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doc/_config.yml

@@ -27,5 +27,9 @@ repository:
 # Add GitHub buttons to your book
 # See https://jupyterbook.org/customize/config.html#add-a-link-to-your-repository
 html:
+  favicon: favicon.ico  # Path to favicon relative to doc/ directory
   use_issues_button: true
   use_repository_button: true
+  extra_navbar: ""
+  extra_footer: ""
+  home_page_in_navbar: false

doc/api_reference.md

@@ -13,125 +13,22 @@ This section provides detailed documentation for all classes, functions, and met
    :show-inheritance:
 
 ```
+### Index Store Module
 
-
-The main class for creating and managing spatial binning operations.
-
-#### Constructor Parameters
-
-- **ds_high** (*xarray.Dataset*): High-resolution input dataset
-- **ds_low** (*xarray.Dataset*): Low-resolution target grid dataset  
-- **var_name** (*str*): Name of the variable to bin
-- **radius** (*float, optional*): Search radius for binning algorithm
-
-#### Key Methods
-
-##### create_binning_index()
-Creates a binning index that maps high-resolution points to low-resolution grid cells.
-
-**Returns**: Binning index object for reuse
-
-##### mean_binning()
-Performs mean binning operation.
-
-**Parameters**:
-- **precomputed_binning_index** (*bool, optional*): Whether to use a precomputed binning index. Defaults to False.
-
-**Returns**: *xarray.Dataset* with binned results
-
-**Usage Example**:
-```python
->>> import xarray as xr
->>> from map_binning.binning import Binning
->>> ds_high = xr.open_dataset('high_res_data.nc')
->>> ds_low = xr.open_dataset('low_res_grid.nc')
->>> binning = Binning(ds_high, ds_low, var_name='temperature')
->>> binned_data = binning.mean_binning()
->>> print(binned_data)
+```{eval-rst}
+.. autoclass:: map_binning.index_store
+   :members:
+   :undoc-members:
+   :show-inheritance:
 ```
 
-## Utility Functions
 
-### save()
+## Utility Functions
 
 ```{eval-rst}
 .. autofunction:: map_binning.save
 ```
 
-Save binning indices or results to disk for later use.
-
-**Parameters**:
-- **obj**: Object to save (binning index or dataset)
-- **filepath** (*str*): Path where to save the file
-
-**Example**:
-```python
-from map_binning.index_store import save
-save(binning_index, 'my_index.pkl')
-```
-
-### load()
-
 ```{eval-rst}
 .. autofunction:: map_binning.load
-```
-
-Load previously saved binning indices or results from disk.
-
-**Parameters**:
-- **filepath** (*str*): Path to the saved file
-
-**Returns**: Loaded object
-
-**Example**:
-```python
-from map_binning.index_store import load
-index = load('my_index.pkl')
-```
-
-## Index Store Module
-
-```{eval-rst}
-.. automodule:: map_binning.index_store
-   :members:
-   :undoc-members:
-   :show-inheritance:
-```
-
-Module for managing persistent storage of binning indices.
-
-## Constants and Configuration
-
-### Default Parameters
-
-- **DEFAULT_RADIUS**: By default, the search radius for binning is set to 60% of the maximum average spacing between latitude and longitude points in the low-resolution grid. This is calculated as `max(mean_lat_spacing, mean_lon_spacing) * 0.6`.
-
-### Supported Save/Load Format
-
-- **SUPPORTED_FORMATS**: Only pickle files (`.pkl`) generated by the package are supported for save and load operations.
-
-## Error Handling
-
-The package defines custom exceptions for different error conditions:
-
-- **BinningError**: Base exception for binning-related errors
-- **InvalidDatasetError**: Raised when input datasets are invalid
-- **IndexError**: Raised when binning index operations fail
-
-## Data Types
-
-### Coordinate Systems
-
-The package supports various coordinate systems and projections:
-
-- Geographic coordinates (latitude/longitude)
-- Projected coordinate systems
-- Custom grid definitions
-
-### Supported Data Formats
-
-Input datasets can be in various formats supported by xarray:
-- NetCDF (.nc, .nc4)
-- Zarr
-- GRIB
-- HDF5
+```