From bd1a21c13ca52c1a31a542fddac3659f892c0749 Mon Sep 17 00:00:00 2001
From: SIKAI ZHANG <34108862+MatthewSZhang@users.noreply.github.com>
Date: Thu, 15 May 2025 13:05:56 +0800
Subject: [PATCH] MNT add codespell

---
 .github/workflows/static.yml |   3 +
 doc/multioutput.rst          |   2 +-
 doc/narx.rst                 |   2 +-
 doc/ols_and_omp.rst          |   2 +-
 doc/pruning.rst              |   4 +-
 examples/plot_fisher.py      |   2 +-
 examples/plot_intuitive.py   |  10 +-
 examples/plot_pruning.py     |   2 +-
 examples/plot_redundancy.py  |   2 +-
 examples/plot_speed.py       |   2 +-
 fastcan/_cancorr_fast.pyx    |   2 +-
 fastcan/_fastcan.py          |   2 +-
 fastcan/narx.py              |   6 +-
 pixi.lock                    | 615 +++++++++++++++++------------------
 pyproject.toml               |  31 +-
 15 files changed, 335 insertions(+), 352 deletions(-)

diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml
index 02dcfbe..f91be89 100644
--- a/.github/workflows/static.yml
+++ b/.github/workflows/static.yml
@@ -30,3 +30,6 @@ jobs:
       - name: Type check
         run: |
           pixi run type
+      - name: Spell check
+        run: |
+          pixi run spell
diff --git a/doc/multioutput.rst b/doc/multioutput.rst
index 26a1220..092bf26 100644
--- a/doc/multioutput.rst
+++ b/doc/multioutput.rst
@@ -12,7 +12,7 @@ MIMO (Multi-Input Multi-Output) data. For classification, it can be used for
 multilabel data. Actually, for multiclass classification, which has one output with
 multiple categories, multioutput feature selection can also be useful. The multiclass
 classification can be converted to multilabel classification by one-hot encoding
-target ``y``. The cannonical correaltion coefficient between the features ``X`` and the
+target ``y``. The canonical correaltion coefficient between the features ``X`` and the
 one-hot encoded target ``y`` has equivalent relationship with Fisher's criterion in
 LDA (Linear Discriminant Analysis) [1]_. Applying :class:`FastCan` to the converted
 multioutput data may result in better accuracy in the following classification task
diff --git a/doc/narx.rst b/doc/narx.rst
index c08f43a..e52d445 100644
--- a/doc/narx.rst
+++ b/doc/narx.rst
@@ -82,7 +82,7 @@ It should also be noted the different types of predictions in model training.
 ARX and OE model
 ----------------
 
-To better understant the two types of training, it is helpful to know two linear time series model structures,
+To better understand the two types of training, it is helpful to know two linear time series model structures,
 i.e., `ARX (AutoRegressive eXogenous) model <https://www.mathworks.com/help/ident/ref/arx.html>`_ and
 `OE (output error) model <https://www.mathworks.com/help/ident/ref/oe.html>`_.
 
diff --git a/doc/ols_and_omp.rst b/doc/ols_and_omp.rst
index b4015bc..5bef340 100644
--- a/doc/ols_and_omp.rst
+++ b/doc/ols_and_omp.rst
@@ -12,7 +12,7 @@ The detailed difference between OLS and OMP can be found in [3]_.
 Here, let's briefly compare the three methods.
 
 
-Assume we have a feature matrix :math:`X_s \in \mathbb{R}^{N\times t}`, which constains
+Assume we have a feature matrix :math:`X_s \in \mathbb{R}^{N\times t}`, which contains
 :math:`t` selected features, and a target vector :math:`y \in \mathbb{R}^{N\times 1}`.
 Then the residual :math:`r \in \mathbb{R}^{N\times 1}` of the least-squares can be
 found by
diff --git a/doc/pruning.rst b/doc/pruning.rst
index aa8a871..75d60ee 100644
--- a/doc/pruning.rst
+++ b/doc/pruning.rst
@@ -22,9 +22,9 @@ should be selected, as any additional samples can be represented by linear combi
 Therefore, the number to select has to be set to small.
 
 To solve this problem, we use :func:`minibatch` to loose the redundancy check of :class:`FastCan`.
-The original :class:`FastCan` checks the redunancy within :math:`X_s \in \mathbb{R}^{n\times t}`, 
+The original :class:`FastCan` checks the redundancy within :math:`X_s \in \mathbb{R}^{n\times t}`, 
 which contains :math:`t` selected samples and n features,
-and the redunancy within :math:`Y \in \mathbb{R}^{n\times m}`, which contains :math:`m` atoms :math:`y_i`.
+and the redundancy within :math:`Y \in \mathbb{R}^{n\times m}`, which contains :math:`m` atoms :math:`y_i`.
 :func:`minibatch` ranks samples with multiple correlation coefficients between :math:`X_b \in \mathbb{R}^{n\times b}` and :math:`y_i`,
 where :math:`b` is batch size and :math:`b <= t`, instead of canonical correlation coefficients between :math:`X_s` and :math:`Y`,
 which is used in :class:`FastCan`.
diff --git a/examples/plot_fisher.py b/examples/plot_fisher.py
index 3df9946..8f2b200 100644
--- a/examples/plot_fisher.py
+++ b/examples/plot_fisher.py
@@ -5,7 +5,7 @@
 
 .. currentmodule:: fastcan
 
-In this examples, we will demonstrate the cannonical correaltion coefficient
+In this examples, we will demonstrate the canonical correaltion coefficient
 between the features ``X`` and the one-hot encoded target ``y`` has equivalent
 relationship with Fisher's criterion in LDA (Linear Discriminant Analysis).
 """
diff --git a/examples/plot_intuitive.py b/examples/plot_intuitive.py
index cd524de..b866c6c 100644
--- a/examples/plot_intuitive.py
+++ b/examples/plot_intuitive.py
@@ -22,10 +22,10 @@
 # the predicted target by a linear regression model) and the target to describe its
 # usefulness, the results are shown in the following figure. It can be seen that
 # Feature 2 is the most useful and Feature 8 is the second. However, does that mean
-# that the total usefullness of Feature 2 + Feature 8 is the sum of their R-squared
+# that the total usefulness of Feature 2 + Feature 8 is the sum of their R-squared
 # scores? Probably not, because there may be redundancy between Feature 2 and Feature 8.
 # Actually, what we want is a kind of usefulness score which has the **superposition**
-# property, so that the usefullness of each feature can be added together without
+# property, so that the usefulness of each feature can be added together without
 # redundancy.
 
 import matplotlib.pyplot as plt
@@ -125,7 +125,7 @@ def plot_bars(ids, r2_left, r2_selected):
 # Select the third feature
 # ------------------------
 # Again, let's compute the R-squared between Feature 2 + Feature 8 + Feature i and
-# the target, and the additonal R-squared contributed by the rest of the features is
+# the target, and the additional R-squared contributed by the rest of the features is
 # shown in following figure. It can be found that after selecting Features 2 and 8, the
 # rest of the features can provide a very limited contribution.
 
@@ -145,8 +145,8 @@ def plot_bars(ids, r2_left, r2_selected):
 # at the RHS of the dashed lines. The fast computational speed is achieved by
 # orthogonalization, which removes the redundancy between the features. We use the
 # orthogonalization first to makes the rest of features orthogonal to the selected
-# features and then compute their additonal R-squared values. ``eta-cosine`` uses
-# the samilar idea, but has an additonal preprocessing step to compress the features
+# features and then compute their additional R-squared values. ``eta-cosine`` uses
+# the similar idea, but has an additional preprocessing step to compress the features
 # :math:`X \in \mathbb{R}^{N\times n}` and the target
 # :math:`X \in \mathbb{R}^{N\times n}` to :math:`X_c \in \mathbb{R}^{(m+n)\times n}`
 # and :math:`Y_c \in \mathbb{R}^{(m+n)\times m}`.
diff --git a/examples/plot_pruning.py b/examples/plot_pruning.py
index db644e6..65d8f3e 100644
--- a/examples/plot_pruning.py
+++ b/examples/plot_pruning.py
@@ -81,7 +81,7 @@ def _fastcan_pruning(
 # %%
 # Compare pruning methods
 # -----------------------
-# 100 samples are seleced from 150 original data with ``Random`` pruning and
+# 100 samples are selected from 150 original data with ``Random`` pruning and
 # ``FastCan`` pruning. The results show that ``FastCan`` pruning gives a higher
 # mean value of R-squared and a lower standard deviation.
 
diff --git a/examples/plot_redundancy.py b/examples/plot_redundancy.py
index 06fd734..24bb5ec 100644
--- a/examples/plot_redundancy.py
+++ b/examples/plot_redundancy.py
@@ -9,7 +9,7 @@
 datasets, which contain redundant features.
 Here four types of features should be distinguished:
 
-* Unuseful features: the features do not contribute to the target
+* Useless features: the features do not contribute to the target
 * Dependent informative features: the features contribute to the target and form
   the redundant features
 * Redundant features: the features are constructed by linear transformation of
diff --git a/examples/plot_speed.py b/examples/plot_speed.py
index 073302e..2ff7e18 100644
--- a/examples/plot_speed.py
+++ b/examples/plot_speed.py
@@ -147,7 +147,7 @@ def baseline(X, y, t):
 r_eta = FastCan(n_features_to_select, eta=True, verbose=0).fit(X, y).indices_
 r_base, _ = baseline(X, y, n_features_to_select)
 
-print("The indices of the seleted features:", end="\n")
+print("The indices of the selected features:", end="\n")
 print(f"h-correlation: {r_h}")
 print(f"eta-cosine: {r_eta}")
 print(f"Baseline: {r_base}")
diff --git a/fastcan/_cancorr_fast.pyx b/fastcan/_cancorr_fast.pyx
index 5d8cea4..1bc5e6e 100644
--- a/fastcan/_cancorr_fast.pyx
+++ b/fastcan/_cancorr_fast.pyx
@@ -1,5 +1,5 @@
 """
-Fast feature selection with sum squared canoncial correlation coefficents
+Fast feature selection with sum squared canonical correlation coefficients
 """
 # Authors: The fastcan developers
 # SPDX-License-Identifier: MIT
diff --git a/fastcan/_fastcan.py b/fastcan/_fastcan.py
index d5145a7..cedb3d7 100644
--- a/fastcan/_fastcan.py
+++ b/fastcan/_fastcan.py
@@ -135,7 +135,7 @@ def __init__(
         self.verbose = verbose
 
     def fit(self, X, y):
-        """Preprare data for h-correlation or eta-cosine methods and select features.
+        """Prepare data for h-correlation or eta-cosine methods and select features.
 
         Parameters
         ----------
diff --git a/fastcan/narx.py b/fastcan/narx.py
index 013df07..d541adb 100644
--- a/fastcan/narx.py
+++ b/fastcan/narx.py
@@ -275,7 +275,7 @@ def make_poly_ids(
     )
 
     const_id = np.where((ids == 0).all(axis=1))
-    return np.delete(ids, const_id, 0)  # remove the constant featrue
+    return np.delete(ids, const_id, 0)  # remove the constant feature
 
 
 def _valiate_time_shift_poly_ids(
@@ -399,7 +399,7 @@ def fd2tp(feat_ids, delay_ids):
     For time_shift_ids, [0, 1], [0, 2], and [1, 3] represents x0(k-1), x0(k-2),
     and x1(k-3), respectively. For poly_ids, [1, 1] and [2, 3] represent the first
     variable multiplying the first variable given by time_shift_ids, i.e.,
-    x0(k-1)*x0(k-1), and the second variable multiplying the thrid variable, i.e.,
+    x0(k-1)*x0(k-1), and the second variable multiplying the third variable, i.e.,
     x0(k-1)*x1(k-3).
 
     Parameters
@@ -475,7 +475,7 @@ def tp2fd(time_shift_ids, poly_ids):
     For time_shift_ids, [0, 1], [0, 2], and [1, 3] represents x0(k-1), x0(k-2),
     and x1(k-3), respectively. For poly_ids, [1, 1] and [2, 3] represent the first
     variable multiplying the first variable given by time_shift_ids, i.e.,
-    x0(k-1)*x0(k-1), and the second variable multiplying the thrid variable, i.e.,
+    x0(k-1)*x0(k-1), and the second variable multiplying the third variable, i.e.,
     x0(k-1)*x1(k-3).
 
     Parameters
diff --git a/pixi.lock b/pixi.lock
index 4c04a78..fb2cbbb 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -11,7 +11,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_gnu.tar.bz2
       - 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/linux-64/cython-3.0.12-py313h5dec8f5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.0-py313h5dec8f5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.43-h712a8e2_4.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/libblas-3.9.0-31_h59b9bed_openblas.conda
@@ -67,7 +67,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-64-18.1.8-hf2b8a54_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/compilers-1.9.0-h694c41f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/cxx-compiler-1.9.0-h20888b2_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/cython-3.0.12-py313h9efc8c2_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/cython-3.1.0-py313h9efc8c2_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/fortran-compiler-1.9.0-h02557f8_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/gfortran-13.3.0-hcc3c99d_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/gfortran_impl_osx-64-13.3.0-hbf5bf67_105.conda
@@ -81,7 +81,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libblas-3.9.0-31_h7f60823_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libcblas-3.9.0-31_hff6cab4_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libclang-cpp18.1-18.1.8-default_h3571c67_9.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-20.1.4-hf95d169_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-20.1.5-hf95d169_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libcxx-devel-18.1.8-h7c275be_8.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libexpat-2.7.0-h240833e_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-64/libffi-3.4.6-h281671d_1.conda
@@ -142,7 +142,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/compiler-rt_osx-arm64-18.1.8-h832e737_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/compilers-1.9.0-hce30654_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cxx-compiler-1.9.0-hba80287_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.0.12-py313hd607753_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/cython-3.1.0-py313hd607753_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/fortran-compiler-1.9.0-h5692697_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gfortran-13.3.0-h3ef1dbf_1.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/gfortran_impl_osx-arm64-13.3.0-h16b3750_105.conda
@@ -155,7 +155,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libblas-3.9.0-31_h10e41b3_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcblas-3.9.0-31_hb3479ef_openblas.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libclang-cpp18.1-18.1.8-default_hf90f093_9.conda
-      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-20.1.4-ha82da77_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-20.1.5-ha82da77_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libcxx-devel-18.1.8-h6dc3340_8.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libexpat-2.7.0-h286801f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/osx-arm64/libffi-3.4.6-h1da3d7d_1.conda
@@ -202,7 +202,7 @@ environments:
       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
-      - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.0.12-py313h11c7957_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cython-3.1.0-py313h11c7957_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/intel-openmp-2024.2.1-h57928b3_1083.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/joblib-1.5.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/libblas-3.9.0-31_h641d27c_mkl.conda
@@ -278,13 +278,13 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/coverage-7.8.0-py313h8060acc_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cyrus-sasl-2.1.27-h54b06d7_7.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.0.12-py313h5dec8f5_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cython-3.1.0-py313h5dec8f5_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/dbus-1.13.6-h5008d03_3.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/debugpy-1.8.14-py313h46c70d0_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/decorator-5.2.1-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/defusedxml-0.7.1-pyhd8ed1ab_0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/linux-64/double-conversion-3.3.1-h5888daf_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.2.2-pyhd8ed1ab_1.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/exceptiongroup-1.3.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/executing-2.2.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/expat-2.7.0-h5888daf_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2
@@ -294,13 +294,13 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.15.0-h7e30c49_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2
       - conda: https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.57.0-py313h8060acc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/fonttools-4.58.0-py313h8060acc_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/fqdn-1.5.1-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/freetype-2.13.3-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h11-0.16.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/h2-4.2.0-pyhd8ed1ab_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.1.0-h3beb420_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/harfbuzz-11.2.1-h3beb420_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/hpack-4.1.0-pyhd8ed1ab_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/httpcore-1.0.9-pyh29332c3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/httpx-0.28.1-pyhd8ed1ab_0.conda
@@ -326,7 +326,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_client-8.6.3-pyhd8ed1ab_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_core-5.7.2-pyh31011fe_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_events-0.12.0-pyh29332c3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.15.0-pyhd8ed1ab_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/noarch/jupyter_server-2.16.0-pyhe01879c_0.conda
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   requires_python: '>=3.10'
-- pypi: https://files.pythonhosted.org/packages/4c/4b/a57f8ddcf48e129e6054fa9899a2a86d1fc6b07a0e15c7eebff7ca94533f/scipy-1.15.2-cp313-cp313t-macosx_10_13_x86_64.whl
+- pypi: https://files.pythonhosted.org/packages/e1/fe/9c4361e7ba2927074360856db6135ef4904d505e9b3afbbcb073c4008328/scipy-1.15.3-cp313-cp313t-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
   name: scipy
-  version: 1.15.2
-  sha256: 447ce30cee6a9d5d1379087c9e474628dab3db4a67484be1b7dc3196bfb2fac9
+  version: 1.15.3
+  sha256: 9db984639887e3dffb3928d118145ffe40eff2fa40cb241a306ec57c219ebbbb
   requires_dist:
   - numpy>=1.23.5,<2.5
   - pytest ; extra == 'test'
@@ -9255,7 +9236,7 @@ packages:
   - jupytext ; extra == 'doc'
   - myst-nb ; extra == 'doc'
   - pooch ; extra == 'doc'
-  - jupyterlite-sphinx>=0.16.5 ; extra == 'doc'
+  - jupyterlite-sphinx>=0.19.1 ; extra == 'doc'
   - jupyterlite-pyodide-kernel ; extra == 'doc'
   - mypy==1.10.0 ; extra == 'dev'
   - typing-extensions ; extra == 'dev'
@@ -9447,16 +9428,18 @@ packages:
   - pkg:pypi/sniffio?source=hash-mapping
   size: 15019
   timestamp: 1733244175724
-- pypi: https://files.pythonhosted.org/packages/ed/dc/c02e01294f7265e63a7315fe086dd1df7dacb9f840a804da846b96d01b96/snowballstemmer-2.2.0-py2.py3-none-any.whl
+- pypi: https://files.pythonhosted.org/packages/c8/78/3565d011c61f5a43488987ee32b6f3f656e7f107ac2782dd57bdd7d91d9a/snowballstemmer-3.0.1-py3-none-any.whl
   name: snowballstemmer
-  version: 2.2.0
-  sha256: c8e1716e83cc398ae16824e5572ae04e0d9fc2c6b985fb0f900f5f0c96ecba1a
+  version: 3.0.1
+  sha256: 6cd7b3897da8d6c9ffb968a6781fa6532dce9c3618a4b127d920dab764a19064
+  requires_python: '!=3.0.*,!=3.1.*,!=3.2.*'
 - conda: https://conda.anaconda.org/conda-forge/noarch/soupsieve-2.7-pyhd8ed1ab_0.conda
   sha256: 7518506cce9a736042132f307b3f4abce63bf076f5fb07c1f4e506c0b214295a
   md5: fb32097c717486aa34b38a9db57eb49e
   depends:
   - python >=3.9
   license: MIT
+  license_family: MIT
   purls:
   - pkg:pypi/soupsieve?source=hash-mapping
   size: 37773
diff --git a/pyproject.toml b/pyproject.toml
index d28af89..8db3947 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -68,6 +68,7 @@ pandas = "*"
 ruff = "*"
 cython-lint = "*"
 mypy = "*"
+codespell = "*"
 
 
 [tool.pixi.feature.build.dependencies]
@@ -115,6 +116,7 @@ fmt = "ruff format"
 lint = "ruff check . --fix"
 cython-lint = { cmd = "cython-lint .", cwd = "fastcan" }
 type = { cmd = "mypy . --ignore-missing-imports", cwd = "fastcan" }
+spell = "codespell"
 
 [tool.pixi.feature.docs.tasks]
 doc = { cmd = "$SPHINXBUILD -M $CMD $SOURCEDIR $BUILDDIR $SPHINXOPTS", cwd = "doc", env = { SPHINXBUILD = "sphinx-build", CMD = "html", SOURCEDIR = ".", BUILDDIR = "_build", SPHINXOPTS = "" } }
@@ -140,23 +142,18 @@ dev = ["docs", "test", "build", "jupyter"]
 static = { features = ["static"], no-default-feature = true }
 nogil = { features = ["nogil"], no-default-feature = true }
 
-[tool.black]
-line-length = 88
-preview = true
-exclude = '''
-/(
-    \.eggs         # exclude a few common directories in the
-  | \.git          # root of the project
-  | \.mypy_cache
-  | \.vscode
-  | \.venv
-  | \.ruff_cache
-  | build
-  | dist
-  | doc/_build
-  | doc/auto_examples
-)/
-'''
+[tool.codespell]
+skip = [
+    "./doc/_build",
+    "./doc/auto_examples",
+    "./doc/modules/generated",
+    "./htmlcov",
+    "./build",
+    "./pixi.lock",
+]
+ignore-words-list = [
+    "MIS", # magnetic impedance spectroscopy (MIS)
+]
 
 [tool.ruff]
 line-length = 88