Postprocessing and Visualizing Results from Aviary

Dashboard

Aviary provides the ability to create a dashboard that lets the user easily browse between the reports and files that are generated during an Aviary run. The dashboard runs locally inside a Web-browser.

The dashboard is organized into 4 sections: Model, Optimization, Results, and Subsystems.

The dashboard assumes these locations for the various reports that are embedded into the dashboard.

Section	File	Location
Model	Inputs	./reports/name_of_run_script/inputs.html
Model	Debug Input List	./input_list.txt
Model	Debug Input List	./output_list.txt
Model	N2	./reports/name_of_run_script/n2.html
Model	Trajectory Linkage Report	./reports/name_of_run_script/traj_linkage_report.html
Optimization	Driver Scaling Report	./reports/name_of_run_script/driver_scaling_report.html
Optimization	Total Coloring Report	./reports/name_of_run_script/total_coloring.html
Optimization	Optimization Report	./reports/name_of_run_script/opt_report.html
Optimization	SNOPT Output (similarly for other optimizers)	./reports/name_of_run_script/SNOPT_print.out
Optimization	Driver recording	Case Recorder file specified by driver_recorder command option
Results	Trajectory Results Report	./reports/name_of_run_script/traj_results_report.html
Results	Subsystem Results	./reports/subsystems/name_of_subsystem.md (or .html)
Results	Mission Results	./reports/subsystems/mission_summary.md
Results	Problem final case recording	Case Recorder file specified by problem_recorder command option, default is problem_history.db

As an example of the workflow for the dashboard, assume that the user has run an Aviary script, run_aviary_example, which records both the Problem final case and also all the cases of the optimization done by the Driver. (To record both the Problem final case and also the Driver optimization iterations, the user must make use of the optimization_history_filename option in the call to run_aviary_problem.)

python run_aviary_example.py

In this example, the case recorder files are named problem_final_case.db and driver_cases.db, respectively. So after the run is completed, the user could run the dashboard using:

aviary dashboard run_aviary_example --problem_recorder=problem_final_case.db  --driver_recorder=driver_cases.db

Note

If you run a Level 1 script, you can access the dashboard using the csv input filename instead of a script name: aviary dashboard <input_filename>

The resulting dashboard would look something like this:

Note

The Problem recorder file is required for the Aircraft 3d model tab to be displayed in the dashboard.

The problem_recorder and driver_recorder options to the dashboard command are used to indicate the file names for those recorder files, if they are not the standard values of problem_history.db and driver_history.db, respectively. If driver_recorder is set to the string "None", then the driver case recorder file is ignored. This is useful if the user is not interested in seeing dashboard tabs related to driver history. If that file is large, it could unnecessarily be read and slow down the generation of the dashboard significantly.

Saving and Sharing Dashboards

The user can also save a dashboard and share it with other users to view. The dashboard is saved as a zip file. To save a dashboard to a file, use the --save option. For example,

aviary dashboard --save run_aviary_example --problem_recorder=problem_final_case.db  --driver_recorder=driver_cases.db

By default, the zip file is named based on the name of the problem. So in this example, the saved zip file will be named run_aviary_example.zip.

If the user wants to save to a different file, they can provide that file name as an argument to the save option as in this example:

aviary dashboard --save saved_dashboard.zip run_aviary_example --problem_recorder=problem_final_case.db  --driver_recorder=driver_cases.db

In this case, the zip file will be named saved_dashboard.zip.

This user can then share the zipped file with another user. That user can view the dashboard using:

aviary dashboard saved_dashboard.zip

Running this form of the dashboard command will result in a new directory being created in the reports directory. For the example, this directory is named reports/saved_dashboard. If that directory existed previously, the command will fail to prevent the existing directory from being overwritten. The user can force that overwriting to happen by using the --force option, as shown in this example:

aviary dashboard --force saved_dashboard.zip

The user can also save the dashboard from within the dashboard itself. There is a button at the top of the page to save the dashboard. The saved zip file will be named based on the problem name.

Accessing Variables

How to get print/access any variable as an output.

Reports and Outputs

Default Reports

Each standard Aviary run generates several output files. Which output files are generated depend on the run options. There is always a sub-folder reports/<name of run script> that contains a few HTML files.

• driver_scaling_report.html

  • This is an OpenMDAO output. After all design variables, objectives, and constraints are declared and the problem has been set up, this report presents all the design variables and constraints in all phases as well as the objectives.

  • It lists design variables and constraints and objectives in three different tables. It contains the following columns: name, size, indices, driver value and units, model value and units, ref, ref0, scaler, adder, etc. It contains a Jacobian Info matrix too.

  • More details can be found at openmdao scaling.

• inputs.html

  • This is a sortable and filterable inputs report of input variables in different phases.

  • It contains the following columns: absolute name, source name, source is IVC, source is DV, units, shape, tags, val, min val, and max val. Here, IVC is the abbreviation of IndepVarComp and DV is the abbreviation of “design variable”.

• n2.html

  • This is an OpenMDAO model hierarchy and an N-squared diagram in the shape of a matrix, representing functional or physical interfaces between system elements. It can be used to systematically identify, define, tabulate, design, and analyze functional and physical interfaces.

  • More information can be found at OpenMDAO’s N2 Basics and N2 Details. There is a tutorial on YouTub e.

• opt_report.html

  • This is the OpenMDAO Optimization Report. It writes a summary of results of an optimization run.

  • OpenMDAO reference is at Optimization Report.

• total_coloring.html

  • OpenMDAO computes a coloring for the total jacobian.

  • More information can be found at Simultaneous Total Derivative Coloring For Separable Problems and Simultaneous Coloring of Approximated Derivatives.

• traj_linkage_report.html

  • This is a dymos linkage report. It provides a report detailing how phases are linked together via constraint or connection. It can be used to identify errant linkages between fixed quantities.

• traj_results_report.html

  • This file contains timeseries and phase parameters in different tabs.

We will show details of the above reports in the onboarding docs.

OpenMDAO has a reports system which will generate reports when you run your model. More on OpenMDAO reports system can be found here.

Database Output Files

There is an SQLite database output. By default, it is problem_history.db. It can be used to rerun your case though we do not detail that here. Users can write separate Python script to create user customized outputs and graphs. We will show how to use the this database to create user’s customized graph in the onboarding docs.

Report Location

Reports are placed a subdirectory called reports under the directory where the script was run. The subdirectory’s name for Level 1 runs is based on the name of the CSV file used to define aircraft and mission properties. For Level 2 and Level 3 runs, the name is based on the name of the Python script being run.

Warning About the Reports Directory

Repeated runs of the same script (Levels 2 and 3) or the same aircraft definition CSV file (Level 1) will overwrite existing reports directories. So to preserve reports from previous runs, the user should make copies of the report directory before starting another run.

Relevant Reports

There is an optimizer output. If SNOPT is the optimizer, SNOPT_print.out is generated. The SNOPT output is a detailed output of the optimizer performance. New users likely will want to exercise caution when viewing this output, as it includes some advanced information.

If IPOPT is the optimizer, IPOPT.out is generated. If SLSQP is the optimizer and pyOptSparseDriver is the driver, SLSQP.out is generated.

If Verbosity is set to VERBOSE or DEBUG, input_list.txt and output_list.txt are generated.

You may notice some warning messages in the Aviary output. Frequently seen warnings are:

• PromotionWarning: Issued when there is ambiguity due to variable promotion (an OpenMDAO warning).

• RuntimeWarning: Issued for warnings about dubious runtime features (a Python warning).

• UserWarning: Issued for warnings about potential OpenMDAO, Dymos, and/or Aviary problems.

• DerivativesWarning: Issued when the approximated partials or coloring cannot be evaluated as expected (an OpenMDAO warning).

Users should pay attention accordingly.

When a problem is setup, we can add an argument check=True. This is an OpenMDAO feature (see Setup Your Model for more information). If we do, a bunch of checks will be performed. You will see those checks on the command line.

After a model is run, a check_partials() method can be applied and results are printed to the command line. This helps us make sure all of our partial derivatives are correct. We refer users to OpenMDAO’s document Working with Derivatives Using Finite Difference or Complex Step.

When Aviary is run, some messages are printed on the command line and they are important. More details are in the onboarding docs.

In addition, users can add their own outputs.

We will cover more details on all those outputs when we show concrete examples in the onboarding docs.

Timeseries Mission Output Report

The outputs of the aircraft trajectory are outputted in a file called mission_timeseries_data.csv within the reports directory. This file contains a comma-separated list of some outputs from the mission. Any value that is included in the timeseries data is included in this file. These files are useful for post-processing and inputting the mission outputs into other tools, especially those used for acoustic analysis.

Note

This feature is under further development. Please let us know if you have any suggestions for functionality or improvements. This feature is not currently supported when using the shooting integration method.

Here is an example of the first few lines of a mission_timeseries_data.csv file:

time (s),mach (unitless),thrust_net_total (lbf),drag (lbf),specific_energy_rate_excess (m/s),fuel_flow_rate_negative_total (lbm/h),altitude_rate (ft/s),throttle (unitless),velocity (m/s),time_phase (s),mach_rate (unitless/s),altitude (ft),mass (kg),distance (m)
0.0,0.2,28478.788920867584,21108.418300418845,12.350271989430475,-10492.593707324704,8.333333333333337,0.565484286063171,68.05737270077049,0.0,0.0001354166666666668,0.0,79560.101698,1.0
160.16130859524844,0.22168851053893995,24443.964075954416,17707.343638310154,13.681656556939739,-9195.769418149232,8.333333333333341,0.5231597651569796,75.090771635822,160.16130859524844,0.0001354166666666669,1334.6775716270722,79362.06974727994,11458.09964519708

If you want to add additional outputs, call add_timeseries_output on the phases. Please refer to the Dymos documentation on timeseries outputs for more information on how to do this.