MNT: Update to shapely 2 API and address lint and type issues raised …

…by the new linting configs. [skip ci]

pydaymet/cli.py

@@ -3,7 +3,7 @@
 
 import itertools
 from pathlib import Path
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, TypeVar
 
 import click
 import geopandas as gpd
@@ -20,7 +20,9 @@
 )
 
 if TYPE_CHECKING:
-    from shapely.geometry import MultiPolygon, Point, Polygon
+    from shapely import MultiPolygon, Point, Polygon
+
+    DFType = TypeVar("DFType", pd.DataFrame, gpd.GeoDataFrame)
 
 
 def parse_snow(target_df: pd.DataFrame) -> pd.DataFrame:
@@ -33,17 +35,15 @@ def parse_snow(target_df: pd.DataFrame) -> pd.DataFrame:
     return target_df
 
 
-def get_target_df(
-    tdf: pd.DataFrame | gpd.GeoDataFrame, req_cols: list[str]
-) -> pd.DataFrame | gpd.GeoDataFrame:
+def get_target_df(tdf: DFType, req_cols: list[str]) -> DFType:
     """Check if all required columns exists in the dataframe.
 
     It also re-orders the columns based on req_cols order.
     """
     missing = [c for c in req_cols if c not in tdf]
     if missing:
         raise MissingItemError(missing)
-    return tdf[req_cols]
+    return tdf[req_cols]  # pyright: ignore[reportGeneralTypeIssues]
 
 
 def get_required_cols(geom_type: str, columns: pd.Index) -> list[str]:
@@ -63,7 +63,8 @@ def _get_region(gid: str, geom: Polygon | MultiPolygon | Point) -> str:
         if bbox.contains(geom):
             return region
     bbox_range = "\n".join(f"{k.upper()}: {v.bounds}" for k, v in region_bbox.items())
-    raise InputRangeError(f"geometry ID of {gid}", f"within\n{bbox_range}")
+    geo_id = f"geometry ID of {gid}"
+    raise InputRangeError(geo_id, f"within\n{bbox_range}")
 
 
 def get_region(geodf: gpd.GeoDataFrame) -> list[str]:
@@ -85,8 +86,12 @@ def get_region(geodf: gpd.GeoDataFrame) -> list[str]:
     "--save_dir",
     type=click.Path(exists=False),
     default="clm_daymet",
-    help="Path to a directory to save the requested files. "
-    + "Extension for the outputs is .nc for geometry and .csv for coords.",
+    help=" ".join(
+        (
+            "Path to a directory to save the requested files.",
+            "Extension for the outputs is .nc for geometry and .csv for coords.",
+        )
+    ),
 )
 
 ssl_opt = click.option(
@@ -122,7 +127,7 @@ def coords(
         - ``lon``: Longitude of the points of interest.
         - ``lat``: Latitude of the points of interest.
         - ``time_scale``: (optional) Time scale, either ``daily`` (default), ``monthly`` or ``annual``.
-        - ``pet``: (optional) Method to compute PET. Suppoerted methods are:
+        - ``pet``: (optional) Method to compute PET. Supported methods are:
                    ``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).
         - ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.
 
@@ -190,7 +195,7 @@ def geometry(
         - ``end``: End time.
         - ``geometry``: Target geometries.
         - ``time_scale``: (optional) Time scale, either ``daily`` (default), ``monthly`` or ``annual``.
-        - ``pet``: (optional) Method to compute PET. Suppoerted methods are:
+        - ``pet``: (optional) Method to compute PET. Supported methods are:
                    ``penman_monteith``, ``hargreaves_samani``, ``priestley_taylor``, and ``none`` (default).
         - ``snow``: (optional) Separate snowfall from precipitation, default is ``False``.
 

pydaymet/core.py

@@ -1,11 +1,12 @@
 """Core class for the Daymet functions."""
+# pyright: reportGeneralTypeIssues=false
 from __future__ import annotations
 
 import functools
 import warnings
 from dataclasses import dataclass
 from datetime import datetime
-from typing import TYPE_CHECKING, Iterable, TypeVar
+from typing import TYPE_CHECKING, Any, Callable, Iterable, TypeVar
 
 import numpy as np
 import numpy.typing as npt
@@ -18,21 +19,24 @@
 
 try:
     from numba import config as numba_config
-    from numba import njit, prange
+    from numba import jit, prange
 
-    ngjit = functools.partial(njit, cache=True, nogil=True)
+    ngjit = functools.partial(jit, nopython=True, cache=True, nogil=True)
     numba_config.THREADING_LAYER = "workqueue"
     has_numba = True
 except ImportError:
     has_numba = False
     prange = range
-    numba_config = None
-    njit = None
 
-    def ngjit(ntypes, parallel=None):  # type: ignore
-        def decorator_njit(func):  # type: ignore
+    T = TypeVar("T")
+    Func = Callable[..., T]
+
+    def ngjit(
+        signature_or_function: str | Func[T], parallel: bool = False
+    ) -> Callable[[Func[T]], Func[T]]:
+        def decorator_njit(func: Func[T]) -> Func[T]:
             @functools.wraps(func)
-            def wrapper_decorator(*args, **kwargs):  # type: ignore
+            def wrapper_decorator(*args: tuple[Any, ...], **kwargs: dict[str, Any]) -> T:
                 return func(*args, **kwargs)
 
             return wrapper_decorator
@@ -125,7 +129,7 @@ def __post_init__(self) -> None:
             raise InputValueError("region", valid_regions)
 
 
-@ngjit("f8[::1](f8[::1], f8[::1], f8, f8)")
+@ngjit("f8[::1](f8[::1], f8[::1], f8, f8)", parallel=True)
 def _separate_snow(
     prcp: npt.NDArray[np.float64],
     tmin: npt.NDArray[np.float64],
@@ -466,5 +470,5 @@ def separate_snow(self, clm: DF, t_rain: float = T_RAIN, t_snow: float = T_SNOW)
             raise InputTypeError("clm", "pandas.DataFrame or xarray.Dataset")
 
         if isinstance(clm, xr.Dataset):
-            return self._snow_gridded(clm, t_rain, t_snow)  # type: ignore
+            return self._snow_gridded(clm, t_rain, t_snow)
         return self._snow_point(clm, t_rain, t_snow)

pydaymet/pet.py

@@ -1,4 +1,5 @@
 """Core class for the Daymet functions."""
+# pyright: reportGeneralTypeIssues=false
 from __future__ import annotations
 
 from typing import (
@@ -15,14 +16,15 @@
 
 import numpy as np
 import pandas as pd
-import py3dep
-import pygeoogc as ogc
-import pyproj
 import xarray as xr
 
+import py3dep
+import pygeoogc as ogc
 from pydaymet.exceptions import InputTypeError, InputValueError, MissingItemError
 
 if TYPE_CHECKING:
+    import pyproj
+
     CRSTYPE = Union[int, str, pyproj.CRS]
     DF = TypeVar("DF", pd.DataFrame, xr.Dataset)
     DS = TypeVar("DS", pd.Series, xr.DataArray)
@@ -62,7 +64,7 @@ def saturation_vapor(temperature: DS) -> DS:
     ----------
     .. footbibliography::
     """
-    return 0.6108 * np.exp(17.27 * temperature / (temperature + 237.3))  # type: ignore
+    return 0.6108 * np.exp(17.27 * temperature / (temperature + 237.3))
 
 
 def actual_vapor_pressure(tmin_c: DS, arid_correction: bool) -> DS:
@@ -93,7 +95,7 @@ def actual_vapor_pressure(tmin_c: DS, arid_correction: bool) -> DS:
     .. footbibliography::
     """
     if arid_correction:
-        return saturation_vapor(tmin_c - 2.0)  # type: ignore
+        return saturation_vapor(tmin_c - 2.0)
     return saturation_vapor(tmin_c)
 
 
@@ -265,7 +267,7 @@ def vapor_slope(tmean_c: DS) -> DS:
     ----------
     .. footbibliography::
     """
-    return 4098 * saturation_vapor(tmean_c) / np.square(tmean_c + 237.3)  # type: ignore
+    return 4098 * saturation_vapor(tmean_c) / np.square(tmean_c + 237.3)
 
 
 def check_requirements(reqs: Iterable[str], cols: KeysView[Hashable] | pd.Index) -> None:
@@ -536,7 +538,7 @@ def __init__(
         self.res = 1.0e3
         self.crs = clm.rio.crs
 
-        if "elevation" not in self.clm.keys():
+        if "elevation" not in self.clm:
             chunksizes = None
             if all(d in self.clm.chunksizes for d in ("time", "x", "y")):
                 chunksizes = self.clm.chunksizes
@@ -629,9 +631,7 @@ def penman_monteith(self) -> xr.Dataset:
             / (self.clm["tmean"] + 273.0)
             * u_2m
             * (self.clm["e_s"] - self.clm["e_a"])
-        ) / (
-            self.clm["vp_slope"] + self.clm["gamma"] * (1.0 + 0.34 * u_2m)  # type: ignore
-        )
+        ) / (self.clm["vp_slope"] + self.clm["gamma"] * (1.0 + 0.34 * u_2m))
 
         self.clm = self.clm.drop_vars(
             ["vp_slope", "gamma", "rad_n", "tmean", "lambda", "e_s", "e_a"]

pydaymet/pydaymet.py

@@ -1,4 +1,5 @@
 """Access the Daymet database for both single single pixel and gridded queries."""
+# pyright: reportGeneralTypeIssues=false
 from __future__ import annotations
 
 import functools
@@ -7,24 +8,24 @@
 import re
 from typing import TYPE_CHECKING, Callable, Generator, Iterable, Sequence, Union, cast
 
-import async_retriever as ar
 import numpy as np
 import pandas as pd
-import pygeoogc as ogc
-import pygeoutils as geoutils
-import pyproj
 import xarray as xr
-from pygeoogc import ServiceError, ServiceURL
-from pygeoutils import Coordinates
 
+import async_retriever as ar
+import pygeoogc as ogc
+import pygeoutils as geoutils
 from pydaymet.core import T_RAIN, T_SNOW, Daymet
 from pydaymet.exceptions import InputRangeError, InputTypeError
 from pydaymet.pet import potential_et
+from pygeoogc import ServiceError, ServiceURL
+from pygeoutils import Coordinates
 
 if TYPE_CHECKING:
     from pathlib import Path
 
-    from shapely.geometry import MultiPolygon, Polygon
+    import pyproj
+    from shapely import MultiPolygon, Polygon
 
     CRSTYPE = Union[int, str, pyproj.CRS]
 
@@ -146,7 +147,7 @@ def _by_coord(
         (
             pd.concat(
                 pd.read_csv(io.StringIO(r), parse_dates=[0], usecols=[0, 3], index_col=[0])
-                for r in retrieve(u, k)  # type: ignore
+                for r in retrieve(u, k)
             )
             for u, k in (zip(*u) for u in url_kwds)
         ),
@@ -161,7 +162,7 @@ def _by_coord(
     clm = clm.where(clm > -9999)
 
     if pet is not None:
-        clm = potential_et(clm, coords, method=pet, params=pet_params)  # type: ignore
+        clm = potential_et(clm, coords, method=pet, params=pet_params)
 
     if snow:
         params = {"t_rain": T_RAIN, "t_snow": T_SNOW} if snow_params is None else snow_params
@@ -318,7 +319,7 @@ def get_bycoords(
     if n_pts == 1:
         clm = next(iter(clm_list), pd.DataFrame())
     else:
-        clm = cast("pd.DataFrame", pd.concat(clm_list, keys=idx, axis=1))
+        clm = pd.concat(clm_list, keys=idx, axis=1)
         clm = clm.columns.set_names(["id", "variable"])
     clm = clm.set_index(pd.DatetimeIndex(pd.to_datetime(clm.index).date))
     return clm
@@ -480,7 +481,7 @@ def get_bygeom(
 
     Examples
     --------
-    >>> from shapely.geometry import Polygon
+    >>> from shapely import Polygon
     >>> import pydaymet as daymet
     >>> geometry = Polygon(
     ...     [[-69.77, 45.07], [-69.31, 45.07], [-69.31, 45.45], [-69.77, 45.45], [-69.77, 45.07]]
@@ -531,9 +532,11 @@ def get_bygeom(
         # https://docs.xarray.dev/en/stable/user-guide/io.html#reading-multi-file-datasets
         clm = xr.merge(_open_dataset(f) for f in clm_files)
     except ValueError as ex:
-        msg = (
-            "Daymet did NOT process your request successfully. "
-            + "Check your inputs and try again."
+        msg = " ".join(
+            (
+                "Daymet did NOT process your request successfully.",
+                "Check your inputs and try again.",
+            )
         )
         raise ServiceError(msg) from ex