def AllMaxesTriax(constit='vm', savedata=False):
'''
Plots the vm eeq at failure for every passing column max
'''
ex = n.genfromtxt('../ExptSummary.dat', delimiter=',')
ex = ex[(ex[:, 1] == 0) & (ex[:, 3] != 0)][:, 0].astype(int)
failz = n.genfromtxt('./failstns.dat', delimiter='\t')
col = {'vm': 15, 'h8': 16, 'anis': 17}
p.style.use('mysty')
fig, ax = p.subplots()
for k, x in enumerate(ex):
lines = []
triax = failz[failz[:, 0] == x, col[constit] - 13]
proj = 'TTGM-{}_FS19SS6'.format(x)
d = n.load(
'../{}/IncrementalAnalysis/NewFilterPassingPoints_3med.npy'.format(
proj))[-1, :, col[constit]]
if x == 10:
d = d[d != d.min()]
p.plot(n.ones_like(d) * triax, d, '.', linestyle='none', color='C0')
p.plot(triax, d.mean(), 'o', color='C1')
ax.axis(xmin=0)
ax.set_xlabel('$\\sigma_{\\mathsf{m}}/\\sigma_{\\mathsf{e}}$')
ax.set_ylabel('$\\mathsf{e}^{\\mathsf{p}}_{\\mathsf{e}}$')
f.eztext(ax, 'Incremental\nAl-6061-T6', 'bl')
f.myax(ax)
p.show()
return fig, ax
def KelinCompare(expt, savedata=False):
proj = 'TTGM-{}_FS19SS6'.format(expt)
key = n.genfromtxt('../ExptSummary.dat', delimiter=',')
alpha = key[key[:, 0] == expt, 3].ravel()
dr = n.genfromtxt('../{}/disp-rot.dat'.format(proj), delimiter=',')[:, 5]
d = n.load(
'../{}/IncrementalAnalysis/NewFilterPassingPoints_3med.npy'.format(
proj))
mnmx = d[..., 17].min(axis=1), d[..., 17].max(axis=1)
cols = {4: (0, 1), 1: (2, 3), 5: (4, 5)}
s = read_excel('../Misc/KelinSim.xlsx', parse_cols=cols[expt]).values
if savedata:
n.savetxt('KelinCompare_Expt-{}.dat'.format(expt),
X=n.c_[dr, d[..., 17].mean(axis=1), mnmx[0], mnmx[1]],
fmt="%.6f",
delimiter=',')
n.savetxt('KelinCompare_Expt-{}_kel.dat'.format(expt),
X=s,
fmt="%.6f",
delimiter=',')
p.style.use('mysty')
fig, ax = p.subplots()
p.fill_between(dr, mnmx[0], mnmx[1], alpha=0.1, color='C0')
p.plot(dr, mnmx[1], 'C0', label='Exp')
p.plot(dr, mnmx[0], 'C0')
p.plot(dr, d[:, :, 17].mean(axis=1), '--', color='C0')
p.plot(s[:, 0], s[:, 1], 'C1', lw=2, label='Anal Anis')
ax.axis(xmin=0, ymin=0)
ax.set_xlabel('$\\Phi$')
ax.set_ylabel('e$_\\mathsf{e}$')
f.ezlegend(ax,
loc=2,
title='TTGM-{}\n$\\alpha$ = {:g}'.format(expt, alpha[0]))
f.myax(ax)
return fig, ax
def CornerFailure(max=False, mean=True, constit='vm'):
key = n.genfromtxt('../ExptSummary.dat', delimiter=',')
key = key[(key[:, 3] != 0)]
key = key[key[:, 3].argsort()]
corneralphas = n.unique(key[n.in1d(key[:, 1], [1, 2])][:, 3])
ex, extype = key[:, :2].astype(int).T
failz = n.genfromtxt('./failstns.dat', delimiter='\t')
columnmapper = {'vm': [0, 6], 'h8': [1, 7], 'anis': [2, 8]}
triaxmapper = {'vm': 2, 'h8': 3, 'anis': 4}
marker = ['C0s', 'C1o', 'C2^']
label = [
'$\\Sigma=\\alpha\\mathcal{T}$', '$\\Sigma\\rightarrow\\mathcal{{T}}$',
'$\\mathcal{{T}}\\rightarrow\\Sigma$'
]
do = False
p.style.use('mysty')
fig, ax = p.subplots()
lines = []
for k, (x, xt, a) in enumerate(zip(ex, extype, key[:, 3])):
triax = failz[failz[:, 0] == x, triaxmapper[constit]]
proj = 'TTGM-{}_FS19SS6'.format(x)
# [0-5]Mean VM-H8-Anis-de00-01-00, [6-11]Max VM-H8-Anis-de00-01-00, [12-13]Mean, max Classic LEp
e = n.genfromtxt(
'../{}/IncrementalAnalysis/NewFilterResults_3med.dat'.format(proj),
delimiter=',')[-1]
c1, c2 = columnmapper[constit.lower()]
if a not in corneralphas:
if mean: ax.plot(triax, e[c1], 'C0s', alpha=0.35)
if max: ax.plot(triax, e[c2], 'C0s', alpha=0.35)
else:
if len(lines) == 3:
lines = []
do = True
if mean:
lines.extend(
ax.plot(triax,
e[c1],
marker[xt],
label=[label[xt] if do else ''][0]))
if max:
lines.extend(
ax.plot(triax,
e[c2],
marker[xt],
label=[label[xt] if do else ''][0]))
f.ezlegend(ax, markers=True, loc=3)
ax.axis(xmin=0, ymin=0, ymax=1.65)
ax.set_xlabel('$\\sigma_{\\mathsf{m}}/\\sigma_{\\mathsf{e}}$')
ax.set_ylabel('$\\mathsf{e}^{\\mathsf{p}}_{\\mathsf{e}}$')
f.eztext(
ax, 'Al-6061-T6\n{}\n{}'.format(
constit.upper(),
'Mean' * mean + '\n' * (max & mean) + 'Max' * max), 'ur')
f.myax(ax)
def slowplots():
'''
Some old plots that take a long time to plot that I don't find useful anymore
'''
# Let's see if old-style calc last stage max is in the new
# datasetmaxloc = n.nonzero( (A[:,0] == maxij[-1,0]) & (A[:,1] == maxij[-1,1]) )[0]
oldmaxloc = n.nonzero( (A[:,0] == maxij[-1,0]) & (A[:,1] == maxij[-1,1]) )[0]
# And calcualte the new maxloc
newmaxloc = n.argmax(de[-1, :, 3])
p.style.use('mysty')
fig1 = p.figure()
ax1 = fig1.add_subplot(111)
for i in de[:,:,3].T:
p.plot(dr, i, 'C1', alpha=0.1)
p.plot(dr, de[:,:,3].mean(axis=1),'C0', label='Increm. Mean')
p.plot(dr, dmean, 'C2', label='Old Mean')
p.plot(dr, dmax, 'C3', label='Old Max')
if len(oldmaxloc) == 1:
p.plot(dr, de[:,oldmaxloc,3], 'C4', label='Old Max Pt')
if oldmaxloc[0] != newmaxloc:
p.plot(dr, de[:,newmaxloc,3], 'C5', label='New Max Pt')
else:
f.eztext(ax1, 'Old pMax pt is same\nas new max pt.', 'ul')
else:
p.plot(dr, de[:,newmaxloc,3], 'C5', label='New Max Pt')
f.eztext(ax1, 'Old Max Pt is not\nin the new dataset', 'ul')
p.xlabel(xlab)
p.ylabel('$\\mathsf{e}_\\mathsf{e}$')
ax1.axis(xmin=0)
f.myax(p.gca())
f.ezlegend(p.gca())
p.savefig('../{}/IncrementalAnalysis/IncrementalAnalysis1.png'.format(pname), dpi=125)
# Epsilon v gamma
fig2 = p.figure()
ax2 = fig2.add_subplot(111)
for gam, eps in zip(-2*de[:,:,1].T, de[:,:,2].T):
ax2.plot(gam, eps, 'C1', alpha=0.05)
ax2.plot(-2*de[:,:,1].mean(axis=1), de[:,:,2].mean(axis=1), label='Mean', zorder=50)
if len(oldmaxloc) == 1:
ax2.plot(-2*de[:,oldmaxloc,1], de[:,oldmaxloc,2], label='Old Max Pt')
if oldmaxloc[0] != newmaxloc:
ax2.plot(-2*de[:,newmaxloc,1], de[:,newmaxloc,2], label='New Max Pt')
else:
ax2.plot(-2*de[:,newmaxloc,1], de[:,newmaxloc,2], label='New Max Pt')
ax2.set_xlabel('$\\mathsf{2e}_{\\theta\\mathsf{x}}$')
ax2.set_ylabel('$\\mathsf{e}_\\mathsf{x}$')
ax2.axis(xmin=0, ymin=0)
f.myax(ax2)
f.ezlegend(ax2)
fig2.savefig('../{}/IncrementalAnalysis/IncrementalAnalysis2.png'.format(pname), dpi=125)
def NewOldMax(expt, mode=None):
'''
Plots the STK16 max point and the new max point eeq vs rot
Can plot either Total or incremental method of calculation
'''
proj = 'TTGM-{}_FS19SS6'.format(expt)
dr = n.genfromtxt('../{}/disp-rot.dat'.format(proj), delimiter=',')[:, 5]
oldmax = n.genfromtxt('../{}/MaxPt.dat'.format(proj), delimiter=',')[:, 10]
oldmax_incr = n.genfromtxt(
'../{}/IncrementalAnalysis/OldFilteringResults.dat'.format(proj),
delimiter=',',
usecols=(10))
maxi, maxj = n.genfromtxt('../{}/max.dat'.format(proj), delimiter=',')[-1,
-2:]
# [0]Index_x [1]Index_y [2,3,4]Undef_X,Y,Z inches
# [5,6,7]Def_X,Y,Z inches [8,9,10,11]DefGrad (11 12 21 22)
# [12,13,14,15,16,17] e00, e01, e11, eeqVM, eeqH8, eeqAnis
D = n.load(
'../{}/IncrementalAnalysis/NewFilterPassingPoints_3med.npy'.format(
proj))
locmax = D[-1, :, 15].argmax()
p.style.use('mysty')
fig, ax = p.subplots()
if mode != 'Total':
ax.plot(dr,
oldmax_incr,
label='STK16 ({:.0f},{:.0f})'.format(maxi, maxj),
zorder=10)
ax.plot(dr,
D[:, locmax, 15],
label='New ({:.0f},{:.0f})'.format(*D[-1, locmax, :2]))
title = 'TTGM-{} Max Incr. Values'.format(expt)
else:
ax.plot(dr,
oldmax,
label='STK16 ({:.0f},{:.0f})'.format(maxi, maxj),
zorder=10)
# [0-5]Mean VM-H8-Anis-de00-01-00, [6-11]Max VM-H8-Anis-de00-01-00, [12-13]Mean, max Classic LEp
h = n.genfromtxt(
'../{}/IncrementalAnalysis/NewFilterResults_3med.dat'.format(proj),
delimiter=',',
usecols=(13, ))
ax.plot(dr, h, label='New ({:.0f},{:.0f})'.format(*D[-1, locmax, :2]))
title = 'TTGM-{} Max Total Stn'.format(expt)
ax.set_xlabel('$\\Phi$')
ax.set_ylabel('e$_\\mathsf{e}$')
ax.axis(xmin=0, ymin=0)
f.ezlegend(ax, title=title, loc=2)
f.myax(ax)
return None
def PlotF(expt, F=None):
'''
Plot Components of the def. grad.
If F isn't given, it gets the expt's old max point F
'''
if F is None:
F = GetMaxPtF(expt)
p.plot(-F[:, 1], F[:, 3], marker='.')
p.axis(xmin=0, ymin=1)
p.xlabel('-F$_\\mathsf{01}$')
p.ylabel('F$_\\mathsf{11}$')
if expt is not None:
f.eztext(p.gca(), 'TTGM-{}'.format(expt), 'ul')
f.myax(p.gca())
return F
def FailureStrains(max=True, mean=True, vm=True, h8=False, anis=False):
ex = n.genfromtxt('../ExptSummary.dat', delimiter=',')
ex = ex[(ex[:, 1] == 0) & (ex[:, 3] != 0)][:, 0].astype(int)
failz = n.genfromtxt('./failstns.dat', delimiter='\t')
p.style.use('mysty')
fig, ax = p.subplots()
for k, x in enumerate(ex):
lines = []
triax = failz[failz[:, 0] == x, 2]
proj = 'TTGM-{}_FS19SS6'.format(x)
# [0-5]Mean VM-H8-Anis-de00-01-00, [6-11]Max VM-H8-Anis-de00-01-00, [12-13]Mean, max Classic LEp
e = n.genfromtxt(
'../{}/IncrementalAnalysis/NewFilterResults_3med.dat'.format(proj),
delimiter=',')[-1]
if x == [35]:
pass
#e = n.genfromtxt('../{}/IncrementalAnalysis/NewFilterResults_4med.dat'.format(proj), delimiter=',')[-1]
if vm:
if mean:
lines.append(ax.plot(triax, e[0], 'gs', label='Mean/VM')[0])
if max: lines.append(ax.plot(triax, e[6], 'rs', label='Max/VM')[0])
if h8:
if mean:
lines.append(
ax.plot(triax, e[1], 'go', mfc='none', label='Mean/H8')[0])
if max:
lines.append(
ax.plot(triax, e[7], 'ro', mfc='none', label='Max/H8')[0])
if anis:
if mean:
lines.append(
ax.plot(triax, e[2], 'g^', mfc='none',
label='Mean/Ani')[0])
if max:
lines.append(
ax.plot(triax, e[8], 'r^', mfc='none', label='Max/Ani')[0])
leg = ax.legend(lines, [i.get_label() for i in lines], loc=1)
[
l.set_color(leg.get_lines()[k].get_mec())
for k, l in enumerate(leg.get_texts())
]
ax.axis(xmin=0, ymin=0, ymax=1.65)
ax.set_xlabel('$\\sigma_{\\mathsf{m}}/\\sigma_{\\mathsf{e}}$')
ax.set_ylabel('$\\mathsf{e}^{\\mathsf{p}}_{\\mathsf{e}}$')
f.eztext(ax, 'Incremental\nAl-6061-T6', 'bl')
f.myax(ax)
return fig, ax
def PlotOldStrainDef(expt, stndef='Haltom'):
'''
Obsolete. See PlotStrainPaths
'''
from FtfEig import LEp
proj = 'TTGM-{}_FS19SS6'.format(expt)
# [0]Index_x [1]Index_y [2,3,4]Undef_X,Y,Z inches
# [5,6,7]Def_X,Y,Z inches [8,9,10,11]DefGrad (11 12 21 22)
# [12,13,14,15,16] e00, e01, e11, eeqVM, eeqH8
d = n.load(
'../{}/IncrementalAnalysis/NewFilterPassingPoints_3med.npy'.format(
proj))
# [0-4]Mean VM-H8-de00-01-00, [5-9]Max VM-H8-de00-01-00, [10-11]Mean, max Classic LEp
maxp = n.genfromtxt(
'../{}/IncrementalAnalysis/NewFilterResults_3med.dat'.format(proj),
delimiter=',')
p.style.use('mysty')
fig, ax = p.subplots()
if expt in [18, '18']:
col = 12
else:
col = 13
if stndef == 'Haltom':
# Haltom definitions
[
p.plot(-n.arctan2(d[:, i, 9], d[:, i, 11]),
d[:, i, 11] - 1,
alpha=0.5) for i in range(d.shape[1])
]
else:
# Aramis definition
for i in range(d.shape[1]):
lep, NEx, NExy, NEy = LEp(d[:, i, 8], d[:, i, 9], d[:, i, 10],
d[:, i, 11], True)
NEx -= 1
NEy -= 1
G = n.arctan(NExy / (1 + NEx)) + n.arctan(NExy / (1 + NEy))
p.plot(-G, NEy, alpha=0.5)
p.axis(xmin=0, ymin=0)
p.xlabel('$\\gamma$')
p.ylabel('$\\epsilon_\\mathsf{x}$')
f.myax(ax)
return fig, ax
def PlotAllEeqRot(expt=24, ij=None, kelin=False):
'''
Plots every maxima's eeq vs rot
'''
proj = 'TTGM-{}_FS19SS6'.format(expt)
dr = n.genfromtxt('../{}/disp-rot.dat'.format(proj), delimiter=',')[:, 5]
# [0]Index_x [1]Index_y [2,3,4]Undef_X,Y,Z inches
# [5,6,7]Def_X,Y,Z inches [8,9,10,11]DefGrad (11 12 21 22)
# [12,13,14,15,16,17] e00, e01, e11, eeqVM, eeqH8, eeqAnis
d = n.load(
'../{}/IncrementalAnalysis/NewFilterPassingPoints_3med.npy'.format(
proj))
locmax = d[-1, :, 15].argmax()
p.style.use('mysty')
fig, ax = p.subplots()
p.plot(dr, d[:, :, 15], alpha=0.2)
p.plot(dr,
d[:, locmax, 15],
'k',
label='Max ({},{})'.format(*d[-1, locmax, :2].astype(int)))
p.plot(dr, d[:, :, 15].mean(axis=1), 'k--', label='Mean')
if ij is not None:
try:
loc = n.nonzero((d[-1, :, 0] == ij[0])
& (d[-1, :, 1] == ij[1]))[0][0]
p.plot(dr, d[:, loc, 15], 'r', label='({},{})'.format(*ij))
except IndexError:
d = n.load(
'../{}/IncrementalAnalysis/PointsInLastWithStrains.npy'.format(
proj))
loc = n.nonzero((d[0, :, 0] == ij[0])
& (d[0, :, 1] == ij[1]))[0][0]
ee = []
for i in range(d.shape[0]):
ee.append(d[i, loc, 15])
p.plot(dr, ee, 'r', label='({},{}) (no pass)'.format(*ij))
if kelin and (expt in [1, 4, 5]):
cols = {4: (0, 1), 1: (2, 3), 5: (4, 5)}
s = read_excel('../Misc/KelinSim.xlsx', parse_cols=cols[expt]).values
p.plot(s[:, 0], s[:, 1], 'b', label='Anal Anis')
ax.axis(xmin=0, ymin=0)
ax.set_xlabel('$\\Phi$')
ax.set_ylabel('e$_\\mathsf{e}$')
f.ezlegend(ax, loc=2, hl=2, title='TTGM-{}'.format(expt))
f.myax(ax)
def SetStrainpaths(Mean=True, Max=False):
'''
Plot axial vs shear strain (mean or max) for all expts)
'''
ex = n.genfromtxt('../ExptSummary.dat', delimiter=',')
ex = ex[ex[:, 3].argsort()][::-1]
ex = ex[(ex[:, 3] != 0)]
alp = ex[:, 3]
ex = ex[:, 0].astype(int)
p.style.use('mysty')
fig, ax = p.subplots()
for k, (x, a) in enumerate(zip(ex, alp)):
proj = 'TTGM-{}_FS19SS6'.format(x)
LL = n.genfromtxt('../{}/prof_stages.dat'.format(proj),
delimiter=',',
dtype=int)[3]
# [0-5]Mean VM-H8-Anis-de00-01-00, [6-11]Max VM-H8-Anis-de00-01-00, [12-13]Mean, max Classic LEp
D = n.genfromtxt(
'../{}/IncrementalAnalysis/NewFilterResults_3med.dat'.format(proj),
delimiter=',')
D[:, [4, 10]] *= -1
if n.isnan(a):
a = '$\\infty$'
line, = p.plot(D[:, 4], D[:, 5], label='{}'.format(a))
LLmark, = p.plot(D[LL, 4], D[LL, 5], 'rs')
if Max:
maxline, = p.plot(D[:, 10],
D[:, 11],
color=line.get_color(),
alpha=0.3)
maxmark, = p.plot(D[LL, 10], D[LL, 11], 'rs', alpha=0.3)
if not Mean:
maxline.set_alpha(1)
maxmark.set_alpha(1)
maxline.set_label(line.get_label())
line.remove()
LLmark.remove()
ax.axis(xmin=-.02, ymin=0)
ax.set_xlabel('e$_{\\theta\\mathsf{x}}$')
ax.set_ylabel('e$_\\mathsf{xx}$')
f.ezlegend(ax,
title='Mean' * Mean + '\n' * (Max & Mean) + 'Max' * Max,
loc=0)
f.myax(ax)
p.show()
def PlotOldMaxTraceBack(expt):
'''
Plots the STK16 max point eeq vs rot and its increm. ex vs 2eqx
'''
p.style.use('mysty')
fig, ax = p.subplots()
labs = [
'Avg_AllPass', 'Avg_P2P', 'LastMax_TraceBk', 'Max_EachStgP2P',
'LastMax_Trace_Nbhd', 'Avg_P2P_PassOnly'
]
alpha = [1, 1, 1, .5, 1, 1]
ls = ['-', '--', '-', '-', '--', '-', '--']
proj = 'TTGM-{}_FS19SS6'.format(expt)
dmean = n.genfromtxt('../{}/mean.dat'.format(proj))[:, -1]
dmax = n.genfromtxt('../{}/MaxPt.dat'.format(proj), delimiter=',')[:, 10]
# [0-4]AvgF-Passing-VM-H8-de00-01-11, [5-9]PassingP2P-VM-H8, [10-14]MaxPtTracedBack-VM-H8,
# [15-19]MaxPtEachStage-VM-H8, [20-24]MaxPtTrace/NbhdFavg, [25-29]
X = n.genfromtxt(
'../{}/IncrementalAnalysis/OldFilteringResults.dat'.format(proj),
delimiter=',')
if expt in ['18', 18]:
dr = n.genfromtxt('../{}/disp-rot.dat'.format(proj), delimiter=',')[:,
4]
xlab = '$\\delta/\\mathsf{L}$'
else:
dr = n.genfromtxt('../{}/disp-rot.dat'.format(proj), delimiter=',')[:,
5]
xlab = '$\\Phi$'
for i in [2]:
ax.plot(dr, X[:, i * 5], label='Incremental\nTracedBack')
ax.plot(dr, dmax, label='STK16')
ax.set_xlabel(xlab)
ax.set_ylabel('e$_\\mathsf{e}$')
f.ezlegend(ax, loc=2, title='TTGM-{} Max Values'.format(expt))
ax.axis(xmin=0, ymin=0)
f.myax(ax)
fig2, ax2 = p.subplots()
ax2.plot(-2 * X[:, 13], X[:, 14])
ax2.set_xlabel('2e$_{\\theta\\mathsf{x}}$')
ax2.set_ylabel('e$_\\mathsf{xx}$')
f.eztext(ax2, 'TTGM-{} Max Values'.format(expt), 'ul')
f.myax(ax2)
p.show()
def YldLocusPlot():
p.style.use('mysty')
fig = p.figure(figsize=(8, 8))
ax = fig.add_axes([.125, .125, .75, .75])
anx, any = CalContour(close=True).T
hx, hy = CalContour(coefs=1, a=8, close=True).T
vx, vy = CalContour(coefs=1, a=2, close=True).T
ax.plot(vx, vy, label='VM')
ax.plot(hx, hy, label='H8')
ax.plot(anx, any, label='Anis')
ax.plot([0, 1.2], [0, 1.2], 'k--', alpha=0.2)
ax.set_xlabel('$\\tau_\\mathsf{x}/\\tau_o$')
ax.set_ylabel('yo')
ax.set_ylabel('$\\frac{\\tau_\\theta}{\\tau_o}$')
import figfun as f
f.ezlegend(ax, loc=2)
ax.set_title("Kelin's Calibration")
f.myax(ax, autoscale=.9)
def PlotAllOldFilters(expt):
p.style.use('mysty-sub')
labs = [
'Avg_AllPass', 'Avg_P2P', 'LastMax_TraceBk', 'Max_EachStgP2P',
'LastMax_Trace_Nbhd', 'Avg_P2P_PassOnly'
]
alpha = [1, 1, 1, .5, 1, 1]
ls = ['-', '--', '-', '-', '--', '-', '--']
proj = 'TTGM-{}_FS19SS6'.format(expt)
dmean = n.genfromtxt('../{}/mean.dat'.format(proj))[:, -1]
dmax = n.genfromtxt('../{}/MaxPt.dat'.format(proj))[:, 10]
X = n.genfromtxt(
'../{}/IncrementalAnalysis/OldFilteringResults.dat'.format(proj),
delimiter=',')
if expt in ['18', 18]:
dr = n.genfromtxt('../{}/disp-rot.dat'.format(proj), delimiter=',')[:,
4]
xlab = '$\\delta/\\mathsf{L}$'
else:
dr = n.genfromtxt('../{}/disp-rot.dat'.format(proj), delimiter=',')[:,
5]
xlab = '$\\Phi$'
fig, ax1, ax2 = f.make21()
for i in [0, 1, 5]:
ax1.plot(dr, X[:, i * 5], label=labs[i], alpha=alpha[i], ls=ls[i])
ax1.plot(dr, dmean, 'k--', label='Old')
ax1.set_xlabel(xlab)
ax1.set_ylabel('e$_\\mathsf{e}$')
f.ezlegend(ax1, loc=2, title='Mean Values')
ax1.axis(xmin=0, ymin=0)
f.myax(ax1)
for i in [2, 3, 4]:
ax2.plot(dr, X[:, i * 5], label=labs[i], alpha=alpha[i], ls=ls[i])
ax2.plot(dr, dmax, 'k--', label='Old')
ax2.set_xlabel(xlab)
ax2.set_ylabel('e$_\\mathsf{e}$')
f.ezlegend(ax2, loc=2, title='Max Values')
ax2.axis(xmin=0, ymin=0)
f.myax(ax2)
p.show()
def SetStrainpaths(Mean=True,
Max=False,
corners=False,
corneronly=False,
export=False):
'''
Plot axial vs shear strain (mean or max) for all expts)
'''
key = n.genfromtxt('../ExptSummary.dat', delimiter=',')
if corneronly:
corners = True
corneralphas = n.unique(key[n.in1d(key[:, 1], [1, 2])][:, 3])
key = key[n.in1d(key[:, 3], corneralphas)]
if not corners:
key = key[key[:, 1] == 0]
key = key[key[:, 3].argsort()][::-1]
key = key[(key[:, 3] != 0)]
alp = key[:, 3]
ex = key[:, 0].astype(int)
extype = key[:, 1].astype(int)
p.style.use('mysty')
fig, ax = p.subplots()
for k, (x, a, xt) in enumerate(zip(ex, alp, extype)):
proj = 'TTGM-{}_FS19SS6'.format(x)
LL = n.genfromtxt('../{}/prof_stages.dat'.format(proj),
delimiter=',',
dtype=int)[3]
# [0-5]Mean VM-H8-Anis-de00-01-00, [6-11]Max VM-H8-Anis-de00-01-00, [12-13]Mean, max Classic LEp
D = n.genfromtxt(
'../{}/IncrementalAnalysis/NewFilterResults_3med.dat'.format(proj),
delimiter=',')
D[:, [4, 10]] *= -1
labeladd = [
'', '($\\Sigma\\rightarrow\\mathcal{{T}}$)',
'($\\mathcal{{T}}\\rightarrow\\Sigma$)'
]
if n.isnan(a):
label = '$\\infty$'
else:
label = '{}'.format(a) + labeladd[xt]
line, = p.plot(D[:, 4], D[:, 5], label=label)
LLmark, = p.plot(D[LL, 4], D[LL, 5], 'rs')
if Max:
maxline, = p.plot(D[:, 10],
D[:, 11],
color=line.get_color(),
alpha=0.3)
maxmark, = p.plot(D[LL, 10], D[LL, 11], 'rs', alpha=0.3)
if not Mean:
maxline.set_alpha(1)
maxmark.set_alpha(1)
maxline.set_label(line.get_label())
line.remove()
LLmark.remove()
if export:
if k == 0:
import pandas as pd
fid = pd.ExcelWriter('StrainPaths.xlsx')
LLdat = n.empty((len(ex), D.shape[1] + 3))
header = "MeanVM,H8,Anis,e11,e12,e22,MaxVM,H8,Anis,e11,e12,e22,MeanClasscLEp,MaxLEp".split(
',')
pd.DataFrame(D).to_excel(fid,
sheet_name='Exp{} _ {}'.format(x, a),
index=False,
header=header)
LLdat[k] = n.hstack(([x, a, xt], D[LL]))
if export:
pd.DataFrame(LLdat).to_excel(fid,
sheet_name='LL',
index=False,
header=["Exp", "Alpha",
"ExpType"].extend(header))
fid.save()
ax.axis(xmin=-.02, ymin=0)
ax.set_xlabel('e$_{\\theta\\mathsf{x}}$')
ax.set_ylabel('e$_\\mathsf{xx}$')
f.ezlegend(ax,
title='Mean' * Mean + '\n' * (Max & Mean) + 'Max' * Max,
loc=0)
f.myax(ax)
p.show()
label=('{}\n$\\sigma_c$,a,c\n' + '{:.3f}\n' * 3).format(
constit, *res.x))
ax1.plot(d.Triax, sts, 'o')
ax1.set_xlabel('$\\sigma_m/\\sigma_e$')
ax1.set_ylabel('$\\sigma_e^f$')
f.ezlegend(ax1)
eef_pred = interp1d(mat[:, 1], mat[:, 0], fill_value='extrapolate').__call__(
plot_HC(res.x, α, constit))
ax2.plot(η, eef_pred)
ax2.axis([0, .6, 0, 2])
ax2.set_xlabel('$\\sigma_m/\\sigma_e$')
ax2.set_ylabel('$e_e^f$')
ax2.plot(d.Triax, d.efmn, 'o')
[f.myax(x) for x in (ax1, ax2)]
fig.savefig('Failure_HC_{}'.format(constit), dpi=125)
# First load up the failure data
p.figure()
def JC(triax, D1, D2, D3):
return D1 + D2 * n.exp(D3 * triax)
bounds = ((0, -n.inf, -n.inf), (n.inf, n.inf, 0))
for efm, lab in zip([efmx, efmn], ['Max', 'Mean']):
params, info = so.curve_fit(JC, Σ, efm, bounds=bounds)
p.style.use('mysty-sub')
p.figure(1, facecolor='w', figsize=(8, 12))
p.subplot(2, 1, 1)
for j in profStg:
p.plot(DR[int(j), 4], DR[int(j), 2], 'o', markersize=7, mec='none')
p.plot(DR[:, 4], DR[:, 2], 'b', zorder=0)
p.xlabel('$\\delta/\\mathsf{L}$')
p.ylabel('$\\Sigma$\n$(\\mathsf{ksi})$')
p.title('TT2-{:.0f}, $\\alpha$ = {}. FS{:.0f}SS{:.0f}. Tube DC-{:.0f}'.format(
expt, alpha, FS, SS, tube),
size=18)
p.gcf().gca().set_ylim([0, 1.2 * n.max(STF[:, 2])])
p.gcf().gca().set_xlim(left=0)
ff.myax(p.gcf(), ff.ksi2Mpa, '$\\Sigma$\n$(\\mathsf{MPa})$')
p.subplot(2, 1, 2)
for j in profStg:
p.plot(DR[j, 5], DR[j, 3], 'o', markersize=7, mec='none')
p.plot(DR[:, 5], DR[:, 3], 'b', zorder=0)
p.xlabel('$\\phi^{\\circ}$')
p.ylabel('$\\mathcal{T}$\n$(\\mathsf{ksi})$')
p.gcf().gca().set_ylim([0, 1.2 * n.max(STF[:, 3])])
p.gcf().gca().set_xlim(left=0)
ff.myax(p.gcf(), ff.ksi2Mpa, '$\\mathcal{T}$\n$(\\mathsf{MPa})$')
if savefigs:
p.savefig('1 - Sts-Delta-Rot.png', dpi=125)
p.close()
def PlotStrainPaths(expt, ij=None, stndef=None, eq=False):
'''
Plots all new passing points incr. strain paths.
Also highlights the max and mean.
if ij is not None, then it will plot a given ij point's path
'''
proj = 'TTGM-{}_FS19SS6'.format(expt)
# [0]Index_x [1]Index_y [2,3,4]Undef_X,Y,Z inches
# [5,6,7]Def_X,Y,Z inches [8,9,10,11]DefGrad (11 12 21 22)
# [12,13,14,15,16,17] e00, e01, e11, eeqVM, eeqH8, eeqAnis
d = n.load(
'../{}/IncrementalAnalysis/NewFilterPassingPoints_3med.npy'.format(
proj))
maxloc = d[-1, :, 15].argmax()
maxij = d[-1, maxloc, :2].astype(int)
# [0-5]Mean VM-H8-Anis-de00-01-11, [6-11]Max VM-H8-Anis-de00-01-00, [12-13]Mean, max Classic LEp
maxp = n.genfromtxt(
'../{}/IncrementalAnalysis/NewFilterResults_3med.dat'.format(proj),
delimiter=',')
p.style.use('mysty')
fig, ax = p.subplots()
if expt in [18, '18']:
col = 12
else:
col = 13
if ij is not None:
loc = n.nonzero((d[-1, :, 0] == ij[0]) & (d[-1, :, 1] == ij[1]))[0][0]
if stndef not in ['Haltom', 'Aramis']:
if eq == True:
# Plots hoops strain vs gamma instead
d[:, :, 12:15] = d[:, :, [14, 13, 12]]
maxp[:, [5, 10]] = maxp[:, [2, 7]]
[
p.plot(-2 * d[:, i, col], d[:, i, 14], alpha=0.3)
for i in range(d.shape[1])
]
p.plot(-2 * maxp[:, col - 3],
maxp[:, 11],
'k',
label='Max ({},{})'.format(*maxij))
p.plot(-2 * maxp[:, col - 9], maxp[:, 5], 'k--', label='Mean')
if ij is not None:
p.plot(-2 * d[:, loc, col],
d[:, loc, 14],
'r',
label='{:.0f},{:.0f}'.format(*ij))
elif stndef == 'Haltom':
[
p.plot(-n.arctan2(d[:, i, 9], d[:, i, 11]),
d[:, i, 11] - 1,
alpha=0.5) for i in range(d.shape[1])
]
p.plot(-n.arctan2(d[:, maxloc, 9], d[:, maxloc, 11]),
d[:, maxloc, 11] - 1,
'k',
label='Max ({},{})'.format(*maxij))
p.plot(-n.arctan2(d[:, :, 9], d[:, :, 11]).mean(axis=1),
d[:, :, 11].mean(axis=1) - 1,
'k--',
label='Mean')
if ij is not None:
p.plot(-n.arctan2(d[:, loc, 9], d[:, loc, 11]),
d[:, loc, 11] - 1,
'r',
label='{:.0f},{:.0f}'.format(*ij))
elif stndef == 'Aramis':
tempG = n.empty(d.shape[:2])
tempNEy = n.empty_like(tempG)
for i in range(d.shape[1]):
lep, NEx, NExy, NEy = LEp(d[:, i, 8], d[:, i, 9], d[:, i, 10],
d[:, i, 11], True)
NEx -= 1
NEy -= 1
G = -n.arctan(NExy / (1 + NEx)) - n.arctan(NExy / (1 + NEy))
tempG[:, i] = G
tempNEy[:, i] = NEy
p.plot(G, NEy, alpha=0.5)
p.plot(tempG[:, maxloc],
tempNEy[:, maxloc],
'k',
label='Max ({},{})'.format(*maxij))
p.plot(tempG.mean(axis=1), tempNEy.mean(axis=1), 'k--', label='Mean')
if ij is not None:
p.plot(tempG[:, loc],
tempNEy[:, loc],
'r',
label='{:.0f},{:.0f}'.format(*ij))
if expt not in [18, '18', 31, '31']:
p.axis(xmin=0, ymin=0)
ax.set_xlabel('2e$_{\\theta\\mathsf{x}}$')
ax.set_ylabel('e$_\\mathsf{xx}$')
f.ezlegend(ax, loc=2, hl=2, title='TTGM-{}'.format(expt))
f.myax(ax)
p.show()
return fig, ax
'LastMax_Trace_Nbhd', 'Avg_P2P_PassOnly'
]
alpha = [1, 1, 1, .5, 1, 1]
ls = ['-', '--', '-', '-', '--', '-', '--']
dmean = n.genfromtxt('../{}/mean.dat'.format(proj))[:, -1]
dmax = n.genfromtxt('../{}/MaxPt.dat'.format(proj))[:, 10]
fig, ax1, ax2 = f.make21()
for i in [0, 1, 5]:
ax1.plot(dr, X[:, i * 5], label=labs[i], alpha=alpha[i], ls=ls[i])
ax1.plot(dr, dmean, 'k--', label='Old')
ax1.set_xlabel(xlab)
ax1.set_ylabel('e$_\\mathsf{e}$')
f.ezlegend(ax1, loc=2, title='Mean Values')
ax1.axis(xmin=0, ymin=0)
f.myax(ax1)
for i in [2, 3, 4]:
ax2.plot(dr, X[:, i * 5], label=labs[i], alpha=alpha[i], ls=ls[i])
ax2.plot(dr, dmax, 'k--', label='Old')
ax2.set_xlabel(xlab)
ax2.set_ylabel('e$_\\mathsf{e}$')
f.ezlegend(ax2, loc=2, title='Max Values')
ax2.axis(xmin=0, ymin=0)
f.myax(ax2)
p.savefig('../{0}/IncrementalAnalysis/OldFilteringResults.png'.format(proj),
dpi=125)
p.close()
#tric = p.tricontourf(x,y,LEp,crange,extend='both',cmap=cmap)
tric = p.tricontourf(x,
y,
Z.triangles,
colorval,
crange,
extend='both',
cmap=cmap)
ax.axis([n.min(x) * 1.1, 1.1 * n.max(x), -.2 / .0461, .2 / .0461])
#ax.plot(box_path[:,0], box_path[:,1], 'w')
#p.axis('equal')
ax.set_xlabel('$\\mathsf{x}_\\mathsf{o}/\\mathsf{R}_\\mathsf{o}$')
ax.set_ylabel('$\\frac{\\mathsf{y}_\\mathsf{o}}{\\mathsf{t}_\\mathsf{o}}$')
f.myax(ax)
ax_bar = fig.add_axes(n.array([9.5, 1, .25, 4]) * mult)
ticklocs = n.abs(n.unique(crange // .1 * .1))
ticklabs = ['{:g}'.format(L) for L in ticklocs]
cbar = p.colorbar(tric,
ax_bar,
extendrect=True,
extendfrac=0,
ticks=ticklocs,
format='%.1f')
cbar.set_label(clabel)
f.colorbar(ax, cbar)
if save:
p.savefig('TTGM-11.png'.format(cmap), bbox_inches='tight', dpi=400)
# This gets rid of the weird aliasing effect for the PDF, but introduces some anomalous things into the png
p.setp(l1.get_texts(),color='r')
l2 = ax11.legend(loc='lower right',fontsize=10,title=legtitle)
p.setp(l2.get_title(),fontsize=10)
ax11.add_artist(l1)
ax12.set_xlabel('$\\phi^\\circ$')
ax12.set_ylabel('$\\mathcal{T}$\n$(\\mathsf{ksi})$')
ax12.set_ylim([0,1.2*n.max(limloads[:,1])])
ax12.set_xlim(left=0)
l1 = ax12.legend([m22,m21],["Station 2", "LL"],loc='upper right',numpoints=1,fontsize=10,frameon=False)
p.setp(l1.get_texts(),color='r')
l2 = ax12.legend(loc='lower right',fontsize=10,title=legtitle)
p.setp(l2.get_title(),fontsize=10)
ax12.add_artist(l1)
p.sca(ax11)
ff.myax(fig1,ff.ksi2Mpa,'$\\Sigma$\n$(\\mathsf{MPa})$')
p.sca(ax12)
ff.myax(fig1,ff.ksi2Mpa,'$\\mathcal{T}$\n$(\\mathsf{MPa})$')
if savefigs:
fig1.savefig('{}/1 - Sts-Delta-Rot.png'.format(savepath),dpi=125)
#p.close()
##################################################
# Figure 2 - Epsilon-Gamma
##################################################
if G == 0:
p.style.use('mysty-sub')
fig2 = p.figure(2,facecolor='w',figsize=(8,12) )
ax21 = fig2.add_subplot(2,1,1)
ax22 = fig2.add_subplot(2,1,2)
#[4]d/Lg lo, [5]d/Lg hi, [6,7,8]S11,22,33,
# [9,10,11]LE11,22,33, [12]Vol'
loc = n.argmax(d[1])
d = d[:, :loc + 1]
# Ax vs hoop sts
fig1 = p.figure()
ax1 = fig1.add_subplot(111)
ax1.plot(d[3], d[2], label='Nominal')
ax1.plot(d[7], d[8], label='True')
ax1.set_xlabel('$\\sigma_\\theta$ (ksi)')
ax1.set_ylabel('$\\sigma_\\mathsf{x}$\n(ksi)')
ax1.legend(loc='lower right')
f.myax(ax1)
# Ax sts vs ax stn
fig2, ax21, ax22 = f.make12()
ax21.plot(d[4], d[2], label='$\\delta/\\mathsf{L}$')
ax21.plot(d[11], d[2], label='$e_\\mathsf{x}$')
[i.set_visible(False) for i in ax21.xaxis.get_ticklabels()[::2]]
ax21.set_xlabel('$\\epsilon_\\mathsf{x}$')
ax21.set_ylabel('$\\sigma_\\mathsf{x}$\n(ksi)')
ax21.legend(loc='lower right')
f.myax(ax21)
ax22.plot(d[10], d[3])
ax22.set_xlabel('$\\mathsf{e}_\\theta$')
ax22.set_ylabel('$\\sigma_\\theta$\n(ksi)')
f.myax(ax22)
ax12.set_ylim([0, 1.2 * n.max(limloads[:, 1])])
ax12.set_xlim(left=0)
l1 = ax12.legend([m22, m21], ["Station 2", "LL"],
loc='upper right',
numpoints=1,
fontsize=10,
frameon=False)
p.setp(l1.get_texts(), color='r')
l2 = ax12.legend(loc='lower right',
fontsize=10,
title='Expt; $\\alpha$; Tube')
p.setp(l2.get_title(), fontsize=10)
ax12.add_artist(l1)
p.sca(ax11)
ff.myax(fig1, ff.ksi2Mpa, '$\\Sigma$\n$(\\mathsf{MPa})$')
p.sca(ax12)
ff.myax(fig1, ff.ksi2Mpa, '$\\mathcal{T}$\n$(\\mathsf{MPa})$')
if savefigs:
#fig1.savefig('{}/1 - Sts-Delta-Rot.png'.format(savepath),dpi=125)
#fig1.savefig('{}/1 - Sts-Delta-Rot.pdf'.format(savepath),dpi=125)
p.close()
##################################################
# Figure 2 - Epsilon-Gamma
##################################################
if G == 0:
p.style.use('mysty-sub')
p.rcParams['axes.prop_cycle'] = cycler('color', colors)
fig2 = p.figure(2, facecolor='w', figsize=(8, 12))
