def sqrt_spectra(examples):
n = examples[0].shape[0]
freq = get_single_side_frequency(n)
spectras = []
fe = fft_data(examples)
for e in fe:
spectras.append((freq, np.absolute(e)))
return spectras
def log_spectra(examples):
n = examples[0].shape[0]
freq = get_single_side_frequency(n)
spectras = []
fe = fft_data(examples)
for e in fe:
m = np.amax(np.absolute(e))
spectras.append((freq, 20 * np.log10(np.absolute(e) / m)))
return spectras
def draw_spectra_of(path):
x = _read_one_file(path)
x, _, _ = standardize_data(x, data_mean(), data_std())
x = fft_data([x])[0]
x = np.absolute(x)
plt.plot(x)
plt.show()
return x
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
from dynamic_reporter import stop_dynamic_report
from dynamic_reporter import report
from data_reader import write_one_file
from multiprocessing import set_start_method
import random
import os
# time.sleep(13500)
# Prepare the training set for this model
print('Preparing the training set...')
if config.TRIM_LENGTH is None:
set_trim_length(300)
train_set = trim_data(standardize_all_data())
train_set, WIN = window(train_set, 'hann')
print(WIN.shape)
train_set = fft_data(train_set)
train_set, dim = lpf_dimension_reduction(train_set, frequency=10)
train_set = flatten_complex_data(train_set)
print(dim)
print('Training set is ready!')
class Complex_Fully_Connected_Linear_Discriminator_LPF(nn.Module):
def __init__(self, dimension):
super(Complex_Fully_Connected_Linear_Discriminator_LPF,
self).__init__()
self.n_in = dimension * 2 * 6 # real part and imaginary part are saperated
# hidden linear layers
self.linear1 = nn.Linear(self.n_in, self.n_in * 12)
self.linear2 = nn.Linear(self.n_in * 12, self.n_in * 12)
def _draw(i):
global AX1
global AX2
global AX3
global AX4
global FIGURE
global QUEUE
global DATA
if not QUEUE.empty():
DATA = QUEUE.get()
if DATA == 'close':
plt.close(FIGURE)
return
example_length = DATA['example'].shape[0]
freq = get_single_side_frequency(example_length)
# DATA['loss_title'] = data['loss_title']
# DATA['losses'].append(data['loss']),
# DATA['loss_labels'] = data['loss_labels']
# DATA['score_title'] = data['score_title']
# DATA['scores'].append(data['score'])
# DATA['score_labels'] = data['score_labels']
# DATA['interval'] = data['interval']
n = len(DATA['losses'])
interval = DATA['interval']
x = np.arange(interval, interval * n + 1, interval)
loss_Y = np.array(DATA['losses']).T
score_Y = np.array(DATA['scores']).T
AX1.clear()
AX2.clear()
AX3.clear()
AX4.clear()
AX1.set_title(DATA['loss_title'])
AX1.set_xlabel('Training Steps')
AX1.set_ylabel('Model Loss')
for i in range(loss_Y.shape[0]):
AX1.plot(x, loss_Y[i].flatten(), label=DATA['loss_labels'][i])
AX1.legend()
AX2.set_title(DATA['score_title'])
AX2.set_xlabel('Training Steps')
AX2.set_ylabel('Score')
for i in range(score_Y.shape[0]):
AX2.plot(x, score_Y[i].flatten(), label=DATA['score_labels'][i])
AX2.legend()
AX3.set_title('Spectra')
AX3.set_xlabel('Frequency')
AX3.set_ylabel('Power (dB)')
example = DATA['example']
spectra = np.absolute(fft_data([DATA['example']])[0]).real
m = np.amax(spectra)
spectra = 20 * np.log10(spectra / m)
AX3.plot(freq, spectra)
AX4.set_title('Example')
AX4.set_xlabel('Time')
AX4.set_ylabel('Axis')
AX4.plot(example.real)
return
import random
import os
# time.sleep(13500)
# Prepare the training set for this model
print('Preparing the training set...')
if config.TRIM_LENGTH is None:
set_trim_length(300)
data = trim_data(standardize_all_data(), 150)
data_set = []
for i in range(len(data) - 2):
d = np.concatenate((data[i], data[i + 1]), 0)
data_set.append(d)
data_set, WIN = window(data_set, 'hann')
data_set = fft_data(data_set)
data_set, dim = lpf_dimension_reduction(data_set, frequency=10)
data_set = flatten_complex_data(data_set)
train_set = []
for i in range(0, len(data_set) - 2, 3):
d = [data_set[i], data_set[i + 1], data_set[i + 2]]
train_set.append(d)
x = torch.tensor(train_set)
print(x[0:10, 2, :].shape)
print(x.shape[1])
print('Training set is ready!')
from dynamic_reporter import stop_dynamic_report
from dynamic_reporter import report
from data_reader import write_one_file
from multiprocessing import set_start_method
import random
import os
from data_processor import window
from data_processor import iwindow
# Prepare the training set for this model
print('Preparing the training set...')
if config.TRIM_LENGTH is None:
set_trim_length(300)
origin = trim_data(standardize_all_data())
windowed, win = window(origin, 'hamming')
data = fft_data(windowed)
train_set = flatten_complex_data(data)
print('Training set is ready!')
class Complex_Fully_Connected_Linear_Discriminator(nn.Module):
def __init__(self, dimension):
super(Complex_Fully_Connected_Linear_Discriminator, self).__init__()
self.n_in = dimension * (
config.TRIM_LENGTH // 2 +
1) * 2 # real part and imaginary part are saperated
# hidden linear layers
self.linear1 = nn.Linear(self.n_in, self.n_in)
self.linear2 = nn.Linear(self.n_in, self.n_in)
self.linear3 = nn.Linear(self.n_in, self.n_in)
from data_processor import trim_data
from dynamic_reporter import init_dynamic_report
from dynamic_reporter import stop_dynamic_report
from dynamic_reporter import report
from data_reader import write_one_file
from multiprocessing import set_start_method
import random
import os
# Prepare the training set for this model
print('Preparing the training set...')
if config.TRIM_LENGTH is None:
set_trim_length(300)
origin = trim_data(standardize_all_data())
data_p = pca_data(origin, 4)
data_pf = fft_data(data_p)
train_set = flatten_complex_data(data_pf)
print(train_set.shape)
print('Training set is ready!')
class Complex_Fully_Connected_Linear_Discriminator(nn.Module):
def __init__(self, dimension):
super(Complex_Fully_Connected_Linear_Discriminator, self).__init__()
self.n_in = dimension * (
config.TRIM_LENGTH // 2 +
1) * 2 # real part and imaginary part are saperated
# hidden linear layers
self.linear1 = nn.Linear(self.n_in, self.n_in)
self.linear2 = nn.Linear(self.n_in, self.n_in)
lpf_compare(samples) # %% WGAN = load_model(Complex_Fully_Connected_WGAN, Choosed_WGAN, (6, )) # %% 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)
# %%
from data_processor import fft_data
from data_processor import ifft_data
import numpy as np
import config
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))
