Drop Python3.8, support Pandas v2.2, reduce Warnings

.github/workflows/main.yml

@@ -41,7 +41,7 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - "3.8"
+          - "3.9"
     steps:
       - name: Harden Runner
         uses: step-security/harden-runner@eb238b55efaa70779f274895e782ed17c84f2895 # v2.6.1
@@ -115,9 +115,6 @@ jobs:
     strategy:
       matrix:
         include:
-          - tox-env: py38-coverage-eofs
-            python-version: "3.8"
-            markers: -m 'not slow'
           - tox-env: py39-coverage-sbck-eofs
             python-version: "3.9"
             markers: -m 'not slow'

CHANGES.rst

@@ -8,6 +8,7 @@ Contributors to this version: Juliette Lavoie (:user:`juliettelavoie`), Pascal B
 
 Announcements
 ^^^^^^^^^^^^^
+* `xclim` no longer supports Python3.8. (:issue:`1268`, :pull:`1565`).
 * `xclim` now officially supports Python3.12 (requires `numba>=0.59.0`). (:pull:`1613`).
 * `xclim` now adheres to the `Semantic Versioning 2.0.0 <https://semver.org/>`_ specification. (:issue:`1556`, :pull:`1569`).
 * The `xclim` repository now uses `GitHub Discussions <https://github.com/Ouranosinc/xclim/discussions>`_ to offer help for users, coordinate translation efforts, and support general Q&A for the `xclim` community. The `xclim` `Gitter` room has been deprecated in favour of GitHub Discussions. (:issue:`1571`, :pull:`1572`).
@@ -21,9 +22,13 @@ New features and enhancements
 
 Breaking changes
 ^^^^^^^^^^^^^^^^
+* With pandas 2.2 and xarray 2023.11.0, many frequency strings where changed : Y becomes YE, M -> ME, Q -> QE. A and AS are removed. T, L, U, N become min, ms, us and ns.
 * `bump2version` has been replaced with `bump-my-version` to bump the version number using configurations set in the `pyproject.toml` file. (:issue:`1557`, :pull:`1569`).
 * `xclim`'s units registry and units formatting are now extended from `cf-xarray`. The exponent sign "^" is now never added in the ``units`` attribute. For example, square meters are given as "m2" instead of "m^2" by xclim, both are still accepted as input. (:issue:`1010`, :pull:`1590`).
 * `yamale` is now listed as a core dependency (was previously listed in the `dev` installation recipe). (:issue:`1595`, :pull:`1596`).
+* The indice and indicator for ``winter_storm`` has been removed (deprecated since `xclim` v0.46.0 in favour of ``snd_storm_days``). (:pull:`1565`).
+* `xclim` base dependencies have risen to `pandas>=2.2.0` and `xarray>=2023.11.0` to reflect changes to time frequency codes introduced in `pandas==2.2.0`. (:issue:`1534`, :pull:`1565`; see also: `pydata/xarray GH/8394 <https://github.com/pydata/xarray/issues/8394>`_).
+* `xclim` has dropped support for `scipy` version below v1.9.0 and `numpy` versions below v1.20.0. (:pull:`1565`).
 
 Bug fixes
 ^^^^^^^^^

CONTRIBUTING.rst

@@ -121,8 +121,8 @@ Ready to contribute? Here's how to set up `xclim` for local development.
 
 #. Create a development environment. We recommend using ``conda``::
 
-    $ conda create -n xclim python=3.8 --file=environment.yml
-    $ pip install -e .[dev]
+    $ conda create -n xclim python=3.9 --file=environment.yml
+    $ python -m pip install -e ".[dev]"
 
 #. Create a branch for local development::
 
@@ -162,10 +162,10 @@ Ready to contribute? Here's how to set up `xclim` for local development.
 
    Alternatively, one can use ``$ tox`` to run very specific testing configurations, as GitHub Workflows would do when a Pull Request is submitted and new commits are pushed::
 
-    $ tox -e py38  # run tests on Python 3.8
-    $ tox -e py39-upstream-doctest  # run tests on Python 3.9, including doctests, with upstream dependencies
-    $ tox -e py310 -- -m "not slow  # run tests on Python 3.10, excluding "slow" marked tests
-    $ tox -e py311  # run tests on Python 3.11
+    $ tox -e py39  # run tests on Python 3.9
+    $ tox -e py310-upstream-doctest  # run tests on Python 3.10, including doctests, with upstream dependencies
+    $ tox -e py311 -- -m "not slow  # run tests on Python 3.11, excluding "slow" marked tests
+    $ tox -e py312-numba -- -m "not slow  # run tests on Python 3.12, installing upstream `numba`, excluding "slow" marked tests
     $ tox -e notebooks_doctests  # run tests using the base Python on doctests and evaluate all notebooks
     $ tox -e offline  # run tests using the base Python, excluding tests requiring internet access
 
@@ -243,7 +243,7 @@ Before you submit a pull request, please follow these guidelines:
     If you aren't accustomed to writing documentation in reStructuredText (`.rst`), we encourage you to spend a few minutes going over the
     incredibly well-summarized `reStructuredText Primer`_ from the sphinx-doc maintainer community.
 
-#. The pull request should work for Python 3.8, 3.9, 3.10, and 3.11 as well as raise test coverage.
+#. The pull request should work for Python 3.9, 3.10, 3.11, and 3.12 as well as raise test coverage.
    Pull requests are also checked for documentation build status and for `PEP8`_ compliance.
 
    The build statuses and build errors for pull requests can be found at: https://github.com/Ouranosinc/xclim/actions

docs/explanation.rst

@@ -5,11 +5,6 @@ Why use xclim?
 Purpose
 =======
 
-.. important::
-
-    The content of this section is actively being developed in the forthcoming paper submission to JOSS.
-    This section will be updated and finalized when the wording has been agreed upon in :pull:`250`
-
 `xclim` aims to position itself as a climate services tool for any researchers interested in using Climate and Forecast Conventions (`CF-Conventions <https://cfconventions.org/>`_) compliant datasets to perform climate analyses. This tool is optimized for working with Big Data in the climate science domain and can function as an independent library for one-off analyses in *Jupyter Notebooks* or as a backend engine for performing climate data analyses via **Web Processing Services** (`WPS <https://www.ogc.org/standard/wps/>`_; e.g. `Finch <https://github.com/bird-house/finch>`_). It was primarily developed targeting Earth and Environmental Science audiences and researchers, originally for calculating climate indicators for the Canadian government web service `ClimateData.ca <https://climatedata.ca/>`_.
 
 The primary domains that `xclim` is built for are in calculating climate indicators, performing statistical correction / bias adjustment of climate model output variables or simulations, and in performing climate model simulation ensemble statistics.

docs/installation.rst

@@ -11,7 +11,7 @@ To install `xclim` via `pip`, run this command in your terminal:
 
 .. code-block:: shell
 
-    $ pip install xclim
+    $ python -m pip install xclim
 
 If you don't have `pip`_ installed, this `Python installation guide`_ can guide you through the process.
 
@@ -55,7 +55,7 @@ Both of these libraries are available on PyPI and conda-forge:
 
 .. code-block:: shell
 
-    $ pip install flox clisops
+    $ python -m pip install flox clisops
     # Or, alternatively:
     $ conda install -c conda-forge flox clisops
 
@@ -70,7 +70,7 @@ For convenience, these libraries can be installed alongside `xclim` using the fo
 
 .. code-block:: shell
 
-    $ pip install -r requirements_upstream.txt
+    $ python -m pip install -r requirements_upstream.txt
 
 Or, alternatively:
 
@@ -105,13 +105,13 @@ Afterwards, `SBCK` can be installed from PyPI using `pip`:
 
 .. code-block:: shell
 
-    $ pip install SBCK
+    $ python -m pip install SBCK
 
 Another experimental function :py:indicator:`xclim.sdba.property.first_eof` makes use of the `eofs`_ library, which is available on both PyPI and conda-forge:
 
 .. code-block:: shell
 
-    $ pip install eofs
+    $ python -m pip install eofs
     # or alternatively,
     $ conda install -c conda-forge eofs
 
@@ -145,7 +145,7 @@ Once you have extracted a copy of the source, you can install it with pip:
 
 .. code-block:: shell
 
-    $ pip install -e ".[dev]"
+    $ python -m pip install -e ".[dev]"
 
 Alternatively, you can also install a local development copy via `flit`_:
 
@@ -166,4 +166,4 @@ To create a conda environment including `xclim`'s dependencies and several optio
 
     $ conda env create -n my_xclim_env python=3.8 --file=environment.yml
     $ conda activate my_xclim_env
-    (my_xclim_env) $ pip install -e .
+    (my_xclim_env) $ python -m pip install -e .

environment.yml

@@ -4,7 +4,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python >=3.8
+  - python >=3.9
   - astroid
   - boltons >=20.1
   - bottleneck >=1.3.1
@@ -16,15 +16,16 @@ dependencies:
   - jsonpickle
   - lmoments3
   - numba
-  - numpy >=1.16
-  - pandas >=0.23,<2.2
+  - numpy >=1.20.0
+  - pandas >=2.2.0
   - pint >=0.9
   - poppler >=0.67
+  - pyarrow  # Strongly encouraged for Pandas v2.2.0+
   - pyyaml
   - scikit-learn >=0.21.3
-  - scipy >=1.2
+  - scipy >=1.10.0
   - statsmodels
-  - xarray >=2022.06.0,<2023.11.0
+  - xarray >=2023.11.0
   - yamale
   # Extras
   - eofs

pyproject.toml

@@ -12,7 +12,7 @@ maintainers = [
   {name = "Pascal Bourgault", email = "bourgault.pascal@ouranos.ca"}
 ]
 readme = {file = "README.rst", content-type = "text/x-rst"}
-requires-python = ">=3.8.0"
+requires-python = ">=3.9.0"
 keywords = ["xclim", "xarray", "climate", "climatology", "bias correction", "ensemble", "indicators", "analysis"]
 license = {file = "LICENSE"}
 classifiers = [
@@ -23,7 +23,6 @@ classifiers = [
   "Natural Language :: English",
   "Operating System :: OS Independent",
   "Programming Language :: Python :: 3",
-  "Programming Language :: Python :: 3.8",
   "Programming Language :: Python :: 3.9",
   "Programming Language :: Python :: 3.10",
   "Programming Language :: Python :: 3.11",
@@ -35,31 +34,29 @@ dependencies = [
   "boltons>=20.1",
   "bottleneck>=1.3.1",
   # cf-xarray is differently named on conda-forge
-  "cf-xarray>=0.6.1,<0.8.5; python_version == '3.8'",
-  "cf-xarray>=0.6.1; python_version >= '3.9'",
+  "cf-xarray>=0.6.1",
   "cftime>=1.4.1",
   "Click>=8.1",
   "dask[array]>=2.6",
-  "importlib-resources; python_version == '3.8'",
   "jsonpickle",
   "lmoments3>=1.0.5",
   "numba",
-  "numpy>=1.16",
-  "pandas>=0.23,<2.0; python_version == '3.8'",
-  "pandas>=0.23,<2.2; python_version >= '3.9'",
+  "numpy>=1.20.0",
+  "pandas>=2.2",
   "pint>=0.10",
+  "pyarrow", # Strongly encouraged for pandas v2.2.0+
   "pyyaml",
   "scikit-learn>=0.21.3",
-  "scipy>=1.2",
+  "scipy>=1.9.0",
   "statsmodels",
-  "xarray>=2022.06.0,<2023.11.0",
+  "xarray>=2023.11.0",
   "yamale"
 ]
 
 [project.optional-dependencies]
 dev = [
   # Dev tools and testing
-  "black >=23.3.0",
+  "black >=23.12.0",
   "blackdoc",
   "bump-my-version",
   "codespell",
@@ -70,6 +67,7 @@ dev = [
   "h5netcdf",
   "ipython",
   "mypy",
+  "nbconvert",
   "nbqa",
   "nbval",
   "netCDF4 >=1.4",
@@ -117,10 +115,10 @@ xclim = "xclim.cli:cli"
 
 [tool.black]
 target-version = [
-  "py38",
   "py39",
   "py310",
-  "py311"
+  "py311",
+  "py312"
 ]
 
 [tool.bumpversion]
@@ -197,12 +195,12 @@ exclude = [
 
 [tool.isort]
 profile = "black"
-py_version = 38
+py_version = 39
 append_only = true
 add_imports = "from __future__ import annotations"
 
 [tool.mypy]
-python_version = 3.8
+python_version = 3.9
 show_error_codes = true
 warn_return_any = true
 warn_unused_configs = true
@@ -250,13 +248,12 @@ markers = [
 [tool.ruff]
 src = ["xclim"]
 line-length = 150
-target-version = "py38"
+target-version = "py39"
 exclude = [
   ".git",
   "docs",
   "build",
-  ".eggs",
-  "tests"
+  ".eggs"
 ]
 ignore = [
   "D205",

tests/conftest.py

@@ -141,7 +141,7 @@ def prsnd_series():
 def pr_hr_series():
     """Return precipitation hourly time series."""
     _pr_hr_series = partial(
-        test_timeseries, start="1/1/2000", variable="pr", units="kg m-2 s-1", freq="1H"
+        test_timeseries, start="1/1/2000", variable="pr", units="kg m-2 s-1", freq="h"
     )
     return _pr_hr_series
 

tests/test_analog.py

@@ -5,8 +5,6 @@
 import pandas as pd
 import pytest
 from numpy.testing import assert_almost_equal
-from packaging.version import Version
-from scipy import __version__ as __scipy_version__
 from scipy import integrate, stats
 from sklearn import datasets
 
@@ -60,11 +58,6 @@ def test_exact_randn(exact_randn):
 @pytest.mark.slow
 @pytest.mark.parametrize("method", xca.metrics.keys())
 def test_spatial_analogs(method, open_dataset):
-    if method in ["nearest_neighbor", "kldiv"] and Version(__scipy_version__) < Version(
-        "1.6.0"
-    ):
-        pytest.skip("Method not supported in scipy<1.6.0")
-
     diss = open_dataset("SpatialAnalogs/dissimilarity")
     data = open_dataset("SpatialAnalogs/indicators")
 
@@ -138,10 +131,6 @@ def test_compare_with_matlab(self):
         assert_almost_equal(dm, 2.8463, 4)
 
 
-@pytest.mark.skipif(
-    Version(__scipy_version__) < Version("1.6.0"),
-    reason="Not supported in scipy<1.6.0",
-)
 class TestNN:
     def test_simple(self, random):
         d = 2
@@ -244,10 +233,6 @@ def func(x):
 
 
 @pytest.mark.slow
-@pytest.mark.skipif(
-    Version(__scipy_version__) < Version("1.6.0"),
-    reason="Not supported in scipy<1.6.0",
-)
 class TestKLDIV:
     #
     def test_against_analytic(self, random):

tests/test_atmos.py

@@ -272,7 +272,7 @@ def test_wind_power_potential_from_3h_series():
     from xclim.testing.helpers import test_timeseries
 
     w = test_timeseries(
-        np.ones(96) * 15, variable="sfcWind", start="7/1/2000", units="m s-1", freq="3H"
+        np.ones(96) * 15, variable="sfcWind", start="7/1/2000", units="m s-1", freq="3h"
     )
     out = atmos.wind_power_potential(wind_speed=w)
 

tests/test_bootstrapping.py

@@ -26,9 +26,9 @@ class Test_bootstrap:
         "var,p,index,freq, cftime",
         (
             ["tas", 98, tg90p, "MS", False],
-            ["tasmin", 98, tn90p, "A-JUL", False],
-            ["tasmax", 98, tx90p, "Q-APR", False],
-            ["tasmax", 98, tx90p, "Q-APR", True],
+            ["tasmin", 98, tn90p, "YS-JUL", False],
+            ["tasmax", 98, tx90p, "QS-APR", False],
+            ["tasmax", 98, tx90p, "QS-APR", True],
             ["tasmin", 2, tn10p, "MS", False],
             ["tasmax", 2, tx10p, "YS", False],
             ["tasmax", 2, tx10p, "YS", True],