From 53b0f7cf8e9e90abb77939416b6c9a9d966c4ef2 Mon Sep 17 00:00:00 2001
From: "C.-W. HSU" <11950284+chiaweh2@users.noreply.github.com>
Date: Wed, 10 Sep 2025 14:02:17 -0600
Subject: [PATCH 1/2] include DOI from zenodo

---
 README.md | 96 +++++++++----------------------------------------------
 1 file changed, 15 insertions(+), 81 deletions(-)

diff --git a/README.md b/README.md
index 454ecc8..c86ec2b 100644
--- a/README.md
+++ b/README.md
@@ -9,6 +9,21 @@
 
 A Python package for spatial resampling and binning of geospatial data, specifically designed for oceanographic datasets. This tool enables efficient downsampling of high-resolution gridded data onto coarser grids while preserving spatial accuracy through intelligent neighborhood averaging.
 
+## Citation
+[![DOI](https://zenodo.org/badge/1050687709.svg)](https://doi.org/10.5281/zenodo.17095448)
+
+If you use this tool in your research, please cite:
+
+```bibtex
+@software{map_binning_2025,
+  author = {Chia-Wei Hsu},
+  title = {Map Binning Tool: Spatial Resampling for Oceanographic Data},
+  url = {https://github.com/chiaweh2/map_binning},
+  doi = {10.5281/zenodo.17095448},
+  year = {2025}
+}
+```
+
 ## Overview
 
 The Map Binning Tool provides a robust solution for spatial data aggregation, particularly useful for:
@@ -52,16 +67,6 @@ pip install map-binning[dev]
 
 ## Developer Installation
 
-### Using conda environment
-```bash
-# Create and activate conda environment
-conda env create -f environment.yml
-conda activate map-binning
-
-# Install the package in development mode
-pip install -e .
-```
-
 ### From source
 ```bash
 git clone <repository-url>
@@ -173,52 +178,6 @@ COPERNICUSMARINE_SERVICE_USERNAME=<your_username>
 COPERNICUSMARINE_SERVICE_PASSWORD=<your_password>
 ```
 
-### Dependencies
-
-Core dependencies:
-- `numpy`: Numerical computing
-- `scipy`: Scientific computing (k-d tree algorithms)
-- `xarray`: Labeled multi-dimensional arrays
-- `netcdf4`: NetCDF file I/O
-- `python-dotenv`: Environment variable management
-
-Development dependencies:
-- `pytest`: Unit testing framework
-- `black`: Code formatting
-- `flake8`: Code linting
-- `mypy`: Static type checking
-
-## Examples
-
-### Working with CMEMS Data
-
-```python
-import xarray as xr
-from map_binning import Binning
-
-# Example with Copernicus Marine data
-ds_high = xr.open_dataset('cmems_high_res_sla.nc')
-ds_low = xr.open_dataset('cmems_low_res_grid.nc')
-
-# Initialize for sea level anomaly processing
-sla_binning = Binning(
-    ds_high=ds_high,
-    ds_low=ds_low,
-    var_name='sla',
-    xdim_name='longitude',
-    ydim_name='latitude'
-)
-
-# Process and cache the result
-binned_sla = sla_binning.mean_binning(
-    pickle_filename="cmems_sla_index.pkl",
-    pickle_location="./cache"
-)
-
-# Save the result
-binned_sla.to_netcdf('binned_sla_data.nc')
-```
-
 ### Time Series Processing
 
 The tool automatically handles time dimensions:
@@ -267,36 +226,11 @@ black map_binning/
 mypy map_binning/
 ```
 
-## Testing
-
-```bash
-# Run all tests
-pytest
-
-# Run with coverage
-pytest --cov=map_binning
-
-# Run specific test file
-pytest tests/test_main.py
-```
 
 ## License
 
 This project is licensed under the MIT License - see the LICENSE file for details.
 
-## Citation
-
-If you use this tool in your research, please cite:
-
-```bibtex
-@software{map_binning_2025,
-  author = {Chia-Wei Hsu},
-  title = {Map Binning Tool: Spatial Resampling for Oceanographic Data},
-  url = {https://github.com/chiaweh2/map_binning},
-  version = {0.3.2},
-  year = {2025}
-}
-```
 
 ## Support
 

From 0088a17fb03a41e933187846e8ed8d160d04d64c Mon Sep 17 00:00:00 2001
From: "C.-W. HSU" <11950284+chiaweh2@users.noreply.github.com>
Date: Wed, 10 Sep 2025 14:04:30 -0600
Subject: [PATCH 2/2] update readme

---
 README.md | 42 +++++++++++++++++++++---------------------
 1 file changed, 21 insertions(+), 21 deletions(-)

diff --git a/README.md b/README.md
index c86ec2b..2e60d10 100644
--- a/README.md
+++ b/README.md
@@ -118,6 +118,27 @@ result = binning.mean_binning(
 )
 ```
 
+### Time Series Processing
+
+The tool automatically handles time dimensions:
+
+```python
+# Works seamlessly with time-varying datasets
+# Input: (time, lat, lon) -> Output: (time, lat_low, lon_low)
+result = binning.mean_binning()
+```
+## Configuration for CMEMS data download
+
+### Environment Variables
+
+Copy `.env.template` to `.env` and configure:
+
+```bash
+# Copernicus Marine Service credentials (if using CMEMS data)
+COPERNICUSMARINE_SERVICE_USERNAME=<your_username>
+COPERNICUSMARINE_SERVICE_PASSWORD=<your_password>
+```
+
 ## API Reference
 
 ### Binning Class
@@ -159,34 +180,13 @@ map_binning/
 ├── tests/                 # Unit tests
 │   ├── __init__.py
 │   └── ...
-├── pickle_folder/         # Default location for cached indices
 ├── pyproject.toml         # Project configuration
 ├── environment.yml        # Conda environment specification
 ├── .env.template          # Environment variables template
 └── README.md              # This file
 ```
 
-## Configuration for CMEMS data download
-
-### Environment Variables
-
-Copy `.env.template` to `.env` and configure:
-
-```bash
-# Copernicus Marine Service credentials (if using CMEMS data)
-COPERNICUSMARINE_SERVICE_USERNAME=<your_username>
-COPERNICUSMARINE_SERVICE_PASSWORD=<your_password>
-```
-
-### Time Series Processing
-
-The tool automatically handles time dimensions:
 
-```python
-# Works seamlessly with time-varying datasets
-# Input: (time, lat, lon) -> Output: (time, lat_low, lon_low)
-result = binning.mean_binning()
-```
 
 ## Performance Considerations