diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index d363182..58234f8 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -11,8 +11,29 @@ jobs:
   call-test:
     uses: ./.github/workflows/test.yml
     secrets: inherit
+  
+  build-sdist:
+    runs-on: ubuntu-latest
+    needs: call-test
+    steps:
+      - uses: actions/checkout@v4
+      - uses: prefix-dev/setup-pixi@v0.8.8
+        with:
+          environments: dev
+          cache: true
+      - name: Re-install local
+        run: |
+          pixi reinstall -e dev --frozen fastcan
+      - name: Build SDist
+        run: |
+          pixi run build-sdist
+      - name: Store artifacts
+        uses: actions/upload-artifact@v4
+        with:
+          name: cibw-sdist
+          path: dist/*.tar.gz
 
-  build:
+  build-wheels:
     strategy:
       fail-fast: false
       matrix:
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
deleted file mode 100644
index 34a1d2b..0000000
--- a/.github/workflows/lint.yml
+++ /dev/null
@@ -1,48 +0,0 @@
-name: Lint
-
-on:
-  workflow_call:
-
-jobs:
-  lint:
-    runs-on: ubuntu-latest
-
-    steps:
-      - uses: actions/checkout@v4
-      - uses: prefix-dev/setup-pixi@v0.8.8
-        with:
-          environments: default
-          cache: true
-
-      - name: Re-install local
-        run: |
-          pixi reinstall -e lint --frozen fastcan
-
-      - name: Lint with ruff
-        run: |
-          pixi run lint
-      - name: Lint with cython-lint
-        run: |
-          pixi run cython-lint
-      - name: Format with black
-        run: |
-          pixi run fmt
-      - name: Type check with mypy
-        run: |
-          pixi run type
-      - name: Test coverage
-        shell: bash
-        run: |
-          FMT=xml pixi run test-coverage
-      - name: Upload coverage reports to Codecov
-        uses: codecov/codecov-action@v5
-        with:
-          token: ${{ secrets.CODECOV_TOKEN }}
-      - name: Build SDist
-        run: |
-          pixi run build-sdist
-      - name: Store artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: cibw-sdist
-          path: dist/*.tar.gz
diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml
new file mode 100644
index 0000000..02dcfbe
--- /dev/null
+++ b/.github/workflows/static.yml
@@ -0,0 +1,32 @@
+name: Static
+
+on:
+  workflow_call:
+
+jobs:
+  static:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - uses: prefix-dev/setup-pixi@v0.8.8
+        with:
+          environments: static
+          cache: true
+
+      - name: Re-install local
+        run: |
+          pixi reinstall -e static --frozen fastcan
+
+      - name: Linter
+        run: |
+          pixi run lint
+      - name: Lint Cython
+        run: |
+          pixi run cython-lint
+      - name: Formatter
+        run: |
+          pixi run fmt
+      - name: Type check
+        run: |
+          pixi run type
diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 374cb7d..0824762 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -9,8 +9,7 @@ on:
 
 jobs:
   call-lint:
-    uses: ./.github/workflows/lint.yml
-    secrets: inherit
+    uses: ./.github/workflows/static.yml
 
   test:
     strategy:
@@ -23,7 +22,7 @@ jobs:
       - uses: actions/checkout@v4
       - uses: prefix-dev/setup-pixi@v0.8.8
         with:
-          environments: default
+          environments: dev
           cache: true
 
       - name: Re-install local
@@ -44,3 +43,13 @@ jobs:
       - name: Test nogil
         run: |
           pixi run nogil-eta
+      - name: Test coverage
+        if: runner.os == 'Linux'
+        shell: bash
+        run: |
+          FMT=xml pixi run test-coverage
+      - name: Upload coverage reports to Codecov
+        if: runner.os == 'Linux'
+        uses: codecov/codecov-action@v5
+        with:
+          token: ${{ secrets.CODECOV_TOKEN }}
diff --git a/examples/plot_narx_msa.py b/examples/plot_narx_msa.py
index 655b019..7d7466a 100644
--- a/examples/plot_narx_msa.py
+++ b/examples/plot_narx_msa.py
@@ -93,7 +93,7 @@ def auto_duffing_equation(y, t):
 
 sol = odeint(duffing_equation, [0.6, -0.8], t)
 u_test = 2.5 * np.cos(2 * np.pi * t).reshape(-1, 1)
-y_test = sol[:, 0]+ e_test
+y_test = sol[:, 0] + e_test
 
 # %%
 # One-step-head VS. multi-step-ahead NARX
diff --git a/examples/plot_speed.py b/examples/plot_speed.py
index 347f3c1..073302e 100644
--- a/examples/plot_speed.py
+++ b/examples/plot_speed.py
@@ -178,12 +178,12 @@ def baseline(X, y, t):
 time_eta = np.zeros(n_features_max, dtype=float)
 for i in range(n_features_max):
     time_h[i] = timeit(
-        f"s = FastCan({i+1}, verbose=0).fit(X, y)",
+        f"s = FastCan({i + 1}, verbose=0).fit(X, y)",
         number=10,
         globals=globals(),
     )
     time_eta[i] = timeit(
-        f"s = FastCan({i+1}, eta=True, verbose=0).fit(X, y)",
+        f"s = FastCan({i + 1}, eta=True, verbose=0).fit(X, y)",
         number=10,
         globals=globals(),
     )
diff --git a/fastcan/__init__.py b/fastcan/__init__.py
index cdd8252..ae06fbc 100644
--- a/fastcan/__init__.py
+++ b/fastcan/__init__.py
@@ -13,8 +13,8 @@
 
 __all__ = [
     "FastCan",
-    "refine",
     "minibatch",
     "narx",
+    "refine",
     "utils",
 ]
diff --git a/fastcan/_fastcan.py b/fastcan/_fastcan.py
index 01af376..d5145a7 100644
--- a/fastcan/_fastcan.py
+++ b/fastcan/_fastcan.py
@@ -17,7 +17,7 @@
 from sklearn.utils._param_validation import Interval
 from sklearn.utils.validation import check_is_fitted, validate_data
 
-from ._cancorr_fast import _forward_search  # type: ignore
+from ._cancorr_fast import _forward_search  # type: ignore[attr-defined]
 
 
 class FastCan(SelectorMixin, BaseEstimator):
diff --git a/fastcan/_minibatch.py b/fastcan/_minibatch.py
index a92d320..305f6d1 100644
--- a/fastcan/_minibatch.py
+++ b/fastcan/_minibatch.py
@@ -13,7 +13,7 @@
 from sklearn.utils._param_validation import Interval, validate_params
 from sklearn.utils.validation import check_X_y
 
-from ._cancorr_fast import _forward_search  # type: ignore
+from ._cancorr_fast import _forward_search  # type: ignore[attr-defined]
 from ._fastcan import _prepare_search
 
 
diff --git a/fastcan/_refine.py b/fastcan/_refine.py
index 0dbd95a..a58e947 100644
--- a/fastcan/_refine.py
+++ b/fastcan/_refine.py
@@ -13,7 +13,7 @@
 from sklearn.utils._param_validation import Interval, StrOptions, validate_params
 from sklearn.utils.validation import check_is_fitted
 
-from ._cancorr_fast import _forward_search  # type: ignore
+from ._cancorr_fast import _forward_search  # type: ignore[attr-defined]
 from ._fastcan import FastCan, _prepare_search
 
 
diff --git a/fastcan/narx.py b/fastcan/narx.py
index ec3775f..013df07 100644
--- a/fastcan/narx.py
+++ b/fastcan/narx.py
@@ -25,7 +25,11 @@
 )
 
 from ._fastcan import FastCan
-from ._narx_fast import _predict_step, _update_cfd, _update_terms  # type: ignore
+from ._narx_fast import (  # type: ignore[attr-defined]
+    _predict_step,
+    _update_cfd,
+    _update_terms,
+)
 from ._refine import refine
 from .utils import mask_missing_values
 
diff --git a/pixi.lock b/pixi.lock
index 41161fe..4c04a78 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -49,7 +49,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.13-noxft_h4845f30_101.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tomli-2.2.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda
-      - pypi: .
+      - pypi: ./
       osx-64:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/bzip2-1.0.8-hfdf4475_7.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/c-compiler-1.9.0-h09a7c41_0.conda
@@ -124,7 +124,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/tzdata-2025b-h78e105d_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/zlib-1.3.1-hd23fc13_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/zstd-1.5.7-h8210216_2.conda
-      - pypi: .
+      - pypi: ./
       osx-arm64:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/bzip2-1.0.8-h99b78c6_7.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/c-compiler-1.9.0-hdf49b6b_0.conda
@@ -198,7 +198,7 @@ environments:
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       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zlib-1.3.1-h8359307_2.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/zstd-1.5.7-h6491c7d_2.conda
-      - pypi: .
+      - pypi: ./
       win-64:
       - conda: https://conda.anaconda.org/conda-forge/win-64/bzip2-1.0.8-h2466b09_7.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-h4c7d964_0.conda
@@ -239,7 +239,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/ucrt-10.0.22621.0-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/vc-14.3-h2b53caa_26.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/vc14_runtime-14.42.34438-hfd919c2_26.conda
-      - pypi: .
+      - pypi: ./
   dev:
     channels:
     - url: https://conda.anaconda.org/conda-forge/
@@ -524,7 +524,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl
-      - pypi: .
+      - pypi: ./
       osx-64:
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda
@@ -774,7 +774,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl
-      - pypi: .
+      - pypi: ./
       osx-arm64:
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/appnope-0.1.4-pyhd8ed1ab_1.conda
@@ -1023,7 +1023,7 @@ environments:
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       - pypi: https://files.pythonhosted.org/packages/27/83/859ecdd180cacc13b1f7e857abf8582a64552ea7a061057a6c716e790fce/sphinxcontrib_qthelp-2.0.0-py3-none-any.whl
       - pypi: https://files.pythonhosted.org/packages/52/a7/d2782e4e3f77c8450f727ba74a8f12756d5ba823d81b941f1b04da9d033a/sphinxcontrib_serializinghtml-2.0.0-py3-none-any.whl
-      - pypi: .
+      - pypi: ./
       win-64:
       - conda: https://conda.anaconda.org/conda-forge/win-64/_openmp_mutex-4.5-2_gnu.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.9.0-pyh29332c3_0.conda
@@ -1263,20 +1263,19 @@ environments:
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-      - pypi: .
-  lint:
+      - pypi: ./
+  nogil:
     channels:
     - url: https://conda.anaconda.org/conda-forge/
+    indexes:
+    - https://pypi.org/simple
     packages:
       linux-64:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/noarch/black-25.1.0-pyh866005b_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h4bc722e_7.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/ca-certificates-2025.4.26-hbd8a1cb_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh707e725_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.12-py313h5dec8f5_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.16.6-pyhff2d567_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/cpython-3.13.3-py313hd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda
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       - conda: https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.6-h2dba641_1.conda
@@ -1289,62 +1288,63 @@ environments:
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-      - conda: https://conda.anaconda.org/conda-forge/linux-64/mypy-1.15.0-py313h536fd9c_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/mypy_extensions-1.1.0-pyha770c72_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/meson-1.8.0-pyh29332c3_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/meson-python-0.18.0-pyh70fd9c4_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h2d0b736_3.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-hff21bea_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/openssl-3.5.0-h7b32b05_1.conda
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-      - pypi: https://files.pythonhosted.org/packages/32/d5/f9a850d79b0851d1d4ef6456097579a9005b31fea68726a4ae5f2d82ddd9/threadpoolctl-3.6.0-py3-none-any.whl
 packages:
 - conda: https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2
   sha256: fe51de6107f9edc7aa4f786a70f4a883943bc9d39b3bb7307c04c41410990726
@@ -1777,51 +1756,6 @@ packages:
   - pkg:pypi/beautifulsoup4?source=compressed-mapping
   size: 146613
   timestamp: 1744783307123
-- conda: https://conda.anaconda.org/conda-forge/noarch/black-25.1.0-pyh866005b_0.conda
-  sha256: c68f110cd491dc839a69e340930862e54c00fb02cede5f1831fcf8a253bd68d2
-  md5: b9b0c42e7316aa6043bdfd49883955b8
-  depends:
-  - click >=8.0.0
-  - mypy_extensions >=0.4.3
-  - packaging >=22.0
-  - pathspec >=0.9
-  - platformdirs >=2
-  - python >=3.11
-  license: MIT
-  license_family: MIT
-  size: 172678
-  timestamp: 1742502887437
-- conda: https://conda.anaconda.org/conda-forge/osx-64/black-25.1.0-py312hb401068_0.conda
-  sha256: e937f18e36e23ecf0ec9ab89fc3ef5263308e88b645c4278fe8807fd95bef4c1
-  md5: d37d5213fcf23a33d946e40937578a02
-  depends:
-  - click >=8.0.0
-  - mypy_extensions >=0.4.3
-  - packaging >=22.0
-  - pathspec >=0.9
-  - platformdirs >=2
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
-  license: MIT
-  license_family: MIT
-  size: 393484
-  timestamp: 1738616259890
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/black-25.1.0-py313h8f79df9_0.conda
-  sha256: ef2f742f6abefc32506038a4c64bf0c086c8e13234c1fe80c8675c7f92589cc2
-  md5: 698e6c77b39a4f3d82c8e2e7d82b81c8
-  depends:
-  - click >=8.0.0
-  - mypy_extensions >=0.4.3
-  - packaging >=22.0
-  - pathspec >=0.9
-  - platformdirs >=2
-  - python >=3.13,<3.14.0a0
-  - python >=3.13,<3.14.0a0 *_cp313
-  - python_abi 3.13.* *_cp313
-  license: MIT
-  license_family: MIT
-  size: 400095
-  timestamp: 1738616517582
 - conda: https://conda.anaconda.org/conda-forge/noarch/bleach-6.2.0-pyh29332c3_4.conda
   sha256: a05971bb80cca50ce9977aad3f7fc053e54ea7d5321523efc7b9a6e12901d3cd
   md5: f0b4c8e370446ef89797608d60a564b3
@@ -2486,27 +2420,6 @@ packages:
   purls: []
   size: 19975
   timestamp: 1742540410050
-- conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh707e725_0.conda
-  sha256: c920d23cd1fcf565031c679adb62d848af60d6fbb0edc2d50ba475cea4f0d8ab
-  md5: f22f4d4970e09d68a10b922cbb0408d3
-  depends:
-  - __unix
-  - python >=3.9
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 84705
-  timestamp: 1734858922844
-- conda: https://conda.anaconda.org/conda-forge/noarch/click-8.1.8-pyh7428d3b_0.conda
-  sha256: c889ed359ae47eead4ffe8927b7206b22c55e67d6e74a9044c23736919d61e8d
-  md5: 90e5571556f7a45db92ee51cb8f97af6
-  depends:
-  - __win
-  - colorama
-  - python >=3.9
-  license: BSD-3-Clause
-  license_family: BSD
-  size: 85169
-  timestamp: 1734858972635
 - conda: https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_1.conda
   sha256: ab29d57dc70786c1269633ba3dff20288b81664d3ff8d21af995742e2bb03287
   md5: 962b9857ee8e7018c22f2776ffa0b2d7
@@ -2817,18 +2730,19 @@ packages:
   - pkg:pypi/cython?source=hash-mapping
   size: 3766349
   timestamp: 1739228643862
-- conda: https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.12-py312hdfbeeba_0.conda
-  sha256: a186d286aedb2230dcdcaf2a8602c098112eaacdf9d8af39da2a474950bf1b98
-  md5: 5801a15eece1bd00c7f6dc0c68640a9f
+- conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.0-py313h5dec8f5_1.conda
+  sha256: 77d6f353cee60a2802d41ef89f56f7c0a5493de2463d42ce76cc1e98210ff520
+  md5: 43ad5286d089949501cf07064693d070
   depends:
-  - __osx >=10.13
-  - libcxx >=18
-  - python >=3.12,<3.13.0a0
-  - python_abi 3.12.* *_cp312
+  - __glibc >=2.17,<3.0.a0
+  - libgcc >=13
+  - libstdcxx >=13
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
   license: Apache-2.0
   license_family: APACHE
-  size: 3455381
-  timestamp: 1739228540351
+  size: 3694348
+  timestamp: 1747172311189
 - conda: https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.12-py313h9efc8c2_0.conda
   sha256: 132d6e81a95c042210f33c3d24f03d52632738434b3ea48cfb184a26684d365e
   md5: ddace7cae5c3073c031ad08ef01881da
@@ -2843,6 +2757,18 @@ packages:
   - pkg:pypi/cython?source=hash-mapping
   size: 3500462
   timestamp: 1739228750512
+- conda: https://conda.anaconda.org/conda-forge/osx-64/cython-3.1.0-py312hdfbeeba_1.conda
+  sha256: 887ef8caea20efd49bfb8ba23a8036eda3ff70180a0c395c930bbca70ee98ab1
+  md5: b09ecb38c03625464a87e3f2b04d7154
+  depends:
+  - __osx >=10.13
+  - libcxx >=18
+  - python >=3.12,<3.13.0a0
+  - python_abi 3.12.* *_cp312
+  license: Apache-2.0
+  license_family: APACHE
+  size: 3438173
+  timestamp: 1747172239562
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.0.12-py313hd607753_0.conda
   sha256: 5dab9497286041af22785e69ec3c4ba96ef06e4498416dac77055ad4ab03fe17
   md5: a05cbfd5f2277521007624ad8ca65f02
@@ -2858,6 +2784,19 @@ packages:
   - pkg:pypi/cython?source=hash-mapping
   size: 3509778
   timestamp: 1739228359500
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.1.0-py313hd607753_1.conda
+  sha256: 0087bc6a4a6dea55d0ffeb22e0350f7b71f18e79b9f0cdbbdbe293a8871474f8
+  md5: 96dbb83d36f74f0faad3a1c5d8ec0843
+  depends:
+  - __osx >=11.0
+  - libcxx >=18
+  - python >=3.13,<3.14.0a0
+  - python >=3.13,<3.14.0a0 *_cp313
+  - python_abi 3.13.* *_cp313
+  license: Apache-2.0
+  license_family: APACHE
+  size: 3412491
+  timestamp: 1747172147455
 - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.0.12-py313h11c7957_0.conda
   sha256: 0c5d28be02ffb8bed56c1d290f0535fe95ed28dffd86eb56f0cf5efaba006060
   md5: 8be86847359ed6c522c23dfce6fa9b90
@@ -2873,6 +2812,19 @@ packages:
   - pkg:pypi/cython?source=hash-mapping
   size: 3202883
   timestamp: 1739228652473
+- conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.1.0-py313h11c7957_1.conda
+  sha256: 6549e02b3874438decc617cd9ef22f1c9150b6b9387c5161cca3ce3774d28091
+  md5: 371976809659c6d55e7f7c5e025d5a21
+  depends:
+  - python >=3.13,<3.14.0a0
+  - python_abi 3.13.* *_cp313
+  - ucrt >=10.0.20348.0
+  - vc >=14.2,<15
+  - vc14_runtime >=14.29.30139
+  license: Apache-2.0
+  license_family: APACHE
+  size: 3238820
+  timestamp: 1747172377863
 - conda: https://conda.anaconda.org/conda-forge/noarch/cython-lint-0.16.6-pyhff2d567_0.conda
   sha256: b8be28885737768620425c3f256c74017d9811fc086f705f5280b2b7bd59536c
   md5: 2d640ab1e2fd57f59e2cd078bc67d99e
@@ -3041,10 +2993,10 @@ packages:
   purls: []
   size: 140050
   timestamp: 1743431809745
-- pypi: .
+- pypi: ./
   name: fastcan
   version: 0.3.2
-  sha256: a518cdeaefefe0fbc7abc8949eedb645b83cdb0e233219c7b84c510cddb7ae21
+  sha256: 9c0571618f9cac84aeba88048b82e830342f1997cab28b05db5e2bca210b8599
   requires_dist:
   - scikit-learn>=1.6.0
   - furo ; extra == 'docs'
@@ -4800,6 +4752,15 @@ packages:
   purls: []
   size: 561294
   timestamp: 1746653898484
+- conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-20.1.5-hf95d169_0.conda
+  sha256: 9003bd12988a54713602999999737590f3b023b0cadb2b316cd3ac256d6740d6
+  md5: 9dde68cee0a231b19e189954ac29027b
+  depends:
+  - __osx >=10.13
+  license: Apache-2.0 WITH LLVM-exception
+  license_family: Apache
+  size: 562408
+  timestamp: 1747262455533
 - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-20.1.4-ha82da77_1.conda
   sha256: 365c2c7bd017ebb8d3605b2f5c23bac7b35e2de8f26ddc46552fa6b4c61c6c13
   md5: 85be146c49d0a2f6ca59cf4c8b58db47
@@ -4810,6 +4771,15 @@ packages:
   purls: []
   size: 567046
   timestamp: 1746653977544
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-20.1.5-ha82da77_0.conda
+  sha256: 2765b6e23da91807ce2ed44587fbaadd5ba933b0269810b3c22462f9582aedd3
+  md5: 4ef1bdb94d42055f511bb358f2048c58
+  depends:
+  - __osx >=11.0
+  license: Apache-2.0 WITH LLVM-exception
+  license_family: Apache
+  size: 568010
+  timestamp: 1747262879889
 - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-devel-18.1.8-h7c275be_8.conda
   sha256: cb3cce2b312aa1fb7391672807001bbab4d6e2deb16d912caecf6219f58ee1f4
   md5: a9513c41f070a9e2d5c370ba5d6c0c00
@@ -7361,15 +7331,6 @@ packages:
   - pkg:pypi/parso?source=hash-mapping
   size: 75295
   timestamp: 1733271352153
-- conda: https://conda.anaconda.org/conda-forge/noarch/pathspec-0.12.1-pyhd8ed1ab_1.conda
-  sha256: 9f64009cdf5b8e529995f18e03665b03f5d07c0b17445b8badef45bde76249ee
-  md5: 617f15191456cc6a13db418a275435e5
-  depends:
-  - python >=3.9
-  license: MPL-2.0
-  license_family: MOZILLA
-  size: 41075
-  timestamp: 1733233471940
 - conda: https://conda.anaconda.org/conda-forge/linux-64/pcre2-10.44-hc749103_2.conda
   sha256: 09717569649d89caafbf32f6cda1e65aef86e5a86c053d30e4ce77fca8d27b68
   md5: 31614c73d7b103ef76faa4d83d261d34
@@ -8759,9 +8720,9 @@ packages:
   - pkg:pypi/rpds-py?source=hash-mapping
   size: 255547
   timestamp: 1743037492141
-- conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.11.8-py313h22842b3_0.conda
-  sha256: 7d0af5acc433527e9b3c5e186c9362cc5b7a9e394b28fbec5d62a269cdcc7316
-  md5: 44c26d389f8ca7021164652849b49598
+- conda: https://conda.anaconda.org/conda-forge/linux-64/ruff-0.11.9-py313h22842b3_0.conda
+  sha256: b560f202f5940857b7a25e708a1734522de59bb024011e45408242bfcd21a22f
+  md5: 3dc9b639c9a5915d48dd69161f6c8a49
   depends:
   - __glibc >=2.17,<3.0.a0
   - libgcc >=13
@@ -8772,11 +8733,11 @@ packages:
   - __glibc >=2.17
   license: MIT
   license_family: MIT
-  size: 9195304
-  timestamp: 1746123693954
-- conda: https://conda.anaconda.org/conda-forge/osx-64/ruff-0.11.8-py312h60e8e2e_0.conda
-  sha256: 13429a5e90436c82530a0da3dae9c51aaa29d2bd9f5d6977928445798ceffb59
-  md5: 8ad1f9fa080144b2efd5a4d2fc5dca82
+  size: 9211172
+  timestamp: 1746841218728
+- conda: https://conda.anaconda.org/conda-forge/osx-64/ruff-0.11.9-py312h60e8e2e_0.conda
+  sha256: 04f11dd2c4dcc9a881a7d25818f4aa7b04654c4853d2101053701476abfef1a4
+  md5: 49736f343a261ee7c73103548614ebd7
   depends:
   - __osx >=10.13
   - libcxx >=18
@@ -8786,11 +8747,11 @@ packages:
   - __osx >=10.13
   license: MIT
   license_family: MIT
-  size: 8585161
-  timestamp: 1746124127838
-- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.11.8-py313hd3a9b03_0.conda
-  sha256: 08b2053648f09b270b90e091a40e4f7dea668c68129848a9c6784d6d9926f17e
-  md5: 2ca5ff0de9dcca7d3f2b03f564074d56
+  size: 8629558
+  timestamp: 1746841407034
+- conda: https://conda.anaconda.org/conda-forge/osx-arm64/ruff-0.11.9-py313hd3a9b03_0.conda
+  sha256: e79d09aad227c97e7a8875865fa2553b076fdc3d8fd3fd4bb173a7fb7169df17
+  md5: a4e3b2f0cd18bc670201877ed3e81086
   depends:
   - __osx >=11.0
   - libcxx >=18
@@ -8801,11 +8762,11 @@ packages:
   - __osx >=11.0
   license: MIT
   license_family: MIT
-  size: 8171789
-  timestamp: 1746123877403
-- conda: https://conda.anaconda.org/conda-forge/win-64/ruff-0.11.8-py313h9f3c1d7_0.conda
-  sha256: 6b37401392d54ee33778827a97d8dbff4c0196ba495a6cdbf239046c9e670e79
-  md5: 1d194c5252ed0adb14d0fc05168c6142
+  size: 8194821
+  timestamp: 1746841562098
+- conda: https://conda.anaconda.org/conda-forge/win-64/ruff-0.11.9-py313h9f3c1d7_0.conda
+  sha256: 5dc8a7428b8681376c351adbfa88a590ffa52fb2760923bbb6f8dc94f91cbfe1
+  md5: cb6ca8d853dea68725b7aa1b93c886e3
   depends:
   - python >=3.13,<3.14.0a0
   - python_abi 3.13.* *_cp313
@@ -8814,8 +8775,8 @@ packages:
   - vc14_runtime >=14.29.30139
   license: MIT
   license_family: MIT
-  size: 8261537
-  timestamp: 1746124842258
+  size: 8273013
+  timestamp: 1746841801808
 - pypi: https://pypi.anaconda.org/scientific-python-nightly-wheels/simple/scikit-learn/1.7.dev0/scikit_learn-1.7.dev0-cp313-cp313t-macosx_10_13_x86_64.whl
   name: scikit-learn
   version: 1.7.dev0
diff --git a/pyproject.toml b/pyproject.toml
index fcd8d29..d28af89 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -63,16 +63,13 @@ pytest = "*"
 pytest-cov = "*"
 pandas = "*"
 
-[tool.pixi.feature.fmt.dependencies]
-black = "*"
-
-[tool.pixi.feature.lint.dependencies]
+[tool.pixi.feature.static.dependencies]
+# Static analysis tools
 ruff = "*"
 cython-lint = "*"
-
-[tool.pixi.feature.type.dependencies]
 mypy = "*"
 
+
 [tool.pixi.feature.build.dependencies]
 python-build = "*"
 pip = "*"
@@ -113,14 +110,10 @@ rebuild = "rm -rf build && pip install --no-deps --force-reinstall -e ."
  [tool.pixi.feature.build.target.win-64.tasks]
  rebuild = "rm -rf build && pip install --no-deps --force-reinstall -e . -Csetup-args=--vsenv"
 
-[tool.pixi.feature.fmt.tasks]
-fmt = { cmd = "black .", cwd = "fastcan" }
-
-[tool.pixi.feature.lint.tasks]
+[tool.pixi.feature.static.tasks]
+fmt = "ruff format"
 lint = "ruff check . --fix"
 cython-lint = { cmd = "cython-lint .", cwd = "fastcan" }
-
-[tool.pixi.feature.type.tasks]
 type = { cmd = "mypy . --ignore-missing-imports", cwd = "fastcan" }
 
 [tool.pixi.feature.docs.tasks]
@@ -144,7 +137,7 @@ nogil-build = { cmd = "pip install --editable . --verbose --no-build-isolation -
 
 [tool.pixi.environments]
 dev = ["docs", "test", "build", "jupyter"]
-lint = { features = ["fmt", "lint", "type"], no-default-feature = true }
+static = { features = ["static"], no-default-feature = true }
 nogil = { features = ["nogil"], no-default-feature = true }
 
 [tool.black]
@@ -166,7 +159,6 @@ exclude = '''
 '''
 
 [tool.ruff]
-# max line length for black
 line-length = 88
 
 exclude = [
@@ -183,16 +175,40 @@ exclude = [
 preview = true
 # This enables us to use the explicit preview rules that we want only
 explicit-preview-rules = true
-# all rules can be found here: https://beta.ruff.rs/docs/rules/
-select = ["E", "F", "W", "I", "CPY001"]
-
-ignore = [
-    # space before : (needed for how black formats slicing)
-    "E203",
+# all rules can be found here: https://docs.astral.sh/ruff/rules/
+extend-select = ["E501", "W", "I", "CPY001", "PGH", "RUF"]
+ignore=[
     # do not assign a lambda expression, use a def
     "E731",
     # do not use variables named 'l', 'O', or 'I'
     "E741",
+    # E721 gives many false positives.
+    # Use `is` and `is not` for type comparisons, or `isinstance()` for
+    # isinstance checks
+    "E721",
+    # We don't care much about F841.
+    # Local variable ... is assigned to but never used
+    "F841",
+    # some RUF rules trigger too many changes
+    "RUF002",
+    "RUF003",
+    "RUF005",
+    "RUF012",
+    "RUF015",
+    "RUF021",
+    # https://docs.astral.sh/ruff/formatter/#conflicting-lint-rules
+    "W191",
+    "E111",
+    "E114",
+    "E117",
+    "D206",
+    "D300",
+    "Q000",
+    "Q001",
+    "Q002",
+    "Q003",
+    "COM812",
+    "COM819",
 ]
 
 [tool.ruff.lint.flake8-copyright]
@@ -205,17 +221,15 @@ notice-rgx = "\\#\\ Authors:\\ The\\ fastcan\\ developers\\\r?\\\n\\#\\ SPDX\\-L
 "doc/conf.py" = ["E402"]
 "tests/*" = ["CPY001"]
 "doc/*" = ["CPY001"]
-
+".github/*"=["CPY001"]
 
 [tool.cython-lint]
 # Ignore the same error codes as ruff
 # + E501 (line too long) because keeping it < 88 in cython
 # often makes code less readable.
-lint.ignore = [
+ignore = [
     # multiple spaces/tab after comma
     'E24',
-    # space before : (needed for how black formats slicing)
-    'E203',
     # line too long
     'E501',
     # do not assign a lambda expression, use a def
diff --git a/tests/test_fastcan.py b/tests/test_fastcan.py
index a8c4475..380ede3 100644
--- a/tests/test_fastcan.py
+++ b/tests/test_fastcan.py
@@ -18,6 +18,7 @@
 def test_fastcan_is_sklearn_estimator():
     check_estimator(FastCan())
 
+
 def test_select_kbest_classif():
     # Test whether the relative univariate feature selection
     # gets the correct items in a simple classification problem
@@ -58,6 +59,7 @@ def test_select_kbest_classif():
     gtruth[:n_informative] = 1
     assert_array_equal(support, gtruth)
 
+
 def test_indices_include_exclude():
     # Test whether fastcan can select informative features based
     # on some pre-include features and pre-exclude features
@@ -78,12 +80,10 @@ def test_indices_include_exclude():
     )
 
     include_filter = FastCan(
-        n_features_to_select=n_informative,
-        indices_include=indices_params
+        n_features_to_select=n_informative, indices_include=indices_params
     )
     exclude_filter = FastCan(
-        n_features_to_select=n_informative,
-        indices_exclude=indices_params
+        n_features_to_select=n_informative, indices_exclude=indices_params
     )
     include_filter.fit(X, y)
     exclude_filter.fit(X, y)
@@ -96,6 +96,7 @@ def test_indices_include_exclude():
     gtruth[indices_params] = 0
     assert_array_equal(exclude_support[:n_informative], gtruth[:n_informative])
 
+
 def test_ssc_consistent_with_cca():
     # Test whether the ssc got from the fastcan is consistent
     # with the ssc got from CCA
@@ -127,6 +128,7 @@ def test_ssc_consistent_with_cca():
     ssc = correlation_filter.scores_.sum()
     assert_almost_equal(actual=ssc, desired=gtruth_ssc)
 
+
 def test_h_eta_consistency():
     # Test whether the ssc got from h-correlation is
     # consistent with the ssc got from eta-cosine
@@ -146,25 +148,20 @@ def test_h_eta_consistency():
         random_state=0,
     )
 
-    h_correlation = FastCan(
-        n_features_to_select=n_to_select,
-        eta=False
-    )
-    eta_cosine = FastCan(
-        n_features_to_select=n_to_select,
-        eta=True
-    )
+    h_correlation = FastCan(n_features_to_select=n_to_select, eta=False)
+    eta_cosine = FastCan(n_features_to_select=n_to_select, eta=True)
     h_correlation.fit(X, y)
     eta_cosine.fit(X, y)
     assert_array_almost_equal(h_correlation.scores_, eta_cosine.scores_)
 
+
 def test_raise_errors():
     # Test whether fastcan raise errors properly
     n_samples = 20
     n_features = 20
     n_classes = 10
     n_informative = 15
-    n_redundant = n_features-n_informative
+    n_redundant = n_features - n_informative
 
     X, y = make_classification(
         n_samples=n_samples,
@@ -178,25 +175,21 @@ def test_raise_errors():
     )
 
     selector_n_select = FastCan(
-        n_features_to_select=n_features+1,
+        n_features_to_select=n_features + 1,
     )
     selector_n_inclusions = FastCan(
-        n_features_to_select=n_features,
-        indices_include=range(n_features+1)
+        n_features_to_select=n_features, indices_include=range(n_features + 1)
     )
     selector_eta_for_small_size_samples = FastCan(
-        n_features_to_select=n_features,
-        eta=True
+        n_features_to_select=n_features, eta=True
     )
 
     selector_indices_include_bounds = FastCan(
-        n_features_to_select=n_features,
-        indices_include=[-1]
+        n_features_to_select=n_features, indices_include=[-1]
     )
 
     selector_indices_include_ndim = FastCan(
-        n_features_to_select=n_features,
-        indices_include=[[0]]
+        n_features_to_select=n_features, indices_include=[[0]]
     )
 
     selector_include_exclude_intersect = FastCan(
@@ -247,13 +240,13 @@ def test_cython_errors():
     x_sub = rng.random((n_samples, n_informative))
     y = rng.random((n_samples))
 
-
     selector_no_cand = FastCan(
-        n_features_to_select=n_informative+1,
+        n_features_to_select=n_informative + 1,
     )
 
     with pytest.raises(RuntimeError, match=r"No candidate feature can .*"):
         # No candidate
-        selector_no_cand.fit(np.c_[x_sub, x_sub[:, 0]+x_sub[:, 1]], y)
+        selector_no_cand.fit(np.c_[x_sub, x_sub[:, 0] + x_sub[:, 1]], y)
+
 
 test_indices_include_exclude()
diff --git a/tests/test_init.py b/tests/test_init.py
index d7f82e0..4b2ed01 100644
--- a/tests/test_init.py
+++ b/tests/test_init.py
@@ -1,8 +1,7 @@
-# ruff: noqa
 """Basic unittests to test functioning of module's top-level"""
 
 try:
-    from fastcan import *
+    from fastcan import *  # noqa: F403
 
     _TOP_IMPORT_ERROR = None
 
diff --git a/tests/test_minibatch.py b/tests/test_minibatch.py
index c650ecd..b5b3a3b 100644
--- a/tests/test_minibatch.py
+++ b/tests/test_minibatch.py
@@ -1,4 +1,5 @@
 """Test feature selection with mini-batch"""
+
 import numpy as np
 import pytest
 from sklearn.cluster import KMeans
@@ -21,14 +22,11 @@ def test_data_pruning():
         random_state=random_state,
     ).fit(X)
     atoms = kmeans.cluster_centers_
-    indices = minibatch(
-        X.T, atoms.T, n_to_select, batch_size=batch_size, verbose=0
-    )
+    indices = minibatch(X.T, atoms.T, n_to_select, batch_size=batch_size, verbose=0)
     assert np.unique(indices).size == n_to_select
     assert indices.size == n_to_select
 
 
-
 def test_select_minibatch_cls():
     # Test whether refine work correctly with random samples.
     n_samples = 200
@@ -61,6 +59,7 @@ def test_select_minibatch_cls():
     assert np.unique(indices).size == n_to_select
     assert indices.size == n_to_select
 
+
 def test_minibatch_error():
     # Test refine raise error.
     n_samples = 200
@@ -83,4 +82,4 @@ def test_minibatch_error():
     )
 
     with pytest.raises(ValueError, match=r"n_features_to_select .*"):
-        _ = minibatch(X, y, n_features+1, batch_size=3)
+        _ = minibatch(X, y, n_features + 1, batch_size=3)
diff --git a/tests/test_narx.py b/tests/test_narx.py
index 339affe..26e827f 100644
--- a/tests/test_narx.py
+++ b/tests/test_narx.py
@@ -58,12 +58,12 @@ def test_narx(nan, multi_output):
             )
             y1[i] = (
                 0.6 * y1[i - 1]
-                - 0.2 * y0[i - 1]*y1[i - 2]
+                - 0.2 * y0[i - 1] * y1[i - 2]
                 + 0.3 * u1[i] ** 2
                 + 1.5 * u1[i - 2] * u0[i - 3]
                 + 0.5
             )
-        y = np.c_[y0[max_delay:]+e0, y1[max_delay:]+e1]
+        y = np.c_[y0[max_delay:] + e0, y1[max_delay:] + e1]
         X = np.c_[u0[max_delay:], u1[max_delay:]]
         n_outputs = 2
     else:
@@ -122,7 +122,7 @@ def test_narx(nan, multi_output):
     narx_default = make_narx(X=X, y=y, **params)
 
     if multi_output:
-        assert narx_default.feat_ids.shape[0] == params["n_terms_to_select"]*2
+        assert narx_default.feat_ids.shape[0] == params["n_terms_to_select"] * 2
     else:
         assert narx_default.feat_ids.shape[0] == params["n_terms_to_select"]
 
@@ -159,9 +159,9 @@ def test_narx(nan, multi_output):
     else:
         output_ids = None
     feat_ids, delay_ids = tp2fd(time_shift_ids, poly_ids)
-    narx_osa = NARX(
-        feat_ids=feat_ids, delay_ids=delay_ids, output_ids=output_ids
-    ).fit(X, y)
+    narx_osa = NARX(feat_ids=feat_ids, delay_ids=delay_ids, output_ids=output_ids).fit(
+        X, y
+    )
     assert narx_osa.coef_.size == poly_ids.shape[0]
     narx_osa_msa = narx_drop.fit(X, y, coef_init="one_step_ahead")
     narx_osa_msa_coef = narx_osa_msa.coef_
@@ -176,7 +176,7 @@ def test_narx(nan, multi_output):
     else:
         y_init = [1] * narx_array_init_msa.max_delay_
     y_hat = narx_array_init_msa.predict(X, y_init=y_init)
-    assert_array_equal(y_hat[:narx_array_init_msa.max_delay_], y_init)
+    assert_array_equal(y_hat[: narx_array_init_msa.max_delay_], y_init)
 
     with pytest.raises(ValueError, match=r"`coef_init` should have the shape of .*"):
         narx_array_init_msa.fit(X, y, coef_init=np.zeros(narx_osa_msa_coef.size))
@@ -258,7 +258,9 @@ def test_mulit_output_warn():
             narx = NARX(feat_ids=feat_ids, delay_ids=delay_ids)
             narx.fit(X, y)
         y_pred = narx.predict(X)
-        assert_almost_equal(np.std(y_pred[narx.max_delay_:, 1] - np.mean(y[:, 1])), 0.0)
+        assert_almost_equal(
+            np.std(y_pred[narx.max_delay_ :, 1] - np.mean(y[:, 1])), 0.0
+        )
 
         X_nan = np.copy(X)
         y_nan = np.copy(y)
@@ -270,12 +272,13 @@ def test_mulit_output_warn():
             y_nan_masked, y_pred_masked = mask_missing_values(y_nan, y_pred)
             assert_almost_equal(
                 np.std(
-                    y_pred_masked[y_pred_masked[:, 0]!=0, 1] -\
-                    np.mean(y_nan_masked[:, 1])
+                    y_pred_masked[y_pred_masked[:, 0] != 0, 1]
+                    - np.mean(y_nan_masked[:, 1])
                 ),
                 0.0,
             )
 
+
 def test_fit_intercept():
     X = np.random.rand(10, 2)
     y = np.random.rand(10, 1)
@@ -316,6 +319,7 @@ def test_fit_intercept():
         narx.fit(X, y, coef_init=[0, 0])
         assert_array_equal(narx.intercept_, [0.0, 0.0])
 
+
 def test_mulit_output_error():
     X = np.random.rand(10, 2)
     y = np.random.rand(10, 2)
@@ -325,20 +329,20 @@ def test_mulit_output_error():
 
     with pytest.raises(ValueError, match="The length of output_ids should"):
         narx = NARX(
-        feat_ids=feat_ids,
-        delay_ids=delay_ids,
-        output_ids=[0],
-    )
+            feat_ids=feat_ids,
+            delay_ids=delay_ids,
+            output_ids=[0],
+        )
         narx.fit(X, y)
 
     with pytest.raises(
         ValueError, match=r"The element x of output_ids should satisfy 0 <=.*"
     ):
         narx = NARX(
-        feat_ids=feat_ids,
-        delay_ids=delay_ids,
-        output_ids=[0, 2],
-    )
+            feat_ids=feat_ids,
+            delay_ids=delay_ids,
+            output_ids=[0, 2],
+        )
         narx.fit(X, y)
 
     with pytest.raises(ValueError, match="The length of `n_terms_to_select` should"):
@@ -409,20 +413,18 @@ def test_sample_weight():
     X_repeated = np.repeat(X, sw, axis=0)
     y_repeated = np.repeat(y, sw)
     narx_osa = NARX().fit(X_repeated, y_repeated)
-    narx_no_sw = NARX().fit(X_repeated, y_repeated, coef_init=[0]*3)
+    narx_no_sw = NARX().fit(X_repeated, y_repeated, coef_init=[0] * 3)
     assert_allclose(
         np.r_[narx_osa.coef_, narx_osa.intercept_],
-        np.r_[narx_no_sw.coef_, narx_no_sw.intercept_]
-    )
-    narx_sw = NARX().fit(
-        X, y, sample_weight=sw,
-        coef_init=[0]*3
+        np.r_[narx_no_sw.coef_, narx_no_sw.intercept_],
     )
+    narx_sw = NARX().fit(X, y, sample_weight=sw, coef_init=[0] * 3)
     assert_allclose(
         np.r_[narx_no_sw.coef_, narx_no_sw.intercept_],
-        np.r_[narx_sw.coef_, narx_sw.intercept_]
+        np.r_[narx_sw.coef_, narx_sw.intercept_],
     )
 
+
 def test_divergence():
     # Test divergence of NARX model
     rng = np.random.default_rng(12345)
@@ -445,7 +447,8 @@ def test_divergence():
     narx = make_narx(X, y, 3, 3, 2)
     narx.fit(X, y, coef_init=[-10, 0, 0, 0])
     y_hat = narx.predict(X, y)
-    assert np.all(y_hat<=1e20)
+    assert np.all(y_hat <= 1e20)
+
 
 def test_tp2fd():
     time_shift_ids = np.array(
@@ -491,6 +494,7 @@ def test_tp2fd():
     with pytest.raises(ValueError, match=r"The element x of poly_ids should.*"):
         _, _ = tp2fd(time_shift_ids, poly_ids)
 
+
 def test_print_narx(capsys):
     X = np.random.rand(10, 2)
     y = np.random.rand(10, 2)
diff --git a/tests/test_narx_jac.py b/tests/test_narx_jac.py
index 3ce2e68..f049741 100644
--- a/tests/test_narx_jac.py
+++ b/tests/test_narx_jac.py
@@ -1,11 +1,11 @@
 """Test Jacobian matrix of NARX"""
 
 import numpy as np
+from fastcan._narx_fast import _predict_step  # type: ignore[attr-defined]
 from numpy.testing import assert_allclose, assert_almost_equal, assert_array_equal
 from scipy.integrate import odeint
 from sklearn.metrics import r2_score
 
-from fastcan._narx_fast import _predict_step  # type: ignore
 from fastcan.narx import NARX, make_narx
 
 
@@ -26,7 +26,6 @@ def test_simple():
     intercept = np.array([1], dtype=float)
     sample_weight = np.array([1, 1, 1], dtype=float).reshape(-1, 1)
 
-
     y_hat = NARX._predict(
         _predict_step,
         X=X,
@@ -41,7 +40,7 @@ def test_simple():
     assert_array_equal(y_hat, y)
 
     delta_w = 0.00001
-    coef_1 = np.array([0.4+delta_w, 1])
+    coef_1 = np.array([0.4 + delta_w, 1])
 
     y_hat_1 = NARX._predict(
         _predict_step,
@@ -54,12 +53,17 @@ def test_simple():
         output_ids=output_ids,
     )
 
-    grad_truth = np.array([
-        np.sum(np.array([0, y_hat_1[0, 0], y_hat_1[1, 0]+coef_1[0]]).reshape(-1, 1)),
-        np.sum(np.array([0, X[0, 0], X[0, 0]*coef_1[0]+X[0, 0]]).reshape(-1, 1)),
-        np.sum(np.array([0, 1, coef_1[0]+1]).reshape(-1, 1)),
-    ])
-
+    grad_truth = np.array(
+        [
+            np.sum(
+                np.array([0, y_hat_1[0, 0], y_hat_1[1, 0] + coef_1[0]]).reshape(-1, 1)
+            ),
+            np.sum(
+                np.array([0, X[0, 0], X[0, 0] * coef_1[0] + X[0, 0]]).reshape(-1, 1)
+            ),
+            np.sum(np.array([0, 1, coef_1[0] + 1]).reshape(-1, 1)),
+        ]
+    )
 
     grad_yyd_ids, grad_coef_ids, grad_feat_ids, grad_delay_ids = NARX._get_cfd_ids(
         feat_ids, delay_ids, output_ids, 1
@@ -113,6 +117,7 @@ def test_simple():
     )
     assert_almost_equal(grad.sum(axis=0), grad_0.sum(axis=0)[:-1])
 
+
 def test_complex():
     """Complex model"""
     # Simulated model
@@ -156,7 +161,7 @@ def test_complex():
             [1, 1],
             [1, 0],
         ],
-        dtype=np.int32
+        dtype=np.int32,
     )
 
     delay_ids = np.array(
@@ -171,7 +176,7 @@ def test_complex():
             [0, 0],
             [2, 3],
         ],
-        dtype=np.int32
+        dtype=np.int32,
     )
 
     output_ids = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1], dtype=np.int32)
@@ -226,7 +231,7 @@ def test_complex():
     e_0 = y_hat_0 - y
 
     delta_w = 0.00001
-    for i in range(len(coef)+len(intercept)):
+    for i in range(len(coef) + len(intercept)):
         if i < len(coef):
             coef_1 = np.copy(coef)
             coef_1[i] += delta_w
@@ -234,7 +239,7 @@ def test_complex():
         else:
             coef_1 = np.copy(coef)
             intercept_1 = np.copy(intercept)
-            intercept_1[i-len(coef)] += delta_w
+            intercept_1[i - len(coef)] += delta_w
 
         y_hat_1 = NARX._predict(
             _predict_step,
@@ -299,8 +304,10 @@ def test_complex():
 
         assert_allclose(grad.sum(axis=0)[i], grad_num.sum(), rtol=1e-1)
 
+
 def test_score_nan():
     """Test fitting scores when data contain nan."""
+
     def duffing_equation(y, t):
         """Non-autonomous system"""
         y1, y2 = y
@@ -308,7 +315,6 @@ def duffing_equation(y, t):
         dydt = [y2, -0.1 * y2 + y1 - 0.25 * y1**3 + u]
         return dydt
 
-
     dur = 10
     n_samples = 1000
 
diff --git a/tests/test_refine.py b/tests/test_refine.py
index 37ee701..3a0dfb6 100644
--- a/tests/test_refine.py
+++ b/tests/test_refine.py
@@ -1,4 +1,5 @@
 """Test refine"""
+
 import pytest
 from sklearn.datasets import make_classification
 
@@ -38,7 +39,7 @@ def test_select_refine_cls():
     assert selector.scores_.sum() <= scores_1.sum()
     assert selector.scores_.sum() <= scores_23.sum()
     assert selector.scores_.sum() <= scores_all.sum()
-    assert (indices_inc[0]==1) and (indices_inc[1]==5)
+    assert (indices_inc[0] == 1) and (indices_inc[1] == 5)
 
 
 def test_refine_error():
diff --git a/tests/test_utils.py b/tests/test_utils.py
index 8baf0e9..8d57c73 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -16,23 +16,27 @@ def test_sum_errs():
 
     indices, scores = ols(X, y, 5)
 
-    y_hat = LinearRegression(fit_intercept=False)\
-                .fit(X[:, indices], y)\
-                .predict(X[:, indices])
-    e = y-y_hat
+    y_hat = (
+        LinearRegression(fit_intercept=False)
+        .fit(X[:, indices], y)
+        .predict(X[:, indices])
+    )
+    e = y - y_hat
     # Sum of Error Reduction Ratio
-    serrs = 1 - np.dot(e, e)/np.dot(y, y)
+    serrs = 1 - np.dot(e, e) / np.dot(y, y)
     assert_almost_equal(actual=scores.sum(), desired=serrs)
 
+
 def test_pearson_r():
     """Test Pearson's correlation."""
     rng = np.random.default_rng(12345)
     X = rng.random(100)
     y = rng.random(100)
     r2 = ssc(X.reshape(-1, 1), y.reshape(-1, 1))
-    gtruth_r2 = np.corrcoef(X, y)[0, 1]**2
+    gtruth_r2 = np.corrcoef(X, y)[0, 1] ** 2
     assert_almost_equal(actual=r2, desired=gtruth_r2)
 
+
 def test_multi_r():
     """Test multiple correlation."""
     rng = np.random.default_rng(12345)
@@ -48,6 +52,7 @@ def test_multi_r():
     gtruth_r2 = LinearRegression().fit(y, X).score(y, X)
     assert_almost_equal(actual=r2, desired=gtruth_r2)
 
+
 def test_mask_missing():
     """Test mask missing values."""
     assert mask_missing_values() is None