Combine comparers¶

ModelSkill comparers can be combined by using the "+" operator. You may want to add a new ModelResult to your existing comparison or a new observation or a new time period:

cc = cc1 + cc2

In [1]:

import modelskill as ms

Observations¶

In [2]:

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")

Model Results¶

In [3]:

fn = "../tests/testdata/SW/CMEMS_DutchCoast_*.nc"
mr1 = ms.model_result(fn, name='CMEMS', item='VHM0')
mr1

Out[3]:

<modelskill.model.grid.GridModelResult at 0x13a26ac96d0>

In [4]:

mr2 = ms.model_result('../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu', name='MIKE21SW', item=0)

Connect and extract¶

Notice that the two ModelResults doesn't cover the exact same period.

In [5]:

ms.plotting.temporal_coverage(obs=[o1,o2,o3], mod=[mr1,mr2], limit_to_model_period=False)

Out[5]:

<Axes: >

In [6]:

cc1 = ms.match(obs=[o1,o2,o3], mod=mr1)#, name='SW')
cc2 = ms.match(obs=[o1,o2,o3], mod=mr2)# , name='SW')

In [7]:

cc1.skill()

Out[7]:

	n	bias	rmse	urmse	mae	cc	si	r2
observation
EPL	43	-0.440930	0.518713	0.273210	0.443256	0.920165	0.089591	0.445177
HKNA	242	-0.741920	0.881698	0.476388	0.741920	0.902987	0.127873	0.221544
c2	39	-0.302117	0.472280	0.363006	0.397694	0.935287	0.091603	0.745605

In [8]:

cc2.skill()

Out[8]:

	n	bias	rmse	urmse	mae	cc	si	r2
observation
EPL	67	-0.066597	0.223597	0.213449	0.188513	0.969846	0.082522	0.932596
HKNA	386	-0.194260	0.351964	0.293499	0.251839	0.971194	0.094489	0.905300
c2	113	-0.001210	0.351797	0.351794	0.294585	0.974335	0.127776	0.899507

Add the two Comparers¶

In [9]:

cc = cc1 + cc2

In [10]:

cc.skill()

Out[10]:

		n	bias	rmse	urmse	mae	cc	si	r2
model	observation
CMEMS	EPL	43	-0.440930	0.518713	0.273210	0.443256	0.920165	0.089591	0.445177
	HKNA	242	-0.741920	0.881698	0.476388	0.741920	0.902987	0.127873	0.221544
	c2	39	-0.302117	0.472280	0.363006	0.397694	0.935287	0.091603	0.745605
MIKE21SW	EPL	43	-0.078281	0.204842	0.189294	0.173804	0.973262	0.062073	0.913476
	HKNA	242	-0.229809	0.411363	0.341185	0.295643	0.948802	0.091582	0.830548
	c2	39	0.321283	0.406855	0.249616	0.353367	0.963817	0.062990	0.811206