typos

_typos.toml

@@ -0,0 +1,3 @@
+[default.extend-words]
+# don't current this name
+Shepard = "Shepard"

src/TopoPlots.jl

@@ -24,7 +24,7 @@ Load EEG example data.
 
 Returns a two-tuple:
   - data: a (64, 400, 3) Float32 array of channel x timepoint x stat array.
-    Timepoints correponds to samples at 500Hz from -0.3s to 0.5s relative to stimulus onset.
+    Timepoints corresponds to samples at 500Hz from -0.3s to 0.5s relative to stimulus onset.
     Stats are mean over subjects, standard errors over subjects, and associated p-value from a t-test.
     For demonstration purposes, the first stat dimension is generally the most applicable.
   - positions: a length-64 Point2f vector of positions for each channel in data.

src/interpolators.jl

@@ -71,8 +71,8 @@ function (sp::SplineInterpolator)(xrange::LinRange, yrange::LinRange,
     return out
 end
 
-# TODO how to properly integrade delauny with the interpolation interface,
-# if the actualy interpolation happens inside the plotting framework (or even on the GPU for (W)GLMakie).
+# TODO how to properly integrated delauny with the interpolation interface,
+# if the actually interpolation happens inside the plotting framework (or even on the GPU for (W)GLMakie).
 
 """
     DelaunayMesh()
@@ -139,7 +139,7 @@ a variety of methods like `Sibson(::Int)`, `Nearest()`, and `Triangle()`.
 
 The advantage of Voronoi-diagram based methods is that they are more robust to 
 irregularly distributed datasets and some discontinuities, which may throw off 
-some polynomial based methods as well as independant distance weighting (kriging). 
+some polynomial based methods as well as independent distance weighting (kriging). 
 See [this Discourse post](https://discourse.julialang.org/t/ann-naturalneighbours-jl-natural-neighbour-interpolation-and-derivative-generation/99164/11) for more information on why NaturalNeighbours are cool! 
 
 To access the methods easily, you should run `using NearestNeighbours`.

test/CondaPkg.toml

@@ -2,7 +2,7 @@ channels = ["anaconda", "conda-forge"]
 
 [deps]
 matplotlib = ""
-python = ">=3.7, <3.13" # we get segaults on 3.13 on Apple Silicon
+python = ">=3.7, <3.13" # we get segfaults on 3.13 on Apple Silicon
 scipy = ">=1.9"
 
 [pip.deps]

test/runtests.jl

@@ -12,7 +12,7 @@ catch e
     # Now, Conda started making problems (in a fresh CI env?!) https://github.com/MakieOrg/TopoPlots.jl/pull/20#issuecomment-1224822002
     # So, lets go back to install matplotlib manually, and let mne install automatically!
     #run(PyCall.python_cmd(`-m pip install matplotlib`))#
-    error("to be adressed")
+    error("to be addressed")
 end
 
 using PyMNE