1 files changed, 87 insertions, 0 deletions
diff --git a/src/avp/toolkit/visualizer.py b/src/avp/toolkit/visualizer.py
new file mode 100644
index 0000000..c55a3f3
--- /dev/null
+++ b/src/avp/toolkit/visualizer.py
@@ -0,0 +1,87 @@
+"""Functions used to transform and manipulate audio for use by visualizers"""
+
+from copy import copy
+import numpy
+
+
+def createSpectrumArray(
+    component,
+    completeAudioArray,
+    sampleSize,
+    smoothConstantDown,
+    smoothConstantUp,
+    scale,
+    progressBarUpdate,
+    progressBarSetText,
+):
+    lastSpectrum = None
+    spectrumArray = {}
+    for i in range(0, len(completeAudioArray), sampleSize):
+        if component.canceled:
+            break
+        lastSpectrum = transformData(
+            i,
+            completeAudioArray,
+            sampleSize,
+            smoothConstantDown,
+            smoothConstantUp,
+            lastSpectrum,
+            scale,
+        )
+        spectrumArray[i] = copy(lastSpectrum)
+
+        progress = int(100 * (i / len(completeAudioArray)))
+        if progress >= 100:
+            progress = 100
+        progressText = f"Analyzing audio: {str(progress)}%"
+        progressBarSetText.emit(progressText)
+        progressBarUpdate.emit(int(progress))
+    return spectrumArray
+
+
+def transformData(
+    i,
+    completeAudioArray,
+    sampleSize,
+    smoothConstantDown,
+    smoothConstantUp,
+    lastSpectrum,
+    scale,
+):
+    if len(completeAudioArray) < (i + sampleSize):
+        sampleSize = len(completeAudioArray) - i
+
+    window = numpy.hanning(sampleSize)
+    data = completeAudioArray[i : i + sampleSize][::1] * window
+    paddedSampleSize = 2048
+    paddedData = numpy.pad(data, (0, paddedSampleSize - sampleSize), "constant")
+    spectrum = numpy.fft.fft(paddedData)
+    sample_rate = 44100
+    frequencies = numpy.fft.fftfreq(len(spectrum), 1.0 / sample_rate)
+
+    y = abs(spectrum[0 : int(paddedSampleSize / 2) - 1])
+
+    # filter the noise away
+    # y[y<80] = 0
+
+    with numpy.errstate(divide="ignore"):
+        y = scale * numpy.log10(y)
+
+    y[numpy.isinf(y)] = 0
+
+    if lastSpectrum is not None:
+        lastSpectrum[y < lastSpectrum] = y[
+            y < lastSpectrum
+        ] * smoothConstantDown + lastSpectrum[y < lastSpectrum] * (
+            1 - smoothConstantDown
+        )
+
+        lastSpectrum[y >= lastSpectrum] = y[
+            y >= lastSpectrum
+        ] * smoothConstantUp + lastSpectrum[y >= lastSpectrum] * (1 - smoothConstantUp)
+    else:
+        lastSpectrum = y
+
+    x = frequencies[0 : int(paddedSampleSize / 2) - 1]
+
+    return lastSpectrum