def _read_ctr_ticks(
task_handle, high_tick, low_tick, num_samps_per_chan, timeout,
interleaved=FillMode.GROUP_BY_CHANNEL):
samps_per_chan_read = ctypes.c_int()
cfunc = lib_importer.windll.DAQmxReadCtrTicks
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_int, ctypes.c_double,
ctypes.c_int,
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
ctypes.c_uint, ctypes.POINTER(ctypes.c_int),
ctypes.POINTER(c_bool32)]
error_code = cfunc(
task_handle, num_samps_per_chan, timeout, interleaved.value,
high_tick, low_tick, numpy.prod(high_tick.shape),
ctypes.byref(samps_per_chan_read), None)
check_for_error(error_code)
return samps_per_chan_read.value
python类uint32()的实例源码
def run(im):
im_disp = im.copy()
window_name = "Draw line here."
cv2.namedWindow(window_name,cv2.WINDOW_AUTOSIZE)
cv2.moveWindow(window_name, 910, 0)
print " Drag across the screen to set lines.\n Do it twice"
print " After drawing the lines press 'r' to resume\n"
l1 = np.empty((2, 2), np.uint32)
l2 = np.empty((2, 2), np.uint32)
list = [l1,l2]
mouse_down = False
def callback(event, x, y, flags, param):
global trigger, mouse_down
if trigger<2:
if event == cv2.EVENT_LBUTTONDOWN:
mouse_down = True
list[trigger][0] = (x, y)
if event == cv2.EVENT_LBUTTONUP and mouse_down:
mouse_down = False
list[trigger][1] = (x,y)
cv2.line(im_disp, (list[trigger][0][0], list[trigger][0][1]),
(list[trigger][1][0], list[trigger][1][1]), (255, 0, 0), 2)
trigger += 1
else:
pass
cv2.setMouseCallback(window_name, callback)
while True:
cv2.imshow(window_name,im_disp)
key = cv2.waitKey(10) & 0xFF
if key == ord('r'):
# Press key `q` to quit the program
return list
exit()
def __init__(self, image, samplefac=10, colors=256):
# Check Numpy
if np is None:
raise RuntimeError("Need Numpy for the NeuQuant algorithm.")
# Check image
if image.size[0] * image.size[1] < NeuQuant.MAXPRIME:
raise IOError("Image is too small")
if image.mode != "RGBA":
raise IOError("Image mode should be RGBA.")
# Initialize
self.setconstants(samplefac, colors)
self.pixels = np.fromstring(image.tostring(), np.uint32)
self.setUpArrays()
self.learn()
self.fix()
self.inxbuild()
def laplace_gpu(y_gpu, mode='valid'):
shape = np.array(y_gpu.shape).astype(np.uint32)
dtype = y_gpu.dtype
block_size = (16,16,1)
grid_size = (int(np.ceil(float(shape[1])/block_size[0])),
int(np.ceil(float(shape[0])/block_size[1])))
shared_size = int((2+block_size[0])*(2+block_size[1])*dtype.itemsize)
preproc = _generate_preproc(dtype, shape)
mod = SourceModule(preproc + kernel_code, keep=True)
if mode == 'valid':
laplace_fun_gpu = mod.get_function("laplace_valid")
laplace_gpu = cua.empty((y_gpu.shape[0]-2, y_gpu.shape[1]-2), y_gpu.dtype)
if mode == 'same':
laplace_fun_gpu = mod.get_function("laplace_same")
laplace_gpu = cua.empty((y_gpu.shape[0], y_gpu.shape[1]), y_gpu.dtype)
laplace_fun_gpu(laplace_gpu.gpudata, y_gpu.gpudata,
block=block_size, grid=grid_size, shared=shared_size)
return laplace_gpu
def remove_artifacts(self, image):
"""
Remove the connected components that are not within the parameters
Operates in place
:param image: sudoku's thresholded image w/o grid
:return: None
"""
labeled, features = label(image, structure=CROSS)
lbls = np.arange(1, features + 1)
areas = extract_feature(image, labeled, lbls, np.sum,
np.uint32, 0)
sides = extract_feature(image, labeled, lbls, min_side,
np.float32, 0, True)
diags = extract_feature(image, labeled, lbls, diagonal,
np.float32, 0, True)
for index in lbls:
area = areas[index - 1] / 255
side = sides[index - 1]
diag = diags[index - 1]
if side < 5 or side > 20 \
or diag < 15 or diag > 25 \
or area < 40:
image[labeled == index] = 0
return None
def remove_artifacts(self, image):
"""
Remove the connected components that are not within the parameters
Operates in place
:param image: sudoku's thresholded image w/o grid
:return: None
"""
labeled, features = label(image, structure=CROSS)
lbls = np.arange(1, features + 1)
areas = extract_feature(image, labeled, lbls, np.sum,
np.uint32, 0)
sides = extract_feature(image, labeled, lbls, min_side,
np.float32, 0, True)
diags = extract_feature(image, labeled, lbls, diagonal,
np.float32, 0, True)
for index in lbls:
area = areas[index - 1] / 255
side = sides[index - 1]
diag = diags[index - 1]
if side < 5 or side > 20 \
or diag < 15 or diag > 25 \
or area < 40:
image[labeled == index] = 0
return None
def _write_binary_u_32(
task_handle, write_array, num_samps_per_chan, auto_start, timeout,
data_layout=FillMode.GROUP_BY_CHANNEL):
samps_per_chan_written = ctypes.c_int()
cfunc = lib_importer.windll.DAQmxWriteBinaryU32
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_int, c_bool32,
ctypes.c_double, ctypes.c_int,
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
ctypes.POINTER(ctypes.c_int), ctypes.POINTER(c_bool32)]
error_code = cfunc(
task_handle, num_samps_per_chan, auto_start, timeout,
data_layout.value, write_array,
ctypes.byref(samps_per_chan_written), None)
check_for_error(error_code)
return samps_per_chan_written.value
def _write_digital_u_32(
task_handle, write_array, num_samps_per_chan, auto_start, timeout,
data_layout=FillMode.GROUP_BY_CHANNEL):
samps_per_chan_written = ctypes.c_int()
cfunc = lib_importer.windll.DAQmxWriteDigitalU32
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_int, c_bool32,
ctypes.c_double, ctypes.c_int,
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
ctypes.POINTER(ctypes.c_int), ctypes.POINTER(c_bool32)]
error_code = cfunc(
task_handle, num_samps_per_chan, auto_start, timeout,
data_layout.value, write_array,
ctypes.byref(samps_per_chan_written), None)
check_for_error(error_code)
return samps_per_chan_written.value
def _read_binary_u_32(
task_handle, read_array, num_samps_per_chan, timeout,
fill_mode=FillMode.GROUP_BY_CHANNEL):
samps_per_chan_read = ctypes.c_int()
cfunc = lib_importer.windll.DAQmxReadBinaryU32
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_int, ctypes.c_double,
ctypes.c_int,
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
ctypes.c_uint, ctypes.POINTER(ctypes.c_int),
ctypes.POINTER(c_bool32)]
error_code = cfunc(
task_handle, num_samps_per_chan, timeout, fill_mode.value,
read_array, numpy.prod(read_array.shape),
ctypes.byref(samps_per_chan_read), None)
check_for_error(error_code)
return samps_per_chan_read.value
def _read_digital_u_32(
task_handle, read_array, num_samps_per_chan, timeout,
fill_mode=FillMode.GROUP_BY_CHANNEL):
samps_per_chan_read = ctypes.c_int()
cfunc = lib_importer.windll.DAQmxReadDigitalU32
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_int, ctypes.c_double,
ctypes.c_int,
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
ctypes.c_uint, ctypes.POINTER(ctypes.c_int),
ctypes.POINTER(c_bool32)]
error_code = cfunc(
task_handle, num_samps_per_chan, timeout, fill_mode.value,
read_array, numpy.prod(read_array.shape),
ctypes.byref(samps_per_chan_read), None)
check_for_error(error_code)
return samps_per_chan_read.value
def _read_counter_u_32(task_handle, read_array, num_samps_per_chan, timeout):
samps_per_chan_read = ctypes.c_int()
cfunc = lib_importer.windll.DAQmxReadCounterU32
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_int, ctypes.c_double,
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
ctypes.c_uint, ctypes.POINTER(ctypes.c_int),
ctypes.POINTER(c_bool32)]
error_code = cfunc(
task_handle, num_samps_per_chan, timeout,
read_array, numpy.prod(read_array.shape),
ctypes.byref(samps_per_chan_read), None)
check_for_error(error_code)
return samps_per_chan_read.value
def _read_counter_u_32_ex(
task_handle, read_array, num_samps_per_chan, timeout,
fill_mode=FillMode.GROUP_BY_CHANNEL):
samps_per_chan_read = ctypes.c_int()
cfunc = lib_importer.windll.DAQmxReadCounterU32Ex
if cfunc.argtypes is None:
with cfunc.arglock:
if cfunc.argtypes is None:
cfunc.argtypes = [
lib_importer.task_handle, ctypes.c_int, ctypes.c_double,
ctypes.c_int,
wrapped_ndpointer(dtype=numpy.uint32, flags=('C', 'W')),
ctypes.c_uint, ctypes.POINTER(ctypes.c_int),
ctypes.POINTER(c_bool32)]
error_code = cfunc(
task_handle, num_samps_per_chan, timeout, fill_mode.value,
read_array, numpy.prod(read_array.shape),
ctypes.byref(samps_per_chan_read), None)
check_for_error(error_code)
return samps_per_chan_read.value
def _get_dtype_maps():
""" Get dictionaries to map numpy data types to ITK types and the
other way around.
"""
# Define pairs
tmp = [ (np.float32, 'MET_FLOAT'), (np.float64, 'MET_DOUBLE'),
(np.uint8, 'MET_UCHAR'), (np.int8, 'MET_CHAR'),
(np.uint16, 'MET_USHORT'), (np.int16, 'MET_SHORT'),
(np.uint32, 'MET_UINT'), (np.int32, 'MET_INT'),
(np.uint64, 'MET_ULONG'), (np.int64, 'MET_LONG') ]
# Create dictionaries
map1, map2 = {}, {}
for np_type, itk_type in tmp:
map1[np_type.__name__] = itk_type
map2[itk_type] = np_type.__name__
# Done
return map1, map2
def report_barcodes_h5(self, filename):
data = self.report('barcodes')
if self.barcode_whitelist:
bc_sequences = cr_utils.format_barcode_seqs(self.barcode_whitelist, self.gem_groups)
elif self.barcode_summary is not None:
bc_sequences = self.barcode_summary
else:
# Get all observed bc sequences
bc_sequences = sorted(list(reduce(lambda x, y: x.union(y.keys()), data.values(), set())))
# Build the columns for the table
bc_table_cols = {cr_constants.H5_BC_SEQUENCE_COL: bc_sequences}
for metric_name, metric_data in data.iteritems():
counts = np.array([metric_data.get(bc, 0) for bc in bc_sequences], dtype=np.uint32)
bc_table_cols[metric_name] = counts
cr_utils.write_h5(filename, bc_table_cols)
def load(shape,vertex_array):
destination = vertex_array[gx.VA_PTNMTXIDX.name]
vertex_index = 0
matrix_table = numpy.zeros(10,numpy.uint32)
for batch in shape.batches:
source = numpy.concatenate([primitive.vertices[gx.VA_PTNMTXIDX.name] for primitive in batch.primitives])
source //= 3
for i,index in enumerate(batch.matrix_table):
if index == 0xFFFF: continue
matrix_table[i] = index
length = sum(len(primitive.vertices) for primitive in batch.primitives)
numpy.take(matrix_table,source,0,destination[vertex_index:vertex_index + length])
vertex_index += length
glEnableVertexAttribArray(MATRIX_INDEX_ATTRIBUTE_LOCATION)
vertex_type = vertex_array.dtype
stride = vertex_type.itemsize
offset = vertex_type.fields[gx.VA_PTNMTXIDX.name][1]
glVertexAttribIPointer(MATRIX_INDEX_ATTRIBUTE_LOCATION,1,GL_UNSIGNED_INT,stride,GLvoidp(offset))
def __init__(self, *args, **kwds):
import numpy
self.dst_types = [numpy.uint8, numpy.uint16, numpy.uint32]
try:
self.dst_types.append(numpy.uint64)
except AttributeError:
pass
pygame.display.init()
try:
unittest.TestCase.__init__(self, *args, **kwds)
self.sources = [self._make_src_surface(8),
self._make_src_surface(16),
self._make_src_surface(16, srcalpha=True),
self._make_src_surface(24),
self._make_src_surface(32),
self._make_src_surface(32, srcalpha=True)]
finally:
pygame.display.quit()
def _check_valid_data(self, data):
"""Checks that the incoming data is a 2 x #elements ndarray of ints.
Parameters
----------
data : :obj:`numpy.ndarray`
The data to verify.
Raises
------
ValueError
If the data is not of the correct shape or type.
"""
if data.dtype.type != np.int8 and data.dtype.type != np.int16 \
and data.dtype.type != np.int32 and data.dtype.type != np.int64 \
and data.dtype.type != np.uint8 and data.dtype.type != np.uint16 \
and data.dtype.type != np.uint32 and data.dtype.type != np.uint64:
raise ValueError('Must initialize image coords with a numpy int ndarray')
if data.shape[0] != 2:
raise ValueError('Illegal data array passed to image coords. Must have 2 coordinates')
if len(data.shape) > 2:
raise ValueError('Illegal data array passed to point cloud. Must have 1 or 2 dimensions')
def to_best_type(array):
'''Convert array to lowest possible bitrate.
'''
ui8 = np.iinfo(np.uint8)
ui8 = ui8.max
ui16 = np.iinfo(np.uint16)
ui16 = ui16.max
ui32 = np.iinfo(np.uint32)
ui32 = ui32.max
ui64 = np.iinfo(np.uint64)
ui64 = ui64.max
if array.max() <= ui64:
new_type = np.uint64
if array.max() <= ui32:
new_type = np.uint32
if array.max() <= ui16:
new_type = np.uint16
if array.max() <= ui8:
new_type = np.uint8
return array.astype(new_type)
def read_uncompressed_patch(pcpatch_wkb, schema):
'''
Patch binary structure uncompressed:
byte: endianness (1 = NDR, 0 = XDR)
uint32: pcid (key to POINTCLOUD_SCHEMAS)
uint32: 0 = no compression
uint32: npoints
pointdata[]: interpret relative to pcid
'''
patchbin = unhexlify(pcpatch_wkb)
npoints = unpack("I", patchbin[9:13])[0]
dt = schema_dtype(schema)
patch = np.fromstring(patchbin[13:], dtype=dt)
# debug
# print(patch[:10])
return patch, npoints
def __init__(self, image, samplefac=10, colors=256):
# Check Numpy
if np is None:
raise RuntimeError("Need Numpy for the NeuQuant algorithm.")
# Check image
if image.size[0] * image.size[1] < NeuQuant.MAXPRIME:
raise IOError("Image is too small")
if image.mode != "RGBA":
raise IOError("Image mode should be RGBA.")
# Initialize
self.setconstants(samplefac, colors)
self.pixels = np.fromstring(image.tostring(), np.uint32)
self.setUpArrays()
self.learn()
self.fix()
self.inxbuild()
def __init__(self, queue, grid_size, program_buffer, block_sizes):
self.queue = queue
self.grid_size = grid_size
self.program_buffer = program_buffer
self.block_sizes = block_sizes
self.integral_one = 0
self.integral_x = 0
self.integral_y = 0
self.integral_z = 0
self.integral_xx = 0
self.integral_yy = 0
self.integral_zz = 0
self.integral_xy = 0
self.integral_xz = 0
self.integral_yz = 0
self.index_sums = cl_util.Buffer(queue, numpy.uint32, 10, pyopencl.mem_flags.READ_WRITE)
self.counter = cl_util.Buffer(queue, numpy.uint32, 1, pyopencl.mem_flags.READ_WRITE)
self.list = cl_util.Buffer(queue, cl_util.Buffer.quad_dtype(numpy.uint8), grid_size * grid_size * grid_size, pyopencl.mem_flags.WRITE_ONLY)
self.box_corner = None
self.level = None
def __init__(self, queue, grid_size, program_buffer, block_sizes):
self.queue = queue
self.grid_size = grid_size
self.program_buffer = program_buffer
self.block_sizes = block_sizes
self.integral_one = 0
self.integral_x = 0
self.integral_y = 0
self.integral_z = 0
self.integral_xx = 0
self.integral_yy = 0
self.integral_zz = 0
self.integral_xy = 0
self.integral_xz = 0
self.integral_yz = 0
self.index_sums = cl_util.Buffer(queue, numpy.uint32, 10, pyopencl.mem_flags.READ_WRITE)
self.counter = cl_util.Buffer(queue, numpy.uint32, 1, pyopencl.mem_flags.READ_WRITE)
self.list = cl_util.Buffer(queue, cl_util.Buffer.quad_dtype(numpy.uint8), grid_size * grid_size * grid_size, pyopencl.mem_flags.WRITE_ONLY)
self.box_corner = None
self.level = None
def generate_trainig_data(self, num_points):
'''
Generate training dataset. Produce random (integer) sequences X, and corresponding
expected output sequences Y = generate_output_sequence(X).
Return xy_data, y_data (both of type uint32)
xy_data = numpy array of shape [num_points, in_seq_len + out_seq_len], with each point being X + Y
y_data = numpy array of shape [num_points, out_seq_len]
'''
x_data = np.random.randint(0, self.in_max_int, size=(num_points, self.in_seq_len)) # shape [num_points, in_seq_len]
x_data = x_data.astype(np.uint32) # ensure integer type
y_data = [ self.sequence_pattern.generate_output_sequence(x) for x in x_data ]
y_data = np.array(y_data)
xy_data = np.append(x_data, y_data, axis=1) # shape [num_points, 2*seq_len]
return xy_data, y_data
def test_int(self):
for st, ut, s in [(np.int8, np.uint8, 8),
(np.int16, np.uint16, 16),
(np.int32, np.uint32, 32),
(np.int64, np.uint64, 64)]:
for i in range(1, s):
assert_equal(hash(st(-2**i)), hash(-2**i),
err_msg="%r: -2**%d" % (st, i))
assert_equal(hash(st(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (st, i - 1))
assert_equal(hash(st(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (st, i))
i = max(i - 1, 1)
assert_equal(hash(ut(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (ut, i - 1))
assert_equal(hash(ut(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (ut, i))
def test_prod(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, a.prod)
self.assertRaises(ArithmeticError, a2.prod, axis=1)
else:
assert_equal(a.prod(axis=0), 26400)
assert_array_equal(a2.prod(axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(a2.prod(axis=-1),
np.array([24, 1890, 600], ctype))
def test_dtype_mix(self):
c = np.array([False, True, False, False, False, False, True, False,
False, False, True, False])
a = np.uint32(1)
b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
dtype=np.float64)
r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
dtype=np.float64)
assert_equal(np.where(c, a, b), r)
a = a.astype(np.float32)
b = b.astype(np.int64)
assert_equal(np.where(c, a, b), r)
# non bool mask
c = c.astype(np.int)
c[c != 0] = 34242324
assert_equal(np.where(c, a, b), r)
# invert
tmpmask = c != 0
c[c == 0] = 41247212
c[tmpmask] = 0
assert_equal(np.where(c, b, a), r)
def test_setdiff1d(self):
a = np.array([6, 5, 4, 7, 1, 2, 7, 4])
b = np.array([2, 4, 3, 3, 2, 1, 5])
ec = np.array([6, 7])
c = setdiff1d(a, b)
assert_array_equal(c, ec)
a = np.arange(21)
b = np.arange(19)
ec = np.array([19, 20])
c = setdiff1d(a, b)
assert_array_equal(c, ec)
assert_array_equal([], setdiff1d([], []))
a = np.array((), np.uint32)
assert_equal(setdiff1d(a, []).dtype, np.uint32)
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int8, np.uint8, np.int16, np.uint16, np.int32,
np.uint32, np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
tgt = np.array([1, 3, 13, 24, 30, 35, 39], ctype)
assert_array_equal(np.cumsum(a, axis=0), tgt)
tgt = np.array(
[[1, 2, 3, 4], [6, 8, 10, 13], [16, 11, 14, 18]], ctype)
assert_array_equal(np.cumsum(a2, axis=0), tgt)
tgt = np.array(
[[1, 3, 6, 10], [5, 11, 18, 27], [10, 13, 17, 22]], ctype)
assert_array_equal(np.cumsum(a2, axis=1), tgt)
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, np.prod, a)
self.assertRaises(ArithmeticError, np.prod, a2, 1)
else:
assert_equal(a.prod(axis=0), 26400)
assert_array_equal(a2.prod(axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(a2.prod(axis=-1),
np.array([24, 1890, 600], ctype))
def test_basic(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
self.assertRaises(ArithmeticError, np.cumprod, a)
self.assertRaises(ArithmeticError, np.cumprod, a2, 1)
self.assertRaises(ArithmeticError, np.cumprod, a)
else:
assert_array_equal(np.cumprod(a, axis=-1),
np.array([1, 2, 20, 220,
1320, 6600, 26400], ctype))
assert_array_equal(np.cumprod(a2, axis=0),
np.array([[1, 2, 3, 4],
[5, 12, 21, 36],
[50, 36, 84, 180]], ctype))
assert_array_equal(np.cumprod(a2, axis=-1),
np.array([[1, 2, 6, 24],
[5, 30, 210, 1890],
[10, 30, 120, 600]], ctype))
