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From: John [H2O] <washakie@gm...>  20090709 22:48:22

Just curious if you're interested in folks contributing to the gallery. I was playing around trying to come up with a routine to automatically choose colors when plotting several datasets, not necessarily from a single array, but rather say iterating through a list of datasets. I came up with the following... maybe it's of interest? And certainly of interest to me... any advice on what could be done better! Thanks! > #!/usr/bin/env python > > import numpy as np > import matplotlib.pyplot as plt > import matplotlib.cm as cm > import matplotlib.colors as colors > > """A script to demonstrate automatically assigning colors based > on the number of x,y pairs to be plotted. """ > > # First example > # set up some example data > x = np.random.random((430,23)) > > # This is the important part for 'autocoloring' > # get an array of 01 values, length of numint (#data sets > # that you will iterate through), these will define the colors > numint = x.shape[1] > Nc = np.array([float(i)/numint for i in range(numint)]) > norm = colors.normalize(Nc.min(),Nc.max()) > > fig = plt.figure() > ax = fig.add_subplot(111) > interval = 0 > for i in range(numint): > #get a new color > cmap = cm.jet(norm(Nc[i])) > ax.scatter(x[:,0],x[:,i],color=cmap) > > > # Second example > # something a little more interesting > fig2 = plt.figure() > ax2 = fig2.add_subplot(111) > X = np.arange(400) > y = np.sin(X) > y2 = X*.2 > x = np.column_stack((y,y2)) > > #define an interval, the dataset is divided by this value > intervalsize = 23 > numint = int(np.round(x.shape[0]/intervalsize)) + 1 > > # This is the important part for 'autocoloring' > # get an array of 01 values, length of numint > # these will define the colors > Nc = np.array([float(i)/numint for i in range(numint)]) > norm = colors.normalize(Nc.min(),Nc.max()) > > interval = 0 > for i in range(0,len(x),intervalsize): > # define the index array (easier than typing) > indx = np.arange(i,i+intervalsize) > #get a new color > cmap = cm.jet(norm(Nc[interval])) > # the indx as defined above may exceed > # the data array > try: > ax2.scatter(x[indx,0],x[indx,1],color=cmap) > #print indx > # case to handle tail of data > except: > #plt.scatter(x[i:,0],x[i:,1],color=cmap) > print 'OOPS, index exceeds dimensions:',indx > pass > # so that you don't miss the last interval > if len(x)i < intervalsize: > ax2.scatter(x[i+1:,0],x[i+1:,1],color=cmap) > print 'last bits...' > interval+=1 > > plt.show() >  View this message in context: http://www.nabble.com/contributetogalleryOr%2Cjustadviceonchangingcolorsautomagically....tp24419101p24419101.html Sent from the matplotlib  users mailing list archive at Nabble.com. 
From: vehemental <jimmy.paillet@gm...>  20090710 13:10:15

Hi, I may be wrong, but arent these already examples of what you trying to show here?: http://matplotlib.sourceforge.net/examples/pylab_examples/line_collection2.html http://matplotlib.sourceforge.net/examples/pylab_examples/ellipse_collection.html if you use a collection you can quickly setup the colors of all your elements..by just passing an array.... jimmy John [H2O] wrote: > > Just curious if you're interested in folks contributing to the gallery. I > was playing around trying to come up with a routine to automatically > choose colors when plotting several datasets, not necessarily from a > single array, but rather say iterating through a list of datasets. I came > up with the following... maybe it's of interest? And certainly of > interest to me... any advice on what could be done better! > > Thanks! > > > >> #!/usr/bin/env python >> >> import numpy as np >> import matplotlib.pyplot as plt >> import matplotlib.cm as cm >> import matplotlib.colors as colors >> >> """A script to demonstrate automatically assigning colors based >> on the number of x,y pairs to be plotted. """ >> >> # First example >> # set up some example data >> x = np.random.random((430,23)) >> >> # This is the important part for 'autocoloring' >> # get an array of 01 values, length of numint (#data sets >> # that you will iterate through), these will define the colors >> numint = x.shape[1] >> Nc = np.array([float(i)/numint for i in range(numint)]) >> norm = colors.normalize(Nc.min(),Nc.max()) >> >> fig = plt.figure() >> ax = fig.add_subplot(111) >> interval = 0 >> for i in range(numint): >> #get a new color >> cmap = cm.jet(norm(Nc[i])) >> ax.scatter(x[:,0],x[:,i],color=cmap) >> >> >> # Second example >> # something a little more interesting >> fig2 = plt.figure() >> ax2 = fig2.add_subplot(111) >> X = np.arange(400) >> y = np.sin(X) >> y2 = X*.2 >> x = np.column_stack((y,y2)) >> >> #define an interval, the dataset is divided by this value >> intervalsize = 23 >> numint = int(np.round(x.shape[0]/intervalsize)) + 1 >> >> # This is the important part for 'autocoloring' >> # get an array of 01 values, length of numint >> # these will define the colors >> Nc = np.array([float(i)/numint for i in range(numint)]) >> norm = colors.normalize(Nc.min(),Nc.max()) >> >> interval = 0 >> for i in range(0,len(x),intervalsize): >> # define the index array (easier than typing) >> indx = np.arange(i,i+intervalsize) >> #get a new color >> cmap = cm.jet(norm(Nc[interval])) >> # the indx as defined above may exceed >> # the data array >> try: >> ax2.scatter(x[indx,0],x[indx,1],color=cmap) >> #print indx >> # case to handle tail of data >> except: >> #plt.scatter(x[i:,0],x[i:,1],color=cmap) >> print 'OOPS, index exceeds dimensions:',indx >> pass >> # so that you don't miss the last interval >> if len(x)i < intervalsize: >> ax2.scatter(x[i+1:,0],x[i+1:,1],color=cmap) >> print 'last bits...' >> interval+=1 >> >> plt.show() >> >  View this message in context: http://www.nabble.com/contributetogalleryOr%2Cjustadviceonchangingcolorsautomagically....tp24419101p24427289.html Sent from the matplotlib  users mailing list archive at Nabble.com. 
From: John [H2O] <washakie@gm...>  20090710 13:46:51

vehemental wrote: > > Hi, I may be wrong, but arent these already examples of what you trying to > show here?: > > http://matplotlib.sourceforge.net/examples/pylab_examples/line_collection2.html > http://matplotlib.sourceforge.net/examples/pylab_examples/ellipse_collection.html > > if you use a collection you can quickly setup the colors of all your > elements..by just passing an array.... > > jimmy > > "same same, but different"... I guess here one would have to assume you're only plotting lines? Or only plotting ellipses, but say for example you had different types of plots you would make, and just need a set of collections.. say you wanted to mix scatter plots with line plots. The point is, yes, you're correct, I think this basically does do what I'm talking about, but having a way to create a 'generic' set of colors that can be indexed I think is nice. There may be a more 'matplotlibish' way to do it, I just didn't find it. Seem ultimately there should be something like: from matplotlib.colors import colorset where colorset could just be defined with an integer as the number of colors, and perhaps a cmap as an option... Maybe it's there?!?! I just haven't found it. My sudo code would be: plotcolors = colorset(10,cmap=cm.jet) for i in range(len(dataset)): x,y = dataset[i] plot(x,y, plotcolors[i]) ... or something. **Note: I realize this isn't the best example, as plot(X,Y) would do it automagically...  View this message in context: http://www.nabble.com/contributetogalleryOr%2Cjustadviceonchangingcolorsautomagically....tp24419101p24427781.html Sent from the matplotlib  users mailing list archive at Nabble.com. 
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