PROSAGA码农传奇-复合模型-在astropy.modeling中绑定参数

<div class =“post-text”itemprop =“text”>
  <P>
    下面的示例类似于astropy文档中给出的示例。
  </p>
  <P>
    复合模型=两个1D高斯函数的和。
  </p>
  <P>
    约束：mean_1 = 2 * mean_0
  </p>
   <pre>
    <code>
      import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling import models, fitting

def tie_center(model):
    mean = 2* model.mean_0
    return mean

tied_parameters = {'mean_1': tie_center}
np.random.seed(42)
g1 = models.Gaussian1D(2, 0.4, 0.3)
g2 = models.Gaussian1D(2.5, 0.2, 0.2)
TwoGaussians = (models.Gaussian1D + 
models.Gaussian1D).rename('TwoGaussians')
x = np.linspace(-1, 1, 200)
y = g1(x) + g2(x) + np.random.normal(0., 0.2, x.shape)

gg_init = TwoGaussians(amplitude_0=1.4, mean_0=1.2, stddev_0=0.1,\
amplitude_1=1.0,stddev_1=0.2, tied=tied_parameters)
fitter = fitting.SLSQPLSQFitter()
gg_fit = fitter(gg_init, x, y)

plt.figure(figsize=(8,5))
plt.plot(x, y, 'ko')
plt.plot(x, gg_fit(x))
plt.xlabel('Position')
plt.ylabel('Flux')
plt.show()
print(gg_fit.mean_0,gg_fit.mean_1)

</code>
  </pre>
  <P>
    当Compund_model =三个1D高斯函数之和时，在星座中绑定参数
约束：所有三种方法的总和应始终等于1。
  </p>
   <pre>
    <code>
      def tie_center(model):
    mean = 1-(model.mean_0+ model.mean_1)
    return mean
tied_parameters = {'mean_2': tie_center}

np.random.seed(42)
g1 = models.Gaussian1D(2, 0.4, 0.3)
g2 = models.Gaussian1D(2.5, 0.2, 0.2)
g3 = models.Gaussian1D(1.5, 0.4, 0.1)
ThreeGaussians = (models.Gaussian1D + models.Gaussian1D + 
models.Gaussian1D).rename('ThreeGaussians')
x = np.linspace(-1, 1, 200)
y = g1(x) + g2(x) + g3(x) + np.random.normal(0., 0.2, x.shape)

gg_init = ThreeGaussians(amplitude_0=1.4, mean_0=0.3, stddev_0=0.1, 
amplitude_1=1.0, mean_1=0.3,stddev_1=0.2, \
amplitude_2=1.5,stddev_2=0.1,tied=tied_parameters)
fitter = fitting.SLSQPLSQFitter()
gg_fit = fitter(gg_init, x, y)
plt.figure(figsize=(8,5))
plt.plot(x, y, 'ko')
plt.plot(x, gg_fit(x))
plt.xlabel('Position')
plt.ylabel('Flux')
plt.show()
print(gg_fit.mean_0,gg_fit.mean_1, gg_fit.mean_2)

</code>
  </pre>
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