Combine comparers¶

FMskill 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]:

from fmskill import ModelResult, PointObservation, TrackObservation, Connector
%matplotlib inline

Observations¶

In [2]:

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

Model Results¶

In [3]:

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

Out[3]:

<XArrayModelResultItem> 'CMEMS'
- Item: VHM0

In [4]:

mr2 = ModelResult('../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]:

con1 = Connector([o1,o2,o3],mr1)
con2 = Connector([o1,o2,o3],mr2)
con1.plot_temporal_coverage(limit_to_model_period=False)
con2.plot_temporal_coverage(limit_to_model_period=False);

In [6]:

cc1 = con1.extract()
cc2 = con2.extract()

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	0.087342	0.445177
HKNA	242	-0.741920	0.881698	0.476388	0.741920	0.902987	0.123507	0.221544
c2	123	-0.349376	0.448139	0.280651	0.383551	0.912560	0.062328	0.529142

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	0.078296	0.932596
HKNA	386	-0.194260	0.351964	0.293499	0.251839	0.971194	0.088669	0.905300
c2	113	-0.001210	0.351796	0.351794	0.294585	0.974335	0.118511	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	0.087342	0.445177
	HKNA	242	-0.741920	0.881698	0.476388	0.741920	0.902987	0.123507	0.221544
	c2	41	-0.311355	0.474520	0.358089	0.402269	0.937073	0.087109	0.740606
MIKE21SW	EPL	43	-0.078281	0.204842	0.189294	0.173804	0.973262	0.060515	0.913476
	HKNA	242	-0.229809	0.411363	0.341185	0.295643	0.948802	0.088455	0.830548
	c2	41	0.327152	0.410106	0.247303	0.357670	0.964173	0.060159	0.806250

In [ ]: