from data_load import getData
import pandas as pd
import matplotlib
matplotlib.use('TkAgg')
import matplotlib.pyplot as plt
df = getData('M')
pdf = df.iloc[:, 0:3]
plt.figure()
pdf.plot()
plt.legend(loc='best')
print('hi')
from conf import Conf
from scenario import SCENARIOS
from model import getModel
from data_load import getData, prepareData, normaliz
from train_data import getTrainData
from resana import ResAnaysis
if __name__ == '__main__':
global C
C = Conf()
res = []
df = getData(C.STRFRQ[C.DATAFREQ])
# df = prepareData(df)
ndf = normaliz(df)
dfY = df[C.Y]
for sn in SCENARIOS:
#encoder.add(Dropout(0.5))
#encoder.add(LSTM(output_dim=C.XOUT_DIM, return_sequences=True, stateful=True))
C.overwrite(sn)
print(" - " + C.SCENARIO)
train_Xs, train_Y = getTrainData(C, ndf, dfY)
val_Xs, val_Y = getTrainData(C, ndf, dfY, 'test')
model = getModel(C)
#cost = model.train_on_batch([train_Xs[i][0:C.BATCH_SIZE] for i in range(len(train_Xs))], train_Y[0:C.BATCH_SIZE])
#print(cost)
re = model.fit(train_Xs,
train_Y,
batch_size=C.BATCH_SIZE,
# Importing dependencies
import numpy as np
import pandas as pd
from data_load import getData
from os import chdir, path
from mne.filter import filter_data
def low_pass(data, sfreq, hfreq):
channels = data.columns[:16].values
for i in channels:
print("Low-pass filtering channel %i / %i: %s" %
(np.where(channels == i)[0] + 1, len(channels), i))
data[i] = filter_data(data[i],
sfreq=sfreq,
l_freq=None,
h_freq=hfreq,
verbose=False)
return data
if __name__ == "__main__":
# Set working directory
chdir(path.dirname(__file__))
data = getData()
data_low_pass = low_pass(data, 64, 8)
print(data.describe())
print(data_low_pass.describe())