org/mei/' \n",
    "    url = prefix + movement\n",
    "    m_movement = importScore(url)\n",
    "    nr = m_movement.notes(combineUnisons=combineUnisons)\n",
    "    mel = m_movement.melodic(df=nr, kind=kind, compound=False, unit=0, end=False)\n",
    "    mass_mvmt_mel_ngrams = m_movement.ngrams(df=mel, n=n)\n",
    "    mass_mvmt_entries = m_movement.entries(df=mel, n=n, thematic=True, anywhere=True)\n",
    "    mass_mvmt_mel_ngrams_duration = m_movement.durations(df=mel, n=n, mask_df=mass_mvmt_entries)\n",
    "    mass_mvmt_entries_stack = mass_mvmt_entries.stack()\n",
    "\n",
    "    \n",
    "    # find shared entries\n",
    "    \n",
    "    shared_entries = list(set(mass_mvmt_entries_stack).intersection(mod_entries_stack))\n",
    "\n",
    "    # print model metadata and chart\n",
    "    print(model.metadata)\n",
    "\n",
    "    display(viz.plot_ngrams_heatmap(mod_entry_ngrams, mod_mel_ngrams_duration, \n",
    "                        selected_patterns=shared_entries,\n",
    "                        voices=[], includeCount=False))\n",
    "\n",
    "\n",
    "    # print mass movement metadata and chart\n",
    "    print(m_movement.metadata)\n",
    "    display(viz.plot_ngrams_heatmap(mass_mvmt_entries, mass_mvmt_mel_ngrams_duration, \n",
    "                                selected_patterns=shared_entries,\n",
    "                                voices=[], includeCount=True)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "40f92945",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf67f7c2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adda854f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bbea9c6c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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