#Gamalyzer#

General-purpose game trace visualization and descriptive analysis. Reads in trace files and spits out pretty pictures via a web server.

##Running the visualization##

Start by running lein deploy project from the metric directory, then cd back to vis, lein cljsbuild once && lein ring server.

To change the game being visualized, you have to modify vis/cli/gamalyzer/ui/core.cljs and change the mode variable to something relevant (also provide a default level and a range for the level slider in the same module); then modify vis/srv/gamalyzer/data.clj to support that mode (functions game-data, data, data-routes). Because I don't have permission to distribute the data sets I've used in the past, you're on your own when it comes to data. Sorry!

You might also need to implement a new reader module if you can't output the native Gamalyzer format provided by gamalyzer.read.edn. This is a series of EDN terms with a header like [:properties ([:game game-name] [:events (:i)] ...)], where (:i) indicates "only inputs are found in this trace" (as opposed to other types of game events). Each individual trace begins with [:log_start UUID], ends with :log_end, and consists of a series of terms [:i player-id decision-module (decision-location-term+) choice-id (choice-term+)]. Every one of those can be any EDN term, as far as I know. See the paper for details.

Note that read-logs can read multiple trace logs from a single file (in its four-argument version) or from a sequence of paths (in its three-argument version).p

##Calculating the metric (Clojure)##

After you've read in some traces and have both a gamalyzer.data.input.Traces record and a gamalyzer.data.input.Domains record (which is a kind of calculated schema that the traces follow), you can run gamalyzer.cmp.tt (trace-to-trace dissimilarity) and call the (distances trace-1 trace-2 domains) function for a time-varying sequence of pairwise distances. You may also want to use the (with-warp-window W ...) macro from gamalyzer.cmp.tt.cced if you plan to use constrained continuous edit distance (CCED); this quantity should be adjusted according to the shape of your data.

##Calculating the metric (Java)##

Similar to the above, but you're on your own when it comes to generating a Traces and a Domains instance (look at how gamalyzer.read.mario exposes its functionality for reference). Once you have them, gamalyzer.cmp.tt exposes both distance (overall dissimilarity) and distances (incremental dissimilarity) static methods which take as arguments two Trace objects, a Domains, and a warp window, assuming that CCED is being used.