timproved plots - sphere - GPU-based 3D discrete element method algorithm with optional fluid coupling
(HTM) git clone git://src.adamsgaard.dk/sphere
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---
(DIR) commit 09657d844261b3e1730f2fb2132d4dd66dba5e5f
(DIR) parent f747e43ef3abf99c7365e9eb6451f7bbd8828304
(HTM) Author: Anders Damsgaard <anders.damsgaard@geo.au.dk>
Date: Tue, 7 Oct 2014 14:33:19 +0200
improved plots
Diffstat:
A python/shear-results-internals.py | 339 +++++++++++++++++++++++++++++++
M python/shear-results-strain.py | 4 ++--
M python/shear-results.py | 123 +++++++++++++++++++++++++------
3 files changed, 443 insertions(+), 23 deletions(-)
---
(DIR) diff --git a/python/shear-results-internals.py b/python/shear-results-internals.py
t@@ -0,0 +1,339 @@
+#!/usr/bin/env python
+import matplotlib
+matplotlib.use('Agg')
+matplotlib.rcParams.update({'font.size': 18, 'font.family': 'serif'})
+matplotlib.rc('text', usetex=True)
+matplotlib.rcParams['text.latex.preamble']=[r"\usepackage{amsmath}"]
+import shutil
+
+import os
+import numpy
+import sphere
+from permeabilitycalculator import *
+import matplotlib.pyplot as plt
+from matplotlib.ticker import MaxNLocator
+
+#steps = [5, 10, 100]
+#steps = [5, 10]
+steps = sys.argv[3:]
+nsteps_avg = 5 # no. of steps to average over
+
+sigma0 = float(sys.argv[1])
+#c_grad_p = 1.0
+c_grad_p = float(sys.argv[2])
+c_phi = 1.0
+
+#sid = 'shear-sigma0=' + str(sigma0) + '-c_phi=' + \
+# str(c_phi) + '-c_grad_p=' + str(c_grad_p) + '-hi_mu-lo_visc'
+sid = 'halfshear-sigma0=' + str(sigma0) + '-c=' + str(c_grad_p) + '-shear'
+sim = sphere.sim(sid, fluid=True)
+sim.readfirst(verbose=False)
+
+# particle z positions
+zpos_p = numpy.zeros((len(steps), sim.np))
+
+# cell midpoint cell positions
+zpos_c = numpy.zeros((len(steps), sim.num[2]))
+dz = sim.L[2]/sim.num[2]
+for i in numpy.arange(sim.num[2]):
+ zpos_c[:,i] = i*dz + 0.5*dz
+
+# particle x displacements
+xdisp = numpy.zeros((len(steps), sim.np))
+
+# particle z velocity
+v_z_p = numpy.zeros((len(steps), sim.np))
+
+# fluid z velocity
+v_z_f = numpy.zeros((len(steps), sim.num[0], sim.num[1], sim.num[2]))
+
+# pressure - hydrostatic pressure
+dev_p = numpy.zeros((len(steps), sim.num[2]))
+
+# mean per-particle values
+v_z_p_bar = numpy.zeros((len(steps), sim.num[2]))
+v_z_f_bar = numpy.zeros((len(steps), sim.num[2]))
+
+# particle-fluid force per particle
+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 porosity change
+dphi_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_str in steps:
+
+ step = int(step_str)
+
+ if os.path.isfile('../output/' + sid + '.status.dat'):
+
+ for substep in numpy.arange(nsteps_avg):
+
+ if step + substep > sim.status():
+ raise Exception(
+ 'Simulation step %d not available (sim.status = %d).'
+ % (step, sim.status()))
+
+ sim.readstep(step + substep, verbose=False)
+
+ zpos_p[s,:] += sim.x[:,2]/nsteps_avg
+
+ xdisp[s,:] += sim.xyzsum[:,0]/nsteps_avg
+ v_z_p[s,:] += sim.vel[:,2]/nsteps_avg
+
+ '''
+ for i in numpy.arange(sim.np):
+ f_pf[s,i] += \
+ sim.f_sum[i].dot(sim.f_sum[i])/nsteps_avg
+ '''
+ f_pf[s,:] += sim.f_sum[:,2]
+
+ dev_p[s,:] += \
+ numpy.average(numpy.average(sim.p_f, axis=0), axis=0)\
+ /nsteps_avg
+
+ v_z_f[s,:] += sim.v_f[:,:,:,2]/nsteps_avg
+
+ v_z_f_bar[s,:] += \
+ numpy.average(numpy.average(sim.v_f[:,:,:,2], axis=0), axis=0)\
+ /nsteps_avg
+
+ phi_bar[s,:] += \
+ numpy.average(numpy.average(sim.phi, axis=0), axis=0)\
+ /nsteps_avg
+
+ dphi_bar[s,:] += \
+ numpy.average(numpy.average(sim.dphi, axis=0), axis=0)\
+ /nsteps_avg/sim.time_dt
+
+
+ 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])
+ v_z_p_bar[s,iz] = numpy.mean(v_z_p[s,I])
+ f_pf_mean[s,iz] = numpy.mean(f_pf[s,I])
+
+ else:
+ print(sid + ' not found')
+ s += 1
+
+#fig = plt.figure(figsize=(8,4*(len(steps))+1))
+#fig = plt.figure(figsize=(8,5*(len(steps))+1))
+fig = plt.figure(figsize=(16,5*(len(steps))+1))
+
+ax = []
+for s in numpy.arange(len(steps)):
+
+ strain_str = 'Shear strain $\\gamma = %.3f$' % (shear_strain[s])
+
+ n = 7
+ if s == 0:
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+1)) # 0: xdisp
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+2, sharey=ax[s*n+0])) # 1: phi
+ ax.append(ax[s*n+1].twiny()) # 2: dphi/dt
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+3, sharey=ax[s*n+0])) # 3: v_z^p
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+4, sharey=ax[s*n+0])) # 4: p_f
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+5, sharey=ax[s*n+0])) # 5: f_pf_z
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+6, sharey=ax[s*n+0])) # 6: v_z^f
+ else:
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+1, sharex=ax[0])) # 0: xdisp
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+2, sharey=ax[s*n+0],
+ sharex=ax[1])) # 1: phi
+ ax.append(ax[s*n+1].twiny()) # 2: dphi/dt
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+3, sharey=ax[s*n+0],
+ sharex=ax[3])) # 3: v_z^p
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+4, sharey=ax[s*n+0],
+ sharex=ax[4])) # 4: p_f
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+5, sharey=ax[s*n+0],
+ sharex=ax[5])) # 5: f_pf_z
+ ax.append(plt.subplot(len(steps), n-1, s*(n-1)+6, sharey=ax[s*n+0],
+ sharex=ax[6])) # 6: v_z^f
+
+ #ax[s*n+0].plot(xdisp[s], zpos_p[s], ',', color = '#888888')
+ ax[s*n+0].plot(xdisp_mean[s], zpos_c[s], color = 'k')
+
+ #ax[s*4+2].plot(dev_p[s]/1000.0, zpos_c[s], 'k')
+ #ax[s*4+2].plot(phi_bar[s,1:], zpos_c[s,1:], '-k', linewidth=3)
+ ax[s*n+1].plot(phi_bar[s,1:], zpos_c[s,1:], '-k')
+
+ #phicolor = '#888888'
+ #ax[s*4+3].plot(phi_bar[s], zpos_c[s], '-', color = phicolor)
+ #for tl in ax[s*4+3].get_xticklabels():
+ #tl.set_color(phicolor)
+ ax[s*n+2].plot(dphi_bar[s,1:], zpos_c[s,1:], '--k')
+ #ax[s*4+3].plot(dphi_bar[s,1:], zpos_c[s,1:], '-k', linewidth=3)
+ #ax[s*4+3].plot(dphi_bar[s,1:], zpos_c[s,1:], '-w', linewidth=2)
+
+ #ax[s*n+3].plot(v_z_p[s]*100.0, zpos_p[s], ',', color = '#888888')
+ ax[s*n+3].plot(v_z_p_bar[s]*100.0, zpos_c[s], color = 'k')
+ #ax[s*n+0].plot([0.0,0.0], [0.0, sim.L[2]], '--', color='k')
+
+ # hydrostatic pressure distribution
+ ax[s*n+4].plot(dev_p[s]/1000.0, zpos_c[s], 'k')
+ y_top = sim.w_x[0]
+ x_top = sim.p_f[0,0,-1]
+ y_bot = 0.0
+ x_bot = x_top + (y_top - y_bot)*sim.rho*numpy.abs(sim.g[2])
+ ax[s*n+4].plot([x_top/1000.0, x_bot/1000.0], [y_top, y_bot], '--', color='k')
+ #ax[s*n+1].set_title(strain_str)
+ #ax[s*n+1].set_title(' ')
+
+ # remove particles with 0.0 pressure force
+ I = numpy.nonzero(numpy.abs(f_pf[s]) > .01)
+ f_pf_nonzero = f_pf[s][I]
+ zpos_p_nonzero = zpos_p[s][I]
+ I = numpy.nonzero(numpy.abs(f_pf_mean[s]) > .01)
+ f_pf_mean_nonzero = f_pf_mean[s][I]
+ zpos_c_nonzero = zpos_c[s][I]
+
+ #ax[s*n+5].plot(f_pf_nonzero, zpos_p_nonzero, ',', color = '#888888')
+ #ax[s*4+1].plot(f_pf_mean[s][1:-2], zpos_c[s][1:-2], color = 'k')
+ ax[s*n+5].plot(f_pf_mean_nonzero, zpos_c_nonzero, color = 'k')
+ #ax[s*4+1].plot([0.0, 0.0], [0.0, sim.L[2]], '--', color='k')
+
+ ax[s*n+6].plot(v_z_f_bar[s]*100.0, zpos_c[s], color = 'k')
+ #ax[s*n+2].plot([0.0,0.0], [0.0, sim.L[2]], '--', color='k')
+
+
+ #plt.plot(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+ #plt.semilogx(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+ #plt.semilogy(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+ #plt.loglog(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+
+ #for x in numpy.arange(sim.num[0]):
+ #for y in numpy.arange(sim.num[1]):
+ #ax[s*n+2].plot(v_z_f[s,x,y,:], zpos_c[s], ',', color = '#888888')
+
+
+ #phicolor = '#888888'
+ #ax[s*n+3].plot(phi_bar[s], zpos_c[s], '-', color = phicolor)
+ #for tl in ax[s*n+3].get_xticklabels():
+ #tl.set_color(phicolor)
+
+ max_z = numpy.max(zpos_p)
+ ax[s*n+0].set_ylim([0, max_z])
+ #ax[s*n+1].set_ylim([0, max_z])
+ #ax[s*n+2].set_ylim([0, max_z])
+
+ #ax[s*n+0].set_xlim([-0.01,0.01])
+ #ax[s*n+0].set_xlim([-0.005,0.005])
+ #ax[s*n+0].set_xlim([-0.25,0.75])
+ ax[s*n+4].set_xlim([595,625]) # p_f
+ #ax[s*n+2].set_xlim([-0.0005,0.0005])
+ #ax[s*n+2].set_xlim([-0.08,0.08])
+
+ #ax[s*4+1].set_xlim([0.15, 0.46]) # f_pf
+ #ax[s*n+1].set_xlim([0.235, 0.409]) # f_pf
+
+ ax[s*n+1].set_xlim([0.33, 0.6]) # phi
+ ax[s*n+2].set_xlim([-0.09, 0.035]) # dphi/dt
+
+
+ #plt.plot(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+ #plt.semilogx(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+ #plt.semilogy(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+ #plt.loglog(dpdz[c], K[c], 'o-', label='$c$ = %.2f' % (cvals[c]))
+
+ ax[s*n+0].set_ylabel('Vertical position $z$ [m]')
+ ax[s*n+0].set_xlabel('$\\boldsymbol{x}^x_\\text{p}$ [m]')
+ ax[s*n+1].set_xlabel('$\\bar{\\phi}$ [-] (solid)')
+ ax[s*n+2].set_xlabel('$\\delta \\bar{\\phi}/\\delta t$ [-] (dashed)')
+ ax[s*n+3].set_xlabel('$\\boldsymbol{v}^z_\\text{p}$ [cms$^-1$]')
+ ax[s*n+4].set_xlabel('$\\bar{p_\\text{f}}$ [kPa]')
+ ax[s*n+5].set_xlabel('$\\boldsymbol{f}^z_\\text{pf}$ [N]')
+ ax[s*n+6].set_xlabel('$\\bar{\\boldsymbol{v}}^z_\\text{f}$ [cms$^-1$]')
+
+ plt.setp(ax[s*n+1].get_yticklabels(), visible=False)
+ plt.setp(ax[s*n+2].get_yticklabels(), visible=False)
+ plt.setp(ax[s*n+3].get_yticklabels(), visible=False)
+ plt.setp(ax[s*n+4].get_yticklabels(), visible=False)
+ plt.setp(ax[s*n+5].get_yticklabels(), visible=False)
+ plt.setp(ax[s*n+6].get_yticklabels(), visible=False)
+
+ #nbins = 4
+ #ax[s*n+0].get_xaxis().set_major_locator(MaxNLocator(nbins=nbins))
+ #ax[s*n+1].get_xaxis().set_major_locator(MaxNLocator(nbins=nbins))
+ #ax[s*n+2].get_xaxis().set_major_locator(MaxNLocator(nbins=nbins))
+
+ plt.setp(ax[s*n+0].xaxis.get_majorticklabels(), rotation=90)
+ plt.setp(ax[s*n+1].xaxis.get_majorticklabels(), rotation=90)
+ plt.setp(ax[s*n+2].xaxis.get_majorticklabels(), rotation=90)
+ plt.setp(ax[s*n+3].xaxis.get_majorticklabels(), rotation=90)
+ plt.setp(ax[s*n+4].xaxis.get_majorticklabels(), rotation=90)
+ plt.setp(ax[s*n+5].xaxis.get_majorticklabels(), rotation=90)
+ plt.setp(ax[s*n+6].xaxis.get_majorticklabels(), rotation=90)
+
+ #if s == 0:
+ #y = 0.95
+ #if s == 1:
+ #y = 0.55
+
+ #plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
+ #ax[s*n+0].ticklabel_format(style='sci', axis='x', scilimits=(-3,3))
+ #ax[s*n+1].ticklabel_format(style='sci', axis='x', scilimits=(-3,3))
+ #ax[s*n+2].ticklabel_format(style='sci', axis='x', scilimits=(-3,3))
+
+ #fig.text(0.1, y, strain_str, horizontalalignment='left', fontsize=22)
+ #ax[s*4+0].annotate(strain_str, xytext=(0,1.1), textcoords='figure fraction',
+ #horizontalalignment='left', fontsize=22)
+ #plt.text(0.05, 1.06, strain_str, horizontalalignment='left', fontsize=22,
+ #transform=ax[s*n+0].transAxes)
+ #ax[s*4+0].set_title(strain_str)
+ ax[s*n+0].set_title('a')
+ ax[s*n+1].set_title('b')
+ ax[s*n+3].set_title('c')
+ ax[s*n+4].set_title('d')
+ ax[s*n+5].set_title('e')
+ ax[s*n+6].set_title('f')
+
+ ax[s*n+0].grid()
+ ax[s*n+1].grid()
+ #ax[s*n+2].grid()
+ ax[s*n+3].grid()
+ ax[s*n+4].grid()
+ ax[s*n+5].grid()
+ ax[s*n+6].grid()
+ #ax1.legend(loc='lower right', prop={'size':18})
+ #ax2.legend(loc='lower right', prop={'size':18})
+
+ strain_str = 'Shear strain $\\gamma = %.3f$' % (shear_strain[s])
+ #fig.text(0.1, y, strain_str, horizontalalignment='left', fontsize=22)
+ #ax[s*4+0].annotate(strain_str, xytext=(0,1.1), textcoords='figure fraction',
+ #horizontalalignment='left', fontsize=22)
+ plt.text(0.05, 1.06, strain_str, horizontalalignment='left', fontsize=22,
+ transform=ax[s*n+0].transAxes)
+
+#plt.title(' ')
+plt.MaxNLocator(nbins=4)
+ #ax1.legend(loc='lower right', prop={'size':18})
+ #ax2.legend(loc='lower right', prop={'size':18})
+
+#plt.title(' ')
+#plt.MaxNLocator(nbins=4)
+#plt.subplots_adjust(wspace = .05)
+#plt.subplots_adjust(hspace = 1.05)
+plt.tight_layout()
+#plt.MaxNLocator(nbins=4)
+
+filename = 'shear-' + str(int(sigma0/1000.0)) + 'kPa-internals.pdf'
+plt.savefig(filename)
+shutil.copyfile(filename, '/home/adc/articles/own/2-org/' + filename)
+print(filename)
(DIR) diff --git a/python/shear-results-strain.py b/python/shear-results-strain.py
t@@ -96,7 +96,7 @@ for s in numpy.arange(len(cvals)):
if cvals[s] == 'dry':
legend = 'dry'
else:
- legend = 'c = ' + str(cvals[s])
+ legend = 'wet, c = ' + str(cvals[s])
ax[0].plot(xdisp[s], zpos_p[s], ',', color = '#888888')
ax[0].plot(xdisp_mean[s], zpos_c[s], linetype[s], color='k', label = legend,
t@@ -134,7 +134,7 @@ for s in numpy.arange(len(cvals)):
#ax2.legend(loc='lower right', prop={'size':18})
legend_alpha=0.5
-ax[0].legend(loc='best', prop={'size':18}, fancybox=True, framealpha=legend_alpha)
+ax[0].legend(loc='lower right', prop={'size':18}, fancybox=True, framealpha=legend_alpha)
ax[0].grid()
plt.tight_layout()
plt.subplots_adjust(wspace = .05)
(DIR) diff --git a/python/shear-results.py b/python/shear-results.py
t@@ -19,8 +19,79 @@ sigma0 = float(sys.argv[1])
cvals = [1.0, 0.1]
#cvals = [1.0]
+# return a smoothed version of in. The returned array is smaller than the
+# original input array
+'''
+def smooth(in_arr, plus_minus_steps):
+ out_arr = numpy.zeros(in_arr.size - 2*plus_minus_steps + 1)
+ s = 0
+ for i in numpy.arange(in_arr.size):
+ if i >= plus_minus_steps and i < plus_minus_steps:
+ for i in numpy.arange(-plus_minus_steps, plus_minus_steps+1):
+ out_arr[s] += in_arr[s+i]/(2.0*plus_minus_steps)
+ s += 1
+'''
+
+def smooth(x, window_len=10, window='hanning'):
+ """smooth the data using a window with requested size.
+
+ This method is based on the convolution of a scaled window with the signal.
+ The signal is prepared by introducing reflected copies of the signal
+ (with the window size) in both ends so that transient parts are minimized
+ in the begining and end part of the output signal.
+
+ input:
+ x: the input signal
+ window_len: the dimension of the smoothing window
+ window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'
+ flat window will produce a moving average smoothing.
+
+ output:
+ the smoothed signal
+
+ example:
+
+ import numpy as np
+ t = np.linspace(-2,2,0.1)
+ x = np.sin(t)+np.random.randn(len(t))*0.1
+ y = smooth(x)
+
+ see also:
+
+ numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convolve
+ scipy.signal.lfilter
+
+ TODO: the window parameter could be the window itself if an array instead of a string
+ """
+
+ if x.ndim != 1:
+ raise ValueError, "smooth only accepts 1 dimension arrays."
+
+ if x.size < window_len:
+ raise ValueError, "Input vector needs to be bigger than window size."
+
+ if window_len < 3:
+ return x
+
+ if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
+ raise ValueError, "Window is on of 'flat', 'hanning', 'hamming', 'bartlett', 'blackman'"
+
+ s=numpy.r_[2*x[0]-x[window_len:1:-1], x, 2*x[-1]-x[-1:-window_len:-1]]
+ #print(len(s))
+
+ if window == 'flat': #moving average
+ w = numpy.ones(window_len,'d')
+ else:
+ w = getattr(numpy, window)(window_len)
+ y = numpy.convolve(w/w.sum(), s, mode='same')
+ return y[window_len-1:-window_len+1]
+
+
+smooth_window = 11
+
shear_strain = [[], [], []]
friction = [[], [], []]
+friction_smooth = [[], [], []]
dilation = [[], [], []]
p_min = [[], [], []]
p_mean = [[], [], []]
t@@ -39,6 +110,7 @@ sim.visualize('shear')
shear_strain[0] = sim.shear_strain
#shear_strain[0] = numpy.arange(sim.status()+1)
friction[0] = sim.tau/sim.sigma_eff
+friction_smooth[0] = smooth(friction[0], smooth_window)
dilation[0] = sim.dilation
f_n_mean[0] = numpy.zeros_like(shear_strain[0])
t@@ -64,6 +136,7 @@ for c in numpy.arange(1,len(cvals)+1):
shear_strain[c] = numpy.zeros(sim.status())
friction[c] = numpy.zeros_like(shear_strain[c])
dilation[c] = numpy.zeros_like(shear_strain[c])
+ friction_smooth[c] = numpy.zeros_like(shear_strain[c])
sim.readlast(verbose=False)
sim.visualize('shear')
t@@ -71,6 +144,7 @@ for c in numpy.arange(1,len(cvals)+1):
#shear_strain[c] = numpy.arange(sim.status()+1)
friction[c] = sim.tau/sim.sigma_eff
dilation[c] = sim.dilation
+ friction_smooth[c] = smooth(friction[c], smooth_window)
# fluid pressures and particle forces
p_mean[c] = numpy.zeros_like(shear_strain[c])
t@@ -100,31 +174,37 @@ for c in numpy.arange(1,len(cvals)+1):
#fig = plt.figure(figsize=(8,8)) # (w,h)
+fig = plt.figure(figsize=(8,10))
#fig = plt.figure(figsize=(8,12))
-fig = plt.figure(figsize=(8,16))
+#fig = plt.figure(figsize=(8,16))
fig.subplots_adjust(hspace=0.0)
#plt.subplot(3,1,1)
#plt.ticklabel_format(style='sci', axis='y', scilimits=(0,0))
-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')
+ax1 = plt.subplot(211)
+ax2 = plt.subplot(212, sharex=ax1)
+#ax3 = plt.subplot(413, sharex=ax1)
+#ax4 = plt.subplot(414, sharex=ax1)
+alpha = 0.5
+#ax1.plot(shear_strain[0], friction[0], label='dry', alpha = 0.5)
+ax1.plot(shear_strain[0], friction_smooth[0], label='dry')
ax2.plot(shear_strain[0], dilation[0], label='dry')
-ax4.plot(shear_strain[0], f_n_mean[0], '-', label='dry', color='blue')
-ax4.plot(shear_strain[0], f_n_max[0], '--', color='blue')
+#ax4.plot(shear_strain[0], f_n_mean[0], '-', label='dry', color='blue')
+#ax4.plot(shear_strain[0], f_n_max[0], '--', color='blue')
color = ['b','g','r']
for c in numpy.arange(1,len(cvals)+1):
- ax1.plot(shear_strain[c][1:], friction[c][1:], \
+ #ax1.plot(shear_strain[c][1:], friction[c][1:], \
+ #label='$c$ = %.2f' % (cvals[c-1]))
+ ax1.plot(shear_strain[c][1:], friction_smooth[c][1:], \
label='$c$ = %.2f' % (cvals[c-1]))
ax2.plot(shear_strain[c][1:], dilation[c][1:], \
label='$c$ = %.2f' % (cvals[c-1]), linewidth=2)
+ '''
alpha = 0.5
ax3.plot(shear_strain[c][1:], p_max[c][1:], '-' + color[c], alpha=alpha)
ax3.plot(shear_strain[c][1:], p_mean[c][1:], '-' + color[c], \
t@@ -139,34 +219,35 @@ for c in numpy.arange(1,len(cvals)+1):
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)
+ '''
-ax4.set_xlabel('Shear strain $\\gamma$ [-]')
+#ax4.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]')
+#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])
-ax3.set_ylim([595,608])
+#ax3.set_ylim([595,608])
plt.setp(ax1.get_xticklabels(), visible=False)
-plt.setp(ax2.get_xticklabels(), visible=False)
-plt.setp(ax3.get_xticklabels(), visible=False)
+#plt.setp(ax2.get_xticklabels(), visible=False)
+#plt.setp(ax3.get_xticklabels(), visible=False)
ax1.grid()
ax2.grid()
-ax3.grid()
-ax4.grid()
+#ax3.grid()
+#ax4.grid()
legend_alpha=0.5
ax1.legend(loc='best', prop={'size':18}, fancybox=True, framealpha=legend_alpha)
ax2.legend(loc='lower right', prop={'size':18}, fancybox=True,
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)
+#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()
plt.subplots_adjust(hspace=0.05)