build: switch to uv and ruff

.github/workflows/ci.yml

@@ -8,51 +8,40 @@ on:
     types: [opened, synchronize, reopened, edited]
   workflow_dispatch: # Allows you to run this workflow manually from the Actions tab
 
-jobs:
-
-  Code_Quality_Check:
 
+jobs:
+  test:
+    name: py${{ matrix.versions.python-version }} ${{ matrix.versions.resolution }}
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.10', 3.11]
+        versions:
+          - python-version: '3.10'
+            resolution: lowest-direct
+          - python-version: '3.11'
+            resolution: highest
+          - python-version: '3.12'
+            resolution: highest
     steps:
-      - name: Checkout Repository
-        uses: actions/checkout@v3
-
-      - name: Setup Python ${{ matrix.python-version }}
-        uses: actions/setup-python@v3
-        with:
-          python-version: ${{ matrix.python-version }}
+      - uses: actions/checkout@v4
 
-      - name: Setup Poetry
-        uses: abatilo/actions[email protected]
+      - name: Setup Python ${{ matrix.versions.python-version }}
+        uses: actions/setup-python@v5
         with:
-          poetry-version: 1.5.1
+          python-version: ${{ matrix.versions.python-version }}
 
-      - name: Configure Poetry Settings
-        shell: bash
-        run: python -m poetry config virtualenvs.in-project true
-
-      - name: Verify Poetry Version
-        run: poetry --version
-
-      - name: Install Project Dependencies
-        run: python -m poetry install --with dev
-
-      - name: Lint Codebase with flake8
+      - name: Install dependencies
         run: |
-          python -m poetry run flake8 . --exclude .venv --count --select=E9,F63,F7,F82 --show-source --statistics
-          python -m poetry run flake8 . --exclude .venv --count --exit-zero --max-complexity=10 --max-line-length=79 --statistics
+          pip install uv
+          uv pip install . -r pyproject.toml --system --extra dev --resolution ${{ matrix.versions.resolution }}
 
-      - name: Execute Tests with pytest and Coverage
+      - name: Execute Tests
         run: |
-          python -m poetry run coverage run -m pytest --doctest-glob="README.md"
-          python -m poetry run coverage report -m
-          python -m poetry run coverage xml
+          coverage run -m pytest -n auto --doctest-glob="README.md"
+          coverage report -m
+          coverage xml
 
       - name: Upload Coverage Report to Codecov
         uses: codecov/codecov-action@v3
         with:
           files: ./coverage.xml
-

.github/workflows/formatting.yml

@@ -0,0 +1,27 @@
+name: Formatting
+
+on:
+  pull_request:
+    branches: [main, develop]
+    types: [opened, synchronize, reopened, edited]
+  workflow_dispatch: # Allows you to run this workflow manually from the Actions tab
+
+
+jobs:
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Install Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.11"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install ruff
+      # Update output format to enable automatic inline annotations.
+      - name: Run Ruff
+        run: |
+          ruff check
+          ruff format --check

.github/workflows/pull-request-linting.yml

@@ -5,15 +5,15 @@
 # Aim: ensure that PR title matches the concentional commits spec
 # More info: https://github.com/marketplace/actions/semantic-pull-request
 
-name: "Pull Request Linting"
+name: "PR Linting"
 
 on:
   pull_request_target:
     types: [opened, edited, synchronize]
 
 jobs:
-  PR_Validation:
-    name: Validate Pull Request Title
+  validate:
+    name: validate conventional commit in title
     runs-on: ubuntu-latest
     steps:
       - name: Run Semantic Pull Request Linting

.pre-commit-config.yaml

@@ -7,16 +7,13 @@ repos:
       - id: conventional-pre-commit
         stages: [commit-msg]
         args: [] # optional: list of Conventional Commits types to allow
-  # Black Code Formatter
-  - repo: https://github.com/psf/black
-    rev: 23.7.0
+  # Lint and format with ruff
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: v0.3.4
     hooks:
-      - id: black
-        # It is recommended to specify the latest version of Python
-        # supported by your project here, or alternatively use
-        # pre-commit's default_language_version, see
-        # https://pre-commit.com/#top_level-default_language_version
-        language_version: python3.11
+      - id: ruff
+        args: [ --fix ]
+      - id: ruff-format
   # Strip output from Jupyter Notebooks
   - repo: https://github.com/kynan/nbstripout
     rev: 0.6.1

docs/auto_examples/1single/plot_complex_eof.py

@@ -2,14 +2,14 @@
 Complex/Hilbert EOF analysis
 ============================================
 
-We demonstrate how to execute a Complex EOF (or Hilbert EOF) analysis [1]_ [2]_ [3]_. 
-This method extends traditional EOF analysis into the complex domain, allowing 
+We demonstrate how to execute a Complex EOF (or Hilbert EOF) analysis [1]_ [2]_ [3]_.
+This method extends traditional EOF analysis into the complex domain, allowing
 the EOF components to have real and imaginary parts. This capability can reveal
-oscillatory patterns in datasets, which are common in Earth observations. 
+oscillatory patterns in datasets, which are common in Earth observations.
 For example, beyond typical examples like seasonal cycles, you can think of
 internal waves in the ocean, or the Quasi-Biennial Oscillation in the atmosphere.
 
-Using monthly sea surface temperature data from 1970 to 2021 as an example, we 
+Using monthly sea surface temperature data from 1970 to 2021 as an example, we
 highlight the method's key features and address edge effects as a common challenge.
 
 .. [1] Rasmusson, E. M., Arkin, P. A., Chen, W.-Y. & Jalickee, J. B. Biennial variations in surface temperature over the United States as revealed by singular decomposition. Monthly Weather Review 109, 587–598 (1981).

docs/auto_examples/1single/plot_eeof.py

@@ -2,12 +2,12 @@
 Extented EOF analysis
 =====================
 
-This example demonstrates Extended EOF (EEOF) analysis on ``xarray`` tutorial 
-data. EEOF analysis, also termed as Multivariate/Multichannel Singular 
-Spectrum Analysis, advances traditional EOF analysis to capture propagating 
-signals or oscillations in multivariate datasets. At its core, this 
-involves the formulation of a lagged covariance matrix that encapsulates 
-both spatial and temporal correlations. Subsequently, this matrix is 
+This example demonstrates Extended EOF (EEOF) analysis on ``xarray`` tutorial
+data. EEOF analysis, also termed as Multivariate/Multichannel Singular
+Spectrum Analysis, advances traditional EOF analysis to capture propagating
+signals or oscillations in multivariate datasets. At its core, this
+involves the formulation of a lagged covariance matrix that encapsulates
+both spatial and temporal correlations. Subsequently, this matrix is
 decomposed to yield its eigenvectors (components) and eigenvalues (explained variance).
 
 Let's begin by setting up the required packages and fetching the data:

docs/auto_examples/1single/plot_eof-smode.py

@@ -5,7 +5,6 @@
 EOF analysis in S-mode maximises the temporal variance.
 """
 
-
 # Load packages and data:
 import xarray as xr
 import matplotlib.pyplot as plt

docs/auto_examples/1single/plot_eof-tmode.py

@@ -6,6 +6,7 @@
 
 Load packages and data:
 """
+
 import xarray as xr
 import matplotlib.pyplot as plt
 from matplotlib.gridspec import GridSpec

docs/auto_examples/1single/plot_gwpca.py

@@ -3,33 +3,34 @@
 ===========================
 Geographically Weighted Principal Component Analysis (GWPCA) is a spatial analysis method that identifies and visualizes local spatial patterns and relationships in multivariate datasets across various geographic areas. It operates by applying PCA within a moving window over a geographical region, which enables the extraction of local principal components that can differ across locations.
 
-TIn this demonstration, we'll apply GWPCA to a dataset detailing the chemical compositions of soils from countries around the Baltic Sea [1]_. This example is inspired by a tutorial originally crafted and published by Chris Brunsdon [2]_. 
-The dataset comprises 10 variables (chemical elements) and spans 768 samples. 
+TIn this demonstration, we'll apply GWPCA to a dataset detailing the chemical compositions of soils from countries around the Baltic Sea [1]_. This example is inspired by a tutorial originally crafted and published by Chris Brunsdon [2]_.
+The dataset comprises 10 variables (chemical elements) and spans 768 samples.
 Here, each sample refers to a pair of latitude and longitude coordinates, representing specific sampling stations.
 
 .. [1] Reimann, C. et al. Baltic soil survey: total concentrations of major and selected trace elements in arable soils from 10 countries around the Baltic Sea. Science of The Total Environment 257, 155–170 (2000).
 .. [2] https://rpubs.com/chrisbrunsdon/99675
 
 
 
-.. note:: The dataset we're using is found in the R package 
-    `mvoutlier <https://cran.r-project.org/web/packages/mvoutlier/mvoutlier.pdf>`_. 
-    To access it, we'll employ the Python package 
-    `rpy2 <https://rpy2.github.io/doc/latest/html/index.html>`_ which facilitates 
-    interaction with R packages from within Python. 
+.. note:: The dataset we're using is found in the R package
+    `mvoutlier <https://cran.r-project.org/web/packages/mvoutlier/mvoutlier.pdf>`_.
+    To access it, we'll employ the Python package
+    `rpy2 <https://rpy2.github.io/doc/latest/html/index.html>`_ which facilitates
+    interaction with R packages from within Python.
 
-.. note:: Presently, there's no support for ``xarray.Dataset`` lacking an explicit feature dimension. 
+.. note:: Presently, there's no support for ``xarray.Dataset`` lacking an explicit feature dimension.
     As a workaround, ``xarray.DataArray.to_array`` can be used to convert the ``Dataset`` to an ``DataArray``.
 
 .. warning:: Bear in mind that GWPCA requires significant computational power.
-    The ``xeofs`` implementation is optimized for CPU efficiency and is best suited 
+    The ``xeofs`` implementation is optimized for CPU efficiency and is best suited
     for smaller to medium data sets. For more extensive datasets where parallel processing becomes essential,
     it's advisable to turn to the R package `GWmodel <https://cran.r-project.org/web/packages/GWmodel/GWmodel.pdf>`_.
     This package harnesses CUDA to enable GPU-accelerated GWPCA for optimized performance.
 
 
 Let's import the necessary packages.
 """
+
 # For the analysis
 import numpy as np
 import xarray as xr

docs/auto_examples/1single/plot_mreof.py

@@ -5,7 +5,6 @@
 Multivariate EOF analysis with additional Varimax rotation.
 """
 
-
 # Load packages and data:
 import xarray as xr
 import matplotlib.pyplot as plt

docs/auto_examples/1single/plot_multivariate-eof.py

@@ -5,7 +5,6 @@
 Multivariate EOF analysis.
 """
 
-
 # Load packages and data:
 import xarray as xr
 import matplotlib.pyplot as plt

docs/auto_examples/1single/plot_rotated_eof.py

@@ -2,25 +2,26 @@
 Rotated EOF analysis
 ========================
 
-EOF (Empirical Orthogonal Function) analysis is commonly used in climate science, interpreting 
-the derived eigenvectors (EOFs) as climatic variability patterns. However, due to 
-the inherent orthogonality constraint in EOF analysis, the interpretation of all 
-but the first EOF can be problematic. Rotated EOF analysis, using optimization criteria 
-like Varimax and Promax, offers a solution by releasing this orthogonality constraint, 
+EOF (Empirical Orthogonal Function) analysis is commonly used in climate science, interpreting
+the derived eigenvectors (EOFs) as climatic variability patterns. However, due to
+the inherent orthogonality constraint in EOF analysis, the interpretation of all
+but the first EOF can be problematic. Rotated EOF analysis, using optimization criteria
+like Varimax and Promax, offers a solution by releasing this orthogonality constraint,
 thus enabling a more accurate interpretation of variability patterns.
 
-Both Varimax (orthogonal) and Promax (oblique) rotations result in "sparse" solutions, 
-meaning the EOFs become more interpretable by limiting the number of variables that 
-contribute to each EOF. This rotation effectively serves as a regularization method 
-for the EOF solution, with the strength of regularization determined by the power parameter; 
+Both Varimax (orthogonal) and Promax (oblique) rotations result in "sparse" solutions,
+meaning the EOFs become more interpretable by limiting the number of variables that
+contribute to each EOF. This rotation effectively serves as a regularization method
+for the EOF solution, with the strength of regularization determined by the power parameter;
 the higher the value, the sparser the EOFs.
 
-Promax rotation, with a small regularization value (i.e., power=1), reverts to Varimax 
-rotation. In this context, we compare the first three modes of EOF analysis: (1) 
+Promax rotation, with a small regularization value (i.e., power=1), reverts to Varimax
+rotation. In this context, we compare the first three modes of EOF analysis: (1)
 without regularization, (2) with Varimax rotation, and (3) with Promax rotation.
 
 We'll start by loading the necessary packages and data:
 """
+
 import xarray as xr
 import matplotlib.pyplot as plt
 import seaborn as sns

docs/auto_examples/1single/plot_weighted-eof.py

@@ -10,6 +10,7 @@
 
 Load packages and data:
 """
+
 import xarray as xr
 import matplotlib.pyplot as plt
 import seaborn as sns

docs/auto_examples/2multi/plot_cca.py

@@ -2,10 +2,10 @@
 Canonical Correlation Analysis
 ==============================
 
-In this example, we're going to perform a Canonical Correlation Analysis (CCA) 
-on three datasets using the ERSSTv5 monthly sea surface temperature (SST) data 
-from 1970 to 2022. We divide this data into three areas: the Indian Ocean, 
-the Pacific Ocean, and the Atlantic Ocean. Our goal is to perform CCA on these 
+In this example, we're going to perform a Canonical Correlation Analysis (CCA)
+on three datasets using the ERSSTv5 monthly sea surface temperature (SST) data
+from 1970 to 2022. We divide this data into three areas: the Indian Ocean,
+the Pacific Ocean, and the Atlantic Ocean. Our goal is to perform CCA on these
 regions.
 
 First, we'll import the necessary modules.

docs/auto_examples/2multi/plot_mca.py

@@ -5,7 +5,6 @@
 Maximum Covariance Analysis (MCA) between two data sets.
 """
 
-
 # Load packages and data:
 import numpy as np
 import xarray as xr

docs/auto_examples/2multi/plot_rotated_mca.py

@@ -5,7 +5,6 @@
 Rotated Maximum Covariance Analysis (MCA) between two data sets.
 """
 
-
 # Load packages and data:
 import numpy as np
 import xarray as xr

docs/auto_examples/3validation/plot_bootstrap.py

@@ -6,7 +6,6 @@
 for both EOFs and PCs.
 """
 
-
 # Load packages and data:
 import numpy as np
 import xarray as xr
@@ -49,7 +48,7 @@
 
 is_significant = q025 - q975.shift({"mode": -1}) > 0
 n_significant_modes = (
-    is_significant.where(is_significant == True).cumsum(skipna=False).max().fillna(0)
+    is_significant.where(is_significant is True).cumsum(skipna=False).max().fillna(0)
 )
 print("{:} modes are significant at alpha=0.05".format(n_significant_modes.values))
 

docs/perf/figure_timings.py

@@ -26,11 +26,14 @@ def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
 
     def __call__(self, value, clip=None):
         result, is_scalar = self.process_value(value)
-        x, y = [np.log(self.vmin), np.log(self.midpoint), np.log(self.vmax)], [
-            0,
-            0.5,
-            1,
-        ]
+        x, y = (
+            [np.log(self.vmin), np.log(self.midpoint), np.log(self.vmax)],
+            [
+                0,
+                0.5,
+                1,
+            ],
+        )
         return np.ma.array(np.interp(np.log(value), x, y), mask=result.mask, copy=False)