def main():
parser = argparse.ArgumentParser()
parser.add_argument("filename")
args = parser.parse_args()
filename = args.filename
record = recording.load(filename)
print_record(record)
fig, ax = plt.subplots()
depths = utils.get_range_depths(record.sensor_config, record.session_info)
if len(record.data.shape) == 3: # Envelope, IQ, Power bins
x = np.arange(len(record.data))
y = depths
z = np.abs(record.data[:, 0, :]).T
ax.pcolormesh(x, y, z)
ax.set_xlabel("Sweep index")
ax.set_ylabel("Depth (m)")
else:
y = np.abs(record.data[:, 0, :, :].mean(axis=1))
ax.plot(y)
ax.set_xlabel("Frame index")
ax.set_ylabel("Mean sweep amplitude")
ax.legend([f"{d:.2f}" for d in depths], title="Depth")
fig.tight_layout()
plt.show()
def test_recording(tmp_path, mode):
config = configs.MODE_TO_CONFIG_CLASS_MAP[mode]()
config.downsampling_factor = 2
mocker = clients.MockClient()
mocker.squeeze = False
session_info = mocker.start_session(config)
recorder = recording.Recorder(
sensor_config=config,
session_info=session_info,
)
for _ in range(10):
data_info, data = mocker.get_next()
recorder.sample(data_info, data)
recorder.close()
record = recorder.record
assert record.mode == mode
assert record.sensor_config_dump == config._dumps()
assert len(record.data) == 10
assert len(record.data_info) == 10
assert isinstance(record.data, np.ndarray)
for ext in ["h5", "npz"]:
filename = os.path.join(tmp_path, "record." + ext)
recording.save(filename, record)
loaded_record = recording.load(filename)
for a in attr.fields(recording.Record):
if a.name == "data":
continue
assert getattr(record, a.name) == getattr(loaded_record, a.name)
assert np.all(record.data == loaded_record.data)
assert record.sensor_config.downsampling_factor == config.downsampling_factor
def main():
parser = argparse.ArgumentParser()
parser.add_argument("filename")
args = parser.parse_args()
filename = args.filename
record = recording.load(filename)
print_record(record)
if len(record.data.shape) == 3: # Envelope, IQ, Power bins
x = np.arange(len(record.data))
y = utils.get_range_depths(record.sensor_config, record.session_info)
z = np.abs(record.data[:, 0, :]).T
fig, ax = plt.subplots()
ax.pcolormesh(x, y, z)
ax.set_xlabel("Sweep index")
ax.set_ylabel("Depth (m)")
fig.tight_layout()
plt.show()
def test_recording(tmp_path, mode, ext, give_pathlib_path):
config = configs.MODE_TO_CONFIG_CLASS_MAP[mode]()
config.downsampling_factor = 2
mocker = clients.MockClient()
mocker.squeeze = False
session_info = mocker.start_session(config)
recorder = recording.Recorder(
sensor_config=config,
session_info=session_info,
)
for _ in range(10):
data_info, data = mocker.get_next()
recorder.sample(data_info, data)
recorder.close()
record = recorder.record
assert record.mode == mode
assert record.sensor_config_dump == config._dumps()
assert len(record.data) == 10
assert len(record.data_info) == 10
assert isinstance(record.data, np.ndarray)
filename = Path(tmp_path).joinpath("record." + ext)
if not give_pathlib_path:
filename = str(filename)
recording.save(filename, record)
loaded_record = recording.load(filename)
for a in attr.fields(recording.Record):
assert np.all(
getattr(record, a.name) == getattr(loaded_record, a.name))
assert record.sensor_config.downsampling_factor == config.downsampling_factor