chord-progression-network 0.2.3

Network transition chord progressions

Chord Progression Network

Network transition chord progression generator

DESCRIPTION

This class generates network transition chord progressions. The transitions are given by a net of scale positions, and the chord "flavors" are defined by a chord_map of types. The chords that are returned are either named chords or lists of three or more named notes with octaves.

The chord types are as follows:

'' (i.e. an empty string) means a major chord.
'm' signifies a minor chord.
'7' is a seventh chord
'M7' is a major 7th chord and 'm7' is a minor 7th.
'dim' is a diminished chord and 'aug' is augmented.
'9', '11', and '13' are extended 7th chords.
'M9', 'M11', and 'M13' are extended major-7th chords.
'm9', 'm11', and 'm13' are extended minor-7th chords.

The scale must be specified with one of the known scales listed on the musical-scales page. A custom scale of named notes (with appropriate net and chord_map attributes) may also be given. Traditional modes (Ionian, Dorian, etc) have known chord_maps. For all other scales, a chord_map should be given in the constructor.

For the traditional modes, the chord_maps are as follows: The "Ionian" mode (major scale) is ['', 'm', 'm', '', '', 'm', 'dim']. "Dorian" is ['m', 'm', '', '', 'm', 'dim', ''], etc. A "chromatic" scale is all minors.

The tonic attribute means that if the first chord of the progression is being generated, then for 0 choose a random successor of the first chord, as defined by the net attribute. For 1, return the first chord in the scale. For any other value, choose a random value of the entire scale.

The resolve attribute means that if the last progression chord is being generated, then for 0 choose a random successor. For 1, return the first chord in the scale, and for any other value, choose a random value of the entire scale. In all other cases (i.e. the middle chords of the progression), choose a random successor.

By default, all chords and notes with accidentals are returned as sharps. If you want flats, set the flat attribute to True in the constructor.

If the substitute attribute is set to True, then the progression chords are subject to extended, "jazz" chord, including tritone substitution. This module performs chord substitution depending on the sub_cond lambda that acts 30% of the time. For now, for this work-in-progress advanced option, please see the substitution() method in the source...

Please see the Tests.py program, in this distribution for usage hints. :)

SYNOPSIS

from chord_progression_network import Generator

neighbors = [ i for i in range(1, 8) ] # 1 through 7
transitions = [ 1 for _ in neighbors ] # equal probability

g = Generator( # defaults
    max=8,
    scale_note='C',
    scale_name='major',
    octave=4,
    net={
        1: neighbors,
        2: neighbors,
        3: neighbors,
        4: neighbors,
        5: neighbors,
        6: neighbors,
        7: neighbors,
    },
    weights={
        1: transitions,
        2: transitions,
        3: transitions,
        4: transitions,
        5: transitions,
        6: transitions,
        7: transitions,
    },
    chord_map=[ '', 'm', 'm', '', '', 'm', 'dim' ],
    tonic=1,
    resolve=1,
    flat=False,
    chord_phrase=False,
    substitute=False,
    verbose=False,
)
phrase = g.generate()

MUSICAL EXAMPLES

from music21 import chord, stream
from chord_progression_network import Generator

g = Generator(verbose=True)
phrase = g.generate()

s = stream.Score()
p = stream.Part()

for notes in phrase:
    p.append(chord.Chord(notes, type='whole'))

s.append(p)
s.show()

from music21 import chord, duration, stream
from chord_progression_network import Generator
from random_rhythms import Rhythm

r = Rhythm(durations=[1, 3/2, 2])
motifs = [ r.motif() for _ in range(4) ]

s = stream.Score()
p = stream.Part()

# simplistic example: all ones = equal probability
weights = [ 1 for _ in range(1,6) ]

g = Generator(
    scale_name='whole-tone scale',
    net={
        1: [2,3,4,5,6],
        2: [1,3,4,5,6],
        3: [1,2,4,5,6],
        4: [1,2,3,5,6],
        5: [1,2,3,4,6],
        6: [1,2,3,4,5],
    },
    weights={
        1: weights,
        2: weights,
        3: weights,
        4: weights,
        5: weights,
        6: weights,
    },
    chord_map=['m'] * 6,
    substitute=True,
)

for m in motifs:
    g.max = len(m)
    phrase = g.generate()
    for i, d in enumerate(m):
        c = chord.Chord(phrase[i])
        c.duration = duration.Duration(d)
        p.append(c)

s.append(p)
s.show()