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{
"cells": [
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"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."
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"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`."
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"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": []
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"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": []
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"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."
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"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": []
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"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": []
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"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."
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"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,
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"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": []
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"cell_type": "code",
"source": "def init_shapefile(path):\n \"\"\"Initializes a shapefile for reading/parsing\"\"\"\n return shapefile.Reader(path)",
"execution_count": 8,
"outputs": []
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"cell_type": "code",
"source": "#Initialize the data sources\nsf = init_shapefile(\"mn_mappluto_16v2/MNMapPLUTO\")\nsr = sf.shapeRecords()",
"execution_count": 9,
"outputs": []
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"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. "
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"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": []
},
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"collapsed": false,
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"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: <C> | Dur: 0.5729 | Octave: 4\n14 WALL STREET | Height: 196.25 | Width: 160.0 | Note: <F> | Dur: 0.8 | Octave: 5\n26 WALL STREET | Height: 170.0 | Width: 90.0 | Note: <D> | Dur: 0.45 | Octave: 5\n37 WALL STREET | Height: 220.0 | Width: 61.08 | Note: <G> | Dur: 0.3054 | Octave: 5\n30 WALL STREET | Height: 120.83 | Width: 86.67 | Note: <G#> | Dur: 0.43335 | Octave: 4\n45 WALL STREET | Height: 185.0 | Width: 107.0 | Note: <E> | Dur: 0.535 | Octave: 5\n40 WALL STREET | Height: 194.58 | Width: 150.0 | Note: <F> | Dur: 0.75 | Octave: 5\n44 WALL STREET | Height: 195.0 | Width: 70.0 | Note: <F> | Dur: 0.35 | Octave: 5\n55 WALL STREET | Height: 171.0 | Width: 204.0 | Note: <D#> | Dur: 1.02 | Octave: 5\n48 WALL STREET | Height: 127.0 | Width: 99.0 | Note: <A> | Dur: 0.495 | Octave: 4\n63 WALL STREET | Height: 213.17 | Width: 112.5 | Note: <F#> | Dur: 0.5625 | Octave: 5\n60 WALL STREET | Height: 195.0 | Width: 296.0 | Note: <F> | Dur: 1.48 | Octave: 5\n67 WALL STREET | Height: 140.0 | Width: 143.0 | Note: <B> | Dur: 0.715 | Octave: 4\n75 WALL STREET | Height: 298.0 | Width: 115.0 | Note: <C> | Dur: 0.575 | Octave: 6\n80 WALL STREET | Height: 76.0 | Width: 48.0 | Note: <C> | Dur: 0.24 | Octave: 4\n95 WALL STREET | Height: 231.0 | Width: 54.0 | Note: <G#> | Dur: 0.27 | Octave: 5\n82 WALL STREET | Height: 52.0 | Width: 92.0 | Note: <F#> | Dur: 0.46 | Octave: 3\n99 WALL STREET | Height: 0.0 | Width: 0.0 | Note: <C> | Dur: 0.125 | Octave: 4\n111 WALL STREET | Height: 232.0 | Width: 215.0 | Note: <G#> | Dur: 1.075 | Octave: 5\n100 WALL STREET | Height: 174.0 | Width: 108.0 | Note: <D#> | Dur: 0.54 | Octave: 5\n110 WALL STREET | Height: 157.0 | Width: 119.0 | Note: <C#> | Dur: 0.595 | Octave: 5\n120 WALL STREET | Height: 0.0 | Width: 0.0 | Note: <C> | 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."
},
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"cell_type": "markdown",
"source": "## The score"
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"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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\n3ghsTykWSflwPyBJk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"text/plain": "<IPython.core.display.Image object>"
},
"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": "<IPython.core.display.HTML object>",
"text/html": "\n <div id='midiPlayerDiv220'></div>\n <link rel=\"stylesheet\" href=\"http://artusi.xyz/music21j/css/m21.css\"\n type=\"text/css\" />\n <script>\n require.config({\n paths: {'music21': 'http://artusi.xyz/music21j/src/music21'}\n });\n require(['music21'], function() {\n mp = new music21.miditools.MidiPlayer();\n mp.addPlayer('#midiPlayerDiv220');\n mp.base64Load('data:audio/midi;base64,TVRoZAAAAAYAAQABBABNVHJrAAAA6gD/AwVQaWFubwDAAADgAEAAwAAA/1EDCiwriACQMGSSAIAwAACQQWSaAIBBAACQPmSOAIA+AACQQ2SKAIBDAACQOGSOAIA4AACQQGSQVZBBZIErgEAAllWAQQAAkEFkilWAQQBWkD9koACAPwAAkDlkkACAOQAAkEJkkgCAQgAAkEFksACAQQAAkDtklgCAOwAAkEhkklWASACBK5AwZIYAkERkggCAMACGAIBEAIIAkCpkjgCAKgAAkDBkhACAMAAAkERkogCQP2RVgEQAkSuAPwAAkD1kklWAPQAAkDBkhACAMACIAP8vAA==');\n });\n </script>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "#Processed MIDI, edited in logic with a few Synthesizers and turned into a WAV.\nipd.Audio('./wallstreet_test2.wav')",
"execution_count": 21,
"outputs": [
{
"execution_count": 21,
"output_type": "execute_result",
"data": {
"text/plain": "<IPython.lib.display.Audio object>",
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