runme.py

# A simple file to produce the results we'll present on 05/07/20

#List of parameters should look like 'o_l, o_m, H0, M'

import numpy as np
import gmpy2
from local_code.pritom.markov_chain import generate_MCMC_chain
from local_code.adam.visualization import plot_markov_chain, split_data, pantheon_scatter, histogram_H0
from local_code.adam.new_parameters import save_chain, load_chain
from matplotlib import pyplot as plt

#[0.8, 0.3, 70000, 19.23]sigmas = [.01,.01,.01,.01]

def reproduce_pantheon_constraints(sys=1):
    '''
    Quickly loads and plots Markov chains.

    Input is sys, a variable which determines whether to load and plot the chain which includes systematic
    error (1) or does not include systematic error (0).
    '''
    my_chain = load_chain(sys=sys)
    #plot_markov_chain(my_chain)
    pantheon_scatter(my_chain)
    #print(len(my_chain))
    return my_chain

'''
#  Optional Convergence Tests, To Be Implemented If Time
#  (Pritom)
def plot_posterior_of_h0(chain_in):
    histogram_H0(chain_in)

def residual_plot_binned_data():
    pass

def chi_squared_and_PTE():
    pass

def noise_and_best_fit():
    pass

def histogram_of_cov_squared_residuals():
    pass

def h0_stability_test():
    pass
'''


'''
#Part 1
my_chain = reproduce_pantheon_constraints()


#Part 2
plot_posterior_of_h0(my_chain)

#Part 3, tests
residual_plot_binned_data()

chi_squared_and_PTE()

noise_and_best_fit()

histogram_of_cov_squared_residuals()

h0_stability_test()
'''