Merge pull request #95 from csiro-coasts/remove-shorthand-imports

Remove shorthand imports

Author: mx-moth

docs/developing/grass.py

@@ -3,8 +3,8 @@
 from functools import cached_property
 from typing import Optional, Tuple, Dict
 
-import numpy as np
-import xarray as xr
+import numpy
+import xarray
 from shapely.geometry import Polygon
 from shapely.geometry.base import BaseGeometry
 
@@ -32,7 +32,7 @@ class Grass(Convention[GrassGridKind, GrassIndex]):
     default_grid_kind = GrassGridKind.blade
 
     @classmethod
-    def check_dataset(cls, dataset: xr.Dataset) -> Optional[int]:
+    def check_dataset(cls, dataset: xarray.Dataset) -> Optional[int]:
         # A Grass dataset is recognised by the 'Conventions' global attribute
         if dataset.attrs['Conventions'] == 'Grass 1.0':
             return Specificity.HIGH
@@ -61,7 +61,7 @@ def unravel_index(
         return (grid_kind, warp, weft)
 
     def get_grid_kind_and_size(
-        self, data_array: xr.DataArray,
+        self, data_array: xarray.DataArray,
     ) -> Tuple[GrassGridKind, int]:
         """
         For the given DataArray from this Dataset,
@@ -75,7 +75,7 @@ def get_grid_kind_and_size(
         raise ValueError(
             "DataArray does not appear to be either a blade or meadow grid")
 
-    def make_linear(self, data_array: xr.DataArray) -> xr.DataArray:
+    def make_linear(self, data_array: xarray.DataArray) -> xarray.DataArray:
         """
         Make the given DataArray linear in its grid dimensions.
         """
@@ -95,12 +95,12 @@ def selector_for_index(self, index: GrassIndex) -> Dict[str, int]:
         return {'warp': warp, 'weft': weft}
 
     @cached_property
-    def polygons(self) -> np.ndarray:
+    def polygons(self) -> numpy.ndarray:
         def make_polygon_for_cell(warp: int, weft: int) -> Polygon:
             # Implementation left as an exercise for the reader
             return Polygon(...)
 
-        return np.array([
+        return numpy.array([
             make_polygon_for_cell(warp, weft)
             for warp in range(self.dataset.dimensions['warp'])
             for weft in range(self.dataset.dimensions['weft'])
@@ -110,7 +110,7 @@ def make_clip_mask(
         self,
         clip_geometry: BaseGeometry,
         buffer: int = 0,
-    ) -> xr.Dataset:
+    ) -> xarray.Dataset:
         # Find all the blades that intersect the clip geometry
         intersecting_blades = [
             item
@@ -143,14 +143,14 @@ def make_clip_mask(
             keep_meadows = blur_mask(keep_meadows, size=buffer)
 
         # Make a dataset out of these masks
-        return xr.Dataset(
+        return xarray.Dataset(
             data_vars={
-                'blades': xr.DataArray(data=keep_blades, dims=['weft', 'warp']),
-                'meadows': xr.DataArray(data=keep_meadows, dims=['warp', 'weft']),
+                'blades': xarray.DataArray(data=keep_blades, dims=['weft', 'warp']),
+                'meadows': xarray.DataArray(data=keep_meadows, dims=['warp', 'weft']),
             },
         )
 
-    def apply_clip_mask(self, clip_mask: xr.Dataset, work_dir: Pathish) -> xr.Dataset:
+    def apply_clip_mask(self, clip_mask: xarray.Dataset, work_dir: Pathish) -> xarray.Dataset:
         # You're on your own, here.
         # This depends entirely on how the mask and datasets interact.
         pass

docs/developing/sea_surface_temperature.py

@@ -4,7 +4,7 @@
 import pathlib
 
 import emsarray
-import xarray as xr
+import xarray
 from emsarray.cli import console_entrypoint
 from emsarray.cli.utils import geometry_argument
 from shapely.geometry.base import BaseGeometry
@@ -64,8 +64,8 @@ def main(options: argparse.Namespace) -> None:
 
 
 def calculate_mean_sea_surface_temperature(
-    dataset: xr.Dataset,
-    temperature: xr.DataArray,
+    dataset: xarray.Dataset,
+    temperature: xarray.DataArray,
     geometry: BaseGeometry,
 ) -> float:
     """

docs/releases/development.rst

@@ -17,3 +17,5 @@ Next release (in development)
 * Fix various small issues with the docs.
   Use newer version of ``sphinx-book-theme`` for documentation
   (:pr:`91`).
+* Remove shorthand imports such as ``import xarray as xr``
+  (:pr:`95`).

src/emsarray/accessors.py

@@ -2,16 +2,16 @@
 
 import logging
 
-import xarray as xr
+import xarray
 
 from .conventions import Convention, get_dataset_convention
 from .state import State
 
 logger = logging.getLogger(__name__)
 
 
-@xr.register_dataset_accessor("ems")
-def ems_accessor(dataset: xr.Dataset) -> Convention:
+@xarray.register_dataset_accessor("ems")
+def ems_accessor(dataset: xarray.Dataset) -> Convention:
     """Provides the ``.ems`` attribute on xarray Datasets.
     This will make a :class:`~emsarray.conventions.Convention` instance for the dataset,
     using the correct :class:`~emsarray.conventions.Convention` subclass depending on the file type.
@@ -33,4 +33,4 @@ def ems_accessor(dataset: xr.Dataset) -> Convention:
     return convention
 
 
-xr.register_dataset_accessor(State.accessor_name)(State)
+xarray.register_dataset_accessor(State.accessor_name)(State)

src/emsarray/cli/commands/export_geometry.py

@@ -3,7 +3,7 @@
 from pathlib import Path
 from typing import Callable, Dict
 
-import xarray as xr
+import xarray
 
 import emsarray
 from emsarray.cli import BaseCommand, CommandException
@@ -12,7 +12,7 @@
 
 logger = logging.getLogger(__name__)
 
-Writer = Callable[[xr.Dataset, Pathish], None]
+Writer = Callable[[xarray.Dataset, Pathish], None]
 format_writers: Dict[str, Writer] = {
     'geojson': geometry.write_geojson,
     'shapefile': geometry.write_shapefile,

src/emsarray/cli/commands/extract_points.py

@@ -2,7 +2,7 @@
 import logging
 from pathlib import Path
 
-import pandas as pd
+import pandas
 
 import emsarray
 from emsarray.cli import BaseCommand, CommandException
@@ -64,7 +64,7 @@ def add_arguments(self, parser: argparse.ArgumentParser) -> None:
     def handle(self, options: argparse.Namespace) -> None:
         logger.info("Extracting points from %r", str(options.input_path))
         dataset = emsarray.open_dataset(options.input_path)
-        dataframe = pd.read_csv(options.points)
+        dataframe = pandas.read_csv(options.points)
 
         try:
             point_data = point_extraction.extract_dataframe(

src/emsarray/compat/shapely.py

@@ -1,7 +1,7 @@
 import warnings
 from typing import Generic, Iterable, Tuple, TypeVar, Union, cast
 
-import numpy as np
+import numpy
 import shapely
 from packaging.version import parse
 from shapely.errors import ShapelyDeprecationWarning
@@ -23,16 +23,16 @@ class SpatialIndex(Generic[T]):
     This also handles the version differences in STRtree between
     shapely ~= 1.8.x and shapely >= 2.0.0
     """
-    items: np.ndarray
+    items: numpy.ndarray
     index: STRtree
 
-    dtype: np.dtype = np.dtype([('geom', np.object_), ('data', np.object_)])
+    dtype: numpy.dtype = numpy.dtype([('geom', numpy.object_), ('data', numpy.object_)])
 
     def __init__(
         self,
-        items: Union[np.ndarray, Iterable[Tuple[BaseGeometry, T]]],
+        items: Union[numpy.ndarray, Iterable[Tuple[BaseGeometry, T]]],
     ):
-        self.items = np.array(items, dtype=self.dtype)
+        self.items = numpy.array(items, dtype=self.dtype)
 
         if shapely_version >= v2:
             self.index = STRtree(self.items['geom'])
@@ -46,19 +46,19 @@ def __init__(
     def query(
         self,
         geom: BaseGeometry,
-    ) -> np.ndarray:
+    ) -> numpy.ndarray:
         if shapely_version >= v2:
             indices = self.index.query(geom)
         else:
             indices = self.index._query(geom)
-        return cast(np.ndarray, self.items.take(indices))
+        return cast(numpy.ndarray, self.items.take(indices))
 
     def nearest(
         self,
         geom: BaseGeometry,
-    ) -> np.ndarray:
+    ) -> numpy.ndarray:
         if shapely_version >= v2:
             indices = self.index.nearest(geom)
         else:
             indices = self.index._nearest(geom)
-        return cast(np.ndarray, self.items.take(indices))
+        return cast(numpy.ndarray, self.items.take(indices))