def tick_formater(self, data_type):
if data_type == 'datetime':
formatter = DatetimeTickFormatter(**dict(days='%Y-%m-%d',
months='%Y-%m',
hours='%Y-%m-%d %H',
minutes='%Y-%m-%d %H:%M'))
elif data_type == 'integer':
print('integer')
formatter = NumeralTickFormatter(format='0,0')
elif data_type == 'float':
print('float')
formatter = NumeralTickFormatter(format='0.000')
return formatter
def make_plot_cntry(data: DF, scale: str = "linear", country="Colombia",
x_tools: List[str] = None):
"""Make a bokeh plot of a certain type (confirmed/recovered/deaths/active)
scale can be "linear" or "log"
for a bunch of countries"""
klasses = ["active", "deaths", "recovered", "confirmed"]
p = init_plot( scale, x_tools=x_tools )
data_source = data_source_ctry(data, country=country)
all_colors = Category10[10]
klass_2_color = {
'active': all_colors[1],
'recovered': all_colors[2],
'deaths': all_colors[3],
'confirmed': all_colors[0],
}
for klass in klasses:
color = klass_2_color[klass]
# visible = country in (ACTIVE_COUNTRIES.union(x_countries))
draw_country_line(p, source=data_source, country=country, klass=klass,
legend=TRANSL.get(klass, klass), color=color, visible=True )
# set_hover_tool(p, klass)
set_hover_tool_ctry( p )
p.legend.location = "top_left"
p.legend.click_policy = "hide"
p.yaxis.formatter = NumeralTickFormatter(format='0,0')
p.xaxis.formatter = DatetimeTickFormatter(days=["%b %d"])
# output_file(OUTPUT_DIR / f"{klass}.html", title=f"Cases by country - {klass}")
return p
def make_cmp_plot(data: DF, klass: str, scale: str = "linear",
x_tools: List[str] = None, countries: List[str] = None,
x_countries: List[str] = None ):
"""Make a bokeh plot of a certain type (confirmed/recovered/deaths/active)
scale can be "linear" or "log"
for a bunch of countries"""
x_countries = x_countries or []
countries = countries or COUNTRIES
print( 'c1', countries )
if scale == 'log' and 'World' not in countries:
countries = ['World'] + countries
print('c2', countries)
countries = x_countries + [ c for c in countries if c not in x_countries ]
countries = countries[:10]
p = init_plot( scale, x_tools )
for country, color in zip(countries, Category10[10]):
data_src = data_source_ctry( data, country )
visible = country in (ACTIVE_COUNTRIES.union(x_countries))
legend = TRANSL.get( country, country )
draw_country_line(p, data_src, country, klass, color, visible, legend )
set_hover_tool(p, klass)
p.legend.location = "top_left"
p.legend.click_policy = "hide"
p.yaxis.formatter = NumeralTickFormatter(format='0,0')
p.xaxis.formatter = DatetimeTickFormatter(days=["%b %d"])
# output_file(OUTPUT_DIR / f"{klass}.html", title=f"Cases by country - {klass}")
return p
def create_figure():
'''
The main function to aggregate the data and create the figure.
'''
use_df = select_data() # Get the filtered dataframe
agg = pivot_data(use_df) # aggregate the dataframe
source = ColumnDataSource(agg) # put into a bokeh data structure
hover = HoverTool( # Set the hover tools, ('Title', '@Column{formatting}'')
tooltips = [
('Year', '@Year'),
('Total Murders','@MRD{0,0}'),
('Solved Murders','@Yes{0,0}'),
('Clearance Rate','@Clearance_Rate{0%}')
]
)
tools=[hover, ResetTool()]
p = figure(plot_width=900, plot_height=550,tools=tools) # Create figure
p.xaxis.axis_label = 'Year'
p.yaxis.axis_label = 'Murders'
# Set the range for the yaxis dynamically. Requires bokeh Rand1d object
ymx = max(agg['MRD'].max(), agg['Yes'].max()) * 1.05
p.y_range=Range1d(0,ymx)
p.yaxis[0].formatter = NumeralTickFormatter(format='0,0')
# Actually plot the lines now.
p.line('Year','MRD',source=source, line_width=3,)
p.line('Year','Yes',source=source, line_width=3, color='orange')
p.title.text = '{} Murders by Year'.format(states.value) # dynamic title
return p
def create_figure():
'''
The main function to aggregate the data and create the figure.
'''
use_df = select_state() # Get the filtered dataframe
agg = use_df.groupby('YEAR',
as_index=False).sum() # aggregate the dataframe
agg['Clearance_Rate'] = agg['CLR'] / agg[
'MRD'] # Set Clearance Rate column
source = ColumnDataSource(agg) # put into a bokeh data structure
hover = HoverTool( # Set the hover tools, ('Title', '@Column{formatting}'')
tooltips=[('Year', '@YEAR'), ('Total Murders', '@MRD{0,0}'),
('Solved Murders', '@CLR{0,0}'),
('Clearance Rate', '@Clearance_Rate{0%}')])
tools = [hover]
p = figure(plot_width=600, plot_height=400, tools=tools) # Create figure
p.xaxis.axis_label = 'Year'
p.yaxis.axis_label = 'Murders'
# Set the range for the yaxis dynamically. Requires bokeh Rand1d object
p.y_range = Range1d(0, agg['MRD'].max() * 1.05)
p.yaxis[0].formatter = NumeralTickFormatter(format='0,0')
# Actually plot the lines now.
p.line(
'YEAR',
'MRD',
source=source,
line_width=3,
)
p.line('YEAR', 'CLR', source=source, line_width=3, color='orange')
p.title.text = '{} Murders by Year'.format(states.value) # dynamic title
return p
def plot_volume(self,
data: bt.AbstractDataBase,
alpha=1.0,
extra_axis=False):
"""extra_axis displays a second axis (for overlay on data plotting)"""
source_id = FigureEnvelope._source_id(data)
self._add_columns([(source_id + 'volume', np.float64),
(source_id + 'colors_volume', np.object)])
kwargs = {
'fill_alpha': alpha,
'line_alpha': alpha,
'name': 'Volume',
'legend_label': 'Volume'
}
ax_formatter = NumeralTickFormatter(format=self._scheme.number_format)
if extra_axis:
source_data_axis = 'axvol'
self.figure.extra_y_ranges = {
source_data_axis:
DataRange1d(
range_padding=1.0 / self._scheme.volscaling,
start=0,
)
}
# use colorup
ax_color = convert_color(self._scheme.volup)
ax = LinearAxis(y_range_name=source_data_axis,
formatter=ax_formatter,
axis_label_text_color=ax_color,
axis_line_color=ax_color,
major_label_text_color=ax_color,
major_tick_line_color=ax_color,
minor_tick_line_color=ax_color)
self.figure.add_layout(ax, 'left')
kwargs['y_range_name'] = source_data_axis
else:
self.figure.yaxis.formatter = ax_formatter
vbars = self.figure.vbar('index',
get_bar_width(),
f'{source_id}volume',
0,
source=self._cds,
fill_color=f'{source_id}colors_volume',
line_color="black",
**kwargs)
# make sure the new axis only auto-scales to the volume data
if extra_axis:
self.figure.extra_y_ranges['axvol'].renderers = [vbars]
self._hoverc.add_hovertip(
"Volume", f"@{source_id}volume{{({self._scheme.number_format})}}",
data)
def _create_price_and_volume_histogram( # pylint: disable=too-many-arguments
data,
price="close",
volume="volume",
bins=150,
color=GRAY,
alpha=0.5):
formatter = NumeralTickFormatter(format="0,0")
hist = np.histogram(data[price], bins=bins, weights=data[volume])
hist_data = zip(hist[1], hist[0])
tooltips = [
("Price", "@{price}"),
("Volume", "@volume{0,0}"),
]
hover = HoverTool(tooltips=tooltips)
return (hv.Histogram(hist_data).redim(Frequency="volume", x="price").opts(
xformatter=formatter,
color=color,
alpha=alpha,
tools=["ybox_zoom", "reset", hover],
default_tools=[],
invert_axes=True,
hooks=[set_toolbar_none],
xticks=2,
))
def initPlot(self):
# TODO adf 14022018, added this for bokeh workaround, remoave after fixing
import warnings
warnings.filterwarnings('ignore')
TickFormatter
self.plot = figure(
plot_height=350,
plot_width=750,
x_axis_type='log',
y_axis_type='log',
title='Detector Noise',
y_axis_label='h [1/\u221AHz]',
x_axis_label='f [Hz]',
toolbar_location='right',
toolbar_sticky=False,
tools="ypan, save, reset")
self.setPlotYLim(self.yLo, self.yHi)
self.plot.title.offset = self.plot.plot_width * (2 / 3)
self.plot.yaxis.major_label_orientation = 'horizontal'
#self.plot.axis.axis_label_text_font = 'italic'
self.plot.axis.axis_label_text_font_size = '11pt'
self.plot.axis.major_label_text_font_size = '10pt'
fi = self.detector.parameters['freqrange'][0]
ff = self.detector.parameters['freqrange'][1]
self.scorecalculator.SetFreqRange(fi, ff)
x, y, self.names = self.scorecalculator.GetNoiseCurves()
self.legends = []
self.colours = {}
self.lines = {}
for i, yi in enumerate(y):
self.colours[self.names[i]] = palette[i]
# TODO: fix this, adf 14.02.2018
# the following is a workaround for a Bokeh bug which
# causes lines to not be clipped at the X/Y limits
# See: https://github.com/bokeh/bokeh/issues/6787
yi=np.array(yi)
x=np.array(x)
idxs = np.nonzero((yi<=self.yHi) & (yi>=self.yLo))[0]
self.lines[self.names[i]] = self.plot.line(x[idxs], yi[idxs], color=palette[i], \
line_width=3)
self.legends.append((self.names[i], [self.lines[self.names[i]]]))
self.layoutLegend(self.legends)
# show freq axis as 1 10 ... 10k
self.plot.xaxis.formatter=NumeralTickFormatter(format="0 a")
self.plot.output_backend = "svg"
self.budget = pywidgets.HTML(
value=self.budgetMsg(),
description='Warning: ',
)
self.score = pywidgets.HTML(
value=' ',
description=' '
)
self.handle = show(self.plot, notebook_handle=True)
return self.budget
def plot_volume(self, obj, alpha, extra_axis=False):
src_prefix = sanitize_source_name(obj._name)
df = convert_to_pandas(obj, self._start, self._end)
if len(nanfilt(df.volume)) == 0:
return
colorup = convert_color(self._scheme.volup)
colordown = convert_color(self._scheme.voldown)
is_up = df.close > df.open
colors = [colorup if x else colordown for x in is_up]
self._add_to_cds(df.volume, src_prefix + 'volume')
self._add_to_cds(colors, src_prefix + 'volume_colors')
kwargs = {
'fill_alpha': alpha,
'line_alpha': alpha,
'name': 'Volume',
'legend': 'Volume'
}
ax_formatter = NumeralTickFormatter(format='0.000 a')
if extra_axis:
self.figure.extra_y_ranges = {'axvol': DataRange1d()}
adapt_yranges(self.figure.extra_y_ranges['axvol'], df.volume)
self.figure.extra_y_ranges['axvol'].end /= self._scheme.volscaling
ax_color = colorup
ax = LinearAxis(y_range_name="axvol",
formatter=ax_formatter,
axis_label_text_color=ax_color,
axis_line_color=ax_color,
major_label_text_color=ax_color,
major_tick_line_color=ax_color,
minor_tick_line_color=ax_color)
self.figure.add_layout(ax, 'left')
kwargs['y_range_name'] = "axvol"
else:
self.figure.yaxis.formatter = ax_formatter
adapt_yranges(self.figure.y_range, df.volume)
self.figure.y_range.end /= self._scheme.volscaling
self.figure.vbar('datetime',
get_bar_length_ms(obj) * 0.7,
src_prefix + 'volume',
0,
source=self._cds,
fill_color=src_prefix + 'volume_colors',
line_color="black",
**kwargs)
self._hoverc.add_hovertip("Volume", f"@{src_prefix}volume{{(0.00 a)}}")
def plot(self):
bar = Bar(\
self.__table,
values = blend('registrations', name = 'registrations', labels_name = 'registrations'),\
label = cat(columns = 'country', sort = False),\
stack = cat(columns = 'registrations'),\
agg = 'mean',\
title = 'Total of new registrations in European Union',\
legend = False,\
tooltips = [('Registrations', '@registrations{1,1}')])
bar._yaxis.formatter = NumeralTickFormatter(format='0,0')
return bar
def plot(self):
bar = Bar(\
self.__fuel_table,
values = blend('percentage', name = 'percentage', labels_name = 'percentage'),\
label = cat(columns = 'fuel'),\
stack = cat(columns = 'percentage'),\
agg = 'mean',\
title = self.__title,\
legend = False,\
tooltips = [('Percentage', '@percentage{0.00%}')])
bar._yaxis.formatter = NumeralTickFormatter(format = '0%')
return bar
def bokeh_wealth_chart(df):
p = (df.wealth + df.cash_flow).rename('Net wealth').plot.line(
show_figure=False,
toolbar_location=None,
hovertool_string=r"""<h4> Net wealth: </h4> £@{Net wealth}{0,0}""",
panning=False,
zooming=False)
p.yaxis[0].formatter = NumeralTickFormatter(format='$0,0')
p.xaxis.major_label_orientation = 3.14 / 5
p.legend.location = "top_left"
# p.yaxis[0].formatter.use_scientific = False
return p
def plotTotal(self):
bar = Bar(\
self.__table,
values = blend('total', name = 'total', labels_name = 'total'),\
label = cat(columns = 'fuel'),\
stack = cat(columns = 'total'),\
agg = 'mean',\
title = 'Total of CO2 emissions (by fuel type) in ' + self.__title,\
legend = False,\
tooltips = [('Total', '@total{1,1}' + ' g/km')])
bar._yaxis.formatter = NumeralTickFormatter(format = '0,0' + ' g/km')
return bar
def generate_grid_scatter_plot(data_frame):
y = data_frame['Diabetes_Pct'].values
x1 = data_frame['Black_Pct'].values
x2 = data_frame['Hispanic_Pct'].values
x3 = data_frame['LowAccess_Pct'].values
x4 = data_frame['HouseholdIncome'].values
source = ColumnDataSource(data=dict(y=y, x1=x1, x2=x2, x3=x3, x4=x4))
TOOLS = "crosshair,wheel_zoom,reset,tap,pan,box_select"
p1 = figure(tools=TOOLS,
width=300,
plot_height=250,
x_range=(-.04, 1.08),
y_range=(0, 0.21))
p1.scatter('x1', 'y', source=source, fill_alpha=0.8, line_color=None)
p1.xaxis.axis_label = 'Percent Black'
p1.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p1.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p2 = figure(tools=TOOLS,
toolbar_location="right",
width=300,
plot_height=250,
x_range=(-.04, 1.08),
y_range=(0, 0.21))
p2.scatter('x2', 'y', source=source, fill_alpha=0.8, line_color=None)
p2.xaxis.axis_label = 'Percent Hispanic'
p2.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p2.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p3 = figure(tools=TOOLS,
width=300,
plot_height=250,
x_range=(-.04, 1.08),
y_range=(0, 0.21))
p3.scatter('x3', 'y', source=source, fill_alpha=0.8, line_color=None)
p3.xaxis.axis_label = 'Low Access Population'
p3.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p3.xaxis[0].formatter = NumeralTickFormatter(format="0.0%")
p4 = figure(tools=TOOLS,
width=300,
plot_height=250,
x_range=(9.8, 11.8),
y_range=(0, 0.21))
p4.scatter('x4', 'y', source=source, fill_alpha=0.8, line_color=None)
p4.xaxis.axis_label = 'Median Household Income (log scale)'
p4.yaxis[0].formatter = NumeralTickFormatter(format="0.0%")
plot_quad = gridplot([[p1, p2], [p3, p4]])
script, dev = components(plot_quad)
return script, dev
def MonthlyGraph():
monthly_graph = figure(x_axis_label='Month',
y_axis_label='Expenses',
x_axis_type='datetime',
sizing_mode='stretch_both',
tools="hover, tap",
tooltips="$name: @$name")
monthly_graph.y_range.start = 0
y_formatter = NumeralTickFormatter(format='0a')
# date_formatter = DatetimeTickFormatter(months=["%m/%y"])
monthly_graph.yaxis.formatter = y_formatter
return monthly_graph
def get_counties_plot(data_frame):
plot = Bar(data_frame,
label='CTYNAME',
values='TOT_POP',
agg='max',
plot_width=1200,
plot_height=500,
title='Population by County',
legend=False)
plot.xaxis.axis_label = 'County'
plot.yaxis.axis_label = 'Population'
plot.yaxis.formatter = NumeralTickFormatter(format="0a")
plot.sizing_mode = 'scale_width'
return plot
def plot_sma(stock):
p = figure(
x_axis_type="datetime",
plot_width=WIDTH_PLOT,
plot_height=300,
title="Simple Moving Average (press the legend to hide/show lines)",
tools=TOOLS,
toolbar_location='above')
p.line(x='date',
y='SMA_5',
line_width=2,
color=BLUE,
source=stock,
legend='5 days',
muted_color=BLUE,
muted_alpha=0.2)
p.line(x='date',
y='SMA_10',
line_width=2,
color=ORANGE,
source=stock,
legend='10 days',
muted_color=ORANGE,
muted_alpha=0.2)
p.line(x='date',
y='SMA_50',
line_width=2,
color=PURPLE,
source=stock,
legend='50 days',
muted_color=PURPLE,
muted_alpha=0.2)
p.line(x='date',
y='SMA_100',
line_width=2,
color=BROWN,
source=stock,
legend='100 days',
muted_color=BROWN,
muted_alpha=0.2)
p.legend.location = "bottom_left"
p.legend.border_line_alpha = 0
p.legend.background_fill_alpha = 0
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
return p
def bokeh_outcomes_chart(df):
p = df.plot(kind='line',
show_figure=False,
toolbar_location=None,
panning=False,
zooming=False,
plot_data_points=True,
plot_data_points_size=5)
p.hover.tooltips = [('year', '$index')] + list(
(c, f'£@{c}' + '{0,0}') for c in df.columns)
p.hover.mode = 'mouse'
p.yaxis[0].formatter = NumeralTickFormatter(format='£0,0')
p.xaxis.major_label_orientation = 3.14 / 5
p.legend.location = "top_left"
# p.yaxis[0].formatter.use_scientific = False
return p
def plot_volume(self, data: bt.feeds.DataBase, strat_clk: array, alpha, extra_axis=False):
source_id = Figure._source_id(data)
df = convert_to_pandas(strat_clk, data, self._start, self._end)
if len(nanfilt(df.volume)) == 0:
return
colorup = convert_color(self._scheme.volup)
colordown = convert_color(self._scheme.voldown)
is_up = df.close > df.open
colors = [colorup if x else colordown for x in is_up]
self._add_to_cds(df.volume, f'{source_id}volume')
self._add_to_cds(colors, f'{source_id}volume_colors')
kwargs = {'fill_alpha': alpha,
'line_alpha': alpha,
'name': 'Volume',
'legend': 'Volume'}
ax_formatter = NumeralTickFormatter(format=self._scheme.number_format)
if extra_axis:
self.figure.extra_y_ranges = {'axvol': DataRange1d()}
adapt_yranges(self.figure.extra_y_ranges['axvol'], df.volume)
self.figure.extra_y_ranges['axvol'].end /= self._scheme.volscaling
ax_color = colorup
ax = LinearAxis(y_range_name="axvol", formatter=ax_formatter,
axis_label_text_color=ax_color, axis_line_color=ax_color, major_label_text_color=ax_color,
major_tick_line_color=ax_color, minor_tick_line_color=ax_color)
self.figure.add_layout(ax, 'left')
kwargs['y_range_name'] = "axvol"
else:
self.figure.yaxis.formatter = ax_formatter
adapt_yranges(self.figure.y_range, df.volume)
self.figure.y_range.end /= self._scheme.volscaling
self.figure.vbar('index', get_bar_width(), f'{source_id}volume', 0, source=self._cds, fill_color=f'{source_id}volume_colors', line_color="black", **kwargs)
hover_target = None if self._scheme.merge_data_hovers else self.figure
self._hoverc.add_hovertip("Volume", f"@{source_id}volume{{({self._scheme.number_format})}}", hover_target)
def get_states_plot():
source = AjaxDataSource(data={
'STATE': [],
'STNAME': [],
'STUSAB': [],
'TOT_POP': [],
'TOT_MALE': [],
'TOT_FEMALE': []
},
data_url='/api/states/',
mode='replace',
method='GET')
hover = HoverTool(tooltips=[
("State", "@STNAME"),
("Population", "@TOT_POP"),
("Female Population", "@TOT_FEMALE"),
("Male Population", "@TOT_MALE"),
])
plot = figure(
title='Population by State',
plot_width=1200,
plot_height=500,
x_range=FactorRange(factors=get_state_abbreviations()),
y_range=(0, 40000000),
tools=[hover, 'tap', 'box_zoom', 'wheel_zoom', 'save', 'reset'])
plot.toolbar.active_tap = 'auto'
plot.xaxis.axis_label = 'State'
plot.yaxis.axis_label = 'Population'
plot.yaxis.formatter = NumeralTickFormatter(format="0a")
plot.sizing_mode = 'scale_width'
plot.vbar(bottom=0,
top='TOT_POP',
x='STUSAB',
legend=None,
width=0.5,
source=source)
url = "/counties/@STATE/"
taptool = plot.select(type=TapTool)
taptool.callback = OpenURL(url=url)
return plot
def get_plot(self, df):
"""
Dataframe df must have columns x0, y0, x1, y1 (in this order) for coordinates
bottom-left (x0, y0) and top right (x1, y1). Optionally a fifth value-column can be provided for colors
Parameters
----------
df
Returns
-------
"""
# processed = {}
# for k, v in self.kwargs.items():
# if k.endswith('formatter') and isinstance(v, str) and '%' not in v:
# v = NumeralTickFormatter(format=v)
# processed[k] = v
# if self.streaming:
# processed['stream'] = self._stream
#hvplots stream? https://holoviews.org/user_guide/Streaming_Data.html
# plot = hv.Rectangles([(0, 0, 1, 1), (2, 3, 4, 6), (0.5, 2, 1.5, 4), (2, 1, 3.5, 2.5)])
processed = {}
for k, v in self.kwargs.items():
if k.endswith('formatter') and isinstance(v, str) and '%' not in v:
v = NumeralTickFormatter(format=v)
processed[k] = v
if self.streaming:
#processed['stream'] = self._stream
plot = hv.DynamicMap(hv.Rectangles, streams=[self._stream])
plot = plot.apply.opts(**self.opts) if self.opts else plot
else:
plot = hv.Rectangles(df)
plot.opts(**self.opts) if self.opts else plot
if self.selection_group or 'selection_expr' in self._param_watchers:
plot = self._link_plot(plot)
return plot
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 _create_normalized_price_and_volume_histogram(hist_data,
color=ACCENT_COLOR,
alpha=0.5):
formatter = NumeralTickFormatter(format="0,0")
tooltips = [
("Price", "@{Price}{0,0.000}"),
("Volume Density", "@{VolumeDensity}{0,0.000}"),
]
hover = HoverTool(tooltips=tooltips)
return (hv.Histogram(hist_data).redim(
Frequency="VolumeDensity", x="Price").opts(
xformatter=formatter,
color=color,
alpha=alpha,
tools=["xbox_zoom", "reset", hover, "save"],
default_tools=[],
ylabel="Volume Density",
# invert_axes=True,
# hooks=[set_toolbar_none],
# xticks=2,
))
def _create_time_and_volume_histogram(data,
time="time",
volume="volume",
color=GRAY,
alpha=0.5):
formatter = NumeralTickFormatter(format="0,0")
hist_data = zip(data[time], data[volume])
tooltips = [
("Time", "@{time}{%F}"),
("Volume", "@volume{0,0}"),
]
hover = HoverTool(tooltips=tooltips, formatters={"@{time}": "datetime"})
return (hv.Histogram(hist_data).redim(Frequency="volume", x="time").opts(
yformatter=formatter,
color=color,
alpha=alpha,
tools=["xbox_zoom", "reset", hover],
default_tools=[],
hooks=[set_toolbar_none],
yticks=4,
))
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 timing_plot(genfn):
"Draw a timing plot for a prime generator function"
if not check_fn(genfn):
return
global _lines
def plot(fig, name, vals, num, dash='solid'):
"Add a line with points to a plot"
col = _palette[num % len(_palette)]
fig.line('x', 'y', legend_label=name, source=vals, line_dash=dash, color=col)
fig.scatter('x', 'y', legend_label=name, source=vals, marker='o', color=col)
name = genfn.__name__
exist = None
args = dict(plot_width=800, plot_height=400, toolbar_location='above', title="Timing")
linfig = figure(y_range=[0, 1], x_range=DataRange1d(start=0), **args)
logfig = figure(y_range=[1e-6, 1], x_range=DataRange1d(start=1),
x_axis_type='log', y_axis_type='log', **args)
num = 0
# add previous lines
for k, v in _lines.items():
plot(linfig, k, v, num, 'dashed')
plot(logfig, k, v, num, 'dashed')
if k == name:
exist = num
num += 1
source = ColumnDataSource(data=dict(x=[], y=[]))
for fig in (linfig, logfig):
plot(fig, name, source, exist or num)
fig.xaxis.axis_label = "Primes"
fig.xaxis.formatter = NumeralTickFormatter(format='0[.]0 a')
fig.xgrid.minor_grid_line_color = 'lightgrey'
fig.xgrid.minor_grid_line_alpha = 0.2
fig.yaxis.axis_label = "Seconds"
fig.legend.location = 'bottom_right'
fig.legend.click_policy = 'hide'
fig.legend.background_fill_alpha = 0.5
linfig.yaxis.formatter = BasicTickFormatter()
logfig.yaxis.formatter = BasicTickFormatter(use_scientific=True, precision=0)
lintab = Panel(child=linfig, title="Linear")
logtab = Panel(child=logfig, title="Log")
tabs = Tabs(tabs=[lintab, logtab])
handle = None
if _incremental:
# Incremental: show plot now, then incrementally add points
handle = show(tabs, notebook_handle=True)
try:
genfn()
combined = True
except TypeError:
combined = False
if combined:
# Generate line in one go
plot_line_combined(genfn, source, handle)
else:
# Generator takes size, need to generate points separately
plot_line_separate(genfn, source, handle)
if not _incremental:
# Plot not shown yet, show it now
show(tabs)
# save line data to show on next plot
_lines[name] = source.data
def plot_volume(self, data, alpha=1.0, extra_axis=False):
'''
Plot method for volume
extra_axis: displays a second axis (for overlay on data plotting)
'''
source_id = get_source_id(data)
self.set_cds_col(source_id + 'volume')
# create color columns
volup = convert_color(self._scheme.volup)
voldown = convert_color(self._scheme.voldown)
self.set_cds_col((source_id + 'colors_volume', source_id + 'open',
source_id + 'close',
partial(cds_op_color,
color_up=volup,
color_down=voldown)))
# prepare bar kwargs
kwargs = {
'x': 'index',
'width': self._bar_width,
'top': source_id + 'volume',
'bottom': 0,
'fill_color': source_id + 'colors_volume',
'line_color': source_id + 'colors_volume',
'fill_alpha': alpha,
'line_alpha': alpha,
'name': 'Volume',
'legend_label': 'Volume'
}
# set axis
ax_formatter = NumeralTickFormatter(format=self._scheme.number_format)
if extra_axis:
source_data_axis = 'axvol'
# use colorup
ax_color = convert_color(self._scheme.volup)
# use only one additional axis to prevent multiple axis being added
# to a single figure
ax = self.figure.select_one({'name': source_data_axis})
if ax is None:
# create new axis if not already available
self.figure.extra_y_ranges = {
source_data_axis:
DataRange1d(range_padding=1.0 / self._scheme.volscaling,
start=0)
}
ax = LinearAxis(name=source_data_axis,
y_range_name=source_data_axis,
formatter=ax_formatter,
axis_label_text_color=ax_color,
axis_line_color=ax_color,
major_label_text_color=ax_color,
major_tick_line_color=ax_color,
minor_tick_line_color=ax_color)
self.figure.add_layout(ax, self._scheme.vol_axis_location)
kwargs['y_range_name'] = source_data_axis
else:
self.figure.yaxis.formatter = ax_formatter
# append renderer
self._figure_append_renderer(self.figure.vbar, **kwargs)
# set hover label
self._fp.hover.add_hovertip(
'Volume', f'@{source_id}volume{{({self._scheme.number_format})}}',
data)
def plot_sv_alleles(annotations, aligned_regions_file):
alignments = list(load_alignments(aligned_regions_file))
query_ids = sorted(list(set(a.query.id for a in alignments)))
plots = []
for query_id in query_ids:
alns = [a for a in alignments if a.query.id == query_id]
x0 = [a.hit.start for a in alns]
x1 = [a.hit.end for a in alns]
y0 = [a.query.start for a in alns]
y1 = [a.query.end for a in alns]
min_x = min(x0 + x1)
max_x = max(x0 + x1)
x_range = (min_x, max_x)
width = x_range[1] - x_range[0]
pad = 0.1 * width
x_range = (int(min_x - pad), int(max_x + pad))
colors = ["red" if a.hit.strand == '-' else "blue" for a in alns]
fig = figure(width=800, height=300, x_range=x_range)
fig.segment(x0,
y0,
x1,
y1,
line_color=colors,
line_width=5,
line_alpha=0.75,
line_cap="butt")
fig.title.text = query_id
fig.yaxis.axis_label = "Amplicon"
fig.xaxis.visible = False
fig.yaxis.ticker = zooming_ticker()
fig.xaxis.formatter = NumeralTickFormatter(format="0,0")
fig.add_tools(hover)
plots.append(fig)
transcripts = transcripts_from_gffutils(annotations,
next(a.hit.id for a in alns),
x_range[0], x_range[1])
fig2 = gene_viz.GenePlot(
dict(row_height=15,
exon_color="Black",
pack=True,
label_horiz_position="center",
label_vert_position="above",
label_justify="center",
label_offset=(0, -4),
label_font_size="6pt",
intron_width_percent=0.001))
fig2.link_x_range(fig)
fig2.figure.xaxis.axis_label = next(a.hit.id for a in alns)
fig2.figure.yaxis.visible = False
fig2.figure.background_fill_color = "black"
fig2.figure.background_fill_alpha = 0.15
fig2.transcripts = transcripts
box = BoxAnnotation(left=min_x,
right=max_x,
fill_color='white',
fill_alpha=1,
level="underlay")
fig2.figure.add_layout(box)
for a in alns:
box = BoxAnnotation(
left=min([a.hit.start, a.hit.end]),
right=max([a.hit.start, a.hit.end]),
fill_color="red" if a.hit.strand == '-' else "blue",
fill_alpha=0.25,
level="underlay")
fig2.figure.add_layout(box)
fig2.update()
plots.append(fig2.figure)
if len(plots) > 0:
save(
gridplot(plots,
ncols=1,
toolbar_location="right",
toolbar_options=dict(logo=None)))
else:
save(Div(text="No Data"))
p = figure(title=title,
x_axis_type="datetime",
plot_width=1200,
plot_height=400,
tools=TOOLS,
toolbar_location='above')
p.line(df_limit.index, df_limit.close, color='black')
p.line(df_limit.index, df.sma_50, legend='SMA 50', color=BLUE)
p.line(df_limit.index, df.SMA_100, legend='SMA 100', color=ORANGE)
p.legend.location = "top_left"
p.legend.border_line_alpha = 1
p.legend.background_fill_alpha = 1
p.legend.click_policy = "mute"
p.yaxis.formatter = NumeralTickFormatter(format='$ 0,0[.]000')
# plot candlesticks
candlestick_width = get_candlestick_width(datetime_interval)
p.segment(df_limit.index,
df_limit.high,
df_limit.index,
df_limit.low,
color="black")
p.vbar(df_limit.index[inc],
candlestick_width,
df_limit.open[inc],
df_limit.close[inc],
fill_color="#D5E1DD",
line_color="black")
p.vbar(df_limit.index[dec],
const arrSum = arr => arr.reduce((a,b) => a + b, 0)
var avg = arrSum(avgList) / avgList.length
for (var i = 0; i < alpha.length; i++){
line_y[i] = avg
}
source.change.emit();
line_source.change.emit();
""")
p.add_tools(bar_hover)
p.add_tools(line_hover)
p.sizing_mode = 'scale_width'
p.background_fill_color = "#fdf6e3"
p.border_fill_color = "#fdf6e3"
p.yaxis[0].formatter = NumeralTickFormatter(format="$0,0")
p.outline_line_color = None
range_slider = RangeSlider(start=0,
end=max(id_range),
value=(0, max(id_range)),
step=1,
title="Showing IDs")
range_slider.js_on_change('value', callback)
# range_slider.js_on_change("value", CustomJS(code="""
# console.log('range_slider: value=' + this.value[0], this.toString())
# """))
range_slider.background = "#fdf6e3"
layout = Column(p, range_slider)
layout.sizing_mode = "scale_width"
# show the results
