import - Working with Java hashmaps from Mathematica

I'm using J/Link to access data from an external Java program. The data are represented as a ConcurrentHashMap (see the official specification here).

link = JavaNew["org.ddpclient.DDPTestClient"];

When I evaluate data = link@mCollections in my Mathematica notebook to access mCollections field where the data of interest are stored, I get a HashMap object:

« JavaObject[java.util.HashMap]»

Is there a way to convert this HashMap into a native Mathematica list (or nested list, to be more precise) so that I could work with it?

I know that one can call a toString[] method on the HashMap object to convert it to a string. In my case, data@toString[] yields something like this:

{G9fuqeYuiQpcmL8MW={playerId=zxcgf24ta, lastSeen=null, status=0.0, \ active=true}, aiej2mQppBAefhad7={playerId=lklu453da1, lastSeen=null, \ status=0.0, active=true}, Bbd8YqAx8yFcdcMcz={playerId=poo10alll235, \ lastSeen=null, status=0.0, active=true}, \ Gnt3KKrrgypgFEorp={playerId=hjffxz535cd, lastSeen=null, status=0.0, \ active=true}, ioydvWm7GacPg5DyM={playerId=asdfa123sa, lastSeen=null, \ status=1.0, active=false}, 8vZ6i73t6mbwTB63D={playerId=66df24hl, \ lastSeen=null, status=0.0, active=true}}

It is probably possible to manually parse this, but I am afraid it would be really difficult to handle all the possible edge cases that could arise during the conversion. Is there a better way?

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plotting - Plot 4D data with color as 4th dimension

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I have several sigmoidal fits to 3 different datasets, with mean fit predictions plus the 95% confidence limits (not symmetrical around the mean) and the actual data. I would now like to show these different 2D plots projected in 3D as in but then using proper perspective. In the link here they give some solutions to combine the plots using isometric perspective, but I would like to use proper 3 point perspective. Any thoughts? Also any way to show the mean points per time point for each series plus or minus the standard error on the mean would be cool too, either using points+vertical bars, or using spheres plus tubes. Below are some test data and the fit function I am using. Note that I am working on a logit(proportion) scale and that the final vertical scale is Log10(percentage). (* some test data *) data = Table[Null, {i, 4}]; data[[1]] = {{1, -5.8}, {2, -5.4}, {3, -0.8}, {4, -0.2}, {5, 4.6}, {1, -6.4}, {2, -5.6}, {3, -0.7}, {4, 0.04}, {5, 1.0}, {1, -6.8}, {2, -4.7}, {3, -1....

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