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| # This file is part of h5py, a Python interface to the HDF5 library. | |
| # | |
| # http://www.h5py.org | |
| # | |
| # Copyright 2008-2013 Andrew Collette and contributors | |
| # | |
| # License: Standard 3-clause BSD; see "license.txt" for full license terms | |
| # and contributor agreement. | |
| """ | |
| Dataset slicing test module. | |
| Tests all supported slicing operations, including read/write and | |
| broadcasting operations. Does not test type conversion except for | |
| corner cases overlapping with slicing; for example, when selecting | |
| specific fields of a compound type. | |
| """ | |
| import numpy as np | |
| from .common import ut, TestCase | |
| import h5py | |
| from h5py import h5s, h5t, h5d | |
| from h5py import File, MultiBlockSlice | |
| class BaseSlicing(TestCase): | |
| def setUp(self): | |
| self.f = File(self.mktemp(), 'w') | |
| def tearDown(self): | |
| if self.f: | |
| self.f.close() | |
| class TestSingleElement(BaseSlicing): | |
| """ | |
| Feature: Retrieving a single element works with NumPy semantics | |
| """ | |
| def test_single_index(self): | |
| """ Single-element selection with [index] yields array scalar """ | |
| dset = self.f.create_dataset('x', (1,), dtype='i1') | |
| out = dset[0] | |
| self.assertIsInstance(out, np.int8) | |
| def test_single_null(self): | |
| """ Single-element selection with [()] yields ndarray """ | |
| dset = self.f.create_dataset('x', (1,), dtype='i1') | |
| out = dset[()] | |
| self.assertIsInstance(out, np.ndarray) | |
| self.assertEqual(out.shape, (1,)) | |
| def test_scalar_index(self): | |
| """ Slicing with [...] yields scalar ndarray """ | |
| dset = self.f.create_dataset('x', shape=(), dtype='f') | |
| out = dset[...] | |
| self.assertIsInstance(out, np.ndarray) | |
| self.assertEqual(out.shape, ()) | |
| def test_scalar_null(self): | |
| """ Slicing with [()] yields array scalar """ | |
| dset = self.f.create_dataset('x', shape=(), dtype='i1') | |
| out = dset[()] | |
| self.assertIsInstance(out, np.int8) | |
| def test_compound(self): | |
| """ Compound scalar is numpy.void, not tuple (issue 135) """ | |
| dt = np.dtype([('a','i4'),('b','f8')]) | |
| v = np.ones((4,), dtype=dt) | |
| dset = self.f.create_dataset('foo', (4,), data=v) | |
| self.assertEqual(dset[0], v[0]) | |
| self.assertIsInstance(dset[0], np.void) | |
| class TestObjectIndex(BaseSlicing): | |
| """ | |
| Feature: numpy.object_ subtypes map to real Python objects | |
| """ | |
| def test_reference(self): | |
| """ Indexing a reference dataset returns a h5py.Reference instance """ | |
| dset = self.f.create_dataset('x', (1,), dtype=h5py.ref_dtype) | |
| dset[0] = self.f.ref | |
| self.assertEqual(type(dset[0]), h5py.Reference) | |
| def test_regref(self): | |
| """ Indexing a region reference dataset returns a h5py.RegionReference | |
| """ | |
| dset1 = self.f.create_dataset('x', (10,10)) | |
| regref = dset1.regionref[...] | |
| dset2 = self.f.create_dataset('y', (1,), dtype=h5py.regionref_dtype) | |
| dset2[0] = regref | |
| self.assertEqual(type(dset2[0]), h5py.RegionReference) | |
| def test_reference_field(self): | |
| """ Compound types of which a reference is an element work right """ | |
| dt = np.dtype([('a', 'i'),('b', h5py.ref_dtype)]) | |
| dset = self.f.create_dataset('x', (1,), dtype=dt) | |
| dset[0] = (42, self.f['/'].ref) | |
| out = dset[0] | |
| self.assertEqual(type(out[1]), h5py.Reference) # isinstance does NOT work | |
| def test_scalar(self): | |
| """ Indexing returns a real Python object on scalar datasets """ | |
| dset = self.f.create_dataset('x', (), dtype=h5py.ref_dtype) | |
| dset[()] = self.f.ref | |
| self.assertEqual(type(dset[()]), h5py.Reference) | |
| def test_bytestr(self): | |
| """ Indexing a byte string dataset returns a real python byte string | |
| """ | |
| dset = self.f.create_dataset('x', (1,), dtype=h5py.string_dtype(encoding='ascii')) | |
| dset[0] = b"Hello there!" | |
| self.assertEqual(type(dset[0]), bytes) | |
| class TestSimpleSlicing(TestCase): | |
| """ | |
| Feature: Simple NumPy-style slices (start:stop:step) are supported. | |
| """ | |
| def setUp(self): | |
| self.f = File(self.mktemp(), 'w') | |
| self.arr = np.arange(10) | |
| self.dset = self.f.create_dataset('x', data=self.arr) | |
| def tearDown(self): | |
| if self.f: | |
| self.f.close() | |
| def test_negative_stop(self): | |
| """ Negative stop indexes work as they do in NumPy """ | |
| self.assertArrayEqual(self.dset[2:-2], self.arr[2:-2]) | |
| def test_write(self): | |
| """Assigning to a 1D slice of a 2D dataset | |
| """ | |
| dset = self.f.create_dataset('x2', (10, 2)) | |
| x = np.zeros((10, 1)) | |
| dset[:, 0] = x[:, 0] | |
| with self.assertRaises(TypeError): | |
| dset[:, 1] = x | |
| class TestArraySlicing(BaseSlicing): | |
| """ | |
| Feature: Array types are handled appropriately | |
| """ | |
| def test_read(self): | |
| """ Read arrays tack array dimensions onto end of shape tuple """ | |
| dt = np.dtype('(3,)f8') | |
| dset = self.f.create_dataset('x',(10,),dtype=dt) | |
| self.assertEqual(dset.shape, (10,)) | |
| self.assertEqual(dset.dtype, dt) | |
| # Full read | |
| out = dset[...] | |
| self.assertEqual(out.dtype, np.dtype('f8')) | |
| self.assertEqual(out.shape, (10,3)) | |
| # Single element | |
| out = dset[0] | |
| self.assertEqual(out.dtype, np.dtype('f8')) | |
| self.assertEqual(out.shape, (3,)) | |
| # Range | |
| out = dset[2:8:2] | |
| self.assertEqual(out.dtype, np.dtype('f8')) | |
| self.assertEqual(out.shape, (3,3)) | |
| def test_write_broadcast(self): | |
| """ Array fill from constant is not supported (issue 211). | |
| """ | |
| dt = np.dtype('(3,)i') | |
| dset = self.f.create_dataset('x', (10,), dtype=dt) | |
| with self.assertRaises(TypeError): | |
| dset[...] = 42 | |
| def test_write_element(self): | |
| """ Write a single element to the array | |
| Issue 211. | |
| """ | |
| dt = np.dtype('(3,)f8') | |
| dset = self.f.create_dataset('x', (10,), dtype=dt) | |
| data = np.array([1,2,3.0]) | |
| dset[4] = data | |
| out = dset[4] | |
| self.assertTrue(np.all(out == data)) | |
| def test_write_slices(self): | |
| """ Write slices to array type """ | |
| dt = np.dtype('(3,)i') | |
| data1 = np.ones((2,), dtype=dt) | |
| data2 = np.ones((4,5), dtype=dt) | |
| dset = self.f.create_dataset('x', (10,9,11), dtype=dt) | |
| dset[0,0,2:4] = data1 | |
| self.assertArrayEqual(dset[0,0,2:4], data1) | |
| dset[3, 1:5, 6:11] = data2 | |
| self.assertArrayEqual(dset[3, 1:5, 6:11], data2) | |
| def test_roundtrip(self): | |
| """ Read the contents of an array and write them back | |
| Issue 211. | |
| """ | |
| dt = np.dtype('(3,)f8') | |
| dset = self.f.create_dataset('x', (10,), dtype=dt) | |
| out = dset[...] | |
| dset[...] = out | |
| self.assertTrue(np.all(dset[...] == out)) | |
| class TestZeroLengthSlicing(BaseSlicing): | |
| """ | |
| Slices resulting in empty arrays | |
| """ | |
| def test_slice_zero_length_dimension(self): | |
| """ Slice a dataset with a zero in its shape vector | |
| along the zero-length dimension """ | |
| for i, shape in enumerate([(0,), (0, 3), (0, 2, 1)]): | |
| dset = self.f.create_dataset('x%d'%i, shape, dtype=int, maxshape=(None,)*len(shape)) | |
| self.assertEqual(dset.shape, shape) | |
| out = dset[...] | |
| self.assertIsInstance(out, np.ndarray) | |
| self.assertEqual(out.shape, shape) | |
| out = dset[:] | |
| self.assertIsInstance(out, np.ndarray) | |
| self.assertEqual(out.shape, shape) | |
| if len(shape) > 1: | |
| out = dset[:, :1] | |
| self.assertIsInstance(out, np.ndarray) | |
| self.assertEqual(out.shape[:2], (0, 1)) | |
| def test_slice_other_dimension(self): | |
| """ Slice a dataset with a zero in its shape vector | |
| along a non-zero-length dimension """ | |
| for i, shape in enumerate([(3, 0), (1, 2, 0), (2, 0, 1)]): | |
| dset = self.f.create_dataset('x%d'%i, shape, dtype=int, maxshape=(None,)*len(shape)) | |
| self.assertEqual(dset.shape, shape) | |
| out = dset[:1] | |
| self.assertIsInstance(out, np.ndarray) | |
| self.assertEqual(out.shape, (1,)+shape[1:]) | |
| def test_slice_of_length_zero(self): | |
| """ Get a slice of length zero from a non-empty dataset """ | |
| for i, shape in enumerate([(3,), (2, 2,), (2, 1, 5)]): | |
| dset = self.f.create_dataset('x%d'%i, data=np.zeros(shape, int), maxshape=(None,)*len(shape)) | |
| self.assertEqual(dset.shape, shape) | |
| out = dset[1:1] | |
| self.assertIsInstance(out, np.ndarray) | |
| self.assertEqual(out.shape, (0,)+shape[1:]) | |
| class TestFieldNames(BaseSlicing): | |
| """ | |
| Field names for read & write | |
| """ | |
| dt = np.dtype([('a', 'f'), ('b', 'i'), ('c', 'f4')]) | |
| data = np.ones((100,), dtype=dt) | |
| def setUp(self): | |
| BaseSlicing.setUp(self) | |
| self.dset = self.f.create_dataset('x', (100,), dtype=self.dt) | |
| self.dset[...] = self.data | |
| def test_read(self): | |
| """ Test read with field selections """ | |
| self.assertArrayEqual(self.dset['a'], self.data['a']) | |
| def test_unicode_names(self): | |
| """ Unicode field names for for read and write """ | |
| self.assertArrayEqual(self.dset['a'], self.data['a']) | |
| self.dset['a'] = 42 | |
| data = self.data.copy() | |
| data['a'] = 42 | |
| self.assertArrayEqual(self.dset['a'], data['a']) | |
| def test_write(self): | |
| """ Test write with field selections """ | |
| data2 = self.data.copy() | |
| data2['a'] *= 2 | |
| self.dset['a'] = data2 | |
| self.assertTrue(np.all(self.dset[...] == data2)) | |
| data2['b'] *= 4 | |
| self.dset['b'] = data2 | |
| self.assertTrue(np.all(self.dset[...] == data2)) | |
| data2['a'] *= 3 | |
| data2['c'] *= 3 | |
| self.dset['a','c'] = data2 | |
| self.assertTrue(np.all(self.dset[...] == data2)) | |
| def test_write_noncompound(self): | |
| """ Test write with non-compound source (single-field) """ | |
| data2 = self.data.copy() | |
| data2['b'] = 1.0 | |
| self.dset['b'] = 1.0 | |
| self.assertTrue(np.all(self.dset[...] == data2)) | |
| class TestMultiBlockSlice(BaseSlicing): | |
| def setUp(self): | |
| super().setUp() | |
| self.arr = np.arange(10) | |
| self.dset = self.f.create_dataset('x', data=self.arr) | |
| def test_default(self): | |
| # Default selects entire dataset as one block | |
| mbslice = MultiBlockSlice() | |
| self.assertEqual(mbslice.indices(10), (0, 1, 10, 1)) | |
| np.testing.assert_array_equal(self.dset[mbslice], self.arr) | |
| def test_default_explicit(self): | |
| mbslice = MultiBlockSlice(start=0, count=10, stride=1, block=1) | |
| self.assertEqual(mbslice.indices(10), (0, 1, 10, 1)) | |
| np.testing.assert_array_equal(self.dset[mbslice], self.arr) | |
| def test_start(self): | |
| mbslice = MultiBlockSlice(start=4) | |
| self.assertEqual(mbslice.indices(10), (4, 1, 6, 1)) | |
| np.testing.assert_array_equal(self.dset[mbslice], np.array([4, 5, 6, 7, 8, 9])) | |
| def test_count(self): | |
| mbslice = MultiBlockSlice(count=7) | |
| self.assertEqual(mbslice.indices(10), (0, 1, 7, 1)) | |
| np.testing.assert_array_equal( | |
| self.dset[mbslice], np.array([0, 1, 2, 3, 4, 5, 6]) | |
| ) | |
| def test_count_more_than_length_error(self): | |
| mbslice = MultiBlockSlice(count=11) | |
| with self.assertRaises(ValueError): | |
| mbslice.indices(10) | |
| def test_stride(self): | |
| mbslice = MultiBlockSlice(stride=2) | |
| self.assertEqual(mbslice.indices(10), (0, 2, 5, 1)) | |
| np.testing.assert_array_equal(self.dset[mbslice], np.array([0, 2, 4, 6, 8])) | |
| def test_stride_zero_error(self): | |
| with self.assertRaises(ValueError): | |
| # This would cause a ZeroDivisionError if not caught | |
| MultiBlockSlice(stride=0, block=0).indices(10) | |
| def test_stride_block_equal(self): | |
| mbslice = MultiBlockSlice(stride=2, block=2) | |
| self.assertEqual(mbslice.indices(10), (0, 2, 5, 2)) | |
| np.testing.assert_array_equal(self.dset[mbslice], self.arr) | |
| def test_block_more_than_stride_error(self): | |
| with self.assertRaises(ValueError): | |
| MultiBlockSlice(block=3) | |
| with self.assertRaises(ValueError): | |
| MultiBlockSlice(stride=2, block=3) | |
| def test_stride_more_than_block(self): | |
| mbslice = MultiBlockSlice(stride=3, block=2) | |
| self.assertEqual(mbslice.indices(10), (0, 3, 3, 2)) | |
| np.testing.assert_array_equal(self.dset[mbslice], np.array([0, 1, 3, 4, 6, 7])) | |
| def test_block_overruns_extent_error(self): | |
| # If fully described then must fit within extent | |
| mbslice = MultiBlockSlice(start=2, count=2, stride=5, block=4) | |
| with self.assertRaises(ValueError): | |
| mbslice.indices(10) | |
| def test_fully_described(self): | |
| mbslice = MultiBlockSlice(start=1, count=2, stride=5, block=4) | |
| self.assertEqual(mbslice.indices(10), (1, 5, 2, 4)) | |
| np.testing.assert_array_equal( | |
| self.dset[mbslice], np.array([1, 2, 3, 4, 6, 7, 8, 9]) | |
| ) | |
| def test_count_calculated(self): | |
| # If not given, count should be calculated to select as many full blocks as possible | |
| mbslice = MultiBlockSlice(start=1, stride=3, block=2) | |
| self.assertEqual(mbslice.indices(10), (1, 3, 3, 2)) | |
| np.testing.assert_array_equal(self.dset[mbslice], np.array([1, 2, 4, 5, 7, 8])) | |
| def test_zero_count_calculated_error(self): | |
| # In this case, there is no possible count to select even one block, so error | |
| mbslice = MultiBlockSlice(start=8, stride=4, block=3) | |
| with self.assertRaises(ValueError): | |
| mbslice.indices(10) | |