Python numpy.intp方法代碼示例

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def check_function(self, function, sz):
        from threading import Thread

        out1 = np.empty((len(self.seeds),) + sz)
        out2 = np.empty((len(self.seeds),) + sz)

        # threaded generation
        t = [Thread(target=function, args=(np.random.RandomState(s), o))
             for s, o in zip(self.seeds, out1)]
        [x.start() for x in t]
        [x.join() for x in t]

        # the same serial
        for s, o in zip(self.seeds, out2):
            function(np.random.RandomState(s), o)

        # these platforms change x87 fpu precision mode in threads
        if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
            assert_array_almost_equal(out1, out2)
        else:
            assert_array_equal(out1, out2) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def _eigen_components(self):
        components = [(0, np.diag([1, 1, 1, 0, 1, 0, 0, 1]))]
        nontrivial_part = np.zeros((3, 3), dtype=np.complex128)
        for ij, w in zip([(1, 2), (0, 2), (0, 1)], self.weights):
            nontrivial_part[ij] = w
            nontrivial_part[ij[::-1]] = w.conjugate()
        assert np.allclose(nontrivial_part, nontrivial_part.conj().T)
        eig_vals, eig_vecs = np.linalg.eigh(nontrivial_part)
        for eig_val, eig_vec in zip(eig_vals, eig_vecs.T):
            exp_factor = -eig_val / np.pi
            proj = np.zeros((8, 8), dtype=np.complex128)
            nontrivial_indices = np.array([3, 5, 6], dtype=np.intp)
            proj[nontrivial_indices[:, np.newaxis], nontrivial_indices] = (
                np.outer(eig_vec.conjugate(), eig_vec))
            components.append((exp_factor, proj))
        return components 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_big_indices(self):
        # ravel_multi_index for big indices (issue #7546)
        if np.intp == np.int64:
            arr = ([1, 29], [3, 5], [3, 117], [19, 2],
                   [2379, 1284], [2, 2], [0, 1])
            assert_equal(
                np.ravel_multi_index(arr, (41, 7, 120, 36, 2706, 8, 6)),
                [5627771580, 117259570957])

        # test overflow checking for too big array (issue #7546)
        dummy_arr = ([0],[0])
        half_max = np.iinfo(np.intp).max // 2
        assert_equal(
            np.ravel_multi_index(dummy_arr, (half_max, 2)), [0])
        assert_raises(ValueError,
            np.ravel_multi_index, dummy_arr, (half_max+1, 2))
        assert_equal(
            np.ravel_multi_index(dummy_arr, (half_max, 2), order='F'), [0])
        assert_raises(ValueError,
            np.ravel_multi_index, dummy_arr, (half_max+1, 2), order='F') 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_invalid(self):
        """ Test it errors when indices has too few dimensions """
        a = np.ones((10, 10))
        ai = np.ones((10, 2), dtype=np.intp)

        # sanity check
        take_along_axis(a, ai, axis=1)

        # not enough indices
        assert_raises(ValueError, take_along_axis, a, np.array(1), axis=1)
        # bool arrays not allowed
        assert_raises(IndexError, take_along_axis, a, ai.astype(bool), axis=1)
        # float arrays not allowed
        assert_raises(IndexError, take_along_axis, a, ai.astype(float), axis=1)
        # invalid axis
        assert_raises(np.AxisError, take_along_axis, a, ai, axis=10) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_count_func(self):
        # Tests count
        assert_equal(1, count(1))
        assert_equal(0, array(1, mask=[1]))

        ott = array([0., 1., 2., 3.], mask=[1, 0, 0, 0])
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)

        ott = ott.reshape((2, 2))
        res = count(ott)
        assert_(res.dtype.type is np.intp)
        assert_equal(3, res)
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_equal([1, 2], res)
        assert_(getmask(res) is nomask)

        ott = array([0., 1., 2., 3.])
        res = count(ott, 0)
        assert_(isinstance(res, ndarray))
        assert_(res.dtype.type is np.intp)
        assert_raises(np.AxisError, ott.count, axis=1) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def _getintp_ctype():
    val = _getintp_ctype.cache
    if val is not None:
        return val
    if ctypes is None:
        import numpy as np
        val = dummy_ctype(np.intp)
    else:
        char = dtype('p').char
        if (char == 'i'):
            val = ctypes.c_int
        elif char == 'l':
            val = ctypes.c_long
        elif char == 'q':
            val = ctypes.c_longlong
        else:
            val = ctypes.c_long
    _getintp_ctype.cache = val
    return val 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_reverse_strides_and_subspace_bufferinit(self):
        # This tests that the strides are not reversed for simple and
        # subspace fancy indexing.
        a = np.ones(5)
        b = np.zeros(5, dtype=np.intp)[::-1]
        c = np.arange(5)[::-1]

        a[b] = c
        # If the strides are not reversed, the 0 in the arange comes last.
        assert_equal(a[0], 0)

        # This also tests that the subspace buffer is initialized:
        a = np.ones((5, 2))
        c = np.arange(10).reshape(5, 2)[::-1]
        a[b, :] = c
        assert_equal(a[0], [0, 1]) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_unaligned(self):
        v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
        d = v.view(np.dtype("S8"))
        # unaligned source
        x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
        x = x.view(np.dtype("S8"))
        x[...] = np.array("b" * 8, dtype="S")
        b = np.arange(d.size)
        #trivial
        assert_equal(d[b], d)
        d[b] = x
        # nontrivial
        # unaligned index array
        b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
        b = b.view(np.intp)[:d.size]
        b[...] = np.arange(d.size)
        assert_equal(d[b.astype(np.int16)], d)
        d[b.astype(np.int16)] = x
        # boolean
        d[b % 2 == 0]
        d[b % 2 == 0] = x[::2] 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_searchsorted(self, data_for_sorting, as_series):
        b, c, a = data_for_sorting
        arr = type(data_for_sorting)._from_sequence([a, b, c])

        if as_series:
            arr = pd.Series(arr)
        assert arr.searchsorted(a) == 0
        assert arr.searchsorted(a, side="right") == 1

        assert arr.searchsorted(b) == 1
        assert arr.searchsorted(b, side="right") == 2

        assert arr.searchsorted(c) == 2
        assert arr.searchsorted(c, side="right") == 3

        result = arr.searchsorted(arr.take([0, 2]))
        expected = np.array([0, 2], dtype=np.intp)

        tm.assert_numpy_array_equal(result, expected)

        # sorter
        sorter = np.array([1, 2, 0])
        assert data_for_sorting.searchsorted(a, sorter=sorter) == 0 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_factorize(self):
        idx1 = TimedeltaIndex(['1 day', '1 day', '2 day', '2 day', '3 day',
                               '3 day'])

        exp_arr = np.array([0, 0, 1, 1, 2, 2], dtype=np.intp)
        exp_idx = TimedeltaIndex(['1 day', '2 day', '3 day'])

        arr, idx = idx1.factorize()
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, exp_idx)

        arr, idx = idx1.factorize(sort=True)
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, exp_idx)

        # freq must be preserved
        idx3 = timedelta_range('1 day', periods=4, freq='s')
        exp_arr = np.array([0, 1, 2, 3], dtype=np.intp)
        arr, idx = idx3.factorize()
        tm.assert_numpy_array_equal(arr, exp_arr)
        tm.assert_index_equal(idx, idx3) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_nat(self):
        assert pd.TimedeltaIndex._na_value is pd.NaT
        assert pd.TimedeltaIndex([])._na_value is pd.NaT

        idx = pd.TimedeltaIndex(['1 days', '2 days'])
        assert idx._can_hold_na

        tm.assert_numpy_array_equal(idx._isnan, np.array([False, False]))
        assert idx.hasnans is False
        tm.assert_numpy_array_equal(idx._nan_idxs,
                                    np.array([], dtype=np.intp))

        idx = pd.TimedeltaIndex(['1 days', 'NaT'])
        assert idx._can_hold_na

        tm.assert_numpy_array_equal(idx._isnan, np.array([False, True]))
        assert idx.hasnans is True
        tm.assert_numpy_array_equal(idx._nan_idxs,
                                    np.array([1], dtype=np.intp)) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_join_left(self):
        # Join with Int64Index
        other = Int64Index(np.arange(25, 14, -1))

        res, lidx, ridx = self.index.join(other, how='left',
                                          return_indexers=True)
        eres = self.index
        eridx = np.array([-1, -1, -1, -1, -1, -1, -1, -1, 9, 7], dtype=np.intp)

        assert isinstance(res, RangeIndex)
        tm.assert_index_equal(res, eres)
        assert lidx is None
        tm.assert_numpy_array_equal(ridx, eridx)

        # Join withRangeIndex
        other = Int64Index(np.arange(25, 14, -1))

        res, lidx, ridx = self.index.join(other, how='left',
                                          return_indexers=True)

        assert isinstance(res, RangeIndex)
        tm.assert_index_equal(res, eres)
        assert lidx is None
        tm.assert_numpy_array_equal(ridx, eridx) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_get_indexer_consistency(self):
        # See GH 16819
        for name, index in self.indices.items():
            if isinstance(index, IntervalIndex):
                continue

            if index.is_unique or isinstance(index, CategoricalIndex):
                indexer = index.get_indexer(index[0:2])
                assert isinstance(indexer, np.ndarray)
                assert indexer.dtype == np.intp
            else:
                e = "Reindexing only valid with uniquely valued Index objects"
                with pytest.raises(InvalidIndexError, match=e):
                    index.get_indexer(index[0:2])

            indexer, _ = index.get_indexer_non_unique(index[0:2])
            assert isinstance(indexer, np.ndarray)
            assert indexer.dtype == np.intp 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_get_indexer(self):
        target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
        indexer = self.index.get_indexer(target)
        expected = np.array([0, -1, 1, 2, 3, 4,
                             -1, -1, -1, -1], dtype=np.intp)
        tm.assert_numpy_array_equal(indexer, expected)

        target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
        indexer = self.index.get_indexer(target, method='pad')
        expected = np.array([0, 0, 1, 2, 3, 4,
                             4, 4, 4, 4], dtype=np.intp)
        tm.assert_numpy_array_equal(indexer, expected)

        target = UInt64Index(np.arange(10).astype('uint64') * 5 + 2**63)
        indexer = self.index.get_indexer(target, method='backfill')
        expected = np.array([0, 1, 1, 2, 3, 4,
                             -1, -1, -1, -1], dtype=np.intp)
        tm.assert_numpy_array_equal(indexer, expected) 

# 需要導入模塊: import numpy [as 別名]
# 或者: from numpy import intp [as 別名]
def test_sort_values(self):
        idx = DatetimeIndex(['2000-01-04', '2000-01-01', '2000-01-02'])

        ordered = idx.sort_values()
        assert ordered.is_monotonic

        ordered = idx.sort_values(ascending=False)
        assert ordered[::-1].is_monotonic

        ordered, dexer = idx.sort_values(return_indexer=True)
        assert ordered.is_monotonic
        tm.assert_numpy_array_equal(dexer, np.array([1, 2, 0], dtype=np.intp))

        ordered, dexer = idx.sort_values(return_indexer=True, ascending=False)
        assert ordered[::-1].is_monotonic
        tm.assert_numpy_array_equal(dexer, np.array([0, 2, 1], dtype=np.intp))