{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": "# The Sounds of Manhattan\n\nThis is a small experiment meant to explore the relationship between architecture and music. Through a series of algorithms, I was able to convert ratios of building heights in Manhattan into musical notes.\n\nI was inspired by the Theremin, an instrument that is controlled by your distance to an antenna. I took this a step further and imagined if you could fly over all of Manhattan and use the distances between you and the buildings as a way to create music. Luckily, we don't actually have to fly! Python does that for us instead." }, { "metadata": {}, "cell_type": "markdown", "source": "# Setup\n\nFirst, let's import a few libraries and define a few constants that will help us later on with the conversion between heights.\n\nFor data visualization and audio analysis we will use Matplotlib, Librosa, and Music21. For creation of midi files we use Pyknon. For parsing through the shapefile of Manhattan (which contains all of the metadata about every building/block), we use Pyshp." }, { "metadata": { "scrolled": true, "collapsed": false, "trusted": true }, "cell_type": "code", "source": "import shapefile\nimport numpy as np\nfrom numpy import log2, power\nimport matplotlib.pyplot as plt\nimport librosa\nimport matplotlib.pyplot as plt\nimport librosa.display\nimport wave\nimport sys\n\nimport IPython.display as ipd\nfrom IPython.core.display import HTML\nfrom IPython.display import Image\nfrom scipy.io import wavfile\n\nfrom pyknon.genmidi import Midi\nfrom pyknon.music import NoteSeq, Note\nfrom music21 import *", "execution_count": 23, "outputs": [] }, { "metadata": { "collapsed": true, "trusted": true }, "cell_type": "code", "source": "#Constants\n#These are the pieces of metadata and their indices in the Manhattan Shape file.\nMETA_DATA = {\n \"ADDRESS\": 15,\n \"BLOCK\": 1,\n \"OWNER\": 31,\n \"HEIGHT\": 50,\n \"FRONT\": 49,\n \"XCOORD\": 73,\n \"YCOORD\": 74\n}\n\n#Musical constants\n#Note durations\nQUARTER = 0.25\nEIGHTH = QUARTER/2\n\n#Middle C Hz\nC4 = 261.63\n#Middle A Hz\nA4 = 440 \nC0 = A4*power(2, -4.75)\nref_notes = [\"C\", \"C#\", \"D\", \"D#\", \"E\", \"F\", \"F#\", \"G\", \"G#\", \"A\", \"A#\", \"B\"]", "execution_count": 3, "outputs": [] }, { "metadata": {}, "cell_type": "markdown", "source": "# Musicology\n\nMy philosophy here was to compare height ratios to Middle C.\n\n#### The frequency ($\\lambda$) of a given building is the ratio between the height (H) of that building and the median height, multiplied by the frequency of middle C.\n\n$$\\lambda_{\\ building} = \\frac{H_{\\ building}}{H_{\\ median\\ of\\ all\\ buildings}} * \\lambda_{\\ C4}$$\n\n#### The \"duration\" (D) of a building is the ratio between the width (W) of that building and the median width, multiplied by the duration of an 8th note.\n\n$$D_{\\ building} = \\frac{W_{\\ building}}{W_{\\ median\\ of\\ all\\ buildings}} * D_{\\ 8th\\ note}$$\n\n## What does this mean?\n\nWhat this implies is that basically, the ratio of a building relative to the median is how far that building is from Middle C. So very tall buildings are very high pitched, and short buildings are very low pitched. Buildings at or around the median more or less sound like Middle C.\n\nLikewise, very wide buildings will last a long time, and shorter buildings won't. In this experiment, since we end up using Wall Street as an example, we multiply every width ratio times an Eighth note to help speed everything up a bit.\n\nOf course, it would be possible to use the ratio between the building height and the average, or compare to the largest/smallest buildings, but I want to eventually explore correlations between building height ratios, corresponding musical pitch, and socioeconomic disparities.\n\nSoon, I will explore the differences between using relative and absolute metrics for comparison. In particular, I want to compare the heights of buildings relative to the streets they're located on and see how that affects the key changes of the music over time.\n\n$$\\lambda_{building_{relative}} = \\frac{H_{\\ building}}{H_{\\ median\\ from\\ building's\\ street}} * \\lambda_{C4}$$\n\nBelow are some functions written to facilitate the mathematical side of the data conversion." }, { "metadata": { "collapsed": false, "trusted": true }, "cell_type": "code", "source": "### MUSIC DATA CONVERSION ###\ndef height_to_freq(height, median):\n \"\"\"Takes a given height, the median for comparison,\n then converts it into a frequency\"\"\"\n #initialize ratio to 1 in case height is 0\n ratio = 1\n if height != 0:\n ratio = height / median\n return ratio * C4\n\ndef front_to_length(front, median):\n \"\"\"Converts the length of the front of a building into the length of a note\"\"\"\n ratio = 1\n if front != 0:\n ratio = front/median\n return ratio * EIGHTH\n\ndef freq_to_note(freq):\n \"\"\"Returns the note of a frequency\"\"\"\n pos = freq_to_position(freq)\n octave = freq_to_octave(freq)\n return ref_notes[pos] + str(octave)\n\ndef freq_to_position(freq):\n \"\"\"Returns the position in the note array of a frequency\"\"\"\n h = round(12*log2(freq/C0))\n n = h % 12\n return int(n)\n\ndef freq_to_octave(freq):\n \"\"\"Returns the octave of a frequency\"\"\"\n h = round(12*log2(freq/C0))\n octave = h // 12\n return int(octave)", "execution_count": 4, "outputs": [] }, { "metadata": { "collapsed": true, "trusted": true }, "cell_type": "code", "source": "### MIDI AND WAV DATA CREATION ###\ndef freq_to_midi(freq):\n \"\"\"Converts a frequency to a midi number\"\"\"\n #MIDI num for A4: 69\n return 12*log2(freq/A4) + 69\n\ndef create_midi(sequence, filename = \"test.midi\"):\n \"\"\"Creates a MIDI file from a given sequence of notes\"\"\"\n midi = Midi(1, tempo = 90)\n midi.seq_notes(sequence, track=0)\n midi.write(filename)\n\ndef play_midi(filepath):\n \"\"\"Embeds a media player to display the MIDI file\"\"\"\n mf = midi.MidiFile()\n mf.open(filepath)\n mf.read()\n mf.close()\n s = midi.translate.midiFileToStream(mf)\n s.show('midi')", "execution_count": 5, "outputs": [] }, { "metadata": {}, "cell_type": "markdown", "source": "# Where do we get the heights from?\n\nI downloaded a shapefile from the NYC Planning website here:\n\nhttps://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page\n\nFrom here, I simply wrote a few functions to aggregate the relevant data, particuarly organized by street. Then, I wrote a few general functions that would sort given building's within a street by x coordinate, since successive addresses are not necessarily geographically sequential." }, { "metadata": { "collapsed": true, "trusted": true }, "cell_type": "code", "source": "### GEOSPATIAL DATA ANALYSIS ###\ndef get_all_street_data(records, *args):\n \"\"\"Takes in shape records and returns all of\n the streets and corresponding metadata\"\"\" \n groups= {}\n for street in get_all_streets(records):\n groups[street] = []\n\n #forms the groups\n for r in records:\n address = r.record[META_DATA[\"ADDRESS\"]]\n for street in groups:\n if street in address:\n groups[street].append(sr_to_metadata(r))\n\n return groups\n\ndef get_street_data_byname(street_data, street_name):\n \"\"\"Returns all of the records along a particular street\"\"\"\n sr = street_data[street_name]\n return sr\n\ndef get_all_streets(records):\n \"\"\"Returns all street names\"\"\"\n streets = []\n for r in records:\n addr = r.record[META_DATA[\"ADDRESS\"]]\n street = addr.split(\" \", 1)[-1]\n if street not in streets:\n streets.append(street)\n return streets", "execution_count": 6, "outputs": [] }, { "metadata": { "collapsed": true, "trusted": true }, "cell_type": "code", "source": "#METADATA METHODS \ndef sort_by_metadata(street_data, metadata):\n \"\"\"Sorts a given set of street data by any piece of metadata\"\"\"\n data = {}\n for i in range(0, len(street_data)):\n data[i] = street_data[i][metadata]\n\n #has the indices\n sorted_data = sorted(data.iteritems(), key=lambda (k,v): (v,k))\n sorted_final = [0] * len(street_data)\n\n for i in range(0, len(sorted_data)):\n sorted_final[i] = street_data[sorted_data[i][0]]\n\n return sorted_final\n\ndef sr_to_metadata(sr):\n \"\"\"Converts a given shape record to its metadata object\"\"\"\n data = {}\n for attr, index in META_DATA.items():\n data[attr] = sr.record[index]\n return data\n\ndef print_metadata_id(records, *args):\n \"\"\"Prints the relevant metadata about specified records\"\"\"\n for arg in args:\n print(\"\\n\")\n r = records[arg]\n for attr, index in META_DATA.items():\n print(\"{}: {}\".format(attr, r.record[index]))\n\ndef print_metadata_all(records):\n \"\"\"Prints all metadata from a given set of shape records\"\"\"\n for r in records:\n print(\"\\n\")\n for attr, index in META_DATA.items():\n print(\"{}: {}\".format(attr, r.record[index]))\n\ndef get_metadata_measure(records, func, metadata):\n \"\"\"Takes in shape records and a function, either max,\n min, median, or avg, then returns the processed data\"\"\"\n \n #Reference each height by the record index\n measure_dic = {}\n\n for i in range(0, len(records)):\n measure_dic[i] = records[i].record[META_DATA[metadata]]\n\n measures = measure_dic.values()\n to_return = 0\n\n #Different statistics on building heights\n if func == \"max\":\n to_return = max(measures)\n elif func == \"min\":\n to_return = min(measures)\n elif func == \"median\":\n to_return = np.median(measures)\n else:\n to_return = np.mean(measures)\n return to_return", "execution_count": 7, "outputs": [] }, { "metadata": { "collapsed": true, "trusted": true }, "cell_type": "code", "source": "def init_shapefile(path):\n \"\"\"Initializes a shapefile for reading/parsing\"\"\"\n return shapefile.Reader(path)", "execution_count": 8, "outputs": [] }, { "metadata": { "collapsed": false, "trusted": true }, "cell_type": "code", "source": "#Initialize the data sources\nsf = init_shapefile(\"mn_mappluto_16v2/MNMapPLUTO\")\nsr = sf.shapeRecords()", "execution_count": 9, "outputs": [] }, { "metadata": { "collapsed": false, "trusted": true }, "cell_type": "code", "source": "median_height = get_metadata_measure(sr, \"median\", \"HEIGHT\")\nmedian_front = get_metadata_measure(sr, \"median\", \"FRONT\")", "execution_count": 10, "outputs": [] }, { "metadata": {}, "cell_type": "markdown", "source": "# Testing on Wall Street\n\nThere are hundreds of streets in Manhattan, so filling a Jupyter notebook with tens of thousands of notes would be ridiculous. So, let's check out Wall Street. " }, { "metadata": { "collapsed": false, "trusted": true }, "cell_type": "code", "source": "#Testing with wall street and sorting by the X coordinates\n#First grab all street data\nstreet_data = get_all_street_data(sr)\n\n#Specific further by grabbing only wall street\nwall_street = get_street_data_byname(street_data, 'WALL STREET')\n\n#Sort the wall street by decreasing X coordinate\nsorted_wall_street = sort_by_metadata(wall_street, 'XCOORD')", "execution_count": 11, "outputs": [] }, { "metadata": { "collapsed": false, "trusted": true }, "cell_type": "code", "source": "#empty melody array\nnotes = NoteSeq()\n\n#wall street test\nfor building in sorted_wall_street:\n \n #Grab the height and front of each building\n height = building['HEIGHT']\n front = building['FRONT'] \n \n #Get the music specific metadata for each building:\n freq = height_to_freq(height, median_height)\n position = freq_to_position(freq)\n octave = freq_to_octave(freq)\n duration = front_to_length(front, median_front)\n \n #TODO: Get the actual volume of the building by multiplying height by area\n volume = 100\n \n #Create a note object using each piece of metadata\n note = Note(position, octave, duration, volume) \n notes.append(note)\n \n #All relevant metadata\n print(\"{} | Height: {} | Width: {} | Note: {} | Dur: {} | Octave: {}\".format(building['ADDRESS'], height, front, note, note.dur, note.octave))", "execution_count": 34, "outputs": [ { "output_type": "stream", "name": "stdout", "text": "2 WALL STREET | Height: 72.33 | Width: 114.58 | Note: | Dur: 0.5729 | Octave: 4\n14 WALL STREET | Height: 196.25 | Width: 160.0 | Note: | Dur: 0.8 | Octave: 5\n26 WALL STREET | Height: 170.0 | Width: 90.0 | Note: | Dur: 0.45 | Octave: 5\n37 WALL STREET | Height: 220.0 | Width: 61.08 | Note: | Dur: 0.3054 | Octave: 5\n30 WALL STREET | Height: 120.83 | Width: 86.67 | Note: | Dur: 0.43335 | Octave: 4\n45 WALL STREET | Height: 185.0 | Width: 107.0 | Note: | Dur: 0.535 | Octave: 5\n40 WALL STREET | Height: 194.58 | Width: 150.0 | Note: | Dur: 0.75 | Octave: 5\n44 WALL STREET | Height: 195.0 | Width: 70.0 | Note: | Dur: 0.35 | Octave: 5\n55 WALL STREET | Height: 171.0 | Width: 204.0 | Note: | Dur: 1.02 | Octave: 5\n48 WALL STREET | Height: 127.0 | Width: 99.0 | Note: | Dur: 0.495 | Octave: 4\n63 WALL STREET | Height: 213.17 | Width: 112.5 | Note: | Dur: 0.5625 | Octave: 5\n60 WALL STREET | Height: 195.0 | Width: 296.0 | Note: | Dur: 1.48 | Octave: 5\n67 WALL STREET | Height: 140.0 | Width: 143.0 | Note: | Dur: 0.715 | Octave: 4\n75 WALL STREET | Height: 298.0 | Width: 115.0 | Note: | Dur: 0.575 | Octave: 6\n80 WALL STREET | Height: 76.0 | Width: 48.0 | Note: | Dur: 0.24 | Octave: 4\n95 WALL STREET | Height: 231.0 | Width: 54.0 | Note: | Dur: 0.27 | Octave: 5\n82 WALL STREET | Height: 52.0 | Width: 92.0 | Note: | Dur: 0.46 | Octave: 3\n99 WALL STREET | Height: 0.0 | Width: 0.0 | Note: | Dur: 0.125 | Octave: 4\n111 WALL STREET | Height: 232.0 | Width: 215.0 | Note: | Dur: 1.075 | Octave: 5\n100 WALL STREET | Height: 174.0 | Width: 108.0 | Note: | Dur: 0.54 | Octave: 5\n110 WALL STREET | Height: 157.0 | Width: 119.0 | Note: | Dur: 0.595 | Octave: 5\n120 WALL STREET | Height: 0.0 | Width: 0.0 | Note: | Dur: 0.125 | Octave: 4\n" } ] }, { "metadata": {}, "cell_type": "markdown", "source": "# The results\n\nAfter running through all of the data and creating the appropriate notes, we end up with a pretty ominous sounding movie sound track. First is the unedited MIDI file, then the processed MIDI edited with some dank synthesizers in Logic Pro X." }, { "metadata": {}, "cell_type": "markdown", "source": "## The score" }, { "metadata": { "scrolled": false, "collapsed": false, "trusted": true }, "cell_type": "code", "source": "#Converting midi into notes\nc = converter.parse('./wallstreet_test2.mid')\nc.show()", "execution_count": 217, "outputs": [ { "output_type": "display_data", "data": { "image/png": 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D3iPQwH5bpzx/A/BGVG8OYD3Kq2c9CfgWKilwDhpgJG36WpXl/KRi3u/k12QzM8vJDsHt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je7ZL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\nPRO/947zp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"text/plain": "" }, "metadata": { "image/png": { "width": 751, "height": 225 } } } ] }, { "metadata": {}, "cell_type": "markdown", "source": "## MIDI creation" }, { "metadata": { "collapsed": false, "trusted": true }, "cell_type": "code", "source": "#Create the unedited MIDI file\ncreate_midi(notes, \"./wallstreet_test2.mid\")\nplay_midi(\"./wallstreet_test2.mid\")", "execution_count": 13, "outputs": [ { "output_type": "display_data", "data": { "text/plain": "", "text/html": "\n