[Numpy-tickets] [NumPy] #966: Enhance meshgrid to generate 3D grids + add option for sparse grids
NumPy
numpy-tickets@scipy....
Mon Dec 8 20:40:55 CST 2008
#966: Enhance meshgrid to generate 3D grids + add option for sparse grids
-------------------------+--------------------------------------------------
Reporter: pbrod | Owner: somebody
Type: enhancement | Status: new
Priority: normal | Milestone: 1.3.0
Component: numpy.lib | Version: devel
Severity: normal | Keywords: meshgrid
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I would suggest to replace numpy's meshgrid with the meshgrid found in
SciTools at:
http://code.google.com/p/scitools/
for the following reasons:
The `meshgrid` function in NumPy is limited to two dimensions only, while
the SciTools version can also work with 3D grids. In addition,
the NumPy version of `meshgrid` has no option for generating sparse
grids to conserve memory, like it is in SciTools by specifying the
`sparse` argument:
{{{
>>> xv, yv = meshgrid(linspace(-2,2,5), linspace(-1,1,3), sparse=True)
>>> xv
array([[-2., -1., 0., 1., 2.]])
>>> yv
array([[-1.],
[ 0.],
[ 1.]])
}}}
Actually, this is the default behavior for the `meshgrid` function in
SciTools. In NumPy, however, we will in this case get a full 2D grid:
{{{
>>> xv, yv = numpy.meshgrid(linspace(-2,2,5), linspace(-1,1,3))
>>> xv
array([[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.]])
>>> yv
array([[-1., -1., -1., -1., -1.],
[ 0., 0., 0., 0., 0.],
[ 1., 1., 1., 1., 1.]])
}}}
This is the same result we get by setting `sparse=False` in `meshgrid`
in SciTools.
The NumPy functions `mgrid` and `ogrid` does provide support for,
respectively, full and sparse n-dimensional meshgrids, however,
these functions uses slices to generate the meshgrids rather than
one-dimensional coordinate arrays such as in Matlab. With slices, the
user does not have the option to generate meshgrid with, e.g.,
irregular spacings, like
{{{
>>> x = array([-1,-0.5,1,4,5], float)
>>> y = array([0,-2,-5], float)
>>> xv, yv = meshgrid(x, y, sparse=False)
>>> xv
array([[-1. , -0.5, 1. , 4. , 5. ],
[-1. , -0.5, 1. , 4. , 5. ],
[-1. , -0.5, 1. , 4. , 5. ]])
>>> yv
array([[ 0., 0., 0., 0., 0.],
[-2., -2., -2., -2., -2.],
[-5., -5., -5., -5., -5.]])
}}}
In addition to the reasons mentioned above, the meshgrid function in
NumPy supports only Cartesian indexing, i.e., x and y, not matrix
indexing, i.e., rows and columns (`mgrid` and `ogrid` supports only
matrix indexing). The `meshgrid` function in SciTools supports both
indexing conventions through the `indexing` keyword argument. Giving
the string `'ij'` returns a meshgrid with matrix indexing, while
`'xy'` returns a meshgrid with Cartesian indexing. The difference is
illustrated by the following code snippet:
{{{
nx = 10
ny = 15
x = linspace(-2,2,nx)
y = linspace(-2,2,ny)
xv, yv = meshgrid(x, y, sparse=False, indexing='ij')
for i in range(nx):
for j in range(ny):
# treat xv[i,j], yv[i,j]
xv, yv = meshgrid(x, y, sparse=False, indexing='xy')
for i in range(nx):
for j in range(ny):
# treat xv[j,i], yv[j,i]
}}}
It is not entirely true that matrix indexing is not supported by the
`meshgrid` function in NumPy because we can just switch the order of
the first two input and output arguments:
{{{
yv, xv = numpy.meshgrid(y, x)
}}}
is the same as
{{{
xv, yv = meshgrid(x, y, sparse=False, indexing='ij')
}}}
However, we think it is clearer to have the logical "x, y"
sequence on the left-hand side and instead adjust a keyword argument.
--
Ticket URL: <http://scipy.org/scipy/numpy/ticket/966>
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