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data_processing.py
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data_processing.py
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import numpy as np
def normalization(datainput):
max_collec = []
min_collec = []
for i in range(len(datainput)):
l = datainput[i]
data_max = max(l)
data_min = min(l)
max_collec.append(data_max)
min_collec.append(data_min)
data_max = float(max(max_collec))
data_min = float(min(min_collec))
range_min = 0
range_max = 1
l = np.array(datainput)
l_float = l.astype(np.float)
scale_ratio = (range_max - range_min) / (data_max - data_min)
output = scale_ratio * (l_float - data_min) + range_min
return output
def scal_mapping(datainput):
scale_factor_x = 50
scale_factor_y = 50
scale_factor_z = 30
nor_x_domi = (max(datainput[0]) - min(datainput[0])) / scale_factor_x
nor_y_domi = (max(datainput[1]) - min(datainput[1])) / scale_factor_y
nor_z_domi = (max(datainput[2]) - min(datainput[2])) / scale_factor_z
return nor_x_domi, nor_y_domi, nor_z_domi
def Place_field_processing(input_pre, place_field_input, place_cell_matrix):
x_pre = input_pre[0]
y_pre = input_pre[1]
z_pre = input_pre[2]
delta_x = place_field_input[0] - x_pre
delta_y = place_field_input[1] - y_pre
delta_z = place_field_input[2] - z_pre
input_matrix = [delta_x, delta_y, delta_z]
error_matrix = np.array(input_matrix).dot(np.array(place_cell_matrix))
return error_matrix
def read_data_files():
f = open("input_signal.txt", "r")
lines = f.readlines()
delta_x_r = []
delta_y_r = []
delta_z_r = []
err_step_x_r = []
err_step_y_r = []
err_step_z_r = []
for x in lines:
delta_x_r.append(x.split(' ')[0])
delta_y_r.append(x.split(' ')[1])
delta_z_r.append(x.split(' ')[2])
err_step_x_r.append(x.split(' ')[3])
err_step_y_r.append(x.split(' ')[4])
err_step_z_r.append(x.split(' ')[5])
f.close()
return delta_x_r, delta_y_r, delta_z_r, err_step_x_r, err_step_y_r, err_step_z_r
def read_data_files_vertical():
f = open("input_signal.txt", "r")
lines = f.readlines()
def read_file(filename):
f = open(filename, "r")
lines = f.readlines()
delta_x_spike = []
delta_y_spike = []
delta_z_spike = []
for x in lines:
delta_x_spike.append(x.split(' ')[0])
delta_y_spike.append(x.split(' ')[1])
delta_z_spike.append(x.split(' ')[2])
f.close()
delta_x_spike = np.array(delta_x_spike).astype(float)
delta_y_spike = np.array(delta_y_spike).astype(float)
delta_z_spike = np.array(delta_z_spike).astype(float)
output = [delta_x_spike, delta_y_spike, delta_z_spike]
return output
def read_groundtruth_file(filename):
f = open(filename, "r")
lines = f.readlines()
delta_x_spike = []
delta_y_spike = []
delta_z_spike = []
for x in lines:
delta_x_spike.append(x.split(' ')[3])
delta_y_spike.append(x.split(' ')[7])
delta_z_spike.append(x.split(' ')[11])
f.close()
delta_x_spike = np.array(delta_x_spike).astype(float)
delta_y_spike = np.array(delta_y_spike).astype(float)
delta_z_spike = np.array(delta_z_spike).astype(float)
output = [delta_x_spike, delta_y_spike, delta_z_spike]
return output
def find_the_valid_idx(input):
sum = []
for i in input:
if len(i) == 0:
pass
else:
sum = sum + i
return sum