Wow. I did not expect such quick fix. Thanks Skilgannon.
Now I would like to complain about this green color :) I personally think it is not very contrast on a white background.
I like our red and blue, but I think it would be better to replace currently light colored green with either much darker green or better yet something else, for example black.
I only speak for my own eyes, but I think our potentially color blinded participants will appreciate it as well.
I am not a plotting wizard but I plot enough in my life to make a fullish attempt to improve it a bit.
Though, it looks like the colors were chosen for the speed and not beauty, and it might be hard to have still fast yet beautiful output.
But I'll try to help. How can I look at the code?
Here is the suggested patch. Note that image array now created in uint8 RGB space, since resulting png image is in RGB space as well. As result it removes unnecessary image manipulation, after the array is prepared.
Beware, I was not able to fully test it since I am missing database and google modules. But it should work fine.
Note that colorAPSvsKNPBI is not used in this script but probably needed for another python script responsible for a given bot stats.
--- BotCompare.py 2014-09-15 15:40:19.000000000 -0400 +++ BotCompare.new.py 2014-11-26 22:28:50.000000000 -0500 @@ -266,28 +266,34 @@ out.append("</td><td rowspan=\"7\">") enemyScores = pickle.loads(zlib.decompress(rumble.ParticipantsScores)) + # RGB color model + # Default colors + # colorSurvival = (0,255,0) + # colorAPS = (255,0,0) + # colorAPSvsKNPBI = (0,0,255) + # Colors suggested at + # http://ksrowell.com/blog-visualizing-data/2012/02/02/optimal-colors-for-graphs/ + colorSurvival = (62,150,81) + colorAPS = (204,37,41) + colorAPSvsKNPBI = (57,106,177) size = 169 - a = numpy.empty((size+1,size+1,4), dtype=numpy.float32) + a = numpy.empty((size+1,size+1,3), dtype=numpy.uint8) a[...,(0,1,2)]=255 for cp in commonList: eScore = enemyScores.get(cp.Name,None) if eScore: - a[max(0,min(size,size-int(round((cp.A_APS - cp.B_APS + 50)*0.01*size)))), - int(round(eScore.APS*0.01*size)),(0)]=0 + x = int(round(eScore.APS*0.01*size)) + y = max(0,min(size,size-int(round((cp.A_APS - cp.B_APS + 50)*0.01*size)))) + a[x,y,(0,1,2)] = colorAPS - a[max(0,min(size,size-int(round((cp.A_Survival - cp.B_Survival + 50)*0.01*size)))), - int(round(eScore.APS*0.01*size)),(1)]=0 + y = [max(0,min(size,size-int(round((cp.A_Survival - cp.B_Survival + 50)*0.01*size)))) + a[x,y,(0,1,2)] = colorSurvival # if eScore.ANPP > 0 and b.NPP >= 0: # a[size-int(round(b.NPP*0.01*size)),int(round(eScore.ANPP*0.01*size)),(0,1)]=0 - b = Image.fromarray(a.astype("uint8"), "CMYK") - c = cStringIO.StringIO() - b = b.convert("RGB") - a = numpy.array(b) - a[(a == (0,0,0)).all(axis=2)] = (255,255,255) a[size - int(round(.5*size)),...] = 127 - a - b = Image.fromarray(a,"RGB") + b = Image.fromarray(a.astype("uint8"),"RGB") + c = cStringIO.StringIO() b.save(c,format="png") d = c.getvalue() c.close()
I'm very busy at the moment, but I'll take a look at this on the weekend.
No problem. I also made a pull request at the bitbucket to simplify import. This request is completely analogous to the presented here patch.
I've integrated your changes, with a few modifications of my own (just implementation details - averaging colours for multi-hit pixels). I also added it to the BotDetails page.
Thanks for taking the patch into the account.
It looks like you do some sort weighting for colors based on counts. But I am afraid my changes to the RGB color model are not very suitable for this task. Since the resulting color is hard to predict and decipher back to APS and Survival.
I would suggest a few things to avoid this:
- (easiest) let single count take it all, chose either APS or Survival
- (better in my opinion) make separate APS and Survival maps. The comparison table underneath is quite wide, we can easily fit 2 maps above. How to handle 3 maps in the bot details page is unclear for me.
- (alternatively) enlarge X-size of the map and use point size as count indicator
I would suggest to increase size of the map and points in any case. The current 169x169 image is painfully small on modern displays. May be we should convert to the vector SVG format supported by modern browsers?