TrackObservation

TrackObservation(
    self,
    data,
    *,
    item=None,
    name=None,
    weight=1.0,
    x_item=0,
    y_item=1,
    keep_duplicates='first',
    quantity=None,
    aux_items=None,
    attrs=None,
)

Class for observation with locations moving in space, e.g. satellite altimetry

The data needs in addition to the datetime of each single observation point also, x and y coordinates.

Create TrackObservation from dfs0 or DataFrame

Parameters

Name	Type	Description	Default
data	(str, Path, mikeio.Dataset, pd.DataFrame, xr.Dataset)	path to dfs0 file or object with track data	required
item	(str, int)	item name or index of values, by default None if data contains more than one item, item must be given	None
name	str	user-defined name for easy identification in plots etc, by default file basename	None
x_item	(str, int)	item name or index of x-coordinate, by default 0	0
y_item	(str, int)	item name or index of y-coordinate, by default 1	1
keep_duplicates	(str, bool)	strategy for handling duplicate timestamps (xarray.Dataset.drop_duplicates): “first” to keep first occurrence, “last” to keep last occurrence, False to drop all duplicates, “offset” to add milliseconds to consecutive duplicates, by default “first”	'first'
quantity	Quantity	The quantity of the observation, for validation with model results For MIKE dfs files this is inferred from the EUM information	None
aux_items	list	list of names or indices of auxiliary items, by default None	None
attrs	dict	additional attributes to be added to the data, by default None	None
weight	float	weighting factor for skill scores, by default 1.0	1.0

Examples

>>> import modelskill as ms
>>> o1 = ms.TrackObservation("track.dfs0", item=2, name="c2")

>>> o1 = ms.TrackObservation("track.dfs0", item="wind_speed", name="c2")

>>> o1 = ms.TrackObservation("lon_after_lat.dfs0", item="wl", x_item=1, y_item=0)

>>> o1 = ms.TrackObservation("track_wl.dfs0", item="wl", x_item="lon", y_item="lat")

>>> df = pd.DataFrame(
...         {
...             "t": pd.date_range("2010-01-01", freq="10s", periods=n),
...             "x": np.linspace(0, 10, n),
...             "y": np.linspace(45000, 45100, n),
...             "swh": [0.1, 0.3, 0.4, 0.5, 0.3],
...         }
... )
>>> df = df.set_index("t")
>>> df
                    x        y  swh
t
2010-01-01 00:00:00   0.0  45000.0  0.1
2010-01-01 00:00:10   2.5  45025.0  0.3
2010-01-01 00:00:20   5.0  45050.0  0.4
2010-01-01 00:00:30   7.5  45075.0  0.5
2010-01-01 00:00:40  10.0  45100.0  0.3
>>> t1 = TrackObservation(df, name="fake")
>>> t1.n_points
5
>>> t1.values
array([0.1, 0.3, 0.4, 0.5, 0.3])
>>> t1.time
DatetimeIndex(['2010-01-01 00:00:00', '2010-01-01 00:00:10',
           '2010-01-01 00:00:20', '2010-01-01 00:00:30',
           '2010-01-01 00:00:40'],
          dtype='datetime64[ns]', name='t', freq=None)
>>> t1.x
array([ 0. ,  2.5,  5. ,  7.5, 10. ])
>>> t1.y
array([45000., 45025., 45050., 45075., 45100.])

Attributes

Name	Description
attrs	Attributes of the observation
gtype	Geometry type
n_points	Number of data points
name	Name of time series (value item name)
plot	Plot using the ComparerPlotter
quantity	Quantity of time series
time	Time index
values	Values as numpy array
weight	Weighting factor for skill scores
x	x-coordinate
y	y-coordinate

Methods

Name	Description
equals	Check if two TimeSeries are equal
sel	Select data by label
to_dataframe	Convert matched data to pandas DataFrame
trim	Trim observation data to a given time interval

equals

TrackObservation.equals(other)

Check if two TimeSeries are equal

sel

TrackObservation.sel(**kwargs)

Select data by label

to_dataframe

TrackObservation.to_dataframe()

Convert matched data to pandas DataFrame

Include x, y coordinates only if gtype=track

Returns

Name	Type	Description
	pd.DataFrame	data as a pandas DataFrame

trim

TrackObservation.trim(start_time=None, end_time=None, buffer='1s')

Trim observation data to a given time interval

Parameters

Name	Type	Description	Default
start_time	pd.Timestamp	start time	None
end_time	pd.Timestamp	end time	None
buffer	str	buffer time around start and end time, by default “1s”	'1s'