Mike Bauer wrote:
Jeff,
Attached you'll find an example and sample data. Sample output is
here: http://gallery.me.com/ohtinsel#100149Below you'll also find an old email about the project that you've
answered before (might help you understand what I'm doing).Thank you so much. This is a huge help.
Mike
Mike: OK, I figure out why I have a black plot. In the latest version of netCDF4, scale_factor and add_offset are applied automatically, so in your script they were effectively being applied twice. Concerning the contours spilling out over the domain, just set the keyword masked to True in the call to transform_scalar. Concerning the "floating whitespace" - your data is on a lat/lon grid, so just plot the data with projection='cyl' and specify the lat/lon values of the lower left and upper right corners of the grid. Then the data will exactly fit the map. Your are using a lambert conformal projection, which covers a much larger area than your data.
-Jeff
···
mbauer wrote:
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
Mike: Thanks for the explanation, I get it now - and I think I have
just the thing for you. First, define a Basemap instance for a Lambert
Conformal projection centered on each of you frames that is 5000 km wide
and 5000 km tall.# for frame centered on lon_0, lat_0.
# resolution=None skips processing of boundary datasets to save time.
m = Basemap(lon_0=lon_0, lat_0=lat_0, projection='lcc', \
width=5000000, height=5000000, resolution=None)Now interpolate your data to a nx by ny grid that is regular in the map
projection region.# data is the lat/lon gridded data, lons and lats are 1D arrays in degrees.
data2 = m.transform_scalar(data, lons, lats, nx, ny)The data2 grids will be approximately equally spaced on the surface of
the earth, regardless on lat_0 and lon_0. Therefore, you should be able
to just ensemble average all the data2 grids and preserve the relative
shapes and sizes of the features (this is helped by the fact that the
projection is conformal, or shape-preserving).HTH,
-Jeff
================================================================================
On Thu, Feb 25, 2010 at 12:16 PM, Jeff Whitaker <jswhit@...146...> wrote:
Mike Bauer wrote:
Howdy All,
I'm hoping someone can give me a quick solution to a couple of
problems. I think I'm just missing an idea or two.Problem 1: I'm creating a map using the 'llc' lambert conformal
projection and pcolormesh. Here is a sampling of the source.self.m =
Basemap(lat_0=self.lat_0,lon_0=self.lon_0,projection='lcc',width=store_comp.base_width,height=store_comp.base_height,
resolution='i',area_thresh=100000)
self.fig = plt.figure(figsize=(width,hieght),frameon=True)
self.ax = self.fig.add_subplot(111)
self.xx, self.yy = self.m(*numpy.meshgrid(self.x,self.y))
self.the_image =self.m.pcolormesh(self.xx,self.yy,z,edgecolors='None',cmap=self.color_scheme)
The problem I have is two fold: 1) the map segment isn't
fully shown unless I drive up the width and size in basemap so that
the map floats in a lot of whitespace. 2) when I use drawparallels
etc. the lines extend beyond the map in a way that I wish they
wouldn't. See map1 in http://gallery.me.com/ohtinsel#100149Problem 2: This problem comes up when I use contourf on the same
data, which occupies only a limited domain (i.e., there is no data
outside the lat/lon bounds shown in map1). Here the contours spill out
onto the plot in a way that I wish they wound't (some of the source
is below). See map2 in http://gallery.me.com/ohtinsel#100149self.x = numpy.where(self.x < 180.0,self.x,self.x-360.0)
scale = 1
dx = self.width/((len(self.x)-1)*scale)
dy = self.height/((len(self.y)-1)*scale)
nx = int((self.m.xmax-self.m.xmin)/dx)+1
ny = int((self.m.ymax-self.m.ymin)/dy)+1
self.z,self.xx,self.yy = self.m.transform_scalar(
self.z,self.x,self.y,nx,ny,returnxy=True)
self.the_image =
self.m.contour(self.xx,self.yy,self.z,colors='k')
self.the_image = self.m.contourf(self.xx,self.yy,self.z,
cmap=self.color_scheme,extend=self.extend)No doubt I'm doing something wrong and probably obvious, but I can't
figure it out. Suggestions are much appreciated.Mike
Mike: It's difficult to tell what's going on (or even what you're really
trying to do) from the code snippets you posted. If you can send a
complete, self-contained example of the problem you're having that anybody
can run, then we can probably help.-Jeff
--
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@...259...
325 Broadway Office : Skaggs Research Cntr 1D-113
Boulder, CO, USA 80303-3328 Web : Jeffrey S. Whitaker: NOAA Physical Sciences Laboratory>
--
Jeffrey S. Whitaker Phone : (303)497-6313
Meteorologist FAX : (303)497-6449
NOAA/OAR/PSD R/PSD1 Email : Jeffrey.S.Whitaker@...259...
325 Broadway Office : Skaggs Research Cntr 1D-113
Boulder, CO, USA 80303-3328 Web : Jeffrey S. Whitaker: NOAA Physical Sciences Laboratory