# data source
source = rcparams.data_sources["mch"]
root = rcparams.data_sources["mch"]["root_path"]
fmt = rcparams.data_sources["mch"]["path_fmt"]
pattern = rcparams.data_sources["mch"]["fn_pattern"]
ext = rcparams.data_sources["mch"]["fn_ext"]
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
data_source = "mch"
# Load data source config
root_path = rcparams.data_sources[data_source]["root_path"]
path_fmt = rcparams.data_sources[data_source]["path_fmt"]
fn_pattern = rcparams.data_sources[data_source]["fn_pattern"]
fn_ext = rcparams.data_sources[data_source]["fn_ext"]
importer_name = rcparams.data_sources[data_source]["importer"]
importer_kwargs = rcparams.data_sources[data_source]["importer_kwargs"]
timestep = rcparams.data_sources[data_source]["timestep"]
# Find the radar files in the archive
fns = io.find_by_date(date,
root_path,
path_fmt,
fn_pattern,
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
###############################################################################
# Read the input rain rate fields
# -------------------------------
date = datetime.strptime("201701311200", "%Y%m%d%H%M")
data_source = "mch"
# Read the data source information from rcparams
datasource_params = rcparams.data_sources[data_source]
# Find the radar files in the archive
fns = io.find_by_date(
date,
datasource_params["root_path"],
datasource_params["path_fmt"],
datasource_params["fn_pattern"],
datasource_params["fn_ext"],
datasource_params["timestep"],
num_prev_files=2,
)
# 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)