from __future__ import print_function
# ****************************************************************************
# 
# ALPS Project: Algorithms and Libraries for Physics Simulations
# 
# ALPS Libraries
# 
# Copyright (C) 2010 by Jan Gukelberger <gukelberger@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 m in [20,40,60]:
    parms.append({ 
        'LATTICE'                   : "open chain lattice", 
        'MODEL'                     : "spin",
        'CONSERVED_QUANTUMNUMBERS'  : 'N,Sz',
        'Sz_total'                  : 0,
        'J'                         : 1,
        'SWEEPS'                    : 4,
        'NUMBER_EIGENVALUES'        : 1,
        'L'                         : 32,
        'MAXSTATES'                 : m
       })

#write the input file and run the simulation
input_file = pyalps.writeInputFiles('parm_spin_one_half_multiple',parms)
res = pyalps.runApplication('dmrg',input_file,writexml=True)

#load all measurements for all states
data = pyalps.loadEigenstateMeasurements(pyalps.getResultFiles(prefix='parm_spin_one_half_multiple'))

# print properties of the eigenvector for each run:
for run in data:
    for s in run:
        print(s.props['observable'], ' : ', s.y[0])