added zenodo artificat

.zenodo.json

@@ -3,14 +3,14 @@
   "description": "SpiPy is a Python implementation of SPIRES (SPectral Inversion of REflectance from Snow), a spectral unmixing algorithm for analyzing snow reflectance data from satellite imagery. It retrieves snow properties (grain size, dust concentration, fractional snow-covered area) using lookup tables generated from Mie-scattering theory. This implementation features a hybrid Python/C++ architecture achieving 3000x speedup over pure Python implementations.",
   "creators": [
     {
-      "name": "Bair, Ned",
+      "name": "Bair, Edward H.",
       "affiliation": "University of California, Santa Barbara",
-      "orcid": "0000-0002-7654-3210"
+      "orcid": "https://orcid.org/0000-0002-6554-387X"
     },
     {
       "name": "Griessbaum, Niklas",
       "affiliation": "Leidos, Inc.",
-      "orcid": "0000-0000-0000-0000"
+      "orcid": "https://orcid.org/0000-0001-5037-6266"
     }
   ],
   "license": "MIT",
@@ -32,9 +32,5 @@
       "scheme": "doi"
     }
   ],
-  "grants": [
-    {
-      "id": "W913E523C0002"
-    }
-  ]
+  "notes": "This work was supported by contract W913E523C0002."
 }

CITATION.cff

@@ -3,19 +3,20 @@ message: "If you use this software, please cite both the software and the algori
 title: "SpiPy: Python implementation of SPIRES snow property inversion"
 authors:
   - family-names: Bair
-    given-names: Ned
+    given-names: Edward H.
     email: edwardbair@ucsb.edu
     affiliation: "University of California, Santa Barbara"
-    orcid: "https://orcid.org/0000-0002-7654-3210"  # Update with actual ORCID if available
+    orcid: "https://orcid.org/0000-0002-6554-387X"
   - family-names: Griessbaum
     given-names: Niklas
     affiliation: "Leidos, Inc."
-    orcid: "https://orcid.org/0000-0000-0000-0000"  # Update with actual ORCID if available
-version: 0.2.0
-date-released: 2024-11-06
-repository-code: "https://github.com/edwardbair/SpiPy"
-url: "https://github.com/edwardbair/SpiPy"
-license: MIT  # Update if different
+    orcid: "https://orcid.org/0000-0001-5037-6266"
+version: 0.2.8
+date-released: 2026-02-23
+repository-code: "https://github.com/NiklasPhabian/SpiPy"
+url: "https://spipy.readthedocs.io"
+license: MIT
+doi: 10.5281/zenodo.18747284
 keywords:
   - remote sensing
   - snow properties

README.md

@@ -1,14 +1,15 @@
 # SpiPy
 
 [![PyPI version](https://badge.fury.io/py/spires.svg)](https://pypi.org/project/spires/)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18747284.svg)](https://doi.org/10.5281/zenodo.18747284)
 [![Documentation Status](https://readthedocs.org/projects/spipy/badge/?version=latest)](https://spipy.readthedocs.io/en/latest/?badge=latest)
 [![Build and Test](https://github.com/NiklasPhabian/SpiPy/workflows/Build%20and%20Test/badge.svg)](https://github.com/NiklasPhabian/SpiPy/actions)
 [![Python 3.9-3.14](https://img.shields.io/badge/python-3.9--3.14-blue.svg)](https://github.com/NiklasPhabian/SpiPy)
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
 **[📦 View Source on GitHub](https://github.com/NiklasPhabian/SpiPy)** | **[📖 Documentation](https://spipy.readthedocs.io)** | **[🐛 Report Issues](https://github.com/NiklasPhabian/SpiPy/issues)**
 
-SpiPy is a Python implementation of [SPIRES](https://ieeexplore.ieee.org/document/9290428) (SPectral Inversion of REflectance from Snow), originally implemented in MATLAB ([SPIRES GitHub repository](https://github.com/edwardbair/SPIRES)).
+SpiPy is a Python implementation of [SPIRES](https://ieeexplore.ieee.org/document/9290428) (Snow Property Inversion From Remote Sensing), originally implemented in MATLAB ([SPIRES GitHub repository](https://github.com/edwardbair/SPIRES)).
 
 ## Overview
 
@@ -239,12 +240,13 @@ If you use this software, please cite the algorithm paper, software implementati
 
 **Software:**
 ```bibtex
-@software{bair2024spipy,
+@software{bair2026spipy,
   title={SpiPy: Python implementation of SPIRES snow property inversion},
-  author={Bair, Ned and Griessbaum, Niklas},
-  year={2024},
-  url={https://github.com/edwardbair/SpiPy},
-  version={0.2.0},
+  author={Bair, Edward H. and Griessbaum, Niklas},
+  year={2026},
+  url={https://github.com/NiklasPhabian/SpiPy},
+  version={0.2.8},
+  doi={10.5281/zenodo.18747284},
   note={See CITATION.cff for full metadata}
 }
 ```

tests/.gitattributes

@@ -0,0 +1 @@
+*.mat filter=lfs diff=lfs merge=lfs -text

tests/data/README.md

@@ -4,13 +4,27 @@ This directory contains test data for the SpiPy package.
 
 ## Files in Repository (via Git LFS)
 
-Small test data files suitable for CI/CD:
+### Lookup Tables
 
 - **lut_sentinel2b_b2to12_3um_dust.mat** (70 MB)
   - Lookup table for Sentinel-2B bands 2-12 with dust parameters
   - Essential for all Sentinel-2 tests
   - Also available on Zenodo (see below)
 
+- **lut_HLSS30_b1to13_3um_dust.mat** (101 MB)
+  - Lookup table for HLS (Harmonized Landsat Sentinel-2) bands 1-13 with dust parameters
+  - For HLS30 product testing
+
+- **lut_modis_b1to7_3um_dust.mat** (537 MB)
+  - Lookup table for MODIS bands 1-7 with dust parameters
+  - For MODIS testing
+
+- **lut_oli_b1to7_3um_dust.mat** (55 MB)
+  - Lookup table for Landsat OLI bands 1-7 with dust parameters
+  - For Landsat 8/9 testing
+
+### Test Imagery Subsets
+
 - **sentinel_r_subset.nc** (2.85 MB)
   - Small spatial subset (50×50 pixels) of full reflectance data
   - For quick integration tests
@@ -26,14 +40,24 @@ Full-resolution test data available on Zenodo:
 ### Lookup Tables
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18701286.svg)](https://doi.org/10.5281/zenodo.18701286)
 
-- **LUT_MODIS.mat** (537 MB)
-  - MODIS lookup table generated from Mie-scattering theory
-  - Download: https://zenodo.org/records/18701286/files/LUT_MODIS.mat
+**Note:** All lookup tables are included in the repository via Git LFS. Download from Zenodo if you have LFS quota issues or prefer direct downloads.
 
 - **lut_sentinel2b_b2to12_3um_dust.mat** (70 MB)
   - Sentinel-2B lookup table (also in repository via LFS)
   - Download: https://zenodo.org/records/18701286/files/lut_sentinel2b_b2to12_3um_dust.mat
 
+- **lut_HLSS30_b1to13_3um_dust.mat** (101 MB)
+  - HLS (Harmonized Landsat Sentinel-2) lookup table (also in repository via LFS)
+  - Download: https://zenodo.org/records/18701286/files/lut_HLSS30_b1to13_3um_dust.mat
+
+- **lut_modis_b1to7_3um_dust.mat** (537 MB)
+  - MODIS lookup table (also in repository via LFS)
+  - Download: https://zenodo.org/records/18701286/files/lut_modis_b1to7_3um_dust.mat
+
+- **lut_oli_b1to7_3um_dust.mat** (55 MB)
+  - Landsat OLI lookup table (also in repository via LFS)
+  - Download: https://zenodo.org/records/18701286/files/lut_oli_b1to7_3um_dust.mat
+
 ### Test Imagery
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.18704072.svg)](https://doi.org/10.5281/zenodo.18704072)
 
@@ -50,26 +74,41 @@ Full-resolution test data available on Zenodo:
 
 ### For Development
 
-The subset files are sufficient for most development and testing:
+All lookup tables and test subsets are included in the repository via Git LFS:
 
 ```python
 import xarray as xr
 import spires
 
-# Use subset files (in repository)
+# Sentinel-2 example
 r = xr.open_dataset('tests/data/sentinel_r_subset.nc')
 r0 = xr.open_dataset('tests/data/sentinel_r0_subset.nc')
-lut = spires.LutInterpolator('tests/data/lut_sentinel2b_b2to12_3um_dust.mat')
+lut_s2 = spires.LutInterpolator('tests/data/lut_sentinel2b_b2to12_3um_dust.mat')
+
+# MODIS example
+lut_modis = spires.LutInterpolator('tests/data/lut_modis_b1to7_3um_dust.mat')
+
+# Landsat OLI example
+lut_oli = spires.LutInterpolator('tests/data/lut_oli_b1to7_3um_dust.mat')
+
+# HLS example
+lut_hls = spires.LutInterpolator('tests/data/lut_HLSS30_b1to13_3um_dust.mat')
 ```
 
 ### For Full Tests
 
-To run tests with full-resolution data, download from Zenodo:
+To run tests with full-resolution imagery or download LUTs without LFS, get from Zenodo:
 
 ```bash
 cd tests/data
 
-# Download lookup tables
+# Download all LUTs from Zenodo (alternative to LFS)
+curl -L -o lut_sentinel2b_b2to12_3um_dust.mat https://zenodo.org/records/18701286/files/lut_sentinel2b_b2to12_3um_dust.mat
+curl -L -o lut_HLSS30_b1to13_3um_dust.mat https://zenodo.org/records/18701286/files/lut_HLSS30_b1to13_3um_dust.mat
+curl -L -o lut_modis_b1to7_3um_dust.mat https://zenodo.org/records/18701286/files/lut_modis_b1to7_3um_dust.mat
+curl -L -o lut_oli_b1to7_3um_dust.mat https://zenodo.org/records/18701286/files/lut_oli_b1to7_3um_dust.mat
+
+# Download legacy MODIS LUT (if needed for compatibility tests)
 curl -L -o LUT_MODIS.mat https://zenodo.org/records/18701286/files/LUT_MODIS.mat
 
 # Download full test imagery (large!)
@@ -90,9 +129,16 @@ python scripts/download_test_data.py --imagery
 ## CI/CD Behavior
 
 GitHub Actions:
-- Uses subset files by default (fast, no quota issues)
-- Skips tests requiring LUT_MODIS.mat (MODIS-specific tests)
-- Can be configured to download full files from Zenodo if needed
+- All lookup tables (Sentinel-2, MODIS, OLI, HLS) are available via Git LFS
+- Uses subset imagery files by default (fast tests)
+- Downloads `lut_sentinel2b_b2to12_3um_dust.mat` from Zenodo to avoid LFS quota
+- Skips tests requiring legacy LUT_MODIS.mat format
+- Full-resolution imagery can be downloaded from Zenodo if needed
+
+GitLab CI:
+- Configured with `GIT_LFS_SKIP_SMUDGE: "1"` to avoid LFS quota issues
+- Downloads required LUTs from Zenodo on-demand
+- Uses same test approach as GitHub Actions
 
 ## Citation