def test(mfault):
from clawpack.clawutil.data import ClawData
probdata = ClawData()
probdata.read('setprob.data', force=True)
fault = dtopotools.Fault(coordinate_specification='top_center')
fault.read('fault.data')
mapping = Mapping(fault)
domain_depth = probdata.domain_depth
domain_width = probdata.domain_width
# num of cells here determined in a similar fashion to that in setrun.py
dx = mapping.fault_width / mfault
num_cells_above = numpy.rint(mapping.fault_depth / dx)
dy = mapping.fault_depth / num_cells_above
mx = int(numpy.ceil(domain_width / dx)) # mx
my = int(numpy.ceil(domain_depth / dy)) # my
mr = mx - mfault
x = linspace(
mapping.xcenter - 0.5 * mapping.fault_width -
numpy.floor(mr / 2.0) * dx, mapping.xcenter +
0.5 * mapping.fault_width + numpy.ceil(mr / 2.0) * dx, mx + 1)
y = linspace(-my * dy, 0.0, my + 1)
xc, yc = meshgrid(x, y)
xp, yp = mapping.mapc2p(xc, yc)
figure()
plot(xp, yp, 'k-')
plot(xp.T, yp.T, 'k-')
plot((mapping.xp1, mapping.xp2), (mapping.yp1, mapping.yp2), '-g')
axis('scaled')
def setplot(plotdata):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of clawpack.visclaw.data.ClawPlotData.
Output: a modified version of plotdata.
"""
fault = dtopotools.Fault()
fault.read(plotdata.outdir + '/fault.data')
mapping = Mapping(fault)
fault_width = mapping.fault_width
xcenter = mapping.xcenter
ycenter = mapping.ycenter
xp1 = mapping.xp1
xp2 = mapping.xp2
yp1 = mapping.yp1
yp2 = mapping.yp2
probdata = ClawData()
probdata.read('setprob.data',force=True)
xlimits = [xcenter-0.5*probdata.domain_width,xcenter+0.5*probdata.domain_width]
ylimits = [-probdata.domain_depth,0.0]
from clawpack.visclaw import colormaps
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'binary'
gaugedata = ClawData()
gaugedata.read('gauges.data',force=True)
ngauges = gaugedata.ngauges
xc = np.zeros(ngauges)
for j in range(ngauges):
g = plotdata.getgauge(j)
xc[j] = g.location[0]
fault.create_dtopography(xc/LAT2METER,np.array([0.]),[1.0],y_disp=True)
def plot_vertical_displacement(current_data):
from pylab import plot,zeros,gca
t = current_data.t
ys = zeros(ngauges)
for gaugeno in range(ngauges):
g = plotdata.getgauge(gaugeno)
for k in range(1,len(g.t)):
if g.t[k] > t:
break
dt = g.t[k] - g.t[k-1]
v = 0.5*(g.q[4,k]+g.q[4,k-1])
ys[gaugeno] += dt*v
ax = gca()
kwargs ={'linestyle':'-','color':'blue','label':'numerical'}
plot(xc[:ngauges],ys,**kwargs)
kwargs = {'linestyle':'--','color':'r','label':'Okada'}
fault.plot_okada(ax,displacement='vertical',kwargs=kwargs)
def plot_interfaces(current_data):
from pylab import plot,zeros,gca,tick_params
t = current_data.t
ys = zeros(ngauges)
for gaugeno in range(ngauges):
g = plotdata.getgauge(gaugeno)
for k in range(1,len(g.t)):
if g.t[k] > t:
break
dt = g.t[k] - g.t[k-1]
v = 0.5*(g.q[4,k]+g.q[4,k-1])
ys[gaugeno] += dt*v
ax = gca()
plot(xc[:ngauges],ys,linewidth=3)
kwargs = {'linestyle':'--','color':'r','linewidth':3}
fault.plot_okada(ax,displacement='vertical',kwargs=kwargs)
tick_params(labelsize=20)
def plot_fault(current_data):
from pylab import linspace, plot
xl = linspace(xp1,xp2,100)
yl = linspace(yp1,yp2,100)
plot(xl,yl,'k')
def sigmatr(current_data):
# return -trace(sigma)
q = current_data.q
return -(q[0,:,:] + q[1,:,:])
# Figure for surfaces and p-waves
plotfigure = plotdata.new_plotfigure(name='surface_and_p_waves', figno=1)
plotfigure.kwargs = {'figsize':(8,8)}
# Set axes for vertical displacement:
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(211)'
plotaxes.xlimits = xlimits
plotaxes.ylimits = [-0.3, 0.5]
plotaxes.title = 'vertical displacement'
plotaxes.scaled = False
plotaxes.afteraxes = plot_vertical_displacement
# Set axes for p waves:
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(212)'
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = '-trace(sigma)'
plotaxes.scaled = True
plotaxes.afteraxes = plot_fault
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = sigmatr
plotitem.pcolor_cmap = colormaps.blue_white_red
plotitem.pcolor_cmin = -1e6
plotitem.pcolor_cmax = 1e6
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
plotitem.MappedGrid = True
plotitem.mapc2p = mapping.mapc2p
# Figure for grid cells
plotfigure = plotdata.new_plotfigure(name='cells', figno=2)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = 'Level 4 grid patches'
plotaxes.scaled = True
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_patch')
plotitem.amr_patch_bgcolor = ['#ffeeee', '#effeee', '#eeffee', '#eeeffe',
'#eeeeff', '#ffffff']
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0,0,0,0,0,1]
plotitem.MappedGrid = True
plotitem.mapc2p = mapping.mapc2p
# Parameters used only when creating html and/or latex hardcopy
# e.g., via clawpack.visclaw.frametools.printframes:
plotdata.printfigs = True # print figures
plotdata.print_format = 'png' # file format
plotdata.print_framenos = 'all' # list of frames to print
plotdata.print_fignos = 'all' # list of figures to print
plotdata.html = True # create html files of plots?
plotdata.html_homelink = '../README.html' # pointer for top of index
plotdata.latex = True # create latex file of plots?
plotdata.latex_figsperline = 2 # layout of plots
plotdata.latex_framesperline = 1 # layout of plots
plotdata.latex_makepdf = False # also run pdflatex?
plotdata.parallel = True
return plotdata
def setplot(plotdata):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of clawpack.visclaw.data.ClawPlotData.
Output: a modified version of plotdata.
"""
fault = dtopotools.Fault()
fault.read(plotdata.outdir + '/fault.data')
mapping = Mapping(fault)
fault_width = mapping.fault_width
xcenter = mapping.xcenter
ycenter = mapping.ycenter
xp1 = mapping.xp1
xp2 = mapping.xp2
yp1 = mapping.yp1
yp2 = mapping.yp2
probdata = ClawData()
probdata.read('setprob.data',force=True)
xlimits = [xcenter-0.5*probdata.domain_width,xcenter+0.5*probdata.domain_width]
ylimits = [-probdata.domain_depth,0.0]
from clawpack.visclaw import colormaps
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'binary'
def plot_fault(current_data):
from pylab import linspace, plot
xl = linspace(xp1,xp2,100)
yl = linspace(yp1,yp2,100)
plot(xl,yl,'k')
def sigmatr(current_data):
# return -trace(sigma)
q = current_data.q
return -(q[0,:,:] + q[1,:,:])
def slip_direction_vel(current_data):
# return vel dot tau, where tau is tangent to fault
tau_x = (xp2 - xp1)/fault_width
tau_y = (yp2 - yp1)/fault_width
tau_x = np.where(current_data.y > ycenter, -tau_x, tau_x)
tau_y = np.where(current_data.y > ycenter, -tau_y, tau_y)
u = current_data.q[3,:,:]
v = current_data.q[4,:,:]
return u*tau_x + v*tau_y
# Figure for waves
plotfigure = plotdata.new_plotfigure(name='waves', figno=1)
plotfigure.kwargs = {'figsize':(10,8)}
# Set up axes for trace(sigma):
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(211)'
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = '-trace(sigma)'
plotaxes.scaled = True
plotaxes.afteraxes = plot_fault
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = sigmatr
plotitem.pcolor_cmap = colormaps.blue_white_red
plotitem.pcolor_cmin = -1e6
plotitem.pcolor_cmax = 1e6
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
plotitem.MappedGrid = True
plotitem.mapc2p = mapping.mapc2p
# Set up axes for slip_direction_velocity:
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(212)'
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = 'slip-direction-velocity'
plotaxes.scaled = True
plotaxes.afteraxes = plot_fault
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = slip_direction_vel
plotitem.pcolor_cmap = colormaps.blue_white_red
plotitem.pcolor_cmin = -0.1
plotitem.pcolor_cmax = 0.1
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
plotitem.MappedGrid = True
plotitem.mapc2p = mapping.mapc2p
# Figure for grid cells
plotfigure = plotdata.new_plotfigure(name='cells', figno=2)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = 'Level 6 grid patches'
plotaxes.scaled = True
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_patch')
plotitem.amr_patch_bgcolor = ['#ffeeee', '#effeee', '#eeffee', '#eeeffe',
'#eeeeff', '#ffffff']
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0,0,0,0,0,1]
plotitem.MappedGrid = True
plotitem.mapc2p = mapping.mapc2p
# Parameters used only when creating html and/or latex hardcopy
# e.g., via clawpack.visclaw.frametools.printframes:
plotdata.printfigs = True # print figures
plotdata.print_format = 'png' # file format
plotdata.print_framenos = 'all' # list of frames to print
plotdata.print_fignos = 'all' # list of figures to print
plotdata.html = True # create html files of plots?
plotdata.html_homelink = '../README.html' # pointer for top of index
plotdata.latex = True # create latex file of plots?
plotdata.latex_figsperline = 2 # layout of plots
plotdata.latex_framesperline = 1 # layout of plots
plotdata.latex_makepdf = False # also run pdflatex?
# plotdata.parallel = True
return plotdata
def setplot(plotdata):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of clawpack.visclaw.data.ClawPlotData.
Output: a modified version of plotdata.
"""
slice_number = 3
os.chdir(plotdata.outdir)
for filename in os.listdir('.'):
if (filename.startswith('slice_%d' % slice_number)):
shutil.copyfile(
filename, filename.replace('slice_%d' % slice_number, 'fort',
1))
os.chdir('..')
fault = dtopotools.Fault()
fault.read('fault.data')
mapping = Mapping(fault)
xp1 = mapping.xp1
xp2 = mapping.xp2
zp1 = mapping.zp1
zp2 = mapping.zp2
xcenter = mapping.xcenter
ycenter = mapping.ycenter
probdata = ClawData()
probdata.read('setprob.data', force=True)
probdata.read(plotdata.outdir + '/setprob.data', force=True)
xlimits = [
xcenter - 0.5 * probdata.domain_width,
xcenter + 0.5 * probdata.domain_width
]
ylimits = [
ycenter - 0.5 * probdata.domain_length,
ycenter + 0.5 * probdata.domain_length
]
zlimits = [-probdata.domain_depth, 0.0]
def plot_fault_xz(current_data):
from pylab import linspace, plot
xl = linspace(xp1, xp2, 100)
zl = linspace(zp1, zp2, 100)
plot(xl, zl, 'k')
if (slice_number is 1):
x1limits = xlimits
x2limits = ylimits
mapc2p = mapping.mapc2p_xy
plot_fault = None
elif (slice_number is 2):
x1limits = ylimits
x2limits = zlimits
mapping.set_slice_xval(0.0)
mapc2p = mapping.mapc2p_yz
plot_fault = None
elif (slice_number is 3):
x1limits = xlimits
x2limits = zlimits
mapc2p = mapping.mapc2p_xz
plot_fault = plot_fault_xz
from clawpack.visclaw import colormaps
plotdata.clearfigures() # clear any old figures,axes,items data
# plotdata.format = 'binary'
def sigmatr(current_data):
# return -trace(sigma)
q = current_data.q
return -(q[0, :, :] + q[1, :, :] + q[2, :, :])
# Figure for trace(sigma)
plotfigure = plotdata.new_plotfigure(name='trace', figno=1)
plotfigure.kwargs = {'figsize': (10, 8)}
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(211)'
plotaxes.xlimits = x1limits
plotaxes.ylimits = x2limits
plotaxes.title = '-trace(sigma)'
plotaxes.scaled = True
plotaxes.afteraxes = plot_fault
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = sigmatr
plotitem.pcolor_cmap = colormaps.blue_white_red
plotitem.pcolor_cmin = -1e6
plotitem.pcolor_cmax = 1e6
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
plotitem.MappedGrid = True
plotitem.mapc2p = mapc2p
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(212)'
plotaxes.xlimits = x1limits
plotaxes.ylimits = x2limits
plotaxes.title = 'x-velocity'
plotaxes.scaled = True
plotaxes.afteraxes = plot_fault
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = 6
plotitem.pcolor_cmap = colormaps.blue_white_red
plotitem.pcolor_cmin = -1e-1
plotitem.pcolor_cmax = 1e-1
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
plotitem.MappedGrid = True
plotitem.mapc2p = mapc2p
# Figure for grid cells
plotfigure = plotdata.new_plotfigure(name='cells', figno=2)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = x1limits
plotaxes.ylimits = x2limits
plotaxes.title = 'Level 4 grid patches'
plotaxes.scaled = True
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_patch')
plotitem.amr_patch_bgcolor = ['#ffeeee', '#eeeeff', '#eeffee', '#ffffff']
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0, 0, 0, 1]
plotitem.MappedGrid = True
plotitem.mapc2p = mapc2p
# Parameters used only when creating html and/or latex hardcopy
# e.g., via clawpack.visclaw.frametools.printframes:
plotdata.printfigs = True # print figures
plotdata.print_format = 'png' # file format
plotdata.print_framenos = 'all' # list of frames to print
plotdata.print_fignos = 'all' # list of figures to print
plotdata.html = True # create html files of plots?
plotdata.html_homelink = '../README.html' # pointer for top of index
plotdata.latex = True # create latex file of plots?
plotdata.latex_figsperline = 2 # layout of plots
plotdata.latex_framesperline = 1 # layout of plots
plotdata.latex_makepdf = False # also run pdflatex?
# plotdata.parallel = True
return plotdata
import clawpack.seismic.dtopotools_horiz_okada_and_1d as dtopotools
from numpy import arange, cos, sin, pi
reload(dtopotools)
from clawpack.geoclaw.data import LAT2METER
fault = dtopotools.Fault(coordinate_specification='top center')
fault.subfaults = []
width = 50000.0
theta = 0.20
fault_top_center = [0.0, -20000.0]
slip = 1.0
mu = 3e10
rupture_time = 0.0
rise_time = 1.0
nsubfaults = 1
longitude0 = fault_top_center[0] / LAT2METER
dlongitude = width * cos(theta) / LAT2METER / nsubfaults
ddepth = width * sin(theta) / nsubfaults
subfault_width = width / nsubfaults
for i in range(nsubfaults):
subfault = dtopotools.SubFault()
subfault.mu = mu
subfault.dip = theta / pi * 180.0
subfault.width = subfault_width
subfault.depth = -fault_top_center[1] + ddepth * i
subfault.slip = slip
subfault.rake = 90
def setplot(plotdata):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of clawpack.visclaw.data.ClawPlotData.
Output: a modified version of plotdata.
"""
slice_number = 1 # set to surface slice number
os.chdir(plotdata.outdir)
for filename in os.listdir('.'):
if (filename.startswith('slice_%d' % slice_number)):
shutil.copyfile(
filename, filename.replace('slice_%d' % slice_number, 'fort',
1))
os.chdir('..')
fault = dtopotools.Fault()
fault.read('fault.data')
mapping = Mapping(fault)
xp1 = mapping.xp1
xp2 = mapping.xp2
zp1 = mapping.zp1
zp2 = mapping.zp2
xcenter = mapping.xcenter
ycenter = mapping.ycenter
probdata = ClawData()
probdata.read('setprob.data', force=True)
probdata.read(plotdata.outdir + '/setprob.data', force=True)
xlimits = [
xcenter - 0.5 * probdata.domain_width,
xcenter + 0.5 * probdata.domain_width
]
ylimits = [
ycenter - 0.5 * probdata.domain_length,
ycenter + 0.5 * probdata.domain_length
]
from clawpack.visclaw import colormaps
xc = np.linspace(-150e3, 200e3, 350)
yc = np.linspace(-87.5e3, 87.5e3, 175)
fault.create_dtopography(xc / LAT2METER, yc / LAT2METER.e3, [1.0])
okada_max = np.max(fault.dtopo.dZ[0, :, :])
clevels = np.linspace(-okada_max, okada_max, 10)
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'binary'
def plot_okada_contour(current_data):
from pylab import gca
kwargs = {'levels': clevels, 'colors': 'g'}
ax = gca()
fault.plot_okada_contour(axes=ax, kwargs=kwargs)
def plot_okada(current_data):
from pylab import gca
kwargs = {
'cmap': colormaps.blue_white_red,
'vmin': clevels[0],
'vmax': clevels[-1]
}
ax = gca()
fault.plot_okada(axes=ax, dim=2, kwargs=kwargs)
kwargs = {'levels': clevels, 'colors': 'g', 'linewidths': 3}
fault.plot_okada_contour(axes=ax, kwargs=kwargs)
# Figure for vertical displacement
plotfigure = plotdata.new_plotfigure(name='trace', figno=1)
plotfigure.kwargs = {'figsize': (10, 8)}
# Set axes for numerical solution:
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(211)'
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = 'Numerical solution'
plotaxes.scaled = True
plotaxes.afteraxes = plot_okada_contour
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = 9
plotitem.pcolor_cmap = colormaps.blue_white_red
plotitem.pcolor_cmin = clevels[0]
plotitem.pcolor_cmax = clevels[-1]
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = 9
plotitem.contour_colors = 'k'
plotitem.contour_levels = clevels
plotitem.kwargs = {'linewidths': 3}
plotitem.amr_contour_show = [0, 0, 0, 1]
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
# Set axes for Okada solution:
plotaxes = plotfigure.new_plotaxes()
plotaxes.axescmd = 'subplot(212)'
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title_with_t = False
plotaxes.title = 'Okada solution'
plotaxes.scaled = True
plotaxes.afteraxes = plot_okada
# Figure for grid cells
plotfigure = plotdata.new_plotfigure(name='cells', figno=2)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = 'Level 4 grid patches'
plotaxes.scaled = True
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_patch')
plotitem.amr_patch_bgcolor = ['#ffeeee', '#eeeeff', '#eeffee', '#ffffff']
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0, 0, 0, 1]
# Parameters used only when creating html and/or latex hardcopy
# e.g., via clawpack.visclaw.frametools.printframes:
plotdata.printfigs = True # print figures
plotdata.print_format = 'png' # file format
plotdata.print_framenos = 'all' # list of frames to print
plotdata.print_fignos = 'all' # list of figures to print
plotdata.html = True # create html files of plots?
plotdata.html_homelink = '../README.html' # pointer for top of index
plotdata.latex = True # create latex file of plots?
plotdata.latex_figsperline = 2 # layout of plots
plotdata.latex_framesperline = 1 # layout of plots
plotdata.latex_makepdf = False # also run pdflatex?
# plotdata.parallel = True
return plotdata
def setrun(claw_pkg='amrclaw'):
#------------------------------
"""
Define the parameters used for running Clawpack.
INPUT:
claw_pkg expected to be "amrclaw" for this setrun.
OUTPUT:
rundata - object of class ClawRunData
"""
from clawpack.clawutil import data
assert claw_pkg.lower() == 'amrclaw', "Expected claw_pkg = 'amrclaw'"
num_dim = 3
rundata = data.ClawRunData(claw_pkg, num_dim)
#------------------------------------------------------------------
# Problem-specific parameters to be written to setprob.data:
#------------------------------------------------------------------
# Sample setup to write one line to setprob.data ...
probdata = rundata.new_UserData(name='probdata',fname='setprob.data')
probdata.add_param('domain_depth', 300e3, 'depth of domain')
probdata.add_param('domain_width', 600e3, 'width of domain (in x direction)')
probdata.add_param('domain_length', 300e3, 'length of domain (in y direction)')
#------------------------------------------------------------------
# Standard Clawpack parameters to be written to claw.data:
#------------------------------------------------------------------
clawdata = rundata.clawdata # initialized when rundata instantiated
#------------------------------------------------------------------
# Read in fault information
#------------------------------------------------------------------
fault = dtopotools.Fault()
fault.read('fault.data')
mapping = Mapping(fault)
fault_length = mapping.fault_length
fault_width = mapping.fault_width
fault_depth = mapping.fault_depth
fault_xcenter = mapping.xcenter
fault_ycenter = mapping.ycenter
rupture_rise_time = 0.0
for subfault in fault.subfaults:
rupture_rise_time = max(rupture_rise_time,subfault.rupture_time
+ subfault.rise_time)
# ---------------
# Spatial domain
# ---------------
# Number of space dimensions:
clawdata.num_dim = num_dim
# Number of grid cells
num_cells_fault_width = 10
num_cells_fault_length = 5
dx = fault_width/num_cells_fault_width
dy = fault_length/num_cells_fault_length
# determine cell number and set computational boundaries
target_num_cells = np.rint(probdata.domain_width/dx) # x direction
num_cells_below = np.rint((target_num_cells - num_cells_fault_width)/2.0)
num_cells_above = target_num_cells - num_cells_below - num_cells_fault_width
if (int(num_cells_below + num_cells_fault_width + num_cells_above) % 2 == 1):
num_cells_above += 1
clawdata.lower[0] = fault_xcenter-0.5*fault_width-num_cells_below*dx
clawdata.upper[0] = fault_xcenter+0.5*fault_width+num_cells_above*dx
clawdata.num_cells[0] = int(num_cells_below + num_cells_fault_width + num_cells_above)
target_num_cells = np.rint(probdata.domain_length/dy) # y direction
num_cells_below = np.rint((target_num_cells - num_cells_fault_length)/2.0)
num_cells_above = target_num_cells - num_cells_below - num_cells_fault_length
if (int(num_cells_below + num_cells_fault_length + num_cells_above) % 2 == 1):
num_cells_above += 1
clawdata.lower[1] = fault_ycenter-0.5*fault_length-num_cells_below*dy
clawdata.upper[1] = fault_ycenter+0.5*fault_length+num_cells_above*dy
clawdata.num_cells[1] = int(num_cells_below + num_cells_fault_length + num_cells_above)
num_cells_above = np.rint(fault_depth/min(dx,dy)) # z direction
dz = fault_depth/num_cells_above
target_num_cells = np.rint(probdata.domain_depth/dz)
num_cells_below = target_num_cells - num_cells_above
if (int(num_cells_below + num_cells_above) % 2 == 1):
num_cells_below += 1
clawdata.lower[2] = -fault_depth - num_cells_below*dz
clawdata.upper[2] = 0.0
clawdata.num_cells[2] = int(num_cells_below + num_cells_above)
# adjust probdata
probdata.domain_width = clawdata.upper[0] - clawdata.lower[0]
probdata.domain_length = clawdata.upper[1] - clawdata.lower[1]
probdata.domain_depth = clawdata.upper[2] - clawdata.lower[2]
# ---------------
# Size of system:
# ---------------
# Number of equations in the system:
clawdata.num_eqn = 10
# Number of auxiliary variables in the aux array (initialized in setaux)
clawdata.num_aux = 2
# Index of aux array corresponding to capacity function, if there is one:
clawdata.capa_index = 2
# -------------
# Initial time:
# -------------
# should be set to final time in reload file if reloading
clawdata.t0 = 0.000000
# Restart from checkpoint file of a previous run?
# Note: If restarting, you must also change the Makefile to set:
# RESTART = True
# If restarting, t0 above should be from original run, and the
# restart_file 'fort.chkNNNNN' specified below should be in
# the OUTDIR indicated in Makefile.
clawdata.restart = False # True to restart from prior results
clawdata.restart_file = 'fort.chk00006' # File to use for restart data
# -------------
# Output times:
#--------------
# Specify at what times the results should be written to fort.q files.
# Note that the time integration stops after the final output time.
clawdata.output_style = 2
if clawdata.output_style==1:
# Output ntimes frames at equally spaced times up to tfinal:
# Can specify num_output_times = 0 for no output
clawdata.num_output_times = 50
clawdata.tfinal = 50.0 #6e-5 #0.0001800000 #0.00003
clawdata.output_t0 = True # output at initial (or restart) time?
elif clawdata.output_style == 2:
# Specify a list or numpy array of output times:
# Include t0 if you want output at the initial time.
clawdata.output_times = [0.0, 4.0, 10.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0]
elif clawdata.output_style == 3:
# Output every step_interval timesteps over total_steps timesteps:
clawdata.output_step_interval = 1
clawdata.total_steps = 1
clawdata.output_t0 = True # output at initial (or restart) time?
clawdata.output_format = 'ascii' # 'ascii' or 'binary'
clawdata.output_q_components = 'all' # could be list such as [True,True]
clawdata.output_aux_components = 'none' # could be list
clawdata.output_aux_onlyonce = True # output aux arrays only at t0
# ---------------------------------------------------
# Verbosity of messages to screen during integration:
# ---------------------------------------------------
# The current t, dt, and cfl will be printed every time step
# at AMR levels <= verbosity. Set verbosity = 0 for no printing.
clawdata.verbosity = 1
# --------------
# Time stepping:
# --------------
# if dt_variable==True: variable time steps used based on cfl_desired,
# if dt_variable==False: fixed time steps dt = dt_initial always used.
clawdata.dt_variable = True
# Initial time step for variable dt.
# (If dt_variable==0 then dt=dt_initial for all steps)
clawdata.dt_initial = 0.25
# Max time step to be allowed if variable dt used:
clawdata.dt_max = 1.000000e+99
# Desired Courant number if variable dt used
clawdata.cfl_desired = 0.900000
# max Courant number to allow without retaking step with a smaller dt:
clawdata.cfl_max = 1.000000
# Maximum number of time steps to allow between output times:
clawdata.steps_max = 1000
# ------------------
# Method to be used:
# ------------------
# Order of accuracy: 1 => Godunov, 2 => Lax-Wendroff plus limiters
clawdata.order = 2
# Use dimensional splitting?
clawdata.dimensional_split = False
# For unsplit method, transverse_waves can be
# 0 or 'none' ==> donor cell (only normal solver used)
# 1 or 'increment' ==> corner transport of waves
# 2 or 'all' ==> corner transport of 2nd order corrections too
clawdata.transverse_waves = 22
# Number of waves in the Riemann solution:
clawdata.num_waves = 6
# List of limiters to use for each wave family:
# Required: len(limiter) == num_waves
# Some options:
# 0 or 'none' ==> no limiter (Lax-Wendroff)
# 1 or 'minmod' ==> minmod
# 2 or 'superbee' ==> superbee
# 3 or 'vanleer' ==> van Leer
# 4 or 'mc' ==> MC limiter
clawdata.limiter = ['mc', 'mc', 'mc', 'mc', 'mc','mc']
clawdata.use_fwaves = False # True ==> use f-wave version of algorithms
# Source terms splitting:
# src_split == 0 or 'none' ==> no source term (src routine never called)
# src_split == 1 or 'godunov' ==> Godunov (1st order) splitting used,
# src_split == 2 or 'strang' ==> Strang (2nd order) splitting used, not recommended.
clawdata.source_split = 'godunov'
# --------------------
# Boundary conditions:
# --------------------
# Number of ghost cells (usually 2)
clawdata.num_ghost = 2
# Choice of BCs at xlower and xupper:
# 0 or 'user' => user specified (must modify bcNamr.f to use this option)
# 1 or 'extrap' => extrapolation (non-reflecting outflow)
# 2 or 'periodic' => periodic (must specify this at both boundaries)
# 3 or 'wall' => solid wall for systems where q(2) is normal velocity
clawdata.bc_lower[0] = 'extrap' # at xlower
clawdata.bc_upper[0] = 'extrap' # at xupper
clawdata.bc_lower[1] = 'extrap' # at ylower
clawdata.bc_upper[1] = 'extrap' # at yupper
clawdata.bc_lower[2] = 'extrap' # at zlower
clawdata.bc_upper[2] = 'user' # at zupper
# --------------
# Checkpointing:
# --------------
# Specify when checkpoint files should be created that can be
# used to restart a computation.
clawdata.checkpt_style = 2
if clawdata.checkpt_style == 0:
# Do not checkpoint at all
pass
elif clawdata.checkpt_style == 1:
# Checkpoint only at tfinal.
pass
elif clawdata.checkpt_style == 2:
# Specify a list of checkpoint times.
clawdata.checkpt_times = [1.0]
elif clawdata.checkpt_style == 3:
# Checkpoint every checkpt_interval timesteps (on Level 1)
# and at the final time.
clawdata.checkpt_interval = 5
# ---------------
# Gauges:
# ---------------
gauges = rundata.gaugedata.gauges
# gauges for surface displacement
#xgauges = np.linspace(-150e3,200e3,350)
#ygauges = np.linspace(-87.5e3,87.5e3,350)
#gcount = 0
#for x in xgauges:
# for y in ygauges:
# gauges.append([gcount, x, y, clawdata.upper[2]-1, 0.0, 1e9])
# gcount = gcount + 1
# ---------------
# AMR parameters:
# ---------------
amrdata = rundata.amrdata
# max number of refinement levels:
amrdata.amr_levels_max = 4
# List of refinement ratios at each level (length at least amr_level_max-1)
amrdata.refinement_ratios_x = [2,2,2]
amrdata.refinement_ratios_y = [2,2,2]
amrdata.refinement_ratios_z = [2,2,2]
amrdata.refinement_ratios_t = [2,2,2]
# Specify type of each aux variable in amrdata.auxtype.
# This must be a list of length num_aux, each element of which is one
# of:
# 'center', 'capacity', 'xleft', or 'yleft' (see documentation).
amrdata.aux_type = ['zleft','capacity']
# Flag for refinement based on Richardson error estimater:
amrdata.flag_richardson = False # use Richardson?
amrdata.flag_richardson_tol = 1.000000e+00 # Richardson to
# Flag for refinement using routine flag2refine:
amrdata.flag2refine = True # use this?
amrdata.flag2refine_tol = 1.0e-4 # tolerance used in this routine
# Flags regions to be refined where tol is roughly the smallest amplitude of normal velocity to capture
# steps to take on each level L between regriddings of level L+1:
amrdata.regrid_interval = 4
# width of buffer zone around flagged points:
# (typically the same as regrid_interval so waves don't escape):
amrdata.regrid_buffer_width = 4
# clustering alg. cutoff for (# flagged pts) / (total # of cells
# refined)
# (closer to 1.0 => more small grids may be needed to cover flagged
# cells)
amrdata.clustering_cutoff = 0.700000
# print info about each regridding up to this level:
amrdata.verbosity_regrid = 4
# -------------------
# Refinement Regions:
# -------------------
regions = rundata.regiondata.regions
# to specify regions of refinement append lines of the form
# [minlevel,maxlevel,t1,t2,x1,x2,y1,y2,z1,z2]
zbuffer = dz
xcb = [fault_xcenter-0.5*fault_width,fault_xcenter+0.5*fault_width]
ycb = [fault_ycenter-0.5*fault_length,fault_ycenter+0.5*fault_length]
# high-resolution region to surround the fault during slip
regions.append([amrdata.amr_levels_max,amrdata.amr_levels_max,
0,1,
xcb[0],xcb[1],
ycb[0],ycb[1],
-fault_depth-dz, -fault_depth+dz])
# decreasing-resolution for cells further away from fault
for j in range(amrdata.amr_levels_max-1):
regions.append([1,amrdata.amr_levels_max-j,
0,1e9,
-1e9,1e9,
-1e9,1e9,
-2.0*fault_depth-j*zbuffer,0.0])
regions.append([1,1, 0,1e9, -1.e9,1e9, -1e9,1e9,-1e9, 0.])
# ----- For developers -----
# Toggle debugging print statements:
amrdata.dprint = False # print domain flags
amrdata.eprint = False # print err est flags
amrdata.edebug = False # even more err est flags
amrdata.gprint = False # grid bisection/clustering
amrdata.nprint = False # proper nesting output
amrdata.pprint = False # proj. of tagged points
amrdata.rprint = False # print regridding summary
amrdata.sprint = False # space/memory output
amrdata.tprint = False # time step reporting each level
amrdata.uprint = False # update/upbnd reporting
# --------------
# Output Slices:
# --------------
slicedata = SliceData()
# use slicedata.add() to add output slices defined
# by a point (x,y,z) and a normal direction (vx,vy,vz)
# e.g. slicedata.add([x,y,z],[nx,ny,nz])
point = [fault_xcenter,fault_ycenter,-0.0001]
# surface slice:
slicedata.add(point,[0.0,0.0,1.0])
# cross section slices:
slicedata.add(point,[1.0,0.0,0.0])
slicedata.add(point,[0.0,1.0,0.0])
slicedata.write()
return rundata
def setplot(plotdata):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of clawpack.visclaw.data.ClawPlotData.
Output: a modified version of plotdata.
"""
fault = dtopotools.Fault()
fault.read(plotdata.outdir + '/fault.data')
mapping = Mapping(fault)
fault_width = mapping.fault_width * length_scale
ycenter = mapping.ycenter
xp1 = mapping.xp1 * length_scale
xp2 = mapping.xp2 * length_scale
yp1 = mapping.yp1 * length_scale
yp2 = mapping.yp2 * length_scale
from clawpack.visclaw import colormaps
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'binary'
def mapc2p(xc, yc):
xp, yp = mapping.mapc2p(xc, yc)
return xp * length_scale, yp * length_scale
def plot_fault(current_data):
from pylab import linspace, plot, xlabel, ylabel, tick_params, annotate
xl = linspace(xp1, xp2, 100)
yl = linspace(yp1, yp2, 100)
plot(xl, yl, 'k', linewidth=3)
if (current_data.t == 0.0):
arrowprops = {'arrowstyle': '->', 'linewidth': 3, 'color': 'g'}
annotate('', (xp1, yp1 + 10), (xp2, yp2 + 10),
arrowprops=arrowprops)
annotate('', (xp2, yp2 - 10), (xp1, yp1 - 10),
arrowprops=arrowprops)
tick_params(labelsize=25)
xlabel('kilometers', fontsize=25)
ylabel('kilometers', fontsize=25)
def sigmatr(current_data):
# return -trace(sigma)
q = current_data.q
return -(q[0, :, :] + q[1, :, :])
def slip_direction_vel(current_data):
# return vel dot tau, where tau is tangent to fault
tau_x = (xp2 - xp1) / fault_width
tau_y = (yp2 - yp1) / fault_width
tau_x = np.where(current_data.y > ycenter, -tau_x, tau_x)
tau_y = np.where(current_data.y > ycenter, -tau_y, tau_y)
u = current_data.q[3, :, :]
v = current_data.q[4, :, :]
return u * tau_x + v * tau_y
# Figure for results
plotfigure = plotdata.new_plotfigure(name='results', figno=1)
plotfigure.kwargs = {'figsize': (11, 8)}
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = xlimits
plotaxes.ylimits = ylimits
plotaxes.title = ''
plotaxes.title_with_t = False
plotaxes.scaled = True
plotaxes.afteraxes = plot_fault
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = sigmatr
plotitem.pcolor_cmap = colormaps.blue_white_red
plotitem.pcolor_cmin = -1e6
plotitem.pcolor_cmax = 1e6
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
plotitem.MappedGrid = True
plotitem.mapc2p = mapc2p
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = slip_direction_vel
plotitem.contour_levels = np.linspace(0.06, 0.2, 3)
plotitem.amr_contour_show = [0, 0, 1, 0]
plotitem.kwargs = {'linewidths': 3}
plotitem.amr_celledges_show = [0]
plotitem.amr_patchedges_show = [0]
plotitem.MappedGrid = True
plotitem.mapc2p = mapc2p
# Parameters used only when creating html and/or latex hardcopy
# e.g., via clawpack.visclaw.frametools.printframes:
plotdata.printfigs = True # print figures
plotdata.print_format = 'png' # file format
plotdata.print_framenos = 'all' # list of frames to print
plotdata.print_fignos = 'all' # list of figures to print
plotdata.html = True # create html files of plots?
plotdata.html_homelink = '../README.html' # pointer for top of index
plotdata.latex = True # create latex file of plots?
plotdata.latex_figsperline = 2 # layout of plots
plotdata.latex_framesperline = 1 # layout of plots
plotdata.latex_makepdf = False # also run pdflatex?
# plotdata.parallel = True
return plotdata
def setrun(claw_pkg='amrclaw'):
#------------------------------
"""
Define the parameters used for running Clawpack.
INPUT:
claw_pkg expected to be "amrclaw" for this setrun.
OUTPUT:
rundata - object of class ClawRunData
"""
from clawpack.clawutil import data
assert claw_pkg.lower() == 'amrclaw', "Expected claw_pkg = 'amrclaw'"
num_dim = 2
rundata = data.ClawRunData(claw_pkg, num_dim)
#------------------------------------------------------------------
# Problem-specific parameters to be written to setprob.data:
#------------------------------------------------------------------
probdata = rundata.new_UserData(name='probdata', fname='setprob.data')
probdata.add_param('domain_depth', 300e3, 'depth of domain')
probdata.add_param('domain_width', 600e3, 'width of domain')
#------------------------------------------------------------------
# Read in fault information
#------------------------------------------------------------------
fault = dtopotools.Fault()
fault.read('fault.data')
mapping = Mapping(fault)
fault_width = mapping.fault_width
fault_depth = mapping.fault_depth
fault_center = mapping.xcenter
rupture_rise_time = 0.0
for subfault in fault.subfaults:
rupture_rise_time = max(rupture_rise_time,
subfault.rupture_time + subfault.rise_time)
#------------------------------------------------------------------
# Standard Clawpack parameters to be written to claw.data:
#------------------------------------------------------------------
clawdata = rundata.clawdata # initialized when rundata instantiated
# ---------------
# Spatial domain:
# ---------------
# Number of space dimensions:
clawdata.num_dim = num_dim
# Number of grid cells:
num_cells_fault = 10
dx = fault_width / num_cells_fault
# determine cell number and set computational boundaries
target_num_cells = np.rint(probdata.domain_width / dx) # x direction
num_cells_below = np.rint((target_num_cells - num_cells_fault) / 2.0)
num_cells_above = target_num_cells - num_cells_below - num_cells_fault
clawdata.lower[0] = fault_center - 0.5 * fault_width - num_cells_below * dx
clawdata.upper[0] = fault_center + 0.5 * fault_width + num_cells_above * dx
clawdata.num_cells[0] = int(num_cells_below + num_cells_fault +
num_cells_above)
num_cells_above = np.rint(fault_depth / dx) # y direction
dy = fault_depth / num_cells_above
target_num_cells = np.rint(probdata.domain_depth / dy)
num_cells_below = target_num_cells - num_cells_above
clawdata.lower[1] = -fault_depth - num_cells_below * dy
clawdata.upper[1] = 0.0
clawdata.num_cells[1] = int(num_cells_below + num_cells_above)
# adjust probdata
probdata.domain_width = clawdata.upper[0] - clawdata.lower[0]
probdata.domain_depth = clawdata.upper[1] - clawdata.lower[1]
# ---------------
# Size of system:
# ---------------
# Number of equations in the system:
clawdata.num_eqn = 5
# Number of auxiliary variables in the aux array (initialized in setaux)
clawdata.num_aux = 13
# Index of aux array corresponding to capacity function, if there is one:
clawdata.capa_index = 12
# -------------
# Initial time:
# -------------
clawdata.t0 = 0.000000
# Restart from checkpoint file of a previous run?
# Note: If restarting, you must also change the Makefile to set:
# RESTART = True
# If restarting, t0 above should be from original run, and the
# restart_file 'fort.qNNNN' specified below should be in
# the OUTDIR indicated in Makefile.
clawdata.restart = False # True to restart from prior results
clawdata.restart_file = 'fort.q0006' # File to use for restart data
# -------------
# Output times:
#--------------
# Specify at what times the results should be written to fort.q files.
# Note that the time integration stops after the final output time.
clawdata.output_style = 1
if clawdata.output_style == 1:
# Output ntimes frames at equally spaced times up to tfinal:
# Can specify num_output_times = 0 for no output
clawdata.num_output_times = 50
clawdata.tfinal = 100.0
clawdata.output_t0 = True # output at initial (or restart) time?
elif clawdata.output_style == 2:
# Specify a list or numpy array of output times:
# Include t0 if you want output at the initial time.
clawdata.output_times = list(np.linspace(0,5,11)) + \
range(6,61)
elif clawdata.output_style == 3:
# Output every step_interval timesteps over total_steps timesteps:
clawdata.output_step_interval = 1
clawdata.total_steps = 40
clawdata.output_t0 = True # output at initial (or restart) time?
clawdata.output_format = 'binary' # 'ascii', 'binary', 'netcdf'
clawdata.output_q_components = 'all' # could be list such as [True,True]
clawdata.output_aux_components = 'none' # could be list
clawdata.output_aux_onlyonce = True # output aux arrays only at t0
# ---------------------------------------------------
# Verbosity of messages to screen during integration:
# ---------------------------------------------------
# The current t, dt, and cfl will be printed every time step
# at AMR levels <= verbosity. Set verbosity = 0 for no printing.
# (E.g. verbosity == 2 means print only on levels 1 and 2.)
clawdata.verbosity = 0
# --------------
# Time stepping:
# --------------
# if dt_variable==True: variable time steps used based on cfl_desired,
# if dt_variable==False: fixed time steps dt = dt_initial always used.
clawdata.dt_variable = True
# Initial time step for variable dt.
# (If dt_variable==0 then dt=dt_initial for all steps)
clawdata.dt_initial = 0.25
# Max time step to be allowed if variable dt used:
clawdata.dt_max = 1.000000e+99
# Desired Courant number if variable dt used
clawdata.cfl_desired = 0.900000
# max Courant number to allow without retaking step with a smaller dt:
clawdata.cfl_max = 1.000000
# Maximum number of time steps to allow between output times:
clawdata.steps_max = 1000
# ------------------
# Method to be used:
# ------------------
# Order of accuracy: 1 => Godunov, 2 => Lax-Wendroff plus limiters
clawdata.order = 2
# Use dimensional splitting? (not yet available for AMR)
clawdata.dimensional_split = 'unsplit'
# For unsplit method, transverse_waves can be
# 0 or 'none' ==> donor cell (only normal solver used)
# 1 or 'increment' ==> corner transport of waves
# 2 or 'all' ==> corner transport of 2nd order corrections too
clawdata.transverse_waves = 2
# Number of waves in the Riemann solution:
clawdata.num_waves = 4
# List of limiters to use for each wave family:
# Required: len(limiter) == num_waves
# Some options:
# 0 or 'none' ==> no limiter (Lax-Wendroff)
# 1 or 'minmod' ==> minmod
# 2 or 'superbee' ==> superbee
# 3 or 'vanleer' ==> van Leer
# 4 or 'mc' ==> MC limiter
clawdata.limiter = ['mc', 'mc', 'mc', 'mc']
clawdata.use_fwaves = False # True ==> use f-wave version of algorithms
# Source terms splitting:
# src_split == 0 or 'none' ==> no source term (src routine never called)
# src_split == 1 or 'godunov' ==> Godunov (1st order) splitting used,
# src_split == 2 or 'strang' ==> Strang (2nd order) splitting used, not recommended.
clawdata.source_split = 0
# --------------------
# Boundary conditions:
# --------------------
# Number of ghost cells (usually 2)
clawdata.num_ghost = 2
# Choice of BCs at xlower and xupper:
# 0 or 'user' => user specified (must modify bcNamr.f to use this option)
# 1 or 'extrap' => extrapolation (non-reflecting outflow)
# 2 or 'periodic' => periodic (must specify this at both boundaries)
# 3 or 'wall' => solid wall for systems where q(2) is normal velocity
clawdata.bc_lower[0] = 'extrap' # at xlower
clawdata.bc_upper[0] = 'extrap' # at xupper
clawdata.bc_lower[1] = 'extrap' # at ylower
clawdata.bc_upper[1] = 'user' # at yupper
# ---------------
# Gauges:
# ---------------
gauges = rundata.gaugedata.gauges
# for gauges append lines of the form [gaugeno, x, y, t1, t2]
# top edge:
xgauges = np.linspace(clawdata.lower[0] + 1, clawdata.upper[0] - 1,
np.rint(probdata.domain_width / 1e3))
for gaugeno, x in enumerate(xgauges):
gauges.append([gaugeno, x, clawdata.upper[1] - 1, 0, 1e10])
## bottom edge:
#for gaugeno,x in enumerate(xgauges):
# gauges.append([200+gaugeno,x,clawdata.lower[1]+1000,0,1e10])
## above fault plane:
#xgauges = np.linspace(probdata.fault_center-0.5*probdata.fault_width,
# probdata.fault_center+0.5*probdata.fault_width)
#for gaugeno,x in enumerate(xgauges):
# gauges.append([300+gaugeno,x,-probdata.fault_depth+1,0,1e10])
# below fault plane:
#for gaugeno,x in enumerate(xgauges):
# gauges.append([400+gaugeno,x,-probdata.fault_depth-1,0,1e10])
# --------------
# Checkpointing:
# --------------
# Specify when checkpoint files should be created that can be
# used to restart a computation.
clawdata.checkpt_style = 2
if clawdata.checkpt_style == 0:
# Do not checkpoint at all
pass
elif clawdata.checkpt_style == 1:
# Checkpoint only at tfinal.
pass
elif clawdata.checkpt_style == 2:
# Specify a list of checkpoint times.
clawdata.checkpt_times = [rupture_rise_time]
elif clawdata.checkpt_style == 3:
# Checkpoint every checkpt_interval timesteps (on Level 1)
# and at the final time.
clawdata.checkpt_interval = 5
# ---------------
# AMR parameters:
# ---------------
amrdata = rundata.amrdata
# max number of refinement levels:
amrdata.amr_levels_max = 6
# List of refinement ratios at each level (length at least
# amr_level_max-1)
amrdata.refinement_ratios_x = [2, 2, 2, 2, 2]
amrdata.refinement_ratios_y = [2, 2, 2, 2, 2]
amrdata.refinement_ratios_t = [2, 2, 2, 2, 2]
# Specify type of each aux variable in amrdata.auxtype.
# This must be a list of length num_aux, each element of which is one
# of:
# 'center', 'capacity', 'xleft', or 'yleft' (see documentation).
amrdata.aux_type = ['center', 'center', 'center', 'center', 'center', \
'center', 'center', 'center', 'center', 'center', 'center', \
'capacity','yleft']
# Flag for refinement based on Richardson error estimater:
amrdata.flag_richardson = False # use Richardson?
amrdata.flag_richardson_tol = 1. # Richardson tolerance
# Flag for refinement using routine flag2refine:
amrdata.flag2refine = True # use this?
amrdata.flag2refine_tol = 1.0e-4 # tolerance used in this routine
# User can modify flag2refine to change the criterion for flagging.
# Default: check maximum absolute difference of first component of q
# between a cell and each of its neighbors.
# steps to take on each level L between regriddings of level L+1:
amrdata.regrid_interval = 2
# width of buffer zone around flagged points:
# (typically the same as regrid_interval so waves don't escape):
amrdata.regrid_buffer_width = 2
# clustering alg. cutoff for (# flagged pts) / (total # of cells
# refined)
# (closer to 1.0 => more small grids may be needed to cover flagged
# cells)
amrdata.clustering_cutoff = 0.7
# print info about each regridding up to this level:
amrdata.verbosity_regrid = 0
# ---------------
# Regions:
# ---------------
regions = rundata.regiondata.regions
# to specify regions of refinement append lines of the form
# [minlevel,maxlevel,t1,t2,x1,x2,y1,y2]
ybuffer = dy
# high-resolution region to surround the fault during slip
regions.append([
amrdata.amr_levels_max, amrdata.amr_levels_max, 0, rupture_rise_time,
fault_center - 0.5 * fault_width, fault_center + 0.5 * fault_width,
-fault_depth - dx, -fault_depth + dx
])
for j in range(amrdata.amr_levels_max - 1):
regions.append([
1, amrdata.amr_levels_max - j, 0, 1e9, -1e9, 1e9,
-2.0 * fault_depth - j * ybuffer, 0.0
])
regions.append([1, 1, 0, 1e9, -1.e9, 1.e9, -1.e9, 0.0])
# ----- For developers -----
# Toggle debugging print statements:
amrdata.dprint = False # print domain flags
amrdata.eprint = False # print err est flags
amrdata.edebug = False # even more err est flags
amrdata.gprint = False # grid bisection/clustering
amrdata.nprint = False # proper nesting output
amrdata.pprint = False # proj. of tagged points
amrdata.rprint = False # print regridding summary
amrdata.sprint = False # space/memory output
amrdata.tprint = False # time step reporting each level
amrdata.uprint = False # update/upbnd reporting
return rundata