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# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
import os
import sys
import pytest
import weakref
import numpy as np
import pyarrow as pa
tensor_type_pairs = [
('i1', pa.int8()),
('i2', pa.int16()),
('i4', pa.int32()),
('i8', pa.int64()),
('u1', pa.uint8()),
('u2', pa.uint16()),
('u4', pa.uint32()),
('u8', pa.uint64()),
('f2', pa.float16()),
('f4', pa.float32()),
('f8', pa.float64())
]
def test_tensor_attrs():
data = np.random.randn(10, 4)
tensor = pa.Tensor.from_numpy(data)
assert tensor.ndim == 2
assert tensor.dim_names == []
assert tensor.size == 40
assert tensor.shape == data.shape
assert tensor.strides == data.strides
assert tensor.is_contiguous
assert tensor.is_mutable
# not writeable
data2 = data.copy()
data2.flags.writeable = False
tensor = pa.Tensor.from_numpy(data2)
assert not tensor.is_mutable
# With dim_names
tensor = pa.Tensor.from_numpy(data, dim_names=('x', 'y'))
assert tensor.ndim == 2
assert tensor.dim_names == ['x', 'y']
assert tensor.dim_name(0) == 'x'
assert tensor.dim_name(1) == 'y'
wr = weakref.ref(tensor)
assert wr() is not None
del tensor
assert wr() is None
def test_tensor_base_object():
tensor = pa.Tensor.from_numpy(np.random.randn(10, 4))
n = sys.getrefcount(tensor)
array = tensor.to_numpy() # noqa
assert sys.getrefcount(tensor) == n + 1
@pytest.mark.parametrize('dtype_str,arrow_type', tensor_type_pairs)
def test_tensor_numpy_roundtrip(dtype_str, arrow_type):
dtype = np.dtype(dtype_str)
data = (100 * np.random.randn(10, 4)).astype(dtype)
tensor = pa.Tensor.from_numpy(data)
assert tensor.type == arrow_type
repr(tensor)
result = tensor.to_numpy()
assert (data == result).all()
def test_tensor_ipc_roundtrip(tmpdir):
data = np.random.randn(10, 4)
tensor = pa.Tensor.from_numpy(data)
path = os.path.join(str(tmpdir), 'pyarrow-tensor-ipc-roundtrip')
mmap = pa.create_memory_map(path, 1024)
pa.ipc.write_tensor(tensor, mmap)
mmap.seek(0)
result = pa.ipc.read_tensor(mmap)
assert result.equals(tensor)
@pytest.mark.gzip
def test_tensor_ipc_read_from_compressed(tempdir):
# ARROW-5910
data = np.random.randn(10, 4)
tensor = pa.Tensor.from_numpy(data)
path = tempdir / 'tensor-compressed-file'
out_stream = pa.output_stream(path, compression='gzip')
pa.ipc.write_tensor(tensor, out_stream)
out_stream.close()
result = pa.ipc.read_tensor(pa.input_stream(path, compression='gzip'))
assert result.equals(tensor)
def test_tensor_ipc_strided(tmpdir):
data1 = np.random.randn(10, 4)
tensor1 = pa.Tensor.from_numpy(data1[::2])
data2 = np.random.randn(10, 6, 4)
tensor2 = pa.Tensor.from_numpy(data2[::, ::2, ::])
path = os.path.join(str(tmpdir), 'pyarrow-tensor-ipc-strided')
mmap = pa.create_memory_map(path, 2048)
for tensor in [tensor1, tensor2]:
mmap.seek(0)
pa.ipc.write_tensor(tensor, mmap)
mmap.seek(0)
result = pa.ipc.read_tensor(mmap)
assert result.equals(tensor)
def test_tensor_equals():
def eq(a, b):
assert a.equals(b)
assert a == b
assert not (a != b)
def ne(a, b):
assert not a.equals(b)
assert not (a == b)
assert a != b
data = np.random.randn(10, 6, 4)[::, ::2, ::]
tensor1 = pa.Tensor.from_numpy(data)
tensor2 = pa.Tensor.from_numpy(np.ascontiguousarray(data))
eq(tensor1, tensor2)
data = data.copy()
data[9, 0, 0] = 1.0
tensor2 = pa.Tensor.from_numpy(np.ascontiguousarray(data))
ne(tensor1, tensor2)
def test_tensor_hashing():
# Tensors are unhashable
with pytest.raises(TypeError, match="unhashable"):
hash(pa.Tensor.from_numpy(np.arange(10)))
def test_tensor_size():
data = np.random.randn(10, 4)
tensor = pa.Tensor.from_numpy(data)
assert pa.ipc.get_tensor_size(tensor) > (data.size * 8)
def test_read_tensor(tmpdir):
# Create and write tensor tensor
data = np.random.randn(10, 4)
tensor = pa.Tensor.from_numpy(data)
data_size = pa.ipc.get_tensor_size(tensor)
path = os.path.join(str(tmpdir), 'pyarrow-tensor-ipc-read-tensor')
write_mmap = pa.create_memory_map(path, data_size)
pa.ipc.write_tensor(tensor, write_mmap)
# Try to read tensor
read_mmap = pa.memory_map(path, mode='r')
array = pa.ipc.read_tensor(read_mmap).to_numpy()
np.testing.assert_equal(data, array)
def test_tensor_memoryview():
# Tensors support the PEP 3118 buffer protocol
for dtype, expected_format in [(np.int8, '=b'),
(np.int64, '=q'),
(np.uint64, '=Q'),
(np.float16, 'e'),
(np.float64, 'd'),
]:
data = np.arange(10, dtype=dtype)
dtype = data.dtype
lst = data.tolist()
tensor = pa.Tensor.from_numpy(data)
m = memoryview(tensor)
assert m.format == expected_format
assert m.shape == data.shape
assert m.strides == data.strides
assert m.ndim == 1
assert m.nbytes == data.nbytes
assert m.itemsize == data.itemsize
assert m.itemsize * 8 == tensor.type.bit_width
assert np.frombuffer(m, dtype).tolist() == lst
del tensor, data
assert np.frombuffer(m, dtype).tolist() == lst
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