Improve typing consistency in codebase

topostats/classes.py

@@ -201,7 +201,7 @@ def __str__(self) -> str:
         )
 
     @property
-    def image(self) -> npt.NDArray[float]:
+    def image(self) -> npt.NDArray[np.floating]:
         """
         Getter for the ``image`` attribute.
 
@@ -213,7 +213,7 @@ def image(self) -> npt.NDArray[float]:
         return self._image
 
     @image.setter
-    def image(self, value: npt.NDArray[float]):
+    def image(self, value: npt.NDArray[np.floating]):
         """
         Setter for the ``image`` attribute.
 
@@ -892,21 +892,21 @@ class MatchedBranch:
 
     Attributes
     ----------
-    ordered_coords : npt.NDArray[np.int32]
+    ordered_coords : npt.NDArray[np.int32], optional
         Numpy array of ordered coordinates.
-    heights : npt.NDArray[np.number]
+    heights : npt.NDArray[np.number], optional
         Numpy array of heights.
-    distances : npt.NDArray[np.number]
+    distances : npt.NDArray[np.number], optional
         Numpy array of distances.
-    fwhm : float
+    fwhm : float, optional
         Full-width half maximum.
-    fwhm_half_maxs : list[float]
+    fwhm_half_maxs : list[float], optional
         Half-maximums from a matched branch.
-    fwhm_peaks : list[int | float]
+    fwhm_peaks : list[float], optional
         Peaks from a matched branch.
-    angles : float
+    angles : float | list[float], optional
         Angle between branches.
-    branch_statistics : dict[str, float | int | list[Any] | str]
+    branch_statistics : dict[str, float | int | list[Any] | str], optional
         Dictionary of branch statistics, ``fwhm``, ``fwhm_half_maxs`` and ``fwhm_peaks``.
     """
 
@@ -971,11 +971,11 @@ class UnMatchedBranch:
 
     Attributes
     ----------
-    angles : float
+    angles : float | list[float]
         Angle between branches.
     """
 
-    angles: float | list[float] | None = None
+    angles: float | list[float]
 
     def __str__(self) -> str:
         """
@@ -1061,23 +1061,23 @@ class OrderedTrace:
     """
     Class for Ordered Trace data and attributes.
 
-    molecule_data : dict[int, Molecule]
+    molecule_data : dict[int, Molecule], optional
         Dictionary of ``Molecule`` objects indexed by molecule number.
-    tracing_stats : dict | None
+    tracing_stats : dict, optional
         Tracing statistics.
-    grain_molstats : Any | None
+    grain_molstats : Any, optional
         Grain molecule statistics.
-    molecules : int
+    molecules : int, optional
         Number of molecules within the grain.
-    writhe : str
+    writhe : str, optional
         The writhe sign, can be either `+`, `-` or `0` for positive, negative or no writhe.
-    pixel_to_nm_scaling: np.float64 | None
+    pixel_to_nm_scaling: np.float64, optional
         Pixel to nm scaling.
-    images: dict[str, npt.NDArray] | None
+    images: dict[str, npt.NDArray], optional
         Diagnostic images produced during processing.
-    error: bool | None
+    error: bool, optional
         Errors encountered?
-    molecule_statistics : dict[int, dict[str, bool | str | float | None]] | None
+    molecule_statistics : dict[int, dict[str, bool | str | float | None]], optional
         Dictionary of molecule statistics, with one entry for each molecule.
     """
 
@@ -1104,7 +1104,7 @@ def __str__(self) -> str:
         writhe = {"+": "positive", "-": "negative", "0": "no writhe"}.get(self.writhe)
         return (
             f"number of molecules : {self.molecules}\n"
-            f"number of images : {len(self.images)}\n"
+            f"number of images : {len(self.images) if self.images is not None else 'None'}\n"
             f"writhe : {writhe}\n"
             f"pixel to nm scaling : {self.pixel_to_nm_scaling}\n"
             f"error : {self.error}"
@@ -1128,17 +1128,20 @@ def require_molecule_data(self) -> dict[int, Molecule]:
             raise RuntimeError("molecule_data is None")
         return self.molecule_data
 
-    def collate_molecule_statistics(self) -> dict[int, dict[str, bool | int | str | None]]:
+    def collate_molecule_statistics(self) -> dict[int, dict[str, bool | int | str | float | None]]:
         """
         Collate molecule statistics for all molecules to dictionary.
 
         The resulting dictionary can be easily converted to Pandas Dataframes for writing to CSV.
 
         Returns
         -------
-        dict[int, dict[str, bool | int | str | None]]
+        dict[int, dict[str, bool | int | str float | None]]
             Dictionary, indexed by molecule where the value is the molecules statistics for the given molecule.
         """
+        if self.molecule_data is None:
+            raise ValueError("No molecule data found")
+
         self.molecule_statistics = {
             molecule_number: molecule.collate_molecule_statistics()
             for molecule_number, molecule in self.molecule_data.items()

topostats/filters.py

@@ -66,9 +66,9 @@ def __init__(
         otsu_threshold_multiplier: float = 1.7,
         threshold_std_dev: dict | None = None,
         threshold_absolute: dict | None = None,
-        gaussian_size: float = None,
+        gaussian_size: float | None = None,
         gaussian_mode: str = "nearest",
-        remove_scars: dict = None,
+        remove_scars: dict | None = None,
     ):
         """
         Initialise the class.
@@ -153,7 +153,7 @@ def __init__(
         }
 
     def median_flatten(
-        self, image: npt.NDArray, mask: npt.NDArray = None, row_alignment_quantile: float = 0.5
+        self, image: npt.NDArray, mask: npt.NDArray | None = None, row_alignment_quantile: float = 0.5
     ) -> npt.NDArray:
         """
         Flatten images using median differences.
@@ -167,7 +167,7 @@ def median_flatten(
         ----------
         image : npt.NDArray
             2-D image of the data to align the rows of.
-        mask : npt.NDArray
+        mask : npt.NDArray, optional
             Boolean array of points to mask (ignore).
         row_alignment_quantile : float
             Quantile (in the range 0.0 to 1.0) used for defining the average background.
@@ -196,7 +196,7 @@ def median_flatten(
 
         return image
 
-    def remove_tilt(self, image: npt.NDArray, mask: npt.NDArray = None) -> npt.NDArray:
+    def remove_tilt(self, image: npt.NDArray, mask: npt.NDArray | None = None) -> npt.NDArray:
         """
         Remove the planar tilt from an image (linear in 2D spaces).
 
@@ -207,7 +207,7 @@ def remove_tilt(self, image: npt.NDArray, mask: npt.NDArray = None) -> npt.NDArr
         ----------
         image : npt.NDArray
             2-D image of the data to remove the planar tilt from.
-        mask : npt.NDArray
+        mask : npt.NDArray, optional
             Boolean array of points to mask (ignore).
 
         Returns
@@ -361,7 +361,7 @@ def model_func(x: float, y: float, a: float, b: float, c: float, d: float) -> fl
 
         return image
 
-    def remove_quadratic(self, image: npt.NDArray, mask: npt.NDArray = None) -> npt.NDArray:
+    def remove_quadratic(self, image: npt.NDArray, mask: npt.NDArray | None = None) -> npt.NDArray:
         """
         Remove the quadratic bowing that can be seen in some large-scale AFM images.
 
@@ -372,7 +372,7 @@ def remove_quadratic(self, image: npt.NDArray, mask: npt.NDArray = None) -> npt.
         ----------
         image : npt.NDArray
             2-D image of the data to remove the quadratic from.
-        mask : npt.NDArray
+        mask : npt.NDArray, optional
             Boolean array of points to mask (ignore).
 
         Returns
@@ -445,15 +445,15 @@ def calc_gradient(self, array: npt.NDArray, shape: int) -> npt.NDArray:
         """
         return self.calc_diff(array) / shape
 
-    def average_background(self, image: npt.NDArray, mask: npt.NDArray = None) -> npt.NDArray:
+    def average_background(self, image: npt.NDArray, mask: npt.NDArray | None = None) -> npt.NDArray:
         """
         Zero the background by subtracting the non-masked mean from all pixels.
 
         Parameters
         ----------
         image : npt.NDArray
             Numpy array representing the image.
-        mask : npt.NDArray
+        mask : npt.NDArray, optional
             Mask of the array, should have the same dimensions as image.
 
         Returns
@@ -515,17 +515,22 @@ def filter_image(self) -> None:  # numpydoc: ignore=GL07
         ...             threshold_method='otsu')
         filter.filter_image()
         """
-        self.images["initial_median_flatten"] = self.median_flatten(
+        self.images["initial_median_flatten"]: npt.NDArray = self.median_flatten(
             self.images["pixels"], mask=None, row_alignment_quantile=self.row_alignment_quantile
         )
-        self.images["initial_tilt_removal"] = self.remove_tilt(self.images["initial_median_flatten"], mask=None)
-        self.images["initial_quadratic_removal"] = self.remove_quadratic(self.images["initial_tilt_removal"], mask=None)
-        self.images["initial_nonlinear_polynomial_removal"] = self.remove_nonlinear_polynomial(
+        self.images["initial_tilt_removal"]: npt.NDArray = self.remove_tilt(
+            self.images["initial_median_flatten"], mask=None
+        )
+        self.images["initial_quadratic_removal"]: npt.NDArray = self.remove_quadratic(
+            self.images["initial_tilt_removal"], mask=None
+        )
+        self.images["initial_nonlinear_polynomial_removal"]: npt.NDArray = self.remove_nonlinear_polynomial(
             self.images["initial_quadratic_removal"], mask=None
         )
 
         # Remove scars
         run_scar_removal = self.remove_scars_config.pop("run")
+        self.images["initial_scar_removal"]: npt.NDArray
         if run_scar_removal:
             LOGGER.debug(f"[{self.filename}] : Initial scar removal")
             self.images["initial_scar_removal"], _ = scars.remove_scars(
@@ -538,7 +543,7 @@ def filter_image(self) -> None:  # numpydoc: ignore=GL07
             self.images["initial_scar_removal"] = self.images["initial_nonlinear_polynomial_removal"]
 
         # Zero the data before thresholding, helps with absolute thresholding
-        self.images["initial_zero_average_background"] = self.average_background(
+        self.images["initial_zero_average_background"]: npt.NDArray = self.average_background(
             self.images["initial_scar_removal"], mask=None
         )
 
@@ -558,19 +563,22 @@ def filter_image(self) -> None:  # numpydoc: ignore=GL07
             thresholds=self.thresholds,
             img_name=self.filename,
         )
-        self.images["masked_median_flatten"] = self.median_flatten(
+        self.images["masked_median_flatten"]: npt.NDArray = self.median_flatten(
             self.images["initial_tilt_removal"],
             self.images["mask"],
             row_alignment_quantile=self.row_alignment_quantile,
         )
-        self.images["masked_tilt_removal"] = self.remove_tilt(self.images["masked_median_flatten"], self.images["mask"])
-        self.images["masked_quadratic_removal"] = self.remove_quadratic(
+        self.images["masked_tilt_removal"]: npt.NDArray = self.remove_tilt(
+            self.images["masked_median_flatten"], self.images["mask"]
+        )
+        self.images["masked_quadratic_removal"]: npt.NDArray = self.remove_quadratic(
             self.images["masked_tilt_removal"], self.images["mask"]
         )
-        self.images["masked_nonlinear_polynomial_removal"] = self.remove_nonlinear_polynomial(
+        self.images["masked_nonlinear_polynomial_removal"]: npt.NDArray = self.remove_nonlinear_polynomial(
             self.images["masked_quadratic_removal"], self.images["mask"]
         )
         # Remove scars
+        self.images["secondary_scar_removal"]: npt.NDArray
         if run_scar_removal:
             LOGGER.debug(f"[{self.filename}] : Secondary scar removal")
             self.images["secondary_scar_removal"], scar_mask = scars.remove_scars(
@@ -582,7 +590,7 @@ def filter_image(self) -> None:  # numpydoc: ignore=GL07
         else:
             LOGGER.debug(f"[{self.filename}] : Skipping scar removal as requested from config")
             self.images["secondary_scar_removal"] = self.images["masked_nonlinear_polynomial_removal"]
-        self.images["final_zero_average_background"] = self.average_background(
+        self.images["final_zero_average_background"]: npt.NDArray = self.average_background(
             self.images["secondary_scar_removal"], self.images["mask"]
         )
         self.images["gaussian_filtered"] = self.gaussian_filter(self.images["final_zero_average_background"])

topostats/grains.py

@@ -1252,7 +1252,7 @@ def keep_largest_labelled_region_classes(
     def calculate_region_connection_regions(
         grain_mask_tensor: npt.NDArray,
         classes: tuple[int, int],
-    ) -> tuple[int, npt.NDArray, dict[int, npt.NDArray[int]]]:
+    ) -> tuple[int, npt.NDArray, dict[int, npt.NDArray[np.integer]]]:
         """
         Get a list of connection regions between two classes.
 

topostats/measure/feret.py

@@ -14,6 +14,7 @@
 from collections.abc import Generator
 from math import sqrt
 from pathlib import Path
+from typing import Any
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -233,7 +234,7 @@ def triangle_height(base1: npt.NDArray | list, base2: npt.NDArray | list, apex:
     base1_base2 = np.asarray(base1) - np.asarray(base2)
     base1_apex = np.asarray(base1) - np.asarray(apex)
     # Ensure arrays are 3-D see note in https://numpy.org/doc/2.0/reference/generated/numpy.cross.html
-    return np.linalg.norm(cross2d(base1_base2, base1_apex)) / np.linalg.norm(base1_base2)
+    return np.linalg.norm(cross2d(base1_base2, base1_apex)) / np.linalg.norm(base1_base2).astype(float)
 
 
 def cross2d(x: npt.NDArray, y: npt.NDArray) -> npt.NDArray:
@@ -349,9 +350,7 @@ def sort_clockwise(coordinates: npt.NDArray) -> npt.NDArray:
     return coordinates[order]
 
 
-def min_max_feret(
-    points: npt.NDArray, axis: int = 0, precision: int = 13
-) -> dict[float, tuple[int, int], float, tuple[int, int]]:
+def min_max_feret(points: npt.NDArray, axis: int = 0, precision: int = 13) -> dict[str, Any]:
     """
     Given a list of 2-D points, returns the minimum and maximum feret diameters.
 
@@ -368,8 +367,8 @@ def min_max_feret(
 
     Returns
     -------
-    dictionary
-        Tuple of the minimum feret distance and its coordinates and the maximum feret distance and  its coordinates.
+    dict
+        Dictionary containing the minimum and maximum feret diameters of the grain and their corresponding coordinates of contact with the calipers.
     """
     caliper_min_feret = list(rotating_calipers(points, axis))
     min_ferets, calipers, min_feret_coords = zip(*caliper_min_feret)
@@ -394,7 +393,7 @@ def min_max_feret(
     }
 
 
-def get_feret_from_mask(mask_im: npt.NDArray, axis: int = 0) -> tuple[float, tuple[int, int], float, tuple[int, int]]:
+def get_feret_from_mask(mask_im: npt.NDArray, axis: int = 0) -> dict[str, Any]:
     """
     Calculate the minimum and maximum feret diameter of the foreground object of a binary mask.
 
@@ -409,8 +408,8 @@ def get_feret_from_mask(mask_im: npt.NDArray, axis: int = 0) -> tuple[float, tup
 
     Returns
     -------
-    Tuple[float, Tuple[int, int], float, Tuple[int, int]]
-        Returns a tuple of the minimum feret and its coordinates and the maximum feret and its coordinates.
+    dict
+        Dictionary containing the minimum and maximum feret diameters of the grain and their corresponding coordinates of contact with the calipers.
     """
     eroded = skimage.morphology.erosion(mask_im)
     outline = mask_im ^ eroded