# Filter multisensor 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. ``` python cml, gauges = open_cml_sample(),open_gauge_sample() ``` ------------------------------------------------------------------------ source ### filter_by_distance_to_nearest_gauge ``` python 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.* ``` python 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

``` python 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"); ```

![](05_data.filter_files/figure-commonmark/cell-5-output-1.png)