Based on the experience from the previous example, it makes sense to write some code to automate the analytical integration process for any choice of finite element basis functions. In addition, we can automate the assembly process and linear system solution. Appropriate functions for this purpose document all details of all steps in the finite element computations and can found in the module file fe_approx1D.py. The key steps in the computational machinery are now explained in detail in terms of code and text.

First we need a Python function for
defining \({\tilde{\varphi}}_r(X)\) in terms of a Lagrange polynomial
of degree `d`:

```
import sympy as sp
import numpy as np
def phi_r(r, X, d):
if isinstance(X, sp.Symbol):
h = sp.Rational(1, d) # node spacing
nodes = [2*i*h - 1 for i in range(d+1)]
else:
# assume X is numeric: use floats for nodes
nodes = np.linspace(-1, 1, d+1)
return Lagrange_polynomial(X, r, nodes)
def Lagrange_polynomial(x, i, points):
p = 1
for k in range(len(points)):
if k != i:
p *= (x - points[k])/(points[i] - points[k])
return p
```

Observe how we construct the `phi_r` function to be
a symbolic expression for \({\tilde{\varphi}}_r(X)\) if `X` is a
`Symbol` object from `sympy`. Otherwise, we assume that `X`
is a `float` object and compute the corresponding
floating-point value of \({\tilde{\varphi}}_r(X)\). Recall that the
`Lagrange_polynomial` function, here simply copied
from the section *Fourier series*,
works with both symbolic and
numeric variables.

The complete basis \({\tilde{\varphi}}_0(X),\ldots,{\tilde{\varphi}}_d(X)\) on the reference element, represented as a list of symbolic expressions, is constructed by

```
def basis(d=1):
X = sp.Symbol('X')
phi = [phi_r(r, X, d) for r in range(d+1)]
return phi
```

Now we are in a position to write the function for computing the element matrix:

```
def element_matrix(phi, Omega_e, symbolic=True):
n = len(phi)
A_e = sp.zeros((n, n))
X = sp.Symbol('X')
if symbolic:
h = sp.Symbol('h')
else:
h = Omega_e[1] - Omega_e[0]
detJ = h/2 # dx/dX
for r in range(n):
for s in range(r, n):
A_e[r,s] = sp.integrate(phi[r]*phi[s]*detJ, (X, -1, 1))
A_e[s,r] = A_e[r,s]
return A_e
```

In the symbolic case (`symbolic` is `True`),
we introduce the element length as a symbol
`h` in the computations. Otherwise, the real numerical value
of the element interval `Omega_e`
is used and the final matrix elements are numbers,
not symbols.
This functionality can be demonstrated:

```
>>> from fe_approx1D import *
>>> phi = basis(d=1)
>>> phi
[1/2 - X/2, 1/2 + X/2]
>>> element_matrix(phi, Omega_e=[0.1, 0.2], symbolic=True)
[h/3, h/6]
[h/6, h/3]
>>> element_matrix(phi, Omega_e=[0.1, 0.2], symbolic=False)
[0.0333333333333333, 0.0166666666666667]
[0.0166666666666667, 0.0333333333333333]
```

The computation of the element vector is done by a similar procedure:

```
def element_vector(f, phi, Omega_e, symbolic=True):
n = len(phi)
b_e = sp.zeros((n, 1))
# Make f a function of X
X = sp.Symbol('X')
if symbolic:
h = sp.Symbol('h')
else:
h = Omega_e[1] - Omega_e[0]
x = (Omega_e[0] + Omega_e[1])/2 + h/2*X # mapping
f = f.subs('x', x) # substitute mapping formula for x
detJ = h/2 # dx/dX
for r in range(n):
b_e[r] = sp.integrate(f*phi[r]*detJ, (X, -1, 1))
return b_e
```

Here we need to replace the symbol `x` in the expression for `f`
by the mapping formula such that `f` can be integrated
in terms of \(X\), cf. the formula
\(\tilde b^{(e)}_{r} = \int_{-1}^1 f(x(X)){\tilde{\varphi}}_r(X)\frac{h}{2}dX\).

The integration in the element matrix function involves only products
of polynomials, which `sympy` can easily deal with, but for the
right-hand side `sympy` may face difficulties with certain types of
expressions `f`. The result of the integral is then an `Integral`
object and not a number or expression
as when symbolic integration is successful.
It may therefore be wise to introduce a fallback on numerical
integration. The symbolic integration can also take much time
before an unsuccessful conclusion so we may also introduce a parameter
`symbolic` and set it to `False` to avoid symbolic integration:

```
def element_vector(f, phi, Omega_e, symbolic=True):
...
if symbolic:
I = sp.integrate(f*phi[r]*detJ, (X, -1, 1))
if not symbolic or isinstance(I, sp.Integral):
h = Omega_e[1] - Omega_e[0] # Ensure h is numerical
detJ = h/2
integrand = sp.lambdify([X], f*phi[r]*detJ)
I = sp.mpmath.quad(integrand, [-1, 1])
b_e[r] = I
...
```

Numerical integration requires that the symbolic
integrand is converted
to a plain Python function (`integrand`) and that
the element length `h` is a real number.

The complete algorithm for computing and assembling the elementwise contributions takes the following form

```
def assemble(nodes, elements, phi, f, symbolic=True):
N_n, N_e = len(nodes), len(elements)
if symbolic:
A = sp.zeros((N_n, N_n))
b = sp.zeros((N_n, 1)) # note: (N_n, 1) matrix
else:
A = np.zeros((N_n, N_n))
b = np.zeros(N_n)
for e in range(N_e):
Omega_e = [nodes[elements[e][0]], nodes[elements[e][-1]]]
A_e = element_matrix(phi, Omega_e, symbolic)
b_e = element_vector(f, phi, Omega_e, symbolic)
for r in range(len(elements[e])):
for s in range(len(elements[e])):
A[elements[e][r],elements[e][s]] += A_e[r,s]
b[elements[e][r]] += b_e[r]
return A, b
```

The `nodes` and `elements` variables represent the finite
element mesh as explained earlier.

Given the coefficient matrix `A` and the right-hand side `b`,
we can compute the coefficients \(\left\{ {c}_i \right\}_{i\in{\mathcal{I}_s}}\) in the expansion
\(u(x)=\sum_jc_j{\varphi}_j\) as the solution vector `c` of the linear
system:

```
if symbolic:
c = A.LUsolve(b)
else:
c = np.linalg.solve(A, b)
```

When `A` and `b` are `sympy` arrays,
the solution procedure implied by `A.LUsolve` is symbolic.
Otherwise, `A` and `b` are `numpy` arrays and a standard
numerical solver is called.
The symbolic version is suited for small problems only
(small \(N\) values) since the calculation time becomes prohibitively large
otherwise. Normally, the symbolic *integration* will be more time
consuming in small problems than the symbolic *solution* of the linear system.

We can exemplify the use of `assemble` on the computational
case from the section *Calculating the linear system* with
two P1 elements (linear basis functions) on the domain \(\Omega=[0,1]\).
Let us first work with a symbolic element length:

```
>>> h, x = sp.symbols('h x')
>>> nodes = [0, h, 2*h]
>>> elements = [[0, 1], [1, 2]]
>>> phi = basis(d=1)
>>> f = x*(1-x)
>>> A, b = assemble(nodes, elements, phi, f, symbolic=True)
>>> A
[h/3, h/6, 0]
[h/6, 2*h/3, h/6]
[ 0, h/6, h/3]
>>> b
[ h**2/6 - h**3/12]
[ h**2 - 7*h**3/6]
[5*h**2/6 - 17*h**3/12]
>>> c = A.LUsolve(b)
>>> c
[ h**2/6]
[12*(7*h**2/12 - 35*h**3/72)/(7*h)]
[ 7*(4*h**2/7 - 23*h**3/21)/(2*h)]
```

We may, for comparison, compute the `c` vector corresponding to
an interpolation/collocation method with finite element basis functions.
Choosing the nodes as points, the principle is

\[u(x_{i}) = \sum_{j\in{\mathcal{I}_s}} c_j{\varphi}_j(x_{i}) = f(x_{i}),\quad
i\in{\mathcal{I}_s}{\thinspace .}\]

The coefficient matrix \(A_{i,j}={\varphi}_j(x_{i})\) becomes the identity matrix because basis function number \(j\) vanishes at all nodes, except node \(j\): \({\varphi}_j(x_{i}=\delta_{ij}\). Therefore, \(c_i = f(x_{i}\).

The associated `sympy` calculations are

```
>>> fn = sp.lambdify([x], f)
>>> c = [fn(xc) for xc in nodes]
>>> c
[0, h*(1 - h), 2*h*(1 - 2*h)]
```

These expressions are much simpler than those based on least squares or projection in combination with finite element basis functions.

The numerical computations corresponding to the
symbolic ones in the section *Example on computing symbolic approximations*,
and still done by `sympy` and the `assemble` function, go as follows:

```
>>> nodes = [0, 0.5, 1]
>>> elements = [[0, 1], [1, 2]]
>>> phi = basis(d=1)
>>> x = sp.Symbol('x')
>>> f = x*(1-x)
>>> A, b = assemble(nodes, elements, phi, f, symbolic=False)
>>> A
[ 0.166666666666667, 0.0833333333333333, 0]
[0.0833333333333333, 0.333333333333333, 0.0833333333333333]
[ 0, 0.0833333333333333, 0.166666666666667]
>>> b
[ 0.03125]
[0.104166666666667]
[ 0.03125]
>>> c = A.LUsolve(b)
>>> c
[0.0416666666666666]
[ 0.291666666666667]
[0.0416666666666666]
```

The `fe_approx1D` module contains functions for generating the
`nodes` and `elements` lists for equal-sized elements with
any number of nodes per element. The coordinates in `nodes`
can be expressed either through the element length symbol `h`
(`symbolic=True`) or by real numbers (`symbolic=False`):

```
nodes, elements = mesh_uniform(N_e=10, d=3, Omega=[0,1],
symbolic=True)
```

There is also a function

```
def approximate(f, symbolic=False, d=1, N_e=4, filename='tmp.pdf'):
```

which computes a mesh with `N_e` elements, basis functions of
degree `d`, and approximates a given symbolic expression
`f` by a finite element expansion \(u(x) = \sum_jc_j{\varphi}_j(x)\).
When `symbolic` is `False`, \(u(x) = \sum_jc_j{\varphi}_j(x)\)
can be computed at a (large)
number of points and plotted together with \(f(x)\). The construction
of \(u\) points from the solution vector `c` is done
elementwise by evaluating \(\sum_rc_r{\tilde{\varphi}}_r(X)\) at a (large)
number of points in each element in the local coordinate system,
and the discrete \((x,u)\) values on
each element are stored in separate arrays that are finally
concatenated to form a global array for \(x\) and for \(u\).
The details are found in the `u_glob` function in
`fe_approx1D.py`.

Let us first see how the global matrix looks like if we assemble
symbolic element matrices, expressed in terms of `h`, from
several elements:

```
>>> d=1; N_e=8; Omega=[0,1] # 8 linear elements on [0,1]
>>> phi = basis(d)
>>> f = x*(1-x)
>>> nodes, elements = mesh_symbolic(N_e, d, Omega)
>>> A, b = assemble(nodes, elements, phi, f, symbolic=True)
>>> A
[h/3, h/6, 0, 0, 0, 0, 0, 0, 0]
[h/6, 2*h/3, h/6, 0, 0, 0, 0, 0, 0]
[ 0, h/6, 2*h/3, h/6, 0, 0, 0, 0, 0]
[ 0, 0, h/6, 2*h/3, h/6, 0, 0, 0, 0]
[ 0, 0, 0, h/6, 2*h/3, h/6, 0, 0, 0]
[ 0, 0, 0, 0, h/6, 2*h/3, h/6, 0, 0]
[ 0, 0, 0, 0, 0, h/6, 2*h/3, h/6, 0]
[ 0, 0, 0, 0, 0, 0, h/6, 2*h/3, h/6]
[ 0, 0, 0, 0, 0, 0, 0, h/6, h/3]
```

The reader is encouraged to assemble the element matrices by hand and verify this result, as this exercise will give a hands-on understanding of what the assembly is about. In general we have a coefficient matrix that is tridiagonal:

(1)\[\begin{split} A = \frac{h}{6}
\left(
\begin{array}{cccccccccc}
2 & 1 & 0
&\cdots & \cdots & \cdots & \cdots & \cdots & 0 \\
1 & 4 & 1 & \ddots & & & & & \vdots \\
0 & 1 & 4 & 1 &
\ddots & & & & \vdots \\
\vdots & \ddots & & \ddots & \ddots & 0 & & & \vdots \\
\vdots & & \ddots & \ddots & \ddots & \ddots & \ddots & & \vdots \\
\vdots & & & 0 & 1 & 4 & 1 & \ddots & \vdots \\
\vdots & & & & \ddots & \ddots & \ddots &\ddots & 0 \\
\vdots & & & & &\ddots & 1 & 4 & 1 \\
0 &\cdots & \cdots &\cdots & \cdots & \cdots & 0 & 1 & 2
\end{array}
\right)\end{split}\]

The structure of the right-hand side is more difficult to reveal since
it involves an assembly of elementwise integrals of
\(f(x(X)){\tilde{\varphi}}_r(X)h/2\), which obviously depend on the
particular choice of \(f(x)\).
Numerical integration can give some insight into the nature of
the right-hand side. For this purpose it
is easier to look at the integration in \(x\) coordinates, which
gives the general formula *(4.4)*.
For equal-sized elements of length \(h\), we can apply the
Trapezoidal rule at the global node points to arrive at

\[b_i = h\left( \frac{1}{2} {\varphi}_i(x_{0})f(x_{0}) +
\frac{1}{2} {\varphi}_i(x_{N})f(x_{N}) + \sum_{j=1}^{N-1}
{\varphi}_i(x_{j})f(x_{j})\right)\]

\[ =
\left\lbrace\begin{array}{ll}
\frac{1}{2} hf(x_i), i=0\hbox{ or }i=N,\]

\[h f(x_i), 1 \leq i \leq N-1
\end{array}\right.\]

The reason for this simple formula is simply that \({\varphi}_i\) is either 0 or 1 at the nodes and 0 at all but one of them.

Going to P2 elements (`d=2`) leads
to the element matrix

\[\begin{split}A^{(e)} = \frac{h}{30}
\left(\begin{array}{ccc}
4 & 2 & -1\\
2 & 16 & 2\\
-1 & 2 & 4
\end{array}\right)\end{split}\]

and the following global assembled matrix from four elements:

\[\begin{split}A = \frac{h}{30}
\left(
\begin{array}{ccccccccc}
4 & 2 & - 1 & 0
& 0 & 0 & 0 & 0 & 0\\
2 & 16 & 2
& 0 & 0 & 0 & 0 & 0 & 0\\- 1 & 2 &
8 & 2 & - 1 & 0 & 0 & 0 &
0\\0 & 0 & 2 & 16 & 2 & 0 & 0
& 0 & 0\\0 & 0 & - 1 & 2 & 8
& 2 & - 1 & 0 & 0\\0 & 0 & 0 & 0 &
2 & 16 & 2 & 0 & 0\\0 & 0 & 0
& 0 & - 1 & 2 & 8 &
2 & - 1\\0 & 0 & 0 & 0 & 0 & 0 &
2 & 16 & 2\\0 & 0 & 0 & 0 & 0
& 0 & - 1 & 2 & 4
\end{array}
\right)\end{split}\]

In general, for \(i\) odd we have the nonzeroes

\[A_{i,i-2} = -1,\quad A_{i-1,i}=2,\quad A_{i,i} = 8,\quad A_{i+1,i}=2,
\quad A_{i+2,i}=-1,\]

multiplied by \(h/30\), and for \(i\) even we have the nonzeros

\[A_{i-1,i}=2,\quad A_{i,i} = 16,\quad A_{i+1,i}=2,\]

multiplied by \(h/30\). The rows with odd numbers correspond to nodes at the element boundaries and get contributions from two neighboring elements in the assembly process, while the even numbered rows correspond to internal nodes in the elements where the only one element contributes to the values in the global matrix.

With the aid of the `approximate` function in the `fe_approx1D`
module we can easily investigate the quality of various finite element
approximations to some given functions. Figure *Comparison of the finite element approximations: 4 P1 elements with 5 nodes (upper left), 2 P2 elements with 5 nodes (upper right), 8 P1 elements with 9 nodes (lower left), and 4 P2 elements with 9 nodes (lower right)*
shows how linear and quadratic elements approximates the polynomial
\(f(x)=x(1-x)^8\) on \(\Omega =[0,1]\), using equal-sized elements.
The results arise from the program

```
import sympy as sp
from fe_approx1D import approximate
x = sp.Symbol('x')
approximate(f=x*(1-x)**8, symbolic=False, d=1, N_e=4)
approximate(f=x*(1-x)**8, symbolic=False, d=2, N_e=2)
approximate(f=x*(1-x)**8, symbolic=False, d=1, N_e=8)
approximate(f=x*(1-x)**8, symbolic=False, d=2, N_e=4)
```

The quadratic functions are seen to be better than the linear ones for the same value of \(N\), as we increase \(N\). This observation has some generality: higher degree is not necessarily better on a coarse mesh, but it is as we refined the mesh.

Some of the examples in the preceding section took several minutes to
compute, even on small meshes consisting of up to eight elements.
The main explanation for slow computations is unsuccessful
symbolic integration: `sympy` may use a lot of energy on
integrals like \(\int f(x(X)){\tilde{\varphi}}_r(X)h/2 dx\) before
giving up, and the program then resorts to numerical integration.
Codes that can deal with a large number of basis functions and
accept flexible choices of \(f(x)\) should compute all integrals
numerically and replace the matrix objects from `sympy` by
the far more efficient array objects from `numpy`.

Another reason for slow code is related to the fact that most of the
matrix entries \(A_{i,j}\) are zero, because \(({\varphi}_i,{\varphi}_j)=0\)
unless \(i\) and \(j\) are nodes in the same element. A matrix whose
majority of entries are zeros, is known as a *sparse* matrix. The
sparsity should be utilized in software as it dramatically decreases
the storage demands and the CPU-time needed to compute the solution of
the linear system. This optimization is not critical in 1D problems
where modern computers can afford computing with all the zeros in the
complete square matrix, but in 2D and especially in 3D, sparse
matrices are fundamental for feasible finite element computations.

In 1D problems, using a
numbering of nodes and elements from left to right over the domain,
the assembled coefficient matrix has only a few diagonals different
from zero. More precisely, \(2d+1\) diagonals are different from
zero. With a different numbering of global nodes, say a random
ordering, the diagonal structure is lost, but the number of
nonzero elements is unaltered. Figures *Matrix sparsity pattern for left-to-right numbering (left) and random numbering (right) of nodes in P1 elements*
and *Matrix sparsity pattern for left-to-right numbering (left) and random numbering (right) of nodes in P3 elements* exemplify sparsity patterns.

The `scipy.sparse` library supports creation of sparse matrices
and linear system solution.

scipy.sparse.diagsfor matrix defined via diagonalsscipy.sparse.lil_matrixfor creation via setting matrix entriesscipy.sparse.dok_matrixfor creation via setting matrix entries