Python numpy.uint64() Examples

def test_hash_collisions():
    # Hash collisions are bad.
    #
    # https://github.com/pandas-dev/pandas/issues/14711#issuecomment-264885726
    hashes = ["Ingrid-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",  # noqa
              "Tim-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"]  # noqa

    # These should be different.
    result1 = hash_array(np.asarray(hashes[0:1], dtype=object), "utf8")
    expected1 = np.array([14963968704024874985], dtype=np.uint64)
    tm.assert_numpy_array_equal(result1, expected1)

    result2 = hash_array(np.asarray(hashes[1:2], dtype=object), "utf8")
    expected2 = np.array([16428432627716348016], dtype=np.uint64)
    tm.assert_numpy_array_equal(result2, expected2)

    result = hash_array(np.asarray(hashes, dtype=object), "utf8")
    tm.assert_numpy_array_equal(result, np.concatenate([expected1,
                                                        expected2], axis=0)) 

def argunique_ctypes(strs):
    nstrs, nset = strs.shape

    sort_idx = numpy.empty(nstrs, dtype=numpy.uint64)

    strs = numpy.asarray(strs, order='C')
    sort_idx = numpy.asarray(sort_idx, order='C')

    nstrs_ = numpy.array([nstrs])

    libhci.argunique(strs.ctypes.data_as(ctypes.c_void_p), 
                     sort_idx.ctypes.data_as(ctypes.c_void_p), 
                     nstrs_.ctypes.data_as(ctypes.c_void_p), 
                     ctypes.c_int(nset))

    sort_idx = sort_idx[:nstrs_[0]]

    return sort_idx.tolist() 

def test_able_int_type():
    # The integer type cabable of containing values
    for vals, exp_out in (
        ([0, 1], np.uint8),
        ([0, 255], np.uint8),
        ([-1, 1], np.int8),
        ([0, 256], np.uint16),
        ([-1, 128], np.int16),
        ([0.1, 1], None),
        ([0, 2**16], np.uint32),
        ([-1, 2**15], np.int32),
        ([0, 2**32], np.uint64),
        ([-1, 2**31], np.int64),
        ([-1, 2**64-1], None),
        ([0, 2**64-1], np.uint64),
        ([0, 2**64], None)):
        assert_equal(able_int_type(vals), exp_out) 

def testChunkReadPoints2D(self):
        chunk_id = "c-00de6a9c-6aff5c35-15d5-3864dd-0740f8_3_4"
        chunk_layout = (100,100)
        chunk_arr = np.zeros((100,100))
        chunk_arr[:,12] = 69
        chunk_arr[12,:] = 96

        point_arr = np.array([[312,498],[312,412],[355,412],[398,497]], dtype=np.uint64)
        arr = chunkReadPoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
        self.assertEqual(arr.tolist(), [96,96,69,0])

        point_arr = np.array([[312,498],[312,412],[355,412],[398,397]], dtype=np.uint64)
        try:
            chunkReadPoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
            self.assertTrue(False)  # expected exception
        except IndexError:
            pass # expected 

def check(self, stream):
        """Check that the CRC at the end of athe stream is correct.

        Parameters
        ----------
        stream : int or array of unsigned int
            For an integer, the value is the stream to check the CRC for.
            For arrays, the dimension is treated as the index into the bits.
            A single stream would thus be of type `bool`. Unsigned integers
            represent multiple streams. E.g., for a 64-track Mark 4 header,
            the stream would be an array of ``np.uint64`` words.

        Returns
        -------
        ok : bool
             `True` if the calculated CRC is all zero (which should be the
             case if the CRC at the end of the stream is correct).
        """
        return self._crc(stream) == 0 

def reorder64_Ft(x):
        """Reorder 64-track bits to bring signs & magnitudes together.

        Special version for the Ft station, which has unusual settings.
        """
        return (((x & 0xFFFFFAAFFFFFFAAF))
                | ((x & 0x0000050000000500) >> 4)
                | ((x & 0x0000005000000050) << 4))
    # Check on 2015-JUL-12: C code: 738811025863578102 -> 738829572664316278
    # 118, 209, 53, 244, 148, 217, 64, 10
    # reorder64(np.array([738811025863578102], dtype=np.uint64))
    # # array([738829572664316278], dtype=uint64)
    # reorder64(np.array([738811025863578102], dtype=np.uint64)).view(np.uint8)
    # # array([118, 209,  53, 244, 148, 217,  64,  10], dtype=uint8)
    # decode_2bit_64track_fanout4(
    #     np.array([738811025863578102], dtype=np.int64)).astype(int).T
    # -1  1  3  1  array([[-1,  1,  3,  1],
    #  1  1  3 -3         [ 1,  1,  3, -3],
    #  1 -3  1  3         [ 1, -3,  1,  3],
    # -3  1  3  3         [-3,  1,  3,  3],
    # -3  1  1 -1         [-3,  1,  1, -1],
    # -3 -3 -3  1         [-3, -3, -3,  1],
    #  1 -1  1  3         [ 1, -1,  1,  3],
    # -1 -1 -3 -3         [-1, -1, -3, -3]]) 

def encode_16chan_2bit_fanout2_ft(values):
    """Encode payload for 16 channels using 2 bits, fan-out 2 (64 tracks)."""
    # words encode 32 values (above) in order ch&0x8, ch&0x3, sample, ch&0x4
    # First reshape goes to time, sample, ch&0x8, ch&0x4, ch&0x3,
    # transpose makes this time, ch&0x8, ch&0x3, sample, ch&0x4.
    # second reshape future bytes, sample+ch&0x4.
    values = (values.reshape(-1, 2, 2, 2, 4).transpose(0, 2, 4, 1, 3)
              .reshape(-1, 4))
    bitvalues = encode_2bit_base(values)
    # values are -3, -1, +1, 3 -> 00, 01, 10, 11;
    # get first bit (sign) as 1, second bit (magnitude) as 16
    reorder_bits = np.array([0, 16, 1, 17], dtype=np.uint8)
    reorder_bits.take(bitvalues, out=bitvalues)
    bitvalues <<= np.array([0, 1, 2, 3], dtype=np.uint8)
    out = np.bitwise_or.reduce(bitvalues, axis=-1).ravel().view(np.uint64)
    return reorder64_Ft(out).view('<u8') 

def squeeze_bits(arr: numpy.ndarray) -> numpy.ndarray:
    """Return a copy of an integer numpy array with the minimum bitness."""
    assert arr.dtype.kind in ("i", "u")
    if arr.size == 0:
        return arr
    if arr.dtype.kind == "i":
        assert arr.min() >= 0
    mlbl = int(arr.max()).bit_length()
    if mlbl <= 8:
        dtype = numpy.uint8
    elif mlbl <= 16:
        dtype = numpy.uint16
    elif mlbl <= 32:
        dtype = numpy.uint32
    else:
        dtype = numpy.uint64
    return arr.astype(dtype) 

def test_int64_uint64_corner_case(self):
        # When stored in Numpy arrays, `lbnd` is casted
        # as np.int64, and `ubnd` is casted as np.uint64.
        # Checking whether `lbnd` >= `ubnd` used to be
        # done solely via direct comparison, which is incorrect
        # because when Numpy tries to compare both numbers,
        # it casts both to np.float64 because there is
        # no integer superset of np.int64 and np.uint64. However,
        # `ubnd` is too large to be represented in np.float64,
        # causing it be round down to np.iinfo(np.int64).max,
        # leading to a ValueError because `lbnd` now equals
        # the new `ubnd`.

        dt = np.int64
        tgt = np.iinfo(np.int64).max
        lbnd = np.int64(np.iinfo(np.int64).max)
        ubnd = np.uint64(np.iinfo(np.int64).max + 1)

        # None of these function calls should
        # generate a ValueError now.
        actual = np.random.randint(lbnd, ubnd, dtype=dt)
        assert_equal(actual, tgt) 

def test_constructor(self):
        idx = UInt64Index([1, 2, 3])
        res = Index([1, 2, 3], dtype=np.uint64)
        tm.assert_index_equal(res, idx)

        idx = UInt64Index([1, 2**63])
        res = Index([1, 2**63], dtype=np.uint64)
        tm.assert_index_equal(res, idx)

        idx = UInt64Index([1, 2**63])
        res = Index([1, 2**63])
        tm.assert_index_equal(res, idx)

        idx = Index([-1, 2**63], dtype=object)
        res = Index(np.array([-1, 2**63], dtype=object))
        tm.assert_index_equal(res, idx) 

def test_sum_uint64_overflow():
    # see gh-14758
    # Convert to uint64 and don't overflow
    df = pd.DataFrame([[1, 2], [3, 4], [5, 6]], dtype=object)
    df = df + 9223372036854775807

    index = pd.Index([9223372036854775808,
                      9223372036854775810,
                      9223372036854775812],
                     dtype=np.uint64)
    expected = pd.DataFrame({1: [9223372036854775809,
                                 9223372036854775811,
                                 9223372036854775813]},
                            index=index)

    expected.index.name = 0
    result = df.groupby(0).sum()
    tm.assert_frame_equal(result, expected) 

def test_setitem(self):

        df = self.df
        idx = self.idx

        # setitem
        df['C'] = idx
        assert_series_equal(df['C'], Series(idx, name='C'))

        df['D'] = 'foo'
        df['D'] = idx
        assert_series_equal(df['D'], Series(idx, name='D'))
        del df['D']

        # With NaN: because uint64 has no NaN element,
        # the column should be cast to object.
        df2 = df.copy()
        df2.iloc[1, 1] = pd.NaT
        df2.iloc[1, 2] = pd.NaT
        result = df2['B']
        assert_series_equal(notna(result), Series(
            [True, False, True], name='B'))
        assert_series_equal(df2.dtypes, Series([np.dtype('uint64'),
                                                np.dtype('O'), np.dtype('O')],
                                               index=['A', 'B', 'C'])) 

def test_constructor_overflow_int64(self):
        # see gh-14881
        values = np.array([2 ** 64 - i for i in range(1, 10)],
                          dtype=np.uint64)

        result = DataFrame({'a': values})
        assert result['a'].dtype == np.uint64

        # see gh-2355
        data_scores = [(6311132704823138710, 273), (2685045978526272070, 23),
                       (8921811264899370420, 45),
                       (long(17019687244989530680), 270),
                       (long(9930107427299601010), 273)]
        dtype = [('uid', 'u8'), ('score', 'u8')]
        data = np.zeros((len(data_scores),), dtype=dtype)
        data[:] = data_scores
        df_crawls = DataFrame(data)
        assert df_crawls['uid'].dtype == np.uint64 

def test_coerce_uint64_conflict(self):
        # see gh-17007 and gh-17125
        #
        # Still returns float despite the uint64-nan conflict,
        # which would normally force the casting to object.
        df = pd.DataFrame({"a": [200, 300, "", "NaN", 30000000000000000000]})
        expected = pd.Series([200, 300, np.nan, np.nan,
                              30000000000000000000], dtype=float, name="a")
        result = to_numeric(df["a"], errors="coerce")
        tm.assert_series_equal(result, expected)

        s = pd.Series(["12345678901234567890", "1234567890", "ITEM"])
        expected = pd.Series([12345678901234567890,
                              1234567890, np.nan], dtype=float)
        result = to_numeric(s, errors="coerce")
        tm.assert_series_equal(result, expected)

        # For completeness, check against "ignore" and "raise"
        result = to_numeric(s, errors="ignore")
        tm.assert_series_equal(result, s)

        msg = "Unable to parse string"
        with pytest.raises(ValueError, match=msg):
            to_numeric(s, errors="raise") 

def test_maybe_convert_objects_uint64(self):
        # see gh-4471
        arr = np.array([2**63], dtype=object)
        exp = np.array([2**63], dtype=np.uint64)
        tm.assert_numpy_array_equal(lib.maybe_convert_objects(arr), exp)

        # NumPy bug: can't compare uint64 to int64, as that
        # results in both casting to float64, so we should
        # make sure that this function is robust against it
        arr = np.array([np.uint64(2**63)], dtype=object)
        exp = np.array([2**63], dtype=np.uint64)
        tm.assert_numpy_array_equal(lib.maybe_convert_objects(arr), exp)

        arr = np.array([2, -1], dtype=object)
        exp = np.array([2, -1], dtype=np.int64)
        tm.assert_numpy_array_equal(lib.maybe_convert_objects(arr), exp)

        arr = np.array([2**63, -1], dtype=object)
        exp = np.array([2**63, -1], dtype=object)
        tm.assert_numpy_array_equal(lib.maybe_convert_objects(arr), exp) 

def _engine(self):
        # Calculate the number of bits needed to represent labels in each
        # level, as log2 of their sizes (including -1 for NaN):
        sizes = np.ceil(np.log2([len(l) + 1 for l in self.levels]))

        # Sum bit counts, starting from the _right_....
        lev_bits = np.cumsum(sizes[::-1])[::-1]

        # ... in order to obtain offsets such that sorting the combination of
        # shifted codes (one for each level, resulting in a unique integer) is
        # equivalent to sorting lexicographically the codes themselves. Notice
        # that each level needs to be shifted by the number of bits needed to
        # represent the _previous_ ones:
        offsets = np.concatenate([lev_bits[1:], [0]]).astype('uint64')

        # Check the total number of bits needed for our representation:
        if lev_bits[0] > 64:
            # The levels would overflow a 64 bit uint - use Python integers:
            return MultiIndexPyIntEngine(self.levels, self.codes, offsets)
        return MultiIndexUIntEngine(self.levels, self.codes, offsets) 

def __init__(self, filename):
        self._filename = filename
        print 'Loading BCF file to memory ... '+filename
        file = open(filename, 'rb')
        size = numpy.fromstring(file.read(8), dtype=numpy.uint64)
        file_sizes = numpy.fromstring(file.read(8*size), dtype=numpy.uint64)
        self._offsets = numpy.append(numpy.uint64(0),
                                     numpy.add.accumulate(file_sizes))
        self._memory = file.read()
        file.close() 

def __init__(self, filename):
        self._filename = filename
        print 'Opening BCF file ... '+filename
        self._file = open(filename, 'rb')
        size = numpy.fromstring(self._file.read(8), dtype=numpy.uint64)
        file_sizes = numpy.fromstring(self._file.read(8*size),
                                      dtype=numpy.uint64)
        self._offsets = numpy.append(numpy.uint64(0),
                                     numpy.add.accumulate(file_sizes)) 

def _toscalar(v):
    if isinstance(v, (np.float16, np.float32, np.float64,
                      np.uint8, np.uint16, np.uint32, np.uint64,
                      np.int8, np.int16, np.int32, np.int64)):
        return np.asscalar(v)
    else:
        return v 

def getindex(self):
        index_size = ctypes.c_uint64(0)
        index_data = ctypes.POINTER(ctypes.c_uint64)()
        check_call(_LIB.MXDataIterGetIndex(self.handle,
                                           ctypes.byref(index_data),
                                           ctypes.byref(index_size)))
        if index_size.value:
            address = ctypes.addressof(index_data.contents)
            dbuffer = (ctypes.c_uint64* index_size.value).from_address(address)
            np_index = np.frombuffer(dbuffer, dtype=np.uint64)
            return np_index.copy()
        else:
            return None 

def cre_des_sign(p, q, string):
    nset = len(string)
    pg, pb = p//64, p%64
    qg, qb = q//64, q%64

    if pg > qg:
        n1 = 0
        for i in range(nset-pg, nset-qg-1):
            n1 += bin(string[i]).count('1')
        n1 += bin(string[-1-pg] & numpy.uint64((1<<pb) - 1)).count('1')
        n1 += string[-1-qg] >> numpy.uint64(qb+1)
    elif pg < qg:
        n1 = 0
        for i in range(nset-qg, nset-pg-1):
            n1 += bin(string[i]).count('1')
        n1 += bin(string[-1-qg] & numpy.uint64((1<<qb) - 1)).count('1')
        n1 += string[-1-pg] >> numpy.uint64(pb+1)
    else:
        if p > q:
            mask = numpy.uint64((1 << pb) - (1 << (qb+1)))
        else:
            mask = numpy.uint64((1 << qb) - (1 << (pb+1)))
        n1 = bin(string[-1-pg]&mask).count('1')

    if n1 % 2:
        return -1
    else:
        return 1 

def toggle_bit(s, place):
    g, b = place//64, place%64
    s[-1-g] ^= numpy.uint64(1<<b)
    return s 

def str2orblst(string, norb):
    occ = []
    vir = []
    nset = len(string)
    off = 0
    for k in reversed(range(nset)):
        s = string[k]
        occ.extend([x+off for x in find1(s)])
        for i in range(0, min(64, norb-off)): 
            if not (s & numpy.uint64(1<<i)):
                vir.append(i+off)
        off += 64
    return occ, vir 

def test_high_int2uint():
    # Need to take care of high values when testing whether values are already
    # in range.  There was a bug here were the comparison was in floating point,
    # and therefore not exact, and 2**63 appeared to be in range for np.int64
    arr = np.array([2**63], dtype=np.uint64)
    out_type = np.int64
    aw = SlopeInterArrayWriter(arr, out_type)
    assert_equal(aw.inter, 2**63) 

def test_byte_orders():
    arr = np.arange(10, dtype=np.int32)
    # Test endian read/write of types not requiring scaling
    for tp in (np.uint64, np.float, np.complex):
        dt = np.dtype(tp)
        for code in '<>':
            ndt = dt.newbyteorder(code)
            for klass in (SlopeInterArrayWriter, SlopeArrayWriter,
                          ArrayWriter):
                aw = klass(arr, ndt)
                data_back = round_trip(aw)
                assert_array_almost_equal(arr, data_back) 

def test_array_to_file():
    arr = np.arange(10).reshape(5,2)
    str_io = BytesIO()
    for tp in (np.uint64, np.float, np.complex):
        dt = np.dtype(tp)
        for code in '<>':
            ndt = dt.newbyteorder(code)
            for allow_intercept in (True, False):
                scale, intercept, mn, mx = calculate_scale(arr,
                                                           ndt,
                                                           allow_intercept)
                data_back = write_return(arr, str_io, ndt,
                                         0, intercept, scale)
                assert_array_almost_equal(arr, data_back) 

def test__inter_type():
    # Test routine to get intercept type
    bf = best_float()
    for in_type, inter, out_type, exp_out in (
        (np.int8, 0, None, np.int8),
        (np.int8, 0, np.int8, np.int8),
        (np.int8, 1, None, np.int16),
        (np.int8, 1, np.int8, bf),
        (np.int8, 1, np.int16, np.int16),
        (np.uint8, 0, None, np.uint8),
        (np.uint8, 1, None, np.uint16),
        (np.uint8, -1, None, np.int16),
        (np.int16, 1, None, np.int32),
        (np.uint16, 0, None, np.uint16),
        (np.uint16, 1, None, np.uint32),
        (np.int32, 1, None, np.int64),
        (np.uint32, 1, None, np.uint64),
        (np.int64, 1, None, bf),
        (np.uint64, 1, None, bf),
    ):
        assert_dt_equal(_inter_type(in_type, inter, out_type), exp_out)
        # Check that casting is as expected
        A = np.zeros((1,), dtype=in_type)
        B = np.array([inter], dtype=exp_out)
        ApBt = (A + B).dtype.type
        assert_dt_equal(ApBt, exp_out) 

def testChunkReadPoints1D(self):
        chunk_id = "c-00de6a9c-6aff5c35-15d5-3864dd-0740f8_12"
        chunk_layout = (100,)
        chunk_arr = np.array(list(range(100)))
        point_arr = np.array([[1200],[1299],[1244],[1222]], dtype=np.uint64)
        arr = chunkReadPoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
        self.assertEqual(arr.tolist(), [0, 99, 44, 22])

        point_arr = np.array([[1200],[1299],[1244],[1322]], dtype=np.uint64)
        try:
            chunkReadPoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
            self.assertTrue(False)  # expected exception
        except IndexError:
            pass # expected 

def testChunkWritePoints1D(self):
        chunk_id = "c-00de6a9c-6aff5c35-15d5-3864dd-0740f8_12"
        chunk_layout = (100,)
        chunk_arr = np.zeros((100,))
        rank = 1
        #       (coord1, coord2, ...) | dset_dtype
        point_dt = np.dtype([("coord", np.uint64), ("val", chunk_arr.dtype)])
        # point_dt = np.dtype([("coord", np.uint64, (rank,)), ("val", chunk_arr.dtype)])
        indexes = (1203,1245,1288,1212,1299)
        num_points = len(indexes)
        point_arr = np.zeros((num_points,), dtype=point_dt)
        print("point_arr.shape:", point_arr.shape)
        print("point_arr.dtype:", point_arr.dtype)
        for i in range(num_points):
            e = point_arr[i]
            e[0] = indexes[i]
            e[1] = 42
        chunkWritePoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
        for i in range(100):
            if i + 1200 in indexes:
                self.assertEqual(chunk_arr[i], 42)
            else:
                self.assertEqual(chunk_arr[i], 0)

        e = point_arr[1]
        e[0] = 99  # index out of range
        try:
            chunkWritePoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
            self.assertTrue(False)  # expected exception
        except IndexError:
            pass  # expected 

def testChunkWritePoints2D(self):
        chunk_id = "c-00de6a9c-6aff5c35-15d5-3864dd-0740f8_3_2"
        chunk_layout = (10,20)
        chunk_arr = np.zeros((10,20))
        rank = 2
        #       (coord1, coord2, ...) | dset_dtype
        point_dt = np.dtype([("coord", np.uint64, (2,)), ("val", chunk_arr.dtype)])
        indexes =((32,46),(38,52),(35,53))
        num_points = len(indexes)
        point_arr = np.zeros((num_points,), dtype=point_dt)
        for i in range(num_points):
            e = point_arr[i]
            e[0] = indexes[i]
            e[1] = 42
        chunkWritePoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
        chunk_index = (30,40)
        for i in range(num_points):
            index = indexes[i]
            x = index[0]- chunk_index[0]
            y = index[1] - chunk_index[1]
            self.assertEqual(chunk_arr[x,y], 42)

        e = point_arr[0]
        e[0] = (42,46)  # index out of range
        try:
            chunkWritePoints(chunk_id=chunk_id, chunk_layout=chunk_layout, chunk_arr=chunk_arr, point_arr=point_arr)
            self.assertTrue(False)  # expected exception
        except IndexError:
            pass  # expected