def export_records(self, level=logging.WARNING):
"""
Convert each log record in the records list with level
greater than or equal to `level` to a
:py:class:`solarforecastarbiter.datamodel.ReportMessage`
and return the tuple of messages.
"""
out = []
for rec in self.records:
if rec.levelno >= level:
out.append(
datamodel.ReportMessage(message=rec.getMessage(),
step=rec.name,
level=rec.levelname,
function=rec.funcName))
return tuple(out)
def wrapper(*args, **kwargs):
try:
out = f(*args, **kwargs)
except Exception:
msg = datamodel.ReportMessage(
message=err_msg,
step='solarforecastarbiter.reports.main',
level='CRITICAL',
function=str(f)
)
raw = datamodel.RawReport(
pd.Timestamp.now(tz='UTC'), 'UTC', (), None,
(), (), (msg,))
session.post_raw_report(report_id, raw, 'failed')
raise
else:
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')