这个问题与另一个问题有关.建议和接受的解决方案是:
Returns:
(tuple): tuple containing:
arg1: First Argument
arg2: Second Argument
这个解决方案不起作用,至少对我而言.不解析带有arg1和arg2描述的缩进子块.
我应如何管理多的回报sphinx,sphinx.ext.napoleon和谷歌文档字符串风格？
在Macports更新后,我认为更新了numpy,我收到了警告:
VisibleDeprecationWarning: boolean index did not match indexed array along dimension 1; dimension is 2 but corresponding boolean dimension is 1
inliers = n.size(pixels[distances <= self.dst])
之前没有提过过.相关代码是:
# Compute distance of all non-zero points from the circumference
distances = guess_feature.points_distance(pixels)
# Check which points are inliers (i.e. near the circle)
inliers = n.size(pixels[distances <= self.dst])
self.dst 是单个标量.
guess_feature.points_distance:
def points_distance(self,points):
r'''
Compute the distance of the points from the feature
:math:`d = \left| \sqrt{(x_i - x_c)^2 + (y_i-y_c)^2} - …我目前正在实现几个抽象来表示3D对象的水平集操作.基本上这个 GLSL着色器的惊人页面中描述了什么.
为了简要概述,可以通过将R ^ 3域映射到称为水平集(或符号距离函数)的标量的函数来描述3D对象.例如,对于球体,水平集函数由phi(X) = X.Norm2() - R*Rwhere Norm2表示R ^ 3中的矢量的平方欧几里德范数来定义.
所以我推出了一个LevelSetObject代表这样一个概念的类:
class LevelSetObject
{
using SDFFunction = std::function<double(double, double, double)>;
protected:
SDFFunction m_SDF;
public:
double SDF(double x, double y, double z) const {
return m_SDF(x, y, z);
}
现在我想在LevelSetObjects 之间定义一些运算符.例如union运算符:
LevelSetObject LevelSetObject::operator+(const LevelSetObject& other) const {
LevelSetObject outObj;
outObj.m_SDF = [this, other]
(double x, double y, double z) {
return std::min(m_SDF(x, y, z), other.m_SDF(x, y, z));
};
return …这是我正在setup.py为其编写文件的模块的树结构：
ls .
LICENSE
README.md
bin
examples
module
scratch
setup.py
tests
tox.ini
我的配置setup.py如下：
from setuptools import setup, find_packages
setup(
name="package_name",
version="0.1",
packages=find_packages(),
install_requires=[
# [...]
],
extras_require={
# [...]
},
tests_require={
'pytest',
'doctest'
},
scripts=['bin/bootstrap'],
data_files=[
('license', ['LICENSE']),
],
# [...]
# could also include long_description, download_url, classifiers, etc.
)
如果我从我的 python 环境（也是一个 virtualenv）安装包
pip install .
该LICENSE文件被正确安装。
但是运行tox：
[tox]
envlist = py27, py35
[testenv]
deps =
pytest
git+https://github.com/djc/couchdb-python
docopt
commands = …我有一个关于4-D numpy tensor字典的表现问题.
我有一个系数名称列表:
cnames = ['CN', 'CM', 'CA', 'CY', 'CLN' ...];
这不是固定大小(取决于上层代码).对于每个系数,我必须生成零的4-D张量[nalpha X nmach X nbeta X nalt](用于预分配目的),所以我这样做:
#Number of coefficients
numofc = len(cnames);
final_data = {};
#I have to generate <numofc> 4D matrixes
for i in range(numofc):
final_data[cnames[i]]=n.zeros((nalpha,nmach,nbeta,nalt));
每个索引是10到30之间的整数.
每个索引是100到200之间的整数
这需要4分钟.我怎样才能加快这个速度？或者我做错了什么？