def test_show_vispy(): """Some basic tests of show_vispy""" if has_matplotlib(): n = 200 t = np.arange(n) noise = np.random.RandomState(0).randn(n) # Need, image, markers, line, axes, figure plt.figure() ax = plt.subplot(211) ax.imshow(read_png(load_data_file('pyplot/logo.png'))) ax = plt.subplot(212) ax.plot(t, noise, 'ko-') plt.draw() canvases = plt.show() canvases[0].close() else: assert_raises(ImportError, plt.show)
from vispy.io import read_png, load_data_file n = 200 freq = 10 fs = 100. t = np.arange(n) / fs tone = np.sin(2*np.pi*freq*t) noise = np.random.RandomState(0).randn(n) signal = tone + noise magnitude = np.abs(np.fft.fft(signal)) freqs = np.fft.fftfreq(n, 1. / fs) flim = n // 2 # Signal fig = plt.figure() ax = plt.subplot(311) ax.imshow(read_png(load_data_file('pyplot/logo.png'))) ax = plt.subplot(312) ax.plot(t, signal, 'k-') # Frequency content ax = plt.subplot(313) idx = np.argmax(magnitude[:flim]) ax.text(freqs[idx], magnitude[idx], 'Max: %s Hz' % freqs[idx], verticalalignment='top') ax.plot(freqs[:flim], magnitude[:flim], 'r-o') plt.draw()
import vispy.mpl_plot as plt from vispy.io import read_png, load_data_file n = 200 freq = 10 fs = 100. t = np.arange(n) / fs tone = np.sin(2 * np.pi * freq * t) noise = np.random.RandomState(0).randn(n) signal = tone + noise magnitude = np.abs(np.fft.fft(signal)) freqs = np.fft.fftfreq(n, 1. / fs) flim = n // 2 # Signal fig = plt.figure() ax = plt.subplot(311) ax.imshow(read_png(load_data_file('pyplot/logo.png'))) ax = plt.subplot(312) ax.plot(t, signal, 'k-') # Frequency content ax = plt.subplot(313) idx = np.argmax(magnitude[:flim]) ax.text(freqs[idx], magnitude[idx], 'Max: %s Hz' % freqs[idx], verticalalignment='top') ax.plot(freqs[:flim], magnitude[:flim], 'k-o')
print("... interpolated in %0.2f s using %0.2f MB" %(t2 - t1, mem[-1])) #============================================================================== # Write resampled data to WAV-File #============================================================================== wavfile.write(file_o, rate_o, data_o.astype('int32')) ############################################################################### # Plot the data ############################################################################### # # Time Domain: Input / output samples and amplitude / time difference #============================================================================== #plt.close('all') # large plots = lots of memory ... if PLT_ENB: fig1 = plt.figure(1) if PLT_ERR: ax1 = fig1.add_subplot(211) else: ax1 = fig1.add_subplot(111) ax1.set_xlabel(t_label) ax1.set_ylabel(r'Sample Amplitude $\rightarrow$') ax1.plot(time_i[PLT_BEG:PLT_END], data_i[:,0][PLT_BEG:PLT_END], 'ro', linestyle = ':', label = 'Original') if PLT_JITTER: # Plot resampled data against ORIGINAL time vector time_i to show # the time displacement (jitter) ax1.step(time_i[r*PLT_BEG:r*PLT_END], data_o[:,0][r*PLT_BEG:r*PLT_END], 'o', where='post', linestyle = '--', label = 'w/ Jitter', color = (0.,0.,1,0.5), markerfacecolor=(0.,0.,1,0.5))