def example(self):
x = self.generate(1).detach().cpu()
x = np.array(x)
x = iflatten_complex_data(x)
x = ifft_data(x)[0]
return x
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 example(self):
# Generate an example with numpy array data type
x = self.generate(1).detach().cpu()
x = np.array(x)
x = iflatten_complex_data(x)
x = ifft_data(x)[0]
return x
def example(self):
# Generate an example with numpy array data type
x = self.generate(1).detach().cpu()
x = np.array(x)
x = istandardize_data_with(x, self.mean, self.std)
x = iflatten_complex_data(x)
x = ifft_data(x)[0]
return x
def example(self):
# Generate an example with numpy array data type
global win
x = self.generate(1).detach().cpu()
x = np.array(x)
x = iflatten_complex_data(x)
x = ifft_data(x)
# x = iwindow(x, win, 0.6)[0]
return x
def example(self):
# Generate an example with numpy array data type
x = self.generate(1).detach().cpu()
x = np.array(x)
x = iflatten_complex_data_with(x, 16)
x = pad_data_zeros(x, get_single_side_frequency().shape[0])
x = ifft_data(x)
# x = iwindow(x, WIN, 0.8)
return x[0]
def example(self, r):
# Generate an example with numpy array data type
global dim
x = self.generate(1, r).detach().cpu()
x = x.squeeze().unsqueeze(0)
x = np.array(x)
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, 0.8)
return x[0]
def example(self):
r = get_random_example(1, config.TRIM_LENGTH)
e = fft_data(r)
e = flatten_complex_data(e)
if self.in_cpu:
e = torch.tensor(e)
else:
e = torch.tensor(e, device=self.device, dtype=torch.float32)
e = self.forward(e).detach().cpu()
e = np.array(e)
e = iflatten_complex_data(e)
e = ifft_data(e)[0]
r = np.array(r[0])
return e - r
# %% samples = sample_net(WGAN, 6) # %% draw_frequency_analysis(samples) # %% draw_frequency_analysis_lpf(samples) # %% lpf_compare(samples) # %% origin = get_random_example(6, 300) originf = fft_data(origin) yf, _ = lpf_dimension_reduction(originf, 5) yf = pad_data_zeros(yf, 151) y = ifft_data(yf) # %% draw_frequency_analysis(origin) # %% draw_frequency_analysis(y) # %% compare(origin, y) # %% originw, win = window(origin, 'hamming') originfw = fft_data(originw) yfw, _ = lpf_dimension_reduction(originfw, 5) yfw = pad_data_zeros(yfw, 151) yw = ifft_data(yfw) yw = iwindow(yw, win, 0.7) # %% draw_frequency_analysis(origin)
from data_reader import get_trajectory
from data_processor import trim_data
from scipy.fft import fft, fftfreq, ifft
# %%
data = get_trajectory('Adam')
data = trim_data(data, 300)
# %%
data[0]
# %%
data_f = fft_data(data)
# %%
data_f[0].shape
# %%
idata = ifft_data(data_f)
# %%
idata[0]
# %%
data[0] - idata[0]
# %%
x = data[0].iloc[:, 0]
# %%
x
# %%
fx = fft(np.array(x))
# %%
x
# %%
pfx = fx[:151]
pfx.shape
