def convolution(signal1, signal2):
result_points = []
for n in range(len(signal1.points) + len(signal2.points) - 1):
y = 0
for k in range(len(signal1.points)):
if 0 <= n - k < len(signal2.points):
y += signal1.points[k].y * signal2.points[n - k].y
result_points.append(Point(n, y))
result_signal = Signal(None, 0, len(signal1.points) + len(signal2.points) - 1, 1)
result_signal.points = result_points
return result_signal
def quantization(signal, Ts, n_bits):
result_signal = Signal(None, None, None, None)
result_signal.points = signal.quantization(Ts, n_bits)
signal_to_draw = Signal(None, None, None, None, False)
signal_to_draw.points.append(signal.points[0])
new_ts = Ts / 2
for i in range(1, len(signal.points)):
signal_to_draw.points.append(
Point(signal.points[i - 1].x + new_ts, signal.points[i - 1].y))
signal_to_draw.points.append(
Point(signal.points[i - 1].x + new_ts, signal.points[i].y))
signal_to_draw.points.append(signal.points[-1])
return result_signal, signal_to_draw
def read_file(file_path):
with open(file_path, 'r') as file:
start_time = file.readline()
duration_time = file.readline()
frequency = file.readline()
points = file.readline()
points = points.split()
signal_points = []
for i in range(0, len(points), 2):
signal_point = Point(float(points[i]), float(points[i + 1]))
signal_points.append(signal_point)
result = Signal(None, start_time, duration_time, frequency)
result.points = signal_points
return result
def foh(signal):
reconstructed_signal = Signal(None, None, None, None, False)
reconstructed_signal.points = deepcopy(signal.points)
return reconstructed_signal
