diff --git a/osculari/datasets/dataset_utils.py b/osculari/datasets/dataset_utils.py
index e4c1064..84ac82e 100644
--- a/osculari/datasets/dataset_utils.py
+++ b/osculari/datasets/dataset_utils.py
@@ -186,6 +186,8 @@ def background_img(bg_type: Any, bg_size: Union[int, Tuple], im2double=True) ->
             num_colours = np.random.randint(3, 25)
             num_patches = np.random.randint(2, bg_size[0] // 20)
             bg_img = _patch_img(bg_size, num_colours, num_patches, channels)
+            if 'achromatic' in bg_type:
+                bg_img = np.repeat(bg_img, 3, axis=2)
         else:
             raise RuntimeError('Unsupported background type %s.' % bg_type)
     # Handle user-specified background values
diff --git a/osculari/datasets/gratings.py b/osculari/datasets/gratings.py
index 8a103f9..1b4fa7d 100644
--- a/osculari/datasets/gratings.py
+++ b/osculari/datasets/gratings.py
@@ -91,7 +91,7 @@ class GratingsDataset(TorchDataset):
     """
 
     def __init__(self, img_size: int, spatial_frequencies: Optional[Sequence[int]] = None,
-                 thetas: Optional[Sequence[int]] = None, gaussian_sigma: Optional[float] = None,
+                 thetas: Optional[Sequence[float]] = None, gaussian_sigma: Optional[float] = None,
                  transform: Optional[Callable] = None) -> None:
         super(GratingsDataset, self).__init__()
         self.img_size = img_size
diff --git a/tests/datasets/dataset_utils_test.py b/tests/datasets/dataset_utils_test.py
new file mode 100644
index 0000000..4be016c
--- /dev/null
+++ b/tests/datasets/dataset_utils_test.py
@@ -0,0 +1,84 @@
+"""
+Unit tests for dataset_utils.py
+"""
+
+import pytest
+import numpy as np
+
+from osculari.datasets import dataset_utils
+
+
+def test_background_uniform_achromatic():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('uniform_achromatic', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.allclose(bg_img, bg_img[0, 0, :])
+
+
+def test_background_uniform_colour():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('uniform_colour', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.allclose(bg_img, bg_img[0, 0, :])
+
+
+def test_background_random_achromatic():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('random_achromatic', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.unique(bg_img).shape[0] > 1
+
+
+def test_background_random_achromatic_pixelwise():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('random_achromatic', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.unique(bg_img).shape[0] > 1
+    assert np.all(np.equal(bg_img[..., 0], bg_img[..., 1]))
+
+
+def test_background_random_colour():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('random_colour', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.unique(bg_img).shape[0] > 1
+
+
+def test_background_patch_achromatic():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('patch_achromatic', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.unique(bg_img).shape[0] > 1
+
+
+def test_background_patch_achromatic_pixelwise():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('patch_achromatic', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.unique(bg_img).shape[0] > 1
+    assert np.all(np.equal(bg_img[..., 0], bg_img[..., 1]))
+
+
+def test_background_patch_colour():
+    bg_size = (256, 256)
+    bg_img = dataset_utils.background_img('patch_colour', bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.unique(bg_img).shape[0] > 1
+
+
+def test_background_uniform_value():
+    bg_size = (256, 256)
+    bg_value = 0.5
+    bg_img = dataset_utils.background_img(bg_value, bg_size)
+    assert bg_img.shape == (*bg_size, 3)
+    assert np.allclose(bg_img, bg_value)
+
+
+def test_background_invalid_type():
+    with pytest.raises(RuntimeError, match='Unsupported background type'):
+        _ = dataset_utils.background_img('invalid_type', (256, 256))
+
+
+def test_background_invalid_value_type():
+    with pytest.raises(RuntimeError, match='Unsupported background type'):
+        _ = dataset_utils.background_img(None, (256, 256))
diff --git a/tests/datasets/gratings_test.py b/tests/datasets/gratings_test.py
new file mode 100644
index 0000000..bbd45dd
--- /dev/null
+++ b/tests/datasets/gratings_test.py
@@ -0,0 +1,61 @@
+"""
+Unit tests for gratings.py
+"""
+
+import pytest
+import numpy as np
+import torch
+import torchvision.transforms as torch_transforms
+
+from osculari.datasets import GratingsDataset
+
+
+def test_gratings_dataset_len():
+    # Test the __len__ method of GratingsDataset
+    img_size = 64
+    dataset = GratingsDataset(img_size=img_size)
+    expected_length = len(dataset.thetas) * len(dataset.sfs)
+    assert len(dataset) == expected_length
+
+
+def test_gratings_dataset_make_grating():
+    # Test the make_grating method of GratingsDataset
+    img_size = 64
+    dataset = GratingsDataset(img_size=img_size)
+    idx = 0
+    amplitude = 1.0
+    channels = 3
+    grating = dataset.make_grating(idx, amplitude, channels)
+    assert isinstance(grating, np.ndarray)
+    assert grating.shape == (img_size, img_size, channels)
+
+
+def test_gratings_dataset_getitem():
+    # Test the __getitem__ method of GratingsDataset
+    img_size = 64
+    dataset = GratingsDataset(img_size=img_size)
+
+    # Test without transformation
+    idx = 0
+    grating = dataset[idx]
+    assert isinstance(grating, np.ndarray)
+    assert grating.shape == (img_size, img_size, 3)
+
+    # Test with transformation
+    transform = torch_transforms.Compose([torch_transforms.ToTensor()])
+    dataset.transform = transform
+    grating = dataset[idx]
+    assert isinstance(grating, torch.Tensor)
+    assert grating.shape == (3, img_size, img_size)
+
+
+def test_gratings_dataset_with_gaussian():
+    # Test the make_grating method of GratingsDataset
+    img_size = 64
+    dataset = GratingsDataset(img_size=img_size, gaussian_sigma=0.5)
+    idx = 0
+    amplitude = 1.0
+    channels = 3
+    grating = dataset.make_grating(idx, amplitude, channels)
+    assert isinstance(grating, np.ndarray)
+    assert grating.shape == (img_size, img_size, channels)