# encoding: utf-8

# periodic triaxial test simulation
#
# The initial packing is either
#
# 1. random cloud with uniform distribution, or
# 2. cloud with specified granulometry (radii and percentages), or
# 3. cloud of clumps, i.e. rigid aggregates of several particles
#
# The triaxial consists of 2 stages:
#
# 1. isotropic compaction, until sigmaIso is reached in all directions;
#    this stage is ended by calling compactionFinished() 
# 2. constant-strain deformation along the z-axis, while maintaining 
#    constant stress (sigmaIso) laterally; this stage is ended by calling
#    triaxFinished()
# 
# Controlling of strain and stresses is performed via PeriTriaxController,
# of which parameters determine type of control and also stability
# condition (maxUnbalanced) so that the packing is considered stabilized
# and the stage is done.
#

from __future__ import print_function
sigmaIso=-1e5

#import matplotlib
#matplotlib.use('Agg')

# generate loose packing
from yade import pack, qt, plot

O.periodic=True
sp=pack.SpherePack()
if 0:
	## uniform distribution
	sp.makeCloud((0,0,0),(2,2,2),rMean=.1,rRelFuzz=.3,periodic=True)
else:
	## create packing from clumps
	# configuration of one clump
	c1=pack.SpherePack([((0,0,0),.03333),((.03,0,0),.017),((0,.03,0),.017)])
	# make cloud using the configuration c1 (there could c2, c3, ...; selection between them would be random)
	sp.makeClumpCloud((0,0,0),(2,2,2),[c1],periodic=True,num=500)

# setup periodic boundary, insert the packing
sp.toSimulation()

O.engines=[
	ForceResetter(),
	InsertionSortCollider([Bo1_Sphere_Aabb()]),
	InteractionLoop(
		[Ig2_Sphere_Sphere_ScGeom()],
		[Ip2_FrictMat_FrictMat_FrictPhys()],
		[Law2_ScGeom_FrictPhys_CundallStrack()]
	),
	PeriTriaxController(label='triax',
		# specify target values and whether they are strains or stresses
		goal=(sigmaIso,sigmaIso,sigmaIso),stressMask=7,
		# type of servo-control
		dynCell=True,maxStrainRate=(10,10,10),
		# wait until the unbalanced force goes below this value
		maxUnbalanced=.1,relStressTol=1e-3,
		# call this function when goal is reached and the packing is stable
		doneHook='compactionFinished()'
	),
	NewtonIntegrator(damping=.2),
	PyRunner(command='addPlotData()',iterPeriod=100),
]
O.dt=.5*PWaveTimeStep()

def addPlotData():
	plot.addData(unbalanced=unbalancedForce(),i=O.iter,
		sxx=triax.stress[0],syy=triax.stress[1],szz=triax.stress[2],
		exx=triax.strain[0],eyy=triax.strain[1],ezz=triax.strain[2],
		# save all available energy data
		Etot=O.energy.total(),**O.energy
	)

# enable energy tracking in the code
O.trackEnergy=True

# define what to plot
plot.plots={'i':('unbalanced',),'i ':('sxx','syy','szz'),' i':('exx','eyy','ezz'),
	# energy plot
	' i ':(O.energy.keys,None,'Etot'),
}
# show the plot
plot.plot()

def compactionFinished():
	# set the current cell configuration to be the reference one
	O.cell.trsf=Matrix3.Identity
	# change control type: keep constant confinement in x,y, 20% compression in z
	triax.goal=(sigmaIso,sigmaIso,-.2)
	triax.stressMask=3
	# allow faster deformation along x,y to better maintain stresses
	triax.maxStrainRate=(1.,1.,.1)
	# next time, call triaxFinished instead of compactionFinished
	triax.doneHook='triaxFinished()'
	# do not wait for stabilization before calling triaxFinished
	triax.maxUnbalanced=10

def triaxFinished():
	print('Finished')
	O.pause()

