print(('aggregating timeseries for comid {} at {}, {}\n'.format(*i)))
# create weighting factors using the inverse-distance squared
weights = []
# get the distances between each station and the centroid
for name, x, y in zip(names, lons, lats):
point = x, y
# the processor has "get_distance" method to estimate the distance in
# kilometers between two points given as (lon1, lat1), (lon2, lat2)
distance = processor.get_distance(point, (lon, lat))
print(
('the distance to station {} is {:.1f} km'.format(name, distance)))
weights.append(1 / distance**2)
# convert weights to a numpy array
weights = numpy.array(weights)
# generate a mask array to hide missing values (as shown previously)
mask = numpy.invert(numpy.isnan(precipitations))
# go through each hour and get the weighted average of the available values
print('aggregating timeseries for comid {} at {}, {}\n'.format(*i))
# create weighting factors using the inverse-distance squared
weights = []
# get the distances between each station and the centroid
for name, x, y in zip(names, lons, lats):
point = x,y
# the processor has "get_distance" method to estimate the distance in
# kilometers between two points given as (lon1, lat1), (lon2, lat2)
distance = processor.get_distance(point, (lon, lat))
print('the distance to station {} is {:.1f} km'.format(name, distance))
weights.append(1 / distance**2)
# convert weights to a numpy array
weights = numpy.array(weights)
# generate a mask array to hide missing values (as shown previously)
mask = numpy.invert(numpy.isnan(precipitations))
# go through each hour and get the weighted average of the available values
