def filter_range(self, start: int, end: int, fir_filter: Filter):
self.iq_array[start:end] = fir_filter.work(self.iq_array[start:end])
self._qad[start:end] = signal_functions.afp_demod(
self.iq_array[start:end], self.noise_threshold,
self.modulation_type, self.modulation_order,
self.costas_loop_bandwidth)
self.__invalidate_after_edit()
def test_plot(self):
modulator = Modulator("gfsk")
modulator.modulation_type = "GFSK"
modulator.samples_per_symbol = 100
modulator.sample_rate = 1e6
modulator.parameters[1] = 20e3
modulator.parameters[0] = 10e3
modulator.carrier_freq_hz = 15e3
modulator.carrier_phase_deg = 90
modulated_samples = modulator.modulate([True, False, True, False, False], 77)
data = copy.deepcopy(modulated_samples)
modulated_samples = modulator.modulate([False, True, True, True, True, False, True], 100, start=len(data))
data = np.concatenate((data, modulated_samples))
plt.subplot(2, 1, 1)
axes = plt.gca()
axes.set_ylim([-2,2])
plt.plot(data.real)
plt.title("Modulated Wave")
plt.subplot(2, 1, 2)
qad = signal_functions.afp_demod(np.ascontiguousarray(data), 0, "FSK")
plt.plot(qad)
plt.title("Quad Demod")
plt.show()
def test_noisy_rect(self):
data = Signal(get_path_for_data_file("fsk.complex")).iq_array.data
rect = afp_demod(data, 0.008, "FSK", 2)[5:15000]
center = detect_center(rect)
self.assertGreaterEqual(center, -0.0587)
self.assertLessEqual(center, 0.02)
def test_ask_center_detection(self):
data = Signal(get_path_for_data_file("ask.complex")).iq_array.data
rect = afp_demod(data, 0.01111, "ASK", 2)
center = detect_center(rect)
self.assertGreaterEqual(center, 0)
self.assertLessEqual(center, 0.06)
def test_fsk_15db_center_detection(self):
data = Signal(get_path_for_data_file("FSK15.complex"),
"").iq_array.data
rect = afp_demod(data, 0, "FSK", 2)
center = detect_center(rect)
self.assertGreaterEqual(center, -0.1979)
self.assertLessEqual(center, 0.1131)
def test_plot(self):
modulator = Modulator("gfsk")
modulator.modulation_type_str = "GFSK"
modulator.samples_per_bit = 100
modulator.sample_rate = 1e6
modulator.param_for_one = 20e3
modulator.param_for_zero = 10e3
modulator.carrier_freq_hz = 15e3
modulator.carrier_phase_deg = 90
modulated_samples = modulator.modulate([True, False, True, False, False], 77)
data = copy.deepcopy(modulated_samples)
modulated_samples = modulator.modulate([False, True, True, True, True, False, True], 100, start=len(data))
data = np.concatenate((data, modulated_samples))
plt.subplot(2, 1, 1)
axes = plt.gca()
axes.set_ylim([-2,2])
plt.plot(data.real)
plt.title("Modulated Wave")
plt.subplot(2, 1, 2)
qad = signal_functions.afp_demod(np.ascontiguousarray(data), 0, 1)
plt.plot(qad)
plt.title("Quad Demod")
plt.show()
def test_ask_center_detection(self):
data = np.fromfile(get_path_for_data_file("ask.complex"),
dtype=np.complex64)
rect = afp_demod(data, 0.01111, 0)
center = detect_center(rect)
self.assertGreaterEqual(center, 0)
self.assertLessEqual(center, 0.06)
def test_noisy_rect(self):
data = np.fromfile(get_path_for_data_file("fsk.complex"),
dtype=np.complex64)
rect = afp_demod(data, 0.008, 1)[5:15000]
center = detect_center(rect)
self.assertGreaterEqual(center, -0.0587)
self.assertLessEqual(center, 0.02)
def test_fsk_live_capture(self):
data = Signal(get_path_for_data_file("fsk_live.coco"), "").data
n = 10
moving_average_filter = Filter([1 / n for _ in range(n)],
filter_type=FilterType.moving_average)
filtered_data = moving_average_filter.apply_fir_filter(data)
rect = afp_demod(filtered_data, 0.0175, 1)
center = detect_center(rect)
self.assertGreaterEqual(center, -0.0148, msg="Filtered")
self.assertLessEqual(center, 0.01, msg="Filtered")
rect = afp_demod(data, 0.0175, 1)
center = detect_center(rect)
self.assertGreaterEqual(center, -0.02, msg="Original")
self.assertLessEqual(center, 0.01, msg="Original")
def test_enocean_center_detection(self):
data = Signal(get_path_for_data_file("enocean.complex")).iq_array.data
rect = afp_demod(data, 0.05, "ASK", 2)
messages = [rect[2107:5432], rect[20428:23758], rect[44216:47546]]
for i, msg in enumerate(messages):
center = detect_center(msg)
self.assertGreaterEqual(center, 0.04, msg=str(i))
self.assertLessEqual(center, 0.072, msg=str(i))
def quad_demod(self):
if self.noise_threshold < self.max_magnitude:
return signal_functions.afp_demod(self.iq_array.data,
self.noise_threshold,
self.modulation_type,
self.modulation_order,
self.costas_loop_bandwidth)
else:
return np.zeros(2, dtype=np.float32)
def test_noised_homematic_center_detection(self):
data = Signal(get_path_for_data_file("noised_homematic.complex"),
"").iq_array.data
rect = afp_demod(data, 0.0, "FSK", 2)
center = detect_center(rect)
self.assertGreater(center, -0.0148)
self.assertLess(center, 0.0024)
def test_enocean_center_detection(self):
data = np.fromfile(get_path_for_data_file("enocean.complex"),
dtype=np.complex64)
rect = afp_demod(data, 0.05, 0)
messages = [rect[2107:5432], rect[20428:23758], rect[44216:47546]]
for i, msg in enumerate(messages):
center = detect_center(msg)
self.assertGreaterEqual(center, 0.04, msg=str(i))
self.assertLessEqual(center, 0.072, msg=str(i))
def test_fsk_live_capture(self):
data = Signal(get_path_for_data_file("fsk_live.coco"),
"").iq_array.data
n = 10
moving_average_filter = Filter([1 / n for _ in range(n)],
filter_type=FilterType.moving_average)
filtered_data = moving_average_filter.apply_fir_filter(
data.flatten()).view(np.float32)
filtered_data = filtered_data.reshape((len(filtered_data) // 2, 2))
rect = afp_demod(filtered_data, 0.0175, "FSK", 2)
center = detect_center(rect)
self.assertGreaterEqual(center, -0.0148, msg="Filtered")
self.assertLessEqual(center, 0.01, msg="Filtered")
rect = afp_demod(data, 0.0175, "FSK", 2)
center = detect_center(rect)
self.assertGreaterEqual(center, -0.02, msg="Original")
self.assertLessEqual(center, 0.01, msg="Original")
def test_ask_50_center_detection(self):
message_indices = [(0, 8000), (18000, 26000), (36000, 44000),
(54000, 62000), (72000, 80000)]
data = Signal(get_path_for_data_file("ask50.complex")).iq_array.data
rect = afp_demod(data, 0.0509, "ASK", 2)
for start, end in message_indices:
center = detect_center(rect[start:end])
self.assertGreaterEqual(center,
0.4,
msg="{}/{}".format(start, end))
self.assertLessEqual(center, 0.65, msg="{}/{}".format(start, end))
def test_homematic_center_detection(self):
data = Signal(get_path_for_data_file("homematic.coco"), "").data
rect = afp_demod(data, 0.0012, 1)
msg1 = rect[17719:37861]
msg2 = rect[70412:99385]
center1 = detect_center(msg1)
self.assertGreaterEqual(center1, -0.1285)
self.assertLessEqual(center1, -0.0413)
center2 = detect_center(msg2)
self.assertGreaterEqual(center2, -0.1377)
self.assertLessEqual(center2, -0.0367)
def test_ask_50_center_detection(self):
message_indices = [(0, 8000), (18000, 26000), (36000, 44000),
(54000, 62000), (72000, 80000)]
data = np.fromfile(get_path_for_data_file("ask50.complex"),
dtype=np.complex64)
rect = afp_demod(data, 0.0509, 0)
for start, end in message_indices:
center = detect_center(rect[start:end])
self.assertGreaterEqual(center,
0.5326,
msg="{}/{}".format(start, end))
self.assertLessEqual(center,
0.9482,
msg="{}/{}".format(start, end))
def filter_range(self, start: int, end: int, fir_filter: Filter):
self._fulldata[start:end] = fir_filter.work(self._fulldata[start:end])
self._qad[start:end] = signal_functions.afp_demod(self.data[start:end],
self.noise_threshold, self.modulation_type)
self.__invalidate_after_edit()
def test_fsk_10db_center_detection(self):
data = Signal(get_path_for_data_file("FSK10.complex"), "").data
rect = afp_demod(data, 0, 1)
center = detect_center(rect)
self.assertGreaterEqual(center, -0.1413)
self.assertLessEqual(center, 0.05)
def quad_demod(self):
return signal_functions.afp_demod(self.iq_array.data,
self.noise_threshold,
self.modulation_type)
def quad_demod(self):
return signal_functions.afp_demod(self.data, self.noise_threshold, self.modulation_type)
def quad_demod(self):
return signal_functions.afp_demod(self.iq_array.data,
self.noise_threshold,
self.modulation_type,
self.modulation_order,
self.costas_loop_bandwidth)
def filter_range(self, start: int, end: int, fir_filter: Filter):
self._fulldata[start:end] = fir_filter.work(self._fulldata[start:end])
self._qad[start:end] = signal_functions.afp_demod(
self.data[start:end], self.noise_threshold, self.modulation_type)
self.__invalidate_after_edit()
def estimate(iq_array: IQArray,
noise: float = None,
modulation: str = None) -> dict:
if isinstance(iq_array, np.ndarray):
iq_array = IQArray(iq_array)
magnitudes = iq_array.magnitudes
# find noise threshold
noise = detect_noise_level(magnitudes) if noise is None else noise
# segment messages
message_indices = segment_messages_from_magnitudes(magnitudes,
noise_threshold=noise)
# detect modulation
modulation = detect_modulation_for_messages(
iq_array, message_indices) if modulation is None else modulation
if modulation is None:
return None
if modulation == "OOK":
message_indices = merge_message_segments_for_ook(message_indices)
if modulation == "OOK" or modulation == "ASK":
data = signal_functions.afp_demod(iq_array.data, noise, 0)
elif modulation == "FSK":
data = signal_functions.afp_demod(iq_array.data, noise, 1)
elif modulation == "PSK":
data = signal_functions.afp_demod(iq_array.data, noise, 2)
else:
raise ValueError("Unsupported Modulation")
centers = []
bit_lengths = []
tolerances = []
for start, end in message_indices:
msg_rect_data = data[start:end]
center = detect_center(msg_rect_data)
if center is None:
continue
plateau_lengths = c_auto_interpretation.get_plateau_lengths(
msg_rect_data, center, percentage=25)
tolerance = estimate_tolerance_from_plateau_lengths(plateau_lengths)
if tolerance is None:
tolerance = 0
else:
tolerances.append(tolerance)
merged_lengths = merge_plateau_lengths(plateau_lengths,
tolerance=tolerance)
if len(merged_lengths) < 2:
continue
bit_length = get_bit_length_from_plateau_lengths(merged_lengths)
min_bit_length = tolerance + 1
if bit_length > min_bit_length:
# only add to score if found bit length surpasses minimum bit length
centers.append(center)
bit_lengths.append(bit_length)
# Since we cannot have different centers per message (yet) we need to combine them to return a common center
if modulation == "OOK" or modulation == "ASK":
# for ask modulations the center tends to be the minimum of all found centers
center = min_without_outliers(np.array(centers), z=2)
if center is None:
# did not find any centers at all so we cannot return a valid estimation
return None
elif len(centers) > 0:
# for other modulations it is a better strategy to take the mean of found centers
center = np.mean(centers)
else:
# did not find any centers at all so we cannot return a valid estimation
return None
bit_length = get_most_frequent_value(bit_lengths)
if bit_length is None:
return None
try:
tolerance = np.percentile(tolerances, 50)
except IndexError:
# no tolerances found, default to 5% of bit length
tolerance = max(1, int(0.05 * bit_length))
result = {
"modulation_type": "ASK" if modulation == "OOK" else modulation,
"bit_length": bit_length,
"center": center,
"tolerance": int(tolerance),
"noise": noise
}
return result
def estimate(signal: np.ndarray, noise: float = None, modulation: str = None) -> dict:
magnitudes = np.abs(signal)
# find noise threshold
noise = detect_noise_level(magnitudes) if noise is None else noise
# segment messages
message_indices = segment_messages_from_magnitudes(magnitudes, noise_threshold=noise)
# detect modulation
modulation = detect_modulation_for_messages(signal, message_indices) if modulation is None else modulation
if modulation is None:
return None
if modulation == "OOK":
message_indices = merge_message_segments_for_ook(message_indices)
if modulation == "OOK" or modulation == "ASK":
data = signal_functions.afp_demod(signal, noise, 0)
elif modulation == "FSK":
data = signal_functions.afp_demod(signal, noise, 1)
elif modulation == "PSK":
data = signal_functions.afp_demod(signal, noise, 2)
else:
raise ValueError("Unsupported Modulation")
centers = []
bit_lengths = []
tolerances = []
for start, end in message_indices:
msg_rect_data = data[start:end]
center = detect_center(msg_rect_data)
if center is None:
continue
plateau_lengths = c_auto_interpretation.get_plateau_lengths(msg_rect_data, center, percentage=25)
tolerance = estimate_tolerance_from_plateau_lengths(plateau_lengths)
if tolerance is None:
tolerance = 0
else:
tolerances.append(tolerance)
merged_lengths = merge_plateau_lengths(plateau_lengths, tolerance=tolerance)
if len(merged_lengths) < 2:
continue
bit_length = get_bit_length_from_plateau_lengths(merged_lengths)
min_bit_length = tolerance + 1
if bit_length > min_bit_length:
# only add to score if found bit length surpasses minimum bit length
centers.append(center)
bit_lengths.append(bit_length)
# Since we cannot have different centers per message (yet) we need to combine them to return a common center
if modulation == "OOK" or modulation == "ASK":
# for ask modulations the center tends to be the minimum of all found centers
center = min_without_outliers(np.array(centers), z=2)
if center is None:
# did not find any centers at all so we cannot return a valid estimation
return None
elif len(centers) > 0:
# for other modulations it is a better strategy to take the mean of found centers
center = np.mean(centers)
else:
# did not find any centers at all so we cannot return a valid estimation
return None
bit_length = get_most_frequent_value(bit_lengths)
if bit_length is None:
return None
try:
tolerance = np.percentile(tolerances, 50)
except IndexError:
# no tolerances found, default to 5% of bit length
tolerance = max(1, int(0.05 * bit_length))
result = {
"modulation_type": "ASK" if modulation == "OOK" else modulation,
"bit_length": bit_length,
"center": center,
"tolerance": int(tolerance),
"noise": noise
}
return result
