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