Update hydrotools.metrics to newer Python versions

.github/workflows/run_metrics.yml

@@ -15,7 +15,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.8", "3.9", "3.10"]
+        python-version: ["3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v2

LICENSE

@@ -1,22 +1,7 @@
-Licensed under the Apache License, Version 2.0 (the “License”);
-you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
-
-http://www.apache.org/licenses/LICENSE-2.0
-
-Unless required by applicable law or agreed to in writing, software
-distributed under the License is distributed on an “AS IS” BASIS,
-WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
-express or implied.
-
-See the License for the specific language governing permission and
-limitations under the License.
-
-- - - - - - - - - - - - - -
-
-“Software code created by U.S. Government employees is not subject to copyright in the
-United States (17 U.S.C. §105). The United States/Department of Commerce reserves all
-rights to seek and obtain copyright protection in countries other than the United States
-for Software authored in its entirety by the Department of Commerce. To this end, the
-Department of Commerce hereby grants to Recipient a royalty-free, nonexclusive license
-to use, copy, and create derivative works of the Software outside of the United States.”
+“Software code created by U.S. Government employees is not subject to copyright
+in the United States (17 U.S.C. §105). The United States/Department of Commerce
+reserve all rights to seek and obtain copyright protection in countries other
+than the United States for Software authored in its entirety by the Department
+of Commerce. To this end, the Department of Commerce hereby grants to Recipient
+a royalty-free, nonexclusive license to use, copy, and create derivative works
+of the Software outside of the United States.”

python/metrics/pyproject.toml

@@ -1,7 +1,41 @@
 [build-system]
-requires = [
-    "setuptools>=42",
-    "wheel",
-    "pip"
-]
+requires = ["setuptools>=70"]
 build-backend = "setuptools.build_meta"
+
+[project]
+name = "hydrotools.metrics"
+authors = [
+    {name = "Jason A. Regina", email = "[email protected]"},
+]
+description = "Variety of standard model evaluation metrics."
+readme = "README.md"
+requires-python = ">=3.9"
+license = {file = "LICENSE"}
+classifiers = [
+    "Development Status :: 4 - Beta",
+    "Intended Audience :: Education",
+    "Intended Audience :: Science/Research",
+    "License :: Free To Use But Restricted",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Topic :: Scientific/Engineering :: Hydrology",
+    "Operating System :: OS Independent"
+]
+dependencies = [
+    "numpy>=1.20.0",
+    "pandas",
+]
+dynamic = ["version"]
+
+[tool.setuptools.dynamic]
+version = {attr = "hydrotools.metrics._version.__version__"}
+
+[project.optional-dependencies]
+develop = ["pytest"]
+
+[project.urls]
+Homepage = "https://github.com/NOAA-OWP/hydrotools"
+Documentation = "https://noaa-owp.github.io/hydrotools/hydrotools.metrics.html"
+Repository = "https://github.com/NOAA-OWP/hydrotools/tree/main/python/metrics"
+"Bug Tracker" = "https://github.com/NOAA-OWP/hydrotools/issues"

python/metrics/src/hydrotools/metrics/_version.py

@@ -1 +1 @@
-__version__ = "1.3.3"
+__version__ = "1.3.5"

python/metrics/src/hydrotools/metrics/metrics.py

@@ -44,7 +44,7 @@ def mean_error(
     power: float = 1.0,
     root: bool = False
     ) -> float:
-    """Compute the mean error or deviation. Default is Mean Absolute Error. The mean error 
+    r"""Compute the mean error or deviation. Default is Mean Absolute Error. The mean error 
     is given by:
 
     $$ME = \frac{1}{n}\sum_{i=1}^{n}\left| y_{s,i} - y_{o,i} \right|^{p}$$
@@ -102,7 +102,7 @@ def mean_error_skill_score(
     power: float = 1.0,
     normalized: bool = False
     ) -> float:
-    """Compute a generic mean error based model skill score. The mean error skill score 
+    r"""Compute a generic mean error based model skill score. The mean error skill score 
     is given by:
 
     $$MESS = 1 - \frac{\sum_{i=1}^{n}\left| y_{p,i} - y_{o,i} \right|^{p}}{\sum_{i=1}^{n}\left| y_{b,i} - y_{o,i} \right|^{p}}$$