API Reference

Quality

Irradiance

The check_*_limits_qcrad functions use the QCRad algorithm 1 to identify irradiance measurements that are beyond physical limits.

quality.irradiance.check_ghi_limits_qcrad(…)	Test for physical limits on GHI using the QCRad criteria.
quality.irradiance.check_dhi_limits_qcrad(…)	Test for physical limits on DHI using the QCRad criteria.
quality.irradiance.check_dni_limits_qcrad(…)	Test for physical limits on DNI using the QCRad criteria.

All three checks can be combined into a single function call.

quality.irradiance.check_irradiance_limits_qcrad(…)

Test for physical limits on GHI, DHI or DNI using the QCRad criteria.

Irradiance measurements can also be checked for consistency.

quality.irradiance.check_irradiance_consistency_qcrad(…)

Check consistency of GHI, DHI and DNI using QCRad criteria.

GHI and POA irradiance can be validated against clearsky values to eliminate data that is unrealistically high.

quality.irradiance.clearsky_limits(measured, …)

Identify irradiance values which do not exceed clearsky values.

You may want to identify entire days that have unrealistically high or low insolation. The following function examines daily insolation, validating that it is within a reasonable range of the expected clearsky insolation for the same day.

quality.irradiance.daily_insolation_limits(…)

Check that daily insolation lies between minimum and maximum values.

Gaps

Identify gaps in the data.

quality.gaps.interpolation_diff(x[, window, …])

Identify sequences which appear to be linear.

Data sometimes contains sequences of values that are “stale” or “stuck.” These are contiguous spans of data where the value does not change within the precision given. The functions below can be used to detect stale values.

Note

If the data has been altered in some way (i.e. temperature that has been rounded to an integer value) before being passed to these functions you may see unexpectedly large amounts of stale data.

quality.gaps.stale_values_diff(x[, window, …])	Identify stale values in the data.
quality.gaps.stale_values_round(x[, window, …])	Identify stale values by rounding.

The following functions identify days with incomplete data.

quality.gaps.completeness_score(series[, …])	Calculate a data completeness score for each day.
quality.gaps.complete(series[, …])	Select data points that are part of days with complete data.

Many data sets may have leading and trailing periods of days with sporadic or no data. The following functions can be used to remove those periods.

quality.gaps.start_stop_dates(series[, days])	Get the start and end of data excluding leading and trailing gaps.
quality.gaps.trim(series[, days])	Mask the beginning and end of the data if not all True.
quality.gaps.trim_incomplete(series[, …])	Trim the series based on the completeness score.

Outliers

Functions for detecting outliers.

quality.outliers.tukey(data[, k])	Identify outliers based on the interquartile range.
quality.outliers.zscore(data[, zmax])	Identify outliers using the z-score.
quality.outliers.hampel(data[, window, …])	Identify outliers by the Hampel identifier.

Time

Quality control related to time. This includes things like time-stamp spacing, time-shifts, and time zone validation.

quality.time.spacing(times, freq)

Check that the spacing between times conforms to freq.

Timestamp shifts, such as daylight savings, can be identified with the following functions.

quality.time.shifts_ruptures(event_times, …)	Identify time shifts using the ruptures library.
quality.time.has_dst(events, tz[, window, …])	Return True if events appears to have daylight savings shifts at the dates on which tz transitions to or from daylight savings time.

Utilities

The quality.util module contains general-purpose/utility functions for building your own quality checks.

quality.util.check_limits(val[, …])	Check whether a value falls withing the given limits.
quality.util.daily_min(series, minimum[, …])	Return True for data on days when the day’s minimum exceeds minimum.

Weather

Quality checks for weather data.

quality.weather.relative_humidity_limits(…)	Identify relative humidity values that are within limits.
quality.weather.temperature_limits(…[, limits])	Identify temperature values that are within limits.
quality.weather.wind_limits(wind_speed[, limits])	Identify wind speed values that are within limits.

In addition to validating temperature by comparing with limits, module temperature should be positively correlated with irradiance. Poor correlation could indicate that the sensor has become detached from the module, for example. Unlike other functions in the quality module which return Boolean masks over the input series, this function returns a single Boolean value indicating whether the entire series has passed (True) or failed (False) the quality check.

quality.weather.module_temperature_check(…)

Test whether the module temperature is correlated with irradiance.

References

1

C. N. Long and Y. Shi, An Automated Quality Assessment and Control Algorithm for Surface Radiation Measurements, The Open Atmospheric Science Journal 2, pp. 23-37, 2008.

Features

Functions for detecting features in the data.

Clipping

Functions for identifying inverter clipping

features.clipping.levels(ac_power[, window, …])	Label clipping in AC power data based on levels in the data.
features.clipping.threshold(ac_power[, …])	Detect clipping based on a maximum power threshold.
features.clipping.geometric(ac_power[, …])	Identify clipping based on a the shape of the ac_power curve on each day.

Clearsky

features.clearsky.reno(ghi, ghi_clearsky)

Identify times when GHI is consistent with clearsky conditions.

Orientation

System orientation refers to mounting type (fixed or tracker) and the azimuth and tilt of the mounting. A system’s orientation can be determined by examining power or POA irradiance on days that are relatively sunny.

This module provides functions that operate on power or POA irradiance to identify system orientation on a daily basis. These functions can tell you whether a day’s profile matches that of a fixed system or system with a single-axis tracker.

Care should be taken when interpreting function output since other factors such as malfunctioning trackers can interfere with identification.

features.orientation.fixed_nrel(…[, …])	Flag days that match the profile of a fixed PV system on a sunny day.
features.orientation.tracking_nrel(…[, …])	Flag days that match the profile of a single-axis tracking PV system on a sunny day.

Daytime

Functions that return a Boolean mask indicating day and night.

features.daytime.power_or_irradiance(series)

Return True for values that are during the day.

System

This module contains functions and classes relating to PV system parameters such as nameplate power, tilt, azimuth, or whether the system is equipped with tracker.

Tracking

system.Tracker	Enum describing the orientation of a PV System.
system.is_tracking_envelope(series, daytime, …)	Infer whether the system is equipped with a tracker.

Orientation

The following function can be used to infer system orientation from power or plane of array irradiance measurements.

system.infer_orientation_daily_peak(…)	Determine system azimuth and tilt from power or POA using solar azimuth at the daily peak.
system.infer_orientation_fit_pvwatts(…[, …])	Get the tilt and azimuth that give PVWatts output that most closely fits the data in power_ac.

Metrics

Performance Ratio

The following functions can be used to calculate system performance metrics.

metrics.performance_ratio_nrel(poa_global, …)

Calculate NREL Performance Ratio.