def plot_bokeh(df, ticker):
p = figure(width=800, height=400, title=ticker.upper(), tools="")
hover = HoverTool(tooltips="""
<div>
<table>
<tr><td class="ttlab">Date:</td><td>@date_str</td></tr>
<tr><td class="ttlab">Close:</td><td>@close_str</td></tr>
</table>
</div>
""")
hover.mode = 'vline'
hover.line_policy = 'nearest'
p.add_tools(hover)
crosshair = CrosshairTool()
crosshair.dimensions = 'height'
crosshair.line_color = "#ffffff"
p.add_tools(crosshair)
dfcds = ColumnDataSource(df)
p.line('date', 'close', source=dfcds, color="#44ddaa")
p.xaxis.formatter = DatetimeTickFormatter(days=["%d %b"])
p.x_range = Range1d(df['date'].min(), df['date'].max())
p.toolbar.logo = None
p.toolbar_location = None
return p
def _add_vlinked_crosshairs(fig1, fig2, x_values):
cross1 = CrosshairTool(dimensions="height")
cross2 = CrosshairTool(dimensions="height")
fig1.add_tools(cross1)
fig2.add_tools(cross2)
js_move = '''
if(cb_obj.x >= fig.x_range.start && cb_obj.x <= fig.x_range.end &&
cb_obj.y >= fig.y_range.start && cb_obj.y <= fig.y_range.end)
{
cross.spans.height.computed_location = cb_obj.sx
// determine closest point in list of x_values in order to render
// the current position when hovering over time series plots
var closest = x_values.reduce(function (prev, curr) {
return (Math.abs(curr - cb_obj.x) < Math.abs(prev - cb_obj.x) ? curr : prev);
});
var index = x_values.indexOf(closest);
// call rendering function, defined in activity_map.html
render_position(index);
}
else
{
cross.spans.height.computed_location = null
}
'''
js_leave = 'cross.spans.height.computed_location = null'
args = {'cross': cross2, 'fig': fig1, "x_values": x_values}
fig1.js_on_event('mousemove', CustomJS(args=args, code=js_move))
fig1.js_on_event('mouseleave', CustomJS(args=args, code=js_leave))
args = {'cross': cross1, 'fig': fig2, "x_values": x_values}
fig2.js_on_event('mousemove', CustomJS(args=args, code=js_move))
fig2.js_on_event('mouseleave', CustomJS(args=args, code=js_leave))
def fn_main():
df = pd.DataFrame(AAPL)[:2000]
df["date"] = pd.to_datetime(df["date"])
df['ToolTipDates'] = df.date.map(
lambda x: x.strftime("%y-%m-%d")) # Saves work with the tooltip later
# print(df.head(3))
TOOL_k = "pan,xwheel_zoom,ywheel_zoom,box_zoom,reset"
TOOL_v = "pan,ywheel_zoom"
TOOL_m = "pan,ywheel_zoom"
p_k = figure(x_axis_type="datetime",
tools=TOOL_k,
plot_width=1504,
plot_height=600) # title = "MSFT Candlestick") # hei 880
p_v = figure(x_axis_type="datetime",
tools=TOOL_v,
plot_width=1504,
plot_height=160) # title="volume"
p_m = figure(x_axis_type="datetime",
tools=TOOL_m,
plot_width=1504,
plot_height=200) # hei 880
p_k.add_tools(CrosshairTool(line_color='#999999'))
p_m.add_tools(CrosshairTool(line_color='#999999'))
p_v.add_tools(CrosshairTool(line_color='#999999'))
p_k.x_range = p_v.x_range = p_m.x_range # 3 link must at one line
p_k.xaxis.visible = p_v.yaxis.visible = p_v.xaxis.visible = False
df['ma20'] = [0.0 for i in range(len(df))]
fn_ma(df, 'ma20', 20)
df['long'] = [0.0 for i in range(len(df))]
df['short'] = [0.0 for i in range(len(df))]
df['diff'] = fn_ema(df, 'close', 'short', 12) - fn_ema(
df, 'close', 'long', 26)
df['dea'] = fn_ema(df, 'diff', 'dea', 9)
df['macd'] = 2 * (df['diff'] - df['dea'])
all_source = ColumnDataSource(df)
tbl_bi = pd.DataFrame(columns=('date', 'price'))
fn_plot_kline(df, p_k, all_source)
fn_plot_fenbi(df, p_k, tbl_bi)
fn_plot_segmt(p_k, tbl_bi)
fn_plot_volume(df, p_v)
fn_plot_macd(df, p_m, all_source)
output_file("chan.html", title="chan", mode='inline')
grid = gridplot([[p_k], [p_v], [p_m]], merge_tools=False, responsive=True)
grid.sizing_mode = 'stretch_both'
show(grid)
pass
def _set_linked_crosshairs(self, figures):
'''
Link crosshairs across all figures
src:
https://stackoverflow.com/questions/37965669/how-do-i-link-the-crosshairtool-in-bokeh-over-several-plots
'''
crosshair_shared = CrosshairTool(dimensions='height')
for f in figures:
crosshair = CrosshairTool(dimensions='width')
f.figure.add_tools(crosshair, crosshair_shared)
def bokehplot(df_1, ticker):
"""Create a time-series line plot in Bokeh."""
p = figure(width=600, height=300, title=ticker.upper(), tools="")
hover = HoverTool(tooltips="""
<div>
<table>
<tr><td class="ttlab">Date:</td><td>@date_str</td></tr>
<tr><td class="ttlab">Close:</td><td>@close</td></tr>
</table>
</div>
""")
hover.mode = 'vline'
hover.line_policy = 'nearest'
p.add_tools(hover)
crosshair = CrosshairTool()
crosshair.dimensions = 'height'
crosshair.line_color = "#ffffff"
p.add_tools(crosshair)
dfcds = ColumnDataSource(df_1)
p.line('date', 'close', source=dfcds, color="#44ddaa")
p.xaxis.formatter = DatetimeTickFormatter(days=["%d %b"])
p.x_range = Range1d(df_1['date'].min(), df_1['date'].max())
p.toolbar.logo = None
p.toolbar_location = None
# Style plot
p.background_fill_color = "#234567"
p.border_fill_color = "#234567"
p.title.text_color = "#ffffff"
p.title.text_font_size = "1.25em"
p.axis.major_label_text_color = "#ffffff"
p.axis.major_label_text_font_size = "0.875em"
p.axis.axis_line_color = "#ffffff"
p.axis.major_tick_line_color = "#ffffff"
p.axis.minor_tick_line_color = "#ffffff"
p.xgrid.grid_line_color = None
p.ygrid.grid_line_alpha = 0.5
p.ygrid.grid_line_dash = [4, 6]
p.outline_line_color = None
p.yaxis.axis_label = "Closing price"
p.yaxis.axis_label_text_color = "#ffffff"
p.yaxis.axis_label_text_font_size = "1em"
p.yaxis.axis_label_text_font_style = "normal"
p.yaxis.axis_label_standoff = 12
return p
def make_plot(yname,
line_color,
tbs=True,
above_axis=False,
below_axis=True,
left_axis=True,
right_axis=False,
border_fill_color="white",
toolbar_location_index=0,
plot_width=600,
plot_height=300):
plot = Plot(x_range=DataRange1d(),
y_range=DataRange1d(),
border_fill_color=border_fill_color,
toolbar_location=toolbar_locations[toolbar_location_index],
plot_width=plot_width,
plot_height=plot_height,
min_border=10,
toolbar_sticky=tbs,
sizing_mode='scale_width')
plot.add_glyph(source, Line(x="x", y=yname, line_color=line_color))
plot.add_tools(PanTool(), BoxSelectTool(), CrosshairTool(), ResetTool())
plot = add_axes(plot,
below_axis=below_axis,
left_axis=left_axis,
right_axis=right_axis,
above_axis=above_axis)
return plot
def add_custom_tools(self):
from bokeh.models import HoverTool, CrosshairTool
hover = HoverTool(tooltips=[("x", "$x"), ("y", "$y")],
mode='vline',
renderers=self.lines)
crosshair = CrosshairTool()
self.plot.add_tools(hover, crosshair)
def plotTraces(et,x='td',y = 'gx',query = '',showplot = True,width=1400,height=800):
from bokeh.plotting import figure,show
from bokeh.models import Span, CrosshairTool, HoverTool, ResetTool, PanTool, BoxZoomTool,WheelZoomTool
from bokeh.transform import factor_cmap
from bokeh.palettes import Spectral6
TOOLS = [CrosshairTool(dimensions='both'),
HoverTool(),PanTool(),BoxZoomTool(),
ResetTool()]
if type(et) != list:
et = [et]
p = figure()
for ix,e in enumerate(et):
#et[ix] = e.query(query)
et[ix]['group'] = str(ix)
#et[ix] = et[ix][[field,'td','group']]
tmp = pd.concat(et,ignore_index=True,)
if len(query)>0:
tmp = tmp.query(query)
# if figure:
#plt.figure()
if tmp.empty:
raise 'Warning: No element left to plot'
p = figure(width=width,tools=TOOLS,height=height)
p.circle(x=x,y=y,source = tmp,legend='group',color=factor_cmap('group',factors=['0','1','2','3','4'], palette=Spectral6))
if showplot:
show(p)
return(p)
def addLinkedCrosshairs(plots,dimensions='both'):
"""
plots: list of figures
"""
js_move = ''' start = fig.x_range.start;
end = fig.x_range.end;
if(cross.dimensions==="both" || cross.dimensions==="height"){
if(cb_obj.x>=start && cb_obj.x<=end && cb_obj.y>=start && cb_obj.y<=end)
{ cross.spans.height.computed_location=cb_obj.sx }
else { cross.spans.height.computed_location = null }
}
if(cross.dimensions==="both" || cross.dimensions==="width"){
if(cb_obj.y>=start && cb_obj.y<=end && cb_obj.x>=start && cb_obj.x<=end)
{ cross.spans.width.computed_location=fig.plot_height-cb_obj.sy }
else { cross.spans.width.computed_location=null }
}'''
js_leave = ''' if(cross.dimensions==="both" || cross.dimensions==="height"){cross.spans.height.computed_location=null};
if(cross.dimensions==="both" || cross.dimensions==="width"){cross.spans.width.computed_location=null}'''
figures = plots[:]
for plot in plots:
crosshair = CrosshairTool(dimensions=dimensions)
plot.add_tools(crosshair)
for figure in figures:
if figure != plot:
args = {'cross': crosshair, 'fig': figure}
figure.js_on_event('mousemove', CustomJS(args = args, code = js_move))
figure.js_on_event('mouseleave', CustomJS(args = args, code = js_leave))
def graphtraceoverlay(trace, new_trace):
x_range = range(0, len(new_trace))
p = figure()
p.add_tools(CrosshairTool())
p.line(x_range, new_trace)
p.line(x_range, trace, line_color='red')
show(p)
def _set_linked_crosshairs(self, figures):
'''
Link crosshairs across all figures
src:
https://stackoverflow.com/questions/37965669/how-do-i-link-the-crosshairtool-in-bokeh-over-several-plots
'''
js_leave = ''
js_move = 'if(cross.spans.height.location){\n'
for i in range(len(figures) - 1):
js_move += '\t\t\tother%d.spans.height.location = cb_obj.sx\n' % i
js_move += '}else{\n'
for i in range(len(figures) - 1):
js_move += '\t\t\tother%d.spans.height.location = null\n' % i
js_leave += '\t\t\tother%d.spans.height.location = null\n' % i
js_move += '}'
crosses = [CrosshairTool() for fig in figures]
for i, fig in enumerate(figures):
fig.figure.add_tools(crosses[i])
args = {'fig': fig.figure, 'cross': crosses[i]}
k = 0
for j in range(len(figures)):
if i != j:
args['other%d' % k] = crosses[j]
k += 1
fig.figure.js_on_event('mousemove',
CustomJS(args=args, code=js_move))
fig.figure.js_on_event('mouseleave',
CustomJS(args=args, code=js_leave))
def build_week_figure(self, week_cds_list):
week_figure = figure(width_policy='max',
height_policy='max',
title='Last Week',
tools='pan,wheel_zoom',
active_scroll='wheel_zoom',
x_axis_type='datetime')
hover_tool = HoverTool(tooltips=TOOLTIPS,
formatters=FORMATTERS,
mode='vline')
cross_hair_tool = CrosshairTool(dimensions='height')
week_figure.add_tools(hover_tool, cross_hair_tool)
weekday_palette = brewer['Blues'][9]
weekend_palette = brewer['OrRd'][5]
for i, (legend_label,
day_cds) in enumerate(zip(WEEK_DAYS, week_cds_list)):
color = weekday_palette[i] if i < 5 else weekend_palette[i - 5]
week_figure.line(x='index',
y=INTEREST_COLUMNS[0],
source=day_cds,
line_width=2,
color=color,
legend_label=legend_label)
week_figure.legend.location = 'top_right'
week_figure.legend.orientation = 'horizontal'
week_figure.legend.click_policy = 'hide'
return week_figure
def event_chart():
factors = ["a","b","c,","d"]
x = [24.3, -22.3, -25, 6]
LineColor=["green" if a>=0 else "red" for a in x]
Tooltips = [
("index", "$index"),
("(x,y)", "($x, $y)")
# ("radius", "@radius"),
# ("fill color", "$color[hex, swatch]:fill_color"),
# ("x", "@x"),
# ("bar", "@bar"),
]
dots_fig = figure(title="exapmple", y_range = factors, x_range = [-30,30], toolbar_location="below", toolbar_sticky=False, \
tools='lasso_select, poly_select, undo, redo, reset')
# , \
# tooltips=Tooltips)
dots_fig.segment(0, factors, x, factors, line_width=2, line_color=LineColor)
c1 = dots_fig.circle(x, factors, size=15, fill_color="orange", line_width=3, line_color=LineColor)
# tool = BoxEditTool(renderers=[c1])
tool2 = BoxSelectTool(dimensions="width") # To make a multiple selection, press the SHIFT key. To clear the selection, press the ESC key
# dots_fig.add_tools(tool) # disappears the points..
dots_fig.add_tools(tool2)
dots_fig.add_tools(CrosshairTool(dimensions='height'))
return dots_fig
def __init__(self,**kwargs):
super(WeatherPlot,self).__init__(**kwargs)
# def __init__(self,
# data,
# column_names=cs.column_names_weewx,
# column_time=cs.time_column_name_weewx,
# plot_height=300,
# plot_width=800,
# border_left=150,
# **kwargs):
# if "tool_events" not in kwargs:
# kwargs["tool_events"] = ToolEvents()
# super(WeatherPlot,self).__init__(x_range=DataRange1d(),
# y_range=DataRange1d(),
# plot_width=800,
# plot_height=500,
# min_border_left=150,
# **kwargs)
time_int = 3600
t_max = int(time.time())
t_min = t_max - 3600 * 24
data = sql_con.get_data_ws(time_int=time_int,
t_max=t_max,
t_min=t_min)
self._data = data
column_names=cs.column_names_weewx
column_time=cs.time_column_name_weewx
data_seconds = data
data_seconds.iloc[:, 0] = data_seconds.iloc[:, 0] * 1000
add_glyphs_to_plot(column_names, column_time, data_seconds, self)
self.add_layout(DatetimeAxis(), 'below')
self.add_tools(PanTool(), WheelZoomTool(), ResizeTool(), CrosshairTool())
def heatmap3(cs3):
from bkcharts import HeatMap, show, output_file
from bokeh.palettes import RdGy11 as palette # @UnresolvedImport
from bokeh.models import HoverTool
from bokeh.models import CrosshairTool
crosshair = CrosshairTool()
hover = HoverTool(tooltips=[
("index", "$index"),
("(x,y)", "(@x,@y)"),
("score", "@values")
])
print (type(cs3))
score = []
for x in c3.apply(tuple):
score.extend(x)
data = {
'utterance1': list(cs3.index) * len(cs3.columns),
'utterance2': [item for item in list(cs3.columns) for i in range(len(c3.index))],
'score': score,
}
output_file('utterance_heatmap.html')
hm = HeatMap(data, x='utterance1', y='utterance2', values='score', title='Cosine Similarity', tools=[hover,crosshair], stat=None, palette=palette)
#show(hm)
return hm
def plot_ticker(ticker):
# Retrieve and process data:
url = urlhead + ticker + urltail
page = requests.get(url)
json = page.json()
df = pd.DataFrame(json['Time Series (Daily)'])
# New DataFrame to append values:
df_1 = pd.DataFrame()
close = np.asarray(df.iloc[3])
df_1['date'] = pd.to_datetime(list(df))
df_1['close'] = close
# Last 30 days:
df_1 = df_1[0:30]
# Create a new column with dates as string:
df_1['date_str'] = df_1['date'].map(lambda x: x.strftime("%Y-%m-%d"))
dfcds = ColumnDataSource(df_1)
# Create Bokeh plot:
p = figure(width=600, height=300, title=ticker.upper(), tools="")
hover = HoverTool(tooltips = [
('Date', '@date_str'),
('Close', '@close')])
hover.mode = 'vline'
hover.line_policy = 'nearest'
p.add_tools(hover)
crosshair = CrosshairTool()
crosshair.dimensions = 'height'
p.add_tools(crosshair)
p.line('date', 'close', source = dfcds)
p.xaxis.formatter=DatetimeTickFormatter(days=["%d %b"])
p.x_range=Range1d(df_1['date'].min(), df_1['date'].max())
p.toolbar.logo = None
p.toolbar_location = None
return p
def add_vlinked_crosshairs(fig1, fig2):
js_move = '''if(cb_obj.x >= fig.x_range.start && cb_obj.x <= fig.x_range.end && cb_obj.y >= fig.y_range.start && cb_obj.y <= fig.y_range.end)
{ cross.spans.height.computed_location = cb_obj.sx }
else
{ cross.spans.height.computed_location = null }'''
js_leave = 'cross.spans.height.computed_location = null'
cross1 = CrosshairTool()
cross2 = CrosshairTool()
fig1.add_tools(cross1)
fig2.add_tools(cross2)
args = {'cross': cross2, 'fig': fig1}
fig1.js_on_event('mousemove', CustomJS(args=args, code=js_move))
fig1.js_on_event('mouseleave', CustomJS(args=args, code=js_leave))
args = {'cross': cross1, 'fig': fig2}
fig2.js_on_event('mousemove', CustomJS(args=args, code=js_move))
fig2.js_on_event('mouseleave', CustomJS(args=args, code=js_leave))
def plot_assets(self):
TOOLS = 'box_select,crosshair,reset'
# output_file("stocks.html", title="Performance tracker")
p = figure(plot_width=700,
plot_height=250,
tools=TOOLS,
toolbar_location=None)
p.line(self.dates,
self.closeprice,
line_width=2,
line_color="greenyellow")
# Title
#p.add_layout(Title(text='last price: ' + str(self.closeprice[-1]) + '€; yield: ' + str(self.ly) + '%; fall from max:' + str(self.loss) +'%', text_font_style="bold", text_font_size="13pt", text_font='Cantarell'), 'above')
p.add_layout(
Title(text=self.label,
text_font_style="bold",
text_font_size="16pt",
text_font='Cantarell'), 'above')
# Axes
p.xaxis.minor_tick_line_color = None
p.yaxis.minor_tick_line_color = None
p.xaxis.ticker.desired_num_ticks = 7
p.xaxis.major_label_text_font_size = "11pt"
p.xaxis.major_label_text_font = "Cantarell"
p.xaxis.major_label_text_font_style = "bold"
p.yaxis.major_label_text_font_size = "11pt"
p.yaxis.major_label_text_font = "Cantarell"
p.yaxis.major_label_text_font_style = "bold"
p.xaxis.formatter = DatetimeTickFormatter(
hours=["%d-%m-%Y"],
days=["%d-%m-%Y"],
months=["%d-%m-%Y"],
years=["%d-%m-%Y"],
)
# Grid
p.grid.grid_line_color = 'gray'
p.grid.grid_line_alpha = 0.5
# Background
p.background_fill_color = "black"
# Horizontal line
hline = Span(location=self.buyprice,
dimension='width',
line_color='red',
line_width=3)
p.renderers.extend([hline])
# Tools: hover and crosshair
p.add_tools(CrosshairTool(line_color="red"))
p.add_tools(
HoverTool(tooltips=[
('date', '@x{%F}'),
('price', '@{y}{%0.2f}'),
],
formatters={
'x': 'datetime',
'y': 'printf',
},
mode='vline'))
return p
def _init_figure(self):
# plot height will be set later
f = figure(tools=Figure._tools, plot_width=self._scheme.plot_width, logo=None, sizing_mode='scale_width', x_axis_type='linear')
# TODO: backend webgl (output_backend="webgl") removed due to this bug:
# https://github.com/bokeh/bokeh/issues/7568
f.border_fill_color = convert_color(self._scheme.border_fill)
f.xaxis.axis_line_color = convert_color(self._scheme.axis_line_color)
f.yaxis.axis_line_color = convert_color(self._scheme.axis_line_color)
f.xaxis.minor_tick_line_color = convert_color(self._scheme.tick_line_color)
f.yaxis.minor_tick_line_color = convert_color(self._scheme.tick_line_color)
f.xaxis.major_tick_line_color = convert_color(self._scheme.tick_line_color)
f.yaxis.major_tick_line_color = convert_color(self._scheme.tick_line_color)
f.xaxis.major_label_text_color = convert_color(self._scheme.axis_label_text_color)
f.yaxis.major_label_text_color = convert_color(self._scheme.axis_label_text_color)
f.xgrid.grid_line_color = convert_color(self._scheme.grid_line_color)
f.ygrid.grid_line_color = convert_color(self._scheme.grid_line_color)
f.title.text_color = convert_color(self._scheme.plot_title_text_color)
f.left[0].formatter.use_scientific = False
f.background_fill_color = convert_color(self._scheme.background_fill)
# mechanism for proper date axis without gaps, thanks!
# https://groups.google.com/a/continuum.io/forum/#!topic/bokeh/t3HkalO4TGA
f.xaxis.formatter = FuncTickFormatter(
args=dict(
axis=f.xaxis[0],
formatter=DatetimeTickFormatter(days=['%d %b', '%a %d'],
months=['%m/%Y', "%b %y"]),
source=self._cds,
),
code="""
axis.formatter.doFormat = function (ticks) {
const dates = ticks.map(i => source.data.datetime[i]),
valid = t => t !== undefined,
labels = formatter.doFormat(dates.filter(valid));
let i = 0;
return dates.map(t => valid(t) ? labels[i++] : '');
};
const axisticks = axis.tick_coords.major[0],
labels = axis.formatter.doFormat(ticks);
return labels[axisticks.indexOf(tick)];
""")
ch = CrosshairTool(line_color=self._scheme.crosshair_line_color)
f.tools.append(ch)
h = HoverTool(tooltips=[('Time', '@datetime{%x %X}')],
mode="vline",
formatters={'datetime': 'datetime'}
)
f.tools.append(h)
self._hover = h
self.figure = f
def create_candlesctick(df, ticker):
title = ticker
df['mid'] = df.apply(lambda x: (x['open'] + x['close']) / 2, axis=1)
df['height'] = df.apply(lambda x: abs(x['close'] - x['open'])
if x['close'] != x['open'] else 0.001,
axis=1)
inc = df.close > df.open
dec = df.open > df.close
width = 0.3
source_inc = ColumnDataSource(df.loc[inc])
source_dec = ColumnDataSource(df.loc[dec])
hover_tools = HoverTool(tooltips=[
('date', '@date'),
('open', '@open'),
('close', '@close'),
])
tools = (CrosshairTool(), hover_tools)
p = figure(plot_width=800,
plot_height=300,
tools=tools,
x_axis_type='datetime',
title=title)
# Style candlesticks:
p.segment(df.timestamp[inc],
df.high[inc],
df.timestamp[inc],
df.low[inc],
color='green') # Positive return
p.segment(df.timestamp[dec],
df.high[dec],
df.timestamp[dec],
df.low[dec],
color='green') # Negative return
p.rect(x='timestamp',
y='mid',
width=width,
height=height,
fill_color='green',
source=source_inc) # Positive return
p.rect(x='timestamp',
y='mid',
width=width,
height=height,
fill_color='red',
source=source_dec) # Positive return
p.yaxis[0].formatter = NumeralTickFormatter(format="$0.00")
p.grid.grid_line_color = None
script, div = components(p)
return script, div
def get_figure_1():
p = figure(x_axis_type='datetime',
sizing_mode='scale_width',
plot_height=50)
p.add_tools(CrosshairTool())
p.xaxis.major_label_orientation = pi / 4
p.grid.grid_line_alpha = 0.6
return p
def add_custom_tools(self):
# N.B. Max hold comes from a different CDS so can't be shown in the same tooltip :(
hover = HoverTool(tooltips=[
("freq", "$x{0,0.00} MHz"),
("power", "@y0 zW / Hz"),
],
mode='vline',
renderers=self.lines)
crosshair = CrosshairTool()
self.plot.add_tools(hover, crosshair)
def plot_k(df):
i_src = ColumnDataSource(df[df.open < df.close])
d_src = ColumnDataSource(df[df.open >= df.close])
w = 16 * 60 * 60 * 1000 # half day in ms
TOOLS = "pan,xwheel_zoom,ywheel_zoom,box_zoom,reset,save"
p = figure(x_axis_type="datetime",
tools=TOOLS,
plot_width=1500,
plot_height=640,
title="MSFT Candlestick") # hei 880
p.toolbar.active_scroll = "auto"
p.xaxis.major_label_orientation = pi / 4
p.grid.grid_line_alpha = 0.3
p.background_fill_color = "black"
p.segment('date', 'high', 'date', 'low', source=i_src, color="red")
p.segment('date', 'high', 'date', 'low', source=d_src, color="green")
p.vbar('date',
w,
'open',
'close',
source=i_src,
name="kline",
fill_color="red",
line_color="red")
p.vbar('date',
w,
'open',
'close',
source=d_src,
name="kline",
fill_color="green",
line_color="green")
p.add_tools(
HoverTool(
tooltips=[("date", "@ToolTipDates"), ("close", "@close{0,0.00}"),
("high", "@high{0,0.00}"), ("low", "@low{0,0.00}")],
names=[
"kline",
],
))
p.add_tools(CrosshairTool(line_color='grey'))
inc_process(df, p)
output_file("candlestick.html",
title="candlestick.py example",
mode='inline')
gridplot()
show(p) # open a browser
def make_vaccine_effectiveness_plot():
"""
This makes the plot that introduces vaccine effectiveness.
"""
# Download and preprocess data.
starttime = datetime.now()
cdc_tables = pd.read_html(
"https://www.cdc.gov/flu/vaccines-work/past-seasons-estimates.html"
) # noqa
cdc_ve = cdc_tables[0]
cdc_ve.columns = cdc_ve.loc[0, :]
cdc_ve = cdc_ve.drop(0).reset_index(drop=True)
cdc_ve.columns = [
"season",
"reference",
"study_sites",
"num_patients",
"overall_ve",
"CI",
]
cdc_ve["season_start"] = (
cdc_ve["season"].str.split("-").str[0].apply(lambda x: str(x)))
# Configure Bokeh Plot
cdc_src = ColumnDataSource(cdc_ve)
hover_tool = HoverTool()
hover_tool.tooltips = [
("Year", "@season_start"),
("Effectiveness (%)", "@overall_ve"),
]
tools = [PanTool(), CrosshairTool(), hover_tool, ResetTool(), SaveTool()]
# Make Bokeh Plot
p = figure(
title="Yearly Vaccine Effectiveness",
plot_height=300,
plot_width=350,
tools=tools,
)
p.xaxis.axis_label = "Year"
p.yaxis.axis_label = "Vaccine Effectiveness (%)"
p.y_range = Range1d(0, 100)
p.line(x="season_start", y="overall_ve", source=cdc_src, line_width=2)
p.circle(
x="season_start",
y="overall_ve",
source=cdc_src,
radius=5,
radius_units="screen",
)
endtime = datetime.now()
elapsed = endtime - starttime
print(f"make_vaccine_effectiveness_plot() took {elapsed} seconds")
return components(p)
def get_ceil_plot(time_int=None, t_min=None, t_max=None):
if time_int == None:
time_int = cs.time_int
plot_height = cs.plot_height
plot_width = cs.plot_width
border_left = cs.border_left
if t_min == None or t_max == None:
t_max, t_min = get_first_time_interval()
t_span = t_max - t_min
data = sql_con.get_data_bs(time_int=time_int, t_min=t_min, t_max=t_max)
# print data
ll = len(data)
toolset = [CrosshairTool(), WheelZoomTool(), PreviewSaveTool(), PanTool()]
plot = figure(x_range=DataRange1d(start=(t_min - t_span * .1) * 1000,
end=(t_max + t_span * .1) * 1000),
y_range=[0, 250 * 15],
plot_height=plot_height,
plot_width=plot_width,
x_axis_type="datetime",
min_border_left=border_left,
toolbar_location="right",
tools=toolset)
plot.yaxis.axis_label = "meters above ground"
dd = []
tt = []
y = []
dw = []
dh = []
for i in range(ll):
dd.append(data.iloc[i:i + 1, 1:].T.values)
tt.append(int(data.iloc[i, 0]) * 1000 - time_int * 1000 / 2)
y.append(0)
dw.append(time_int * 1000)
dh.append(250 * 15)
plot.image(image=dd,
x=tt,
y=y,
dw=dw,
dh=dh,
palette="Spectral11",
dilate=True)
plot.title = cs.plot_title_ceil + ' averaged to {} seconds'.format(
time_int)
return plot
def get_weather_plot(time_int=None, t_min=None, t_max=None):
plot_height = cs.plot_height
plot_width = cs.plot_width
border_left = cs.border_left
column_names = cs.column_names_weewx
column_time = cs.time_column_name_weewx
if t_max == None or t_min == None:
t_max, t_min = get_first_time_interval()
t_span = t_max - t_min
if time_int == None:
time_int = cs.time_int
data_seconds = sql_con.get_data_ws(time_int=time_int,
t_min=t_min,
t_max=t_max)
data_seconds.iloc[:, 0] = data_seconds.iloc[:, 0] * 1000
plot = Plot(
x_range=DataRange1d(start=(t_min - t_span * .1) * 1000,
end=(t_max + t_span * .1) * 1000),
y_range=DataRange1d(),
plot_width=plot_width,
plot_height=plot_height,
# x_axis_type="datetime",
min_border_left=border_left,
toolbar_location="right")
add_glyphs_to_plot(column_names, column_time, data_seconds, plot,
'source_weather')
plot.add_layout(DatetimeAxis(name="date_time_axis"), 'below')
plot.add_tools(
PanTool(),
WheelZoomTool(),
# ResizeTool(),
CrosshairTool(),
PreviewSaveTool())
Grid(plot=plot,
dimension=0,
ticker=plot.select('date_time_axis')[0].ticker)
Grid(plot=plot,
dimension=1,
ticker=plot.select(type=LinearAxis, name=column_names[0])[0].ticker)
set_legends(plot)
plot.title = cs.plot_title_weewx + ' averaged to {} seconds'.format(
time_int)
return plot
def make_num_sequences_per_year_plot():
starttime = datetime.now()
# Download and Preprocess Data
sequences, metadata = load_sequence_and_metadata()
metadata["Year"] = metadata["Collection Date"].apply(lambda x: x.year)
metadata = metadata[metadata["Host Species"] == "IRD:Human"]
gb = metadata.groupby("Year").count().reset_index()
# Configure Bokeh Plot
seqperyear_src = ColumnDataSource(gb)
hover_tool = HoverTool()
hover_tool.tooltips = [("Year", "@Year"), ("Num. Sequences", "@Name")]
tools = [PanTool(), CrosshairTool(), hover_tool, ResetTool(), SaveTool()]
# Make figure
p = figure(
plot_height=300,
plot_width=350,
tools=tools,
title="Num. Sequences Per Year",
)
p.line(x="Year", y="Name", source=seqperyear_src, line_width=2)
p.circle(
x="Year",
y="Name",
source=seqperyear_src,
radius=5,
radius_units="screen",
)
p.xaxis.axis_label = "Year"
p.yaxis.axis_label = "Number of Sequences"
# Collate metadata dictionary.
meta = dict()
meta["n_seqs"] = len(metadata)
meta["min_year"] = min(metadata["Year"])
meta["max_year"] = max(metadata["Year"])
endtime = datetime.now()
elapsed = endtime - starttime
print(f"make_num_sequences_per_year_plot() took {elapsed} seconds.")
return components(p), meta
def make_vaccine_effectiveness_plot():
"""
This makes the plot that introduces vaccine effectiveness.
"""
# Download and preprocess data.
starttime = datetime.now()
cdc_tables = pd.read_html(
'https://www.cdc.gov/flu/professionals/vaccination/effectiveness-studies.htm'
) # noqa
cdc_ve = cdc_tables[0]
cdc_ve.columns = cdc_ve.loc[0, :]
cdc_ve = cdc_ve.drop(0).reset_index(drop=True)
cdc_ve.columns = [
'season', 'reference', 'study_sites', 'num_patients', 'overall_ve',
'CI'
]
cdc_ve['season_start'] = cdc_ve['season'].str.split('-').str[0]\
.apply(lambda x: str(x))
# Configure Bokeh Plot
cdc_src = ColumnDataSource(cdc_ve)
hover_tool = HoverTool()
hover_tool.tooltips = [("Year", "@season_start"),
("Effectiveness (%)", "@overall_ve")]
tools = [PanTool(), CrosshairTool(), hover_tool, ResetTool(), SaveTool()]
# Make Bokeh Plot
p = figure(title='Yearly Vaccine Effectiveness',
plot_height=300,
plot_width=350,
tools=tools)
p.xaxis.axis_label = 'Year'
p.yaxis.axis_label = 'Vaccine Effectiveness (%)'
p.y_range = Range1d(0, 100)
p.line(x='season_start', y='overall_ve', source=cdc_src, line_width=2)
p.circle(x='season_start',
y='overall_ve',
source=cdc_src,
radius=5,
radius_units='screen')
endtime = datetime.now()
elapsed = endtime - starttime
print(f'make_vaccine_effectiveness_plot() took {elapsed} seconds')
return components(p)
def build_live_figure(self, cds):
live_figure = figure(width_policy='max',
height_policy='max',
tools='pan,wheel_zoom',
active_scroll='wheel_zoom',
title='Live',
x_axis_type='datetime')
live_figure.line(x='index',
y=INTEREST_COLUMNS[0],
source=cds,
line_color='green',
line_width=2)
hover_tool = HoverTool(tooltips=TOOLTIPS,
formatters=FORMATTERS,
mode='vline')
cross_hair_tool = CrosshairTool(dimensions='height')
live_figure.add_tools(hover_tool, cross_hair_tool)
return live_figure
def _init_figure(self):
# plot height will be set later
f = figure(tools=Figure._tools,
plot_width=self._scheme.plot_width,
logo=None,
sizing_mode='scale_width',
x_axis_type='datetime',
output_backend="webgl")
f.border_fill_color = convert_color(self._scheme.border_fill)
f.xaxis.axis_line_color = convert_color(self._scheme.axis_line_color)
f.yaxis.axis_line_color = convert_color(self._scheme.axis_line_color)
f.xaxis.minor_tick_line_color = convert_color(
self._scheme.tick_line_color)
f.yaxis.minor_tick_line_color = convert_color(
self._scheme.tick_line_color)
f.xaxis.major_tick_line_color = convert_color(
self._scheme.tick_line_color)
f.yaxis.major_tick_line_color = convert_color(
self._scheme.tick_line_color)
f.xaxis.major_label_text_color = convert_color(
self._scheme.axis_label_text_color)
f.yaxis.major_label_text_color = convert_color(
self._scheme.axis_label_text_color)
f.xgrid.grid_line_color = convert_color(self._scheme.grid_line_color)
f.ygrid.grid_line_color = convert_color(self._scheme.grid_line_color)
f.title.text_color = convert_color(self._scheme.plot_title_text_color)
f.left[0].formatter.use_scientific = False
f.background_fill_color = convert_color(self._scheme.background_fill)
ch = CrosshairTool(line_color=self._scheme.crosshair_line_color)
f.tools.append(ch)
h = HoverTool(tooltips=[('Time', '@datetime{%x %X}')],
mode="vline",
formatters={'datetime': 'datetime'})
f.tools.append(h)
self._hover = h
self.figure = f
