mirror of
https://github.com/wassname/simpeg.git
synced 2026-06-28 03:19:21 +08:00
Rowan Cockett
271 lines
7.1 KiB
Python
271 lines
7.1 KiB
Python
import numpy as np
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import scipy.ndimage as ndi
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import scipy.sparse as sp
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def getIndecesBlock(p0,p1,ccMesh):
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"""
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Creates a vector containing the block indexes in the cell centerd mesh.
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Returns a tuple
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The block is defined by the points
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p0, describe the position of the left upper front corner, and
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p1, describe the position of the right bottom back corner.
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ccMesh represents the cell-centered mesh
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The points p0 and p1 must live in the the same dimensional space as the mesh.
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"""
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# Validation: p0 and p1 live in the same dimensional space
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assert len(p0) == len(p1), "Dimension mismatch. len(p0) != len(p1)"
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# Validation: mesh and points live in the same dimensional space
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dimMesh = np.size(ccMesh[0,:])
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assert len(p0) == dimMesh, "Dimension mismatch. len(p0) != dimMesh"
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if dimMesh == 1:
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# Define the reference points
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x1 = p0[0]
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x2 = p1[0]
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indX = (x1 <= ccMesh[:,0]) & (ccMesh[:,0] <= x2)
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ind = np.where(indX)
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elif dimMesh == 2:
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# Define the reference points
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x1 = p0[0]
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y1 = p0[1]
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x2 = p1[0]
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y2 = p1[1]
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indX = (x1 <= ccMesh[:,0]) & (ccMesh[:,0] <= x2)
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indY = (y1 <= ccMesh[:,1]) & (ccMesh[:,1] <= y2)
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ind = np.where(indX & indY)
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elif dimMesh == 3:
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# Define the points
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x1 = p0[0]
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y1 = p0[1]
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z1 = p0[2]
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x2 = p1[0]
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y2 = p1[1]
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z2 = p1[2]
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indX = (x1 <= ccMesh[:,0]) & (ccMesh[:,0] <= x2)
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indY = (y1 <= ccMesh[:,1]) & (ccMesh[:,1] <= y2)
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indZ = (z1 <= ccMesh[:,2]) & (ccMesh[:,2] <= z2)
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ind = np.where(indX & indY & indZ)
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# Return a tuple
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return ind
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def defineBlockConductivity(p0,p1,ccMesh,condVals):
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"""
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Build a block with the conductivity specified by condVal. Returns an array.
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condVals[0] conductivity of the block
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condVals[1] conductivity of the ground
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"""
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sigma = np.zeros(ccMesh.shape[0]) + condVals[1]
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ind = getIndecesBlock(p0,p1,ccMesh)
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sigma[ind] = condVals[0]
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return sigma
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def defineTwoLayeredConductivity(depth,ccMesh,condVals):
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"""
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Define a two layered model. Depth of the first layer must be specified.
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CondVals vector with the conductivity values of the layers. Eg:
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Convention to number the layers::
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<----------------------------|------------------------------------>
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0 depth zf
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1st layer 2nd layer
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"""
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sigma = np.zeros(ccMesh.shape[0]) + condVals[1]
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dim = np.size(ccMesh[0,:])
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p0 = np.zeros(dim)
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p1 = np.zeros(dim)
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# Identify 1st cell centered reference point
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p0[0] = ccMesh[0,0]
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if dim>1: p0[1] = ccMesh[0,1]
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if dim>2: p0[2] = ccMesh[0,2]
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# Identify the last cell-centered reference point
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p1[0] = ccMesh[-1,0]
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if dim>1: p1[1] = ccMesh[-1,1]
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if dim>2: p1[2] = ccMesh[-1,2]
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# The depth is always defined on the last one.
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p1[len(p1)-1] -= depth
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ind = getIndecesBlock(p0,p1,ccMesh)
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sigma[ind] = condVals[0];
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return sigma
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def scalarConductivity(ccMesh,pFunction):
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"""
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Define the distribution conductivity in the mesh according to the
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analytical expression given in pFunction
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"""
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dim = np.size(ccMesh[0,:])
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CC = [ccMesh[:,0]]
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if dim>1: CC.append(ccMesh[:,1])
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if dim>2: CC.append(ccMesh[:,2])
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sigma = pFunction(*CC)
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return sigma
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def randomModel(shape, seed=None, anisotropy=None, its=100, bounds=[0,1]):
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"""
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Create a random model by convolving a kernal with a
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uniformly distributed model.
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:param int,tuple shape: shape of the model.
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:param int seed: pick which model to produce, prints the seed if you don't choose.
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:param numpy.ndarray,list anisotropy: this is the (3 x n) blurring kernal that is used.
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:param int its: number of smoothing iterations
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:param list bounds: bounds on the model, len(list) == 2
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:rtype: numpy.ndarray
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:return: M, the model
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.. plot::
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import matplotlib.pyplot as plt
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import SimPEG.utils.ModelBuilder as MB
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plt.colorbar(plt.imshow(MB.randomModel((50,50),bounds=[-4,0])))
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plt.title('A very cool, yet completely random model.')
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plt.show()
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"""
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if seed is None:
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seed = np.random.randint(1e3)
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print 'Using a seed of: ', seed
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if type(shape) in [int, long, float]:
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shape = (shape,) # make it a tuple for consistency
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np.random.seed(seed)
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mr = np.random.rand(*shape)
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if anisotropy is None:
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if len(shape) is 1:
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smth = np.array([1,10.,1],dtype=float)
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elif len(shape) is 2:
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smth = np.array([[1,2,1],[7,10,7],[1,2,1]],dtype=float)
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elif len(shape) is 3:
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kernal = np.array([1,4,1], dtype=float).reshape((1,3))
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smth = np.array(sp.kron(sp.kron(kernal,kernal.T).todense()[:],kernal).todense()).reshape((3,3,3))
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else:
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assert len(anisotropy.shape) is len(shape), 'Anisotropy must be the same shape.'
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smth = np.array(anisotropy,dtype=float)
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smth = smth/smth.sum() # normalize
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mi = mr
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for i in range(its):
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mi = ndi.convolve(mi, smth)
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# scale the model to live between the bounds.
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mi = (mi - mi.min())/(mi.max()-mi.min()) # scaled between 0 and 1
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mi = mi*(bounds[1]-bounds[0])+bounds[0]
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return mi
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if __name__ == '__main__':
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from SimPEG.mesh import TensorMesh
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from matplotlib import pyplot as plt
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# Define the mesh
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testDim = 2
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h1 = 0.3*np.ones(7)
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h1[0] = 0.5
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h1[-1] = 0.6
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h2 = .5 * np.ones(4)
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h3 = .4 * np.ones(6)
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x0 = np.zeros(3)
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if testDim == 1:
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h = [h1]
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x0 = x0[0]
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elif testDim == 2:
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h = [h1, h2]
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x0 = x0[0:2]
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else:
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h = [h1, h2, h3]
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M = TensorMesh(h, x0)
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ccMesh = M.gridCC
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# ------------------- Test conductivities! --------------------------
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print('Testing 1 block conductivity')
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p0 = np.array([0.5,0.5,0.5])[:testDim]
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p1 = np.array([1.0,1.0,1.0])[:testDim]
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condVals = np.array([100,1e-6])
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sigma = defineBlockConductivity(p0,p1,ccMesh,condVals)
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# Plot sigma model
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print sigma.shape
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M.plotImage(sigma)
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print 'Done with block! :)'
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plt.show()
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# -----------------------------------------
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print('Testing the two layered model')
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condVals = np.array([100,1e-5]);
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depth = 1.0;
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sigma = defineTwoLayeredConductivity(depth,ccMesh,condVals)
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M.plotImage(sigma)
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print sigma
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print 'layer model!'
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plt.show()
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# -----------------------------------------
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print('Testing scalar conductivity')
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if testDim == 1:
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pFunction = lambda x: np.exp(x)
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elif testDim == 2:
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pFunction = lambda x,y: np.exp(x+y)
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elif testDim == 3:
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pFunction = lambda x,y,z: np.exp(x+y+z)
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sigma = scalarConductivity(ccMesh,pFunction)
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# Plot sigma model
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M.plotImage(sigma)
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print sigma
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print 'Scalar conductivity defined!'
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plt.show()
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# -----------------------------------------
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