Python has a package SymPy that offers symbolic computing. Here is a simple introductory example where we differentiate a quadratic polynomial, integrate it again, and find the roots:
>>> import sympy as sp
>>> x, a = sp.symbols('x a') # Define mathematical symbols
>>> Q = a*x**2 - 1 # Quadratic function
>>> dQdx = sp.diff(Q, x) # Differentiate wrt x
>>> dQdx
2*a*x
>>> Q2 = sp.integrate(dQdx, x) # Integrate (no constant)
>>> Q2
a*x**2
>>> Q2 = sp.integrate(Q, (x, 0, a)) # Definite integral
>>> Q2
a**4/3 - a
>>> roots = sp.solve(Q, x) # Solve Q = 0 wrt x
>>> roots
[-sqrt(1/a), sqrt(1/a)]
One can easily convert a SymPy expression like Q into
a Python function Q(x, a) to be used for further numerical
computing:
>>> Q = sp.lambdify([x, a], Q) # Turn Q into Py func.
>>> Q(x=2, a=3) # 3*2**2 - 1 = 11
11
Sympy can do a lot of other things. Here is an example on computing the Taylor series of \( e^{-x}\sin(rx) \), where \( r \) is the smallest root of \( Q=ax^2-1=0 \) as computed above:
>>> f = sp.exp(-a*x)*sp.sin(roots[0]*x)
>>> f.series(x, 0, 4)
-x*sqrt(1/a) + x**3*(-a**2*sqrt(1/a)/2 + (1/a)**(3/2)/6) +
a*x**2*sqrt(1/a) + O(x**4)
Software testing in Python is best done with a unit test framework
such as
nose or
pytest.
These frameworks can automatically
run all functions starting with test_ recursively in files in a
directory tree. Each test_* function is called a test function
and must take no arguments and apply assert to a boolean expression
that is True if the test passes and False if it fails.
def halve(x):
"""Return half of x."""
return x/2.0
def test_halve():
x = 4
expected = 2
computed = halve(x)
# Compare real numbers using tolerance
tol = 1E-14
diff = abs(computed - expected)
assert diff < tol
A frequently used technique to test differential equation solvers is to just specify a solution and fit a source term in the differential equation such that the specified solution solves the equation. The initial conditions must be set according to the specified solution. This technique is known as the method of manufactured solutions.
Here we shall specify a solution that is quadratic or linear in \( t \)
because such lower-order polynomials will often be an exact solution
of both the differential equation and the finite difference
equations. This means that the polynomial should be reproduced to
machine precision by our solver function.
We can use SymPy to find an appropriate \( F(t) \) term in the differential equation such that a specified solution \( u(t)=I + Vt + qt^2 \) fits the equation and initial conditions. With quadratic damping, only a linear \( u \) will solve the discrete equation so in this case we choose \( u=I+Vt \).
We embed the SymPy calculations and the numerical calculations in a test function:
def lhs_eq(t, m, b, s, u, damping='linear'):
"""Return lhs of differential equation as sympy expression."""
v = sp.diff(u, t)
d = b*v if damping == 'linear' else b*v*sp.Abs(v)
return m*sp.diff(u, t, t) + d + s(u)
def test_solver():
"""Verify linear/quadratic solution."""
# Set input data for the test
I = 1.2; V = 3; m = 2; b = 0.9; k = 4
s = lambda u: k*u
T = 2
dt = 0.2
N = int(round(T/dt))
time_points = np.linspace(0, T, N+1)
# Test linear damping
t = sp.Symbol('t')
q = 2 # arbitrary constant
u_exact = I + V*t + q*t**2 # sympy expression
F_term = lhs_eq(t, m, b, s, u_exact, 'linear')
print 'Fitted source term, linear case:', F_term
F = sp.lambdify([t], F_term)
u, t_ = solver(I, V, m, b, s, F, time_points, 'linear')
u_e = sp.lambdify([t], u_exact, modules='numpy')
error = abs(u_e(t_) - u).max()
tol = 1E-13
assert error < tol
# Test quadratic damping: u_exact must be linear
u_exact = I + V*t
F_term = lhs_eq(t, m, b, s, u_exact, 'quadratic')
print 'Fitted source term, quadratic case:', F_term
F = sp.lambdify([t], F_term)
u, t_ = solver(I, V, m, b, s, F, time_points, 'quadratic')
u_e = sp.lambdify([t], u_exact, modules='numpy')
error = abs(u_e(t_) - u).max()
assert error < tol
We recommend to use pytest as test framework. Subdirectories test*
are examined for files test_*.py that have test functions
test_*(), and all such tests are executed by the following command:
Terminal> py.test -s
The -s option makes all output from the tests appear in the terminal
window.
To run the tests in a specific file tests/test_bumpy.py, do
Terminal> py.test -s tests/test_bumpy.py
Python software is frequently organized as modules, or in collection of modules, called packages. A module enables sharing functions, variables, and classes between programs and other modules.
It is very easy to make a module in Python. Just put all the functions and global variables (and classes, if you have) you want to include in the module in a file. If you have a main program calling functions or using global variables, these statements will be executed with you import the module in other programs, which is not what you want. Therefore, move the main program to a so-called test block at the end of the module:
if __name__ == '__main__':
<statements in the main program>
The module file, say its name is mymod.py, now typically looks like
import module1
from module2 import func1, func2
def myfunc1(...):
...
def myfunc2(...):
...
if __name__ == '__main__':
<statements in the main program>
The name of the module mymod if the filename is mymod.py.
During import mymod or from mymod import *, the special Python
variable __name__
equals the name of the module and the test block with statements
in the main program will not be executed. However, if you run
mymod.py as a program, __name__ equals __main__ and
the main program will be executed. In this way, you can have a single
file that both acts as a library and that is an executable program.
A from mymod import * will import the global functions
myfunc1 and myfunc2, plus
the global names module1, somefunc1, and somefunc2.