soundpointsgenerators

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Classes

AtaxicSoundPointsGenerator	Ataxic sound points generator.
GRWSoundPointsGenerator	Gaussian (sampled) random walk sound points generator.
ManualSoundPointsGenerator	Manual sound-point generator.
RandomWalkSoundPointsGenerator	Sound points generator, with pitches chosen with a random walk process.
SoundPoint	SoundPoint / Event.
SoundPointsGenerator	Abstract base sound-point generator.

class pang.soundpointsgenerators.AtaxicSoundPointsGenerator(arrival_rate=1, service_rate=1, pitch_set=[0], arrival_model='markov', service_model='markov', seed=123456, order='idp')

Ataxic sound points generator.

Initializing an ataxic cloud.

>>> pitch_set = list(range(24))
>>> sound_points_generator = pang.AtaxicSoundPointsGenerator(
...     pitch_set=pitch_set,
... )
>>> sequence = pang.Sequence(
...     sound_points_generator=sound_points_generator,
...     sequence_duration=4,
... )
>>> sequence.simulate_queue()
>>> server = sequence.servers[0]
>>> q_event_sequence = server.q_event_sequence
>>> quantizer = nauert.Quantizer()
>>> optimizer = nauert.MeasurewiseAttackPointOptimizer()
>>> result = quantizer(q_event_sequence, attack_point_optimizer=optimizer)
>>> abjad.show(result)  

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Special methods

(SoundPointsGenerator).__call__(sequence_duration)

Call self as a function.

(SoundPointsGenerator).__repr__()

Gets interpreter representation.

class pang.soundpointsgenerators.GRWSoundPointsGenerator(arrival_rate=1, service_rate=1, arrival_model='markov', service_model='markov', pitch_set=[0], origin=None, mean=0, standard_deviation=1, seed=123456, order='idp')

Gaussian (sampled) random walk sound points generator.

>>> pitch_set = list(range(20))
>>> sound_points_generator = pang.GRWSoundPointsGenerator(
...     pitch_set=pitch_set,
... )
>>> sequence = pang.Sequence(
...     sound_points_generator=sound_points_generator,
...     sequence_duration=4,
... )
>>> sequence.simulate_queue()
>>> server = sequence.servers[0]
>>> q_event_sequence = server.q_event_sequence
>>> quantizer = nauert.Quantizer()
>>> optimizer = nauert.MeasurewiseAttackPointOptimizer()
>>> result = quantizer(q_event_sequence, attack_point_optimizer=optimizer)
>>> abjad.show(result)  

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Special methods

(SoundPointsGenerator).__call__(sequence_duration)

Call self as a function.

(SoundPointsGenerator).__repr__()

Gets interpreter representation.

class pang.soundpointsgenerators.ManualSoundPointsGenerator(instances=[], durations=[], pitches=None)

Manual sound-point generator.

Initializing a sequence manually.

>>> instances = [0, 1, 2, 3]
>>> durations = [1, 1, 0.5, 0.5]
>>> sound_points_generator = pang.ManualSoundPointsGenerator(
...     instances=instances,
...     durations=durations,
... )
>>> sequence = pang.Sequence(
...     sound_points_generator=sound_points_generator,
... )
>>> print(sequence.instances)
[0, 1, 2, 3]

>>> print(sequence.durations)
[1, 1, 0.5, 0.5]

>>> print(sequence.sequence_duration)
3.5

>>> sequence.simulate_queue()
>>> server = sequence.servers[0]
>>> q_event_sequence = server.q_event_sequence
>>> quantizer = nauert.Quantizer()
>>> optimizer = nauert.MeasurewiseAttackPointOptimizer()
>>> result = quantizer(q_event_sequence, attack_point_optimizer=optimizer)
>>> abjad.show(result)  

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Attributes Summary

__call__

Call self as a function.

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Special methods

overridden __call__(sequence_duration)

Call self as a function.

(SoundPointsGenerator).__repr__()

Gets interpreter representation.

class pang.soundpointsgenerators.RandomWalkSoundPointsGenerator(arrival_rate=1, service_rate=1, arrival_model='markov', service_model='markov', pitch_set=[0], origin=None, seed=123456, order='idp')

Sound points generator, with pitches chosen with a random walk process.

Initializing an ataxic cloud.

>>> pitch_set = list(range(20))
>>> sound_points_generator = pang.RandomWalkSoundPointsGenerator(
...     pitch_set=pitch_set,
... )
>>> sequence = pang.Sequence(
...     sound_points_generator=sound_points_generator,
...     sequence_duration=4,
... )
>>> sequence.simulate_queue()
>>> server = sequence.servers[0]
>>> q_event_sequence = server.q_event_sequence
>>> quantizer = nauert.Quantizer()
>>> optimizer = nauert.MeasurewiseAttackPointOptimizer()
>>> result = quantizer(q_event_sequence, attack_point_optimizer=optimizer)
>>> abjad.show(result)  

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Special methods

(SoundPointsGenerator).__call__(sequence_duration)

Call self as a function.

(SoundPointsGenerator).__repr__()

Gets interpreter representation.

class pang.soundpointsgenerators.SoundPoint(instance, duration, pitch)

SoundPoint / Event.

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Attributes Summary

duration
instance
pitch

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Read/write properties

duration

instance

pitch

class pang.soundpointsgenerators.SoundPointsGenerator

Abstract base sound-point generator.

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Attributes Summary

__call__	Call self as a function.
__repr__	Gets interpreter representation.

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Special methods

overridden __call__(sequence_duration)

Call self as a function.

overridden __repr__()

Gets interpreter representation.