如何翻译此 SageMath 代码以在 Python 中运行？答案

【问题标题】：How can I translate this SageMath code to run in Python?如何翻译此 SageMath 代码以在 Python 中运行？
【发布时间】：2023-03-29 11:00:02
【问题描述】：

我必须做些什么才能让这段代码在 Python 中运行？我从here 获得了代码。如果我在CoCalc 的 SageMath 工作簿中运行它，它无需调整即可工作。导入 sage 和 numpy 后在 Python 中运行时，出现各种名称和属性错误。

def mean_x(factor, values):
    return sum([cos(2*pi*v/factor) for v in values])/len(values)

def mean_y(factor, values):
    return sum([sin(2*pi*v/factor) for v in values])/len(values)

def calculatePeriodAppeal(factor, values):
    mx = mean_x(factor, values)
    my = mean_y(factor, values)
    appeal = sqrt(mx^2+my^2)
    return appeal

def calculateBestLinear(factor, values):
    mx = mean_x(factor, values).n()
    my = mean_y(factor, values).n()
    y0 = factor*atan2(my,mx)/(2*pi).n()
    err = 1-sqrt(mx^2+my^2).n()
    return [factor*x + y0, err]

def calculateGCDAppeal(factor, values):
    mx = mean_x(factor, values)
    my = mean_y(factor, values)
    appeal = 1 - sqrt((mx-1)^2+my^2)/2
    return appeal

testSeq = [0,125,211,287,408,520,650,735,816,942,1060] 
gcd = calculateGCDAppeal(x,testSeq)
agcd = find_local_maximum(gcd,2,100)
print(agcd)
plot(gcd,(x,2,100))

【问题讨论】：

标签： python translate sage

【解决方案1】：

第一个错误是

RuntimeError: Use ** for exponentiation, not '^', which means xor
in Python, and has the wrong precedence.

一旦修复，就会有各种缺失的导入。

使用 Sage 的 import_statements 函数找到它们中的每一个。

sage: import_statements(x)
from sage.calculus.predefined import x

sage: import_statements(sum)
from sage.misc.functional import symbolic_sum

sage: import_statements(cos)
from sage.functions.trig import cos

sage: import_statements(sin)
from sage.functions.trig import sin

最终代码可能如下所示：

from sage.calculus.predefined import x
from sage.misc.functional import symbolic_sum as sum
from sage.functions.trig import cos, sin

def mean_x(factor, values):
    return sum(cos(2*pi*v/factor) for v in values) / len(values)

def mean_y(factor, values):
    return sum(sin(2*pi*v/factor) for v in values) / len(values)

def calculatePeriodAppeal(factor, values):
    mx = mean_x(factor, values)
    my = mean_y(factor, values)
    appeal = sqrt(mx**2 + my**2)
    return appeal

def calculateBestLinear(factor, values):
    mx = mean_x(factor, values).n()
    my = mean_y(factor, values).n()
    y0 = factor*atan2(my, mx)/(2*pi).n()
    err = 1-sqrt(mx**2 + my**2).n()
    return [factor*x + y0, err]

def calculateGCDAppeal(factor, values):
    mx = mean_x(factor, values)
    my = mean_y(factor, values)
    appeal = 1 - sqrt((mx - 1)**2 + my**2)/2
    return appeal

testSeq = [0, 125, 211, 287, 408, 520, 650, 735, 816, 942, 1060]
gcd = calculateGCDAppeal(x, testSeq)
agcd = find_local_maximum(gcd, 2, 100)
print(agcd)
plot(gcd, (x, 2, 100))

在 Ask Sage 中搜索“import_statements”，那里有一些答案详细了解该主题。

Search import_statements on Ask Sage

【讨论】：