Filter multisensor data

This module groups all the functions used to filter data based in other sensor data

Filter links based on distance to nearest gauge

Check the Sample data doc page to see how cml and gauges datasets are structured and what variables they contain.

cml, gauges = open_cml_sample(),open_gauge_sample()

source

filter_by_distance_to_nearest_gauge


def filter_by_distance_to_nearest_gauge(
    cml:Dataset, # CML dataset containing link information with coordinates
    gauges:Dataset, # Dataset containing rain gauge locations
    mx_dist:float, # Maximum allowed distance (in km) from a link center to its nearest gauge. Links farther than this are filtered out.
    gauge_lat:str='lat', # Name of the latitude coordinate in the gauges dataset
    gauge_lon:str='lon', # Name of the longitude coordinate in the gauges dataset
)->Dataset: # Filtered CML dataset, containing only links whose center is within `mx_dist` km of at least one gauge

Filter CML links based on their distance to the nearest rain gauge.

filtered_cml = filter_by_distance_to_nearest_gauge(cml, gauges, 2.5)
filtered_cml

<xarray.Dataset> Size: 62MB
Dimensions:      (cml_id: 72, sublink_id: 6, time: 2964)
Coordinates: (10)
Data variables:
    rsl_avg      (cml_id, sublink_id, time) float64 10MB -54.1 -54.1 ... nan nan
    tsl_avg      (cml_id, sublink_id, time) float64 10MB 10.5 10.5 ... nan nan
    rsl_min      (cml_id, sublink_id, time) float64 10MB -54.3 -54.3 ... nan nan
    tsl_min      (cml_id, sublink_id, time) float64 10MB 10.5 10.5 ... nan nan
    rsl_max      (cml_id, sublink_id, time) float64 10MB -53.8 -53.8 ... nan nan
    tsl_max      (cml_id, sublink_id, time) float64 10MB 10.5 10.5 ... nan nan
Attributes:
    title:                 East side Douala CML links sample data
    file author(s):        Orange Cameroun and IRD Rainsmore Group
    institution:           Orange Cameroun and IRD Rainsmore Group
    date:                  2025-11-07
    source:                Modified Orange Cameroun CML data for example purp...
    naming convention:     COST ACTION OPENSENSE V2
    license restrictions:  CC BY-NC-ND 4.0

Code

links = cml.cml_id.to_dataframe()
links_geo = gpd.GeoDataFrame(index=links.index, geometry=[LineString([(r.site_0_lon, r.site_0_lat), (r.site_1_lon, r.site_1_lat)]) for idx, r in links.iterrows()])
filt_links = filtered_cml.cml_id.to_dataframe()
filt_links_geo = gpd.GeoDataFrame(index=filt_links.index, geometry=[LineString([(r.site_0_lon, r.site_0_lat), (r.site_1_lon, r.site_1_lat)]) for idx, r in filt_links.iterrows()])
gauge_geo = gpd.GeoDataFrame(index=gauge_coords.index, geometry=gpd.points_from_xy(gauge_coords.lon, gauge_coords.lat))

fig, ax = plt.subplots(figsize=(12, 9))
links_geo.plot(ax=ax, color='red', linewidth=0.8, label="Source")
filt_links_geo.plot(ax=ax, color='black', linewidth=0.8, label="Filtered")
gauge_geo.plot(ax=ax, color='yellow', edgecolor='black', markersize=80, zorder=5, label="Gauges")
ax.legend(loc="upper left")
ax.set_title("CML Links and Rain Gauges");