def test_decompose_recompose():
"""Tests cascade decomposition."""
pytest.importorskip("netCDF4")
root_path = pysteps.rcparams.data_sources["bom"]["root_path"]
rel_path = os.path.join("prcp-cscn", "2", "2018", "06", "16")
filename = os.path.join(root_path, rel_path, "2_20180616_120000.prcp-cscn.nc")
precip, _, metadata = pysteps.io.import_bom_rf3(filename)
# Convert to rain rate from mm
precip, metadata = pysteps.utils.to_rainrate(precip, metadata)
# Log-transform the data
precip, metadata = pysteps.utils.dB_transform(
precip, metadata, threshold=0.1, zerovalue=-15.0
)
# Set Nans as the fill value
precip[~np.isfinite(precip)] = metadata["zerovalue"]
# Set number of cascade levels
num_cascade_levels = 9
# Construct the Gaussian bandpass filters
_filter = filter_gaussian(precip.shape, num_cascade_levels)
# Decompose precip
decomp = decomposition_fft(precip, _filter)
# Recomposed precip from decomp
recomposed = recompose_fft(decomp)
# Assert
assert_array_almost_equal(recomposed.squeeze(), precip)
cmap = cm.RdBu_r
vmin = -3
vmax = 3
fn = io.archive.find_by_date(date, root_path, "%Y%m%d", fn_pattern, fn_ext, 5)
R, _, metadata = io.read_timeseries(fn, import_fmi_pgm, gzipped=True)
R = R.squeeze()
R[R < 10.0] = 5.0
R[~np.isfinite(R)] = 5.0
R = (R - np.mean(R)) / np.std(R)
filter = filter_gaussian(R.shape, 8)
decomp = decomposition_fft(R, filter)
for i in range(8):
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)
im = ax[0, 0].imshow(R, cmap=cmap, vmin=vmin, vmax=vmax)
ax[0, 1].imshow(decomp["cascade_levels"][0],
cmap=cmap,
vmin=vmin,
vmax=vmax,
rasterized=True)
ax[0, 2].imshow(decomp["cascade_levels"][1],
)
ax.set_xlim(1, L / 2)
ax.set_ylim(0, 1)
xt = np.hstack([[1.0], filter["central_wavenumbers"][1:]])
ax.set_xticks(xt)
ax.set_xticklabels(["%.2f" % cf for cf in filter["central_wavenumbers"]])
ax.set_xlabel("Radial wavenumber $|\mathbf{k}|$")
ax.set_ylabel("Normalized weight")
ax.set_title("Bandpass filter weights")
plt.show()
###############################################################################
# Finally, apply the 2D Gaussian filters to decompose the radar rainfall field
# into a set of cascade levels of decreasing spatial scale and plot them.
decomp = decomposition_fft(R, filter, compute_stats=True)
# Plot the normalized cascade levels
for i in range(num_cascade_levels):
mu = decomp["means"][i]
sigma = decomp["stds"][i]
decomp["cascade_levels"][i] = (decomp["cascade_levels"][i] - mu) / sigma
fig, ax = plt.subplots(nrows=2, ncols=4)
ax[0, 0].imshow(R, cmap=cm.RdBu_r, vmin=-5, vmax=5)
ax[0, 1].imshow(decomp["cascade_levels"][0], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[0, 2].imshow(decomp["cascade_levels"][1], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[0, 3].imshow(decomp["cascade_levels"][2], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[1, 0].imshow(decomp["cascade_levels"][3], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[1, 1].imshow(decomp["cascade_levels"][4], cmap=cm.RdBu_r, vmin=-3, vmax=3)
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)
ax[0, 1].imshow(decomp["cascade_levels"][0], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[0, 2].imshow(decomp["cascade_levels"][1], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[0, 3].imshow(decomp["cascade_levels"][2], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[1, 0].imshow(decomp["cascade_levels"][3], cmap=cm.RdBu_r, vmin=-3, vmax=3)
ax[1, 1].imshow(decomp["cascade_levels"][4], cmap=cm.RdBu_r, vmin=-3, vmax=3)