def run_script(args):
# ## Make a new string buffer
# in_buf = StringIO.StringIO()
# ## Write the decoded string into the buffer
# buf.write(base64.b64decode(args.wireframe))
# ## Move to the start of the buffer
# in_buf.seek(0)
## Open the image in PIL
#image = Image.open(buf)
processed_data = image_segment(args.wireframe)
#plt.imshow(processed_data)
#image = get_figure_data()
dt = .01
t = 1.0
f = 1.
wave = ricker(t, dt, f)
seismic = [
np.convolve(wave, i, mode='same') for i in np.transpose(processed_data)
]
plt.imshow(seismic)
image = get_figure_data()
return (image, )
def run_script(args):
# ## Make a new string buffer
# in_buf = StringIO.StringIO()
# ## Write the decoded string into the buffer
# buf.write(base64.b64decode(args.wireframe))
# ## Move to the start of the buffer
# in_buf.seek(0)
## Open the image in PIL
#image = Image.open(buf)
processed_data = image_segment(args.wireframe)
#plt.imshow(processed_data)
#image = get_figure_data()
dt = .01
t = 1.0
f= 1.
wave = ricker(t,dt,f)
seismic = [np.convolve(wave, i, mode='same') for i in np.transpose(processed_data)]
plt.imshow(seismic)
image = get_figure_data()
return (image,)
def run_script(earth_model,
seismic_model,
args):
fig =plt.figure()
gs = gridspec.GridSpec(1, 2,width_ratios=[4,1])
axarr = [plt.subplot(gs[0]), plt.subplot(gs[1])]
seismic_model.go(earth_model,
theta=args.theta)
seismic_data = seismic_model.seismic
t = seismic_model.dt * np.arange(seismic_data.shape[0])
axarr[0].imshow(seismic_data[:,:, 0, 0],aspect='auto',
extent=[0, seismic_data.shape[1],
t[-1], t[0]],
cmap='Greys')
axarr[0].axvline(x=args.trace, lw=3, color='b')
axarr[0].set_title('spatial cross-section')
axarr[1].plot(seismic_data[:,args.trace,0,0],
t)
plt.gca().invert_yaxis()
axarr[1].axis('tight')
"""
seismic_model.go(earth_model,
traces=[args.trace])
seismic_data = seismic_model.seismic
axarr[1].imshow(seismic_data[:, 0, :, 0],
aspect='auto')
axarr[1].axvline(x=args.theta, lw=3, color='b')
axarr[1].set_title('angle cross-section')
seismic_model.go(earth_model,
theta=args.theta,
traces=[args.trace])
seismic_data = seismic_model.seismic
"""
"""
axarr[2].imshow(seismic_data[:,0, 0, :],
aspect='auto')
axarr[2].axvline(x=args.f, lw=3, color='b')
axarr[2].set_title('wavelet cross-section')
"""
fig.tight_layout()
return get_figure_data()
def run_script(args):
matplotlib.interactive(False)
args.plot_type = 'dashboard'
Rprop0 = args.Rock0
Rprop1 = args.Rock1
theta = np.arange(0,90)
vp0, vs0, rho0 = make_normal_dist( Rprop0, args.iterations )
vp1, vs1, rho1 = make_normal_dist( Rprop1, args.iterations )
reflect = []
hist_titles = [[r'$V_\mathrm{P}$' , r'$m/s$'],
[r'$V_\mathrm{S}$' , r'$m/s$'],
[r'$\rho$' , r'$kg / m^3$']]
nbins = 15
vp_lim = ( np.amin((Rprop1.vp - ( 3.* Rprop1.vp_sig ),
Rprop0.vp - ( 3.* Rprop1.vp_sig ) ) ),
np.amax((Rprop1.vp + ( 3.* Rprop1.vp_sig ),
Rprop0.vp + ( 3.* Rprop1.vp_sig ) ) ) )
vs_lim = ( np.amin((Rprop1.vs - ( 3.* Rprop1.vs_sig ),
Rprop0.vs - ( 3.* Rprop1.vs_sig ) ) ),
np.amax((Rprop1.vs + ( 3.* Rprop1.vs_sig ),
Rprop0.vs + ( 3.* Rprop1.vs_sig ) ) ) )
rho_lim = ( np.amin((Rprop1.rho - ( 3.* Rprop1.rho_sig ),
Rprop0.rho - ( 3.* Rprop1.rho_sig ) ) ),
np.amax((Rprop1.rho + ( 3.* Rprop1.rho_sig ),
Rprop0.rho + ( 3.* Rprop1.rho_sig ) ) ) )
limits = np.array([[ vp_lim, vs_lim, rho_lim ],
[ vp_lim, vs_lim, rho_lim ] ])
for i in range( args.iterations ):
reflect.append(args.reflectivity_method(vp0[i], vs0[i],
rho0[i], vp1[i],
vs1[i], rho1[i],
theta))
reflect = np.array(reflect)
reflect = np.nan_to_num(reflect)
#temp = np.concatenate( (vp0, rho0, vs0, vp1, rho1, vs1,), axis=0)
temp = np.concatenate((vp0, vs0, rho0, vp1, vs1, rho1), axis=0)
prop_samples = np.reshape(temp, (6, args.iterations))
ave_reflect = np.mean(reflect,axis=0)
nbins = 15
# DO PLOTTING
plt.figure(figsize = (5,13))
plt.subplots_adjust(bottom=0.1, left=0.1, top = 1, right=0.9)
plt.hold(True)
if args.plot_type == 'dashboard':
G = matplotlib.gridspec.GridSpec(9,2, hspace=0.5)
shift=3
else:
G = matplotlib.gridspec.GridSpec(2,6)
shift=0
# histogram plots (ax_3, ax_4, ax_5, ax_6, ax_7, ax_8)
hist_max = 0
for k in np.arange(len(prop_samples)):
# find the max bar height of the histogram for scaling the plots
hist_max = max(hist_max,max(np.histogram(prop_samples[k],
density=True)[0]))
for j in np.arange(2):
upper_color = 'blue' #color of upper histogram
lower_color = 'green' #color of lower histogram
for i in np.arange(3):
plt.subplot(G[3+i+shift,0])
plt.hist( prop_samples[i], nbins,
facecolor = upper_color,
histtype='stepfilled',
alpha = 0.25
,normed = True
)
plt.hist( prop_samples[i+(3)], nbins,
facecolor = lower_color,
histtype='stepfilled',
alpha = 0.25
,normed = True
)
temp = plt.gca()
# Annotation and making it look nice
plt.axis([limits[j][i][0], limits[j][i][1],
temp.axis()[2], hist_max ])
plt.yticks([])
plt.xticks( rotation=90,horizontalalignment='left' )
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_alpha(0.5)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(6)
label.set_alpha(0.5)
for tick in ax.xaxis.get_major_ticks():
tick.tick1On = True
tick.tick2On = False
# upper text label
mean_props = [ [ Rprop0.vp, Rprop1.vp ],
[ Rprop0.vs, Rprop1.vs ],
[ Rprop0.rho, Rprop1.rho ]
]
which_label = ['upper','lower']
# Main label
ax.text( x = limits[0,i,1], y = hist_max * 0.5, s = hist_titles[i][0],
color='black', fontsize=14, alpha=0.75,
horizontalalignment = 'left', verticalalignment = 'center' )
# Label for units
ax.text( x = limits[0,i,1], y = hist_max * 0.25, s = hist_titles[i][1],
color='black', fontsize=10, alpha=0.75,
horizontalalignment = 'left', verticalalignment = 'center' )
ax.text( x=float(mean_props[i][0]),
y=hist_max / 5.0, s = which_label[0],
alpha=0.75, color=upper_color,
fontsize = '9',
horizontalalignment = 'center',
verticalalignment = 'center',
)
#lower text label
ax.text( x = float(mean_props[i][1]), y = hist_max / 5.0, s = which_label[1],
alpha=0.75, color=lower_color,
fontsize = '9',
horizontalalignment = 'center',
verticalalignment = 'center'
)
#
# ax_1 the AVO plot
#
plt.subplot(G[0:3,:])
plt.hold(True)
critical_angles = []
for i in range( args.iterations -1):
# Do the AVO template as an underlay
# HERE
# Do the plots --> This step might not need to be in a loop
plt.plot( theta, reflect[i] ,color = 'grey', lw = 1.0, alpha = np.min((30./args.iterations, 0.08)))
if vp1[i] > vp0[i]:
theta_crit = arcsin( vp0[i] / vp1[i] )*180/np.pi
plt.axvline( x= theta_crit , color='black', lw = 1.0, alpha = np.min((30./args.iterations, 0.5)))
critical_angles.append(theta_crit)
if len(critical_angles) > 0:
critical_angle = np.mean(critical_angles)
else:
critical_angle = 'N/A'
plt.plot( theta, ave_reflect, color='black', alpha = 0.5, lw= 1.5 )
plt.grid()
# Annotation and making it look nice
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.spines['bottom'].set_alpha(0.5)
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
ax.spines['left'].set_alpha(0.5)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(6)
label.set_alpha(0.5)
plt.grid()
plt.ylim((-.5,.5))
ax.text(0.95, 0.45, 'angle',
verticalalignment='top', horizontalalignment='right',
transform=ax.transAxes,
color='black', fontsize=8, alpha=0.5)
ax.text(0.05, 0.95, 'amplitude',
verticalalignment='top', horizontalalignment='right',
transform=ax.transAxes, rotation=90,
color='black', fontsize=8, alpha=0.5)
#
# Patches for the Background template AVO plot STARTS here
#
a1 = 0.10 # transparency for AVO background template patches
rangex = 90
band = 0.04 # thickness of Class 2 band
# CLASS 1
Path1 = mpath.Path
path_data1 = [
(Path1.MOVETO, (rangex * 0, 0.04)),
(Path1.CURVE4, (rangex *0.4, 0.05)),
(Path1.CURVE4, (rangex *0.6, -0.015)),
(Path1.CURVE4, (rangex *1.0, -band)),
(Path1.LINETO, (rangex *1.0, 1.0)),
(Path1.LINETO, (rangex *0.0, 1.0)),
(Path1.CLOSEPOLY, (rangex *0.0, 1.0)),
]
codes1, verts1 = zip(*path_data1)
path1 = mpath.Path(verts1, codes1)
patch1 = mpatches.PathPatch(path1, facecolor='r', alpha=a1, ec = 'none')
ax.add_patch(patch1)
# plot control points and connecting lines
x1, y1 = zip(*path1.vertices)
#line1, = ax.plot(x1, y1, 'go-')
# CLASS 2p
Path2P = mpath.Path
path_data2P = [
(Path2P.MOVETO, (rangex * 0, band)),
(Path2P.CURVE4, (rangex * 0.4, 0.05)),
(Path2P.CURVE4, (rangex * 0.6, -0.015)),
(Path2P.CURVE4, (rangex * 1.0, - band)),
(Path2P.LINETO, (rangex * 1.0, - (band + band) )),
(Path2P.CURVE4, (rangex * 0.6, -(0.015 + band))),
(Path2P.CURVE4, (rangex * 0.4, 0.05 - band)),
(Path2P.CURVE4, (rangex * 0.0, 0.0)),
(Path2P.CLOSEPOLY, (rangex * 0.0, 0.0)),
]
codes2P, verts2P = zip(*path_data2P)
path2P = mpath.Path(verts2P, codes2P)
patch2P = mpatches.PathPatch(path2P, facecolor='yellow', alpha=a1, ec = 'none')
ax.add_patch(patch2P)
# plot control points and connecting lines
x2, y2 = zip(*path2P.vertices)
#line2, = ax.plot(x2, y2, 'ro-')
# CLASS 2
Path2 = mpath.Path
path_data2 = [
(Path2.MOVETO, (rangex * 0.0, 0.0)),
(Path2.CURVE4, (rangex * 0.4, 0.05 - band)),
(Path2.CURVE4, (rangex * 0.6, -(0.015 + band))),
(Path2.CURVE4, (rangex * 1.0, - (band + band))),
(Path2.LINETO, (rangex * 1.0, - (3 * band))),
(Path2.CURVE4, (rangex * 0.6, - (0.015 + 2*band))),
(Path2.CURVE4, (rangex * 0.4, 0.05 - (0.0 + 2*band))),
(Path2.CURVE4, (rangex * 0.0, - band)),
(Path2.CLOSEPOLY, (rangex * 0.0, 0.0)),
]
codes2, verts2 = zip(*path_data2)
path2 = mpath.Path(verts2, codes2)
patch2 = mpatches.PathPatch(path2, facecolor='green', alpha=a1, ec = 'none')
ax.add_patch(patch2)
# plot control points and connecting lines
x2, y2 = zip(*path2.vertices)
#line2, = ax.plot(x2, y2, 'ro-')
# CLASS 3
Path3 = mpath.Path
path_data3 = [
(Path3.MOVETO, (rangex * 0.0, - band)),
(Path3.CURVE4, (rangex * 0.4, 0.05 - (0.0 + 2*band))),
(Path3.CURVE4, (rangex * 0.6, - (0.015 + 2*band))),
(Path3.CURVE4, (rangex * 1.0, - (3 * band))),
(Path3.LINETO, (rangex * 1.0, -1.0)),
(Path3.LINETO, (rangex * 0.0, -1.0)),
(Path3.CLOSEPOLY, (rangex * 0.0, -1.0)),
]
codes3, verts3 = zip(*path_data3)
path3 = mpath.Path(verts3, codes3)
patch3 = mpatches.PathPatch(path3, facecolor='blue', alpha=a1, ec = 'none')
ax.add_patch(patch3)
# plot control points and connecting lines
x3, y3 = zip(*path3.vertices)
#line2, = ax.plot(x2, y2, 'ro-')
ax.grid()
ax.text(0.98, 0.98, 'Amplitude vs angle',
verticalalignment='top',
horizontalalignment='right',
transform=ax.transAxes,
color='black', fontsize=9, fontweight = 'bold', alpha=0.5)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(8)
label.set_alpha(0.5)
plt.grid()
# Class 1 label
# y-values for class labels 1,2p, 2, 3, and 4 respectively
ylabelcntrs = [ 0.35, 0.025, -0.025, -0.4, -0.2 ]
xctrs = 10
fs = 10 # fontsize
a2 = 0.4 # transparency value for Gradient vs Intercept text
ax.text( xctrs, ylabelcntrs[0], 'CLASS 1',
verticalalignment='center',
horizontalalignment='left',
color='red', fontsize=fs, fontweight = 'bold', alpha=a2)
# Class 2p label
ax.text( xctrs, ylabelcntrs[1], 'CLASS 2p',
verticalalignment='center',
horizontalalignment='left',
rotation = -3,
color='#EEC900', fontsize=fs, fontweight = 'bold', alpha=a2 * 1.5)
# Class 2 label
ax.text( xctrs, ylabelcntrs[2], 'CLASS 2',
verticalalignment='center',
horizontalalignment='left',
rotation = -3,
color='green', fontsize=fs, fontweight = 'bold', alpha=a2)
# Class 3 label
ax.text( xctrs, ylabelcntrs[3], 'CLASS 3',
verticalalignment='center',
horizontalalignment='left',
rotation = -15,
color='blue', fontsize=fs, fontweight = 'bold', alpha=a2)
# Class 4 label
ax.text( xctrs, ylabelcntrs[4], 'CLASS 4',
verticalalignment='center',
horizontalalignment='left',
rotation = 15,
color='#B048B5', fontsize=fs, fontweight = 'bold', alpha=a2)
#
# Patches for background template AVO plot ENDS here
#
# ax_2 the AB plot
plt.subplot(G[0+shift:3+shift,:])
plt.hold(True)
max_ang = args.max_angle # Max ang for computing gradient
for i in range( args.iterations -1):
plt.scatter( reflect[i,0], (reflect[i,max_ang]-reflect[i,0] ),
color = 'grey' , s = 20,
alpha = np.max((30./args.iterations, 0.2)) )
# Plot the average of the dots
plt.scatter( ave_reflect[0], ave_reflect[max_ang]- ave_reflect[0],
color = 'black' , s = 40, alpha= 0.5 )
# Annotation and making it nice
plt.xticks([]), plt.yticks([])
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.spines['bottom'].set_alpha(0.5)
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
ax.spines['left'].set_alpha(0.5)
plt.grid()
# axis limits
ylimits = (np.amin((-.3,np.nanmin(reflect[:,50]-reflect[:,0]))),
np.amax((.3,np.nanmax(reflect[:,50]-reflect[:,0]))))
xlimits = (np.amin((-.3,np.nanmin(reflect[:,0]))),
np.amax((.3,np.nanmax(reflect[:,0]))))
plt.ylim( ylimits )
plt.xlim( xlimits )
# axis labels
ax.text(xlimits[1]-0.05, 0.025, 'intercept',
verticalalignment='center',
horizontalalignment='right',
#transform=ax.transAxes,
color='black', fontsize=8, alpha=0.5)
ax.text( 0.025, ylimits[1]-0.05, 'gradient', rotation=90,
verticalalignment='top', horizontalalignment='center',
#transform=ax.transAxes,
color='black', fontsize=8, alpha=0.5)
#
# Patches for background Gradient vs Intercept template STARTS here
#
x = np.arange(xlimits[0], xlimits[1], 0.01)
y = np.arange(ylimits[0], ylimits[1], 0.01)
s0 = -x
shift2 = 0.04 #width for class2 width in plot
height_ellipse = 0.05
width_ellipse = 3.0
# Plot background trend (diagonal line)
ax.plot(x, s0, color='black', alpha = a1)
# add a rectangle for class 2 neg
lowleft2 = (-shift2,-1.0)
class2neg = mpatches.Rectangle( lowleft2, width = abs(lowleft2[0]), height = 1.0,
color = 'green',
alpha = a1,
ec = "white",
lw = 4)
ax.add_patch(class2neg)
# add a rectange for class 2 pos
lowleft2pos = (0.0, 0.0)
class2pos = mpatches.Rectangle( lowleft2pos, width = abs(lowleft2[0]), height = 1.0,
color = 'green',
alpha = a1,
ec="white",
lw = 4)
ax.add_patch(class2pos)
# add a rectange for class 2p pos
lowleft2Ppos = (-shift2, 0.0)
class2Ppos = mpatches.Rectangle( lowleft2Ppos, width = abs(lowleft2[0]), height = 1.0,
color = 'yellow',
alpha = a1,
ec="white",
lw = 4)
ax.add_patch(class2Ppos)
# add a rectange for class 2p neg
lowleft2Pneg = (0.0, -1.0)
class2Ppos = mpatches.Rectangle( lowleft2Pneg, width = abs(lowleft2[0]), height = 1.0,
color = 'yellow',
alpha = a1,
ec="white",
lw = 4)
ax.add_patch(class2Ppos)
# add rectangle for lower left quadrant class 3
lowleft3neg = (-1.0, -1.0)
class3neg = mpatches.Rectangle(lowleft3neg, width = 1.0 + lowleft2[0], height = 1.0,
color = 'blue',
alpha = a1,
ec = 'none')
ax.add_patch(class3neg)
# add rectange for upper right quadrant class 3
lowleft3pos = (shift2, 0)
class3pos = mpatches.Rectangle(lowleft3pos, width = 1.0 + lowleft2[0], height = 1.0,
color = 'blue',
alpha = a1,
ec = 'none')
ax.add_patch(class3pos)
# add a Polygon for Class 4 upper left quadrant
# add a path patch
Path4u = mpath.Path
path_data4u = [
(Path4u.MOVETO, [ -1.0, 0.0 ]),
(Path4u.LINETO, [-1.0, 1.0]),
(Path4u.LINETO, [ -shift2, shift2]),
(Path4u.LINETO, [ -shift2, 0]),
(Path4u.CLOSEPOLY, [-shift2, 0.0])
]
codes4u, verts4u = zip(*path_data4u)
path4u = mpath.Path(verts4u, codes4u)
patch4u = mpatches.PathPatch(path4u, facecolor = '#B048B5', # purple
alpha = a1,
ec = 'none')
ax.add_patch(patch4u)
# add a Polygon for Class 4 lower right quadrant
Path4l = mpath.Path
path_data4l = [
(Path4l.MOVETO, [ shift2, 0.0 ]),
(Path4l.LINETO, [ 1.0, 0.0]),
(Path4l.LINETO, [ 1.0, -1.0]),
(Path4l.LINETO, [ shift2, -shift2 ]),
(Path4l.CLOSEPOLY, [shift2, -shift2])
]
codes4l, verts4l = zip(*path_data4l)
path4l = mpath.Path(verts4l, codes4l)
patch4l = mpatches.PathPatch(path4l, facecolor = '#B048B5', # purple
alpha = a1,
ec = 'none')
ax.add_patch(patch4l)
# Add a Polygon for the Class 1 upper right quadrant
Path1u = mpath.Path
path_data1u = [
(Path1u.MOVETO, [ -shift2, shift2 ]),
(Path1u.LINETO, [ -1.0, 1.0]),
(Path1u.LINETO, [ -shift2, 1.0]),
(Path1u.CLOSEPOLY, [-shift2, shift2])
]
codes1u, verts1u = zip(*path_data1u)
path1u = mpath.Path(verts1u, codes1u)
patch1u = mpatches.PathPatch(path1u, facecolor = 'red',
alpha = a1,
ec = 'none')
ax.add_patch(patch1u)
# Add a Polygone for the Class 1 lower left quadrant
Path1l = mpath.Path
path_data1l = [
(Path1l.MOVETO, [ shift2, -shift2 ]),
(Path1l.LINETO, [ shift2, -1.0]),
(Path1l.LINETO, [ 1.0, -1.0]),
(Path1l.CLOSEPOLY, [shift2, -shift2])
]
codes1l, verts1l = zip(*path_data1l)
path1l = mpath.Path(verts1l, codes1l)
patch1l = mpatches.PathPatch(path1l, facecolor = 'red',
alpha = a1,
ec = 'none')
ax.add_patch(patch1l)
# Draw ellipse
xy = np.hstack((0,0))
bkgd = collections.EllipseCollection(
widths = width_ellipse,
heights = height_ellipse,
angles = 135, units = 'xy',
offsets = xy,
transOffset = ax.transData,
facecolor = 'grey',
edgecolor = 'none',
alpha = 0.15
)
ax.add_collection(bkgd)
#Get rid of axes spines
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data',0))
ax.spines['bottom'].set_alpha(0.5)
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data',0))
ax.spines['left'].set_alpha(0.5)
# Annotation for Gradient vs Intercept annotation
ax.text(0.98, 0.98, 'Gradient vs intercept',
verticalalignment='top',
horizontalalignment='right',
transform=ax.transAxes,
color='black', fontsize=9, fontweight = 'bold', alpha=0.50)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(8)
label.set_alpha(0.5)
# Class 1 label
ax.text(3 * shift2, ylimits[0]+shift2, 'CLASS 1',
verticalalignment='center',
horizontalalignment='center',
color='red', fontsize=fs, fontweight = 'bold', alpha=a2)
# Class 2 label
ax.text( -0.5 * shift2, ylimits[0] + 0.5 * shift2, 'CLASS 2',
verticalalignment='bottom',
horizontalalignment='center',
rotation = 90,
color='green', fontsize=fs, fontweight = 'bold', alpha=a2)
# Class 2P label
ax.text( 0.5 * shift2, ylimits[0] + 0.5 * shift2, 'CLASS 2p',
verticalalignment='bottom',
horizontalalignment='center',
rotation = 90,
color='#EEC900', fontsize=fs, fontweight = 'bold', alpha=a2 * 1.5)
# Class 3 label
ax.text(- 3 * shift2, ylimits[0]+shift2, 'CLASS 3',
verticalalignment='center',
horizontalalignment='right',
color='blue', fontsize=fs, fontweight = 'bold', alpha=a2)
# Class 4 label
ax.text(-3 * shift2, shift2, 'CLASS 4',
verticalalignment='center',
horizontalalignment='right',
color='#B048B5', fontsize=fs, fontweight = 'bold', alpha=a2)
# Background label
angle = -45
ax.text( 0.1 * height_ellipse, 0.1 * height_ellipse, 'background',
verticalalignment='center',
horizontalalignment='center',
rotation = angle,
transform=ax.transData,
color='black', fontsize=fs, fontweight = 'bold',
alpha=a2/2.0)
return get_figure_data(), {"mean critical angle": critical_angle}
def run_script(earth_model, seismic_model,
args):
matplotlib.interactive(False)
cmap = 'seismic_r'
#max/min amplitude for plot and colorbar scaling
extr1 = 1 / (10.0 * (args.gain + 0.0001) / 1000.0)
# Define the figure layout
fig = plt.figure(figsize=[15,10], facecolor = 'white')
width_ratios = [3,1,1]
gs = gridspec.GridSpec(2, 3, width_ratios = width_ratios,
height_ratios = [2,1])
# Make the array of subplot
ax11 = plt.subplot(gs[0])
ax12 = plt.subplot(gs[1])
ax13 = plt.subplot(gs[2])
ax21 = plt.subplot(gs[3])
ax22 = plt.subplot(gs[4])
ax23 = plt.subplot(gs[5])
axarr = [[ax11, ax12, ax13],[ax21, ax22, ax23]]
# spatial
seismic_model.go(earth_model, theta=args.theta)
seismic_data = np.nan_to_num(seismic_model.seismic)
seis_ratios = [ seismic_data.shape[1], seismic_data.shape[2] ,
seismic_data.shape[3] ]
dec = 5 # number of traces to skip, hack for decimation of jitteryness
im = axarr[0][0].imshow(seismic_data[:,::dec, 0, 0],
aspect='auto', cmap=cmap,
extent =[0, seismic_data.shape[1],
1000*seismic_model.dt*seismic_data.shape[0],
0],
vmin = -extr1,
vmax = extr1,
interpolation='spline16')
axarr[0][0].set_xlim(left=0, right=seismic_data.shape[1])
axarr[0][0].set_ylim(top=0,
bottom=1000*seismic_model.dt*seismic_data.shape[0])
axarr[0][0].axvline(x=args.trace, lw=3, color='k', alpha=0.25)
axarr[0][0].axhline(y=args.time*seismic_model.dt*1000, lw=3,
color='g')
axarr[0][0].set_title('spatial cross-section')
axarr[0][0].set_ylabel('time [ms]')
axarr[0][0].set_xticklabels(' ')
axarr[0][0].grid()
# Put wiggle trace on seismic cross-section
trace1 = seismic_data[:,args.trace, 0, 0]
x = np.arange(seismic_data.shape[0]) * seismic_model.dt * 1000.0
c = 20 # fractional denominator of cross section width
gain1 = float(seismic_data.shape[1]/c)
axarr[0][0].plot(args.trace + gain1 * trace1, x, 'k',
alpha = 0.9)
axarr[0][0].fill_betweenx(x, args.trace+gain1 * trace1,
args.trace,
gain1 * trace1 >= 0.01,
color = 'k', alpha = 0.5)
# Put colorbar legend on spatial cross section
colorbar_ax = fig.add_axes([0.565,0.825,0.010,0.08])
fig.colorbar(im, cax=colorbar_ax)
colorbar_ax.text( 0.5, -0.1, '%3.1f' % -1,
transform=colorbar_ax.transAxes,
horizontalalignment='center',
verticalalignment='top')
colorbar_ax.text(0.5, 1.1, '%3.1f' % 1,
transform=colorbar_ax.transAxes,
horizontalalignment='center')
colorbar_ax.set_axis_off()
if args.time > (seismic_data.shape[0]-1):
args.time = seismic_data.shape[0]-1
axarr[1][0].plot(seismic_data[args.time,:,
0, 0], 'g',
lw = 3)
axarr[1][0].set_ylim(-extr1,extr1)
axarr[1][0].set_xlim(0,seismic_data.shape[1])
axarr[1][0].set_ylabel('amplitude')
axarr[1][0].set_xlabel('trace')
axarr[1][0].grid()
# angle column
seismic_model.go(earth_model,
traces=args.trace)
seismic_data = seismic_model.seismic
axarr[0][1].imshow(seismic_data[:, 0, :, 0],
aspect='auto', cmap=cmap,
extent=[0, seismic_data.shape[2],
1000*seismic_model.dt*seismic_data.shape[0],
0],
vmin = -extr1, vmax = extr1,
interpolation='spline16'
)
axarr[0][1].set_xlim(left=0, right=seismic_data.shape[1])
axarr[0][1].set_ylim(top=0,
bottom=1000*seismic_model.dt*seismic_data.shape[0])
axarr[0][1].axvline(x=args.theta, lw=3, color='r', alpha = 0.25)
axarr[0][1].axhline(y=args.time*seismic_model.dt*1000,
lw=3, color='g')
axarr[0][1].set_title('angle gather')
axarr[0][1].set_yticklabels(' ')
axarr[0][1].set_xticklabels(' ')
axarr[0][1].grid()
# Put wiggle trace on AVO cross-section
trace2 = seismic_data[:,0, args.theta, 0]
x = np.arange(seismic_data.shape[0]) * 1000.0 * seismic_model.dt
gain2 = (float(width_ratios[0])/width_ratios[1]) * (seismic_data.shape[2] / gain1)
axarr[0][1].plot(args.theta + gain2 * trace2, x, 'k', alpha = 0.9)
axarr[0][1].fill_betweenx(x, args.theta + gain2 * trace2,
args.theta,
gain2 * trace2 >= -0.01,
color = 'k', alpha = 0.5)
axarr[0][1].set_xlim(left=0, right=seismic_data.shape[2])
# line plot
data2 = seismic_data[args.time, 0,:, 0]
axarr[1][1].plot(data2, 'g', lw = 3)
axarr[1][1].set_ylim(-extr1,extr1)
axarr[1][1].set_xlim(0,seismic_data.shape[2])
axarr[1][1].set_xlabel(r'$\theta$'+' '+r'$^{\circ}$')
axarr[1][1].set_yticklabels(' ')
axarr[1][1].set_xticks([10,20,30,40])
axarr[1][1].grid()
# wavelet
f = seismic_model.f
seismic_model.f = np.arange(0,50)
freq = seismic_model.wavelet_cf()[f]
seismic_model.go(earth_model, traces=args.trace,
theta=args.theta)
seismic_data = seismic_model.seismic
axarr[0][2].imshow(seismic_data[:, 0, 0, :],
aspect='auto', cmap=cmap,
extent = [seismic_model.wavelet_cf()[0],
seismic_model.wavelet_cf()[-1],
seismic_data.shape[0]*seismic_model.dt*1000,
0],
vmin = -extr1, vmax = extr1,
interpolation='spline16')
axarr[0][2].set_xlim(left=0, right=seismic_data.shape[3])
axarr[0][2].set_ylim(top=0,
bottom=1000*seismic_model.dt*seismic_data.shape[0])
axarr[0][2].axvline(x=freq-1, lw=3, color='m', alpha = 0.25)
axarr[0][2].axhline(y=args.time*seismic_model.dt*1000.0,
lw=3, color='g')
axarr[0][2].set_xscale('log', basex=2)
axarr[0][2].set_title('wavelet gather')
axarr[0][2].set_yticklabels(' ')
axarr[0][2].set_xticklabels(' ')
axarr[0][2].set_xticks((16,32,64))
# Put wiggle trace on wavelet cross-section
trace3 = seismic_data[:,0, 0, f]
x = np.arange(seismic_data.shape[0]) * seismic_model.dt * 1000.0
gain3 = 2*(float(width_ratios[0])/width_ratios[2]) * (seismic_data.shape[3] / gain1)
freq = seismic_model.wavelet_cf()[f]
axarr[0][2].plot(freq -1 + gain3 * trace3, x, 'k', alpha = 0.9)
axarr[0][2].fill_betweenx(x, freq -1 + gain3 * trace3, freq -1,
gain3 * trace3 -1 >= -1,
color = 'k', alpha = 0.5)
#
axarr[0][2].set_xlim(left = 8, right = 99)
axarr[0][2].grid()
#line plot
data3 = seismic_data[args.time, 0, 0, :]
axarr[1][2].plot(seismic_model.wavelet_cf(),
data3, 'g', lw = 3)
axarr[1][2].set_ylim(-extr1,extr1)
axarr[1][2].set_xlabel('centre frequency ' + 'Hz')
axarr[1][2].set_xscale('log', basex=2)
axarr[1][2].set_xlim(seismic_model.wavelet_cf()[0],
seismic_model.wavelet_cf()[-1])
axarr[1][2].set_yticklabels(' ')
axarr[1][2].grid()
# remove some whitespace between the axes
gs.update(hspace=0.05,wspace=0.05)
seismic_model.start_f = 8
seismic_model.end_f = 100
fig.subplots_adjust(left=0.05, right=0.98, top=0.95, bottom=0.07)
return get_figure_data()
def run_script(earth_model, seismic_model, args):
matplotlib.interactive(False)
cmap = 'seismic_r'
#max/min amplitude for plot and colorbar scaling
extr1 = 1 / (10.0 * (args.gain + 0.0001) / 1000.0)
# Define the figure layout
fig = plt.figure(figsize=[15, 10], facecolor='white')
width_ratios = [3, 1, 1]
gs = gridspec.GridSpec(2,
3,
width_ratios=width_ratios,
height_ratios=[2, 1])
# Make the array of subplot
ax11 = plt.subplot(gs[0])
ax12 = plt.subplot(gs[1])
ax13 = plt.subplot(gs[2])
ax21 = plt.subplot(gs[3])
ax22 = plt.subplot(gs[4])
ax23 = plt.subplot(gs[5])
axarr = [[ax11, ax12, ax13], [ax21, ax22, ax23]]
# spatial
seismic_model.go(earth_model, theta=args.theta)
seismic_data = np.nan_to_num(seismic_model.seismic)
seis_ratios = [
seismic_data.shape[1], seismic_data.shape[2], seismic_data.shape[3]
]
dec = 5 # number of traces to skip, hack for decimation of jitteryness
im = axarr[0][0].imshow(seismic_data[:, ::dec, 0, 0],
aspect='auto',
cmap=cmap,
extent=[
0, seismic_data.shape[1], 1000 *
seismic_model.dt * seismic_data.shape[0], 0
],
vmin=-extr1,
vmax=extr1,
interpolation='spline16')
axarr[0][0].set_xlim(left=0, right=seismic_data.shape[1])
axarr[0][0].set_ylim(top=0,
bottom=1000 * seismic_model.dt *
seismic_data.shape[0])
axarr[0][0].axvline(x=args.trace, lw=3, color='k', alpha=0.25)
axarr[0][0].axhline(y=args.time * seismic_model.dt * 1000, lw=3, color='g')
axarr[0][0].set_title('spatial cross-section')
axarr[0][0].set_ylabel('time [ms]')
axarr[0][0].set_xticklabels(' ')
axarr[0][0].grid()
# Put wiggle trace on seismic cross-section
trace1 = seismic_data[:, args.trace, 0, 0]
x = np.arange(seismic_data.shape[0]) * seismic_model.dt * 1000.0
c = 20 # fractional denominator of cross section width
gain1 = float(seismic_data.shape[1] / c)
axarr[0][0].plot(args.trace + gain1 * trace1, x, 'k', alpha=0.9)
axarr[0][0].fill_betweenx(x,
args.trace + gain1 * trace1,
args.trace,
gain1 * trace1 >= 0.01,
color='k',
alpha=0.5)
# Put colorbar legend on spatial cross section
colorbar_ax = fig.add_axes([0.565, 0.825, 0.010, 0.08])
fig.colorbar(im, cax=colorbar_ax)
colorbar_ax.text(0.5,
-0.1,
'%3.1f' % -1,
transform=colorbar_ax.transAxes,
horizontalalignment='center',
verticalalignment='top')
colorbar_ax.text(0.5,
1.1,
'%3.1f' % 1,
transform=colorbar_ax.transAxes,
horizontalalignment='center')
colorbar_ax.set_axis_off()
if args.time > (seismic_data.shape[0] - 1):
args.time = seismic_data.shape[0] - 1
axarr[1][0].plot(seismic_data[args.time, :, 0, 0], 'g', lw=3)
axarr[1][0].set_ylim(-extr1, extr1)
axarr[1][0].set_xlim(0, seismic_data.shape[1])
axarr[1][0].set_ylabel('amplitude')
axarr[1][0].set_xlabel('trace')
axarr[1][0].grid()
# angle column
seismic_model.go(earth_model, traces=args.trace)
seismic_data = seismic_model.seismic
axarr[0][1].imshow(seismic_data[:, 0, :, 0],
aspect='auto',
cmap=cmap,
extent=[
0, seismic_data.shape[2],
1000 * seismic_model.dt * seismic_data.shape[0], 0
],
vmin=-extr1,
vmax=extr1,
interpolation='spline16')
axarr[0][1].set_xlim(left=0, right=seismic_data.shape[1])
axarr[0][1].set_ylim(top=0,
bottom=1000 * seismic_model.dt *
seismic_data.shape[0])
axarr[0][1].axvline(x=args.theta, lw=3, color='r', alpha=0.25)
axarr[0][1].axhline(y=args.time * seismic_model.dt * 1000, lw=3, color='g')
axarr[0][1].set_title('angle gather')
axarr[0][1].set_yticklabels(' ')
axarr[0][1].set_xticklabels(' ')
axarr[0][1].grid()
# Put wiggle trace on AVO cross-section
trace2 = seismic_data[:, 0, args.theta, 0]
x = np.arange(seismic_data.shape[0]) * 1000.0 * seismic_model.dt
gain2 = (float(width_ratios[0]) /
width_ratios[1]) * (seismic_data.shape[2] / gain1)
axarr[0][1].plot(args.theta + gain2 * trace2, x, 'k', alpha=0.9)
axarr[0][1].fill_betweenx(x,
args.theta + gain2 * trace2,
args.theta,
gain2 * trace2 >= -0.01,
color='k',
alpha=0.5)
axarr[0][1].set_xlim(left=0, right=seismic_data.shape[2])
# line plot
data2 = seismic_data[args.time, 0, :, 0]
axarr[1][1].plot(data2, 'g', lw=3)
axarr[1][1].set_ylim(-extr1, extr1)
axarr[1][1].set_xlim(0, seismic_data.shape[2])
axarr[1][1].set_xlabel(r'$\theta$' + ' ' + r'$^{\circ}$')
axarr[1][1].set_yticklabels(' ')
axarr[1][1].set_xticks([10, 20, 30, 40])
axarr[1][1].grid()
# wavelet
f = seismic_model.f
seismic_model.f = np.arange(0, 50)
freq = seismic_model.wavelet_cf()[f]
seismic_model.go(earth_model, traces=args.trace, theta=args.theta)
seismic_data = seismic_model.seismic
axarr[0][2].imshow(seismic_data[:, 0, 0, :],
aspect='auto',
cmap=cmap,
extent=[
seismic_model.wavelet_cf()[0],
seismic_model.wavelet_cf()[-1],
seismic_data.shape[0] * seismic_model.dt * 1000, 0
],
vmin=-extr1,
vmax=extr1,
interpolation='spline16')
axarr[0][2].set_xlim(left=0, right=seismic_data.shape[3])
axarr[0][2].set_ylim(top=0,
bottom=1000 * seismic_model.dt *
seismic_data.shape[0])
axarr[0][2].axvline(x=freq - 1, lw=3, color='m', alpha=0.25)
axarr[0][2].axhline(y=args.time * seismic_model.dt * 1000.0,
lw=3,
color='g')
axarr[0][2].set_xscale('log', basex=2)
axarr[0][2].set_title('wavelet gather')
axarr[0][2].set_yticklabels(' ')
axarr[0][2].set_xticklabels(' ')
axarr[0][2].set_xticks((16, 32, 64))
# Put wiggle trace on wavelet cross-section
trace3 = seismic_data[:, 0, 0, f]
x = np.arange(seismic_data.shape[0]) * seismic_model.dt * 1000.0
gain3 = 2 * (float(width_ratios[0]) /
width_ratios[2]) * (seismic_data.shape[3] / gain1)
freq = seismic_model.wavelet_cf()[f]
axarr[0][2].plot(freq - 1 + gain3 * trace3, x, 'k', alpha=0.9)
axarr[0][2].fill_betweenx(x,
freq - 1 + gain3 * trace3,
freq - 1,
gain3 * trace3 - 1 >= -1,
color='k',
alpha=0.5)
#
axarr[0][2].set_xlim(left=8, right=99)
axarr[0][2].grid()
#line plot
data3 = seismic_data[args.time, 0, 0, :]
axarr[1][2].plot(seismic_model.wavelet_cf(), data3, 'g', lw=3)
axarr[1][2].set_ylim(-extr1, extr1)
axarr[1][2].set_xlabel('centre frequency ' + 'Hz')
axarr[1][2].set_xscale('log', basex=2)
axarr[1][2].set_xlim(seismic_model.wavelet_cf()[0],
seismic_model.wavelet_cf()[-1])
axarr[1][2].set_yticklabels(' ')
axarr[1][2].grid()
# remove some whitespace between the axes
gs.update(hspace=0.05, wspace=0.05)
seismic_model.start_f = 8
seismic_model.end_f = 100
fig.subplots_adjust(left=0.05, right=0.98, top=0.95, bottom=0.07)
return get_figure_data()
def run_script(earth_model,
seismic_model,
args):
# Hard code colormap
cmap='seismic_r'
# min / max scaling for colormap and wiggles
extr1 = 1.0 / (10.0 * args.gain / 1000.0)
# decimate number of columns in synthetic
dec = 5
# Create figure
fig =plt.figure(figsize=[15,10], facecolor = 'white')
# Create two axes within figure
gs = gridspec.GridSpec(1, 2,width_ratios=[19,1])
axarr = [plt.subplot(gs[0]), plt.subplot(gs[1])]
seismic_model.go(earth_model,
theta=args.theta)
seismic_data = seismic_model.seismic
t = seismic_model.dt * np.arange(seismic_data.shape[0])
img = seismic_data[:,:, 0, 0]
# synthetic cross section
im = axarr[0].imshow(img[:,::dec],
aspect='auto', cmap=cmap,
extent =[0, seismic_data.shape[1],
1000*seismic_model.dt*seismic_data.shape[0],0],
vmin = -extr1,
vmax = extr1,
interpolation='spline16')
axarr[0].set_xlim(left=0, right=seismic_data.shape[1])
axarr[0].set_ylim(top=0,
bottom=1000*seismic_model.dt*seismic_data.shape[0])
axarr[0].grid()
axarr[0].axvline(x=args.trace, lw=1, color='k', alpha=0.25)
axarr[0].set_title('spatial cross-section')
axarr[0].set_ylabel('time [ms]')
axarr[0].set_xlabel('trace')
# Put colorbar legend on spatial cross section
colorbar_ax = fig.add_axes([0.85,0.825,0.010,0.08])
fig.colorbar(im, cax=colorbar_ax)
colorbar_ax.invert_yaxis()
colorbar_ax.text( 0.5, -0.1, '%3.1f' % -1,
transform=colorbar_ax.transAxes,
horizontalalignment='center',
verticalalignment='top')
colorbar_ax.text(0.5, 1.1, '%3.1f' % 1,
transform=colorbar_ax.transAxes,
horizontalalignment='center')
colorbar_ax.set_axis_off()
# find max and min of section slice
ampmin = np.amin(img)
ampmax = np.amax(img)
biggest = max(abs(ampmin),abs(ampmax))
# Put wiggle trace on seismic cross-section
trace1 = seismic_data[:,args.trace-1, 0, 0]
tt = np.arange(seismic_data.shape[0]) * seismic_model.dt * 1000.0
c = 20 # fractional denominator of cross section width
gain1 = float(seismic_data.shape[1]/c)
axarr[0].plot(args.trace + gain1 * trace1, tt, 'k',alpha = 0.9)
axarr[0].fill_betweenx(tt, args.trace + gain1 * trace1,
args.trace,
gain1 * trace1 > 0.01,
color = 'k', alpha = 0.5)
# Put wiggle in right panel
#get y-axis limits, so can reverse y-axis of wiggle plot
a1ymin, a1ymax = axarr[1].get_ylim()
axarr[1].plot(trace1,tt,'k')
axarr[1].fill_betweenx(tt, trace1, 0,
trace1 > 0.01,
color = 'k', alpha = 0.5)
axarr[1].axvline(x=0, lw=1, color='k', alpha=0.25)
axarr[1].yaxis.tick_right()
axarr[1].set_xticks([-1.0, 0, 1.0])
#axarr[1].get_xaxis().set_visible(False)
axarr[1].set_ylim((a1ymax, a1ymin))
axarr[1].set_xlim((-biggest, biggest))
axarr[1].set_ylabel('time [ms]')
axarr[1].yaxis.set_label_position("right")
axarr[1].grid()
axarr[1].axis('tight')
plt.gca().invert_yaxis()
fig.tight_layout()
# set the frequency options (need to do this more elegantly)
seismic_model.f = np.arange(0,50)
return get_figure_data()
def run_script(earth_model, seismic_model, args):
# Hard code colormap
cmap = 'seismic_r'
# min / max scaling for colormap and wiggles
extr1 = 1.0 / (10.0 * args.gain / 1000.0)
# decimate number of columns in synthetic
dec = 5
# Create figure
fig = plt.figure(figsize=[15, 10], facecolor='white')
# Create two axes within figure
gs = gridspec.GridSpec(1, 2, width_ratios=[19, 1])
axarr = [plt.subplot(gs[0]), plt.subplot(gs[1])]
seismic_model.go(earth_model, theta=args.theta)
seismic_data = seismic_model.seismic
t = seismic_model.dt * np.arange(seismic_data.shape[0])
img = seismic_data[:, :, 0, 0]
# synthetic cross section
im = axarr[0].imshow(img[:, ::dec],
aspect='auto',
cmap=cmap,
extent=[
0, seismic_data.shape[1],
1000 * seismic_model.dt * seismic_data.shape[0], 0
],
vmin=-extr1,
vmax=extr1,
interpolation='spline16')
axarr[0].set_xlim(left=0, right=seismic_data.shape[1])
axarr[0].set_ylim(top=0,
bottom=1000 * seismic_model.dt * seismic_data.shape[0])
axarr[0].grid()
axarr[0].axvline(x=args.trace, lw=1, color='k', alpha=0.25)
axarr[0].set_title('spatial cross-section')
axarr[0].set_ylabel('time [ms]')
axarr[0].set_xlabel('trace')
# Put colorbar legend on spatial cross section
colorbar_ax = fig.add_axes([0.85, 0.825, 0.010, 0.08])
fig.colorbar(im, cax=colorbar_ax)
colorbar_ax.invert_yaxis()
colorbar_ax.text(0.5,
-0.1,
'%3.1f' % -1,
transform=colorbar_ax.transAxes,
horizontalalignment='center',
verticalalignment='top')
colorbar_ax.text(0.5,
1.1,
'%3.1f' % 1,
transform=colorbar_ax.transAxes,
horizontalalignment='center')
colorbar_ax.set_axis_off()
# find max and min of section slice
ampmin = np.amin(img)
ampmax = np.amax(img)
biggest = max(abs(ampmin), abs(ampmax))
# Put wiggle trace on seismic cross-section
trace1 = seismic_data[:, args.trace - 1, 0, 0]
tt = np.arange(seismic_data.shape[0]) * seismic_model.dt * 1000.0
c = 20 # fractional denominator of cross section width
gain1 = float(seismic_data.shape[1] / c)
axarr[0].plot(args.trace + gain1 * trace1, tt, 'k', alpha=0.9)
axarr[0].fill_betweenx(tt,
args.trace + gain1 * trace1,
args.trace,
gain1 * trace1 > 0.01,
color='k',
alpha=0.5)
# Put wiggle in right panel
#get y-axis limits, so can reverse y-axis of wiggle plot
a1ymin, a1ymax = axarr[1].get_ylim()
axarr[1].plot(trace1, tt, 'k')
axarr[1].fill_betweenx(tt, trace1, 0, trace1 > 0.01, color='k', alpha=0.5)
axarr[1].axvline(x=0, lw=1, color='k', alpha=0.25)
axarr[1].yaxis.tick_right()
axarr[1].set_xticks([-1.0, 0, 1.0])
#axarr[1].get_xaxis().set_visible(False)
axarr[1].set_ylim((a1ymax, a1ymin))
axarr[1].set_xlim((-biggest, biggest))
axarr[1].set_ylabel('time [ms]')
axarr[1].yaxis.set_label_position("right")
axarr[1].grid()
axarr[1].axis('tight')
plt.gca().invert_yaxis()
fig.tight_layout()
# set the frequency options (need to do this more elegantly)
seismic_model.f = np.arange(0, 50)
return get_figure_data()
def run_script(args):
matplotlib.interactive(False)
args.plot_type = 'dashboard'
Rprop0 = args.Rock0
Rprop1 = args.Rock1
theta = np.arange(0, 90)
vp0, vs0, rho0 = make_normal_dist(Rprop0, args.iterations)
vp1, vs1, rho1 = make_normal_dist(Rprop1, args.iterations)
reflect = []
hist_titles = [[r'$V_\mathrm{P}$', r'$m/s$'],
[r'$V_\mathrm{S}$', r'$m/s$'], [r'$\rho$', r'$kg / m^3$']]
nbins = 15
vp_lim = (np.amin(
(Rprop1.vp - (3. * Rprop1.vp_sig), Rprop0.vp - (3. * Rprop1.vp_sig))),
np.amax((Rprop1.vp + (3. * Rprop1.vp_sig),
Rprop0.vp + (3. * Rprop1.vp_sig))))
vs_lim = (np.amin(
(Rprop1.vs - (3. * Rprop1.vs_sig), Rprop0.vs - (3. * Rprop1.vs_sig))),
np.amax((Rprop1.vs + (3. * Rprop1.vs_sig),
Rprop0.vs + (3. * Rprop1.vs_sig))))
rho_lim = (np.amin((Rprop1.rho - (3. * Rprop1.rho_sig),
Rprop0.rho - (3. * Rprop1.rho_sig))),
np.amax((Rprop1.rho + (3. * Rprop1.rho_sig),
Rprop0.rho + (3. * Rprop1.rho_sig))))
limits = np.array([[vp_lim, vs_lim, rho_lim], [vp_lim, vs_lim, rho_lim]])
for i in range(args.iterations):
reflect.append(
args.reflectivity_method(vp0[i], vs0[i], rho0[i], vp1[i], vs1[i],
rho1[i], theta))
reflect = np.array(reflect)
reflect = np.nan_to_num(reflect)
#temp = np.concatenate( (vp0, rho0, vs0, vp1, rho1, vs1,), axis=0)
temp = np.concatenate((vp0, vs0, rho0, vp1, vs1, rho1), axis=0)
prop_samples = np.reshape(temp, (6, args.iterations))
ave_reflect = np.mean(reflect, axis=0)
nbins = 15
# DO PLOTTING
plt.figure(figsize=(5, 13))
plt.subplots_adjust(bottom=0.1, left=0.1, top=1, right=0.9)
plt.hold(True)
if args.plot_type == 'dashboard':
G = matplotlib.gridspec.GridSpec(9, 2, hspace=0.5)
shift = 3
else:
G = matplotlib.gridspec.GridSpec(2, 6)
shift = 0
# histogram plots (ax_3, ax_4, ax_5, ax_6, ax_7, ax_8)
hist_max = 0
for k in np.arange(len(prop_samples)):
# find the max bar height of the histogram for scaling the plots
hist_max = max(hist_max,
max(np.histogram(prop_samples[k], density=True)[0]))
for j in np.arange(2):
upper_color = 'blue' #color of upper histogram
lower_color = 'green' #color of lower histogram
for i in np.arange(3):
plt.subplot(G[3 + i + shift, 0])
plt.hist(prop_samples[i],
nbins,
facecolor=upper_color,
histtype='stepfilled',
alpha=0.25,
normed=True)
plt.hist(prop_samples[i + (3)],
nbins,
facecolor=lower_color,
histtype='stepfilled',
alpha=0.25,
normed=True)
temp = plt.gca()
# Annotation and making it look nice
plt.axis(
[limits[j][i][0], limits[j][i][1],
temp.axis()[2], hist_max])
plt.yticks([])
plt.xticks(rotation=90, horizontalalignment='left')
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['left'].set_color('none')
ax.spines['top'].set_color('none')
ax.spines['bottom'].set_alpha(0.5)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(6)
label.set_alpha(0.5)
for tick in ax.xaxis.get_major_ticks():
tick.tick1On = True
tick.tick2On = False
# upper text label
mean_props = [[Rprop0.vp, Rprop1.vp], [Rprop0.vs, Rprop1.vs],
[Rprop0.rho, Rprop1.rho]]
which_label = ['upper', 'lower']
# Main label
ax.text(x=limits[0, i, 1],
y=hist_max * 0.5,
s=hist_titles[i][0],
color='black',
fontsize=14,
alpha=0.75,
horizontalalignment='left',
verticalalignment='center')
# Label for units
ax.text(x=limits[0, i, 1],
y=hist_max * 0.25,
s=hist_titles[i][1],
color='black',
fontsize=10,
alpha=0.75,
horizontalalignment='left',
verticalalignment='center')
ax.text(
x=float(mean_props[i][0]),
y=hist_max / 5.0,
s=which_label[0],
alpha=0.75,
color=upper_color,
fontsize='9',
horizontalalignment='center',
verticalalignment='center',
)
#lower text label
ax.text(x=float(mean_props[i][1]),
y=hist_max / 5.0,
s=which_label[1],
alpha=0.75,
color=lower_color,
fontsize='9',
horizontalalignment='center',
verticalalignment='center')
#
# ax_1 the AVO plot
#
plt.subplot(G[0:3, :])
plt.hold(True)
critical_angles = []
for i in range(args.iterations - 1):
# Do the AVO template as an underlay
# HERE
# Do the plots --> This step might not need to be in a loop
plt.plot(theta,
reflect[i],
color='grey',
lw=1.0,
alpha=np.min((30. / args.iterations, 0.08)))
if vp1[i] > vp0[i]:
theta_crit = arcsin(vp0[i] / vp1[i]) * 180 / np.pi
plt.axvline(x=theta_crit,
color='black',
lw=1.0,
alpha=np.min((30. / args.iterations, 0.5)))
critical_angles.append(theta_crit)
if len(critical_angles) > 0:
critical_angle = np.mean(critical_angles)
else:
critical_angle = 'N/A'
plt.plot(theta, ave_reflect, color='black', alpha=0.5, lw=1.5)
plt.grid()
# Annotation and making it look nice
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))
ax.spines['bottom'].set_alpha(0.5)
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))
ax.spines['left'].set_alpha(0.5)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(6)
label.set_alpha(0.5)
plt.grid()
plt.ylim((-.5, .5))
ax.text(0.95,
0.45,
'angle',
verticalalignment='top',
horizontalalignment='right',
transform=ax.transAxes,
color='black',
fontsize=8,
alpha=0.5)
ax.text(0.05,
0.95,
'amplitude',
verticalalignment='top',
horizontalalignment='right',
transform=ax.transAxes,
rotation=90,
color='black',
fontsize=8,
alpha=0.5)
#
# Patches for the Background template AVO plot STARTS here
#
a1 = 0.10 # transparency for AVO background template patches
rangex = 90
band = 0.04 # thickness of Class 2 band
# CLASS 1
Path1 = mpath.Path
path_data1 = [
(Path1.MOVETO, (rangex * 0, 0.04)),
(Path1.CURVE4, (rangex * 0.4, 0.05)),
(Path1.CURVE4, (rangex * 0.6, -0.015)),
(Path1.CURVE4, (rangex * 1.0, -band)),
(Path1.LINETO, (rangex * 1.0, 1.0)),
(Path1.LINETO, (rangex * 0.0, 1.0)),
(Path1.CLOSEPOLY, (rangex * 0.0, 1.0)),
]
codes1, verts1 = zip(*path_data1)
path1 = mpath.Path(verts1, codes1)
patch1 = mpatches.PathPatch(path1, facecolor='r', alpha=a1, ec='none')
ax.add_patch(patch1)
# plot control points and connecting lines
x1, y1 = zip(*path1.vertices)
#line1, = ax.plot(x1, y1, 'go-')
# CLASS 2p
Path2P = mpath.Path
path_data2P = [
(Path2P.MOVETO, (rangex * 0, band)),
(Path2P.CURVE4, (rangex * 0.4, 0.05)),
(Path2P.CURVE4, (rangex * 0.6, -0.015)),
(Path2P.CURVE4, (rangex * 1.0, -band)),
(Path2P.LINETO, (rangex * 1.0, -(band + band))),
(Path2P.CURVE4, (rangex * 0.6, -(0.015 + band))),
(Path2P.CURVE4, (rangex * 0.4, 0.05 - band)),
(Path2P.CURVE4, (rangex * 0.0, 0.0)),
(Path2P.CLOSEPOLY, (rangex * 0.0, 0.0)),
]
codes2P, verts2P = zip(*path_data2P)
path2P = mpath.Path(verts2P, codes2P)
patch2P = mpatches.PathPatch(path2P,
facecolor='yellow',
alpha=a1,
ec='none')
ax.add_patch(patch2P)
# plot control points and connecting lines
x2, y2 = zip(*path2P.vertices)
#line2, = ax.plot(x2, y2, 'ro-')
# CLASS 2
Path2 = mpath.Path
path_data2 = [
(Path2.MOVETO, (rangex * 0.0, 0.0)),
(Path2.CURVE4, (rangex * 0.4, 0.05 - band)),
(Path2.CURVE4, (rangex * 0.6, -(0.015 + band))),
(Path2.CURVE4, (rangex * 1.0, -(band + band))),
(Path2.LINETO, (rangex * 1.0, -(3 * band))),
(Path2.CURVE4, (rangex * 0.6, -(0.015 + 2 * band))),
(Path2.CURVE4, (rangex * 0.4, 0.05 - (0.0 + 2 * band))),
(Path2.CURVE4, (rangex * 0.0, -band)),
(Path2.CLOSEPOLY, (rangex * 0.0, 0.0)),
]
codes2, verts2 = zip(*path_data2)
path2 = mpath.Path(verts2, codes2)
patch2 = mpatches.PathPatch(path2, facecolor='green', alpha=a1, ec='none')
ax.add_patch(patch2)
# plot control points and connecting lines
x2, y2 = zip(*path2.vertices)
#line2, = ax.plot(x2, y2, 'ro-')
# CLASS 3
Path3 = mpath.Path
path_data3 = [
(Path3.MOVETO, (rangex * 0.0, -band)),
(Path3.CURVE4, (rangex * 0.4, 0.05 - (0.0 + 2 * band))),
(Path3.CURVE4, (rangex * 0.6, -(0.015 + 2 * band))),
(Path3.CURVE4, (rangex * 1.0, -(3 * band))),
(Path3.LINETO, (rangex * 1.0, -1.0)),
(Path3.LINETO, (rangex * 0.0, -1.0)),
(Path3.CLOSEPOLY, (rangex * 0.0, -1.0)),
]
codes3, verts3 = zip(*path_data3)
path3 = mpath.Path(verts3, codes3)
patch3 = mpatches.PathPatch(path3, facecolor='blue', alpha=a1, ec='none')
ax.add_patch(patch3)
# plot control points and connecting lines
x3, y3 = zip(*path3.vertices)
#line2, = ax.plot(x2, y2, 'ro-')
ax.grid()
ax.text(0.98,
0.98,
'Amplitude vs angle',
verticalalignment='top',
horizontalalignment='right',
transform=ax.transAxes,
color='black',
fontsize=9,
fontweight='bold',
alpha=0.5)
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(8)
label.set_alpha(0.5)
plt.grid()
# Class 1 label
# y-values for class labels 1,2p, 2, 3, and 4 respectively
ylabelcntrs = [0.35, 0.025, -0.025, -0.4, -0.2]
xctrs = 10
fs = 10 # fontsize
a2 = 0.4 # transparency value for Gradient vs Intercept text
ax.text(xctrs,
ylabelcntrs[0],
'CLASS 1',
verticalalignment='center',
horizontalalignment='left',
color='red',
fontsize=fs,
fontweight='bold',
alpha=a2)
# Class 2p label
ax.text(xctrs,
ylabelcntrs[1],
'CLASS 2p',
verticalalignment='center',
horizontalalignment='left',
rotation=-3,
color='#EEC900',
fontsize=fs,
fontweight='bold',
alpha=a2 * 1.5)
# Class 2 label
ax.text(xctrs,
ylabelcntrs[2],
'CLASS 2',
verticalalignment='center',
horizontalalignment='left',
rotation=-3,
color='green',
fontsize=fs,
fontweight='bold',
alpha=a2)
# Class 3 label
ax.text(xctrs,
ylabelcntrs[3],
'CLASS 3',
verticalalignment='center',
horizontalalignment='left',
rotation=-15,
color='blue',
fontsize=fs,
fontweight='bold',
alpha=a2)
# Class 4 label
ax.text(xctrs,
ylabelcntrs[4],
'CLASS 4',
verticalalignment='center',
horizontalalignment='left',
rotation=15,
color='#B048B5',
fontsize=fs,
fontweight='bold',
alpha=a2)
#
# Patches for background template AVO plot ENDS here
#
# ax_2 the AB plot
plt.subplot(G[0 + shift:3 + shift, :])
plt.hold(True)
max_ang = args.max_angle # Max ang for computing gradient
for i in range(args.iterations - 1):
plt.scatter(reflect[i, 0], (reflect[i, max_ang] - reflect[i, 0]),
color='grey',
s=20,
alpha=np.max((30. / args.iterations, 0.2)))
# Plot the average of the dots
plt.scatter(ave_reflect[0],
ave_reflect[max_ang] - ave_reflect[0],
color='black',
s=40,
alpha=0.5)
# Annotation and making it nice
plt.xticks([]), plt.yticks([])
ax = plt.gca()
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))
ax.spines['bottom'].set_alpha(0.5)
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))
ax.spines['left'].set_alpha(0.5)
plt.grid()
# axis limits
ylimits = (np.amin((-.3, np.nanmin(reflect[:, 50] - reflect[:, 0]))),
np.amax((.3, np.nanmax(reflect[:, 50] - reflect[:, 0]))))
xlimits = (np.amin(
(-.3, np.nanmin(reflect[:,
0]))), np.amax((.3, np.nanmax(reflect[:, 0]))))
plt.ylim(ylimits)
plt.xlim(xlimits)
# axis labels
ax.text(
xlimits[1] - 0.05,
0.025,
'intercept',
verticalalignment='center',
horizontalalignment='right',
#transform=ax.transAxes,
color='black',
fontsize=8,
alpha=0.5)
ax.text(
0.025,
ylimits[1] - 0.05,
'gradient',
rotation=90,
verticalalignment='top',
horizontalalignment='center',
#transform=ax.transAxes,
color='black',
fontsize=8,
alpha=0.5)
#
# Patches for background Gradient vs Intercept template STARTS here
#
x = np.arange(xlimits[0], xlimits[1], 0.01)
y = np.arange(ylimits[0], ylimits[1], 0.01)
s0 = -x
shift2 = 0.04 #width for class2 width in plot
height_ellipse = 0.05
width_ellipse = 3.0
# Plot background trend (diagonal line)
ax.plot(x, s0, color='black', alpha=a1)
# add a rectangle for class 2 neg
lowleft2 = (-shift2, -1.0)
class2neg = mpatches.Rectangle(lowleft2,
width=abs(lowleft2[0]),
height=1.0,
color='green',
alpha=a1,
ec="white",
lw=4)
ax.add_patch(class2neg)
# add a rectange for class 2 pos
lowleft2pos = (0.0, 0.0)
class2pos = mpatches.Rectangle(lowleft2pos,
width=abs(lowleft2[0]),
height=1.0,
color='green',
alpha=a1,
ec="white",
lw=4)
ax.add_patch(class2pos)
# add a rectange for class 2p pos
lowleft2Ppos = (-shift2, 0.0)
class2Ppos = mpatches.Rectangle(lowleft2Ppos,
width=abs(lowleft2[0]),
height=1.0,
color='yellow',
alpha=a1,
ec="white",
lw=4)
ax.add_patch(class2Ppos)
# add a rectange for class 2p neg
lowleft2Pneg = (0.0, -1.0)
class2Ppos = mpatches.Rectangle(lowleft2Pneg,
width=abs(lowleft2[0]),
height=1.0,
color='yellow',
alpha=a1,
ec="white",
lw=4)
ax.add_patch(class2Ppos)
# add rectangle for lower left quadrant class 3
lowleft3neg = (-1.0, -1.0)
class3neg = mpatches.Rectangle(lowleft3neg,
width=1.0 + lowleft2[0],
height=1.0,
color='blue',
alpha=a1,
ec='none')
ax.add_patch(class3neg)
# add rectange for upper right quadrant class 3
lowleft3pos = (shift2, 0)
class3pos = mpatches.Rectangle(lowleft3pos,
width=1.0 + lowleft2[0],
height=1.0,
color='blue',
alpha=a1,
ec='none')
ax.add_patch(class3pos)
# add a Polygon for Class 4 upper left quadrant
# add a path patch
Path4u = mpath.Path
path_data4u = [(Path4u.MOVETO, [-1.0, 0.0]), (Path4u.LINETO, [-1.0, 1.0]),
(Path4u.LINETO, [-shift2, shift2]),
(Path4u.LINETO, [-shift2, 0]),
(Path4u.CLOSEPOLY, [-shift2, 0.0])]
codes4u, verts4u = zip(*path_data4u)
path4u = mpath.Path(verts4u, codes4u)
patch4u = mpatches.PathPatch(
path4u,
facecolor='#B048B5', # purple
alpha=a1,
ec='none')
ax.add_patch(patch4u)
# add a Polygon for Class 4 lower right quadrant
Path4l = mpath.Path
path_data4l = [(Path4l.MOVETO, [shift2, 0.0]), (Path4l.LINETO, [1.0, 0.0]),
(Path4l.LINETO, [1.0, -1.0]),
(Path4l.LINETO, [shift2, -shift2]),
(Path4l.CLOSEPOLY, [shift2, -shift2])]
codes4l, verts4l = zip(*path_data4l)
path4l = mpath.Path(verts4l, codes4l)
patch4l = mpatches.PathPatch(
path4l,
facecolor='#B048B5', # purple
alpha=a1,
ec='none')
ax.add_patch(patch4l)
# Add a Polygon for the Class 1 upper right quadrant
Path1u = mpath.Path
path_data1u = [(Path1u.MOVETO, [-shift2, shift2]),
(Path1u.LINETO, [-1.0, 1.0]),
(Path1u.LINETO, [-shift2, 1.0]),
(Path1u.CLOSEPOLY, [-shift2, shift2])]
codes1u, verts1u = zip(*path_data1u)
path1u = mpath.Path(verts1u, codes1u)
patch1u = mpatches.PathPatch(path1u, facecolor='red', alpha=a1, ec='none')
ax.add_patch(patch1u)
# Add a Polygone for the Class 1 lower left quadrant
Path1l = mpath.Path
path_data1l = [(Path1l.MOVETO, [shift2, -shift2]),
(Path1l.LINETO, [shift2, -1.0]),
(Path1l.LINETO, [1.0, -1.0]),
(Path1l.CLOSEPOLY, [shift2, -shift2])]
codes1l, verts1l = zip(*path_data1l)
path1l = mpath.Path(verts1l, codes1l)
patch1l = mpatches.PathPatch(path1l, facecolor='red', alpha=a1, ec='none')
ax.add_patch(patch1l)
# Draw ellipse
xy = np.hstack((0, 0))
bkgd = collections.EllipseCollection(widths=width_ellipse,
heights=height_ellipse,
angles=135,
units='xy',
offsets=xy,
transOffset=ax.transData,
facecolor='grey',
edgecolor='none',
alpha=0.15)
ax.add_collection(bkgd)
#Get rid of axes spines
ax.spines['right'].set_color('none')
ax.spines['top'].set_color('none')
ax.xaxis.set_ticks_position('bottom')
ax.spines['bottom'].set_position(('data', 0))
ax.spines['bottom'].set_alpha(0.5)
ax.yaxis.set_ticks_position('left')
ax.spines['left'].set_position(('data', 0))
ax.spines['left'].set_alpha(0.5)
# Annotation for Gradient vs Intercept annotation
ax.text(0.98,
0.98,
'Gradient vs intercept',
verticalalignment='top',
horizontalalignment='right',
transform=ax.transAxes,
color='black',
fontsize=9,
fontweight='bold',
alpha=0.50)
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(8)
label.set_alpha(0.5)
# Class 1 label
ax.text(3 * shift2,
ylimits[0] + shift2,
'CLASS 1',
verticalalignment='center',
horizontalalignment='center',
color='red',
fontsize=fs,
fontweight='bold',
alpha=a2)
# Class 2 label
ax.text(-0.5 * shift2,
ylimits[0] + 0.5 * shift2,
'CLASS 2',
verticalalignment='bottom',
horizontalalignment='center',
rotation=90,
color='green',
fontsize=fs,
fontweight='bold',
alpha=a2)
# Class 2P label
ax.text(0.5 * shift2,
ylimits[0] + 0.5 * shift2,
'CLASS 2p',
verticalalignment='bottom',
horizontalalignment='center',
rotation=90,
color='#EEC900',
fontsize=fs,
fontweight='bold',
alpha=a2 * 1.5)
# Class 3 label
ax.text(-3 * shift2,
ylimits[0] + shift2,
'CLASS 3',
verticalalignment='center',
horizontalalignment='right',
color='blue',
fontsize=fs,
fontweight='bold',
alpha=a2)
# Class 4 label
ax.text(-3 * shift2,
shift2,
'CLASS 4',
verticalalignment='center',
horizontalalignment='right',
color='#B048B5',
fontsize=fs,
fontweight='bold',
alpha=a2)
# Background label
angle = -45
ax.text(0.1 * height_ellipse,
0.1 * height_ellipse,
'background',
verticalalignment='center',
horizontalalignment='center',
rotation=angle,
transform=ax.transData,
color='black',
fontsize=fs,
fontweight='bold',
alpha=a2 / 2.0)
return get_figure_data(), {"mean critical angle": critical_angle}
