# 
# ALPS Project: Algorithms and Libraries for Physics Simulations
# 
# ALPS Libraries
# 
# Copyright (C) 2010      by Brigitte Surer <surerb@phys.ethz.ch> 
#               2012-2013 by Jakub Imriska  <jimriska@phys.ethz.ch>
# 
# ALPS Project: https://alps.comp-phys.org/
# SPDX-License-Identifier: MIT
# 
# ****************************************************************************

import pyalps
import numpy as np
import matplotlib.pyplot as plt
import pyalps.plot


## Please run the tutorial4a.py before this one

listobs = ['0']   # we look at convergence of a single flavor (=0) 

## load all results
data = pyalps.loadDMFTIterations(pyalps.getResultFiles(pattern='parm_u_*.h5'), measurements=listobs, verbose=True)

## create a figure for each BETA
grouped = pyalps.groupSets(pyalps.flatten(data), ['U','observable'])
for sim in grouped:
    common_props = pyalps.dict_intersect([ d.props for d in sim ])
    
    ## rescale x-axis and set label
    for d in sim:
        d.x = d.x * d.props['BETA']/float(d.props['N'])
        d.y *= -1.
        d.props['label'] = 'it'+d.props['iteration']
    
    ## plot all iterations for this BETA
    plt.figure()
    plt.xlabel(r'$\tau$')
    plt.ylabel(r'$-G_{flavor=%8s}(\tau)$' % common_props['observable'])
    plt.title('DMFT-04: ' + r'$U = %.4s$' % common_props['U'])
    pyalps.plot.plot(sim)
    plt.legend()
    plt.yscale("log")

plt.show()