Python numpy.int_() Examples

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.float64)
        assert_(res.dtype.type is np.float64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.complex64)
        assert_(res.dtype.type is np.float32)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray)) 

def test_dti_custom_getitem(self):
        rng = pd.bdate_range(START, END, freq='C')
        smaller = rng[:5]
        exp = DatetimeIndex(rng.view(np.ndarray)[:5])
        tm.assert_index_equal(smaller, exp)
        assert smaller.freq == rng.freq

        sliced = rng[::5]
        assert sliced.freq == CDay() * 5

        fancy_indexed = rng[[4, 3, 2, 1, 0]]
        assert len(fancy_indexed) == 5
        assert isinstance(fancy_indexed, DatetimeIndex)
        assert fancy_indexed.freq is None

        # 32-bit vs. 64-bit platforms
        assert rng[4] == rng[np.int_(4)] 

def test_dti_business_getitem(self):
        rng = pd.bdate_range(START, END)
        smaller = rng[:5]
        exp = DatetimeIndex(rng.view(np.ndarray)[:5])
        tm.assert_index_equal(smaller, exp)

        assert smaller.freq == rng.freq

        sliced = rng[::5]
        assert sliced.freq == BDay() * 5

        fancy_indexed = rng[[4, 3, 2, 1, 0]]
        assert len(fancy_indexed) == 5
        assert isinstance(fancy_indexed, DatetimeIndex)
        assert fancy_indexed.freq is None

        # 32-bit vs. 64-bit platforms
        assert rng[4] == rng[np.int_(4)] 

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        self.assertTrue(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        self.assertTrue(not allclose(a, b))
        b[0] = np.inf
        self.assertTrue(allclose(a, b))
        # Test allclose w/ masked
        a = masked_array(a)
        a[-1] = masked
        self.assertTrue(allclose(a, b, masked_equal=True))
        self.assertTrue(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        self.assertTrue(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        self.assertTrue(allclose(a, a)) 

def test_no_seq_repeat_basic_array_like(self):
        # Test that an array-like which does not know how to be multiplied
        # does not attempt sequence repeat (raise TypeError).
        # See also gh-7428.
        class ArrayLike(object):
            def __init__(self, arr):
                self.arr = arr
            def __array__(self):
                return self.arr

        # Test for simple ArrayLike above and memoryviews (original report)
        for arr_like in (ArrayLike(np.ones(3)), memoryview(np.ones(3))):
            assert_array_equal(arr_like * np.float32(3.), np.full(3, 3.))
            assert_array_equal(np.float32(3.) * arr_like, np.full(3, 3.))
            assert_array_equal(arr_like * np.int_(3), np.full(3, 3))
            assert_array_equal(np.int_(3) * arr_like, np.full(3, 3)) 

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        self.assertTrue(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        self.assertTrue(not allclose(a, b))
        b[0] = np.inf
        self.assertTrue(allclose(a, b))
        # Test allclose w/ masked
        a = masked_array(a)
        a[-1] = masked
        self.assertTrue(allclose(a, b, masked_equal=True))
        self.assertTrue(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        self.assertTrue(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        self.assertTrue(allclose(a, a)) 

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 initialize(self):
        self.pcoord_ndim = 1
        self.pcoord_dtype = numpy.float32
        self.pcoord_len = 5
        self.bin_mapper = RectilinearBinMapper([ list(numpy.arange(0.0, 10.1, 0.1)) ] )
        self.bin_target_counts = numpy.empty((self.bin_mapper.nbins,), numpy.int_)
        self.bin_target_counts[...] = 10
        self.test_variable_2 = "And I'm the second one"

# YAML Front end tests

# Implemented basic tests
#  - returns the correct system 
#    given a system driver
#  - returns the correct system
#    given a yaml system
#  - returns the correct system
#    given both

# A class to test both paths at the same time
# if it works we assure we can load the driver
# AND overwrite it properly 

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.float64)
        assert_(res.dtype.type is np.float64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res, res_v = linalg.eigh(a)
        assert_(res_v.dtype.type is np.complex64)
        assert_(res.dtype.type is np.float32)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray)) 

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res, res_v = linalg.eig(a)
        assert_(res_v.dtype.type is np.float64)
        assert_(res.dtype.type is np.float64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res, res_v = linalg.eig(a)
        assert_(res_v.dtype.type is np.complex64)
        assert_(res.dtype.type is np.complex64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray)) 

def test_empty_tuple_index(self):
        # Empty tuple index creates a view
        a = np.array([1, 2, 3])
        assert_equal(a[()], a)
        assert_(a[()].base is a)
        a = np.array(0)
        assert_(isinstance(a[()], np.int_))

        # Regression, it needs to fall through integer and fancy indexing
        # cases, so need the with statement to ignore the non-integer error.
        with warnings.catch_warnings():
            warnings.filterwarnings('ignore', '', DeprecationWarning)
            a = np.array([1.])
            assert_(isinstance(a[0.], np.float_))

            a = np.array([np.array(1)], dtype=object)
            assert_(isinstance(a[0.], np.ndarray)) 

def boundary_conditions(self, bc):
        global bc_choices
        if bc in list(bc_choices.keys()):
            bc = [bc_choices[bc]] * self.dim
            self.bc_shift = None
        else:
            bc = list(bc)  # we modify entries...
            self.bc_shift = np.zeros(self.dim - 1, np.int_)
            for i, bc_i in enumerate(bc):
                if isinstance(bc_i, int):
                    if i == 0:
                        raise ValueError("Invalid bc: first entry can't be a shift")
                    self.bc_shift[i - 1] = bc_i
                    bc[i] = bc_choices['periodic']
                else:
                    bc[i] = bc_choices[bc_i]
            if not np.any(self.bc_shift != 0):
                self.bc_shift = None
        self.bc = np.array(bc) 

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 testLinspaceExecution(self):
        a = linspace(2.0, 9.0, num=11, chunk_size=3)

        res = self.executor.execute_tensor(a, concat=True)[0]
        expected = np.linspace(2.0, 9.0, num=11)
        np.testing.assert_allclose(res, expected)

        a = linspace(2.0, 9.0, num=11, endpoint=False, chunk_size=3)

        res = self.executor.execute_tensor(a, concat=True)[0]
        expected = np.linspace(2.0, 9.0, num=11, endpoint=False)
        np.testing.assert_allclose(res, expected)

        a = linspace(2.0, 9.0, num=11, chunk_size=3, dtype=int)

        res = self.executor.execute_tensor(a, concat=True)[0]
        self.assertEqual(res.dtype, np.int_) 

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res = linalg.eigvalsh(a)
        assert_(res.dtype.type is np.float64)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(res, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res = linalg.eigvalsh(a)
        assert_(res.dtype.type is np.float32)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(res, np.ndarray)) 

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res, res_v = linalg.eig(a)
        assert_(res_v.dtype.type is np.float64)
        assert_(res.dtype.type is np.float64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res, res_v = linalg.eig(a)
        assert_(res_v.dtype.type is np.complex64)
        assert_(res.dtype.type is np.complex64)
        assert_equal(a.shape, res_v.shape)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(a, np.ndarray)) 

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        assert_(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        assert_(not allclose(a, b))
        b[0] = np.inf
        assert_(allclose(a, b))
        # Test allclose w/ masked
        a = masked_array(a)
        a[-1] = masked
        assert_(allclose(a, b, masked_equal=True))
        assert_(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        assert_(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        assert_(allclose(a, a)) 

def draw_lane_fit(undist, warped ,Minv, left_fitx, right_fitx, ploty):
	# Drawing
	# Create an image to draw the lines on
	warp_zero = np.zeros_like(warped).astype(np.uint8)
	color_warp = np.dstack((warp_zero, warp_zero, warp_zero))

	# Recast the x and y points into usable format for cv2.fillPoly()
	pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
	pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
	pts = np.hstack((pts_left, pts_right))

	# Draw the lane onto the warped blank image
	cv2.fillPoly(color_warp, np.int_([pts]), (0,255,0))

	# Warp the blank back to original image space using inverse perspective matrix(Minv)
	newwarp = cv2.warpPerspective(color_warp, Minv, (undist.shape[1], undist.shape[0]))
	# Combine the result with the original image
	result = cv2.addWeighted(undist, 1, newwarp, 0.3, 0)

	return result 

def test_allclose(self):
        # Tests allclose on arrays
        a = np.random.rand(10)
        b = a + np.random.rand(10) * 1e-8
        assert_(allclose(a, b))
        # Test allclose w/ infs
        a[0] = np.inf
        assert_(not allclose(a, b))
        b[0] = np.inf
        assert_(allclose(a, b))
        # Test allclose w/ masked
        a = masked_array(a)
        a[-1] = masked
        assert_(allclose(a, b, masked_equal=True))
        assert_(not allclose(a, b, masked_equal=False))
        # Test comparison w/ scalar
        a *= 1e-8
        a[0] = 0
        assert_(allclose(a, 0, masked_equal=True))

        # Test that the function works for MIN_INT integer typed arrays
        a = masked_array([np.iinfo(np.int_).min], dtype=np.int_)
        assert_(allclose(a, a)) 

def test_0_size(self):
        # Check that all kinds of 0-sized arrays work
        class ArraySubclass(np.ndarray):
            pass
        a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
        res = linalg.eigvals(a)
        assert_(res.dtype.type is np.float64)
        assert_equal((0, 1), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(res, np.ndarray))

        a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
        res = linalg.eigvals(a)
        assert_(res.dtype.type is np.complex64)
        assert_equal((0,), res.shape)
        # This is just for documentation, it might make sense to change:
        assert_(isinstance(res, np.ndarray)) 

def get_sum_dtype(dtype):
    """Mimic numpy's casting for np.sum"""
    if np.issubdtype(dtype, np.float_):
        return np.float_
    if dtype.kind == 'u' and np.can_cast(dtype, np.uint):
        return np.uint
    if np.can_cast(dtype, np.int_):
        return np.int_
    return dtype 

def test_intp(self,level=rlevel):
        # Ticket #99
        i_width = np.int_(0).nbytes*2 - 1
        np.intp('0x' + 'f'*i_width, 16)
        self.assertRaises(OverflowError, np.intp, '0x' + 'f'*(i_width+1), 16)
        self.assertRaises(ValueError, np.intp, '0x1', 32)
        assert_equal(255, np.intp('0xFF', 16))
        assert_equal(1024, np.intp(1024)) 

def testCreateSparseExecution(self):
        mat = sps.csr_matrix([[0, 0, 2], [2, 0, 0]])
        t = tensor(mat, dtype='f8', chunk_size=2)

        res = self.executor.execute_tensor(t)
        self.assertIsInstance(res[0], SparseNDArray)
        self.assertEqual(res[0].dtype, np.float64)
        np.testing.assert_array_equal(res[0].toarray(), mat[..., :2].toarray())
        np.testing.assert_array_equal(res[1].toarray(), mat[..., 2:].toarray())

        t2 = ones_like(t, dtype='f4')

        res = self.executor.execute_tensor(t2)
        expected = sps.csr_matrix([[0, 0, 1], [1, 0, 0]])
        self.assertIsInstance(res[0], SparseNDArray)
        self.assertEqual(res[0].dtype, np.float32)
        np.testing.assert_array_equal(res[0].toarray(), expected[..., :2].toarray())
        np.testing.assert_array_equal(res[1].toarray(), expected[..., 2:].toarray())

        t3 = tensor(np.array([[0, 0, 2], [2, 0, 0]]), chunk_size=2).tosparse()

        res = self.executor.execute_tensor(t3)
        self.assertIsInstance(res[0], SparseNDArray)
        self.assertEqual(res[0].dtype, np.int_)
        np.testing.assert_array_equal(res[0].toarray(), mat[..., :2].toarray())
        np.testing.assert_array_equal(res[1].toarray(), mat[..., 2:].toarray()) 

def test_out_dtype_error(self):
        for f in self.nanfuncs:
            for dtype in [np.bool_, np.int_, np.object_]:
                out = np.empty(_ndat.shape[0], dtype=dtype)
                assert_raises(TypeError, f, _ndat, axis=1, out=out) 

def test_dtype_error(self):
        for f in self.nanfuncs:
            for dtype in [np.bool_, np.int_, np.object_]:
                assert_raises(TypeError, f, _ndat, axis=1, dtype=dtype) 

def _rvs(self, low, high):
        """An array of *size* random integers >= ``low`` and < ``high``."""
        if self._size is not None:
            # Numpy's RandomState.randint() doesn't broadcast its arguments.
            # Use `broadcast_to()` to extend the shapes of low and high
            # up to self._size.  Then we can use the numpy.vectorize'd
            # randint without needing to pass it a `size` argument.
            low = broadcast_to(low, self._size)
            high = broadcast_to(high, self._size)
        randint = np.vectorize(self._random_state.randint, otypes=[np.int_])
        return randint(low, high) 

def find_successful_trajs(self):
        pcoord_formats = {'u8': '%20d',
                          'i8': '%20d',
                          'u4': '%10d',
                          'i4': '%11d',
                          'u2': '%5d',
                          'i2': '%6d',
                          'f4': '%14.7g',
                          'f8': '%23.15g'}
        
        if not self.output_suppress_headers:
            self.output_file.write('''\
# successful (recycled) segments 
# column 0:    iteration
# column 1:    seg_id
# column 2:    weight
# column>2:    final progress coordinate value
''')
        for n_iter in range(1, self.data_manager.current_iteration):
            seg_index = self.get_seg_index(n_iter)
            all_seg_ids = numpy.arange(len(seg_index), dtype=numpy.int_)
            recycled_seg_ids = all_seg_ids[seg_index[:]['endpoint_type'] == west.Segment.SEG_ENDPOINT_RECYCLED]

            if len(recycled_seg_ids) == 0:
                # Attemping to retrieve a 0-length selection from HDF5 (the pcoords below) fails
                continue
            
            pcoord_ds = self.get_pcoord_dataset(n_iter)
            pcoord_len = pcoord_ds.shape[1]
            pcoord_ndim = pcoord_ds.shape[2]
            final_pcoords = self.get_pcoord_dataset(n_iter)[recycled_seg_ids,pcoord_len-1,:]
            # The above HDF5 selection always returns a vector; we want a 2-d array
            final_pcoords.shape = (len(recycled_seg_ids),pcoord_ndim)
            
            for (ipc, seg_id) in enumerate(recycled_seg_ids):
                self.output_file.write('%8d    %8d    %20.14g' % (n_iter, seg_id, seg_index[seg_id]['weight']))
                fields = ['']
                for field in final_pcoords[ipc]:
                    fields.append(pcoord_formats.get(field.dtype.str[1:], '%s') % field)
                self.output_file.write('    '.join(fields))
                self.output_file.write('\n') 

def sparse_stats(self):
        """Returns a string detailing the sparse statistics."""
        total = np.prod(self.shape)
        if total == 0:
            return "Array without entries, one axis is empty."
        nblocks = self.stored_blocks
        stored = self.size
        nonzero = np.sum([np.count_nonzero(t) for t in self._data], dtype=np.int_)
        bs = np.array([t.size for t in self._data], dtype=np.float)
        if nblocks > 0:
            captsparse = float(nonzero) / stored
            bs_min = int(np.min(bs))
            bs_max = int(np.max(bs))
            bs_mean = np.sum(bs) / nblocks
            bs_med = np.median(bs)
            bs_var = np.var(bs)
        else:
            captsparse = 1.
            bs_min = bs_max = bs_mean = bs_med = bs_var = 0
        res = "{nonzero:d} of {total:d} entries (={nztotal:g}) nonzero,\n" \
            "stored in {nblocks:d} blocks with {stored:d} entries.\n" \
            "Captured sparsity: {captsparse:g}\n"  \
            "Block sizes min:{bs_min:d} mean:{bs_mean:.2f} median:{bs_med:.1f} " \
            "max:{bs_max:d} var:{bs_var:.2f}"

        return res.format(nonzero=nonzero,
                          total=total,
                          nztotal=nonzero / total,
                          nblocks=nblocks,
                          stored=stored,
                          captsparse=captsparse,
                          bs_min=bs_min,
                          bs_max=bs_max,
                          bs_mean=bs_mean,
                          bs_med=bs_med,
                          bs_var=bs_var)

    # accessing entries ======================================================= 

def test_casting(self):
        ints = np.array(range(10))
        self.assertEqual(ints.dtype, np.int_)
        floats = np.arange(0,5,.5)
        self.assertEqual(floats.dtype, np.float_)
        strings = np.array(list('testing'))
        self.assertEqual(strings.dtype.type, np.str_)

        self.assertEqual(ObsAr(ints).dtype, np.float_)
        self.assertEqual(ObsAr(floats).dtype, np.float_)
        self.assertEqual(ObsAr(strings).dtype.type, np.str_) 

def intarray_default_factory():
    import numpy as np
    return np.int_([])