Standardised terminology: now using just "axis"/"axes", not "dimension"

docs/api/array.md

@@ -19,12 +19,12 @@ When calling a function, variable-size axes and symbolic axes will be matched up
 
 In addition some modifiers can be applied:
 
-- Prepend `*` to a dimension to indicate that it can match multiple axes, e.g. `"*batch c h w"` will match zero or more batch axes.
-- Prepend `#` to a dimension to indicate that it can be that size *or* equal to one -- i.e. broadcasting is acceptable, e.g.  
+- Prepend `*` to an axis to indicate that it can match multiple axes, e.g. `"*batch c h w"` will match zero or more batch axes.
+- Prepend `#` to an axis to indicate that it can be that size *or* equal to one -- i.e. broadcasting is acceptable, e.g.  
     `def add(x: Float[Array, "#foo"], y: Float[Array, "#foo"]) -> Float[Array, "#foo"]`.
-- Prepend `_` to a dimension to disable any runtime checking of that dimension (so that it can be used just as documentation). This can also be used as just `_` on its own: e.g. `"b c _ _"`.
+- Prepend `_` to an axis to disable any runtime checking of that axis (so that it can be used just as documentation). This can also be used as just `_` on its own: e.g. `"b c _ _"`.
 - Documentation-only names (i.e. they're ignored by jaxtyping) can be handled by prepending a name followed by `=` e.g. `Float[Array, "rows=4 cols=3"]`.
-- Prepend `?` to a dimension to indicate that its size can vary within a PyTree structure. (See [PyTree annotations](../pytree/).)
+- Prepend `?` to an axis to indicate that its size can vary within a PyTree structure. (See [PyTree annotations](../pytree/).)
 
 When using multiple modifiers, their order does not matter.
 
@@ -37,7 +37,7 @@ As a special case:
 - To denote a scalar shape use `""`, e.g. `Float[Array, ""]`.
 - To denote an arbitrary shape (and only check dtype) use `"..."`, e.g. `Float[Array, "..."]`.
 - You cannot have more than one use of multiple-axes, i.e. you can only use `...` or `*name` at most once in each array.
-- An example of broadcasting multiple dimensions:  
+- An example of broadcasting multiple axes:  
     `def add(x: Float[Array, "*#foo"], y: Float[Array, "*#foo"]) -> Float[Array, "*#foo"]`.
 - A symbolic expression cannot be evaluated unless all of the axes sizes it refers to have already been processed. In practice this usually means that they should only be used in annotations for the return type, and only use axes declared in the arguments.
 

docs/index.md

@@ -24,7 +24,7 @@ The annotations provided by jaxtyping are compatible with runtime type-checking
 ```python
 from jaxtyping import Array, Float, PyTree
 
-# Accepts floating-point 2D arrays with matching dimensions
+# Accepts floating-point 2D arrays with matching axes
 def matrix_multiply(x: Float[Array, "dim1 dim2"],
                     y: Float[Array, "dim2 dim3"]
                   ) -> Float[Array, "dim1 dim3"]: