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()
def pie_viz(
df: pd.DataFrame,
col: str,
miss_pct: float,
plot_width: int,
plot_height: int,
) -> Panel:
"""
Render a pie chart
"""
title = f"{col} ({miss_pct}% missing)" if miss_pct > 0 else f"{col}"
tooltips = [(f"{col}", "@col"), ("Count", "@cnt"),
("Percent", "@pct{0.2f}%")]
df["angle"] = df["cnt"] / df["cnt"].sum() * 2 * pi
fig = Figure(
title=title,
plot_width=plot_width,
plot_height=plot_height,
tools="hover",
toolbar_location=None,
tooltips=tooltips,
)
color_list = PALETTE * (len(df) // len(PALETTE) + 1)
df["colour"] = color_list[0:len(df)]
if df.iloc[-1]["cnt"] == 0: # no "Others" group
df = df[:-1]
pie = fig.wedge(
x=0,
y=1,
radius=0.9,
start_angle=cumsum("angle", include_zero=True),
end_angle=cumsum("angle"),
line_color="white",
fill_color="colour",
source=df,
)
legend = Legend(
items=[LegendItem(label=dict(field="col"), renderers=[pie])])
legend.label_text_font_size = "8pt"
fig.add_layout(legend, "right")
tweak_figure(fig, "pie")
fig.axis.major_label_text_font_size = "0pt"
fig.axis.major_tick_line_color = None
return Panel(child=fig, title="pie chart")
def custom_reports(report_id):
if report_id == 'A':
# result = db_session.execute('''select ga_date,sum(page_views),floor(dbms_random.value(2000, 6000)) as sales
# from ga_sink
# group by ga_date''' ).fetchall()
result = db_session.execute(
'''select T1.ga_date,T1.page_views, T2.total_sale
from (select ga_date,sum(page_views) as page_views from ga_sink group by ga_date) T1
join (select sale_date,sum(amount) as total_sale from demo_sales group by sale_date) T2
on T1.ga_date=T2.sale_date''').fetchall(
)
# result = db_session.execute('''select T1."date",T1.page_views, T2.total_sale
# from (select "date",sum(page_views) as page_views from test group by "date") T1
# join (select sale_date,sum(amount) as total_sale from demo_sales group by sale_date) T2
# on T1."date"=T2.sale_date''' ).fetchall()
print(result)
test = pd.DataFrame(result,
columns=['date', 'page_views', 'total_sale'])
test['date'] = pd.to_datetime(test['date'])
test.set_index(keys=['date'], inplace=True)
test.sort_index(inplace=True)
cds = ColumnDataSource(test)
p = Figure(plot_width=1000,
plot_height=500,
title="Sales Vs Views",
y_range=Range1d(start=2500, end=33000),
x_axis_type='datetime',
x_axis_label='Date',
y_axis_label='Revenue($)')
l1 = p.line('date',
'page_views',
source=cds,
line_color=d3['Category10'][10][0],
line_width=5,
legend="Page Views")
l2 = p.line('date',
'total_sale',
source=cds,
line_color=d3['Category10'][10][1],
line_width=5,
legend="Revenue")
p.extra_y_ranges = {"foo": Range1d(start=0, end=6000)}
p.add_layout(
LinearAxis(y_range_name='foo', axis_label="Number of Views"),
'right')
p.legend.location = "bottom_right"
p.background_fill_color = "beige"
p.background_fill_alpha = 0.5
p.border_fill_color = "#F8F8FF"
p.add_tools(
HoverTool(
renderers=[l1],
tooltips=[
('date',
'@date{%F}'), # use @{ } for field names with spaces
('views', '@page_views'),
],
formatters={
'date':
'datetime', # use 'datetime' formatter for 'date' field
# use default 'numeral' formatter for other fields
},
# display a tooltip whenever the cursor is vertically in line with a glyph
mode='vline'))
p.add_tools(
HoverTool(
renderers=[l2],
tooltips=[
# ( 'date', '@date{%F}' ),
('revenue', '$@{total_sale}'
), # use @{ } for field names with spaces
],
formatters={
# 'date' : 'datetime', # use 'datetime' formatter for 'date' field
'revenue':
'printf', # use 'printf' formatter for 'adj close' field
# use default 'numeral' formatter for other fields
},
# display a tooltip whenever the cursor is vertically in line with a glyph
mode='vline'))
return json.dumps(json_item(p))
if report_id == "B":
result = db_session.execute(
'''select product_id,sum(page_views) as views
from ga_sink
group by product_id
order by views desc ''').fetchall()
# result = db_session.execute('''select product_id,sum(page_views) as views
# from test
# group by product_id
# order by views desc ''' ).fetchall()
test = pd.DataFrame(result, columns=['product_id', 'page_views'])
test.set_index(keys=['product_id'], inplace=True)
cds = ColumnDataSource(test)
p = Figure(x_range=cds.data['product_id'],
plot_height=350,
title="Top Products by Views",
tools="")
p.vbar(x='product_id',
top='page_views',
source=cds,
width=0.9,
fill_color=factor_cmap(field_name='product_id',
palette=d3['Category10'][10],
factors=cds.data['product_id']))
p.xgrid.grid_line_color = None
p.y_range.start = 0
p.background_fill_color = "beige"
p.background_fill_alpha = 0.5
p.border_fill_color = "#F8F8FF"
return json.dumps(json_item(p))
if report_id == "C":
# cdata= [{'product_id':'BGB-US-001','total_sale': random.randint(1000,8000)},
# {'product_id':'BGB-US-002','total_sale': random.randint(1000,8000)},
# {'product_id':'BGB-US-003','total_sale': random.randint(1000,8000)},
# {'product_id':'BGB-US-004','total_sale': random.randint(1000,8000)},
# {'product_id':'BGB-US-005','total_sale': random.randint(1000,8000)},
# {'product_id':'BGB-US-006','total_sale': random.randint(1000,8000)},
# {'product_id':'BGB-US-007','total_sale': random.randint(1000,8000)}]
cdata = db_session.execute('''select product_id,sum(amount)
from demo_sales
group by product_id''').fetchall()
c = pd.DataFrame(cdata, columns=['product_id', 'amount'])
c.rename(columns={"amount": "total_sale"}, inplace=True)
print(c)
c.set_index(keys=['product_id'], inplace=True)
c['angle'] = c['total_sale'] / c['total_sale'].sum() * 2 * pi
c['color'] = d3['Category10'][10][len(c) - 1::-1]
c['percent'] = round(c['total_sale'] / c['total_sale'].sum() * 100, 0)
cds = ColumnDataSource(c)
p = Figure(plot_height=350,
title="Revenue Breakdown by Product",
tools="hover",
tooltips="@product_id: @percent %",
x_range=(-0.5, 1.0))
p.wedge(x=0,
y=1,
radius=0.4,
start_angle=cumsum('angle', include_zero=True),
end_angle=cumsum('angle'),
line_color="white",
fill_color='color',
legend='product_id',
source=cds)
p.axis.axis_label = None
p.axis.visible = False
p.grid.grid_line_color = None
p.background_fill_color = "beige"
p.background_fill_alpha = 0.5
p.border_fill_color = "#F8F8FF"
return json.dumps(json_item(p))