fft_thresh = 2.0
fft_set_thresh = 0.0
#Constants
fs = 250.0 #Frequency in Hz
sample_time = dataset.shape[1]/fs #Total time for sample
presample_time = predataset.shape[1]/fs #Total time for sample
#To get alpha and beta waves
print("Applying filters...")
dataset.flags['WRITEABLE'] = True
predataset.flags['WRITEABLE'] = True
for i in range(0,dataset.shape[0]):
for j in range(0,dataset.shape[2]):
if enable_dc:
predataset[i,:,j] = apply_dc_filter(predataset[i,:,j], fs, dc_lowcut, dc_highcut, dc_order, dc_type, dc_func_type)
dataset[i,:,j] = apply_dc_filter(dataset[i,:,j], fs, dc_lowcut, dc_highcut, dc_order, dc_type, dc_func_type)
if enable_dwt:
predataset[i,:,j] = apply_dwt_filter(predataset[i,:,j], dwt_type, dwt_level, dwt_thresh_func, dwt_thresh_type)
dataset[i,:,j] = apply_dwt_filter(dataset[i,:,j], dwt_type, dwt_level, dwt_thresh_func, dwt_thresh_type)
if enable_fft:
predataset[i,:,j] = apply_stfft_filter(predataset[i,:,j], fs, presample_time, fft_window, fft_step, fft_thresh, fft_set_thresh)
dataset[i,:,j] = apply_stfft_filter(dataset[i,:,j], fs, sample_time, fft_window, fft_step, fft_thresh, fft_set_thresh)
#Normalize
dataset[i,:,j] = dataset[i,:,j]/np.linalg.norm(dataset[i,:,j])
#predataset[i,:,j] = predataset[i,:,j]/np.linalg.norm(predataset[i,:,j])
#np.mean(predataset[i,:,j])
dataset.shape = (dataset.shape[0], num_channels * max_samples)
data.shape = (max_rows, cols)
min_thresh = -60000
max_thresh = -10000
#min_thresh = -38000
#max_thresh = -12000
for i in range(0,data.shape[1]):
if max(data[:,i]) < max_thresh and min(data[:,i]) > min_thresh:
electrodes[i] = True
#DC Filter
fs = 250
enable_dc = True
dc_lowcut = 8.0 #Only get alpha and beta, most related to movement
dc_highcut = 30.0
dc_order = 2
dc_type = "bandpass"
dc_func_type = "butter"
data.flags['WRITEABLE'] = True
for i in range(0,data.shape[1]):
if enable_dc:
data[:,i] = apply_dc_filter(data[:,i], fs, dc_lowcut, dc_highcut, dc_order, dc_type, dc_func_type)
#data = np.delete(data, range(data.shape[0] - 25, data.shape[0]), 0)
#data = np.delete(data, range(0,25), 0)
np.savetxt(path + "/../public/data/test.csv", data, delimiter=",",newline="\n")
with open(path+"/testing/out.log", "w") as writestream:
writestream.write(json.dumps({"electrodes": electrodes}))
#Constants
fs = 250.0 #Frequency in Hz
sample_time = dataset.shape[1] / fs #Total time for sample
presample_time = predataset.shape[1] / fs #Total time for sample
#To get alpha and beta waves
print("Applying filters...")
dataset.flags['WRITEABLE'] = True
predataset.flags['WRITEABLE'] = True
for i in range(0, dataset.shape[0]):
for j in range(0, dataset.shape[2]):
if enable_dc:
predataset[i, :,
j] = apply_dc_filter(predataset[i, :, j], fs, dc_lowcut,
dc_highcut, dc_order, dc_type,
dc_func_type)
dataset[i, :, j] = apply_dc_filter(dataset[i, :, j], fs, dc_lowcut,
dc_highcut, dc_order, dc_type,
dc_func_type)
if enable_dwt:
predataset[i, :, j] = apply_dwt_filter(predataset[i, :,
j], dwt_type,
dwt_level, dwt_thresh_func,
dwt_thresh_type)
dataset[i, :, j] = apply_dwt_filter(dataset[i, :, j], dwt_type,
dwt_level, dwt_thresh_func,
dwt_thresh_type)
if enable_fft:
predataset[i, :,
j] = apply_stfft_filter(predataset[i, :, j], fs,
with open(testingpath + "/" + sys.argv[1], "rb") as readstream:
magic = _read32(readstream)
rows = _read32(readstream)
cols = _read32(readstream)
buf = readstream.read(max_rows * cols * dt_f32.itemsize)
data = np.frombuffer(buf, dtype=dt_f32)
data.shape = (max_rows, cols)
#DC Filter
fs = 250
enable_dc = True
dc_lowcut = 1.0 #Only get alpha and beta, most related to movement
dc_highcut = 13.0
dc_order = 2
dc_type = "bandpass"
dc_func_type = "butter"
data.flags['WRITEABLE'] = True
for i in range(0, data.shape[1]):
if enable_dc:
data[:, i] = apply_dc_filter(data[:, i], fs, dc_lowcut, dc_highcut,
dc_order, dc_type, dc_func_type)
data[:, i] = data[:, i] / np.linalg.norm(data[:, i])
data.shape = (1, max_rows * num_channels)
#Read Model
with open(path + '/predicting/eeg.model', 'rb') as readstream:
ensemble = pickle.load(readstream)
print(ensemble.predict(data)[0])
