Changes for Python 3.13 support

.github/workflows/unit-tests-workflow.yml

@@ -6,7 +6,7 @@ jobs:
   test:
     strategy:
       matrix:
-        python-version: ['3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.10', '3.11', '3.12', '3.13']
 
     runs-on: ubuntu-latest
 

pyproject.toml

@@ -23,7 +23,7 @@ exclude = '''
 
 [tool.poetry]
 name = 'climate_indices'
-version = '2.0.1'
+version = '2.0.2'
 description = 'Reference implementations of various climate indices typically used for drought monitoring'
 authors = ['James Adams <[email protected]>']
 readme = 'README.md'
@@ -33,27 +33,27 @@ classifiers = [
      'Intended Audience :: Education',
      'Intended Audience :: Science/Research',
      'License :: OSI Approved :: BSD License',
-     'Programming Language :: Python :: 3.8',
-     'Programming Language :: Python :: 3.9',
      'Programming Language :: Python :: 3.10',
      'Programming Language :: Python :: 3.11',
+     'Programming Language :: Python :: 3.12',
+     'Programming Language :: Python :: 3.13',
      'Topic :: Software Development :: Libraries :: Python Modules',
-     'Topic :: Scientific/Engineering :: Atmospheric Science',
-     'Topic :: Scientific/Engineering :: Physics'
+     'Topic :: Scientific/Engineering :: Climate Science',
 ]
 packages = [{include = 'climate_indices', from = 'src'}]
 
 [tool.poetry.dependencies]
 # only Python and scipy are required for the base library code
-python = '>=3.8,<3.12'
-scipy = '1.10.1'
+python = '>=3.10,<3.14'
+scipy = '>=1.14.1'
 # remaining dependencies are required for the CLI (console) scripts
 cftime = '>=1.6.4'
-dask = '>=2023.5.0'
-h5netcdf = '>=1.1.0'
-xarray = '>=2023.1.0'
+dask = '>=2024.12.0'
+h5netcdf = '>=1.4.0'
+xarray = '>=2024.11.0'
 
 [tool.poetry.dev-dependencies]
+coverage = '^7.6.9'
 pytest = '8.3.3'
 toml = '>=0.10.2'
 
@@ -68,3 +68,72 @@ spi = 'climate_indices.__spi__:main'
 filterwarnings = [
     'ignore::FutureWarning',
 ]
+
+## ==========================   below for conversion to uv instead of poetry  ==========================
+#[project]
+#    authors = [
+#        {name = "James Adams", email = "[email protected]"},
+#    ]
+#    requires-python = ">=3.10,<3.14"
+#    dependencies = [
+#        "scipy>=1.14.1",
+#        "cftime",
+#        "dask",
+#        "h5py",
+#        "xarray",
+#    ]
+#    name = "climate_indices"
+#    version = "2.0.2"
+#    description = "Reference implementations of various climate indices typically used for drought monitoring"
+#    readme = "README.md"
+#    classifiers = [
+#        "Development Status :: 5 - Production/Stable",
+#        "Intended Audience :: Developers",
+#        "Intended Audience :: Education",
+#        "Intended Audience :: Science/Research",
+#        "License :: OSI Approved :: BSD License",
+#        "Programming Language :: Python :: 3.10",
+#        "Programming Language :: Python :: 3.11",
+#        "Programming Language :: Python :: 3.12",
+#        "Programming Language :: Python :: 3.13",
+#        "Topic :: Software Development :: Libraries :: Python Modules",
+#        "Topic :: Scientific/Engineering :: Climate Science",
+#    ]
+#
+#[dependency-groups]
+#    dev = [
+#        "sphinx-autodoc-typehints==2.0.1",
+#        "pytest==8.3.3",
+#        "toml>=0.10.2",
+#    ]
+#
+#[project.urls]
+#    'Homepage' = 'https://github.com/monocongo/climate_indices'
+#    'Bug Tracker' = 'https://github.com/monocongo/climate_indices/issues'
+#
+#[project.scripts]
+#    process_climate_indices = "climate_indices.__main__:main"
+#    spi = "climate_indices.__spi__:main"
+#
+#[tool.black]
+#line-length = 120
+#target-version = ['py39', 'py310', 'py311', 'py312', 'py313']
+#include = '\.pyi?$'
+#exclude = '''
+#/(
+#    \.git
+#  | \.mypy_cache
+#  | \.tox
+#  | \.venv
+#  | build
+#  | dist
+#)/
+#'''
+#
+#[tool.pytest.ini_options]
+#filterwarnings = [
+#    'ignore::FutureWarning',
+#]
+#
+#[tool.pdm.build]
+#includes = ["src/climate_indices"]

src/climate_indices/compute.py

@@ -2,7 +2,6 @@
 Common classes and functions used to compute the various climate indices.
 """
 from enum import Enum
-from distutils.version import LooseVersion
 import logging
 from typing import Tuple
 
@@ -21,9 +20,6 @@
     "transform_fitted_pearson",
 ]
 
-# depending on the version of scipy we may need to use a workaround due to a bug in some versions of scipy
-_do_pearson3_workaround = LooseVersion(scipy.version.version) < "1.6.0"
-
 # Retrieve logger and set desired logging level
 _logger = utils.get_logger(__name__, logging.WARN)
 
@@ -501,30 +497,11 @@ def _pearson_fit(
         values[zero_mask] = 0.0
         values[trace_mask] = 0.0005
 
-        if _do_pearson3_workaround:
-            # Before scipy 1.6.0, there were a few bugs in pearson3.
-            # Looks like https://github.com/scipy/scipy/pull/12640 fixed them.
-
-            # compute the minimum value possible, and if any values are below
-            # that threshold then we set the corresponding CDF to a floor value.
-            # This was not properly done in older scipy releases.
-            # TODO ask Richard Heim why the use of this floor value, matching
-            #  that used for the trace amount?
-            nans_mask = np.isnan(values)
-            values[np.logical_and(minimums_mask, nans_mask)] = 0.0005
-            # This will get turned into 0.9995 when the negative
-            # skew bug is worked around a few lines from here.
-            values[np.logical_and(maximums_mask, nans_mask)] = 0.0005
-
-            # account for negative skew
-            skew_mask = skew < 0.0
-            values[:, skew_mask] = 1 - values[:, skew_mask]
-        else:
-            # The original values were found to be outside the
-            # range of the fitted distribution, so we will set
-            # the probabilities to something just within the range.
-            values[minimums_mask] = 0.0005
-            values[maximums_mask] = 0.9995
+        # The original values were found to be outside the
+        # range of the fitted distribution, so we will set
+        # the probabilities to something just within the range.
+        values[minimums_mask] = 0.0005
+        values[maximums_mask] = 0.9995
 
         if not np.all(np.isnan(values)):
             # calculate the probability value, clipped between 0 and 1