Fix docs

src/pixelator/annotate/aggregates.py

@@ -1,4 +1,4 @@
-"""This module contains functions for finding aggregates.
+"""Functions for finding aggregates.
 
 Copyright (c) 2022 Pixelgen Technologies AB.
 """
@@ -46,7 +46,9 @@ def specificity_tau(matrix: np.ndarray) -> np.ndarray:
 
 
 def call_aggregates(adata: AnnData, inplace: bool = True) -> Optional[AnnData]:
-    """We defined aggregates as components where either:
+    """Call aggregates on the adata instance.
+
+    We defined aggregates as components where either:
      - A single or a handful of markers account for almost all of the count data.
        These can likely be attributed to single antibodies forming aggregates
      - Low tau scores, meaning a an even number of counts for multiple markers.

src/pixelator/annotate/cell_calling.py

@@ -1,4 +1,4 @@
-"""This module contains functions for doing size-based cell calling.
+"""Functions for doing size-based cell calling.
 
 Copyright (c) 2022 Pixelgen Technologies AB.
 """
@@ -25,7 +25,9 @@ def find_component_size_limits(
     component_sizes: np.ndarray,
     direction: Literal["lower", "upper"],
 ) -> Optional[int]:
-    """This function will attempt to find a cutoff for a distribution of component sizes.
+    """Find component size limits.
+
+    This function will attempt to find a cutoff for a distribution of component sizes.
     The direction of the cut-off is determined by the `direction` parameter (lower for
     min size and upper for max size).
 
@@ -64,7 +66,9 @@ def find_component_size_limits(
     """
 
     def log_size_and_rank(df: pd.DataFrame) -> pd.DataFrame:
-        """Rank component sizes by size, sort by their rank, and compute the
+        """Rank components by size.
+
+        Rank component sizes by size, sort by their rank, and compute the
         log10 of both the sizes and ranks. The input dataframe must contain
         a `size` column with the component sizes. A new dataframe is
         returned with two new columns `log10_size` and `log10_rank`.
@@ -76,7 +80,9 @@ def log_size_and_rank(df: pd.DataFrame) -> pd.DataFrame:
         return df
 
     def smooth(df: pd.DataFrame, x_var: str, y_var: str) -> pd.DataFrame:
-        """Calculate a smoothing spline of df[x_var] ~ df[y_var]
+        """Create a smoothing spline.
+
+        Calculate a smoothing spline of df[x_var] ~ df[y_var]
         to make it possible to calculate a less unstable derivate.
         The input dataframe must contain the `x_var` and `y_var`
         columns, a new column `smooth` is added to the returned
@@ -92,7 +98,9 @@ def smooth(df: pd.DataFrame, x_var: str, y_var: str) -> pd.DataFrame:
         return df
 
     def derivatives(df: pd.DataFrame, x_var: str) -> pd.DataFrame:
-        """Calculate the first and second derivatives of the smoothed
+        """Calculate first and second derivatives.
+
+        Calculate the first and second derivatives of the smoothed
         `x_var` variable. The input dataframe must contain the
         `x_var` and `smooth` columns. The returned dataframe
         contains two new columns `der1` and `der2` with the
@@ -103,7 +111,9 @@ def derivatives(df: pd.DataFrame, x_var: str) -> pd.DataFrame:
         return df
 
     def find_der1_vs_der2_outliers(df: pd.DataFrame) -> pd.Series:
-        """Find the distance from origo to each component in
+        """Find the distance from origo to each component.
+
+        Find the distance from origo to each component in
         the space df[der1] ~ df[der2], then try to find
         outliers in the upper part of component ranks, by
         looking at the standard deviation of the distances.
@@ -122,10 +132,10 @@ def find_der1_vs_der2_outliers(df: pd.DataFrame) -> pd.Series:
         return df["rank"] == rank - 1
 
     def minimum_der2(df: pd.DataFrame) -> pd.Series:
-        """Find argmin element. The function returns
-        a boolean Series where the global minimum
-        of `der2` evaluates to True. The input dataframe
-        must contain the `der2` column.
+        """Find argmin element.
+
+        The function returns a boolean Series where the global minimum of `der2` evaluates
+        to True. The input dataframe must contain the `der2` column.
         """
         return df["der2"] == np.nanmin(df["der2"])