def make_basic_timeseries(source, object_name, variable, interval_label,
plot_width):
"""
Make a basic timeseries plot (with either a step or line)
and add a hover tool.
Parameters
----------
source : bokeh.models.ColumnDataSource
The datasource with 'timestamp' and 'value' columns that will be
plotted.
object_name : str
Name of the object to be plotted so an appropriate title can be made.
variable : str
Variable of the plotted object
interval_label : str
Interval label of the object to determine whether a line or step
is most appropriate.
plot_width : int
The width of the output figure
Returns
-------
bokeh.models.Figure
The figure with the timeseries
Raises
------
KeyError
If timestamp is not a column in source
IndexError
If the timestamp column is empty
"""
timestamps = source.data['timestamp']
first = pd.Timestamp(timestamps[0], tz='UTC')
last = pd.Timestamp(timestamps[-1], tz='UTC')
plot_method, plot_kwargs, hover_kwargs = plot_utils.line_or_step(
interval_label)
figure_title = build_figure_title(object_name, first, last)
fig = figure(title=figure_title,
sizing_mode='scale_width',
plot_width=plot_width,
plot_height=300,
x_range=(first, last),
x_axis_type='datetime',
tools='pan,wheel_zoom,box_zoom,zoom_in,zoom_out,reset,save',
toolbar_location='above',
min_border_bottom=50)
getattr(fig, plot_method)(x='timestamp',
y='value',
source=source,
**plot_kwargs)
fig.yaxis.axis_label = plot_utils.format_variable_name(variable)
fig.xaxis.axis_label = 'Time (UTC)'
if variable == 'event':
fig.yaxis.ticker = [0, 1]
fig.yaxis.major_label_overrides = {1: 'True', 0: 'False'}
add_hover_tool(fig, source, **hover_kwargs)
return fig
def generate_probabilistic_forecast_figure(forecast,
data,
limit=pd.Timedelta('3d')):
"""
Creates a plotly figure spec from api response for a probabilistic forecast
group.
Parameters
----------
forecast : datamodel.ProbabilisticForecast
data : pandas.DataFrame
DataFrame with forecast values in each column, column names as the
constant values and a datetime index.
limit : pandas.Timedelta or None
Returns
-------
None
When the data is empty.
figure: Plotly.graph_objects.Figure
Plotly json specification for the plot.
"""
logger.info('Starting probabilistic forecast figure generation...')
if len(data.index) == 0:
return None
fig = go.Figure()
if 'x' in forecast.axis:
ylabel = 'Probability (%)'
_plot_probabilsitic_distribution_axis_x(fig, forecast, data)
else:
ylabel = plot_utils.format_variable_name(forecast.variable)
_plot_probabilsitic_distribution_axis_y(fig, forecast, data)
fig.update_xaxes(title_text=f'Time (UTC)',
showgrid=True,
gridwidth=1,
gridcolor='#CCC',
showline=True,
linewidth=1,
linecolor='black',
ticks='outside')
fig.update_yaxes(title_text=ylabel,
showgrid=True,
gridwidth=1,
gridcolor='#CCC',
showline=True,
linewidth=1,
linecolor='black',
ticks='outside',
fixedrange=True)
first = data.index[0]
last = data.index[-1]
fig.update_layout(
title=build_figure_title(forecast.name, first, last),
legend=dict(font=dict(size=10)),
**PLOTLY_LAYOUT_DEFAULTS,
)
return fig
def test_format_variable_name(var, exp):
out = utils.format_variable_name(var)
assert out == exp