fix(docs): replace pd.DataFrame/Index with fully qualified pandas types

Sphinx cannot resolve the pd alias; using pandas.DataFrame and
pandas.Index allows intersphinx to link them correctly across all
public-facing modules.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: PauBadiaM

src/gretapy/pl/_distance.py

@@ -5,7 +5,7 @@
 
 
 def cre_to_tss_distance(
-    df: pd.DataFrame,
+    df: pandas.DataFrame,
     order: list | None = None,
     palette: str | None = None,
     thr_distance: int | None = 250_000,

src/gretapy/pl/_links.py

@@ -35,7 +35,7 @@ def _get_tss_window(gannot: pr.PyRanges, gene: str, w_size: int) -> pr.PyRanges:
     return tss_window
 
 
-def _get_gannot_data(gannot: pr.PyRanges, target: str, w_size: int, atac_var_names: pd.Index):
+def _get_gannot_data(gannot: pr.PyRanges, target: str, w_size: int, atac_var_names: pandas.Index):
     """Extract genomic annotation data for plotting."""
     target_gr = gannot[gannot.df["Name"] == target]
     wind = _get_tss_window(gannot, target, w_size)
@@ -56,13 +56,13 @@ def _get_gannot_data(gannot: pr.PyRanges, target: str, w_size: int, atac_var_nam
         cre_chr, cre_start, cre_end = cre.split("-")
         if cre_chr == chromosome:
             cres_gr.append([cre_chr, cre_start, cre_end, cre])
-    cres_gr = pr.PyRanges(pd.DataFrame(cres_gr, columns=["Chromosome", "Start", "End", "Name"]))
+    cres_gr = pr.PyRanges(pandas.DataFrame(cres_gr, columns=["Chromosome", "Start", "End", "Name"]))
 
     return x_min, x_max, gs_gr, tss, chromosome, strand, cres_gr
 
 
 def _plot_links(
-    links: pd.DataFrame,
+    links: pandas.DataFrame,
     tf: str,
     tss: int,
     strand: str,
@@ -191,7 +191,7 @@ def _plot_omic(
 
 def links(
     mdata,
-    grn: pd.DataFrame | dict[str, pd.DataFrame],
+    grn: pandas.DataFrame | dict[str, pandas.DataFrame],
     target: str,
     tfs: list[str],
     gannot: pr.PyRanges | str = "hg38",
@@ -279,7 +279,7 @@ def links(
         gannot = gt.show_genome_annotation(organism=gannot)
 
     # Normalize GRN input to dict
-    if isinstance(grn, pd.DataFrame):
+    if isinstance(grn, pandas.DataFrame):
         grn_dict = {"grn": grn}
     else:
         grn_dict = grn

src/gretapy/pl/_stats.py

@@ -14,7 +14,7 @@
 
 
 def stats(
-    df: pd.DataFrame,
+    df: pandas.DataFrame,
     order: list | None = None,
     palette: str | None = None,
     **kwargs,

src/gretapy/pp/_check.py

@@ -117,8 +117,8 @@ def _check_metrics(
 
 
 def _check_grn(
-    grn: pd.DataFrame,
-) -> pd.DataFrame:
+    grn: pandas.DataFrame,
+) -> pandas.DataFrame:
     """
     Validate GRN DataFrame structure.
 
@@ -131,7 +131,7 @@ def _check_grn(
     -------
     Validated and deduplicated GRN DataFrame.
     """
-    assert isinstance(grn, pd.DataFrame), f"grn must be pd.DataFrame, got {type(grn)}"
+    assert isinstance(grn, pandas.DataFrame), f"grn must be pandas.DataFrame, got {type(grn)}"
     assert {"source", "target"}.issubset(grn.columns), (
         f'grn must contain "source" and "target" column names, got {grn.columns}'
     )
@@ -187,7 +187,7 @@ def _check_dataset(
 
 def _check_dts_grn(
     dataset: mu.MuData | ad.AnnData,
-    grn: pd.DataFrame,
+    grn: pandas.DataFrame,
 ) -> None:
     """
     Validate gene overlap between dataset and GRN.

src/gretapy/tl/_eval.py

@@ -54,7 +54,7 @@ def benchmark(
     metrics: str | list | None = None,
     min_edges: int = 5,
     verbose: bool = True,
-) -> pd.DataFrame:
+) -> pandas.DataFrame:
     """
     Run the benchmark for one or multiple GRNs across one or multiple datasets.
 
@@ -119,7 +119,7 @@ def benchmark(
             datasets={"PBMC": mudata_obj, "Lung": mudata_obj2},
         )
     """
-    # Validate grns: must be dict[str, dict[str, dict[str, pd.DataFrame]]]
+    # Validate grns: must be dict[str, dict[str, dict[str, pandas.DataFrame]]]
     if not isinstance(grns, dict):
         raise ValueError(f"grns must be dict[str, dict[str, dict[str, DataFrame]]], got {type(grns)}")
     for grn_name, grn_inner in grns.items():
@@ -217,21 +217,21 @@ def benchmark(
     _log(f"Benchmark complete ({len(all_results)} result(s), {elapsed:.1f}s)", level="info", verbose=verbose)
     _log(_SEP, level="info", verbose=verbose)
     if not all_results:
-        return pd.DataFrame(columns=["grn", "organism", "dataset", "class", "task", "db", "precision", "recall", "f01"])
+        return pandas.DataFrame(columns=["grn", "organism", "dataset", "class", "task", "db", "precision", "recall", "f01"])
     return pd.concat(all_results, ignore_index=True)
 
 
 def eval_grn_dataset(
     organism: str,
-    grn: pd.DataFrame,
+    grn: pandas.DataFrame,
     dataset: str | mu.MuData | ad.AnnData,
     terms: dict | None,
     metrics: str | list | None = None,
     min_edges: int = 5,
     grn_name: str | None = None,
     dataset_name: str | None = None,
     verbose: bool = True,
-) -> pd.DataFrame:
+) -> pandas.DataFrame:
     """
     Evaluate a GRN against a dataset using multiple metrics.
 
@@ -290,7 +290,7 @@ def eval_grn_dataset(
             level="warning",
             verbose=verbose,
         )
-        return pd.DataFrame(columns=result_cols)
+        return pandas.DataFrame(columns=result_cols)
     # Resolve dataset_name for logging
     if dataset_name is None and isinstance(dataset, str):
         dataset_name = dataset
@@ -355,14 +355,14 @@ def eval_grn_dataset(
     _log(_SEP, level="info", verbose=verbose)
     _log(f"Evaluation complete{label_suffix} ({len(results)} metrics, {elapsed:.1f}s)", level="info", verbose=verbose)
     _log(_SEP, level="info", verbose=verbose)
-    return pd.DataFrame(results, columns=result_cols)
+    return pandas.DataFrame(results, columns=result_cols)
 
 
 def _run_metric(
     metric_type: str,
     db_name: str,
-    grn: pd.DataFrame,
-    db: pd.DataFrame | pr.PyRanges | ad.AnnData,
+    grn: pandas.DataFrame,
+    db: pandas.DataFrame | pr.PyRanges | ad.AnnData,
     genes: list,
     peaks: list,
     cats: list | None,
@@ -395,7 +395,7 @@ def _run_fileless_metric(
     metric_type: str,
     db_name: str,
     dataset: mu.MuData | ad.AnnData,
-    grn: pd.DataFrame,
+    grn: pandas.DataFrame,
     adata: ad.AnnData,
     is_mudata: bool,
     has_cre: bool,
@@ -412,7 +412,7 @@ def _run_fileless_metric(
 def _run_omics_metric(
     db_name: str,
     dataset: mu.MuData | ad.AnnData,
-    grn: pd.DataFrame,
+    grn: pandas.DataFrame,
     is_mudata: bool,
     has_cre: bool,
     verbose: bool = True,