def test_weights(self): # tests are tuple consisting of array containing s, array containing correct weight output, and order of derivative tests = ((np.array([-2,-1,0]),np.array([[1,-2,1]]),2), (np.array([-4,-3,-2,-1,0,1,2,3,4]),np.array([[1./280,-4./105,1./5,-4./5,0.,4./5,-1./5,4./105,-1./280]]),1), (np.array([-1,0,1]),np.array([[1,-2,1]]),2)) for test in tests: stcl = stencil.Stencil(test[0]) weights = stcl.get_weights(test[2]) np.testing.assert_almost_equal(weights,test[1])
def test_stencil_matrix(self):
# tests are tuples consisting of array containing indicies s and an array corresponding to the correct output of s_matrix
tests = (([-2, -1, 0, 1, 2],
np.array([[1, 1, 1, 1, 1], [-2, -1, 0, 1, 2],
[4, 1, 0, 1, 4], [-8, -1, 0, 1, 8],
[16, 1, 0, 1, 16]])), (np.array([-2, -1, 0]),
np.array([[1, 1, 1],
[-2, -1, 0],
[4, 1, 0]])))
for test in tests:
stcl = stencil.Stencil(test[0])
stcl._stencil_matrix()
np.testing.assert_array_equal(stcl.s_matrix, test[1])
def __init__(self, s_list, deg):
## \var stncl A stencil object
self.stncl = stencil.Stencil(s_list)
## \var deg The derivative degree
self.deg = deg
## \var accuracy Accuracy of the approximation, which is
# equal to the number of stencil points minus the
# derivative degree
self.accuracy = self.stncl.N - self.deg
assert self.accuracy > 0, '''Improper 1D operator formation:
increase number of stencil points!'''
## \var weights The appropriate weighting coefficients for
# the finite difference approximation
self.weights = self.stncl.get_weights(deg)