Model skill visualisation

import modelskill as ms

Model skill visualisation#

fn = 'data/SW/HKZN_local_2017_DutchCoast.dfsu'
mr = ms.model_result(fn, name='HKZN_local', item=0)

obs = [ms.PointObservation('data/SW/HKZA_Hm0.dfs0', item=0, x=3.9, y=52.7, name="HKZA"),
        ms.PointObservation('data/SW/HKZA_Hm0.dfs0', item=0, x=3.8, y=52.5, name="HKZA_2"),
        ms.PointObservation('data/SW/HKZA_Hm0.dfs0', item=0, x=3.5, y=52.6, name="HKZA_3"),
        ms.PointObservation('data/SW/HKNA_Hm0.dfs0', item=0, x=4.2420, y=52.6887, name="HKNA"),
        ms.PointObservation('data/SW/HKNA_Hm0.dfs0', item=0, x=4.2, y=52.6, name="HKNA_2"),
        ms.PointObservation('data/SW/HKNA_Hm0.dfs0', item=0, x=4.3, y=52.7, name="HKNA_3"),
        ms.PointObservation("data/SW/eur_Hm0.dfs0", item=0, x=3.2760, y=51.9990, name="EPL"),
        ms.PointObservation("data/SW/eur_Hm0.dfs0", item=0, x=3.2, y=51.9, name="EPL_2"),
        ms.PointObservation("data/SW/eur_Hm0.dfs0", item=0, x=3.3, y=51.95, name="EPL_3")
]

cc = ms.match(obs=obs, mod=mr)
cc

<ComparerCollection>
Comparers:
0: HKZA - Significant wave height [m]
1: HKZA_2 - Significant wave height [m]
2: HKZA_3 - Significant wave height [m]
3: HKNA - Significant wave height [m]
4: HKNA_2 - Significant wave height [m]
5: HKNA_3 - Significant wave height [m]
6: EPL - Significant wave height [m]
7: EPL_2 - Significant wave height [m]
8: EPL_3 - Significant wave height [m]

Data analysis#

cc.skill()
n bias rmse urmse mae cc si r2
observation
HKZA 397 0.052742 0.287870 0.282997 0.224974 0.965311 0.097643 0.929193
HKZA_2 397 -0.056992 0.281897 0.276075 0.216178 0.968424 0.095254 0.932101
HKZA_3 397 -0.046658 0.267980 0.263886 0.207180 0.971200 0.091049 0.938640
HKNA 386 -0.202413 0.355195 0.291877 0.255866 0.971708 0.093967 0.903554
HKNA_2 386 -0.263090 0.403130 0.305446 0.300016 0.970160 0.098335 0.875765
HKNA_3 386 -0.221611 0.367311 0.292926 0.267783 0.971869 0.094304 0.896862
EPL 66 -0.070746 0.226261 0.214916 0.190754 0.969425 0.082737 0.931269
EPL_2 66 -0.155260 0.266044 0.216041 0.229844 0.968181 0.083170 0.904974
EPL_3 66 -0.071587 0.226266 0.214643 0.191164 0.969477 0.082632 0.931266 
s = cc.skill()
type(s)

modelskill.skill.SkillTable

s.rmse.plot.bar();
_images/8de98a01a64d301801857f5547efe7b30c09daf22e191ef274e353895f3d851d.png 
s['urmse'].plot.line();
_images/ee17bab1f48b6ee03f46bf53d3faf61729788287a5ec270816e29897d589fadf.png

Custom plot#

All skill statistics are available in a dataframe, and in case you need a tailor-made plot, you can get data and use matplotlib to get exactly what you need.

import matplotlib.pyplot as plt

df = s.to_dataframe().sort_values('bias')
x = df.bias
y = df.index

plt.subplots(figsize=(6,6))
plt.scatter(x,y,marker='D',c='red',s=100)
plt.xlim(-0.35,0.35)
plt.axvline(0,linestyle='--')
plt.xlabel("Bias (m)")
plt.title(mr.name)
plt.grid()
_images/3eff4d5ff07fc5dcb6b0fbb90fd32c5046ead5c9712e854436de2a9f4223fa12.png
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