Thanks Jeff, To clarify, I'm sampling a numpy array (regular lon/lat grid) and extracting a series of same size frames (say 60 longitude grids and 30 latitude grids) around a feature of interest, which can be centered somewhere on the map. What I want to do is accumulate statistics with these frames such that the relative size/distances are persevered, which of course means that I can't just add a frame centered on 30N with one centered on 80N. Ideally, I'd like to interpolate each frame to a common point (lon/lat) and display the results either in the common grid space or as radial distances from the common point.
Since you're a meteorologist I can simply say I'm creating an ensemble average of extra tropical cyclones from a dozen or so computer models (each with very different resolutions). I want to see how cloud and precipitation features in each model's cyclones compare to a similar product I'm producing from satellite data using weather model output to locate the cyclones. Much the same thing as the link I provided. Thanks for your suggests as transform_scalar sounds like a good place to begin. Mike On Dec 11, 2007, at 4:57 PM, Jeff Whitaker wrote: > mbauer wrote: >> Matplotlib users, I looking to tap your wealth of ideas and >> experience to help solve a problem I'm working on. >> >> The problem: I have a series of 2d scalar arrays representing a >> fixed width/height lon/lat box centered on an arbitrary lon/lat. I >> need to average these composites on a common basis that >> accommodates the scale changes due to latitude, preferably by >> shifting everything to a common central lon/lat (a polar/radial >> distance basis would work too). I want a plot of the end result >> too and I'm like to do everything with matplotlib and python so >> that it folds into the rest of my program. >> >> Something similar can be seen at >> http://www.atmos.washington.edu/~robwood/topic_cyclones.htm >> >> I've been looking at transform_scalar from basemap but I'm not >> quite sure this is what I should use. >> > Mike: > > transform_scalar does simple bilinear interpolation from a lat/lon > grid to a regular grid in map projection coordinates. If your map > projection is just a lat/lon projection, then this amounts to > interpolating from one lat/lon grid to another. >> If anyone can offer a solution, a point in the right direction, or >> just wave me off this path I'd be most appreciative. >> > I'm sure numpy/matplotlib can do what you need to do. Matplotlib > can certainly make a plot similar to the one given in your link. I > think you question relates more to the processing of your arrays > though, and not specifically the plotting. Are all your 2d arrays > the same shape (the same number of lats and lons)? Are they just > centered on different regions? If so, I think you can just multiply > each grid point by the cosine of latitude to get the proper area > weighting before summing them together. But perhaps I'm missing the > essence of your question .... > > -Jeff > > > -- > Jeffrey S. Whitaker Phone : (303)497-6313 > Meteorologist FAX : (303)497-6449 > NOAA/OAR/PSD R/PSD1 Email : [EMAIL PROTECTED] > 325 Broadway Office : Skaggs Research Cntr 1D-124 > Boulder, CO, USA 80303-3328 Web : http://tinyurl.com/5telg > > ------------------------------------------------------------------------- SF.Net email is sponsored by: Check out the new SourceForge.net Marketplace. It's the best place to buy or sell services for just about anything Open Source. http://sourceforge.net/services/buy/index.php _______________________________________________ Matplotlib-users mailing list Matplotlib-users@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/matplotlib-users
