Merge pull request #64 from d-v-b/feat/sentinel2-pydantic-model

feat/sentinel2 pydantic model

Author: d-v-b

.pre-commit-config.yaml

@@ -25,5 +25,5 @@ repos:
         exclude: tests/.*
         additional_dependencies:
         - types-attrs
+        - typing-extensions>=4.15.0
         - pydantic>=2.12
-        - typing-extensions>=4.15

docs/api-reference.md

@@ -9,6 +9,7 @@ Complete reference for the EOPF GeoZarr library's Python API.
 The main function for converting EOPF datasets to GeoZarr format.
 
 ```python
+# test: skip
 def create_geozarr_dataset(
     dt_input: xr.DataTree,
     groups: List[str],
@@ -39,6 +40,7 @@ def create_geozarr_dataset(
 **Example:**
 
 ```python
+# test: skip
 import xarray as xr
 from eopf_geozarr import create_geozarr_dataset
 
@@ -58,6 +60,7 @@ dt_geozarr = create_geozarr_dataset(
 Sets up GeoZarr-compliant metadata for a DataTree.
 
 ```python
+# test: skip
 def setup_datatree_metadata_geozarr_spec_compliant(
     dt: xr.DataTree,
     geozarr_groups: Dict[str, xr.Dataset]
@@ -69,6 +72,7 @@ def setup_datatree_metadata_geozarr_spec_compliant(
 Writes a single group to GeoZarr format with proper metadata.
 
 ```python
+# test: skip
 def write_geozarr_group(
     group_path: str,
     datasets: Dict[str, xr.Dataset],
@@ -84,6 +88,7 @@ def write_geozarr_group(
 Creates multiscales metadata compliant with GeoZarr specification.
 
 ```python
+# test: skip
 def create_geozarr_compliant_multiscales(
     datasets: Dict[str, xr.Dataset],
     tile_width: int = 256
@@ -97,6 +102,7 @@ def create_geozarr_compliant_multiscales(
 Calculates optimal chunk size that aligns with data dimensions.
 
 ```python
+# test: skip
 def calculate_aligned_chunk_size(
     dimension_size: int,
     target_chunk_size: int
@@ -127,6 +133,7 @@ print(chunk_size)  # Returns 3660 (10980 / 3 = 3660)
 Downsamples a 2D array by factor of 2 using mean aggregation.
 
 ```python
+# test: skip
 def downsample_2d_array(
     data: np.ndarray,
     factor: int = 2
@@ -138,6 +145,7 @@ def downsample_2d_array(
 Validates existing band data against expected specifications.
 
 ```python
+# test: skip
 def validate_existing_band_data(
     dataset: xr.Dataset,
     band_name: str,
@@ -151,6 +159,7 @@ def validate_existing_band_data(
 ### Storage Path Utilities
 
 ```python
+# test: skip
 # Path normalization and validation
 def normalize_path(path: str) -> str
 def is_s3_path(path: str) -> bool
@@ -164,6 +173,7 @@ def get_s3_storage_options(s3_path: str, **s3_kwargs: Any) -> Dict[str, Any]
 ### S3 Operations
 
 ```python
+# test: skip
 # S3 store creation and validation
 def validate_s3_access(s3_path: str, **s3_kwargs: Any) -> tuple[bool, Optional[str]]
 def s3_path_exists(s3_path: str, **s3_kwargs: Any) -> bool
@@ -181,6 +191,7 @@ def read_s3_json_metadata(s3_path: str, **s3_kwargs: Any) -> Dict[str, Any]
 ### Zarr Operations
 
 ```python
+# test: skip
 # Zarr group operations
 def open_zarr_group(path: str, mode: str = "r", **kwargs: Any) -> zarr.Group
 def open_s3_zarr_group(s3_path: str, mode: str = "r", **s3_kwargs: Any) -> zarr.Group
@@ -195,6 +206,7 @@ async def async_consolidate_metadata(output_path: str, **storage_kwargs) -> None
 ### Coordinate Metadata
 
 ```python
+# test: skip
 def _add_coordinate_metadata(ds: xr.Dataset) -> None
 ```
 
@@ -207,13 +219,15 @@ Adds proper coordinate metadata including:
 ### Grid Mapping
 
 ```python
+# test: skip
 def _setup_grid_mapping(ds: xr.Dataset, grid_mapping_var_name: str) -> None
 def _add_geotransform(ds: xr.Dataset, grid_mapping_var: str) -> None
 ```
 
 ### CRS and Tile Matrix
 
 ```python
+# test: skip
 def create_native_crs_tile_matrix_set(
     crs: Any,
     transform: Any,
@@ -230,6 +244,7 @@ Creates a tile matrix set for native CRS (non-Web Mercator).
 ### calculate_overview_levels
 
 ```python
+# test: skip
 def calculate_overview_levels(
     width: int,
     height: int,
@@ -242,6 +257,7 @@ Calculates appropriate overview levels based on data dimensions.
 ### create_overview_dataset_all_vars
 
 ```python
+# test: skip
 def create_overview_dataset_all_vars(
     ds: xr.Dataset,
     overview_factor: int
@@ -255,6 +271,7 @@ Creates overview dataset with all variables downsampled.
 ### Retry Logic
 
 ```python
+# test: skip
 def write_dataset_band_by_band_with_validation(
     ds: xr.Dataset,
     output_path: str,
@@ -270,6 +287,7 @@ Writes dataset with robust error handling and retry logic.
 ### Coordinate Attributes
 
 ```python
+# test: skip
 def _get_x_coord_attrs() -> Dict[str, Any]
 def _get_y_coord_attrs() -> Dict[str, Any]
 ```
@@ -279,6 +297,7 @@ Returns standard attributes for X and Y coordinates.
 ### Grid Mapping Detection
 
 ```python
+# test: skip
 def is_grid_mapping_variable(ds: xr.Dataset, var_name: str) -> bool
 ```
 
@@ -289,6 +308,7 @@ Determines if a variable is a grid mapping variable.
 ### Basic Conversion
 
 ```python
+# test: skip
 import xarray as xr
 from eopf_geozarr import create_geozarr_dataset
 
@@ -304,6 +324,7 @@ dt_geozarr = create_geozarr_dataset(
 ### Advanced S3 Usage
 
 ```python
+# test: skip
 from eopf_geozarr.conversion.fs_utils import (
     validate_s3_access,
     get_s3_storage_options
@@ -316,7 +337,7 @@ is_valid, error = validate_s3_access(s3_path)
 if is_valid:
     # Get storage options
     storage_opts = get_s3_storage_options(s3_path)
-    
+
     # Convert with S3
     dt_geozarr = create_geozarr_dataset(
         dt_input=dt,
@@ -329,6 +350,7 @@ if is_valid:
 ### Custom Chunking
 
 ```python
+# test: skip
 from eopf_geozarr.conversion.utils import calculate_aligned_chunk_size
 
 # Calculate optimal chunks for your data
@@ -348,6 +370,7 @@ dt_geozarr = create_geozarr_dataset(
 The library uses comprehensive type hints. Import types as needed:
 
 ```python
+# test: skip
 from typing import Dict, List, Optional, Tuple, Any
 import xarray as xr
 import numpy as np

docs/architecture.md

@@ -59,6 +59,7 @@ graph TB
 The main conversion engine orchestrates the transformation process:
 
 ```python
+# test: skip
 def create_geozarr_dataset(
     dt_input: xr.DataTree,
     groups: List[str],
@@ -102,17 +103,19 @@ Core processing algorithms:
 **Chunking:**
 
 ```python
+# test: skip
 def calculate_aligned_chunk_size(
-    dimension_size: int, 
+    dimension_size: int,
     target_chunk_size: int
 ) -> int
 ```
 
 **Downsampling:**
 
 ```python
+# test: skip
 def downsample_2d_array(
-    data: np.ndarray, 
+    data: np.ndarray,
     factor: int = 2
 ) -> np.ndarray
 ```
@@ -218,6 +221,7 @@ y_attrs = {
 Each dataset includes proper grid mapping information:
 
 ```python
+# test: skip
 grid_mapping_attrs = {
     'grid_mapping_name': 'transverse_mercator',  # or appropriate mapping
     'projected_crs_name': crs.to_string(),
@@ -232,6 +236,7 @@ grid_mapping_attrs = {
 GeoZarr-compliant multiscales structure:
 
 ```python
+# test: skip
 multiscales = [{
     'version': '0.4',
     'name': group_name,
@@ -289,6 +294,7 @@ def calculate_aligned_chunk_size(dimension_size: int, target_chunk_size: int) ->
 **Band-by-Band Processing:**
 
 ```python
+# test: skip
 def write_dataset_band_by_band_with_validation(
     ds: xr.Dataset,
     output_path: str,
@@ -317,6 +323,7 @@ def write_dataset_band_by_band_with_validation(
 The library provides a unified interface for different storage backends:
 
 ```python
+# test: skip
 def get_storage_options(path: str, **kwargs) -> Optional[Dict[str, Any]]:
     """Get storage options based on path type."""
     if is_s3_path(path):
@@ -336,6 +343,7 @@ def get_storage_options(path: str, **kwargs) -> Optional[Dict[str, Any]]:
 **Configuration:**
 
 ```python
+# test: skip
 s3_options = {
     'key': os.environ.get('AWS_ACCESS_KEY_ID'),
     'secret': os.environ.get('AWS_SECRET_ACCESS_KEY'),

docs/converter.md

@@ -45,6 +45,7 @@ The converter also provides a Python API for programmatic usage:
 ### Example: Basic Conversion
 
 ```python
+# test: skip
 import xarray as xr
 from eopf_geozarr import create_geozarr_dataset
 

docs/examples.md

@@ -9,6 +9,7 @@ Practical examples demonstrating common use cases for the EOPF GeoZarr library.
 Convert a local EOPF dataset to GeoZarr format:
 
 ```python
+# test: skip
 import xarray as xr
 from eopf_geozarr import create_geozarr_dataset