def test_raw_report_plots_invalid_versions(plotly_report_figure,
plotly_version, bokeh_version,
script):
with pytest.raises(KeyError):
datamodel.RawReportPlots(figures=(plotly_report_figure, ),
plotly_version=plotly_version,
bokeh_version=bokeh_version,
script=script)
def test_raw_report_plots(plotly_report_figure, plotly_version, bokeh_version,
script):
dm_obj = datamodel.RawReportPlots(figures=(plotly_report_figure, ),
plotly_version=plotly_version,
bokeh_version=bokeh_version,
script=script)
assert dm_obj.figures == (plotly_report_figure, )
assert dm_obj.plotly_version == plotly_version
assert dm_obj.bokeh_version == bokeh_version
assert dm_obj.script == script
def raw_report_plots(report, metrics):
"""Create a RawReportPlots object from the metrics of a report.
Parameters
----------
report: :py:class:`solarforecastarbiter.datamodel.Report`
metrics: tuple of :py:class:`solarforecastarbiter.datamodel.MetricResult`
Returns
-------
:py:class:`solarforecastarbiter.datamodel.RawReportPlots`
"""
cds = construct_metrics_cds(metrics, rename=abbreviate)
# Create initial bar figures
figure_dict = {}
# Components for other metrics
for category in report.report_parameters.categories:
for metric in report.report_parameters.metrics:
if category == 'total':
fig = bar(cds, metric)
figure_dict[f'total::{metric}::all'] = fig
else:
figs = bar_subdivisions(cds, category, metric)
for name, fig in figs.items():
figure_dict[f'{category}::{metric}::{name}'] = fig
script, divs = components(figure_dict)
mplots = []
with _make_webdriver() as driver:
for k, v in divs.items():
cat, met, name = k.split('::', 2)
fig = figure_dict[k]
svg = output_svg(fig, driver=driver)
mplots.append(
datamodel.BokehReportFigure(name=name,
category=cat,
metric=met,
div=v,
svg=svg,
figure_type='bar'))
out = datamodel.RawReportPlots(bokeh_version=bokeh_version,
script=script,
figures=tuple(mplots))
return out
def raw_report_plots(report, metrics):
"""Create a RawReportPlots object from the metrics of a report.
Parameters
----------
report: :py:class:`solarforecastarbiter.datamodel.Report`
metrics: tuple of :py:class:`solarforecastarbiter.datamodel.MetricResult`
Returns
-------
:py:class:`solarforecastarbiter.datamodel.RawReportPlots`
"""
metrics_df = construct_metrics_dataframe(metrics, rename=abbreviate)
# Create initial bar figures
figure_dict = {}
# Components for other metrics
for category in report.report_parameters.categories:
for metric in report.report_parameters.metrics:
if category == 'total':
fig = bar(metrics_df, metric)
figure_dict[f'total::{metric}::all'] = fig
else:
figs = bar_subdivisions(metrics_df, category, metric)
for name, fig in figs.items():
figure_dict[f'{category}::{metric}::{name}'] = fig
mplots = []
for k, v in figure_dict.items():
cat, met, name = k.split('::', 2)
figure_spec = v.to_json()
pdf = output_pdf(v)
mplots.append(
datamodel.PlotlyReportFigure(name=name,
category=cat,
metric=met,
spec=figure_spec,
pdf=pdf,
figure_type='bar'))
out = datamodel.RawReportPlots(tuple(mplots), plotly_version)
return out
def test_render_pdf_special_chars(
ac_power_observation_metadata, ac_power_forecast_metadata, dash_url,
fail_pdf, preprocessing_result_types, report_metrics):
if shutil.which('pdflatex') is None: # pragma: no cover
pytest.skip('pdflatex must be on PATH to generate PDF reports')
quality_flag_filter = datamodel.QualityFlagFilter(
(
"USER FLAGGED",
)
)
forecast = ac_power_forecast_metadata.replace(
name="ac_power forecast (why,) ()'-_,")
observation = ac_power_observation_metadata.replace(
name="ac_power observations ()'-_,")
fxobs = datamodel.ForecastObservation(forecast,
observation)
tz = 'America/Phoenix'
start = pd.Timestamp('20190401 0000', tz=tz)
end = pd.Timestamp('20190404 2359', tz=tz)
report_params = datamodel.ReportParameters(
name="NREL MIDC OASIS GHI Forecast Analysis ()'-_,",
start=start,
end=end,
object_pairs=(fxobs,),
metrics=("mae", "rmse", "mbe", "s"),
categories=("total", "date", "hour"),
filters=(quality_flag_filter,)
)
report = datamodel.Report(
report_id="56c67770-9832-11e9-a535-f4939feddd83",
report_parameters=report_params
)
qflags = list(
f.quality_flags for f in report.report_parameters.filters if
isinstance(f, datamodel.QualityFlagFilter)
)
qflags = list(qflags[0])
ser_index = pd.date_range(
start, end,
freq=to_offset(forecast.interval_length),
name='timestamp')
ser = pd.Series(
np.repeat(100, len(ser_index)), name='value',
index=ser_index)
pfxobs = datamodel.ProcessedForecastObservation(
forecast.name,
fxobs,
forecast.interval_value_type,
forecast.interval_length,
forecast.interval_label,
valid_point_count=len(ser),
validation_results=tuple(datamodel.ValidationResult(
flag=f, count=0) for f in qflags),
preprocessing_results=tuple(datamodel.PreprocessingResult(
name=t, count=0) for t in preprocessing_result_types),
forecast_values=ser,
observation_values=ser
)
figs = datamodel.RawReportPlots(
(
datamodel.PlotlyReportFigure.from_dict(
{
'name': 'mae tucson ac_power',
'spec': '{"data":[{"x":[1],"y":[1],"type":"bar"}]}',
'pdf': fail_pdf,
'figure_type': 'bar',
'category': 'total',
'metric': 'mae',
'figure_class': 'plotly',
}
),), '4.5.3',
)
raw = datamodel.RawReport(
generated_at=report.report_parameters.end,
timezone=tz,
plots=figs,
metrics=report_metrics(report),
processed_forecasts_observations=(pfxobs,),
versions=(('test', 'test_with_underscore?'),),
messages=(datamodel.ReportMessage(
message="Failed to make metrics for ac_power forecast ()'-_,",
step='', level='', function=''),))
rr = report.replace(raw_report=raw)
rendered = template.render_pdf(rr, dash_url)
assert rendered.startswith(b'%PDF')