#!/usr/bin/python
# -*- coding: UTF-8 no BOM -*-

import numpy as np
from scipy.optimize import curve_fit
from scipy.linalg import svd
import threading,time,os,subprocess,shlex
import damask

def execute(cmd,dir='./'):
      
  initialPath=os.getcwd()
  out = ''
  line = True
  process = subprocess.Popen(shlex.split(cmd),stdout=subprocess.PIPE,stderr = subprocess.STDOUT)
  while line:
    line = process.stdout.readline()
    out += line
  os.chdir(initialPath)

def asFullTensor(voigt):
  return np.array([[voigt[0],voigt[3],voigt[5]],\
          [voigt[3],voigt[1],voigt[4]],\
          [voigt[5],voigt[4],voigt[2]]])

def Hill48(x, F,G,H,L,M,N):
  a= F*(x[1]-x[2])**2 + G*(x[2]-x[0])**2 + H*(x[0]-x[1])** + \
         2*L*x[4]**2 + 2*M*x[5]**2 + 2*N*x[3]**2 -1.
  return a.ravel()

def vonMises(x, S_y):
  sv=np.zeros(0,'d')
  for i in xrange(np.shape(x)[1]):
    U, l, Vh = svd(np.array(x[:,i]).reshape(3,3))
    sv = np.append(sv,l)
  sv = sv.reshape(3,np.shape(x)[1])
  ooo= (sv[2,:]-sv[1,:])**2+(sv[1,:]-sv[0,:])**2+(sv[0,:]-sv[2,:])**2-2*S_y**2
  return ooo

#---------------------------------------------------------------------------------------------------
class Loadcase():
#---------------------------------------------------------------------------------------------------
  '''
     Class for generating load cases for the spectral solver
  '''

# ------------------------------------------------------------------
  def __init__(self):
    print('using the random load case generator')

  def getNext(self,N=0):
    defgrad=['*']*9
    stress =[0]*9
    values=(np.random.random_sample(9)-.5)*scale*2

    main=np.array([0,4,8])
    np.random.shuffle(main)
    for i in main[:2]: # fill 2 out of 3 main entries
      defgrad[i]=1.+values[i]
      stress[i]='*'
    for off in [[1,3,0],[2,6,0],[5,7,0]]: # fill 3 off-diagonal pairs of defgrad (1 or 2 entries)
      off=np.array(off)
      np.random.shuffle(off)
      for i in off[0:2]:
        if i != 0: 
          defgrad[i]=values[i]
          stress[i]='*'

    return 'f '+' '.join(str(c) for c in defgrad)+\
          ' p '+' '.join(str(c) for c in stress)+\
          ' incs %s'%incs+\
          ' time %s'%duration

#---------------------------------------------------------------------------------------------------
class Criterion(object):
#---------------------------------------------------------------------------------------------------
  '''
     Fitting to certain criterion
  '''
  def __init__(self,name):
    self.name = name.lower()
    if self.name not in ['hill48','vonmises']: print('Mist')
    print('using the %s criterion'%self.name)
    self.popt = 0.0

  def fit(self,stress):
    try:
      popt, pcov = curve_fit(Hill48, stress, np.zeros(np.shape(stress)[1]))
      print 'Hill 48', popt
    except Exception as detail:
      print detail
      pass
    popt, pcov = curve_fit(vonMises, stress, np.zeros(np.shape(stress)[1]))
    print 'von Mises', popt


#---------------------------------------------------------------------------------------------------

#---------------------------------------------------------------------------------------------------
  '''
     Runner class
  '''
class myThread (threading.Thread):
  def __init__(self, threadID):
    threading.Thread.__init__(self)
    self.threadID = threadID
  def run(self):
    s.acquire()
    conv=converged()
    s.release()
    while not conv:
      doSim(4.,self.name)
      s.acquire()
      conv=converged()
      s.release()

def doSim(delay,thread):

  s.acquire()
  me=getLoadcase()
  print('generating loadcase for sim %i from %s'%(me,thread))
  f=open('%s.load'%me,'w')
  f.write(myLoad.getNext(me))
  f.close()

  print('starting simulation %i from %s'%(me,thread))
  s.release()

  execute('DAMASK_spectral -l %i -g 20grains16x16x16'%me)

  s.acquire()
  print('startin post processing for sim %i from %s'%(me,thread))
  s.release()
  execute('postResults --cr f,p 20grains16x16x16_%i.spectralOut'%me)
  execute('addCauchy ./postProc/20grains16x16x16_%i.txt'%me)
  execute('addStrainTensors -l -v ./postProc/20grains16x16x16_%i.txt'%me)
  execute('addMises -s Cauchy -e ln(V) ./postProc/20grains16x16x16_%i.txt'%me)

  refFile = open('./postProc/20grains16x16x16_%i.txt'%me)
  table = damask.ASCIItable(refFile)
  table.head_read()
  for l in ['Mises(ln(V))','1_Cauchy']:
    if l not in table.labels: print '%s not found'%l
  while table.data_read():
    if float(table.data[table.labels.index('Mises(ln(V))')]) > 0.002:
      yieldStress = np.array(table.data[table.labels.index('1_Cauchy'):table.labels.index('9_Cauchy')+1],'d').reshape(3,3)

  s.acquire()
  print('startin fitting for sim %i from %s'%(me,thread))
  global stressAll
  stressAll=np.append(stressAll,yieldStress.reshape(9))
  stressAll=stressAll.reshape(9,len(stressAll)//9)
  myFit.fit(stressAll)
  s.release()

def getLoadcase():
  global N_simulations
  N_simulations+=1
  return N_simulations

def converged():
  global N_simulations
  global maxN_simulations
  if N_simulations < maxN_simulations:
    return False
  else:
    return True

# main

minN_simulations=20
maxN_simulations=20
N_simulations=0
s=threading.Semaphore(1)
scale = 0.02
incs = 10
duration = 10
stressAll=np.zeros(0,'d')

myLoad = Loadcase()
myFit = Criterion('Hill48')

N_threads=3
t=[]

for i in range(N_threads):
  t.append(myThread(i))
  t[i].start()

for i in range(N_threads):
  t[i].join()

print "Exiting Main Thread"