Add python typing

doubletdetection/doubletdetection.py

@@ -1,12 +1,14 @@
 """Doublet detection in single-cell RNA-seq data."""
 
 import collections
+from collections.abc import Callable
 import io
 import warnings
 from contextlib import redirect_stdout
 
 import anndata
 import numpy as np
+from numpy.typing import NDArray
 import phenograph
 import scanpy as sc
 import scipy.sparse as sp_sparse
@@ -83,20 +85,20 @@ class BoostClassifier:
 
     def __init__(
         self,
-        boost_rate=0.25,
-        n_components=30,
-        n_top_var_genes=10000,
-        replace=False,
-        clustering_algorithm="phenograph",
-        clustering_kwargs=None,
-        n_iters=10,
-        normalizer=None,
-        pseudocount=0.1,
-        random_state=0,
-        verbose=False,
-        standard_scaling=False,
-        n_jobs=1,
-    ):
+        boost_rate: float = 0.25,
+        n_components: int = 30,
+        n_top_var_genes: int = 10000,
+        replace: bool = False,
+        clustering_algorithm: str = "phenograph",
+        clustering_kwargs: dict | None = None,
+        n_iters: int = 10,
+        normalizer: Callable | None = None,
+        pseudocount: float = 0.1,
+        random_state: int = 0,
+        verbose: bool = False,
+        standard_scaling: bool = False,
+        n_jobs: int = 1,
+    ) -> None:
         self.boost_rate = boost_rate
         self.replace = replace
         self.clustering_algorithm = clustering_algorithm
@@ -145,7 +147,7 @@ def __init__(
             n_components, n_top_var_genes
         )
 
-    def fit(self, raw_counts):
+    def fit(self, raw_counts: NDArray | sp_sparse.csr_matrix) -> "BoostClassifier":
         """Fits the classifier on raw_counts.
 
         Args:
@@ -226,7 +228,7 @@ def fit(self, raw_counts):
 
         return self
 
-    def predict(self, p_thresh=1e-7, voter_thresh=0.9):
+    def predict(self, p_thresh: float = 1e-7, voter_thresh: float = 0.9) -> NDArray:
         """Produce doublet calls from fitted classifier
 
         Args:
@@ -266,7 +268,7 @@ def predict(self, p_thresh=1e-7, voter_thresh=0.9):
 
         return self.labels_
 
-    def doublet_score(self):
+    def doublet_score(self) -> NDArray:
         """Produce doublet scores
 
         The doublet score is the average negative log p-value of doublet enrichment
@@ -284,7 +286,7 @@ def doublet_score(self):
 
         return -avg_log_p
 
-    def _one_fit(self):
+    def _one_fit(self) -> tuple[NDArray, NDArray]:
         if self.verbose:
             print("\nCreating synthetic doublets...")
         self._createDoublets()
@@ -395,7 +397,7 @@ def _one_fit(self):
 
         return scores, log_p_values
 
-    def _createDoublets(self):
+    def _createDoublets(self) -> None:
         """Create synthetic doublets.
 
         Sets .parents_
@@ -414,7 +416,7 @@ def _createDoublets(self):
         self._raw_synthetics = synthetic
         self.parents_ = parents
 
-    def _set_clustering_kwargs(self):
+    def _set_clustering_kwargs(self) -> None:
         """Sets .clustering_kwargs"""
         if self.clustering_algorithm == "phenograph":
             if "prune" not in self.clustering_kwargs:

doubletdetection/plot.py

@@ -1,8 +1,11 @@
 import os
 import warnings
+from typing import Any
 
 import matplotlib
 import numpy as np
+from numpy.typing import NDArray
+from matplotlib.figure import Figure
 
 try:
     os.environ["DISPLAY"]
@@ -11,7 +14,7 @@
 import matplotlib.pyplot as plt
 
 
-def normalize_counts(raw_counts, pseudocount=0.1):
+def normalize_counts(raw_counts: NDArray, pseudocount: float = 0.1) -> NDArray:
     """Normalize count array. Default normalizer used by BoostClassifier.
 
     Args:
@@ -22,7 +25,6 @@ def normalize_counts(raw_counts, pseudocount=0.1):
         ndarray: Normalized data.
     """
     # Sum across cells
-
     cell_sums = np.sum(raw_counts, axis=1)
 
     # Mutiply by median and divide each cell by cell sum
@@ -34,7 +36,13 @@ def normalize_counts(raw_counts, pseudocount=0.1):
     return normed
 
 
-def convergence(clf, show=False, save=None, p_thresh=1e-7, voter_thresh=0.9):
+def convergence(
+    clf: Any,
+    show: bool = False,
+    save: str | None = None,
+    p_thresh: float = 1e-7,
+    voter_thresh: float = 0.9,
+) -> Figure:
     """Produce a plot showing number of cells called doublet per iter
 
     Args:
@@ -81,15 +89,15 @@ def convergence(clf, show=False, save=None, p_thresh=1e-7, voter_thresh=0.9):
 
 
 def threshold(
-    clf,
-    show=False,
-    save=None,
-    log10=True,
-    log_p_grid=None,
-    voter_grid=None,
-    v_step=2,
-    p_step=5,
-):
+    clf: Any,
+    show: bool = False,
+    save: str | None = None,
+    log10: bool = True,
+    log_p_grid: NDArray | None = None,
+    voter_grid: NDArray | None = None,
+    v_step: int = 2,
+    p_step: int = 5,
+) -> Figure:
     """Produce a plot showing number of cells called doublet across
        various thresholds