# 
# 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>
# 
# This software is part of the ALPS libraries, published under the ALPS
# Library License; you can use, redistribute it and/or modify it under
# the terms of the license, either version 1 or (at your option) any later
# version.
#  
# You should have received a copy of the ALPS Library License along with
# the ALPS Libraries; see the file LICENSE.txt. If not, the license is also
# available from http://alps.comp-phys.org/.
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
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# ****************************************************************************

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


## Please run the tutorial5a.py before this one

listobs = ['0', '2']   # 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-05: Orbitally Selective Mott Transition on the Bethe lattice: ' + r'$U = %.4s$' % common_props['U'])
    pyalps.plot.plot(sim)
    plt.legend()
    plt.yscale("log")

plt.show()