myline = np.linspace(0, 6, 100)

This code creates an array called "myline" with 100 equally spaced numbers between 0 and 6 (inclusive). The numpy function "linspace" is used to generate this array.

相关问题

以下代码哪里出错了：import numpy as np from scipy.integrate import odeint from matplotlib import animation import matplotlib.pyplot as plt def f(y,t,G,M): return np.array([y[1], y[0]*y[3]**2-G*M/y[0]**2, y[3], -2*y[1]*y[3]/y[0]]) #初始条件 v0=0 #解微分方程 G,M=6.67e-11,2e30 theta0=0 t= np.linspace(0,20,200) r0=1.5e11 y0=[r0,0,theta0,np.sqrt(2*G*M/r0**3)] res=odeint(f,y0,t,args=(G,M)) x=res[:,0]*np.sin(res[:,2]) y=res[:,0]*np.cos(res[:,2]) #画图 fig=plt.figure() plt.axis([-5*r0,5*r0,-5*r0,5*r0]) myline,=plt.plot([],[],'b',lw=2) mypoint,=plt.plot([],[],'ro',ms=20) def animate(i): mypoint.set_data([x[i],y[i]]) myline.set_data(x[:i],y[:i]) return mypoint,myline anim=animation.FuncAnimation(fig,animate,frames=len(t),interval=100) plt.show()

代码中缺少空格，应该在第一行的 import numpy as np 和 from scipy.integrate import odeint 之间加一个空格。正确代码如下：

import numpy as np 
from scipy.integrate import odeint 
from matplotlib import animation 
import matplotlib.pyplot as plt 

def f(y,t,G,M):
    return np.array([y[1],
                     y[0]*y[3]**2-G*M/y[0]**2,
                     y[3],
                     -2*y[1]*y[3]/y[0]])

#初始条件 v0=0
#解微分方程
G,M=6.67e-11,2e30 
theta0=0 
t= np.linspace(0,20,200) 
r0=1.5e11 
y0=[r0,0,theta0,np.sqrt(2*G*M/r0**3)] 
res=odeint(f,y0,t,args=(G,M))

x=res[:,0]*np.sin(res[:,2])
y=res[:,0]*np.cos(res[:,2])

#画图
fig=plt.figure()
plt.axis([-5*r0,5*r0,-5*r0,5*r0])
myline,=plt.plot([],[],'b',lw=2)
mypoint,=plt.plot([],[],'ro',ms=20)

def animate(i):
    mypoint.set_data([x[i],y[i]])
    myline.set_data(x[:i],y[:i])
    return mypoint,myline

anim=animation.FuncAnimation(fig,animate,frames=len(t),interval=100)
plt.show()

def temp(filename,x,dic): path='excelFile/'+filename df = pandas.read_excel(path,header=None) data = [] datax = df.iloc[0].tolist() datay=df.iloc[1].tolist() transInt(datax) transInt(datay) slope,intercept,r,p,std_err=stats.linregress(datax,datay) def myfunc(datax): return slope*datax+intercept mymodel=list(map(myfunc,datax)) plt.scatter(datax,datay) plt.plot(datax,mymodel) print(x) result=myfunc(int(x)) if r>=0.7 or r<=-0.7: count=2 while count<6 : mymodel_2=numpy.poly1d(numpy.polyfit(datax,datay,count)) myline=numpy.linspace(min(datax),max(datay),100) r2=r2_score(datay,mymodel_2(datax)) plt.scatter(datax,datay) plt.plot(myline,mymodel_2(myline)) result=mymodel_2(int(x)) if r2>=0.7 or r2<=-0.7: break plt.savefig('figs/result.png') dic['Y']=str(result)这段代码报错main thread is not in main loop

这个错误通常是由于在非主线程中使用了 matplotlib 库的某些功能导致的。为了避免这个错误，您可以在调用 `plt` 绘图函数之前，添加以下两行代码：

import matplotlib
matplotlib.use('Agg')

这样就可以将 matplotlib 的后端设置为 "Agg"，这是一种不需要图形界面的后端，可以避免在非主线程中使用 matplotlib 函数时出现报错。最终代码应该是这样的：

import matplotlib
matplotlib.use('Agg')
import pandas
import numpy
from scipy import stats
from sklearn.metrics import r2_score
import matplotlib.pyplot as plt

def temp(filename, x, dic):
    path = 'excelFile/' + filename
    df = pandas.read_excel(path, header=None)
    data = []
    datax = df.iloc[0].tolist()
    datay = df.iloc[1].tolist()
    transInt(datax)
    transInt(datay)
    slope, intercept, r, p, std_err = stats.linregress(datax, datay)

    def myfunc(datax):
        return slope * datax + intercept

    mymodel = list(map(myfunc, datax))
    plt.scatter(datax, datay)
    plt.plot(datax, mymodel)

    print(x)
    result = myfunc(int(x))

    if r >= 0.7 or r <= -0.7:
        count = 2
        while count < 6:
            mymodel_2 = numpy.poly1d(numpy.polyfit(datax, datay, count))
            myline = numpy.linspace(min(datax), max(datay), 100)
            r2 = r2_score(datay, mymodel_2(datax))
            plt.scatter(datax, datay)
            plt.plot(myline, mymodel_2(myline))
            result = mymodel_2(int(x))
            if r2 >= 0.7 or r2 <= -0.7:
                break

    plt.savefig('figs/result.png')
    dic['Y'] = str(result)

这样就可以避免这个报错了。