# -*- coding: utf-8 -*-

#  Copyright (C) 2010 by Bruno Chareyre                                  *
#  bruno.chareyre_at_grenoble-inp.fr                                     *

from yade import pack

############################################
###   DEFINING VARIABLES AND MATERIALS   ###
############################################

# Batch execution
nRead=utils.readParamsFromTable(
    num_spheres=10000,# number of spheres
    compFricDegree = 5, # contact friction during the confining phase
    unknownOk=True,
    isoForce=100000
)
from yade.params import table

num_spheres=table.num_spheres# number of spheres
targetPorosity = 0.382 #the porosity we want for the packing
compFricDegree = table.compFricDegree # initial contact friction during the confining phase (will be decreased during the REFD compaction process)
finalFricDegree = 35 # contact friction during the deviatoric loading
rate=0.002 # loading rate (strain rate)
damp=0.2 # damping coefficient
stabilityThreshold=0.01 # we test unbalancedForce against this value in different loops (see below)
key='_triax_draine_e618_100_psd' # put you simulation's name here
young=2006060 # contact stiffness
mn,mx=Vector3(-0.15,-0.15,-0.15),Vector3(0.15,0.15,0.15) # corners of the initial packing
thick = 0.01 # thickness of the plates


## create materials for spheres and plates
O.materials.append(FrictMat(young=young,poisson=0.42,frictionAngle=radians(compFricDegree),density=3000,label='spheres'))
O.materials.append(FrictMat(young=young,poisson=0.5,frictionAngle=0,density=0,label='walls'))

## create walls around the packing
walls=utils.aabbWalls([mn,mx],thickness=thick,oversizeFactor=1.5,material='walls')
wallIds=O.bodies.append(walls)

## use a SpherePack object to generate a random loose particles packing
sp=pack.SpherePack()
psdSizes=[0.002,0.003,0.004,0.005,0.006,0.007,0.008,0.0095] # (sizes or radii of the grains vary from 2mm to 9.5mm)
psdCumm=[1,9,25,50,69,90,95,100] # the correspondent amount (percentage) of each diameter
#psdCumm=[0.01,0.09,0.25,0.50,0.69,0.90,0.95,1.00] # for the code do not use percentage
#---------------------------------------------
#psdSizes=[0.002,0.004,0.008,0.0095] # (sizes or radii of the grains vary from 2mm to 9.5mm)
#psdCumm=[1,25,95,100] # the correspondent amount (percentage) of each diameter

#clumps=False #turn this true for the same example with clumps
#if clumps:
# ## approximate mean rad of the futur dense packing for latter use
# volume = (mx[0]-mn[0])*(mx[1]-mn[1])*(mx[2]-mn[2])
# mean_rad = pow(0.09*volume/num_spheres,0.3333)
# ## define a unique clump type (we could have many, see clumpCloud documentation)
# c1=pack.SpherePack([((-0.2*mean_rad,0,0),0.5*mean_rad),((0.2*mean_rad,0,0),0.5*mean_rad)])
# ## generate positions and input them in the simulation
# sp.makeClumpCloud(mn,mx,[c1],periodic=False)
# sp.toSimulation(material='spheres')
#else:

###################################################
#method 01
#sp.makeCloud(mn,mx,-1,0,num_spheres,False, 0.95,psdSizes,psdCumm,False,seed=1) #"seed" make the "random" generation always the same

#method 02
sp.particleSD(mn,mx,0.00575,False,'triaxial_test',10000,psdSizes,psdCumm,False,seed=1)
#method 03
#sp.particleSD2(psdSizes,psdCumm,10000,False,cloudPorosity=0.95,0)

# O.bodies.append([utils.sphere(center,rad,material='spheres') for center,rad in sp])
 #or alternatively (higher level function doing exactly the same):
sp.toSimulation(material='spheres')

############################
###   DEFINING ENGINES   ###
############################

triax=TriaxialStressController(
    maxMultiplier=1.001, # spheres growing factor (fast growth)
    finalMaxMultiplier=1.01, # spheres growing factor (slow growth)
    thickness = thick,
    ## switch stress/strain control using a bitmask. What is a bitmask, huh?!
    ## Say x=1 if stress is controlled on x, else x=0. Same for for y and z, which are 1 or 0.
    ## Then an integer uniquely defining the combination of all these tests is: mask = x*1 + y*2 + z*4
    ## to put it differently, the mask is the integer whose binary representation is xyz, i.e.
    ## "100" (1) means "x", "110" (3) means "x and y", "111" (7) means "x and y and z", etc.
    stressMask = 7,
    #the value of confining stress for the intitial (growth) phase
    goal1=table.isoForce,
    goal2=table.isoForce,
    goal3=table.isoForce,
    max_vel=0.005,
    internalCompaction=False, # If true the confining pressure is generated by growing particles
#   Key=key # passed to the engine so that the output file will have the correct name
)

newton=NewtonIntegrator(damping=damp)

O.engines=[
    ForceResetter(),
    InsertionSortCollider([Bo1_Sphere_Aabb(),Bo1_Box_Aabb()]),
    InteractionLoop(
        [Ig2_Sphere_Sphere_ScGeom(),Ig2_Box_Sphere_ScGeom()],
        [Ip2_FrictMat_FrictMat_FrictPhys()],
        [Law2_ScGeom_FrictPhys_CundallStrack()]
    ),
    ## We will use the global stiffness of each body to determine an optimal timestep (see https://yade-dem.org/w/images/1/1b/Chareyre&Villard2005_licensed.pdf)
    GlobalStiffnessTimeStepper(active=1,timeStepUpdateInterval=50,timestepSafetyCoefficient=0.8),
    triax,
    TriaxialStateRecorder(iterPeriod=50,file='WallStresses'+key),
    newton
]

#Display spheres with 2 colors for seeing rotations better
Gl1_Sphere.stripes=0
if nRead==0: yade.qt.Controller(), yade.qt.View()

## UNCOMMENT THE FOLLOWING SECTIONS ONE BY ONE
## DEPENDING ON YOUR EDITOR, IT COULD BE DONE
## BY SELECTING THE CODE BLOCKS BETWEEN THE SUBTITLES
## AND PRESSING CTRL+SHIFT+D

#######################################
###   APPLYING CONFINING PRESSURE   ###
#######################################

while 1:
  O.run(1000, True)
  #the global unbalanced force on dynamic bodies, thus excluding boundaries, which are not at equilibrium
  unb=unbalancedForce()
  #average stress
  #note: triax.stress(k) returns a stress vector, so we need to keep only the normal component
  meanS=(triax.stress(triax.wall_right_id)[0]+triax.stress(triax.wall_top_id)[1]+triax.stress(triax.wall_front_id)[2])/3
  print 'unbalanced force:',unb,' mean stress: ',meanS, 'void ratio=', triax.porosity/(1-triax.porosity)
  if unb<stabilityThreshold and abs(meanS-table.isoForce)/table.isoForce<0.001:
    break

O.save('confinedState'+key+'.yade.gz')
print "###      Isotropic state saved      ###"
print 'current porosity=',triax.porosity
print 'current void ratio=',triax.porosity/(1-triax.porosity)

###################################################
###   REACHING A SPECIFIED POROSITY PRECISELY   ###
###################################################

### We will reach a prescribed value of porosity with the REFD algorithm
### (see http://dx.doi.org/10.2516/ogst/2012032 and
### http://www.geosyntheticssociety.org/Resources/Archive/GI/src/V9I2/GI-V9-N2-Paper1.pdf)

import sys #this is only for the flush() below
while triax.porosity>targetPorosity:
    # we decrease friction value and apply it to all the bodies and contacts
    compFricDegree = 0.95*compFricDegree
    setContactFriction(radians(compFricDegree))
    print "\r Friction: ",compFricDegree," porosity:",triax.porosity,
    sys.stdout.flush()
    # while we run steps, triax will tend to grow particles as the packing
    # keeps shrinking as a consequence of decreasing friction. Consequently
    # porosity will decrease
    O.run(500,1)

O.save('compactedState'+key+'.yade.gz')
print "###    Compacted state saved      ###"
print 'current porosity=',triax.porosity
print 'current void ratio=',triax.porosity/(1-triax.porosity)

##############################
###   DEVIATORIC LOADING   ###
##############################
triax.goal1=triaxgoal2=triax.goal3=100000
#We move to deviatoric loading, let us turn internal compaction off to keep particles sizes constant
#triax.internalCompaction=False

# Change contact friction (remember that decreasing it would generate instantaneous instabilities)
#setContactFriction(radians(finalFricDegree))
setContactFriction(radians(35))

#set stress control on x and z, we will impose strain rate on y (5)
triax.stressMask = 5
#now goal2 is the target strain rate
triax.goal2=-rate
#we assign constant stress to the other directions
triax.goal1=100000
triax.goal3=100000

##we can change damping here. What is the effect in your opinion?
newton.damping=0.1

##Save temporary state in live memory. This state will be reloaded from the interface with the "reload" button.
O.saveTmp()

#####################################################
###    Example of how to record and plot data     ###
#####################################################

from yade import plot

## a function saving variables
def history():
    plot.addData(e11=triax.strain[0], e22=triax.strain[1], e33=triax.strain[2],
            ev=-triax.strain[0]-triax.strain[1]-triax.strain[2],
            s11=triax.stress(triax.wall_right_id)[0],
            s22=triax.stress(triax.wall_top_id)[1],
            s33=triax.stress(triax.wall_front_id)[2],
            p=(triax.stress(triax.wall_right_id)[0]+triax.stress(triax.wall_top_id)[1]+triax.stress(triax.wall_front_id)[2])/3000,
            q=(triax.stress(triax.wall_top_id)[1]-triax.stress(triax.wall_front_id)[2])/1000,
            i=O.iter)

if 1:
  # include a periodic engine calling that function in the simulation loop
  O.engines=O.engines[0:5]+[PyRunner(iterPeriod=20,command='history()',label='recorder')]+O.engines[5:7]
  O.engines.insert(4,PyRunner(iterPeriod=20,command='history()',label='recorder'))
else:
  # With the line above, we are recording some variables twice. We could in fact replace the previous
  # TriaxialRecorder
  # by our periodic engine. Uncomment the following line:
  O.engines[4]=PyRunner(iterPeriod=20,command='history()',label='recorder')

O.run(100,True)

### declare what is to plot. "None" is for separating y and y2 axis
#plot.plots={'i':('e11','e22','e33',None,'s11','s22','s33')}
### the traditional triaxial curves would be more like this:
plot.plots={'e22':('q')}

## display on the screen (doesn't work on VMware image it seems)
plot.plot()

#####  PLAY THE SIMULATION HERE WITH "PLAY" BUTTON OR WITH THE COMMAND O.run(N)  #####

## In that case we can still save the data to a text file at the the end of the simulation, with:
plot.saveDataTxt('results'+key)
##or even generate a script for gnuplot. Open another terminal and type  "gnuplot plotScriptKEY.gnuplot:
plot.saveGnuplot('plotScript'+key)