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_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,
)
timestep = rcparams.data_sources["mch"]["timestep"]
importer_name = rcparams.data_sources["mch"]["importer"]
importer_kwargs = rcparams.data_sources["mch"]["importer_kwargs"]
# read precip field
date = datetime.strptime("201607112100", "%Y%m%d%H%M")
fns = io.find_by_date(date,
root,
fmt,
pattern,
ext,
timestep,
num_prev_files=2)
importer = io.get_method(importer_name, "importer")
precip, __, metadata = io.read_timeseries(fns, importer, **importer_kwargs)
precip, metadata = utils.to_rainrate(precip, metadata)
# precip[np.isnan(precip)] = 0
# motion
motion = dense_lucaskanade(precip)
# parameters
nleadtimes = 6
thr = 1 # mm / h
slope = 1 * timestep # km / min
# compute probability forecast
extrap_kwargs = dict(allow_nonfinite_values=True)
fct = forecast(precip[-1],
motion,
nleadtimes,
###############################################################################
# Read the input data
# -------------------
#
# As first step, we need to import the precipitation field that we are going
# to use in this example.
# Import the example radar composite
root_path = rcparams.data_sources["mch"]["root_path"]
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"]
###############################################################################
datasource["fn_ext"],
datasource["timestep"],
num_prev_files=2)
R, _, metadata = io.readers.read_timeseries(
fns, importer, **datasource["importer_kwargs"])
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
# convert to mm/h
R, metadata = utils.to_rainrate(R, metadata)
# threshold the data
R[R < 0.1] = 0.0
metadata["threshold"] = 0.1
# set NaN equal to zero
R[~np.isfinite(R)] = 0.0
# transform to dBR
R, metadata = utils.dB_transform(R, metadata, zerovalue=zero_value_dbr)
R_ = R.copy()
# Remove rain/no-rain discontinuity so that dBR field starts from 0
if remove_norain_step:
importer_name = data_source["importer"]
importer_kwargs = data_source["importer_kwargs"]
# Find the input files in the archive. Use history length of 5 timesteps
filenames = io.archive.find_by_date(
date, root_path, path_fmt, fn_pattern, fn_ext, timestep=5, num_prev_files=5
)
# Read the input time series
importer = io.get_method(importer_name, "importer")
rainrate_field, quality, metadata = io.read_timeseries(
filenames, importer, **importer_kwargs
)
# Convert to rain rate (mm/h)
rainrate_field, metadata = utils.to_rainrate(rainrate_field, metadata)
################################################################################
# Compute the advection field
# ---------------------------
#
# Apply the Lucas-Kanade method with the parameters given in Pulkkinen et al.
# (2020) to compute the advection field.
fd_kwargs = {}
fd_kwargs["max_corners"] = 1000
fd_kwargs["quality_level"] = 0.01
fd_kwargs["min_distance"] = 2
fd_kwargs["block_size"] = 8
lk_kwargs = {}
