def short_time_haar(x, window_len=4096, window_shift=2048):
w = list(x[0:len(x) - len(x) % window_shift])
transformed = []
for t in range(0, (len(w) - window_len)/window_shift + 1):
window_haar = haar_transform.haar(w[window_shift * t: window_shift * t + window_len])
transformed.append(window_haar)
return scipy.array(transformed)
negative[j] = data[i]
n_entries[negative[j]] = i + base
break
negative.sort(reverse=True)
return p_entries, n_entries
if __name__ == '__main__':
f = open(os.path.join(dir, file_name), 'r')
# Get just the displacement in the x coordinate
x = []
for line in f:
x.append(float(line.split(',')[6]))
haar_x = haar_transform.haar(haar_transform.trim_n2(x))
positive_largest, negative_largest = find_edges(haar_x[len(haar_x)/2:], len(haar_x)/2, 5)
haar_x1 = smooth(haar_x, 1)
haar_x2 = smooth(haar_x, 2)
haar_x3 = smooth(haar_x, 3)
haar_x4 = smooth(haar_x, 4)
#Graph it, and save figure as a .png
graph_accel(x)
graph_accel(haar_x)
graph_accel(haar_transform.inverse_haar(haar_x))
graph_accel(haar_transform.inverse_haar(haar_x1))
graph_accel(haar_transform.inverse_haar(haar_x2))
graph_accel(haar_transform.inverse_haar(haar_x3))
graph_accel(haar_transform.inverse_haar(haar_x4))