release(v0.3.3)

docs/CHANGELOG.md

@@ -4,6 +4,17 @@ All notable changes to this project will be documented in this file.
 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).
 
+## [0.3.3] - 2024-12-13
+
+### Fixed
+
+- `mode` in `zonal_stats`
+- missing support for `zonal_stats` when dataset had no time dimension
+
+### Changed
+
+- xarray accessor classes are now privates to avoid having them in autocomplementation.
+
 ## [0.3.2] - 2024-12-10
 
 ### Added

earthdaily/__init__.py

@@ -1,13 +1,13 @@
 from typing import Optional
 from pathlib import Path
 from . import earthdatastore, datasets
-from .accessor import EarthDailyAccessorDataArray, EarthDailyAccessorDataset
+from .accessor import __EarthDailyAccessorDataArray, __EarthDailyAccessorDataset
 
 # import warnings
 # to hide warnings from rioxarray or nano seconds conversion
 # warnings.filterwarnings("ignore")
 
-__version__ = "0.3.2"
+__version__ = "0.3.3"
 
 
 def EarthDataStore(

earthdaily/accessor/__init__.py

@@ -78,7 +78,7 @@ def _xr_rio_clip(datacube, geom):
 
 
 @xr.register_dataarray_accessor("ed")
-class EarthDailyAccessorDataArray:
+class __EarthDailyAccessorDataArray:
     def __init__(self, xarray_obj):
         self._obj = xarray_obj
 
@@ -206,7 +206,7 @@ def drop_unfrozen_coords(self, keep_spatial_ref=True):
 
 
 @xr.register_dataset_accessor("ed")
-class EarthDailyAccessorDataset(EarthDailyAccessorDataArray):
+class __EarthDailyAccessorDataset(__EarthDailyAccessorDataArray):
     def __init__(self, xarray_obj):
         self._obj = xarray_obj
 

earthdaily/earthdatastore/cube_utils/_zonal.py

@@ -14,6 +14,7 @@
 from . import custom_reducers
 from .preprocessing import rasterize
 from scipy.sparse import csr_matrix
+from scipy.stats import mode
 
 
 def _compute_M(data):
@@ -26,13 +27,6 @@ def _indices_sparse(data):
     return [np.unravel_index(row.data, data.shape) for row in M]
 
 
-def _np_mode(arr, **kwargs):
-    values, counts = np.unique(arr, return_counts=True)
-    isnan = np.isnan(values)
-    values, counts = values[~isnan], counts[~isnan]
-    return values[np.argmax(counts)]
-
-
 def datacube_time_stats(datacube, operations):
     datacube = datacube.groupby("time")
     stats = []
@@ -76,8 +70,11 @@ def _zonal_stats_ufunc(dataset, positions, reducers):
             field_stats = []
             for reducer in reducers:
                 field_arr = dataset[(...,) + tuple(positions[idx])]
-                func = f"nan{reducer}" if hasattr(np, f"nan{reducer}") else reducer
-                field_arr = getattr(np, func)(field_arr, axis=-1)
+                if reducer == "mode":
+                    field_arr = mode(field_arr, axis=-1, nan_policy="omit").mode
+                else:
+                    func = f"nan{reducer}" if hasattr(np, f"nan{reducer}") else reducer
+                    field_arr = getattr(np, func)(field_arr, axis=-1)
                 field_stats.append(field_arr)
             field_stats = np.asarray(field_stats)
             zs.append(field_stats)
@@ -187,8 +184,8 @@ def _loop_time_chunks(dataset, method, smart_load, time_chunks):
         positions = [np.asarray(yx_pos[i + 1]) for i in np.arange(geoms.shape[0])]
         positions = [position for position in positions if position.size > 0]
         del yx_pos
-        time_chunks = _memory_time_chunks(dataset, memory)
-        if smart_load:
+        if "time" in dataset.dims and smart_load:
+            time_chunks = _memory_time_chunks(dataset, memory)
             zs = xr.concat(
                 [
                     z

requirements.yml

@@ -6,7 +6,7 @@ dependencies:
 - numpy
 - matplotlib
 - joblib
-- gdal=3.7.3
+- gdal
 - scipy
 - psutil
 - pystac-client

requirements_dev.yml

@@ -6,7 +6,7 @@ dependencies:
   - numpy
   - pandas
   - libtiff
-  - gdal=3.7.3
+  - gdal
   - geopandas>=0.11
   - dask>=2024.3
   - shapely