Enhance line_based_frc function with detailed docstring and input validation checks

src/cdtools/tools/analysis/analysis.py

@@ -1530,7 +1530,8 @@ def calc_spectral_info(dataset, nbins=50):
 
 def line_based_frc(image1, image2, axis=1, n_bins=None, thresholds={}, pixel_size=1.0, unit="pixels"):
     """
-    Compute line-based FRC with multiple threshold methods
+    Compute line-based FRC with multiple threshold methods. This works great for highly anisotropic
+    images, such as those with vertical or horizontal stripes.
 
     Parameters
     ----------
@@ -1563,6 +1564,9 @@ def line_based_frc(image1, image2, axis=1, n_bins=None, thresholds={}, pixel_siz
     if isinstance(image2, t.Tensor):
         image2 = image2.numpy()
 
+    assert isinstance(image1, np.ndarray), "image1 must be a numpy array or torch tensor"
+    assert isinstance(image2, np.ndarray), "image2 must be a numpy array or torch tensor"
+
     assert image1.shape == image2.shape, "Images must have same shape"
 
     if image1.ndim != 2:

tests/tools/test_analysis.py

@@ -1,7 +1,8 @@
 import numpy as np
+import pytest
+import torch as t
 from scipy import linalg as la
 from scipy.sparse import linalg as spla
-import torch as t
 
 from cdtools.tools import analysis, initializers
 from cdtools.tools.analysis import line_based_frc
@@ -612,3 +613,10 @@ def test_line_based_frc():
     assert res_dict["1/2"] > 70.0 and res_dict["1/2"] < 75.0
     assert res_dict["1/4"] > 52.0 and res_dict["1/4"] < 57.0
     assert res_dict["1/7"] > 45.0 and res_dict["1/7"] < 48.0
+
+    # Now lets check if we catch some errors correctly
+    # Check if it raises an error for non-2D input
+    with pytest.raises(ValueError):
+        line_based_frc(np.random.rand(30, 17, 3), np.random.rand(30, 17, 3))
+        line_based_frc(np.random.rand(30, 17, 3), np.random.rand(30, 17))
+        line_based_frc("string", np.random.rand(30, 17))