pyemu.ErrVar

class pyemu.ErrVar(jco, **kwargs)

FOSM-based error variance 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.

  • omitted_parameters ([str]) – list of parameters to treat as “omitted”. Passing this argument activates 3-term error variance analysis.

  • omitted_parcov (varies) – an argument that can be cast to a parcov for the omitted parameters. If None, omitted_parcov will be formed by extracting a sub-matrix from the LinearAnalsis.parcov attribute.

  • omitted_predictions (varies) – an argument that can be cast to a “predictions” (e.g. “forecasts”) attribute to form prediction sensitivity vectors with respec to the omitted parameters. If None, these vectors will be extracted from the pyemu.LinearAnalysis.predictions attribute

  • kl (bool, optional) – flag to perform Karhunen-Loeve scaling on the jacobian before error variance calculations. If True, the pyemu.ErrVar.jco and pyemu.ErrVar.parcov are altered in place. Default is False.

Example:

ev = pyemu.ErrVar(jco="my.jco",omitted_parameters=["wel1","wel2"])
df = ev.get_errvar_dataframe()