def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
python类min_scalar_type()的实例源码
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
def deposit(self, frequencies): # TODO: adjust signature with UniversalData
frequencies = np.array(frequencies, dtype=self.composition_type) # freqencies must not exceed 4,294,967,295
maxtype = np.min_scalar_type(np.max(frequencies))
row = frequencies.astype(maxtype) # TODO: support sparse features
self._frequencies.append(row)
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
def render(self):
# Convert data to QImage for display.
profile = debug.Profiler()
if self.image is None or self.image.size == 0:
return
if isinstance(self.lut, collections.Callable):
lut = self.lut(self.image)
else:
lut = self.lut
if self.autoDownsample:
# reduce dimensions of image based on screen resolution
o = self.mapToDevice(QtCore.QPointF(0,0))
x = self.mapToDevice(QtCore.QPointF(1,0))
y = self.mapToDevice(QtCore.QPointF(0,1))
w = Point(x-o).length()
h = Point(y-o).length()
if w == 0 or h == 0:
self.qimage = None
return
xds = max(1, int(1.0 / w))
yds = max(1, int(1.0 / h))
axes = [1, 0] if self.axisOrder == 'row-major' else [0, 1]
image = fn.downsample(self.image, xds, axis=axes[0])
image = fn.downsample(image, yds, axis=axes[1])
self._lastDownsample = (xds, yds)
else:
image = self.image
# if the image data is a small int, then we can combine levels + lut
# into a single lut for better performance
levels = self.levels
if levels is not None and levels.ndim == 1 and image.dtype in (np.ubyte, np.uint16):
if self._effectiveLut is None:
eflsize = 2**(image.itemsize*8)
ind = np.arange(eflsize)
minlev, maxlev = levels
levdiff = maxlev - minlev
levdiff = 1 if levdiff == 0 else levdiff # don't allow division by 0
if lut is None:
efflut = fn.rescaleData(ind, scale=255./levdiff,
offset=minlev, dtype=np.ubyte)
else:
lutdtype = np.min_scalar_type(lut.shape[0]-1)
efflut = fn.rescaleData(ind, scale=(lut.shape[0]-1)/levdiff,
offset=minlev, dtype=lutdtype, clip=(0, lut.shape[0]-1))
efflut = lut[efflut]
self._effectiveLut = efflut
lut = self._effectiveLut
levels = None
# Assume images are in column-major order for backward compatibility
# (most images are in row-major order)
if self.axisOrder == 'col-major':
image = image.transpose((1, 0, 2)[:image.ndim])
argb, alpha = fn.makeARGB(image, lut=lut, levels=levels)
self.qimage = fn.makeQImage(argb, alpha, transpose=False)
def render(self):
# Convert data to QImage for display.
profile = debug.Profiler()
if self.image is None or self.image.size == 0:
return
if isinstance(self.lut, collections.Callable):
lut = self.lut(self.image)
else:
lut = self.lut
if self.autoDownsample:
# reduce dimensions of image based on screen resolution
o = self.mapToDevice(QtCore.QPointF(0,0))
x = self.mapToDevice(QtCore.QPointF(1,0))
y = self.mapToDevice(QtCore.QPointF(0,1))
w = Point(x-o).length()
h = Point(y-o).length()
if w == 0 or h == 0:
self.qimage = None
return
xds = max(1, int(1.0 / w))
yds = max(1, int(1.0 / h))
axes = [1, 0] if self.axisOrder == 'row-major' else [0, 1]
image = fn.downsample(self.image, xds, axis=axes[0])
image = fn.downsample(image, yds, axis=axes[1])
self._lastDownsample = (xds, yds)
else:
image = self.image
# if the image data is a small int, then we can combine levels + lut
# into a single lut for better performance
levels = self.levels
if levels is not None and levels.ndim == 1 and image.dtype in (np.ubyte, np.uint16):
if self._effectiveLut is None:
eflsize = 2**(image.itemsize*8)
ind = np.arange(eflsize)
minlev, maxlev = levels
levdiff = maxlev - minlev
levdiff = 1 if levdiff == 0 else levdiff # don't allow division by 0
if lut is None:
efflut = fn.rescaleData(ind, scale=255./levdiff,
offset=minlev, dtype=np.ubyte)
else:
lutdtype = np.min_scalar_type(lut.shape[0]-1)
efflut = fn.rescaleData(ind, scale=(lut.shape[0]-1)/levdiff,
offset=minlev, dtype=lutdtype, clip=(0, lut.shape[0]-1))
efflut = lut[efflut]
self._effectiveLut = efflut
lut = self._effectiveLut
levels = None
# Assume images are in column-major order for backward compatibility
# (most images are in row-major order)
if self.axisOrder == 'col-major':
image = image.transpose((1, 0, 2)[:image.ndim])
argb, alpha = fn.makeARGB(image, lut=lut, levels=levels)
self.qimage = fn.makeQImage(argb, alpha, transpose=False)
def __init__(self, coords, data=None, shape=None, has_duplicates=True,
sorted=False, cache=False):
self._cache = None
if cache:
self.enable_caching()
if data is None:
# {(i, j, k): x, (i, j, k): y, ...}
if isinstance(coords, dict):
coords = list(coords.items())
has_duplicates = False
if isinstance(coords, np.ndarray):
result = COO.from_numpy(coords)
self.coords = result.coords
self.data = result.data
self.has_duplicates = result.has_duplicates
self.sorted = result.sorted
self.shape = result.shape
return
# []
if not coords:
data = []
coords = []
# [((i, j, k), value), (i, j, k), value), ...]
elif isinstance(coords[0][0], Iterable):
if coords:
assert len(coords[0]) == 2
data = [x[1] for x in coords]
coords = [x[0] for x in coords]
coords = np.asarray(coords).T
# (data, (row, col, slab, ...))
else:
data = coords[0]
coords = np.stack(coords[1], axis=0)
self.data = np.asarray(data)
self.coords = np.asarray(coords)
if self.coords.ndim == 1:
self.coords = self.coords[None, :]
if shape and not np.prod(self.coords.shape):
self.coords = np.zeros((len(shape), 0), dtype=np.uint64)
if shape is None:
if self.coords.nbytes:
shape = tuple((self.coords.max(axis=1) + 1).tolist())
else:
shape = ()
self.shape = tuple(shape)
if self.shape:
dtype = np.min_scalar_type(max(self.shape))
else:
dtype = np.int_
self.coords = self.coords.astype(dtype)
assert not self.shape or len(data) == self.coords.shape[1]
self.has_duplicates = has_duplicates
self.sorted = sorted
def _get_expanded_coords_data(coords, data, params, broadcast_shape):
"""
Expand coordinates/data to broadcast_shape. Does most of the heavy lifting for broadcast_to.
Produces sorted output for sorted inputs.
Parameters
----------
coords : np.ndarray
The coordinates to expand.
data : np.ndarray
The data corresponding to the coordinates.
params : list
The broadcast parameters.
broadcast_shape : tuple[int]
The shape to broadcast to.
Returns
-------
expanded_coords : np.ndarray
List of 1-D arrays. Each item in the list has one dimension of coordinates.
expanded_data : np.ndarray
The data corresponding to expanded_coords.
"""
first_dim = -1
expand_shapes = []
for d, p, l in zip(range(len(broadcast_shape)), params, broadcast_shape):
if p and first_dim == -1:
expand_shapes.append(coords.shape[1])
first_dim = d
if not p:
expand_shapes.append(l)
all_idx = COO._cartesian_product(*(np.arange(d, dtype=np.min_scalar_type(d - 1)) for d in expand_shapes))
dt = np.result_type(*(np.min_scalar_type(l - 1) for l in broadcast_shape))
false_dim = 0
dim = 0
expanded_coords = np.empty((len(broadcast_shape), all_idx.shape[1]), dtype=dt)
expanded_data = data[all_idx[first_dim]]
for d, p, l in zip(range(len(broadcast_shape)), params, broadcast_shape):
if p:
expanded_coords[d] = coords[dim, all_idx[first_dim]]
else:
expanded_coords[d] = all_idx[false_dim + (d > first_dim)]
false_dim += 1
if p is not None:
dim += 1
return np.asarray(expanded_coords), np.asarray(expanded_data)
# (c) senderle
# Taken from https://stackoverflow.com/a/11146645/774273
# License: https://creativecommons.org/licenses/by-sa/3.0/
