mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-27 21:38:39 +08:00
Rowan Cockett
b33b04d9a6
ndgrid returns vectors in a matrix by default, but input kwarg of vector=False, and you can return the matrices.
100 lines
2.4 KiB
Python
100 lines
2.4 KiB
Python
import numpy as np
|
|
|
|
|
|
def reshapeF(x, size):
|
|
return np.reshape(x, size, order='F')
|
|
|
|
|
|
def mkvc(x, numDims=1):
|
|
"""Creates a vector with the number of dimension specified
|
|
|
|
e.g.:
|
|
|
|
a = np.array([1, 2, 3])
|
|
|
|
mkvc(a, 1).shape
|
|
> (3, )
|
|
|
|
mkvc(a, 2).shape
|
|
> (3, 1)
|
|
|
|
mkvc(a, 3).shape
|
|
> (3, 1, 1)
|
|
|
|
"""
|
|
assert type(x) == np.ndarray, "Vector must be a numpy array"
|
|
|
|
if numDims == 1:
|
|
return x.flatten(order='F')
|
|
elif numDims == 2:
|
|
return x.flatten(order='F')[:, np.newaxis]
|
|
elif numDims == 3:
|
|
return x.flatten(order='F')[:, np.newaxis, np.newaxis]
|
|
|
|
|
|
def ndgrid(*args, **kwargs):
|
|
"""
|
|
Form tensorial grid for 1, 2, or 3 dimensions.
|
|
|
|
Returns as column vectors by default.
|
|
|
|
To return as matrix input:
|
|
|
|
ndgrid(..., vector=False)
|
|
|
|
The inputs can be a list or separate arguments.
|
|
|
|
e.g.
|
|
|
|
a = np.array([1, 2, 3])
|
|
b = np.array([1, 2])
|
|
|
|
XY = ndgrid(a, b)
|
|
> [[1 1]
|
|
[2 1]
|
|
[3 1]
|
|
[1 2]
|
|
[2 2]
|
|
[3 2]]
|
|
|
|
X, Y = ndgrid(a, b, vector=False)
|
|
> X = [[1 1]
|
|
[2 2]
|
|
[3 3]]
|
|
> Y = [[1 2]
|
|
[1 2]
|
|
[1 2]]
|
|
|
|
"""
|
|
|
|
# Read the keyword arguments, and only accept a vector=True/False
|
|
vector = kwargs.pop('vector', True)
|
|
assert type(vector) == bool, "'vector' keyword must be a bool"
|
|
assert len(kwargs) == 0, "Only 'vector' keyword accepted"
|
|
|
|
# you can either pass a list [x1, x2, x3] or each seperately
|
|
if type(args[0]) == list:
|
|
xin = args[0]
|
|
else:
|
|
xin = args
|
|
|
|
# Each vector needs to be a numpy array
|
|
assert np.all([type(x) == np.ndarray for x in xin]), "All vectors must be numpy arrays."
|
|
|
|
if len(xin) == 1:
|
|
return xin[0]
|
|
elif len(xin) == 2:
|
|
XY = np.broadcast_arrays(mkvc(xin[1], 1), mkvc(xin[0], 2))
|
|
if vector:
|
|
X2, X1 = [mkvc(x) for x in XY]
|
|
return np.c_[X1, X2]
|
|
else:
|
|
return XY[1], XY[0]
|
|
elif len(xin) == 3:
|
|
XYZ = np.broadcast_arrays(mkvc(xin[2], 1), mkvc(xin[1], 2), mkvc(xin[0], 3))
|
|
if vector:
|
|
X3, X2, X1 = [mkvc(x) for x in XYZ]
|
|
return np.c_[X1, X2, X3]
|
|
else:
|
|
return XYZ[2], XYZ[1], XYZ[0]
|