pastas.model.Model

class pastas.model.Model(oseries: pandas.Series, constant: bool = True, name: str | None = None, metadata: dict[str, Any] | None = None, freq: str = 'D')

Class that initiates a Pastas time series model.

Parameters:

• oseries (pandas.Series) – pandas.Series object containing the dependent time series. The observation can be non-equidistant.

• constant (bool, optional) – Add a constant to the model (Default=True).

• name (str, optional) – String with the name of the model, used in plotting and saving.

• metadata (dict, optional) – Dictionary containing metadata of the oseries, passed on to the oseries when creating a pastas TimeSeries object. hence, ml.oseries.metadata will give you the metadata.

• freq (str, optional) – String with the frequency the stressmodels are simulated. Must be one of the following: (D, h, m, s, ms, us, ns) or a multiple of that e.g. “7D”. Default is “D”. New in 0.18.0.

Returns:

ml – Pastas Model instance, the base object in Pastas.

Return type:

pastas.model.Model

Examples

A minimal working example of the Model class is shown below:

>>> oseries = pd.Series([1,2,1], index=pd.to_datetime(range(3), unit="D"))
>>> ml = Model(oseries)

property parameters: pandas.DataFrame

Get model parameters DataFrame.

Returns:

parameters – DataFrame containing the model parameters. This is a read-only view; modifications to this DataFrame will not affect the model.

Return type:

pandas.DataFrame

Notes

The parameters attribute is read-only. To modify parameter properties, use the set_parameter method instead.

See also

set_parameter

Method to change parameter properties.

property settings: dict[str, Any]

Get model settings dictionary.

Returns:

settings – Dictionary containing the model settings. This is a read-only view; modifications to this dictionary will not affect the model.

Return type:

dict

Notes

The settings attribute is read-only. Model settings are automatically updated through methods like ml.solve() and ml.set_settings().

property oseries_calib

Deprecated property for calibration observations.

Use ml.observations() instead.

property time_offset: pandas.Timedelta

Property to get the time offset from the settings.

property sim_index: pandas.DatetimeIndex

Property that returns the simulation index, including the warmup.

Using the tmin, tmax, freq, and warmup from the model settings, a DatetimeIndex is created that includes the warmup period. This index is used for simulating the model and calculating the residuals.

Returns:

sim_index – Pandas DatetimeIndex instance with the datetimes values for which the model is simulated.

Return type:

pandas.DatetimeIndex

Methods

add_stressmodel(→ None)	Add a stressmodel to the main model.
add_constant(→ None)	Add a Constant to the time series Model.
add_transform(transform)	Add a Transform to the time series Model.
add_noisemodel(→ None)	Add a noisemodel to the time series Model.
add_solver(→ None)	Add a solver to the model.
del_stressmodel(name)	Safely delete a stress model from the Model.
del_constant(→ None)	Safely delete the Constant from the Model.
del_transform(→ None)	Safely delete the transform from the Model.
del_noisemodel(→ None)	Safely delete the noise model from the Model.
simulate(→ pandas.Series)	Simulate the time series model.
residuals(→ pandas.Series)	Calculate the residual series.
noise(→ pandas.Series)	Simulate the noise when a noisemodel is present.
observations(→ pandas.Series)	Return the observations series used for calibration.
initialize(→ None)	Initialize the model.
solve(→ None)	Solve the time series model.
reset_settings(→ None)	Reset the model settings to the default settings.
set_settings(→ None)	Change the model settings.
set_parameter(→ None)	Change the parameter properties.
set_oseries(→ None)	Set a new oseries for an existing Model.
get_tmin(→ pandas.Timestamp)	Check and return valid values for tmin.
get_tmax(→ pandas.Timestamp)	Check and return valid values for tmax.
get_init_parameters(→ pandas.DataFrame)	Get all initial parameters from the individual objects.
get_parameters(→ pastas.typing.ArrayLike)	Obtain the parameters needed for calculation.
get_stressmodel_names(→ list[str])	Return list of stressmodel names.
get_stressmodel_settings(→ dict[str, Any] | None)	Obtain the time series settings for a stress model.
get_contribution(→ pandas.Series)	Get contribution of a stressmodel.
get_contributions(→ list[pandas.Series])	Get contributions of all stressmodels.
get_transform_contribution(→ pandas.Series)	Get contribution of a transform.
get_output_series(→ pandas.DataFrame)	Get all the modeled output time series from the Model.
get_block_response(→ pandas.Series | None)	Obtain the block response for a stressmodel.
get_step_response(→ pandas.Series | None)	Obtain the step response for a stressmodel.
get_response_tmax(→ float | None)	Get tmax used for the response function.
get_stress(→ pandas.Series | list[pandas.Series])	Obtain the stress(es) from the stressmodel.
fit_report(→ str)	Fit report of the model solve.
to_dict(→ dict)	Export a model to a dictionary.
to_file(→ None)	Save the Pastas model to a file.
copy(→ pastas.typing.Model)	Copy a model.