Reputation: 42329
I need to generate a grid for an array with a general/variable number of dimensions. In the 2D case, I know I can use mgrid:
# Some 2D data
N = 1000
x = np.random.uniform(0., 1., N)
y = np.random.uniform(10., 100., N)
xmin, xmax, ymin, ymax = x.min(), x.max(), y.min(), y.max()
# Obtain 2D grid
xy_grid = np.mgrid[xmin:xmax:10j, ymin:ymax:10j]
How can I scale this approach when the number of dimensions is variable? Ie: my data could be (x, y)
or (x, y, z)
or (x, y, z, q)
, etc.
The naive approach of:
# Md_data.shape = (M, N), for M dimensions
dmin, dmax = np.amin(Md_data, axis=1), np.amax(Md_data, axis=1)
Md_grid = np.mgrid[dmin:dmax:10j]
does not work.
Upvotes: 1
Views: 270
Reputation: 221504
We could use a list comprehension looping through the list of variables : x,y,z,q,etc.
to create the slice notation and then simply feed it to mgrid
-
L = [x,y,z,q] # list of variables
out = np.mgrid[[np.s_[A.min():A.max():10j] for A in L]]
With the slice
constructor :
np.mgrid[[slice(A.min(),A.max(),10j) for A in L]]
Upvotes: 1