rationale for contour() argument dimensions?

Community matplotlib-users
1

I may be a bit thick, but I am having a heck of a time figuring out how to
use contour() properly.
Specifically, I do not see why the z dimension should be a 2d array.
It should only take a set of x,y,z
coordinates to produce a surface -- what is the extra dimension for? More
importantly, how do I take
"canonical" 3-d data, e.g.:

x y z
1 1 2.3
1 2 4.5
1 3 6.1
2 1 7.3
.
.
.

and get it into the form that matplotlib demands?

Thanks,
cf

2

Chris,

The point is that contouring and gridding are two entirely separate operations--and this is true in general, not just for matplotlib. Contouring algorithms--at least all the ones I have looked at--work with data on a regular grid. There are many ways to map scattered data to a regular grid, so it makes sense to do this as a separate step, so you can choose a gridding method that works well for your particular type of data set. See http://www.scipy.org/Cookbook/Matplotlib/Gridding_irregularly_spaced_data

Now, if your data are already on a quadrilateral grid, as in the example you give, but your arrays have been flattened, then all you need to do is reshape your arrays so that they are 2-D, with the row number corresponding to Y (vertical on the plot) and the column index corresponding to X (horizontal). Filling in your example below to include 2 values of x, and assuming all three variables are numpy ndarrays, you could do something like this:

x.shape = 2,3
y.shape = 2,3
z.shape = 2,3
contour(x.transpose(), y.transpose(), z.transpose())

The transposes are needed because your original z is arranged with y varying fastest.

Eric

Chris wrote:

ยทยทยท

I may be a bit thick, but I am having a heck of a time figuring out how to use contour() properly. Specifically, I do not see why the z dimension should be a 2d array. It should only take a set of x,y,z coordinates to produce a surface -- what is the extra dimension for? More importantly, how do I take "canonical" 3-d data, e.g.:

x y z
1 1 2.3
1 2 4.5
1 3 6.1
2 1 7.3
.

and get it into the form that matplotlib demands?

Thanks,
cf

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