def test_tracking_tdating_dating(source, dry_input):
pytest.importorskip("skimage")
pandas = pytest.importorskip("pandas")
if not dry_input:
input, metadata = get_precipitation_fields(0, 2, True, True, 4000, source)
input, __ = to_reflectivity(input, metadata)
else:
input = np.zeros((3, 50, 50))
metadata = {"timestamps": ["00", "01", "02"]}
timelist = metadata["timestamps"]
output = dating(input, timelist, mintrack=1)
# Check output format
assert isinstance(output, tuple)
assert len(output) == 3
assert isinstance(output[0], list)
assert isinstance(output[1], list)
assert isinstance(output[2], list)
assert len(output[1]) == input.shape[0]
assert len(output[2]) == input.shape[0]
assert isinstance(output[1][0], pandas.DataFrame)
assert isinstance(output[2][0], np.ndarray)
assert output[1][0].shape[1] == 8
assert output[2][0].shape == input.shape[1:]
if not dry_input:
assert len(output[0]) > 0
assert isinstance(output[0][0], pandas.DataFrame)
assert output[0][0].shape[1] == 8
else:
assert len(output[0]) == 0
assert output[1][0].shape[0] == 0
assert output[2][0].sum() == 0
time=time,
)
###############################################################################
# Properties of one of the identified cells:
print(cells_id.iloc[0])
###############################################################################
# Example of thunderstorm tracking over a timeseries
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# The tstorm-dating function requires the entire pre-loaded time series.
# The first two timesteps are required to initialize the
# flow prediction and are not used to compute tracks.
track_list, cell_list, label_list = tstorm_dating.dating(
input_video=Z,
timelist=timelist,
)
###############################################################################
# Plotting the results
# ~~~~~~~~~~~~~~~~~~~~
# Plot precipitation field
plot_precip_field(Z[2, :, :], geodata=metadata, units=metadata["unit"])
plt.xlabel("Swiss easting [m]")
plt.ylabel("Swiss northing [m]")
# Add the identified cells
plot_cart_contour(cells_id.cont, geodata=metadata)
# Filter the tracks to only contain cells existing in this timestep