Python numpy.longlong() Examples

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) 

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) 

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) 

def _open_and_load(f, dtype, multilabel, zero_based, query_id,
                   offset=0, length=-1):
    if hasattr(f, "read"):
        actual_dtype, data, ind, indptr, labels, query = \
            _load_svmlight_file(f, dtype, multilabel, zero_based, query_id,
                                offset, length)
    else:
        with closing(_gen_open(f)) as f:
            actual_dtype, data, ind, indptr, labels, query = \
                _load_svmlight_file(f, dtype, multilabel, zero_based, query_id,
                                    offset, length)

    # convert from array.array, give data the right dtype
    if not multilabel:
        labels = np.frombuffer(labels, np.float64)
    data = np.frombuffer(data, actual_dtype)
    indices = np.frombuffer(ind, np.longlong)
    indptr = np.frombuffer(indptr, dtype=np.longlong)   # never empty
    query = np.frombuffer(query, np.int64)

    data = np.asarray(data, dtype=dtype)    # no-op for float{32,64}
    return data, indices, indptr, labels, query 

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) 

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) 

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) 

def concatenateNeighborLists(meshPaths):
    neighbor = []
    for path in meshPaths:
        mesh = om.MFnMesh(path)
        _, indices = mesh.getTriangles()
        offset = len(neighbor)
        neighbor = neighbor + [set() for v in xrange(mesh.numVertices)]
        for l in xrange(len(indices) / 3):
            i0 = indices[l * 3 + 0] + offset
            i1 = indices[l * 3 + 1] + offset
            i2 = indices[l * 3 + 2] + offset
            neighbor[i0].add(i1)
            neighbor[i0].add(i2)
            neighbor[i1].add(i0)
            neighbor[i1].add(i2)
            neighbor[i2].add(i0)
            neighbor[i2].add(i1)
    maxlen = 0
    for i in xrange(len(neighbor)):
        maxlen = max(maxlen, len(neighbor[i]))
    retval = -np.ones([len(neighbor), maxlen], dtype = np.longlong)
    for i in xrange(len(neighbor)):
        retval[i, 0:len(neighbor[i])] = list(neighbor[i])
    return retval 

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) 

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) 

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) 

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) 

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) 

def test_signed_overflow_bounds(self):
        self.do_signed_overflow_bounds(np.byte)
        self.do_signed_overflow_bounds(np.short)
        self.do_signed_overflow_bounds(np.intc)
        self.do_signed_overflow_bounds(np.int_)
        self.do_signed_overflow_bounds(np.longlong) 

def test_platform_dependent_aliases(self):
        if np.int64 is np.int_:
            assert_('int64' in np.int_.__doc__)
        elif np.int64 is np.longlong:
            assert_('int64' in np.longlong.__doc__) 

def test_signed_overflow_bounds(self):
        self.do_signed_overflow_bounds(np.byte)
        self.do_signed_overflow_bounds(np.short)
        self.do_signed_overflow_bounds(np.intc)
        self.do_signed_overflow_bounds(np.int_)
        self.do_signed_overflow_bounds(np.longlong) 

def test_platform_dependent_aliases(self):
        if np.int64 is np.int_:
            assert_('int64' in np.int_.__doc__)
        elif np.int64 is np.longlong:
            assert_('int64' in np.longlong.__doc__) 

def testLongLong(self):
    self.assertAllClose(
        onp.int64(7), npe.jit(lambda x: x)(onp.longlong(7)), check_dtypes=True) 

def test_signed_overflow_bounds(self):
        self.do_signed_overflow_bounds(np.byte)
        self.do_signed_overflow_bounds(np.short)
        self.do_signed_overflow_bounds(np.intc)
        self.do_signed_overflow_bounds(np.int_)
        self.do_signed_overflow_bounds(np.longlong) 

def test_eigvals_banded(self):
        """Compare eigenvalues of eigvals_banded with those of linalg.eig."""
        w_sym = eigvals_banded(self.bandmat_sym)
        w_sym = w_sym.real
        assert_array_almost_equal(sort(w_sym), self.w_sym_lin)

        w_herm = eigvals_banded(self.bandmat_herm)
        w_herm = w_herm.real
        assert_array_almost_equal(sort(w_herm), self.w_herm_lin)

        # extracting eigenvalues with respect to an index range
        ind1 = 2
        ind2 = np.longlong(6)
        w_sym_ind = eigvals_banded(self.bandmat_sym,
                                    select='i', select_range=(ind1, ind2))
        assert_array_almost_equal(sort(w_sym_ind),
                                  self.w_sym_lin[ind1:ind2+1])
        w_herm_ind = eigvals_banded(self.bandmat_herm,
                                    select='i', select_range=(ind1, ind2))
        assert_array_almost_equal(sort(w_herm_ind),
                                  self.w_herm_lin[ind1:ind2+1])

        # extracting eigenvalues with respect to a value range
        v_lower = self.w_sym_lin[ind1] - 1.0e-5
        v_upper = self.w_sym_lin[ind2] + 1.0e-5
        w_sym_val = eigvals_banded(self.bandmat_sym,
                                select='v', select_range=(v_lower, v_upper))
        assert_array_almost_equal(sort(w_sym_val),
                                  self.w_sym_lin[ind1:ind2+1])

        v_lower = self.w_herm_lin[ind1] - 1.0e-5
        v_upper = self.w_herm_lin[ind2] + 1.0e-5
        w_herm_val = eigvals_banded(self.bandmat_herm,
                                select='v', select_range=(v_lower, v_upper))
        assert_array_almost_equal(sort(w_herm_val),
                                  self.w_herm_lin[ind1:ind2+1])

        w_sym = eigvals_banded(self.bandmat_sym, check_finite=False)
        w_sym = w_sym.real
        assert_array_almost_equal(sort(w_sym), self.w_sym_lin) 

def test_signed_overflow_bounds(self):
        self.do_signed_overflow_bounds(np.byte)
        self.do_signed_overflow_bounds(np.short)
        self.do_signed_overflow_bounds(np.intc)
        self.do_signed_overflow_bounds(np.int_)
        self.do_signed_overflow_bounds(np.longlong) 

def test_platform_dependent_aliases(self):
        if np.int64 is np.int_:
            assert_('int64' in np.int_.__doc__)
        elif np.int64 is np.longlong:
            assert_('int64' in np.longlong.__doc__) 

def test_signed_overflow_bounds(self):
        self.do_signed_overflow_bounds(np.byte)
        self.do_signed_overflow_bounds(np.short)
        self.do_signed_overflow_bounds(np.intc)
        self.do_signed_overflow_bounds(np.int_)
        self.do_signed_overflow_bounds(np.longlong) 

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) 

def get_dims(self):
        """ Gets the dimensions of the KHIVA array.

        :return: The dimensions of the KHIVA array.
        """
        c_array_n = (ctypes.c_longlong * 4)(*(np.zeros(4)).astype(np.longlong))
        error_code = ctypes.c_int(0)
        error_message = ctypes.create_string_buffer(KHIVA_ERROR_LENGTH)
        KhivaLibrary().c_khiva_library.get_dims(ctypes.pointer(self.arr_reference),
                                                ctypes.pointer(c_array_n),
                                                ctypes.pointer(error_code),
                                                error_message)
        return np.array(c_array_n) 

def test_return_arrcrt_zeros(a, b, c):
    d = np.zeros(3)
    d[:] = 2
    d[0] = 1
    return d

# TODO: fix for np.ulonglong, np.longlong and uint64 

def test_return_scalar(dtype):
    def fkt(a): return a
    ao, ah = random(dtype, [])
    ro, rh = fkt(ao), hope.jit(fkt)(ah)
    assert check(ro, rh)

# TODO: fix for np.ulonglong, np.longlong and uint64 

def test_return_arrayscalar(dtype):
    def fkt(a): return a[2]
    ao, ah = random(dtype, [10])
    ro, rh = fkt(ao), hope.jit(fkt)(ah)
    assert check(ro, rh)

# TODO: fix for np.ulonglong, np.longlong and uint64 

def _to_qtimestamp(dt):
    t_dt = type(dt)
    if t_dt == numpy.int64:
        return dt
    elif t_dt == numpy.datetime64:
        return (dt - _EPOCH_TIMESTAMP).astype(longlong) if not dt == _NUMPY_NULL[QTIMESTAMP] else _QTIMESTAMP_NULL
    else:
        raise ValueError('Cannot convert %s of type %s to q value.' % (dt, type(dt))) 

def _to_qtimespan(dt):
    t_dt = type(dt)
    if t_dt == numpy.int64:
        return dt
    elif t_dt == numpy.timedelta64:
        return dt.astype(longlong) if not dt == _NUMPY_NULL[QTIMESPAN] else _QTIMESTAMP_NULL
    else:
        raise ValueError('Cannot convert %s of type %s to q value.' % (dt, type(dt)))