Update for 0.5.1 release (#7)

* Print first-time notice to correct file
* Add 0.5.1 highlights
* Add newline after new contributors notice

additional_notes/0.5.1/header.md

@@ -0,0 +1,14 @@
+⚠️ *Note: these release notes are still in draft while 0.5.0 is in alpha/release
+candidate testing.* ⚠️
+
+*Wednesday, Jul 24, 2024*
+
+We're happy to announce the release of napari 0.5.1!
+
+napari is a fast, interactive, multi-dimensional image viewer for Python.
+It's designed for exploring, annotating, and analyzing multi-dimensional
+images. It's built on Qt (for the GUI), VisPy (for performant GPU-based
+rendering), and the scientific Python stack (NumPy, SciPy, and friends).
+
+For more information, examples, and documentation, please visit our website:
+https://napari.org/

additional_notes/0.5.1/highlights.md

@@ -0,0 +1,15 @@
+napari 0.5.1 is a bugfix release hot on the heels of
+[napari 0.5.0](release_0_5_0). It fixes a critical bug with creating viewers
+multiple times within a single IPython/Jupyter session
+([#7106](https://github.com/napari/napari/pull/7106)), as well as regressions
+with viewing multiscale 3D time series
+([#7103](https://github.com/napari/napari/pull/7103)) and with converting image
+layers to labels layers ([#7095](https://github.com/napari/napari/pull/7095)).
+
+It also fixes a bug with NumPy 2 support
+([#7104](https://github.com/napari/napari/pull/7104) and our storing of layer
+axis info when using the `channel_axis` keyword argument for images
+([#7089](https://github.com/napari/napari/pull/7089)).
+
+Read on for the full list of changes since the last version from just two weeks
+ago!

generate_release_notes.py

@@ -328,7 +328,8 @@ def parse_pull(pull: PullRequest, repo_: Repository = repo):
     )
     print(committer_str, file=file_handle)
     print('', file=file_handle)
-    print('(+) denotes first-time contributors 🥳')
+    print('(+) denotes first-time contributors 🥳', file=file_handle)
+    print('', file=file_handle)
 
     for c in sorted(contributor_set, key=lambda x: users[x].lower()):
         if c in authors and c in docs_authors: