def ex_01(fns=[
'powder_collection_25JUL12/25JUL12_0002Data1Phi-90.txt',
'powder_collection_25JUL12/25JUL12_0002Data2Phi0.txt',
'powder_collection_25JUL12/25JUL12_0002Data3Phi45.txt',
'powder_collection_25JUL12/25JUL12_0002Data4Phi135.txt'
]):
from MP.lib.mpl_lib import wide_fig as wf
from MP.lib.mpl_lib import fancy_legend
from MP.lib.axes_label import __deco__ as deco
fig = wf(nh=1, nw=1, uw=4, left=0.2)
ax = fig.axes[0]
ehkl_dat = []
for i in xrange(len(fns)):
sign_sin2psi, ehkl, dspacing, psi = read(fns[i], iopt=0, isort=True)
ax.plot(sign_sin2psi,
ehkl,
'-x',
label=fns[i].split('Phi')[1].split('.')[0])
ehkl_dat.append(ehkl)
print 'mean:', np.array(ehkl_dat).mean()
print 'std:', np.array(ehkl_dat).std()
deco(iopt=0, ft=15, ax=ax, ipsi_opt=1)
fancy_legend(ax=ax, size=10, ncol=2)
def influence_of_cnts_stats(
ss=3,bounds=[0.,0.3257],
nbins=13, sigmas=[1e-5, 2e-5, 5e-5, 1e-4],
nsample=5,intp_opt=0,iplot=False):
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
if iplot==False:
plt.ioff()
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=2,nh=1);
ax1=fig.axes[0]; ax2=fig.axes[1]
M = []
for i in range(len(sigmas)):
Y = []
for j in range(nsample):
x, y = influence_of_nbin_scatter(
ss=ss,bounds=bounds,
nbins=[nbins],
iscatter=True,nsample=1,
iwgt=True,sigma=sigmas[i],
intp_opt=intp_opt,
iplot=False)
Y.append(y)
Y = np.array(Y).T
M = []; S=[]
for k in range(len(x)):
M.append(Y[k].mean())
S.append(Y[k].std())
ax1.plot(x,M,label='%6.0e'%sigmas[i])
ax2.errorbar(x,M,yerr=S,label='%6.0e'%sigmas[i])
if type(ss).__name__=='int':
ax1.plot(x[::ss],np.zeros((len(x[::ss]),)),'o',mec='r',mfc='None',ms=8)
ax2.plot(x[::ss],np.zeros((len(x[::ss]),)),'o',mec='r',mfc='None',ms=8)
elif type(ss).__name__=='list':
ax1.plot(x[ss],np.zeros((len(x[ss]),)),'o',mec='r',mfc='None',ms=8)
ax2.plot(x[ss],np.zeros((len(x[ss]),)),'o',mec='r',mfc='None',ms=8)
ax1.set_ylim(0.,); ax1.set_xlim(0.,ax1.get_xlim()[1]*1.05)
ax1.set_title(r'Error in $\varepsilon^\mathrm{hkl}$')
deco(iopt=8,ft=15,ax=ax1)
fancy_legend(ax=ax1, size=7,ncol=2)
ax2.set_ylim(0.,); ax2.set_xlim(0.,ax2.get_xlim()[1]*1.05)
ax2.set_title(r'Error in $\varepsilon^\mathrm{hkl}$')
deco(iopt=8,ft=15,ax=ax2)
fancy_legend(ax=ax2, size=7,ncol=2)
fig.savefig('ee.pdf')
if iplot==False:
plt.close('all')
plt.ion()
return x, M, S
def influence_of_nbin(
ss=3,bounds = [0,0.5],
nbins = [11, 19, 25, 51],
iscatter=False,
sigma=1e-5,
iwgt=False,
intp_opt=0,
iplot=False,):
"""
Influence of psi bin size
Arguments
=========
ss = 3
bounds = [0.0, 0.5]
nbins = [11, 19, 25, 51]
iscatter = False
iwgt = False
intp_opt = 0 (Interpolation option)
iplot = False
"""
if iplot:
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=1,nh=1);ax=fig.axes[0]
Y = []
for i in range(len(nbins)):
nb = nbins[i]
fw, e = influence_of_intp(
ss=ss, bounds=bounds,
psi_nbin = nb,iplot=False,
iscatter=iscatter,sigma=sigma,
iwgt=iwgt,
intp_opt=intp_opt,
)
x = fw.epsilon_vm[::]
y = e[::]
if iplot: ax.plot(x,y,label=nb)
Y.append(y)
if type(ss).__name__=='int': dum=ss
elif type(ss).__name__=='list': dum =len(ss)
if iplot:
fancy_legend(ax=ax,size=10)
deco(iopt=8,ft=15,ax=ax)
fig.savefig('ss%i_err.pdf'%dum)
plt.close('all')
## Y [nbins, nsteps]
return x, Y
def influence_of_nbin(
ss=3,bounds = [0,0.5],
nbins = [11, 19, 25, 51],
iscatter=False,
sigma=1e-5,
iwgt=False,
intp_opt=0,
iplot=False,):
"""
Influence of psi bin size
Arguments
=========
ss = 3
bounds = [0.0, 0.5]
nbins = [11, 19, 25, 51]
iscatter = False
iwgt = False
intp_opt = 0 (Interpolation option)
iplot = False
"""
if iplot:
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=1,nh=1);ax=fig.axes[0]
Y = []
for i in xrange(len(nbins)):
nb = nbins[i]
fw, e = influence_of_intp(
ss=ss, bounds=bounds,
psi_nbin = nb,iplot=False,
iscatter=iscatter,sigma=sigma,
iwgt=iwgt,
intp_opt=intp_opt,
)
x = fw.epsilon_vm[::]
y = e[::]
if iplot: ax.plot(x,y,label=nb)
Y.append(y)
if type(ss).__name__=='int': dum=ss
elif type(ss).__name__=='list': dum =len(ss)
if iplot:
fancy_legend(ax=ax,size=10)
deco(iopt=8,ft=15,ax=ax)
fig.savefig('ss%i_err.pdf'%dum)
plt.close('all')
## Y [nbins, nsteps]
return x, Y
def ex_01(fns=[
'powder_collection_25JUL12/25JUL12_0002Data1Phi-90.txt',
'powder_collection_25JUL12/25JUL12_0002Data2Phi0.txt',
'powder_collection_25JUL12/25JUL12_0002Data3Phi45.txt',
'powder_collection_25JUL12/25JUL12_0002Data4Phi135.txt']):
from MP.lib.mpl_lib import wide_fig as wf
from MP.lib.mpl_lib import fancy_legend
from MP.lib.axes_label import __deco__ as deco
fig = wf(nh=1,nw=1,uw=4,left=0.2); ax=fig.axes[0]
ehkl_dat = []
for i in range(len(fns)):
sign_sin2psi,ehkl,dspacing,psi = read(fns[i],iopt=0,isort=True)
ax.plot(sign_sin2psi,ehkl,'-x',label=fns[i].split('Phi')[1].split('.')[0])
ehkl_dat.append(ehkl)
print 'mean:', np.array(ehkl_dat).mean()
print 'std:', np.array(ehkl_dat).std()
deco(iopt=0,ft=15,ax=ax,ipsi_opt=1)
fancy_legend(ax=ax,size=10,ncol=2)
def error_est(
path='BB',psi_nbin=21,
sin2psimx=0.5,
hkls=['200','220','211','310'],
iplot_rs=False):
"""
"""
from MP.lib.axes_label import __deco__ as deco
fig = wf(nw=1,nh=1,left=0.2,uw=3.5,
w0=0,w1=0.3,right=0,iarange=True)
ax=fig.axes[0]
for i in range(len(hkls)):
err = main_plot_flow_all(
hkl=hkls[i],sin2psimx=sin2psimx,
psi_nbin=psi_nbin,pmargin=None,
iplot_rs=iplot_rs,path=path)
eps_vm, e = err
ax.plot(eps_vm,e,label=hkls[i])
deco(iopt=8,ft=15,ax=ax)
ax.legend(loc='best',fontsize=9)
fig.savefig('error_dsa_%s.pdf'%path)
return fig
def error_est(
path='BB',psi_nbin=21,
sin2psimx=0.5,
hkls=['200','220','211','310'],
iplot_rs=False):
"""
"""
from MP.lib.axes_label import __deco__ as deco
fig = wf(nw=1,nh=1,left=0.2,uw=3.5,
w0=0,w1=0.3,right=0,iarange=True)
ax=fig.axes[0]
for i in xrange(len(hkls)):
err = main_plot_flow_all(
hkl=hkls[i],sin2psimx=sin2psimx,
psi_nbin=psi_nbin,pmargin=None,
iplot_rs=iplot_rs,path=path)
eps_vm, e = err
ax.plot(eps_vm,e,label=hkls[i])
deco(iopt=8,ft=15,ax=ax)
ax.legend(loc='best',fontsize=9)
fig.savefig('error_dsa_%s.pdf'%path)
return fig
def influence_of_cnts_stats(
## characteristics of an ensemble for stress data
sigmas=[1e-5, 2e-5, 5e-5, 1e-4],
bounds=[0.,0.5],
ss=3,
nbins=10,
iwgt=False,
nsample=4,
intp_opt=0,
iplot=False,
## diffraction condition
bragg=78.2, ## Fe {211} using Cr K-alpha
ird=0.182, ## Intensity of random distribution
## for {211} using window of 10 degree.
DEC_freq_sym=True,
NCPU=0):
"""
Influence of counting statistics uncertainty
With fixing other variables, investigates the
propagation of counting stat error on to the final
diffraction stress by examining a number of
statistical ensembles (nsample) characterized
by given arguments
Arguments
=========
sigmas : Pure CSE
bounds : Bounds of tilting range
ss : Sampling frequency in DECs
nbins : How many tilting angles in diffraction
iwgt : Do we weight (E-e) by peak intensity?
nsample : The number of ensembles.
intp_opt : DEC interpolation method
iplot : Flag for plotting
bragg : Bragg's angle
ird : Intensity of random distribution
DEC_freq_sym: Plotting option for where DECs are sampled.
NCPU : The number of CPU cores allowed to run
"""
from RS.rs_ex import ex_consistency as func
if iplot:
import matplotlib.pyplot as plt
from MP.lib import mpl_lib,axes_label
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=2,nh=1)
ax1,ax2 = fig.axes[0], fig.axes[1]
print '\n\n****************'
print 'test run started'
print '****************\n\n'
myrs,flow1,flow2 =func(
sin2psimx=bounds[1],
iscatter=True,
sigma=sigmas[0],
psi_nbin=nbins,
ig_sub=True,
iplot=False, # iplot=True
dec_inv_frq=ss,
dec_interp=intp_opt,
)
print '\n\n**************'
print 'test completed'
print '**************\n\n'
# return ## debugging
Y_all = np.zeros((len(sigmas), nsample, flow1.nstp))
M = []
ls=['-+','-s','-o','-d','-t']
import multiprocessing
from multiprocessing import Pool
if NCPU==0: NCPU = multiprocessing.cpu_count()
print 'NCPU: %i'%NCPU
pool = Pool(processes = NCPU)
## function is now wrap_func
results = []
for i in range(len(sigmas)):
results.append([])
for j in range(nsample):
results[i].append(
pool.apply_async(
wrap_func,
args=(
sigmas[i],
ss,
intp_opt,
bounds,
nbins
)
,))
## close/join
pool.close()
pool.join()
## terminate
pool.terminate()
## below is to post-process the results
for i in range(len(sigmas)):
for j in range(nsample):
x,y = results[i][j].get()
Y_all[i][j][:] = y[::]
M = np.zeros((len(sigmas),len(x)))
S = np.zeros((len(sigmas),len(x)))
for i in range(len(sigmas)):
y = Y_all[i][:][:]
y = y.T[::] ## len(x), nsample
for k in range(len(x)):
M[i][k] = y[k].mean()
S[i][k] = y[k].std()
nbins = 10
H = np.zeros((len(sigmas), len(x), nbins))
BE = np.zeros((len(sigmas), len(x), nbins+1))
for i in range(len(sigmas)):
y = Y_all[i][:][:]
y = y.T[::] ## len(x), nsample
for k in range(len(x)):
hist,bin_edges = np.histogram(y[k],bins=10)
H[i,k,:] = hist[::]
BE[i,k,:] = bin_edges[::]
if iplot:
for i in range(len(sigmas)):
ax1.plot(x,M[i],ls[i],mfc='None',color='k',label='%6.0e'%sigmas[i])
ax2.errorbar(x,M[i],yerr=S[i],color='k',ls=ls[i])
if type(ss).__name__=='int' and DEC_freq_sym:
ax1.plot(x[::ss],np.zeros((len(x[::ss]),)),'o',mec='r',mfc='None',ms=8)
ax2.plot(x[::ss],np.zeros((len(x[::ss]),)),'o',mec='r',mfc='None',ms=8)
elif type(ss).__name__=='list' and DEC_freq_sym:
ax1.plot(x[ss],np.zeros((len(x[ss]),)),'o',mec='r',mfc='None',ms=8)
ax2.plot(x[ss],np.zeros((len(x[ss]),)),'o',mec='r',mfc='None',ms=8)
ax1.set_ylim(0.,); ax1.set_xlim(0.,ax1.get_xlim()[1]*1.05)
deco(iopt=9,ft=15,ax=ax1)
fancy_legend(ax=ax1, size=7,ncol=2,nscat=1)
ax2.set_ylim(0.,); ax2.set_xlim(0.,ax2.get_xlim()[1]*1.05)
deco(iopt=9,ft=15,ax=ax2)
fig.savefig('ee.pdf')
return x, M, S, H, BE
def influence_of_nbin_scatter(
##
sigma=5e-5,
ss=1,
bounds=[0.0, 0.5],
nbins=[10,10], ## main arg
iscatter=True,
nsample=1,
iwgt=False,
intp_opt=0,
iplot=False,):
"""
Repeat influence_of_nbin examination
to verify the error in stress analysis
pertaining to that in eps(hkl,phi,psi)
Arguments
=========
sigma = 5e-5
ss = 1
bounds = [0,0.5]
nbins = [10,10]
iscatter = True
nsample = 1
iwgt = False
intp_opt = 0
iplot = False
"""
if iplot:
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=2,nh=1);
ax = fig.axes[0]
ax1 = fig.axes[1]
for i in range(len(nbins)):
nbin = nbins[i]
for j in range(nsample):
x, y = influence_of_nbin(
ss=ss,bounds=bounds,
nbins=[nbin], iscatter=iscatter,
sigma=sigma,
iwgt=iwgt,
intp_opt=intp_opt,)
if i==0 and j==0:
## Getting Y array shaped.
Y = np.zeros((len(nbins),len(x),nsample))
Y[i,:,j] = y[0][:]
nbin, nstp, nsamp = Y.shape
for i in range(nbin):
e = []; s = []
for j in range(nstp):
mean = Y[i,j,:].mean()
std = Y[i,j,:].std()
e.append(mean)
s.append(std)
if iplot:
ax1.plot(x,e,'-o',label=nbins[i])
ax.errorbar(x,e,yerr=std,fmt='x',label=nbins[i])
if iplot:
if type(ss).__name__=='int':
ax1.plot(x[::ss],np.zeros((len(x[::ss]),)),'r|',ms=12)
ax.plot(x[::ss],np.zeros((len(x[::ss]),)),'r|',ms=8)
elif type(ss).__name__=='list':
ax1.plot(x[ss],np.zeros((len(x[ss]),)),'r|',ms=12)
ax.plot(x[ss],np.zeros((len(x[ss]),)),'r|',ms=8)
ax.set_ylim(0.,); ax.set_xlim(0.,ax.get_xlim()[1]*1.05)
ax1.set_ylim(0.,); ax1.set_xlim(0.,ax1.get_xlim()[1]*1.05)
fancy_legend(ax=ax, size=7,ncol=2)
fancy_legend(ax=ax1,size=7,ncol=2)
deco(iopt=8,ft=15,ax=ax); deco(iopt=8,ft=15,ax=ax1)
if type(ss).__name__=='int': dum=ss
elif type(ss).__name__=='list': dum =len(ss)
fig.savefig('ss_%i_err_scatter_nsamp_%i.pdf'%(dum,nsample))
plt.close('all')
return x, e
def influence_of_intp(ss=2,bounds=[0,0.5],
psi_nbin=13,iplot=False,
hkl=None,
iscatter=False,iwgt=False,
sigma=5e-5,
intp_opt=0):
"""
Parametric study to demonstrate the uncertainty
for a fixed intp_opt
Arguments
=========
ss = 2 (step size)
bounds = sin2psi bounds
psi_nbin = Number of psi data points in use
iplot = False
hkl = None
iscatter = False
iwgt = False
sigma = 5e-5
intp_opt = 0
"""
from rs import filter_psi2
from rs_ex import ex_consistency as main
from MP.mat.mech import find_err
if iplot:
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=3,nh=1)
axs = fig.axes
## Use the reduced set over the consistency check
sf, ig = use_intp_sfig(
ss=ss,iopt=intp_opt,iplot=False,
iwgt=False)
sf_ext = sf.sf.swapaxes(1,-1).swapaxes(-2,-1)[::]*1e6
ig_ext = ig.ig[::]
##
# Filtering against sin2psi
sf_ext = filter_psi2(
obj=sf_ext,sin2psi=sf.sin2psi,
bounds=bounds)
ig_ext = filter_psi2(
obj=ig_ext,sin2psi=sf.sin2psi,
bounds=bounds)
### Reduce binning
## consistency check
rst = main(sin2psimx=bounds[1],
psi_nbin=psi_nbin,
sf_ext=sf_ext,ig_ext=ig_ext,
iplot=iplot,hkl=hkl,
iscatter=iscatter,
sigma=sigma,iwgt=iwgt,
vf_ext=None)
fw, fd = rst[1], rst[2]
if iplot:
axs[0].plot(fw.epsilon_vm,fw.sigma_vm,
'b-x',label='Weight Avg.')
axs[0].plot(fd.epsilon_vm,fd.sigma_vm,
'k+',label='Diff Stress')
if type(ss).__name__=='int':
x = fd.epsilon_vm[::ss];
y = fd.sigma_vm[::ss]
elif type(ss).__name__=='list':
x = fd.epsilon_vm[ss];
y = fd.sigma_vm[ss]
if iplot:
label='SF/IG acqusition'
axs[0].plot(x,y,'o',mec='r',mfc='None',
alpha=0.8,label=label)
axs[1].plot(fw.sigma[0,0],fw.sigma[1,1],'b-x')
axs[1].plot(fd.sigma[0,0],fd.sigma[1,1],'k+')
npoints = len(fw.sigma[0,0])
wgtx, wgty = fw.sigma[0,0], fw.sigma[1,1]
dsax, dsay = fd.sigma[0,0], fd.sigma[1,1]
if iplot:
for i in range(npoints):
axs[1].plot([wgtx[i],dsax[i]],
[wgty[i],dsay[i]],
'k-',alpha=0.2)
e = find_err(fw,fd)
if iplot: axs[2].plot(fw.epsilon_vm, e, 'x')
if type(ss).__name__=='int' and iplot:
axs[2].plot(fw.epsilon_vm[::ss],e[::ss],
'o',mec='r',mfc='None',label=label)
elif type(ss).__name__=='list' and iplot:
axs[2].plot(fw.epsilon_vm[ss],e[ss],
'o',mec='r',mfc='None',label=label)
if type(ss).__name__=='int': dum=ss
elif type(ss).__name__=='list': dum =len(ss)
if iplot:
axes_label.__eqv__(axs[0],ft=10)
axs[1].set_aspect('equal')
axs[1].set_xlabel(r'$\bar{\Sigma}_{11}$',dict(fontsize=15))
axs[1].set_ylabel(r'$\bar{\Sigma}_{22}$',dict(fontsize=15))
axs[1].set_ylim(-100,700); axs[1].set_xlim(-100,700)
axs[0].legend(loc='best',fontsize=10).get_frame().set_alpha(0.5)
deco(iopt=8,ft=15,ax=axs[2])
fig.savefig('flow_dd_bin%i_ss%i.pdf'%(psi_nbin,dum))
plt.close(fig)
return fw, e
def plot_sf_psis(
sff_fn='temp.sff',
psi_ref=[45.0, 42.4, 39.8, 37.1, 34.3, 31.5,
28.5, 25.2, 21.7, 17.5, 12.3, 0]):
import numpy as np
import matplotlib.pyplot as plt
from pepshkl import reader4
from RS import sff_converter
from rs_exp import read_IGSF
from MP.lib import mpl_lib, axes_label
from RS.rs import find_nearest
deco = axes_label.__deco__
wf = mpl_lib.wide_fig
sff_converter.main(fn=sff_fn,difile=None,itab=True,
ieps0=4, fn_str='STR_STR.OUT')
SF, IG = read_IGSF(fn=sff_fn,fn_str='STR_STR.OUT')
tdat,_psis_,_vdat_,_ngrd_ = reader4(
fn='int_els_ph1.out',ndetector=2,iopt=1)
## sorting
import MP.ssort as sort
shsort = sort.shellSort
ss = sort.ind_swap
## sort psi and find ind
psis, ind = shsort(_psis_)
nstp, nphi, npsis = np.shape(_vdat_)
vdat = np.zeros((nstp,nphi,npsis))
ngrd = np.zeros((nstp,nphi,npsis))
for istp in range(nstp):
for iphi in range(nphi):
dum_v = _vdat_[istp,iphi,:][::]
vdat[istp,iphi,:] = ss(dum_v[::],ind)[::]
dum_n = _ngrd_[istp,iphi,:][::]
ngrd[istp,iphi,:] = ss(dum_n[::],ind)[::]
## Find only psi_ref values
## sin2psi_ref = np.sin(psi_ref*np.pi/180.)**2
inds = []
for i in range(len(psi_ref)):
inds.append(find_nearest(SF.psi, psi_ref[i]))
#SF.mask_vol() ## shake off all zero values
fig=wf(nh=nphi,nw=len(psi_ref),iarange=True)
figv=wf(nh=nphi,nw=len(psi_ref),iarange=True)
fe0=wf(nh=nphi,nw=len(psi_ref),iarange=True)
for iphi in range(nphi):
for ipsi in range(len(psi_ref)):
#ax = fig.axes[iphi+nphi*ipsi]
ax = fig.axes[ipsi+len(psi_ref)*iphi]
axt = figv.axes[ipsi+len(psi_ref)*iphi]
aig = fe0.axes[ipsi+len(psi_ref)*iphi]
if iphi==0:
ax.set_title(
r'$\psi= %3.1f^\circ{}$'%\
SF.psi[inds[ipsi]]
)
axt.set_title(
r'$\psi= %3.1f^\circ{}$'%\
SF.psi[inds[ipsi]]
)
aig.set_title(
r'$\psi= %3.1f^\circ{}$'%\
SF.psi[inds[ipsi]]
)
if ipsi==0:
ax.text(0,0,r'$\phi=%3.1f^\circ{}$'%\
SF.phi[iphi],transform=ax.transAxes)
axt.text(0,0,r'$\phi=%3.1f^\circ{}$'%\
SF.phi[iphi],transform=axt.transAxes)
aig.text(0,0,r'$\phi=%3.1f^\circ{}$'%\
SF.phi[iphi],transform=aig.transAxes)
deco(ax,iopt=6)
deco(axt,iopt=7)
x = SF.flow.epsilon_vm
y = SF.sf[:,iphi,inds[ipsi],0] # f11
ig = IG.ig[:,iphi,inds[ipsi]]
v = vdat[:,iphi,inds[ipsi]]
ax.plot(x,y*1e12,'b-o')
aig.plot(x,ig,'b-o')
axt.plot(x,v,'r-x')
axt.set_ylim(0.,0.10)
ax.set_xlim(0.,1.0)
aig.set_xlim(0.,1.0)
aig.set_ylim(-0.002,0.002)
def influence_of_cnts_stats(
## characteristics of an ensemble for
## stress data
sigmas=[1e-5, 2e-5, 5e-5, 1e-4],
bounds=[0.,0.5],
ss=3,
nbins=10,
iwgt=False,
nsample=4,
intp_opt=0,
iplot=False,
## diffraction condition
bragg=78.2*np.pi/180., ## Fe {211} using Cr K-alpha
#bragg=162.2*np.pi/180., ## Fe {310} using Cobalt Radiation.
ird=0.182, ## Intensity of random distribution
## for {211} using window of 10 degree.
## sample a portion of data.
nfrq=None,
DEC_freq_sym=True,
NCPU=0):
"""
Propagate errors due to
1) counting statistics error (CSE)
2) incomplete DECs (by utilizing only
a portion of model DECs)
3) Infinite number of tilting angles in use
4) Incomplete range of psi angle ranges in use
5) Geometric distortion (Bragg angle / psi)
6) Peak intensity: I(phi,psi) / Ird
With fixing other variables, investigates the
propagation of counting stat error to final
diffraction stress by examining a number of
statistical ensembles <nsample> characterized
by given arguments
## diffraction condition
#bragg=78.2, ## Fe {211} using Cr K-alpha
#bragg=78.2, ## Fe {310} using Cobalt
#ird=0.182, ## Intensity of random distribution
Arguments
=========
sigmas : Counting Stat Error
bounds : Bounds of tilting range
(low psi to high psi)
ss : Sampling frequency in DECs
nbins : How many tilting angles in
diffraction
iwgt : Do we weight (E-e) by peak intensity?
nsample : The number of ensembles
intp_opt : DEC interpolation method
iplot : Flag for plotting
bragg : Bragg's angle
ird : Intensity of random distribution
nfrq : if not (None) but an integer,
use it as denominator to 'continue' to skip
--------------------------
if istp % nfrq != 0 :
continue ! skip
else:
run stress analysis
--------------------------
DEC_freq_sym: Plotting option for where DECs are sampled.
NCPU : The number of CPU cores allowed to run
"""
from RS.rs_ex import ex_consistency as func
if iplot:
import matplotlib.pyplot as plt
from MP.lib import mpl_lib,axes_label
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=2,nh=1)
ax1,ax2 = fig.axes[0], fig.axes[1]
print '\n\n****************'
print 'test run started'
print '****************\n\n'
myrs,flow_raw,flow2 =func(
sin2psimx=bounds[1],
iscatter=False,
sigma=sigmas[0],
psi_nbin=nbins,
ig_sub=True,
iplot=False, # iplot=True
dec_inv_frq=ss,
dec_interp=intp_opt,
nfrq=nfrq)
print '\n\n**************'
print 'test completed'
print '**************\n\n'
Y_all = np.zeros((len(sigmas), nsample, flow2.nstp))
M = []
ls=['-+','-s','-o','-d','-t']
import multiprocessing
from multiprocessing import Pool
if NCPU==0: NCPU = multiprocessing.cpu_count()
print 'NCPU: %i'%NCPU
pool = Pool(processes = NCPU)
## function is now wrap_func
results = []
for i in xrange(len(sigmas)):
results.append([])
for j in xrange(nsample):
results[i].append(
pool.apply_async(
wrap_func,
args=(
sigmas[i],
ss,
intp_opt,
bounds,
nbins,
bragg,
ird,nfrq),))
## close/join
pool.close(); pool.join()
## terminate
pool.terminate()
## below is to post-process the results
for i in xrange(len(sigmas)):
for j in xrange(nsample):
x,y = results[i][j].get()
Y_all[i][j][:] = y[::]
M = np.zeros((len(sigmas),len(x)))
S = np.zeros((len(sigmas),len(x)))
for i in xrange(len(sigmas)):
y = Y_all[i][:][:]
y = y.T[::] ## len(x), nsample
for k in xrange(len(x)):
M[i][k] = y[k].mean()
S[i][k] = y[k].std()
nbins = 10
H = np.zeros((len(sigmas), len(x), nbins))
BE = np.zeros((len(sigmas), len(x), nbins+1))
for i in xrange(len(sigmas)):
y = Y_all[i][:][:]
y = y.T[::] ## len(x), nsample
for k in xrange(len(x)):
hist,bin_edges = np.histogram(y[k],bins=10)
H[i,k,:] = hist[::]
BE[i,k,:] = bin_edges[::]
if iplot:
for i in xrange(len(sigmas)):
ax1.plot(x,M[i],ls[i],mfc='None',color='k',label='%6.0e'%sigmas[i])
ax2.errorbar(x,M[i],yerr=S[i],color='k',ls=ls[i])
if type(ss).__name__=='int' and DEC_freq_sym:
ax1.plot(x[::ss],np.zeros((len(x[::ss]),)),'o',mec='r',mfc='None',ms=8)
ax2.plot(x[::ss],np.zeros((len(x[::ss]),)),'o',mec='r',mfc='None',ms=8)
elif type(ss).__name__=='list' and DEC_freq_sym:
ax1.plot(x[ss],np.zeros((len(x[ss]),)),'o',mec='r',mfc='None',ms=8)
ax2.plot(x[ss],np.zeros((len(x[ss]),)),'o',mec='r',mfc='None',ms=8)
# ax1.set_ylim(0.,); ax1.set_xlim(0.,ax1.get_xlim()[1]*1.05)
deco(iopt=9,ft=15,ax=ax1)
fancy_legend(ax=ax1, size=7,ncol=2,nscat=1)
ax2.set_ylim(0.,); ax2.set_xlim(0.,ax2.get_xlim()[1]*1.05)
deco(iopt=9,ft=15,ax=ax2)
fig.savefig('ee.pdf')
return x, M, S, H, BE, flow_raw
def influence_of_nbin_scatter(
##
sigma=5e-5,
ss=1,
bounds=[0.0, 0.5],
nbins=[10,10], ## main arg
iscatter=True,
nsample=1,
iwgt=False,
intp_opt=0,
iplot=False,):
"""
Repeat influence_of_nbin examination
to verify the error in stress analysis
pertaining to that in eps(hkl,phi,psi)
Arguments
=========
sigma = 5e-5
ss = 1
bounds = [0,0.5]
nbins = [10,10]
iscatter = True
nsample = 1
iwgt = False
intp_opt = 0
iplot = False
"""
if iplot:
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=2,nh=1);
ax = fig.axes[0]
ax1 = fig.axes[1]
for i in xrange(len(nbins)):
nbin = nbins[i]
for j in xrange(nsample):
x, y = influence_of_nbin(
ss=ss,bounds=bounds,
nbins=[nbin], iscatter=iscatter,
sigma=sigma,
iwgt=iwgt,
intp_opt=intp_opt,)
if i==0 and j==0:
## Getting Y array shaped.
Y = np.zeros((len(nbins),len(x),nsample))
Y[i,:,j] = y[0][:]
nbin, nstp, nsamp = Y.shape
for i in xrange(nbin):
e = []; s = []
for j in xrange(nstp):
mean = Y[i,j,:].mean()
std = Y[i,j,:].std()
e.append(mean)
s.append(std)
if iplot:
ax1.plot(x,e,'-o',label=nbins[i])
ax.errorbar(x,e,yerr=std,fmt='x',label=nbins[i])
if iplot:
if type(ss).__name__=='int':
ax1.plot(x[::ss],np.zeros((len(x[::ss]),)),'r|',ms=12)
ax.plot(x[::ss],np.zeros((len(x[::ss]),)),'r|',ms=8)
elif type(ss).__name__=='list':
ax1.plot(x[ss],np.zeros((len(x[ss]),)),'r|',ms=12)
ax.plot(x[ss],np.zeros((len(x[ss]),)),'r|',ms=8)
ax.set_ylim(0.,); ax.set_xlim(0.,ax.get_xlim()[1]*1.05)
ax1.set_ylim(0.,); ax1.set_xlim(0.,ax1.get_xlim()[1]*1.05)
fancy_legend(ax=ax, size=7,ncol=2)
fancy_legend(ax=ax1,size=7,ncol=2)
deco(iopt=8,ft=15,ax=ax); deco(iopt=8,ft=15,ax=ax1)
if type(ss).__name__=='int': dum=ss
elif type(ss).__name__=='list': dum =len(ss)
fig.savefig('ss_%i_err_scatter_nsamp_%i.pdf'%(dum,nsample))
plt.close('all')
return x, e
def influence_of_intp(ss=2,bounds=[0,0.5],
psi_nbin=13,iplot=False,
hkl=None,
iscatter=False,iwgt=False,
sigma=5e-5,
intp_opt=0):
"""
Parametric study to demonstrate the uncertainty
for a fixed intp_opt
Arguments
=========
ss = 2 (step size)
bounds = sin2psi bounds
psi_nbin = Number of psi data points in use
iplot = False
hkl = None
iscatter = False
iwgt = False
sigma = 5e-5
intp_opt = 0
"""
from rs import filter_psi2
from rs_ex import ex_consistency as main
from MP.mat.mech import find_err
if iplot:
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
fig = wide_fig(nw=3,nh=1)
axs = fig.axes
## Use the reduced set over the consistency check
sf, ig = use_intp_sfig(
ss=ss,iopt=intp_opt,iplot=False,
iwgt=False)
sf_ext = sf.sf.swapaxes(1,-1).swapaxes(-2,-1)[::]*1e6
ig_ext = ig.ig[::]
##
# Filtering against sin2psi
sf_ext = filter_psi2(
obj=sf_ext,sin2psi=sf.sin2psi,
bounds=bounds)
ig_ext = filter_psi2(
obj=ig_ext,sin2psi=sf.sin2psi,
bounds=bounds)
### Reduce binning
## consistency check
rst = main(sin2psimx=bounds[1],
psi_nbin=psi_nbin,
sf_ext=sf_ext,ig_ext=ig_ext,
iplot=iplot,hkl=hkl,
iscatter=iscatter,
sigma=sigma,iwgt=iwgt,
vf_ext=None)
fw, fd = rst[1], rst[2]
if iplot:
axs[0].plot(fw.epsilon_vm,fw.sigma_vm,
'b-x',label='Weight Avg.')
axs[0].plot(fd.epsilon_vm,fd.sigma_vm,
'k+',label='Diff Stress')
if type(ss).__name__=='int':
x = fd.epsilon_vm[::ss];
y = fd.sigma_vm[::ss]
elif type(ss).__name__=='list':
x = fd.epsilon_vm[ss];
y = fd.sigma_vm[ss]
if iplot:
label='SF/IG acqusition'
axs[0].plot(x,y,'o',mec='r',mfc='None',
alpha=0.8,label=label)
axs[1].plot(fw.sigma[0,0],fw.sigma[1,1],'b-x')
axs[1].plot(fd.sigma[0,0],fd.sigma[1,1],'k+')
npoints = len(fw.sigma[0,0])
wgtx, wgty = fw.sigma[0,0], fw.sigma[1,1]
dsax, dsay = fd.sigma[0,0], fd.sigma[1,1]
if iplot:
for i in xrange(npoints):
axs[1].plot([wgtx[i],dsax[i]],
[wgty[i],dsay[i]],
'k-',alpha=0.2)
e = find_err(fw,fd)
if iplot: axs[2].plot(fw.epsilon_vm, e, 'x')
if type(ss).__name__=='int' and iplot:
axs[2].plot(fw.epsilon_vm[::ss],e[::ss],
'o',mec='r',mfc='None',label=label)
elif type(ss).__name__=='list' and iplot:
axs[2].plot(fw.epsilon_vm[ss],e[ss],
'o',mec='r',mfc='None',label=label)
if type(ss).__name__=='int': dum=ss
elif type(ss).__name__=='list': dum =len(ss)
if iplot:
axes_label.__eqv__(axs[0],ft=10)
axs[1].set_aspect('equal')
axs[1].set_xlabel(r'$\bar{\Sigma}_{11}$',dict(fontsize=15))
axs[1].set_ylabel(r'$\bar{\Sigma}_{22}$',dict(fontsize=15))
axs[1].set_ylim(-100,700); axs[1].set_xlim(-100,700)
axs[0].legend(loc='best',fontsize=10).get_frame().set_alpha(0.5)
deco(iopt=8,ft=15,ax=axs[2])
fig.savefig('flow_dd_bin%i_ss%i.pdf'%(psi_nbin,dum))
plt.close(fig)
return fw, e
def plot_sf_psis(
sff_fn='temp.sff',
psi_ref=[45.0, 42.4, 39.8, 37.1, 34.3, 31.5,
28.5, 25.2, 21.7, 17.5, 12.3, 0]):
import numpy as np
import matplotlib.pyplot as plt
from pepshkl import reader4
from RS import sff_converter
from rs_exp import read_IGSF
from MP.lib import mpl_lib, axes_label
from RS.rs import find_nearest
deco = axes_label.__deco__
wf = mpl_lib.wide_fig
sff_converter.main(fn=sff_fn,difile=None,itab=True,
ieps0=4, fn_str='STR_STR.OUT')
SF, IG = read_IGSF(fn=sff_fn,fn_str='STR_STR.OUT')
tdat,_psis_,_vdat_,_ngrd_ = reader4(
fn='int_els_ph1.out',ndetector=2,iopt=1)
## sorting
import MP.ssort as sort
shsort = sort.shellSort
ss = sort.ind_swap
## sort psi and find ind
psis, ind = shsort(_psis_)
nstp, nphi, npsis = np.shape(_vdat_)
vdat = np.zeros((nstp,nphi,npsis))
ngrd = np.zeros((nstp,nphi,npsis))
for istp in xrange(nstp):
for iphi in xrange(nphi):
dum_v = _vdat_[istp,iphi,:][::]
vdat[istp,iphi,:] = ss(dum_v[::],ind)[::]
dum_n = _ngrd_[istp,iphi,:][::]
ngrd[istp,iphi,:] = ss(dum_n[::],ind)[::]
## Find only psi_ref values
## sin2psi_ref = np.sin(psi_ref*np.pi/180.)**2
inds = []
for i in xrange(len(psi_ref)):
inds.append(find_nearest(SF.psi, psi_ref[i]))
#SF.mask_vol() ## shake off all zero values
fig=wf(nh=nphi,nw=len(psi_ref),iarange=True)
figv=wf(nh=nphi,nw=len(psi_ref),iarange=True)
fe0=wf(nh=nphi,nw=len(psi_ref),iarange=True)
for iphi in xrange(nphi):
for ipsi in xrange(len(psi_ref)):
#ax = fig.axes[iphi+nphi*ipsi]
ax = fig.axes[ipsi+len(psi_ref)*iphi]
axt = figv.axes[ipsi+len(psi_ref)*iphi]
aig = fe0.axes[ipsi+len(psi_ref)*iphi]
if iphi==0:
ax.set_title(
r'$\psi= %3.1f^\circ{}$'%\
SF.psi[inds[ipsi]]
)
axt.set_title(
r'$\psi= %3.1f^\circ{}$'%\
SF.psi[inds[ipsi]]
)
aig.set_title(
r'$\psi= %3.1f^\circ{}$'%\
SF.psi[inds[ipsi]]
)
if ipsi==0:
ax.text(0,0,r'$\phi=%3.1f^\circ{}$'%\
SF.phi[iphi],transform=ax.transAxes)
axt.text(0,0,r'$\phi=%3.1f^\circ{}$'%\
SF.phi[iphi],transform=axt.transAxes)
aig.text(0,0,r'$\phi=%3.1f^\circ{}$'%\
SF.phi[iphi],transform=aig.transAxes)
deco(ax,iopt=6)
deco(axt,iopt=7)
x = SF.flow.epsilon_vm
y = SF.sf[:,iphi,inds[ipsi],0] # f11
ig = IG.ig[:,iphi,inds[ipsi]]
v = vdat[:,iphi,inds[ipsi]]
ax.plot(x,y*1e12,'b-o')
aig.plot(x,ig,'b-o')
axt.plot(x,v,'r-x')
axt.set_ylim(0.,0.10)
ax.set_xlim(0.,1.0)
aig.set_xlim(0.,1.0)
aig.set_ylim(-0.002,0.002)
def influence_of_nbin_scatter(
ss=3,bounds=[0.0, 0.5],
nbins=[7, 13, 23, 33],
iscatter=True,nsample=5,iwgt=True,
sigma=5e-5,intp_opt=0,iplot=False):
"""
Repeat influence_of_nbin examination
to verify the error in stress analysis
pertaining to that in eps(hkl,phi,psi)
"""
from MP.lib import mpl_lib,axes_label
import matplotlib.pyplot as plt
fancy_legend = mpl_lib.fancy_legend
wide_fig = mpl_lib.wide_fig
deco = axes_label.__deco__
if iplot:
fig = wide_fig(nw=2,nh=1);
ax=fig.axes[0]
ax1=fig.axes[1]
Y = []
for i in range(nsample):
x, y = influence_of_nbin(
ss=ss,bounds=bounds,
nbins=nbins, iscatter=iscatter,
sigma=sigma,
iwgt=iwgt,intp_opt=intp_opt)
Y.append(y)
## Y [nsample, nbin, nstp]
Y = np.array(Y).swapaxes(0,-1).swapaxes(0,1)
## Y [nbin, nstp, nsample]
nbin, nstp, nsamp = Y.shape
for i in range(nbin):
e = []; s = []
for j in range(nstp):
mean = Y[i,j,:].mean()
std = Y[i,j,:].std()
e.append(mean)
s.append(std)
if iplot:
ax1.plot(x,e,'-o',label=nbins[i])
ax.errorbar(x,e,yerr=std,fmt='x',label=nbins[i])
if iplot:
if type(ss).__name__=='int':
ax1.plot(x[::ss],np.zeros((len(x[::ss]),)),'r|',ms=12)
ax.plot(x[::ss],np.zeros((len(x[::ss]),)),'r|',ms=8)
elif type(ss).__name__=='list':
ax1.plot(x[ss],np.zeros((len(x[ss]),)),'r|',ms=12)
ax.plot(x[ss],np.zeros((len(x[ss]),)),'r|',ms=8)
ax.set_ylim(0.,); ax.set_xlim(0.,ax.get_xlim()[1]*1.05)
ax1.set_ylim(0.,); ax1.set_xlim(0.,ax1.get_xlim()[1]*1.05)
fancy_legend(ax=ax, size=7,ncol=2)
fancy_legend(ax=ax1,size=7,ncol=2)
deco(iopt=8,ft=15,ax=ax); deco(iopt=8,ft=15,ax=ax1)
if type(ss).__name__=='int': dum=ss
elif type(ss).__name__=='list': dum =len(ss)
fig.savefig('ss_%i_err_scatter_nsamp_%i.pdf'%(dum,nsample))
plt.close('all')
return x, e
