Starting with the release of Pastas 0.15 changes to the API are reported here. The release notes for previous releases up to 0.14 can be found at the GitHub Release page. For full details of all changes check the commit log.
Note
This release will introduce backward incompatible changes to Pastas, most notably due to the renaming of the parameter input argument. This change is mostly internally and will only affect users that explicitly pass parameters into a method.
A new stress model (ps.LinearTrend) to simulate linear trends is added to the list of stable stress models.
ps.LinearTrend
New method to compute the Standardized Groundwater Index. See (ps.stats.sgi) for more details.
ps.stats.sgi
Most of the goodness-of-fit metrics now allow providing a “weighted” argument. This may result in more realistic values for time series with irregular time steps.
The documentation website is further improved. We now separate ‘Examples’ and ‘Concepts’. The First are worked-out example notebooks using Pastas to analyse a problem, the second are notebooks showing a underlying methods.
The following methods to set parameter properties are now deprecated and replaced by the single method ml.set_parameter: ml.set_vary, ml.set_pmin, ml.set_pmax, ml.set_initial.
The name of the input argument for the parameters was made consistent throughout Pastas. If the input argument is named p an array-like object is expected, whereas if the input is parameters a Pandas DataFrame object is expected.
ps.FactorModel is deprecated and will be removed in a future version. Use ps.StressModel with rfunc=ps.One instead.
ps.FactorModel
ps.StressModel
New notebook on computing Standardized Groundwater Index using Pastas.
New Notebook on simulated step and linear trend.
This release will introduce backward incompatible changes to Pastas, most notably due to the weighting of the first value of the noise. This will cause the calibrated values to be slightly different but better for most models. It is highly recommended to upgrade to this new version of Pastas.
Model.noise now returns the noise and not the weighted noise. Weights may now be obtained through Model.noise_weights.
Model.noise
Model.noise_weights
Private methods are now identified by a leading underscore issue 74.
Model.set_parameter method on the Model class is introduced to set the initial, minimum, maximum and vary settings for a parameters in one line.
Model.set_parameter
the ps.stats subpackage has been completely restructured. All methods may now also be used as separate methods.
ps.stats.diagnostics: perform multiple diagnostic tests at once. ps.stats.stoffer_toloi: Ljung-box test adapted for missing data. ps.stats.plot_diagnostics: stand-alone version of the plot for diagnostic checking ps.stats.plot_acf: convenience method to plot the autocorrelation function. all goodness-of-fit metrics are now available as separate functions e.g., ps.stats.nse. See the API docs for all available methods.
ps.stats.diagnostics: perform multiple diagnostic tests at once.
ps.stats.diagnostics
ps.stats.stoffer_toloi: Ljung-box test adapted for missing data.
ps.stats.stoffer_toloi
ps.stats.plot_diagnostics: stand-alone version of the plot for diagnostic checking
ps.stats.plot_diagnostics
ps.stats.plot_acf: convenience method to plot the autocorrelation function.
ps.stats.plot_acf
all goodness-of-fit metrics are now available as separate functions e.g., ps.stats.nse. See the API docs for all available methods.
ps.stats.nse
A new experimental noise model is added: ArmaModel. This model computes the noise from the residuals according to a autoregressive-moving-average model (ARMA(1,1)). Currently this method is experimental and only applicable to time series with equidistant time steps.
ArmaModel
The response functions have been standardized to all fit the same formula for the impulse response function, when some parameters are fixed to certain values.
new function ps.show_versions is introduced. This function may be used to show the version of package dependencies that are installed.
ps.show_versions
New method ml.get_response_tmax is introduced. This method may be used to obtain the tmax of the response function.
ml.get_response_tmax
ml.set_vary, ml.set_initial, ml.set_pmin, and ml.set_pmax are deprecated and will be removed in a future release . The use of ml.set_parameter method is now recommended.
ml.set_vary
ml.set_initial
ml.set_pmin
ml.set_pmax
ml.set_parameter
The parameters of the Hantush response function have new names. This will cause problems when loading models using this function to be loaded from .pas-file. No fix is available for this.
The first value of the noise series has changes (see issue 152 for details), causing changes in the optimal parameter values.
Notebook on diagnostic checking of Pastas models.
Notebook on the new ArmaModel noise model.
Notebook on reading Dutch datasets.
Notebook on the autocorrelation function with irregular time steps.