Module test_numpy

Functions

• ▷ def test_compress_memoryview()

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• ▷ def test_compress_numpy()

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• ▷ def test_compression_takes_advantage_of_itemsize()

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• ▷ def test_dont_compress_uncompressable_data() @pytest.mark.skip

  @pytest.mark.skip
  def test_dont_compress_uncompressable_data()

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• ▷ def test_dumps_numpy_writable(writeable) @pytest.mark.parametrize("writeable", [True, False])

  @pytest.mark.parametrize("writeable", [True, False])
  def test_dumps_numpy_writable(writeable)

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• ▷ def test_dumps_serialize_numpy(x) @pytest.mark.parametrize( "x", [ np.ones(5), np.array(5), np.random.random((5, 5)), np.random.random((5, 5))[::2, :], np.random.random((5, 5))[:, ::2], np.asfortranarray(np.random.random((5, 5))), np.asfortranarray(np.random.random((5, 5)))[::2, :], np.asfortranarray(np.random.random((5, 5)))[:, ::2], np.random.random(5).astype("f4"), np.random.random(5).astype(">i8"), np.random.random(5).astype("<i8"), np.arange(5).astype("M8[us]"), np.arange(5).astype("M8[ms]"), np.arange(5).astype("m8"), np.arange(5).astype("m8[s]"), np.arange(5).astype("c16"), np.arange(5).astype("c8"), np.array([True, False, True]), np.ones(shape=5, dtype=[("a", "i4"), ("b", "M8[us]")]), np.array(["abc"], dtype="S3"), np.array(["abc"], dtype="U3"), np.array(["abc"], dtype=object), np.array([np.arange(3), np.arange(4, 6)], dtype=object), np.ones(shape=(5,), dtype=("f8", 32)), np.ones(shape=(5,), dtype=[("x", "f8", 32)]), np.ones(shape=(5,), dtype=np.dtype([("a", "i1"), ("b", "f8")], align=False)), np.ones(shape=(5,), dtype=np.dtype([("a", "i1"), ("b", "f8")], align=True)), np.ones(shape=(5,), dtype=np.dtype([("a", "m8[us]")], align=False)), # this dtype fails unpickling np.ones(shape=(5,), dtype=np.dtype([("a", "m8")], align=False)), np.array([(1, "abc")], dtype=[("x", "i4"), ("s", object)]), np.zeros(5000, dtype=[("x%d" % i, "<f8") for i in range(4)]), np.zeros(5000, dtype="S32"), np.zeros((1, 1000, 1000)), np.arange(12)[::2], # non-contiguous array np.ones(shape=(5, 6)).astype(dtype=[("total", "<f8"), ("n", "<f8")]), np.broadcast_to(np.arange(3), shape=(10, 3)), # zero-strided array ], )

  @pytest.mark.parametrize( "x", [ np.ones(5), np.array(5), np.random.random((5, 5)), np.random.random((5, 5))[::2, :], np.random.random((5, 5))[:, ::2], np.asfortranarray(np.random.random((5, 5))), np.asfortranarray(np.random.random((5, 5)))[::2, :], np.asfortranarray(np.random.random((5, 5)))[:, ::2], np.random.random(5).astype("f4"), np.random.random(5).astype(">i8"), np.random.random(5).astype("<i8"), np.arange(5).astype("M8[us]"), np.arange(5).astype("M8[ms]"), np.arange(5).astype("m8"), np.arange(5).astype("m8[s]"), np.arange(5).astype("c16"), np.arange(5).astype("c8"), np.array([True, False, True]), np.ones(shape=5, dtype=[("a", "i4"), ("b", "M8[us]")]), np.array(["abc"], dtype="S3"), np.array(["abc"], dtype="U3"), np.array(["abc"], dtype=object), np.array([np.arange(3), np.arange(4, 6)], dtype=object), np.ones(shape=(5,), dtype=("f8", 32)), np.ones(shape=(5,), dtype=[("x", "f8", 32)]), np.ones(shape=(5,), dtype=np.dtype([("a", "i1"), ("b", "f8")], align=False)), np.ones(shape=(5,), dtype=np.dtype([("a", "i1"), ("b", "f8")], align=True)), np.ones(shape=(5,), dtype=np.dtype([("a", "m8[us]")], align=False)), # this dtype fails unpickling np.ones(shape=(5,), dtype=np.dtype([("a", "m8")], align=False)), np.array([(1, "abc")], dtype=[("x", "i4"), ("s", object)]), np.zeros(5000, dtype=[("x%d" % i, "<f8") for i in range(4)]), np.zeros(5000, dtype="S32"), np.zeros((1, 1000, 1000)), np.arange(12)[::2], # non-contiguous array np.ones(shape=(5, 6)).astype(dtype=[("total", "<f8"), ("n", "<f8")]), np.broadcast_to(np.arange(3), shape=(10, 3)), # zero-strided array ], )
  def test_dumps_serialize_numpy(x)

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• ▷ def test_dumps_serialize_numpy_custom_dtype()

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• ▷ def test_dumps_serialize_numpy_large() @pytest.mark.slow

  @pytest.mark.slow
  def test_dumps_serialize_numpy_large()

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• ▷ def test_itemsize(dt, size) @pytest.mark.parametrize( "dt,size", [ ("f8", 8), ("i4", 4), ("c16", 16), ("b", 1), ("S3", 3), ("M8[us]", 8), ("M8[s]", 8), ("U3", 12), ([("a", "i4"), ("b", "f8")], 12), (("i4", 100), 4), ([("a", "i4", 100)], 8), ([("a", "i4", 20), ("b", "f8")], 20 * 4 + 8), ([("a", "i4", 200), ("b", "f8")], 8), ], )

  @pytest.mark.parametrize( "dt,size", [ ("f8", 8), ("i4", 4), ("c16", 16), ("b", 1), ("S3", 3), ("M8[us]", 8), ("M8[s]", 8), ("U3", 12), ([("a", "i4"), ("b", "f8")], 12), (("i4", 100), 4), ([("a", "i4", 100)], 8), ([("a", "i4", 20), ("b", "f8")], 20 * 4 + 8), ([("a", "i4", 200), ("b", "f8")], 8), ], )
  def test_itemsize(

  dt,

  size,

  )

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• ▷ def test_large_numpy_array()

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• ▷ def test_memmap()

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• ▷ def test_non_zero_strided_array()

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• ▷ def test_serialize()

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• ▷ def test_serialize_numpy_ma_masked()

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• ▷ def test_serialize_numpy_ma_masked_array(x) @pytest.mark.parametrize( "x", [ np.ma.masked_array([5, 6], mask=[True, False], fill_value=10, dtype="i4"), np.ma.masked_array([5.0, 6.0], mask=[True, False], fill_value=10, dtype="f4"), np.ma.masked_array( [5.0, 6.0], mask=[True, False], fill_value=np.nan, dtype="f8" ), np.ma.masked_array( [5.0, 6.0], mask=np.ma.nomask, fill_value=np.nan, dtype="f8" ), np.ma.masked_array( [True, False], mask=np.ma.nomask, fill_value=True, dtype="bool" ), np.ma.masked_array(["a", "b"], mask=[True, False], fill_value="c", dtype="O"), ], )

  @pytest.mark.parametrize( "x", [ np.ma.masked_array([5, 6], mask=[True, False], fill_value=10, dtype="i4"), np.ma.masked_array([5.0, 6.0], mask=[True, False], fill_value=10, dtype="f4"), np.ma.masked_array( [5.0, 6.0], mask=[True, False], fill_value=np.nan, dtype="f8" ), np.ma.masked_array( [5.0, 6.0], mask=np.ma.nomask, fill_value=np.nan, dtype="f8" ), np.ma.masked_array( [True, False], mask=np.ma.nomask, fill_value=True, dtype="bool" ), np.ma.masked_array(["a", "b"], mask=[True, False], fill_value="c", dtype="O"), ], )
  def test_serialize_numpy_ma_masked_array(x)

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• ▷ def test_serialize_writeable_array_readonly_base_object()

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• ▷ def test_zero_strided_numpy_array(x, writeable) @pytest.mark.parametrize( "x", [ np.broadcast_to(np.arange(10), (20, 10)), # Some strides are 0 np.broadcast_to(1, (3, 4, 2)), # All strides are 0 np.broadcast_to(np.arange(100)[:1], 5), # x.base is larger than x np.broadcast_to(np.arange(5), (4, 5))[:, ::-1], ], ) @pytest.mark.parametrize("writeable", [True, False])

  @pytest.mark.parametrize( "x", [ np.broadcast_to(np.arange(10), (20, 10)), # Some strides are 0 np.broadcast_to(1, (3, 4, 2)), # All strides are 0 np.broadcast_to(np.arange(100)[:1], 5), # x.base is larger than x np.broadcast_to(np.arange(5), (4, 5))[:, ::-1], ], )
  @pytest.mark.parametrize("writeable", [True, False])
  def test_zero_strided_numpy_array(

  x,

  writeable,

  )

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Generated by nedoc v0.9 at 2020-12-29 14:05