chore: [pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -4,7 +4,7 @@ default_language_version:
   python: python3.10
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v5.0.0
+    rev: v6.0.0
     hooks:
       - id: check-added-large-files
       - id: check-toml
@@ -14,7 +14,7 @@ repos:
       - id: end-of-file-fixer
       - id: trailing-whitespace
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.13
+    rev: v0.16.5
     hooks:
       - id: ruff
         args:
@@ -23,17 +23,17 @@ repos:
         files: ^(siapy|tests)/
       - id: ruff-format
   - repo: https://github.com/gitleaks/gitleaks
-    rev: v8.27.2
+    rev: v8.30.0
     hooks:
       - id: gitleaks
   - repo: https://github.com/codespell-project/codespell
-    rev: v2.4.1
+    rev: v2.4.3
     hooks:
       - id: codespell
         additional_dependencies:
           - tomli
   - repo: https://github.com/compilerla/conventional-pre-commit
-    rev: v4.2.0
+    rev: v4.4.0
     hooks:
       - id: conventional-pre-commit
         stages: [commit-msg]

docs/concepts/datasets.md

@@ -6,5 +6,5 @@
 The datasets module provides structured containers and utilities for transforming spectral image data into formats optimized for analysis and machine learning. It bridges the gap between raw spectral data and analytical workflows.
 
 ```python
---8<-- "docs/concepts/src/datasets_01.py"
+--8 < --"docs/concepts/src/datasets_01.py"
 ```

docs/concepts/entities.md

@@ -55,7 +55,7 @@ Since spectral images often contain distinct objects with different spectral pro
 The `Pixels` class represents spatial coordinates within spectral image, providing a container for *(x, y)* coordinate pairs. It uses pandas DataFrame internally for storage, enabling high-performance operations. The class provides multiple initialization methods and conversion functions to work with different data representations (i.e. DataFrames, list, arrays)
 
 ```python
---8<-- "docs/concepts/src/pixels_01.py"
+--8 < --"docs/concepts/src/pixels_01.py"
 ```
 
 ## Signals
@@ -66,7 +66,7 @@ The `Pixels` class represents spatial coordinates within spectral image, providi
 The `Signals` class stores spectral data for each pixel in a pandas DataFrame, allowing you to use any column names you choose (e.g. "band_1", "nir", "red_edge"). You can initialize it from a DataFrame, lists, dicts or NumPy arrays.
 
 ```python
---8<-- "docs/concepts/src/signals_01.py"
+--8 < --"docs/concepts/src/signals_01.py"
 ```
 
 However, direct initialization of `Signals` is typically not necessary in practice. When you create a `Signatures` instance, the underlying `Signals` object is automatically generated and managed for you. This section demonstrates the `Signals` class primarily to illustrate how the `Signatures` class (discussed next) is composed internally and to provide insight into the data structure that powers spectral analysis.
@@ -81,19 +81,19 @@ The `Signatures` class represents spectral data collections by combining spatial
 `Signatures` can be initialized in multiple ways. The explicit approach creates each component separately before combining them, providing clarity about the composition:
 
 ```python
---8<-- "docs/concepts/src/signatures_01.py:long"
+--8 < --"docs/concepts/src/signatures_01.py:long"
 ```
 
 For more concise code, you can initialize a `Signatures` object directly from coordinate and signal values:
 
 ```python
---8<-- "docs/concepts/src/signatures_01.py:short"
+--8 < --"docs/concepts/src/signatures_01.py:short"
 ```
 
 Both approaches yield equivalent results when initialized with the same data. You can access and work with the data using various DataFrame operations and conversion methods:
 
 ```python
---8<-- "docs/concepts/src/signatures_01.py:assert"
+--8 < --"docs/concepts/src/signatures_01.py:assert"
 ```
 
 ## Shape
@@ -104,7 +104,7 @@ Both approaches yield equivalent results when initialized with the same data. Yo
 The `Shape` class represents geometric shapes that can be associated with images, such as points, lines, and polygons.
 
 ```python
---8<-- "docs/concepts/src/shapes_01.py"
+--8 < --"docs/concepts/src/shapes_01.py"
 ```
 
 ## Spectral Image
@@ -121,31 +121,31 @@ A `SpectralImage` is the primary container for spectral image data. It's a gener
 This is commonly used for hyperspectral imagery from airborne or satellite sensors.
 
 ```python
---8<-- "docs/concepts/src/spectral_image_01.py"
+--8 < --"docs/concepts/src/spectral_image_01.py"
 ```
 
 #### 2. Load from GeoTIFF or other geospatial formats (using rasterio)
 
 Perfect for georeferenced data with spatial information.
 
 ```python
---8<-- "docs/concepts/src/spectral_image_02.py"
+--8 < --"docs/concepts/src/spectral_image_02.py"
 ```
 
 #### 3. Create from numpy array
 
 Useful for testing or when you already have image data in memory.
 
 ```python
---8<-- "docs/concepts/src/spectral_image_03.py"
+--8 < --"docs/concepts/src/spectral_image_03.py"
 ```
 
 #### 4. Create your own custom image class
 
 For specialized file formats or custom processing needs, you can extend the ImageBase class.
 
 ```python
---8<-- "docs/concepts/src/spectral_image_04.py"
+--8 < --"docs/concepts/src/spectral_image_04.py"
 ```
 
 ### Data conversion methods
@@ -156,7 +156,7 @@ The example below demonstrates two key conversion methods of `SpectralImage` ins
 2. `to_subarray()`: Converts selected pixel data to a NumPy array for numerical processing or integration with other scientific libraries
 
 ```python
---8<-- "docs/concepts/src/spectral_image_05.py"
+--8 < --"docs/concepts/src/spectral_image_05.py"
 ```
 
 ### Manipulation of Shapes
@@ -166,13 +166,13 @@ Each `SpectralImage` instance automatically initializes a `GeometricShapes` obje
 The `GeometricShapes` class provides a list-like interface that wraps a standard Python list, enhancing it with specialized functionality for manipulating geometric shapes while preserving standard list behavior. The list of shapes can be accessed via the `image.geometric_shapes.shapes` property.
 
 ```python
---8<-- "docs/concepts/src/spectral_image_shapes_01.py:init"
+--8 < --"docs/concepts/src/spectral_image_shapes_01.py:init"
 ```
 
 As a result, shapes can be added to the spectral image using standard list operations. The example below demonstrates how this can be done:
 
 ```python
---8<-- "docs/concepts/src/spectral_image_shapes_01.py:operations"
+--8 < --"docs/concepts/src/spectral_image_shapes_01.py:operations"
 ```
 
 ## Spectral Image Set
@@ -183,5 +183,5 @@ As a result, shapes can be added to the spectral image using standard list opera
 The `SpectralImageSet` class manages a collection of spectral images.
 
 ```python
---8<-- "docs/concepts/src/spectral_image_set_01.py"
+--8 < --"docs/concepts/src/spectral_image_set_01.py"
 ```

docs/concepts/features.md

@@ -17,23 +17,23 @@ Spectral indices are mathematical combinations of spectral bands that highlight
 The `get_spectral_indices()` function returns all spectral indices that can be computed from the available bands:
 
 ```python
---8<-- "docs/concepts/src/features_01.py"
+--8 < --"docs/concepts/src/features_01.py"
 ```
 
 ### Computing spectral indices
 
 The `compute_spectral_indices()` function calculates spectral indices from DataFrame data:
 
 ```python
---8<-- "docs/concepts/src/features_02.py"
+--8 < --"docs/concepts/src/features_02.py"
 ```
 
 ### Band mapping
 
 When your data uses non-standard column names, use the `bands_map` parameter:
 
 ```python
---8<-- "docs/concepts/src/features_03.py:map"
+--8 < --"docs/concepts/src/features_03.py:map"
 ```
 
 ## Automatic features generation
@@ -49,21 +49,21 @@ When your data uses non-standard column names, use the `bands_map` parameter:
 The AutoFeat classes provide deterministic wrappers around the AutoFeat library, which automatically generates and selects engineered features through symbolic regression.
 
 ```python
---8<-- "docs/concepts/src/features_04.py"
+--8 < --"docs/concepts/src/features_04.py"
 ```
 
 ### Features extracted using spectral indices
 
 These classes integrate spectral index computation with automated feature selection, offering end-to-end pipelines for identifying the most relevant spectral indices.
 
 ```python
---8<-- "docs/concepts/src/features_05.py"
+--8 < --"docs/concepts/src/features_05.py"
 ```
 
 ## Integration with siapy enitites
 
 The features module integrates seamlessly with siapy entity system.
 
 ```python
---8<-- "docs/concepts/src/features_06.py"
+--8 < --"docs/concepts/src/features_06.py"
 ```

docs/concepts/optimizers.md

@@ -14,7 +14,7 @@ The optimizers module provides hyperparameter optimization capabilities for mach
 The `TabularOptimizer` class provides automated hyperparameter optimization for sklearn-compatible models using tabular spectral data.
 
 ```python
---8<-- "docs/concepts/src/optimizers_01.py"
+--8 < --"docs/concepts/src/optimizers_01.py"
 ```
 
 ## Trial Parameters
@@ -28,7 +28,7 @@ The `TabularOptimizer` class provides automated hyperparameter optimization for
 Trial parameters define the hyperparameter search space for optimization. You can specify integer, float, and categorical parameters:
 
 ```python
---8<-- "docs/concepts/src/optimizers_02.py"
+--8 < --"docs/concepts/src/optimizers_02.py"
 ```
 
 ## Scorers
@@ -43,21 +43,21 @@ Scorers define how model performance is evaluated during optimization.
 Use cross-validation for robust model evaluation:
 
 ```python
---8<-- "docs/concepts/src/optimizers_03.py"
+--8 < --"docs/concepts/src/optimizers_03.py"
 ```
 
 ### Hold-out scorer
 
 Use hold-out validation for faster evaluation:
 
 ```python
---8<-- "docs/concepts/src/optimizers_04.py"
+--8 < --"docs/concepts/src/optimizers_04.py"
 ```
 
 ## Integration with siapy entities
 
 The optimizers module integrates seamlessly with the siapy entity system.
 
 ```python
---8<-- "docs/concepts/src/optimizers_05.py"
+--8 < --"docs/concepts/src/optimizers_05.py"
 ```

docs/concepts/transformations.md

@@ -13,23 +13,23 @@ The transformations module provides essential image processing and co-registrati
 ### Basic transformations
 
 ```python
---8<-- "docs/concepts/src/transformations_01.py"
+--8 < --"docs/concepts/src/transformations_01.py"
 ```
 
 ### Data augmentation
 
 Data augmentation transformations are useful for expanding training datasets and testing algorithm robustness.
 
 ```python
---8<-- "docs/concepts/src/transformations_02.py"
+--8 < --"docs/concepts/src/transformations_02.py"
 ```
 
 ### Normalization
 
 The `area_normalization` function normalizes spectral signals by their area under the curve, which is particularly useful for comparing spectral shapes regardless of overall intensity.
 
 ```python
---8<-- "docs/concepts/src/transformations_03.py"
+--8 < --"docs/concepts/src/transformations_03.py"
 ```
 
 ## Co-registration
@@ -44,13 +44,13 @@ Co-registration enables alignment and coordinate transformation between differen
 The typical co-registration workflow involves selecting corresponding points in both images and computing a transformation matrix:
 
 ```python
---8<-- "docs/concepts/src/transformations_04.py"
+--8 < --"docs/concepts/src/transformations_04.py"
 ```
 
 ### Applying transformations
 
 Once you have a transformation matrix, you can transform pixel coordinates between image spaces:
 
 ```python
---8<-- "docs/concepts/src/transformations_05.py"
+--8 < --"docs/concepts/src/transformations_05.py"
 ```

docs/concepts/utils_image.md

@@ -12,27 +12,27 @@ The image utilities module provides functions for saving, loading, and processin
 The SPy backend saves images in ENVI format.
 
 ```python
---8<-- "docs/concepts/src/utils_image_01.py"
+--8 < --"docs/concepts/src/utils_image_01.py"
 ```
 
 ### Rasterio backend
 
 The Rasterio backend provides geospatial capabilities and supports various formats.
 
 ```python
---8<-- "docs/concepts/src/utils_image_02.py"
+--8 < --"docs/concepts/src/utils_image_02.py"
 ```
 
 ## Radiance to Reflectance Conversion
 
 Converting radiance measurements to reflectance using reference panels is essential for quantitative spectral analysis.
 
 ```python
---8<-- "docs/concepts/src/utils_image_03.py"
+--8 < --"docs/concepts/src/utils_image_03.py"
 ```
 
 ## Additional Utility Functions
 
 ```python
---8<-- "docs/concepts/src/utils_image_04.py"
+--8 < --"docs/concepts/src/utils_image_04.py"
 ```

docs/concepts/utils_plotting.md

@@ -12,15 +12,15 @@ The plotting utilities module provides interactive tools for pixel and area sele
 Select individual pixels from an image by clicking on them.
 
 ```python
---8<-- "docs/concepts/src/utils_plotting_01.py"
+--8 < --"docs/concepts/src/utils_plotting_01.py"
 ```
 
 ### Area-based Selection
 
 Select irregular areas from an image using lasso selection tool.
 
 ```python
---8<-- "docs/concepts/src/utils_plotting_02.py"
+--8 < --"docs/concepts/src/utils_plotting_02.py"
 ```
 
 ## Image Visualization
@@ -30,21 +30,21 @@ Select irregular areas from an image using lasso selection tool.
 Visualize spectral images with overlaid selected pixels or areas.
 
 ```python
---8<-- "docs/concepts/src/utils_plotting_03.py"
+--8 < --"docs/concepts/src/utils_plotting_03.py"
 ```
 
 ### Multiple Image Comparison
 
 Display multiple images side by side with their corresponding selected areas.
 
 ```python
---8<-- "docs/concepts/src/utils_plotting_04.py"
+--8 < --"docs/concepts/src/utils_plotting_04.py"
 ```
 
 ## Signal Visualization
 
 Plot mean spectral signatures with standard deviation bands for different classes.
 
 ```python
---8<-- "docs/concepts/src/utils_plotting_05.py"
+--8 < --"docs/concepts/src/utils_plotting_05.py"
 ```