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add changelog, bump version, add additional level of nesting in changelog

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sneakers-the-rat 2024-09-03 13:18:00 -07:00
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docs/changelog.md
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@ -2,7 +2,17 @@
## 1.* ## 1.*
### 1.5.0 - 24-09-02 - `str` support for HDF5 ### 1.5.*
#### 1.5.1 - 24-09-03 - Fix revalidation with proxy classes
Bugfix:
- [#14](https://github.com/p2p-ld/numpydantic/pull/14): Allow revalidation of proxied arrays
Tests:
- Add test module for tests against all interfaces, test for above bug
#### 1.5.0 - 24-09-02 - `str` support for HDF5
Strings in hdf5 are tricky! HDF5 doesn't have native support for unicode, 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. but it can be persuaded to store data in ASCII or virtualized utf-8 under somewhat obscure conditions.
@ -36,7 +46,9 @@ instance[0,0] = 'hey'
assert instance[0,0] == 'hey' assert instance[0,0] == 'hey'
``` ```
### 1.4.1 - 24-09-02 - `len()` support and dunder method testing ### 1.4.*
#### 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]`, It's pretty natural to want to do `len(array)` as a shorthand for `array.shape[0]`,
but since some of the numpydantic classes are passthrough proxy objects, but since some of the numpydantic classes are passthrough proxy objects,
@ -52,7 +64,7 @@ There is a certain combinatoric explosion when we start testing across all inter
for all input types, for all dtype and all shape cases, for all input types, for all dtype and all shape cases,
but for now numpydantic is fast enough that this doesn't matter <3. but for now numpydantic is fast enough that this doesn't matter <3.
### 1.4.0 - 24-09-02 - HDF5 Compound Dtype Support #### 1.4.0 - 24-09-02 - HDF5 Compound Dtype Support
HDF5 can have compound dtypes like: HDF5 can have compound dtypes like:
@ -107,7 +119,9 @@ my_model = MyModel(
np.dtype('int64') np.dtype('int64')
``` ```
### 1.3.3 - 24-08-13 - Callable type annotations ### 1.3.*
#### 1.3.3 - 24-08-13 - Callable type annotations
Problem, when you use a numpydantic `"wrap"` validator, it gives the annotation as a `handler` function. Problem, when you use a numpydantic `"wrap"` validator, it gives the annotation as a `handler` function.
@ -162,11 +176,11 @@ ShapeError: Invalid shape! expected shape ['3'], got shape (4,)
using it. This shaves ~600ms off import time. using it. This shaves ~600ms off import time.
### 1.3.2 - 24-08-12 - Allow subclasses of dtypes #### 1.3.2 - 24-08-12 - Allow subclasses of dtypes
(also when using objects for dtypes, subclasses of that object are allowed to validate) (also when using objects for dtypes, subclasses of that object are allowed to validate)
### 1.3.1 - 24-08-12 - Allow arbitrary dtypes, pydantic models as dtypes #### 1.3.1 - 24-08-12 - Allow arbitrary dtypes, pydantic models as dtypes
Previously we would only allow dtypes if we knew for sure that there was some Previously we would only allow dtypes if we knew for sure that there was some
python base type to generate a schema with. python base type to generate a schema with.
@ -183,7 +197,7 @@ Only one substantial change, and that is a `get_object_dtype` method which
interfaces can override if there is some fancy way they have of getting interfaces can override if there is some fancy way they have of getting
types/items from an object array. types/items from an object array.
### 1.3.0 - 24-08-05 - Better string dtype handling #### 1.3.0 - 24-08-05 - Better string dtype handling
API Changes: API Changes:
- Split apart the validation methods into smaller chunks to better support - Split apart the validation methods into smaller chunks to better support
@ -194,7 +208,9 @@ Bugfix:
- [#4](https://github.com/p2p-ld/numpydantic/issues/4) - Support dtype checking - [#4](https://github.com/p2p-ld/numpydantic/issues/4) - Support dtype checking
for strings in zarr and numpy arrays for strings in zarr and numpy arrays
### 1.2.3 - 24-07-31 - Vendor `nptyping` ### 1.2.*
#### 1.2.3 - 24-07-31 - Vendor `nptyping`
`nptyping` vendored into `numpydantic.vendor.nptyping` - `nptyping` vendored into `numpydantic.vendor.nptyping` -
`nptyping` is no longer maintained, and pins `numpy<2`. `nptyping` is no longer maintained, and pins `numpy<2`.
@ -221,22 +237,24 @@ Tidying:
- Remove `monkeypatch` module! we don't need it anymore! - Remove `monkeypatch` module! we don't need it anymore!
everything has either been upstreamed or vendored. everything has either been upstreamed or vendored.
### 1.2.2 - 24-07-31 #### 1.2.2 - 24-07-31
Add `datetime` map to numpy's :class:`numpy.datetime64` type Add `datetime` map to numpy's :class:`numpy.datetime64` type
### 1.2.1 - 24-06-27 #### 1.2.1 - 24-06-27
Fix a minor bug where {class}`~numpydantic.exceptions.DtypeError` would not cause Fix a minor bug where {class}`~numpydantic.exceptions.DtypeError` would not cause
pydantic to throw a {class}`pydantic.ValidationError` because custom validator functions pydantic to throw a {class}`pydantic.ValidationError` because custom validator functions
need to raise either `AssertionError` or `ValueError` - made `DtypeError` also need to raise either `AssertionError` or `ValueError` - made `DtypeError` also
inherit from `ValueError` because that is also technically true. inherit from `ValueError` because that is also technically true.
### 1.2.0 - 24-06-13 - Shape ranges #### 1.2.0 - 24-06-13 - Shape ranges
- Add ability to specify shapes as ranges - see [shape ranges](shape-ranges) - Add ability to specify shapes as ranges - see [shape ranges](shape-ranges)
### 1.1.0 - 24-05-24 - Instance Checking ### 1.1.*
#### 1.1.0 - 24-05-24 - Instance Checking
https://github.com/p2p-ld/numpydantic/pull/1 https://github.com/p2p-ld/numpydantic/pull/1

2
pyproject.toml
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@ -1,6 +1,6 @@
[project] [project]
name = "numpydantic" name = "numpydantic"
version = "1.5.0" version = "1.5.1"
description = "Type and shape validation and serialization for arbitrary array types in pydantic models" description = "Type and shape validation and serialization for arbitrary array types in pydantic models"
authors = [ authors = [
{name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"}, {name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"},