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
