wradlib Documentation - Bitbucket

wradlib Documentation, Release 0.1.1
number of neighbours which should be considered in the vicinity of each point in obs
stat: string :
function name
__call__(raw[, obs])
Returns the values of raw at the observation locations
wradlib.adjust.Raw_at_obs.__call__
Raw_at_obs.__call__(raw, obs=None)
Returns the values of raw at the observation locations
Parameters raw : array of float
raw values
3.13.4 References
3.14 Verification
Verification mainly refers to the comparison of radar-based precipitation estimates to ground truth.
ErrorMetrics
PolarNeighbours
Compute quality metrics from a set of observations (obs) and estimates (est).
For a set of projected point coordinates, extract the neighbouring bin values from a data set in polar coordina
3.14.1 wradlib.verify.ErrorMetrics
class wradlib.verify.ErrorMetrics(obs, est, minval=None)
Compute quality metrics from a set of observations (obs) and estimates (est).
First create an instance of the class using the set of observations and estimates. Then compute quality metrics
using the class methods. A dictionary of all available quality metrics is returned using the all method. Method
report pretty prints all these metrics over a scatter plot.
Parameters obs: array of floats :
observations (e.g. rain gage observations)
est: array of floats :
estimates (e.g. radar, adjusted radar, ...)
minval : float
threshold value in order to compute metrics only for values larger than minval
Examples
>>> obs = np.random.uniform(0,10,100)
>>> est = np.random.uniform(0,10,100)
>>> metrics = ErrorMetrics(obs,est)
>>> metrics.all()
>>> metrics.pprint()
3.14. Verification 79