def setplot(plotdata=None):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
from clawpack.visclaw import colormaps, geoplot
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
plotdata.clearfigures() # clear any old figures,axes,items data
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user["drytol"] = 1.e-3
plotdata.beforeframe = set_drytol
#-----------------------------------------
# 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
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.surface
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -0.1
plotitem.pcolor_cmax = 0.1
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 = [-2, 2]
plotaxes.ylimits = [-2, 2]
# Add contour lines of bathymetry:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = linspace(-.1, 0.5, 20)
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linestyles': 'solid'}
plotitem.amr_contour_show = [1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
#-----------------------------------------
# Figure for cross section
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='cross-section', figno=1)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [-2, 2]
plotaxes.ylimits = [-0.15, 0.3]
plotaxes.title = 'Cross section at y=0'
def plot_topo_xsec(current_data):
from pylab import plot, hold, cos, sin, where, legend, nan
t = current_data.t
hold(True)
x = linspace(-2, 2, 201)
y = 0.
B = h0 * (x**2 + y**2) / a**2 - h0
eta1 = sigma * h0 / a**2 * (2. * x * cos(omega * t) +
2. * y * sin(omega * t) - sigma)
etatrue = where(eta1 > B, eta1, nan)
plot(x, etatrue, 'r', label="true solution", linewidth=2)
plot(x, B, 'g', label="bathymetry")
## plot([0],[-1],'kx',label="Level 1") # shouldn't show up in plots,
## plot([0],[-1],'bo',label="Level 2") # but will produced desired legend
plot([0], [-1], 'bo', label="Computed") ## need to fix plotstyle
legend()
hold(False)
plotaxes.afteraxes = plot_topo_xsec
plotitem = plotaxes.new_plotitem(plot_type='1d_from_2d_data')
def xsec(current_data):
# Return x value and surface eta at this point, along y=0
from pylab import find, ravel
x = current_data.x
y = current_data.y
dy = current_data.dy
q = current_data.q
ij = find((y <= dy / 2.) & (y > -dy / 2.))
x_slice = ravel(x)[ij]
eta_slice = ravel(q[3, :, :])[ij]
return x_slice, eta_slice
plotitem.map_2d_to_1d = xsec
plotitem.plotstyle = 'kx' ## need to be able to set amr_plotstyle
plotitem.kwargs = {'markersize': 3}
plotitem.amr_show = [1] # plot on all levels
#-----------------------------------------
# Figure for grids alone
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='grids', figno=2)
plotfigure.show = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [-2, 2]
plotaxes.ylimits = [-2, 2]
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]
#-----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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 = [] # 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?
plotdata.parallel = True # make multiple frame png's at once
return plotdata
def setplot(plotdata=None):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
from clawpack.visclaw import colormaps, geoplot
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'ascii' # Format of output
# plotdata.format = 'netcdf'
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user['drytol'] = 1.e-2
plotdata.beforeframe = set_drytol
# 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
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
#plotitem.plot_var = geoplot.surface
plotitem.plot_var = geoplot.surface_or_depth
plotitem.pcolor_cmap = colormaps.make_colormap({
1.0: 'r',
0.5: 'w',
0.0: 'b'
})
plotitem.pcolor_cmin = -0.3
plotitem.pcolor_cmax = 0.3
plotitem.add_colorbar = True
# 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 = 2.0
plotitem.add_colorbar = False
plotaxes.xlimits = [-1, 1]
plotaxes.ylimits = [-1, 1]
#-----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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 = [4, 5, 104, 105] # 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?
plotdata.parallel = True # make multiple frame png's at once
return plotdata
def setplot(plotdata=None):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
from clawpack.visclaw import colormaps, geoplot
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
plotdata.clearfigures() # clear any old figures,axes,items data
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user["drytol"] = 1.e-3
plotdata.beforeframe = set_drytol
#-----------------------------------------
# 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
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.surface
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -0.1
plotitem.pcolor_cmax = 0.1
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 = [-2,2]
plotaxes.ylimits = [-2,2]
# Add contour lines of bathymetry:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = linspace(-.1, 0.5, 20)
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linestyles':'solid'}
plotitem.amr_contour_show = [1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
#-----------------------------------------
# Figure for cross section
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='cross-section', figno=1)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [-2,2]
plotaxes.ylimits = [-0.15,0.3]
plotaxes.title = 'Cross section at y=0'
def plot_topo_xsec(current_data):
from pylab import plot, cos,sin,where,legend,nan
t = current_data.t
x = linspace(-2,2,201)
y = 0.
B = h0*(x**2 + y**2)/a**2 - h0
eta1 = sigma*h0/a**2 * (2.*x*cos(omega*t) + 2.*y*sin(omega*t) -sigma)
etatrue = where(eta1>B, eta1, nan)
plot(x, etatrue, 'r', label="true solution", linewidth=2)
plot(x, B, 'g', label="bathymetry")
## plot([0],[-1],'kx',label="Level 1") # shouldn't show up in plots,
## plot([0],[-1],'bo',label="Level 2") # but will produced desired legend
plot([0],[-1],'bo',label="Computed") ## need to fix plotstyle
legend()
plotaxes.afteraxes = plot_topo_xsec
plotitem = plotaxes.new_plotitem(plot_type='1d_from_2d_data')
def xsec(current_data):
# Return x value and surface eta at this point, along y=0
from pylab import find,ravel
x = current_data.x
y = current_data.y
dy = current_data.dy
q = current_data.q
ij = find((y <= dy/2.) & (y > -dy/2.))
x_slice = ravel(x)[ij]
eta_slice = ravel(q[3,:,:])[ij]
return x_slice, eta_slice
plotitem.map_2d_to_1d = xsec
plotitem.plotstyle = 'kx' ## need to be able to set amr_plotstyle
plotitem.kwargs = {'markersize':3}
plotitem.amr_show = [1] # plot on all levels
#-----------------------------------------
# Figure for grids alone
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='grids', figno=2)
plotfigure.show = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [-2,2]
plotaxes.ylimits = [-2,2]
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]
#-----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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 = [] # 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?
plotdata.parallel = True # make multiple frame png's at once
return plotdata
def setplot(plotdata=None):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
from clawpack.visclaw import colormaps, geoplot
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = 'ascii' # Format of output
# plotdata.format = 'netcdf'
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user['drytol'] = 1.e-2
plotdata.beforeframe = set_drytol
#-----------------------------------------
# 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
# 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 = -0.9
plotitem.pcolor_cmax = 0.9
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_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 = [1, 1, 0]
plotaxes.xlimits = [-100, 100]
plotaxes.ylimits = [-100, 100]
#-----------------------------------------
# Figure for zoom
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Zoom', figno=10)
#plotfigure.show = False
plotfigure.kwargs = {'figsize': [12, 7]}
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('diag zoom')
plotaxes.axescmd = 'axes([0.0,0.1,0.6,0.6])'
plotaxes.title = 'On diagonal'
plotaxes.scaled = True
plotaxes.xlimits = [55, 66]
plotaxes.ylimits = [55, 66]
def addgauges(current_data):
from clawpack.visclaw import gaugetools
gaugenos = range(101, 110) # on diagonal
gaugetools.plot_gauge_locations(current_data.plotdata, \
gaugenos=gaugenos, format_string='ko', add_labels=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.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -0.9
plotitem.pcolor_cmax = 0.9
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_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 = [1, 1, 0]
# Add contour lines of bathymetry:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(-10., 0., 1.)
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linestyles': 'solid'}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add contour lines of topography:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(0., 11., 1.)
plotitem.amr_contour_colors = ['g'] # color on each level
plotitem.kwargs = {'linestyles': 'solid'}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add dashed contour line for shoreline
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = [0.]
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linestyles': 'dashed'}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
#-----------------------------------------
# Figure for zoom near axis
#-----------------------------------------
#plotfigure = plotdata.new_plotfigure(name='Zoom2', figno=11)
# now included in same figure as zoom on diagonal
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('x zoom')
plotaxes.show = True
plotaxes.axescmd = 'axes([0.5,0.1,0.6,0.6])'
plotaxes.title = 'On x-axis'
plotaxes.scaled = True
plotaxes.xlimits = [82, 93]
plotaxes.ylimits = [-5, 6]
def addgauges(current_data):
from clawpack.visclaw import gaugetools
gaugenos = range(1, 10) # on x-axis
gaugetools.plot_gauge_locations(current_data.plotdata, \
gaugenos=gaugenos, format_string='ko', add_labels=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.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -0.9
plotitem.pcolor_cmax = 0.9
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_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 = [1, 1, 0]
# Add contour lines of bathymetry:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(-10., 0., 1.)
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linestyles': 'solid'}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add contour lines of topography:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(0., 11., 1.)
plotitem.amr_contour_colors = ['g'] # color on each level
plotitem.kwargs = {'linestyles': 'solid'}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add dashed contour line for shoreline
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = [0.]
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linestyles': 'dashed'}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
#-----------------------------------------
# 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 = 'auto'
plotaxes.ylimits = [-2.0, 2.0]
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 patches alone
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='patches', 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 = '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']
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_patchedges_show = [1]
#-----------------------------------------
# Scatter plot of surface for radially symmetric
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Scatter', figno=200)
plotfigure.show = False
# Note: will not look very good unless more of domain is refined
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0., 100.]
plotaxes.ylimits = [-1.5, 2.]
plotaxes.title = 'Scatter plot of surface'
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type='1d_from_2d_data')
plotitem.plot_var = geoplot.surface
def q_vs_radius(current_data):
from numpy import sqrt
x = current_data.x
y = current_data.y
r = sqrt(x**2 + y**2)
q = current_data.var
return r, q
plotitem.map_2d_to_1d = q_vs_radius
plotitem.plotstyle = 'o'
plotitem.amr_color = ['b', 'r', 'g']
plotaxes.afteraxes = "import pylab; pylab.legend(['Level 1','Level 2'])"
#-----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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 = [4, 5, 104, 105] # 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?
plotdata.parallel = True # make multiple frame png's at once
plotdata.html_movie_width = 800 # width for js movie
return plotdata
def setplot(plotdata=None):
# --------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
from clawpack.visclaw import colormaps, geoplot
plotdata.clearfigures() # clear any old figures,axes,items data
plotdata.format = "ascii" # Format of output
# plotdata.format = 'netcdf'
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user["drytol"] = 1.0e-2
plotdata.beforeframe = set_drytol
# 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
# 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 = -0.9
plotitem.pcolor_cmax = 0.9
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_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 = [1, 1, 0]
plotaxes.xlimits = [-100, 100]
plotaxes.ylimits = [-100, 100]
# -----------------------------------------
# Figure for zoom
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name="Zoom", figno=10)
# plotfigure.show = False
plotfigure.kwargs = {"figsize": [12, 7]}
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes("diag zoom")
plotaxes.axescmd = "axes([0.0,0.1,0.6,0.6])"
plotaxes.title = "On diagonal"
plotaxes.scaled = True
plotaxes.xlimits = [55, 66]
plotaxes.ylimits = [55, 66]
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.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -0.9
plotitem.pcolor_cmax = 0.9
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_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 = [1, 1, 0]
# Add contour lines of bathymetry:
plotitem = plotaxes.new_plotitem(plot_type="2d_contour")
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(-10.0, 0.0, 1.0)
plotitem.amr_contour_colors = ["k"] # color on each level
plotitem.kwargs = {"linestyles": "solid"}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add contour lines of topography:
plotitem = plotaxes.new_plotitem(plot_type="2d_contour")
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(0.0, 11.0, 1.0)
plotitem.amr_contour_colors = ["g"] # color on each level
plotitem.kwargs = {"linestyles": "solid"}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add dashed contour line for shoreline
plotitem = plotaxes.new_plotitem(plot_type="2d_contour")
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = [0.0]
plotitem.amr_contour_colors = ["k"] # color on each level
plotitem.kwargs = {"linestyles": "dashed"}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# -----------------------------------------
# Figure for zoom near axis
# -----------------------------------------
# plotfigure = plotdata.new_plotfigure(name='Zoom2', figno=11)
# now included in same figure as zoom on diagonal
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes("x zoom")
plotaxes.show = True
plotaxes.axescmd = "axes([0.5,0.1,0.6,0.6])"
plotaxes.title = "On x-axis"
plotaxes.scaled = True
plotaxes.xlimits = [82, 93]
plotaxes.ylimits = [-5, 6]
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.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -0.9
plotitem.pcolor_cmax = 0.9
plotitem.add_colorbar = True
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_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 = [1, 1, 0]
# Add contour lines of bathymetry:
plotitem = plotaxes.new_plotitem(plot_type="2d_contour")
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(-10.0, 0.0, 1.0)
plotitem.amr_contour_colors = ["k"] # color on each level
plotitem.kwargs = {"linestyles": "solid"}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add contour lines of topography:
plotitem = plotaxes.new_plotitem(plot_type="2d_contour")
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = arange(0.0, 11.0, 1.0)
plotitem.amr_contour_colors = ["g"] # color on each level
plotitem.kwargs = {"linestyles": "solid"}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# Add dashed contour line for shoreline
plotitem = plotaxes.new_plotitem(plot_type="2d_contour")
plotitem.plot_var = geoplot.topo
plotitem.contour_levels = [0.0]
plotitem.amr_contour_colors = ["k"] # color on each level
plotitem.kwargs = {"linestyles": "dashed"}
plotitem.amr_contour_show = [0, 0, 1] # show contours only on finest level
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
# -----------------------------------------
# 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 = "auto"
plotaxes.ylimits = [-2.0, 2.0]
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 patches alone
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name="patches", 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 = "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"]
plotitem.amr_celledges_show = [1, 1, 0]
plotitem.amr_patchedges_show = [1]
# -----------------------------------------
# Scatter plot of surface for radially symmetric
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name="Scatter", figno=200)
plotfigure.show = False
# Note: will not look very good unless more of domain is refined
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0.0, 100.0]
plotaxes.ylimits = [-1.5, 2.0]
plotaxes.title = "Scatter plot of surface"
# Set up for item on these axes:
plotitem = plotaxes.new_plotitem(plot_type="1d_from_2d_data")
plotitem.plot_var = geoplot.surface
def q_vs_radius(current_data):
from numpy import sqrt
x = current_data.x
y = current_data.y
r = sqrt(x ** 2 + y ** 2)
q = current_data.var
return r, q
plotitem.map_2d_to_1d = q_vs_radius
plotitem.plotstyle = "o"
plotitem.amr_color = ["b", "r", "g"]
plotaxes.afteraxes = "import pylab; pylab.legend(['Level 1','Level 2'])"
# -----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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 = [4, 5, 104, 105] # 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?
plotdata.parallel = True # make multiple frame png's at once
return plotdata
def setplot(plotdata=None):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
from clawpack.visclaw import colormaps, geoplot
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
plotdata.clearfigures() # clear any old figures,axes,items data
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user["drytol"] = 1.e-10
plotdata.beforeframe = set_drytol
#-----------------------------------------
# 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 = 'Depth'
plotaxes.scaled = False
def max_cmap(current_data):
q = current_data.q
return ((q[1, :, :].max()) * 1.1)
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.depth
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = cmax1
plotitem.add_colorbar = True
plotitem.colorbar_label = 'm'
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [-domain_y / 2.0, domain_y / 2.0]
# 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 = cmax1
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [-domain_y / 2.0, domain_y / 2.0]
# # Add contour lines of bathymetry:
# plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
# plotitem.plot_var = geoplot.topo
# from numpy import arange, linspace
# plotitem.contour_levels = linspace(-.1, 0.5, 20)
# plotitem.amr_contour_colors = ['k'] # color on each level
# plotitem.kwargs = {'linestyles':'solid'}
# plotitem.amr_contour_show = [1]
# plotitem.celledges_show = 0
# plotitem.patchedges_show = 0
# plotitem.show = True
#-----------------------------------------
# Figure for zoomed area pcolor plot
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='zoomed_pcolor', figno=1)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Depth for Zoomed area'
plotaxes.scaled = True
def max_cmap(current_data):
q = current_data.q
return ((q[1, :, :].max()) * 1.1)
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.depth
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = cmax1
plotitem.add_colorbar = True
plotitem.colorbar_label = 'm'
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [39.6, 42.0]
plotaxes.ylimits = [-domain_y / 2.0, domain_y / 2.0]
# 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 = cmax1
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [39.6, 42.0]
plotaxes.ylimits = [-domain_y / 2.0, domain_y / 2.0]
#-----------------------------------------
# Figure for centerline slice
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Centerline_slice', figno=2)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [0.0, hn * 2.0]
plotaxes.title = 'Centerline slice'
#def depth_plot(current_data):
#from pylab import plot, cos,sin,where,legend,nan
#t = current_data.t
#q = current_data.q
#q1 = q[1,:,centerline_index]
#x = numpy.linspace(0,domain_x,len(q1))
#plot(x, q1, 'k.', label="true solution", linewidth=2)
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = 0
plotitem.plotstyle = 'ko' ## need to be able to set amr_plotstyle
#plotitem.kwargs = {'markersize':3}
#plotitem.amr_show = [1] # plot on all levels
#plotaxes.afteraxes = depth_plot
#-----------------------------------------
# Figure for grids alone
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='grids', figno=3)
plotfigure.show = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [-domain_y / 2.0, domain_y / 2.0]
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]
#-----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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 = [] # 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?
plotdata.parallel = True # make multiple frame png's at once
return plotdata
def setplot(plotdata=None):
# --------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
from clawpack.visclaw import colormaps, geoplot
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
def change_fonts(current_data):
pylab.xticks(fontsize=21, fontname="Tex Gyre Pagella")
pylab.yticks(fontsize=21, fontname="Tex Gyre Pagella")
# "Fill the step area with a black rectangle."
import matplotlib.pyplot as plt
rectangle = plt.Rectangle((x1, y1), 0.4, 0.4, color="k", fill=True)
plt.gca().add_patch(rectangle)
def change_fonts2(current_data):
pylab.xticks(fontsize=17, fontname="Tex Gyre Pagella")
pylab.yticks(fontsize=17, fontname="Tex Gyre Pagella")
# "Fill the step area with a black rectangle."
import matplotlib.pyplot as plt
rectangle = plt.Rectangle((x1,y1),0.4,0.4,color="k",fill=True)
plt.gca().add_patch(rectangle)
plotdata.clearfigures() # clear any old figures,axes,items data
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user["drytol"] = 1.e-20
plotdata.beforeframe = set_drytol
# -----------------------------------------
# Figure for pcolor plot
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name='pcolor', figno=0)
plotfigure.kwargs = {'figsize':[10,10],'facecolor':'white'}
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Depth'
plotaxes.scaled = False
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.depth
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = cmax1
plotitem.add_colorbar = True
plotitem.colorbar_label = 'm'
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [-domain_y/2.0, domain_y/2.0]
# 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 = cmax1
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [-domain_y/2.0, domain_y/2.0]
# # Add contour lines of bathymetry:
# plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
# plotitem.plot_var = geoplot.topo
# from numpy import arange, linspace
# plotitem.contour_levels = linspace(-.1, 0.5, 20)
# plotitem.amr_contour_colors = ['k'] # color on each level
# plotitem.kwargs = {'linestyles':'solid'}
# plotitem.amr_contour_show = [1]
# plotitem.celledges_show = 0
# plotitem.patchedges_show = 0
# plotitem.show = True
plotaxes.afteraxes = change_fonts
# -----------------------------------------
# Figure for zoomed area pcolor plot
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name='zoomed_pcolor', figno=1)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes('pcolor')
plotaxes.title = 'Depth for Zoomed area'
plotaxes.scaled = False
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.depth
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = 0.0
plotitem.pcolor_cmax = cmax1
plotitem.add_colorbar = True
plotitem.colorbar_label = 'm'
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [39.4, 42.0]
plotaxes.ylimits = [-domain_y/2.0, domain_y/2.0]
# 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 = cmax1
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 0
plotaxes.xlimits = [39.5, 42.0]
plotaxes.ylimits = [-domain_y/2.0, domain_y/2.0]
plotaxes.afteraxes = change_fonts2
# -----------------------------------------
# Figure for centerline slice
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Centerline_slice', figno=2)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [0.0, hn*2.0]
plotaxes.title = 'Centerline slice'
# def depth_plot(current_data):
#from pylab import plot, cos,sin,where,legend,nan
#t = current_data.t
#q = current_data.q
#q1 = q[1,:,centerline_index]
#x = numpy.linspace(0,domain_x,len(q1))
#plot(x, q1, 'k.', label="true solution", linewidth=2)
plotitem = plotaxes.new_plotitem(plot_type='1d_plot')
plotitem.plot_var = 0
plotitem.plotstyle = 'ko' # need to be able to set amr_plotstyle
#plotitem.kwargs = {'markersize':3}
# plotitem.amr_show = [1] # plot on all levels
#plotaxes.afteraxes = depth_plot
# -----------------------------------------
# Figure for grids alone
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name='grids', figno=3)
plotfigure.show = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [-domain_y/2.0, domain_y/2.0]
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]
# -----------------------------------------
# Figure for cross section at y=0
# -----------------------------------------
plotfigure = plotdata.new_plotfigure(name='cross-section', figno=4)
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = [0.0, domain_x]
plotaxes.ylimits = [0.0, cmax1*1.80]
plotaxes.title = 'Cross section at y=0'
def plot_topo_xsec(current_data):
from pylab import plot, cos, sin, where, legend, nan
t = current_data.t
x = numpy.linspace(0.0, domain_x, 201)
#y = 0.
B = where(x > 40.0, where(x < 40.50, 7.0, 0.0), 0.0)
plot(x, B, 'g', label="bathymetry")
legend()
pylab.xticks(fontsize=21, fontname="Tex Gyre Pagella")
pylab.yticks(fontsize=21, fontname="Tex Gyre Pagella")
plotaxes.afteraxes = plot_topo_xsec
plotitem = plotaxes.new_plotitem(plot_type='1d_from_2d_data')
def xsec(current_data):
# Return x value and surface depth at this point, along y=0
from pylab import where, ravel
x = current_data.x
y = ravel(current_data.y)
dy = current_data.dy
q = current_data.q
ij = where((y <= dy/2.) & (y > -dy/2.))
x_slice = ravel(x)[ij]
ij1 = where((x_slice > 40.50) | (x_slice < 40.0))
x_slice = x_slice[ij1]
depth_slice = ravel(q[0, :, :])[ij]
depth_slice = depth_slice[ij1]
return x_slice, depth_slice
plotitem.map_2d_to_1d = xsec
plotitem.plotstyle = 'k-x' # need to be able to set amr_plotstyle
plotitem.kwargs = {'markersize': 4}
plotitem.amr_show = [1] # plot on all levels
# -----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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 = [] # 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?
plotdata.parallel = True # make multiple frame png's at once
return plotdata
def setplot(plotdata=None):
#--------------------------
"""
Specify what is to be plotted at each frame.
Input: plotdata, an instance of pyclaw.plotters.data.ClawPlotData.
Output: a modified version of plotdata.
"""
from clawpack.visclaw import colormaps, geoplot
from bay import Bay
mobile = Bay('bay.info')
if plotdata is None:
from clawpack.visclaw.data import ClawPlotData
plotdata = ClawPlotData()
plotdata.clearfigures() # clear any old figures,axes,items data
def set_drytol(current_data):
# The drytol parameter is used in masking land and water and
# affects what color map is used for cells with small water depth h.
# The cell will be plotted as dry if h < drytol.
# The best value to use often depends on the application and can
# be set here (measured in meters):
current_data.user["drytol"] = 1.e-3
plotdata.beforeframe = set_drytol
#-----------------------------------------
# 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
# Water
plotitem = plotaxes.new_plotitem(plot_type='2d_pcolor')
plotitem.plot_var = geoplot.surface
plotitem.pcolor_cmap = geoplot.tsunami_colormap
plotitem.pcolor_cmin = -0.5
plotitem.pcolor_cmax = 0.5
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 = mobile.z0
plotitem.pcolor_cmax = mobile.z_r
plotitem.add_colorbar = False
plotitem.amr_celledges_show = [0, 0, 0]
plotitem.patchedges_show = 1
plotaxes.xlimits = [mobile.x_o1, mobile.x_o2]
plotaxes.ylimits = [mobile.y0, mobile.y_r]
# Add contour lines of bathymetry:
plotitem = plotaxes.new_plotitem(plot_type='2d_contour')
plotitem.plot_var = geoplot.topo
from numpy import arange, linspace
plotitem.contour_levels = linspace(mobile.z0, mobile.z_r, 10)
plotitem.amr_contour_colors = ['k'] # color on each level
plotitem.kwargs = {'linestyles': 'solid'}
plotitem.amr_contour_show = [1]
plotitem.celledges_show = 0
plotitem.patchedges_show = 0
plotitem.show = True
#-----------------------------------------
# Figures for gauges
#-----------------------------------------
plotfigure = plotdata.new_plotfigure(name='Surface & topo',
figno=1,
type='each_gauge')
plotfigure.clf_each_gauge = True
# Set up for axes in this figure:
plotaxes = plotfigure.new_plotaxes()
plotaxes.xlimits = 'auto'
plotaxes.ylimits = [-1.5, 1.5]
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
#-----------------------------------------
# Parameters used only when creating html and/or latex hardcopy
# e.g., via pyclaw.plotters.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?
plotdata.parallel = True # make multiple frame png's at once
return plotdata
