In [73]:

    

from pearce.emulator import OriginalRecipe, ExtraCrispy, SpicyBuffalo
from pearce.mocks import cat_dict
import numpy as np
from os import path


    

In [74]:

    

import matplotlib
#matplotlib.use('Agg')
from matplotlib import pyplot as plt
%matplotlib inline
import seaborn as sns
sns.set()


    

In [75]:

    

#xi gg
training_file = '/scratch/users/swmclau2/xi_zheng07_cosmo_lowmsat/PearceRedMagicXiCosmoFixedNd.hdf5'
#test_file = '/scratch/users/swmclau2/xi_zheng07_cosmo_test2/PearceRedMagicXiCosmoTest.hdf5'


    

In [76]:

    

em_method = 'gp'
split_method = 'random'


    

In [77]:

    

a = 1.0
z = 1.0/a - 1.0


    

In [78]:

    

fixed_params = {'z':z, 'cosmo': 0}#, 'r':24.06822623}


    

In [79]:

    

np.random.seed(0)
emu = SpicyBuffalo(training_file, method = em_method, fixed_params=fixed_params,
                 custom_mean_function = 'linear', downsample_factor = 1.0)


    

    




<type 'numpy.ndarray'> 18000 (18000, 5)

In [80]:

    

emu0 = emu._emulators[5]
emu1 = emu._emulators[7]


    

In [81]:

    

x0, y0 = emu.x[5], emu.y[5]
x1, y1 = emu.x[7], emu.y[7]


    

In [82]:

    

x0.shape, x1.shape


    

    
Out[82]:





((1000, 4), (1000, 4))

In [83]:

    

np.all(x0 == x1)


    

    
Out[83]:





True

In [84]:

    

y0.shape, y1.shape


    

    
Out[84]:





((1000,), (1000,))

In [85]:

    

np.all(x0==x1)


    

    
Out[85]:





True

In [86]:

    

emu0._x.shape


    

    
Out[86]:





(1000, 4)

In [87]:

    

emu0.predict(y0, np.zeros((1, x0.shape[1])), return_cov = False)


    

    
Out[87]:





array([ 0.05295054])

In [88]:

    

emu0.predict(y1, np.zeros((1, x0.shape[1])), return_cov = False)


    

    
Out[88]:





array([ 0.0243183])

In [89]:

    

emu1.predict(y0, np.zeros((1, x0.shape[1])), return_cov = False)


    

    
Out[89]:





array([ 0.05296291])

In [90]:

    

emu1.predict(y1, np.zeros((1, x0.shape[1])), return_cov = False)


    

    
Out[90]:





array([ 0.02432987])

In [ ]: