# 
# 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/.
# 
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 
# FITNESS FOR A PARTICULAR PURPOSE, TITLE AND NON-INFRINGEMENT. IN NO EVENT 
# SHALL THE COPYRIGHT HOLDERS OR ANYONE DISTRIBUTING THE SOFTWARE BE LIABLE 
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# DEALINGS IN THE SOFTWARE.
# 
# ****************************************************************************

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


#prepare the input parameters
parms=[]
for u,j in [[1.8,0.45],[2.2,0.55],[2.8,0.7]]:
    parms.append(
            { 
              'CONVERGED'           : 0.001,
              'FLAVORS'             : 4,
              'H'                   : 0,
              'H_INIT'              : 0.,
              'MAX_IT'              : 15,
              'MAX_TIME'            : 600,
              'MU'                  : 0,
              'N'                   : 500,
              'NMATSUBARA'          : 500,
              'N_MEAS'              : 2000,
              'N_ORDER'             : 50,
              'SEED'                : 0,
              'SOLVER'              : 'hybridization',
              'SC_WRITE_DELTA'      : 1,
              'SYMMETRIZATION'      : 1,
              'SWEEPS'              : 10000,
              'BETA'                : 30,
              'THERMALIZATION'      : 500,
              'U'                   : u,
              'J'                   : j,
              't0'                  : 0.5,
              't1'                  : 1
        }
        )

# For more precise calculations we propose to enhance the SWEEPS

#write the input file and run the simulation
for p in parms:
    input_file = pyalps.writeParameterFile('parm_u_'+str(p['U'])+'_j_'+str(p['J']),p)
    res = pyalps.runDMFT(input_file)

listobs = ['0', '2']   # flavor 0 is SYMMETRIZED with 1, flavor 2 is SYMMETRIZED with 3
    
data = pyalps.loadMeasurements(pyalps.getResultFiles(pattern='parm_u_*h5'), respath='/simulation/results/G_tau', what=listobs, verbose=True)
for d in pyalps.flatten(data):
    d.x = d.x*d.props["BETA"]/float(d.props["N"])
    d.y = -d.y
    d.props['label'] = r'$U=$'+str(d.props['U'])+'; flavor='+str(d.props['observable'][len(d.props['observable'])-1])
plt.figure()
plt.yscale('log')
plt.xlabel(r'$\tau$')
plt.ylabel(r'$G_{flavor}(\tau)$')
plt.title('DMFT-05: Orbitally Selective Mott Transition on the Bethe lattice')
pyalps.plot.plot(data)
plt.legend()
plt.show()