Thanks Matt. Also, I am not able to loop over method 1. I am using the stupid way as shown below. And method 2 is not showing the grids. from yt.mods import * # method1 p = plots.get_slice("RedshiftOutput0002", "Density", 0) p.modify["grids"]() p.modify["contour"]("Density") p.save_image("a") p = plots.get_slice("RedshiftOutput0002", "Density", 1) p.modify["grids"]() p.modify["contour"]("Density") p.save_image("b") p = plots.get_slice("RedshiftOutput0002", "Density", 2) p.modify["grids"]() p.modify["contour"]("Density") p.save_image("c") # method2 pf = load("RedshiftOutput0002") pc = PlotCollection(pf) for ax in range(3): pc.add_slice("Density", ax).modify["grids"]() pc.add_slice("Density", ax).modify["contour"]("Density") pc.save("plot2") shankar -----Original Message----- From: yt-users-bounces@lists.spacepope.org on behalf of Matthew Turk Sent: Tue 11/24/2009 10:44 AM To: Discussion of the yt analysis package Subject: Re: [yt-users] plotting questions Hi Shankar,
My simulation box is 200 Mpc/h. I wanted to know what are the default span (length scales) for the 2 methods ? For plot 1, how can I control the spacing of contours ? And where is plot 1 centered at ?
It took me a moment to track down what's happening. The default length scale is "unitary" -- which is the entire domain. However, the difference between the two plots is because plot collection-mediated plots default to being periodic, and this will reset . I have changed it so that the behavior between the two plot generation mechanisms is identical, in r1543. However, if you do not wish to upgrade, you can feed the argument "periodic=True" to the get_slice_plot call.
For plot 2, it is centered at the most dense point in the box. But how can I add density contours ? Also, how to control the spacing ?
You can add density contours the same way you did for the first mechanism -- through the modify["contours"] call. :) You can control the spacing a couple ways, as mentioned in the manual: http://yt.enzotools.org/doc/advanced/callbacks.html#available-callbacks through the ncont, clim, and take_log functions. If you look at the source code, you can see that ncont is fed directly to the matplotlib contour command -- so you can get greater control by feeding it, for instance, a list of density contours. Note that the mechanism for log-spaced contours is to log the data, not request log-spaced contours, so you will have to keep that in mind if you choose to request specific contour levels. -Matt _______________________________________________ yt-users mailing list yt-users@lists.spacepope.org http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org