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From: Gary <pajer@in...>  20050114 19:59:14

John, I've been meaning to ask you ... how did you produce the very fine User Guide? Is that TeXmacs? LyX? raw LaTeX? ConTeXt? emacs magic? Is there some slick way of getting the listings from the command line window into the document, especially with the comments colorized? I'm writing a small local guide, and was wondering ... gary 
From: John Hunter <jdhunter@ac...>  20050114 17:44:22

>>>>> "James" == James Boyle <boyle5@...> writes: James> Is there anyway to place the tick marks so that they are James> located outside the axes, i.e. on the same side of the axis James> line as the axis labels? James> With plots such as imshow and pcolor and even some busy James> line plots, the interior minor ticks are completely James> obscured and the exact location of the major ticks is James> ambiguous. James> It would be nice to be able to specify the ticks as inside James> or outside (or both), right or left (or both), top or James> bottom (or both). This functionality may already be present James> but I cannot figure out how to invoke it if it is. I would like to make tick placement more flexible, for example to support a detachable tick line so the axis line, tick lines and labels float below the axes boundary. In addition, I would like the ability to position ticks along this line as above, centered or below, as you suggest. But for now this doesn't exist, but you can hack an approximation. The tick markers are TICKUP, TICKDOWN, TICKLEFT, and TICKRIGHT, and these are constants in matplotlib.lines. You can set the tick markers, for example, to be TICKDOWN. But you'll have to manually adjust the y position of the labels to be below them. The second hack is this only works in interactive mode. ticks are generated dynamically (eg for panning and zooming) and the ticks aren't generated until the plot is show. In noninteractive mode, the change of the default tick's line style is not propogating to the new ticks that are dynamically generated when the line is shown. This appears to be a bug so I'll look into it. For now, though, you should be able to get something that works in noninteractive mode. import matplotlib matplotlib.interactive(True) import matplotlib.lines as mpllines import pylab as pl ax = pl.subplot(111) pl.plot([1,2,3]) lines = ax.get_xticklines() labels = ax.get_xticklabels() for line in lines: line.set_marker(mpllines.TICKDOWN) # labels are in axes coords, where 0,0 is lower left of axes rectangle # and 1,1 is upper right for label in labels: label.set_y(0.02) pl.show() 
From: John Hunter <jdhunter@ac...>  20050114 17:12:43

>>>>> "seberino" == seberino <seberino@...> writes: seberino> Imagine your arrays had points (Cartesian position seberino> vectors) all over the place at completely random points seberino> in space. The 'shape' of this plot depends on max and seberino> min values of each coordinate. I believe Mathematica seberino> plotting would automagically calculate these max and min seberino> values and set plot ranges for you. This is why 'shape' seberino> attribute of Matplotlib/Numarray seems awkward and seberino> unnecessary to me unless I'm missing something. There are a variety of issues here.  The "shape" attribute comes form Numeric/numarray and is outside the realm of matplotlib. matplotlib plots numerix arrays.  The pcolor interface is determined by matlab. matlab has a pcolor function which I have tried to implement faithfully. To the extent that matplotlib has been successful, this is due in part because matlab has a good interface for plotting and replicating it generally, is a good thing.  Storing the "shape" of a data set allows for memory and efficiency savings. To take your example of a set of x,y,z points, you are right you cold reconstruct rectilinear grid from this data  one might have to use interpolation but it can be done  but it would require a lot of unnecessary computation for data which already lives on a grid. So pcolor assumes your data are on a rectilinear grid and it is incumbent upon you to get it into that form. The meshgrid function takes regularly sampled vector data and turns it into a rectilinear grid (this is also a matlab function). The matlab griddata function (which is not yet implemented in matplotlib) does the same for irregularly sampled data. JDH 
From: Dominique Orban <Dominique.Orban@po...>  20050114 16:49:08

Hi, When trying to plot the contours of the famous Rosenbrock function:  from matplotlib.pylab import * def rosenbrock(x,y): return 10.0 * (yx**2)**2 + (x1)**2 x = arange( 1.5, 1.5, 0.01 ) y = arange( 0.5, 1.5, 0.01 ) [X,Y] = meshgrid( x, y ) Z = rosenbrock( X, Y ) contour( Z, x=X, y=Y, levels = 50 ) show()  I notice some spurious zigzagging lines towards the top of the plot. Any idea where those might be coming from? Also, the figure produced by the above script is flipped horizontally. The corresponding Matlab script produces the correct plot. Thanks, Dominique 