In [1]:

%load_ext autoreload
%autoreload 2

In [2]:

import numpy as np
from fmskill import PointObservation, TrackObservation
from fmskill import ModelResult, Connector

In [3]:

import matplotlib.pyplot as plt
from matplotlib_inline.backend_inline import set_matplotlib_formats
set_matplotlib_formats('png')
%matplotlib inline 

In [4]:

fn = '../tests/testdata/SW/HKZN_local_2017_DutchCoast.dfsu'
mr = ModelResult(fn, name='HKZN_local', item=0)
mr.dfs

Out[4]:

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

In [5]:

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")

In [6]:

o1.hist();

In [7]:

o1.plot(); 

In [8]:

o3 = TrackObservation("../tests/testdata/SW/Alti_c2_Dutch.dfs0", item=3, name="c2")
o3.df.head()

Out[8]:

	Longitude	Latitude	swh
2017-10-27 12:52:52.337	2.422854	51.253353	1.638
2017-10-27 12:52:53.280	2.413789	51.310268	1.383
2017-10-27 12:52:54.224	2.404711	51.367184	1.791
2017-10-27 12:52:55.167	2.395619	51.424099	1.700
2017-10-27 12:52:56.111	2.386516	51.481014	1.800

In [9]:

con = Connector([o1, o2, o3], mr)
con.observations

Out[9]:

{'HKNA': PointObservation: HKNA, x=4.242, y=52.6887,
 'EPL': PointObservation: EPL, x=3.276, y=51.999,
 'c2': TrackObservation: c2, n=298}

In [10]:

con.plot_observation_positions();

In [11]:

cc = con.extract()

In [12]:

cc

Out[12]:

<ComparerCollection>
PointComparer: HKNA
PointComparer: EPL
TrackComparer: c2

In [13]:

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

Out[13]:

	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	0.12	0.90

In [14]:

cc["c2"].skill(metrics="mean_absolute_error")

Out[14]:

	n	mean_absolute_error
observation
c2	113	0.294585

In [15]:

cc["HKNA"].plot_timeseries(figsize=(10,5));

In [16]:

cc["HKNA"].plot_timeseries(width=1000,backend="plotly")

In [17]:

cc["EPL"].scatter(figsize=(8,8), show_hist=True) #, ylim=[2,8], xlim=[1,10])

In [18]:

cc["EPL"].scatter(show_hist=True, backend='plotly', width=600, height=600) #,xlim=[-1,11])

In [19]:

cc["c2"].hist(bins=20)

In [20]:

cc["HKNA"].scatter(binsize=0.25, cmap="viridis")

In [21]:

con.plot_temporal_coverage();

In [22]:

con.plot_temporal_coverage(limit_to_model_period=False);

Automatic reporting¶

In [24]:

from fmskill.report import Reporter
rep = Reporter(con)

In [25]:

%%capture 
rep.to_markdown(); 

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