486 lines
15 KiB
Python
486 lines
15 KiB
Python
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# This file is part of h5py, a Python interface to the HDF5 library.
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#
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# http://www.h5py.org
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#
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# Copyright 2008-2013 Andrew Collette and contributors
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#
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# License: Standard 3-clause BSD; see "license.txt" for full license terms
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# and contributor agreement.
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"""
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Tests the h5py.Dataset.__getitem__ method.
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This module does not specifically test type conversion. The "type" axis
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therefore only tests objects which interact with the slicing system in
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unreliable ways; for example, compound and array types.
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See test_dataset_getitem_types for type-conversion tests.
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Tests are organized into TestCases by dataset shape and type. Test
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methods vary by slicing arg type.
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1. Dataset shape:
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Empty
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Scalar
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1D
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3D
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2. Type:
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Float
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Compound
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Array
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3. Slicing arg types:
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Ellipsis
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Empty tuple
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Regular slice
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Indexing
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Index list
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Boolean mask
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Field names
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"""
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from __future__ import absolute_import
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import sys
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import numpy as np
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import h5py
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from ..common import ut, TestCase
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class TestEmpty(TestCase):
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def setUp(self):
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TestCase.setUp(self)
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sid = h5py.h5s.create(h5py.h5s.NULL)
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tid = h5py.h5t.C_S1.copy()
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tid.set_size(10)
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dsid = h5py.h5d.create(self.f.id, b'x', tid, sid)
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self.dset = h5py.Dataset(dsid)
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self.empty_obj = h5py.Empty(np.dtype("S10"))
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def test_ndim(self):
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""" Verify number of dimensions """
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self.assertEquals(self.dset.ndim, 0)
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def test_shape(self):
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""" Verify shape """
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self.assertEquals(self.dset.shape, None)
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def test_ellipsis(self):
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""" Ellipsis -> ValueError """
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self.assertEquals(self.dset[...], self.empty_obj)
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def test_tuple(self):
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""" () -> IOError """
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self.assertEquals(self.dset[()], self.empty_obj)
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def test_slice(self):
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""" slice -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0:4]
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def test_index(self):
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""" index -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0]
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def test_indexlist(self):
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""" index list -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[[1,2,5]]
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def test_mask(self):
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""" mask -> ValueError """
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mask = np.array(True, dtype='bool')
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with self.assertRaises(ValueError):
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self.dset[mask]
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def test_fieldnames(self):
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""" field name -> ValueError """
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with self.assertRaises(ValueError):
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self.dset['field']
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class TestScalarFloat(TestCase):
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def setUp(self):
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TestCase.setUp(self)
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self.data = np.array(42.5, dtype='f')
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self.dset = self.f.create_dataset('x', data=self.data)
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def test_ndim(self):
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""" Verify number of dimensions """
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self.assertEquals(self.dset.ndim, 0)
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def test_shape(self):
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""" Verify shape """
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self.assertEquals(self.dset.shape, tuple())
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def test_ellipsis(self):
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""" Ellipsis -> scalar ndarray """
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out = self.dset[...]
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self.assertArrayEqual(out, self.data)
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def test_tuple(self):
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""" () -> bare item """
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out = self.dset[()]
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self.assertArrayEqual(out, self.data.item())
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def test_slice(self):
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""" slice -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0:4]
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def test_index(self):
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""" index -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0]
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# FIXME: NumPy has IndexError instead
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def test_indexlist(self):
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""" index list -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[[1,2,5]]
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# FIXME: NumPy permits this
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def test_mask(self):
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""" mask -> ValueError """
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mask = np.array(True, dtype='bool')
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with self.assertRaises(ValueError):
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self.dset[mask]
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def test_fieldnames(self):
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""" field name -> ValueError (no fields) """
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with self.assertRaises(ValueError):
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self.dset['field']
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class TestScalarCompound(TestCase):
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def setUp(self):
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TestCase.setUp(self)
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self.data = np.array((42.5, -118, "Hello"), dtype=[('a', 'f'), ('b', 'i'), ('c', '|S10')])
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self.dset = self.f.create_dataset('x', data=self.data)
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def test_ndim(self):
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""" Verify number of dimensions """
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self.assertEquals(self.dset.ndim, 0)
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def test_shape(self):
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""" Verify shape """
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self.assertEquals(self.dset.shape, tuple())
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def test_ellipsis(self):
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""" Ellipsis -> scalar ndarray """
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out = self.dset[...]
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# assertArrayEqual doesn't work with compounds; do manually
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self.assertIsInstance(out, np.ndarray)
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self.assertEqual(out.shape, self.data.shape)
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self.assertEqual(out.dtype, self.data.dtype)
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def test_tuple(self):
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""" () -> np.void instance """
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out = self.dset[()]
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self.assertIsInstance(out, np.void)
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self.assertEqual(out.dtype, self.data.dtype)
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def test_slice(self):
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""" slice -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0:4]
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def test_index(self):
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""" index -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0]
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# FIXME: NumPy has IndexError instead
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def test_indexlist(self):
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""" index list -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[[1,2,5]]
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# FIXME: NumPy permits this
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def test_mask(self):
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""" mask -> ValueError """
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mask = np.array(True, dtype='bool')
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with self.assertRaises(ValueError):
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self.dset[mask]
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# FIXME: NumPy returns a scalar ndarray
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def test_fieldnames(self):
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""" field name -> bare value """
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out = self.dset['a']
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self.assertIsInstance(out, np.float32)
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self.assertEqual(out, self.dset['a'])
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class TestScalarArray(TestCase):
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def setUp(self):
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TestCase.setUp(self)
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self.dt = np.dtype('(3,2)f')
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self.data = np.array([(3.2, -119), (42, 99.8), (3.14, 0)], dtype='f')
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self.dset = self.f.create_dataset('x', (), dtype=self.dt)
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self.dset[...] = self.data
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def test_ndim(self):
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""" Verify number of dimensions """
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self.assertEquals(self.data.ndim, 2)
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self.assertEquals(self.dset.ndim, 0)
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def test_shape(self):
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""" Verify shape """
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self.assertEquals(self.data.shape, (3, 2))
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self.assertEquals(self.dset.shape, tuple())
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def test_ellipsis(self):
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""" Ellipsis -> ndarray promoted to underlying shape """
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out = self.dset[...]
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self.assertArrayEqual(out, self.data)
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def test_tuple(self):
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""" () -> same as ellipsis """
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out = self.dset[...]
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self.assertArrayEqual(out, self.data)
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def test_slice(self):
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""" slice -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0:4]
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def test_index(self):
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""" index -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[0]
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def test_indexlist(self):
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""" index list -> ValueError """
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with self.assertRaises(ValueError):
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self.dset[[]]
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def test_mask(self):
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""" mask -> ValueError """
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mask = np.array(True, dtype='bool')
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with self.assertRaises(ValueError):
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self.dset[mask]
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def test_fieldnames(self):
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""" field name -> ValueError (no fields) """
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with self.assertRaises(ValueError):
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self.dset['field']
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@ut.skipUnless(h5py.version.hdf5_version_tuple >= (1, 8, 7), 'HDF5 1.8.7+ required')
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class Test1DZeroFloat(TestCase):
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def setUp(self):
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TestCase.setUp(self)
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self.data = np.ones((0,), dtype='f')
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self.dset = self.f.create_dataset('x', data=self.data)
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def test_ndim(self):
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""" Verify number of dimensions """
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self.assertEquals(self.dset.ndim, 1)
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def test_shape(self):
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""" Verify shape """
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self.assertEquals(self.dset.shape, (0,))
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def test_ellipsis(self):
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""" Ellipsis -> ndarray of matching shape """
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self.assertNumpyBehavior(self.dset, self.data, np.s_[...])
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def test_tuple(self):
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""" () -> same as ellipsis """
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self.assertNumpyBehavior(self.dset, self.data, np.s_[()])
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def test_slice(self):
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""" slice -> ndarray of shape (0,) """
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self.assertNumpyBehavior(self.dset, self.data, np.s_[0:4])
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def test_slice_stop_less_than_start(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[7:5])
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# FIXME: NumPy raises IndexError
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def test_index(self):
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""" index -> out of range """
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with self.assertRaises(ValueError):
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self.dset[0]
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# FIXME: Under NumPy this works and returns a shape-(0,) array
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# Also, at the moment it rasies UnboundLocalError (!)
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@ut.expectedFailure
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def test_indexlist(self):
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""" index list """
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with self.assertRaises(ValueError):
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self.dset[[]]
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def test_mask(self):
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""" mask -> ndarray of matching shape """
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mask = np.ones((0,), dtype='bool')
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self.assertNumpyBehavior(self.dset, self.data, np.s_[mask])
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def test_fieldnames(self):
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""" field name -> ValueError (no fields) """
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with self.assertRaises(ValueError):
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self.dset['field']
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class Test1DFloat(TestCase):
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def setUp(self):
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TestCase.setUp(self)
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self.data = np.arange(13).astype('f')
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self.dset = self.f.create_dataset('x', data=self.data)
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def test_ndim(self):
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""" Verify number of dimensions """
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self.assertEquals(self.dset.ndim, 1)
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def test_shape(self):
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""" Verify shape """
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self.assertEquals(self.dset.shape, (13,))
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def test_ellipsis(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[...])
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def test_tuple(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[()])
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def test_slice_simple(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[0:4])
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def test_slice_zerosize(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[4:4])
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def test_slice_strides(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[1:7:3])
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def test_slice_negindexes(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[-8:-2:3])
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def test_slice_stop_less_than_start(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[7:5])
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def test_slice_outofrange(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[100:400:3])
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def test_slice_backwards(self):
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""" we disallow negative steps """
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with self.assertRaises(ValueError):
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self.dset[::-1]
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def test_slice_zerostride(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[::0])
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def test_index_simple(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[3])
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def test_index_neg(self):
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self.assertNumpyBehavior(self.dset, self.data, np.s_[-4])
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|
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||
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|
# FIXME: NumPy permits this... it adds a new axis in front
|
||
|
|
def test_index_none(self):
|
||
|
|
with self.assertRaises(TypeError):
|
||
|
|
self.dset[None]
|
||
|
|
|
||
|
|
# FIXME: NumPy raises IndexError
|
||
|
|
# Also this currently raises UnboundLocalError. :(
|
||
|
|
@ut.expectedFailure
|
||
|
|
def test_index_illegal(self):
|
||
|
|
""" Illegal slicing argument """
|
||
|
|
with self.assertRaises(TypeError):
|
||
|
|
self.dset[{}]
|
||
|
|
|
||
|
|
# FIXME: NumPy raises IndexError
|
||
|
|
def test_index_outofrange(self):
|
||
|
|
with self.assertRaises(ValueError):
|
||
|
|
self.dset[100]
|
||
|
|
|
||
|
|
def test_indexlist_simple(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[[1,2,5]])
|
||
|
|
|
||
|
|
def test_indexlist_single_index_ellipsis(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[[0], ...])
|
||
|
|
|
||
|
|
def test_indexlist_numpyarray_single_index_ellipsis(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[np.array([0]), ...])
|
||
|
|
|
||
|
|
def test_indexlist_numpyarray_ellipsis(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[np.array([1, 2, 5]), ...])
|
||
|
|
|
||
|
|
# Another UnboundLocalError
|
||
|
|
@ut.expectedFailure
|
||
|
|
def test_indexlist_empty(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[[]])
|
||
|
|
|
||
|
|
# FIXME: NumPy has IndexError
|
||
|
|
def test_indexlist_outofrange(self):
|
||
|
|
with self.assertRaises(ValueError):
|
||
|
|
self.dset[[100]]
|
||
|
|
|
||
|
|
def test_indexlist_nonmonotonic(self):
|
||
|
|
""" we require index list values to be strictly increasing """
|
||
|
|
with self.assertRaises(TypeError):
|
||
|
|
self.dset[[1,3,2]]
|
||
|
|
|
||
|
|
def test_indexlist_repeated(self):
|
||
|
|
""" we forbid repeated index values """
|
||
|
|
with self.assertRaises(TypeError):
|
||
|
|
self.dset[[1,1,2]]
|
||
|
|
|
||
|
|
def test_mask_true(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[self.data > -100])
|
||
|
|
|
||
|
|
def test_mask_false(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[self.data > 100])
|
||
|
|
|
||
|
|
def test_mask_partial(self):
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[self.data > 5])
|
||
|
|
|
||
|
|
def test_mask_wrongsize(self):
|
||
|
|
""" we require the boolean mask shape to match exactly """
|
||
|
|
with self.assertRaises(TypeError):
|
||
|
|
self.dset[np.ones((2,), dtype='bool')]
|
||
|
|
|
||
|
|
def test_fieldnames(self):
|
||
|
|
""" field name -> ValueError (no fields) """
|
||
|
|
with self.assertRaises(ValueError):
|
||
|
|
self.dset['field']
|
||
|
|
|
||
|
|
|
||
|
|
@ut.skipUnless(h5py.version.hdf5_version_tuple >= (1, 8, 7), 'HDF5 1.8.7+ required')
|
||
|
|
class Test2DZeroFloat(TestCase):
|
||
|
|
|
||
|
|
def setUp(self):
|
||
|
|
TestCase.setUp(self)
|
||
|
|
self.data = np.ones((0,3), dtype='f')
|
||
|
|
self.dset = self.f.create_dataset('x', data=self.data)
|
||
|
|
|
||
|
|
def test_ndim(self):
|
||
|
|
""" Verify number of dimensions """
|
||
|
|
self.assertEquals(self.dset.ndim, 2)
|
||
|
|
|
||
|
|
def test_shape(self):
|
||
|
|
""" Verify shape """
|
||
|
|
self.assertEquals(self.dset.shape, (0, 3))
|
||
|
|
|
||
|
|
@ut.expectedFailure
|
||
|
|
def test_indexlist(self):
|
||
|
|
""" see issue #473 """
|
||
|
|
self.assertNumpyBehavior(self.dset, self.data, np.s_[:,[0,1,2]])
|
||
|
|
|
||
|
|
|
||
|
|
class TestVeryLargeArray(TestCase):
|
||
|
|
|
||
|
|
def setUp(self):
|
||
|
|
TestCase.setUp(self)
|
||
|
|
self.dset = self.f.create_dataset('x', shape=(2**15, 2**16))
|
||
|
|
|
||
|
|
@ut.skipIf(sys.maxsize < 2**31, 'Maximum integer size >= 2**31 required')
|
||
|
|
def test_size(self):
|
||
|
|
self.assertEqual(self.dset.size, 2**31)
|