OptView is designed to quickly and interactively visualize optimization histories.
OptView has the following dependency tree:
matplotlib (OptView) \-backends | \-backend_tkagg | \-FigureCanvasTkAgg (OptView) | \-NavigationToolbar2TkAgg (OptView) \-pyplot (OptView) mpl_toolkits \-axes_grid1 | \-host_subplot (OptView) \-axisartist (OptView) numpy (OptView)
If you are successfully running
pyOptSparse, these packages are most likely
Although not necessary for most usage, the
dill package is needed
if you wish to save an editable version of the graph produced in
dill can be installed via
pip in a terminal using:
pip install dill
view_saved_figure.py can be used to reformat and view the saved figure.
OptView can be run via terminal as:
python OptView.py --histFile --outputDirectory
histFile is the name of the history file to be examined
(default is ‘opt_hist.hst’).
outputDirectory is the name of the desired output directory for
saved images (default is within the same folder as
OptView can also be ran from any directory by adding an alias line
.bashrc file such as:
alias OptView='python ~/hg/pyoptsparse/postprocessing/OptView.py
Through this usage,
OptView can be called from any directory as:
OptView histFile --outputDirectory
Additionally, you can open multiple history files in the same
by calling them via the command line:
OptView histFile1 histFile2 histFile3 --outputDirectory
Each file’s contents will be loaded into
OptView with a flag appended to the end
of each variable or function name corresponding to the history file. The first one
listed will have ‘_A’ added to the name, the second will have ‘_B’ added, etc.
There is currently no limit to the number of history files than can be loaded.
OptView has many options and features, including:
- plotting multiple variables on a single plot
- producing stacked plots
- live searchable variable names
- hovering plot labels
- saving the figure to an image or pickling it for later formatting
- refreshing the optimization history on the fly
Although some of these are self-explanatory, the layout and usage of
will be explained below.
The window is divided into two sections. The top is the canvas where the figure and graphs will be produced, while the bottom grayed section contains user-selectable options. Here, we will focus on the user options.
The selectable variables are contained on the lefthand side of the options panel in scrollable listboxes. You can select multiple items from the listboxes using the normal selection operators such as control and shift. If a selected variable is an array, a third listbox should appear on the righthand side of the options panel, allowing you to select specific subvariables within the single array variable.
There are three main options when selecting how to produce the graph(s):
- Shared axes - all selected variables are plotted on a single pair of axes
- Multiple axes - each selected variable gets its own y-axis while all selected data shares an x-axis
- Stacked plots - each variable gets its own individual plot and the set is stacked vertically
Most checkbox options should play well with any of these three main options, though there are known issues with using the ‘multiple axes’ option and delta values or for displaying arrays.
There are seven checkbox options:
- Absolute delta values - displays the absolute difference between one iteration’s value and the previous
- Log scale - sets the y-axis as a log scale
- Min/max for arrays - only shows the minimum and maximum value of a variable for each iteration
- Show all for arrays - plots all variables within an array
- Show legend - reveals the legend for the plotted data
- Show bounds - shows the variable bounds as dashed lines
- Show ‘major’ iterations - a heuristic filter to remove the line search iterations from the plotting results; especially useful for SNOPT output
Additionally, four buttons allow control of the plot:
- Refresh history - reloads the history file; used if checking on an optimization run on the fly
- Save all figures - saves .png versions of a basic plot for each variable in the history file
- Save figure - saves a .png and .pickle version of the current plot (the .pickle version can be reformatted afterwards)
- Quit - exits the program
Lastly, there some miscellaneous features:
- A search box to cull the selectable variables
- A font size slider to control the text size on the plot
- Hoverable tooltips when the cursor is on a plot line
- A variable called actual_iteration_number that gives a translation between history file iteration number and run file iteration number. This is especially useful for debugging specific steps of an optimization or comparing values across different histories.
More features are being developed on an as-needed basis. Feel free to edit the code as you see fit and submit a pull request if you would like to see a feature added. Alternatively, you can submit an issue ticket to discuss possible features.
Parsing SNOPT Printout files¶
SNOPT_parse.py has been included in the
postprocessing folder for extracting the optimality, feasibility and meric function values for each major iteration. It then generates a
.dat file for use with Tecplot.
The file can be run via terminal as:
python SNOPT_parse.py filename
filename is the name of the SNOPT printout file to be examined. If no filename is provided the default name
SNOPT_print.out will be assumed.