def make_candlesticks_cs(price: pd.DataFrame) -> Figure:
TOOLTIPS = [
("index", "$index"),
("(x,y)", "($x, $y)"),
("open", "@Open"),
("close", "@Close")
]
# have to fix y-range here as we are adding extra_y_range later
p: Figure = figure(
x_axis_type="datetime", tools=TOOLS, plot_width=1000, tooltips=TOOLTIPS,
title = "ETH prices", height=400, y_range=(150, price.High.max() * 1.1))
p.grid.grid_line_alpha = 0.7
# this is the basic bokeh data container
# even when we pass dataframe, CDS is created under the covers
cds = ColumnDataSource(price)
# line segment glyphs
p.segment("Date", "High", "Date", "Low", color="black", source=cds)
# green bar glyphs
# you can't pass a mix of data source and iterable values, i.e. df + cds not gonna fly
inc_view = CDSView(source=cds, filters=[BooleanFilter(price.Close > price.Open)])
p.vbar("Date", w, "Open", "Close",
fill_color="#06982d", line_color="black", source=cds, view=inc_view)
# red bar glyphs
dec_view = CDSView(source=cds, filters=[BooleanFilter(price.Open > price.Close)])
p.vbar("Date", w, "Open", "Close",
fill_color="#ae1325", line_color="black", source=cds, view=dec_view)
return p
def get_expenses_table(selected_month=None, category=None):
table_columns = [
TableColumn(field='Date', title='Date', formatter=DateFormatter()),
TableColumn(field='Description', title='Description'),
TableColumn(field='Category', title='Category'),
TableColumn(field='Subcategory', title='Subcategory'),
TableColumn(field='Cost', title='Cost')
]
# which month is currently selected? subset data according to selection
if selected_month is None:
month_filter = [True] * len(expenses_source.data['Date'])
else:
source_datetimes = pd.DatetimeIndex(expenses_source.data['Date'])
month_filter = [
date.month == selected_month for date in source_datetimes
]
month_filter = BooleanFilter(month_filter)
# filter for current category
if category is None:
category_filter = [True] * len(expenses_source.data['Category'])
else:
category_filter = [
cat == category for cat in expenses_source.data['Category']
]
category_filter = BooleanFilter(category_filter)
view = CDSView(source=expenses_source,
filters=[month_filter, category_filter])
return DataTable(source=expenses_source,
columns=table_columns,
view=view,
sizing_mode='stretch_both')
def render(**figure_kwargs):
figure = Figure(**figure_kwargs)
figure.segment(source=onesource,
legend_label="OHLC H/L",
x0='datetime',
x1='datetime',
y0='low',
y1='high',
line_width=1,
color='black')
updown = [
o < c
for o, c in zip(onesource.data['open'], onesource.data['close'])
]
figure.vbar(
legend_label="OHLC Up",
source=onesource,
view=CDSView(source=onesource, filters=[BooleanFilter(updown)]),
width=(onesource.data['datetime'][1] -
onesource.data['datetime'][0]) / 2,
x='datetime',
bottom='open',
top='close',
fill_color="#D5E1DD",
line_color="black",
)
figure.vbar(
legend_label="OHLC Down",
source=onesource,
view=CDSView(source=onesource,
filters=[BooleanFilter([not b for b in updown])]),
width=(onesource.data['datetime'][1] -
onesource.data['datetime'][0]) / 2,
x='datetime',
top='open',
bottom='close',
fill_color="#F2583E",
line_color="black",
)
# point of calling super.__call__() ?? Legend ??
figure.legend.location = "top_left"
figure.legend.click_policy = "hide"
return figure
def update_bird(attr, old, new):
bird = self.select.value
bird_aou = int(
species[species['english_common_name'] == bird]['aou'])
yr = self.slider.value
new_bird_df = bird_lookup(bird_aou)[[
'countrynum', 'statenum', 'route', 'longitude', 'latitude',
'speciestotal', 'routename', 'english_common_name', 'year',
'species_size'
]]
print(bird, yr)
self.bird_source.data = ColumnDataSource(new_bird_df).data
self.booleans = [
True if int(x) == yr else False
for x in self.bird_source.data['year']
]
self.view1.filters = [BooleanFilter(booleans)]
try:
#table_source.data = ColumnDataSource(bird_table_data(yr)).data
#wiki_page = wikipedia.page(bird)
#image_summary.text = wiki_page.html()
self.Elevated.title.text = 'Sightings of ' + str(
bird) + ', %d' % yr
#data_table.visible = True
except:
#wiki_page = wikipedia.page(bird)
#image_summary.text = wiki_page.html()
self.Elevated.title.text = 'Sightings of ' + str(
bird) + ', %d' % yr
def make_div_plot(data, symbol=''):
data['formatted_ex_date'] = \
[x.strftime("%Y-%m-%d") for x in data.index]
source = ColumnDataSource(data)
booleans = [not np.isnan(amount) for amount in source.data['div_amount']]
div_view = CDSView(source=source, filters=[BooleanFilter(booleans)])
plot = figure(
x_axis_type='datetime',
plot_height=500,
plot_width=1000,
)
div_circle = plot.circle(
x='index',
y='div_amount',
source=source,
view=div_view,
name='Divident Amount',
size=7.5,
color='maroon',
)
hover = HoverTool(
renderers=[div_circle],
tooltips=[("ex_date", "@formatted_ex_date"),
("amount", "@div_amount")],
)
plot.add_tools(hover)
plot.title.text = f"{symbol.upper()} Dividend History" if symbol else "Dividend History"
plot.legend.location = "top_left"
plot.xaxis.axis_label = 'Date'
plot.yaxis.axis_label = 'Dividend Amount'
plot.ygrid.band_fill_color = "olive"
plot.ygrid.band_fill_alpha = 0.1
return plot
def cb_trigger_callback(attr, old, new):
data = doc.get_model_by_name('tsne_glyphs').data_source.data
filter_bool = [(float(x) in new) for x in data['color']]
ds = doc.get_model_by_name('tsne_glyphs').data_source
doc.get_model_by_name('tsne_glyphs').view = CDSView(
source=ds, filters=[BooleanFilter(filter_bool)])
def update_plot(attrname, old, new):
mask = [True] * len(gdf)
for key, filter in filter_list.items():
if filter.toggle_.active:
mask = mask & (gdf[key] >= filter.slider_.value[0]) & (
gdf[key] <= filter.slider_.value[1])
test_view.filters[0] = BooleanFilter(booleans=mask)
def update_filter(self):
"""Get mask to filter record by current number of hours for display."""
current_time = dt.datetime.now(self.tz).replace(tzinfo=None)
time_filter = (current_time -
self.data["datetime"]) < pd.Timedelta(hours=self.hrs)
self.data_view.filters = [BooleanFilter(time_filter.values)]
return time_filter
def callback_point_view(event):
booleans = [
True if frame == im_num[-1] else False
for frame in point_labels.data['frame']
]
view = CDSView(source=point_labels,
filters=[BooleanFilter(booleans)])
renderer.view = view
def update_filter(self):
priority_filter = []
for priority in self.map_data.categories['priority']:
priority_filter.append(self.priority_toggles[priority].active)
view = CDSView(source=self.table_source, filters=[BooleanFilter(priority_filter)])
self.table.view = view
for priority in self.map_data.priorities:
self.priority_groups[priority].visible = self.priority_toggles[priority].active
self.priority_groups_rect[priority].visible = self.priority_toggles[priority].active
def update_plot(attr, old, new):
circle = p.select({"name": "circle"})
src_heatmap.data["total"] = [
i[radio_button.active + 3] for i in src_heatmap.data["aggr_delta_after_eti"]
]
booleans = [
True if total > -0.5 and total < 0.5 else False
for total in src_heatmap.data["total"]
]
circle.view.filters[0] = BooleanFilter(booleans)
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT, plot_height=H_PLOT, tools=TOOLS,
title="Stock price", toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {i+int(stock.data['index'][0]): date.strftime('%b %d') for i, date in enumerate(pd.to_datetime(stock.data["Date"]))}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
p.segment(x0='index', x1='index', y0='Low', y1='High', color=RED, source=stock, view=view_inc)
p.segment(x0='index', x1='index', y0='Low', y1='High', color=GREEN, source=stock, view=view_dec)
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=BLUE, line_color=BLUE,source=stock,view=view_inc, name="price")
p.vbar(x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=RED, line_color=RED,source=stock,view=view_dec, name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14/4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ["price"]
# Creating tooltips
price_hover.tooltips = [("Datetime", "@Date{%Y-%m-%d}"),
("Open", "@Open{$0,0.00}"),
("Close", "@Close{$0,0.00}"),("Volume", "@Volume{($ 0.00 a)}")]
price_hover.formatters={"Date": 'datetime'}
return p
def plot_with_slider(dataframe, country_name, y_axis_name):
""""
this function takes a dataframe, y-axis name and country as a parameter,
creates a plot with a slider and returns the plot.
"""
# create a figure object with width and height
plot = figure(x_axis_type="datetime",
width=1000,
height=400,
sizing_mode='fixed')
# creating columnDataSource object, for the dataframes
source = ColumnDataSource(dataframe)
# initialize the min and max value of the date
init_value = (dataframe['Date'].min(), dataframe['Date'].max())
# configuring date range slider with start date end date and value
date_range_slider = DateRangeSlider(start=init_value[0],
end=init_value[1],
value=init_value)
date_filter = BooleanFilter(booleans=[True] * dataframe.shape[0])
# not use scientific numbers on Y-axis
plot.yaxis.formatter = BasicTickFormatter(use_scientific=False)
# whenever a slider value updates. The date range sliders, value changes.
date_range_slider.js_on_change(
"value",
CustomJS(args=dict(f=date_filter, cases_source=source),
code="""\
const [start, end] = cb_obj.value;
f.booleans = Array.from(cases_source.data['Date']).map(d => (d >= start
&& d <= end));
// Needed because of https://github.com/bokeh/bokeh/issues/7273
cases_source.change.emit();
"""))
# add a circle renderer using the source's two columns.
plot.circle(x='Date',
y=country_name,
source=source,
view=CDSView(source=source, filters=[date_filter]),
color='Pink',
line_width=0.5)
# name and field pairs for the Hover tool
tooltips = [('Date', '@Date{%F}'), (country_name, "$y{int}")]
# formatting scheme of date column
formatters = {'@Date': 'datetime'}
# create a Hover tool for the figure with the tooltips and specify the formatting scheme
plot.add_tools(
HoverTool(tooltips=tooltips, formatters=formatters, mode='vline'))
plot.title.text_color = "midnightblue"
plot.title.text_font_size = "25px"
plot.toolbar.active_drag = None
plot.toolbar_location = None
plot.xaxis.axis_label = 'Date'
plot.yaxis.axis_label = y_axis_name
return column(plot, date_range_slider)
def plot_stock_price(stock):
p = figure(plot_width=W_PLOT,
plot_height=H_PLOT,
tools=TOOLS,
title="Stock price",
toolbar_location='above')
inc = stock.data['close'] > stock.data['open']
dec = stock.data['open'] > stock.data['close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
# p.segment(x0='index', x1='index', y0='Low', y1='High',
# color=RED, source=stock, view=view_inc)
# p.segment(x0='index', x1='index', y0='Low', y1='High',
# color=GREEN, source=stock, view=view_dec)
p.vbar(x='index',
width=VBAR_WIDTH,
top='open',
bottom='close',
fill_color=BLUE,
line_color=BLUE,
source=stock,
view=view_inc,
name="price")
p.vbar(x='index',
width=VBAR_WIDTH,
top='open',
bottom='close',
fill_color=RED,
line_color=RED,
source=stock,
view=view_dec,
name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
return p
def make_wind_plot(src):
TOOLS = "pan,wheel_zoom,box_select,lasso_select,reset,save,box_zoom"
p = figure(
width=2 * 900,
height=900,
title='Layer travel time from ' + file1_name + ' to ' + file2_name,
y_axis_label='Time in minutes',
x_axis_label='Height from sea level -2600m (approx Paranal) in meters',
tools=TOOLS)
mypalette = [
'#e41a1c', '#377eb8', '#4daf4a', '#984ea3', '#ff7f00', '#525252',
'#a65628', '#f781bf', '#999999'
]
legend = []
for i, date_time in enumerate(np.unique(src.data['datetime'])):
boolean = np.zeros((len(src.data['datetime'])), dtype=bool)
filter_bool = np.where(src.data['datetime'] == date_time)
colors = np.unique(src.data['color_line'][filter_bool]).tolist()[0]
boolean[filter_bool] = True
view = CDSView(source=src, filters=[BooleanFilter(boolean)])
p.line(x='height_over_sea_level -2600m',
y='time_predicted',
line_width=2,
view=view,
source=src,
color=colors)
legend.append(
LegendItem(label=src.data['datetime'][boolean][0],
renderers=[p.renderers[i]]))
legend1 = Legend(items=legend, location='top_right')
p.add_layout(legend1)
print(src.data['max_corr_lag'].shape)
hline = Span(location=src.data['max_corr_lag'][0],
dimension='width',
line_color='black',
line_width=1.5,
line_dash='dashed')
p.add_layout(hline)
for i, h in enumerate([500, 1000, 2000, 4000, 8000, 16000]):
vline = Span(location=h,
dimension='height',
line_color=mypalette[i],
line_width=2.5,
line_dash='dashed')
p.add_layout(vline)
return p
def make_price_plot(data, symbol='', add_div=True):
source = ColumnDataSource(data)
plot = figure(
x_axis_type='datetime',
plot_height=500,
plot_width=1000,
)
price_line = plot.line(x='index',
y='ohlc_avg',
source=source,
legend='OHLC AVG',
muted_alpha=0.2,
name='price')
price_hover = HoverTool(renderers=[price_line],
mode='vline',
tooltips=[
("open: ", "@open"),
("close: ", "@close"),
("high: ", "@high"),
("low: ", "@low"),
])
plot.add_tools(price_hover)
if 'div_amount' in data.columns and add_div:
booleans = [
not np.isnan(amount) for amount in source.data['div_amount']
]
div_view = CDSView(source=source, filters=[BooleanFilter(booleans)])
dividend = plot.circle(x='index',
y='ohlc_avg',
source=source,
view=div_view,
color='maroon',
legend='Dividend',
muted_alpha=0.2,
size=5,
name='div')
dividend_hover = HoverTool(renderers=[dividend],
tooltips=[
("dividend amount: ", "@div_amount"),
])
plot.add_tools(dividend_hover)
plot.title.text = f"{symbol.upper()} Price" if symbol else "Price"
plot.legend.location = "top_left"
plot.xaxis.axis_label = 'Date'
plot.yaxis.axis_label = 'Price'
plot.ygrid.band_fill_color = "olive"
plot.ygrid.band_fill_alpha = 0.1
plot.legend.click_policy = "mute"
return plot
def dot_marker(p, source, col_name, color):
circle_filter = [
True if val != 0 else False for val in source.data[col_name]
]
circle_view = CDSView(source=source,
filters=[BooleanFilter(circle_filter)])
p.circle(name=col_name,
x='DATA',
y=col_name,
size=10,
source=source,
color=color,
view=circle_view)
return p
def plot_stock_price (stock):
p = figure (plot_width=W_PLOT, plot_height=H_PLOT, tools=TOOLS,
title="Stock price", toolbar_location='above')
inc = stock.data['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView (source=stock, filters=[BooleanFilter (inc)])
view_dec = CDSView (source=stock, filters=[BooleanFilter (dec)])
p.segment (x0='index', x1='index', y0='Low', y1='High', color=RED, source=stock, view=view_inc)
p.segment (x0='index', x1='index', y0='Low', y1='High', color=GREEN, source=stock, view=view_dec)
p.vbar (x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=BLUE, line_color=BLUE,
source=stock, view=view_inc, name="price")
p.vbar (x='index', width=VBAR_WIDTH, top='Open', bottom='Close', fill_color=RED, line_color=RED,
source=stock, view=view_dec, name="price")
p.legend.location = "top_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {
i + int (stock.data['index'][0]): date.strftime ('%b %d') for i, date in
enumerate (pd.to_datetime (stock.data["Date"]))
}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
# Add more ticks in the plot
p.x_range.range_padding = 0.05
p.xaxis.ticker.desired_num_ticks = 40
p.xaxis.major_label_orientation = 3.14 / 4
p.yaxis.formatter = NumeralTickFormatter (format='$ 0,0[.]000')
return p
def update_year(attr, old, new):
yr = self.slider.value
print(yr)
self.booleans = [
True if int(x) == yr else False
for x in bird_source.data['year']
]
self.view1.filters = [BooleanFilter(self.booleans)]
try:
#table_source.data = ColumnDataSource(bird_table_data(yr)).data
# view = CDSView(source = all_source, filters=[BooleanFilter(booleans)])
self.Elevated.title.text = 'Sightings of ' + str(
select.value) + ', %d' % yr
#data_table.visible = True
except:
#table_source.data = ColumnDataSource(None).data
self.Elevated.title.text = 'No Sightings of ' + str(
select.value) + ', %d' % yr
def bokeh_view(self, ignore_filters=False):
""" because we need a new view for each document request...
ignore filters allow some glyph renderer not supporting filters to ignore them
rendering everything instead of breaking...
"""
if ignore_filters or self.filter is None:
view = CDSView(
source=self.model.source) # defaults to complete view.
else:
boolfilter = [
self.filter(r) for r in self.model.data.itertuples()
] # TODO : This should be recreated at everytick ?!?!
# TODO : model iterable on rows ?
view = CDSView(source=self.model.source,
filters=[BooleanFilter(boolfilter)])
return view
def create_figure():
active = {}
for w in sort_widgets:
active[w.name] = converters[w.name][w.active]
key = '{} {} {}'.format(active['Model'], active['Dataset'], active['Filter'])
xrange = sorts[key]
alpha_dict = {'Support vector machine': 1.0, 'Random forest': 0.1}
color_dict = {'Split': Dark2[5][0], 'Mixed': Dark2[5][1],
'Reverse split': Dark2[5][2], 'US': Dark2[5][3],
'UK': Dark2[5][4]}
shape_dict = {'Complete': 'circle', 'Complete filtered': 'diamond',
'Filtered': 'square'}
visible_models = [modelboxes.labels[x] for x in modelboxes.active]
visible_datasets = [datasetboxes.labels[x] for x in datasetboxes.active]
visible_filters = [filterboxes.labels[x] for x in filterboxes.active]
height = 600 + (22*(len(visible_models) * len(visible_datasets) * len(visible_filters)))
p = figure(x_range=FactorRange(factors=xrange),
x_axis_label='Kmer', y_axis_label='Score', plot_width=900,
plot_height=height, output_backend='webgl')
p.xaxis.major_label_orientation = 3.1415/4
legend = []
for model in visible_models:
for dataset in visible_datasets:
for f in visible_filters:
curr_filter = [GroupFilter(column_name='Model', group=model),
GroupFilter(column_name='Dataset', group=dataset),
GroupFilter(column_name='Filter', group=f),
bools = [True if k in xrange else False for k in source.data['Kmer']]
BooleanFilter(bools)]
view = CDSView(source=source, filters=curr_filter)
alpha = alpha_dict[model]
color = color_dict[dataset]
shape = shape_dict[f]
size=10
glyph = p.scatter(x='Kmer', y='Score', source=source, view=view,
color=color, fill_alpha=alpha, size=size,
marker=shape)
legend.append(("{} {} {}".format(model, dataset, f), [glyph]))
p.add_layout(Legend(items=legend), 'below')
return p
def get_layout():
"""Create layout and app elements."""
data_scr = ColumnDataSource({
"x": np.random.normal(size=(10, )),
"y": np.random.normal(size=(10, ))
})
columns = [
TableColumn(field="x", title="First"),
TableColumn(field="y", title="Second")
]
full_table = DataTable(columns=columns, source=data_scr)
mask = [True for el in data_scr.data["y"]]
data_view = CDSView(source=data_scr, filters=[BooleanFilter(mask)])
filtered_table = DataTable(columns=columns,
source=data_scr,
view=data_view)
lt = layout([[full_table], [filtered_table]])
return data_scr, filtered_table, lt
def draw_heatmap(self, xaxis, source, genes):
cds = ColumnDataSource(source)
TOOLTIPS = [
("Sample", "@Sample"),
("Gene", "@Gene"),
("Foldchange", "@mean"),
]
p = figure(x_range=xaxis,
y_range=FactorRange(factors=genes),
frame_width=self._width * len(self._samples),
frame_height=self._height * len(self._genes),
tooltips=TOOLTIPS)
self._heatmap_plot = p
p.xaxis.visible = False
p.min_border_left = MIN_BORDER_LEFT
for gene in genes:
view = CDSView(
source=cds,
filters=[BooleanFilter([x == gene for x in source["Gene"]])])
color = linear_cmap('mean',
Viridis256,
low=0,
high=self._maxvalues.get(gene, 1))
self._linear_color_mapper = color["transform"]
p.rect(x='Sample',
y='Gene',
width=1,
height=1,
color=color,
source=cds,
view=view)
return p
def periodic_callback():
"""Update view."""
mask = np.random.randint(0, 2, (10, ), dtype=bool)
view = CDSView(source=data_scr, filters=[BooleanFilter(mask)])
filtered_table.view = view
def plot_stock_price_bar(df, ticker, features):
stock = ColumnDataSource(df)
p = figure(
plot_width=W_PLOT,
plot_height=H_PLOT,
tools=TOOLS,
title=f'Alpha-Vantage Real-Time Stock Price - Bar Chart - {ticker}',
x_axis_label='date',
y_axis_label='price')
p.grid.grid_line_alpha = 0.3
inc = df['Close'] > stock.data['Open']
dec = stock.data['Open'] > stock.data['Close']
view_inc = CDSView(source=stock, filters=[BooleanFilter(inc)])
view_dec = CDSView(source=stock, filters=[BooleanFilter(dec)])
p.segment(x0='index',
x1='index',
y0='Low',
y1='High',
color=RED,
source=stock,
view=view_inc)
p.segment(x0='index',
x1='index',
y0='Low',
y1='High',
color=GREEN,
source=stock,
view=view_dec)
p.vbar(x='index',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color=BLUE,
line_color=BLUE,
source=stock,
view=view_inc,
name="price")
p.vbar(x='index',
width=VBAR_WIDTH,
top='Open',
bottom='Close',
fill_color=RED,
line_color=RED,
source=stock,
view=view_dec,
name="price")
# map dataframe indices to date strings and use as label overrides
p.xaxis.major_label_overrides = {
i + int(stock.data['index'][0]): date.strftime('%b %d')
for i, date in enumerate(pd.to_datetime(stock.data["Date"]))
}
p.xaxis.bounds = (stock.data['index'][0], stock.data['index'][-1])
# Add more ticks in the plot
p.x_range.range_padding = 0.05
p.xaxis[
0].ticker.desired_num_ticks = 40 # ticker is not the same stock ticker but a bokeh axis term
p.xaxis.major_label_orientation = 3.14 / 4
# Select specific tool for the plot
price_hover = p.select(dict(type=HoverTool))
# Choose, which glyphs are active by glyph name
price_hover.names = ['price']
# Creating tooltips
price_hover.tooltips = [('Datetime', '@Date{%F}'),
('Open', '@Open{$0,0.00}'),
('Close', '@Close{$0,0.00}'),
('Volume', '@Volume{($0.00 a)}')]
price_hover.formatters = {'@Date': 'datetime'}
return p
from bokeh.layouts import gridplot
from bokeh.models import ColumnDataSource, CDSView, BooleanFilter
from bokeh.plotting import figure, show
source = ColumnDataSource(data=dict(x=[1, 2, 3, 4, 5], y=[1, 2, 3, 4, 5]))
booleans = [True if y_val > 2 else False for y_val in source.data['y']]
view = CDSView(source=source, filters=[BooleanFilter(booleans)])
tools = ["box_select", "hover", "reset"]
p = figure(plot_height=300, plot_width=300, tools=tools)
p.circle(x="x", y="y", size=10, hover_color="red", source=source)
p_filtered = figure(plot_height=300,
plot_width=300,
tools=tools,
x_range=p.x_range,
y_range=p.y_range)
p_filtered.circle(x="x",
y="y",
size=10,
hover_color="red",
source=source,
view=view)
show(gridplot([[p, p_filtered]]))
from bokeh.layouts import gridplot
from bokeh.models import BooleanFilter, CDSView, ColumnDataSource
from bokeh.plotting import figure, output_file, show
output_file("linked_selection_subsets.html")
x = list(range(-20, 21))
y0 = [abs(xx) for xx in x]
y1 = [xx**2 for xx in x]
# create a column data source for the plots to share
source = ColumnDataSource(data=dict(x=x, y0=y0, y1=y1))
# create a view of the source for one plot to use
view = CDSView(filters=[
BooleanFilter([True if y > 250 or y < 100 else False for y in y1])
])
TOOLS = "box_select,lasso_select,hover,help"
# create a new plot and add a renderer
left = figure(tools=TOOLS, width=300, height=300, title=None)
left.circle('x', 'y0', size=10, hover_color="firebrick", source=source)
# create another new plot, add a renderer that uses the view of the data source
right = figure(tools=TOOLS, width=300, height=300, title=None)
right.circle('x',
'y1',
size=10,
hover_color="firebrick",
source=source,
def modify_doc(doc):
im_num = [
0,
]
images = [
np.pad(im, ((max_height - im.shape[0], 0),
(0, max_width - im.shape[1])), 'constant')
for im in ims
]
plot, source = bootcamp_utils.viz.bokeh_imshow(images[im_num[-1]],
return_im=True)
source = source.data_source
booleans = [
True if frame == im_num[-1] else False
for frame in point_labels.data['frame']
]
view = CDSView(source=point_labels, filters=[BooleanFilter(booleans)])
renderer = plot.scatter(x='x',
y='y',
source=point_labels,
view=view,
color=point_tool_color,
size=point_size)
columns = [
TableColumn(field="x", title="x"),
TableColumn(field="y", title="y"),
TableColumn(field='frame', title='frame')
]
table = DataTable(source=point_labels,
columns=columns,
editable=True,
height=table_height)
draw_tool = PointDrawTool(renderers=[renderer], empty_value=im_num[-1])
plot.add_tools(draw_tool)
plot.add_tools(CrosshairTool(line_alpha=crosshair_tool_alpha))
plot.toolbar.active_tap = draw_tool
def update_image(new_ind):
_, data = bootcamp_utils.viz.bokeh_imshow(images[new_ind],
return_im=True)
data = data.data_source
source.data = data.data
def callback_point_view(event):
booleans = [
True if frame == im_num[-1] else False
for frame in point_labels.data['frame']
]
view = CDSView(source=point_labels,
filters=[BooleanFilter(booleans)])
renderer.view = view
def callback_slider(attr, old, new):
update_image(new)
im_num.append(int(new))
draw_tool.empty_value = im_num[-1]
callback_point_view('tap')
def callback_button(direction):
new = im_num[-1] + direction
if (((len(images) - 1) < new and direction == 1)
or (new == -1 and direction == -1)):
return None
update_image(new)
im_num.append(new)
draw_tool.empty_value = im_num[-1]
callback_point_view('tap')
slider = Slider(start=0,
end=len(images),
value=0,
step=1,
title="Frame Number")
slider.on_change('value', callback_slider)
button_back = Button(label='back', button_type="success")
button_back.on_click(partial(callback_button, direction=-1))
button_forward = Button(label='forward', button_type="success")
button_forward.on_click(partial(callback_button, direction=1))
plot.on_event('tap', callback_point_view)
doc.add_root(
column(row(slider), plot, row(button_back, button_forward), table))
def plot_multi_hail_hist(
hist_data: Dict[str, hl.Struct],
title: str = "Plot",
log: bool = False,
fill_color: Dict[str, str] = None,
outlier_fill_color: Dict[str, str] = None,
line_color: str = "#033649",
hover_mode: str = "mouse",
hide_zeros: bool = False,
alpha: float = None,
) -> bokeh.plotting.Figure:
"""
Plot multiple histograms on the same plot.
Each histogram can (and should) come straight from ht.aggregate(hl.agg.hist(ht.data, start, end, bins))
Example usage:
.. code-block:: python
plot_multi_hail_hist(ht.aggregate(hl.agg.group_by(ht.pop, hl.agg.hist(ht.data, start, end, bins))))
:param hist_data: Data to plot
:param title: Plot title
:param log: Whether the y-axis should be log
:param fill_color: Color to fill the histogram bars that fall within the hist boundaries
:param outlier_fill_color: Color to fill the histogram bars that fall outside the hist boundaries
:param line_color: Color of the lines around the histogram bars
:param hover_mode: Hover mode; one of 'mouse' (default), 'vline' or 'hline'
:param hide_zeros: Remove hist bars with 0 count
:param alpha: Alpha value (if None, then 1.0/len(hist_data) is used)
:return: Histogram plot
"""
low = int(log)
if alpha is None:
alpha = 1.0 / len(hist_data)
if fill_color is None:
color_palette = d3["Category10"][max(3, len(hist_data))]
fill_color = {
hist_name: color_palette[i]
for i, hist_name in enumerate(hist_data.keys())
}
if outlier_fill_color is None:
outlier_fill_color = fill_color
p = (figure(title=title, y_axis_type="log", tools=TOOLS)
if log else figure(title=title, tools=TOOLS))
hists = []
for label, hist in hist_data.items():
data = {}
distance = abs(hist.bin_edges[0] - hist.bin_edges[1])
data["top"] = [x + low for x in hist.bin_freq]
data["left"] = hist.bin_edges[:-1]
data["right"] = hist.bin_edges[1:]
data["color"] = [fill_color[label]] * len(hist.bin_freq)
if hist.n_smaller > 0:
data["top"].insert(0, hist.n_smaller + low)
data["left"].insert(0, hist.bin_edges[0] - distance)
data["right"].insert(0, hist.bin_edges[0])
data["color"].insert(0, outlier_fill_color[label])
if hist.n_larger > 0:
data["top"].append(hist.n_larger + low)
data["left"].append(hist.bin_edges[-1])
data["right"].append(hist.bin_edges[-1] + distance)
data["color"].append(outlier_fill_color[label])
data["bottom"] = [low] * len(data["top"])
data["label"] = [label] * len(data["top"])
hist_source = ColumnDataSource(data)
# pylint: disable=unsubscriptable-object
hide_zeros_filter = BooleanFilter(
[top > 0 for top in hist_source.data["top"]])
view = (CDSView(source=hist_source, filters=[hide_zeros_filter])
if hide_zeros else CDSView(source=hist_source))
hists.append((
label,
[
p.quad(
top="top",
bottom="bottom",
left="left",
right="right",
source=hist_source,
view=view,
fill_color="color",
alpha=alpha,
line_color=line_color,
)
],
))
tooltips = [("bin", "$index"), ("bin_edges", "(@left, @right)"),
("freq", "@top")]
if len(hist_data) > 1:
tooltips.insert(0, ("label", "@label"))
p.add_layout(
Legend(
items=hists,
location=(0, 0),
orientation="horizontal",
click_policy="hide",
),
"above",
)
p.select_one(HoverTool).tooltips = tooltips
p.select_one(HoverTool).mode = hover_mode
num_data_points = sum([sum(x.bin_freq) for x in hist_data.values()])
p.add_layout(Title(text=f"({num_data_points:,} data points)"), "above")
return p
def figures_chisq_simple(init_group, df_chisq):
df_chisq["case_ma07"] = df_chisq["case_ma07"] / 1000
df_chisq["threshold_min_eps"] = df_chisq["threshold_min_eps"] / 1000
df_chisq["threshold_max_eps"] = df_chisq["threshold_max_eps"] / 1000
source = ColumnDataSource(df_chisq)
gf = GroupFilter(column_name='CountryProv', group=init_group)
view1 = CDSView(source=source, filters=[gf])
booleans = [
True if float(case_detrended) >= 0 else False
for case_detrended in source.data['case_detrended']
]
t1_view = CDSView(source=source, filters=[gf, BooleanFilter(booleans)])
booleans = [
True if float(case_detrended) < 0 else False
for case_detrended in source.data['case_detrended']
]
t2_view = CDSView(source=source, filters=[gf, BooleanFilter(booleans)])
p_b1_tooltip = [
("Date", "@Date{%F}"),
("Thresholds Min", "@threshold_min_eps"),
("Thresholds Max", "@threshold_max_eps"),
("Positive Cases", "@case_ma07"),
]
p_b2_tooltip = [
("Date", "@Date{%F}"),
("Detrended Cases", "@case_detrended"),
]
p_formatters = {'@Date': 'datetime'}
plot_size_and_tools = {
'tools': ['box_select', 'reset', 'help', 'box_zoom'],
'x_axis_type': 'datetime'
}
# FIXME couldnt do p_a1.line below, so using hack of varea
p_b1 = figure(title="Positive cases", **plot_size_and_tools)
c_b1b = p_b1.varea(x='Date',
y1='threshold_min_eps',
y2='threshold_max_eps',
source=source,
color='grey',
view=view1,
legend_label="Range of 14-day expectation")
c_b1a = p_b1.circle(x='Date',
y='case_ma07',
source=source,
color='red',
view=view1,
legend_label="7-day average")
p_b2 = figure(
title="7-day moving average cases minus thresholds -> Excess cases",
**plot_size_and_tools)
c_b2a = p_b2.scatter(x='Date',
y='case_detrended',
source=source,
color='#73b2ff',
legend_label="Above Threshold",
view=t2_view)
c_b2a = p_b2.scatter(x='Date',
y='case_detrended',
source=source,
color='#ff7f7f',
legend_label="Below Threshold",
view=t1_view)
editplotcolors(p_b1)
editplotcolors(p_b2)
p_b1.xaxis.axis_label = 'Date'
p_b1.yaxis.axis_label = 'Positive Cases (in thousands)'
p_b2.xaxis.axis_label = 'Date'
p_b2.yaxis.axis_label = 'Detrended Number of Cases'
p_b2.x_range = p_b1.x_range
p_b1.add_tools(HoverTool(tooltips=p_b1_tooltip, formatters=p_formatters))
p_b2.add_tools(HoverTool(tooltips=p_b2_tooltip, formatters=p_formatters))
p_b1.legend.location = 'top_left'
p_b1.legend.background_fill_alpha = 0.8
p_b1.legend.background_fill_color = "#262626"
p_b1.legend.border_line_alpha = 0
p_b1.legend.label_text_color = "whitesmoke"
p_b2.legend.background_fill_alpha = 0.8
p_b2.legend.background_fill_color = "#262626"
p_b2.legend.border_line_alpha = 0
p_b2.legend.label_text_color = "whitesmoke"
p_b2.legend.location = 'bottom_left'
return source, c_b1b, p_b1, p_b2
