CBD.lib.endpoints module

class CBD.lib.endpoints.CollectorBlock(block_name, input_ports, data=None)

Bases: CBD.Core.BaseBlock

Abstract class, representing a basic collection unit. In the data attribute, a set of values is stored for external access (possibly during the simulation). This should allow for live plots w.r.t. an independent plotting framework.

Parameters

• block_name (str) – The name of the block.

• input_ports (list) – The inputs for the collector.

• data (Any) – The data that is being tracked.

compute(curIteration)

Computes this block’s operation, based on its inputs and store it as an output signal.

Parameters

curIteration (int) – The iteration at which we must compute this value.

clear()

Clears the contents of the block. Must be called in-between simulations if the obtainable statistics do not need to interfere/retain information about previous simulations.

property data

Property to obtain the internal data.

class CBD.lib.endpoints.SignalCollectorBlock(block_name, buffer_size=- 1)

Bases: CBD.lib.endpoints.CollectorBlock

Collects a single signal to store w.r.t. the arrival time.

Parameters

• block_name (str) – The name of the block.

• buffer_size (int) – The maximal amount of values to keep track of. When negative, no buffering will happen. Defaults to -1.

compute(curIteration)

Computes this block’s operation, based on its inputs and store it as an output signal.

Parameters

curIteration (int) – The iteration at which we must compute this value.

clear()

Clears the contents of the block. Must be called in-between simulations if the obtainable statistics do not need to interfere/retain information about previous simulations.

property data_xy

The collected data, as a pair of lists; i.e. the unzipped form of xs, ys.

class CBD.lib.endpoints.PositionCollectorBlock(block_name, buffer_size=- 1)

Bases: CBD.lib.endpoints.CollectorBlock

Collects a X/Y position, to be used for a parametric plot.

Parameters

• block_name (str) – The name of the block.

• buffer_size (int) – The maximal amount of values to keep track of. When negative, no buffering will happen. Defaults to -1.

compute(curIteration)

Computes this block’s operation, based on its inputs and store it as an output signal.

Parameters

curIteration (int) – The iteration at which we must compute this value.

clear()

Clears the contents of the block. Must be called in-between simulations if the obtainable statistics do not need to interfere/retain information about previous simulations.

distance_from_last(x1, y1)

Computes the distance between the last point and the given point.

Parameters

• x1 (numeric) – The x position of the given point.

• y1 (numeric) – The y position of the given point.

distance_from_start()

Obtains the distance from the starting position (in the buffer).

property data_xy

The collected data, as a pair of lists; i.e. the unzipped form of xs, ys.

class CBD.lib.endpoints.StatisticsCollectorBlock(name)

Bases: CBD.lib.endpoints.CollectorBlock

Collects all inputs and allows for statistical data summaries.

While this can be done on the SignalCollectorBlock, this class uses a less memory-intensive algorithm to do so. When only statistical info is required and a lot of data needs to be analyzed, this algorithm should be used!

Note

This block only works for NUMERICAL data.

compute(curIteration)

Computes this block’s operation, based on its inputs and store it as an output signal.

Parameters

curIteration (int) – The iteration at which we must compute this value.

clear()

Resets the values of the statistics. Should be called when the model is used in multiple simulations back-to-back to make sure the statistics remain valid.

count()

Returns the amount of items that have been captured.

min()

Returns the smallest value that was seen.

max()

Returns the largest value that was seen.

sum()

Returns the sum of all items that have been captured.

sumOfSquares()

Returns the sum of the squares for all items that have been captured.

mean()

Returns the average/mean value of the data.

variance()

Returns the variance of the data.

std()

Returns the standard deviation value of the data.