def layout_padding(plots):
"""
Temporary workaround to allow empty plots in a
row of a bokeh GridPlot type. Should be removed
when https://github.com/bokeh/bokeh/issues/2891
is resolved.
"""
widths, heights = defaultdict(int), defaultdict(int)
for r, row in enumerate(plots):
for c, p in enumerate(row):
if p is not None:
width = p.plot_width if isinstance(p, Plot) else p.width
height = p.plot_height if isinstance(p, Plot) else p.height
widths[c] = max(widths[c], width)
heights[r] = max(heights[r], height)
expanded_plots = []
for r, row in enumerate(plots):
expanded_plots.append([])
for c, p in enumerate(row):
if p is None:
p = Figure(plot_width=widths[c],
plot_height=heights[r])
p.text(x=0, y=0, text=[' '])
p.xaxis.visible = False
p.yaxis.visible = False
p.outline_line_color = None
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
expanded_plots[r].append(p)
return expanded_plots
def layout_padding(plots):
"""
Temporary workaround to allow empty plots in a
row of a bokeh GridPlot type. Should be removed
when https://github.com/bokeh/bokeh/issues/2891
is resolved.
"""
widths, heights = defaultdict(int), defaultdict(int)
for r, row in enumerate(plots):
for c, p in enumerate(row):
if p is not None:
width = p.plot_width if isinstance(p, Plot) else p.width
height = p.plot_height if isinstance(p, Plot) else p.height
widths[c] = max(widths[c], width)
heights[r] = max(heights[r], height)
expanded_plots = []
for r, row in enumerate(plots):
expanded_plots.append([])
for c, p in enumerate(row):
if p is None:
p = Figure(plot_width=widths[c], plot_height=heights[r])
p.text(x=0, y=0, text=[' '])
p.xaxis.visible = False
p.yaxis.visible = False
p.outline_line_color = None
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
expanded_plots[r].append(p)
return expanded_plots
def make_plots(linesources, pointsources):
plots = []
i=0
for linesource, pointsource in zip(linesources, pointsources):
fig = Figure(title=None, toolbar_location=None, tools=[],
x_axis_type="datetime",
width=300, height=70)
fig.xaxis.visible = False
if i in [0, 9] :
fig.xaxis.visible = True
fig.height = 90
fig.yaxis.visible = False
fig.xgrid.visible = True
fig.ygrid.visible = False
fig.min_border_left = 10
fig.min_border_right = 10
fig.min_border_top = 5
fig.min_border_bottom = 5
if not i in [0, 9]:
fig.xaxis.major_label_text_font_size = "0pt"
#fig.yaxis.major_label_text_font_size = "0pt"
fig.xaxis.major_tick_line_color = None
fig.yaxis.major_tick_line_color = None
fig.xaxis.minor_tick_line_color = None
fig.yaxis.minor_tick_line_color = None
fig.background_fill_color = "whitesmoke"
fig.line(x='date', y="y", source=linesource)
fig.circle(x='date', y='y', size=5, source=pointsource)
fig.text(x='date', y='y', text='text', x_offset=5, y_offset=10, text_font_size='7pt', source=pointsource)
fig.title.align = 'left'
fig.title.text_font_style = 'normal'
plots.append(fig)
i+=1
return plots
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_bokeh.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 inds = Math.round(cb_obj.selected['1d'].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()
""")
# ==============================
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, callback=period_slider_callback)
# ==============================
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, callback=curve_slider_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_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)
def create_pie_chart(dataframe, title, palette):
radio_tarta = 0.4
df_pie_plot = dataframe.groupby("origen").size().sort_values(
ascending=False).reset_index()
df_pie_plot.columns = ["origen", "conteo"]
df_pie_plot["porcentaje"] = df_pie_plot["conteo"] / df_pie_plot[
"conteo"].sum() * 100
df_pie_plot["porcentaje"] = df_pie_plot["porcentaje"].apply(
lambda x: "{0:.2f}%".format(x))
df_pie_plot["porcentaje_texto"] = df_pie_plot[
"origen"] + "\n" + df_pie_plot["porcentaje"]
df_pie_plot["angulo"] = df_pie_plot["conteo"] / df_pie_plot["conteo"].sum(
) * 2 * np.pi
df_pie_plot["angulo_acumulado"] = df_pie_plot["angulo"].cumsum()
df_pie_plot["angulo_bisectriz"] = df_pie_plot[
"angulo_acumulado"] - df_pie_plot["angulo"] / 2.0
df_pie_plot["posicion_x_texto"] = radio_tarta * 0.7 * np.cos(
df_pie_plot["angulo_bisectriz"])
df_pie_plot["posicion_y_texto"] = radio_tarta * 0.7 * np.sin(
df_pie_plot["angulo_bisectriz"])
df_pie_plot["color"] = palette[:len(df_pie_plot)]
df_pie_plot_source = ColumnDataSource(data=df_pie_plot)
pie_plot_bokeh = Figure(title=title,
x_range=(-0.45, 0.45),
y_range=(-0.45, 0.45))
sectores = pie_plot_bokeh.annular_wedge(
x=0,
y=0,
inner_radius=radio_tarta / 3.0,
outer_radius=radio_tarta,
start_angle=bokeh_cumsum("angulo", include_zero=True),
end_angle=bokeh_cumsum('angulo'),
line_color="white",
line_width=3,
fill_color='color',
#legend_field="origen",
alpha=0.8,
hover_color="color",
hover_alpha=1.0,
source=df_pie_plot_source)
textos = pie_plot_bokeh.text(x='posicion_x_texto',
y='posicion_y_texto',
text="porcentaje_texto",
text_color="white",
text_font_style="bold",
text_align="center",
text_baseline="middle",
source=df_pie_plot_source)
# Old hover:
#hover_pie = HoverTool(tooltips="@{origen}: @{conteo}",
# renderers=[sectores])
# New hover:
hover_pie = HoverTool(tooltips=[("Origen", "@{origen}"),
("Clientes", "@{conteo}"),
("Porcentaje", "@{porcentaje}")],
renderers=[sectores])
pie_plot_bokeh.add_tools(hover_pie)
pie_plot_bokeh.axis.axis_label = None
pie_plot_bokeh.axis.visible = False
pie_plot_bokeh.grid.grid_line_color = None
return pie_plot_bokeh, df_pie_plot_source
def create_figure(self):
analog_glyphs = {}
binary_glyphs = {}
multistates_glyphs = {}
multistates_labels = {}
virtuals_glyphs = {}
self._log.debug("Creating figure")
TOOLS = "pan,box_zoom,wheel_zoom,save,reset"
p = Figure(
x_axis_type="datetime",
x_axis_label="Time",
y_axis_label="Value",
title="Live trends",
tools=TOOLS,
plot_width=1200,
plot_height=800,
toolbar_location="right",
)
p.title.text_font_size = "24pt"
p.xaxis.axis_label_text_font_size = "18pt"
p.yaxis.axis_label_text_font_size = "18pt"
p.xaxis.axis_label_text_font_style = "normal"
p.yaxis.axis_label_text_font_style = "normal"
p.xaxis.major_label_text_font_size = "12pt"
p.yaxis.major_label_text_font_size = "12pt"
p.background_fill_color = "#f4f3ef"
p.border_fill_color = "#f4f3ef"
p.extra_y_ranges = {
"bool": Range1d(start=0, end=1.1),
"enum": Range1d(start=0, end=10),
}
p.add_layout(
LinearAxis(y_range_name="bool", axis_label="Binary",
visible=False), "left")
p.add_layout(
LinearAxis(
y_range_name="enum",
axis_label="Enumerated",
visible=False,
# ticker=list(range(11)),
),
"right",
)
hover_common = HoverTool(
tooltips=[
("name", "$name"),
("value", "$data_y"),
# ('state', "@$name_state"),
# ("units", "$tags"),
("time", "@time_s"),
],
renderers=[],
toggleable=False,
formatters={"@time_s": "datetime"},
mode="mouse",
)
hover_multi = {}
p.add_tools(hover_common)
for name in self._binary_name:
binary_glyphs[name] = p.step(
x="index",
y=name,
source=self.cds,
name=name,
color=self.color_mappers["binary"][name],
y_range_name="bool",
mode="after",
line_width=8,
visible=False,
tags=["unit", "description"],
)
# binary_glyphs[name].add_tool(hover_common)
hover_common.renderers.append(binary_glyphs[name])
for name in self._multistates_name:
multistates_glyphs[name] = p.step(
x="index",
y=name,
source=self.cds,
name=name,
color=self.color_mappers["multistates"][name],
y_range_name="enum",
line_dash="dashed",
line_width=7,
visible=False,
tags=["unit", "description"],
mode="after",
)
# for name in self._multistates_labels:
# multistates_labels[name] = LabelSet(x="index", y=name.split('_')[0], text=name, level='glyph',
# x_offset=0, y_offset=1, source=self.cds, render_mode='canvas', visible=False)
# p.add_layout(multistates_labels[name])
# for name in self._multistates_labels:
# _msname = name.split("_")[0]
# multistates_labels[name] = p.circle(
# x="index",
# y=_msname,
# source=self.cds,
# color=self.color_mappers["multistates"][_msname],
# size=10,
# alpha=0.1,
# y_range_name="enum",
# visible=False,
# )
# hover_multi[name] = HoverTool(
# tooltips=[
# ("name", "$name"),
# ("value", "@" + name),
# ("time", "@time_s"),
# ],
# mode="mouse",
# renderers=[multistates_labels[name]],
# toggleable=False,
# )
# p.add_tools(hover_multi[name])
for name in self._multistates_labels:
_msname = name.split("_")[0]
multistates_labels[name] = p.text(
x="index",
y=_msname,
text=name,
source=self.cds,
text_color=self.color_mappers["multistates"][_msname],
angle=0.7835,
# size=10,
# alpha=0.1,
y_range_name="enum",
visible=False,
)
hover_multi[name] = HoverTool(
tooltips=[
("name", "$name"),
("value", "@" + name),
("time", "@time_s"),
],
mode="mouse",
renderers=[multistates_labels[name]],
toggleable=False,
)
p.add_tools(hover_multi[name])
for name in self._analog_name:
analog_glyphs[name] = p.line(
x="index",
y=name,
source=self.cds,
name=name,
color=self.color_mappers["analog"][name],
line_width=2,
visible=False,
tags=["unit", "description"],
)
# analog_glyphs[name].add_tool(hover_common)
hover_common.renderers.append(analog_glyphs[name])
for name in self._virtuals_name:
virtuals_glyphs[name] = p.line(
x="index",
y=name,
source=self.cds,
name=name,
color=self.color_mappers["virtual"][name],
line_width=2,
visible=False,
tags=["unit", "description"],
)
# virtuals_glyphs[name].add_tool(hover_common)
hover_common.renderers.append(virtuals_glyphs[name])
self.glyphs = {
"analog": analog_glyphs,
"binary": binary_glyphs,
"multistates": multistates_glyphs,
"multistates_labels": multistates_labels,
"virtual": virtuals_glyphs,
}
legend = Legend(items=[])
legend.click_policy = "hide"
p.add_layout(legend, "below")
return p
# initialise the text color as black. This will be switched to white if the block color gets dark enough
text_color = '#000000'
# create a data source to enable refreshing of fill & text color
source = ColumnDataSource(data=dict(color=[hex_color], text_color=[text_color]))
# create first plot, as a rect() glyph and centered text label, with fill and text color taken from source
p1 = Figure(x_range=(-8, 8), y_range=(-4, 4),
plot_width=600, plot_height=300,
title='move sliders to change', tools='')
p1.rect(0, 0, width=18, height=10, fill_color='color',
line_color = 'black', source=source)
p1.text(0, 0, text='color', text_color='text_color',
alpha=0.6667, text_font_size='36pt', text_baseline='middle',
text_align='center', source=source)
# the callback function to update the color of the block and associated label text
# NOTE: the JS functions for converting RGB to hex are taken from the excellent answer
# by Tim Down at http://stackoverflow.com/questions/5623838/rgb-to-hex-and-hex-to-rgb
callback = CustomJS(args=dict(source=source), code="""
function componentToHex(c) {
var hex = c.toString(16);
return hex.length == 1 ? "0" + hex : hex;
}
function rgbToHex(r, g, b) {
return "#" + componentToHex(r) + componentToHex(g) + componentToHex(b);
}
var data = source.get('data');
color = data['color'];
plot1.yaxis.axis_line_width = 0
plot1.xaxis.axis_line_color = 'black'
plot1.yaxis.axis_line_color = 'black'
plot1.border_fill_color = "black"
plot1.min_border_left = 80
# main glyphs for planet circles
star_syms = plot1.circle('x', 'y', source=star_points, name="star_points_to_hover", \
fill_color='color', line_color='color', radius=0.5, line_alpha=0.5, fill_alpha=0.7)
star_syms.selection_glyph = Circle(fill_alpha=0.8,
fill_color="purple",
radius=1.5,
line_color='purple',
line_width=3)
plot1.text(0.95*0.707*np.array([10., 20., 30., 40.]), 0.707*np.array([10., 20., 30., 40.]), \
text=['10 pc', '20 pc', '30 pc', '40 pc'], text_color="white", text_font_style='bold', text_font_size='12pt', text_alpha=0.8)
plot1.text([48.5], [47], ['Chance Of Detecting'],
text_color="white",
text_align="right",
text_alpha=1.0)
plot1.text([48.5], [44.5], ['an Earth Twin if Present'],
text_color="white",
text_align="right",
text_alpha=1.0)
plot1.text([48.5], [44.5], ['___________________'],
text_color="white",
text_align="right",
text_alpha=1.0)
plot1.text(np.array([48.5]),
np.array([41.5]), ["80-100%"],
text_color='lightgreen',
def periodic_plot(cds, title="Periodic Table", width=PLOT_WIDTH,
height=PLOT_HEIGHT, cmap="viridis",
showfblock=True, long_version=False,
color_mapper=None):
"""
Create the periodic plot
Args:
df : DataFrame
Pandas DataFrame with the data on elements
tile : str
Title to appear above the periodic table
colorby : str
Name of the column containig the colors
width : int
Width of the figure in pixels
height : int
Height of the figure in pixels
cmap : str
Colormap to use, see matplotlib colormaps
long_version : bool
Show the long version of the periodic table with the f block between
the s and d blocks
showfblock : bool
Show the elements from the f block
.. note::
`property` attribute holds the current property to be displayed
"""
fig = Figure(title=title,
x_axis_location="above",
x_range=(0.5, 18.5),
y_range=(10.0, 0.5),
plot_width=width,
plot_height=height,
tools="box_zoom,pan,resize,save,reset",
toolbar_location="above",
toolbar_sticky=False,
)
if color_mapper is None:
color_dict = "#1F77B4"
else:
color_dict = {"field": "value", "transform": color_mapper}
fig.rect("x", "y", 0.9, 0.9, source=cds, fill_alpha=0.6,
fill_color=color_dict, line_color=color_dict)
# adjust the ticks and axis bounds
fig.yaxis.bounds = (1, 7)
fig.axis[1].ticker.num_minor_ticks = 0
fig.axis[0].ticker = FixedTicker(ticks=list(range(1, 19)))
text_props = {
"source": cds,
"angle": 0,
"color": "black",
"text_align": "center",
"text_baseline": "middle"
}
fig.text(x="x", y="y_symbol", text="symbol",
text_font_style="bold", text_font_size="15pt", **text_props)
fig.text(x="x", y="y_anumber", text="atomic_number",
text_font_size="9pt", **text_props)
fig.text(x="x", y="y_name", text="name",
text_font_size="7pt", **text_props)
fig.text(x="x", y="y_prop", text="value_str",
text_font_size="8pt", **text_props)
fig.grid.grid_line_color = None
hover = HoverTool(tooltips=HOVER_TOOLTIPS)
fig.add_tools(hover)
return fig
def plot_cross_section_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, cross_data, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and cross-sections using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all is a list of velocity profiles
cross_lat_data_all is a list of cross-sections along latitude
lat_value_all is a list of corresponding latitudes for these cross-sections
cross_lon_data_all is a list of cross-sections along longitude
lon_value_all is a list of corresponding longitudes for these cross-sections
boundary_data is a list of boundaries
style_parameter contains parameters to customize the plots
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
palette_r = palette[::-1]
ncolor = len(palette_r)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min)*1./ncolor
colorbar_left = np.linspace(color_min,color_max-color_step,ncolor)
colorbar_right = np.linspace(color_min+color_step,color_max,ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append('Depth: {0:8.0f} km'.format(map_depth))
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_all_slices.append(map_color)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']],\
dh=[style_parameter['nlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
#
plot_depth = np.shape(cross_data)[0] * style_parameter['cross_ddepth']
plot_lon = great_arc_distance(style_parameter['cross_default_lat0'], style_parameter['cross_default_lon0'],\
style_parameter['cross_default_lat1'], style_parameter['cross_default_lon1'])
vs_min = style_parameter['cross_view_vs_min']
vs_max = style_parameter['cross_view_vs_max']
cross_color = val_to_rgb(cross_data, palette_r, vmin, vmax)
cross_data_bokeh = ColumnDataSource(data=dict(x=[0],\
y=[plot_depth],dw=[plot_lon],\
dh=[plot_depth],cross_data=[cross_color]))
map_line_bokeh = ColumnDataSource(data=dict(lat=[style_parameter['cross_default_lat0'], style_parameter['cross_default_lat1']],\
lon=[style_parameter['cross_default_lon0'], style_parameter['cross_default_lon1']]))
#
ncolor_cross = len(my_palette)
colorbar_top_cross = [0.1 for i in range(ncolor_cross)]
colorbar_bottom_cross = [0 for i in range(ncolor_cross)]
color_min_cross = style_parameter['cross_view_vs_min']
color_max_cross = style_parameter['cross_view_vs_max']
color_step_cross = (color_max_cross - color_min_cross)*1./ncolor_cross
colorbar_left_cross = np.linspace(color_min_cross, color_max_cross-color_step_cross, ncolor_cross)
colorbar_right_cross = np.linspace(color_min_cross+color_step_cross, color_max_cross, ncolor_cross)
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',\
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50, \
callback=depth_slider_callback)
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.line('lon', 'lat', source=map_line_bokeh, line_dash=[8,2,8,2], line_color='#00ff00',\
nonselection_line_alpha=1.0, line_width=5.,\
nonselection_line_color='black')
map_view.text([style_parameter['cross_default_lon0']],[style_parameter['cross_default_lat0']], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
map_view.text([style_parameter['cross_default_lon1']],[style_parameter['cross_default_lat1']], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left')
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location=None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text="""<p style="font-size:16px">""", \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
# plot cross-section along latitude
cross_section_plot_width = int(style_parameter['cross_plot_height']*1.0/plot_depth*plot_lon/10.)
cross_view = Figure(plot_width=cross_section_plot_width, plot_height=style_parameter['cross_plot_height'], \
tools=style_parameter['cross_view_tools'], title=style_parameter['cross_view_title'], \
y_range=[plot_depth, -30],\
x_range=[0, plot_lon])
cross_view.image_rgba('cross_data',x='x',\
y='y',dw='dw',dh='dh',\
source=cross_data_bokeh, level='image')
cross_view.text([plot_lon*0.1], [-10], ['A'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
cross_view.text([plot_lon*0.9], [-10], ['B'], \
text_font_size=style_parameter['title_font_size'],text_align='left',level='underlay')
# ------------------------------
# change style
cross_view.title.text_font_size = style_parameter['title_font_size']
cross_view.title.align = 'center'
cross_view.title.text_font_style = 'normal'
cross_view.xaxis.axis_label = style_parameter['cross_view_xlabel']
cross_view.xaxis.axis_label_text_font_style = 'normal'
cross_view.xaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.xaxis.major_label_text_font_size = xlabel_fontsize
cross_view.yaxis.axis_label = style_parameter['cross_view_ylabel']
cross_view.yaxis.axis_label_text_font_style = 'normal'
cross_view.yaxis.axis_label_text_font_size = xlabel_fontsize
cross_view.yaxis.major_label_text_font_size = xlabel_fontsize
cross_view.xgrid.grid_line_color = None
cross_view.ygrid.grid_line_color = None
cross_view.toolbar.logo = None
cross_view.toolbar_location = 'right'
cross_view.toolbar_sticky = False
# ==============================
colorbar_fig_right = Figure(tools=[], y_range=(0,0.1),plot_width=cross_section_plot_width, \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig_right.toolbar_location=None
colorbar_fig_right.quad(top=colorbar_top_cross,bottom=colorbar_bottom_cross,\
left=colorbar_left_cross,right=colorbar_right_cross,\
color=my_palette)
colorbar_fig_right.yaxis[0].ticker=FixedTicker(ticks=[])
colorbar_fig_right.xgrid.grid_line_color = None
colorbar_fig_right.ygrid.grid_line_color = None
colorbar_fig_right.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig_right.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig_right.title.text_font_size = xlabel_fontsize
colorbar_fig_right.title.align = 'center'
colorbar_fig_right.title.text_font_style = 'normal'
# ==============================
output_file(filename,title=style_parameter['html_title'], mode=style_parameter['library_source'])
left_column = Column(depth_slider, map_view, colorbar_fig, annotating_fig01, width=style_parameter['left_column_width'])
right_column = Column(annotating_fig02, cross_view, colorbar_fig_right, width=cross_section_plot_width)
layout = Row(left_column, right_column, height=800)
save(layout)
def plot_3DModel_bokeh(filename, map_data_all_slices, map_depth_all_slices, \
color_range_all_slices, profile_data_all, boundary_data, \
style_parameter):
'''
Plot shear velocity maps and velocity profiles using bokeh
Input:
filename is the filename of the resulting html file
map_data_all_slices contains the velocity model parameters saved for map view plots
map_depth_all_slices is a list of depths
color_range_all_slices is a list of color ranges
profile_data_all constains the velocity model parameters saved for profile plots
boundary_data is a list of boundaries
style_parameter contains plotting parameters
Output:
None
'''
xlabel_fontsize = style_parameter['xlabel_fontsize']
#
colorbar_data_all_left = []
colorbar_data_all_right = []
map_view_ndepth = style_parameter['map_view_ndepth']
ncolor = len(palette)
colorbar_top = [0.1 for i in range(ncolor)]
colorbar_bottom = [0 for i in range(ncolor)]
map_data_all_slices_depth = []
for idepth in range(map_view_ndepth):
color_min = color_range_all_slices[idepth][0]
color_max = color_range_all_slices[idepth][1]
color_step = (color_max - color_min) * 1. / ncolor
colorbar_left = np.linspace(color_min, color_max - color_step, ncolor)
colorbar_right = np.linspace(color_min + color_step, color_max, ncolor)
colorbar_data_all_left.append(colorbar_left)
colorbar_data_all_right.append(colorbar_right)
map_depth = map_depth_all_slices[idepth]
map_data_all_slices_depth.append(
'Depth: {0:8.1f} km'.format(map_depth))
#
palette_r = palette[::-1]
# data for the colorbar
colorbar_data_one_slice = {}
colorbar_data_one_slice['colorbar_left'] = colorbar_data_all_left[
style_parameter['map_view_default_index']]
colorbar_data_one_slice['colorbar_right'] = colorbar_data_all_right[
style_parameter['map_view_default_index']]
colorbar_data_one_slice_bokeh = ColumnDataSource(data=dict(colorbar_top=colorbar_top,colorbar_bottom=colorbar_bottom,\
colorbar_left=colorbar_data_one_slice['colorbar_left'],\
colorbar_right=colorbar_data_one_slice['colorbar_right'],\
palette_r=palette_r))
colorbar_data_all_slices_bokeh = ColumnDataSource(data=dict(colorbar_data_all_left=colorbar_data_all_left,\
colorbar_data_all_right=colorbar_data_all_right))
#
map_view_label_lon = style_parameter['map_view_depth_label_lon']
map_view_label_lat = style_parameter['map_view_depth_label_lat']
map_data_one_slice_depth = map_data_all_slices_depth[
style_parameter['map_view_default_index']]
map_data_one_slice_depth_bokeh = ColumnDataSource(data=dict(lat=[map_view_label_lat], lon=[map_view_label_lon],
map_depth=[map_data_one_slice_depth],
left=[style_parameter['profile_plot_xmin']], \
right=[style_parameter['profile_plot_xmax']]))
#
map_view_default_index = style_parameter['map_view_default_index']
#map_data_one_slice = map_data_all_slices[map_view_default_index]
#
map_color_all_slices = []
for i in range(len(map_data_all_slices)):
vmin, vmax = color_range_all_slices[i]
map_color = val_to_rgb(map_data_all_slices[i], palette_r, vmin, vmax)
map_color_2d = map_color.view('uint32').reshape(map_color.shape[:2])
map_color_all_slices.append(map_color_2d)
map_color_one_slice = map_color_all_slices[map_view_default_index]
#
map_data_one_slice_bokeh = ColumnDataSource(data=dict(x=[style_parameter['map_view_image_lon_min']],\
y=[style_parameter['map_view_image_lat_min']],dw=[style_parameter['nlon']*style_parameter['dlon']],\
dh=[style_parameter['nlat']*style_parameter['dlat']],map_data_one_slice=[map_color_one_slice]))
map_data_all_slices_bokeh = ColumnDataSource(data=dict(map_data_all_slices=map_color_all_slices,\
map_data_all_slices_depth=map_data_all_slices_depth))
# ------------------------------
nprofile = len(profile_data_all)
grid_lat_list = []
grid_lon_list = []
width_list = []
height_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
grid_lat_list.append(aprofile['lat'])
grid_lon_list.append(aprofile['lon'])
width_list.append(style_parameter['map_view_grid_width'])
height_list.append(style_parameter['map_view_grid_height'])
grid_data_bokeh = ColumnDataSource(data=dict(lon=grid_lon_list,lat=grid_lat_list,\
width=width_list, height=height_list))
profile_default_index = style_parameter['profile_default_index']
selected_dot_on_map_bokeh = ColumnDataSource(data=dict(lat=[grid_lat_list[profile_default_index]], \
lon=[grid_lon_list[profile_default_index]], \
width=[style_parameter['map_view_grid_width']],\
height=[style_parameter['map_view_grid_height']],\
index=[profile_default_index]))
# ------------------------------
profile_vs_all = []
profile_depth_all = []
profile_ndepth = style_parameter['profile_ndepth']
profile_lat_label_list = []
profile_lon_label_list = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
vs_raw = aprofile['vs']
top_raw = aprofile['top']
profile_lat_label_list.append('Lat: {0:12.1f}'.format(aprofile['lat']))
profile_lon_label_list.append('Lon: {0:12.1f}'.format(aprofile['lon']))
vs_plot = []
depth_plot = []
for idepth in range(profile_ndepth):
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth])
vs_plot.append(vs_raw[idepth])
depth_plot.append(top_raw[idepth + 1])
profile_vs_all.append(vs_plot)
profile_depth_all.append(depth_plot)
profile_data_all_bokeh = ColumnDataSource(data=dict(profile_vs_all=profile_vs_all, \
profile_depth_all=profile_depth_all))
selected_profile_data_bokeh = ColumnDataSource(data=dict(vs=profile_vs_all[profile_default_index],\
depth=profile_depth_all[profile_default_index]))
selected_profile_lat_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lat_label_x']], y=[style_parameter['profile_lat_label_y']],\
lat_label=[profile_lat_label_list[profile_default_index]]))
selected_profile_lon_label_bokeh = ColumnDataSource(data=\
dict(x=[style_parameter['profile_lon_label_x']], y=[style_parameter['profile_lon_label_y']],\
lon_label=[profile_lon_label_list[profile_default_index]]))
all_profile_lat_label_bokeh = ColumnDataSource(data=dict(
profile_lat_label_list=profile_lat_label_list))
all_profile_lon_label_bokeh = ColumnDataSource(data=dict(
profile_lon_label_list=profile_lon_label_list))
#
button_ndepth = style_parameter['button_ndepth']
button_data_all_vs = []
button_data_all_vp = []
button_data_all_rho = []
button_data_all_top = []
for iprofile in range(nprofile):
aprofile = profile_data_all[iprofile]
button_data_all_vs.append(aprofile['vs'][:button_ndepth])
button_data_all_vp.append(aprofile['vp'][:button_ndepth])
button_data_all_rho.append(aprofile['rho'][:button_ndepth])
button_data_all_top.append(aprofile['top'][:button_ndepth])
button_data_all_bokeh = ColumnDataSource(data=dict(button_data_all_vs=button_data_all_vs,\
button_data_all_vp=button_data_all_vp,\
button_data_all_rho=button_data_all_rho,\
button_data_all_top=button_data_all_top))
# ==============================
map_view = Figure(plot_width=style_parameter['map_view_plot_width'], plot_height=style_parameter['map_view_plot_height'], \
tools=style_parameter['map_view_tools'], title=style_parameter['map_view_title'], \
y_range=[style_parameter['map_view_figure_lat_min'], style_parameter['map_view_figure_lat_max']],\
x_range=[style_parameter['map_view_figure_lon_min'], style_parameter['map_view_figure_lon_max']])
#
map_view.image_rgba('map_data_one_slice',x='x',\
y='y',dw='dw',dh='dh',
source=map_data_one_slice_bokeh, level='image')
depth_slider_callback = CustomJS(args=dict(map_data_one_slice_bokeh=map_data_one_slice_bokeh,\
map_data_all_slices_bokeh=map_data_all_slices_bokeh,\
colorbar_data_all_slices_bokeh=colorbar_data_all_slices_bokeh,\
colorbar_data_one_slice_bokeh=colorbar_data_one_slice_bokeh,\
map_data_one_slice_depth_bokeh=map_data_one_slice_depth_bokeh), code="""
var d_index = Math.round(cb_obj.value)
var map_data_all_slices = map_data_all_slices_bokeh.data
map_data_one_slice_bokeh.data['map_data_one_slice'] = [map_data_all_slices['map_data_all_slices'][d_index]]
map_data_one_slice_bokeh.change.emit()
var color_data_all_slices = colorbar_data_all_slices_bokeh.data
colorbar_data_one_slice_bokeh.data['colorbar_left'] = color_data_all_slices['colorbar_data_all_left'][d_index]
colorbar_data_one_slice_bokeh.data['colorbar_right'] = color_data_all_slices['colorbar_data_all_right'][d_index]
colorbar_data_one_slice_bokeh.change.emit()
map_data_one_slice_depth_bokeh.data['map_depth'] = [map_data_all_slices['map_data_all_slices_depth'][d_index]]
map_data_one_slice_depth_bokeh.change.emit()
""")
depth_slider = Slider(start=0, end=style_parameter['map_view_ndepth']-1, \
value=map_view_default_index, step=1, \
width=style_parameter['map_view_plot_width'],\
title=style_parameter['depth_slider_title'], height=50)
depth_slider.js_on_change('value', depth_slider_callback)
depth_slider_callback.args["depth_index"] = depth_slider
# ------------------------------
# add boundaries to map view
# country boundaries
map_view.multi_line(boundary_data['country']['longitude'],\
boundary_data['country']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# marine boundaries
map_view.multi_line(boundary_data['marine']['longitude'],\
boundary_data['marine']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# shoreline boundaries
map_view.multi_line(boundary_data['shoreline']['longitude'],\
boundary_data['shoreline']['latitude'],color='black',\
line_width=2, level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# state boundaries
map_view.multi_line(boundary_data['state']['longitude'],\
boundary_data['state']['latitude'],color='black',\
level='underlay',nonselection_line_alpha=1.0,\
nonselection_line_color='black')
# ------------------------------
# add depth label
map_view.rect(style_parameter['map_view_depth_box_lon'], style_parameter['map_view_depth_box_lat'], \
width=style_parameter['map_view_depth_box_width'], height=style_parameter['map_view_depth_box_height'], \
width_units='screen',height_units='screen', color='#FFFFFF', line_width=1., line_color='black', level='underlay')
map_view.text('lon', 'lat', 'map_depth', source=map_data_one_slice_depth_bokeh,\
text_font_size=style_parameter['annotating_text_font_size'],text_align='left',level='underlay')
# ------------------------------
map_view.rect('lon', 'lat', width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='gray', line_alpha=0.5, \
selection_line_color='gray', selection_line_alpha=0.5, selection_fill_color=None,\
nonselection_line_color='gray',nonselection_line_alpha=0.5, nonselection_fill_color=None,\
source=grid_data_bokeh, color=None, line_width=1, level='glyph')
map_view.rect('lon', 'lat',width='width', \
width_units='screen', height='height', \
height_units='screen', line_color='#00ff00', line_alpha=1.0, \
source=selected_dot_on_map_bokeh, fill_color=None, line_width=3.,level='glyph')
# ------------------------------
grid_data_js = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh, \
grid_data_bokeh=grid_data_bokeh,\
profile_data_all_bokeh=profile_data_all_bokeh,\
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh, \
), code="""
var inds = cb_obj.indices
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][inds]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][inds]]
selected_dot_on_map_bokeh.data['index'] = [inds]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][inds]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][inds]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[inds]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[inds]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
grid_data_bokeh.selected.js_on_change('indices', grid_data_js)
# ------------------------------
# change style
map_view.title.text_font_size = style_parameter['title_font_size']
map_view.title.align = 'center'
map_view.title.text_font_style = 'normal'
map_view.xaxis.axis_label = style_parameter['map_view_xlabel']
map_view.xaxis.axis_label_text_font_style = 'normal'
map_view.xaxis.axis_label_text_font_size = xlabel_fontsize
map_view.xaxis.major_label_text_font_size = xlabel_fontsize
map_view.yaxis.axis_label = style_parameter['map_view_ylabel']
map_view.yaxis.axis_label_text_font_style = 'normal'
map_view.yaxis.axis_label_text_font_size = xlabel_fontsize
map_view.yaxis.major_label_text_font_size = xlabel_fontsize
map_view.xgrid.grid_line_color = None
map_view.ygrid.grid_line_color = None
map_view.toolbar.logo = None
map_view.toolbar_location = 'above'
map_view.toolbar_sticky = False
# ==============================
# plot colorbar
colorbar_fig = Figure(tools=[], y_range=(0,0.1),plot_width=style_parameter['map_view_plot_width'], \
plot_height=style_parameter['colorbar_plot_height'],title=style_parameter['colorbar_title'])
colorbar_fig.toolbar_location = None
colorbar_fig.quad(top='colorbar_top',bottom='colorbar_bottom',left='colorbar_left',right='colorbar_right',\
color='palette_r',source=colorbar_data_one_slice_bokeh)
colorbar_fig.yaxis[0].ticker = FixedTicker(ticks=[])
colorbar_fig.xgrid.grid_line_color = None
colorbar_fig.ygrid.grid_line_color = None
colorbar_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis.major_label_text_font_size = xlabel_fontsize
colorbar_fig.xaxis[0].formatter = PrintfTickFormatter(format="%5.2f")
colorbar_fig.title.text_font_size = xlabel_fontsize
colorbar_fig.title.align = 'center'
colorbar_fig.title.text_font_style = 'normal'
# ==============================
profile_xrange = Range1d(start=style_parameter['profile_plot_xmin'],
end=style_parameter['profile_plot_xmax'])
profile_yrange = Range1d(start=style_parameter['profile_plot_ymax'],
end=style_parameter['profile_plot_ymin'])
profile_fig = Figure(plot_width=style_parameter['profile_plot_width'], plot_height=style_parameter['profile_plot_height'],\
x_range=profile_xrange, y_range=profile_yrange, tools=style_parameter['profile_tools'],\
title=style_parameter['profile_title'])
profile_fig.line('vs',
'depth',
source=selected_profile_data_bokeh,
line_width=2,
line_color='black')
# ------------------------------
# add lat, lon
profile_fig.rect([style_parameter['profile_label_box_x']], [style_parameter['profile_label_box_y']],\
width=style_parameter['profile_label_box_width'], height=style_parameter['profile_label_box_height'],\
width_units='screen', height_units='screen', color='#FFFFFF', line_width=1., line_color='black',\
level='underlay')
profile_fig.text('x',
'y',
'lat_label',
source=selected_profile_lat_label_bokeh)
profile_fig.text('x',
'y',
'lon_label',
source=selected_profile_lon_label_bokeh)
# ------------------------------
# change style
profile_fig.xaxis.axis_label = style_parameter['profile_xlabel']
profile_fig.xaxis.axis_label_text_font_style = 'normal'
profile_fig.xaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.xaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.axis_label = style_parameter['profile_ylabel']
profile_fig.yaxis.axis_label_text_font_style = 'normal'
profile_fig.yaxis.axis_label_text_font_size = xlabel_fontsize
profile_fig.yaxis.major_label_text_font_size = xlabel_fontsize
profile_fig.xgrid.grid_line_dash = [4, 2]
profile_fig.ygrid.grid_line_dash = [4, 2]
profile_fig.title.text_font_size = style_parameter['title_font_size']
profile_fig.title.align = 'center'
profile_fig.title.text_font_style = 'normal'
profile_fig.toolbar_location = 'above'
profile_fig.toolbar_sticky = False
profile_fig.toolbar.logo = None
# ==============================
profile_slider_callback = CustomJS(args=dict(selected_dot_on_map_bokeh=selected_dot_on_map_bokeh,\
grid_data_bokeh=grid_data_bokeh, \
profile_data_all_bokeh=profile_data_all_bokeh, \
selected_profile_data_bokeh=selected_profile_data_bokeh,\
selected_profile_lat_label_bokeh=selected_profile_lat_label_bokeh,\
selected_profile_lon_label_bokeh=selected_profile_lon_label_bokeh, \
all_profile_lat_label_bokeh=all_profile_lat_label_bokeh, \
all_profile_lon_label_bokeh=all_profile_lon_label_bokeh), code="""
var p_index = Math.round(cb_obj.value)
var grid_data = grid_data_bokeh.data
selected_dot_on_map_bokeh.data['lat'] = [grid_data['lat'][p_index]]
selected_dot_on_map_bokeh.data['lon'] = [grid_data['lon'][p_index]]
selected_dot_on_map_bokeh.data['index'] = [p_index]
selected_dot_on_map_bokeh.change.emit()
var profile_data_all = profile_data_all_bokeh.data
selected_profile_data_bokeh.data['vs'] = profile_data_all['profile_vs_all'][p_index]
selected_profile_data_bokeh.data['depth'] = profile_data_all['profile_depth_all'][p_index]
selected_profile_data_bokeh.change.emit()
var all_profile_lat_label = all_profile_lat_label_bokeh.data['profile_lat_label_list']
var all_profile_lon_label = all_profile_lon_label_bokeh.data['profile_lon_label_list']
selected_profile_lat_label_bokeh.data['lat_label'] = [all_profile_lat_label[p_index]]
selected_profile_lon_label_bokeh.data['lon_label'] = [all_profile_lon_label[p_index]]
selected_profile_lat_label_bokeh.change.emit()
selected_profile_lon_label_bokeh.change.emit()
""")
profile_slider = Slider(start=0, end=nprofile-1, value=style_parameter['profile_default_index'], \
step=1, title=style_parameter['profile_slider_title'], \
width=style_parameter['profile_plot_width'], height=50)
profile_slider_callback.args['profile_index'] = profile_slider
profile_slider.js_on_change('value', profile_slider_callback)
# ==============================
simple_text_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "# Layer Top (km) Vs(km/s) Vp(km/s) Rho(g/cm^3) \\n"
while(button_data_vp[i]) {
temp+=button_data_top[i].toPrecision(6) + " " + button_data_vs[i].toPrecision(4) + " " + \
button_data_vp[i].toPrecision(4) + " " + button_data_rho[i].toPrecision(4) + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
simple_text_button = Button(
label=style_parameter['simple_text_button_label'],
button_type='default',
width=style_parameter['button_width'])
simple_text_button.js_on_click(simple_text_button_callback)
# ------------------------------
model96_button_callback = CustomJS(args=dict(button_data_all_bokeh=button_data_all_bokeh,\
selected_dot_on_map_bokeh=selected_dot_on_map_bokeh), \
code="""
var index = selected_dot_on_map_bokeh.data['index']
var lat = selected_dot_on_map_bokeh.data['lat']
var lon = selected_dot_on_map_bokeh.data['lon']
var button_data_vs = button_data_all_bokeh.data['button_data_all_vs'][index]
var button_data_vp = button_data_all_bokeh.data['button_data_all_vp'][index]
var button_data_rho = button_data_all_bokeh.data['button_data_all_rho'][index]
var button_data_top = button_data_all_bokeh.data['button_data_all_top'][index]
var csvContent = ""
var i = 0
var temp = csvContent
temp += "MODEL." + index + " \\n"
temp += "ShearVelocityModel Lat: "+ lat +" Lon: " + lon + "\\n"
temp += "ISOTROPIC \\n"
temp += "KGS \\n"
temp += "SPHERICAL EARTH \\n"
temp += "1-D \\n"
temp += "CONSTANT VELOCITY \\n"
temp += "LINE08 \\n"
temp += "LINE09 \\n"
temp += "LINE10 \\n"
temp += "LINE11 \\n"
temp += " H(KM) VP(KM/S) VS(KM/S) RHO(GM/CC) QP QS ETAP ETAS FREFP FREFS \\n"
while(button_data_vp[i+1]) {
var thickness = button_data_top[i+1] - button_data_top[i]
temp+=" " +thickness.toPrecision(6) + " " + button_data_vp[i].toPrecision(4) + " " + button_data_vs[i].toPrecision(4) \
+ " " + button_data_rho[i].toPrecision(4) + " 0.00 0.00 0.00 0.00 1.00 1.00" + "\\n"
i = i + 1
}
const blob = new Blob([temp], { type: 'text/csv;charset=utf-8;' })
const link = document.createElement('a');
link.href = URL.createObjectURL(blob);
link.download = 'vel_model96.txt';
link.target = '_blank'
link.style.visibility = 'hidden'
link.dispatchEvent(new MouseEvent('click'))
""")
model96_button = Button(label=style_parameter['model96_button_label'],
button_type='default',
width=style_parameter['button_width'])
model96_button.js_on_click(model96_button_callback)
# ==============================
# annotating text
annotating_fig01 = Div(text=style_parameter['annotating_html01'], \
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
annotating_fig02 = Div(text=style_parameter['annotating_html02'],\
width=style_parameter['annotation_plot_width'], height=style_parameter['annotation_plot_height'])
# ==============================
output_file(filename,
title=style_parameter['html_title'],
mode=style_parameter['library_source'])
left_column = Column(depth_slider,
map_view,
colorbar_fig,
annotating_fig01,
width=style_parameter['left_column_width'])
button_pannel = Row(simple_text_button, model96_button)
right_column = Column(profile_slider,
profile_fig,
button_pannel,
annotating_fig02,
width=style_parameter['right_column_width'])
layout = Row(left_column, right_column)
save(layout)
size=8)
# imaging
ulens = Table.read(
'/Users/tumlinson/Dropbox/LUVOIR_STDT/luvoir_simtools/planetspace/imaging.dat',
format='ascii',
names=['name', 'msini', 'semi', 'mstar'])
a_rad = 2.7 * ulens['mstar']
ulens_syms = p1.square(ulens['semi'] / a_rad,
ulens['msini'] * mjup,
fill_alpha=0.4,
line_alpha=0.9,
color='purple',
size=8)
p1.text([5.2 / 2.7], [317.8], ['J'], text_color="red", text_align="center")
#xyouts, 5.2/2.7, 317.8, 'J', align=0.5
#xyouts, 9.6/2.7, 95.2, 'S', align=0.5
#xyouts, 19.2/2.7, 14.5, 'U', align=0.5
#xyouts, 30.1/2.7, 17.1, 'N', align=0.5
#xyouts, 1.0/2.7, 1.0, 'E', align=0.5
#xyouts, 0.72/2.7, 0.8, 'V', align=0.5
#xyouts, 1.5/2.7, 0.1, 'M', align=0.5
#xyouts, 0.39/2.7, 0.055, 'M', align=0.5
def update_image(active):
print 'wHAT?'
print active
print active.__contains__(1)
sample_curve_input = Dropdown(label="choose a sample function pair or enter one below",
menu=leibnitz_settings.sample_curve_names)
sample_curve_input.on_click(sample_curve_change)
# initialize plot
toolset = "crosshair,pan,reset,resize,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=400, plot_width=400, tools=toolset,
title="Leibnitz sector formula",
x_range=[leibnitz_settings.x_min_view, leibnitz_settings.x_max_view],
y_range=[leibnitz_settings.y_min_view, leibnitz_settings.y_max_view])
# Plot the line by the x,y values in the source property
plot.line('x', 'y', source=source_curve, line_width=3, line_alpha=1, color='black', legend='curve')
plot.scatter('x', 'y', source=source_point, color='blue', legend='point at t')
plot.scatter([0], [0], color='black', marker='x')
pat = plot.patch('x', 'y', source=source_sector, fill_color='blue', fill_alpha=.2, legend='area')
plot.line('x_start', 'y_start', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.line('x_end', 'y_end', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.text('x', 'y', text='text', text_color='text_color', source=source_text)
# calculate data
update_curve()
update_point()
# lists all the controls in our app
controls = widgetbox(t_value_input, sample_curve_input, x_component_input, y_component_input)
# make layout
curdoc().add_root(row(plot, controls))
str(int(np.sum(yields['complete9'][:]))),
str(int(np.sum(yields['complete10'][:]))),
str(int(np.sum(yields['complete11'][:]))),
str(int(np.sum(yields['complete12'][:]))),
str(int(np.sum(yields['complete13'][:]))),
str(int(np.sum(yields['complete14'][:])))
]
hist_plot.quad(top='yields', bottom=0., left='left', right='right', source=total_yield_label, \
color='color', fill_alpha=0.9, line_alpha=1., name='total_yield_label_hover')
hist_plot.text('xlabels',
'yields',
'labels',
0.,
20,
-3,
text_align='center',
source=total_yield_label,
text_color='black')
yield_hover = HoverTool(names=["total_yield_label_hover"],
mode='mouse',
tooltips="""
<div>
<span style="font-size: 20px; font-weight: bold; color:@color">N = </span>
<span style="font-size: 20px; font-weight: bold; color:@color">@yields</span>
</div>
<div>
<span style="font-size: 20px; font-weight: bold; color:@color">@temp </span>
</div>
p.rect("x",
"y",
.98,
.98,
0,
source=source,
fill_color={
'field': 'z',
'transform': mapper
},
fill_alpha=0.9) #, color="color")
p.axis.minor_tick_line_color = None
p.text(x="x",
y="y_offset2",
text="ztext",
text_font_style="bold",
text_font_size="20pt",
**text_props)
p.text(x="x", y="y_offset1", text="amp", text_font_size="18pt", **text_props)
formatter = PrintfTickFormatter(format='%2.1e')
color_bar = ColorBar(color_mapper=mapper,
major_label_text_align='left',
major_label_text_font_size='10pt',
label_standoff=2,
location=(0, 0),
formatter=formatter,
title="(ADU)",
title_text_baseline="alphabetic")
p.add_layout(color_bar, 'right')
plot_width=600,
plot_height=300,
title=None,
tools=tools1)
color_block = p1.rect(x='x',
y='y',
width=18,
height=10,
fill_color='color',
line_color='black',
source=source)
hex_code_text = p1.text('x',
'y',
text='color',
text_color='text_color',
alpha=0.6667,
text_font_size='36pt',
text_baseline='middle',
text_align='center',
source=source)
# the callback function to update the color of the block and associated label text
# NOTE: the JS functions for converting RGB to hex are taken from the excellent answer
# by Tim Down at http://stackoverflow.com/questions/5623838/rgb-to-hex-and-hex-to-rgb
callback = CustomJS(args=dict(source=source),
code="""
function componentToHex(c) {
var hex = c.toString(16);
return hex.length == 1 ? "0" + hex : hex;
}
function rgbToHex(r, g, b) {
menu=leibnitz_settings.sample_curve_names)
sample_curve_input.on_change('value', sample_curve_change)
# initialize plot
toolset = "crosshair,pan,reset,save,wheel_zoom"
# Generate a figure container
plot = Figure(plot_height=400, plot_width=400, tools=toolset,
title="Leibnitz sector formula",
x_range=[leibnitz_settings.x_min_view, leibnitz_settings.x_max_view],
y_range=[leibnitz_settings.y_min_view, leibnitz_settings.y_max_view])
# Plot the line by the x,y values in the source property
plot.line('x', 'y', source=source_curve, line_width=3, line_alpha=1, color='black', legend_label='curve')
plot.scatter('x', 'y', source=source_point, color='blue', legend_label='point at t')
plot.scatter([0], [0], color='black', marker='x')
pat = plot.patch('x', 'y', source=source_sector, fill_color='blue', fill_alpha=.2, legend_label='area')
plot.line('x_start', 'y_start', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.line('x_end', 'y_end', source=source_lines, line_width=1, line_alpha=1, color='blue')
plot.text('x', 'y', text='text', text_color='text_color', source=source_text)
# calculate data
update_curve()
update_point()
# lists all the controls in our app
controls = widgetbox(t_value_input, sample_curve_input, x_component_input, y_component_input)
# make layout
curdoc().add_root(row(plot, controls))
curdoc().title = split(dirname(__file__))[-1].replace('_',' ').replace('-',' ') # get path of parent directory and only use the name of the Parent Directory for the tab name. Replace underscores '_' and minuses '-' with blanks ' '
class pf_graph:
def end_date(self):
now = date.today() + timedelta(hours=4)
return now.strftime("%Y%m%d")
def get_quotes(self):
self.data_slice = slice(-self.data_length, None)
self.pf.asset_ticker = self.ticker
quote = fn.get_quotes_finam(self.ticker,
start_date=self.from_date,
end_date=self.end_date(),
period=self.quote_period,
verbose=False,
timeout=30)
self.quote = quote[self.data_slice]
#print(self.quote.tail())
#self.quote.to_csv('/home/jupyter/bokeh/pf/quotes_{}'.format(self.quote_period))
def update_quotes(self):
quote = self.quote
last_day = quote.ix[-1].name
last_day_begin = datetime(last_day.year, last_day.month, last_day.day)
quote_base = quote.ix[:last_day_begin + timedelta(seconds=-1)]
last_day_str = last_day_begin.strftime('%Y%m%d')
#while True:
#print('get quotes')
try:
quote = fn.get_quotes_finam(self.ticker,
start_date=last_day_str,
end_date=self.end_date(),
period=self.quote_period,
verbose=False,
timeout=5)
except timeout as e:
print('get quotes: timeout, period={}!'.format(self.quote_period))
self.text.value = 'timeout!'
return
except:
self.text.value = 'get quote error!'
return
#print('obtain quotes')
self.quote = quote_base.append(quote)[self.data_slice]
time_str = quote.ix[-1].name.strftime('%Y-%m-%d %H:%M')
if self.text_marker == len(markers) - 1:
self.text_marker = 0
else:
self.text_marker += 1
self.text.value = '{} [{}] {}'.format(
markers[self.text_marker], time_str,
quote.ix[-1][self.ticker + '_Close'])
#time.sleep(1)
def __init__(self, quote_period, box_size, graph_name, candles=30):
# Set up data
self.graph_counter = 0
self.data_length = candles
self.data_slice = slice(-self.data_length, None)
self.quote_period = quote_period
self.ticker = 'SPFB.RTS'
self.box_size_divider = 1.
self.pf = point_and_figure.Point_and_Figure()
#print(quote_period)
if (quote_period == 'daily' or quote_period == 'hour'):
self.from_date = (date.today() - timedelta(
days=days_look_back_min_candles * 5)).strftime('%Y%m%d')
else:
self.from_date = (
date.today() -
timedelta(days=days_look_back_min_candles)).strftime('%Y%m%d')
self.get_quotes()
self.last_quote_date = None
self.pf.box_size = box_size
self.pf.process_df(self.quote)
self.pf.prepare_datasource()
self.plot = Figure(
plot_height=400,
plot_width=600,
title=graph_name,
tools="crosshair,pan,reset,resize,wheel_zoom,save",
)
self.yticker = bokeh.models.FixedTicker()
#print pf.yticks
self.yticker.ticks = list(self.pf.yticks)
self.xticker = bokeh.models.FixedTicker()
self.xticker.ticks = list(self.pf.xticks)
# ticker.interval = 1
#p.xaxis.ticker = ticker
self.plot.ygrid.ticker = self.yticker
self.plot.xgrid.ticker = self.xticker
self.plot.yaxis.formatter = bokeh.models.NumeralTickFormatter(
format=self.pf.bokeh_ticks_format)
self.plot.yaxis.ticker = self.yticker
#self.plot.ygrid.band_fill_alpha = 0.05
#self.plot.ygrid.band_fill_color = "navy"
#self.glyphs_size = 100.
#self.glyphs_size = self.pf.box_size / 1. / len(self.pf.yticks) * self.pf.scale_factor
self.scale = Slider(title="scale",
value=300,
start=300,
end=3000,
step=1)
self.scale.on_change('value', self.scale_change)
self.recalc_glyphs_size = True
self.calc_glyphs()
self.source_x = ColumnDataSource(
data=dict(x=self.pf.x_x, y=self.pf.x_y, size=self.glyphs_size_x))
self.source_o = ColumnDataSource(
data=dict(x=self.pf.o_x, y=self.pf.o_y, size=self.glyphs_size_o))
self.source_digits = ColumnDataSource(
data=dict(x=self.pf.digits_x,
y=self.pf.digits_y,
text=self.pf.digits_text,
size=self.font_size))
self.graph_o = self.plot.circle('x',
'y',
source=self.source_o,
alpha=0.5,
size='size',
color="navy")
self.graph_x = self.plot.cross('x',
'y',
source=self.source_x,
alpha=0.5,
size='size',
color="navy",
angle=np.pi / 4)
self.graph_digits = self.plot.text(
'x',
'y',
source=self.source_digits,
alpha=0.5,
text='text',
color="navy",
text_font_size='size') #text_font_size
self.box = Slider(title="box",
value=self.pf.box_size,
start=10,
end=500,
step=1)
self.box.on_change('value', self.box_change)
self.text = TextInput()
self.text_marker = 0
def calc_glyphs(self):
#self.glyphs_size = self.pf.box_size / 4. / len(self.pf.yticks) * self.pf.scale_factor
#print self.glyphs_size
if self.recalc_glyphs_size:
self.glyphs_size = min(
self.plot.plot_height / 1.2 / (len(self.pf.yticks) + 3),
self.plot.plot_width / 1.2 / (len(self.pf.xticks) + 3))
self.scale.value = int(self.glyphs_size * 100)
self.recalc_glyphs_size = False
if self.glyphs_size < 3:
self.glyphs_size = 3
self.glyphs_size_x = [self.glyphs_size for _ in self.pf.x_x]
self.glyphs_size_o = [self.glyphs_size for _ in self.pf.o_x]
self.font_size = [
'{}px'.format(self.glyphs_size) for _ in self.pf.digits_x
]
#print(self.glyphs_size_x)
def scale_change(self, attrname, old, new):
self.glyphs_size = self.scale.value / 100.
#print(self.glyphs_size)
def box_change(self, attrname, old, new):
self.pf.box_size = self.box.value / self.box_size_divider
self.recalc_glyphs_size = True
def update_data(self, attrname, old, new):
#print('box')
# Get the current slider values
# Generate the new curve
self.pf.process_df(self.quote)
self.pf.prepare_datasource()
self.yticker.ticks = list(self.pf.yticks)
self.xticker.ticks = list(self.pf.xticks)
self.calc_glyphs()
#self.graph_o.glyph.size = self.glyphs_size
#self.graph_x.glyph.size = self.glyphs_size
#self.shuffle(x=self.pf.x_x, y=self.pf.x_y)
data = dict(x=self.pf.x_x, y=self.pf.x_y, size=self.glyphs_size_x)
self.source_x.data = data
#self.shuffle(x=self.pf.o_x, y=self.pf.o_y)
data = dict(x=self.pf.o_x, y=self.pf.o_y, size=self.glyphs_size_o)
self.source_o.data = data
data = dict(x=self.pf.digits_x,
y=self.pf.digits_y,
text=self.pf.digits_text,
size=self.font_size)
self.source_digits.data = data
title='move sliders to change',
tools='')
p1.rect(0,
0,
width=18,
height=10,
fill_color='color',
line_color='black',
source=source)
p1.text(0,
0,
text='color',
text_color='text_color',
alpha=0.6667,
text_font_size='36pt',
text_baseline='middle',
text_align='center',
source=source)
# the callback function to update the color of the block and associated label text
# NOTE: the JS functions for converting RGB to hex are taken from the excellent answer
# by Tim Down at http://stackoverflow.com/questions/5623838/rgb-to-hex-and-hex-to-rgb
callback = CustomJS(args=dict(source=source),
code="""
function componentToHex(c) {
var hex = c.toString(16);
return hex.length == 1 ? "0" + hex : hex;
}
function rgbToHex(r, g, b) {
def plot_amp(self,
dz,
refexp,
name="",
font_size="1.2vw",
description="",
nrg=[],
wrg=[],
status_plot=False):
''' Initializing AMP plot
'''
ztext, cbarformat = self.set_amp(dz)
dx = [0, 1, 0, 1]
dy = [1, 1, 0, 0]
zvalid = np.array([x if x > -999 else np.nan for x in dz])
data_source = dict(
x=dx,
y=dy,
z=dz,
zvalid=zvalid,
ref=["{:.2f}".format(x) for x in refexp],
zdiff=zvalid - np.array(refexp),
y_offset1=[i + 0.15 for i in dy],
y_offset2=[i - 0.10 for i in dy],
amp=['AMP %s' % i for i in range(1, 5)],
amp_number=['%s' % i for i in range(1, 5)],
ztext=ztext,
)
if status_plot:
text_val = 'status'
color_status = ['green', 'darkgrey', 'yellow', 'red']
color = {
'NaN': 'darkgrey',
'NORMAL': 'green',
'WARNING': 'yellow',
'ALARM': 'red',
}
status = [
self.val_status(x, nrg=nrg, wrg=wrg)
for x in zvalid - np.array(refexp)
]
color_status = [
color[self.val_status(x, nrg=nrg, wrg=wrg)]
for x in zvalid - np.array(refexp)
]
fill_color = "color_status"
data_source.update({
'status': status,
'color_status': color_status,
})
fill_alpha = 0.8
if not status_plot:
text_val = 'ztext'
cmap = get_palette("RdBu_r")
mapper = LinearColorMapper(palette=cmap,
low=wrg[0],
high=wrg[1],
nan_color="darkgrey")
formatter = PrintfTickFormatter(format=cbarformat)
color_bar = ColorBar(color_mapper=mapper,
major_label_text_align='left',
major_label_text_font_size='10pt',
label_standoff=2,
location=(0, 0),
formatter=formatter,
title="(Val-Ref)",
title_standoff=15,
title_text_baseline="alphabetic")
fill_color = {'field': 'zdiff', 'transform': mapper}
fill_alpha = 0.9
cmap_tooltip = """
<div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">AMP: </span>
<span style="font-size: 1vw; color: #515151;">@amp_number</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">counts: </span>
<span style="font-size: 1vw; color: #515151">@text_val</span>
</div>
<div>
<span style="font-size: 1vw; font-weight: bold; color: #303030;">Reference: </span>
<span style="font-size: 1vw; color: #515151;">@ref</span>
</div>
</div>
""".replace("counts:",
name.replace("_AMP", "") + ":").replace(
"text_val", text_val)
hover = HoverTool(tooltips=cmap_tooltip)
p = Figure(title=name,
tools=[hover],
x_range=list([-0.5, 1.5]),
y_range=list([-0.5, 1.5]),
plot_width=450,
plot_height=400)
source = ColumnDataSource(data=data_source)
text_props = {
"source": source,
"angle": 0,
"color": "black",
"text_color": "black",
"text_align": "center",
"text_baseline": "middle"
}
p.rect("x",
"y",
.98,
.98,
0,
source=source,
fill_color=fill_color,
fill_alpha=fill_alpha)
p.text(x="x",
y="y_offset1",
text="amp",
text_font_size="2vw",
**text_props)
p.text(x="x",
y="y_offset2",
text="ztext",
text_font_style="bold",
text_font_size="2.5vw",
**text_props)
if not status_plot:
p.add_layout(color_bar, 'right')
# Format:
p.xaxis.axis_label_text_font_size = font_size
p.legend.label_text_font_size = font_size
p.title.text_font_size = font_size
p.xaxis.axis_label = description
# Clear Axis:
p.grid.grid_line_color = None
p.outline_line_color = None
p.axis.minor_tick_line_color = None
p.axis.major_label_text_font_size = '0pt'
p.yaxis.major_label_text_font_size = '0pt'
p.xaxis.major_tick_line_color = None
p.xaxis.minor_tick_line_color = None
p.yaxis.major_tick_line_color = None
p.yaxis.minor_tick_line_color = None
p.yaxis.visible = False
p.xaxis.visible = True
p.axis.clear
return p
""")
# Set up plot
snr_plot = Figure(plot_height=400,
plot_width=800,
tools="crosshair,pan,reset,resize,save,box_zoom,wheel_zoom",
x_range=[120, 2300],
y_range=[0, 40],
toolbar_location='right')
snr_plot.x_range = Range1d(120, 2300, bounds=(120, 2300))
snr_plot.add_tools(hover)
snr_plot.background_fill_color = "beige"
snr_plot.background_fill_alpha = 0.5
snr_plot.yaxis.axis_label = 'SNR'
snr_plot.xaxis.axis_label = 'Wavelength (nm)'
snr_plot.text(5500, 20, text=['V'], text_align='center', text_color='red')
snr_plot.line('x', 'y', source=source, line_width=3, line_alpha=1.0)
snr_plot.circle('x',
'y',
source=source,
fill_color='white',
line_color='blue',
size=10)
snr_plot.line('x',
'y',
source=source2,
line_width=3,
line_color='orange',
line_alpha=1.0)
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, callback=curve_slider_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_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)
# initialise the text color as black. This will be switched to white if the block color gets dark enough
text_color = '#000000'
# create a data source to enable refreshing of fill & text color
source = ColumnDataSource(data=dict(x = x, y = y, color = [hex_color], text_color = [text_color]))
tools1 = 'reset, save'
# create first plot, as a rect() glyph and centered text label, with fill and text color taken from source
p1 = Figure(x_range=(-8, 8), y_range=(-4, 4), plot_width=600, plot_height=300,
title=None, tools=tools1)
color_block = p1.rect(x='x', y='y', width=18, height=10, fill_color='color',
line_color = 'black', source=source)
hex_code_text = p1.text('x', 'y', text='color', text_color='text_color',
alpha=0.6667, text_font_size='36pt', text_baseline='middle',
text_align='center', source=source)
# the callback function to update the color of the block and associated label text
# NOTE: the JS functions for converting RGB to hex are taken from the excellent answer
# by Tim Down at http://stackoverflow.com/questions/5623838/rgb-to-hex-and-hex-to-rgb
callback = CustomJS(args=dict(source=source), code="""
function componentToHex(c) {
var hex = c.toString(16);
return hex.length == 1 ? "0" + hex : hex;
}
function rgbToHex(r, g, b) {
return "#" + componentToHex(r) + componentToHex(g) + componentToHex(b);
}
var data = source.get('data');
var RS = red_slider;
class AppView(object):
def __init__(self, app_model):
self.model = app_model
self.create_layout()
def create_layout(self):
# create figure
self.x_range = Range1d(start=self.model.map_extent[0],
end=self.model.map_extent[2], bounds=None)
self.y_range = Range1d(start=self.model.map_extent[3],
end=self.model.map_extent[1], bounds=None)
self.fig = Figure(tools='wheel_zoom,pan,save,box_zoom', title='Color Magnitude Diagram',
x_range=self.x_range,
y_range=self.y_range,
lod_threshold=None,
plot_width=self.model.plot_width,
plot_height=self.model.plot_height,
background_fill_color='white')
# Labeling will be used to deal with the fact that we cannot get the axes right
# this source can then be adjusted with sliders as necessary to reset axis.
label_source = ColumnDataSource(data={'x': [-5,-5,-5,-5,-5],
'y': [-10-0.2,-5-0.2,0-0.2,5-0.2,10-0.2], 'text':['10','5','0','-5','-10']})
label1 = self.fig.text('x','y','text', source=label_source, text_font_size='8pt', text_color='deepskyblue')
# edit all the usual bokeh figure properties here
self.fig.xaxis.axis_label=self.model.xtitle
self.fig.yaxis.axis_label=self.model.ytitle
self.fig.yaxis.axis_label_text_font= 'PT Serif'
self.fig.yaxis.major_label_text_font = 'PT Serif'
self.fig.xaxis.axis_label_text_font= 'PT Serif'
self.fig.xaxis.major_label_text_font = 'PT Serif'
self.fig.min_border_top = 20
self.fig.min_border_bottom = 10
self.fig.min_border_left = 10
self.fig.min_border_right = 10
self.fig.axis.visible = False # use this to flip the axis labeling on and off
self.fig.xgrid.grid_line_color = '#aaaaaa'
self.fig.ygrid.grid_line_color = '#aaaaaa'
self.fig.ygrid.grid_line_alpha = 0.1
self.fig.xgrid.grid_line_alpha = 0.1
# add tiled basemap to class AppView
image_url = 'http://server.arcgisonline.com/arcgis//rest/services/World_Imagery/MapServer/tile/{Z}/{Y}/{X}.png'
self.tile_source = WMTSTileSource(url=image_url)
self.tile_renderer = TileRenderer(tile_source=self.tile_source)
self.tile_renderer.alpha = 0.02
self.fig.renderers.append(self.tile_renderer) # comment this out and it takes the ds points with it! WHY?
# add datashader layer - these are the aggregated data points to class AppView
self.image_source = ImageSource(url=self.model.service_url, extra_url_vars=self.model.shader_url_vars)
self.image_renderer = DynamicImageRenderer(image_source=self.image_source)
self.image_renderer.alpha = 1.00
self.fig.renderers.append(self.image_renderer)
# add controls
controls = [] # empty list for astronomy controls
visual_controls = [] # empty list for visual controls
self.parameters = {} # empty dict for astrophyscal pars
age_slider = Slider(title="Log Age [Gyr]", value=9.0, start=5.5, end=10.1, step=0.05)
age_slider.on_change('value', self.on_age_change)
controls.append(age_slider)
self.parameters['age'] = age_slider.value
aperture_size_slider = Slider(title="Aperture [m]", value=10, start=2,end=20, step=1)
aperture_size_slider.on_change('value', self.on_aperture_size_change)
controls.append(aperture_size_slider)
self.parameters['aperture'] = aperture_size_slider.value
exposure_time_slider = Slider(title="Exposure Time [hr]", value=0.1, start=1.0,end=10.0, step=0.1)
exposure_time_slider.on_change('value', self.on_exposure_time_change)
controls.append(exposure_time_slider)
distance_slider = Slider(title="Distance [kpc]", value=100. , start=10.0,end=1000.0, step=100.)
distance_slider.on_change('value', self.on_distance_change)
controls.append(distance_slider)
axes_select = Select(title='Variables', options=list(self.model.axes.keys()))
axes_select.on_change('value', self.on_axes_change)
controls.append(axes_select)
self.field_select = Select(title='Field', options=list(self.model.fields.keys()))
self.field_select.on_change('value', self.on_field_change)
#controls.append(self.field_select) - chooses wich field to weight by in aggregation, temporarily omitted for devel
self.aggregate_select = Select(title='Aggregate', options=list(self.model.aggregate_functions.keys()))
self.aggregate_select.on_change('value', self.on_aggregate_change)
controls.append(self.aggregate_select)
spread_size_slider = Slider(title="Spread Size (px)", value=1, start=0,end=5, step=1)
spread_size_slider.on_change('value', self.on_spread_size_change)
visual_controls.append(spread_size_slider)
image_opacity_slider = Slider(title="Opacity", value=100, start=0,end=100, step=1)
image_opacity_slider.on_change('value', self.on_image_opacity_slider_change)
visual_controls.append(image_opacity_slider)
transfer_select = Select(title='Transfer Function', options=list(self.model.transfer_functions.keys()))
transfer_select.on_change('value', self.on_transfer_function_change)
visual_controls.append(transfer_select)
color_ramp_select = Select(title='Colormap', name='Color Ramp', options=list(self.model.color_ramps.keys()))
color_ramp_select.on_change('value', self.on_color_ramp_change)
visual_controls.append(color_ramp_select)
astro_tab = Panel(child=Column(children=controls), title='Stars')
visual_tab = Panel(child=Column(children=visual_controls), title='Visuals', width=450)
self.controls = Tabs(tabs=[astro_tab, visual_tab], width=350)
self.map_area = Column(width=900, height=600,children=[self.fig])
self.layout = Row(width=1300, height=600,children=[self.controls, self.fig])
self.model.fig = self.fig # identify the fig defined here as the one that will be passed to AppView
def update_image(self):
self.model.shader_url_vars['cachebust'] = str(uuid.uuid4())
self.image_renderer.image_source = ImageSource(url=self.model.service_url, extra_url_vars=self.model.shader_url_vars)
self.fig.xaxis.axis_label=self.model.active_axes['xtitle']
self.fig.yaxis.axis_label=self.model.active_axes['ytitle']
def on_field_change(self, attr, old, new):
self.model.field_title = new
self.model.field = self.model.fields[new]
self.update_image()
if not self.model.field:
self.aggregate_select.options = [("No Aggregates Available", "")]
elif self.model.field in self.model.categorical_fields:
self.aggregate_select.options = [("Categorical", "count_cat")]
else:
opts = [(k, k) for k in self.model.aggregate_functions.keys()]
self.aggregate_select.options = opts
def on_spread_size_change(self, attr, old, new):
self.model.spread_size = int(new)
self.update_image()
def on_aperture_size_change(self, attr, old, new):
self.model.aperture = int(new)
self.update_image()
def on_age_change(self, attr, old, new):
self.model.age = new
self.update_image()
def on_exposure_time_change(self, attr, old, new):
self.model.exposure_time = int(new)
self.update_image()
def on_distance_change(self, attr, old, new):
self.model.distance = int(new)
self.update_image()
def on_axes_change(self, attr, old, new):
print("self.model.axes ", self.model.axes)
self.model.active_axes = self.model.axes[new]
self.update_image()
def on_aggregate_change(self, attr, old, new):
self.model.agg_function_name = new
self.update_image()
def on_transfer_function_change(self, attr, old, new):
self.model.transfer_function = self.model.transfer_functions[new]
self.update_image()
def on_color_ramp_change(self, attr, old, new):
self.model.color_ramp = self.model.color_ramps[new]
self.update_image()
def on_image_opacity_slider_change(self, attr, old, new):
self.image_renderer.alpha = new / 100
def _segment_plot(self, source):
"""Plot showing the mirror segments.
The plot shows the 91 segment positions with their position number. The colour of the segments indicates how
long ago they have been recoated.
Params:
-------
source: pandas.DataFrame
Data source.
Returns:
--------
bokeh.plotting.Figure
Plot showing the mirror segments.
"""
def segment_color(date):
"""Fill colour of a mirror segment."""
days = (datetime.datetime.now().date() - date).days
days = min(days, self.RECOATING_PERIOD)
days = max(days, 0)
def hex_value(v):
"""Convert integer into a hexadecimal string suitable for specifying an RGB value in a css rule."""
s = format(int(round(255 * v)), 'x')
if len(s) < 2:
s = '0' + s
return s
# The colour ranges from dark green for recently recoated segments to white for segments which need to be
# recoated.
h = 99
s = 100
l = 33 + 67 * days / self.RECOATING_PERIOD
rgb = colorsys.hls_to_rgb(h / 360, l / 100, s / 100)
return '#{r}{g}{b}'.format(r=hex_value(rgb[0]),
g=hex_value(rgb[1]),
b=hex_value(rgb[2]))
plot = Figure(tools='tap', toolbar_location=None)
colors = [segment_color(d) for d in source.data['ReplacementDate']]
plot.patches(xs='SegmentPositionCornerXs',
ys='SegmentPositionCornerYs',
source=source,
color=colors,
line_color='#dddddd')
plot.text(x='SegmentPositionX',
y='SegmentPositionY',
text='SegmentPosition',
source=source,
text_color='black',
text_align='center',
text_baseline='middle')
plot.grid.grid_line_color = None
plot.axis.visible = False
plot.min_border_top = 20
plot.min_border_bottom = 30
plot.outline_line_color = None
return plot
