mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-27 20:23:01 +08:00
Improved ndgrid, and pushed unused utilities to EldadsCode folder.
ndgrid returns vectors in a matrix by default, but input kwarg of vector=False, and you can return the matrices.
This commit is contained in:
Rowan Cockett
@@ -0,0 +1,87 @@
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from numpy import *
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import numpy as np
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def diff(A, d):
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if(d == 1):
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return A[1:, :, :] - A[:-1, :, :]
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elif(d == 2):
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return A[:, 1:, :] - A[:, :-1, :]
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else:
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return A[:, :, 1:] - A[:, :, :-1]
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#else:
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# print('d must be 1,2 or 3')
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def diffp(A, d1, d2):
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if(d1 == 1 and d2 == 2):
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return A[1:, 1:, :] - A[:-1, :-1, :]
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elif(d1 == 1 and d2 == 3):
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return A[1:, :, 1:] - A[:-1, :, :-1]
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else:
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return A[:, 1:, 1:] - A[:, :-1, :-1]
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def diffm(A, d1, d2):
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if(d1 == 3 and d2 == 2):
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return A[:, :-1, 1:] - A[:, 1:, :-1]
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elif(d1 == 1 and d2 == 3):
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return A[1:, :, :-1] - A[:-1, :, 1:]
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elif(d1 == 2 and d2 == 1):
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return A[:-1, 1:, :] - A[1:, :-1, :]
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else:
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print('d must be 1, 2 or 3')
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def ave(A, d):
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if(d == 1):
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return 0.5*(A[1:, :, :] + A[:-1, :, :])
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elif(d == 2):
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return 0.5*(A[:, 1:, :] + A[:, :-1, :])
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elif(d == 3):
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return 0.5*(A[:, :, 1:] + A[:, :, :-1])
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else:
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print('d must be 1,2 or 3')
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def mkmat(x):
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return reshape(matrix(x), (size(x), 1), 'F')
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def hstack3(a, b, c):
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a = mkvc(a)
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b = mkvc(b)
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c = mkvc(c)
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a = mkmat(a)
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b = mkmat(b)
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c = mkmat(c)
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return hstack((hstack((a, b)), c))
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def ind2sub(shape, ind):
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"""From the given shape, returns the subscrips of the given index"""
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revshp = []
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revshp.extend(shape)
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mult = [1]
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for i in range(0, len(revshp)-1):
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mult.extend([mult[i]*revshp[i]])
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mult = array(mult).reshape(len(mult))
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sub = []
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for i in range(0, len(shape)):
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sub.extend([math.floor(ind / mult[i])])
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ind = ind - (math.floor(ind/mult[i]) * mult[i])
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return sub
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def sub2ind(shape, subs):
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"""From the given shape, returns the index of the given subscript"""
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revshp = list(shape)
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mult = [1]
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for i in range(0, len(revshp)-1):
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mult.extend([mult[i]*revshp[i]])
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mult = array(mult).reshape(len(mult), 1)
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idx = dot((subs), (mult))
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return idx
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+56
-104
@@ -1,49 +1,6 @@
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from numpy import *
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import numpy as np
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def diff(A, d):
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if(d == 1):
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return A[1:, :, :] - A[:-1, :, :]
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elif(d == 2):
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return A[:, 1:, :] - A[:, :-1, :]
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else:
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return A[:, :, 1:] - A[:, :, :-1]
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#else:
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# print('d must be 1,2 or 3')
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def diffp(A, d1, d2):
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if(d1 == 1 and d2 == 2):
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return A[1:, 1:, :] - A[:-1, :-1, :]
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elif(d1 == 1 and d2 == 3):
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return A[1:, :, 1:] - A[:-1, :, :-1]
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else:
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return A[:, 1:, 1:] - A[:, :-1, :-1]
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def diffm(A, d1, d2):
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if(d1 == 3 and d2 == 2):
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return A[:, :-1, 1:] - A[:, 1:, :-1]
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elif(d1 == 1 and d2 == 3):
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return A[1:, :, :-1] - A[:-1, :, 1:]
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elif(d1 == 2 and d2 == 1):
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return A[:-1, 1:, :] - A[1:, :-1, :]
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else:
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print('d must be 1, 2 or 3')
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def ave(A, d):
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if(d == 1):
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return 0.5*(A[1:, :, :] + A[:-1, :, :])
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elif(d == 2):
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return 0.5*(A[:, 1:, :] + A[:, :-1, :])
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elif(d == 3):
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return 0.5*(A[:, :, 1:] + A[:, :, :-1])
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else:
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print('d must be 1,2 or 3')
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def reshapeF(x, size):
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return np.reshape(x, size, order='F')
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@@ -53,7 +10,7 @@ def mkvc(x, numDims=1):
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e.g.:
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a = np.array(1,2,3)
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a = np.array([1, 2, 3])
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mkvc(a, 1).shape
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> (3, )
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@@ -75,8 +32,45 @@ def mkvc(x, numDims=1):
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return x.flatten(order='F')[:, np.newaxis, np.newaxis]
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def ndgrid(*args):
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"""Form tensorial grid for 1, 2 and 3 dimensions. Return X1,X2,X3 arrays depending on the dimension"""
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def ndgrid(*args, **kwargs):
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"""
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Form tensorial grid for 1, 2, or 3 dimensions.
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Returns as column vectors by default.
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To return as matrix input:
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ndgrid(..., vector=False)
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The inputs can be a list or separate arguments.
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e.g.
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a = np.array([1, 2, 3])
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b = np.array([1, 2])
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XY = ndgrid(a, b)
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> [[1 1]
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[2 1]
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[3 1]
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[1 2]
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[2 2]
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[3 2]]
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X, Y = ndgrid(a, b, vector=False)
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> X = [[1 1]
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[2 2]
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[3 3]]
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> Y = [[1 2]
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[1 2]
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[1 2]]
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"""
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# Read the keyword arguments, and only accept a vector=True/False
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vector = kwargs.pop('vector', True)
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assert type(vector) == bool, "'vector' keyword must be a bool"
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assert len(kwargs) == 0, "Only 'vector' keyword accepted"
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# you can either pass a list [x1, x2, x3] or each seperately
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if type(args[0]) == list:
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@@ -84,64 +78,22 @@ def ndgrid(*args):
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else:
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xin = args
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# Each vector needs to be a numpy array
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assert np.all([type(x) == np.ndarray for x in xin]), "All vectors must be numpy arrays."
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if len(xin) == 1:
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return xin
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return xin[0]
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elif len(xin) == 2:
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X2, X1 = [mkvc(x) for x in np.broadcast_arrays(mkvc(xin[1], 1), mkvc(xin[0], 2))]
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return np.c_[X1, X2]
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XY = np.broadcast_arrays(mkvc(xin[1], 1), mkvc(xin[0], 2))
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if vector:
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X2, X1 = [mkvc(x) for x in XY]
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return np.c_[X1, X2]
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else:
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return XY[1], XY[0]
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elif len(xin) == 3:
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X3, X2, X1 = [mkvc(x) for x in np.broadcast_arrays(mkvc(xin[2], 1), mkvc(xin[1], 2), mkvc(xin[0], 3))]
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return np.c_[X1, X2, X3]
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def ind2sub(shape, ind):
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# From the given shape, returns the subscrips of the given index
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revshp = []
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revshp.extend(shape)
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mult = [1]
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for i in range(0, len(revshp)-1):
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mult.extend([mult[i]*revshp[i]])
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mult = array(mult).reshape(len(mult))
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sub = []
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for i in range(0, len(shape)):
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sub.extend([math.floor(ind / mult[i])])
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ind = ind - (math.floor(ind/mult[i]) * mult[i])
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return sub
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def sub2ind(shape, subs):
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# From the given shape, returns the index of the given subscript
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revshp = list(shape)
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mult = [1]
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for i in range(0, len(revshp)-1):
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mult.extend([mult[i]*revshp[i]])
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mult = array(mult).reshape(len(mult), 1)
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idx = dot((subs), (mult))
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return idx
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def mkmat(x):
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return reshape(matrix(x), (size(x), 1), 'F')
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def hstack3(a, b, c):
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a = mkvc(a)
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b = mkvc(b)
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c = mkvc(c)
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a = mkmat(a)
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b = mkmat(b)
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c = mkmat(c)
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return hstack((hstack((a, b)), c))
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if __name__ == '__main__':
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X, Y, Z = mgrid[0:4, 0:5, 0:6]
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print Z
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t = ave(X, 1)
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print t
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XYZ = np.broadcast_arrays(mkvc(xin[2], 1), mkvc(xin[1], 2), mkvc(xin[0], 3))
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if vector:
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X3, X2, X1 = [mkvc(x) for x in XYZ]
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return np.c_[X1, X2, X3]
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else:
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return XYZ[2], XYZ[1], XYZ[0]
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