label_data = ColumnDataSource(
data=dict(x=[1, 2, 3], y=[0, 0, 0], t=['Original', 'Normal', 'Uniform']))
label_set = LabelSet(x='x',
y='y',
text='t',
y_offset=-4,
source=label_data,
render_mode='css',
text_baseline="top",
text_align='center')
p.add_layout(label_set)
callback = CustomJS(args=dict(source=source, normal=normal, uniform=uniform),
code="""
const data = source.data;
for (var i = 0; i < data.y.length; i++) {
data.xn[i] = normal.compute(data.x[i] + 1);
}
for (var i = 0; i < data.y.length; i++) {
data.xu[i] = uniform.compute(data.x[i] + 2);
}
source.change.emit();
""")
button = Button(label='Press to apply Jitter!', width=300)
button.js_on_click(callback)
output_file("transform_jitter.html", title="Example Jitter Transform")
show(Column(button, p))
# Set up callbacks
def update_title(attrname, old, new):
plot.title.text = text.value
text.on_change('value', update_title)
def update_data(attrname, old, new):
# Get the current slider values
a = amplitude.value
b = offset.value
w = phase.value
k = freq.value
# Generate the new curve
x = np.linspace(0, 4 * np.pi, N)
y = a * np.sin(k * x + w) + b
source.data = dict(x=x, y=y)
for w in [offset, amplitude, phase, freq]:
w.on_change('value', update_data)
# Set up layouts and add to document
inputs = Column(text, offset, amplitude, phase, freq)
curdoc().add_root(row(inputs, plot, width=800))
curdoc().title = 'Sliders'
def plot_waveform_bokeh(filename,waveform_list,metadata_list,station_lat_list,\
station_lon_list, event_lat, event_lon, boundary_data, style_parameter):
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
map_station_location_bokeh = ColumnDataSource(data=dict(map_lat_list=station_lat_list,\
map_lon_list=station_lon_list))
dot_default_index = 0
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[station_lat_list[dot_default_index]],\
lon=[station_lon_list[dot_default_index]],\
index=[dot_default_index]))
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['map_view_plot_height'], \
y_range=[style_parameter['map_view_lat_min'],\
style_parameter['map_view_lat_max']], x_range=[style_parameter['map_view_lon_min'],\
style_parameter['map_view_lon_max']], tools=style_parameter['map_view_tools'],\
title=style_parameter['map_view_title'])
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='gray',\
line_width=2, level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='gray',\
level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='gray',\
line_width=2, nonselection_line_alpha=1.0, level='underlay',
nonselection_line_color='gray')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='gray',\
level='underlay', nonselection_line_alpha=1.0,\
nonselection_line_color='gray')
#
map_view.triangle('map_lon_list', 'map_lat_list', source=map_station_location_bokeh, \
line_color='gray', size=style_parameter['marker_size'], fill_color='black',\
selection_color='black', selection_line_color='gray',\
selection_fill_alpha=1.0,\
nonselection_fill_alpha=1.0, nonselection_fill_color='black',\
nonselection_line_color='gray', nonselection_line_alpha=1.0)
map_view.triangle('lon','lat', source=selected_dot_on_map_bokeh,\
size=style_parameter['selected_marker_size'], line_color='black',fill_color='red')
map_view.asterisk([event_lon], [event_lat], size=style_parameter['event_marker_size'], line_width=3, line_color='red', \
fill_color='red')
# 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
# --------------------------------------------------------
max_waveform_length = 0
max_waveform_amp = 0
ncurve = len(waveform_list)
for a_sta in waveform_list:
for a_trace in a_sta:
if len(a_trace) > max_waveform_length:
max_waveform_length = len(a_trace)
if np.max(np.abs(a_trace)) > max_waveform_amp:
max_waveform_amp = np.max(np.abs(a_trace))
#
plotting_list = []
for a_sta in waveform_list:
temp = []
for a_trace in a_sta:
if len(a_trace) < max_waveform_length:
a_trace = np.append(
a_trace, np.zeros([(max_waveform_length - len(a_trace)),
1]))
temp.append(list(a_trace))
plotting_list.append(temp)
#
time_list = []
for ista in range(len(plotting_list)):
a_sta = plotting_list[ista]
temp = []
for itr in range(len(a_sta)):
a_trace = a_sta[itr]
delta = metadata_list[ista][itr]['delta']
time = list(np.arange(len(a_trace)) * delta)
temp.append(time)
#
time_list.append(temp)
#
reftime_label_list = []
channel_label_list = []
for ista in range(len(metadata_list)):
temp_ref = []
temp_channel = []
a_sta = metadata_list[ista]
for a_trace in a_sta:
temp_ref.append('Starting from ' + a_trace['starttime'])
temp_channel.append(a_trace['network'] + '_' + a_trace['station'] +
'_' + a_trace['channel'])
reftime_label_list.append(temp_ref)
channel_label_list.append(temp_channel)
# --------------------------------------------------------
curve_fig01 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 0
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh01 = ColumnDataSource(
data=dict(time=select_curve_time, amp=select_curve_data))
select_reftime_label = reftime_label_list[dot_default_index][curve_index]
selected_reftime_label_bokeh01 = ColumnDataSource(data=dict(x=[style_parameter['curve_reftime_label_x']],\
y=[style_parameter['curve_reftime_label_y']],\
label=[select_reftime_label]))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh01 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
all_curve_data_bokeh = ColumnDataSource(
data=dict(t=time_list, amp=plotting_list))
all_reftime_label_bokeh = ColumnDataSource(data=dict(
label=reftime_label_list))
all_channel_label_bokeh = ColumnDataSource(data=dict(
label=channel_label_list))
# plot waveform
curve_fig01.line('time','amp', source=selected_curve_data_bokeh01,\
line_color='black')
# add refference time as a label
curve_fig01.text('x', 'y', 'label', source=selected_reftime_label_bokeh01)
# add channel label
curve_fig01.text('x', 'y', 'label', source=selected_channel_label_bokeh01)
# change style
curve_fig01.title.text_font_size = style_parameter['title_font_size']
curve_fig01.title.align = 'center'
curve_fig01.title.text_font_style = 'normal'
curve_fig01.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig01.xaxis.axis_label_text_font_style = 'normal'
curve_fig01.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig01.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig01.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig01.yaxis.axis_label_text_font_style = 'normal'
curve_fig01.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig01.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig01.toolbar.logo = None
curve_fig01.toolbar_location = 'above'
curve_fig01.toolbar_sticky = False
# --------------------------------------------------------
curve_fig02 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 1
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh02 = ColumnDataSource(
data=dict(time=select_curve_time, amp=select_curve_data))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh02 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
# plot waveform
curve_fig02.line('time','amp', source=selected_curve_data_bokeh02,\
line_color='black')
# add channel label
curve_fig02.text('x', 'y', 'label', source=selected_channel_label_bokeh02)
# change style
curve_fig02.title.text_font_size = style_parameter['title_font_size']
curve_fig02.title.align = 'center'
curve_fig02.title.text_font_style = 'normal'
curve_fig02.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig02.xaxis.axis_label_text_font_style = 'normal'
curve_fig02.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig02.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig02.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig02.yaxis.axis_label_text_font_style = 'normal'
curve_fig02.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig02.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig02.toolbar.logo = None
curve_fig02.toolbar_location = 'above'
curve_fig02.toolbar_sticky = False
# --------------------------------------------------------
curve_fig03 = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(-max_waveform_amp*1.05,max_waveform_amp*1.05), \
x_range=(0,max_waveform_length),\
tools=['save','box_zoom','ywheel_zoom','xwheel_zoom','reset','crosshair','pan'])
#
curve_index = 2
select_curve_data = plotting_list[dot_default_index][curve_index]
select_curve_time = time_list[dot_default_index][curve_index]
selected_curve_data_bokeh03 = ColumnDataSource(
data=dict(time=select_curve_time, amp=select_curve_data))
select_channel_label = channel_label_list[dot_default_index][curve_index]
selected_channel_label_bokeh03 = ColumnDataSource(data=dict(x=[style_parameter['curve_channel_label_x']],\
y=[style_parameter['curve_channel_label_y']],\
label=[select_channel_label]))
# plot waveform
curve_fig03.line('time','amp', source=selected_curve_data_bokeh03,\
line_color='black')
# add channel label
curve_fig03.text('x', 'y', 'label', source=selected_channel_label_bokeh03)
# change style
curve_fig03.title.text_font_size = style_parameter['title_font_size']
curve_fig03.title.align = 'center'
curve_fig03.title.text_font_style = 'normal'
curve_fig03.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig03.xaxis.axis_label_text_font_style = 'normal'
curve_fig03.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig03.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig03.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig03.yaxis.axis_label_text_font_style = 'normal'
curve_fig03.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig03.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig03.toolbar.logo = None
curve_fig03.toolbar_location = 'above'
curve_fig03.toolbar_sticky = False
# --------------------------------------------------------
map_station_location_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_station_location_bokeh=map_station_location_bokeh,\
selected_curve_data_bokeh01=selected_curve_data_bokeh01,\
selected_curve_data_bokeh02=selected_curve_data_bokeh02,\
selected_curve_data_bokeh03=selected_curve_data_bokeh03,\
selected_channel_label_bokeh01=selected_channel_label_bokeh01,\
selected_channel_label_bokeh02=selected_channel_label_bokeh02,\
selected_channel_label_bokeh03=selected_channel_label_bokeh03,\
selected_reftime_label_bokeh01=selected_reftime_label_bokeh01,\
all_reftime_label_bokeh=all_reftime_label_bokeh,\
all_channel_label_bokeh=all_channel_label_bokeh,\
all_curve_data_bokeh=all_curve_data_bokeh), code="""
var inds = cb_obj.indices
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_loc = map_station_location_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_loc['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_loc['map_lon_list'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh01.data['t'] = all_curve_data_bokeh.data['t'][inds][0]
selected_curve_data_bokeh01.data['amp'] = all_curve_data_bokeh.data['amp'][inds][0]
selected_curve_data_bokeh01.change.emit()
selected_curve_data_bokeh02.data['t'] = all_curve_data_bokeh.data['t'][inds][1]
selected_curve_data_bokeh02.data['amp'] = all_curve_data_bokeh.data['amp'][inds][1]
selected_curve_data_bokeh02.change.emit()
selected_curve_data_bokeh03.data['t'] = all_curve_data_bokeh.data['t'][inds][2]
selected_curve_data_bokeh03.data['amp'] = all_curve_data_bokeh.data['amp'][inds][2]
selected_curve_data_bokeh03.change.emit()
selected_reftime_label_bokeh01.data['label'] = [all_reftime_label_bokeh.data['label'][inds][0]]
selected_reftime_label_bokeh01.change.emit()
selected_channel_label_bokeh01.data['label'] = [all_channel_label_bokeh.data['label'][inds][0]]
selected_channel_label_bokeh01.change.emit()
selected_channel_label_bokeh02.data['label'] = [all_channel_label_bokeh.data['label'][inds][1]]
selected_channel_label_bokeh02.change.emit()
selected_channel_label_bokeh03.data['label'] = [all_channel_label_bokeh.data['label'][inds][2]]
selected_channel_label_bokeh03.change.emit()
""")
#
map_station_location_bokeh.selected.js_on_change('indices',
map_station_location_js)
#
curve_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_station_location_bokeh=map_station_location_bokeh,\
selected_curve_data_bokeh01=selected_curve_data_bokeh01,\
selected_curve_data_bokeh02=selected_curve_data_bokeh02,\
selected_curve_data_bokeh03=selected_curve_data_bokeh03,\
selected_channel_label_bokeh01=selected_channel_label_bokeh01,\
selected_channel_label_bokeh02=selected_channel_label_bokeh02,\
selected_channel_label_bokeh03=selected_channel_label_bokeh03,\
selected_reftime_label_bokeh01=selected_reftime_label_bokeh01,\
all_reftime_label_bokeh=all_reftime_label_bokeh,\
all_channel_label_bokeh=all_channel_label_bokeh,\
all_curve_data_bokeh=all_curve_data_bokeh),code="""
var inds = Math.round(cb_obj.value)
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_loc = map_station_location_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_loc['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_loc['map_lon_list'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh01.data['t'] = all_curve_data_bokeh.data['t'][inds][0]
selected_curve_data_bokeh01.data['amp'] = all_curve_data_bokeh.data['amp'][inds][0]
selected_curve_data_bokeh01.change.emit()
selected_curve_data_bokeh02.data['t'] = all_curve_data_bokeh.data['t'][inds][1]
selected_curve_data_bokeh02.data['amp'] = all_curve_data_bokeh.data['amp'][inds][1]
selected_curve_data_bokeh02.change.emit()
selected_curve_data_bokeh03.data['t'] = all_curve_data_bokeh.data['t'][inds][2]
selected_curve_data_bokeh03.data['amp'] = all_curve_data_bokeh.data['amp'][inds][2]
selected_curve_data_bokeh03.change.emit()
selected_reftime_label_bokeh01.data['label'] = [all_reftime_label_bokeh.data['label'][inds][0]]
selected_reftime_label_bokeh01.change.emit()
selected_channel_label_bokeh01.data['label'] = [all_channel_label_bokeh.data['label'][inds][0]]
selected_channel_label_bokeh01.change.emit()
selected_channel_label_bokeh02.data['label'] = [all_channel_label_bokeh.data['label'][inds][1]]
selected_channel_label_bokeh02.change.emit()
selected_channel_label_bokeh03.data['label'] = [all_channel_label_bokeh.data['label'][inds][2]]
selected_channel_label_bokeh03.change.emit()
""")
curve_slider = Slider(start=0, end=ncurve-1, value=style_parameter['curve_default_index'], \
step=1, title=style_parameter['curve_slider_title'], width=style_parameter['map_view_plot_width'],\
height=50)
curve_slider.js_on_change('value', curve_slider_callback)
curve_slider_callback.args['curve_index'] = curve_slider
# ==============================
# 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_fig = Column(curve_slider,
map_view,
annotating_fig01,
width=style_parameter['left_column_width'])
right_fig = Column(curve_fig01,
curve_fig02,
curve_fig03,
annotating_fig02,
width=style_parameter['right_column_width'])
layout = Row(left_fig, right_fig)
save(layout)
selectType.on_change("value",typeChange)
def updatePlot():
setMapColorRange()
newfig=plotAvgTrend()
graphColumn.children[2]=newfig
new_countryMap=createMap()
mapColum.children[0]=new_countryMap
def slider_update(attrname, old, new):
global year
year=new
new_countryMap=createMap()
mapColum.children[0]=new_countryMap
slider = Slider(start=1973, end=2017, value=2017, step=1, title="Year")
slider.on_change('value', slider_update)
div = Div(text="""
<p>All source data is from the Begian governement at <a href=https://statbel.fgov.be/en target="_blank">https://statbel.fgov.be/en</a>.</br>
All data are averages.</br>
For communes where no data was available at all the average was set at 0.</br>
For years where no data was available the average was set to the same value as the year before.</br>
<b>The interactive graph was made by <a href=mailto:[email protected] >Joris Meert</a> for educational purpose.</b></p>
""",width=700, height=100)
graphColumn=Column(selectType,Row(selectCommune1,selectCommune2),fig,selectYaxis,div)
mapColum=Column(countryMap,Row(slider,button))
layout=Row(graphColumn,mapColum)
curdoc().add_root(layout)
curdoc().title="Belgian house market"
def initialize_plot(self, plots=None, ranges=None):
ranges = self.compute_ranges(self.layout, self.keys[-1], None)
opts = self.layout.opts.get('plot', self.backend)
opts = {} if opts is None else opts.kwargs
plot_grid = self._compute_grid()
passed_plots = [] if plots is None else plots
r_offset = 0
col_offsets = defaultdict(int)
tab_plots = []
stretch_width = False
stretch_height = False
for r in range(self.rows):
# Compute row offset
row = [(k, sp) for k, sp in self.subplots.items() if k[0] == r]
row_padded = any(len(sp.layout) > 2 for k, sp in row)
if row_padded:
r_offset += 1
for c in range(self.cols):
subplot = self.subplots.get((r, c), None)
# Compute column offset
col = [(k, sp) for k, sp in self.subplots.items() if k[1] == c]
col_padded = any(len(sp.layout) > 1 for k, sp in col)
if col_padded:
col_offsets[r] += 1
c_offset = col_offsets.get(r, 0)
if subplot is None:
continue
shared_plots = list(passed_plots) if self.shared_axes else None
subplots = subplot.initialize_plot(ranges=ranges,
plots=shared_plots)
nsubplots = len(subplots)
modes = {
sp.sizing_mode
for sp in subplots
if sp.sizing_mode not in (None, 'auto', 'fixed')
}
sizing_mode = self.sizing_mode
if modes:
responsive_width = any(s in m for m in modes
for s in ('width', 'both'))
responsive_height = any(s in m for m in modes
for s in ('height', 'both'))
stretch_width |= responsive_width
stretch_height |= responsive_height
if responsive_width and responsive_height:
sizing_mode = 'stretch_both'
elif responsive_width:
sizing_mode = 'stretch_width'
elif responsive_height:
sizing_mode = 'stretch_height'
# If tabs enabled lay out AdjointLayout on grid
if self.tabs:
title = subplot.subplots['main']._format_title(
self.keys[-1], dimensions=False)
if not title:
title = ' '.join(self.paths[r, c])
if nsubplots == 1:
grid = subplots[0]
elif nsubplots == 2:
grid = gridplot([subplots],
merge_tools=self.merge_tools,
toolbar_location=self.toolbar,
sizing_mode=sizing_mode)
else:
grid = [[subplots[2], None], subplots[:2]]
grid = gridplot(children=grid,
merge_tools=self.merge_tools,
toolbar_location=self.toolbar,
sizing_mode=sizing_mode)
tab_plots.append((title, grid))
continue
# Situate plot in overall grid
if nsubplots > 2:
plot_grid[r + r_offset - 1][c + c_offset - 1] = subplots[2]
plot_column = plot_grid[r + r_offset]
if nsubplots > 1:
plot_column[c + c_offset - 1] = subplots[0]
plot_column[c + c_offset] = subplots[1]
else:
plot_column[c + c_offset - int(col_padded)] = subplots[0]
passed_plots.append(subplots[0])
if 'sizing_mode' in opts:
sizing_mode = opts['sizing_mode']
elif stretch_width and stretch_height:
sizing_mode = 'stretch_both'
elif stretch_width:
sizing_mode = 'stretch_width'
elif stretch_height:
sizing_mode = 'stretch_height'
else:
sizing_mode = None
# Wrap in appropriate layout model
if self.tabs:
plots = filter_toolboxes([p for t, p in tab_plots])
panels = [Panel(child=child, title=t) for t, child in tab_plots]
layout_plot = Tabs(tabs=panels, sizing_mode=sizing_mode)
else:
plot_grid = filter_toolboxes(plot_grid)
layout_plot = gridplot(children=plot_grid,
toolbar_location=self.toolbar,
merge_tools=self.merge_tools,
sizing_mode=sizing_mode)
title = self._get_title_div(self.keys[-1])
if title:
self.handles['title'] = title
layout_plot = Column(title, layout_plot, sizing_mode=sizing_mode)
self.handles['plot'] = layout_plot
self.handles['plots'] = plots
if self.shared_datasource:
self.sync_sources()
if self.top_level:
self.init_links()
self.drawn = True
return self.handles['plot']
def large_plot(n: int) -> Tuple[Model, Set[Model]]:
from bokeh.models import (
BoxSelectTool,
BoxZoomTool,
Column,
ColumnDataSource,
DataRange1d,
GlyphRenderer,
Grid,
Line,
LinearAxis,
PanTool,
Plot,
ResetTool,
SaveTool,
WheelZoomTool,
ZoomInTool,
ZoomOutTool,
)
col = Column()
objects: Set[Model] = {col}
for i in range(n):
source = ColumnDataSource(data=dict(x=[0, i + 1], y=[0, i + 1]))
xdr = DataRange1d()
ydr = DataRange1d()
plot = Plot(x_range=xdr, y_range=ydr)
xaxis = LinearAxis()
plot.add_layout(xaxis, "below")
yaxis = LinearAxis()
plot.add_layout(yaxis, "left")
xgrid = Grid(dimension=0)
plot.add_layout(xgrid, "center")
ygrid = Grid(dimension=1)
plot.add_layout(ygrid, "center")
tickers = [
xaxis.ticker, xaxis.formatter, yaxis.ticker, yaxis.formatter
]
glyph = Line(x='x', y='y')
renderer = GlyphRenderer(data_source=source, glyph=glyph)
plot.renderers.append(renderer)
pan = PanTool()
zoom_in = ZoomInTool()
zoom_out = ZoomOutTool()
wheel_zoom = WheelZoomTool()
box_zoom = BoxZoomTool()
box_select = BoxSelectTool()
save = SaveTool()
reset = ResetTool()
tools = [
pan, zoom_in, zoom_out, wheel_zoom, box_zoom, box_select, save,
reset
]
plot.add_tools(*tools)
col.children.append(plot)
objects |= set([
xdr,
ydr,
xaxis,
xaxis.major_label_policy,
yaxis,
yaxis.major_label_policy,
xgrid,
ygrid,
renderer,
renderer.view,
glyph,
source,
source.selected,
source.selection_policy,
plot,
plot.x_scale,
plot.y_scale,
plot.toolbar,
plot.title,
box_zoom.overlay,
box_select.overlay,
] + tickers + tools)
return col, objects
def FrecuenciaCardiaca(DatosBokeh, EjeX, MetricasAuxiliares):
"""
GRAFICO AREA | FRECUENCIA CARDIACA
"""
# Creacion del diccionario de metricas auxiliares
DiccionarioVariables = ParametrosVariables(DatosBokeh)
# Generacion del codigo JavaScript que habilita la visualizacion de metricas auxiliares
CodigoJS = GeneracionCodigoJS(MetricasAuxiliares)
# Asignacion de tamaño del punto en graficas discretas
SizeCircle = FunctionSizeCircle(DatosBokeh)
# Creacion de un grafica
PLT = figure(width=1000,
height=400,
x_range=(DatosBokeh.data[EjeX].min(),
DatosBokeh.data[EjeX].max()),
y_range=(LimiteEjeY(DatosBokeh, 'FRECUENCIA CARDIACA',
'Inferior'),
LimiteEjeY(DatosBokeh, 'FRECUENCIA CARDIACA',
'Superior')),
tools='',
toolbar_location=None)
# Creacion del area bajo la linea de la metrica a partir del CDS
Area = DatosBokeh.to_df().copy().reset_index()[[
EjeX, 'FrecuenciaCardiaca[ppm]'
]].set_index(EjeX)
Area.rename(columns={'FrecuenciaCardiaca[ppm]': 'Area'}, inplace=True)
AreaBottom = Area[::-1]
AreaBottom['Area'] = 0
PLT.patch(x=hstack((AreaBottom.index, Area.index)),
y=hstack((AreaBottom['Area'], Area['Area'])),
color=Grapefruit[1],
alpha=1,
line_color=None)
# Inclusion de datos
PLT_Linea = PLT.line(EjeX,
'FrecuenciaCardiaca[ppm]',
source=DatosBokeh,
color=Grapefruit[2],
line_width=2,
line_cap='round')
PLT.add_tools(
HoverTool(tooltips=[('', '@{FrecuenciaCardiaca[ppm]}{int} ppm')],
renderers=[PLT_Linea],
mode='vline'))
PLT_Max = PLT.inverted_triangle(
DatosBokeh.data[EjeX][list(
DatosBokeh.data['FrecuenciaCardiaca[ppm]']).index(
DatosBokeh.data['FrecuenciaCardiaca[ppm]'].max())],
DatosBokeh.data['FrecuenciaCardiaca[ppm]'].max(),
size=10,
line_color=DarkGray[1],
line_width=2,
fill_color=LightGray[1])
PLT.add_tools(
HoverTool(tooltips=[
('Maximo',
str(DatosBokeh.data['FrecuenciaCardiaca[ppm]'].max()) + ' ppm')
],
renderers=[PLT_Max],
mode='mouse'))
PLT.add_layout(
Span(location=DatosBokeh.data['FrecuenciaCardiaca[ppm]'].mean(),
dimension='width',
line_color=Grapefruit[0],
line_dash='dashed',
line_width=1,
line_alpha=1))
# Inclusion de lineas auxiliares
ListadoMetricasAuxiliares = {}
for Metrica in MetricasAuxiliares:
if DiccionarioVariables[Metrica]['Tipo'] == 'circle':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.circle(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['FRECUENCIA CARDIACA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
size=SizeCircle,
line_color=DiccionarioVariables[Metrica]['Color'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
elif DiccionarioVariables[Metrica]['Tipo'] == 'step':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.step(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['FRECUENCIA CARDIACA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
else:
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.line(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['FRECUENCIA CARDIACA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
# Atributos
PLT.title.text = 'FRECUENCIA CARDIACA'
PLT.sizing_mode = 'fixed'
PLT.yaxis.axis_label = 'Frecuencia cardiaca [ppm]'
PLT.yaxis.formatter = NumeralTickFormatter(format='0')
PLT.grid.visible = False
PLT.yaxis.minor_tick_line_color = None
PLT.yaxis.major_label_overrides = FormateoEjes(
DatosBokeh.data['FrecuenciaCardiaca[ppm]'], 10, 1)
# Asignacion de opciones de visualizacion del eje X
if EjeX == 'Distancia[m]':
PLT.xaxis.axis_label = 'Distancia'
PLT.xaxis.formatter = NumeralTickFormatter(format='0')
if DatosBokeh.data['Distancia[m]'].max() >= 4000:
PLT.xaxis.ticker = SingleIntervalTicker(interval=1000)
PLT.xaxis.major_label_overrides = FormateoEjes(
DatosBokeh.data['Distancia[m]'], 1000, 1000, 0, 0)
elif EjeX == 'TiempoActividad':
PLT.xaxis.axis_label = 'Tiempo actividad'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
elif EjeX == 'TiempoTotal':
PLT.xaxis.axis_label = 'Tiempo total'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
#Botones
Botones = CheckboxGroup(labels=MetricasAuxiliares,
active=[],
width=100,
height=380)
ListadoMetricasAuxiliares['checkbox'] = Botones
CodigoJSFrecuenciaCardiaca = CustomJS(code=CodigoJS,
args=ListadoMetricasAuxiliares)
Botones.js_on_click(CodigoJSFrecuenciaCardiaca)
#Layout
GridBotones = layout(
[Spacer(width=100, height=25),
Column(Botones, width=100, height=375)],
sizing_mode='fixed',
width=100,
height=400)
GridGrafica = gridplot([PLT, GridBotones],
ncols=2,
sizing_mode='stretch_width',
toolbar_location=None,
plot_width=1100,
plot_height=400)
return GridGrafica
args = document.session_context.request.arguments
try:
file = args['file'][0].decode('ascii')
user = args['user'][0].decode('ascii')
except KeyError as e:
raise KeyError(
str(e) +
'. Filename and username must be provided as request parameters.')
# find path for result data
results_path = join(UPLOAD_FOLDER, user, file, 'results')
p = Paragraph(text="", width=500)
layout = Column(children=[p])
layer_activation_source = ColumnDataSource(data=dict())
def create_image_grid(filters):
no_of_images = len(filters)
if no_of_images < 4:
no_of_rows = 1
elif no_of_images < 64:
no_of_rows = 4
else:
no_of_rows = 8
var data = source.data;
var dx = 6 / %d;
if (mode == 'None') {
data['x'] = [];
data['y'] = [];
}
else {
if (mode == 'Linear') { interp = linear; }
else if (mode == 'Step (before)') { interp = step; step.mode = 'before'; }
else if (mode == 'Step (center)') { interp = step; step.mode = 'center'; }
else if (mode == 'Step (after)') { interp = step; step.mode = 'after'; }
for (var i = 0; i < %d; i++) {
data['x'][i] = i * dx
data['y'][i] = interp.compute(data['x'][i])
}
}
source.change.emit()
""" % (N, N))
mode = Select(
title='Interpolation Mode',
value='None',
options=['None', 'Linear', 'Step (before)', 'Step (center)', 'Step (after)'],
callback=callback)
output_file("transform_interpolator.html", title="Example Transforms")
show(Column(WidgetBox(mode,width=300), p))
else:
#If the number of stations deployed is max, display message
print("Max number of stations reached")
#
#-----------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------
#-------------------------------This allows you to interact with the drone sim button to start the drone simulation-----------------------
#-----------------------------------------------------------------------------------------------------------------------------------------
#
def callbackSimRun(event):
eventTrigger(coordList)
#
#-----------------------------------------------------------------------------------------------------------------------------------------
#-----------------------------------------------------------------------------------------------------------------------------------------
#These plot the new stations and the button to run the simulation
p.on_event(DoubleTap, callback)
button_widget.on_event(ButtonClick, callbackSimRun)
#This sets the layout of the plot
layout=Column(p,widgetbox(button_widget))
#This updates the plot to the server
curdoc().add_root(layout)
def bar_cam(sni_csub, sni_dsub):
# make data sources
def make_camr_src(cmr):
data = {
'NVF': sni_csub[sni_csub['CAMR'] == cmr]['NVF'],
'NB': sni_csub[sni_csub['CAMR'] == cmr]['Count']
}
return data
def make_dr_src(dr):
data = {
'NVF': sni_dsub[sni_dsub['DR'] == dr]['NVF'],
'NB': sni_dsub[sni_dsub['DR'] == dr]['Count']
}
return data
# make plots
def make_plots(src1, src2):
nvf = list(sni_csub['NVF'].unique())
color_map = factor_cmap(field_name='NVF', palette=GnBu[8], factors=nvf)
# define figure and plot p1
p1 = figure(
title='Répartition du PTF de la CAM CORPORATE par classe de risque',
plot_height=450,
plot_width=600,
x_axis_label='Rating Class',
y_axis_label='Number of rated clients',
x_range=nvf,
toolbar_location='below')
p1.vbar(x='NVF', top='NB', source=src1, width=0.5, color='#2B7C75')
#p1.line('NVF', 'NB', source=src1, color='red')
p1.toolbar.active_drag = None
hover1 = HoverTool(tooltips=[('NVF', '@NVF'), ('NB Rated', '@NB')])
p1.add_tools(hover1)
labels1 = LabelSet(x='NVF',
y='NB',
text='NB',
level='glyph',
x_offset=0,
y_offset=1,
source=src1,
render_mode='canvas',
text_align='center',
text_font_style='bold',
text_font_size='10pt')
p1.add_layout(labels1)
# define figure and plot p2
p2 = figure(
title='Répartition du PTF de la CAM CORPORATE par classe de risque',
plot_height=450,
plot_width=600,
x_axis_label='Rating Class',
y_axis_label='Number of rated clients',
x_range=nvf,
toolbar_location='below')
p2.vbar(x='NVF', top='NB', source=src2, width=0.5, color='#2D7D3E')
#p2.line('NVF', 'NB', source=src2, color='black')
p2.toolbar.active_drag = None
hover2 = HoverTool(tooltips=[('NVF', '@NVF'), ('NB Rated', '@NB')])
p2.add_tools(hover2)
labels2 = LabelSet(x='NVF',
y='NB',
text='NB',
level='glyph',
x_offset=0,
y_offset=1,
source=src2,
render_mode='canvas',
text_align='center',
text_font_style='bold',
text_font_size='10pt')
p2.add_layout(labels2)
return p1, p2
# plot style
def style(p):
p.xgrid.visible = False
p.ygrid.visible = False
p.yaxis.visible = False
# title
p.title.align = 'center'
p.title.text_font_size = '12pt'
p.title.text_font = 'times'
# Axis titles
p.xaxis.axis_label_text_font_size = '11pt'
p.xaxis.axis_label_text_font_style = 'bold italic'
p.yaxis.axis_label_text_font_size = '11pt'
p.yaxis.axis_label_text_font_style = 'bold italic'
# Tick labels
p.xaxis.major_label_text_font_size = '9pt'
p.yaxis.major_label_text_font_size = '9pt'
p.border_fill_color = None
p.outline_line_color = None
return p
# callback 1
def update_plot1(attr, old, new):
# read the current value of the dropdown
cmr = camr_select.value
# set new_data
new_data = make_camr_src(cmr)
# Assign new_data to the original source
src1.data = new_data
p1.title.text = 'Répartition du PTF de la %s par classe de risque' % cmr
# callback 2
def update_plot2(attr, old, new):
# read the current value of the dropdown
dr = dr_select.value
# set new_data
new_data = make_dr_src(dr)
# Assign new_data to the original source
src2.data = new_data
p2.title.text = 'Répartition du PTF de la %s par classe de risque' % dr
def table(src1, src2):
col1 = [
TableColumn(field='NVF', title='Rating Class'),
TableColumn(field='NB', title='Clients Rated')
]
tbl1 = DataTable(source=src1, columns=col1, editable=True, width=600)
col2 = [
TableColumn(field='NVF', title='Rating Class'),
TableColumn(field='NB', title='Clients Rated')
]
tbl2 = DataTable(source=src2, columns=col2, editable=True, width=600)
return tbl1, tbl2
camr_select = Select(options=list(sni_csub['CAMR'].unique()),
value='CAM CORPORATE',
title='Choisir une CAM Region')
# attach the update to the value
camr_select.on_change('value', update_plot1)
dr_select = Select(options=list(sni_dsub['DR'].unique()),
value='CAM CORPORATE',
title='Choisir une DR')
# attach the update to the value
dr_select.on_change('value', update_plot2)
camr_select.width = 600
# Data
data1 = make_camr_src(camr_select.value)
data2 = make_dr_src(dr_select.value)
src1 = ColumnDataSource(data=data1)
src2 = ColumnDataSource(data=data2)
p1, p2 = make_plots(src1, src2)
p1 = style(p1)
p2 = style(p2)
tbl1, tbl2 = table(src1, src2)
layout = row([
column([Column(camr_select, width=500), p1, tbl1]),
column([Column(dr_select, width=500), p2, tbl2])
])
tab = Panel(child=layout, title='Rating class distribution')
return tab
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_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']*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, \
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.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,\
callback=profile_slider_callback)
profile_slider_callback.args['profile_index'] = profile_slider
# ==============================
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 = "data:text;charset=utf-8,"
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
}
var encodedUri = encodeURI(temp)
link = document.createElement('a');
link.setAttribute('href', encodedUri);
link.setAttribute('download', 'vel_model.txt');
link.click();
""")
simple_text_button = Button(label=style_parameter['simple_text_button_label'], button_type='default', width=style_parameter['button_width'],\
callback=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 = "data:text;charset=utf-8,"
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
}
var encodedUri = encodeURI(temp)
link = document.createElement('a');
link.setAttribute('href', encodedUri);
link.setAttribute('download', 'vel_model96.txt');
link.click();
""")
model96_button = Button(label=style_parameter['model96_button_label'], button_type='default', width=style_parameter['button_width'],\
callback=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)
ys=[[y0, y0, Y[i+1], Y[i]] for i in range(len(Y[:-1])) ],
color=data.colors[:-1]
))
patches = Patches(xs="xs", ys="ys", fill_color="color", line_color="color")
plot.add_glyph(patches_source, patches)
line_source = ColumnDataSource(dict(x=data.dist, y=data.alt))
line = Line(x='x', y='y', line_color="black", line_width=1)
plot.add_glyph(line_source, line)
return plot
data = prep_data(obiszow_mtb_xcm)
trail = trail_map(data)
altitude = altitude_profile(data)
layout = Column(children=[altitude, trail])
doc = Document()
doc.add_root(layout)
if __name__ == "__main__":
doc.validate()
filename = "trail.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Trail map and altitude profile"))
print("Wrote %s" % filename)
view(filename)
def Zancada(DatosBokeh, EjeX, MetricasAuxiliares):
"""
GRAFICO DISCRETO | LONGITUD DE ZANCADA
"""
# Creacion del diccionario de metricas auxiliares
DiccionarioVariables = ParametrosVariables(DatosBokeh)
# Generacion del codigo JavaScript que habilita la visualizacion de metricas auxiliares
CodigoJS = GeneracionCodigoJS(MetricasAuxiliares)
# Asignacion de tamaño del punto en graficas discretas
SizeCircle = FunctionSizeCircle(DatosBokeh)
# Creacion de un grafica
PLT = figure(width=1000,
height=400,
x_range=(DatosBokeh.data[EjeX].min(),
DatosBokeh.data[EjeX].max()),
y_range=(LimiteEjeY(DatosBokeh, 'LONGITUD ZANCADA',
'Inferior'),
LimiteEjeY(DatosBokeh, 'LONGITUD ZANCADA',
'Superior')),
tools='',
toolbar_location=None)
# Inclusion de datos
PLT.circle(EjeX,
'LongitudZancada[m]',
source=DatosBokeh,
size=SizeCircle,
line_color=transform(
'LongitudZancada[m]',
LinearColorMapper(palette=PaletaColoresLinea,
low=0.8,
high=2)),
color=transform(
'LongitudZancada[m]',
LinearColorMapper(palette=PaletaColores, low=0.8, high=2)),
fill_alpha=1)
PLT_Linea = PLT.line(EjeX,
'LongitudZancada[m]',
source=DatosBokeh,
color='white',
line_width=0,
line_alpha=0)
PLT.add_tools(
HoverTool(tooltips=[('', '@{LongitudZancada[m]}{0,0.00} m')],
renderers=[PLT_Linea],
mode='vline'))
# Inclusion de lineas auxiliares
ListadoMetricasAuxiliares = {}
for Metrica in MetricasAuxiliares:
if DiccionarioVariables[Metrica]['Tipo'] == 'circle':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.circle(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['LONGITUD ZANCADA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
size=SizeCircle,
line_color=DiccionarioVariables[Metrica]['Color'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
elif DiccionarioVariables[Metrica]['Tipo'] == 'step':
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.step(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['LONGITUD ZANCADA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
else:
ListadoMetricasAuxiliares[
'l' + str(MetricasAuxiliares.index(Metrica))] = PLT.line(
EjeX,
DiccionarioVariables[Metrica]['Variable'].split('[', 1)[0]
+ DiccionarioVariables['LONGITUD ZANCADA']['Sufijo'],
source=DiccionarioVariables[Metrica]['CDS'],
color=DiccionarioVariables[Metrica]['Color'],
**DiccionarioVariables[Metrica]['Propiedades'])
# Atributos
PLT.title.text = 'LONGITUD DE ZANCADA'
PLT.sizing_mode = 'fixed'
PLT.yaxis.axis_label = 'Longitud de zancada [m]'
PLT.yaxis.formatter = NumeralTickFormatter(format='0.0')
PLT.grid.visible = False
PLT.yaxis.minor_tick_line_color = None
# Asignacion de opciones de visualizacion del eje X
if EjeX == 'Distancia[m]':
PLT.xaxis.axis_label = 'Distancia'
PLT.xaxis.formatter = NumeralTickFormatter(format='0')
if DatosBokeh.data['Distancia[m]'].max() >= 4000:
PLT.xaxis.ticker = SingleIntervalTicker(interval=1000)
PLT.xaxis.major_label_overrides = FormateoEjes(
DatosBokeh.data['Distancia[m]'], 1000, 1000, 0, 0)
elif EjeX == 'TiempoActividad':
PLT.xaxis.axis_label = 'Tiempo actividad'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
elif EjeX == 'TiempoTotal':
PLT.xaxis.axis_label = 'Tiempo total'
PLT.xaxis.formatter = DatetimeTickFormatter(hourmin='%H:%M:%S',
minutes='%M:%S',
seconds='%Ss')
PLT.xaxis.ticker = DatetimeTicker()
#Botones
Botones = CheckboxGroup(labels=MetricasAuxiliares,
active=[],
width=100,
height=380)
ListadoMetricasAuxiliares['checkbox'] = Botones
CodigoJSFrecuenciaCardiaca = CustomJS(code=CodigoJS,
args=ListadoMetricasAuxiliares)
Botones.js_on_click(CodigoJSFrecuenciaCardiaca)
#Layout
GridBotones = layout(
[Spacer(width=100, height=25),
Column(Botones, width=100, height=375)],
sizing_mode='fixed',
width=100,
height=400)
GridGrafica = gridplot([PLT, GridBotones],
ncols=2,
sizing_mode='stretch_width',
toolbar_location=None,
plot_width=1100,
plot_height=400)
return GridGrafica
checkbox_group = CheckboxGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_group.js_on_click(CustomJS(code="console.log('checkbox_group: active=' + this.active, this.toString())"))
radio_group = RadioGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_group.js_on_click(CustomJS(code="console.log('radio_group: active=' + this.active, this.toString())"))
checkbox_button_group = CheckboxButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=[0, 1])
checkbox_button_group.js_on_click(CustomJS(code="console.log('checkbox_button_group: active=' + this.active, this.toString())"))
radio_button_group = RadioButtonGroup(labels=["Option 1", "Option 2", "Option 3"], active=0)
radio_button_group.js_on_click(CustomJS(code="console.log('radio_button_group: active=' + this.active, this.toString())"))
widget_box = Column(children=[
button, button_disabled,
toggle_inactive, toggle_active,
dropdown, dropdown_disabled, dropdown_split,
checkbox_group, radio_group,
checkbox_button_group, radio_button_group,
])
doc = Document()
doc.add_root(widget_box)
if __name__ == "__main__":
doc.validate()
filename = "buttons.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Button widgets"))
print("Wrote %s" % filename)
view(filename)
bar_chart_source = ColumnDataSource(data=bar_chart_data)
bar_chart = create_bar_chart(bar_chart_data, bar_chart_source)
stacked_chart_positive_data = wrangle_pos_data_for_stacked_chart(user_inputs)
stacked_chart_negative_data = wrangle_neg_data_for_stacked_chart(user_inputs)
stacked_chart_positive_source = ColumnDataSource(
data=stacked_chart_positive_data)
stacked_chart_negative_source = ColumnDataSource(
data=stacked_chart_negative_data)
stacked_chart = create_stacked_chart(
stacked_chart_positive_data,
stacked_chart_negative_data,
stacked_chart_positive_source,
stacked_chart_negative_source,
)
inputs = Column(change_air_travel_slider, )
# layout of charts
bar_chart.margin = (0, 0, 15, 0)
stacked_chart.margin = (0, 0, 15, 0)
charts = column(
bar_chart,
stacked_chart,
sizing_mode="stretch_width",
margin=(0, 15, 0, 15),
)
# doc.theme = Theme(filename="main/static/main/ghg_bokeh_theme.yaml")
curdoc().add_root(layout([[inputs, charts]], css_classes=["center"]))
def compute(t):
value = np.sin(xx / 50 + t / 10) * np.cos(yy / 50 + t / 10) * 50 + 50
return dict(x=xx, y=yy, z=value, color=value)
source = ColumnDataSource(data=compute(0))
content_filename = join(dirname(__file__), "description.html")
description = Div(text=open(content_filename).read(),
render_as_text=False,
width=600)
surface = Surface3d(x="x", y="y", z="z", color="color", data_source=source)
curdoc().add_root(Column(description, surface))
@count()
def update(t):
source.data = compute(t)
curdoc().add_periodic_callback(update, 100)
curdoc().title = "Surface3dYo"
session = push_session(curdoc(), url="http://127.0.0.1:5006/extension_loader")
session.show() # open the document in a browser
session.loop_until_closed() # run forever
source.data = { value: [cb_obj.value] }
""")
exposure_controls.append(sn_slider)
noise_slider = Slider(title="Noise to Add In",
value=500,
start=0.0,
end=1000.0,
step=50.0,
callback_policy='mouseup')
noise_slider.callback = CustomJS(args=dict(source=fake_callback_source3),
code="""
source.data = { value: [cb_obj.value] }
""")
#exposure_controls.append(noise_slider)
sp.set_plot_options(
plot.state
) # plot.state has bokeh type Figure, so can be manipulated in the usual way
astro_tab = Panel(child=Column(children=astro_controls), title='Stars')
exposure_tab = Panel(child=Column(children=exposure_controls),
title='Exposure')
info_tab = Panel(child=Div(text=h.help(), width=300), title='Info')
visual_tab = Panel(child=Column(children=[widget]), title='Visuals')
controls = Tabs(tabs=[astro_tab, exposure_tab, visual_tab, info_tab],
width=400)
layout = layout([[controls, plot.state]], sizing_mode='fixed')
curdoc().add_root(layout)
def initialize_plot(self, plots=None, ranges=None):
ranges = self.compute_ranges(self.layout, self.keys[-1], None)
passed_plots = [] if plots is None else plots
plots = [[] for _ in range(self.rows)]
tab_titles = {}
insert_rows, insert_cols = [], []
offset = 0
for r, c in self.coords:
subplot = self.subplots.get((r, c), None)
if subplot is not None:
shared_plots = passed_plots if self.shared_axes else None
subplots = subplot.initialize_plot(ranges=ranges,
plots=shared_plots)
# Computes plotting offsets depending on
# number of adjoined plots
offset = sum(r >= ir for ir in insert_rows)
if len(subplots) > 2:
# Add pad column in this position
insert_cols.append(c)
if r not in insert_rows:
# Insert and pad marginal row if none exists
plots.insert(r + offset,
[None for _ in range(len(plots[r]))])
# Pad previous rows
for ir in range(r):
plots[ir].insert(c + 1, None)
# Add to row offset
insert_rows.append(r)
offset += 1
# Add top marginal
plots[r + offset - 1] += [subplots.pop(-1), None]
elif len(subplots) > 1:
# Add pad column in this position
insert_cols.append(c)
# Pad previous rows
for ir in range(r):
plots[r].insert(c + 1, None)
# Pad top marginal if one exists
if r in insert_rows:
plots[r + offset - 1] += 2 * [None]
else:
# Pad top marginal if one exists
if r in insert_rows:
plots[r + offset - 1] += [None] * (1 +
(c in insert_cols))
plots[r + offset] += subplots
if len(subplots) == 1 and c in insert_cols:
plots[r + offset].append(None)
passed_plots.append(subplots[0])
if self.tabs:
title = subplot.subplots['main']._format_title(
self.keys[-1], dimensions=False)
if not title:
title = ' '.join(self.paths[r, c])
tab_titles[r, c] = title
else:
plots[r + offset] += [empty_plot(0, 0)]
# Replace None types with empty plots
# to avoid bokeh bug
plots = layout_padding(plots, self.renderer)
# Wrap in appropriate layout model
kwargs = dict(sizing_mode=self.sizing_mode)
if self.tabs:
panels = [
Panel(child=child, title=str(tab_titles.get((r, c))))
for r, row in enumerate(plots) for c, child in enumerate(row)
if child is not None
]
layout_plot = Tabs(tabs=panels)
else:
plots = filter_toolboxes(plots)
plots, width = pad_plots(plots)
layout_plot = gridplot(children=plots,
width=width,
toolbar_location=self.toolbar,
merge_tools=self.merge_tools,
**kwargs)
title = self._get_title(self.keys[-1])
if title:
self.handles['title'] = title
layout_plot = Column(title, layout_plot, **kwargs)
self.handles['plot'] = layout_plot
self.handles['plots'] = plots
self._update_callbacks(layout_plot)
if self.shared_datasource:
self.sync_sources()
self.drawn = True
return self.handles['plot']
if not warning_devices:
alarm.visible = False
alarm_list.visible = False
t += 1
layout = layout([[fig_tanque3, fig_tanque4], [fig_tanque1, fig_tanque2],
[fig_vol1, fig_vol2]])
panel1 = Panel(child=row(
Column(
label1,
row(Column(dataRecordingButton, dataRecordingLabel),
Column(extensionsDropdown)), refEst1, refEst2,
row(Column(valvula1Label, Kp1, Ki1, Kd1, Kw1),
Column(valvula2Label, Kp2, Ki2, Kd2, Kw2)),
row(alarm, alarm_list)), layout),
title='Modo Automático')
panel2 = Panel(child=row(
Column(
label2,
row(Column(dataRecordingButton, dataRecordingLabel),
Column(extensionsDropdown)),
row(Column(valvula1Label, voltageV1, razonFlujoV1),
Column(valvula2Label, voltageV2, razonFlujoV2)),
row(alarm, alarm_list)), layout),
title='Modo Manual')
# Tabs
widgets = Column(children=[
Row(children=[
Column(children=[
click_button,
disabled_button,
toggle,
dropdown,
dropdown_split,
checkbox_group,
radio_group,
checkbox_button_group,
radio_button_group,
Row(children=[
checkbox_button_group_vertical, radio_button_group_vertical
]),
]),
Column(children=[
text_input,
autocomplete_input,
text_area,
select,
multi_select,
multi_choice,
slider,
range_slider,
date_slider,
date_range_slider,
spinner,
color_picker,
date_picker,
Row(children=[switch_0, switch_1]),
paragraph,
div,
pre_text,
]),
tabs,
]),
table,
])
div.background = `rgb(${r}, ${g}, ${b})`
""")
red.js_on_change('value', cb)
green.js_on_change('value', cb)
blue.js_on_change('value', cb)
return Row(children=[red, green, blue, div])
sliders = Row(children=[
Column(children=[
slider,
disabled_slider,
range_slider,
date_slider,
date_range_slider,
only_value_slider,
no_title_slider,
]),
color_picker(),
])
doc = Document()
doc.add_root(sliders)
if __name__ == "__main__":
doc.validate()
filename = "sliders.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "sliders"))
def initialize_plot(self, plots=None, ranges=None):
ranges = self.compute_ranges(self.layout, self.keys[-1], None)
plot_grid = self._compute_grid()
passed_plots = [] if plots is None else plots
r_offset = 0
col_offsets = defaultdict(int)
tab_plots = []
for r in range(self.rows):
# Compute row offset
row = [(k, sp) for k, sp in self.subplots.items() if k[0] == r]
row_padded = any(len(sp.layout) > 2 for k, sp in row)
if row_padded:
r_offset += 1
for c in range(self.cols):
subplot = self.subplots.get((r, c), None)
# Compute column offset
col = [(k, sp) for k, sp in self.subplots.items() if k[1] == c]
col_padded = any(len(sp.layout) > 1 for k, sp in col)
if col_padded:
col_offsets[r] += 1
c_offset = col_offsets.get(r, 0)
if subplot is None:
continue
shared_plots = list(passed_plots) if self.shared_axes else None
subplots = subplot.initialize_plot(ranges=ranges, plots=shared_plots)
nsubplots = len(subplots)
# If tabs enabled lay out AdjointLayout on grid
if self.tabs:
title = subplot.subplots['main']._format_title(self.keys[-1],
dimensions=False)
if not title:
title = ' '.join(self.paths[r,c])
if nsubplots == 1:
grid = subplots[0]
elif nsubplots == 2:
grid = gridplot([subplots], merge_tools=self.merge_tools,
toolbar_location=self.toolbar)
else:
grid = [[subplots[2], None], subplots[:2]]
grid = gridplot(children=grid, merge_tools=self.merge_tools,
toolbar_location=self.toolbar)
tab_plots.append((title, grid))
continue
# Situate plot in overall grid
if nsubplots > 2:
plot_grid[r+r_offset-1][c+c_offset-1] = subplots[2]
plot_column = plot_grid[r+r_offset]
if nsubplots > 1:
plot_column[c+c_offset-1] = subplots[0]
plot_column[c+c_offset] = subplots[1]
else:
plot_column[c+c_offset-int(col_padded)] = subplots[0]
passed_plots.append(subplots[0])
# Wrap in appropriate layout model
kwargs = dict(sizing_mode=self.sizing_mode)
if self.tabs:
plots = filter_toolboxes([p for t, p in tab_plots])
panels = [Panel(child=child, title=t) for t, child in tab_plots]
layout_plot = Tabs(tabs=panels)
else:
plot_grid = layout_padding(plot_grid, self.renderer)
plot_grid = filter_toolboxes(plot_grid)
plot_grid, width = pad_plots(plot_grid)
layout_plot = gridplot(children=plot_grid, width=width,
toolbar_location=self.toolbar,
merge_tools=self.merge_tools, **kwargs)
title = self._get_title(self.keys[-1])
if title:
self.handles['title'] = title
layout_plot = Column(title, layout_plot, **kwargs)
self.handles['plot'] = layout_plot
self.handles['plots'] = plots
self._update_callbacks(layout_plot)
if self.shared_datasource:
self.sync_sources()
if self.top_level:
self.init_links()
self.drawn = True
return self.handles['plot']
def Mapa(dfBokeh, DatosBokeh):
"""
PREPARACION DE DATOS
"""
# Calculo de los valores agregados
AVG_Altitud, MAX_Altitud, MIN_Altitud, \
AVG_Velocidad, MAX_Velocidad, MIN_Velocidad, \
AVG_Ritmo, MAX_Ritmo, MIN_Ritmo, \
AVG_FrecuenciaCardiaca, MAX_FrecuenciaCardiaca, MIN_FrecuenciaCardiaca, \
AVG_Cadencia, MAX_Cadencia, MIN_Cadencia, \
AVG_Temperatura, MAX_Temperatura, MIN_Temperatura, \
AVG_LongitudZancada, MAX_LongitudZancada, MIN_LongitudZancada, \
AVG_Pendiente, MAX_Pendiente , MIN_Pendiente = CalculosVectoresAgregados(dfBokeh)
AltitudInicio, AltitudFin = dfBokeh.loc[dfBokeh.index.min() == dfBokeh.index, ['Altitud']].min()[0], dfBokeh.loc[dfBokeh.index.max() == dfBokeh.index, ['Altitud']].min()[0]
# Calculo de desniveles finales
DesnivelPositivo = dfBokeh['DesnivelPositivoAcumulado'].max()
DesnivelNegativo = dfBokeh['DesnivelNegativoAcumulado'].max()
DesnivelAcumulado = DesnivelPositivo + DesnivelNegativo
DesnivelPorKilometro = (DesnivelAcumulado/dfBokeh['Distancia'].max())*1000
"""
MAPA
CARTODBPOSITRON
CARTODBPOSITRON_RETINA
STAMEN_TERRAIN
STAMEN_TERRAIN_RETINA
STAMEN_TONER
STAMEN_TONER_BACKGROUND
STAMEN_TONER_LABELS
"""
# Creacion de un grafica
PLT_Mapa = figure(width=900, height=430, x_range=(DatosBokeh.data['LongitudMercator'].min()-100, DatosBokeh.data['LongitudMercator'].max()+100), y_range=(DatosBokeh.data['LatitudMercator'].min()-100, DatosBokeh.data['LatitudMercator'].max()+100), x_axis_type= 'mercator', y_axis_type= 'mercator', tools= 'wheel_zoom, reset, hover')
PLT_Mapa.add_tile(get_provider('STAMEN_TERRAIN'))
# Inclusion de datos
PLT_MP_Linea = PLT_Mapa.line(x= 'LongitudMercator', y= 'LatitudMercator', source= DatosBokeh, line_color= Grapefruit[2], line_width= 3, line_cap= 'round')
"""
#PLT_Mapa.circle(x= 'LongitudMercator', y= 'LatitudMercator', source= DatosBokeh, size= 5, line_color= None, fill_color= None, fill_alpha= 0, hover_fill_color= 'yellow', hover_line_color = 'black', hover_alpha= 1)
#PLT_Mapa.add_tools(HoverTool(tooltips=None, mode='mouse'))
"""
CoordenadasHitosKm, TiempoTotalKm, TiempoActividadKm, MinDistanciaKm = HitosKilometricos(dfBokeh)
CoordenadasPausas, TiempoTotalPausas, TiempoActividadPausas, DistanciasPausas = HitosPausas(dfBokeh)
# Ubicacion de puntos de inicio, fin y kilometros
LongitudKm =[]
LatitudKm =[]
PuntoKilometrico = []
for i, Km in enumerate(CoordenadasHitosKm):
if i == 0:
PLT_Mapa_Inicio = PLT_Mapa.circle(ConversorCoordenadasMercator(Km[1], Km[0])[0], ConversorCoordenadasMercator(Km[1], Km[0])[1], size= 8, line_color= 'black', fill_color= Spectral[2], visible= True)
elif i == len(CoordenadasHitosKm)-1:
PLT_Mapa_Fin = PLT_Mapa.circle(ConversorCoordenadasMercator(Km[1], Km[0])[0], ConversorCoordenadasMercator(Km[1], Km[0])[1], size= 8, line_color= 'black', fill_color= Spectral[7], visible= True)
else:
LongitudKm.append(ConversorCoordenadasMercator(Km[1], Km[0])[0])
LatitudKm.append(ConversorCoordenadasMercator(Km[1], Km[0])[1])
PuntoKilometrico.append(str(i))
CDS_PuntosKm = ColumnDataSource(data= dict(Longitud= LongitudKm, Latitud= LatitudKm, PuntoKilometrico= PuntoKilometrico))
PLT_Mapa_PuntoKm = PLT_Mapa.circle(x= 'Longitud', y= 'Latitud', source= CDS_PuntosKm, color= 'white', size= 8, line_color= 'black', fill_color= 'white', visible= True)
PLT_Mapa_PuntoKm_TXT = LabelSet(x= 'Longitud', y= 'Latitud', text='PuntoKilometrico', level='glyph', x_offset= 5, y_offset= 0, source= CDS_PuntosKm, render_mode='canvas', text_font_size= '10pt', text_color= 'black', text_align= 'left', text_baseline= 'middle', text_font_style= 'bold', visible= True)
PLT_Mapa.add_layout(PLT_Mapa_PuntoKm_TXT)
# Ubicacion de pausas
LongitudPausa =[]
LatitudPausa =[]
for i, Km in enumerate(CoordenadasPausas):
LongitudPausa.append(ConversorCoordenadasMercator(Km[1], Km[0])[0])
LatitudPausa.append(ConversorCoordenadasMercator(Km[1], Km[0])[1])
PLT_Mapa_Pausas = PLT_Mapa.x(LongitudPausa, LatitudPausa, line_color= 'black', line_width= 2, fill_color= None, visible= False)
# Identificacion de pico y valle en trails
for index, row in dfBokeh.iterrows():
LongitudMercator, LatitudMercator = ConversorCoordenadasMercator(row.Longitud, row.Latitud)
dfBokeh.at[index,'LongitudMercator'] = LongitudMercator
dfBokeh.at[index,'LatitudMercator'] = LatitudMercator
if (DesnivelPorKilometro > 40) and (MAX_Altitud[0] >= (AltitudInicio + 50) and MAX_Altitud[0] >= (AltitudFin + 50)):
PLT_Mapa_Cima = PLT_Mapa.triangle(dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LongitudMercator'].min(), dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LatitudMercator'].min(), size= 10, line_color= 'black', line_width= 2, fill_color= Spectral[4], visible= False)
PLT_Mapa_Cima_TXT = Label(x= dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LongitudMercator'].min(), y= dfBokeh[dfBokeh['Altitud']==MAX_Altitud[0]]['LatitudMercator'].min(), text= str(round(MAX_Altitud[0])), x_offset= 5, y_offset= 0, text_font_size= '10pt', text_color= 'black', text_align= 'left', text_baseline= 'middle', text_font_style= 'bold', visible= False)
PLT_Mapa.add_layout(PLT_Mapa_Cima_TXT)
else:
PLT_Mapa_Cima = PLT_Mapa.triangle(0, 0, size= 0, line_alpha= 0, visible= False)
PLT_Mapa_Cima_TXT = Label(x= 0, y= 0, text= '', text_font_size= '0pt', text_alpha= 0, visible= False)
if (DesnivelPorKilometro > 40) and (MIN_Altitud[0] <= (AltitudInicio - 50) and MIN_Altitud[0] <= (AltitudFin - 50)):
PLT_Mapa_Valle = PLT_Mapa.inverted_triangle(dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LongitudMercator'].min(), dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LatitudMercator'].min(), size= 10, line_color= 'black', line_width= 2, fill_color= Spectral[0], visible= False)
PLT_Mapa_Valle_TXT = Label(x= dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LongitudMercator'].min(), y= dfBokeh[dfBokeh['Altitud']==MIN_Altitud[0]]['LatitudMercator'].min(), text= str(round(MIN_Altitud[0])), x_offset= 5, y_offset= 0, text_font_size= '10pt', text_color= 'black', text_align= 'left', text_baseline= 'middle', text_font_style= 'bold', visible= False)
PLT_Mapa.add_layout(PLT_Mapa_Valle_TXT)
else:
PLT_Mapa_Valle = PLT_Mapa.inverted_triangle(0, 0, size= 0, line_alpha= 0, visible= False)
PLT_Mapa_Valle_TXT = Label(x= 0, y= 0, text= '', text_font_size= '0pt', text_alpha= 0, visible= False)
# Atributos
PLT_Mapa.sizing_mode = 'fixed'
PLT_Mapa.xaxis.major_tick_line_color = None
PLT_Mapa.xaxis.minor_tick_line_color = None
PLT_Mapa.yaxis.major_tick_line_color = None
PLT_Mapa.yaxis.minor_tick_line_color = None
PLT_Mapa.xaxis.major_label_text_font_size = '0pt'
PLT_Mapa.yaxis.major_label_text_font_size = '0pt'
PLT_Mapa.grid.visible = False
PLT_Mapa.toolbar.autohide = True
# Sin bordes
PLT_Mapa.min_border_left = 0
PLT_Mapa.min_border_right = 0
PLT_Mapa.min_border_top = 0
PLT_Mapa.min_border_bottom = 0
# Linea exterior
PLT_Mapa.outline_line_width = 3
PLT_Mapa.outline_line_alpha = 0.3
PLT_Mapa.outline_line_color = 'black'
"""
BOTONES
"""
CodigoJS = """
var indexOf = [].indexOf || function(item) { for (var i = 0, l = this.length; i < l; i++) { if (i in this && this[i] === item) return i; } return -1; };
l0.visible = indexOf.call(checkbox.active,0)>=0;
l1.visible = indexOf.call(checkbox.active,0)>=0;
l2.visible = indexOf.call(checkbox.active,1)>=0;
l3.visible = indexOf.call(checkbox.active,1)>=0;
l4.visible = indexOf.call(checkbox.active,2)>=0;
l5.visible = indexOf.call(checkbox.active,2)>=0;
l6.visible = indexOf.call(checkbox.active,2)>=0;
l7.visible = indexOf.call(checkbox.active,2)>=0;
l8.visible = indexOf.call(checkbox.active,3)>=0;
"""
BotonesMapa = CheckboxButtonGroup(labels=["INICIO/FIN", "PUNTOS KILOMETRICOS", "CIMA/VALLE", 'PAUSAS'], active=[0, 1], width=300, height=30)
CodigoJSMapa = CustomJS(code=CodigoJS, args=dict(l0=PLT_Mapa_Inicio, l1=PLT_Mapa_Fin, l2=PLT_Mapa_PuntoKm, l3= PLT_Mapa_PuntoKm_TXT, l4=PLT_Mapa_Cima, l5=PLT_Mapa_Cima_TXT, l6=PLT_Mapa_Valle, l7=PLT_Mapa_Valle_TXT, l8=PLT_Mapa_Pausas, checkbox= BotonesMapa))
BotonesMapa.js_on_click(CodigoJSMapa)
GridMapa = layout([PLT_Mapa, Column(BotonesMapa, width=300, height=35)], sizing_mode='stretch_width', width=900, height=470)
return GridMapa
plot.add_layout(Grid(dimension=0, ticker=xaxis.ticker))
plot.add_layout(Grid(dimension=1, ticker=yaxis.ticker))
plot.add_tools(HoverTool())
tab = Panel(child=plot, title=title, closable=True)
return tab
def make_tabs(objs):
return Tabs(tabs=[make_tab(title, obj) for title, obj in objs], width=600)
layout = Column(children=[
Paragraph(text="Only Image and ImageRGBA glyphs are not demonstrated."),
make_tabs(glyphs),
make_tabs(markers)
])
doc = Document()
doc.add_root(layout)
if __name__ == "__main__":
doc.validate()
filename = "glyphs.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Glyphs"))
print("Wrote %s" % filename)
view(filename)
plot_1.title.text = "Circular Layout (NodesAndLinkedEdges inspection policy)"
plot_1.add_tools(HoverTool(tooltips=None))
plot_2 = create_graph(nx.spring_layout,
selection_policy=NodesAndLinkedEdges(),
scale=2,
center=(0, 0))
plot_2.title.text = "Spring Layout (NodesAndLinkedEdges selection policy)"
plot_2.add_tools(TapTool(), BoxSelectTool())
plot_3 = create_graph(nx.random_layout,
inspection_policy=EdgesAndLinkedNodes(),
center=(0, 0))
plot_3.title.text = "Random Layout (EdgesAndLinkedNodes inspection policy)"
plot_3.add_tools(HoverTool(tooltips=None))
plot_4 = create_graph(nx.fruchterman_reingold_layout,
selection_policy=EdgesAndLinkedNodes(),
scale=2,
center=(0, 0),
dim=2)
plot_4.title.text = "FR Layout (EdgesAndLinkedNodes selection policy)"
plot_4.add_tools(TapTool())
layout = Column(Row(plot_1, plot_2), Row(plot_3, plot_4))
doc = curdoc()
doc.add_root(layout)
show(layout)
def plot_dispersion_bokeh(filename, period_array, curve_data_array,
boundary_data, style_parameter):
'''
Plot dispersion maps and curves using bokeh
Input:
filename is the filename of the resulting html file
period_array is a list of period
curve_data_array is a list of dispersion curves
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
# ==============================
# prepare data
map_data_all_slices_velocity = []
map_data_all_slices_period = []
map_data_all_slices_color = []
colorbar_data_all_left = []
colorbar_data_all_right = []
nperiod = len(period_array)
ncurve = len(curve_data_array)
ncolor = len(palette)
palette_r = palette[::-1]
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
for iperiod in range(nperiod):
one_slice_lat_list = []
one_slice_lon_list = []
one_slice_vel_list = []
map_period = period_array[iperiod]
for icurve in range(ncurve):
acurve = curve_data_array[icurve]
curve_lat = acurve['latitude']
curve_lon = acurve['longitude']
curve_vel = acurve['velocity']
curve_period = acurve['period']
one_slice_lat_list.append(curve_lat)
one_slice_lon_list.append(curve_lon)
if map_period in curve_period:
curve_period_index = curve_period.index(map_period)
one_slice_vel_list.append(curve_vel[curve_period_index])
else:
one_slice_vel_list.append(style_parameter['nan_value'])
# get color for dispersion values
one_slice_vel_mean = np.nanmean(one_slice_vel_list)
one_slice_vel_std = np.nanstd(one_slice_vel_list)
color_min = one_slice_vel_mean - one_slice_vel_std * style_parameter[
'spread_factor']
color_max = one_slice_vel_mean + one_slice_vel_std * style_parameter[
'spread_factor']
color_step = (color_max - color_min) * 1. / ncolor
one_slice_color_list = get_color_list(one_slice_vel_list,color_min,color_max,palette_r,\
style_parameter['nan_value'],style_parameter['nan_color'])
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
if one_slice_lat_list:
map_data_all_slices_velocity.append(one_slice_vel_list)
map_data_all_slices_period.append(
'Period: {0:6.1f} s'.format(map_period))
map_data_all_slices_color.append(one_slice_color_list)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
# get location for all points
map_lat_list, map_lon_list = [], []
map_lat_label_list, map_lon_label_list = [], []
for i in range(ncurve):
acurve = curve_data_array[i]
map_lat_list.append(acurve['latitude'])
map_lon_list.append(acurve['longitude'])
map_lat_label_list.append('Lat: {0:12.3f}'.format(acurve['latitude']))
map_lon_label_list.append('Lon: {0:12.3f}'.format(acurve['longitude']))
# data for the map view plot
map_view_label_lon = style_parameter['map_view_period_label_lon']
map_view_label_lat = style_parameter['map_view_period_label_lat']
map_data_one_slice = map_data_all_slices_color[
style_parameter['map_view_default_index']]
map_data_one_slice_period = map_data_all_slices_period[
style_parameter['map_view_default_index']]
map_data_one_slice_bokeh = ColumnDataSource(data=dict(map_lat_list=map_lat_list,\
map_lon_list=map_lon_list,\
map_data_one_slice=map_data_one_slice))
map_data_one_slice_period_bokeh = ColumnDataSource(
data=dict(lat=[map_view_label_lat],
lon=[map_view_label_lon],
map_period=[map_data_one_slice_period]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices_color=map_data_all_slices_color,\
map_data_all_slices_period=map_data_all_slices_period))
# 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))
# data for dispersion curves
curve_default_index = style_parameter['curve_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[map_lat_list[curve_default_index]],\
lon=[map_lon_list[curve_default_index]],\
color=[map_data_one_slice[curve_default_index]],\
index=[curve_default_index]))
selected_curve_data = curve_data_array[curve_default_index]
selected_curve_data_bokeh = ColumnDataSource(data=dict(curve_period=selected_curve_data['period'],\
curve_velocity=selected_curve_data['velocity']))
period_all = []
velocity_all = []
for acurve in curve_data_array:
period_all.append(acurve['period'])
velocity_all.append(acurve['velocity'])
curve_data_all_bokeh = ColumnDataSource(
data=dict(period_all=period_all, velocity_all=velocity_all))
selected_curve_lat_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lat_label_x']], \
y=[style_parameter['curve_lat_label_y']],\
lat_label=[map_lat_label_list[curve_default_index]]))
selected_curve_lon_label_bokeh = ColumnDataSource(data=dict(x=[style_parameter['curve_lon_label_x']], \
y=[style_parameter['curve_lon_label_y']],\
lon_label=[map_lon_label_list[curve_default_index]]))
all_curve_lat_label_bokeh = ColumnDataSource(data=dict(
map_lat_label_list=map_lat_label_list))
all_curve_lon_label_bokeh = ColumnDataSource(data=dict(
map_lon_label_list=map_lon_label_list))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['map_view_plot_height'], \
y_range=[style_parameter['map_view_lat_min'],\
style_parameter['map_view_lat_max']], x_range=[style_parameter['map_view_lon_min'],\
style_parameter['map_view_lon_max']], tools=style_parameter['map_view_tools'],\
title=style_parameter['map_view_title'])
# ------------------------------
# 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 period label
map_view.rect(style_parameter['map_view_period_box_lon'], style_parameter['map_view_period_box_lat'], \
width=style_parameter['map_view_period_box_width'], height=style_parameter['map_view_period_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_period', source=map_data_one_slice_period_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
# plot dots
map_view.circle('map_lon_list', 'map_lat_list', color='map_data_one_slice', \
source=map_data_one_slice_bokeh, size=style_parameter['marker_size'],\
line_width=0.2, line_color='black', alpha=1.0,\
selection_color='map_data_one_slice', selection_line_color='black',\
selection_fill_alpha=1.0,\
nonselection_fill_alpha=1.0, nonselection_fill_color='map_data_one_slice',\
nonselection_line_color='black', nonselection_line_alpha=1.0)
map_view.circle('lon', 'lat', color='color', source=selected_dot_on_map_bokeh, \
line_color='#00ff00', line_width=4.0, alpha=1.0, \
size=style_parameter['selected_marker_size'])
# ------------------------------
# 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',\
fill_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'
# ==============================
curve_fig = Figure(plot_width=style_parameter['curve_plot_width'], plot_height=style_parameter['curve_plot_height'], \
y_range=(style_parameter['curve_y_min'],style_parameter['curve_y_max']), \
x_range=(style_parameter['curve_x_min'],style_parameter['curve_x_max']),x_axis_type='log',\
tools=['save','box_zoom','wheel_zoom','reset','crosshair','pan'],
title=style_parameter['curve_title'])
# ------------------------------
curve_fig.rect([style_parameter['curve_label_box_x']], [style_parameter['curve_label_box_y']], \
width=style_parameter['curve_label_box_width'], height=style_parameter['curve_label_box_height'], \
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
curve_fig.text('x', 'y', \
'lat_label', source=selected_curve_lat_label_bokeh)
curve_fig.text('x', 'y', \
'lon_label', source=selected_curve_lon_label_bokeh)
# ------------------------------
curve_fig.line('curve_period',
'curve_velocity',
source=selected_curve_data_bokeh,
color='black')
curve_fig.circle('curve_period',
'curve_velocity',
source=selected_curve_data_bokeh,
size=5,
color='black')
# ------------------------------
curve_fig.title.text_font_size = style_parameter['title_font_size']
curve_fig.title.align = 'center'
curve_fig.title.text_font_style = 'normal'
curve_fig.xaxis.axis_label = style_parameter['curve_xlabel']
curve_fig.xaxis.axis_label_text_font_style = 'normal'
curve_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.xaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.axis_label = style_parameter['curve_ylabel']
curve_fig.yaxis.axis_label_text_font_style = 'normal'
curve_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
curve_fig.yaxis.major_label_text_font_size = xlabel_fontsize
curve_fig.xgrid.grid_line_dash = [4, 2]
curve_fig.ygrid.grid_line_dash = [4, 2]
curve_fig.xaxis[0].formatter = PrintfTickFormatter(format="%4.0f")
curve_fig.toolbar.logo = None
curve_fig.toolbar_location = 'above'
curve_fig.toolbar_sticky = False
# ==============================
map_data_one_slice_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh), code="""
var inds = cb_obj.indices
selected_dot_on_map_bokeh.data['index'] = [inds]
var new_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [new_slice['map_lat_list'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [new_slice['map_lon_list'][inds]]
selected_dot_on_map_bokeh.data['color'] = [new_slice['map_data_one_slice'][inds]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][inds]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][inds]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[inds]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[inds]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
map_data_one_slice_bokeh.selected.js_on_change('indices',
map_data_one_slice_js)
# ==============================
period_slider_callback = CustomJS(args=dict(map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh, \
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_period_bokeh=map_data_one_slice_period_bokeh),\
code="""
var p_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
var map_data_new_slice = map_data_all_slices['map_data_all_slices_color'][p_index]
map_data_one_slice_bokeh.data['map_data_one_slice'] = map_data_new_slice
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'][p_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][p_index]
colorbar_data_one_slice_bokeh.change.emit()
var selected_index = selected_dot_on_map_bokeh.data['index']
selected_dot_on_map_bokeh.data['color'] = [map_data_new_slice[selected_index]]
selected_dot_on_map_bokeh.change.emit()
map_data_one_slice_period_bokeh.data['map_period'] = [map_data_all_slices['map_data_all_slices_period'][p_index]]
map_data_one_slice_period_bokeh.change.emit()
""")
period_slider = Slider(start=0, end=nperiod-1, value=style_parameter['map_view_default_index'], \
step=1, title=style_parameter['period_slider_title'], \
width=style_parameter['period_slider_plot_width'],\
height=50)
period_slider.js_on_change('value', period_slider_callback)
period_slider_callback.args['period_index'] = period_slider
# ==============================
curve_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
selected_curve_data_bokeh=selected_curve_data_bokeh,\
curve_data_all_bokeh=curve_data_all_bokeh,\
selected_curve_lat_label_bokeh=selected_curve_lat_label_bokeh,\
selected_curve_lon_label_bokeh=selected_curve_lon_label_bokeh,\
all_curve_lat_label_bokeh=all_curve_lat_label_bokeh,\
all_curve_lon_label_bokeh=all_curve_lon_label_bokeh),\
code="""
var c_index = Math.round(cb_obj.value)
var one_slice = map_data_one_slice_bokeh.data
selected_dot_on_map_bokeh.data['index'] = [c_index]
selected_dot_on_map_bokeh.data['lat'] = [one_slice['map_lat_list'][c_index]]
selected_dot_on_map_bokeh.data['lon'] = [one_slice['map_lon_list'][c_index]]
selected_dot_on_map_bokeh.data['color'] = [one_slice['map_data_one_slice'][c_index]]
selected_dot_on_map_bokeh.change.emit()
selected_curve_data_bokeh.data['curve_period'] = curve_data_all_bokeh.data['period_all'][c_index]
selected_curve_data_bokeh.data['curve_velocity'] = curve_data_all_bokeh.data['velocity_all'][c_index]
selected_curve_data_bokeh.change.emit()
var all_lat_labels = all_curve_lat_label_bokeh.data['map_lat_label_list']
var all_lon_labels = all_curve_lon_label_bokeh.data['map_lon_label_list']
selected_curve_lat_label_bokeh.data['lat_label'] = [all_lat_labels[c_index]]
selected_curve_lon_label_bokeh.data['lon_label'] = [all_lon_labels[c_index]]
selected_curve_lat_label_bokeh.change.emit()
selected_curve_lon_label_bokeh.change.emit()
""")
curve_slider = Slider(start=0, end=ncurve-1, value=style_parameter['curve_default_index'], \
step=1, title=style_parameter['curve_slider_title'], width=style_parameter['curve_plot_width'],\
height=50)
curve_slider.js_on_change('value', curve_slider_callback)
curve_slider_callback.args['curve_index'] = curve_slider
# ==============================
# 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_fig = Column(period_slider, map_view, colorbar_fig, annotating_fig01,\
width=style_parameter['left_column_width'] )
right_fig = Column(curve_slider, curve_fig, annotating_fig02, \
width=style_parameter['right_column_width'] )
layout = Row(left_fig, right_fig)
save(layout)
y_axis=Select(title="Y Axis",
options=[
"Tomato Meter", "Number of Review",
"Dollars at box office"
],
value="Number of Reviews",
sizing_mode=sizing_mode))
w1 = make_widgets(sizing_mode)
w2 = make_widgets(sizing_mode)
check = Icon(icon_name='check')
Column(w1['genre'],
w1['director'],
w1['x_axis'],
w1['y_axis'],
sizing_mode=sizing_mode)
Column(w2['genre'],
w2['director'],
w2['x_axis'],
w2['y_axis'],
sizing_mode=sizing_mode)
layout = Column(Row(intro, sizing_mode=sizing_mode),
Row(WidgetBox(w1['reviews'],
w1['genre'],
w1['oscars'],
w1['director'],
w1['x_axis'],
w1['y_axis'],
step.mode = 'before';
break;
case 'Step (center)':
interp = step;
step.mode = 'center';
break;
case 'Step (after)':
interp = step;
step.mode = 'after';
break;
}
for (var i = 0; i < N; i++) {
data['x'][i] = i * dx
data['y'][i] = interp.compute(data['x'][i])
}
}
source.change.emit()
""")
mode = Select(
title='Interpolation Mode',
value='None',
options=['None', 'Linear', 'Step (before)', 'Step (center)', 'Step (after)'],
width=300)
mode.js_on_change('value', callback)
show(Column(mode, p))
# Add the tools
tooltips = [
("Manufacturer", "@manufacturer"),
("Model", "@model"),
("Displacement", "@displ"),
("Year", "@year"),
("Cylinders", "@cyl"),
("Transmission", "@trans"),
("Drive", "@drv"),
("Class", "@class"),
]
cty_hover_tool = HoverTool(renderers=[cty],
tooltips=tooltips + [("City MPG", "@cty")])
hwy_hover_tool = HoverTool(renderers=[hwy],
tooltips=tooltips + [("Highway MPG", "@hwy")])
select_tool = BoxSelectTool(renderers=[cty, hwy], dimensions='width')
plot.add_tools(cty_hover_tool, hwy_hover_tool, select_tool)
layout = Column(plot, data_table)
doc = Document()
doc.add_root(layout)
if __name__ == "__main__":
doc.validate()
filename = "data_tables.html"
with open(filename, "w") as f:
f.write(file_html(doc, INLINE, "Data Tables"))
print("Wrote %s" % filename)
view(filename)