本文整理汇总了Python中numpy.full方法的典型用法代码示例。如果您正苦于以下问题:Python numpy.full方法的具体用法?Python numpy.full怎么用?Python numpy.full使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numpy
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在下文中一共展示了numpy.full方法的15个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: query
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def query(self, coords, order=1):
"""
Returns the map value at the specified location(s) on the sky.
Args:
coords (`astropy.coordinates.SkyCoord`): The coordinates to query.
order (Optional[int]): Interpolation order to use. Defaults to `1`,
for linear interpolation.
Returns:
A float array containing the map value at every input coordinate.
The shape of the output will be the same as the shape of the
coordinates stored by `coords`.
"""
out = np.full(len(coords.l.deg), np.nan, dtype='f4')
for pole in self.poles:
m = (coords.b.deg >= 0) if pole == 'ngp' else (coords.b.deg < 0)
if np.any(m):
data, w = self._data[pole]
x, y = w.wcs_world2pix(coords.l.deg[m], coords.b.deg[m], 0)
out[m] = map_coordinates(data, [y, x], order=order, mode='nearest')
return out
示例2: find_subject_path
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def find_subject_path(sid, check_path=True):
'''
find_subject_path(sub) yields the full path of a HCP subject with the name given by the string
sub, if such a subject can be found in the HCP search paths. See also add_subject_path.
If no subject is found, then None is returned.
'''
# if it's a full/relative path already, use it:
sub = str(sid)
if ((not check_path or is_hcp_subject_path(sub)) and
(check_path is None or os.path.isdir(sub))):
return sub
# check the subject directories:
sdirs = config['hcp_subject_paths']
return next((os.path.abspath(p) for sd in sdirs
for p in [os.path.join(sd, sub)]
if ((not check_path or is_hcp_subject_path(p)) and
(check_path is None or os.path.isdir(p)))),
None)
示例3: cos_edge
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def cos_edge(f=Ellipsis, width=np.pi, offset=0, scale=1):
'''
cos_edge() yields a potential function g(x) that calculates 0 for x < pi/2, 1 for x > pi/2, and
0.5*(1 + cos(pi/2*(1 - x))) for x between -pi/2 and pi/2.
The full formulat of the cosine well is, including optional arguments:
scale/2 * (1 + cos(pi*(0.5 - (x - offset)/width)
The following optional arguments may be given:
* width (default: pi) specifies that the frequency of the cos-curve should be pi/width; the
width is the distance between the points on the cos-curve with the value of 1.
* offset (default: 0) specifies the offset of the minimum value of the coine curve on the
x-axis.
* scale (default: 1) specifies the height of the cosine well.
'''
f = to_potential(f)
freq = np.pi/2
(xmn,xmx) = (offset - width/2, offset + width/2)
F = piecewise(scale,
((-np.inf, xmn), 0),
((xmn,xmx), scale/2 * (1 + cos(np.pi*(0.5 - (identity - offset)/width)))))
if is_const_potential(f): return const_potential(F.value(f.c))
elif is_identity_potential(f): return F
else: return compose(F, f)
示例4: signed_face_areas
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def signed_face_areas(faces, axis=1):
'''
signed_face_areas(faces) yields a potential function f(x) that calculates the signed area of
each face represented by the simplices matrix faces.
If faces is None, then the parameters must arrive in the form of a flattened (n x 3 x 2) matrix
where n is the number of triangles. Otherwise, the faces matrix must be either (n x 3) or (n x 3
x s); if the former, each row must list the vertex indices for the faces where the vertex matrix
is presumed to be shaped (V x 2). Alternately, faces may be a full (n x 3 x 2) simplex array of
the indices into the parameters.
The optional argument axis (default: 1) may be set to 0 if the faces argument is a matrix but
the coordinate matrix will be (2 x V) instead of (V x 2).
'''
faces = np.asarray(faces)
if len(faces.shape) == 2:
if faces.shape[1] != 3: faces = faces.T
n = 2 * (np.max(faces) + 1)
if axis == 0: tmp = np.reshape(np.arange(n), (2,-1)).T
else: tmp = np.reshape(np.arange(n), (-1,2))
faces = np.reshape(tmp[faces.flat], (-1,3,2))
faces = faces.flatten()
return compose(TriangleSignedArea2DPotential(), part(Ellipsis, faces))
示例5: face_areas
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def face_areas(faces, axis=1):
'''
face_areas(faces) yields a potential function f(x) that calculates the unsigned area of each
faces represented by the simplices matrix faces.
If faces is None, then the parameters must arrive in the form of a flattened (n x 3 x 2) matrix
where n is the number of triangles. Otherwise, the faces matrix must be either (n x 3) or (n x 3
x s); if the former, each row must list the vertex indices for the faces where the vertex matrix
is presumed to be shaped (V x 2). Alternately, faces may be a full (n x 3 x 2) simplex array of
the indices into the parameters.
The optional argument axis (default: 1) may be set to 0 if the faces argument is a matrix but
the coordinate matrix will be (2 x V) instead of (V x 2).
'''
faces = np.asarray(faces)
if len(faces.shape) == 2:
if faces.shape[1] != 3: faces = faces.T
n = 2 * (np.max(faces) + 1)
if axis == 0: tmp = np.reshape(np.arange(n), (2,-1)).T
else: tmp = np.reshape(np.arange(n), (-1,2))
faces = np.reshape(tmp[faces.flat], (-1,3,2))
faces = faces.flatten()
return compose(TriangleArea2DPotential(), part(Ellipsis, faces))
示例6: _retinotopic_field_sign_triangles
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def _retinotopic_field_sign_triangles(m, retinotopy):
t = m.tess if isinstance(m, geo.Mesh) or isinstance(m, geo.Topology) else m
# get the polar angle and eccen data as a complex number in degrees
if pimms.is_str(retinotopy):
(x,y) = as_retinotopy(retinotopy_data(m, retinotopy), 'geographical')
elif retinotopy is Ellipsis:
(x,y) = as_retinotopy(retinotopy_data(m, 'any'), 'geographical')
else:
(x,y) = as_retinotopy(retinotopy, 'geographical')
# Okay, now we want to make some coordinates...
coords = np.asarray([x, y])
us = coords[:, t.indexed_faces[1]] - coords[:, t.indexed_faces[0]]
vs = coords[:, t.indexed_faces[2]] - coords[:, t.indexed_faces[0]]
(us,vs) = [np.concatenate((xs, np.full((1, t.face_count), 0.0))) for xs in [us,vs]]
xs = np.cross(us, vs, axis=0)[2]
xs[np.isclose(xs, 0)] = 0
return np.sign(xs)
示例7: test_gluon_trainer_step
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def test_gluon_trainer_step():
def check_trainer_step():
ctx = mx.cpu(0)
shape = (10, 1)
x = mx.gluon.Parameter('x', shape=shape)
x.initialize(ctx=ctx, init='ones')
trainer = mx.gluon.Trainer([x], 'sgd', {'learning_rate': 1.0, 'multi_precision': False}, kvstore=kv)
with mx.autograd.record():
w = x.data(ctx)
y = (my_rank + 1) * w
y.backward()
trainer.step(1)
expected = 1 - (1 + nworker) * nworker / 2
assert_almost_equal(x.data(ctx).asnumpy(), np.full(shape, expected))
check_trainer_step()
print('worker ' + str(my_rank) + ' passed test_gluon_trainer_step')
示例8: test_gluon_trainer_sparse_step
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def test_gluon_trainer_sparse_step():
def check_trainer_sparse_step():
ctx = mx.cpu(0)
shape = (2, 10)
all_rows = mx.nd.arange(0, shape[0], ctx=ctx)
x = mx.gluon.Parameter('x', shape=shape, stype='row_sparse', grad_stype='row_sparse')
x.initialize(ctx=ctx, init='ones')
trainer = mx.gluon.Trainer([x], 'sgd', {'learning_rate': 1.0}, kvstore=kv)
with mx.autograd.record():
w = x.row_sparse_data(all_rows)
y = (my_rank + 1) * w
y.backward()
trainer.step(1)
expected = 1 - (1 + nworker) * nworker / 2
assert_almost_equal(x.row_sparse_data(all_rows).asnumpy(), np.full(shape, expected))
check_trainer_sparse_step()
print('worker ' + str(my_rank) + ' passed test_gluon_trainer_sparse_step')
示例9: create_colorful_test_image
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def create_colorful_test_image(self):
"""This function creates an image that can be used to test vis functions.
It makes an image composed of four colored rectangles.
Returns:
colorful test numpy array image.
"""
ch255 = np.full([100, 200, 1], 255, dtype=np.uint8)
ch128 = np.full([100, 200, 1], 128, dtype=np.uint8)
ch0 = np.full([100, 200, 1], 0, dtype=np.uint8)
imr = np.concatenate((ch255, ch128, ch128), axis=2)
img = np.concatenate((ch255, ch255, ch0), axis=2)
imb = np.concatenate((ch255, ch0, ch255), axis=2)
imw = np.concatenate((ch128, ch128, ch128), axis=2)
imu = np.concatenate((imr, img), axis=1)
imd = np.concatenate((imb, imw), axis=1)
image = np.concatenate((imu, imd), axis=0)
return image
示例10: make_source_target_alignment
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def make_source_target_alignment(self, sequence):
space_char_code = self._char_map_inverse[' ']
unknown_word_code = self._word_map_inverse['<unknown>']
source = []
target = []
length = 0
for word in sequence.split(' '):
source.append(
np.array([space_char_code] + self.encode_source(word),
dtype='int32')
)
target.append(
np.full(len(word) + 1, self.encode_target([word])[0],
dtype='int32')
)
length += 1 + len(word)
# concatenate data
return (
length,
np.concatenate(source),
np.concatenate(target)
)
示例11: __init__
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def __init__(self, epsilon=1e-4, shape=(), scope=''):
sess = get_session()
self._new_mean = tf.placeholder(shape=shape, dtype=tf.float64)
self._new_var = tf.placeholder(shape=shape, dtype=tf.float64)
self._new_count = tf.placeholder(shape=(), dtype=tf.float64)
with tf.variable_scope(scope, reuse=tf.AUTO_REUSE):
self._mean = tf.get_variable('mean', initializer=np.zeros(shape, 'float64'), dtype=tf.float64)
self._var = tf.get_variable('std', initializer=np.ones(shape, 'float64'), dtype=tf.float64)
self._count = tf.get_variable('count', initializer=np.full((), epsilon, 'float64'), dtype=tf.float64)
self.update_ops = tf.group([
self._var.assign(self._new_var),
self._mean.assign(self._new_mean),
self._count.assign(self._new_count)
])
sess.run(tf.variables_initializer([self._mean, self._var, self._count]))
self.sess = sess
self._set_mean_var_count()
示例12: _positional_to_optimal
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def _positional_to_optimal(self, K):
k, l = self.k, self.l
suffix = np.full((len(K), self.l), 0.0)
X = np.column_stack([K, suffix])
X[:, self.k + self.l - 1] = 0.35
for i in range(self.k + self.l - 2, self.k - 1, -1):
m = X[:, i + 1:k + l]
val = m.sum(axis=1) / m.shape[1]
X[:, i] = 0.35 ** ((0.02 + 1.96 * val) ** -1)
ret = X * (2 * (np.arange(self.n_var) + 1))
return ret
# ---------------------------------------------------------------------------------------------------------
# TRANSFORMATIONS
# ---------------------------------------------------------------------------------------------------------
示例13: _do
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def _do(self, problem, pop, off, **kwargs):
ret = np.full((len(pop), 1), False)
pop_F, pop_CV, pop_feasible = pop.get("F", "CV", "feasible")
off_F, off_CV, off_feasible = off.get("F", "CV", "feasible")
if problem.n_constr > 0:
# 1) Both infeasible and constraints have been improved
ret[(~pop_feasible & ~off_feasible) & (off_CV < pop_CV)] = True
# 2) A solution became feasible
ret[~pop_feasible & off_feasible] = True
# 3) Both feasible but objective space value has improved
ret[(pop_feasible & off_feasible) & (off_F < pop_F)] = True
else:
ret[off_F < pop_F] = True
return ret[:, 0]
示例14: _do
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def _do(self, problem, X, **kwargs):
n_parents, n_matings, n_var = X.shape
_X = np.full((self.n_offsprings, n_matings, problem.n_var), False)
for k in range(n_matings):
p1, p2 = X[0, k], X[1, k]
both_are_true = np.logical_and(p1, p2)
_X[0, k, both_are_true] = True
n_remaining = problem.n_max - np.sum(both_are_true)
I = np.where(np.logical_xor(p1, p2))[0]
S = I[np.random.permutation(len(I))][:n_remaining]
_X[0, k, S] = True
return _X
示例15: _do
# 需要导入模块: import numpy [as 别名]
# 或者: from numpy import full [as 别名]
def _do(self, problem, X, **kwargs):
_, n_matings, n_var = X.shape
def fun(mask, operator):
return operator._do(problem, X[..., mask], **kwargs)
ret = apply_mixed_variable_operation(problem, self.process, fun)
# for the crossover the concatenation is different through the 3d arrays.
X = np.full((self.n_offsprings, n_matings, n_var), np.nan, dtype=np.object)
for i in range(len(self.process)):
mask, _X = self.process[i]["mask"], ret[i]
X[..., mask] = _X
return X