Quick and dirty compare¶

Sometimes all your need is a simple comparison of two time series. The modelskill.match() method does just that.

In [1]:

import mikeio 
import modelskill as ms

The model¶

Can be either a dfs0 or a DataFrame.

In [2]:

fn_mod = '../tests/testdata/SW/ts_storm_4.dfs0'
df_mod = mikeio.read(fn_mod, items=0).to_dataframe()  # select 1 item

The observation¶

Can be either a dfs0, a DataFrame or a PointObservation object.

In [3]:

fn_obs = '../tests/testdata/SW/eur_Hm0.dfs0'

match()¶

The match() method will return an object that can be used for scatter plots, skill assessment, time series plots etc.

If the observation or model has more than 1 item, the obs_item or mod_item can be given to specify the relevant item.

In [4]:

cmp = ms.match(fn_obs, df_mod)   # both with 1 item only

In [5]:

cmp.plot.timeseries();

In [6]:

cmp.skill()

Out[6]:

	n	bias	rmse	urmse	mae	cc	si	r2
observation
eur_Hm0	67	0.052239	0.22824	0.222181	0.174851	0.968321	0.085898	0.929767

In [7]:

cmp.plot.scatter();

By default the regression line is fitted based on all the data. In order to fit do a weighted regression based on the quantiles, use the fit_to_quantiles option.

In [8]:

cmp.plot.scatter(fit_to_quantiles=True);

An example including the background data and skill table using the show_points use the skill_table options:

In [9]:

ms.settings.load_style(name='MOOD')

In [10]:

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

In [11]:

cmp.plot.taylor(normalize_std=True)

Out[11]:

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