Merge pull request #8 from derollins/ready_for_v004

Tidy up ready for patch release v0.0.4

Author: derollins

.markdownlint.yaml

@@ -6,3 +6,8 @@ MD013:
 MD033:
   allowed_elements:
     - div
+
+# Exclude files
+ignore:
+  - LICENSE
+  - LICENSE.md

.pre-commit-config.yaml

@@ -38,7 +38,7 @@ repos:
     hooks:
       - id: black
         language_version: python3
-        exclude: (^docs/|^notebooks/|^LICENSE$)
+        exclude: (^docs/|^notebooks/|^LICENSE$|build/)
 
   # --- Mypy (type checking) ---
   - repo: https://github.com/pre-commit/mirrors-mypy

pyproject.toml

@@ -7,7 +7,7 @@ name = "pnanolocz_lib"
 description = "A Python library for AFM, HS-AFM, and Localization AFM data analysis. Based on NanoLocz"
 authors = [
     { name = "George Heath", email = "G.R.Heath@leeds.ac.uk" },
-    { name = "D. E. Rollins", email = "d.e.rollins@leeds.ac.uk" }
+    { name = "Daniel E. Rollins", email = "d.e.rollins@leeds.ac.uk" }
 ]
 dynamic = ["version"]
 readme = "README.md"

src/pnanolocz_lib/level.py

@@ -82,7 +82,6 @@
 from typing import Any, Literal, Optional
 
 import numpy as np
-from numpy.polynomial.polyutils import RankWarning  # type: ignore[attr-defined]
 from scipy.optimize import curve_fit
 
 # Constants
@@ -193,7 +192,8 @@ def level_plane(
     # ========== X DIRECTION ==========
     # Column-wise masked mean with NaN-outside semantics
     with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=RuntimeWarning)
+        warnings.simplefilter("ignore", RuntimeWarning)
+        warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
         column_means = np.nanmean(np.where(m, arr, np.nan), axis=0)
 
     valid_columns = ~np.isnan(column_means)
@@ -213,7 +213,8 @@ def level_plane(
     standardized_columns = (column_indices - col_centroid) / col_scale
 
     with warnings.catch_warnings():
-        warnings.simplefilter("ignore", RankWarning)
+        warnings.simplefilter("ignore", RuntimeWarning)
+        warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
         x_coeffs = np.polyfit(standardized_columns, column_means[valid_columns], polyx)
 
     # Evaluate polynomial at every column (1..W) using the same mu
@@ -226,7 +227,8 @@ def level_plane(
     # ========== Y DIRECTION ==========
     # Row-wise masked mean after X subtraction (NaN-outside semantics)
     with warnings.catch_warnings():
-        warnings.simplefilter("ignore", category=RuntimeWarning)
+        warnings.simplefilter("ignore", RuntimeWarning)
+        warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
         row_means = np.nanmean(np.where(m, leveled, np.nan), axis=1)
 
     valid_rows = ~np.isnan(row_means)
@@ -245,7 +247,8 @@ def level_plane(
     standardized_rows = (row_indices - row_centroid) / row_scale
 
     with warnings.catch_warnings():
-        warnings.simplefilter("ignore", RankWarning)
+        warnings.simplefilter("ignore", RuntimeWarning)
+        warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
         y_coeffs = np.polyfit(standardized_rows, row_means[valid_rows], polyy)
 
     # Evaluate polynomial at every row (1..H) with the same mu
@@ -333,7 +336,8 @@ def level_line(
                 xs = (x_idx - mu) / sd  # xs: standardized x indices used for fitting
 
                 with warnings.catch_warnings():
-                    warnings.simplefilter("ignore", RankWarning)
+                    warnings.simplefilter("ignore", RuntimeWarning)
+                    warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
                     p_coeff = np.polyfit(xs, y_vals, polyx)
 
                 all_cols = (np.arange(img_width) + 1).astype(np.float64)
@@ -378,7 +382,8 @@ def level_line(
             ys = (yl - mu) / sd  # ys: standardized y indices used for fitting
 
             with warnings.catch_warnings():
-                warnings.simplefilter("ignore", RankWarning)
+                warnings.simplefilter("ignore", RuntimeWarning)
+                warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
                 p_coeff = np.polyfit(ys, y_vals, polyy)
 
             all_rows = (np.arange(img_height) + 1).astype(np.float64)

src/pnanolocz_lib/level_weighted.py

@@ -70,7 +70,6 @@
 from typing import Any, List, Optional, Tuple
 
 import numpy as np
-from numpy.polynomial.polyutils import RankWarning  # type: ignore[attr-defined]
 from scipy import ndimage
 
 # ---------------------
@@ -151,7 +150,8 @@ def _polyfit_centered(
     std_x = (x - centroid) / scale
 
     with warnings.catch_warnings():
-        warnings.simplefilter("ignore", RankWarning)
+        warnings.simplefilter("ignore", RuntimeWarning)
+        warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
         coeffs = np.polyfit(std_x, y, order)
 
     return np.asarray(coeffs, dtype=np.float64), (centroid, scale)
@@ -314,7 +314,8 @@ def level_weighted_plane(
 
         # X-direction: mean of each column within region
         with warnings.catch_warnings():
-            warnings.simplefilter("ignore", category=RuntimeWarning)
+            warnings.simplefilter("ignore", RuntimeWarning)
+            warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
             mean_by_col = np.nanmean(
                 region_masked, axis=0
             )  # Nanolocz-mean_by_col is xp
@@ -336,7 +337,8 @@ def level_weighted_plane(
 
         # Y-direction: mean of each row within region
         with warnings.catch_warnings():
-            warnings.simplefilter("ignore", category=RuntimeWarning)
+            warnings.simplefilter("ignore", RuntimeWarning)
+            warnings.filterwarnings("ignore", message=".*[Rr]ank.*")
             mean_by_row = np.nanmean(region_masked, axis=1)
         valid_rows = ~np.isnan(mean_by_row)
         row_values = mean_by_row[valid_rows]

src/pnanolocz_lib/thresholder.py

@@ -73,7 +73,7 @@
 Authors
 -------
 George Heath, University of Leeds (MATLAB reference implementation)
-D. E. Rollins, University of Leeds (Python implementation)
+Daniel E. Rollins, University of Leeds (Python implementation)
 
 Part of the pNanoLocz-Lib Python library for AFM analysis.
 """