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tupdate plots - sphere - GPU-based 3D discrete element method algorithm with op… |
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git clone git://src.adamsgaard.dk/sphere |
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LICENSE |
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--- |
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commit 47859ce34c1917a9b4a2f277fd750f9086401e60 |
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parent e5de47d2e2884179b7956ee59e5b26d4ccf9ebd3 |
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Author: Anders Damsgaard <[email protected]> |
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Date: Tue, 16 Jun 2015 09:09:48 +0200 |
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update plots |
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Diffstat: |
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M python/halfshear-darcy-creep-dynam… | 11 +++++++---- |
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M python/halfshear-darcy-strength-di… | 34 ++++++++++++++++-----------… |
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M python/halfshear-darcy-strength-di… | 34 ++++++++++++++++-----------… |
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3 files changed, 43 insertions(+), 36 deletions(-) |
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--- |
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diff --git a/python/halfshear-darcy-creep-dynamics.py b/python/halfshear-darcy-… |
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t@@ -162,9 +162,11 @@ for step in steps: |
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scalingfactor = 1./t_DEM_to_t_real / (24.*3600.) |
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t_scaled = t*scalingfactor |
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-# Normal stress plot |
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fig = plt.figure(figsize=[3.5, 3.5]) |
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+plt.figtext(0.05, 0.95, 'A', horizontalalignment='left', weight='bold') |
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+plt.figtext(0.05, 0.35, 'B', horizontalalignment='left', weight='bold') |
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+ |
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# ax1 = plt.subplot(1, 1, 1) |
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ax1 = plt.subplot2grid((2, 3), (0, 0), colspan=3) |
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t@@ -187,10 +189,10 @@ ax1.set_ylabel('Effective normal stress $N$ [kPa]') |
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ax2 = ax1.twinx() |
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lns1 = ax2.semilogy(t_scaled, numpy.abs(vel_avg)*timescaling, '-b', |
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- label='$\\bar{\\boldsymbol{v}}_x$', |
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+ label='$|\\bar{\\boldsymbol{v}}_x|$', |
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clip_on=False) |
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lns2 = ax2.semilogy(t_scaled, numpy.abs(angvel_avg)*timescaling, '-r', |
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- label='$\\bar{\\boldsymbol{\\omega}}_y$', |
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+ label='$|\\bar{\\boldsymbol{\\omega}}_y|$', |
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clip_on=False) |
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ax2.set_ylabel('Linear $\\bar{\\boldsymbol{v}}_x$ [m/s] or ' |
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+ 'angular velocity $\\bar{\\boldsymbol{\\omega}}_y$ [rad/s]') |
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t@@ -207,7 +209,8 @@ ax1.xaxis.set_ticks_position('bottom') |
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lns = lns0+lns1+lns2 |
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labs = [l.get_label() for l in lns] |
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-ax2.legend(lns, labs, loc='upper center', ncol=3, bbox_to_anchor=(0.5, 1.12), |
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+# bbox_to_anchor=(0.5, 1.12) for legend centered above box |
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+ax2.legend(lns, labs, loc='upper center', ncol=3, bbox_to_anchor=(0.5, 1.25), |
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fancybox=True, framealpha=1.0) |
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ax1.set_xlim([numpy.min(t_scaled), numpy.max(t_scaled)]) |
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diff --git a/python/halfshear-darcy-strength-dilation-rate.py b/python/halfshea… |
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t@@ -44,34 +44,34 @@ velfac = 1.0 |
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# original input array |
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def smooth(x, window_len=10, window='hanning'): |
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"""smooth the data using a window with requested size. |
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- |
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+ |
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This method is based on the convolution of a scaled window with the signal. |
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- The signal is prepared by introducing reflected copies of the signal |
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+ The signal is prepared by introducing reflected copies of the signal |
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(with the window size) in both ends so that transient parts are minimized |
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in the begining and end part of the output signal. |
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- |
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+ |
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input: |
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- x: the input signal |
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+ x: the input signal |
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window_len: the dimension of the smoothing window |
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window: the type of window from 'flat', 'hanning', 'hamming', 'bartlet… |
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flat window will produce a moving average smoothing. |
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output: |
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the smoothed signal |
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- |
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+ |
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example: |
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- import numpy as np |
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+ import numpy as np |
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t = np.linspace(-2,2,0.1) |
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x = np.sin(t)+np.random.randn(len(t))*0.1 |
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y = smooth(x) |
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- |
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- see also: |
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- |
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+ |
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+ see also: |
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+ |
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numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convol… |
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scipy.signal.lfilter |
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- |
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- TODO: the window parameter could be the window itself if an array instead … |
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+ |
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+ TODO: the window parameter could be the window itself if an array instead … |
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""" |
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if x.ndim != 1: |
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t@@ -250,17 +250,19 @@ for sigma0 in sigma0_list: |
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label=label, linewidth=1) |
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if pressures: |
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- #ax3.plot(shear_strain[c], p_max[c], '-', color=color[c], alpha=0.… |
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+ ax3.plot(shear_strain[c], p_max[c], ':', color=color[c], alpha=0.5, |
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+ linewidth=0.5) |
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ax3.plot(shear_strain[c], p_mean[c], '-', color=color[c], \ |
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label=label, linewidth=1) |
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- #ax3.plot(shear_strain[c], p_min[c], '-', color=color[c], alpha=0.… |
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+ ax3.plot(shear_strain[c], p_min[c], ':', color=color[c], alpha=0.5, |
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+ linewidth=0.5) |
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- ax3.fill_between(shear_strain[c], p_min[c], p_max[c], |
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- where=p_min[c]<=p_max[c], facecolor=color[c], edgecolor='N… |
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- interpolate=True, alpha=0.3) |
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+ #ax3.fill_between(shear_strain[c], p_min[c], p_max[c], |
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+ # where=p_min[c]<=p_max[c], facecolor=color[c], edgecolor='… |
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+ # interpolate=True, alpha=0.3) |
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#ax4.plot(shear_strain[c][1:], f_n_mean[c][1:], '-' + color[c], |
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#label='$c$ = %.2f' % (cvals[c-1]), linewidth=2) |
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diff --git a/python/halfshear-darcy-strength-dilation.py b/python/halfshear-dar… |
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t@@ -43,34 +43,34 @@ velfac = 1.0 |
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def smooth(x, window_len=10, window='hanning'): |
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#def smooth(x, window_len=10, window='flat'): |
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"""smooth the data using a window with requested size. |
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- |
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+ |
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This method is based on the convolution of a scaled window with the signal. |
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- The signal is prepared by introducing reflected copies of the signal |
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+ 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. |
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- |
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+ |
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input: |
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- x: the input signal |
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+ x: the input signal |
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window_len: the dimension of the smoothing window |
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window: the type of window from 'flat', 'hanning', 'hamming', 'bartlet… |
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flat window will produce a moving average smoothing. |
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output: |
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the smoothed signal |
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- |
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+ |
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example: |
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|
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- import numpy as np |
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+ import numpy as np |
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t = np.linspace(-2,2,0.1) |
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x = np.sin(t)+np.random.randn(len(t))*0.1 |
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y = smooth(x) |
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- |
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- see also: |
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- |
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+ |
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+ see also: |
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+ |
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numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman, numpy.convol… |
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scipy.signal.lfilter |
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- |
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- TODO: the window parameter could be the window itself if an array instead … |
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+ |
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+ TODO: the window parameter could be the window itself if an array instead … |
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""" |
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if x.ndim != 1: |
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t@@ -248,17 +248,19 @@ for sigma0 in sigma0_list: |
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label=label, linewidth=1) |
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if fluid and pressures: |
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- #ax3.plot(shear_strain[c], p_max[c], '-', color=color[c], alpha=0.… |
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+ ax3.plot(shear_strain[c], p_max[c], ':', color=color[c], alpha=0.5, |
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+ linewidth=0.5) |
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ax3.plot(shear_strain[c], p_mean[c], '-', color=color[c], \ |
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label=label, linewidth=1) |
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- #ax3.plot(shear_strain[c], p_min[c], '-', color=color[c], alpha=0.… |
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+ ax3.plot(shear_strain[c], p_min[c], ':', color=color[c], alpha=0.5, |
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+ linewidth=0.5) |
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- ax3.fill_between(shear_strain[c], p_min[c], p_max[c], |
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- where=p_min[c]<=p_max[c], facecolor=color[c], edgecolor='N… |
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- interpolate=True, alpha=0.3) |
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+ #ax3.fill_between(shear_strain[c], p_min[c], p_max[c], |
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+ # where=p_min[c]<=p_max[c], facecolor=color[c], edgecolor='… |
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+ # interpolate=True, alpha=0.3) |
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#ax4.plot(shear_strain[c][1:], f_n_mean[c][1:], '-' + color[c], |
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#label='$c$ = %.2f' % (cvals[c-1]), linewidth=2) |
