pyemu.Schur

class pyemu.Schur(jco, **kwargs)

FOSM-based uncertainty and data-worth analysis

Parameters:

  • jco (varies, optional) – something that can be cast or loaded into a pyemu.Jco. Can be a str for a filename or pyemu.Matrix/pyemu.Jco object.

  • pst (varies, optional) – something that can be cast into a pyemu.Pst. Can be an str for a filename or an existing pyemu.Pst. If None, a pst filename is sought with the same base name as the jco argument (if passed)

  • parcov (varies, optional) – prior parameter covariance matrix. If str, a filename is assumed and the prior parameter covariance matrix is loaded from a file using the file extension (“.jcb”/”.jco” for binary, “.cov”/”.mat” for PEST-style ASCII matrix, or “.unc” for uncertainty files). If None, the prior parameter covariance matrix is constructed from the parameter bounds in LinearAnalysis.pst. Can also be a pyemu.Cov instance

  • obscov (varies, optional) – observation noise covariance matrix. If str, a filename is assumed and the noise covariance matrix is loaded from a file using the file extension (“.jcb”/”.jco” for binary, “.cov”/”.mat” for PEST-style ASCII matrix, or “.unc” for uncertainty files). If None, the noise covariance matrix is constructed from the obsevation weights in LinearAnalysis.pst. Can also be a pyemu.Cov instance

  • forecasts (varies, optional) – forecast sensitivity vectors. If str, first an observation name is assumed (a row in LinearAnalysis.jco). If that is not found, a filename is assumed and predictions are loaded from a file using the file extension. If [str], a list of observation names is assumed. Can also be a pyemu.Matrix instance, a numpy.ndarray or a collection. Note if the PEST++ option “++forecasts()” is set in the pest control file (under the pyemu.Pst.pestpp_options dictionary), then there is no need to pass this argument (unless you want to analyze different forecasts) of pyemu.Matrix or numpy.ndarray.

  • ref_var (float, optional) – reference variance. Default is 1.0

  • verbose (bool) – controls screen output. If str, a filename is assumed and and log file is written.

  • sigma_range (float, optional) – defines range of upper bound - lower bound in terms of standard deviation (sigma). For example, if sigma_range = 4, the bounds represent 4 * sigma. Default is 4.0, representing approximately 95% confidence of implied normal distribution. This arg is only used if constructing parcov from parameter bounds.

  • scale_offset (bool, optional) – flag to apply parameter scale and offset to parameter bounds when calculating prior parameter covariance matrix from bounds. This arg is onlyused if constructing parcov from parameter bounds.Default is True.

Note

This class is the primary entry point for FOSM-based uncertainty and dataworth analyses

This class replicates and extends the behavior of the PEST PREDUNC utilities.

Example:

#assumes "my.pst" exists
sc = pyemu.Schur(jco="my.jco",forecasts=["fore1","fore2"])
print(sc.get_forecast_summary())
print(sc.get_parameter_contribution())