Python numpy.uintc() Examples

The following are 16 code examples for showing how to use numpy.uintc(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: mars   Author: mars-project   File: histogram.py    License: Apache License 2.0 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:  # pragma: no cover
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 2
Project: lambda-packs   Author: ryfeus   File: histograms.py    License: MIT License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 3
Project: Mastering-Elasticsearch-7.0   Author: PacktPublishing   File: histograms.py    License: MIT License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 4
Project: GraphicDesignPatternByPython   Author: Relph1119   File: histograms.py    License: MIT License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 5
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 6
Project: pySINDy   Author: luckystarufo   File: histograms.py    License: MIT License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 7
Project: coffeegrindsize   Author: jgagneastro   File: histograms.py    License: MIT License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 8
Project: Carnets   Author: holzschu   File: histograms.py    License: BSD 3-Clause "New" or "Revised" License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 9
Project: Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda   Author: PacktPublishing   File: histograms.py    License: MIT License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 10
Project: twitter-stock-recommendation   Author: alvarobartt   File: histograms.py    License: MIT License 6 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 11
Project: recruit   Author: Frank-qlu   File: histograms.py    License: Apache License 2.0 5 votes vote down vote up
def _unsigned_subtract(a, b):
    """
    Subtract two values where a >= b, and produce an unsigned result

    This is needed when finding the difference between the upper and lower
    bound of an int16 histogram
    """
    # coerce to a single type
    signed_to_unsigned = {
        np.byte: np.ubyte,
        np.short: np.ushort,
        np.intc: np.uintc,
        np.int_: np.uint,
        np.longlong: np.ulonglong
    }
    dt = np.result_type(a, b)
    try:
        dt = signed_to_unsigned[dt.type]
    except KeyError:
        return np.subtract(a, b, dtype=dt)
    else:
        # we know the inputs are integers, and we are deliberately casting
        # signed to unsigned
        return np.subtract(a, b, casting='unsafe', dtype=dt) 
Example 12
Project: eliot   Author: itamarst   File: test_json.py    License: Apache License 2.0 5 votes vote down vote up
def test_numpy(self):
        """NumPy objects get serialized to readable JSON."""
        l = [
            np.float32(12.5),
            np.float64(2.0),
            np.float16(0.5),
            np.bool(True),
            np.bool(False),
            np.bool_(True),
            np.unicode_("hello"),
            np.byte(12),
            np.short(12),
            np.intc(-13),
            np.int_(0),
            np.longlong(100),
            np.intp(7),
            np.ubyte(12),
            np.ushort(12),
            np.uintc(13),
            np.ulonglong(100),
            np.uintp(7),
            np.int8(1),
            np.int16(3),
            np.int32(4),
            np.int64(5),
            np.uint8(1),
            np.uint16(3),
            np.uint32(4),
            np.uint64(5),
        ]
        l2 = [l, np.array([1, 2, 3])]
        roundtripped = loads(dumps(l2, cls=EliotJSONEncoder))
        self.assertEqual([l, [1, 2, 3]], roundtripped) 
Example 13
Project: pyslam   Author: luigifreda   File: test_stl_binders.py    License: GNU General Public License v3.0 5 votes vote down vote up
def test_vector_buffer_numpy():
    a = np.array([1, 2, 3, 4], dtype=np.int32)
    with pytest.raises(TypeError):
        m.VectorInt(a)

    a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], dtype=np.uintc)
    v = m.VectorInt(a[0, :])
    assert len(v) == 4
    assert v[2] == 3
    ma = np.asarray(v)
    ma[2] = 5
    assert v[2] == 5

    v = m.VectorInt(a[:, 1])
    assert len(v) == 3
    assert v[2] == 10

    v = m.get_vectorstruct()
    assert v[0].x == 5
    ma = np.asarray(v)
    ma[1]['x'] = 99
    assert v[1].x == 99

    v = m.VectorStruct(np.zeros(3, dtype=np.dtype([('w', 'bool'), ('x', 'I'),
                                                   ('y', 'float64'), ('z', 'bool')], align=True)))
    assert len(v) == 3 
Example 14
Project: pyslam   Author: luigifreda   File: test_stl_binders.py    License: GNU General Public License v3.0 5 votes vote down vote up
def test_vector_buffer_numpy():
    a = np.array([1, 2, 3, 4], dtype=np.int32)
    with pytest.raises(TypeError):
        m.VectorInt(a)

    a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], dtype=np.uintc)
    v = m.VectorInt(a[0, :])
    assert len(v) == 4
    assert v[2] == 3
    ma = np.asarray(v)
    ma[2] = 5
    assert v[2] == 5

    v = m.VectorInt(a[:, 1])
    assert len(v) == 3
    assert v[2] == 10

    v = m.get_vectorstruct()
    assert v[0].x == 5
    ma = np.asarray(v)
    ma[1]['x'] = 99
    assert v[1].x == 99

    v = m.VectorStruct(np.zeros(3, dtype=np.dtype([('w', 'bool'), ('x', 'I'),
                                                   ('y', 'float64'), ('z', 'bool')], align=True)))
    assert len(v) == 3 
Example 15
Project: pyslam   Author: luigifreda   File: test_stl_binders.py    License: GNU General Public License v3.0 5 votes vote down vote up
def test_vector_buffer_numpy():
    a = np.array([1, 2, 3, 4], dtype=np.int32)
    with pytest.raises(TypeError):
        m.VectorInt(a)

    a = np.array([[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]], dtype=np.uintc)
    v = m.VectorInt(a[0, :])
    assert len(v) == 4
    assert v[2] == 3
    ma = np.asarray(v)
    ma[2] = 5
    assert v[2] == 5

    v = m.VectorInt(a[:, 1])
    assert len(v) == 3
    assert v[2] == 10

    v = m.get_vectorstruct()
    assert v[0].x == 5
    ma = np.asarray(v)
    ma[1]['x'] = 99
    assert v[1].x == 99

    v = m.VectorStruct(np.zeros(3, dtype=np.dtype([('w', 'bool'), ('x', 'I'),
                                                   ('y', 'float64'), ('z', 'bool')], align=True)))
    assert len(v) == 3 
Example 16
Project: segyio   Author: equinor   File: tools.py    License: GNU Lesser General Public License v3.0 4 votes vote down vote up
def native(data,
           format = segyio.SegySampleFormat.IBM_FLOAT_4_BYTE,
           copy = True):
    """Convert numpy array to native float

    Converts a numpy array from raw segy trace data to native floats. Works for numpy ndarrays.

    Parameters
    ----------

    data : numpy.ndarray
    format : int or segyio.SegySampleFormat
    copy : bool
        If True, convert on a copy, and leave the input array unmodified

    Returns
    -------

    data : numpy.ndarray

    Notes
    -----

    .. versionadded:: 1.1

    Examples
    --------

    Convert mmap'd trace to native float:

    >>> d = np.memmap('file.sgy', offset = 3600, dtype = np.uintc)
    >>> samples = 1500
    >>> trace = segyio.tools.native(d[240:240+samples])

    """

    data = data.view( dtype = np.single )
    if copy:
        data = np.copy( data )

    format = int(segyio.SegySampleFormat(format))
    return segyio._segyio.native(data, format)