def draw_frequency_analysis_lpf(examples):
N = len(examples)
examples_f = []
for i in range(N):
examples_f.append(low_pass_filter(examples[i]))
spectras = sqrt_spectra(examples_f)
log_spectras = log_spectra(examples_f)
figure, axs = plt.subplots(N, 3, figsize=(20, 45))
for i in range(N):
ax = axs[i]
ax[0].set_title('Spectra')
ax[1].set_title('Spectra')
ax[2].set_title('Standardized trajectory')
ax[0].set_ylabel('Sqrt power')
ax[1].set_ylabel('Log power')
ax[0].set_xlabel('Frequency(Hz)')
ax[1].set_xlabel('Frequency(Hz)')
ax[2].set_xlabel('Frame')
ax[0].plot(*spectras[i])
ax[1].plot(*log_spectras[i])
ax[2].plot(examples_f[i])
plt.show()
return
def play_rnn_OLA(model, path, n, win, format='rov', args=None, translation=True, lpf=True):
net = model(*args)
net.load_state_dict(torch.load(path))
x = net.generate(1, n).detach().squeeze().numpy()
x = iflatten_complex_data_with(x, 31)
x = pad_data_zeros(x, get_single_side_frequency().shape[0])
x = ifft_data(x)
x = iwindow(x, win + 0.001, 0.9)
x = istandardize_data(x)
x = overlap_and_add_data(x)
if lpf:
x = low_pass_filter(x)
write_one_file('example', x, format=format)
cmd = CMD + PAR1 + 'example' + PAR2
if translation:
cmd += TRANSLATION
os.system(cmd)
return x
def play_long_video_from(model, path, length, format='rov', args=None, translation=True, lpf=True):
net = model(*args)
net.load_state_dict(torch.load(path))
y = get_real_example(net)
while y.shape[0] < length:
y = overlap_and_add(y, get_real_example(net))
if lpf:
y = low_pass_filter(y)
write_one_file('example', y, format=format)
cmd = CMD + PAR1 + 'example' + PAR2
if translation:
cmd += TRANSLATION
os.system(cmd)
return y
def lpf_compare(examples):
N = len(examples)
examples_lpf = []
for e in examples:
examples_lpf.append(low_pass_filter(e))
figure, axs = plt.subplots(N, 2, figsize=(15, 45))
for i in range(N):
ax = axs[i]
ax[0].set_title('Standardized trajectory')
ax[1].set_title('Filtered trajectory')
ax[0].set_xlabel('Frame')
ax[1].set_xlabel('Frame')
ax[0].plot(examples[i])
ax[1].plot(examples_lpf[i])
plt.show()
return
def play_long_video_istft(model, path, n, win, format='rov', args=None, translation=True, lpf=True):
net = model(*args)
net.load_state_dict(torch.load(path))
data = []
for i in range(n):
data.append(net.example()[0])
print('data ' + str(len(data)))
result = griffin(data, win)
result = istandardize_data([result])[0]
if lpf:
result = low_pass_filter(result)
write_one_file('example', result, format=format)
cmd = CMD + PAR1 + 'example' + PAR2
if translation:
cmd += TRANSLATION
os.system(cmd)
return result
from play import draw_spectra_of
from play import draw_log_spectra_of
from play import draw_trajectory_of
from data_processor import low_pass_filter
import matplotlib.pyplot as plt
from data_reader import write_one_file
# %%
draw_spectra_of('/home/tai/Desktop/Examples/WGAN-Gesture1.rov')
# %%
draw_log_spectra_of('/home/tai/Desktop/Examples/WGAN-Stable1.rov')
# %%
x = draw_trajectory_of('/home/tai/Desktop/Examples/MLP-Gesture4.rov')
# %%
y = low_pass_filter(x)
plt.plot(y)
# %%
write_one_file('/home/tai/Desktop/Examples/MLP-Gesture4-lpf.rov', y)
# %%
from scipy import signal
import matplotlib.pyplot as plt
# %%
a = signal.get_window('hamming', 300)
# %%
plt.plot(a)
# %%
a.shape
# %%
import numpy as np