lair.air.background#

Calculate background concentrations.

Functions

`get_well_mixed`(data[, hours])	Subset the data to the well-mixed hours of the day.
`phase_shift_corrected_baseline`(data[, n, q])	Derive a baseline concentration using a low quantile approach to minimize phase shift effects.
`rolling_baseline`(data[, window, q, ...])	Calculate the baseline concentration as the {q} quantile of a rolling window of size {window} hours.
`thoning`(data[, smooth_time])	Thoning curve fitting.
`thoning_filter`(data, **kwargs)	Create a Thoning filter object from a time series of data.

lair.air.background.get_well_mixed(data: Series | DataFrame, hours: list[int] = [12, 13, 14, 15, 16]) → Series | DataFrame[source]#

Subset the data to the well-mixed hours of the day.

Parameters:

datapd.Series | pd.DataFrame: Time series data to subset. Must have a datetime index.
hourslist[int], optional: Hours of the day to subset. Default is LST afternoon hours.

Returns:

pd.Series | pd.DataFrame: Subset of the data for the well-mixed hours.

lair.air.background.rolling_baseline(data: Series, window: Any = '24h', q: float = 0.01, min_periods: int = 1, center: bool = True) → Series[source]#

Calculate the baseline concentration as the {q} quantile of a rolling window of size {window} hours.

Parameters:

datapd.Series: Time series of data to calculate the baseline from.
windowAny, optional: Window size for the rolling calculation. Must be passable to pd.Timedelta. Default is ‘24h’.
qfloat, optional: Quantile to calculate the baseline from. Default is 0.01.
min_periodsint, optional: Minimum number of periods within each window required to have a value. Default is 1.
centerbool, optional: Center the window on the timestamp. Default is True.

Returns:

pd.Series: Baseline concentration

lair.air.background.phase_shift_corrected_baseline(data: Series, n: int = 3600, q: float = 0.01) → Series[source]#

Derive a baseline concentration using a low quantile approach to minimize phase shift effects. This method uses forward-looking and backward-looking windows to better represent the lowest observed concentrations during periods of rapid change.

Note

Original developed by Ben Fasoli for the Google Street View project. See supplementary material in: https://doi.org/10.1016/j.atmosenv.2023.119995

Parameters:

datapd.Series: Signal time series with local datetime index.
nint: Window size in seconds.
qfloat: Quantile to extract from signal.
timezonestr, optional: Timezone to convert the datetime index to.

Returns:

pd.Series: Baseline concentration

lair.air.background.thoning_filter(data: Series, **kwargs) → ccgFilter[source]#

Create a Thoning filter object from a time series of data.

Parameters:

datapd.Series: Time series of data to be smoothed. Must have a datetime index.
**kwargs: Additional keyword arguments to pass to the ccgFilter class.

Returns:

ccgFilter: Thoning filter object.

lair.air.background.thoning(data: Series, smooth_time: list[datetime] | None = None, **kwargs) → Series[source]#

Thoning curve fitting.

Wraps code published by NOAA GML: https://gml.noaa.gov/ccgg/mbl/crvfit/crvfit.html

Thoning, K.W., P.P. Tans, and W.D. Komhyr, 1989,: Atmospheric carbon dioxide at Mauna Loa Observatory, 2. Analysis of the NOAA/GMCC data, 1974 1985., J. Geophys. Res. ,94, 8549 8565.

Parameters:

datapd.Series: Time series of data to be smoothed. Must have a datetime index.
**kwargs: Additional keyword arguments to pass to the ccgFilter class.

Returns:

pd.Series: Smoothed data.