def test_dB_transform(R, metadata, threshold, zerovalue, inverse, expected):
"""Test the dB_transform."""
assert_array_almost_equal(
transformation.dB_transform(R, metadata, threshold, zerovalue,
inverse)[0],
expected,
)
# Compute 30-minute LINDA nowcast with 8 parallel workers
# Restrict the number of features to 15 to reduce computation time
nowcast_linda = linda.forecast(
rainrate,
advection,
6,
max_num_features=15,
add_perturbations=False,
num_workers=8,
measure_time=True,
)[0]
# Compute S-PROG nowcast for comparison
rainrate_db, _ = transformation.dB_transform(rainrate,
metadata,
threshold=0.1,
zerovalue=-15.0)
nowcast_sprog = sprog.forecast(
rainrate_db[-3:, :, :],
advection,
6,
n_cascade_levels=6,
R_thr=-10.0,
)
# Convert reflectivity nowcast to rain rate
nowcast_sprog = transformation.dB_transform(nowcast_sprog,
threshold=-10.0,
inverse=True)[0]
# Plot the nowcasts
else:
datasource = datasources.mch
precipevents = precipevents.mch
root_path = os.path.join(datasources.root_path, datasource["root_path"])
importer = io.get_method(datasource["importer"], "importer")
results = {}
for m in oflow_methods:
results[m] = {}
results[m]["CSI"] = [0.0] * num_timesteps
results[m]["MAE"] = [0.0] * num_timesteps
results[m]["n_samples"] = [0.0] * num_timesteps
R_min_dB = transformation.dB_transform(np.array([R_min]))[0][0]
for pei, pe in enumerate(precipevents):
curdate = datetime.strptime(pe[0], "%Y%m%d%H%M")
enddate = datetime.strptime(pe[1], "%Y%m%d%H%M")
while curdate <= enddate:
print("Computing nowcasts for event %d, start date %s..." %
(pei + 1, str(curdate)),
end="")
sys.stdout.flush()
if curdate + num_timesteps * timedelta(minutes=5) > enddate:
break
fns = io.archive.find_by_date(curdate,
rainrate_field_log, _ = utils.transformation.dB_transform(
rainrate_field, metadata=metadata
)
velocity = oflow(rainrate_field_log, **oflow_kwargs)
###############################################################################
# Compute the nowcasts and threshold rain rates below 0.5 mm/h
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
forecast_extrap = extrapolation.forecast(
rainrate_field[-1], velocity, 3, extrap_kwargs={"allow_nonfinite_values": True}
)
forecast_extrap[forecast_extrap < 0.5] = 0.0
# log-transform the data and the threshold value to dBR units for S-PROG
rainrate_field_db, _ = transformation.dB_transform(
rainrate_field, metadata, threshold=0.1, zerovalue=-15.0
)
rainrate_thr, _ = transformation.dB_transform(
np.array([0.5]), metadata, threshold=0.1, zerovalue=-15.0
)
forecast_sprog = sprog.forecast(
rainrate_field_db[-3:], velocity, 3, n_cascade_levels=8, R_thr=rainrate_thr[0]
)
forecast_sprog, _ = transformation.dB_transform(
forecast_sprog, threshold=-10.0, inverse=True
)
forecast_sprog[forecast_sprog < 0.5] = 0.0
forecast_anvil = anvil.forecast(
rainrate_field[-4:], velocity, 3, ar_window_radius=25, ar_order=2
)
vsf = 60.0 / datasource["timestep"] * metadata["xpixelsize"] / 1000.0
missing_data = False
for i in range(R.shape[0]):
if not np.any(np.isfinite(R[i, :, :])):
missing_data = True
break
if missing_data:
curdate += timedelta(minutes=datasource["timestep"])
continue
R[~np.isfinite(R)] = metadata["zerovalue"]
if use_precip_mask:
MASK = np.any(R < R_min, axis=0)
R = transformation.dB_transform(R)[0]
if args.oflow == "vet":
R_ = R[-2:, :, :]
else:
R_ = R
# TODO: Allow the user to supply parameters for the optical flow.
V = oflow(R_) * vsf
# discard the motion field if the mean velocity is abnormally large
if np.nanmean(np.linalg.norm(V, axis=0)) > 0.5 * R.shape[1]:
curdate += timedelta(minutes=datasource["timestep"])
continue
if use_precip_mask:
V[0, :, :][MASK] = np.nan
def compute(nowcast_method, config_number):
# the optical flow methods to use
oflow_methods = ["darts"]
# time step between computation of each nowcast (minutes)
timestep = 30
# the number of time steps for each nowcast (5 minutes for the MeteoSwiss and
# FMI data)
num_timesteps = 24
# the threshold to use for precipitation/no precipitation
R_min = 0.1
R_min_dB = transformation.dB_transform(np.array([R_min]))[0][0]
precip_events = [
("201104160800", "201104170000"),
("201111152300", "201111161000"),
("201304110000", "201304120000"),
("201304041800", "201304051800"),
("201305180600", "201305181200"),
("201305270000", "201305271200"),
]
for pei, pe in enumerate(precip_events):
start_date = datetime.strptime(pe[0], "%Y%m%d%H%M")
curdate = datetime.strptime(pe[0], "%Y%m%d%H%M")
enddate = datetime.strptime(pe[1], "%Y%m%d%H%M")
results = {}
for m in oflow_methods:
results[m] = {}
results[m]["comptimes"] = 0.0
results[m]["CSI"] = [0.0] * num_timesteps
results[m]["RMSE"] = [0.0] * num_timesteps
results[m]["n_samples"] = [0.0] * num_timesteps
my_observations = LowAltCompositeCollection()
while curdate <= enddate:
print("Computing nowcasts for event %d, start date %s..." %
(pei + 1, str(curdate)),
end="")
sys.stdout.flush()
if curdate + num_timesteps * timedelta(minutes=5) > enddate:
break
time_step_in_sec = 5 * 60
times = [
curdate - timedelta(seconds=time_step_in_sec * i)
for i in range(9)
]
times += [
curdate + timedelta(seconds=time_step_in_sec * i)
for i in range(1, num_timesteps + 1)
]
times.sort()
# Add elements to the collection if they don't exists
for _time in times:
my_observations.add(get_lowalt_file(_time))
# First 9 times
R = my_observations.get_data('Reflectivity', date=times[:9])
R = dbz_to_r(R, a=300., b=1.5)
_R = list()
# The original data is at 1km resolutions
# Downscale to 5 km resolution by 5x5 averaging
for i in range(9):
_R.append(downscale_local_mean(R[i, :-1, :], (5, 5)))
R = np.asarray(_R)
my_observations.clean_buffers() # release memory
missing_data = False
for i in range(R.shape[0]):
if not np.any(np.isfinite(R[i, :, :])):
print("Skipping, no finite values found for time step %d" %
(i + 1))
missing_data = True
break
if missing_data:
curdate += timedelta(minutes=timestep)
continue
R = transformation.dB_transform(R)[0]
# Forecast times
fcts_times = times[9:]
R_obs = my_observations.get_data('Reflectivity', date=times[9:])
R_obs = dbz_to_r(R_obs, a=300., b=1.5)
# The original data is at 1km resolutions
# Downscale to 5 km resolution by 5x5 averaging
_R = list()
for i in range(len(fcts_times)):
_R.append(downscale_local_mean(R_obs[i, :-1, :], (5, 5)))
R_obs = np.asarray(_R)
my_observations.clean_buffers() # release memory
for oflow_method in oflow_methods:
oflow = motion.get_method(oflow_method)
if oflow_method == "vet":
R_ = R[-2:, :, :]
else:
R_ = R
starttime = time.time()
V = oflow(R_, **configurations[config_number])
print("%s optical flow computed in %.3f seconds." % \
(oflow_method, time.time() - starttime))
if nowcast_method == "advection":
nc = nowcasts.get_method("extrapolation")
R_fct = nc(R[-1, :, :], V, num_timesteps)
else:
nc = nowcasts.get_method("steps")
R_fct = nc(R[-3:, :, :],
V,
num_timesteps,
noise_method=None,
vel_pert_method=None,
n_ens_members=1,
mask_method="sprog",
R_thr=R_min_dB,
probmatching_method="mean",
fft_method="numpy")[0, :, :, :]
R_fct = transformation.dB_transform(R_fct, inverse=True)[0]
for lt in range(num_timesteps):
if not np.any(np.isfinite(R_obs[lt, :, :])):
print(
"Warning: no finite verifying observations for lead time %d."
% (lt + 1))
continue
csi = det_cat_fcst(R_fct[lt, :, :], R_obs[lt, :, :], R_min,
["CSI"])[0]
MASK = np.logical_and(R_fct[lt, :, :] > R_min,
R_obs[lt, :, :] > R_min)
if np.sum(MASK) == 0:
print("Skipping, no precipitation for lead time %d." %
(lt + 1))
continue
rmse = det_cont_fcst(R_fct[lt, :, :][MASK],
R_obs[lt, :, :][MASK], ["MAE_add"])[0]
results[oflow_method]["CSI"][lt] += csi
results[oflow_method]["RMSE"][lt] += rmse
results[oflow_method]["n_samples"][lt] += 1
print("Done.")
curdate += timedelta(minutes=timestep)
data_dir = './data/dart_tests/config_{:d}'.format(config_number)
create_dir(data_dir)
file_name = "optflow_comparison_results_%s_%s.dat" % (
nowcast_method, get_timestamp(start_date))
with open(join(data_dir, file_name), "wb") as f:
pickle.dump(results, f)
fn_pattern = "%Y%m%d%H%M_fmi.radar.composite.lowest_FIN_SUOMI1"
fn_ext = "pgm.gz"
# find the input files from the archive
fns = io.archive.find_by_date(date,
root_path,
"%Y%m%d",
fn_pattern,
fn_ext,
5,
num_prev_files=9)
# read the radar composites and apply thresholding
Z, _, metadata = io.read_timeseries(fns, import_fmi_pgm, gzipped=True)
R = conversion.to_rainrate(Z, metadata, 223.0, 1.53)[0]
R = transformation.dB_transform(R, threshold=0.1, zerovalue=-15.0)[0]
R[~np.isfinite(R)] = -15.0
# construct bandpass filter and apply the cascade decomposition
filter = filter_gaussian(R.shape[1:], 7)
decomp = decomposition_fft(R[-1, :, :], filter)
# plot the normalized cascade levels
for i in range(7):
mu = decomp["means"][i]
sigma = decomp["stds"][i]
decomp["cascade_levels"][i] = (decomp["cascade_levels"][i] - mu) / sigma
fig, ax = pyplot.subplots(nrows=2, ncols=4)
ax[0, 0].imshow(R[-1, :, :], cmap=cm.RdBu_r, vmin=-3, vmax=3)
seed = 24
map_plotter = "basemap"
basemap_resolution = 'h'
inputfns = find_by_date(date,
root_path,
path_fmt,
fn_pattern,
fn_ext,
timestep,
num_prev_files=9)
Z, _, metadata = read_timeseries(inputfns, import_fmi_pgm, gzipped=True)
R = conversion.to_rainrate(Z, metadata, 223.0, 1.53)[0]
R = transformation.dB_transform(R, threshold=0.1, zerovalue=-15.0)[0]
R[~np.isfinite(R)] = -15.0
V = dense_lucaskanade(R)
# the S-PROG nowcast
nowcast_method = nowcasts.get_method("sprog")
R_f = nowcast_method(R[-3:, :, :],
V,
12,
n_cascade_levels=8,
R_thr=-10.0,
decomp_method="fft",
bandpass_filter_method="gaussian",
probmatching_method="mean",
fft_method="pyfftw")
