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Fix sawtooth and triangle wave generation

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Both were using incorrect frequency scaling (magic numbers instead of
deriving cycle length from sample rate / hz). Now they match the sine
wave approach: compute one cycle at the correct frequency, then resize.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Kenneth Reitz
2026-03-22 20:31:36 -04:00
parent 6f7f9008b0
commit fd82dccbfd
1 changed files with 15 additions and 19 deletions
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pytheory/play.py
+15 -19
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@@ -20,32 +20,28 @@ def sine_wave(hz, peak=SAMPLE_PEAK, n_samples=SAMPLE_RATE):
return numpy.resize(onecycle, (n_samples,)).astype(numpy.int16)
def sawtooth_wave(hz, peak=SAMPLE_PEAK, rising_ramp_width=1, n_samples=SAMPLE_RATE):
"""Compute N samples of a sine wave with given frequency and peak amplitude.
def sawtooth_wave(hz, peak=SAMPLE_PEAK, n_samples=SAMPLE_RATE):
"""Compute N samples of a sawtooth wave with given frequency and peak amplitude.
Defaults to one second.
rising_ramp_width is the percentage of the ramp spend rising:
.5 is a triangle wave with equal rising and falling times.
"""
t = numpy.linspace(0, 1, int(500 * 440 / hz), endpoint=False)
wave = scipy.signal.sawtooth(2 * numpy.pi * 5 * t, width=rising_ramp_width)
wave = numpy.resize(wave, (n_samples,))
# Sawtooth waves sound very quiet, so multiply peak by 4.
return peak * 6 * wave.astype(numpy.int16)
length = SAMPLE_RATE / float(hz)
omega = numpy.pi * 2 / length
xvalues = numpy.arange(int(length)) * omega
onecycle = scipy.signal.sawtooth(xvalues, width=1)
onecycle = (peak * onecycle).astype(numpy.int16)
return numpy.resize(onecycle, (n_samples,))
def triangle_wave(hz, peak=SAMPLE_PEAK, rising_ramp_width=0.5, n_samples=SAMPLE_RATE):
def triangle_wave(hz, peak=SAMPLE_PEAK, n_samples=SAMPLE_RATE):
"""Compute N samples of a triangle wave with given frequency and peak amplitude.
Defaults to one second.
rising_ramp_width is the percentage of the ramp spend rising:
.5 is a triangle wave with equal rising and falling times.
"""
hz_value = float(hz)
num_samples = int(500 * 440 / hz_value)
t = numpy.linspace(0, 1, num_samples, endpoint=False)
wave = scipy.signal.sawtooth(2 * numpy.pi * 5 * t, width=rising_ramp_width)
wave = numpy.resize(wave, (n_samples,))
# Use same amplitude as sawtooth_wave for testing
return peak * 6 * wave.astype(numpy.int16)
length = SAMPLE_RATE / float(hz)
omega = numpy.pi * 2 / length
xvalues = numpy.arange(int(length)) * omega
onecycle = scipy.signal.sawtooth(xvalues, width=0.5)
onecycle = (peak * onecycle).astype(numpy.int16)
return numpy.resize(onecycle, (n_samples,))
def _play_for(sample_wave, ms):