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lynting series.py
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@scfleming
scfleming committed Jan 16, 2025
1 parent 0282c6f commit 0e1397f
Showing 1 changed file with 28 additions and 23 deletions.
51 changes: 28 additions & 23 deletions astronify/series/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -255,7 +255,8 @@ def sonify(self):

data["asf_pitch"] = self.pitch_mapper(data[self.val_col])
data["asf_onsets"] = [
x for x in (data[self.time_col] - data[self.time_col][0])
x
for x in (data[self.time_col] - data[self.time_col][0])
/ exptime
* self.note_spacing
]
Expand Down Expand Up @@ -288,7 +289,7 @@ def play(self):
(duration - 0.005, 0),
],
mul=[self.gain for i in range(len(pitches))],
).play(delay=list(delays), dur=duration)
).play(delay=list(delays), dur=duration)

self.streams = pyo.Sine(list(pitches), 0, env).out(
delay=list(delays), dur=duration
Expand Down Expand Up @@ -336,7 +337,7 @@ def write(self, filepath):
).play(delay=list(delays), dur=duration)
sine = pyo.Sine(list(pitches), 0, env).out(
delay=list(delays), dur=duration
) # noqa: F841
) # noqa: F841
self.server.start()

# Clean up
Expand Down Expand Up @@ -386,46 +387,48 @@ def plot_preview(self, xdata_bin_ranges):
plt.plot(
self._soniseries.data[self._soniseries.time_col],
self._soniseries.data[self._soniseries.val_col],
color="k"
color="k",
)

plt.axvspan(
xdata_bin_ranges[0][0],
xdata_bin_ranges[0][1],
color="royalblue",
alpha=0.5,
lw=0
lw=0,
)

plt.axvspan(
xdata_bin_ranges[1][0],
xdata_bin_ranges[1][1],
color="green",
alpha=0.5,
lw=0
lw=0,
)

plt.axvspan(
xdata_bin_ranges[2][0],
xdata_bin_ranges[2][1],
color="yellow",
alpha=0.5,
lw=0
lw=0,
)

plt.axvspan(
xdata_bin_ranges[3][0],
xdata_bin_ranges[3][1],
color="orange",
alpha=0.5,
lw=0)
lw=0,
)

plt.axvspan(
xdata_bin_ranges[4][0],
xdata_bin_ranges[4][1],
color="red",
alpha=0.5,
lw=0)
lw=0,
)

plt.show()

Expand All @@ -447,7 +450,9 @@ def sonify_preview(self, plotting=True, verbose=False):
# Split the data into `n_pitch_values` equal-sized pieces.
bin_size = int(np.round(len(xdata) // self.n_pitch_values, 1))
# Split the y-values into pieces.
ydata_bins = [ydata_norm[i : i+bin_size] for i in range(0, len(ydata_norm), bin_size)]
ydata_bins = [
ydata_norm[i : i + bin_size] for i in range(0, len(ydata_norm), bin_size)
]
# Split the x-values into pieces.
xdata_bins = [xdata[i : i + bin_size] for i in range(0, len(xdata), bin_size)]

Expand Down Expand Up @@ -478,7 +483,8 @@ def sonify_preview(self, plotting=True, verbose=False):
# area.
self.amplitudes = np.asarray(area_vals) / total_area

if std_dev_norm == 0.0: std_dev_norm = 1.0
if std_dev_norm == 0.0:
std_dev_norm = 1.0

# Set the tremolo values based on the standard deviation of the piece normalized by the
# `std_dev_norm` factor.
Expand Down Expand Up @@ -511,7 +517,7 @@ def sonify_preview(self, plotting=True, verbose=False):
print("Tremolo Vals (x10) = ", self.tremolo_vals)

def play_preview(self):
""" Play the sound of a "preview-style" sonification.
"""Play the sound of a "preview-style" sonification.
The assigned pitch for each section of the spectra will begin
to play, with the calculated amplitude and frequency, one
Expand Down Expand Up @@ -540,7 +546,6 @@ def play_preview(self):
d = pyo.Phasor(self.pitch_values[3], mul=np.pi * 2)
e = pyo.Phasor(self.pitch_values[4], mul=np.pi * 2)


# TODO: Make everything below iterable to it's cleaner and takes up less lines
lfo1 = (
pyo.Sine(float(self.tremolo_vals[0]), 0, float(self.amplitudes[0]), 0)
Expand Down Expand Up @@ -570,34 +575,34 @@ def play_preview(self):

self.stream1 = pyo.Sine(
freq=[self.pitch_values[0], self.pitch_values[0]], mul=lfo1
).out(delay=self.delays[0], dur=2.0)
).out(delay=self.delays[0], dur=2.0)
self.stream2 = pyo.Sine(
freq=[self.pitch_values[1], self.pitch_values[1]], mul=lfo2
).out(delay=self.delays[1], dur=2.0)
).out(delay=self.delays[1], dur=2.0)
self.stream3 = pyo.Sine(
freq=[self.pitch_values[2], self.pitch_values[2]], mul=lfo3
).out(delay=self.delays[2], dur=2.0)
).out(delay=self.delays[2], dur=2.0)
self.stream4 = pyo.Sine(
freq=[self.pitch_values[3], self.pitch_values[3]], mul=lfo4
).out(delay=self.delays[3], dur=2.0)
).out(delay=self.delays[3], dur=2.0)
self.stream5 = pyo.Sine(
freq=[self.pitch_values[4], self.pitch_values[4]], mul=lfo5
).out(delay=self.delays[4], dur=2.0)
).out(delay=self.delays[4], dur=2.0)

# All together, if in ensemble mode.
if self._soniseries.preview_type == "ensemble":
self.stream6 = pyo.Sine(
freq=[self.pitch_values[0], self.pitch_values[0]], mul=lfo1
).out(delay=10, dur=4)
).out(delay=10, dur=4)
self.stream7 = pyo.Sine(
freq=[self.pitch_values[1], self.pitch_values[1]], mul=lfo2
).out(delay=10, dur=4)
).out(delay=10, dur=4)
self.stream8 = pyo.Sine(
freq=[self.pitch_values[2], self.pitch_values[2]], mul=lfo3
).out(delay=10, dur=4)
).out(delay=10, dur=4)
self.stream9 = pyo.Sine(
freq=[self.pitch_values[3], self.pitch_values[3]], mul=lfo4
).out(delay=10, dur=4)
).out(delay=10, dur=4)
self.stream10 = pyo.Sine(
freq=[self.pitch_values[4], self.pitch_values[4]], mul=lfo5
).out(delay=10, dur=4)
).out(delay=10, dur=4)

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