feat: Updating pre-commit hooks and fixing errors.

Refs: #13

Author: Thomas Zilio

.pre-commit-config.yaml

@@ -1,6 +1,6 @@
 repos:
   - repo: https://github.com/pre-commit/pre-commit-hooks
-    rev: v4.6.0
+    rev: v5.0.0
     hooks:
       - id: check-case-conflict
       - id: check-docstring-first
@@ -15,16 +15,16 @@ repos:
       - id: trailing-whitespace
         exclude: conda/meta.yaml
   - repo: https://github.com/asottile/pyupgrade
-    rev: "v3.17.0"
+    rev: "v3.19.1"
     hooks:
     - id: pyupgrade
       args: [--py310-plus]
   - repo: https://github.com/PyCQA/isort
-    rev: 5.13.2
+    rev: 6.0.1
     hooks:
       - id: isort
   - repo: https://github.com/PyCQA/flake8
-    rev: 7.1.1
+    rev: 7.1.2
     hooks:
       - id: flake8
         exclude: tests
@@ -47,11 +47,11 @@ repos:
     hooks:
     - id: docformatter
   - repo: https://github.com/codespell-project/codespell
-    rev: "v2.3.0"
+    rev: "v2.4.1"
     hooks:
     - id: codespell
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.11.2
+    rev: v1.15.0
     hooks:
       - id: mypy
         exclude: docs

conda/meta.yaml

@@ -23,7 +23,7 @@ requirements:
     - numpy >=1.20
     - python
     - xarray
-    - zarr
+    - zarr < 3
 test:
   requires:
     - pytest

setup.cfg

@@ -33,7 +33,7 @@ install_requires =
     numpy>=1.20
     pandas
     xarray
-    zarr>=2.11
+    zarr>=2.11,<3
 package_dir =
     = .
 packages = find:

zcollection/merging/period.py

@@ -8,7 +8,7 @@
 """
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, Final, Literal
 from collections.abc import Callable
 import enum
 import re
@@ -23,8 +23,8 @@
     r'(?:datetime|timedelta)64\[(\w+)\]').search
 
 #: Numpy time units
-RESOLUTION = ('as', 'fs', 'ps', 'ns', 'us', 'ms', 's', 'm', 'h', 'D', 'W', 'M',
-              'Y')
+RESOLUTION: Final = ('as', 'fs', 'ps', 'ns', 'us', 'ms', 's', 'm', 'h', 'D',
+                     'W', 'M', 'Y')
 
 
 def _time64_unit(dtype: DType[Any]) -> str:
@@ -35,7 +35,10 @@ def _time64_unit(dtype: DType[Any]) -> str:
     return match.group(1)
 
 
-def _min_time64_unit(*args: DType[Any]) -> str:
+def _min_time64_unit(
+    *args: DType[Any]
+) -> Literal['as', 'fs', 'ps', 'ns', 'us', 'ms', 's', 'm', 'h', 'D', 'W', 'M',
+             'Y']:
     """Get the minimum unit of time."""
     index = min(RESOLUTION.index(_time64_unit(item)) for item in args)
     return RESOLUTION[index]
@@ -212,7 +215,7 @@ def __init__(self,
                  end: numpy.datetime64,
                  *,
                  within: bool = False) -> None:
-        duration_unit: str = _min_time64_unit(begin.dtype, end.dtype)
+        duration_unit = _min_time64_unit(begin.dtype, end.dtype)
 
         #: The beginning of the period.
         self._begin: numpy.datetime64 = begin

zcollection/partitioning/tests/test_date.py

@@ -8,6 +8,7 @@
 """
 from __future__ import annotations
 
+from typing import Literal
 import dataclasses
 import pickle
 import random
@@ -84,29 +85,30 @@ def test_split_dataset(
     delayed: bool,
 ) -> None:
     """Test the split_dataset method."""
-    for end_date, indices, resolution in [
-        (
-            numpy.datetime64('2001-12-31', 'Y'),
-            slice(0, 1),
-            'Y',
-        ),
-        (
-            numpy.datetime64('2000-12-31', 'M'),
-            slice(0, 2),
-            'M',
-        ),
-        (
-            numpy.datetime64('2000-12-31', 'D'),
-            slice(0, 3),
-            'D',
-        ),
-        (
-            numpy.datetime64('2000-01-31', 'h'),
-            slice(0, 4),
-            'h',
-        ),
-    ]:
-
+    splits: list[tuple[numpy.datetime64, slice,
+                       Literal['Y', 'M', 'D', 'h']]] = [
+                           (
+                               numpy.datetime64('2001-12-31', 'Y'),
+                               slice(0, 1),
+                               'Y',
+                           ),
+                           (
+                               numpy.datetime64('2000-12-31', 'M'),
+                               slice(0, 2),
+                               'M',
+                           ),
+                           (
+                               numpy.datetime64('2000-12-31', 'D'),
+                               slice(0, 3),
+                               'D',
+                           ),
+                           (
+                               numpy.datetime64('2000-01-31', 'h'),
+                               slice(0, 4),
+                               'h',
+                           ),
+                       ]
+    for end_date, indices, resolution in splits:
         # Time delta between two partitions
         timedelta = numpy.timedelta64(1, resolution)
 
@@ -128,7 +130,7 @@ def test_split_dataset(
         assert len(partitioning) == len(range(indices.start, indices.stop))
 
         # Date of the current partition
-        date = numpy.datetime64(START_DATE, resolution)
+        date = START_DATE.astype(f'datetime64[{resolution}]')
 
         # Build the test dataset
         zds = dataset.Dataset.from_xarray(xds)

zcollection/tests/test_expression.py

@@ -11,6 +11,7 @@
 import timeit
 
 import numpy
+import numpy as np
 import pytest
 import xarray
 
@@ -25,11 +26,12 @@
 
 def make_dataset(num_samples: int | None = None) -> dataset.Dataset:
     """Creation of a data set for testing purposes."""
-    dates = numpy.arange(numpy.datetime64('2000-01-01', 'ns'),
-                         numpy.datetime64('2009-12-31', 'ns'),
-                         numpy.timedelta64(1, 'h')).astype('datetime64[ns]')
+    dates: np.ndarray = numpy.arange(numpy.datetime64('2000-01-01', 'ns'),
+                                     numpy.datetime64('2009-12-31', 'ns'),
+                                     numpy.timedelta64(
+                                         1, 'h')).astype('datetime64[ns]')
     if num_samples is not None:
-        dates = dates[:num_samples + 1]
+        dates = dates[0:num_samples + 1]
     observation = numpy.random.rand(dates.size)  # type: ignore
     return dataset.Dataset.from_xarray(
         xarray.Dataset({