def modify_doc(doc):
plot = Plot(height=400, width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300)
def cb(attr, old, new):
slider.title = "baz"
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
def modify_doc(doc):
plot = Plot(plot_height=400, plot_width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300, bar_color="red")
def cb(attr, old, new):
slider.bar_color = "rgba(255, 255, 0, 1)"
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
def modify_doc(doc):
plot = Plot(height=400, width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300)
def cbv(attr, old, new): junk['v'] += 1
def cbvt(attr, old, new): junk['vt'] += 1
slider.on_change('value', cbv)
slider.on_change('value_throttled', cbvt)
doc.add_root(column(slider, plot))
def modify_doc(doc):
source = ColumnDataSource(dict(x=[1, 2], y=[1, 1], val=["a", "b"]))
plot = Plot(height=400, width=400, x_range=Range1d(0, 1), y_range=Range1d(0, 1), min_border=0)
plot.add_glyph(source, Circle(x='x', y='y', size=20))
plot.add_tools(CustomAction(callback=CustomJS(args=dict(s=source), code=RECORD("data", "s.data"))))
slider = DateSlider(start=start, end=end, value=value, css_classes=["foo"], width=300, step=24*3600*1000)
def cb(attr, old, new):
source.data['val'] = [slider.value_as_date.isoformat()]
slider.on_change('value', cb)
doc.add_root(column(slider, plot))
fill_color={
'field': 'temp_anomalies',
'transform': color_mapper
},
fill_alpha=0.5,
line_color=None)
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=12,
border_line_color=None,
location=(0, 0))
p.add_layout(color_bar, 'right')
# Make a slider object: slider
slider = DateSlider(title="Date",
value=min(climate_data.index),
start=min(climate_data.index),
end=max(climate_data.index),
step=1,
width=900)
# Attach the callback to the 'value' property of slider
slider.on_change('value', update_plot)
# Make a row layout of widgetbox(slider) and plot and add it to the current document
layout = column(widgetbox(slider, sizing_mode='scale_width'), p)
# Add the plot to the current document and add a title
curdoc().add_root(layout)
curdoc().title = 'Global Warming'
max(df_map[plot_var[8]]),
max(df_map[plot_var[9]]),
max(df_map[plot_var[10]]),
max(df_map[plot_var[11]])
]
# Make a selection of the date to plot
slider = DateSlider(title='Date',
start=first_dt,
end=last_dt,
step=1,
value=last_dt,
height=20,
margin=(20, 50, 20, 50),
sizing_mode="stretch_width")
slider.on_change('value_throttled', update_map)
# Make a span to show current date in plots
dt_span = Span(location=slider.value_as_date,
dimension='height',
line_color='red',
line_dash='solid',
line_width=2)
# Update timeseries plots based on selection
source_map.selected.on_change('indices', update_plot)
# Make a set of labels to show some totals on the map
source_out = ColumnDataSource(get_stats())
columns_out = [
TableColumn(field='stat', title="Statistic"),
new_data = filtered_data(selectedDate)
new_data['date'] = new_data['date'].to_string()
new_json = new_data.to_json()
geosource.geojson = new_json
#Datepicker
datepicker = DateSlider(start="2020-12-13",
end=current_date,
step=1,
value=current_date,
width=200,
align="center",
title="Currently Displaying")
datepicker.on_change('value', update_plot)
# Last Updated on Current Date Text
update_text = "Last Updated on " + str(current_date)
p.add_layout(Title(text=update_text, align="center", text_alpha=0.5), "below")
#Authorship
p.add_layout(Title(text="Made by Frank Hoang", align="center", text_alpha=0.5),
"below")
# Make a column layout of widgetbox(slider) and plot, and add it to the current document
layout = Column(datepicker, p)
curdoc().add_root(layout)
show(layout)
# In[ ]:
def update_date(attr, old, new):
yr = date_slider.value
new_data = json_data(yr)
geosource.geojson = new_data
p.title.text = 'covid 19 deaths, %s' %yr
def update_color(attr, old, new):
color_mapper.high = color_slider.value
merged = read_all_data()
geosource = GeoJSONDataSource(geojson = json_data(str(date(2020, 4,25))))
palette = brewer['YlGnBu'][8]
palette = palette[::-1]
color_cap_deaths = round(np.nanmax(merged["Deaths"]) + 500, -3)
color_mapper = LinearColorMapper(palette = palette, low = 0, high = color_cap_deaths, nan_color = '#d9d9d9')
hover = HoverTool(tooltips = [ ('Country/region','@country'),('deaths', '@Deaths')])
color_bar = ColorBar(color_mapper=color_mapper, label_standoff=8,width = 900, height = 20, border_line_color=None,location = (0,0), orientation = 'horizontal')
p = figure(title = 'Covid 19 deaths', plot_height = 900 , plot_width = 1600, tools = [BoxZoomTool(), ResetTool(), hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
p.patches('xs','ys', source = geosource,fill_color = {'field' :'Deaths', 'transform' : color_mapper}, line_color = 'black', line_width = 0.25, fill_alpha = 1)
p.add_layout(color_bar, 'below')
date_slider = DateSlider(title="Date Range: ", start=date(2020, 1, 31), end=date(2020, 4, 25), value=date(2020, 4, 25), step=1)
date_slider.on_change('value', update_date)
color_slider = Slider(title="Max value", start=0, end=color_cap_deaths, value=color_cap_deaths, step=100)
color_slider.on_change('value', update_color)
layout = column(p,widgetbox(date_slider), widgetbox(color_slider))
curdoc().add_root(layout)
show(layout)
def build_time_evolution_tab():
# Importing geographical shapefile
s3_root = 'https://covid19-bokeh-app.s3.eu-west-2.amazonaws.com'
geo_data_gdf = gpd.read_file(f'{s3_root}/data/_geo_data/ne_50m_land.zip')
geosource = GeoJSONDataSource(geojson=geo_data_gdf.to_json())
# Importing geo-evolutions cases/deaths data
time_evol_df = pd.read_csv(f'{s3_root}/data/geo_time_evolution.csv')
# Selecting earliest snapshot
time_evol_df.date = pd.to_datetime(time_evol_df.date, format="%Y-%m-%d")
snapshot_df = time_evol_df[time_evol_df.date == min(time_evol_df.date)]
global_by_day_df = pd.read_csv(f'{s3_root}/data/global_by_day.csv')
global_by_day_df.date = pd.to_datetime(global_by_day_df.date,
format="%Y-%m-%d")
global_totals_df = global_by_day_df.loc[global_by_day_df.date == min(
time_evol_df.date)]
global_cases = int(global_totals_df.iloc[0]['cases'])
global_deaths = int(global_totals_df.iloc[0]['deaths'])
# Applying bubble-size mapping
bubble_size = snapshot_df['cases'].apply(lambda x: 0.5 * math.log(x, 1.1)
if x > 0 else 0)
snapshot_df = snapshot_df.assign(size=bubble_size.values)
# Creating ColumnDataSource for visualisation
cases_cds = ColumnDataSource(snapshot_df)
# Adding figure and geographical patches from shapefile
geo_plot = figure(plot_height=450,
plot_width=720,
x_range=(-180, 180),
y_range=(-90, 90),
name="time_evolution_geo_plot",
sizing_mode="scale_width")
geo_patches = geo_plot.patches('xs', 'ys', source=geosource, alpha=0.5)
# Adding circle glyph to create bubble plot
cases_circles = geo_plot.circle(x='long',
y='lat',
size='size',
source=cases_cds,
color='red',
alpha=0.2)
# Adding hover tool
hover = HoverTool(tooltips=[('Country/Region', '@region'),
('Province/State', '@province'),
('Cases', '@cases')],
renderers=[cases_circles])
geo_plot.add_tools(hover)
# Adding hbar
countries_df = snapshot_df.loc[:, ['date', 'region', 'cases']]
countries_df.rename(columns={"cases": "value"}, inplace=True)
countries_df = countries_df.groupby(['date', 'region']).sum().reset_index()
countries_df.sort_values(by='value', ascending=False, inplace=True)
countries_df = countries_df.reset_index(drop=True)
countries_cds = ColumnDataSource(countries_df)
hbar_plot = figure(plot_height=450,
plot_width=475,
y_range=(10.5, -0.5),
x_range=(0, countries_df.value.max() * 1.2),
name="time_evolution_hbar_plot",
sizing_mode="scale_width")
hbar = hbar_plot.hbar(left=0,
right='value',
y='index',
source=countries_cds,
height=0.5,
line_color='white')
labels = LabelSet(x='value',
y='index',
text='region',
text_font_size='10pt',
text_color='white',
x_offset=5,
y_offset=0,
source=countries_cds,
level='glyph',
render_mode='canvas')
hbar_plot.add_layout(labels)
# Adding hover tool
hover_hbar = HoverTool(tooltips=[('Country/Region', '@region'),
('Cases', '@value')],
renderers=[hbar])
hbar_plot.add_tools(hover_hbar)
# Adding callback for updating data
def data_view_callback(attr, old, new):
"""Callback function to update data source:
- Updates source data to selected data view
(cases/deaths/new cases/new deaths)
and selected snapshot date on date slider.
- Updates HoverTool to reflect data view change
- Updates Divs for total cases/deaths
"""
# Determine data view selection
if cases_deaths_button.active == 0:
data_view = "cases"
elif cases_deaths_button.active == 1:
data_view = "deaths"
if total_new_button.active == 1:
data_view = f"new_{data_view}"
# Determine date selection
slider_date = date_slider.value_as_datetime.date()
# Filter data for selected date
snapshot_df = time_evol_df[time_evol_df.date == pd.Timestamp(
slider_date)]
# Map bubble size on selected data view
bubble_size = snapshot_df[data_view].apply(
lambda x: 0.5 * math.log(x, 1.1) if x > 0 else 0)
snapshot_df = snapshot_df.assign(size=bubble_size.values)
cases_cds.data = snapshot_df
hover.tooltips = [('Country/Region', '@region'),
('Province/State', '@province'),
(data_view.replace('_',
' ').title(), f'@{data_view}')]
# Update hbar data
countries_df = snapshot_df.loc[:, ['date', 'region', data_view]]
countries_df.rename(columns={data_view: "value"}, inplace=True)
countries_df = countries_df.groupby(['date',
'region']).sum().reset_index()
countries_df.sort_values(by="value", ascending=False, inplace=True)
countries_df = countries_df.reset_index(drop=True)
countries_cds.data = countries_df
hbar_plot.x_range.end = countries_df.value.max() * 1.2
hover_hbar.tooltips = [('Country/Region', '@region'),
(data_view.replace('_', ' ').title(), '@value')]
# Filter for totals
global_totals_df = global_by_day_df.loc[(
global_by_day_df.date == pd.Timestamp(slider_date))]
global_cases = int(global_totals_df.iloc[0]['cases'])
global_deaths = int(global_totals_df.iloc[0]['deaths'])
cases_div.text = (f'<h3 class="card-text">' f'{global_cases:,}</h3>')
deaths_div.text = (f'<h3 class="card-text">' f'{global_deaths:,}</h3>')
# Adding Date slider
date_range = [
pd.Timestamp(date_val) for date_val in time_evol_df.date.unique()
]
date_slider = DateSlider(title="Date",
start=min(date_range),
end=max(date_range),
value=min(date_range),
sizing_mode="scale_width")
date_slider.on_change('value', data_view_callback)
# Adding Cases/Deaths toggle
cases_deaths_button = RadioButtonGroup(labels=["Cases", "Deaths"],
active=0,
sizing_mode="scale_width")
cases_deaths_button.on_change('active', data_view_callback)
# Adding Total/New toggle
total_new_button = RadioButtonGroup(labels=["Total", "New"],
active=0,
sizing_mode="scale_width")
total_new_button.on_change('active', data_view_callback)
# Adding callback for zooming into a selected continent
def continent_zoom_callback(attr, old, new):
continent_map_ref = {
# Worldwide
0: {
'x_range': [-200, 200],
'y_range': [-100, 100]
},
# Europe
1: {
'x_range': [-30, 50],
'y_range': [30, 70]
},
# North America
2: {
'x_range': [-175, -15],
'y_range': [0, 80]
},
# South America
3: {
'x_range': [-140, 10],
'y_range': [-60, 15]
},
# Africa
4: {
'x_range': [-55, 105],
'y_range': [-40, 40]
},
# Asia
5: {
'x_range': [40, 140],
'y_range': [-5, 45]
},
# Oceania
6: {
'x_range': [80, 200],
'y_range': [-55, 5]
}
}
map_ref = continent_map_ref[continent_button.active]
geo_plot.x_range.start = map_ref['x_range'][0]
geo_plot.x_range.end = map_ref['x_range'][1]
geo_plot.y_range.start = map_ref['y_range'][0]
geo_plot.y_range.end = map_ref['y_range'][1]
# Adding continent toggle
continent_button = RadioGroup(labels=[
"Worldwide", "Europe", "North America", "South America", "Africa",
"Asia", "Oceania"
],
active=0)
continent_button.on_change('active', continent_zoom_callback)
# Adding animation with Play/Pause button
callback_id = None
def animate():
def animate_update():
date = date_slider.value_as_datetime.date() + timedelta(days=1)
date = pd.Timestamp(date)
if date >= max(date_range):
date = min(date_range)
date_slider.value = date
global callback_id
if play_button.label == '► Play':
play_button.label = '❚❚ Pause'
callback_id = curdoc().add_periodic_callback(animate_update, 300)
else:
play_button.label = '► Play'
curdoc().remove_periodic_callback(callback_id)
play_button = Button(label='► Play', width=60, button_type="success")
play_button.on_click(animate)
# Adding Cases/Deaths count
cases_div = Div(text=f'<h3 class="card-text">'
f'{global_cases:,}</h3>',
sizing_mode="scale_width",
name="cases_div")
deaths_div = Div(text=f'<h3 class="card-text">'
f'{global_deaths:,}</h3>',
sizing_mode="scale_width",
name="deaths_div")
# Defining layout of tab
widgets = widgetbox(date_slider,
cases_deaths_button,
total_new_button,
play_button,
continent_button,
name="time_evolution_widgetbox",
sizing_mode="scale_width")
# Add the plot to the current document
curdoc().add_root(geo_plot)
curdoc().add_root(hbar_plot)
curdoc().add_root(widgets)
curdoc().add_root(cases_div)
curdoc().add_root(deaths_div)
def create_us_map_tab():
"Factory for creating second tab of app: US Only Data"
## Data Sources
source_df_confirmed, source_CDS = get_time_series_confirmed_US_data()
source_CDS.data['number_per_capita'] = source_df_confirmed[
START_DATE_STRING] / source_df_confirmed['population']
## Map
color_mapper = log_cmap(field_name='number',
palette=Spectral6,
low=1,
high=1e6)
TOOLTIPS = [('County/Region', '@county'), ('State/Province', '@region'),
('Population', '@population'), ('Cases', '@number'),
('Cases Per Capita', '@number_per_capita')]
map_figure = figure(
title='Confirmed COVID-19 Cases in the United States',
tooltips=TOOLTIPS,
x_range=(-18367715, -6901808.43),
y_range=(0, 13377019.78),
x_axis_type='mercator',
y_axis_type='mercator',
active_scroll='wheel_zoom',
)
tile_provider = get_provider(CARTODBPOSITRON)
map_figure.add_tile(tile_provider)
map_figure.circle(x='web_mercator_x',
y='web_mercator_y',
source=source_CDS,
size='sizes',
color=color_mapper)
## Colorbar
color_bar = ColorBar(title='Num. Cases',
title_standoff=20,
color_mapper=color_mapper['transform'],
label_standoff=20,
width=8,
location=(0, 0),
ticker=LogTicker())
color_bar.formatter.use_scientific = False
map_figure.add_layout(color_bar, 'right')
## Slider
def slider_callback(attr, old, new):
delta = datetime.timedelta(milliseconds=new)
date = datetime.date(1970, 1, 1) + delta
date_string = date.strftime('%Y-%m-%d')
try:
source_CDS.data['number'] = source_df_confirmed[date_string]
source_CDS.data['sizes'] = source_df_confirmed[date_string].apply(
scale)
source_CDS.data['number_per_capita'] = source_df_confirmed[
date_string] / source_df_confirmed['population']
except KeyError:
pass
slider = DateSlider(title='Date',
start=START_DATE,
end=datetime.date.today(),
step=1,
value=START_DATE)
slider.on_change('value', slider_callback)
## Data Table
columns = [
TableColumn(field='county', title='County/Region'),
TableColumn(field='region', title='State/Province'),
TableColumn(field='population', title='Population'),
TableColumn(field='number', title='Cases'),
TableColumn(field='number_per_capita', title='Cases Per Capita'),
]
data_table = DataTable(
source=source_CDS,
columns=columns,
)
## Cancel Selection Button
def cancel_selection_callback():
source_CDS.selected.indices = []
cancel_selection_button = Button(label='Clear Selection',
button_type='warning')
cancel_selection_button.on_click(cancel_selection_callback)
child = row([
column([slider, map_figure]),
column([data_table, cancel_selection_button])
])
return Panel(child=child, title='United States Map')
# update columns 'size', 'dnc' with the column named '%Y-%m-%d' in merged
# update geosource with new merged
def callback(attr, old, new):
# Convert timestamp to datetime
# https://stackoverflow.com/questions/9744775/how-to-convert-integer-timestamp-to-python-datetime
date = datetime.fromtimestamp(new / 1e3)
i = date.strftime('%Y-%m-%d')
merged.size = merged[i] * 1e5 / 5 + 10
merged.dnc = merged[i]
geosource.geojson = merged.to_json()
# Circles change on mouse move
timeslider.on_change('value', callback)
# T2.6 Add a play button to change slider value and update the map plot dynamically
# https://stackoverflow.com/questions/46420606/python-bokeh-add-a-play-button-to-a-slider
# https://stackoverflow.com/questions/441147/how-to-subtract-a-day-from-a-date
# Update the slider value with one day before current date
# somehow the timeslider.value is also an int and thus needs to be converted
def animate_update_slider():
# Extract date from slider's current value and convert it to datetime
date = datetime.fromtimestamp(timeslider.value / 1e3)
# Subtract one day from date and do not exceed the allowed date range
day = date - timedelta(days=1)
day = day.replace(hour=0, minute=0, second=0, microsecond=0)
# Handle out of range
def create_world_map_tab():
"Factory for creating first tab of app."
## Data Sources
source_df, source_CDS = get_time_series_confirmed_data()
## Map
color_mapper = log_cmap(field_name='number',
palette=Spectral6,
low=1,
high=1e6)
TOOLTIPS = [('Region', '@full_name'), ('Num. Cases', '@number{0,0}')]
map_figure = figure(title='Confirmed COVID-19 Cases by Region',
tooltips=TOOLTIPS,
x_range=(-16697923.62, 18924313),
y_range=(-8399737.89, 8399737.89),
x_axis_type='mercator',
y_axis_type='mercator',
active_scroll='wheel_zoom')
tile_provider = get_provider(CARTODBPOSITRON)
map_figure.add_tile(tile_provider)
map_figure.circle(x='web_mercator_x',
y='web_mercator_y',
source=source_CDS,
size='sizes',
color=color_mapper)
## Colorbar
color_bar = ColorBar(title='Num. Cases',
title_standoff=20,
color_mapper=color_mapper['transform'],
label_standoff=20,
width=8,
location=(0, 0),
ticker=LogTicker())
color_bar.formatter.use_scientific = False
map_figure.add_layout(color_bar, 'right')
## Slider
def slider_callback(attr, old, new):
delta = datetime.timedelta(milliseconds=new)
date = datetime.date(1970, 1, 1) + delta
date_string = date.strftime('%Y-%m-%d')
try:
source_CDS.data['number'] = source_df[date_string]
source_CDS.data['sizes'] = source_df[date_string].apply(scale)
except KeyError:
pass
slider = DateSlider(title='Date',
start=START_DATE,
end=datetime.date.today(),
step=1,
value=START_DATE)
slider.on_change('value', slider_callback)
## Data Table
columns = [
TableColumn(field='full_name', title='Region'),
TableColumn(field='number', title='Cases')
]
data_table = DataTable(
source=source_CDS,
columns=columns,
)
## Cancel Selection Button
def cancel_selection_callback():
source_CDS.selected.indices = []
cancel_selection_button = Button(label='Clear Selection',
button_type='warning')
cancel_selection_button.on_click(cancel_selection_callback)
child = row([
column([slider, map_figure]),
column([data_table, cancel_selection_button]),
])
return Panel(child=child, title='World Map')
update_legend_color_bar(select_feature.value, day)
def update_src_after_day_change(attr, old, new):
day = ts_to_day(new)
geosource.geojson = create_sample(day).to_json()
# def update_label_figures_after_feature_change(attr, old, new):
# label_figures.text = new
TOOLTIPS = [('Область', '@ADM1_UA'),
(select_feature.value, '@{' + str(select_feature.value) + '}')]
plot.add_tools(HoverTool(tooltips=TOOLTIPS))
tabs.on_change('active', update_tabs)
select_feature.on_change('value', update_color_mapper_and_legend_color_bar_after_feature_change)
select_feature.on_change('value', update_tooltips_after_feature_change)
select_feature.js_link('value', plot.title, 'text')
# select_feature.on_change('value', update_label_figures_after_feature_change)
slider_time.on_change('value_throttled', update_src_after_day_change)
slider_time.on_change('value_throttled', update_color_mapper_and_legend_color_bar_after_day_change)
curdoc().add_root(column(tabs))
curdoc().title = 'Ukraine | Hospitals | Supply'
def _plot_choropleth(geoSource, prefectureBoundaries,
geographicDistributionData):
#Define a sequential multi-hue color palette and reverse order so that dark => high # of cases.
base_colors = brewer['YlOrRd'][8]
base_colors = base_colors[::-1]
#Transform palette to create non-uniform intervals.
palette, low, high = _transform_color_intervals(base_colors, 0, 800)
#Instantiate LinearColorMapper that maps numbers in a range, into a sequence of colors. Input nan_color.
color_mapper = LinearColorMapper(palette=palette,
low=low,
high=high,
nan_color='#d9d9d9')
#Add hover tool.
hover = HoverTool(tooltips=[('prefecture',
'@prefecture'), ('# of cases', '@cases')])
#Define custom ticks for colorbar.
ticks = np.array([0, 20, 50, 100, 200, 400, 800])
#Create color bar.
color_bar = ColorBar(color_mapper=color_mapper,
label_standoff=8,
width=800,
height=20,
border_line_color=None,
location=(0, 0),
orientation='horizontal',
ticker=FixedTicker(ticks=ticks))
#Create figure object.
p = figure(title='COVID-19 cases in Greece, %s' % DATES[-1],
plot_height=600,
plot_width=950,
toolbar_location=None,
tools=[hover])
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
#Add patch renderer to figure.
p.patches('xs',
'ys',
source=geoSource,
fill_color={
'field': 'cases',
'transform': color_mapper
},
line_color='black',
line_width=0.25,
fill_alpha=1)
#Specify figure layout.
p.add_layout(color_bar, 'below')
def _update_plot(attr, old, new):
"""Callback function to update the plot using a slider.
"""
date = dt.utcfromtimestamp(slider.value / 1000).strftime('%Y_%m_%d')
new_data = _merge_and_convert_to_json(prefectureBoundaries,
geographicDistributionData, date)
geoSource.geojson = new_data
p.title.text = 'COVID-19 cases in Greece, %s' % date
#Make a slider object to trigger _update_plot.
slider = DateSlider(title='Date',
start=dt.strptime(DATES[0], '%Y_%m_%d'),
end=dt.strptime(DATES[-1], '%Y_%m_%d'),
step=int(
datetime.timedelta(days=1).total_seconds() * 1000),
value=dt.strptime(DATES[-1], '%Y_%m_%d'))
slider.on_change('value', _update_plot)
#Make a column layout of plot and slider, and add it to the current document.
layout = column(p, column(slider))
curdoc().add_root(layout)
curdoc().title = 'COVID-19 cases in Greece'
#Crete an HTML of the static choropleth for 2020-03-29.
save(p,
OUTPUT_FILE_PATH,
resources=None,
title='COVID-19 cases in Greece, 2020_03_29')
# update geosource with new merged
def callback(attr, old, new):
# Convert timestamp to datetime
# https://stackoverflow.com/questions/9744775/how-to-convert-integer-timestamp-to-python-datetime
date = ...
i = date.strftime('%Y-%m-%d')
merged.size = ...
merged.dnc = ...
geosource.geojson = ...
# Circles change on mouse move
timeslider.on_change(...)
# T2.6 Add a play button to change slider value and update the map plot dynamically
# https://stackoverflow.com/questions/46420606/python-bokeh-add-a-play-button-to-a-slider
# https://stackoverflow.com/questions/441147/how-to-subtract-a-day-from-a-date
# Update the slider value with one day before current date
def animate_update_slider():
# Extract date from slider's current value
date = ...
# Subtract one day from date and do not exceed the allowed date range
day = ...
...
def gen_bokeh(
df,
il_center=(3879662.3653308493, 3694724.561061665),
marg_x=2.0e5,
marg_y=2.0e5,
init_date=datetime.datetime(year=2021, month=5, day=10),
MAX_W=1000,
MAX_H=800,
):
# main figure with world map
tile_provider = get_provider(CARTODBPOSITRON)
p = figure(x_range=(il_center[0] - marg_x, il_center[0] + marg_x),
y_range=(il_center[1] - marg_y, il_center[1] + marg_y),
x_axis_type="mercator",
y_axis_type="mercator")
p.plot_height = MAX_H // 2
p.plot_width = MAX_W
p.add_tile(tile_provider)
# process df for plotting
df['mydate'] = pd.to_datetime(df['date'])
df = df.loc[df['mydate'] > init_date]
merc = longlat2mercator.transform(df['lat'].values, df['long'].values)
df['x'] = merc[0]
df['y'] = merc[1]
df['name'] = df['location']
df[['x', 'y', 'mydate_f', 'name']] = df[['x', 'y', 'mydate',
'name']].fillna('')
df['fill_alpha'] = .5
df['line_alpha'] = .0
df['fill_color'] = df['eta'].apply(eta2color)
src_cols = [
'x', 'y', 'mydate_f', 'name', 'fill_alpha', 'line_alpha', 'fill_color'
]
source0 = df[src_cols].to_dict('list')
source = ColumnDataSource(data=source0)
# add alarms on map
p.circle(x="x",
y="y",
size=4,
fill_color="fill_color",
fill_alpha='fill_alpha',
line_alpha='line_alpha',
source=source)
p.add_tools(
HoverTool(
tooltips=[
('time', '@mydate_f{%H:%M}'),
('date', '@mydate_f{%d/%m/%Y}'),
('name', '@name'),
],
formatters={'@mydate_f': 'datetime'},
))
# build other elements
date_slider = DateSlider(start=df['mydate'].min(),
end=date.today(),
step=24,
value=date.today(),
title='Date',
width=int(MAX_W * .9))
dur_slider = Slider(start=0,
end=168,
step=1,
value=24,
title='Hour duration',
width=MAX_W // 2)
delay_slider = Slider(start=0,
end=1000,
step=10,
value=200,
title='Animation delay (ms)',
width=MAX_W // 2)
label_xy = longlat2mercator.transform(31., 34)
label_src = ColumnDataSource(data=dict(text=['Hello, use the sliders']))
label = LabelSet(text='text',
x=label_xy[0],
y=label_xy[1],
source=label_src)
p.add_layout(label)
button = Button(label="Play", button_type="success", width=MAX_W // 10)
# add plot for number of alarms per day
ff = figure(x_range=(df['mydate_f'].min(), df['mydate_f'].max()),
x_axis_type="datetime")
df['day'] = df['mydate_f'].dt.day
agg = df.groupby('day').agg('count')
agg['date'] = agg.index.to_series().apply(
lambda x: df.loc[df['mydate_f'].dt.day == x, 'mydate_f'].iloc[0])
ff.scatter(agg['date'], agg['Unnamed: 0'], size=10)
ff.plot_height = MAX_H // 3
ff.plot_width = MAX_W
ff.title = 'alarms per day'
# callback functions
callback_id = None
def update_map_callback(attr, old, new):
st, en = date_slider.value, date_slider.value + dur_slider.value * 60 * 60 * 1000
st = datetime.datetime.fromtimestamp(st / 1000.)
en = datetime.datetime.fromtimestamp(en / 1000.)
ddf = df.loc[(df['mydate_f'] >= st) & (df['mydate_f'] < en)]
source.data = ddf[src_cols].to_dict('list')
sts = st.strftime("%d/%m, %H:%M")
ens = en.strftime("%d/%m, %H:%M")
label_src.data = dict(
text=[f'Range: {sts} to {ens}, total: {len(ddf)}'])
def restart_animation(attr, old, new):
global callback_id
if button.label == 'Stop':
curdoc().remove_periodic_callback(callback_id)
callback_id = curdoc().add_periodic_callback(
adv_slider, delay_slider.value)
def adv_slider():
date_slider.value += 1 * 1000 * 60 * 60
if date_slider.value >= date_slider.end:
date_slider.value = date_slider.start
def button_callback():
global callback_id
if button.label == 'Play':
button.label = 'Stop'
callback_id = curdoc().add_periodic_callback(
adv_slider, delay_slider.value)
else:
button.label = 'Play'
curdoc().remove_periodic_callback(callback_id)
# callback assignment
delay_slider.on_change('value', restart_animation)
date_slider.on_change('value', update_map_callback)
dur_slider.on_change('value', update_map_callback)
button.on_click(button_callback)
lay = column(p, row(date_slider, button), row(dur_slider, delay_slider),
ff)
return lay
