def fixed_viewpoint_selector(
base: alt.Chart,
active_fixed_viewpoint_selector: bool = False) -> alt.Chart:
"""Transparent selectors across the chart (visible on hover).
This is what tells us the belief time for a given x-value of the cursor.
:param active_fixed_viewpoint_selector: if False, return an idle colored version without tooltip
"""
selector = base.mark_rule().encode(
x=alt.X("belief_time:T", scale={"domain": time_selection_brush.ref()}),
color=alt.ColorValue(idle_color)
if not active_fixed_viewpoint_selector else alt.ColorValue("#c21431"),
opacity=alt.condition(nearest_x_hover_brush, alt.value(1),
alt.value(0)),
tooltip=[
alt.Tooltip(
"belief_time:T",
timeUnit="yearmonthdatehoursminutes",
title="Click to select belief time",
)
] if active_fixed_viewpoint_selector is True else None,
)
return selector.add_selection(nearest_x_select_brush).add_selection(
nearest_x_hover_brush)
def value_vs_time_chart(
base: alt.Chart,
active_fixed_viewpoint_selector: bool,
sensor_name: str,
sensor_unit: str,
belief_horizon_unit: str,
intuitive_forecast_horizon: bool,
interpolate: bool,
ci: float,
event_value_range: Tuple[float, float],
) -> alt.LayerChart:
# Configure the stepwise line for the reference
if interpolate is True:
ts_line_reference_chart = base.mark_line(interpolate="monotone")
else:
ts_line_reference_chart = base.mark_rule().encode(x2=alt.X2("event_end:T"))
ts_line_reference_chart = ts_line_reference_chart.encode(
y=alt.Y(
"reference_value",
scale=alt.Scale(domain=(event_value_range[0], event_value_range[-1])),
),
color=alt.ColorValue("black"),
tooltip=[
alt.Tooltip(
"event_start:T",
timeUnit="yearmonthdatehoursminutes",
title="Event start",
),
alt.Tooltip(
"event_end:T", timeUnit="yearmonthdatehoursminutes", title="Event end"
),
alt.Tooltip("reference_value:Q", title="Real value", format=".2f"),
],
)
# Configure the stepwise line for the beliefs
if interpolate is True:
ts_line_chart = base.mark_line(interpolate="monotone")
else:
ts_line_chart = base.mark_rule().encode(x2=alt.X2("event_end:T"))
ts_line_chart = ts_line_chart.encode(
y=alt.Y("expected_value", title="%s (%s)" % (sensor_name, sensor_unit))
)
if active_fixed_viewpoint_selector is True:
ts_line_chart = (
ts_line_chart.transform_filter(
"datum.belief_time <= nearest_x_select.belief_time"
)
.transform_joinaggregate(
most_recent_belief_time="max(belief_time)",
groupby=["event_start", "source"],
)
.transform_filter("datum.belief_time == datum.most_recent_belief_time")
)
# Configure the confidence intervals
if interpolate is True:
confidence_interval = ts_line_chart.mark_area(
interpolate="monotone", opacity=0.3
)
else:
confidence_interval = ts_line_chart.mark_bar(opacity=0.3)
confidence_interval = confidence_interval.encode(
y="lower_value",
y2="upper_value",
tooltip=[
alt.Tooltip(
"event_start:T",
timeUnit="yearmonthdatehoursminutes",
title="Event start",
),
alt.Tooltip(
"event_end:T", timeUnit="yearmonthdatehoursminutes", title="Event end"
),
alt.Tooltip("expected_value:Q", title="Expected value", format=".2f"),
alt.Tooltip(
"belief_time:T",
timeUnit="yearmonthdatehoursminutes",
title="Belief time",
),
alt.Tooltip(
"belief_horizon:Q",
title="%s (%s)"
% (
"Forecast horizon"
if intuitive_forecast_horizon
else "Belief horizon",
belief_horizon_unit,
),
),
alt.Tooltip("source", title="Source"),
alt.Tooltip(
"upper_value:Q",
format=".2f",
title="Upper value of {0:.0f}% confidence interval".format(100 * ci),
),
alt.Tooltip(
"lower_value:Q",
format=".2f",
title="Lower value of {0:.0f}% confidence interval".format(100 * ci),
),
],
)
return (ts_line_reference_chart + ts_line_chart + confidence_interval).properties(
title="Model results"
)
def horizon_selector(
base: alt.Chart,
horizon_selection_brush: alt.MultiSelection,
belief_horizon_unit: str,
intuitive_forecast_horizon: bool,
unique_belief_horizons,
) -> alt.LayerChart:
bar_chart = (
base.mark_rule(orient="vertical").transform_filter(
time_selection_brush
) # Apply brush before calculating accuracy metrics for the selected events on the fly
.transform_calculate(
constant=1 + alt.datum.event_start -
alt.datum.event_start).transform_calculate(
belief_horizon_str='datum.belief_horizon + " %s"' %
belief_horizon_unit).
encode(
opacity=alt.condition(
time_selection_brush,
alt.Opacity("event_start:T",
scale=alt.Scale(domain=(0.9999, 1)),
legend=None),
alt.value(0),
),
# Trick to be able to apply the selection filter for event_start (event_start must be a field in one of the encoding channels)
x=alt.X(
"belief_horizon:Q",
axis=alt.Axis(labelFlush=False),
scale=alt.Scale(
zero=False,
domain=(unique_belief_horizons[0],
unique_belief_horizons[-1]),
),
title="",
),
y=alt.Y(
"constant:Q",
title=" ",
axis=alt.Axis(values=[], domain=False, ticks=False),
),
color=alt.condition(
horizon_selection_brush | horizon_hover_brush,
alt.ColorValue("#c21431"),
alt.ColorValue(idle_color),
),
size=alt.value(1),
tooltip=[
alt.Tooltip(
"belief_horizon_str:N",
title="Click to select %s" %
("forecast horizon"
if intuitive_forecast_horizon else "belief horizon"),
)
],
).properties(
height=30,
title="Select %s" %
("forecast horizon"
if intuitive_forecast_horizon else "belief horizon"),
).transform_filter(time_selection_brush))
circle_chart = (bar_chart.mark_circle().transform_calculate(
half_constant=alt.datum.constant / 2).encode(
y=alt.Y("half_constant:Q", title="", axis=alt.Axis(values=[])),
size=alt.value(100),
))
return (
bar_chart.add_selection(horizon_selection_brush, horizon_hover_brush) +
circle_chart)
