我知道这里有一些类似的问题,但似乎没有一个能真正解决我的问题。
我的代码是这样的:
import numpy
import matplotlib.pyplot as plt
from scipy import integrate as integrate
def H(z , omega_m , H_0 = 70):
omega_lambda=1-omega_m
z_prime=((1+z)**3)
wurzel=numpy.sqrt(omega_m*z_prime + omega_lambda)
return H_0*wurzel
def H_inv(z, omega_m , H_0=70):
return 1/(H(z, omega_m, H_0=70))
def integral(z, omega_m , H_0=70):
I=integrate.quad(H_inv,0,z,args=(omega_m,))
return I
def d_L(z, omega_m , H_0=70):
distance=(2.99*(10**8))*(1+z)*integral(z, omega_m, H_0=70)
return distance
函数确实有效,我的问题是:如何绘制 d_L 与 z 的关系图?就像我在 d_L 的定义中有这个积分函数显然是一个问题,它取决于 z 和一些 args=(omega_m, )。
最佳答案
要构建@eyllanesc 的解决方案,请按以下方式绘制 omega 的多个值:
import numpy
import matplotlib.pyplot as plt
from scipy import integrate
def H(z, omega_m, H_0=70):
omega_lambda = 1 - omega_m
z_prime = ((1 + z) ** 3)
wurzel = numpy.sqrt(omega_m * z_prime + omega_lambda)
return H_0 * wurzel
def H_inv(z, omega_m, H_0=70):
return 1 / (H(z, omega_m, H_0=70))
def integral(z, omega_m, H_0=70):
I = integrate.quad(H_inv, 0, z, args=(omega_m,))[0]
return I
def d_L(z, omega_m, H_0=70):
distance = (2.99 * (10 ** 8)) * (1 + z) * integral(z, omega_m, H_0)
return distance
z0 = -1.8
zf = 10
zs = numpy.linspace(z0, zf, 1000)
fig, ax = plt.subplots(nrows=1,ncols=1, figsize=(16,9))
for omega_m in np.linspace(0, 1, 10):
d_Ls = numpy.linspace(z0, zf, 1000)
for index in range(zs.size):
d_Ls[index] = d_L(zs[index], omega_m=omega_m)
ax.plot(zs,d_Ls, label='$\Omega$ = {:.2f}'.format(omega_m))
ax.legend(loc='best')
plt.show()
关于Python:绘制积分,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/40960968/