<jupyter_start><jupyter_text>Log event model df.head(1)  Average duration over cycles<jupyter_code>df_avg_over_cycles = df.groupby(['cycle_no'])['command_duration'].mean()
ax = df_avg_over_cycles.plot()
ax.set_ylabel('avg. duration in seconds')<jupyter_output><empty_output><jupyter_text>Average duration over number of actors<jupyter_code>ax = df\
    .groupby(['cycle_actor_count'])['command_duration']\
    .mean()\
    .plot()
ax.set_ylabel('avg. duration in seconds')
ax.set_xlabel('number of actors')<jupyter_output><empty_output><jupyter_text>Nearly linear average command execution time increase is noticeable.  Each command average duration over number of actors<jupyter_code>ax = df\
    .groupby(['cycle_actor_count', 'command_type'])['command_duration']\
    .mean()\
    .unstack()\
    .plot()
ax.set_ylabel('avg. duration in seconds')
ax.set_xlabel('number of actors')<jupyter_output><empty_output><jupyter_text>Some command types are liable to changes in the quantity of actors (clone, fetch, pull, push), some are not (merge).The liable commans are those interacting with the server.Fetch, pull and push commands do not occur for scenario with one actor. Obviously, this happens due to lack of merge conflicts in 'single client' scenario.<jupyter_code>df_avg_over_cycles = df.groupby(['command_type'])['command_exit_code'].mean()
ax = df_avg_over_cycles.plot()
ax.set_ylabel('exit code')<jupyter_output><empty_output><jupyter_text>In most cases commands recive 0 exit code that means commands were executed in a best way, but in defined situations, commands recive 32768 and 256 which means that actors subjected a failure.<jupyter_code>df_avg_over_cycles = df.groupby(['actor_interval'])['cycle_actor_count'].mean()
ax = df_avg_over_cycles.plot()
ax.set_ylabel('number of actors')
ax.set_xlabel('actor\'s interval')
# ax.set_ylabel('Command result (0.2 = False, 1.0 = True)')
With increasing number of actors, the actor interval is increasing too.
ax = df.groupby(['cycle_no', 'command_type'])['command_duration']\
    .mean()\
    .unstack()\
    .plot()
ax.set_ylabel('command duration')
ax.set_xlabel('cycle no')<jupyter_output><empty_output><jupyter_text>Every new actor creation and command execution time duretion increase proportionally.<jupyter_code>ax = df.groupby(['cycle_actor_count', 'command_type'])['command_exit_code']\
    .mean()\
    .unstack()\
    .plot()
ax.set_ylabel('number of actors')
ax.set_xlabel('Command exit code')<jupyter_output><empty_output>
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