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Message flow#

Plot.create_message_flow_plot() function shows how input messages are received to nodes and output messages are transmitted to next nodes. You can confirm bottleneck of response in your application.

from caret_analyze.plot import Plot
from caret_analyze import Application, Architecture, Lttng
from bokeh.plotting import output_notebook, figure, show

arch = Architecture('yaml', '/path/to/architecture_file')
lttng = Lttng('/path/to/trace_data')
app = Application(arch, lttng)
path = app.get_path('target_path')

plot = Plot.create_message_flow_plot(path, granularity='node', lstrip_s=1, rstrip_s=1)


The horizontal axis means time, labeled as Time [s]. The vertical axis lists names of nodes and topics in a target path. A colored line is corresponded to an input message and represents its propagation. With tracing a colored line, you can find when a message input is processed in a certain node. Gray rectangles indicate callback executions.

Plot.create_message_flow_plot() function has following arguments.

  • granularity is served to adjusts granularity of chain with two value; raw and node
    • With raw, callback-level message flow is generated
    • With node, node-level message flow is generated
  • treat_drop_as_delay is a boolean value for the connection of the flow to the next flow if there is a drop
  • lstrip_s is float value for selecting start time of cropping time range
  • rstrip_s is float value for selecting end time of cropping time range

Message flow diagram let you operate as follows.

  • Scrolling upper or lower on x-axis for scaling up or down on horizontal direction
  • Scrolling upper or lower on y-axis for scaling up or down on vertical direction
  • Scrolling upper of lower on a graph for scaling up or down on both horizontal and vertical direction
  • Hovering over a line in message flow or a gray rectangle give you details