def verbose_formatter():
'''
Format bokeh time axis verbosely
'''
vf = DatetimeTickFormatter()
vf.microseconds = ['%f us']
vf.milliseconds = ['%S.%3N s']
# vf.milliseconds = ['%H:%M:%S.%3N']
vf.seconds = ['%H:%M:%S']
vf.minsec = ['%H:%M:%S']
vf.minutes = ['%m/%d %H:%M']
vf.hourmin = ['%m/%d %H:%M']
vf.hours = ['%m/%d %H:%M']
vf.days = ['%m/%d', '%a%d']
vf.months = ['%m/%Y', '%b %Y']
vf.years = ['%Y']
return vf
def plot_time_series(activity: models.Activity):
"""
Plotting function to create the time series plots shown in tha activity page. Depending
on what data is available this creates the following plots:
- Altitude
- Heart Rate
- Speed
- Cadence
- Temperature
All plots share a connected vertical cross hair tools.
Parameters
----------
activity : models.Activity
Activity model containing the required activity for which the plots should be generated
Returns
-------
script, div : tuple(str, str)
the html script and div elements used to render the plots in the html templates
"""
attributes = activity.trace_file.__dict__
lap_data = models.Lap.objects.filter(trace=activity.trace_file)
plots = []
lap_lines = []
timestamps = pd.to_datetime(pd.Series(json.loads(
attributes["timestamps_list"]),
dtype=float),
unit="s")
x_axis = pd.to_datetime(timestamps).dt.tz_localize("utc").dt.tz_convert(
settings.TIME_ZONE)
x_axis = x_axis - x_axis.min()
for attribute, values in attributes.items():
if attribute in attributes_to_create_time_series_plot_for:
values = pd.Series(json.loads(values), dtype=float)
if values.any():
attribute = attribute.replace("_list", "")
p = figure(
x_axis_type="datetime",
plot_height=int(settings.PLOT_HEIGHT / 2.5),
sizing_mode="stretch_width",
y_axis_label=plot_matrix[attribute]["axis"],
)
lap = _add_laps_to_plot(laps=lap_data, plot=p, y_values=values)
lap_lines += lap
if attribute == "altitude":
p.varea(
x=x_axis,
y1=values,
y2=values.min(),
color=plot_matrix[attribute]["second_color"],
fill_alpha=0.5,
)
p.line(
x_axis,
values,
line_width=2,
color=plot_matrix[attribute]["color"],
legend_label=plot_matrix[attribute]["title"],
)
else:
p.line(
x_axis,
values,
line_width=2,
color=plot_matrix[attribute]["color"],
legend_label=plot_matrix[attribute]["title"],
)
x_hover = ("Time", "@x")
hover = HoverTool(
tooltips=[(plot_matrix[attribute]["title"],
f"@y {plot_matrix[attribute]['axis']}"),
x_hover],
mode="vline",
)
p.add_tools(hover)
cross = CrosshairTool(dimensions="height")
p.add_tools(cross)
p.toolbar.logo = None
p.toolbar_location = None
p.xgrid.grid_line_color = None
p.legend.location = "top_left"
p.legend.label_text_font = "ubuntu"
p.legend.background_fill_alpha = 0.7
dtf = DatetimeTickFormatter()
dtf.minutes = ["%M:%S"]
p.xaxis.formatter = dtf
p.xaxis.major_label_overrides = {0: "0:00"}
plots.append(p)
values.ffill(inplace=True)
values.bfill(inplace=True)
x_axis.ffill(inplace=True)
x_axis.bfill(inplace=True)
_link_all_plots_with_each_other(all_plots=plots, x_values=x_axis)
all_plots = column(*plots)
all_plots.sizing_mode = "stretch_width"
if lap_data:
# include button to toggle rendering of laps
log.debug(f"found some Lap data for {activity}: {lap_data}")
checkbox = CheckboxButtonGroup(labels=["Show Laps"],
active=[0],
width=100)
js = """
for (line in laps) {
laps[line].visible = false;
if (typeof markerGroup != "undefined") {
markerGroup.removeFrom(map);
}
}
for (i in cb_obj.active) {
if (cb_obj.active[i] == 0) {
for (line in laps) {
laps[line].visible = true;
if (typeof markerGroup != "undefined") {
markerGroup.addTo(map);
}
}
}
}
"""
callback = CustomJS(args=dict(laps=lap_lines, checkbox=checkbox),
code=js)
checkbox.js_on_change("active", callback)
layout = column(all_plots, checkbox)
layout.sizing_mode = "stretch_width"
script, div = components(layout)
else:
script, div = components(all_plots)
return script, div
