def create_legend(self, img, x, y, dw, dh, x_start, x_end, y_range):
x_axis_type = 'linear' if self.transfer_function == 'linear' else 'log'
legend_fig = Figure(x_range=(x_start, x_end),
plot_height=max(dh, 50),
plot_width=self.plot_width,
lod_threshold=None,
toolbar_location=None,
y_range=y_range,
x_axis_type=x_axis_type)
legend_fig.min_border_top = 0
legend_fig.min_border_bottom = 10
legend_fig.min_border_left = 15
legend_fig.min_border_right = 15
legend_fig.yaxis.visible = False
legend_fig.grid.grid_line_alpha = 0
legend_fig.image_rgba(image=[img],
x=[x],
y=[y],
dw=[dw],
dh=[dh],
dw_units='screen')
return legend_fig
def create_ramp_legend(agg, cmap, how='linear', width=600):
'''
Helper function to create a Bokeh ``Figure`` object
with a color ramp corresponding to input aggregate and transfer function.
Parameters
----------
agg : xarray
Datashader aggregate object (e.g. result of Canvas.points())
cmap : list of colors or matplotlib.colors.Colormap, optional
The colormap to use. Can be either a list of colors (in any of the
formats described above), or a matplotlib colormap object.
how : str
Datashader transfer function name (either linear or log)
width : int
Width in pixels of resulting legend figure (default=600)
'''
vals_arr, min_val, max_val = summarize_aggregate_values(agg, how=how)
img = tf.shade(vals_arr, cmap=cmap, how=how)
x_axis_type = how
assert x_axis_type == 'linear' or x_axis_type == 'log'
legend_fig = Figure(x_range=(min_val, max_val),
plot_height=50,
plot_width=width,
lod_threshold=None,
toolbar_location=None,
y_range=(0, 18),
x_axis_type=x_axis_type)
legend_fig.min_border_top = 0
legend_fig.min_border_bottom = 10
legend_fig.min_border_left = 15
legend_fig.min_border_right = 15
legend_fig.yaxis.visible = False
legend_fig.grid.grid_line_alpha = 0
legend_fig.image_rgba(image=[img.values],
x=[min_val],
y=[0],
dw=[max_val - min_val],
dh=[18],
dw_units='screen')
return legend_fig
def create_ramp_legend(agg, cmap, how='linear', width=600):
'''
Helper function to create a Bokeh ``Figure`` object
with a color ramp corresponding to input aggregate and transfer function.
Parameters
----------
agg : xarray
Datashader aggregate object (e.g. result of Canvas.points())
cmap : list of colors or matplotlib.colors.Colormap, optional
The colormap to use. Can be either a list of colors (in any of the
formats described above), or a matplotlib colormap object.
how : str
Datashader transfer function name (either linear or log)
width : int
Width in pixels of resulting legend figure (default=600)
'''
vals_arr, min_val, max_val = summarize_aggregate_values(agg, how=how)
img = tf.shade(vals_arr, cmap=cmap, how=how)
x_axis_type = how
assert x_axis_type == 'linear' or x_axis_type == 'log'
legend_fig = Figure(x_range=(min_val, max_val),
plot_height=50,
plot_width=width,
lod_threshold=None,
toolbar_location=None,
y_range=(0, 18),
x_axis_type=x_axis_type)
legend_fig.min_border_top = 0
legend_fig.min_border_bottom = 10
legend_fig.min_border_left = 15
legend_fig.min_border_right = 15
legend_fig.yaxis.visible = False
legend_fig.grid.grid_line_alpha = 0
legend_fig.image_rgba(image=[img.values],
x=[min_val],
y=[0],
dw=[max_val - min_val],
dh=[18],
dw_units='screen')
return legend_fig
ymax = 4979238.441
path = './data/projected.tif'
fig = Figure(x_range=(xmin, xmax),
y_range=(ymin, ymax),
plot_height=600,
plot_width=900,
tools='pan,wheel_zoom')
fig.background_fill_color = 'black'
fig.add_tile(STAMEN_TONER, alpha=0) # used to set axis ranges
fig.x_range.callback = CustomJS(code=dims_jscode, args=dict(plot=fig, dims=dims))
fig.y_range.callback = CustomJS(code=dims_jscode, args=dict(plot=fig, dims=dims))
fig.axis.visible = False
fig.grid.grid_line_alpha = 0
fig.min_border_left = 0
fig.min_border_right = 0
fig.min_border_top = 0
fig.min_border_bottom = 0
image_source = ColumnDataSource(dict(image=[], x=[], y=[], dw=[], dh=[]))
fig.image_rgba(source=image_source,
image='image',
x='x',
y='y',
dw='dw',
dh='dh',
dilate=False)
curdoc().add_root(fig)
plot_width=900,
tools='pan,wheel_zoom')
fig.background_fill_color = 'black'
fig.add_tile(get_provider("STAMEN_TONER"), alpha=.3)
fig.x_range.callback = CustomJS(code=dims_jscode,
args=dict(plot=fig, dims=dims))
fig.y_range.callback = CustomJS(code=dims_jscode,
args=dict(plot=fig, dims=dims))
fig.axis.visible = False
fig.grid.grid_line_alpha = 0
fig.min_border_left = 0
fig.min_border_right = 0
fig.min_border_top = 0
fig.min_border_bottom = 0
image_source = ColumnDataSource(dict(image=[], x=[], y=[], dw=[], dh=[]))
fig.image_rgba(source=image_source,
image='image',
x='x',
y='y',
dw='dw',
dh='dh',
dilate=False)
time_text = Paragraph(text='Time Period: 00:00 - 00:00')
controls = HBox(children=[time_text, time_select], width=fig.plot_width)
layout = VBox(children=[fig, controls])
curdoc().add_root(layout)
curdoc().add_periodic_callback(update_data, 1000)
def plot_cross_section_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, cross_data, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and cross-sections using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all is a list of velocity profiles
cross_lat_data_all is a list of cross-sections along latitude
lat_value_all is a list of corresponding latitudes for these cross-sections
cross_lon_data_all is a list of cross-sections along longitude
lon_value_all is a list of corresponding longitudes for these cross-sections
boundary_data is a list of boundaries
style_parameter contains parameters to customize the plots
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
palette_r = palette[::-1]
ncolor = len(palette_r)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min)*1./ncolor
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append('Depth: {0:8.0f} km'.format(map_depth))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_all_slices.append(map_color)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']],\
dh=[style_parameter['nlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
#
plot_depth = np.shape(cross_data)[0] * style_parameter['cross_ddepth']
plot_lon = great_arc_distance(style_parameter['cross_default_lat0'], style_parameter['cross_default_lon0'],\
style_parameter['cross_default_lat1'], style_parameter['cross_default_lon1'])
vs_min = style_parameter['cross_view_vs_min']
vs_max = style_parameter['cross_view_vs_max']
cross_color = val_to_rgb(cross_data, palette_r, vmin, vmax)
cross_data_bokeh = ColumnDataSource(data=dict(x=[0],\
y=[plot_depth],dw=[plot_lon],\
dh=[plot_depth],cross_data=[cross_color]))
map_line_bokeh = ColumnDataSource(data=dict(lat=[style_parameter['cross_default_lat0'], style_parameter['cross_default_lat1']],\
lon=[style_parameter['cross_default_lon0'], style_parameter['cross_default_lon1']]))
#
ncolor_cross = len(my_palette)
colorbar_top_cross = [0.1 for i in range(ncolor_cross)]
colorbar_bottom_cross = [0 for i in range(ncolor_cross)]
color_min_cross = style_parameter['cross_view_vs_min']
color_max_cross = style_parameter['cross_view_vs_max']
color_step_cross = (color_max_cross - color_min_cross)*1./ncolor_cross
colorbar_left_cross = np.linspace(color_min_cross, color_max_cross-color_step_cross, ncolor_cross)
colorbar_right_cross = np.linspace(color_min_cross+color_step_cross, color_max_cross, ncolor_cross)
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',\
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50, \
callback=depth_slider_callback)
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.line('lon', 'lat', source=map_line_bokeh, line_dash=[8,2,8,2], line_color='#00ff00',\
nonselection_line_alpha=1.0, line_width=5.,\
nonselection_line_color='black')
map_view.text([style_parameter['cross_default_lon0']],[style_parameter['cross_default_lat0']], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
map_view.text([style_parameter['cross_default_lon1']],[style_parameter['cross_default_lat1']], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location=None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text="""<p style="font-size:16px">""", \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
# plot cross-section along latitude
cross_section_plot_width = int(style_parameter['cross_plot_height']*1.0/plot_depth*plot_lon/10.)
cross_view = Figure(plot_width=cross_section_plot_width, plot_height=style_parameter['cross_plot_height'], \
tools=style_parameter['cross_view_tools'], title=style_parameter['cross_view_title'], \
y_range=[plot_depth, -30],\
x_range=[0, plot_lon])
cross_view.image_rgba('cross_data',x='x',\
y='y',dw='dw',dh='dh',\
source=cross_data_bokeh, level='image')
cross_view.text([plot_lon*0.1], [-10], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
cross_view.text([plot_lon*0.9], [-10], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
# ------------------------------
# change style
cross_view.title.text_font_size = style_parameter['title_font_size']
cross_view.title.align = 'center'
cross_view.title.text_font_style = 'normal'
cross_view.xaxis.axis_label = style_parameter['cross_view_xlabel']
cross_view.xaxis.axis_label_text_font_style = 'normal'
cross_view.xaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.xaxis.major_label_text_font_size = xlabel_fontsize
cross_view.yaxis.axis_label = style_parameter['cross_view_ylabel']
cross_view.yaxis.axis_label_text_font_style = 'normal'
cross_view.yaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.yaxis.major_label_text_font_size = xlabel_fontsize
cross_view.xgrid.grid_line_color = None
cross_view.ygrid.grid_line_color = None
cross_view.toolbar.logo = None
cross_view.toolbar_location = 'right'
cross_view.toolbar_sticky = False
# ==============================
colorbar_fig_right = Figure(tools=[], y_range=(0,0.1),plot_width=cross_section_plot_width, \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig_right.toolbar_location=None
colorbar_fig_right.quad(top=colorbar_top_cross,bottom=colorbar_bottom_cross,\
left=colorbar_left_cross,right=colorbar_right_cross,\
color=my_palette)
colorbar_fig_right.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig_right.xgrid.grid_line_color = None
colorbar_fig_right.ygrid.grid_line_color = None
colorbar_fig_right.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig_right.title.text_font_size = xlabel_fontsize
colorbar_fig_right.title.align = 'center'
colorbar_fig_right.title.text_font_style = 'normal'
# ==============================
output_file(filename,title=style_parameter['html_title'], mode=style_parameter['library_source'])
left_column = Column(depth_slider, map_view, colorbar_fig, annotating_fig01, width=style_parameter['left_column_width'])
right_column = Column(annotating_fig02, cross_view, colorbar_fig_right, width=cross_section_plot_width)
layout = Row(left_column, right_column, height=800)
save(layout)
def plot_3DModel_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, profile_data_all, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and velocity profiles using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all constains the velocity model parameters saved for profile plots
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
ncolor = len(palette)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min) * 1. / ncolor
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append(
'Depth: {0:8.1f} km'.format(map_depth))
#
palette_r = palette[::-1]
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[
style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[
style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[
style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth],
left=[style_parameter['profile_plot_xmin']], \
right=[style_parameter['profile_plot_xmax']]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
#
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_2d = map_color.view('uint32').reshape(map_color.shape[:2])
map_color_all_slices.append(map_color_2d)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']*style_parameter['dlon']],\
dh=[style_parameter['nlat']*style_parameter['dlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
# ------------------------------
nprofile = len(profile_data_all)
grid_lat_list = []
grid_lon_list = []
width_list = []
height_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
grid_lat_list.append(aprofile['lat'])
grid_lon_list.append(aprofile['lon'])
width_list.append(style_parameter['map_view_grid_width'])
height_list.append(style_parameter['map_view_grid_height'])
grid_data_bokeh = ColumnDataSource(data=dict(lon=grid_lon_list,lat=grid_lat_list,\
width=width_list, height=height_list))
profile_default_index = style_parameter['profile_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[grid_lat_list[profile_default_index]], \
lon=[grid_lon_list[profile_default_index]], \
width=[style_parameter['map_view_grid_width']],\
height=[style_parameter['map_view_grid_height']],\
index=[profile_default_index]))
# ------------------------------
profile_vs_all = []
profile_depth_all = []
profile_ndepth = style_parameter['profile_ndepth']
profile_lat_label_list = []
profile_lon_label_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
vs_raw = aprofile['vs']
top_raw = aprofile['top']
profile_lat_label_list.append('Lat: {0:12.1f}'.format(aprofile['lat']))
profile_lon_label_list.append('Lon: {0:12.1f}'.format(aprofile['lon']))
vs_plot = []
depth_plot = []
for idepth in range(profile_ndepth):
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth])
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth + 1])
profile_vs_all.append(vs_plot)
profile_depth_all.append(depth_plot)
profile_data_all_bokeh = ColumnDataSource(data=dict(profile_vs_all=profile_vs_all, \
profile_depth_all=profile_depth_all))
selected_profile_data_bokeh = ColumnDataSource(data=dict(vs=profile_vs_all[profile_default_index],\
depth=profile_depth_all[profile_default_index]))
selected_profile_lat_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lat_label_x']], y=[style_parameter['profile_lat_label_y']],\
lat_label=[profile_lat_label_list[profile_default_index]]))
selected_profile_lon_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lon_label_x']], y=[style_parameter['profile_lon_label_y']],\
lon_label=[profile_lon_label_list[profile_default_index]]))
all_profile_lat_label_bokeh = ColumnDataSource(data=dict(
profile_lat_label_list=profile_lat_label_list))
all_profile_lon_label_bokeh = ColumnDataSource(data=dict(
profile_lon_label_list=profile_lon_label_list))
#
button_ndepth = style_parameter['button_ndepth']
button_data_all_vs = []
button_data_all_vp = []
button_data_all_rho = []
button_data_all_top = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
button_data_all_vs.append(aprofile['vs'][:button_ndepth])
button_data_all_vp.append(aprofile['vp'][:button_ndepth])
button_data_all_rho.append(aprofile['rho'][:button_ndepth])
button_data_all_top.append(aprofile['top'][:button_ndepth])
button_data_all_bokeh = ColumnDataSource(data=dict(button_data_all_vs=button_data_all_vs,\
button_data_all_vp=button_data_all_vp,\
button_data_all_rho=button_data_all_rho,\
button_data_all_top=button_data_all_top))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50)
depth_slider.js_on_change('value', depth_slider_callback)
depth_slider_callback.args["depth_index"] = depth_slider
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.rect('lon', 'lat', width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='gray', line_alpha=0.5, \
selection_line_color='gray', selection_line_alpha=0.5, selection_fill_color=None,\
nonselection_line_color='gray',nonselection_line_alpha=0.5, nonselection_fill_color=None,\
source=grid_data_bokeh, color=None, line_width=1, level='glyph')
map_view.rect('lon', 'lat',width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='#00ff00', line_alpha=1.0, \
source=selected_dot_on_map_bokeh, fill_color=None, line_width=3.,level='glyph')
# ------------------------------
grid_data_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh, \
grid_data_bokeh=grid_data_bokeh,\
profile_data_all_bokeh=profile_data_all_bokeh,\
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh, \
), code="""
var inds = cb_obj.indices
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][inds]]
selected_dot_on_map_bokeh.data['index'] = [inds]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][inds]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][inds]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[inds]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[inds]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
grid_data_bokeh.selected.js_on_change('indices', grid_data_js)
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location = None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
profile_xrange = Range1d(start=style_parameter['profile_plot_xmin'],
end=style_parameter['profile_plot_xmax'])
profile_yrange = Range1d(start=style_parameter['profile_plot_ymax'],
end=style_parameter['profile_plot_ymin'])
profile_fig = Figure(plot_width=style_parameter['profile_plot_width'], plot_height=style_parameter['profile_plot_height'],\
x_range=profile_xrange, y_range=profile_yrange, tools=style_parameter['profile_tools'],\
title=style_parameter['profile_title'])
profile_fig.line('vs',
'depth',
source=selected_profile_data_bokeh,
line_width=2,
line_color='black')
# ------------------------------
# add lat, lon
profile_fig.rect([style_parameter['profile_label_box_x']], [style_parameter['profile_label_box_y']],\
width=style_parameter['profile_label_box_width'], height=style_parameter['profile_label_box_height'],\
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black',\
level='underlay')
profile_fig.text('x',
'y',
'lat_label',
source=selected_profile_lat_label_bokeh)
profile_fig.text('x',
'y',
'lon_label',
source=selected_profile_lon_label_bokeh)
# ------------------------------
# change style
profile_fig.xaxis.axis_label = style_parameter['profile_xlabel']
profile_fig.xaxis.axis_label_text_font_style = 'normal'
profile_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.xaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.axis_label = style_parameter['profile_ylabel']
profile_fig.yaxis.axis_label_text_font_style = 'normal'
profile_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.xgrid.grid_line_dash = [4, 2]
profile_fig.ygrid.grid_line_dash = [4, 2]
profile_fig.title.text_font_size = style_parameter['title_font_size']
profile_fig.title.align = 'center'
profile_fig.title.text_font_style = 'normal'
profile_fig.toolbar_location = 'above'
profile_fig.toolbar_sticky = False
profile_fig.toolbar.logo = None
# ==============================
profile_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
grid_data_bokeh=grid_data_bokeh, \
profile_data_all_bokeh=profile_data_all_bokeh, \
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh), code="""
var p_index = Math.round(cb_obj.value)
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][p_index]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][p_index]]
selected_dot_on_map_bokeh.data['index'] = [p_index]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][p_index]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][p_index]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[p_index]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[p_index]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
profile_slider = Slider(start=0, end=nprofile-1, value=style_parameter['profile_default_index'], \
step=1, title=style_parameter['profile_slider_title'], \
width=style_parameter['profile_plot_width'], height=50)
profile_slider_callback.args['profile_index'] = profile_slider
profile_slider.js_on_change('value', profile_slider_callback)
# ==============================
simple_text_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "# Layer Top (km) Vs(km/s) Vp(km/s) Rho(g/cm^3) \\n"
while(button_data_vp[i]) {
temp+=button_data_top[i].toPrecision(6) + " " + button_data_vs[i].toPrecision(4) + " " + \
button_data_vp[i].toPrecision(4) + " " + button_data_rho[i].toPrecision(4) + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
simple_text_button = Button(
label=style_parameter['simple_text_button_label'],
button_type='default',
width=style_parameter['button_width'])
simple_text_button.js_on_click(simple_text_button_callback)
# ------------------------------
model96_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var lat = selected_dot_on_map_bokeh.data['lat']
var lon = selected_dot_on_map_bokeh.data['lon']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "MODEL." + index + " \\n"
temp += "ShearVelocityModel Lat: "+ lat +" Lon: " + lon + "\\n"
temp += "ISOTROPIC \\n"
temp += "KGS \\n"
temp += "SPHERICAL EARTH \\n"
temp += "1-D \\n"
temp += "CONSTANT VELOCITY \\n"
temp += "LINE08 \\n"
temp += "LINE09 \\n"
temp += "LINE10 \\n"
temp += "LINE11 \\n"
temp += " H(KM) VP(KM/S) VS(KM/S) RHO(GM/CC) QP QS ETAP ETAS FREFP FREFS \\n"
while(button_data_vp[i+1]) {
var thickness = button_data_top[i+1] - button_data_top[i]
temp+=" " +thickness.toPrecision(6) + " " + button_data_vp[i].toPrecision(4) + " " + button_data_vs[i].toPrecision(4) \
+ " " + button_data_rho[i].toPrecision(4) + " 0.00 0.00 0.00 0.00 1.00 1.00" + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model96.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
model96_button = Button(label=style_parameter['model96_button_label'],
button_type='default',
width=style_parameter['button_width'])
model96_button.js_on_click(model96_button_callback)
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
left_column = Column(depth_slider,
map_view,
colorbar_fig,
annotating_fig01,
width=style_parameter['left_column_width'])
button_pannel = Row(simple_text_button, model96_button)
right_column = Column(profile_slider,
profile_fig,
button_pannel,
annotating_fig02,
width=style_parameter['right_column_width'])
layout = Row(left_column, right_column)
save(layout)
x0=[mandelbrot_settings.x0], # image origin x
y0=[mandelbrot_settings.y0], # image origin y
xw=[mandelbrot_settings.xw], # image width
yw=[mandelbrot_settings.yw], # image height
freq=[mandelbrot_settings.freq_init] # frequency of the colormap
))
source_view = ColumnDataSource(data=dict(x_start=[mandelbrot_settings.x0], # image origin x
y_start=[mandelbrot_settings.y0], # image origin y
x_end=[mandelbrot_settings.x1], # image final x
y_end=[mandelbrot_settings.y1], # image final y
))
plot.image_rgba(image='image', # image data from data source
x='x0', # image origin x
y='y0', # image origin y
dw='xw', # image width
dh='yw', # image height
source=source_image) # corresponding data source
# Turn off tick labels
plot.axis.formatter = PrintfTickFormatter(format=" ") # create formatter
plot.axis.major_tick_line_color = None # turn off major ticks
plot.axis.minor_tick_line_color = None # turn off minor ticks
def update_colormap(attrname, old, new_frequency):
"""
updates the coloring of the plot.
:param attrname: unused, but needed for bokeh callback functions
:param old: unused, but needed for bokeh callback functions
:param new_frequency: new value for the frequency
xw=[mandelbrot_settings.xw], # image width
yw=[mandelbrot_settings.yw], # image height
freq=[mandelbrot_settings.freq_init] # frequency of the colormap
))
source_view = ColumnDataSource(data=dict(
x_start=[mandelbrot_settings.x0], # image origin x
y_start=[mandelbrot_settings.y0], # image origin y
x_end=[mandelbrot_settings.x1], # image final x
y_end=[mandelbrot_settings.y1], # image final y
))
plot.image_rgba(
image='image', # image data from data source
x='x0', # image origin x
y='y0', # image origin y
dw='xw', # image width
dh='yw', # image height
source=source_image) # corresponding data source
# Turn off tick labels
plot.axis.formatter = PrintfTickFormatter(format=" ") # create formatter
plot.axis.major_tick_line_color = None # turn off major ticks
plot.axis.minor_tick_line_color = None # turn off minor ticks
def update_colormap(attrname, old, new_frequency):
"""
updates the coloring of the plot.
:param attrname: unused, but needed for bokeh callback functions
:param old: unused, but needed for bokeh callback functions
