def test_polyfit(self): # Tests polyfit # On ndarrays x = np.random.rand(10) y = np.random.rand(20).reshape(-1, 2) assert_almost_equal(polyfit(x, y, 3), np.polyfit(x, y, 3)) # ON 1D maskedarrays x = x.view(MaskedArray) x[0] = masked y = y.view(MaskedArray) y[0, 0] = y[-1, -1] = masked # (C, R, K, S, D) = polyfit(x, y[:, 0], 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:], y[1:, 0].compressed(), 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_) # (C, R, K, S, D) = polyfit(x, y[:, -1], 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, -1], 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_) # (C, R, K, S, D) = polyfit(x, y, 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, :], 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_) # w = np.random.rand(10) + 1 wo = w.copy() xs = x[1:-1] ys = y[1:-1] ws = w[1:-1] (C, R, K, S, D) = polyfit(x, y, 3, full=True, w=w) (c, r, k, s, d) = np.polyfit(xs, ys, 3, full=True, w=ws) assert_equal(w, wo) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_)
def test_polyfit(self): # Tests polyfit # On ndarrays x = np.random.rand(10) y = np.random.rand(20).reshape(-1, 2) assert_almost_equal(polyfit(x, y, 3), np.polyfit(x, y, 3)) # ON 1D maskedarrays x = x.view(MaskedArray) x[0] = masked y = y.view(MaskedArray) y[0, 0] = y[-1, -1] = masked # (C, R, K, S, D) = polyfit(x, y[:, 0], 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:], y[1:, 0].compressed(), 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_) # (C, R, K, S, D) = polyfit(x, y[:, -1], 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, -1], 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_) # (C, R, K, S, D) = polyfit(x, y, 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1,:], 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_) # w = np.random.rand(10) + 1 wo = w.copy() xs = x[1:-1] ys = y[1:-1] ws = w[1:-1] (C, R, K, S, D) = polyfit(x, y, 3, full=True, w=w) (c, r, k, s, d) = np.polyfit(xs, ys, 3, full=True, w=ws) assert_equal(w, wo) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_)
def test_polyfit_with_masked_NaNs(self): x = np.random.rand(10) y = np.random.rand(20).reshape(-1, 2) x[0] = np.nan y[-1, -1] = np.nan x = x.view(MaskedArray) y = y.view(MaskedArray) x[0] = masked y[-1, -1] = masked (C, R, K, S, D) = polyfit(x, y, 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1, :], 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_)
def test_polyfit_with_masked_NaNs(self): x = np.random.rand(10) y = np.random.rand(20).reshape(-1, 2) x[0] = np.nan y[-1,-1] = np.nan x = x.view(MaskedArray) y = y.view(MaskedArray) x[0] = masked y[-1,-1] = masked (C, R, K, S, D) = polyfit(x, y, 3, full=True) (c, r, k, s, d) = np.polyfit(x[1:-1], y[1:-1,:], 3, full=True) for (a, a_) in zip((C, R, K, S, D), (c, r, k, s, d)): assert_almost_equal(a, a_)