Period

CARET is capable of showing periods of callback start, message transmission, invocation of publisher or subscription. Plot.create_period_timeseries_plot(target_object) interface is provided for it. This section describes sample visualization scripts for them. Execute the following script code to load trace data and an architecture object before calling this method.

from caret_analyze.plot import Plot
from caret_analyze import Application, Architecture, Lttng
from bokeh.plotting import output_notebook, figure, show
output_notebook()
arch = Architecture('yaml', '/path/to/architecture_file')
lttng = Lttng('/path/to/trace_data')
app = Application(arch, lttng)

Callback

Plot.create_period_timeseries_plot(callbacks: Collections[CallbackBase]) and Plot.create_period_histogram_plot(callbacks: Collections[CallbackBase]) are introduced to check period between invocation of callback function . Period is more detailed metrics than frequency.

### Timestamp tables
plot = Plot.create_period_timeseries_plot(app.callbacks)
period_df = plot.to_dataframe()
period_df

# ---Output in jupyter-notebook as below---

Time Series

### Time-series graph
plot = Plot.create_period_timeseries_plot(app.callbacks)
plot.show()

# ---Output in jupyter-notebook as below---

The horizontal axis means time, labeled as Time [s]. xaxis_type argument is prepared to select index of x-axis among Linux system time, ROS simulation time, and 0-based ordering. One of 'system_time', 'sim_time' and 'index' is chosen as xaxis_type though 'system_time' is the default value. The vertical axis means period of callback start, labeled as Period [ms]. It is plotted per sample.

Histogram

### histogram graph
plot = Plot.create_period_histogram_plot(app.callbacks)
plot.show()

# ---Output in jupyter-notebook as below---

The horizontal axis represents the period, labeled as period [ms]. The vertical axis represents the number of samples executed at each period, labeled as The number of samples.

Communication

Plot.create_period_timeseries_plot(communications: Collection[Communication]) and Plot.create_period_histogram_plot(communications: Collection[Communication]) are helpful if you want to see that communication period is stable or not. Here, CARET takes into account communication when both transmission and reception on a message are performed successfully without being lost. See Premise of communication for more details.

### Timestamp tables
plot = Plot.create_period_timeseries_plot(app.communications)
period_df = plot.to_dataframe()
period_df

# ---Output in jupyter-notebook as below---

Time Series

### Time-series graph
plot = Plot.create_period_timeseries_plot(app.communications)
plot.show()

# ---Output in jupyter-notebook as below---

The horizontal axis means time, labeled as Time [s] while the vertical axis means period, labeled as Period [ms], from communication of a certain message to that of next one. xaxis_type argument is provided as well as callback execution.

Histogram

### Histogram graph
plot = Plot.create_period_histogram_plot(app.communications)
plot.show()

# ---Output in jupyter-notebook as below---

The horizontal axis represents the period, labeled as period [ms]. The vertical axis represents the number of samples executed at each period, labeled as The number of samples.

Publish and Subscription

Plot.create_period_timeseries_plot(Collection[publish: Publisher or subscription: Subscriber]) is useful to check how stable invocation cycle of publisher or subscription is.

### Timestamp tables
plot = Plot.create_period_timeseries_plot(*app.publishers, *app.subscriptions)
period_df = plot.to_dataframe()
period_df

# ---Output in jupyter-notebook as below---

### Time-series graph
plot = Plot.create_period_timeseries_plot(*app.publishers, *app.subscriptions)
plot.show()

# ---Output in jupyter-notebook as below---

The horizontal axis means time, labeled as Time [s], while the vertical axis means invocation period of publish or subscription, labeled as Period [ms]. xaxis_type argument is prepared as well.