From 067ffa0342fce95e99d0c210e52c827c1eaec058 Mon Sep 17 00:00:00 2001
From: sneakers-the-rat <sneakers-the-rat@protonmail.com>
Date: Mon, 2 Sep 2024 22:23:41 -0700
Subject: [PATCH] bump changelog, version

---
 docs/changelog.md | 33 +++++++++++++++++++++++++++++++++
 pyproject.toml    |  2 +-
 2 files changed, 34 insertions(+), 1 deletion(-)

diff --git a/docs/changelog.md b/docs/changelog.md
index b93adb2..5dfe547 100644
--- a/docs/changelog.md
+++ b/docs/changelog.md
@@ -2,6 +2,39 @@
 
 ## 1.*
 
+### 1.5.0 - 24-09-02 - `str` support for HDF5
+
+Strings in hdf5 are tricky! HDF5 doesn't have native support for unicode, 
+but it can be persuaded to store data in ASCII or virtualized utf-8 under somewhat obscure conditions.
+
+This PR uses h5py's string methods to expose string datasets (compound or not) 
+via the h5proxy with the `asstr()` view method. 
+This also allows us to set strings with normal python strings. 
+
+Since numpydantic isn't necessarily a tool for *creating* hdf5 files 
+(nobody should be doing that), but rather an interface to them, 
+tests are included for reading and validating (unskip the existing string tests) 
+as well as setting/getting.
+
+```python
+import h5py
+import numpy as np
+from pydantic import BaseModel
+from numpydantic import NDArray
+from typing import Any
+
+class MyModel(BaseModel):
+  array: NDArray[Any, str]
+
+h5f = h5py.File('my_data.h5', 'w')
+data = np.random.random((10,10)).astype(str)
+_ = h5f.create_dataset('/dataset', data=data)
+
+instance = MyModel(array=('my_data.h5', '/dataset'))
+instance[0,0] = 'hey'
+assert instance[0,0] == 'hey'
+```
+
 ### 1.4.1 - 24-09-02 - `len()` support and dunder method testing
 
 It's pretty natural to want to do `len(array)` as a shorthand for `array.shape[0]`, 
diff --git a/pyproject.toml b/pyproject.toml
index 57ae6be..59f725c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [project]
 name = "numpydantic"
-version = "1.4.1"
+version = "1.5.0"
 description = "Type and shape validation and serialization for numpy arrays in pydantic models"
 authors = [
     {name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"},