In [1]:

import numpy as np
import modelskill as ms

In [2]:

fn = '../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu'
mr = ms.model_result(fn, name='HKZN_local', item=0)
mr.data

Out[2]:

Dfsu2D
number of elements: 958
number of nodes: 570
projection: LONG/LAT
number of items: 15
time: 23 steps with dt=10800.0s
      2017-10-27 00:00:00 -- 2017-10-29 18:00:00

Configuration of comparison, see SW_DutchCoast.ipynb for more details.

In [3]:

o1 = ms.PointObservation('../tests/testdata/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name="HKNA")
o2 = ms.PointObservation("../tests/testdata/SW/eur_Hm0.dfs0", item=0, x=3.2760, y=51.9990, name="EPL")
o3 = ms.TrackObservation("../tests/testdata/SW/Alti_c2_Dutch.dfs0", item=3, name="c2")
cc = ms.match([o1, o2, o3], mr)
cc

Out[3]:

<ComparerCollection>
Comparer: HKNA
Comparer: EPL
Comparer: c2

Standard set of metrics

In [4]:

cc.skill().style(precision=2)

Out[4]:

	n	bias	rmse	urmse	mae	cc	si	r2
observation
EPL	67	-0.07	0.22	0.21	0.19	0.97	0.08	0.93
HKNA	386	-0.19	0.35	0.29	0.25	0.97	0.09	0.91
c2	113	-0.00	0.35	0.35	0.29	0.97	0.13	0.90

Select a specific metric

In [5]:

cc.skill(metrics="mean_absolute_error")

Out[5]:

	n	mean_absolute_error
observation
EPL	67	0.188513
HKNA	386	0.251839
c2	113	0.294585

Some metrics has parameters, which require a bit special treatment.

In [6]:

import modelskill.metrics as mtr
from modelskill.metrics import hit_ratio

def hit_ratio_05_pct(obs, model):
    return hit_ratio(obs, model, 0.5) * 100

def hit_ratio_01_pct(obs, model):
    return hit_ratio(obs, model, 0.1) * 100


mtr.add_metric(hit_ratio_05_pct)
mtr.add_metric(hit_ratio_01_pct)

cc.skill(metrics=[hit_ratio_05_pct, hit_ratio_01_pct]).style(precision=0)

Out[6]:

	n	hit_ratio_05_pct	hit_ratio_01_pct
observation
EPL	67	99	27
HKNA	386	87	30
c2	113	86	17

And you are of course always free to specify your own special metric.

In [7]:

def my_special_metric(obs, model):

    res = obs - model

    res_clipped = np.clip(res,0,np.inf)

    return np.mean(np.abs(res_clipped))

mtr.add_metric(my_special_metric)

cc.skill(metrics=my_special_metric)

Out[7]:

	n	my_special_metric
observation
EPL	67	0.127555
HKNA	386	0.223049
c2	113	0.147897