Python fft_all_data Examples

Example #1

File: Simple_GAN.py Project: TYTTYTTYT/Training_System

from data_processor import iflatten_complex_data
from data_processor import time_stamp
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)
fft_all_data()
data = trim_data(standardize_all_data())
train_set = flatten_real_data(data)
print('Training set is ready!')


class Complex_Fully_Connected_Discriminator(nn.Module):
    def __init__(self, dimension):
        super(Complex_Fully_Connected_Discriminator, self).__init__()
        self.n_in = dimension * config.TRIM_LENGTH

        # 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, 1)
        self.drop_layer = nn.Dropout(0.87)

Example #2

from data_processor import time_stamp
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 = fft_all_data()
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)
        self.linear4 = nn.Linear(self.n_in, 1)

        self.criterion = nn.BCELoss()

Example #3

File: play_ground.py Project: TYTTYTTYT/Training_System

import config
from data_reader import get_trajectory
from data_processor import trim_data
from scipy.fft import fft, fftfreq, ifft
from data_analyzer import spectra_diff
from data_processor import fft_all_data
from data_analyzer import average_spectra_diff
from config import set_trim_length
from data_processor import standardize_all_data

# %%
set_trim_length(300)
data = get_trajectory('Adam')
data = trim_data(data)
standardize_data()
fft_all_data()

# %%
dataf = fft_data(data)
X = dataf[0]
Y = dataf[5]
# %%
X
# %%
Y
# %%
spectra_diff(X, Y)
# %%
R = [np.zeros((300, 6))]
Rf = fft_data(R)
# %%