init commmit

README.md

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+# Guitar tunner
+This a guitar tunner program based on python.

main.py

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+import pyaudio
+import numpy as np 
+from scipy import signal
+import matplotlib as mpl
+import matplotlib.pyplot as plt
+
+END = False
+
+def d2r(degree): return degree * np.pi / 180.
+
+def on_press(event):
+    global END, in_stream, p
+    # print('Quit')
+    if event.key == 'q':
+        END = True
+        plt.close()
+        in_stream.stop_stream()
+        in_stream.close()
+        p.terminate()
+
+#=============================================
+# Notes_guitar = ['E2','A2','D3','G3','B3','E4']
+Notes_guitar = ['E','A','D','G','B','E']
+freq_guitar = np.array([82.4069, 110.0000, 146.8324,\
+                        195.9977, 246.9417, 329.6276])
+
+freq_ticks = np.array([0, 82.4069, 90, 110.0000, 146.8324, 180,\
+                        195.9977, 246.9417, 270, 329.6276])
+tick_notes = ['0 Hz','E','90','A','D','180','G','B','270','E']
+
+# freq_ticks = np.array([82.4069, 110.0000, 146.8324,\
+#                         195.9977, 246.9417, 329.6276])
+# tick_notes = ['E','A','D','G','B','E']
+#=============================================
+
+FORMAT = pyaudio.paInt16 # 16 bit int
+CHANNELS = 1 # Number of input channels
+SAMPLE_RATE = 44100  # Sampling rate/frequency (Hz)?
+CHUNK = 1024*16*2 # Number of sample frames per buffer?
+SAMPLE_INTERVAL = 1/SAMPLE_RATE 
+
+p = pyaudio.PyAudio()
+in_stream = p.open(format=FORMAT, channels=CHANNELS, rate=SAMPLE_RATE,\
+                input=True, frames_per_buffer=CHUNK)
+
+r_panel = 30
+pointer_len = r_panel - 1
+pointer_color = '#2b2b23'
+pointer_width = 1
+spectrum_base = 15
+divide_factor = 40
+mpl.rcParams['toolbar'] = 'None'
+fig = plt.figure()
+fig.patch.set_facecolor('#958f82')
+fig.canvas.toolbar_visible = False
+ax = plt.subplot(projection='polar')
+ax.set_facecolor("#818077")
+plt.get_current_fig_manager().set_window_title('Guitar Tuner')
+fig.canvas.mpl_connect('key_press_event', on_press)
+ax.set_xticks(d2r(freq_ticks)) 
+ax.set(xticklabels=tick_notes)
+ax.set_ylim(0,30)
+ax.set_yticks([30]) 
+ax.set(yticklabels=[])
+# plt.grid(linestyle='--')
+plt.grid()
+# ax.tick_params(left=False)
+
+scale = np.arange(0, 360, 10)
+scale_end_r = r_panel
+scale_start_r = r_panel - 1
+scale_w_min = 0.7
+scale_w_max = 2.0
+
+# Center
+ax.scatter(0, 0, c=pointer_color, s=32, cmap='hsv', alpha=1)
+
+for s in scale:
+    ax.vlines(d2r(s), scale_end_r, scale_start_r, colors=pointer_color, linewidth= scale_w_min, zorder=3)
+
+for f in freq_guitar:
+    ax.vlines(d2r(f), scale_end_r, scale_start_r, colors=pointer_color, linewidth= scale_w_max, zorder=3)
+
+
+
+# Cut freq. of band pass filter
+lowcut, highcut = 75.0, 1250.0
+freq_range = [75, 350]
+freq = np.fft.rfftfreq(CHUNK, d=1./SAMPLE_RATE)
+mask = (freq < freq_range[0]) + (freq > freq_range[1])
+
+mask_plot = freq < 360
+freq_to_plot = freq[mask_plot]
+line0, = ax.plot(d2r(freq_to_plot), 50*np.random.rand(len(freq_to_plot)), color=pointer_color, linewidth= pointer_width)
+
+plt.ion()
+plt.tight_layout()
+plt.show()
+
+while END==False:
+    buffer = in_stream.read(CHUNK, exception_on_overflow = False)
+    y = np.frombuffer(buffer, dtype = np.int16)
+    Y = np.fft.rfft(y)/CHUNK
+    Y_a = np.abs(Y)
+    sos = signal.butter(10, [lowcut, highcut], 'bp', fs=SAMPLE_RATE, output='sos')
+    filtered = signal.sosfilt(sos, y)
+    FILTERED = np.fft.rfft(filtered)/CHUNK
+    FILTERED_a = np.abs(FILTERED)
+    line0.set_ydata(spectrum_base+FILTERED_a[mask_plot]/divide_factor)
+    S_a = FILTERED_a
+    S_a[mask] = 0
+    main_freq = freq[np.argmax(S_a)] 
+
+    vline = ax.vlines(d2r(main_freq), 0 , pointer_len, colors=pointer_color, linewidth= pointer_width, zorder=3)
+
+    fig.canvas.draw()
+    fig.canvas.flush_events()
+    plt.pause(0.0001)
+    vline.remove()