用序列创建的 numpy 数组 [英] numpy array creating with a sequence
问题描述
我正在从 MATLAB 过渡到 scipy(+numpy)+matplotlib.在实施某些事情时,我一直遇到问题.我想在三个不同的部分创建一个简单的向量数组.在 MATLAB 中,我会做类似的事情:
vector=[0.2,1:60,60.8];
这会产生一个包含 62 个位置的一维数组.我正在尝试使用 scipy 来实现这一点.我现在最接近的是这个:
a=[[0.2],linspace(1,60,60),[60.8]]
然而,这会创建一个列表,而不是一个数组,因此我无法将其重塑为向量数组.但是,当我这样做时,我收到一个错误
a=array([[0.2],linspace(1,60,60),[60.8]])ValueError:使用序列设置数组元素.
我相信我的主要障碍是我无法弄清楚如何在 MATLAB 中翻译这个简单的操作:
a=[1:2:20];
麻木.我知道如何访问数组中的位置,尽管在创建序列时不知道.任何帮助将不胜感激,谢谢!
NumPy 实现了 MATLAB 的数组创建函数,vector,使用两个函数而不是一个函数——每个都隐含地指定了一个特定的轴,串联应该沿着这个轴发生.这些功能是:
r_(逐行串联)和
c_(按列)
因此,对于您的示例,NumPy 等效项是:
<预><代码>>>>将 numpy 导入为 NP>>>v = NP.r_[.2, 1:10, 60.8]>>>打印(五)[ 0.2 1. 2. 3. 4. 5. 6. 7. 8. 9. 60.8 ]按列对应的是:
<预><代码>>>>NP.c_[.2, 1:10, 60.8]slice 表示法按预期工作 [start:stop:step]:
<预><代码>>>>v = NP.r_[.2, 1:25:7, 60.8]>>>v数组([ 0.2, 1. , 8. , 15. , 22. , 60.8])虽然如果虚数用作第三个参数,切片符号的行为类似于linspace:
<预><代码>>>>v = NP.r_[.2, 1:25:7j, 60.8]>>>v数组([ 0.2, 1. , 5. , 9. , 13. , 17. , 21. , 25. , 60.8])
否则,它的行为类似于 arange:
I am on my transitional trip from MATLAB to scipy(+numpy)+matplotlib. I keep having issues when implementing some things. I want to create a simple vector array in three different parts. In MATLAB I would do something like:
vector=[0.2,1:60,60.8];
This results in a one dimensional array of 62 positions. I'm trying to implement this using scipy. The closest I am right now is this:
a=[[0.2],linspace(1,60,60),[60.8]]
However this creates a list, not an array, and hence I cannot reshape it to a vector array. But then, when I do this, I get an error
a=array([[0.2],linspace(1,60,60),[60.8]])
ValueError: setting an array element with a sequence.
I believe my main obstacle is that I can't figure out how to translate this simple operation in MATLAB:
a=[1:2:20];
to numpy. I know how to do it to access positions in an array, although not when creating a sequence. Any help will be appreciated, thanks!
Well NumPy implements MATLAB's array-creation function, vector, using two functions instead of one--each implicitly specifies a particular axis along which concatenation ought to occur. These functions are:
r_ (row-wise concatenation) and
c_ (column-wise)
So for your example, the NumPy equivalent is:
>>> import numpy as NP
>>> v = NP.r_[.2, 1:10, 60.8]
>>> print(v)
[ 0.2 1. 2. 3. 4. 5. 6. 7. 8. 9. 60.8]
The column-wise counterpart is:
>>> NP.c_[.2, 1:10, 60.8]
slice notation works as expected [start:stop:step]:
>>> v = NP.r_[.2, 1:25:7, 60.8]
>>> v
array([ 0.2, 1. , 8. , 15. , 22. , 60.8])
Though if an imaginary number of used as the third argument, the slicing notation behaves like linspace:
>>> v = NP.r_[.2, 1:25:7j, 60.8]
>>> v
array([ 0.2, 1. , 5. , 9. , 13. , 17. , 21. , 25. , 60.8])
Otherwise, it behaves like arange:
>>> v = NP.r_[.2, 1:25:7, 60.8]
>>> v
array([ 0.2, 1. , 8. , 15. , 22. , 60.8])
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