MIKE21 HD¶

In [1]:

import modelskill as ms

In [2]:

mr = ms.model_result('../tests/testdata/Oresund2D.dfsu',
                     item='Surface elevation')
mr

Out[2]:

<DfsuModelResult>: Oresund2D
Time: 2018-03-04 00:00:00 - 2018-03-10 22:40:00
Quantity: Surface Elevation [m]

In [3]:

fn = '../tests/testdata/smhi_2095_klagshamn.dfs0'
o1 = ms.PointObservation(fn, x=366844.15, y=6154291.6, item=0) 
o1

Out[3]:

<PointObservation>: smhi_2095_klagshamn
Location: 366844.15, 6154291.6
Time: 2015-01-01 01:00:00 - 2020-09-28 00:00:00
Quantity: Water Level [m]

In [4]:

ms.plotting.spatial_overview(o1, mr, figsize=(8, 8));

In [5]:

cmp = ms.match(o1, mr)
cmp

Out[5]:

<Comparer>
Quantity: Water Level [m]
Observation: smhi_2095_klagshamn, n_points=167
Model(s):
0: Oresund2D

Most use cases will compare many observed locations to a one or more models.

In this case we only have one observed location.

In [6]:

cmp.plot.timeseries(figsize=(10,4));

In [7]:

ub_cmp = cmp.remove_bias()
ub_cmp.plot.timeseries(figsize=(10,4));

In [8]:

ub_cmp.score("bias")

Out[8]:

{'Oresund2D': -2.9251385079944247e-17}

In [9]:

cmp.score("bias")

Out[9]:

{'Oresund2D': 0.1868744370781926}

In [10]:

cmp.plot.timeseries()

Out[10]:

<Axes: title={'center': 'smhi_2095_klagshamn'}, xlabel='time', ylabel='Water Level [m]'>

Get skill for a commonly used set of metrics

In [11]:

cmp.skill()

Out[11]:

	n	bias	rmse	urmse	mae	cc	si	r2
observation
smhi_2095_klagshamn	167	0.186874	0.191303	0.040924	0.186874	0.838306	0.378995	-5.505521

Or choose specific metrics

In [12]:

cmp.skill(metrics = ["bias","rmse"])

Out[12]:

	n	bias	rmse
observation
smhi_2095_klagshamn	167	0.186874	0.191303

In [13]:

cmp.plot.scatter(bins=0.02, cmap='YlOrRd', show_points=True);

In [14]:

cmp.plot.scatter(skill_table=True, show_points=True);