property TensorMesh.average_edge_y_to_cell

Averaging operator from y-edges to cell centers (scalar quantities).

This property constructs a 2nd order averaging operator that maps scalar quantities from y-edges to cell centers. This averaging operator is used when a discrete scalar quantity defined on y-edges must be projected to cell centers. Once constructed, the operator is stored permanently as a property of the mesh. See notes.

(n_cells, n_edges_y) scipy.sparse.csr_matrix

The scalar averaging operator from y-edges to cell centers


Let \(\boldsymbol{\phi_y}\) be a discrete scalar quantity that lives on y-edges. average_edge_y_to_cell constructs a discrete linear operator \(\mathbf{A_{yc}}\) that projects \(\boldsymbol{\phi_y}\) to cell centers, i.e.:

\[\boldsymbol{\phi_c} = \mathbf{A_{yc}} \, \boldsymbol{\phi_y}\]

where \(\boldsymbol{\phi_c}\) approximates the value of the scalar quantity at cell centers. For each cell, we are simply averaging the values defined on its y-edges. The operation is implemented as a matrix vector product, i.e.:

phi_c = Ayc @ phi_y


Here we compute the values of a scalar function on the y-edges. We then create an averaging operator to approximate the function at cell centers. We choose to define a scalar function that is strongly discontinuous in some places to demonstrate how the averaging operator will smooth out discontinuities.

We start by importing the necessary packages and defining a mesh.

>>> from discretize import TensorMesh
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> h = np.ones(40)
>>> mesh = TensorMesh([h, h], x0="CC")

Then we create a scalar variable on y-edges,

>>> phi_y = np.zeros(mesh.nEy)
>>> xy = mesh.edges_y
>>> phi_y[(xy[:, 1] > 0)] = 25.0
>>> phi_y[(xy[:, 1] < -10.0) & (xy[:, 0] > -10.0) & (xy[:, 0] < 10.0)] = 50.0

Next, we construct the averaging operator and apply it to the discrete scalar quantity to approximate the value at cell centers.

>>> Ayc = mesh.average_edge_y_to_cell
>>> phi_c = Ayc @ phi_y

And plot the results,

Expand to show scripting for plot
>>> fig = plt.figure(figsize=(11, 5))
>>> ax1 = fig.add_subplot(121)
>>> v = np.r_[np.zeros(mesh.nEx), phi_y]  # create vector for plotting function
>>> mesh.plot_image(v, ax=ax1, v_type="Ey")
>>> ax1.set_title("Variable at y-edges", fontsize=16)
>>> ax2 = fig.add_subplot(122)
>>> mesh.plot_image(phi_c, ax=ax2, v_type="CC")
>>> ax2.set_title("Averaged to cell centers", fontsize=16)
>>> plt.show()

(Source code, png, pdf)


Below, we show a spy plot illustrating the sparsity and mapping of the operator

Expand to show scripting for plot
>>> fig = plt.figure(figsize=(9, 9))
>>> ax1 = fig.add_subplot(111)
>>> ax1.spy(Ayc, ms=1)
>>> ax1.set_title("Y-Edge Index", fontsize=12, pad=5)
>>> ax1.set_ylabel("Cell Index", fontsize=12)
>>> plt.show()

(png, pdf)