from_matched

from_matched(
    data,
    *,
    obs_item=0,
    mod_items=None,
    aux_items=None,
    quantity=None,
    name=None,
    weight=1.0,
    x=None,
    y=None,
    z=None,
    x_item=None,
    y_item=None,
)

Create a Comparer from data that is already matched (aligned).

Parameters

Name	Type	Description	Default
data	[pd.DataFrame, str, Path, mikeio.Dfs0, mikeio.Dataset]	DataFrame (or object that can be converted to a DataFrame e.g. dfs0) with columns obs_item, mod_items, aux_items	required
obs_item	[str, int]	Name or index of observation item, by default first item	0
mod_items	Iterable[str, int]	Names or indicies of model items, if None all remaining columns are model items, by default None	None
aux_items	Iterable[str, int]	Names or indicies of auxiliary items, by default None	None
quantity	Quantity	Quantity of the observation and model results, by default Quantity(name=“Undefined”, unit=“Undefined”)	None
name	str	Name of the comparer, by default None (will be set to obs_item)	None
x	float	x-coordinate of observation, by default None	None
y	float	y-coordinate of observation, by default None	None
z	float	z-coordinate of observation, by default None	None
x_item	str | int | None	Name of x item, only relevant for track data	None
y_item	str | int | None	Name of y item, only relevant for track data	None

Examples

>>> import pandas as pd
>>> import modelskill as ms
>>> df = pd.DataFrame({'stn_a': [1,2,3], 'local': [1.1,2.1,3.1]}, index=pd.date_range('2010-01-01', periods=3))
>>> cmp = ms.from_matched(df, obs_item='stn_a') # remaining columns are model results
>>> cmp
<Comparer>
Quantity: Undefined [Undefined]
Observation: stn_a, n_points=3
 Model: local, rmse=0.100
>>> df = pd.DataFrame({'stn_a': [1,2,3], 'local': [1.1,2.1,3.1], 'global': [1.2,2.2,3.2], 'nonsense':[1,2,3]}, index=pd.date_range('2010-01-01', periods=3))
>>> cmp = ms.from_matched(df, obs_item='stn_a', mod_items=['local', 'global'])
>>> cmp
<Comparer>
Quantity: Undefined [Undefined]
Observation: stn_a, n_points=3
    Model: local, rmse=0.100
    Model: global, rmse=0.200