Linear regression fill_between with matplotlib

I am currently performing linear regression on my data with the following code (from the stats models.api statistics):

import statsmodels.api
from statsmodels.stats.outliers_influence import summary_table

X = statsmodels.api.add_constant(log_sSFR_dAGN)
res = statsmodels.api.OLS(Dn4000_dAGN, X).fit()

xlin = numpy.linspace(-15,-6, num=1000)

st, data, ss2 = summary_table(res, alpha=0.05)
fittedvalues = data[:,2]
predict_mean_se  = data[:,3]
predict_mean_ci_low, predict_mean_ci_upp = data[:,4:6].T
predict_ci_low, predict_ci_upp = data[:,6:8].T

      

        
        
        
      

    

Now, what I did to plot this with my data is the following:

pyplot_left.plot(X, fittedvalues, 'k-', label='OLS')
pyplot_left.plot(X, predict_ci_low, 'k--')
pyplot_left.plot(X, predict_ci_upp, 'k--')
pyplot_left.plot(X, predict_mean_ci_low, 'k:')
pyplot_left.plot(X, predict_mean_ci_upp, 'k:')
pyplot_left.set_xlim([-14,-8])
pyplot_left.fill_between(log_sSFR_dAGN, predict_mean_ci_low, predict_mean_ci_upp, facecolor='grey', alpha=0.5, edgecolor='none')

      

        
        
        
      

    

This gives the following output:

I'm sure many of you will now know what I want to fix. I would like the lower and upper limit regression lines to go from "-14 to -6" to cover the entire range of my data, and the gray shaded area (ie fill_between(log_sSFR_dAGN, predict_mean_ci_low, predict_mean_ci_upp, facecolor='grey', alpha=0.5, edgecolor='none')

) also covers the full range of my selected x-axis values.

I tried using the method np.linspace

, but I got the following errors for several methods that worked for me earlier.

Method 1 error:

ValueError: Argument dimensions are incompatible

      

        
        
        
      

    

Method 2 error:

TypeError: 'numpy.ndarray' object is not callable 

      

        
        
        
      

    

Any solutions or pointers are greatly appreciated!