Some test cases for the peicewise polynomials

With the existing tests, this gets us to 100% test
coverage. However, some edge cases need addressing.

tests/test_peice_poly.py

@@ -0,0 +1,77 @@
+"""
+Test cases for PeicewisePolynomial class
+
+"""
+import numpy as np
+import numpy.testing as npt
+
+import earth_model.peice_poly as pp
+
+# FIXME: when run on it's own this does not
+# test 32-33, 82, 111-129, 132-144 in peice_poly
+# (although we get 100% coverage from earth_model
+# tests). Also see comments below that indicate edge
+# cases that need thinking about
+
+def test_constant():
+    """
+    Check that a constant function gives allways gives it's value
+    """
+    poly = pp.PeicewisePolynomial(np.array([[2.0], [2.0]]),
+                                  np.array([0.0, 0.5, 1.0]))
+    assert poly(0.0) == 2.0
+    assert poly(0.25) == 2.0
+    assert poly(0.5) == 2.0
+    assert poly(0.5, break_down=False) == 2.0
+    assert poly(0.5, break_down=True) == 2.0
+    assert poly(0.75) == 2.0
+    assert poly(1.0) == 2.0
+
+
+def test_step():
+    """
+    Check that two peicewise constants give the right values
+    """
+    poly = pp.PeicewisePolynomial(np.array([[2.0], [20.0]]),
+                                  np.array([0.0, 0.5, 1.0]))
+    assert poly(0.0) == 2.0
+    assert poly(0.25) == 2.0
+    assert poly(0.5) == 20.0
+    assert poly(0.5, break_down=False) == 20.0
+    assert poly(0.5, break_down=True) == 2.0
+    assert poly(0.75) == 20.0
+    assert poly(1.0) == 20.0
+    npt.assert_allclose(poly(np.array([0.0, 0.25, 0.5, 0.75, 1.0])),
+                        np.array([2.0, 2.0, 20.0, 20.0, 20.0]))
+    npt.assert_allclose(poly(np.array([0.0, 0.25, 0.5, 0.75, 1.0]),
+                             break_down=False),
+                        np.array([2.0, 2.0, 20.0, 20.0, 20.0]))
+    npt.assert_allclose(poly(np.array([0.25, 0.5, 0.75, 1.0]),
+                             break_down=True),
+                        np.array([2.0, 2.0, 20.0, 20.0]))
+    # What should we do on the edges
+    # How should we report being outside the bounds
+    # How should we handle the lower boundary with break_down=True
+
+
+def test_deriv():
+    poly = pp.PeicewisePolynomial(np.array([[4.0, 3.0, 2.0],
+                                            [40.0, 30.0, 20.0]]),
+                                  np.array([0.0, 2.0, 4.0]))
+    expected_deriv_coefs = np.array([[3.0, 4.0], [30.0, 40.0]])
+    calc_dpoly = poly.derivative()
+
+    npt.assert_allclose(calc_dpoly.coeffs, expected_deriv_coefs)
+    assert calc_dpoly(0.5) == 5.0
+    assert calc_dpoly(3.0) == 150.0
+    # What should we do on a breakpoint? 
+
+
+def test_antideriv():
+    poly = pp.PeicewisePolynomial(np.array([[4.0, 3.0, 2.0],
+                                            [40.0, 30.0, 20.0]]),
+                                  np.array([0.0, 2.0, 4.0]))
+    expected_antideriv_coefs = np.array([[0.0, 4.0, 3.0/2.0, 2.0/3.0], 
+                                         [0.0, 40.0, 30.0/2.0, 20.0/3.0]])
+    calc_antideriv = poly.antiderivative()
+    npt.assert_allclose(calc_antideriv.coeffs, expected_antideriv_coefs)