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