# Create a Parameter Image Model¶

The Slycat Parameter Image Model associates images with feature vectors, and would typically be used to explore the input parameters for an ensemble of image-generating simulations. For this type of model, you’ll use one Python script to synthesize image and parameter data in a format suitable for use with Slycat, then import the data using a web browser user interface.

## Generate Image Data¶

• If you haven’t already ssh into the Slycat server:

$ssh slycat@<docker ip address> -p2222  • Switch to the Slycat source code directory containing sample client scripts: $ cd src/slycat/web-client

• Synthesize some parameter image data, organized for use with Slycat:

\$ python slycat-create-sample-parameter-image-csv.py

• The script creates a sample-parameter-images directory containing a set of randomly-generated images, and a sample-parameter-images.csv file that contains links to the images, plus randomly-generated numeric, string, and categorical parameters (the script includes optional command line parameters to control how much data is generated). Now that you have some sample data, you’re ready to pull it into Slycat.

## Create a Project¶

• Point a web browser to the Slycat web server at https://<docker ip address>
• Use Create > New Project on the Slycat navbar, enter “My PI Project” as the project name in the wizard, and click Finish.
• The browser switches to a separate page for the new project.

## Ingest a Parameter Image Model¶

• In the project page, choose Create > New Remote Parameter Image Model. This wizard is used to ingest a file from a machine other than the host running the web browser.
• In the wizard that opens, enter “MyPI” as the model name and click Next.
• In the login screen that follows, choose hostname “localhost”, enter username “slycat” and password “slycat” and choose Next. Note that these credentials will be used to SSH to another machine to load the parameter image data (in this case, the “other” machine happens to be localhost, but the Slycat server can be configured to connect to any other host that’s accessible via SSH).
• In the remote file browser that opens, navigate to the /home/slycat/src/slycat/web-client directory, and double-click the sample-parameter-images.csv file that you generated in a previous step.
• A list of the variables (columns) from the file appears, along with five columns of checkboxes, allowing you to designate each variable as in input, output, rating, categorical, or image variable. Slycat trys to guess the types of the individual variables, but you will need to make some manual changes. Use the checkboxes to designate “category0” and “category1” as Category variables, and “rating0” and “rating1” as Rating variables. Change “output0”, “output1”, and “output2” to Output variables, and uncheck “unused0”, “unused1”, “unused2”.
• Note that the “image0”, “image1”, and “image2” columns are already correctly identified as Image variables, so leave them alone, and click Finish.
• As before, you can navigate to the newly created model using the link in the last page of the wizard, the link on the underlying project page, or the link in the status dropdown in the navbar.

## View a Parameter Image Model¶

• The bottom third of the model page features a table containing the raw data used to compute the model. Input variables are color-coded green, output variables are color-coded purple, and the remaining variables are color-coded white.
• The rest of the page contains a scatterplot with a point for each observation (row) in the data table.

## Interact with a Parameter Image Model¶

• If you hover over any of the scatterplot points, you will be prompted for a username and password to retrieve the corresponding image - when this happens use slycat and slycat as you’ve done before.
• Use the “X Axis” and “Y Axis” dropdown menus at the top of the display to use any two numeric variables for the scatterplot axes.
• Click variable names in the raw data table or use the “Point Color” dropdown menu to color the scatterplot points using any numeric variable.
• Hover over columns in the raw data table to reveal sorting widgets.
• Click observations in the scatterplot to highlight the corresponding entry in the raw data table.
• Click and drag in the scatterplot to rubber-band-select multiple observations.
• Click rows or shift-click ranges of rows in the raw data table to highlight corresponding observations in the scatterplot.
• Choose an image variable using the “Image Set” dropdown at the top of the display, then hover the mouse over observations in the scatterplot to see the corresponding images.
• Click the “pin” icon in the upper-left-corner of an image to display it permanently.
• Click the “close” icon in the upper-left-corner of a pinned image to close it.
• Drag the “resize” icon in the lower-right-corner of a pinned image to resize it.
• Click-and-drag anywhere else within a pinned image to reposition it on the page.
• Click-and-drag the colorbar to reposition it on the page.

## Next Steps¶

That’s it for the tutorial ... now on to Managing Docker.