The following are code examples for showing how to use . They are extracted from open source Python projects. You can vote up the examples you like or vote down the exmaples you don’t like. You can also save this page to your account.
Example 1
def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' %
(magic, filename))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
Example 2
def gl_init(self,array_table):
self.gl_hide = False
self.gl_vertex_array = gl.VertexArray()
glBindVertexArray(self.gl_vertex_array)
self.gl_vertex_buffer = gl.Buffer()
glBindBuffer(GL_ARRAY_BUFFER,self.gl_vertex_buffer)
self.gl_element_count = 3*gl_count_triangles(self)
self.gl_element_buffer = gl.Buffer()
glBindBuffer(GL_ELEMENT_ARRAY_BUFFER,self.gl_element_buffer)
vertex_type = numpy.dtype([array_table[attribute].field() for attribute in self.attributes])
vertex_count = sum(len(primitive.vertices) for primitive in self.primitives)
vertex_array = numpy.empty(vertex_count,vertex_type)
for attribute in self.attributes:
array_table[attribute].load(self,vertex_array)
vertex_array,element_map = numpy.unique(vertex_array,return_inverse=True)
element_array = gl_create_element_array(self,element_map,self.gl_element_count)
glBufferData(GL_ARRAY_BUFFER,vertex_array.nbytes,vertex_array,GL_STATIC_DRAW)
glBufferData(GL_ELEMENT_ARRAY_BUFFER,element_array.nbytes,element_array,GL_STATIC_DRAW)
Example 3
def extract_images(filename):
"""Extract the images into a 4D uint8 numpy array [index, y, x, depth]."""
print('Extracting', filename)
with gzip.open(filename) as bytestream:
magic = _read32(bytestream)
if magic != 2051:
raise ValueError(
'Invalid magic number %d in MNIST image file: %s' %
(magic, filename))
num_images = _read32(bytestream)
rows = _read32(bytestream)
cols = _read32(bytestream)
buf = bytestream.read(rows * cols * num_images)
data = numpy.frombuffer(buf, dtype=numpy.uint8)
data = data.reshape(num_images, rows, cols, 1)
return data
Example 4
def __keytransform__(self, key):
if isinstance(key[0], np.ndarray):
shape = key[0].shape
dtype = key[0].dtype
i = key[1]
zero = True if len(key) == 2 else key[2]
elif isinstance(key[0], tuple):
if len(key) == 3:
shape, dtype, i = key
zero = True
elif len(key) == 4:
shape, dtype, i, zero = key
else:
raise TypeError("Wrong type of key for work array")
assert isinstance(zero, bool)
assert isinstance(i, int)
self.fillzero = zero
return (shape, np.dtype(dtype), i)
Example 5
def accumulate_strings(values, name="strings"):
"""Accumulates strings into a vector.
Args:
values: A 1-d string tensor that contains values to add to the accumulator.
Returns:
A tuple (value_tensor, update_op).
"""
tf.assert_type(values, tf.string)
strings = tf.Variable(
name=name,
initial_value=[],
dtype=tf.string,
trainable=False,
collections=[],
validate_shape=True)
value_tensor = tf.identity(strings)
update_op = tf.assign(
ref=strings, value=tf.concat([strings, values], 0), validate_shape=False)
return value_tensor, update_op
Example 6
def test_expect_dtypes_with_tuple(self):
allowed_dtypes = (dtype('datetime64[ns]'), dtype('float'))
@expect_dtypes(a=allowed_dtypes)
def foo(a, b):
return a, b
for d in allowed_dtypes:
good_a = arange(3).astype(d)
good_b = object()
ret_a, ret_b = foo(good_a, good_b)
self.assertIs(good_a, ret_a)
self.assertIs(good_b, ret_b)
with self.assertRaises(TypeError) as e:
foo(arange(3, dtype='uint32'), object())
expected_message = (
"{qualname}() expected a value with dtype 'datetime64[ns]' "
"or 'float64' for argument 'a', but got 'uint32' instead."
).format(qualname=qualname(foo))
self.assertEqual(e.exception.args[0], expected_message)
Example 7
def _classify_gems(counts0, counts1):
""" Infer number of distinct transcriptomes present in each GEM (1 or 2) and
report cr_constants.GEM_CLASS_GENOME0 for a single cell w/ transcriptome 0,
report cr_constants.GEM_CLASS_GENOME1 for a single cell w/ transcriptome 1,
report cr_constants.GEM_CLASS_MULTIPLET for multiple transcriptomes """
# Assumes that most of the GEMs are single-cell; model counts independently
thresh0, thresh1 = [cr_constants.DEFAULT_MULTIPLET_THRESHOLD] * 2
if sum(counts0 > counts1) >= 1 and sum(counts1 > counts0) >= 1:
thresh0 = np.percentile(counts0[counts0 > counts1], cr_constants.MULTIPLET_PROB_THRESHOLD)
thresh1 = np.percentile(counts1[counts1 > counts0], cr_constants.MULTIPLET_PROB_THRESHOLD)
doublet = np.logical_and(counts0 >= thresh0, counts1 >= thresh1)
dtype = np.dtype('|S%d' % max(len(cls) for cls in cr_constants.GEM_CLASSES))
result = np.where(doublet, cr_constants.GEM_CLASS_MULTIPLET, cr_constants.GEM_CLASS_GENOME0).astype(dtype)
result[np.logical_and(np.logical_not(result == cr_constants.GEM_CLASS_MULTIPLET), counts1 > counts0)] = cr_constants.GEM_CLASS_GENOME1
return result
Example 8
def widen_cat_column(old_ds, new_type):
name = old_ds.name
tmp_name = "__tmp_" + old_ds.name
grp = old_ds.parent
ds = grp.create_dataset(tmp_name,
data = old_ds[:],
shape = old_ds.shape,
maxshape = (None,),
dtype = new_type,
compression = COMPRESSION,
shuffle = True,
chunks = (CHUNK_SIZE,))
del grp[name]
grp.move(tmp_name, name)
return ds
Example 9
def create_levels(ds, levels):
# Create a dataset in the LEVEL_GROUP
# and store as native numpy / h5py types
level_grp = ds.file.get(LEVEL_GROUP)
if level_grp is None:
# Create a LEVEL_GROUP
level_grp = ds.file.create_group(LEVEL_GROUP)
ds_name = ds.name.split("/")[-1]
dt = h5py.special_dtype(vlen=str)
level_grp.create_dataset(ds_name,
shape = [len(levels)],
maxshape = (None,),
dtype = dt,
data = levels,
compression = COMPRESSION,
chunks = (CHUNK_SIZE,))
Example 10
def reg2bin_vector(begin, end):
'''Vectorized tabix reg2bin -- much faster than reg2bin'''
result = np.zeros(begin.shape)
# Entries filled
done = np.zeros(begin.shape, dtype=np.bool)
for (bits, bins) in rev_bit_bins:
begin_shift = begin >> bits
new_done = (begin >> bits) == (end >> bits)
mask = np.logical_and(new_done, np.logical_not(done))
offset = ((1 << (29 - bits)) - 1) / 7
result[mask] = offset + begin_shift[mask]
done = new_done
return result.astype(np.int32)
Example 11
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 Non
