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))
