minor_adjust_docs

Author: Sergio Salas

src/troutpy/pp/aggregate.py

@@ -12,7 +12,6 @@
 from spatialdata import SpatialData
 from spatialdata.models import ShapesModel
 from tqdm import tqdm
-from anndata import AnnData
 
 
 def _make_squares(centroid_coordinates: np.ndarray, half_widths: list[float]) -> ShapesModel:

src/troutpy/tl/.ipynb_checkpoints/quantify_urna-checkpoint.py

@@ -369,7 +369,7 @@ def in_out_correlation(
 
     Parameters
     ----------
-    sdata : SpatialData
+    sdata : spatialdata.SpatialData
         A SpatialData object containing both extracellular and cellular AnnData objects.
 
     extracellular_layer : str
@@ -555,7 +555,7 @@ def cluster_distribution_from_source(
         A DataFrame with columns 'gene' and 'cluster' indicating the cluster assignment.
     hist_df : pandas.DataFrame
         A DataFrame where each row is a gene and the columns are the normalized histogram counts.
-    bin_edges : numpy.array
+    bin_edges : numpy.ndarray
         The bin edges used for the histograms.
     """
     # Get the observation DataFrame from the 'source_score' layer.

src/troutpy/tl/quantify_urna.py

@@ -369,7 +369,7 @@ def in_out_correlation(
 
     Parameters
     ----------
-    sdata : SpatialData
+    sdata : spatialdata.SpatialData
         A SpatialData object containing both extracellular and cellular AnnData objects.
 
     extracellular_layer : str
@@ -555,7 +555,7 @@ def cluster_distribution_from_source(
         A DataFrame with columns 'gene' and 'cluster' indicating the cluster assignment.
     hist_df : pandas.DataFrame
         A DataFrame where each row is a gene and the columns are the normalized histogram counts.
-    bin_edges : numpy.array
+    bin_edges : numpy.ndarray
         The bin edges used for the histograms.
     """
     # Get the observation DataFrame from the 'source_score' layer.