In [1]:

import modelskill as ms

In [2]:

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

Out[2]:

Dfsu2D
number of elements: 3612
number of nodes: 2046
projection: UTM-33
items:
  0:  Surface elevation <Surface Elevation> (meter)
  1:  Total water depth <Water Depth> (meter)
  2:  U velocity <u velocity component> (meter per sec)
  3:  V velocity <v velocity component> (meter per sec)
  4:  Wind U velocity <Wind Velocity> (meter per sec)
  5:  Wind V velocity <Wind Velocity> (meter per sec)
  6:  Air pressure <Pressure2> (pascal)
time: 51 steps with dt=12000.0s
      2018-03-04 00:00:00 -- 2018-03-10 22:40:00

In [3]:

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

PointObservation: smhi_2095_klagshamn, x=366844.15, y=6154291.6
50328 2015-01-01 01:00:00

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 [meter]
Observation: smhi_2095_klagshamn, n_points=167
 Model: Oresund2D, rmse=0.189

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': -2.6592168254494768e-17}

In [17]:

cmp.skill().style()

Out[17]:

	n	bias	rmse
observation
smhi_2095_klagshamn	167	0.184	0.189

In [15]:

cmp.score("bias")

Out[15]:

{'Oresund2D': 0.18441130340484135}

In [9]:

cmp.plot.timeseries()

Out[9]:

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

In [10]:

cmp.plot.residual_hist(bins=40);

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	0.184411	0.188837	0.040645	0.184411	0.841135	0.376413	-5.338911

Or choose specific metrics

In [12]:

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

Out[12]:

	n	bias	rmse
observation
smhi_2095_klagshamn	167	0.184411	0.188837

In [13]:

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

In [14]:

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

In [ ]: