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.
"""
from clawpack.visclaw import colormaps, geoplot
from numpy import linspace
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'binary'
#plotdata.format = 'ascii'
# Load data from output
clawdata = clawutil.ClawInputData(2)
clawdata.read(os.path.join(plotdata.outdir, 'claw.data'))
ocean_xlimits = [clawdata.lower[0], clawdata.upper[0]]
ocean_ylimits = [clawdata.lower[1], clawdata.upper[1]]
amrdata = amrclaw.AmrclawInputData(clawdata)
amrdata.read(os.path.join(plotdata.outdir, 'amr.data'))
physics = geodata.GeoClawData()
physics.read(os.path.join(plotdata.outdir, 'geoclaw.data'))
surge_data = geodata.SurgeData()
surge_data.read(os.path.join(plotdata.outdir, 'surge.data'))
probdata = clawutil.ClawData()
#probdata.read('setprob.data', force=True)
#theta_island = probdata.theta_island
# To plot gauge locations on pcolor or contour plot, use this as
# an afteraxis function:
def addgauges(current_data):
from clawpack.visclaw import gaugetools
gaugetools.plot_gauge_locations(current_data.plotdata, \
gaugenos='all', format_string='ko', add_labels=True)
def mynewafteraxes(current_data):
addgauges(current_data)
bigfont(current_data)
#-----------------------------------------
# Some global KML settings
#-----------------------------------------
#plotdata.kml_publish = 'http://math.boisestate.edu/~calhoun/visclaw/GoogleEarth/kmz/' #public html
plotdata.kml_publish = None
plotdata.kml_name = "NYC Asteroid" # name appears in Google Earth display only
plotdata.kml_index_fname = "NYC_Asteroid" # name for .kmz and .kml files ["_GoogleEarth"]
# specify beginning of slider time. if not used, assumes Jan. 1 1970
plotdata.kml_starttime = [2115, 4, 29, 7, 32,
0] # Asteroid hits at 1:32AM, 4/29/2115 (UTC)
plotdata.kml_tz_offset = 6 # off set to UTC
# for todays date as the default use
kml_cmin = -0.005 #colorbar min and max
kml_cmax = 0.005
kml_dpi = 400 # only used if individual figures dpi not set
kml_cmap = geoplot.googleearth_lightblue
# kml_cmap = geoplot.googleearth_darkblue
# kml_cmap = geoplot.googleearth_transparent
# kml_cmap = geoplot.googleearth_white
def kml_colorbar(filename):
#cmin = -0.01
#cmax = 0.01
geoplot.kml_build_colorbar(filename, kml_cmap, kml_cmin, kml_cmax)
#-----------------------------------------
# Figure for pcolor plot for surface
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='pcolor', figno=0)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Surface'
plotaxes.scaled = True
#plotaxes.afteraxes = addgauges
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
#plotitem.plot_var = 0/1/2 or plot that entry into q instead of a function
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -.8
plotitem.pcolor_cmax = .8
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.amr_patchedges_show = [0, 0, 0]
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.land
plotitem.show = True
# plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
plotitem.pcolor_cmap = geoplot.land_colors
#plotitem.pcolor_cmap = geoplot.googleearth_transparent
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0, 0, 0]
#plotitem.patchedges_show = 1
plotitem.amr_patchedges_show = [0, 0, 0]
#plotaxes.xlimits = [-85,-55]
#plotaxes.ylimits = [25,45]
plotaxes.xlimits = ocean_xlimits
plotaxes.ylimits = ocean_ylimits
#plotaxes.xlimits = [-75,-70]
#plotaxes.ylimits = [38,43]
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = True
plotitem.plot_var = geoplot.topo
#plotitem.contour_levels = [-500,-250,-100, -50, 0, 50]
plotitem.contour_levels = linspace(-1000, -400, 3)
plotitem.amr_contour_colors = ['g'] # color on each level
#plotitem.kwargs = {'linestyles':'dashed','linewidths':2,'colors' : 'red' }
plotitem.kwargs = {
'linestyles': 'dashed',
'linewidths': 1,
'colors': 'magenta'
}
plotitem.amr_contour_show = [1, 1, 1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------------------------
# Figure for KML files - Sea Surface Height
#----------------------------------------------------------
#plotfigure = plotdata.new_plotfigure(name='Sea Surface',figno=1)
#plotfigure.show = True
#plotfigure.use_for_kml = True
#plotfigure.kml_use_for_initial_view = True
# These overide any values set in the plotitems below
#plotfigure.kml_xlimits = [-80,-55]
#plotfigure.kml_ylimits = [25, 45]
# Resolution
#plotfigure.kml_dpi = 300
#plotfigure.kml_tile_images = False
def kml_colorbar_transparent(filename):
#cmin = -0.01
#cmax = 0.01
geoplot.kml_build_colorbar(filename, geoplot.googleearth_transparent,
kml_cmin, kml_cmax)
#plotfigure.kml_colorbar = kml_colorbar_transparent
# Set up for axes in this figure:
#plotaxes = plotfigure.new_plotaxes('kml')
#plotaxes.scaled = True
# Water
#plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
#plotitem.show = True
#plotitem.plot_var = geoplot.surface_or_depth
#plotitem.pcolor_cmap = geoplot.googleearth_transparent
#plotitem.pcolor_cmin = kml_cmin
#plotitem.pcolor_cmax = kml_cmax
#plotitem.amr_celledges_show = [0,0,0]
#plotitem.patchedges_show = 0
#plotfigure.kml_colorbar = kml_colorbar
#-----------------------------------------
# Figure for speeds
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Speeds', figno=10)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Speed'
plotaxes.scaled = True
#def fixup(current_data):
# import pylab
# addgauges(current_data)
# t = current_data.t
# t = t / 3600. # hours
# pylab.title('Speed at %4.2f hours' % t, fontsize=20)
# pylab.xticks(fontsize=15)
# pylab.yticks(fontsize=15)
#plotaxes.afteraxes = fixup
def speed(current_data):
from pylab import where, sqrt
q = current_data.q
h = q[0, :]
dry_tol = 0.001
u = q[1, :] # this is just to initialize
v = q[2, :] # to correct size
s = 0 * q[2, :] # to correct size
nq = len(q[1, :])
[n, m] = h.shape
for ii in range(0, n):
for jj in range(0, m):
if h[ii, jj] > dry_tol:
u[ii, jj] = q[1, ii, jj] / h[ii, jj]
v[ii, jj] = q[2, ii, jj] / h[ii, jj]
s[ii,
jj] = sqrt(u[ii, jj] * u[ii, jj] + v[ii, jj] * v[ii, jj])
else:
u[ii, jj] = 0.
v[ii, jj] = 0.
s[ii, jj] = 0
#print("max of u = " + str(max(u)))
#u = where(h>dry_tol, q[1,:]/h, 0.)
#v = where(h>dry_tol, q[2,:]/h, 0.)
#s = sqrt(u**2 + v**2)
#s = sqrt(u*2+v*v) #try not dividing or using where
return s
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = False
plotitem.plot_var = speed
plotitem.pcolor_cmap = geoplot.tsunami_colormap
#plotitem.pcolor_cmap = \
# colormaps.make_colormap({0:[1,1,1],0.5:[0.5,0.5,1],1:[1,0.3,0.3]})
plotitem.pcolor_cmin = 0.
plotitem.pcolor_cmax = .10
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0, 0, 0]
#plotitem.patchedges_show = 1
plotitem.amr_patchedges_show = [0, 0, 0]
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.land
plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = ocean_xlimits
plotaxes.ylimits = ocean_ylimits
#-----------------------------------------
# Figure for zoom around NYC
#-----------------------------------------
zoomWanted = True
if zoomWanted:
plotfigure = plotdata.new_plotfigure(name='Zoom1', figno=7)
# add another figure
#plotfigure.use_for_kml = True
#plotfigure.kml_use_for_initial_view = False
# These overide any values set in the plotitems below
#plotfigure.kml_xlimits = [-74.5, -73.5]
#plotfigure.kml_ylimits = [40.4,40.9]
# Resolution
#plotfigure.kml_dpi = 300
#plotfigure.kml_tile_images = True
#plotfigure.kml_colorbar = kml_colorbar # defined above
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('zoom on nyc')
plotaxes.title = 'Surface elevation'
plotaxes.scaled = True
manhattan_island = -73.5
xisland, yisland = latlong(1600e3, manhattan_island, 40., Rearth)
#plotaxes.xlimits = [xisland-0.6, xisland+0.6]
#plotaxes.xlimits = [manhattan_island-1, manhattan_island+1]
#plotaxes.ylimits = [40.15,41.5]
plotaxes.xlimits = [-74.5, -72.5] # really zoom in on lower manhattan]
plotaxes.ylimits = [40.25, 41.5]
plotaxes.afteraxes = addgauges
def bigfont(current_data):
import pylab
t = current_data.t
pylab.title("Surface at t = %8.1f" % t, fontsize=20)
pylab.xticks(fontsize=15)
pylab.yticks(fontsize=15)
#plotaxes.afteraxes = bigfont
#plotaxes.afteraxes = mynewafteraxes # after axes functions mess with GE plots
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = False
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
#plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmap = kml_cmap
#plotitem.pcolor_cmin = kml_cmin # same as above
#plotitem.pcolor_cmax = kml_cmax
plotitem.pcolor_cmin = -.2
plotitem.pcolor_cmax = .2
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = False
plotitem.plot_var = geoplot.land
# plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0, 0, 0]
plotitem.patchedges_show = 0
# contour lines:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.surface
plotitem.contour_levels = [-0.8, -0.4, 0.4, 0.8]
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linewidths': 1}
plotitem.amr_contour_show = [0, 0, 0, 1, 1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = linspace(-30, -10, 3)
plotitem.amr_contour_colors = ['m'] # color on each level
plotitem.kwargs = {'linestyles': 'dashed', 'linewidths': 1}
plotitem.amr_contour_show = [1, 1, 1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------
# Figures for gauges
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Surface & topo', figno=300, \
type='each_gauge')
plotfigure.clf_each_gauge = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
#plotaxes.xlimits = [0000, 6500]
#plotaxes.ylimits = [-.10, .15]
plotaxes.xlimits = 'auto'
plotaxes.ylimits = 'auto'
plotaxes.title = 'Surface'
# Plot surface as blue curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = 3
plotitem.plotstyle = 'b-'
# Plot topo as green curve:
#plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
def gaugetopo(current_data):
q = current_data.q
h = q[0, :]
eta = q[3, :]
topo = eta - h
return topo
def gaugedpress(current_data):
q = current_data.q
#dpress = (q[4,:] - 101300)/101300
dpress = q[4, :] # already output as relative: dp/amb_pr
return dpress
def gs(current_data):
q = current_data.q
# different than speed function because q is function of time, not
# x,y at the gauges.
from numpy import where, sqrt
h = q[0, :]
#print('shape of h ' + str(h.shape))
dry_tol = 0.001
u = where(h > dry_tol, q[1, :] / h, 0.)
v = where(h > dry_tol, q[2, :] / h, 0.)
ssq = sqrt(u * u + v * v)
#s = sqrt(u**2 + v**2)
s = sqrt(ssq)
return ssq
#plotitem.plot_var = gaugetopo
#plotitem.plotstyle = 'g-'
# Plot relative delta pressure as red curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = gaugedpress
plotitem.plotstyle = 'r-'
# add speed to this plot since cant get new one going
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = gs
plotitem.plotstyle = 'g-'
def add_zeroline(current_data):
from pylab import plot, legend
t = current_data.t
# legend(('surface','topography','dp'),loc='lower left')
legend(('surface', 'dp', 'speed'), loc='upper right')
#plot(t, 0*t, 'k')
plotaxes.afteraxes = add_zeroline
# -------------------------------
# Figure for speed at gauges
#--------------------------------
# plotfigure = plotdata.new_plotfigure(name='Speed at gauges', figno=310, \
# type='each_gauge')
#
#
# # Set up for axes in this figure:
# plotaxes = plotfigure.new_plotaxes()
# plotaxes.xlimits = 'auto'
# plotaxes.ylimits = 'auto'
# #plotaxes.xlimits = [0000, 7000]
# #plotaxes.ylimits = 'auto'
# plotaxes.title = 'Speed'
#
# # Plot surface as blue curve:
# plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
# #plotitem.plot_var = speed
# plotitem.plot_var = gs #gauge_speed
# plotitem.plotstyle = 'b-'
#
# plotfigure.show = True
#
#-----------------------------------------
# Figure for bathy alone
#-----------------------------------------
def bathy(current_data):
return current_data.aux[0, :, :]
plotfigure = plotdata.new_plotfigure(name='bathymetry', figno=3)
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Bathymetry'
plotaxes.scaled = True
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = bathy
plotitem.pcolor_cmin = -3000.00
plotitem.pcolor_cmax = 500
plotitem.add_colorbar = True
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = linspace(-2000, 2000, 21)
plotitem.amr_contour_colors = ['k'] # color on each level
#plotitem.contour_levels = [-1000 -500,-250,-100, -5, 5]
#-----------------------------------------
# Figure for grids alone
#-----------------------------------------
#plotfigure = plotdata.new_plotfigure(name='grids', figno=2)
#plotfigure.show = False
# Set up for axes in this figure:
#plotaxes = plotfigure.new_plotaxes()
#plotaxes.xlimits = [0,1]
#plotaxes.ylimits = [0,1]
#plotaxes.title = 'grids'
#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']
#plotitem.amr_celledges_show = [0,0,0]
#plotitem.amr_patchedges_show = [0,0,0]
def normalized_pressure(current_data):
pressure_index = 6 #7 in fortran, but python is 0 based
return current_data.aux[
pressure_index, :, :] / surge_data.ambient_pressure
# Pressure field
plotfigure = plotdata.new_plotfigure(name='Pressure', figno=33)
plotfigure.show = True
plotaxes = plotfigure.new_plotaxes('normalized_pressure')
plotaxes.xlimits = ocean_xlimits
plotaxes.ylimits = ocean_ylimits
plotaxes.title = 'Pressure Field'
plotaxes.scaled = True
plotaxes.afteraxes = addgauges
pressure_limits = [.99, 1.10]
#pressure_limits = [.999*surge_data.ambient_pressure / 100.0,
# 1.001 * surge_data.ambient_pressure / 100.0]
#pressure_limits = [-.000001*surge_data.ambient_pressure,
# .000001 * surge_data.ambient_pressure]
surgeplot.add_pressure(plotaxes, bounds=pressure_limits)
surgeplot.add_land(plotaxes)
#plotitem = plotaxes.new_plotitem(plot_type='2d_patch')
plotitem = plotaxes.plotitem_dict['pressure']
plotitem.show = False
plotitem.plot_var = normalized_pressure
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.amr_patchedges_show = [0, 0, 0]
plotaxes.plotitem_dict['land'].amr_celledges_show = [0, 0, 0]
plotaxes.plotitem_dict['land'].amr_patchedges_show = [0, 0, 0]
#-----------------------------------------
# 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_framenos = [30,50,70] # list of frames to print
#plotdata.print_framenos = [1] # list of frames to print
plotdata.print_gaugenos = 'all' # list of gauges to print
#plotdata.print_fignos = [0,7,10,33,300] # list of figures to print
#plotdata.print_fignos = [0,7,10,300] # list of figures to print
plotdata.print_fignos = [0, 300] # 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.kml = False
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.
"""
from clawpack.visclaw import colormaps, geoplot
from numpy import linspace
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'binary'
#plotdata.format = 'ascii'
# Load data from output
clawdata = clawutil.ClawInputData(2)
clawdata.read(os.path.join(plotdata.outdir,'claw.data'))
ocean_xlimits = [clawdata.lower[0],clawdata.upper[0]]
ocean_ylimits = [clawdata.lower[1],clawdata.upper[1]]
amrdata = amrclaw.AmrclawInputData(clawdata)
amrdata.read(os.path.join(plotdata.outdir,'amr.data'))
physics = geodata.GeoClawData()
physics.read(os.path.join(plotdata.outdir,'geoclaw.data'))
surge_data = geodata.SurgeData()
surge_data.read(os.path.join(plotdata.outdir,'surge.data'))
probdata = clawutil.ClawData()
#probdata.read('setprob.data', force=True)
#theta_island = probdata.theta_island
# To plot gauge locations on pcolor or contour plot, use this as
# an afteraxis function:
def addgauges(current_data):
from clawpack.visclaw import gaugetools
gaugetools.plot_gauge_locations(current_data.plotdata, \
gaugenos='all', format_string='ko', add_labels=True)
def mynewafteraxes(current_data):
addgauges(current_data)
bigfont(current_data)
#-----------------------------------------
# Some global KML settings
#-----------------------------------------
#plotdata.kml_publish = 'http://math.boisestate.edu/~calhoun/visclaw/GoogleEarth/kmz/' #public html
plotdata.kml_publish = None
plotdata.kml_name = "NYC Asteroid" # name appears in Google Earth display only
plotdata.kml_index_fname = "NYC_Asteroid" # name for .kmz and .kml files ["_GoogleEarth"]
# specify beginning of slider time. if not used, assumes Jan. 1 1970
plotdata.kml_starttime = [2115,4,29,7,32,0] # Asteroid hits at 1:32AM, 4/29/2115 (UTC)
plotdata.kml_tz_offset = 6 # off set to UTC
# for todays date as the default use
kml_cmin = -0.005 #colorbar min and max
kml_cmax = 0.005
kml_dpi = 400 # only used if individual figures dpi not set
kml_cmap = geoplot.googleearth_lightblue
# kml_cmap = geoplot.googleearth_darkblue
# kml_cmap = geoplot.googleearth_transparent
# kml_cmap = geoplot.googleearth_white
def kml_colorbar(filename):
#cmin = -0.01
#cmax = 0.01
geoplot.kml_build_colorbar(filename,
kml_cmap,
kml_cmin,kml_cmax)
#-----------------------------------------
# Figure for pcolor plot for surface
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='pcolor', figno=0)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Surface'
plotaxes.scaled = True
plotaxes.afteraxes = addgauges
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
#plotitem.plot_var = 0/1/2 or plot that entry into q instead of a function
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -.08
plotitem.pcolor_cmax = .08
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0,0,0]
plotitem.amr_patchedges_show = [0,0,0]
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.land
plotitem.show = True
# plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
plotitem.pcolor_cmap = geoplot.land_colors
#plotitem.pcolor_cmap = geoplot.googleearth_transparent
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0,0,0,0,0]
#plotitem.patchedges_show = 1
plotitem.amr_patchedges_show = [0,0,0]
#plotaxes.xlimits = [-85,-55]
#plotaxes.ylimits = [25,45]
plotaxes.xlimits = ocean_xlimits
plotaxes.ylimits = ocean_ylimits
#plotaxes.xlimits = [-75,-70]
#plotaxes.ylimits = [38,43]
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = True
plotitem.plot_var = geoplot.topo
#plotitem.contour_levels = [-500,-250,-100, -50, 0, 50]
plotitem.contour_levels = linspace(-1000,-200,4)
plotitem.amr_contour_colors = ['g'] # color on each level
#plotitem.kwargs = {'linestyles':'dashed','linewidths':2,'colors' : 'red' }
plotitem.kwargs = {'linestyles':'dashed','linewidths':1,'colors' : 'magenta' }
plotitem.amr_contour_show = [1,1,1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------------------------
# Figure for KML files - Sea Surface Height
#----------------------------------------------------------
#plotfigure = plotdata.new_plotfigure(name='Sea Surface',figno=1)
#plotfigure.show = True
#plotfigure.use_for_kml = True
#plotfigure.kml_use_for_initial_view = True
# These overide any values set in the plotitems below
#plotfigure.kml_xlimits = [-80,-55]
#plotfigure.kml_ylimits = [25, 45]
# Resolution
#plotfigure.kml_dpi = 300
#plotfigure.kml_tile_images = False
def kml_colorbar_transparent(filename):
#cmin = -0.01
#cmax = 0.01
geoplot.kml_build_colorbar(filename,
geoplot.googleearth_transparent,
kml_cmin,kml_cmax)
#plotfigure.kml_colorbar = kml_colorbar_transparent
# Set up for axes in this figure:
#plotaxes = plotfigure.new_plotaxes('kml')
#plotaxes.scaled = True
# Water
#plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
#plotitem.show = True
#plotitem.plot_var = geoplot.surface_or_depth
#plotitem.pcolor_cmap = geoplot.googleearth_transparent
#plotitem.pcolor_cmin = kml_cmin
#plotitem.pcolor_cmax = kml_cmax
#plotitem.amr_celledges_show = [0,0,0]
#plotitem.patchedges_show = 0
#plotfigure.kml_colorbar = kml_colorbar
#-----------------------------------------
# Figure for speeds
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Speeds', figno=10)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Speed'
plotaxes.scaled = True
#def fixup(current_data):
# import pylab
# addgauges(current_data)
# t = current_data.t
# t = t / 3600. # hours
# pylab.title('Speed at %4.2f hours' % t, fontsize=20)
# pylab.xticks(fontsize=15)
# pylab.yticks(fontsize=15)
#plotaxes.afteraxes = fixup
def speed(current_data):
from pylab import where,sqrt
q = current_data.q
h = q[0,:]
dry_tol = 0.001
u = q[1,:] # this is just to initialize
v = q[2,:] # to correct size
s = 0*q[2,:] # to correct size
nq = len(q[1,:])
[n,m] = h.shape
for ii in range(0,n):
for jj in range(0,m):
if h[ii,jj] > dry_tol:
u[ii,jj] = q[1,ii,jj]/h[ii,jj]
v[ii,jj] = q[2,ii,jj]/h[ii,jj]
s[ii,jj] = sqrt(u[ii,jj]* u[ii,jj] + v[ii,jj]*v[ii,jj])
else:
u[ii,jj] = 0.
v[ii,jj] = 0.
s[ii,jj] = 0
#print("max of u = " + str(max(u)))
#u = where(h>dry_tol, q[1,:]/h, 0.)
#v = where(h>dry_tol, q[2,:]/h, 0.)
#s = sqrt(u**2 + v**2)
#s = sqrt(u*2+v*v) #try not dividing or using where
return s
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = speed
plotitem.pcolor_cmap = geoplot.tsunami_colormap
#plotitem.pcolor_cmap = \
# colormaps.make_colormap({0:[1,1,1],0.5:[0.5,0.5,1],1:[1,0.3,0.3]})
plotitem.pcolor_cmin = 0.
plotitem.pcolor_cmax = .02
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0,0,0]
#plotitem.patchedges_show = 1
plotitem.amr_patchedges_show = [0,0,0]
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.land
plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0,0,0]
plotitem.patchedges_show = 0
plotaxes.xlimits = ocean_xlimits
plotaxes.ylimits = ocean_ylimits
#-----------------------------------------
# Figure for zoom around NYC
#-----------------------------------------
zoomWanted = True
if zoomWanted:
plotfigure = plotdata.new_plotfigure(name='Zoom1', figno=7)
# add another figure
#plotfigure.use_for_kml = True
#plotfigure.kml_use_for_initial_view = False
# These overide any values set in the plotitems below
#plotfigure.kml_xlimits = [-74.5, -73.5]
#plotfigure.kml_ylimits = [40.4,40.9]
# Resolution
#plotfigure.kml_dpi = 300
#plotfigure.kml_tile_images = True
#plotfigure.kml_colorbar = kml_colorbar # defined above
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('zoom on nyc')
plotaxes.title = 'Surface elevation'
plotaxes.scaled = True
manhattan_island = -73.5
xisland,yisland = latlong(1600e3, manhattan_island, 40., Rearth)
#plotaxes.xlimits = [xisland-0.6, xisland+0.6]
#plotaxes.xlimits = [manhattan_island-1, manhattan_island+1]
#plotaxes.ylimits = [40.15,41.5]
plotaxes.xlimits = [-74.5, -72.5] # really zoom in on lower manhattan]
plotaxes.ylimits = [40.25,41.5]
plotaxes.afteraxes = addgauges
def bigfont(current_data):
import pylab
t = current_data.t
pylab.title("Surface at t = %8.1f" % t, fontsize=20)
pylab.xticks(fontsize=15)
pylab.yticks(fontsize=15)
#plotaxes.afteraxes = bigfont
#plotaxes.afteraxes = mynewafteraxes # after axes functions mess with GE plots
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
#plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmap = kml_cmap
plotitem.pcolor_cmin = kml_cmin # same as above
plotitem.pcolor_cmax = kml_cmax
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0,0,0]
plotitem.patchedges_show = 0
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
plotitem.plot_var = geoplot.land
# plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0,0,0,0,0]
plotitem.patchedges_show = 0
# contour lines:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = True
plotitem.plot_var = geoplot.surface
plotitem.contour_levels = [-0.8, -0.4, 0.4, 0.8]
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linewidths':1}
plotitem.amr_contour_show = [0,0,0,1,1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = linspace(-30, -10,3)
plotitem.amr_contour_colors = ['m'] # color on each level
plotitem.kwargs = {'linestyles':'dashed','linewidths':1}
plotitem.amr_contour_show = [1,1,1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------
# Figures for gauges
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Surface & topo', figno=300, \
type='each_gauge')
plotfigure.clf_each_gauge = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0000, 6100]
plotaxes.ylimits = [-.10, .15]
#plotaxes.xlimits = 'auto'
#plotaxes.ylimits = 'auto'
plotaxes.title = 'Surface'
# Plot surface as blue curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = 3
plotitem.plotstyle = 'b-'
# Plot topo as green curve:
#plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
def gaugetopo(current_data):
q = current_data.q
h = q[0,:]
eta = q[3,:]
topo = eta - h
return topo
def gaugedpress(current_data):
q = current_data.q
#dpress = (q[4,:] - 101300)/101300
dpress = q[4,:] # already output as relative: dp/amb_pr
return dpress
def gs(current_data):
q = current_data.q
# different than speed function because q is function of time, not
# x,y at the gauges.
from numpy import where, sqrt
h = q[0,:]
#print('shape of h ' + str(h.shape))
dry_tol = 0.001
u = where(h>dry_tol, q[1,:]/h, 0.)
v = where(h>dry_tol, q[2,:]/h, 0.)
ssq = sqrt(u*u+v*v)
#s = sqrt(u**2 + v**2)
s = sqrt(ssq)
return ssq
#plotitem.plot_var = gaugetopo
#plotitem.plotstyle = 'g-'
# Plot relative delta pressure as red curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = gaugedpress
plotitem.plotstyle = 'r-'
# add speed to this plot since cant get new one going
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = gs
plotitem.plotstyle = 'g-'
def add_zeroline(current_data):
from pylab import plot, legend
t = current_data.t
# legend(('surface','topography','dp'),loc='lower left')
legend(('surface','dp','speed'),loc='upper right')
#plot(t, 0*t, 'k')
plotaxes.afteraxes = add_zeroline
# -------------------------------
# Figure for speed at gauges
#--------------------------------
# plotfigure = plotdata.new_plotfigure(name='Speed at gauges', figno=310, \
# type='each_gauge')
#
#
# # Set up for axes in this figure:
# plotaxes = plotfigure.new_plotaxes()
# plotaxes.xlimits = 'auto'
# plotaxes.ylimits = 'auto'
# #plotaxes.xlimits = [0000, 7000]
# #plotaxes.ylimits = 'auto'
# plotaxes.title = 'Speed'
#
# # Plot surface as blue curve:
# plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
# #plotitem.plot_var = speed
# plotitem.plot_var = gs #gauge_speed
# plotitem.plotstyle = 'b-'
#
# plotfigure.show = True
#
#-----------------------------------------
# Figure for bathy alone
#-----------------------------------------
def bathy(current_data):
return current_data.aux[0,:,:]
plotfigure = plotdata.new_plotfigure(name='bathymetry', figno=3)
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Bathymetry'
plotaxes.scaled = True
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = bathy
plotitem.pcolor_cmin = -3000.00
plotitem.pcolor_cmax = 500
plotitem.add_colorbar = True
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = linspace(-2000,2000,21)
plotitem.amr_contour_colors = ['k'] # color on each level
#plotitem.contour_levels = [-1000 -500,-250,-100, -5, 5]
#-----------------------------------------
# Figure for grids alone
#-----------------------------------------
#plotfigure = plotdata.new_plotfigure(name='grids', figno=2)
#plotfigure.show = False
# Set up for axes in this figure:
#plotaxes = plotfigure.new_plotaxes()
#plotaxes.xlimits = [0,1]
#plotaxes.ylimits = [0,1]
#plotaxes.title = 'grids'
#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']
#plotitem.amr_celledges_show = [0,0,0]
#plotitem.amr_patchedges_show = [0,0,0]
def normalized_pressure(current_data):
pressure_index = 6 #7 in fortran, but python is 0 based
return current_data.aux[pressure_index,:,:]/surge_data.ambient_pressure
# Pressure field
plotfigure = plotdata.new_plotfigure(name='Pressure',figno=33)
plotfigure.show = False
plotaxes = plotfigure.new_plotaxes('normalized_pressure')
plotaxes.xlimits = ocean_xlimits
plotaxes.ylimits = ocean_ylimits
plotaxes.title = 'Pressure Field'
plotaxes.scaled = True
plotaxes.afteraxes = addgauges
pressure_limits = [.995,1.005]
#pressure_limits = [.999*surge_data.ambient_pressure / 100.0,
# 1.001 * surge_data.ambient_pressure / 100.0]
#pressure_limits = [-.000001*surge_data.ambient_pressure,
# .000001 * surge_data.ambient_pressure]
surgeplot.add_pressure(plotaxes, bounds=pressure_limits)
surgeplot.add_land(plotaxes)
#plotitem = plotaxes.new_plotitem(plot_type='2d_patch')
plotitem = plotaxes.plotitem_dict['pressure']
plotitem.show = True
plotitem.plot_var = normalized_pressure
plotitem.amr_celledges_show = [0,0,0]
plotitem.amr_patchedges_show = [0,0,0]
plotaxes.plotitem_dict['land'].amr_celledges_show=[0,0,0]
plotaxes.plotitem_dict['land'].amr_patchedges_show=[0,0,0]
#-----------------------------------------
# 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_gaugenos = 'all' # list of gauges to print
#plotdata.print_fignos = [0,7,10,33,300] # list of figures to print
#plotdata.print_fignos = [0,7,10,300] # list of figures to print
plotdata.print_fignos = [7] # 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.kml = False
return plotdata
def setrun(claw_pkg='geoclaw'):
#------------------------------
"""
Define the parameters used for running Clawpack.
INPUT:
claw_pkg expected to be "geoclaw" for this setrun.
OUTPUT:
rundata - object of class ClawRunData
"""
from clawpack.clawutil import data
assert claw_pkg.lower() == 'geoclaw', "Expected claw_pkg = 'geoclaw'"
num_dim = 2
rundata = data.ClawRunData(claw_pkg, num_dim)
#------------------------------------------------------------------
# Problem-specific parameters to be written to setprob.data:
#------------------------------------------------------------------
# theta_island locates the island: 220 or 260 in the paper (x axis is latter)
# to get rid of island altogether, change make topo file to not include routine that
# adds island to topography
# if changed here need to do make data then make topo
# then make .output as usual, or make .plots
theta_island = 260.
probdata = rundata.new_UserData(name='probdata',fname='setprob.data')
probdata.add_param('theta_island', theta_island, 'angle to island')
#------------------------------------------------------------------
# GeoClaw specific parameters:
#------------------------------------------------------------------
rundata = setgeo(rundata)
#------------------------------------------------------------------
# Standard Clawpack parameters to be written to claw.data:
#------------------------------------------------------------------
clawdata = rundata.clawdata # initialized when rundata instantiated
# Set single grid parameters first.
# See below for AMR parameters.
# ---------------
# Spatial domain:
# ---------------
# Number of space dimensions:
clawdata.num_dim = num_dim
# Lower and upper edge of computational domain:
clawdata.lower[0] = -20.0 # xlower
clawdata.upper[0] = 20.0 # xupper
clawdata.lower[1] = 20.0 # ylower
clawdata.upper[1] = 60.0 # yupper
#clawdata.lower[0] = -5.0 # xlower
#clawdata.upper[0] = 5.0 # xupper
#clawdata.lower[1] = 35.0 # ylower
#clawdata.upper[1] = 45.0 # yupper
# Number of grid cells:
#clawdata.num_cells[0] = 40 # mx
#clawdata.num_cells[1] = 40 # my
clawdata.num_cells[0] = 100 # mx
clawdata.num_cells[1] = 100 # my
# ---------------
# Size of system:
# ---------------
# Number of equations in the system:
clawdata.num_eqn = 3
# Number of auxiliary variables in the aux array (initialized in setaux)
clawdata.num_aux = 3 + 1 + 3
# Index of aux array corresponding to capacity function, if there is one:
clawdata.capa_index = 2
# -------------
# Initial time:
# -------------
clawdata.t0 = 0.0
# 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.chk00021' # 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 = 3
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 = 14
#clawdata.tfinal = 14000.0
clawdata.tfinal = 7000.0
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 = [0., 0.1]
elif clawdata.output_style == 3:
# Output every step_interval timesteps over total_steps timesteps:
clawdata.output_step_interval = 100
clawdata.total_steps = 4000/1 # final time 6000 sec / dtinit of 2 sec
clawdata.output_t0 = True # output at initial (or restart) time?
#clawdata.output_format = 'binary' # 'ascii', 'binary'
clawdata.output_format = 'ascii' # 'ascii', 'binary'
clawdata.output_q_components = 'all' # could be list such as [True,True]
clawdata.output_aux_components = 'all' # could be list
clawdata.output_aux_onlyonce = False # 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 = 1
# --------------
# Time stepping:
# --------------
# if dt_variable==True: variable time steps used based on cfl_desired,
# if dt_variable==Falseixed time steps dt = dt_initial always used.
#clawdata.dt_variable = True
clawdata.dt_variable = False
# Initial time step for variable dt.
# (If dt_variable==0 then dt=dt_initial for all steps)
#clawdata.dt_initial = 16.0
clawdata.dt_initial = 1.0
# Max time step to be allowed if variable dt used:
clawdata.dt_max = 1e+99
# Desired Courant number if variable dt used
clawdata.cfl_desired = 0.75
# max Courant number to allow without retaking step with a smaller dt:
clawdata.cfl_max = 1.0
# Maximum number of time steps to allow between output times:
clawdata.steps_max = 5000
# ------------------
# 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 = 3
# 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 = ['vanleer', 'vanleer', 'vanleer']
clawdata.use_fwaves = True # 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 = 1
# --------------------
# 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
# ---------------
# Gauges:
# ---------------
gauges = rundata.gaugedata.gauges
# for gauges append lines of the form [gaugeno, x, y, t1, t2]
gaugeno = 0
for d in [1570e3, 1590e3, 1610e3, 1630e3]:
gaugeno = gaugeno+1
x,y = latlong(d, theta_island, 40., Rearth)
gauges.append([gaugeno, x, y, 0., 1e10])
# for radially symmetric traveling wave test
#gauges.append([1, 0.01, 40.01, 0, 1e10])
#gauges.append([2, 1.01, 40.01, 0, 1e10])
#gauges.append([3, 2.01, 40.01, 0, 1e10])
#gauges.append([4, 3.01, 40.01, 0, 1e10])
#for 1d test problem i use these gauges
#gauges.append([1, -17.50, 40, 0, 1e10])
#gauges.append([2, -15.5, 40, 0, 1e10])
#gauges.append([3, -13.5, 40, 0, 1e10])
#gauges.append([4, -11.5, 40, 0, 1e10])
# --------------
# Checkpointing:
# --------------
# Specify when checkpoint files should be created that can be
# used to restart a computation.
clawdata.checkpt_style = 1
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 = [0.1,0.15]
elif clawdata.checkpt_style == 3:
# Checkpoint every checkpt_interval timesteps (on Level 1)
# and at the final time.
clawdata.checkpt_interval = 5
# ---------------
# AMR parameters: (written to amr.data)
# ---------------
amrdata = rundata.amrdata
# max number of refinement levels:
#amrdata.amr_levels_max = 5
amrdata.amr_levels_max = 4
# List of refinement ratios at each level (length at least amr_level_max-1)
amrdata.refinement_ratios_x = [4, 4, 4, 4]
amrdata.refinement_ratios_y = [4, 4, 4, 4]
amrdata.refinement_ratios_t = [4, 4, 4, 1]
# 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', 'capacity', 'yleft', 'center', 'center',
'center', 'center']
# Flag for refinement based on Richardson error estimater:
amrdata.flag_richardson = False # use Richardson?
amrdata.flag_richardson_tol = 1.0 # Richardson tolerance
# Flag for refinement using routine flag2refine:
amrdata.flag2refine = True # use this?
amrdata.flag2refine_tol = 0.5 # tolerance used in this routine
# Note: in geoclaw the refinement tolerance is set as wave_tolerance below
# and flag2refine_tol is unused!
# steps to take on each level L between regriddings of level L+1:
amrdata.regrid_interval = 3
# 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 = 5
# ---------------
# Regions:
# ---------------
regions = rundata.regiondata.regions
# to specify regions of refinement append lines of the form
# [minlevel,maxlevel,t1,t2,x1,x2,y1,y2]
#regions.append([1, 3, 0., 5000., -5., 20., 35., 45.])
#regions.append([1, 2, 5000., 6900., 10., 20., 35., 45.])
#regions.append([1, 3, 5000., 6900., 12., 20., 39., 43.])
regions.append([1, 2, 0., 100000000., -20., 20., 20., 60.])
# Force refinement near the island as the wave approaches:
(xisland,yisland) = latlong(1600.e3, theta_island, 40., Rearth)
x1 = xisland - 1.
x2 = xisland + 1.
y1 = yisland - 1.
y2 = yisland + 1.
regions.append([4, 4, 2500., 1.e10, x1,x2,y1,y2])
x1 = xisland - 0.2
x2 = xisland + 0.2
y1 = yisland - 0.2
y2 = yisland + 0.2
regions.append([4, 5, 3000., 1.e10, x1,x2,y1,y2])
# ----- 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 = True # 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
from clawpack.geoclaw.data import Rearth # radius of earth
from clawpack.clawutil.data import ClawData
from mapper import latlong
import maketopo
probdata = ClawData()
probdata.read('setprob.data', force=True)
theta_island = probdata.theta_island
print "theta_island = ", theta_island
#close(1)
#figure(1, figsize=(10,6))
#axes([.1,.1,.5,.8])
clf()
s = linspace(0, 360., 1001)
xshore, yshore = latlong(1645.e3, s, 40., Rearth)
plot(xshore, yshore, 'k', linewidth=2)
xshelf, yshelf = latlong(1560.e3, s, 40., Rearth)
plot(xshelf, yshelf, 'k--', linewidth=2)
theta_island = 220.
(xi, yi) = latlong(1600.e3, theta_island, 40., Rearth)
xb = [xi - 1, xi + 1, xi + 1, xi - 1, xi - 1]
yb = [yi - 1, yi - 1, yi + 1, yi + 1, yi - 1]
plot(xb, yb, 'b', linewidth=1.5)
text(5, 49, 'Test 1', fontsize=20)
theta_island = 260.
(xi, yi) = latlong(1600.e3, theta_island, 40., Rearth)
xb = [xi - 1, xi + 1, xi + 1, xi - 1, xi - 1]
"""
from clawpack.geoclaw import topotools
from clawpack.geoclaw.data import Rearth # radius of earth
from interp import pwcubic
from clawpack.clawutil.data import ClawData
from numpy import *
from mapper import latlong, gcdist
probdata = ClawData()
probdata.read('setprob.data', force=True)
theta_island = probdata.theta_island
print "theta_island = ", theta_island
(xisland, yisland) = latlong(1600.e3, theta_island, 40., Rearth)
def maketopo():
"""
Output topography file for the entire domain
and near-shore in one location.
"""
nxpoints = 400
nypoints = 400
xlower = -30.e0
xupper = 30.e0
ylower = 20.e0
yupper = 60.e0
outfile = "ocean.topotype2"
topotools.topo2writer(outfile, topo, xlower, xupper, ylower, yupper,
from clawpack.clawutil.data import ClawData
from mapper import latlong
import maketopo
probdata = ClawData()
probdata.read('setprob.data', force=True)
theta_island = probdata.theta_island
print "theta_island = ",theta_island
#close(1)
#figure(1, figsize=(10,6))
#axes([.1,.1,.5,.8])
clf()
s = linspace(0, 360., 1001)
xshore,yshore = latlong(1645.e3, s, 40., Rearth)
plot(xshore,yshore,'k',linewidth=2)
xshelf,yshelf = latlong(1560.e3, s, 40., Rearth)
plot(xshelf,yshelf,'k--',linewidth=2)
theta_island = 220.
(xi,yi) = latlong(1600.e3, theta_island, 40., Rearth)
xb = [xi-1, xi+1, xi+1, xi-1, xi-1]
yb = [yi-1, yi-1, yi+1, yi+1, yi-1]
plot(xb,yb,'b',linewidth=1.5)
text(5,49,'Test 1', fontsize=20)
theta_island = 260.
(xi,yi) = latlong(1600.e3, theta_island, 40., Rearth)
xb = [xi-1, xi+1, xi+1, xi-1, xi-1]
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.
"""
from clawpack.visclaw import colormaps, geoplot
from numpy import linspace
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'binary'
# To plot gauge locations on pcolor or contour plot, use this as
# an afteraxis function:
def addgauges(current_data):
from clawpack.visclaw import gaugetools
gaugetools.plot_gauge_locations(current_data.plotdata, \
gaugenos='all', format_string='ko', add_labels=True)
#-----------------------------------------
# Figure for pcolor plot
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='pcolor', figno=0)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Surface'
plotaxes.scaled = True
def fixup(current_data):
import pylab
t = current_data.t
#addgauges(current_data)
pylab.title("Surface at t = %8.1f" % t, fontsize=20)
#pylab.xticks(fontsize=15)
#pylab.yticks(fontsize=15)
pylab.gca().set_aspect(1. / pylab.cos(40 * pylab.pi / 180.))
plotaxes.afteraxes = fixup
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -1.0
plotitem.pcolor_cmax = 1.0
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 1
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.land
plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
#plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [1, 0]
plotitem.patchedges_show = 1
plotaxes.xlimits = [-20, 20]
plotaxes.ylimits = [20, 60]
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = linspace(-1000, -1000, 1)
plotitem.amr_contour_colors = ['g'] # color on each level
plotitem.kwargs = {'linestyles': 'dashed', 'linewidths': 2}
plotitem.amr_contour_show = [1, 0, 0]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------
# Figure for zoom
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Zoom1', figno=7)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('zoom on island')
plotaxes.title = 'Surface elevation'
xisland, yisland = latlong(1600e3, theta_island, 40., Rearth)
plotaxes.xlimits = [xisland - 0.6, xisland + 0.8]
plotaxes.ylimits = [yisland - 0.6, yisland + 0.8]
def fixup(current_data):
import pylab
t = current_data.t
addgauges(current_data)
pylab.title("Surface at t = %8.1f" % t, fontsize=20)
#pylab.xticks(fontsize=15)
#pylab.yticks(fontsize=15)
pylab.gca().set_aspect(1. / pylab.cos(50 * pylab.pi / 180.))
plotaxes.afteraxes = fixup
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -1.0
plotitem.pcolor_cmax = 1.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.patchedges_show = 1
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
plotitem.plot_var = geoplot.land
plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
#plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [1, 0, 0, 0, 0]
plotitem.patchedges_show = 1
# contour lines:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = True
plotitem.plot_var = geoplot.surface
plotitem.contour_levels = [-0.8, -0.4, 0.4, 0.8]
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linewidths': 2}
plotitem.amr_contour_show = [0, 0, 0, 1, 1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = linspace(-1000, -1000, 1)
plotitem.amr_contour_colors = ['g'] # color on each level
plotitem.kwargs = {'linestyles': 'dashed', 'linewidths': 2}
plotitem.amr_contour_show = [0, 0, 1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------
# Figures for gauges
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Surface & topo', figno=300, \
type='each_gauge')
plotfigure.clf_each_gauge = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [6000, 14000]
plotaxes.ylimits = [-2.0, 3.0]
plotaxes.title = 'Surface'
# Plot surface as blue curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = 3
plotitem.plotstyle = 'b-'
# Plot topo as green curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
def gaugetopo(current_data):
q = current_data.q
h = q[0, :]
eta = q[3, :]
topo = eta - h
return topo
plotitem.plot_var = gaugetopo
plotitem.plotstyle = 'g-'
def add_zeroline(current_data):
from pylab import plot, legend
t = current_data.t
#legend(('surface','topography'),loc='lower left')
plot(t, 0 * t, 'k')
plotaxes.afteraxes = add_zeroline
#-----------------------------------------
# Figure for grids alone
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='grids', figno=2)
plotfigure.show = False
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0, 1]
plotaxes.ylimits = [0, 1]
plotaxes.title = 'grids'
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']
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_patchedges_show = [1]
#-----------------------------------------
# Figures for fgmax plots
#-----------------------------------------
# Note: You need to move fgmax png files into _plots/fgmax_plots after
# creating them, e.g., by running the process_fgmax notebook or script.
# The lines below just create links to these figures from _PlotIndex.html
otherfigure = plotdata.new_otherfigure(
name='max depth on fgmax grid 1',
fname='fgmax_plots/fgmax0001_h_onshore.png')
otherfigure = plotdata.new_otherfigure(
name='max speed on fgmax grid 1',
fname='fgmax_plots/fgmax0001_speed.png')
otherfigure = plotdata.new_otherfigure(
name='max elevation on fgmax grid 2',
fname='fgmax_plots/fgmax0002_surface.png')
otherfigure = plotdata.new_otherfigure(
name='max depth on fgmax grid 3',
fname='fgmax_plots/fgmax0003_h_onshore.png')
otherfigure = plotdata.new_otherfigure(
name='max speed on fgmax grid 3',
fname='fgmax_plots/fgmax0003_speed.png')
# add additional lines for any other figures you want added to the index.
#-----------------------------------------
# Plots of timing (CPU and wall time):
def make_timing_plots(plotdata):
import os
from clawpack.visclaw import plot_timing_stats
try:
timing_plotdir = plotdata.plotdir + '/timing_figures'
os.system('mkdir -p %s' % timing_plotdir)
units = {
'comptime': 'seconds',
'simtime': 'hours',
'cell': 'billions'
}
plot_timing_stats.make_plots(outdir=plotdata.outdir,
make_pngs=True,
plotdir=timing_plotdir,
units=units)
os.system('cp %s/timing.* %s' % (plotdata.outdir, timing_plotdir))
except:
print('*** Error making timing plots')
# create a link to this webpage from _PlotIndex.html:
otherfigure = plotdata.new_otherfigure(name='timing',
fname='timing_figures/timing.html')
otherfigure.makefig = make_timing_plots
#-----------------------------------------
# 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_gaugenos = 'all' # list of gauges 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?
return plotdata
def setrun(claw_pkg='geoclaw'):
#------------------------------
"""
Define the parameters used for running Clawpack.
INPUT:
claw_pkg expected to be "geoclaw" for this setrun.
OUTPUT:
rundata - object of class ClawRunData
"""
from clawpack.clawutil import data
assert claw_pkg.lower() == 'geoclaw', "Expected claw_pkg = 'geoclaw'"
num_dim = 2
rundata = data.ClawRunData(claw_pkg, num_dim)
#------------------------------------------------------------------
# Problem-specific parameters to be written to setprob.data:
#------------------------------------------------------------------
theta_island = 220.
probdata = rundata.new_UserData(name='probdata', fname='setprob.data')
probdata.add_param('theta_island', theta_island, 'angle to island')
#------------------------------------------------------------------
# Standard Clawpack parameters to be written to claw.data:
#------------------------------------------------------------------
clawdata = rundata.clawdata # initialized when rundata instantiated
# Set single grid parameters first.
# See below for AMR parameters.
# ---------------
# Spatial domain:
# ---------------
# Number of space dimensions:
clawdata.num_dim = num_dim
# Lower and upper edge of computational domain:
clawdata.lower[0] = -20.0 # xlower
clawdata.upper[0] = 20.0 # xupper
clawdata.lower[1] = 20.0 # ylower
clawdata.upper[1] = 60.0 # yupper
# Number of grid cells:
clawdata.num_cells[0] = 40 # mx
clawdata.num_cells[1] = 40 # my
# ---------------
# Size of system:
# ---------------
# Number of equations in the system:
clawdata.num_eqn = 3
# Number of auxiliary variables in the aux array (initialized in setaux)
clawdata.num_aux = 3
# Index of aux array corresponding to capacity function, if there is one:
clawdata.capa_index = 2
# -------------
# Initial time:
# -------------
clawdata.t0 = 0.0
# 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 = 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 = 7
clawdata.tfinal = 14000.
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., 0.1]
elif clawdata.output_style == 3:
# Output every step_interval timesteps over total_steps timesteps:
clawdata.output_step_interval = 2
clawdata.total_steps = 4
clawdata.output_t0 = True # output at initial (or restart) time?
clawdata.output_format = 'binary' # 'ascii', '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.
# (E.g. verbosity == 2 means print only on levels 1 and 2.)
clawdata.verbosity = 1
# --------------
# Time stepping:
# --------------
# if dt_variable==True: variable time steps used based on cfl_desired,
# if dt_variable==Falseixed 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 = 16.0
# Max time step to be allowed if variable dt used:
clawdata.dt_max = 1e+99
# Desired Courant number if variable dt used
clawdata.cfl_desired = 0.75
# max Courant number to allow without retaking step with a smaller dt:
clawdata.cfl_max = 1.0
# Maximum number of time steps to allow between output times:
clawdata.steps_max = 5000
# ------------------
# 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 = 3
# 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 = ['vanleer', 'vanleer', 'vanleer']
clawdata.use_fwaves = True # 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 = 1
# --------------------
# 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
# ---------------
# Gauges:
# ---------------
gauges = rundata.gaugedata.gauges
# for gauges append lines of the form [gaugeno, x, y, t1, t2]
gaugeno = 0
for d in [1570e3, 1590e3, 1610e3, 1630e3]:
gaugeno = gaugeno + 1
x, y = latlong(d, theta_island, 40., Rearth)
gauges.append([gaugeno, x, y, 0., 1e10])
# --------------
# Checkpointing:
# --------------
# Specify when checkpoint files should be created that can be
# used to restart a computation.
clawdata.checkpt_style = 1
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 = [0.1, 0.15]
elif clawdata.checkpt_style == 3:
# Checkpoint every checkpt_interval timesteps (on Level 1)
# and at the final time.
clawdata.checkpt_interval = 5
# ---------------
# AMR parameters: (written to amr.data)
# ---------------
amrdata = rundata.amrdata
# max number of refinement levels:
amrdata.amr_levels_max = 5
# List of refinement ratios at each level (length at least amr_level_max-1)
amrdata.refinement_ratios_x = [4, 3, 5, 4]
amrdata.refinement_ratios_y = [4, 3, 5, 4]
amrdata.refinement_ratios_t = [1, 1, 1, 1]
# 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', 'capacity', 'yleft']
# Flag for refinement based on Richardson error estimater:
amrdata.flag_richardson = False # use Richardson?
amrdata.flag_richardson_tol = 1.0 # Richardson tolerance
# Flag for refinement using routine flag2refine:
amrdata.flag2refine = True # use this?
amrdata.flag2refine_tol = 0.5 # tolerance used in this routine
# Note: in geoclaw the refinement tolerance is set as wave_tolerance below
# and flag2refine_tol is unused!
# steps to take on each level L between regriddings of level L+1:
amrdata.regrid_interval = 3
# 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]
regions.append([1, 3, 0., 5000., -5., 20., 35., 55.])
regions.append([1, 2, 5000., 6900., -5., 20., 35., 55.])
regions.append([1, 3, 5000., 6900., 10., 20., 45., 55.])
regions.append([1, 2, 6900., 9000., 10., 20., 47., 52.])
# Force refinement near the island as the wave approaches:
(xisland, yisland) = latlong(1600.e3, theta_island, 40., Rearth)
x1 = xisland - 1.
x2 = xisland + 1.
y1 = yisland - 1.
y2 = yisland + 1.
regions.append([4, 4, 7000., 1.e10, x1, x2, y1, y2])
x1 = xisland - 0.2
x2 = xisland + 0.2
y1 = yisland - 0.2
y2 = yisland + 0.2
regions.append([4, 5, 8000., 1.e10, x1, x2, y1, y2])
# -----------------------------------------------
# GeoClaw specific parameters:
try:
geo_data = rundata.geo_data
except:
print("*** Error, this rundata has no geo_data attribute")
raise AttributeError("Missing geo_data attribute")
# == Physics ==
geo_data.gravity = 9.81
geo_data.coordinate_system = 2
geo_data.earth_radius = 6367500.0
# == Forcing Options
geo_data.coriolis_forcing = False
# == Algorithm and Initial Conditions ==
geo_data.sea_level = 0.0
geo_data.dry_tolerance = 0.001
geo_data.friction_forcing = True
geo_data.manning_coefficient = 0.025
geo_data.friction_depth = 1000000.0
# Refinement settings
refinement_data = rundata.refinement_data
refinement_data.variable_dt_refinement_ratios = True
refinement_data.wave_tolerance = 0.01
refinement_data.deep_depth = 100.0
refinement_data.max_level_deep = 3
# == settopo.data values ==
topofiles = rundata.topo_data.topofiles
# for topography, append lines of the form
# [topotype, minlevel, maxlevel, t1, t2, fname]
topofiles.append([3, 1, 1, 0.0, 10000000000.0, 'ocean.tt3'])
topofiles.append([3, 1, 1, 0.0, 10000000000.0, 'island.tt3'])
# == setdtopo.data values ==
rundata.dtopo_data.dtopofiles = []
dtopofiles = rundata.dtopo_data.dtopofiles
# for moving topography, append lines of the form :
# [topotype, minlevel,maxlevel,fname]
# == setqinit.data values ==
rundata.qinit_data.qinit_type = 4
qinitfiles = rundata.qinit_data.qinitfiles
# for qinit perturbations, append lines of the form: (<= 1 allowed for now!)
# [minlev, maxlev, fname]
rundata.qinit_data.qinitfiles = [[1, 2, 'hump.xyz']]
# == fgmax_grids.data values ==
# NEW STYLE STARTING IN v5.7.0
# set num_fgmax_val = 1 to save only max depth,
# 2 to also save max speed,
# 5 to also save max hs,hss,hmin
rundata.fgmax_data.num_fgmax_val = 2 # Save depth and speed
fgmax_grids = rundata.fgmax_data.fgmax_grids # empty list to start
# Now append to this list objects of class fgmax_tools.FGmaxGrid
# specifying any fgmax grids.
# Here several different ones are specified to illustrate:
tstart_max = 8000. # when to start monitoring fgmax grids
# Points on a uniform 2d grid:
fg = fgmax_tools.FGmaxGrid()
fg.point_style = 2 # uniform rectangular x-y grid
fg.x1 = 14.25
fg.x2 = 14.65
fg.y1 = 50.10
fg.y2 = 50.35
fg.dx = 15 / 3600. # desired resolution of fgmax grid
fg.min_level_check = amrdata.amr_levels_max # which levels to monitor max on
fg.tstart_max = tstart_max # just before wave arrives
fg.tend_max = 1.e10 # when to stop monitoring max values
fg.dt_check = 20. # how often to update max values
fg.interp_method = 0 # 0 ==> pw const in cells, recommended
fgmax_grids.append(fg) # written to fgmax_grids.data
# Points on a 1d transect from (x1,y1) to (x2,y2):
fg = fgmax_tools.FGmaxGrid()
fg.point_style = 1 # equally spaced points on a transect
fg.x1 = 14.25
fg.x2 = 14.65
fg.y1 = 50.10
fg.y2 = 50.35
fg.npts = 50
fg.min_level_check = amrdata.amr_levels_max # which levels to monitor max on
fg.tstart_max = tstart_max # just before wave arrives
fg.tend_max = 1.e10 # when to stop monitoring max values
fg.dt_check = 20. # how often to update max values
fg.interp_method = 0 # 0 ==> pw const in cells, recommended
fgmax_grids.append(fg) # written to fgmax_grids.data
# fgmax grid point_style==4 means grid specified as topo_type==3 file:
fg = fgmax_tools.FGmaxGrid()
fg.point_style = 4
fg.min_level_check = amrdata.amr_levels_max # which levels to monitor max on
fg.tstart_max = tstart_max # just before wave arrives
fg.tend_max = 1.e10 # when to stop monitoring max values
fg.dt_check = 20. # how often to update max values
fg.interp_method = 0 # 0 ==> pw const in cells, recommended
fg.xy_fname = 'fgmax_pts_island.data' # file of 0/1 values in tt3 format
fgmax_grids.append(fg) # written to fgmax_grids.data
# fgmax grid point_style==0 means list of points:
fg = fgmax_tools.FGmaxGrid()
fg.point_style = 0
fg.min_level_check = amrdata.amr_levels_max # which levels to monitor max on
fg.tstart_max = tstart_max # just before wave arrives
fg.tend_max = 1.e10 # when to stop monitoring max values
fg.dt_check = 20. # how often to update max values
fg.interp_method = 0 # 0 ==> pw const in cells, recommended
# can set list of points here:
fg.npts = 2
fg.X = np.array([14.4, 14.5])
fg.Y = np.array([50.13, 50.13])
fgmax_grids.append(fg) # written to fgmax_grids.data
# fgmax grid point_style==0 means list of points:
fg = fgmax_tools.FGmaxGrid()
fg.point_style = 0
fg.min_level_check = amrdata.amr_levels_max # which levels to monitor max on
fg.tstart_max = tstart_max # just before wave arrives
fg.tend_max = 1.e10 # when to stop monitoring max values
fg.dt_check = 20. # how often to update max values
fg.interp_method = 0 # 0 ==> pw const in cells, recommended
# can specify that list of points is in a different file:
fg.npts = 0
fg.xy_fname = 'fgmax_ps0.txt'
fgmax_grids.append(fg) # written to fgmax_grids.data
# ----- 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
from clawpack.geoclaw.data import Rearth # radius of earth
from interp import pwcubic
from clawpack.clawutil.data import ClawData
import numpy as np
from matplotlib import pylab as plt
from mapper import latlong
import maketopo
probdata = ClawData()
probdata.read('setprob.data', force=True)
theta_island = probdata.theta_island
print "theta_island = ",theta_island
(xisland,yisland) = latlong(1600.e3, theta_island, 40., Rearth)
def ocean():
dmax = 1650.e3
dp = np.linspace(0., dmax, 3301)
zp = maketopo.shelf1(dp)
plt.clf()
plt.plot(dp*1e-3,zp,'k-',linewidth=3)
plt.fill_between(dp*1e-3,zp,0.,where=(zp<0.), color=[0,0.5,1])
plt.xlim([0.,1650])
plt.ylim([-4500.,500])
plt.title("Topography as function of radius",fontsize=18)
plt.xlabel("kilometers from center",fontsize=15)
plt.ylabel("meters",fontsize=15)
plt.xticks(fontsize=15)
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.
"""
from clawpack.visclaw import colormaps, geoplot
from numpy import linspace
plotdata.clearfigures() # clear any old figures,axes,items data
#plotdata.format = 'binary'
plotdata.format = 'ascii'
# Load data from output
clawdata = clawutil.ClawInputData(2)
clawdata.read(os.path.join(plotdata.outdir,'claw.data'))
amrdata = amrclaw.AmrclawInputData(clawdata)
amrdata.read(os.path.join(plotdata.outdir,'amr.data'))
physics = geodata.GeoClawData()
physics.read(os.path.join(plotdata.outdir,'geoclaw.data'))
surge_data = surgedata.SurgeData()
surge_data.read(os.path.join(plotdata.outdir,'surge.data'))
probdata = clawutil.ClawData()
probdata.read('setprob.data', force=True)
theta_island = probdata.theta_island
# To plot gauge locations on pcolor or contour plot, use this as
# an afteraxis function:
def addgauges(current_data):
from clawpack.visclaw import gaugetools
gaugetools.plot_gauge_locations(current_data.plotdata, \
gaugenos='all', format_string='ko', add_labels=True)
#-----------------------------------------
# Figure for pcolor plot for surface
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='pcolor', figno=0)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Surface'
plotaxes.scaled = True
plotaxes.afteraxes = addgauges
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
#plotitem.plot_var = 0/1/2 or plot that entry into q instead of a function
plotitem.pcolor_cmap = geoplot.tsunami_colormap
#plotitem.pcolor_cmin = -1.0
#plotitem.pcolor_cmax = 1.0
plotitem.pcolor_cmin = -.005
plotitem.pcolor_cmax = .005
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0,0,0]
plotitem.patchedges_show = 1
#plotitem.patchedges_show = 0
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.land
# plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0,0]
#plotitem.patchedges_show = 1
plotitem.patchedges_show = 0
plotaxes.xlimits = [-20,20]
plotaxes.ylimits = [20,60]
#plotaxes.xlimits = [-5,5]
#plotaxes.ylimits = [35,45]
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = True #False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = [-3500,-2500,-1500, -500, 0, 500]
plotitem.amr_contour_colors = ['g'] # color on each level
plotitem.kwargs = {'linestyles':'dashed','linewidths':2,'colors' : 'red' }
plotitem.amr_contour_show = [0,0,0]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------
# Figure for speeds
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Speeds', figno=10)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Speed'
plotaxes.scaled = True
#def fixup(current_data):
# import pylab
# addgauges(current_data)
# t = current_data.t
# t = t / 3600. # hours
# pylab.title('Speed at %4.2f hours' % t, fontsize=20)
# pylab.xticks(fontsize=15)
# pylab.yticks(fontsize=15)
#plotaxes.afteraxes = fixup
def speed(current_data):
from pylab import where,sqrt
q = current_data.q
h = q[0,:]
dry_tol = 0.001
u = q[1,:] # this is just to initialize
v = q[2,:] # to correct size
s = 0*q[2,:] # to correct size
nq = len(q[1,:])
[n,m] = h.shape
for ii in range(0,n):
for jj in range(0,m):
if h[ii,jj] > dry_tol:
u[ii,jj] = q[1,ii,jj]/h[ii,jj]
v[ii,jj] = q[2,ii,jj]/h[ii,jj]
s[ii,jj] = sqrt(u[ii,jj]* u[ii,jj] + v[ii,jj]*v[ii,jj])
else:
u[ii,jj] = 0.
v[ii,jj] = 0.
s[ii,jj] = 0
#print("max of u = " + str(max(u)))
#u = where(h>dry_tol, q[1,:]/h, 0.)
#v = where(h>dry_tol, q[2,:]/h, 0.)
#s = sqrt(u**2 + v**2)
#s = sqrt(u*2+v*v) #try not dividing or using where
return s
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = speed
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmap = \
colormaps.make_colormap({0:[1,1,1],0.5:[0.5,0.5,1],1:[1,0.3,0.3]})
plotitem.pcolor_cmin = 0.
plotitem.pcolor_cmax = .01
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [0,0,0]
plotitem.patchedges_show = 1
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.land
plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0,0,0]
plotitem.patchedges_show = 1
plotaxes.xlimits = [-20,20]
plotaxes.ylimits = [20,60]
#-----------------------------------------
# Figure for zoom at island
#-----------------------------------------
zoomWanted = True
if zoomWanted:
plotfigure = plotdata.new_plotfigure(name='Zoom1', figno=7)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('zoom on island')
plotaxes.title = 'Surface elevation'
plotaxes.scaled = True
xisland,yisland = latlong(1600e3, theta_island, 40., Rearth)
plotaxes.xlimits = [xisland-0.6, xisland+0.6]
plotaxes.ylimits = [yisland-0.6, yisland+0.6]
#plotaxes.afteraxes = addgauges
def bigfont(current_data):
import pylab
t = current_data.t
pylab.title("Surface at t = %8.1f" % t, fontsize=20)
pylab.xticks(fontsize=15)
pylab.yticks(fontsize=15)
plotaxes.afteraxes = bigfont
plotaxes.afteraxes = addgauges
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -1.0
plotitem.pcolor_cmax = 1.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0]
plotitem.patchedges_show = 1
# Land
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.show = True
plotitem.plot_var = geoplot.land
# plotitem.pcolor_cmap = colormaps.all_white # to print better in B&W
plotitem.pcolor_cmap = geoplot.land_colors
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = 100.0
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [1,0,0,0,0]
plotitem.patchedges_show = 1
# contour lines:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = True
plotitem.plot_var = geoplot.surface
plotitem.contour_levels = [-0.8, -0.4, 0.4, 0.8]
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linewidths':2}
plotitem.amr_contour_show = [0,0,0,1,1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
# add contour lines of bathy if desired:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.show = False
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = linspace(-1000,-1000,1)
plotitem.amr_contour_colors = ['g'] # color on each level
plotitem.kwargs = {'linestyles':'dashed','linewidths':2}
plotitem.amr_contour_show = [0,0,1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
#-----------------------------------------
# Figures for gauges
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Surface & topo', figno=300, \
type='each_gauge')
plotfigure.clf_each_gauge = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0000, 5000]
plotaxes.ylimits = [-.10, .10]
#plotaxes.xlimits = 'auto'
#plotaxes.ylimits = 'auto'
plotaxes.title = 'Surface'
# Plot surface as blue curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = 3
plotitem.plotstyle = 'b-'
# Plot topo as green curve:
#plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
def gaugetopo(current_data):
q = current_data.q
h = q[0,:]
eta = q[3,:]
topo = eta - h
return topo
def gaugedpress(current_data):
q = current_data.q
#dpress = (q[4,:] - 101300)/101300
dpress = q[4,:] # already output as relative: dp/amb_pr
return dpress
def gs(current_data):
q = current_data.q
# different than speed function because q is function of time, not
# x,y at the gauges.
from numpy import where, sqrt
h = q[0,:]
#print('shape of h ' + str(h.shape))
dry_tol = 0.001
u = where(h>dry_tol, q[1,:]/h, 0.)
v = where(h>dry_tol, q[2,:]/h, 0.)
ssq = sqrt(u*u+v*v)
#s = sqrt(u**2 + v**2)
s = sqrt(ssq)
return ssq
#plotitem.plot_var = gaugetopo
#plotitem.plotstyle = 'g-'
# Plot relative delta pressure as red curve:
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = gaugedpress
plotitem.plotstyle = 'r-'
# add speed to this plot since cant get new one going
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = gs
plotitem.plotstyle = 'g-'
def add_zeroline(current_data):
from pylab import plot, legend
t = current_data.t
# legend(('surface','topography','dp'),loc='lower left')
legend(('surface','dp','speed'),loc='upper right')
plot(t, 0*t, 'k')
plotaxes.afteraxes = add_zeroline
# -------------------------------
# Figure for speed at gauges
#--------------------------------
# plotfigure = plotdata.new_plotfigure(name='Speed at gauges', figno=310, \
# type='each_gauge')
#
#
# # Set up for axes in this figure:
# plotaxes = plotfigure.new_plotaxes()
# plotaxes.xlimits = 'auto'
# plotaxes.ylimits = 'auto'
# #plotaxes.xlimits = [0000, 7000]
# #plotaxes.ylimits = 'auto'
# plotaxes.title = 'Speed'
#
# # Plot surface as blue curve:
# plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
# #plotitem.plot_var = speed
# plotitem.plot_var = gs #gauge_speed
# plotitem.plotstyle = 'b-'
#
# plotfigure.show = True
#
#-----------------------------------------
# Figure for bathy alone
#-----------------------------------------
def bathy(current_data):
return current_data.aux[0,:,:]
plotfigure = plotdata.new_plotfigure(name='bathymetry', figno=3)
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Bathymetry'
plotaxes.scaled = True
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = bathy
plotitem.pcolor_cmin = -4000.00
plotitem.pcolor_cmax = 100
plotitem.add_colorbar = True
#-----------------------------------------
# Figure for grids alone
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='grids', figno=2)
plotfigure.show = False
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0,1]
plotaxes.ylimits = [0,1]
plotaxes.title = 'grids'
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']
plotitem.amr_celledges_show = [1,1,0]
#plotitem.amr_patchedges_show = [1]
plotitem.amr_patchedges_show = [0]
# Pressure field
plotfigure = plotdata.new_plotfigure(name='Pressure')
plotfigure.show = True
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [-20,20]
plotaxes.ylimits = [20,60]
#plotaxes.xlimits = [-5,5]
#plotaxes.ylimits = [35,45]
plotaxes.title = "Pressure Field"
# plotaxes.afteraxes = gulf_after_axes
plotaxes.scaled = True
#pressure_limits = [surge_data.ambient_pressure / 100.0,
# 2.0 * surge_data.ambient_pressure / 100.0]
pressure_limits = [.999*surge_data.ambient_pressure / 100.0,
1.001 * surge_data.ambient_pressure / 100.0]
#pressure_limits = [-.000001*surge_data.ambient_pressure,
# .000001 * surge_data.ambient_pressure]
surgeplot.add_pressure(plotaxes, bounds=pressure_limits)
surgeplot.add_land(plotaxes)
#-----------------------------------------
# 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_gaugenos = 'all' # list of gauges 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?
return plotdata
