Better formatting of scope

1 files changed, 35 insertions, 22 deletions

diff --git a/doc/neps/scope.rst b/doc/neps/scope.rst
index 1b7e906f6..2a3b6abe0 100644
--- a/doc/neps/scope.rst
+++ b/doc/neps/scope.rst
@@ -4,25 +4,38 @@ Scope of NumPy
 
 Here, we describe aspects of N-d array computation that are within scope for NumPy development. This is *not* an aspirational definition of where NumPy should aim, but instead captures the status quo—areas which we have decided to continue supporting, at least for the time being.
 
-- In-memory, N-dimensional, homogeneously typed (single pointer + strided) arrays on CPUs
-    - Support for a wide range of data types
-    - Not specialized hardware such as GPUs
-    - But, do support wide range of CPUs (e.g. ARM, PowerX)
-- Higher level APIs for N-dimensional arrays
-    - NumPy is a *de facto* standard for array APIs in Python
-    - Indexing and fast iteration over elements (ufunc)
-    - Interoperability protocols with other data container implementations (like `__array_ufunc__`).
-- Python API and a C API to the ndarray's methods and attributes.
-- More specialized types of N-dimensional arrays:
-    - Masked arrays
-    - Structured arrays
-- Historically, NumPy has included the following basic functionality in support of scientific computation. We intend to keep supporting (but not to expand) what is currently included:
-    - Linear algebra
-    - Fast Fourier transforms and windowing
-    - Pseudo-random number generators
-    - Polynomial fitting
-- NumPy provides some infrastructure for other packages in the scientific Python ecosystem:
-    - numpy.distutils (build support for C++, Fortran, BLAS/LAPACK, and other relevant libraries for scientific computing
-    - f2py (generating bindings for Fortran code)
-    - testing utilities
-- Speed: while we take performance concerns seriously, where conflict arises maintenance and portability take precedence over performance. We aim to prevent regressions where possible (e.g., through asv).
+- **In-memory, N-dimensional, homogeneously typed (single pointer + strided) arrays on CPUs**
+
+  - Support for a wide range of data types
+  - Not specialized hardware such as GPUs
+  - But, do support wide range of CPUs (e.g. ARM, PowerX)
+
+- **Higher level APIs for N-dimensional arrays**
+
+  - NumPy is a *de facto* standard for array APIs in Python
+  - Indexing and fast iteration over elements (ufunc)
+  - Interoperability protocols with other data container implementations (like `__array_ufunc__`).
+
+- **Python API and a C API** to the ndarray's methods and attributes.
+
+- Other **specialized types of N-dimensional arrays**:
+
+  - Masked arrays
+  - Structured arrays
+
+- Historically, NumPy has included the following **basic functionality
+  in support of scientific computation**. We intend to keep supporting
+  (but not to expand) what is currently included:
+
+  - Linear algebra
+  - Fast Fourier transforms and windowing
+  - Pseudo-random number generators
+  - Polynomial fitting
+
+- NumPy provides some **infrastructure for other packages in the scientific Python ecosystem**:
+
+  - numpy.distutils (build support for C++, Fortran, BLAS/LAPACK, and other relevant libraries for scientific computing
+  - f2py (generating bindings for Fortran code)
+  - testing utilities
+
+- **Speed**: while we take performance concerns seriously, where conflict arises maintenance and portability take precedence over performance. We aim to prevent regressions where possible (e.g., through asv).