1 files changed, 77 insertions, 0 deletions
diff --git a/python/demos/demo_spectrogram.py b/python/demos/demo_spectrogram.py
new file mode 100755
index 0000000..51be917
--- /dev/null
+++ b/python/demos/demo_spectrogram.py
@@ -0,0 +1,77 @@
+#! /usr/bin/env python
+
+import sys, os.path
+from aubio import pvoc, source, float_type
+from numpy import zeros, log10, vstack
+import matplotlib.pyplot as plt
+
+def get_spectrogram(filename, samplerate = 0):
+    win_s = 512                                        # fft window size
+    hop_s = win_s // 2                                 # hop size
+    fft_s = win_s // 2 + 1                             # spectrum bins
+
+    a = source(filename, samplerate, hop_s)            # source file
+    if samplerate == 0: samplerate = a.samplerate
+    pv = pvoc(win_s, hop_s)                            # phase vocoder
+    specgram = zeros([0, fft_s], dtype=float_type)     # numpy array to store spectrogram
+
+    # analysis
+    while True:
+        samples, read = a()                              # read file
+        specgram = vstack((specgram,pv(samples).norm))   # store new norm vector
+        if read < a.hop_size: break
+
+    # plotting
+    fig = plt.imshow(log10(specgram.T + .001), origin = 'bottom', aspect = 'auto', cmap=plt.cm.gray_r)
+    ax = fig.axes
+    ax.axis([0, len(specgram), 0, len(specgram[0])])
+    # show axes in Hz and seconds
+    time_step = hop_s / float(samplerate)
+    total_time = len(specgram) * time_step
+    outstr = "total time: %0.2fs" % total_time
+    print(outstr + ", samplerate: %.2fkHz" % (samplerate / 1000.))
+    n_xticks = 10
+    n_yticks = 10
+
+    def get_rounded_ticks( top_pos, step, n_ticks ):
+        top_label = top_pos * step
+        # get the first label
+        ticks_first_label = top_pos * step / n_ticks
+        # round to the closest .1
+        ticks_first_label = round ( ticks_first_label * 10. ) / 10.
+        # compute all labels from the first rounded one
+        ticks_labels = [ ticks_first_label * n for n in range(n_ticks) ] + [ top_label ]
+        # get the corresponding positions
+        ticks_positions = [ ticks_labels[n] / step for n in range(n_ticks) ] + [ top_pos ]
+        # convert to string
+        ticks_labels = [  "%.1f" % x for x in ticks_labels ]
+        # return position, label tuple to use with x/yticks
+        return ticks_positions, ticks_labels
+  
+    # apply to the axis
+    x_ticks, x_labels = get_rounded_ticks ( len(specgram), time_step, n_xticks )
+    y_ticks, y_labels = get_rounded_ticks ( len(specgram[0]), (samplerate / 1000. / 2.) / len(specgram[0]), n_yticks )
+    ax.set_xticks( x_ticks )
+    ax.set_yticks ( y_ticks )
+    ax.set_xticklabels( x_labels )
+    ax.set_yticklabels ( y_labels )
+    ax.set_ylabel('Frequency (kHz)')
+    ax.set_xlabel('Time (s)')
+    ax.set_title(os.path.basename(filename))
+    for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
+            ax.get_xticklabels() + ax.get_yticklabels()):
+        item.set_fontsize('x-small')
+    return fig
+
+if __name__ == '__main__':
+    if len(sys.argv) < 2:
+        print("Usage: %s <filename>" % sys.argv[0])
+    else:
+        for soundfile in sys.argv[1:]:
+            fig = get_spectrogram(soundfile)
+            # display graph
+            plt.show()
+            #outimage = os.path.basename(soundfile) + '.png'
+            #print ("writing: " + outimage)
+            #plt.savefig(outimage)
+            plt.close()