Update round_params too.

cubids/tests/test_utils.py

@@ -2,11 +2,54 @@
 
 import pandas as pd
 
-from cubids.utils import cluster_single_parameters
+from cubids import utils
+from cubids.tests.utils import compare_group_assignments
+
+
+def test_round_params():
+    """Test the cubids.utils.round_params function."""
+    # Example DataFrame
+    df = pd.DataFrame(
+        {
+            "A": [1.12345, 2.23456, 3.34567],
+            "B": [[1.12345, 2.23456], [3.34567, 4.45678], [5.56789, 6.67890]],
+            "C": ["text", "more text", "even more text"],
+            "D": [1.12345, 2.23456, 3.34567],
+        }
+    )
+
+    # Example config
+    config = {
+        "sidecar_params": {
+            "modality1": {
+                "A": {"precision": 2},
+                "B": {"precision": 2},
+            },
+        },
+        "derived_params": {
+            "modality1": {},
+        },
+    }
+
+    # Expected DataFrame after rounding
+    expected_df = pd.DataFrame(
+        {
+            "A": [1.12, 2.23, 3.35],
+            "B": [[1.12, 2.23], [3.35, 4.46], [5.57, 6.68]],
+            "C": ["text", "more text", "even more text"],
+            "D": [1.12345, 2.23456, 3.34567],
+        }
+    )
+
+    # Round columns
+    rounded_df = utils.round_params(df, config, "modality1")
+
+    # Assert that the rounded DataFrame matches the expected DataFrame
+    pd.testing.assert_frame_equal(rounded_df, expected_df)
 
 
 def test_cluster_single_parameters():
-    """Test the cluster_single_parameters function.
+    """Test the cubids.utils.cluster_single_parameters function.
 
     We want to test that the function correctly clusters parameters based on the
     configuration dictionary.
@@ -86,7 +129,7 @@ def test_cluster_single_parameters():
     modality = "func"
 
     # Run the function
-    out_df = cluster_single_parameters(
+    out_df = utils.cluster_single_parameters(
         files_df=files_df,
         config=config,
         modality=modality,
@@ -113,7 +156,7 @@ def test_cluster_single_parameters():
 
     # Change the tolerance for SliceTiming
     config["sidecar_params"]["func"]["SliceTiming"]["tolerance"] = 0.5
-    out_df = cluster_single_parameters(
+    out_df = utils.cluster_single_parameters(
         files_df=files_df,
         config=config,
         modality=modality,
@@ -139,56 +182,3 @@ def test_cluster_single_parameters():
         out_df["Cluster_ImageType"].values.astype(int),
         [0, 0, 0, 0, 0, 0, 1, 2],
     )
-
-
-def compare_group_assignments(list1, list2):
-    """Compare two lists for equality based on group assignments.
-
-    This function checks if two lists can be considered equal based on their group assignments.
-    The actual values in the lists do not matter, only the group assignments do. Each unique value
-    in the first list is mapped to a unique value in the second list, and the function checks if
-    this mapping is consistent throughout the lists.
-
-    Parameters
-    ----------
-    list1 : list
-        The first list to compare.
-    list2 : list
-        The second list to compare.
-
-    Returns
-    -------
-    bool
-        True if the lists are equal based on group assignments, False otherwise.
-
-    Examples
-    --------
-    >>> list1 = [1, 2, 1, 3, 2]
-    >>> list2 = ['a', 'b', 'a', 'c', 'b']
-    >>> compare_group_assignments(list1, list2)
-    True
-
-    >>> list1 = [1, 2, 1, 3, 2]
-    >>> list2 = ['b', 'd', 'b', 'q', 'd']
-    >>> compare_group_assignments(list1, list2)
-    True
-
-    >>> list1 = [1, 2, 1, 3, 2]
-    >>> list2 = ['a', 'b', 'a', 'c', 'd']
-    >>> compare_group_assignments(list1, list2)
-    False
-    """
-    if len(list1) != len(list2):
-        return False
-
-    mapping = {}
-    for a, b in zip(list1, list2):
-        if a in mapping:
-            if mapping[a] != b:
-                return False
-        else:
-            if b in mapping.values():
-                return False
-            mapping[a] = b
-
-    return True

cubids/tests/utils.py

@@ -167,3 +167,56 @@ def chdir(path):
         yield
     finally:
         os.chdir(oldpwd)
+
+
+def compare_group_assignments(list1, list2):
+    """Compare two lists for equality based on group assignments.
+
+    This function checks if two lists can be considered equal based on their group assignments.
+    The actual values in the lists do not matter, only the group assignments do. Each unique value
+    in the first list is mapped to a unique value in the second list, and the function checks if
+    this mapping is consistent throughout the lists.
+
+    Parameters
+    ----------
+    list1 : list
+        The first list to compare.
+    list2 : list
+        The second list to compare.
+
+    Returns
+    -------
+    bool
+        True if the lists are equal based on group assignments, False otherwise.
+
+    Examples
+    --------
+    >>> list1 = [1, 2, 1, 3, 2]
+    >>> list2 = ['a', 'b', 'a', 'c', 'b']
+    >>> compare_group_assignments(list1, list2)
+    True
+
+    >>> list1 = [1, 2, 1, 3, 2]
+    >>> list2 = ['b', 'd', 'b', 'q', 'd']
+    >>> compare_group_assignments(list1, list2)
+    True
+
+    >>> list1 = [1, 2, 1, 3, 2]
+    >>> list2 = ['a', 'b', 'a', 'c', 'd']
+    >>> compare_group_assignments(list1, list2)
+    False
+    """
+    if len(list1) != len(list2):
+        return False
+
+    mapping = {}
+    for a, b in zip(list1, list2):
+        if a in mapping:
+            if mapping[a] != b:
+                return False
+        else:
+            if b in mapping.values():
+                return False
+            mapping[a] = b
+
+    return True

cubids/utils.py

@@ -377,13 +377,13 @@ def _get_param_groups(
     return ordered_labeled_files, param_groups_with_counts
 
 
-def round_params(param_group_df, config, modality):
+def round_params(df, config, modality):
     """Round columns' values in a DataFrame according to requested precision.
 
     Parameters
     ----------
-    param_group_df : pandas.DataFrame
-        DataFrame containing the parameters to be rounded.
+    df : pandas.DataFrame
+        DataFrame containing the parameters to be rounded, with one row per file.
     config : dict
         Configuration dictionary containing rounding precision information.
     modality : str
@@ -398,16 +398,19 @@ def round_params(param_group_df, config, modality):
     to_format.update(config["derived_params"][modality])
 
     for column_name, column_fmt in to_format.items():
-        if column_name not in param_group_df:
+        if column_name not in df:
             continue
 
         if "precision" in column_fmt:
-            if isinstance(param_group_df[column_name], float):
-                param_group_df[column_name] = param_group_df[column_name].round(
-                    column_fmt["precision"]
+            precision = column_fmt["precision"]
+            if isinstance(df[column_name], float):
+                df[column_name] = df[column_name].round(precision)
+            elif df[column_name].apply(lambda x: isinstance(x, (list, np.ndarray))).any():
+                df[column_name] = df[column_name].apply(
+                    lambda x: np.round(x, precision) if isinstance(x, (list, np.ndarray)) else x
                 )
 
-    return param_group_df
+    return df
 
 
 def get_sidecar_metadata(json_file):