docs: update migration guide

PR-URL: #1000
Reviewed-by: Athan Reines <kgryte@gmail.com>
Reviewed-by: Lucas Colley

Author: mtsokol

spec/draft/migration_guide.md

@@ -2,115 +2,116 @@
 
 # Migration Guide
 
-This page is meant to help migrate your codebase to an Array API compliant
-implementation. The guide is divided into two parts and, depending on your
-exact use-case, you should look thoroughly into at least one of them.
+This page is meant to help migrate your codebase to an array API standard
+compliant implementation or become interoperable with compliant
+implementations. The guide is divided into three parts.
 
-The first part is dedicated for {ref}`array-producers`. If your library
-mimics, for example, NumPy's or Dask's functionality, then you can find in
-the first part additional instructions and guidance on how to ensure
-downstream users can easily pick your solution as an array provider for
-their system/algorithm.
+The first part gives an overview of the {ref}`ecosystem` libraries, that
+are helpful in different contexts when working with the array API standard.
 
-The second part delves into details for Array API compatibility for
+The second part is dedicated to {ref}`array-producers`. If your library
+mimics, for example, NumPy's or PyTorch's functionality, you can find
+additional instructions and guidance here on how to ensure downstream users
+can easily pick your solution as an array provider for their system/algorithm.
+
+The third part delves into details for array API standard compatibility for
 {ref}`array-consumers`. This pertains to any software that performs
 multidimensional array manipulation in Python, such as may be found in
 scikit-learn, SciPy, or statsmodels. If your software relies on a certain
 array producing library, such as NumPy or JAX, then you can use the second
-part to learn how to make it library agnostic and interchange array
-namespaces with significantly less friction.
+part to learn how to make it library agnostic and, as a result, use array
+namespaces interchangeably with significantly less friction.
+
+
+(ecosystem)=
 
 ## Ecosystem
 
-Apart from the documented standard, the Array API ecosystem also provides
+Apart from the documented standard, the array API ecosystem also provides
 a set of tools and packages to help you with the migration process:
 
 
 (array-api-compat)=
 
-### Array API Compat
+### array-api-compat
 
 GitHub: [array-api-compat](https://github.com/data-apis/array-api-compat)
 
 User group: Array Consumers
 
-Although NumPy, Dask, CuPy, and PyTorch support the Array API Standard, there
-are still some corner cases where their behavior diverges from the standard.
-`array-api-compat` provides a compatibility layer to cover these cases.
-This is also accompanied by a few utility functions for easier introspection
-into array objects. As an array consumer, you can still rely on the original
-API while having access to the standard compatible one.
+Although NumPy or CuPy support the array API standard, there are still some
+corner cases where their behavior diverges from the standard.
+`array-api-compat` provides a compatibility layer to cover an additional subset
+of such corner cases for supported libraries. This is also accompanied by a few
+utility functions for easier introspection into array objects. As an array
+consumer, you can consume standard-compliant namespaces as well as the wrapped
+namespaces in `array-api-compat` at the same time.
 
 
 (array-api-strict)=
 
-### Array API Strict
+### array-api-strict
 
 GitHub: [array-api-strict](https://github.com/data-apis/array-api-strict)
 
-User group: Array Consumers, Array Producers (for testing)
+User group: Array Consumers
 
 `array-api-strict` is a library that provides a strict and minimal
-implementation of the Array API Standard. For array producers, it is designed
-to be used as a reference implementation for testing and development purposes.
-You can compare your API calls with `array-api-strict` counterparts and
-ensure that your library is fully compliant with the standard and can
-serve as a reliable reference for other developers in the ecosystem.
-For consumers, you can use `array-api-strict` during the development as an
-array provider to ensure your code uses APIs compliant with the standard.
+implementation of the array API standard. As a consumer, you can use
+`array-api-strict` in parametrising tests over the array namespace
+to ensure your code uses only APIs that are compliant with the standard.
 
 
 (array-api-tests)=
 
-### Array API Test
+### array-api-tests
 
 GitHub: [array-api-tests](https://github.com/data-apis/array-api-tests)
 
 User group: Array Producers
 
 `array-api-tests` is a collection of tests that can be used to verify the
-compliance of your library with the Array API Standard. It includes tests
+compliance of your library with the array API standard. It includes tests
 for array producers, covering a wide range of functionalities and use cases.
 By running these tests, you can ensure that your library adheres to the
 standard and can be used with compatible array consumer libraries.
 
 
 (array-api-extra)=
 
-### Array API Extra
+### array-api-extra
 
 GitHub: [array-api-extra](https://github.com/data-apis/array-api-extra)
 
 User group: Array Consumers
 
 `array-api-extra` is a collection of additional utilities and tools that are
-missing from the Array API Standard but can be useful for compliant array
-consumers. It includes additional array manipulation and statistical functions.
-It is already used by SciPy and scikit-learn.
-
-The sections below mention when and how to use them.
+not present in the array API standard but can be useful for compliant array
+consumers. It includes additional array manipulation and statistical
+functions, support for lazy backends, and useful testing utilities. It is
+already used by SciPy and scikit-learn.
 
 
 (array-producers)=
 
 ## Array Producers
 
 For array producers, the central task during the development/migration process
-is ensuring that the user-facing API adheres to the Array API Standard.
+is ensuring that the user-facing API adheres to the array API standard.
 
 The complete API of the standard is documented in the
 [API specification](https://data-apis.org/array-api/latest/API_specification/index.html).
 
 There, each function, constant, and object is described with details
 on parameters, return values, and special cases.
 
-### Testing against Array API
+### Testing against array API
 
 There are two main ways to test your API for compliance: either using
 `array-api-tests` suite or testing your API manually against the
 `array-api-strict` reference implementation.
 
-#### Array API Test suite (Recommended)
+#### array-api-tests suite (Recommended)
 
 {ref}`array-api-tests` is a test suite which verifies that your API
 adheres to the standard. For each function or method, it confirms
@@ -126,10 +127,10 @@ cover only the minimal workflow:
    variable to your package import name.
 3. Inside the `array-api-tests` directory run the command for running pytest: `pytest`. There are
    multiple useful options delivered by the test suite. A few worth mentioning:
-   - `--max-examples=1000` - maximal number of test cases to generate when using
+   - `--max-examples=1000` - maximum number of test cases to generate when using
      hypothesis. This allows you to balance between execution time of the test
      suite and thoroughness of the testing. It's advised to use as many examples
-     as the time buget can fit. Each test case is a random combination of
+     as the time budget can fit. Each test case is a random combination of
      possible inputs: the more cases, the higher chance of finding an
      unsupported edge case.
    - With the `--xfails-file` option, you can describe which tests are expected
@@ -144,13 +145,15 @@ cover only the minimal workflow:
      option is to skip these for the time being.
 
 We strongly advise you to embed this setup in your CI as well. This will allow
-you to continuously monitor Array API coverage, and make sure new changes don't break existing
-APIs. As a reference, see [NumPy's Array API Tests CI setup](https://github.com/numpy/numpy/blob/581d10f43b539a189a2d37856e5130464de9e5f6/.github/workflows/linux.yml#L296).
+you to continuously monitor array API standard coverage, and make sure new
+changes don't break existing APIs. As a reference, see
+[NumPy's array-api-tests CI setup](https://github.com/numpy/numpy/blob/581d10f43b539a189a2d37856e5130464de9e5f6/.github/workflows/linux.yml#L296)
+and [a Pixi workspace setup](https://github.com/mdhaber/mparray/blob/0ef47e008fef92c605f73907436d4c6617419161/pixi.toml#L119-L179).
 
 
-#### Array API Strict
+#### array-api-strict
 
-A simpler, and more manual, way of testing Array API coverage is to
+A simpler, and more manual, way of testing array API standard coverage is to
 run your API calls along with the {ref}`array-api-strict` Python implementation.
 
 This way, you can ensure that the outputs coming from your API match the minimal
@@ -163,10 +166,9 @@ cases.
 
 ## Array Consumers
 
-For array consumers, the main premise is to keep in mind that your **array
-manipulation operations should not lock in for a particular array producing
-library**. For instance, if you use NumPy for arrays, then your code could
-contain:
+For array consumers, the main premise is that your **array manipulation operations
+should not be specific to one particular array producing library**. For instance,
+if your code is specific to NumPy, it might contain:
 
 ```python
 import numpy as np
@@ -178,12 +180,12 @@ return np.dot(c, b)
 ```
 
 The first step should be as simple as assigning the `np` namespace to a dedicated
-namespace variable. The convention used in the ecosystem is to name it `xp`. Then,
-it is vital to ensure that each method and function call is something that the Array API
-supports. For example, `dot` is present in the NumPy's API, but the standard
-doesn't support it. For the sake of simplicity, let's assume both `c` and `b`
-are `ndim=2`; therefore, we select `tensordot` instead, as both NumPy and the
-standard define it:
+namespace variable. The convention used in the ecosystem is to name it `xp`.
+Then, it is vital to ensure that each method and function call is something that
+the array API standard supports. For example, `dot` is present in the NumPy
+API, but the standard doesn't support it. For the sake of simplicity, let's
+assume both `c` and `b` are `ndim=2`; therefore, we select `tensordot` instead,
+as both NumPy and the standard define it:
 
 ```python
 import numpy as np
@@ -196,18 +198,19 @@ c = xp.mean(a, axis=0)
 return xp.tensordot(c, b, axes=1)
 ```
 
-At this point, replacing one backend with another one should only require providing a different
-namespace, such as `xp = torch` (e.g., via an environment variable). This can be useful
-if you're writing a script or in your custom software. The other alternatives are:
+At this point, replacing one backend with another one should only require
+providing a different namespace, such as `xp = torch` (e.g., via an environment
+variable). This can be useful if you're writing a script or in your custom
+software. The other alternatives are:
 
-- If you are building a library where the backend is determined by input arrays,
-  and your function accepts array arguments, then a recommended way is to ask
-  your input arrays for a namespace to use: `xp = arr.__array_namespace__()`.
-  If the given library doesn't have it, then [`array_api_compat.array_namespace()`](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.array_namespace)
-  should be used instead:
+- If you are building a library where the backend is determined by input
+  arrays, and your function accepts array arguments, then a recommended way to
+  fetch the namespace is to use [`array_api_compat.array_namespace()`](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.array_namespace).
+  In case you don't want to introduce a new package dependency, you can rely
+  on a plain `xp = arr.__array_namespace__()`:
   ```python
   def func(array1, scalar1, scalar2):
-    xp = array1.__array_namespace__()  # or array_namespace(array1)
+    xp = array_namespace(array1)  # or array1.__array_namespace__()
     return xp.arange(scalar1, scalar2) @ array1
   ```
 - For a function that accepts scalars and returns arrays, use namespace `xp` as
@@ -227,7 +230,7 @@ offers a set of useful utility functions, such as:
 - [array_namespace()](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.array_namespace)
   for fetching the namespace based on input arrays.
 - [is_array_api_obj()](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.is_array_api_obj)
-  for inspecting whether a given object is Array API compatible.
+  for inspecting whether a given object is array API compatible.
 - [device()](https://data-apis.org/array-api-compat/helper-functions.html#array_api_compat.device)
   for retrieving the device on which an array resides.