pastas.modelstats.Statistics

class pastas.modelstats.Statistics(ml: pastas.typing.Model)

This class provides statistics to pastas Model class.

Parameters:

ml (Pastas.model.Model) – ml is a time series Model that is calibrated.

Notes

To obtain a list of all statistics that are included type:

>>> print(ml.stats.ops)

Methods

rmse(→ float)	Root mean squared error of the residuals.
rmsn(→ float)	Root mean squared error of the noise.
sse(→ float)	Sum of the squares of the error (SSE)
mae(→ float)	Mean Absolute Error (MAE) of the residuals.
nse(→ float)	Nash-Sutcliffe Efficiency for model fit .
nnse(→ float)	Normalized Nash-Sutcliffe Efficiency for model fit .
pearsonr(→ float)	Compute the (weighted) Pearson correlation (r).
evp(→ float)	Explained variance percentage.
rsq(→ float)	R-squared.
kge(→ float)	Kling-Gupta Efficiency.
kge_2012(→ float)	Kling-Gupta Efficiency.
bic(→ float)	Bayesian Information Criterium (BIC).
aic(→ float)	Akaike Information Criterium (AIC).
aicc(→ float)	Akaike Information Criterium with second order bias correction (AICc).
summary(→ pandas.DataFrame)	Returns a Pandas DataFrame with goodness-of-fit metrics.
diagnostics(, float_fmt)	Methods to compute various diagnostics checks for the noise time series. If