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add changelog, bump version, add additional level of nesting in changelog
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2 changed files with 31 additions and 13 deletions
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@ -2,7 +2,17 @@
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## 1.*
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### 1.5.0 - 24-09-02 - `str` support for HDF5
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### 1.5.*
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#### 1.5.1 - 24-09-03 - Fix revalidation with proxy classes
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Bugfix:
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- [#14](https://github.com/p2p-ld/numpydantic/pull/14): Allow revalidation of proxied arrays
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Tests:
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- Add test module for tests against all interfaces, test for above bug
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#### 1.5.0 - 24-09-02 - `str` support for HDF5
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Strings in hdf5 are tricky! HDF5 doesn't have native support for unicode,
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but it can be persuaded to store data in ASCII or virtualized utf-8 under somewhat obscure conditions.
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@ -36,7 +46,9 @@ instance[0,0] = 'hey'
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assert instance[0,0] == 'hey'
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```
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### 1.4.1 - 24-09-02 - `len()` support and dunder method testing
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### 1.4.*
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#### 1.4.1 - 24-09-02 - `len()` support and dunder method testing
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It's pretty natural to want to do `len(array)` as a shorthand for `array.shape[0]`,
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but since some of the numpydantic classes are passthrough proxy objects,
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@ -52,7 +64,7 @@ There is a certain combinatoric explosion when we start testing across all inter
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for all input types, for all dtype and all shape cases,
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but for now numpydantic is fast enough that this doesn't matter <3.
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### 1.4.0 - 24-09-02 - HDF5 Compound Dtype Support
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#### 1.4.0 - 24-09-02 - HDF5 Compound Dtype Support
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HDF5 can have compound dtypes like:
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@ -107,7 +119,9 @@ my_model = MyModel(
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np.dtype('int64')
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```
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### 1.3.3 - 24-08-13 - Callable type annotations
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### 1.3.*
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#### 1.3.3 - 24-08-13 - Callable type annotations
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Problem, when you use a numpydantic `"wrap"` validator, it gives the annotation as a `handler` function.
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@ -162,11 +176,11 @@ ShapeError: Invalid shape! expected shape ['3'], got shape (4,)
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using it. This shaves ~600ms off import time.
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### 1.3.2 - 24-08-12 - Allow subclasses of dtypes
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#### 1.3.2 - 24-08-12 - Allow subclasses of dtypes
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(also when using objects for dtypes, subclasses of that object are allowed to validate)
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### 1.3.1 - 24-08-12 - Allow arbitrary dtypes, pydantic models as dtypes
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#### 1.3.1 - 24-08-12 - Allow arbitrary dtypes, pydantic models as dtypes
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Previously we would only allow dtypes if we knew for sure that there was some
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python base type to generate a schema with.
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@ -183,7 +197,7 @@ Only one substantial change, and that is a `get_object_dtype` method which
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interfaces can override if there is some fancy way they have of getting
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types/items from an object array.
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### 1.3.0 - 24-08-05 - Better string dtype handling
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#### 1.3.0 - 24-08-05 - Better string dtype handling
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API Changes:
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- Split apart the validation methods into smaller chunks to better support
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@ -194,7 +208,9 @@ Bugfix:
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- [#4](https://github.com/p2p-ld/numpydantic/issues/4) - Support dtype checking
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for strings in zarr and numpy arrays
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### 1.2.3 - 24-07-31 - Vendor `nptyping`
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### 1.2.*
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#### 1.2.3 - 24-07-31 - Vendor `nptyping`
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`nptyping` vendored into `numpydantic.vendor.nptyping` -
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`nptyping` is no longer maintained, and pins `numpy<2`.
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@ -221,22 +237,24 @@ Tidying:
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- Remove `monkeypatch` module! we don't need it anymore!
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everything has either been upstreamed or vendored.
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### 1.2.2 - 24-07-31
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#### 1.2.2 - 24-07-31
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Add `datetime` map to numpy's :class:`numpy.datetime64` type
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### 1.2.1 - 24-06-27
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#### 1.2.1 - 24-06-27
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Fix a minor bug where {class}`~numpydantic.exceptions.DtypeError` would not cause
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pydantic to throw a {class}`pydantic.ValidationError` because custom validator functions
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need to raise either `AssertionError` or `ValueError` - made `DtypeError` also
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inherit from `ValueError` because that is also technically true.
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### 1.2.0 - 24-06-13 - Shape ranges
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#### 1.2.0 - 24-06-13 - Shape ranges
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- Add ability to specify shapes as ranges - see [shape ranges](shape-ranges)
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### 1.1.0 - 24-05-24 - Instance Checking
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### 1.1.*
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#### 1.1.0 - 24-05-24 - Instance Checking
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https://github.com/p2p-ld/numpydantic/pull/1
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@ -1,6 +1,6 @@
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[project]
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name = "numpydantic"
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version = "1.5.0"
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version = "1.5.1"
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description = "Type and shape validation and serialization for arbitrary array types in pydantic models"
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authors = [
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{name = "sneakers-the-rat", email = "sneakers-the-rat@protonmail.com"},
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