Update shift inversion unit tests to interface change and use more realistic parameters

Made test stricter. The previous parameters used a maximum shift too small to increase
the required margin size, thus not testing anything related to its size.

tests/test_shift_inversion_dask.py

@@ -8,28 +8,43 @@ from lisainstrument.shift_inversion_dask import make_shift_inverse_lagrange_dask
 from lisainstrument.shift_inversion_numpy import make_shift_inverse_lagrange_numpy
 
 
-def test_shift_inversion_numpy():
+def test_shift_inversion_dask():
     """Test basic functioning of shift_inversion_dask on analytic coordinate transform"""
-    order = 31
-    nsamp = 600
     chunks = 70
-    k_mod = 8 * np.pi
-    a_mod = 1e-3
-    max_it = 6
-    tol = 1e-12
+    order = 31
+    nsamp = 3000
+    fsample = 16.0
+    dt = 1 / fsample
+
+    f_mod = 0.005
+    a_mod = 1e-2 / (2 * np.pi * f_mod)
+    max_it = 5
+    tol = 1e-10
 
     def dx_from_x(x):
-        return np.sin(x * k_mod) * a_mod
+        return np.sin(2 * np.pi * f_mod * x) * a_mod
+
+    xi = np.arange(nsamp) * dt
 
-    xi, dt = np.linspace(0, 1, nsamp, retstep=True)
-    fsample = 1 / dt
     dxi = dx_from_x(xi)
 
-    op_np = make_shift_inverse_lagrange_numpy(order, a_mod * 1.01 / dt, max_it, tol)
-    op_da = make_shift_inverse_lagrange_dask(order, a_mod * 1.01 / dt, max_it, tol)
+    op_np = make_shift_inverse_lagrange_numpy(
+        order=order,
+        fsample=fsample,
+        max_abs_shift=a_mod * 1.01,
+        max_iter=max_it,
+        tolerance=tol,
+    )
+    op_da = make_shift_inverse_lagrange_dask(
+        order=order,
+        fsample=fsample,
+        max_abs_shift=a_mod * 1.01,
+        max_iter=max_it,
+        tolerance=tol,
+    )
     op_na = numpyfy_dask_multi(op_da, chunks)
 
-    ai_np = op_np(dxi, fsample)
-    ai_na = op_na(dxi, fsample)
+    ai_np = op_np(dxi)
+    ai_na = op_na(dxi)
 
     assert ai_np == pytest.approx(ai_na, abs=tol, rel=0)

tests/test_shift_inversion_numpy.py

@@ -9,22 +9,30 @@ from lisainstrument.shift_inversion_numpy import make_shift_inverse_lagrange_num
 def test_shift_inversion_numpy():
     """Test basic functioning of shift_inversion_numpy on analytic coordinate transform"""
     order = 31
-    nsamp = 200
-    k_mod = 8 * np.pi
-    a_mod = 1e-3
+    nsamp = 3000
+    fsample = 16.0
+    dt = 1 / fsample
+
+    f_mod = 0.005
+    a_mod = 1e-2 / (2 * np.pi * f_mod)
     max_it = 5
     tol = 1e-10
 
     def dx_from_x(x):
-        return np.sin(x * k_mod) * a_mod
+        return np.sin(2 * np.pi * f_mod * x) * a_mod
 
-    xi, dt = np.linspace(0, 1, nsamp, retstep=True)
-    fsample = 1 / dt
+    xi = np.arange(nsamp) * dt
     dxi = dx_from_x(xi)
 
-    op_np = make_shift_inverse_lagrange_numpy(order, a_mod * 1.01 / dt, max_it, tol)
+    op_np = make_shift_inverse_lagrange_numpy(
+        order=order,
+        fsample=fsample,
+        max_abs_shift=a_mod * 1.01,
+        max_iter=max_it,
+        tolerance=tol,
+    )
 
-    ai_np = op_np(dxi, fsample)
+    ai_np = op_np(dxi)
 
     ai_ex = dx_from_x(xi - ai_np)