Python numpy.uint64方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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() 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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]]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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'])) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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") 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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-b9MddTxOWW2AT1Py6vtVbZwGAmYCjbp89p8mxsiFoVX4FyDOF3wFiAkyQTUgwg9sVqVYOZo09Dh1AzhFHbgij52ylF0SEwgzjzHH8TGY8Lypart4p4onnDoDvVMBa0kdthVGKl6K0BDVGzyOXPXKpmnMF1H6rJzqHJ0HywfwS4XYpVwlAkoeNsiicHkJUFdUAhG229INzvIAiJuAHeJDUoyO4DCBqtoZ5TDend6TK7Y914yHlfH3g1WZu5LksKv68VQHJriWFYusW5e6ZZ6dKaMjTwEGuRgdT66iU5nqWTHRH8WSzpXoCFwGcTOwyuqPSe0fTe21DVtJn1FKj9F9nEnR9xOvJUO7E0piCIF4Ad9yAIDY4DBimpsTfKXCu1vdHpKYerzbndfuFe5AhfMduLYZJi5iAw8qKSwR5h86ttXV0Mc0QmXz8dsRvDgxjXSmupPxBggdlqUlC828hXiTPD7am0yETBV0F3bEtvPiNJfremszcV8NcqAoARMe"]  # 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)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import uint64 [as 別名]
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)