hypothesis-awkward 0.1.0

Hypothesis strategies for Awkward Array

hypothesis-awkward

Hypothesis strategies for Awkward Arrays.

Hypothesis is a property-based testing library. Its strategies are Python functions that strategically generate test data that can fail test cases in pytest and other testing frameworks. Once a test fails, Hypothesis searches for the simplest sample that causes the same error. Hypothesis automatically explores edge cases; developers do not need to craft test data manually.

Property-based testing is useful for finding edge cases in array libraries and in code that uses them. In fact, Hypothesis strategies for NumPy and pandas data types are included in Hypothesis itself. Xarray provides strategies for its data structure. The Apache Arrow codebase has strategies for PyArrow, which are not officially documented in its API reference.

This package, hypothesis-awkward, is a collection of Hypothesis strategies for Awkward Array, which can represent a wide variety of layouts of nested, variable-length, and mixed-type data. The current version of this package includes strategies that generate samples with certain types of layouts. The goal is to develop strategies that can generate fully general Awkward Arrays with multiple options to control the layout, data types, missing values, masks, and other array attributes. These strategies can help close in on edge cases in tools that use Awkward Array, and Awkward Array itself.

[!NOTE]

This package is early work in progress and still experimental. The APIs may change over time.

Installation

You can install the package from PyPI using pip:

pip install hypothesis-awkward

This also installs Hypothesis and Awkward Array as dependencies unless they are already installed.

The strategy arrays()

The function arrays() is the main strategy. It is currently experimental and developed in strategies/constructors/. The plan is to have arrays() generate fully general Awkward Arrays with many options to control the output arrays.

Sample outputs of arrays()

You can see sample outputs of the current version of arrays() in the test case:

from hypothesis import given

import awkward as ak
import hypothesis_awkward.strategies as st_ak


@given(array=st_ak.constructors.arrays())
def test_array(array: ak.Array) -> None:
    print(f'{array=!r}')

For example, this might print:

array=<Array [] type='0 * bool'>
array=<Array [] type='0 * var * bool'>
array=<Array [[], [], [], []] type='4 * var * 2 * timedelta64[W]'>
array=<Array [] type='0 * var * bool'>
array=<Array [] type='0 * var * timedelta64[us]'>
array=<Array [] type='0 * var * bool'>
array=<Array [[[52, 51113, 30]], []] type='2 * var * var * uint64'>
array=<Array [] type='0 * timedelta64[ns]'>
array=<Array [] type='0 * 0 * bool'>
array=<Array [[[-9223372036854773716], ...]] type='1 * 3 * 1 * datetime64[Y]'>
array=<Array [] type='0 * int32'>
array=<Array [9223372036854775807 seconds, ...] type='3 * timedelta64[s]'>
array=<Array [-9223372036854775590 seconds, ...] type='3 * timedelta64[s]'>
array=<Array [-9223372036854775590 seconds] type='1 * timedelta64[s]'>

The current version generates arrays with NumpyArray as leaf contents that can be nested multiple levels deep in RegularArray, ListOffsetArray, and ListArray lists.

The API of arrays()

def arrays(
    dtypes: st.SearchStrategy[np.dtype] | None = None,
    max_size: int = 10,
    allow_nan: bool = False,
    allow_regular: bool = True,
    allow_list_offset: bool = True,
    allow_list: bool = True,
    max_depth: int = 5,
):

Parameter	Description
dtypes	A strategy for NumPy scalar dtypes used in NumpyArray. If None, the default strategy that generates any scalar dtype supported by Awkward Array is used.
max_size	Maximum total number of scalar values in the generated array.
allow_nan	No NaN/NaT values are generated if False.
allow_regular	No RegularArray is generated if False.
allow_list_offset	No ListOffsetArray is generated if False.
allow_list	No ListArray is generated if False.
max_depth	Maximum depth of nested arrays.

Other strategies

In addition to arrays() mentioned above, this package includes other strategies that generate Awkward Arrays and related data types.

NumPy

These strategies are related to the section of Awkward Array User Guide "How to convert to/from NumPy".

Strategy	Data type
from_numpy	Awkward Arrays created from NumPy arrays
numpy_arrays	NumPy arrays that can be converted to Awkward Array
numpy_dtypes	NumPy dtypes (simple or array) supported by Awkward Array
supported_dtypes	NumPy dtypes (simple only) supported by Awkward Array
supported_dtype_names	Names of NumPy dtypes (simple only) supported by Awkward Array

Python lists

These strategies are related to the section of Awkward Array User Guide "How to convert to/from Python objects".

Strategy	Data type
from_list	Awkward Arrays created from Python lists
lists	Nested Python lists for which Awkward Arrays can be created
items_from_dtype	Python built-in type values for a given NumPy dtype
builtin_safe_dtypes	NumPy dtypes with corresponding Python built-in types
builtin_safe_dtype_names	Names of NumPy dtypes with corresponding Python built-in types