In [1]:

    

import arviz as az
import pandas as pd
import pystan
import seaborn as sns


    

In [2]:

    

import numpy as np
import matplotlib.pyplot as plt


    

In [3]:

    

%matplotlib inline


    

In [4]:

    

model = pystan.StanModel(file='galactic.stan')


    

    




INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_e901c4fe2de64b805e4b7fba7588a11a NOW.

In [5]:

    

n=10


    

In [6]:

    

data= {
    'nobs': n,
    'robs':10**np.random.uniform(low=-1, high=4, size=n)[0],
    'zobs':10**np.random.uniform(low=-1, high=4, size=n)[0]
}


    

In [7]:

    

gaus_data= {
    'N':n,
    'x': 10**np.random.uniform(low=-1, high=4, size=n),
    'y':10**np.random.uniform(low=-1, high=4, size=n),
}


    

In [8]:

    

g_model = pystan.StanModel(file='gaussian.stan')


    

    




INFO:pystan:COMPILING THE C++ CODE FOR MODEL anon_model_f37b33c2481468380d3d5cceb736ce7d NOW.

In [9]:

    

fit = g_model.sampling(data=gaus_data)


    

In [10]:

    

az.plot_trace(fit,  var_names=['alpha'])


    

    
Out[10]:





array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1c299d2470>,
        <matplotlib.axes._subplots.AxesSubplot object at 0x1c299a0cc0>]],
      dtype=object)






    

In [17]:

    

fit['alpha']


    

    
Out[17]:





array([16.20330792, 17.81967344, 15.48517861, ..., 18.09006379,
       14.07359717, 14.36157213])

In [25]:

    

#plt.plot(gaus_data['x'], gaus_data['y'], '.')
plt.plot(fit['rho'], fit['alpha'], '.')


    

    
Out[25]:





[<matplotlib.lines.Line2D at 0x1c2a592c50>]






    

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