Fix/get df with default args

scvelo/core/_anndata.py

@@ -1,4 +1,5 @@
 import re
+import warnings
 from typing import List, Literal, Optional, Union
 
 import numpy as np
@@ -178,6 +179,13 @@ def get_df(
  :class:`pd.DataFrame`
  A dataframe.
  """
+ warnings.warn(
+ "`get_df` is deprecated since scvelo==0.4.0 and will be removed in a future version "
+ "of scVelo. Please `AnnData::get_df` or Scanpy's `scanpy.get.obs_df` or `scanpy.get.var_df`.",
+ DeprecationWarning,
+ stacklevel=2,
+ )
+
  if precision is not None:
  pd.set_option("display.precision", precision)
 
@@ -188,8 +196,6 @@ def get_df(
  keys, key_add = (
  keys.split("/") if isinstance(keys, str) and "/" in keys else (keys, None)
  )
- keys = [keys] if isinstance(keys, str) else keys
- key = keys[0]
 
  s_keys = ["obs", "var", "obsm", "varm", "uns", "layers"]
  d_keys = [
@@ -207,62 +213,68 @@ def get_df(
 
  if keys is None:
  df = data.to_df()
- elif key in data.var_names:
- df = obs_df(data, keys, layer=layer)
- elif key in data.obs_names:
- df = var_df(data, keys, layer=layer)
  else:
- if keys_split is not None:
- keys = [
- k
- for k in list(data.obs.keys()) + list(data.var.keys())
- if key in k and keys_split in k
- ]
- key = keys[0]
- s_key = [s for (s, d_key) in zip(s_keys, d_keys) if key in d_key]
- if len(s_key) == 0:
- raise ValueError(f"'{key}' not found in any of {', '.join(s_keys)}.")
- if len(s_key) > 1:
- logg.warn(f"'{key}' found multiple times in {', '.join(s_key)}.")
-
- s_key = s_key[-1]
- df = getattr(data, s_key)[keys if len(keys) > 1 else key]
- if key_add is not None:
- df = df[key_add]
- if index is None:
- index = (
- data.var_names
- if s_key == "varm"
- else data.obs_names
- if s_key in {"obsm", "layers"}
- else None
- )
- if index is None and s_key == "uns" and hasattr(df, "shape"):
- key_cats = np.array(
- [
- key
- for key in data.obs.keys()
- if is_categorical_dtype(data.obs[key])
- ]
- )
- num_cats = [
- len(data.obs[key].cat.categories) == df.shape[0]
- for key in key_cats
+ keys = [keys] if isinstance(keys, str) else keys
+ key = keys[0]
+
+ if key in data.var_names:
+ df = obs_df(data, keys, layer=layer)
+ elif key in data.obs_names:
+ df = var_df(data, keys, layer=layer)
+ else:
+ if keys_split is not None:
+ keys = [
+ k
+ for k in list(data.obs.keys()) + list(data.var.keys())
+ if key in k and keys_split in k
  ]
- if np.sum(num_cats) == 1:
- index = data.obs[key_cats[num_cats][0]].cat.categories
- if (
- columns is None
- and len(df.shape) > 1
- and df.shape[0] == df.shape[1]
- ):
- columns = index
- elif isinstance(index, str) and index in data.obs.keys():
- index = pd.Categorical(data.obs[index]).categories
- if columns is None and s_key == "layers":
- columns = data.var_names
- elif isinstance(columns, str) and columns in data.obs.keys():
- columns = pd.Categorical(data.obs[columns]).categories
+ key = keys[0]
+ s_key = [s for (s, d_key) in zip(s_keys, d_keys) if key in d_key]
+ if len(s_key) == 0:
+ raise ValueError(
+ f"'{key}' not found in any of {', '.join(s_keys)}."
+ )
+ if len(s_key) > 1:
+ logg.warn(f"'{key}' found multiple times in {', '.join(s_key)}.")
+
+ s_key = s_key[-1]
+ df = getattr(data, s_key)[keys if len(keys) > 1 else key]
+ if key_add is not None:
+ df = df[key_add]
+ if index is None:
+ index = (
+ data.var_names
+ if s_key == "varm"
+ else data.obs_names
+ if s_key in {"obsm", "layers"}
+ else None
+ )
+ if index is None and s_key == "uns" and hasattr(df, "shape"):
+ key_cats = np.array(
+ [
+ key
+ for key in data.obs.keys()
+ if is_categorical_dtype(data.obs[key])
+ ]
+ )
+ num_cats = [
+ len(data.obs[key].cat.categories) == df.shape[0]
+ for key in key_cats
+ ]
+ if np.sum(num_cats) == 1:
+ index = data.obs[key_cats[num_cats][0]].cat.categories
+ if (
+ columns is None
+ and len(df.shape) > 1
+ and df.shape[0] == df.shape[1]
+ ):
+ columns = index
+ elif isinstance(index, str) and index in data.obs.keys():
+ index = pd.Categorical(data.obs[index]).categories
+ if columns is None and s_key == "layers":
+ columns = data.var_names
+ elif isinstance(columns, str) and columns in data.obs.keys():
+ columns = pd.Categorical(data.obs[columns]).categories
  elif isinstance(data, pd.DataFrame):
  if isinstance(keys, str) and "*" in keys:
  keys, keys_split = keys.split("*")

tests/core/test_anndata.py

@@ -573,6 +573,20 @@ def test_data_as_array(
  else:
  assert (df.columns == ["col_1", "col_2"]).all()
 
+ @given(
+ adata=get_adata(
+ max_obs=5,
+ max_vars=5,
+ layer_keys=["layer_1", "layer_2"],
+ ),
+ modality=st.sampled_from([None, "X", "layer_1", "layer_2"]),
+ )
+ def test_default(self, adata: AnnData, modality: Optional[None]):
+ df = get_df(adata, layer=modality)
+
+ assert isinstance(df, pd.DataFrame)
+ np.testing.assert_equal(adata.to_df().values, df.values)
+
 
 class TestGetInitialSize(TestBase):
  @given(