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	timproved plots with means and porosities - sphere - GPU-based 3D discrete elem…
	git clone git://src.adamsgaard.dk/sphere
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	---
	commit ce6e548cf6900e585d97dbe8b5482a428c96600e
	parent 41f960f604e07ec961ae9fa30468295627f7eb4e
	Author: Anders Damsgaard <[email protected]>
	Date: Tue, 9 Sep 2014 15:40:37 +0200

	improved plots with means and porosities

	Diffstat:
	M python/shear-results-forces.py \| 56 ++++++++++++++++++++++++++---…

	1 file changed, 48 insertions(+), 8 deletions(-)
	---
	diff --git a/python/shear-results-forces.py b/python/shear-results-forces.py
	t@@ -44,6 +44,15 @@ f_pf = numpy.zeros_like(xdisp)
	# pressure - hydrostatic pressure
	dev_p = numpy.zeros((len(steps), sim.num[2]))

	+# mean porosity
	+phi_bar = numpy.zeros((len(steps), sim.num[2]))
	+
	+# mean per-particle values
	+xdisp_mean = numpy.zeros((len(steps), sim.num[2]))
	+f_pf_mean = numpy.zeros((len(steps), sim.num[2]))
	+
	+shear_strain = numpy.zeros(len(steps))
	+
	s = 0
	for step in steps:

	t@@ -73,6 +82,21 @@ for step in steps:
	numpy.average(numpy.average(sim.p_f, axis=0), axis=0)\
	/nsteps_avg

	+ phi_bar[s,:] += \
	+ numpy.average(numpy.average(sim.phi, axis=0), axis=0)\
	+ /nsteps_avg
	+
	+ shear_strain[s] += sim.shearStrain()/nsteps_avg
	+
	+ # calculate mean values of xdisp and f_pf
	+ for iz in numpy.arange(sim.num[2]):
	+ z_bot = iz*dz
	+ z_top = (iz+1)*dz
	+ I = numpy.nonzero((zpos_p[s,:] >= z_bot) & (zpos_p[s,:] < z_top))
	+ if len(I) > 0:
	+ xdisp_mean[s,iz] = numpy.mean(xdisp[s,I])
	+ f_pf_mean[s,iz] = numpy.mean(f_pf[s,I])
	+
	else:
	print(sid + ' not found')
	s += 1
	t@@ -83,10 +107,20 @@ for s in numpy.arange(len(steps)):
	ax1 = plt.subplot((s+1)*100 + 31)
	ax2 = plt.subplot((s+1)*100 + 32, sharey=ax1)
	ax3 = plt.subplot((s+1)*100 + 33, sharey=ax1)
	+ ax4 = ax3.twiny()
	+
	+ ax1.plot(xdisp[s], zpos_p[s], '+', color = '#888888')
	+ ax1.plot(xdisp_mean[s], zpos_c[s], color = 'k')
	+
	+ ax2.plot(f_pf[s], zpos_p[s], '+', color = '#888888')
	+ ax2.plot(f_pf_mean[s], zpos_c[s], color = 'k')

	- ax1.plot(xdisp[s], zpos_p[s], '+')
	- ax2.plot(f_pf[s], zpos_p[s], '+')
	- ax3.plot(dev_p[s]/1000.0, zpos_c[s])
	+ ax3.plot(dev_p[s]/1000.0, zpos_c[s], 'k')
	+
	+ phicolor = '#666666'
	+ ax4.plot(phi_bar[s], zpos_c[s], '--', color = phicolor)
	+ for tl in ax4.get_xticklabels():
	+ tl.set_color(phicolor)

	max_z = numpy.max(zpos_p)
	ax1.set_ylim([0, max_z])
	t@@ -98,26 +132,32 @@ for s in numpy.arange(len(steps)):
	#plt.loglog(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))

	ax1.set_ylabel('Vertical position $z$ [m]')
	- ax1.set_xlabel('$x^3_p$ [m]')
	+ ax1.set_xlabel('$x^3_\\text{p}$ [m]')
	ax2.set_xlabel('$\\boldsymbol{f}_\\text{pf}$ [N]')
	ax3.set_xlabel('$\\bar{p_\\text{f}}$ [kPa]')
	+ ax4.set_xlabel('$\\bar{\\phi}$ [-]', color=phicolor)
	plt.setp(ax2.get_yticklabels(), visible=False)
	plt.setp(ax3.get_yticklabels(), visible=False)

	- ax1.get_xaxis().set_major_locator(MaxNLocator(nbins=4))
	- ax2.get_xaxis().set_major_locator(MaxNLocator(nbins=4))
	- ax3.get_xaxis().set_major_locator(MaxNLocator(nbins=4))
	+ ax1.get_xaxis().set_major_locator(MaxNLocator(nbins=5))
	+ ax2.get_xaxis().set_major_locator(MaxNLocator(nbins=5))
	+ ax3.get_xaxis().set_major_locator(MaxNLocator(nbins=5))

	plt.setp(ax1.xaxis.get_majorticklabels(), rotation=90)
	plt.setp(ax2.xaxis.get_majorticklabels(), rotation=90)
	plt.setp(ax3.xaxis.get_majorticklabels(), rotation=90)
	+ plt.setp(ax4.xaxis.get_majorticklabels(), rotation=90)
	+
	+ fig.text(0.1, 0.9,
	+ 'Shear strain $\\gamma = %.3f$' % (shear_strain[s]),
	+ horizontalalignment='left', fontsize=22)
	#ax1.grid()
	#ax2.grid()
	#ax1.legend(loc='lower right', prop={'size':18})
	#ax2.legend(loc='lower right', prop={'size':18})

	plt.tight_layout()
	-plt.subplots_adjust(wspace = .001)
	+plt.subplots_adjust(wspace = .05)
	plt.MaxNLocator(nbins=4)
	filename = 'shear-10kPa-forces.pdf'
	plt.savefig(filename)