#!/usr/bin/env python
"""Simple trapezoid-rule integrator."""
import numpy as N
def trapz(x, y):
"""Simple trapezoid integrator for sequence-based innput.
Inputs:
- x,y: arrays of the same length.
Output:
- The result of applying the trapezoid rule to the input, assuming that
y[i] = f(x[i]) for some function f to be integrated.
Minimally modified from matplotlib.mlab."""
raise NotImplementedError
def trapzf(f,a,b,npts=100):
"""Simple trapezoid-based integrator.
Inputs:
- f: function to be integrated.
- a,b: limits of integration.
Optional inputs:
- npts(100): the number of equally spaced points to sample f at, between
a and b.
Output:
- The value of the trapezoid-rule approximation to the integral."""
# you will need to apply the function f to easch element of the
# vector x. What are several ways to do this? Can you profile
# them to see what differences in timings result for long vectors
# x?
raise NotImplementedError
#-----------------------------------------------------------------------------
# Tests
#-----------------------------------------------------------------------------
import nose, nose.tools as nt
import numpy.testing as nptest
def square(x): return x**2
def test_err():
nt.assert_raises(ValueError,trapz,range(2),range(3))
def test_call():
x = np.linspace(0,1,100)
y = np.array(map(square,x))
nptest.assert_almost_equal(trapz(x,y),1./3,4)
def test_square():
nptest.assert_almost_equal(trapzf(square,0,1),1./3,4)
def test_square2():
nptest.assert_almost_equal(trapzf(square,0,3,350),9.0,4)
# If called from the command line, run all the tests
if __name__ == '__main__':
# This call form is ipython-friendly
nose.runmodule(argv=['-s','--with-doctest'],
exit=False)
