def getData2(variable1,variable2,data):
fudge = 1.1 # Srinath added 10% to wcell??
keys = ['increment','time','exx','sxx','epamp','sxxT']
indexdict = Mdict.instantiate(keys,list(range(len(keys))))
scaledict = Mdict.instantiate(keys,[1,1.e9,1.e2*fudge,1.e6,1.e2*fudge,1.e6])
xy = data[:,[indexdict[variable1],indexdict[variable2]]]
scale = [scaledict[variable1],scaledict[variable2]]
return xy, scale
def solveEqnsNew(ep,
r1bar=r1bartry,
r2bar=r2bartry,
r3bar=r3bartry,
alphabar=alphabartry,
offset=0,
step=0.01,
tol=1.e-8,
r3barfac=0.98,
n1=3,
n2=3):
# largest cutoff radius possible is r3bar
# smallest cutoff radius possible is r2bar
# tries to solve for largest cutoff radius possible
# constructs smooth potential so that value of potential at r2bar is -ep
# n1, n2 are order of splines
while r3barfac > 0:
r3barcurr = r2bar + (r3bar - r2bar) * r3barfac
func = lambda coeff: solveEqnsSub(coeff, ep, r1bar, r2bar, r3barcurr,
alphabar, offset, n1, n2)
coeffsol = spo.fsolve(func, np.zeros(n1 + n2 + 2))
spline1, spline2 = coeffsol[0:n1 + 1], coeffsol[n1 + 1:n1 + n2 + 2]
maxval2 = Mmath.maxPolyRoot(spline2)
if maxval2 > tol:
r3barfac = r3barfac - step
else:
keyslist = [
'spline1', 'spline2', 'r1bar', 'r2bar', 'r3bar', 'alphabar',
'offset'
]
valslist = [
spline1, spline2, r1bar, r2bar, r3barcurr, alphabar, offset
]
return Mdict.instantiate(keyslist, valslist)
print('Bad input')
def solveEqnsWarner(
r3bar,
r1bar=r1bartrywarner,
alphabar=alphabartry,
offset=0
): # as r3bar (cutoff) increases, potential changes from brittle to ductile; brittle - r3bar = r2bartry; ductile - r3bar = 0.99*r3bartry (values outside bounds lead to bad potentials)
func = lambda coeff: solveEqnsSubWarner(coeff, r1bar, r3bar, alphabar,
offset)
coeffsol = spo.fsolve(func, np.zeros(4))
keyslist = ['spline1', 'r1bar', 'r3bar', 'alphabar', 'offset']
valslist = [coeffsol, r1bar, r3bar, alphabar, offset]
return Mdict.instantiate(keyslist, valslist)
def createPlotDicts():
colorlist = ['r','g','b','m','y']
markersizedisl = 100 # in points
markersizesn = 25 # in points
plotkeys = ['edgecolor','facecolor','s']
markerkeys = ['markertype', 'rotation']
objectkeys = ['spos','opos','pos','neg']
nucdictp = Mdict.instantiate(plotkeys,['k','k',markersizesn])
obsdictp = Mdict.instantiate(plotkeys,['k','None',markersizesn])
disldictp = Mdict.instantiate(plotkeys,[colorlist,'None',markersizedisl])
nucdictm = Mdict.instantiate(markerkeys,['o',0])
obsdictm = Mdict.instantiate(markerkeys,['o',0])
dislposdictm = Mdict.instantiate(markerkeys,['disl',0])
dislnegdictm = Mdict.instantiate(markerkeys,['disl',180])
plotdict = Mdict.instantiate(objectkeys,[nucdictp,obsdictp,disldictp,disldictp])
markerdict = Mdict.instantiate(objectkeys,[nucdictm,obsdictm,dislposdictm,dislnegdictm])
return (plotdict, markerdict)
def createPlotDicts():
colorlist = ['r', 'g', 'b', 'm', 'y']
markersizedisl = 100 # in points
markersizesn = 25 # in points
plotkeys = ['edgecolor', 'facecolor', 's']
markerkeys = ['markertype', 'rotation']
objectkeys = ['spos', 'opos', 'pos', 'neg']
nucdictp = Mdict.instantiate(plotkeys, ['k', 'k', markersizesn])
obsdictp = Mdict.instantiate(plotkeys, ['k', 'None', markersizesn])
disldictp = Mdict.instantiate(plotkeys,
[colorlist, 'None', markersizedisl])
nucdictm = Mdict.instantiate(markerkeys, ['o', 0])
obsdictm = Mdict.instantiate(markerkeys, ['o', 0])
dislposdictm = Mdict.instantiate(markerkeys, ['disl', 0])
dislnegdictm = Mdict.instantiate(markerkeys, ['disl', 180])
plotdict = Mdict.instantiate(objectkeys,
[nucdictp, obsdictp, disldictp, disldictp])
markerdict = Mdict.instantiate(
objectkeys, [nucdictm, obsdictm, dislposdictm, dislnegdictm])
return (plotdict, markerdict)
def solveEqnsNew(ep,r1bar=r1bartry,r2bar=r2bartry,r3bar=r3bartry,alphabar=alphabartry,offset=0,step=0.01,tol=1.e-8,r3barfac=0.98,n1=3,n2=3):
# largest cutoff radius possible is r3bar
# smallest cutoff radius possible is r2bar
# tries to solve for largest cutoff radius possible
# constructs smooth potential so that value of potential at r2bar is -ep
# n1, n2 are order of splines
while r3barfac > 0:
r3barcurr = r2bar + (r3bar - r2bar)*r3barfac
func = lambda coeff: solveEqnsSub(coeff,ep,r1bar,r2bar,r3barcurr,alphabar,offset,n1,n2)
coeffsol = spo.fsolve(func,np.zeros(n1+n2+2))
spline1, spline2 = coeffsol[0:n1+1], coeffsol[n1+1:n1+n2+2]
maxval2 = Mmath.maxPolyRoot(spline2)
if maxval2 > tol:
r3barfac = r3barfac - step
else:
keyslist = ['spline1','spline2','r1bar','r2bar','r3bar','alphabar','offset']
valslist = [spline1,spline2,r1bar,r2bar,r3barcurr,alphabar,offset]
return Mdict.instantiate(keyslist,valslist)
print('Bad input')
def getData1(variable,data):
keys = ['sxx', 'syy', 'sxy', 'u', 'v']
indexdict = Mdict.instantiate(keys,[2,3,4,2,3])
return data[:,0], data[:,1], data[:,indexdict[variable]]
def getFileInfo(variable):
keys = ['sxx', 'syy', 'sxy', 'u', 'v']
filetypedict = Mdict.instantiate(keys,['stress']*3 + ['displ']*2) # this depends on what's used in GetDDFileName
datakeydict = Mdict.instantiate(keys,['Stress']*3 + ['Displacement']*2) # this depends on the key used in .plt file
return (filetypedict[variable], datakeydict[variable])
def solveEqnsWarner(r3bar,r1bar=r1bartrywarner,alphabar=alphabartry,offset=0): # as r3bar (cutoff) increases, potential changes from brittle to ductile; brittle - r3bar = r2bartry; ductile - r3bar = 0.99*r3bartry (values outside bounds lead to bad potentials)
func = lambda coeff: solveEqnsSubWarner(coeff,r1bar,r3bar,alphabar,offset)
coeffsol = spo.fsolve(func,np.zeros(4))
keyslist = ['spline1','r1bar','r3bar','alphabar','offset']
valslist = [coeffsol,r1bar,r3bar,alphabar,offset]
return Mdict.instantiate(keyslist,valslist)