def run_test_matmul_aa_correlator_kernel(self, ntime, nstand, nchan, misalign=0):
x_shape = (ntime, nchan, nstand*2)
perm = [1,0,2]
x8 = ((np.random.random(size=x_shape+(2,))*2-1)*127).astype(np.int8)
x = x8.astype(np.float32).view(np.complex64).reshape(x_shape)
x = x.transpose(perm)
x = x[..., misalign:]
b_gold = np.matmul(H(x), x)
triu = np.triu_indices(x.shape[-1], 1)
b_gold[..., triu[0], triu[1]] = 0
x = x8.view(bf.DataType.ci8).reshape(x_shape)
x = bf.asarray(x, space='cuda')
x = x.transpose(perm)
x = x[..., misalign:]
b = bf.zeros_like(b_gold, space='cuda')
self.linalg.matmul(1, None, x, 0, b)
b = b.copy('system')
np.testing.assert_allclose(b, b_gold, RTOL*10, ATOL)
python类int8()的实例源码
def test_quantize_from_probs2(size, resolution):
set_random_seed(make_seed(size, resolution))
probs = np.exp(np.random.random(size)).astype(np.float32)
probs2 = probs.reshape((1, size))
quantized = quantize_from_probs2(probs2, resolution)
assert quantized.shape == probs2.shape
assert quantized.dtype == np.int8
assert np.all(quantized.sum(axis=1) == resolution)
# Check that quantized result is closer to target than any other value.
quantized = quantized.reshape((size, ))
target = resolution * probs / probs.sum()
distance = np.abs(quantized - target).sum()
for combo in itertools.combinations(range(size), resolution):
other = np.zeros(size, np.int8)
for i in combo:
other[i] += 1
assert other.sum() == resolution
other_distance = np.abs(other - target).sum()
assert distance <= other_distance
def test_server_logprob_normalized(N, V, C, M):
model = generate_fake_model(N, V, C, M)
config = TINY_CONFIG.copy()
config['model_num_clusters'] = M
model['config'] = config
server = TreeCatServer(model)
# The total probability of all categorical rows should be 1.
ragged_index = model['suffstats']['ragged_index']
factors = []
for v in range(V):
C = ragged_index[v + 1] - ragged_index[v]
factors.append([one_hot(c, C) for c in range(C)])
data = np.array(
[np.concatenate(columns) for columns in itertools.product(*factors)],
dtype=np.int8)
logprobs = server.logprob(data)
logtotal = np.logaddexp.reduce(logprobs)
assert logtotal == pytest.approx(0.0, abs=1e-5)
def test_server_median(N, V, C, M):
model = generate_fake_model(N, V, C, M)
config = TINY_CONFIG.copy()
config['model_num_clusters'] = M
model['config'] = config
server = TreeCatServer(model)
# Evaluate on random data.
counts = np.random.randint(10, size=[V], dtype=np.int8)
table = generate_dataset(N, V, C)['table']
median = server.median(counts, table.data)
assert median.shape == table.data.shape
assert median.dtype == np.int8
for v in range(V):
beg, end = table.ragged_index[v:v + 2]
totals = median[:, beg:end].sum(axis=1)
assert np.all(totals == counts[v])
def observed_perplexity(self, counts):
"""Compute perplexity = exp(entropy) of observed variables.
Perplexity is an information theoretic measure of the number of
clusters or latent classes. Perplexity is a real number in the range
[1, M], where M is model_num_clusters.
Args:
counts: A [V]-shaped array of multinomial counts.
Returns:
A [V]-shaped numpy array of perplexity.
"""
V, E, M, R = self._VEMR
if counts is not None:
counts = np.ones(V, dtype=np.int8)
assert counts.shape == (V, )
assert counts.dtype == np.int8
assert np.all(counts > 0)
observed_entropy = np.empty(V, dtype=np.float32)
for v in range(V):
beg, end = self._ragged_index[v:v + 2]
probs = np.dot(self._feat_cond[beg:end, :], self._vert_probs[v, :])
observed_entropy[v] = multinomial_entropy(probs, counts[v])
return np.exp(observed_entropy)
def quantize_from_probs2(probs, resolution):
"""Quantize multiple non-normalized probs to given resolution.
Args:
probs: An [N, M]-shaped numpy array of non-normalized probabilities.
Returns:
An [N, M]-shaped array of quantized probabilities such that
np.all(result.sum(axis=1) == resolution).
"""
assert len(probs.shape) == 2
N, M = probs.shape
probs = probs / probs.sum(axis=1, keepdims=True)
result = np.zeros(probs.shape, np.int8)
range_N = np.arange(N, dtype=np.int32)
for _ in range(resolution):
sample = probs.argmax(axis=1)
result[range_N, sample] += 1
probs[range_N, sample] -= 1.0 / resolution
return result
def count_observations(ragged_index, data):
"""Count the observations in each cell of a ragged data array.
Args:
ragged_index: A [V+1]-shaped numpy array as returned by
make_ragged_index.
data: A [N, R]-shaped ragged array of multinomial count data, where
N is the number of rows and R = ragged_index[-1].
Returns:
A [N, V]-shaped array whose entries are the number of observations
in each cell of data.
"""
N, R = data.shape
assert R == ragged_index[-1]
V = len(ragged_index) - 1
counts = np.zeros([N, V], np.int8)
for v in range(V):
beg, end = ragged_index[v:v + 2]
counts[:, v] = data[:, beg:end].sum(axis=1)
return counts
def __getitem__(self, index):
img_name = self.files[self.split][index]
img_path = self.root + '/' + self.split + '/' + img_name
lbl_path = self.root + '/' + self.split + 'annot/' + img_name
img = m.imread(img_path)
img = np.array(img, dtype=np.uint8)
lbl = m.imread(lbl_path)
lbl = np.array(lbl, dtype=np.int8)
if self.augmentations is not None:
img, lbl = self.augmentations(img, lbl)
if self.is_transform:
img, lbl = self.transform(img, lbl)
return img, lbl
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 flip_code(code):
if isinstance(code, (numpy.dtype,type)):
# since several things map to complex64 we must carefully select
# the opposite that is an exact match (ticket 1518)
if code == numpy.int8:
return gdalconst.GDT_Byte
if code == numpy.complex64:
return gdalconst.GDT_CFloat32
for key, value in codes.items():
if value == code:
return key
return None
else:
try:
return codes[code]
except KeyError:
return None
def UnHidePdfintoPng(img,x,y):
counterx = 0
countery = 0
h , w , c = img.shape
data = []
while counterx <= x:
temp1 = (img[counterx][countery][0] & 0x03) << 6;
temp2 = (img[counterx][countery][ 1] & 0x03) << 4
temp3 = (img[counterx][countery ][2] & 0x03) << 2
temp4 = (img[counterx][countery ][3] & 0x03)
data.append(temp1 | temp2 | temp3 | temp4)
if counterx == x and countery == y:
#print ('EOF Found')
break
if(countery == w - 1):
countery = 0
counterx += 1
else:
countery += 1
data = np.int8(data)
return data
def inc_region(self, dst, y, x, h, w):
'''Incremets dst in the specified region. Runs fastest on np.int8, but not much slower on
np.int16.'''
dh, dw = dst.shape
h2 = h // 2
w2 = w // 2
py = y - h2
px = x - w2
y_min = max(0, py)
y_max = min(dh, y + h2)
x_min = max(0, px)
x_max = min(dw, x + w2)
if y_max - y_min <= 0 or x_max - x_min <= 0:
return
dst[y_min:y_max, x_min:x_max] += 1
def __read_spike_fixed(self, numpts=40):
"""
Read a spike with a fixed waveform length (40 time bins)
-------------------------------------------
Returns the time, waveform and trig2 value.
The returned objects must be converted to a SpikeTrain then
added to the Block.
ID: 29079
"""
# float32 -- spike time stamp in ms since start of SpikeTrain
time = np.fromfile(self._fsrc, dtype=np.float32, count=1)
# int8 * 40 -- spike shape -- use numpts for spike_var
waveform = np.fromfile(self._fsrc, dtype=np.int8,
count=numpts).reshape(1, 1, numpts)
# uint8 -- point of return to noise
trig2 = np.fromfile(self._fsrc, dtype=np.uint8, count=1)
return time, waveform, trig2
def __read_spike_fixed(self, numpts=40):
"""
Read a spike with a fixed waveform length (40 time bins)
-------------------------------------------
Returns the time, waveform and trig2 value.
The returned objects must be converted to a SpikeTrain then
added to the Block.
ID: 29079
"""
# float32 -- spike time stamp in ms since start of SpikeTrain
time = np.fromfile(self._fsrc, dtype=np.float32, count=1)
# int8 * 40 -- spike shape -- use numpts for spike_var
waveform = np.fromfile(self._fsrc, dtype=np.int8,
count=numpts).reshape(1, 1, numpts)
# uint8 -- point of return to noise
trig2 = np.fromfile(self._fsrc, dtype=np.uint8, count=1)
return time, waveform, trig2
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 _gene_signature(self,wm,size,key):
'''????????????????????????'''
wm = cv2.resize(wm,(size,size))
wU,_,wV = np.linalg.svd(np.mat(wm))
sumU = np.sum(np.array(wU),axis=0)
sumV = np.sum(np.array(wV),axis=0)
sumU_mid = np.median(sumU)
sumV_mid = np.median(sumV)
sumU=np.array([1 if sumU[i] >sumU_mid else 0 for i in range(len(sumU)) ])
sumV=np.array([1 if sumV[i] >sumV_mid else 0 for i in range(len(sumV)) ])
uv_xor=np.logical_xor(sumU,sumV)
np.random.seed(key)
seq=np.random.randint(2,size=len(uv_xor))
signature = np.logical_xor(uv_xor, seq)
sqrts = int(np.sqrt(size))
return np.array(signature,dtype=np.int8).reshape((sqrts,sqrts))
def _gene_signature(self,wm,key):
'''????????????????????????'''
wm = cv2.resize(wm,(256,256))
wU,_,wV = np.linalg.svd(np.mat(wm))
sumU = np.sum(np.array(wU),axis=0)
sumV = np.sum(np.array(wV),axis=0)
sumU_mid = np.median(sumU)
sumV_mid = np.median(sumV)
sumU=np.array([1 if sumU[i] >sumU_mid else 0 for i in range(len(sumU)) ])
sumV=np.array([1 if sumV[i] >sumV_mid else 0 for i in range(len(sumV)) ])
uv_xor=np.logical_xor(sumU,sumV)
np.random.seed(key)
seq=np.random.randint(2,size=len(uv_xor))
signature = np.logical_xor(uv_xor, seq)
return np.array(signature,dtype=np.int8)
def _gene_signature(self,wU,wV,key):
'''????????????????????????'''
sumU = np.sum(wU,axis=0)
sumV = np.sum(wV,axis=0)
sumU_mid = np.median(sumU)
sumV_mid = np.median(sumV)
sumU=np.array([1 if sumU[i] >sumU_mid else 0 for i in range(len(sumU)) ])
sumV=np.array([1 if sumV[i] >sumV_mid else 0 for i in range(len(sumV)) ])
uv_xor=np.logical_xor(sumU,sumV)
np.random.seed(key)
seq=np.random.randint(2,size=len(uv_xor))
signature = np.logical_xor(uv_xor, seq)
return np.array(signature,dtype=np.int8)
def test_bin_counts(self):
metadata2 = {'cellType': 'int32ud-500',
'extent': self.extent,
'crs': '+proj=longlat +datum=WGS84 +no_defs ',
'bounds': {
'minKey': {'col': 0, 'row': 0},
'maxKey': {'col': 0, 'row': 0}},
'layoutDefinition': {
'extent': self.extent,
'tileLayout': {'tileCols': 4, 'tileRows': 4, 'layoutCols': 1, 'layoutRows': 1}}}
arr2 = np.int8([[[1, 1, 1, 1],
[3, 1, 1, 1],
[4, 3, 1, 1],
[5, 4, 3, 1]]])
tile2 = Tile(arr2, 'INT', -500)
rdd2 = BaseTestClass.pysc.parallelize([(self.spatial_key, tile2)])
tiled2 = TiledRasterLayer.from_numpy_rdd(LayerType.SPATIAL, rdd2,
metadata2)
hist2 = tiled2.get_class_histogram()
bin_counts = hist2.bin_counts()
self.assertEqual(bin_counts, [(1, 10), (3, 3), (4, 2), (5, 1)])
def load_texture(image_file, repeat=False):
"""Carga una textura desde un archivo image_file"""
img = Image.open(image_file)
data = numpy.array(list(img.getdata()), numpy.int8)
tex = glGenTextures(1)
glPixelStorei(GL_UNPACK_ALIGNMENT, 1)
glBindTexture(GL_TEXTURE_2D, tex)
if repeat:
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_REPEAT)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_REPEAT)
else:
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)
glTexParameterf(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGB, img.size[0], img.size[1], 0, GL_RGB, GL_UNSIGNED_BYTE, data)
glTexEnvf(GL_TEXTURE_ENV, GL_TEXTURE_ENV_MODE, GL_MODULATE)
return tex
def wavWrite(y, fs, nbits, audioFile):
""" Write samples to WAV file
Args:
samples: (ndarray / 2D ndarray) (floating point) sample vector
mono: DIM: nSamples
stereo: DIM: nSamples x nChannels
fs: (int) Sample rate in Hz
nBits: (int) Number of bits
fnWAV: (string) WAV file name to write
"""
if nbits == 8:
intsamples = (y+1.0) * AudioIO.normFact['int' + str(nbits)]
fX = np.int8(intsamples)
elif nbits == 16:
intsamples = y * AudioIO.normFact['int' + str(nbits)]
fX = np.int16(intsamples)
elif nbits > 16:
fX = y
write(audioFile, fs, fX)
def test_copy_from(self):
a = DynamicArray(0, numpy.dtype([
('a', numpy.int8),
('b', numpy.float32),
('c', numpy.float64),
]))
a.extend([
(4, 3.932, 902.345),
(7, 1.016, 548.229),
(2, 0.542, 771.031),
(8, 5.429, 858.063),
])
b = DynamicArray(0, numpy.dtype([
('a', numpy.int8),
('c', numpy.float64),
]))
b.copy_from(a.data)
self.assertEqual(len(b), 4)
self.assertEqual(b.data.tolist(), [(4, 902.345), (7, 548.229), (2, 771.031), (8, 858.063),])
def test_resize(self):
a = DynamicArray(0, numpy.dtype(numpy.int8))
a.extend([0, 1, 4, 9])
self.assertEqual(len(a), 4)
self.assertEqual(len(a.data), 4)
self.assertEqual(a.data.tolist(), [0, 1, 4, 9])
a.resize(2)
self.assertEqual(len(a), 2)
self.assertEqual(len(a.data), 2)
self.assertEqual(a.data.tolist(), [0, 1])
request = a.capacity * 2
a.resize(request)
self.assertEqual(len(a), request)
self.assertEqual(len(a.data), request)
self.assertGreaterEqual(a.capacity, request)
self.assertEqual(a.data.tolist()[:2], [0, 1])
def test_append(self):
b = OrderedBuffer(3, numpy.dtype(numpy.int8))
data = b.append(9)
self.assertEqual(data, 9)
self.assertIsInstance(data, numpy.int8)
self.assertEqual(len(b), 1)
self.assertEqual([chunk.tolist() for chunk in b.chunks], [[9]])
data = b.append(1)
self.assertIsInstance(data, numpy.int8)
self.assertEqual(len(b), 2)
self.assertEqual([chunk.tolist() for chunk in b.chunks], [[9, 1]])
data = b.append(4)
self.assertIsInstance(data, numpy.int8)
self.assertEqual(len(b), 3)
self.assertEqual([chunk.tolist() for chunk in b.chunks], [[9, 1, 4]])
data = b.append(0)
self.assertIsInstance(data, numpy.int8)
self.assertEqual(len(b), 4)
self.assertEqual([chunk.tolist() for chunk in b.chunks], [[9, 1, 4], [0]])
def test_clear(self):
b = OrderedBuffer(3, numpy.dtype(numpy.int8))
b.extend([9, 1])
b.extend([1, 2, 3, 4, 5])
b.append(4)
self.assertEqual(len(b), 8)
self.assertEqual([chunk.tolist() for chunk in b.chunks], [[9, 1], [1, 2, 3, 4, 5], [4]])
b.clear()
self.assertEqual(len(b), 0)
self.assertEqual([chunk.tolist() for chunk in b.chunks], [])
b.append(0)
b.extend([7, 8, 9])
b.append(1)
b.extend([2])
self.assertEqual(len(b), 6)
self.assertEqual([chunk.tolist() for chunk in b.chunks], [[0], [7, 8, 9], [1, 2]])
def set_prior(self, bernoulli_prior=None, density_conditions=None):
"""
density_conditions is the max no of ones in each dimension
[min_row, min_col, max_row, max_col].
zero means unrestricted
"""
if density_conditions is None:
self.density_conditions = np.array([0,0,0,0], dtype=np.int8)
else:
assert len(density_conditions) == 4
self.density_conditions = np.array(density_conditions, dtype=np.int8)
self.bernoulli_prior = bernoulli_prior
if bernoulli_prior is None:
self.logit_bernoulli_prior = 0
else:
self.logit_bernoulli_prior = np.log(bernoulli_prior/(1-bernoulli_prior))
def test_float(self):
# offset for alignment test
for i in range(4):
assert_array_equal(self.f[i:] > 0, self.ef[i:])
assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:])
assert_array_equal(self.f[i:] == 0, ~self.ef[i:])
assert_array_equal(-self.f[i:] < 0, self.ef[i:])
assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:])
r = self.f[i:] != 0
assert_array_equal(r, self.ef[i:])
r2 = self.f[i:] != np.zeros_like(self.f[i:])
r3 = 0 != self.f[i:]
assert_array_equal(r, r2)
assert_array_equal(r, r3)
# check bool == 0x1
assert_array_equal(r.view(np.int8), r.astype(np.int8))
assert_array_equal(r2.view(np.int8), r2.astype(np.int8))
assert_array_equal(r3.view(np.int8), r3.astype(np.int8))
# isnan on amd64 takes the same code path
assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:])
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]
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_big_binary(self):
"""Test workarounds for 32-bit limited fwrite, fseek, and ftell
calls in windows. These normally would hang doing something like this.
See http://projects.scipy.org/numpy/ticket/1660"""
if sys.platform != 'win32':
return
try:
# before workarounds, only up to 2**32-1 worked
fourgbplus = 2**32 + 2**16
testbytes = np.arange(8, dtype=np.int8)
n = len(testbytes)
flike = tempfile.NamedTemporaryFile()
f = flike.file
np.tile(testbytes, fourgbplus // testbytes.nbytes).tofile(f)
flike.seek(0)
a = np.fromfile(f, dtype=np.int8)
flike.close()
assert_(len(a) == fourgbplus)
# check only start and end for speed:
assert_((a[:n] == testbytes).all())
assert_((a[-n:] == testbytes).all())
except (MemoryError, ValueError):
pass
