fix(zonal_stats id)

Author: nicolasK

CHANGELOG.md

@@ -5,11 +5,17 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
 
+## [0.0.11] - Unreleased
+
+### Fixed
+
+- `zonal_stats` manages index independently from row.
+
 ## [0.0.10] - 2024-03-05
 
 ### Added
 
--  `ed.whittaker` adapted from pywapor github.
+- `ed.whittaker` adapted from pywapor github.
 - `ed.zonal_stats` using new `geocube` zonal_stats engine.
 
 ### Changed

earthdaily/__init__.py

@@ -5,4 +5,4 @@
 # to hide warnings from rioxarray or nano seconds conversion
 warnings.filterwarnings("ignore")
 
-__version__ = "0.0.10"
+__version__ = "0.0.11"

earthdaily/earthdatastore/cube_utils/_zonal.py

@@ -113,18 +113,20 @@ def zonal_stats(
     if method == "geocube":
         from geocube.api.core import make_geocube
 
-        gdf["feature"] = list(gdf.index)
+        gdf["index"] = np.arange(gdf.shape[0])
         out_grid = make_geocube(
             gdf,
-            measurements=["feature"],
+            measurements=["index"],
             like=dataset,  # ensure the data are on the same grid
         )
-        cube = dataset.groupby(out_grid.feature)
+        cube = dataset.groupby(out_grid.index)
         zonal_stats = xr.concat(
             [getattr(cube, operation)() for operation in operations], dim="stats"
         )
         zonal_stats["stats"] = operations
-        return zonal_stats
+        zonal_stats["index"] = gdf.index
+        return zonal_stats.rename(dict(index="feature"))
+    
     tqdm_bar = tqdm.tqdm(total=gdf.shape[0])
 
     if dataset.rio.crs != gdf.crs:

examples/field_evolution.py

@@ -45,16 +45,15 @@
 # ----------------------------------------------------
 pivot_cube = pivot_cube.load()
 pivot_cube.ed.plot_rgb(col_wrap=3)
-
-plt.title("Clear cover percent with SCL")
-plt.title("Pivot evolution with SCL masks")
+plt.title("Pivot evolution masked with native cloudmasks")
 plt.show()
 
 
 ##############################################################################
 # Compute zonal stats for the pivot
 # ----------------------------------------------------
 
+pivot.index = ['my_pivot']
 zonal_stats = pivot_cube.ed.zonal_stats(pivot, ['mean','max','min'])
 
 zonal_stats.isel(feature=0).to_array(dim="band").plot.line(