Sameer,
They are fundamentally different in what they do; this is most clear
if you experiment with using both methods on a low-resolution field
(say 10 by 12 points), and with a small number of contours (5 or
10). Contouring is most typically done with a moderate number of
contours, maybe 30 or fewer. It is most immediately appropriate for
data values at discrete points, since it is interpolating.
pcolormesh does not interpolate, but shows the value in each
quadrilateral as a block of solid color, so it is more like an image.
There are data sets and situations for which either of the two
methods can be appropriate, and other cases where one is clearly
better than the other. For a somewhat noisy field, pcolormesh is
usually better; it allows one to see the signal and the noise, and
let one's eye pick out the former. Isolated extreme values are
represented better with pcolormesh. For smoother fields and for
seeing large-scale structure, contourf may be more appropriate.
What kind of data are you plotting?
Eric
Hello,
I'd like to know about the difference between contourf and
pcolormesh
and their intended uses. So far, I've been using contourf with a
large
number of levels (150 - 200) to plot two dimensional data. Are
their any
disadvantages of this and is pcolormesh better suited for this task?
As far as I can make out, contourf displays a "smoothened-out"
image and
pcolormesh is more "boxy".
Thanks.
Sameer
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Hi Eric,
The data is usually around 1000 X 1000 large or smaller, mainly
experimental or theoretical data obtained by varying two independent
parameters. With a large number of contours, the visual output from them
doesn't look too different, especially if pcolormesh is used with
gouraud shading. But I realize they do things differently. Thank you
for the clarification.
My only comment is that contourf(x,y,z) expects z of shape(size(y),
size(x)) whereas pcolormesh (x,y,z) expects z of shape (size(y)+1,
size(x)+1), for obvious reasons.
It would be nice if pcolormesh could adjust x and y so that the centres
of each quadrilateral is at the value of x and y, so that both functions
could be called in the same way.
If x and y are uniformly spaced, then imshow does that (and much
faster), with suitable specification of extent, aspect, and interpolation.
I view the way pcolormesh handles x and y as fundamental, so I am
reluctant to add an option to interpolate/extrapolate a pixel-centered
grid to the the edge grid that pcolormesh absolutely requires. In the
simplest cases, such as a uniform rectangular grid, such a
transformation is straightforward, but in the more general cases that
pcolormesh handles, it is not; there is no single algorithm that would
"do the right thing" in all cases.
There is scope for making it easier to do common things, but rather than
fold it into functions like pcolormesh, I think it is better to break it
out into standalone grid manipulation functions.
We also have a NonuniformImage class that handles irregular (and
regular) rectangular pixel-centered grids, and can be used directly.
That class needs an Axes method and a pyplot function.
Eric
···
On 2017/01/07 9:24 AM, Sameer Grover wrote:
On 6 January 2017 at 06:50, Eric Firing <efiring at hawaii.edu > <mailto:efiring at hawaii.edu>> wrote:
On 2017/01/05 2:40 PM, Sameer Grover wrote:
It's not hard to do but something that could be considered if there are
enough use cases.
Sameer