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	timproved plots - sphere - GPU-based 3D discrete element method algorithm with …
	git clone git://src.adamsgaard.dk/sphere
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	---
	commit 64696bcfffc330d89cebbd151b578e8b282282ca
	parent 38c4e8e94d69431d60105de0ec5583cf5ede7acc
	Author: Anders Damsgaard <[email protected]>
	Date: Wed, 1 Oct 2014 10:00:15 +0200

	improved plots

	Diffstat:
	M python/shear-results.py \| 39 +++++++++++++++++++++++------…
	M python/sphere.py \| 15 +++++++++++++++

	2 files changed, 44 insertions(+), 10 deletions(-)
	---
	diff --git a/python/shear-results.py b/python/shear-results.py
	t@@ -26,6 +26,8 @@ dilation = [[], [], []]
	p_min = [[], [], []]
	p_mean = [[], [], []]
	p_max = [[], [], []]
	+f_n_mean = [[], [], []]
	+f_n_max = [[], [], []]

	fluid=True

	t@@ -61,15 +63,22 @@ for c in numpy.arange(1,len(cvals)+1):
	friction[c] = sim.tau/sim.sigma_eff
	dilation[c] = sim.dilation

	- # fluid pressures
	- p_mean[c] = numpy.zeros_like(shear_strain[c])
	- p_min[c] = numpy.zeros_like(shear_strain[c])
	- p_max[c] = numpy.zeros_like(shear_strain[c])
	+ # fluid pressures and particle forces
	+ p_mean[c] = numpy.zeros_like(shear_strain[c])
	+ p_min[c] = numpy.zeros_like(shear_strain[c])
	+ p_max[c] = numpy.zeros_like(shear_strain[c])
	+ f_n_mean[c] = numpy.zeros_like(shear_strain[c])
	+ f_n_max[c] = numpy.zeros_like(shear_strain[c])
	for i in numpy.arange(sim.status()):
	+ sim.readstep(i, verbose=False)
	iz_top = int(sim.w_x[0]/(sim.L[2]/sim.num[2]))-1
	p_mean[c][i] = numpy.mean(sim.p_f[:,:,0:iz_top])/1000
	- p_min[c][i] = numpy.min(sim.p_f[:,:,0:iz_top])/1000
	- p_max[c][i] = numpy.max(sim.p_f[:,:,0:iz_top])/1000
	+ p_min[c][i] = numpy.min(sim.p_f[:,:,0:iz_top])/1000
	+ p_max[c][i] = numpy.max(sim.p_f[:,:,0:iz_top])/1000
	+
	+ sim.findNormalForces()
	+ f_n_mean[c][i] = numpy.mean(sim.f_n_magn)
	+ f_n_max[c][i] = numpy.max(sim.f_n_magn)

	else:
	print(sid + ' not found')
	t@@ -82,14 +91,16 @@ for c in numpy.arange(1,len(cvals)+1):


	#fig = plt.figure(figsize=(8,8)) # (w,h)
	-fig = plt.figure(figsize=(8,12))
	+#fig = plt.figure(figsize=(8,12))
	+fig = plt.figure(figsize=(8,16))

	#plt.subplot(3,1,1)
	#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))

	-ax1 = plt.subplot(311)
	-ax2 = plt.subplot(312, sharex=ax1)
	-ax3 = plt.subplot(313, sharex=ax1)
	+ax1 = plt.subplot(411)
	+ax2 = plt.subplot(412, sharex=ax1)
	+ax3 = plt.subplot(413, sharex=ax1)
	+ax4 = plt.subplot(414, sharex=ax1)
	ax1.plot(shear_strain[0], friction[0], label='dry')
	ax2.plot(shear_strain[0], dilation[0], label='dry')

	t@@ -112,11 +123,17 @@ for c in numpy.arange(1,len(cvals)+1):
	where=p_min[c][1:]<=p_max[c][1:], facecolor=color[c],
	interpolate=True, alpha=alpha)

	+ ax4.plot(shear_strain[c][1:], f_n_mean[c][1:], '-' + color[c],
	+ label='$c$ = %.2f' % (cvals[c-1]), linewidth=2)
	+ ax4.plot(shear_strain[c][1:], f_n_max[c][1:], '--' + color[c])
	+ #label='$c$ = %.2f' % (cvals[c-1]), linewidth=2)
	+
	ax3.set_xlabel('Shear strain $\\gamma$ [-]')

	ax1.set_ylabel('Shear friction $\\tau/\\sigma\'$ [-]')
	ax2.set_ylabel('Dilation $\\Delta h/(2r)$ [-]')
	ax3.set_ylabel('Fluid pressure $p_\\text{f}$ [kPa]')
	+ax4.set_ylabel('Particle contact force $\|\|\\boldsymbol{f}_\\text{p}\|\|$ [N]')

	#ax1.set_xlim([200,300])

	t@@ -133,6 +150,8 @@ ax2.legend(loc='lower right', prop={'size':18}, fancybox=T…
	framealpha=legend_alpha)
	ax3.legend(loc='lower right', prop={'size':18}, fancybox=True,
	framealpha=legend_alpha)
	+ax4.legend(loc='best', prop={'size':18}, fancybox=True,
	+ framealpha=legend_alpha)

	plt.tight_layout()
	filename = 'shear-' + str(int(sigma0/1000.0)) + 'kPa-stress-dilation.pdf'
	diff --git a/python/sphere.py b/python/sphere.py
	t@@ -3662,6 +3662,8 @@ class sim:
	done in C++. The particle pair indexes and the distance of the overlaps
	is saved in the object itself as the ``.pairs`` and ``.overlaps``
	members.
	+
	+ See also: :func:`findNormalForces()`
	'''
	self.writebin(verbose=False)
	subprocess.call('cd .. && ./sphere --contacts input/' + self.sid
	t@@ -3671,6 +3673,19 @@ class sim:
	dtype=numpy.int32)
	self.overlaps = numpy.array(contactdata[:,2])

	+ def findNormalForces(self):
	+ '''
	+ Finds all particle-particle overlaps (by first calling
	+ :func:`findOverlaps()`) and calculating the normal magnitude by
	+ multiplying the overlaps with the elastic stiffness ``self.k_n``.
	+
	+ The result is saved in ``self.f_n_magn``.
	+
	+ See also: :func:`findOverlaps()`
	+ '''
	+ self.findOverlaps()
	+ self.f_n_magn = self.k_n * numpy.abs(self.overlaps)
	+

	def forcechains(self, lc=200.0, uc=650.0, outformat='png', disp='2d'):
	'''