Matplotlib - 在同一图表上绘制多条线

问题描述

很难找到相关信息.我有两个函数要一起绘制，enumeration() 和 betterEnumeration()

This has been surprisingly difficult to find information on. I have two functions that I want to chart together, enumeration() and betterEnumeration()

import matplotlib.pyplot as plt
import time
import numpy as np
import sympy
from sympy import S, symbols
import random
from math import floor

def enumeration(array):
    max = None
    to_return = (max, 0, 0)
    for i in range(0, len(array) + 1):
        for j in range(0, i):
            currentSum = 0
            for k in range(j, i):
                currentSum += array[k]
                if (max is None) or (currentSum > max):
                    max = currentSum
                    to_return = (max, j, k)
    return to_return

def betterEnumeration(array):
    max = None
    to_return = (max, 0, 0)
    for i in range(1, len(array) + 1):
        currentSum = 0
        for j in range(i, len(array) + 1):
            currentSum += array[j - 1]
            if (max is None) or (currentSum > max):
                max = currentSum
                to_return = (max, i-1, j-1)
    return to_return

我还有两个辅助函数 randomArray() 和 regressionCurve().

I also have two helper functions randomArray() and regressionCurve().

def randomArray(totalNumbers,min,max):
    array = []
    while totalNumbers > 0:
        array.append(random.randrange(min,max))
        totalNumbers -= 1
    return array

def regressionCurve(x,y):
    # calculate polynomial
    p = np.polyfit(x, y, 3)
    f = np.poly1d(p)

    # calculate new x's and y's
    x_new = np.linspace(x[0], x[-1], 50)
    y_new = f(x_new)

    x = symbols("x")
    poly = sum(S("{:6.5f}".format(v))*x**i for i, v in enumerate(p[::-1]))
    eq_latex = sympy.printing.latex(poly)

    plt.plot(x_new, y_new, label="${}$".format(eq_latex))
    plt.legend(fontsize="small")
    plt.show()

我想在同一张图表上绘制这两个函数，包括原始数据点和回归曲线.以下代码将绘制 enumeration() 的数据点，然后为它们绘制回归曲线，但我不确定如何绘制 enumeration() 和 betterEnumeration() 在同一图表上.

I want to plot both of these functions on the same chart, both the raw data points as well as the regression curves. The following code will chart the data points for enumeration() and then make a regression curve for them, but I'm not sure how to plot both enumeration() and betterEnumeration() on the same chart.

def chart():
    nValues = [10,25,50,100,250,500,1000]
    avgExecTimes = []
    for n in nValues: # For each n value
        totals = []
        sum = 0
        avgExecTime = 0
        for i in range(0,10): # Create and test 10 random arrays
            executionTimes = []
            array = randomArray(n,0,10)
            t1 = time.clock()
            enumeration(array)
            t2 = time.clock()
            total = t2-t1
            totals.append(total)
            executionTimes.append(total)
            print("Time elapsed(n=" + str(n) + "): " + str(total))
        for t in totals: # Find avg running time for each n's 10 executions
            sum += t
        avgExecTime = sum/10
        avgExecTimes.append(avgExecTime)
        print("Avg execution time: " + str(avgExecTime))

    # Chart execution times
    plt.plot(nValues,avgExecTimes)
    plt.ylabel('Seconds')
    plt.xlabel('n')
    plt.show()

    # Chart curve that fits
    x = np.array(nValues)
    y = np.array(avgExecTimes)
    regressionCurve(x,y)

推荐答案

在绘图中添加一条线:

plt.plot(x,y)

所以，如果你想绘制 x1, y1 然后添加 x2,y2:

so, if you wanted to plot x1, y1 and then add x2,y2:

plt.plot(x1,y1)
plt.plot(x2,y2)

但是，这将以默认颜色绘制第二行.您将要添加颜色组件:

However, that's going to plot the second line in the default color. You're going to want to add a color component:

plt.plot(x1,y1, c='b')
plt.plot(x2,y2, c= 'g')

如果单位不同，您将需要查看 twinx，这将允许您使用 2 个不同的 y 轴但相同的 x 轴进行绘图.

and if the units are different, you'll want to look into twinx, which will allow you to plot with 2 different y axes but the same x axis.

您将要绘制来自同一函数内或函数外的两组数据.否则，您也会遇到本地与全球问题.

You're going to want to plot both sets of data from within the same function or both outside of the function. Otherwise, you're running into a local vs. global issue as well.

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