pathsim.blocks.scope module

class pathsim.blocks.scope.Scope(sampling_rate=None, t_wait=0.0, labels=None)

Bases: Block

Block for recording time domain data with variable sampling rate.

A time threshold can be set by t_wait to start recording data after the simulation time is larger then the specified waiting time, i.e. t - t_wait > 0. This is useful for recording data only after all the transients have settled.

The block uses an interal Schedule event, when sampling_rate is provided, otherwise it just samples at every simulation timestep.

Parameters:

• sampling_rate (int, None) – number of samples per time unit, default is every timestep

• t_wait (float) – wait time before starting recording, optional

• labels (list[str]) – labels for the scope traces, and for the csv, optional

recording

recording, where key is time, and value the recorded values

Type:

dict

events

internal scheduled event for periodic input sampling when sampling_rate is provided

Type:

list[Schedule]

reset()

Reset the blocks inputs and outputs and also its internal solver, if the block has a solver instance.

read()

Return the recorded time domain data and the corresponding time for all input ports

Returns:

• time (array[float]) – recorded time points

• data (array[obj]) – recorded data points

sample(t, dt)

Sample the data from all inputs, and overwrites existing timepoints, since we use a dict for storing the recorded data.

If sampling_rate is provided, this does nothing, since the sampling is handled by the internal Schedule event.

Parameters:

t (float) – evaluation time for sampling

plot(*args, **kwargs)

Directly create a plot of the recorded data for quick visualization and debugging.

Parameters:

• args (tuple) – args for ax.plot

• kwargs (dict) – kwargs for ax.plot

Returns:

• fig (matplotlib.figure) – internal figure instance

• ax (matplotlib.axis) – internal axis instance

plot2D(*args, axes=(0, 1), **kwargs)

Directly create a 2D plot of the recorded data for quick visualization and debugging.

Parameters:

• args (tuple) – args for ax.plot

• axes (tuple[int]) – axes / ports to select for 2d plot

• kwargs (dict) – kwargs for ax.plot

Returns:

• fig (matplotlib.figure) – internal figure instance

• ax (matplotlib.axis) – internal axis instance

plot3D(*args, axes=(0, 1, 2), **kwargs)

Directly create a 3D plot of the recorded data for quick visualization.

Parameters:

• args (tuple) – args for ax.plot

• axes (tuple[int]) – indices of the three data channels (ports) to plot (default: (0, 1, 2)).

• kwargs (dict) – kwargs for ax.plot

Returns:

• fig (matplotlib.figure) – internal figure instance.

• ax (matplotlib.axes._axes.Axes3D) – internal 3D axis instance.

save(path='scope.csv')

Save the recording of the scope to a csv file.

Parameters:

path (str) – path where to save the recording as a csv file

update(t)

update system equation for fixed point loop, here just setting the outputs

Note

Scope has no passthrough, so the ‘update’ method is optimized for this case (does nothing)

Parameters:

t (float) – evaluation time

Returns:

error – absolute error to previous iteration for convergence control (always ‘0.0’ because sink-type)

Return type:

float

class pathsim.blocks.scope.RealtimeScope(sampling_rate=None, t_wait=0.0, labels=[], max_samples=None)

Bases: Scope

An extension of the ‘Scope’ block that also initializes a realtime plotter that creates an interactive plotting window while the simulation is running.

Otherwise implements the same functionality as the regular ‘Scope’ block.

Note

Due to the plotting being relatively expensive, including this block slows down the simulation significantly but may still be valuable for debugging and testing.

Parameters:

• sampling_rate (int, None) – number of samples time unit, default is every timestep

• t_wait (float) – wait time before starting recording

• labels (list[str]) – labels for the scope traces, and for the csv

• max_samples (int, None) – number of samples for realtime display, all per default

plotter

instance of a RealtimePlotter

Type:

RealtimePlotter

sample(t, dt)

Sample the data from all inputs, and overwrites existing timepoints, since we use a dict for storing the recorded data.

Parameters:

t (float) – evaluation time for sampling