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


    

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import numpy as np
import h5py
#from chainconsumer import ChainConsumer
from corner import corner


    

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training_file = '/scratch/users/swmclau2/xi_gg_corrabzheng07/PearceXiggCosmoCorrAB.hdf5'


    

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from pearce.emulator import NashvilleHot
fixed_params = {'z':0.0}
emu_hps = {}


    

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emu = NashvilleHot(training_file, fixed_params = fixed_params, hyperparams = emu_hps)


    

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emu.get_param_names()


    

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true_param_dict = dict(zip(emu.get_param_names(), [np.mean(emu.get_param_bounds(p)) for p in emu.get_param_names()]))


    

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N = 6
cmap = sns.color_palette("BrBG_d", N)


    

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fig = plt.figure(figsize=(10,7))
varied_pname = 'ln10As'
lower, upper = emu.get_param_bounds(varied_pname)
mean = (upper+lower)/2.0
param_dict[varied_pname] = mean
mean_pred = emu.emulate_wrt_r(param_dict)[0]
for c, val in zip(cmap, np.linspace(lower, upper, N) ):
    param_dict = true_param_dict.copy()
    param_dict[varied_pname] = val
    pred = emu.emulate_wrt_r(param_dict)[0]
    plt.plot(emu.scale_bin_centers, 10**(pred-mean_pred), alpha = 1.0,label = val, color =c)
    
plt.title(r'$\ln(10^{10} A_s)$')
plt.xscale('log')
plt.xlabel('r [Mpc]')
plt.ylabel(r'$\xi_{gg}(r)/\xi_{mean}(r)$')
plt.legend(loc='best')
plt.show();


    

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cmap = sns.color_palette("OrRd_d", N)


    

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fig = plt.figure(figsize=(10,7))
varied_pname = 'mean_occupation_satellites_assembias_corr1'
lower, upper = emu.get_param_bounds(varied_pname)
mean = (upper+lower)/2.0
param_dict[varied_pname] = mean
mean_pred = emu.emulate_wrt_r(param_dict)[0]
for c, val in zip(cmap, np.linspace(lower, upper, N) ):
    param_dict = true_param_dict.copy()
    param_dict[varied_pname] = val
    pred = emu.emulate_wrt_r(param_dict)[0]

    plt.plot(emu.scale_bin_centers, 10**(pred-mean_pred), alpha = 1.0,label = val, color =c)
    
plt.title(r'$\mathcal{A}_{sat}$')
plt.xscale('log')
plt.legend(loc='best')
plt.show();


    

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