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 test_line_or_step(label, method):
out = utils.line_or_step(label)
assert out[0] == method
assert isinstance(out[1], dict)
assert isinstance(out[2], dict)
def timeseries(timeseries_value_cds,
timeseries_meta_cds,
start,
end,
units,
timezone='UTC'):
"""
Timeseries plot of one or more forecasts and observations.
Parameters
----------
timeseries_value_cds: bokeh.models.ColumnDataSource
ColumnDataSource of timeseries data. See :py:func:`solarforecastarbiter.reports.reoports.figures.construct_timeseries_cds` for format.
timeseries_meta_cds: bokeh.models.ColumnDataSource
ColumnDataSource of metadata for each Observation Forecast pair. See :py:func:`solarforecastarbiter.reports.reoports.figures.construct_timeseries_cds` for format.
start : pandas.Timestamp
Report start time
end : pandas.Timestamp
Report end time
timezone : str
Timezone consistent with the data in the obs_fx_cds.
Returns
-------
fig : bokeh.plotting.figure
""" # NOQA
palette = cycle(PALETTE)
fig = figure(
sizing_mode='scale_width',
plot_width=900,
plot_height=300,
x_range=(start, end),
x_axis_type='datetime',
tools='pan,xwheel_zoom,box_zoom,box_select,lasso_select,reset,save',
name='timeseries')
plotted_objects = 0
for obs_hash in np.unique(timeseries_meta_cds.data['observation_hash']):
metadata = _extract_metadata_from_cds(timeseries_meta_cds, obs_hash,
'observation_hash')
pair_indices = _boolean_filter_indices_by_pair(timeseries_value_cds,
metadata['pair_index'])
view = CDSView(source=timeseries_value_cds,
filters=[BooleanFilter(pair_indices)])
plot_method, plot_kwargs, hover_kwargs = line_or_step(
metadata['interval_label'])
legend_label = metadata['observation_name']
color = metadata['observation_color']
getattr(fig, plot_method)(x='timestamp',
y='observation_values',
source=timeseries_value_cds,
view=view,
color=color,
legend_label=legend_label,
**plot_kwargs)
plotted_objects += 1
for fx_hash in np.unique(timeseries_meta_cds.data['forecast_hash']):
metadata = _extract_metadata_from_cds(timeseries_meta_cds, fx_hash,
'forecast_hash')
pair_indices = _boolean_filter_indices_by_pair(timeseries_value_cds,
metadata['pair_index'])
view = CDSView(source=timeseries_value_cds,
filters=[BooleanFilter(pair_indices)])
plot_method, plot_kwargs, hover_kwargs = line_or_step(
metadata['interval_label'])
legend_label = metadata['forecast_name']
color = next(palette)
getattr(fig, plot_method)(x='timestamp',
y='forecast_values',
source=timeseries_value_cds,
view=view,
color=color,
legend_label=legend_label,
**plot_kwargs)
plotted_objects += 1
fig.legend.location = "top_left"
fig.legend.click_policy = "hide"
if plotted_objects > 10:
fig.legend.label_height = 10
fig.legend.label_text_font_size = '8px'
fig.legend.glyph_height = 10
fig.legend.spacing = 1
fig.legend.margin = 0
fig.xaxis.axis_label = f'Time ({timezone})'
fig.yaxis.axis_label = f'Data ({units})'
return fig