def test_aggregate_fields_space(R, metadata, space_window, ignore_nan,
expected):
"""Test the aggregate_fields_space."""
assert_array_equal(
dimension.aggregate_fields_space(R, metadata, space_window,
ignore_nan)[0],
expected,
)
root_path,
path_fmt,
fn_pattern,
fn_ext,
timestep,
num_prev_files=2)
# Read the data from the archive
importer = io.get_method(importer_name, "importer")
R, _, metadata = io.read_timeseries(fns, importer, **importer_kwargs)
# Convert to rain rate
R, metadata = conversion.to_rainrate(R, metadata)
# Upscale data to 2 km to limit memory usage
R, metadata = dimension.aggregate_fields_space(R, metadata, 2000)
# Plot the rainfall field
plot_precip_field(R[-1, :, :], geodata=metadata)
plt.show()
# Log-transform the data to unit of dBR, set the threshold to 0.1 mm/h,
# set the fill value to -15 dBR
R, metadata = transformation.dB_transform(R,
metadata,
threshold=0.1,
zerovalue=-15.0)
# Set missing values with the fill value
R[~np.isfinite(R)] = -15.0
datasource_params["fn_pattern"],
datasource_params["fn_ext"],
datasource_params["timestep"],
num_prev_files=2,
)
# Read the data from the archive
importer = io.get_method(datasource_params["importer"], "importer")
reflectivity, _, metadata = io.read_timeseries(
fns, importer, **datasource_params["importer_kwargs"])
# Convert reflectivity to rain rate
rainrate, metadata = conversion.to_rainrate(reflectivity, metadata)
# Upscale data to 2 km to reduce computation time
rainrate, metadata = dimension.aggregate_fields_space(rainrate, metadata, 2000)
# Plot the most recent rain rate field
plt.figure()
plot_precip_field(rainrate[-1, :, :])
plt.show()
###############################################################################
# Estimate the advection field
# ----------------------------
# The advection field is estimated using the Lucas-Kanade optical flow
advection = dense_lucaskanade(rainrate, verbose=True)
###############################################################################
# Deterministic nowcast