attackpointoptimizers

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Abstract Classes

AttackPointOptimizer

Abstract attack-point optimizer.

abstract class abjadext.nauert.attackpointoptimizers.AttackPointOptimizer

Abstract attack-point optimizer.

Attack-point optimizers may alter the number, order, and individual durations of leaves in a logical tie, but may not alter the overall duration of that logical tie.

They effectively “clean up” notation, post-quantization.

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Attributes Summary

__call__

Calls attack-point optimizer.

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Special methods

overridden abstract __call__(argument)

Calls attack-point optimizer.

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Classes

MeasurewiseAttackPointOptimizer	Measurewise attack-point optimizer.
NaiveAttackPointOptimizer	Naive attack-point optimizer.
NullAttackPointOptimizer	Null attack-point optimizer.

class abjadext.nauert.attackpointoptimizers.MeasurewiseAttackPointOptimizer

Measurewise attack-point optimizer.

Attempts to optimize attack points in an expression with regard to the effective time signature of that expression.

Only acts on measures.

>>> staff = abjad.Staff("c'8 d'8 e'8 f'8 g'8 a'8 b'8 c''8")
>>> abjad.show(staff)  

>>> source_tempo = abjad.MetronomeMark(abjad.Duration(1, 4), 60)
>>> q_events = nauert.QEventSequence.from_tempo_scaled_leaves(
...     staff[:],
...     tempo=source_tempo,
... )
>>> target_tempo = abjad.MetronomeMark(abjad.Duration(1, 4), 54)
>>> q_schema = nauert.MeasurewiseQSchema(
...     tempo=target_tempo,
... )

Without the measure-wise attack-point optimizer:

>>> result = nauert.quantize(q_events, q_schema=q_schema)
>>> abjad.show(result)  

With the measure-wise attack-point optimizer:

>>> optimizer = nauert.MeasurewiseAttackPointOptimizer()
>>> result = nauert.quantize(
...     q_events,
...     attack_point_optimizer=optimizer,
...     q_schema=q_schema,
... )
>>> abjad.show(result)  

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Attributes Summary

__call__

Calls measurewise attack-point optimizer.

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Special methods

overridden __call__(argument, time_signature=None)

Calls measurewise attack-point optimizer.

Return type:

None

class abjadext.nauert.attackpointoptimizers.NaiveAttackPointOptimizer

Naive attack-point optimizer. (The default attack-point optimizer)

Optimizes attack points by fusing tie leaves within logical ties with leaf durations decreasing monotonically.

Logical ties will be partitioned into sub-logical-ties if leaves are found with metronome marks attached.

>>> staff = abjad.Staff("c'8 d'8 e'8 f'8 g'8 a'8 b'8 c''8")
>>> abjad.show(staff)  

>>> source_tempo = abjad.MetronomeMark(abjad.Duration(1, 4), 60)
>>> q_events = nauert.QEventSequence.from_tempo_scaled_leaves(
...     staff[:],
...     tempo=source_tempo,
... )
>>> target_tempo = abjad.MetronomeMark(abjad.Duration(1, 4), 54)
>>> q_schema = nauert.MeasurewiseQSchema(
...     tempo=target_tempo,
... )

>>> optimizer = nauert.NaiveAttackPointOptimizer()
>>> result = nauert.quantize(
...     q_events,
...     attack_point_optimizer=optimizer,
...     q_schema=q_schema,
... )
>>> abjad.show(result)  

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Attributes Summary

__call__

Calls naive attack-point optimizer.

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Special methods

overridden __call__(argument)

Calls naive attack-point optimizer.

class abjadext.nauert.attackpointoptimizers.NullAttackPointOptimizer

Null attack-point optimizer.

Performs no attack point optimization.

>>> staff = abjad.Staff("c'8 d'8 e'8 f'8 g'8 a'8 b'8 c''8")
>>> abjad.show(staff)  

>>> source_tempo = abjad.MetronomeMark(abjad.Duration(1, 4), 60)
>>> q_events = nauert.QEventSequence.from_tempo_scaled_leaves(
...     staff[:],
...     tempo=source_tempo,
... )
>>> target_tempo = abjad.MetronomeMark(abjad.Duration(1, 4), 54)
>>> q_schema = nauert.MeasurewiseQSchema(
...     tempo=target_tempo,
... )

>>> optimizer = nauert.NullAttackPointOptimizer()
>>> result = nauert.quantize(
...     q_events,
...     attack_point_optimizer=optimizer,
...     q_schema=q_schema,
... )
>>> abjad.show(result)  

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Attributes Summary

__call__

Calls null attack-point optimizer.

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Special methods

overridden __call__(argument)

Calls null attack-point optimizer.