def test_ndgrid_2D(self):
XY = ndgrid([self.a, self.b])
X1_test = np.array([1, 2, 3, 1, 2, 3])
X2_test = np.array([1, 1, 1, 2, 2, 2])
self.assertTrue(np.all(XY[:, 0] == X1_test))
self.assertTrue(np.all(XY[:, 1] == X2_test))
def test_ndgrid_2D(self):
XY = ndgrid([self.a, self.b])
X1_test = np.array([1, 2, 3, 1, 2, 3])
X2_test = np.array([1, 1, 1, 2, 2, 2])
self.assertTrue(np.all(XY[:, 0] == X1_test))
self.assertTrue(np.all(XY[:, 1] == X2_test))
def test_ndgrid_3D(self):
XYZ = ndgrid([self.a, self.b, self.c])
X1_test = np.array([1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3])
X2_test = np.array([1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2])
X3_test = np.array([1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4])
self.assertTrue(np.all(XYZ[:, 0] == X1_test))
self.assertTrue(np.all(XYZ[:, 1] == X2_test))
self.assertTrue(np.all(XYZ[:, 2] == X3_test))
def test_indexCube_3D(self):
nN = np.array([3, 3, 3])
self.assertTrue(
np.all(
indexCube('A', nN) == np.array([0, 1, 3, 4, 9, 10, 12, 13])))
self.assertTrue(
np.all(
indexCube('B', nN) == np.array([3, 4, 6, 7, 12, 13, 15, 16])))
self.assertTrue(
np.all(
indexCube('C', nN) == np.array([4, 5, 7, 8, 13, 14, 16, 17])))
self.assertTrue(
np.all(
indexCube('D', nN) == np.array([1, 2, 4, 5, 10, 11, 13, 14])))
self.assertTrue(
np.all(
indexCube('E', nN) == np.array([9, 10, 12, 13, 18, 19, 21, 22
])))
self.assertTrue(
np.all(
indexCube('F', nN) == np.array(
[12, 13, 15, 16, 21, 22, 24, 25])))
self.assertTrue(
np.all(
indexCube('G', nN) == np.array(
[13, 14, 16, 17, 22, 23, 25, 26])))
self.assertTrue(
np.all(
indexCube('H', nN) == np.array(
[10, 11, 13, 14, 19, 20, 22, 23])))
def test_indexCube_3D(self):
nN = np.array([3, 3, 3])
self.assertTrue(np.all(indexCube('A', nN) == np.array([0, 1, 3, 4, 9, 10, 12, 13])))
self.assertTrue(np.all(indexCube('B', nN) == np.array([3, 4, 6, 7, 12, 13, 15, 16])))
self.assertTrue(np.all(indexCube('C', nN) == np.array([4, 5, 7, 8, 13, 14, 16, 17])))
self.assertTrue(np.all(indexCube('D', nN) == np.array([1, 2, 4, 5, 10, 11, 13, 14])))
self.assertTrue(np.all(indexCube('E', nN) == np.array([9, 10, 12, 13, 18, 19, 21, 22])))
self.assertTrue(np.all(indexCube('F', nN) == np.array([12, 13, 15, 16, 21, 22, 24, 25])))
self.assertTrue(np.all(indexCube('G', nN) == np.array([13, 14, 16, 17, 22, 23, 25, 26])))
self.assertTrue(np.all(indexCube('H', nN) == np.array([10, 11, 13, 14, 19, 20, 22, 23])))
def test_ndgrid_3D(self):
XYZ = ndgrid([self.a, self.b, self.c])
X1_test = np.array([
1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1, 2, 3, 1,
2, 3
])
X2_test = np.array([
1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2, 2, 2, 1, 1, 1, 2,
2, 2
])
X3_test = np.array([
1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4,
4, 4
])
self.assertTrue(np.all(XYZ[:, 0] == X1_test))
self.assertTrue(np.all(XYZ[:, 1] == X2_test))
self.assertTrue(np.all(XYZ[:, 2] == X3_test))
def _warn_non_axis_aligned_sources(self, change):
value = change['value']
axaligned = [
True for vec in
[np.r_[1., 0., 0.], np.r_[0., 1., 0.], np.r_[0., 0., 1.]]
if np.all(value == vec)
]
if len(axaligned) != 1:
warnings.warn('non-axes aligned orientations {} are not rigorously'
' tested'.format(value))
def test_asArray_N_x_Dim(self):
true = np.array([[1, 2, 3]])
listArray = asArray_N_x_Dim([1, 2, 3], 3)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
listArray = asArray_N_x_Dim(np.r_[1, 2, 3], 3)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
listArray = asArray_N_x_Dim(np.array([[1, 2, 3.]]), 3)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
true = np.array([[1, 2], [4, 5]])
listArray = asArray_N_x_Dim([[1, 2], [4, 5]], 2)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
def test_asArray_N_x_Dim(self):
true = np.array([[1,2,3]])
listArray = asArray_N_x_Dim([1,2,3],3)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
listArray = asArray_N_x_Dim(np.r_[1,2,3],3)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
listArray = asArray_N_x_Dim(np.array([[1,2,3.]]),3)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
true = np.array([[1,2],[4,5]])
listArray = asArray_N_x_Dim([[1,2],[4,5]],2)
self.assertTrue(np.all(true == listArray))
self.assertTrue(true.shape == listArray.shape)
def test_sub2ind(self):
x = np.ones((5,2))
self.assertTrue(np.all(sub2ind(x.shape, [0,0]) == [0]))
self.assertTrue(np.all(sub2ind(x.shape, [4,0]) == [4]))
self.assertTrue(np.all(sub2ind(x.shape, [0,1]) == [5]))
self.assertTrue(np.all(sub2ind(x.shape, [4,1]) == [9]))
self.assertTrue(np.all(sub2ind(x.shape, [[4,1]]) == [9]))
self.assertTrue(np.all(sub2ind(x.shape, [[0,0],[4,0],[0,1],[4,1]]) == [0,4,5,9]))
def getInterpolationMat(self, loc, locType, zerosOutside=False):
""" Produces interpolation matrix
:param numpy.ndarray loc: Location of points to interpolate to
:param str locType: What to interpolate (see below)
:rtype: scipy.sparse.csr.csr_matrix
:return: M, the interpolation matrix
locType can be::
'Ex' -> x-component of field defined on edges
'Ey' -> y-component of field defined on edges
'Ez' -> z-component of field defined on edges
'Fx' -> x-component of field defined on faces
'Fy' -> y-component of field defined on faces
'Fz' -> z-component of field defined on faces
'N' -> scalar field defined on nodes
'CC' -> scalar field defined on cell centers
"""
if self._meshType == 'CYL' and self.isSymmetric and locType in ['Ex','Ez','Fy']:
raise Exception('Symmetric CylMesh does not support %s interpolation, as this variable does not exist.' % locType)
loc = Utils.asArray_N_x_Dim(loc, self.dim)
if zerosOutside is False:
assert np.all(self.isInside(loc)), "Points outside of mesh"
else:
indZeros = np.logical_not(self.isInside(loc))
loc[indZeros, :] = np.array([v.mean() for v in self.getTensor('CC')])
if locType in ['Fx','Fy','Fz','Ex','Ey','Ez']:
ind = {'x':0, 'y':1, 'z':2}[locType[1]]
assert self.dim >= ind, 'mesh is not high enough dimension.'
nF_nE = self.vnF if 'F' in locType else self.vnE
components = [Utils.spzeros(loc.shape[0], n) for n in nF_nE]
components[ind] = Utils.interpmat(loc, *self.getTensor(locType))
# remove any zero blocks (hstack complains)
components = [comp for comp in components if comp.shape[1] > 0]
Q = sp.hstack(components)
elif locType in ['CC', 'N']:
Q = Utils.interpmat(loc, *self.getTensor(locType))
else:
raise NotImplementedError('getInterpolationMat: locType=='+locType+' and mesh.dim=='+str(self.dim))
if zerosOutside:
Q[indZeros, :] = 0
return Q.tocsr()
def test_sub2ind(self):
x = np.ones((5, 2))
self.assertTrue(np.all(sub2ind(x.shape, [0, 0]) == [0]))
self.assertTrue(np.all(sub2ind(x.shape, [4, 0]) == [4]))
self.assertTrue(np.all(sub2ind(x.shape, [0, 1]) == [5]))
self.assertTrue(np.all(sub2ind(x.shape, [4, 1]) == [9]))
self.assertTrue(np.all(sub2ind(x.shape, [[4, 1]]) == [9]))
self.assertTrue(
np.all(
sub2ind(x.shape, [[0, 0], [4, 0], [0, 1], [4, 1]]) ==
[0, 4, 5, 9]))
def test_numpy_one(self):
o = Identity()
n = np.r_[2.,3]
assert np.all(n+1 == n+o)
assert np.all(1+n == o+n)
assert np.all(n-1 == n-o)
assert np.all(1-n == o-n)
assert np.all(n/1 == n/o)
assert np.all(n/-1 == n/-o)
assert np.all(1/n == o/n)
assert np.all(-1/n == -o/n)
assert np.all(n*1 == n*o)
assert np.all(n*-1 == n*-o)
assert np.all(1*n == o*n)
assert np.all(-1*n == -o*n)
def test_indexCube_2D(self):
nN = np.array([3, 3])
self.assertTrue(np.all(indexCube('A', nN) == np.array([0, 1, 3, 4])))
self.assertTrue(np.all(indexCube('B', nN) == np.array([3, 4, 6, 7])))
self.assertTrue(np.all(indexCube('C', nN) == np.array([4, 5, 7, 8])))
self.assertTrue(np.all(indexCube('D', nN) == np.array([1, 2, 4, 5])))
def test_ind2sub(self):
x = np.ones((5, 2))
self.assertTrue(
np.all(ind2sub(x.shape, [0, 4, 5, 9])[0] == [0, 4, 0, 4]))
self.assertTrue(
np.all(ind2sub(x.shape, [0, 4, 5, 9])[1] == [0, 0, 1, 1]))
def getInterpolationMat(self, loc, locType='CC', zerosOutside=False):
""" Produces interpolation matrix
:param numpy.ndarray loc: Location of points to interpolate to
:param str locType: What to interpolate (see below)
:rtype: scipy.sparse.csr.csr_matrix
:return: M, the interpolation matrix
locType can be::
'Ex' -> x-component of field defined on edges
'Ey' -> y-component of field defined on edges
'Ez' -> z-component of field defined on edges
'Fx' -> x-component of field defined on faces
'Fy' -> y-component of field defined on faces
'Fz' -> z-component of field defined on faces
'N' -> scalar field defined on nodes
'CC' -> scalar field defined on cell centers
'CCVx' -> x-component of vector field defined on cell centers
'CCVy' -> y-component of vector field defined on cell centers
'CCVz' -> z-component of vector field defined on cell centers
"""
if self._meshType == 'CYL' and self.isSymmetric and locType in [
'Ex', 'Ez', 'Fy'
]:
raise Exception(
'Symmetric CylMesh does not support %s interpolation, as this variable does not exist.'
% locType)
loc = Utils.asArray_N_x_Dim(loc, self.dim)
if zerosOutside is False:
assert np.all(self.isInside(loc)), "Points outside of mesh"
else:
indZeros = np.logical_not(self.isInside(loc))
loc[indZeros, :] = np.array(
[v.mean() for v in self.getTensor('CC')])
if locType in ['Fx', 'Fy', 'Fz', 'Ex', 'Ey', 'Ez']:
ind = {'x': 0, 'y': 1, 'z': 2}[locType[1]]
assert self.dim >= ind, 'mesh is not high enough dimension.'
nF_nE = self.vnF if 'F' in locType else self.vnE
components = [Utils.spzeros(loc.shape[0], n) for n in nF_nE]
components[ind] = Utils.interpmat(loc, *self.getTensor(locType))
# remove any zero blocks (hstack complains)
components = [comp for comp in components if comp.shape[1] > 0]
Q = sp.hstack(components)
elif locType in ['CC', 'N']:
Q = Utils.interpmat(loc, *self.getTensor(locType))
elif locType in ['CCVx', 'CCVy', 'CCVz']:
Q = Utils.interpmat(loc, *self.getTensor('CC'))
Z = Utils.spzeros(loc.shape[0], self.nC)
if locType == 'CCVx':
Q = sp.hstack([Q, Z, Z])
elif locType == 'CCVy':
Q = sp.hstack([Z, Q, Z])
elif locType == 'CCVz':
Q = sp.hstack([Z, Z, Q])
else:
raise NotImplementedError('getInterpolationMat: locType==' +
locType + ' and mesh.dim==' +
str(self.dim))
if zerosOutside:
Q[indZeros, :] = 0
return Q.tocsr()
def check(exp,ans):
assert np.all((exp).todense() == ans)
def test_indexCube_2D(self):
nN = np.array([3, 3])
self.assertTrue(np.all(indexCube('A', nN) == np.array([0, 1, 3, 4])))
self.assertTrue(np.all(indexCube('B', nN) == np.array([3, 4, 6, 7])))
self.assertTrue(np.all(indexCube('C', nN) == np.array([4, 5, 7, 8])))
self.assertTrue(np.all(indexCube('D', nN) == np.array([1, 2, 4, 5])))
def xy_2_lineID(DCsurvey):
"""
Read DC survey class and append line ID.
Assumes that the locations are listed in the order
they were collected. May need to generalize for random
point locations, but will be more expensive
Input:
:param DCdict Vectors of station location
Output:
:param LineID Vector of integers
:return
Created on Thu Feb 11, 2015
@author: dominiquef
"""
# Compute unit vector between two points
nstn = DCsurvey.nSrc
# Pre-allocate space
lineID = np.zeros(nstn)
linenum = 0
indx = 0
for ii in range(nstn):
if ii == 0:
A = DCsurvey.srcList[ii].loc[0]
B = DCsurvey.srcList[ii].loc[1]
xout = np.mean([A[0:2],B[0:2]], axis = 0)
xy0 = A[:2]
xym = xout
# Deal with replicate pole location
if np.all(xy0==xym):
xym[0] = xym[0] + 1e-3
continue
A = DCsurvey.srcList[ii].loc[0]
B = DCsurvey.srcList[ii].loc[1]
xin = np.mean([A[0:2],B[0:2]], axis = 0)
# Compute vector between neighbours
vec1, r1 = r_unit(xout,xin)
# Compute vector between current stn and mid-point
vec2, r2 = r_unit(xym,xin)
# Compute vector between current stn and start line
vec3, r3 = r_unit(xy0,xin)
# Compute vector between mid-point and start line
vec4, r4 = r_unit(xym,xy0)
# Compute dot product
ang1 = np.abs(vec1.dot(vec2))
ang2 = np.abs(vec3.dot(vec4))
# If the angles are smaller then 45d, than next point is on a new line
if ((ang1 < np.cos(np.pi/4.)) | (ang2 < np.cos(np.pi/4.))) & (np.all(np.r_[r1,r2,r3,r4] > 0)):
# Re-initiate start and mid-point location
xy0 = A[:2]
xym = xin
# Deal with replicate pole location
if np.all(xy0==xym):
xym[0] = xym[0] + 1e-3
linenum += 1
indx = ii
else:
xym = np.mean([xy0,xin], axis = 0)
lineID[ii] = linenum
xout = xin
return lineID
def getInterpolationMat(self, loc, locType="CC", zerosOutside=False):
""" Produces interpolation matrix
:param numpy.ndarray loc: Location of points to interpolate to
:param str locType: What to interpolate (see below)
:rtype: scipy.sparse.csr_matrix
:return: M, the interpolation matrix
locType can be::
'Ex' -> x-component of field defined on edges
'Ey' -> y-component of field defined on edges
'Ez' -> z-component of field defined on edges
'Fx' -> x-component of field defined on faces
'Fy' -> y-component of field defined on faces
'Fz' -> z-component of field defined on faces
'N' -> scalar field defined on nodes
'CC' -> scalar field defined on cell centers
'CCVx' -> x-component of vector field defined on cell centers
'CCVy' -> y-component of vector field defined on cell centers
'CCVz' -> z-component of vector field defined on cell centers
"""
if self._meshType == "CYL" and self.isSymmetric and locType in ["Ex", "Ez", "Fy"]:
raise Exception(
"Symmetric CylMesh does not support {0!s} interpolation, as this variable does not exist.".format(
locType
)
)
loc = Utils.asArray_N_x_Dim(loc, self.dim)
if zerosOutside is False:
assert np.all(self.isInside(loc)), "Points outside of mesh"
else:
indZeros = np.logical_not(self.isInside(loc))
loc[indZeros, :] = np.array([v.mean() for v in self.getTensor("CC")])
if locType in ["Fx", "Fy", "Fz", "Ex", "Ey", "Ez"]:
ind = {"x": 0, "y": 1, "z": 2}[locType[1]]
assert self.dim >= ind, "mesh is not high enough dimension."
nF_nE = self.vnF if "F" in locType else self.vnE
components = [Utils.spzeros(loc.shape[0], n) for n in nF_nE]
components[ind] = Utils.interpmat(loc, *self.getTensor(locType))
# remove any zero blocks (hstack complains)
components = [comp for comp in components if comp.shape[1] > 0]
Q = sp.hstack(components)
elif locType in ["CC", "N"]:
Q = Utils.interpmat(loc, *self.getTensor(locType))
elif locType in ["CCVx", "CCVy", "CCVz"]:
Q = Utils.interpmat(loc, *self.getTensor("CC"))
Z = Utils.spzeros(loc.shape[0], self.nC)
if locType == "CCVx":
Q = sp.hstack([Q, Z, Z])
elif locType == "CCVy":
Q = sp.hstack([Z, Q, Z])
elif locType == "CCVz":
Q = sp.hstack([Z, Z, Q])
else:
raise NotImplementedError("getInterpolationMat: locType==" + locType + " and mesh.dim==" + str(self.dim))
if zerosOutside:
Q[indZeros, :] = 0
return Q.tocsr()
def test_ind2sub(self):
x = np.ones((5,2))
self.assertTrue(np.all(ind2sub(x.shape, [0,4,5,9])[0] == [0,4,0,4]))
self.assertTrue(np.all(ind2sub(x.shape, [0,4,5,9])[1] == [0,0,1,1]))