def show(sample_size):
global session
global scatter_plot
global source
global pie_chart_source
global line_chart_source
global slider
DB.__init__(sample_size)
min_time = DB.min_time()
max_time = DB.max_time()
print min_time
print min_time
xs, ys, color, time = DB.get_current()
xs = [xs[i] for i,v in enumerate(time) if time[i] == min_time]
ys = [ys[i] for i,v in enumerate(time) if time[i] == min_time]
color = [color[i] for i,v in enumerate(time) if time[i] == min_time]
time_dict = Counter(time)
pie_chart_source = ColumnDataSource(data=ChartMath.compute_color_distribution('x', 'y', 'color', color))
line_chart_source = ColumnDataSource(data=dict(x=[key for key in time_dict], y=[time_dict[key] for key in time_dict]))
source = ColumnDataSource(data=dict(x=xs, y=ys, color=color))
scatter_plot = Figure(plot_height=800,
plot_width=1200,
title="Plot of Voters",
tools="pan, reset, resize, save, wheel_zoom",
)
scatter_plot.circle('x', 'y', color='color', source=source, line_width=0, line_alpha=0.001, fill_alpha=0.5, size=15)
scatter_plot.patches('x', 'y', source=state_source, fill_alpha=0.1, line_width=3, line_alpha=1)
scatter_plot.x_range.on_change('end', update_coordinates)
line_chart = Figure(title="Distribution over Time", plot_width=350, plot_height=350)
line_chart.line(x='x', y='y', source=line_chart_source)
pie_chart_plot = Figure(plot_height=350,
plot_width=350,
title="Voter Distribution",
x_range=(-1, 1),
y_range=(-1, 1))
pie_chart_plot.wedge(x=0, y=0, source=pie_chart_source, radius=1, start_angle="x", end_angle="y", color="color")
slider = Slider(start=min_time, end=max_time, value=min_time, step=1, title="Time")
slider.on_change('value', update_coordinates)
h = hplot(scatter_plot, vplot(pie_chart_plot, line_chart))
vplot(slider, h, width=1600, height=1800)
session = push_session(curdoc())
session.show()
#script = autoload_server(scatter_plot, session_id=session.id)
session.loop_until_closed()
class MapPlot(object):
def __init__(self):
self.map_plot = Figure(
tools="pan,wheel_zoom,tap", toolbar_location="right",
logo=None, min_border=0, min_border_left=0,
**get_paris_extent(0.6), # x_range & y_range
plot_width=1100, plot_height=650)
self.map_plot.add_tile(tile_source=OSM_TILE_SOURCE)
self.map_plot_renderer = self.map_plot.patches(
source=geo_source,
xs="xs",
ys="ys",
line_width=2,
line_color=None,
selection_line_color="firebrick",
nonselection_line_color=None,
fill_color="colors",
selection_fill_color="colors",
nonselection_fill_color="colors",
alpha=0.85,
selection_alpha=0.9,
nonselection_alpha=0.85)
self.map_plot.axis.visible = None
self.map_plot_ds = self.map_plot_renderer.data_source
def get_plot(self):
return self.map_plot
def get_data_source(self):
return self.map_plot_ds
def build_local_uk_tab():
s3_root = 'https://covid19-bokeh-app.s3.eu-west-2.amazonaws.com'
la_boundaries_gdf = gpd.read_file(
f'{s3_root}/data/_geo_data/la_districts_dec19.zip'
).loc[:, ['lad19cd', 'lad19nm', 'geometry']]
# Importing uk local authority data
la_cases_df = pd.read_csv(f'{s3_root}/data/local_uk.csv')
# Filter for latest date
la_cases_latest_df = la_cases_df.loc[la_cases_df.date ==
la_cases_df.date.max()]
# Import local authority population data
la_pop_df = pd.read_csv(f'{s3_root}/data/local_authority_populations.csv'
).loc[:, ['code', 'population']]
# Remove commas and convert to numeric dtype
la_pop_df['population'] = pd.to_numeric(
(la_pop_df['population'].str.replace(",", "")))
# Merge cases and population datasets
la_cases_latest_df = la_cases_latest_df.merge(la_pop_df,
left_on="area_code",
right_on="code",
how="left")
# Merge geo-boundaries GeoDataFrame with cases, pop dataset
la_cases_gdf = la_boundaries_gdf.merge(la_cases_latest_df,
left_on="lad19cd",
right_on="area_code",
how="left")
# Calculate weekly cases per 100,000
la_cases_gdf['cases_per_pop'] = (
100000 * la_cases_gdf['weekly_cases'] /
la_cases_gdf['population']).fillna(0).astype(int)
# Convert dataset to GeoJSONDataSource to feed geo-plot
geosource = GeoJSONDataSource(geojson=la_cases_gdf.to_json())
# Adding figure and geographical patches from shapefile
local_uk_geo_plot = Figure(title="Weekly Cases per 100,000",
plot_height=500,
plot_width=500,
name="local_uk_geo_plot",
output_backend="webgl")
# Add linear colour mapper for case density
mapper = linear_cmap(field_name='cases_per_pop',
palette=brewer['YlOrRd'][9][:7][::-1],
low=min(la_cases_gdf['cases_per_pop']),
high=max(la_cases_gdf['cases_per_pop']))
# Add local authority patches to figure
geo_patches = local_uk_geo_plot.patches('xs',
'ys',
source=geosource,
alpha=0.8,
color=mapper)
# Adding hover tool and tap tool
hover = HoverTool(
tooltips=[('Local Authority', '@lad19nm'), (
'Daily Cases',
'@new_cases'), ('Population', '@population{0,0}'
), ('Weekly Cases per 100,000', '@cases_per_pop')])
local_uk_geo_plot.add_tools(hover)
local_uk_geo_plot.add_tools(TapTool())
# Adding color bar
color_bar = ColorBar(color_mapper=mapper['transform'], location=(0, 0))
local_uk_geo_plot.add_layout(color_bar, 'right')
# Adding recent trend figure
area_name = "Wandsworth"
cases_trend_df = la_cases_df.loc[la_cases_df.area_name == area_name]
cases_trend_df.date = pd.to_datetime(cases_trend_df.date,
format="%Y-%m-%d")
ninety_days_back = cases_trend_df.date.max() - timedelta(days=90)
cases_trend_df = cases_trend_df.loc[
cases_trend_df.date >= ninety_days_back]
cases_trend_cds = ColumnDataSource(cases_trend_df)
cases_trend_plot = Figure(title=f"New Cases in {area_name}",
plot_height=500,
plot_width=500,
name="cases_trend_plot",
x_axis_type="datetime")
cases_trend_plot.line(x='date',
y='new_cases',
legend="New Cases",
source=cases_trend_cds)
cases_trend_plot.line(x='date',
y='weekly_average',
line_color="orange",
legend="Weekly Average",
source=cases_trend_cds)
cases_trend_hover = HoverTool(tooltips=[('Date', '@date{%F}'),
('Daily Cases', '@new_cases'),
('Weekly Average',
'@weekly_average{int}')],
formatters={"@date": "datetime"})
cases_trend_plot.add_tools(cases_trend_hover)
def callback(attr, old, new):
index = geosource.selected.indices[0]
geojson = json.loads(geosource.geojson)
area_name = geojson['features'][index]['properties']['area_name']
# Adding recent trend figure
cases_trend_df = la_cases_df.loc[la_cases_df.area_name == area_name]
cases_trend_df.date = pd.to_datetime(cases_trend_df.date,
format="%Y-%m-%d")
ninety_days_back = cases_trend_df.date.max() - timedelta(days=90)
cases_trend_df = cases_trend_df.loc[
cases_trend_df.date >= ninety_days_back]
cases_trend_cds.data = cases_trend_df
cases_trend_plot.title.text = f"New Cases in {area_name}"
geosource.selected.on_change('indices', callback)
# Add the plots to the current document
curdoc().add_root(local_uk_geo_plot)
curdoc().add_root(cases_trend_plot)
b = pickle.load(f)
patch_source = ColumnDataSource(data=dict(borders_x=b["x"],
borders_y=b["y"],
colors=no_colors,
alpha=alpha,
state_names=state_names,
state_totals=state_totals,
state_drunk=state_drunk,
state_percent=state_percent,
state_percent_sp=state_percent_sp))
patches = fig.patches(xs="borders_x",
ys="borders_y",
source=patch_source,
fill_alpha="alpha",
fill_color="colors",
line_alpha=0,
hover_alpha=.3,
line_width=5)
fig.add_tools(
HoverTool(renderers=[patches],
tooltips=[("State", "@state_names"), ("Total", "@state_totals"),
("Drunk%", "@state_percent{1.11}" + "%"),
("Speeding%", "@state_percent_sp{1.11}" + "%")]))
select = Select(title="Color States By:",
value="None",
options=["None", "Drunk%", "Speeding%"])
def update_color(attrname, old, new):
# Generate a figure container for the field
plot_field = Figure(plot_height=400,
plot_width=400,
tools=toolset,
title="Vector valued function",
x_range=[curveintegral_settings.x_min, curveintegral_settings.x_max],
y_range=[curveintegral_settings.y_min, curveintegral_settings.y_max]
)
# remove grid from plot
plot_field.grid[0].grid_line_alpha = 0.0
plot_field.grid[1].grid_line_alpha = 0.0
# Plot the direction field
plot_field.segment('x0', 'y0', 'x1', 'y1', source=source_segments)
plot_field.patches('xs', 'ys', source=source_patches)
plot_field.circle('x', 'y', source=source_basept, color='blue', size=1.5)
# Plot curve
plot_field.line('x', 'y', source=source_curve, color='black', legend='curve')
# Plot parameter point
plot_field.scatter('x', 'y', source=source_param, color='black', legend='c(t)')
# Plot corresponding tangent vector
plot_field.segment('x0', 'y0', 'x1', 'y1', source=source_param, color='black')
plot_field.patches('xs', 'ys', source=source_param, color='black')
# Generate a figure container for the integral value
plot_integral = Figure(title_text_font_size="12pt",
plot_height=200,
plot_width=400,
title="Integral along curve",
x_range=[curveintegral_settings.parameter_min, curveintegral_settings.parameter_max],
b = pickle.load(f)
patch_source = ColumnDataSource(
data=dict(borders_x=b["x"],
borders_y=b["y"],
colors=no_colors,
alpha=alpha,
state_names=state_names,
state_totals=state_totals,
state_drunk=state_drunk,
state_percent=state_percent,
state_percent_sp=state_percent_sp
)
)
patches = fig.patches(xs="borders_x", ys="borders_y", source=patch_source, fill_alpha="alpha",
fill_color="colors", line_alpha=0, hover_alpha=.3, line_width=5)
fig.add_tools(HoverTool(renderers=[patches], tooltips=[("State", "@state_names"),
("Total", "@state_totals"),
("Drunk%", "@state_percent{1.11}" + "%"),
("Speeding%", "@state_percent_sp{1.11}" + "%")]))
select = Select(title="Color States By:", value="None", options=["None", "Drunk%", "Speeding%"])
def update_color(attrname, old, new):
if select.value == "Drunk%":
patch_source.data["colors"] = drunk_colors
patch_source.data["alpha"] = [.3]*len(state_names)
elif select.value == "Speeding%":
patch_source.data["colors"] = speeding_colors
patch_source.data["alpha"] = [.3]*len(state_names)
else:
# Generate a figure container for the field
plot_field = Figure(
plot_height=400,
plot_width=400,
tools=toolset,
title="Vector valued function",
x_range=[curveintegral_settings.x_min, curveintegral_settings.x_max],
y_range=[curveintegral_settings.y_min, curveintegral_settings.y_max])
# remove grid from plot
plot_field.grid[0].grid_line_alpha = 0.0
plot_field.grid[1].grid_line_alpha = 0.0
# Plot the direction field
plot_field.segment('x0', 'y0', 'x1', 'y1', source=source_segments)
plot_field.patches('xs', 'ys', source=source_patches)
plot_field.circle('x', 'y', source=source_basept, color='blue', size=1.5)
# Plot curve
plot_field.line('x', 'y', source=source_curve, color='black', legend='curve')
# Plot parameter point
plot_field.scatter('x', 'y', source=source_param, color='black', legend='c(t)')
# Plot corresponding tangent vector
plot_field.segment('x0', 'y0', 'x1', 'y1', source=source_param, color='black')
plot_field.patches('xs', 'ys', source=source_param, color='black')
# Generate a figure container for the integral value
plot_integral = Figure(title_text_font_size="12pt",
plot_height=200,
plot_width=400,
title="Integral along curve",
x_range=[
'Delta Housing Units': delta_housing_units}
agg_method = Select(title='Aggregation Method', value="Number of Permits", options=list(methods))
# Create Column Data Source that will be used by the plot
source = ColumnDataSource(data=dict(xs=[], ys=[], color=[], alpha=[], value=[]))
hover = HoverTool(tooltips=[
("Value", "@value"),
])
p = Figure(plot_height=700, plot_width=700, title="",
x_range=[-94.9, -94.25], y_range=[38.8, 39.45],
tools=[hover, 'wheel_zoom', 'reset', 'pan'])
p.patches(xs="xs", ys="ys", source=source, fill_color="color",
fill_alpha='alpha', line_color=None)
controls = [min_year, max_year, min_permit_cost, max_permit_cost,
agg_method, permit_type_checkbox, res_non_checkbox]
for control in controls:
if hasattr(control, 'value'):
control.on_change('value', update)
elif hasattr(control, 'active'):
control.on_change('active', update)
inputs = HBox(VBoxForm(controls), width=300)
update(None, None, None) # initial load of the data
del states["HI"]
del states["AK"]
EXCLUDED = ("ak", "hi", "pr", "gu", "vi", "mp", "as")
state_xs = [states[code]["lons"] for code in states]
state_ys = [states[code]["lats"] for code in states]
state_xs = state_xs[0:5] + state_xs[6:16] + state_xs[17:18] + state_xs[19:31] + state_xs[32:46]
state_ys = state_ys[0:5] + state_ys[6:16] + state_ys[17:18] + state_ys[19:31] + state_ys[32:46]
x = [-100,-80, -70, -125, -100]
y = [30,40,45, 40, 45]
#color = ["blue", "red", "green", "gold", "black"]
source = ColumnDataSource(data=dict(x=state_xs, y=state_ys))
source2 = ColumnDataSource(data=dict(x=latitude, y=longitude, color=colors))
p = Figure(title="US Unemployment 2009", toolbar_location="left",
plot_width=1100, plot_height=700)
p.patches('x', 'y', source=source, fill_alpha=0.1, line_width=1, line_alpha=0.3)
p.circle('x', 'y', color='color', source=source2, fill_alpha=0.1, line_width=1, line_alpha=0.001, size=3)
curdoc().add_root(HBox(children=[p], width=1100))
session.show()
session.loop_until_closed()
def on_slider_change(attr, old, new):
simplify_tolerance = new
simplify_features(simplify_tolerance)
# let's read some shapefile metadata
crs = states.crs
simplify_slider = Slider(title='Simplify Tolerance',
value=0,
start=0,
end=1,
step=.01)
simplify_slider.on_change('value', on_slider_change)
save_button = Button(label='Save')
save_button.on_click(save_to_shapefile)
p = Figure(plot_height=600,
plot_width=900,
x_range=(bounds[0], bounds[2]),
y_range=(bounds[1], bounds[3]))
polys = p.patches(xs='xs', ys='ys', alpha=0.9, source=geo_source)
controls = HBox(width=p.plot_width, children=[simplify_slider, save_button])
layout = VBox(width=p.plot_width, children=[controls, p])
curdoc().add_root(layout)
# convert to strings for tooltip
total = [str(x) for x in total_acc]
perc_drunk = [str(x*100)+"%" for x in perc_drunk]
perc_speeding = [str(x*100)+"%" for x in perc_speeding]
border_source = ColumnDataSource(dict(
xs=x_vals,
ys=y_vals,
total=total_acc,
state = us_states.keys(),
perc_drunk = perc_drunk,
perc_speeding = perc_speeding
))
states = fig.patches("xs", "ys", source=border_source, alpha=.5, line_color="red", hover_color="green", hover_alpha=.8, hover_line_color='black')
speeding_source = ColumnDataSource(dict(
x=speeding_accidents['x'],
y=speeding_accidents['y'] ))
drinking_source = ColumnDataSource(dict(
x=drinking_accidents['x'],
y=drinking_accidents['y'] ))
other_source = ColumnDataSource(dict(
x=other_accidents['x'],
y=other_accidents['y'] ))
fig.circle('x', 'y', source=speeding_source, fill_color="red", size=3)
fig.circle('x', 'y', source=drinking_source, fill_color="green", size=3)
# output_file("nyc_basemap.html", title="NYC census tracts")
source = ColumnDataSource(
data=dict(
QI_colmap=ct_colors, ct_x=ct_x, ct_y=ct_y, NicheScore=qivals, Price=pricevals, Neighborhood=extrainfo["NTAName"]
)
) # .loc[recdf.index]
# print source
hover = HoverTool(tooltips=[("Niche Score", "@NicheScore"), ("Price", "@Price")]) # ("Area", "@Neighborhood"),
tools = [PanTool(), BoxZoomTool(), WheelZoomTool(), ResetTool(), hover, PreviewSaveTool()]
p = Figure(title="NicheLife Map", plot_width=800, plot_height=700, tools=tools)
# tools="pan,wheel_zoom,reset,box_zoom,save") # toolbar_location="top", #box_select,
p.grid.grid_line_alpha = 0
p.patches("ct_x", "ct_y", source=source, fill_color="QI_colmap", fill_alpha=0.7, line_color="white", line_width=0.5)
# image1 = ImageURL(url=source.data["image"], x=-74.04, y=40.85)
# p.add_glyph(source, image1)
p.image_url(url=imageurl, x="-74.04", y="40.85")
# Set up widgets
text = TextInput(title="Map Name", value="NicheLife Map")
feature1 = Slider(title="Subway Accessibility", value=0.5, start=0, end=1, step=0.1)
feature2 = Slider(title="Safety", value=0.5, start=0, end=1, step=0.1)
feature3 = Slider(title="Public Satisfaction", value=0.5, start=0, end=1, step=0.1)
feature4 = Slider(title="Restaurants", value=0.5, start=0, end=1, step=0.1)
feature5 = Slider(title="Grocery Stores", value=0.5, start=0, end=1, step=0.1)
feature6 = Slider(title="Nightlife", value=0.5, start=0, end=1, step=0.1)
price = Select(title="Show Affordability", options=["Yes", "No"])
source = ColumnDataSource(
data=dict(xs=[], ys=[], color=[], alpha=[], value=[]))
hover = HoverTool(tooltips=[
("Value", "@value"),
])
p = Figure(plot_height=700,
plot_width=700,
title="",
x_range=[-94.9, -94.25],
y_range=[38.8, 39.45],
tools=[hover, 'wheel_zoom', 'reset', 'pan'])
p.patches(xs="xs",
ys="ys",
source=source,
fill_color="color",
fill_alpha='alpha',
line_color=None)
controls = [
min_year, max_year, min_permit_cost, max_permit_cost, agg_method,
permit_type_checkbox, res_non_checkbox
]
for control in controls:
if hasattr(control, 'value'):
control.on_change('value', update)
elif hasattr(control, 'active'):
control.on_change('active', update)
inputs = HBox(VBoxForm(controls), width=300)
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
geo_source.geojson = states_view.to_json()
def save_to_shapefile():
states_view.to_file('fout.shp')
def on_slider_change(attr, old, new):
simplify_tolerance = new
simplify_features(simplify_tolerance)
# let's read some shapefile metadata
crs = states.crs
simplify_slider = Slider(title='Simplify Tolerance',
value=0, start=0, end=1, step=.01)
simplify_slider.on_change('value', on_slider_change)
save_button = Button(label='Save')
save_button.on_click(save_to_shapefile)
p = Figure(plot_height=600, plot_width=900,
x_range=(bounds[0], bounds[2]), y_range=(bounds[1], bounds[3]))
polys = p.patches(xs='xs', ys='ys', alpha=0.9, source=geo_source)
controls = HBox(width=p.plot_width, children=[simplify_slider, save_button])
layout = VBox(width=p.plot_width, children=[controls, p])
curdoc().add_root(layout)
