fix: randn arguments

* Uses positional arguments instead of `n` as int | tuple, which is the correct usage with `np.random.randn`
* Corrects input/output types

pylops/utils/backend.py

@@ -610,29 +610,29 @@ def inplace_divide(x: npt.ArrayLike, y: npt.ArrayLike, idx: list) -> NDArray:
         return y
 
 
-def randn(n: Union[int, tuple], backend: str = "numpy") -> Callable:
+def randn(*n: int, backend: str = "numpy") -> NDArray:
     """Returns randomly generated number
 
     Parameters
     ----------
-    n : :obj:`int` or :obj:`tuple`
-        Number of samples to generate
+    *n : :obj:`int`
+        Number of samples to generate in each dimension
     backend : :obj:`str`, optional
         Backend used for dot test computations (``numpy`` or ``cupy``). This
         parameter will be used to choose how to create the random vectors.
 
     Returns
     -------
-    x : :obj:`numpy.ndarray` or :obj:`jax.numpy.ndarray`
+    x : :obj:`numpy.ndarray` or :obj:`jax.Array`
         Generated array
 
     """
     if backend == "numpy":
-        x = np.random.randn(n)
+        x = np.random.randn(*n)
     elif backend == "cupy":
-        x = cp.random.randn(n)
+        x = cp.random.randn(*n)
     elif backend == "jax":
-        x = jnp.array(np.random.randn(n))
+        x = jnp.array(np.random.randn(*n))
     else:
         raise ValueError("backend must be numpy, cupy, or jax")
     return x

pylops/utils/dottest.py

@@ -93,13 +93,13 @@ def dottest(
     # make u and v vectors
     rdtype = np.ones(1, Op.dtype).real.dtype
 
-    u = randn(nc, backend).astype(rdtype)
+    u = randn(nc, backend=backend).astype(rdtype)
     if complexflag not in (0, 2):
-        u = u + 1j * randn(nc, backend).astype(rdtype)
+        u = u + 1j * randn(nc, backend=backend).astype(rdtype)
 
-    v = randn(nr, backend).astype(rdtype)
+    v = randn(nr, backend=backend).astype(rdtype)
     if complexflag not in (0, 1):
-        v = v + 1j * randn(nr, backend).astype(rdtype)
+        v = v + 1j * randn(nr, backend=backend).astype(rdtype)
 
     y = Op.matvec(u)  # Op * u
     x = Op.rmatvec(v)  # Op'* v