Why bi visibility matters
October 3rd, 2024
4 min
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Hi, I’m Guillermina, product specialist at Datawrapper, and today I’m combining my love of cello and data vis.
Have you ever wondered what sound looks like? For a long time, I’ve been captivated by the idea of seeing sound. I believe that patterns exist in almost everything we hear — intonation, silences, speed, and volume. But could these patterns be visualized? If so, what would they look like?
After running into Nicholas Rougeux’s visual music scores, I decided to explore this idea further and try to achieve something similar in Datawrapper with one of the most famous pieces ever written for cello.
Here’s a visual representation of Bach’s Cello Suite No. 1 in G major. It has seven movements: Prelude (the most well-known), Allemande, Courante, Sarabande, Menuet I and II, and Gigue. You can listen to the full suite (and Bach’s five other cello suites, too!) interpreted by Pieter Wispelwey here.
Creating this visualization was a multistep process:
pretty_midi
to read into the file. For each note, I extracted the start and end times, pitch, and velocity, and exported everything as a CSV. I tried to match the data structure that Nicholas explains here. Here’s the Python script for reading into the MIDI file.
import pretty_midi import pandas as pd
# load MIDI file
midi_data = pretty_midi.PrettyMIDI('BACH_SUITE.mid')
# get notes
notes = []
for instrument in midi_data.instruments: if not instrument.is_drum: for note in instrument.notes: notes.append([note.start, note.end, note.pitch, note.velocity])
# create df
df = pd.DataFrame(notes, columns=['start_secs', 'end_secs', 'pitch', 'velocity'])
# calculate start and duration of notes, and transform pitch into exact note
df['start_ticks'] = (df['start_secs'] * midi_data.resolution).astype(int) df['duration_secs'] = df['end_secs'] - df['start_secs'] df['duration_ticks'] = (df['duration_secs'] * midi_data.resolution).astype(int) df['fullNoteOctave'] = df['pitch'].apply(lambda x: pretty_midi.note_number_to_name(x))
# reorder df
df = df[['start_ticks', 'start_secs', 'duration_ticks', 'duration_secs', 'pitch', 'fullNoteOctave', 'velocity']]
# save csv
df.to_csv('BACH_SUITE.csv', index=False)
Here’s the Python script for calculating the (x,y) coordinates.
import pandas as pd
import numpy as np
files = ['BACH_SUITE.csv', 'PRELUDE.csv', 'ALLEMANDE.csv', 'COURANTE.csv', 'SARABANDE.csv', 'MENUET1.csv', 'MENUET2.csv', 'GIGUE.csv']
# load the data
for i in files: df = pd.read_csv(i)
# normalize pitch radius
min_pitch = df['pitch'].min()
max_pitch = df['pitch'].max()
df['normalized_pitch'] = ((df['pitch'] - min_pitch) / (max_pitch - min_pitch) * 10) + 2
# scale to 0-10 and +2 to shift towards outer circle
# generate angles (total secs mapped to 0 to 2pi)
max_time = df['start_secs'].max()
df['angle'] = (df['start_secs'] / max_time) * 2 * np.pi
# calculate coordinates using normalized_pitch as radius * sine (x) and cosine (y)
df['x'] = df['normalized_pitch'] * np.sin(df['angle'])
df['y'] = df['normalized_pitch'] * np.cos(df['angle'])
csv_path = '{0}_circle.csv'.format(i)
df.to_csv(csv_path, index=False)
That’s it for my Weekly Chart! Now I invite you to sit back, relax, and enjoy some Bach. See you next week!
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