inversion_ideas.InversionLogRich

class inversion_ideas.InversionLogRich(columns, **kwargs)

Log the outputs of an inversion.

Parameters:

columnsdict

Dictionary with specification for the columns of the log table. The keys are the column titles as strings. The values are callables that will be used to generate the value for each row and column. Each callable should take two arguments: iteration (an integer with the number of the iteration) and model (the inverted model as a 1d array).

kwargs

Pass extra options to rich.table.Table.

Attributes

columns	Column specifiers.
has_records	Whether the log has recorded values or not.
log	Inversion log.
table	Table for the inversion log.

Methods

add_column(name, column)	Add column to the log.
create_from(objective_function, **kwargs)	Create the standard log for a classic inversion.
live(**kwargs)	Context manager for live update of the table.
show()	Show log through a Rich console.
to_pandas([index_col])	Generate a pandas.DataFrame out of the log.
update(iteration, model)	Update the log.
update_table()	Add row to the table given the latest inverted model.

Attributes

InversionLogRich.columns

Column specifiers.

InversionLogRich.has_records

Whether the log has recorded values or not.

InversionLogRich.log

Inversion log.

InversionLogRich.table

Table for the inversion log.

Methods

Methods documentation

InversionLogRich.add_column(name, column)

Add column to the log.

Parameters:

namestr

Name of the column, used in the InversionLog.log dictionary to access the recorded values.

columnCallable | Column

A callable that takes the iteration and the model as arguments, or a Column.

Returns:

self

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classmethod InversionLogRich.create_from(objective_function, **kwargs)

Create the standard log for a classic inversion.

Parameters:

objective_functionCombo

Combo objective function with two elements: the data misfit and the regularization (including a trade-off parameter).

kwargsdict

Keyword arguments passed to the constructor of the class.

Returns:

Self

Notes

The objective function should be of the type:

\[\phi(\mathbf{m}) = \phi_d(\mathbf{m}) + \beta \phi_m(\mathbf{m})\]

where \(\phi_d(m)\) is the data misfit term, \(\phi_m(\mathbf{m})\) is the model norm, and \(\beta\) is the trade-off parameter.

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InversionLogRich.live(**kwargs)

Context manager for live update of the table.

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InversionLogRich.show()

Show log through a Rich console.

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InversionLogRich.to_pandas(index_col=0)

Generate a pandas.DataFrame out of the log.

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InversionLogRich.update(iteration, model)

Update the log.

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InversionLogRich.update_table()

Add row to the table given the latest inverted model.

Parameters:

model(n_params) array

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