v0.0.14

v0.0.14

Author: nkarasiak

CHANGELOG.md

@@ -4,6 +4,27 @@ 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).
 
+## [0.0.14] - 2024-04-11
+
+### Fixed
+
+- `GeoSeries` supported in GeometryManager.
+- `ed.sel_nearest_dates` accessor avoid duplicated times.
+- Issue when managing multiple indices with accessor `xr.ed`.
+- Issue when same datetime when rescaling dataset.
+
+### Added
+
+- `mode` for zonal stats in `operations.reducers`.
+
+### Changed
+
+- `ag_cloud_mask_items` queries items per batch.
+
+### Removed
+
+- Typing decorator, expected new typing library.
+
 ## [0.0.13] - 2024-03-06
 
 ### Fixed

earthdaily/__init__.py

@@ -5,4 +5,4 @@
 # to hide warnings from rioxarray or nano seconds conversion
 warnings.filterwarnings("ignore")
 
-__version__ = "0.0.13"
+__version__ = "0.0.14"

earthdaily/accessor/__init__.py

@@ -12,86 +12,7 @@
 from xarray.core.extensions import AccessorRegistrationWarning
 from ..earthdatastore.cube_utils import GeometryManager
 
-warnings.filterwarnings("ignore", category=AccessorRegistrationWarning)
-
-
-class MisType(Warning):
-    pass
-
-
-def _typer(raise_mistype=False, custom_types={}):
-    """
-
-    Parameters
-    ----------
-    raise_mistype : TYPE, optional
-        DESCRIPTION. The default is False.
-    custom_types : TYPE, optional
-        DESCRIPTION. The default is {}.
-        Example : {
-            np.ndarray:{"func":np.asarray},
-            xr.Dataset:{"func":xr.DataArray.to_dataset,"kwargs":{"dim":"band"}}
-            }
-    Raises
-    ------
-    MisType
-        DESCRIPTION.
-
-    Returns
-    -------
-    TYPE
-        DESCRIPTION.
-
-    """
-
-    def decorator(func):
-        def force(*args, **kwargs):
-            _args = list(args)
-            for key, vals in func.__annotations__.items():
-                if not isinstance(vals, (list, tuple)):
-                    vals = [vals]
-                val = vals[0]
-                idx = func.__code__.co_varnames.index(key)
-                is_kwargs = key in kwargs.keys()
-                if not is_kwargs and idx >= len(_args):
-                    break
-                value = kwargs.get(key, None) if is_kwargs else args[idx]
-                if type(value) in vals:
-                    continue
-                if (
-                    type(kwargs.get(key)) not in vals
-                    if is_kwargs
-                    else type(args[idx]) not in vals
-                ):
-                    if raise_mistype:
-                        if is_kwargs:
-                            expected = f"{type(kwargs[key]).__name__} ({kwargs[key]})"
-                        else:
-                            expected = f"{type(args[idx]).__name__} ({args[idx]})"
-                        raise MisType(f"{key} expected {val.__name__}, not {expected}.")
-                    if any(val == k for k in custom_types.keys()):
-                        exp = custom_types[val]
-                        var = args[idx]
-                        res = exp["func"](var, **exp.get("kwargs", {}))
-                        if is_kwargs:
-                            kwargs[key] = res
-                        else:
-                            _args[idx] = res
-                    elif is_kwargs:
-                        kwargs[key] = (
-                            val(kwargs[key]) if val is not list else [kwargs[key]]
-                        )
-                    else:
-                        _args[idx] = val(args[idx]) if val is not list else [args[idx]]
-            args = tuple(_args)
-            return func(*args, **kwargs)
-
-        return force
-
-    return decorator
-
-
-@_typer()
+
 def xr_loop_func(
     dataset: xr.Dataset,
     func,
@@ -122,7 +43,6 @@ def _xr_loop_func(dataset, metafunc, loop_dimension, **kwargs):
     )
 
 
-@_typer()
 def _lee_filter(img, window_size: int):
     img_ = img.copy()
     if isinstance(img, np.ndarray):
@@ -166,7 +86,6 @@ def clip(self, geom):
     def _max_time_wrap(self, wish=5, col="time"):
         return np.min((wish, self._obj[col].size))
 
-    @_typer()
     def plot_band(self, cmap="Greys", col="time", col_wrap=5, **kwargs):
         return self._obj.plot.imshow(
             cmap=cmap,
@@ -175,6 +94,29 @@ def plot_band(self, cmap="Greys", col="time", col_wrap=5, **kwargs):
             **kwargs,
         )
 
+    def whittaker(
+        self,
+        lmbd: float,
+        weights: np.ndarray = None,
+        a: float = 0.5,
+        min_value: float = -np.inf,
+        max_value: float = np.inf,
+        max_iter: int = 10,
+        time="time",
+    ):
+        from . import whittaker
+
+        return whittaker.xr_wt(
+            self._obj.to_dataset(name="index"),
+            lmbd,
+            time=time,
+            weights=weights,
+            a=a,
+            min_value=min_value,
+            max_value=max_value,
+            max_iter=max_iter,
+        )["index"]
+
 
 @xr.register_dataset_accessor("ed")
 class EarthDailyAccessorDataset:
@@ -187,7 +129,6 @@ def clip(self, geom):
     def _max_time_wrap(self, wish=5, col="time"):
         return np.min((wish, self._obj[col].size))
 
-    @_typer()
     def plot_rgb(
         self,
         red: str = "red",
@@ -205,7 +146,6 @@ def plot_rgb(
             )
         )
 
-    @_typer()
     def plot_band(self, band, cmap="Greys", col="time", col_wrap=5, **kwargs):
         return self._obj[band].plot.imshow(
             cmap=cmap,
@@ -214,7 +154,6 @@ def plot_band(self, band, cmap="Greys", col="time", col_wrap=5, **kwargs):
             **kwargs,
         )
 
-    @_typer()
     def lee_filter(self, window_size: int):
         return xr.apply_ufunc(
             _lee_filter,
@@ -225,7 +164,6 @@ def lee_filter(self, window_size: int):
             kwargs=dict(window_size=window_size),
         )
 
-    @_typer()
     def centroid(self, to_wkt: str = False, to_4326: bool = True):
         """Return the geographic center point in 4326/WKT of this dataset."""
         # we can use a cache on our accessor objects, because accessors
@@ -288,7 +226,6 @@ def available_indices(self, details=False):
                 available_indices.append(spyndex.indices[k] if details else k)
         return available_indices
 
-    @_typer()
     def add_indices(self, indices: list, **kwargs):
         """
         Uses spyndex to compute and add index.
@@ -313,12 +250,11 @@ def add_indices(self, indices: list, **kwargs):
         idx = spyndex.computeIndex(index=indices, params=params, **kwargs)
 
         if len(indices) == 1:
-            idx = idx.expand_dims(indices=indices)
-        idx = idx.to_dataset(dim="indices")
+            idx = idx.expand_dims(index=indices)
+        idx = idx.to_dataset(dim="index")
 
         return xr.merge((self._obj, idx))
 
-    @_typer()
     def sel_nearest_dates(
         self,
         target: (xr.Dataset, xr.DataArray),
@@ -334,14 +270,14 @@ def sel_nearest_dates(
             for i, j in enumerate(pos)
             if j.days <= max_delta
         ]
+        pos = np.unique(pos)
         if return_target:
             method_convert = {"bfill": "ffill", "ffill": "bfill", "nearest": "nearest"}
             return self._obj.sel(time=pos), target.sel(
                 time=pos, method=method_convert[method]
             )
         return self._obj.sel(time=pos)
 
-    @_typer()
     def whittaker(
         self,
         lmbd: float,
@@ -350,15 +286,16 @@ def whittaker(
         min_value: float = -np.inf,
         max_value: float = np.inf,
         max_iter: int = 10,
+        time="time",
     ):
         from . import whittaker
 
         return whittaker.xr_wt(
             self._obj,
             lmbd,
-            time="time",
-            weights=None,
-            a=0.5,
+            time=time,
+            weights=weights,
+            a=a,
             min_value=min_value,
             max_value=max_value,
             max_iter=max_iter,

earthdaily/accessor/whittaker/__init__.py

@@ -110,4 +110,4 @@ def xr_wt(
         output_core_dims=ocd,
         dask="allowed",
     )
-    return xr.where(np.isnan(datacube_), datacube_, datacube)
+    return xr.where(np.isnan(datacube_).all(dim=time), datacube_, datacube)

earthdaily/earthdatastore/cube_utils/_zonal.py

@@ -9,7 +9,8 @@
 import numpy as np
 import xarray as xr
 import tqdm
-from . import custom_operations
+
+from . import custom_reducers
 from .preprocessing import rasterize
 from scipy.sparse import csr_matrix
 

earthdaily/earthdatastore/cube_utils/asset_mapper/__update_asset_mapper_config_json.py

@@ -13,7 +13,7 @@
     earthdaily.earthdatastore.cube_utils.asset_mapper.__asset_mapper_config_path
 )
 asset_mapper_config = (
-    earthdaily.earthdatastore.cube_utils.asset_mapper.__asset_mapper_config
+    earthdaily.earthdatastore.cube_utils.asset_mapper._asset_mapper_config
 )
 
 for collection in eds.explore():
@@ -23,6 +23,7 @@
         print(f"collection {collection} has no items")
         continue
     for asset_name in assets_name:
+        print(asset_name)
         if asset_mapper_config.get(collection) is None:
             asset_mapper_config[collection] = [{}]
         if asset_name not in asset_mapper_config[collection][0].values():

earthdaily/earthdatastore/cube_utils/custom_operations.py earthdaily/earthdatastore/cube_utils/custom_reducers.py

@@ -3,7 +3,7 @@
 import numpy as np
 
 
-class CustomOperations:
+class CustomReducers:
     @staticmethod
     def _np_mode(arr, **kwargs):
         if isinstance(arr, list):
@@ -22,17 +22,17 @@ def _np_mode(arr, **kwargs):
     def mode(data_array_grouped, optional_arg=None):
         # Apply _xrmode to DataArrayGroupBy object
         result = data_array_grouped.reduce(
-            CustomOperations._np_mode,
+            CustomReducers._np_mode,
             list(dim for dim in data_array_grouped.dims if dim != "time"),
         )
         return result
 
     @staticmethod
-    def register_custom_operations():
+    def register_custom_reducers():
         # register custom methods fo DataArrayGroupBy
-        xr.core.groupby.DataArrayGroupBy.mode = CustomOperations.mode
-        xr.core.groupby.DatasetGroupBy.mode = CustomOperations.mode
-        np.mode = CustomOperations._np_mode
+        xr.core.groupby.DataArrayGroupBy.mode = CustomReducers.mode
+        xr.core.groupby.DatasetGroupBy.mode = CustomReducers.mode
+        np.mode = CustomReducers._np_mode
 
 
-CustomOperations.register_custom_operations()
+CustomReducers.register_custom_reducers()

earthdaily/earthdatastore/cube_utils/geometry_manager.py

@@ -9,6 +9,9 @@ def __init__(self, geometry):
         self.geometry = geometry
         self._obj = self.to_geopandas()
 
+    def __call__(self):
+        return self._obj
+
     def to_intersects(self, crs="EPSG:4326"):
         return json.loads(self._obj.to_crs(crs).dissolve().to_json(drop_id=True))[
             "features"
@@ -55,8 +58,12 @@ def _unknow_geometry_to_geodataframe(self, geometry):
                             [geometry["coordinates"][0]]
                         )
                         return gpd.GeoDataFrame(geometry=[geom], crs="EPSG:4326")
-                    elif isinstance(geometry, gpd.GeoSeries):
-                        self.input_type = "GeoSeries"
-                        return gpd.GeoDataFrame(geometry=geometry, crs="EPSG:4326")
+        elif isinstance(geometry, gpd.GeoSeries):
+            self.input_type = "GeoSeries"
+
+            return gpd.GeoDataFrame(
+                geometry=geometry,
+                crs="EPSG:4326" if geometry.crs is None else geometry.crs,
+            )
         else:
             raise NotImplementedError("Couldn't guess your geometry type")