[英]Print two arrays side by side using numpy
I'm trying to create a table of cosines using numpy in python.我正在尝试在 python 中使用 numpy 创建一个余弦表。 I want to have the angle next to the cosine of the angle, so it looks something like this:我想在角度的余弦旁边有角度,所以它看起来像这样:
0.0 1.000 5.0 0.996 10.0 0.985 15.0 0.966
20.0 0.940 25.0 0.906 and so on.
I'm trying to do it using a for loop but I'm not sure how to get this to work.我正在尝试使用 for 循环来做到这一点,但我不确定如何让它发挥作用。 Currently, I have目前,我有. .
Any suggestions?有什么建议吗?
Let's say you have:假设你有:
>>> d = np.linspace(0, 360, 10, endpoint=False)
>>> c = np.cos(np.radians(d))
If you don't mind having some brackets and such on the side, then you can simply concatenate column-wise using np.c_
, and display:如果您不介意旁边有一些括号等,那么您可以简单地使用np.c_
列连接,并显示:
>>> print(np.c_[d, c])
[[ 0.00000000e+00 1.00000000e+00]
[ 3.60000000e+01 8.09016994e-01]
[ 7.20000000e+01 3.09016994e-01]
[ 1.08000000e+02 -3.09016994e-01]
[ 1.44000000e+02 -8.09016994e-01]
[ 1.80000000e+02 -1.00000000e+00]
[ 2.16000000e+02 -8.09016994e-01]
[ 2.52000000e+02 -3.09016994e-01]
[ 2.88000000e+02 3.09016994e-01]
[ 3.24000000e+02 8.09016994e-01]]
But if you care about removing them, one possibility is to use a simple regex:但是如果你关心删除它们,一种可能性是使用一个简单的正则表达式:
>>> import re
>>> print(re.sub(r' *\n *', '\n',
np.array_str(np.c_[d, c]).replace('[', '').replace(']', '').strip()))
0.00000000e+00 1.00000000e+00
3.60000000e+01 8.09016994e-01
7.20000000e+01 3.09016994e-01
1.08000000e+02 -3.09016994e-01
1.44000000e+02 -8.09016994e-01
1.80000000e+02 -1.00000000e+00
2.16000000e+02 -8.09016994e-01
2.52000000e+02 -3.09016994e-01
2.88000000e+02 3.09016994e-01
3.24000000e+02 8.09016994e-01
I'm removing the brackets, and then passing it to the regex to remove the spaces on either side in each line.我正在删除括号,然后将其传递给正则表达式以删除每行中任一侧的空格。
np.array_str
also lets you set the precision. np.array_str
还可以让您设置精度。 For more control, you can use np.array2string
instead.如需更多控制,您可以使用np.array2string
代替。
You can use python's zip
function to go through the elements of both lists simultaneously.您可以使用 python 的zip
函数同时浏览两个列表的元素。
import numpy as np
degreesVector = np.linspace(0.0, 360.0, 73.0)
cosinesVector = np.cos(np.radians(degreesVector))
for d, c in zip(degreesVector, cosinesVector):
print d, c
And if you want to make a numpy array out of the degrees and cosine values, you can modify the for
loop in this way:如果你想用度数和余弦值创建一个 numpy 数组,你可以这样修改for
循环:
table = []
for d, c in zip(degreesVector, cosinesVector):
table.append([d, c])
table = np.array(table)
And now on one line!现在在一条线上!
np.array([[d, c] for d, c in zip(degreesVector, cosinesVector)])
Pandas is very convenient module for such tasks: Pandas 是用于此类任务的非常方便的模块:
In [174]: import pandas as pd
...:
...: x = pd.DataFrame({'angle': np.linspace(0, 355, 355//5+1),
...: 'cos': np.cos(np.deg2rad(np.linspace(0, 355, 355//5+1)))})
...:
...: pd.options.display.max_rows = 20
...:
...: x
...:
Out[174]:
angle cos
0 0.0 1.000000
1 5.0 0.996195
2 10.0 0.984808
3 15.0 0.965926
4 20.0 0.939693
5 25.0 0.906308
6 30.0 0.866025
7 35.0 0.819152
8 40.0 0.766044
9 45.0 0.707107
.. ... ...
62 310.0 0.642788
63 315.0 0.707107
64 320.0 0.766044
65 325.0 0.819152
66 330.0 0.866025
67 335.0 0.906308
68 340.0 0.939693
69 345.0 0.965926
70 350.0 0.984808
71 355.0 0.996195
[72 rows x 2 columns]
A built-in Numpy approach using the column_stack((...))
method.使用column_stack((...))
方法的内置 Numpy 方法。
numpy.column_stack((A, B))
is a column stack with Numpy which allows you to compare two or more matrices/arrays.numpy.column_stack((A, B))
是一个带有 Numpy 的列堆栈,它允许您比较两个或多个矩阵/数组。
Use the numpy.column_stack((A, B))
method with a tuple.将numpy.column_stack((A, B))
方法与元组一起使用。 The tuple must be represented with ()
parenthesizes representing a single argument with as many matrices/arrays as you want .元组必须用()
括号表示,该括号表示具有任意数量的矩阵/数组的单个参数。
import numpy as np
A = np.random.uniform(size=(10,1))
B = np.random.uniform(size=(10,1))
C = np.random.uniform(size=(10,1))
np.column_stack((A, B, C)) ## <-- Compare Side-by-Side
The result looks like this:结果如下所示:
array([[0.40323596, 0.95947336, 0.21354263],
[0.18001121, 0.35467198, 0.47653884],
[0.12756083, 0.24272134, 0.97832504],
[0.95769626, 0.33855075, 0.76510239],
[0.45280595, 0.33575171, 0.74295859],
[0.87895151, 0.43396391, 0.27123183],
[0.17721346, 0.06578044, 0.53619146],
[0.71395251, 0.03525021, 0.01544952],
[0.19048783, 0.16578012, 0.69430883],
[0.08897691, 0.41104408, 0.58484384]])
Numpy column_stack
is useful for AI/ML applications when comparing the predicted results with the expected answers.将预测结果与预期答案进行比较时,Numpy column_stack
对于 AI/ML 应用程序很有用。 This determines the effectiveness of the Neural Net training.这决定了神经网络训练的有效性。 It is a quick way to detect where errors are in the network calculations.这是一种检测网络计算中错误位置的快速方法。
You were close - but if you iterate over angles, just generate the cosine
for that angle:你很接近 - 但如果你迭代角度,只需生成该角度的cosine
:
In [293]: for angle in range(0,60,10):
...: print('{0:8}{1:8.3f}'.format(angle, np.cos(np.radians(angle))))
...:
0 1.000
10 0.985
20 0.940
30 0.866
40 0.766
50 0.643
To work with arrays, you have lots of options:要使用数组,您有很多选择:
In [294]: angles=np.linspace(0,60,7)
In [295]: cosines=np.cos(np.radians(angles))
iterate over an index:迭代索引:
In [297]: for i in range(angles.shape[0]):
...: print('{0:8}{1:8.3f}'.format(angles[i],cosines[i]))
Use zip
to dish out the values 2 by 2:使用zip
将值 2 乘以 2:
for a,c in zip(angles, cosines):
print('{0:8}{1:8.3f}'.format(a,c))
A slight variant on that:一个轻微的变化:
for ac in zip(angles, cosines):
print('{0:8}{1:8.3f}'.format(*ac))
You could concatenate the arrays together into a 2d array, and display that:您可以将数组连接到一个二维数组中,并显示:
In [302]: np.vstack((angles, cosines)).T
Out[302]:
array([[ 0. , 1. ],
[ 10. , 0.98480775],
[ 20. , 0.93969262],
[ 30. , 0.8660254 ],
[ 40. , 0.76604444],
[ 50. , 0.64278761],
[ 60. , 0.5 ]])
In [318]: print(np.vstack((angles, cosines)).T)
[[ 0. 1. ]
[ 10. 0.98480775]
[ 20. 0.93969262]
[ 30. 0.8660254 ]
[ 40. 0.76604444]
[ 50. 0.64278761]
[ 60. 0.5 ]]
np.column_stack
can do that without the transpose. np.column_stack
可以在没有转置的情况下做到这一点。
And you can pass that array to your formatting with:您可以将该数组传递给您的格式:
for ac in np.vstack((angles, cosines)).T:
print('{0:8}{1:8.3f}'.format(*ac))
or you could write that to a csv
style file with savetxt
(which just iterates over the 'rows' of the 2d array and writes with fmt
):或者您可以使用savetxt
将其写入csv
样式文件(它只是遍历二维数组的“行”并使用fmt
写入):
In [310]: np.savetxt('test.txt', np.vstack((angles, cosines)).T, fmt='%8.1f %8.3f')
In [311]: cat test.txt
0.0 1.000
10.0 0.985
20.0 0.940
30.0 0.866
40.0 0.766
50.0 0.643
60.0 0.500
Unfortunately savetxt
requires the old style formatting.不幸的是, savetxt
需要旧式格式。 And trying to write to sys.stdout
runs into byte v unicode string issues in Py3.尝试写入sys.stdout
在 Py3 中遇到 byte v unicode 字符串问题。
Just in numpy with some format ideas, to use @MaxU 's syntax只是在有一些格式想法的 numpy 中,使用 @MaxU 的语法
a = np.array([[i, np.cos(np.deg2rad(i)), np.sin(np.deg2rad(i))]
for i in range(0,361,30)])
args = ["Angle", "Cos", "Sin"]
frmt = ("{:>8.0f}"+"{:>8.3f}"*2)
print(("{:^8}"*3).format(*args))
for i in a:
print(frmt.format(*i))
Angle Cos Sin
0 1.000 0.000
30 0.866 0.500
60 0.500 0.866
90 0.000 1.000
120 -0.500 0.866
150 -0.866 0.500
180 -1.000 0.000
210 -0.866 -0.500
240 -0.500 -0.866
270 -0.000 -1.000
300 0.500 -0.866
330 0.866 -0.500
360 1.000 -0.000
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