deepcave.plugins.summary.footprint

# FootPrint

This module provides utilities to visualize a configuration footprint.

The module contains a static plugin class for defining the footprint.

## Classes

  • FootPrint: A static plugin for the footprint of a configuration.

Classes

FootPrint()

Visualize the footprint of a configuration.
class deepcave.plugins.summary.footprint.FootPrint[source]

Bases: StaticPlugin

Visualize the footprint of a configuration.

A static plugin for the footprint.

static get_filter_layout(register)[source]

Get layout for the filter block.

Parameters:

register (Callable) – Method to register (user) variables. The register_input function is located in the Plugin superclass.

Returns:

The layouts for the filter block.

Return type:

List[dbc.Row]

static get_input_layout(register)[source]

Get the layout for the input block.

Parameters:

register (Callable) – Method to register (user) variables. The register_input function is located in the Plugin superclass.

Returns:

The layouts for the input block.

Return type:

List[Any]

static get_mpl_output_layout(register)[source]

Get the layout for the matplotlib output block.

Parameters:

register (Callable) – Method to register the outputs. The register_output function is located in the Plugin superclass.

Returns:

The layout for the output block.

Return type:

List[dbc.Tabs]

static get_output_layout(register)[source]

Get the layout for the output block.

Parameters:

register (Callable) – Method for registering outputs. The register_output function is located in the Plugin superclass.

Returns:

The layout for the output block.

Return type:

dbc.Tabs

load_dependency_inputs(run, previous_inputs, inputs)[source]

Work like ‘load_inputs’ but called after inputs have changed.

Note

Only the changes have to be returned. The returned dictionary will be merged with the inputs.

Parameters:

  • run – The selected run.

  • previous_inputs – Previous content of the inputs. Not used in this specific function.

  • inputs – Current content of the inputs.

Returns:

The dictionary with the changes.

Return type:

Dict[str, Any]

load_inputs()[source]

Load the content for the defined inputs in ‘get_input_layout’ and ‘get_filter_layout’.

This method is necessary to pre-load contents for the inputs. So, if the plugin is called for the first time or there are no results in the cache, the plugin gets its content from this method.

Returns:

The content to be filled.

Return type:

Dict[str, Dict[str, Any]]

static load_mpl_outputs(run, inputs, outputs)[source]

Read in the raw data and prepare them for the layout.

Note

The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ or ‘load_dependency_inputs’. Please see ‘_clean_inputs’ for more information.

Parameters:

  • run – The selected run.

  • inputs – Input and filter values from the user.

  • outputs – Raw outputs from the run.

Return type:

The rendered matplotlib figure of the footprint

static load_outputs(run, inputs, outputs)[source]

Read in the raw data and prepare them for the layout.

Note

The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ or ‘load_dependency_inputs’. Please see ‘_clean_inputs’ for more information.

Parameters:

  • run – The selected run.

  • inputs – Input and filter values from the user.

  • outputs – Raw output from the run.

Returns:

The plotly figure of the footprint performance and area.

Return type:

List[Any]

static process(run, inputs)[source]

Return raw data based on a run and input data.

Warning

The returned data must be JSON serializable.

Note

The passed inputs are cleaned and therefore differs compared to ‘load_inputs’ or ‘load_dependency_inputs’. Please see ‘_clean_inputs’ for more information.

Parameters:

  • run – The selected run.

  • inputs – The input data.

Returns:

A serialized dictionary.

Return type:

Dict[str, Any]

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