diff --git a/.github/workflows/black_formatting.yml b/.github/workflows/black_formatting.yml deleted file mode 100644 index 85a9ce92..00000000 --- a/.github/workflows/black_formatting.yml +++ /dev/null @@ -1,20 +0,0 @@ -name: Code Formatting Check - -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: - black_formatting: - runs-on: ubuntu-latest - steps: - - name: Checkout Code - uses: actions/checkout@v3 - - name: Check Code Formatting with Black - uses: psf/black@stable - with: - src: "./xeofs" - version: "~= 23.7.0" \ No newline at end of file diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 11465f93..1d2f743b 100644 --- a/.github/workflows/ci.yml +++ b/.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-poetry@v2.1.0 + - 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 - diff --git a/.github/workflows/formatting.yml b/.github/workflows/formatting.yml new file mode 100644 index 00000000..54149bdc --- /dev/null +++ b/.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 diff --git a/.github/workflows/pull-request-linting.yml b/.github/workflows/pull-request-linting.yml index 9616f09b..c71bee4a 100644 --- a/.github/workflows/pull-request-linting.yml +++ b/.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 diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0a709243..fd0494ab 100644 --- a/.pre-commit-config.yaml +++ b/.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 diff --git a/docs/auto_examples/1single/plot_complex_eof.py b/docs/auto_examples/1single/plot_complex_eof.py index d07be820..d72e8132 100644 --- a/docs/auto_examples/1single/plot_complex_eof.py +++ b/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). diff --git a/docs/auto_examples/1single/plot_eeof.py b/docs/auto_examples/1single/plot_eeof.py index 2a7ab1d1..ab804f5b 100644 --- a/docs/auto_examples/1single/plot_eeof.py +++ b/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: diff --git a/docs/auto_examples/1single/plot_eof-smode.py b/docs/auto_examples/1single/plot_eof-smode.py index 4a947fc8..f9cd5f38 100644 --- a/docs/auto_examples/1single/plot_eof-smode.py +++ b/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 diff --git a/docs/auto_examples/1single/plot_eof-tmode.py b/docs/auto_examples/1single/plot_eof-tmode.py index 2afe0fdc..6a618e3e 100644 --- a/docs/auto_examples/1single/plot_eof-tmode.py +++ b/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 diff --git a/docs/auto_examples/1single/plot_gwpca.py b/docs/auto_examples/1single/plot_gwpca.py index a0842832..f1542228 100644 --- a/docs/auto_examples/1single/plot_gwpca.py +++ b/docs/auto_examples/1single/plot_gwpca.py @@ -3,8 +3,8 @@ =========================== 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). @@ -12,17 +12,17 @@ -.. note:: The dataset we're using is found in the R package - `mvoutlier `_. - To access it, we'll employ the Python package - `rpy2 `_ which facilitates - interaction with R packages from within Python. +.. note:: The dataset we're using is found in the R package + `mvoutlier `_. + To access it, we'll employ the Python package + `rpy2 `_ 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 `_. This package harnesses CUDA to enable GPU-accelerated GWPCA for optimized performance. @@ -30,6 +30,7 @@ Let's import the necessary packages. """ + # For the analysis import numpy as np import xarray as xr diff --git a/docs/auto_examples/1single/plot_mreof.py b/docs/auto_examples/1single/plot_mreof.py index cafbe67e..5edce5b0 100644 --- a/docs/auto_examples/1single/plot_mreof.py +++ b/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 diff --git a/docs/auto_examples/1single/plot_multivariate-eof.py b/docs/auto_examples/1single/plot_multivariate-eof.py index 85331795..ebf4058a 100644 --- a/docs/auto_examples/1single/plot_multivariate-eof.py +++ b/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 diff --git a/docs/auto_examples/1single/plot_rotated_eof.py b/docs/auto_examples/1single/plot_rotated_eof.py index f28a2446..0f8b296b 100644 --- a/docs/auto_examples/1single/plot_rotated_eof.py +++ b/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 diff --git a/docs/auto_examples/1single/plot_weighted-eof.py b/docs/auto_examples/1single/plot_weighted-eof.py index 23721267..fbdc5a54 100644 --- a/docs/auto_examples/1single/plot_weighted-eof.py +++ b/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 diff --git a/docs/auto_examples/2multi/plot_cca.py b/docs/auto_examples/2multi/plot_cca.py index b84cf0dc..e57af825 100644 --- a/docs/auto_examples/2multi/plot_cca.py +++ b/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. diff --git a/docs/auto_examples/2multi/plot_mca.py b/docs/auto_examples/2multi/plot_mca.py index 9b804644..d3b48de7 100644 --- a/docs/auto_examples/2multi/plot_mca.py +++ b/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 diff --git a/docs/auto_examples/2multi/plot_rotated_mca.py b/docs/auto_examples/2multi/plot_rotated_mca.py index 59d337bc..a7ec89fe 100644 --- a/docs/auto_examples/2multi/plot_rotated_mca.py +++ b/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 diff --git a/docs/auto_examples/3validation/plot_bootstrap.py b/docs/auto_examples/3validation/plot_bootstrap.py index 5a53af99..02cec5dc 100644 --- a/docs/auto_examples/3validation/plot_bootstrap.py +++ b/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)) diff --git a/docs/perf/figure_timings.py b/docs/perf/figure_timings.py index e0f0ecc9..3dd4beda 100644 --- a/docs/perf/figure_timings.py +++ b/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) diff --git a/docs/perf/xeofs_timings.py b/docs/perf/xeofs_timings.py index 8e8b9796..9898a233 100644 --- a/docs/perf/xeofs_timings.py +++ b/docs/perf/xeofs_timings.py @@ -4,7 +4,6 @@ import xeofs as xe import xarray as xr import dask -import matplotlib.pyplot as plt from tqdm import tqdm import timeit diff --git a/examples/1single/plot_complex_eof.py b/examples/1single/plot_complex_eof.py index d07be820..d72e8132 100644 --- a/examples/1single/plot_complex_eof.py +++ b/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). diff --git a/examples/1single/plot_eeof.py b/examples/1single/plot_eeof.py index 2a7ab1d1..ab804f5b 100644 --- a/examples/1single/plot_eeof.py +++ b/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: diff --git a/examples/1single/plot_eof-smode.py b/examples/1single/plot_eof-smode.py index 4a947fc8..f9cd5f38 100644 --- a/examples/1single/plot_eof-smode.py +++ b/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 diff --git a/examples/1single/plot_eof-tmode.py b/examples/1single/plot_eof-tmode.py index 2afe0fdc..6a618e3e 100644 --- a/examples/1single/plot_eof-tmode.py +++ b/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 diff --git a/examples/1single/plot_gwpca.py b/examples/1single/plot_gwpca.py index a0842832..f1542228 100644 --- a/examples/1single/plot_gwpca.py +++ b/examples/1single/plot_gwpca.py @@ -3,8 +3,8 @@ =========================== 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). @@ -12,17 +12,17 @@ -.. note:: The dataset we're using is found in the R package - `mvoutlier `_. - To access it, we'll employ the Python package - `rpy2 `_ which facilitates - interaction with R packages from within Python. +.. note:: The dataset we're using is found in the R package + `mvoutlier `_. + To access it, we'll employ the Python package + `rpy2 `_ 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 `_. This package harnesses CUDA to enable GPU-accelerated GWPCA for optimized performance. @@ -30,6 +30,7 @@ Let's import the necessary packages. """ + # For the analysis import numpy as np import xarray as xr diff --git a/examples/1single/plot_mreof.py b/examples/1single/plot_mreof.py index cafbe67e..5edce5b0 100644 --- a/examples/1single/plot_mreof.py +++ b/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 diff --git a/examples/1single/plot_multivariate-eof.py b/examples/1single/plot_multivariate-eof.py index 85331795..ebf4058a 100644 --- a/examples/1single/plot_multivariate-eof.py +++ b/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 diff --git a/examples/1single/plot_rotated_eof.py b/examples/1single/plot_rotated_eof.py index f28a2446..0f8b296b 100644 --- a/examples/1single/plot_rotated_eof.py +++ b/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 diff --git a/examples/1single/plot_weighted-eof.py b/examples/1single/plot_weighted-eof.py index 23721267..fbdc5a54 100644 --- a/examples/1single/plot_weighted-eof.py +++ b/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 diff --git a/examples/2multi/plot_cca.py b/examples/2multi/plot_cca.py index b84cf0dc..e57af825 100644 --- a/examples/2multi/plot_cca.py +++ b/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. diff --git a/examples/2multi/plot_mca.py b/examples/2multi/plot_mca.py index 9b804644..d3b48de7 100644 --- a/examples/2multi/plot_mca.py +++ b/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 diff --git a/examples/2multi/plot_rotated_mca.py b/examples/2multi/plot_rotated_mca.py index 59d337bc..a7ec89fe 100644 --- a/examples/2multi/plot_rotated_mca.py +++ b/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 diff --git a/examples/3validation/plot_bootstrap.py b/examples/3validation/plot_bootstrap.py index 5a53af99..02cec5dc 100644 --- a/examples/3validation/plot_bootstrap.py +++ b/examples/3validation/plot_bootstrap.py @@ -6,7 +6,6 @@ for both EOFs and PCs. 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-[metadata] -lock-version = "2.0" -python-versions = ">=3.10,<3.12" -content-hash = "bc43e276a5376c47b37e4e8f6f455eadc7bf8c13da28bd5caf94e71feb84d176" diff --git a/pyproject.toml b/pyproject.toml index 6f14ae2d..2168602a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,64 +1,72 @@ -[tool.poetry] +[project] name = "xeofs" version = "2.3.2" description = "Comprehensive EOF analysis in Python with xarray: A versatile, multidimensional, and scalable tool for advanced climate data analysis" -authors = ["Niclas Rieger ", "Samuel J. Levang "] -license = "MIT" +authors = [ + {name = "Niclas Rieger", email = "niclasrieger@gmail.com"}, + {name = "Samuel J. Levang", email = "slevang@gmail.com"}, +] +license = {text = "MIT"} readme = "README.md" -homepage = "https://github.com/xarray-contrib/xeofs" -repository = "https://github.com/xarray-contrib/xeofs" -documentation = "https://xeofs.readthedocs.io/en/latest/" +requires-python = ">=3.10" -[tool.poetry.dependencies] -python = ">=3.10,<3.12" -poetry = "^1.6" -numpy = "^1.24" -pandas = "^2" -xarray = ">=2023" -scikit-learn = "^1.0.2" -pooch = "^1.6.0" -tqdm = "^4.64.0" -dask = ">=2023.0.1" -statsmodels = "^0.14.0" -netCDF4 = "^1.5.7" -numba = ">=0.57" -typing-extensions = "^4.8.0" -zarr = "^2.0.0" -xarray-datatree = ">=0.0.5" - -[tool.poetry.group.dev] -optional = true - -[tool.poetry.group.dev.dependencies] -flake8 = "^4.0" -pytest = "^7.0" -coverage = "^6.3" -black = "~23.7.0" -pre-commit = "^3.3" -nbstripout = "^0.6" +dependencies = [ + "numpy>=1.24", + "pandas>=2", + "xarray>=2023.04.0", + "scikit-learn>=1.0.2", + "tqdm>=4.64.0", + "dask>=2023.0.1", + "statsmodels>=0.14.0", + "netCDF4>=1.5.8", + "numba>=0.57", + "typing-extensions>=4.8.0", + "zarr>=2.14.0", + "xarray-datatree>=0.0.12", +] -[tool.poetry.group.docs] -optional = true +[project.optional-dependencies] +dev = [ + "build>=1.0.0", + "ruff>=0.3", + "pytest>=7", + "pytest-xdist>=3", + "coverage>=6", + "pre-commit>=3", + "nbstripout>=0.6", + "pooch>=1.6.0", +] +docs = [ + "rpy2>=3.5", + "sphinx-gallery>=0.14", + "sphinx-design>=0.5", + "sphinx-copybutton>=0.5", + "nbsphinx>=0.9", + "pydata-sphinx-theme>=0.14", + "sphinx>=7.2", + "nbconvert>=7.9", +] -[tool.poetry.group.docs.dependencies] -rpy2 = "^3.5" -sphinx-gallery = "^0.14" -sphinx-design = "^0.5" -sphinx-copybutton = "^0.5" -nbsphinx = "^0.9" -pydata-sphinx-theme = "^0.14" -sphinx = "^7.2" -nbconvert = "^7.9" +[project.urls] +homepage = "https://github.com/xarray-contrib/xeofs" +repository = "https://github.com/xarray-contrib/xeofs" +documentation = "https://xeofs.readthedocs.io/en/latest/" [build-system] -requires = ["setuptools", "poetry-core>=1.0.0"] -build-backend = "poetry.core.masonry.api" +build-backend = "setuptools.build_meta" +requires = [ + "setuptools>=42", + "setuptools-scm>=7", +] + +[tool.ruff] +target-version = "py311" [tool.semantic_release] -version_toml = [ "pyproject.toml:tool.poetry.version" ] +version_toml = [ "pyproject.toml:project.version" ] version_source = "tag" commit_message = "chore(release): v{version}" -build_command = "pip install poetry && poetry build" +build_command = "python -m build" branch = "main" [tool.semantic_release.commit_parser_options] @@ -76,4 +84,3 @@ allowed_tags = [ ] minor_tags = ["feat"] patch_tags = ["fix", "perf"] - diff --git a/tests/conftest.py b/tests/conftest.py index c2d66230..7aa11922 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,5 +1,3 @@ -from typing import Dict, Optional - import numpy as np import pytest import warnings diff --git a/tests/models/test_cca.py b/tests/models/test_cca.py index 620e0a0e..ea77c2af 100644 --- a/tests/models/test_cca.py +++ b/tests/models/test_cca.py @@ -1,9 +1,4 @@ -import numpy as np -import xarray as xr import pytest -import dask.array as da -from numpy.testing import assert_allclose -from ..conftest import generate_list_of_synthetic_dataarrays from xeofs.models.cca import CCA diff --git a/tests/models/test_complex_eof.py b/tests/models/test_complex_eof.py index c55912cf..812b8966 100644 --- a/tests/models/test_complex_eof.py +++ b/tests/models/test_complex_eof.py @@ -1,11 +1,8 @@ import numpy as np -import xarray as xr import pytest -import dask.array as da import warnings -from numpy.testing import assert_allclose -from xeofs.models import EOF, ComplexEOF +from xeofs.models import ComplexEOF warnings.filterwarnings("ignore", message="numpy.dtype size changed") warnings.filterwarnings("ignore", message="numpy.ufunc size changed") @@ -99,7 +96,9 @@ def test_scores_phase(mock_data_array, dim): scores_phase = ceof.scores_phase() assert scores_phase is not None - assert ((-np.pi <= scores_phase.fillna(0)) & (scores_phase.fillna(0) <= np.pi)).all() # type: ignore + assert ( + (-np.pi <= scores_phase.fillna(0)) & (scores_phase.fillna(0) <= np.pi) + ).all() # type: ignore @pytest.mark.parametrize( diff --git a/tests/models/test_complex_eof_rotator.py b/tests/models/test_complex_eof_rotator.py index 2e9e4542..8e195dca 100644 --- a/tests/models/test_complex_eof_rotator.py +++ b/tests/models/test_complex_eof_rotator.py @@ -1,7 +1,5 @@ import pytest -import numpy as np import xarray as xr -from dask.array import Array as DaskArray # type: ignore from xeofs.models import ComplexEOF, ComplexEOFRotator from xeofs.data_container import DataContainer diff --git a/tests/models/test_complex_mca.py b/tests/models/test_complex_mca.py index bfe348b5..6d40c256 100644 --- a/tests/models/test_complex_mca.py +++ b/tests/models/test_complex_mca.py @@ -1,8 +1,5 @@ import pytest -import numpy as np import xarray as xr -import dask.array as da -from numpy.testing import assert_allclose from xeofs.models import ComplexMCA @@ -213,7 +210,7 @@ def test_fit_empty_data(dim): @pytest.mark.parametrize( "dim", [ - (("invalid_dim")), + ("invalid_dim"), ], ) def test_fit_invalid_dims(mca_model, mock_data_array, dim): diff --git a/tests/models/test_complex_mca_rotator.py b/tests/models/test_complex_mca_rotator.py index 14e6ca29..ad852577 100644 --- a/tests/models/test_complex_mca_rotator.py +++ b/tests/models/test_complex_mca_rotator.py @@ -1,7 +1,6 @@ import pytest import numpy as np import xarray as xr -from dask.array import Array as DaskArray # type: ignore # Import the classes from your modules from xeofs.models import ComplexMCA, ComplexMCARotator @@ -27,7 +26,7 @@ def test_init(): assert mca_rotator._params["power"] == 1 assert mca_rotator._params["max_iter"] == 1000 assert mca_rotator._params["rtol"] == 1e-8 - assert mca_rotator._params["squared_loadings"] == False + assert mca_rotator._params["squared_loadings"] is False @pytest.mark.parametrize( @@ -59,9 +58,7 @@ def test_transform(mca_model, mock_data_array): mca_rotator.fit(mca_model) with pytest.raises(NotImplementedError): - projections = mca_rotator.transform( - data1=mock_data_array, data2=mock_data_array - ) + mca_rotator.transform(data1=mock_data_array, data2=mock_data_array) @pytest.mark.parametrize( diff --git a/tests/models/test_decomposer.py b/tests/models/test_decomposer.py index bb19a99a..c1dcef9e 100644 --- a/tests/models/test_decomposer.py +++ b/tests/models/test_decomposer.py @@ -1,10 +1,6 @@ import numpy as np -import xarray as xr import pytest from dask.array import Array as DaskArray # type: ignore -from sklearn.utils.extmath import randomized_svd as svd -from scipy.sparse.linalg import svds as complex_svd # type: ignore -from dask.array.linalg import svd_compressed as dask_svd from xeofs.models.decomposer import Decomposer from ..utilities import data_is_dask diff --git a/tests/models/test_eeof.py b/tests/models/test_eeof.py index ee2c20dd..1c150a2c 100644 --- a/tests/models/test_eeof.py +++ b/tests/models/test_eeof.py @@ -1,8 +1,6 @@ import numpy as np import xarray as xr import pytest -import dask.array as da -from numpy.testing import assert_allclose from xeofs.models.eeof import ExtendedEOF @@ -377,13 +375,13 @@ def test_transform(dim, mock_data_array): # Create a xarray DataArray with random data model = ExtendedEOF(n_modes=5, tau=2, embedding=2, solver="full") model.fit(mock_data_array, dim) - scores = model.scores() + model.scores() # Create a new xarray DataArray with random data new_data = mock_data_array with pytest.raises(NotImplementedError): - projections = model.transform(new_data) + model.transform(new_data) # # Check that the projection has the right dimensions # assert projections.dims == scores.dims, "Projection has wrong dimensions" # type: ignore diff --git a/tests/models/test_eof.py b/tests/models/test_eof.py index b1d7e27e..d1403e80 100644 --- a/tests/models/test_eof.py +++ b/tests/models/test_eof.py @@ -1,10 +1,6 @@ -from copy import deepcopy - import numpy as np import xarray as xr import pytest -from dask.array import Array as DaskArray # type: ignore -from numpy.testing import assert_allclose from xeofs.models.eof import EOF diff --git a/tests/models/test_eof_rotator.py b/tests/models/test_eof_rotator.py index 948dd74c..b95589ff 100644 --- a/tests/models/test_eof_rotator.py +++ b/tests/models/test_eof_rotator.py @@ -1,7 +1,6 @@ import pytest import numpy as np import xarray as xr -from dask.array import Array as DaskArray # type: ignore from xeofs.models import EOF, EOFRotator from xeofs.data_container import DataContainer diff --git a/tests/models/test_gwpca.py b/tests/models/test_gwpca.py index c3e87d5c..8b3fc353 100644 --- a/tests/models/test_gwpca.py +++ b/tests/models/test_gwpca.py @@ -1,7 +1,6 @@ import pytest import xeofs as xe -from ..utilities import assert_expected_dims, data_is_dask, data_has_multiindex # ============================================================================= # GENERALLY VALID TEST CASES @@ -36,8 +35,8 @@ def test_fit(mock_data_array, kernel): n_modes=2, metric="haversine", kernel=kernel, bandwidth=5000 ) gwpca.fit(mock_data_array, dim=("lat", "lon")) - comps = gwpca.components() - llwc = gwpca.largest_locally_weighted_components() + gwpca.components() + gwpca.largest_locally_weighted_components() @pytest.mark.parametrize( @@ -50,6 +49,4 @@ def test_fit(mock_data_array, kernel): ) def test_fit_invalid(mock_data_array, metric, kernel, bandwidth): with pytest.raises(ValueError): - gwpca = xe.models.GWPCA( - n_modes=2, metric=metric, kernel=kernel, bandwidth=bandwidth - ) + xe.models.GWPCA(n_modes=2, metric=metric, kernel=kernel, bandwidth=bandwidth) diff --git a/tests/models/test_mca.py b/tests/models/test_mca.py index 579c787e..09aba4d7 100644 --- a/tests/models/test_mca.py +++ b/tests/models/test_mca.py @@ -1,8 +1,6 @@ import pytest import numpy as np import xarray as xr -from dask.array import Array as DaskArray # type: ignore -from numpy.testing import assert_allclose from xeofs.models.mca import MCA from ..utilities import data_is_dask diff --git a/tests/models/test_mca_rotator.py b/tests/models/test_mca_rotator.py index 3cf1f1d2..61198549 100644 --- a/tests/models/test_mca_rotator.py +++ b/tests/models/test_mca_rotator.py @@ -1,7 +1,6 @@ import pytest import numpy as np import xarray as xr -from dask.array import Array as DaskArray # type: ignore # Import the classes from your modules from xeofs.models import MCA, MCARotator @@ -28,7 +27,7 @@ def test_init(): assert mca_rotator._params["power"] == 1 assert mca_rotator._params["max_iter"] == 1000 assert mca_rotator._params["rtol"] == 1e-8 - assert mca_rotator._params["squared_loadings"] == False + assert mca_rotator._params["squared_loadings"] is False @pytest.mark.parametrize( diff --git a/tests/models/test_opa.py b/tests/models/test_opa.py index 35803614..57991bbb 100644 --- a/tests/models/test_opa.py +++ b/tests/models/test_opa.py @@ -1,8 +1,6 @@ import numpy as np import xarray as xr import pytest -import dask.array as da -from numpy.testing import assert_allclose from xeofs.models import OPA diff --git a/tests/models/test_orthogonality.py b/tests/models/test_orthogonality.py index 75ae56e5..e1d25e88 100644 --- a/tests/models/test_orthogonality.py +++ b/tests/models/test_orthogonality.py @@ -1,8 +1,5 @@ import numpy as np -import xarray as xr import pytest -import dask.array as da -from numpy.testing import assert_allclose from xeofs.models import EOF, ComplexEOF, EOFRotator, ComplexEOFRotator from xeofs.models import MCA, ComplexMCA, MCARotator, ComplexMCARotator @@ -486,9 +483,9 @@ def test_ceof_transform(dim, use_coslat, mock_data_array, normalized): """Not implemented yet""" model = ComplexEOF(n_modes=5, standardize=True, use_coslat=use_coslat) model.fit(mock_data_array, dim=dim) - scores = model.scores(normalized=normalized) + model.scores(normalized=normalized) with pytest.raises(NotImplementedError): - pseudo_scores = model.transform(mock_data_array, normalized=normalized) + model.transform(mock_data_array, normalized=normalized) # Rotated EOF @@ -539,9 +536,9 @@ def test_creof_transform(dim, use_coslat, power, mock_data_array, normalized): model.fit(mock_data_array, dim=dim) rot = ComplexEOFRotator(n_modes=5, power=power) rot.fit(model) - scores = rot.scores(normalized=normalized) + rot.scores(normalized=normalized) with pytest.raises(NotImplementedError): - pseudo_scores = rot.transform(mock_data_array, normalized=normalized) + rot.transform(mock_data_array, normalized=normalized) # MCA diff --git a/tests/models/test_rotator.py b/tests/models/test_rotator.py index fe5806a9..3f2d71a2 100644 --- a/tests/models/test_rotator.py +++ b/tests/models/test_rotator.py @@ -1,5 +1,4 @@ import pytest -from unittest.mock import Mock from xeofs.models import EOF, ComplexEOF, MCA, ComplexMCA from xeofs.models import EOFRotator, ComplexEOFRotator, MCARotator, ComplexMCARotator diff --git a/tests/preprocessing/test_dataarray_multiindex_converter.py b/tests/preprocessing/test_dataarray_multiindex_converter.py index 876ec5f9..d939a642 100644 --- a/tests/preprocessing/test_dataarray_multiindex_converter.py +++ b/tests/preprocessing/test_dataarray_multiindex_converter.py @@ -1,12 +1,9 @@ import pytest -import pandas as pd from xeofs.preprocessing.multi_index_converter import ( MultiIndexConverter, ) -from ..conftest import generate_synthetic_dataarray -from xeofs.utils.data_types import DataArray -from ..utilities import assert_expected_dims, data_is_dask, data_has_multiindex +from ..utilities import data_is_dask, data_has_multiindex # ============================================================================= # GENERALLY VALID TEST CASES diff --git a/tests/preprocessing/test_dataarray_sanitizer.py b/tests/preprocessing/test_dataarray_sanitizer.py index b3388651..3972b8f5 100644 --- a/tests/preprocessing/test_dataarray_sanitizer.py +++ b/tests/preprocessing/test_dataarray_sanitizer.py @@ -1,6 +1,5 @@ import pytest import numpy as np -import xarray as xr from xeofs.preprocessing.sanitizer import Sanitizer from xeofs.utils.data_types import DataArray diff --git a/tests/preprocessing/test_dataarray_stacker.py b/tests/preprocessing/test_dataarray_stacker.py index 1eb531ee..10f3f4bd 100644 --- a/tests/preprocessing/test_dataarray_stacker.py +++ b/tests/preprocessing/test_dataarray_stacker.py @@ -1,6 +1,4 @@ import pytest -import numpy as np -import xarray as xr from xeofs.preprocessing import Stacker from xeofs.utils.data_types import DataArray diff --git a/tests/preprocessing/test_dataset_multiindex_converter.py b/tests/preprocessing/test_dataset_multiindex_converter.py index b93e75b0..d7057652 100644 --- a/tests/preprocessing/test_dataset_multiindex_converter.py +++ b/tests/preprocessing/test_dataset_multiindex_converter.py @@ -1,11 +1,8 @@ import pytest -import pandas as pd from xeofs.preprocessing.multi_index_converter import ( MultiIndexConverter, ) -from ..conftest import generate_synthetic_dataset -from xeofs.utils.data_types import DataArray from ..utilities import assert_expected_dims, data_is_dask, data_has_multiindex # ============================================================================= diff --git a/tests/preprocessing/test_dataset_stacker.py b/tests/preprocessing/test_dataset_stacker.py index 5eed6c23..44d61b16 100644 --- a/tests/preprocessing/test_dataset_stacker.py +++ b/tests/preprocessing/test_dataset_stacker.py @@ -1,9 +1,7 @@ import pytest -import xarray as xr -import numpy as np from xeofs.preprocessing import Stacker -from xeofs.utils.data_types import DataSet, DataArray +from xeofs.utils.data_types import DataArray from ..conftest import generate_synthetic_dataset from ..utilities import ( get_dims_from_data, diff --git a/tests/preprocessing/test_preprocessor_dataarray.py b/tests/preprocessing/test_preprocessor_dataarray.py index 2873423c..9db17c72 100644 --- a/tests/preprocessing/test_preprocessor_dataarray.py +++ b/tests/preprocessing/test_preprocessor_dataarray.py @@ -1,6 +1,5 @@ import pytest import numpy as np -import xarray as xr from xeofs.preprocessing.preprocessor import Preprocessor from ..conftest import generate_synthetic_dataarray diff --git a/tests/preprocessing/test_preprocessor_datalist.py b/tests/preprocessing/test_preprocessor_datalist.py index 8602d08b..7be277d2 100644 --- a/tests/preprocessing/test_preprocessor_datalist.py +++ b/tests/preprocessing/test_preprocessor_datalist.py @@ -64,7 +64,7 @@ def test_fit_transform_scalings( ): prep = Preprocessor(with_std=with_std, with_coslat=with_coslat) - n_data = len(mock_data_array_list) + len(mock_data_array_list) sample_dims = ("time",) weights = None if with_weights: diff --git a/tests/preprocessing/test_preprocessor_dataset.py b/tests/preprocessing/test_preprocessor_dataset.py index 9c047f3b..930bdd6d 100644 --- a/tests/preprocessing/test_preprocessor_dataset.py +++ b/tests/preprocessing/test_preprocessor_dataset.py @@ -1,6 +1,5 @@ import pytest import numpy as np -import xarray as xr from xeofs.preprocessing.preprocessor import Preprocessor from ..conftest import generate_synthetic_dataset @@ -66,7 +65,7 @@ def test_fit_transform_scalings(with_std, with_coslat, with_weights, mock_datase weights = None if with_weights: weights = mock_dataset.mean("time").copy() - weights = weights.where(weights == True, 0.5) + weights = weights.where(weights is True, 0.5) data_trans = prep.fit_transform(mock_dataset, "time", weights) diff --git a/tests/utilities.py b/tests/utilities.py index b69380fa..1243cc56 100644 --- a/tests/utilities.py +++ b/tests/utilities.py @@ -1,18 +1,15 @@ -from typing import Tuple, List, Hashable import numpy as np import pandas as pd -import xarray as xr from xeofs.utils.data_types import ( DataArray, DataSet, DataList, - DaskArray, Dims, DimsList, DimsTuple, DimsListTuple, ) -from xeofs.utils.xarray_utils import data_is_dask +from xeofs.utils.xarray_utils import data_is_dask # noqa: F401 def is_xdata(data): diff --git a/tests/utils/test_dimension_renamer.py b/tests/utils/test_dimension_renamer.py index 4ce1ca42..f61b0c1e 100644 --- a/tests/utils/test_dimension_renamer.py +++ b/tests/utils/test_dimension_renamer.py @@ -3,9 +3,7 @@ from xeofs.utils.dimension_renamer import DimensionRenamer -import xarray as xr import pandas as pd -import pytest @pytest.fixture diff --git a/tests/utils/test_total_variance.py b/tests/utils/test_total_variance.py index f0e7232e..0aa44d7f 100644 --- a/tests/utils/test_total_variance.py +++ b/tests/utils/test_total_variance.py @@ -1,4 +1,3 @@ -import pytest import numpy as np import xarray as xr diff --git a/tests/validation/test_eof_bootstrapper.py b/tests/validation/test_eof_bootstrapper.py index e78ef126..c4501c03 100644 --- a/tests/validation/test_eof_bootstrapper.py +++ b/tests/validation/test_eof_bootstrapper.py @@ -2,7 +2,7 @@ import xarray as xr import numpy as np -from xeofs.models import EOF, ComplexEOF +from xeofs.models import EOF from xeofs.validation import EOFBootstrapper @@ -307,7 +307,7 @@ def test_scores(eof_model): bootstrapper.fit(eof_model) scores = bootstrapper.scores() - assert isinstance(scores, xr.DataArray), f"scores is not a xr.DataArray" + assert isinstance(scores, xr.DataArray), "scores is not a xr.DataArray" # check for expected dimensions ref = eof_model.scores() diff --git a/xeofs/__init__.py b/xeofs/__init__.py index 867b6054..ac3e5d18 100644 --- a/xeofs/__init__.py +++ b/xeofs/__init__.py @@ -1,2 +1,4 @@ from xeofs import models, validation from xeofs._version import __version__ + +__all__ = ["models", "validation", "__version__"] diff --git a/xeofs/data_container/__init__.py b/xeofs/data_container/__init__.py index 3a8fcd5c..a375973e 100644 --- a/xeofs/data_container/__init__.py +++ b/xeofs/data_container/__init__.py @@ -1 +1,3 @@ from .data_container import DataContainer + +__all__ = ["DataContainer"] diff --git a/xeofs/data_container/data_container.py b/xeofs/data_container/data_container.py index b45f70c4..2c30d4f4 100644 --- a/xeofs/data_container/data_container.py +++ b/xeofs/data_container/data_container.py @@ -2,7 +2,6 @@ from typing_extensions import Self import dask -import xarray as xr from dask.diagnostics.progress import ProgressBar from datatree import DataTree diff --git a/xeofs/models/_base_cross_model.py b/xeofs/models/_base_cross_model.py index 25b05dd6..a8fa74c8 100644 --- a/xeofs/models/_base_cross_model.py +++ b/xeofs/models/_base_cross_model.py @@ -1,10 +1,9 @@ -from typing import Tuple, Hashable, Sequence, Dict, Optional, List, Literal +from typing import Tuple, Hashable, Sequence, Dict, Optional, Literal from typing_extensions import Self from abc import ABC, abstractmethod from datetime import datetime import dask -import numpy as np import xarray as xr from datatree import DataTree from dask.diagnostics.progress import ProgressBar @@ -12,7 +11,7 @@ from .eof import EOF from ..preprocessing.preprocessor import Preprocessor from ..data_container import DataContainer -from ..utils.data_types import DataObject, DataArray, DataSet +from ..utils.data_types import DataObject, DataArray from ..utils.io import insert_placeholders, open_model_tree, write_model_tree from ..utils.xarray_utils import convert_to_dim_type, data_is_dask from ..utils.sanity_checks import validate_input_type diff --git a/xeofs/models/_base_model.py b/xeofs/models/_base_model.py index c5433bee..424c8bd1 100644 --- a/xeofs/models/_base_model.py +++ b/xeofs/models/_base_model.py @@ -7,31 +7,23 @@ Any, List, Literal, - TypeVar, - Tuple, ) from typing_extensions import Self from abc import ABC, abstractmethod from datetime import datetime import dask -import numpy as np import xarray as xr from datatree import DataTree from dask.diagnostics.progress import ProgressBar from ..preprocessing.preprocessor import Preprocessor from ..data_container import DataContainer -from ..utils.data_types import DataObject, Data, DataArray, DataSet, DataList, Dims +from ..utils.data_types import DataObject, Data, DataArray from ..utils.io import insert_placeholders, open_model_tree, write_model_tree from ..utils.sanity_checks import validate_input_type from ..utils.xarray_utils import ( convert_to_dim_type, - get_dims, - feature_ones_like, - convert_to_list, - process_parameter, - _check_parameter_number, data_is_dask, ) from .._version import __version__ diff --git a/xeofs/models/decomposer.py b/xeofs/models/decomposer.py index 42db742a..42da537e 100644 --- a/xeofs/models/decomposer.py +++ b/xeofs/models/decomposer.py @@ -3,7 +3,6 @@ import dask from dask.array import Array as DaskArray # type: ignore from dask.diagnostics.progress import ProgressBar -from numpy.linalg import svd from sklearn.utils.extmath import randomized_svd from scipy.sparse.linalg import svds as complex_svd # type: ignore from dask.array.linalg import svd_compressed as dask_svd diff --git a/xeofs/models/eeof.py b/xeofs/models/eeof.py index bc6c1604..77e6859e 100644 --- a/xeofs/models/eeof.py +++ b/xeofs/models/eeof.py @@ -4,12 +4,9 @@ import numpy as np import xarray as xr -from ._base_model import _BaseModel from .eof import EOF -from .decomposer import Decomposer -from ..utils.data_types import DataArray, Data, Dims +from ..utils.data_types import DataArray from ..data_container import DataContainer -from ..utils.xarray_utils import total_variance as compute_total_variance class ExtendedEOF(EOF): @@ -78,7 +75,7 @@ def __init__( solver: str = "auto", random_state: Optional[int] = None, solver_kwargs: dict = {}, - **kwargs + **kwargs, ): super().__init__( n_modes=n_modes, @@ -92,7 +89,7 @@ def __init__( solver=solver, random_state=random_state, solver_kwargs=solver_kwargs, - **kwargs + **kwargs, ) self.attrs.update({"model": "Extended EOF Analysis"}) self._params.update( diff --git a/xeofs/models/eof.py b/xeofs/models/eof.py index ba1cfe4f..3ab4cfb0 100644 --- a/xeofs/models/eof.py +++ b/xeofs/models/eof.py @@ -5,7 +5,7 @@ from ._base_model import _BaseModel from .decomposer import Decomposer -from ..utils.data_types import DataObject, DataArray, Dims +from ..utils.data_types import DataObject, DataArray from ..utils.hilbert_transform import hilbert_transform from ..utils.xarray_utils import total_variance as compute_total_variance from ..utils.sanity_checks import assert_not_complex diff --git a/xeofs/models/eof_rotator.py b/xeofs/models/eof_rotator.py index ffb6e5af..eb3e555e 100644 --- a/xeofs/models/eof_rotator.py +++ b/xeofs/models/eof_rotator.py @@ -140,7 +140,7 @@ def _fit_algorithm(self, model) -> Self: loadings, feature_dim=self.feature_name, compute=self._params["compute"], - **promax_kwargs + **promax_kwargs, ) # Assign coordinates to the rotation/correlation matrices diff --git a/xeofs/models/gwpca.py b/xeofs/models/gwpca.py index 4e123a9b..f9813ce3 100644 --- a/xeofs/models/gwpca.py +++ b/xeofs/models/gwpca.py @@ -1,20 +1,15 @@ -from typing import Sequence, Hashable, Optional, Callable from typing_extensions import Self -from sklearn.utils.extmath import randomized_svd from xeofs.utils.data_types import DataArray -from xeofs.utils.data_types import Data from ._base_model import _BaseModel from ..utils.sanity_checks import assert_not_complex -from ..utils.xarray_utils import convert_to_dim_type from ..utils.constants import ( VALID_CARTESIAN_X_NAMES, VALID_CARTESIAN_Y_NAMES, VALID_LATITUDE_NAMES, VALID_LONGITUDE_NAMES, ) -from .eof import EOF import numpy as np import xarray as xr @@ -115,12 +110,12 @@ def __init__( self.attrs.update({"model": "GWPCA"}) - if not kernel in VALID_KERNELS: + if kernel not in VALID_KERNELS: raise ValueError( f"Invalid kernel: {kernel}. Must be one of {VALID_KERNELS}." ) - if not metric in VALID_METRICS: + if metric not in VALID_METRICS: raise ValueError( f"Invalid metric: {metric}. Must be one of {VALID_METRICS}." ) diff --git a/xeofs/models/mca.py b/xeofs/models/mca.py index 6ab7da99..a00b5efb 100644 --- a/xeofs/models/mca.py +++ b/xeofs/models/mca.py @@ -371,7 +371,7 @@ def covariance_fraction(self): change_in_cf_in_last_mode = change_per_mode.isel(mode=-1) if change_in_cf_in_last_mode > 0.001: print( - f"Warning: CF is sensitive to the number of modes retained. Please increase `n_modes` for a better estimate." + "Warning: CF is sensitive to the number of modes retained. Please increase `n_modes` for a better estimate." ) cov_frac = svals / tot_var cov_frac.name = "covariance_fraction" diff --git a/xeofs/models/mca_rotator.py b/xeofs/models/mca_rotator.py index 4c15fae1..6215ff88 100644 --- a/xeofs/models/mca_rotator.py +++ b/xeofs/models/mca_rotator.py @@ -195,7 +195,7 @@ def _fit_algorithm(self, model) -> Self: loadings=loadings, feature_dim=self.feature_name, compute=self._params["compute"], - **promax_kwargs + **promax_kwargs, ) # Assign coordinates to the rotation/correlation matrices diff --git a/xeofs/preprocessing/concatenator.py b/xeofs/preprocessing/concatenator.py index 0c6f514d..e8e25c69 100644 --- a/xeofs/preprocessing/concatenator.py +++ b/xeofs/preprocessing/concatenator.py @@ -1,7 +1,6 @@ from typing import List, Optional, Dict from typing_extensions import Self -import pandas as pd import numpy as np import xarray as xr @@ -10,13 +9,6 @@ Dims, DimsList, DataArray, - DataSet, - Data, - DataVar, - DataList, - DataArrayList, - DataSetList, - DataVarList, ) diff --git a/xeofs/preprocessing/dimension_renamer.py b/xeofs/preprocessing/dimension_renamer.py index 7ef729d3..537c5815 100644 --- a/xeofs/preprocessing/dimension_renamer.py +++ b/xeofs/preprocessing/dimension_renamer.py @@ -2,7 +2,7 @@ from typing_extensions import Self from .transformer import Transformer -from ..utils.data_types import Dims, DataArray, DataSet, Data, DataVar, DataVarBound +from ..utils.data_types import Dims, DataArray, Data, DataVarBound class DimensionRenamer(Transformer): diff --git a/xeofs/preprocessing/list_processor.py b/xeofs/preprocessing/list_processor.py index 0dd12e93..45fed8b3 100644 --- a/xeofs/preprocessing/list_processor.py +++ b/xeofs/preprocessing/list_processor.py @@ -9,9 +9,7 @@ from ..utils.data_types import ( Data, DataVar, - DataVarBound, DataArray, - DataSet, Dims, DimsList, ) diff --git a/xeofs/preprocessing/multi_index_converter.py b/xeofs/preprocessing/multi_index_converter.py index b27817a1..65c01db4 100644 --- a/xeofs/preprocessing/multi_index_converter.py +++ b/xeofs/preprocessing/multi_index_converter.py @@ -1,9 +1,9 @@ -from typing import List, Optional, Dict +from typing import Optional, Dict from typing_extensions import Self import pandas as pd from .transformer import Transformer -from ..utils.data_types import Dims, DataArray, DataSet, Data, DataVar, DataVarBound +from ..utils.data_types import Dims, DataArray, Data, DataVar, DataVarBound class MultiIndexConverter(Transformer): diff --git a/xeofs/preprocessing/preprocessor.py b/xeofs/preprocessing/preprocessor.py index 9ee57539..22fc2cf7 100644 --- a/xeofs/preprocessing/preprocessor.py +++ b/xeofs/preprocessing/preprocessor.py @@ -2,7 +2,6 @@ from typing_extensions import Self import numpy as np -import xarray as xr from datatree import DataTree from .list_processor import GenericListTransformer diff --git a/xeofs/preprocessing/sanitizer.py b/xeofs/preprocessing/sanitizer.py index 08bccc27..71e5a40e 100644 --- a/xeofs/preprocessing/sanitizer.py +++ b/xeofs/preprocessing/sanitizer.py @@ -5,7 +5,7 @@ import xarray as xr from .transformer import Transformer -from ..utils.data_types import Dims, DataArray, DataSet, Data, DataVar +from ..utils.data_types import Dims, DataArray, Data class Sanitizer(Transformer): @@ -65,7 +65,7 @@ def fit( X: Data, sample_dims: Optional[Dims] = None, feature_dims: Optional[Dims] = None, - **kwargs + **kwargs, ) -> Self: # Check if input is a DataArray self._check_input_type(X) diff --git a/xeofs/preprocessing/scaler.py b/xeofs/preprocessing/scaler.py index 8da97e64..c2d4284d 100644 --- a/xeofs/preprocessing/scaler.py +++ b/xeofs/preprocessing/scaler.py @@ -6,7 +6,7 @@ import xarray as xr from .transformer import Transformer -from ..utils.data_types import Dims, DataArray, DataSet, Data, DataVar, DataVarBound +from ..utils.data_types import Dims, DataArray, DataVar, DataVarBound from ..utils.xarray_utils import compute_sqrt_cos_lat_weights, feature_ones_like diff --git a/xeofs/preprocessing/stacker.py b/xeofs/preprocessing/stacker.py index f2a57b04..76838bae 100644 --- a/xeofs/preprocessing/stacker.py +++ b/xeofs/preprocessing/stacker.py @@ -98,13 +98,13 @@ def _validate_dims(self, X: Data, sample_dims, feature_dims): invalid_feature_dims = True if len(feature_dims) < 1 else False if invalid_sample_dims: - raise ValueError(f"Sample dimension must not be empty.") + raise ValueError("Sample dimension must not be empty.") if invalid_feature_dims: match X: case xr.DataArray(): - raise ValueError(f"Feature dimension must not be empty.") + raise ValueError("Feature dimension must not be empty.") case xr.Dataset(): - err_msg = f"Dataset without feature dimension is currently not supported. Please convert your Dataset to a DataArray first, e.g. by using `to_array()`." + err_msg = "Dataset without feature dimension is currently not supported. Please convert your Dataset to a DataArray first, e.g. by using `to_array()`." raise ValueError(err_msg) case _: raise TypeError(f"Invalid data type {type(X)}.") @@ -112,7 +112,7 @@ def _validate_dims(self, X: Data, sample_dims, feature_dims): def _validate_indices(self, X: Data): """Check that the indices of the data are no MultiIndex""" if any([isinstance(index, pd.MultiIndex) for index in X.indexes.values()]): - raise ValueError(f"Cannot stack data containing a MultiIndex.") + raise ValueError("Cannot stack data containing a MultiIndex.") def _sanity_check(self, X: Data, sample_dims, feature_dims): self._validate_dims(X, sample_dims, feature_dims) @@ -186,7 +186,7 @@ def _stack(self, X: Data, sample_dims: Dims, feature_dims: Dims) -> DataArray: # There's only one sample dimension and it's already named correctly pass else: - raise ValueError(f"Sample dimension must not be empty.") + raise ValueError("Sample dimension must not be empty.") # Stack FEATURE dimension match X: @@ -200,7 +200,7 @@ def _stack(self, X: Data, sample_dims: Dims, feature_dims: Dims) -> DataArray: # There's only one feature dimension and it's already named correctly pass else: - raise ValueError(f"Feature dimension must not be empty.") + raise ValueError("Feature dimension must not be empty.") case xr.Dataset(): X = X.to_stacked_array( diff --git a/xeofs/preprocessing/transformer.py b/xeofs/preprocessing/transformer.py index 3da45868..354ad1bd 100644 --- a/xeofs/preprocessing/transformer.py +++ b/xeofs/preprocessing/transformer.py @@ -8,7 +8,7 @@ from datatree import DataTree from sklearn.base import BaseEstimator, TransformerMixin -from ..utils.data_types import Dims, DataVar, DataArray, DataSet, Data, DataVarBound +from ..utils.data_types import Dims, DataArray, DataSet, Data class Transformer(BaseEstimator, TransformerMixin, ABC): diff --git a/xeofs/utils/data_types.py b/xeofs/utils/data_types.py index 4e77a82a..175eb10a 100644 --- a/xeofs/utils/data_types.py +++ b/xeofs/utils/data_types.py @@ -8,7 +8,6 @@ ) import dask.array as da -import xarray as xr from xarray.core import dataarray as xr_dataarray from xarray.core import dataset as xr_dataset diff --git a/xeofs/utils/numba_utils.py b/xeofs/utils/numba_utils.py index bbac0e18..4902545a 100644 --- a/xeofs/utils/numba_utils.py +++ b/xeofs/utils/numba_utils.py @@ -184,4 +184,4 @@ def local_pca_vectorized( for i in range(n_out.shape[0]): expvar[i] = lbda[i] comps[:, i] = Vt[i, :] - totvar = lbda.sum() + lbda.sum() diff --git a/xeofs/utils/sanity_checks.py b/xeofs/utils/sanity_checks.py index 975622ee..0cdd2bff 100644 --- a/xeofs/utils/sanity_checks.py +++ b/xeofs/utils/sanity_checks.py @@ -1,4 +1,4 @@ -from typing import Sequence, Hashable, Tuple, Any +from typing import Any import xarray as xr @@ -98,5 +98,5 @@ def validate_input_type(X) -> None: def assert_not_complex(da: xr.DataArray) -> None: if da.dtype == "complex": raise TypeError( - f"Invalid input type. This method does not support complex data types." + "Invalid input type. This method does not support complex data types." ) diff --git a/xeofs/utils/statistics.py b/xeofs/utils/statistics.py index adeb035c..1ac6eca2 100644 --- a/xeofs/utils/statistics.py +++ b/xeofs/utils/statistics.py @@ -1,5 +1,3 @@ -from typing import Tuple - import xarray as xr import scipy as sc from statsmodels.stats.multitest import multipletests as statsmodels_multipletests diff --git a/xeofs/utils/xarray_utils.py b/xeofs/utils/xarray_utils.py index d2dfbc84..8b169e03 100644 --- a/xeofs/utils/xarray_utils.py +++ b/xeofs/utils/xarray_utils.py @@ -188,7 +188,7 @@ def get_dims( return sample_dims, feature_dims else: err_message = f"Invalid input type: {type(data).__name__}. Expected one of " - err_message += f"of the following: list of DataArrays or Datasets." + err_message += "of the following: list of DataArrays or Datasets." raise TypeError(err_message) diff --git a/xeofs/validation/__init__.py b/xeofs/validation/__init__.py index 1b7f1ab9..ffcd0c8e 100644 --- a/xeofs/validation/__init__.py +++ b/xeofs/validation/__init__.py @@ -1 +1,3 @@ from .bootstrapper import EOFBootstrapper + +__all__ = ["EOFBootstrapper"] diff --git a/xeofs/validation/bootstrapper.py b/xeofs/validation/bootstrapper.py index a5c2111c..8a1c78a4 100644 --- a/xeofs/validation/bootstrapper.py +++ b/xeofs/validation/bootstrapper.py @@ -57,7 +57,6 @@ def fit(self, model: EOF): self.model = model self.preprocessor = model.preprocessor sample_name = model.sample_name - feature_name = model.feature_name input_data = model.data["input_data"] n_samples = input_data.sample.size