How to use solarforecastarbiter - 10 common examples

metrics : list of str
        List of metrics to be computed.

    Returns
    -------
    list
        List of datamodel.MetricResult for each
        datamodel.ProcessedForecastObservation
    """
    calc_metrics = []

    for proc_fxobs in processed_pairs:

        # determine type of metrics to calculate
        if isinstance(proc_fxobs.original.forecast,
                      (datamodel.ProbabilisticForecast,
                       datamodel.ProbabilisticForecastConstantValue)):
            try:
                calc_metrics.append(calculate_probabilistic_metrics(
                    proc_fxobs,
                    categories,
                    metrics))
            except RuntimeError as e:
                logger.error('Failed to calculate probabilistic metrics'
                             ' for %s: %s', proc_fxobs.name, e)
        elif isinstance(proc_fxobs.original.forecast, datamodel.EventForecast):
            try:
                calc_metrics.append(calculate_event_metrics(
                    proc_fxobs, categories, metrics
                ))
            except RuntimeError as e:
                logger.error('Failed to calculate event metrics for %s: %s',

----------
    modeling_parameters : datamodel.FixedTiltModelingParameters or
                          datamodel.SingleAxisModelingParameters

    Returns
    -------
    function
        Function that accepts two arguments (apparent_zenith, azimuth)
        and returns three series (surface_tilt, surface_azimuth, aoi)

    Raises
    ------
    TypeError if modeling_parameters is invalid.
    """
    if isinstance(modeling_parameters,
                  datamodel.FixedTiltModelingParameters):
        return partial(
            aoi_fixed,
            modeling_parameters.surface_tilt,
            modeling_parameters.surface_azimuth
        )
    elif isinstance(modeling_parameters,
                    datamodel.SingleAxisModelingParameters):
        return partial(
            aoi_tracking,
            modeling_parameters.axis_tilt,
            modeling_parameters.axis_azimuth,
            modeling_parameters.max_rotation_angle,
            modeling_parameters.backtrack,
            modeling_parameters.ground_coverage_ratio
        )
    else:

obs = self.get_observation(o['observation'])
                pair = datamodel.ForecastObservation(
                    fx, obs, normalization=norm, uncertainty=unc,
                    reference_forecast=ref_fx, cost=cost)
            elif 'aggregate' in o:
                agg = self.get_aggregate(o['aggregate'])
                pair = datamodel.ForecastAggregate(
                    fx, agg, normalization=norm, uncertainty=unc,
                    reference_forecast=ref_fx, cost=cost)
            else:
                raise ValueError('must provide observation or aggregate in all'
                                 'object_pairs')
            pairs.append(pair)
        rep_params['object_pairs'] = tuple(pairs)
        req_dict['report_parameters'] = rep_params
        return datamodel.Report.from_dict(req_dict)

res = _apply_event_metric_func(
                    metric_, group.forecast, group.observation
                )

                # Change category label of the group from numbers
                # to e.g. January or Monday
                if category == 'month':
                    cat = calendar.month_abbr[cat]
                elif category == 'weekday':
                    cat = calendar.day_abbr[cat]

                metric_vals.append(datamodel.MetricValue(
                    category, metric_, str(cat), res))

    out['values'] = _sort_metrics_vals(metric_vals,
                                       datamodel.ALLOWED_EVENT_METRICS)
    calc_metrics = datamodel.MetricResult.from_dict(out)
    return calc_metrics

Parameters
        ----------
        report_id : string
            UUID of the report to retrieve

        Returns
        -------
        datamodel.Report
        """
        req = self.get(f'/reports/{report_id}')
        resp = req.json()
        raw = resp.pop('raw_report', None)
        report = self.process_report_dict(resp)
        if raw is not None:
            raw_report = datamodel.RawReport.from_dict(raw)
            processed_fxobs = self.get_raw_report_processed_data(
                report_id, raw_report, resp['values'])
            report = report.replace(raw_report=raw_report.replace(
                processed_forecasts_observations=processed_fxobs))
        return report

handle_func : function
        Function that handles how `quality_flags` will be used.
        See solarforecastarbiter.metrics.preprocessing.exclude as an
        example.

    Returns
    -------
    validated_obs : pandas.Series
        The validated timeseries data as pandas.Series.
    counts : dict
        Dict where keys are qfilter.quality_flags and values
        are integers indicating the number of points filtered
        for the given flag.
    """
    # List of flags from filter
    if not isinstance(qfilter, datamodel.QualityFlagFilter):
        raise TypeError(f"{qfilter} not a QualityFlagFilter")
    filters = qfilter.quality_flags

    if obs_df.empty:
        return obs_df.value, {f: 0 for f in filters}
    else:
        validation_df = quality_mapping.convert_mask_into_dataframe(
            obs_df['quality_flag'])
        validation_df = validation_df[list(filters)]
        validated_obs = handle_func(obs_df.value, validation_df)
        counts = validation_df.astype(int).sum(axis=0).to_dict()
        return validated_obs, counts

Fields must be kind and the names of the forecasts
    category : str
        One of the available metrics grouping categories (e.g., total)

    Returns
    -------
    figs : dict of figures
    """
    palette = cycle(PALETTE)
    tools = 'pan,xwheel_zoom,box_zoom,reset,save'
    fig_kwargs = dict(tools=tools, toolbar_location='above')
    figs = {}

    width = 0.8

    human_category = datamodel.ALLOWED_CATEGORIES[category]
    metric_name = datamodel.ALLOWED_DETERMINISTIC_METRICS[metric]

    fig_kwargs['x_axis_label'] = human_category
    fig_kwargs['y_axis_label'] = metric_name

    filter_ = ((np.asarray(cds.data['category']) == category) &
               (np.asarray(cds.data['metric']) == metric))
    # Special handling for x-axis with dates
    if category == 'date':
        fig_kwargs['x_axis_type'] = 'datetime'
        width = width * pd.Timedelta(days=1)
        fig_kwargs['x_range'] = DataRange1d()
    elif category == 'month':
        fig_kwargs['x_range'] = FactorRange(
            factors=calendar.month_abbr[1:])
    elif category == 'weekday':

def infer_timezone(report_parameters):
    # maybe not ideal when comparing across sites. might need explicit
    # tz options ('infer' or spec IANA tz) in report interface.
    fxobs_0 = report_parameters.object_pairs[0]
    if isinstance(fxobs_0, datamodel.ForecastObservation):
        timezone = fxobs_0.observation.site.timezone
    else:
        timezone = fxobs_0.aggregate.timezone
    return timezone

Get Aggregate metadata from the API for the given aggregate_id

        Parameters
        ----------
        aggregate_id : string
            UUID of the aggregate to get metadata for

        Returns
        -------
        datamodel.Aggregate
        """
        req = self.get(f'/aggregates/{aggregate_id}/metadata')
        agg_dict = req.json()
        for o in agg_dict['observations']:
            o['observation'] = self.get_observation(o['observation_id'])
        return datamodel.Aggregate.from_dict(agg_dict)

def _fx_name(forecast, data_object):
    # TODO: add code to ensure fx names are unique
    forecast_name = forecast.name
    if isinstance(forecast, datamodel.ProbabilisticForecastConstantValue):
        if forecast.axis == 'x':
            forecast_name += \
                f' Prob(x <= {forecast.constant_value} {forecast.units})'
        else:
            forecast_name += f' Prob(f <= x) = {forecast.constant_value}%'
    if forecast_name == data_object.name:
        forecast_name += ' Forecast'
    return forecast_name