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_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)
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()
# Estimate the motion field with Lucas-Kanade
oflow_method = motion.get_method("LK")
V = oflow_method(R[-3:, :, :])
# Extrapolate the last radar observation
extrapolate = nowcasts.get_method("extrapolation")
R[~np.isfinite(R)] = metadata["zerovalue"]
R_f = extrapolate(R[-1, :, :], V, n_leadtimes)
# Back-transform to rain rate
R_f = transformation.dB_transform(R_f, threshold=-10.0, inverse=True)[0]
# Plot the motion field
plot_precip_field(R_, geodata=metadata)
quiver(V, geodata=metadata, step=50)
###############################################################################
# Verify with FSS
# ---------------
#
# The fractions skill score (FSS) provides an intuitive assessment of the
# dependency of skill on spatial scale and intensity, which makes it an ideal
# skill score for high-resolution precipitation forecasts.
# Find observations in the data archive
fns = io.archive.find_by_date(
date,
root_path,
path_fmt,
fn_pattern,
################################################################################
# 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")
V2 = oflow_method(R[-3:, :, :])
