ErrorModel

ErrorModel is the abstract base class for computing the error estimate for a given discretization level. Below is the description of the abstract base class followed by the concrete classes.

class cfdverify.discretization.ErrorModel(parent: DiscretizationError)

Abstract base class for response error models

abstract error(key: str | None = None, index: int | None = None) → floating | Series | DataFrame

Error method

Parameters:

• key (str | None) – Key of system response quantity of interest or None for all SRQs

• index (int | None) – Index of discretization level of interest or None for all levels

Returns:

Error of requested values

Return type:

np.floating | pd.Series | pd.DataFrame

get_data(key: str | None) → Series | DataFrame

Return either all discretization data or key data

Parameters:

key (str | None) – Key for system response quantity or None for all data

Returns:

data – DataFrame of system response quantities of interest

Return type:

pd.Series | pd.DataFrame

class cfdverify.discretization.EstimatedError(parent: DiscretizationError)

Bases: ErrorModel

Compute errors relative to estimated response value

error(key: str | None = None, index: int | None = None) → floating | Series | DataFrame

Compute error relative to estimated zero discretization error value

\[\epsilon_i = f_i - f_0.\]

Parameters:

• key (str | None) – Key of system response quantity of interest or None for all SRQs

• index (int | None) – Index of discretization level of interest or None for all levels

Returns:

err – Estimated error of requested values

Return type:

np.floating | pd.Series | pd.DataFrame

class cfdverify.discretization.RelativeError(parent: DiscretizationError)

Bases: ErrorModel

Compute errors relative to coarser response value

error(key: str | None = None, index: int | None = None) → floating | Series | DataFrame

Compute error relative to coarser discretization level

Errors for all but the coarsest level are computed as

\[\epsilon_i = f_i - f_{i+1},\]

while the error for the coarsest level is computed as

\[\epsilon_i = f_{i-1} - f_{i}.\]

Parameters:

• key (str | None) – Key of system response quantity of interest or None for all SRQs

• index (int | None) – Index of discretization level of interest or None for all levels

Returns:

rel_err – Relative error of requested values

Return type:

np.floating | pd.Series | pd.DataFrame