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Python Matplotlib-在同一图表上绘制多条直线_Python_Numpy_Matplotlib_Plot_Regression - Fatal编程技术网

Python Matplotlib-在同一图表上绘制多条直线

Python Matplotlib-在同一图表上绘制多条直线,python,numpy,matplotlib,plot,regression,Python,Numpy,Matplotlib,Plot,Regression,令人惊讶的是,很难找到这方面的信息。我有两个函数要一起绘制,enumeration()和betternumeration() 我还有两个助手函数randomArray()和regressionCurve() def randomArray(totalNumbers,min,max): array = [] while totalNumbers > 0: array.append(random.randrange(min,max)) total

令人惊讶的是,很难找到这方面的信息。我有两个函数要一起绘制,
enumeration()
betternumeration()

我还有两个助手函数
randomArray()
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。下面的代码将绘制
enumeration()
的数据点图表,然后为它们绘制回归曲线,但我不确定如何在同一图表上同时绘制
enumeration()
betternumeration()

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)

要将线添加到绘图,请执行以下操作:

plt.plot(x,y)
所以,如果你想画x1,y1,然后加上x2,y2:

plt.plot(x1,y1)
plt.plot(x2,y2)
但是,这将以默认颜色绘制第二行。您将要添加一个颜色组件:

plt.plot(x1,y1, c='b')
plt.plot(x2,y2, c= 'g')
如果单位不同,你需要查看twinx,这将允许你用两个不同的y轴但相同的x轴来绘图


您需要从同一函数内部或函数外部绘制两组数据。否则,您也会遇到本地与全球的问题

要将线添加到绘图,请执行以下操作:

plt.plot(x,y)
所以,如果你想画x1,y1,然后加上x2,y2:

plt.plot(x1,y1)
plt.plot(x2,y2)
但是,这将以默认颜色绘制第二行。您将要添加一个颜色组件:

plt.plot(x1,y1, c='b')
plt.plot(x2,y2, c= 'g')
如果单位不同,你需要查看twinx,这将允许你用两个不同的y轴但相同的x轴来绘图

您需要从同一函数内部或函数外部绘制两组数据。否则,您也会遇到本地与全球的问题