def test_visualization_plot_precip_field(source, map_kwargs, pass_geodata):
field, metadata = get_precipitation_fields(0, 0, True, True, None, source)
field = field.squeeze()
field, __ = to_rainrate(field, metadata)
if not pass_geodata:
metadata = None
plot_precip_field(field, ptype="intensity", geodata=metadata, map_kwargs=map_kwargs)
def test_visualization_motionfields_quiver(source, axis, step, quiver_kwargs,
map_kwargs, upscale, pass_geodata):
if source is not None:
fields, geodata = get_precipitation_fields(0, 2, False, True, upscale,
source)
if not pass_geodata:
geodata = None
ax = plot_precip_field(fields[-1], geodata=geodata)
oflow_method = motion.get_method("LK")
UV = oflow_method(fields)
else:
shape = (100, 100)
geodata = None
ax = None
u = np.ones(shape[1]) * shape[0]
v = np.arange(0, shape[0])
U, V = np.meshgrid(u, v)
UV = np.concatenate([U[None, :], V[None, :]])
UV_orig = UV.copy()
__ = quiver(UV,
ax,
geodata,
axis,
step,
quiver_kwargs,
map_kwargs=map_kwargs)
# Check that quiver does not modify the input data
assert np.array_equal(UV, UV_orig)
def test_visualization_plot_precip_field(
source, type, bbox, colorscale, probthr, title, colorbar, axis,
):
if type == "intensity":
field, metadata = get_precipitation_fields(0, 0, True, True, None, source)
field = field.squeeze()
field, metadata = conversion.to_rainrate(field, metadata)
elif type == "depth":
field, metadata = get_precipitation_fields(0, 0, True, True, None, source)
field = field.squeeze()
field, metadata = conversion.to_raindepth(field, metadata)
elif type == "prob":
field, metadata = get_precipitation_fields(0, 10, True, True, None, source)
field, metadata = conversion.to_rainrate(field, metadata)
field = ensemblestats.excprob(field, probthr)
ax = plot_precip_field(
field,
type=type,
bbox=bbox,
geodata=metadata,
colorscale=colorscale,
probthr=probthr,
units=metadata["unit"],
title=title,
colorbar=colorbar,
axis=axis,
)
pl.close()
def test_visualization_plot_precip_field(
source,
plot_type,
bbox,
colorscale,
probthr,
title,
colorbar,
axis,
):
if plot_type == "intensity":
field, metadata = get_precipitation_fields(0, 0, True, True, None,
source)
field = field.squeeze()
field, metadata = conversion.to_rainrate(field, metadata)
elif plot_type == "depth":
field, metadata = get_precipitation_fields(0, 0, True, True, None,
source)
field = field.squeeze()
field, metadata = conversion.to_raindepth(field, metadata)
elif plot_type == "prob":
field, metadata = get_precipitation_fields(0, 10, True, True, None,
source)
field, metadata = conversion.to_rainrate(field, metadata)
field = ensemblestats.excprob(field, probthr)
field_orig = field.copy()
ax = plot_precip_field(
field.copy(),
type=plot_type,
bbox=bbox,
geodata=None,
colorscale=colorscale,
probthr=probthr,
units=metadata["unit"],
title=title,
colorbar=colorbar,
axis=axis,
)
# Check that plot_precip_field does not modify the input data
field_orig = np.ma.masked_invalid(field_orig)
field_orig.data[field_orig.mask] = -100
field = np.ma.masked_invalid(field)
field.data[field.mask] = -100
assert np.array_equal(field_orig.data, field.data)
def test_visualization_plot_precip_field(source, map, drawlonlatlines, lw):
field, metadata = get_precipitation_fields(0, 0, True, True, None, source)
field = field.squeeze()
field, __ = to_rainrate(field, metadata)
ax = plot_precip_field(
field,
type="intensity",
geodata=metadata,
map=map,
drawlonlatlines=drawlonlatlines,
lw=lw,
)
def test_visualization_motionfields_streamplot(
source, axis, streamplot_kwargs, map_kwargs, upscale, pass_geodata
):
if source is not None:
fields, geodata = get_precipitation_fields(0, 2, False, True, upscale, source)
if not pass_geodata:
pass_geodata = None
ax = plot_precip_field(fields[-1], geodata=geodata)
oflow_method = motion.get_method("LK")
UV = oflow_method(fields)
else:
shape = (100, 100)
geodata = None
ax = None
u = np.ones(shape[1]) * shape[0]
v = np.arange(0, shape[0])
U, V = np.meshgrid(u, v)
UV = np.concatenate([U[None, :], V[None, :]])
__ = streamplot(UV, ax, geodata, axis, streamplot_kwargs, map_kwargs=map_kwargs,)
def test_visualization_motionfields_quiver(
source,
map,
drawlonlatlines,
lw,
axis,
step,
quiver_kwargs,
upscale,
):
if map == "cartopy":
pytest.importorskip("cartopy")
elif map == "basemap":
pytest.importorskip("basemap")
if source is not None:
fields, geodata = get_precipitation_fields(0, 2, False, True, upscale,
source)
ax = plot_precip_field(
fields[-1],
map=map,
geodata=geodata,
)
oflow_method = motion.get_method("LK")
UV = oflow_method(fields)
else:
shape = (100, 100)
geodata = None
ax = None
u = np.ones(shape[1]) * shape[0]
v = np.arange(0, shape[0])
U, V = np.meshgrid(u, v)
UV = np.concatenate([U[None, :], V[None, :]])
__ = quiver(UV, ax, map, geodata, drawlonlatlines, lw, axis, step,
quiver_kwargs)
12,
n_ens_members=24,
n_cascade_levels=8,
R_thr=-10.0,
kmperpixel=1.0,
timestep=5)
# plot the S-PROG nowcast, one ensemble member of the STEPS nowcast and the exceedance
# probability of 0.1 mm/h computed from the ensemble
R_f_sprog = transformation.dB_transform(R_f_sprog,
threshold=-10.0,
inverse=True)[0]
pyplot.figure()
plot_precip_field(R_f_sprog,
map="basemap",
geodata=metadata,
drawlonlatlines=True,
basemap_resolution='h')
pyplot.savefig("SPROG_nowcast.png", bbox_inches="tight", dpi=300)
R_f = transformation.dB_transform(R_f, threshold=-10.0, inverse=True)[0]
R_f_mean = np.mean(R_f[:, -1, :, :], axis=0)
pyplot.figure()
plot_precip_field(R_f_mean,
map="basemap",
geodata=metadata,
drawlonlatlines=True,
basemap_resolution='h')
pyplot.savefig("STEPS_ensemble_mean.png", bbox_inches="tight", dpi=300)
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
# Nicely print the metadata
pprint(metadata)
plt.subplot(2, 3, n + 1)
plt.imshow(frame, interpolation="nearest", vmin=0, vmax=1)
plt.xticks([])
plt.yticks([])
plt.show()
################################################################################
# Let's plot one single leadtime in more detail using the pysteps visualization
# functionality.
plt.close()
# Plot the field of probabilities
plot_precip_field(
fct[2],
geodata=metadata,
ptype="prob",
probthr=thr,
title="Exceedence probability (+ %i min)" % (nleadtimes * timestep),
)
plt.show()
###############################################################################
# Verification
# ------------
# verifying observations
importer = io.get_method(importer_name, "importer")
fns = io.find_by_date(date,
root,
fmt,
pattern,
# Make sure the units are in mm/h
converter = pysteps.utils.get_method("mm/h")
radar_precip, radar_metadata = converter(radar_precip, radar_metadata)
nwp_precip, nwp_metadata = converter(nwp_precip, nwp_metadata)
# Threshold the data
radar_precip[radar_precip < 0.1] = 0.0
nwp_precip[nwp_precip < 0.1] = 0.0
# Plot the radar rainfall field and the first time step of the NWP forecast.
date_str = datetime.strftime(date_radar, "%Y-%m-%d %H:%M")
plt.figure(figsize=(10, 5))
plt.subplot(121)
plot_precip_field(
radar_precip[-1, :, :],
geodata=radar_metadata,
title=f"Radar observation at {date_str}",
)
plt.subplot(122)
plot_precip_field(
nwp_precip[0, :, :], geodata=nwp_metadata, title=f"NWP forecast at {date_str}"
)
plt.tight_layout()
plt.show()
# transform the data to dB
transformer = pysteps.utils.get_method("dB")
radar_precip, radar_metadata = transformer(radar_precip, radar_metadata, threshold=0.1)
nwp_precip, nwp_metadata = transformer(nwp_precip, nwp_metadata, threshold=0.1)
# r_nwp has to be four dimentional (n_models, time, y, x).
# Import the example radar composite
root_path = rcparams.data_sources["mch"]["root_path"]
filename = os.path.join(root_path, "20160711", "AQC161932100V_00005.801.gif")
R, _, metadata = io.import_mch_gif(filename,
product="AQC",
unit="mm",
accutime=5.0)
# Convert to mm/h
R, metadata = conversion.to_rainrate(R, metadata)
# Nicely print the metadata
pprint(metadata)
# Plot the rainfall field
plot_precip_field(R, geodata=metadata)
plt.show()
# Log-transform the data
R, metadata = transformation.dB_transform(R,
metadata,
threshold=0.1,
zerovalue=-15.0)
# Assign the fill value to all the Nans
R[~np.isfinite(R)] = metadata["zerovalue"]
###############################################################################
# Parametric filter
# -----------------
#
del quality # Not used
reference_field = np.squeeze(reference_field) # Remove time dimension
###############################################################################
# Preprocess the data
# ~~~~~~~~~~~~~~~~~~~
# Convert to mm/h
reference_field, metadata = stp.utils.to_rainrate(reference_field, metadata)
# Mask invalid values
reference_field = np.ma.masked_invalid(reference_field)
# Plot the reference precipitation
plot_precip_field(reference_field, title="Reference field")
plt.show()
# Log-transform the data [dBR]
reference_field, metadata = stp.utils.dB_transform(reference_field,
metadata,
threshold=0.1,
zerovalue=-15.0)
print("Precip. pattern shape: " + str(reference_field.shape))
# This suppress nan conversion warnings in plot functions
reference_field.data[reference_field.mask] = np.nan
################################################################################
)
# 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
# ---------------------
# Compute 30-minute LINDA nowcast with 8 parallel workers
# Restrict the number of features to 15 to reduce computation time
root_path,
path_fmt,
fn_pattern,
fn_ext,
timestep,
num_prev_files=2)
# Read the radar composites
importer = io.get_method(importer_name, "importer")
Z, _, metadata = io.read_timeseries(fns, importer, **importer_kwargs)
# Convert to rain rate using the finnish Z-R relationship
R, metadata = conversion.to_rainrate(Z, metadata, 223.0, 1.53)
# Plot the rainfall field
plot_precip_field(R[-1, :, :], geodata=metadata)
# Store the last frame for plotting it later later
R_ = R[-1, :, :].copy()
# 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)
# Nicely print the metadata
pprint(metadata)
###############################################################################
ax.add_artist(circle)
circle = plt.Circle(
(590, 240), 30, color="r", clip_on=False, fill=False, zorder=1e9
)
ax.add_artist(circle)
circle = plt.Circle(
(585, 160), 15, color="r", clip_on=False, fill=False, zorder=1e9
)
ax.add_artist(circle)
fig = plt.figure(figsize=(10, 13))
ax = fig.add_subplot(321)
rainrate_field[-1][rainrate_field[-1] < 0.5] = 0.0
plot_precip_field(rainrate_field[-1])
plot_growth_decay_circles(ax)
ax.set_title("Obs. %s" % str(date))
ax = fig.add_subplot(322)
plot_precip_field(refobs_field)
plot_growth_decay_circles(ax)
ax.set_title("Obs. %s" % str(date + timedelta(minutes=15)))
ax = fig.add_subplot(323)
plot_precip_field(forecast_extrap[-1])
plot_growth_decay_circles(ax)
ax.set_title("Extrapolation +15 minutes")
ax = fig.add_subplot(324)
plot_precip_field(forecast_sprog[-1])
R_ac += advection_correction(R[i:(i + 2)], T=10, t=1) R_ac /= R.shape[0] ############################################################################### # Results # ------- # # We compare the two accumulation maps. The first map on the left is # computed without advection correction and we can therefore see that the shift # between successive images 10 minutes apart produces irregular accumulations. # Conversely, the rainfall accumulation of the right is produced using advection # correction to account for this spatial shift. The final result is a smoother # rainfall accumulation map. pl.figure(figsize=(9, 4)) pl.subplot(121) plot_precip_field(R.mean(axis=0), title="3-h rainfall accumulation") pl.subplot(122) plot_precip_field(R_ac, title="Same with advection correction") pl.tight_layout() pl.show() ################################################################################ # Reference # ~~~~~~~~~ # # Anagnostou, E. N., and W. F. Krajewski. 1999. "Real-Time Radar Rainfall # Estimation. Part I: Algorithm Formulation." Journal of Atmospheric and # Oceanic Technology 16: 189–97. # https://doi.org/10.1175/1520-0426(1999)016<0189:RTRREP>2.0.CO;2
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# 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
IDs = cells_id.ID.values
track_filt = []
for track in track_list:
if np.unique(track.ID) in IDs:
track_filt.append(track)
# Add their tracks
plot_track(track_filt, geodata=metadata)
filename = os.path.join(root_path, "20160711", "AQC161932100V_00005.801.gif")
precip, _, metadata = io.import_mch_gif(
filename, product="AQC", unit="mm", accutime=5.0
)
# Convert to mm/h
precip, metadata = to_rainrate(precip, metadata)
# Reduce to a square domain
precip, metadata = square_domain(precip, metadata, "crop")
# Nicely print the metadata
pprint(metadata)
# Plot the original rainfall field
plot_precip_field(precip, geodata=metadata)
plt.show()
# Assign the fill value to all the Nans
precip[~np.isfinite(precip)] = metadata["zerovalue"]
###############################################################################
# Upscale the field
# -----------------
#
# To test our downscaling method, we first need to upscale the original field to
# a lower resolution. We are going to use an upscaling factor of 16 x.
upscaling_factor = 16
upscale_to = metadata["xpixelsize"] * upscaling_factor # upscaling factor : 16 x
precip_lr, metadata_lr = aggregate_fields_space(precip, metadata, upscale_to)
################################################################################
# Lucas-Kanade (LK)
# -----------------
#
# The Lucas-Kanade optical flow method implemented in pysteps is a local
# tracking approach that relies on the OpenCV package.
# Local features are tracked in a sequence of two or more radar images. The
# scheme includes a final interpolation step in order to produce a smooth
# field of motion vectors.
oflow_method = motion.get_method("LK")
V1 = oflow_method(R[-3:, :, :])
# Plot the motion field on top of the reference frame
plot_precip_field(R_, geodata=metadata, title="LK")
quiver(V1, geodata=metadata, step=25)
plt.show()
################################################################################
# Variational echo tracking (VET)
# -------------------------------
#
# This module implements the VET algorithm presented
# by Laroche and Zawadzki (1995) and used in the McGill Algorithm for
# Prediction by Lagrangian Extrapolation (MAPLE) described in
# Germann and Zawadzki (2002).
# The approach essentially consists of a global optimization routine that seeks
# at minimizing a cost function between the displaced and the reference image.
oflow_method = motion.get_method("VET")
def plot_optflow_method_convergence(input_precip,
optflow_method_name,
motion_type):
"""
Test the convergence to the actual solution of the optical flow method used.
Parameters
----------
input_precip: numpy array (lat, lon)
Input precipitation field.
optflow_method_name: str
Optical flow method name
motion_type : str
The supported motion fields are:
- linear_x: (u=2, v=0)
- linear_y: (u=0, v=2)
- rotor: rotor field
"""
if optflow_method_name.lower() != "darts":
num_times = 2
else:
num_times = 9
ideal_motion, precip_obs = create_observations(input_precip,
motion_type,
num_times=num_times)
oflow_method = motion.get_method(optflow_method_name)
computed_motion = oflow_method(precip_obs, verbose=False)
precip_obs, _ = stp.utils.dB_transform(precip_obs, inverse=True)
precip_data = precip_obs.max(axis=0)
precip_data.data[precip_data.mask] = 0
precip_mask = ((uniform_filter(precip_data, size=20) > 0.1)
& ~precip_obs.mask.any(axis=0))
cmap = get_cmap('jet')
cmap.set_under('grey', alpha=0.25)
cmap.set_over('none')
# Compare retrieved motion field with the ideal one
plt.figure(figsize=(9, 4))
plt.subplot(1, 2, 1)
ax = plot_precip_field(precip_obs[0], title="Reference motion")
quiver(ideal_motion, step=25, ax=ax)
plt.subplot(1, 2, 2)
ax = plot_precip_field(precip_obs[0], title="Retrieved motion")
quiver(computed_motion, step=25, ax=ax)
# To evaluate the accuracy of the computed_motion vectors, we will use
# a relative RMSE measure.
# Relative MSE = < (expected_motion - computed_motion)^2 > / <expected_motion^2 >
# Relative RMSE = sqrt(Relative MSE)
mse = ((ideal_motion - computed_motion)[:, precip_mask] ** 2).mean()
rel_mse = mse / (ideal_motion[:, precip_mask] ** 2).mean()
plt.suptitle(f"{optflow_method_name} "
f"Relative RMSE: {np.sqrt(rel_mse) * 100:.2f}%")
plt.show()
R_thr=-10.0,
decomp_method="fft",
bandpass_filter_method="gaussian",
probmatching_method="mean",
fft_method="pyfftw")
R_f = transformation.dB_transform(R_f, threshold=-10.0, inverse=True)[0]
fig = figure(figsize=(9, 9))
ax = fig.add_subplot(221)
ax.set_title("a)", loc="left", fontsize=12)
if map_plotter == "cartopy":
plot_precip_field(R_f[-1, :, :],
map="cartopy",
geodata=metadata,
drawlonlatlines=True,
cartopy_scale="50m")
else:
bm = plot_precip_field(R_f[-1, :, :],
map="basemap",
geodata=metadata,
drawlonlatlines=False,
basemap_resolution=basemap_resolution,
basemap_scale_args=[30.0, 58.5, 30.2, 58.5, 120])
# the STEPS nowcast
nowcast_method = nowcasts.get_method("steps")
R_f = nowcast_method(R[-3:, :, :],
V,
12,
