def daily_plot(self, days):
"""Bar plot displaying the number of block visits per day.
The number of block visits is shown for the `days` days leading to but excluding `self.date`. A day here refers
to the time from noon to noon. For example, 22 May 2016 refers to the period from 22 May 2016, 12:00 to 23 May
2016, 12:00.
Params:
-------
days : int
Number of days.
Returns:
--------
app.plot.plot.TimeBarPlot
Plot of number of block visits as a function of the day.
"""
start_date, end_date = day_range(self.date, days)
trend_func = functools.partial(day_running_average, ignore_missing_values=False)
df = self.df.copy()
df['EngineeringTimePercentage'] = 100 * np.divide(df.EngineeringTime, df.NightLength)
df['EngineeringTime'] /= 60
return daily_bar_plot(df=df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column=['EngineeringTime', 'EngineeringTimePercentage'],
y_range=[Range1d(start=0, end=200), Range1d(start=0, end=30)],
y_formatters=[PrintfTickFormatter(format='%.0fm'), PrintfTickFormatter(format='%.0f%%')],
trend_func=trend_func,
**self.kwargs)
def daily_plot(self, days):
"""Bar plot displaying the operation efficiency per day.
The operation efficiency is shown for the `days` days leading to but excluding `self.date`. A day here refers
to the time from noon to noon. For example, 22 May 2016 refers to the period from 22 May 2016, 12:00 to 23 May
2016, 12:00.
Params:
-------
days : int
Number of days.
Returns:
--------
app.plot.plot.TimeBarPlot
Plot of operation efficiency as a function of the day.
"""
df = self.df.copy()
df['OperationEfficiency'] = 100 * np.divide(df.ObsTime, df.ScienceTime)
start_date, end_date = day_range(self.date, days)
trend_func = functools.partial(day_running_average,
ignore_missing_values=True)
return daily_bar_plot(
df=df,
start_date=start_date,
end_date=end_date,
date_column='Date',
y_column='OperationEfficiency',
y_range=Range1d(start=0, end=140),
trend_func=trend_func,
y_formatters=[PrintfTickFormatter(format='%d%%')],
**self.kwargs)
def plot_rsi(stock):
p = figure(x_axis_type="datetime",
plot_width=WIDTH_PLOT,
plot_height=200,
title="RSI 15 days",
tools=TOOLS,
toolbar_location='above')
p.line(x='date', y='rsi_15', line_width=2, color=BLUE, source=stock)
low_box = BoxAnnotation(top=30, fill_alpha=0.1, fill_color=RED)
p.add_layout(low_box)
high_box = BoxAnnotation(bottom=70, fill_alpha=0.1, fill_color=GREEN)
p.add_layout(high_box)
# Horizontal line
hline = Span(location=50,
dimension='width',
line_color='black',
line_width=0.5)
p.renderers.extend([hline])
p.y_range = Range1d(0, 100)
p.yaxis.ticker = [30, 50, 70]
p.yaxis.formatter = PrintfTickFormatter(format="%f%%")
p.grid.grid_line_alpha = 0.3
return p
def ridgeplot(courses_obj):
# first format 'courses_obj' into 'probly' DataFrame format
# courses_obj: [{'course_name': 'Calculus...', ...}, ...]
grades = [[100*y[2]/y[3] for y in x['assignments']] for x in courses_obj]
# turn this list of lists into a complete NumPy array
length = len(sorted(grades, key=len, reverse=True)[0])
grades = np.array([xi+[None]*(length-len(xi)) for xi in grades], dtype='float')
columns = [x['course_name'] for x in courses_obj]
grades = grades.transpose()
probly = pd.DataFrame(grades, columns=columns)
cats = list(reversed(probly.keys()))
palette = [cc.rainbow[i*15] for i in range(17)]
x = np.linspace(-20,110, 500)
source = ColumnDataSource(data=dict(x=x))
p = figure(y_range=cats, plot_width=900, plot_height = 300, x_range=(-5, 120))#, toolbar_location=None)
for i, cat in enumerate(reversed(cats)):
adjusted = probly[cat].replace([np.inf, -np.inf], np.nan).dropna(how="all")
if adjusted.size == 1 or pd.unique(adjusted).size == 1: # this means we can't compute
continue
pdf = gaussian_kde(adjusted)
#p = figure(plot_width=400, plot_height=400)
#p.line(x, pdf(x))
y = ridge(cat, pdf(x), scale=2)
#p.line(x, y, color='black')
#show(p)
source.add(y, cat)
p.patch('x', cat, color=palette[i], alpha=0.6, line_color="black", source=source)
p.outline_line_color = None
p.background_fill_color = "#efefef"
p.tools = [PanTool(), CrosshairTool(), HoverTool(), SaveTool(), BoxZoomTool(), WheelZoomTool(), ResetTool()]
ticks = list(np.array([np.array([0,3,7])+i*10 for i in range(10)]).flatten()) + [100]
p.xaxis.ticker = FixedTicker(ticks=ticks)
p.xaxis.formatter = PrintfTickFormatter(format="%d")
p.xaxis.axis_label = "Your Grade Distribution"
p.yaxis.axis_label = "Your Courses"
p.ygrid.grid_line_color = None
p.xgrid.grid_line_color = "Grey"
p.xgrid.ticker = p.xaxis[0].ticker
p.axis.minor_tick_line_color = None
p.axis.major_tick_line_color = None
p.axis.axis_line_color = None
p.y_range.range_padding = 0.12
return p
def monthly_plot(self, months):
"""Bar plot displaying the shutter open efficiency per momth.
The operation efficiency is shown for the `months` months leading to but excluding the month containing
`self.date`. A month here refers start at noon of the first of the month. For example, May 2016 refers to the
period from 1 May 2016, 12:00 to 1 June 2016, 12:00.
Params:
-------
months : int
Number of months.
Returns:
--------
app.plot.plot.TimeBarPlot
Plot of shutter open efficiency as a function of the month.
"""
start_date, end_date = month_range(self.date, months)
trend_func = functools.partial(month_running_average,
ignore_missing_values=False)
def post_binning_func(df):
df['TimeLostToProblemsPercentage'] = 100 * np.divide(
df.TimeLostToProblems, df.NightLength)
df.TimeLostToProblems /= 3600
return monthly_bar_plot(
df=self.df,
start_date=start_date,
end_date=end_date,
date_column='Date',
month_column='Month',
y_column=['TimeLostToProblems', 'TimeLostToProblemsPercentage'],
y_range=[Range1d(start=0, end=60),
Range1d(start=0, end=20)],
y_formatters=[
PrintfTickFormatter(format='%.0fh'),
PrintfTickFormatter(format='%.0f%%')
],
trend_func=trend_func,
post_binning_func=post_binning_func,
**self.kwargs)
def wrap_formatter(formatter, axis):
"""
Wraps formatting function or string in
appropriate bokeh formatter type.
"""
if isinstance(formatter, TickFormatter):
pass
elif isinstance(formatter, FunctionType):
msg = ('%sformatter could not be '
'converted to tick formatter. ' % axis)
jsfunc = py2js_tickformatter(formatter, msg)
if jsfunc:
formatter = FuncTickFormatter(code=jsfunc)
else:
formatter = None
else:
formatter = PrintfTickFormatter(format=formatter)
return formatter
def make_tpf_figure_elements(tpf,
tpf_source,
pedestal=None,
fiducial_frame=None,
plot_width=370,
plot_height=340,
scale='log',
vmin=None,
vmax=None,
cmap='Viridis256',
tools='tap,box_select,wheel_zoom,reset'):
"""Returns the lightcurve figure elements.
Parameters
----------
tpf : TargetPixelFile
TPF to show.
tpf_source : bokeh.plotting.ColumnDataSource
TPF data source.
pedestal: float
A scalar value to be added to the TPF flux values, often to avoid
taking the log of a negative number in colorbars.
Defaults to `-min(tpf.flux) + 1`
fiducial_frame: int
The tpf slice to start with by default, it is assumed the WCS
is exact for this frame.
scale: str
Color scale for tpf figure. Default is 'log'
vmin: int [optional]
Minimum color scale for tpf figure
vmax: int [optional]
Maximum color scale for tpf figure
cmap: str
Colormap to use for tpf plot. Default is 'Viridis256'
tools: str
Bokeh tool list
Returns
-------
fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider
"""
if pedestal is None:
pedestal = -np.nanmin(tpf.flux.value) + 1
if scale == 'linear':
pedestal = 0
if tpf.mission in ['Kepler', 'K2']:
title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)
elif tpf.mission == 'TESS':
title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)
else:
title = "Pixel data"
# We subtract 0.5 from the range below because pixel coordinates refer to
# the middle of a pixel, e.g. (col, row) = (10.0, 20.0) is a pixel center.
fig = figure(plot_width=plot_width,
plot_height=plot_height,
x_range=(tpf.column - 0.5, tpf.column + tpf.shape[2] - 0.5),
y_range=(tpf.row - 0.5, tpf.row + tpf.shape[1] - 0.5),
title=title,
tools=tools,
toolbar_location="below",
border_fill_color="whitesmoke")
fig.yaxis.axis_label = 'Pixel Row Number'
fig.xaxis.axis_label = 'Pixel Column Number'
vlo, lo, hi, vhi = np.nanpercentile(tpf.flux.value + pedestal,
[0.2, 1, 95, 99.8])
if vmin is not None:
vlo, lo = vmin, vmin
if vmax is not None:
vhi, hi = vmax, vmax
if scale == 'log':
vstep = (np.log10(vhi) -
np.log10(vlo)) / 300.0 # assumes counts >> 1.0!
if scale == 'linear':
vstep = (vhi - vlo) / 300.0 # assumes counts >> 1.0!
if scale == 'log':
color_mapper = LogColorMapper(palette=cmap, low=lo, high=hi)
elif scale == 'linear':
color_mapper = LinearColorMapper(palette=cmap, low=lo, high=hi)
else:
raise ValueError(
'Please specify either `linear` or `log` scale for color.')
fig.image([tpf.flux.value[fiducial_frame, :, :] + pedestal],
x=tpf.column - 0.5,
y=tpf.row - 0.5,
dw=tpf.shape[2],
dh=tpf.shape[1],
dilate=True,
color_mapper=color_mapper,
name="tpfimg")
# The colorbar will update with the screen stretch slider
# The colorbar margin increases as the length of the tick labels grows.
# This colorbar share of the plot window grows, shrinking plot area.
# This effect is known, some workarounds might work to fix the plot area:
# https://github.com/bokeh/bokeh/issues/5186
if scale == 'log':
ticker = LogTicker(desired_num_ticks=8)
elif scale == 'linear':
ticker = BasicTicker(desired_num_ticks=8)
color_bar = ColorBar(color_mapper=color_mapper,
ticker=ticker,
label_standoff=-10,
border_line_color=None,
location=(0, 0),
background_fill_color='whitesmoke',
major_label_text_align='left',
major_label_text_baseline='middle',
title='e/s',
margin=0)
fig.add_layout(color_bar, 'right')
color_bar.formatter = PrintfTickFormatter(format="%14i")
if tpf_source is not None:
fig.rect('xx',
'yy',
1,
1,
source=tpf_source,
fill_color='gray',
fill_alpha=0.4,
line_color='white')
# Configure the stretch slider and its callback function
if scale == 'log':
start, end = np.log10(vlo), np.log10(vhi)
values = (np.log10(lo), np.log10(hi))
elif scale == 'linear':
start, end = vlo, vhi
values = (lo, hi)
stretch_slider = RangeSlider(start=start,
end=end,
step=vstep,
title='Screen Stretch ({})'.format(scale),
value=values,
orientation='horizontal',
width=200,
direction='ltr',
show_value=True,
sizing_mode='fixed',
height=15,
name='tpfstretch')
def stretch_change_callback_log(attr, old, new):
"""TPF stretch slider callback."""
fig.select('tpfimg')[0].glyph.color_mapper.high = 10**new[1]
fig.select('tpfimg')[0].glyph.color_mapper.low = 10**new[0]
def stretch_change_callback_linear(attr, old, new):
"""TPF stretch slider callback."""
fig.select('tpfimg')[0].glyph.color_mapper.high = new[1]
fig.select('tpfimg')[0].glyph.color_mapper.low = new[0]
if scale == 'log':
stretch_slider.on_change('value', stretch_change_callback_log)
if scale == 'linear':
stretch_slider.on_change('value', stretch_change_callback_linear)
return fig, stretch_slider
def make_tpf_figure_elements(tpf,
tpf_source,
pedestal=None,
fiducial_frame=None,
plot_width=370,
plot_height=340):
"""Returns the lightcurve figure elements.
Parameters
----------
tpf : TargetPixelFile
TPF to show.
tpf_source : bokeh.plotting.ColumnDataSource
TPF data source.
pedestal: float
A scalar value to be added to the TPF flux values, often to avoid
taking the log of a negative number in colorbars.
Defaults to `-min(tpf.flux) + 1`
fiducial_frame: int
The tpf slice to start with by default, it is assumed the WCS
is exact for this frame.
Returns
-------
fig, stretch_slider : bokeh.plotting.figure.Figure, RangeSlider
"""
if pedestal is None:
pedestal = -np.nanmin(tpf.flux) + 1
if tpf.mission in ['Kepler', 'K2']:
title = 'Pixel data (CCD {}.{})'.format(tpf.module, tpf.output)
elif tpf.mission == 'TESS':
title = 'Pixel data (Camera {}.{})'.format(tpf.camera, tpf.ccd)
else:
title = "Pixel data"
fig = figure(plot_width=plot_width,
plot_height=plot_height,
x_range=(tpf.column, tpf.column + tpf.shape[2]),
y_range=(tpf.row, tpf.row + tpf.shape[1]),
title=title,
tools='tap,box_select,wheel_zoom,reset',
toolbar_location="below",
border_fill_color="whitesmoke")
fig.yaxis.axis_label = 'Pixel Row Number'
fig.xaxis.axis_label = 'Pixel Column Number'
vlo, lo, hi, vhi = np.nanpercentile(tpf.flux + pedestal,
[0.2, 1, 95, 99.8])
vstep = (np.log10(vhi) - np.log10(vlo)) / 300.0 # assumes counts >> 1.0!
color_mapper = LogColorMapper(palette="Viridis256", low=lo, high=hi)
fig.image([tpf.flux[fiducial_frame, :, :] + pedestal],
x=tpf.column,
y=tpf.row,
dw=tpf.shape[2],
dh=tpf.shape[1],
dilate=True,
color_mapper=color_mapper,
name="tpfimg")
# The colorbar will update with the screen stretch slider
# The colorbar margin increases as the length of the tick labels grows.
# This colorbar share of the plot window grows, shrinking plot area.
# This effect is known, some workarounds might work to fix the plot area:
# https://github.com/bokeh/bokeh/issues/5186
color_bar = ColorBar(color_mapper=color_mapper,
ticker=LogTicker(desired_num_ticks=8),
label_standoff=-10,
border_line_color=None,
location=(0, 0),
background_fill_color='whitesmoke',
major_label_text_align='left',
major_label_text_baseline='middle',
title='e/s',
margin=0)
fig.add_layout(color_bar, 'right')
color_bar.formatter = PrintfTickFormatter(format="%14u")
if tpf_source is not None:
fig.rect('xx',
'yy',
1,
1,
source=tpf_source,
fill_color='gray',
fill_alpha=0.4,
line_color='white')
# Configure the stretch slider and its callback function
stretch_slider = RangeSlider(start=np.log10(vlo),
end=np.log10(vhi),
step=vstep,
title='Screen Stretch (log)',
value=(np.log10(lo), np.log10(hi)),
orientation='horizontal',
width=200,
height=10,
direction='ltr',
show_value=True,
sizing_mode='fixed',
name='tpfstretch')
def stretch_change_callback(attr, old, new):
"""TPF stretch slider callback."""
fig.select('tpfimg')[0].glyph.color_mapper.high = 10**new[1]
fig.select('tpfimg')[0].glyph.color_mapper.low = 10**new[0]
stretch_slider.on_change('value', stretch_change_callback)
return fig, stretch_slider
def get_data():
cursor = collection.find()
data = pd.DataFrame(list(cursor))
data = data.set_index('_id')
data = data.sort_index(ascending=True)
timestamps = pd.to_datetime(data.index, unit='s').to_series()
returns = data.returns.cumsum()*100
return timestamps, returns
timestamps, returns = get_data()
output_server('bitpredict_performance')
background = '#f2f2f2'
ylabel_standoff = 0
xformatter = DatetimeTickFormatter(formats=dict(hours=["%H:%M"]))
yformatter = PrintfTickFormatter(format="%8.1f")
p = figure(title=None,
plot_width=750,
plot_height=500,
x_axis_type='datetime',
min_border_top=5,
min_border_bottom=10,
background_fill=background,
x_axis_label='Date',
tools='',
toolbar_location=None)
p.line(x=timestamps,
y=returns,
name='returns',
color='#8959a8',
#high_box = BoxAnnotation(bottom=70, fill_alpha=0.1, fill_color=GREEN)
#p3.add_layout(high_box)
box = BoxAnnotation(bottom=30, top=70, fill_alpha=0.25, fill_color=PURPLE)
p3.add_layout(box)
hline = Span(location=30,
dimension='width',
line_color='black',
line_dash=[5, 10],
line_width=0.5)
p3.renderers.extend([hline])
hline = Span(location=50,
dimension='width',
line_color='black',
line_width=0.5)
p3.renderers.extend([hline])
hline = Span(location=70,
dimension='width',
line_color='black',
line_dash=[5, 10],
line_width=0.5)
p3.renderers.extend([hline])
#p3.y_range = Range1d(0, 100)
p3.yaxis.ticker = [30, 50, 70]
p3.yaxis.formatter = PrintfTickFormatter(format="%f%%")
p3.grid.grid_line_alpha = 0.3
output_file("Crypto_ta.html", title="Crypto TA")
show(column(p, p2, p3))
def run_chart(exchange, asset, limit):
client = pymongo.MongoClient()
db = client['bitpredict_' + exchange]
collection = db[asset_ + '_predictions']
def get_data():
cursor = collection.find().limit(limit).sort('_id', pymongo.DESCENDING)
data = pd.DataFrame(list(cursor))
data = data.set_index('_id')
data = data.sort_index(ascending=True)
timestamps = pd.to_datetime(data.index, unit='s').to_series()
prices = data.price
predictions = data.prediction * 10000
returns = (data.position * data.change).cumsum() * 10000
return timestamps, prices, predictions, returns
timestamps, prices, predictions, returns = get_data()
output_server('bitpredict_' + exchange + '_extended')
background = '#f2f2f2'
ylabel_standoff = 0
xformatter = DatetimeTickFormatter(formats=dict(hours=["%H:%M"]))
yformatter = PrintfTickFormatter(format="%8.1f")
p1 = figure(title=None,
plot_width=750,
plot_height=300,
x_axis_type='datetime',
min_border_top=10,
min_border_bottom=33,
background_fill=background,
tools='',
toolbar_location=None)
p1.line(x=timestamps,
y=prices,
name='prices',
color='#4271ae',
line_width=1,
legend='Bitcoin Bid/Ask Midpoint',
line_cap='round',
line_join='round')
p1.legend.orientation = 'top_left'
p1.legend.border_line_color = background
p1.outline_line_color = None
p1.xgrid.grid_line_color = 'white'
p1.ygrid.grid_line_color = 'white'
p1.axis.axis_line_color = None
p1.axis.major_tick_line_color = None
p1.axis.minor_tick_line_color = None
p1.yaxis.axis_label = 'Price'
p1.yaxis.axis_label_standoff = ylabel_standoff
p1.xaxis.formatter = xformatter
p1.yaxis.formatter = PrintfTickFormatter(format='%8.2f')
p1.yaxis.major_label_text_font = 'courier'
p1.xaxis.major_label_text_font = 'courier'
p2 = figure(title=None,
plot_width=750,
plot_height=295,
x_axis_type='datetime',
min_border_top=5,
min_border_bottom=33,
background_fill=background,
tools='',
toolbar_location=None)
p2.line(x=timestamps,
y=predictions,
name='predictions',
color='#c82829',
line_width=1,
legend='30 Second Prediction',
line_cap='round',
line_join='round')
p2.legend.orientation = 'top_left'
p2.legend.border_line_color = background
p2.outline_line_color = None
p2.xgrid.grid_line_color = 'white'
p2.ygrid.grid_line_color = 'white'
p2.axis.axis_line_color = None
p2.axis.major_tick_line_color = None
p2.axis.minor_tick_line_color = None
p2.yaxis.axis_label = 'Basis Points'
p2.yaxis.axis_label_standoff = ylabel_standoff
p2.xaxis.formatter = xformatter
p2.yaxis.formatter = yformatter
p2.yaxis.major_label_text_font = 'courier'
p2.xaxis.major_label_text_font = 'courier'
p2.x_range = p1.x_range
p3 = figure(title=None,
plot_width=750,
plot_height=320,
x_axis_type='datetime',
min_border_top=5,
min_border_bottom=10,
background_fill=background,
x_axis_label='Greenwich Mean Time',
tools='',
toolbar_location=None)
p3.line(x=timestamps,
y=returns,
name='returns',
color='#8959a8',
line_width=1,
legend='Cumulative Return',
line_cap='round',
line_join='round')
p3.legend.orientation = 'top_left'
p3.legend.border_line_color = background
p3.outline_line_color = None
p3.xgrid.grid_line_color = 'white'
p3.ygrid.grid_line_color = 'white'
p3.axis.axis_line_color = None
p3.axis.major_tick_line_color = None
p3.axis.minor_tick_line_color = None
p3.yaxis.axis_label = 'Basis Points'
p3.yaxis.axis_label_standoff = ylabel_standoff
p3.xaxis.formatter = xformatter
p3.yaxis.formatter = yformatter
p3.xaxis.axis_label_standoff = 12
p3.yaxis.major_label_text_font = 'courier'
p3.xaxis.major_label_text_font = 'courier'
p3.x_range = p1.x_range
vp = vplot(p1, p2, p3)
push()
ip = load(urlopen('http://jsonip.com'))['ip']
ssn = cursession()
ssn.publish()
embed.autoload_server(vp, ssn, public=True)
renderer = p1.select(dict(name='prices'))
ds_prices = renderer[0].data_source
renderer = p2.select(dict(name='predictions'))
ds_predictions = renderer[0].data_source
renderer = p3.select(dict(name='returns'))
ds_returns = renderer[0].data_source
while True:
timestamps, prices, predictions, returns = get_data(exchange, limit)
ds_prices.data['x'] = timestamps
ds_predictions.data['x'] = timestamps
ds_returns.data['x'] = timestamps
ds_prices.data['y'] = prices
ds_predictions.data['y'] = predictions
ds_returns.data['y'] = returns
ssn.store_objects(ds_prices)
ssn.store_objects(ds_predictions)
ssn.store_objects(ds_returns)
time.sleep(60)
data = data.set_index('_id')
data = data.sort_index(ascending=True)
timestamps = pd.to_datetime(data.index, unit='s').to_series()
prices = data.price
predictions = data.prediction * 10000
returns = (data.position * data.change).cumsum() * 10000
return timestamps, prices, predictions, returns
timestamps, prices, predictions, returns = get_data()
output_server('bitpredict')
background = '#f2f2f2'
ylabel_standoff = 0
xformatter = DatetimeTickFormatter(formats=dict(minutes=["%H:%M"]))
yformatter = PrintfTickFormatter(format="%8.1f")
p1 = figure(title=None,
plot_width=750,
plot_height=300,
x_axis_type='datetime',
min_border_top=10,
min_border_bottom=33,
background_fill=background,
tools='',
toolbar_location=None)
p1.line(x=timestamps,
y=prices,
name='prices',
color='#4271ae',
line_width=1,
legend='Bitcoin Bid/Ask Midpoint',
def __make_daybyday_interactive_timeline(
df: pd.DataFrame,
*,
geo_df: geopandas.GeoDataFrame,
value_col: str,
transform_df_func: Callable[[pd.DataFrame], pd.DataFrame] = None,
stage: Union[DiseaseStage, Literal[Select.ALL]] = Select.ALL,
count: Union[Counting, Literal[Select.ALL]] = Select.ALL,
out_file_basename: str,
subplot_title_prefix: str,
plot_aspect_ratio: float = None,
cmap=None,
n_cbar_buckets: int = None,
n_buckets_btwn_major_ticks: int = None,
n_minor_ticks_btwn_major_ticks: int = None,
per_capita_denominator: int = None,
x_range: Tuple[float, float],
y_range: Tuple[float, float],
min_visible_y_range: float,
should_make_video: bool,
) -> InfoForAutoload:
"""Create the bokeh interactive timeline plot(s)
This function takes the given DataFrame, which must contain COVID data for locations
on different dates, and a GeoDataFrame, which contains the long/lat coords for those
locations, and creates an interactive choropleth of the COVID data over time.
:param df: The COVID data DataFrame
:type df: pd.DataFrame
:param geo_df: The geometry GeoDataFrame for the locations in `df`
:type geo_df: geopandas.GeoDataFrame
:param value_col: The column of `df` containing the values to plot in the
choropleth; should be something like "Case_Counts" or "Case_Diff_From_Prev_Day"
:type value_col: str
:param stage: The DiseaseStage to plot, defaults to Select.ALL. If ALL, then all
stages are plotted and are stacked vertically.
:type stage: Union[DiseaseStage, Literal[Select.ALL]], optional
:param count: The Counting to plot, defaults to Select.ALL. If ALL, then all
count types are plotted and are stacked horizontally.
:type count: Union[Counting, Literal[Select.ALL]], optional
:param out_file_basename: The basename of the file to save the interactive plots to
(there are two components, the JS script and the HTML <div>)
:type out_file_basename: str
:param subplot_title_prefix: What the first part of the subplot title should be;
probably a function of `value_col` (if value_col is "Case_Counts" then this param
might be "Cases" or "# of Cases")
:type subplot_title_prefix: str
:param x_range: The range of the x-axis as (min, max)
:type x_range: Tuple[float, float]
:param y_range: The range of the y-axis as (min, max)
:type y_range: Tuple[float, float]
:param min_visible_y_range: The minimum height (in axis units) of the y-axis; it
will not be possible to zoom in farther than this on the choropleth.
:type min_visible_y_range: float
:param should_make_video: Optionally run through the timeline day by day, capture
a screenshot for each day, and then stitch the screenshots into a video. The video
shows the same info as the interactive plots, but not interactively. This easily
takes 20x as long as just making the graphs themselves, so use with caution.
:type should_make_video: bool
:param transform_df_func: This function expects data in a certain format, and does
a bunch of preprocessing (expected to be common) before plotting. This gives you a
chance to do any customization on the postprocessed df before it's plotted. Defaults
to None, in which case no additional transformation is performed.
:type transform_df_func: Callable[[pd.DataFrame], pd.DataFrame], optional
:param plot_aspect_ratio: The aspect ratio of the plot as width/height; if set, the
aspect ratio will be fixed to this. Defaults to None, in which case the aspect ratio
is determined from the x_range and y_range arguments
:type plot_aspect_ratio: float, optional
:param cmap: The colormap to use as either a matplotlib-compatible colormap or a
list of hex strings (e.g., ["#ae8f1c", ...]). Defaults to None in which case a
reasonable default is used.
:type cmap: Matplotlib-compatible colormap or List[str], optional
:param n_cbar_buckets: How many colorbar buckets to use. Has little effect if the
colormap is continuous, but always works in conjunction with
n_buckets_btwn_major_ticks to determine the number of major ticks. Defaults to 6.
:type n_cbar_buckets: int, optional
:param n_buckets_btwn_major_ticks: How many buckets are to lie between colorbar
major ticks, determining how many major ticks are drawn. Defaults to 1.
:type n_buckets_btwn_major_ticks: int, optional
:param n_minor_ticks_btwn_major_ticks: How many minor ticks to draw between colorbar
major ticks. Defaults to 8 (which means each pair of major ticks has 10 ticks
total).
:type n_minor_ticks_btwn_major_ticks: int, optional
:param per_capita_denominator: When describing per-capita numbers, what to use as
the denominator (e.g., cases per 100,000 people). If None, it is automatically
computed per plot to be appropriately scaled for the data.
:type per_capita_denominator: int, optional
:raises ValueError: [description]
:return: The two pieces of info required to make a Bokeh autoloading HTML+JS plot:
the HTML div to be inserted somewhere in the HTML body, and the JS file that will
load the plot into that div.
:rtype: InfoForAutoload
"""
Counting.verify(count, allow_select=True)
DiseaseStage.verify(stage, allow_select=True)
# The date as a string, so that bokeh can use it as a column name
STRING_DATE_COL = "String_Date_"
# A column whose sole purpose is to be a (the same) date associated with each
# location
FAKE_DATE_COL = "Fake_Date_"
# The column we'll actually use for the colors; it's computed from value_col
COLOR_COL = "Color_"
# Under no circumstances may you change this date format
# It's not just a pretty date representation; it actually has to match up with the
# date strings computed in JS
DATE_FMT = r"%Y-%m-%d"
ID_COLS = [
REGION_NAME_COL,
Columns.DATE,
Columns.STAGE,
Columns.COUNT_TYPE,
]
if cmap is None:
cmap = cmocean.cm.matter
if n_cbar_buckets is None:
n_cbar_buckets = 6
if n_buckets_btwn_major_ticks is None:
n_buckets_btwn_major_ticks = 1
if n_minor_ticks_btwn_major_ticks is None:
n_minor_ticks_btwn_major_ticks = 8
n_cbar_major_ticks = n_cbar_buckets // n_buckets_btwn_major_ticks + 1
try:
color_list = [
# Convert matplotlib colormap to bokeh (list of hex strings)
# https://stackoverflow.com/a/49934218
RGB(*rgb).to_hex()
for i, rgb in enumerate((255 * cmap(range(256))).astype("int"))
]
except TypeError:
color_list = cmap
color_list: List[BokehColor]
if stage is Select.ALL:
stage_list = list(DiseaseStage)
else:
stage_list = [stage]
if count is Select.ALL:
count_list = list(Counting)
else:
count_list = [count]
stage_list: List[DiseaseStage]
count_list: List[Counting]
stage_count_list: List[Tuple[DiseaseStage, Counting]] = list(
itertools.product(stage_list, count_list))
df = df.copy()
# Unadjust dates (see SaveFormats._adjust_dates)
normalized_dates = df[Columns.DATE].dt.normalize()
is_at_midnight = df[Columns.DATE] == normalized_dates
df.loc[is_at_midnight, Columns.DATE] -= pd.Timedelta(days=1)
df.loc[~is_at_midnight, Columns.DATE] = normalized_dates[~is_at_midnight]
min_date, max_date = df[Columns.DATE].agg(["min", "max"])
dates: List[pd.Timestamp] = pd.date_range(start=min_date,
end=max_date,
freq="D")
max_date_str = max_date.strftime(DATE_FMT)
# Get day-by-day case diffs per location, date, stage, count-type
# Make sure data exists for every date for every state so that the entire country is
# plotted each day; fill missing data with 0 (missing really *is* as good as 0)
# enums will be replaced by their name (kind of important)
id_cols_product: pd.MultiIndex = pd.MultiIndex.from_product(
[
df[REGION_NAME_COL].unique(),
dates,
[s.name for s in DiseaseStage],
[c.name for c in Counting],
],
names=ID_COLS,
)
df = (id_cols_product.to_frame(index=False).merge(
df,
how="left",
on=ID_COLS,
).sort_values(ID_COLS))
df[STRING_DATE_COL] = df[Columns.DATE].dt.strftime(DATE_FMT)
df[Columns.CASE_COUNT] = df[Columns.CASE_COUNT].fillna(0)
if transform_df_func is not None:
df = transform_df_func(df)
df = geo_df.merge(df, how="inner", on=REGION_NAME_COL)[[
REGION_NAME_COL,
Columns.DATE,
STRING_DATE_COL,
Columns.STAGE,
Columns.COUNT_TYPE,
value_col,
]]
dates: List[pd.Timestamp] = [
pd.Timestamp(d) for d in df[Columns.DATE].unique()
]
values_mins_maxs = (df[df[value_col] > 0].groupby(
[Columns.STAGE, Columns.COUNT_TYPE])[value_col].agg(["min", "max"]))
vmins: pd.Series = values_mins_maxs["min"]
vmaxs: pd.Series = values_mins_maxs["max"]
pow10s_series: pd.Series = vmaxs.map(
lambda x: int(10**(-np.floor(np.log10(x)))))
# _pow_10s_series_dict = {}
# for stage in DiseaseStage:
# _pow_10s_series_dict.update(
# {
# (stage.name, Counting.TOTAL_CASES.name): 100000,
# (stage.name, Counting.PER_CAPITA.name): 10000,
# }
# )
# pow10s_series = pd.Series(_pow_10s_series_dict)
vmins: dict = vmins.to_dict()
vmaxs: dict = vmaxs.to_dict()
for stage in DiseaseStage:
_value_key = (stage.name, Counting.PER_CAPITA.name)
if per_capita_denominator is None:
_max_pow10 = pow10s_series.loc[(slice(None),
Counting.PER_CAPITA.name)].max()
else:
_max_pow10 = per_capita_denominator
vmins[_value_key] *= _max_pow10
vmaxs[_value_key] *= _max_pow10
pow10s_series[_value_key] = _max_pow10
percap_pow10s: pd.Series = df.apply(
lambda row: pow10s_series[
(row[Columns.STAGE], row[Columns.COUNT_TYPE])],
axis=1,
)
_per_cap_rows = df[Columns.COUNT_TYPE] == Counting.PER_CAPITA.name
df.loc[_per_cap_rows, value_col] *= percap_pow10s.loc[_per_cap_rows]
# Ideally we wouldn't have to pivot, and we could do a JIT join of state longs/lats
# after filtering the data. Unfortunately this is not possible, and a long data
# format leads to duplication of the very large long/lat lists; pivoting is how we
# avoid that. (This seems to be one downside of bokeh when compared to plotly)
df = (df.pivot_table(
index=[REGION_NAME_COL, Columns.STAGE, Columns.COUNT_TYPE],
columns=STRING_DATE_COL,
values=value_col,
aggfunc="first",
).reset_index().merge(
geo_df[[REGION_NAME_COL, LONG_COL, LAT_COL]],
how="inner",
on=REGION_NAME_COL,
))
# All three oclumns are just initial values; they'll change with the date slider
df[value_col] = df[max_date_str]
df[FAKE_DATE_COL] = max_date_str
df[COLOR_COL] = np.where(df[value_col] > 0, df[value_col], "NaN")
# Technically takes a df but we don't need the index
bokeh_data_source = ColumnDataSource(
{k: v.tolist()
for k, v in df.to_dict(orient="series").items()})
filters = [[
GroupFilter(column_name=Columns.STAGE, group=stage.name),
GroupFilter(column_name=Columns.COUNT_TYPE, group=count.name),
] for stage, count in stage_count_list]
figures = []
for subplot_index, (stage, count) in enumerate(stage_count_list):
# fig = bplotting.figure()
# ax: plt.Axes = fig.add_subplot(
# len(stage_list), len(count_list), subplot_index
# )
# # Add timestamp to top right axis
# if subplot_index == 2:
# ax.text(
# 1.25, # Coords are arbitrary magic numbers
# 1.23,
# f"Last updated {NOW_STR}",
# horizontalalignment="right",
# fontsize="small",
# transform=ax.transAxes,
# )
view = CDSView(source=bokeh_data_source,
filters=filters[subplot_index])
vmin = vmins[(stage.name, count.name)]
vmax = vmaxs[(stage.name, count.name)]
# Compute and set axes titles
if stage is DiseaseStage.CONFIRMED:
fig_stage_name = "Cases"
elif stage is DiseaseStage.DEATH:
fig_stage_name = "Deaths"
else:
raise ValueError
fig_title_components: List[str] = []
if subplot_title_prefix is not None:
fig_title_components.append(subplot_title_prefix)
fig_title_components.append(fig_stage_name)
if count is Counting.PER_CAPITA:
_per_cap_denom = pow10s_series[(stage.name, count.name)]
fig_title_components.append(f"Per {_per_cap_denom:,d} people")
formatter = PrintfTickFormatter(format=r"%2.3f")
label_standoff = 12
tooltip_fmt = "{0.000}"
else:
formatter = NumeralTickFormatter(format="0.0a")
label_standoff = 10
tooltip_fmt = "{0}"
color_mapper = LogColorMapper(
color_list,
low=vmin,
high=vmax,
nan_color="#f2f2f2",
)
fig_title = " ".join(fig_title_components)
if plot_aspect_ratio is None:
if x_range is None or y_range is None:
raise ValueError("Must provide both `x_range` and `y_range`" +
" when `plot_aspect_ratio` is None")
plot_aspect_ratio = (x_range[1] - x_range[0]) / (y_range[1] -
y_range[0])
# Create figure object
p = bplotting.figure(
title=fig_title,
title_location="above",
tools=[
HoverTool(
tooltips=[
("Date", f"@{{{FAKE_DATE_COL}}}"),
("State", f"@{{{REGION_NAME_COL}}}"),
("Count", f"@{{{value_col}}}{tooltip_fmt}"),
],
toggleable=False,
),
PanTool(),
BoxZoomTool(match_aspect=True),
ZoomInTool(),
ZoomOutTool(),
ResetTool(),
],
active_drag=None,
aspect_ratio=plot_aspect_ratio,
output_backend="webgl",
lod_factor=4,
lod_interval=400,
lod_threshold=1000,
lod_timeout=300,
)
p.xgrid.grid_line_color = None
p.ygrid.grid_line_color = None
# Finally, add the actual choropleth data we care about
p.patches(
LONG_COL,
LAT_COL,
source=bokeh_data_source,
view=view,
fill_color={
"field": COLOR_COL,
"transform": color_mapper
},
line_color="black",
line_width=0.25,
fill_alpha=1,
)
# Add evenly spaced ticks and their labels to the colorbar
# First major, then minor
# Adapted from https://stackoverflow.com/a/50314773
bucket_size = (vmax / vmin)**(1 / n_cbar_buckets)
tick_dist = bucket_size**n_buckets_btwn_major_ticks
# Simple log scale math
major_tick_locs = (
vmin * (tick_dist**np.arange(0, n_cbar_major_ticks))
# * (bucket_size ** 0.5) # Use this if centering ticks on buckets
)
# Get minor locs by linearly interpolating between major ticks
minor_tick_locs = []
for major_tick_index, this_major_tick in enumerate(
major_tick_locs[:-1]):
next_major_tick = major_tick_locs[major_tick_index + 1]
# Get minor ticks as numbers in range [this_major_tick, next_major_tick]
# and exclude the major ticks themselves (once we've used them to
# compute the minor tick locs)
minor_tick_locs.extend(
np.linspace(
this_major_tick,
next_major_tick,
n_minor_ticks_btwn_major_ticks + 2,
)[1:-1])
color_bar = ColorBar(
color_mapper=color_mapper,
ticker=FixedTicker(ticks=major_tick_locs,
minor_ticks=minor_tick_locs),
formatter=formatter,
label_standoff=label_standoff,
major_tick_out=0,
major_tick_in=13,
major_tick_line_color="white",
major_tick_line_width=1,
minor_tick_out=0,
minor_tick_in=5,
minor_tick_line_color="white",
minor_tick_line_width=1,
location=(0, 0),
border_line_color=None,
bar_line_color=None,
orientation="vertical",
)
p.add_layout(color_bar, "right")
p.hover.point_policy = "follow_mouse"
# Bokeh axes (and most other things) are splattable
p.axis.visible = False
figures.append(p)
# Make all figs pan and zoom together by setting their axes equal to each other
# Also fix the plots' aspect ratios
figs_iter = iter(np.ravel(figures))
anchor_fig = next(figs_iter)
if x_range is not None and y_range is not None:
data_aspect_ratio = (x_range[1] - x_range[0]) / (y_range[1] -
y_range[0])
else:
data_aspect_ratio = plot_aspect_ratio
if x_range is not None:
anchor_fig.x_range = Range1d(
*x_range,
bounds="auto",
min_interval=min_visible_y_range * data_aspect_ratio,
)
if y_range is not None:
anchor_fig.y_range = Range1d(*y_range,
bounds="auto",
min_interval=min_visible_y_range)
for fig in figs_iter:
fig.x_range = anchor_fig.x_range
fig.y_range = anchor_fig.y_range
# 2x2 grid (for now)
gp = gridplot(
figures,
ncols=len(count_list),
sizing_mode="scale_both",
toolbar_location="above",
)
plot_layout = [gp]
# Ok, pause
# Now we're going into a whole other thing: we're doing all the JS logic behind a
# date slider that changes which date is shown on the graphs. The structure of the
# data is one column per date, one row per location, and a few extra columns to
# store the data the graph will use. When we adjust the date of the slider, we copy
# the relevant column of the df into the columns the graphs are looking at.
# That's the easy part; the hard part is handling the "play button" functionality,
# whereby the user can click one button and the date slider will periodically
# advance itself. That requires a fair bit of logic to schedule and cancel the
# timers and make it all feel right.
# Create unique ID for the JS playback info object for this plot (since it'll be on
# the webpage with other plots, and their playback info isn't shared)
_THIS_PLOT_ID = uuid.uuid4().hex
__TIMER = "'timer'"
__IS_ACTIVE = "'isActive'"
__SELECTED_INDEX = "'selectedIndex'"
__BASE_INTERVAL_MS = "'BASE_INTERVAL'" # Time (in MS) btwn frames when speed==1
__TIMER_START_DATE = "'startDate'"
__TIMER_ELAPSED_TIME_MS = "'elapsedTimeMS'"
__TIMER_ELAPSED_TIME_PROPORTION = "'elapsedTimeProportion'"
__SPEEDS_KEY = "'SPEEDS'"
__PLAYBACK_INFO = f"window._playbackInfo_{_THIS_PLOT_ID}"
_PBI_TIMER = f"{__PLAYBACK_INFO}[{__TIMER}]"
_PBI_IS_ACTIVE = f"{__PLAYBACK_INFO}[{__IS_ACTIVE}]"
_PBI_SELECTED_INDEX = f"{__PLAYBACK_INFO}[{__SELECTED_INDEX}]"
_PBI_TIMER_START_DATE = f"{__PLAYBACK_INFO}[{__TIMER_START_DATE}]"
_PBI_TIMER_ELAPSED_TIME_MS = f"{__PLAYBACK_INFO}[{__TIMER_ELAPSED_TIME_MS}]"
_PBI_TIMER_ELAPSED_TIME_PROPORTION = (
f"{__PLAYBACK_INFO}[{__TIMER_ELAPSED_TIME_PROPORTION}]")
_PBI_BASE_INTERVAL = f"{__PLAYBACK_INFO}[{__BASE_INTERVAL_MS}]"
_PBI_SPEEDS = f"{__PLAYBACK_INFO}[{__SPEEDS_KEY}]"
_PBI_CURR_INTERVAL_MS = (
f"{_PBI_BASE_INTERVAL} / {_PBI_SPEEDS}[{_PBI_SELECTED_INDEX}]")
_SPEED_OPTIONS = [0.25, 0.5, 1.0, 2.0]
_DEFAULT_SPEED = 1.0
_DEFAULT_SELECTED_INDEX = _SPEED_OPTIONS.index(_DEFAULT_SPEED)
_SETUP_WINDOW_PLAYBACK_INFO = f"""
if (typeof({__PLAYBACK_INFO}) === 'undefined') {{
{__PLAYBACK_INFO} = {{
{__TIMER}: null,
{__IS_ACTIVE}: false,
{__SELECTED_INDEX}: {_DEFAULT_SELECTED_INDEX},
{__TIMER_START_DATE}: null,
{__TIMER_ELAPSED_TIME_MS}: 0,
{__TIMER_ELAPSED_TIME_PROPORTION}: 0,
{__BASE_INTERVAL_MS}: 1000,
{__SPEEDS_KEY}: {_SPEED_OPTIONS}
}};
}}
"""
_DEFFUN_INCR_DATE = f"""
// See this link for why this works (it's an undocumented feature?)
// https://discourse.bokeh.org/t/5254
// Tl;dr we need this to automatically update the hover as the play button plays
// Without this, the hover tooltip only updates when we jiggle the mouse
// slightly
let prev_val = null;
source.inspect.connect(v => prev_val = v);
function updateDate() {{
{_PBI_TIMER_START_DATE} = new Date();
{_PBI_TIMER_ELAPSED_TIME_MS} = 0
if (dateSlider.value < maxDate) {{
dateSlider.value += 86400000;
}}
if (dateSlider.value >= maxDate) {{
console.log(dateSlider.value, maxDate)
console.log('reached end')
clearInterval({_PBI_TIMER});
{_PBI_IS_ACTIVE} = false;
playPauseButton.active = false;
playPauseButton.change.emit();
playPauseButton.label = 'Restart';
}}
dateSlider.change.emit();
// This is pt. 2 of the prev_val/inspect stuff above
if (prev_val !== null) {{
source.inspect.emit(prev_val);
}}
}}
"""
_DO_START_TIMER = f"""
function startLoopTimer() {{
updateDate();
if ({_PBI_IS_ACTIVE}) {{
{_PBI_TIMER} = setInterval(updateDate, {_PBI_CURR_INTERVAL_MS})
}}
}}
{_PBI_TIMER_START_DATE} = new Date();
// Should never be <0 or >1 but I am being very defensive here
const proportionRemaining = 1 - (
{_PBI_TIMER_ELAPSED_TIME_PROPORTION} <= 0
? 0
: {_PBI_TIMER_ELAPSED_TIME_PROPORTION} >= 1
? 1
: {_PBI_TIMER_ELAPSED_TIME_PROPORTION}
);
const remainingTimeMS = (
{_PBI_CURR_INTERVAL_MS} * proportionRemaining
);
const initialInterval = (
{_PBI_TIMER_ELAPSED_TIME_MS} === 0
? 0
: remainingTimeMS
);
{_PBI_TIMER} = setTimeout(
startLoopTimer,
initialInterval
);
"""
_DO_STOP_TIMER = f"""
const now = new Date();
{_PBI_TIMER_ELAPSED_TIME_MS} += (
now.getTime() - {_PBI_TIMER_START_DATE}.getTime()
);
{_PBI_TIMER_ELAPSED_TIME_PROPORTION} = (
{_PBI_TIMER_ELAPSED_TIME_MS} / {_PBI_CURR_INTERVAL_MS}
);
clearInterval({_PBI_TIMER});
"""
update_on_date_change_callback = CustomJS(
args={"source": bokeh_data_source},
code=f"""
{_SETUP_WINDOW_PLAYBACK_INFO}
const sliderValue = cb_obj.value;
const sliderDate = new Date(sliderValue)
// Ugh, actually requiring the date to be YYYY-MM-DD (matching DATE_FMT)
const dateStr = sliderDate.toISOString().split('T')[0]
const data = source.data;
{_PBI_TIMER_ELAPSED_TIME_MS} = 0
if (typeof(data[dateStr]) !== 'undefined') {{
data['{value_col}'] = data[dateStr]
const valueCol = data['{value_col}'];
const colorCol = data['{COLOR_COL}'];
const fakeDateCol = data['{FAKE_DATE_COL}']
for (var i = 0; i < data['{value_col}'].length; i++) {{
const value = valueCol[i]
if (value == 0) {{
colorCol[i] = 'NaN';
}} else {{
colorCol[i] = value;
}}
fakeDateCol[i] = dateStr;
}}
source.change.emit();
}}
""",
)
# Taking day-over-day diffs means the min slider day is one more than the min data
# date (might be off by 1 if not using day over diffs but in practice not an issue)
min_slider_date = min_date + pd.Timedelta(days=1)
date_slider = DateSlider(
start=min_slider_date,
end=max_date,
value=max_date,
step=1,
sizing_mode="stretch_width",
width_policy="fit",
)
date_slider.js_on_change("value", update_on_date_change_callback)
play_pause_button = Toggle(
label="Start playing",
button_type="success",
active=False,
sizing_mode="stretch_width",
)
animate_playback_callback = CustomJS(
args={
"source": bokeh_data_source,
"dateSlider": date_slider,
"playPauseButton": play_pause_button,
"maxDate": max_date,
"minDate": min_slider_date,
},
code=f"""
{_SETUP_WINDOW_PLAYBACK_INFO}
{_DEFFUN_INCR_DATE}
if (dateSlider.value >= maxDate) {{
if (playPauseButton.active) {{
dateSlider.value = minDate;
dateSlider.change.emit();
// Hack to get timer to wait after date slider wraps; any positive
// number works but the smaller the better
{_PBI_TIMER_ELAPSED_TIME_MS} = 1;
}}
}}
const active = cb_obj.active;
{_PBI_IS_ACTIVE} = active;
if (active) {{
playPauseButton.label = 'Playing – Click/tap to pause'
{_DO_START_TIMER}
}} else {{
playPauseButton.label = 'Paused – Click/tap to play'
{_DO_STOP_TIMER}
}}
""",
)
play_pause_button.js_on_click(animate_playback_callback)
change_playback_speed_callback = CustomJS(
args={
"source": bokeh_data_source,
"dateSlider": date_slider,
"playPauseButton": play_pause_button,
"maxDate": max_date,
},
code=f"""
{_SETUP_WINDOW_PLAYBACK_INFO}
{_DEFFUN_INCR_DATE}
// Must stop timer before handling changing the speed, as stopping the timer
// saves values based on the current (unchaged) speed selection
if ({_PBI_TIMER} !== null) {{
{_DO_STOP_TIMER}
}}
const selectedIndex = cb_obj.active;
{_PBI_SELECTED_INDEX} = selectedIndex;
if ({_PBI_IS_ACTIVE}) {{
{_DO_START_TIMER}
}} else {{
{_PBI_TIMER_ELAPSED_TIME_MS} = 0
}}
console.log({__PLAYBACK_INFO})
""",
)
playback_speed_radio = RadioButtonGroup(
labels=[f"{speed:.2g}x speed" for speed in _SPEED_OPTIONS],
active=_DEFAULT_SELECTED_INDEX,
sizing_mode="stretch_width",
)
playback_speed_radio.js_on_click(change_playback_speed_callback)
plot_layout.append(
layout_column(
[
date_slider,
layout_row(
[play_pause_button, playback_speed_radio],
height_policy="min",
),
],
width_policy="fit",
height_policy="min",
))
plot_layout = layout_column(plot_layout, sizing_mode="scale_both")
# grid = gridplot(figures, ncols=len(count_list), sizing_mode="stretch_both")
# Create the autoloading bokeh plot info (HTML + JS)
js_path = str(Path(out_file_basename + "_autoload").with_suffix(".js"))
tag_html_path = str(
Path(out_file_basename + "_div_tag").with_suffix(".html"))
js_code, tag_code = autoload_static(plot_layout, CDN, js_path)
tag_uuid = re.search(r'id="([^"]+)"', tag_code).group(1)
tag_code = re.sub(r'src="([^"]+)"', f'src="\\1?uuid={tag_uuid}"', tag_code)
with open(Paths.DOCS / js_path,
"w") as f_js, open(Paths.DOCS / tag_html_path, "w") as f_html:
f_js.write(js_code)
f_html.write(tag_code)
# Create the video by creating stills of the graphs for each date and then stitching
# the images into a video
if should_make_video:
save_dir: Path = PNG_SAVE_ROOT_DIR / out_file_basename
save_dir.mkdir(parents=True, exist_ok=True)
STILL_WIDTH = 1500
STILL_HEIGHT = int(np.ceil(STILL_WIDTH / plot_aspect_ratio) *
1.05) # Unclear why *1.05 is necessary
gp.height = STILL_HEIGHT
gp.width = STILL_WIDTH
gp.sizing_mode = "fixed"
orig_title = anchor_fig.title.text
for date in dates:
date_str = date.strftime(DATE_FMT)
anchor_fig.title = Title(text=f"{orig_title} {date_str}")
for p in figures:
p.title = Title(text=p.title.text, text_font_size="20px")
# Just a reimplementation of the JS code in the date slider's callback
data = bokeh_data_source.data
data[value_col] = data[date_str]
for i, value in enumerate(data[value_col]):
if value == 0:
data[COLOR_COL][i] = "NaN"
else:
data[COLOR_COL][i] = value
data[FAKE_DATE_COL][i] = date_str
save_path: Path = (save_dir / date_str).with_suffix(".png")
export_png(gp, filename=save_path)
resize_to_even_dims(save_path, pad_bottom=0.08)
if date == max(dates):
poster_path: Path = (
PNG_SAVE_ROOT_DIR /
(out_file_basename + "_poster")).with_suffix(".png")
poster_path.write_bytes(save_path.read_bytes())
make_video(save_dir, out_file_basename, 0.9)
print(f"Did interactive {out_file_basename}")
return (js_code, tag_code)
def run_chart(exchange, asset, limit):
client = pymongo.MongoClient()
db = client['bitpredict_'+exchange]
collection = db[asset_+'_predictions']
def get_data():
if (limit)
cursor = collection.find()
else
cursor = collection.find().limit(limit)
data = pd.DataFrame(list(cursor))
data = data.set_index('_id')
data = data.sort_index(ascending=True)
timestamps = pd.to_datetime(data.index, unit='s').to_series()
returns = data.returns.cumsum()*100
return timestamps, returns
timestamps, returns = get_data()
output_server('bitpredict_'+exchange+'_performance')
background = '#f2f2f2'
ylabel_standoff = 0
xformatter = DatetimeTickFormatter(formats=dict(hours=["%H:%M"]))
yformatter = PrintfTickFormatter(format="%8.1f")
p = figure(title=None,
plot_width=750,
plot_height=500,
x_axis_type='datetime',
min_border_top=5,
min_border_bottom=10,
background_fill=background,
x_axis_label='Date',
tools='',
toolbar_location=None)
p.line(x=timestamps,
y=returns,
name='returns',
color='#8959a8',
line_width=1,
legend='Cumulative Return',
line_cap='round',
line_join='round')
p.legend.orientation = 'top_left'
p.legend.border_line_color = background
p.outline_line_color = None
p.xgrid.grid_line_color = 'white'
p.ygrid.grid_line_color = 'white'
p.axis.axis_line_color = None
p.axis.major_tick_line_color = None
p.axis.minor_tick_line_color = None
p.yaxis.axis_label = 'Percent'
p.yaxis.axis_label_standoff = ylabel_standoff
p.xaxis.formatter = xformatter
p.yaxis.formatter = yformatter
p.xaxis.axis_label_standoff = 12
p.yaxis.major_label_text_font = 'courier'
p.xaxis.major_label_text_font = 'courier'
push()
ip = load(urlopen('http://jsonip.com'))['ip']
ssn = cursession()
ssn.publish()
tag = embed.autoload_server(p, ssn, public=True)
renderer = p.select(dict(name='returns'))
ds_returns = renderer[0].data_source
while True:
timestamps, returns = get_data()
ds_returns.data['x'] = timestamps
ds_returns.data['y'] = returns
ssn.store_objects(ds_returns)
time.sleep(300)